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.

ObjectiveTo investigate the modifiable areal unit problem （MAUP） in the spatial pattern of Pseudostellaria heterophylla production regions and reveal the impact of statistical scales on the spatial distribution characteristics of this medicinal plant species. MethodsUsing multi-source data （literature records， field surveys， and statistical data）， we systematically analyzed the spatial patterns across three administrative levels （provincial， prefectural， and county scales）. Spatial autocorrelation （Moran's I） analysis， high-low clustering （Getis-Ord General G）， and hot/cold spot analysis （Getis-Ord Gi*） were employed. ResultsThe literature-based analysis showed that the production regions of P. heterophylla presented random distribution on the provincial scale and significant aggregation on the prefectural scale. The field survey data showed that the production regions displayed random distribution on the provincial scale but significant aggregation on both prefectural and county scales. The statistical data revealed that the production regions lacked spatial autocorrelation on the provincial scale but demonstrated significant aggregation on prefectural and county scales. ConclusionMAUP effects have substantive implications for understanding and decision-making in the arrangement of medicinal plant production regions. The county scale proves to be the most sensitive and explanatory level for analyzing the spatial pattern of P. heterophylla production regions， providing a critical foundation for habitat modeling， suitability evaluation， and ecological cultivation planning of medicinal plants.

Pyroptosis is an identified programmed cell death that has been highly linked to endoplasmic reticulum (ER) dynamics. However, the crucial proteins for modulating dynamic ER membrane curvature change that trigger pyroptosis are currently not well understood. In this study, a biotin-labeled chemical probe of potent pyroptosis inducer α-mangostin (α-MG) was synthesized. Through protein microarray analysis, reticulon-4 (RTN4/Nogo), a crucial regulator of ER membrane curvature, was identified as a target of α-MG. We observed that chemically induced proteasome degradation of RTN4 by α-MG through recruiting E3 ligase UBR5 significantly enhances the pyroptosis phenotype in cancer cells. Interestingly, the downregulation of RTN4 expression significantly facilitated a dynamic remodeling of ER membrane curvature through a transition from tubules to sheets, consequently leading to rapid fusion of the ER with the cell plasma membrane. In particular, the ER-to-plasma membrane fusion process is supported by the observed translocation of several crucial ER markers to the "bubble" structures of pyroptotic cells. Furthermore, α-MG-induced RTN4 knockdown leads to pyruvate kinase M2 (PKM2)-dependent conventional caspase-3/gasdermin E (GSDME) cleavages for pyroptosis progression. In vivo, we observed that chemical or genetic RTN4 knockdown significantly inhibited cancer cells growth, which further exhibited an antitumor immune response with anti-programmed death-1 (anti-PD-1). In translational research, RTN4 high expression was closely correlated with the tumor metastasis and death of patients. Taken together, RTN4 plays a fundamental role in inducing pyroptosis through the modulation of ER membrane curvature remodeling, thus representing a prospective druggable target for anticancer immunotherapy.
Pyroptosis/immunology* ; Humans ; Endoplasmic Reticulum/immunology* ; Animals ; Nogo Proteins/antagonists & inhibitors* ; Mice ; Cell Line, Tumor ; Xanthones/pharmacology* ; Neoplasms/pathology* ; Mice, Nude

Lung cancer exhibits the highest incidence and mortality rates among cancers globally, with a five-year overall survival rate alarmingly below 20%. Targeting autophagy, though a controversial therapeutic strategy, is extensively employed in clinical practice. Current research is actively pursuing various therapeutic strategies using small molecules to exploit the dual function of autophagy. Nevertheless, the pivotal question of enhancing or inhibiting autophagy in cancer therapy merits further attention. This review aims to provide a comprehensive overview of the mechanisms of autophagy in lung cancer. It also explores recent advances in targeting cytotoxic autophagy and inhibiting protective autophagy with small molecules to induce cell death in lung cancer cells. Notably, most autophagy-targeting drugs, primarily natural small molecules, have demonstrated that activating cytotoxic autophagy effectively induces cell death in lung cancer, as opposed to inhibiting protective autophagy. These insights contribute to identifying druggable targets and drug candidates for potential autophagy-related lung cancer therapies, offering promising approaches to combat this disease.

Japanese spotted fever(JSF)is an infectious disease caused by Rickettsia japonica,with nonspecific clinical symptoms and a high risk of misdiagnosis.We reported a case of JSF,in which Rickettsia japonica was detected in blood cells by metagenomic next-generation sequencing.The patient recovered after treatment with doxycycline.This report provides a reference for the clinical diagnosis and treatment of JSF.
Humans ; High-Throughput Nucleotide Sequencing ; Metagenomics ; Rickettsia/isolation & purification* ; Spotted Fever Group Rickettsiosis/microbiology*

Objective:To explore the potential prognostic value of the HAMP(hepcidin)gene in gastric adenocarci-noma and its correlation with immune infiltration in gastric cancer.Methods:This study systematically analyzed the ex-pression characteristics of the HAMP gene using bioinformatics approaches based on mRNA data from 448 gastric ad-enocarcinoma tissues and non-tumor tissues in the TCGA database.Firstly,the cBioPortal platform was employed to analyze the genetic variation features of the HAMP gene,and the LinkedOmics database was used to evaluate the corre-lation between its methylation status and expression levels.Kaplan-Meier survival analysis with log-rank test was per-formed to assess the relationship between HAMP expression levels and patient prognosis.Furthermore,by integrating TIMER2.0 and TISIDB databases,we systematically evaluated the correlation between HAMP expression and immune-related genes as well as immune cell infiltration.Gene Set Enrichment Analysis(GSEA)was conducted to investigate HAMP-associated signaling pathway characteristics.Finally,STRING and Gepia databases were utilized to construct a protein-protein interaction network of HAMP and identify core interacting genes,comprehensively evaluating the role of HAMP in immune infiltration in gastric adenocarcinoma.Results:The expression level of HAMP was significantly higher in gastric adenocarcinoma tissues compared to normal tissues(P<0.01),and its elevated expression was strongly associ-ated with poor patient prognosis,manifested by significantly shorter overall survival(OS),progression-free survival(PFS),and post-progression survival(PPS)(all P<0.05).Genomic analysis revealed that HAMP mutations in gastric cancer were predominantly amplification-type,and its methylation level showed a positive correlation with mRNA expression(r=0.14,P<0.001).Immunological analysis demonstrated that high HAMP expression was significantly correlated with multiple key immune checkpoint molecules(PD-1:rho=0.274;PD-L1:rho=0.211;CTLA-4:rho=0.199,all P<0.001)and immune cell infiltration(dendritic cells:r=0.548;macrophages:r=0.414;neutrophils:r=0.374,all P<0.001).Pathway enrichment analy-sis indicated that the high HAMP expression group was significantly enriched in immune-related pathways including anti-gen presentation and NK cell-mediated cytotoxicity.Furthermore,protein-protein interaction network analysis identified core interacting genes such as TREM2 and TYROBP,suggesting that HAMP may participate in tumor immune regulation through specific molecular networks.Conclusion:HAMP is highly expressed in gastric cancer,and its high expression significantly reduces the survival time of gastric adenocarcinoma patients,demonstrating prognostic value.HAMP ex-pression is positively correlated with most immune-related genes in STAD and significantly associated with the abun-dance of multiple immune cell infiltration levels,serving as an independent prognostic factor related to immune infiltration.

Objective To develop dynamic prediction models based on multiple postnatal time points to support early diagnosis and individualized intervention for bronchopulmonary dysplasia(BPD)in preterm infants with gestational age<32 weeks.Methods Clinical data of 472 preterm infants with gestational age<32 weeks admitted to the Second Xiangya Hospital of Central South University between January 2016 and November 2020 were retrospectively analyzed.Multivariable logistic regression was applied to develop five independent prediction models at postnatal days 1,7,14,21,and 28.The performance of the models was assessed using the area under the receiver operating characteristic curve(AUC)and the Hosmer-Lemeshow test.Results Baseline characteristics such as gestational age and birth weight differed significantly between the BPD group(n=147)and the non-BPD group(n=325)(P<0.05).Predictors of BPD evolved across time points:on day 1,key predictors included gestational age,birth weight,Score for Neonatal Acute Physiology II(SNAP-II),invasive mechanical ventilation,and fraction of inspired oxygen>30%;by day 7,additional variables emerged,including fasting duration>2 days,mean feeding advancement rate<8.5 mL/(kg·d),neonatal respiratory distress syndrome,apnea of prematurity,and positive sputum culture;from day 14 onward,nutrition-and treatment-related indicators were incorporated additionally.The models demonstrated good discrimination at postnatal days 1,7,14,21,and 28,with AUCs of 0.917,0.927,0.939,0.944,and 0.968,respectively,and good calibration(Hosmer-Lemeshow P>0.05).Internal validation showed AUCs ranging from 0.899 to 0.958,indicating robust performance.Conclusions Dynamic postnatal prediction models incorporating indicators spanning perinatal factors,respiratory support,nutritional management,and therapeutic interventions demonstrate high predictive performance and facilitate dynamic risk assessment for BPD in preterm infants with gestational age<32 weeks.

Abnormal expression of CMTM4 protein is closely related to tumour occurrence,development and prognosis.Although the important role of CMTM4 in tumours has been gradually manifested,its spe-cific mechanism of action in cervical cancer remains unclear.The aim of this study was to investigate the relationship between CMTM4 expression and clinicopathological features in cervical cancer and study its mechanism.Immunohistochemistry and Western blot were used to detect the expression level of CMTM4 in cancer tissues and paracancerous tissues,and it was found that CMTM4 was significantly under-ex-pressed in cervical cancer tissues(P＜0.001).The chi-square test analysed the relationship between high and low CMTM4 expression and the clinical and pathological characteristics of cervical cancer pa-tients and results found that CMTM4 expression was correlated with the number of births,HPV infection status,pathological type,FIGO stage and lymph node metastasis.Data from Western blot and RT-qPCR found that CMTM4 protein and mRNA levels in HaCaT cells were significantly higher than that of C-33A cells,HeLa cells,U14 cells,and HT-3 cells.Among them,the most significant change in CMTM4 ex-pression was observed in C-33A cells,so the C-33A cell line was selected for subsequent overexpression experiments.CCK-8 analysis found that the proliferation ability of C-33A cervical cancer cells in the pcDNA-CMTM4 group was significantly lower than that in the pcDNA-NC group(P＜0.001).Flow cy-tometry and Western blot results indicated that CMTM4 overexpression promoted apoptosis(P＜0.001),significantly increased Bax(P＜0.001)and cleaved caspase 3(P＜0.05)protein levels,and significant-ly decreased Bcl-2 protein level(P＜0.01).Western blot results further found that CMTM4 overexpres-sion significantly reduced the protein levels of p-PI3K(P＜0.001)and p-AKT(P＜0.01),but did not affect the protein levels of PI3K and AKT(P＞0.05).The above findings indicated that CMTM4 gene expression was down-regulated in cervical cancer,and CMTM4 overexpression inhibited cervical cancer cell proliferation and induced apoptosis by inhibiting the PI3K/AKT pathway.Therefore,CMTM4 may be used as a biological marker for screening cervical cancer.