Childhood simple obesity has become a significant public health issue in China. Modern medicine primarily relies on lifestyle interventions and often suffers from poor long-term compliance， while pharmacological options are limited and associated with potential adverse effects. Traditional Chinese Medicine （TCM） has a long history in the prevention and management of this condition， demonstrating eight distinct advantages， including systematic theoretical foundation， diversified therapeutic approaches， definite therapeutic efficacy， high safety profile， good patient compliance， comprehensive intervention strategies， emphasis on prevention， and stepwise treatment protocols. Additionally， TCM is characterized by six distinctive features： the use of natural medicinal substances， non-invasive external therapies， integration of medicinal dietetics， simple exercise regimens， precise syndrome differentiation， and diverse dosage forms. By combining internal and external treatments， TCM facilitates individualized regimen adjustment and holistic regulation， demonstrating remarkable effects in improving obesity-related metabolic indicators， regulating constitutional imbalance， and promoting healthy behaviors. However， challenges remain， such as inconsistent operational standards， insufficient high-quality clinical evidence， and a gap between basic research and clinical application. Future efforts should focus on accelerating the standardization of TCM diagnosis and treatment， conducting multicenter randomized controlled trials， and fostering interdisciplinary integration， so as to enhance the scientific validity and international recognition of TCM in the prevention and treatment of childhood obesity.

Objective: To explore the application method and clinical efficacy of endoscopic and computerized navigation technology in maxillofacial foreign body removal surgery, and to provide a reference for the clinical application of this technology. Methods: This study, which was approved by the Medical Ethics Committee of the hospital, retrospectively analyzed the data of five patients with maxillofacial foreign bodies who were admitted to Sun Yat-sen Memorial Hospital, Sun Yat-sen University from January 2018 to December 2024. All patients underwent preoperative CT scanning. Intraoperatively, endoscopic and computer navigation techniques were used in combination or separately according to the location, size, and adjacency of the foreign body to important neurovascular vessels. The foreign body was precisely localized by endoscopic magnification and direct visualization, and the optimal surgical path was designed and verified under the real-time guidance of computerized navigation to accurately remove the foreign body. The type of foreign body, location, length and diameter, duration of surgery, length of incision, success rate of foreign body removal, postoperative complications, and follow-up were recorded and analyzed. Results: The foreign body was successfully removed in all five patients with a success rate of 100%. The intraoperative computerized navigation system was accurate in positioning, and the alignment stability was not significantly affected by mandibular movement; the endoscope provided good illumination and exposure of the operative field. All surgical incisions were small, and no serious complications, such as foreign body residue, important neurovascular injury, or infection, occurred after surgery. One month after the operation, the patients were followed up and recovered well. Conclusion The combination of endoscopy and computer navigation or separately assisted technology can provide a clear field and real-time positioning for maxillofacial foreign body removal, effectively avoiding important anatomical structures, thus realizing safe and complete foreign body removal with minimized trauma. This assistive technology significantly improves the accuracy and safety of the operation and has clinical promotion value.

Objective: Data on syncopal donation-related vasovagal reaction (DRVR) collected from 74 blood centers between 2020 and 2023 was statistically analyzed to provide a reference for developing preventive strategies against syncopal DRVR. Methods: Data on blood donation adverse reactions and basic information of donors from 2020 to 2023 were collected through the information management system at monitoring sentinel sites. Statistical analysis was performed on the following aspects of syncopal DRVR: characteristics of donors who experienced syncope, reported incidence, triggers, duration, presence and occurrence time of syncope-related trauma, clinical management including outpatient and inpatient treatment, and severity grading. Results: From 2020 to 2023, 45 966 donation-related adverse reactions were recorded. Of these, 1 665 (3.72%) cases were syncopal DRVR. The incidence of syncopal DRVR decreased with age, being the highest in the 18-22 age group. Incidence was significantly higher in female donors than male donors, in first-time donors than repeat donors, and in university and individual donors than group donors (all P<0.05). There was no statistically significant difference among different blood donation locations (P>0.05). The top three triggers were tension, fatigue, and needle phobia or fear of blood. Among syncopal DRVR cases, 60.36% occurred during blood collection, 87.63% lasted for less than 60 seconds, and 5.05% were accompanied by trauma. Notably, 57.14% of these traumas occurred after donor had left the blood collection site. Syncope severity was graded based on required treatment: grade 1 (fully recovered without treatment, 95.50%); grade 2 (recovered after outpatient treatment, 4.02%); and grade 3 (recovered after inpatient treatment, 0.48%). Conclusion: By analyzing the data of syncopal DRVR cases, it is possible to provide a reference for formulating blood donor safety policies.

Objective To observe the value of intratumoral and peritumoral ROI-based CT radiomics combined with clinical and imaging features for preoperatively predicting renal capsule invasion of clear cell renal cell carcinoma(ccRCC).Methods Totally 105 ccRCC patients were retrospectively collected and divided into invasion group(n=70)and non-invasion group(n=35)according to post operation pathology,also divided into training set(n=84,including 56 cases of invasion group and 28 of non-invasion group)and test set(n=21,including 14 cases of invasion group and 7 of non-invasion group)at a ratio of 8∶2.A clinical-imaging model was constructed based on clinical and CT features being significantly different between groups.Radiomics features related to renal capsule invasion were extracted and selected from intratumoral and of 1-6 mm peritumoral ROI on unenhanced phase(UP),corticomedullary phase(CMP)and nephrographic phase(NP)CT images,respectively.The optimal algorithm was selected among 6 machine learning algorisms based on CMP intratumoral ROI.With the optimal and selected features,single intratumoral or peritumoral models,combined intratumoral and peritumoral models within the same phase and combined pairwise models within the same range across different phases images were established.The best performing radiomics model was chosen and integrated with clinical and imaging features to form a combined model.Receiver operating characteristic(ROC)curves were drawn,the area under the curve(AUC)was calculated to evaluate the efficacy of model for predicting renal capsule invasion of ccRCC,which were compared using DeLong's test.Results Hypertension,presence of clinical symptoms and high enhancement degree on CMP images were all independent predicting factors for renal capsule invasion of ccRCC,which were used to establish clinical-imaging model.Support vector machine(SVM)was the optimal algorithm.CMP peritumoral 3 mm model,CMP intratumoral model,NP peritumoral 4 mm model,NP intratumoral+peritumoral 4 mm model and CMP peritumoral 3 mm+NP peritumoral 3 mm model showed higher performance than the others,with AUC being not significantly different(all P＞0.05).CMP peritumoral 3 mm model was the optimal radiomics model,with the highest AUC(0.898)in test set.The combined model demonstrated superior AUC(0.979)in training set compared to both clinical-imaging model and the optimal radiomics model alone(both P＜0.05),while in test set(AUC 0.918)showed comparable performance with the latter two(both P＞0.05).Conclusion CT-based peritumoral radiomics models were equally effective for preoperatively predicting renal capsule invasion of ccRCC.Combining with clinical and imaging features might further enhance diagnostic performance.

Objective To explore the application of cell block(CB)technology in the diagnosis of papillary thyroid carcinoma(PTC).Methods A total of 86 patients with thyroid nodules who were treated at Luoyang Central Hospital Affiliated to Zhengzhou University from February 2023 to December 2024 were retrospectively selected.Before the operation,all patients underwent ultrasound-guided fine needle aspiration biopsy.After the biopsy,the specimens were respectively subjected to cell smear(CS)and CB pathological examinations.Using postoperative pathology as the gold standard,the clinical application value of CS,CB,BRAFV600E gene detection,and combined detection in the preoperative diagnosis of PTC was evaluated.Results A total of 86 patients were included.Among them,42 cases were diagnosed as PTC by postoperative pathology,and 44 cases were benign lesions.The sensitivity(73.81％),specificity(97.73％),positive predictive value(96.88％),negative predictive value(79.63％),and accuracy(86.05％)of CB in the preoperative diagnosis of PTC were significantly higher than those of CS.The Kappa consistency test results showed that the consistency between CB pathological results and postoperative pathological results was better than that of CS.Receiver operating characteristic curve analysis indicated that the area under the curve(AUC)of CB was significantly higher than that of CS(0.858 vs.0.743,Z=2.391,P=0.017).Furthermore,the combination of CB and BRAFV600E gene detection further improved preoperative diagnostic performance,with a consistency with postoperative pathological results(Kappa=0.837)superior to other examination methods,and an AUC(0.918)significantly higher than other examination methods.Conclusion CB has significant clinical application value in the preoperative diagnosis of PTC,with diagnostic performance significantly superior to CS.Further combination with BRAFV600E gene detection can significantly improve preoperative diagnostic accuracy.

Spleen-stomach damp-heat syndrome is currently the most prevalent TCM pattern in patients with chronic atrophic gastritis (CAG), with internal damp-heat accumulation regarded as a key factor contributing to its prolonged and refractory course. This syndrome represents a critical stage in the progressive pathogenesis of CAG, characterized by a deepening pathological evolution. Modern TCM practitioners generally agree that its core pathogenesis lies in "deficiency in root and excess in superficiality, with internal damp-heat retention", and emphasize a treatment strategy that combines eliminating pathogenic factors and reinforcing the body's healthy qi through dynamic syndrome differentiation. Chinese materia medica used in treating CAG with spleen-stomach damp-heat syndrome can effectively relieve clinical symptoms, improve the internal damp-heat environment, mitigate gastric mucosal atrophy and intestinal metaplasia, and delay the inflammation-to-cancer transformation. Its mechanisms may involve eradication of Helicobacter pylori, repair of gastric mucosal injury, regulation of immune inflammatory response and other aspects, which has the advantages of multi-channel and multi-target.

OBJECTIVE To explore the effect of fluconazole on proteomics of Trichosporon asahii so as to reveal the responding process of T.asahii to fluconazole stress and the resistance mechanisms to azoles on the protein level.METHODS T.asahii AS 2.2174 was chosen as the research subject,the minimum inhibitory concentration(MIC)of fluconazole was determined by broth microdilution assay.The protein abundance of T.asahii was detec-ted by means of tandem mass tag(TMT)technique combined with liquid chromatography-tandem mass spectrom-etry(LC-MS/MS)before and after the treatment with fluconazole(1× MIC).The differentially expressed pro-teins(DEPs)were identified based on the screening standards of fold change ≥1.20 or ≤0.83 and P＜0.05.Gene ontlogy(GO)and Kyoto encyclopedia of genes and genomes(KEGG)enrichment analysis were performed for the DEPs so as to understand the biological property of the DEPs and the major biological pathways that the DEPs in-volved in.Finally,the targeted validation was carried out for the targeted differentially expressed proteins by using multiple reaction monitoring(MRM).RESULTS The MIC of fluconazole to T.asahii AS 2.2174 was 8 μg/ml.Totally 196 DEPs were identified,including 93 upregulated DEPs and 103 downregulated DEPs.The function en-richment analysis showed that the DEPs mainly participated in synthesis and metabolism of sterols,drug metabo-lism,stress response,energy metabolism and intertranslation.The targeted DEPs showed the consistent expres-sion trends in MRM target validation and TMT-LC-MS/MS.CONCLUSIONS The protein abundance of T.asahii has remarkable change under the fluconazole stress.The bioinformatics analysis reveals the complicated molecular mechanisms of T.asahii in response to the fluconazole stress,which may offer valuable ideas for understanding the drug resistance to azoles and developing new drug targets.

Purpose To investigate the value of radiomics nomogram for the prediction of p53 abnormal in patient with endometrial carcinoma based on intratumoral and peritumoral MRI.Materials and Methods A total of 145 female patients were pathologically confirmed endometrial carcinoma who underwent pelvic MRI before treatment in Baoding First Central Hospital from January 2020 to April 2024,including 96 patients with p53 wild type and 49 with p53 abnormal.Radiomics features were extracted from both intratumoral and peritumoral regions(2 mm)in diffusion weighted imaging and equilibrium phase of dynamic contrast enhanced MRI,which were selected using least absolute shrinkage and selection operator.Three machine learning algorithms of random forest,K-nearest neighbors and extra trees were conducted to develop the intratumoral,peritumoral and intratumoral combined peritumoral radiomics models.Multivariate Logistic regression was used to constitute the clinical model and nomogram.The performance of these models was evaluated using receiver operating characteristic curve,decision curve analysis and calibration curve.Results The K-nearest neighbors model of the intratumoral combined peritumoral regions performed the best in all radiomics models,the area under the curve were 0.921 and 0.773 in the training cohorts and test cohorts.The radiomics nomogram,which was composed of age,apparent diffusion coefficient and radiomics signatures,achieved the best performance with area under the curve of 0.970 and 0.777 in the training cohorts and test cohorts,respectively.Calibration curve analysis and decision curve analysis demonstrated favorable calibration and clinical utility of the nomogram model.Conclusion The nomogram based on intratumoral and peritumoral MRI radiomics yields a favorable diagnostic value for predicting p53 abnormal in patient with endometrial carcinoma.

Objective To observe the value of intratumoral and peritumoral ROI-based CT radiomics combined with clinical and imaging features for preoperatively predicting renal capsule invasion of clear cell renal cell carcinoma(ccRCC).Methods Totally 105 ccRCC patients were retrospectively collected and divided into invasion group(n=70)and non-invasion group(n=35)according to post operation pathology,also divided into training set(n=84,including 56 cases of invasion group and 28 of non-invasion group)and test set(n=21,including 14 cases of invasion group and 7 of non-invasion group)at a ratio of 8∶2.A clinical-imaging model was constructed based on clinical and CT features being significantly different between groups.Radiomics features related to renal capsule invasion were extracted and selected from intratumoral and of 1-6 mm peritumoral ROI on unenhanced phase(UP),corticomedullary phase(CMP)and nephrographic phase(NP)CT images,respectively.The optimal algorithm was selected among 6 machine learning algorisms based on CMP intratumoral ROI.With the optimal and selected features,single intratumoral or peritumoral models,combined intratumoral and peritumoral models within the same phase and combined pairwise models within the same range across different phases images were established.The best performing radiomics model was chosen and integrated with clinical and imaging features to form a combined model.Receiver operating characteristic(ROC)curves were drawn,the area under the curve(AUC)was calculated to evaluate the efficacy of model for predicting renal capsule invasion of ccRCC,which were compared using DeLong's test.Results Hypertension,presence of clinical symptoms and high enhancement degree on CMP images were all independent predicting factors for renal capsule invasion of ccRCC,which were used to establish clinical-imaging model.Support vector machine(SVM)was the optimal algorithm.CMP peritumoral 3 mm model,CMP intratumoral model,NP peritumoral 4 mm model,NP intratumoral+peritumoral 4 mm model and CMP peritumoral 3 mm+NP peritumoral 3 mm model showed higher performance than the others,with AUC being not significantly different(all P＞0.05).CMP peritumoral 3 mm model was the optimal radiomics model,with the highest AUC(0.898)in test set.The combined model demonstrated superior AUC(0.979)in training set compared to both clinical-imaging model and the optimal radiomics model alone(both P＜0.05),while in test set(AUC 0.918)showed comparable performance with the latter two(both P＞0.05).Conclusion CT-based peritumoral radiomics models were equally effective for preoperatively predicting renal capsule invasion of ccRCC.Combining with clinical and imaging features might further enhance diagnostic performance.

Purpose To investigate the value of radiomics nomogram for the prediction of p53 abnormal in patient with endometrial carcinoma based on intratumoral and peritumoral MRI.Materials and Methods A total of 145 female patients were pathologically confirmed endometrial carcinoma who underwent pelvic MRI before treatment in Baoding First Central Hospital from January 2020 to April 2024,including 96 patients with p53 wild type and 49 with p53 abnormal.Radiomics features were extracted from both intratumoral and peritumoral regions(2 mm)in diffusion weighted imaging and equilibrium phase of dynamic contrast enhanced MRI,which were selected using least absolute shrinkage and selection operator.Three machine learning algorithms of random forest,K-nearest neighbors and extra trees were conducted to develop the intratumoral,peritumoral and intratumoral combined peritumoral radiomics models.Multivariate Logistic regression was used to constitute the clinical model and nomogram.The performance of these models was evaluated using receiver operating characteristic curve,decision curve analysis and calibration curve.Results The K-nearest neighbors model of the intratumoral combined peritumoral regions performed the best in all radiomics models,the area under the curve were 0.921 and 0.773 in the training cohorts and test cohorts.The radiomics nomogram,which was composed of age,apparent diffusion coefficient and radiomics signatures,achieved the best performance with area under the curve of 0.970 and 0.777 in the training cohorts and test cohorts,respectively.Calibration curve analysis and decision curve analysis demonstrated favorable calibration and clinical utility of the nomogram model.Conclusion The nomogram based on intratumoral and peritumoral MRI radiomics yields a favorable diagnostic value for predicting p53 abnormal in patient with endometrial carcinoma.