There is a range of pathogenic bacteria worldwide that can affect all age groups of both sexes. In the worst cases, certain bacterial infections may cause severe morbidity and mortality in human population. Moreover, some pathogenic bacteria have acquired the genes to become multidrug-resistant (MDR) strains due to an extensive use of antimicrobials. The MDR bacterial strains have posed serious challenges to the scientists and clinicians in the last few decades. There is a dire need either to find new antimicrobials or replace or modify the currently available antimicrobials to combat drug-resistant bacterial infections.

Recent advances in the field of Nanotechnology have provided new hopes for patients and practitioners to overcome the problem of drug resistance. The effectiveness of nanoparticles (NPs) depends on the interaction between the NPs and the microorganisms. The NPs have extensively been studied for acting as antimicrobials to fight against drug-resistant bacteria. The NPs can be defined as intentionally produced particles that have characteristics dimension from 1 to 100 nm and have properties that are not shared by non-nanoscale particles with the same chemical composition.

The synthesis and biomedical application of noble metal NPs, especially gold nanoparticles (AuNPs) is a promising field of research and has garnered the attention of vast groups of researchers. The tailored ability to ensure cellular uptake with controlled release of the drug to specific cell target, significant stability, bio-inertness with non-toxicity of gold have made AuNPs a popular candidate for biomedical applications such as biosensors, drug delivery for cancer chemotherapy and radiotherapy.

There are many characteristics making NPs strong alternatives for traditional antibiotic drugs. Firstly, they have a high surface area to volume ratio, which can increase the contact area with target organisms. Additionally, they may be synthesised from polymers, lipids, and metals. Furthermore, a multitude of chemical structures, such as fullerenes and metal oxides, allow for a diverse set of chemical functionalities.

Nanomaterials have become a promising and efficient drug candidate that can replace conventional materials with most applications in the field of science and technology. Commonly used nanoparticles are formulated with silver, gold and zinc, each with different properties and spectrum activities. AuNPs are widely used in enormous biological applications mainly in medical and gene therapy and biosensors for diagnosis. AuNPs are easy to be prepared by co-precipitation approach and may have lower toxicity compared to other metallic nanomaterials such as silver nanoparticles (AgNPs). The emergence of antibiotic-resistant bacteria and their emphasis on healthcare costs, has encouraged researchers to innovate new approaches to develop more effective antimicrobial agents to overcome the bacterial resistance and reduce their cost. The present study was aimed to assess the effectiveness and mechanism of the antimicrobial activity of AuNPs as a novel therapeutic formulation against E. coli strains.

Methods

The stool, urine and wound samples were collected from patients from different hospitals in Qassim region, Saudi Arabia. The samples were collected during the period between September and December 2018. The samples were then processed using appropriate methods. They were cultured on blood and MacConkey agar plates and incubated at 37-degree C for 24 hours. Gram stain, API20E and other biochemical tests were then performed to confirm E. coli isolation.

Determination of Minimum inhibitory concentration (MIC) of AuNPs against E. coli strains

The macrodilution method was used to establish the minimum inhibitory concentrations (MICs) of AuNPs. The antibacterial effect of AuNPs against E. coli strains was evaluated at the MICs of the NPs. In brief, two-fold serial dilutions (200–0.39 μg/ml) was prepared. Each well was consequently filled with 100 μl of inoculum. To evaluate the antibacterial action of AuNPs on bacterial growth, the MICs for all isolates were determined after 24 hours using the optical density (OD600) of the bacterial culture solutions. The final cell concentrations of bacterial inoculants were 106–107 CFU/ml.

Determination of Minimum Bactericidal Concentration (MBC) of AuNPs against E. coli strains 

To determine MBC, 50 μl aliquot from the tubes not showing any turbidity was transferred onto Tryptone Soya Agar (TSA) plates not supplemented with AuNPs and then incubated at 37-degree C for 24 hours. All plates were examined before and after incubation for the presence or absence of bacterial growth. The number of surviving organisms was determined by viability counts. The lowest concentration of NPs that inhibited the growth of ≥99.99 per cent of E. coli was defined as MBC. All of the experiments were duplicated on two different days.

Synthesis of nanoparticle formulation

Aqueous colloidal AuNPs suspension (0.05 mg/ml) was purchased from PlasmaChem GmbH (Berlin, Germany) to use in this study. Chemical reduction method was used for synthesis of vwAuNPs. This was carried out by a reduction of 10-mM tetrachloroauric acid (HAuCl4) using sodium citrate (Sigma-Aldrich, USA). Briefly, an aqueous solution of HAuCl4.3H2O was boiled under reflux while being stirred. Changing the colour of the solution from yellow to deep red after adding 10-ml trisodium citrate (1 per cent) indicated the formation of spherical AuNPs. The solution was refluxed for 20 min, then left to cool at 25°C. Afterward, the solution was filtered through a 0.45-μm acetate filter and stored at 4°C. Morphology of the synthesised NPs was examined by transmission electron microscopy (Oberkochen, Germany). NPs size distribution was measured according to the dynamic light scattering using a Malvern Zeta sizer Nano ZS device (Sysmex, the Netherlands).

Results

Twenty-eight E. coli strains (N=28) were successfully isolated. Fourteen E. coli strains were isolated from the stool samples, while 10 strains were isolated from the urine samples. Only 5 strains were isolated from the wound samples. All isolated strains were initially stained with Gram stain to confirm that they are negative. All isolated strains were then checked by API20E and other biochemical tests for confirming that they were E. coli.

As shown in Figure 1, doses of 100 and 200 µg/ml were found to be effective. The results showed that the majority of E. coli strains (60 per cent) were sensitive to the dose of 100 µg/ml, whereas remaining 40 per cent showed resistance to the same concentration. The dose of 200 µg/ml was found to be effective against the E. coli strains that were resistant to a dose of 100 µg/ml as shown in Figure 2.

The suspension from the tubes of 100 and 200 µg/ml was inoculated in nutrient agar plate and incubated for 24 hours. No growth of bacteria was observed that confirmed as bactericidal concentration. The presence or absence of turbidity was denoted as + or – respectively. As bacterial growth at different concentrations of AuNPs was assessed after 24 hours, the MIC and MBC of E. coli was observed maximum for a concentration of 100 µg/ml, and 200 µg/ml, indicating that it has both bacteriostatic and bactericidal activity (Figure 1).

Statistical analysis of MBC for different concentrations when assessed showed significant inhibition of growth for both 100 µg/ml and 200 µg/ml when compared to 0.09 to 50 µg/ml, however, the optimum MIC was obtained with 100 µg/ml. Therefore, these results confirm that the MIC and MBC of AuNPs can be effective at dilution of 100 µg/ml.

Conclusions

This study investigated the antimicrobial activity of AuNPs against E. coli isolated strains. The current study determined the MIC value by observing the turbidity to determine bacterial growth inhibition in the liquid. The results suggested that the sensitivity of E. coli strains against AuNPs in concentration 100 µg/ml was higher than 200 µg/ml at MIC.

NPs can be used either by combining it with various antimicrobials or capping material of NPs. Coating or capping of NPs often ensures an increase in biocompatibility and reduction of toxicity. The combination of NPs with other antimicrobials can help to reduce the intrinsic toxicity of NPs for mammalian cells. Therefore, the synthesis of AuNPs can be performed either by chemicals or biosynthesis techniques.

This study suggests that AuNPs showed strong antibacterial effect against E. coli strains. It can also be assumed that AuNPs have created immense interest in the field of medicine as an antimicrobial agent. The assessment of the potential toxicity of AuNPs remains a major concern before their clinical applications. However, further studies are required to determine the activities of AgNPs against the same E. coli isolates.

Point-of-care testing (POCT) is diagnostic testing performed close to the site of patient care. POCT uses small, portable devices to obtain rapid test results while the clinician is still examining the patient. The analysis uses small amounts of unprocessed blood and urine samples, does not require continuous power or purified water and can be performed by non-laboratory clinical staff with minimal training. POCT is simple and results are sufficiently reliable to meet medical needs for triage, diagnosis and management of patients in the field, outside of a laboratory. This allows for POCT to be performed on hospital wards and outpatient clinics without having to collect and transport specimens to a laboratory. The portability and rapid return of test results has found application of POCT in schools, charity or free clinics, athletic events, concerts, helicopter and air transport, ambulances, cruise ships, and remote settings such as military field hospitals, disaster relief, and expeditions. POCT has even been conducted on a space shuttle and Mount Everest.

Early healthcare featured a doctor who made house-calls to a sick child or injured farmer. A family would call the doctor who brought his black bag and medicines to diagnose and manage the patient in their home. With increasing numbers of patients in need of healthcare, it became more efficient for patients to visit the physician in an office or hospital for the management of illness. In this modern healthcare model, the patient must wait for an appointment and laboratory tests are ordered when the physician examines the patient. Blood is collected and the specimen is processed, transported to a laboratory for analysis, and test results are communicated back to the physician. Then, the patient waits to hear back from their physician for the test results and follow-up. This process delays laboratory results, impedes access to healthcare, and depersonalises the healthcare experience.

Recent regulatory changes in the United States allow patients the right to access personal health information under the Health Insurance Portability and Accountability Act (HIPAA). Together with changes to the interpretation of the Clinical Laboratory Improvement Amendments of 1988 (CLIA), patients or their representative can now access completed test reports. Prior to these changes, laboratory test results could only be released to the ordering physician. Kathleen Sebelius, the former United States Secretary of Health and Human Services, stated that “information like lab results empowers patients to track their health progress, make decisions with healthcare professionals and adhere to treatment plans”. In response, many hospitals and health systems have opened patient portals over the Internet. These portals provide patients easy access to personal health information and laboratory results.

Role of the patient

POCT promotes the personal role of the patient in their healthcare. For example, women concerned about pregnancy can purchase a test over-the counter at their pharmacy or grocery store without a prescription. The test can be conducted without a doctor’s appointment and privately without results being recorded in their medical record. Several products are now available for over-the-counter purchase such as tests for glucose, lipids, fertility, drugs of abuse and human immunodeficiency virus (HIV) infection. A challenge with over-the-counter POCT and home self-testing is the interpretation of results. In one study, 638 laywomen using 11 different pregnancy tests, falsely interpreted 230/478 (48 per cent) negative urine results as positive due to the difficulty of understanding explanatory leaflets and reading results. Review of 16 pregnancy tests found test instructions were written at the 7 – 10th grade reading level and the question and answer sections were written at the 11 – 14th grade level, while 7th grade literacy was the target. In addition, the typeset, graphics and paper size were too small for user friendliness. Of note, digitally read results offer more clarity over non-digital pregnancy tests that are manually interpreted.

Direct-to-consumer (DTC) testing facilities offer laboratory testing directly to the patient without a physician order. The menu varies by location depending on regional laws, but most facilities collect blood on-site, send the sample to a CLIA-certified laboratory for analysis, and either mail results to the patient or post results online. A full menu of tests such as paternity testing, health and wellness, drug and steroid testing as well as sexually transmitted disease tests are available without physician interaction. The advantage of direct-to-consumer testing is professional performance of the test. This allows access of patients to laboratory quality testing for disease prevention and management. However, the patient must still interpret the test result. This can be complicated. For example, fertility testing with follicle stimulating hormone (FSH) and luteinizing hormone (LH). These hormones are affected by stress, irregular eating, alcohol use as well as endocrine function. Results from either home POCT or direct-to-consumer FSH and LH may require follow-up tests such as progesterone and oestrogen to diagnose menopause. In this case, test results are best interpreted in conjunction with past medical history and physical examination by a clinician rather than by a consumer without medical training.

When patients need non-acute healthcare (not warranting an emergency department visit), but can’t get an appointment at their own clinic, a pharmacy clinic may be an option. A clinician, generally a nurse practitioner, examines the patient, orders the appropriate test and even conducts a range of POCT in the clinic. Results can then be interpreted by the clinician on-site in conjunction with the patient’s physical symptoms and medical history. This option offers patients a way to receive the care they need all from one place. When a child wakes up with a sore throat, the parent can take them to a pharmacy clinic, have the child examined, get rapid strep POCT and leave with prescribed antibiotics all in the same visit. The pharmacy clinic streamlines care and minimises the amount of time the parent must take off from work, the child missing school and limits the illness and duration of symptoms by receiving prompt treatment.

Personalised experience

There is now a wave of healthcare on the rise, doctors making house calls! This may not be new, as our predecessors relied on doctor house-calls for care in the past, but there is a current resurgence. This time, the doctors come equipped with POCT devices to provide on-site testing as well as a short supply of drugs to treat common conditions. These on-call services can be accessed through mobile phone applications or through a hotel concierge, if traveling. A nurse practitioner will call back within the hour and come directly to the patient’s home, office or hotel to provide non-emergent focused assessment and triage the patient. A doctor house call provides a more personalised healthcare experience than current clinic or hospital visits. For patients who are frail and/or have limited mobility, house calls provide safe access to healthcare. Additionally, healthcare providers equipped with POCT devices can travel to remote areas to provide healthcare services to patients without the means for transportation to a clinic or hospital.

POCT devices with wireless or cellular capabilities can link test results with the patient’s electronic medical record. This ensures dissemination of results across their healthcare team. The future of POCT may take the form of smartphone attachment/readers to provide automated test interpretation and data transmission. Devices with glucose readers can analyse the test and phone results to the physician. Digital pregnancy tests are available that can call a patient’s phone with the test interpretation. Even microscope lenses have been attached to phones to provide inexpensive POCT devices for developing counties. Watches, wearable devices and smart contacts and patches are in development that can connect to other digital health applications.

The growth of social media provides even more personalised healthcare with the growing opportunity to access digital health resources. Social media can be used to promote disease state discussions, disseminate peer published literature and exchange professional education. Patients with the same disease can connect and share experiences through blogs, Facebook, Twitter, Instagram, and other social media sites.

Patients can seek laboratory test information from sites such as LabTestsOnline.org, a website by the American Association for Clinical Chemistry (AACC), which provides information on laboratory tests and is currently available in 14 countries and in 12 languages. As the availability of POCT and direct-to-consumer testing expands, trusted resources on laboratory testing will be in high demand.

POCT is at the cusp of a digital revolution merging laboratory diagnostics with information technologies. As POCT grows, the technology will expand to new locations finding greater applications for improving healthcare. This is an exciting time for POCT innovations that are yet to come.

References available on request.

In 2018, Lucile Packard Children’s Hospital (LPCH) at Stanford welcomed two siblings with Schimke immuno-osseous dysplasia (SIOD), an extremely rare form of dwarfism that affects just seven children in the U.S. The two siblings, Kruz and Paizlee Davenport, are the first brother and sister pair with SIOD in the country to become ambassadors for the condition.

“There was a one in 80 million chance both Kruz and Paizlee would have SIOD,” says their mother, Jessica. Today the siblings are both being treated at LPCH, where they are receiving a ground-breaking treatment combining stem cell and kidney transplant from their HLA-partially matched parents. This innovative approach, which eliminates the use of post-transplant immunosuppressive drugs, is being overseen by a multidisciplinary team led by the author.

Children with SIOD have a life expectancy of 11 years and normally experience conditions such as kidney failure, severe T-cell deficiency, and hip dysplasia. So far, only five patients in the world have been reported to receive both a stem cell and kidney transplant. Four patients died because of post-transplant complications including severe graft-versus-host disease (GvHD). The fifth patient, Kruz, has fully recovered from a living donor kidney transplant that took place in July 2019, five months after the stem cell transplant where his mother Jessica was the donor. Paizlee, the younger sister, is now three months out of a paternal stem cell transplant and will receive the kidney from the father early next year.

These siblings are benefiting from a method of graft engineering pioneered by the author that makes it safer to receive stem cells from a donor who does not have an exact HLA match. In fact, to overcome the mismatch between donor and recipient, a novel method of ex vivo T- and B-cell depletion based on the selective elimination of αβ+ T cells (the lymphocyte subset responsible for GvHD occurrence) was implemented. After this manipulation, the graft contains not only hematopoietic stem cells (i.e. CD34+ cells), but also large numbers of effector cells such as Natural Killer (NK) cells and gd T cells. These lymphocyte’s subsets, promptly available after the transplants infusion, can effectively control severe infections and leukaemia recurrence. Using this graft manipulation’s strategy, the extensive depletion of αβ+ T cells (up to 5 logs) abrogates the risk of severe GvHD.

The Davenport family

Thanks to this approach, a donor is virtually available for every patient in the need of a transplant. A fully matched related donor (sibling), is present in the family for only 25 per cent of the patients, and less than the remaining 60 per cent can allocate a suitable unrelated donor in an acceptable frame time. The likelihood of finding an optimal donor, varies among racial and ethnic groups, with the probability of identifying an appropriate donor being highest among whites of European descent (75 per cent) and lowest among blacks of South or Central American descent (16 per cent). Thus, there is an urgent clinical need for more broadly applicable hematopoietic stem cell transplant (HSCT) methods that can reach a wider range of patients. Such a development has a major medical impact in patients lacking a fully matched donor, a situation that is especially true for ethnic minorities.

However, this is not only a donor’s matter. The fast and robust neutrophil and platelet engraftment (13 and 11 days, respectively), the absence of the need of post-transplant pharmacological GvHD prophylaxis as well as the negligible risk of chronic GvHD, render this approach ideal for indications previously not for candidates for transplant due to the high burden of complications. SIOD is just one of the multiple different diseases that now can be treated with an αβ haplo-HSCT. Remarkably , the exciting result of this pilot experience combining stem cell and kidney transplantation from the same donor, has laid the foundation at Lucile Packard Children’s Hospital for expanding the use of αβ  haplo-HSCT to diseases routinely not for candidates for allogeneic HSCT, to patients in very poor clinical condition and in the need of living donor solid organ transplant.

What about αβ haplo-HSCT and malignancies?

Peter, now 15, has been a lifelong patient at Lucile Packard Children’s Hospital. He needed a heart transplant at the age of two, and suffered from chronic ear infections, a broken leg, repeated bouts of pneumonia, and a challenging genetic lung disorder (primary ciliary dyskinesia). At the age of eight, he developed a very rare cancer — angioimmunoblastic T-cell lymphoma.

Standard cancer treatments were not working on Peter’s lymphoma and he suffered multiple relapses. He needed a stem cell transplant but having already received a heart transplant made it more difficult to introduce another immune system from a separate donor. In 2017, Peter received an αβ haplo-HSCT from his mother.

Today—two years later—Peter’s a sophomore in high school who enjoys playing video games with friends, practicing for his driving test in parking lots, excelling at school, and thinking about his future career and personal interests. And for the first time, his lymphoma is on molecular remission.

Despite marked improvement in the treatment of children affected by haematological malignancies with chemotherapy, a significant proportion of patients still require HSCT. Haploidentical transplantation opens the possibility to offer this treatment to every child in need of an allograft lacking an HLA-matched sibling, a matched unrelated donor, or a suitable umbilical cord blood unit. However, early attempts at haploidentical HSCT in leukaemia patients were associated with high rates of graft rejection and GvHD, leading to high transplant-related mortality and, consequently, poor survival. In the last two decades, novel insights in transplant immunology, continuing advances in graft-manipulation technology, and improved supportive care strategies have led to significantly better outcomes, so that, with further refinements, it is possible that haplo-HSCT become the preferred transplant option for children with hematologic malignancies without an HLA-identical relative.

In order to remove T cells, responsible for GvHD, and B cells, from which post-transplant lymphoproliferative disease (PTLD) can arise, positive selection of CD34+ hematopoietic stem cells (HSCs) has been employed for many years in haplo-HSCT. Although the administration of CD34+ cell megadoses has been demonstrated to be a valid approach for preventing both graft failure and severe GvHD in haplo-HSCT recipients, removal of lymphoid cells and committed hematopoietic progenitors from the graft cause prolonged lymphopenia and delay immune reconstitution, resulting in an increased risk of non-relapse mortality (NRM), mainly from opportunistic infections. In order to circumvent this delay in immune recovery, in 2010, a more sophisticated method of graft manipulation based on selective depletion of ab T lymphocytes, and of B cells (ab haplo-HSCT) was developed. This refined technique of graft engineering reduces the problems associated with delayed immune recovery, which is typical in the CD34+ cell selection approach. Indeed, using ab haplo-HSCT, it is possible to transfer to the recipient not only donor hematopoietic stem cells but also committed hematopoietic progenitors, as well as mature NK and gd T cells.

These lymphocyte subsets may provide a protective effect against both leukaemia relapse and severe infections. Human gd T cells orchestrate both innate and adaptive immunity and, unlike ab T cells, recognise tumours in a MHC-independent manner without causing GvHD, giving them immense clinical appeal. Both NK and gd T cells exert a potent antileukemia effect able to prevent the risk of relapse after HSCT.

The author recently validated her single-centre results in a multi-centre setting, conducting a retrospective comparative analysis within 13 Italian centres. Evaluating 245 matched unrelated donor (MUD) and 98 ab haplo-HSCT recipients we demonstrated that: first, this approach is associated with a cumulative incidence of NRM and disease recurrence comparable to that of children transplanted from a fully MUD. Second, ab haplo-HSCT abrogates the risk of developing severe acute GvHD and is also associated with a faster neutrophil and platelet recovery than MUD-HSCT. Finally, when compared to a mismatched unrelated donor HSCT, αβ haplo-HSCT is clearly superior, showing a significantly lower NRM and a better chronic GvHD-free/relapse-free survival (GFRS).

In light of all these considerations, αβ haplo-HSCT represents the ideal platform for post-HSCT adoptive immunotherapy for treating either malignant or non-malignant disorders.

References available on request.

Invasive aspergillosis (IA) is a severe and often life-threatening infection in immunocompromised individuals. Early diagnosis is critical to enable prompt initiation of treatment and prevention of fatalities. Alongside culture and microscopic investigation, detection of Aspergillus antigens in body fluids is a mainstay of laboratory diagnostics for IA. The detected antigens are polysaccharides or glycoproteins of the cell wall, which are produced during active fungal growth. A newly developed ELISA provides quantitative detection of Aspergillus galactomannoprotein in serum or fluid from the lungs. With the option of semi-automated processing, it is especially suitable for high-throughput IA screening in high-risk persons.

Aspergillosis

Aspergillus spp. are ubiquitous sac fungi which are found, for example, in soil, compost or on damp walls. Almost 200 species have been described, of which A. fumigatus, A. terreus, A. flavus, A. niger and A. nidulans are considered to be human pathogenic. Aspergillus forms single-cell spores, which are spread through the air and are not very susceptible to environmental factors. Their concentration is especially elevated in summer.

In individuals with an intact immune system the inhalation of spores does not generally cause any health problems, although a permanent load can lead to sensitivity or allergic reactions. Immunocompromised individuals, however, cannot mount an adequate immune response, resulting in severe or even life-threatening infections. The spores are deposited in the lung tissue, germinate and grow hyphae. These penetrate the tissue and spread to other parts of the body via the blood. This disseminated form, known as IA, represents the most severe clinical manifestation of Aspergillus infection. It most often affects the nervous system, eyes, heart, kidneys and skin. The symptoms, which are often unspecific at onset, encompass cough, breathing difficulties, dyspnoea with hypoxia, sustained fever of longer than 72 hours, lung infiltration under antibiotic treatment, pain in the chest and haemoptysis. An estimated 200,000 to 300,000 cases of life-threatening IA occur each year worldwide. Mortality is high, ranging from 40 to 95 per cent depending on the immune status of the patient, distribution of the infection and drug treatment. IA with involvement of the central nervous system is nearly always fatal.

High-risk patients

Persons with neutropaenia, leukaemia, or chronic granulomatous disease (CGD) are considered at high risk of IA. It is also a frequent life-threatening complication in advanced stages of AIDS, during chemotherapy or following bone marrow or organ transplants.

The incidence of IA amounts to 40 per cent in CGD patients and 12 per cent in AIDS patients. Seven to 13 per cent of allogenic bone marrow transplant recipients develop IA, either after two weeks of aplasia or around three months later during immunosuppressive therapy. IA also affects 0.5 to 8 per cent of autologous transplantation patients during neutropaenia, as well as 2 to 3 per cent of organ recipients, with lung transplant patients at highest risk. Further risk factors for IA are chronic lung diseases, CMV infection, tumours and autoimmune diseases. In recent years an increased number of nosocomial infections in patients in intensive care has been observed.

IA diagnostics

IA can be difficult to diagnose due to the unspecific initial symptoms such as fever or inflammatory reactions. Diagnosis is based on a combination of clinical manifestation, radiology, culture, microscopy and serology. Since clinical and radiological signs of IA are often non-specific, laboratory tests are nearly always required to substantiate diagnosis. Culture of Aspergillus spp. in combination with histopathologic evidence of tissue invasion by hyphae provides a definitive evidence of IA. However, culture is time-consuming and has a success rate of only around 50 per cent, and biopsy is not always feasible due to the risk of complications. In vitro detection of antigen biomarkers provides a first-line, non-invasive method to screen patients for IA.

Antigen detection yields results much faster than culture and is thus particularly helpful for early diagnosis. For this reason it has been incorporated into guidelines of the European Organization for Research and Treatment of Cancer (EORTC) and the National Institute of Allergy and Infectious Diseases Mycoses Study Group (MSG) as a criterion for probable IA. In high-risk patients regular analysis of blood for Aspergillus antigen is recommended. Polymerase chain reaction (PCR) is not recommended for routine IA diagnostics as there are few standardised test systems and PCR does not allow differentiation between non-invasive and invasive infections.

Aspergillus Antigen ELISA

The new CE-marked Aspergillus Antigen ELISA from EUROIMMUN provides detection of extracellular galactomannoprotein of different growing Aspergillus spp. The analysis is performed on patient serum or bronchoalveolar lavage (BAL) fluid samples. Results can be evaluated either quantitatively in pg/ml using a 6-point calibration curve or semiquantitatively by means of a cut-off ratio. The test can be semi-automated, for example, on the EUROIMMUN Analyzer I, increasing the efficiency of screening.

Sensitivity and specificity

In the most comprehensive clinical study to date based on 120 sera from 45 patients with proven IA as well as control sera, the EUROIMMUN Aspergillus Antigen ELISA yielded comparable sensitivity and specificity to another commercially available Aspergillus antigen test (Platelia Aspergillus Ag Assay, Bio-Rad). The EUROIMMUN assay identified 56 per cent of the cases, while the Bio-Rad test detected 47 per cent (Table 1). The specificity amounted to 97 per cent for the EUROIMMUN assay and 99 per cent for the Bio-Rad test. To overcome the relatively low sensitivity of Aspergillus antigen detection, the author recommends serial testing of patients at risk. Overall, the positive predictive value of the EUROIMMUN Aspergillus Antigen ELISA was 77.8 per cent for serum and 86.2 per cent for BAL, while the negative predictive value was 92.9 per cent for serum and 95.2 per cent for BAL.

Perspectives

In recent decades the incidence of IA has surged due to the rising number of patients undergoing organ and stem cell transplantation or aggressive cancer chemotherapy regimes. Given the growing population of chronically ill and elderly individuals, the burden of IA will likely increase further in the future, including in critical care settings. Deciding on the appropriate antifungal medication, dose and duration relies on fast and reliable diagnosis. Aspergillus antigen detection has been integrated into diagnostic algorithms and is now employed in most medical centres in Europe for routine diagnostics and surveillance of high-risk patients. The antigen test can also play a role in trials of antifungal drug efficacy, strategy trials and epidemiological studies.

Digital pathology is changing the practice of pathology. The initial benefits cited for digital pathology include educational and archival use cases. Additionally, creating digital representations of glass slides transforms the morphologic features contained on the physical glass slides into digital data. This allows for easier collaboration and increased access to the pathology material. All of these factors represent improvements to the traditional tasks that are performed by pathologists in generating diagnostic reports for our patients. The conversion of glass slides (analogue data) to whole slide images (digital data) represent a new opportunity to provide enhanced diagnostic reports. Quantitative image analysis represents one of the new opportunities especially given the recent development of immune based therapy for malignancies.

Prior efforts for quantitative image analysis have principally involved breast cancer – specifically quantification of oestrogen receptor, progesterone receptor and HER2 expression. This image analysis has been limited by technical restrictions. Previously, only small fields could be analysed. This limitation required that a pathologist select a region to be analysed. In this workflow, a mundane task of selecting a field is dependent on the pathologist. Although selecting the field is a minor task, the pathologist represents a bottleneck in efficiently getting the tissue analysed. An additional by-product of having the pathologist select a field is that the pathologist can just simply interpret the results manually. If the digital assessment does not provide substantial improvement over the manual assessment, it is very difficult to push adoption. Oestrogen and progesterone receptors are both nuclear immunostains that represent relatively simplistic stains to interpret, however, HER2 is a membranous protein and the intensity of the immunostain is critical in the interpretation. Additionally, breast carcinoma is often intermingled with benign tissue elements that must be separated from the carcinoma for report purposes. This intermingling causes difficulty with tumour selection and impedes the use of digital quantification. However, increased billing revenue has been attributed to the digital quantification and so some institutions have begun using digital quantification for reporting out results.

Proliferative index of malignancies can be assessed by Ki-67 immunostain – neuroendocrine neoplasms represent the most common tumour type that utilises this for prognosis. Although this is a nuclear stain, it is still largely reported out by “eyeball” method. The reporting interpretations do not require a fine assessment beyond “eyeball” method and so this has become the practice. Within the last few years, technical advances have been made that allow for whole slide image analysis to be performed. Additionally, artificial intelligence algorithms are being developed that may minimise the need for a pathologist to perform field selection before image analysis can be performed. These advancements are making quantitative image analysis a valuable reporting tool for pathology departments.

At the University of Pittsburgh Medical Center, we were interested in providing a quantitative CD8 cell count for our oncologists. We were approached to develop quantitative image analysis for CD8 cells within oropharyngeal squamous cell carcinoma. We began with a tissue microarray (TMA) from multiple tumours. We performed a CD8 immunostain on the TMA tissue section. Each core was manually counted, and this was used to assess the reliability of quantitative image analysis for the cores. The image analysis algorithm was optimised, and the parameters of the image analysis algorithm were fixed. Next, this same algorithm was used to quantify CD8 concentrations within a TMA section that had been previously quantified using a different method (AQUA). The results provided a similar predictive ability between the two methods. Based on these findings, we then decided to use the same quantitative algorithm on whole slide CD8 immunostain sections from a population with known outcomes. Based on the outcomes of the population, we were able to establish cut-offs associated with good vs poor prognosis. The unique aspect of this analysis was that we performed it on a whole tissue section – no fields for selection were necessary.

This represents a major improvement in the workflow because it does not require a pathologist to intervene prior to performing the image analysis. This however does not remove the pathologist from the process. We generate a procedure for performing the analysis and then an image analysis procedure report is signed out by a pathologist. This sign-out pathologist has the obligation to review the analysis for accuracy and confirm the results. The number of cells quantitated in this method can reach 10-100 thousand cells. This amount of cells would be impossible to count by manual (analogue) method. We have elected to divide the number of cells by the area analysed in order to generate the density of the CD8 inflammatory infiltrate. Providing the area measurement is also a task that is difficult by manual (analogue) methods. Historically, a high-power field is often recommended but variability within microscope components has added more complexity to using this standard measurement area. It is very easy to generate measurements from digital slides. Therefore, the only method that we could use to generate our results was to use automated image analysis. By using the entire tissue section, we have also eliminated selection bias introduced by choosing fields to analyse.

We have continued working to expand our image analysis in other types of malignancies. Many papers have been published about the quantification of CD8 cells in colon carcinoma. We have begun work on using quantification of CD8 cells within colon carcinoma. The system that we use for image analysis is a commercially available system that provides the image analysis tools and so we feel that our work for quantifying CD8 cells represents a more generalised method than those published previously. Although it is unlikely that every pathology department will perform quantitative image analysis, the technology should only be available from a few commercial entities. We have also worked to expand quantitative image analysis of CD8 cells in other types of solid malignancies.

Quantifying CD8 cells was initially described as a positive prognostic factor and our initial study found similar findings. However, immune based therapies are now being used for solid malignancies and the quantification of CD8 cells within the solid malignancies may have relevance to using immune based therapies. We continue to explore the possible role that quantification of CD8 cells may have in immune based therapy – either in determining the type of therapy to use or response to therapy. Although CD8 has an expansive amount of literature about its relevance in solid malignancies, there may be other immune cells that have relevance in predicting immune based therapy. These immune cells may already be known, or they may yet to be discovered. Given the nature of the immune system, digital quantification of the cells will be critical to using these cells in diagnostic reporting.

Immuno-therapy is changing the practice of oncologic treatment. As more immunotherapies are released, it will become important to assist our clinical colleagues with deciding which therapy to use in order to optimise patient outcomes. We have developed an automated quantification method for CD8 cells on whole tissue sections that can aid in the prognostic assessment of solid malignancies. We are using this same method to further evaluate whether it can be used in prediction of the types of therapies that should be used for solid malignancies.

The 18th edition of the recently concluded Medlab Exhibition & Congress 2019 generated a total business of US$ 152 million and proved to be an instrumental platform for business exchange and education. His Excellency Humaid Al Qatami, Director General of the Dubai Health Authority (DHA) inaugurated the event, which is the MENA region’s largest medical laboratory exhibition and congress. Organised by Informa Markets – Healthcare, the event was officially supported by the UAE Ministry of Health, Government of Dubai, DHA, Health Authority Abu Dhabi, and Dubai Healthcare City Authority.

The event hosted 608 exhibitors, 51 exhibiting countries, 13 country pavilions, 4,673 delegates and 25,661 professional visits. The four-day event provided the MENA medical laboratory industry a platform to build relationships with international stakeholders and saw clinical laboratory manufacturers from around the world display their latest devices, equipment, innovations and solutions. It enabled companies to showcase progress and achievements in the sector, as well as make the most of new business opportunities in the global medical laboratory field.

A survey at the show found that 97 per cent rated the exhibition as an important platform for their business, 94 per cent will be exhibiting again in 2020, 89 per cent considered the show a success, while 80 per cent of exhibitors were seeking new contacts for future business.

At the show, visitors were able to explore some of the latest laboratory medicine solutions, including diagnostic tests, reagents, disposables and equipment. The event welcomed thousands of medical laboratory professionals right from purchase managers responsible for negotiating supplier contracts, to leading manufacturers, to distributors and trade professionals in search of equipment worldwide to support their clients needs and budgets.

Industry update

According to a report by Colliers International, the MENA region’s clinical laboratory services market is estimated to be worth between US$8 to 10 billion, with approximately 70 per cent of doctor’s decisions regarding a patient’s diagnosis, treatment, hospital admission and discharge, being based on laboratory test results.

The research predicted that the clinical laboratory services market in MENA is expected to grow at a Compound Annual Growth Rate (CAGR) of 6 to 8 per cent until 2025 in line with global projections. Furthermore, figures from the report titled “Clinical Laboratory Services in the MENA Region 2019” and published as part of the Medlab Market Report series, revealed the number of inpatients and outpatients in the UAE (Abu Dhabi and Dubai) is expected to grow from 29.3 million in 2016 to 95.3 million in 2030. Similarly, the UAE is projected to have a bed capacity of 25,300 by 2030, up from 12,500 in 2016.

Reportedly, in Dubai, public sector healthcare facilities conducted around 25.5 million tests in 2017 while the private healthcare sector performed over 6.2 million tests during the same period.

Focus on automation

Laboratory automation, which involves the use of technologies such as robotic devices to achieve greater efficiencies when carrying out diagnostic testing on humans, emerged as a major trend at Medlab 2019. Clinical laboratory automation plays a major role in reducing avoidable human error and diagnosis delays as well as improving turnaround time, increasing productivity, effectively utilising resources and enhancing patient safety.

Commenting on the role of automation in the clinical laboratory on the sidelines of the event, Dr. Shakoor Malik, Chief Scientific Officer, Pure Health, said: “Automation has moved from “nice-to-have” for large reference laboratories to “must-have” for any clinical laboratory. Automation, robotics and laboratory information systems are dominating the industry, which in return leads to quick and independent sample testing and reporting with a reduction in operational costs.”

The Laboratory Management conference featured talks that stressed that as more and more money is being spent on introducing newer technologies in clinical laboratories, automation is expected to present several benefits, including improving resourcing and enhancing the accuracy and efficiency of laboratory operations.

Dr. Shakoor Malik, Chief Scientific Officer, Pure Health, said: “Automation has moved from “nice-to have” for large reference laboratories to “must-have” for any clinical laboratory."

Multi-disciplinary congress

With 11 conferences and more than 4,673 delegates and 120 international and regional speakers, Medlab Middle East Congress is the only multi-disciplinary CME accredited medical laboratory congress in the region.Day one of the Congress saw the launch of its inaugural Artificial Intelligence (AI) Conference. Exploring the potential for AI to transform the medical laboratory industry in the UAE, the conference also assessed how diagnosis can be revolutionised through futuristic tech such as data robots and “bloodless blood tests”.Speaking during the conference, Dr. Alain Pluquet, Director of Innovation, Institut Merieux, Lyon, France, said: “AI is playing a huge role in microbiology and there are several commercial applications that are already bringing medical and economic benefits to the industry.”

The Immunology Conference also made its debut at the show. The conference provided a space for discussion on the latest trends and important issues concerning the widespread utilisation of immunological techniques for healthcare advancement. Regional and international pioneers within immunology and relevant fields presented their research and shared novel case-based knowledge throughout the exclusive scientific programme.

Other featured topics included microbiology, molecular diagnostics and genetics, laboratory informatics, haematology and blood transfusion, point of care testing (POCT) and cytogenetics and IVF.

A report titled “In Vitro Fertilization (IVF) & Fertility in the MENA region”, by Colliers International, revealed that compared to 10 per cent worldwide, infertility in the MENA region is 15 per cent or higher, with male infertility a growing problem occurring in approximately 50 per cent of the cases in the GCC and Middle East due to lifestyle, diabetes, obesity and genetics related factors, as GCC countries have one of the highest diabetic and obesity rates in the world.

According to the report, new innovations and improved testing techniques are gradually creating paradigm shifts in the field of assistive reproductive technology. These were visible at the Cytogenetics & IVF conference that shed light on topics such as pre-marital screening for consanguineous (relatives) couples and the development of new genetic tests for screening of the embryos that can greatly improve the chance of minimising certain genetic diseases common in this region.

The conference also emphasised advances in molecular cytogenetic diagnostic tests, which includes karyotyping, Fluorescence in situ hybridization (FISH) testing and advanced Chromosomal Microarray testing (CMA) to help improve services in the growing number of fertility centres and genetic labs in the region.

In the clinical laboratory, Chemistry and Haematology departments have been the earliest to adapt robotics and algorithms into its workflow. As early as 1984, the “EXPERT”, a consultation system-building tool, which is a knowledge-based Artificial Intelligence (AI) programme was developed at Rutgers University for enabling sequential laboratory testing and interpretation. 

AI technologies are now commonly termed ‘knowledge engineering’ and the intelligent computer software that embodies knowledge are called ‘expert systems’. Because it has been rather difficult to develop practical applications of automatic learning, expert systems often don’t include the ability to learn by themselves. Nevertheless, such expert systems are able to make decisions based on the accumulated knowledge with which they are programmed and are therefore commonly included within the definition of AI systems. An ever-increasing number of publications in the area of AI show the increasing interest and scope of its application in healthcare. A quick search of Pubmed reveals almost 83,000 publications related to AI in healthcare over the past few years. 

With the staggering increase in volume of patient healthcare data, a constant increase in patient expectations and scarcity of resources, AI will be the engine driving improvements across the care continuum. Doctors will adapt to and use AI technology in their day-to-day work. Nurses and other healthcare workers augmented by AI can deliver a higher level of care for a larger populace. 

With rapid advances in pathology such as paradigm shifts in digital pathology, next-gen sequencing, precision medicine and personalised treatments, pathologists will be the first point where clinical decisions will be made. Computational Pathology applies to computational models, machine learning and visualisations to make the lab output both more useful and easily understood for the clinical decision maker. Computational pathology has clinical value in all aspects of medicine via a focus on computational methods that incorporate clinical pathology, anatomic pathology (including digital imaging), and molecular/genomic pathology data sets. 

Continuous remote sensing of patients using “wearables” such as glucose monitoring devices, oximetry, temperature, heart rate and respiratory rate monitors connected to a central computing device via the ubiquitous “Internet of things” will be the norm, with AI aided “ambient computing” changing the way futuristic patient care will be provided. Prediction of sepsis is an important diagnostic conundrum where early appropriate therapy can save lives. A randomised controlled trial by Shimabukuro et al at the UCSF Medical Centre in 2017 used a machine learning-based predictor, which resulted in significant decreases in length of stay and in-hospital mortality rate. The study demonstrated the superiority of using an algorithmic predictor relative to the hospital’s current Electronic Health Record native, rules-based, severe sepsis surveillance system. AI enhanced microfluidics and compact small interactive POCT labs are set to alter the way diagnostics is carried out. An example is the “Maverick Detection System” from Genalyte. Using biological probes bound on silicon biosensors chips, it binds macromolecules in the serum, the binding of which detected by a change in light resonance, which is determined photometrically. They plan to detect up to 128 analytes using disposable chips from a single sample. 

Tumour DNA changes are important since it influences therapy. Following Next-Gen Sequencing, large amounts of genomic data are generated, which are then analysed by a combination of computational tools and human experts to understand the types of genetic mutations present in the tumour. These act as a guide to prognosis and personalised therapy. Newer methods of analysis involve machine learning, which automates the tumour DNA diagnostic process and improves the accuracy of that identification as compared with existing techniques enabling accurate prescription of mutation-specific therapies. 

Today’s clinical labs are already using advanced robotics to test minute volumes of blood, serum and other body fluids from thousands of samples in a day to give highly accurate and reproducible answers to clinical questions, in scales almost difficult to emulate humanly.

These machines are driven by conventional algorithmic programmes, which represent and use data, iterate repetitively and exhaustively using a decision sequence, using numbers and equations, finally presenting a number or result within confidence limits. In the future, robots used in the clinical laboratory will be heuristic (self-learning), using inferential processes, with numerous ways to derive the best decision possible even allowing for missing information. Artificial Intelligence programmes combined with data bases, data mining, statistics, mathematical modelling, pattern recognition, computer vision, natural language processing, mixed reality and ambient computing will change the way our laboratories generate and display clinical information in the future. 

The Future 

Pathologist augmented with AI is the future. AI will help leverage human knowledge, wisdom, and experience. Findings suggest that instead of replacing doctors, AI algorithms might work best alongside them in healthcare. AI and machine learning software are beginning to integrate themselves as tools for efficiency and accuracy within pathology. Software is being developed by start-ups, often in tandem with prominent educational institutions or large hospital research laboratories, addressing different diseases and conditions, most notably forms of cancer. A review of the functionalities of AI and machine learning software in the field of pathology reveal predominant usage in whole slide imaging analysis and diagnosis, tumour tissue genomics and its correlation to therapy, and finally companion diagnostic devices. The ICU of the future will have AI programmes, which will concurrently evaluate the continuous streams of data from multiple monitors and data collection devices to pool their information and present a comprehensive picture of the patient’s health to doctors autonomously adjusting equipment settings to keep the patient in optimal condition. 

I would like to conclude by quoting a concept on “Singularity” by Ray Kurzweil dated to occur by 2045. Technological singularity is a hypothesis that AI will trigger logarithmic technological growth, resulting in unfathomable changes to human civilisation. The changes to healthcare and longevity once we attain “Singularity” is beyond our current understanding. 

He said: “2029 is the consistent date I have predicted for when an AI will pass a valid Turing test and therefore achieve human levels of intelligence. I have set the date 2045 for the “Singularity”, which is when we will multiply our effective intelligence a billion-fold by merging with the intelligence we have created.”

Rising medical costs have become a huge social burden throughout the world, having grown four to five times on average during the past three decades. The situation in the U.S. has been the most extreme with eight to nine times in growth rate, translated into total medical costs that totalled $3 trillion, amounting to 17.8 per cent of the gross domestic product in 2015. 

The two main causes of high medical costs are one, the high price of medical services, and the high number of diagnostic tests performed. As a good example, in 2006 the total number of CT scans taken in the U.S. was 62 million, compared to three million taken in 1980, a 20-fold increase in just 25 years. 

The issue with overutilisation of diagnostic tests is truly remarkable. Up to 30-50 per cent of diagnostic tests in the U.S. is considered to have been unnecessary according to various reports. There are various reasons for this, but the bottom line is there is lack of objective guidance for these new diagnostic tests, and due to the fee-for-service payment system and defensive medicine associated with the medical liability environment in the U.S., it’s only natural diagnostic tests are often overutilised. 

Government policies, exemplified by the Patient Protection and Affordable Care Act of 2010, play an important role in shaping healthcare expenditure. Beyond policy, a potential solution to reduce medical costs is to use informatics to provide quantified objective guidance, as suggested by the Institute of Medicine of the National Academy of Sciences. 

Biomarkers Validated to Save Medical Costs

Genomic Health’s Oncotype Dx and Heartflow’s CT-FFR are both good examples of biomarkers that have been successfully commercialised, reimbursed both by private and public insurance in reference to extensive evidence supported by favourable results from various prospective clinical studies. 

Oncotype Dx is a molecular test based on genomic analysis that predicts individual response to chemotherapy in early breast cancer patients. Because historically around 50 per cent of hormone-receptor-positive early breast cancer patients received adjuvant chemotherapy in which only two per cent would benefit from the therapy, there has been a significant cost-inefficiency involved. Studies show application of Oncotype Dx reduced approximately 60 per cent of unnecessary chemotherapy. 

Heartflow’s CT-FFR is the only image-based analytics widely accepted as a biomarker on the market that predicts fractional flow reserve values through analysis of coronary CT angiography images based on computational fluid dynamics. Among stable angina patients, approximately 60 per cent of patients undergo invasive coronary angiography to evaluate the coronary vessels, in which only a small minority are found to have significant coronary stenosis subject to revascularisation. According to various studies, Heartflow’s CT-FFR helped avoid unnecessary invasive angiograms in 61 per cent of patients. 

AI-powered Imaging Biomarkers in Radiology Can Save Medical Costs 

The high-performance level achieved by newly developed AI-powered data-driven solutions can be attributed to the power of deep learning technology. Semi-supervised learning has been mainly applied, in which only a small proportion of trained data are annotated by experts to guide the training process, in turn allowing the AI algorithm to discover subtle image features and patterns hardly recognised through ordinary human vision associated with target lesions to be detected, interpreted, and diagnosed. 

In Lunit, a Seoul-based start-up company, we believe AI-powered imaging biomarkers that accurately evaluate and generate objective quantification for specific tasks of imaging, e.g. breast cancer assessment in mammography, are a viable solution to significantly reduce medical costs. Lunit’s research and development efforts involve a wide variety of image modalities that include chest x-ray, mammography, chest CT, digital breast tomosynthesis, coronary CT angiography, as well as digital pathology, in which accuracy levels have reached 97-99 per cent in ROC AUC, significantly exceeding expert-level accuracy. 

Among the 40 million mammograms performed each year in the U.S., on average around 10 per cent are recalled for a subsequent cancer detection rate of around five per cent, meaning a 95 per cent false positive rate. It can be estimated that from these unnecessary recalls, around $4 billion is wasted each year. Lunit’s mammography product, Lunit INSIGHT MMG, claims to be 98 per cent accurate (ROC AUC), and depending on the level of trust ultimately given by radiologists, the recall rate is expected to be significantly reduced by more than 50 per cent. If Lunit INSIGHT MMG is applied to all mammograms taken annually, this may lead to over $2 billion medical costs saved per year in the U.S. alone. Similar estimations for all products currently being developed by Lunit leads to an approximation of more than $20 billion in medical cost potentially saved per year by Lunit’s products combined. 

Digital Pathology: A New Promising Frontier 

A great majority of research in medical image AI has been focused on radiology images, but with recent technological advances that enabled high throughput scanning of pathology slides, AI applied to digital pathology images has garnered much interest in recent years.

The digitisation of tissue slides marks the inception of a new era when a myriad of new information will be at disposal for pathologists. This is especially true because pathology slides entail vast amounts of data, involving 10+ gigapixels when digitised at 40x magnification. Historically pathologists, responsible to review such slides through microscopes, were forced to simplify the overall characterisation of the tissue for consistent description that needed to be accurately conveyed and perceived. Even so, the discordance rate between pathologists has been reported to be high, ranging from four per cent for clearly cancerous cases like invasive carcinoma to 48 per cent for marginal cases like atypia. 

Through AI-powered comprehensive, consistent, and quantitative analysis of digital pathology slides, diagnostic, prognostic, and predictive histomorphological features that have not been previously characterised may bring unprecedented clinical value. H&E slides, historically used to simply detect the presence of disease (e.g. cancer or not), and classify the basic types of disease (e.g. adenocarcinoma vs. squamous cell carcinoma), may be used to collect more information pertinent to even making treatment decisions, such as whether a cancer patient may respond to chemotherapy or not, hence functioning as imaging biomarker. 

In fact, histomorphological features based on traditional pathology and AI-based analysis have both been shown to reflect the biological nature of cancer, and highly predictive of survival. Lunit’s preliminary research has demonstrated high level of correlation (R>0.7) between AI-powered analysis of H&E slides with RNA sequencing data of genes related to tumour proliferation, which is the foremost factor predictive of chemotherapy response. 

Alike Lunit, many companies, both newly founded start-ups like PathAI, Paige.ai, and Proscia, and relatively older image analytics companies such as Indica Labs, Definiens, and Visiopharm, have been active in their own ways to find means to develop clinically useful AI-powered solutions in digital pathology. 

The Future of AI-powered Imaging Biomarkers Is Bright 

AI-powered imaging biomarkers will need to be properly validated through clinical studies before they can be widely used clinically. Nonetheless, the potential benefit is clear: valuable information through accurate, consistent, and objective analysis of images may be easily used and applied clinically for more cost-efficient diagnostic and therapeutic decision making. The data-driven nature of AI is what makes it so effective, but what is more remarkable is its applicability in readily available data, especially imaging data, already deeply incorporated in routine clinical practice, allowing it to be inherently cost-efficient.

The diagnostic laboratory has always been a key source of data that informs clinical decisions. Clinical pathology tests generate discrete results with numeric or coded values that can be classified as normal or abnormal. Anatomic pathology analysis results in a report based on visual analysis of tissues based on the application of specialised stains, probes or other resources that help evaluate the sample for malignancy, inflammation or other clinically significant findings. Recent advances in molecular methods, including diagnostic genomic sequencing, as well as advanced imaging methods such as digital pathology, generate orders of magnitude more data than traditional methods. These advances have created exciting opportunities and some challenges for the laboratory community. The emerging discipline of data science offers a valuable toolkit to maximise the value of all modalities of laboratory data and to improve the diagnostic and operational functions of a modern lab. 

Data science refers to the combination of computational, statistical and subject matter expertise necessary to recognise subtle patterns in high volume, complex data and then to develop predictive models based on those analyses. Some common categories of data science approaches include artificial intelligence (AI), machine learning and deep learning. A data science project typically begins with a large data set that is divided into a training segment and a test segment. The training segment is used to iteratively design, develop and tune an algorithm. A high volume of data to drive the statistical power of any analysis is critical, however data sets in the dozens or even hundreds are often not deep enough to support the complex analyses. For example, a data set combining haemoglobin A1c, glucose values, body mass index and dates of diabetes diagnosis with other clinical information could be used to identify early indicators of the onset of diabetes. The degree of expert involvement in this process depends on the specific data science methodology. Some AI approaches involve expert curation of the training data set and algorithm development, these are considered “supervised” methods. In contrast, deep learning is generally driven by inherent attributes of the source data. Specialised analysis approaches, including bioinformatics, can also fit into the broad category of data science. 

The incorporation of pathology information into electronic health records creates the opportunity to query this data for subtle patterns. At the local level, data analysis can help in quality control, for example in determining whether there has been drift in the results from an instrument indicating a need for calibration. Some analyses require more data than is available from a single organisation. Initiatives in which de-identified electronic health record (EHR) data is aggregated from multiple organisations can provide a valuable resource for gaining new insights into the role of laboratory data in clinical decision making. For example, we recently used this approach to demonstrate that magnesium, both high and low levels of magnesium, in patients with a myocardial infarction correlates with higher mortality. We have also used aggregate EHR data to demonstrate that A1c tests are frequently ordered for sickle cell patients, a practice that should be avoided. The unstructured data found on the text content of pathology reports can also be evaluated using natural language processing methods. EHR data analysis is increasingly recognised as an important source of phenotype information to complement genomic analysis. 

Data science is also being applied to automate the analysis of diagnostic images, including pathology slides. The application of data science methods such as deep learning to these images has the potential to improve the accuracy of interpretation and to assist in the recognition of subtle but potentially significant patterns that elude the human eye or brain. AI based methods use pathologist annotations of slides to train an algorithm. Early efforts in this area include the use of deep learning to recognise micrometastases of breast cancer in lymph node biopsies and demonstrated increased sensitivity and reduced time to review. Other work has explored the use of data science to enhance blurry regions in slide images and to support quality control. 

Molecular diagnostic testing has become standard practice in diagnostic laboratories. Increasingly, clinical full exome or genome sequencing is also becoming widely available for the management of cancer and the diagnosis of complex cases that do not yield to traditional methods. These methods generate massive volumes of high complexity data. For example, a genomic analysis for a single patient can generate more than one terabyte of data. Data science methods assist in the analysis of these raw sequences as laboratories search for variants that may be clinically significant. Recent advances in single cell sequencing will introduce another major shift in the volume of data that will ultimately be applied to reach a diagnosis. Genomic analysis has had many notable successes in single variant, Mendelian conditions, and in assisting in the management of cancer. Common chronic diseases with known hereditary, polygenic, influences remain difficult to characterise and will require the continued application of data science and bioinformatics methods to identify multifactorial contributions to diseases such as diabetes and asthma. 

Data science methods have a wide variety of applications relevant to the laboratory. First, they can enhance the diagnostic capacity of the lab by offering novel means to improve accuracy and speed. Second, with the increasing complexity of data generated by laboratory processes such as high-resolution images and genomic data, subtle patterns are increasingly likely to elude human perception. Data science can help augment the clinical expert as they navigate these new sources of diagnostic data. Third, emerging methods will support the integration of lab data with other clinical data to develop comprehensive predictive algorithms capable of early detection of disease risk or identifying optimal treatment strategies in support of precision medicine. Finally, there are numerous applications of data science that can promote the administrative process of operating a clinical lab. For example, understanding subtle patterns in test utilisation can help in inventory management. Likewise, models that predict patients at risk of being a no-show for specimen collection can help manage call centre reminders. 

Laboratory professionals seeking to apply data science to address complex questions can take a number of approaches. The best approach is to form a collaborative team with computational and statistical experts to address a clearly defined problem. The team would identify and characterise the data available to them as they design their strategy. The team approach helps mitigate concerns that laboratorians have to become programmers to participate in data science. For those who do want to develop some of the technical skills, high quality online data science training resources such as those provided by Coursera or edX provide an excellent starting point for learning more about the principles and methods of data science. Open source applications such as R and Python are widely available to perform complex data analysis, as are commercial packages. Laboratories that embrace data science will be well positioned to engage in the next generation of diagnostic technologies and methods.

Laboratory testing is dependent upon preanalytical, analytical and postanalytical test phases. In the laboratory, we are focused upon the analytical phase and often ignore the preanalytical and postanalytical portion of testing. This is a drastic error, even though both the pre and post phases are more difficult to control, they account for many errors, and for delays in caregivers reacting to test results. Seventy per cent of laboratory errors are associated with pre-analytical errors. Much work has been done to speed the results of laboratory testing in the analytical phase, however if the results are not available to care givers soon after they are posted in the laboratory information system, then the effort expended to speed the testing will be wasted. When possible, electronic results sent directly to the caregiver will aid in care, i.e. TheraDoc system. Also, if test results can be in the medical record when the caregiver can review them or rounds with patients, it will speed appropriate care. 

Over the years, we have often offered testing and methodologies that can be described as “we have always done it that way”. The use of evidenced based medicine should replace that approach so that providers test the appropriate patient, use the best method to diagnose disease and also treat patients appropriately. Microbiologists, infectious disease physicians and pharmacists have recently been engaged in antibiotic stewardship programmes to better use appropriate antimicrobials to treat patients and not to use them on patients that do not need them. By doing so, a significant cost savings will be seen as well as better outcomes for patients. Too often laboratory medical and administrative staff make decisions in a void without using expertise from other disciplines. The use of multidisciplinary teams and diagnostic management teams offer a great advantage in treating patients and offering relevant laboratory testing. 

In addition to some practices mentioned above, here is a look at some of the “stuff we should not be doing”. 

Examples: 

1. Accepting samples that are not appropriate for testing either due to poor collection, storage, or from unapproved sources without proper validation studies. 

2. Adding newer methodologies without addressing with affected medical professionals preanalytical specimen collection errors and other pre-analytical steps. 

3. Shipping samples many hours to the main laboratory if processing, testing or screening can be done locally. If they cannot be done in the laboratory, then in the point of care.

4. Adding new methodologies or procedures without discussing them with a multidisciplinary team including: Infectious disease, the physician specialty affected by the change and administration (i.e., the post-analytical impact of a new test). 

5. Agreeing to perform AST on drug-bug combinations for which there are no standards. 

6. Other “do not do” items will be discussed. 

Antibiotic Stewardship and the Laboratory

The laboratory has played a key role in dispensing antimicrobial results as well as interpretation for clinicians, nurses and pharmacists. The explosion of antibiotic stewardship has occurred in the last few years with increasing full-time equivalents (FTE) as well as increasing budgets in pharmacy, infectious disease and nursing. However, since the Joint Commission issued a new standard for antimicrobial stewardship programmes in hospitals, critical access hospitals, and nursing care centres, the laboratory has been excluded from the “table”. The members of the team as mentioned in the standard are an infectious disease physician, pharmacist, practitioners and infection preventionists. For several of the members of the team, “if available in the setting,” are to be included. The laboratory, and in particular the microbiology laboratory, is excluded from the required active members. Ideally a doctoral level microbiologist should be on the team as is recognised by the Centers for Disease Control and Prevention (CDC). Although the Joint Commission states that they mimic the requirements for the team published by CDC, however, a member of microbiology laboratory or the diagnostic laboratory in general is not mentioned as key members of their stewardship team. 

Laboratory Stewardship 

Over the last 30 years an explosion in the cost of healthcare has occurred, and the laboratory is not exempt to this issue. In fact, an estimated four to five billion laboratory tests are performed annually with 30 per cent of them being unnecessary. The clinical microbiology laboratory is to provide appropriate tests to aid in diagnosis and therapeutic management of a patient with an infectious disease. Evidence-based approaches are key to this success as well as using a multidisciplinary team and also diagnostic management teams to treat patients. The clinical microbiologist should not only be dispensing test results but also should be key in determining which tests are useful in a patient scenario along with other caregivers. Many of the tests that could be ordered are not pertinent to the disease but rather simply just “ordered by habit”. Stewardship programmes rely on the clinical microbiology laboratory to direct actions needed for effective stewardship activities. 

Examples of those contributions to stewardship are: 1. preanalytical, analytical and postanalytical test phases all influence the value of a test result; 2. Rapid result reporting can be an extremely important parameter for care as it can lead to effective interventions; 3. Guidance in selection of tests and interpretive assistance of the results; 4. Implementation of the most currently effective testing modalities and interpretation of the results; reporting appropriate antibiotics associated with positive outcomes for the patient; 5. Detection of antibiotic resistance begins in the Clinical Microbiology laboratory. 

By working with other healthcare specialties, the microbiologist can serve as a valuable resource to the healthcare system. 

The clinical microbiologist plays an important role in antibiotic stewardship and contributes to the antibiotic and laboratory stewardship teams in many ways, some of which are detailed above. They should be part of hospital, state, regional and federal expert panels to come up with guidelines on all areas of microbiology, as demonstrated in North Carolina’s CRE guidance. To that point, clinical microbiology should be represented on all committees discussing antibiotic resistance and stewardship and infection control, so the clinical microbiologist can describe how they can help.