In recent years， repetitive transcranial magnetic stimulation （rTMS） has garnered significant attention as a therapeutic approach for sleep disorders in the elderly. However， the prevailing rTMS protocols are predominantly developed based on normative neurophysiological data derived from young adults and fail to incorporate individualized parameters tailored to the brain characteristics of the elderly. To address this gap， the consensus development group synthesized the latest evidence from 2010 to 2025 and established a standardized rTMS protocol specifically for elderly patients with sleep disorders. Adhering to the Appraisal of Guidelines for Research and Evaluation II （AGREE II） framework， systematically screened randomized controlled trials （RCTs） and systematic reviews regarding rTMS in the treatment of sleep disorders across various conditions. Meanwhile， the Grading of Recommendations Assessment， Development and Evaluation （GRADE） system was employed to rigorously grade the quality of evidence and the strength of recommendations. This consensus guideline delineates precise rTMS protocols for the management of sleep disorders in the elderly， highlights the adjustment of stimulation intensity according to scalp-cortex distance recommends either MRI‑guided neuronavigation or the Beam F3/F4 heuristic approach for accurate target localization， thereby providing precise rTMS intervention protocol for sleep disorders in the elderly， aiming to enhance clinical efficacy while ensuring treatment safety. ［Funded by National Key Research and Development Program （number， 2023YFC3603200）； General Program of Shenzhen Science and Technology Innovation Commission （number， JCYJ20240813112859008， JCYJ20240813112900002）； Youth Program of Shenzhen Kangning Hospital （number， KN2023A004）； www.guidelines-registry.cn number， PREPARE-2026CN530］

Owing to its inherent advantages—such as being non-destructive, rapid, highly molecule-specific, and minimally interfered with by moisture—Raman spectroscopy has been widely adopted in the fields of skin barrier function assessment, monitoring the transdermal penetration of active cosmetic ingredients, and the identification and quality control of cosmetic products. Despite these strengths, the practical application of this technique faces a significant bottleneck: the strong fluorescence background generated by endogenous skin components and exogenous cosmetic additives. Endogenous skin substances, such as structural proteins (e.g., collagen and elastin), metabolic coenzymes (e.g., nicotinamide adenine dinucleotide), and pigments (e.g., melanin), together with exogenous cosmetic constituents like organic colorants, chemical sunscreens, and fragrances, often possess strong absorption and emission characteristics. When excited by lasers, these components produce a fluorescence background that can be 10⁶ to 10⁸ times stronger than the Raman scattering signals, effectively masking the inherently weak vibrational fingerprint information. In recent years, driven by the rapid development of optoelectronic hardware and artificial intelligence algorithms, fluorescence suppression strategies have evolved from isolated, single-method approaches into comprehensive, multi-level synergistic systems. These systems are categorized into three distinct tiers: sample preparation, signal acquisition, and data processing. At the sample preparation level, techniques such as photobleaching and surface-enhanced Raman spectroscopy (SERS) are employed to eliminate or bypass the generation of fluorescence at the source. At the signal acquisition level, instrumental improvements—including the use of long-wavelength near-infrared excitation (typically 785 nm or 1 064 nm), confocal spatial filtering, and shifted excitation Raman difference spectroscopy (SERDS)— are utilized to physically isolate Raman signals from the fluorescence background. Furthermore, at the data processing level, numerical baseline correction methods such as polynomial fitting, penalized least squares (e.g., airPLS, arPLS), wavelet transform, and derivative algorithms are increasingly integrated into the analytical pipeline to extract Raman spectral features from mixed signals without increasing hardware costs or acquisition time. This review provides a systematic categorization and critical evaluation of these fluorescence suppression methods, detailing their underlying principles, technical advantages, and inherent limitations in diverse experimental setups. By focusing on critical application scenarios—including skin barrier assessment, percutaneous absorption monitoring, the routine quality control of cosmetics, and the emerging field of portable on-site detection—this paper explores the current state of technique selection and optimization. Finally, the article discusses future development trends, emphasizing the necessity of constructing adaptive, tiered suppression strategies, developing intelligent and automated data processing algorithms, and promoting the integration of portable, multi-modal diagnostic devices. The objective of this review is to provide a comprehensive technical reference to facilitate the transition of Raman spectroscopy from a specialized laboratory tool into a routine, robust analytical platform for advancements in skin science and cosmetic research.

Objective:To construct and validate a predictive model and application program for stone recurrence after endoscopic retrograde cholangiopancreatography (ERCP) based on machine learning algorithms in patients with common bile duct stones (CBDS).Methods:A multicenter retrospective cohort study was conducted, 862 CBDS patients underwent ERCP from June 2020 to September 2023 in Changshu First People′s Hospital (data set 1, 759 cases, including a training set of 588 cases and a validation set of 171 cases) and Changshu Hospital of Traditional Chinese Medicine (data set 2, 103 cases, used as a test set). The demographics, medical history, ERCP procedural records and laboratory indices were collected. All patients were followed up for 1 year, and the stone recurrence was recorded. In training set, the feature selection was conducted by the least absolute shrinkage and selection operator (LASSO) algorithm, and a conventional Logistic regression model was constructed based on selected features. The 3 machine learning algorithms (gradient boosting machine model, extreme gradient boosting model and random forest model) and a conventional Logistic regression model (LASSO model) were trained to fit predictive models. The model performance was assessed by area under curve (AUC) of receiver operating characteristic curve. The model interpretability was analyzed by feature importance evaluation, Shapley additive explanations (SHAP) and force plots. The best-performing model was deployed as an online application by Streamlit framework (V1.36.0).Results:Among the 862 patients, 158 patients (18.33%) developed stone recurrence after ERCP. There were no statistical difference in demographics, medical history, ERCP procedural records and laboratory indices between training set and a validation set ( P>0.05). LASSO regression analysis result showed that 6 key variables (in descending order of significance: endoscopic sphincterotomy, common bile duct angulation, stone diameter, stone count, common bile duct diameter, and periampullary diverticulum) influencing stone recurrence. ROC curve analysis result showed that the random forest model exhibited the highest predictive performance (it had the largest AUC of 0.900). SHAP analysis result showed that common bile duct angulation, common bile duct diameter, stone diameter, endoscopic sphincterotomy and stone count were the top 5 contributing factors in the random forest model. Using Python, the random forest model was implemented into a Streamlit-based application with a user-friendly visual interface, providing predictive outcomes, confidence levels, SHAP force diagram and health recommendations. In the test set, the application program achieved an accuracy of 84.5% (87/103), sensitivity of 82.6% (19/23), and specificity of 85.0% (68/80). SHAP plots and force diagram intuitively illustrated the impact of key features on stone recurrence prediction, offering a clear visualization of each variable′s role within the model. Conclusions:The predictive model and application program based on the random forest machine learning algorithms demonstrate excellent predictive performance and practical usability in predicting stone recurrence after ERCP in patients with CBDS.

Objective To compare the effects of dexmedetomidine and midazolam on intraoperative blood pressure and postoperative 90-day outcome of endovascular treatment(EVT)in patients with acute anterior circulation large vessel occlusive stroke.Methods Retrospective consecutive patients with acute anterior circulation large vessel occlusion stroke who received EVT within 24 hours of onset,admitted to the Department of Neurology at the Second People's Hospital of Hefei from January 2024 to February 2025 were included.Patients were divided into the dexmedetomidine group and the midazolam group based on the choice of sedative in EVT.Baseline and clinical data were collected from patients,including sex,age,medical history(hypertension,diabetes,atrial fibrillation,stroke history),smoking history,blood pressure at admission(systolic,diastolic,mean arterial pressure),National Institutes of Health stroke scale(NIHSS)score at admission,trial of Org 10172 in acute stroke treatment(TOAST)classification,and site of vascular occlusion(internal carotid artery,M1 segment of the middle cerebral artery).Procedure related parameters,including intravenous thrombolysis before EVT,intraoperative use of tirofiban,modified thrombolysis in cerebral infarction(mTICI)grade,thrombectomy techniques(stent-retriever thrombectomy,aspiration thrombectomy,combined stent-retriever and aspiration thrombectomy,and other salvage measures),number of thrombectomy,time from onset to revascularization,time from puncture to revascularization,blood pressure during EVT(minimum systolic,minimum diastolic,and minimum mean arterial pressure),and blood pressure at the end of EVT(systolic,diastolic,and mean arterial pressure).The primary outcome was good prognosis at 90 days after EVT(modified Rankin scale score of 0-2 at 90 days),while secondary outcome was＞20％decrease in mean arterial pressure during EVT,early neurological improvement(ENI;a decrease on NIHSS score no less than 8 or a reduction of NIHSS score to 0-1 at 24 hours after EVT),and early neurological deterioration(END;an increase of more than 2 points on the NIHSS at 24 hours after procedure).Safety outcomes included any intracranial hemorrhage within 48 hours after EVT,symptomatic intracranial hemorrhage within 48 hours after EVT(sICH;intracranial hemorrhage confirmed by head CT leading to neurological deterioration,with an increase in NIHSS score of at least 4 points,or the presence of potentially fatal intracranial hemorrhage on head CT),pneumonia within 2 weeks after EVT,and the 90-day mortality after EVT.The baseline and clinical data,EVT conditions,primary outcome,secondary outcome,and safety indicators were compared between the two groups.Univariate Logistic regression analysis was used to screen the variables associated with a decrease in mean arterial pressure＞20％during EVT in patients with acute anterior circulation large vessel occlusive stroke.Variables with P＜0.15 and those considered potentially influential based on clinical experience were included in multivariate Logistic regression analysis to identify predictors of a＞20％decrease in mean arterial pressure during EVT in patients with acute anterior circulation large vessel occlusive stroke.Results A total of 93 patients with acute anterior circulation large vessel occlusive stroke who underwent EVT were included,comprising 51 males and 42 females,aged 34 to 99 years,with an average of(71±13)years old.Among them,63 patients were in the dexmedetomidine group,and 30 patients were in the midazolam group.33 patients showed＞20％decreases in mean arterial pressure during EVT,while 60 patients had ≤20％decreases.(1)Compare with the midazolam group,the proportion of female patients in the dexmedetomidine group was lower(36.5％[23/63]vs.63.3％[19/30],P=0.015),and the age was younger([69±13]years vs.[77±13]years,P=0.005).There were no statistically significant differences in other baseline and clinical data(all P＞0.05).(2)In comparison with the midazolam group,the dexmedetomidine group had a higher proportion of patients with more thrombectomy procedures(1.00[1.00,2.00]times vs.1.00[1.00,1.25]times,P=0.011),END(27.0％[17/63]vs.6.7％[2/30],P=0.023),sICH within 48 hours(19.0％[12/63]vs.3.3％[1/30],P=0.041),and a decrease in mean arterial pressure＞20％during EVT(42.9％[27/63]vs.20.0％[6/30],P=0.031).There were no statistically significant differences in the remaining EVT conditions,primary outcome,secondary outcome,and safety indicators(all P＞0.05).(3)The results of univariate Logistic regression analysis showed that diastolic blood pressure at admission(P=0.002),mean arterial pressure at admission(P=0.009),and dexmedetomidine sedation(P=0.036)were the influencing factors of a decrease＞20％in mean arterial pressure during EVT in patients with acute anterior circulation large vessel occlusion stroke.(4)The results of multivariate Logistic regression analysis showed that dexmedetomidine sedation(OR,3.271,95％CI 1.057-10.126,P=0.040)and higher diastolic blood pressure on admission(OR,1.105,95％CI 1.006-1.213,P=0.037)were independent predictors of a decrease over 20％in mean arterial pressure during EVT in patients with acute anterior circulation large vessel occlusive stroke.Conclusions Dexmedetomidine is an independent predictor of an over 20％decrease in mean arterial pressure during EVT in patients with acute anterior circulation large vessel occlusive stroke,but there is no statistically significant differences in the rate of good neurological function at 90 days and 90-day mortality postoperatively between the two groups.Further prospective randomized controlled studies are needed.

Machine learning,as a core branch of artificial intelligence,has gradually demonstrated significant potential in the field of dentition defect repair in recent years.By employing techniques such as data mining and pattern recognition,machine learning has been applied in various aspects including oral implant restoration,fixed bridge design,and fabrication of removable partial dentures.This paper seeks to provide a comprehensive review of machine learning applications within the aforementioned three aspects.It endeavors to assess the current developmental landscape and chart a course for future investigative endeavors,in hopes of offering a reference for re-search on machine learning in prosthodontics.

Aim To explore the protective effect of icariin on the damage of tight junctional function of Sertoli cells in naturally aging mice and the related mechanism.Methods 15-month-old C57BL/6J male mice were randomly divided into three groups:aging model group,low-dose and high-dose icariin treatment group(5 and 20 mg·kg-1).Another 1-month-old C57BL/6J male mice were considered as adult control group(n=10).The mice in adult control group and aging model group were given the vehicle(0.5％sodi-um carboxymethyl cellulose solution)by intragastric administration,while the mice in icariin-treated groups were given different concentrations of icariin,respec-tively.After continuous administration of icariin for three months,the testes and epididymis were immedi-ately removed,weighed,and the organ index was calcu-lated.Sperm viability and sperm concentration in epi-didymis were measured.The morphological changes of testes were observed by HE staining.The ultrastructur-al changes of tight junctions of Sertoli cells were ob-served by transmission electron microscopy.The ex-pression levels of tight junction-related proteins ZO-1,Occludin,and Claudin11 of testicular Sertoli cells were detected by Western blot.The expression and localiza-tion of ZO-1,Occludin,Raptor,Rictor,p-70S6K,and p-rps6 were detected by immunofluorescence.Results Compared with the aging model group,icariin signifi-cantly increased testicular weight and its index,and ep-ididymal index,improved sperm viability and increased sperm concentration in naturally aging mice.In addi-tion,icariin improved the degeneration of testicular morphology and the damage of ultrastructure of Sertoli cell tight junction with aging.Furthermore,Western blot results showed that icariin up-regulated the expres-sion of ZO-1 and Occludin,but had no significant effect on the expression of Claudin 11.Immunofluorescence assay showed that icariin up-regulated the expression of Rictor,and down-regulated the expression of p-70S6K,p-rps6 and Raptor.Conclusions Icariin improves the tight junction damage of Sertoli cells in naturally aging mice,and its mechanism may be related to restoring the balance between mTORC1 and mTORC2.

Objective:To compare the effectiveness of a new virtual reality (VR) working memory test system with that of the traditional E-prime working memory test system in terms of evaluating executive function in cognitive rehabilitation.Methods:Two experienced rehabilitation therapists tested 30 healthy subjects. The average reaction time and accuracy of the traditional and the VR-based working memory test systems in completing the 2-back task were compared. The usability and user experience of the VR-based working memory test system were also evaluated using the system usability scale (SUS), a simulator discomfort scale (SSQ) and semi-structured interviews.Results:There was no significant difference in the average reaction time and accuracy of the 30 subjects between the two different tests. The SUS score of the VR working memory test was at the " Good-Excellent" level, and the SSQ scores demonstrated that no significant discomfort was observed. In the interviews the VR working memory test system was described as easy to operate and as having a strong sense of immersion.Conclusions:The VR-based working memory test system has good usability and user acceptance, and can provide subjects with an immersive and persona-lized test scenario. Therefore, it can be used as a tool for testing working memory.

Objective To construct YOLOv10 based artificial intelligence(AI)models for the automatic detection in small bowel capsule endoscopy(SBCE)images.Methods SBCE data from two centers was collected,including 23 115 images and 35 412 annotated labels covering 11 categories of small bowel lesions.The images were annotated using the LabelMe tool and converted into the YOLO format required for deep learning model development.The pre-trained YOLOv10 and YOLOv8 models were used for transfer learning training on the constructed dataset.Model performance was comprehensively evaluated using metrics such as precision,accuracy,sensitivity,specificity,false-positive rate,and detection speed.Finally,the models were deployed on local computers for real-time detection of SBCE images and videos.Results Six different versions of YOLO object detection models were developed,namely YOLOv8n,YOLOv8s,YOLOv8m,YOLOv10n,YOLOv10s,and YOLOv10m.On the validation set,YOLOv10s model achieved the best mAP50(0.795);although its inference latency was not the fastest(4.803 ms/img),it met the requirements for clinical application.On the test set,YOLOv10s performed well,with an accuracy of 92.69%,a sensitivity of 89.23%,and a false-positive rate of 4.78%.Especially,in category-specific inference,the highest sensitivity was for"bleeding"at 96.41%,while the lowest was for"narrowing"at 82.29%.Conclusion The model constructed based on YOLOv10 neural network can rapidly and accurately detect and classify various small bowel lesions,exhibiting significant clinical application potential.

Aim To explore the protective effect of icariin on the damage of tight junctional function of Sertoli cells in naturally aging mice and the related mechanism.Methods 15-month-old C57BL/6J male mice were randomly divided into three groups:aging model group,low-dose and high-dose icariin treatment group(5 and 20 mg·kg-1).Another 1-month-old C57BL/6J male mice were considered as adult control group(n=10).The mice in adult control group and aging model group were given the vehicle(0.5％sodi-um carboxymethyl cellulose solution)by intragastric administration,while the mice in icariin-treated groups were given different concentrations of icariin,respec-tively.After continuous administration of icariin for three months,the testes and epididymis were immedi-ately removed,weighed,and the organ index was calcu-lated.Sperm viability and sperm concentration in epi-didymis were measured.The morphological changes of testes were observed by HE staining.The ultrastructur-al changes of tight junctions of Sertoli cells were ob-served by transmission electron microscopy.The ex-pression levels of tight junction-related proteins ZO-1,Occludin,and Claudin11 of testicular Sertoli cells were detected by Western blot.The expression and localiza-tion of ZO-1,Occludin,Raptor,Rictor,p-70S6K,and p-rps6 were detected by immunofluorescence.Results Compared with the aging model group,icariin signifi-cantly increased testicular weight and its index,and ep-ididymal index,improved sperm viability and increased sperm concentration in naturally aging mice.In addi-tion,icariin improved the degeneration of testicular morphology and the damage of ultrastructure of Sertoli cell tight junction with aging.Furthermore,Western blot results showed that icariin up-regulated the expres-sion of ZO-1 and Occludin,but had no significant effect on the expression of Claudin 11.Immunofluorescence assay showed that icariin up-regulated the expression of Rictor,and down-regulated the expression of p-70S6K,p-rps6 and Raptor.Conclusions Icariin improves the tight junction damage of Sertoli cells in naturally aging mice,and its mechanism may be related to restoring the balance between mTORC1 and mTORC2.

Objective:To construct and validate a predictive model and application program for stone recurrence after endoscopic retrograde cholangiopancreatography (ERCP) based on machine learning algorithms in patients with common bile duct stones (CBDS).Methods:A multicenter retrospective cohort study was conducted, 862 CBDS patients underwent ERCP from June 2020 to September 2023 in Changshu First People′s Hospital (data set 1, 759 cases, including a training set of 588 cases and a validation set of 171 cases) and Changshu Hospital of Traditional Chinese Medicine (data set 2, 103 cases, used as a test set). The demographics, medical history, ERCP procedural records and laboratory indices were collected. All patients were followed up for 1 year, and the stone recurrence was recorded. In training set, the feature selection was conducted by the least absolute shrinkage and selection operator (LASSO) algorithm, and a conventional Logistic regression model was constructed based on selected features. The 3 machine learning algorithms (gradient boosting machine model, extreme gradient boosting model and random forest model) and a conventional Logistic regression model (LASSO model) were trained to fit predictive models. The model performance was assessed by area under curve (AUC) of receiver operating characteristic curve. The model interpretability was analyzed by feature importance evaluation, Shapley additive explanations (SHAP) and force plots. The best-performing model was deployed as an online application by Streamlit framework (V1.36.0).Results:Among the 862 patients, 158 patients (18.33%) developed stone recurrence after ERCP. There were no statistical difference in demographics, medical history, ERCP procedural records and laboratory indices between training set and a validation set ( P>0.05). LASSO regression analysis result showed that 6 key variables (in descending order of significance: endoscopic sphincterotomy, common bile duct angulation, stone diameter, stone count, common bile duct diameter, and periampullary diverticulum) influencing stone recurrence. ROC curve analysis result showed that the random forest model exhibited the highest predictive performance (it had the largest AUC of 0.900). SHAP analysis result showed that common bile duct angulation, common bile duct diameter, stone diameter, endoscopic sphincterotomy and stone count were the top 5 contributing factors in the random forest model. Using Python, the random forest model was implemented into a Streamlit-based application with a user-friendly visual interface, providing predictive outcomes, confidence levels, SHAP force diagram and health recommendations. In the test set, the application program achieved an accuracy of 84.5% (87/103), sensitivity of 82.6% (19/23), and specificity of 85.0% (68/80). SHAP plots and force diagram intuitively illustrated the impact of key features on stone recurrence prediction, offering a clear visualization of each variable′s role within the model. Conclusions:The predictive model and application program based on the random forest machine learning algorithms demonstrate excellent predictive performance and practical usability in predicting stone recurrence after ERCP in patients with CBDS.