Pre-hospital delay intention and its associated factors in the high-risk population of stroke: A latent profile analysis.

AIM: To explore the possible latent classifications of pre-hospital delayed intention at high risk of stroke and to analyze the characteristics of different profiles. METHODS AND RESULTS: A cross-sectional study was conducted in one community in Shanghai, China. 470 individuals at high risk of stroke were recruited, and self-reported questionnaires (including socio-demographic, stroke knowledge, health belief, and pre-hospital delay behavior intention scale) were distributed between April and June 2023. A latent profile analysis was employed to identify the delay intention clusters, and multinomial logistic regression was utilized to ascertain the factors influencing the latent classes of delay intention. 457 high-risk populations with a response rate of 97.23% were finally enrolled in this study. Four distinct classes were identified: High warning signs-Low delay intention (26.3%), Low warning signs-Low delay intention (17.7%), moderate level of delay intention (37.3%), and high level of delay intention (18.7%). The influencing factors included stroke knowledge, health belief, age, education background, the nearest distance to the medical institution, and household income. CONCLUSION: The pre-hospital delay intention among high-risk populations of stroke was classified into four distinct classes. It is crucial for individuals at high risk to remain vigilant towards stroke symptoms and to take prompt action. Health promotion education may be explored as a strategy to bridge the gap between the recognition of stroke symptoms and the low pre-hospital delay intention.

CRISPR-GPT: An LLM Agent for Automated Design of Gene-Editing Experiments.

The introduction of genome engineering technology has transformed biomedical research, making it possible to make precise changes to genetic information. However, creating an efficient gene-editing system requires a deep understanding of CRISPR technology, and the complex experimental systems under investigation. While Large Language Models (LLMs) have shown promise in various tasks, they often lack specific knowledge and struggle to accurately solve biological design problems. In this work, we introduce CRISPR-GPT, an LLM agent augmented with domain knowledge and external tools to automate and enhance the design process of CRISPR-based gene-editing experiments. CRISPR-GPT leverages the reasoning ability of LLMs to facilitate the process of selecting CRISPR systems, designing guide RNAs, recommending cellular delivery methods, drafting protocols, and designing validation experiments to confirm editing outcomes. We showcase the potential of CRISPR-GPT for assisting non-expert researchers with gene-editing experiments from scratch and validate the agent's effectiveness in a real-world use case. Furthermore, we explore the ethical and regulatory considerations associated with automated gene-editing design, highlighting the need for responsible and transparent use of these tools. Our work aims to bridge the gap between biological researchers across various fields with CRISPR genome engineering technology and demonstrate the potential of LLM agents in facilitating complex biological discovery tasks.

Characterization of morphological and chemical changes using atomic force microscopy and metabolism assays: the relationship between surface wax and skin greasiness in apple fruit.

Introduction: Skin greasiness occurred on stored apples (Malus domestica Borkh.) is generally believed to result from changes in surface wax components. Previous reports have typically correlated wax changes with greasiness scores to reveal the contributing wax components. A notable limitation of this approach is that greasiness scores are highly subjective and influenced by individual perception. Methods: This study aimed to assess skin quality by quantitatively analyzing wax morphology changes in greasy 'Jonagold' apples using Atomic Force Microscopy (AFM) roughness parameters Ra, Rq, Rmax, and Rz, and to correlate these changes with wax composition. Results: AFM results revealed that wax crystals disappeared as skin greasiness increased, accompanied by significant declines in roughness parameters Ra, Rq, Rmax, and Rz, which decreased by 70% to 85%. Chemical analysis showed a significant increase in liquid esters, including linoleate and oleate esters, in the surface waxes, which negatively correlated with the decline in roughness parameters. Key genes related to ester production, such as MdFAD2, MdWSD1, and MdWBC11, exhibited increased expression and were also negatively correlated with decreases in Rq, Ra and Rz. Additionally, 1-Methylcyclopropene (1-MCP) treatment suppressed both the development of greasiness and the associated changes. Discussion: Our findings suggest that the increased liquid esters contribute to alternations in wax morphology in greasy apples, and that MdFAD2, MdWSD1, and MdWBC11, play crucial roles in ester biosynthesis. These results highlight the effectiveness of AFM roughness parameters Ra, Rq, Rmax, and Rz in quantifying wax morphology changes in apples during skin greasiness development.

MAPbrain: a multi-omics atlas of the primate brain.

The brain is the central hub of the entire nervous system. Its development is a lifelong process guided by a genetic blueprint. Understanding how genes influence brain development is critical for deciphering the formation of human cognitive functions and the underlying mechanisms of neurological disorders. Recent advances in multi-omics techniques have now made it possible to explore these aspects comprehensively. However, integrating and analyzing extensive multi-omics data presents significant challenges. Here, we introduced MAPbrain (http://bigdata.ibp.ac.cn/mapBRAIN/), a multi-omics atlas of the primate brain. This repository integrates and normalizes both our own lab's published data and publicly available multi-omics data, encompassing 21 million brain cells from 38 key brain regions and 436 sub-regions across embryonic and adult stages, with 164 time points in humans and non-human primates. MAPbrain offers a unique, robust, and interactive platform that includes transcriptomics, epigenomics, and spatial transcriptomics data, facilitating a comprehensive exploration of brain development. The platform enables the exploration of cell type- and time point-specific markers, gene expression comparison between brain regions and species, joint analyses across transcriptome and epigenome, and navigation of cell types across species, brain regions, and development stages. Additionally, MAPbrain provides an online integration module for users to navigate and analyze their own data within the platform.

GIAE-DTI: Predicting Drug-Target Interactions Based on Heterogeneous Network and GIN-based Graph Autoencoder.

Accurate prediction of drug-target interactions (DTIs) is essential for advancing drug discovery and repurposing. However, the sparsity of DTI data limits the effectiveness of existing computational methods, which primarily focus on sparse DTI networks and have poor performance in aggregating information from neighboring nodes and representing isolated nodes within the network. In this study, we propose a novel deep learning framework, named GIAE-DTI, which considers cross-modal similarity of drugs and targets and constructs a heterogeneous network for DTI prediction. Firstly, the model calculates the cross-modal similarity of drugs and proteins from the relationships among drugs, proteins, diseases, and side effects, and performs similarity integration by taking the average. Then, a drug-target heterogeneous network is constructed, including drug-drug interactions, protein-protein interactions, and drug-target interactions processed by weighted K nearest known neighbors. In the heterogeneous network, a graph autoencoder based on a graph isomorphism network is employed for feature extraction, while a dual decoder is utilized to achieve better self-supervised learning, resulting in latent feature representations for drugs and targets. Finally, a deep neural network is employed to predict DTIs. The experimental results indicate that on the benchmark dataset, GIAE-DTI achieves AUC and AUPR scores of 0.9533 and 0.9619, respectively, in DTI prediction, outperforming the current state-of-the-art methods. Additionally, case studies on four 5-hydroxytryptamine receptor-related targets and five drugs related to mental diseases show the great potential of the proposed method in practical applications.

The association between telomere length and diabetes mellitus: accumulated evidence from observational studies.

OBJECTIVE: In order to assess the associations between telomere length (TL) and diabetes mellitus (DM), especially type 2 diabetes (T2DM), we performed this systematic review and meta-analysis. METHODS: PubMed, Embase, and Web of Science were thoroughly searched up to July 11, 2023. The pooled standardized mean difference (SMD) and the 95% confidence interval (CI) were evaluated using the random-effects model. Age, sex, study design, duration of diabetes, region, sample size, and body mass index (BMI) were used to stratify subgroup analyses. RESULTS: A total of 37 observational studies involving 18,181 participants from 14 countries were included in the quantitative meta-analysis. In this study, patients with diabetes had shorter TL than the non-diabetic, whether those patients had T1DM (-2.70; 95% CI: -4.47, -0.93; P<0.001), T2DM (-3.70; 95% CI: -4.20, -3.20; P<0.001), or other types of diabetes (-0.71; 95% CI: -1.10, -0.31; P<0.001). Additionally, subgroup analysis of T2DM showed that TL was significantly correlated with age, sex, study design, diabetes duration, sample size, detection method, region, and BMI. CONCLUSION: A negative correlation was observed between TL and DM. To validate this association in the interim, more extensive, superior prospective investigations and clinical trials are required.

Racial and social-economic inequalities in systemic chemotherapy use among adult glioblastoma patients following surgery and radiotherapy.

Not all patients with glioblastoma multiforme (GBM) eligible for systemic chemotherapy after upfront surgery and radiotherapy finally receive it. The information on patients with GBM was retrieved from the surveillance, epidemiology, and end results database. Patients who underwent upfront surgery or biopsy and external beam radiotherapy between 2010 and 2019 were eligible for systemic chemotherapy. The available patient and tumor characteristics were assessed using multivariable logistic regression and chi-squared test. Out of the 16,682 patients eligible, 92.1% underwent systemic chemotherapy. The characteristics linked to the lowest systemic chemotherapy utilization included tumors of the brain stem/cerebellum (P = 0.01), former years of diagnosis (P = 0.001), ≥ 80 years of age (P < 0.001), Hispanic, Non-Hispanic Asian, Pacific Islander, or Black race (P < 0.001), non-partnered status (P < 0.001), and low median household income (P = 0.006). Primary tumor site, year of diagnosis, age, race, partnered status, and median household income correlated with the omission of systemic chemotherapy in GBM in adult patients.

Optimization research on multi-trip distribution of reverse logistics terminal for automobile scrap parts under the background of sustainable development strategy.

To effectively solve the reverse logistics distribution problem caused by the increasing number of scrapped parts in the automotive market, this study constructs a multi-trip green vehicle routing problem model with time windows by comprehensively considering the coordination between carbon dioxide emissions and cost efficiency. A hybrid adaptive genetic algorithm is proposed to solve this problem, featuring innovative improvements in the nearest neighbor rule based on minimum cost, adaptive strategies, bin packing algorithm based on the transfer-of-state equation, and large-scale neighborhood search. Additionally, to efficiently obtain location data for supplier factory sites in the distribution network, a coordinate extraction method based on image recognition technology is proposed. Finally, the scientific validity of this study is verified based on the actual case data, and the robust optimization ability of the algorithm is verified by numerical calculations of different examples. This research not only enriches the study of green vehicle routing problems but also provides valuable insights for the industry to achieve cost reduction, efficiency enhancement, and sustainable development in reverse logistics.

Efficacy and Safety of Alprostadil in Microcirculatory Disturbances During Emergency PCI: A Meta-Analysis of Randomized Controlled Trials.

OBJECTIVE: The clinical advantage of alprostadil [prostaglandin E1 (PGE1)] in the treatment of microcirculatory disturbances (defined as no-reflow or slow-flow) in acute percutaneous coronary intervention (PCI) is still disputed. The purpose of our study was to review the efficacy of PGE1 supplements in patients with acute myocardial infarction (AMI) who had urgent PCI. DESIGN: This study was a meta-analysis of randomized controlled trials. DATA SOURCES: PubMed, Embase, the Cochrane Library, Ovid, ProQuest, Scopus, the Chinese BioMedical Literature Database, China National Knowledge Internet, the China Science and Technology Journal Database, and the Wanfang Data Knowledge Service Platform were used as sources. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: We included randomized controlled trials including PGE1 for the treatment of intraoperative microcirculatory disorders and major cardiovascular adverse events in emergency PCI in people with AMI. Independent data extraction was conducted, and study quality was assessed. The meta-analysis was carried out by using random effects models to calculate the risk ratio (RR) of microcirculatory disorders between groups receiving PGE1 and those receiving placebo, nitroglycerin, or tirofiban. MAIN OUTCOME MEASURES: The primary endpoint of the study was the incidence of microcirculatory disturbances. Secondary outcomes included corrected thrombolysis in myocardial infarction (TIMI) frame count (cTFC), the percentage of patients with TIMI myocardial perfusion grade 3 (TMPG3), and the percentage of patients with myocardial blush grade 3 (MBG3) as efficacy indicators. Additionally, major adverse cardiovascular events (MACE) at 30 days and 180 days were assessed as safety indicators. RESULTS: There were 18 trials involving a total of 1458 participants. PGE1 significantly reduced the occurrence of microcirculation disorders compared with conventional medications and placebo [risk ratio 0.48, 95% confidence interval (CI) 0.36-0.63, I2 = 46%; cTFC (RR -4.74, 95% -6.85 to -2.63, I2 93%); percentage of patients with TMPG3 (RR 1.34, 95% CI 1.07-1.68, I2 70%) or MBG3 (RR 1.33, 95% CI 1.19-1.49, I2 0%); major adverse cardiovascular events (MACEs) in 30 days (RR 0.48, 95% CI 0.27-0.86, I2 0%); and MACEs in 180 days (RR 0.41, 95% CI 0.28-0.60, I2 0%)]. CONCLUSIONS: We found that PGE1 decreased the occurrence of micro-circulation disturbance in AMI and enhanced the outcome of PCI. Additional studies should be conducted to confirm these findings.

Determinants influencing health-promoting behaviors in individuals at high risks of stroke: a cross-sectional study.

Background: Quit smoking, moderate drinking, exercise, and healthy eating habits are all known to decrease the risk of stroke. As a result, understanding the health behaviors of high risk groups for stroke is crucial. Health behavior is influenced by knowledge, social environment, and health beliefs. However, little research has been done on these relationships. For a better grasp of the relationships mentioned above, consider using the COM-B model (capability, opportunity, motivation, and behavior). The purpose of this study was to investigate the variables related to health behavior and to test the mediating effect of health beliefs. Methods: The cross-sectional study was carried out at a physical examination center of a tertiary hospital in Shanghai, China. 986 high-risk populations of stroke have been tested using the Health Behavior Scale (HBS-SP), Stroke Knowledge Questionnaire (SKQ), Health Beliefs Questionnaire (HBS), and Multidimensional Scale of Perceived Social Support (MSPSS). The structural equation modeling was used in this study. Results: The scores for MSPSS, SKQ, HBS, and HBS-SP were 60.64 ± 13.72, 26.60 ± 9.77, 157.71 ± 34.34, and 2.46 ± 0.41, respectively. The revised model fits well (approximate root mean square error = 0.042; comparative fit index = 0.946). The health behavior was obviously and positively correlated to social Support, stroke knowledge, and health beliefs. Moreover, health belief has a mediating effect on the relation of social support, stroke knowledge, and health behavior. Conclusion: Chinese high risk groups for stroke have a mediate level of health behaviors. Factors associated with health behaviors are knowledge of stroke, health beliefs, and social support. The COM-B-based model can be used to explain the health behavior of individuals at risk of stroke and to guide the formulation of effective health management programs.

Prognostic significance of platelet­to­albumin ratio in patients with nasopharyngeal carcinoma receiving concurrent chemoradiotherapy: a retrospective study of 858 cases.

BACKGROUND: Despite evidence supporting the high correlation of the novel platelet-to-albumin ratio (PAR) with survival in diverse malignancies, its prognostic relevance in nasopharyngeal carcinoma (NPC) remains underexplored. This study aimed to examine the link between PAR and overall survival (OS) in NPC and to establish a predictive model based on this biomarker. METHODS: We retrospectively assembled a cohort consisting of 858 NPC patients who underwent concurrent chemoradiotherapy (CCRT). Utilizing the maximally selected log-rank method, we ascertained the optimal cut-off point for the PAR. Subsequently, univariate and multivariate Cox proportional hazards models were employed to discern factors significantly associated with OS and to construct a predictive nomogram. Further, we subjected the nomogram's predictive accuracy to rigorous independent validation. RESULTS: The discriminative optimal PAR threshold was determined to be 4.47, effectively stratifying NPC patients into two prognostically distinct subgroups (hazard ratio [HR] = 0.53; 95% confidence interval [CI]: 0.28-0.98, P = 0.042). A predictive nomogram was formulated using the results from multivariate analysis, which revealed age greater than 45 years, T stage, N stage, and PAR score as independent predictors of OS. The nomogram demonstrated a commendable predictive capability for OS, with a C-index of 0.69 (95% CI: 0.64-0.75), surpassing the performance of the conventional staging system, which had a C-index of 0.56 (95% CI: 0.65-0.74). CONCLUSIONS: In the context of NPC patients undergoing CCRT, the novel nutritional-inflammatory biomarker PAR emerges as a promising, cost-efficient, easily accessible, non-invasive, and potentially valuable predictor of prognosis. The predictive efficacy of the nomogram incorporating the PAR score exceeded that of the conventional staging approach, thereby indicating its potential as an enhanced prognostic tool in this clinical setting.

A 5' UTR Language Model for Decoding Untranslated Regions of mRNA and Function Predictions.

The 5' UTR, a regulatory region at the beginning of an mRNA molecule, plays a crucial role in regulating the translation process and impacts the protein expression level. Language models have showcased their effectiveness in decoding the functions of protein and genome sequences. Here, we introduced a language model for 5' UTR, which we refer to as the UTR-LM. The UTR-LM is pre-trained on endogenous 5' UTRs from multiple species and is further augmented with supervised information including secondary structure and minimum free energy. We fine-tuned the UTR-LM in a variety of downstream tasks. The model outperformed the best known benchmark by up to 5% for predicting the Mean Ribosome Loading, and by up to 8% for predicting the Translation Efficiency and the mRNA Expression Level. The model also applies to identifying unannotated Internal Ribosome Entry Sites within the untranslated region and improves the AUPR from 0.37 to 0.52 compared to the best baseline. Further, we designed a library of 211 novel 5' UTRs with high predicted values of translation efficiency and evaluated them via a wet-lab assay. Experiment results confirmed that our top designs achieved a 32.5% increase in protein production level relative to well-established 5' UTR optimized for therapeutics.

An Au bipyramids@CuZn MOF core-shell nanozyme enables universal SERS and a colorimetric dual-model bioassay.

In this study, a new type of gold nano-bipyramids@CuZn bimetallic organic framework (AuNBPs@CuZn MOF) nanozyme with high peroxidase (POD)-like activity and surface enhanced Raman scattering (SERS) activity was constructed with a special core-shell structure, which can catalyze the oxidation of TMB (colourless and Raman-inactive) into ox-TMB (blue and Raman-active). An AuNBPs@CuZn MOF-enabling universal SERS and colorimetric dual-model bioassay was thus developed for biomolecules with excellent performance, and has promising application prospects in the biosensing fields.

Surface Modification of Polyimide Aerogel by Thermoplastic Polyurethane for Enhanced Mechanical Strength and Thermal Insulation Performance.

Polyimide (PI) aerogel is a good thermal insulation material with the highest temperature resistance in practical application. But the mechanical strength of PI aerogels prepared by freeze-drying or thermoimide methods is weak. In this research, TPU was selected as an aging solution to solve the problem of the low mechanical strength of PI aerogel prepared by the freeze-drying method. Previous work has certified that the coupling of PI and thermoplastic polyurethane (TPU) can enhance the mechanical strength of PI aerogel to a certain extent due to the flexibility of TPU. But excessive TPU will change the PI structure in the cross-linking process and decrease the mechanical strength of the aerogel. Thus, a new kind of PI gel modification method was provided by using TPU as an aging solution, and the mechanical strength of PI aerogel is improved to 3.06 MPa. Furthermore, the shrinkage, specific surface area, waterproof angle, and thermal conductivity all show good performance, thus enabling PI aerogel to be used in many aspects. Specially, the method is simple and can be used to prepare some other high-strength aerogels.

The role of evidence-based practice and empathy in shared decision-making attitudes among newly graduated nurses: A cross-sectional study.

AIM: Examine the levels of variables and explore drivers associated with shared decision-making attitudes among newly graduated nurses. DESIGN: This was a descriptive and cross-sectional study. METHODS: From August 2022 to October 2022, a cross-section of 216 newly graduated nurses from four comprehensive A-level hospitals in northern China was recruited using convenience sampling. Newly graduated nurses are generally defined as nurses with a service period of six months to one year. Data were collected using an online questionnaire support platform, including the Nursing Shared Decision-Making Attitude scale, Jefferson scale of Empathy-Health profession students and the Health Sciences Evidence-Based Practice questionnaire. All data were analysed descriptively, and correlational analysis and hierarchical regression were used to make identical connections between variables. RESULTS: Newly graduated nurses supported shared decision-making. Perceptions of shared decision-making were correlated with the experiences of empathy and evidence-based practice. Additionally, perspective-taking of empathy and beliefs, and the ability to search for and apply existing scientific findings of evidence-based practice had a significant impact on more positive attitudes. CONCLUSION: The survey showed that acceptance of shared decision-making was positive among newly graduated nurses. Clinical nursing managers and teachers should pay attention to cultivating the evidence-based practice and empathy of newly graduated nurses to adopt an optimistic attitude towards shared decision-making in the long term. IMPACT: The survey addresses attitudes of shared decision-making among newly graduated nurses and determines whether empathy and evidence-based practice has an impact on it. The main finding is that newly graduated nurses have an optimistic outlook on the implementation of shared decision-making. This survey showed that empathy and evidence-based practice competencies are associated with shared decision-making attitudes among newly graduated nurses. The results of this survey have an impact on educational institutions and hospitals in the form of recommendations. Several training programmes on empathy and evidence-based practice can help adopt the shared decision-making attitudes of newly graduated nurses. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution.

Causal association of plasma circulating metabolites with nephritis: a Mendelian randomization study.

Background: Nephritis is a pivotal catalyst in chronic kidney disease (CKD) progression. Although epidemiological studies have explored the impact of plasma circulating metabolites and drugs on nephritis, few have harnessed genetic methodologies to establish causal relationships. Methods: Through Mendelian randomization (MR) in two substantial cohorts, spanning large sample sizes, we evaluated over 100 plasma circulating metabolites and 263 drugs to discern their causal effects on nephritis risk. The primary analytical tool was the inverse variance weighted (IVW) analysis. Our bioinformatic scrutiny of GSE115857 (IgA nephropathy, 86 samples) and GSE72326 (lupus nephritis, 238 samples) unveiled anomalies in lipid metabolism and immunological characteristics in nephritis. Thorough sensitivity analyses (MR-Egger, MR-PRESSO, leave-one-out analysis) were undertaken to verify the instrumental variables' (IVs) assumptions. Results: Unique lipoprotein-related molecules established causal links with diverse nephritis subtypes. Notably, docosahexaenoic acid (DHA) emerged as a protective factor for acute tubulointerstitial nephritis (ATIN) (OR1 = 0.84, [95% CI 0.78-0.90], p1 = 0.013; OR2 = 0.89, [95% CI 0.82-0.97], p2 = 0.007). Conversely, multivitamin supplementation minus minerals notably increased the risk of ATIN (OR = 31.25, [95% CI 9.23-105.85], p = 0.004). Reduced α-linolenic acid (ALA) levels due to lipid-lowering drugs were linked to both ATIN (OR = 4.88, [95% CI 3.52-6.77], p < 0.001) and tubulointerstitial nephritis (TIN) (OR = 7.52, [95% CI 2.78-20.30], p = 0.042). While the non-renal drug indivina showed promise for TIN treatment, the use of digoxin, hydroxocobalamin, and liothyronine elevated the risk of chronic tubulointerstitial nephritis (CTIN). Transcriptome analysis affirmed that anomalous lipid metabolism and immune infiltration are characteristic of IgA nephropathy and lupus nephritis. The robustness of these causal links was reinforced by sensitivity analyses and leave-one-out tests, indicating no signs of pleiotropy. Conclusion: Dyslipidemia significantly contributes to nephritis development. Strategies aimed at reducing plasma low-density lipoprotein levels or ALA supplementation may enhance the efficacy of existing lipid-lowering drug regimens for nephritis treatment. Renal functional status should also be judiciously considered with regard to the use of nonrenal medications.

Mesenchymal stem cell-derived extracellular vesicles relieve endothelial cell senescence via recovering CTRP9 upon repressing miR-674-5p in atherosclerosis.

Background: The senescence of endothelial cells is of great importance involving in atherosclerosis (AS) development. Recent studies have proved the protective role of mesenchymal stem cell-derived extracellular vesicles in AS, herein, we further desired to unvei their potential regulatory mechanisms in endothelial cell senescence. Methods: Senescence induced by H2O2 in primary mouse aortic endothelial cells (MAECs) was evaluated by SA-ß-gal staining. Targeted molecule expression was detected by qRT-PCR and Western blotting. The biological functions of MAECs were determined by CCK-8, flow cytometry, transwell, and tube formation assays. Oxidative injury was assessed by LDH, total and lipid ROS, LPO and MDA levels. The proliferation of adipose-derived mesenchymal stem cell (ADSCs) was analyzed by EdU assay. Effect of ADSCs-derived extracellular vesicles (ADSC-EVs) on AS was investigated in HFD-fed ApoE-/- mice. Results: miR-674-5p was up-regulated, while C1q/TNF-related protein 9 (CTRP9) was down-regulated in H2O2-induced senescent MAECs. CTRP9 was demonstrated as a target gene of miR-674-5p. miR-674-5p inhibition restrained senescence, oxidative stress, promoted proliferation, migration, and angiogenesis of H2O2-stimulated MAECs via enhancing CTRP9 expression. Moreover, treatment with ADSC-EVs inhibited H2O2-induced senescence and dysfunction of MAECs through regulating miR-674-5p/CTRP9 axis. In the in vivo AS mouse model, ADSC-EVs combination with miR-674-5p silencing slowed down AS progression via up-regulation of CTRP9. Conclusion: ADSC-EVs repressed endothelial cell senescence and improved dysfunction via promotion of CTRP9 expression upon miR-674-5p deficiency during AS progression, which might provide vital evidence for ADSC-EVs as a promising therapy for AS.

Deep reinforcement learning identifies personalized intermittent androgen deprivation therapy for prostate cancer.

The evolution of drug resistance leads to treatment failure and tumor progression. Intermittent androgen deprivation therapy (IADT) helps responsive cancer cells compete with resistant cancer cells in intratumoral competition. However, conventional IADT is population-based, ignoring the heterogeneity of patients and cancer. Additionally, existing IADT relies on pre-determined thresholds of prostate-specific antigen to pause and resume treatment, which is not optimized for individual patients. To address these challenges, we framed a data-driven method in two steps. First, we developed a time-varied, mixed-effect and generative Lotka-Volterra (tM-GLV) model to account for the heterogeneity of the evolution mechanism and the pharmacokinetics of two ADT drugs Cyproterone acetate and Leuprolide acetate for individual patients. Then, we proposed a reinforcement-learning-enabled individualized IADT framework, namely, I$^{2}$ADT, to learn the patient-specific tumor dynamics and derive the optimal drug administration policy. Experiments with clinical trial data demonstrated that the proposed I$^{2}$ADT can significantly prolong the time to progression of prostate cancer patients with reduced cumulative drug dosage. We further validated the efficacy of the proposed methods with a recent pilot clinical trial data. Moreover, the adaptability of I$^{2}$ADT makes it a promising tool for other cancers with the availability of clinical data, where treatment regimens might need to be individualized based on patient characteristics and disease dynamics. Our research elucidates the application of deep reinforcement learning to identify personalized adaptive cancer therapy.

Effect of Intraorbital Mechanical Compression on Retinal Microvascular Perfusion in Quiescent Thyroid-Associated Ophthalmopathy Based on Ocular Biomechanics Measured by Corvis ST.

INTRODUCTION: To analyze the correlation between orbital compliance and retinal vessel density (VD) based on dynamic Scheimpflug analyzer (Corvis ST) and optical coherence tomographic angiography (OCT-A). METHODS: In this prospective observational study, 65 eyes of 44 patients with thyroid-associated ophthalmopathy (TAO) in quiescent stage were included (15 males and 29 females). The whole eye movement (WEM) was detected by Corvis ST. The superficial capillary plexus VD (SCP-VD) and deep capillary plexus VD (DCP-VD) were obtained by scanning the 3 × 3 mm area around the fovea using OCT-A, while the peripapillary vessel density (ppVD) was obtained by scanning the 4.5 × 4.5 mm area around the optic disk. Covariances including biomechanically corrected intraocular pressure (bIOP), axial length, age and gender were adjusted during data analysis. RESULTS: The mean WEM of the participants was 0.235 ± 0.066 mm. The mean SCP-VD and DCP-VD in whole image were 46.20% ± 3.77% and 50.51% ± 3.96%; the mean whole pp-VD was 49.75% ± 2.01%. WEM was positively correlated with SCP-VD (r = 0.327, p = 0.01) and the whole pp-VD (r = 0.394, p < 0.01) after adjusting by gender, axial length (AL), age and bIOP, but it was not significantly correlated with DCP-VD (r = 0.072 p = 0.581). CONCLUSION: Increase in orbital pressure might reduce retinal microvascular perfusion. Our data suggest orbital mechanical compression may be an important cause of retinal VD changes in quiescent patients with TAO.

Design, synthesis and biological evaluation of quinazoline and pyrrolo[3,2-d]pyrimidine derivatives as TLR7 agonists for antiviral agents.

Pattern recognition receptors (PRRs) play a critical role in the innate immune response, and toll-like receptor 7 (TLR7) is an important member of PRRs. Although several TLR7 agonists are available, most of them are being tested clinically, with only one available on the market. Thus, it is imperative to develop new TLR7 agonists. In this study, we designed and synthesized three kinds of quinazoline derivatives and five kinds of pyrrolo[3,2-d]pyrimidine derivatives targeting TLR7. The antiviral efficacy of these compounds was evaluated in vitro and in vivo. Our findings indicated that four kinds of compounds showed exceptional antiviral activity. Furthermore, molecular docking studies confirmed that compound 11 successfully positioned itself in the pocket of the TLR7 guanosine loading site with a binding energy of -4.45 kcal mol-1. These results suggested that these compounds might be potential antiviral agents.