Medical review (Berlin, Germany)最新文献

Objectives: Kasai portoenterostomy (KPE) is the primary treatment for biliary atresia (BA). However, postoperative complications such as cholangitis with bile lake formation frequently lead to liver failure. This study aimed to evaluate the therapeutic efficacy and significance of revision portoenterostomy in patients with bile lakes at the porta hepatis following KPE.

Methods: A retrospective analysis of patients with BA who underwent revision portoenterostomy from January 2011 to December 2021 was conducted. Patient data, including laboratory tests, imaging findings, and follow-up records, were comprehensively reviewed. The jaundice clearance rate and autologous liver survival rate were assessed using the Kaplan-Meier method. The Cox proportional hazards model was employed to identify factors influencing revision portoenterostomy outcomes.

Results: Twenty patients with cholangitis and bile lakes underwent revision portoenterostomy. Sixteen patients (80 %) achieved jaundice clearance, and 14 (60 %) attained autologous liver survival, resulting in an overall survival rate of 90 %. All patients with refractory cholangitis preoperatively showed improvement following revision portoenterostomy. Additionally, the revision portoenterostomy procedure did not lead to increased blood loss or extended operation times in subsequent liver transplants.

Conclusions: Revision portoenterostomy for patients with bile lakes effectively alleviates intractable cholangitis and can delay or eliminate the necessity for liver transplantation.

Macroautophagy/autophagy is a lysosome-dependent degradation process involved in cellular energy metabolism, recycling and quality control. Autophagy is a highly dynamic and precisely regulated process, which contains four major steps: autophagic membrane initiation and cargo recognition, autophagosome formation, autophagosome-lysosome fusion and lysosomal degradation. During the terminal phase of autophagy, the merging of the autophagosome and lysosome membranes is critical for the effective breakdown of sequestered cargoes. However, the participated molecules and the interplay among them have not been fully uncovered. The spatiotemporal property of these molecules is crucial for maintaining the orderly fusion of autophagosomes and lysosomes, otherwise it may lead to fusion disorders. In this article, we tend to summarize the molecules mediating autophagosome-lysosome fusion into two categories: effector molecules and regulatory molecules. The effector molecules are soluble N-ethylmaleimide-sensitive factor attachment protein receptor and tethering proteins, and the latter category contains phosphatidylinositol, Rab GTPases and ATG8-family proteins. The spatio-temporal properties of these autophagosome-lysosome fusion mediating molecules will be featured in this review.

Thyroid-associated ophthalmopathy (TAO), also known as Graves' ophthalmopathy (GO) is an autoimmune disease (AD) with abnormal thyroid function typically. Currently, intravenous glucocorticoid therapy remains the first-line treatment for moderate-to-severe active TAO. Second-line treatments, including immunosuppressants and biological agents, are being explored in depth. However, like other ADs, the adverse effects of these therapies, little impact on long-term sequelae, and the irreversible progression of the disease remain significant limitations. As a result, the development of new therapeutic strategies for TAO is essential. Chimeric antigen receptors (CAR)-based adoptive cell therapy has emerged as an innovative approach for ADs treatment, capitalizing on its principles of genetically modifying immune cells to specifically target pathogenic cells. This approach aims to reduce autoimmune response or eliminate effective cells, CAR-based therapies of both T-cell-mediated and B-cell-mediated ADs have shown promising results in wide clinical trial. CAR-based therapy obviously become a rising star on refractory and relapsed ADs. TAO is no exception in terms of the potential for improvement through CAR-based therapy. However, the success of CAR-based therapy in TAO depends critically on identifying appropriate targets. Selected targets need to be coverage to ensure the therapeutic efficiency while specificity to preserve safety. Furthermore, the target cells must be relevant to the pathogenesis of TAO. Except target selection, adopting advanced and effective strategies for CAR design is also crucial. For example, dual-target approaches involving thyroid-stimulating hormone receptor (TSHR) or insulin-like growth factor-1 receptor (lGF-1R), off-the-shelf CAR-based cells, or leveraging artificial intelligence (Al) to predict optimal targets could enhance the specificity and effectiveness of CAR-based, therapies in TAO treatment.

The integration of artificial intelligence (AI) in medical diagnostics represents a transformative advancement in healthcare, with projected market growth reaching $188 billion by 2030. This comprehensive review examines the latest developments in AI-driven diagnostic technologies across multiple disease domains, particularly focusing on cancer, Alzheimer's disease (AD), and diabetes. Through systematic bibliometric analysis using GraphRAG methodology, we analyzed research publications from 2022 to 2024, revealing the distribution and impact of AI applications across various medical fields. In cancer diagnostics, AI systems have achieved breakthrough performances in analyzing medical imaging and molecular data, with notable advances in early detection capabilities across 19 different cancer types. For AD diagnosis, AI-powered tools have demonstrated up to 90 % accuracy in risk detection through non-invasive methods, including speech pattern analysis and blood-based biomarkers. In diabetes care, AI-integrated systems incorporating deep neural networks and electronic nose technology have shown remarkable accuracy in predicting disease onset before clinical manifestation. These developments collectively indicate a paradigm shift toward more precise, efficient, and accessible diagnostic approaches. However, challenges remain in standardization, data quality, and clinical implementation. This review synthesizes current progress while highlighting the potential for AI to revolutionize medical diagnostics through enhanced accuracy, early detection, and personalized patient care.

Natural products, the most important chemical library with magical structures and unique functions, have long been playing significant roles in contributing to the discovery of novel drugs. The complexity and diversity of natural products present great challenges regarding the exploration of their potential targets. Identifying the targets of natural products not only enhances our understanding of biological functions and molecular mechanisms, but also paves the way for discovering novel lead compounds for disease treatment. Recent advances in technologies like chemical biology, structural biology, and artificial intelligence have provided powerful tools for pinpointing natural product target and unraveling molecular mechanisms. This review aims to comprehensively summarize the innovative strategies employed in recent years to identify natural product targets, and evaluate their impact on biological pathways by modulating target functions for pharmacological effects. Moreover, we also discuss the challenges encountered in this field and outline future research prospects, aiming to offer guidance for researchers in natural product chemical biology.

B7-H3 (CD276) is one of the immune checkpoint molecules at the forefront of cancer biology, plays a diverse role in immune regulation and cancer progression, while its immunosuppressive functions enable tumors to escape immune detection, its contribution to processes such as angiogenesis, metabolic reprogramming and chemoresistance underscores its broader impact on the tumor microenvironment (TME). These properties make B7-H3 an attractive target for cancer therapy. This perspective discusses the immune and non-immune related functions of B7-H3, the challenges in tapping its therapeutic potential.

Cardiac ion channels are critical transmembrane proteins that mediate almost all aspects of cardiac function including generation and propagation of cardiac action potential (AP) as well as maintenance of normal heart excitability and contraction. In addition, the pivotal role of cardiac ion channels in cardiac health and disease is underscored by the profound effects of their dysfunctional mutations on various arrhythmias. Hence, ion channels are vital targets for antiarrhythmic drugs. In this review, we first summarize the characteristics, structure of the various cardiac ion channels and their specific roles in cardiac electrophysiology. Subsequently, we highlight the implications of genetic mutations that disrupt ion channel function, which are associated with inherited cardiac arrhythmias. Finally, we address antiarrhythmic drugs acting on cardiac ion channels respectively, according to their therapeutic targets. In conclusion, this manuscript aims to review the physiology, pathophysiology and pharmacology of the most prominent ventricular Na_V, Ca_V, K_V, and K_ir ion channels.

Cancer remains a substantial global health challenge, with steadily increasing incidence rates. Radiotherapy (RT) is a crucial component in cancer treatment. Nevertheless, due to limited resources, there is an urgent need to enhance both its efficiency and therapeutic efficacy. The integration of Artificial Intelligence (AI) into RT has proven to significantly improve treatment efficiency, especially in time-consuming tasks. This perspective demonstrates how AI enhances the efficiency of target delineation and treatment planning, and introduces the concept of All-in-One RT, which may greatly improve RT efficiency. Furthermore, the concept of Radiotherapy Digital Twins (RDTs) is introduced. By integrating patient-specific data with AI, RDTs enable personalized and precise treatment, as well as the evaluation of therapeutic efficacy. This perspective highlights the transformative impact of AI and digital twin technologies in revolutionizing cancer RT, with the aim of making RT more accessible and effective on a global scale.

The diagnosis and prognosis of Prostate cancer (PCa) have undergone a significant transformation with the advent of prostate-specific membrane antigen (PSMA)-targeted positron emission tomography (PET) imaging. PSMA-PET imaging has demonstrated superior performance compared to conventional imaging methods by detecting PCa, its biochemical recurrence, and sites of metastasis with higher sensitivity and specificity. That transformation now intersects with rapid advances in artificial intelligence (AI) - including the emergence of generative AI. However, there are unique clinical challenges associated with PSMA-PET imaging that still need to be addressed to ensure its continued widespread integration into clinical care and research trials. Some of those challenges are the very wide dynamic range of lesion uptake, benign uptake in organs that may be adjacent to sites of disease, insufficient large datasets for training AI models, as well as artifacts in the images. Generative AI models, e.g., generative adversarial networks, variational autoencoders, diffusion models, and large language models have played crucial roles in overcoming many such challenges across various imaging modalities, including PET, computed tomography, magnetic resonance imaging, ultrasound, etc. In this review article, we delve into the potential role of generative AI in enhancing the robustness and widespread utilization of PSMA-PET imaging and image analysis, drawing insights from existing literature while also exploring current limitations and future directions in this domain.

Current two-dimensional (2D) cell models for effective drug screening suffer from significant limitations imposed by the lack of realism in the physiological environment. Three-dimensional (3D) organoids models hold immense potential in mimicking the key functions of human organs by overcoming the limitations of traditional 2D cell models. However, current techniques for preparation of 3D organoids models had limitations in reproducibility, scalability, and the ability to closely replicate the complex microenvironment found in vivo. Additionally, traditional 3D cell culture systems often involve lengthy and labor-intensive processes that hinder high-throughput applications necessary for a large-scale drug screening. Advancements in 3D bioprinting technologies offer promising solutions to these challenges by enabling precise spatial control over cell placement and material composition, thereby facilitating the creation of more physiologically relevant organoids than current techniques. This review provides a comprehensive summary of recent advances in 3D bioprinting technologies for creating organoids models, which begins with an introduction to different types of 3D bioprinting techniques (especially focus on volumetric bioprinting (VBP) technique), followed by an overview of bioinks utilized for organoids bioprinting. Moreover, we also introduce the applications of 3D bioprinting organoids in disease models, drug efficiency evaluation and regenerative medicine. Finally, the challenges and possible strategies for the development and clinical translation of 3D bioprinting organoids are concluded.