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BackgroundIn the era of information explosion, the use of the internet to assist with clinical practice and diagnosis has become a cutting-edge area of research. The application of medical informatics allows patients to be aware of their clinical conditions, which may contribute toward the prevention of several chronic diseases and disorders. ObjectiveIn this study, we applied machine learning techniques to construct a medical database system from electronic medical records (EMRs) of subjects who have undergone health examination. This system aims to provide online self-health evaluation to clinicians and patients worldwide, enabling personalized health and preventive health. MethodsWe built a medical database system based on the literature, and data preprocessing and cleaning were performed for the database. We utilized both supervised and unsupervised machine learning technology to analyze the EMR data to establish prediction models. The models with EMR databases were then applied to the internet platform. ResultsThe validation data were used to validate the online diagnosis prediction system. The accuracy of the prediction model for metabolic syndrome reached 91%, and the area under the receiver operating characteristic (ROC) curve was 0.904 in this system. For chronic kidney disease, the prediction accuracy of the model reached 94.7%, and the area under the ROC curve (AUC) was 0.982. In addition, the system also provided disease diagnosis visualization via clustering, allowing users to check their outcome compared with those in the medical database, enabling increased awareness for a healthier lifestyle. ConclusionsOur web-based health care machine learning system allowed users to access online diagnosis predictions and provided a health examination report. Users could understand and review their health status accordingly. In the future, we aim to connect hospitals worldwide with our platform, so that health care practitioners can make diagnoses or provide patient education to remote patients. This platform can increase the value of preventive medicine and telemedicine.

BackgroundMetabolic syndrome is a cluster of disorders that significantly influence the development and deterioration of numerous diseases. FibroScan is an ultrasound device that was recently shown to predict metabolic syndrome with moderate accuracy. However, previous research regarding prediction of metabolic syndrome in subjects examined with FibroScan has been mainly based on conventional statistical models. Alternatively, machine learning, whereby a computer algorithm learns from prior experience, has better predictive performance over conventional statistical modeling. ObjectiveWe aimed to evaluate the accuracy of different decision tree machine learning algorithms to predict the state of metabolic syndrome in self-paid health examination subjects who were examined with FibroScan. MethodsMultivariate logistic regression was conducted for every known risk factor of metabolic syndrome. Principal components analysis was used to visualize the distribution of metabolic syndrome patients. We further applied various statistical machine learning techniques to visualize and investigate the pattern and relationship between metabolic syndrome and several risk variables. ResultsObesity, serum glutamic-oxalocetic transaminase, serum glutamic pyruvic transaminase, controlled attenuation parameter score, and glycated hemoglobin emerged as significant risk factors in multivariate logistic regression. The area under the receiver operating characteristic curve values for classification and regression trees and for the random forest were 0.831 and 0.904, respectively. ConclusionsMachine learning technology facilitates the identification of metabolic syndrome in self-paid health examination subjects with high accuracy.

Maleic acid (MA) induces renal tubular cell dysfunction directed to acute kidney injury (AKI). AKI is an increasing global health burden due to its association with mortality and morbidity. However, targeted therapy for AKI is lacking. Previously, we determined mitochondrial-associated proteins are MA-induced AKI affinity proteins. We hypothesized that mitochondrial dysfunction in tubular epithelial cells plays a critical role in AKI. In vivo and in vitro systems have been used to test this hypothesis. For the in vivo model, C57BL/6 mice were intraperitoneally injected with 400 mg/kg body weight MA. For the in vitro model, HK-2 human proximal tubular epithelial cells were treated with 2 mM or 5 mM MA for 24 h. AKI can be induced by administration of MA. In the mice injected with MA, the levels of blood urea nitrogen (BUN) and creatinine in the sera were significantly increased (p

Background Knee joint injury is a common military training injury, which seriously affects the combat effectiveness of soldiers. In recent years, the prevention and treatment of military training injury becomes more and more important. Ultrasound therapy and Qingpeng ointment are commonly used to treat sports training injuries. However, the protective effectiveness of ultrasound combined with the external use of Qingpeng ointment for knee injury in sports training injury patients is less reported. Objective To comprehensively assess the clinical efficacy of Qingpeng ointment combined with ultrasound therapy in the treatment of knee injuries caused by military training. Methods Fifty-six patients with knee injury who were treated in the Fifth Pentry Outpatient Clinic, the General Hospital of the Western Theater Command PLA from May to July 2022 were selected and randomly grouped into control group (4 cases fell off during the study, n=24) and treatment group (n=28) using IBM-SPSS 25.0 statistical software to generate random numbers. The control group was given ordinary coupling agent ultrasonic therapy, and the treatment group was given Qingpeng ointment ultrasonic therapy, once a day, 10 times a course, a total of 2 courses. The Lysholm knee joint score, VAS score, Hamilton Depression Scale-17 (HAMD-17), and knee range of motion were compared between the two groups after a twenty-minute intervention. Results Before treatment, there was no significant difference in Lysholm knee joint score, VAS score, HAMD-17 score and knee joint motion between the two groups (P>0.05). After 2 courses of treatment, the passive joint motion of the knee in the treatment group was better than that in the control group (P

Abstract Background Atherosclerotic cardiovascular diseases remain the leading cause of mortality in diabetic patients, with endothelial cell (EC) dysfunction serving as the initiating step of atherosclerosis, which is exacerbated in diabetes. Krüppel-like factor 11 (KLF11), known for its missense mutations leading to the development of diabetes in humans, has also been identified as a novel protector of vascular homeostasis. However, its role in diabetic atherosclerosis remains unexplored. Methods Diabetic atherosclerosis was induced in both EC-specific KLF11 transgenic and knockout mice in the Ldlr−/− background by feeding a diabetogenic diet with cholesterol (DDC). Single-cell RNA sequencing (scRNA-seq) was utilized to profile EC dysfunction in diabetic atherosclerosis. Additionally, gain- and loss-of-function experiments were conducted to investigate the role of KLF11 in hyperglycemia-induced endothelial cell dysfunction. Results We found that endothelial KLF11 deficiency significantly accelerates atherogenesis under diabetic conditions, whereas KLF11 overexpression remarkably inhibits it. scRNA-seq profiling demonstrates that loss of KLF11 increases endothelial-to-mesenchymal transition (EndMT) during atherogenesis under diabetic conditions. Utilizing gain- and loss-of-function approaches, our in vitro study reveals that KLF11 significantly inhibits EC inflammatory activation and TXNIP-induced EC oxidative stress, as well as Notch1/Snail-mediated EndMT under high glucose exposure. Conclusion Our study demonstrates that endothelial KLF11 is an endogenous protective factor against diabetic atherosclerosis. These findings indicate that manipulating KLF11 could be a promising approach for developing novel therapies for diabetes-related cardiovascular complications.

Abstract Eels migrate along largely unknown routes to their spawning ground. By coupling Zermelo’s navigation solution and data from the Japan Coastal Ocean Predictability Experiment 2 (JCOPE2M), we simulated a range of seasonal scenarios, swimming speeds, and swimming depths to predict paths that minimize migration duration and energy cost. Our simulations predict a trade-off between migration duration and energy cost. Given that eels do not refuel during their migration, our simulations suggest eels should travel at speeds of 0.4–0.6 body-length per second to retain enough energy reserves for reproduction. For real eels without full information of the ocean currents, they cannot optimize their migration in strong surface currents, thus when swimming at slow swimming speeds, they should swim at depths of 200 m or greater. Eels swimming near the surface are also influenced by seasonal factors, however, migrating at greater depths mitigates these effects. While greater depths present more favorable flow conditions, water temperature may become increasingly unfavorable, dropping near or below 5 °C. Our results serve as a benchmark, demonstrating the complex interplay between swimming speed, depth, seasonal factors, migration time, and energy consumption, to comprehend the migratory behaviors of Japanese eels and other migratory fish.

Abstract Coherent optics has profoundly impacted diverse applications ranging from communications, LiDAR to quantum computations. However, developing coherent systems in integrated photonics comes at great expense in hardware integration and energy efficiency. Here we demonstrate a high-coherence parallelization strategy for advanced integrated coherent systems at minimal cost. By using a self-injection locked microcomb to injection lock distributed feedback lasers, we achieve a record high on-chip gain of 60 dB with no degradation in coherence. This strategy enables highly coherent channels with linewidths down to 10 Hz and power over 20 dBm. The overall electrical-to-optical efficiency reaches 19%, comparable to that of advanced semiconductor lasers. This method supports a silicon photonic communication link with an unprecedented data rate beyond 60 Tbit/s and reduces phase-related DSP consumption by 99.99999% compared to traditional III-V laser pump schemes. This work paves the way for realizing scalable, high-performance coherent integrated photonic systems, potentially benefiting numerous applications.

Nocturia is defined as the nocturnal frequency of one or more voiding episodes per night. It increases with aging and has an impact on sleep quality and the risks of falling and mortality. Nocturia disorder involves nighttime frequency, nocturnal polyuria, and nocturnal enuresis. In older adults with nocturia disorder, multiple factors could contribute to nocturia severity and characteristics, including poor sleep quality, lower urinary tract dysfunction, and excessive fluid output. Several nonurological medical diseases have been found to result in nocturia, such as hypertension, congestive heart failure, chronic kidney disease, chronic obstructive pulmonary disease, metabolic syndrome, and diabetes. Urological and medical assessments should be performed to diagnose nocturia disorder. A frequency volume chart to evaluate the nocturnal polyuria index, functional bladder capacity, and urodynamic study can reveal the presence of nocturnal polyuria and lower urinary tract dysfunction. Treatment should be based on multiple nocturia etiologies, and a combination of multiple therapies for individual pathophysiology will achieve a better treatment outcome.

Abstract The presence of glucose-6-phosphate dehydrogenase (G6PD) deficiency may increase the risk of type 2 diabetes mellitus (T2DM), with differing prevalence between males and females. Although G6PD deficiency is an X-linked genetic condition, its interaction with sex regarding T2DM risk among the Taiwanese population has not been fully explored. This study aimed to investigate the association between G6PD deficiency and T2DM risk in the Taiwanese population, focusing on the potential influence of sex. Data were obtained from the Taiwan Biobank (TWB) database, involving 85,334 participants aged 30 to 70 years. We used multiple logistic regression analysis to assess the interaction between G6PD rs72554664 and sex in relation to T2DM risk. The T2DM cohort comprised 55.35% females and 44.65% males (p

Pseudorabies virus (PRV) is the pathogenic agent of pseudorabies, causing serious reproductive failure in swine. However, it is still unknown whether PRV uterine inoculation impairs blastocyst implantation. In the present study, a PRV infection mouse model was developed. Pregnant mice were inoculated with either 104 or 105 50% tissue culture infectious dose (TCID50) units of PRV on gestation day 2 (GD2). Viral DNA was detected in tissues by PCR and/or in situ hybridization. Histopathological change and expression of proinflammatory cytokines in uterus were analyzed by H.E. staining and qPCR, respectively. Apoptosis was also investigated by TUNEL assay, and the expression of apoptosis-related proteins including Bax and Bcl-2 was detected by Western blot. The results showed that intrauterine exposure of PRV on GD2 reduced the number of embryo implantation site. Abundant viral DNA was detected in spinal marrow and brain, and small amounts of PRV genomes were detected in embryo implantation site, ovary as well as thymus. Considerable inflammatory cells infiltrating in the endometrium, with high levels of pro-inflammatory cytokines of interleukin (IL)-6, IL-1β and tumor necrosis factor-α mRNA after infection. In addition, PRV infection promoted apoptosis in stroma and endothelium of the mouse endometrium. Collectively, intrauterine inoculation of PRV during early pregnancy causes cytokine release syndrome and apoptosis in endometrium, which impairs mouse embryo implantation.

Objectives: The relationship between left atrial (LA) size and atrial fibrillation (AF) is well-established. However, the specifics of LA regional remodeling and its connection to left atrial tachycardia (LA-AT) post-ablation in patients with non-paroxysmal AF are less understood. This study aims to explore how LA dimensions are related to the development of LA-AT following AF ablation procedures in these patients. Methods: This study focused on 73 patients with non-paroxysmal atrial fibrillation (average age 52, predominantly male, with a nearly even split between persistent and long-lasting persistent AF), all undergoing their first catheter ablation for AF. Prior to the ablation, left atrial dimensions were determined through computed tomography, measuring the maximal transverse, anteroposterior, and superoinferior diameters. Results: Over an average follow-up period of 23 months, 31.5% of the patients (Group 1) experienced left atrial tachycardia (LA-AT) that required a second linear ablation procedure. This group had significantly larger left atrial (LA) dimensions in terms of transverse, anteroposterior, and superoinferior measurements compared to the other group (Group 2). However, the recurrence of atrial fibrillation (AF) was not linked to any specific LA diameter. The anteroposterior diameter was identified as a significant predictor (p=0.002, HR 2.3, 95% CI 1.3-3.8) for LA-AT occurrence through multivariate analysis. Conclusions: Eccentric dilatation involving the anteroposterior diameter is a significant predictor for the occurrence of LA-AT in patients with non-paroxysmal AF after catheter ablation. Keywords Atrial fibrillation; Atrial flutter; Atrial tachycardia; Left atrial diameter; Catheter ablation; Computed tomography