To explore the effects of hypoxia on spermatid differentiation and stability of sperm flagellar microtubule, and investigate the underlying molecular mechanisms in order to clarify the potential adverse effects of hypoxia on male reproductive function.

Forty-eight 8-week-old healthy male SD rats (weighing 300~399 g) were subjected in this study. The experiments included ①an oxygen concentration gradient experiment (n=6): 21% oxygen was regarded as normoxia (control), and 13.5%, 11.8%, and 10.4% oxygen were used to simulate hypoxic environments at altitudes of 3 500, 4 500 and 5 500 m, respectively, for a continuous exposure of 2 months; ② a time gradient experiment (n=6): the rats were exposed to 10.4% oxygen for 0, 0.5, 1, and 2 months, respectively. Flow cytometry was employed to isolate round spermatids, and the following methods were employed to measure relevant indicators: ① RNA sequencing to analyze gene expression profile changes related to impaired spermatogenesis and abnormal flagellar structure under hypoxic stress; ②Western blotting to detect the expression levels of key proteins CEP290, RING 1A, and H2AK119ub; ③fluorescence recovery after photobleaching (FRAP) to monitor microtubule assembly dynamics and assess the immediate impact of hypoxia on microtubule stability.

In the oxygen concentration gradient experiment, after 2 months of exposure to 10.4% oxygen, the proportions of spermatogonia, secondary spermatocytes, and round spermatids in rat seminiferous tubules were significantly increased (P<0.05), reaching 1.33±0.04, 1.06±0.01 and 1.60±0.02 times higher, respectively than that of the 21% normoxia group. Conversely, the proportions of primary spermatocytes and elongated spermatids were obviously decreased (P<0.05), taking 0.89±0.01 and 0.88±0.000 2 times respectively when compared with that of the 21% normoxia group, in a oxygen concentration-depended manner. In the time gradient experiment, after 0.5 months of exposure to 10.4% oxygen, the proportions of spermatogonia, secondary spermatocytes, and round spermatids began to increase (P<0.05), reaching 1.11±0.03, 1.04±0.01 and 1.29±0.003 times higher, respectively than that of the 0-month control group. The proportions of primary spermatocytes and elongated spermatids started to significantly decrease (P<0.05) after 1 month of exposure, only 0.94±0.03 and 0.95±0.008 times, respectively than that of the 0-month control group. After 2 months of exposure to 10.4% oxygen, the rate of sperm tail abnormalities in the epididymis of rats was significantly increased (P<0.05), rising from (12.1±1.7)% in the 21% normoxia group to (30.8±3.7)%. In G2 spermatocytes exposed to 1% hypoxia for 24 h, FRAP revealed a decrease in microtubule assembly rate and enhanced microtubule dynamic instability, with the maximum fluorescence recovery value decreasing from 0.37±0.02 in the normoxia group to 0.29±0.01. The results of RNA sequencing showed that under hypoxic condition, the transcription level of the key cilium basal body molecule CEP290 was increased, with an upregulation of 1.81±0.11 times than that of the 21% normoxia group. In contrast, the expression levels of PRC1 complex members RING 1A, RING 1B, CBX2, PHC1, and PCGF1 were decreased, to 0.74±0.02, 0.73±0.01, 0.78±0.02, 0.71±0.01 and 0.86±0.03 times of that of the 21% normoxia group, respectively. Western blotting indicated that the protein level of CEP290 was up-regulated in the hypoxia group, while that of RING 1A was down-regulated. ChIP-qPCR experiments showed that the binding of RING 1A and its product H2AK119ub to the CEP290 promoter were significantly decreased (P<0.000 1), with binding strengths of 0.38±0.02 and 0.52±0.06 times of that of the 21% normoxia group, respectively. In siRING 1A-treated G2 cells, the binding of H2AK119ub to the CEP290 promoter was significantly decreased (P<0.000 1), with a binding strength of 0.74±0.06 times of that of the control group, while CEP290 mRNA level was significantly increased (P<0.000 1), with an up-regulation of 3.35±0.37 times.

Hypoxic environment impair sperm flagellar microtubule stability via the RING 1A-H2AK119ub-CEP290 signaling axis, which affects spermatid differentiation and leads to spermatogenic dysfunction.

To investigate the changes in the tumor microenvironment of pancreatic cancer (PDAC) complicated with diabetes mellitus (DM) in a mouse model of hyperglycemia and orthotopic pancreatic cancer by analyzing transcriptome and single-cell transcriptome data in order to identify potential therapeutic targets.

By integrating single-cell transcriptome and bulk transcriptome data, bioinformatics analysis was conducted to compare the characteristics of tumor cells and tumor immune microenvironment between PDAC patients with DM (DM group) and those without DM (non-DM group). Twenty male C57BL/6 mice (6 weeks old, weighing 18~20 g) were randomly divided into a hyperglycemic group [STZ group, continuous intraperitoneal injection of 50 mg/kg streptozocin (STZ) (final concentration of 1%) dissolved in citrate buffer], and a control group (Control group, an equivalent volume of citrate buffer without STZ at the same time points), with 10 mice in each group. Tail-tip blood glucose level was measured to monitor glycemic status. After orthotopic inoculation of pancreatic cancer cells in both Control and STZ groups, tumor-infiltrating immune cells were harvested. Flow cytometry was employed to determine the effects of hyperglycemia on: total CD8⁺ T cell and Treg cell populations; CD8⁺ T cell subsets expressing Ki67, TNF-α, granzyme B (GZMB) and IFN-γ; surface expression of PD-1, lymphocyte activation gene-3 (LAG-3) and T cell immunoglobulin and mucin domain-3 (Tim-3) on CD8⁺ T cells; programmed death-ligand 1 (PD-L1) expression on tumor cells; and tumor-associated macrophage surface expression of major histocompatibility complex class I (MHC-Ⅰ) and cluster of differentiation 206 (CD206).

Bioinformatics analysis revealed that, compared to the non-DM group, the genes significantly up-regulated in the DM group were associated with poor prognosis (P<0.001). The proportion of type 2 ductal cells was increased in the DM group, exhibiting higher levels of copy number variation (P<0.001). In the tumor immune microenvironment of the DM group, there was an increase in the proportion of Treg cells (P<0.05) and an elevated exhaustion score for CD8⁺ T cells (P<0.001), accompanied by down-regulated expression of effector molecules, up-regulated expression of inhibitory checkpoints, and a significant increase in the M2 score of M2-like macrophages (P<0.001). Animal experiments and flow cytometry found that, compared to the Control group, the STZ group had a shorter survival time (P<0.001), with decreased proportions of total CD8⁺ T cells (P<0.01) and CD8⁺ T cells expressing Ki67, TNF-α, GZMB and IFN-γ (P<0.01), increased proportion of Treg cells (P<0.001), up-regulated expression of PD-1, LAG-3 and Tim-3 on the surface of CD8⁺ T cells (P<0.001), and up-regulation of PD-L1 on tumor cell surface (P<0.001) and enhanced expression of CD206 on the surface of tumor-associated macrophages, while down-regulated expression of MHC-I (P<0.001).

High glucose promotes the formation of an immunosuppressive microenvironment in PDAC, and targeting type 2 ductal cells and immunosuppressive cells in the tumor microenvironment, combined with dual immune checkpoint antibody therapy, may improve patient prognosis.

To investigate the efficacy of varying doses of methylprednisolone (MP) on mice with acute exacerbations of chronic obstructive pulmonary disease (AECOPD) induced with influenza A virus (IAV).

Mouse model of COPD was established using LPS combined with smoking for 12 weeks, and then these COPD mice were treated with administration of 40 μL IAV via nasal drip to establish a AECOPD model. A total of 15 AECOPD mice were randomly divided into low-, medium- and high-dose MP groups, oseltamivir group and blank group. The body weight and survival time were monitored within 10 d after IAV infection. On days 1, 3, and 5 post-treatment, lung function was assessed using whole-body plethysmography (WBP), inflammatory factors in bronchoalveolar lavage fluid (BALF) were quantified with ELISA, viral titers in BALF were determined using plaque assays, and colony-forming units were evaluated with blood agar plates. Immunofluorescence analysis: ①Pulmonary immunofluorescence assay: Mice were randomly categorized into (n=4): LPS 1-day group, LPS 3-day group, and LPS+MP treatment group. All groups received an initial dose of LPS via atomization; subsequently, the LPS+MP treatment group received a single gavage dose of MP. Lung tissues were harvested from the 1-day LPS group on 1 d post-treatment, and from the 3-day LPS and LPS+MP groups on 3 d for immunofluorescence staining. ②Cellular immunofluorescence assay: Mouse bone marrow neutrophils were classified into blank control (no intervention), LPS stimulation (LPS group), MP intervention with LPS stimulation (LPS+MP group), and MP intervention alone (MP group). The above cells were collected in 4 h after corresponding interventions for subsequent cellular immunofluorescence analysis.

①The medium-dose MP group demonstrated the most significant improvement in survival rate, weight recovery, and lung function when compared to other groups (P<0.05). ② Treatment of medium-dose MP obviously reduced the levels of IL-6 and neutrophil extracellular traps (NETs) (P<0.05), while, elevated inflammatory factors and NETs were observed in the high-dose MP group on day 5 post-treatment. ③ Notable decline in the lung injury score was found in the medium-dose MP group than the other groups (P<0.05). ④The high-dose MP group exhibited substantial bacterial proliferation and delayed viral clearance since day 5 after treatment.

Medium-dose MP shows best efficacy in treatment of IAV-induced AECOPD, and the dose neither delays viral clearance nor increases the risk of bacterial infection following viral infection.

To explore the related factors of postoperative nutritional risk in elderly patients with proximal femoral nail anti rotation (PFNA) internal fixation and establish a prediction model of malnutrition.

A total of 574 elderly patients who underwent PFNA internal fixation in the First Medical Center of Chinese PLA General Hospital from January 2021 to June 2024 were included and divided into malnutrition group (n=389) and good nutrition group (n=185). The differences in 39 indicators in aspects of physiological, psychological, social, economic, environmental and medical fields were compared between the 2 groups. Logistic analysis was used to screen the nutritional risk factors, and then a nomogram model was constructed based on these factors.

Advanced age, lower BMI, higher postoperative Self-Rating Anxiety Scale (SAS) score, less exercise before fracture, being farmers, higher economic pressure, lower preoperative albumin, preprotein and hemoglobin, and lower Barthel index before fracture were independent risk factors for nutritional risk in patients undergoing PFNA internal fixation (P<0.05). The nomogram prediction model based on the above factors had an AUC value of 0.995 (95%CI: 0.987~1.000) in predicting the risk of malnutrition in these patients. When the threshold probability >0.02, this model could be clinically beneficial in predicting the risk of postoperative malnutrition in patients after PFNA internal fixation.

Our nutritional risk prediction model based on age, BMI, economic pressure, pre-fracture exercise and preoperative albumin and other indicators is constructed for the elderly patients after PFNA internal fixation, and the model has high accuracy and clinical application value.

To construct and validate a nomogram-based prediction model of vascular plaque stability in patients with progressive cerebral infarction based on serum monocyte chemotactic protein-1 (MCP-1), monocyte chemotactic protein-1 inducible protein 1 (MCPIP1) combined with inflammatory factors.

A retrospective cohort study was conducted on 200 patients with progressive cerebral infarction admitted to our department from January to December 2023. All of them were assigned into a modeling group, and were divided into a stable plaque subgroup and the unstable plaque subgroup according to results of carotid multilayer spiral CT angiography. Their general data, results of laboratory tests, and other clinical indicators were collected to identify the influencing factors for vascular plaque instability with single-factor and multifactor analyses. Then a nomogram model for predicting vascular plaque stability was constructed for patients with progressive cerebral infarction. Receiver operating characteristics (ROC) curve was plotted to evaluate the predictive performance of the nomogram model. Subsequently, in a ratio of 7∶3 between the cases in the modeling group and the validation group, another 86 patients with progressive cerebral infarction admitted to our department from January to June 2024 were enrolled and served as the validation group. Their clinical data were collected for external validation of the model.

In the modeling group, there were 68 patients (34.00%) in the stable plaque subgroup and 132 patients (66.00%) in the unstable plaque subgroup. Univariate analysis showed that there were significant differences between the 2 subgroups in terms of age (65.31±6.74 vs 67.52±7.14 years, t=2.113), comorbid diabetes mellitus [35 (48.53%) vs 80 (60.61%) cases, Chi-square=7.182], MCP-1 (570.67±104.23 vs 693.94±128.45 pg/mL, t=6.836), MCPIP1 (2.93±0.58 vs 4.08±0.75 ng/mL, t=11.051), homocysteine (Hcy, 10.56±2.38 vs 16.04±3.54 μmol/L, t=11.491), C-reactive protein (CRP, 6.16±2.03 vs 8.05±2.67 mg/L, t=5.122) and TNF-ɑ (1.31±0.29 vs 1.79±0.47 ng/mL, t=7.696) (all P˂0.05). Multivariate analysis indicated that age (β=0.103, OR=1.109, 95%CI=1.012~1.215), comorbid diabetes (β=2.135, OR=8.461, 95%CI=1.866~38.353), Hcy (β=0.706, OR=2.026, 95%CI=1.550~2.650), MCP-1 (β=0.011, OR=1.011, 95%CI=1.004~1.018), MCPIP1 (β=1.928, OR=6.875, 95%CI=2.765~17.094), CRP (β=0.327, OR=1.387, 95%CI=1.022~1.883) and TNF-ɑ (β=1.491, OR=4.443, 95%CI=1.389~14.212) were independent influencing factors for vascular plaque instability in the patients with progressive cerebral infarction (all P<0.05). The modeling formula based on these factors was Logit (P)=0.103×(age)+2.135×(combined diabetes)+0.706×(Hcy)+0.011×(MCP-1)+1.928×(MCPIP1)+0.327×(CRP)+1.491×(TNF-ɑ)-34.684. ROC curve analysis revealed that the area under curve (AUC) of the model in the modeling group was 0.956 (95%CI: 0.931~0.981, P<0.001), with a sensitivity of 0.841 and a specificity of 0.926, and the AUC value in validation group was 0.960 (95%CI=0.925~0.996, P<0.001).

Our nomogram prediction model has a good predictive performance for vascular plaque instability in patients with progressive stroke, and it can be used to identify high-risk patients for vascular plaque instability in clinical practice.

To develop a machine learning-based risk prediction model for postoperative acute kidney injury (AKI) and a model for mortality in obese patients admitted to intensive care unit (ICU) in order to improve early warning and prognostic evaluation to support clinical decision-making.

Data of obese postoperative ICU patients were retrospectively retrieved from the MIMIC-IV and eICU databases for statistical analysis. Ultimately, 2 520 patients (670 from MIMIC-IV and 1 850 from eICU databases) were included to build the risk prediction models for AKI and mortality. The data included demographic information, vital signs, laboratory findings, surgical types, comorbidities, and medication use. After data cleaning and preprocessing, Boruta feature selection was applied, followed by the construction of prediction models using 7 machine learning algorithms, that is, Gradient Boosting Machine (GBM), Generalized Linear Model (GLM), k-Nearest Neighbors (KNN), Naïve Bayes (NB), Neural Network (NNET), Support Vector Machine (SVM), and XGBoost. Model performance was evaluated through cross-validation and external validation.

In the risk prediction models of AKI, the SVM model achieved the highest AUC value of 0.80 in the testing set and 0.71 in the external validation test. For the risk prediction models of mortality, the GBM model outperformed others in the prediction, attaining an AUC value of 0.91 in the testing set.

Risk predictive models for postoperative AKI and mortality in obese ICU patients are successfully constructed, and are valuable tools for clinicians to optimize early intervention and improve clinical outcomes for the patients.

‍To investigate the status of pain and influencing factors of patients with chronic wounds.

A convenience sampling was used to recruit 186 patients with chronic wounds from a tertiary hospital in Chongqing between January and August 2024. General information questionnaire, Numeric Rating Scale (NRS), and Chronic Wound Pain Status Questionnaire were used to collect data.

The 186 patients had a median score for pain knowledge of 15 (12, 19), for pain attitude of 40 (37, 45), and for coping behaviors of 21 (16, 26). Generalized linear model analysis showed that age, education level, and per capita monthly household income were independent factors influencing pain knowledge (standardized regression coefficients: -1.625, 2.071, 1.209; 95%CI: -2.479~-0.770, 1.431~2.711, 0.160~2.258, respectively; P<0.05). Education level and baseline pain intensity were independent factors influencing pain attitude (standardized regression coefficients: 3.036, -2.211; 95%CI: 2.146~3.926, -3.568~-0.854; P<0.05). Education level and type of pain persistence were independent factors influencing coping behaviors to pain (standardized regression coefficients: 1.001, -1.694; 95%CI: 0.194~1.809, -3.262~-0.126; P<0.05).

Patients with chronic wounds have generally low levels of pain-related knowledge, attitudes, and coping behaviors. Age, per capita monthly household income, education level, and pain characteristics significantly influence their pain status. Education level plays a key role in influencing pain among patients with chronic wounds by shaping their access to pain-related knowledge, development of their attitudes toward pain, and execution of pain management behaviors.

To construct an intelligent segmentation and T-stage diagnostic model for esophageal cancer based on the DAEUnet and ConvNeXt networks using transfer learning.

Dicom raw data from 126 patients diagnosed with esophageal cancer between January 2018 and April 2022 were collected, including 100 cases from Department of Thoracic Surgery at the First Affiliated Hospital of Army Medical University and 26 cases from the Department of Thoracic Surgery at Shanxi Cancer Hospital. After data augmentation, a total of 60 275 images were obtained. The DAEUnet esophageal cancer intelligent segmentation network was built, and on this basis, 3 classification networks, ConvNeXt, Swin Transformer, and ResNet were constructed for T-stage diagnosis of esophageal cancer.

The Dice similarity coefficient (DSC) for esophageal cancer intelligent segmentation using the DAEUnet network was 0.82, and the DSC value of the esophagus, aorta, normal esophagus, mediastinal lymph nodes, and heart was 72.4%, 87.5%, 79.3%, 60.5% and 96.8%, respectively. Among the 3 T-stage diagnosis models for esophageal cancer, the ConvNeXt model performed the best, with a precision value for T1~T4 stages of 0.65, 0.727, 0.889 and 0.92, respectively, and an AUC value of 0.892, which were superior to the ResNet and Swin Transformer networks.

The proposed DAEUnet and ConvNeXt-based intelligent segmentation and T-stage diagnosis model for esophageal cancer improves T-stage accuracy and treatment efficiency.

‍To analyze the composition and function of residual cells in pre-transplantation human thymic slices by single-cell transcriptomics sequencing (scRNA-seq), and established a quality assessment method for thymic slices based on the expression levels of molecular markers in the culture supernatant.

The discarded thymus from 18 patients with congenital heart disease undergoing surgical treatment in Department of Cardiothoracic Surgery of Children’s Hospital Affiliated to Chongqing Medical University from May 2023 to January 2024 were collected and prepared into thymic slices. After the slices were cultured in vitro for 14 d, scRNA-seq was employed to identify the residual cell types, and gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis was performed to analyze the biological function of the residual cells. Then based on the literature concerning thymic slice culture, the molecular markers indicating thymocyte function were screened out. ELISA was applied to detect the changes in protein levels of molecular markers in the supernatant. Receiver operating characteristic (ROC) curve was plotted and assess the value of the molecular markers in the supernatant in evaluating the quality of thymic slices with area under the curve (AUC). Then, the qualified and unqualified thymic slices determined by our obtained molecular markers were transplanted subcutaneously into male nude mice (6~8 weeks old, weighing 14~17 g), respectively, and the male nude mice without transplantation of the thymic slices served as control group. Flow cytometry and histologic analysis were utilized to observe the immune reconstitution after transplantation.

① scRNA-seq identified 11 cell types in thymic slices, dominated with epithelial cells, fibroblasts, and T cells. GO and KEGG enrichment analysis showed that epithelial cells were involved in enrichment entries related to chemotaxis, epithelial cell development, cell matrix adhesion and tight junction; fibroblasts were involved in enrichment entries related to extracellular matrix, epithelial cell proliferation, negative regulation of cell migration, and regulation of actin cytoskeleton; T cells were mainly related to T cell differentiation, regulation of T cell activation, T cell apoptosis, and T cell receptor signaling. ② Molecular markers, CCL19, CCL21,CXCL12, CXCL16, IL16 and SELL were identified to indicate thymocyte function. Compared with the levels of the first day, the protein secretions of CCL19, CCL21, CXCL12 and CXCL16 were significantly increased during in vitro culture (P<0.05), while the protein secretions of IL16 and L-selectin (protein form of SELL) were significantly decreased (P<0.05). The combined predictor Pre1 from subset of cytokines (IL16and L-selectin) had the highest value in the quality assessment of thymic slices after 1 d of culture (AUC=0.883), and the combined predictor Pre2 from subset of cytokines (CCL19, CCL21, CXCL12 and CXCL16) had the highest value in the quality assessment after 14 d of culture (AUC=0.948). ③ Transplantation in nude mice indicated that the qualified thymic slices could develop to thymus structure in vivo, and effectively increase the proportion of T cells in peripheral blood (P<0.01), while the unqualified thymic slices could not obtain the reconstitution of T cell development.

The main residual component cells in thymic slices are epithelial cells, fibroblasts and T cells. IL16 and L-selectin can be used as potential indicators to determine the quality of donor thymic samples. CCL19, CCL21, CXCL12 and CXCL16 can effectively evaluate the quality of thymic slices before transplantation.

To investigate the effects of hypoxia on the transcriptional phenotype and ultrastructure of tumor-associated macrophages (TAMs) in glioma.

CD14⁺ monocytes were isolated from healthy human peripheral blood samples collected from the Blood Bank of the First Affiliated Hospital of Army Medical University, and the cells were induced to differentiate into TAMs through co-culture with glioma cell-conditioned medium. Hypoxic TAM models were established using varying concentrations of cobalt chloride hexahydrate (CoCl₂, 50~400 μmol/L) or hypoxic conditions (1%, 5%, 10%O₂) for 48 h, while normoxic TAM models (21%O₂) served as controls. RT-qPCR and transcriptome sequencing were employed to analyze transcriptional changes in TAMs under normoxic and hypoxic conditions. Gene set enrichment analysis (GSEA) was applied to compare the differences in angiogenesis, glycolysis and other hypoxia-responsive pathways between the 2 conditions. Transmission electron microscopy (TEM) or immunofluorescence staining was conducted to assess the ultrastructural alterations in cytoskeleton, endoplasmic reticulum (ER), and mitochondria in normoxic and hypoxic TAMs (1%O₂).

Hypoxic TAMs exhibited up-regulated transcription of hypoxia-responsive markers (oxygen transport, glycolysis, pro-angiogenesis), with the effects correlating with hypoxia severity（P<0.05）. GSEA revealed significant up-regulation of hypoxia, angiogenesis regulation, glycolysis and gluconeogenesis, and starvation stress pathways, alongside down-regulation of innate immunity, macrophage activation, cytoskeleton, and protein maturation pathways in hypoxic TAMs (P<0.05). TEM and immunofluorescence staining demonstrated obvious ultrastructure changes, including disrupted cytoskeletal organization, shortened rough ER with reduced ribosomes, mitochondrial swelling with cristae damage, and diminished ER-mitochondria contacts in hypoxic TAMs.

CoCl₂ and hypoxia induce a hypoxic transcriptional phenotype in TAMs, which may potentially associated with ultrastructural remodeling of the cytoskeleton, ER, and mitochondria.