A global perspective on the status of clinical metabolomics in laboratory medicine - a survey by the IFCC metabolomics working group.

OBJECTIVES: Metabolomics aims for comprehensive characterization and measurement of small molecule metabolites (<1700 Da) in complex biological matrices. This study sought to assess the current understanding and usage of metabolomics in laboratory medicine globally and evaluate the perception of its promise and future implementation. METHODS: A survey was conducted by the IFCC metabolomics working group that queried 400 professionals from 79 countries. Participants provided insights into their experience levels, knowledge, and usage of metabolomics approaches, along with detailing the applications and methodologies employed. RESULTS: Findings revealed a varying level of experience among respondents, with varying degrees of familiarity and utilization of metabolomics techniques. Targeted approaches dominated the field, particularly liquid chromatography coupled to a triple quadrupole mass spectrometer, with untargeted methods also receiving significant usage. Applications spanned clinical research, epidemiological studies, clinical diagnostics, patient monitoring, and prognostics across various medical domains, including metabolic diseases, endocrinology, oncology, cardiometabolic risk, neurodegeneration and clinical toxicology. CONCLUSIONS: Despite optimism for the future of clinical metabolomics, challenges such as technical complexity, standardization issues, and financial constraints remain significant hurdles. The study underscores the promising yet intricate landscape of metabolomics in clinical practice, emphasizing the need for continued efforts to overcome barriers and realize its full potential in patient care and precision medicine.

Advancing Renal Amyloidosis Care: The Role of Modern Diagnostic Techniques with the Potential of Enhancing Patient Outcomes.

Renal amyloidosis is a set of complex disorders characterized by the deposition of amyloid proteins in the kidneys, which causes gradual organ damage and potential kidney failure. Recent developments in diagnostic methods, particularly mass spectrometry and proteome profiling, have greatly improved the accuracy of amyloid typing, which is critical for disease management. These technologies provide extensive insights into the specific proteins involved, allowing for more targeted treatment approaches and better patient results. Despite these advances, problems remain, owing to the heterogeneous composition of amyloid proteins and the varying efficacy of treatments based on amyloid type. Access to sophisticated diagnostics and therapy varies greatly, highlighting the global difference in renal amyloidosis management. Future research is needed to investigate next-generation sequencing and gene-editing technologies, like clustered regularly interspaced short palindromic repeats (CRISPR), which promise more profound insights into the genetic basis of amyloidosis.

Infrared Spectroscopy in Gynecological Oncology: A Comprehensive Review of Diagnostic Potentials and Challenges.

The early detection of gynecological cancers, which is critical for improving patient survival rates, is challenging because of the vague early symptoms and the diagnostic limitations of current approaches. This comprehensive review delves into the game-changing potential of infrared (IR) spectroscopy, a noninvasive technology used to transform the landscape of cancer diagnosis in gynecology. By collecting the distinctive vibrational frequencies of chemical bonds inside tissue samples, Fourier-transform infrared (FTIR) spectroscopy provides a 'molecular fingerprint' that outperforms existing diagnostic approaches. We highlight significant advances in this field, particularly the identification of discrete biomarker bands in the mid- and near-IR spectra. Proteins, lipids, carbohydrates, and nucleic acids exhibited different absorption patterns. These spectral signatures not only serve to distinguish between malignant and benign diseases, but also provide additional information regarding the cellular changes associated with cancer. To underscore the practical consequences of these findings, we examined studies in which IR spectroscopy demonstrated exceptional diagnostic accuracy. This review supports the use of IR spectroscopy in normal clinical practice, emphasizing its capacity to detect and comprehend the intricate molecular underpinnings of gynecological cancers.

Application of Machine Learning in Chronic Kidney Disease: Current Status and Future Prospects.

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.

From Vibrations to Visions: Raman Spectroscopy's Impact on Skin Cancer Diagnostics.

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.

Infrared Spectroscopy: A New Frontier in Hematological Disease Diagnosis.

Hematological diseases, due to their complex nature and diverse manifestations, pose significant diagnostic challenges in healthcare. The pressing need for early and accurate diagnosis has driven the exploration of novel diagnostic techniques. Infrared (IR) spectroscopy, renowned for its noninvasive, rapid, and cost-effective characteristics, has emerged as a promising adjunct in hematological diagnostics. This review delves into the transformative role of IR spectroscopy and highlights its applications in detecting and diagnosing various blood-related ailments. We discuss groundbreaking research findings and real-world applications while providing a balanced view of the potential and limitations of the technique. By integrating advanced technology with clinical needs, we offer insights into how IR spectroscopy may herald a new era of hematological disease diagnosis.

Unveiling the Hidden Power of Uromodulin: A Promising Potential Biomarker for Kidney Diseases.

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.

Applications of Artificial Intelligence in Urinalysis: Is the Future Already Here?

BACKGROUND: Artificial intelligence (AI) has emerged as a promising and transformative tool in the field of urinalysis, offering substantial potential for advancements in disease diagnosis and the development of predictive models for monitoring medical treatment responses. CONTENT: Through an extensive examination of relevant literature, this narrative review illustrates the significance and applicability of AI models across the diverse application area of urinalysis. It encompasses automated urine test strip and sediment analysis, urinary tract infection screening, and the interpretation of complex biochemical signatures in urine, including the utilization of cutting-edge techniques such as mass spectrometry and molecular-based profiles. SUMMARY: Retrospective studies consistently demonstrate good performance of AI models in urinalysis, showcasing their potential to revolutionize clinical practice. However, to comprehensively evaluate the real clinical value and efficacy of AI models, large-scale prospective studies are essential. Such studies hold the potential to enhance diagnostic accuracy, improve patient outcomes, and optimize medical treatment strategies. By bridging the gap between research and clinical implementation, AI can reshape the landscape of urinalysis, paving the way for more personalized and effective patient care.

Unlocking the Diagnostic Potential of Saliva: A Comprehensive Review of Infrared Spectroscopy and Its Applications in Salivary Analysis.

Infrared (IR) spectroscopy is a noninvasive and rapid analytical technique that provides information on the chemical composition, structure, and conformation of biomolecules in saliva. This technique has been widely used to analyze salivary biomolecules, owing to its label-free advantages. Saliva contains a complex mixture of biomolecules including water, electrolytes, lipids, carbohydrates, proteins, and nucleic acids which are potential biomarkers for several diseases. IR spectroscopy has shown great promise for the diagnosis and monitoring of diseases such as dental caries, periodontitis, infectious diseases, cancer, diabetes mellitus, and chronic kidney disease, as well as for drug monitoring. Recent advancements in IR spectroscopy, such as Fourier-transform infrared (FTIR) spectroscopy and attenuated total reflectance (ATR) spectroscopy, have further enhanced its utility in salivary analysis. FTIR spectroscopy enables the collection of a complete IR spectrum of the sample, whereas ATR spectroscopy enables the analysis of samples in their native form, without the need for sample preparation. With the development of standardized protocols for sample collection and analysis and further advancements in IR spectroscopy, the potential for salivary diagnostics using IR spectroscopy is vast.

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).

Potentials and pitfalls of ChatGPT and natural-language artificial intelligence models for the understanding of laboratory medicine test results. An assessment by the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) Working Group on Artificial Intelligence (WG-AI).

OBJECTIVES: ChatGPT, a tool based on natural language processing (NLP), is on everyone's mind, and several potential applications in healthcare have been already proposed. However, since the ability of this tool to interpret laboratory test results has not yet been tested, the EFLM Working group on Artificial Intelligence (WG-AI) has set itself the task of closing this gap with a systematic approach. METHODS: WG-AI members generated 10 simulated laboratory reports of common parameters, which were then passed to ChatGPT for interpretation, according to reference intervals (RI) and units, using an optimized prompt. The results were subsequently evaluated independently by all WG-AI members with respect to relevance, correctness, helpfulness and safety. RESULTS: ChatGPT recognized all laboratory tests, it could detect if they deviated from the RI and gave a test-by-test as well as an overall interpretation. The interpretations were rather superficial, not always correct, and, only in some cases, judged coherently. The magnitude of the deviation from the RI seldom plays a role in the interpretation of laboratory tests, and artificial intelligence (AI) did not make any meaningful suggestion regarding follow-up diagnostics or further procedures in general. CONCLUSIONS: ChatGPT in its current form, being not specifically trained on medical data or laboratory data in particular, may only be considered a tool capable of interpreting a laboratory report on a test-by-test basis at best, but not on the interpretation of an overall diagnostic picture. Future generations of similar AIs with medical ground truth training data might surely revolutionize current processes in healthcare, despite this implementation is not ready yet.

Urinary Extracellular Vesicles in Chronic Kidney Disease: From Bench to Bedside?

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.

A Tissue Section-Based Near-Infrared Spectroscopical Analysis of Salivary Gland Tumors.

SGTs vary in histological behavior. Mucins, a major component in salivary glands, consist of a glycosylated and sialylated protein core. Rapid evolutions in glycobiology have demonstrated the important role of glycoproteins in cancer development. NIR spectroscopy is a method for the biochemical analysis of substrates. NIR spectra can be analyzed using specific chemometrics. Our aim was to explore the diagnostic possibilities of NIR spectroscopy in SGTs. 238 Hematoxylin and Eosine stained (H&E) SGT tissue sections were examined using NIR spectroscopy. 45 deparaffinized tissue sections were treated with neuraminidase to identify wavelengths in the NIR spectrum related to sialylation. NIR spectra were analyzed with chemometrics. NIR spectra could distinguish malignant SGTs from controls and benign SGTs. Prediction models based on the entire spectral range resulted in a 73.1% accurate classification of malignant SGTs and controls, while, based on neuraminidase experimental spectral peak differences (1436 nm; 1713 nm; 1783 nm; 1924 nm; 2032 nm; 2064 nm; 2178 nm; 2216 nm), an improved overall correct classification rate of 91.9% was obtained between healthy subjects and malignant tumors. H&E tissue section-based NIR spectroscopy can identify malignant SGTs from controls, promising an alternative method in the diagnosis of SGTs.

On the nature of peculiar expectorated bronchial casts: Can infrared spectroscopy enlighten us?

BACKGROUND: Bronchial casts can be defined as gelatinous structures originating from the airways. While several cases of bronchial cast formation have been reported in literature, unravelling its nature is often a difficult task. METHODS: In this case report, we applied infrared (IR) spectroscopy on a bronchial cast fragment originating from a patient who suffered from a 2-y history of frequent coughing accompanied by the occasional expectoration of viscous and thick white-yellow bronchial-like structures. RESULTS: Based on a markedly high lipid to protein ratio and presence of long-chain fatty acids, the resulting IR spectrum appeared to be very suggestive for chyloptysis. CONCLUSION: Taking into account the patient's prior history of radiation therapy for a lymphoma complicated by congestive heart failure, we hypothesized that an impairment of adequate lymphatic flow into the venous system due to a combination of lymphatic obstruction and high venous pressures is the most plausible culprit.