ObjectiveTo investigate the mechanism of Yangxin Tongmai Formula （养心通脉方， YTF） in trea-ting coronary heart disease with blood stasis syndrome based on DNA methylation. MethodsSeventy-two SD rats were randomly divided into a control group （n=12） and a modeling group （n=60）. The modeling group was subjected to a high-fat diet， intragastric administration of vitamin D3， and subcutaneous injection of isoprenaline to establish the rat model of coronary heart disease with blood stasis syndrome. Forty-one successfully modeled rats were then randomly allocated into model group， YTF low-， medium-， and high-dose groups， and the atorvastatin calcium group， with 8 rats in each group and 1 rat reserved. The YTF low-， medium-， and high-dose groups received YTF at 6， 12， and 18 g/（kg·d） by gavage， respectively. The atorvastatin calcium group received atorvastatin calcium tablets at 1.8 mg/（kg·d） by gavage. The control group and the model group received 0.9% sodium chloride injection at 4 ml/（kg·d） by gavage. All administrations were performed once daily for 3 weeks. Twenty-four hours after the last administration， serum lipid levels including total cholesterol （TC）， triglycerides （TG）， low-density lipoprotein cholesterol （LDL-C）， and high-density lipoprotein cholesterol （HDL-C）， myocardial enzymes including cardiac troponin T （cTnT）， creatine kinase MB （CK-MB）， and lactate dehydrogenase （LDH）， and inflammatory factors including interleukin-1β （IL-1β） and interleukin-10 （IL-10） were detected by ELISA. Pathological changes in myocardial tissue were observed via HE staining. Whole blood DNA methylation sequencing was used to analyze differential methylation gene expression among the control group， model group， and YTF high-dose group. Western Blotting was used to verify the protein levels of the key genes and downstream signaling pathways. ResultsCompared to the control group， the model group showed increased levels of TC， TG， LDL-C， cTnT， CK-MB， LDH， and IL-1β， along with decreased levels of HDL-C and IL-10 （P＜0.05 or P＜0.01）. Compared to the model group， all treatment groups exhibited decreased levels of TC， LDL-C， CK-MB， and LDH， along with increased IL-10 levels. Among these， the high-dose YTF group demonstrated superior efficacy in reducing cTnT levels compared to the other TCM groups （P＜0.05 or P＜0.01）. HE staining indicated that the YTF high-dose group ameliorated myocardial cell swelling， disordered arrangement， pyknosis， and disappearance of nuclei， thereby reducing myocardial cell damage. Whole blood DNA methylation sequencing identified 240 differentially methylated genes shared by the control group， model group， and YTF high-dose group， including 109 hypermethylated and 131 hypomethylated genes； eif2ak3 was identified as a key differentially methylated gene. Compared to the control group， the model group exhibited increased protein levels of eukaryotic translation initiation factor 2 alpha kinase 3 （eIf2ak3）， phosphorylated protein kinase RNA-like endoplasmic reticulum kinase （p-PERK）， activating transcription factor 4 （ATF4）， C/EBP homologous protein （CHOP）， and Bax， along with a decreased level of B-cell lymphoma-2 （Bcl-2） protein （P＜0.05 or P＜0.01）. Compared to the model group， the YTF high-dose group showed decreased protein levels of eIf2ak3， p-PERK， ATF4， CHOP， and Bax， and an increased level of Bcl-2 protein （P＜0.05 or P＜0.01）. ConclusionYTF may regulate key differentially methylated genes such as eIf2ak3 and the PERK/ATF4/CHOP signaling pathway， thereby inhibiting endoplasmic reticulum stress， reducing myocardial cell apoptosis， and exerting therapeutic effects in coronary heart disease blood stasis syndrome.

Purpose: This study aimed to evaluate the safety and efficacy of gemcitabine and cisplatin (GP) regimen in combination with immune checkpoint inhibitor sintilimab as neoadjuvant therapy for muscle-invasive bladder cancer (MIBC) patients and the feasibility of the following selective bladder sparing surgery. Materials and Methods: Patients with histopathologically confirmed urothelial carcinoma without distant metastases (T2-4a, N ≤ 1, M0, American Joint Committee of Cancer 8th) and with adequate organ function will be enrolled. The therapeutic regimen was sintilimab 200 mg once on day 8, gemcitabine 1,000 mg/m2 and cisplatin 35 mg/m2 once on days 1 and 8, every 21 days for four cycles. The primary endpoint was pathologic complete response (pCR, pT0N0) rate. The secondary end points were ypT < 2 rate, R0 resection rate, event-free survival, and safety. Results: From May 4, 2020, to May 20, 2023, 55 patients were enrolled. Forty-six patients were evaluated for efficacy. Among the 42 patients who underwent surgery, 16 patients (38.0%) achieved pCR. Thirty-three patients (78.6%) achieved pT < 2. With a median follow-up of 15.7 months, the 1-year event-free survival was 91.3%. Notwithstanding the poor pathological baseline characteristic of a high T3-T4a proportion (39.1%), a promising bladder preservation (including 22 patients transurethral resection of bladder tumor, 5 patients partial cystectomy, and 4 surveillances) rate was achieved (67.4%). The most common grade ≥ 3 treatment-related adverse events was neutropenia (n=15, 27.3%), which was related to chemotherapy. There were no grade 3 immune-related adverse events. Conclusion Neoadjuvant GP plus sintilimab is a promising regimen for MIBC patients, with relatively high pT < 2 rate and triggering the emerging roles for the multi-disciplinary team decision-making for bladder sparing surgery.

In view of the few studies on the influence of Armillaria spp. infection on the content of the chemical components in different parts of Polyporus umbellatus sclerotia, this study determined the biomass of P. umbellatus sclerotia and the contents of ergosterol, polyporusterone A, polyporusterone B and polysaccharide in the separated cavity wall of the sclerotia and the uninfected part of the sclerotia in different harvesting years under the conditions of A. gallica and A. mellea infection respectively. According to the difference of content and dynamic changes of the polysaccharide and the steroid substances, the superior Armillaria sp. was screened to obtain the best harvest years of P. umbellatus. Using HPLC and UV-VIS spectrophotometry methods, the contents of ergosterol, polyporusterone A, polyporusterone B and polysaccharide in P. umbellatus sclerotia infected by the two Armillaria spp. in different years were determined. In addition, the differentially expressed genes related to P. umbellatus polysaccharide synthesis were screened according to the transcriptomic data of different parts of P. umbellatus after A. mellea infection. With the increase of years, the biomass of sclerotia infected by different Armillaria spp. had significant differences, and there were significant differences in the four components of sclerotia. The four components of the separated cavity wall of the sclerotia were significantly higher than those of the uninfected part. The best harvest time was the third year after cultivation. Transcriptomic analysis showed that the infection of Armillaria spp. could significantly promote polysaccharide synthesis, which provided a basis for polysaccharide content determination at the molecular level. The study clarified the influence of different Armillaria spp. infection on the accumulation of chemical components of P. umbellatus sclerotia, laying a foundation for exploring the symbiosis mechanism and provided a scientific clue for screening superior Armillaria sp. and guiding the artificial cultivation of P. umbellatus sclerotia.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective To compare the capture performance differences between TruSeq? Exome and NimbleGen SeqCap EZ Human Exome kits in children with cerebral palsy(CP),and to provide a technical selection basis for clinical genetic research and diagnosis.Methods Peripheral blood samples from 48 sporadic CP patients were included.Exome libraries were constructed using TruSeq(DNA probes)and NimbleGen(RNA probes),followed by sequencing on the Illumina HiSeq 2000 platform.Bioinformatics analysis was applied to evaluate mapping rate,target region coverage,variant concordance,and clinical relevance based on a CP-related gene set(2 293 genes).The statistical analysis was performed using a paired t-test with a significance threshold of α=0.05.Results The results showed no significant differences between NimbleGen and TruSeq exome capture kits in basic data quality(alignment rate,insert size)and GC content.However,they exhibited complementary characteristics in key performance metrics:NimbleGen demonstrated superior performance in specific depth coverage(1×coverage rate,P=1.84×10-5;20×coverage rate,P=1.49×10-20).TruSeq,on the other hand,showed higher sensitivity in Indel detection(TruSeq vs.NimbleGen:11 371±1 689 vs.11 274±1 670,P=3.24×10-7)and rare variant capture(TruSeq vs.NimbleGen:3 164±766 vs.3 072±774,P=1.20×10-4),successfully identifying all 11 pathogenic variants(including 2 missed by NimbleGen).Conclusion TruSeq,with its superior variant detection rate,is more suitable for clinical diagnostic applications,while NimbleGen's coverage stability may be advantageous for research-oriented projects.

Aim To design and synthesize a series of glycyrrhetinic acid derivatives by using glycyrrhetinic acid as the parent nucleus,screen their antitumor activ-ities,and investigate the in vitro and in vivo antitumor effects and mechanisms of the most active compound.Methods MTT assay was used to screen for the com-pound with the most potent antitumor activity.MTT as-say,wound healing assay,colony formation assay and Transwell migration assay were used to evaluate the effects of the compound on tumor cell viability and mi-gration.Flow cytometry was employed to assess the im-pact of the compound on tumor cell cycle progression and apoptosis.Western blot was conducted to verify the effects on the expression of pro-apoptotic proteins Bax,caspase-3 and cleaved caspase-3.A mouse model of hepatocellular carcinoma ascites tumor was estab-lished to examine the antitumor effects of the compound in vivo.Results Compound C22 was identified as having the most significant inhibitory effect on hepato-cellular carcinoma cells.C22 inhibited the viability and migration of hepatocellular carcinoma cells in a time and concentration-dependent manner.C22 upreg-ulated the expression of pro-apoptotic proteins Bax,caspase-3 and cleaved caspase-3 in hepatocellular car-cinoma cells,induced apoptosis,and arrested the cell cycle in the G0/G1 and S phases.C22 significantly re-duced the growth of mouse hepatocellular carcinoma as-cites tumors and prolonged survival.Conclusion Glycyrrhetinic acid derivative C22 significantly inhibits the viability and migration of hepatocellular carcinoma cells in vitro and in vivo,and induces cell cycle arrest and apoptosis.

The preferred treatment for adrenal functional tumors is laparoscopic surgical resection. However, in recent years, various ablation techniques have gradually been applied in the treatment of adrenal tumors. This report presents the successful diagnosis and treatment experience of 4 aldosterone-producing adenomas and 1 cortisol-secreting adenoma treated with a combined cold and hot ablation system at Xi’an Daxing Hospital from Dec. 2023 to Dec. 2024, providing a new treatment approach for clinical exploration.