Fourier transform mid-infrared (MIR-FTIR) spectroscopy is a nondestructive, label-free, highly sensitive and specific technique that provides complete information on the chemical composition of biological samples. The technique both can offer fundamental structural information and serve as a quantitative analysis tool. Therefore, it has many potential applications in different fields of clinical laboratory science. Although considerable technological progress has been made to promote biomedical applications of this powerful analytical technique, most clinical laboratory analyses are based on spectroscopic measurements in the visible or ultraviolet (UV) spectrum and the potential role of FTIR spectroscopy still remains unexplored. In this review, we present some general principles of FTIR spectroscopy as a useful method to study molecules in specimens by MIR radiation together with a short overview of methods to interpret spectral data. We aim at illustrating the wide range of potential applications of the proposed technique in the clinical laboratory setting with a focus on its advantages and limitations and discussing the future directions. The reviewed applications of MIR spectroscopy include (1) quantification of clinical parameters in body fluids, (2) diagnosis and monitoring of cancer and other diseases by analysis of body fluids, cells, and tissues, (3) classification of clinically relevant microorganisms, and (4) analysis of kidney stones, nails, and faecal fat.

The emergence of artificial intelligence and machine learning (ML) has revolutionized the landscape of clinical medicine, offering opportunities to improve medical practice and research. This narrative review explores the current status and prospects of applying ML to chronic kidney disease (CKD). ML, at the intersection of statistics and computer science, enables computers to derive insights from extensive datasets, thereby presenting an interesting landscape for constructing statistical models and improving data interpretation. The integration of ML into clinical algorithms aims to increase efficiency and promote its adoption as a standard approach to data interpretation in nephrology. As the field of ML continues to evolve, collaboration between clinicians and data scientists is essential for defining data-sharing and usage policies, ultimately contributing to the advancement of precision diagnostics and personalized medicine in the context of CKD.

Uromodulin, also known as Tamm-Horsfall protein, represents the predominant urinary protein in healthy individuals. Over the years, studies have revealed compelling associations between urinary and serum concentrations of uromodulin and various parameters, encompassing kidney function, graft survival, cardiovascular disease, glucose metabolism, and overall mortality. Consequently, there has been a growing interest in uromodulin as a novel and effective biomarker with potential applications in diverse clinical settings. Reduced urinary uromodulin levels have been linked to an elevated risk of acute kidney injury (AKI) following cardiac surgery. In the context of chronic kidney disease (CKD) of different etiologies, urinary uromodulin levels tend to decrease significantly and are strongly correlated with variations in estimated glomerular filtration rate. The presence of uromodulin in the serum, attributable to basolateral epithelial cell leakage in the thick ascending limb, has been observed. This serum uromodulin level is closely associated with kidney function and histological severity, suggesting its potential as a biomarker capable of reflecting disease severity across a spectrum of kidney disorders. The UMOD gene has emerged as a prominent locus linked to kidney function parameters and CKD risk within the general population. Extensive research in multiple disciplines has underscored the biological significance of the top UMOD gene variants, which have also been associated with hypertension and kidney stones, thus highlighting the diverse and significant impact of uromodulin on kidney-related conditions. UMOD gene mutations are implicated in uromodulin-associated kidney disease, while polymorphisms in the UMOD gene show a significant association with CKD. In conclusion, uromodulin holds great promise as an informative biomarker, providing valuable insights into kidney function and disease progression in various clinical scenarios. The identification of UMOD gene variants further strengthens its relevance as a potential target for better understanding kidney-related pathologies and devising novel therapeutic strategies. Future investigations into the roles of uromodulin and regulatory mechanisms are likely to yield even more profound implications for kidney disease diagnosis, risk assessment, and management.

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.

Raman spectroscopy, a non-invasive diagnostic technique capturing molecular vibrations, offers significant advancements in skin cancer diagnostics. This review delineates the ascent of Raman spectroscopy from classical methodologies to the forefront of modern technology, emphasizing its precision in differentiating between malignant and benign skin tissues. Our study offers a detailed examination of distinct Raman spectroscopic signatures found in skin cancer, concentrating specifically on squamous cell carcinoma, basal cell carcinoma, and melanoma, across both in vitro and in vivo research. The discussion extends to future possibilities, spotlighting enhancements in portable Raman instruments, the adoption of machine learning for spectral data refinement, and the merging of Raman imaging with other diagnostic techniques. The review culminates by contemplating the broader implications of these advancements, suggesting a trajectory that may significantly optimize the accuracy and efficiency of skin cancer diagnostics.

Extracellular vesicles are a diverse group of particles that include exosomes, microvesicles, and apoptotic bodies and are defined by size, composition, site of origin, and density. They incorporate various bioactive molecules from their cell of origin during formation, such as soluble proteins, membrane receptors, nucleic acids (mRNAs and miRNAs), and lipids, which can then be transferred to target cells. Extracellular vesicles/exosomes have been extensively studied as a critical factor in pathophysiological processes of human diseases. Urinary extracellular vesicles could be a promising liquid biopsy for determining the pattern and/or severity of kidney histologic injury. The signature of urinary extracellular vesicles may pave the way for noninvasive methods to supplement existing testing methods for diagnosing kidney diseases. We discuss the potential role of urinary extracellular vesicles in various chronic kidney diseases in this review, highlighting open questions and discussing the potential for future research.