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.

Objective To analyze the characteristics of viral hepatitis mortality and life loss among residents in Pudong New Area from 2003 to 2023, and to provide a basis for related prevention and control work. Methods Viral hepatitis mortality data were obtained from the Pudong New Area mortality monitoring system. The crude mortality rate (CMR), standardized mortality rate (SMR), potential years of life lost (PYLL), average years of life lost (AYLL), and standardized potential years of life lost (SPYLL) were calculated to analyze viral hepatitis deaths. The average annual change (AAPC) and annual percentage change (APC) of the mortality rate were calculated by Joinpoint regression analysis to analyze the trend of mortality. Results The CMR and SMR of viral hepatitis among residents in Pudong New Area from 2003 to 2023 were 3.89/100000 and 1.98/100000, respectively. Both CMR and SMR of viral hepatitis showed a decreasing trend over time (CMR:APC=-5.476, t=-13.581, P<0.001; SMR:APC=- 7.624, t= -21.253, P<0.001). The CMR for males was 4.75/100000 and the SMR for males was 2.65/100000; the CMR for females was 3.04/100000 and the SMR for females was 1.32/100000, with a higher mortality rate for males than for females（Z_CME=12.094，P<0.001; Z_SMR=-14.718，P<0.001). Deaths were concentrated in the age groups of 45-64 years old and 65 years old and above, accounting for 91.62% of the total deaths. The PYLL of deaths due to viral hepatitis among residents in Pudong New Area from 2003 to 2023 was 26912 person-years, with a PYLLR of 0.45% and an AYLL of 8.88 years per person. Conclusion The mortality rate of viral hepatitis among the residents of Pudong New Area in 2003-2023 shows a decreasing trend over time. The mortality rate of males is higher than that of females, and the deaths of middle-aged and elderly people account for a large proportion of the total deaths. Chronic hepatitis B is the main cause of death.

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.

Background The crude mortality rate of sudden death has shown a steady upward trend. Pudong New Area, Shanghai, is a highly urbanized and densely populated district that is substantially affected by the urban heat island effect and has experienced frequent extreme temperature events in recent years. However, evidence on the association between ambient temperatures and sudden death among local residents remains limited. Objective To examine the association between daily mean temperature and sudden death in Pudong New Area, and to provide evidence for reducing temperature-related risks of sudden death. Methods Data on sudden death, meteorological factors (daily average temperature, daily average relative humidity, and daily average air pressure), and air pollutants [inhalable particulate matter (PM₁₀), sulfur dioxide (SO₂), and nitrogen dioxide (NO₂)] in Pudong New Area from 2016 to 2021 were collected. A distributed lag non-linear model was used to examine the associations of daily mean temperature with total sudden death and sudden death due to cardiovascular and cerebrovascular diseases, including lagged and cumulative effects (lag0, 0-3, 0-7, 0-14, and 0-21 d). Stratified analyses were performed by sex, age, and education level to identify vulnerable populations. The temperature attributable burden of sudden death was assessed by calculating attributable number (AN) and attribute fraction (AF). Results A total of 19458 sudden deaths were reported in Pudong New Area from 2016 to 2021, of which 11082 (56.95%) were due to cardiovascular and cerebrovascular diseases. Both low (P₅, 4.6 ℃) and high (P₉₅, 30.2 ℃) temperatures were associated with increased risks of sudden death. The cumulative risks associated with low temperature peaked at lag 0-21 d for both total sudden death and sudden death due to cardiovascular and cerebrovascular diseases. In contrast, the cumulative risks associated with high temperature peaked at lag 0-7 d for total sudden death and at lag 0-3 d for sudden death due to cardiovascular and cerebrovascular diseases. Stratified analyses showed that females, individuals aged ≥65 years, and those with primary school education or below were more sensitive to low temperature; whereas females, individuals aged <65 years, and those with secondary school education or above were more sensitive to high temperature. Using 21.5 ℃ as the minimum mortality temperature, the AN of total sudden deaths attributable to non-optimal temperature was 3791 (95%CI: 1853, 5587), with an AF of 20.41% (95%CI: 9.47%, 28.94%); the AN attributable to low temperature was 3358 (95%CI: 1087, 5062), with an AF of 17.26% (95%CI: 5.89%, 26.01%), whereas the AN attributable to high temperature was 225 (95%CI: −274, 655), with an AF of 2.03% (95%CI: −2.53%, 5.78%). For sudden deaths due to cardiovascular and cerebrovascular diseases, the AN attributable to non-optimal temperature was 2721 (95%CI: 1747, 5575), with an AF of 24.55% (95%CI: 9.59%, 28.65%); the AN attributable to low temperature was 2496 (95%CI: 988, 3562), with an AF of 22.53% (95%CI: 8.97%, 32.41%), whereas the AN attributable to high temperature was 614 (95%CI: −15, 1176), with an AF of 3.15% (95%CI: −0.08%, 6.02%). Conclusions Both low (P₅, 4.6 ℃) and high (P₉₅, 30.2 ℃) temperatures are associated with increased risks of sudden death in Pudong New Area, Shanghai. High temperature shows a relative short-term effect, whereas low temperature shows a stronger cumulative effect. Females, people ≥65 years, and individuals with primary school educational attainment or below are more vulnerable to low temperature, while females, people <65 years, and those with secondary school educational attainment or above are more vulnerable to high temperature. The attributable burden of low temperature is greater than that of high temperature. Targeted health protection measures should be strengthened for vulnerable populations during extreme temperature events.

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.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

Objective To summarize the changing prevalence of carbapenem resistance in Enterobacterales based on the data of CHINET Antimicrobial Resistance Surveillance Program from 2015 to 2021 for improving antimicrobial treatment in clinical practice.Methods Antimicrobial susceptibility testing was performed using a commercial automated susceptibility testing system according to the unified CHINET protocol.The results were interpreted according to the breakpoints of the Clinical & Laboratory Standards Institute(CLSI)M100 31st ed in 2021.Results Over the seven-year period(2015-2021),the overall prevalence of carbapenem-resistant Enterobacterales(CRE)was 9.43％(62 342/661 235).The prevalence of CRE strains in Klebsiella pneumoniae,Citrobacter freundii,and Enterobacter cloacae was 22.38％,9.73％,and 8.47％,respectively.The prevalence of CRE strains in Escherichia coli was 1.99％.A few CRE strains were also identified in Salmonella and Shigella.The CRE strains were mainly isolated from respiratory specimens(44.23±2.80)％,followed by blood(20.88±3.40)％and urine(18.40±3.45)％.Intensive care units(ICUs)were the major source of the CRE strains(27.43±5.20)％.CRE strains were resistant to all the β-lactam antibiotics tested and most non-β-lactam antimicrobial agents.The CRE strains were relatively susceptible to tigecycline and polymyxins with low resistance rates.Conclusions The prevalence of CRE strains was increasing from 2015 to 2021.CRE strains were highly resistant to most of the antibacterial drugs used in clinical practice.Clinicians should prescribe antimicrobial agents rationally.Hospitals should strengthen antibiotic stewardship in key clinical settings such as ICUs,and take effective infection control measures to curb CRE outbreak and epidemic in hospitals.

Objective To characterize the changing species distribution and antibiotic resistance profiles of respiratory isolates in hospitals participating in the CHINET Antimicrobial Resistance Surveillance Program from 2015 to 2021.Methods Commercial automated antimicrobial susceptibility testing systems and disk diffusion method were used to test the susceptibility of respiratory bacterial isolates to antimicrobial agents following the standardized technical protocol established by the CHINET program.Results A total of 589 746 respiratory isolates were collected from 2015 to 2021.Overall,82.6％of the isolates were Gram-negative bacteria and 17.4％were Gram-positive bacteria.The bacterial isolates from outpatients and inpatients accounted for(6.0±0.9)％and(94.0±0.1)％,respectively.The top microorganisms were Klebsiella spp.,Acinetobacter spp.,Pseudomonas aeruginosa,Staphylococcus aureus,Haemophilus spp.,Stenotrophomonas maltophilia,Escherichia coli,and Streptococcus pneumoniae.Each microorganism was isolated from significantly more males than from females(P＜0.05).The overall prevalence of methicillin-resistant S.aureus(MRSA)was 39.9％.The prevalence of penicillin-resistant S.pneumoniae was 1.4％.The prevalence of extended-spectrum β-lactamase(ESBL)-producing E.coli and K.pneumoniae was 67.8％and 41.3％,respectively.The overall prevalence of carbapenem-resistant E.coli,K.pneumoniae,Enterobacter cloacae,Pseudomonas aeruginosa,and Acinetobacter baumannii was 3.7％,20.8％,9.4％,29.8％,and 73.3％,respectively.The prevalence of β-lactamase was 96.1％in Moraxella catarrhalis and 60.0％in Haemophilus influenzae.The H.influenzae isolates from children(＜18 years)showed significantly higher resistance rates to β-lactam antibiotics than the isolates from adults(P＜0.05).Conclusions Gram-negative bacteria are still predominant in respiratory isolates associated with serious antibiotic resistance.Antimicrobial resistance surveillance should be strengthened in clinical practice to support accurate etiological diagnosis and appropriate antimicrobial therapy based on antimicrobial susceptibility testing results.

AIM To compare the chemical constituents in traditional decoction and formula granule decoction of classical famous prescription Wendan Decoction.METHODS The HPLC fingerprints were established,after which the contents of adenosine,synephrine,liquiritin,naringin,hesperidin,6-gingerol and adenosine cyclophosphate were determined,cluster analysis,principal component analysis and multidimensional scaling analysis were adopted in the investigation of component differences,and the equivalent of formula granules was adjusted.RESULTS The similarities of HPLC fingerprints for 10 batches of traditional decoctions were higher than those of HPLC fingerprints for 9 batches of formula granule decoctions(P＜0.01).Adenosine,synephrine,liquiritin,hesperidin and cyclic adenosine monophosphate demonstrated higher contents in traditional decoctions than those in formula granule decoctions(P＜0.05),6-gingerol displayed lower content than that in the latter produced by manufacturers A,C(P＜0.05),which was higher than that in the latter produced by manufacturer B(P＜0.01).Various batches of traditional decoctions and formula granule decoctions could be obviously distinguished,adenosine,synephrine and hesperidin exhibited great influences on the classification of principal component analysis,and the quality of formula granule decoctions produced by manufacturer C was closer to that of traditional decoctions.After equivalent correction,the contents of various constituents in formula granule decoctions produced by manufacturers A,C showed no significant differences as compared with those in traditional decoction(P＞0.05).CONCLUSION The formula granules of Wendan Decoction from different manufacturers exist quality differences,so the preparation process and extraction process of this preparation should be optimized to improve quality,and equivalent ratio should be adjusted according to actual requirements to ensure its scientific and rational clinical application.

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.