Search

The White Blood Cell (WBC) differential test ranks as the second most frequently performed diagnostic assay. It requires manual confirmation of the peripheral blood smear by experts to identify signs of abnormalities. Automated digital microscopy has emerged as a solution to reduce this labor-intensive process and improve efficiency. Several publicly available datasets provide various WBC subtypes of differing quality and resolution. These datasets have contributed to advancing WBC classification using machine learning techniques. However, digital microscopy of blood cells with high magnification often requires a wider depth of field, posing challenges for automatic digital microscopy that necessitates capturing multiple stacks of focal planes to obtain complete images of specific blood cells. Our dataset provides 25,773 image stacks from 72 patients. The image labels consist of 18 classes encompassing normal and abnormal cells, with two experts reviewing each label. Each image includes 10 z-stacks of cropped 200 by 200 pixel images, captured using a 50X microscope with 400 nm intervals. This study presents a comprehensive multi-focus dataset for WBC classification., (© 2024. The Author(s).)

Improvements in digital microscopy are critical for the development of a malaria diagnosis method that is accurate at the cellular level and exhibits satisfactory clinical performance. Digital microscopy can be enhanced by improving deep learning algorithms and achieving consistent staining results. In this study, a novel miLab™ device incorporating the solid hydrogel staining method was proposed for consistent blood film preparation, eliminating the use of complex equipment and liquid reagent maintenance. The miLab™ ensures consistent, high-quality, and reproducible blood films across various hematocrits by leveraging deformable staining patches. Embedded-deep-learning-enabled miLab™ was utilized to detect and classify malarial parasites from autofocused images of stained blood cells using an internal optical system. The results of this method were consistent with manual microscopy images. This method not only minimizes human error but also facilitates remote assistance and review by experts through digital image transmission. This method can set a new paradigm for on-site malaria diagnosis. The miLab™ algorithm for malaria detection achieved a total accuracy of 98.86% for infected red blood cell (RBC) classification. Clinical validation performed in Malawi demonstrated an overall percent agreement of 92.21%. Based on these results, miLab™ can become a reliable and efficient tool for decentralized malaria diagnosis., Competing Interests: Authors CYB, YMS, MK, YS, HJL, KWK, HWL, YJK, B-IK, SH, S-HC, BMW, CYL, K-HC were employed by Noul Co. Ltd. H-PB, DKL were a member of Scientific Advisory Board (Technical Consultant) in Noul Co., Ltd. The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Bae, Shin, Kim, Song, Lee, Kim, Lee, Kim, Kanyemba, Lungu, Kang, Han, Beck, Cho, Woo, Lim and Choi.)

The imaging and quantification of stained red blood cells (RBCs) are important for identifying RBCs in hematology and for diagnosing diseased RBCs or parasites in cytopathology. Romanowsky staining has been used traditionally to produce hues in blood cells using a mixture of anionic eosin Y and cationic methylene blue and azure B. While Romanowsky stains have been widely used in cytopathology, end-users have experienced problems with varying results in staining due to the premature precipitation or evaporation of methanol, leading to the inherent inconsistency of solution-based Romanowsky staining. Herein, we demonstrate that the staining and destaining of blood smears are controllable by the contact time of agarose gel stamps. While the extent of staining and destaining is discernable by the hue values of stamped red blood cells in micrographs, the quantification of adsorbed and desorbed Romanowsky dye molecules (in particular, eosin Y, methylene blue and azure B) from and to the agarose gel stamps needs a model that can explain the sorption process. We found predictable sorption of the Romanowsky dye molecules from the pseudo-second-order kinetic model for adsorption and the one phase decay model for desorption. Thus, the method of agarose gel stamping demonstrated here could be an alternative to solution-based Romanowsky staining with the predictable quantity of sorption and timing of contact.

Background: Cancer is one of the main global health threats. Early personalized prediction of cancer incidence is crucial for the population at risk. This study introduces a novel cancer prediction model based on modern recurrent survival deep learning algorithms. Methods: The study includes 160,407 participants from the blood-based cohort of the Korea Cancer Prevention Research-II Biobank, which has been ongoing since 2004. Data linkages were designed to ensure anonymity, and data collection was carried out through nationwide medical examinations. Predictive performance on ten cancer sites, evaluated using the concordance index (c-index), was compared among nDeep and its multitask variation, Cox proportional hazard (PH) regression, DeepSurv, and DeepHit. Results: Our models consistently achieved a c-index of over 0.8 for all ten cancers, with a peak of 0.8922 for lung cancer. They outperformed Cox PH regression and other survival deep neural networks. Conclusion: This study presents a survival deep learning model that demonstrates the highest predictive performance on censored health dataset, to the best of our knowledge. In the future, we plan to investigate the causal relationship between explanatory variables and cancer to reduce cancer incidence and mortality.

Bacterial two-component signal transduction systems provide sensory inputs for appropriately adapting gene expression. These systems rely on a histidine kinase that phosphorylates a response regulator which alters gene expression. Several two-component systems include additional sensory components that can activate the histidine kinase. In Escherichia coli, the lipoprotein NlpE was identified as a sensor for the Cpx cell envelope stress response. It has remained unclear how NlpE signals to Cpx in the periplasm. In this study, we used a combination of genetics, biochemistry, and AlphaFold2 complex modeling to uncover the molecular details of how NlpE triggers the Cpx response through an interaction with the CpxA histidine kinase. Remarkably, only a short loop of NlpE is required to activate the Cpx response. A single substitution in this loop inactivates NlpE signaling to Cpx and abolishes an in vivo biochemical NlpE:CpxA interaction. An independent AlphaFold multimer prediction supported a role for the loop and predicted an interaction interface at CpxA. Mutations in this CpxA region specifically blind the histidine kinase to NlpE activation but preserve the ability to respond to other cell envelope stressors. Hence, our work additionally reveals a previously unrecognized complexity in signal integration by the CpxA periplasmic sensor domain., (© 2023 John Wiley & Sons Ltd.)

Objectives: The world is witnessing a sharp increase in its elderly population, accelerated by longer life expectancy and lower birth rates, which in turn imposes enormous medical burden on society. Although numerous studies have predicted medical expenses based on region, gender, and chronological age (CA), any attempt has rarely been made to utilize biological age (BA)-an indicator of health and aging-to ascertain and predict factors related to medical expenses and medical care use. Thus, this study employs BA to predict factors that affect medical expenses and medical care use., Materials and Methods: Referring to the health screening cohort database of the National Health Insurance Service (NHIS), this study targeted 276,723 adults who underwent health check-ups in 2009-2010 and kept track of the data on their medical expenses and medical care use up to 2019. The average follow-up period is 9.12 years. Twelve clinical indicators were used to measure BA, while the total annual medical expenses, total annual number of outpatient days, total annual number of days in hospital, and average annual increases in medical expenses were used as the variables for medical expenses and medical care use. For statistical analysis, this study employed Pearson correlation analysis and multiple regression analysis., Results: Regression analysis of the differences between corrected biological age (cBA) and CA exhibited statistically significant increases (p<0.05) in all the variables of the total annual medical expenses, total annual number of outpatient days, total annual number of days in hospital, and average annual increases in medical expenses., Conclusions: This study quantified decreases in the variables for medical expenses and medical care use based on improved BA, thereby motivating people to become more health-conscious. In particular, this study is significant in that it is the first of its kind to predict medical expenses and medical care use through BA., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Bae et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Purpose: As the population ages rapidly, the incidence of age-related diseases (ARDs) is also increasing fast. Predicting the incidence of ARDs is a challenge since the rates of individual aging vary, and objective assessments of the stages of aging based on chronological age (CA) may be inaccurate. Thus, in this study, we developed a biological age (BA) model based on the National Health Examination (NHE) data and analyzed the model prediction results for the incidence of 16 ARDs., Methods: This study was based on the 2002-2019 National Health Information Databases of the National Health Insurance Service (NHIS-NHID). The data from a total of 10,002,494 subjects were selected between 2009 and 2010, and the principal component analysis (PCA) was performed to develop the BA model. The Cox-proportional hazard model was used to perform predictive analysis of the ARD incidence., Results: For the unit increase in the difference between corrected biological age (cBA) and chronological age (CA), the hazard ratios (HRs) of ARDs increased significantly for both sexes (p < 0.001). In descending order, the corresponding ARDs' HRs were obesity (1.655), chronic renal failure (1.362), hypertension (1.301), hyperlipidemia (1.264), diabetes mellitus (1.261), fracture (1.119), dementia (1.163), cataract (1.116), myocardial infarction (1.097), stroke (1.169), macular degeneration (1.075), osteoarthritis (1.059), osteoporosis (1.124), Parkinson's disease (1.048), and chronic obstructive pulmonary disease (1.026)., Conclusions: In this study, the incidence of 16 ARDs were analyzed based on BA. Therefore, conducting the NHIS health examination can facilitate the prevention of ARDs by estimating HRs for at least 16 diseases., Competing Interests: Declaration of Competing Interest Declarations of interest: none., (Copyright © 2022. Published by Elsevier B.V.)

Peroxiredoxin-2 (Prx-2) is a critical antioxidant protein in red blood cells (RBC). Prx-2 is oxidized to a disulfide covalently-bound dimer by H 2 O 2 , and then reduced back by the NADPH-dependent thioredoxin-thioredoxin reductase system. The reduction of oxidized Prx-2 is relatively slow in RBCs. Since Prx-2 is highly abundant, Prx-2s' peroxidase catalytic cycle is not considered to be limiting under normal conditions. However, whether Prx-2 recycling becomes limiting when RBCs are exposed to stress is not known. Using three different model systems characterized by increased oxidative damage to RBCs spanning the physiologic (endogenous RBCs of different ages), therapeutic (cold-stored RBCs in blood banks) and pathologic (RBCs from sickle cell disease (SCD) patients and humanized SCD mice) spectrum, basal levels of Prx-2 oxidation and Prx-2 recycling kinetics after addition of H 2 O 2 were determined. The reduction of oxidized Prx-2 was significantly slower in older versuin older versus younger RBCs, in RBCs stored for 4-5 weeks compared to 1 week, and in RBC from pediatric SCD patients compared to RBCs from control non-SCD patients. Similarly, the rate of Prx-2 recycling was slower in humanized SCD mice compared to WT mice. Treatment of RBC with carbon monoxide (CO) to limit heme-peroxidase activity had no effect on Prx-2 recycling kinetics. Treatment with glucose attenuated slowed Prx-2 recycling in older RBCs and SCD RBCs, but not stored RBCs. In conclusion, the reduction of oxidized Prx-2 can be further slowed in RBCs, which may limit the protection afforded by this antioxidant protein in settings associated with erythrocyte stress., (© 2022 Federation of American Societies for Experimental Biology.)