Project description:When starting a new collaborative endeavor, it pays to establish upfront how strongly your partner commits to the common goal and what compensation can be expected in case the collaboration is violated. Diverse examples in biological and social contexts have demonstrated the pervasiveness of making prior agreements on posterior compensations, suggesting that this behavior could have been shaped by natural selection. Here, we analyze the evolutionary relevance of such a commitment strategy and relate it to the costly punishment strategy, where no prior agreements are made. We show that when the cost of arranging a commitment deal lies within certain limits, substantial levels of cooperation can be achieved. Moreover, these levels are higher than that achieved by simple costly punishment, especially when one insists on sharing the arrangement cost. Not only do we show that good agreements make good friends, agreements based on shared costs result in even better outcomes.
Project description:Systems biology is an approach to biology that emphasizes the structure and dynamic behavior of biological systems and the interactions that occur within them. To succeed, systems biology crucially depends on the accessibility and integration of data across domains and levels of granularity. Biomedical ontologies were developed to facilitate such an integration of data and are often used to annotate biosimulation models in systems biology.We provide a framework to integrate representations of in silico systems biology with those of in vivo biology as described by biomedical ontologies and demonstrate this framework using the Systems Biology Markup Language. We developed the SBML Harvester software that automatically converts annotated SBML models into OWL and we apply our software to those biosimulation models that are contained in the BioModels Database. We utilize the resulting knowledge base for complex biological queries that can bridge levels of granularity, verify models based on the biological phenomenon they represent and provide a means to establish a basic qualitative layer on which to express the semantics of biosimulation models.We establish an information flow between biomedical ontologies and biosimulation models and we demonstrate that the integration of annotated biosimulation models and biomedical ontologies enables the verification of models as well as expressive queries. Establishing a bi-directional information flow between systems biology and biomedical ontologies has the potential to enable large-scale analyses of biological systems that span levels of granularity from molecules to organisms.
Project description:This reproducibility study presents an algorithm to weigh in race distribution data of clinical research study samples when training biomedical embeddings. We extracted 12,864 PubMed abstracts published between January 1st, 2000 and January 1st, 2022 and weighed them based on the race distribution data extracted from their corresponding clinical trials registered on ClinicalTrials.gov. We trained Word2vec and BERT embeddings and evaluated their performance on predicting length of hospital stay (LHS) and intensive care unit (ICU) readmission using MIMIC-IV electronic health record data. We observed that models trained using race-sensitive embeddings do not consistently outperform the neutral embeddings ones when used for LHS prediction (with similar Mean Absolute Error 1.975 vs. 2.008) or ICU readmission prediction (with similar accuracy 74.61% vs. 75.17% and the same AUC 0.775), respectively. We conclude that demographic sensitive embeddings do not necessarily significantly improve the accuracy of health predictive models as previously reported in the literature.
Project description:BackgroundThe tile-based approach has been widely used for slide-level predictions in whole slide image (WSI) analysis. However, the irregular shapes and variable dimensions of tumor regions pose challenges for the process. To address this issue, we proposed PathEX, a framework that integrates intersection over tile (IoT) and background over tile (BoT) algorithms to extract tile images around boundaries of annotated regions while excluding the blank tile images within these regions.MethodsWe developed PathEX, which incorporated IoT and BoT into tile extraction, for training a classification model in CAM (239 WSIs) and PAIP (40 WSIs) datasets. By adjusting the IoT and BoT parameters, we generated eight training sets and corresponding models for each dataset. The performance of PathEX was assessed on the testing set comprising 13,076 tile images from 48 WSIs of CAM dataset and 6,391 tile images from 10 WSIs of PAIP dataset.ResultsPathEX could extract tile images around boundaries of annotated region differently by adjusting the IoT parameter, while exclusion of blank tile images within annotated regions achieved by setting the BoT parameter. As adjusting IoT from 0.1 to 1.0, and 1-BoT from 0.0 to 0.5, we got 8 train sets. Experimentation revealed that set C demonstrates potential as the most optimal candidate. Nevertheless, a combination of IoT values ranging from 0.2 to 0.5 and 1-BoT values ranging from 0.2 to 0.5 also yielded favorable outcomes.ConclusionsIn this study, we proposed PathEX, a framework that integrates IoT and BoT algorithms for tile image extraction at the boundaries of annotated regions while excluding blank tiles within these regions. Researchers can conveniently set the thresholds for IoT and BoT to facilitate tile image extraction in their own studies. The insights gained from this research provide valuable guidance for tile image extraction in digital pathology applications.
Project description:Gene deletion has been a valuable tool for unraveling the mysteries of molecular biology. Early approaches included gene trapping and gene targetting to disrupt or delete a gene randomly or at a specific location, respectively. Using these technologies in mouse embryos led to the generation of mouse knockout models and many scientific discoveries. The efficacy and specificity of these approaches have significantly increased with the advent of new technology such as clustered regularly interspaced short palindromic repeats for targetted gene deletion. However, several limitations including unwanted off-target gene deletion have hindered their widespread use in the field. Cre-recombinase technology has provided additional capacity for cell-specific gene deletion. In this review, we provide a summary of currently available literature on the application of this system for targetted deletion of neuronal genes. This article has been constructed to provide some background information for the new trainees on the mechanism and to provide necessary information for the design, and application of the Cre-recombinase system through reviewing the most frequent promoters that are currently available for genetic manipulation of neurons. We additionally will provide a summary of the latest technological developments that can be used for targeting neurons. This may also serve as a general guide for the selection of appropriate models for biomedical research.
Project description:Illness narratives invite practitioners to understand how biomedical and traditional health information is incorporated, integrated, or otherwise internalized into a patient's own sense of self and social identity. Such narratives also reveal cultural values, underlying patterns in society, and the overall life context of the narrator. Most illness narratives have been examined from the perspective of European-derived genres and literary theory, even though theorists from other parts of the globe have developed locally relevant literary theories. Further, illness narratives typically examine only the experience of illness through acute or chronic suffering (and potential recovery). The advent of biomedical disease prevention methods like post- and pre-exposure prophylaxis (PEP and PrEP) for HIV, which require daily pill consumption or regular injections, complicates the notion of an illness narrative by including illness prevention in narrative accounts. This paper has two aims. First, we aim to rectify the Eurocentrism of existing illness narrative theory by incorporating insights from African literary theorists; second, we complicate the category by examining prevention narratives as a subset of illness narratives. We do this by investigating several narratives of HIV prevention from informants enrolled in an HIV prevention trial in Kenya and Uganda in 2022.
Project description:BackgroundGender dynamics influence household-level decision-making about health behaviors and subsequent outcomes. Health and development programs in Niger are addressing gender norms through social and behavior change (SBC) approaches, yet not enough is known about how health care decisions are made and if gender-sensitive programs influence the decision-making process.MethodsWe qualitatively explored how households make decisions about family planning, child health, and nutrition in the Maradi and Zinder regions, Niger, within the context of a multi-sectoral integrated SBC program. We conducted 40 in-depth interviews with married women (n = 20) and men (n = 20) between 18 and 61 years of age.ResultsMale heads of household were central in health decisions, yet women were also involved and expressed the ability to discuss health issues with their husbands. Participants described three health decision-making pathways: (1st pathway) wife informs husband of health issue and husband solely decides on the solution; (2nd pathway) wife informs husband of health issue, proposes the solution, husband decides; and (3rd pathway) wife identifies the health issue and both spouses discuss and jointly identify a solution. Additionally, the role of spouses, family members, and others varied depending on the health topic: family planning was generally discussed between spouses, whereas couples sought advice from others to address common childhood illnesses. Many participants expressed feelings of shame when asked about child malnutrition. Participants said that they discussed health more frequently with their spouses' following participation in health activities, and some men who participated in husbands' schools (a group-based social and behavior change approach) reported that this activity influenced their approach to and involvement with household responsibilities. However, it is unclear if program activities influenced health care decision-making or women's autonomy.ConclusionsWomen are involved to varying degrees in health decision-making. Program activities that focus on improving communication among spouses should be sustained to enhance women role in health decision-making. Male engagement strategies that emphasize spousal communication, provide health information, discuss household labor may enhance couple communication in Niger. Adapting the outreach strategies and messages by healthcare topic, such as couples counseling for family planning versus community-based nutrition messaging, are warranted.
Project description:MotivationSignificant progress has been achieved in biomedical text mining using deep learning methods, which rely heavily on large amounts of high-quality data annotated by human experts. However, the reality is that obtaining high-quality annotated data is extremely challenging due to data scarcity (e.g. rare or new diseases), data privacy and security concerns, and the high cost of data annotation. Additionally, nearly all researches focus on predicting labels without providing corresponding explanations. Therefore, in this paper, we investigate a more realistic scenario, biomedical few-shot learning, and explore the impact of interpretability on biomedical few-shot learning.ResultsWe present LetEx-Learning to explain-a novel multi-task generative approach that leverages reasoning explanations from large language models (LLMs) to enhance the inductive reasoning ability of few-shot learning. Our approach includes (1) collecting high-quality explanations by devising a suite of complete workflow based on LLMs through CoT prompting and self-training strategies, (2) converting various biomedical NLP tasks into a text-to-text generation task in a unified manner, where collected explanations serve as additional supervision between text-label pairs by multi-task training. Experiments are conducted on three few-shot settings across six biomedical benchmark datasets. The results show that learning to explain improves the performances of diverse biomedical NLP tasks in low-resource scenario, outperforming strong baseline models significantly by up to 6.41%. Notably, the proposed method makes the 220M LetEx perform superior reasoning explanation ability against LLMs.Availability and implementationOur source code and data are available at https://github.com/cpmss521/LetEx.
Project description:To summarize the current literature on racial and gender disparities in critical care and the mechanisms underlying these disparities in the course of acute critical illness.MEDLINE search on the published literature addressing racial, ethnic, or gender disparities in acute critical illness, such as sepsis, acute lung injury, pneumonia, venous thromboembolism, and cardiac arrest.Clinical studies that evaluated general critically ill patient populations in the United States as well as specific critical care conditions were reviewed with a focus on studies evaluating factors and contributors to health disparities.Study findings are presented according to their association with the prevalence, clinical presentation, management, and outcomes in acute critical illness.This review presents potential contributors for racial and gender disparities related to genetic susceptibility, comorbidities, preventive health services, socioeconomic factors, cultural differences, and access to care. The data are organized along the course of acute critical illness.The literature to date shows that disparities in critical care are most likely multifactorial involving individual, community, and hospital-level factors at several points in the continuum of acute critical illness. The data presented identify potential targets as interventions to reduce disparities in critical care and future avenues for research.
Project description:Marine resources in unique marine environments provide abundant, cost-effective natural biomaterials with distinct structures, compositions, and biological activities compared to terrestrial species. These marine-derived raw materials, including polysaccharides, natural protein components, fatty acids, and marine minerals, etc., have shown great potential in preparing, stabilizing, or modifying multifunctional nano-/micro-systems and are widely applied in drug delivery, theragnostic, tissue engineering, etc. This review provides a comprehensive summary of the most current marine biomaterial-based nano-/micro-systems developed over the past three years, primarily focusing on therapeutic delivery studies and highlighting their potential to cure a variety of diseases. Specifically, we first provided a detailed introduction to the physicochemical characteristics and biological activities of natural marine biocomponents in their raw state. Furthermore, the assembly processes, potential functionalities of each building block, and a thorough evaluation of the pharmacokinetics and pharmacodynamics of advanced marine biomaterial-based systems and their effects on molecular pathophysiological processes were fully elucidated. Finally, a list of unresolved issues and pivotal challenges of marine-derived biomaterials applications, such as standardized distinction of raw materials, long-term biosafety in vivo, the feasibility of scale-up, etc., was presented. This review is expected to serve as a roadmap for fundamental research and facilitate the rational design of marine biomaterials for diverse emerging applications.