ObjectiveGastric hemorrhage is one of the most common and life-threatening emergencies of the upper digestive tract. Early identification and continuous monitoring are essential for reducing rebleeding rates and mortality, particularly within the critical early hours after onset. Although endoscopy and radiological imaging can accurately localize bleeding sites, these approaches are invasive, resource-intensive, and unsuitable for continuous bedside monitoring. Electrical impedance tomography (EIT), as a noninvasive and radiation-free functional imaging technique, offers real-time visualization of conductivity distribution and has the potential for detecting intragastric bleeding based on the electrical contrast between blood and surrounding gastric tissues. In this study, a three-dimensional gastric EIT (3D-gEIT) framework is proposed to achieve noninvasive, real-time, and dynamic monitoring of gastric hemorrhage, with emphasis on spatial localization and quantitative volume assessment. MethodsA three-dimensional upper-abdominal simulation model incorporating the stomach, gastric wall, gastric contents, and surrounding tissues was established. Three electrode configurations, namely the dual layer ring, the four layer staggered ring, and the opposed dual plane array, were designed and systematically compared to evaluate their influence on depth sensitivity and spatial resolution. Based on the Tikhonov-Noser hybrid regularization scheme, a region-clustering constraint was introduced to develop the TK-Noser-RCC algorithm. This approach aggregates spatially adjacent elements with similar conductivity variations, thereby enhancing structural continuity and suppressing isolated noise artifacts. To validate the proposed framework, an upper-abdominal physical phantom was constructed using agar to simulate background tissue conductivity. Hemispherical high-conductivity inclusions with volumes ranging from 10 ml to 50 ml were attached to the inner gastric wall to mimic localized bleeding under different gastric filling states. Boundary voltages were acquired under a 120 kHz excitation current and reconstructed using the TK-Noser-RCC algorithm. Furthermore, an in vivo animal experiment was performed using a porcine model with adult-scale abdominal dimensions. A total of 100 ml of autologous blood was injected incrementally into the stomach to simulate progressive gastric hemorrhage, and time-difference EIT reconstruction was conducted at each injection stage to assess the dynamic system response under physiological conditions. ResultsSimulation results demonstrated that the opposed dual-plane electrode array achieved superior depth sensitivity distribution and spatial resolution. For a 40 ml hemorrhage model, the average ICC and SSIM improved by 55.9% and 38.8% compared with the dual-layer ring configuration, and by 64.0% and 39.5% compared with the four-layer staggered configuration. The proposed region-clustering constraint significantly enhanced reconstruction stability. Under added Gaussian noise of 40 dB and 30 dB, ICC values remained approximately 0.85, indicating effective artifact suppression and preservation of boundary integrity. In physical phantom experiments, reconstructed hemorrhage volumes increased approximately linearly with the preset hemispherical volumes, and the reconstructed high-conductivity regions closely matched the actual bleeding locations. Both empty-stomach and full-stomach conditions were evaluated, demonstrating that the opposed dual-plane configuration maintained stable imaging performance across varying gastric contents. In the animal experiment, reconstructed low-impedance regions expanded progressively with increasing injected blood volume. The spatial localization of the hemorrhage remained stable throughout the procedure, and no significant artifacts were observed. Quantitative analysis showed that reconstructed volume and average conductivity variation exhibited an approximately linear growth trend with injected blood volume, confirming the sensitivity of the system to dynamic intragastric conductivity changes. ConclusionThe proposed 3D-gEIT framework enables quantitative reconstruction of gastric hemorrhage volume and spatial distribution with improved depth sensitivity, structural continuity, and noise robustness compared with conventional EIT approaches. By integrating optimized electrode configuration and a region-clustering-constrained reconstruction algorithm, the system provides stable dynamic monitoring under both controlled phantom conditions and in vivo physiological environments. This method offers a noninvasive, real-time, and low-cost imaging strategy for early diagnosis, postoperative monitoring, and bedside surveillance of gastric bleeding.

With the rapid advancement of hemodynamic indices and monitoring technologies, their classification methods and application processes have become increasingly complex. Currently, no unified standard hasbeen established, making it difficult to fully meet the clinical requirements for hemodynamic management. To assist in hemodynamic monitoring assessment and therapeutic decision-making in critically ill patients, the Critical Hemodynamic Therapy Collaborative Group, in conjunction with the Critical Ultrasound Study Group, has jointly developed the Standard for the Application of Hemodynamic Monitoring Techniques in Critical Care. The first part of this standard systematically categorizes hemodynamic indicators into flow indicators, pressure and its derivative indicators, and tissue perfusion indicators, while elaborating on the clinical application of each. The second part establishes a standardized clinical implementation pathway for hemodynamic monitoring. It proposes a tiered monitoring strategy-comprising basic, advanced, indication-specific, and special scenario monitoring-tailored to different clinical settings. It emphasizes the central role of critical care ultrasound across all levels of monitoring and establishes hemodynamic assessment standards for organs such as the brain, kidneys, and gastrointestinal tract. This standard aims to provide a unified framework for clinical practice, teaching, training, and research in critical care medicine, thereby promoting standardized development within the discipline.

Obesity represents a critical global health challenge characterized by a complex pathogenesis involving dysregulated adipogenesis and lipid metabolism. In recent years, long non-coding RNAs (lncRNAs) have been established as crucial regulators in the initiation and progression of obesity. These RNA molecules, typically exceeding 200 nucleotides in length, have emerged as key modulators of various biological processes through multiple molecular mechanisms. This review innovatively defines lncRNAs as “molecular switches” in energy metabolism—they regulate adipogenesis and lipid metabolism through key signaling pathways, and exert bidirectional control over obesity via ceRNA mechanisms or recruitment of chromatin-modifying complexes in tissues such as adipose and liver. Additionally, circulating lncRNAs, owing to their tissue specificity and stability, hold promise as non-invasive liquid biopsy biomarkers for obesity and related metabolic disorders. Furthermore, we systematically summarize lncRNA-based intervention strategies, including targeting pathogenic lncRNAs using antisense oligonucleotides (ASOs) or CRISPR/Cas gene editing systems, utilizing viral vectors (such as adeno-associated virus, AAV) to deliver or mimic beneficial lncRNAs in target tissues, and employing exercise as a non-pharmacological intervention that ameliorates obesity and its related complications at multiple levels, offering novel insights for personalized therapeutic approaches. We also critically assess the current challenges in clinical translation, particularly addressing issues related to delivery efficiency, target specificity, and long-term safety concerns. Future research should focus on the following directions: integrating multi-omics with functional screening to elucidate the regulatory networks of lncRNAs in obesity and its complications; leveraging artificial intelligence to construct predictive models of lncRNA-target gene interactions; developing efficient and safein vivo delivery systems, and optimizing drug design to enhance specificity and safety; establishing highly sensitive detection methods and stable circulating lncRNA biomarkers to enable precise patient stratification and real-time monitoring of therapeutic responses; investigating the synergistic effects of lncRNAs with existing treatments (e.g., GLP-1 receptor agonists, lifestyle interventions) to develop combination therapies and establish a multidimensional, personalized precision medicine framework for obesity. This review aims to provide novel perspectives for understanding the molecular mechanisms underlying obesity and to establish a solid theoretical foundation for developing lncRNA-targeted precision medicine strategies against obesity and its associated metabolic complications.

Obesity represents a critical global health challenge characterized by a complex pathogenesis involving dysregulated adipogenesis and lipid metabolism. In recent years, long non-coding RNAs (lncRNAs) have been established as crucial regulators in the initiation and progression of obesity. These RNA molecules, typically exceeding 200 nucleotides in length, have emerged as key modulators of various biological processes through multiple molecular mechanisms. This review innovatively defines lncRNAs as “molecular switches” in energy metabolism—they regulate adipogenesis and lipid metabolism through key signaling pathways, and exert bidirectional control over obesity via ceRNA mechanisms or recruitment of chromatin-modifying complexes in tissues such as adipose and liver. Additionally, circulating lncRNAs, owing to their tissue specificity and stability, hold promise as non-invasive liquid biopsy biomarkers for obesity and related metabolic disorders. Furthermore, we systematically summarize lncRNA-based intervention strategies, including targeting pathogenic lncRNAs using antisense oligonucleotides (ASOs) or CRISPR/Cas gene editing systems, utilizing viral vectors (such as adeno-associated virus, AAV) to deliver or mimic beneficial lncRNAs in target tissues, and employing exercise as a non-pharmacological intervention that ameliorates obesity and its related complications at multiple levels, offering novel insights for personalized therapeutic approaches. We also critically assess the current challenges in clinical translation, particularly addressing issues related to delivery efficiency, target specificity, and long-term safety concerns. Future research should focus on the following directions: integrating multi-omics with functional screening to elucidate the regulatory networks of lncRNAs in obesity and its complications; leveraging artificial intelligence to construct predictive models of lncRNA-target gene interactions; developing efficient and safein vivo delivery systems, and optimizing drug design to enhance specificity and safety; establishing highly sensitive detection methods and stable circulating lncRNA biomarkers to enable precise patient stratification and real-time monitoring of therapeutic responses; investigating the synergistic effects of lncRNAs with existing treatments (e.g., GLP-1 receptor agonists, lifestyle interventions) to develop combination therapies and establish a multidimensional, personalized precision medicine framework for obesity. This review aims to provide novel perspectives for understanding the molecular mechanisms underlying obesity and to establish a solid theoretical foundation for developing lncRNA-targeted precision medicine strategies against obesity and its associated metabolic complications.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

OBJECTIVE: To screen anti-tumor drugs that improve antigen processing and presentation in acute myeloid leukemia (AML) cells. METHODS: A TCR-like or TCR mimic antibody that can specifically recognize HLA-A*0201:WT1_126-134 ( RMFPNAPYL) complex (hereafter referred to as HLA-A2:WT1) was synthesized to evaluate the function of antigen processing and presentation machinery (APM) in AML cells. AML cell line THP1 was incubated with increasing concentrations of IFN-γ, hypomethylating agents (HMA), immunomodulatory drugs (IMiD), proteasome inhibitors (PI) and γ-secretase inhibitors (GSI), followed by measuring of HLA-ABC, HLA-A2 and HLA-A2:WT1 levels by flow cytometry at consecutive time points. RESULTS: The TCR-like antibody we generated only binds to HLA-A*0201⁺WT1⁺ cells, indicating the specificity of the antibody. HLA-A2:WT1 level of THP-1 cells detected with the TCR-like antibody was increased significantly after co-incubation with IFN-γ, showing that the HLA-A2:WT1 TCR like antibody could evaluate the function of APM. Among the anti-tumor agents screened in this study, GSI (LY-411575) and HMA (decitabine and azacitidine) could significantly increase the HLA-A2:WT1 level. The IMiD lenalidomide and pomalidomide could aslo upregulate the expression of HLA-A2:WT1 complex under certain concentrations of the drugs and incubation time. As proteasome inhibitors, carfilzomib could significantly decreased the expression of HLA-A2:WT1, while bortezomib had no significant effect on HLA-A2:WT1 expression. CONCLUSION HLA-A2:WT1 TCR-like antibody can effectively reflect the APM function. Some of the anti-tumor drugs can affect the APM function and immunogenicity of tumor cells.
Humans ; Leukemia, Myeloid, Acute/immunology* ; Antineoplastic Agents/pharmacology* ; Antigen Presentation/drug effects* ; HLA-A2 Antigen/immunology* ; Receptors, Antigen, T-Cell/immunology* ; Cell Line, Tumor ; Interferon-gamma

Systemic lupus erythematosus (SLE) is a chronic autoimmune disorder affecting multiple systems, characterized by the development of harmful autoantibodies and immune complexes that lead to damage in organs and tissues. Chinese medicine (CM) plays a role in mitigating complications, enhancing treatment effectiveness, and reducing toxicity of concurrent medications, and ensuring a safe pregnancy. However, CM mainly solves the disease comprehensively through multi-target and multi-channel regulation process, therefore, its treatment mechanism is often complicated, involving many molecular links. This review introduces the research progress of pathogenesis of SLE from the aspects of genetics, epigenetics, innate immunity and acquired immunity, and then discusses the molecular mechanism and target of single Chinese herbal medicine and prescription that are commonly used and effective in clinic to treat SLE.
Lupus Erythematosus, Systemic/immunology* ; Humans ; Medicine, Chinese Traditional ; Drugs, Chinese Herbal/pharmacology* ; Animals