bluuebunny/crossref_metadata_2025_split huggingface dataset:
Development of medical device software for the screening and assessment of depression severity using data collected from a wristband-type wearable device: SWIFT study protocol
Authors: Kishimoto Taishiro , Kinoshita Shotaro , Kikuchi Toshiaki , Bun Shogyoku , Kitazawa Momoko , Horigome Toshiro , Tazawa Yuki , Takamiya Akihiro , Hirano Jinichi , Mimura Masaru , Liang Kuo-ching , Koga Norihiro , Ochiai Yasushi , Ito Hiromi , Miyamae Yumiko , Tsujimoto Yuiko , Sakuma Kei , Kida Hisashi , Miura Gentaro , Kawade Yuko , Goto Akiko , Yoshino Fumihiro 
Published: March 2022

ABSTRACTIntroductionFew biomarkers can be clinically used to diagnose and assess the severity of depression. However, a decrease in activity and sleep efficiency can be observed in depressed patients, and recent technological developments have made it possible to measure these changes. In addition, physiological changes, such as heart rate variability, can be used to distinguish depressed patients from normal persons; these parameters can be used to improve diagnostic accuracy. The proposed research will explore and construct machine learning models capable of detecting depressive episodes and assessing their severity using data collected from wristband-type wearable devices.MethodsPatients with depressive symptoms and healthy subjects will wear a wristband-type wearable device for 7 days; data on triaxial acceleration, pulse rate, skin temperature, and ultraviolet light will be collected. On the seventh day of wearing, the severity of depressive episodes will be assessed using SCID-5, HAMD, and other scales. Data for up to five 7-day periods of device wearing will be collected from each subject. Using wearable device data associated with clinical symptoms as supervisory data, we will explore and build a machine learning model capable of identifying the presence or absence of depressive episodes and predicting the HAMD scores for an unknown data set.DiscussionOur machine learning model could improve the clinical diagnosis and management of depression through the use of a wearable medical device.Trial RegistrationjRCT1031210478, Japan Registry of Clinical Trials (jRCT)
Plasma metabolites associated with cognitive function across race/ethnicities affirming the importance of healthy nutrition
Authors: Granot-Hershkovitz Einat , He Shan , Bressler Jan , Yu Bing , Tarraf Wassim , Rebholz Casey M. , Cai Jianwen , Chan Queenie , Garcia Tanya P. , Mosley Thomas , Kristal Bruce S. , DeCarli Charles , Fornage Myriam , Chen Guo-Chong , Qi Qibin , Kaplan Robert , Gonzalez Hector M. , Sofer Tamar 
Published: March 2022

AbstractINTRODUCTIONWe studied the replication and generalization of previously identified metabolites potentially associated with global cognitive function in multiple race/ethnicities and assessed the contribution of diet to these associations.METHODSWe tested metabolite-cognitive function associations in U.S.A. Hispanic/Latino adults (n= 2,222) from the Community Health Study/ Study of Latinos (HCHS/SOL) and in European (n=1,365) and African (n=478) Americans from the Atherosclerosis Risk In Communities (ARIC) Study. We applied Mendelian Randomization (MR) analyses to assess causal associations between the metabolites and cognitive function and between Mediterranean diet and cognitive function.RESULTSSix metabolites were consistently associated with lower global cognitive function across all studies. Of these, four were sugar related (e.g., ribitol). MR analyses provided weak evidence for a potential causal effect of ribitol on cognitive function and bi-directional effects of cognitive performance on diet.DISCUSSIONSeveral diet-related metabolites were associated with global cognitive function across studies with different race/ethnicities.
Comparing “people-like-me” and linear mixed model predictions of functional recovery following knee arthroplasty
Authors: Graber Jeremy , Kittelson Andrew , Juarez-Colunga Elizabeth , Jin Xin , Bade Michael , Stevens-Lapsley Jennifer 
Published: March 2022

ABSTRACTObjectivePrediction models can be useful tools for monitoring patient status and personalizing treatment in health care. The goal of this study was to compare the relative strengths and weaknesses of two different approaches for predicting functional recovery after knee arthroplasty: a neighbors-based “people-like-me” (PLM) approach and a linear mixed model (LMM) approach.Materials and MethodsWe used two distinct datasets to train and then test PLM and LMM prediction approaches for functional recovery following knee arthroplasty. We used Timed Up and Go (TUG)—a commonly used test of mobility—to operationalize physical function. Both approaches used patient characteristics and baseline postoperative TUG values to predict TUG recovery from days 1-425 following surgery. We compared the accuracy and precision of PLM and LMM predictions in the testing dataset.ResultsA total of 317 patient records with 1379 TUG observations were used to train PLM and LMM approaches, and 456 patient records with 1244 TUG observations were used to test the predictions. The approaches performed similarly in terms of mean squared error and bias, but the PLM approach provided more accurate and precise estimates of prediction uncertainty.Discussion and ConclusionOverall, the PLM approach more accurately and precisely predicted TUG recovery following knee arthroplasty. These results suggest PLM predictions may be more clinically useful for monitoring recovery and personalizing care following knee arthroplasty. However, clinicians and organizations seeking to use predictions in practice should consider additional factors (e.g., resource requirements) when selecting a prediction approach.
Parkinson’s disease causality and heterogeneity: a proteogenomic view
Authors: Kaiser Sergio , Zhang Luqing , Mollenhauer Brit , Jacob Jaison , Longerich Simonne , Del-Aguila Jorge , Marcus Jacob , Raghavan Neha , Stone David , Fagboyegun Olumide , Galasko Douglas , Dakna Mohammed , Bilican Bilada , Dovlatyan Mary , Kostikova Anna , Li Jingyao , Peterson Brant , Rotte Michael , Sanz Vinicius , Foroud Tatiana , Hutten Samantha J. , Frasier Mark , Iwaki Hirotaka , Singleton Andrew , Marek Ken , Crawford Karen , Elwood Fiona , Messa Mirko , Serrano-Fernandez Pablo 
Published: March 2022

AbstractThe pathogenesis and clinical heterogeneity of Parkinson’s disease have been evaluated from molecular, pathophysiological, and clinical perspectives. High-throughput proteomic analysis of CSF has opened new opportunities for scrutinizing this heterogeneity. To date, this is the most comprehensive CSF-based proteomics profiling study in Parkinson’s disease (1103 patients, 4135 proteins). Combining CSF aptamer-based proteomics with genetics we determined protein quantitative trait loci (pQTLs). Analyses of pQTLs together with summary statistics from the largest Parkinson’s disease genome wide association study (GWAS) identified 68 potential causal proteins by Mendelian randomization. The top causal protein, GPNMB was previously reported to be upregulated in the substantia nigra of Parkinson’s disease patients.We also compared the CSF proteomes of patients and controls. The Parkinson’s disease cohort comprised not only LRRK2+ and GBA+ mutation carriers but also idiopathic patients. Proteome differences between GBA+ patients and unaffected GBA+ controls suggest degeneration of dopaminergic neurons, altered dopamine metabolism and increased brain inflammation. The proteins discriminating LRRK2+ patients from unaffected LRRK2+ controls, revealed dysregulated lysosomal degradation, as well as altered alpha-synuclein processing, and neurotransmission. Proteome differences between idiopathic patients and controls suggest increased neuroinflammation, mitochondrial dysfunction / oxidative stress, altered iron metabolism and potential neuroprotection mediated by vasoactive substances.Finally, we used proteomic data to stratify idiopathic patients into “endotypes”. The identified endotypes show differences in cognitive and motor disease progression based on the use of previously reported protein-based risk scores.In summary, we: i) identified causal proteins for Parkinson’s disease, ii) assessed CSF proteome differences in Parkinson’s disease patients of genetic and idiopathic etiology, and. iii) stratified idiopathic patients into robust clinically relevant subtypes. Our findings not only contribute to the identification of new therapeutic targets but also to shaping personalized medicine in CNS neurodegeneration.
Bacterial culture use, etiology and antibiotic susceptibility of common bacterial infections in Indonesian hospitals in 2019
Authors: de Brabander Justin , Nelwan Erni J. , Limato Ralalicia , Alamanda Monik , Mudia Manzilina , Tjoa Enty , Mauleti Ifael Y. , Mayasari Maria , Firmansyah Iman , Jayati T. Mannaria , van Vugt Michèle , van Doorn H. Rogier , Hamers Raph L. 
Published: March 2022

AbstractObjectivesTo describe the use of bacterial cultures, and the etiology and antibiotic susceptibility of common high-priority bacteria isolated from hospitalized patients in Jakarta, Indonesia.MethodsWe conducted a hospital-wide cross-sectional study of all inpatients receiving systemic antibiotic treatment (WHO ATC J01) in six hospitals in 2019, capturing routine data on antibiotic treatment and cultures. We reported bug-drug combinations for Escherichia coli and the ESKAPE group of bacteria.Results562 patients (52% women, median age 46 years) had 587 diagnoses, with pneumonia (258, 44%) most common. One or more culture specimens were taken in 38% (215/562) overall, a sputum culture in 25% (64/258) of pneumonia patients; and a blood culture in 52% (16/31) of sepsis patients. 50% of positive blood culture results were reported after 4 days. From 670 culture specimens, 279 bacteria were isolated, 214 (77%) were Gram-negative, including Klebsiella pneumoniae (70, 25%), Pseudomonas aeruginosa (36, 13%), and E. coli (21, 11%). Resistance included third-generation cephalosporin-resistant K. pneumoniae (77%), E. coli (65%) and Enterobacter spp (81%); carbapenem-resistant K. pneumoniae (26%), P. aeruginosa (24%), E. coli (33%), Acinetobacter spp (57%), and Enterobacter spp (60%); and meticillin-resistant S. aureus (71%). Vancomycin-resistant S. aureus (0%) and Enterococcus faecalis (12%) were uncommon. Multi-drug resistance was 30% for K. pneumoniae, 29% for P. aeruginosa, 49% for E. coli, 42% for Acinetobacter spp, and 71% for S. aureus.ConclusionsIn Indonesian hospitals, bacterial cultures were underused and antibiotic resistance is at alarming levels. Enhanced context-specific infection prevention, diagnostic and antibiotic stewardship interventions are urgently needed.
Associations between maternal infections during pregnancy and childhood IQ scores using the ALSPAC birth cohort
Authors: Kwok Janell , Hall Hildigunnur Anna , Murray Aja Louise , Lombardo Michael Vincent , Auyeung Bonnie 
Published: March 2022

AbstractMaternal prenatal infections have been linked to children’s neurodevelopment and cognitive outcomes. It remains unclear, however, whether infections occurring during specific vulnerable gestational periods can affect children’s cognitive outcomes. The study aimed to examine maternal infections in each trimester of pregnancy and associations with children’s verbal, performance, and total IQ scores. The ALSPAC birth cohort was used to investigate associations between maternal infections in pregnancy and childhood IQ outcomes. Infection data from mothers and cognition data from children were included with the final study sample size comprising 7,410 mother-child participants. Regression analysis was used to examine links between maternal infections occurring at each trimester of pregnancy and children’s cognitive IQ scores at 18 months, 4 years, and 8 years. Infections in the third trimester were significantly associated with decreased verbal IQ at age 4 (p<.05, adjusted R2 = .004); decreased verbal IQ (p<.01, adjusted R2 = .001), performance IQ (p<.01, adjusted R2 = .0008), and total IQ at age 8 (p<.01, adjusted R2 = .001). Results suggest that later maternal infections could have a latent effect on cognitive development, only emerging when cognitive load increases over time, though magnitude of effect appears to be small. Performance IQ may be more vulnerable to trimester-specific exposure to maternal infection as compared to verbal IQ. Future research could include examining potential mediating mechanisms on childhood cognition, such as possible moderating effects of early childhood environmental factors, and if effects persist in future cognitive outcomes.
Reversal of increases in methadone distribution for opioid use disorder in the US during the COVID-19 pandemic
Authors: Kennalley Amy L. , Fanelli Jessica L. , Furst John A. , Mynarski Nicholas J. , Jarvis Margaret A. , Nichols Stephanie D. , McCall Kenneth L. , Piper Brian J. 
Published: March 2022

AbstractOpioid use disorder (OUD) is a major public health concern in the United States (US), resulting in high rates of overdose and other negative outcomes. Methadone, a treatment for OUD, has been shown to be effective in reducing the risk of overdose and improving overall health and quality of life. This study analyzed the distribution of methadone for the treatment of OUD across the US using data from the Drug Enforcement Administration’s Automated Reports and Consolidated Ordering System, Medicaid’s State Drug Utilization Data, and the US Census Bureau. Analysis revealed that methadone distribution for OUD has expanded significantly over the past decade, with an average state increase of +96.96% from 2010 to 2020, and there was a significant increase in overall distribution of methadone to opioid treatment programs (OTP) in the US from 2010 to 2020 (+61.00%) and from 2015 to 2020 (+26.22%). However, the distribution to OTPs did not significantly change from 2019 to 2021 (−5.15%). Furthermore, pronounced variation in methadone distribution among states were observed, with some states having no OTPs or Medicaid coverage. New policies are urgently needed to increase access to methadone treatment and address the opioid overdose crisis in the US.
Identification and Quantification of Bioactive Compounds Suppressing SARS-CoV-2 Signals in Wastewater-based Epidemiology Surveillance
Authors: Bayati Mohamed , Hsieh Hsin-Yeh , Hsu Shu-Yu , Li Chenhui , Rogers Elizabeth , Belenchia Anthony , Zemmer Sally A. , Blanc Todd , LePage Cindy , Klutts Jessica , Reynolds Melissa , Semkiw Elizabeth , Johnson Hwei-Yiing , Foley Trevor , Wieberg Chris G. , Wenzel Jeff , Lyddon Terri , LePique Mary , Rushford Clayton , Salcedo Braxton , Young Kara , Graham Madalyn , Suarez Reinier , Ford Anarose , Lei Zhentian , Sumner Lloyd , Mooney Brian P. , Wei Xing , Greenlief C. Michael , Johnson Marc C. , Lin Chung-Ho 
Published: March 2022

AbstractRecent SARS-CoV-2 wastewater-based epidemiology (WBE) surveillance have documented a positive correlation between the number of COVID-19 patients in a sewershed and the level of viral genetic material in the wastewater. Efforts have been made to use the wastewater SARS-CoV-2 viral load to predict the infected population within each sewershed using a multivariable regression approach. However, reported clear and sustained variability in SARS-CoV-2 viral load among treatment facilities receiving industrial wastewater have made clinical prediction challenging. Several classes of molecules released by regional industries and manufacturing facilities, particularly the food processing industry, can significantly suppress the SARS-CoV-2 signals in wastewater by breaking down the lipid-bilayer of the membranes. Therefore, a systematic ranking process in conjugation with metabolomic analysis was developed to identify the wastewater treatment facilities exhibiting SARS-CoV-2 suppression and identify and quantify the chemicals suppressing the SARS-COV-2 signals. By ranking the viral load per diagnosed case among the sewersheds, we successfully identified the wastewater treatment facilities in Missouri, USA that exhibit SARS-CoV-2 suppression (significantly lower than 5 × 1011gene copies/reported case) and determined their suppression rates. Through both untargeted global chemical profiling and targeted analysis of wastewater samples, 40 compounds were identified as candidates of SARS-CoV-2 signal suppression. Among these compounds, 14 had higher concentrations in wastewater treatment facilities that exhibited SARS-CoV-2 signal suppression compared to the unsuppressed control facilities. Stepwise regression analyses indicated that 4-nonylphenol, palmitelaidic acid, sodium oleate, and polyethylene glycol dioleate are positively correlated with SARS-CoV-2 signal suppression rates. Suppression activities were further confirmed by incubation studies, and the suppression kinetics for each bioactive compound were determined. According to the results of these experiments, bioactive molecules in wastewater can significantly reduce the stability of SARS-CoV-2 genetic marker signals. Based on the concentrations of these chemical suppressors, a correction factor could be developed to achieve more reliable and unbiased surveillance results for wastewater treatment facilities that receive wastewater from similar industries.
The impact of the one child policy on China’s infant mortality from 1970-1989: a quasi-experimental study
Authors: Chakrabarti Suman , Choo Esther , He Yao 
Published: March 2022

AbstractBackgroundChina’s One Child Policy (OCP), enforced in 1980, was the largest fertility control policy in human history, and its effects are still widely debated. Previous analyses of the OCP have focused on population reduction and sex ratios, among others. This study evaluates the impact of the OCP on infant mortality in China using quasi-experimental methods.MethodsCountry-level panel data for China and 106 other countries were extracted from the World Bank data repository for the years 1970-1989. The primary outcome was infant mortality rate (IMR) defined as deaths among children <1 year of age per 1000 live births. The impact of the OCP was estimated using uncontrolled and controlled interrupted time series (ITS) designs that control for within group time varying characteristics. Models were further adjusted for a comprehensive set of covariates and corrected for first-order autocorrelation. Countries included in the control groups were identified using the method of synthetic controls and propensity score matching (PSM). Valid control groups reduced threats from selection bias and unobserved time invariant confounders.ResultsAdjusting for GDP per capita, GDP growth, women’s education, and population density, IMR decreased on average by 2.5 deaths per 1,000 annually before the OCP was enforced in China. The pre-OCP trends in IMR moved in tandem across China, synthetic and PSM controls, validating the parallel trends assumption. After the OCP, the trend in IMR in China increased by 2.24 (95% CI 1.39-3.09) per year, compared to the IMR trend in the pre-OCP period, comparing differences in China to synthetic controls. Results were similar and confirmed across PSM (1.96, 0.56-3.37) and uncontrolled ITS (2.14, 1.39-2.89) models. No significant immediate level change in IMR was observed in the controlled ITS models.ConclusionsThe reduction in IMR slowed down significantly after the OCP was enforced in China. The slowdown is likely due to a relative increase in the ratio of the IMR of females to males suggesting that practices such as female infanticide, abandonment, and neglect, stemming from a strong son preference were primary contributors. Coercive policies to reduce fertility can have unintended consequences.
Cross-sector Decision Landscape in Response to COVID-19: A Qualitative Analysis of North Carolina Decision-Makers
Authors: Biddell Caitlin B. , Johnson Karl T. , Patel Mehul D. , Smith Raymond L. , Hecht Hillary K. , Swann Julie L. , Mayorga Maria E. , Lich Kristen Hassmiller 
Published: March 2022

AbstractContextThe COVID-19 pandemic response has demonstrated the interconnectedness of individuals, organizations, and other entities jointly contributing to the production of community health. This response has involved stakeholders from numerous sectors who have been faced with new decisions, objectives, and constraints.ObjectiveWe aimed to examine the cross-sector organizational decision landscape that formed in response to the COVID-19 pandemic.DesignWe applied a systems approach to the qualitative analysis of semi-structured interviews on the cross-sector, organizational response to the COVID-19 pandemic. We analyzed transcribed interviews using conventional content analysis to synthesize key themes.SettingSemi-structured interviews were conducted via secure, video-conferencing platform between October 2020 and January 2021.ParticipantsForty-four state and local decision-makers representing organizations from nine sectors in North Carolina participated.Main Outcome MeasuresWe defined the decision landscape as including decision-maker roles, key decisions, and inter-relationships involved in producing community health.ResultsDecision-maker roles were characterized by underlying tensions between balancing organizational mission with employee/community health and navigating organizational versus individual responsibility for reducing transmission. Key Decisions fell into several broad categories, including how to translate public health guidance into practice; when to institute, and subsequently loosen, public health restrictions; and how to address downstream social and economic impacts of public health restrictions. Lastly, given limited and changing information, as well as limited resources and expertise, the COVID-19 response required cross-sector collaboration, which was commonly coordinated by local health departments.ConclusionsBy documenting the local, cross-sector decision landscape that formed in response to COVID-19, we illuminate the impacts different organizations may have on information/misinformation, prevention behaviors, and, ultimately, health. Public health researchers and practitioners must understand, and work within, this complex decision landscape when responding to COVID-19 and future community health challenges.
Preliminary Rasch analysis of the Multidimensional Assessment of Interoceptive Awareness in adults with stroke
Authors: Blackwood Jena , Carpentier Sydney , Deng Wei , Van de Winckel Ann 
Published: March 2022

AbstractPurposeThe Multidimensional Assessment of Interoceptive Awareness (MAIA) measures interoceptive body awareness, which includes aspects such as attention regulation, self-regulation, and body listening. Our purpose was to validate the MAIA in adults with stroke using Rasch Measurement Theory.MethodsThe original MAIA has 32 items grouped into 8 separately scored subscales that measure aspects of body awareness. Using Rasch Measurement Theory we evaluated the unidimensionality of the entire scale and investigated person and item fit, person separation reliability, targeting, local item dependence, and principal components analysis of residuals.ResultsForty-one adults with chronic stroke (average 3.8 years post-stroke, 13 women, average age 57±13 years) participated in the study. Overall fit (χ2=62.26,p=0.26) and item fit were obtained after deleting 3 items and rescoring 26 items. One participant did not fit the model (2.44%). There were no floor (0.00%) or ceiling effects (0.00%). Local item dependence was found in 42 pairs. The person separation reliability was 0.91, and the person mean location was 0.06±1.12 logits.ConclusionsThe MAIA demonstrated good targeting and reliability, as well as good item and person fit in adults with chronic stroke. A study with a larger sample size is needed to validate our findings.