ObjectiveThe malaria parasites remodel the host erythrocyte structure by exporting parasite proteins that interact with the membrane skeleton proteins of red blood cells (RBCs), facilitating their intracellular survival and pathogenicity. Skeleton-binding protein 1 (SBP1) is a conserved exported protein across Plasmodium species. In Plasmodium falciparum, SBP1 has been reported to interact with erythrocyte membrane skeleton proteins 4.1R and spectrin, while its contribution to erythrocyte remodeling and parasite virulence in Plasmodium berghei (Pb) remains unclear. This study aims to determine whether PbSBP1 associates with the host cytoskeletal protein 4.1R and to investigate its role in the remodeling of host RBCs and the pathogenicity of Plasmodium berghei. MethodsIn Plasmodium berghei, the relationship between PbSBP1 and the erythrocyte cytoskeletal protein 4.1R was examined using co-immunoprecipitation. A Pbsbp1 gene knockout mutant of Plasmodium berghei (Pbsbp1∆) was generated based on the principle of double crossover homologous recombination. The deformability of erythrocytes infected with Pbsbp1∆ parasites was assessed using microfluidic methods. Microchannels with an array of cylindrical pillars were used to detect modifications in infected RBC deformability. The infected RBCs were squashed between the rows and recovered between the columns and the transit velocity (μm/s) of infected RBCs travelling through the microchannel was recorded. The component of the erythrocyte membrane skeleton junctional complex, tropomodulin (TMOD), was fluorescently labeled, and the cytoskeletal network of infected erythrocytes was imaged using super-resolution stochastic optical reconstruction microscopy (STORM) to analyze ultrastructural changes in the cytoskeleton of wild-type (WT) and Pbsbp1∆-infected erythrocytes. Actin-based junctional complexes were displayed as individual clusters by the labeled TMOD in the STORM images, and the cluster densities and distances between adjacent clusters of infected RBCs were calculated. Additionally, rodent malaria models (BALB/c mice) and experimental cerebral malaria models (C57BL/6 mice) were employed to monitor the growth of Pbsbp1∆ and WT parasites during the intraerythrocytic stage and their capacity to induce cerebral malaria in mice. ResultsPbSBP1 may participate in the remodeling of infected erythrocytes through direct or indirect interaction with the erythrocyte cytoskeletal protein 4.1R. Microfluidic assays revealed that the deformability of erythrocytes infected with Pbsbp1∆ parasites was significantly enhanced compared to those infected with WT parasites. STORM imaging further demonstrated that the ultrastructure of the erythrocyte cytoskeleton in Pbsbp1∆-infected cells was altered relative to that in WT-infected erythrocytes. The distances between nearest neighbors of clusters had a tendency to increase while the cluster densities were decreased in Pbsbp1∆-infected RBCs compared to WT-infected RBCs. Subsequent phenotypic analysis indicated that the growth rate of Pbsbp1∆ parasites during the intraerythrocytic stage was significantly slower than that of WT parasites, and their ability to induce cerebral malaria in mice was also attenuated. These findings suggest that PbSBP1 is involved in the remodeling of the erythrocyte membrane skeleton, likely through its direct or indirect interaction with protein 4.1R, thereby regulating the deformability of infected erythrocytes and influencing the pathogenicity of the blood-stage parasites. ConclusionThis study establishes a role for PbSBP1 in host erythrocyte remodeling and parasite virulence, providing new research strategies for the prevention and treatment of malaria.

ObjectiveTo describe the prevalence and dynamic pain characteristics of dysmenorrhea among female medical students through a menstrual diary-based observational study. MethodsFrom April to June 2021, female students were recruited from a medical college in Shanghai to complete menstrual diaries and record dysmenorrhea-related characteristics for two menstrual cycles. Dysmenorrhea severity was assessed using the visual analogue scale (VAS) (no pain: 0; mild: 1‒3; moderate: 4‒6; severe: 7‒10). A total of 323 diaries were collected, of which 303 were 93.81% valid (valid response rate: 93.81%), covering 596 complete menstrual cycles. Among them, 293 participants completed diaries for two cycles and were included in the analysis of persistent dysmenorrhea. Period, point, and persistent prevalence of dysmenorrhea were calculated using the number of participants as the denominator. Paired comparisons were performed using Cochran’s Q test and Bonferroni-adjusted McNemar tests. Using menstrual cycles with dysmenorrhea as the unit of analysis, the distributions of pain onset, peak timing, and pain duration across different severities were compared using the χ² tests or Fisher’s precision probability tests. ResultsAmong 303 female college students, 269 experienced dysmenorrhea during the first recorded cycle, and 288 had dysmenorrhea in any cycle. Among 596 complete menstrual cycles recorded, 527 were associated with dysmenorrhea (88.42%), and among 293 students who completed 2 complete recorded cycles, 239 had persistent dysmenorrhea. The period, point, and persistent prevalence of dysmenorrhea among female college students were 95.05% (288/303), 88.78% (269/303), and 81.57% (239/293), respectively (Q=61.61, P<0.001). The prevalence of persistently severe dysmenorrhea was 5.12% (15/293). Among the 527 dysmenorrheic cycles, there were statistically significant differences in the distribution of pain onset time (P=0.003) and pain duration (P<0.001) among different degrees of dysmenorrhea; mild dysmenorrhea mostly started on the first day of menstruation (52.83%), and moderate dysmenorrhea mostly started before menstruation (50.19%). Pain duration increased with severity; the proportions of cycles with pain lasting ≥4 days were 13.20%, 43.03%, and 51.56% for mild, moderate, and severe dysmenorrhea cycles, respectively. For all severity groups, mean pain scores peaked on the first day of menstruation and then decreased day by day to a stable level. ConclusionDysmenorrhea is highly prevalent among female medical students, and differences exist across pain severity levels in onset time, duration, and dynamic changes in pain. Identification and health guidance for those with persistently moderate-to-severe dysmenorrhea should be strengthened, and medical evaluation should be conducted when necessary.

Readiness for Hospital Discharge(RHD)refers to a multidimensional assessment of a patient's ability to transition safely from hospital to home,encompassing physiological stability,psychological preparedness,and social support adequacy.For patients requiring Home Nutrition Support(HNS),discharge readiness is particularly critical due to their heightened need for post-discharge specialized care,which significantly influences long-term recovery and quality of life.This paper reviews the concept,influencing factors,and unmet needs of RHD in patients with HNS and proposes targeted strategies to enhance discharge preparedness.By addressing gaps in current practices,we aim to optimize RHD in this vulnerable population and provide clinicians with evidence-based guidance for developing effective discharge plans.

Objective To develop a machine learning-based risk prediction model for postoperative acute kidney injury(AKI)and a model for mortality in obese patients admitted to intensive care unit(ICU)in order to improve early warning and prognostic evaluation to support clinical decision-making.Methods Data of obese postoperative ICU patients were retrospectively retrieved from the MIMIC-Ⅳ and eICU databases for statistical analysis.Ultimately,2 520 patients(670 from MIMIC-Ⅳ and 1 850 from eICU databases)were included to build the risk prediction models for AKI and mortality.The data included demographic information,vital signs,laboratory findings,surgical types,comorbidities,and medication use.After data cleaning and preprocessing,Boruta feature selection was applied,followed by the construction of prediction models using 7 machine learning algorithms,that is,Gradient Boosting Machine(GBM),Generalized Linear Model(GLM),k-Nearest Neighbors(KNN),Na?ve Bayes(NB),Neural Network(NNET),Support Vector Machine(SVM),and XGBoost.Model performance was evaluated through cross-validation and external validation.Results In the risk prediction models of AKI,the SVM model achieved the highest AUC value of 0.80 in the testing set and 0.71 in the external validation test.For the risk prediction models of mortality,the GBM model outperformed others in the prediction,attaining an AUC value of 0.91 in the testing set.Conclusion Risk predictive models for postoperative AKI and mortality in obese ICU patients are successfully constructed,and are valuable tools for clinicians to optimize early intervention and improve clinical outcomes for the patients.

Chrysophanol(Chr)and physcion(Phy)are primary active ingredients of a well-known traditional Chinese medicine namely rhubarb(Chinese name:Dahuang),and their glucuronides have been revealed as the dominant forms presenting in rats after oral administration.Either Chr or Phy has two glycosylation sites,resulting in a pair of positional isomers for glucuronides of either compound(CG1&CG2 and PG1&PG2).To confirmatively identify these glucuronides,energy-resolved mass spectrometry(ER-MS)was used to pursue the fragmentation trajectories of the targeted fragment ions,and the resultant breakdown graphs that were described by the optimal collision energy(OCE)were expected to exhibit the differences of glycosidic bond cleavage between the isomers.Quantum chemical calculation was thereafter conducted to produce the bond dissociation energy(BDE)of the glycosidic bonds.The isomers were unambiguously identified through applying the positive correlation rule between OCE and BDE.Fortunately,the glucuronides of Chr and Phy in vivo were observed through liver microsomes incubationin vitro.ER-MS was utilized to collect the Gaussian-shaped breakdown graphs in response to the neutral loss of 176 Da,and the absolute values of OCE were compared between positional isomers.The results revealed that CG1(-32.31 eV)>CG2(-31.61 eV),and nonetheless,PG1(-30.00 eV)Chr-8-GluA(-48.787 kJ/mol),and nonetheless,Phy-1-GluA(-48.672 kJ/mol)

Objective:To investigate the predictive value of three-dimensional arterial spin labeling (3D-ASL) for posterior circulation ischemia (PCI) and posterior circulation stroke (PCS) in patients with isolated vertigo.Methods:Patients with isolated vertigo underwent 3D-ASL imaging at Drum Tower Hospital Affiliated to Nanjing University School of Medicine from January 1, 2022 to December 31, 2024 were included retrospectively. According to the imaging findings, the patients with isolated vertigo were divided into PCI group and non-PCI group. The PCI group was further divided into PCS group and non-PCS group. The baseline clinical data and laboratory examination data were collected. Cerebral blood flow (CBF) in different brain regions of the posterior circulation was obtained through 3D-ASL related parameters to evaluate the posterior circulation perfusion, including CBF at two post-labeling delay times (PLD) (1.5 s and 2.5 s), delayed perfusion CBF (ΔCBF), multisequence PLD (Multi-PLD) CBF, and CBF under arterial transit time (ATT). Multivariate logistic regression analysis was used to determine the association of different CBF values with PCI and PCS in patients with isolated vertigo. Receiver operating characteristic (ROC) curve was used to evaluate the predictive value of different CBF values for PCI and PCS. Results:A total of 81 patients with isolated vertigo were included, aged 63.0±12.1 years, 44 were males (54.3%); 58 (71.6%) had PCI and 27 (25.9%) had PCS. Multivariate logistic regression analysis showed that PLD 1.5 s-CBF (odds ratio [ OR] 1.372, 95% confidence interval [ CI] 1.169-1.611; P<0.001), ΔCBF ( OR 1.197, 95% CI 1.072-1.336; P=0.001), and Multi-PLD-CBF ( OR 2.099, 95% CI 1.257-3.504; P=0.005) were the independent predictive factors of PCI. ROC curve analysis showed that the area under the curve for predicting PCI using the above three parameters alone and in combination were 0.962 (95% CI 0.915-1.000), 0.683 (95% CI 0.543-0.823), 0.944 (95% CI 0.985-1.000), and 0.999 (95% CI 0.997-1.000), respectively. Multivariate logistic regression analysis showed that the PLD 1.5 s-CBF ( OR 1.246, 95% CI 1.030-2.089; P=0.002), ΔCBF ( OR 1.153, 95% CI 1.038-1.281; P=0.008), and multi-PLD-CBF ( OR 1.388, 95% CI 1.219-1.689; P=0.001) in cerebellar region were the independent predictors of PCS. ROC curve analysis showed that the area under the curve for predicting PCS using the above three parameters alone and in combination were 0.956 (95% CI 0.911-1.00), 0.802 (95% CI 0.685-0.920), 0.972 (95% CI 0.923-1.000), and 0.977 (95% CI 0.937-1.00), respectively. Conclusion:3D-ASL can predict PCI and PCS early, and combining multiple parameters can improve the predictive ability for PCI and PCS.

OBJECTIVE: To assess the cardioprotective effect and impact of Qishen Granules (QSG) on different ischemic areas of the myocardium in heart failure (HF) rats by evaluating its metabolic pattern, substrate utilization, and mechanistic modulation. METHODS: In vivo, echocardiography and histology were used to assess rat cardiac function; positron emission tomography was performed to assess the abundance of glucose metabolism in the ischemic border and remote areas of the heart; fatty acid metabolism and ATP production levels were assessed by hematologic and biochemical analyses. The above experiments evaluated the cardioprotective effect of QSG on left anterior descending ligation-induced HF in rats and the mode of energy metabolism modulation. In vitro, a hypoxia-induced H9C2 model was established, mitochondrial damage was evaluated by flow cytometry, and nuclear translocation of hypoxia-inducible factor-1 α (HIF-1 α) was observed by immunofluorescence to assess the mechanism of energy metabolism regulation by QSG in hypoxic and normoxia conditions. RESULTS: QSG regulated the pattern of glucose and fatty acid metabolism in the border and remote areas of the heart via the HIF-1 α pathway, and improved cardiac function in HF rats. Specifically, QSG promoted HIF-1 α expression and entry into the nucleus at high levels of hypoxia (P<0.05), thereby promoting increased compensatory glucose metabolism; while reducing nuclear accumulation of HIF-1 α at relatively low levels of hypoxia (P<0.05), promoting the increased lipid metabolism. CONCLUSIONS QSG regulates the protein stability of HIF-1 α, thereby coordinating energy supply balance between the ischemic border and remote areas of the myocardium. This alleviates the energy metabolism disorder caused by ischemic injury.
Animals ; Myocardial Infarction/physiopathology* ; Male ; Hypoxia-Inducible Factor 1, alpha Subunit/metabolism* ; Rats, Sprague-Dawley ; Glucose/metabolism* ; Drugs, Chinese Herbal/therapeutic use* ; Energy Metabolism/drug effects* ; Rats ; Fatty Acids/metabolism* ; Myocardium/pathology*

Juvenile idiopathic arthritis (JIA) is the most common condition of chronic rheumatic disease in children. JIA is an autoimmune or autoinflammatory disease, with unclear mechanism and limited treatment efficacy. Recent studies have found a number of alterations in gut microbiota and its metabolites in children with JIA, which are related to the development and progression of JIA. This review focuses on the influence of the gut microbiota and its metabolites on immune function and the intestinal mucosal barrier and discuss the key role of the gut-joint axis in the pathogenesis of JIA and emerging treatment methods based on gut microbiota and its metabolites. This review could help elucidate the pathogenesis of JIA and identify the potential therapeutic targets for the prevention and treatment of JIA.
Humans ; Arthritis, Juvenile/physiopathology* ; Gastrointestinal Microbiome/physiology* ; Child ; Intestinal Mucosa

To facilitate the early intelligent screening of pediatric genu valgum, this study develops a deep learning-based gait recognition model tailored for clinical application. The model is constructed upon a three-dimensional residual network architecture and incorporates a triplet attention module alongside a spatial hierarchical pooling module, jointly enhancing feature interaction across temporal, spatial, and channel dimensions. This design ensures an optimal balance between representational capacity and computational efficiency. Evaluated on a self-constructed dataset, the model achieves precision of 98.0%, 97.1%, and 96.5%, recall rates of 97.5%, 97.0%, and 95.0%, and F ₁-scores of 0.98, 0.97, and 0.96 on the training, validation, and test sets, respectively, demonstrating excellent recognition performance and strong generalization ability. Ablation experiments confirm the importance of the proposed model's core components in improving performance, and comparative experiments further highlight its significant advantages in recognition accuracy and robustness. Visualization experiments reveal that the model effectively focuses on key regions of gait images, with attention regions aligning closely with clinical anatomical landmarks, thereby enhancing the interpretability of the model's decision-making in clinical applications. In summary, the proposed model not only offers an efficient and reliable technical solution for early intelligent screening of genu valgum in children, but also provides a practical pathway for applying gait recognition technology in medical diagnosis.
Humans ; Gait ; Deep Learning ; Genu Valgum/physiopathology* ; Child ; Neural Networks, Computer ; Algorithms

Objective:This study was aimed to explore the preference and heterogeneity in community health management service model selection among older patients with multiple chronic diseases,and to provide scientific evidence for optimizing the model.Methods:A multi-stage stratified and convenience sampling approach was adopted.A discrete choice experiment was conducted with 360 elderly patients with multiple chronic diseases from six regions in Jiangsu Province.The Mixed Logit Model was used to analyze service selection preferences and willingness to pay,while the Latent Class Logit Model was applied to explore heterogeneity among patient groups.Results:Patients showed a stronger preference for a service model featuring"twice-monthly visits,medication guidance+lifestyle counseling,and face-to-face consultations,"with willingness to pay values of 170.18 CNY,162.90 CNY,and 112.70 CNY,respectively.Willingness to pay decreased as out-of-pocket costs increased.Heterogeneity analysis identified three distinct preference groups,with statistically significant differences in urban-rural distribution,income levels,and health insurance types.Conclusions and suggestions:Medication and lifestyle guidance are the most valued components of community health management services among older patients with multiple chronic diseases.Patients'demographic and socioeconomic characteristics have a structural influence on their service preferences,highlighting the need to tailor service provision to different population groups.It is recommended to enhance medication guidance capacity at the primary care level,especially in rural areas;promote physical medicine integration to improve the accuracy and effectiveness of lifestyle guidance provided by primary healthcare personnel;strengthen digital infrastructure and streamline service processes to moderately increase the frequency of face-to-face consultations;and improve the integration of medical insurance and public health funding mechanisms to enhance service accessibility and equity.