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.

Introduction: The 2021 IDF-DAR risk calculator had been previously evaluated in multiple studies and subsequently widely accepted and applied in clinical practice as a practical standardized tool for patient risk stratification. Recently updated, the 2026 IDF-DAR Risk calculator enables a more individualized, evidence-related evaluation of patientrelated and disease-related risk factors, incorporating modern diabetes technologies, including continuous glucose monitoring (CGM), automated insulin delivery (AID) systems, and advanced insulin formulations to enhance risk stratification. This tool allows medical professionals to tailor Ramadan practices based on overall factors toward promoting safe fasting. Methodology: This prospective multicentre observational study recruited adults with Type 1 and Type 2 diabetes attending public hospitals nationwide. People with diabetes (PwD) intending to perform Ramadan fasting were invited to participate and assessed using the 2026 IDF-DAR Risk Calculator in the 6-week pre-Ramadan period between 30th January and 19th March 2026. Results: A total of 458 PwD were evaluated and stratified into low (15.7%), moderate (41%), and high risk (43.3%) categories. Most participants had Type 2 diabetes (83.6%), with 60.3% having a disease duration exceeding 10 years and 43% exhibiting poor glycemic control (hemoglobin A1c >9%). Insulin therapy was used by 76.4% of participants, including two individuals with Type 1 diabetes using AID systems. Most participants reported no recent hypoglycemia (76.4%), 81.0% performed glucose monitoring, and 3.3% used CGM. Severe comorbidities were uncommon, with 1.1% having unstable macrovascular disease and 4.4% advanced chronic kidney disease (estimated glomerular filtration rate <30). Notably, 72.2% received structured Ramadan education. Conclusion Majority of PwD attending tertiary diabetes clinics were in the moderate- to high-risk category and intended to fast despite medical advice against fasting in some cases. Although most participants were on insulin therapy, hypoglycemia was low in the pre-Ramadan period. Integration of modern technologies, advanced insulin therapies, and structured education may support safer fasting practices.
Risk Assessment ; Diabetes Mellitus ; Hospitals ; Fasting

Molecularly imprinted polymers(MIPs)of colchicine(COL)were prepared by precipitation polymerization with COL as template,methacrylic acid(MAA)as monomer,ethylene glycol dimethacrylate(EGDMA)as crosslinking agent,azobisisobutyronitrile(AIBN)as initiator and chloroform as porogen.The basic structure of the prepared MIPs was characterized by scanning electron microscopy(SEM),transmission electron microscopy(TEM)and Fourier transform infrared(FT-IR)spectrometry,and the adsorption properties of MIPs were evaluated by kinetic adsorption,isothermal adsorption and selective adsorption experiments.The results indicated that the prepared MIPs had uniform particles size,with a maximum adsorption capacity of 62.61 mg/g for COL and an imprinting factor of 3.74,and was suitable for sample pretreatment in determination of COL blood concentration,enabling the enrichment and separation of trace or even ultra-trace target components,thereby improving analytical capabilities.

Objective To investigate the correlation of monocyte count(MONO)to high density lipopro-tein-cholesterol(HDL-C)ratio(MHR)and systemic immune-inflammation index(SII)with diabetic retinop-athy(DR).Methods Patients with type 2 diabetes mellitus(T2DM)admitted to the hospital from June 2020 to May 2023 were selected as the research objects.According to the presence or absence of DR,the patients were divided into non-retinopathy group(NDR group)and DR Group.The differences in basic information,blood routine,and biochemical indexes between the two groups were analyzed,and the MHR and SII were cal-culated.Multivariate Logistic regression was used to analyze the risk factors for DR.Spearman correlation a-nalysis was used to analyze the correlation between risk factors and DR.The receiver operating characteristic(ROC)curve was used to evaluate the value of MHR and SII in predicting DR in T2DM patients.Results A total of 291 T2DM patients were enrolled,including 135 patients in the NDR group and 156 patients in the DR group.Compared with the NDR group,duration of diabetes was significantly prolonged(P<0.05),glycosy-lated hemoglobin(HbA1c),creatinine,fasting plasma glucose(FPG),total cholesterol(TC),platelet count(PLT),MHR and SII were increased(P<0.05),and high density lipoprotein-cholesterol(HDL-C)was de-creased(P<0.05)in the DR Group.Spearman correlation analysis showed that DR was positively correlated with duration of diabetes,FPG,HbA1c,PLT,MHR and SII(P<0.05),and negatively correlated with HDL-C(P<0.05).Multivariate Logistic regression analysis showed that gender(OR=0.151,95%CI 0.052-0.432,P<0.001),history of heavy drinking(OR=7.199,95%CI 2.845-18.216,P<0.001),duration of di-abetes(OR=1.570,95%CI 1.354-1.821,P<0.001),HbA1c(OR=1.218,95%CI 1.013-1.464,P=0.036),MHR(OR=1.054,95%CI 1.028-1.080,P<0.001)and SII(OR=1.002,95%CI 1.001-1.003,P=0.002)were independent influencing factors for DR patients.ROC curve analysis showed that the area un-der the curve(AUC)of MHR and SII in predicting the development of T2DM to DR was 0.696 and 0.567,re-spectively.The AUC of MHR and SII combined in predicting DR was 0.702.Conclusion MHR and SII are closely related to the incidence of DR,and both have certain predictive value for DR,and the predictive value of the combined of MHR and SII is higher.

Objective To investigate the predictive value of serum lactate dehydrogenase(LDH)level com-bined with high-throughput sequencing in children with severe Mycoplasma pneumoniae pneumonia(SMPP).Methods The clinical data of 99 children with Mycoplasma pneumoniae pneumonia(MPP)admitted to Hua-zhong University of Science and Technology Union Hospital from October 2023 to March 2024 were retro-spectively analyzed.According to the severity of the disease,the children were divided into mild group(33 ca-ses)and severe group(66 cases).Spearman correlation analysis was used for correlation analysis,Logistic re-gression was used to analyze the influencing factors of SMPP,and the receiver operating characteristic(ROC)curve was used to evaluate the value of LDH in predicting SMPP.Results The level of LDH in the severe group was higher than that in the mild group(P<0.05).The results of high-throughput sequencing showed that the proportions of virulence positive and drug resistance gene A2063G mutation in the severe group were higher than those in the mild group(P<0.05).Multivariate Logistic regression analysis showed that LDH level and drug resistance gene A2063G mutation were related to the SMPP(P<0.05).ROC curve analysis showed that LDH combined with high-throughput sequencing detection of drug resistance gene A2063G muta-tion had a high value in predicting SMPP,and the area under the curve was 0.724.Conclusion Serum LDH combined with high-throughput sequencing detection of drug resistance gene A2063G mutation can be used as an effective indicator to predict SMPP.

Objective To examine the changing prevalence and antimicrobial resistance profiles of Burkholderia cepacia in 52 hospitals across China from 2015 to 2021.Methods A total of 9 261 strains of B.cepacia were collected from 52 hospitals between January 1,2015 and December 31,2021.Antimicrobial susceptibility of the strains was tested using Kirby-Bauer method or automated antimicrobial susceptibility testing systems according to a unified protocol.The results were interpreted according to the breakpoints released in the Clinical & Laboratory Standards Institute(CLSI)guidelines(2023 edition).Results A total of 9 261 strains of B.cepacia were isolated from all age groups,especially elderly patients.The proportion was 11.1％(1 032 strains)in children,significantly lower than the proportion in adults.About half(46.5％,4 310/9 261)of the strains were isolated from patients at least 60 years old and 42.3％(3 919/9 261)of the strains were isolated from young adults.Most isolates(71.1％)were isolated from sputum and respiratory secretions,followed by urine(10.7％)and blood samples(8.1％).B.cepacia isolates were highly susceptible to the five antimicrobial agents recommended in the CLSI M100 document(33rd edition,2023).B.cepacia isolates showed relatively higher resistance rates to meropenem and levofloxacin.However,the resistance rates to ceftazidime,trimethoprim-sulfamethoxazole,and minocycline remained below 8.1％.The percentage of B.cepacia strains resistant to levofloxacin was the highest compared to other antibiotics in any of the three age groups(from 12.4％in the patients＜18 years old to 20.6％in the patients aged 60 years or older).Conclusions B.cepacia is one of the clinically important non-fermenting gram-negative bacteria.Accurate and timely reporting of antimicrobial susceptibility test results and ongoing antimicrobial resistance surveillance are helpful for rational prescription of antimicrobial agents and proper prevention and control of nosocomial infections.

Objective To review the application of sexual dysfunction intervention in cervical cancer patients,and refine the intervention elements,so as to provide ideas and methods for the development of personalized intervention measures.Methods Guided by the 2021 updated methodological guidelines for scoping reviews from the Joanna Briggs Institute in Australia,CINAHL,PubMed,Web of Science,Embase,Cochrane Library,CNKI,Wanfang and VIP databases were systematically searched from inception to February 29,2024,and the included literature were analyzed and summarized.Results A total of 17 studies were included,involving 9 randomized controlled trials,7 quasi-experimental studies and 1 pilot study.The theoretical basis of the intervention includes PLISSIT model,BETTER model,FOCUS program,PERMA flourish theory,empowerment theory,the trans-theoretical model and narrative therapy.The intervention content involves 4 themes:sexual problem disclosing,sexual science education,sexual physiological rehabilitation and sexual psychological rehabilitation.The main forms are offline and online independent or combined intervention.The outcomes include sexual health function,social function,psychological status and self-management.Conclusion Sexual dysfunction interventions have a positive impact on cervical cancer patients.Future research should develop sexual dysfunction interventions with more comprehensive content,richer forms and more accurate evaluation based on the in-depth understanding of the theoretical connotation,and play the role of nurse-led multidisciplinary teams to maintain the long-term sexual and reproductive health of cervical cancer patients.

Objective:To preliminarily evaluate the lumen gain of sonodynamic therapy (SDT) mediated by sinoporphyrin sodium at carotid and femoral atherosclerotic plaque sites, and to assess whether concomitant statin use, lesion location, plaque echogenicity/type, and baseline stenosis severity modify the therapeutic response.Methods:This single-center, prospective, exploratory pilot clinical study enrolled patients with peripheral artery disease who attended the outpatient cardiology clinic of the First Affiliated Hospital of Harbin Medical University between February and September 2016. All enrolled patients received optimized oral medical therapy in combination with a single session of SDT. Vascular evaluation was performed using color Doppler ultrasound before treatment and 1 and 4 weeks after treatment. The primary efficacy endpoint was the percent change from baseline in luminal diameter stenosis at the site of the atherosclerotic plaque (%Δ) at week 4, while the secondary efficacy endpoint was %Δ at week 1. Subgroup analyses were conducted according to prior statin use, plaque location, plaque characteristics, and baseline degree of luminal stenosis.Results:A total of 24 patients, aged (70.7±2.2) years were enrolled. There were 20 (83%) males. Compared to baseline, luminal diameter stenosis at the plaque site reduced by week 4 ((50.1±1.2)% vs. (57.2±1.1)%, P<0.001), %Δ was(12.32±1.05)%; and luminal diameter stenosis also reduced by week 1 ((51.7±1.2)% vs. (57.2±1.1)%, P<0.001)), %Δ was(9.61±0.85)%. In subgroup analyses, the treatment effect on diameter stenosis was independent of prior statin use; SDT reduced stenosis in both carotid and femoral plaques; with superior efficacy observed in hypoechoic and mixed-echo plaques; and efficacy was observed across mild, moderate, and severe baseline stenosis categories (all P<0.05). Conclusion:In this single-center pilot study, SDT demonstrates therapeutic efficacy across mild, moderate, and severe stenoses, as well as in hypoechoic and mixed-echo plaques, showing potential to rapidly promote luminal gain at carotid and femoral atherosclerotic plaque sites.

Objective:To screen novel diagnostic marker or therapeutic target for multiple myeloma(MM).Methods:Sel1L,SPAG4,KCNN3 and PARM1 were identified by bioinformatics method based on GEO database as high expression genes in MM.Their RNA and protein expression levels in bone marrow mononuclear cells from myeloma cell lines U266,NCI-H929,MM.1s,RPMI8226 and leukemia cell line THP1,as well as 31 MM patients were evaluated by RT-PCR and Western blot,respectively.Meanwhile,5 samples of bone marrow from healthy donors for allogeneic hematopoietic stem cell transplantation were employed as controls.Results:Compared with leukemia cell line THP1,the expression levels of KCNN3,PARM1 and Sel1L mRNA were significantly increased in myeloma cell lines U266,NCI-H929 and MM.1s,while PARM1 was further increased in myeloma cell lines 8226.Western blot showed that the 4 genes were all expressed in the 4 myeloma cell lines.Compared with healthy controls,the expression levels of Sel1L,SPAG4,KCNN3 and PARM1 mRNA were significantly higher in MM patients(all P＜0.05).Western blot showed that the 4 genes were all expressed in MM patients,and the protein expression level of Sel1L and KCNN3 were significantly different compared with healthy donors(all P＜0.01).Conclusion:Sel1L,SPAG4,KCNN3 and PARM1 may be potential diagnostic markers and therapeutic targets for MM.