BioMEL: a translational research biobank of melanocytic lesions and melanoma.

INTRODUCTION: Diagnosing invasive cutaneous melanoma (CM) can be challenging due to subjectivity in distinguishing equivocal nevi, melanoma in situ and thin CMs. The underlying molecular mechanisms of progression from nevus to melanoma must be better understood. Identifying biomarkers for treatment response, diagnostics and prognostics is crucial. Using biomedical data from biobanks and population-based healthcare data, translational research can improve patient care by implementing evidence-based findings. The BioMEL biobank is a prospective, multicentre, large-scale biomedical database on equivocal nevi and all stages of primary melanoma to metastases. Its purpose is to serve as a translational resource, enabling researchers to uncover objective molecular, genotypic, phenotypic and structural differences in nevi and all stages of melanoma. The main objective is to leverage BioMEL to significantly improve diagnostics, prognostics and therapy outcomes of patients with melanoma. METHODS AND ANALYSIS: The BioMEL biobank contains biological samples, epidemiological information and medical data from adult patients who receive routine care for melanoma. BioMEL is focused on primary and metastatic melanoma, but equivocal pigmented lesions such as clinically atypical nevi and melanoma in situ are also included. BioMEL data are gathered by questionnaires, blood sampling, tumour imaging, tissue sampling, medical records and histopathological reports. ETHICS AND DISSEMINATION: The BioMEL biobank project is approved by the national Swedish Ethical Review Authority (Dnr. 2013/101, 2013/339, 2020/00469, 2021/01432 and 2022/02421-02). The datasets generated are not publicly available due to regulations related to the ethical review authority. TRIAL REGISTRATION NUMBER: NCT05446155.

Optimal policy tree to assist in adjuvant therapy decision-making after resection of colorectal liver metastases.

BACKGROUND: Although systemic postoperative therapy after surgery for colorectal liver metastases is generally recommended, the benefit of adjuvant chemotherapy has been debated. We used machine learning to develop a decision tree and define which patients may benefit from adjuvant chemotherapy after hepatectomy for colorectal liver metastases. METHODS: Patients who underwent curative-intent resection for colorectal liver metastases between 2000 and 2020 were identified from an international multi-institutional database. An optimal policy tree analysis was used to determine the optimal assignment of the adjuvant chemotherapy to subgroups of patients for overall survival and recurrence-free survival. RESULTS: Among 1,358 patients who underwent curative-intent resection of colorectal liver metastases, 1,032 (76.0%) received adjuvant chemotherapy. After a median follow-up of 28.7 months (interquartile range 13.7-52.0), 5-year overall survival was 67.5%, and 3-year recurrence-free survival was 52.6%, respectively. Adjuvant chemotherapy was associated with better recurrence-free survival (3-year recurrence-free survival: adjuvant chemotherapy, 54.4% vs no adjuvant chemotherapy, 46.8%; P < .001) but no overall survival significant improvement (5-year overall survival: adjuvant chemotherapy, 68.1% vs no adjuvant chemotherapy, 65.7%; P = .15). Patients were randomly allocated into 2 cohorts (training data set, n = 679, testing data set, n = 679). The random forest model demonstrated good performance in predicting counterfactual probabilities of death and recurrence relative to receipt of adjuvant chemotherapy. According to the optimal policy tree, patient demographics, secondary tumor characteristics, and primary tumor characteristics defined the subpopulation that would benefit from adjuvant chemotherapy. CONCLUSION: A novel artificial intelligence methodology based on patient, primary tumor, and treatment characteristics may help clinicians tailor adjuvant chemotherapy recommendations after colorectal liver metastases resection.

MultifacetedProtDB: a database of human proteins with multiple functions.

MultifacetedProtDB is a database of multifunctional human proteins deriving information from other databases, including UniProt, GeneCards, Human Protein Atlas (HPA), Human Phenotype Ontology (HPO) and MONDO. It collects under the label 'multifaceted' multitasking proteins addressed in literature as pleiotropic, multidomain, promiscuous (in relation to enzymes catalysing multiple substrates) and moonlighting (with two or more molecular functions), and difficult to be retrieved with a direct search in existing non-specific databases. The study of multifunctional proteins is an expanding research area aiming to elucidate the complexities of biological processes, particularly in humans, where multifunctional proteins play roles in various processes, including signal transduction, metabolism, gene regulation and cellular communication, and are often involved in disease insurgence and progression. The webserver allows searching by gene, protein and any associated structural and functional information, like available structures from PDB, structural models and interactors, using multiple filters. Protein entries are supplemented with comprehensive annotations including EC number, GO terms (biological pathways, molecular functions, and cellular components), pathways from Reactome, subcellular localization from UniProt, tissue and cell type expression from HPA, and associated diseases following MONDO, Orphanet and OMIM classification. MultiFacetedProtDB is freely available as a web server at: https://multifacetedprotdb.biocomp.unibo.it/.

Analysis of immune-related adverse events in gastrointestinal malignancy patients treated with immune checkpoint inhibitors.

Immune checkpoint inhibitors are becoming an increasingly common treatment for advanced gastrointestinal cancer, but the possibility of immune-related adverse events has raised concerns. This study aimed to evaluate the risks of immune-related adverse events between patients who received immune checkpoint inhibitors and those who received chemotherapy among different types of gastrointestinal cancer. The study utilized data from the multicenter TriNetX database in the United States covering the period between 2015 and 2022. Hazard ratios and 95% confidence intervals were used to describe the relative hazard of immune-related adverse events based on comparing time-to-event rates. Our study revealed that the incidence of immune-related adverse events was significantly higher in patients who received immune checkpoint inhibitors and chemotherapy compared to those who received chemotherapy only in treating gastrointestinal cancer. CTLA-4 inhibitors tended to have a higher rate of immune-related adverse events compared to PD-1/PD-L1 inhibitors. Our study found a lower mortality rate among patients who developed immune-related adverse events compared to those who did not after propensity score matching (HR, 0.661; 95% CI 0.620-0.704; p < .01). We provide important real-world data on the incidence and impact of immune-related adverse events in patients with advanced gastrointestinal cancer treated with immune checkpoint inhibitors. Our study's results support clinicians in making informed decisions about the potential benefits and risks of immune checkpoint inhibitor therapy for patients with gastrointestinal cancer.

"Repository of Cochlear Implant Information:" A Multi-Institutional REDCap Database.

PURPOSE: To describe the creation of a multi-center cochlear implant database as a template for future medical database design. The first clinical question examined was the association between BMI on cochlear implant surgical time and postoperative outcome. MATERIALS AND METHODS: A retrospective repository in REDCap, named the "Repository of Cochlear Implant Information" (ROCII), was created and collected de-identified data on patients who underwent cochlear implantation. Data was exported and stratified into three BMI groupings (<25, 25.0-29.9, ≥ 30.0). Differences in surgical time and AZBio Sentence Test postoperative score changes were analyzed using the mixed-effect model. RESULTS: The mean BMI (n = 145) was 28.52, and the mean surgical time was 128.9 min. The BMI < 25 reference group (n = 50) and the BMI 25.0-29.9 group (n = 50) had an identical mean surgical time of 127.5 min. The BMI ≥30.0 group (n = 45) had a mean surgical time of 132 min, however this difference was not statistically significant when compared to the reference group (p = 0.4727). The mean AZBio postoperative score change (n = 74) was 63.32. The BMI < 25 reference group (n = 29) had a mean postoperative change of 56.66. The BMI 25.0-29.9 group (n = 22) and BMI ≥30.0 group (n = 23) had mean postoperative changes of 61.32 and 73.65 respectively, however these differences were not statistically significant compared to the reference group (p = 0.5847, 0.0637). CONCLUSION: BMI did not have a significant association with surgical time or postoperative outcome and therefore should not be a contraindication for implantation. ROCII will facilitate a deeper understanding of the evaluation process, outcomes, and patient experience of cochlear implantation across institutions. LEVEL OF EVIDENCE: Level 1.

The role of randomized controlled trials, registries, observational databases in evaluating new interventions.

Approaches to comparing safety and efficacy of interventions include analyzing data from randomized controlled trials (RCTs), registries and observational databases (ODBs). RCTs are regarded as the gold standard but data from such trials are sometimes unavailable because a disease is uncommon, because the intervention is uncommon, because of structural limitations or because randomization cannot be done for practical or (seemingly) ethical reasons. There are many examples of an unproved intervention being so widely-believed to be effective that clinical trialists and potential subjects decline randomization. Often, when a RCT is finally done the intervention is proved ineffective or even harmful. These situations are termed medical reversals and are not uncommon [1,2]. There is also the dilemma of when seemingly similar RCTs report discordant conclisions Data from high-quality registries, especially ODBs can be used when data from RCTs are unavailable but also have limitations. Biases and confounding co-variates may be unknown, difficult or impossible to identify and/or difficult to adjust for adequately. However, ODBs sometimes have large numbers of diverse subjects and often give answers more useful to clinicians than RCTs. Side-by-side comparisons suggest analyses from high-quality ODBs often give similar conclusions from high quality RCTs. Meta-analyses combining data from RCTs, registries and ODBs are sometimes appropriate. We suggest increased use of registries and ODBs to compare efficacy of interventions.

Predictive Value of the Hemoglobin-Geriatric Nutritional Risk Index in Patients with Heart Failure.

Malnutrition prevails among patients with heart failure (HF), increasing the likelihood of functional decline. We assessed the predictive value of the Hemoglobin-Geriatric Nutritional Risk Index (H-GNRI)-combining hemoglobin and the Geriatric Nutritional Risk Index (GNRI)-on prognosis in older patients with HF. We used the JMDC multicenter database to examine the potential associations between malnutrition risk and other outcome measures. The patients were categorized as low- (H-GNRI score = 0), intermediate- (H-GNRI score = 1), or high-risk (H-GNRI score = 2) based on their H-GNRI scores. The primary outcome measure was the Barthel Index (BI) gain; the secondary outcomes included the BI at discharge, the BI efficiency, length of hospital stay, in-hospital mortality, discharge to home or a nursing home, and hospitalization-associated disability. We analyzed 3532 patients, with 244 being low-risk, 952 being intermediate-risk, and 2336 being high-risk patients. The high-risk group of patients had significantly lower BI values at discharge, lower BI gains, reduced BI efficiency values, and prolonged hospital stays compared to those in the low-risk group. The high-risk patients also had higher in-hospital mortality rates, lower rates of discharge to home or a nursing home, and greater incidences of a hospitalization-associated disability in comparison to the low-risk group. The H-GNRI may serve as a valuable tool for determining prognoses for patients with HF.

PLANTdataHUB: a collaborative platform for continuous FAIR data sharing in plant research.

In modern reproducible, hypothesis-driven plant research, scientists are increasingly relying on research data management (RDM) services and infrastructures to streamline the processes of collecting, processing, sharing, and archiving research data. FAIR (i.e., findable, accessible, interoperable, and reusable) research data play a pivotal role in enabling the integration of interdisciplinary knowledge and facilitating the comparison and synthesis of a wide range of analytical findings. The PLANTdataHUB offers a solution that realizes RDM of scientific (meta)data as evolving collections of files in a directory - yielding FAIR digital objects called ARCs - with tools that enable scientists to plan, communicate, collaborate, publish, and reuse data on the same platform while gaining continuous quality control insights. The centralized platform is scalable from personal use to global communities and provides advanced federation capabilities for institutions that prefer to host their own satellite instances. This approach borrows many concepts from software development and adapts them to fit the challenges of the field of modern plant science undergoing digital transformation. The PLANTdataHUB supports researchers in each stage of a scientific project with adaptable continuous quality control insights, from the early planning phase to data publication. The central live instance of PLANTdataHUB is accessible at (https://git.nfdi4plants.org), and it will continue to evolve as a community-driven and dynamic resource that serves the needs of contemporary plant science.

The accuracy of an Online Sequential Extreme Learning Machine in detecting voice pathology using the Malaysian Voice Pathology Database.

BACKGROUND: A multidimensional voice quality assessment is recommended for all patients with dysphonia, which requires a patient visit to the otolaryngology clinic. The aim of this study was to determine the accuracy of an online artificial intelligence classifier, the Online Sequential Extreme Learning Machine (OSELM), in detecting voice pathology. In this study, a Malaysian Voice Pathology Database (MVPD), which is the first Malaysian voice database, was created and tested. METHODS: The study included 382 participants (252 normal voices and 130 dysphonic voices) in the proposed database MVPD. Complete data were obtained for both groups, including voice samples, laryngostroboscopy videos, and acoustic analysis. The diagnoses of patients with dysphonia were obtained. Each voice sample was anonymized using a code that was specific to each individual and stored in the MVPD. These voice samples were used to train and test the proposed OSELM algorithm. The performance of OSELM was evaluated and compared with other classifiers in terms of the accuracy, sensitivity, and specificity of detecting and differentiating dysphonic voices. RESULTS: The accuracy, sensitivity, and specificity of OSELM in detecting normal and dysphonic voices were 90%, 98%, and 73%, respectively. The classifier differentiated between structural and non-structural vocal fold pathology with accuracy, sensitivity, and specificity of 84%, 89%, and 88%, respectively, while it differentiated between malignant and benign lesions with an accuracy, sensitivity, and specificity of 92%, 100%, and 58%, respectively. Compared to other classifiers, OSELM showed superior accuracy and sensitivity in detecting dysphonic voices, differentiating structural versus non-structural vocal fold pathology, and between malignant and benign voice pathology. CONCLUSION: The OSELM algorithm exhibited the highest accuracy and sensitivity compared to other classifiers in detecting voice pathology, classifying between malignant and benign lesions, and differentiating between structural and non-structural vocal pathology. Hence, it is a promising artificial intelligence that supports an online application to be used as a screening tool to encourage people to seek medical consultation early for a definitive diagnosis of voice pathology.

Examination of reporting status of pediatric oncology trials within the national library of medicine's trial database.

Finding safer and more effective treatment options are critical in progressing the field of pediatric oncology. These treatment options are discovered through completion and publication of clinical trials. The primary objective of this study was to assess the overall study characteristics of pediatric oncology clinical trials initiated between 2008 and 2021. The secondary objective of our study was to assess rates of discontinuation and reporting of results as required by the Food and Drug Administration (FDA). After acquiring pediatric oncology clinical trials from ClinicalTrials.gov, a cross-sectional study was performed. Included trials have an intervention exclusive to pediatrics and were conducted between 2008 and 2021. The results measured were characteristics of the clinical trials and their rate of discontinuation. Of the 7,930 trials originally returned from the search, 349 trials met inclusion criteria. The majority of the trials were phase 1 and 2 pharmaceutical interventions studying brain and blood cancer. Our study found that 14.9% (52) of the pediatric oncology trials were discontinued. Given the breadth of study within pediatric oncology, our overarching assessment shows that drug trials geared toward treating cancers of the brain and blood were dominant in the field. It is crucial for the advancement of science that results of trials are known. This avoids duplication of studies and waste of funds. Of the trials that were completed, 40.3% (58) did not report results to ClinicalTrials.gov. The nonreporting of this data limits the information available delaying the advancement of treatment options.

Accelerating Food Allergy Research: Need for a Data Commons.

Food allergy is a significant health problem affecting approximately 8% of children and 11% of adults in the United States. It exhibits all the characteristics of a "complex" genetic trait; therefore, it is necessary to look at very large numbers of patients, far more than exist at any single organization, to eliminate gaps in the current understanding of this complex chronic disorder. Advances may be achieved by bringing together food allergy data from large numbers of patients into a Data Commons, a secure and efficient platform for researchers, comprising standardized data, available in a common interface for download and/or analysis, in accordance with the FAIR (Findable, Accessible, Interoperable, and Reusable) principles. Prior data commons initiatives indicate that research community consensus and support, formal food allergy ontology, data standards, an accepted platform and data management tools, an agreed upon infrastructure, and trusted governance are the foundation of any successful data commons. In this article, we will present the justification for the creation of a food allergy data commons and describe the core principles that can make it successful and sustainable.

Involving multiple stakeholders in assessing and reviewing a novel data visualisation tool for a national neonatal data asset.

OBJECTIVES: We involved public and professional stakeholders to assess a novel data interrogation tool, the Neonatal Health Intelligence Tool, for a National Data Asset, the National Neonatal Research Database. METHODS: We recruited parents, preterm adults, data managers, clinicians, network managers and researchers (trialists and epidemiologists) for consultations demonstrating a prototype tool and semi-structured discussion. A thematic analysis of consultations is reported by stakeholder group. RESULTS: We held nine on-line consultations (March-December 2021), with 24 stakeholders: parents (n=8), preterm adults (n=2), data managers (n=3), clinicians (n=3), network managers (n=2), triallists (n=3) and epidemiologists (n=3). We identified four themes from parents/preterm adults: struggling to consume information, Dads and data, bring data to life and yearning for predictions; five themes from data managers/clinicians/network managers: benchmarking, clinical outcomes, transfers and activity, the impact of socioeconomic background and ethnicity, and timeliness of updates and widening availability; and one theme from researchers: interrogating the data. DISCUSSION: Other patient and public involvement (PPI) studies have reported that data tools generate concerns; our stakeholders had none. They were unanimously supportive and enthusiastic, citing visualisation as the tool's greatest strength. Stakeholders had no criticisms; instead, they recognised the tool's potential and wanted more features. Parents saw the tool as an opportunity to inform themselves without burdening clinicians, while clinicians welcomed an aid to explaining potential outcomes to parents. CONCLUSION: All stakeholder groups recognised the need for the tool, praising its content and format. PPI consultations with all key groups, and their synthesis, illustrated desire for additional uses from it.

The Mandarin Chinese auditory emotions stimulus database: A validated set of Chinese pseudo-sentences.

Emotional prosody is fully embedded in language and can be influenced by the linguistic properties of a specific language. Considering the limitations of existing Chinese auditory stimulus database studies, we developed and validated an emotional auditory stimuli database composed of Chinese pseudo-sentences, recorded by six professional actors in Mandarin Chinese. Emotional expressions included happiness, sadness, anger, fear, disgust, pleasant surprise, and neutrality. All emotional categories were vocalized into two types of sentence patterns, declarative and interrogative. In addition, all emotional pseudo-sentences, except for neutral, were vocalized at two levels of emotional intensity: normal and strong. Each recording was validated with 40 native Chinese listeners in terms of the recognition accuracy of the intended emotion portrayal; finally, 4361 pseudo-sentence stimuli were included in the database. Validation of the database using a forced-choice recognition paradigm revealed high rates of emotional recognition accuracy. The detailed acoustic attributes of vocalization were provided and connected to the emotion recognition rates. This corpus could be a valuable resource for researchers and clinicians to explore the behavioral and neural mechanisms underlying emotion processing of the general population and emotional disturbances in neurological, psychiatric, and developmental disorders. The Mandarin Chinese auditory emotion stimulus database is available at the Open Science Framework ( https://osf.io/sfbm6/?view_only=e22a521e2a7d44c6b3343e11b88f39e3 ).

Improving Research Patient Data Repositories From a Health Data Industry Viewpoint.

Organizational, administrative, and educational challenges in establishing and sustaining biomedical data science infrastructures lead to the inefficient use of Research Patient Data Repositories (RPDRs). The challenges, including but not limited to deployment, sustainability, cost optimization, collaboration, governance, security, rapid response, reliability, stability, scalability, and convenience, restrict each other and may not be naturally alleviated through traditional hardware upgrades or protocol enhancements. This article attempts to borrow data science thinking and practices in the business realm, which we call the data industry viewpoint, to improve RPDRs.

ChIP-Atlas 2021 update: a data-mining suite for exploring epigenomic landscapes by fully integrating ChIP-seq, ATAC-seq and Bisulfite-seq data.

ChIP-Atlas (https://chip-atlas.org) is a web service providing both GUI- and API-based data-mining tools to reveal the architecture of the transcription regulatory landscape. ChIP-Atlas is powered by comprehensively integrating all data sets from high-throughput ChIP-seq and DNase-seq, a method for profiling chromatin regions accessible to DNase. In this update, we further collected all the ATAC-seq and whole-genome bisulfite-seq data for six model organisms (human, mouse, rat, fruit fly, nematode, and budding yeast) with the latest genome assemblies. These together with ChIP-seq data can be visualized with the Peak Browser tool and a genome browser to explore the epigenomic landscape of a query genomic locus, such as its chromatin accessibility, DNA methylation status, and protein-genome interactions. This epigenomic landscape can also be characterized for multiple genes and genomic loci by querying with the Enrichment Analysis tool, which, for example, revealed that inflammatory bowel disease-associated SNPs are the most significantly hypo-methylated in neutrophils. Therefore, ChIP-Atlas provides a panoramic view of the whole epigenomic landscape. All datasets are free to download via either a simple button on the web page or an API.

SerumCovid database: Description and preliminary analysis of serological COVID-19 diagnosis in healthcare workers.

Serological databases represent an important source of information to perceive COVID-19 impact on health professionals involved in combating the disease. This paper describes SerumCovid, a COVID-19 serological database focused on the diagnosis of health professionals, providing a preliminary analysis to contribute to the understanding of the antibody response to the SARS-CoV-2. The study population comprises 321 samples from 236 healthcare and frontline workers fighting COVID-19 in Vitória de Santo Antão, Brazil. Samples were collected from at least six days of symptoms to more than 100 days. The used immunoenzymatic assays were Euroimmun Anti-SARS-CoV-2 ELISA IgG and IgA. The most common gender in SerumCovid is female, while the most common age group is between 30 and 39 years old. However, no statistical differences were observed in either genders or age categories. The most reported symptoms were fatigue, headaches, and myalgia. Still, some subjects presented positive results for IgA after 130 days. Based on a temporal analysis, we have not identified general patterns as subjects presented high and low values of IgA and IgG with different evolution trends. Unexpectedly, for subjects with both serological tests, the outcome of IgA and IgG tests were the same (either positive or negative) for more than 80% of the samples. Therefore, SerumCovid helps better understand how COVID-19 affected healthcare and frontline workers, which increases knowledge about the infection and enables direct prevention actions.