Idiopathic pulmonary fibrosis (IPF), a chronic interstitial lung disease, is characterized by aberrant wound healing, excessive scarring and the formation of myofibroblastic foci. Although the role of alternative splicing (AS) in the pathogenesis of organ fibrosis has garnered increasing attention, its specific contribution to pulmonary fibrosis remains incompletely understood. In this study, we identified an up-regulation of serine/arginine-rich splicing factor 7 (SRSF7) in lung fibroblasts derived from IPF patients and a bleomycin (BLM)-induced mouse model, and further characterized its functional role in both human fetal lung fibroblasts and mice. We demonstrated that enhanced expression of Srsf7 in mice spontaneously induced alveolar collagen accumulation. Mechanistically, we investigated alternative splicing events and revealed that SRSF7 modulates the alternative splicing of pyruvate kinase (PKM), leading to metabolic dysregulation and fibroblast activation. In vivo studies showed that fibroblast-specific knockout of Srsf7 in conditional knockout mice conferred resistance to bleomycin-induced pulmonary fibrosis. Importantly, through drug screening, we identified lomitapide as a novel modulator of SRSF7, which effectively mitigated experimental pulmonary fibrosis. Collectively, our findings elucidate a molecular pathway by which SRSF7 drives fibroblast metabolic dysregulation and propose a potential therapeutic strategy for pulmonary fibrosis.

OBJECTIVE: To compare the efficacy and safety of intravenous colistin sulfate combined with nebulized inhalation versus intravenous monotherapy for pulmonary infections caused by carbapenem-resistant organism (CRO). METHODS: A multicenter retrospective cohort study was conducted. Clinical data were collected from patients admitted to the intensive care unit (ICU) of 10 tertiary class-A hospitals in Henan Province between July 2021 and May 2023, who received colistin sulfate for CRO pulmonary infections. Data included baseline characteristics, inflammatory markers [white blood cell count (WBC), neutrophil count (NEU), procalcitonin (PCT), C-reactive protein (CRP)], renal function indicators [serum creatinine (SCr), blood urea nitrogen (BUN)], life support measures, anti-infection regimens, clinical efficacy, microbiological clearance rate, and prognostic outcomes. Patients were divided into two groups: intravenous group (colistin sulfate monotherapy via intravenous infusion) and combination group ((intravenous infusion combined with nebulized inhalation of colistin sulfate). Changes in parameters before and after treatment were analyzed. RESULTS: A total of 137 patients with CRO pulmonary infections were enrolled, including 89 in the intravenous group and 48 in the combination group. Baseline characteristics, life support measures, daily colistin dose, and combination regimens (most commonly colistin sulfate plus carbapenems in both groups) showed no significant differences between two groups. The combination group exhibited higher clinical efficacy [77.1% (37/48) vs. 59.6% (52/89)] and microbiological clearance rate [60.4% (29/48) vs. 39.3% (35/89)], both P < 0.05. Pre-treatment inflammatory and renal parameters showed no significant differences between two groups. Post-treatment, the combination group showed significantly lower WBC and CRP [WBC (×10⁹/L): 8.2±0.5 vs. 10.9±0.6, CRP (mg/L): 14.0 (5.7, 26.6) vs. 52.1 (24.4, 109.6), both P < 0.05], whereas NEU, PCT, SCr, and BUN levels showed no significant between two groups. ICU length of stay was shorter in the combination group [days: 16 (10, 25) vs. 21 (14, 29), P < 0.05], although mechanical ventilation duration and total hospitalization showed no significant differences between two groups. CONCLUSIONS Intravenous colistin sulfate combined with nebulized inhalation improved clinical efficacy and microbiological clearance in CRO pulmonary infections with an acceptable safety profile.
Humans ; Colistin/therapeutic use* ; Retrospective Studies ; Administration, Inhalation ; Anti-Bacterial Agents/therapeutic use* ; Carbapenems/pharmacology* ; Male ; Female ; Middle Aged ; Gram-Negative Bacteria/drug effects* ; Aged ; Treatment Outcome ; Respiratory Tract Infections/drug therapy*

OBJECTIVE: This study aimed to explore the association between body mass index (BMI) and mortality based on the 10-year population-based multicenter prospective study. METHODS: A general population-based multicenter prospective study was conducted at four sites in rural China between 2013 and 2023. Multivariate Cox proportional hazards models and restricted cubic spline analyses were used to assess the association between BMI and mortality. Stratified analyses were performed based on the individual characteristics of the participants. RESULTS: Overall, 19,107 participants with a sum of 163,095 person-years were included and 1,910 participants died. The underweight (< 18.5 kg/m ²) presented an increase in all-cause mortality (adjusted hazards ratio [ aHR] = 2.00, 95% confidence interval [ CI]: 1.66-2.41), while overweight (≥ 24.0 to < 28.0 kg/m ²) and obesity (≥ 28.0 kg/m ²) presented a decrease with an aHR of 0.61 (95% CI: 0.52-0.73) and 0.51 (95% CI: 0.37-0.70), respectively. Overweight ( aHR = 0.76, 95% CI: 0.67-0.86) and mild obesity ( aHR = 0.72, 95% CI: 0.59-0.87) had a positive impact on mortality in people older than 60 years. All-cause mortality decreased rapidly until reaching a BMI of 25.7 kg/m ² ( aHR = 0.95, 95% CI: 0.92-0.98) and increased slightly above that value, indicating a U-shaped association. The beneficial impact of being overweight on mortality was robust in most subgroups and sensitivity analyses. CONCLUSION This study provides additional evidence that overweight and mild obesity may be inversely related to the risk of death in individuals older than 60 years. Therefore, it is essential to consider age differences when formulating health and weight management strategies.
Humans ; Body Mass Index ; China/epidemiology* ; Male ; Female ; Middle Aged ; Prospective Studies ; Rural Population/statistics & numerical data* ; Aged ; Follow-Up Studies ; Adult ; Mortality ; Cause of Death ; Obesity/mortality* ; Overweight/mortality*

CAPOS syndrome is an autosomal dominant neurological disorder caused by mutations in the ATP1A3 gene. Initial symptoms, often fever-induced, include recurrent acute ataxic encephalopathy in childhood, featuring cerebellar ataxia, optic atrophy, areflflexia, sensorineural hearing loss, and in some cases, pes cavus. This report details a case of CAPOS syndrome resulting from a maternal ATP1A3 gene mutation. Both the child and her mother exhibited symptoms post-febrile induction,including severe sensorineural hearing loss in both ears, ataxia, areflexia, and decreased vision. Additionally, the patient's mother presented with pes cavus. Genetic testing revealed a c. 2452G>A（Glu818Lys） heterozygous mutation in theATP1A3 gene in the patient . This article aims to enhance clinicians' understanding of CAPOS syndrome, emphasizing the case's clinical characteristics, diagnostic process, treatment, and its correlation with genotypeic findings.
Humans ; Child ; Female ; Cerebellar Ataxia/diagnosis* ; Talipes Cavus ; Hearing Loss, Sensorineural/diagnosis* ; Optic Atrophy/diagnosis* ; Mutation ; Phenotype ; Sodium-Potassium-Exchanging ATPase/genetics* ; Foot Deformities, Congenital ; Reflex, Abnormal

Objective To evaluate the correlation and consistency between an artificial intelligence(AI)bone mineral density(BMD)measurement system based on 3D-Densenet neural network technology and quantitative computed tomography(QCT)in measuring BMD,as well as to assess its effectiveness in diagnosing osteoporosis(OP).Methods A total of 1 201 participants who underwent low dose computed tomography(LDCT)were retrospectively included.The AI BMD measurement system and QCT were utilized to measure the BMD of T12,L1,L2 vertebrae,and the average BMD.Consistency and correlation of BMD measurements between the two methods were assessed using Bland-Altman,Pearson,and Kappa analyses.With QCT results as the reference standard,the receiver operating characteristic(ROC)curve was drawn to evaluate the accuracy of AI BMD measurement system in diagnosing OP.Results The r and r2 for the average BMD measured by the two methods were 0.997 and 0.993,respectively.The Kappa value for the diagnosis of normal BMD,low bone mass,and OP using the AI BMD measurement system was 0.905.The area under the curve(AUC)for diagnosing OP using the AI BMD measurement system was 0.998,with a sensitivity of 0.888 and specificity of 0.997.Conclusion The AI BMD measurement system based on 3D-Densenet neural network technology has a high correlation and consistency with the QCT measurement result,which can accurately diagnose normal BMD,low bone mass,and OP.

Objective:To establish a predictive model for post-induction hypotension (PIH) in the patients undergoing colorectal cancer resection using machine learning algorithms based on muscle CT parameters.Methods:This was a single-center, retrospective study. Electronic medical records from 318 patients who underwent colorectal cancer resection from September 1, 2018 to September 30, 2021 at our hospital were collected. Predictive variables included age, gender, body mass index, hemoglobin, American Society of Anesthesiologists Physical Status classification, TNM staging, age-adjusted Charlson comorbidity index, prognostic nutritional index, L 3 level skeletal muscle index, and muscle quality assessed by Hounsfield unit average calculation. The outcome variable was PIH. The training and testing sets were divided based on the timeline (patients before September 1, 2020 were included in the training set, and those after that date were included in the testing set). The filtering method was used to screen the feature variables. Eight models, including logistic regression, Bayesian models, K-nearest neighbors, support vector machines, neural networks, decision trees, extreme gradient boosting trees, and random forests, were established in the training set using over-sampling technique, repeated cross-validation and hyperparameter optimization. After selecting the best model, a sorting chart of the feature variables, a univariate partial dependency profile, and a breakdown profile were drawn. In the testing set, the confusion matrix and parameters were calculated, and the receiver operating characteristic curve, precision recall curve, calibration curve, and decision curve were drawn to evaluate the performance of the predictive model. Results:The screened feature variables were Hounsfield unit average calculation value, age, L 3 level skeletal muscle index, prognostic nutritional index, hemoglobin and body mass index. The random forest was the optimal model, with an accuracy of 0.985 9, a MCC of 0.970 8, the area under the receiver operating characteristic curve was 1.0, and the area under the precision recall curve was 1.0. The Brier score of the calibration curve was 0.076 6; the decision curve showed the highest clinical net benefit of 0.6. Conclusions:In this study, machine learning algorithm is used to identify the important characteristic variables and establish a high-performance PIH prediction model based on muscle CT parameters.

To develop a rapid differential detection method for porcine epidemic diarrhea virus(PEDV)and transmissible gastroenteritis virus(PEDV),M gene sequences of PEDV and TGEV were compared,the inner and outer primer pairs and probes were designed according to the highly conserved region.PEDV-Probe was labeled with FAM at5'end and BHQ1 at 3'end.TGEV-Probe was labeled with CY5.5 at the 5'end and BHQ2 at the 3'end.After optimizing the reaction condi-tions and system,a dual fluorescent RT-LAMP assay for PEDV and TGEV rapid identification was established.The amplification could be completed within 60 min in a 63 ℃ water bath and then stopped at 85 ℃ for 10 min.Then the tubes were placed in a multicolor imaging system,and the re-sults could be observed under 520 nm and 690 nm dual channels.There was no cross-reaction when other common swine viral pathogens were detected by this method.The sensitivity of the assay was evaluated with a 10-fold diluted recombinant plasmid as templates.The lowest detection limit was 102 copies/μL recombinant plasmid,which was 10 times more sensitive than the conventional RT-PCR method.Seventy-two PEDV-positive samples,49 TGEV-positive samples,and 40 PEDV and TGEV co-infected samples were detected from 175 anal swab samples of diarrheic piglets by the established method,which were all higher than the detection rates of the conventional RT-PCR method.The dual fluorescent RT-LAMP method established in this study can be used to amplify the target gene in an ordinary water bath without gel electrophoresis,which provides technical sup-port for rapid and convenient differential diagnosis of PED and TGE and simultaneous detection of PEDV and TGEV co-infection.

Protein phosphorylation plays a key role in Mycobacterium tuberculosis, the pathogen of tuberculosis, holding promise as a new target of anti-tuberculosis drugs. We used M. smegmatis, a close relative of M. tuberculosis, as a model organism to study the protein phosphorylation at different growth phases. We identified 573 phosphorylated peptides and 816 phosphorylated sites of 385 proteins in the M. smegmatis samples at both logarithmic and stationary phases, and then established a comprehensive dataset of phosphorylated proteins in M. smegmatis. By comparing the expression levels of phosphorylated proteins between the logarithmic and the stationary phase with the selected ion monitoring (SIM) strategy, we verified 68 upregulated proteins involved in cell division and protein translation, and 69 downregulated proteins mainly involved in the tricarboxylic acid cycle pathway. The differentially expressed phosphorylated proteins were significantly enriched in important cellular cycle events such as cell elongation and division. The findings of this study provide proteome evidence for elucidating the phosphorylation in both M. smegmatis and M. tuberculosis.
Mycobacterium smegmatis/genetics* ; Bacterial Proteins/genetics* ; Phosphorylation ; Phosphoproteins/metabolism* ; Mycobacterium tuberculosis/growth & development* ; Proteome/metabolism* ; Proteomics

Objective:To investigate the high-risk factors affecting the prognosis of patients with pT 1-2N 1M 0 after mastectomy, establish a nomogram prediction model, perform risk stratification, and screen the radiotherapy benefit populations. Methods:Clinical data of 936 patients with pT 1-2N 1M 0 breast cancer undergoing mastectomy in the Fourth Hospital of Hebei Medical University from January 2010 to December 2016 were retrospectively analyzed and 908 cases had complete follow-up data. They were divided into the radiotherapy (RT) group ( n=583) and non radiotherapy (NRT) group ( n=325) according to the radiotherapy. After propensity score matching (PSM) was performed 1 vs. 1, 298 cases were assigned into the RT group and 298 in the NRT group. Overall survival (OS) and disease-free survival (DFS) were compared between two groups using log-rank test. Nomogram prediction model was established, the survival differences were compared among different risk groups, and the radiotherapy benefit populations were screened. Results:Univariate analysis showed that the 5- and 8-year OS and DFS in the RT group were significantly better than those in the NRT group (both P<0.001). Multivariate analysis showed that age, tumor quadrant, number of lymph node metastases, T staging, and Ki-67 level were the independent prognostic factors for OS. Age, tumor quadrant, and T staging were the independent prognostic factors for DFS. The OS nomogram analysis showed that the OS of patients in the high-risk group was significantly improved by post-mastectomy radiotherapy (PMRT) ( P=0.001), while PMRT did not show an advantage in the low- and medium-risk groups ( P=0.057, P=0.099). The DFS nomogram analysis showed that DFS was significantly improved by PMRT in patients in the medium- and high-risk groups ( P=0.036, P=0.001), whereas the benefits from PMRT were not significant in the low-risk group ( P=0.475). Conclusions:For patients with pT 1-2N 1M 0 breast cancer after mastectomy, age ≤ 40 years, tumor located in the inner quadrant or central area, T 2 staging, 2-3 lymph node metastases, Ki-67>30% are the high-risk factors affecting clinical prognosis. The nomogram prediction model can screen the populations that can benefit from PMRT, providing reference for clinical decision-making.

Objective: To investigate adolescent haze weather health protection behavior, and to provide scientific basis for behavioral intervention and health guidance for adolescents in haze weather. Methods: From June 2015 to April 2016, 1 025 adolescents were selected from 22 classes in two middle schools of Baoding City, Hebei Province, by stratified cluster sampling method. General information questionnaire and the Brief Haze Weather Health Protection Behavior Assessment Scale Adolescent Version (BHWHPBAS AV) were used. Multiple linear regressions were conducted to explore factors affecting adolescent haze weather health protection behavior. Different models were used to confirm associations between influencing factors and BHWHPBAS AV scores. Results: Adolescents had a low overall score of BHWHPBASAV (45.81±13.16). The score rate of self adjustment after haze weather was the highest (64.54%). The score rate of obtaining relevant knowledge before haze weather was the lowest (50.28%). Compared with adolescents in urban area, rural adolescents had a lower BHWHPBAS AV score ( β=-3.20, P <0.01). Compared with students (living with parents), those living without parents had a lower BHWHPBAS AV score ( β=-4.16, P =0.01). Compared with students never receive physical examination,those had received physical examination during the past years had a higher BHWHPBAS AV score ( β=4.44，5.66，9.04, P <0.01). Compared with students with no knowledge of respiratory system diseases, those with moderate to sufficient knowledge had a higher BHWHPBAS AV score ( β=9.34，12.19，P <0.01). These associations were stable and consistent.Multiple linear regression analysis showed that residence, residence with parents, physical examination and knowledge of respiratory diseases were the relevant factors of BHWHPBAS AV score ( P <0.05). Conclusion Adolescent haze weather health protection behavior level is low and is affected by many factors. Cooperation should be strengthened to conduct behavioral interventions and health guidance on haze health protection for adolescents, so as to promote healthy growth of adolescents.