Arginine methylation is a critical post-translational modification that plays multifaceted biological functions. However, the manipulation of protein arginine methylation largely depends on genetic or pharmaceutic inhibition of the regulatory enzymes, protein arginine methyltransferases (PRMTs), or non-methylation substitution of corresponding arginine residue to lysine or alanine of protein of interest (POI), which inevitably affects other substrates, or disrupts the structure of POI. Thus, it urges an approach to specifically modulate the arginine methylation of a POI under physiological conditions. To this end, we report the discovery of a methylation tagging system (MeTAG), that enables targeted modification of protein arginine methylation. Through bridging the methyltransferase PRMT5 proximity to a POI, MeTAG facilitates the arginine methylation of POIs, including known arginine methylated proteins, androgen receptor (AR) and protein kinase B (AKT), as well as a neo-substrate E1A binding protein (p300), in a reversible and PRMT5-dependent manner. Moreover, MeTAG can regulate downstream signaling in a methylation dependent manner, leading to downregulation of PSMA mRNA level and activation of AKT. Therefore, MeTAG represents a feasible approach to modulate protein methylation and thereby perturbs protein function in biological and therapeutic contexts.

Objective To propose a lung artery segmentation method that integrates shape and position prior knowledge, aiming to solve the issues of inaccurate segmentation caused by the high similarity and small size differences between the lung arteries and surrounding tissues in CT images. Methods Based on the three-dimensional U-Net network architecture and relying on the PARSE 2022 database image data, shape and position prior knowledge was introduced to design feature extraction and fusion strategies to enhance the ability of lung artery segmentation. The data of the patients were divided into three groups: a training set, a validation set, and a test set. The performance metrics for evaluating the model included Dice Similarity Coefficient (DSC), sensitivity, accuracy, and Hausdorff distance (HD95). Results The study included lung artery imaging data from 203 patients, including 100 patients in the training set, 30 patients in the validation set, and 73 patients in the test set. Through the backbone network, a rough segmentation of the lung arteries was performed to obtain a complete vascular structure; the branch network integrating shape and position information was used to extract features of small pulmonary arteries, reducing interference from the pulmonary artery trunk and left and right pulmonary arteries. Experimental results showed that the segmentation model based on shape and position prior knowledge had a higher DSC (82.81%±3.20% vs. 80.47%±3.17% vs. 80.36%±3.43%), sensitivity (85.30%±8.04% vs. 80.95%±6.89% vs. 82.82%±7.29%), and accuracy (81.63%±7.53% vs. 81.19%±8.35% vs. 79.36%±8.98%) compared to traditional three-dimensional U-Net and V-Net methods. HD95 could reach (9.52±4.29) mm, which was 6.05 mm shorter than traditional methods, showing excellent performance in segmentation boundaries. Conclusion The lung artery segmentation method based on shape and position prior knowledge can achieve precise segmentation of lung artery vessels and has potential application value in tasks such as bronchoscopy or percutaneous puncture surgery navigation.

Transformer models have emerged as pivotal tools within the realm of drug discovery,distinguished by their unique architectural features and exceptional performance in managing intricate data landscapes.Leveraging the innate capabilities of transformer architectures to comprehend intricate hierarchical dependencies inherent in sequential data,these models showcase remarkable efficacy across various tasks,including new drug design and drug target identification.The adaptability of pre-trained trans-former-based models renders them indispensable assets for driving data-centric advancements in drug discovery,chemistry,and biology,furnishing a robust framework that expedites innovation and dis-covery within these domains.Beyond their technical prowess,the success of transformer-based models in drug discovery,chemistry,and biology extends to their interdisciplinary potential,seamlessly combining biological,physical,chemical,and pharmacological insights to bridge gaps across diverse disciplines.This integrative approach not only enhances the depth and breadth of research endeavors but also fosters synergistic collaborations and exchange of ideas among disparate fields.In our review,we elucidate the myriad applications of transformers in drug discovery,as well as chemistry and biology,spanning from protein design and protein engineering,to molecular dynamics(MD),drug target iden-tification,transformer-enabled drug virtual screening(VS),drug lead optimization,drug addiction,small data set challenges,chemical and biological image analysis,chemical language understanding,and single cell data.Finally,we conclude the survey by deliberating on promising trends in transformer models within the context of drug discovery and other sciences.

Objective To evaluate the diagnostic value of intestinal ultrasound (IUS) for organic lesions in the intestines of patients with chronic abdominal pain or diarrhea. Methods The IUS signs in 263 patients with chronic abdominal pain or diarrhea were retrospectively analyzed.With the endoscopic examination results as the gold standard,comparison was performed for the IUS signs between the groups with positive and negative endoscopic results,as well as between the inflammatory bowel disease group and the non-specific intestinal inflammation group of positive cases.Furthermore,the detection rates of IUS in different intestinal segments were analyzed to evaluate the accuracy of IUS in the diagnosis and localization of intestinal lesions. Results Among the 263 patients,194 (73.8%) and 69 (26.2%) patients were in the groups with positive and negative endoscopic results,respectively.The diagnosis sensitivity,specificity,and accuracy of IUS were 82.0%,71.0%,and 79.1%,respectively.The proportions of positive IUS signs in the group with positive endoscopic results were higher than that in the group with negative endoscopic results (all P<0.001).The proportions of positive IUS signs in the inflammatory bowel disease group were higher than those in the non-specific bowel inflammation group (all P<0.001).When the lesion was located in the ileum,ileocecal region,and colon,IUS demonstrated good consistency with endoscopic results in locating the lesion (kappa=0.642,0.686,and 0.601,respectively),with sensitivity and specificity of 82.7% (95%CI=75.4%-88.6%) and 81.5% (95%CI=73.5%-87.9%),73.7% (95%CI=62.3%-83.1%) and 93.0% (95%CI=88.4%-96.2%),and 68.9% (95%CI=58.3%-78.2%) and 89.6% (95%CI=84.1%-93.7%),respectively. Conclusions IUS can be used for screening the patients with chronic abdominal pain or diarrhea to detect organic lesions in the intestines. Moreover,it can effectively locate the affected intestinal segment,which is helpful for the monitoring and follow-up of intestinal diseases.
Humans ; Diarrhea/diagnostic imaging* ; Female ; Male ; Abdominal Pain/diagnostic imaging* ; Middle Aged ; Adult ; Ultrasonography ; Retrospective Studies ; Aged ; Young Adult ; Intestines/diagnostic imaging* ; Adolescent ; Chronic Disease ; Sensitivity and Specificity ; Aged, 80 and over

Transformer models have emerged as pivotal tools within the realm of drug discovery, distinguished by their unique architectural features and exceptional performance in managing intricate data landscapes. Leveraging the innate capabilities of transformer architectures to comprehend intricate hierarchical dependencies inherent in sequential data, these models showcase remarkable efficacy across various tasks, including new drug design and drug target identification. The adaptability of pre-trained transformer-based models renders them indispensable assets for driving data-centric advancements in drug discovery, chemistry, and biology, furnishing a robust framework that expedites innovation and discovery within these domains. Beyond their technical prowess, the success of transformer-based models in drug discovery, chemistry, and biology extends to their interdisciplinary potential, seamlessly combining biological, physical, chemical, and pharmacological insights to bridge gaps across diverse disciplines. This integrative approach not only enhances the depth and breadth of research endeavors but also fosters synergistic collaborations and exchange of ideas among disparate fields. In our review, we elucidate the myriad applications of transformers in drug discovery, as well as chemistry and biology, spanning from protein design and protein engineering, to molecular dynamics (MD), drug target identification, transformer-enabled drug virtual screening (VS), drug lead optimization, drug addiction, small data set challenges, chemical and biological image analysis, chemical language understanding, and single cell data. Finally, we conclude the survey by deliberating on promising trends in transformer models within the context of drug discovery and other sciences.

Objective:To investigate the incidence, clinical characteristics, and impact of spontaneous portosystemic shunt (SPSS) in patients with liver cirrhosis combined with hepatic encephalopathy (HE).Methods:The basic clinical and follow-up data were retrospectively analyzed for patients diagnosed with cirrhosis combined with HE at Mengchao Hepatobiliary Hospital of Fujian Medical University from January 2017 to December 2022. The patients were divided into large and small SPSS groups and a control group based on the results of abdominal enhanced CT or MRI.The clinical characteristics and outcome differences were compared among the three groups. Kaplan-Meier survival curves were used to compare HE-free survival time and overall survival time among the three groups. The log-rank test was used to compare the differences between groups. Cox regression analysis was used to identify the relevant risk factors affecting HE-free survival time and overall survival time.Results:A total of 223 cases with liver cirrhosis combined with HE were enrolled, including 150 in the SPSS and 73 in the control groups. The incidence rate of SPSS was 67.3% (150/223). The group was divided into small SPSS (79/150, 52.7%) and large SPSS group (71/150, 47.3%) according to the cross-sectional area of the diversion channel. The HE-free survival was shorter in the small and large SPSS groups compared with the control group (35.5 months in the small SPSS group and 21.3 months in the large SPSS group; P<0.001). The HE-free survival time was shorter in the large SPSS than with small SPSS group ( P=0.003). The overall survival time in the small SPSS group and the large SPSS group was shorter compared with the control group (small SPSS group: 39.4 months, large SPSS group: 52.9 months; P<0.001). There was no statistically significant difference in overall survival time between the small SPSS and large SPSS groups ( P=0.700). Cox regression analysis showed that SPSS was an independent risk factor affecting patients' HE-free survival time and overall survival time ( P<0.05). Conclusion:SPSS is more common in patients with liver cirrhosis combined with HE. Patients who combined with SPSS showed significant reductions in both HE-free survival time and overall survival time, especially evident in those with combined large SPSS.

Hepatic encephalopathy (HE) is a kind of neuropsychiatric syndrome caused by acute or chronic liver failure or portosystemic venous shunt (hereinafter referred to as portosystemic shunt), which can lead to the occurrence of functional impairment, personality and behavioral abnormalities, coma, and even death. Most patients with cirrhosis combined with HE have spontaneous portosystemic shunt (SPSS), especially those with recurrent or persistent HE. Internal medicine's current standard of treatment for HE associated with SPSS in cirrhotic patients is unsatisfactory, and even after treatment, recurrent HE episodes may still occur. Although interventional therapy has shown significant results and has been applied in clinical practice for many years for SPSS-associated HE, the number of treatment-related cases is relatively small, and there is a lack of large samples and well-designed research. Currently, interventional therapy for SPSS-associated HE in patients with cirrhosis is still under continuous exploration.

China has become the country with the highest global burden of heart failure(HF).Despite the widespread use of prognostic-improving medications today,the mortality rate of HF remains high,reaching 13.7％at one year-particularly among patients with heart failure with reduced ejection fraction(HFrEF).HF interventional device therapy(structural intervention)targets the structural factors underlying HF,including atrial pressure,ventricular remodeling,and valvular intervention.It leverages the heart's intrinsic physiological properties and pathological progression mechanisms to deliver treatments through interventions without external active forces,achieving anatomical or functional repair.This field has emerged as a rapidly growing area and plays an increasingly critical role in HF management.This article provides a comprehensive review and summary of the latest advancements in HF and cardiomyopathy interventional therapy over the past year.It covers various novel technologies and products currently in the research phase,aiming to provide an in-depth analysis of the current status and future directions of HF interventional therapy,and further advance the development of this discipline.

China has become the country with the highest global burden of heart failure(HF).Despite the widespread use of prognostic-improving medications today,the mortality rate of HF remains high,reaching 13.7％at one year-particularly among patients with heart failure with reduced ejection fraction(HFrEF).HF interventional device therapy(structural intervention)targets the structural factors underlying HF,including atrial pressure,ventricular remodeling,and valvular intervention.It leverages the heart's intrinsic physiological properties and pathological progression mechanisms to deliver treatments through interventions without external active forces,achieving anatomical or functional repair.This field has emerged as a rapidly growing area and plays an increasingly critical role in HF management.This article provides a comprehensive review and summary of the latest advancements in HF and cardiomyopathy interventional therapy over the past year.It covers various novel technologies and products currently in the research phase,aiming to provide an in-depth analysis of the current status and future directions of HF interventional therapy,and further advance the development of this discipline.

Objective:To investigate the incidence, clinical characteristics, and impact of spontaneous portosystemic shunt (SPSS) in patients with liver cirrhosis combined with hepatic encephalopathy (HE).Methods:The basic clinical and follow-up data were retrospectively analyzed for patients diagnosed with cirrhosis combined with HE at Mengchao Hepatobiliary Hospital of Fujian Medical University from January 2017 to December 2022. The patients were divided into large and small SPSS groups and a control group based on the results of abdominal enhanced CT or MRI.The clinical characteristics and outcome differences were compared among the three groups. Kaplan-Meier survival curves were used to compare HE-free survival time and overall survival time among the three groups. The log-rank test was used to compare the differences between groups. Cox regression analysis was used to identify the relevant risk factors affecting HE-free survival time and overall survival time.Results:A total of 223 cases with liver cirrhosis combined with HE were enrolled, including 150 in the SPSS and 73 in the control groups. The incidence rate of SPSS was 67.3% (150/223). The group was divided into small SPSS (79/150, 52.7%) and large SPSS group (71/150, 47.3%) according to the cross-sectional area of the diversion channel. The HE-free survival was shorter in the small and large SPSS groups compared with the control group (35.5 months in the small SPSS group and 21.3 months in the large SPSS group; P<0.001). The HE-free survival time was shorter in the large SPSS than with small SPSS group ( P=0.003). The overall survival time in the small SPSS group and the large SPSS group was shorter compared with the control group (small SPSS group: 39.4 months, large SPSS group: 52.9 months; P<0.001). There was no statistically significant difference in overall survival time between the small SPSS and large SPSS groups ( P=0.700). Cox regression analysis showed that SPSS was an independent risk factor affecting patients' HE-free survival time and overall survival time ( P<0.05). Conclusion:SPSS is more common in patients with liver cirrhosis combined with HE. Patients who combined with SPSS showed significant reductions in both HE-free survival time and overall survival time, especially evident in those with combined large SPSS.