Diagnosis and management of benign recurrent intrahepatic cholestasis and psychosocial stressors in an adolescent: A case report.

BACKGROUND: Benign recurrent intrahepatic cholestasis (BRIC) is a rare autosomal recessive disorder, characterized by episodes of intense pruritus, elevated serum levels of alkaline phosphatase and bilirubin, and near-normal -glutamyl transferase. These episodes may persist for weeks to months before spontaneously resolving, with patients typically remaining asymptomatic between occurrences. Diagnosis entails the evaluation of clinical symptoms and targeted genetic testing. Although BRIC is recognized as a benign genetic disorder, the triggers, particularly psychosocial factors, remain poorly understood. CASE SUMMARY: An 18-year-old Chinese man presented with recurrent jaundice and pruritus after a cold, which was exacerbated by self-medication involving vitamin B and paracetamol. Clinical and laboratory evaluations revealed elevated levels of bilirubin and liver enzymes, in the absence of viral or autoimmune liver disease. Imaging excluded biliary and pancreatic abnormalities, and liver biopsy demonstrated centrilobular cholestasis, culminating in a BRIC diagnosis confirmed by the identification of a novel ATP8B1 gene mutation. Psychological assessment of the patient unveiled stress attributable to academic and familial pressures, regarded as potential triggers for BRIC. Initial relief was observed with ursodeoxycholic acid and cetirizine, followed by an adjustment of the treatment regimen in response to elevated liver enzymes. The patient's condition significantly improved following a stress-related episode, thanks to a comprehensive management approach that included psychosocial support and medical treatment. CONCLUSION: Our research highlights genetic and psychosocial influences on BRIC, emphasizing integrated diagnostic and management strategies.

Spatial and Temporal Patterns of Disease Burden attributable to High Body Mass Index in Belt and Road Initiative Countries, 1990-2019.

OBJECTIVE: This study aimed to analyze the spatial and temporal patterns of disease burden attributed to high body mass index (DB-hBMI) from 1990 to 2019 in Belt and Road Initiative (BRI) countries, in light of increasing hBMI prevalence worldwide. DESIGN: The study was a secondary analysis of global burden of disease 2019 (GBD 2019) that analyzed (using Joinpoint regression analysis) numbers and the age-standardized rate of mortality and disability-adjusted life years (DALYs) of hBMI-induced diseases and their trends from 1990 to 2019 and in the final decade. SETTING: GBD 2019 study data for BRI countries were categorized by country, age, gender, and disease. PARTICIPANTS: GBD 2019 data were used to analyze DB-hBMI in BRI countries. RESULTS: In 2019, China, India, and Russia reported the highest mortality and DALYs among BRI countries. From 1990 to 2019, the age-standardized DALYs increased in Southeast Asia and South Asia, whereas many European countries saw declines. Notably, Bangladesh, Nepal, and Vietnam showed the steepest increases, with AAPC values of 4.42%, 4.19%, and 4.28%, respectively (all P<0.05). In contrast, Israel, Slovenia, and Poland experienced significant reductions, with APCC values of -1.70%, -1.63%, and -1.58%, respectively (all P<0.05). The most rapid increases among males were seen in Vietnam, Nepal, and Bangladesh, while Jordan, Poland, and Slovenia recorded the fastest declines among females. Across most BRI countries, the burden of diabetes and kidney diseases related to hBMI showed a significant uptrend. CONCLUSION: DB-hBMI varies significantly by region, age, gender, and disease type across BRI countries. It can pose a substantial threat to public health.

miR-486-5p diagnosed atrial fibrillation, predicted the risk of left atrial fibrosis, and regulated angiotensin II-induced cardiac fibrosis via modulating PI3K/Akt signaling through targeting FOXO1.

This study focused on miR-486-5p in atrial fibrillation (AF) evaluating its clinical significance and revealing its regulatory mechanism in cardiac fibroblasts, aiming to explore a novel biomarker for AF. The study enrolled 131 AF patients and 77 non-AF individuals. With the help of polymerase chain reaction (PCR), the expression of miR-486-5p was evaluated. The significance of miR-486-5p in the diagnosis of AF and the occurrence of left atrial fibrosis (LAF) was assessed by receiver operating curve (ROC) and logistic analyses. The regulatory effect and mechanism of miR-486-5p on cardiac fibrosis were investigated in human cardiac fibroblasts treated with angiotensin II. miR-486-5p was significantly upregulated in AF patients and discriminated AF patients from non-AF individuals. Increasing miR-486-5p showed a significant association with decreasing left ventricular ejection fraction (LVEF), increasing left atrial diameter (LAD) and left ventricular end-diastolic diameter (LVEDd), and the high incidence of LAF in AF patients. Moreover, miR-486-5p was identified as a risk factor for LAF and could distinguish AF patients with LAF and without LAF. In cardiac fibroblasts, angiotensin II induced the upregulation of miR-486-5p and promoted cell proliferation, migration, and collagen synthesis. miR-486-5p negatively regulated forkhead box O1 (FOXO1) and its knockdown could reverse the promoted effect of angiotensin II. FOXO1 alleviated the effect of miR-486-5p, and the miR-486-5p/FOXO1 could activate PI3K/Akt signaling. The activation of PI3K/Akt signaling alleviated the enhanced proliferation, migration, and collagen synthesis of cardiac fibroblasts induced by angiotensin II, and its inhibition showed opposite effects. Increased miR-486-5p served as a biomarker for the diagnosis and development prediction of AF. miR-486-5p regulated cardiac fibroblast viability and collagen synthesis via modulating the PI3K/Akt signaling through targeting FOXO1.

Alcohol Exposure and Disease Associations: A Mendelian Randomization and Meta-Analysis on Weekly Consumption and Problematic Drinking.

Alcohol consumption significantly impacts disease burden and has been linked to various diseases in observational studies. However, comprehensive meta-analyses using Mendelian randomization (MR) to examine drinking patterns are limited. We aimed to evaluate the health risks of alcohol use by integrating findings from MR studies. A thorough search was conducted for MR studies focused on alcohol exposure. We utilized two sets of instrumental variables-alcohol consumption and problematic alcohol use-and summary statistics from the FinnGen consortium R9 release to perform de novo MR analyses. Our meta-analysis encompassed 64 published and 151 de novo MR analyses across 76 distinct primary outcomes. Results show that a genetic predisposition to alcohol consumption, independent of smoking, significantly correlates with a decreased risk of Parkinson's disease, prostate hyperplasia, and rheumatoid arthritis. It was also associated with an increased risk of chronic pancreatitis, colorectal cancer, and head and neck cancers. Additionally, a genetic predisposition to problematic alcohol use is strongly associated with increased risks of alcoholic liver disease, cirrhosis, both acute and chronic pancreatitis, and pneumonia. Evidence from our MR study supports the notion that alcohol consumption and problematic alcohol use are causally associated with a range of diseases, predominantly by increasing the risk.

CLIP170 inhibits the metastasis and EMT of papillary thyroid cancer through the TGF-ß pathway.

Metastasis poses a significant challenge in combating tumors. Even in papillary thyroid cancer (PTC), which typically exhibits a favorable prognosis, high recurrence rates are attributed to metastasis. Cytoplasmic linker protein 170 (CLIP170) functions as a classical microtubule plus-end tracking protein (+TIP) and has shown close association with cell migration. Nevertheless, the specific impact of CLIP170 on PTC cells remains to be elucidated. Our analysis of the GEO and TCGA databases unveiled an association between CLIP170 and the progression of PTC. To explore the impact of CLIP170 on PTC cells, we conducted various assays. We evaluated its effects through CCK-8, wound healing assay, and transwell assay after knocking down CLIP170. Additionally, the influence of CLIP170 on the cellular actin structure was examined via immunofluorescence; we further investigated the molecular expressions of epithelial-mesenchymal transition (EMT) and the transforming growth factor-ß (TGF-ß) signaling pathways through Western blotting and RT-qPCR. These findings were substantiated through an in vivo nude mouse model of lung metastasis. We observed a decreased expression of CLIP170 in PTC in contrast to normal thyroid tissue. Functionally, the knockdown of CLIP170 (CLIP170KD) notably enhanced the metastatic potential and EMT of PTC cells, both in vitro and in vivo. Mechanistically, CLIP170KD triggered the activation of the TGF-ß pathway, subsequently promoting tumor cell migration, invasion, and EMT. Remarkably, the TGF-ß inhibitor LY2157299 effectively countered TGF-ß activity and significantly reversed tumor metastasis and EMT induced by CLIP170 knockdown. In summary, these findings collectively propose CLIP170 as a promising therapeutic target to mitigate metastatic tendencies in PTC.

Chemical Proteomics Reveals Nε-Fatty-Acylation of Septins by Rho Inactivation Domain (RID) of the Vibrio MARTX Toxin to Alter Septin Localization and Organization.

Vibrio species, the Gram-negative bacterial pathogens causing cholera and sepsis, produce multiple secreted virulence factors for infection and pathogenesis. Among these is the multifunctional-autoprocessing repeats-in-toxin (MARTX) toxin that releases several critical effector domains with distinct functions inside eukaryotic host cells. One such effector domain, the Rho inactivation domain (RID), has been discovered to catalyze long-chain Nε-fatty-acylation on lysine residues of Rho GTPases, causing inactivation of Rho GTPases and disruption of the host actin cytoskeleton. However, whether RID modifies other host proteins to exert additional functions remains to be determined. Herein, we describe the integration of bioorthogonal chemical labeling and quantitative proteomics to globally profile the target proteins modified by RID in living cells. More than 246 proteins are identified as new RID substrates, including many involved in GTPase regulation, cytoskeletal organization, and cell division. We demonstrate that RID extensively Nε-fatty-acylates septin proteins, the fourth cytoskeletal component of mammalian cells with important roles in diverse cellular processes. While affinity purification and mass spectrometry analysis show that RID-mediated Nε-fatty-acylation does not affect septin-septin interactions, this modification increases the membrane association of septins and confers localization to detergent-resistant membrane rafts. As a result, the filamentous assembly and organization of septins are disrupted by RID-mediated Nε-fatty-acylation, further contributing to cytoskeletal and mitotic defects that phenocopy the effects of septin depletion. Overall, our work greatly expands the substrate scope and function of RID and demonstrates the role of RID-mediated Nε-fatty-acylation in manipulating septin localization and organization.

Major Depressive Disorder Prediction Based on Sleep-Wake Disorders Symptoms in US Adolescents: A Machine Learning Approach from National Sleep Research Resource.

Background: There is substantial evidence from previous studies that abnormalities in sleep parameters associated with depression are demonstrated in almost all stages of sleep architecture. Patients with symptoms of sleep-wake disorders have a much higher risk of developing major depressive disorders (MDD) compared to those without. Objective: The aim of the present study is to establish and compare the performance of different machine learning models based on sleep-wake disorder symptoms data and to select the optimal model to interpret the importance of sleep-wake disorder symptoms to predict MDD occurrence in adolescents. Methods: We derived data for this work from 2020 to 2021 Assessing Nocturnal Sleep/Wake Effects on Risk of Suicide Phase I Study from National Sleep Research Resource. Using demographic and sleep-wake disorder symptoms data as predictors and the occurrence of MDD measured base on the center for epidemiologic studies depression scale as an outcome, the following six machine learning predictive models were developed: eXtreme Gradient Boosting model (XGBoost), Light Gradient Boosting mode, AdaBoost, Gaussian Naïve Bayes, Complement Naïve Bayes, and multilayer perceptron. The models' performance was assessed using the AUC and other metrics, and the final model's predictor importance ranking was explained. Results: XGBoost is the optimal predictive model in comprehensive performance with the AUC of 0.804 in the test set. All sleep-wake disorder symptoms were significantly positively correlated with the occurrence of adolescent MDD. The insomnia severity was the most important predictor compared with the other predictors in this study. Conclusion: This machine learning predictive model based on sleep-wake disorder symptoms can help to raise the awareness of risk of symptoms between sleep-wake disorders and MDD in adolescents and improve primary care and prevention.

scEM: A New Ensemble Framework for Predicting Cell Type Composition Based on scRNA-Seq Data.

With the advent of single-cell RNA sequencing (scRNA-seq) technology, many scRNA-seq data have become available, providing an unprecedented opportunity to explore cellular composition and heterogeneity. Recently, many computational algorithms for predicting cell type composition have been developed, and these methods are typically evaluated on different datasets and performance metrics using diverse techniques. Consequently, the lack of comprehensive and standardized comparative analysis makes it difficult to gain a clear understanding of the strengths and weaknesses of these methods. To address this gap, we reviewed 20 cutting-edge unsupervised cell type identification methods and evaluated these methods comprehensively using 24 real scRNA-seq datasets of varying scales. In addition, we proposed a new ensemble cell-type identification method, named scEM, which learns the consensus similarity matrix by applying the entropy weight method to the four representative methods are selected. The Louvain algorithm is adopted to obtain the final classification of individual cells based on the consensus matrix. Extensive evaluation and comparison with 11 other similarity-based methods under real scRNA-seq datasets demonstrate that the newly developed ensemble algorithm scEM is effective in predicting cellular type composition.

Causal associations between digital device use and suicide risk: A bidirectional Mendelian randomization study.

BACKGROUND: The popularity of digital devices seems to provide a new observational variable for early identification and prevention of suicide with the development of the information technology era. Nevertheless, whether it is the use of digital devices that alters suicide risk or suicide risk manifests itself through change digital device use needs to be further explored. METHODS: Bidirectional Mendelian randomization (MR) analysis was used to explore potential causal relationships in the perspective of genetic prediction. We collected publicly available digital device use and suicide risk summary statistics genome-wide association data from UK Biobank, Neale Lab and FinnGen genetic databases. We used inverse variance weighting methods to assess MR estimates. For robustness of the results, we performed further tests of heterogeneity and pleiotropy. RESULTS: In the Phase 1 results, we did not observe any effect of the length of digital device use on the suicide risk, while the results of Phase 2 suggested a significant positive association between suicide risk and the length of mobile phone use (IVW OR, 1.04; 95%CI, 1.01-1.06; P = 0.002), but this significance disappeared after adjusting for confounders of mental and affective disorders. CONCLUSIONS: In this bidirectional MR analysis, we observed that People at high risk of suicide may be more addicted to digital device use, but more detailed GWAS data and research methods to validate this finding are required.

Narcolepsy and cardiovascular disease: A two-sample Mendelian randomization study.

BACKGROUND: Observational findings suggest that patients with narcolepsy are at higher risk for cardiovascular diseases (CVDs), but the potential causal relationship between narcolepsy and CVDs is unclear. Therefore, Mendelian randomization (MR) was used to explore the association between narcolepsy and CVDs. METHODS: Summary statistics related to narcolepsy, coronary artery disease (CAD), myocardial infarction (MI), heart failure (HF), any stroke (AS), and any ischemic stroke (AIS) were extracted from the public database of relevant published genome-wide association studies (GWAS). Independent single nucleotide polymorphisms were selected as instrumental variables under strict quality control criteria. Inverse variance-weighted (IVW) was the main analytical method to assess causal effects. In addition, we conducted MR pleiotropy residual sum and outlier (MR-PRESSO), weighted median, MR-Egger, and leave-one-out sensitivity analysis to verify the robustness and reliability of the results. RESULTS: The results of the MR study revealed that narcolepsy was significantly associated with an increased risk of HF (OR = 1.714; 95%CI [1.031-2.849]; P = 0.037), CAD (OR = 1.702; 95%CI [1.011-2.864]; P = 0.045). There was no statistically significant causal association between narcolepsy and MI, AS, and AIS. In addition, further sensitivity analysis showed robust results. CONCLUSIONS: The results of the two-sample MR study reveal a potential causal relationship between the increased risk of HF and CAD in narcolepsy. These findings emphasize the importance of early monitoring and assessment of cardiovascular risk in patients with narcolepsy.

Combining Global-Constrained Concept Factorization and a Regularized Gaussian Graphical Model for Clustering Single-Cell RNA-seq Data.

Single-cell RNA sequencing technology is one of the most cost-effective ways to uncover transcriptomic heterogeneity. With the rapid rise of this technology, enormous amounts of scRNA-seq data have been produced. Due to the high dimensionality, noise, sparsity and missing features of the available scRNA-seq data, accurately clustering the scRNA-seq data for downstream analysis is a significant challenge. Many computational methods have been designed to address this issue; nevertheless, the efficacy of the available methods is still inadequate. In addition, most similarity-based methods require a number of clusters as input, which is difficult to achieve in real applications. In this study, we developed a novel computational method for clustering scRNA-seq data by considering both global and local information, named GCFG. This method characterizes the global properties of data by applying concept factorization, and the regularized Gaussian graphical model is utilized to evaluate the local embedding relationship of data. To learn the cell-cell similarity matrix, we integrated the two components, and an iterative optimization algorithm was developed. The categorization of single cells is obtained by applying Louvain, a modularity-based community discovery algorithm, to the similarity matrix. The behavior of the GCFG approach is assessed on 14 real scRNA-seq datasets in terms of ACC and ARI, and comparison results with 17 other competitive methods suggest that GCFG is effective and robust.

Melatonin Promotes Iron Reactivation and Reutilization in Peach Plants under Iron Deficiency.

The yellowing of leaves due to iron deficiency is a prevalent issue in peach production. Although the capacity of exogenous melatonin (MT) to promote iron uptake in peach plants has been demonstrated, its underlying mechanism remains ambiguous. This investigation was carried out to further study the effects of exogenous MT on the iron absorption and transport mechanisms of peach (Prunus persica) plants under iron-deficient conditions through transcriptome sequencing. Under both iron-deficient and iron-supplied conditions, MT increased the content of photosynthetic pigments in peach leaves and decreased the concentrations of pectin, hemicellulose, cell wall iron, pectin iron, and hemicellulose iron in peach plants to a certain extent. These effects stemmed from the inhibitory effect of MT on the polygalacturonase (PG), cellulase (Cx), phenylalanine ammonia-lyase (PAL), and cinnamoyl-coenzyme A reductase (CCR) activities, as well as the promotional effect of MT on the cinnamic acid-4-hydroxylase (C4H) activity, facilitating the reactivation of cell wall component iron. Additionally, MT increased the ferric-chelate reductase (FCR) activity and the contents of total and active iron in various organs of peach plants under iron-deficient and iron-supplied conditions. Transcriptome analysis revealed that the differentially expressed genes (DEGs) linked to iron metabolism in MT-treated peach plants were primarily enriched in the aminoacyl-tRNA biosynthesis pathway under iron-deficient conditions. Furthermore, MT influenced the expression levels of these DEGs, regulating cell wall metabolism, lignin metabolism, and iron translocation within peach plants. Overall, the application of exogenous MT promotes the reactivation and reutilization of iron in peach plants.

CHD-related/specific mortality of 3.17 million people in a transitioning region: trends, risk factors, and prevention.

BACKGROUND: Demographic shifts cause uncertain changes in the burden of coronary heart disease (CHD) in transitioning regions. We aimed to analyze the trends of CHD burden and its risk factors in Pudong, Shanghai, and explore prevention strategies for transitioning regions. METHODS: We analyzed CHD-related and CHD-specific deaths in Pudong from 2005 to 2020, including the crude mortality rate (CMR), age-standardized mortality rate worldwide (ASMRW), years of life lost (YLL), and age-specific proportions. We also examined the impact of population aging on the burden of CHD. The Joinpoint Regression Program was used to analyze trends, and the decomposition method was used to evaluate the impact of demographics on the mortality rate. RESULTS: Of the 86,171 CHD-related deaths, 52,152 (60.52%) were CHD-specific deaths. For both CHD-related and CHD-specific deaths, there was a significant increase in the CMR, ASMRW, and YLL rate, except in the 70-79-year age group, which exhibited a distinctive and statistically significant decline in these rates (all P < 0.05). There were steadily increasing trends in the rates caused by aging from 2005 to 2020, with average annual percent changes (AAPCs) of 42.59% and 41.43%, respectively (all P < 0.05). CONCLUSIONS: Our results indicate that the CHD burden in Pudong has been persistently increasing, but in the age group of 70-79 years, substantial declines were observed. The quality of primary healthcare services may be a critical point in addressing the overwhelming CHD burden.

Nonequilibrium interactions between multi-scale colloids regulate the suspension microstructure and rheology.

Understanding nonequilibrium interactions of multi-component colloidal suspensions is critical for many dynamical settings such as self-assembly and material processing. A key question is how the nonequilibrium distributions of individual components influence the effective interparticle interactions and flow behavior. In this work, we develop a first-principle framework to study a bidisperse suspension of colloids and depletants using a Smoluchowski equation and corroborated by Brownian dynamics (BD) simulations. Using nonlinear microrheology as a case study, we demonstrate that effective depletion interactions between driven colloids are sensitive to particle timescales out of equilibrium and cannot be predicted by equilibrium-based pair potentials like Asakura-Oosawa. Furthermore, we show that the interplay between Brownian relaxation timescales of different species plays a critical role in governing the viscosity of multi-component suspensions. Our model highlights the limitations of using equilibrium pair potentials to approximate interparticle interactions in nonequilibrium processes such as hydrodynamic flows and presents a useful framework for studying the transport of driven, interacting suspensions.

Revisiting Supersaturation of a Biopharmaceutical Classification System IIB Drug: Evaluation via a Multi-Cup Dissolution Approach and Molecular Dynamic Simulation.

As a subclass of the biopharmaceutical classification system (BCS) class II, basic drugs (BCS IIB) exhibit pH-dependent solubility and tend to generate supersaturation in the gastrointestinal tract, leading to less qualified in vitro-in vivo correlation (IVIVC). This study aims to develop a physiologically based multi-cup dissolution approach to improve the evaluation of the supersaturation for a higher quality of IVIVC and preliminarily explores the molecular mechanism of supersaturation and precipitation of ketoconazole affected by Polyvinylpyrrolidone-vinyl acetate copolymer (PVPVA) and hydroxypropyl methyl-cellulose (HPMC). The concentration of ketoconazole in each cup of the dynamic gastrointestinal model (DGIM) was measured using fiber optical probes. Molecular interactions between ketoconazole and PVPVA or HPMC were simulated by Materials Studio. The results demonstrated that PVPVA and HPMC improved and maintained the supersaturation of ketoconazole. PVPVA exhibited superior precipitation inhibitory effect on ketoconazole molecule aggregation due to slightly stronger van der Waals forces as well as unique electrostatic forces, thereby further enhancing in vitro drug absorption, which correlated well with in vivo drug absorption. Compared with a conventional dissolution apparatus paddle method, the DGIM improved the mean prediction error through the IVIVC from 19.30% to 9.96%, reaching the qualification criteria. In conclusion, the physiologically based multi-cup dissolution approach enables improved evaluation of supersaturation in gastrointestinal transportation of BCS IIB drug ketoconazole, enabling screening screen precipitation inhibitors and achieving qualified IVIVC for drug formulation studies.

Transcriptome Profiles Reveal the Promoting Effects of Exogenous Melatonin on Fruit Softening of Chinese Plum.

In this study, we investigated the effect of exogenous melatonin (MT) on cell wall metabolism leading to Chinese plum (Prunus salicina Lindl.) fruit softening. Exogenous MT treatment increased the endogenous MT content in plum fruits before fruit ripening. However, in mature plum fruits, exogenous MT treatment decreased the fruit hardness, pulp hardness, fruit elasticity, contents of ion-bound pectin, covalently-bound pectin, hemicellulose, and cellulose, and activities of xyloglucan endotransglycosylase/hydrolase and endo-ß-1,4-glucanase, and increased the water-soluble pectin content, and activities of pectin methyl esterase, pectin lyase, polygalacturonase, ß-galactopyranosidase, and α-L-arabinofuranosidase. Transcriptome analysis revealed that the differentially expressed genes (DEGs) associated with cell wall metabolism in the exogenous MT-treated plum fruits were mainly enriched in the pentose and glucuronate interconversions, phenylpropanoid biosynthesis, cyanoamino acid metabolism, and galactose metabolism pathways. Analysis of these DEGs revealed that exogenous MT treatment affected the expression of genes regulating the cell wall metabolism. Overall, exogenous MT treatment promotes the fruit softening of Chinese plum.

Patterns and Trends in Mortality Associated With and Due to Diabetes Mellitus in a Transitioning Region With 3.17 Million People: Observational Study.

BACKGROUND: Diabetes mellitus (DM) imposes a significant disease burden in economically transitioning regions. Most transitioning regions share similar experience in urbanization processes. Shanghai's Pudong district serves as a representative area of such regions. OBJECTIVE: We aimed to assess the burden of and trends in DM mortality in Shanghai's Pudong district and analyze the impact of aging and multimorbidity. METHODS: A longitudinal, population-based study was conducted to analyze DM mortality in Pudong from 2005 to 2020. We used joinpoint regression to analyze epidemiological features and long-term trends in crude mortality rate (CMR), age-standardized mortality rate worldwide (ASMRW), and years of life lost (YLL). Furthermore, the decomposition method was used to evaluate the contribution of demographic and nondemographic factors associated with mortality. RESULTS: There were 49,414 deaths among individuals with DM, including 15,512 deaths due to DM. The CMR and ASMRW were 109.55/105 and 38.01/105 person-years, respectively. Among the mortality associated with and due to DM, the total annual ASMRW increased by 3.65% (95% CI 3.25%-4.06%) and 1.38% (95% CI 0.74%-2.02%), respectively. Additionally, the total annual YLL rate increased by 4.98% (95% CI 3.92%-6.05%) and 2.68% (95% CI 1.34%-4.04%). The rates of YLL increase in persons aged 30 to 44 years (3.98%, 95% CI 0.32%-7.78%) and 45 to 59 years (4.31%, 95% CI 2.95%-5.69%) were followed by the increase in persons aged 80 years and older (10.53%, 95% CI 9.45%-11.62%) for deaths associated with DM. The annual CMR attributable to demographic factors increased by 41.9% (95% CI 17.73%-71.04%) and 36.72% (95% CI 16.69%-60.2%) for deaths associated with and due to DM, respectively. Hypertension, cerebrovascular disease, and ischemic heart disease were the top 3 comorbidities. CONCLUSIONS: Aging and multimorbidity played essential roles in changing the burden of DM in an urbanizing and transitioning region. There is an increasing disease burden among young and middle-aged people, emphasizing the need for greater attention to these groups. Health management is an emerging method that holds important implications for alleviating the future burden of DM.

Network module analysis and molecular docking-based study on the mechanism of astragali radix against non-small cell lung cancer.

BACKGROUND: Most lung cancer patients worldwide (stage IV non-small cell lung cancer, NSCLC) have a poor survival: 25%-30% patients die < 3 months. Yet, of those surviving > 3 months, 10%-15% patients survive (very) long. Astragali radix (AR) is an effective traditional Chinese medicine widely used for non-small cell lung cancer (NSCLC). However, the pharmacological mechanisms of AR on NSCLC remain to be elucidated. METHODS: Ultra Performance Liquid Chromatography system coupled with Q-Orbitrap HRMS (UPLC-Q-Orbitrap HRMS) was performed for the qualitative analysis of AR components. Then, network module analysis and molecular docking-based approach was conducted to explore underlying mechanisms of AR on NSCLC. The target genes of AR were obtained from four databases including TCMSP (Traditional Chinese Medicine Systems Pharmacology) database, ETCM (The Encyclopedia of TCM) database, HERB (A high-throughput experiment- and reference-guided database of TCM) database and BATMAN-TCM (a Bioinformatics Analysis Tool for Molecular mechanism of TCM) database. NSCLC related genes were screened by GEO (Gene Expression Omnibus) database. The STRING database was used for protein interaction network construction (PIN) of AR-NSCLC shared target genes. The critical PIN were further constructed based on the topological properties of network nodes. Afterwards the hub genes and network modules were analyzed, and enrichment analysis were employed by the R package clusterProfiler. The Autodock Vina was utilized for molecular docking, and the Gromacs was utilized for molecular dynamics simulations Furthermore, the survival analysis was performed based on TCGA (The Cancer Genome Atlas) database. RESULTS: Seventy-seven AR components absorbed in blood were obtained. The critical network was constructed with 1447 nodes and 28,890 edges. Based on topological analysis, 6 hub target genes and 7 functional modules were gained. were obtained including TP53, SRC, UBC, CTNNB1, EP300, and RELA. After module analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed that AR may exert therapeutic effects on NSCLC by regulating JAK-STAT signaling pathway, PI3K-AKT signaling pathway, ErbB signaling pathway, as well as NFkB signaling pathway. After the intersection calculation of the hub targets and the proteins participated in the above pathways, TP53, SRC, EP300, and RELA were obtained. These proteins had good docking affinity with astragaloside IV. Furthermore, RELA was associated with poor prognosis of NSCLC patients. CONCLUSIONS: This study could provide chemical component information references for further researches. The potential pharmacological mechanisms of AR on NSCLC were elucidated, promoting the clinical application of AR in treating NSCLC. RELA was selected as a promising candidate biomarker affecting the prognosis of NSCLC patients.

Dynamic interfaces for contact-time control of colloidal interactions.

Understanding pairwise interactions between colloidal particles out of equilibrium has a profound impact on dynamical processes such as colloidal self assembly. However, traditional colloidal interactions are effectively quasi-static on colloidal timescales and cannot be modulated out of equilibrium. A mechanism to dynamically tune the interactions during colloidal contacts can provide new avenues for self assembly and material design. In this work, we develop a framework based on polymer-coated colloids and demonstrate that in-plane surface mobility and mechanical relaxation of polymers at colloidal contact interfaces enable an effective, dynamic interaction. Combining analytical theory, simulations, and optical tweezer experiments, we demonstrate precise control of dynamic pair interactions over a range of pico-Newton forces and seconds timescales. Our model helps further the general understanding of out-of-equilibrium colloidal assemblies while providing extensive design freedom via interface modulation and nonequilibrium processing.

Global comparison of the effect of non-pharmacological interventions on glycemic control in patients with type 2 diabetes: A network meta-analysis from 107 randomized controlled trials.

OBJECTIVE: To investigate the effectiveness of non-pharmacological interventions (NPIs) on glycemic control in patients with type 2 diabetes (T2D) and to provide guidance for clinical healthcare-giver. DESIGN: Network meta-analysis (NMA). SETTING AND PARTICIPANTS: Randomized controlled trials comparing the effect of NPIs with usual care, waitlist, or other NPIs on glycemic control in patients with T2D. METHODS: This NMA was guided by frequentist framework. PubMed, Embase, the Cochrane Library Central Register of Controlled Trials, Cumulated Index to Nursing and Allied Health Literature, and Web of Science were searched from their inception until January 2023. The primary outcome was HbA1c and secondary outcomes were cardiovascular risk scores and related psychosocial scores. Mean differences and standardized mean differences were pooled using NMA. Study quality was assessed with the Confidence in Network Meta-analysis. RESULTS: A total of 107 studies (10,496 participants) were included. The median sample size of the included studies was 64 (range, 10-563) and the median duration was 3 months (range, 1-24). Compared to usual care, all NPIs except acupuncture (MD: -0.28; 95 % CI: -1.02, 0.26) and psychological therapy (MD: -0.29; 95 % CI: -0.66, 0.08) showed significantly differences in improving glycemic control in patients with T2D. And according to the results of surface under the cumulative ranking analysis and Cluster ranking, meditation therapy was considered to the best choice when balancing the efficacy of glycemic control with self-efficacy and diabetes related problems, while nutrition therapy was considered to the best choice when balancing quality of life with risk of cardiovascular complications. CONCLUSIONS: These findings validate the efficacy of NPIs for glycemic control in patients with T2D and suggest that healthcare-giver should consider both the efficacy of interventions and the psychosocial needs of patients when developing NPIs programs.