Objective: To quantify the spatiotemporal factors and built environment effects on daily blood collection volumes at fixed sites using multi-source spatiotemporal data, explore the influence radii of different point of interest(POI) categories, and develop a classification framework for sites, thereby providing data references for efficiency diagnosis and network optimization of blood collection sites. Methods: We collected daily blood donation records from 16 fixed whole blood collection sites in Guangzhou in 2023, constructing a balanced “site-day” panel dataset (16 sites × 365 days = 5 840 observations). Multi-source geospatial data, including Amap POI, OpenStreetMap (OSM) road network, and meteorological data were integrated to establish 300 m, 500 m, 800 m, and 1 000 m buffer zones around each site. Variables extracted included counts of surrounding commercial facilities, distance to the nearest subway station, rainfall, and temperature. A two-way fixed effects panel model was employed with a first-order lag term of the dependent variable to capture the inertial effect of blood donation behavior. Based on the significant regression coefficients, a "Structure-Function Index (SFI)" was constructed for each site to identify its optimal buffer radius. Combining inherent site capacity (fixed effects) with the optimal SFI, a two-dimensional quadrant diagram was created to classify the sites. Results: The optimized model showed an adjusted R of 0.89 and a Durbin-Watson statistic of 1.90, with the lag term coefficient stable at 0.36 (P<0.05). Temporal effects were significant: campaign days increased blood collection by 1 717.75 mL, holidays by 3 755.53 mL, and overlapping holidays with campaign days by 5 650.49 mL (P<0.05). Rainy days reduced collection by approximately 419 mL (P<0.05), and each 1℃ rise in daily average temperature decreased collection by approximately 31 mL (P<0.05). Distance to the nearest subway station was significantly positive (coefficient 0.95-1.39, P<0.05). POI effects showed scale-dependent differences: within 300 m, leisure and entertainment (+173.65 mL/unit) and shopping (+2.95 mL/unit) were significantly positive; within 500 m, tourist attractions (+72.55 mL/unit) and hotels (+40.09 mL/unit) were significantly positive; within 1 000 m, these effects diminished. Based on the two-dimensional classification of inherent collection capacity and optimal SFI, sites were categorized into four types: core, potential, adjustment, and resource-dependent. Conclusion: This study elucidates the multidimensional determinants of blood collection site efficiency, and provides quantitative evidence for performance diagnosis. It proposes a "concentric-zone planning" approach for site selection assessment alongside a" station-specific strategy" classification framework, offering data-driven references for network planning and resource allocation in blood collection and supply institutions. It should be noted that blood donation site selection also involves complex factors such as budgetary constraints, interdepartmental coordination, and venue conditions. This study provides a spatial econometric perspective for the quantitative optimization of blood collection networks.

Chemical constituents of n-butanol fraction of ethanol extract from the leaves of Cyclocarya paliurus (Batalin) Iljinskaja were studied. Ten compounds were purified by silica gel, MCI, ODS, Sephadex LH-20 column chromatography and semi-preparative high-performance liquid chromatography. Based on the physicochemical properties and spectroscopic data, these compounds were identified as (4S)-4,8-dihydroxy-α-tetralone-5-[6′-(3′′,5′′-dimethoxy-(E)-p-coumaroyl)]-β-D-glucopyranoside (1), (4S)-4,8-dihydroxy-α-tetralone-5-[6′-(E)-feruloyl]-β-D-glucopyranoside (2), (1S,3R,4S,5R)-4,5-di-O-caffeoyl quinic acid methyl ester (3), (1R,3R,4S,5R)-3,4-di-O-caffeoyl quinic acid methyl ester (4), (3R,5R)-3,4,5-tri-O-caffeoylquinic acid methyl ester (5), (1R,3R,4S,5R)-3-O-caffeoyl-5-O-p-coumaroyl quinic acid methyl ester (6), (1R,3R,4S,5R)-3-O-p-coumaroy-4-O-caffeoyl quinic acid methyl ester (7), (1R,3R,4S,5R)-3-O-caffeoyl-4-O-p-coumaroy quinic acid methyl ester (8), (3R,5R)-3,5-di-O-caffeoyl quinic acid methyl ester (9) and (1S,3R,4R,5R)-4-O-caffeoyl-5-O-feruloyl quinic acid methyl ester (10). Among them, compound 1 was a new compound, and compounds 3–10 were isolated from the genus Cyclocarya for the first time.

In March 2024, three male and two female specimens of the genus Lispe were intercepted on cargo ships inbound for Wan Zai Port under Gongbei Customs. Based on morphological characteristics and molecular analysis, the specimens were identified as Lispe pumila. In this study, we describe the morphological features, diagnostic characteristics, and geographical distribution of L. pumila, with the aim of providing a reference for the identification of this species when intercepted at ports.

Quantum dots (QDs), nanoscale semiconductor crystals, have emerged as a revolutionary class of nanomaterials with unique optical and electrochemical properties, making them highly promising for applications in disease diagnosis and treatment. Their tunable emission spectra, long-term photostability, high quantum yield, and excellent charge carrier mobility enable precise control over light emission and efficient charge utilization, which are critical for biomedical applications. This article provides a comprehensive review of recent advancements in the use of quantum dots for disease diagnosis and therapy, highlighting their potential and the challenges involved in clinical translation. Quantum dots can be classified based on their elemental composition and structural configuration. For instance, IB-IIIA-VIA group quantum dots and core-shell structured quantum dots are among the most widely studied types. These classifications are essential for understanding their diverse functionalities and applications. In disease diagnosis, quantum dots have demonstrated remarkable potential due to their high brightness, photostability, and ability to provide precise biomarker detection. They are extensively used in bioimaging technologies, enabling high-resolution imaging of cells, tissues, and even individual biomolecules. As fluorescent markers, quantum dots facilitate cell tracking, biosensing, and the detection of diseases such as cancer, bacterial and viral infections, and immune-related disorders. Their ability to provide real-time, in vivo tracking of cellular processes has opened new avenues for early and accurate disease detection. In the realm of disease treatment, quantum dots serve as versatile nanocarriers for targeted drug delivery. Their nanoscale size and surface modifiability allow them to transport therapeutic agents to specific sites, improving drug bioavailability and reducing off-target effects. Additionally, quantum dots have shown promise as photosensitizers in photodynamic therapy (PDT). When exposed to specific wavelengths of light, quantum dots interact with oxygen molecules to generate reactive oxygen species (ROS), which can selectively destroy malignant cells, vascular lesions, and microbial infections. This targeted approach minimizes damage to healthy tissues, making PDT a promising strategy for treating complex diseases. Despite these advancements, the translation of quantum dots from research to clinical application faces significant challenges. Issues such as toxicity, stability, and scalability in industrial production remain major obstacles. The potential toxicity of quantum dots, particularly to vital organs, has raised concerns about their long-term safety. Researchers are actively exploring strategies to mitigate these risks, including surface modification, coating, and encapsulation techniques, which can enhance biocompatibility and reduce toxicity. Furthermore, improving the stability of quantum dots under physiological conditions is crucial for their effective use in biomedical applications. Advances in surface engineering and the development of novel encapsulation methods have shown promise in addressing these stability concerns. Industrial production of quantum dots also presents challenges, particularly in achieving consistent quality and scalability. Recent innovations in synthesis techniques and manufacturing processes are paving the way for large-scale production, which is essential for their widespread adoption in clinical settings. This article provides an in-depth analysis of the latest research progress in quantum dot applications, including drug delivery, bioimaging, biosensing, photodynamic therapy, and pathogen detection. It also discusses the multiple barriers hindering their clinical use and explores potential solutions to overcome these challenges. The review concludes with a forward-looking perspective on the future directions of quantum dot research, emphasizing the need for further studies on toxicity mitigation, stability enhancement, and scalable production. By addressing these critical issues, quantum dots can realize their full potential as transformative tools in disease diagnosis and treatment, ultimately improving patient outcomes and advancing biomedical science.

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.

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.

Type 2 diabetes (T2D) is an independent risk factor for cognitive impairment. The dysregulation of hypoxia inducible factor (HIF) signaling in T2D patients results in impaired adaptive responses to hypoxia, thereby accelerating the progression of complications. However, limited knowledge is available regarding its precise function in diabetes-associated cognitive impairment (DACI). Here, elevated HIF-1α levels were observed in brain endothelial cells (ECs) of db/db mice. Functionally, brain ECs-specific knockdown of H if1 a significantly ameliorated T2D-induced memory loss and neuronal damage. Glycolysis in brain ECs was inhibited in this process, as indicated by RNA-seq, leading to decreased hippocampal lactate production through reduced LDHA expression. Notably, T2D patients showed increased cerebrospinal fluid lactate levels, which were strongly associated with their cognitive dysfunction. Intrahippocampal injection of lactate accelerated cognitive dysfunction and impaired adult hippocampal neurogenesis (AHN) in db/db mice. Conversely, reducing hippocampal lactate levels through the intrahippocampal injection of oxamate delayed the onset of memory deficits. Furthermore, asiatic acid was discovered to protect db/db mice from cognitive impairment by decreasing brain endothelial HIF-1α expression and subsequently reducing hippocampal lactate-induced AHN damage. Overall, this study elucidates the inhibiting role played by endothelial HIF-1α-driven lactate in AHN and highlights a potential tactic of targeting HIF-1α in brain ECs for treating cognitive impairment.

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: The relationship between fish consumption and stroke is inconsistent, and it is uncertain whether this association varies across predicted stroke risks. METHODS: A cohort study comprising 95,800 participants from the Prediction for Atherosclerotic Cardiovascular Disease Risk in China project was conducted. A standardized questionnaire was used to collect data on fish consumption. Participants were stratified into low- and moderate-to-high-risk categories based on their 10-year stroke risk prediction scores. Hazard ratios ( HRs) and 95% confidence intervals ( CIs) were estimated using Cox proportional hazard models and additive interaction by relative excess risk due to interaction (RERI), attributable proportion (AP), and synergy index (SI). RESULTS: During 703,869 person-years of follow-up, 2,773 incident stroke events were identified. Higher fish consumption was associated with a lower risk of stroke, particularly among moderate-to-high-risk individuals ( HR = 0.53, 95% CI: 0.47-0.60) than among low-risk individuals ( HR = 0.64, 95% CI: 0.49-0.85). A significant additive interaction between fish consumption and predicted stroke risk was observed (RERI = 4.08, 95% CI: 2.80-5.36; SI = 1.64, 95% CI: 1.42-1.89; AP = 0.36, 95% CI: 0.28-0.43). CONCLUSION Higher fish consumption was associated with a lower risk of stroke, and this beneficial association was more pronounced in individuals with moderate-to-high stroke risk.
Humans ; China/epidemiology* ; Male ; Female ; Stroke/etiology* ; Middle Aged ; Prospective Studies ; Incidence ; Aged ; Animals ; Fishes ; Risk Factors ; Diet ; Seafood ; Adult ; Cohort Studies