Activity

Such journals have represented and hopefully will continue to represent the privileged place of welcome for future scientific research in hemocytometry. Improved technologies, attention to quality, new reagents and electronics, information technology, and scientist talent ensure a more profound and deeper knowledge of cell properties current laboratory devices measure and count even minor immature or pathological cell subpopulations. Full-field hemocytometry includes the analysis of nonhematic fluids, digital adds to the microscope, and the development of effective point-of-care devices.TTP is a life-threatening disorder diagnosed using a combination of clinical information and laboratory results. ADAMTS13 activity and antibody testing represent a major advance in the field, but results can sometimes be difficult to interpret due to technical aspects of the tests and characteristics of the causative antibodies in acquired TTP. Genetic testing for ADAMTS13 mutations is also now available to assist with the diagnosis of inherited TTP. This review will focus on ADAMTS13 testing and will highlight patient and laboratory aspects that can lead to diagnostic difficulty. The effects of TTP therapies on test results will also be discussed.Ever since hematopoietic cells became “events” enumerated and characterized in suspension by cell counters or flow cytometers, researchers and engineers have strived to refine the acquisition and display of the electronic signals generated. A large array of solutions was then developed to identify at best the numerous cell subsets that can be delineated, notably among hematopoietic cells. As instruments became more and more stable and robust, the focus moved to analytic software. Almost concomitantly, the capacity increased to use large panels (both with mass and classical cytometry) and to apply artificial intelligence/machine learning for their analysis. The combination of these concepts raised new analytical possibilities, opening an unprecedented field of subtle exploration for many conditions, including hematopoiesis and hematological disorders. In this review, the general concepts and progress achieved in the development of new analytical approaches for exploring high-dimensional data sets at the single-cell level will be described as they appeared over the past few years. A larger and more practical part will detail the various steps that need to be mastered, both in data acquisition and in the preanalytical check of data files. Finally, a step-by-step explanation of the solution in development to combine the Bioconductor clustering algorithm FlowSOM and the popular and widely used software Kaluza® (Beckman Coulter) will be presented. The aim of this review was to point out that the day when these progresses will reach routine hematology laboratories does not seem so far away.Artificial Intelligence (AI) and machine learning (ML) have now spawned a new field within health care and health science research. These new predictive analytics tools are starting to change various facets of our clinical care domains including the practice of laboratory medicine. Many of these ML tools and studies are also starting to populate our literature landscape as we know it but unfamiliarity of the average reader to the basic knowledge and critical concepts within AI/ML is now demanding a need to better prepare our audience to such relatively unfamiliar concepts. A fundamental knowledge of such platforms will inevitably enhance cross-disciplinary literacy and ultimately lead to enhanced integration and understanding of such tools within our discipline. read more In this review, we provide a general outline of AI/ML along with an overview of the fundamental concepts of ML categories, specifically supervised, unsupervised, and reinforcement learning. Additionally, since the vast majority of our current approaches within ML in laboratory medicine and health care involve supervised algorithms, we will predominantly concentrate on such platforms. Finally, the need for making such tools more accessible to the average investigator is becoming a major driving force for the need of automation within these ML platforms. This has now given rise to the automated ML (Auto-ML) world which will undoubtedly help shape the future of ML within health care. Hence, an overview of Auto-ML is also covered within this manuscript which will hopefully enrich the reader’s understanding, appreciation, and the need for embracing such tools.

Relationships between stress urinary incontinence (SUI) and physical function and spinal alignment have not been fully elucidated; therefore, we examined these relationships in older women.

The participants of this cross-sectional study comprised 21 women with SUI (SUI group) and 41 continent women (continent group) aged >65 years who participated in a community-based health-check survey from 2018 to 2019. We examined age, body mass index, number of deliveries, age at first childbirth, and medical histories as participants’ characteristics. SUI was evaluated using the International Consultation on Incontinence Questionnaire-Short Form (ICIQ-SF). We also assessed spinal alignment and physical activity, grip strength, trunk and lower limb muscle mass, gait speed, and one-leg standing time as measures of participants’ physical function.

Body mass index was significantly higher in the SUI group compared with continents (P=0.04), and trunk muscle mass in the SUI group was significantly lower (P < 0.01). Additionally, the thoracic kyphosis angle in the SUI group was significantly larger (P=0.02). In the logistic regression analysis, trunk muscle mass (odds ratio=0.546, P=0.03) and increased thoracic kyphosis angle (odds ratio=1.066, P=0.045) were independent factors affecting SUI. Furthermore, there was a negative weak correlation between total ICIQ-SF score and trunk muscle mass (r=-0.36, P < 0.01), and a positive weak correlation between total ICIQ-SF score and thoracic kyphosis angle (r=0.27, P < 0.05).

Trunk muscle mass and thoracic kyphosis angle relate to SUI status and severity among Japanese community-dwelling older women.

Trunk muscle mass and thoracic kyphosis angle relate to SUI status and severity among Japanese community-dwelling older women.