Objective To investigate the epidemiological characteristics and changing trend of typhus in Shaanxi Province from 2005 to 2023 to provide a scientific basis for its prevention and control. Methods Excel 2007, SPSS 25.0, Joinpoint 4.9.1.0, and Geoda 1.6 were used for data collection and statistical analysis. Super Map was used for data visualization to describe the changing characteristics of the disease. Results A total of 394 typhus cases were reported in Shaanxi Province from 2005 to 2023. The average annual incidence of typhus was 0.054/100 000, showing a dynamic fluctuation trend(AAPC=-3.3, t=-0.3, P>0.05). The cases were mainly concentrated in Baoji, Hanzhong and Xi′an, accounting for 78.68%. The incidence peak was from May to October, accounting for 64.21% of annual incidence. The epidemic season was from May to October and December. The incidence of the disease was concentrated in the 40-69 age group, accounting for 58.88%, and the sex was 1.07:1. The main occupation was farmers, accounting for 72.08%. The median time from onset to diagnosis was 7 days. Global spatial autocorrelation analysis showed that there were significant spatial autocorrelations in 11 years from 2005 to 2023(P<0.05). Local spatial autocorrelation analysis detected a total of 43“high-high”clustering areas, mainly concentrated in Baoji City. Conclusions The overall incidence of typhus in Shaanxi Province showed a dynamic fluctuation trend, with notable seasonal and regional aggregation. The incidence of typhus was higher in middle-aged and elderly people in rural areas. Surveillance should be strengthened in typhus endemic areas in summer and autumn, and health education should be conducted for key population to form good health habits and reduce the incidence of typhus.

Chronic diabetic wounds, severely complicated by multidrug-resistant (MDR) bacterial infections, represent a profound and escalating global health crisis. The intrinsically hostile microenvironment of diabetic wounds, characterized by localized hypoxia, persistent oxidative stress, and poor vascularization, creates an ideal niche for opportunistic pathogens such as Staphylococcus aureus and Pseudomonas aeruginosa. These bacteria readily construct dense extracellular polymeric substance (EPS) biofilms, which not only physically shield the microbes from host immune responses but also actively trap the wound in a state of chronic, unresolved inflammation. Consequently, conventional systemic and topical antibiotic therapies are becoming increasingly futile, as poor perfusion at the wound site restricts drug bioavailability, while the rapid genetic evolution of bacteria and the impenetrable nature of biofilms lead to catastrophic treatment failures, often culminating in severe tissue necrosis and lower-extremity amputations. To circumvent the limitations of traditional antimicrobials, therapeutic gas delivery has emerged as a highly promising, paradigm-shifting strategy. Gaseous signaling molecules, particularly nitric oxide (NO), carbon monoxide (CO), hydrogen sulfide (H₂S), and hydrogen (H₂), possess unique physicochemical properties that allow them to seamlessly penetrate dense biofilm matrices and cellular membranes. Once inside, these gases operate via multi-targeted mechanisms that are incredibly difficult for bacteria to develop resistance against; for instance, NO induces severe lipid peroxidation and DNA cleavage in bacteria, CO downregulates pro-inflammatory cytokines, H₂S significantly accelerates endothelial cell migration for neovascularization, and H₂ acts as a powerful selective antioxidant to neutralize tissue-damaging reactive oxygen species (ROS). Together, these therapeutic gases not only exert broad-spectrum bactericidal effects but also actively reprogram the wound bed by promoting the critical M1-to-M2 macrophage polarization and stimulating angiogenesis. Despite their immense biological potential, the direct clinical translation of gas therapies is severely hindered by inherent physicochemical drawbacks, including extreme volatility, short physiological half-lives, poor aqueous solubility, and the high risk of off-target systemic toxicity, if applied indiscriminately. To conquer these immense pharmacokinetic barriers, cutting-edge advancements in materials science have driven the development of gas-releasing micro- and nanoplatforms. Utilizing sophisticated carriers such as metal-organic frameworks (MOFs), mesoporous silica, polymeric nanoparticles, liposomes, and injectable hydrogels, researchers can now encapsulate gas-donor molecules to achieve sustained, localized delivery. More importantly, these advanced nanoplatforms are ingeniously engineered to be stimuli-responsive. By exploiting the pathological hallmarks of the diabetic wound environment, such as elevated glucose concentrations, acidic pH, and overexpressed ROS, or by utilizing external triggers like near-infrared (NIR) light irradiation and ultrasound, these intelligent platforms ensure on-demand, precise spatio-temporal gas release. This often allows for powerful synergistic combinations, such as photothermal or photodynamic therapy coupled with gas release, thereby obliterating biofilms while sparing healthy tissue. While the therapeutic outcomes of these smart delivery systems in eradicating MDR infections and accelerating tissue repair are unprecedented, several critical challenges remain before widespread clinical adoption, as long-term biosafety profiles of the carrier nanomaterials, complexities in large-scale good manufacturing practice (GMP) production, and stringent regulatory hurdles must be rigorously addressed. Looking forward, the next frontier lies in the realm of precision medicine and theranostics, where future research must focus on the seamless integration of these gas-releasing platforms with flexible, wearable biosensors capable of continuously monitoring wound biomarkers (e.g., pH, temperature, uric acid) in real-time. Coupled with artificial intelligence algorithms to govern automated, closed-loop adaptive dosing, these next-generation smart dressings hold the ultimate potential to comprehensively transform the clinical management of complex, infected diabetic wounds.

Chronic diabetic wounds, severely complicated by multidrug-resistant (MDR) bacterial infections, represent a profound and escalating global health crisis. The intrinsically hostile microenvironment of diabetic wounds, characterized by localized hypoxia, persistent oxidative stress, and poor vascularization, creates an ideal niche for opportunistic pathogens such as Staphylococcus aureus and Pseudomonas aeruginosa. These bacteria readily construct dense extracellular polymeric substance (EPS) biofilms, which not only physically shield the microbes from host immune responses but also actively trap the wound in a state of chronic, unresolved inflammation. Consequently, conventional systemic and topical antibiotic therapies are becoming increasingly futile, as poor perfusion at the wound site restricts drug bioavailability, while the rapid genetic evolution of bacteria and the impenetrable nature of biofilms lead to catastrophic treatment failures, often culminating in severe tissue necrosis and lower-extremity amputations. To circumvent the limitations of traditional antimicrobials, therapeutic gas delivery has emerged as a highly promising, paradigm-shifting strategy. Gaseous signaling molecules, particularly nitric oxide (NO), carbon monoxide (CO), hydrogen sulfide (H₂S), and hydrogen (H₂), possess unique physicochemical properties that allow them to seamlessly penetrate dense biofilm matrices and cellular membranes. Once inside, these gases operate via multi-targeted mechanisms that are incredibly difficult for bacteria to develop resistance against; for instance, NO induces severe lipid peroxidation and DNA cleavage in bacteria, CO downregulates pro-inflammatory cytokines, H₂S significantly accelerates endothelial cell migration for neovascularization, and H₂ acts as a powerful selective antioxidant to neutralize tissue-damaging reactive oxygen species (ROS). Together, these therapeutic gases not only exert broad-spectrum bactericidal effects but also actively reprogram the wound bed by promoting the critical M1-to-M2 macrophage polarization and stimulating angiogenesis. Despite their immense biological potential, the direct clinical translation of gas therapies is severely hindered by inherent physicochemical drawbacks, including extreme volatility, short physiological half-lives, poor aqueous solubility, and the high risk of off-target systemic toxicity, if applied indiscriminately. To conquer these immense pharmacokinetic barriers, cutting-edge advancements in materials science have driven the development of gas-releasing micro- and nanoplatforms. Utilizing sophisticated carriers such as metal-organic frameworks (MOFs), mesoporous silica, polymeric nanoparticles, liposomes, and injectable hydrogels, researchers can now encapsulate gas-donor molecules to achieve sustained, localized delivery. More importantly, these advanced nanoplatforms are ingeniously engineered to be stimuli-responsive. By exploiting the pathological hallmarks of the diabetic wound environment, such as elevated glucose concentrations, acidic pH, and overexpressed ROS, or by utilizing external triggers like near-infrared (NIR) light irradiation and ultrasound, these intelligent platforms ensure on-demand, precise spatio-temporal gas release. This often allows for powerful synergistic combinations, such as photothermal or photodynamic therapy coupled with gas release, thereby obliterating biofilms while sparing healthy tissue. While the therapeutic outcomes of these smart delivery systems in eradicating MDR infections and accelerating tissue repair are unprecedented, several critical challenges remain before widespread clinical adoption, as long-term biosafety profiles of the carrier nanomaterials, complexities in large-scale good manufacturing practice (GMP) production, and stringent regulatory hurdles must be rigorously addressed. Looking forward, the next frontier lies in the realm of precision medicine and theranostics, where future research must focus on the seamless integration of these gas-releasing platforms with flexible, wearable biosensors capable of continuously monitoring wound biomarkers (e.g., pH, temperature, uric acid) in real-time. Coupled with artificial intelligence algorithms to govern automated, closed-loop adaptive dosing, these next-generation smart dressings hold the ultimate potential to comprehensively transform the clinical management of complex, infected diabetic wounds.

This study was aimed at understanding the Babesia species makeup and distribution in small mammals in Jinsha River Basin of Yunnan Province,and the Babesia carriage status in small mammals in this area,to provide a scientific basis for the preven-tion and control of Babesia disease.A total of 1 493 small mammals belonging to 5 orders,10 families,25 genera,and 54 species were captured from 10 counties(cities)in the Jinsha River Basin of Yunnan Province in various agricultural and forest environments.DNA was extracted from liver and tick tissues,and 150 bp fragments of Babesia 18S rRNA were detected through molecular biological methods.The positive samples showed amplification of a 1 600 bp target fragment of 18S rRNA.Species characteristics were assessed through sequence comparison and phylogenetic analysis.A total of 14 small mammals infected with Babesia were detected in six coun-ties(cities)of Jinsha River Basin,Yunnan Province,with a positivity rate of 0.93%(14/1 493).The Otsu and Kobe types of Babesia voles were analyzed,and their sequences were compared with the sequences from human Babesia cases with high similarity and close evolutionary relationships.The positivity rates were 2.34%(3/128)in Qiaojia County,2.06%(2/97)in Yongshan County,1.88%(4/213)in Yuanmou County,1.03%(3/291)in Deqin County,0.95%(1/105)in Shangri-La City,and 0.78%(1/128)in Shuifu County.The positive small mammals belonged to one order,two families,six genera,and the following eight species:P.leucurus 5.56%(1/18),R.brunneusculus 3.36%(4/119),M.minutus 3.33%(1/30),E.custos 2.94%(1/34),N.confucianus 2.65%(3/113),N.fulvescens 2.35%(2/85),A.latronum 1.16%(1/86),and A.draco 0.98%(1/102).The detection of Babesia in M.minutus was re-poorted first time.Small animals infected with Babesia were detected in all three habitats and altitudes,and higher infection rates were observed in forest regions between 1 500 and 2 500 meters and high-altitude residential areas.Babesia infection was found in many small mammals in several counties(cities)along Jinsha River in Yunnan Province,and the epidemic status of Babesia in these areas warrants attention.

Objective:A prognosis prediction model for pancreatic cancer was constructed based on multi-scale radiomics combined with deep learning, and the prediction effect of the model was evaluated.Methods:A retrospective analysis was conducted on the clinical data of 215 patients who underwent radical resection of pancreatic cancer at the First Affiliated Hospital of Naval Medical University from January 2017 to December 2017. Among them, 134 were male and 81 were female, with an age of (61.9±9.2) years. Patients were randomly divided into the training set ( n=151) and the test set ( n=64) in a ratio of 7: 3. Habitat features, peritumoral radiomics features, 3D radiomics features, and 2.5D deep learning features were extracted from preoperative CT images respectively. After feature screening, a survival prediction model was constructed using the CoxBoost machine learning algorithm that integrated the Boosting algorithm and the Cox proportional hazards model. The performance of the model was evaluated using the area under the time-dependent receiver operating characteristic curve and the consistency index. The clinical benefits of the model were evaluated using decision curve analysis. The survival curves were plotted using the Kaplan-Meier method, and the log-rank test was used for the comparison of survivals between groups. Results:The LASSO, random forest and extreme gradient boosting models were each used to screen out the top 10 most important features and take the union, ultimately obtaining 20 radiomics features for modeling. In the training set and test set, the consistency index of the CoxBoost model in predicting overall survival was 0.717 (95% CI: 0.669-0.765) and 0.688 (95% CI: 0.610-0.766), respectively, and the area under the curve for predicting overall survival at 1, 2, and 3 years after surgery was 0.830 (95% CI: 0.752-0.898), 0.753 (95% CI: 0.665-0.833), 0.828 (95% CI: 0.735-0.908) and 0.690 (95% CI: 0.549-0.824), 0.780 (95% CI: 0.649-0.887 and 0.793 (95% CI: 0.660-0.897), respectively. The area under the curve for predicting long-term survival after surgery (≥40 months) was above 0.8. Based on the optimal cutoff value of -0.19 for the predicted value of the CoxBoost model calculated by the R package " survminer", the patients were divided into high-risk (predicted value >-0.19) and low-risk (predicted value <-0.19) groups. In both the training set and the test set, the survival of patients in the low-risk group was better than that in the high-risk group (training set: χ2=39.01, P<0.001; test set: χ2=12.34, P<0.001). The median survival period of patients in the high-risk group was lower than that in the low-risk group (training set: 15.80 vs 34.07 months; test set: 16.87 vs 43.07; months). Decision curve analysis shows that patients obtain survival benefit when the threshold probability of the training set is greater than 0.25 and that of the test set is greater than 0.45. Conclusion:The CoxBoost model has a good predictive ability for the overall survival of pancreatic cancer patients after surgery and can effectively screen out patient subgroups that may significantly benefit from surgical treatment.

Objective To construct radiomics-semantic models to predict the treatment resistance of chemoradiotherapy in brain gliomas based on MRI and clinical data of multicenter patients.Methods Among 2 108 brain gliomas patients from five medical institutions,132 patients had residual gliomas after surgery.The clinical risk factors and multimodal MRI were collected.All patients were divided into training set(n=95)and validation set(n=37).The treatment response of gliomas after standardized chemoradiotherapy were divided into resistant and non-resistant types.The semantic features of MRI were evaluated by two radiologists.Three different segmentation regions of interest(ROI)were delineated to extract radiomics features.And that three groups of radiomics models were con-structed based on different sequence MRIs.The radiomics model with the best predictive efficacy in each group was selected and combined with MRI semantic features,three radiomics-semantic models(combined models)were established.Finally,a MRI semantic model,three groups of radiomics models and three combined models were developed.Results Comparisons between the different models showed that the radiomics-semantic model based on pre-operative T2-fluid attenuated inversion recovery(FLAIR)sequence,had the best predictive efficacy,the area under the curve(AUC)in the training and validation sets were 0.866[95％confidence interval(CI)0.790-0.942]and 0.810(95％CI 0.667-0.952),respectively.The radiomics-semantic model based on postoperative T1 WI sequence performed the second best,with the AUC of the training and validation sets being 0.812(95％CI 0.726-0.898)and 0.711(95％CI 0.541-0.881),respectively.Conclusion The combined models based on MRI radiomics and semantic features are able to predict the treatment resistance of chemoradiotherapy in brain gliomas patients,and may be used as an important basis for optimizing treatment.

Immobilized enzyme-based enzyme electrode biosensors, characterized by high sensitivity and efficiency, strong specificity, and compact size, demonstrate broad application prospects in life science research, disease diagnosis and monitoring, etc. Immobilization of enzyme is a critical step in determining the performance (stability, sensitivity, and reproducibility) of the biosensors. Random immobilization (physical adsorption, covalent cross-linking, etc.) can easily bring about problems, such as decreased enzyme activity and relatively unstable immobilization. Whereas, directional immobilization utilizing amino acid residue mutation, affinity peptide fusion, or nucleotide-specific binding to restrict the orientation of the enzymes provides new possibilities to solve the problems caused by random immobilization. In this paper, the principles, advantages and disadvantages and the application progress of enzyme electrode biosensors of different directional immobilization strategies for enzyme molecular sensing elements by specific amino acids (lysine, histidine, cysteine, unnatural amino acid) with functional groups introduced based on site-specific mutation, affinity peptides (gold binding peptides, carbon binding peptides, carbohydrate binding domains) fused through genetic engineering, and specific binding between nucleotides and target enzymes (proteins) were reviewed, and the application fields, advantages and limitations of various immobilized enzyme interface characterization techniques were discussed, hoping to provide theoretical and technical guidance for the creation of high-performance enzyme sensing elements and the manufacture of enzyme electrode sensors.

Objective To investigate the regulatory mechanism of the central nervous system in patients with refractory overactive bladder (rOAB) using diffusion tensor imaging (DTI) and graph theory analysis. Methods A total of 43 rOAB patients (rOAB group) and 46 matched healthy controls (HC group) were recruited during May and Nov.2024. All participants were scanned with DTI, and surveyed with the overactive bladder symptom score (OABSS), and overactive bladder questionnaire (OAB-q). Their age, gender, height, weight, and educational years were collected.DTI plus graph theory analysis was employed to explore the alterations in global and local topological properties of the brain structural network in rOAB patients. Brain regions showing significant group differences in structural metrics [specifically, the right paracentral lobule (PCL.R) ]were further used as seed points for functional connectivity (FC) analysis. Correlations between the nodal clustering coefficient (NCp) of the identified region, FC strength, OABSS, and OAB-q score were investigated. Results The OABSS [8 (6,10) vs.0 (0,1) ]and OAB-q [71 (53,80) vs.20 (19,24) ]were higher in the rOAB group than the HC group (P<0.001). Graph theory analysis revealed no statistically significant differences in global network metrics between the two groups (P>0.05). However, the NCp was significantly higher in the PCL.R of rOAB group compared to HC group (P<0.05, FDR-corrected).FC analysis using the PCL.R as a seed region demonstrated significantly reduced FC value in the left cerebellar crus Ⅱ (Cerebelum_Crus2_L) of the rOAB group (P<0.05, FDR-corrected). Partial correlation analysis showed that the NCp of PCL.R was positively correlated with both OABSS (r=0.255, P=0.018) and OAB-q score (r=0.257, P=0.017). Conversely, the FC of Cerebelum_Crus2_L was significantly negatively correlated with OABSS (r=-0.545, P<0.001) and OAB-q score (r=-0.535, P<0.001). Conclusion Patients with rOAB exhibit distinct brain structural network alterations compared to healthy individuals, primarily manifestation in the NCp value of PCL.R increased, and the FC intensity of Cerebelum_Crus2_L is significantly weakened. These alterations in the topological properties of the structural network may be implicated in the pathogenesis of rOAB.

Methods: The clinical data of 26 patients diagnosed with irreducible atlantoaxial dislocation complicated by atlantodental bony obstruction were analyzed retrospectively. All patients underwent anterior retropharyngeal atlantodentoplasty, followed by posterior occipitocervical fusion. Details including surgical duration and blood loss volume were recorded. Radiographic data such as the anterior atlantodental interval, O–C2 angle, space available for the cord, clivus–canal angle, and cervical medullary angle, and clinical data including the Japanese Orthopedic Association (JOA) score were assessed. The fusion time of the grafted bone and the development of complications were examined. Results: In patients undergoing anterior retropharyngeal atlantodentoplasty, the surgical duration and blood loss volume were 120.1±16.4 minutes and 100.6±33.5 mL, respectively. The anterior atlantodental interval decreased significantly after the surgery (p <0.001). The O–C2 angle, space available for the cord, clivus–canal angle, and cervical medullary angle increased significantly after the surgery (p <0.001). The JOA score during the latest follow-up significantly increased compared with that before the surgery (p <0.001). The improvement rate of the JOA score was 80.8%±18.1%. The fusion time of the grafted bone was 3–8 months, with an average of 5.7±1.5 months. In total, 11 patients presented with postoperative dysphagia and three with irritating cough. However, none of them exhibited other major complications. Conclusions Anterior retropharyngeal atlantodentoplasty can anatomically reduce the atlantoaxial joint with a satisfactory clinical outcome in patients with irreducible atlantoaxial dislocation with atlantodental bony obstruction.

Microglia, the resident immune cells in the central nervous system (CNS), rapidly transition from a resting to an active state in the acute phase of ischemic brain injury. This active state mediates a pro-inflammatory response that can exacerbate the injury. Targeting the pro-inflammatory response of microglia in the semi-dark band during this acute phase may effectively reduce brain injury. Shionone (SH), an active ingredient extracted from the dried roots and rhizomes of the genus Aster (Asteraceae), has been reported to regulate the inflammatory response of macrophages in sepsis-induced acute lung injury. However, its function in post-stroke neuroinflammation, particularly microglia-mediated neuroinflammation, remains uninvestigated. This study found that SH significantly inhibited lipopolysaccharide (LPS)-induced elevation of inflammatory cytokines, including interleukin-1β (IL-1β), tumor necrosis factor-α (TNF-α), and inducible nitric oxide synthase (iNOS), in microglia in vitro. Furthermore, the results demonstrated that SH alleviated infarct volume and improved behavioral performance in middle cerebral artery occlusion (MCAO) mice, which may be attributed to the inhibition of the microglial inflammatory response induced by SH treatment. Mechanistically, SH potently inhibited the phosphorylation of serine-threonine protein kinase B (AKT), mammalian target of rapamycin (mTOR), and signal transducer and activator of transcription 3 (STAT3). These findings suggest that SH may be a potential therapeutic agent for relieving ischemic stroke (IS) by alleviating microglia-associated neuroinflammation.
Animals ; Microglia/immunology* ; Mice ; Male ; Mice, Inbred C57BL ; Brain Ischemia/immunology* ; Neuroinflammatory Diseases/drug therapy* ; Neuroprotective Agents/administration & dosage* ; Interleukin-1beta/genetics* ; STAT3 Transcription Factor/genetics* ; TOR Serine-Threonine Kinases/genetics* ; Tumor Necrosis Factor-alpha/genetics* ; Proto-Oncogene Proteins c-akt/immunology* ; Nitric Oxide Synthase Type II/genetics* ; Lipopolysaccharides