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.

Testicular histology based on testicular biopsy is an important factor for determining appropriate testicular sperm extraction surgery and predicting sperm retrieval outcomes in patients with azoospermia. Therefore, we developed a deep learning (DL) model to establish the associations between testicular grayscale ultrasound images and testicular histology. We retrospectively included two-dimensional testicular grayscale ultrasound from patients with azoospermia (353 men with 4357 images between July 2017 and December 2021 in The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China) to develop a DL model. We obtained testicular histology during conventional testicular sperm extraction. Our DL model was trained based on ultrasound images or fusion data (ultrasound images fused with the corresponding testicular volume) to distinguish spermatozoa presence in pathology (SPP) and spermatozoa absence in pathology (SAP) and to classify maturation arrest (MA) and Sertoli cell-only syndrome (SCOS) in patients with SAP. Areas under the receiver operating characteristic curve (AUCs), accuracy, sensitivity, and specificity were used to analyze model performance. DL based on images achieved an AUC of 0.922 (95% confidence interval [CI]: 0.908-0.935), a sensitivity of 80.9%, a specificity of 84.6%, and an accuracy of 83.5% in predicting SPP (including normal spermatogenesis and hypospermatogenesis) and SAP (including MA and SCOS). In the identification of SCOS and MA, DL on fusion data yielded better diagnostic performance with an AUC of 0.979 (95% CI: 0.969-0.989), a sensitivity of 89.7%, a specificity of 97.1%, and an accuracy of 92.1%. Our study provides a noninvasive method to predict testicular histology for patients with azoospermia, which would avoid unnecessary testicular biopsy.
Humans ; Male ; Azoospermia/diagnostic imaging* ; Deep Learning ; Testis/pathology* ; Retrospective Studies ; Adult ; Ultrasonography/methods* ; Sperm Retrieval ; Sertoli Cell-Only Syndrome/diagnostic imaging*

Objective To explore the networked association among the dimensions of self-efficacy in patients with rheumatoid arthritis(RA)and to identify core efficacy and bridge efficacy,and provide a basis for formulating precise nursing intervention strategies.Methods A total of 652 RA patients admitted in our hospital from September 2024 to January 2025 were enrolled with convenience sampling.The general information questionnaire and Rheumatoid Arthritis Self-Efficacy Scale(RASE)were used for assessment.Exploratory factor analysis was used to extract efficacy symptom clusters.With aid of R project,network analysis was employed to construct an association network among efficacy dimensions to calculate centrality indicators(strength,closeness,betweenness)to identify core efficacy and bridge efficacy.Results Exploratory factor analysis extracted 7 efficacy symptom clusters,with a cumulative variance contribution rate of 64.539%(P<0.001).Network analysis showed that the network density was 0.143,suggesting that there were moderate correlations among the self-efficacy dimensions."Relaxation efficacy 1(r1)"and"pain efficacy 1(a1)"had the strongest correlation(r=0.73).Interpersonal efficacy 2(i2)had the highest intensity centrality(6.88),and relaxation efficacy 3(r3)had the highest tightness(0.0125),and medication efficacy 1(m1)had the highest mediation(116),which were the core efficacy and bridge efficacy in this network group.Conclusion There are complex network-like correlations among the various dimensions of self-efficacy in RA patients.Interpersonal efficacy is the core driving factor,while relaxation and medication efficacies play the bridging role,jointly influencing the overall level of patients'self-management ability.

ObjectiveAt present, the most commonly used photosensitizers in photodynamic therapy are still chemical photosensitizers, such as porphyrin and methylene blue, in order to specifically target cellular tissues, and thus poison cells, chemical photosensitizers need to use antibody conjugation or a transgenically encoded tag with affinity for the modified photosensitizing ligand, e.g. FlAsH, ReAsh or Halo Tag. Gene-encoded photosensitizers can directly poison cells by targeting specific cell compartments or organelles. However, currently developed gene-encoded photosensitizers have low reactive oxygen species production and low cytotoxicity, so it is necessary to continue to develop and obtain photosensitizers with higher reactive oxygen species production for the treatment of microbial infections and tumors. MethodsIn this study, we developed a photosensitizer LovPSO2 based on the light-oxygen-voltage (LOV) structural domain of phototropin-1B-like from Oryza sativa japonica. LovPSO2 was expressed in E. coli BL21(DE3) and purified to obtain protein samples, the purified protein samples were added 3 µmol/L singlet oxygen probe of SOSG and 5 µmol/L superoxide anion probe of DHE after fixed to A₄₄₅=0.063±0.003, respectively, then measured every 2 min of singlet oxygen production for 10 min and every 1 min of superoxide anion production for 5 min under blue light irradiation at 445 nm, 70 µmol·m^-2·s^-1. ResultsThe results showed that LovPSO2 could produce a large amount of singlet oxygen under blue light irradiation at 445 nm, 70 µmol·m^-2·s^-1, and its singlet oxygen quantum yield was 0.61, but its superoxide anion yield was low, so in order to improve the superoxide anion yield of LovPSO2, a mutant with a relatively high superoxide anion yield was obtained by further development and design on its basis LovPRO2. The stability of proteins is crucial for research in drug development and drug delivery, among others. Temperature and light are the key factors affecting the production of reactive oxygen species (ROS) by photosensitive proteins and their stability, while the temperature in cell culture and mammals in vivo is about 37°C, and the temperature inside tumor cells is about 42-45°C. Therefore, we further analyzed the photostability of miniSOG, SOPP3, LovPSO2, and LovPRO2 and their thermostability at 37℃ and 45℃. The analysis of proteins thermostability showed that LovPSO2 and LovPRO2 had better thermostability at 37℃ and 45℃, respectively. Analysis of the photostability of the proteins showed that LovPRO2 had better photostability. In addition, to further determine the phototoxic effects of photosensitizers, LovPSO2 and LovPRO2 were expressed in E. coli BL21(DE3) and HeLa cells, respectively. The results showed that LovPSO2 and LovPRO2 had better phototoxicity to E. coli BL21(DE3) under blue light irradiation, and the cellular phototoxicity lethality was as high as 90% after 30 min of continuous light irradiation, but the phototoxicity was weaker in HeLa cells. The reason for this result may be that the intracellular environment exacerbated the photobleaching of FMN encapsulated by LovPSO2 and LovPRO2, respectively, which attenuated the damage of reactive oxygen species to animal cellular tissues, limiting its use as a mechanistic tool to study oxidative stress. ConclusionLovPSO2 and LovPRO2 can be used as antibacterial photosensitizers, which have broader application prospects in the food and medical fields.

Objective:To develop an ultrasound multi-view fusion recognition model and evaluate its clinical value in diagnosing fetal conotruncal defects (CTD).Methods:This prospective study collected cardiac ultrasound images from fetuses at 20-32 weeks of gestation undergoing prenatal ultrasound at Dongguan Maternal and Child Health Hospital between September 2022 and May 2024. The case group comprised fetuses diagnosed with CTD, while controls with normal cardiac structures were collected at a 1∶2 ratio. Both groups were divided into modeling training and validation sets at a 3∶1 ratio. One optimal standard image each from the four-chamber view, left ventricular outflow tract view, right ventricular outflow tract view, and three vessels and trachea view was included per fetus. A deep learning-based multi-view fusion recognition model was developed to differentiate normal conotruncal anatomy from CTD. Model performance was validated against post-abortion pathology or postnatal echocardiography results. SAS software was used for statistical analysis to calculate the sensitivity and specificity of three fusion models (based on positivity in any two, three, or four views, and were designated as Fusion Model 1, Fusion Model 2, and Fusion Model 3, respectively), with the optimal model determined by the maximum Youden index. Senior, intermediate, and junior prenatal sonologists independently diagnosed cases in the validation set under blinding conditions. Their diagnostic results were compared with those of the optimal model. Paired Chi-square test (Cochran's Q test) was employed to compare the differences between the diagnostic accuracy rates of sonologists at different experience levels and the sensitivity of the optimal model, thereby analyzing the auxiliary diagnostic value of the multi-view fusion recognition model. Results:The study included 88 CTD cases, excluding six cases (non-CTD diagnosed by post-abortion pathology or postnatal echocardiography or poor image quality), divided into 60 training and 22 validation cases (12 tetralogy of Fallot, four double outlet right ventricle, three transposition of great arteries, three persistent truncus arteriosus). The control group included 176 cases, excluding 15 cases (other cardiac abnormalities confirmed postnatally or poor image quality after re-evaluation), divided into 120 training and 41 validation cases. The sensitivities of Fusion Model 1, Fusion Model 2, and Fusion Mudel 3 were 0.86, 0.64, and 0.27, while their specificities were 0.76, 0.95, and 1.00, respectively. Fusion Model 1 demonstrated the highest Youden index (0.62) and was selected as optimal. Its diagnostic sensitivity showed no significant difference from senior sonologists [86% vs. 91% (20/22), Bonferroni-corrected P>0.999], but was significantly higher than intermediate [55% (12/22), Bonferroni-corrected P=0.049] and junior sonologists [32% (7/22), Bonferroni-corrected P=0.003]. Conclusion:The deep learning multi-view fusion model achieved diagnostic performance comparable to senior sonologists, demonstrating potential value in assisting CTD diagnosis, training less experienced sonologists, and supporting research and education.

Immunometabolism studies focus on the relationships between immune cell functions and cellular energy metabolism pathways.Immunometabolism plays an important regulatory role in immune-related diseases.Mycobacterium tuberculosis(M.tb),an important intracellular pathogenic bacterium,enters alveolar macrophages after infection.The confrontation between M.tb and the host is a complex and dynamic process involving multiple aspects and mechanisms,such as the immune response,granuloma formation,and immune evasion.M.tb effector proteins play key roles in maintaining bacterial virulence and regulating host cell metabolism.This article reviews the reprogramming process of glucose metabolism,lipid metabolism,and immunometabolism,as well as changes in mi-tochondrial function in M.tb-infected host cells,thereby revealing the relationship between M.tb pathogenicity and host metabolic regu-lation,which is important for understanding tuberculosis.

Objective To investigate the feasibility of magnetic tracer technique for locating pulmonary nodules.Methods A tracer magnet and a matching puncture instrument were designed by ourselves for locating pulmonary nodules.After preliminarily verifying the feasibility of the operation in the isolated lung,four beagle dogs were used as animal models to perform puncture localization of the assumed lesions in the upper lobe of the right lung under the guidance of X-ray by using self-designed tracer magnets and puncture instruments,and the positioning effect was observed and evaluated after thorax opening.The operation time required for the tracer magnet implantation,whether there is bleeding at the puncture site,whether the tracer magnet is displaced,and the positioning time of pulmonary nodules after thorax opening were recorded.Results Two tracer magnets were successfully inserted into the upper lobe of the right lung under X-ray guidance in four beagle dogs,and the magnets were successfully attracted and fixed.The median insertion time of the tracer magnet was 5 minutes(4 to 7 minutes),and the insertion process was smooth without bleeding at the puncture site.After thorax opening,oval forceps were used to conveniently locate the location of the tracer magnet,achieving accurate positioning of pulmonary nodules with a median positioning time of 13 seconds(10 to 17 seconds),and the tracer magnet did not shift during the whole process.Conclusion The magnetic tracer technique is simple to operate and pricise for localization of pulmonary nodules.With further optimization of the operation process,this technique is expected to be applied in clinic.

Objective To investigate the feasibility of magnetic tracer technique for locating pulmonary nodules.Methods A tracer magnet and a matching puncture instrument were designed by ourselves for locating pulmonary nodules.After preliminarily verifying the feasibility of the operation in the isolated lung,four beagle dogs were used as animal models to perform puncture localization of the assumed lesions in the upper lobe of the right lung under the guidance of X-ray by using self-designed tracer magnets and puncture instruments,and the positioning effect was observed and evaluated after thorax opening.The operation time required for the tracer magnet implantation,whether there is bleeding at the puncture site,whether the tracer magnet is displaced,and the positioning time of pulmonary nodules after thorax opening were recorded.Results Two tracer magnets were successfully inserted into the upper lobe of the right lung under X-ray guidance in four beagle dogs,and the magnets were successfully attracted and fixed.The median insertion time of the tracer magnet was 5 minutes(4 to 7 minutes),and the insertion process was smooth without bleeding at the puncture site.After thorax opening,oval forceps were used to conveniently locate the location of the tracer magnet,achieving accurate positioning of pulmonary nodules with a median positioning time of 13 seconds(10 to 17 seconds),and the tracer magnet did not shift during the whole process.Conclusion The magnetic tracer technique is simple to operate and pricise for localization of pulmonary nodules.With further optimization of the operation process,this technique is expected to be applied in clinic.

Immunometabolism studies focus on the relationships between immune cell functions and cellular energy metabolism pathways.Immunometabolism plays an important regulatory role in immune-related diseases.Mycobacterium tuberculosis(M.tb),an important intracellular pathogenic bacterium,enters alveolar macrophages after infection.The confrontation between M.tb and the host is a complex and dynamic process involving multiple aspects and mechanisms,such as the immune response,granuloma formation,and immune evasion.M.tb effector proteins play key roles in maintaining bacterial virulence and regulating host cell metabolism.This article reviews the reprogramming process of glucose metabolism,lipid metabolism,and immunometabolism,as well as changes in mi-tochondrial function in M.tb-infected host cells,thereby revealing the relationship between M.tb pathogenicity and host metabolic regu-lation,which is important for understanding tuberculosis.

Objective:To develop an ultrasound multi-view fusion recognition model and evaluate its clinical value in diagnosing fetal conotruncal defects (CTD).Methods:This prospective study collected cardiac ultrasound images from fetuses at 20-32 weeks of gestation undergoing prenatal ultrasound at Dongguan Maternal and Child Health Hospital between September 2022 and May 2024. The case group comprised fetuses diagnosed with CTD, while controls with normal cardiac structures were collected at a 1∶2 ratio. Both groups were divided into modeling training and validation sets at a 3∶1 ratio. One optimal standard image each from the four-chamber view, left ventricular outflow tract view, right ventricular outflow tract view, and three vessels and trachea view was included per fetus. A deep learning-based multi-view fusion recognition model was developed to differentiate normal conotruncal anatomy from CTD. Model performance was validated against post-abortion pathology or postnatal echocardiography results. SAS software was used for statistical analysis to calculate the sensitivity and specificity of three fusion models (based on positivity in any two, three, or four views, and were designated as Fusion Model 1, Fusion Model 2, and Fusion Model 3, respectively), with the optimal model determined by the maximum Youden index. Senior, intermediate, and junior prenatal sonologists independently diagnosed cases in the validation set under blinding conditions. Their diagnostic results were compared with those of the optimal model. Paired Chi-square test (Cochran's Q test) was employed to compare the differences between the diagnostic accuracy rates of sonologists at different experience levels and the sensitivity of the optimal model, thereby analyzing the auxiliary diagnostic value of the multi-view fusion recognition model. Results:The study included 88 CTD cases, excluding six cases (non-CTD diagnosed by post-abortion pathology or postnatal echocardiography or poor image quality), divided into 60 training and 22 validation cases (12 tetralogy of Fallot, four double outlet right ventricle, three transposition of great arteries, three persistent truncus arteriosus). The control group included 176 cases, excluding 15 cases (other cardiac abnormalities confirmed postnatally or poor image quality after re-evaluation), divided into 120 training and 41 validation cases. The sensitivities of Fusion Model 1, Fusion Model 2, and Fusion Mudel 3 were 0.86, 0.64, and 0.27, while their specificities were 0.76, 0.95, and 1.00, respectively. Fusion Model 1 demonstrated the highest Youden index (0.62) and was selected as optimal. Its diagnostic sensitivity showed no significant difference from senior sonologists [86% vs. 91% (20/22), Bonferroni-corrected P>0.999], but was significantly higher than intermediate [55% (12/22), Bonferroni-corrected P=0.049] and junior sonologists [32% (7/22), Bonferroni-corrected P=0.003]. Conclusion:The deep learning multi-view fusion model achieved diagnostic performance comparable to senior sonologists, demonstrating potential value in assisting CTD diagnosis, training less experienced sonologists, and supporting research and education.