Subconjunctival delivery of p75NTR antagonists reduces the inflammatory, vascular, and neurodegenerative pathologies of diabetic retinopathy

The p75NTR is a novel therapeutic target validated in a streptozotocin mouse model of diabetic retinopathy. Intravitreal (IVT) injection of small molecule p75NTR antagonist THX-B was therapeutic and resolved the inflammatory, vascular, and neurodegenerative phases of the retinal pathology. To simplify clinical translation, we sought a superior drug delivery method that circumvents risks associated with IVT injections. METHODS. We compared the pharmacokinetics of a single 40 lg subconjunctival (SCJ) depot to the reported effective 5 lg IVT injections of THX-B. We quantified therapeutic efficacy, with endpoints of inflammation, edema, and neuronal death. RESULTS. The subconjunctival depot affords retinal exposure equal to IVT injection, without resulting in detectable drug in circulation. At week 2 of diabetic retinopathy, the SCJ depot provided therapeutic efficacy similar to IVT injections, with reduced inflammation, reduced edema, reduced neuronal death, and a long-lasting protection of the retinal structure. CONCLUSIONS. Subconjunctival injections are a safe and effective route for retinal delivery of p75NTR antagonists. The subconjunctival route offers an advantageous, less-invasive, more compliant, and nonsystemic method to deliver p75NTR antagonists for the treatment of retinal diseases.Fil: Galan, Alba. Mc Gill University. Lady Davis Research Intitute; CanadáFil: Barcelona, Pablo Federico. Mc Gill University. Lady Davis Research Intitute; Canadá. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Córdoba. Centro de Investigaciones en Bioquímica Clínica e Inmunología; ArgentinaFil: Nedev, Hinyu. Mc Gill University. Lady Davis Research Intitute; CanadáFil: Sarunic, Marinko V.. University Fraser Simon; CanadáFil: Jian, Yifan. University Fraser Simon; CanadáFil: Saragovi, H. Uri. Mc Gill University. Lady Davis Research Intitute; Canad

Demonstration-based learning for few-shot biomedical named entity recognition under machine reading comprehension

Although deep learning techniques have shown significant achievements, they frequently depend on extensive amounts of hand-labeled data and tend to perform inadequately in few-shot scenarios. The objective of this study is to devise a strategy that can improve the model's capability to recognize biomedical entities in scenarios of few-shot learning. By redefining biomedical named entity recognition (BioNER) as a machine reading comprehension (MRC) problem, we propose a demonstration-based learning method to address few-shot BioNER, which involves constructing appropriate task demonstrations. In assessing our proposed method, we compared the proposed method with existing advanced methods using six benchmark datasets, including BC4CHEMD, BC5CDR-Chemical, BC5CDR-Disease, NCBI-Disease, BC2GM, and JNLPBA. We examined the models' efficacy by reporting F1 scores from both the 25-shot and 50-shot learning experiments. In 25-shot learning, we observed 1.1% improvements in the average F1 scores compared to the baseline method, reaching 61.7%, 84.1%, 69.1%, 70.1%, 50.6%, and 59.9% on six datasets, respectively. In 50-shot learning, we further improved the average F1 scores by 1.0% compared to the baseline method, reaching 73.1%, 86.8%, 76.1%, 75.6%, 61.7%, and 65.4%, respectively. We reported that in the realm of few-shot learning BioNER, MRC-based language models are much more proficient in recognizing biomedical entities compared to the sequence labeling approach. Furthermore, our MRC-language models can compete successfully with fully-supervised learning methodologies that rely heavily on the availability of abundant annotated data. These results highlight possible pathways for future advancements in few-shot BioNER methodologies

Adversarial Preference Optimization

Human preference alignment is a crucial training step to improve the interaction quality of large language models (LLMs). Existing aligning methods depend on manually annotated preference data to guide the LLM optimization directions. However, in practice, continuously updating LLMs raises a distribution gap between model-generated samples and human-preferred responses, which hinders model fine-tuning efficiency. To mitigate this issue, previous methods require additional preference annotation on generated samples to adapt the shifted distribution, which consumes a large amount of annotation resources. Targeting more efficient human preference optimization, we propose an adversarial preference optimization (APO) framework, where the LLM agent and the preference model update alternatively via a min-max game. Without additional annotation, our APO method can make a self-adaption to the generation distribution gap through the adversarial learning process. In experiments, we empirically verify the effectiveness of APO in improving LLM's helpfulness and harmlessness compared with rejection sampling baselines.Comment: In proces

A WRF-UCM-SOLWEIG framework of 10m resolution to quantify the intra-day impact of urban features on thermal comfort

City-scale outdoor thermal comfort diagnostics are essential for understanding actual heat stress. However, previous research primarily focused on the street scale. Here, we present the WRF-UCM-SOLWEIG framework to achieve fine-grained thermal comfort mapping at the city scale. The background climate condition affecting thermal comfort is simulated by the Weather Research and Forecasting (WRF) model coupled with the urban canopy model (UCM) at a local-scale (500m). The most dominant factor, mean radiant temperature, is simulated using the Solar and Longwave Environmental Irradiance Geometry (SOLWEIG) model at the micro-scale (10m). The Universal Thermal Climate Index (UTCI) is calculated based on the mean radiant temperature and local climate parameters. The influence of different ground surface materials, buildings, and tree canopies is simulated in the SOLWEIG model using integrated urban morphological data. We applied this proposed framework to the city of Guangzhou, China, and investigated the intra-day variation in the impact of urban morphology during a heat wave period. Through statistical analysis, we found that the elevation in UTCI is primarily attributed to the increase in the fraction of impervious surface (ISF) during daytime, with a maximum correlation coefficient of 0.80. Tree canopy cover has a persistent cooling effect during the day. Implementing 40% of tree cover can reduce the daytime UTCI by 1.5 to 2.0 K. At nighttime, all urban features have a negligible contribution to outdoor thermal comfort. Overall, the established framework provides essential input data and references for studies and urban planners in the practice of urban (micro)climate diagnostics and planning