Critical reviews in clinical laboratory sciences最新文献

This review attempts to find a crucial set of criteria that can be applied to diagnose infections caused by Trichophyton indotineae, based on laboratory and clinical characteristics derived from other studies. T. indotineae has emerged as a new species from the anthropophilic Trichophyton mentagrophytes and Trichophyton interdigitale. The close relationship between T. indotineae and these two species makes it difficult to distinguish them based on morphological and physiological features. Resistance to terbinafine was first observed in T. indotineae, and later to other antifungals, which increased the treatment failure rate in several dermatophytoses. In conclusion; identifying criteria specific to infections with T. indotineae can provide a framework for physicians to readily diagnose such infections and distinguish them from other dermatophytoses. Categorization guidelines for all diagnostic characteristics of T. indotineae and its infection can make a forward step in prescribing effective therapies.

Inherited familial tumors are linked to distinct germline mutations that result in different syndromic phenotypes, increasing cancer risk in patients. Recent findings have unveiled new evidence of genotype-phenotype correlations and highlighted the essential role of biomolecular and immunohistochemical (IHC) analyses in pinpointing predictive markers for immune-therapy responses. Lynch Syndrome (LS), recently identified as four unique hereditary cancer syndromes, is defined by specific germline mutations in Mismatch Repair Genes MLH1, MSH2, MSH6, PMS2. It features distinctive characteristics such as the early development of visceral tumors (primarily colorectal cancer, endometrial, ovarian, urothelial) and a high incidence of synchronous and metachronous cancers. LS cancers exhibit microsatellite instability (MSI). MSI was first used as an indicator of a genetic predisposition, but it is now recognized as an essential predictive marker for therapy response to immune checkpoint inhibitors. These findings resulted in the approval of immune checkpoint inhibitors, such as anti-programmed cell death 1 (anti-PD1) or anti-programmed cell death ligand 1 (anti-PD-L1) by regulatory bodies solely based on MSI status, independent of the type of cancer. In this case, a transition to a universal LS molecular screening method for all newly diagnosed colorectal and endometrial cancers has been effectively promoted. This shift in viewpoint will necessitate a thorough examination of present guidelines, encompassing recommendations on the optimal timing and approach for the implementation of biomolecular and IHC analyses to evaluate MSI status in additional cancers associated with LS. In this review, we offer a clinical summary of the primary MSI-H/dMMR cancers linked to LS and present a detailed description of immunotherapy, recently introduced for some neoplastic entities but increasingly extended to other tumors. We additionally emphasize the absence of existing molecular screening for rarer LS-associated tumors, like sebaceous cancers, which could gain the most from universal IHC screening and contemporary immuno-therapeutic strategies.

Despite advancements in medical care, acute kidney injury (AKI) remains a major contributor to adverse patient outcomes and presents a significant challenge due to its associated morbidity, mortality, and financial cost. Machine learning (ML) is increasingly being recognized for its potential to transform AKI care by enabling early prediction, detection, and facilitating an individualized approach to patient management. This scoping review aims to provide a comprehensive analysis of externally validated ML models for the prediction, detection, and management of AKI. We systematically searched for relevant literature from inception to 15 February 2024, using four databases-MEDLINE, EMBASE, Web of Science, and Scopus. We focused solely on models that had undergone external validation, employed Kidney Disease Improving Global Outcomes (KDIGO) definitions for AKI, and utilized ML models (excluding logistic regression models). A total of 44 studies encompassing 161 ML models for AKI prediction, severity assessment, and outcomes in both adult and pediatric populations were included in the review. These studies encompassed 4,153,424 patient admissions, with 1,209,659 in the development and internal validation cohorts and 2,943,765 in the external validation cohorts. The ML models demonstrated significant variability in performance owing to differing clinical settings, populations, and predictors used. Most of the included models were developed in specialized patient populations, such as those in intensive care units, post-surgical settings, and specific disease states (e.g. congestive heart failure, traumatic brain injury, etc.). Moreover, only a few models incorporated dynamic predictors of AKI which are crucial for improving clinical utility in rapidly evolving clinical conditions like AKI. The variable performance of these models when applied to external validation cohorts highlights the challenges of reproducibility and generalizability in implementing ML models in AKI care. Despite acceptable performance metrics, none of the models assessed in this review underwent validation or implementation in real-world clinical workflows. These findings underscore the need for standardized performance metrics and validation protocols to enhance the generalizability and clinical applicability of these models. Future efforts should focus on enhancing model adaptability by incorporating dynamic predictors and unstructured data and by ensuring that models are developed in diverse patient populations. Moreover, collaboration between clinicians and data scientists is critical to ensure the development of models that are clinically relevant, fair, and tailored to real-world healthcare environments.

Given recent economic concerns, there has been pressure on the health-care system to improve efficiency, quality and reduce cost. The clinical laboratory is now under close scrutiny to adopt "practicing to value" which involves shifting its focus from performing many tests to performing only necessary tests. To achieve this, clinical laboratories have been implementing strategies for effective laboratory test utilization and participating in health outcome studies to provide evidenced-based insights on test utilization, clinical decision-making and policy improvements. It is essential to highlight the full spectrum of this additional role of the clinical laboratory to administrators, policy makers and other health-care stakeholders, as clinical laboratories are an easy target for economic restrictions. This review highlights how strategic stewardship implementation by a clinical laboratory improves clinical outcomes in a pediatric setting.

Kynurenine pathway (KP) metabolites are implicated in various disorders, including Alzheimer's disease, schizophrenia, and adverse pregnancy outcomes. Simultaneous measurement of multiple KP metabolites offers valuable insight into the pathway's role in health and disease, would improve this relatively undeveloped field. This systematic review aim was to summarize the state of the art for measuring the eight key KP metabolites, using liquid chromatography-mass spectrometry (LC-MS), explicitly focusing on whether methods were validated using established guidelines with superior sensitivity and selectivity. We undertook a comprehensive review of the literature using the PRISMA guidelines. Our search uncovered 66 publications, and 39 qualified the defined key criteria. We summarized each publication's method development parameters, analytical design, and method performance specifications. We found notable variability in sample preparation techniques and analytical design across biological matrices, underscoring a lack of universally established and validated methods for KP metabolite quantification. We also identified significant gaps in the basic method evaluation. Our findings highlight that no single method has been evaluated for quantifying the eight key KP metabolites across three or more biological sample types, revealing a critical gap in the field. Our review emphasizes the need for robust analytical methods to quantify KP metabolites across multiple biological matrices, facilitating a better understanding of their roles in health and disease. Given the diversity of disorders involving the KP in the clinical testing lab, developing such methods will reduce diagnostic errors and advance KP metabolite research, supporting more precise, and personalized medical care.

Small cell lung cancer (SCLC) is one of the deadliest types of lung cancer, with most cases being diagnosed at advanced stages. The gold standard approach in SCLC treatment has been chemotherapy, although it has been associated with limited efficacy and significant toxicity. In recent years, the integration of immunotherapies coupled with traditional chemotherapy has expanded the treatment landscape for SCLC. Nevertheless, a major challenge remains in accurately predicting which patients will benefit from these treatment strategies. However, the paucity of available tumor tissue in some patients requires the exploration of alternative approaches. In this context, liquid biopsy provides a minimally invasive tool for earlier diagnosis and treatment decision-making. Peripheral blood contains several tumor-derived elements, such as circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), extracellular vesicles (EVs), and platelets, which provide real-time insights into the tumor, offering a dynamic alternative to traditional tissue biopsies. This article aims to comprehensively review the latest research on the application of liquid biopsy in SCLC. Specifically, the studies reviewed here focus on the detection, counting, and profiling of CTCs and the genomic, fragmentomic, and methylomic patterns of ctDNA across various patient cohorts and treatment settings. These studies reported promising results, particularly in the areas of early diagnosis and prognosis, suggesting that liquid biopsies could significantly enhance the management of SCLC patients. Additionally, emerging biomarkers such as serum/plasma-derived EV proteins and miRNA signatures, and the platelet-lymphocyte ratio have shown potential, however, their clinical application is still in the early stages. Although the findings regarding liquid biopsy-based markers are encouraging, their translation into the clinics is not yet achieved, mainly due to the low number and high variability of enrolled patients along with the lack of universal isolation strategies and univocal cut-offs for diagnosis and prognosis. Thus, large-scale, multi-institutional studies are essential to validate these markers and explore their integration into comprehensive multi-parameter scores. Finally, in-vitro/in-vivo CTC-derived cell lines/xenografts (CDX) might be used as pre-clinical "tumor-twin" models to understand SCLC biology as well as to test therapeutic options and comprehend the mechanisms of drug resistance, resulting in the expansion of alternative tools to improve precision medicine of this lethal neoplasm.

High-sensitivity assays for cardiac troponin (cTn) have improved rule-out algorithms for acute myocardial infarction (AMI). However, reduced specificity specifically to AMI posed new clinical challenges. Studies involving the composition of troponin released into the circulation after injury may provide insights to improve specificity. In MI patients, cTnI primarily exists of cTnIC and truncated cTnTIC complexes. Larger-sized cTnT forms, as part of the cTnTIC complex, predominate with shorter ischemic time windows. Over time, mildly and heavily truncated cTnT forms increase, whereas for cTnI this is less certain. Targeting the central part of cTnT, the current high-sensitivity assay also identifies heavily truncated forms as seen in end-stage renal disease and after exercise. This review on composition of circulating troponin covers different populations and outlines first initiatives toward more specific assays by targeting larger-sized troponin forms.

Transgender and gender diverse (TGD) individuals seeking gender affirming treatment are an increasing demographic in today's society; such treatments include hormonal and surgical interventions aimed at alleviating gender dysphoria and increasing quality of life. A number of diagnostic pathology tests are provided to medical professionals with sex specific reference intervals (RIs) for interpretation, due to sex specific physiological differences, organ size and hormone levels for example. These tests may be reported with RIs that are not appropriate, and interpretation for the medical professional can be challenging. From the laboratory perspective, there are limitations in Laboratory Information Management Systems (LIMS) and the ability of these databases to record both sex and gender identifiers, as well as the reporting of appropriate RIs. The use of RIs derived from the transgender population is complex, studies generally have a low sample size and include adults with long established hormonal treatments. The age of an individual undergoing gender affirming therapy has decreased, and the use of Gonadotrophin Releasing Hormone analogues adds complexity. In this review, we will discuss the current challenges and perspectives regarding the reporting of reference intervals in the TGD population, the derivation of personalized or transgender specific RIs and interpretation of specific diagnostic tests.

Acute liver failure (ALF) is an uncommon but severe condition with high morbidity and mortality. Advances in supportive care have improved patient outcomes, but liver transplantation remains the only life-saving intervention in many cases. Unfortunately, healthy donor livers are in short supply. In addition, transplant recipients face several potentially fatal risks including organ rejection, biliary and vascular complications, and infection. It is therefore critical to accurately identify patients who need a new liver while sparing those who do not. This also needs to be done quickly, within the first few days of hospital admission, due to the rapid progression of ALF. Prognostic tools, like the Clichy criteria, the King's College Criteria (KCC), the model for end-stage liver disease (MELD) score, the Acute Liver Failure Study Group Prognostic Index (ALFSGPI), and others have been available for this purpose since at least the 1980s and are commonly used today, but their performance is imperfect, leading to many efforts over the last several decades - and especially in recent years - to identify new noninvasive biomarkers. This review begins with a description of the earliest liver function (e.g. the levulose [fructose] test) and liver injury (e.g. alkaline phosphatase [ALP] and alanine aminotransferase [ALT]) tests and continues through the most recent proposed biomarkers, with critical evaluation of the prognostic utility of each using the KCC and MELD as benchmarks for comparison. Overall, there is as-yet no single biomarker that clearly and consistently performs better than the latter tools, though many may modestly improve the performance of the KCC or MELD when used in combination with them. The search for a better, single biomarker is therefore likely to continue.

Multiple sclerosis (MS) is a chronic autoimmune disorder affecting the central nervous system, often emerging in early adulthood and representing a leading cause of neurological disability in young adults. Diagnosing MS involves a combination of clinical assessment, imaging and laboratory tests, with cerebrospinal fluid (CSF)-specific immunoglobulin G (IgG) oligoclonal bands (OCB) being an important marker for fulfilling the dissemination in time criteria. A recent survey of Canadian clinical laboratories highlighted considerable variation in OCB reporting practices nationwide, spanning quality control (QC) practices, acceptable time limits between paired CSF and serum sample collections, protocols for reporting band counts, interpretation and reporting of mirrored patterns, testing panels, and interpretive thresholds. These inconsistencies impact patient care and the comparability of laboratory results across different laboratories. The Harmonized CSF Analysis for MS Investigation (hCAMI) subcommittee of the Canadian Society of Clinical Chemists Reference Interval Harmonization Working Group was established to generate recommendations for laboratory processes and reporting of CSF OCB and associated tests supporting MS diagnosis. This review serves as a foundation for these efforts, summarizing the available evidence in areas where practice variations have been noted. This review begins by examining current practices and guidelines for standardized quality assurance, including optimal QC materials, frequency, documentation, and participation in external quality assurance programs. The disparity between paired CSF and serum sample acceptability time limits was further examined by reviewing current practices and recommendations as well as compiling evidence on IgG synthesis, turnover rate, biological variation, and stability in CSF and serum samples. Additionally, this review addresses the lack of consensus on reporting the number of CSF-specific and CSF-serum matched bands, focusing on interpreter variability and clinical utility. Contributing factors and clinical implications of mirror patterns, including discussion on monoclonal gammopathies and cases of matched bands of differing staining intensity, is provided. Testing panel components including adjunctive CSF tests, such as the IgG index, to support MS investigations despite their absence from clinical guidelines is also discussed. This review also provides a comprehensive analysis of current practices, guidelines, and the evidence surrounding different cutoffs for IgG index and CSF-specific bands.

Finally, the review considers emerging biomarkers, such as the kappa free light chain index and serum neurofilament light chain, which show promise for MS diagnosis and management. This comprehensive review of current practices, guidelines, and evolving evidence will guide the hCAMI subcommittee's efforts to harmonize CSF OCB analysis and improve MS diagnosis.