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 This study reports on the obligatory parasitism of water mites Arrenurus sp. on Anopheles messeae at the Manzhouli Port, Inner Mongolia, China. Methods Duing July 2024, a survey on the mosquito diversity was conducted at the Manzhouli Port. Captured mosquitoes and their ectoparasites were identified to species level. Results A total of 1840 adult mosquitoes were collected, representing species from three genera: Culex(Cx. modestus, Cx. pipiens pallens), Aedes(Ae. dorsalis, Ae. flavidorsalis, Ae. flavescens), and Anopheles (An. messeae). Among all the mosqutioes specimens,3 out of 150 captured An. messeae were found to carry ectoparasitic mites, with number of 2,4,27 mites separately. Morphological and molecular identification reached the same result as water mites(Hydrachnidiae, Hydracrina). COI gene sequence showed 94% similarity with the closest species Arrenurus truncatellus. Conlusions Literature review suggests water mites are host-specific parasitism of mosquito species and herein with the first record of Arrenurus sp. parasiting on An. Messeae in the most high-latitude region globally.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

Objective To develop a risk management system for complications associated with peripheral venous indwelling needles and to evaluate its application effect in order to reduce the risk of related complications.Methods We designed a comprehensive risk management system for peripheral intravenous indwelling needle complications,integrating specialized evidence-based guidelines with clinical practice insights,including 5 modules:intelligent assessment and decision support,intelligent inspection reminders,educational modules,collaborative case management procedures,and a knowledge base.Patients admitted to the neurology and internal classification metabolism departments of a tertiary hospital in Jilin Province were conveniently selected as the research subjects.Patients were assigned to a control group(from May to July 2022,before the implementation of the system)and an experimental group(from September to December 2022,after the implementation of the system).The incidence of complications,duration of indwelling needle,compliance rate of core indicators for nursing quality and number of nursing consultations were compared between the groups.Additionally,a self-administered questionnaire was used to assess clinical nurses'evaluations of the system.Results A total of 189 patients were included in the experimental group and 177 patients in the control group.The incidence of complications associated with peripheral intravenous indwelling needles was lower than that in the control group.The duration of indwelling peripheral intravenous needles was longer than that in the control group.The quality compliance rate of peripheral venous indwelling needle care in the experimental group was higher than that in the control group,and the number of consultation cases in the experimental group was higher than that in the control group.Comparisons between the 2 groups showed statistically significant differences for all indicators(P＜0.05).The nurses'recognition rates for the system's user-friendliness,professional guidance,and patient benefit were 77.78％,81.74％,and 82.13％,respectively.Conclusion The application of a peripheral vein indwelling needle complication risk management system can reduce the incidence of complications,prolong the duration of indwelling,and improve nursing quality.Nurses have a high recognition of this system.

Objective To observe the impact factors of vascular heat sink effect during in vitro microwave ablation(MWA)of porcine lung.Methods Simulation models were established using in vitro porcine lung tissue blocks based on isobaric inflation with an air pump and cyclic perfusion of duck blood with a glass tube and peristaltic pump,etc.MWA was performed under 8 different combining conditions(vessel diameter of 3 or 5 mm,blood perfusion of 30 or 50 cm/s,as well as distance between vessel and ablation antenna of 5 or 10 mm)each for 3 times.The highest temperature TV on vessel side and TC on control side during MWA,and ablation depth DV on vessel side and DC on control side after MWA were recorded.Multi-factor linear regression equations were constructed based on simulated vessel diameters,blood perfusion and distance between vessel and ablation antenna,and the impact factors of|TC-TV|and|DC-DV|were screened,respectively.Results Simulated vessel diameter showed linear positive correlation with both|TC-TV|and|DC-DV|(both P＜0.001).Simulated distance between vessel and ablation antenna showed linear negative correlation with both|TC-TV|and|DC-DV|(both P＜0.001),and the latter had more obvious impact on vascular heat sink effect than the former.Meanwhile,no significant linear relationship was found between simulated blood perfusion and|TC-TV|nor|DC-DV|(both P＞0.05).Conclusion Simulated vessel diameter and distance between vessel and ablation antenna were both impact factors of vascular heat sink effect during in vitro MWA of porcine lung,and the latter was more influential,whereas simulated blood perfusion showed no significant impact on it.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

Objective:To systematically evaluate the association between maternal arsenic exposure and spontaneous abortion, stillbirth and preterm birth.Methods:Web of Science, PubMed, CNKI database, the VIP Chinese Journal Service Platform and Wanfang Data Knowledge Service Platform were searched for studies on the association between maternal arsenic exposure and spontaneous abortion, stillbirth, and preterm birth before December 2023. Meta-analysis of binary variables was conducted using Stata MP 17.0 software, with OR value(95% CI) as the effect analysis indicator, and subgroup analysis was used to explore the sources of heterogeneity. Results:A total of 21 articles were included ( n = 5 150 286). The meta-analysis results showed that maternal arsenic exposure was positively correlated with spontaneous abortion, stillbirth, and preterm birth, with pooled OR values (95% CI) of 1.48 (1.28 - 1.70), 1.98 (1.60 - 2.45), and 1.05 (1.02 - 1.07), respectively. Subgroup analyses showed that arsenic exposure was positively correlated with the risk of preterm birth in both prospective cohort studies ( OR = 1.18, 95% CI: 1.09 - 1.27) and cross-sectional studies ( OR = 1.10, 95% CI: 1.06 - 1.15). Internal exposure to arsenic ( OR = 1.15, 95% CI: 1.04 - 1.28) presented a more pronounced risk of preterm birth compared to external exposure ( OR = 1.05, 95% CI: 1.01 - 1.10). Moreover, the impact was more substantial in developing countries ( OR = 1.17, 95% CI: 1.06 - 1.29), while it was not statistically significant in developed countries [1.04 (1.00 - 1.09), P > 0.05]. Conclusion:There is a positive association between maternal arsenic exposure and spontaneous abortion, stillbirth and preterm birth.

Aim To investigate the intervention effect of Rehmannia radix water extract on bleomycin(BLM)-induced pulmonary fibrosis in mice combined with metabolomics and to reveal the potential mechanism,in order to provide new ideas for clinical treatment of pul-monary fibrosis.Methods Male C57BL/6N mice were randomly divided into the control group,model group,pirfenidone group(positive control,PFD,270 mg·kg-1),and low dose(DH-L,4.55 g·kg-1)group,medium dose(DH-M,9.1 g·kg-1)group and high dose(DH-H,18.2 g·kg-1)group of Rehman-nia.Except for the control group,BLM(5 mg·kg-1)was instilled into the trachea to establish the model of pulmonary fibrosis in the other groups.The survival rate,lung index and blood oxygen saturation of mice in each group were evaluated.HE and Masson staining were used to observe the pathological changes of lung tissue.WBP was used to detect lung function.Flow cytometry was used to detect the apoptosis of primary lung cells,ROS and immune cells.ELISA was used to detect the levels of fibrosis markers and inflammatory factors(α-SMA,collagen Ⅰ,collagen Ⅲ,TGF-β1,TNF-α,IL-1 β,and IL-6).Biochemical method was employed to detect the contents of GSH-Px,T-SOD and MDA.Liquid chromatograph mass spectrometer(LC-MS)metabolomics was used to analyze the changes of serum metabolic profile.Results Water extract of Re-hmannia significantly increased the survival rate,oxy-gen saturation and lung function of mice with pulmona-ry fibrosis,reduced the lung coefficient,ameliorated pathological damage and collagen deposition in lung tissue,reduced the levels of apoptosis and oxidative stress,and down-regulated the levels of inflammatory factors in lung tissue.It regulated the levels of metabo-lites such as bile acid metabolism,sphingolipid metabo-lism,and unsaturated fatty acid metabolism.Conclu-sions Water extract of Rehmannia inhibits lung injury and collagen deposition in mice with pulmonary fibrosis by inhibiting inflammatory response,which may be a-chieved by regulating the levels of inflammatory factors through the metabolic pathways of bile acid and sphin-golipid.

Advances in genomics,biochemistry,and genetics have deepened our understanding of epigenetic mechanisms.These mechanisms play a crucial role in life,heredity,and evo-lution.Their growing significance is driving biomedical research toward personalized and precise medicine.Renal diseases,par-ticularly chronic kidney disease and acute kidney injury,require new treatment strategies.Their subtle clinical symptoms and challenges in early diagnosis limit current therapeutic options.Research on epigenetic modifications in renal diseases is expan-ding rapidly.This field is emerging as a promising approach for kidney disease treatment.The transition from basic mechanistic studies to clinical applications is underway.Epigenetic modifica-tions hold great potential for improving early diagnosis,enabling personalized treatment,and advancing precision medicine in re-nal diseases.

Advances in genomics,biochemistry,and genetics have deepened our understanding of epigenetic mechanisms.These mechanisms play a crucial role in life,heredity,and evo-lution.Their growing significance is driving biomedical research toward personalized and precise medicine.Renal diseases,par-ticularly chronic kidney disease and acute kidney injury,require new treatment strategies.Their subtle clinical symptoms and challenges in early diagnosis limit current therapeutic options.Research on epigenetic modifications in renal diseases is expan-ding rapidly.This field is emerging as a promising approach for kidney disease treatment.The transition from basic mechanistic studies to clinical applications is underway.Epigenetic modifica-tions hold great potential for improving early diagnosis,enabling personalized treatment,and advancing precision medicine in re-nal diseases.