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.

Objective This study investigated the diversity of rodent populations and the infection status of Bartonella at three ports along the China-Mongolia border in 2022. Methods Rodents at three Belt and Road ports along the China-Mongolia border, namely Ganqimaodu, Erenhot, and Zhuengadabuqi, were morphologically identified. The Bartonella citrate synthase(gltA)gene was amplified by nested polymerase chain reaction(PCR), and PCR-positive products were sequenced. The resulting sequences were then analyzed for genetic characteristics. Phylogenetic analysis was performed using MEGA 11.0 software using Neighbor-Joining and Maximum-Likelihood method. Results A total of 94 rodents were captured, representing seven species from five families and six genera: Mus musculus, Meriones unguiculatus, Meriones meridianus, Spermophilus dauricus, Allactaga sibirica, Dipus sagitta, and Phodopus roborovskii. Among them, M. unguiculatus was the most abundant species, with 58 rodents, accounting for 61.70% of the total. Overall,11 positive Bartonella pathogen sequences were obtained from the three ports, with a positive detection rate of 11.70%(11/94). The infected rodent species included M. unguiculatus, M. meridianus, and A. sibirica, and two species of Bartonella were detected. Conclusions Rodents at Ganqimaodu, Erenhot, and Zhuengadabuqi ports along the China-Mongolia border were naturally infected with Bartonella. Rodent monitoring and pathogen prevention and control in this area should be strengthened.

Objective This study aimed to investigate the genetic diversity, differentiation, and population structure of Aedes albopictus across different geographical regions in Shandong Province, and to explore the relationship between genetic diversity and geographical distribution. Methods Between July and August 2024, ten Ae. albopictus populations were sampled from seven cities in Shandong Province. Genomic DNA was extracted from individual mosquitoes, and the mitochondrial cytochrome c oxidase subunit I(COⅠ)gene was amplified using PCR and sequenced. The obtained sequences were verified using BLAST and analyzed with MAFFT, MEGA 11, DnaSP v6.12, Arlequin 3.5, PopART 1.7, STRUCTURE 2.3.4 and the R packages adegenet and vegan to assess genetic diversity and population structure. Results A total of 229 COⅠ sequences(662 bp) was obtained, revealing ten variable sites with no insertions or deletions. The overall base composition showed an AT bias of 67.7%. Haplotype analysis identified 11 haplotypes, with Hap2 being the dominant and most widely distributed haplotype across all populations. Neutrality tests showed significant population expansion only in the Rizhao population. Mantel testing result revealed that geographical distance does not significantly impede gene flow. Overall, genetic differentiation among populations was low, indicating frequent gene flow. When combined with additional samples from Shanghai, Fujian, Guangdong, Yunnan, Hainan and Guangxi, STRUCTURE, UPGMA and DAPC analyses revealed two primary genetic clusters of Ae. albopictus. Conclusions Ae. albopictus populations in Shandong Province exhibit frequent gene flow, low genetic differentiation, and relatively low overall genetic diversity. However, the Rizhao population showed signs of recent expansion, highlighting the need for enhanced surveillance and targeted control measures in this area.

ObjectiveTo investigate the potential therapeutic effects of Dengzhan Shengmai capsules （DZSM） on myocardial fibrosis after myocardial infarction and decipher the underlying molecular mechanisms by virtue of network robustness algorithm. MethodsA rat model of myocardial infarction was established by ligating the left anterior descending coronary artery. Successfully modeled Sprague-Dawley rats were randomly allocated into a model group， an enalapril group（3.15 mg·kg^-1）， and high- and low-dose DZSM groups（226.8，113.4 mg·kg^-1）. In addition， a sham group was set up. After four consecutive weeks of intervention， the cardiac function was assessed via echocardiography， and myocardial collagen deposition was observed through Sirius red staining. The key regulatory pathways of DZSM were analyzed by the network robustness algorithm. Enzyme-linked immunosorbent assay （ELISA） and immunofluorescence assay were employed to validate the mechanisms. ResultsCompared with the sham group， the model group exhibited decreased left ventricular ejection fraction （LVEF%） and fractional shortening （FS%） （P<0.01） and increased myocardial collagen volume fraction （CVF%） （P<0.01）. Compared with the model group， high-dose DZSM and enalapril improved LVEF% and FS% （P<0.01）. Both high- and low-dose DZSM reduced the CVF% （P<0.05， P<0.05，）. Network analysis indicated that DZSM exerted its effects by regulating neutrophil infiltration and related inflammatory pathways. Experimental validation showed that DZSM significantly reduced the protein levels of nuclear factor （NF）-κB， Janus kinase 2 （JAK2）， and signal transduction activator and transducer 3 （STAT3） in the cardiac tissue， as well as the levels of chemokine （C-X-C motif） ligand 1 （CXCL1）， interleukin （IL）-1β， neutrophil elastase-DNA complex （NE-DNA）， and myeloperoxidase-DNA complex （MPO-DNA） in the plasma. ConclusionDZSM may ameliorate myocardial fibrosis by inhibiting neutrophil infiltration and extracellular trap formation and regulating the NF-κB/JAK2/STAT3 signaling pathway.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Escherichia coli(E.coli)is a key pathogenic bacterium responsible for postpartum endo-metritis,with its colonization in the reproductive tract closely associated with endometrial damage and disruption of the ovarian cycle.This ultimately leads to infertility,causing significant economic losses to the dairy industry.Macrophages play a pivotal role in the inflammatory response.This study aims to investigate the mRNA expression profile of bovine bone marrow-derived macropha-ges following E.coli infection using RNA sequencing(RNA-seq)technology.Additionally,it seeks to identify the biological functions and signaling pathways of differentially expressed genes(DEGs)through Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analyses.The results demonstrated that E.coli infection induced differential expression of 4 522 genes,with 2 141 upregulated and 2 381 downregulated.These genes were primarily asso-ciated with inflammatory responses,where TLR2,TLR4,NLRP3,and PTGS2 played pivotal roles.PGD2 synthesis was mediated by TLR2,TLR4,and NLRP3.Transcriptome sequencing of bovine bone marrow-derived macrophages infected with E.coli and treated with a PGD2 inhibitor revealed a marked downregulation of TLR2 and TLR4 gene expression.qPCR validation results were highly consistent with the RNA-seq findings.This study elucidates the interactive regulatory roles of TLR2,TLR4,and NLRP3 in conjunction with PGD2,which collectively modulate bovine endome-tritis.These findings offer significant molecular insights that enhance our understanding of the pathological mechanisms underlying bovine endometritis,thereby informing its prevention and treatment strategies.

Objective:To investigate the clinical efficacy of extended abdominal wall resec-tion combined with reconstruction for abdominal wall aggressive fibromatosis (AF).Methods:The retrospective and descriptive study was conducted. The clinical data of 70 patients with abdominal wall AF who were admitted to 3 medical centers, including The First Affiliated Hospital of the University of Science and Technology of China, between January 2009 and July 2024 were collected. There were 6 males and 64 females, aged (36±13)years. All patients underwent extended abdominal wall resection combined with abdominal wall reconstruction. Observation indicators: (1) surgical situations; (2) tumor recurrence and postoperative complications. Comparisons of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test. Results:(1)Surgical situations. All 70 patients underwent extended abdominal wall resection combined with abdominal wall recons-truction. The operation time was 90(91)minutes and duration of postoperative hospital stay was 10(6)days. Of the 70 patients, 41 patients underwent abdominal wall AF resection plus polypropylene mesh abdominal wall reconstruction, with a defect area of 60(54)cm2. The mesh placement method was uniformly Sublay repair. The remaining 29 patients underwent abdominal wall AF resection plus direct suture repair, with a defect area of 34(31)cm2. There was a significant difference in the abdominal wall defect area between the two groups ( U=291.00, P<0.05). All 70 patients achieved R 0 resection. The distance from surgical margin to tumor edge was 2-3 cm in 39 cases and >3 cm in 31 cases. (2) Tumor recurrence and postoperative complications. All 70 patients were followed up for 78(90)months. During follow-up, 10 patients developed tumor recurrence (5 cases with mesh reinforced abdominal wall reconstruction and 5 cases with direct suture repair). Among them, one case was monitored, one case underwent radiotherapy, and neither received further surgical treatment. The remaining 8 patients underwent repeat R 0 resection, and no further recurrence occurred. There was no significant difference in recurrence rate between the patients with mesh reconstruction and patients with direct suture repair ( χ2=0.06, P>0.05). The postoperative recurrence rate was 9.7%(3/31) in patients with the distance from surgical margin to tumor edge >3 cm, versus 17.9%(7/39) in patients with the distance from surgical margin to tumor edge of 2-3 cm, showing no significant difference between them ( χ2=0.97, P>0.05). Sixty patients had no tumor recurrence. During follow-up, none of the 70 patients developed incisional hernia. Two patients experienced postoperative wound infection, and 6 cases developed postoperative chronic pain. Conclusion:Extended abdominal wall resection combined with reconstruction is safe and feasible for abdominal wall AF.