Approximately 20% to 30% of the global workforce is engaged in shift work. As a significant cause of circadian disruption, shift work is closely associated with an increased risk for periodontitis. Nevertheless, how shift work-related circadian disruption functions in periodontitis remains unknown. Herein, we employed a simulated shift work model constructed by controlling the environmental light-dark cycles and revealed that shift work-related circadian disruption exacerbated the progression of experimental periodontitis. RNA sequencing and in vitro experiments indicated that downregulation of the core circadian protein brain and muscle ARNT-like protein 1 (BMAL1) and activation of the Gasdermin D (GSDMD)-mediated pyroptosis were involved in the pathogenesis of that. Mechanically, BMAL1 regulated GSDMD-mediated pyroptosis by suppressing NOD-like receptor protein 3 (NLRP3) inflammasome signaling through modulating nuclear receptor subfamily 1 group D member 1 (NR1D1), and inhibiting Gsdmd transcription via directly binding to the E-box elements in its promoter. GSDMD-mediated pyroptosis accelerated periodontitis progression, whereas downregulated BMAL1 under circadian disruption further aggravated periodontal destruction by increasing GSDMD activity. And restoring the level of BMAL1 by circadian recovery and SR8278 injection alleviated simulated shift work-exacerbated periodontitis via lessening GSDMD-mediated pyroptosis. These findings provide new evidence and potential interventional targets for circadian disruption-accelerated periodontitis.
Pyroptosis/physiology* ; ARNTL Transcription Factors/metabolism* ; Animals ; Periodontitis/etiology* ; Mice ; Phosphate-Binding Proteins/metabolism* ; Shift Work Schedule/adverse effects* ; Intracellular Signaling Peptides and Proteins/metabolism* ; Mice, Inbred C57BL ; Male ; Disease Models, Animal ; Gasdermins

Drug development remains a critical issue in the field of biomedicine. With the rapid advancement of information technologies such as artificial intelligence (AI) and the advent of the big data era, AI-assisted drug development has become a new trend, particularly in predicting drug-target associations. To address the challenge of drug-target prediction, AI-driven models have emerged as powerful tools, offering innovative solutions by effectively extracting features from complex biological data, accurately modeling molecular interactions, and precisely predicting potential drug-target outcomes. Traditional machine learning (ML), network-based, and advanced deep learning architectures such as convolutional neural networks (CNNs), graph convolutional networks (GCNs), and transformers play a pivotal role. This review systematically compiles and evaluates AI algorithms for drug- and drug combination-target predictions, highlighting their theoretical frameworks, strengths, and limitations. CNNs effectively identify spatial patterns and molecular features critical for drug-target interactions. GCNs provide deep insights into molecular interactions via relational data, whereas transformers increase prediction accuracy by capturing complex dependencies within biological sequences. Network-based models offer a systematic perspective by integrating diverse data sources, and traditional ML efficiently handles large datasets to improve overall predictive accuracy. Collectively, these AI-driven methods are transforming drug-target predictions and advancing the development of personalized therapy. This review summarizes the application of AI in drug development, particularly in drug-target prediction, and offers recommendations on models and algorithms for researchers engaged in biomedical research. It also provides typical cases to better illustrate how AI can further accelerate development in the fields of biomedicine and drug discovery.

Objective : To investigate the factors affecting the outcome of high-risk human papillomavirus ( HR- HPV) infection in patients with normal cervix examined by colposcopy in Hefei area and the relationship between persistent HR-HPV infection and cervical cytology． Methods : Data of colposcopy patients were collected from 478 HR-HPV infected patients with normal cervix through colposcopy．Their age，number of sexual partners，contracep- tive methods and other relevant basic information were recorded．Vaginal interferon use，HR-HPV infection at year 1 and year 2，and cervical liquid-based cytology test ( LCT) results were tracked，univariate and multivariate ana- lyses were performed based on basic information，and ROC curves were plotted． Results : The HR-HPV clearance rate at 1 year was 59. 41% ，and the clearance rate at 2 years was 66. 75%．The other 12 types of infection ( 31， 33，35，39，45，51，52，56，58，59，66，68) were more common than the 16 and 18 types．Univariate and mult- ivariate analyses showed that age＞50 years，number of sexual partners ≥2，and history of cervical conectomy in- creased the risk of persistent HR-HPV infection ( χge = 21. 676，P ＜0. 001; χumber of sexual partners = 8. 262，P =0. 004; χistory of cervical conectomy = 11. 267，P = 0. 001 ) ． The risk of HR-HPV infection was significantly lower when condom or vaginal interferon was used ( χondom use = 10. 885，P = 0. 001; χnterferon use = 4. 099，P = 0. 043) ．The area under the ROC curve (AUC) of combined diagnosis of HR-HPV persistent infection was higher than that of single diagnosis，and the AUC of combined diagnosis was 0. 737．Persistent HR-HPV infection was an independent risk factor for abnormal LCT，and the AUC predicted by the model was 0. 755．No cancer was found in patients with persistent HR-HPV infection for 2 years，and the proportion of abnormal LCT was higher than that in patients with negative HR-HPV．The difference was statistically significant ( χ2 = 39. 64，P＜0. 001) ． Conclusion The combined ROC model constructed for patients＞50 years old，with multiple sexual partners，history of cervical surgery， no vaginal interferon use，and no condom use has certain value in predicting persistent HR-HPV infection，and per- sistent HR-HPV infection has predictive value in predicting LCT abnormalities．

Objective To explore the potential role and underlying mechanism of the functionally uncharacterized gene Gm49394 on regulating β-cell apoptosis under diabetic conditions.Methods The expression and translational activity of Gm49394 in pancreatic β-cell lines and non-β-cell lines were validated using RNA fluorescence in situ hybridization(RNA-FISH),quantitative real-time PCR(qPCR),Western blotting,and immunofluorescence(IF)assay.The β-cell lines(NIT-1/Min6)with Gm49394 overexpression or knockdown were constructed.The proliferation,apoptosis,mitochondrial function,as well as oxidative stress and endoplasmic reticulum stress markers in these β-cell lines under physiological homeostasis or pathological stress conditions,such as high glucose(30 mmol/L),inflammation(10 ng/mL IFN-γ alone or combined with 10 ng/mL IL-6),and hydrogen peroxide(100 μmol/L H2O2)were detected by flow cytometry and Western blotting.Results RNA-FISH and qPCR indicated that Gm49394 was specifically expressed in pancreatic β-cell lines and up-regulated under high glucose or inflammatory stimulation.IF assay and Western blotting showed that Gm49394 had protein-coding activity.Flow cytometry and Western blotting identified that Gm49394 overexpression did not affect β-cell proliferation,but promoted β-cell apoptosis and increased reactive oxygen species(ROS)and mitochondrial superoxide(MitoSOX)levels in β cells under physiological homeostasis or pathological stress conditions(P<0.05).Under physiological conditions,Gm49394 knockdown failed to induce significant alterations on β-cell apoptosis,ROS,or MitoSOX levels.Under pathological stress conditions,Gm49394 knockdown significantly suppressed β-cell proliferation,apoptosis,as well as oxidative and endoplasmic reticulum stress(P<0.05).Conclusion Gm49394 may promote β-cell apoptosis via oxidative stress and endoplasmic reticulum stress.

By combining the origin and research progress of the combination of disease and syndrome, the core pathogenesis, this article explored the research ideas and methods of the core pathogenesis of TCM. It is found that modern TCM is mostly guided by the idea of classification-staging-syndrome differentiation, the main prescription of the main disease, the special prescription of the special disease, and the idea of "dynamic-fixed sequential". The tongue image syndrome differentiation method, clustering analysis method, drug test syndrome method, compound pathogenesis method, "evidence-based pathogenesis-syndrome treatment system" research model, and the integration of traditional Chinese and Western medicine theory were used to explore the core pathogenesis of TCM under the condition of disease. Combined with the advantages of modern medical disease differentiation and TCM syndrome differentiation, the individualized diagnosis and treatment methods of integrated traditional Chinese and Western medicine have been continuously improved, in order to solve the stage contradictions of different clinical stages, effectively delay the progression of the disease and improve the prognosis of the disease.

Drug development remains a critical issue in the field of biomedicine.With the rapid advancement of information technologies such as artificial intelligence(AI)and the advent of the big data era,AI-assisted drug development has become a new trend,particularly in predicting drug-target associations.To address the challenge of drug-target prediction,AI-driven models have emerged as powerful tools,of-fering innovative solutions by effectively extracting features from complex biological data,accurately modeling molecular interactions,and precisely predicting potential drug-target outcomes.Traditional machine learning(ML),network-based,and advanced deep learning architectures such as convolutional neural networks(CNNs),graph convolutional networks(GCNs),and transformers play a pivotal role.This review systematically compiles and evaluates AI algorithms for drug-and drug combination-target predictions,highlighting their theoretical frameworks,strengths,and limitations.CNNs effectively identify spatial patterns and molecular features critical for drug-target interactions.GCNs provide deep insights into molecular interactions via relational data,whereas transformers increase prediction accu-racy by capturing complex dependencies within biological sequences.Network-based models offer a systematic perspective by integrating diverse data sources,and traditional ML efficiently handles large datasets to improve overall predictive accuracy.Collectively,these AI-driven methods are transforming drug-target predictions and advancing the development of personalized therapy.This review summa-rizes the application of AI in drug development,particularly in drug-target prediction,and offers rec-ommendations on models and algorithms for researchers engaged in biomedical research.It also provides typical cases to better illustrate how AI can further accelerate development in the fields of biomedicine and drug discovery.

Objective To explore the safety and effectiveness of transvaginal ischia spinous fascia fixation for pelvic organ prolapse.Methods The retrospective analysis of 124 patients who underwent surgical treatment for stage Ⅲ-Ⅳ pelvic organ prolapse was conducted.Among them, 53 cases of transvaginal ischia spinous fascia fixation (IS-FF) were performed as a study group (ISFF group) while 71 cases of transvaginal sacrospinous ligament fixation (SSLF) were performed as a control group (SSLF group) .The operation time, postoperative hospitalization days, preoperative and postoperative hemoglobin values, indwelling urinary catheter time, postoperative pain scores, and the occurrence of complications were compared between the two groups, and the efficacy of the operation was objec-tively evaluated by using the staging method of pelvic organ prolapse (POP-Q) .Also the scores of the pelvic floor impact questionnaire-7 (PFIQ-7) , the pelvic floor dysfunction questionnaire-20 (PFDI-20) , and the questionnaire of quality of life12 (PISQ-12) were used to evaluate the patients' postoperative quality of life.Results The oper-ation time and postoperative hospitalization days of patients in the ISFF group were less than those in the SSLF group , and the differences were statistically significant (P<0.05) .The preoperative and postoperative hemoglobin values, retention time of urinary catheter, postoperative pain scores, and hospitalization costs of patients in the two groups were compared, and the differences were not statistically significant.At the 3-month postoperative outpatient follow-up, the objective success rate was 100％ in two groups.The median follow-up time of patients in both groups was 24 months (12-41 months) , and there were 2 cases of recurrence in the ISFF group, with a recurrence rate of 3.77％ and a subjective success rate of 96.23％.While there were 3 cases of recurrence in the SSLF group and 2 cases of loss of visit, with a recurrence rate of 4.34％ and a subjective success rate of 95.65％.1 patient in the SSLF group presented with a pelvic hematoma with a diameter of about 5 cm after surgery.The hematoma disap-peared after hemostasis and other symptomatic treatment.There was no organ injury or blood transfusion in both groups.Conclusion Transvaginal ischia spinous fascia fixation is a safe and effective treatment for pelvic organ prolapse, and it has the advantages of short operation time, fast postoperative recovery, fewer complications, and improvement of patients' quality of life.

ObjectiveTo systematically evaluate the efficacy and safety of omental wrapping technique for pancreaticojejunal anastomosis in preventing complications after pancreaticoduodenectomy. MethodsThis study was conducted according to the PRISMA guideline. English and Chinese databases including CNKI， Wanfang Data， VIP， CBM， the Cochrane Library， PubMed， Embase， and Web of Science were searched for clinical studies on omental wrapping technique for pancreaticojejunal anastomosis in preventing complications after pancreaticoduodenectomy published up to November 2022， and Stata 16 and Review Manager 5.4 were used to perform the meta-analysis. ResultsA total of 15 studies with 1 830 patients were included in this study. The meta-analysis showed that the omental wrapping group had a significantly lower overall incidence rate of postoperative pancreatic fistula （POPF） than the non-omental wrapping group （odds ratio ［OR］=0.30， 95% confidence interval ［CI］： 0.22‍ ‍—‍ ‍0.41， P<0.001）， and the subgroup analysis showed that the omental wrapping group had a significantly lower incidence rate of grade B/C POPF than the non-omental wrapping group （OR=0.29， 95%CI： 0.21‍ ‍—‍ ‍0.39， P<0.001）. Compared with the non-omental wrapping group， the omental wrapping group had significantly lower incidence rates of postoperative bile leakage （OR=0.30， 95%CI： 0.16‍ ‍—‍ ‍0.56， P<0.001）， postoperative hemorrhage （OR=0.35， 95%CI： 0.24‍ ‍—‍ ‍0.53， P<0.001）， delayed gastric emptying （OR=0.45， 95%CI： 0.31‍ ‍—‍ ‍0.64， P<0.001）， abdominal infection （OR=0.55， 95%CI： 0.40‍ ‍—‍ ‍0.75， P<0.001）， reoperation （OR=0.31， 95%CI： 0.18‍ ‍—‍ ‍0.54， P<0.001）， and death within 30 days after surgery （OR=0.42， 95%CI： 0.22‍ ‍—‍ ‍0.80， P=0.009）， a significantly earlier time to diet （mean difference ［MD］=-0.98， 95%CI： -1.84 to -0.11， P=0.03）， and a significantly shorter length of postoperative hospital stay （MD=-2.44， 95%CI： -4.10 to -0.77， P=0.004）. There were no significant differences between the two groups in the time of operation （MD=-13.68， 95%CI： -28.31 to -0.95， P=0.07） and intraoperative blood loss （MD=-17.26， 95%CI： -57.55 to -23.03， P=0.40）. ConclusionOmental wrapping can reduce the incidence rates of postoperative complications such as pancreatic fistula， bile leakage， postoperative hemorrhage， abdominal infection， and delayed gastric emptying， improve the prognosis of patients， and shorten the length of hospital stay， without increasing surgical difficulty or time of operation.