ObjectiveTo observe the clinical efficacy and radiographic outcomes of manipulative reduction combined with small splint fixation in the treatment of distal radius fractures. MethodsThe clinical data of 1051 patients with distal radius fractures were retrospectively collected from five hospitals included in the Jiangsu Diagnosis and Treatment Data Platform for Traditional Chinese Medicine（TCM） Dominant Diseases. Propensity score matching at a 1∶4 ratio was applied， resulting in 580 cases selected for final analysis， which comprised 448 patients in the TCM group（manipulative reduction plus small splint fixation） and 132 in the surgical treatment group（open reduction and internal fixation）. Each group was further stratified into type A， B， and C subgroups based on AO fracture classification. Radiographic indicators including palmar tilt， radial inclination， and radial height were compared between groups before treatment and 1 day， 1 week， and 4-6 weeks after treatment， and pain visual analog scale（VAS） scores before treatment and 1 week and 4-6 weeks after treatment were also compared. Wrist joint function was assessed 12 weeks after treatment， using the Dienst wrist function score and the Gartland and Werley（G-W） wrist function score. Additionally， the radiographic indicators at different timepoints and the 12-week wrist function levels were compared between groups across different fracture types. ResultsNo statistically significant difference was observed in radiographic indicators and VAS scores at all timepoints before and after treatment， as well as wrist joint function grades assessed by the Dienst score and the G-W score at 12 weeks after treatment （P＞0.05）. Compared to those before treatment， both groups showed increased palmar tilt， radial inclination， and radial height 1 week and 4-6 weeks after treatment， and decreased VAS scores （P＜0.05）. Compared to those 1 week after treatment， both groups showed a decrease in palmar tilt， an increase in radial inclination and radial height， and a reduction in VAS score 4-6 weeks after treatment（P＜0.05）. In type A and B subgroups， the surgical treatment group had a higher radial inclination than the TCM group 4-6 weeks after treatment， while in the type C subgroup， a higher radial height was shown in the surgical treatment group than in the TCM group 4-6 weeks after treatment（P＜0.05）. In type C subgroup， there was significant difference between groups in the wrist joint function by G-W scores 12 weeks after treatment（P＜0.05）. ConclusionManipulative reduction combined with small splint fixation can maintain fracture alignment and alleviate pain in treating distal radius fractures， which achieves therapeutic outcomes comparable to surgical treatment. It is particularly suitable for type A and B fractures and can be considered an effective treatment option for distal radius fractures.

ObjectiveTo construct an automatic measurement model for palmar tilt and radial inclination suitable for traditional Chinese medicine （TCM） clinical scenarios， and to validate its accuracy and efficiency in TCM manipulative reduction settings. MethodsData on anteroposterior （AP） and lateral X-rays of distal radius fractures were collected from patients admitted to 18 TCM/ integrated TCM and western medicine hospitals in Jiangsu province between September 1st， 2023， and September 1st， 2024， via the Jiangsu Diagnosis and Treatment Big Data Platform for TCM Dominant Diseases. A medical image segmentation framework based on multi-scale feature fusion and edge-awareness was employed， combined with anatomical knowledge specific to TCM orthopedics， to optimize the feature extraction strategy of an artificial intelligence （AI） model. This framework enabled automatic segmentation of fracture regions and measurement of distal radius palmar tilt and radial inclination. The accuracy of the AI model in measuring radial inclination and volar tilt was validated， and the measurement time and average time gain rate of the AI model were compared to those of manual measurement. ResultsA total of 15,444 AP and lateral X-ray images of distal radius fractures were collected， and were divided into a training set （11,144 images， 5066 AP and 6078 lateral）， a validation set （3700 images， 1840 AP and 1860 lateral）， and an independent test set （600 images， 300 AP and 300 lateral） after preprocessing. In the measurement of 300 AP X-rays in the independent test set for radial inclination， when the degree error between AI measurement and manual measurement was ＜3° and ＜5°， AI measurement accuracy was 83% and 93%， respectively. In 300 lateral X-rays in the test set for palmar tilt， when AI measurements had an error of ＜3° and ＜5° compared to manual measurements， corresponding accuracy rate was 78% and 90%， respectively. For 50 X-ray images， AI measurement time was （1.37±0.05） min for radial inclination while manual measurement time was （22.57±2.52） min （P＜0.001）； in terms of palmar tilt， the AI measurement time was （1.33±0.14） min， shorter than （23.70±2.80） min for manual measurement time （P＜0.001）. Average time gain rates for manual and AI measurements were 93.93% and 94.39% respectively. ConclusionAn automatic measurement model for palmar tilt and radial inclination in distal radius fractures has been established， enabling more accurate and efficient assessment as well as providing a tool to support the quantitative evaluation of the efficacy of TCM manipulative reduction and large-sample clinical research.

Objective To design an individually portable oral treatment device to solve the problems of oral diagnosis and treatment under field conditions in alpine regions.Methods The individually portable oral treatment device had a trolley box structure and consisted of an outer box,an inner framework and an operation panel.The outer box was made of low-density polyethylene material and formed by by one-time rotational moulding process;the inner framework integrated a plateau com-pressor,an independent negative-pressure compressor,an integrated control system for programmable logic controller(PLC),an individually portable respiratory synchronized pulsed oxygen supply module for plateau application;there were several curative devices equipped in the operation panel,including a 3-way syringe,a high-speed turbine handpiece,an electric variable-speed handpiece,a water control switch,a light curing machine and an ultrasonic dental cleaning handpiece.Trials were carried out with the test-phase prototype in alpine regions so as to verify the performance of the device.Results Trials proved that the prototype gained advantages in mobility,multifunctionality and pressure supply facilitating continuous operation of power gas source for oral diagnosis and treatment in alpine regions.Conclusion The device developed solves the problems in pressure insufficiency and instability,control system integration,portability and oxygen supply for medical staffs,improves the mobility of oral diagnosis and treatment in alpine regions and enhances the oral support service and equipment effectively.[Chinese Medical Equipment Journal,2025,46(1):108-113]

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.

Objective To investigate the value of fractional-order calculus(FROC)and continuous-time random-walk(CTRW)diffusion models based on readout segmentation of long variable echo-trains(RESOLVE)in identifying benign and malignant lesions in the head and neck.Methods A retrospective analysis was conducted on 61 patients pathologically confirmed head and neck lesions,including 19 benign lesions(BL)and 42 malignant lesions(ML).The ML were further divided into a lymphoma subgroup(LS)with 9 cases(14 lesions)and a non-lymphoma malignant lesion subgroup(MLS)with 33 cases.The parameters of DFROC,βFROC,μFROC,DCTRW,αCTRW and βCTRW were obtained from the two diffusion models;Independent sample t-tests or U tests were used to compare the differences in each parameter between benign and malignant groups and among various subgroups,and the receiver operating characteristic(ROC)curve was used to analyze the diagnostic efficacy of each parameter.Area under the curve(AUC)was compared by DeLong test.Results DFROC,μFROC,DCTRW and αCTRW showed significant differences between benign and malignant,BL and LS,BL and MLS and LS and MLS,with αCTRW showed the highest diagnostic efficacy;βFROC showed differences between BL and LS,BL and MLS,whileβCTRW did not show differences between benign and malignant groups,and among subgroups.Conclusion FROC and CTRW diffusion models based on RESOLVE can distinguish between benign and malignant head and neck lesions with multiple parameters,and provide metrics reflecting tissue heterogeneity.

Objective To develop and validate a nomogram model that integrates systemic inflammatory markers to predict the likelihood of pelvic lymph node metastasis(PLNM)in prostate cancer patients prior to surgery.Methods This study retrospectively analyzed the clinical data and preoperative inflammatory markers—including neutrophil-to-lymphocyte ratio(NLR),platelet-to-lymphocyte ratio(PLR),systemic immune-inflammation index(SII),and monocyte-to-lymphocyte ratio(MLR)—of patients diagnosed with prostate cancer.Univariate and multi-variate logistic regression analyses were conducted to identify markers that were significantly associated with PLNM.Based on the results of the multivariate analysis,a nomogram was developed and its predictive accuracy was assessed using receiver operating characteristic curves(ROC)and calibration plots.Results Among the 334 enrolled patients with prostate cancer,107 were identified with PLNM.Univariate analysis revealed statistically significant differences in free prostate-specific antigen(fPSA),Gleason score,NLR,PLR,MLR,and SII between the PLNM and non-pelvic lymph node metastasis(NPLNM)groups(P<0.05).Multivariate analysis confirmed that fPSA,Gleason score,and SII were independent predictors of PLNM(P<0.05).A nomogram incorporating these predic-tors exhibited strong discriminative ability,with an area under the ROC curve(AUC)of 0.79(95%CI:0.73～0.84).Calibration analysis further demonstrated good consistency between the predicted and observed probabilities of PLNM.Conclusions This study successfully developed a nomogram model based on systemic inflammatory markers for preoperative prediction of pelvic lymph node metastasis in prostate cancer.Owing to its user-friendly design and high predictive accuracy,this tool may serve as a valuable complementary method to conventional imaging techniques,thereby supporting personalized treatment decision-making.

Objective To develop and validate a nomogram model that integrates systemic inflammatory markers to predict the likelihood of pelvic lymph node metastasis(PLNM)in prostate cancer patients prior to surgery.Methods This study retrospectively analyzed the clinical data and preoperative inflammatory markers—including neutrophil-to-lymphocyte ratio(NLR),platelet-to-lymphocyte ratio(PLR),systemic immune-inflammation index(SII),and monocyte-to-lymphocyte ratio(MLR)—of patients diagnosed with prostate cancer.Univariate and multi-variate logistic regression analyses were conducted to identify markers that were significantly associated with PLNM.Based on the results of the multivariate analysis,a nomogram was developed and its predictive accuracy was assessed using receiver operating characteristic curves(ROC)and calibration plots.Results Among the 334 enrolled patients with prostate cancer,107 were identified with PLNM.Univariate analysis revealed statistically significant differences in free prostate-specific antigen(fPSA),Gleason score,NLR,PLR,MLR,and SII between the PLNM and non-pelvic lymph node metastasis(NPLNM)groups(P<0.05).Multivariate analysis confirmed that fPSA,Gleason score,and SII were independent predictors of PLNM(P<0.05).A nomogram incorporating these predic-tors exhibited strong discriminative ability,with an area under the ROC curve(AUC)of 0.79(95%CI:0.73～0.84).Calibration analysis further demonstrated good consistency between the predicted and observed probabilities of PLNM.Conclusions This study successfully developed a nomogram model based on systemic inflammatory markers for preoperative prediction of pelvic lymph node metastasis in prostate cancer.Owing to its user-friendly design and high predictive accuracy,this tool may serve as a valuable complementary method to conventional imaging techniques,thereby supporting personalized treatment decision-making.

Objective To investigate the value of fractional-order calculus(FROC)and continuous-time random-walk(CTRW)diffusion models based on readout segmentation of long variable echo-trains(RESOLVE)in identifying benign and malignant lesions in the head and neck.Methods A retrospective analysis was conducted on 61 patients pathologically confirmed head and neck lesions,including 19 benign lesions(BL)and 42 malignant lesions(ML).The ML were further divided into a lymphoma subgroup(LS)with 9 cases(14 lesions)and a non-lymphoma malignant lesion subgroup(MLS)with 33 cases.The parameters of DFROC,βFROC,μFROC,DCTRW,αCTRW and βCTRW were obtained from the two diffusion models;Independent sample t-tests or U tests were used to compare the differences in each parameter between benign and malignant groups and among various subgroups,and the receiver operating characteristic(ROC)curve was used to analyze the diagnostic efficacy of each parameter.Area under the curve(AUC)was compared by DeLong test.Results DFROC,μFROC,DCTRW and αCTRW showed significant differences between benign and malignant,BL and LS,BL and MLS and LS and MLS,with αCTRW showed the highest diagnostic efficacy;βFROC showed differences between BL and LS,BL and MLS,whileβCTRW did not show differences between benign and malignant groups,and among subgroups.Conclusion FROC and CTRW diffusion models based on RESOLVE can distinguish between benign and malignant head and neck lesions with multiple parameters,and provide metrics reflecting tissue heterogeneity.

Testicular histology based on testicular biopsy is an important factor for determining appropriate testicular sperm extraction surgery and predicting sperm retrieval outcomes in patients with azoospermia. Therefore, we developed a deep learning (DL) model to establish the associations between testicular grayscale ultrasound images and testicular histology. We retrospectively included two-dimensional testicular grayscale ultrasound from patients with azoospermia (353 men with 4357 images between July 2017 and December 2021 in The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China) to develop a DL model. We obtained testicular histology during conventional testicular sperm extraction. Our DL model was trained based on ultrasound images or fusion data (ultrasound images fused with the corresponding testicular volume) to distinguish spermatozoa presence in pathology (SPP) and spermatozoa absence in pathology (SAP) and to classify maturation arrest (MA) and Sertoli cell-only syndrome (SCOS) in patients with SAP. Areas under the receiver operating characteristic curve (AUCs), accuracy, sensitivity, and specificity were used to analyze model performance. DL based on images achieved an AUC of 0.922 (95% confidence interval [CI]: 0.908-0.935), a sensitivity of 80.9%, a specificity of 84.6%, and an accuracy of 83.5% in predicting SPP (including normal spermatogenesis and hypospermatogenesis) and SAP (including MA and SCOS). In the identification of SCOS and MA, DL on fusion data yielded better diagnostic performance with an AUC of 0.979 (95% CI: 0.969-0.989), a sensitivity of 89.7%, a specificity of 97.1%, and an accuracy of 92.1%. Our study provides a noninvasive method to predict testicular histology for patients with azoospermia, which would avoid unnecessary testicular biopsy.
Humans ; Male ; Azoospermia/diagnostic imaging* ; Deep Learning ; Testis/pathology* ; Retrospective Studies ; Adult ; Ultrasonography/methods* ; Sperm Retrieval ; Sertoli Cell-Only Syndrome/diagnostic imaging*

Background As electronic cigarettes (e-cigarettes) are becoming increasingly prevalent, adolescents are experiencing growing levels of environmental exposure to them. Investigating the status and influencing factors of such exposure is essential to inform the development of targeted tobacco control strategies for youth. Objective To investigate the environmental exposure to electronic cigarettes among vocational school students in Shanghai, identify its influencing factors, and assess its impact on e-cigarette use behavior. Methods By cluster sampling, a total of 4890 students were recruited from 4 vocational schools in Shanghai. A questionnaire was designed to collect information on general characteristics, tobacco use (cigarettes and e-cigarettes), peer/parental e-cigarette use, and environmental e-cigarette exposure (environmental aerosol exposure, e-cigarette sales exposure and e-cigarette-related information exposure). Data were collected via Wenjuanxing (an online survey platform) anonymously and analyzed using SPSS 24.0. Chi-square test and logistic regressionwere used to analyze the data. Results The current vaping rate and vaping attempt rate were 2.29% and 8.94% among all participants. The proportions of students exposed to e-cigarette aerosol, e-cigarette sales, and e-cigarette-related information were 41.0%, 63.3%, and 77.3%, respectively. The exposure to environmental e-cigarette aerosol was higher among male students (45.3%) than among female students (34.5%), while the exposure to e-cigarette sales (65.4%) and e-cigarette-related information (78.8%) were higher among female students than among males (61.9%, 76.2%), respectively (P<0.05). Having friends using e-cigarettes increased the risk of exposure to e-cigarette aerosol (OR=7.32, 95%CI: 6.37, 8.41), exposure to e-cigarette sales (OR=3.10, 95%CI: 2.70, 3.56), and exposure to e-cigarette-related information (OR=1.45, 95%CI: 1.25, 1.69). Current cigarette smoking, parental e-cigarette use, and not living on campus also increased the risk of environmental e-cigarette aerosol exposure and sales exposure among the participants (P<0.01). Higher pocket money was significantly associated with increased risks of exposure to e-cigarette sales and e-cigarette information (P<0.001). Environmental e-cigarette aerosol exposure increased the risk of current (OR=6.21, 95%CI: 3.75, 10.29) and attempt (OR=2.25, 95%CI: 1.82, 2.77) e-cigarette use among the participants. Furthermore, reporting exposure to two or more categories of e-cigarette sales and information was associated with a higher risk of e-cigarette use than those reporting no exposure (P<0.05). Conclusion The adolescents are severely exposed to environmental e- cigarette, which increases their risk of e-cigarette use. We recommend implementing measures to mitigate environmental exposures, strengthen tobacco control education, and reduce peer influence related to e-cigarette use, with particular attention to female adolescents and those with higher pocket money.