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Abstract Objective This study aimed to investigate the relationship between serum lactate levels at admission to the intensive care unit (ICU) and the clinical outcomes of children with sepsis. Methods We used the Pediatric Intensive Care (PIC) database to identify 288 pediatric patients with sepsis. We analyzed the relationship between lactate levels and hospital and ICU mortality in sepsis by establishing a segmented multivariable Cox regression model. We also conducted subgroup analysis as well as analyzed the restricted cubic spline curves. Results After adjusting for all potential confounding factors, an increase of 1 mmol/L in lactate levels was found to be associated with an 17% rise in the 28-day hospital mortality risk among sepsis patients (HR: 1.17, 95% CI: 1.08–1.27, P = 0.0002). When considering lactate as a categorical variable, the mortality risk of patients with lactate levels ≥ 2.2 mmol/L was significantly increased (HR: 3.61, 95% CI: 1.24–10.54, P = 0.0189). The restricted cubic spline curve analysis revealed a nonlinear correlation between lactate and 28-day mortality, with an inflection point at 2.2 mmol/L. Similar findings were also observed in the assessment of secondary outcomes. Conclusions Our study demonstrates a non-linear correlation between lactate levels and 28-day mortality in pediatric sepsis, with a critical threshold of 2.2mmol/l for lactate levels in septic patients. Early assessment of lactate levels is recommended for children with sepsis to facilitate prompt intervention and mitigate the risk of fatality.

Oxidative stress is closely associated with the estimated glomerular filtration rate (eGFR) levels. A comprehensive indicator for evaluating dietary and lifestyle exposures to oxidative stress is the Oxidative Balance Score (OBS), with higher OBS suggesting more substantial antioxidant exposures. The aim of this study is to explore the relationship between OBS and eGFR levels. A total of 20,285 subjects were selected from the National Health and Nutrition Examination Survey (NHANES) from 2007 to 2018. One-way and multifactorial linear regression analyses were used to assess the association of OBS with eGFR. The relationship between OBS and eGFR levels was also assessed using restricted cubic splines (RCS) analysis. OBS levels were significantly and positively correlated with eGFR levels, and eGFR values were more susceptible to OBS in males than in females. Our findings suggest that higher OBS scores may be associated with higher eGFR levels, highlighting the importance of adhering to an antioxidant diet and lifestyle, which can help prevent kidney disease.

ObjectivesThere is limited amount of research on the association between fecal incontinence (FI) and cardiovascular disease (CVD). This study aims to evaluate whether there is a relationship between FI and CVD among adults in the United States.MethodsThis study employed a cross-sectional design, encompassing 11,237 adults aged 20 years and older, drawn from the National Health and Nutrition Survey conducted from 2005 to 2010. FI was defined as the involuntary monthly leakage of solid, liquid, or mucus stool. The presence of CVD was evaluated through a questionnaire. Adjusted odds ratios (OR) were computed utilizing a multivariate logistic regression model. Subgroup analyses were conducted to ascertain the stability of the results.ResultsFollowing adjustments for population characteristics, lifestyle habits, laboratory tests, and comorbidities, a significant association was observed between FI and elevated CVD risk (OR: 1.47, 95% CI: 1.21–1.79, P

BackgroundInsomnia (IS) and circadian rhythm sleep-wake disorders (CRSWD) are complex disorders with limited and unsatisfactory treatment options that can even cause some side effects. By analyzing blood metabolites to reveal underlying biological processes, studies of sleep and the complex interactions between its influencing factors can be elucidated. Therefore, we hope to bring new hope for the treatment of these diseases through blood metabolites.AimsInvestigating the causal link between blood metabolites and IS and CRSWD.MethodsA genome-wide association study (GWAS) for 486 metabolites was used as the exposure, whereas two different GWAS datasets for sleep disorders were the outcome, and all datasets were obtained from publicly available databases. We employed the standard inverse variance weighting (IVW) method for causal analysis, supported by the MR-Egger method, weighted median (WM) method, and MR-PRESSO method for sensitivity analysis to mitigate the impact of pleiotropy. Genetic correlation between IS, CRSWD, and blood metabolites was explored through linkage disequilibrium analysis (LDSC), while Multivariable MR analysis (MVMR) elucidated whether these metabolites exhibit a direct association with IS and CRSWD. Further, we conducted metabolic pathway analysis to identify the specific metabolites driving these relationships.ResultsEmploying meticulous MVMR analysis, we have identified specific metabolites that independently influence IS, including 2-hydroxypalmitate (OR 2.95, 95%CI 1.05–8.31 P = 0.040), X-11786-Methylcysteine (OR = 0.25, 95%CI 0.08–0.76 P = 0.014), and salicylate (OR 0.89, 95%CI 0.83–0.95 P = 9 × 10–4). In the context of CRSWD, our findings reveal direct associations with metabolites such as carnitine (OR 0.02, 95%CI: 0.00–0.20, P = 0.002), levulinate (OR 0.06, 95%CI: 0.01–0.64, P = 0.020), p-cresol sulfate (OR 0.25, 95% CI: 0.09–0.67, P = 0.006), and X-14208-Phenylalanylserine (OR 0.36, 95% CI: 0.16–0.81, P = 0.014). These discoveries contribute to a nuanced understanding of the distinct metabolic signatures underlying IS and CRSWD.

ObjectivesThe aim of this study was to evaluate the potential association between early onset puberty and the risk of different forms of obesity in children.MethodsThe databases PubMed, EMBASE, Web of Science and Cochrane Library were systematically searched for relevant studies. The odds ratio (OR) and 95% confidence interval (CI) of obesity in precocious puberty were calculated using Stata software 14.0. A fixed-effects model was used if P > 0.1 and I2 ≤ 50%. Otherwise, a random-effects model was used. Publication bias was assessed using funnel plots and Egger's test.ResultThe pooling analysis showed that precocious puberty in girls was associated with a higher risk of obesity (OR = 1.98; 95% CI: 1.76–2.24; I2 = 0.00%, P

The application of non-cognitive factors represented by facial emotion in educational evaluation has attracted much attention in recent years. There are many existing studies on facial emotion assisted education evaluation, but most of them are based on virtual learning environments, which means that the research on facial emotion and learning effect in offline learning environments is sparse. In order to solve this problem, this study designed an emotion observation experiment based on the offline learning environment, obtained the type of learner facial emotion and learning effect of 127 college students, and further explored the relationship between the two. The results show that: 1) We obtained eight types of learner emotion through the combined description method: joy, relaxation, surprise, meekness, contempt, disgust, sadness, anxiety and their respective PAD emotional mean. 2) We obtained the correlation results of the six emotions of joy, relaxation, surprise, meekness, contempt, and anxiety with the learning effect and the predicted value of the learning effect. 3) We then constructed an explanatory model of learner emotion and learning effect based on the offline learning environment.

In the research of Facial Expression Recognition (FER), the inter-class of facial expression data is not evenly distributed, the features extracted by networks are insufficient, and the FER accuracy and speed are relatively low for practical applications. Therefore, a lightweight and efficient method based on class-rebalancing fusion cumulative learning for FER is proposed in our research. A dual-branch network (Regular feature learning and Rebalancing-Cumulative learning Network, RLR-CNet) is proposed, where the RLR-CNet uses the improvement in the lightweight ShuffleNet with two branches (feature learning and class-rebalancing) based on cumulative learning, which improves the efficiency of our model recognition. Then, to enhance the generalizability of our model and pursue better recognition efficiency in real scenes, a random masking method is improved to process datasets. Finally, in order to extract local detailed features and further improve FER efficiency, a shuffle attention module (SA) is embedded in the model. The results demonstrate that the recognition accuracy of our RLR-CNet is 71.14%, 98.04%, and 87.93% on FER2013, CK+, and RAF-DB, respectively. Compared with other FER methods, our method has great recognition accuracy, and the number of parameters is only 1.02 MB, which is 17.74% lower than that in the original ShuffleNet.