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.

Objective To investigate the distribution characteristics of Oncomelania hupensis snails in Yunnan Province fol-lowing interruption of schistosomiasis transmission, so as to provide the evidence for assessing the risk of schistosomiasis transmission and scientifically formulating the schistosomiasis surveillance program. Methods According to the requirements of the National Schistosomiasis Surveillance Scheme (2020 Edition), O. hupensis snail surveillance data were collected from 18 schistosomiasis-endemic counties (cities, districts) in Yunnan Province from 2020 to 2024, including area of snail survey, area of snail habitats, area of re-emerging snail habitats, number of frames surveyed, number of frames with O. hupensis snails, number of O. hupensis snails captured, and number of living snails, and the occurrence of frames with snails and mean density of living snails were calculated. Changes in snail status over the 5-year period from 2020 to 2024 and the differences in snail distributions specified by epidemic intensity, environmental type, and vegetation type were analyzed. Results The areas of snail survey increased from 1 727.96 hm² in 2020 to 3 894.45 hm² in 2024 (peak) across 18 schistosomiasis-endemic counties (cities, districts) in Yunnan Province during the period from 2020 through 2024. The areas of snail habitats increased from 70.36 hm² in 2020 to a peak in 2023 (172.04 hm²), followed by a reduction to 132.36 hm² in 2024, and the areas of re-emerging snail habitats increased from 42.71 hm² in 2020 to a peak in 2022 (78.43 hm²), followed by a reduction to 40.21 hm² in 2024. The occurrence of frames with snails and mean density of living snails increased from 1.24% (3 025/244 404) and (0.033 2 ± 0.038 7) snails/0.1 m² in 2020 to peaks at 2.03% (6 231/307 563) and (0.066 9 ± 0.068 4) snails/0.1 m² in 2023, followed by reductions to 1.04% (5 829/559 941) and (0.032 6 ± 0.057 7) snails/0.1 m² in 2024, respectively. There was a significant difference in the occurrence of frames with snails over the 5-year study period (χ² = 1 962.95, P < 0.05), and the occurrence of frames with snails reduced by 48.71% in 2024 relative to in 2023 (χ² = 1 411.05, P < 0.005); however, there was no significant difference in the mean density of living snails over the 5 years (H = 5.310, P > 0.05). There were significant differences in the occurrence of frames with snails (χ² = 481.27, P < 0.05) and mean density of living snails (H = 6.872, P < 0.05) in schistosomiasis-endemic areas with different epidemic intensities. The occurrence of frames with snails (χ² = 25.32 and 38.70, both P values < 0.017) and mean density of living snails (Z = 28.55 and 49.96, both P values < 0.017) were higher in schistosomiasis transmission-interrupted and eliminated areas with snails than in schistosomiasis-eliminated areas without snails, and the occurrence of frames with snails (χ² = 453.54, P < 0.017) and mean density of living snails (Z = −56.97, P < 0.017) were higher in schistosomiasis-eliminated areas with snails than in schistosomiasis transmission-interrupted areas with snails. O. hupensis snails were mainly distributed in paddy fields, dry farmlands and ditches; however, the occurrence of frames with snails (13.40%, 424/3 164) and mean density of living snails [(0.252 8 ± 0.158 7) snails/0.1 m²] were higher in ponds/weirs than in other types of environments (both P values < 0.05). Rice, dry farmland crops and weeds were main vegetations in which O. hupensis snails were distributed, and the occurrence of frames with snails (2.29%, 7 111/310 140) and mean density of living snails [(0.072 3 ± 0.018 9) snails/0.1 m²] were higher in weeds than in other types of environments (both P values < 0.05). Conclusions O. hupensis snails have been effectively controlled in Yunnan Province following implementation of integrated schistosomiasis control measures; however, there are still risk factors for schistosomiasis transmission, including reduced attention to schistosomiasis control and snail re-emergence. Improved control efforts and surveillance system construction and timely identification of risk factors of snail status and timely management are recommended to ensure the achievement of the target of schistosomiasis elimination as scheduled.

Objective To investigate the prevalence of Schistosoma japonicum infections in wild murines in schistosomiasisendemic areas of Yunnan Province, so as to provide the evidence for improving the schistosomiasis surveillance and early warning capability and implementing precise interventions in the province. Methods A total of 56 natural villages were sampled from 14 schistosomiasis-endemic counties (cities, districts) in Yunnan Province as surveillance sites from 2024 to 2025. Wild murines were captured using traps at night and characterized for species. Liver samples were collected from wild murines, S. japonicum eggs and miracidia were simultaneously detected using liver tissue compression microscopy, liver tissue homogenate microscopy, and liver tissue homogenate hatching. In addition, stool samples were collected from wild murine rectum, with S. japonicum eggs detected using a modified Kato-Katz thick smear technique and S. japonicum miracidia detected using the hatching test with nylon gauzes. Results A total of 15 577 valid murine traps were recovered, and 891 wild murines belonging to 16 species were captured, with a capture rate of 5.72%(891/15 577). The captured wild murines were predominantly districted in paddy fields, dry lands, ditches, ponds and marshlands, with Apodemus chevrieri, Rattus norvegicus, Crocidura attenuata, R. rattus sladeni, R. flavipectus, and Eothenomys miletus as dominant species. No S. japonicum eggs or miradicia were in laboratory; however, other parasites were identified in 25 wild murines. The prevalence rates of Schistosoma sinensis, Capillaria hepatica and Hymenolepis nana were 2.24% (20/891), 0.90% (8/891) and 0.56% (5/891), and the prevalence of S. sinensis and C. hepatica co-infection was 0.90% (8/891), respectively. Conclusions S. sinensis, C. hepatica and H. nana are predominant parasite species in wild murines in schistosomiasis-endemic areas of Yunan Province. No S. japonicum infection was detected during the study period; however, there is still a risk of wild murines as a potential source of infection for schistosomiasis transmission. Continuous intensified surveillance of S. japonicum infections in wild murines is a critical programme in schistosomiasis surveillance and early warning in Yunnan Province.

Objective: To examine the association of physical activity with cognitive function and mental health among primary school students based on latent subgroup identification and dose response modeling, and to clarify cognitive-psychological profiles and the protective role of physical exercise, so as to provide evidence based support for physical exercise promoting mental health among primary school students. Methods: A study was conducted among 546 primary school students aged 8-12 years old from a primary school in Shanghai from March to June 2025. Physical fitness indicators, including vital capacity, 50 m sprint, sit and reach, and 1 min rope skipping were measured. Physical exercise was assessed by using the Physical Activity Rating Scale-3 (PARS-3), and mental health was assessed by using the Revised Child Anxiety and Depression Scale, 47-item version (RCADS-47). Cognitive function was assessed by using Stroop, N-back, and task-switching tasks. Latent profile analysis (LPA) was performed for cognitive-psychological health of primary school students based on seven cognitive indicators and six psychological indicators. Multivariable Logistic regression, trend test, and restricted cubic spline (RCS) analysis were used to examine associations between physical exercise and psychological highrisk status. Results: Five cognitive psychological health subgroups were identified:high symptom and impaired cognition group ( n =40, 7.3%), low cognition and low symptom group ( n =108, 19.8% ), and high cognition and high symptom group ( n =110, 20.2%), cognitive advantage and psychologically healthy group ( n = 272, 49.8%), and extremely high symptom and cognitively vulnerable group ( n =16, 2.9%). Among them, the high symptom and impaired-cognition group and extremely high symptom and cognitively vulnerable group were classified as psychologically high risk types. Significant differences were observed across subgroups in psychological symptoms, cognitive function, some physical fitness indicators and physical exercise level, except for body weight, body mass index, and sit and reach ( F =2.64-297.53, all P < 0.05). The results of the multivariate Logistic regression analysis indicated that high level sports, including high total volume of physical exercise, single session duration >30 min, high intensity, and frequency ≥3 times/week, was associated with lower psychological high risk status ( OR =0.20, 0.36, 0.33, 0.40, all P <0.05). Trend analysis showed that total volume of physical exercise and single session exercise duration were negatively associated with psychological high risk status among primary school students ( OR =0.98, 0.79, both P trend <0.05). RCS analysis showed no statistically significant overall or nonlinear association between total volume of physical exercise and psychological high risk status ( χ 2=2.92, 0.01, both P >0.05). Conclusions Cognitive-psychological health among primary school students is heterogeneous. Physical exercise is associated with psychological health improvement and may show a threshold effect, with total volume of physical exercise and single session exercise duration being the key dimensions.

Objective:To investigate the association between serum 25-hydroxyvitamin D [25(OH)D] levels and 24-h urinary calcium excretion (24-h UCaE) and hypercalciuria in Chinese adults.Methods:This cross-sectional study was conducted from March 2022 to March 2023 in nine cities in China and included 1 239 residents. Demographic characteristics were collected through questionnaires and physical examinations, fasting blood samples were assessed for bone metabolism indicators, and 24-h urine samples were used to determine the 24-h UCaE. Multiple linear regression analysis was used to explore the relationship between serum 25(OH)D and 24-h UCaE and bone metabolism indexes. The relationship between serum 25(OH)D and hypercalciuria was analyzed using a multiple logistic regression model combined with restricted cubic spline modeling.Results:The mean participant age was (47.9±18.1) years, of which 453 (36.6%) were male. The percentages of vitamin D sufficiency, insufficiency, and deficiency were 7.6% (94/1 239), 29.0% (359/1 239), and 63.4% (786/1 239), respectively. The multiple linear regression model showed that after adjusting for the covariates the 24-h UCaE gradually increased with higher levels of 25(OH)D ( P overall <0.001, P nonlinear <0.001). The logistic regression analysis revealed that compared with the vitamin D deficient group, the OR for the prevalence of hypercalciuria in the vitamin D sufficient and vitamin D insufficient groups were 3.290 (95% CI 1.745 to 6.202) and 3.742 (95% CI 2.458 to 5.697), respectively. The results of the restricted cubic spline modeling showed a positive nonlinear relationship between 25(OH)D and the prevalence of hypercalciuria ( P overall <0.001, P nonlinear <0.001). The prevalence of hypercalciuria increased when 25(OH)D was >17.00 μg/L and peaked at 26.71 μg/L, after which there was a decreasing trend in the prevalence of hypercalciuria with increasing 25(OH)D. Conclusion:Associations between serum 25(OH)D levels and urinary calcium excretion and the prevalence of hypercalciuria were observed in the Chinese adult population.

OBJECTIVE To provide scientific and reasonable recommendations for the identification of Anaerobio-spirillum succiniciproducens in clinical microbiology laboratories by comparing the effectiveness of biochemical methods,matrix-assisted laser desorption ionization time-of-flight mass spectrometry(MALDI-TOF MS),and 16S rRNA gene sequencing.METHODS Methodological evaluation was conducted.A strain of A.succiniciprodu-cens was isolated on Mar.27,2023 from a patient with sepsis caused by a dog bite,who was admitted to Guang-dong Nongken Central Hospital,and analyzed for the morphological characteristics,culture methods,and colony features.The identification of the isolated strain was conducted by the automated microbial identification and anal-ysis system(VITEK 2 Compact 2,bioMerieux),MALDI-TOF MS including VITEK MS,MALDI Biotyper,Au-tof ms 1000,and EX-Accuspec,as well as 16S ribosomal RNA(rRNA)gene sequencing.The performance of each method was evaluated.RESULTSA.succiniciproducens was a strictly anaerobic gram-negative spiral bacteri-um,characterized by slow growth,flat and colorless transparent colonies,and negative oxidase and catalase activ-ity.When using VITEK 2 Compact ANC card,it was not successfully recognized;however,through the identifi-cation by VITEK MS,MALDI Biotyper,Autof ms 1000 and EX-Accuspec,it was all confirmed as A.succinicip-roducens.Additionally,16S rRNA gene sequencing technology also identified it as A.succiniciproducens.CONCLUSIONS Current automated biochemical identification systems are unable to accurately identify A.succi-niciproducens.MALDI-TOF MS and 16S rRNA gene sequencing techniques can reliably confirm A.succinicipro-ducens.Clinical microbiologists should select the appropriate identification method based on their available re-sources.

Gender recognition based on the analysis of fingerprint residue can assist investigators in narrowing down the scope of investigation and play an important role in the field of criminal investigation.This study established a quantitative analysis method for lipid substances in fingerprints based on gas chromatography-mass spectrometry(GC-MS).Fatty acids in fingerprints were methylated using sulfuric acid methanol derivatization reagent(7%,V/V),the extraction reagent was dichloromethane-methanol(1∶1,V/V)solution,the reaction temperature was 70℃and the heating time was 45 min.Quantitative analysis of the relative content of 23 kinds of fatty acids and squalene in fingerprints residue by different genders was conducted,and orthogonal partial least squares-discriminant analysis(OPLS-DA)was used to reduce the dimensionality of the quantitative results.A total of 13 kinds of components in the fingerprints were selected to maximize the difference in relative content between male and female fingerprints.Three machine learning models,including binary logistic regression(BLR),support vector machine(SVM)and random forest(RF),were further used as feature variables to classify the gender of fingerprints.The classification performance of each model was compared through five indicators,and it was found that the most suitable model for binary classification of fingerprint gender was SVM model.The results showed that the SVM fingerprint residual gender binary classification model established based on the relative content data of 13 kinds of lipid substances in fingerprints achieved a classification accuracy of 90%and an area under the receiver operating characteristic curve(AUC)value of 0.98.This study provided a new research method for detecting lipid components in fingerprints and a methodological basis for gender recognition of fingerprints.

Forensic medicine has been designated as a first-level discipline,presenting new opportunities and challenges for the development of forensic medicine.Since the 1980s,the establishment of foren-sic medicine discipline and the cultivation of high-level forensic talents have become hot topics in the development of forensic medicine in China.Since the 13th Five-Year Plan,the forensic team of Shanxi Medical University has been aiming at the forefront,proposing the development goals of"Five First-class"and the discipline development path"Six Major Achievements".It has selected benchmark disci-plines,identified gaps in disciplinary development,unified thoughts,formulated completion timelines,concentrated superior resources,assigned tasks to individuals,and created an"Organized Fill-in-the-Blank Format"forensic medicine discipline construction model with the characteristics of the new era.The construction model of forensic medicine has achieved good results in the goals,discipline frame-work,scientific research,talent cultivation,discipline team and platform construction,forming a rela-tively complete discipline construction and management system,and accumulating valuable experience for the construction of first-level discipline and high-level talent cultivation of forensic medicine.

OBJECTIVE: Humans are exposed to complex mixtures of environmental chemicals and other factors that can affect their health. Analysis of these mixture exposures presents several key challenges for environmental epidemiology and risk assessment, including high dimensionality, correlated exposure, and subtle individual effects. METHODS: We proposed a novel statistical approach, the generalized functional linear model (GFLM), to analyze the health effects of exposure mixtures. GFLM treats the effect of mixture exposures as a smooth function by reordering exposures based on specific mechanisms and capturing internal correlations to provide a meaningful estimation and interpretation. The robustness and efficiency was evaluated under various scenarios through extensive simulation studies. RESULTS: We applied the GFLM to two datasets from the National Health and Nutrition Examination Survey (NHANES). In the first application, we examined the effects of 37 nutrients on BMI (2011-2016 cycles). The GFLM identified a significant mixture effect, with fiber and fat emerging as the nutrients with the greatest negative and positive effects on BMI, respectively. For the second application, we investigated the association between four pre- and perfluoroalkyl substances (PFAS) and gout risk (2007-2018 cycles). Unlike traditional methods, the GFLM indicated no significant association, demonstrating its robustness to multicollinearity. CONCLUSION GFLM framework is a powerful tool for mixture exposure analysis, offering improved handling of correlated exposures and interpretable results. It demonstrates robust performance across various scenarios and real-world applications, advancing our understanding of complex environmental exposures and their health impacts on environmental epidemiology and toxicology.
Humans ; Environmental Exposure/analysis* ; Linear Models ; Nutrition Surveys ; Environmental Pollutants ; Body Mass Index