Explore the vast range of titles available.

We aimed to systematically identify the possible risk factors responsible for severe cases. We searched PubMed, Embase, Web of science and Cochrane Library for epidemiological studies of confirmed COVID-19, which include information about clinical characteristics and severity of patients' disease. We analyzed the potential associations between clinical characteristics and severe cases. We identified a total of 41 eligible studies including 21060 patients with COVID-19. Severe cases were potentially associated with advanced age (Standard Mean Difference (SMD) = 1.73, 95% CI: 1.34-2.12), male gender (Odds Ratio (OR) = 1.51, 95% CI:1.33-1.71), obesity (OR = 1.89, 95% CI: 1.44-2.46), history of smoking (OR = 1.40, 95% CI:1.06-1.85), hypertension (OR = 2.42, 95% CI: 2.03-2.88), diabetes (OR = 2.40, 95% CI: 1.98-2.91), coronary heart disease (OR: 2.87, 95% CI: 2.22-3.71), chronic kidney disease (CKD) (OR = 2.97, 95% CI: 1.63-5.41), cerebrovascular disease (OR = 2.47, 95% CI: 1.54-3.97), chronic obstructive pulmonary disease (COPD) (OR = 2.88, 95% CI: 1.89-4.38), malignancy (OR = 2.60, 95% CI: 2.00-3.40), and chronic liver disease (OR = 1.51, 95% CI: 1.06-2.17). Acute respiratory distress syndrome (ARDS) (OR = 39.59, 95% CI: 19.99-78.41), shock (OR = 21.50, 95% CI: 10.49-44.06) and acute kidney injury (AKI) (OR = 8.84, 95% CI: 4.34-18.00) were most likely to prevent recovery. In summary, patients with severe conditions had a higher rate of comorbidities and complications than patients with non-severe conditions. Patients who were male, with advanced age, obesity, a history of smoking, hypertension, diabetes, malignancy, coronary heart disease, hypertension, chronic liver disease, COPD, or CKD are more likely to develop severe COVID-19 symptoms. ARDS, shock and AKI were thought to be the main hinderances to recovery.

Recent high-throughput techniques have generated a flood of biological data in all aspects. The transformation and visualization of multi-dimensional and numerical gene or protein expression data in a single heatmap can provide a concise but comprehensive presentation of molecular dynamics under different conditions. In this work, we developed an easy-to-use tool named HemI (Heat map Illustrator), which can visualize either gene or protein expression data in heatmaps. Additionally, the heatmaps can be recolored, rescaled or rotated in a customized manner. In addition, HemI provides multiple clustering strategies for analyzing the data. Publication-quality figures can be exported directly. We propose that HemI can be a useful toolkit for conveniently visualizing and manipulating heatmaps. The stand-alone packages of HemI were implemented in Java and can be accessed at http://hemi.biocuckoo.org/down.php.

As academic pressure continues to rise, college students’ academic performance has attracted increasing attention. Although previous studies have examined emotional regulation, learning motivation, and self-efficacy, limited research has distinguished the roles of specific regulation strategies or explored the moderating function of social support. This study integrates the emotional regulation model, self-determination theory, and self-efficacy theory to propose a theoretical model with both mediating and moderating mechanisms. The study gathered data from 866 students across several Chinese universities. Validated instruments were employed to assess cognitive reappraisal, expressive suppression, learning motivation, self-efficacy, social support, and academic performance. Data analysis was conducted using partial least squares structural equation modelling (PLS-SEM). Cognitive reappraisal, expressive suppression, and learning motivation were significantly associated with academic performance. Self-efficacy mediated these effects, and social support moderated the relationship between self-efficacy and academic performance. This study develops an integrated model of academic adaptation by linking emotional regulation, motivation, and support resources, thereby extending the theoretical scope of self-efficacy and social support. The findings suggest that students who report higher levels of emotional regulation, self-efficacy, and social support also tend to show better academic performance, highlighting potential areas for future support efforts in higher education.

The growth of plants is threatened by numerous diseases. Accurate and timely identification of these diseases is crucial to prevent disease spreading. Many deep learning-based methods have been proposed for identifying leaf diseases. However, these methods often combine plant, leaf disease, and severity into one category or treat them separately, resulting in a large number of categories or complex network structures. Given this, this paper proposes a novel leaf disease identification network (LDI-NET) using a multi-label method. It is quite special because it can identify plant type, leaf disease and severity simultaneously using a single straightforward branch model without increasing the number of categories and avoiding extra branches. It consists of three modules, i.e., a feature tokenizer module, a token encoder module and a multi-label decoder module. The LDI-NET works as follows: Firstly, the feature tokenizer module is designed to enhance the capability of extracting local and long-range global contextual features by leveraging the strengths of convolutional neural networks and transformers. Secondly, the token encoder module is utilized to obtain context-rich tokens that can establish relationships among the plant, leaf disease and severity. Thirdly, the multi-label decoder module combined with a residual structure is utilized to fuse shallow and deep contextual features for better utilization of different-level features. This allows the identification of plant type, leaf disease, and severity simultaneously. Experiments show that the proposed LDI-NET outperforms the prevalent methods using the publicly available AI challenger 2018 dataset.

Some articles have examined perfluorooctanoic acid (PFOA) exposure in early life in relation to risk of childhood adiposity. Nevertheless, the results from epidemiological studies exploring the associations remain inconsistent and contradictory. We thus conducted an analysis of data currently available to examine the association between PFOA exposure in early life and risk of childhood adiposity. The PubMed, EMBASE, and Web of Science databases were searched to identify studies that examined the impact of PFOA exposure in early life on childhood adiposity. A random-effects meta-analysis model was used to pool the statistical estimates. We identified ten prospective cohort studies comprising 6076 participants with PFOA exposure. The overall effect size (relative risk or odds ratio) for childhood overweight was 1.25 (95% confidence interval (CI): 1.04, 1.50; I2 = 40.5%). In addition, exposure to PFOA in early life increased the z-score of childhood body mass index (β = 0.10, 95% CI: 0.03, 0.17; I2 = 27.9%). Accordingly, exposure to PFOA in early life is associated with an increased risk for childhood adiposity. Further research is needed to verify these findings and to shed light on the molecular mechanism of PFOA in adiposity.

Glutamine synthetase (GS) catalyzes de novo synthesis of glutamine that facilitates cancer cell growth. In the liver, GS functions next to the urea cycle to remove ammonia waste. As a dysregulated urea cycle is implicated in cancer development, the impact of GS's ammonia clearance function has not been explored in cancer. Here, we show that oncogenic activation of β-catenin (encoded by CTNNB1) led to a decreased urea cycle and elevated ammonia waste burden. While β-catenin induced the expression of GS, which is thought to be cancer promoting, surprisingly, genetic ablation of hepatic GS accelerated the onset of liver tumors in several mouse models that involved β-catenin activation. Mechanistically, GS ablation exacerbated hyperammonemia and facilitated the production of glutamate-derived nonessential amino acids, which subsequently stimulated mechanistic target of rapamycin complex 1 (mTORC1). Pharmacological and genetic inhibition of mTORC1 and glutamic transaminases suppressed tumorigenesis facilitated by GS ablation. While patients with hepatocellular carcinoma, especially those with CTNNB1 mutations, have an overall defective urea cycle and increased expression of GS, there exists a subset of patients with low GS expression that is associated with mTORC1 hyperactivation. Therefore, GS-mediated ammonia clearance serves as a tumor-suppressing mechanism in livers that harbor β-catenin activation mutations and a compromised urea cycle.

Objective This study used a three-level meta-analysis to examine the impact of parent-adolescent conflicts on depressive mood, specifically analyzing differences between father-adolescent conflict and mother-adolescent conflict, as well as the moderating factors affecting their relationship. Positive parent-adolescent relationships are vital for adolescent development, and understanding the negative impact of parent-adolescent conflict on mental health is crucial for developing effective interventions and fostering positive family relationships. Methods A systematic search of literature published before December 2024 identified 46 studies with 31,147 participants and 157 independent effect sizes. A three-level meta-analysis was conducted to assess the relationship between parent- adolescent conflict and depression. Results The overall analysis revealed a moderate positive correlation between parent- adolescent conflict and depression ( r  = 0.267, p  < 0.001). The study design influenced this association, with a stronger correlation in cross-sectional studies ( r  = 0.320, p  = 0.004) compared to longitudinal studies ( r  = 0.227, p  = 0.004). Moderator analysis revealed that the measurement tool and publication year were significant moderators. The correlation was stronger when measured with the NRI ( r  = 0.341, p  = 0.005) and in more recent studies ( r  = 0.264, p  = 0.002). Conclusion These findings show that research design and measurement tools affect the link between parent- adolescent conflict and adolescent depressive mood, highlighting the need for future studies to consider these factors and their broader implications for fostering positive parent- adolescent relationships.

Dietary betaine supplement could ameliorate insulin resistance (IR) in animals, but no data are available for choline. Reports on humans are rare. The aim of this study was to investigate the association between dietary choline and betaine intake and IR in humans. We assessed 2394 adults from the CODING (Complex Diseases in the Newfoundland population: Environment and Genetics) study. Intake of dietary choline and betaine was evaluated from the Willett Food Frequency Questionnaire. IR was estimated by homeostatic model assessment (HOMA-IR) and the quantitative insulin-sensitivity check index (QUICKI). Partial correlation analysis was used to determine the correlations of dietary choline and betaine intake with IR adjusted for major confounding factors. Dietary choline and betaine intake was inversely correlated with levels of fasting glucose and insulin, HOMA-IR, HOMA-β (r = −0.08 to −0.27 for choline and r = −0.06 to −0.16 for betaine; P < 0.05) and positively related to QUICKI (r = 0.16–0.25 for choline and r = 0.11–0.16 for betaine; P < 0.01) in both sexes after controlling for age, total calorie intake, and physical activity level. The significant associations disappeared in men after percent trunk fat was added as a confounding factor. Furthermore, individuals with the highest tertile of dietary choline and betaine intake had the lowest IR severity. Dietary choline and betaine intake, however, was the lowest in the high IR group, intermediate in the medium group, and the highest in the low IR group. This study demonstrated that higher intake of dietary choline and betaine is associated with lower IR in the general population. •We first investigated the association of dietary choline and betaine intake with insulin resistance.•Dietary choline and betaine intake was negatively correlated with insulin resistance.•Associations were more pronounced in women than men.

Tabata, which is involved 20 seconds of maximum-intensity exercise followed by 10 seconds of complete rest, repeated for 8 cycles totaling 4 minutes, has been identified to enhance energy expenditure and fat oxidation in humans. The study aims to find an optimal Tabata volume for weight loss. 32 male university students with overweight/obesity participated in three tests. Test I consisted of a single Tabata cycle, Test II consisted of two, and Test III consisted of three. Each cycle was separated by a 10-minute interval, and each test was was separated by 7 days. Gas exchange indices were monitored during the last Tabata cycle of the test and the 30-minute recovery period. Subsequently, fat and glucose oxidation amounts, rates, and energy expenditure were calculated. During the 10-minute recovery period, the fat oxidation amounts of Test II (0.80±0.26 g) and Test III (0.87±0.24 g) were higher than Test I (0.50±0.11 g, p<0.001). There was no significant difference between Test II and Test III. During the 20-minute and 30-minute recovery periods, Test II (3.21±0.50 g; 5.04±1.02 g) showed significantly higher fat oxidation amount than Test I (2.47±0.59 g, p<0.001; 4.41±0.98 g, p<0.05) and Test III (2.80±0.43 g, p<0.001; 4.11±0.96 g, p<0.001), there was no significant difference between Test I and Test III (p>0.05). No significant differences in energy expenditure were observed among the three tests during recovery periods (p>0.05). We conclude that two Tabata cycles show the highest fat oxidation amount with the same energy expenditure amount during the recovery period, which is the optimal Tabata volume for weight loss.

Proxy-based reconstructions and modeling of Holocene spatiotemporal precipitation patterns for China and Mongolia have hitherto yielded contradictory results indicating that the basic mechanisms behind the East Asian Summer Monsoon and its interaction with the westerly jet stream remain poorly understood. We present quantitative reconstructions of Holocene precipitation derived from 101 fossil pollen records and analyse them with the help of a minimal empirical model. We show that the westerly jet-stream axis shifted gradually southward and became less tilted since the middle Holocene. This was tracked by the summer monsoon rain band resulting in an early-Holocene precipitation maximum over most of western China, a mid-Holocene maximum in north-central and northeastern China, and a late-Holocene maximum in southeastern China. Our results suggest that a correct simulation of the orientation and position of the westerly jet stream is crucial to the reliable prediction of precipitation patterns in China and Mongolia. The basic mechanisms behind the East Asian Summer Monsoon remain poorly understood. Using proxy-based reconstructions and simulations, here the authors show that changes in the orientation and position of the westerly jet stream resulted in regionally asynchronous Holocene precipitation maxima.