ObjectiveTo investigate the role of runt-related transcription factor 3 （RUNX3） in transforming growth factor-β₁ （TGF-β₁）-induced activation of mouse primary pulmonary fibroblasts （PFs）， and its effects on fibroblast activation protein （FAP） expression， cell proliferation， and collagen synthesis. MethodsPFs were isolated from C57BL/6 mice and cultured. A RUNX3 knockdown model was established using small interfering RNA （siRNA）. Cells were assigned to the control group （Control）， TGF-β₁-treated group （TGF-β₁）， negative control group （TGF-β₁+siRNA-NC）， and RUNX3-silenced group （TGF-β₁+si-RUNX3）. In addition， a RUNX3 overexpression rescue experiment was performed based on TGF-β₁ stimulation. Protein and mRNA levels of RUNX3， FAP， and typeⅠcollagen （COL1A1） were measured by Western blot and reverse transcription quantitative real-time PCR （RT-qPCR）. Cell proliferation was assessed using CCK-8 and EdU assays. Co-expression of COL1A1 and FAP was examined by double immunofluorescence staining. ResultsCompared with the Control group， RUNX3， FAP， and COL1A1 expression levels were upregulated in PFs in the TGF-β₁ group （P<0.01）. The CCK-8 assay showed that the absorbance value was reduced in the RUNX3 knockdown group compared with the negative control group （P<0.01）. Consistently， the EdU assay demonstrated a lower proportion of EdU-positive cells in the RUNX3 knockdown group than in the negative control group （P<0.01）. Immunofluorescence double staining revealed decreased fluorescence intensities of COL1A1 and FAP in the RUNX3 knockdown group relative to the negative control. Under RUNX3 overexpression conditions， these fluorescence signals exhibited a partial rebound （P<0.01）. ConclusionRUNX3 in TGF-β1-induced PFs may promote cell proliferation and collagen synthesis by positively regulating FAP expression. Targeting the RUNX3/FAP axis may represent a potential therapeutic strategy for pulmonary fibrosis.

ObjectiveTo investigate the effects of targeted silencing of Runt-related Transcription Factor 3 （RUNX3） on the proliferation and migration of Mouse Hepatic Stellate Cells （HSCs）， as well as subsequent collagen deposition. MethodsMouse hepatic stellate cell line （JS-1） was selected and then morphologically observed and identified under a microscope. After the cells had fully adhered， they were treated with 5 ng/mL of transforming growth factor beta 1 （TGF-β₁） for 24 hours to induce hepatic stellate cell activation. Furthermore， a RUNX3 silencing model was established using RUNX3 lentiviral infection. The experiment was divided into four groups： Control group， TGF-β₁ group， TGF-β₁+siRNA-NC group， and TGF-β₁+siRNA-RUNX3 group. Protein expression changes of RUNX3， alpha-smooth muscle actin （α-SMA）， and Alpha 1 type I collagen （Collagen I） were detected using Western blot method. Cellular immunofluorescence assays were employed to investigate the deposition changes of α-SMA and RUNX3 in hepatic stellate cells. RT-qPCR was utilized to examine the mRNA expression changes of RUNX3， α-SMA， and Collagen I. The proliferative capacity of hepatic stellate cells was assessed using Edu staining. The migratory ability of hepatic stellate cells was evaluated through wound healing assays and Transwell migration experiments. ResultsCompared with Control group， a significant elevation in RUNX3 was observed in the TGF-β₁-induced activated HSCs （P<0.01）. Meanwhile， the protein and mRNA levels of fibrosis-related markers and α-SMA and Collagen I were significantly upregulated （P<0.001）. Additionally， the proliferation and migration capabilities of HSCs were significantly enhanced （P<0.001）. In contrast， when compared to TGF-β₁+siRNA-NC group， TGF-β₁+siRNA-RUNX3 group exhibited a notable decrease in RUNX3 and other related indicators， such as the protein and mRNA levels of α-SMA and Collagen I （P<0.05）. Concurrently， the proliferation and migration capabilities of HSCs were significantly inhibited in TGF-β₁+siRNA-RUNX3 group （P<0.01）. ConclusionSilencing RUNX3 can inhibit the deposition of collagen and the proliferation and migration of hepatic stellate cells. Conversely， RUNX3 promotes the proliferation and migration capabilities of HSCs， thereby facilitating the activation of HSC.

With the rapid development of artificial intelligence and machine learning algorithms in the med-ical field,as well as the high-quality development requirements put forward by the state for hospitals and vari-ous departments,the development of laboratory medicine shows a new trend.The development of clinical labo-ratory has evolved from the initial test suite to the test pathway based on clinical pathway and diagnosis-relat-ed groups payment,and then to clinical laboratory omics.Clinical laboratory omics refers to the use of high-throughput methods to obtain a large number of laboratory project results data.Combined with the clinical characteristics of patients,statistical methods and machine learning algorithms are commonly used to reveal the information behind a large number of medical data to assist clinicians in diagnosis and treatment.Clinical laboratory omics may be a new trend in the future development of laboratory medicine,and it is likely to play an important role in the field of laboratory medicine.

Background: Despite the fact that an increasing number of studies have focused on developing therapies for acute lung injury, managing acute respiratory distress syndrome (ARDS) remains a challenge in intensive care medicine.Whether the pathology of animal models with acute lung injury in prior studies differed from clinical symptoms of ARDS, resulting in questionable management for human ARDS. To evaluate precisely the therapeutic effect of trans‑ planted stem cells or medications on acute lung injury, we developed an animal model of severe ARDS with lower lung function, capable of keeping the experimental animals survive with consistent reproducibility. Establishing this animal model could help develop the treatment of ARDS with higher efficiency. Results: In this approach, we intratracheally delivered bleomycin (BLM, 5 mg/rat) into rats’ left trachea via a needle connected with polyethylene tube, and simultaneously rotated the rats to the left side by 60 degrees. Within sevendays after the injury, we found that arterial blood oxygen saturation ­(SpO2 ) significantly decreased to 83.7%, partial pressure of arterial oxygen ­(PaO2 ) markedly reduced to 65.3 mmHg, partial pressure of arterial carbon dioxide ­(PaCO2 )amplified to 49.2 mmHg, and the respiratory rate increased over time. Morphologically, the surface of the left lung appeared uneven on Day 1, the alveoli of the left lung disappeared on Day 2, and the left lung shrank on Day 7. A his‑ tological examination revealed that considerable cell infiltration began on Day 1 and lasted until Day 7, with a larger area of cell infiltration. Serum levels of IL-5, IL-6, IFN-γ, MCP-1, MIP-2, G-CSF, and TNF-α substantially rose on Day 7. Conclusions This modified approach for BLM-induced lung injury provided a severe, stable, and one-sided (left-lobe) ARDS animal model with consistent reproducibility. The physiological symptoms observed in this severe ARDS animal model are entirely consistent with the characteristics of clinical ARDS. The establishment of this ARDS animal model could help develop treatment for ARDS.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Galectin-9 (Gal-9), a member of the β-galactoside-binding lectin family, is widely expressed in various tissues and cells. It can specifically bind to multiple glycoprotein receptors, including the receptors of Tim-3, CD44, 4-1BB/CD137, and Dectin-1, thereby regulating the activity of immune cells and participating in crucial physiological and pathological processes such as immune regulation and tumor development. Given its role in immunomodulation, Gal-9 is considered a potential target for immunotherapy, showing promising prospects in the treatment of various diseases, including autoimmune disorders, transplantation rejection, pregnancy complications, inflammation, infection, and cancer. This review summarizes the biological effects mediated by Gal-9 upon binding to its receptors, which may help to explore the potential application value of Gal-9 in clinical diagnosis and therapy.

Objective To understand the current status of work-related musculoskeletal disorders (WMSDs) among operating room doctors,thereby analyzing its effect on mental health. Methods A total of 1013 doctors in the operating rooms of 30 hospitals in 24 provinces from September to December 2022 were selected by cross-sectional survey method. The domestic musculoskeletal disease questionnaire was used to investigate the occurrence of WMSDs in operating room doctors,the symptom check list-90 (SCL-90) was used to evaluate the psychological status of operating room doctors,the relationship between WMSDs related items and SCL-90 scores was analyzed,the correla-tion between the rest time of each shift and SCL-90 factors was analyzed by Pearson method,and the influencing factors of mental health of operating room doctors were analyzed by multivariate Logistic regression.Results A total of 1100 questionnaires were distributed and 1013 valid questionnaires were recovered,with an effective rate of 92.10％. The incidence of WMSDs among operating room doctors in the past 1 year was 95.06％. The body parts with incidences of WMSDs from high to low were as follows:waist (36.92％),neck (35.54％),shoulder (33.56％),and upper back (32.77％). In the past 1 year,the SCL-90 scores and total scores of patients with musculoskeletal pain in various body parts,keeping the same back posture for a long time while work,bending the back at the same time of turning frequently while working,bowing the head for a long time while working,and bending the knee for a long time while working were significantly higher than those who were without the above conditions (P<0.05). Shorter rest time of each shift and longer periods of keeping the same back posture were the risk factors for the development of mental illness in operating room doctors. Conclusion The incidence of WMSDs in operating room doctors is high,which can influence the mental health of operating room doctors. Therefore,operating room doctors should avoid keeping the same back posture for a long time,bending the back at the same time of turning frequently,bowing the head for a long time,and bending the knee for a long time while working,and increase the rest time of each shift,in order to improve the mental condition of the operating room doctors.

Objective: To investigate the effect of targeted silencing of DNA methyltransferase 3A(DNMT3A) on collagen deposition, proliferation and migration activity of mouse lung fibroblasts(PFs). Methods: In order to ensure the proliferation and migration activity of primary fibroblasts, the lung tissues of neonatal C57 suckling mice were taken, PFs were extracted after being sheared, and the morphology was observed and identified under the microscope. PFs cells were activated by 5 ng/ml TGF-β1for 24 h after cell attachment, and DNMT3A silencing model was constructed by small interfering RNA; The experiment was divided into control group, TGF-β1group, TGF-β1+ siRNA-NC group and TGF-β1+ siRNA-DNMT3A group. The protein expressions of DNMT3A, α-smooth muscle actin(α-SMA) and Collagen Ⅰ were detected by Western blot; Real time quantitative reverse transcription polymerase chain reaction(RT-qPCR) was used to detect the mRNA expression changes ofDNMT3A,α-SMAandCollagenⅠ. The proliferation ability of PFs was detected by CCK-8 and EdU staining; the migration ability of PFs was detected by scratch test and Transwell migration test. Results: Compared with the control group, TGF-β1induced the increase of DNMT3A in the activated PFs cell group(P<0.01), the protein and mRNA levels of fibrosis and proliferation related indicators α-SMA and Collagen Ⅰ also increased(allP<0.05), and the proliferation and migration ability of PFs increased(allP<0.000 1). Compared with the siRNA-NC group, the protein expression levels of DNMT3A(P<0.000 1) and related indicators α-SMA(P<0.01) and Collagen Ⅰ(P<0.01) significantly decreased in the DNMT3A silencing group by Western blot, and the mRNA levels ofDNMT3A,α-SMAandCollagenⅠby RT-qPCR also decreased(allP<0.001), and the proliferation(P<0.01) and migration ability(P<0.05) of PFs cells decreased compared with the control group. Conclusion Silencing DNMT3A can inhibit the deposition of collagen and the proliferation of PFs. DNMT3A can promote the proliferation and migration of PFs, and then promote the activation of PFs and the development of pulmonary fibrosis. This process may be regulated by DNA methylation modification.

Objective:To analyze the prognostic factors of patients with liver cirrhosis and portal vein thrombosis (PVT), and to construct a prognostic prediction model based on machine learning methods.Methods:The clinical data of 388 patients with liver cirrhosis and PVT admitted to the Fifth Medical Center of PLA General Hospital from January 2022 to April 2024 were retrospectively collected and analyzed, including 243 males and 145 females, aged (56.9±10.9) years. A total of 388 patients were randomly divided into the training set ( n=310) and the testing set ( n=78) in a 4∶1 ratio. The Boruta algorithm was used to screen the key features in the training set, and then four machine learning algorithms, including random forest, support vector machine, generalized linear model and Bayesian, were used to establish a survival prediction model. Model performance was evaluated by the receiver operating characteristic (ROC) curves of the test set and the training set. The patients were followed up for 1 year for survival. Sort the importance of features based on the SHAP value. Results:There were 250 patients (80.6%) who survived and 60 (19.4%) who died. The model for end-stage liver disease score, total bilirubin, serum creatinine, prothrombin time, international normalized ratio, D-dimer, white blood cell count, severe ascites ratio, and Child-Pugh grade C ratio of liver function in the death group were higher than those in the survival group, and the red blood cell count and hematocrit were lower than those in the survival group, and the differences were statistically significant (all P<0.05). The areas under the ROC curve for predicting survival by random forest, support vector machine, generalized linear model and Bayesian model were 0.92, 0.78, 0.81 and 0.71 in the training set, and the area under the ROC curve in the testing set were 0.81, 0.72, 0.67 and 0.68, respectively. Random forest had the best prediction performance, with an accuracy of 81.7%, a sensitivity of 84.6%, and a specificity of 76.9% in the testing set. In the analysis of the importance of characteristic parameters of the random forest model, total bilirubin, red blood cells, hematocrit, serum creatinine, ascites classification, etc. had a relatively high contribution to the model. Conclusion:In the survival prediction model of patients with liver cirrhosis and PVT based on machine learning algorithm, the random forest model had high prediction performance, and total bilirubin may be the most important factor affecting the survival prognosis of patients.

OBJECTIVES: To explore the risk factors of feeding intolerance (FI) in critically ill children receiving enteral nutrition (EN) and to construct a prediction nomogram model for FI. METHODS: A retrospective study was conducted to collect data from critically ill children admitted to the Pediatric Intensive Care Unit of Xiangya Hospital, Central South University, between January 2015 and October 2020. The children were randomly divided into a training set (346 cases) and a validation set (147 cases). The training set was further divided into a tolerance group (216 cases) and an intolerance group (130 cases). Multivariate logistic regression analysis was used to screen for risk factors for FI in critically ill children receiving EN. A nomogram was constructed using R language, which was then validated on the validation set. The model's discrimination, calibration, and clinical net benefit were evaluated using receiver operating characteristic curves, calibration curves, and decision curves. RESULTS: Duration of bed rest, shock, gastrointestinal decompression, use of non-steroidal anti-inflammatory drugs, and combined parenteral nutrition were identified as independent risk factors for FI in critically ill children receiving EN (P<0.05). Based on these factors, a nomogram prediction model for FI in critically ill children receiving EN was developed. The area under the receiver operating characteristic curve for the training set and validation set was 0.934 (95%CI: 0.906-0.963) and 0.852 (95%CI: 0.787-0.917), respectively, indicating good discrimination of the model. The Hosmer-Lemeshow goodness-of-fit test showed that the model had a good fit (χ 2=12.559, P=0.128). Calibration curve and decision curve analyses suggested that the model has high predictive efficacy and clinical application value. CONCLUSIONS Duration of bed rest, shock, gastrointestinal decompression, use of non-steroidal anti-inflammatory drugs, and combined parenteral nutrition are independent risk factors for FI in critically ill children receiving EN. The nomogram model developed based on these factors exhibits high predictive efficacy and clinical application value.
Humans ; Critical Illness ; Enteral Nutrition/adverse effects* ; Male ; Risk Factors ; Female ; Child, Preschool ; Infant ; Nomograms ; Retrospective Studies ; Child ; Logistic Models