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Objective To explore the effect of sleep duration and body mass index (BMI) on hypertension. Methods The data was collected from the database of the China Health and Retirement Longitudinal Study (CHARLS), and the data of the 2015 National Longitudinal Study were selected. A total of 8, 889 subjects were included in this study. Questionnaires, health status, physical examination and laboratory information were collected. The interactive effect of sleep duration and BMI on hypertension was analyzed by multiplicative model and additive model. Results The prevalence rate of hypertension was 28.86%. Logistic regression results showed that night sleep duration ＜6 h (OR=1.138, 95% CI: 1.024-1.265) and ＞8 h (OR=1.136 5, 95% CI: 1.167-1.598); overweight (OR=1.736, 95% CI: 1.557-1.936) and obesity (OR=2.076, 95% CI: 1.973-2.710) were all risk factors for hypertension (P＜0.05). Stratified results showed that the risk of hypertension in the normal group was 1.193(95% CI: 1.023-1.393) times and 1.555(95% CI: 1.244-1.943) times of those with sleep duration 6-8 h, respectively. The risk of hypertension was 1.392(95% CI: 1.066-1.818) times higher than that of sleep duration 6-8 h. The interaction showed that night sleep duration and BMI had a multiplicative interaction (OR=2.120, 95% CI: 1.767-2.543) and a additive interaction on hypertension [RERI=1.242(95% CI: 0.326-2.157), SI=3.697 (95% CI: 2.658-5.143), AP=0.460 (95% CI: 0.324-0.595)]. Conclusions Insufficient and excessive sleep duration at night and BMI both increase the risk of hypertension, and have a multiplicative and additive interaction. Reasonable adjustment of nighttime sleep duration and BMI may prevent the occurrence of hypertension.

Background As the gatekeepers for rural residents' health, rural doctors are the main providers of health management services for rural populations. Their community health management ability directly affects the health level of rural residents. Objective This study aims to investigate the current status of health management ability of rural doctors in China and explore strategies for enhancing their community health management ability, thus providing a basis for strengthening the building of a rural doctor team. Methods In August 2020, a multi-stage stratified cluster sampling and purposive sampling methods were used to select 3 916 rural doctors from five provinces in east, central, and western China. Rural doctors were surveyed using an online questionnaire platform called "Wenjuanxing". The questionnaire content included the rural doctors' ability to manage the health of children, women, elderly, and chronic disease patients, and the degree to which these needs were met, all of which were self-evaluated by the rural doctors. Results Of the 3 916 rural doctors surveyed, 3 494 (89.22%), 3 175 (81.08%), 3 775 (96.40%), and 3 738 (95.45%) were found to had the ability to manage the health of children, women, the elderly, and patients with chronic diseases, respectively. The ability of female rural doctors to provide family planning guidance, prenatal health guidance, prevention and treatment of menopause-related diseases, and screening for common gynecological diseases was better than that of male rural doctors (P

BackgroundThe newly defined superphylum Patescibacteria such as Parcubacteria (OD1) and Microgenomates (OP11) has been found to be prevalent in groundwater, sediment, lake, and other aquifer environments. Recently increasing attention has been paid to this diverse superphylum including > 20 candidate phyla (a large part of the candidate phylum radiation, CPR) because it refreshed our view of the tree of life. However, adaptive traits contributing to its prevalence are still not well known.ResultsHere, we investigated the genomic features and metabolic pathways of Patescibacteria in groundwater through genome-resolved metagenomics analysis of > 600 Gbp sequence data. We observed that, while the members of Patescibacteria have reduced genomes (~ 1 Mbp) exclusively, functions essential to growth and reproduction such as genetic information processing were retained. Surprisingly, they have sharply reduced redundant and nonessential functions, including specific metabolic activities and stress response systems. The Patescibacteria have ultra-small cells and simplified membrane structures, including flagellar assembly, transporters, and two-component systems. Despite the lack of CRISPR viral defense, the bacteria may evade predation through deletion of common membrane phage receptors and other alternative strategies, which may explain the low representation of prophage proteins in their genomes and lack of CRISPR. By establishing the linkages between bacterial features and the groundwater environmental conditions, our results provide important insights into the functions and evolution of this CPR group.ConclusionsWe found that Patescibacteria has streamlined many functions while acquiring advantages such as avoiding phage invasion, to adapt to the groundwater environment. The unique features of small genome size, ultra-small cell size, and lacking CRISPR of this large lineage are bringing new understandings on life of Bacteria. Our results provide important insights into the mechanisms for adaptation of the superphylum in the groundwater environments, and demonstrate a case where less is more, and small is mighty.

Suppose $p$ is a prime, $t$ is a positive integer, and$f\!\in\!\mathbb{Z}[x]$ is a univariate polynomial of degree $d$ withcoefficients of absolute value $

Suppose $p$ is a prime, $t$ is a positive integer, and$f\!\in\!\mathbb{Z}[x]$ is a univariate polynomial of degree $d$ withcoefficients of absolute value $

Suppose $q$ is a prime power and $f\in\mathbb{F}_q[x]$ is a univariatepolynomial with exactly $t$ monomial terms and degree $

In recent years, accreditation standards for international engineering education have led to a dramatic rise in the use of outcome-based education at universities. In this system, enticing new undergraduate students to science and engineering, although challenging, is the first important step toward building students' career competencies. An ongoing effort to attract students to biotechnology was initiated 13 years ago in the School of Biotechnology at the East China University of Science and Technology in Shanghai. We describe the design and organization of the Microbe Competition, a program attracting a total of nearly 6,500 students as of 2018. In the competition, students need to pass the microbiology knowledge test, provide a practical experiment proposal related to the topic of competition, and finish the experiment under the supervision of teachers before getting final prizes. The competition develops students' competencies in acquiring and applying knowledge, problem solving, teamwork, communication, and experimental skills. By investigating students' feedback, we have been continuously improving the quality of competition to attract more students from the biotechnology major. We hope that by sharing our experience, we can help educators at other universities organize similar introductory activities on their own campuses.

Candidemia often poses a diagnostic challenge due to the lack of specific clinical features, and delayed antifungal therapy can significantly increase mortality rates, particularly in the intensive care unit (ICU). This study aims to develop a machine learning predictive model for early candidemia diagnosis in ICU patients, leveraging their clinical information and findings. We conducted this study with a cohort of 334 patients admitted to the ICU unit at Ji Ning NO.1 people's hospital in China from Jan. 2015 to Dec. 2022. To ensure the model's reliability, we validated this model with an external group consisting of 77 patients from other sources. The candidemia to bacteremia ratio is 1:1. We collected relevant clinical procedures and eighteen key examinations or tests features to support the recursive feature elimination (RFE) algorithm. These features included total bilirubin, age, platelet count, hemoglobin, CVC, lymphocyte, Duration of stay in ICU and so on. To construct the candidemia diagnosis model, we employed random forest (RF) algorithm alongside other machine learning methods and conducted internal and external validation with training and testing sets allocated in a 7:3 ratio. The RF model demonstrated the highest area under the receiver operating characteristic (AUC) with values of 0.87 and 0.83 for internal and external validation, respectively. To evaluate the importance of features in predicting candidemia, Shapley additive explanation (SHAP) values were calculated and results revealed that total bilirubin and age were the most important factors in the prediction model. This advancement in candidemia prediction holds significant promise for early intervention and improved patient outcomes in the ICU setting, where timely diagnosis is of paramount crucial. [ABSTRACT FROM AUTHOR]