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.

In this paper, near-infrared spectroscopy(NIRS) was employed to analyze 129 batches of commercial products of Reduning Injection. The batch reporting rate was estimated according to the report of Reduning Injection in the direct adverse drug reaction(ADR) reporting system of the drug marketing authorization holder of the Center for Drug Reevaluation of the National Medical Products Administration(National Center for ADR Monitoring) from August 2021 to August 2022. According to the batch reporting rate, the samples of Reduning Injection were classified into those with potential risks and those being safe. No processing, random oversampling(ROS), random undersampling(RUS), and synthetic minority over-sampling technique(SMOTE) were then employed to balance the unbalanced data. After the samples were classified according to appropriate sampling methods, competitive adaptive reweighted sampling(CARS), successive projections algorithm(SPA), uninformative variables elimination(UVE), and genetic algorithm(GA) were respectively adopted to screen the features of spectral data. Then, support vector machine(SVM), logistic regression(LR), k-nearest neighbors(KNN), naive bayes(NB), random forest(RF), and artificial neural network(ANN) were adopted to establish the risk prediction models. The effects of the four feature extraction methods on the accuracy of the models were compared. The optimal method was selected, and bayesian optimization was performned to optimize the model parameters to improve the accuracy and robustness of model prediction. To explore the correlations between potential risks of clinical use and quality test data, TreeNet was employed to identify potential quality parameters affecting the clinical safety of Reduning Injection. The results showed that the models established with the SVM, LR, KNN, NB, RF, and ANN algorithms had the F1 scores of 0.85, 0.85, 0.86, 0.80, 0.88, and 0.85 and the accuracy of 88%, 88%, 88%, 85%, 91%, and 88%, respectively, and the prediction time was less than 5 s. The results indicated that the established models were accurate and efficient. Therefore, near infrared spectroscopy combined with machine learning algorithms can quickly predict the potential risks of clinical use of Reduning Injection in batches. Three key quality parameters that may affect clinical safety were identified by TreeNet, which provided a scientific basis for improving the safety standards of Reduning Injection.
Spectroscopy, Near-Infrared/methods* ; Drugs, Chinese Herbal/administration & dosage* ; Machine Learning ; Algorithms ; Humans ; Quality Control

A combination of the "disease-syndrome-symptom" approach was used to study the syndrome characterization and features of dampness-heat obstruction syndrome in papain-induced knee osteoarthritis(KOA) model rats during the disease process. Forty-eight male SD rats were randomly divided into sham and model groups. The KOA model was established by injecting a mixture of papain and L-cysteine into the joint cavity on days 1, 3, and 5. During the 8 weeks following model establishment, the rats were assessed weekly for the plantar mechanical pain threshold, knee joint diameter, local skin temperature of the knee joint, weight-bearing difference between the two hind feet, and the modified Lequesne MG score of the knee joint. Samples were collected at 1, 2, 4, 6, and 8 weeks after model establishment to observe the gross lesions in cartilage and synovium. Histopathological changes in joint tissues were examined using hematoxylin-eosin, Masson's trichrome, and Senna red O-solid green staining. ELISA and immunohistochemical analysis were performed to detect the levels of interleukin(IL)-1β, IL-6, tumor necrosis factor(TNF)-α, prostaglandin E2(PGE2), and the expression of aquaporins(AQP) 1 and 3 in serum and synovium. The results showed that the ink score of articular cartilage in the model group significantly increased from 4 to 8 weeks, the cartilage Mankin's score and the percentage of Masson-positive area in cartilage increased significantly from 1 to 8 weeks. The percentage of red-stained area for cartilage proteoglycans decreased significantly from 1 to 8 weeks. The synovitis score from 1 to 6 weeks and the percentage of blue-stained collagen fibers in the synovium from 1 to 8 weeks increased significantly, with statistically significant differences compared to the sham group. The mechanical pain threshold in the model group significantly decreased from 1 to 8 weeks, the knee joint diameter significantly increased from 1 to 6 weeks, and the local skin temperature of the knee joint, the weight-bearing difference between the two hind feet, and the modified Lequesne MG score from 1 to 5 weeks significantly increased, all with statistically significant differences compared to the sham group. The levels of IL-1β, IL-6, TNF-α, and PGE2 in serum and synovium of the model group significantly increased from 1 to 6 weeks. Serum TNF-α and PGE2, and synovial IL-1β, also significantly increased at 8 weeks. The levels of cartilage AQP1 and AQP3 significantly increased from 1 to 4 weeks, while synovial AQP1 and AQP3 increased significantly from 1 to 6 weeks, with all differences statistically significant compared to the sham group. In conclusion, papain-induced KOA rats exhibited pathological changes, including articular cartilage degeneration and synovial inflammation, within 1 week of induction. The KOA rats showed characteristics of dampness-heat obstruction syndrome, such as joint pain, swelling, elevated skin temperature, and decreased function, as well as increased inflammatory factors and AQP1、AQP3 in serum and joint tissues within 5 to 6 weeks of disease onset. These results provide an experimental model for studying the syndromes of KOA with dampness-heat obstruction syndrome.
Animals ; Male ; Rats, Sprague-Dawley ; Rats ; Osteoarthritis, Knee/physiopathology* ; Disease Models, Animal ; Humans ; Interleukin-1beta/metabolism* ; Interleukin-6/metabolism* ; Tumor Necrosis Factor-alpha/metabolism* ; Knee Joint/pathology*

The patient was a girl aged 10 years and 10 months, with weakness, pale complexion, and rash as the initial presentation. She had the manifestations of anemia, thrombocytopenia, hematuria-proteinuria with renal insufficiency, hypocomplementemia, polyserositis, and positive anti-nuclear antibody and anti-dsDNA antibody. The girl was initially diagnosed with systemic lupus erythematosus and lupus nephritis. She demonstrated a suboptimal response to methylprednisolone pulse therapy, intravenous immunoglobulin administration, and therapeutic plasma exchange. She had persistent anemia, thrombocytopenia, abnormal renal function, elevated lactate dehydrogenase, decreased complement factors H and I, increased antibodies to C3 converting enzyme, and normal ADAMTS13 activity. She was diagnosed with complement-mediated hemolytic thrombotic microangiopathy secondary to systemic lupus erythematosus. The patient's condition improved after treatment with two doses of eculizumab (600 mg per dose). Patients with systemic lupus erythematosus complicated by thrombotic microangiopathy often have a severe disease course and poor prognosis; therefore, early recognition and aggressive intervention are crucial for improving outcomes.
Humans ; Female ; Lupus Erythematosus, Systemic/drug therapy* ; Thrombotic Microangiopathies/etiology* ; Antibodies, Monoclonal, Humanized/therapeutic use* ; Child

ObjectiveTo explore the effects of angle and original moisture content on the moisture distribution， migration and contents of effective components in the drying process of sliced Salviae Miltiorrhizae Radix et Rhizoma（SMRR）. MethodsSet the slicing angles of SMRR at 30°， 45°， and 90°. Cut the fresh samples， 1/3 dehydrated samples， and 2/3 dehydrated samples， dry them in an oven at 40 ℃ and take samples at the set time points. Low-field nuclear magnetic resonance（LF-NMR） and magnetic resonance imaging（MRI） were used to analyze the changes in transverse relaxation time（T₂） of SMRR samples in 9 treatment groups at specific times， as well as the distribution and migration of water in the samples. The contents of tanshinone Ⅱ_A， tanshinone Ⅰ， cryptotanshinone， and salvianolic acid B in samples from 9 different treatment groups were determined by high performance liquid chromatography（HPLC）， and the best processing technology of SMRR was screened by combining with One-way ANOVA， Duncan multiple comparison and principal component analysis（PCA）. ResultsThe moisture content of dry basis of SMRR in each treatment group decreased with the extension of drying time. The drying rate of fresh cut group decreased slowly at first， while the drying rate of water loss group showed a trend of increasing at first and then decreasing. The internal water of SMRR could be divided into three states， including bound water， non flowing water and free water. During the drying process， the water migration law showed that the free water of fresh cut group disappeared after drying for 12 h， the content of bound water gradually decreased， and the overall fluidity deteriorated. In the water loss group， part of the free water was transformed into more cohesive and non flowing water after drying for 3 h， and the three kinds of water basically disappeared after drying for 12 h. The MRI results showed that the entire dehydration process slowly moved from the outer side to the center， and the internal water eventually dissipated. In terms of the contents of active ingredients， the order of the effect of slicing angle on the total content of active ingredients in SMRR was 30°>45°>90°. The content of tanshinones was ranked as 1/3 dehydrated group>2/3 dehydrated group>fresh cut group， and the content of salvianolic acid B was ranked as 1/3 dehydrated group>fresh cut group>2/3 dehydrated group. Combined with the results of PCA and comprehensive scoring results， the overall level of effective component content in SMRR was the highest when cut at 30° after 1/3 of water loss. ConclusionAfter comprehensive evaluation， SMRR can be sliced at 30° after 1/3 of water loss. It is not only easy to cut， but also the surface and cross-sectional colors remain basically unchanged after drying， which is similar to the color under traditional processing， and the effective ingredients are preserved the highest. This study can provide a basis for the optimization of processing technology of SMRR.

Objective:This study aimed to validated the diagnostic accuracy of Global Leadership Initiative on Malnutrition(GLIM)criteria for malnutrition in pancreatic cancer patients undergoing pancreaticoduodenectomy and to evaluated its prognostic value for postoperative outcome.Methods:A retrospective analysis was conducted on 230 consecutive pancreatic cancer patients who underwent pancreaticoduodenectomy at the Department of Pancreatobiliary Surgery,Sun Yat-sen University Cancer Center,between January 2018 to January 2024.Patients were stratified into malnutrition group and non-malnutrition group using Nutritional Risk Screening 2002(NRS 2002)and GLIM criteria.Multivariable logistic regression identified independent risk factors for postoperative morbidity.Results:GLIM criteria identified malnutrition in 96 patients(41.7％).Compared with the non-malnourished group,the number of preoperative nutritional support(t=20.038,P<0.001),the number of preoperative enteral nutrition support(t=8.377,P=0.004),the number of preoperative parenteral nutrition support(t=22.302,P<0.001),the number of anemia(t=8.037,P=0.005)and preoperative parenteral nutrition use days(t=-2.898,P=0.009),the difference was statistically significant.There were statistically significant differences in C-reactive protein(t=10.944,P=0.008),NLR(t=-2.523,P=0.012)and PNI(t=-2.397,P=0.017)between the two groups before surgery.Preoperative BMI(t=-4.410,P<0.001)was significantly lower in the malnourished group.The number of postoperative parenteral nutrition days(Z=-2.283,P=0.022)and amino acid supplementation during postoperative hospitalization were significantly higher in the malnourished group(Z=-2.309,P=0.021).The incidence of malnutrition was higher in patients with Clavien-Dindo grade≥Ⅲ(P=0.030)and intra-abdominal infections(P=0.049).Multivariable analysis identified preoperative weight loss(OR=2.154,95％CI:1.158～4.005;P=0.015)and BMI reduction(OR=0.175,95％CI:0.040～0.775;P=0.022)as independent predictors of postoperative complications.Conclusions:The GLIM standard effectively characterize malnutrition status in pancreatic cancer patients after pancreaticoduodenectomy patients and demonstrate superior predictive performance for postoperative morbidity.It has good predictive performance and clinical application value.

OBJECTIVE: To explore the accuracy of human-computer interaction software in identifying and locating type C1 distal radius fractures. METHODS: Based on relevant inclusion and exclusion criteria, 14 cases of type C1 distal radius fractures between September 2023 and March 2024 were retrospectively analyzed, comprising 3 males and 11 females(aged from 27 to 82 years). The data were assigned randomized identifiers. A senior orthopedic physician reviewed the films and measured the ulnar deviation angle, radial height, palmar inclination angle, intra-articular step, and intra-articular gap for each case on the hospital's imaging system. Based on the reduction standard for distal radius fractures, cases were divided into reduction group and non-reduction group. Then, the data were sequentially imported into a human-computer interaction intelligent software, where a junior orthopedic physician analyzed the same radiological parameters, categorized cases, and measured fracture details. The categorization results from the software were consistent with manual classifications (6 reduction cases and 8 non-reduction cases). For non-reduction cases, the software performed further analyses, including bone segmentation and fracture recognition, generating 8 diagnostic reports containing fracture recognition information. For the 6 reduction cases, the senior and junior orthopedic physicians independently analyzed the data on the hospital's imaging system and the AI software, respectively. Bone segments requiring reduction were identified, verified by two senior physicians, and measured for displacement and rotation along the X (inward and outward), Z (front and back), and Y (up and down) axes. The AI software generated comprehensive diagnostic reports for these cases, which included all measurements and fracture recognition details. RESULTS: Both the manual and AI software methods consistently categorized the 14 cases into 6 reduction and 8 non-reduction groups, with identical data distributions. A paired sample t-test revealed no statistically significant differences (P>0.05) between the manual and software-based measurements for ulnar deviation angle, radial ulnar bone height, palmar inclination angle, intra-articular step, and joint space. In fracture recognition, the AI software correctly identified 10 C-type fractures and 4 B-type fractures. For the 6 reduction cases, a total of 24 bone fragments were analyzed across both methods. After verification, it was found that the bone fragments identified by the two methods were consistent. A paired sample t-tests revealed that the identified bone fragments and measured displacement and rotation angles along the X, Y, and Z axes were consistent between the two methods. No statistically significant differences(P>0.05) were found between manual and software measurements for these parameters. CONCLUSION Human-computer interaction software employing AI technology demonstrated comparable accuracy to manual measurement in identifying and locating type C1 distal radius fractures on CT imaging.
Humans ; Male ; Female ; Radius Fractures/surgery* ; Middle Aged ; Adult ; Aged ; Aged, 80 and over ; Tomography, X-Ray Computed/methods* ; Retrospective Studies ; Software ; Wrist Fractures

Gene regulation relies on the precise binding of transcription factors (TFs) at regulatory elements, but simultaneously detecting hundreds of TFs on chromatin is challenging. We developed cFOOT-seq, a cytosine deaminase-based TF footprinting assay, for high-resolution, quantitative genome-wide assessment of TF binding in both open and closed chromatin regions, even with small cell numbers. By utilizing the dsDNA deaminase SsdAtox, cFOOT-seq converts accessible cytosines to uracil while preserving genomic integrity, making it compatible with techniques like ATAC-seq for sensitive and cost-effective detection of TF occupancy at the single-molecule and single-cell level. Our approach enables the delineation of TF footprints, quantification of occupancy, and examination of chromatin influences on TF binding. Notably, cFOOT-seq, combined with FootTrack analysis, enables de novo prediction of TF binding sites and tracking of TF occupancy dynamics. We demonstrate its application in capturing cell type-specific TFs, analyzing TF dynamics during reprogramming, and revealing TF dependencies on chromatin remodelers. Overall, cFOOT-seq represents a robust approach for investigating the genome-wide dynamics of TF occupancy and elucidating the cis-regulatory architecture underlying gene regulation.
Transcription Factors/genetics* ; Humans ; Chromatin/genetics* ; Animals ; Binding Sites ; Mice ; DNA Footprinting/methods*

Objective To determine the knee joint cartilage thickness using different methods and explore the feasibility of mathematical statistical models of dataset for the prediction of cartilage thickness.Methods A total of 304 patients diagnosed as knee osteoarthritis(OA)combined with varus deformity and undergoing unilateral total knee arthroplasty at the Second Affiliated Hospital of Army Medical University from March 2023 to March 2024 were selected for the study.All patients had complete preoperative and postoperative clinical data.The healthy cartilage at four anatomical sites of patients,including the distal femur lateral condyle,lateral tibial plateau,posterior medial femoral condyle,and posterior lateral femoral condyle were selected,and the knee joint cartilage thickness was determined based on preoperative MRI analysis,robotic navigation system tracing,tissue section of surgical specimen and digital vernier caliper.The baseline indicators of demographics,disease and imaging ffor patients were collected to construct a dataset,and four models of linear regression analysis,principal component analysis,Least Absolute Shrinkage and Selection Operator(LASSO)regression analysis,and K-nearest neighbors(KNN)analysis were established for predicting the accuracy,determination coefficient(R2)and root mean square error(RMSE),and the regression equation for predicting cartilage thickness was established.Results The knee joint cartilage thicknesses determined by preoperative MRI analysis,robotic navigation system tracing,tissue section of surgical specimen had no statistically significant difference with that by digital vernier caliper(P>0.05).The predictive efficiencies of models of linear regression analysis,principal component analysis,and LASSO regression analysis for the knee joint cartilage thickness all failed to meet the expectations(R2<0.3,RMSE>0.03).The predictive effect of KNN model on the cartilage thickness of the distal femur lateral condyle and lateral tibial plateau was not ideal(R2=0.23,RMSE=0.29),while it had potential predictive value(accuracy=0.21,accuracy=0.15).Conclusion The prediction model of knee joint cartilage thickness based on individual parameters has certain scientificity,and the feasibility of KNN model is relatively high.However,due to insufficient sample size and unclear individual parameter weight,the efficiencies of the four established prediction models are not ideal,which fails to provide definite prediction equations.Therefore,the construction scheme of the prediction model still needs to be further optimized.

Objective:To investigate the changes in brain functional network and structural-functional network coupling in children with drug-resistant epilepsy (DRE), and to analyze their correlation with cognitive function, disease duration, and age of onset.Methods:This study was a cross-sectional study. Clinical and imaging data of 19 children with DRE who received consultation and treatment at the Affiliated Hospital of Zunyi Medical University from August 2021 to August 2023 (DRE group) were prospectively included. Another 27 age-and sex-matched healthy children were collected as the healthy control group. All subjects had 3D-T 1WI, T 2 fluid-attenuated inversion recovery, diffusion tensor imaging (DTI), resting-state functional magnetic resonance imaging (rs-fMRI) scans and Wechsler Intelligence Scale assessments. Independent sample t-test and Mann-Whitney U test were used to analyze the global and local topological attributes, as well as the structural-functional coupling (SFC) values at the whole brain and modular levels in two groups. Correlations between abnormal resting state brain functional network indicators and the Wechsler Intelligence Scale score [verbal comprehension index (VCI), perceptual reasoning index (PRI), working memory index (WMI), processing speed index (PSI), full scale intelligence quotient (FSIQ)], disease duration and age of onset was evaluated using a Spearman or Pearson correlation analysis. Results:Compared to the healthy control group, DRE group exhibited decreased VCI, PRI, WMI, PSI, FSIQ and the differences were all statistically significant (all P<0.05). Both brain functional networks had small world attributes. There was a statistically significant difference in the area under the curve of sparsity of degree centrality (DC) in the left pallidum between the DRE group and healthy control group (2.998±0.942, 4.992±1.945, t=-4.07, FDR corrected P<0.05). Compared with the control group, the DRE group had decreased SFC within the limbic network (LN) ( P<0.05), increased SFC within the sensorimotor (SMN) ( P<0.05), decreased SFC between the default mode network-LN ( P<0.05), and increased SFC between the SMN-attentional network (AN) ( P<0.05). There was no statistically significant difference in SFC at the whole brain level between the two groups. Correlation analysis indicated that DC in left pallidum in DRE group negatively correlated with the PSI ( r=-0.537, P=0.018), and SFC between the SMN and AN demonstrated a negative correlation with age of onset ( r=-0.537, P=0.018). Conclusion:The altered DC in left pallidum may be related to cognitive impairment in children with DRE, providing biomarker information for the study of neural mechanisms in children with DRE.