Carbamoylated Nail Proteins as Assessed by Near-Infrared Analysis are Associated with Load of Uremic Toxins and Mortality in Hemodialysis Patients

Artificial intelligence in the clinical laboratory

Laboratory medicine has become a highly automated medical discipline. Nowadays, artificial intelligence (AI) applied to laboratory medicine is also gaining more and more attention, which can optimize the entire laboratory workflow and even revolutionize laboratory medicine in the future. However, only a few commercially available AI models are currently approved for use in clinical laboratories and have drawbacks such as high cost, lack of accuracy, and the need for manual review of model results. Furthermore, there are a limited number of literature reviews that comprehensively address the research status, challenges, and future opportunities of AI applications in laboratory medicine. Our article begins with a brief introduction to AI and some of its subsets, then reviews some AI models that are currently being used in clinical laboratories or that have been described in emerging studies, and explains the existing challenges associated with their application and possible solutions, finally provides insights into the future opportunities of the field. We highlight the current status of implementation and potential applications of AI models in different stages of the clinical testing process.

The Potential Use of Near- and Mid-Infrared Spectroscopy in Kidney Diseases

Traditional renal biomarkers such as serum creatinine and albuminuria/proteinuria are rather insensitive since they change later in the course of the disease. In order to determine the extent and type of kidney injury, as well as to administer the proper therapy and enhance patient management, new techniques for the detection of deterioration of the kidney function are urgently needed. Infrared spectroscopy is a label-free and non-destructive technique having the potential to be a vital tool for quick and inexpensive routine clinical diagnosis of kidney disorders. The aim of this review is to provide an overview of near- and mid-infrared spectroscopy applications in patients with acute kidney injury and chronic kidney disease (e.g., diabetic nephropathy and glomerulonephritis).

Biomarkers of disease in human nails: a comprehensive review

Diagnostic, monitoring, response, predictive, risk, and prognostic biomarkers of disease are all widely studied, for the most part in biological fluids or tissues, but there is steadily growing interest in alternative matrices such as nails. Here we comprehensively review studies dealing with molecular or elemental biomarkers of disease, as opposed to semiological, pharmacological, toxicological, or biomonitoring studies. Nails have a long history of use in medicine as indicators of pathological processes and have also been used extensively as a matrix for monitoring exposure to environmental pollution. Nail clippings are simple to collect noninvasively as well as to transport and store, and the matrix itself is relatively stable. Nails incorporate, and are influenced by, circulating molecules and elements over their several months of growth, and it is widely held that markers of biological processes will remain in the nail, even when their levels in blood have declined. Nails thus offer the possibility to not only look back into a subject's metabolic history but also to study biomarkers of processes that operate over a longer time scale such as the post-translational modification of proteins. Reports on ungual biomarkers of metabolic and endocrine diseases, cancer, and psychological and neurological disorders will be presented, and an overview of the sampling and analytical techniques provided.

Recent evolutions of machine learning applications in clinical laboratory medicine

Machine learning (ML) is gaining increased interest in clinical laboratory medicine, mainly triggered by the decreased cost of generating and storing data using laboratory automation and computational power, and the widespread accessibility of open source tools. Nevertheless, only a handful of ML-based products are currently commercially available for routine clinical laboratory practice. In this review, we start with an introduction to ML by providing an overview of the ML landscape, its general workflow, and the most commonly used algorithms for clinical laboratory applications. Furthermore, we aim to illustrate recent evolutions (2018 to mid-2020) of the techniques used in the clinical laboratory setting and discuss the associated challenges and opportunities. In the field of clinical chemistry, the reviewed applications of ML algorithms include quality review of lab results, automated urine sediment analysis, disease or outcome prediction from routine laboratory parameters, and interpretation of complex biochemical data. In the hematology subdiscipline, we discuss the concepts of automated blood film reporting and malaria diagnosis. At last, we handle a broad range of clinical microbiology applications, such as the reduction of diagnostic workload by laboratory automation, the detection and identification of clinically relevant microorganisms, and the detection of antimicrobial resistance.

Near-infrared spectroscopy as a potential non-invasive tool in the assessment of disease activity in vitiligo patients

Vitiligo is a common chronic depigmenting skin disease. We explored the utility of near-infrared (NIR) spectroscopy in the identification of spectral changes associated with disease activity in vitiligo patients. In vivo spectral measurements were performed directly on the perilesional skin of 70 vitiligo patients. Relative intensities (second derivative) at 1139, 1344, 1646 and 1839 nm appeared to be significantly lower in the perilesional region of patients with active vitiligo compared with stable disease, while the intensity at 1884 nm seemed to be significantly higher. A classification model based on the spectral ranges around those peaks generated a correct prediction in 82.9% of the cases. In conclusion, we can state that NIR spectroscopy could have potential in the assessment of disease activity. However, large-scale prospective studies are necessary to confirm our preliminary results.