Development and validation of the core life activities scale.

Life activities profoundly influence well-being, mental health, and quality of life. The COVID-19 pandemic has heightened the importance of monitoring these activities for psychological and emotional health. However, existing measurement tools are limited, particularly for assessing psychological health. To address this gap, we developed and validated the Core Life Activities (CORE) scale, comprising five key factors (sleep, exercise, learning, diet, and social relationships) identified in neuroscience, cognitive psychology, and gerontology. In Study 1 (n = 1,137), exploratory and confirmatory factor analyses supported a single-factor structure with good model fit (χ2 = 6.377, df = 3, TLI = 0.992, CFI = 0.998, RMSEA = 0.031), demonstrating robust internal consistency (Cronbach's alpha = 0.776) and test-retest reliability (intraclass correlation coefficient = 0.522, p < 0.001). The CORE exhibited significant convergent validity with mental health screening tools for depressive and anxiety disorders and suicidality. Study 2 (n = 684) confirmed a significant correlation between CORE and the World Health Organisation Quality of Life Brief Version, complementing the convergent validity found in Study 1. In addition, discriminant validity was confirmed by a non-significant correlation with the COVID-19 Preventive Behavior Scale. The findings establish the CORE as a reliable and valid tool, offering a simple yet comprehensive measure for assessing core life activities with potential applications in diverse environments.

Daily vitality fluctuations in older adults with depressive symptoms: A multilevel location-scale model.

BACKGROUND: Examining the daily experiences of older adults with depression facilitates the development and application of personalized effective treatments for them. In previous clinical research on depression, traditional mean-based approaches have mainly been employed. However, the within-person residual variance as a random effect provides greater insight into the heterogeneity of daily experiences among geriatric samples. OBJECTIVE: This study aimed to examine the relationship between depression and daily vitality in older adults. Specifically, it focused on the mean and residual variance of daily vitality measured by the Ecological Momentary Assessment (EMA). METHODS: Data from 64 older adults aged 65 years or more, who participated in community welfare centers or retirees' associations, were used. Daily vitality was examined using EMA surveys for seven consecutive days (four random surveys per day). The data were analyzed using a location-scale model. RESULTS: The intraclass correlation computed from the empty model for the EMA data was 0.488, indicating significant variances in daily vitality across time between individuals. Older adults with higher levels of depressive symptoms showed low mean levels of daily vitality and a large log-residual variance of daily vitality. CONCLUSIONS: The findings from the current study suggest that individuals experiencing depression not only exhibit low vitality in their daily lives but also struggle to maintain stable levels of vitality in their lives. These insights could contribute to the facilitation and advancement of personalized interventions tailored for older adults.

Mechanistic insight into human androgen receptor-mediated endocrine disrupting potential of cyclic depsipeptide mycotoxin, beauvericin, and influencing environmental factors for its biosynthesis in Fusarium oxysporum KFCC 11363P on rice cereal.

In current study, Fusarium mycotoxin, beauvericin (BEA), has endocrine disrupting potential through suppressing the exogenous androgen receptor (AR)-mediated transcriptional activation. BEA was classified as an AR antagonist, with IC30 and IC50 values indicating that it suppressed AR dimerization in the cytosol. BEA suppress the translocation of cytosolic activated ARs to the nucleus via exogenous androgens. Furthermore, we investigated the impact of environmental conditions for BEA production on rice cereal using response surface methodology. The environmental factors affecting the production of BEA, namely temperature, initial moisture content, and growth time were optimized at 20.28 °C, 42.79 % (w/w), and 17.31 days, respectively. To the best of our knowledge, this is the first report showing that BEA has endocrine disrupting potential through suppressing translocation of cytosolic ARs to nucleus, and temperature, initial moisture content, and growth time are important influencing environmental factors for its biosynthesis in Fusarium strains on cereal.

Deciphering the mechanisms and interactions of the endocrine disruptor bisphenol A and its analogs with the androgen receptor.

Bisphenol A (BPA) and its various forms used as BPA alternatives in industries are recognized toxic compounds and antiandrogenic endocrine disruptors. These chemicals are widespread in the environment and frequently detected in biological samples. Concerns exist about their impact on hormones, disrupting natural biological processes in humans, together with their negative impacts on the environment and biotic life. This study aims to characterize the interaction between BPA analogs and the androgen receptor (AR) and the effect on the receptor's normal activity. To achieve this goal, molecular docking was conducted with BPA and its analogs and dihydrotestosterone (DHT) as a reference ligand. Four BPA analogs exhibited higher affinity (-10.2 to -8.7 kcal/mol) for AR compared to BPA (-8.6 kcal/mol), displaying distinct interaction patterns. Interestingly, DHT (-11.0 kcal/mol) shared a binding pattern with BPA. ADMET analysis of the top 10 compounds, followed by molecular dynamics simulations, revealed toxicity and dynamic behavior. Experimental studies demonstrated that only BPA disrupts DHT-induced AR dimerization, thereby affecting AR's function due to its binding nature. This similarity to DHT was observed during computational analysis. These findings emphasize the importance of targeted strategies to mitigate BPA toxicity, offering crucial insights for interventions in human health and environmental well-being.

Stimulation of estrogen receptor-alpha by hydroxyanilide fungicide, fenhexamid promotes lipid accumulation in 3 T3-L1 adipocyte.

Fenhexamid are fungicides that act against plant pathogens by inhibiting sterol biosynthesis. Nonetheless, it can trigger endocrine disruption and promote breast cancer cell growth. In a recent study, we investigated the mechanism underlying the lipid accumulation induced by fenhexamid hydroxyanilide fungicides in 3 T3-L1 adipocytes. To examine the estrogen receptor alpha (ERα)-agonistic effect, ER transactivation assay using the ERα-HeLa-9903 cell line was applied, and fenhexamid-induced ERα agonist effect was confirmed. Further confirmation that ERα-dependent lipid accumulation occurred was provided by treating 3 T3-L1 adipocytes with Methyl-piperidino-pyrazole hydrate (MPP), an ERα-selective antagonist. Fenhexamid mimicked the actions of ERα agonists and impacted lipid metabolism, and its mechanism involves upregulation of the expression of transcription factors that facilitate adipogenesis and lipogenesis. Additionally, it stimulated the expression of peroxisome proliferator-activated receptor (PPARγ), CCAAT/enhancer-binding protein α (C/EBPα), fatty acid synthase (FAS), and sterol regulatory element-binding protein 1 (SREBP1) and significantly elevated the expression of fatty acid-binding protein 4 (FABP4). In contrast, in combination with an ERα-selective antagonist, fenhexamid suppressed the expression of adipogenic/lipogenic transcription factors. These results suggest that fenhexamid affects the endocrine system and leads to lipid accumulation by interfering with processes influenced by ERα activation.

Transgelin-2, a novel cancer stem cell-related biomarker, is a diagnostic and therapeutic target for biliary tract cancer.

BACKGROUND: Biliary tract cancer (BTC) is a relatively rare but aggressive gastrointestinal cancer with a high mortality rate. Cancer stem cell (CSC) populations play crucial roles in tumor biology and are responsible for the low response to anti-cancer treatment and the high recurrence rate. This study investigated the role of Transgelin-2 (TAGLN2), overexpressed in CSC in BTC cells, and analyzed its expression in patient tissues and serum to identify potential new targets for BTC. METHODS: TAGLN2 expression was suppressed by small-interfering or short hairpin RNAs, and its effects on tumor biology were assessed in several BTC cell lines. Furthermore, the effects of TAGLN2 silencing on gemcitabine-resistant BTC cells, differentially expressed genes, proteins, and sensitivity to therapeutics or radiation were assessed. TAGLN2 expression was also assessed using western blotting and immunohistochemistry in samples obtained from patients with BTC to validate its clinical application. RESULTS: Suppression of TAGLN2 in BTC cell lines decreased cell proliferation, migration, invasion, and tumor size, in addition to a reduction in CSC features, including clonogenicity, radioresistance, and chemoresistance. TAGLN2 was highly expressed in BTC tissues, especially in cancer-associated fibroblasts in the stroma. Patients with a low stromal immunohistochemical index had prolonged disease-free survival compared to those with a high stromal immunohistochemical index (11.5 vs. 7.4 months, P = 0.013). TAGLN2 expression was higher in the plasma of patients with BTC than that in those with benign diseases. TAGLN2 had a higher area under the curve (0.901) than CA19-9, a validated tumor biomarker (0.799; P < 0.001). CONCLUSION: TAGLN2 plays a critical role in promoting BTC cell growth and motility and is involved in regulating BTC stemness. Silencing TAGLN2 expression enhanced cell sensitivity to radiation and chemotherapeutic drugs. The expression of TAGLN2 in patient tissue and plasma suggests its potential to serve as a secretory biomarker for BTC. Overall, targeting TAGLN2 could be an appropriate therapeutic strategy against advanced cancer following chemotherapy failure.

sEMG-based Sarcopenia risk classification using empirical mode decomposition and machine learning algorithms.

Early detection of the risk of sarcopenia at younger ages is crucial for implementing preventive strategies, fostering healthy muscle development, and minimizing the negative impact of sarcopenia on health and aging. In this study, we propose a novel sarcopenia risk detection technique that combines surface electromyography (sEMG) signals and empirical mode decomposition (EMD) with machine learning algorithms. First, we recorded and preprocessed sEMG data from both healthy and at-risk individuals during various physical activities, including normal walking, fast walking, performing a standard squat, and performing a wide squat. Next, electromyography (EMG) features were extracted from a normalized EMG and its intrinsic mode functions (IMFs) were obtained through EMD. Subsequently, a minimum redundancy maximum relevance (mRMR) feature selection method was employed to identify the most influential subset of features. Finally, the performances of state-of-the-art machine learning (ML) classifiers were evaluated using a leave-one-subject-out cross-validation technique, and the effectiveness of the classifiers for sarcopenia risk classification was assessed through various performance metrics. The proposed method shows a high accuracy, with accuracy rates of 0.88 for normal walking, 0.89 for fast walking, 0.81 for a standard squat, and 0.80 for a wide squat, providing reliable identification of sarcopenia risk during physical activities. Beyond early sarcopenia risk detection, this sEMG-EMD-ML system offers practical values for assessing muscle function, muscle health monitoring, and managing muscle quality for an improved daily life and well-being.

Beauvericin, produced by Fusarium oxysporum inhibits bisphenol A-induced proliferation of human breast cancer cell line by regulating ERα/p38 pathway.

Beauvericin (BEA) is a cyclic depsipeptide secondary metabolite of Fusarium species. It causes chemical hazards in food products and exists in an environment containing soil and various food types. On the other hand, the purified BEA has various biological activities and is regarded as a potential candidate for pharmaceutical research. This study was performed to assess the anti-proliferation activity of BEA against human breast cancer cells by regulating the estrogen receptor-alpha (ERα)/p38 pathway. TA and BA assays verified that BEA is a completed ER antagonist. Additionally, BEA suppressed cell proliferation in the anti-proliferation assay involving ER-positive human breast cancer cells co-treated with BPA and BEA. In respect to an anti-proliferation activity, the BPA-induced phosphorylation of p38 protein was inhibited in the presence of BEA. These results suggested that BEA exerts inhibitory potentials on endocrine disrupting effect and possibly acts as a natural therapeutic material for human estrogen hormonal health.

Differential DNA methylation and metabolite profiling of Atlantic killifish (Fundulus heteroclitus) from the New Bedford Harbor Superfund site.

Atlantic killifish (Fundulus heteroclitus) is a valuable model in evolutionary toxicology to study how the interactions between genetic and environmental factors serve the adaptive ability of organisms to resist chemical pollution. Killifish populations inhabiting environmental toxicant-contaminated New Bedford Harbor (NBH) show phenotypes tolerant to polychlorinated biphenyls (PCBs) and differences at the transcriptional and genomic levels. However, limited research has explored epigenetic alterations and metabolic effects in NBH killifish. To identify the involvement of epigenetic and metabolic regulation in the adaptive response of killifish, we investigated tissue- and sex-specific differences in global DNA methylation and metabolomic profiles of NBH killifish populations, compared to sensitive populations from a non-polluted site, Scorton Creek (SC). The results revealed that liver-specific global DNA hypomethylation and differential metabolites were evident in fish from NBH compared with those from SC. The sex-specific differences were not greater than the tissue-specific differences. We demonstrated liver-specific enriched metabolic pathways (e.g., amino acid metabolic pathways converged into the urea cycle and glutathione metabolism), suggesting possible crosstalk between differential metabolites and DNA hypomethylation in the livers of NBH killifish. Additional investigation of methylated gene regions is necessary to understand the functional role of DNA hypomethylation in the regulation of enzyme-encoding genes associated with metabolic processes and physiological changes in NBH populations.

Integrated drug response prediction models pinpoint repurposed drugs with effectiveness against rhabdomyosarcoma.

Targeted therapies for inhibiting the growth of cancer cells or inducing apoptosis are urgently needed for effective rhabdomyosarcoma (RMS) treatment. However, identifying cancer-targeting compounds with few side effects, among the many potential compounds, is expensive and time-consuming. A computational approach to reduce the number of potential candidate drugs can facilitate the discovery of attractive lead compounds. To address this and obtain reliable predictions of novel cell-line-specific drugs, we apply prediction models that have the potential to improve drug discovery approaches for RMS treatment. The results of two prediction models were ensemble and validated via in vitro experiments. The computational models were trained using data extracted from the Genomics of Drug Sensitivity in Cancer database and tested on two RMS cell lines to select potential RMS drug candidates. Among 235 candidate drugs, 22 were selected following the result of the computational approach, and three candidate drugs were identified (NSC207895, vorinostat, and belinostat) that showed selective effectiveness in RMS cell lines in vitro via the induction of apoptosis. Our in vitro experiments have demonstrated that our proposed methods can effectively identify and repurpose drugs for treating RMS.

Diagnosis of high-Ni NCA/Gr-Si cells before rapid capacity drop by monitoring the heterogeneous degradation.

Understanding the degradation of lithium-ion batteries is of utmost significance for preventing unexpected capacity drops and addressing safety concerns. The manner in which batteries degrade during operation has a notable influence on their subsequent cycle performance. In particular, the rapid capacity drop related to the spatial heterogeneity of the anode degradation highlights the necessity of a health indicator for an accurate battery diagnosis. A novel health indicator established in this study, the Dominant degradation factors among Negative and Positive electrodes (DNP) scores, enables clear identification of degraded states despite comparable capacity levels. Specifically, batteries with heterogeneous anode degradation exhibited negative scores and the aggravation of the cycle performance. It is anticipated that this health indicator can provide a distinct evaluation of batteries based on their degraded states, supporting onboard battery management and the efficient allocation of resources for the battery reuse industry.

Image-based Gait Spatiotemporal Parameters Estimation using a Single Camera and CNN-Transformer Hybrid Network.

Vision-based gait analysis can play an important role in the remote and continuous monitoring of the elderly's health conditions. However, most vision-based approaches compute gait spatiotemporal parameters using human pose information and provide average parameters. This study aimed to propose a straightforward method for stride-by-stride gait spatiotemporal parameters estimation. A total of 160 elderly individuals participated in this study. Data were gathered with a GAITRite system and a mobile camera simultaneously. Three deep learning networks were trained with a few RGB frames as input and a continuous 1D signal containing both spatial and temporal gait parameters as output. The trained networks estimated the stride lengths with correlations of 0.938 and more and detected gait events with F1-scores of 0.914 and more.Clinical relevance- The proposed method showed excellent agreements with the GAITRite system in analyzing spatiotemporal gait parameters. Our approach can be applied to monitor the elderly's health conditions based on their gait parameters for early diagnosis of diseases, proper treatment, and timely intervention.

Age Is Just a Number: Progress and Obstacles in the Discovery of New Candidate Drugs for Sarcopenia.

Sarcopenia is a disease characterized by the progressive loss of skeletal muscle mass and function that occurs with aging. The progression of sarcopenia is correlated with the onset of physical disability, the inability to live independently, and increased mortality. Due to global increases in lifespan and demographic aging in developed countries, sarcopenia has become a major socioeconomic burden. Clinical therapies for sarcopenia are based on physical therapy and nutritional support, although these may suffer from low adherence and variable outcomes. There are currently no clinically approved drugs for sarcopenia. Consequently, there is a large amount of pre-clinical research focusing on discovering new candidate drugs and novel targets. In this review, recent progress in this research will be discussed, along with the challenges that may preclude successful translational research in the clinic. The types of drugs examined include mitochondria-targeting compounds, anti-diabetes agents, small molecules that target non-coding RNAs, protein therapeutics, natural products, and repositioning candidates. In light of the large number of drugs and targets being reported, it can be envisioned that clinically approved pharmaceuticals to prevent the progression or even mitigate sarcopenia may be within reach.

View-independent gait events detection using CNN-transformer hybrid network.

Accurate gait detection is crucial in utilizing the ample health information embedded in it. Vision-based approaches for gait detection have emerged as an alternative to the exacting sensor-based approaches, but their application has been rather limited due to complicated feature engineering processes and heavy reliance on lateral views. Thus, this study aimed to find a simple vision-based approach that is view-independent and accurate. A total of 22 participants performed six different actions representing standard and peculiar gaits, and the videos acquired from these actions were used as the input of the deep learning networks. Four networks, including a 2D convolutional neural network and an attention-based deep learning network, were trained with standard gaits, and their detection performance for both standard and peculiar gaits was assessed using measures including F1-scores. While all networks achieved remarkable detection performance, the CNN-Transformer network achieved the best performance for both standard and peculiar gaits. Little deviation by the speed of actions or view angles was found. The study is expected to contribute to the wider application of vision-based approaches in gait detection and gait-based health monitoring both at home and in clinical settings.

Social distancing policy and mental health during COVID-19 pandemic: an 18-month longitudinal cohort study in South Korea.

Background: Despite the effectiveness of social distancing policies in preventing the spread of Coronavirus Disease 2019 (COVID-19), their impact on mental health remains a concern. Longitudinal studies investigating the psychological effects of social distancing are limited. Methods: Longitudinal data on psychological variables were collected eight times between May 2020 and November 2021 through online surveys in South Korea. Results: The participants in the study reported a worsening of depressive and anxiety symptoms, suicide risk, and psychological distress with increasing levels of social distancing. Specifically, during the third wave, when social distancing levels peaked, the highest levels of depression, anxiety, and psychological distress were observed, and the second-lowest levels of vitality were reported. Furthermore, psychological risk factors, such as depressive symptoms, anxiety symptoms, and suicidal risk, were closely associated with vitality levels in daily life. Discussions: During the pandemic, although social distancing helped prevent the spread of COVID-19, it also led to increased depression, anxiety, suicide risk, psychological distress, and decreased vitality. Engagement at a personal level in fundamental daily activities is important to cope with psychological distress. Our results indicate that commitment to fundamental daily activities and following routines is an important protective factor against psychological distress, notwithstanding COVID-19.

Phenotypic characteristics of circulating tumor cells and predictive impact for efficacy of chemotherapy in patients with pancreatic cancer: a prospective study.

Objective: Early chemoresistance and tumor mass progression are associated with poor prognosis in pancreatic ductal adenocarcinoma (PDAC). Circulating tumor cells (CTCs) have been studied as potential predictors of treatment response and prognosis in PDAC; however, this approach has yet to be applied in clinical practice. The aim of our study was to investigate the phenotypic characteristics of CTCs and determine their predictive value for PDAC progression. Methods: We prospectively enrolled 40 patients who were pathologically diagnosed with PDAC and collected blood samples at diagnosis, 2 months after diagnosis, and during disease progression or recurrence. We used a microfabricated filter-based enrichment system to retrieve and analyze CTCs, which were classified using immunofluorescence staining (CD45, EpCAM, and vimentin). Results: Our study included 20 women and 20 men (median age, 66 years). Overall, 45% of the patients (18/40) had disseminated disease, and 77.5% (31/40) received chemotherapy. Multivariate analysis revealed that the total CTC count and carbohydrate antigen 19-9 level at 2 months after diagnosis were associated with disease progression (P<0.05). Linear mixed model analysis revealed that the total CTC count and vimentin-positive CTCs were significantly correlated with treatment response during chemotherapy (P=0.024 and 0.017, respectively). Kaplan-Meier analysis showed that total CTC positivity at 2 months was significantly associated with poor progression-free survival (P=0.038). Conclusion: Our study's findings suggest that CTCs can serve as predictive biomarkers of clinical outcomes in patients with PDAC receiving palliative chemotherapy. In particular, the total CTC count and vimentin-positive CTCs showed changes associated with the chemotherapy response.

Muricauda meishanensis sp. nov., a facultative anaerobic bacterium belonging to the family Flavobacteriaceae isolated from marine sediment in the East China Sea.

A facultative anaerobic, Gram-strain-negative, rod-shaped bacterium (strain NBU2970T) was isolated by using modified ichip in situ cultivation from a marine sediment sample collected from Meishan Island in the East China Sea. Strain NBU2970T grew optimally at 37 °C, with a NaCl concentration of 2.0 % (w/v) and at pH 7.0. The 16S rRNA gene sequence analyses revealed that strain NBU2970T represents a novel species with the genus Muricauda, sharing highest sequence identities with Muricauda beolgyonensis BB-My12T (96.1 %), Muricauda alvinocaridis SCR12T (96.0 %), Muricauda taeanensis 105T (96.0 %) and Muricauda ruestringensis B1T (95.6 %). Phylogenetic analyses also indicated that strain NBU2970T clustered with the genus Muricauda and was closely related to M. beolgyonensis BB-My12T and M. ruestringensis B1T. The draft genome sequence of strain NBU2970T was composed of six contigs with a size of 3.2 Mbp, containing 3045 protein-coding genes and 38 RNA genes. The DNA G+C content was 43.8 mol%. The average nucleotide identity and digital DNA-DNA hybridization values between strain NBU2970T and related species of the genus Muricauda were well below the threshold limit for prokaryotic species delineation. The major cellular fatty acids were iso-C15 : 0, iso-C15 : 1 G and iso-C17 : 0 3-OH. The only respiratory quinone was MK-6. The major polar lipid was phosphatidylethanolamine. Based on its phenotypic, chemotaxonomic and genotypic data, strain NBU2970T is considered to be a representative of a novel species in the genus Muricauda, for which the name Muricauda meishanensis sp. nov. is proposed. The type strain is NBU2970T (=KCTC 82915T=MCCC 1K06394T).

Microbial Interaction is Among the Key Factors for Isolation of Previous Uncultured Microbes.

Pure cultivation of microbes is still limited by the challenges of microbial uncultivability, with most microbial strains unable to be cultivated under standard laboratory conditions. The experience accumulated from advanced techniques such as in situ cultivation has identified that microbial interactions exist in natural habitats but are absent in laboratory cultures. These microbial interactions are likely one of the key factors in isolating previously uncultured microbes. The need for better knowledge of the mechanisms operating in microbial interactions has led to various experiments that have utilized microbial interactions in different approaches to microbial cultivation. These new attempts to understand microbial interactions not only present a new perspective on microbial uncultivability but also provide an opportunity to access uncultured phylogenetically novel microbes with their potential biotechnology applications. In this review, we focus on studies of the mechanisms of microbial interaction where the growth of other microbes is affected. Additionally, we review some successful applications of microbial interactions in cultivation methods, an approach that can play an important role in the bioprospecting of untapped microbial resources.

Improving older adults' autobiographical memory through video-conferencing intervention during COVID-19.

BACKGROUND: Autobiographical memory (AM) is valuable not only as an indicator of mental health and cognitive function, but also as a target of therapeutic intervention for older adults. In the context of the COVID-19 pandemic, the demand for online psychosocial interventions and assessment services has sharply increased. Thus, the present study examined the effectiveness of videoconferencing AM (vAM) intervention using the Ecological Momentary Assessment (EMA) method in addition to the traditional paper-and-pencil assessment among samples of community dwelling older adults. METHODS: Twenty-seven older adults (aged 66-86 years) participated in a vAM intervention composed of 4 weekly 90-min sessions. The primary outcome was AM specificity, with secondary outcomes as depressive symptom and cognitive function, measured before and after the intervention. In addition, daily emotions were measured through EMA over 4 weeks of intervention. The EMA data were analyzed using a multilevel analysis. RESULTS: The results showed low dropout rates (7%) and high EMA response rates (85%). Autobiographical memory specificity increased (Cohen's d = 0.678), and the level of depression declined significantly (Cohen's d = 0.375) after the program. Additionally, measures assessing cognitive function, such as Seoul Verbal Learning Test and DSC (Digit Symbol Coding), showed significant improvements. The EMA results indicated a decrease in the intensity and proportion of negative emotions experienced during the program. CONCLUSIONS: This study is the first to utilize videoconferencing and EMA to deliver an AM intervention targeting older adults. The intervention was effective in improving mental health and cognitive function, including AM in older adults. Additionally, EMA was found to be a feasible tool for use in older adults.

RelCurator: a text mining-based curation system for extracting gene-phenotype relationships specific to neurodegenerative disorders.

BACKGROUND: The identification of gene-phenotype relationships is important in medical genetics as it serves as a basis for precision medicine. However, most of the gene-phenotype relationship data are buried in the biomedical literature in textual form. OBJECTIVE: We propose RelCurator, a curation system that extracts sentences including both gene and phenotype entities related to specific disease categories from PubMed articles, provides rich additional information such as entity taggings, and predictions of gene-phenotype relationships. METHODS: We targeted neurodegenerative disorders and developed a deep learning model using Bidirectional Gated Recurrent Unit (BiGRU) networks and BioWordVec word embeddings for predicting gene-phenotype relationships from biomedical texts. The prediction model is trained with more than 130,000 labeled PubMed sentences including gene and phenotype entities, which are related to or unrelated to neurodegenerative disorders. RESULTS: We compared the performance of our deep learning model with those of Bidirectional Encoder Representations from Transformers (BERT), Support Vector Machine (SVM), and simple Recurrent Neural Network (simple RNN) models. Our model performed better with an F1-score of 0.96. Furthermore, the evaluation done using a few curation cases in the real scenario showed the effectiveness of our work. Therefore, we conclude that RelCurator can identify not only new causative genes, but also new genes associated with neurodegenerative disorders' phenotype. CONCLUSION: RelCurator is a user-friendly method for accessing deep learning-based supporting information and a concise web interface to assist curators while browsing the PubMed articles. Our curation process represents an important and broadly applicable improvement to the state of the art for the curation of gene-phenotype relationships.