Optimizing personalized psychological well-being interventions through digital phenotyping: results from a randomized non-clinical trial.

Background: Digital technologies, including smartphones, hold great promise for expanding mental health services and improving access to care. Digital phenotyping, which involves the collection of behavioral and physiological data using smartphones, offers a novel way to understand and monitor mental health. This study examines the feasibility of a psychological well-being program using a telegram-integrated chatbot for digital phenotyping.

Exploring the Latest Highlights in Medical Natural Language Processing across Multiple Languages: A Survey.

Objectives: This survey aims to provide an overview of the current state of biomedical and clinical Natural Language Processing (NLP) research and practice in Languages other than English (LoE). We pay special attention to data resources, language models, and popular NLP downstream tasks.

Methods: We explore the literature on clinical and biomedical NLP from the years 2020-2022, focusing on the challenges of multilinguality and LoE.

Automatic Detection of Adverse Drug Events in Geriatric Care: Study Proposal.

Background: One-third of older inpatients experience adverse drug events (ADEs), which increase their mortality, morbidity, and health care use and costs. In particular, antithrombotic drugs are among the most at-risk medications for this population. Reporting systems have been implemented at the national, regional, and provider levels to monitor ADEs and design prevention strategies.

Toward the design and development of peptidomimetic inhibitors of the Ataxin-1 aggregation pathway.

Spinocerebellar ataxia type 1 is a degenerative disorder caused by polyglutamine expansions and aggregation of Ataxin-1. The interaction between Capicua (CIC) and the AXH domain of Ataxin-1 protein has been suggested as a possible driver of aggregation for the expanded Ataxin-1 protein and the subsequent onset of spinocerebellar ataxia 1. Experimental studies have demonstrated that short constructs of CIC may prevent such aggregation and suggested this as a possible candidate to inspire the rational design of peptidomimetics.

Parallel sequence tagging for concept recognition.

Background: Named Entity Recognition (NER) and Normalisation (NEN) are core components of any text-mining system for biomedical texts. In a traditional concept-recognition pipeline, these tasks are combined in a serial way, which is inherently prone to error propagation from NER to NEN. We propose a parallel architecture, where both NER and NEN are modeled as a sequence-labeling task, operating directly on the source text.

Genetic, cellular, and structural characterization of the membrane potential-dependent cell-penetrating peptide translocation pore.

Cell-penetrating peptides (CPPs) allow intracellular delivery of bioactive cargo molecules. The mechanisms allowing CPPs to enter cells are ill-defined. Using a CRISPR/Cas9-based screening, we discovered that KCNQ5, KCNN4, and KCNK5 potassium channels positively modulate cationic CPP direct translocation into cells by decreasing the transmembrane potential (V).

Improving classification of low-resource COVID-19 literature by using Named Entity Recognition.

Automatic document classification for highly interrelated classes is a demanding task that becomes more challenging when there is little labeled data for training. Such is the case of the coronavirus disease 2019 (COVID-19) Clinical repository-a repository of classified and translated academic articles related to COVID-19 and relevant to the clinical practice-where a 3-way classification scheme is being applied to COVID-19 literature. During the 7th Biomedical Linked Annotation Hackathon (BLAH7) hackathon, we performed experiments to explore the use of named-entity-recognition (NER) to improve the classification.

Lisen&Curate: A platform to facilitate gathering textual evidence for curation of regulation of transcription initiation in bacteria.

The number of published papers in biomedical research makes it rather impossible for a researcher to keep up to date. This is where manually curated databases contribute facilitating the access to knowledge. However, the structure required by databases strongly limits the type of valuable information that can be incorporated.

Learning adaptive representations for entity recognition in the biomedical domain.

Background: Named Entity Recognition is a common task in Natural Language Processing applications, whose purpose is to recognize named entities in textual documents. Several systems exist to solve this task in the biomedical domain, based on Natural Language Processing techniques and Machine Learning algorithms. A crucial step of these applications is the choice of the representation which describes data.

Pre-Existing Cardiovascular Conditions as Clinical Predictors of Myocarditis Reporting with Immune Checkpoint Inhibitors: A VigiBase Study.

Although rare, immune checkpoint inhibitor (ICI)-related myocarditis can be life-threatening, even fatal. In view of increased ICI prescription, identification of clinical risk factors for ICI-related myocarditis is of primary importance. This study aimed to assess whether pre-existing cardiovascular (CV) patient conditions are associated with the reporting of ICI-related myocarditis in VigiBase, the WHO global database of suspected adverse drug reactions (ADRs).

Cone-beam computed tomography-based radiomics in prostate cancer: a mono-institutional study.

Purpose: The purpose of the reported study was to investigate the value of cone-beam computed tomography (CBCT)-based radiomics for risk stratification and prediction of biochemical relapse in prostate cancer.

Methods: The study population consisted of 31 prostate cancer patients. Radiomics features were extracted from weekly CBCT scans performed for verifying treatment position.

Network Structures of Symptoms From the Zung Depression Scale.

The Self-rating Depression Scale (SDS) is a psychometric tool composed of 20 items used to assess depression symptoms. The aim of this work is to perform a network analysis of this scale in a large sample composed of 1090 French-speaking Belgian university students. We estimated a regularized partial correlation network and a Directed Acyclic Graph for the 20 items of the questionnaire.

A new approach and gold standard toward author disambiguation in MEDLINE.

Objective: Author-centric analyses of fast-growing biomedical reference databases are challenging due to author ambiguity. This problem has been mainly addressed through author disambiguation using supervised machine-learning algorithms. Such algorithms, however, require adequately designed gold standards that reflect the reference database properly.

PCSF: An R-package for network-based interpretation of high-throughput data.

With the recent technological developments a vast amount of high-throughput data has been profiled to understand the mechanism of complex diseases. The current bioinformatics challenge is to interpret the data and underlying biology, where efficient algorithms for analyzing heterogeneous high-throughput data using biological networks are becoming increasingly valuable. In this paper, we propose a software package based on the Prize-collecting Steiner Forest graph optimization approach.