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 analyze the effect of releasing the lower pulmonary ligament on right residual lung expansion after right upper lobe resection under different body mass index (BMI) levels. Methods The clinical data of patients who underwent thoracoscopic right upper lobe resection in the First Affiliated Hospital with Nanjing Medical University from 2021 to 2022 were retrospectively analyzed. Patients were divided into a group A (17 kg/m2＜BMI≤23 kg/m2), a group B (23 kg/m2＜BMI≤29 kg/m2) and a group C (BMI＞29 kg/m2) according to BMI. The presence of residual cavity was judged by chest X-ray at 7-10 days after operation, the degree of compensation change of the right main bronchus angle was measured, and the changes in lung volume were determined by CT three-dimensional reconstruction. Results A total of 157 patients who underwent thoracoscopic right upper lobe resection were included, including 71 males and 86 females, with an average age of (59.7±11.2) years. There were 50 patients in the group A, 75 patients in the group B, and 32 patients in the group C. In the group A, compared with those without releasing the lower pulmonary ligament, patients with releasing had a lower incidence of postoperative residual cavity (P=0.016), greater changes in bronchus angle (P<0.001), and smaller changes in lung volume (P<0.001). In the group B and C, there was no significant effect of releasing the lower pulmonary ligament on postoperative residual cavity, bronchus angle, and lung volume changes (P>0.05). Conclusion For patients with thin and long body shape and low BMI, releasing the lower pulmonary ligament is helpful to promote the expansion of the residual lung after right upper lobe resection and reduce the occurrence of postoperative residual cavity in patients.

The pathogenesis of metabolic dysfunction-associated steatotic liver disease （MASLD） is fundamentally rooted in spleen deficiency and is closely associated with phlegm turbidity， damp-heat and blood stasis. Clinically， liver constraint with spleen deficiency and internal retention of damp turbidity represent the predominant traditional Chinese medicine （TCM） syndrome patterns. Researches have indicated intrinsic connections between the syndrome patterns and biological indicators such as gut microbiota and metabolic profiles. Regarding treatment， classical famous formulas， modern empirical formulas， and newly developed TCM drugs show positive effects in regulating glucose and lipid metabolism， improving insulin resistance， and alleviating metabolic inflammation， exhibiting multi-target mechanisms of action； acupuncture and other external therapies also provide adjunctive value. Nevertheless， current researches still have limitations such as the lack of high-quality clinical evidence and insufficient systematic elucidation of the uncerlying mechanisms. Future efforts should focus on conducting high-quality TCM clinical trials with hard endpoint outcomes such as hepatic histology outcomes， and utilizing modern technologies like multi-omics to elucidate TCM's mechanisms of action， thereby advancing the position of TCM as a first-line therapeutic strategy for MASLD.

Zoledronic acid (ZA), a nitrogen-containing bisphosphonate with established clinical utility in osteoporosis management, exhibits emerging antitumor potential in estrogen receptor-positive breast cancer. However, the molecular mechanisms underlying its nonapoptotic anticancer effects remain poorly characterized. This study revealed that ZA induced ferroptosis in ER+ breast cancer cells through dual suppression of cystine-glutamate antiporter SLC7A11 and glutathione peroxidase 4 (GPX4), key repressors of ferroptosis. Pharmacological inhibition of ferroptosis using Ferrostatin-1 significantly attenuated ZA-induced cytotoxicity, while combinatorial treatment with the GPX4 inhibitor RSL3 synergistically enhanced lipid peroxidation and cell death. Mechanistically, ZA activated the Hippo-YAP signaling pathway, promoting YAP phosphorylation, proteasomal degradation, and cytoplasmic retention, thereby silencing SLC7A11 and GPX4. We established a novel metabolic vulnerability in hormone-responsive malignancies.These findings position ZA as a bifunctional ferroptosis inducer in ER+ breast cancer, offering a promising strategy to overcome endocrine resistance.

CRISPR-Cas-based biosensing systems have become important platforms for rapid nucleic acid detection because of their programmable sequence recognition and high specificity. In many diagnostic workflows, the CRISPR reaction is coupled with polymerase chain reaction or isothermal pre-amplification to enrich low-abundance targets before readout. This design improves sensitivity, but it also introduces additional primers and enzymes, requirements for temperature control and reaction compatibility, and a higher demand for contamination management. These factors make the whole assay less convenient for integrated and field-deployable point-of-care testing. Developing pre-amplification-free CRISPR assays is therefore not simply a pursuit of shorter protocols, but a way to simplify molecular diagnosis at the system level, especially in settings where rapid decisions, closed workflows, and minimal manual operation are required. Microfluidic devices provide an important engineering route for this purpose. Their small reaction volumes, precise fluid manipulation, high-throughput partitioning, and compatibility with portable readout make it possible to integrate target confinement, reaction control, signal acquisition, and quantitative analysis on a chip. Droplet microfluidics and microwell arrays can divide a sample into large numbers of independent microreactors, enabling digital counting of rare recognition events. Continuous-flow, centrifugal, and paper-based microfluidic formats further provide options for automated operation, low-cost fabrication, and on-site use. In this context, microfluidics is not only a signal-enhancement method, but also a platform that connects CRISPR molecular recognition with practical assay implementation. This review summarizes recent progress in microfluidic-based pre-amplification-free CRISPR-Cas biosensing for rapid detection. We focus on four related technical directions. crRNA engineering, including spacer-length tuning, multi-crRNA design, chemical modification, and allosteric regulation, can improve recognition kinetics, nuclease stability, and mismatch discrimination. Digital microfluidic detection based on droplets or microwell arrays converts single-molecule recognition events into countable positive partitions, thereby improving quantitative capability without target pre-amplification. Physical signal transduction interfaces couple CRISPR activity to electrochemical, electrochemiluminescent, fluorescent, or surface-enhanced Raman scattering readouts, making weak molecular signals easier to detect with portable instruments. On-chip cascade signal amplification strategies further enhance output intensity through enzymatic reactions, DNA circuits, nanomaterials, or cross-domain amplification modules while avoiding direct amplification of the target nucleic acid. Together, these strategies show that the performance of a pre-amplification-free assay depends not only on biochemical recognition, but also on how the reaction is confined, amplified, transduced, and operated. In addition to describing these mechanisms, this review compares different strategies in terms of sensitivity improvement, operational complexity, cost, and suitability for point-of-care testing. Representative applications in food authenticity identification and rapid pathogen screening are also discussed to illustrate how these platforms perform in practical scenarios. Finally, we analyze the remaining challenges, including the detection of low-abundance targets in complex matrices, on-chip sample preparation, device-to-device reproducibility, long-term reagent storage, and standardization. Future development will likely depend on tighter integration of sample processing, CRISPR reaction, signal readout, and data interpretation, ultimately moving pre-amplification-free CRISPR diagnostics toward automated and user-friendly sample-to-answer systems.

A method for determination and targeted screening of diflorasone components in health products using ultra performance liquid chromatography-quadrupole time of flight mass spectrometry(UPLC-Q-TOF/MS)was established.Four representative diflorasone and esters(diflorasone,diflorasone diacetate,diflorasone-17-propionate,and diflorasone-21-propionate)were selected to optimize the pretreatment conditions,and 10 mL of extraction solvent dosage,15 min of extraction time and 5 g of salting-out agent as the optimal conditions were selected by response surface methodology.The results showed that the four analytes exhibited good linearity within the concentration range of 2.0?100 μg/L with the chromatographic peak area,and the correlation coefficients(R2)were all greater than 0.9990,while the results of recovery and relative standard deviation could satisfy the requirements of determination.The common characteristic ions of diflorasone and esters werem/z121 andm/z335,and their specific structures were obtained by analyzing the cleavage pathway based on the optimized determination conditions.A targeted screening method for other esters of diflorasone based on characteristic ions guidance strategy was established.This method had many advantages such as high efficiency,high sensitivity and good reproducibility,and could be used for targeted screening of diflorasone and esters in health products.The developed characteristic ion guided strategy could be employed to construct mass spectral databases for various glucocorticoids,enabling comprehensive targeted screening across a broad range of compounds.

Trauma can disrupt the body′s internal environment, resulting in organ dysfunction. This may manifest as symptoms such as acute respiratory distress, liver and kidney dysfunction, circulatory disorders and neurological damage. Mitochondrial quality control (MQC), encompassing processes like autophagy, dynamic changes, and biogenesis, plays an essential role in maintaining mitochondrial function. Stress responses following trauma can cause mitochondrial dysfunction and MQC imbalance, thereby exacerbating organ injury. Correcting MQC imbalance can improve organ function. Current researches on MQC in post-traumatic organ dysfunction mainly focuses on imbalance in specific MQC mechanism, lacking a systemic understanding of its incidence and progression. To this end, the authors reviewed the progress in researches on the impact of MQC imbalance on post-traumatic organ dysfunction, aiming to provide insights for clinical treatment.

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 develop a predictive model integrating clinical features,deep learning(DL),and radiomics based on T2-weighted imaging for prenatal assessment of postpartum hemorrhage(PPH)risk in high-risk pregnant women.Methods A total of 538 pregnant women with ultrasound-reported high-risk placenta accrete were retrospectively enrolled and divided into training,internal test,and external test cohorts.A nnUNet model was trained for automatic placental segmentation.Univariate and multivariate analyses were conducted on clinical features to identify those associated with PPH.Quantitative radiomic features were extracted from the placental region,and a random forest model was developed to predict estimated blood loss(EBL)and PPH risk.A DenseNet-based multi-task DL model was trained to predict PPH risk,EBL,and placenta previa status.Finally,a DL-radiomics ensemble(DRE)model was constructed by integrating clinical features,DL outputs,and radiomics scores.Diagnostic performance was evaluated using the area under the receiver operating characteristic curve(AUC)and DeLong test.Results The DRE model achieved AUC values of 0.874(95%CI:0.792-0.951)and 0.836(95%CI:0.648-0.974)in the internal and external test cohorts,respectively,significantly outperforming the standalone clinical,DL,and radiomics models.Incorporation of EBL regression improved the performance of the PPH classification model,with the external test AUC increasing from 0.261-0.788 to 0.836.Conclusion The DRE model integrating DL and radiomics can efficiently predict PPH risk and assist in the clinical management of high-risk pregnancies.