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BackgroundThe increasing number of patients treated with peritoneal dialysis (PD) and their consistently high rate of hospital admissions have placed a large burden on the health care system. Early clinical interventions and optimal management of patients at a high risk of prolonged length of stay (pLOS) may help improve the medical efficiency and prognosis of PD-treated patients. If timely clinical interventions are not provided, patients at a high risk of pLOS may face a poor prognosis and high medical expenses, which will also be a burden on hospitals. Therefore, physicians need an effective pLOS prediction model for PD-treated patients. ObjectiveThis study aimed to develop an optimal data-driven model for predicting the pLOS risk of PD-treated patients using basic admission data. MethodsPatient data collected using the Hospital Quality Monitoring System (HQMS) in China were used to develop pLOS prediction models. A stacking model was constructed with support vector machine, random forest (RF), and K-nearest neighbor algorithms as its base models and traditional logistic regression (LR) as its meta-model. The meta-model used the outputs of all 3 base models as input and generated the output of the stacking model. Another LR-based pLOS prediction model was built as the benchmark model. The prediction performance of the stacking model was compared with that of its base models and the benchmark model. Five-fold cross-validation was employed to develop and validate the models. Performance measures included the Brier score, area under the receiver operating characteristic curve (AUROC), estimated calibration index (ECI), accuracy, sensitivity, specificity, and geometric mean (Gm). In addition, a calibration plot was employed to visually demonstrate the calibration power of each model. ResultsThe final cohort extracted from the HQMS database consisted of 23,992 eligible PD-treated patients, among whom 30.3% had a pLOS (ie, longer than the average LOS, which was 16 days in our study). Among the models, the stacking model achieved the best calibration (ECI 8.691), balanced accuracy (Gm 0.690), accuracy (0.695), and specificity (0.701). Meanwhile, the stacking and RF models had the best overall performance (Brier score 0.174 for both) and discrimination (AUROC 0.757 for the stacking model and 0.756 for the RF model). Compared with the benchmark LR model, the stacking model was superior in all performance measures except sensitivity, but there was no significant difference in sensitivity between the 2 models. The 2-sided t tests revealed significant performance differences between the stacking and LR models in overall performance, discrimination, calibration, balanced accuracy, and accuracy. ConclusionsThis study is the first to develop data-driven pLOS prediction models for PD-treated patients using basic admission data from a national database. The results indicate the feasibility of utilizing a stacking-based pLOS prediction model for PD-treated patients. The pLOS prediction tools developed in this study have the potential to assist clinicians in identifying patients at a high risk of pLOS and to allocate resources optimally for PD-treated patients.

BackgroundLong-distance running can be a form of stress to the heart. Technological improvements combined with the public’s gradual turn toward mobile health (mHealth), self-health, and exercise effectiveness have resulted in the widespread use of wearable exercise products. The monitoring of dynamic cardiac function changes during running and running performance should be further studied. ObjectiveWe investigated the relationship between dynamic cardiac function changes and finish time for 3000-meter runs. Using a wearable device based on a novel cardiac force index (CFI), we explored potential correlations among 3000-meter runners with stronger and weaker cardiac functions during running. MethodsThis study used the American product BioHarness 3.0 (Zephyr Technology Corporation), which can measure basic physiological parameters including heart rate, respiratory rate, temperature, maximum oxygen consumption, and activity. We investigated the correlations among new physiological parameters, including CFI = weight * activity / heart rate, cardiac force ratio (CFR) = CFI of running / CFI of walking, and finish times for 3000-meter runs. ResultsThe results showed that waist circumference, smoking, and CFI were the significant factors for qualifying in the 3000-meter run. The prediction model was as follows: ln (3000 meters running performance pass probability / fail results probability) = –2.702 – 0.096 × [waist circumference] – 1.827 × [smoke] + 0.020 × [ACi7]. If smoking and the ACi7 were controlled, contestants with a larger waist circumference tended to fail the qualification based on the formula above. If waist circumference and ACi7 were controlled, smokers tended to fail more often than nonsmokers. Finally, we investigated a new calculation method for monitoring cardiac status during exercise that uses the CFI of walking for the runner as a reference to obtain the ratio between the cardiac force of exercise and that of walking (CFR) to provide a standard for determining if the heart is capable of exercise. A relationship is documented between the CFR and the performance of 3000-meter runs in a healthy 22-year-old person. During the running period, data are obtained while participant slowly runs 3000 meters, and the relationship between the CFR and time is plotted. The runner’s CFR varies with changes in activity. Since the runner’s acceleration increases, the CFR quickly increases to an explosive peak, indicating the runner’s explosive power. At this period, the CFI revealed a 3-fold increase (CFR=3) in a strong heart. After a time lapse, the CFR is approximately 2.5 during an endurance period until finishing the 3000-meter run. Similar correlation is found in a runner with a weak heart, with the CFR at the beginning period being 4 and approximately 2.5 thereafter. ConclusionsIn conclusion, the study results suggested that measuring the real-time CFR changes could be used in a prediction model for 3000-meter running performance.

Abstract Late Holocene relative sea-level (RSL) data are important to understand the drivers of RSL change, but there is a lack of precise RSL records from the Sunda Shelf. Here, we produced a Late Holocene RSL reconstruction from coral microatolls in Singapore, demonstrating for the first time the utility of Diploastrea heliopora microatolls as sea-level indicators. We produced 12 sea-level index points and three marine limiting data with a precision of

Human respiratory syncytial virus (HRSV) is the most prevalent pathogen contributing to acute respiratory tract infections (ARTI) in infants and young children and can lead to significant financial and medical costs. Here, we developed a simultaneous, dual-gene and ultrasensitive detection system for typing HRSV within 60 minutes that needs only minimum laboratory support. Briefly, multiplex integrating reverse transcription-recombinase polymerase amplification (RT-RPA) was performed with viral RNA extracted from nasopharyngeal swabs as a template for the amplification of the specific regions of subtypes A (HRSVA) and B (HRSVB) of HRSV. Next, the Pyrococcus furiosus Argonaute (PfAgo) protein utilizes small 5’-phosphorylated DNA guides to cleave target sequences and produce fluorophore signals (FAM and ROX). Compared with the traditional gold standard (RT-qPCR) and direct immunofluorescence assay (DFA), this method has the additional advantages of easy operation, efficiency and sensitivity, with a limit of detection (LOD) of 1 copy/μL. In terms of clinical sample validation, the diagnostic accuracy of the method for determining the HRSVA and HRSVB infection was greater than 95%. This technique provides a reliable point-of-care (POC) testing for the diagnosis of HRSV-induced ARTI in children and for outbreak management, especially in resource-limited settings.

AbstractBauhinia glauca subsp. hupehana (Craib) T. C. Chen 1988, a member of the Leguminosae family, Cercidoideae subfamily, and Bauhinia genus, has a rich history of traditional usage in Chinese medicine. Renowned for its analgesic properties, it is commonly employed for managing inflammation and pain. This study aimed to sequence the complete chloroplast genome of B. glauca subsp. hupehana using Illumina paired-end sequencing data. The chloroplast genome spans 156,967 bp and consists of four main regions: the large single-copy (LSC) region (89,185 bp), the small single-copy (SSC) region (19,146 bp), and a pair of inverted repeats (IRs) (24,318 bp). The overall GC content of the chloroplast genome is 36.19%, with specific values of 33.99%, 29.79%, and 42.76% for the LSC, SSC, and IR regions, respectively. A total of 128 genes were annotated in the chloroplast genome, including 83 protein-coding genes, 37 tRNA genes, and eight rRNA genes. Phylogenetic analysis revealed that B. glauca subsp. hupehana is closely related to Bauhinia racemose, indicating a sister taxon relationship between the two species. This study significantly contributes to the chloroplast genomic resource for Bauhinia, laying the groundwork for future phylogenetic investigations within the genus.

To assess the effectiveness of subgrid scale (SGS) models on the prediction results of unsteady loads and turbulent fluctuation of pumpjet propulsors equipped with both front and rear stators, a pumpjet propulsor computational model with attached parts at the model scale is developed using a fully structured mesh, and large eddy simulations are conducted. The computational results of the different SGS models are compared based on five aspects: open water characteristics, turbulence parameters, incoming turbulence spectrum, vortex structure, and fluctuating pressure. Their results are also compared with the experimental values, and the correlation between the internal flow characteristics of the pumpjet propulsor and the turbulent fluctuation is analyzed. According to the results, as regards the prediction of the open water performance of the pumpjet propulsor containing both front and rear stators, the overall trend obtained by the three subgrid models is similar, and the error between the values predicted by the SL model and the experimental ones is the smallest. At the same mesh level, the turbulent fluctuating scale obtained by the SL model is larger than that obtained by the WALE and DSL models, and the turbulent time scale obtained by the DSL model has the smallest fluctuation in the circumferential direction. Among the three SGS models, the turbulent fluctuating scale of the SL model is larger than those of the WALE and DSL models. The SL model exhibits the largest energy dissipation among the three SGS models, followed by the DSL model, while that of the WALE model is the smallest. In the WALE model, the leakage vortex at the top of the blade is the longest, followed by the DSL model, while it is the shortest in the SL model. In the WALE and DSL models, the fluctuating load fluctuates more in the transition region from the middle section to the trailing edge of the blade.