ObjectiveTo investigate the characteristics and distribution of traditional Chinese medicine （TCM） syndromes in the patients with esophageal squamous cell carcinoma （ESCC）. MethodsAn electronic questionnaire was developed to collect the general data and four examination information of ESCC patients treated in 10 areas with high incidence of esophageal cancer in China from June 2020 to March 2021. Multiple analyses including frequency analysis， factor analysis， and hierarchical cluster analysis were performed to analyze the potential syndrome elements， disease location， and common syndromes of ESCC. ResultsA total of 2 027 patients with ESCC were included. Statistical analysis was performed on 113 symptoms， physical signs， 33 tongue manifestation variables， and 23 pulse manifestation variables of the patients’ four examination information. Factor analysis was performed on 55 variables with frequency>10%， extracting 19 common factors. According to clinical experience and expert opinions， the main lesions of patients with ESCC were in the spleen and stomach， and the main syndrome elements were Qi stagnation， blood stasis， phlegm， dampness， and Qi deficiency， with the syndrome element combination of phlegm obstruction + Qi stagnation + blood stasis being the most common. The syndromes can be classified into four categories of liver-stomach disharmony + combined phlegm and Qi obstruction， kidney-spleen dysfunction + combined phlegm and stasis， spleen-kidney Yang deficiency + obstinate phlegm and blood stasis， and liver-kidney Yin deficiency + obstinate phlegm and blood stasis. The main syndrome of ESCC was liver-stomach disharmony + combined phlegm and Qi obstruction in the early stage， liver-spleen dysfunction + combined phlegm and stasis in the middle stage， and spleen-kidney Yang deficiency + obstinate phlegm and blood stasis in the late stage. ConclusionESCC mainly has main pathological features of internal deficiency and external excess and combined deficiency and excess， with the key syndrome elements being phlegm obstruction， Qi stagnation， and blood stasis. The main disease locations are in the spleen and stomach， involving the liver， kidney， chest and diaphragm， heart， and lung. The main syndrome is liver-stomach disharmony + combined phlegm and Qi obstruction. In clinical practice， it is necessary to grasp the pathogenesis dynamics of the disease and use prescriptions according to patients’ syndromes.

OBJECTIVE: To investigate the midterm clinical efficacy of medial patellofemoral complex (MPFC) reconstruction for recurrent patellar dislocation with high-grade trochlear dysplasia. METHODS: A retrospective analysis was carried out among adult patients who underwent arthroscopically assisted MPFC reconstruction between January 2014 and December 2020. Dejour classification was evaluated to grade trochlear dysplasia; tibial tubercle-trochlear groove (TT-TG) distance and Insall-Salvati index were measured. Preoperative and postoperative patient-reported outcome measures (PROMs) were compared, including International Knee Documentation Committee (IKDC) score, Kujala score, Lysholm score and Tegner score. Information regarding returning-to-sport rate, re-instability events and complications was collected. Patellar tilt (PT), lateral patellar displacement (LPD) and bisect offset (BSO) ratio were measured based on axial computed tomography before and after surgery to assess the patellofemoral congruence. RESULTS: A total of 46 MPFC reconstructions in 43 patients were enrolled, including 16 male and 27 female. Mean age at surgery was (22.2±7.6) years (range: 14-44 years). Mean follow-up was (49.9±22.6) months (range: 18-102 months). The percentages of Dejour B, C and D dysplasia were 37.0% (17/46), 43.5% (20/46), and 19.6% (9/46), respectively. Mean Insall-Salvati index was 1.2±0.2 (range: 0.85-1.44), and mean TT-TG distance was (19.6±3.5) mm (range: 10.6-28.7 mm). At latest follow-up, there were significant improvements in all PROMs (P < 0.001): IKDC score, from 56.3±15.1 to 86.2±8.1; Kujala score, from 58.9±15.6 to 92.6±5.4; Lysholm score, from 63.7±15.0 to 94.0±5.7; Tegner score, from 3.1±1.4 to 4.7±1.4, and there were no significant differences in the improvements of the scores between the patients with Dejour B, C and D dysplasia. Overall, ninety percent of the patients returned to their preoperative sports level. One patient reported a postoperative subluxation, while no cases of infection, limited range of motion or patella fracture were observed. PT, LPD and BSO ratio were all significant altered (P < 0.001) after MPFC reconstruction. CONCLUSION Arthroscopically assisted MPFC reconstruction yielded satisfactory midterm clinical results for recurrent patellar dislocation with high-grade trochlear dysplasia. No significant differences of improvements in knee function were observed among the three types of high-grade trochlear dysplasia.
Humans ; Patellar Dislocation/surgery* ; Male ; Female ; Adult ; Arthroscopy/methods* ; Retrospective Studies ; Adolescent ; Young Adult ; Patellofemoral Joint/surgery* ; Recurrence ; Plastic Surgery Procedures/methods* ; Patella/surgery* ; Treatment Outcome

As the brain's resident immune cells, microglia perform crucial functions such as phagocytosis, neuronal network maintenance, and injury restoration by adopting various phenotypes. Dynamic imaging of these phenotypes is essential for accessing brain diseases and therapeutic responses. Although numerous probes are available for imaging pro-inflammatory microglia, no PET tracers have been developed specifically to visualize anti-inflammatory microglia. In this study, we present an ¹⁸F-labeled PET tracer (QTFT) that targets the P2Y₁₂, a receptor highly expressed on anti-inflammatory microglia. [¹⁸F]QTFT exhibited high binding affinity to the P2Y₁₂ (14.43 nmol/L) and superior blood-brain barrier permeability compared to other candidates. Micro-PET imaging in IL-4-induced neuroinflammation models showed higher [¹⁸F]QTFT uptake in lesions compared to the contralateral normal brain tissues. Importantly, this specific uptake could be blocked by QTFT or a P2Y₁₂ antagonist. Furthermore, [¹⁸F]QTFT visualized brain lesions in mouse models of epilepsy, glioma, and aging by targeting the aberrantly expressed P2Y₁₂ in anti-inflammatory microglia. In a pilot clinical study, [¹⁸F]QTFT successfully located epileptic foci, showing enhanced radioactive signals in a patient with epilepsy. Collectively, these studies suggest that [¹⁸F]QTFT could serve as a valuable diagnostic tool for imaging various brain disorders by targeting P2Y₁₂ overexpressed in anti-inflammatory microglia.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline and memory loss, with few effective treatments currently available. The multifactorial nature of AD, shaped by genetic, environmental, and biological factors, complicates both research and clinical management. Recent advances in artificial intelligence (AI) and multi-omics technologies provide new opportunities to elucidate the molecular mechanisms of AD and identify early biomarkers for diagnosis and prognosis. AI-driven approaches such as machine learning, deep learning, and network-based models have enabled the integration of large-scale genomic, transcriptomic, proteomic, metabolomic, and microbiomic datasets. These efforts have facilitated the discovery of novel molecular signatures and therapeutic targets. Methods including deep belief networks and joint deep semi-non-negative matrix factorization have contributed to improvements in disease classification and patient stratification. However, ongoing challenges remain. These include data heterogeneity, limited interpretability of complex models, a lack of large and diverse datasets, and insufficient clinical validation. The absence of standardized multi-omics data processing methods further restricts progress. This review systematically summarizes recent advances in AI-driven multi-omics research in AD, highlighting achievements in early diagnosis and biomarker discovery while discussing limitations and future directions needed to advance these approaches toward clinical application.

Periodontal bone defects, primarily caused by periodontitis, are highly prevalent in clinical settings and manifest as bone fenestration, dehiscence, or attachment loss, presenting a significant challenge to oral health. In regenerative medicine, harnessing developmental principles for tissue repair offers promising therapeutic potential. Of particular interest is the condensation of progenitor cells, an essential event in organogenesis that has inspired clinically effective cell aggregation approaches in dental regeneration. However, the precise cellular coordination mechanisms during condensation and regeneration remain elusive. Here, taking the tooth as a model organ, we employed single-cell RNA sequencing to dissect the cellular composition and heterogeneity of human dental follicle and dental papilla, revealing a distinct Platelet-derived growth factor receptor alpha (PDGFRA) mesenchymal stem/stromal cell (MSC) population with remarkable odontogenic potential. Interestingly, a reciprocal paracrine interaction between PDGFRA⁺ dental follicle stem cells (DFSCs) and CD31⁺ Endomucin⁺ endothelial cells (ECs) was mediated by Vascular endothelial growth factor A (VEGFA) and Platelet-derived growth factor subunit BB (PDGFBB). This crosstalk not only maintains the functionality of PDGFRA⁺ DFSCs but also drives specialized angiogenesis. In vivo periodontal bone regeneration experiments further reveal that communication between PDGFRA⁺ DFSC aggregates and recipient ECs is essential for effective angiogenic-osteogenic coupling and rapid tissue repair. Collectively, our results unravel the importance of MSC-EC crosstalk mediated by the VEGFA and PDGFBB-PDGFRA reciprocal signaling in orchestrating angiogenesis and osteogenesis. These findings not only establish a framework for deciphering and promoting periodontal bone regeneration in potential clinical applications but also offer insights for future therapeutic strategies in dental or broader regenerative medicine.
Receptor, Platelet-Derived Growth Factor alpha/metabolism* ; Humans ; Neovascularization, Physiologic/physiology* ; Dental Sac/cytology* ; Single-Cell Analysis ; Transcriptome ; Mesenchymal Stem Cells/metabolism* ; Bone Regeneration ; Animals ; Dental Papilla/cytology* ; Periodontium/physiology* ; Stem Cells/metabolism* ; Regeneration ; Angiogenesis

Transoral endoscopic thyroidectomy vestibular approach (TOETVA) is a novel endoscopic thyroid surgery method. TOETVA can completely dissect the lymph nodes in the central area and Ⅳ area. TOETVA has both advantages of beauty and curative effect. Based on the clinical experience of this technique, the author reviewed the development, indications, complications and surgical skills of TOETVA in recent years, and looked forward to the development trend of this technique.

Objective To assess the value of cardiac magnetic resonance(CMR)imaging for predicting adverse left ventricular remodeling in patients with ST-segment elevation myocardial infarction(STEMI).Methods We retrospectively analyzed the clinical data and serial CMR(cine and LGE sequences)images of 86 STEMI patients within 1 week and 5 months after percutaneous coronary intervention(PCI),including 25 patients with adverse LV remodeling and 61 without adverse LV remodeling,defined as an increase of left ventricular end-systolic volume(LVESV)over 15%at the second CMR compared to the initial CMR.The CMR images were analyzed for LV volume,infarct characteristics,and global and infarct zone myocardial function.The independent predictors of adverse LV remodeling following STEMI were analyzed using univariate and multivariate Logistic regression methods.Results The initial CMR showed no significant differences in LV volume or LV ejection fraction(LVEF)between the two groups,but the infarct mass and microvascular obstructive(MVO)mass were significantly greater in adverse LV remodeling group(P<0.05).Myocardial injury and cardiac function of the patients recovered over time in both groups.At the second CMR,the patients with adverse LV remodeling showed a significantly lower LVEF,a larger left ventricular end-systolic volume index(LVESVI)and a greater extent of infarct mass(P<0.001)with lower global peak strains and strain rates in the radial,circumferential,and longitudinal directions(P<0.05),infarct zone peak strains in the 3 directions,and infarct zone peak radial and circumferential strain rates(P<0.05).The independent predictors for adverse LV remodeling following STEMI included the extent of infarct mass(AUC=0.793,95%CI:0.693-0.873;cut-off value:30.67%),radial diastolic peak strain rate(AUC=0.645,95%CI:0.534-0.745;cut-off value:0.58%),and RAAS inhibitor(AUC= 0.699,95%CI:0.590-0.793).Conclusion The extent of infarct mass,peak radial diastolic strain rate,and RAAS inhibitor are independent predictors of adverse LV remodeling following STEMI.

Objective The best evidence of respiratory muscle training for patients with mechanical ventilation in ICU after machine withdrawal was extracted and summarized to provide evidence-based evidence for respiratory muscle training for patients with mechanical ventilation after machine withdrawal.Methods We searched relevant guideline networks and association websites,as well as PubMed,Web of Science,Embase,CINAHL,CNKI,VIP,Wanfang and other databases to collect relevant guidelines,clinical decisions,evidence summaries,expert consensuses,systematic reviews and randomized controlled studies,and the search time limit is from the establishment of the databases to July 30,2023.There were 2 researchers who independently evaluated the literature quality and extracted data.Results A total of 13 articles were included,including 2 guidelines,2 clinical decisions,5 systematic reviews and 4 expert consensuses.There were 24 pieces of evidence being summarized in 7 categories,including training team,training evaluation,training methods,training frequency,training safety,training effect evaluation and health education.Conclusion This study summarizes the best evidence for respiratory muscle training in patients with mechanical ventilation after withdrawal,which can provide references for medical staffs to conduct respiratory muscle training for patients after withdrawal.It is recommended that medical staff should consider the clinical situation when applying the evidence,and selectively apply the best evidence.

Objective To explore the value of machine learning(ML)models based on non-contrast CT(NCCT)radiomics features for predicting the severity of acute phase traumatic brain injury(TBI).Methods Totally 600 TBI patients were retrospectively collected as observation group,other 65 TBI patients were taken as external validation set,while 50 TBI patients were prospectively enrolled as prospective validation set.Patients in observation group were divided into high-risk subgroup(n=240)and low-risk subgroup(n=360)according to Glasgow outcome scale(GOS)at discharge.The severity of acute phase TBI in observation group was assessed by doctor A and B with the same criteria,then an artificial model was established based on clinical and NCCT data at the time of first diagnosis using logistic regression(LR)method for predicting the severity of acute phase TBI.Patients in observation group were divided into training set(n=420,including 168 in high-risk subgroup and 252 in low-risk subgroup)and test set(n=180,including 72 in high-risk subgroup and 108 in low-risk subgroup)at the ratio of 7∶3.Based on NCCT of training set,radiomics features were extracted and selected,and LR,support vector machine(SVM),random forest(RF)and K-nearest neighbor(KNN)were used to establish 4 ML models.The efficacies of the above models were validated in test set,external validation set(including 34 cases of high-risk and 31 cases of low-risk TBI)and prospective validation set(including 21 cases of high-risk and 29 cases of low-risk TBI),respectively.Results The area under the curve(AUC)of doctor A and B for evaluating the severity of acute phase TBI in observation group was 0.606 and 0.771,respectively,of artificial model was 0.824.Based on NCCT in training set,6 optimal radiomics features were selected to construct LR,SVM,RF and KNN ML models,with AUC of 0.983,0.971,0.970 and 0.984 in test set,respectively,while the AUC of artificial model was 0.708.The AUC of LR,SVM,RF,KNN ML models and artificial model in external validation set was 0.879,0.881,0.984,0.863 and 0.733,while in prospective validation set was 0.984,0.873,0.982,0.897 and 0.704,respectively.Conclusion ML models based on CT radiomics could effectively predict the severity of acute phase TBI.

Objective To investigate the application efficiency and potential of CT radiomics in differentiating malignant and benign sub-centimeter solid pulmonary nodules. Methods A retrospective study was performed on the sub-centimeter ( ≤ 10 mm) solid pulmonary nodules detected by enhanced CT in our hospital from March 2020 to January 2023. Malignancy was confirmed by surgical pathology, and benignity was confirmed by surgical pathology or follow-up. Lesions were manually segmented and radiomic features were extracted. The feature dimension was reduced via feature correlation analysis and least absolute shrinkage and selection operator (LASSO). The 5-fold cross validation was used to validate the model. Support vector machine, logistic regression, linear classification support vector machine, gradient boosting, and random forest models were established for CT radiomics. Receiver operating characteristic curves were drawn. Delong test was used to compare the diagnostic performance of the five classifiers. The optimal model was selected and compared to radiologists with medium and high seniority. Results A total of 303 nodules, 136 of which were malignant, were examined. Radiomics models were established after feature extraction and selection. On test set, the areas under the receiver operating characteristic curves of support vector machine, logistic regression, linear classification support vector machine, random forest, and gradient boosting models were 0.922 (95%CI: 0.893, 0.950), 0.910 (95%CI: 0.878, 0.942), 0.905 (95%CI: 0.872, 0.938), 0.899 (95%CI: 0.865, 0.933), and 0.896 (95%CI: 0.862, 0.930), respectively. Delong test indicated no significant differences in the performance of the five radiomics models, and the support vector machine model showed the highest accuracy and F1 score. The support vector machine model showed significantly higher diagnostic accuracy as compared to radiologists (83.8% vs. 55.4%, P < 0.001). Conclusion The radiomics models achieved high diagnostic efficiency and may help to reduce the uncertainty in diagnosis of malignant and benign sub-centimeter solid nodules by radiologists.