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.

Background: Malignant pleural effusion (MPE), a common complication of advanced cancers, is associated with poor prognosis and reduced quality of life. Although host–tumor interactions are known to drive MPE development, the associated immune dynamics during disease progression remain unclear. Using a Lewis lung carcinoma-induced MPE model in C57BL/6JNidfc mice, we systematically evaluated general parameters and immune cell changes at two-day intervals throughout disease progression. Results: The day of Lewis lung carcinoma cell injection into the pleural space was designated as day 0. By day 10 post-injection (p.i.), MPE-bearing mice exhibited ~ 10% body weight loss, marking the experimental endpoint. Pleural tumor mass and pleural effusion volume were minimal up to day 4 p.i. but increased sharply from day 6 onward.CD45⁺ immune cell counts rose over time, and days 6, 8, and 10 p.i. marked key stages of MPE progression. On day 6, B cells, T cells, and natural killer cells, but not macrophages and neutrophils, increased significantly compared to earlier timepoints. By day 8, all immune cell subsets except T cells exceeded day 6 levels, and at day 10, natural killer cell numbers declined while others continued to increase. Besides, the numbers of CD8⁺ T cells, Th1 cells, regulatory T cells, and M2 macrophages progressively increased from day 6 to 10. Based on these data, days 6 and 10 were defined as early and advanced MPE stages, respectively, with distinct immune phenotypes. In advanced MPE, CD8⁺ T cells displayed reduced IFN-γ, TNF-α, Granzyme B, Perforin, FasL, and Ki-67, but upregulated PD-1 and CTLA-4 relative to early stage. Similarly, Th1 cells showed decreased IFN-γ, TNF-α, and IL-2 production along with reduced Ki-67 expression. Advanced-stage M2 macrophages exhibited lower MHC-II levels and impaired phagocytosis, but higher PD-L1 and IL-10 production, while neutrophils showed reduced TNF-α release and phagocytic activity. Conclusions Our findings characterize the temporal immune dynamics associated with MPE progression in a mouse model, revealing a transition from an early immunostimulatory state to a late immunosuppressive state. This study enhances our understanding of MPE immunopathogenesis and provides a foundation for developing precise, stagespecific therapeutic strategies.

Although global brain science research has progressed rapidly in recent decades, several fundamental questions in neuroscience remain unresolved. In particular, the physical mechanism underlying neural signal transmission remains controversial, and the carriers responsible for neural information storage and retrieval have not yet been fully clarified. These unresolved issues motivate us to re-examine the processes of neural information generation, transmission, integration, storage, and retrieval from multiple perspectives. A key observation is that neural electromagnetic activities are closely associated with time. Their duration, temporal structure, and dynamic evolution play crucial roles in neural information processing. In this work, we analyze neural electromagnetic activities from the perspective of temporal scales (referred to here as the “time course”). By reviewing and integrating findings from previous studies, we examine the characteristic time requirements and dynamic features of neural processes occurring at different stages of information processing. These stages include neural signal generation, signal transmission along axons, synaptic integration, synaptic plasticity, and memory formation and retrieval. Based on this temporal analysis, we outline a framework describing neural electromagnetic activities across a wide range of time scales, spanning from microseconds to minutes, hours, or even longer periods associated with long-term memory, which suggests that neural information processing involves multiple physical processes operating at different time levels. Rapid electromagnetic events may occur on microsecond scales, whereas electrophysiological phenomena such as action potentials typically last on the order of milliseconds. Longer time scales are associated with synaptic plasticity and memory-related processes. From this perspective, we propose that the physical carrier of neural information may be transient electromagnetic pulses with durations on the microsecond scale. In this framework, action potentials can be interpreted as the macroscopic electrophysiological manifestation of underlying electromagnetic processes triggered by ionic currents across neuronal membranes. Rather than being the fundamental neural signal itself, the action potential may represent a measurable membrane-level response associated with the successful activation of these electromagnetic events. Moreover, we discuss a possible mechanism for long-term memory storage. Considering the apparent temporal contradiction between the millisecond-scale excitation of neurons and the long-term persistence of memories, we believe that long-term memory information may be stored within neural network topologies formed by electrical synapse coupling. Such structures, referred to as electrically coupled memory networks (ECMNs), may enable neurons within the same network to respond rapidly and synchronously to stimuli, thereby facilitating efficient memory retrieval. Overall, this study emphasizes the importance of considering the temporal organization of neural electromagnetic activities when interpreting neural signaling mechanisms. It may provide new insights into the physical nature of neural information carriers and the mechanisms of memory storage and retrieval. Furthermore, highlighting the potential role of electromagnetic interactions in neural activity may contribute to the development of new theoretical frameworks and experimental approaches in neuroscience. Such perspectives may also offer valuable references for future research on neural coding, brain function mechanisms, and neuromodulation technologies.

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 evaluate the efficacy and safety of recombinant staphylokinase in patients with acute ST-segment elevation myocardial infarction(STEMI)by a multi-center,randomized,position-controlled,parallel post-marketing clinical trial.Methods This study was a multi-center,randomized,positive drug parallel control,non-inferiority clinical trial.From July 2019 to June 2022,a total of 251 patients with STEMI were enrolled in 31 hospitals.Patients were randomly assigned to receive intravenous staphylokinase or alteplase in a ratio of 1∶1.Vascular recanalization was evaluated by clinical indicators 30 minutes,60 minutes and 120 minutes after the initiation of thrombolysis.Coronary angiography was performed 90 to 120 minutes after the initiation of thrombolysis.The proportion of infarct-related artery(IRA)with thrombolysis in myocardial infarction(TIMI)grade Ⅱ and Ⅲ,corrected TIMI frame count(CTFC)and TIMI myocardial perfusion grade(TMPG)were analyzed Major adverse cardiac events(MACE,including all-cause death,rehospitalization,reinfarction,urgent target vessel revascularization)and bleeding events were followed up at 30 days(±2 days)after thrombolysis.Results After excluding 7 subjects who did not use thrombolytic drugs,244 subjects were finally eligibled from 31 hospitals(117 in trial group and 127 in control group),and 232 subjects completed the follow-up(111 in trial group and 121 in control group).The vascular recanalization rate evaluated by clinical indicators at 120 minutes after thrombolysis was 85.6％ in trial group and 83.5％ in control group(P=0.657).The difference between the two groups was 2.11(95％CI-7.19-11.41).Given that the lower confidence limit of the 95％CI was greater than-12％,the non-inferiority of the vascular recanalization rate was established based on clinical judgment.Coronary angiography showed that the total patency rate of IRA(TIMIⅡ-Ⅲ)was 77.5％ in trial group and 77.7％ in control group(P=0.970).The difference between the two groups was-0.21(95％CI-10.95-10.54),with the lower bound of the 95％CI exceeding-12％.Therefore,the non-inferiority of the TIMI blood flow grade was confirmed,indicating that the total patency rate of IRA in the trial group was not inferior to that in the control group.The CTFC was(32.7±17.6)frames in trial group and(37.6±16.6)frames in control group,with no statistically significant difference between the two groups(P=0.054).The difference between the two groups was-4.9(95％CI-10.0-0.1).As the lower limit of the 95％CI exceeded-12％,the noninferiority of CTFC was successfully demonstrated.The proportions of TMPG 0-Ⅲ were 20.7％,6.3％,2.7％and 69.4％in trial group,and 22.3％,4.1％,6.6％ and 66.9％ in control group,respectively.There was no significant difference in TIMI myocardial perfusion grade between the two groups(P=0.086).The incidence of MACE was 7.7％ in trial group and 7.1％ in control group within 30 days after the initiation of thrombolysis,and there was no significant difference between the two groups(P=0.857).Further analysis showed that there was no significant difference in cardiovascular mortality(3.4％ vs.4.7％,P=0.751).All 244 subjects were included in the safety analysis set.There was no significant difference in the total incidence of bleeding events between the two groups(22.2％ vs.15.0％,P=0.144).There was no significant difference in the incidence of major bleeding(1.7％ vs.0.8％,P=0.609).Conclusions Recombinant staphylokinase is simple to use and has a rapid onset of action.The efficacy and safety of recombinant staphylokinase are not inferior to alteplase in the treatment of acute STEMI.

This study was aimed at determining the genetic characteristics of Yersinia pestis in Subei County through differ-ential region(DFR),clustered regularly interspaced short palindromic repeats(CRISPR),and variable number tandem repeat(VNTR)analyses,to guide the tracing of plague outbreaks.The DNA of 89 Yersinia pestis strains isolated from various ani-mal in foci of Subei County from 1973 to 2017 was extracted.Primers for genotyping by DFR,CRISPR,and MLVA were used in PCR,and agarose electrophoresis was used to determine whether the amplified products were present.Genotypes were deter-mined through comparison against the DFR database of Yersinia pestis in China.The PCR products were sequenced and com-pared against the online CRISPR database for Yersinia pestis to determine the genotype.The number of VNTR repeats in each strain was calculated through capillary electrophoresis,and the minimum spanning tree was constructed with BioNumerics 7.6 according to the numbers of VNTR repeats from 89 Yersinia pestis strains in Subei County and 11 strains from a Marmota hi-malayana focus in Qinghai Province.The Yersinia pestis strains in Subei County were divided into six main genotypes by DFR:8,7,lb,5,32,and 44.The Yersinia pestis strains were divided into three gene clusters and three genotypes by CRISPR.Ca35'was the main gene cluster in Subei County;the genotype was 26';and the distribution was primarily in Dangchengwan Town,Yuerhong Township,and Shibaocheng Township.Ca7 and CaΔ5'comprised secondary gene clusters,with genotypes 22 and 24,and were distributed in Dangchengwan Town,Yuerhong Township,and Shibaocheng Township.The Yersinia pes-tis strains in Subei County were divided primarily into three clusters:the Dangchengwan Machang cluster,Yuerhong Township cluster and Dangchengwan Town cluster.Therefore,the Yersinia pestis strains in Subei County,divided into major and minor genotypes according to DFR,CRISPR and MLVA,showed different regional distribution characteristics,highly diverse geno-types,and complex population characteristics.These aspects are particularly important in tracing the sources of plague out-breaks.

Objective:This study aims to investigate the expression of uridine diphosphate-glucose[]pyrophos-phorylase 2(UGP2)in breast cancer(BC)tissues and its oncogenic mechanism,assessing its potential value as a diag-nostic and prognostic biomarker for breast cancer.Methods:(1)Online database analysis was conducted to assess UGP2 mRNA and protein expression levels in breast cancer and explore their correlation with clinical characteristics.Im-munohistochemistry(IHC)was used to verify UGP2 expression in human breast cancer tumor tissues and evaluate its relationship with clinicopathological features.(2)Kaplan-Meier survival analysis and COX regression models were used to analyze the impact of UGP2 expression on breast cancer patient prognosis.(3)Bioinformatics methods were em-ployed to investigate the correlation between UGP2 and tumor immune cell infiltration,and to predict the biological func-tions and associated signaling pathways of UGP2 in breast cancer.Results:(1)The mRNA and protein expression levels of UGP2 were upregulated in breast cancer tissues(both P<0.05),and were negatively correlated with ER-positive and PR-positive status(OR<1,P<0.05),while positively correlated with Ki-67 levels and the triple-negative breast cancer(TNBC)subtype(OR>1,P<0.05).(2)Elevated expression levels of UGP2 were associated with poorer survival rates in breast cancer patients(both P<0.05)and were identified as an independent adverse prognostic factor for breast cancer(HR=1.40,P<0.05).(3)Functional analysis results suggested that UGP2 may promote tumor progression by regulating metabolism,hormone signaling,and the immune microenvironment.Additionally,UGP2 expression was negatively cor-related with NK cell activation status and positively correlated with the inhibitory state.Conclusion:UGP2 expression is elevated in breast cancer tissues and is closely associated with poor patient prognosis.It may promote cancer pro-gression through mechanisms such as metabolic reprogramming and immune suppression.UGP2 shows promise as a potential biomarker and therapeutic target in breast cancer,providing a basis for personalized treatment.

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.