X-3D: Explicit 3D Structure Modeling for Point Cloud Recognition

Numerous prior studies predominantly emphasize constructing relation vectors for individual neighborhood points and generating dynamic kernels for each vector and embedding these into high-dimensional spaces to capture implicit local structures. However, we contend that such implicit high-dimensional structure modeling approch inadequately represents the local geometric structure of point clouds due to the absence of explicit structural information. Hence, we introduce X-3D, an explicit 3D structure modeling approach. X-3D functions by capturing the explicit local structural information within the input 3D space and employing it to produce dynamic kernels with shared weights for all neighborhood points within the current local region. This modeling approach introduces effective geometric prior and significantly diminishes the disparity between the local structure of the embedding space and the original input point cloud, thereby improving the extraction of local features. Experiments show that our method can be used on a variety of methods and achieves state-of-the-art performance on segmentation, classification, detection tasks with lower extra computational cost, such as \textbf{90.7\%} on ScanObjectNN for classification, \textbf{79.2\%} on S3DIS 6 fold and \textbf{74.3\%} on S3DIS Area 5 for segmentation, \textbf{76.3\%} on ScanNetV2 for segmentation and \textbf{64.5\%} mAP , \textbf{46.9\%} mAP on SUN RGB-D and \textbf{69.0\%} mAP , \textbf{51.1\%} mAP on ScanNetV2 . Our code is available at \href{https://github.com/sunshuofeng/X-3D}{https://github.com/sunshuofeng/X-3D}

Berberine Could Ameliorate Cardiac Dysfunction via Interfering Myocardial Lipidomic Profiles in the Rat Model of Diabetic Cardiomyopathy

Background: Diabetic cardiomyopathy (DCM) is considered to be a distinct clinical entity independent of concomitant macro- and microvascular disorders, which is initiated partly by disturbances in energy substrates. This study was to observe the dynamic modulations of berberine in DCM rats and explore the changes of lipidomic profiles of myocardial tissue.Methods: Sprague-Dawley (SD) rats were fed high-sucrose and high-fat diet (HSHFD) for totally 22 weeks and intraperitoneally (i.p.) injected with 30 mg/kg of streptozotocin (STZ) at the fifth week to induce DCM. Seventy-two hours after STZ injection, the rats were orally given with berberine at 10, 30 mg/kg and metformin at 200 mg/kg, respectively. Dynamic changes of cardiac function, heart mass ratios and blood lipids were observed at f 4, 10, 16, and 22, respectively. Furthermore, lipid metabolites in myocardial tissue at week 16 were profiled by the ultra-high-performance liquid chromatography coupled to a quadruple time of flight mass spectrometer (UPLC/Q-TOF/MS) approach.Results: Berberine could protect against cardiac diastolic and systolic dysfunctions, as well as cardiac hypertrophy, and the most effective duration is with 16-week of administration. Meanwhile, 17 potential biomarkers of phosphatidylcholines (PCs), phosphatidylethanolamines (PEs) and sphingolipids (SMs) of DCM induced by HSFD/STZ were identified. The perturbations of lipidomic profiles could be partly reversed with berberine intervention, i.e., PC (16:0/20:4), PC (18:2/0:0), PC (18:0/18:2), PC (18:0/22:5), PC (20:4/0:0), PC (20:4/18:0), PC (20:4/18:1), PC (20:4/20:2), PE (18:2/0:0), and SM (d18:0/16:0).Conclusions: These results indicated a close relationship between PCs, PEs and SMs and cardiac damage mechanisms during development of DCM. The therapeutic effects of berberine on DCM are partly caused by interferences with PCs, PEs, and SMs metabolisms

STAGER checklist: Standardized testing and assessment guidelines for evaluating generative artificial intelligence reliability

Generative artificial intelligence (AI) holds immense potential for medical applications, but the lack of a comprehensive evaluation framework and methodological deficiencies in existing studies hinder its effective implementation. Standardized assessment guidelines are crucial for ensuring reliable and consistent evaluation of generative AI in healthcare. Our objective is to develop robust, standardized guidelines tailored for evaluating generative AI performance in medical contexts. Through a rigorous literature review utilizing the Web of Sciences, Cochrane Library, PubMed, and Google Scholar, we focused on research testing generative AI capabilities in medicine. Our multidisciplinary team of experts conducted discussion sessions to develop a comprehensive 32‐item checklist. This checklist encompasses critical evaluation aspects of generative AI in medical applications, addressing key dimensions such as question collection, querying methodologies, and assessment techniques. The checklist and its broader assessment framework provide a holistic evaluation of AI systems, delineating a clear pathway from question gathering to result assessment. It guides researchers through potential challenges and pitfalls, enhancing research quality and reporting and aiding the evolution of generative AI in medicine and life sciences. Our framework furnishes a standardized, systematic approach for testing generative AI's applicability in medicine. For a concise checklist, please refer to Table S or visit GenAIMed.org . Highlights This work formulates the standardized testing and assessment guidelines for evaluating generative artificial intelligence (AI) reliability (STAGER) checklist, a 32‐item framework offering standardized assessment guidelines tailored for evaluating generative AI systems in medical and life science contexts. It consists of key aspects, including question collection, querying approaches, and assessment techniques. It enhances research quality and facilitates advances in this emerging field

Cellular Sensors and Viral Countermeasures: A Molecular Arms Race between Host and SARS-CoV-2

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of the coronavirus disease 2019 (COVID-19) pandemic that has caused disastrous effects on the society and human health globally. SARS-CoV-2 is a sarbecovirus in the Coronaviridae family with a positive-sense single-stranded RNA genome. It mainly replicates in the cytoplasm and viral components including RNAs and proteins can be sensed by pattern recognition receptors including toll-like receptors (TLRs), RIG-I-like receptors (RLRs), and NOD-like receptors (NLRs) that regulate the host innate and adaptive immune responses. On the other hand, the SARS-CoV-2 genome encodes multiple proteins that can antagonize the host immune response to facilitate viral replication. In this review, we discuss the current knowledge on host sensors and viral countermeasures against host innate immune response to provide insights on virus–host interactions and novel approaches to modulate host inflammation and antiviral responses

Metal-based strategies for the fight against COVID-19

Metallodrugs and metal complexes kill SARS-CoV-2 through inhibition of multiple viral proteins/enzymes.</jats:p

Emodin Improves Glucose and Lipid Metabolism Disorders in Obese Mice via Activating Brown Adipose Tissue and Inducing Browning of White Adipose Tissue

Abstract Background: Obesity has become a worldwide health threat related to type 2 diabetes, hypertension, cardiovascular disease, etc. Activating brown adipocytes and inducing browning of white adipocytes has been proposed as a potential molecular target for obesity treatment. In the present study, we investigated the effects of emodin on browning in mice with high-fat diet (HFD) and explore its underlying pharmacological mechanisms. Methods: The positive effects of emodin (40, 80 mg/kg/day, i.g. for 6 weeks) on lipid metabolism were evaluated in mice model of hyperlipidemia. Hyperlipidemia mice were induced by high-fat diet (60% of kilocalories from fat, 5.24 Kcal/kg) for 8 weeks. Body weight and food intake were monitored every week. After 6 weeks of treatment, fasting blood glucose, oral glucose tolerance, Lee's index, the ratio of fat weight to body weight, blood lipids, and adipose tissues morphology were assayed. Then uncoupling protein 1 (UCP1), CD36, fatty acid transporter 4 (FATP4), peroxisome proliferator activated receptor α (PPARα) and prohibitin (PHB) protein of subcutaneous white adipose tissue (scWAT) and brown adipose tissue (BAT) were analyzed. In addition, the lipid metabolites in adipose tissues were analyzed by ultra-high- performance liquid chromatography with electrospray ionization tandem mass spectrometry.Results: Emodin treatment decreased body weight gain, fasting blood glucose, Lee's index, the ratio of scWAT weight to body weight, and the levels of total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-c) and Leptin in serum, and increased serum adiponectin content and improved glucose tolerance. Furthermore, emodin enhanced the expression of UCP1, CD36, FATP4, PPARα and PHB protein in scWAT and BAT. Meanwhile, emodin can significantly up-regulated lipid levels in scWAT of mice fed with HFD such as PC(O-18:2/22:5), PE(O-18:1/18:2), PE(O-18:2/20:4), PE(O-20:1/20:5), Cer(d14:1/20:0) and SM(d18:0/23:0), and reduced the lipid levels such as PC(O-18:0/20:0), PE(O-18:2/22:2), PE(O-18:0/22:5). In addition, emodin significantly up-regulate lipid levels in BAT of mice fed with HFD such as PC(14:0/16:0), PC(16:0/16:1), PC(16:1/16:1), PC(15:1/18:3), PC(18:0/20:0), LysoPC(20:0), LysoPC(22:0) and LysoPC(22:1), and reduced the lipid levels PC(12:0/20:4) and PC(17:0/22:5). Conclusions: These results indicated that hyperlipidemia could be alleviated by treatment of emodin via promoting browning of white adipose tissue. In addition, the disturbance of some small lipid metabolites in adipose also could be reversed by emodin.</jats:p

Photoresponsive polymers with multi-azobenzene groups

Photoresponsive polymers with multi-azobenzene groups are reviewed and their potential applications in photoactuation, photo-patterning, and photoinduced birefringence are introduced.</p