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 understand the current status of neglect among rural preschool children in Xi an under the multi child policy and the association with family characteristics, so as to provide a reference for preventing and reducing the occurrence of child neglect. Methods: A total of 7 052 parents of preschool children were selected using stratified cluster sampling across 9 suburban counties/districts in Xi an from March to April 2025. A questionnaire survey was administered using the Chinese Norm Scale for Neglect Assessment of Rural(Preschool) Children Aged 3-6. The t-test, Chi-quare test, and analysis of variance (ANOVA) were used for inter group comparisons. Results: The overall prevalence rate and mean score of neglect among rural preschool aged children in Xi an were 32.4% and 38.27±6.70, respectively. Statistically significant differences were detected in neglect rates and neglect degrees among preschool children of different genders and grade levels ( χ 2=30.41, 15.15, t/F =4.92,7.03, all P <0.05). Statistically significant differences were also detected in neglect rates and neglect degrees among preschool children from whether only one child, different family structures, numbers of children in a family and families with different annual incomes ( χ 2=29.22, 10.41 , 31.99, 186.47, t/F =-9.96, 5.50, 33.57, 68.63, all P <0.05). In multi child families, there was a statistically significant difference in neglect degree among children with different birth orders ( F =4.25, P <0.05), but there was no statistically significant difference in neglect rate ( χ 2=5.73, P >0.05). Among all subgroups, the highest neglect rates and neglect degrees were observed in children from multi child families(35.0%,39.00±6.71), other family types(50.0%，42.38±12.34) and families with three children(39.9%，39.50±7.43). Lower annual family income was associated with higher neglect rates and neglect degrees among preschool children( χ 2 trend =186.47， F =270.68，both P <0.05). Conclusions Under the multiple child policy, the neglect of preschool children in rural areas of Xi an is quite severe, particularly in families with multiple children and low income households. Targeted interventions should be implemented for high risk groups.

Background: and Purpose Bilirubin has potent antioxidant, anti-inflammatory, and neuroprotective effects. Herein, we investigated whether total bilirubin (TBIL) modifies the association between hypertension and cerebral small vessel disease (CSVD). Methods: Data were obtained from the PolyvasculaR Evaluation for Cognitive Impairment and vaScular Events study. TBIL and direct bilirubin (DBIL) levels were assayed using fasting venous blood samples. Indirect bilirubin (IBIL) was calculated by subtracting DBIL from TBIL. TBIL was stratified as ≤17 μmol/L and >17 μmol/L based on the biological relevance of Gilbert’s syndrome. Hypertension was defined as blood pressure ≥140/90 mm Hg, self-reported hypertension history, or current use of antihypertensive agents. White matter hyperintensity, lacunes, cerebral microbleeds, and enlarged perivascular spaces were evaluated using magnetic resonance imaging and used to rate CSVD burden according to the criteria proposed by Wardlaw et al. and Rothwell et al. Results: This study included 3,061 participants, with a mean age of 61.2±6.7 years and 46.5% males. After adjusting for confounders, hypertension was associated with increased odds of presence of CSVD (Wardlaw: odds ratio [OR]=1.86, 95% confidence interval [CI] 1.41–2.44, P<0.001; Rothwell: OR=1.84, 95% CI 1.43–2.38, P<0.001) and higher modified total CSVD burden (common OR: 1.85, 95% CI 1.45–2.36, P<0.001) in participants with TBIL ≤17 μmol/L but not in TBIL >17 μmol/L (P for interaction <0.05). Johnson–Neyman analyses showed cut-off concentrations of 22.3–22.4 μmol/L for effect modification by TBIL. IBIL contributed to effect modification, whereas DBIL did not. Conclusions Mildly elevated TBIL may modify the association between hypertension and CSVD.

Adipose tissue macrophages (ATMs) are crucial immunomodulatory factors in the adipose tissue (AT) microenvironment, playing an irreplaceable role in maintaining the balance of the local immune system and regulating metabolic homeostasis. Under obese conditions, the excessive accumulation of lipids leads to abnormal expansion of adipose tissue, which further disrupts the homeostasis of the local microenvironment, including the imbalance of inflammatory factors, the occurrence of oxidative stress, and the damage of microcirculation. As an essential immune cell population in adipose tissue, ATMs are deeply involved in the occurrence and progression of metabolic disorders and multiple obesity-related diseases, and their functional abnormalities are closely related to the initiation and development of adipose tissue inflammation and systemic metabolic disorders. The interaction between ATMs and adipokines, including adiponectin, leptin, resistin and retinol-binding protein 4 (RBP4), acts as the core immunological and molecular mechanism linking adipose tissue inflammation to continuous disease deterioration, and this interaction is also a key research focus in the field of obesity-related diseases in recent years. This review discusses in detail the facilitating effect of the AT inflammatory microenvironment on the differentiation of peripheral monocytes into ATMs, elaborating on the specific molecular mechanisms by which various inflammatory mediators and abnormal metabolic products in the inflammatory microenvironment induce the differentiation of peripheral monocytes into functional ATMs. It also focuses on the regulatory mechanisms of chemokine-mediated ATM polarization and recruitment, including the specific roles of key chemokines like monocyte chemoattractant protein‑1 (MCP-1) and C‑X‑C motif chemokine ligand 10 (CXCL10) in mediating the recruitment of ATMs to adipose tissue, as well as the molecular pathways that regulate the switch between M1 pro-inflammatory phenotype and M2 anti-inflammatory phenotype of ATMs. In addition, this review explores the specific process by which ATMs trigger AT inflammation by secreting various pro-inflammatory factors such as tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6), and IL-1β, which further amplify the local inflammatory response and disrupt the metabolic homeostasis of adipose tissue. Furthermore, it analyzes the specific binding patterns and molecular characteristics of adiponectin, leptin, resistin and RBP4 with ATM surface receptors, and clarifies how the activation of downstream immune signaling pathways, such as Janus kinase‑signal transducer and activator of transcription (JAK-STAT), nuclear factor‑κB (NF‑κB), and phosphatidylinositol 3‑kinase‑protein kinase B (PI3K-AKT), triggered by these binding processes, induces the occurrence and development of various obesity-related diseases, including insulin resistance, cancer, metabolic-associated steatohepatitis (MASH) and cardiovascular diseases. This paper highlights the unique and crucial role of the interaction between ATMs and adipokines in the occurrence and progression of obesity-related diseases, emphasizing that this interaction is a key link connecting local adipose tissue inflammation to systemic metabolic disorders. It further summarizes the potential therapeutic strategies targeting the ATM-adipokine axis, and comprehensively illustrates three major clinical application barriers, namely biological complexity, technical bottlenecks, and clinical translation obstacles, in the process of applying these therapeutic strategies. For each barrier, this review puts forward corresponding feasible solutions and clear research directions, which not only enrich the theoretical system of the ATM-adipokine axis in the field of obesity research, but also provide a solid theoretical basis and practical ideas for the clinical intervention, prevention and treatment of obesity and its related diseases.

ObjectiveMagnetoencephalography (MEG), a non-invasive neuroimaging technique, meticulously captures the magnetic fields emanating from brain electrical activity. Compared with MEG based on superconducting quantum interference devices (SQUID), MEG based on optically pump magnetometer (OPM) has the advantages of higher sensitivity, better spatial resolution and lower cost. However, most of the current studies are clinical studies, and there is a lack of animal studies on MEG based on OPM technology. Pain, a multifaceted sensory and emotional phenomenon, induces intricate alterations in brain activity, exhibiting notable sex differences. Despite clinical revelations of pain-related neuronal activity through MEG, specific properties remain elusive, and comprehensive laboratory studies on pain-associated brain activity alterations are lacking. The aim of this study was to investigate the effects of inflammatory pain (induced by Complete Freund’s Adjuvant (CFA)) on brain activity in a rat model using the MEG technique, to analysis changes in brain activity during pain perception, and to explore sex differences in pain-related MEG signaling. MethodsThis study utilized adult male and female Sprague-Dawley rats. Inflammatory pain was induced via intraplantar injection of CFA (100 μl, 50% in saline) in the left hind paw, with control groups receiving saline. Pain behavior was assessed using von Frey filaments at baseline and 1 h post-injection. For MEG recording, anesthetized rats had an OPM positioned on their head within a magnetic shield, undergoing two 15-minute sessions: a 5-minute baseline followed by a 10-minute mechanical stimulation phase. Data analysis included artifact removal and time-frequency analysis of spontaneous brain activity using accumulated spectrograms, generating spectrograms focused on the 4-30 Hz frequency range. ResultsMEG recordings in anesthetized rats during resting states and hind paw mechanical stimulation were compared, before and after saline/CFA injections. Mechanical stimulation elevated alpha activity in both male and female rats pre- and post-saline/CFA injections. Saline/CFA injections augmented average power in both sexes compared to pre-injection states. Remarkably, female rats exhibited higher average spectral power 1 h after CFA injection than after saline injection during resting states. Furthermore, despite comparable pain thresholds measured by classical pain behavioral tests post-CFA treatment, female rats displayed higher average power than males in the resting state after CFA injection. ConclusionThese results imply an enhanced perception of inflammatory pain in female rats compared to their male counterparts. Our study exhibits sex differences in alpha activities following CFA injection, highlighting heightened brain alpha activity in female rats during acute inflammatory pain in the resting state. Our study provides a method for OPM-based MEG recordings to be used to study brain activity in anaesthetized animals. In addition, the findings of this study contribute to a deeper understanding of pain-related neural activity and pain sex differences.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

In-depth study of the components of polymyxins is the key to controlling the quality of this class of antibiotics. Similarities and variations of components present significant analytical challenges. A two-dimensional (2D) liquid chromatography-mass spectrometr (LC-MS) method was established for screening and comprehensive profiling of compositions of the antibiotic colistimethate sodium (CMS). A high concentration of phosphate buffer mobile phase was used in the first-dimensional LC system to get the components well separated. For efficient and high-accuracy screening of CMS, a targeted method based on a self-constructed high resolution (HR) mass spectrum database of CMS components was established. The database was built based on the commercial MassHunter Personal Compound Database and Library (PCDL) software and its accuracy of the compound matching result was verified with six known components before being applied to genuine sample screening. On this basis, the unknown peaks in the CMS chromatograms were deduced and assigned. The molecular formula, group composition, and origins of a total of 99 compounds, of which the combined area percentage accounted for more than 95% of CMS components, were deduced by this 2D-LC-MS method combined with the MassHunter PCDL. This profiling method was highly efficient and could distinguish hundreds of components within 3 h, providing reliable results for quality control of this kind of complex drugs.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.