Can social health insurance improve mental health? An analysis of supplementary high-cost illness insurance in China

ObjectivesChina introduced an innovative Supplementary High-Cost Illness Insurance (SHCII) programme to enhance existing social health insurance by providing extra financial support for individuals facing catastrophic illnesses in 2015. The SHCII has notably increased access to healthcare and alleviated financial strain for economically disadvantaged individuals. However, there is a lack of information regarding the programme's impact on the mental health of its beneficiaries. This study aims to assess the impact of SHCII on the mental well-being of middle-aged and older individuals.MethodsUsing data from the China Health and Retirement Longitudinal Study (CHARLS; 2011, 2013, 2015 and 2018), this study examined how SHCII affects mental health among middle-aged and older individuals in China using propensity score matching with the time-varying difference-in-differences (DID) method.ResultsWe found that SHCII implementation can significantly reduce the Center for Epidemiologic Studies Depression Scale (CESD-10) scores of middle-aged and older individuals. This reduction was more pronounced among older individuals with poor self-rated health, chronic disease, and low household wealth when compared to their counterparts.DiscussionThe implementation of SHCII has had a significant and positive impact on mental health outcomes. We recommend that governments consider expanding the programme to other areas within China, focusing especially on the most economically disadvantaged segments of the population

Study on Yield Stress and Thixotropy of Hydroxypropyl Distarch Phosphate Paste

In order to study the yield stress and thixotropic behavior of the hydroxypropyl distarch phosphate (HPDSP) paste, HPDSP respectively derived from corn starch (CS) and waxy corn starch (WS) with different ratios of amylopectin were investigated. The critical mass fractions, yield stress, and thixotropic behavior of HPDSP pastes under various temperatures were studied. The results showed that, the critical mass fractions for the transition of the HPDSP solution at 5 ℃ from dilute to semi-dilute, and from semi-dilute to concentrated were 3wt% and 6wt%, respectively. The yield stress of 5wt% corn starch-hydroxypropyl distarch phosphate (CS-HPDSP) and waxy corn starch-hydroxypropyl distarch phosphate (WS-HPDSP) paste both showed weak correlations with temperature. However, at 6wt% concentration, the yield stress significantly decreased (P<0.05) by 69.52% and 77.95% respectively at 85 ℃. Additionally, the thixotropic behavior of HPDSP was influenced by both mass fraction and temperature. At 5 ℃, 5wt% CS-HPDSP and WS-HPDSP showed limited thixotropy, while at 6wt% of mass fraction, the areas of thixotropic loops of CS-HPDSP and WS-HPDSP were 163.49 and 85.00 Pa/s, respectively, and decreased by 86.38% and 92.18% at 85 ℃, respectively. WS-HPDSP exhibited less thixotropic behavior than CS-HPDSP, and showed better stability in three interval thixotropy test (3iTT). In conclusion, WS-HPDSP showed less yield stress and thixotropy compared with CS-HPDSP. This study provides theoretical supports for practical application of HPDSP as thickening agents in food products

Rethinking the Metric in Few-shot Learning: From an Adaptive Multi-Distance Perspective

Few-shot learning problem focuses on recognizing unseen classes given a few labeled images. In recent effort, more attention is paid to fine-grained feature embedding, ignoring the relationship among different distance metrics. In this paper, for the first time, we investigate the contributions of different distance metrics, and propose an adaptive fusion scheme, bringing significant improvements in few-shot classification. We start from a naive baseline of confidence summation and demonstrate the necessity of exploiting the complementary property of different distance metrics. By finding the competition problem among them, built upon the baseline, we propose an Adaptive Metrics Module (AMM) to decouple metrics fusion into metric-prediction fusion and metric-losses fusion. The former encourages mutual complementary, while the latter alleviates metric competition via multi-task collaborative learning. Based on AMM, we design a few-shot classification framework AMTNet, including the AMM and the Global Adaptive Loss (GAL), to jointly optimize the few-shot task and auxiliary self-supervised task, making the embedding features more robust. In the experiment, the proposed AMM achieves 2% higher performance than the naive metrics fusion module, and our AMTNet outperforms the state-of-the-arts on multiple benchmark datasets

The clinical implications of fasting serum insulin levels in patients with insulin-treated type 2 diabetes: a cross-sectional survey

ObjectiveThis study aimed to investigate the clinical implications of fasting serum insulin (FINS) levels in subjects with type 2 diabetes who were receiving insulin therapy.MethodsA total of 1,553 subjects with type 2 diabetes [774 subjects who had never received insulin treatment (N-INS) and 779 subjects who were receiving insulin therapy (constant insulin treatment, C-INS)] admitted to the Department of Endocrinology and Metabolism of Peking University People’s Hospital were enrolled in this study. Their FINS levels were measured and those with hyperinsulinemia were identified. The underlying mechanisms of hyperinsulinemia were revealed by measuring insulin antibodies (IAs) and analyzing changes in FINS levels before and after polyethylene glycol (PEG) precipitation. In addition, the clinical characteristics of patients with different types of hyperinsulinemia were compared.ResultsHigher FINS levels and a higher incidence (43.8%, 341/779) of hyperinsulinemia (FINS &gt; 15μIU/mL) were observed in subjects with C-INS than in subjects with N-INS. Among subjects with C-INS and hyperinsulinemia, 66.9% (228/341) were IAs positive, and the incidence of IAs was found to be positively associated with FINS level. By performing PEG precipitation, we found that all subjects without IAs (i.e., those with real hyperinsulinemia) and 31.1% of subjects (71/228) with IAs (i.e., those with both real and IAs-related hyperinsulinemia) still had hyperinsulinemia after PEG precipitation, whereas FINS levels in the other 68.9% of subjects (157/228) with IAs were normal (IAs-related hyperinsulinemia) after PEG precipitation. Comparisons between the groups showed that subjects with real hyperinsulinemia showed more obvious insulin resistance characteristics, including higher lipid levels, BMIs, and homoeostasis model assessment2-estimated insulin resistance (HOMA2-IR) index, and were more likely to have hypertension, obesity, and metabolic syndromes (p &lt; 0.05). However, the risk of hypoglycemia and glucose variability increased significantly in subjects with IAs compared with those without IAs. A cutoff of FINS to serum C-peptide ratio (≥ 9.3μIU/ng) could be used to screen IAs in clinical practice with 83.3% sensitivity and 70% specificity.ConclusionsIt is necessary to measure FINS in subjects with C-INS to distinguish between types of hyperinsulinemia, which should help to tailor treatment regimens

MMDB: annotating protein sequences with Entrez's 3D-structure database

Three-dimensional (3D) structure is now known for a large fraction of all protein families. Thus, it has become rather likely that one will find a homolog with known 3D structure when searching a sequence database with an arbitrary query sequence. Depending on the extent of similarity, such neighbor relationships may allow one to infer biological function and to identify functional sites such as binding motifs or catalytic centers. Entrez's 3D-structure database, the Molecular Modeling Database (MMDB), provides easy access to the richness of 3D structure data and its large potential for functional annotation. Entrez's search engine offers several tools to assist biologist users: (i) links between databases, such as between protein sequences and structures, (ii) pre-computed sequence and structure neighbors, (iii) visualization of structure and sequence/structure alignment. Here, we describe an annotation service that combines some of these tools automatically, Entrez's ‘Related Structure’ links. For all proteins in Entrez, similar sequences with known 3D structure are detected by BLAST and alignments are recorded. The ‘Related Structure’ service summarizes this information and presents 3D views mapping sequence residues onto all 3D structures available in MMDB ()

Novel hypoxia-related gene signature for predicting prognoses that correlate with the tumor immune microenvironment in NSCLC

Background: Intratumoral hypoxia is widely associated with the development of malignancy, treatment resistance, and worse prognoses. The global influence of hypoxia-related genes (HRGs) on prognostic significance, tumor microenvironment characteristics, and therapeutic response is unclear in patients with non-small cell lung cancer (NSCLC).Method: RNA-seq and clinical data for NSCLC patients were derived from The Cancer Genome Atlas (TCGA) database, and a group of HRGs was obtained from the MSigDB. The differentially expressed HRGs were determined using the limma package; prognostic HRGs were identified via univariate Cox regression. Using the least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression, an optimized prognostic model consisting of nine HRGs was constructed. The prognostic model’s capacity was evaluated by Kaplan‒Meier survival curve analysis and receiver operating characteristic (ROC) curve analysis in the TCGA (training set) and GEO (validation set) cohorts. Moreover, a potential biological pathway and immune infiltration differences were explained.Results: A prognostic model containing nine HRGs (STC2, ALDOA, MIF, LDHA, EXT1, PGM2, ENO3, INHA, and RORA) was developed. NSCLC patients were separated into two risk categories according to the risk score generated by the hypoxia model. The model-based risk score had better predictive power than the clinicopathological method. Patients in the high-risk category had poor recurrence-free survival in the TCGA (HR: 1.426; 95% CI: 0.997–2.042; p = 0.046) and GEO (HR: 2.4; 95% CI: 1.7–3.2; p &lt; 0.0001) cohorts. The overall survival of the high-risk category was also inferior to that of the low-risk category in the TCGA (HR: 1.8; 95% CI: 1.5–2.2; p &lt; 0.0001) and GEO (HR: 1.8; 95% CI: 1.4–2.3; p &lt; 0.0001) cohorts. Additionally, we discovered a notable distinction in the enrichment of immune-related pathways, immune cell abundance, and immune checkpoint gene expression between the two subcategories.Conclusion: The proposed 9-HRG signature is a promising indicator for predicting NSCLC patient prognosis and may be potentially applicable in checkpoint therapy efficiency prediction

CDD: a Conserved Domain Database for protein classification

The Conserved Domain Database (CDD) is the protein classification component of NCBI's Entrez query and retrieval system. CDD is linked to other Entrez databases such as Proteins, Taxonomy and PubMed®, and can be accessed at http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=cdd. CD-Search, which is available at http://www.ncbi.nlm.nih.gov/Structure/cdd/wrpsb.cgi, is a fast, interactive tool to identify conserved domains in new protein sequences. CD-Search results for protein sequences in Entrez are pre-computed to provide links between proteins and domain models, and computational annotation visible upon request. Protein–protein queries submitted to NCBI's BLAST search service at http://www.ncbi.nlm.nih.gov/BLAST are scanned for the presence of conserved domains by default. While CDD started out as essentially a mirror of publicly available domain alignment collections, such as SMART, Pfam and COG, we have continued an effort to update, and in some cases replace these models with domain hierarchies curated at the NCBI. Here, we report on the progress of the curation effort and associated improvements in the functionality of the CDD information retrieval system

PubChem3D: a new resource for scientists

<p>Abstract</p> <p>Background</p> <p>PubChem is an open repository for small molecules and their experimental biological activity. PubChem integrates and provides search, retrieval, visualization, analysis, and programmatic access tools in an effort to maximize the utility of contributed information. There are many diverse chemical structures with similar biological efficacies against targets available in PubChem that are difficult to interrelate using traditional 2-D similarity methods. A new layer called PubChem3D is added to PubChem to assist in this analysis.</p> <p>Description</p> <p>PubChem generates a 3-D conformer model description for 92.3% of all records in the PubChem Compound database (when considering the parent compound of salts). Each of these conformer models is sampled to remove redundancy, guaranteeing a minimum (non-hydrogen atom pair-wise) RMSD between conformers. A diverse conformer ordering gives a maximal description of the conformational diversity of a molecule when only a subset of available conformers is used. A pre-computed search per compound record gives immediate access to a set of 3-D similar compounds (called "Similar Conformers") in PubChem and their respective superpositions. Systematic augmentation of PubChem resources to include a 3-D layer provides users with new capabilities to search, subset, visualize, analyze, and download data.</p> <p>A series of retrospective studies help to demonstrate important connections between chemical structures and their biological function that are not obvious using 2-D similarity but are readily apparent by 3-D similarity.</p> <p>Conclusions</p> <p>The addition of PubChem3D to the existing contents of PubChem is a considerable achievement, given the scope, scale, and the fact that the resource is publicly accessible and free. With the ability to uncover latent structure-activity relationships of chemical structures, while complementing 2-D similarity analysis approaches, PubChem3D represents a new resource for scientists to exploit when exploring the biological annotations in PubChem.</p

The impact of entrepreneurship of farmers on agriculture and rural economic growth: Innovation-driven perspective

This research delves into the underlying impacts of farmers' innovative entrepreneurship on agricultural and rural economic development in China, adopting a dynamic and spatio-temporal perspective. The study utilizes panel data encompassing 30 provinces (cities and autonomous regions) from 2015 to 2020, with a systematic consideration of diversified spatial weight matrices. The empirical findings reveal that the spatial distribution of rural innovative entrepreneurship demonstrates a characteristic of low-low agglomeration, accompanied by evident positive spatial spillover effects and radiation-driving effects, especially in regions with similar urbanization levels. Additionally, the study identifies heterogeneous effects across regions with different grain production patterns and household income levels. Ultimately, the research underscores the significance of deeply integrating farmers' innovation and entrepreneurship and provides empirical evidence to support the necessity of adopting differentiated and specialized incentive measures for rural entrepreneurship amid the context of the new economic normal