ObjectiveTo investigate the protective effect of α-ketoglutarate （α-KG） on hepatic lipid deposition in offspring caused by arsenic exposure during pregnancy. Methods8-week-old institute of cancer research （ICR） mice were mated in a ratio of 2∶1 between females and males， and the detection of vaginal plugs confirmed pregnant. A total of 32 pregnant mice were randomly divided into four groups： control group， arsenic group， α-KG group， arsenic+α-KG group. On gestational day 0-16 （GD0-GD16）， the arsenic and arsenic+α-KG groups were exposed to sodium arsenite （NaAsO₂ ，15 mg/L） in drinking water everyday， and the α-KG and arsenic+α-KG groups were gavaged with α-KG （2 g/kg） everyday. On GD16， pregnant mice were euthanized to collect fetal liver， and fetal body weight and crown-rump length were measured. Gene expression differences between the control group and the arsenic group were analyzed by transcriptome. The total triglycerides （TGs） and subtypes in fetal liver were detected by liquid chromatography tandem mass spectrometry （LC-MS/MS）. Oil red O staining was used to observe the histopathological changes in the liver. Quantitative polymerase chain reaction （qPCR） was used to detect the expression level of genes related to lipid synthesis， transport， and degradation， and phosphatidylinositol 3' -kinase/ protein kinase B （PI3K/AKT） in the liver of fetus. ResultsTranscriptomics analysis showed that 2 144 genes were downregulated and 1 675 genes were upregulated in the arsenic exposed fetal liver； body weight and crown-rump length were reduced （P_TuKey<0.05）； the level of hepatic TGs was elevated in arsenic group （P_TuKey<0.05）； oil-red O staining showed a significant increase in lipid droplets in arsenic group （P_TuKey<0.01）； the expression of lipid synthesis-related genes were significantly upregulated （P_TuKey<0.05）； the expression of β-oxidation-related genes and lipid degradation-related genes were downregulated （P_TuKey<0.05）； the expression of PI3K， AKT decreased（P_TuKey<0.05）. Compared with the arsenic group， the body weight and crown-rump length of fetus increased in the arsenic+α-KG group （P_TuKey<0.05）； the level of hepatic TGs decreased in the arsenic+α-KG group （P_TuKey<0.05）； oil red O staining showed lipid droplets significantly decreased （P_TuKey<0.01）； the expression of lipid synthesis-related genes were downregulated （P_TuKey<0.05）， the expression of β-oxidation-related genes and lipid degradation-related genes were upregulated （P_TuKey<0.05）； the expression levels of PI3K and AKT increased （P_TuKey<0.05）. Conclusionα-KG alleviated hepatic lipid deposition in offspring exposed to arsenic during pregnancy through activating PI3K/AKT signaling pathway.

ObjectiveObesity, a global chronic metabolic disease, is closely associated with disruptions in lipid metabolism and gut microbiota. Current intervention strategies still have limitations in terms of safety and microecological regulation, necessitating the exploration of novel natural regulatory approaches. Based on the early pathological characteristics of obesity, this study innovatively employs a rectal delivery method alongside a high-fat diet (HFD)-induced obesity model to systematically evaluate the inhibitory effects, safety, and gut microbiota regulation mechanisms of leek-derived and konjac-derived extracellular vesicles on obesity development. By simulating early clinical intervention scenarios, this study aims to explore the preventive potential of plant-derived extracellular vesicles during the initial stages of obesity onset. MethodsExtracellular vesicles from leek and konjac were isolated using ultracentrifugation combined with density gradient centrifugation. Their nanoscale properties were characterized by dynamic light scattering (DLS), transmission electron microscopy (TEM), and nanoparticle tracking analysis (NTA). Male C57BL/6J mice were randomly divided into four groups: normal control (NC), high-fat diet (HFD), leek-derived extracellular vesicles (LEVs), and konjac-derived extracellular vesicles (KEVs). Beginning simultaneously with HFD feeding, mice in the intervention groups received 20 g/L vesicles rectally every 3 d for 4 weeks. Body mass and body composition were monitored throughout. At endpoint, mouse serum, adipose tissue, and colonic contents were collected. Serum biochemical indices (lipid profile, liver and kidney function, cardiac markers) were assessed to evaluate safety and metabolic efficacy, while 16S rRNA sequencing was employed to analyze gut microbial structure and diversity. ResultsDLS, NTA, and TEM confirmed that both LEVs and KEVs exhibited typical cup-shaped nanostructures with average particle sizes of approximately 284 nm and 223 nm, respectively. LEVs and KEVs treatment significantly suppressed HFD-induced weight gain and elevation of body-fat percentage (P<0.05), and reduced accumulation of abdominal white and epididymal adipose tissue. Serological analyses showed that both vesicles lowered total cholesterol, triglycerides and LDL-cholesterol, and ameliorated liver enzyme profiles (ALT, AST), demonstrating lipid-metabolic regulation and hepatoprotective effects. No hepatic, renal or cardiac dysfunction was observed, indicating favorable safety. Gut microbiota analyses revealed that vesicle intervention partially restored HFD-depleted microbial diversity and reshaped community structure. Notably, LEVs markedly increased the relative abundance of the beneficial taxon Lachnospiraceae at the family level, which is known for producing short-chain fatty acids and enhancing intestinal barrier function. Furthermore, Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) functional prediction suggested that LEVs and KEVs modulated gut microbial functions through distinct mechanisms: LEVs downregulated pathways related to ribosomes and DNA replication while enhancing xenobiotic degradation, whereas KEVs tended to upregulate energy metabolism and protein synthesis toward healthy levels. ConclusionRectally administered LEVs and KEVs exhibit excellent safety and pronounced metabolic benefits during the early phase of obesity, suppressing weight gain, correcting lipid dysregulation, and exerting effects via modulation of gut microbial composition and function. This study provides systematic experimental evidence supporting plant-derived exosome-like vesicles as an early intervention strategy against obesity.

ObjectiveObesity, a global chronic metabolic disease, is closely associated with disruptions in lipid metabolism and gut microbiota. Current intervention strategies still have limitations in terms of safety and microecological regulation, necessitating the exploration of novel natural regulatory approaches. Based on the early pathological characteristics of obesity, this study innovatively employs a rectal delivery method alongside a high-fat diet (HFD)-induced obesity model to systematically evaluate the inhibitory effects, safety, and gut microbiota regulation mechanisms of leek-derived and konjac-derived extracellular vesicles on obesity development. By simulating early clinical intervention scenarios, this study aims to explore the preventive potential of plant-derived extracellular vesicles during the initial stages of obesity onset. MethodsExtracellular vesicles from leek and konjac were isolated using ultracentrifugation combined with density gradient centrifugation. Their nanoscale properties were characterized by dynamic light scattering (DLS), transmission electron microscopy (TEM), and nanoparticle tracking analysis (NTA). Male C57BL/6J mice were randomly divided into four groups: normal control (NC), high-fat diet (HFD), leek-derived extracellular vesicles (LEVs), and konjac-derived extracellular vesicles (KEVs). Beginning simultaneously with HFD feeding, mice in the intervention groups received 20 g/L vesicles rectally every 3 d for 4 weeks. Body mass and body composition were monitored throughout. At endpoint, mouse serum, adipose tissue, and colonic contents were collected. Serum biochemical indices (lipid profile, liver and kidney function, cardiac markers) were assessed to evaluate safety and metabolic efficacy, while 16S rRNA sequencing was employed to analyze gut microbial structure and diversity. ResultsDLS, NTA, and TEM confirmed that both LEVs and KEVs exhibited typical cup-shaped nanostructures with average particle sizes of approximately 284 nm and 223 nm, respectively. LEVs and KEVs treatment significantly suppressed HFD-induced weight gain and elevation of body-fat percentage (P<0.05), and reduced accumulation of abdominal white and epididymal adipose tissue. Serological analyses showed that both vesicles lowered total cholesterol, triglycerides and LDL-cholesterol, and ameliorated liver enzyme profiles (ALT, AST), demonstrating lipid-metabolic regulation and hepatoprotective effects. No hepatic, renal or cardiac dysfunction was observed, indicating favorable safety. Gut microbiota analyses revealed that vesicle intervention partially restored HFD-depleted microbial diversity and reshaped community structure. Notably, LEVs markedly increased the relative abundance of the beneficial taxon Lachnospiraceae at the family level, which is known for producing short-chain fatty acids and enhancing intestinal barrier function. Furthermore, Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) functional prediction suggested that LEVs and KEVs modulated gut microbial functions through distinct mechanisms: LEVs downregulated pathways related to ribosomes and DNA replication while enhancing xenobiotic degradation, whereas KEVs tended to upregulate energy metabolism and protein synthesis toward healthy levels. ConclusionRectally administered LEVs and KEVs exhibit excellent safety and pronounced metabolic benefits during the early phase of obesity, suppressing weight gain, correcting lipid dysregulation, and exerting effects via modulation of gut microbial composition and function. This study provides systematic experimental evidence supporting plant-derived exosome-like vesicles as an early intervention strategy against obesity.

Anti-tumor drug research and development in non-small cell lung cancer (NSCLC) is rapidly developing, and the clinical application of high-throughput sequencing technology is also becoming widespread. Accordingly, researchers are focusing on human epidermal growth factor receptor-2 (HER-2) gene as a rare target of NSCLC, and a series of exploratory studies has been performed. Traditional chemotherapy and immunotherapy are unsatisfactory in the HER-2 mutant population, whereas the survival improvement of anti-HER-2 monoclonal antibodies and pan-HER inhibitors is limited. The development of antibody drug conjugate (ADC) ushers in a turning point for HER-2-mutated NSCLC, and new ADC drugs represented by trastuzumab deruxtecan are making a breakthrough. It opens up a new era of precision therapy for advanced HER-2-mutated NSCLC. Additionally, novel HER-2 inhibitors show very encouraging initial efficacy and safety, and clinical trials are ongoing. This review focuses on the latest progress of research on HER-2-mutated NSCLC.

BACKGROUND:The plantarflexor weakness is a common muscle defect in patients with spastic cerebral palsy and Charcot-Marie-Tooth,which clinically manifests abnormal gaits,and the relationship between plantarflexor weakness and abnormal gaits is unclear. OBJECTIVE:To explore the biomechanical behavior of the lower limb under the action of a single factor of plantarflexor weakness to reveal the mechanism of abnormal gait induced by plantarflexor weakness and to provide guidance for the rehabilitation training of patients with plantarflexor weakness. METHODS:A predictive framework of musculoskeletal multibody dynamics in the sagittal plane was established based on OpenSim Moco to predict lower limb joint angles and muscle activation changes during walking in normal subjects.The validity of the framework was verified by combining the inverse kinematics and electromyogram activation time of the experimental data.Reduced isometric muscle forces were used to model plantarflexor weakness and to compare predicted lower extremity joint angles,joint moments,and muscle energy expenditure with normal subjects to analyze the effects of plantarflexor weakness on lower extremity biomechanics. RESULTS AND CONCLUSION:(1)The Moco-based prediction framework realistically predicted the biomechanical changes of the lower limbs during walking in normal subjects(joint angles:normalized correlation coefficient≥0.73,root mean square error≤7.10°).(2)The musculoskeletal model used a small stride support phase to increase the"heel-walking"gait during plantarflexor weakness.When the plantarflexor weakness reached 80%,the muscle energy expenditure was 5.691 4 J/kg/m,and the maximum activation levels of the gastrocnemius and soleus muscles were 0.72 and 0.53,which might cause the plantarflexor weakness patients to be more prone to fatigue when walking.(3)Muscle energy expenditure was significantly higher when the weakness of plantarflexors exceeded 40%,and the joint angles and moments of the lower limbs deteriorated significantly when the weakness of plantarflexors exceeded 60%,suggesting that there may be a"threshold"for the effect of plantarflexor weakness on gait,which may correspond to the point at which health care professionals should intervene in the clinical setting.

Background Other infectious diarrhea (OID) refers to infectious diarrhea other than cholera, dysentery, and typhoid/paratyphoid fever. In the global climate change context, frequent extreme temperature events pose a significant threat to population health, yet the association between extreme temperatures and OID remains unclear. Objective To understand the impact of extreme temperature events on the incidence of OID in Taizhou region, and provide a scientific basis for formulating targeted public health intervention strategies. Methods Case data of OID from 2016 to 2024 and meteorological and pollutant data of Taizhou, Zhejiang during the same period were collected. The case data were derived from National Notifiable Infectious Diseases Reporting System, while the meteorological data and pollutant data were obtained from National Meteorological Science Data Center and Taizhou Ecology and Environment Bureau, respectively. An extreme heat event is defined as a period of two or more consecutive days in the warm season during which the daily mean temperature exceeds the 90th percentile value for that season. Similarly, an extreme cold event is defined as a period of two or more consecutive days in the cold season during which the daily mean temperature falls below the 10th percentile value. A distributed lag nonlinear model based on Poisson regression was used to assess the impact of extreme temperatures on OID incidence. Furthermore, subgroup analyses were conducted by gender and age. Results From 2016 to 2024, a total of 108 670 OID cases were reported in Taizhou, with higher incidence in males (61 253 cases) than females (47 417 cases). In terms of single-day lag effect, exposure to extreme cold presented an inverted U-shaped risk pattern: a significant risk effect began to emerge on the 4th day after exposure, continued to rise, and reached a peak on the 7th day (RR=1.044, 95%CI: 1.029, 1.058), whereas extreme heat had no significant impact. In terms of cumulative lag effect, exposure to extreme cold presented a J-shaped cumulative effect curve: short-term exposure (0-2 days) showed no risk effect, but as time extended (0-9 days), a risk effect emerged and reached the maximum risk value during the 0-12 d cumulative period (RR=1.243, 95%CI: 1.057, 1.461). In contrast, extreme heat had no cumulative lag effect. The subgroup analysis revealed that under extreme cold exposure, the single-day lag effects for both males and females exhibited an inverted U-shaped curve, with the risk peaking on day 7 for males (RR=1.045, 95%CI: 1.026, 1.065) and day 8 for females (RR=1.041, 95%CI: 1.019, 1.064). The risk pattern in the 1-5 years age group was similar to that of the overall population but with a higher effect size (RR=1.063, 95%CI: 1.041, 1.085). In terms of the cumulative lag effects, the cumulative risk curves for both males and females under extreme cold exposure showed a J-shaped pattern with varied risk profiles. Significant variations in risk patterns were observed across different age groups. No significant cumulative lag effects were observed for extreme heat exposure in any subgroup. Conclusion Extreme cold can increase the incidence of OID in Taizhou, and the risk effects are heterogeneous among different groups. No risk effect is observed for extreme heat.

Objective: To establish a machine learning-based precision blood donor recruitment model at the county level and assess its generalizability and applicability. Methods: A retrospective study was conducted using blood donation and SMS recruitment data from the Taicang Branch of the Suzhou Blood Center between 2019 and 2024. Multiple machine learning algorithms were employed, including extreme gradient boosting, support vector machine, k-nearest neighbor, logistic regression, decision tree, random forest, and multilayer perceptron. These were combined with techniques such as synthetic minority oversampling, undersampling, and cost-sensitive learning (using MFE and MSFE loss functions). Model parameters were optimized through grid search to identify the best-performing model. Results: In a prospective comparative study against conventional methods, the machine learning models increased the recruitment success rate among high-willingness donors by an average of 129.15%, and the recruitment efficiency per SMS improved by 125.02% compared with the traditional method. Under full-scale SMS sending, the recruitment rate per SMS increased by 42.61%, and SMS sending efficiency improved by 31.77%, significantly enhancing recruitment performance. Conclusion: This study represents the first application of a machine learning-based precision donor recruitment model at the county-level in China. The precise recruitment framework not only improves recruitment efficiency and reduces recruitment costs but also demonstrates strong scalability and generalizability. It provides a scientific and feasible intelligent pathway to ensure the safety and sustainability of the blood supply.

Prostate cancer (PCa) ranks as the second most prevalent malignancy among men worldwide. Early diagnosis, personalized treatment, and prognosis prediction of PCa play a crucial role in improving patients' survival rates. The advancement of artificial intelligence (AI), particularly the utilization of deep learning (DL) algorithms, has brought about substantial progress in assisting the diagnosis, treatment, and prognosis prediction of PCa. The introduction of the foundation model has revolutionized the application of AI in medical treatment and facilitated its integration into clinical practice. This review emphasizes the clinical application of AI in PCa by discussing recent advancements from both pathological and imaging perspectives. Furthermore, it explores the current challenges faced by AI in clinical applications while also considering future developments, aiming to provide a valuable point of reference for the integration of AI and clinical applications.
Humans ; Prostatic Neoplasms/diagnosis* ; Male ; Artificial Intelligence ; Deep Learning ; Prognosis

In this study, we constructed a GLP-2R-HEK293 cell line and established a method for the determination of the in vitro biological activity of teduglutide based on HTRF, after optimizing experimental conditions and methodological verification. We also carried out relative potency detection of teduglutide pharmaceutical products using this method. The result showed that there was a quantitative-efficient relationship between the teduglutide activity and cAMP contents in GLP-2R-HEK293 cells, which conformed to four-parameter model. Method verification results of five concentrations of teduglutide (64%, 80%, 100%, 125% and 156%) met the requirements of the General Rules of Chinese Pharmacopoeia, 2020 edition, Volume IV (9401). We then analyzed the relative potency of three batches of teduglutide drug substances and three batches of drug products. The linearity, regression and parallelism of the obtained curves all fit the system suitability requirements. The relative potency of six batches of teduglutide was from 83% to 105%. In summary, the biological activity detection method established in this study was accurate, precise, simple and time-saving, which can be used for quality control of teduglutide pharmaceutical products.