PeTailor: Improving Large Language Model by Tailored Chunk Scorer in Biomedical Triple Extraction

The automatic extraction of biomedical entities and their interaction from unstructured data remains a challenging task due to the limited availability of expert-labeled standard datasets. In this paper, we introduce PETAI-LOR, a retrieval-based language framework that is augmented by tailored chunk scorer. Unlike previous retrieval-augmented language models (LM) that retrieve relevant documents by calculating the similarity between the input sentence and the candidate document set, PETAILOR segments the sentence into chunks and retrieves the relevant chunk from our pre-computed chunk-based relational key-value memory. Moreover, in order to comprehend the specific requirements of the LM, PETAI-LOR adapt the tailored chunk scorer to the LM. We also introduce GM-CIHT, an expert annotated biomedical triple extraction dataset with more relation types. This dataset is centered on the non-drug treatment and general biomedical domain. Additionally, we investigate the efficacy of triple extraction models trained on general domains when applied to the biomedical domain. Our experiments reveal that PETAI-LOR achieves state-of-the-art performance on GM-CIHTComment: this is the first preprint versio

EHRTutor: Enhancing Patient Understanding of Discharge Instructions

Large language models have shown success as a tutor in education in various fields. Educating patients about their clinical visits plays a pivotal role in patients' adherence to their treatment plans post-discharge. This paper presents EHRTutor, an innovative multi-component framework leveraging the Large Language Model (LLM) for patient education through conversational question-answering. EHRTutor first formulates questions pertaining to the electronic health record discharge instructions. It then educates the patient through conversation by administering each question as a test. Finally, it generates a summary at the end of the conversation. Evaluation results using LLMs and domain experts have shown a clear preference for EHRTutor over the baseline. Moreover, EHRTutor also offers a framework for generating synthetic patient education dialogues that can be used for future in-house system training.Comment: To appear in NeurIPS'23 Workshop on Generative AI for Education (GAIED

W-procer: Weighted Prototypical Contrastive Learning for Medical Few-Shot Named Entity Recognition

Contrastive learning has become a popular solution for few-shot Name Entity Recognization (NER). The conventional configuration strives to reduce the distance between tokens with the same labels and increase the distance between tokens with different labels. The effect of this setup may, however, in the medical domain, there are a lot of entities annotated as OUTSIDE (O), and they are undesirably pushed apart to other entities that are not labeled as OUTSIDE (O) by the current contrastive learning method end up with a noisy prototype for the semantic representation of the label, though there are many OUTSIDE (O) labeled entities are relevant to the labeled entities. To address this challenge, we propose a novel method named Weighted Prototypical Contrastive Learning for Medical Few Shot Named Entity Recognization (W-PROCER). Our approach primarily revolves around constructing the prototype-based contractive loss and weighting network. These components play a crucial role in assisting the model in differentiating the negative samples from OUTSIDE (O) tokens and enhancing the discrimination ability of contrastive learning. Experimental results show that our proposed W-PROCER framework significantly outperforms the strong baselines on the three medical benchmark datasets

Complementary and Integrative Health Lexicon (CIHLex) and Entity Recognition in the Literature

Objective: Our study aimed to construct an exhaustive Complementary and Integrative Health (CIH) Lexicon (CIHLex) to better represent the often underrepresented physical and psychological CIH approaches in standard terminologies. We also intended to apply advanced Natural Language Processing (NLP) models such as Bidirectional Encoder Representations from Transformers (BERT) and GPT-3.5 Turbo for CIH named entity recognition, evaluating their performance against established models like MetaMap and CLAMP. Materials and Methods: We constructed the CIHLex by integrating various resources, compiling and integrating data from biomedical literature and relevant knowledge bases. The Lexicon encompasses 198 unique concepts with 1090 corresponding unique terms. We matched these concepts to the Unified Medical Language System (UMLS). Additionally, we developed and utilized BERT models and compared their efficiency in CIH named entity recognition to that of other models such as MetaMap, CLAMP, and GPT3.5-turbo. Results: From the 198 unique concepts in CIHLex, 62.1% could be matched to at least one term in the UMLS. Moreover, 75.7% of the mapped UMLS Concept Unique Identifiers (CUIs) were categorized as "Therapeutic or Preventive Procedure." Among the models applied to CIH named entity recognition, BLUEBERT delivered the highest macro average F1-score of 0.90, surpassing other models. Conclusion: Our CIHLex significantly augments representation of CIH approaches in biomedical literature. Demonstrating the utility of advanced NLP models, BERT notably excelled in CIH entity recognition. These results highlight promising strategies for enhancing standardization and recognition of CIH terminology in biomedical contexts

Engineering polyamide nanofiltration membrane with bifunctional terpolymer brushes for antifouling and antimicrobial properties

In order to alleviate the organic/biological membrane fouling, we propose a difunctional nanofiltration membrane with integrating antifouling and antibacterial properties. In this work, the random terpolymer containing quaternary ammonium and zwitterionic moieties is fabricated via free radical polymerization and followed by grafting on the surface of nanofiltration membrane via coupling reaction. Surface characterization demonstrates the enhanced hydrophilicity and higher surface charge after the membrane modification. Benefiting from the co-existence of quaternary ammonium and zwitterionic moieties on the membrane surface, the terpolymer-modified membrane (i.e., TP-TFC) can not only effectively inhibit the protein and bacterial adhesion, but also possess high bacteria inhibiting efficiency. In the protein/bacterial dynamic fouling test, the water flux maintenance and flux recovery of TP-TFC membranes are among the best, which is of great significance for the long-term antibacterial properties. Results reported here shed light on a novel approach for fabricating the antifouling and antimicrobial nanofiltration membranes.</p

Engineering polyamide nanofiltration membrane with bifunctional terpolymer brushes for antifouling and antimicrobial properties

In order to alleviate the organic/biological membrane fouling, we propose a difunctional nanofiltration membrane with integrating antifouling and antibacterial properties. In this work, the random terpolymer containing quaternary ammonium and zwitterionic moieties is fabricated via free radical polymerization and followed by grafting on the surface of nanofiltration membrane via coupling reaction. Surface characterization demonstrates the enhanced hydrophilicity and higher surface charge after the membrane modification. Benefiting from the co-existence of quaternary ammonium and zwitterionic moieties on the membrane surface, the terpolymer-modified membrane (i.e., TP-TFC) can not only effectively inhibit the protein and bacterial adhesion, but also possess high bacteria inhibiting efficiency. In the protein/bacterial dynamic fouling test, the water flux maintenance and flux recovery of TP-TFC membranes are among the best, which is of great significance for the long-term antibacterial properties. Results reported here shed light on a novel approach for fabricating the antifouling and antimicrobial nanofiltration membranes.</p

A Review of Reinforcement Learning for Natural Language Processing, and Applications in Healthcare

Reinforcement learning (RL) has emerged as a powerful approach for tackling complex medical decision-making problems such as treatment planning, personalized medicine, and optimizing the scheduling of surgeries and appointments. It has gained significant attention in the field of Natural Language Processing (NLP) due to its ability to learn optimal strategies for tasks such as dialogue systems, machine translation, and question-answering. This paper presents a review of the RL techniques in NLP, highlighting key advancements, challenges, and applications in healthcare. The review begins by visualizing a roadmap of machine learning and its applications in healthcare. And then it explores the integration of RL with NLP tasks. We examined dialogue systems where RL enables the learning of conversational strategies, RL-based machine translation models, question-answering systems, text summarization, and information extraction. Additionally, ethical considerations and biases in RL-NLP systems are addressed

Inhibition of MicroRNA-96 Ameliorates Cognitive Impairment and Inactivation Autophagy Following Chronic Cerebral Hypoperfusion in the Rat

Background/Aims: Chronic cerebral hypoperfusion (CCH) is a high-risk factor for vascular dementia and Alzheimer’s disease. Autophagy plays a critical role in the initiation and progression of CCH. However, the underlying mechanisms remain unclear. In this study, we identified the effect of a microRNA (miR) on autophagy under CCH. Methods: A CCH rat model was established by two-vessel occlusion (2VO). Learning and memory abilities were assessed by the Morris water maze. The protein levels of LC3, beclin-1, and mTOR were detected by western blotting and immunofluorescence assays, miR-96 expression was assessed by real-time PCR, luciferase assays were used to determine the effect of miR-96 on the 3′ untranslated region (UTR) of mTOR, and the number of autophagosomes was examined by electron microscopy. Results: The level of miR-96 was significantly increased in 2VO rats, and inhibition of miR-96 ameliorated the cognitive impairment induced by 2VO. Furthermore, the number of LC3- and beclin-1-positive autophagosomes was increased in 2VO rats, and was decreased after miR-96 antagomir injection. However, the protein level of mTOR was reduced in 2VO rats, and it was down-regulated by miR-96 overexpression and up-regulated by miR-96 inhibition in 2VO rats and primary culture cells. Moreover, the luciferase activity of the 3′-UTR of mTOR was suppressed by miR-96, which was relieved by mutation of the miR-96 binding sites. Conclusion: Our study demonstrated that miR-96 may play a key role in autophagy under CCH by regulating mTOR; therefore, miR-96 may represent a potential therapeutic target for CCH

The impact of copper on bone metabolism

Copper is an essential trace element for the human body. Abnormalities in copper metabolism can lead to bone defects, mainly by directly affecting the viability of osteoblasts and osteoclasts and their bone remodeling function, or indirectly regulating bone metabolism by influencing enzyme activities as cofactors. Copper ions released from biological materials can affect osteoblasts and osteoclasts, either directly or indirectly by modulating the inflammatory response, oxidative stress, and rapamycin signaling. This review presents an overview of recent progress in the impact of copper on bone metabolism.Translational potential of this article: The impact of copper on bone metabolism can provide insights into clinical application of copper-containing supplements and biomaterials

Mesenchymal Stem Cell-Derived Apoptotic Bodies: Biological Functions and Therapeutic Potential

Mesenchymal stem cells (MSCs) are non-hematopoietic progenitor cells with self-renewal ability and multipotency of osteogenic, chondrogenic, and adipogenic differentiation. MSCs have appeared as a promising approach for tissue regeneration and immune therapies, which are attributable not only to their differentiation into the desired cells but also to their paracrine secretion. MSC-sourced secretome consists of soluble components including growth factors, chemokines, cytokines, and encapsulated extracellular vesicles (EVs). Apoptotic bodies (ABs) are large EVs (diameter 5002000 nm) harboring a variety of cellular components including microRNA, mRNA, DNA, protein, and lipids related to the characteristics of the originating cell, which are generated during apoptosis. The released ABs as well as the genetic information they carry are engulfed by target cells such as macrophages, dendritic cells, epithelial cells, and fibroblasts, and subsequently internalized and degraded in the lysosomes, suggesting their ability to facilitate intercellular communication. In this review, we discuss the current understanding of the biological functions and therapeutic potential of MSC-derived ABs, including immunomodulation, tissue regeneration, regulation of inflammatory response, and drug delivery system