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 compare the difference,agreement,and axial length measurement success rate between biometers ZW-30 and IOLMaser 700 based on swept-source optical coherence tomography for the measurement of ocular bi-ological parameters in patients with cataracts.Methods A total of 126 cataract patients(233 eyes)who were advised to undergo cataract surgery at the Department of Ophthalmology at Beijing Tongren Hospital,Capital Medical University from January to February 2024 were included in this study.Two biometers were used to measure the axial length(AL),mean keratometry(Km),anterior chamber depth(ACD),lens thickness(LT),central corneal thickness(CCT),and horizontal corneal diameter[namely,the white-to-white(WTW)distance].The axial measurement success rate of the two biometers and the difference and agreement between the parameters were calculated.Results The mean difference between ZW-30 and IOLMaster 700 was(-0.006±0.042)mm for AL,(-0.074±0.204)D for Km,(0.031±0.051)mm for ACD,(0.001±0.005)mm for CCT,and(-0.286±0.337)mm for WTW,and the differences were statistically significant(all P＜0.05).The mean difference between ZW-30 and IOLMaster 700 was(0.008±0.215)mm for LT,and the difference was not statis-tically significant(t=0.579,P=0.563).The 95％limits of agreement range was between-0.011 mm and 0.000 mm for AL,between-0.474 D and 0.326 D for Km,between-0.010 mm and 0.012 mm for CCT,between-0.068 mm and 0.131 mm for ACD,between-0.116 mm and 0.159 mm for LT,and between-0.947 mm and 0.376 mm for WTW.The intra-class correlation coefficient of all measurements ranged from 0.790 to 1.000.The AL measurement success rate of IOLMaster 700 and ZW-30 was 95.3％and 95.7％,respectively.The latter had an AL measurement success rate of 98.7％after manually marking the position of the retinal identification line.Conclusion There were statistically significant differences between ZW-30 and IOLMaster 700 in the measurement of the AL,Km,ACD,and CCT,which,however,were not clinically significant.The agreement between both was good.ZW-30 had a higher AL measurement success rate,espe-cially for the manual identification function of eyes with opacified refractive media,which can further improve the AL meas-urement success rate and provide reference for clinical work.

Objective To compare the difference,agreement,and axial length measurement success rate between biometers ZW-30 and IOLMaser 700 based on swept-source optical coherence tomography for the measurement of ocular bi-ological parameters in patients with cataracts.Methods A total of 126 cataract patients(233 eyes)who were advised to undergo cataract surgery at the Department of Ophthalmology at Beijing Tongren Hospital,Capital Medical University from January to February 2024 were included in this study.Two biometers were used to measure the axial length(AL),mean keratometry(Km),anterior chamber depth(ACD),lens thickness(LT),central corneal thickness(CCT),and horizontal corneal diameter[namely,the white-to-white(WTW)distance].The axial measurement success rate of the two biometers and the difference and agreement between the parameters were calculated.Results The mean difference between ZW-30 and IOLMaster 700 was(-0.006±0.042)mm for AL,(-0.074±0.204)D for Km,(0.031±0.051)mm for ACD,(0.001±0.005)mm for CCT,and(-0.286±0.337)mm for WTW,and the differences were statistically significant(all P＜0.05).The mean difference between ZW-30 and IOLMaster 700 was(0.008±0.215)mm for LT,and the difference was not statis-tically significant(t=0.579,P=0.563).The 95％limits of agreement range was between-0.011 mm and 0.000 mm for AL,between-0.474 D and 0.326 D for Km,between-0.010 mm and 0.012 mm for CCT,between-0.068 mm and 0.131 mm for ACD,between-0.116 mm and 0.159 mm for LT,and between-0.947 mm and 0.376 mm for WTW.The intra-class correlation coefficient of all measurements ranged from 0.790 to 1.000.The AL measurement success rate of IOLMaster 700 and ZW-30 was 95.3％and 95.7％,respectively.The latter had an AL measurement success rate of 98.7％after manually marking the position of the retinal identification line.Conclusion There were statistically significant differences between ZW-30 and IOLMaster 700 in the measurement of the AL,Km,ACD,and CCT,which,however,were not clinically significant.The agreement between both was good.ZW-30 had a higher AL measurement success rate,espe-cially for the manual identification function of eyes with opacified refractive media,which can further improve the AL meas-urement success rate and provide reference for clinical work.

Objective:To analyze the biological parameters of the anterior segment of age-related cataracts patients with or without zonular weakness with the new generation of sweep-source anterior optical coherence tomography (OCT) device CASIA2 to provide a basis for the diagnosis of zonular weakness.Methods:A case-control study was conducted.A total of 158 cases (186 eyes) of patients with age-related cataracts having zonular weakness as a zonular weakness group, and 80 cases (80 eyes) of cataract with age-related cataracts without zonular weakness as a normal zonule group were enroll from June 2022 to June 2023 at Beijing Tongren Hospital.All patients underwent routine preoperative ophthalmological examination including slit lamp microsopy, IOLMaster 700, ocular B-ultrasound and OCT.The anterior chamber depth (ACD), anterior chamber volume (ACV), lens thickness (LT), lens vault (LV), anterior chamber angle opening distance at 750 μm (AOD750), lens decentration and tilt, radius of curvature of the anterior lens surface (RAL), and radius of curvature of the posterior lens surface (RPL) of patients were measured with CASIA2 and compared between the two groups.The relationship between zonular weakness and anterior chamber parameters and lens parameters was evaluated by logistic regression analysis.This study followed the Declaration of Helsinki.The study protocol was approved by the Ethics Committee of Beijing Tongren Hospital (No.TRECKY2018-049), and all enrolled patients signed the informed consent form.Results:In the zonular weakness group, ACD, ACV, RAL, and AOD750 were (2.34±0.56)mm, (85.05±40.19)mm 3, 7.52 (7.13, 8.08)mm, and 0.27 (0.07, 0.30)mm, respectively, which were smaller than (3.13±0.38)mm, (127.75±38.15)mm 3, 9.28(8.51, 9.76)mm, and 0.52 (0.31, 0.65)mm in the normal zonule group, with statistically significant differences (all P<0.05).LT and LV in the zonular weakness group were (5.14±0.45)mm and (1.22±0.53)mm, respectively, which were larger than (4.27±0.52)mm and (0.32±0.30)mm in the normal zonule group, and the differences were statistically significant (both P<0.05).Magnitude of lens decentration and tilt in the zonular weakness were 0.34 (0.13, 0.45)mm and 6.44 (3.67, 7.32)°, respectively, which were significantly larger than 0.19 (0.12, 0.25)mm and 4.88 (3.85, 5.65)° in the normal zonule group (both P<0.05).No obvious pattern was found in the direction of decentration and tilt.Logistic regression analysis showed that LV and lens decentration were risk factors of zonular weakness (odds ratios [ OR]=706.170, 335.339; both P<0.05), and RAL was a protective factor of zonular weakness ( OR=0.239, P<0.05). Conclusions:Age-related cataract patients with zonular weakness often present with a decrease in ACV due to increased anterior convexity of the lens.Decreased RAL, increased lens decentration and elevated LV are risk factors for zonular weakness.

Objective:To analyze the biological parameters of the anterior segment of age-related cataracts patients with or without zonular weakness with the new generation of sweep-source anterior optical coherence tomography (OCT) device CASIA2 to provide a basis for the diagnosis of zonular weakness.Methods:A case-control study was conducted.A total of 158 cases (186 eyes) of patients with age-related cataracts having zonular weakness as a zonular weakness group, and 80 cases (80 eyes) of cataract with age-related cataracts without zonular weakness as a normal zonule group were enroll from June 2022 to June 2023 at Beijing Tongren Hospital.All patients underwent routine preoperative ophthalmological examination including slit lamp microsopy, IOLMaster 700, ocular B-ultrasound and OCT.The anterior chamber depth (ACD), anterior chamber volume (ACV), lens thickness (LT), lens vault (LV), anterior chamber angle opening distance at 750 μm (AOD750), lens decentration and tilt, radius of curvature of the anterior lens surface (RAL), and radius of curvature of the posterior lens surface (RPL) of patients were measured with CASIA2 and compared between the two groups.The relationship between zonular weakness and anterior chamber parameters and lens parameters was evaluated by logistic regression analysis.This study followed the Declaration of Helsinki.The study protocol was approved by the Ethics Committee of Beijing Tongren Hospital (No.TRECKY2018-049), and all enrolled patients signed the informed consent form.Results:In the zonular weakness group, ACD, ACV, RAL, and AOD750 were (2.34±0.56)mm, (85.05±40.19)mm 3, 7.52 (7.13, 8.08)mm, and 0.27 (0.07, 0.30)mm, respectively, which were smaller than (3.13±0.38)mm, (127.75±38.15)mm 3, 9.28(8.51, 9.76)mm, and 0.52 (0.31, 0.65)mm in the normal zonule group, with statistically significant differences (all P<0.05).LT and LV in the zonular weakness group were (5.14±0.45)mm and (1.22±0.53)mm, respectively, which were larger than (4.27±0.52)mm and (0.32±0.30)mm in the normal zonule group, and the differences were statistically significant (both P<0.05).Magnitude of lens decentration and tilt in the zonular weakness were 0.34 (0.13, 0.45)mm and 6.44 (3.67, 7.32)°, respectively, which were significantly larger than 0.19 (0.12, 0.25)mm and 4.88 (3.85, 5.65)° in the normal zonule group (both P<0.05).No obvious pattern was found in the direction of decentration and tilt.Logistic regression analysis showed that LV and lens decentration were risk factors of zonular weakness (odds ratios [ OR]=706.170, 335.339; both P<0.05), and RAL was a protective factor of zonular weakness ( OR=0.239, P<0.05). Conclusions:Age-related cataract patients with zonular weakness often present with a decrease in ACV due to increased anterior convexity of the lens.Decreased RAL, increased lens decentration and elevated LV are risk factors for zonular weakness.

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 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 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.

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.