ObjectiveTo evaluate the clinical efficacy and safety of acupuncture in treating severe Bell's palsy and to explore its potential mechanism by investigating the effect on serum levels of glial cell line-derived neurotrophic factor （GDNF） and nerve growth factor （NGF）. MethodsA randomized， subject-blinded， sham-acupuncture controlled trial was conducted. A total of 130 patients with severe Bell's palsy were randomly allocated into a treatment group or a control group at a 1∶1 ratio. Both groups received conventional western medicine. In addition， the treatment group received acupuncture， while the control group received sham acupuncture， with each session lasting 30 minutes. The treatment course lasted 8 weeks for both groups， followed by a follow-up assessment at week 12. The primary outcome was the proportion of patients achieving House-Brackmann （H-B） grade Ⅱ or lower at week 8. Secondary outcomes included Sunnybrook facial grading system scores at week 0， 4， 8， and 12， the time to satisfactory recovery（the time required to achieve H-B grade≤Ⅱ）， distribution of H-B grades and facial disability index （FDI） scores including the physical function subscale （FDIP） and social/well-being function subscale （FDIS） scores at week 0， 4， 8， and 12， and serum GDNF and NGF levels at week 0， 4， and 8. Adverse events and participants' self-assessments of treatment efficacy were also recorded. ResultsA total of 122 participants completed the study， including 62 in the treatment group and 60 in the control group. An intention-to-treat （ITT） analysis was performed， and missing data were handled using the last observation carried forward （LOCF） method. The proportion of patients achieving H-B grade ≤grade Ⅱ at week 8 was 78.5% （51/65） in the treatment group， significantly higher than 49.2% （32/65） in the control group （P＜0.05）. The Sunnybrook scores， FDIP and FDIS scores increased， while H-B grades decreased at week 4， 8， and 12 in both groups compared to week 0； moreover， improvements in all outcome measures were significantly greater in the treatment group than in the control group （P＜0.05）. The median time to satisfactory recovery was 6 weeks （95%CI： 5.697-6.303） in the treatment group， significantly shorter than 12 weeks （95%CI： 8.314-15.686） in the control group （P＜0.05）. Serum levels of GDNF and NGF were significantly higher in the treatment group at weeks 4 and 8 （P＜0.05）. No serious acupuncture-related adverse events occurred in either group. Adverse events were reported in 5 patients （7.69%） in the treatment group and 4 patients （6.15%） in the control group， with no statistically significant difference between groups （P＞0.05）. Patients' self-assessment of treatment efficacy after 8 weeks treatment was significantly better in the treatment group （P＜0.05）. ConclusionAcupuncture can effectively improve facial nerve function and shorten recovery time in patients with severe Bell's palsy， with a favorable safety profile. The therapeutic mechanism may be associated with the upregulation of serum GDNF and NGF levels.

BACKGROUND: Cervical squamous cell carcinoma (CESC), the most common subtype of cervical cancer, is primarily caused by the high-risk human papillomavirus (HPV) infection and genetic susceptibility. Single-cell RNA sequencing (scRNA-seq) has been widely used in CESC research to uncover the diversity of cell types and states within tumor tissues, enabling a detailed study of the tumor microenvironment (TME). This technology allows precise mapping of HPV infection in cervical tissues, providing valuable insights into the initiation and progression of HPV-mediated malignant transformation. METHODS: We performed the scRNA-seq to characterize gene expression in tumor tissues and paired adjacent para-cancerous tissues from four patients with early-stage CESC using the 10× Genomics platform. The HPV infection and its subtypes were identified using the scRNA data and viral sequence mapping, and trajectory analyses were performed using HPV+ or HPV- cells. Interactions between different types of keratinized cells and their interactions with other cell types were identified, and pathways and specificity markers were screened for proliferating keratinized cells. The Cancer Genome Atlas (TCGA) dataset was used to verify the prognostic correlation between tumor-specific PI3+S100A7+ keratinocyte infiltration and CESC, and the localization relationship between PI3+S100A7+ keratinocytes and macrophages was verified by immunofluorescence staining. RESULTS: Various types of keratinocytes and fibroblasts were the two cell types with the most significant differences in percentage between the tumor tissue samples and paired adjacent non-cancerous tissue samples in the early stages of CESC. We found that PI3+S100A7+ keratinocytes were associated with early HPV-positive CESC, and PI3+S100A7+ keratinocytes were more abundant in tumors than in adjacent normal tissues in the TCGA-CESC dataset. Analysis of clinical information revealed that the infiltration of PI3+S100A7+ keratinocytes was notably higher in tumors with poor prognosis than in those with good prognosis. Additionally, multiplex immunofluorescence analysis showed a specific increase in PI3+S100A7+ expression within tumor tissues, with PI3+S100A7+ keratinocytes and CD163+ macrophages being spatially very close to each other. In the analysis of cell-cell interactions, macrophages exhibited strong crosstalk with PI3+S100A7+ proliferating keratinocytes in HPV-positive CESC tumors, mediated by tumor necrosis factor (TNF), CCL2, CXCL8, and IL10, highlighting the dynamic and tumor-specific enhancement of macrophage-keratinocyte interactions, which are associated with poor prognosis and immune modulation. Using CIBERSORTx, we discovered that patients with high infiltration of both PI3+S100A7+ proliferating keratinocytes and macrophages had the shortest overall survival. In the analysis of cell-cell interactions, PI3+S100A7+ proliferating keratinocytes and macrophages were found to be involved in highly active pathways that promote differentiation and structure formation, including cytokine receptor interactions, the Nuclear Factor kappa-light-chain-enhancer of activated B cells (NF-κB) signaling pathway, and TNF signaling pathway regulation. Further subtyping of fibroblast populations identified four subtypes. The C1 group, characterized by its predominance in tumor tissues, is a subtype enriched with cancer-associated fibroblasts (CAFs), whereas the C3 group is primarily enriched in adjacent non-cancerous tissues and consists of undifferentiated cells. Moreover, the distinct molecular and cellular differences between HPV16- and HPV66-associated tumors were demonstrated, emphasizing the unique tumor-promoting mechanisms and microenvironmental influences driven by each HPV subtype. CONCLUSIONS We discovered a heterogeneous population of keratinocytes between tumor and adjacent non-cancerous tissues caused by HPV infection and identified macrophages and specific CAFs that play a crucial role during the early stage in promoting the inflammatory response and remodeling the cancer-promoting TME. Our findings provide new insights into the transcriptional landscape of early-stage CESC to understand the mechanism of HPV-mediated malignant transformation in cervical cancer.
Humans ; Female ; Papillomavirus Infections/genetics* ; Uterine Cervical Neoplasms/genetics* ; Carcinoma, Squamous Cell/pathology* ; Keratinocytes/metabolism* ; Single-Cell Analysis/methods* ; Tumor Microenvironment/genetics*

Dysfunction of drug transporters significantly affects therapeutic outcomes and drug efficacy in patients with liver injury. Clinical and experimental evidence demonstrates that liver injury involves complex inter-organ interactions among the brain, eye, liver, intestine, and kidney. Recent advances in basic and clinical research have illuminated the physiologic and molecular mechanisms underlying transporter alterations in liver injury, particularly those associated with bilirubin, reactive oxygen species, ammonia, bile acid, and inflammatory factors. Notably, the influence of these transporter modifications on drug pharmacokinetics in liver injury patients remains inadequately understood. Additional research is necessary to fully comprehend these effects and their therapeutic implications. The documented alterations of transporters in distant organs across various liver diseases indicate that dosage modifications may be required when administering transporter-substrate drugs, including both traditional Chinese and Western medicines, to patients with liver dysfunction. This strategy helps maintain drug concentrations within therapeutic ranges while reducing adverse reactions. Furthermore, when utilizing transporter inducers or inhibitors clinically, consideration of their long-term effects on transporters and subsequent therapeutic impact is essential. Careful attention must be paid to avoid compromising the elimination of toxic metabolites and proteins when inhibiting these transporters. Similarly, prudent use of inducers or inducer-type therapeutic drugs is necessary to prevent enhanced drug resistance. This review examines recent clinical and experimental findings regarding the inter-organ interaction of drug transporters in liver injury conditions and their clinical relevance.
Humans ; Liver/drug effects* ; Animals ; Chemical and Drug Induced Liver Injury/metabolism* ; Membrane Transport Proteins/metabolism* ; Biological Transport ; Liver Diseases/drug therapy* ; Pharmaceutical Preparations/metabolism*

Objective To investigate the executive condition of Enhanced Recovery After Surgery(ERAS)measures among the hospitalized surgical patients in secondary and tertiary medical institutions of Chongqing City.Methods Using a multicenter cross-sectional survey approach,patients undergoing elective surgeries admitted and treated in 40 member units under the Chongqing Anesthesiology and Perioperative Medicine Specialized Alliance from July 11 to 30,2024 were selected as the survey subjects.Adherence to and completion of ERAS measures were calculated.Factors influencing measures with low completion rates were analyzed.Results A total of 2 100 questionnaires were issued,1 708 effective questionnaires were recovered with an effective recovery rate of 81.33％.Among them,there were 1 017 questionnaires in the tertiary medi-cal institutions and 691 questionnaires in the secondary medical institutions.The age of 1 708 patients ranged from 19-78 years old with a median age of 52 years old.Females were dominant.The proportion of patients from gastrointestinal surgery and those with secondary school education or above was high.Hypertension and diabetes were the main complication types.The surgical grade was concentrated at grades Ⅲ and Ⅳ.The ASA grading was concentrated at the grade Ⅰ/Ⅱ.The NYHA heart function grade was mainly the grade Ⅰ/Ⅱ.The ERAS measures compliance rate ranged from 36.36％to 95.45％,averaged 73.47％.The compliance rate of ERAS measures in the tertiary hospitals was higher than that in the secondary hospitals(75.82％vs.70.01％),and the difference was statistically significant(P<0.05).The average completion rate of ERAS measures was 74.08％.The top three in the completion rate were preoperative education(95.78％),preven-tive antibiotics and skin preparation(92.62％),and preoperative interview and evaluation(88.58％).The completion rates of Prehabilitation(55.27％)and preoperative fasting(57.67％)urgently needed to be in-creased.The completion rate of other measures was lower than 60％.Conclusion The compliance rate of ERAS measures needs to be increased,moreover there are significant differences among various hospitals.Fu-ture practices should focus on two measures:preoperative pre-rehabilitation exercises and preoperative oral intake of carbohydrates.

OBJECTIVE To investigate the correlation of serum P-Tau protein and β-amyloid protein expression levels with obstructive sleep apnea syndrome(OSAS)and mild cognitive impairment(MCI)patients,and their diagnostic value.METHODS From December 2020 to December 2023,120 patients with OSAS admitted to Third Hospital of Shijiazhuang were collected as the case group.According to the diagnostic criteria for MCI,patients were grouped into OSAS without MCI group(40 cases)and OSAS with MCI group(80 cases).ELISA method was applied to detect the levels of serum P-Tau protein and β-amyloid protein.Spearman method was applied to analyze the correlation between serum P-Tau protein,β-amyloid protein,and MCI.Multivariate logistic regression was applied to analyze the influencing factors of OSAS patients with MCI.ROC curve was applied to evaluate the diagnostic efficacy of serum P-Tau protein and β-amyloid protein in OSAS patients with MCI.RESULTS The Montreal Cognitive Assessment(MoCA)score in the OSAS with MCI group was obviously lower than that in the OSAS without MCI group(P<0.05).The expression levels of P-Tau protein and β-amyloid protein in the OSAS with MCI group were obviously higher than those in the OSAS without MCI group(P<0.05).The expression levels of serum P-Tau protein and β-amyloid protein in OSAS patients were negatively correlated with MoCA score(r=-0.346,-0.565,P<0.001).Serum P-Tau protein and β-amyloid protein were risk factors for OSAS with MCI(P<0.05).The AUC of the expression levels of serum P-Tau protein,β-amyloid protein,and their combination for OSAS with MCI was 0.751,0.848,and 0.928,respectively.The combined evaluation of the two showed better results(Zcombination-P-Tau protein=4.102,P<0.001;Zcombination-β amyloid protein=2.147,P=0.032).CONCLUSION The expression of serum P-Tau protein and β-amyloid protein is upregulated in OSAS patients with MCI,they are risk factors for the development of MCI in OSAS patients.The combined detection of the two has higher diagnostic efficacy.

Objective:To develop and assess a three-class machine learning model for predicting wild-type, 19 del, and 21 L858R mutations of the epidermal growth factor receptor (EGFR) in lung adenocarcinoma using 18F-FDG PET/CT radiomic features and clinical features. Methods:The retrospective data was collected from 703 patients (346 males, 357 females; age (64.3±9.0) years) with lung adenocarcinoma at the First People′s Hospital of Changzhou from January 2018 to June 2023. Patients were divided into the training set (563 cases) and test set (140 cases) at the ratio of 8∶2. Clinical features were selected using recursive feature elimination (RFE). Radiomic features were extracted from PET and CT images, and the optimal feature sets were selected using minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) methods. Base models were constructed by using random forest (RF), logistic regression (LR), support vector machine (SVM), K-nearest neighbors (KNN), and multi-layer perceptron (MLP), and the stacking method was applied to establish the CT and PET ensemble models. Delong test was used to compare the AUC differences between the PET/CT combined model and the clinical + PET/CT integrated model.Results:Among 703 patients, 273 were with EGFR wild-type, 202 were with 19 del mutation, and 228 were with 21 L858R mutation. In the single-modal analysis, the AUCs of CT ensemble model in the training and test sets were 0.893 and 0.667, respectively, while the AUCs of PET ensemble model were 0.692 and 0.660. The AUC of PET/CT combined model were 0.897 in training set and 0.672 in test set. The AUC of clinical + PET/CT integrated model showed further improvement, with AUCs of 0.902 and 0.721 in training and test sets, respectively. Notably, the clinical + PET/CT integrated model outperformed PET/CT combined model in predicting wild-type EGFR (test set AUC: 0.784 vs 0.707; Z=3.28, P=0.001). Conclusion:The three-class model (clinical + PET/CT integrated model) based on 18F-FDG PET/CT radiomics and clinical features effectively predicts EGFR mutation subtypes in lung adenocarcinoma.

Objective:To explore the value of 18F-fluorodeoxyglucose ( 18F-FDG) PET-CT dual-modality habitat imaging technology in predicting the epidermal growth factor receptor (EGFR) mutation status in lung adenocarcinoma. Methods:This study was designed as a cross-sectional study. Clinical and imaging data of 403 patients with lung adenocarcinoma who underwent 18F-FDG PET-CT imaging with definitive EGFR results from January 2018 to April 2022 at the Third Affiliated Hospital of Soochow University were retrospectively analyzed.The patients were divided into a development set (282 cases) and a validation set (121 cases) using a stratified random sampling method at a 7∶3 ratio. An adaptive clustering algorithm was used to segment the regions of interest, forming different habitats and obtaining derived parameters. Independent samples t-test or Mann-Whitney U test were used to compare clinical, imaging indicators, and habitat-derived parameters between EGFR mutant and wild-type patient. The clinical, imaging indicators, and habitat-derived parameters that showed statistically significant differences in univariate analysis were included in multivariate logistic regression to construct clinical and clinical-habitat combined models, respectively. The receiver operating characteristic curve and area under the curve (AUC) were used to evaluate the model′s ability to predict EGFR mutations in lung adenocarcinoma. Additionally, the net reclassification index (NRI) was employed to assess the model′s classification improvement capability. Results:There were 249 cases of EGFR mutation and 154 cases of wild type. The optimal number of habitats was two, namely Habitat 1 and Habitat 2. The parameters included in the clinical model were smoking history, bronchial sign, pleural indentation sign, and tumor diameter. The parameters incorporated into the clinical-habitat combined model were smoking history, bronchial sign, pleural indentation sign, Habitat 2, and Habitat 1 voxel count. In the development set, the AUCs for predicting EGFR mutations in lung adenocarcinoma using the clinical model and the clinical-habitat combined model were 0.723 and 0.733, respectively, with no statistically significant difference ( Z=0.60, P=0.549); In the validation set, the AUCs were 0.684 and 0.715, respectively, with no statistically significant difference ( Z=1.32, P=0.186). The accuracy (0.694) and specificity (0.609) of the clinical-habitat combined model in the validation set were slightly higher than those of the clinical model (0.686 and 0.565, respectively). NRI analysis confirmed that the clinical-habitat combined model improved the correct classification of EGFR wild-type lung adenocarcinoma by 10.9% compared to the clinical model ( P=0.018). Conclusion:18F-FDG PET-CT dual-modality habitat imaging technology can be used to analyze the microenvironment of lung adenocarcinoma and has the potential in non-invasively predicting EGFR mutation status, providing an important basis for personalized and accurate treatment of patients with lung adenocarcinoma.

Objective:To develop and assess a three-class machine learning model for predicting wild-type, 19 del, and 21 L858R mutations of the epidermal growth factor receptor (EGFR) in lung adenocarcinoma using 18F-FDG PET/CT radiomic features and clinical features. Methods:The retrospective data was collected from 703 patients (346 males, 357 females; age (64.3±9.0) years) with lung adenocarcinoma at the First People′s Hospital of Changzhou from January 2018 to June 2023. Patients were divided into the training set (563 cases) and test set (140 cases) at the ratio of 8∶2. Clinical features were selected using recursive feature elimination (RFE). Radiomic features were extracted from PET and CT images, and the optimal feature sets were selected using minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) methods. Base models were constructed by using random forest (RF), logistic regression (LR), support vector machine (SVM), K-nearest neighbors (KNN), and multi-layer perceptron (MLP), and the stacking method was applied to establish the CT and PET ensemble models. Delong test was used to compare the AUC differences between the PET/CT combined model and the clinical + PET/CT integrated model.Results:Among 703 patients, 273 were with EGFR wild-type, 202 were with 19 del mutation, and 228 were with 21 L858R mutation. In the single-modal analysis, the AUCs of CT ensemble model in the training and test sets were 0.893 and 0.667, respectively, while the AUCs of PET ensemble model were 0.692 and 0.660. The AUC of PET/CT combined model were 0.897 in training set and 0.672 in test set. The AUC of clinical + PET/CT integrated model showed further improvement, with AUCs of 0.902 and 0.721 in training and test sets, respectively. Notably, the clinical + PET/CT integrated model outperformed PET/CT combined model in predicting wild-type EGFR (test set AUC: 0.784 vs 0.707; Z=3.28, P=0.001). Conclusion:The three-class model (clinical + PET/CT integrated model) based on 18F-FDG PET/CT radiomics and clinical features effectively predicts EGFR mutation subtypes in lung adenocarcinoma.

Objective:To explore the value of 18F-fluorodeoxyglucose ( 18F-FDG) PET-CT dual-modality habitat imaging technology in predicting the epidermal growth factor receptor (EGFR) mutation status in lung adenocarcinoma. Methods:This study was designed as a cross-sectional study. Clinical and imaging data of 403 patients with lung adenocarcinoma who underwent 18F-FDG PET-CT imaging with definitive EGFR results from January 2018 to April 2022 at the Third Affiliated Hospital of Soochow University were retrospectively analyzed.The patients were divided into a development set (282 cases) and a validation set (121 cases) using a stratified random sampling method at a 7∶3 ratio. An adaptive clustering algorithm was used to segment the regions of interest, forming different habitats and obtaining derived parameters. Independent samples t-test or Mann-Whitney U test were used to compare clinical, imaging indicators, and habitat-derived parameters between EGFR mutant and wild-type patient. The clinical, imaging indicators, and habitat-derived parameters that showed statistically significant differences in univariate analysis were included in multivariate logistic regression to construct clinical and clinical-habitat combined models, respectively. The receiver operating characteristic curve and area under the curve (AUC) were used to evaluate the model′s ability to predict EGFR mutations in lung adenocarcinoma. Additionally, the net reclassification index (NRI) was employed to assess the model′s classification improvement capability. Results:There were 249 cases of EGFR mutation and 154 cases of wild type. The optimal number of habitats was two, namely Habitat 1 and Habitat 2. The parameters included in the clinical model were smoking history, bronchial sign, pleural indentation sign, and tumor diameter. The parameters incorporated into the clinical-habitat combined model were smoking history, bronchial sign, pleural indentation sign, Habitat 2, and Habitat 1 voxel count. In the development set, the AUCs for predicting EGFR mutations in lung adenocarcinoma using the clinical model and the clinical-habitat combined model were 0.723 and 0.733, respectively, with no statistically significant difference ( Z=0.60, P=0.549); In the validation set, the AUCs were 0.684 and 0.715, respectively, with no statistically significant difference ( Z=1.32, P=0.186). The accuracy (0.694) and specificity (0.609) of the clinical-habitat combined model in the validation set were slightly higher than those of the clinical model (0.686 and 0.565, respectively). NRI analysis confirmed that the clinical-habitat combined model improved the correct classification of EGFR wild-type lung adenocarcinoma by 10.9% compared to the clinical model ( P=0.018). Conclusion:18F-FDG PET-CT dual-modality habitat imaging technology can be used to analyze the microenvironment of lung adenocarcinoma and has the potential in non-invasively predicting EGFR mutation status, providing an important basis for personalized and accurate treatment of patients with lung adenocarcinoma.

Objective:To investigate the value of intratumoral and peritumoral radiomics models based on 18F-FDG PET-CT in predicting epidermal growth factor receptor (EGFR) mutation status in lung adenocarcinoma and interpret peritumoral radiomics features. Methods:This study was a cross-sectional study. Patients with lung adenocarcinoma who underwent 18F-FDG PET-CT at the Third Affiliated Hospital of Soochow University between January 2018 and April 2022 were retrospectively collected and samplied into a training set (309 cases) and a test set (206 cases) in a 6∶4 ratio randomly. Radiomics features were extracted from the intratumoral and peritumoral regions of interest based on PET and CT images, respectively, and the optimal feature sets were selected. Radiomics models were established using the XGBoost algorithm, and radiomics scores (intratumoral CT label, peritumoral CT label, intratumoral PET label, peritumoral PET label) were calculated. Logistic regression analysis was used to construct a clinical model and a combined model (incorporating PET-CT intratumoral and peritumoral radiomics, clinical features, and CT semantic features). The predictive performance of the models was evaluated using receiver operating characteristic curves and the area under the curve (AUC). Unsupervised clustering, Spearman correlation analysis, and visualization methods were used for the interpretability of peritumoral radiomics features. Results:In both the training and test sets, the AUC value of CT peritumoral labels was greater than that of CT intratumoral labels for predicting EGFR mutation status in lung adenocarcinoma (training set: Z=3.84, P<0.001; test set: Z=1.99, P=0.046). In the test set, the AUC value of PET intratumoral labels (0.684) was slightly higher than that of PET peritumoral labels (0.672) for predicting EGFR mutation status, but the difference was not statistically significant ( P>0.05). The combined model had the highest AUC value for predicting EGFR mutation status of lung adenocarcinoma in both the training and test sets and was significantly better than the clinical model (training set: Z=6.52, P<0.001; test set: Z=2.31, P=0.021). Interpretability analysis revealed that CT peritumoral radiomics features were correlated with CT shape features, and there were significant differences in CT peritumoral features between different EGFR mutation statuses. Conclusions:The value of CT peritumoral labels is superior to that of CT intratumoral labels in predicting EGFR mutation status in lung adenocarcinoma. The predictive performance of the model can be improved by combining PET-CT intratumoral and peritumoral radiomics, clinical features, and CT semantic features.