ObjectiveThis study aims to develop a prediction model for the compatibility of Chinese medicinal pairs based on Graph Convolutional Networks （GCN）， named HC-GCN. The model integrates the properties of herbs with modern pharmacological mechanisms to predict pairs with specific therapeutic effects. It serves as a demonstration by applying the model to predict and validate the efficacy of blood-activating herb pairs. MethodsThe training dataset for herb pair prediction was constructed by systematically collecting commonly used herb pairs along with their characteristic data， including Qi， flavor， meridian tropism， and target genes. Integrating traditional characteristics of herb with modern bioinformatics， we developed an efficacy-oriented herb pair compatibility prediction model （HC-GCN） using graph convolutional networks （GCN）. This model leverages machine learning to capture the complex relationships in herb pair compatibility， weighted by efficacy features. The performance of the HC-GCN model was evaluated using accuracy （ACC）， recall， precision， F1 score （F1）， and area under the ROC curve （AUC）. Its predictive effectiveness was then compared to five other machine learning models： eXtreme Gradient Boosting （XGBoost）， logistic regression （LR）， Naive Bayes， K-nearest neighbor （KNN）， and support vector machine （SVM）. ResultsUsing herb pairs with blood-activating effects as a demonstration， a prediction model was constructed based on a foundational dataset of 46 blood-activating herb pairs， incorporating their Qi， flavor， meridian tropism， and target gene characteristics. The HC-GCN model outperforms other commonly used machine learning models in key performance metrics， including ACC， recall， precision， F1 score， and AUC. Through the predictive analysis of the HC-GCN model， 60 herb pairs with blood-activating effects were successfully identified. Among of these potential herb pairs， 44 include at least one herb with blood-activating effects. ConclusionIn this study， we established an efficacy-oriented compatibility prediction model for herb pairs based on GCN by integrating the unique characteristics of traditional herbs with modern pharmacological mechanisms. This model demonstrated high predictive performance， offering a novel approach for the intelligent screening and optimization of traditional Chinese medicine prescriptions， as well as their clinical applications.

BACKGROUND: There is little literature describing the artificial intelligence (AI)-aided diagnosis of severe pneumonia (SP) subphenotypes and the association of the subphenotypes with the ventilatory treatment efficacy. The aim of our study is to illustrate whether clinical and biological heterogeneity, such as ventilation and gas-exchange, exists among patients with SP using chest computed tomography (CT)-based AI-aided latent class analysis (LCA). METHODS: This retrospective study included 413 patients hospitalized at Xinhua Hospital diagnosed with SP from June 1, 2015 to May 30, 2020. AI quantification results of chest CT and their combination with additional clinical variables were used to develop LCA models in an SP population. The optimal subphenotypes were determined though evaluating statistical indicators of all the LCA models, and clinical implications of them such as guiding ventilation strategies were further explored by statistical methods. RESULTS: The two-class LCA model based on AI quantification results of chest CT can describe the biological characteristics of the SP population well and hence yielded the two clinical subphenotypes. Patients with subphenotype-1 had milder infections ( P <0.001) than patients with subphenotype-2 and had lower 30-day ( P <0.001) and 90-day ( P <0.001) mortality, and lower in-hospital ( P = 0.001) and 2-year ( P <0.001) mortality. Patients with subphenotype-1 showed a better match between the percentage of non-infected lung volume (used to quantify ventilation) and oxygen saturation (used to reflect gas exchange), compared with patients with subphenotype-2. There were significant differences in the matching degree of lung ventilation and gas exchange between the two subphenotypes ( P <0.001). Compared with patients with subphenotype-2, those with subphenotype-1 showed a relatively better match between CT-based AI metrics of the non-infected region and oxygenation, and their clinical outcomes were effectively improved after receiving invasive ventilation treatment. CONCLUSIONS A two-class LCA model based on AI quantification results of chest CT in the SP population particularly revealed clinical heterogeneity of lung function. Identifying the degree of match between ventilation and gas-exchange may help guide decisions about assisted ventilation.
Humans ; Tomography, X-Ray Computed/methods* ; Male ; Female ; Retrospective Studies ; Middle Aged ; Artificial Intelligence ; Aged ; Pneumonia/diagnosis* ; Latent Class Analysis ; Adult

Objective To establish a rat model of Alzheimer's disease (AD) expressing human triple mutant amyloid precursor protein (APP) in the hippocampus, and to provide a model for the study of disease mechanisms and drug development. Methods Twenty-four 12-week-old SPF-grade female SD rats were randomly divided into a blank control group, a virus control group and an experimental group, with eight rats in each group; among them, the experimental group received a stereotaxic injection of adeno-associated virus (AAV) carrying the human triple mutant APP and NanoLuc luciferase genes into the hippocampus. In vivo imaging was used to observe viral expression in the brains of rats in each group, the novel object recognition test was used to assess the recognition memory of the rats in each group, real-time fluorescent quantitative PCR was used to detect the expression level of the APP gene, HE staining was used to examine the brain histopathology, Nissl staining was used to assess the hippocampal lesions, and immunohistochemistry was used to detect the deposition of amyloid β-protein (Aβ). Results In vivo imaging showed that reporter fluorescence was detected in the brains of rats in both experimental and virus control groups. Fluorescence quantitative PCR showed that the expression level of the APP gene was significantly increased in the brains of rats in the experimental group (P<0.01). Novel object recognition test revealed that the recognition memory of rats in the experimental group was significantly reduced compared with that of the blank control group (P<0.01). Six months after recombinant AAV virus infection, HE staining and Nissl staining of brain tissues showed that the number of neurons and Nissl bodies in the CA1 region of the hippocampus in the experimental group was reduced and disorganized; immuno-histochemistry testing of the CA1 region of the hippocampus and the pyramidal cell layer of the experimental group revealed prominent brown deposits, indicating Aβ protein deposition. Conclusion The rat model successfully established by stereotaxic injection and AAV-mediated delivery of human triple mutant APP gene exhibits typical AD features, providing a valuable animal model for studying AD pathology and developing drug therapies targeting Aβ protein deposition.

Objective To establish a rat model of Alzheimer's disease (AD) expressing human triple mutant amyloid precursor protein (APP) in the hippocampus, and to provide a model for the study of disease mechanisms and drug development. Methods Twenty-four 12-week-old SPF-grade female SD rats were randomly divided into a blank control group, a virus control group and an experimental group, with eight rats in each group; among them, the experimental group received a stereotaxic injection of adeno-associated virus (AAV) carrying the human triple mutant APP and NanoLuc luciferase genes into the hippocampus. In vivo imaging was used to observe viral expression in the brains of rats in each group, the novel object recognition test was used to assess the recognition memory of the rats in each group, real-time fluorescent quantitative PCR was used to detect the expression level of the APP gene, HE staining was used to examine the brain histopathology, Nissl staining was used to assess the hippocampal lesions, and immunohistochemistry was used to detect the deposition of amyloid β-protein (Aβ). Results In vivo imaging showed that reporter fluorescence was detected in the brains of rats in both experimental and virus control groups. Fluorescence quantitative PCR showed that the expression level of the APP gene was significantly increased in the brains of rats in the experimental group (P<0.01). Novel object recognition test revealed that the recognition memory of rats in the experimental group was significantly reduced compared with that of the blank control group (P<0.01). Six months after recombinant AAV virus infection, HE staining and Nissl staining of brain tissues showed that the number of neurons and Nissl bodies in the CA1 region of the hippocampus in the experimental group was reduced and disorganized; immuno-histochemistry testing of the CA1 region of the hippocampus and the pyramidal cell layer of the experimental group revealed prominent brown deposits, indicating Aβ protein deposition. Conclusion The rat model successfully established by stereotaxic injection and AAV-mediated delivery of human triple mutant APP gene exhibits typical AD features, providing a valuable animal model for studying AD pathology and developing drug therapies targeting Aβ protein deposition.

Objective: To investigate the role of ferroptosis in transfusion-related acute lung injury (TRALI) and evaluate the efficacy of the specific inhibitor Ferrostatin-1 (Fer-1), thereby to provide a basis for the prevention and treatment of TRALI. Methods: This study utilized a ”2-hit” model to induce TRALI in mice. The mouse model of TRALI was validated through survival curve analysis, lung tissue wet/dry weight ratio (W/D), myeloperoxidase (MPO) activity, and total protein concentration in lung tissue. Samples from the TRALI model group, LPS group, and control group (n=6) were collected. The occurrence of ferroptosis in TRALI was confirmed by measuring key ferroptosis indicators, including iron concentration in lung tissue, malondialdehyde (MDA) level, lipid peroxidation products (LPO) level, and expression levels of related proteins (GPX4, ACSL4). Additionally, a Fer-1 intervention group was added to evaluate its preventive and therapeutic effects. The survival rates and clinical symptoms of the four groups (n=6) were dynamically monitored, and the degrees of lung injury were assessed. Ferroptosis-related indicators were also measured to elucidate the protective mechanism of Fer-1. Results: A mouse model of TRALI was successfully established. Compared to the control and LPS groups, the TRALI group showed significantly higher levels of ferrous iron [(18.32±1.11) nmol/well, MDA [(14.68±0.96) μmol/L], and LPO [(1.60±0.02) μmol/L] in lung tissue (all P<0.01), along with a downregulation of GPX4 and an upregulation of ACSL4. Fer-1 pretreatment significantly reversed these abnormalities: the W/D ratio decreased to 4.01±0.43, and MPO activity significantly decreased [Fer-1 group: (21 606±4 235) pg/mL vs TRALI group: (30 724±2 616) pg/mL], the total protein concentration in lung tissue of the Fer-1 group decreased by approximately 40.8% compared to the TRALI group (all P<0.01). These changes indicate that the lung injury in mice was alleviated after treatment. Following Fer-1 intervention, ferrous iron concentration [(7.46±1.83) nmol/well] was restored to a level close to that of the control group [(5.48±0.70) nmol/well]. Lipid peroxidation tests further revealed that Fer-1 intervention reduced MDA and LPO levels by 35.8% and 29.4%, respectively (P<0.001). Additionally, the expression levels of GPX4 and ACSL4 proteins returned to near-normal levels in the treated mice (both P>0.05). Conclusion: The progression of TRALI is closely related to the activation of ferroptosis, characterized by iron overload, lipid peroxidation accumulation, and the imbalance of GPX4/ACSL4. Ferrostatin-1 significantly alleviates pulmonary edema and inflammatory damage by inhibiting the ferroptosis pathway, suggesting that targeting ferroptosis may provide a new therapeutic strategy for TRALI.

Objective: To explore the independent effects and gender differences of different types of adverse childhood experiences (ACEs) on the co-occurrence of non-suicidal self-injury (NSSI) and suicide attempts (SA), so as to provide a reference for the precise prevention and control of self-harm in junior high school students. Methods: From May to June 2023, a total of 7 360 junior high school students were selected from 12 schools in three districts/counties of Chongqing using a combination of stratified cluster sampling and convenience sampling methods. Information on NSSI, SA, ACEs, and depressive symptom, as well as other related data were collected through the Adolescent Non-suicidal Self-injury Assessment Questionnaire (ANSAQ), suicide related section of the Chinese Adolescent Health related Behavior Questionnaire (Junior High School Version), Childhood Trauma Questionnaire-Short Form ( CTQ- SF), and Center for Epidemiologic Studies-Depression Scale (CES-D). Statistical analyses of the data were performed using the Chi-square test and multiple Logistic regression. Results: The detection rates of NSSI, SA, NSSI+SA and ACEs in junior high school students were 19.2%, 4.6%, 3.5% and 57.9% respectively. After controlling for factors such as gender, grade, family type, self rated family economic status, self rated academic performance, self rated academic pressure, number of close friends, and depressive symptom scores, results from the multiple Logistic regression analysis showed that junior high school students with physical abuse ( OR = 1.98, 95% CI =1.23-3.18), emotional abuse ( OR =2.83, 95% CI =1.92-4.19), sexual abuse ( OR = 1.70, 95% CI =1.07- 2.69 ), physical neglect ( OR =1.67, 95% CI =1.20-2.33) and witnessing domestic violence ( OR =2.10, 95% CI =1.41-2.87) in childhood had higher risks for the occurrence of NSSI+SA (all P <0.05). After stratification by gender, boys with sexual abuse in childhood had a high risk for the occurrence of NSSI+SA ( OR =2.17, 95% CI =1.06-4.43), whereas girls with emotional abuse ( OR =3.69, 95% CI =2.29-5.94), physical neglect ( OR =1.62, 95% CI =1.07-2.45) and witnessing domestic violence ( OR =2.17, 95% CI =1.41-3.34) in childhood had hgih risks for the occurrence of NSSI+SA (all P <0.05). Conclusions Different types of ACEs have different effects on the co-occurrence of self-harm in junior high school students and there are gender differences. When family interventions are conducted for the combined model, emphasis should be placed on aspects of emotional abuse and domestic violence while optimizing the interventions based on gender differences.

Objective: To understand the current situation and influencing factors of comorbidity of depressive and anxiety symptoms among middle school students in Chongqing, so as to provide a scientific basis for formulating a comprehensive strategy for the co prevention of multiple diseases among middle school students. Methods: From September to December 2024, 12 327 middle school students were selected from 6 districts and counties in Chongqing by the combination of stratified cluster sampling and convenience sampling method. The current status of depressive and anxiety symptoms was investigated by using the Center for Epidemiological Survey-Depression Scale (CES-D) and the Generalized Anxiety Disorder-7 (GAD-7). The Chi-squared test was used to compare the differences between groups with comorbidity of depressive and anxiety symptoms, multivariate Logistic regression analysis was used to analyze its related factors, and a nomogram prediction model was drawn. Results: The detection rates of depressive symptoms, anxiety symptoms and comorbidity among middle school students in Chongqing were 26.34%, 34.55% and 21.16%, respectively. Among them, the detection rates of the three types of symptoms in girls (29.80%, 40.99%, 25.15%) were all higher than those in boys (23.22%, 28.73%, 17.55%) ( χ 2=68.61, 204.23, 106.51, all P <0.01). Statistical significance was observed in the distribution of depressive and anxious symptoms among middle school students across different gender, academic stage, school district, family type, physical activity levels, parental discipline, smoking, alcohol consumption, sleep deprivation, excessive screen time, Internet addiction, and bullying ( χ 2=14.49-991.46, all P <0.01). Multivariate Logistic regression analysis showed that compared with junior high school students, ordinary high school students had a higher risk of comorbidity ( OR=2.71, 95% CI = 2.41-3.05); girls ( OR=2.17, 95%CI =1.95-2.40), non-core family ( OR=1.20, 95%CI =1.08-1.32), and good neighborhood ( OR=1.16, 95%CI =1.02-1.30), campus bullying ( OR=4.88, 95%CI =4.32-5.50), Internet addiction ( OR=4.77, 95%CI = 3.41 -6.68), parental beating and scolding ( OR=3.18, 95%CI =2.72-3.71), alcohol consumption ( OR=2.10, 95%CI =1.86- 2.37 ), and insufficient sleep ( OR=1.73, 95%CI =1.54-1.95) had higher risks with comorbidity of depression and anxiety symptoms (all P <0.05). A nomogram prediction model was constructed based on significant variables shows that C-index=0.75 (AUC= 0.75 , 95% CI=0.74-0.76, P <0.05), and the model had good predictive performance. Conclusions The current situation of comorbidity of depressive and anxiety symptoms among middle school students in Chongqing is not optimistic. The nomograms can be used to effectively predict the risk of comorbidity of depressive and anxiety symptoms in middle school students.

Medical visual question answering (MVQA) plays a crucial role in the fields of computer-aided diagnosis and telemedicine. Due to the limited size and uneven annotation quality of the MVQA datasets, most existing methods rely on additional datasets for pre-training and use discriminant formulas to predict answers from a predefined set of labels. This approach makes the model prone to overfitting in low resource domains. To cope with the above problems, we propose an image-aware generative MVQA method based on image caption prompts. Firstly, we combine a dual visual feature extractor with a progressive bilinear attention interaction module to extract multi-level image features. Secondly, we propose an image caption prompt method to guide the model to better understand the image information. Finally, the image-aware generative model is used to generate answers. Experimental results show that our proposed method outperforms existing models on the MVQA task, realizing efficient visual feature extraction, as well as flexible and accurate answer outputs with small computational costs in low-resource domains. It is of great significance for achieving personalized precision medicine, reducing medical burden, and improving medical diagnosis efficiency.
Humans ; Image Processing, Computer-Assisted/methods* ; Diagnosis, Computer-Assisted/methods* ; Algorithms ; Telemedicine

OBJECTIVES: To propose a hypothesis that aloe-emodin may inhibit scar tissue fibrosis through thrombospondin-1(THBS1)-PI3K-Akt pathway. METHODS: By cultivating fibroblasts derived from scar tissue after cleft palate surgery in humans, aloe emodin of different concentrations (10, 20, 30, 40 and 50 μmol/L) was added to the cells which activity was detected. At the same time, transcriptome sequencing was performed on scar tissue and cells, and bioinformatics methods were used to explore potential targets and signaling pathways of scar tissue fibrosis. RESULTS: Aloe-emodin had a concentration dependent inhibitory effect on fibroblast proliferation,with the 40 μmol/L concentration group showing the most significant effect. The results of tissue and cell sequencing indicated that differentially expressed genes were significantly enriched in extracellular matrix-receptor interaction pathway, and shared a common differential gene which was THBS1. The ORA analysis results indicated that differentially expressed genes, including THBS1, were significantly enriched in the PI3K-Akt signaling pathway. CONCLUSIONS Aloe emodin may inhibit the PI3K-Akt pathway by downregulating THBS1, thereby reducing the proliferation activity of fibroblasts derived from postoperative palatal scar tissue.
Thrombospondin 1/genetics* ; Humans ; Signal Transduction/drug effects* ; Fibroblasts/cytology* ; Proto-Oncogene Proteins c-akt/metabolism* ; Fibrosis ; Phosphatidylinositol 3-Kinases/metabolism* ; Cicatrix/metabolism* ; Cell Proliferation/drug effects* ; Anthraquinones/pharmacology* ; Cells, Cultured

Conventional cancer therapies, such as radiotherapy and chemotherapy, often damage normal cells and may induce new tumors. Oncolytic viruses (OVs) selectively target tumor cells while sparing normal cells. Most OVs used in clinical trials have been genetically engineered to enhance their ability to target tumor cells and activate immune responses. To develop a specific OV-based approach for treating cervical cancer, this study constructed an oncolytic adenovirus that delivered a base editor targeting oncogenes to achieve efficient killing of tumor cells through inhibiting tumor growth and directly lysing tumor cells. We utilized the human telomerase reverse transcriptase (TERT) promoter to drive the expression of adenovirus early region 1A (E1A) and successfully constructed the P-hTERT-E1A-GFP vector, which was validated for its activity in cervical cancer cells. Given the critical role of the MYC oncogene in the research of oncology, identifying efficient editing sites for the MYC oncogene is a key step in this study.Three MYC-targeting gRNAs were engineered and co-delivered with ABE8e base editor plasmids into HEK293T cells. Following puromycin selection, Sanger sequencing demonstrated differential editing efficiencies: MYC-1 (43%), MYC-2 (25%), and MYC-3 (35%), identifying MYC-1 as the most efficient editing locus. By constructing the P-ABEs-hTERT-E1A-GFP and P-MYC gRNA-hTERT-E1A-GFP vectors, we successfully packaged the virus and confirmed its specificity and efficacy. The experimental results demonstrate that this novel oncolytic adenovirus effectively inhibits the growth of HeLa cells in vitro, providing new experimental evidence and potential strategies for treating cervical cancer based on the HeLa cell model.
Humans ; Uterine Cervical Neoplasms/pathology* ; Oncolytic Viruses/genetics* ; Female ; HEK293 Cells ; Oncolytic Virotherapy/methods* ; Adenoviridae/genetics* ; Gene Editing/methods* ; Telomerase/genetics* ; Adenovirus E1A Proteins/genetics* ; Genetic Vectors/genetics* ; HeLa Cells