ObjectiveTo investigate the independent predictive factors for 90-day mortality in patients with acute-on-chronic liver failure （ACLF）， to establish a risk predictive model， and to assess its predictive efficacy in comparison with MELD， MELD-Na， MELD 3.0， and COSSH-ACLF Ⅱ. MethodsA retrospective analysis was performed for the clinical data of 394 patients with ACLF who were admitted to The Affiliated Hospital of Inner Mongolia Medical University and Hohhot Second Hospital from July 2018 to July 2024， and general information and laboratory markers on admission were collected from all patients. The independent-samples t test or the Mann-Whitney U test was used for comparison of quantitative data between two groups， and the chi-square test or the adjusted chi-square test was used for comparison of qualitative data between two groups. The LASSO regression analysis was used to identify related variables， and the multivariate logistic regression analysis was used to establish a predictive model and generate a nomogram. The receiver operating characteristic （ROC） curve， the area under the ROC curve （AUC）， calibration curve， and clinical decision curve were used to assess the performance of the model. ResultsA total of 394 patients with ACLF were included in this study， with 136 patients in the training set， 58 in the internal validation set， and 200 in the external validation set. The cohort had a mean age of 52.9±11.7 years， among whom male patients accounted for 72.84% （287/394）， the patients with HBV infection accounted for 22.33% （88/394）， the patients with alcohol-related causes accounted for 45.94% （181/394）， and the patients with other causes （including drug-induced and autoimmune diseases） accounted for 31.73% （125/394）. The overall 90-day mortality rate was 27.41% （108/394）. The multivariate logistic regression analysis showed that diabetes （odds ratio ［OR］= 5.831， 95% confidence interval ［CI］： 1.587 — 21.424， P=0.008）， cystatin C （Cys-C） （OR=2.984， 95%CI： 1.501 — 5.933， P=0.002）， and spontaneous peritonitis （SBP） （OR=5.692， 95%CI： 2.150 — 15.071， P<0.001） were independent risk factors， and a nomogram was generated based on these factors. This model had an AUC of 0.836 in the training set， 0.881 in the internal validation set， and 0.878 in the external validation set， showing a good discriminatory ability. The calibration curve showed a good degree of fitting， with a relatively high net clinical benefit. The subgroup analysis based on etiology showed that the model had an AUC of 0.850 in the patients with HBV infection， 0.858 in the patients with alcohol-induced ACLF， and 0.908 in the patients with other etiologies， indicating that the model had a good discriminatory ability across the populations with different etiologies. Compared with traditional scores， the model （AUC=0.836） had a significantly better predictive value than MELD （AUC=0.619， Z=3.197， P=0.001）， MELD-Na （AUC=0.651， Z=2.998， P=0.003）， MELD 3.0 （AUC=0.601， Z=3.682， P<0.001）， and COSSH-ACLF Ⅱ （AUC=0.719， Z=2.396， P=0.017） alone. ConclusionDiabetes， SBP， and Cys-C are independent risk factors for 90-day mortality in patients with ACLF. Compared with MELD， MELD-Na， MELD 3.0， and COSSH-ACLF Ⅱ scores， this model has a higher predictive value for 90-day prognosis in patients with ACLF and is suitable for patients with ACLF caused by various etiologies.

BACKGROUND:Curcumin is the main active ingredient of turmeric and has significant medicinal value in anti-tumor,anti-inflammatory,antioxidant and other aspects.However,its poor water solubility,unstable chemical properties and easy decomposition lead to difficulty in extracting curcumin and low extraction yield.Therefore,it is particularly important to optimize the curcumin extraction method.OBJECTIVE:To enhance the extraction yield and utilization value of curcumin and optimize the curcumin extraction process and curcumin nanoparticle preparation process.METHODS:Curcumin was extracted from turmeric by ethanol extraction,ultrasonic extraction,ionic liquid extraction,enzyme extraction,and ionic liquid combined with ultrasonic assisted enzyme extraction.The curcumin extraction yield was detected by high performance liquid chromatography;the best extraction method was determined,and subsequent process optimization experiments were carried out.The curcumin extraction yield was the response value with the type of ionic liquid,reaction temperature,ultrasonic time,liquid-to-solid ratio,ionic liquid concentration,and enzyme-drug mass ratio as parameters.The optimal production process of ionic liquid combined with ultrasonic assisted enzyme extraction was determined by single factor combined response surface experiment.The optimal process for preparing curcumin nanoparticles by ionic crosslinking method was determined by single factor combined response surface experiment with acetic acid concentration,chitosan to sodium tripolyphosphate mass ratio,stirring rate,curcumin mass concentration,sodium tripolyphosphate mass concentration,and chitosan mass concentration as parameters,and drug encapsulation efficiency as response value.Curcumin nanoparticles were prepared under the optimal process,and the particle size,polydispersity index,Zata potential value,drug loading,stability,hemolysis rate,and antioxidant capacity in vivo and in vitro of the nanoparticles were detected.RESULTS AND CONCLUSION:(1)Among the five extraction methods,the curcumin yield of ionic liquid combined with ultrasound-assisted enzyme extraction was the highest,and this method was selected as the curcumin extraction method for subsequent experiments.The results of single factor combined response surface experiment showed that the optimal process for curcumin extraction was:ionic liquid selected 1-hexyl-3-methylimidazolium chloride,reaction temperature 55 ℃,liquid-to-solid ratio 40 mL/g,ultrasound time 57 minutes,ionic liquid concentration 57％,enzyme-drug mass ratio 3.5:10,and the obtained turmeric extraction yield was 3.10％.The optimal preparation process of curcumin nanoparticles was:glacial acetic acid concentration 0.5％,chitosan and sodium tripolyphosphate mass ratio 5.0:1,stirring speed 150 r/min,curcumin mass concentration 2.23 mg/mL,sodium tripolyphosphate mass concentration 1.45 mg/mL,chitosan mass concentration 3.63 mg/mL,and the obtained drug encapsulation efficiency was 90.61％.(2)The drug loading of curcumin nanoparticles was(14.49±0.23)％,the average particle size was(76.95±1.65)nm,the polydispersity coefficient was 0.15±0.02,and the Zata potential value was(32.37±1.46)mV.The curcumin nanoparticles had good stability and blood compatibility,did not induce hemolysis,and had stronger antioxidant capacity in vivo and in vitro than free curcumin.(3)The results show that the process optimization not only solves the problems of low extraction yield,poor solubility,and low bioavailability of curcumin,but also enhances its antioxidant activity in vivo and in vitro.

BACKGROUND:Nanocell vesicles possess functions such as re-epithelialization,antioxidation,anti-inflammation,and regulation of extracellular matrix remodeling.Meanwhile,apoptotic bodies have the immunomodulatory effects.Therefore,the combination of the two to form nanofusion vesicles can synergistically promote the healing of diabetic skin wounds.OBJECTIVE:To elucidate the impact of nanofusion vesicles on skin wound healing in a diabetic murine model.METHODS:(1)Material preparation and characterization:The primary bone marrow mesenchymal stem cells of C57BL/6J neonatal mice and the neutrophil apoptotic bodies of C57BL/6J mice were isolated and extracted.The nanofusion vesicles were prepared by micro-extrusion mechanism.(2)In vitro experiment:MTT assay was used to detect the proliferative effect of different concentrations of nanofusion vesicles on NIH-3T3 cells and human umbilical vein endothelial cells.Reactive oxygen species fluorescence probe was used to detect the antioxidant effect of nano-fusion vesicles on NIH-3T3 cells treated with hydrogen peroxide(H2O2).The inhibitory effect of nanofusion vesicles on RAW 264.7 macrophage inflammation induced by lipopolyside was detected by real-time quantitative RT-qPCR.(3)In vivo experiment:36 male C57BL/6J mice were employed to develop a murine model of diabetes mellitus.Following the successful induction of diabetes,two circular full-thickness wounds,each with a diameter of 6 mm,were created on either side of the diabetic mice's spine using a skin punch.The mice were divided into three groups by random number table method.The control group was injected with 0.1 mL of phosphate buffer solution.The nanovesicle group was injected with 0.1 mL nanovesicles(25 μg/mL).The nanofusion vesicle group was injected with 0.1 mL nanofusion(25 μg/mL)vesicles.After treatment for three consecutive days,the wound healing and histomorphological changes were observed.RESULTS AND CONCLUSION:(1)In vitro experiment:nanofusion vesicles,when administered at concentrations ranging from 0 to 100 μg/mL,exhibited no toxic effects and promoted the proliferation of NIH-3T3 and HUVEC cell lines.Notably,a concentration of 25 μg/mL nanofusion vesicle significantly enhanced the proliferation of NIH-3T3 cells.Furthermore,the survival rate of human umbilical vein endothelial cells was observed to increase in correlation with escalating concentrations of nanofusion vesicles.Nanofusion vesicles had a good antioxidant effect.In comparison to the H2O2 group,the fluorescence signal indicative of reactive oxygen species was progressively diminished in both the nanovesicle group and the nanofusion vesicle group.Furthermore,nanofusion vesicles possessed anti-inflammatory capabilities,effectively mitigating the inflammatory response in macrophages triggered by lipopolysaccharide stimulation.(2)In vivo experiment:Hematoxylin-eosin and Masson's trichrome staining revealed that in comparison to the control group,both the nanovesicle group and the nanofusion vesicle group exhibited a significant increase in granulation tissue formation and collagen fiber deposition within the wounds by day 6.Notably,the nanofusion vesicle group displayed the most pronounced effects.On day 12,the wound of nanofusion vesicle group was significantly reduced,and the healing rate was significantly faster than that of other groups(P＜0.01),and the effect of promoting wound healing was the most significant.Our findings demonstrated that nanofusion vesicles exhibited superior pro-cell proliferative,antioxidant,and anti-inflammatory properties,thereby exerting a beneficial effect on the promotion of skin wound healing in diabetic mouse models.

ObjectiveCleft palate (CP) is a common congenital deformity often associated with velopharyngeal insufficiency (VPI), which disrupts the physiological coupling between respiration and speech. Conventional clinical assessments, such as nasometry and spirometry, provide limited static data and fail to visualize the dynamic spatiotemporal distribution of lung ventilation during phonation. This study introduces spatiotemporal electrical impedance tomography (ST-EIT) to evaluate speech-respiratory functional features in CP patients compared to normal controls (NC). The aim is to characterize multi-domain respiratory patterns and to validate an interpretable machine learning framework for providing objective, quantitative evidence for clinical assessment. MethodsSeventy-five participants were enrolled in this study, comprising 37 patients with surgically repaired CP and 38 healthy volunteers matched for age, gender, and body mass index (BMI). All subjects performed standardized sustained phonation tasks while undergoing synchronous monitoring with a 16-electrode EIT system and a pneumotachograph. A comprehensive feature engineering pipeline was developed to extract physiological parameters across 3 complementary domains. (1) Temporal domain: including inspiratory/expiratory phase duration (tPhase), time constants (Tau), and inspiratory-to-expiratory time ratios (TI/TE); (2) airflow domain: comprising mean flow, peak flow, and instantaneous flow at 25%, 50%, and 75% of tidal volume; and (3) spatial domain: quantifying global and regional tidal impedance variation (TIV), global inhomogeneity (GI), and center of ventilation (CoV). Extreme Gradient Boosting (XGBoost) classifiers were trained using 5 distinct data sources (Spirometry, Nasometry, Inspiratory-EIT, Expiratory-EIT, and fused ST-EIT). Model performance was rigorously evaluated via stratified 5-fold cross-validation, and Shapley additive explanations (SHAP) were employed to quantify global and local feature contributions. ResultsThe CP group exhibited a distinct respiratory phenotype compared to controls. In the temporal domain, CP patients showed significantly shorter inspiratory (1.60 s vs.1.85 s, P<0.001) and expiratory phase durations (2.45 s vs. 3.95 s, P<0.001), indicating a rapid, shallow breathing rhythm. In the airflow domain, while inspiratory flows were comparable, the CP group demonstrated significantly elevated mean and peak flows during the expiratory phase (P<0.001), reflecting compensatory respiratory effort. Spatially, CP patients presented significant ventilation redistribution, characterized by higher regional TIV in the right-anterior (ROI1) and left-posterior (ROI4) quadrants, but lower TIV in the left-anterior (ROI2) quadrant. In terms of diagnostic accuracy, the multi-modal ST-EIT model achieved the highest performance (AUC: 0.915±0.012, Accuracy: 0.843±0.019, F1-score: 0.872±0.017), substantially outperforming models based on spirometry (AUC: 0.721) or nasometry (AUC: 0.625) alone. Interpretability analysis revealed that spatial domain features were the most critical, contributing 53.4% to the model’s decision-making, followed by temporal (25.0%) and airflow (21.6%) features. ConclusionST-EIT successfully captures the temporal, airflow, and spatial deviations in CP speech respiration that are undetectable by conventional methods—specifically, rapid phase transitions, hyperdynamic expiratory airflow, and regional ventilation heterogeneity. This study validates ST-EIT as a robust, non-invasive, and radiation-free tool for characterizing speech-respiratory dysfunction, offering high clinical value for bedside screening, rehabilitation planning, and longitudinal monitoring of patients with cleft palate.

ObjectiveCleft palate (CP) is a common congenital deformity often associated with velopharyngeal insufficiency (VPI), which disrupts the physiological coupling between respiration and speech. Conventional clinical assessments, such as nasometry and spirometry, provide limited static data and fail to visualize the dynamic spatiotemporal distribution of lung ventilation during phonation. This study introduces spatiotemporal electrical impedance tomography (ST-EIT) to evaluate speech-respiratory functional features in CP patients compared to normal controls (NC). The aim is to characterize multi-domain respiratory patterns and to validate an interpretable machine learning framework for providing objective, quantitative evidence for clinical assessment. MethodsSeventy-five participants were enrolled in this study, comprising 37 patients with surgically repaired CP and 38 healthy volunteers matched for age, gender, and body mass index (BMI). All subjects performed standardized sustained phonation tasks while undergoing synchronous monitoring with a 16-electrode EIT system and a pneumotachograph. A comprehensive feature engineering pipeline was developed to extract physiological parameters across 3 complementary domains. (1) Temporal domain: including inspiratory/expiratory phase duration (tPhase), time constants (Tau), and inspiratory-to-expiratory time ratios (TI/TE); (2) airflow domain: comprising mean flow, peak flow, and instantaneous flow at 25%, 50%, and 75% of tidal volume; and (3) spatial domain: quantifying global and regional tidal impedance variation (TIV), global inhomogeneity (GI), and center of ventilation (CoV). Extreme Gradient Boosting (XGBoost) classifiers were trained using 5 distinct data sources (Spirometry, Nasometry, Inspiratory-EIT, Expiratory-EIT, and fused ST-EIT). Model performance was rigorously evaluated via stratified 5-fold cross-validation, and Shapley additive explanations (SHAP) were employed to quantify global and local feature contributions. ResultsThe CP group exhibited a distinct respiratory phenotype compared to controls. In the temporal domain, CP patients showed significantly shorter inspiratory (1.60 s vs.1.85 s, P<0.001) and expiratory phase durations (2.45 s vs. 3.95 s, P<0.001), indicating a rapid, shallow breathing rhythm. In the airflow domain, while inspiratory flows were comparable, the CP group demonstrated significantly elevated mean and peak flows during the expiratory phase (P<0.001), reflecting compensatory respiratory effort. Spatially, CP patients presented significant ventilation redistribution, characterized by higher regional TIV in the right-anterior (ROI1) and left-posterior (ROI4) quadrants, but lower TIV in the left-anterior (ROI2) quadrant. In terms of diagnostic accuracy, the multi-modal ST-EIT model achieved the highest performance (AUC: 0.915±0.012, Accuracy: 0.843±0.019, F1-score: 0.872±0.017), substantially outperforming models based on spirometry (AUC: 0.721) or nasometry (AUC: 0.625) alone. Interpretability analysis revealed that spatial domain features were the most critical, contributing 53.4% to the model’s decision-making, followed by temporal (25.0%) and airflow (21.6%) features. ConclusionST-EIT successfully captures the temporal, airflow, and spatial deviations in CP speech respiration that are undetectable by conventional methods—specifically, rapid phase transitions, hyperdynamic expiratory airflow, and regional ventilation heterogeneity. This study validates ST-EIT as a robust, non-invasive, and radiation-free tool for characterizing speech-respiratory dysfunction, offering high clinical value for bedside screening, rehabilitation planning, and longitudinal monitoring of patients with cleft palate.

Malignant tumors represent a major threat to global health. Conventional anti-tumor pharmacotherapy often encounters challenges such as drug resistance, highlighting an urgent need for the development of novel therapeutic strategies. Fatty acid synthase (FASN), the key enzyme catalyzing de novo fatty acid synthesis, is subject to precise regulation at multiple levels, including transcriptional control, various post-translational modifications such as ubiquitination and phosphorylation, as well as modulation by diverse signaling pathways. Recent studies have revealed that FASN is aberrantly overexpressed in various malignant tumors and is closely associated with tumor progression and poor patient prognosis. FASN is a homodimer composed of seven functional domains that catalyzes the NADPH-dependent condensation of acetyl-CoA and malonyl-CoA to generate saturated fatty acids, primarily palmitic acid. Its stability is regulated by multiple ubiquitin ligases and deubiquitinating enzymes. Additionally, FASN is subject to upstream regulation via neural precursor cell-expressed developmentally downregulated 8 (Nedd8) modification and the phosphatidylinositol 3-kinase (PI3K)/protein kinase B (AKT)/mammalian target of rapamycin (mTOR) pathway, thereby establishing a metabolic-signaling positive feedback loop. As a core executor of metabolic reprogramming, FASN promotes tumorigenesis through dual mechanisms. First, its fatty acid synthesis product, palmitate, participates in membrane phospholipid synthesis, lipid raft formation, and protein palmitoylation, thereby activating several key oncogenic signaling pathways, including PI3K/AKT/mTOR, wingless-type MMTV integration site family member (Wnt)/β‑catenin, and signal transducer and activator of transcription 3 (STAT3)/matrix metalloproteinase (MMP), leading to tumor development and progression. Second, FASN plays a pivotal role in modulating the anti-tumor functions of immune cells and remodeling the tumor immune microenvironment. Specifically, FASN enhances immune checkpoint inhibition by inducing programmed death-ligand 1 (PD-L1) palmitoylation, suppresses the activation of cytotoxic T lymphocytes and natural killer cells, and promotes the polarization of M2-type macrophages, consequently facilitating tumor immune evasion and malignant progression. Precisely due to its significant overexpression in tumor cells, its critical functional role, and its differential expression compared to normal cells, FASN has emerged as a highly promising target for anti-tumor drug development. Highly selective small-molecule inhibitors, notably represented by TVB-2640, have advanced to clinical trial stages and demonstrated favorable anti-tumor activity. Furthermore, the combination of FASN inhibitors with other chemotherapeutic agents or targeted drugs can overcome the limitations of monotherapy through synergistic effects or by resensitizing tumor cells to conventional drugs, achieving a “1+1>2” therapeutic outcome. With the advancement of modern traditional Chinese medicine (TCM), numerous active ingredients derived from TCM have been confirmed to exert anti-tumor effects by modulating FASN-related pathways. This integrated approach leverages the precision of Western medicine while simultaneously harnessing the holistic regulatory benefits of TCM to alleviate the side effects of radiotherapy and chemotherapy. Despite the promising prospects of FASN-targeted therapies, challenges remain, including tumor cell metabolic plasticity, tumor context-dependent responses, and heterogeneity. This review systematically summarizes the molecular structure, physiological functions, and mechanisms of FASN in tumorigenesis, as well as recent advances in targeted therapies. Future directions—including the precise identification of responsive patient populations using spatial transcriptomics, the development of novel combination regimens, and the active exploration of integrative strategies combining traditional Chinese and Western medicine—will facilitate the clinical translation of FASN-targeted therapies and open new avenues for improving the quality of life and prognosis of cancer patients.

ObjectiveGastric hemorrhage is one of the most common and life-threatening emergencies of the upper digestive tract. Early identification and continuous monitoring are essential for reducing rebleeding rates and mortality, particularly within the critical early hours after onset. Although endoscopy and radiological imaging can accurately localize bleeding sites, these approaches are invasive, resource-intensive, and unsuitable for continuous bedside monitoring. Electrical impedance tomography (EIT), as a noninvasive and radiation-free functional imaging technique, offers real-time visualization of conductivity distribution and has the potential for detecting intragastric bleeding based on the electrical contrast between blood and surrounding gastric tissues. In this study, a three-dimensional gastric EIT (3D-gEIT) framework is proposed to achieve noninvasive, real-time, and dynamic monitoring of gastric hemorrhage, with emphasis on spatial localization and quantitative volume assessment. MethodsA three-dimensional upper-abdominal simulation model incorporating the stomach, gastric wall, gastric contents, and surrounding tissues was established. Three electrode configurations, namely the dual layer ring, the four layer staggered ring, and the opposed dual plane array, were designed and systematically compared to evaluate their influence on depth sensitivity and spatial resolution. Based on the Tikhonov-Noser hybrid regularization scheme, a region-clustering constraint was introduced to develop the TK-Noser-RCC algorithm. This approach aggregates spatially adjacent elements with similar conductivity variations, thereby enhancing structural continuity and suppressing isolated noise artifacts. To validate the proposed framework, an upper-abdominal physical phantom was constructed using agar to simulate background tissue conductivity. Hemispherical high-conductivity inclusions with volumes ranging from 10 ml to 50 ml were attached to the inner gastric wall to mimic localized bleeding under different gastric filling states. Boundary voltages were acquired under a 120 kHz excitation current and reconstructed using the TK-Noser-RCC algorithm. Furthermore, an in vivo animal experiment was performed using a porcine model with adult-scale abdominal dimensions. A total of 100 ml of autologous blood was injected incrementally into the stomach to simulate progressive gastric hemorrhage, and time-difference EIT reconstruction was conducted at each injection stage to assess the dynamic system response under physiological conditions. ResultsSimulation results demonstrated that the opposed dual-plane electrode array achieved superior depth sensitivity distribution and spatial resolution. For a 40 ml hemorrhage model, the average ICC and SSIM improved by 55.9% and 38.8% compared with the dual-layer ring configuration, and by 64.0% and 39.5% compared with the four-layer staggered configuration. The proposed region-clustering constraint significantly enhanced reconstruction stability. Under added Gaussian noise of 40 dB and 30 dB, ICC values remained approximately 0.85, indicating effective artifact suppression and preservation of boundary integrity. In physical phantom experiments, reconstructed hemorrhage volumes increased approximately linearly with the preset hemispherical volumes, and the reconstructed high-conductivity regions closely matched the actual bleeding locations. Both empty-stomach and full-stomach conditions were evaluated, demonstrating that the opposed dual-plane configuration maintained stable imaging performance across varying gastric contents. In the animal experiment, reconstructed low-impedance regions expanded progressively with increasing injected blood volume. The spatial localization of the hemorrhage remained stable throughout the procedure, and no significant artifacts were observed. Quantitative analysis showed that reconstructed volume and average conductivity variation exhibited an approximately linear growth trend with injected blood volume, confirming the sensitivity of the system to dynamic intragastric conductivity changes. ConclusionThe proposed 3D-gEIT framework enables quantitative reconstruction of gastric hemorrhage volume and spatial distribution with improved depth sensitivity, structural continuity, and noise robustness compared with conventional EIT approaches. By integrating optimized electrode configuration and a region-clustering-constrained reconstruction algorithm, the system provides stable dynamic monitoring under both controlled phantom conditions and in vivo physiological environments. This method offers a noninvasive, real-time, and low-cost imaging strategy for early diagnosis, postoperative monitoring, and bedside surveillance of gastric bleeding.

ObjectiveThis study aims to explore the potential of different orders of magnitude single-nucleotide polymorphism (SNP) locus combinations for predicting distant kinship relationships. A high-density SNP locus set was constructed, and a comprehensive assessment of its inference capability was conducted. MethodsFirstly, we selected three commercial chip panels, CGA (Chinese genotyping array, Illumina), GSA (Global screening array, Illumina), Affy (23MF_V2 high-density SNP array, Affymetrix) and merged them after quality control, forming a high-density SNP locus panel(1 180 k). Secondly, we selected 161 samples and collected their peripheral blood samples by using whole-genome sequencing technology. Within this sample population, the levels of kinship relationships fully covered the range from level 1 to level 9, and the number of kinship pairs at each level was consistently maintained at over 50 pairs. From 161 samples data of whole-genome sequencing, the 1 180 k locus set was extracted, which is referred to as the high-density SNP locus set in the following text. The kinship inference was conducted using the identity-by-descent (IBD) algorithm with the selected optimal parameters. To comprehensively evaluate the performance of the high-density SNP locus set in kinship inference, we compared it with the three commercial chip panels, the intersection of these three chip loci, and the control sets constructed by randomly reducing the number of the high-density SNP locus set. Based on the changes in the IBD lengths, as well as the dynamic trends in prediction accuracy, we conducted a scientific assessment of the kinship inference capability of the high-density SNP locus set. ResultsAfter screening, a set of 1 184 334 autosomal SNPs was obtained. During the process of screening the optimal IBD length threshold, the result revealed that 0 cM, 1 cM, and 2 cM all demonstrated good applicability. However, to avoid the issue of a large amount of redundant information caused by setting a too low IBD length threshold, this study ultimately selected 2 cM as the optimal threshold. Compared with the average results of three chip panels, the high-density SNP locus set increased the total IBD length and the average IBD length across levels 1-9; the accuracy of the confidence interval for level 8 was 70.97%, which represented a 3.50% improvement; the average confidence interval accuracy for levels 1-8 was 91.39%, representing a 1.00% increase; and the false negative rates at levels 8 and 9 were reduced by 2.42% and 6.76%, respectively. The system efficacy of the high-density SNP locus set for kinship inference of first to eighth degree relationships reached 98.91%. Through random reduction of the high-density SNP locus set results, it is found that increasing the number of SNPs with the panel, the detection efficiency of IBD length showed a significant upward trend. At the same time, the overall trend in the accuracy of kinship relationship prediction as well as the confidence interval accuracy also indicated that both metrics steadily increased with the addition of more loci. ConclusionThe results show that the high-density SNPs panel significantly enhances the efficacy of distant kinship inference, accurately covering kinship degrees, with the average confidence interval accuracy for first to eighth degree relationships stably above 90%. The study finds that increasing the number of SNPs panel can improve the ability to predict distant kinship.

OBJECTIVE To investigate the protective effect of 2-dodecyl-6-methoxy-2，5-diene-1，4-cyclohexanedione （DMDD）， an active component from Averrhoa carambola root， on myocardial injury in diabetic mice based on the nuclear receptor coactivator 4/ferritin heavy chain 1/autophagy-related protein 8 （NCOA4/FTH1/ATG8） axis. METHODS The successfully modeled diabetic mice were randomly divided into model group and DMDD low-， medium-， and high-dose （12.5， 25， 50 mg/kg） groups， while an additional non-modeled control group was established， with 6 mice in each group. Each group received the corresponding drug solution or an equal volume of normal saline intragastically once daily for 21 consecutive days. After the administration， the levels of fasting blood glucose （FBG）， serum lactate dehydrogenase （LDH）， and creatine kinase isoenzyme MB （CK-MB） were measured. Myocardial pathological changes， degree of fibrosis， and myocardial cell ultrastructure were observed. Myocardial cell death index and NCOA4 protein positive index were detected. The protein expression levels of NCOA4， FTH1， ATG8， solute carrier family 7 member 11 （SLC7A11）， and glutathione peroxidase 4 （GPX4） in cardiac tissue were measured. RESULTS Compared with model group， each DMDD group showed significant alleviation of cardiac pathological injury and varying degrees of improvement in the myocardial cell ultrastructure. The FBG and serum LDH and CK-MB levels， the myocardial cell death index and NCOA4 protein positive index，the protein expression levels of NCOA4， FTH1， and ATG8 in cardiac tissue were significantly decreased （ P ＜0.001）， while the protein expression levels of SLC7A11 and GPX4 were significantly increased （ P ＜0.001）. CONCLUSIONS DMDD can reduce blood glucose levels， alleviate myocardial histopathological injury， and inhibit cell death in diabetic mice. The mechanism is associated with inhibiting excessive activation of the NCOA4/FTH1/ATG8 axis and reducing ferritinophagy.

ObjectiveTo investigate the regulatory effects of Huanglian Jiedutang （HLJDT） on immune cell migration， blood-brain barrier protection， and cellular functional recovery in a model of ischemic stroke. MethodsA transient middle cerebral artery occlusion （tMCAO） model was established in mice to induce ischemic stroke. Cerebral blood flow and neurological function were evaluated using laser speckle imaging and neurological deficit scoring. Histopathological damage in brain tissues was assessed by hematoxylin-eosin （HE） and Nissl staining. Mice were divided into a sham group， a model group， an HLJDT group， and a Ginkgo biloba extract （GBE） group. After one week of acclimatization， intragastric administration was initiated. The sham and model groups received normal saline， the HLJDT group received HLJDT at 1.82 g·kg^-¹， and the GBE group received GBE at 0.432 g·kg^-¹. Administration was continued for 5 consecutive days， and the tMCAO model was established after the final dose on day 6. Single-cell RNA sequencing was performed on brain tissues and peripheral immune cells. UMAP and odds ratio （OR） indices were used to analyze cell distribution. Differential expression analysis was conducted to evaluate the effects of HLJDT on endothelial cells， pericytes， and macrophages， combined with CellChat and decoupler to analyze cell-cell communication and transcription factor regulation. Finally， PCR and ELISA were used to validate the mRNA and protein expression of relevant genes. ResultsCompared with the sham group， the model group showed significantly increased neurological deficit scores （P<0.01） and significantly decreased cerebral blood flow （P<0.01）， accompanied by cortical structural disorder， aggravated cytoplasmic vacuolization， and increased numbers of Nissl bodies. Compared with the model group， both the HLJDT and GBE groups exhibited significantly reduced neurological deficit scores （P<0.01） and markedly improved cerebral blood flow （P<0.01）， along with amelioration of cortical structural disorder， alleviated cytoplasmic vacuolization， and reduced numbers of Nissl bodies. Single-cell analysis showed that HLJDT protected endothelial cells and pericytes by preventing their reduction， restored the expression of functional genes in these cells （e.g.， PECAM1 and NOS3）， and downregulated the expression of chemokines and adhesion-related factors （e.g.， CCL2 and CXCL2）. In macrophages， HLJDT reduced their recruitment to the central nervous system and downregulated the expression of chemokine receptors and inflammatory factors （e.g.， IL-6， CCR2， and CXCR2）. Cell-cell communication analysis further indicated that HLJDT， through the above mechanisms， alleviated damage to pericytes and endothelial cells， reduced their recruitment of macrophages， and decreased ligand-receptor interactions in chemokine signaling pathways （including CCL， CXCL， and CSF3） between pericytes/endothelial cells and macrophages， thereby preventing secondary injury. Compared with the sham group， the model group showed significantly upregulated mRNA expression levels of IL-1β， IL-6， TNF-α， CCL2， CXCL2， and CSF3 （P<0.01）， while mRNA expression levels of endothelial- and pericyte function-related genes （RGS5， PECAM1， VEGFB， and NOS3） were significantly downregulated （P<0.01）. In contrast， compared with the model group， the HLJDT and GBE groups exhibited significantly decreased mRNA expression levels of IL-1β， IL-6， TNF-α， CCL2， CXCL2， and CSF3 （P<0.01）， and significantly increased expression of RGS5， PECAM1， VEGFB， and NOS3 （P<0.01）. At the protein level， compared with the sham group， the model group showed significantly increased expression of IL-1β， IL-6， and TNF-α （P<0.01）， whereas these protein levels were significantly reduced in the HLJDT and GBE groups compared with the model group （P<0.01）. ConclusionHLJDT reduces neuronal damage in ischemic stroke by protecting endothelial cells and pericytes， while inhibiting their interaction with macrophages， thereby mitigating secondary injury in the central nervous system.