Objective To investigate the heterogeneity and key molecular features of epithelial cells in triple-negative breast cancer (TNBC), identify prognostic biomarkers, and develop a robust survival prediction model. Methods Using TNBC single-cell transcriptomic data, epithelial cells were extracted, normalized, and subclustered to characterize their molecular signatures and functional differences. High-dimensional weighted gene co-expression network analysis (hdWGCNA) was applied to establish co-expression modules in epithelial cells. Multiple machine learning algorithms were integrated to select key prognostic genes and develop a risk-score model, whose performance was evaluated using receiver operating characteristic (ROC) curves and Kaplan-Meier (K-M) survival analysis. In addition, the immune microenvironment features and potential drug-response differences between the high- and low-risk groups were systematically assessed. Finally, PCR was performed to validate the expression differences of the key genes between tumor and normal tissues. Results We characterized the composition and molecular features of TNBC epithelial subpopulations and identified a TNBC-associated epithelial subset. By integrating hdWGCNA with machine learning approaches, 10 key genes were selected to construct a prognostic model, which effectively stratified patients into distinct survival-risk groups and demonstrated favorable predictive performance in ROC and K-M analyses. Immune profiling revealed the differences in the infiltration levels of seven immune cell types and immune function-related features between the high- and low-risk groups. Drug-sensitivity analysis suggested potential differential responses to eight agents across the risk groups. PCR validation further confirmed the differential expression of the ten signature genes between tumor and normal tissues. Conclusion This study reveals epithelial heterogeneity in TNBC at single-cell resolution and establishes a 10-gene prognostic model, which may facilitate the stratification of TNBC risk and the evaluation of immune characteristics and potential therapeutic strategies.

Hepatocellular carcinoma (HCC) develops within a profoundly immunosuppressive tumor immune microenvironment (TIME), which limits the efficacy of immunotherapy. Polarization of tumor-associated macrophages (TAMs) toward a pro-tumorigenic M2 phenotype is a major driver of immune escape. Ferroptosis, an iron-dependent regulated cell death program, intersects with hepatic iron metabolism and immune regulation and thus offers promising points of therapeutic intervention. This review systematically elucidates the mechanistic role of TAM ferroptosis in HCC immune evasion and highlights a “bidirectional regulation” intervention strategy grounded in the Traditional Chinese medicine (TCM) principle of “fortifying healthy qi and eliminating pathogens” (Fuzheng Quxie). This strategy employs “eliminating pathogens” (Quxie) approaches to exploit the metabolic vulnerability of M2-like TAMs and precisely induce their ferroptosis. Moreover, it utilizes “fortifying healthy qi” (Fuzheng) approaches to protect M1-like TAMs and CD8⁺ T cells from oxidative damage. This parallel “induction-protection” paradigm demonstrates the unique advantages of TCM in systemically remodeling TIME through multitarget synergistic actions. Accordingly, precision regulation of TAM ferroptosis based on the Fuzheng Quxie theory represents a promising integrative Chinese-Western medicine strategy for overcoming current bottlenecks in HCC immunotherapy, although its clinical translational potential warrants further validation.

Background In 2021, chronic obstructive pulmonary disease (COPD) emerged as the forth leading cause of death in the world. However, the impact of air pollutants on COPD is still inconsistent across current studies. Objective To analyze the relationship between ambient sulfur dioxide (SO₂) exposure and hospital admissions for COPD in Lanzhou, and to examine the modified effects of SO₂ across different genders, age groups, and seasons. Methods A total of 20718 hospital admission records for COPD were collected from Lanzhou between 2016 and 2020, alongside synchronized data on SO₂ concentration and meteorological variables. A generalized additive model integrated with a distributed lag nonlinear model was used to assess the relationship and the lag effects between SO₂ exposure and COPD admissions. Potential confounders, including meteorological factors, day-of-the-week effect, and holidays, were controlled for, with a maximum lag period set at 7 d. Two-pollutant models and sensitivity analyses were conducted to ensure robustness. Results are expressed as relative risks (RR) with 95% confidence intervals (95%CI). Results During the study period, the average daily number of COPD admissions was 11.34±7.03. The average mass concentration of SO₂ was (23.72±12.35) μg·m⁻³. SO₂ exposure was positively correlated with COPD admissions; specifically, each 10 μg·m⁻³ increase in SO₂ concentration was associated with an RR of 1.440 (95%CI: 1.317, 1.573). Stratified analyses found that at a cumulative lag of 7 d, the RRs were higher among females, individuals aged ≥65 years, and during the cold season. Conversely, no significant increase in COPD admission risk related to SO₂ was observed during the warm season. For every 10 μg·m⁻³ increase in SO₂ concentration, the RR was 1.483 (95%CI: 1.311, 1.678) for females, 1.441 (95%CI: 1.311, 1.583) for those aged > 65 years， and 1.595 (95%CI: 1.313, 1.937) during the cold season. Conclusion Short-term exposure to ambient SO₂ exposure in Lanzhou is associated with an increased risk of COPD admission. The association is more pronounced among females, the elderly (aged> 65 years) and during the cold season.

Background Meteorological factors are among the key extrinsic triggers for the onset and exacerbation of cardiovascular and cerebrovascular diseases (CVD). Against the backdrop of sustained global warming, elucidating the impact of ambient temperature and atmospheric pressure on CVD, especially on pre-hospital CVD emergent events, has become imperative for evidence-based prevention and emergency preparedness. Objective To quantify the temporal trends of daily mean temperature and atmospheric pressure and their associations with pre-hospital CVD emergent events in Yantai, and to explore effect modification by demographic subgroups and geographic areas, thereby providing an empirical basis for the rational allocation of emergency medical resources. Methods Pre-hospital CVD emergency data from January 1, 2016 to December 31, 2022 were selected from the Yantai 120 Emergency Medical Command System. Synchronous meteorological factors and environmental pollutant data were obtained from the websites of the National Oceanic and Atmospheric Administration and the National Centers for Environmental Information of the United States. Time-series analysis combined with distributed lag non-linear model was used to analyze the association between daily temperature, atmospheric pressure, and pre-hospital CVD emergencies. Average annual percentage changes (AAPC) were calculated using Joinpoint (version 5.2.0.0) to reflect temporal trends. Spearman correlation analysis was employed to screen variables with low collinearity for inclusion in the multi-pollutant adjusted models. Results From 2016 to 2022, a total of 268275 pre-hospital CVD emergency cases were recorded in Yantai City, showing an increasing annual trend (AAPC=7.16%, P=0.002). Emergency volume, daily temperature, atmospheric pressure, and PM₁₀ displayed marked seasonal patterns, with summer bottoms and winter peaks for emergencies, daily temperature higher in summer and lower in winter, and the inverse for atmospheric pressure. Correlation analysis showed that both daily temperature and atmospheric pressure were positively correlated with O₃, with correlation coefficients of 0.745 and 0.723, respectively; negatively correlated with PM_2.5, with correlation coefficients of −0.246 and −0.235, respectively; and negatively correlated with PM₁₀, with correlation coefficients of −0.241 and −0.229, respectively (P < 0.05). A U-shaped correlation was revealed between daily temperature and pre-hospital CVD emergency risk. Using 24 °C as the reference, risk was elevated at both low and high daily temperatures, with the strongest cold effect at –11 ℃ (RR=1.53, 95%CI: 1.35, 1.73) and strongest heat effect at 30 ℃ (RR=1.06, 95%CI: 1.01, 1.11); cold effects were prolonged (lag0–14d) compared to heat effects. Atmospheric pressure exhibited a positive association with emergencies; using a median of 1012.2 hPa as the reference, the maximum risk occurred at 1037 hPa (RR=1.25, 95%CI: 1.03, 1.52), with a 0–5 d lag. In the subgroup analysis, the impact of low temperature was more pronounced among females (RR_{−9 ℃}=1.66), people over 60 years (RR_{−9 ℃}=1.91), and rural residents (RR_{−9 ℃}=1.76). High atmospheric pressure significantly increased the risk of pre-hospital CVD emergency among females (RR_{1035 hPa}=1.54), people under 18 years (RR_{1035 hPa}=3.62), and rural residents (RR_{1035 hPa}=1.46). The sensitivity analysis results indicated that, after excluding the impact of pandemic and the degrees of freedom for time variations, the effects of daily average temperature and atmospheric pressure on pre-hospital CVD emergency volume were consistent with the primary analysis findings. Conclusion Both non-optimal temperature and atmospheric pressure significantly increase the pre-hospital CVD emergency volume; therefore, emergency medical services should develop targeted response protocols, optimize resource allocation, and intensify public health education to mitigate the pre-hospital burden attributable to temperature and pressure fluctuations.

ObjectiveCleft palate (CP) is a common congenital deformity often associated with velopharyngeal insufficiency (VPI), which disrupts the physiological coupling between respiration and speech. Conventional clinical assessments, such as nasometry and spirometry, provide limited static data and fail to visualize the dynamic spatiotemporal distribution of lung ventilation during phonation. This study introduces spatiotemporal electrical impedance tomography (ST-EIT) to evaluate speech-respiratory functional features in CP patients compared to normal controls (NC). The aim is to characterize multi-domain respiratory patterns and to validate an interpretable machine learning framework for providing objective, quantitative evidence for clinical assessment. MethodsSeventy-five participants were enrolled in this study, comprising 37 patients with surgically repaired CP and 38 healthy volunteers matched for age, gender, and body mass index (BMI). All subjects performed standardized sustained phonation tasks while undergoing synchronous monitoring with a 16-electrode EIT system and a pneumotachograph. A comprehensive feature engineering pipeline was developed to extract physiological parameters across 3 complementary domains. (1) Temporal domain: including inspiratory/expiratory phase duration (tPhase), time constants (Tau), and inspiratory-to-expiratory time ratios (TI/TE); (2) airflow domain: comprising mean flow, peak flow, and instantaneous flow at 25%, 50%, and 75% of tidal volume; and (3) spatial domain: quantifying global and regional tidal impedance variation (TIV), global inhomogeneity (GI), and center of ventilation (CoV). Extreme Gradient Boosting (XGBoost) classifiers were trained using 5 distinct data sources (Spirometry, Nasometry, Inspiratory-EIT, Expiratory-EIT, and fused ST-EIT). Model performance was rigorously evaluated via stratified 5-fold cross-validation, and Shapley additive explanations (SHAP) were employed to quantify global and local feature contributions. ResultsThe CP group exhibited a distinct respiratory phenotype compared to controls. In the temporal domain, CP patients showed significantly shorter inspiratory (1.60 s vs.1.85 s, P<0.001) and expiratory phase durations (2.45 s vs. 3.95 s, P<0.001), indicating a rapid, shallow breathing rhythm. In the airflow domain, while inspiratory flows were comparable, the CP group demonstrated significantly elevated mean and peak flows during the expiratory phase (P<0.001), reflecting compensatory respiratory effort. Spatially, CP patients presented significant ventilation redistribution, characterized by higher regional TIV in the right-anterior (ROI1) and left-posterior (ROI4) quadrants, but lower TIV in the left-anterior (ROI2) quadrant. In terms of diagnostic accuracy, the multi-modal ST-EIT model achieved the highest performance (AUC: 0.915±0.012, Accuracy: 0.843±0.019, F1-score: 0.872±0.017), substantially outperforming models based on spirometry (AUC: 0.721) or nasometry (AUC: 0.625) alone. Interpretability analysis revealed that spatial domain features were the most critical, contributing 53.4% to the model’s decision-making, followed by temporal (25.0%) and airflow (21.6%) features. ConclusionST-EIT successfully captures the temporal, airflow, and spatial deviations in CP speech respiration that are undetectable by conventional methods—specifically, rapid phase transitions, hyperdynamic expiratory airflow, and regional ventilation heterogeneity. This study validates ST-EIT as a robust, non-invasive, and radiation-free tool for characterizing speech-respiratory dysfunction, offering high clinical value for bedside screening, rehabilitation planning, and longitudinal monitoring of patients with cleft palate.

ObjectiveCleft palate (CP) is a common congenital deformity often associated with velopharyngeal insufficiency (VPI), which disrupts the physiological coupling between respiration and speech. Conventional clinical assessments, such as nasometry and spirometry, provide limited static data and fail to visualize the dynamic spatiotemporal distribution of lung ventilation during phonation. This study introduces spatiotemporal electrical impedance tomography (ST-EIT) to evaluate speech-respiratory functional features in CP patients compared to normal controls (NC). The aim is to characterize multi-domain respiratory patterns and to validate an interpretable machine learning framework for providing objective, quantitative evidence for clinical assessment. MethodsSeventy-five participants were enrolled in this study, comprising 37 patients with surgically repaired CP and 38 healthy volunteers matched for age, gender, and body mass index (BMI). All subjects performed standardized sustained phonation tasks while undergoing synchronous monitoring with a 16-electrode EIT system and a pneumotachograph. A comprehensive feature engineering pipeline was developed to extract physiological parameters across 3 complementary domains. (1) Temporal domain: including inspiratory/expiratory phase duration (tPhase), time constants (Tau), and inspiratory-to-expiratory time ratios (TI/TE); (2) airflow domain: comprising mean flow, peak flow, and instantaneous flow at 25%, 50%, and 75% of tidal volume; and (3) spatial domain: quantifying global and regional tidal impedance variation (TIV), global inhomogeneity (GI), and center of ventilation (CoV). Extreme Gradient Boosting (XGBoost) classifiers were trained using 5 distinct data sources (Spirometry, Nasometry, Inspiratory-EIT, Expiratory-EIT, and fused ST-EIT). Model performance was rigorously evaluated via stratified 5-fold cross-validation, and Shapley additive explanations (SHAP) were employed to quantify global and local feature contributions. ResultsThe CP group exhibited a distinct respiratory phenotype compared to controls. In the temporal domain, CP patients showed significantly shorter inspiratory (1.60 s vs.1.85 s, P<0.001) and expiratory phase durations (2.45 s vs. 3.95 s, P<0.001), indicating a rapid, shallow breathing rhythm. In the airflow domain, while inspiratory flows were comparable, the CP group demonstrated significantly elevated mean and peak flows during the expiratory phase (P<0.001), reflecting compensatory respiratory effort. Spatially, CP patients presented significant ventilation redistribution, characterized by higher regional TIV in the right-anterior (ROI1) and left-posterior (ROI4) quadrants, but lower TIV in the left-anterior (ROI2) quadrant. In terms of diagnostic accuracy, the multi-modal ST-EIT model achieved the highest performance (AUC: 0.915±0.012, Accuracy: 0.843±0.019, F1-score: 0.872±0.017), substantially outperforming models based on spirometry (AUC: 0.721) or nasometry (AUC: 0.625) alone. Interpretability analysis revealed that spatial domain features were the most critical, contributing 53.4% to the model’s decision-making, followed by temporal (25.0%) and airflow (21.6%) features. ConclusionST-EIT successfully captures the temporal, airflow, and spatial deviations in CP speech respiration that are undetectable by conventional methods—specifically, rapid phase transitions, hyperdynamic expiratory airflow, and regional ventilation heterogeneity. This study validates ST-EIT as a robust, non-invasive, and radiation-free tool for characterizing speech-respiratory dysfunction, offering high clinical value for bedside screening, rehabilitation planning, and longitudinal monitoring of patients with cleft palate.

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.

Hepatocellular carcinoma (HCC) is a lethal cancer with high morbidity rates worldwide. It is a major threat to public health in China, due to the combination of known and new risk factors, such as endemic hepatitis B virus (HBV), dietary aflatoxin exposure, and the occurrence of metabolic dysfunction-associated steatotic liver disease (MASLD). Although many methods for surveillance and multimodal therapies, such as surgery, local ablation, transarterial therapy, and new systemic agents, have been available, the survival rates of HCC remains poor. They have very limited durable responses, long post-treatment recurrence rates, and high resistance to treatment. This reflects an imperfect picture of the biological cause of the disease and a need for new mechanistic or targeted techniques. A significant characteristic of HCC, in common with other aggressive cancers, is the presence of reprogrammed, hyperactive cell metabolism. Tumor cells hijack metabolic pathways to promote their uncontrolled growth, stress survival, invasion and metastasis. While classical mechanisms such as the Warburg effect, lipid metabolism and glutamine utilization have been understood, the lysosome, which was once viewed as a static “waste disposal unit” to remove old organelles and proteins, is instead a dynamic signaling and metabolic core. The lysosomes incorporate nutrients, energy and stress signals by master regulators such as mTORC1 (activated on its surface) that balance anabolic growth and catabolic recycling to the cellular demands. In HCC, lysosomes are not passive, but are highly active and dysregulated. HCC cells upregulate lysosomes, which scavenge intracellular components via enhanced autophagy and engulf extracellular proteins via macropinocytosis, crucial for survival in the nutrient-poor, hypoxic tumor microenvironment. In addition to metabolism, lysosomes exhibit pro-invasive functions by secreting hydrolases to remodel the extracellular matrix, promote angiogenesis, and suppress stromal immune cells to foster a pro-tumor microenvironment. In a clinical context, lysosomes play an important role in therapeutic resistance: they sequester and inactivate chemotherapeutics via lysosomal sequestration, and enhanced autophagic flux protects the cell from therapy-induced damage, contributing to relapse, as lysosomal dysfunction is a key cause of treatment failure. This makes lysosomes promising yet challenging therapeutic targets in HCC. Recent preclinical and early clinical studies investigate multiple strategies to exploit the susceptibility of lysosomes: lysosome-specific agents, alkalinizing the lysosome lumen or inducing membrane permeabilization and lysosome-dependent cell death; pharmacological inhibition of key lysosomal enzymes or autophagy to impair nutrient recycling and stress adaptation; smart nanotherapeutic agents or antibody-drug conjugates, specifically activated in the acidic lysosomal environment or utilizing lysosomal pathways for efficient intracellular drug release; and combination strategies of lysosome-targeting agents with tyrosine kinase inhibitors or immunotherapy to overcome resistance and achieve synergistic antitumor effects. In summary, our review systematically presents the role of lysosomes in HCC, from metabolic reprogramming and microenvironmental adaptation to therapeutic resistance. By synthesizing the latest mechanistic insights and preclinical advances, this review highlights the indispensable role of lysosomes in the complex HCC biological network, emphasizing that an in-depth understanding of this dynamic organelle holds great promise for developing innovative, targeted therapies, offering new hope for improving the poor prognosis of global HCC patients.

Metabolic dysfunction-associated steatotic liver disease (MASLD) has become the most prevalent chronic liver disease worldwide, affecting approximately 32%-38% of the adult population and posing a growing public health burden. MASLD represents a continuous disease spectrum ranging from simple steatosis to metabolic dysfunction-associated steatohepatitis (MASH), progressive hepatic fibrosis, cirrhosis, and ultimately hepatocellular carcinoma (HCC). The pathological core of MASLD lies in disruption of hepatic lipid metabolic homeostasis, characterized by an imbalance among de novo lipogenesis, fatty acid β-oxidation, and very-low-density lipoprotein (VLDL)-mediated lipid export. This metabolic disequilibrium subsequently drives inflammatory injury and fibrotic progression. Among the multiple regulatory pathways involved, thyroid hormone (TH) signaling has emerged as a central regulator of hepatic metabolic homeostasis. The liver is a major peripheral target organ of TH action, where TH predominantly exerts its metabolic effects through thyroid hormone receptor β (TRβ). Large-scale epidemiological studies and meta-analyses have demonstrated that hypothyroidism is significantly associated with increased MASLD prevalence, more severe histological injury, and advanced hepatic fibrosis, suggesting that dysregulation of TH signaling may participate throughout the entire MASLD disease spectrum. At the molecular level, TH regulates hepatic lipid metabolism by coordinating suppression of lipogenesis, enhancement of mitochondrial fatty acid oxidation, and promotion of VLDL assembly and secretion through integrated genomic actions of the T3-TRβ axis and non-genomic signaling pathways. Across different stages of MASLD, TH signaling exerts stage-dependent protective effects. In the steatosis stage, TH improves metabolic flexibility by modulating insulin sensitivity, glucose metabolism, and lipid droplet clearance, thereby alleviating early lipotoxic stress. During progression to MASH, TH attenuates inflammatory amplification by improving mitochondrial homeostasis, suppressing activation of the NOD-like receptor family pyrin domain containing 3 (NLRP3) inflammasome, and modulating the gut-liver axis microenvironment. In advanced stages, TH signaling influences hepatic stellate cell activation and extracellular matrix deposition, partly through interaction with the transforming growth factor-β (TGF-β)/SMAD pathway, while alterations in intrahepatic TH availability, mediated by dynamic changes in iodothyronine deiodinase 1 (DIO1), contribute to fibrosis progression and hepatocellular dedifferentiation. In hepatocellular carcinoma, coordinated downregulation of TRβ and DIO1 establishes a tumor-associated hypothyroid state that promotes metabolic reprogramming and tumor progression. The clinical relevance of TH signaling in MASLD has been underscored by the recent approval of Resmetirom, a liver-targeted TRβ‑selective agonist, for the treatment of non-cirrhotic MASH with moderate-to-severe fibrosis (F2-F3). This approval represents a landmark transition from mechanistic understanding to metabolism-centered precision therapy in MASLD. Clinical trials have demonstrated that Resmetirom not only improves key histological endpoints, including MASH resolution and fibrosis regression, but also favorably modulates atherogenic lipid profiles, highlighting the therapeutic potential of selectively targeting hepatic TH pathways. This review systematically summarizes the multidimensional regulatory roles of TH across the MASLD disease spectrum and discusses emerging diagnostic and therapeutic implications of TH-based interventions, aiming to inform future mechanistic research and optimize clinical management strategies.

This paper introduces a real-world cohort research model for community-acquired pneumonia （CAP） based on the Jiangsu Traditional Chinese Medicine （TCM） Dominant Diseases Diagnosis and Treatment Data Platform. Firstly， data cleaning is performed by standardizing diagnosis， symptoms， treatment and imaging， intelligently extracting unstructured information， and cleaning and constructing a standardized database. Secondly， for cohort establishment， CAP patients across the province are screened in accordance with CAP diagnostic criteria to build a high-quality disease-specific cohort. Lastly， in terms of protocol design， the characteristics of TCM research and the CAP disease profile are considered to determine appropriate inclusion and exclusion criteria， estimate sample size， define interventions， outcomes and economic evaluations， providing a reference for real-world TCM research on CAP.