ObjectiveTo predict the mechanism through which Shasheng Maidong Tang enhances the efficacy of chemotherapy for lung cancer via network pharmacology and validate the prediction results in animal experiments. MethodsThe potential mechanism through which Shasheng Maidong Tang enhances the efficacy of chemotherapy for lung cancer was predicted by network pharmacology， liquid chromatography-mass spectrometry （LC-MS）， and molecular docking methods. C57/BL6 mice were assigned into normal， model， cisplatin， and Shasheng Maidong Tang+cisplatin groups. In addition to the normal group， the remaining groups were injected subcutaneously with 0.2 mL of 1×10⁷ cells·mL^-1 Lewis lung cancer cells to establish the Lewis lung cancer model. The daily gavage dose of Shasheng Maidong Tang was 3.58 g·kg^-1， and the concentration of cisplatin intraperitoneally injected on every other day was 2 mg·kg^-1. Drugs were administered for 14 d. The changes in the tumor volume and the rate of tumor suppression were monitored， and the tumor histopathological changes were observed by hematoxylin-eosin （HE） staining. Enzyme-linked immunosorbent assay was employed to measure the interleukin （IL）-6 and interferon （IFN）-γ levels in peripheral blood. Real-time PCR was performed to quantify the mRNA levels of Janus kinase 2 （JAK2）， signal transducer and activator of transcription 1 （STAT1）， and signal transducer and activator of transcription 3 （STAT3） in the tumor tissue of mice. Western blot was employed to determine the protein levels of JAK2， STAT3， B-cell lymphoma-2 （Bcl-2）， cysteinyl aspartate-specific proteinase-3 （Caspase-3）， and Pim-1 proto1 （PIM1） in the tumor tissue. Immunohistochemistry was employed to detect the expression of Bcl-2 and PIM1 in the tumor tissue. ResultsNetwork pharmacological predictions indicated that Shasheng Maidong Tang might enhance the efficacy of chemotherapy for lung cancer by regulating nitrogen metabolism， AGE-RAGE signaling pathway， cancer pathway， and JAK/STAT signaling pathway. The experimental results demonstrated that tumor volume in the cisplatin group and Shasheng Maidong Tang+cisplatin group was reduced compared with the model group， with statistically distinct differences observed on days 14， 17， 20 post modeling （P<0.05）. Notably， the Shasheng Maidong Tang+cisplatin therapy further decreased tumor volume compared with the cisplatin group， showing marked reductions on days 17 and 20 （P<0.05）， consistent with trends visualized in tumor volume comparison charts. The Shasheng Maidong Tang+cisplatin group exhibited higher tumor inhibition rate than the cisplatin group （P<0.05）. Histopathological analysis via HE staining revealed that the tumors in the model group displayed frequent nuclear mitosis， densely arranged cells， hyperchromatic nuclei， and no necrosis. Cisplatin treatment induced partial necrosis and vacuolization， while the Shasheng Maidong Tang+cisplatin group exhibited extensive necrotic regions， maximal vacuolization， disarranged tumor cells， and minimal mitotic activity. Compared with the model group， the cisplatin group and the Shasheng Maidong Tang+cisplatin group showed elevated level of IFN-γ （P<0.01） and declined level of IL-6 （P<0.01） in the peripheral blood. Compared with the cisplatin group， the Shasheng Maidong Tang+cisplatin group presented elevated level of IFN-γ （P<0.01） and lowered level of IL-6 （P<0.01） in the peripheral blood. Compared with the model group， the cisplatin group and the Shasheng Maidong Tang+cisplatin groups showed down-regulated mRNA levels of JAK2 and STAT3 （P<0.01） and up-regulated mRNA level STAT1 （P<0.01）. Compared with the cisplatin group， the Shasheng Maidong Tang+cisplatin group presented down-regulated mRNA levels of JAK2 and STAT3 （P<0.01） and up-regulated mRNA level of STAT1 （P<0.01）. Compared with the model group， the cisplatin group and the Shasheng Maidong Tang+cisplatin group showed down-regulated protein levels of JAK2 （P<0.01）， Bcl-2 （P<0.01）， PIM1 （P<0.01）， and STAT3 （P<0.05）， and up-regulated protein level of Caspase-3 （P<0.01）. Compared with the cisplatin group， Shasheng Maidong Tang+cisplatin group presented down-regulated protein levels of JAK2 （P<0.01）， Bcl-2 （P<0.01）， PIM1 （P<0.01）， STAT3 （P<0.05）， and up-regulated protein level of Caspase-3 （P<0.01）. The Bcl-2 and PIM1 expression results obtained by immunohistochemistry were consistent with those of Western blot. ConclusionShasheng Maidong Tang may enhance the efficacy of chemotherapy in the mouse model of Lewis lung cancer by regulating the JAK2/STAT3 signaling pathway.

Objective: To prepare the erythrocyte-liposome drug delivery system to enhance the therapeutic effect of drugs on tumors and inhibit tumor metastasis. Methods: This study prepared and characterized paclitaxel (PTX)-plerixafor (AMD3100) liposomes (Lips), developed the erythrocyte-liposome drug delivery system, and evaluated its targeting efficiency and therapeutic efficacy through a series of in vitro cellular and in vivo animal experiments. Results: The particle size of PTX-AMD-Lips was (186.4±0.83) nm. Drug encapsulation efficiency of PTX-AMD-Lips was (75.50±5.27)% for PTX and (88.31±2.45)% for AMD. The Binding efficiency between RBC and liposomes in the drug delivery system was (69.93±2.55)%. Vitro cellular experiments revealed that PTX-AMD-Lips significantly inhibited tumor cell migration. In vivo animal experiments, the erythrocyte-liposome drug delivery system significantly increased drug accumulation in the lungs. At the experimental endpoint, the quantitative fluorescence signal of tumor size measured (4.04±0.44)×10 for the PTX-Lips group, and (5.14±3.40)×10 for the RBC-PTX-AMD-Lips group. Conclusion: The erythrocyte-liposome drug delivery system could enhance the lung-specific targeting capability of liposomes, kill tumor cells and suppress further metastasis effectively.

Objective: To develop a depression recognition model by integrating the spirit-expression diagnostic framework of traditional Chinese medicine (TCM) with machine learning algorithms. The proposed model seeks to establish a TCM-informed tool for early depression screening, thereby bridging traditional diagnostic principles with modern computational approaches. Methods: The study included patients with depression who visited the Shanghai Pudong New Area Mental Health Center from October 1, 2022 to October 1, 2023, as well as students and teachers from Shanghai University of Traditional Chinese Medicine during the same period as the healthy control group. Videos of 3 – 10 s were captured using a Xiaomi Pad 5, and the TCM spirit and expressions were determined by TCM experts (at least 3 out of 5 experts agreed to determine the category of TCM spirit and expressions). Basic information, facial images, and interview information were collected through a portable TCM intelligent analysis and diagnosis device, and facial diagnosis features were extracted using the Open CV computer vision library technology. Statistical analysis methods such as parametric and non-parametric tests were used to analyze the baseline data, TCM spirit and expression features, and facial diagnosis feature parameters of the two groups, to compare the differences in TCM spirit and expression and facial features. Five machine learning algorithms, including extreme gradient boosting (XGBoost), decision tree (DT), Bernoulli naive Bayes (BernoulliNB), support vector machine (SVM), and k-nearest neighbor (KNN) classification, were used to construct a depression recognition model based on the fusion of TCM spirit and expression features. The performance of the model was evaluated using metrics such as accuracy, precision, and the area under the receiver operating characteristic (ROC) curve (AUC). The model results were explained using the Shapley Additive exPlanations (SHAP). Results: A total of 93 depression patients and 87 healthy individuals were ultimately included in this study. There was no statistically significant difference in the baseline characteristics between the two groups (P > 0.05). The differences in the characteristics of the spirit and expressions in TCM and facial features between the two groups were shown as follows. (i) Quantispirit facial analysis revealed that depression patients exhibited significantly reduced facial spirit and luminance compared with healthy controls (P < 0.05), with characteristic features such as sad expressions, facial erythema, and changes in the lip color ranging from erythematous to cyanotic. (ii) Depressed patients exhibited significantly lower values in facial complexion L, lip L, and a values, and gloss index, but higher values in facial complexion a and b, lip b, low gloss index, and matte index (all P < 0.05). (iii) The results of multiple models show that the XGBoost-based depression recognition model, integrating the TCM “spirit-expression” diagnostic framework, achieved an accuracy of 98.61% and significantly outperformed four benchmark algorithms—DT, BernoulliNB, SVM, and KNN (P < 0.01). (iv) The SHAP visualization results show that in the recognition model constructed by the XGBoost algorithm, the complexion b value, categories of facial spirit, high gloss index, low gloss index, categories of facial expression and texture features have significant contribution to the model. Conclusion This study demonstrates that integrating TCM spirit-expression diagnostic features with machine learning enables the construction of a high-precision depression detection model, offering a novel paradigm for objective depression diagnosis.

Based on the interactive ritual chain theory， this paper deeply analyzed the interactive characteristics between doctors， patients， and their families in the palliative care environment， as well as explored the role of emotional resonance， symbolic representation， and situational creation in psychological support. It also sorted out four primary issues currently present in psychological support for palliative care patients， including insufficient recognition of caregivers regarding patients’ psychological needs， limited psychological intervention methods， inadequate psychological support capabilities among medical staff， and an imperfect family and social support system. On this basis， a five-dimensional psychological support mechanism was constructed， encompassing emotional resonance， situational creation， team collaboration， environment building， and technological application. This aimed to provide palliative care patients with comprehensive and continuous psychological intervention by optimizing doctor-patient interaction， strengthening emotional connection， improving physical environment， and utilizing information technology， thereby contributing to alleviating the psychological distress patients confront in the terminal stage and improving their life dignity and quality of life.

ObjectiveTo investigate the mechanism underlying the weight loss effect of electrothermal acupuncture （ETA） in diet-induced obese （DIO） model mice based on the hypoxia-inducible factor-1α （HIF-1α）/sphingosine-1-phosphate （S1P）sphingosine-1-phosphate receptor 3 （S1PR3） signaling pathway-mediated macrophage polarization. MethodsAn obesity mouse model was established by continuously feeding at a high-fat diet （HFD） for 8 weeks. Twenty successfully modeled obese mice were randomly divided into a model group and an electrothermal acupuncture （ETA） group with 10 mice per group， both of which continued to receive the HFD till week 12. Another 10 mice were assigned to the normal group and fed a standard chow diet continuously for 12 weeks. Starting from week 9 of the experiment， mice in the ETA group received electrothermal acupuncture stimulation at Zhongwan （CV12）， bilateral Tianshu （ST25）， and bilateral Zusanli （ST36） for 30 minutes per session， with the instrument current set at （40±0.5）mA and a constant temperature of （40±0.5）℃. Mice in the normal group and the model group were restrained in the same manner for 30 minutes without acupuncture. All interventions were performed once every other day for a total of 4 weeks. Ten hours after the final intervention， the body weight， waist circumference， and body length of mice in each group were measured， and Lee's index was calculated. Serum biochemical analysis was performed to assess fasting blood glucose （FBG）， total cholesterol （TC）， triglycerides （TG）， high-density lipoprotein cholesterol （HDL-C）， low-density lipoprotein cholesterol （LDL-C）， alanine aminotransferase （ALT）， aspartate aminotransferase （AST）， blood urea nitrogen （BUN）， and creatinine （Cr）. Serum leptin （LEP） and adiponectin （ADPN） levels， as well as sphingosine-1-phosphate （S1P） levels in epididymal white adipose tissue （eWAT）， were detected by ELISA. Histomorphological changes in eWAT were monitored via HE staining. Flow cytometry was used to analyze macrophage subtype-related indexes in eWAT， including the total proportion of macrophages， the proportion of M1 macrophages， the proportion of M2 macrophages， and the M1/M2 ratio. Antibody array technology was used to determine the levels of differentially expressed inflammatory factors in eWAT between the model and blank groups， as well as between the ETA and model groups. Western blot and qPCR were used to detect the protein and mRNA expression levels of HIF-1α and S1PR3 in the eWAT， respectively. ResultsCompared to the normal group， the model group showed significantly increased body weight， Lee's index， waist circumference， and FBG， TC， TG， HDL-C， LDL-C， AST， and ALT levels， upgraded LEP and S1P levels， decreased ADPN level and ADPN/LEP ratio， elevated proportion of M1 macrophages and the M1/M2 ratio， reduced proportion of M2 macrophages， and upregulated protein and mRNA expression levels of HIF-1α and S1PR3 （P＜0.05 or P＜0.01）. With the exception of HDL-C， all the abovementioned indicators in the ETA group were significantly improved compared to the model group （P＜0.05 or P＜0.01）. Analysis of differentially expressed inflammatory cytokines revealed that， compared to the normal group， the levels of interleukin-1β （IL-1β）， interferon-γ （IFN-γ）， interleukin-6 （IL-6）， and interleukin-12p70 （IL-12p70） in the eWAT of the model group were elevated； conversely， the levels of interleukin-17 （IL-17）， interleukin-5 （IL-5）， IL-6， and interleukin-23 （IL-23） in the eWAT of the ETA group decreased compared to the model group （P＜0.05 or P＜0.01）. ConclusionETA exerts a weight-reducing effect in obese model mice， and the mechanism may involve inhibition of the HIF-1α/S1P/S1PR3 signaling pathway activation， which shifts macrophage polarization from the M1 to M2 phenotype， thereby alleviating chronic inflammation.

Children， as a special group， frequently experience of off-label drug use worldwide. Common reasons for off-label drug use in children include the lack of data on pediatric patients during the clinical trial stage of drug development， delayed updates to drug instructions， and the non-standard professional behavior of some doctors. Off-label drug use in children is a double-edged sword. It could save lives and provide a way to explore additional functions of drugs， while it may also lead to the phenomenon of hyper-indication abuse， increasing the risk of adverse drug events. Regulating off-label drug use in children can safeguard the best treatment rights and interests of children. It is recommended to encourage pharmaceutical enterprises to conduct research and development of pediatric new drugs， simplify the approval process for drug instructions amendments， accumulate evidence-based medical evidence for off-label drug use in children， standardize the process of off-label drug use in children in medical institutions， continuously improve the standardized diagnosis and treatment capabilities of pediatricians， and actively cooperate with the families of pediatric patients in diagnosis and treatment， so as to comprehensively safeguard the rights and interests of both doctors and patients.

Purpose: Gold-standard urodynamic examination is widely used in the diagnosis and treatment of lower urinary tract dysfunction. The purpose of urodynamic quality control is to standardize urodynamic examination and ensure its clinical reference value. In our study, we attempted to use a deep learning (DL) algorithm model, mainly for the recognition of typical urodynamic signal, to help physicians complete high-quality urodynamic examinations. Methods: Urodynamic image data from 2 cohorts of adult patients with neurogenic bladder were used: (1) 300 patients with neurogenic bladder in our center from 2012 to 2018 (1,960 images used to train and validate the DL model); and (2) 100 patients with neurogenic bladder from 2020 to 2021 (695 images used to test the performance of the DL model). This resulted in a total of 2,655 images to train, validate and test the DL algorithm to predict the urdynamic signals. Results: Yolov5l had the best detection performance and the highest comprehensive index score (F1, 0.81; mean average precision, 0.83). Our study is a retrospective single-center study, and the generalization ability of the model has not been verified. Conclusions DL algorithms can help operators identify typical urodynamic signals in real time, improve the interpretation and quality of urodynamic examination, and benefit patients.

Purpose: Gold-standard urodynamic examination is widely used in the diagnosis and treatment of lower urinary tract dysfunction. The purpose of urodynamic quality control is to standardize urodynamic examination and ensure its clinical reference value. In our study, we attempted to use a deep learning (DL) algorithm model, mainly for the recognition of typical urodynamic signal, to help physicians complete high-quality urodynamic examinations. Methods: Urodynamic image data from 2 cohorts of adult patients with neurogenic bladder were used: (1) 300 patients with neurogenic bladder in our center from 2012 to 2018 (1,960 images used to train and validate the DL model); and (2) 100 patients with neurogenic bladder from 2020 to 2021 (695 images used to test the performance of the DL model). This resulted in a total of 2,655 images to train, validate and test the DL algorithm to predict the urdynamic signals. Results: Yolov5l had the best detection performance and the highest comprehensive index score (F1, 0.81; mean average precision, 0.83). Our study is a retrospective single-center study, and the generalization ability of the model has not been verified. Conclusions DL algorithms can help operators identify typical urodynamic signals in real time, improve the interpretation and quality of urodynamic examination, and benefit patients.

Objective: To investigate the predictive value of EZH2 expression for malignant transformation in oral leukoplakia (OLK) and to provide a reference for clinical practice. Methods: This study was approved by the institutional ethics committee, and informed consent was obtained from all participants. A total of 114 patients diagnosed with OLK by pathological examination and treated at our hospital between November 2020 and July 2022 were initially enrolled. After excluding those with incomplete data or follow-up, 105 participants were included in the final analysis, comprising 14 in the high EZH2 expression group and 91 in the low EZH2 expression group. Histopathological examination of oral mucosa and immunohistochemical detection of EZH2 protein expression were performed. The follow-up period was 30 months; participants were followed until malignant transformation occurred or until the end of follow-up, at which point they were withdrawn from the study. The exposure factor was the level of EZH2 protein expression, and the outcome was the malignant transformation rate of OLK. Differences in EZH2 expression levels and transformation outcomes were analyzed. Results: There were no statistically significant differences between the high and low EZH2 expression groups in terms of age, sex, history of systemic disease, lifestyle habits, psychological status, diet, and sleep conditions (P > 0.05). Lesions in the high EZH2 expression group were mainly located on the ventral tongue, while in the low EZH2 expression group, they were more commonly found on the dorsal tongue and buccal mucosa. The malignant transformation rate was 28.6% (4/14) in the high expression group and 8.8% (8/91) in the low expression group; these differences were not statistically significant (P=0.053). In univariate Cox regression analysis, the risk of malignant transformation in the high EZH2 expression group was 3.647 times that of the low EZH2 expression group (HR = 3.647, 95% CI: 1.097-12.120, P<0.05). Kaplan-Meier survival analysis showed that over the 30-month follow-up period, the cancer-free survival rate in the high EZH2 expression group was 19.8% lower than in the low expression group, and the difference was statistically significant (P<0.05). In multivariate Cox regression analysis, only moderate and severe epithelial dysplasia were identified as independent risk factors for malignant transformation. The risk of malignant transformation in the moderate and severe dysplasia groups was 10.695 and 13.623 times higher, respectively, than in the mild dysplasia group (HR = 10.695, 95% CI: 2.270-50.396, P<0.05; HR=13.623, 95% CI: 1.918-96.774, P<0.05). EZH2 high expression was not an independent risk factor in the multivariate model (HR= 2.528, 95% CI: 0.752-8.500, P = 0.134). Conclusion High EZH2 protein expression is a risk factor for the malignant transformation of OLK but does not have independent predictive value.

Purpose: Gold-standard urodynamic examination is widely used in the diagnosis and treatment of lower urinary tract dysfunction. The purpose of urodynamic quality control is to standardize urodynamic examination and ensure its clinical reference value. In our study, we attempted to use a deep learning (DL) algorithm model, mainly for the recognition of typical urodynamic signal, to help physicians complete high-quality urodynamic examinations. Methods: Urodynamic image data from 2 cohorts of adult patients with neurogenic bladder were used: (1) 300 patients with neurogenic bladder in our center from 2012 to 2018 (1,960 images used to train and validate the DL model); and (2) 100 patients with neurogenic bladder from 2020 to 2021 (695 images used to test the performance of the DL model). This resulted in a total of 2,655 images to train, validate and test the DL algorithm to predict the urdynamic signals. Results: Yolov5l had the best detection performance and the highest comprehensive index score (F1, 0.81; mean average precision, 0.83). Our study is a retrospective single-center study, and the generalization ability of the model has not been verified. Conclusions DL algorithms can help operators identify typical urodynamic signals in real time, improve the interpretation and quality of urodynamic examination, and benefit patients.