Critical reviews in clinical laboratory sciences最新文献

Haptoglobin (Hp) is a polymorphic protein that was initially described as a hemoglobin (Hb)-binding protein. The major functions of Hp are to scavenge Hb, prevent iron loss, and prevent heme-based oxidation. Hp regulates angiogenesis, nitric oxide homeostasis, immune responses, and prostaglandin synthesis. Genetic polymorphisms in the Hp gene give rise to different phenotypes, including Hp 1-1, Hp 2-1, and Hp 2-2. Extensive research has been conducted to investigate the association between Hp polymorphisms and several medical conditions including cardiovascular disease, inflammatory bowel disease, cancer, transplantation, and hemoglobinopathies. Generally, the Hp 2-2 phenotype is associated with increased disease risk and poor outcomes. Over the years, the Hp 2 allele has spread under genetic pressures. Individuals with the Hp 2-2 phenotype generally exhibit lower levels of CD163 expression in macrophages. The decreased expression of CD163 may be associated with the poor antioxidant capacity in the serum of subjects carrying the Hp 2-2 phenotype. However, the Hp 1-1 phenotype may confer protection in some cases. The Hp1 allele has strong antioxidant, anti-inflammatory, and immunomodulatory properties. It is important to note that the benefits of the Hp1 allele may vary depending on genetic and environmental factors as well as the specific disease or condition under consideration. Therefore, the Hp1 allele may not necessarily confer advantages in all situations, and its effects may be context-dependent. This review highlights the current understanding of the role of Hp polymorphisms in cardiovascular disease, inflammatory bowel disease, cancer, transplantation, hemoglobinopathies, and polyuria.

Point-of-care testing (POCT) is the fastest-growing segment of laboratory medicine. This review focuses on the essential aspects of setting analytical performance specifications (APS) and performing quality assurance for POCT in primary healthcare. In-vitro diagnostic medical devices for POCT are typically small and easy to operate. Users often have little to no laboratory experience and may not necessarily see the value of conducting quality assurance on their devices. Therefore, training, guidance, and motivation should be integral parts of the total quality management system, as they are vital for managing errors and ensuring reliable results. It is common to believe that the analytical quality of POCT should be comparable to that of laboratory testing, and as a result, APS should be the same. This paper challenges this concept. The APS for POCT can often be less stringent compared to those used in a central laboratory because the requester is closer to both the analytical and clinical situation. Point-of-care instruments should be selected based on clinical needs, the required analytical quality and user-friendliness in the intended usage setting.Quality assurance should include both internal quality control (IQC) and external quality assessment (EQA). It is recommended that IQC protocols should be dependent on the complexity of the POCT device. A scoring system to determine how frequent IQC should be analyzed in primary healthcare on different types of POCT devices has been suggested. The main challenge in EQA for POCT involves using suitable control materials that reflect instrument performance on patient samples. Obtaining commutable control materials for POCT is difficult since the matrix often is whole blood. An essential aspect of EQA for POCT is that feedback reports should be easily interpretable. Users should receive advice from the EQA organizer regarding the root causes of deviating results. Quality assurance for POCT is not an easy task and presents numerous challenges. However, there is evidence that quality assurance improves the quality of POCT measurements and, consequently, can enhance patient outcomes.

Serum protein electrophoresis (SPEP) is a valuable laboratory test that separates proteins from the blood based on their electrical charge and size. The test can detect and analyze various protein abnormalities, and the interpretation of graphic SPEP features plays a crucial role in the diagnosis and monitoring of conditions, such as myeloma. Furthermore, the advancement of artificial intelligence (AI) technology presents an opportunity to enhance the organization and optimization of analytical procedures by streamlining the process and reducing the potential for human error in SPEP analysis, thereby making the process more efficient and reliable. For instance, AI can assist in the identification of protein peaks, the calculation of their relative proportions, and the detection of abnormalities or inconsistencies. This review explores the characteristics and limitations of AI in SPEP, and the role of standardization in improving its clinical utility. It also offers guidance on the rational ordering and interpreting of SPEP results in conjunction with AI. Such integration can effectively reduce the time and resources required for manual analysis while improving the accuracy and consistency of the results.

Endometriosis, an enigmatic and chronic disorder, is considered a debilitating condition despite being benign. Globally, this gynecologic disorder affects up to 10% of females of reproductive age, impacting almost 190 million individuals. A variety of genetic and environmental factors are involved in endometriosis development, hence the pathophysiology and etiology of endometriosis remain unclear. The uncertainty of the etiology of the disease and its complexity along with nonspecific symptoms have led to misdiagnosis or lack of diagnosis of affected people. Biopsy and laparoscopy are referred to as the gold standard for endometriosis diagnosis. However, the invasiveness of the procedure, the unnecessary operation in disease-free women, and the dependence of the reliability of diagnosis on experience in this area are considered the most significant limitations. Therefore, continuous studies have attempted to offer a noninvasive and reliable approach. The recent advances in modern technologies have led to the generation of large-scale biological data sets, known as -omics data, resulting in the proceeding of the -omics century in biomedical sciences. Thereby, the present study critically reviews novel and noninvasive biomarkers that are based on -omics approaches from 2020 onward. The findings reveal that biomarkers identified based on genomics, epigenomics, transcriptomics, proteomics, and metabolomics are potentially able to diagnose endometriosis, predict prognosis, and stage patients, and potentially, in the near future, a multi-panel of these biomarkers will generate clinical benefits.

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.