Objective: To investigate factors influencing perioperative blood transfusion in patients with scarred uterus during pregnancy undergoing cesarean section, construct and validate a transfusion risk prediction model, and provide evidence for preoperative assessment and blood management. Methods: Clinical data of 405 patients undergoing cesarean section for scarred uterus during pregnancy at the First Affiliated Hospital of Xi'an Jiaotong University from January 2020 to December 2024 were retrospectively collected. The dataset was randomly divided into a training set (n=284) and a validation set (n=121) at a 7∶3 ratio. Within the training set, Firth-penalized logistic regression was employed for multivariate analysis to identify independent factors influencing perioperative blood transfusion and construct a predictive model. Model performance was evaluated in the validation set. Results: Multivariate Firth regression analysis showed that severe placenta previa (OR=75.566, 95%CI: 8.603-9979.174) and placenta accreta (OR=4.591, 95%CI: 1.120-19.416) were independent risk factors for perioperative blood transfusion, while preoperative red blood cell count (OR=0.189, 95%CI: 0.083-0.405) and fibrinogen levels (OR=0.588, 95%CI: 0.395-0.855) were protective factors. The predictive model constructed based on these four variables demonstrated good discriminatory performance, with areas under the receiver operating characteristic curves of 0.803 (95%CI: 0.740-0.867) and 0.753 (95%CI: 0.644-0.862) in the training and validation sets, respectively. Conclusion: For patients with scarred uterus during pregnancy undergoing cesarean section, severe placenta previa and placenta accreta significantly increase the risk of transfusion, while higher preoperative red blood cell count and fibrinogen levels exert a protective effect. The predictive model established in this study facilitates the identification of patients requiring transfusion, thereby enabling preoperative blood preparation and optimized blood management.

Objective To explore the short-term efficacy of endoscopic submucosal dissection (ESD) in the treatment of early carcinoma in the remnant stomach. Methods A retrospective study was conducted on 45 patients with early residual gastric cancer underwent ESD at the Endoscopy Center of Zhongshan Hospital, Fudan University from December 2014 to April 2024, with a total of 45 lesions. The patients were divided into an anastomotic group (n=15) and a non-anastomotic group (n=30) based on the location of tumor occurrence, and their clinical data, endoscopic diagnosis and treatment, and histopathological conditions were compared between the two groups. Results All 45 patients had lesions with redness and erosion. There were 9 cases of poor lifting of submucosal injection in the anastomotic group and 2 cases in the non-anastomotic group, respectively, and the difference was statistically significant (P<0.05). ESD surgery was performed on 13 lesions in the anastomotic group and 28 lesions in the non-anastomotic group, with surgery times of 80.00 (50.00, 100.00) min and 55.00 (43.75, 80.00) min, respectively. The difference in surgery time between the two groups was statistically significant (P=0.03). Among the 45 patients, ESD surgery achieved curative resection in 35 cases, including 11 cases in the anastomotic group and 24 cases in the non-anastomotic group, with no statistically significant difference. Conclusions Careful preoperative evaluation of early carcinoma in the remnant stomach is essential to prevent oversight. Lesions at anastomotic sites and suture lines present higher technical challenges for complete resection. ESD is safe and effective, with auxiliary traction technique available when necessary.

OBJECTIVE: To observe the effect of electroacupuncture (EA) on the intestinal flora in rats with chronic obstructive pulmonary disease (COPD) and explore its possible mechanism based on the gut-lung axis theory. METHODS: A total of 30 male SD rats of SPF grade were randomly divided into a normal control (NC) group, a model group and an EA group, 10 rats in each one. In the model group and the EA group, COPD model was established by intratracheal instillation of lipopolysaccharide combined with cigarette fumigation. In the EA group, EA was applied at bilateral "Feishu" (BL13) and "Zusanli" (ST36), with disperse-dense waves, in frequency of 4 Hz/20 Hz, current of 1-3 mA, 20 min a time, once a day for 14 days continuously. Before and after modeling, as well as after intervention, body weight was observed; after intervention, the lung function indexes (forced expiratory volume in 0.1 second [FEV_0.1], FEV_0.1/forced vital capacity [FVC]%, forced expiratory volume in 0.3 second [FEV_0.3] and FEV_0.3/FVC%) were measured, serum levels of inflammatory factors (tumor necrosis factor-α[TNF-α], interleukin-6[IL-6], interleukin-1β[IL-1β] and interleukin-10[IL-10]) were detected by ELISA, histopathology of lung and colon tissues was observed by HE staining, the intestinal flora were analyzed by 16S rRNA, and the correlations between lung function and intestinal flora were analyzed. RESULTS: Compared with the NC group, in the COPD group, the body weight and lung function indexes were reduced (P<0.01); the lung and colon tissues were damaged, the mean linear intercept (MLI) of alveolus and inflammatory cell numbers of 100 μm² in lung tissue were increased (P<0.01); the serum levels of TNF-α, IL-6 and IL-1β were increased (P<0.01, P<0.05), and the serum level of IL-10 was decreased (P<0.01); α-diversity indexes of intestinal flora were increased (P<0.01); the relative abundance of Bacteroidetes, Proteobacteria and Oscillospira, Bacteroides, Coprococcus was increased (P<0.01), the relative abundance of Firmicutes, Actinobacteria, Tenericutes, TM7 and Lactobacillus, Allobaculum, Bifidobacterium, YRC22 was decreased (P<0.01, P<0.05); 31 different expressed metabolic pathways were identified between the two groups. Compared with the COPD group, in the EA group, the body weight and lung function indexes were increased (P<0.01); the damage of lung and colon tissues was improved, the MLI of alveolus was decreased (P<0.05); the serum levels of TNF-α, IL-6 and IL-1β were decreased (P<0.05), and the serum level of IL-10 was increased (P<0.05); α-diversity indexes of intestinal flora were decreased (P<0.01); the relative abundance of Bacteroidetes, Proteobacteria and Oscillospira, Bacteroides, Coprococcus was decreased (P<0.01, P<0.05), the relative abundance of Firmicutes, Actinobacteria, Tenericutes, TM7 and Lactobacillus, Allobaculum, Bifidobacterium, YRC22 was increased (P<0.01); 35 different expressed metabolic pathways were identified between the two groups. The lung function was positive related with Actinobacteria, Tenericutes, TM7 and YRC22, and was negative related with Bacteroidetes, Proteobacteria and Oscillospira, Bacteroides, Coprococcus. CONCLUSION EA may ameliorate lung function and tissue injury of COPD by regulating intestinal flora dysbiosis and inflammatory response, suggesting an anti-inflammatory effect mediated via "gut-lung" axis.
Animals ; Pulmonary Disease, Chronic Obstructive/genetics* ; Male ; Electroacupuncture ; Rats ; Rats, Sprague-Dawley ; Lung/metabolism* ; Gastrointestinal Microbiome ; Humans ; Interleukin-6/immunology* ; Tumor Necrosis Factor-alpha/immunology* ; Intestines/microbiology* ; Interleukin-10/immunology*

Aim To investigate the effect of linarin on improving cognitive behavior of APP/PS1 mice,and to explore the therapeutic effect of linarin on A β deposi-tion and neuroinflammation and its correlation.Meth-ods APP/PS1 transgenic mice were randomly divid-ed into the model group,high-dose group,medium-dose group,low-dose group and positive control group.C57BL/6J mice were set as the normal group.Morris water maze was used to evaluate the learning and mem-ory abilities of mice.TUNEL staining was used to de-tect the apoptosis of neurons in the CA1 region of mice.IHC was used to detect the expression levels of Aβ42 and GFAP.Western blot was used to detect the expression levels of BACE1 and PS-1.Results Com-pared with the normal group,mice of the model group showed lower NCP,shorter target quadrant travel,less target quadrant residence time percentage(all P＜0.01),higher apoptosis rate of neurons in the CA1 re-gion(P＜0.01),significantly higher protein expres-sion levels of A β42 and GFAP(all P＜0.01),and significantly higher protein expression levels of BACE1 and PS-1(all P＜0.01).Compared with the model group,the medium-dose group,high-dose group and positive control group showed higher NCP,longer tar-get quadrant travel,more target quadrant residence time percentage(all P＜0.05),lower apoptosis rate of neurons in the CA1 region(P＜0.01),significantly lower protein expression levels of A β42 and GFAP(all P＜0.01),and significantly lower protein expression levels of BACE1 and PS-1(all P＜0.01).Conclu-sions Linarin can inhibit two key enzymes to reduce the decomposition of APP and the generation of A β42,thereby inhibiting the activation of astrocytes,allevia-ting neuroinflammation,improving the core pathologi-cal features of AD,and thus significantly improving learning and memory impairment in APP/PS1 mice.

Aim To study the effect of anti-PD-L1 monoclonal antibody on high-fat diet-induced athero-sclerosis in ApoE-/-mice.Methods Twenty-four ApoE-/-mice were randomly divided into the normal group,high-fat group,and high-fat+anti-PD-L1 mAb group.After 70 days,the blood samples were harves-ted.Blood vessels(aortic root to abdominal aorta)and liver from each groups were stained with Oil Red O.Hematoxylin-eosin staining(HE)was employed to vis-ualize structural changes in liver.Enzyme-linked im-munosorbent assay(ELISA)was applied to detect the serum levels of total cholesterol(CHO),triglyceride(TG),high-density lipoprotein(HDL-c),low-density lipoprotein(LDL-c)and inflammatory factors(IFN-γ,TNF-α,IL-1 β).Flow cytometry was used to detect the proportion of lymphocytes(CD4 and CD8).RT-PCR was utilized to assess the expressions of IFN-γ,TNF-α,IL-1 β,CD4 and CD8 in liver.Results Compared with the high-fat group,the treatment with anti-PD-L1 monoclonal antibody promoted vascular wall and liver lipid accumulation,and also up-regulated serum and liver content of cholesterol(CHO),triglyceride(TG)and high-density lipoprotein(HDL-c).Treatment with anti-PD-L1 monoclonal antibody up-regulated the con-tent of alanine aminotransferase(GPT)and aspartate aminotransferase(GOT)in serum and liver,but not al-kaline phosphatase(AKP).ELISA test indicated that treatment with anti-PD-L1 monoclonal antibody stimu-lated the serum level of IFN-γ,TNF-α and IL-1 β.Fur-thermore,the mRNA level of IFN-γ,TNF-α and IL-1 βin liver was also up-regulated after treatment with anti-PD-L1 monoclonal antibody.With flow cytometry,we observed that treatment with anti-PD-L1 monoclonal antibody promoted hepatic CD8+T and CD8+IFN-γ+T cell activation,but had no effect on CD4+IFN-γ+T cell activation under high-fat feeding conditions.Con-clusions Anti-PD-L1 monoclonal antibody adminis-tered under high-fat feeding conditions can damage liv-er function and aggravate atherosclerosis by activating liver CD8+IFN-γ+T cells.

Natural products are an important source for innovative drugs,but unclear molecular targets and mechanisms limit their further development and application.The authors proposed a new method for the target identification of natural products based on proteolysis-targeting chimera(PROTAC)technology and quantitative proteomics,and established the targeted degradomics(TGDO) technology for the identification of weak-affinity tar-gets.This article summarizes the standardized workflow and the application of TGDO for target identification of natural products.

Objective To develop a deep learning method for small sample multi-omics data using attention mechanism and Meta-learning for the establishment of autism diagnosis model.Methods MLAN(Meta-learning based attentive network)consisting of the omics feature pre-reduction module,the multi-omics data fusion and feature learning module,and the parameter optimization module was designed.Firstly,differential expression analysis was performed on high-dimensional multi-omics data to preliminarily screen out unimportant features.Secondly,a multi-channel attention mechanism was used to learn the importances of every set of omics data and to realize data fusion,and a two-layer fully connected network was constructed to further extract latent features and realize the diagnosis task.Finally,the Meta-learning algorithm Reptile was used to optimize the initial parameters of the above model to obtain the optimal parameters.A total of 58 children's saliva samples were collected,including 21 children diagnosed with autism,12 children with social disorders,and 25 healthy controls,and the protein and metabolomics data were detected by mass spectrometry.All data were randomly divided into training set and test set by 4∶1,and the training set was divided into training data and validation data in the same way for model training and validation.The test set was used for the final evaluation of the model effect.Five baseline models and three ablated models were constructed and evaluated along with MLAN based on metrics including multi-classification accuracy,F1-macro and F1-weighted scores.Results The constructed multi-classification autism diagnosis model MLAN achieved multi-classification accuracy,F1-macro and F1-weighted scores of 0.850±0.066,0.817±0.103 and 0.834±0.087.The values of all three indicators were better than those of baseline models and the ablated models.Conclusion The proposed MLAN can effectively deal with heterogeneous multi-omics data with small samples and achieve good results,which is expected to provide assistance for the clinical diagnosis of autism.

Objective To explore the feasibility and corresponding implementation methods of constructing a sample resource bank based on individual-level data of completed clinical trials and using it to construct external controls for drug/device clinical trials.Methods Taking the pre-marketing clinical trial of transcatheter active valve replacement(TAVR)for the treatment of aortic valve stenosis as an example,the individual-level databases of multiple trials were standardized to form a sample bank.The original data of any trial in the sample bank were selected as the experimental group,and the remaining samples were selected as the control group.The potential confounding was handled by using the propensity score matching and stratification methods to clarify the process of constructing external controls based on the sample bank of individual-level data of clinical trials.Results This study included individual-level data of single-group trials of 4 TAVR devices,with a total of 569 subjects(59.2%male).The number of subjects in Trials 1 to 4 was 120,120,163,and 166,respectively.Propensity score matching enabled the matching of 113,117,125,and 147 subjects with comparable or similar characteristics from individual-level data from other trials,respectively,demonstrating a high matching success rate.The PS score distribution plot after stratification showed that the proportions of subjects in the experimental and control groups in strata 1 to 5 in scheme 1 were 4/103,11/103,22/92,32/87,and 51/64,respectively.For all constructed external controlled trials,a certain number of control samples with similar baseline characteristics to the experimental groups were distributed within each propensity score stratum.The results of the simulation test also reflected the potential differences between different devices in the 12-month all-cause mortality rate.Conclusions The sample bank constructed with individual-level data from clinical trials,as a high-quality data source,can serve as a source of external control for single-arm trials in the same field,and as a useful supplement to the external control scenario of real-world evidence to support drug and device development.At the same time,targeted research on research methods and bias control measures in related fields is also needed.

Objective To investigate the effects of goal-directed fluid therapy(GDFT)combined with ultrasound-guided erector spinae plane block(ESPB)on perioperative stress response and postoperative recovery in patients undergoing robot-assisted pedicle screw fixation surgery.Methods Eighty patients scheduled for elec-tive surgery were randomly divided into two groups:control(general anesthesia+conventional fluid management,n=40)and intervention(general anesthesia+GDFT+ESPB,n=40).Heart rate(HR),mean arterial pressure(MAP),serum cortisol(Cor),norepinephrine(NE),blood glucose(GLU)levels,and resting VAS scores were measured and compared between the two groups preoperatively(T?),at screw placement(T?),at end of surgery(T?),30 minutes after extubation(T?),at 24 hours(T?),and 48 hours(T?)postoperatively.Intraoperative fluid volume,remifentanil consumption,postoperative rescue analgesia frequency,time to first ambulation,length of stay,and complications(agitation,nausea and vomiting,respiratory depression)were recorded.Results There were no differences in baseline(T?)indicators between the two groups(P>0.05).The intervention group exhibited significantly lower levels of HR,MAP,Cor,NE,and GLU at T?,T?,and T? compared with the control group(P<0.05),with Cor,NE,and GLU reduced by>30%at T?(P<0.05).Intraoperative fluid volume and remi-fentanil dosage were significantly reduced(both P<0.05).VAS scores and the number of rescue analgesia requests were lower at T?,T?,and T?(P<0.01).Time to first ambulation time and postoperative hospital stay were shortened(both P<0.01).The incidence of agitation,nausea/vomiting and overall complications was lower in the interven-tion group(P<0.05),whereas respiratory depression rates did not differ(P>0.05).Conclusion GDFT plus ESPB can effectively suppress perioperative stress response in robot-assisted spinal surgery,significantly reduce intraoperative opioid dosage and fluid volume,optimize postoperative analgesia,enhance recovery,and lower complication risk.This combination provides an effective ERAS strategy for improving surgical safety and recovery quality.

Objective:To evaluate the value of ultrasound radiomics combined with machine learning for early diagnosis of seronegative Hashimoto’s thyroiditis (SN-HT) .Methods:This retrospective study included 164 patients from Liaoning Provincial People’s Hospital , Lixin County People’s Hospital, Linghai Dalinghe Hospital, Fengcheng Phoenix Hospital, who underwent thyroidectomy for solitary nodules with normal thyroid function between Nov. 2016 and Jan. 2024. Postoperative pathology confirmed Hashimoto’s thyroiditis (HT) in some cases, who were further categorized into antibody-positive and antibody-negative groups based on serum antibody status. Patients without Hashimoto’s thyroiditis served as the control group. A total of 298 ultrasound images were analyzed. Radiomics features were extracted from hypoechoic non-nodular areas within 0.5 cm surrounding the tumor. Two senior pathologists and two senior ultrasound physicians independently assessed lymphocytic infiltration, eosinophilic changes of follicular epithelium, and the proportion of hypoechoic areas in pathology and ultrasound images, respectively. A machine learning model, CCH-NET, was developed using linear regression and t-distributed stochastic neighbor embedding (t-SNE) techniques. The dataset was divided into a training set (80%) and a validation set (20%) to compare the diagnostic accuracy of CCH-NET with that of senior ultrasound physicians. Results:In internal validation, CCH-NET achieved a diagnostic accuracy of 88.89% for both antibody-positive and antibody-negative groups, significantly higher than the 66.67% accuracy of senior ultrasound physicians ( P<0.01). In external validation, CCH-NET achieved 75.00% and 66.67% accuracy for the two groups, compared to 50.00% by senior ultrasound physicians. For the control group, both methods achieved 93.33% accuracy. The AUC of CCH-NET was 0.848, outperforming senior ultrasound physicians (0.681) ,demonstrating superior diagnostic performance. Conclusion:The radiomics-based CCH-NET model, using non-nodular hypoechoic areas as a specific indicator, can accurately identify early SN-HT in euthyroid patients. It significantly outperforms senior ultrasound physicians, improving diagnostic accuracy and reducing missed diagnoses.