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*

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 screen the primary processing methods of Ligusticum sinense slice based on differential metabolites， and provide theoretical basis for the scientific processing of L. sinense. METHODS Using 13 groups of L. sinense slice processed by fresh-cutting or traditional methods as samples， UHPLC-QE-MS was employed for metabolite identification. Multivariate statistical analysis was applied to screen differential metabolites among the 13 sample groups， analyzing the effects of washing， soaking， drying methods， and drying cycles on both the relative expressions of differential metabolites and the contents of carboxylic acids and their derivatives in the samples （to reflect the total amino acid content）. RESULTS Principal component analysis and partial least squares-discriminant analysis both showed significant intergroup differences among the 13 sample groups. A total of 688 differential metabolites were screened from the 13 sample groups， with carboxylic acids and their derivatives showing the highest proportion. The relative expression levels of phosphatidylcholine significantly increased after washing treatment， while tryptophan expression significantly decreased after soaking treatment. Samples dried at 50-60 ℃ showed significantly increased expression of psoralen， whereas those dried at 40 ℃ showed significantly decreased expression of methyl -p- methoxycinnamate. Both washing and soaking treatments significantly reduced the total amino acid content in samples， while secondary drying significantly increased it. The three controlled-temperature drying methods maintained relatively stable total content of amino acids in samples. CONCLUSIONS The optimal processing protocol for L. sinense slice is as follows: fresh L. sinense slice should be freshly cut at the production site， undergo quick washing after soil removal， and be dried twice at 40 ℃ （before and after slicing）.

The identification and optimization of mutations in nanobodies are crucial for enhancing their therapeutic potential in disease prevention and control. However, this process is often complex and time-consuming, which limit its widespread application in practice. In this study, we developed a workflow, named Evolutionary-Nanobody (EvoNB), to predict key mutation sites of nanobodies by combining protein language models (PLMs) and molecular dynamic (MD) simulations. By fine-tuning the ESM2 model on a large-scale nanobody dataset, the ability of EvoNB to capture specific sequence features of nanobodies was significantly enhanced. The fine-tuned EvoNB model demonstrated higher predictive accuracy in the conserved framework and highly variable complementarity-determining regions of nanobodies. Additionally, we selected four widely representative nanobody-antigen complexes to verify the predicted effects of mutations. MD simulations analyzed the energy changes caused by these mutations to predict their impact on binding affinity to the targets. The results showed that multiple mutations screened by EvoNB significantly enhanced the binding affinity between nanobody and its target, further validating the potential of this workflow for designing and optimizing nanobody mutations. Additionally, sequence-based predictions are generally less dependent on structural absence, allowing them to be more easily integrated with tools for structural predictions, such as AlphaFold 3. Through mutation prediction and systematic analysis of key sites, we can quickly predict the most promising variants for experimental validation without relying on traditional evolutionary or selection processes. The EvoNB workflow provides an effective tool for the rapid optimization of nanobodies and facilitates the application of PLMs in the biomedical field.

The identification and optimization of mutations in nanobodies are crucial for enhancing their thera-peutic potential in disease prevention and control.However,this process is often complex and time-consuming,which limit its widespread application in practice.In this study,we developed a work-flow,named Evolutionary-Nanobody(EvoNB),to predict key mutation sites of nanobodies by combining protein language models(PLMs)and molecular dynamic(MD)simulations.By fine-tuning the ESM2 model on a large-scale nanobody dataset,the ability of EvoNB to capture specific sequence features of nanobodies was significantly enhanced.The fine-tuned EvoNB model demonstrated higher predictive accuracy in the conserved framework and highly variable complementarity-determining regions of nanobodies.Additionally,we selected four widely representative nanobody-antigen complexes to verify the predicted effects of mutations.MD simulations analyzed the energy changes caused by these mu-tations to predict their impact on binding affinity to the targets.The results showed that multiple mu-tations screened by EvoNB significantly enhanced the binding affinity between nanobody and its target,further validating the potential of this workflow for designing and optimizing nanobody mutations.Additionally,sequence-based predictions are generally less dependent on structural absence,allowing them to be more easily integrated with tools for structural predictions,such as AlphaFold 3.Through mutation prediction and systematic analysis of key sites,we can quickly predict the most promising variants for experimental validation without relying on traditional evolutionary or selection processes.The EvoNB workflow provides an effective tool for the rapid optimization of nanobodies and facilitates the application of PLMs in the biomedical field.

Objective To construct a nomogram model for prolonged length of stay in patients with acute cerebral infarction(ACI)based on total cerebral small vessel disease(CSVD)burden scores,and validate its effectiveness.Methods A total of 462 ACI patients admitted to the Department of Neurology of South Taihu Hospital Affiliated To Huzhou College from January 2021 to December 2023 were selected as the study subjects.According to the ratio of 7:3,patients were divided into training group of 323 cases and validation group of 139 cases.Lasso-Logistic regression was used to analyze the risk factors for prolonged length of stay in ACI patients,construct a nomogram model and validate the model using validation data.Receiver operating characteristic(ROC)curve were used to evaluate the predictive performance of the model.Results Based on the training group data,Lasso regression screened four non-zero coefficient indicators,including baseline National Institutes of Health stroke scale(NIHSS)score,age-adjusted Charlson comorbidity index(aCCI)score,neutrophil to lymphocyte ratio(NLR)and total CSVD burden score.Multivariate Logistic regression analysis showed that baseline NIHSS score,aCCI score,NLR and total CSVD burden score were independent risk factors for prolonged length of stay in ACI patients(P＜0.05).Based on the above four indicators,a nomogram model was constructed.The results showed that the ROC curve area of the model predicted prolonged length of stay between training group and validation group were 0.812(95％CI:0.756-0.868)and 0.820(95％CI:0.730-0.909).Conclusion The nomogram model for prolonged length of stay in ACI patients based on total CSVD burden score has good predictive performance and can be used as a screening tool for evaluating the prolonged length of stay in ACI patients.

Objective To construct a nomogram model for prolonged length of stay in patients with acute cerebral infarction(ACI)based on total cerebral small vessel disease(CSVD)burden scores,and validate its effectiveness.Methods A total of 462 ACI patients admitted to the Department of Neurology of South Taihu Hospital Affiliated To Huzhou College from January 2021 to December 2023 were selected as the study subjects.According to the ratio of 7:3,patients were divided into training group of 323 cases and validation group of 139 cases.Lasso-Logistic regression was used to analyze the risk factors for prolonged length of stay in ACI patients,construct a nomogram model and validate the model using validation data.Receiver operating characteristic(ROC)curve were used to evaluate the predictive performance of the model.Results Based on the training group data,Lasso regression screened four non-zero coefficient indicators,including baseline National Institutes of Health stroke scale(NIHSS)score,age-adjusted Charlson comorbidity index(aCCI)score,neutrophil to lymphocyte ratio(NLR)and total CSVD burden score.Multivariate Logistic regression analysis showed that baseline NIHSS score,aCCI score,NLR and total CSVD burden score were independent risk factors for prolonged length of stay in ACI patients(P＜0.05).Based on the above four indicators,a nomogram model was constructed.The results showed that the ROC curve area of the model predicted prolonged length of stay between training group and validation group were 0.812(95％CI:0.756-0.868)and 0.820(95％CI:0.730-0.909).Conclusion The nomogram model for prolonged length of stay in ACI patients based on total CSVD burden score has good predictive performance and can be used as a screening tool for evaluating the prolonged length of stay in ACI patients.

Schwann cells (SCs), a type of glial cell in the peripheral nervous system, can serve as a source of mesenchymal stem cells (MSCs) to repair injured pulp. This study aimed to investigate the role of SCs in tooth germ development and repair of pulp injury. We performed RNA-seq and immunofluorescent staining on tooth germs at different developmental stages. The effect of L-type calcium channel (LTCC) blocker nimodipine on SCs odontogenic differentiation was analyzed by real-time polymerase chain reaction and Alizarin Red S staining. We used the PLP1-CreERT2/ Rosa26-GFP tracing mice model to examine the role of SCs and Cav 1.2 in self-repair after pulp injury. SC-specific markers expressed in rat tooth germs at different developmental stages. Nimodipine treatment enhanced mRNA levels of osteogenic markers (DSPP, DMP1, and Runx2) but decreased calcium nodule formation. SCs-derived cells increased following pulp injury and Ca v 1.2 showed a similar response pattern as SCs. The different SCs phenotypes are coordinated in the whole process to ensure tooth development. Blocking the LTCC with nimodipine promoted SCs odontogenic differentiation. Moreover, SCs participate in the process of injured dental pulp repair as a source of MSCs, and Cav 1.2 may regulate this process.

Schwann cells (SCs), a type of glial cell in the peripheral nervous system, can serve as a source of mesenchymal stem cells (MSCs) to repair injured pulp. This study aimed to investigate the role of SCs in tooth germ development and repair of pulp injury. We performed RNA-seq and immunofluorescent staining on tooth germs at different developmental stages. The effect of L-type calcium channel (LTCC) blocker nimodipine on SCs odontogenic differentiation was analyzed by real-time polymerase chain reaction and Alizarin Red S staining. We used the PLP1-CreERT2/ Rosa26-GFP tracing mice model to examine the role of SCs and Cav 1.2 in self-repair after pulp injury. SC-specific markers expressed in rat tooth germs at different developmental stages. Nimodipine treatment enhanced mRNA levels of osteogenic markers (DSPP, DMP1, and Runx2) but decreased calcium nodule formation. SCs-derived cells increased following pulp injury and Ca v 1.2 showed a similar response pattern as SCs. The different SCs phenotypes are coordinated in the whole process to ensure tooth development. Blocking the LTCC with nimodipine promoted SCs odontogenic differentiation. Moreover, SCs participate in the process of injured dental pulp repair as a source of MSCs, and Cav 1.2 may regulate this process.