ObjectiveTo explore the construction of a machine learning model for the diagnosis of traditional Chinese medicine （TCM） syndromes in chronic cough and the optimization of this model using the Voting ensemble algorithm. MethodsA retrospective analysis was conducted using clinical data from 921 patients with chronic cough treated at the Respiratory Department of Dongfang Hospital， Beijing University of Chinese Medicine. After standardized processing， 84 clinical features were extracted to determine TCM syndrome types. A specialized dataset for TCM syndrome diagnosis in chronic cough was formed by selecting syndrome types with more than 50 cases. The synthetic minority over-sampling technique （SMOTE） was employed to balance the dataset. Four base models， logistic regression （LR）， decision tree （dt）， multilayer perceptron （MLP）， and Bagging， were constructed and integrated using a hard voting strategy to form a Voting ensemble model. Model performance was evaluated using accuracy， recall， precision， F1-score， receiver operating characteristic （ROC） curve， area under the curve （AUC）， and confusion matrix. ResultsAmong the 921 cases， six syndrome types had over 50 cases each， phlegm-heat obstructing the lung （294 cases）， wind pathogen latent in the lung （103 cases）， cold-phlegm obstructing the lung （102 cases）， damp-heat stagnating in the lung （64 cases）， lung yang deficiency （54 cases）， and phlegm-damp obstructing the lung （53 cases）， yielding a total of 670 cases in the specialized dataset. High-frequency symptoms among these patients included cough， expectoration， odor-induced cough， throat itchiness， itch-induced cough， and cough triggered by cold wind. Among the four base models， the MLP model showed the best diagnostic performance （test accuracy： 0.9104； AUC： 0.9828）. Compared with the base models， the Voting ensemble model achieved superior performance with an accuracy of 0.9289 on the training set and 0.9253 on the test set， showing a minimal overfitting gap of 0.0036. It also achieved the highest AUC （0.9836） in the test set， outperforming all base models. The model exhi-bited especially strong diagnostic performance for damp-heat stagnating in the lung （AUC： 0.9984） and wind pathogen latent in the lung （AUC： 0.9970）. ConclusionThe Voting ensemble algorithm effectively integrates the strengths of multiple machine learning models， resulting in an optimized diagnostic model for TCM syndromes in chronic cough with high accuracy and enhanced generalization ability.

OBJECTIVE: To explore the clinical application of transurethral steam thermal ablation of the prostate as an ultra-minimally invasive treatment of benign prostatic hyperplasia (BPH). METHODS: We treated 18 BPH patients by transurethral steam thermal ablation of the prostate in our hospital, and followed them up for 6－12 months after operation. We obtained the IPSS, maximum urinary flow rate (Qmax), IIEF-5 scores, Male Sexual Health Questionnaire-Ejaculatory Dysfunction-Short Form (MSHQ-EjD-SF) scores and quality of life (QOL) scores from the patients and compared them before and after surgery. RESULTS: Operations were successfully completed in all the cases, with no intraoperative complications, and all the patients were discharged on the second day after surgery. At the 6-month follow-up after surgery, the Qmax increased from (10.08 ± 2.06) ml/s before surgery to (7.51 ± 3.21) ml/s, the IPSS decreased from 23.72 ± 1.87 to 8.06 ± 1.39, and the QOL score decreased from 5.11 ± 0.58 before surgery to 1.28 ± 0.46. The differences in these indicators were statistically significant (P<0.05). And these is no significant difference in the MSHQ-EjD-SF and IIEF-5 score(P>0.05). CONCLUSION Transurethral steam thermal ablation of the prostate is a safe, effective and almost non-invasive surgical strategy for the treatment of BPH, with a good prospect of clinical application.
Humans ; Male ; Prostatic Hyperplasia/surgery* ; Aged ; Middle Aged ; Treatment Outcome ; Steam ; Transurethral Resection of Prostate/methods* ; Quality of Life

Objective:This study aims to investigate the influence of microvessel density（MVD） and microlymphatic vessel density（MLVD） on the prognosis of patients with hypopharyngeal squamous cell carcinoma（HPSCC） and to develop a nomogram prediction model for prognosis based on pathological characteristics. Methods:A retrospective analysis was conducted on clinicopathological and follow-up data from HPSCC patients who underwent surgical treatment at our institution between June 2010 and June 2020. Immunohistochemical staining was performed on tumor tissues and adjacent normal margin tissues to evaluate MVD and MLVD. The associations among MVD, MLVD, and clinicopathological features were analyzed. Univariate and multivariate Cox regression analyses were conducted to identify independent risk factors affecting overall survival（OS）. Based on these findings, a nomogram model was constructed and its predictive accuracy was assessed using C-index, receiver operating characteristic（ROC） curve, and calibration curve. Results:Both MVD and MLVD were significantly higher in HPSCC tumor tissues compared to normal tissues. Patients in the high MVD and high MLVD groups exhibited significantly lower OS rates than those in the low MVD and low MLVD groups. Multivariate Cox regression analysis revealed that N stage, recurrence, nerve invasion, lymph node capsule invasion, MVD, and MLVD were independent prognostic factors of OS. Based on these factors, a nomogram prognosis model was successfully constructed. The nomograms demonstrated superior performance in terms of C-index, area under the ROC curve, and calibration, outperforming the AJCC TNM staging system. Conclusion:Elevated MVD and MLVD levels are associated with poorer prognosis in HPSCC patients. The nomogram model based on pathological features provides valuable insights for clinical assessment and decision-making.
Humans ; Hypopharyngeal Neoplasms/blood supply* ; Prognosis ; Retrospective Studies ; Microvascular Density ; Nomograms ; Lymphatic Vessels/pathology* ; Male ; Female ; Middle Aged ; Carcinoma, Squamous Cell/blood supply* ; Microvessels/pathology* ; Lymphatic Metastasis ; Survival Rate

The prevalence of Class III malocclusion varies among different countries and regions. The populations from Southeast Asian countries (Chinese and Malaysian) showed the highest prevalence rate of 15.8%, which can seriously affect oral function, facial appearance, and mental health. As anterior crossbite tends to worsen with growth, early orthodontic treatment can harness growth potential to normalize maxillofacial development or reduce skeletal malformation severity, thereby reducing the difficulty and shortening the treatment cycle of later-stage treatment. This is beneficial for the physical and mental growth of children. Therefore, early orthodontic treatment for Class III malocclusion is particularly important. Determining the optimal timing for early orthodontic treatment requires a comprehensive assessment of clinical manifestations, dental age, and skeletal age, and can lead to better results with less effort. Currently, standardized treatment guidelines for early orthodontic treatment of Class III malocclusion are lacking. This review provides a comprehensive summary of the etiology, clinical manifestations, classification, and early orthodontic techniques for Class III malocclusion, along with systematic discussions on selecting early treatment plans. The purpose of this expert consensus is to standardize clinical practices and improve the treatment outcomes of Class III malocclusion through early orthodontic treatment.
Humans ; Malocclusion, Angle Class III/classification* ; Orthodontics, Corrective/methods* ; Consensus ; Child

Digital technologies have become an integral part of complete denture restoration. With advancement in computer-aided design and computer-aided manufacturing (CAD/CAM), tools such as intraoral scanning, facial scanning, 3D printing, and numerical control machining are reshaping the workflow of complete denture restoration. Unlike conventional methods that rely heavily on clinical experience and manual techniques, digital technologies offer greater precision, predictability, and efficacy. They also streamline the process by reducing the number of patient visits and improving overall comfort. Despite these improvements, the clinical application of digital complete denture restoration still faces challenges that require further standardization. The major issues include appropriate case selection, establishing consistent digital workflows, and evaluating long-term outcomes. To address these challenges and provide clinical guidance for practitioners, this expert consensus outlines the principles, advantages, and limitations of digital complete denture technology. The aim of this review was to offer practical recommendations on indications, clinical procedures and precautions, evaluation metrics, and outcome assessment to support digital restoration of complete denture in clinical practice.
Humans ; Denture, Complete ; Computer-Aided Design ; Denture Design/methods* ; Consensus ; Printing, Three-Dimensional

The 2-(2-phenylethyl)chromones were separated from agarwood of Aquilaria agallocha Roxb. and their anti-KRAS mutant non-small cell lung cancer (NSCLC) activities were evaluated. 2-(2-Phenyethyl)chromones in agarwood were separated and purified by silica gel, RP-18 reverse phase silica gel, MCI gel CH20P, Diol, and semi preparative HPLC chromatography techniques, while the structures of the compounds were identified by extensive spectroscopic analysis, such as 1D and 2D NMR and ESI-MS. The cell counting kit 8 (CCK-8) assay was used to screen the anti-tumor activity of the isolated monomeric compounds on three KRAS-mutant NSCLC cells. The cell proliferation, cloning formation, adhesion ability, and cell cycle arrest activity of compound 8 were analyzed. Molecular docking and Western blot experiments were used to study the mechanism of compound 8. The in vivo anti-tumor activity of the compound 8 was evaluated by zebrafish cell derived xenograft (CDX) model. Nineteen known 2-(2-phenylethyl)chromones were isolated from the ethyl acetate extract of agarwood. On A549 (KRAS G12S) cells, compounds 8, 10, and 19 showed good inhibitory activity, on H23 (KRAS G12C) cells, compounds 7, 8, and 19 showed good inhibitory activity, and on H358 (KRAS G12C) cells, compounds 8, 10, and 16 showed good inhibitory activity. Compound 8 had the best inhibitory activity in all three cell lines. It effectively inhibited cell proliferation, clone formation, adhesion ability, and arrested the H23 cell cycle at G2/M phase. Compound 8 could also inhibit the expression of c-Met and its downstream signaling pathways, effectively inhibiting tumor growth in zebrafish CDX model. In conclusion, among the nineteen known 2-(2-phenylethyl)chromones, compound 8 had the best activity and significantly inhibited the proliferation of KRAS-mutant NSCLC cells by arresting the cells in the G2/M phase.

Objective To evaluate the short-term outcomes and hospital costs of robot-assisted thoracic surgery (RATS) versus video-assisted thoracoscopic surgery (VATS) for non-small cell lung cancer. Methods The clinical data of patients who underwent lobectomy or sublobar resection for non-small cell lung cancer completed by the same operator in the Department of Thoracic Surgery, Gansu Provincial Hospital from June 2016 to June 2022 were retrospectively analyzed. According to the surgery approach, the patients were divided into a RATS group and a VATS group. The clinical data of the two groups were compared. Results A total of 516 patients were enrolled. There were 254 patients in the RATS group, including 103 males and 151 females, with a mean age of 60.0±4.2 years, and 262 patients in the VATS group, including 126 males and 136 females, with a mean age of 59.5±4.3 years. All patients in both groups successfully completed radical lung cancer surgery with no perioperative death. In terms of intraoperative bleeding (63.4±13.3 mL vs. 92.5±23.5 mL), postoperative drainage time (4.1±0.9 d vs. 4.7±1.2 d), postoperative hospital stay time (5.6±1.1 d vs. 6.7±1.4 d), number of lymph nodes dissected (17.9±2.1 vs. 13.9±1.4) and groups of lymph nodes dissected (5.4±0.8 groups vs. 4.4±1.0 groups), the RATS group had an advantage, and the difference was statistically significant (P<0.05). In terms of operative time and total postoperative chest drainage, the VATS group had an advantage, and the difference was statistically significant (P<0.05). There was no statistical difference between the two groups in terms of postoperative complications or intraoperative conversion to thoractomy (P>0.05). The total hospitalization, surgical and total consumables costs of the RATS group were higher than those in the VATS group (P<0.05). In terms of other costs and consumables costs (one-time costs of purely high-value consumables after deduction of robot-specific costs), the VATS group was higher than the RATS group (P<0.05). Conclusion RATS offers technical and short-term efficacy advantages, but comes with the disadvantage of high costs. Thoracic surgeons can make full use of the features of the robotic surgery system, exploiting its potential to continuously improve and optimize techniques and reduce the use of high-value consumables, thus achieving efficiency and cost reductions and allowing robotic surgery to reach more patients.

Background Mercury, as a global heavy metal pollutant, poses a serious threat to human health. The toxicity of mercury depends on its chemical form. Distinguishing the forms of mercury in the environment is of great significance for mercury management and reducing human mercury exposure risks. Objective To establish a non-targeted metallomics method based on synchrotron radiation X-ray fluorescence (SRXRF) spectroscopy combined with machine learning to screen inorganic mercury (IHg) or methylmercury (MeHg) exposed rice plants. Methods Rice seeds were exposed to ultra-pure water (control group), 0.1 mg·L⁻¹ IHg (IHg group) or MeHg (MeHg group) solutions, respectively. After germination, the seedlings were cultured for 21 d, and rice leaves were collected, dried, weighed, and pressed. The content of metallome in rice leaves was determined by SRXRF. Machine learning models including soft independent modeling cluster analysis (SIMCA), partial least squares discriminant analysis (PLS-DA), and logistic regression (LR) were used to classify the SRXRF full spectra of different groups and find the best model to distinguish rice exposed to IHg or MeHg. Besides, characteristic elements were selected as input parameters to optimize the model by improving computing speed and reducing model calculation. Results The SRXRF spectral intensities of the control group, IHg group, and MeHg group were different, indicating that exposure to IHg and MeHg can interfere the homeostasis of metallome in rice leaves. The results of principal component analysis (PCA) of SRXRF spectra showed that the control group could be well distinguished from the mercury exposed groups, but the IHg group and the MeHg group were mostly overlapped. The accuracy rates of the three models (PLS-DA, SIMCA, and LR) were higher than 98% for the training set, higher than 95% for the validation set, and higher than 94% for the cross-validation set. Besides, the accuracy of the LR model was higher than that of the PLS-DA model and the SIMCA model. Furthermore, the accuracy was 92.05% when using characteristic elements K, Ca, Mn, Fe, and Zn selected by LR to distinguish the IHg group and the MeHg group. Compared with the full spectra model, although the prediction accuracy of the characteristic spectral model decreased, the input parameters of the model decreased by 99.51%, and precision, recall, and F1 score were above 84.48%, indicating that the model could distinguish rice exposed to different mercury forms. Conclusion Non-targeted metallomics method based on SRXRF and machine learning can be applied for high-throughput screening of rice exposed to different forms of mercury and thus decrease the risks of people being exposed to mercury.

Objective Tissue uptake and distribution of nano-/microplastics was studied at a single high dose by gavage in vivo.Methods Fluorescent microspheres (100 nm, 3 μm, and 10 μm) were given once at a dose of 200 mg/(kg·body weight). The fluorescence intensity (FI) in observed organs was measured using the IVIS Spectrum at 0.5, 1, 2, and 4 h after administration. Histopathology was performed to corroborate these findings.Results In the 100 nm group, the FI of the stomach and small intestine were highest at 0.5 h, and the FI of the large intestine, excrement, lung, kidney, liver, and skeletal muscles were highest at 4 h compared with the control group (P < 0.05). In the 3 μm group, the FI only increased in the lung at 2 h (P < 0.05). In the 10 μm group, the FI increased in the large intestine and excrement at 2 h, and in the kidney at 4 h (P < 0.05). The presence of nano-/microplastics in tissues was further verified by histopathology. The peak time of nanoplastic absorption in blood was confirmed.Conclusion Nanoplastics translocated rapidly to observed organs/tissues through blood circulation;however, only small amounts of MPs could penetrate the organs.

Objective To evaluate the application effect of joint inventory management method in drug procurement and control management in multiple hospital areas.Methods Based on the joint inventory management model,four pilot drugs were selected from a certain group hospital by reasonable data processing methods for research.The effects of the model application were compared and analyzed from aspects such as inventory cost,turnover situation,and supply situation.Results After applying the joint inventory management model led by the central hospital,the inventory and amount of drugs in the three pilot hospitals were significantly reduced,with the inventory reduced by 31.93%and the average inventory amount decreased by 16.23%;The inventory turnover days had significantly decreased,with the turnover days of all three branches decreasing by more than one day;The drug shortage rate had significantly decreased,with the most significant change among the pilot drugs being the doxorubicin liposome injection,which had a 6.7%decrease in the shortage rate;The comparison results of each group of data showed statistical significance(P＜0.05).Conclusion Adopting a central hospital led joint inventory management model in multiple hospital areas could significantly improve the effectiveness of drug procurement management and inventory management,which enhanced the efficiency of hospital fund utilization.