bluuebunny/crossref_metadata_2025_split huggingface dataset:
Development and Evaluation of the DMIND Questionnaire: Preparing for AI Integration into an Effective Depression Screening Tool
Authors: Hemrungrojn Solaphat , Saengsai Kittipoch , Jakkrawankul Pasit , Kiattiporn-Opas Chanyanart , Chaichareenon Kantapat , Amrapala Arisara , Lapanan Kulvara , Hengprapom Sarunya , Hiransuthikul Narin , Achakulvisut Titipat , Nupairoj Natawut , Phutrakool Phanupong , Yodlorchai Rapinpat , Vateekul Peerapol 
Published: June 2024

AbstractObjectiveThailand’s mental health crisis is exacerbated by high demand and a shortage of mental health professionals. The research objective was to develop and validate the Detection and Monitoring Intelligence Network for Depression (DMIND) questionnaire, designed to be culturally relevant and easily administered in clinical settings. Crafted with expert input, items specifically conducive to artificial intelligence (AI) analysis were selected to facilitate the future development of an AI-assisted depression scoring model. This approach underscores the tool’s dual utility in both human-led and technology-enhanced diagnostics.Materials and MethodsWe enrolled 81 participants from psychiatric and tertiary care hospitals in Bangkok. Participants were assessed using the DMIND questionnaire, followed by the Hamilton Depression Rating Scale (HDRS-17). Statistical analyses included the content validity index (CVI), Cronbach’s alpha, Pearson’s correlation coefficient, Cohen’s kappa, and receiver operating characteristic (ROC) analysis. The Liu method, Youden index, and nearest neighbor method were used to determine the optimal cut-off pointResultsThe DMIND questionnaire showed strong validity, with an item-level CVI (I- CVI) and scale-level CVI (S-CVI) exceeding 1.0, indicating strong consensus on its relevance and utility. The tool also demonstrated high internal consistency (Cronbach’s alpha = 0.96). ROC analysis showed an AUC of 0.88, indicating high accuracy in depression screening. An optimal cut-off score of 11.5 was identified, balancing predictive value and sensitivity.ConclusionThe DMIND questionnaire represents a significant advancement in innovative mental health diagnostics, addressing unmet clinical needs by providing accurate and efficient assessments capable of AI integration for further enhancing mental health service delivery in Thailand.
Analysis of Unmapped RNA-seq Data from Cancer Spatial Transcriptome to Decipher Cancer Microbiome
Authors: Park Seo Hye , Park Jeongbin , Kim Jiwon , Choi Hongyoon , Kim In Gul , Chung Eun-Jae , Na Kwon Joong 
Published: June 2024

AbstractRecent research increasingly emphasizes the importance of the microbiome in the development and progression of cancer. Thus, exploring the microbiome modulation in tumor microenvironment and understanding its composition and function are becoming important. The introduction of spatial transcriptomics has provided new insights into microbiome research, as it enables how microbiome affects tumor microenvironment. In this study, we analyzed the spatial distribution of microbial RNA observed through PathSeq and our extended method for unmapped RNA reads in oral squamous cell carcinoma, head and neck cancer, and colorectal cancer samples. Our novel method is designed to identify and classify microbial RNA, using a custom reference that includes species-specific microbial 16S rRNA sequences to enhance the accuracy of species-level microbiome analysis. The results of this study showed a potential for a deeper understanding of the microbial distribution and their functional roles within cancer tissues. These findings will reevaluate the role of the microbiome in cancer research, providing an insight for the development of microbiome-based therapies in the future.
Routine Blood Tests as Predictors of Periodontal Disease Risk Using a Machine Learning Approach
Authors: Ahmad Bushra , Saleh Khaled , Alharbi Saleh , Jeong Y. Natalie , Zavras Athanasios , Alqaderi Hend 
Published: June 2024

ABSTRACTBackgroundPeriodontal disease is a prevalent chronic inflammatory condition associated with systemic health complications. Early identification of high-risk individuals is crucial for targeted preventive and therapeutic interventions. In this study, we aimed to develop and evaluate machine learning models using routine blood tests to predict periodontal disease risk. This will help to develop accessible and cost-effective screening tools for early detection in a non-dental setting.MethodsThis study utilized data from the 2013-2014 National Health and Nutrition Examination Survey (NHANES), including full-mouth periodontal examinations, demographic variables, and routine blood tests (complete blood count [CBC], lipid profile, liver and kidney function tests). Periodontitis was defined as a binary outcome based on attachment loss and probing depth at interproximal sites. Individuals meeting any of these criteria were classified as having periodontitis. Seven machine learning models were developed and evaluated using precision, recall, and accuracy metrics.ResultsThe Random Forest Classifier achieved the highest performance, scoring 0.91 in precision, recall, and accuracy for predicting periodontitis. Key predictive features included smoking status, age, education level, gamma glutamyl transferase, albumin, blood urea nitrogen, glucose, phosphorus, creatinine, basophil count, and total calcium levels.ConclusionThis study underscores the promise of machine learning, especially the Random Forest Classifier, in predicting periodontal disease risk using routine blood tests and demographics. The model accurately identified periodontitis cases, with key features revealing the complex relationship between systemic health and periodontal disease. This suggests the potential for developing machine learning-based screening tools for early periodontal disease detection in non-dental settings.
To be climate-friendly, food-based dietary guidelines must include limits on total meat consumption – modeling from the case of France
Authors: Kesse-Guyot Emmanuelle , Baudry Julia , Berlivet Justine , Perraud Elie , Julia Chantal , Touvier Mathilde , Allès Benjamin , Lairon Denis , Hercberg Serge , Fouillet Hélène , Pointereau Philippe , Mariotti François 
Published: June 2024

AbstractAlthough food-based dietary guidelines (FBDG) include guidelines for meat consumption, their setting most often do not explicitly include environmental considerations. For instance, in France, FBDG recommend consuming no more than 500 g of red meat and 150 g of processed meat per week. This study uses modeling to investigate the range of greenhouse gas emissions (GHGe) that can be achieved under FBDG compliance.The study analyzed data collected in 2014 from 29,413 NutriNet-Santé participants to assess their adherence to the French FBDG. GHGe, cumulative energy demand (CED), and land occupation (LO) for organic and conventional foods were obtained from the DIALECTE database. Diets adequate in nutrients, culturally acceptable, and consistent with FBDG were modeled under different GHGe constraints. Environmental, nutritional, and health criteria were then calculated.The average observed adequacy to FBDG was low (19%, SD=25%) and GHGe were 4.34 (SD=2.7%) kgCO2eq/d. The GHGe range of the diets varied from 1.16 to 6.99 kgCO2eq/d, depending up to ∼85% on the level of meat consumption. Similar associations were observed for CED, LO and Health Risk Score. At isoenergetic diets, the diet with the lowest emissions had a higher proportion of vegetables, whole grains, and plant-based substitutes. This diet had a lower CED, LO, and a greater proportion of organic foods when compared to the diet with the highest emissions.While French dietary guidelines contribute, on average, to mitigating climate change and promoting health, this study emphasizes levers in recommended food consumption to more efficiently reduce diets’ GHGe and points to total meat as the critical issue to better account for pressure on climate change. Other environmental pressures should also be taken into account when designing dietary guidelines.HighlightsThe average greenhouse gas emissions of the observed diets was 4.34 (SD=2.70) kgCO2eq/d with an energy intake of 2080 Kcal/dThe diet that closely resembled the observed diet under the dietary guidelines, nutrient and acceptability constraints (TD model) had emissions of 5.15 kgCO2eq/d .Modeled diets that complied with dietary guidelines and nutrient and acceptability constraints had emissions ranging from 1.16 kgCO2eq/d (model MinGHGe) to 6.99 kgCO2eq/d (model MaxGHGe).All modeled diets had higher consumption levels of fruit, vegetable oils, pulses, and wholegrain products.The MinGHGe and MaxGHGe diets, and the range of identified models in between, differed in their level of beef/lamb, refined cereals, fruit, pork, and snack products.The level of meat, especially beef/lamb, explained most of the difference (up to ≈85%) in GHGe across models.The level of total meat consumption varied progressively across models that imposed an increase in GHGe.
Public involvement to enhance care home research; collaboration on a minimum data set for care homes
Authors: Killett Anne , Micklewright Kerry , Carroll Rachael , Akdur Gizdem , Allinson Emily , Crellin Liz , de Corte Kaat , Fox Margaret , Hanratty Barbara , Irvine Lisa , Jones Liz , Auburn Mere Marlene Kelly , Lloyd Therese , Meyer Julienne , Spilsbury Karen , Towers Ann-Marie , Tracey Freya , Wilmott John , Goodman Claire 
Published: June 2024

AbstractIntroductionInformation on care home residents is captured in lots of datasets (care home records, GP records, community nursing etc) but little of this information is currently analysed in a way that is useful for care providers, current or future residents and families or that realises the potential of data to enhance care provision. The DACHA study aimed to develop and test a minimum data set (MDS) which would bring together data that is useful to support and improve care and facilitate research. It is that utility that underscores the importance of meaningful public involvement with the range of groups of people affected. This paper analyses the public involvement of family members of care home residents and care home staff through a Public Involvement (PI) Panel.ObjectivesThe objective for the PI activities was to consistently bring the knowledge and perspectives of family members and care home staff to influence the ongoing design and conduct of the DACHA study.MethodsThe bespoke methods of PI included a dedicated PI team and a PI Panel of public involvement contributors. Meetings were recorded and minutes agreed, resulting actions were tracked, and reflections on the PI recorded. A democratic, social relations approach was used to frame the analysis.ResultsA PI panel met 17 times. All meetings included both family members and care home staff. The public involvement deepened the research team’s understanding of the data environment in care homes, influenced the inclusion of quality of life and community health data in the pilot MDS and shaped research practices with care homes. Some panel members expressed personal and professional development from their involvement. Expectations of what the project could achieve had to be negotiated.ConclusionsPI shaped the design and conduct of the DACHA study, grounding it in the needs and perspectives of people using and providing social care. Data research has a huge responsibility to accurately incorporate relevant public perspectives. There is an implicit assumption that records and data are objective and “speak for themselves” however there can be unintended consequences from introduction of new data requirements in practice.Patient or Public ContributionPublic contributors to this manuscript include family members of older people living in care homes and staff of care homes. The wider study also involved as the public, older people living in care homes. Public contributors helped develop the project, contributed throughout the conduct of the study and some chose to be involved in preparing this manuscript.
Vitamin D levels and COVID-19 severe pneumonia: a prospective case-control study
Authors: Cardoso Fortunato José , Araújo Carla Adriane Leal de , Silva Junior José Roberto da , Guimarães Angélica , Taveiro Michelle Viana , Figueiroa José Natal , Bezerra Alves João Guilherme 
Published: June 2024

AbstractIntroductionThe hypothesis that a low vitamin D levels is associated with a higher risk for severe COVID-19 has not been completely proven, especially with severe pneumonia.ObjectiveThe goal of this study was to confirm the link between vitamin D levels and COVID-19 severe pneumonia.MethodsThis prospective case-control study involved 307 patients who developed severe SARS-CoV-2 pneumonia and were hospitalized in an intensive care unit. Age- and sex-matched controls (307) were selected from the same population; 307 patients with mild to moderate forms of COVID-19 who were not hospitalized. Vitamin D levels were assessed during the duration of the disease.ResultsThe mean vitamin D level was lower in the severe COVID-19 pneumonia group as compared to the control group; 26.8 ± 7.6 ng/mLvs28.6 ± 7.4 ng/mL, p<0.002. There were more patients with a sufficient level of vitamin D in the control group as compared to the control group; 127 (20.6%) vs 89 (14.5%), p<0.001. Multivariable analysis showed that a deficient vitamin D level was associated with a higher risk for severe COVID-19 pneumonia (OR=3.0; 95% CI: 1.79, 5.10CI), p<0.001.ConclusionA sufficient vitamin D level is linked to a lower risk of COVID-19 severe pneumonia.
Neuroimaging Insights into Subjective Cognitive Decline: Differential Sensitivity of Cognitive Change Index and Everyday Cognition Scale
Authors: Morrison Cassandra , Anderson John A. E. , Dadar Mahsa ,   the Alzheimer’s Disease Neuroimaging Initiative
Published: June 2024

AbstractIntroductionCognitively healthy older adults may experience self-perceived memory and cognitive deficits, known as subjective cognitive decline (SCD), increasing their risk for dementia- related brain and cognitive changes. This study investigated if questions from the Cognitive Change Index (CCI) and Everyday Cognition Scale (ECog) show similar associations with dementia-related changes.MethodsCognitively healthy older adults (n=332) from the Alzheimer’s Disease Neuroimaging Initiative were included. Partial-least-squares observed the latent variables (LVs) that maximize the relationship between the two questionnaires.ResultsTwo LVs (p’s<0.001) explained 85.89% and 8.30% of the cross-block covariance. In the first LV, several CCI questions correlated with older age and frontal, parietal, and temporal WMHs, lower hippocampal and entorhinal cortex volume, and larger ventricles. The second LV showed younger individuals with higher SCD scores on three CCI questions correlated with temporal and parietal WMHs and entorhinal cortex volumes.ConclusionMore questions from the CCI are associated with neuroimaging markers, unlike the ECog questions. These questionnaires may thus be measuring different neural decline patterns and may be sensitive to different etiologies.
Improved user-friendliness in the design and analysis of FAVITES-Lite simulations
Authors: Stadler Kate , Phillips Kayla , Moshiri Niema 
Published: June 2024

AbstractAfter the rise of several different simulation tools that can simulate different aspects of an epidemic, FAVITES provides a framework to simulate an end-to-end epidemic (from contact network generation to infection transmission to viral phylogenies). To increase accessibility, FAVITES-Lite was developed with the key functionalities of FAVITES with the sacrifice of some flexibility. It remains that configuring and analyzing a complicated simulation like FAVITES or FAVITES-Lite can be extremely daunting, so we created example configuration files to help users learn the process behind parameter choice and a graph-visualization web application to help users visualize the results of their simulations.
Transcutaneous Electrical Spinal Cord Stimulation Promotes Focal Sensorimotor Activation that Accelerates Brain-Computer Interface Skill Learning
Authors: Alawieh Hussein , Liu Deland , Madera Jonathan , Kumar Satyam , Racz Frigyes Samuel , Fey Ann Majewicz , Millán José del R. 
Published: June 2024

AbstractInjuries affecting the central nervous system may disrupt neural pathways to muscles causing motor deficits. Yet the brain exhibits sensorimotor rhythms (SMRs) during movement intents, and brain-computer interfaces (BCIs) can decode SMRs to control assistive devices and promote functional recovery. However, non-invasive BCIs suffer from the instability of SMRs, requiring longitudinal training for users to learn proper SMR modulation. Here, we accelerate this skill learning process by applying cervical transcutaneous electrical spinal stimulation (TESS) to inhibit the motor cortex prior to longitudinal upper-limb BCI training. Results support a mechanistic role for cortical inhibition in significantly increasing focality and strength of SMRs leading to accelerated BCI control in healthy subjects and an individual with spinal cord injury. Improvements were observed following only two TESS sessions and were maintained for at least one week in users who could not otherwise achieve control. Our findings provide promising possibilities for advancing BCI-based motor rehabilitation.
Wavelet transform of single-trial vestibular short-latency evoked potential reveals temporary reduction in signal detectability and temporal precision following noise exposure
Authors: Niwa Mamiko , Bauer David , Anderson Marie , Kanicki Ariane , Altschuler Richard A. , Stewart Courtney E. , King W. Michael 
Published: June 2024

AbstractThe vestibular short-latency evoked potential (VsEP) reflects the activity of irregular vestibular afferents and their target neurons in the brain stem. Attenuation of trial-averaged VsEP waveforms is widely accepted as an indicator of vestibular dysfunction, however, more quantitative analyses of VsEP waveforms could reveal underlying neural properties of VsEP waveforms. Here, we present a time-frequency analysis of the VsEP with a wavelet transform on a single-trial basis, which allows us to examine trial-by-trial variability in the strength of VsEP waves as well as their temporal coherence across trials. Using this method, we examined changes in the VsEP following 110 dB SPL noise exposure in rats. We found detectability of head jerks based on the power of wavelet transform coefficients was significantly reduced 1 day after noise exposure but recovered nearly to pre-exposure level in 3 - 7 days and completely by 28 days after exposure. Temporal coherence of VsEP waves across trials was also significantly reduced on 1 day after exposure but recovered with a similar time course. Additionally, we found a significant reduction in the number of calretinin-positive calyces in the sacculi collected 28 days after noise exposure. Furthermore, the number of calretinin-positive calyces was significantly correlated with the degree of reduction in temporal coherence and/or signal detectability of the smallest-amplitude jerks. This new analysis of the VsEP provides more quantitative descriptions of noise-induced changes as well as new insights into potential mechanisms underlying noise-induced vestibular dysfunction.Significance StatementOur study presents a new method of VsEP quantification using wavelet transform on a single-trial basis. It also describes a novel approach to determine the stimulus threshold of the VsEP based on signal-detection theory and Rayleigh statistics. The present analysis could also be applied to analysis of auditory brain stem response (ABR). Thus, it has the potential to provide new insights into the physiological properties that underlie peripheral vestibular and auditory dysfunction.
Mg2+-dependent mechanism of environmental versatility in a multidrug efflux pump
Authors: Russell Lewis Benjamin , Uddin Muhammad R. , Kuo Katie M. , Shah Laila M. N. , Harris Nicola J. , Booth Paula J. , Hammerschmid Dietmar , Gumbart James C. , Zgurskaya Helen I. , Reading Eamonn 
Published: June 2024

AbstractTripartite resistance nodulation and cell division multidrug efflux pumps span the periplasm and are a major driver of multidrug resistance among Gram-negative bacteria. The periplasm provides a distinct environment between the inner and outer membranes of Gram-negative bacteria. Cations, such as Mg2+, become concentrated within the periplasm and, in contrast to the cytoplasm, its pH is sensitive to conditions outside the cell. Here, we reveal an interplay between Mg2+and pH in modulating the dynamics of the periplasmic adaptor protein, AcrA, and its function within the prototypical AcrAB-TolC multidrug efflux pump fromEscherichia coli. In the absence of Mg2+, AcrA becomes increasingly plastic within acidic conditions, but when Mg2+is bound this is ameliorated, resulting in domain specific organisation in neutral to weakly acidic regimes. We establish a unique histidine residue directs these structural dynamics and is essential for sustaining pump efflux activity across acidic, neutral, and alkaline conditions. Overall, we propose Mg2+conserves the structural mobility of AcrA to ensure optimal AcrAB-TolC function within rapid changing environments commonly faced by the periplasm during bacterial infection and colonization. This work highlights that Mg2+is an important mechanistic component in this pump class and possibly across other periplasmic lipoproteins.
Pharmacologic Activation of Integrated Stress Response Kinases Inhibits Pathologic Mitochondrial Fragmentation
Authors: Baron Kelsey R. , Oviedo Samantha , Krasny Sophia , Zaman Mashiat , Aldakhlallah Rama , Bora Prerona , Mathur Prakhyat , Pfeffer Gerald , Bollong Michael J. , Shutt Timothy E. , Grotjahn Danielle A. , Wiseman R. Luke 
Published: June 2024

SUMMARYExcessive mitochondrial fragmentation is associated with the pathologic mitochondrial dysfunction implicated in the pathogenesis of etiologically-diverse diseases, including many neurodegenerative disorders. The integrated stress response (ISR) – comprising the four eIF2α kinases PERK, GCN2, PKR, and HRI – is a prominent stress-responsive signaling pathway that regulates mitochondrial morphology and function in response to diverse types of pathologic insult. This suggests that pharmacologic activation of the ISR represents a potential strategy to mitigate pathologic mitochondrial fragmentation associated with human disease. Here, we show that pharmacologic activation of the ISR kinases HRI or GCN2 promotes adaptive mitochondrial elongation and prevents mitochondrial fragmentation induced by the calcium ionophore ionomycin. Further, we show that pharmacologic activation of the ISR reduces mitochondrial fragmentation and restores basal mitochondrial morphology in patient fibroblasts expressing the pathogenic D414V variant of the pro-fusion mitochondrial GTPase MFN2 associated with neurological dysfunctions including ataxia, optic atrophy, and sensorineural hearing loss. These results identify pharmacologic activation of ISR kinases as a potential strategy to prevent pathologic mitochondrial fragmentation induced by disease-relevant chemical and genetic insults, further motivating the pursuit of highly selective ISR kinase-activating compounds as a therapeutic strategy to mitigate mitochondrial dysfunction implicated in diverse human diseases.