ObjectiveTo establish a regional risk assessment system for hospital-acquired infections in neurosurgery department of general hospital, and to evaluate its prevention and control effectiveness. MethodsFailure mode and effect analysis (FMEA) was used to identify the core risk factors for infections in neurosurgery department. The risk priority number (RPN) of each risk factor was calculated to determine the priority intervention targets. Targeted interventions were developed and continuously refined through the plan-do-check-act (PDCA) cycles. Data from January to June 2023 (control group) and July to December 2023 (intervention group) were collected to compare the differences in environmental hygiene monitoring qualification rate, incidence rate of hospital-acquired infections among inpatients, and detection rate of bacterial antimicrobial resistance. ResultsHigh-risk factors for hospital-acquired infections in neurosurgery department included patient-related risk factors, inadequate implementation of isolation measures for special infections, and poor compliance with surgical site infection (SSI) prevention protocols. After intervention, the environmental hygiene qualification rate significantly increased from 81.55% to 100.00% (χ²=120.49, P<0.001). The overall hospital-acquired infection rate among inpatients decreased from 2.62% to 2.45%, the infection rate of per case declined from 3.12% to 2.84%, and the detection rate of multidrug-resistant organism infections reduced from 43.72% to 36.79%. Additionally, antimicrobial utilization rate decreased from 48.75% to 42.53% (χ²=34.09, P<0.001). ConclusionThe FMEA-based risk assessment system can effectively identify critical infection risks in neurosurgery department, and targeted interventions can significantly improve infection prevention and control performance.

Objective:To evaluate the effectiveness of anti-G straining maneuver (AGSM) in pilots by surface electromyography (sEMG), and to explore the relationships between characteristics of sEMG and anti-G endurance in pilots.Methods:Thirty-eight male high-performance fighter pilots who completed the human centrifuge test at the Air Force Medical Center were selected. Among them, 25 completed the 8.0 G for 10 s anti-G endurance test and 13 completed the 9.0 G for 10 s anti-G endurance test. The sEMG tester was used to keep track of the changes of sEMG in rectus abdominis, rectus femoris, anterior tibial and gastrocnemius muscles while pilots were engaged in AGSM. The anti-G endurance was evaluated according to the changes of visual fields and consciousness. The pilots were divided into 3 groups: the good vision and consciousness group, peripheral visual field narrowing group and endurance endpoint group. The differences in the integral electromyogram (iEMG), mean power frequency (MPF) and muscle input rates between the 3 groups were investigated.Results:A total of 25 pilots completed the 8.0 G for 10 s anti-G endurance test. Among them, 8 (32.0%) were in the good vision and consciousness group, 13 (52.0%) in the peripheral visual field narrowing group and 4 (16.0%) reached the endurance endpoint. Among the 13 pilots who completed the 9.0 G for 10 s anti-G endurance test, 3 (23.1%) were in the good vision and consciousness group, 6 (46.1%) in the peripheral visual field narrowing group, and 4 (30.8%) in the endurance endpoint group. The results of sEMG showed that the iEMG values of the anterior tibialis muscle in pilots under the 9.0 G for 10 s load were significantly different across endurance groups ( H=7.54, P=0.023), and that the iEMG values of the tibialis anterior muscle in the good vision and consciousness group were higher than those in the endurance endpoint group ( P=0.036). The negative slopes of MPF for the rectus abdominis, rectus femoris, anterior tibialis, and gastrocnemius muscles were higher in the good vision and consciousness group than in the other 2 groups, but the differences were not statistically significant ( P>0.05). During the 8.0 G for 10 s anti-G endurance test, there were significant differences in lower limb muscle contribution rates between the 3 groups ( F=4.19, P=0.029). The endurance endpoint group exhibited a lower contribution rate than the good vision and consciousness group ( P=0.025). During the 9.0 G for 10 s anti-G endurance test, there were significant differences in tibialis anterior muscle contribution rates between the 3 groups ( F=4.16, P=0.049). The endurance endpoint group demonstrated a lower contribution rate than the good vision and consciousness group ( P=0.049). Conclusions:The full and balanced activation of abdominal muscles and lower limb muscles, especially the effective mobilization of calf muscles, plays a pivotal role in improving pilots′ AGSM efficiency in high G environments.

Objective:To evaluate the effectiveness of anti-G straining maneuver (AGSM) in pilots by surface electromyography (sEMG), and to explore the relationships between characteristics of sEMG and anti-G endurance in pilots.Methods:Thirty-eight male high-performance fighter pilots who completed the human centrifuge test at the Air Force Medical Center were selected. Among them, 25 completed the 8.0 G for 10 s anti-G endurance test and 13 completed the 9.0 G for 10 s anti-G endurance test. The sEMG tester was used to keep track of the changes of sEMG in rectus abdominis, rectus femoris, anterior tibial and gastrocnemius muscles while pilots were engaged in AGSM. The anti-G endurance was evaluated according to the changes of visual fields and consciousness. The pilots were divided into 3 groups: the good vision and consciousness group, peripheral visual field narrowing group and endurance endpoint group. The differences in the integral electromyogram (iEMG), mean power frequency (MPF) and muscle input rates between the 3 groups were investigated.Results:A total of 25 pilots completed the 8.0 G for 10 s anti-G endurance test. Among them, 8 (32.0%) were in the good vision and consciousness group, 13 (52.0%) in the peripheral visual field narrowing group and 4 (16.0%) reached the endurance endpoint. Among the 13 pilots who completed the 9.0 G for 10 s anti-G endurance test, 3 (23.1%) were in the good vision and consciousness group, 6 (46.1%) in the peripheral visual field narrowing group, and 4 (30.8%) in the endurance endpoint group. The results of sEMG showed that the iEMG values of the anterior tibialis muscle in pilots under the 9.0 G for 10 s load were significantly different across endurance groups ( H=7.54, P=0.023), and that the iEMG values of the tibialis anterior muscle in the good vision and consciousness group were higher than those in the endurance endpoint group ( P=0.036). The negative slopes of MPF for the rectus abdominis, rectus femoris, anterior tibialis, and gastrocnemius muscles were higher in the good vision and consciousness group than in the other 2 groups, but the differences were not statistically significant ( P>0.05). During the 8.0 G for 10 s anti-G endurance test, there were significant differences in lower limb muscle contribution rates between the 3 groups ( F=4.19, P=0.029). The endurance endpoint group exhibited a lower contribution rate than the good vision and consciousness group ( P=0.025). During the 9.0 G for 10 s anti-G endurance test, there were significant differences in tibialis anterior muscle contribution rates between the 3 groups ( F=4.16, P=0.049). The endurance endpoint group demonstrated a lower contribution rate than the good vision and consciousness group ( P=0.049). Conclusions:The full and balanced activation of abdominal muscles and lower limb muscles, especially the effective mobilization of calf muscles, plays a pivotal role in improving pilots′ AGSM efficiency in high G environments.

Objective:To observe the ultrasonographic features of idiopathic uveal effusion syndrome (IUES).Methods:A retrospective controlled study. From January 2012 to December 2023, 13 patients with IUES (26 eyes) diagnosed by ophthalmology examination in Department of Ophthalmonogy of Beijing Tongren Hospital (IUES group) and 22 healthy people with 30 eyes (control group) were included in the study. Both eyes of all participants were examined by color Doppler ultrasound (CDU) and ultrasonic biomicroscopy (UBM). The thickness of the ocular wall at 300 μm on the temporal side of the optic disc was measured by CDU. UBM was used to measure the thickness of the nasal and temporal scleral processes. Corneal thickness (CT), anterior chamber depth (AD), lens thickness (LT) and axial length (AL) were measured by A-mode ultrasound. There were no significant differences in age ( t=0.842), sex component ratio ( χ2=0.540), eye difference ( χ2=0.108) and AL ( t=0.831) between IUES group and control group ( P>0.05). The CDU and UBM imaging features and biometrics of IUES affected eyes were observed. Independent sample t test was used for comparison between groups. Results:CDU examination results showed that in 26 eyes of IUES group, choroidal detachment occurred in 20 eyes (76.9%, 20/26), which showed arc-shaped band echo connected with peripheral and equatorial eye wall echo, with uniform low echo area below, and blood flow signal could be seen on the band echo. The echo thickened and decreased in 4 eyes (15.3%, 4/26). Nine eyes (33.3%, 9/26) were accompanied by retinal detachment, which showed that the posterior pole vitreous echo was connected to the optic disc echo, and the blood flow signal was seen on the ribbon echo. UBM results showed ciliary detachment in 22 eyes (84.7%, 22/26), showing a spongy thickening of the ciliary body with interlamellar echo separation and an echoless area between the sclera. Ciliary body echo thickened and decreased in 2 eyes (7.7%, 2/26). Shallow space between ciliary body and sclera was observed in 2 eyes (7.7%, 2/26). Compared with the control group, CT ( Z=2.054), LT ( Z=1.867), scleral thickness ( Z=2.536) and ocular wall thickness ( Z=2.094) were thickened in IUES group, and AD ( Z=1.888) were decreased, with statistical significance ( P<0.05). Conclusions:The CDU of IUES is characterized by a thickened echo of the ocular wall and a uniform low echo area under the detached choroid. UBM is characterized by a spongy thickening of the ciliary body echo with interlaminar echo separation.

Objective:To observe the color Doppler flow imaging (CDFI) features of vitreoretinal lymphoma (VRL).Methods:Retrospective case series. From January 2022 to December 2024, 71 eyes of 42 patients diagnosed with VRL at the Eye Center of Beijing Tongren Hospital were enrolled. Among them, 17 were male and 25 female; 29 had bilateral and 13 unilateral involvement. Age ranged 17-78 years (median 59 years). Eleven cases had histopathologic confirmation and 31 were clinically diagnosed. All patients underwent CDFI and optical coherence tomography (OCT). CDFI findings were analyzed, noting the presence or absence of vitreous opacities (centrifugal distribution), posterior vitreous detachment (PVD), retinal detachment, and retinal elevated lesions. With Doppler overlay, blood flow within retinal lesions was assessed. The χ 2 test was used to compare the detection rates of retinal lesions by CDFI and OCT, while Cohen’s Kappa assessed agreement in identifying the depth of lymphoma cell infiltration. Results:Among 71 eyes, vitreous opacity occurred in 66 eyes (93.0%, 66/71), of which 40 eyes (60.6%, 40/66) showed centrifugal opacity. 58 eyes (81.7%, 58/71) had posterior vitreous detachment. Retinal detachment occurred in 7 eyes (9.9%, 7/71). Retinal occupying lesions occurred in 23 eyes (32.4%, 23/61), of which 15 eyes (65.2%, 15/23) showed blood flow signals on the surface of the lesions but no blood flow signals inside the lesions by CDFI, and OCT showed that tumor cells gathered under the retinal pigment epithelium. CDFI showed blood flow signals inside the lesions in 8 eyes (34.8%, 8/23), and OCT showed that tumor cells gathered between retinal neuroepithelial layers. The lesion detection rate of OCT (69.6%, 16/23) was significantly lower than that of CDFI (100.0%, 23/23) ( χ 2= 6.066, P=0.014). OCT and CDFI showed perfect agreement in determining the depth of tumor cell infiltration (Kappa=1.0). Conclusions:The ultrasonographic manifestations of VRL include vitreous opacity, PVD, and some retinal occupying lesions. Vitreous opacity often presents a characteristic centrifugal distribution. By observing whether there is blood flow signal in retinopathy using Doppler, the level of lymphoma cell infiltration can be suggested.

Objective To reveal the scientific output and trends in pulmonary nodules/early-stage lung cancer prediction models. Methods Publications on predictive models of pulmonary nodules/early lung cancer between January 1, 2002 and June 3, 2023 were retrieved and extracted from CNKI, Wanfang, VIP and Web of Science database. CiteSpace 6.1.R3 and VOSviewer 1.6.18 were used to analyze the hotspots and theme trends. Results A marked increase in the number of publications related to pulmonary nodules/early-stage lung cancer prediction models was observed. A total of 12581 authors from 2711 institutions in 64 countries/regions published 2139 documents in 566 academic journals in English. A total of 282 articles from 1256 authors were published in 176 journals in Chinese. The Chinese and English journals which published the most pulmonary nodules/early-stage lung cancer prediction model-related papers were Journal of Clinical Radiology and Frontiers in Oncology, respectively. Chest was the most frequently cited journal. China and the United States were the leading countries in the field of pulmonary nodules/early-stage lung cancer prediction models. The institutions represented by Fudan University had significant academic influence in the field. Analysis of keywords revealed that multi-omics, nomogram, machine learning and artificial intelligence were the current focus of research. Conclusion Over the last two decades, research on risk-prediction models for pulmonary nodules/early-stage lung cancer has attracted increasing attention. Prognosis, machine learning, artificial intelligence, nomogram, and multi-omics technologies are both current hotspots and future trends in this field. In the future, in-depth explorations using different omics should increase the sensitivity and accuracy of pulmonary nodules/early-stage lung cancer prediction models. More high-quality future studies should be conducted to validate the efficacy and safety of pulmonary nodules/early-stage lung cancer prediction models further and reduce the global burden of lung cancer.

Objective: To evaluate the clinical efficacy of digitally guided precision crown lengthening in secondary aesthetic rehabilitation cases, and to provide a clinical reference for digitally guided crown lengthening procedures and secondary aesthetic restorations. Methods: We present a case of a patient with tetracycline-stained teeth, partial detachment of anterior resin veneers, and gingival margin discrepancies. The patient underwent digitally guided precision crown lengthening followed by secondary aesthetic rehabilitation. Multimodal data, including intraoral, facial, and CBCT scans, were integrated to construct a four-dimensional virtual patient model (incorporating teeth, face, bone, and occlusion) for surgical planning and 3D-printed guide fabrication. Secondary aesthetic restoration was performed after achieving stable post-surgical outcomes. Based on this case, we conducted a detailed analysis and reviewed relevant literature on crown lengthening in secondary aesthetic rehabilitation. Results: The gingival contour of the anterior teeth exhibited significant improvement, with enhanced symmetry and stable gingival margin positioning that closely matched the preoperative design. The crown lengthening procedure demonstrated high precision, and the final outcome was aesthetic and functional. Literature review indicated that secondary restorations frequently present challenges such as gingival contour discrepancies and inflammation. Aesthetic crown lengthening in the anterior region should optimize both soft and hard tissue morphology to meet aesthetic standards, with digital technology improving procedural accuracy. Conclusion Precision crown lengthening effectively addresses gingival margin discrepancies in secondary aesthetic rehabilitation, ensuring stable gingival positioning and superior aesthetic outcomes. This approach is particularly suitable for cases with high aesthetic demands.

Objective To analyze the differences in distribution of traditional Chinese medicine (TCM) syndrome elements and salivary microbiota between the individuals with pulmonary nodules and those without, and to explore the potential correlation between the distribution of TCM syndrome elements and salivary microbiota in patients with pulmonary nodules. Methods We retrospectively recruited 173 patients with pulmonary nodules (PN) and 40 healthy controls (HC). The four diagnostic information was collected from all participants, and syndrome differentiation method was used to analyze the distribution of TCM syndrome elements in both groups. Saliva samples were obtained from the subjects for 16S rRNA high-throughput sequencing to obtain differential microbiota and to explore the correlation between TCM syndrome elements and salivary microbiota in the evolution of the pulmonary nodule disease. Results The study found that in the PN group, the primary TCM syndrome elements related to disease location were the lung and liver, and the primary TCM syndrome elements related to disease nature were yin deficiency and phlegm. In the HC group, the primary TCM syndrome elements related to disease location were the lung and spleen, and the primary TCM syndrome elements related to disease nature were dampness and qi deficiency. There were differences between the two groups in the distribution of TCM syndrome elements related to disease location (lung, liver, kidney, exterior, heart) and disease nature (yin deficiency, phlegm, qi stagnation, qi deficiency, dampness, blood deficiency, heat, blood stasis) (P<0.05). The species abundance of the salivary microbiota was higher in the PN group than that in the HC group (P<0.05), and there was significant difference in community composition between the two groups (P<0.05). Correlation analysis using multiple methods, including Mantel test network heatmap analysis and Spearman correlation analysis and so on, the results showed that in the PN group, Prevotella and Porphyromonas were positively correlated with disease location in the lung, and Porphyromonas and Granulicatella were positively correlated with disease nature in yin deficiency (P<0.05). Conclusion The study concludes that there are notable differences in the distribution of TCM syndrome elements and the species abundance and composition of salivary microbiota between the patients with pulmonary nodules and the healthy individuals. The distinct external syndrome manifestations in patients with pulmonary nodules, compared to healthy individuals, may be a cascade event triggered by changes in the salivary microbiota. The dual correlation of Porphyromonas with both disease location and nature suggests that changes in its abundance may serve as an objective indicator for the improvement of symptoms in patients with yin deficiency-type pulmonary nodules.

Objective To construct a "disease-syndrome combination" mathematical representation model for pulmonary nodules based on oral microbiome data, utilizing a multimodal data algorithm framework centered on dynamic systems theory. Furthermore, to compare predictive models under various algorithmic frameworks and validate the efficacy of the optimal model in predicting the presence of pulmonary nodules. Methods A total of 213 subjects were prospectively enrolled from July 2022 to March 2023 at the Hospital of Chengdu University of Traditional Chinese Medicine, Sichuan Cancer Hospital, and the Chengdu Integrated Traditional Chinese and Western Medicine Hospital. This cohort included 173 patients with pulmonary nodules and 40 healthy subjects. A novel multimodal data algorithm framework centered on dynamic systems theory, termed VAEGANTF (Variational Auto Encoder-Generative Adversarial Network-Transformer), was proposed. Subsequently, based on a multi-dimensional integrated dataset of “clinical features-syndrome elements-microorganisms”, all subjects were divided into training (70%) and testing (30%) sets for model construction and efficacy testing, respectively. Using pulmonary nodules as dependent variables, and combining candidate markers such as clinical features, lesion location, disease nature, and microbial genera, the independent variables were screened based on variable importance ranking after identifying and addressing multicollinearity. Missing values were then imputed, and data were standardized. Eight machine learning algorithms were then employed to construct pulmonary nodule risk prediction models: random forest, least absolute shrinkage and selection operator (LASSO) regression, support vector machine, multilayer perceptron, eXtreme Gradient Boosting (XGBoost), VAE-ViT (Vision Transformer), GAN-ViT, and VAEGANTF. K-fold cross-validation was used for model parameter tuning and optimization. The efficacy of the eight predictive models was evaluated using confusion matrices and receiver operating characteristic (ROC) curves, and the optimal model was selected. Finally, goodness-of-fit testing and decision curve analysis (DCA) were performed to evaluate the optimal model. Results There were no statistically significant differences between the two groups in demographic characteristics such as age and sex. The 213 subjects were randomly divided into training and testing sets (7 : 3), and prediction models were constructed using the eight machine learning algorithms. After excluding potential problems such as multicollinearity, a total of 301 clinical feature information, syndrome elements, and microbial genera markers were included for model construction. The area under the curve (AUC) values of the random forest, LASSO regression, support vector machine, multilayer perceptron, and VAE-ViT models did not reach 0.85, indicating poor efficacy. The AUC values of the XGBoost, GAN-ViT, and VAEGANTF models all reached above 0.85, with the VAEGANTF model exhibiting the highest AUC value (AUC=0.923). Goodness-of-fit testing indicated good calibration ability of the VAEGANTF model, and decision curve analysis showed a high degree of clinical benefit. The nomogram results showed that age, sex, heart, lung, Qixu, blood stasis, dampness, Porphyromonas genus, Granulicatella genus, Neisseria genus, Haemophilus genus, and Actinobacillus genus could be used as predictors. Conclusion The “disease-syndrome combination” risk prediction model for pulmonary nodules based on the VAEGANTF algorithm framework, which incorporates multi-dimensional data features of “clinical features-syndrome elements-microorganisms”, demonstrates better performance compared to other machine learning algorithms and has certain reference value for early non-invasive diagnosis of pulmonary nodules.

Objective To construct and validate a prediction model for urosepsis in patients after upper urinary tract stone surgery using various machine learning algorithms.Methods A total of 7 464 upper urinary tract stone patients who underwent surgery at the Sixth Affiliated Hospital of Guangxi Medical University from Jun.2018 to Jun.2023 were enrolled and randomly assigned to training(5 224 cases)or validation sets(2 240 cases)at a ratio of 7∶3.Among them,622(8.33%)cases developed urosepsis postoperatively.Six machine learning algorithms,including extreme gradient boosting(XGBoost),logistic regression,light gradient boosting machine(LightGBM),random forest(RF),adaptive boosting(AdaBoost),and gradient boosting decision tree(GBDT),were used to construct prediction models for postoperative urosepsis.The model's predictive ability and clinical benefits were evaluated using receiver operating characteristic(ROC)curves,Shapley additive explanation(SHAP)analysis,calibration curves,and decision curve analysis(DCA).Results The clinical features included body mass index(BMI),number of surgeries,heart rate,Barthel index,venous thrombo embolism(VTE)risk assessment,gender,American Society of Anesthesiologists(ASA)grade,urinary nitrite,and urinary leukocyte in the models.In the training set,the XGBoost,LightGBM,and RF models performed excellently,with area under curve(AUC)values of ROC curves reaching 1.00.In the validation set,the logistic regression model performed the best,with an AUC value of ROC curve of 0.76,showing good predictive stability and calibration.The AdaBoost and GBDT models followed with AUC values of 0.74 and 0.75,respectively,while the AUC values of the LightGBM,XGBoost,and RF models were 0.71,0.70,and 0.68.In terms of model interpretability,SHAP analysis showed the contribution of variables in a descending order as:heart rate,urinary leukocytes,gender,BMI,Barthel index,VTE risk assessment,urinary nitrite,number of surgeries,and ASA grade.Conclusion A logistic regression model for early risk prediction of postoperative urosepsis in upper urinary tract stone patients has been successfully constructed.This model has good predictive performance and calibration,and can effectively assist clinical diagnosis.