ObjectiveTo investigate the present situation of input, output, outcome and impact of all registered community-based rehabilitation stations in Inner Mongolia in China, and analyze how the input predict the output, outcome and impact. MethodsFrom March 1st to April 30th, 2025, a questionnaire survey was conducted on all registered community-based rehabilitation stations in Inner Mongolia, covering four dimensions: input, output, outcome and impact. A total of 1 365 questionnaires were distributed. The input included four items: laws and policies, human resources, equipment and facilities, and rehabilitation information management. The output included two items: technical paths and benefits/effectiveness. The outcome included three items: coverage rates, rehabilitation interventions and functional results. The impact included two items: health and sustainability. Each item contained several questions, all of which were described in a positive way. Each question was scored from one to five. A lower score indicated that the situation of the community-based rehabilitation station was more in line with the content described in the question. Regression analysis was performed using the total score of each item of input dimension as independent variables, and the total scores of the output, outcome and impact dimensions as dependent variables. ResultsA total of 1 262 valid questionnaires were collected. The mean values of input, output, outcome and impact of community-based rehabilitation stations were 1.827 to 1.904, with coefficient of variation of 45.892% to 49.239%. The regression analysis showed that, rehabilitation information management, human resources, and laws and policies significantly predicted the output dimension (R² = 0.910, P < 0.001). Meanwhile, all four items in the input dimension predicted both the outcome (R² = 0.850, P < 0.001) and impact dimensions (R² = 0.833, P < 0.001). ConclusionInput, output, outcome and impact of the community-based rehabilitation stations in Inner Mongolia were generally in line with the content of the questions, although some imbalances were observed. Additionally, the input of community-based rehabilitation stations could significantly predict their output, outcome and impact.

ObjectiveDiscriminating atypical hepatocellular carcinoma (HCC) from other malignancies in liver nodules classified as Liver Imaging Reporting and Data System category M (LR-M) remains a significant diagnostic challenge on conventional ultrasound examination. The LR-M category, originally intended to capture non-HCC malignancies, paradoxically contains up to 63% of atypical HCCs that deviate from classic enhancement patterns, leading to potential misdiagnosis and suboptimal treatment planning. While deep learning has shown promise in HCC diagnosis, most existing models rely exclusively on single-modality ultrasound, overlooking the diagnostic benefits of integrating complementary information from multiple imaging sources. To address this gap, we propose a novel attention-weighted tri-modal ultrasound network (TUS-Net) that integrates contrast-enhanced ultrasound (CEUS), B-mode ultrasound (BUS), and time-intensity curves (TICs) to improve diagnostic accuracy for these clinically challenging lesions. MethodsOur framework incorporates a three-dimensional convolutional neural network (C3D) backbone to extract spatiotemporal features from CEUS videos, capturing dynamic vascular patterns critical for lesion characterization. To effectively fuse complementary modalities, we introduce a dual-channel feature fusion module (DCFFM) that adaptively combines features from CEUS and BUS through channel-wise attention mechanisms, allowing the model to dynamically weigh the contribution of each modality based on diagnostic relevance. Additionally, we propose a temporal intensity feature fusion module (TIFFM) that leverages quantitative hemodynamic information from TICs to guide the model’s attention toward diagnostically critical temporal phases, such as arterial wash-in and portal venous washout. The model is further enhanced by automated lesion localization using YOLOX and class activation mapping for interpretability, ensuring that predictions align with clinically meaningful imaging features. ResultsEvaluated on a tri-modal ultrasound dataset comprising 161 patients with pathologically confirmed LR-M nodules (131 atypical HCC and 30 non-HCC malignancies), our model achieved an accuracy of 86.83%, a sensitivity of 92.50%, a specificity of 75.50%, and an AUC of 89.32% in screening atypical HCC. Compared to single-modality baselines, TUS-Net demonstrated superior specificity, a clinically critical metric given the higher risk associated with misclassifying non-HCC malignancies. Ablation studies confirmed the contribution of each module, with the full model outperforming both standard C3D and 3D ResNet backbones integrated with attention mechanisms. A reader study involving junior and senior radiologists further validated the clinical utility of AI assistance, showing consistent improvements in specificity and inter-reader consistency, particularly for less experienced clinicians. ConclusionThese results surpass existing benchmark models and demonstrate the potential of our approach to enhance diagnostic precision in clinically specific cases. By intelligently fusing multi-modal ultrasound data with attention-guided mechanisms, TUS-Net offers a reliable and interpretable tool that holds promise for improving the non-invasive diagnosis of atypical HCC in challenging LR-M liver nodules.

ObjectiveDiscriminating atypical hepatocellular carcinoma (HCC) from other malignancies in liver nodules classified as Liver Imaging Reporting and Data System category M (LR-M) remains a significant diagnostic challenge on conventional ultrasound examination. The LR-M category, originally intended to capture non-HCC malignancies, paradoxically contains up to 63% of atypical HCCs that deviate from classic enhancement patterns, leading to potential misdiagnosis and suboptimal treatment planning. While deep learning has shown promise in HCC diagnosis, most existing models rely exclusively on single-modality ultrasound, overlooking the diagnostic benefits of integrating complementary information from multiple imaging sources. To address this gap, we propose a novel attention-weighted tri-modal ultrasound network (TUS-Net) that integrates contrast-enhanced ultrasound (CEUS), B-mode ultrasound (BUS), and time-intensity curves (TICs) to improve diagnostic accuracy for these clinically challenging lesions. MethodsOur framework incorporates a three-dimensional convolutional neural network (C3D) backbone to extract spatiotemporal features from CEUS videos, capturing dynamic vascular patterns critical for lesion characterization. To effectively fuse complementary modalities, we introduce a dual-channel feature fusion module (DCFFM) that adaptively combines features from CEUS and BUS through channel-wise attention mechanisms, allowing the model to dynamically weigh the contribution of each modality based on diagnostic relevance. Additionally, we propose a temporal intensity feature fusion module (TIFFM) that leverages quantitative hemodynamic information from TICs to guide the model’s attention toward diagnostically critical temporal phases, such as arterial wash-in and portal venous washout. The model is further enhanced by automated lesion localization using YOLOX and class activation mapping for interpretability, ensuring that predictions align with clinically meaningful imaging features. ResultsEvaluated on a tri-modal ultrasound dataset comprising 161 patients with pathologically confirmed LR-M nodules (131 atypical HCC and 30 non-HCC malignancies), our model achieved an accuracy of 86.83%, a sensitivity of 92.50%, a specificity of 75.50%, and an AUC of 89.32% in screening atypical HCC. Compared to single-modality baselines, TUS-Net demonstrated superior specificity, a clinically critical metric given the higher risk associated with misclassifying non-HCC malignancies. Ablation studies confirmed the contribution of each module, with the full model outperforming both standard C3D and 3D ResNet backbones integrated with attention mechanisms. A reader study involving junior and senior radiologists further validated the clinical utility of AI assistance, showing consistent improvements in specificity and inter-reader consistency, particularly for less experienced clinicians. ConclusionThese results surpass existing benchmark models and demonstrate the potential of our approach to enhance diagnostic precision in clinically specific cases. By intelligently fusing multi-modal ultrasound data with attention-guided mechanisms, TUS-Net offers a reliable and interpretable tool that holds promise for improving the non-invasive diagnosis of atypical HCC in challenging LR-M liver nodules.

Hypertension ranks among the most prevalent chronic non-communicable diseases worldwide, with its prevalence rising steadily and exerting substantial impacts on the demographic composition of voluntary blood donors and the safety of blood supply. The eligibility of hypertensive individuals for safe blood donation and the relevant screening and management strategies have long remained controversial in transfusion medicine. This article systematically reviews the epidemiological characteristics of hypertension, donation-related physiological alterations and underlying risk mechanisms. By synthesizing evidence from international multicenter studies and regulatory practices across major nations, it conducts a comprehensive analysis of blood donation safety and donor admission criteria for hypertensive patients. Available evidence demonstrates that for hypertensives with well-stabilized blood pressure and no severe cardiac, cerebral or renal complications or target organ damage, the overall incidence of adverse reactions following blood donation is comparable to that in healthy donors, and no definitive correlation has been identified between blood pressure levels and vasovagal responses. Current mainstream international management frameworks are gradually shifting from screening reliant on a single blood pressure cutoff value toward an integrated strategy combining stratified risk classification and individualized dynamic assessment, with core focus on long-term blood pressure control, consistency of medication regimens and complication status. Meanwhile, emerging technologies including artificial intelligence, ambulatory blood pressure monitoring and wearable devices have opened innovative avenues for blood donation risk evaluation and full-cycle donor management. In light of existing clinical evidence, future efforts should further refine the multi-dimensional risk stratification system, facilitate the implementation of intelligent screening and continuous monitoring technologies, and scale up health education and follow-up management for hypertensive populations. Such measures aim to appropriately expand the pool of eligible blood donors while safeguarding donation safety, ultimately achieving coordinated improvement in blood safety and blood supply capacity.

Listeria brainstem encephalitis with myelitis is extremely rare in clinical practice.Since the clinical manifestations are non-specific,MRI is helpful for diagnosis.Positive cerebrospinal fluid culture is considered the gold standard for diagnosis.This article reports a case of an immunocompetent individual with listeria brainstem encephalitis with myelitis,aiming to enhance the awareness of this condition.
Humans ; Brain Stem/pathology* ; Diagnostic Errors ; Encephalitis/complications* ; Encephalomyelitis, Acute Disseminated/diagnosis* ; Listeriosis/complications* ; Myelitis/complications*

Objective:To investigate the current situation of nursing information ability of head nurses in different grades of hospitals in Jiaozuo City, and analyze its influencing variables based on the random forest model, so as to provide evidence-based basis for the construction of nursing information ability improvement strategies and training programs for head nurses.Methods:From August to September 2024, the head nurses of 20 hospitals of different grades were selected by cluster sampling method in Jiaozuo City as the research subjects. General Data Questionnaire and Nursing Information Ability Scale of Head Nurses were used to conduct a cross-sectional survey. Random forest model was used to evaluate the importance of variables, lasso regression analysis was applied to complete the variable screening, and the influencing factors of nursing information ability of head nurses were explored through stepwise multiple linear regression analysis.Results:A total of 305 questionnaires were ultimately collected, of which 297 were valid, the effective rate was 97.38%. Out of 297 head nurses, 6 were male and 291 were female; 49 individuals aged 20-29 years old, 127 individuals aged 30-39 years old, 92 individuals aged 40-49 years old, and 29 individuals aged ≥ 50 years old. The total score of nursing information ability was (139.06 ± 24.62). Random forest model and lasso regression analysis showed that the top 6 variables in terms of importance were the number of years serving as head nurses, participation in information function development, number of academic conferences in the past three years, hospital grade, participating in information training, and chairing or participating in scientific research in the past three years. Stepwise multiple linear regression analysis showed that the number of years serving as head nurses, participating in information training, participating in information function development and number of academic conferences in the past three years had significant effects on nursing information ability of head nurses (t values were -4.66 to 3.81, all P<0.05). Conclusions:The nursing information ability of head nurses in Jiaozuo city is in the middle level, and the nursing information ability of head nurses in different grades of hospitals is different. It is suggested to construct a stepped information ability continuing education framework and a research-information ability coupling development mechanism, and incorporate nursing information ability into the necessary conditions for the appointment of nursing management posts, so as to realize the balanced development of nursing information management ability and the continuous iteration of nursing manager information ability.

Aim To investigate the effect of PU.1 in-hibitor DB2313 on lupus nephritis in MRL/lpr mice and its mechanism.Methods Thirty female MRL/lpr mice were randomly divided into the model group,DB2313 group and TACI-Ig group,with 10 mice in each group.Another 10 female BALB/c mice were se-lected as normal control groups.Mice in the DB2313 group received intraperitoneal DB2313 injections every two days,and those in the TACI-Ig group received subcutaneous injections of TACI-Ig every two days.Mice in the control group and model group were intra-gastrically given the same amount of 0.9％NaCl injec-tion every day.Before the drug intervention and for 1 to 5 weeks after the intervention,the urine of mice was collected regularly,the urine protein content was meas-ured,and the renal damage index was evaluated.The histopathological changes of kidney were observed by HE,Masson and PAS staining.The expression levels of immune complex of C3 in kidney tissue were detec-ted by immunohistochemistry.The concentrations of u-rea nitrogen(BUN),serum creatinine(Scr),inter-leukin-6(IL-6),and tumor necrosis factor alpha(TNF-α)in the serum samples were assayed utilizing the respective kits.The expression levels of PU.1 and FLT3 in kidney tissues were determined by immunoflu-orescence technology,and the protein expressions of PU.1,FLT3,PI3K,AKT and phosphorylated AKT(p-AKT)in kidney tissues were detected by Western blot.Results DB2313 treatment significantly allevia-ted the pathological damage of kidney in MRL/lpr mice,and reduced the deposition of C3,kidney injury index and 24-hour urine protein in renal tissue.The results of ELISA showed that DB2313 administration could significantly reduce the serum levels of BUN,Scr,IL-6 and TNF-α in MRL/lpr mice.The results of immunofluorescence and Western blot further showed that DB2313 treatment could significantly down-regu-late the protein expression of PU.1,PI3K and p-AKT,and up-regulate the protein expression of FLT3.Con-clusion DB2313 has an ameliorating effect on lupus nephritis in MRL/lpr mice,and its underlying mecha-nism may involve the inhibition of the transcription fac-tor PU.1-mediated signaling pathway.

Objective To investigate the expression of arginase Ⅱ(ArgⅡ)in kidney tissue of rats with diabetic nephropathy(DN)and its significance in the development of DN.Methods A total of 10 male SD rats were randomly divided into the control group and the model group,with 5 rats in each group.An rat model of DN was developed by feeding with high-sugar and high-fat diet combined with intra-peritoneal injection of low-dose streptozotocin(45 mg/kg),and they were sacrificed after 11 weeks of continued feeding.The body weight,and biochemical indexes of blood and urine of rats were determined.The right kidney was weighed and histopathological examination was performed.The pathological changes of kidney tissues and protein expression of ArgⅡ and CD68+were observed,and the immunofluores-cence double staining was used to observe the distribution and expression of ArgⅡand a marker of renal macrophage activation CD68+;the protein expression of ArgⅡ,NF-κB,TNF-α and IL-6 in kidney tissues was determined by Western blot.Results Compared with the control group,the ratio of kidney weight to body weight,24-hour urine volume,24-hour urine protein,fasting blood glucose,urea nitrogen and insulin level in the model group were significantly increased(P<0.05).The renal histopathology showed that the mesangial cells of the renal glomerular were necrotic with vascular dilatation,and the renal tubular epithelial cells were steatosis and congestion.Compared with the control group,the protein expression of ArgⅡ,CD68+,NF-κB,TNF-α and IL-6 in the kidney tissues of the model group were significantly increased(P<0.05).Immunofluorescence double staining demonstrated the co-expression of ArgⅡ and CD68+in renal tissue,and the fluorescence intensities of both ArgⅡ and CD68+in the model group were significantly stronger than those in the control group(P<0.01).Conclusion The expression of ArgⅡ is increased in DN,which may be participated in the occurrence of inflammatory lesions in DN.

Objective To understand the changing composition and antibiotic resistance of bacterial species in the clinical isolates from outpatient and emergency department(hereinafter referred to as outpatients)and inpatient children over time in various hospitals,and to provide laboratory evidence for rational antibiotic use.Methods The data on clinically isolated pathogenic bacteria and antimicrobial susceptibility of isolates from outpatients and inpatient children in the CHINET program from 2015 to 2021 were collected and analyzed.Results A total of 278 471 isolates were isolated from pediatric patients in the CHINET program from 2015 to 2021.About 17.1％of the strains were isolated from outpatients,primarily group A β-hemolytic Streptococcus,Escherichia coli,and Staphylococcus aureus.Most of the strains(82.9％)were isolated from inpatients,mainly SS.aureus,E.coli,and H.influenzae.The prevalence of methicillin-resistant S.aureus(MRSA)in outpatients(24.5％)was lower than that in inpatient children(31.5％).The MRSA isolates from outpatients showed lower resistance rates to the antibiotics tested than the strains isolated from inpatient children.The prevalence of vancomycin-resistant Enterococcus faecalis or E.faecium and penicillin-resistant S.pneumoniae was low in either outpatients or inpatient children.S.pneumoniae,β-hemolytic Streptococcus and S.viridans showed high resistance rates to erythromycin.The prevalence of erythromycin-resistant group A β-hemolytic Streptococcus was higher in outpatients than that in inpatient children.The prevalence of β-lactamase-producing H.influenzae showed an overall upward trend in children,but lower in outpatients(45.1％)than in inpatient children(59.4％).The prevalence of carbapenem-resistant Klebsiella pneumoniae(CRKpn),carbapenem-resistant Pseudomonas aeruginosa(CRPae)and carbapenem-resistant Acinetobacter baumannii(CRAba)was 14％,11.7％,47.8％in outpatients,but 24.2％,20.6％,and 52.8％in inpatient children,respectively.The prevalence of multidrug-resistant E.coli,K.pneumoniae,Proteus mirabilis,P.aeruginosa and A.baumannii strains was lower in outpatients than in inpatient children.The prevalence of fluoroquinolone-resistant E.coli,ESBLs-producing K.pneumoniae,ESBLs-producing P.mirabilis,carbapenem-resistant E.coli(CREco),CRKpn,and CRPae was lower in children in outpatients than in inpatient children,but the prevalence of CRAba in 2021 was higher than in inpatient children.Conclusions The distribution of clinical isolates from children is different between outpatients and inpatients.The prevalence of MRSA,ESBL,and CRO was higher in inpatient children than in outpatients.Antibiotics should be used rationally in clinical practice based on etiological diagnosis and antimicrobial susceptibility test results.Ongoing antimicrobial resistance surveillance and prevention and control of hospital infections are crucial to curbing bacterial resistance.

Objective To investigate the changing antibiotic resistance profiles of E.coli isolated from patients in the 52 hospitals participating in the CHINET program from 2015 to 2021.Methods Antimicrobial susceptibility was tested for clinical isolates of E.coli according to the unified protocol of CHINET program.WHONET 5.6 and SPSS 20.0 software were used for data analysis.Results Atotal of 289 760 nonduplicate clinical strains ofE.coli were isolated from 2015 to 2021,mainly from urine samples(44.7±3.2)％.The proportion of E.coli strains isolated from urine samples was higher in females than in males(59.0％vs 29.5％).The proportion of E.coli strains isolated from respiratory tract and cerebrospinal fluid samples was significantly higher in children than in adults(16.7％vs 7.8％,0.8％vs 0.1％,both P＜0.05).The isolates from internal medicine department accounted for the largest proportion(28.9±2.8)％with an increasing trend over years.Overall,the prevalence of ESBLs-producing E.coli and carbapenem resistant E.coli(CREco)was 55.9％and 1.8％,respectively during the 7-year period.The prevalence of ESBLs-producing E.coli was the highest in tertiary hospitals each year from 2015 to 2021 compared to secondary hospitals.The prevalence of CREco was higher in children's hospitals compared to secondary and tertiary hospitals each year from 2015 to 2021.The prevalence of ESBLs-producing E.coli in tertiary hospitals and children's hospitals and the prevalence of CREco in children's hospitals showed a decreasing trend over the 7-year period.The prevalence of CREco in secondary and tertiary hospitals increased slowly.Antibiotic resistance rates changed slowly from 2015 to 2021.Carbapenem drugs(imipenem,meropenem)were the most active drugs amongβ-lactams against E.coli(resistance rate≤2.1％).The resistance rates of E.coli to β-lactam/β-lactam inhibitor combinations(piperacillin-tazobactam,cefoperazone-sulbactam),aminoglycosides(amikacin),nitrofurantoin and fosfomycin(for urinary isolates only)were all less than 10％.The resistance rate of E.coli strains to antibiotics varied with the level of hospitals and the departments where the strains were isolated,especially for cefazolin and ciprofloxacin,to which the resistance rate of E.coli strains from children in non-ICU departments was significantly lower than that of the strains isolated from other departments(P＜0.05).The E.coli isolates from ICU showed higher resistance rate to most antimicrobial agents tested(excluding tigecycline)than the strains isolated from other departments.The E.coli strains isolated from tertiary hospitals showed higher resistance rates to the antimicrobial agents tested(excluding tigecycline,polymyxin B,cefepime and carbapenems)than the strains from secondary hospitals and children's hospitals.Conclusions E.coli is an important pathogen causing clinical infection.More than half of the clinical isolates produced ESBL.The prevalence of CREco is increasing in secondary and tertiary hospitals over the 7-year period even though the overall prevalence is still low.This is an issue of concern.