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.

Fifty whitespotted bamboo sharks (Chiloscyllium playgiosum) of both sexes were used to establish a large capacity variable domain of the new antigen receptor (VNAR) library with a total capacity of over 10⁹ colony-forming units (CFU). It was applied to screen VNARs against human serum albumin (HSA) and human transcription factor EB (TFEB), respectively. Meanwhile, VNAR libraries specific to HSA and TFEB with capacities above 10⁸ CFU were obtained following conventional immunization. These two approaches were systematically studied in terms of VNAR yield and composition. By comparing the VNAR sequences obtained from naïve and antigen-immunized libraries, we found that the complementary-determining region 3 (CDR3) of the former differs in composition from that of the latter. It shares a higher degree of homology with the naïve library. Meanwhile, the binding efficiency assessed by ELISA is also different between the naïve and antigen-immunized libraries. The binding of VNARs from the TFEB-immunized library appeared to surpass that observed with the naïve libraries, whereas the performance of VNARs from the HSA-immunized library indicated that both the immunized and naïve libraries for HSA had positive binding responses in polyclonal and monoclonal ELISA. The results are useful to develop novel diagnostic and therapeutic products based on shark VNARs.

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Adenomyosis is a common gynecological disease characterized by the invasion of endometrial glands and stroma into the myometrium of uterus, the pathological mechanism of which remains unclear yet. Disturbed lipid metabolism extensively affects abnormal cell proliferation and invasion in various diseases. However, the lipidome signature of human myometrium, which could be crucial in the development of adenomyosis, remains unknown. In this study, we generated the first lipidome profiling of human myometrium using a high-coverage and quantitative lipidomics approach based on ultra-performance liquid chromatography (UPLC) coupled with triple quadrupole (QqQ)-mass spectrometry (MS). A total of 317 lipid species were successfully quantified in the myometrial tissues from women with (n = 38) or without (n = 65) adenomyosis who underwent hysterectomy at Peking Union Medical College Hospital (Bejing, China). Up to 83 lipid species showed significant alternations in content between the two groups. These lipid aberrations involved multiple metabolic pathways, and emphasized inflammation, cell migration, and immune dysregulation upon adenomyosis. Moreover, receiver operating characteristic (ROC) curve analysis found that the combination of five lipid species could accurately distinguished the myometrial samples from women with and without adenomyosis with an area under the curve (AUC) of 0.906. Desorption electrospray ionization MS imaging (MSI) further underscored the heterogeneous distributions of these lipid markers in the adenomyosis lesion and adjacent myometrial tissue. Collectively, these results extremely improved our understanding on the molecular basis of adenomyosis, and could shed light on developing potential biomarkers and new therapeutic directions for adenomyosis.

OBJECTIVE: This study aimed to explore the association between body mass index (BMI) and mortality based on the 10-year population-based multicenter prospective study. METHODS: A general population-based multicenter prospective study was conducted at four sites in rural China between 2013 and 2023. Multivariate Cox proportional hazards models and restricted cubic spline analyses were used to assess the association between BMI and mortality. Stratified analyses were performed based on the individual characteristics of the participants. RESULTS: Overall, 19,107 participants with a sum of 163,095 person-years were included and 1,910 participants died. The underweight (< 18.5 kg/m ²) presented an increase in all-cause mortality (adjusted hazards ratio [ aHR] = 2.00, 95% confidence interval [ CI]: 1.66-2.41), while overweight (≥ 24.0 to < 28.0 kg/m ²) and obesity (≥ 28.0 kg/m ²) presented a decrease with an aHR of 0.61 (95% CI: 0.52-0.73) and 0.51 (95% CI: 0.37-0.70), respectively. Overweight ( aHR = 0.76, 95% CI: 0.67-0.86) and mild obesity ( aHR = 0.72, 95% CI: 0.59-0.87) had a positive impact on mortality in people older than 60 years. All-cause mortality decreased rapidly until reaching a BMI of 25.7 kg/m ² ( aHR = 0.95, 95% CI: 0.92-0.98) and increased slightly above that value, indicating a U-shaped association. The beneficial impact of being overweight on mortality was robust in most subgroups and sensitivity analyses. CONCLUSION This study provides additional evidence that overweight and mild obesity may be inversely related to the risk of death in individuals older than 60 years. Therefore, it is essential to consider age differences when formulating health and weight management strategies.
Humans ; Body Mass Index ; China/epidemiology* ; Male ; Female ; Middle Aged ; Prospective Studies ; Rural Population/statistics & numerical data* ; Aged ; Follow-Up Studies ; Adult ; Mortality ; Cause of Death ; Obesity/mortality* ; Overweight/mortality*

OBJECTIVE: The study aim was to investigate the effects of exposure to multiple environmental organic pollutants on cardiopulmonary health with a focus on the potential mediating role of oxidative stress. METHODS: A repeated-measures randomized crossover study involving healthy college students in Beijing was conducted. Biological samples, including morning urine and venous blood, were collected to measure concentrations of 29 typical organic pollutants, including hydroxy polycyclic aromatic hydrocarbons (OH-PAHs), bisphenol A and its substitutes, phthalates and their metabolites, parabens, and five biomarkers of oxidative stress. Health assessments included blood pressure measurements and lung function indicators. RESULTS: Urinary concentrations of 2-hydroxyphenanthrene (2-OH-PHE) ( β = 4.35% [95% confidence interval ( CI): 0.85%, 7.97%]), 3-hydroxyphenanthrene ( β = 3.44% [95% CI: 0.19%, 6.79%]), and 4-hydroxyphenanthrene (4-OH-PHE) ( β = 5.78% [95% CI: 1.27%, 10.5%]) were significantly and positively associated with systolic blood pressure. Exposures to 1-hydroxypyrene (1-OH-PYR) ( β = 3.05% [95% CI: -4.66%, -1.41%]), 2-OH-PHE ( β = 2.68% [95% CI: -4%, -1.34%]), and 4-OH-PHE ( β = 3% [95% CI: -4.68%, -1.29%]) were negatively associated with the ratio of forced expiratory volume in the first second to forced vital capacity. These findings highlight the adverse effects of exposure to multiple pollutants on cardiopulmonary health. Biomarkers of oxidative stress, including 8-hydroxy-2'-deoxyguanosine and extracellular superoxide dismutase, mediated the effects of multiple OH-PAHs on blood pressure and lung function. CONCLUSION Exposure to multiple organic pollutants can adversely affect cardiopulmonary health. Oxidative stress is a key mediator of the effects of OH-PAHs on blood pressure and lung function.
Humans ; Oxidative Stress/drug effects* ; Male ; Cross-Over Studies ; Female ; Young Adult ; Environmental Pollutants/toxicity* ; Environmental Exposure/adverse effects* ; Biomarkers/blood* ; Adult ; Blood Pressure/drug effects* ; Polycyclic Aromatic Hydrocarbons/urine* ; Beijing