ObjectiveThis study aimed to investigate the anti-Mycoplasma pneumoniae (MP) activity of luteolin and elucidate its underlying mechanisms. MethodsLuteolin was identified as the primary active compound from the polyphenol extract ofF. diotrys using network pharmacology. Its efficacy was evaluated against two MP strains: the standard strain M129 and the multidrug-resistant strain M19. A modified culture medium with visual characteristics was employed to determine the minimum inhibitory concentration (MIC) of luteolin. The expression of key proteins involved in MP growth and pathogenicity was assessed by qRT-PCR following luteolin treatment. Additionally, the viability of A549 cells infected with MP was compared between luteolin-treated and untreated groups. In vivo anti-MP activity was evaluated using a mouse model, and the expression of inflammatory cytokines in lung tissues was analyzed. ResultsLuteolin effectively inhibited both MP strains, with MIC₉₀ values of 100 mg/L for M19 and M129. Treatment with luteolin significantly downregulated the expression of adhesion proteins P1 and P30 in both strains. However, the expression of P65, HMW3, TrmB, and CARDS TX was reduced only in the M19 strain following luteolin intervention. Luteolin also enhanced the growth and viability of A549 cells infected with MP. In the mouse model, luteolin treatment resulted in steady weight gain and was well tolerated. The bacteriostatic rate of luteolin in lung tissues was 50.7%, significantly higher than the 25.2% observed in the roxithromycin group. Furthermore, luteolin reduced the expression of inflammatory factors, including IL-6, TNF-α, and HMGB1, in MP-infected mice. ConclusionLuteolin effectively and safely inhibits the proliferation and pathogenicity of MP, particularly the drug-resistant M19 strain, by downregulating the expression of toxicity-associated proteins (P1, P30, P65, HMW3, TrmB, CARDS TX) and modulating host inflammatory responses. These findings suggest that luteolin may offer a novel therapeutic strategy for treating MP infections, especially those caused by drug-resistant strains.

ObjectiveThis study aimed to investigate the anti-Mycoplasma pneumoniae (MP) activity of luteolin and elucidate its underlying mechanisms. MethodsLuteolin was identified as the primary active compound from the polyphenol extract ofF. diotrys using network pharmacology. Its efficacy was evaluated against two MP strains: the standard strain M129 and the multidrug-resistant strain M19. A modified culture medium with visual characteristics was employed to determine the minimum inhibitory concentration (MIC) of luteolin. The expression of key proteins involved in MP growth and pathogenicity was assessed by qRT-PCR following luteolin treatment. Additionally, the viability of A549 cells infected with MP was compared between luteolin-treated and untreated groups. In vivo anti-MP activity was evaluated using a mouse model, and the expression of inflammatory cytokines in lung tissues was analyzed. ResultsLuteolin effectively inhibited both MP strains, with MIC₉₀ values of 100 mg/L for M19 and M129. Treatment with luteolin significantly downregulated the expression of adhesion proteins P1 and P30 in both strains. However, the expression of P65, HMW3, TrmB, and CARDS TX was reduced only in the M19 strain following luteolin intervention. Luteolin also enhanced the growth and viability of A549 cells infected with MP. In the mouse model, luteolin treatment resulted in steady weight gain and was well tolerated. The bacteriostatic rate of luteolin in lung tissues was 50.7%, significantly higher than the 25.2% observed in the roxithromycin group. Furthermore, luteolin reduced the expression of inflammatory factors, including IL-6, TNF-α, and HMGB1, in MP-infected mice. ConclusionLuteolin effectively and safely inhibits the proliferation and pathogenicity of MP, particularly the drug-resistant M19 strain, by downregulating the expression of toxicity-associated proteins (P1, P30, P65, HMW3, TrmB, CARDS TX) and modulating host inflammatory responses. These findings suggest that luteolin may offer a novel therapeutic strategy for treating MP infections, especially those caused by drug-resistant strains.

BACKGROUND: Chronic kidney disease (CKD) is a progressive disorder that leads to significant structural and functional changes in the kidneys, posing a major global health concern and contributing to high mortality rates. METHODS: The urgent need for innovative treatments is evident. Mesenchymal stem cells (MSCs) are well-regarded in regenerative medicine for their ability to repair tissue and modulate immune responses. Emerging research indicates that the therapeutic benefits of MSCs are largely mediated by the secretion of extracellular vesicles (EVs), particularly exosomes (MSC-Exos), which replicate the effects of MSCs by delivering genetic materials and proteins to target cells. RESULTS: MSC-Exos are novel natural carriers for targeted gene or drug delivery, offering biocompatibility, intrinsic targeting capabilities, and bioactive cargo to modulate recipient cells. They represent a groundbreaking platform for precision medicine, enhancing therapeutic efficacy with minimal immunogenicity and off-target effects. Moreover, embedding exosomes within hydrogels has emerged as a promising strategy to maintain their biological activity and enable a controlled release. CONCLUSION This review explores the roles of MSC-Exos in CKD pathophysiology, highlights the renoprotective effects of MSC-Exos for various sources, and provides a comprehensive overview of how hydrogel biomaterials present a promising approach for integrating exosomes to enhance therapeutic outcomes. The use of hydrogels to encapsulate exosomes improves their sustained release and stability in diseased kidney tissues, providing an innovative strategy to enhance precision therapies.

Background Against the backdrop of global warming, extreme high-temperature weather events are becoming more frequent and pose a substantial threat to human health. Urumqi is located in a typical arid climate zone, but the health effects of heatwaves in this region remain insufficiently understood. Objective To evaluate the association between heatwave events and non-accidental mortality among residents in Urumqi, quantify the attributable mortality burden, identify vulnerable populations, and provide scientific evidence to support the development of targeted regional intervention strategies. Methods Data on non-accidental mortality, meteorological variables, and air pollutants in Urumqi from 2018 to 2024 were collected. A heatwave was defined as three or more consecutive days with daily mean temperature exceeding the 90th percentile of the study period. Under a time-stratified case-crossover design, a generalized additive model incorporating a cross-basis function within the distributed lag nonlinear model framework was applied to simultaneously assess the single-day (lag0-lag7) and cumulative (lag01-lag07) lag effects and exposure-response relationship of heatwave exposure with non-accidental mortality. Heatwave-attributable excess deaths was further estimated. Results The exposure-response relationship between daily mean temperature exposure and non-accidental mortality in Urumqi exhibited a J-shaped curve. Using 25 ℃ as the reference temperature, mortality risk increased when temperature exceeded 27.4 ℃. The cumulative relative mortality risk over lag07 following heatwave exposure was 1.100 (95%CI: 1.059, 1.142), with the highest effect observed at lag06 (RR=1.151, 95%CI: 1.095, 1.209). Subgroup analyses showed that females (lag06, RR=1.169, 95%CI: 1.082, 1.262) and residents aged ≥65 years (lag06, RR=1.156, 95%CI: 1.092, 1.224) had higher mortality risks than males (lag06, RR=1.140, 95%CI: 1.069, 1.216) and residents aged <65 years group (lag06, RR=1.141, 95%CI: 1.035, 1.257), respectively. The increased risk appeared earlier among females, beginning at lag02, compared with lag03 among males. Heatwave-attributable excess deaths showed an overall upward trend, peaking at 248 (95%CI: 152, 341) deaths in 2023 and 229 (95%CI: 141, 314) deaths in 2024. Conclusion Heatwave events are significantly associated with an increased risk of non-accidental mortality among residents in Urumqi, primarily through cumulative lag effects under consecutive days of exposure. Females and residents aged ≥65 years are identified as vulnerable populations. The estimated excess deaths attributable to heatwaves show an overall upward trend.

AIM:This study aims to investigate the changes in the lactylation levels of mitsugumin 53(MG53)protein during myocardial ischemia/reperfusion(I/R)and to further explore the potential molecular mechanisms by which MG53 lactylation impacts myocardial cell injury resulting from I/R.METHODS:A myocardial I/R model was established in mice through ligation of the left anterior descending coronary artery followed by reperfusion.Simultaneous-ly,a hypoxia/reoxygenation(H/R)model of rat myocardial H9C2 cells was created in vitro.In the mouse model,TTC and HE staining were employed to assess myocardial ischemia and injury.The levels of creatine kinase and cardiac troponin I were quantified using ELISA,while MG53 expression was evaluated through immunofluorescence.In the in vitro cell model,CCK-8 assay and flow cytometry were utilized to measure cell viability and apoptosis level,respectively.The levels of MG53 protein,its lactylation,and ferroptosis-related proteins in both mouse myocardial tissue and in vitro rat cardiomyo-cytes were analyzed using immunoprecipitation and Western blot.Additionally,the levels of reactive oxygen species,fer-rous ion,lactate dehydrogenase and malondialdehyde in tissues and cells were measured using appropriate assay kits.RE-SULTS:In the I/R group,mouse myocardial tissue exhibited significant injury following cardiac I/R.The expression of MG53 protein was reduced,while the lactylation level of MG53 protein and the ferroptosis of myocardial cells were in-creased.In the in vitro cardiomyocyte experiments,H/R exposure resulted in decreased cell viability,increased apoptosis and ferroptosis levels,reduced MG53 protein expression,and increased MG53 lactylation level.Treatment with 2-deoxy-D-glucose(2-DG)improved cardiomyocyte viability,decreased apoptosis and ferroptosis levels,increased MG53 expres-sion,and lowered MG53 lactylation level.Furthermore,the addition of exogenous lactate significantly countered the ef-fects of 2-DG on cell viability and MG53 expression,and increased the cell apoptosis,ferroptosis and MG53 lactylation levels.CONCLUSION:During myocardial I/R,there is a down-regulation of MG53 protein expression accompanied by an increase in the lactylation level of MG53 protein.The elevation of MG53 lactylation may exacerbate myocardial I/R inju-ry by facilitating the ferroptosis of cardiomyocytes.

Brain age prediction is of great significance to the in-depth understanding of individual neurodevelopment,early diagnosis of neuropsychiatric disorders,and formulation of personalized treatment plans. With the continuous advancement of deep learning,more and more researches focus on using such algorithms to predict brain age. Compared with traditional regression algorithms,deep learning which has the advantages of complex pattern learning,end-to-end learning and high adaptability can more accurately reveal the neuropathological mechanisms of neuropsychiatric disorders,and provide more precise tools for clinical assessment,assisted diagnosis and prognosis prediction. Herein the study reviews the recent advances in the application of deep learning algorithms in brain age prediction,introduces the achievements in deep learning model optimization,multimodal data inputs and interpretability studies for brain age prediction,discusses the methods for the establishment of integrated deep learning architectures and the future challenges of developing unified benchmarking,and provides an outlook on the application of deep learning in brain age prediction.

Knee osteoarthritis(OA)is a primary cause of joint dysfunction.Knee osteotomy has garnered significant attention due to its potential to delay the progression of knee OA and enhance joint function.As a pivotal biomechanical factor in the onset and progression of OA,the accurate correction of abnormal knee alignment is the central objective of knee osteotomy.This article systematically reviews the biomechanical research progress related to knee osteotomy,with a focus on the precision and personalized correction of force line.The development of new classification system and measurement technology of force line is summarized,the biomechanical mechanism of knee OA induced by abnormal mechanical load is analyzed,and the goal of force line and clinical application progress of knee osteotomy is discusses,so as to provide a new perspective and idea for the clinical treatment of knee OA with knee osteotomy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

[Objective] To analyse and predict the tendencies of using erythrocyte blood in Changsha based on the autoregressive integrated moving average (ARIMA) model, long short-term memory (LSTM) and ARIMA-LSTM combination model, so as to provide reliable basis for designing a feasible and effective blood inventory management strategy. [Methods] The data of erythrocyte usage from hospitals in Changsha between January 2012 and December 2023 were collected, and ARIMA model, LSTM model and ARIMA-LSTM combination model were established. The actual erythrocyte consumption from January to May 2024 were used to assess and verify the prediction effect of the models. The extrapolation prediction accuracy of the models were tested using two evaluation indicators: mean absolute percentage error (MAPE) and root mean square error (RMSE), and then the prediction performance of the model was compared. [Results] The RMSE of LSTM model, optimal model ARIMA(1,1,1)(1,1,1)12 and ARIMA-LSTM combination model were respectively 5 206.66, 3 096.43 and 2 745.75, and the MAPE were 18.78%,11.54% and 9.76% respectively, which indicated that the ARIMA-LSTM combination model was more accurate than the ARIMA model and LSTM model, and the prediction results was basically consistent with the actual situation. [Conclusion] The ARIMA-LSTM model can better predict the clinical erythrocyte consumption in Changsha in the short term.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.