Optical imaging is highly valued for its superior temporal and spatial resolution. This is particularly important in near-infrared II (NIR-II, 1 000-3 000 nm) imaging, which offers advantages such as reduced tissue absorption, minimal scattering, and low autofluorescence. These characteristics make NIR-II imaging especially suitable for deep tissue visualization, where high contrast and minimal background interference are critical for accurate diagnosis and monitoring. Currently, inorganic fluorescent probes—such as carbon nanotubes, rare earth nanoparticles, and quantum dots—offer high brightness and stability. However, they are hindered by ambiguous structures, larger sizes, and potential accumulation toxicity in vivo. In contrast, organic fluorescent probes, including small molecules and polymers, demonstrate higher biocompatibility but are limited by shorter emission wavelengths, lower quantum yields, and reduced stability. Recently, gold clusters have emerged as a promising class of nanomaterials with potential applications in biocatalysis, fluorescence sensing, biological imaging, and more. Water-soluble gold clusters are particularly attractive as fluorescent probes due to their remarkable optical properties, including strong photoluminescence, large Stokes shifts, and excellent photostability. Furthermore, their outstanding biocompatibility—attributed to good aqueous stability, ultra-small hydrodynamic size, and high renal clearance efficiency—makes them especially suitable for biomedical applications. Gold clusters hold significant potential for NIR-II fluorescence imaging. Atomic-precision gold clusters, typically composed of tens to hundreds of gold atoms and measuring only a few nanometers in diameter, possess well-defined three-dimensional structures and clear spatial coordination. This atomic-level precision enables fine-tuned structural regulation, further enhancing their fluorescence properties. Variations in cluster size, surface ligands, and alloying elements can result in distinct physicochemical characteristics. The incorporation of different atoms can modulate the atomic and electronic structures of gold clusters, while diverse ligands can influence surface polarity and steric hindrance. As such, strategies like alloying and ligand engineering are effective in enhancing both fluorescence and catalytic performance, thereby meeting a broader range of clinical needs. In recent years, gold clusters have attracted growing attention in the biomedical field. Their application in NIR-II imaging has led to significant progress in vascular, organ, and tumor imaging. The resulting high-resolution, high signal-to-noise imaging provides powerful tools for clinical diagnostics. Moreover, biologically active gold clusters can aid in drug delivery and disease diagnosis and treatment, offering new opportunities for clinical therapeutics. Despite the notable achievements in fundamental research and clinical translation, further studies are required to address challenges related to the standardized synthesis and complex metabolic behavior of gold clusters. Resolving these issues will help accelerate their clinical adoption and broaden their biomedical applications.

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.

Cardiovascular disease (CVD) remains one of the leading causes of mortality among adults globally, with continuously rising morbidity and mortality rates. Metabolic disorders are closely linked to various cardiovascular diseases and play a critical role in their pathogenesis and progression, involving multifaceted mechanisms such as altered substrate utilization, mitochondrial structural and functional dysfunction, and impaired ATP synthesis and transport. In recent years, the potential role of peroxisome proliferator-activated receptors (PPARs) in cardiovascular diseases has garnered significant attention, particularly peroxisome proliferator-activated receptor alpha (PPARα), which is recognized as a highly promising therapeutic target for CVD. PPARα regulates cardiovascular physiological and pathological processes through fatty acid metabolism. As a ligand-activated receptor within the nuclear hormone receptor family, PPARα is highly expressed in multiple organs, including skeletal muscle, liver, intestine, kidney, and heart, where it governs the metabolism of diverse substrates. Functioning as a key transcription factor in maintaining metabolic homeostasis and catalyzing or regulating biochemical reactions, PPARα exerts its cardioprotective effects through multiple pathways: modulating lipid metabolism, participating in cardiac energy metabolism, enhancing insulin sensitivity, suppressing inflammatory responses, improving vascular endothelial function, and inhibiting smooth muscle cell proliferation and migration. These mechanisms collectively reduce the risk of cardiovascular disease development. Thus, PPARα plays a pivotal role in various pathological processes via mechanisms such as lipid metabolism regulation, anti-inflammatory actions, and anti-apoptotic effects. PPARα is activated by binding to natural or synthetic lipophilic ligands, including endogenous fatty acids and their derivatives (e.g., linoleic acid, oleic acid, and arachidonic acid) as well as synthetic peroxisome proliferators. Upon ligand binding, PPARα activates the nuclear receptor retinoid X receptor (RXR), forming a PPARα-RXR heterodimer. This heterodimer, in conjunction with coactivators, undergoes further activation and subsequently binds to peroxisome proliferator response elements (PPREs), thereby regulating the transcription of target genes critical for lipid and glucose homeostasis. Key genes include fatty acid translocase (FAT/CD36), diacylglycerol acyltransferase (DGAT), carnitine palmitoyltransferase I (CPT1), and glucose transporter (GLUT), which are primarily involved in fatty acid uptake, storage, oxidation, and glucose utilization processes. Advancing research on PPARα as a therapeutic target for cardiovascular diseases has underscored its growing clinical significance. Currently, PPARα activators/agonists, such as fibrates (e.g., fenofibrate and bezafibrate) and thiazolidinediones, have been extensively studied in clinical trials for CVD prevention. Traditional PPARα agonists, including fenofibrate and bezafibrate, are widely used in clinical practice to treat hypertriglyceridemia and low high-density lipoprotein cholesterol (HDL-C) levels. These fibrates enhance fatty acid metabolism in the liver and skeletal muscle by activating PPARα, and their cardioprotective effects have been validated in numerous clinical studies. Recent research highlights that fibrates improve insulin resistance, regulate lipid metabolism, correct energy metabolism imbalances, and inhibit the proliferation and migration of vascular smooth muscle and endothelial cells, thereby ameliorating pathological remodeling of the cardiovascular system and reducing blood pressure. Given the substantial attention to PPARα-targeted interventions in both basic research and clinical applications, activating PPARα may serve as a key therapeutic strategy for managing cardiovascular conditions such as myocardial hypertrophy, atherosclerosis, ischemic cardiomyopathy, myocardial infarction, diabetic cardiomyopathy, and heart failure. This review comprehensively examines the regulatory roles of PPARα in cardiovascular diseases and evaluates its clinical application value, aiming to provide a theoretical foundation for further development and utilization of PPARα-related therapies in CVD treatment.

ObjectiveTobacco-related diseases remain one of the leading preventable public health challenges worldwide and are among the primary causes of premature death. In recent years, accumulating evidence has supported the classification of nicotine addiction as a chronic brain disease, profoundly affecting both brain structure and function. Despite the urgency, effective diagnostic methods for smoking addiction remain lacking, posing significant challenges for early intervention and treatment. To address this issue and gain deeper insights into the neural mechanisms underlying nicotine dependence, this study proposes a novel graph neural network framework, termed TI-GNN. This model leverages functional magnetic resonance imaging (fMRI) data to identify complex and subtle abnormalities in brain connectivity patterns associated with smoking addiction. MethodsThe study utilizes fMRI data to construct functional connectivity matrices that represent interaction patterns among brain regions. These matrices are interpreted as graphs, where brain regions are nodes and the strength of functional connectivity between them serves as edges. The proposed TI-GNN model integrates a Transformer module to effectively capture global interactions across the entire brain network, enabling a comprehensive understanding of high-level connectivity patterns. Additionally, a spatial attention mechanism is employed to selectively focus on informative inter-regional connections while filtering out irrelevant or noisy features. This design enhances the model’s ability to learn meaningful neural representations crucial for classification tasks. A key innovation of TI-GNN lies in its built-in causal interpretation module, which aims to infer directional and potentially causal relationships among brain regions. This not only improves predictive performance but also enhances model interpretability—an essential attribute for clinical applications. The identification of causal links provides valuable insights into the neuropathological basis of addiction and contributes to the development of biologically plausible and trustworthy diagnostic tools. ResultsExperimental results demonstrate that the TI-GNN model achieves superior classification performance on the smoking addiction dataset, outperforming several state-of-the-art baseline models. Specifically, TI-GNN attains an accuracy of 0.91, an F1-score of 0.91, and a Matthews correlation coefficient (MCC) of 0.83, indicating strong robustness and reliability. Beyond performance metrics, TI-GNN identifies critical abnormal connectivity patterns in several brain regions implicated in addiction. Notably, it highlights dysregulations in the amygdala and the anterior cingulate cortex, consistent with prior clinical and neuroimaging findings. These regions are well known for their roles in emotional regulation, reward processing, and impulse control—functions that are frequently disrupted in nicotine dependence. ConclusionThe TI-GNN framework offers a powerful and interpretable tool for the objective diagnosis of smoking addiction. By integrating advanced graph learning techniques with causal inference capabilities, the model not only achieves high diagnostic accuracy but also elucidates the neurobiological underpinnings of addiction. The identification of specific abnormal brain networks and their causal interactions deepens our understanding of addiction pathophysiology and lays the groundwork for developing targeted intervention strategies and personalized treatment approaches in the future.

Objective:To standardize the bacterial endotoxin testing criteria for zoledronic acid injection and estab-lish a detection method using recombinant factor C(rFC).Methods:The gel-clot method(BET)was utilized to test 13 batches of zoledronic acid injection from national supervision and random inspection.Interference tests were conducted on zoledronic acid injections from three manufacturers at different concentrations(500,100,50,25 μg·mL-1)using rFC test kits from two manufacturers.Results:Detection was performed for the specification of 100 mL∶5 mg and other specifications according to＜0.50 EU per 1 mL and＜5.0 EU per 1 mg,respectively,and all results met the criteria.The recovery rate for 25 μg·mL-1 using rFC kits from both manufacturers ranged between 50％and 200％.Validation of rFC.Methods:Eight batches of zoledronic acid injection were validated at 25 μg·mL-1,five batches of zoledronic acid injection(100 mL∶5 mg)were validated at 0.5 EU·mL-1,and all recovery rates were between 50％and 200％.Conclusion:The bacterial endotoxin testing method for zoledronic acid injection can be established as follows:for large volume injection products with 100 mL or more,each 1 mL should contain less than 0.50 EU of endotoxin(following the Chinese Pharmacopoeia 2020,general chapter 1143).For other specifications,each 1 mg of zoledronic acid should contain less than 10.0 EU of endotoxin.The rFC test kit method for bacterial endotoxins involved diluting the sample with water for bacterial endotoxin testing to contain 25 μg of zoledronic acid per 1 mL,or performing 1∶1 dilution for large volume injections(100 mL∶5.0 mg specifi-cation),with recovery rate between 50％and 200％as per the kit instructions.

To understand the distribution of ticks in the Wuwei Region,enrich tick species data,and provide a basis for the prevention of tick-borne diseases,tick were collected using flagging and tick-picking methods during the highest activity period from April to September in 2021 and 2022 in the mountainous areas of Wuwei City.The ticks were identified based on morpho-logical and molecular biological characteristics,and characteristic sequences were obtained.A systematic evolutionary tree was constructed using the neighbor-joining method in MEGA 11.0 software.In total,7 342 ticks collected in Wuwei,which be-longed to 5 species from 4 genera with in the Ixodidae family,which included Dermacentor nuttalli,Hyalomma asiaticum,Ixodes canisuga,Haemaphysalis longicornis and Haema-physalis danieli.Ticks of the same species clustered together into the same branch of an evolutionary tree.In the Wuwei Re-gion,five common tick species are found across various habi-tats,with each habitat featuring different distributions of tick species and populations.The Dermacentor nuttalli is the dom-inant tick species in this area.

Molecular Biology is a key basic professional course for all the students specializing in Biolo-gy,Biotechnology,and Bioengineering.With the promotion of double world-class project and first-class undergraduate construction,the development of English-taught course faces challenges.We started to teach the Molecular Biology course in English at the East China University of Science and Technology since 2019,the construction of Molecular Biology course has been reformed and practiced,including the combination of imagery,vividness and classroom teaching,the combination of advanced,cutting-edge and classical theories,and the comprehensive coverage of the teaching process,which has effectively pro-moted the construction and practice of Molecular Biology course.The Molecular Biology course taught in English greatly increased the students'professional and scientific research ability,international vision and English academic communication ability,comprehensive ability and satisfaction,and teachers'teaching and research ability.This course provides an effective reference for fostering innovative profes-sional first-class undergraduates and the construction of Molecular Biology course.

Objective:To investigate the effect of feeder layer cells expressing interleukin(IL)-21 on the amplification of NK cells in vitro.Methods:The K562 cell line with IL-21 expression on its membrane was constructed by electroporation,and co-cultured with NK cells after inactivation.The proliferation of NK cells was observed.The killing function of the amplified NK cells in vitro was evaluated by the lactate dehydrogenase(LDH)and interferon-γ(IFN-y)release assay.A colorectal cancer xenograft model in NOD/SCID mice was established,and a blank control group,a NK cell group and an amplified NK cell group were set up to detect the tumor killing effect of amplified NK cells in vivo.Results:K562 cells expressing IL-21 on the membrane were successfully constructed by electroporation.After co-culturing with K562 cells expressing IL-21 on the membrane for 17 days,the NK cells increased to 700 times,which showed an enhanced amplification ability compared with control group(P＜0.001).In the tumor cell killing experiment in vitro,there was no significant difference in the killing activity on tumor cells between NK cells and amplified NK cells,and there was also no significant difference in mice in vivo.Conclusion:K562 cells expressing IL-21 on the membrane can significantly increase the amplification ability of NK cells in vitro,but do not affect the killing function of NK cells in vitro and in vivo.It can be used for the subsequent large-scale production of NK cells in vitro.

Objective:To investigate the effect of sertraline hydrochloride combined with compound chamomile lidocaine gel in the treatment of premature ejaculation(PE).Methods:We selected 80 cases of PE treated in our hospital from June 2021 to May 2023 and equally randomized them into a control and an observation group,the former medicated with compound chamomile lidocaine gel while the latter with sertraline hydrochloride in addition,both for 6 weeks.We recorded and compared the intravaginal ejaculation latency time(IELT),the number of successful sexual intercourses per week,the Premature Ejaculation Diagnostic Tool(PEDT)scores,and the incidence of adverse reactions between the two groups of patients.Results:After the treatment,the IELT was signif-icantly longer([5.39±1.17]vs[2.49±0.73]min,P＜0.05),the weekly number of successful sexual intercourses remarkably higher(1.82±0.45 vs 0.93±0.19,P＜0.05)and the PEDT scores markedly lower(7.42±2.04 vs 9.85±2.36,P＜0.05)in the observation than in the control group,but no statistically significant differences were observed in the baseline PEDT scores or the incidence of adverse reactions between the two groups(P＞0.05).Conclusion:Sertraline hydrochloride combined with com-pound chamomile lidocaine gel is definitely effective in the treatment of PE,which can significantly improve the patients'quality of sexual life,with a high safety and low incidence of adverse reactions.

Objective To explore the factors affecting the use of medical sutures in medical institutions based on the autore-gressive integrated moving average(ARIMA)model so as to provide basis and optimization strategy for the rational use of medical sutures in medical institutions.Methods The monthly usage amount of medical sutures in some hospital from January 2014 to November 2023 was used as the research subject.An ARIMA model was established by R language,with the data on medical suture usage amount from January 2014 to December 2018 used as the training set;the reliability of the ARIMA model was verified with the data on medical suture usage amount from January to December 2019 as the validation set.The monthly usage amount of medical sutures from January 2021 to November 2023 was predicted with the validated ARIMA model,then the predictive value was compared with the actual one to evaluate the benefits of implementing the medical suture optimization policy in January 2021.Results The established ARIMA model passed the white noise test(P＞0.05),and the difference between the predicted and actual values for the whole year of 2019 was not statistically significant(P＞0.05);after the implementation of the medical suture optimization policy,the actual usage amount of medical sutures in some hospital decreased.The actual value from January 2021 to November 2023 was much lower than the predicted value,and the difference was statistically significant(P＜0.05).Conclusion The ARIMA model-based medical suture usage data prediction and configuration optimization scheme contributes to clarifying the law of medical suture usage in some hospital,and provides references for optimization and precision managment of medical suture configuration.[Chinese Medical Equipment Journal,2024,45(8):73-77]