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.

Objective To construct 5 machine-learning models and compare their performance in predicting the associations between pre-pregnancy socio-psycho-behavioral exposures of both spouses and preconception outcomes.Methods Based on Chongqing Preconception Reproductive Health and Birth Outcome Cohort of volunteers recruited from Chongqing Health Center for Women and Children during January 2019 and March 2022,5 447 couples were recruited and surveyed through interviewer-interview for the demographic and social-psychological-behavioral data of both spouses(221 variables).According to the inclusion and exclusion criteria,4 097 couples were finally included,and randomly assigned into a training set(n=2 867 spouses)and a validation set(n=1 230 spouses)at a ratio of 7∶3.Feature analysis and collinear screening were applied to select the potential exposure factors.In consideration of difficulty to carry out semen parameters analysis in primary healthcare institutions,feature Set 1 including sperm parameters and feature Set 2 excluding semen parameters were constructed by including or excluding sperm quality simultaneously in the training set and the validation set.Five algorithms,that is,Logistic Regression,Naive Bayes,Random Forest,Gradient Boosting Machine,and Support Vector Machine,were used to construct preconception outcome prediction models,and the parameters of each model were optimized using random search combined with grid search.The predictive performance of each model was compared using precision,recall,F1 score,area under the receiver operating characteristic curve(AUC),and calibration curve.The optimal model was then selected by comparing the changes in the predictive ability of the questionnaire data for fertility outcomes with or without semen parameters.Results There were 24 variables screened out in feature Set 1,and 16 variables in feature Set 2.In feature Set 1,the gradient boosting machine performed better,with a relatively higher AUC value(0.651)and better F1 score(0.61).The logistic regression model performed stably(AUC value=0.647)and was suitable as the reference model.The random forest(AUC value=0.641),Naive Bayes(AUC value=0.641),and support vector machine(AUC value=0.634)performed second-best.By utilizing the gradient boosting machine,comparable results were found between the predictions from feature sets with or without semen parameters,as in feature Set 1,the AUC value of its validation set was 0.651(95%CI:0.629～0.681),the prediction accuracy was 0.63,the recall rate was 0.65,and the average precision value F1 was 0.61;and in feature Set 2,the AUC value of its validation set was 0.649(95%CI:0.624～0.663),and both the calibration curves were close to the ideal curve.The prediction results indicated that in feature Set 1,the features highly negatively correlated with preconception outcomes were female age,male age,and no pregnancy within 1 year without contraception,while the features highly positively correlated with preconception outcomes were female pregnancy history,total sperm vitality,and use of contraceptive measures before enrollment.Conclusion Among the 5 machine-learning algorithms performed in this cohort data,the gradient boosting machine shows slightly better performance.There are 24 factors being associated with preconception outcomes in both spouses,and the performance of the simplified model excluding semen parameters is not significantly declined.It is feasible to use machine-learning methods to predict human preconception outcomes through social-psychological-behavioral questionnaires.

Polymer magnetic microspheres are a new type of composite functional materials,which have the advantages of stable structure,diverse modification methods,and magnetic response.These characteristics make polymer magnetic microspheres have the advantages of high sensitivity,high-specificity and fast response when detecting logo carriers or modified materials,and shows great application prospects in the fields of biomedicine and medical diagnosis.In this review,the basic properties of polymer magnetic microspheres were summarized,the research progresses of magnetic microspheres in the fields of biomedicine and medical diagnosis were emphatically introduced,and the improvement of preparation methods and development trends in the future were prospected.

Glutathione(GSH)is a tripeptide containing sulfhydryl groups,which has the function of antioxidant,detoxifying,and immune enhancing effects,and its expression is closely associated with many diseases such as cancer,etc.Therefore,it is of great significance for clinical diagnosis by developing a highly sensitive,simple,and rapid method for detecting GSH.Herein,two-dimensional(2D)Zn-TCPP(Fe)nanosheets employing Zn2+and Fe-bound tetrakis(4-carboxyphenyl)porphyrin ligands were obtained by a surfactant-assisted synthetic method.The 2D Zn-TCPP(Fe)nanosheets exhibited excellent peroxidase-like activity,which allowed the catalysis of the H2O2-initiated oxidation of colorless 3,3',5,5'-tetramethylbenzidine(TMB)to blue oxidized TMB(ox-TMB).In the presence of GSH,GSH underwent a reduction reaction with oxTMB,resulting in the blue color of the solution fading.The proposed colorimetric sensor exhibited favorable sensitivity for GSH in the linear range of 0.1-200 μmol/L with a limit of detection(S/N=3)of 0.042 μmol/L,indicating excellent selectivity.The developed sensor was successfully applied to detect GSH level in the human serum samples with recoveries of 93.0％-107.7％,showing satisfactory results.The developed colorimetric sensor provided a new approach for detecting GSH.

ObjectiveTo explore the effect of respiratory training based on core stabilization training on lumbar disc herniation. MethodsFrom January, 2023 to October, 2024, 96 patients with lumbar disc herniation admitted to the First Affiliated Hospital of Bengbu Medical University were divided into control group (n = 32), core group (n = 32) and respiratory group (n = 32). All the groups underwent conventional rehabilitation therapy, with core stabilization training in the core group and respiratory training combined with core stabilization training in the respiratory group, additionally, for four weeks. Before and after training, the scores of Visual Analogue Scale, Japanese Orthopaedic Association (JOA) and Oswestry Dysfunction Index (ODI) were compared, the average electromyographic value (AEMG) and root mean square (RMS) value of the multifidus and transversus abdominis were detected by surface electromyography (sEMG); and the thickness of the multifidus and transversus abdominis were measured by musculoskeletal ultrasonography bilaterally. ResultsThe intra-group effect (F > 597.796, P < 0.001), inter-group effect (F > 16.535, P < 0.001) and interaction effect (F > 49.622, P < 0.001) were significant in the scores of VAS, JOA and ODI; which were better in the respiratory group than in the control group and the core group (P < 0.05), and were better in the core group than in the control group (P < 0.001). The intra-group effect (F > 7971.631, P < 0.001), inter-group effect (F > 177.760, P < 0.001) and interaction effect (F > 478.771, P < 0.001) were significant in the thickness of the transversus abdominis and multifidus; which were better in the respiratory group than in the control group and the core group (P < 0.001), and were better in the core group than in the control group (P < 0.001). The intra-group effect (F > 144303.007, P < 0.001), inter-group effect (F > 1495.458, P < 0.001) and interaction effect (F > 3121.361, P < 0.001) were significant in the RMS of the multifidus and transversus abdominis; which were better in the respiratory group than in the control group and the core group (P < 0.001), and were better in the core group than in the control group (P < 0.001). The intra-group effect (F > 1890.532, P < 0.001), inter-group effect (F > 607.132, P < 0.001) and interaction effect (F > 824.923, P < 0.001) were significant in the AEMG of the multifidus and transversus abdominis; which were better in the respiratory group than in the control group and core group (P < 0.001), and were better in the core group than in the control group (P < 0.001). ConclusionCore training combined with respiratory training can more effectively reduce pain and improve dysfunction by enhancing the strength and control of the core muscles， thus improving the quality of life of patients with lumbar disc herniation.

Objective: Inflammatory cascade reactions play a crucial role in secondary neuronal injury in acute ischemic stroke (AIS). The aim of this study was to explore the correlations between specific serological indicators, early neurological deterioration (END), disease prognosis, and syndrome factors in AIS based on this injury mechanism. Methods: The data for this study were collected from 135 patients with AIS admitted to the emergency department of Fangshan Hospital, Beijing University of Chinese Medicine, within 24 h of onset between November 2019 and May 2021. Among these, 29 patients had complete data and experienced END. Additionally, 9 non-END patients were matched from the remaining 90 patients with complete data, resulting in a total of 38 patients for statistical analysis. Statistical methods, including logistic regression and receiver operating curves, were used to analyze the correlation between serum levels of tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6), interleukin-10 (IL-10), vascular endothelial growth factor (VEGF), and intercellular adhesion molecule-1 (ICAM-1) within 24 h of END onset, disease prognosis, and syndrome factors. Grouping criteria included END occurrence, presence of syndrome elements on the first and third day post-onset, and prognosis at 90 days post-onset. Results: All 38 cases had onset time of less than 12 h, and there were no significant differences in age, gender, and onset time between the END and non-END groups. The TNF-α serum level within 24 h of onset was not associated with the occurrence of END but was negatively correlated with all-cause mortality at 90 days [0.110; P<0.05). An elevated IL-10 serum level within 24 h of onset showed a strong negative correlation with non-disabling and good functional outcomes at 90 days post-onset (0.110; P<0.05). When the ICAM-1 serum levels exceeded 233 599.500 μg/L and 125 141.500 μg/L, respectively, the susceptibility to endogenous wind and endogenous fire on the third day of onset was 15.364 times and 6.071 times higher, respectively, than that in patients with serum levels below these thresholds. Conclusion As accessible and objective biomarkers, inflammatory serum indicators are closely associated with both disease progression and traditional Chinese medicine (TCM) syndrome elements. They enhance AIS diagnosis, assessment, and treatment and contribute to a deeper understanding of the role of TCM syndrome differentiation in AIS diagnosis and treatment.

Dipsaci Radix is a commonly used Chinese herbal medicine in China, with triterpenoid saponins as the main active components. β-Amyrin synthase, a member of the oxidosqualene cyclase superfamily, plays a crucial role in the biosynthesis of oleanane-type triterpenoid saponins. Asperosaponin Ⅵ is an oleanane-type triterpenoid saponin. To explore the β-amyrin synthase genes involved in the biosynthesis of asperosaponin Ⅵ in Dipsacus asper, this study screened the candidate genes from the transcriptome data of D. asper. Two β-amyrin synthase genes, Da OSC1 and Da OSC2, were identified by phylogenetic analysis and correlation analysis. The coding sequences of Da OSC1 and Da OSC2 were 2 286 bp and 2 295 bp in length, encoding 761 and 764 amino acids,respectively. Multiple sequence alignments showed that Da OSC1 and Da OSC2 had three conserved motifs( DCTAE, QW, and MWCYCR) unique to the oxidosqualene cyclase family. Real-time quantitative PCR results showed that Da OSC1 and Da OSC2 had the highest expression levels in the roots. Compared with normal growth conditions, the low-temperature treatment significantly upregulated the expression of Da OSC1 and Da OSC2. Agrobacterium-mediated transient expression of Da OSC1 and Da OSC2 in Nicotiana benthamiana resulted in the production of β-amyrin, which suggested that Da OSC1 and Da OSC2 were able to catalyze the synthesis of β-amyrin. This study clarified the catalytic functions of two β-amyrin synthases in D. asper, analyzed their expression patterns in different tissue and at low temperatures. The findings provide a foundation for further studying the biosynthetic pathway and regulatory mechanism of asperosaponin Ⅵ in D. asper.
Intramolecular Transferases/chemistry* ; Phylogeny ; Plant Proteins/chemistry* ; Gene Expression Regulation, Plant ; Dipsacaceae/classification* ; Saponins/metabolism* ; Oleanolic Acid/metabolism*

The pathogenesis of neurodegenerative diseases (NDDs) is fundamentally linked to complex and profound alterations in metabolic networks within the brain, which exhibit marked spatial heterogeneity. While conventional bulk metabolomics is powerful for detecting global metabolic shifts, it inherently lacks spatial resolution. This methodological limitation hampers the ability to interrogate critical metabolic dysregulation within discrete anatomical brain regions and specific cellular microenvironments, thereby constraining a deeper understanding of the core pathological mechanisms that initiate and drive NDDs. To address this critical gap, spatial metabolomics, with mass spectrometry imaging (MSI) at its core, has emerged as a transformative approach. It uniquely overcomes the limitations of bulk methods by enabling high-resolution, simultaneous detection and precise localization of hundreds to thousands of endogenous molecules—including primary metabolites, complex lipids, neurotransmitters, neuropeptides, and essential metal ions—directly in situ from tissue sections. This powerful capability offers an unprecedented spatial perspective for investigating the intricate and heterogeneous chemical landscape of NDD pathology, opening new avenues for discovery. Accordingly, this review provides a comprehensive overview of the field, beginning with a discussion of the technical features, optimal application scenarios, and current limitations of major MSI platforms. These include the widely adopted matrix-assisted laser desorption/ionization (MALDI)-MSI, the ultra-high-resolution technique of secondary ion mass spectrometry (SIMS)-MSI, and the ambient ionization method of desorption electrospray ionization (DESI)-MSI, along with other emerging technologies. We then highlight the pivotal applications of spatial metabolomics in NDD research, particularly its role in elucidating the profound chemical heterogeneity within distinct pathological microenvironments. These applications include mapping unique molecular signatures around amyloid β‑protein (Aβ) plaques, uncovering the metabolic consequences of neurofibrillary tangles composed of hyperphosphorylated tau protein, and characterizing the lipid and metabolite composition of Lewy bodies. Moreover, we examine how spatial metabolomics contributes to constructing detailed metabolic vulnerability maps across the brain, shedding light on the biochemical factors that render certain neuronal populations and anatomical regions selectively susceptible to degeneration while others remain resilient. Looking beyond current applications, we explore the immense potential of integrating spatial metabolomics with other advanced research methodologies. This includes its combination with three-dimensional brain organoid models to recapitulate disease-relevant metabolic processes, its linkage with multi-organ axis studies to investigate how systemic metabolic health influences neurodegeneration, and its convergence with single-cell and subcellular analyses to achieve unprecedented molecular resolution. In conclusion, this review not only summarizes the current state and critical role of spatial metabolomics in NDD research but also offers a forward-looking perspective on its transformative potential. We envision its continued impact in advancing our fundamental understanding of NDDs and accelerating translation into clinical practice—from the discovery of novel biomarkers for early diagnosis to the development of high-throughput drug screening platforms and the realization of precision medicine for individuals affected by these devastating disorders.

Iron metabolism is a critical factor in tumorigenesis and development. Although TP53 mutations are prevalent in glioblastoma (GBM), the mechanisms by which TP53 regulates iron metabolism remain elusive. We reveal an imbalance iron homeostasis in GBM via TCGA database analysis. TP53 mutations disrupted iron homeostasis in GBM, characterized by elevated total iron levels and reduced ferritin (FTH). The gain-of-function effect triggered by TP53 mutations upregulates itchy E3 ubiquitin-protein ligase (ITCH) protein expression in astrocytes, leading to FTH degradation and an increase in free iron levels. TP53-mut astrocytes were more tolerant to the high iron environment induced by exogenous ferric ammonium citrate (FAC), but the increase in intracellular free iron made them more sensitive to Erastin-induced ferroptosis. Interestingly, we found that Erastin combined with FAC treatment significantly increased ferroptosis. These findings provide new insights for drug development and therapeutic modalities for GBM patients with TP53 mutations from iron metabolism perspectives.
Ferroptosis/drug effects* ; Humans ; Iron/metabolism* ; Glioblastoma/metabolism* ; Tumor Suppressor Protein p53/metabolism* ; Homeostasis/physiology* ; Ferritins/metabolism* ; Brain Neoplasms/genetics* ; Mutation ; Astrocytes/drug effects* ; Cell Line, Tumor ; Piperazines/pharmacology* ; Quaternary Ammonium Compounds/pharmacology* ; Ferric Compounds

Histone deacetylase 3 (HDAC3) is an epigenetic modification enzyme that plays a crucial role in the development and progression of diabetes and its complications. Studies have reported that increased HDAC3 activity is associated with pancreatic β-cell dysfunction in type 1 diabetes, while in type 2 diabetes, HDAC3 affects insulin resistance and signaling by regulating the metabolism of the liver, adipose tissue, and muscle. Additionally, HDAC3 plays a key role in diabetic complications such as cardiomyopathy, retinopathy, and nephropathy. Selective inhibition of HDAC3 has the potential to improve insulin sensitivity, reduce chronic inflammation, and enhance pancreatic cell function, offering a promising new therapeutic strategy for diabetes and its complications.