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Association between body mass index and long-term all-cause mortality in critically ill patients without malignant tumors

Background The “obesity paradox” in certain diseases has been reported in previous studies. This study aimed to investigate the relationship between BMI and long-term mortality in all critically ill patients without malignant tumors who were admitted to the ICU. Methods Using the MIMIC-IV 2.2 database, we included all ICU admissions for patients without malignant tumors and categorized them into four groups based on the World Health Organization (WHO) obesity criteria. The relationship between BMI and 90-day, 180-day, and 1-year mortality was analyzed using univariate and multivariate Cox regression models, along with restricted cubic spline (RCS) models to account for potential non-linear associations. Results A total of 19,089 patients were included, with 90-day, 180-day, and 1-year mortality rates of 18.35%, 20.80%, and 23.96%, respectively. Overweight and obese patients exhibited significantly lower mortality rates compared to underweight and normal-weight individuals at all time points. After adjusting for confounders, higher BMI remained a protective factor for long-term mortality (HR 0.65–0.72, P < 0.001). RCS curves demonstrated a U-shaped relationship between BMI and mortality, and subgroup analyses confirmed the protective effect of higher BMI in different subgroups. Conclusion The “obesity paradox” may apply to critically ill patients without malignant tumors.

Photoinduced Copper-Catalyzed Enantioselective Alkylalkynylation of Alkenes via Polarity-Matched Hydrogen Atom Transfer

Demographic forecast modelling using SSA-XGBoost for smart population management based on multi-sources data

Population prediction could provide effective data support for social and economic planning and decision-making, especially for the sub-national population forecasting accurately. In addition to realizing efficient smart population management, this research focuses primarily on the combination model for forecasting demographic data based on machine learning. As to the higher error of population forecasts due to high population density and mobility, a dynamic monitoring method based on mobile communication big data such as mobile phone signals is proposed, combined with more structurally stable traditional statistical data, it forms a multi-source dataset that possesses both accuracy and real-time characteristics. In the study, the Extreme Gradient Boosting tree (XGBoost) model is used to identify the base model to create a reliable predictive model for population dynamic monitoring. The sparrow search algorithm (SSA) is investigated to obtain more reasonable parameters of XGBoost to improve forecast accuracy. The combination model is verified based on the data of the 6th and 7th national population census and mobile phone signal data in Hebei Province, obtained the predicted data for mortality and migration, categorized by age and gender, for the following year. Subsequently, the research compared the performance of different metaheuristic algorithms and various gradient-boosting machine-learning models on the dataset. The SSA-XGBoost model demonstrates a better prediction performance in the demographic data forecast with better R2 0.9984 and a lower mean absolute error of 0.0002 and a mean squared error of 6.9184. The results of the comparative experiments and cross-validation show that the proposed predictive model can effectively forecast the demographic data for sub-national regions to realize smart population management.

Automatic Discovery and Optimal Generation of Amorphous High-Entropy Electrocatalysts

Mental health disorders among children with special health needs: A population-based cohort study using linked administrative data from Manitoba, Canada

Objective An estimated 15–22% of Canadian kindergarten-age children have a special health need (SHN), defined as a clinical diagnosis, a functional need requiring special accommodation at school, or a health condition leading to increased needs. Children with SHN may be more likely to experience mental health disorders than their peers without SHN, placing them at risk for further health and academic challenges. Our objective was to determine the odds of children with SHN identified in kindergarten being diagnosed with a mental health disorder by age 16. Methods In this retrospective cohort study using population-based, linked administrative data, we identified children with SHN born 1995–2020 in Manitoba, Canada, and enrolled in kindergarten from 2006–2011. The SHN designation is derived from the Early Development Instrument. We measured prevalence of common childhood mental health disorders (ADHD, mood/anxiety disorders, conduct disorders) in children with SHN to age 16. Using binary logistic regressions, we calculated crude odds ratios (OR) for children with vs. without SHN being diagnosed with a mental health disorder, then adjusted for age, sex, and neighbourhood-level income. Results Among 42,766 children, 13.8% had a SHN designation in kindergarten. Among these, 41.0% were diagnosed with a mental health disorder by age 16. The odds of a mental health diagnosis by SHN category were: special needs designation in kindergarten (OR 1.75, 95%CI 1.53–2.01); learning impairment (OR 1.61, 95%CI 1.39–1.86); behavioural impairment (OR 3.27, 95%CI 2.87–3.72); and emotional impairment (OR 2.01, 95%CI 1.75–2.32). Children with SHN (vs. none) had higher odds of a mental health disorder if they had 1 + impairment (OR 1.67, 95%CI 1.50–1.85). Adjusting for sociodemographic characteristics did not change the estimates. Conclusions The study highlights important kindergarten predictors of future mental health disorders in children, which should be used to inform preventive and supportive strategies for children with SHN and help generate wider mental health supports in schools.

Chemoselective Electrochemical Coupling of Thioethers and Primary Amines for Accessing Sulfilimines and Sulfoximines

Motives of Chinese foreign direct investment in Africa: With regulation effects of institutional quality

This article analyzes the motives of Chinese foreign direct investment (FDI) in Africa and the impact of the institutional quality of the host country for the period between 2003 and 2022 from both static and dynamic perspectives by using the OLS, PPML, and GMM methods. The results show that: China has obvious market-, and efficiency-seeking motives, but weak resource- and strategic asset-seeking motives. There is a tipping point for market- and efficiency-seeking motives. Good institutional quality of the host country (region) facilitates FDI expansion. Investment inertia and factor endowments affect long-term FDI. We also examine the moderating effects of institutional quality and the Belt and Road Initiative (BRI) and found that: the host country’s (region’s) institutional environment optimization makes market size more attractive and labor costs less attractive. The BRI itself has no obvious influence on Chinese investment in Africa, while the impact of market size and labor costs became more significant after 2013. Hence, when selecting investment locations, Chinese enterprises should prioritize the host country’s (region’s) market size, labor costs, and institutional quality. Additionally, they should utilize the moderating effect of institutional quality to mitigate the disadvantages associated with higher labor costs.

Hydrophobic Cation-Immobilized Covalent Organic Frameworks Enable Selective and Stable Electrosynthesis of Ethylene from CO₂

Effect of high-intensity anaerobic exercise on electrocortical activity in athletes and non-athletes

Aim The present study aims to verify the information processing in athletes through electroencephalography, analyze cortical areas responsible for cognitive functions related to attentional processing of visual stimuli, and investigate motor activity’s influence on cognitive aspects. Additionally, we aimed to analyze the acute effect of physical exercise after the high-intensity anaerobic effort, evaluating how a short-duration Wingate test influences cortical activity and attentional processing immediately following exertion. Materials and methods The sample consisted of 29 subjects, divided into an experimental group (n = 13 modern pentathlon athletes) and a control group (n = 16 non-athletes). We collected the electrocortical activity before and after the Wingate Anaerobic Test. During the electrophysiological measures, the volunteers performed a saccadic eye movement paradigm. They also performed cognitive tasks, resting heart rate, and anthropometric measurements. Results A mixed ANOVA was applied to analyze the statistical differences between groups (athletes and control) and moments (before and after exercise) for F3, F4, P3, and P4 electrodes during rest one and task (pre-stimulus GO). There was an interaction for the group vs. moment factors in F3 [F = 17,129; p = 0,000; η² = 0.512], F4 [F = 22,774; p = 0,000; η² = 0.510], P3 [F = 11,429; p = 0,001; η² = 0.405], and P4 electrodes [F = 18,651; p = 0,000; η² = 0.379]. We found the main effect for group factors in the frontal and parietal electrodes of the right hemisphere (F4 and P4) and a main effect of the moment factor on the frontal (F3 and F4) and parietal (P3 and P4) electrodes. There was an interaction between the group vs. moment factors for the reaction time. The groups were different in Peak Power (Watts/kg), Average Power (Watts/kg), Fatigue Index (%), and Maximum Power (ms). Conclusions We identified chronic effects of exercise training on the cortical activity of modern pentathlon athletes, read-through differences in absolute alpha power, and acute effects of a high-intensity exercise session for athletes and non-athletes for electrocortical and behavioral responses.

In Situ Encapsulation of Cationic [2]Catenane in a Stable Zirconium Metal–Organic Framework

Correction: Killer whale respiration rates

Discovery of High-Capacity Asymmetric Three-Stage Redox Reactions of Iodine for Aqueous Batteries

Identification of differentially expressed genes and proteins related to diapause in Lymantria dispar: Insights for the mechanism of diapause from transcriptome and proteome analyses

Spongy moth (Lymantria dispar Linnaeus) is a globally recognized quarantine leaf-eating pest. Spongy moths typically enter diapause after completing embryonic development and overwinter in the egg stage. They spend three-quarters of their life cycle (approximately nine months) in the egg stage, which requires a period of low-temperature stimulation to break diapause and continue growth and development. In this study, we explored the molecular mechanism underlying the diapause process in spongy moth. We performed bioinformatics analysis on four Asian populations of spongy moth and one Asian–European hybrid population through a transcriptome analysis combined with proteomics. The results revealed that 1,842 genes were differentially expressed upon diapause initiation, while 264 genes were identified upon diapause termination. Eight diapause-related genes were screened out from the three-level pathways that were significantly enriched by differentially expressed genes at the time of diapause and diapause termination, and the phylogenetic tree and protein three-dimensional structure model were constructed. This study elucidates the diapause mechanism of spongy moth at the gene and protein levels, providing theoretical insights into the early and precise prevention and control of spongy moth. This study can facilitate the development of an efficient, environmentally friendly control system for managing spongy moth populations in the field.

Intrinsically Adaptive Salicylaldimine: Mechanically and Thermally Induced Switching between Photochromism and Photoluminescence

The global impact of non-alcoholic fatty liver disease (including cirrhosis) in the elderly from 1990 to 2021 and future projections of disease burden

Background Nonalcoholic fatty liver disease (NAFLD) is a metabolic disorder characterized by hepatic steatosis and inflammation in individuals with no significant alcohol consumption history. Predominantly affecting middle-aged and elderly populations, particularly those with obesity or metabolic syndrome, this condition represents a spectrum ranging from benign fatty accumulation to progressive liver damage. In advanced stages, NAFLD may progress to cirrhosis and hepatocellular carcinoma. This study systematically examines the global incidence patterns and epidemiological characteristics of NAFLD in older adults(>60 years), while establishing predictive models for its future disease burden. Methods Data on NAFLD in the Elderly(>60 years), from 1990 to 2021, was obtained from the Global Burden of Disease (GBD) study, encompassing 204 countries and territories. This dataset includes incidence rates of NAFLD. The Joinpoint regression model was utilized to detect turning points in the epidemiological trends of NAFLD, and decomposition analysis was performed to analyze the factors influencing these trends. To evaluate potential health disparities related to NAFLD, the Slope Index and Concentration Index were calculated. Additionally, the Norpred and Bayesian age-period-cohort (BAPC) models were employed to forecast future incidence rates of NAFLD. Results In 1990, the global NAFLD incidence in the elderly was 2819125(3972309 ± 1807520), with an ASIR of 568.46(803.33 ± 364.10). The global NAFLD prevalence in the elderly was 132549345(166820867 ± 102941502), with an ASPR of 27284.94(34321.92 ± 21200.69). The global NAFLD deaths in the elderly were 27864(45975 ± 15898), with an age-standardized death rate of 6.20(10.18 ± 3.55). The global NAFLD DALYs in the elderly were 559945(931920 ± 319045), with an age-standardized DALYs rate of 116.78(193.49 ± 66.73). In 2021, the global NAFLD incidence in the elderly was 7,012,128 (9,896,736 ± 4,480,162), with an ASIR of 636.90 (900.15 ± 406.76). The global NAFLD prevalence in the elderly was 366,363,498 (454,385,769 ± 287,891,088), with an ASPR of 33,576.22 (41,647.44 ± 26,372.13). The global NAFLD deaths in the elderly were 63,313 (99,891 ± 37,267), with an age-standardized death rate of 5.95 (9.39 ± 3.50). The global NAFLD DALYs in the elderly were 1,238,927 (1,973,042 ± 729,228), with an age-standardized DALYs rate of 113.95 (181.50 ± 66.91). From 1990 to 2021, the AAPC of ASIR for NAFLD in the elderly globally was 0.37(0.36 to 0.38), with a p-value < 0.05. The AAPC of ASPR for NAFLD in the elderly globally was 0.67(0.65 to 0.68), with a p-value < 0.05. The AAPC of age-standardized deaths rate for NAFLD in the elderly globally was −0.13(−0.16 to −0.1), with a p-value < 0.05. The AAPC of age-standardized DALYs rate for NAFLD in the elderly globally was −0.05(−0.07 to −0.02), with a p-value < 0.05. The decomposition analysis results indicate that population growth is the primary driver of increased disease burden in older NAFLD patients. It is expected that in the future, the disease burden of NAFLD in elderly people worldwide will continue to rise. Conclusions Over the past three decades, the annual age-standardized incidence rate and total number of cases of NAFLD, including cirrhosis, have increased among the elderly population, irrespective of gender. This upward trend is consistent across all SDI regions. Furthermore, future projections indicate that both the annual age-standardized incidence rate and the case numbers of NAFLD, including cirrhosis, in the elderly are likely to continue rising.

Spatially Aligned Binary Single-Site Catalyst on Defective SiO ₂ for Cascading Reactions

Computer-aided discovery of dual-target compounds for Alzheimer’s from ayurvedic medicinal plants

Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by cognitive decline, driven by the accumulation of amyloid-beta plaques and neurofibrillary tangles. It involves the dysfunction of key enzymes such as Acetylcholinesterase (AChE) and β-secretase (BACE1), making them critical targets for therapeutic intervention. In this study we investigated an in-house library of 820 secondary metabolites obtained from Ayurvedic plants against AChE and BACE1 with the aim to discover novel leads for AD. Virtual screening resulted in 15 ligands, mostly belonging to the ursane-type or dammarene-type triterpene saponins of Centella asiatica, reestablishing the potency of this plant in drug discovery against AD. The binding affinities were further verified by molecular dynamics (MD) simulation trajectories, including root mean square fluctuations (RMSF), root mean square deviation (RMSD), hydrogen bonding analysis, Coulomb interaction calculation, Lennard-Jones interactions, and the total interaction energy. Moreover, extensive Principal Component Analysis (PCA) and Gibbs free energy landscape were performed. Our results demonstrated three compounds, namely (S)-eriodictyol 7-O-(6-β-O-trans-p-coumaroyl)-β-d-glucopyranoside, sitoindoside-X and 1,5-di-o-caffeoyl quinic acid as more effective in treating AD due to their comparable drug-like properties. Drug-likeness, structural chemistry, pharmacophore, and ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) analysis support their potential for future drug development. To establish the effectiveness of these lead compounds against AD, additional experimental testing should be performed.

A Linear Metallocene of Sc(II) Supported by [C₅Me₄(SiMe₂^<i>t</i>Bu)]^1– Ligands

Identification of novel biomarkers related to pathogenesis and treatment of psoriasis based on integrated analysis of weighted gene co-expression network analysis and LASSO

Background Psoriasis is an inflammatory skin disease, and current treatments have their own limitations, including moderate treatment effectiveness, poor compliance, and potential safety risks, etc. Therefore, the primary focus of this study is to explore novel molecular targets and improve the diagnosis and treatment of psoriasis patients. Method In this study, comprehensive bioinformatics analysis was performed on the expression profiles of tissue samples from patients with psoriasis in the clinical trial of TYK2/JAK1 inhibitor treatment (NCT02310750). Weighted gene co-expression network analysis (WGCNA) and least absolute shrinkage and selection operator (LASSO) regression were performed to identify characteristic genes and construct the diagnostic models. Gene set enrichment analysis (GSEA) was used to identify the biological processes of psoriasis characteristic gene sets. GO and KEGG pathway analysis were combined to elucidate the potential biological significance of differentially expressed genes (DEGs). The accuracy of biomarker identification was further validated using immune cell infiltration and receiver operating characteristic (ROC) curves based on external data (GSE6710\GSE30999\GSE14905). Results A total of 5 genes (DEFB103A, OAS3, OASL, SAMD9, STAT1) were co-identified as characteristic genes in psoriasis progression and treatment. The feature of the immune cell infiltration was highly consistent with association of characteristic biomarkers with immune cells. A total of 14 up-regulated genes and 5 down-regulated genes were identified in respective modules (AUC NL/LS = 0.9783; AUC pre/post = 0.9395; AUC external = 0.9469). In addition, 8 genes (DEFB103A, OASL, HERC6, ISG15, MKI67, MX1, MXD1, SCO2) were considered to have statistically significant differences in sensitivity of short-term treatment for psoriasis. Conclusion The research findings provide an understanding of the role of novel biomarkers and offer a perspective for further in-depth investigation into the progression and treatment of psoriasis.

Self-Assembly of Alkynylplatinum(II) Complexes for Sialic Acid Detection and Differentiation of Cancer Cells from Normal Cells