Baicalin-aluminum complex on the regulation of IPEC-1 infected with enterotoxigenic Escherichia coli.

Enterotoxigenic Escherichia coli (ETEC) is the main bacterial cause of diarrhea in weaned piglets. Baicalin-aluminum (BA) complex is the main active ingredient of Scutellaria baicalensis Georgi extracted-aluminum complex, which has been used to treat diarrhea in weaning piglets, however the underlying mechanism remains unclear. To investigate the effects of the BA complex on the regulation of porcine intestinal epithelial (IPEC-1) cells infected with ETEC, IPEC-1 cells were incubated with an ETEC bacterial strain at a multiplicity of infection of 1 for 6 h and then treated with different concentrations of the BA complex for 6 h. ETEC infection increased the levels of cAMP and cGMP, upregulated CFTR (cystic fibrosis transmembrane conductance regulator) mRNA, and downregulated NHE4 mRNA in IPEC-1 cells. Treatment with the BA complex inhibited ETEC adhesion and the production of cAMP and cGMP, reduced CFTR mRNA expression, and increased NHE4 mRNA expression. Overall, the BA complex weakened the adhesion of ETEC to IPEC-1 cells, and inhibited cAMP/cGMP-CFTR signaling in IPEC-1 cells.

A diffusion MRI tractography atlas for concurrent white matter mapping across Eastern and Western populations.

The study of brain differences across Eastern and Western populations provides vital insights for understanding potential cultural and genetic influences on cognition and mental health. Diffusion MRI (dMRI) tractography is an important tool in assessing white matter (WM) connectivity and brain tissue microstructure across different populations. However, a comprehensive investigation into WM fiber tracts between Eastern and Western populations is challenged due to the lack of a cross-population WM atlas and the large site-specific variability of dMRI data. This study presents a dMRI tractography atlas, namely the East-West WM Atlas, for concurrent WM mapping between Eastern and Western populations and creates a large, harmonized dMRI dataset (n=306) based on the Human Connectome Project and the Chinese Human Connectome Project. The curated WM atlas, as well as subject-specific data including the harmonized dMRI data, the whole brain tractography data, and parcellated WM fiber tracts and their diffusion measures, are publicly released. This resource is a valuable addition to facilitating the exploration of brain commonalities and differences across diverse cultural backgrounds.

Radiogenomics nomogram based on MRI and microRNAs to predict microvascular invasion of hepatocellular carcinoma.

Purpose: This study aimed to develop and validate a radiogenomics nomogram for predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC) on the basis of MRI and microRNAs (miRNAs). Materials and methods: This cohort study included 168 patients (training cohort: n = 116; validation cohort: n = 52) with pathologically confirmed HCC, who underwent preoperative MRI and plasma miRNA examination. Univariate and multivariate logistic regressions were used to identify independent risk factors associated with MVI. These risk factors were used to produce a nomogram. The performance of the nomogram was evaluated by receiver operating characteristic curve (ROC) analysis, sensitivity, specificity, accuracy, and F1-score. Decision curve analysis was performed to determine whether the nomogram was clinically useful. Results: The independent risk factors for MVI were maximum tumor length, rad-score, and miRNA-21 (all P < 0.001). The sensitivity, specificity, accuracy, and F1-score of the nomogram in the validation cohort were 0.970, 0.722, 0.884, and 0.916, respectively. The AUC of the nomogram was 0.900 (95% CI: 0.808-0.992) in the validation cohort, higher than that of any other single factor model (maximum tumor length, rad-score, and miRNA-21). Conclusion: The radiogenomics nomogram shows satisfactory predictive performance in predicting MVI in HCC and provides a feasible and practical reference for tumor treatment decisions.

TractoSCR: a novel supervised contrastive regression framework for prediction of neurocognitive measures using multi-site harmonized diffusion MRI tractography.

Neuroimaging-based prediction of neurocognitive measures is valuable for studying how the brain's structure relates to cognitive function. However, the accuracy of prediction using popular linear regression models is relatively low. We propose a novel deep regression method, namely TractoSCR, that allows full supervision for contrastive learning in regression tasks using diffusion MRI tractography. TractoSCR performs supervised contrastive learning by using the absolute difference between continuous regression labels (i.e., neurocognitive scores) to determine positive and negative pairs. We apply TractoSCR to analyze a large-scale dataset including multi-site harmonized diffusion MRI and neurocognitive data from 8,735 participants in the Adolescent Brain Cognitive Development (ABCD) Study. We extract white matter microstructural measures using a fine parcellation of white matter tractography into fiber clusters. Using these measures, we predict three scores related to domains of higher-order cognition (general cognitive ability, executive function, and learning/memory). To identify important fiber clusters for prediction of these neurocognitive scores, we propose a permutation feature importance method for high-dimensional data. We find that TractoSCR obtains significantly higher accuracy of neurocognitive score prediction compared to other state-of-the-art methods. We find that the most predictive fiber clusters are predominantly located within the superficial white matter and projection tracts, particularly the superficial frontal white matter and striato-frontal connections. Overall, our results demonstrate the utility of contrastive representation learning methods for regression, and in particular for improving neuroimaging-based prediction of higher-order cognitive abilities. Our code will be available at: https://github.com/SlicerDMRI/TractoSCR.

Microfluidic-Assisted Self-Assembly of Molecular Hydrogelator at Water-Water Interfaces for Continuous Fabrication of Supramolecular Microcapsules.

Control over the self-assembly of small molecules at specific areas is of great interest for many high-tech applications, yet remains a formidable challenge. Here, how the self-assembly of hydrazone-based molecular hydrogelators can be specifically triggered at water-water interfaces for the continuous fabrication of supramolecular microcapsules by virtue of the microfluidic technique is demonstrated. The non-assembling hydrazide- and aldehyde-based hydrogelator precursors are distributed in two immiscible aqueous polymer solutions, respectively, through spontaneous phase separation. In the presence of catalysts, hydrazone-based hydrogelators rapidly form and self-assemble into hydrogel networks at the generated water-water interfaces. Relying on the microfluidic technique, microcapsules bearing a shell of supramolecular hydrogel are continuously produced. The obtained microcapsules can effectively load enzymes, enabling localized enzymatic growth of supramolecular fibrous supramolecular structures, reminiscent of the self-assembly of biological filaments within living cells. This work may contribute to the development of biomimetic supramolecular carriers for applications in biomedicine and fundamental research, for instance, the construction of protocells.

Facile Electret-Based Self-Powered Soft Sensor for Noncontact Positioning and Information Translation.

Noncontact sensors have demonstrated significant potential in human-machine interactions (HMIs) in terms of hygiene and less wear and tear. The development of soft, stable, and simply structured noncontact sensors is highly desired for their practical applications in HMIs. This work reports on electret-based self-powered noncontact sensors that are soft, transparent, stable, and easy to manufacture. The sensors contain a three-layer structure with a thickness of 0.34 mm that is fabricated by simply stacking a polymeric electret layer, an electrode layer, and a substrate layer together. The fabricated sensors show high charge-retention capability, keeping over 98% of the initial surface potential even after 90 h, and can accurately and repeatedly sense external approaching objects with impressive durability. The intensity of the detected signal shows a strong dependence on the distance between the object and the sensor, capable of sensing a distance as small as 2 mm. Furthermore, the sensors can report stable signals in response to external objects over 3000 cycles. By virtue of the signal dependence on distance, an intelligent noncontact positioning system is developed that can precisely detect the location of an approaching object. Finally, by integrating with eyeglasses, the transparent sensor successfully captures the movements of blinks for information translation. This work may contribute to the development of stable and easily manufactured noncontact soft sensors for HMI applications, for instance, assisting with communication for locked-in syndrome patients.

TFAM is an autophagy receptor that limits inflammation by binding to cytoplasmic mitochondrial DNA.

When cells are stressed, DNA from energy-producing mitochondria can leak out and drive inflammatory immune responses if not cleared. Cells employ a quality control system called autophagy to specifically degrade damaged components. We discovered that mitochondrial transcription factor A (TFAM)-a protein that binds mitochondrial DNA (mtDNA)-helps to eliminate leaked mtDNA by interacting with the autophagy protein LC3 through an autolysosomal pathway (we term this nucleoid-phagy). TFAM contains a molecular zip code called the LC3 interacting region (LIR) motif that enables this binding. Although mutating TFAM's LIR motif did not affect its normal mitochondrial functions, more mtDNA accumulated in the cell cytoplasm, activating inflammatory signalling pathways. Thus, TFAM mediates autophagic removal of leaked mtDNA to restrict inflammation. Identifying this mechanism advances understanding of how cells exploit autophagy machinery to selectively target and degrade inflammatory mtDNA. These findings could inform research on diseases involving mitochondrial damage and inflammation.

Progress in the Regulation of Immune Cells in the Tumor Microenvironment by Bioactive Compounds of Traditional Chinese Medicine.

The tumor microenvironment (TME) can aid tumor cells in evading surveillance and clearance by immune cells, creating an internal environment conducive to tumor cell growth. Consequently, there is a growing focus on researching anti-tumor immunity through the regulation of immune cells within the TME. Various bioactive compounds in traditional Chinese medicine (TCM) are known to alter the immune balance by modulating the activity of immune cells in the TME. In turn, this enhances the body's immune response, thus promoting the effective elimination of tumor cells. This study aims to consolidate recent findings on the regulatory effects of bioactive compounds from TCM on immune cells within the TME. The bioactive compounds of TCM regulate the TME by modulating macrophages, dendritic cells, natural killer cells and T lymphocytes and their immune checkpoints. TCM has a long history of having been used in clinical practice in China. Chinese medicine contains various chemical constituents, including alkaloids, polysaccharides, saponins and flavonoids. These components activate various immune cells, thereby improving systemic functions and maintaining overall health. In this review, recent progress in relation to bioactive compounds derived from TCM will be covered, including TCM alkaloids, polysaccharides, saponins and flavonoids. This study provides a basis for further in-depth research and development in the field of anti-tumor immunomodulation using bioactive compounds from TCM.

Cross-domain Fiber Cluster Shape Analysis for Language Performance Cognitive Score Prediction.

Shape plays an important role in computer graphics, offering informative features to convey an object's morphology and functionality. Shape analysis in brain imaging can help interpret structural and functionality correlations of the human brain. In this work, we investigate the shape of the brain's 3D white matter connections and its potential predictive relationship to human cognitive function. We reconstruct brain connections as sequences of 3D points using diffusion magnetic resonance imaging (dMRI) tractography. To describe each connection, we extract 12 shape descriptors in addition to traditional dMRI connectivity and tissue microstructure features. We introduce a novel framework, Shape--fused Fiber Cluster Transformer (SFFormer), that leverages a multi-head cross-attention feature fusion module to predict subject-specific language performance based on dMRI tractography. We assess the performance of the method on a large dataset including 1065 healthy young adults. The results demonstrate that both the transformer-based SFFormer model and its inter/intra feature fusion with shape, microstructure, and connectivity are informative, and together, they improve the prediction of subject-specific language performance scores. Overall, our results indicate that the shape of the brain's connections is predictive of human language function.

Activation of CaMKII/HDAC4 by SDF1 contributes to pulmonary arterial hypertension via stabilization Runx2.

Stromal derived factor 1 (SDF1) has been shown to be involved in the pathogenesis of pulmonary artery hypertension (PAH). However, the detailed molecular mechanisms remain unclear. To address this, we utilized primary cultured rat pulmonary artery smooth muscle cells (PASMCs) and monocrotaline (MCT)-induced PAH rat models to investigate the mechanisms of SDF1 driving PASMCs proliferation and pulmonary arterial remodeling. SDF1 increased runt-related transcription factor 2 (Runx2) acetylation by Calmodulin (CaM)-dependent protein kinase II (CaMKII)-dependent HDAC4 cytoplasmic translocation, elevation of Runx2 acetylation conferred its resistance to proteasome-mediated degradation. The accumulation of Runx2 further upregulated osteopontin (OPN) expression, finally leading to PASMCs proliferation. Blocking SDF1, suppression of CaMKII, inhibition the nuclear export of HDAC4 or silencing Runx2 attenuated pulmonary arterial remodeling and prevented PAH development in MCT-induced PAH rat models. Our study provides novel sights for SDF1 induction of PASMCs proliferation and suggests that targeting SDF1/CaMKII/HDAC4/Runx2 axis has potential value in the management of PAH.

The global burden of ischemic heart disease attributed to high fasting plasma glucose: Data from 1990 to 2019.

Background: Ischemic heart disease (IHD) is the leading cause of death worldwide. High fasting plasma glucose (FPG) is an increasing risk factor for IHD. We aimed to explore the long-term trends of high FPG-attributed IHD mortality during 1990-2019. Methods: Data were obtained from the Global Burden of Disease Study 2019 database. Deaths, disability-adjusted life-years (DALYs), the age-standardized mortality rate (ASMR) and age-standardized DALY rate (ASDR) of IHD attributable to high FPG were estimated by sex, socio-demographic index (SDI), regions and age. Estimated annual percentage changes (EAPCs) were calculated to assess the trends of ASMR and ASDR of IHD attributable to high FPG. Results: IHD attributable to high FPG deaths increased from 1.04 million (0.62-1.63) in 1990 to 2.35 million (1.4-3.7) in 2019, and the corresponding DALYs rose from 19.82 million (12.68-29.4) to 43.3 million (27.8-64.2). In 2019, ASMR and ASDR of IHD burden attributable to high FPG were 30.45 (17.09-49.03) and 534.8 (340.7-792.2), respectively. The highest ASMR and ASDR of IHD attributable to high FPG occurred in low-middle SDI quintiles, with 39.28 (22.40-62.76) and 742.3 (461.5-1117.5), respectively, followed by low SDI quintiles and middle SDI quintiles. Males had higher ASMR and ASDR compared to females across the past 30 years. In addition, ASRs of DALYs and deaths were highest in those over 95 years old. Conclusion: High FPG-attributed IHD mortality and DALYs have increased dramatically and globally, particularly in low, low-middle SDI quintiles and among the elderly. High FPG remains a great concern on the global burden of IHD and effective prevention and interventions are urgently needed to curb the ranking IHD burden, especially in lower SDI regions.

TractGeoNet: A geometric deep learning framework for pointwise analysis of tract microstructure to predict language assessment performance.

We propose a geometric deep-learning-based framework, TractGeoNet, for performing regression using diffusion magnetic resonance imaging (dMRI) tractography and associated pointwise tissue microstructure measurements. By employing a point cloud representation, TractGeoNet can directly utilize tissue microstructure and positional information from all points within a fiber tract without the need to average or bin data along the streamline as traditionally required by dMRI tractometry methods. To improve regression performance, we propose a novel loss function, the Paired-Siamese Regression loss, which encourages the model to focus on accurately predicting the relative differences between regression label scores rather than just their absolute values. In addition, to gain insight into the brain regions that contribute most strongly to the prediction results, we propose a Critical Region Localization algorithm. This algorithm identifies highly predictive anatomical regions within the white matter fiber tracts for the regression task. We evaluate the effectiveness of the proposed method by predicting individual performance on two neuropsychological assessments of language using a dataset of 20 association white matter fiber tracts from 806 subjects from the Human Connectome Project Young Adult dataset. The results demonstrate superior prediction performance of TractGeoNet compared to several popular regression models that have been applied to predict individual cognitive performance based on neuroimaging features. Of the twenty tracts studied, we find that the left arcuate fasciculus tract is the most highly predictive of the two studied language performance assessments. Within each tract, we localize critical regions whose microstructure and point information are highly and consistently predictive of language performance across different subjects and across multiple independently trained models. These critical regions are widespread and distributed across both hemispheres and all cerebral lobes, including areas of the brain considered important for language function such as superior and anterior temporal regions, pars opercularis, and precentral gyrus. Overall, TractGeoNet demonstrates the potential of geometric deep learning to enhance the study of the brain's white matter fiber tracts and to relate their structure to human traits such as language performance.

Jasmonate signaling pathway confers salt tolerance through a NUCLEAR FACTOR-Y trimeric transcription factor complex in Arabidopsis.

Jasmonate (JA) is a well-known phytohormone essential for plant response to biotic stress. Recently, a crucial role of JA signaling in salt resistance has been highlighted; however, the specific regulatory mechanism remains largely unknown. In this study, we found that the NUCLEAR FACTOR-Y (NF-Y) subunits NF-YA1, NF-YB2, and NF-YC9 form a trimeric complex that positively regulates the expression of salinity-responsive genes, whereas JASMONATE-ZIM DOMAIN protein 8 (JAZ8) directly interacts with three subunits and acts as the key repressor to suppress both the assembly of the NF-YA1-YB2-YC9 trimeric complex and the transcriptional activation activity of the complex. When plants encounter high salinity, JA levels are elevated and perceived by the CORONATINE INSENSITIVE (COI) 1 receptor, leading to the degradation of JAZ8 via the 26S proteasome pathway, thereby releasing the activity of the NF-YA1-YB2-YC9 complex, initiating the activation of salinity-responsive genes, such as MYB75, and thus enhancing the salinity tolerance of plants.

Global burden of MDR-TB and XDR-TB attributable to high fasting plasma glucose from 1990 to 2019: a retrospective analysis based on the global burden of disease study 2019.

PURPOSE: High fasting plasma glucose (HFPG) has been identified as a risk factor for drug-resistant tuberculosis incidence and mortality. However, the epidemic characteristics of HFPG-attributable multidrug-resistant tuberculosis (MDR-TB) and extensively drug-resistant tuberculosis (XDR-TB) remain unclear. We aimed to analyze the global spatial patterns and temporal trends of HFPG-attributable MDR-TB and XDR-TB from 1990 to 2019. METHODS: Utilizing data from the Global Burden of Disease 2019 project, annual deaths and disability-adjusted life years (DALYs) of HFPG-attributable MDR-TB and XDR-TB were conducted from 1990 to 2019. Joinpoint regression was employed to quantify trends over time. RESULTS: From 1990 to 2019, the deaths and DALYs due to HFPG-attributable MDR-TB and XDR-TB globally showed an overall increasing trend, with a significant increase until 2003 to 2004, followed by a gradual decline or stability thereafter. The low sociodemographic index (SDI) region experienced the most significant increase over the past 30 years. Regionally, Sub-Saharan Africa, Central Asia and Oceania remained the highest burden. Furthermore, there was a sex and age disparity in the burden of HFPG-attributable MDR-TB and XDR-TB, with young males in the 25-34 age group experiencing higher mortality, DALYs burden and a faster increasing trend than females. Interestingly, an increasing trend followed by a stable or decreasing pattern was observed in the ASMR and ASDR of HFPG-attributable MDR-TB and XDR-TB with SDI increasing. CONCLUSION: The burden of HFPG-attributable MDR-TB and XDR-TB rose worldwide from 1990 to 2019. These findings emphasize the importance of routine bi-directional screening and integrated management for drug-resistant TB and diabetes.

Tractography-based automated identification of the retinogeniculate visual pathway with novel microstructure-informed supervised contrastive learning.

The retinogeniculate visual pathway (RGVP) is responsible for carrying visual information from the retina to the lateral geniculate nucleus. Identification and visualization of the RGVP are important in studying the anatomy of the visual system and can inform the treatment of related brain diseases. Diffusion MRI (dMRI) tractography is an advanced imaging method that uniquely enables in vivo mapping of the 3D trajectory of the RGVP. Currently, identification of the RGVP from tractography data relies on expert (manual) selection of tractography streamlines, which is time-consuming, has high clinical and expert labor costs, and is affected by inter-observer variability. In this paper, we present a novel deep learning framework, DeepRGVP , to enable fast and accurate identification of the RGVP from dMRI tractography data. We design a novel microstructure-informed supervised contrastive learning method that leverages both streamline label and tissue microstructure information to determine positive and negative pairs. We propose a simple and successful streamline-level data augmentation method to address highly imbalanced training data, where the number of RGVP streamlines is much lower than that of non-RGVP streamlines. We perform comparisons with several state-of-the-art deep learning methods that were designed for tractography parcellation, and we show superior RGVP identification results using DeepRGVP. In addition, we demonstrate a good generalizability of DeepRGVP to dMRI tractography data from neurosurgical patients with pituitary tumors and we show DeepRGVP can successfully identify RGVPs despite the effect of lesions affecting the RGVPs. Overall, our study shows the high potential of using deep learning to automatically identify the RGVP.

Multitask Deep Learning-Based Whole-Process System for Automatic Diagnosis of Breast Lesions and Axillary Lymph Node Metastasis Discrimination from Dynamic Contrast-Enhanced-MRI: A Multicenter Study.

BACKGROUND: Accurate diagnosis of breast lesions and discrimination of axillary lymph node (ALN) metastases largely depend on radiologist experience. PURPOSE: To develop a deep learning-based whole-process system (DLWPS) for segmentation and diagnosis of breast lesions and discrimination of ALN metastasis. STUDY TYPE: Retrospective. POPULATION: 1760 breast patients, who were divided into training and validation sets (1110 patients), internal (476 patients), and external (174 patients) test sets. FIELD STRENGTH/SEQUENCE: 3.0T/dynamic contrast-enhanced (DCE)-MRI sequence. ASSESSMENT: DLWPS was developed using segmentation and classification models. The DLWPS-based segmentation model was developed by the U-Net framework, which combined the attention module and the edge feature extraction module. The average score of the output scores of three networks was used as the result of the DLWPS-based classification model. Moreover, the radiologists' diagnosis without and with the DLWPS-assistance was explored. To reveal the underlying biological basis of DLWPS, genetic analysis was performed based on RNA-sequencing data. STATISTICAL TESTS: Dice similarity coefficient (DI), area under receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and kappa value. RESULTS: The segmentation model reached a DI of 0.828 and 0.813 in the internal and external test sets, respectively. Within the breast lesions diagnosis, the DLWPS achieved AUCs of 0.973 in internal test set and 0.936 in external test set. For ALN metastasis discrimination, the DLWPS achieved AUCs of 0.927 in internal test set and 0.917 in external test set. The agreement of radiologists improved with the DLWPS-assistance from 0.547 to 0.794, and from 0.848 to 0.892 in breast lesions diagnosis and ALN metastasis discrimination, respectively. Additionally, 10 breast cancers with ALN metastasis were associated with pathways of aerobic electron transport chain and cytoplasmic translation. DATA CONCLUSION: The performance of DLWPS indicates that it can promote radiologists in the judgment of breast lesions and ALN metastasis and nonmetastasis. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY STAGE: 3.

ROCK1 is a multifunctional factor maintaining the primordial follicle reserve and follicular development in mice.

The follicle is the basic structural and functional unit of the ovary in female mammals. The excessive depletion of follicles will lead to diminished ovarian reserve or even premature ovarian failure, resulting in diminished ovarian oogenesis and endocrine function. Excessive follicular depletion is mainly due to loss of primordial follicles. Our analysis of published human ovarian single-cell sequencing results by others revealed a significant increase in rho-associated protein kinase 1 (ROCK1) expression during primordial follicle development. However, the role of ROCK1 in primordial follicle development and maintenance is not clear. This study revealed a gradual increase in ROCK1 expression during primordial follicle activation. Inhibition of ROCK1 resulted in reduced primordial follicle activation, decreased follicular reserve, and delayed development of growing follicles. This effect may be achieved through the HIPPO pathway. The present study indicates that ROCK1 is a key molecule for primordial follicular reserve and follicular development.NEW & NOTEWORTHY ROCK1, one of the Rho GTPases, plays an important role in primordial follicle reserve and follicular development. ROCK1 was primarily expressed in the cytoplasm of oocytes and granulosa cell in mice. Inhibition of ROCK1 significantly reduced the primordial follicle reserve and delayed growing follicle development. ROCK1 regulates primordial follicular reserve and follicle development through the HIPPO signaling pathway. These findings shed new lights on the physiology of sustaining female reproduction.

Health Poverty Alleviation Project in Rural China: Impact on Poverty Vulnerability, Health Status, Healthcare Utilization, Health Expenditures.

Background: China has lead to the inception of the Health Poverty Alleviation Project (HPAP) in 2015. While the previous studies suggest that, despite its apparent reduction in patients' financial strain, the long-term poverty reduction effects are yet to be fully elucidated. This study explores HPAP's enduring impact on poverty reduction and the potential moral hazards. Methods: Data were obtained from four waves of the China Health and Retirement Longitudinal Study (CHARLS) spanning 2011-2018. We employed difference-in-differences (DID) models to gauge HPAP's influence on participants' poverty vulnerability, health outcomes, and healthcare utilization. The dynamic DID model is employed to test the robustness of HPAP policy effects. The mediation effect models were used to understand HPAP policy outcomes through physical examinations and inpatient care. Results: Our dataset encompassed 40,384 participants, of which 5946 (14.72%) had been exposed to HPAP and 34,438 (85.28%) had not access. Our findings reveal that HPAP decreases poverty vulnerability by 3.3% (p < 0.01) and attenuates health deterioration by 1.84% (p < 0.01). Furthermore, HPAP enhances inpatient care utilization by 9.34% (p < 0.01) and self-treatment behaviors by 4.1% (p < 0.01) while significantly slashing outpatient and inpatient expenses (p < 0.05). The implementation of HPAP has significantly reduced healthcare costs by 72.8% (p < 0.05) out-of-pocket (OOP) payments of outpatient care during the past month for the last time, and 89.39% (p < 0.05) out-of-pocket (OOP) payments of inpatient care during past the year for the last time. Mechanistic analyses have shown that the indirect effect of the HPAP policy decreases poverty vulnerability by -0.132% (p < 0.05) physical examinations and -0.309% (p < 0.05) inpatient care. Conclusion: The HPAP initiative markedly attenuates poverty vulnerability and forestalls health decline among the rural populace. Moreover, HPAP bolsters healthcare service use, such as physical examinations and inpatient care, primarily attributed to the release of pent-up demand rather than moral hazards.

Impact of health insurance equity on poverty vulnerability: evidence from urban-rural health insurance integration in rural China.

Background: In 2016, the Chinese government introduced an integration reform of the health insurance system with the aim to enhance equity in healthcare coverage and reduce disparities between urban and rural sectors. The gradual introduction of the policy integrating urban and rural medical insurance in pilot cities provides an opportunity to evaluate the policy impact. This study attempts to assess the policy impact of urban-rural health insurance integration on the chronic poverty of rural residents and to analyze the mechanisms. Method: Based on the four waves of data from the China Health and Retirement Longitudinal Study (CHARLS) conducted in 2011, 2013, 2015, and 2018, we employed a staggered difference-in-differences (staggered DID) model to assess the impact of integrating urban-rural health insurance on poverty vulnerability among rural inhabitants and a mediation model to analyze the mechanism channel of the policy impact. Results: (1) Baseline regression analysis revealed that the urban-rural health insurance integration significantly reduced the poverty vulnerability of rural residents by 6.32% (p < 0.01). The one health insurance system with one unified scheme of contributions and benefits package (OSOS, 6.27%, p < 0.01) is more effective than the transitional one health insurance system with multiple schemes (OSMS, 3.25%, p < 0.01). (2) The heterogeneity analysis results showed that the urban-rural health insurance integration had a more significant impact on vulnerable groups with relatively poor health (7.84%, p < 0.1) than those with fairly good health (6.07%, p < 0.01), and it also significantly reduced the poverty vulnerability of the group with chronic diseases by 9.59% (p < 0.01). The integration policy can significantly reduce the poverty vulnerability of the low consumption and low medical expenditure groups by 8.6% (p < 0.01) and 7.64% (p < 0.01), respectively, compared to their counterparts. (3) The mechanism analysis results showed that the urban-rural health insurance integration can partially enhance labor supply (14.23%, p < 0.01) and physical examinations (6.28%, p < 0.01). The indirect effects of labor supply and physical examination in reducing poverty vulnerability are 0.14%, 0.13% respectively. Conclusion: The urban-rural health insurance integration policy significantly reduced poverty vulnerability, and the OSOS is more effective than the OSMS. The urban-rural health insurance integration policy can significantly reduce poverty vulnerability for low consumption and poor health groups. Labor supply and physical examination are indirect channels of the impact. Both channels potentially increase rural household income and expectations of investment in human health capital to achieve the policy objective of eliminating chronic poverty.