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

Triple-negative breast cancer (TNBC) represents a distinctive subtype, characterized by the absence of estrogen receptors, progesterone receptors, and human epidermal growth factor receptor 2 (HER2). Due to its high inter-tumor and intra-tumor heterogeneity, TNBC poses significant chanllenges for personalized diagnosis and treatment. The advant of clustered regular interspaced short palindromic repeats (CRISPR) technology has profoundly enhanced our understanding of the structure and function of the TNBC genome, providing a powerful tool for investigating the occurrence and development of diseases. This review focuses on the application of CRISPR/Cas technology in the personalized diagnosis and treatment of TNBC. We begin by discussing the unique attributes of TNBC and the limitations of current diagnostic and treatment approaches: conventional diagnostic methods provide limited insights into TNBC, while traditional chemotherapy drugs are often associated with low efficacy and severe side effects. The CRISPR/Cas system, which activates Cas enzymes through complementary guide RNAs (gRNAs) to selectively degrade specific nucleic acids, has emerged as a robust tool for TNBC research. This technology enables precise gene editing, allowing for a deeper understanding of TNBC heterogeneity by marking and tracking diverse cell clones. Additionally, CRISPR facilitates high-throughput screening to promptly identify genes involved in TNBC growth, metastasis, and drug resistance, thus revealing new therapeutic targets and strategies. In TNBC diagnostics, CRISPR/Cas was applied to develop molecular diagnostic systems based on Cas9, Cas12, and Cas13, each employing distinct detection principles. These systems can sensitively and specifically detect a variety of TNBC biomarkers, including cell-specific DNA/RNA and circulating tumor DNA (ctDNA). In the realm of precision therapy, CRISPR/Cas has been utilized to identify key genes implicated in TNBC progression and treatment resistance. CRISPR-based screening has uncovered potential therapeutic targets, while its gene-editing capabilities have facilitated the development of combination therapies with traditional chemotherapy drugs, enhancing their efficacy. Despite its promise, the clinical translation of CRISPR/Cas technology remains in its early stages. Several clinical trials are underway to assess its safety and efficacy in the treatment of various genetic diseases and cancers. Challenges such as off-target effects, editing efficiency, and delivery methods remain to be addressed. The integration of CRISPR/Cas with other technologies, such as 3D cell culture systems, human induced pluripotent stem cells (hiPSCs), and artificial intelligence (AI), is expected to further advance precision medicine for TNBC. These technological convergences can offer deeper insights into disease mechanisms and facilitate the development of personalized treatment strategies. In conclusion, the CRISPR/Cas system holds immense potential in the precise diagnosis and treatment of TNBC. As the technology progresses and becomes more costs-effective, its clinical relevance will grow, and the translation of CRISPR/Cas system data into clinical applications will pave the way for optimal diagnosis and treatment strategies for TNBC patients. However, technical hurdles and ethical considerations require ongoing research and regulation to ensure safety and efficacy.

This study investigates the reproductive protective effect and potential mechanism of Cistanches Herba extract(CHE) on a rat model of kidney-Yang deficiency induced by adenine. Rats were randomly divided into five groups: normal, model, low-dose CHE(0.6 g·kg~(-1)·d~(-1)), high-dose CHE(1.2 g·kg~(-1)·d~(-1)), and L-carnitine(100 mg·kg~(-1)·d~(-1)). The rats were administered adenine(200 mg·kg~(-1)·d~(-1)) by gavage for the first 14 days to induce kidney-Yang deficiency, while simultaneously receiving drug treatment. After 14 days, the modeling was discontinued, but drug treatment continued to 49 days. The content of components in CHE was analyzed by high-performance liquid chromatography. The adenine-induced kidney-Yang deficiency model was assessed through symptom characterization and measurement of testosterone(T) levels using an enzyme-linked immunosorbent assay kit. Pathological damage to the testis and epididymis was evaluated based on the wet weight and performing hematoxylin-eosin staining. Sperm density and motility were measured using computer-aided sperm analysis, and sperm viability was assessed using live/dead sperm staining kits, and sperm morphology was evaluated using eosin staining, thereby determining rat sperm quality. Metabolomics was used to analyze changes in serum metabolites, enrich related metabolic pathways, and explore the mechanism of CHE in improving reproductive function damage in rats with kidney-Yang deficiency syndrome. Compared to the normal group, the model group exhibited significant kidney-Yang deficiency symptoms, reduced T levels, decreased testicular and epididymal wet weights, and significant pathological damage to the testis and epididymis. The sperm density, motility, and viability decreased, with an increased rate of sperm abnormalities. In contrast, rats treated with CHE showed marked improvements in kidney-Yang deficiency symptoms, restored T levels, alleviated pathological damage to the testis and epididymis, and improved various sperm parameters. Metabolomics results revealed 286 differential metabolites between the normal and model groups(191 upregulated and 95 downregulated). Seventy-five differential metabolites were identified between the model and low-dose CHE groups(21 upregulated and 54 downregulated). A total of 24 common differential metabolites were identified across the three groups, with 22 of these metabolites exhibiting opposite regulation trends between the two comparison groups. These metabolites were primarily involved in linoleic acid metabolism, ether lipid metabolism, and pantothenic acid and coenzyme A biosynthesis, as well as metabolites including 13-hydroperoxylinoleic acid, lysophosphatidylcholine, and pantethine. CHE can improve kidney-Yang deficiency symptoms in rats, alleviate reproductive organ damage, and enhance sperm quality. The regulation of lipid metabolism may be a potential mechanism through which CHE improves reproductive function in rats with kidney-Yang deficiency. The potential bioactive compounds of CHE include echinacoside, verbascoside, salidroside, betaine, and cistanoside A.
Animals ; Male ; Rats ; Yang Deficiency/physiopathology* ; Metabolomics ; Kidney/physiopathology* ; Rats, Sprague-Dawley ; Drugs, Chinese Herbal/administration & dosage* ; Cistanche/chemistry* ; Kidney Diseases/metabolism* ; Testis/metabolism* ; Humans ; Reproduction/drug effects* ; Testosterone/blood*

To investigate the effects of Biyan Jiedu Capsules on the proliferation and apoptosis of nasopharyngeal carcinoma cells and their molecular mechanism, nasopharyngeal carcinoma cells CNE1 and CNE2 were used. They were divided into control group(30% blank serum medium), low-(10% drug-containing serum + 20% blank serum medium), medium-(20% drug-containing serum + 10% blank serum medium), and high-(30% drug-containing serum medium) concentration group of Biyan Jiedu Capsules according to in vitro experiment. After 24 h of intervention, the effects of Biyan Jiedu Capsules on the proliferation of CNE1 and CNE2 were detected by CCK-8 assay, clonal formation experiment, and EdU staining. The effect of Biyan Jiedu Capsules on apoptosis of CNE1 and CNE2 was detected by flow cytometry. Western blot was used to detect the effect of Biyan Jiedu Capsules on the expression of X-linked apoptosis inhibitor protein(XIAP), survivin, proliferating cell nuclear antigen(PCNA), and PI3K/Akt pathway-related proteins in CNE1 and CNE2. The results showed that compared with the control group, the survival rate of CNE1 and CNE2 in the medium and high concentration groups of Biyan Jiedu Capsules could be decreased in a concentration-dependent way(P<0.05, P<0.01). At the same time, EdU staining and clonal formation experiments showed that the proliferation of CNE1 and CNE2 was significantly inhibited in the medium and high concentration groups of Biyan Jiedu Capsules(P<0.05, P<0.01). Flow cytometry showed that the apoptosis rate of CNE1 and CNE2 was significantly increased in all concentration groups of Biyan Jiedu Capsules(P<0.01), and the apoptosis rate was concentration-dependent. Western blot showed that the expressions of XIAP, survivin, PCNA, p-PI3K, and p-Akt in all concentration groups of Biyan Jiedu Capsules were significantly down-regulated(P<0.05, P<0.01). In conclusion, Biyan Jiedu Capsules can inhibit the proliferation and induce apoptosis of nasopharyngeal carcinoma cells possibly by down-regulating the PI3K/Akt signaling pathway.
Humans ; Apoptosis/drug effects* ; Cell Proliferation/drug effects* ; Nasopharyngeal Carcinoma ; Nasopharyngeal Neoplasms/physiopathology* ; Proto-Oncogene Proteins c-akt/genetics* ; Cell Line, Tumor ; Drugs, Chinese Herbal/pharmacology* ; Phosphatidylinositol 3-Kinases/genetics* ; Signal Transduction/drug effects* ; Capsules ; Carcinoma/drug therapy*

A high-performance liquid chromatography method using pre-column derivatization with 6-aminoquinolyl-N-hydroxysuccinimidyl carbamate was developed to determine the content of 15 amino acids in the medicinal snakes Bungarus Parvus, Agkistrodon, and Zaocys. The results showed that the total amino acid(TAA) content ranged from 277.13 to 515.05 mg·g~(-1), with the top four amino acids in all three species being glutamic acid(Glu), glycine(Gly), aspartic acid(Asp), and lysine(Lys). The essential amino acid(EAA) content ranged from 74.56 to 203.94 mg·g~(-1), with Agkistrodon exhibiting the highest content. The non-essential amino acid(NEAA), semi-essential amino acid(semi-EAA), and medicinal amino acid(MAA) content ranged from 189.06 to 318.23, 12.89 to 33.53, and 179.83 to 342.33 mg·g~(-1), respectively, with Zaocys having the highest content in these categories. Amino acid nutritional value was evaluated using the amino acid ratio(RAA), amino acid ratio coefficient(RCAA), and amino acid ratio coefficient score(SRCAA), and the results indicated that all three medicinal snakes possessed good nutritional value. The amino acid composition was similar across the species, though significant differences in content were observed. Based on these differences, an orthogonal partial least squares-discriminant analysis(OPLS-DA) model was established, which could clearly distinguish between the three medicinal snake species. The key differences in amino acid content included Gly, tyrosine(Tyr), Glu, and serine(Ser), which may be related to the observed clinical application differences among the species. Further research into the mechanisms of these differential amino acids is expected to provide more insights into the clinical application disparities of these three medicinal snake species.
Amino Acids/chemistry* ; Animals ; Nutritive Value ; Chromatography, High Pressure Liquid ; Snakes/classification* ; Bungarus

ObjectivePrimary liver cancer, predominantly hepatocellular carcinoma (HCC), is a significant global health issue, ranking as the sixth most diagnosed cancer and the third leading cause of cancer-related mortality. Accurate and early diagnosis of HCC is crucial for effective treatment, as HCC and non-HCC malignancies like intrahepatic cholangiocarcinoma (ICC) exhibit different prognoses and treatment responses. Traditional diagnostic methods, including liver biopsy and contrast-enhanced ultrasound (CEUS), face limitations in applicability and objectivity. The primary objective of this study was to develop an advanced, light-weighted classification network capable of distinguishing HCC from other non-HCC malignancies by leveraging the automatic analysis of brightness changes in CEUS images. The ultimate goal was to create a user-friendly and cost-efficient computer-aided diagnostic tool that could assist radiologists in making more accurate and efficient clinical decisions. MethodsThis retrospective study encompassed a total of 161 patients, comprising 131 diagnosed with HCC and 30 with non-HCC malignancies. To achieve accurate tumor detection, the YOLOX network was employed to identify the region of interest (ROI) on both B-mode ultrasound and CEUS images. A custom-developed algorithm was then utilized to extract brightness change curves from the tumor and adjacent liver parenchyma regions within the CEUS images. These curves provided critical data for the subsequent analysis and classification process. To analyze the extracted brightness change curves and classify the malignancies, we developed and compared several models. These included one-dimensional convolutional neural networks (1D-ResNet, 1D-ConvNeXt, and 1D-CNN), as well as traditional machine-learning methods such as support vector machine (SVM), ensemble learning (EL), k-nearest neighbor (KNN), and decision tree (DT). The diagnostic performance of each method in distinguishing HCC from non-HCC malignancies was rigorously evaluated using four key metrics: area under the receiver operating characteristic (AUC), accuracy (ACC), sensitivity (SE), and specificity (SP). ResultsThe evaluation of the machine-learning methods revealed AUC values of 0.70 for SVM, 0.56 for ensemble learning, 0.63 for KNN, and 0.72 for the decision tree. These results indicated moderate to fair performance in classifying the malignancies based on the brightness change curves. In contrast, the deep learning models demonstrated significantly higher AUCs, with 1D-ResNet achieving an AUC of 0.72, 1D-ConvNeXt reaching 0.82, and 1D-CNN obtaining the highest AUC of 0.84. Moreover, under the five-fold cross-validation scheme, the 1D-CNN model outperformed other models in both accuracy and specificity. Specifically, it achieved accuracy improvements of 3.8% to 10.0% and specificity enhancements of 6.6% to 43.3% over competing approaches. The superior performance of the 1D-CNN model highlighted its potential as a powerful tool for accurate classification. ConclusionThe 1D-CNN model proved to be the most effective in differentiating HCC from non-HCC malignancies, surpassing both traditional machine-learning methods and other deep learning models. This study successfully developed a user-friendly and cost-efficient computer-aided diagnostic solution that would significantly enhances radiologists’ diagnostic capabilities. By improving the accuracy and efficiency of clinical decision-making, this tool has the potential to positively impact patient care and outcomes. Future work may focus on further refining the model and exploring its integration with multimodal ultrasound data to maximize its accuracy and applicability.

OBJECTIVE: To explore and quantify the association of hot night exposure during the sperm development period (0-90 lag days) with semen quality. METHODS: A total of 6,640 male sperm donors from 6 human sperm banks in China during 2014-2020 were recruited in this multicenter study. Two indices (i.e., hot night excess [HNE] and hot night duration [HND]) were used to estimate the heat intensity and duration during nighttime. Linear mixed models were used to examine the association between hot nights and semen quality parameters. RESULTS: The exposure-response relationship revealed that HNE and HND during 0-90 days before semen collection had a significantly inverse association with sperm motility. Specifically, a 1 °C increase in HNE was associated with decreased sperm progressive motility of 0.0090 (95% confidence interval [ CI]: -0.0147, -0.0033) and decreased total motility of 0.0094 (95% CI: -0.0160, -0.0029). HND was significantly associated with reduced sperm progressive motility and total motility of 0.0021 (95% CI: -0.0040, -0.0003) and 0.0023 (95% CI: -0.0043, -0.0002), respectively. Consistent results were observed at different temperature thresholds on hot nights. CONCLUSION Our findings highlight the need to mitigate nocturnal heat exposure during spermatogenesis to maintain optimal semen quality.
Humans ; Male ; Semen Analysis ; Adult ; Sperm Motility ; Hot Temperature/adverse effects* ; China ; Middle Aged ; Spermatozoa/physiology* ; Young Adult