Diabetic cardiomyopathy (DCM) is a medical condition characterized by cardiac remodeling and dysfunction in individuals with diabetes mellitus. Sarcoplasmic reticulum (SR) and mitochondrial Ca²⁺ overload in cardiomyocytes have been recognized as biological hallmarks in DCM; however, the specific factors underlying these abnormalities remain largely unknown. In this study, we aimed to investigate the role of a cardiac-specific long noncoding RNA, D830005E20Rik (Trdn-as), in DCM. Our results revealed the remarkably upregulation of Trdn-as in the hearts of the DCM mice and cardiomyocytes treated with high glucose (HG). Knocking down Trdn-as in cardiac tissues significantly improved cardiac dysfunction and remodeling in the DCM mice. Conversely, Trdn-as overexpression resulted in cardiac damage resembling that observed in the DCM mice. At the cellular level, Trdn-as induced Ca²⁺ overload in the SR and mitochondria, leading to mitochondrial dysfunction. RNA-seq and bioinformatics analyses identified calsequestrin 2 (Casq2), a primary calcium-binding protein in the junctional SR, as a potential target of Trdn-as. Further investigations revealed that Trdn-as facilitated the recruitment of METTL14 to the Casq2 mRNA, thereby enhancing the m⁶A modification of Casq2. This modification increased the stability of Casq2 mRNA and subsequently led to increased protein expression. When Casq2 was knocked down, the promoting effects of Trdn-as on Ca²⁺ overload and mitochondrial damage were mitigated. These findings provide valuable insights into the pathogenesis of DCM and suggest Trdn-as as a potential therapeutic target for this condition.
Animals ; Diabetic Cardiomyopathies/pathology* ; RNA, Long Noncoding/genetics* ; Myocytes, Cardiac/metabolism* ; Mice ; Calsequestrin/genetics* ; Calcium/metabolism* ; Male ; Sarcoplasmic Reticulum/metabolism* ; Methyltransferases/metabolism* ; Mice, Inbred C57BL ; Mitochondria, Heart/metabolism* ; Disease Models, Animal ; Mitochondria/metabolism*

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

In recent years， the incidence of pulmonary nodules has kept rising. To give full play to the advantages of traditional Chinese medicine （TCM） in the treatment of pulmonary nodules and identify the breakthrough points of integrating TCM with Western medicine， the China Association of Chinese Medicine organized medical experts in TCM and western medicine to carry out in-depth discussion regarding this disease. The discussion encompassed the modern medical advances， TCM theories of etiology and pathogenesis， the role and advantages of TCM in the whole course management of pulmonary nodules， contents and methods of research on pulmonary nodules， and science popularization work， aiming to provide a reference for clinical practice and scientific research. After discussion， the experts concluded that the occurrence of pulmonary nodules was rooted in the deficiency of the lung and spleen and triggered by phlegm dampness， blood stasis， and Qi stagnation. TCM can treat pulmonary nodules by controlling and reducing nodules， improving physical constitution， ameliorating multi-system nodular diseases， reducing anxiety and avoiding excessive diagnosis and treatment， and serving as an alternative for patients who are unwilling or unfit for surgical treatment. At present， the optimal diagnosis and treatment strategy for pulmonary nodules has not been formed， which needs to be further studied from multiple perspectives such as clinical epidemiology， biology， and evidence-based medicine. The primary task of current research is to find out the advantages， effective prescriptions， and target populations and determine the effective outcomes of TCM in the treatment of pulmonary nodules. At the same time， basic research should be carried out to explore the etiology and biological behaviors of pulmonary nodules. The expert consensus on the diagnosis and treatment of pulmonary nodules with integrated TCM and Western medicine needs to be continuously revised to guide clinicians to conduct standardized， scientific， and accurate effective diagnosis and treatment.

In this study, deep eutectic solvents (DESs) were used as excipients to prepare solid dispersion (SD) of scutellarin. The SD of scutellarin were prepared by melting method with cumulative dissolution rate as the index of investigation. The preparation conditions of SD of scutellarin were optimized by single factor experiment, which investigated the type of the carrier material, the type of DESs, and the ratio of the drug to the carrier. The optimum preparation conditions of DESs-SD were as follows: using Poloxamer 407 as the carrier material, PEG 200/urea (2∶1) as the DESs system, and the ratio of carrier, DESs, and drug was 6∶1∶1. The drug loading capacity of scutellarin in SD was 12.53% under the optimum preparation conditions. Differential scanning calorimetry, Fourier transform infrared spectroscopy, X-ray powder diffraction and scanning electron microscope exhibited that scutellarin was amorphous form in the SD system. Furthermore, the stability of the DESs-based SD of scutellarin was evaluated by high temperature, high humidity, and strong light tests, which showed that the cumulative dissolution rate and scutellarin content of SD decreased with time under these conditions. Finally, the result of pharmacokinetic studies indicated that the oral absorption of the scutellarin could be increased using DESs as an excipient in the preparation of SD. The animal experiment was approved by the Experimental Animal Ethics Committee of Fujian University of Traditional Chinese Medicine (approval number: FJTCMIACUC 2023048). Consequently, this research offers a novel and effective approach for using DESs to enhance the oral bioavailability of active substances with low water solubility.

Aim To investigate the regulatory mecha-nism of butin on the proliferation,migration,cycle blockage and pyroptosis related inflammatory factors in human fibroblast-like synoviocytes of rheumatoid arthri-tis(HFLS-RA).Methods Cell proliferation,migra-tion and invasion were studied using cell migration and invasion assays.Cell cycle was detected by flow cytom-etry,and the expression of the pyroptosis-associated in-flammatory factors IL-1β,IL-18,caspase-1 and caspase-3 was detected by ELISA,RT-qPCR and West-ern blot.Results Migration and invasion experiments showed that the cell proliferation rate of the butin group was lower than that of the blank control group(P＜0.05).Cell cycle analysis demonstrated that in the G0/G1 phase,the DNA expression was elevated in the medium and high-dose groups of butin(P＜0.05),while in the G2 and S phases,the DNA expression was reduced in the medium and high-dose groups of butin(P＜0.05).The results of ELISA,RT-qPCR and Western blot assay revealed that the expression of IL-1β,IL-1 8,caspase-1,and caspase-3 decreased in the butin group compared with the IL-1β+caspase-3 in-hibitor group(P＜0.05).Conclusions Butin inhib-its HFLS-RA proliferation by inhibiting the synthesis of inflammatory vesicles by caspase-1 in the pyroptosis pathway,thereby reducing the production and release of inflammatory factors such as IL-1β and IL-18 down-stream of the pathway,and also inhibits HFLS-RA pro-liferation by exerting a significant blocking effect in the G1 phase,which may be one of the potential mecha-nisms of butin in the treatment of RA.

Objective:To investigate the effect of microgravity-mediated Notch1 signaling on macrophage polarization on bone homeostasis.Methods:The animal model was constructed by tail-limb suspension(HLS)to simulate the microgravity environment.The animals were grouped into Control group,HLS group,HLS+NC group,HLS+si group,HLS+rhNF-κB group.ELISA was used to detect the content of TNF-α and IL-1β in serum of rats.TUNEL staining was used to detect the apoptosis of bone tissue.Immunofluo-rescence was used to detect the polarization of macrophages in bone tissue.The rat osteoblast CP-R091 microgravity model was con-structed by simulating the microgravity environment with a rotating wall bioreactor.The cell experiments were divided into Control group,HLS group,HLS+NC group,HLS+si group,HLS+rhNF-κB group.CCK-8 test was used to detect the proliferation activity of cells in each group,and AO test was used to test the apoptosis rate of cells in each group.PCR was used to detect the expression of os-teogenesis-related genes in bone tissues and cells.Western blot was used to detect the expression of Notch1,hair division-related en-hancer-1(HES-1),and Notch pathway ligand 1(Jagged1)in bone tissues and cells of each group.Results:Compared with control group,the contents of TNF-α and IL-1β in the serum of the rats in the HLS group,the apoptosis rate,and the proportion of M1 macro-phages were significantly increased.Compared with HLS group,the HLS+si group could obviously partially reverse the change trend of the above parameters,while HLS+rhNF-κB group significantly changed the above parameter values.Compared with control group,the proliferation activity of the cells in the HLS group was significantly reduced,and the apoptosis rate was significantly increased.Com-pared with HLS group,the HLS+si group could obviously partially reverse the change trend of the above parameters,while the HLS+rhNF-κB group made the above parameter values worse.The expressions of the osteogenesis-related genes collagen type Ⅰ(COL1),osteocalcin(OCN)and Runt-related transcription factor 2(RUNX2)in bone tissues and cells in the microgravity environment were significantly decreased,while the expressions of Notch-1,Hes-1 and Jagged1 were significantly increased,and the differences were statistically significant(all P<0.05).Conclusion:Microgravity-mediated Notch1 signaling regulates M1/M2 polarization of macro-phages,participates in cell proliferation and apoptosis in bone tissue,and affects the progress of bone homeostasis.

AIM To study the chemical constituents from the branches and leaves of Toona ciliata Roem.var.pubescens(Franch.)Hand-Mazz.and their antitumor activities.METHODS The compounds were isolated and purified by silica gel,RP-18 reverse phase silica gel and semi-preparative HPLC,the structures of compounds were identified by physicochemical properties and spectral data.The antitumor activities were determined by MTT method.RESULTS Fifteen compounds were isolated and identified as toonaolide D(1),toonaciliatin E(2),bourjotinolone A(3),(21R,23R)-epoxy-21α-ethoxy-24S,25-dihydroxyapotirucalla-7-en-3-one(4),(Z)-toonasterone C(5),(E)-toonasterone(6),3-epi-dyscusin C(7),(Z)-aglawone(8),(E)-volkendousin(9),8(14),15-isopimaradiene-2α,3α,19-triol(10),(-)-loliolide(11),cyclohexenone(12),pubinernoid A(13),quercetin-3-O-(4″-methoxy)-α-L-rahmnopyranosyl(14),5-hydroxymethyl-2-furancarboxaldehyde(15).The IC50 values of compounds 3 and 4 on K562 cells were 54.2 and 47.3 μmol/L,respectively,and the IC50 values on HEL cells were 47.3 and 61.1 μmol/L,respectively.CONCLUTION Compounds 4,7,10 and 11 are isolated from Toona genus for the first time,and compounds 2,15 are first isolated from this plant.Compounds 3 and 4 show weak antitumor activities.