T7 RNA polymerase (T7 RNAP) is one of the simplest known RNA polymerases. Its unique structural features make it a critical model for studying the mechanisms of RNA synthesis. This review systematically examines the static crystal structure of T7 RNAP, beginning with an in-depth examination of its characteristic “thumb”, “palm”, and “finger” domains, which form the classic “right-hand-like” architecture. By detailing these structural elements, this review establishes a foundation for understanding the overall organization of T7 RNAP. This review systematically maps the functional roles of secondary structural elements and their subdomains in transcriptional catalysis, progressively elucidating the fundamental relationships between structure and function. Further, the intrinsic flexibility of T7 RNAP and its applications in research are also discussed. Additionally, the review presents the structural diagrams of the enzyme at different stages of the transcription process, and through these diagrams, it provides a detailed description of the complete transcription process of T7 RNAP. By integrating structural dynamics and kinetics analyses, the review constructs a comprehensive framework that bridges static structure to dynamic processes. Despite its advantages, T7 RNAP has a notable limitation: it generates double-stranded RNA (dsRNA) as a byproduct. The presence of dsRNA not only compromises the purity of mRNA products but also elicits nonspecific immune responses, which pose significant challenges for biotechnological and therapeutic applications. The review provides a detailed exploration of the mechanisms underlying dsRNA formation during T7 RNAP catalysis, reviews current strategies to mitigate this issue, and highlights recent progress in the field. A key focus is the semi-rational design of T7 RNAP mutants engineered to minimize dsRNA generation and enhance catalytic performance. Beyond its role in transcription, T7 RNAP exhibits rapid development and extensive application in fields, including gene editing, biosensing, and mRNA vaccines. This review systematically examines the structure-function relationships of T7 RNAP, elucidates the mechanisms of dsRNA formation, and discusses engineering strategies to optimize its performance. It further explores the engineering optimization and functional expansion of T7 RNAP. Furthermore, this review also addresses the pressing issues that currently need resolution, discusses the major challenges in the practical application of T7 RNAP, and provides an outlook on potential future research directions. In summary, this review provides a comprehensive analysis of T7 RNAP, ranging from its structural architecture to cutting-edge applications. We systematically examine: (1) the characteristic right-hand domains (thumb, palm, fingers) that define its minimalistic structure; (2) the structure-function relationships underlying transcriptional catalysis; and (3) the dynamic transitions during the complete transcription cycle. While highlighting T7 RNAP’s versatility in gene editing, biosensing, and mRNA vaccine production, we critically address its major limitation—dsRNA byproduct formation—and evaluate engineering solutions including semi-rationally designed mutants. By synthesizing current knowledge and identifying key challenges, this work aims to provide novel insights for the development and application of T7 RNAP and to foster further thought and progress in related fields.

Generative artificial intelligence (GAI) represents a prominent research focus in medicine, with medical education being a key application area. GAI demonstrates potential to enhance residency training efficacy through personalized instruction, automated assessment item generation, question bank updating, and intelligent scoring systems. However, current limitations exist regarding output accuracy and content consistency. To address these constraints, strategic measures are required: continuous GAI model refinement, development of standardized usage guidelines, enhanced data quality control, and implementation of human verification protocols for generated content. Concurrently, residents should proactively acquire GAI utilization skills to strengthen the practical application of theoretical knowledge. With these advancements, GAI is anticipated to evolve into a valuable asset for improving the efficiency and quality of residency training programs.

Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by progressive cognitive decline, functional impairment, and neuropsychiatric symptoms. It represents the most prevalent form of dementia among the elderly population. Accumulating evidence indicates that oxidative stress plays a pivotal role in the pathogenesis of AD. Notably, elevated levels of oxidative stress have been observed in the brains of AD patients, where excessive reactive oxygen species (ROS) can cause extensive damage to lipids, proteins, and DNA, ultimately compromising neuronal structure and function. Amyloid β‑protein (Aβ) has been shown to induce mitochondrial dysfunction and calcium overload, thereby promoting the generation of ROS. This, in turn, exacerbates Aβ aggregation and enhances tau phosphorylation, leading to the formation of two pathological features of AD: extracellular Aβ plaque deposition and intracellular neurofibrillary tangles (NFTs). These events ultimately culminate in neuronal death, forming a vicious cycle. The interplay between oxidative stress and these pathological processes constitutes a core link in the pathogenesis of AD. The signaling pathways mediating oxidative stress in AD include Nrf2, RCAN1, PP2A, CREB, Notch1, NF‑κB, ApoE, and ferroptosis. Nrf2 signaling pathway serves as a key regulator of cellular redox homeostasis, exerts important antioxidant capacity and protective effects in AD. RCAN1 signaling pathway, as a calcineurin inhibitor, and modulates AD progression through multiple mechanisms. PP2A signaling pathway is involved in regulating tau phosphorylation and neuroinflammation processes. CREB signaling pathway contributes to neuroplasticity and memory formation; activation of CREB improves cognitive function and reduce oxidative stress. Notch1 signaling pathway regulates neuronal development and memory, participates in modulation of Aβ production, and interacts with Nrf2 toco-regulate antioxidant activity. NF‑κB signaling pathway governs immune and inflammatory responses; sustained activation of this pathway forms “inflammatory memory”, thereby exacerbating AD pathology. ApoE signaling pathway is associated with lipid metabolism; among its isoforms, ApoE-ε4 significantly increases the risk of AD, leading to elevated oxidative stress, abnormal lipid metabolism, and neuroinflammation. The ferroptosis signaling pathway is driven by iron-dependent lipid peroxidation, and the subsequent release of lipid peroxidation products and ROS exacerbate oxidative stress and neuronal damage. These interconnected pathways form a complex regulatory network that regulates the progression of AD through oxidative stress and related pathological cascades. In terms of therapeutic strategies targeting oxidative stress, among the drugs currently used in clinical practice for AD treatment, memantine and donepezil demonstrate significant therapeutic efficacy and can improve the level of oxidative stress in AD patients. Some compounds with antioxidant effects (such asα-lipoic acid and melatonin) have shown certain potential in AD treatment research and can be used as dietary supplements to ameliorate AD symptoms. In addition, non-drug interventions such as calorie restriction and exercise have been proven to exerted neuroprotective effects and have a positive effect on the treatment of AD. By comprehensively utilizing the therapeutic characteristics of different signaling pathways, it is expected that more comprehensive multi-target combination therapy regimens and combined nanomolecular delivery systems will be developed in the future to bypass the blood-brain barrier, providing more effective therapeutic strategies for AD.

Clinical practice guidelines represent the best recommendations for patient care. They are developed through systematically reviewing currently available clinical evidence and weighing the relative benefits and risks of various interventions. However, clinical practice guidelines have to go through a long translation cycle from development and revision to clinical promotion and application, facing problems such as scattered distribution, high duplication rate, and low actual utilization. At present, the clinical practice guideline information platform can directly or indirectly solve the problems related to the lengthy revision cycles, decentralized dissemination and limited application of clinical practice guidelines. Therefore, this paper systematically examines different types of clinical practice guideline information platforms and investigates their corresponding challenges and emerging trends in platform design, data integration, and practical implementation, with the aim of clarifying the current status of this field and providing valuable reference for future research on clinical practice guideline information platforms.

Pulmonary electrical impedance tomography (EIT), a noninvasive, continuous, dynamic, and radiation-free bedside imaging technique for monitoring pulmonary ventilation, is now widely utilized in the diagnosis and management of critically ill patients. Beyond ventilation monitoring, hypertonic saline contrast-enhanced EIT for bedside pulmonary perfusion assessment has recently garnered significant attention. This article describes the application of hypertonic saline contrast-enhanced EIT to evaluate pulmonary perfusion in two patients following pulmonary endarterectomy, providing a reference for its perioperative application in such patients.

Immobilized enzyme-based enzyme electrode biosensors, characterized by high sensitivity and efficiency, strong specificity, and compact size, demonstrate broad application prospects in life science research, disease diagnosis and monitoring, etc. Immobilization of enzyme is a critical step in determining the performance (stability, sensitivity, and reproducibility) of the biosensors. Random immobilization (physical adsorption, covalent cross-linking, etc.) can easily bring about problems, such as decreased enzyme activity and relatively unstable immobilization. Whereas, directional immobilization utilizing amino acid residue mutation, affinity peptide fusion, or nucleotide-specific binding to restrict the orientation of the enzymes provides new possibilities to solve the problems caused by random immobilization. In this paper, the principles, advantages and disadvantages and the application progress of enzyme electrode biosensors of different directional immobilization strategies for enzyme molecular sensing elements by specific amino acids (lysine, histidine, cysteine, unnatural amino acid) with functional groups introduced based on site-specific mutation, affinity peptides (gold binding peptides, carbon binding peptides, carbohydrate binding domains) fused through genetic engineering, and specific binding between nucleotides and target enzymes (proteins) were reviewed, and the application fields, advantages and limitations of various immobilized enzyme interface characterization techniques were discussed, hoping to provide theoretical and technical guidance for the creation of high-performance enzyme sensing elements and the manufacture of enzyme electrode sensors.

Accurate prediction of drug-target interactions (DTIs) plays a pivotal role in drug discovery, facilitating optimization of lead compounds, drug repurposing and elucidation of drug side effects. However, traditional DTI prediction methods are often limited by incomplete biological data and insufficient representation of protein features. In this study, we proposed KG-CNNDTI, a novel knowledge graph-enhanced framework for DTI prediction, which integrates heterogeneous biological information to improve model generalizability and predictive performance. The proposed model utilized protein embeddings derived from a biomedical knowledge graph via the Node2Vec algorithm, which were further enriched with contextualized sequence representations obtained from ProteinBERT. For compound representation, multiple molecular fingerprint schemes alongside the Uni-Mol pre-trained model were evaluated. The fused representations served as inputs to both classical machine learning models and a convolutional neural network-based predictor. Experimental evaluations across benchmark datasets demonstrated that KG-CNNDTI achieved superior performance compared to state-of-the-art methods, particularly in terms of Precision, Recall, F1-Score and area under the precision-recall curve (AUPR). Ablation analysis highlighted the substantial contribution of knowledge graph-derived features. Moreover, KG-CNNDTI was employed for virtual screening of natural products against Alzheimer's disease, resulting in 40 candidate compounds. 5 were supported by literature evidence, among which 3 were further validated in vitro assays.
Alzheimer Disease/drug therapy* ; Biological Products/therapeutic use* ; Humans ; Neural Networks, Computer ; Machine Learning ; Drug Discovery/methods* ; Algorithms ; Drug Evaluation, Preclinical/methods*

Objective:To evaluate the efficacy of multimodal prehabilitation in patients with refractory functional constipation undergoing Jinling procedure(modified Duhamel surgery).Methods:In this prospective randomized controlled trial,80 patients with refractory functional constipation scheduled for Jinling procedure at the Department of General Surgery,the General Hospital of Eastern Theater Command between January 2020 and December 2021 were enrolled.Participants were randomly assigned to either the observation group(n=40,multimodal prehabilitation)or control group(n=40,routine nursing care).Outcome measures included:time to first flatus,time to first ambulation,defecation volume on postoperative day 5,length of hospitalization,nutritional markers(hemoglobin,albumin,total protein at postoperative day 7),anxiety/depression scores(Hospital Anxiety and Depression Scale,HADS),and total complication rates.Results:Compared to controls,the first ventilation time(48.02±6.15)h,first ambulation time(49.92±5.58)h,defecation volume on the fifth day(234.50±51.03)mL,hospital stay(13.15±2.64)d,anxiety score(43.68±3.45)points,depression score(43.81±1.58)points,and the total incidence of postoperative complications(15%)were significantly lower in the observation group(all p values<0.05).By contrast,the serum levels of hemoglobin(115.60±11.60)g/l,albumin(41.19±5.79)g/L and total protein(61.64±4.94)g/L on day 7 post-operatively were significantly higher in the observation group than those in the control group(P<0.05).Conclusions:Multimodal prehabilitation enhances postoperative intestinal recovery,reduces complications,improves nutritional status,and shortens hospital stays in refractory functional constipation patients undergoing Jinling procedure,supporting its clinical adoption.

The life view of physique-qi-spirit trinity is the core theory for explaining the physiological activities of human body and the evolution of disease pathology in traditional Chinese medicine(TCM),and will bring about an overview of TCM tumorigenesis.This paper explores the biological basis of tumor from the perspective of disorders of physique,qi and spirit:there is a correlation between qi-physique transformation and energy and substance metabolism,and between spirit-emotion and neuromodulation;the nerve-metabolism pathway contributes to partial biological basis of tumor from the perspective of disorders of physique,qi and spirit.Furthermore,it puts forward the strategies for prevention and treatment with TCM through the simultaneous regulation of physique,qi and spirit:eliminating the mass to inhibit the tumor,and improving physique to preserve life in the view of treating physique;replenishing qi to strengthen the body,and ventilating qi to remove toxins in the view of treating qi;regulating the spirit to treat cancer through comprehensive therapy in the view of treating spirit.The exploration on the biological basis of tumor from the perspective of disorders of physique,qi and spirit will further embody the unique advantages of TCM theories in understanding malignant tumors,and will provide useful references for the model of synergistic prevention and treatment of malignant tumors with TCM.

To explore the application effectiveness of generative artificial intelligence(GAI) in the standardized training assessment of critical care medicine residents.