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Background With modernization and ageing in China, the population of older adults living alone is increasing. Living alone may be a potential risk factor for depressive symptoms. However, no parallel mediation model analysis has investigated the mediating factors for living alone or not (living arrangements) and depressive symptoms. Methods This cross-sectional study included a total number of 10,980 participants from the Chinese Longitudinal Healthy Longevity Survey (CLHLS), 1699 of whom lived alone and 9281 of whom did not live alone. Binary logistic regression and parallel mediation effect model were used to explore the relationship between living alone or not and depressive symptoms and possible mediation effects. Bootstrap analysis was used to examine the mediation effect of living alone or not on depressive symptoms. Results Compared to the participants who were not living alone, the living alone group had a higher rate of depressive symptoms. The binary logistic regression showed that after adjusting for other covariates, the risk of depressive symptoms was approximately 0.21 times higher for living alone compared to not living alone (OR = 1.21, 95% CI: 1.06, 1.37). Further, the results of the bootstrap analysis supported the partial mediating role of sleep quality and anxiety. Mediation analysis revealed that sleep quality and anxiety partially mediate the relationship between living alone and depressive symptoms (β = 0.008, 95% CI [0.003, 0.014]; β = 0.015, 95% CI [0.008, 0.024], respectively). Conclusions Sleep quality and anxiety were identified as partially parallel mediators between living alone or not and depressive symptoms. Older adults living alone with poorer sleep quality and more pronounced anxiety were positively associated with higher levels of depressive symptoms. Older adults living alone should be encouraged to engage in social activities that may improve sleep quality, relieve anxiety, and improve feelings of loneliness caused by living alone. Meanwhile, older adults living alone should receive attention and support to alleviate their depressive symptoms.

The predicted 80 open reading frames (ORFs) of herpes simplex virus 1 (HSV-1) have been intensively studied for decades. Here, we unravel the complete viral transcriptome and translatome during lytic infection with base-pair resolution by computational integration of multi-omics data. We identify a total of 201 transcripts and 284 ORFs including all known and 46 novel large ORFs. This includes a so far unknown ORF in the locus deleted in the FDA-approved oncolytic virus Imlygic. Multiple transcript isoforms expressed from individual gene loci explain translation of the vast majority of ORFs as well as N-terminal extensions (NTEs) and truncations. We show that NTEs with non-canonical start codons govern the subcellular protein localization and packaging of key viral regulators and structural proteins. We extend the current nomenclature to include all viral gene products and provide a genome browser that visualizes all the obtained data from whole genome to single-nucleotide resolution. Here, using computational integration of multi-omics data, the authors provide a detailed transcriptome and translatome of herpes simplex virus 1 (HSV-1), including previously unidentified ORFs and N-terminal extensions. The study also provides a HSV-1 genome browser and should be a valuable resource for further research.

Nucleic acid motifs consist of conserved and variable nucleotide regions. For functional action, several motifs are combined to modules. The tool AIModules allows identification of such motifs including combinations of them and conservation in several nucleic acid stretches. AIModules recognizes conserved motifs and combinations of motifs (modules) allowing a number of interesting biological applications such as analysis of promoter and transcription factor binding sites (TFBS), identification of conserved modules shared between several gene families, e.g. promoter regions, but also analysis of shared and conserved other DNA motifs such as enhancers and silencers, in mRNA (motifs or regulatory elements e.g. for polyadenylation) and lncRNAs. The tool AIModules presented here is an integrated solution for motif analysis, offered as a Web service as well as downloadable software. Several nucleotide sequences are queried for TFBSs using predefined matrices from the JASPAR DB or by using one’s own matrices for diverse types of DNA or RNA motif discovery. Furthermore, AIModules can find TFBSs common to two or more sequences. Demanding high or low conservation, AIModules outperforms other solutions in speed and finds more modules (specific combinations of TFBS) than alternative available software. The application also searches RNA motifs such as polyadenylation site or RNA–protein binding motifs as well as DNA motifs such as enhancers as well as user-specified motif combinations ( https://bioinfo-wuerz.de/aimodules/ ; alternative entry pages: https://aimodules.heinzelab.de or https://www.biozentrum.uni-wuerzburg.de/bioinfo/computing/aimodules ). The application is free and open source whether used online, on-site, or locally.

For SARS-CoV-2, R0 calculations in the range of 2–3 dominate the literature, but much higher estimates have also been published. Because capacity for RT-PCR testing increased greatly in the early phase of the Covid-19 pandemic, R0 determinations based on these incidence values are subject to strong bias. We propose to use Covid-19-induced excess mortality to determine R0 regardless of RT-PCR testing capacity. We used data from the Robert Koch Institute (RKI) on the incidence of Covid cases, Covid-related deaths, number of RT-PCR tests performed, and excess mortality calculated from data from the Federal Statistical Office in Germany. We determined R0 using exponential growth estimates with a serial interval of 4.7 days. We used only datasets that were not yet under the influence of policy measures (e.g., lockdowns or school closures). The uncorrected R0 value for the spread of SARS-CoV-2 based on RT-PCR incidence data was 2.56 (95% CI 2.52–2.60) for Covid-19 cases and 2.03 (95% CI 1.96–2.10) for Covid-19-related deaths. However, because the number of RT-PCR tests increased by a growth factor of 1.381 during the same period, these R0 values must be corrected accordingly (R0corrected = R0uncorrected/1.381), yielding 1.86 for Covid-19 cases and 1.47 for Covid-19 deaths. The R0 value based on excess deaths was calculated to be 1.34 (95% CI 1.32–1.37). A sine-function-based adjustment for seasonal effects of 40% corresponds to a maximum value of R0 January  = 1.68 and a minimum value of R0 July  = 1.01. Our calculations show an R0 that is much lower than previously thought. This relatively low range of R0 fits very well with the observed seasonal pattern of infection across Europe in 2020 and 2021, including the emergence of more contagious escape variants such as delta or omicron. In general, our study shows that excess mortality can be used as a reliable surrogate to determine the R0 in pandemic situations.

Background Antibiotic resistance is one of the greatest threats to global public health. Inappropriate use of antibiotics can lead to an increase in antibiotic resistance. Individual self-efficacy in the appropriate use of antibiotics plays a key role, especially in China where the population has easy access to antibiotics. However, there are no tools available to assess the self-efficacy of appropriate antibiotic use for Chinese adults. We aimed to translate and develop a Chinese version of the Appropriate Antibiotic Use Self-Efficacy Scale (AAUSES), and validate its reliability and validity. Methods A total of 659 adults were recruited to participate in the questionnaire. The original version scale was first translated into Chinese using the backward and forward translation procedures. The internal consistency reliability of the scale was measured by the Cronbach alpha coefficient, the test-retest reliability, and the corrected item-total correlation. The validity of the scale was assessed by the content validity index, exploratory factor analysis, and confirmatory factor analysis. Results The content validity index of the scale was 0.96. Exploratory factor analysis (EFA) supported a 4-factor structure of the translated questionnaire, and the discriminant validity of the scale was good. Confirmatory factor analysis (CFA) showed in the model fitness index, the chi-square degree of freedom was 2.940, the goodness-of-fit index(GFI) was 0.929, the incremental fit index (IFI) was 0.908, the comparative fit index(CFI) was 0.906, root mean square error of approximation(RMSEA) was 0.077, and standardized root mean residual (SRMR) was 0.0689, and the model fitting indexes were all in the acceptable range. Cronbach alpha coefficient for the scale was 0.910. The test-retest reliability was 0.947, and the corrected item-total correlations for the items ranged from 0.488 to 0.736. Self-efficacy for appropriate antibiotic use in adults varied by education, occupation, income, place of residence, and whether or not they had heard of antibiotic resistance. Conclusions The results indicated that the Chinese version of the AAUSES had good reliability and validity. Therefore, it can be considered a tool to evaluate the appropriate antibiotic use self-efficacy of adults in China.

Obstructive sleep apnea (OSA) is a well-known risk factor for hypertension. Moderate-to-severe OSA is more likely to lead to resistant hypertension (RH) compared to the absence of moderate-to-severe OSA. Early identification of patients with OSA among those with RH is crucial for prioritizing diagnosis and reducing the burden. However, currently, there is a lack of specific tools for assessing the risk of moderate-to-severe OSA in patients with RH. In this retrospective cohort study conducted from October 2023 to August 2024, 659 patients with RH from the health examination center of a tertiary hospital in Northeast China completed polysomnography. Based on the polysomnography results, the participants were divided into a group without moderate-to-severe OSA (control group) and a moderate-to-severe OSA group. The sample was randomly divided into a development cohort (461 patients) and a validation cohort (198 patients), and the incidence of OSA in the two groups was comparable ( P  > 0.05). Relevant clinical data of patients with RH were collected. The Least Absolute Shrinkage and Selection Operator method was used to identify independent risk factors. Subsequently, three predictive models were developed based on ten variables, including waist circumference, waist-to-hip ratio, low-density lipoprotein, morning dry mouth, serum creatinine, homocysteine, drinking, cholesterol, triglycerides and smoking. Among these models, the random forest model showed excellent discrimination and calibration in development and validation cohorts. Additionally, decision curve analysis was performed on the random forest model, as well as the STOP-Bang questionnaire and the Berlin questionnaire, to evaluate their clinical benefits. Finally, Shapley Additive Explanations analysis clearly indicated that waist circumference was the most important factor in predicting comorbid moderate-to-severe OSA in RH patients.

Background It is becoming increasingly acknowledged that obstructive sleep apnea (OSA) is a variable factor influencing cognitive health. The aims of this study were to explore whether the severity of OSA is related to the occurrence of mild cognitive impairment (MCI) in people with OSA and whether different degrees of daytime sleepiness and nighttime sleep quality are related to MCI. Methods The study was cross-sectional. For our subjects, we selected individuals who visited the Sleep Medicine Center of Jinzhou Medical University’s First Affiliated Hospital between May 2023 and October 2024, underwent polysomnography (PSG) or the home sleep apnea test (HSAT), and were diagnosed with OSA. The patients were split into two groups: one for normal cognitive function (NC) and the other for MCI. MCI was defined as Montreal Cognitive Assessment (MOCA) < 26 points. The apnea-hypopnea index (AHI) and the oxygen desaturation index (ODI) were used to assess the severity of sleep apnea. The Epworth Sleepiness Scale (ESS) was used to assess patients’ daytime sleepiness, and the Pittsburgh Sleep Quality Index Scale (PSQI) was used to assess their sleep quality. Multivariate logistic regression analysis was used to evaluate the correlation between the variables. Results In this study, 387 patients with OSA (45.3 ± 12.6 years, 82.4% male) were included, of whom 38% had MCI (52.4 ± 11.9 years, 74.1% male). In the unadjusted model, the sleep apnea severity, daytime sleepiness severity, and different sleep quality at night were positively related to MCI. After controlling for confounding factors, this correlation was no longer significant. Only severe sleep apnea (AHI ≥ 30/h, p  < 0.001), poor nighttime sleep quality (PSQI ≥ 9, p  = 0.020), and sleepiness (ESS ≥ 11, p  < 0.05) were associated with increased risk of MCI. Conclusions Severe sleep apnea, poor sleep quality, and sleepiness were relevant to increased risk of MCI. It provides a basis for a more comprehensive understanding of the relationship between OSA and MCI.

Once biological systems are modeled by regulatory networks, the next step is to include external stimuli, which model the experimental possibilities to affect the activity level of certain network's nodes, in a mathematical framework. Then, this framework can be interpreted as a mathematical optimal control framework such that optimization algorithms can be used to determine external stimuli which cause a desired switch from an initial state of the network to another final state. These external stimuli are the intervention points for the corresponding biological experiment to obtain the desired outcome of the considered experiment. In this work, the model of regulatory networks is extended to controlled regulatory networks. For this purpose, external stimuli are considered which can affect the activity of the network's nodes by activation or inhibition. A method is presented how to calculate a selection of external stimuli which causes a switch between two different steady states of a regulatory network. A software solution based on Jimena and Mathworks Matlab is provided. Furthermore, numerical examples are presented to demonstrate application and scope of the software on networks of 4 nodes, 11 nodes and 36 nodes. Moreover, we analyze the aggregation of platelets and the behavior of a basic T-helper cell protein-protein interaction network and its maturation towards Th0, Th1, Th2, Th17 and Treg cells in accordance with experimental data.

Background Adequate sleep and rest are essential for patient recovery; however, lack of sleep has become a common problem faced by Chinese patients during hospital stays. Reduced sleep is often associated with a higher risk of disease progression and is strongly associated with increased hospital stay. However, there is no specific tool in China to assess short-term insomnia caused by hospitalization. This study aimed to translate the Hospital-acquired Insomnia Scale (HAIS) into Chinese, test its applicability to Chinese inpatients through reliability and validity indicators, and investigate the potential influencing factors of hospital-acquired insomnia. Methods Psychometric analysis from a sample of 679 hospitalized patients to whom the HAIS questionnaire was applied. The structural validity was assessed by exploratory factor analysis (EFA) and confirmatory factor analysis (CFA), and the content validity of the scale was assessed using the content validity index. Cronbach’s alpha coefficient, split-half reliability and test–retest reliability were calculated to evaluate the internal consistency of the scale. Multiple stepwise linear regression analysis was conducted to determine the potential correlates of hospital-acquired insomnia. Results EFA supported a four-factor structure with factor loadings for all dimensions greater than 0.40. CFA showed good indicators of model fit. The content validity index of the scale was 0.94. the Cronbach’s alpha of the scale was 0.915, the split-half reliability coefficient was 0.819, and the retest reliability was 0.844. Gender, age, total hours of sleep during the night, medical insurance, length of hospital stay, perceived stress level, and perceptions about sleep explained 46.2% of the variance in hospital-acquired insomnia. Conclusion The Chinese version of HAIS has good psychometric characteristics and is an effective instrument for evaluating hospital-acquired insomnia. In addition, hospital-acquired insomnia is more common in women, of younger age, less than 5 h of sleep a night, without medical insurance, stressed, and patients with more misconceptions about sleep.

Background Although vaccination is one of the critical interventions to address global health issues, inadequate vaccination rates has become an international challenge. Vaccine hesitancy is the key to affecting inadequate vaccination rates. According to the WHO SAGE working group’s definition, vaccine hesitancy refers to delaying or refusing vaccination and has been ranked as one of the top 10 health threats. There has yet to be a scale that evaluates vaccination attitudes among Chinese adults. However, an attitude quantity, the adult vaccination attitude scale, has been developed to assess adult vaccination attitudes and reasons for vaccine hesitancy. Objective The Adult Attitudes to Vaccination Scale (ATAVAC) was initially developed by Professor Zoi Tsimtsiou et al. This study aimed to analyze the structure of the Chinese version of the ATAVAC and explore the relationship between adult vaccination attitudes, e-health literacy, and medical distrust. Methods After obtaining author permission for the initial scales, the study was translated using the Brislin back-translation method. 693 adults were enrolled to the study. To validate this hypothesis, participants finished the socio-demographic questionnaire, the Chinese version of the ATAVAC, the electronic Health Literacy Scale (e-HEALS) and the Medical Mistrust Index (MMI). The exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were used to examine the underlying structure of the factors of the Chinese version of the Adult Vaccination Attitude Scale and to measure its reliability and validity. Results The Cronbach’s alpha coefficient for the Chinese version of the ATAVAC was 0.885, with Cronbach’s alpha coefficients ranging from 0.850 to 0.958 for each dimension. The content validity index was 0.90, and the retest reliability was 0.943. The exploratory factor analysis (EFA) supported the 3-factor structure of the translation instrument, and the scale had good discriminant validity. The confirmatory factor analysis (CFA) revealed a degree of freedom of 1.219, a model fit index (GFI) of 0.979, a normative fit index (NFI) of 0.991, a Tucker-Lewis index (TLI) of 0.998, a comparability index (CFI) of 0.998 and a root mean square error of approximation (RMSEA) of 0.026. Conclusion The results show that the Chinese version of the ATAVAC has demonstrated good reliability and validity. Hence, it can be used as an effective tool to assess vaccination attitudes among Chinese adults.