Critical reviews in clinical laboratory sciences最新文献

There is a growing focus on understanding the role of the male microbiome in fertility issues. Although research on the bacterial communities within the male reproductive system is in its initial phases, recent discoveries highlight notable variations in the microbiome's composition and abundance across distinct anatomical regions like the skin, foreskin, urethra, and coronary sulcus. To assess the relationship between male genitourinary microbiome and reproduction, we queried various databases, including MEDLINE (available via PubMed), SCOPUS, and Web of Science to obtain evidence-based data. The literature search was conducted using the following terms "gut/intestines microbiome," "genitourinary system microbiome," "microbiome and female/male infertility," "external genital tract microbiome," "internal genital tract microbiome," and "semen microbiome." Fifty-one relevant papers were analyzed, and eleven were strictly semen quality or male fertility related. The male microbiome, especially in the accessory glands like the prostate, seminal vesicles, and bulbourethral glands, has garnered significant interest because of its potential link to male fertility and reproduction. Studies have also found differences in bacterial diversity present in the testicular tissue of normozoospermic men compared to azoospermic suggesting a possible role of bacterial dysbiosis and reproduction. Correlation between the bacterial taxa in the genital microbiota of sexual partners has also been found, and sexual activity can influence the composition of the urogenital microbiota. Exploring the microbial world within the male reproductive system and its influence on fertility opens doors to developing ways to prevent, diagnose, and treat infertility. The present work emphasizes the importance of using consistent methods, conducting long-term studies, and deepening our understanding of how the reproductive tract microbiome works. This helps make research comparable, pinpoint potential interventions, and smoothly apply microbiome insights to real-world clinical practices.

Great strides have been made in the past decade to lower barriers to clinical pharmacogenomics implementation. Nevertheless, PGx consultation prior to prescribing therapeutics is not yet mainstream. This review addresses the current climate surrounding PGx implementation, focusing primarily on strategies for implementation at academic institutions, particularly at The University of Chicago, and provides an up-to-date guide of resources supporting the development of PGx programs. Remaining challenges and recent strategies for overcoming these challenges to implementation are discussed.

The integration of artificial intelligence technologies has propelled the progress of clinical and genomic medicine in recent years. The significant increase in computing power has facilitated the ability of artificial intelligence models to analyze and extract features from extensive medical data and images, thereby contributing to the advancement of intelligent diagnostic tools. Artificial intelligence (AI) models have been utilized in the field of personalized medicine to integrate clinical data and genomic information of patients. This integration allows for the identification of customized treatment recommendations, ultimately leading to enhanced patient outcomes. Notwithstanding the notable advancements, the application of artificial intelligence (AI) in the field of medicine is impeded by various obstacles such as the limited availability of clinical and genomic data, the diversity of datasets, ethical implications, and the inconclusive interpretation of AI models' results. In this review, a comprehensive evaluation of multiple machine learning algorithms utilized in the fields of clinical and genomic medicine is conducted. Furthermore, we present an overview of the implementation of artificial intelligence (AI) in the fields of clinical medicine, drug discovery, and genomic medicine. Finally, a number of constraints pertaining to the implementation of artificial intelligence within the healthcare industry are examined.

Direct access testing (DAT) is an emerging care model that provides on-demand laboratory services for certain preventative, diagnostic, and monitoring indications. Unlike conventional testing models where health care providers order tests and where sample collection is performed onsite at the clinic or laboratory, most interactions between DAT consumers and the laboratory are virtual. Tests are ordered and results delivered online, and specimens are frequently self-collected at home with virtual support. Thus, DAT depends on high-quality information technology (IT) tools and optimized data utilization to a greater degree than conventional laboratory testing. This review critically discusses the United States DAT landscape in relation to IT to highlight digital challenges and opportunities for consumers, health care systems, providers, and laboratories. DAT offers consumers increased autonomy over the testing experience, cost, and data sharing, but the current capacity to integrate DAT as a care option into the conventional patient-provider model is lacking and will require innovative approaches to accommodate. Likewise, both consumers and health care providers need transparent information about the quality of DAT laboratories and clinical decision support to optimize appropriate use of DAT as a part of comprehensive care. Interoperability barriers will require intentional approaches to integrating DAT-derived data into the electronic health records of health systems nationally. This includes ensuring the laboratory results are appropriately captured for downstream data analytic pipelines that are used to satisfy population health and research needs. Despite the data- and IT-related challenges for widespread incorporation of DAT into routine health care, DAT has the potential to improve health equity by providing versatile, discreet, and affordable testing options for patients who have been marginalized by the current limitations of health care delivery in the United States.

Monoclonal gammopathy (MG) is a spectrum of diseases ranging from the benign asymptomatic monoclonal gammopathy of undetermined significance to the malignant multiple myeloma. Clinical guidelines and laboratory recommendations have been developed to inform best practices in the diagnosis, monitoring, and management of MG. In this review, the pathophysiology, relevant laboratory testing recommended in clinical practice guidelines and laboratory recommendations related to MG testing and reporting are examined. The clinical guidelines recommend serum protein electrophoresis, serum immunofixation and serum free light chain measurement as initial screening. The laboratory recommendations omit serum immunofixation as it offers limited additional diagnostic value. The laboratory recommendations offer guidance on reporting findings beyond monoclonal protein, which was not required by the clinical guidelines. The clinical guidelines suggested monitoring total IgA concentration by turbidimetry or nephelometry method if the monoclonal protein migrates in the non-gamma region, whereas the laboratory recommendations make allowance for involved IgM and IgG. Additionally, several external quality assurance programs for MG protein electrophoresis and free light chain testing are also appraised. The external quality assurance programs show varied assessment criteria for protein electrophoresis reporting and unit of measurement. There is also significant disparity in reported monoclonal protein concentrations with wide inter-method analytical variation noted for both monoclonal protein quantification and serum free light chain measurement, however this variation appears smaller when the same method was used. Greater harmonization among laboratory recommendations and reporting format may improve clinical interpretation of MG testing.

Laboratory testing has been a key tool in managing the SARS-CoV-2 global pandemic. While rapid antigen and PCR testing has proven useful for diagnosing acute SARS-CoV-2 infections, additional testing methods are required to understand the long-term impact of SARS-CoV-2 infections on immune response. Serological testing, a well-documented laboratory practice, measures the presence of antibodies in a sample to uncover information about host immunity. Although proposed applications of serological testing for clinical use have previously been limited, current research into SARS-CoV-2 has shown growing utility for serological methods in these settings. To name a few, serological testing has been used to identify patients with past infections and long-term active disease and to monitor vaccine efficacy. Test utility and result interpretation, however, are often complicated by factors that include poor test sensitivity early in infection, lack of immune response in some individuals, overlying infection and vaccination responses, lack of standardization of antibody titers/levels between instruments, unknown titers that confer immune protection, and large between-individual biological variation following infection or vaccination. Thus, the three major components of this review will examine (1) factors that affect serological test utility: test performance, testing matrices, seroprevalence concerns and viral variants, (2) patient factors that affect serological response: timing of sampling, age, sex, body mass index, immunosuppression and vaccination, and (3) informative applications of serological testing: identifying past infection, immune surveillance to guide health practices, and examination of protective immunity. SARS-CoV-2 serological testing should be beneficial for clinical care if it is implemented appropriately. However, as with other laboratory developed tests, use of SARS-CoV-2 serology as a testing modality warrants careful consideration of testing limitations and evaluation of its clinical utility.

Autoimmune encephalitis (AE) is a group of inflammatory conditions that can associate with the presence of antibodies directed to neuronal intracellular, or cell surface antigens. These disorders are increasingly recognized as an important differential diagnosis of infectious encephalitis and of other common neuropsychiatric conditions. Autoantibody diagnostics plays a pivotal role for accurate diagnosis of AE, which is of utmost importance for the prompt recognition and early treatment. Several AE subgroups can be identified, either according to the prominent clinical phenotype, presence of a concomitant tumor, or type of neuronal autoantibody, and recent diagnostic criteria have provided important insights into AE classification. Antibodies to neuronal intracellular antigens typically associate with paraneoplastic neurological syndromes and poor prognosis, whereas antibodies to synaptic/neuronal cell surface antigens characterize many AE subtypes that associate with tumors less frequently, and that are often immunotherapy-responsive. In addition to the general features of AE, we review current knowledge on the pathogenic mechanisms underlying these disorders, focusing mainly on the potential role of neuronal antibodies in the most frequent conditions, and highlight current theories and controversies. Then, we dissect the crucial aspects of the laboratory diagnostics of neuronal antibodies, which represents an actual challenge for both pathologists and neurologists. Indeed, this diagnostics entails technical difficulties, along with particularly interesting novel features and pitfalls. The novelties especially apply to the wide range of assays used, including specific tissue-based and cell-based assays. These assays can be developed in-house, usually in specialized laboratories, or are commercially available. They are widely used in clinical immunology and in clinical chemistry laboratories, with relevant differences in analytic performance. Indeed, several data indicate that in-house assays could perform better than commercial kits, notwithstanding that the former are based on non-standardized protocols. Moreover, they need expertise and laboratory facilities usually unavailable in clinical chemistry laboratories. Together with the data of the literature, we critically evaluate the analytical performance of the in-house vs commercial kit-based approach. Finally, we propose an algorithm aimed at integrating the present strategies of the laboratory diagnostics in AE for the best clinical management of patients with these disorders.

Acute myocardial infarction (AMI) is a leading cause of mortality globally, highlighting the need for timely and accurate diagnostic strategies. Cardiac troponin has been the biomarker of choice for detecting myocardial injury. A dynamic change in concentrations supports the diagnosis of AMI in the setting of evidence of acute myocardial ischemia. The new generation of high-sensitivity cardiac troponin (hs-cTn) assays has significantly improved analytical sensitivity but at the expense of decreased clinical specificity. As a result, sophisticated algorithms are required to differentiate AMI from non-AMI patients. Establishing optimal hs-cTn cutoffs for these algorithms to rule out and rule in AMI has been the subject of intensive investigations. These efforts have evolved from examining the utility of the hs-cTn 99th percentile upper reference limit, comparing the percentage versus absolute delta thresholds, and evaluating the performance of an early European Society of Cardiology-recommended 3 h algorithm, to the development of accelerated 1 h and 2 h algorithms that combine the admission hs-cTn concentrations and absolute delta cutoffs to rule out and rule in AMI. Specific cutoffs for individual confounding factors such as sex, age, and renal insufficiency have also been investigated. At the same time, concerns such as whether the small delta thresholds exceed the analytical and biological variations of hs-cTn assays and whether the algorithms developed in European study populations fit all other patient cohorts have been raised. In addition, the accelerated algorithms leave a substantial number of patients in a non-diagnostic observation zone. How to properly diagnose patients falling in this zone and those presenting with elevated baseline hs-cTn concentrations due to the presence of confounding factors or comorbidities remain open questions. Here we discuss the developments described above, focusing on criteria and underlying considerations for establishing optimal cutoffs. In-depth analyses are provided on the influence of biological variation, analytical imprecision, local AMI rate, and the timing of presentation on the performance metrics of the accelerated hs-cTn algorithms. Developing diagnostic strategies for patients who remain in the observation zone and those presenting with confounding factors are also reviewed.