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.

Background: The association of changes in metabolic syndrome (MetS) with cognitive function remains unclear. We explored this association using prospective and Mendelian randomization (MR) studies. Methods: MetS components including high-density lipoprotein cholesterol (HDL-C), systolic blood pressure (SBP), waist circumference (WC), fasting plasma glucose (FPG), and triglycerides were measured at baseline and two follow-ups, constructing a MetS index. Immediate, delayed memory recall, and cognitive function along with its dimensions were assessed by immediate 10- word recall test (IWRT) and delayed 10-word recall test (DWRT), and mini-mental state examination (MMSE), respectively, at baseline and follow-ups. Linear mixed-effect model was used. Additionally, the genome-wide association study (GWAS) of MetS was conducted and one-sample MR was performed to assess the causality between MetS and cognitive function. Results: Elevated MetS index was associated with decreasing annual change rates (decrease) in DWRT and MMSE scores, and with decreases in attention, calculation and recall dimensions. HDL-C was positively associated with an increase in DWRT scores, while SBP and FPG were negatively associated. HDL-C showed a positive association, whereas WC was negatively associated with increases in MMSE scores, including attention, calculation and recall dimensions. Interaction analysis indicated that the association of MetS index on cognitive decline was predominantly observed in low family income group. The GWAS of MetS identified some genetic variants. MR results showed a non-significant causality between MetS and decrease in DWRT, IWRT, nor MMSE scores. Conclusion Our study indicated a significant association of MetS and its components with declines in memory and cognitive function, especially in delayed memory recall.

Obstructive sleep apnea (OSA) is an increasingly widespread sleep-breathing disordered disease, and is an independent risk factor for many high-risk chronic diseases such as hypertension, coronary heart disease, stroke, arrhythmias and diabetes, which is potentially fatal. The key to the prevention and treatment of OSA is early diagnosis and treatment, so the assessment and diagnostic technologies of OSA have become a research hotspot. This paper reviews the research progresses of severity assessment parameters and diagnostic technologies of OSA, and discusses their future development trends. In terms of severity assessment parameters of OSA, apnea hypopnea index (AHI), as the gold standard, together with the percentage of duration of apnea hypopnea (AH%), lowest oxygen saturation (LSpO₂), heart rate variability (HRV), oxygen desaturation index (ODI) and the emerging biomarkers, constitute a multi-dimensional evaluation system. Specifically, the AHI, which measures the frequency of sleep respiratory events per hour, does not fully reflect the patients’ overall sleep quality or the extent of their daytime functional impairments. To address this limitation, the AH%, which measures the proportion of the entire sleep cycle affected by apneas and hypopneas, deepens our understanding of the impact on sleep quality. The LSpO₂ plays a critical role in highlighting the potential severe hypoxic episodes during sleep, while the HRV offers a different perspective by analyzing the fluctuations in heart rate thereby revealing the activity of the autonomic nervous system. The ODI provides a direct and objective measure of patients’ nocturnal oxygenation stability by calculating the number of desaturation events per hour, and the biomarkers offers novel insights into the diagnosis and management of OSA, and fosters the development of more precise and tailored OSA therapeutic strategies. In terms of diagnostic techniques of OSA, the standardized questionnaire and Epworth sleepiness scale (ESS) is a simple and effective method for preliminary screening of OSA, and the polysomnography (PSG) which is based on recording multiple physiological signals stands for gold standard, but it has limitations of complex operations, high costs and inconvenience. As a convenient alternative, the home sleep apnea testing (HSAT) allows patients to monitor their sleep with simplified equipment in the comfort of their own homes, and the cardiopulmonary coupling (CPC) offers a minimal version that simply analyzes the electrocardiogram (ECG) signals. As an emerging diagnostic technology of OSA, machine learning (ML) and artificial intelligence (AI) adeptly pinpoint respiratory incidents and expose delicate physiological changes, thus casting new light on the diagnostic approach to OSA. In addition, imaging examination utilizes detailed visual representations of the airway’s structure and assists in recognizing structural abnormalities that may result in obstructed airways, while sound monitoring technology records and analyzes snoring and breathing sounds to detect the condition subtly, and thus further expands our medical diagnostic toolkit. As for the future development directions, it can be predicted that interdisciplinary integrated researches, the construction of personalized diagnosis and treatment models, and the popularization of high-tech in clinical applications will become the development trends in the field of OSA evaluation and diagnosis.

This study aims to explore the mechanism of lung and gut protection by Tanreqing Capsules on the mice infected with influenza virus based on "the lung-gut axis". A total of 110 C57BL/6J mice were randomized into control group, model group, oseltamivir group, and low-and high-dose Tanreqing Capsules groups. Ten mice in each group underwent body weight protection experiments, and the remaining 12 mice underwent experiments for mechanism exploration. Mice were infected with influenza virus A/Puerto Rico/08/1934(PR8) via nasal inhalation for the modeling. The lung tissue was collected on day 3 after gavage, and the lung tissue, colon tissue, and feces were collected on day 7 after gavage for subsequent testing. The results showed that Tanreqing Capsules alleviated the body weight reduction and increased the survival rate caused by PR8 infection. Compared with model group, Tanreqing Capsules can alleviate the lung injury by reducing the lung index, alleviating inflammation and edema in the lung tissue, down-regulating viral gene expression at the late stage of infection, reducing the percentage of neutrophils, and increasing the percentage of T cells. Tanreqing Capsules relieved the gut injury by restoring the colon length, increasing intestinal lumen mucin secretion, alleviating intestinal inflammation, and reducing goblet cell destruction. The gut microbiota analysis showed that Tanreqing Capsules increased species diversity compared with model group. At the phylum level, Tanreqing Capsules significantly increased the abundance of Firmicutes and Actinobacteria, while reducing the abundance of Bacteroidota and Proteobacteria to maintain gut microbiota balance. At the genus level, Tanreqing Capsules significantly increased the abundance of unclassified＿f＿Lachnospiraceae while reducing the abundance of Bacteroides, Eubacterium, and Phocaeicola to maintain gut microbiota balance. In conclusion, Tanreqing Capsules can alleviate mouse lung and gut injury caused by influenza virus infection and restore the balance of gut microbiota. Treating influenza from the lung and gut can provide new ideas for clinical practice.
Animals ; Drugs, Chinese Herbal/administration & dosage* ; Mice ; Lung/metabolism* ; Mice, Inbred C57BL ; Capsules ; Orthomyxoviridae Infections/virology* ; Gastrointestinal Microbiome/drug effects* ; Male ; Humans ; Female ; Influenza A virus/physiology* ; Influenza, Human/virology*

To explore the variation laws of volatile oil during the extraction process of Artemisiae Argyi Folium and its impact on the quality of the medicinal solution, as well as to achieve precise control of the extraction process, this study employed headspace solid phase microextraction gas chromatography-mass spectrometry(HS-SPME-GC-MS) in combination with multiple light scattering techniques to conduct a comprehensive analysis, identification, and characterization of the changes in volatile components and the physical properties of the medicinal solution during the extraction process. A total of 82 volatile compounds were identified using the HS-SPME-GC-MS technique, including 21 alcohols, 15 alkenes, 14 ketones, 9 acids, 6 aldehydes, 5 phenols, 3 esters, and 9 other types of compounds. At different extraction time points(15, 30, 45, and 60 min), 71, 72, 64, and 44 compounds were identified in the medicinal solution, respectively. It was observed that the content of volatile components gradually decreased with the extension of extraction time. Through multivariate statistical analysis, four compounds with significant differences during different extraction time intervals were identified, namely 1,8-cineole, terpinen-4-ol, 3-octanone, and camphor. RESULTS:: from multiple light scattering techniques indicated that at 15 minutes of extraction, the transmittance of the medicinal solution was the lowest(25%), the particle size was the largest(0.325-0.350 nm), and the stability index(turbiscan stability index, TSI) was the highest(0-2.5). With the extension of extraction time, the light transmittance of the medicinal solution improved, stability was enhanced, and the particle size decreased. These laws of physicochemical property changes provide important basis for the control of Artemisiae Argyi Folium extraction process. In addition, the changes in the bioactivity of Artemisiae Argyi Folium extracts during the extraction process were investigated through mouse writhing tests and antimicrobial assays. The results indicated that the analgesic and antimicrobial effects of the medicinal solution were strongest at the 15-minute extracting point. In summary, the findings of this study demonstrate that the content of volatile oil in Artemisiae Argyi Folium extracts gradually decreases with the extension of extraction time, and the variation in volatile oil content directly influences the physicochemical properties and pharmacological efficacy of the medicinal solution. This discovery provides important scientific reference for the optimization of Artemisiae Argyi Folium extraction processes and the development and application of process analytical technologies.
Oils, Volatile/pharmacology* ; Artemisia/chemistry* ; Gas Chromatography-Mass Spectrometry ; Drugs, Chinese Herbal/pharmacology* ; Chlorogenic Acid/pharmacology* ; Solid Phase Microextraction ; Quality Control

The chemical components of Carthami Flos were investigated by using macroporous resin, silica gel column chromatography, reversed-phase octadecylsilane(ODS) column chromatography, Sephadex LH-20, and semi-preparative high-performance liquid chromatography(HPLC). The planar structures of the compounds were established based on their physicochemical properties and ultraviolet-visible(UV-Vis), infrared(IR), high-resolution electrospray ionization mass spectrometry(HR-ESI-MS), and nuclear magnetic resonance(NMR) spectroscopic technology. The absolute configurations were determined by comparing the calculated and experimental electronic circular dichroism(ECD). Six flavonoid C-glycosides were isolated from the 30% ethanol elution fraction of macroporous resin obtained from the 95% ethanol extract of Carthami Flos, and identified as saffloquinoside F(1), 5-hydroxysaffloneoside(2), iso-5-hydroxysaffloneoside(3), isosafflomin C(4), safflomin C(5), and vicenin 2(6). Among these, the compounds 1 to 3 were new chalcone C-glycosides. The compounds 1, 2, 4, and 5 could significantly increase the viability of H9c2 cardiomyocytes damaged by oxygen-glucose deprivation/reoxygenation(OGD/R) at a concentration of 50 μmol·L~(-1), showing their good cardioprotective activity.
Glycosides/pharmacology* ; Flowers/chemistry* ; Drugs, Chinese Herbal/pharmacology* ; Carthamus tinctorius/chemistry* ; Chalcones/pharmacology* ; Animals

BACKGROUND AND OBJECTIVE: Patients with glioma experience a high symptom burden and have diverse palliative care needs. However, the assessment scales used in palliative care remain non-standardized and highly heterogeneous. To evaluate the application patterns of the current scales used in palliative care for glioma, we aim to identify gaps and assess the need for disease-specific scales in glioma palliative care. METHODS: We conducted a systematic search of five databases including PubMed, Web of Science, Medline, EMBASE, and CINAHL for quantitative studies that reported scale-based assessments in glioma palliative care. We extracted data on scale characteristics, domains, frequency, and psychometric properties. Quality assessments were performed using the Cochrane ROB 2.0 and ROBINS-I tools. RESULTS: Of the 3,405 records initially identified, 72 studies were included. These studies contained 75 distinct scales that were used 193 times. Mood (21.7%), quality of life (24.4%), and supportive care needs (5.2%) assessments were the most frequently assessed items, exceeding half of all scale applications. Among the various assessment dimensions, the Distress Thermometer (DT) was the most frequently used tool for assessing mood, while the Short Form-36 Health Survey Questionnaire (SF-36) was the most frequently used tool for assessing quality of life. The Mini Mental Status Examination (MMSE) was the most common tool for cognitive assessment. Performance status (5.2%) and social support (6.8%) were underrepresented. Only three brain tumor-specific scales were identified. Caregiver-focused scales were limited and predominantly burden-oriented. CONCLUSIONS: There are significant heterogeneity, domain imbalances, and validation gaps in the current use of assessment scales for patients with glioma receiving palliative care. The scale selected for use should be comprehensive and user-friendly.
Humans ; Glioma/psychology* ; Palliative Care/methods* ; Quality of Life ; Psychometrics ; Brain Neoplasms/psychology*

With the rise of data-intensive research, data literacy has become a critical capability for improving scientific data quality and achieving artificial intelligence (AI) readiness. In the biomedical domain, data are characterized by high complexity and privacy sensitivity, calling for robust and systematic data management skills. This paper reviews current trends in scientific data governance and the evolving policy landscape, highlighting persistent challenges such as inconsistent standards, semantic misalignment, and limited awareness of compliance. These issues are largely rooted in the lack of structured training and practical support for researchers. In response, this study builds on existing data literacy frameworks and integrates the specific demands of biomedical research to propose a comprehensive, lifecycle-oriented data literacy competency model with an emphasis on ethics and regulatory awareness. Furthermore, it outlines a tiered training strategy tailored to different research stages-undergraduate, graduate, and professional, offering theoretical foundations and practical pathways for universities and research institutions to advance data literacy education.
Artificial Intelligence ; Humans ; Biomedical Research

Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by synovial inflammation, joint destruction, and functional impairment. Angiogenesis plays a key role in the pathological progression of RA with dysfunction of endothelial cells to promote synovial inflammation, sustain pannus formation, subsequently leading to joint damage. Colquhounia Root Tablets (CRT), a Chinese patent drug, has shown a satisfying clinical efficacy in treating RA, while the underlying mechanism by which CRT inhibits RA-associated angiogenesis remains unclear. In this study, we applied a research approach combining transcriptomic data analysis, bio-network mapping, and in vivo and in vitro experiments to explore the molecular mechanisms of CRT in suppressing angiogenesis in RA. Animal welfare and experimental procedures follow the regulations of the Animal Ethics Committee of Institute of Basic Theory for Chinese Medicine, China Academy of Chinese Medical Sciences (ratification number: IBTCMCACMS21-2307-06). Network analysis identified that key genes such as nucleotide-binding oligomerization domain-containing protein 2 (NOD2), SMAD family member 3 (SMAD3), and vascular endothelial growth factor A (VEGFA) significantly enriched in pathways related to NOD-like receptor signaling and VEGF signaling, indicating that CRT may inhibit angiogenesis by regulating vascular endothelial cell function with modulating angiogenesis-related pathways. In vivo data showed that CRT significantly reduced the positive expression of CD31 and VEGF in the ankle joint of adjuvant-induced arthritis (AIA) rats. In vitro data further confirmed that CRT effectively inhibited VEGF-induced migration, invasion, and tube formation in HUVECs, while significantly reduced the expression of angiogenesis-related factors VEGF/CD31/Ang-1, as well as the positive expression of VEGF and CD31 in HUVECs. Furthermore, CRT markedly decreased the protein expression of NOD2, VEGFA, and SMAD3. In conclusion, these findings indicate that CRT may inhibit the RA-related angiogenesis by targeting the NOD2/SMAD3/VEGF signaling axis to improve endothelial cell function, enriching the scientific connotation of CRT in inhibiting pathological angiogenesis in RA and also offer new insights for clinical prevention and treatment of RA.