Pulmonary fibrosis（PF） is a progressive and life-threatening disease with limited therapeutic options， highlighting the urgent need for innovative drug discovery strategies. To address this challenge， the authors propose the formula-originated rational intelligent screening&translation（FIRST）， a systematic framework for developing anti-fibrotic monomers derived from classical traditional Chinese medicine（TCM）. The strategy integrates three key dimensions， including tissue-oriented intelligent screening of active compounds， structural optimization based on drug-target spatial interactions and plant biosynthetic pathways， and cross-scale validation of drug. We further highlight its applications in discovering tissue-oriented novel drugs from clinically validated TCM， the development and mechanistic elucidation of anti-fibrotic therapeutics， as well as the clinical translation and secondary development of candidate drugs. This strategy paves the way for first-in-class， formula-derived monomeric drugs with defined structures， clarified mechanisms， and proven safety， offering a transformative avenue to meet the urgent therapeutic needs of PF and setting a new paradigm for TCM-based drug innovation.

Pulmonary fibrosis（PF） is a progressive and life-threatening disease with limited therapeutic options， highlighting the urgent need for innovative drug discovery strategies. To address this challenge， the authors propose the formula-originated rational intelligent screening&translation（FIRST）， a systematic framework for developing anti-fibrotic monomers derived from classical traditional Chinese medicine（TCM）. The strategy integrates three key dimensions， including tissue-oriented intelligent screening of active compounds， structural optimization based on drug-target spatial interactions and plant biosynthetic pathways， and cross-scale validation of drug. We further highlight its applications in discovering tissue-oriented novel drugs from clinically validated TCM， the development and mechanistic elucidation of anti-fibrotic therapeutics， as well as the clinical translation and secondary development of candidate drugs. This strategy paves the way for first-in-class， formula-derived monomeric drugs with defined structures， clarified mechanisms， and proven safety， offering a transformative avenue to meet the urgent therapeutic needs of PF and setting a new paradigm for TCM-based drug innovation.

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.

Hepatocellular carcinoma(HCC)expresses abundant glycolytic enzymes and displays comprehensive glucose metabolism reprogramming.Aldolase A(ALDOA)plays a prominent role in glycolysis;however,little is known about its role in HCC development.In the present study,we aim to explore how ALDOA is involved in HCC proliferation.HCC proliferation was markedly suppressed both in vitro and in vivo following ALDOA knockout,which is consistent with ALDOA overexpression encouraging HCC prolifera-tion.Mechanistically,ALDOA knockout partially limits the glycolytic flux in HCC cells.Meanwhile,ALDOA translocated to nuclei and directly interacted with c-Jun to facilitate its Thr93 phosphorylation by P21-activated protein kinase;ALDOA knockout markedly diminished c-Jun Thr93 phosphorylation and then dampened c-Jun transcription function.A crucial site Y364 mutation in ALDOA disrupted its interaction with c-Jun,and Y364S ALDOA expression failed to rescue cell proliferation in ALDOA deletion cells.In HCC patients,the expression level of ALDOA was correlated with the phosphorylation level of c-Jun(Thr93)and poor prognosis.Remarkably,hepatic ALDOA was significantly upregulated in the promotion and progression stages of diethylnitrosamine-induced HCC models,and the knockdown of Aldoa strikingly decreased HCC development in vivo.Our study demonstrated that ALDOA is a vital driver for HCC development by activating c-Jun-mediated oncogene transcription,opening additional avenues for anti-cancer therapies.

The Huangdi Neijing proposes the " using bitter herbs to nourish or purge" theory to guide clinical prescription and formulation of herbal remedies based on the physiological characteristics and functions of the five zang viscera, along with the properties and flavors of medicinal herbs. This study explored diabetic kidney disease pathogenesis and treatment based on the " using bitter herbs to nourish or purge" theory. Kidney dryness is a key pathological factor in diabetic kidney disease, and the disharmony of kidney dryness is an essential aspect of its pathogenesis. Strengthening is the primary therapeutic principle, and kidney dryness is a persistent factor throughout the occurrence and progression of diabetic kidney disease. In the early stage, the pathogenesis involves heat-consuming qi and injuring yin, leading to kidney dryness. In the middle stage, the pathogenesis manifests as qi deficiency and blood stasis in the collaterals, resulting in turbidity owing to kidney dryness. In the late stage, the pathogenesis involves yin and yang deficiency, with kidney dryness and disharmony. This study proposes the staging-based treatment based on the " need for firmness" characteristic of the kidney. The aim is to provide new insights for clinical diagnosis and treatment in traditional Chinese medicine by rationally using pungent, bitter, and salty medicinal herbs to nourish and moisturize the kidney. This approach seeks to promote precise syndrome differentiation and personalized treatment for different stages of diabetic kidney disease, thereby enhancing clinical efficacy.

Objective To analyze the epidemiological distribution,microbiological characteristics,drug-resistance status,and risk factors for mortality in adult intensive care unit(ICU)patients with Klebsiella pneumoniae infection.Methods This multi-center prospective cohort study included ICU patients with suspected infection from 67 hospitals across 16 Chinese provinces/municipalities between July 1,2021 and December 31,2022.Clinical data and microbiological results were collected,and patients were divided into survival and non-survival groups according to their survival status and drug-resistance situation.Risk factors for mortality and drug resistance in ICU patients with Klebsiella pneumonia infection were determined through univariate and multivariate logistic regression analyses.Results A total of 2964 ICU-infected patients were enrolled,with 12 175 microbial specimens submitted for testing.Among these,487 specimens tested positive for Klebsiella pneumoniae.Ultimately,314 patients with Klebsiella pneumoniae infection were identified,primarily from lung infections,with a drug-resistance rate of 78.3%.The in-hospital mortality rate of ICU patients infected with Klebsiella pneumoniae was 19.8%.Univariate and multivariate logistic regression analyses revealed that older age(P=0.027),high drug-resistance rate(P=0.028),and low clinical-effectiveness rate(P<0.001)were independent risk factors for mortality in ICU patients infected with Klebsiella pneumoniae.Drug-resistance analysis showed that,compared with non-resistant cases,ICU patients with drug-resistant Klebsiella pneumoniae infection had lower pathogen-clearance rates(P=0.003),clinical-effectiveness rates(P=0.004),and antibiotic-effectiveness rates(P<0.010),and higher mortality rates(P=0.006).Patients with Klebsiella pneumoniae abdominal infection(P=0.003)and urinary tract infection(P=0.007)had higher drug-resistance incidences.There were no statistically significant differences in clinical-effectiveness rate,Klebsiella pneumoniae clearance,drug-resistance incidence,mortality rate,or hospital-stay length between patients with lung infection and those with non-lung infection of Klebsiella pneumoniae(P>0.05).Compared with patients with non-bloodstream infection,patients with bloodstream infection of Klebsiella pneumoniae had lower clinical-effectiveness rates(P=0.027)and higher mortality rates(P=0.021).Conclusions Older age,high drug-resistance rate,and low clinical-effectiveness rate are independent risk factors for mortality in ICU patients infected with Klebsiella pneumoniae.ICU patients with bloodstream infection of Klebsiella pneumoniae may have lower clinical-effectiveness rates and higher mortality rates.ICU patients with abdominal and urinary tract infections caused by Klebsiella pneumoniae are more likely to develop drug resistance.

Alzheimer's disease (AD) is the major form of dementia in the elderly and is closely related to the toxic effects of microglia sustained activation. In AD, sustained microglial activation triggers impaired synaptic pruning, neuroinflammation, neurotoxicity, and cognitive deficits. Accumulating evidence has demonstrated that aberrant expression of deubiquitinating enzymes is associated with regulating microglia function. Here, we use RNA sequencing to identify a deubiquitinase YOD1 as a regulator of microglial function and AD pathology. Further study showed that YOD1 knockout significantly improved the migration, phagocytosis, and inflammatory response of microglia, thereby improving the cognitive impairment of AD model mice. Through LC-MS/MS analysis combined with Co-IP, we found that Myosin heavy chain 9 (MYH9), a key regulator maintaining microglia homeostasis, is an interacting protein of YOD1. Mechanistically, YOD1 binds to MYH9 and maintains its stability by removing the K48 ubiquitin chain from MYH9, thereby mediating the microglia polarization signaling pathway to mediate microglia homeostasis. Taken together, our study reveals a specific role of microglial YOD1 in mediating microglia homeostasis and AD pathology, which provides a potential strategy for targeting microglia to treat AD.

Protein arginine methyltransferase 5 (PRMT5) acts as an oncogene in liver cancer, yet its roles and in-depth molecular mechanisms within the liver cancer immune microenvironment remain mostly undefined. Here, we demonstrated that disruption of tumor-intrinsic PRMT5 enhances CD8⁺ T-cell-mediated antitumor immunity both in vivo and in vitro. Further experiments verified that this effect is achieved through downregulation of the inhibitory immune checkpoint molecule, fibrinogen-like protein 1 (FGL1). Mechanistically, PRMT5 catalyzed symmetric dimethylation of transcription factor 12 (TCF12) at arginine 554 (R554), prompting the binding of TCF12 to FGL1 promoter region, which transcriptionally activated FGL1 in tumor cells. Methylation deficiency at TCF12-R554 residue downregulated FGL1 expression, which promoted CD8⁺ T-cell-mediated antitumor immunity. Notably, combining the PRMT5 methyltransferase inhibitor GSK591 with PD-L1 blockade efficiently inhibited liver cancer growth and improved overall survival in mice. Collectively, our findings reveal the immunosuppressive role and mechanism of PRMT5 in liver cancer and highlight that targeting PRMT5 could boost checkpoint immunotherapy efficacy.

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.