bluuebunny/crossref_metadata_2025_split huggingface dataset:
Psilocybin desynchronizes brain networks
Authors: Siegel Joshua S , Subramanian Subha , Perry Demetrius , Kay Benjamin , Gordon Evan , Laumann Timothy , Reneau Rick , Gratton Caterina , Horan Christine , Metcalf Nicholas , Chacko Ravi , Schweiger Julie , Wong Dean , Bender David , Padawer-Curry Jonah , Raison Charles , Raichle Marcus , Lenze Eric J. , Snyder Abraham Z , Dosenbach Nico U.F. , Nicol Ginger 
Published: August 2023

1SummaryThe relationship between the acute effects of psychedelics and their persisting neurobiological and psychological effects is poorly understood. Here, we tracked brain changes with longitudinal precision functional mapping in healthy adults before, during, and for up to 3 weeks after oral psilocybin and methylphenidate (17 MRI visits per participant) and again 6+ months later. Psilocybin disrupted connectivity across cortical networks and subcortical structures, producing more than 3-fold greater acute changes in functional networks than methylphenidate. These changes were driven by desynchronization of brain activity across spatial scales (area, network, whole brain). Psilocybin-driven desynchronization was observed across association cortex but strongest in the default mode network (DMN), which is connected to the anterior hippocampus and thought to create our sense of self. Performing a perceptual task reduced psilocybin-induced network changes, suggesting a neurobiological basis forgrounding, connecting with physical reality during psychedelic therapy. The acute brain effects of psilocybin are consistent with distortions of space-time and the self. Psilocybin induced persistent decrease in functional connectivity between the anterior hippocampus and cortex (and DMN in particular), lasting for weeks but normalizing after 6 months. Persistent suppression of hippocampal-DMN connectivity represents a candidate neuroanatomical and mechanistic correlate for psilocybin’s pro-plasticity and anti-depressant effects.
Genomic epidemiology of the cholera outbreak in Malawi 2022-2023
Authors: Chabuka Lucious , Choga Wonderful T. , Mavian Carla N. , Moir Monika , Tegally Houriiyah , Wilkinson Eduan , Naidoo Yeshnee , Inward Rhys , Morgenstern Christian , Bhatt Samir , WilliamWint G. R. , Khan Kamran , Bogoch Isaac I. , Kraemer Moritz U.G. , Baxter Cheryl , Tagliamonte Massimiliano , Salemi Marco , Lessells Richard , Mitambo Collins , Chitatanga Ronald , Bango Joseph Bitilinyu- , Chiwaula Mabvuto , Chavula Yollamu , Bukhu Mphatso , Manda Happy , Chitenje Moses , Malolo Innocent , Mwanyongo Alex , Mvula Dr Bernard , Nyenje Dr Mirrium , Oliveira Tulio de , Kagoli Dr Mathew 
Published: August 2023

AbstractSince early 2022, in the aftermath of two extreme weather events, Malawi experienced its largest ever cholera outbreak, with over 58,000 reported cases and 1,761 deaths as of May 2023. We generated 49 high-quality, near-complete Vibrio cholerae genomes in Malawi from isolates collected between December 2022 and March 2023 from all three regions of Malawi. Using phylogenetic methods with 2159 publicly available genomes, we present evidence suggesting that the Malawi outbreak strains originated from the Pakistan outbreak, the estimated most recent ancestor of this lineage, named T15, was during the Pakistan floods, and once introduced into Malawi was exacerbated by major floods between June and October 2022. The extreme weather events and humanitarian crises in Malawi provided the environment for the spread of Vibrio cholerae, and the subsequent movement of large numbers of people may have facilitated its spread to susceptible populations in areas relatively unaffected by cholera for over a decade.
Characterizing patterns of diffusion tensor imaging variance in aging brains
Authors: Gao Chenyu , Yang Qi , Kim Michael E. , Khairi Nazirah Mohd , Cai Leon Y. , Newlin Nancy R. , Kanakaraj Praitayini , Remedios Lucas W. , Krishnan Aravind R. , Yu Xin , Yao Tianyuan , Zhang Panpan , Schilling Kurt G. , Moyer Daniel , Archer Derek B. , Resnick Susan M. , Landman Bennett A. ,   the Alzheimer’s Disease Neuroimaging Initiative,   the BIOCARD Study team
Published: August 2023

AbstractPurposeAs large analyses merge data across sites, a deeper understanding of variance in statistical assessment across the sources of data becomes critical for valid analyses. Diffusion tensor imaging (DTI) exhibits spatially varying and correlated noise, so care must be taken with distributional assumptions. Here we characterize the role of physiology, subject compliance, and the interaction of subject with the scanner in the understanding of DTI variability, as modeled in spatial variance of derived metrics in homogeneous regions.ApproachWe analyze DTI data from 1035 subjects in the Baltimore Longitudinal Study of Aging (BLSA), with ages ranging from 22.4 to 103 years old. For each subject, up to 12 longitudinal sessions were conducted. We assess variance of DTI scalars within regions of interest (ROIs) defined by four segmentation methods and investigate the relationships between the variance and covariates, including baseline age, time from the baseline (referred to as “interval”), motion, sex, and whether it is the first scan or the second scan in the session.ResultsCovariate effects are heterogeneous and bilaterally symmetric across ROIs. Inter-session interval is positively related (p≪ 0.001) to FA variance in the cuneus and occipital gyrus, but negatively (p≪ 0.001) in the caudate nucleus. Males show significantly (p≪ 0.001) higher FA variance in the right putamen, thalamus, body of the corpus callosum, and cingulate gyrus. In 62 out of 176 ROIs defined by the Eve type-1 atlas, an increase in motion is associated (p< 0.05) with a decrease in FA variance. Head motion increases during the rescan of DTI (Δμ= 0.045 millimeters per volume).ConclusionsThe effects of each covariate on DTI variance, and their relationships across ROIs are complex. Ultimately, we encourage researchers to include estimates of variance when sharing data and consider models of heteroscedasticity in analysis. This work provides a foundation for study planning to account for regional variations in metric variance.
Prediction of COVID-19 infection risk using personal mobile location data only
Authors: Jang Ahreum , Kim Sungtae , Baek Hyeongwoo , Kim Hyejung , Park Hae-Lee 
Published: August 2023

AbstractPredicting an individual’s risk of infectious disease is a critical technology in infectious disease response. During the COVID-19 pandemic, identifying and isolating individuals at high risk of infection was an essential task for epidemic control. We introduce a new machine learning model that predicts the risk of COVID-19 infection using only individuals’ mobile cell tower location information. This model distinguishes the cell tower location information of an individual into residential and non-residential areas and calculates whether the cell tower locations overlapped with other individuals. It then generates various variables from the information of overlapping and predicts the possibility of COVID-19 infection using a machine learning algorithm. The predictive model we developed showed performance comparable to models using individual’s clinical information. This predictive model, which can be used to predict infections of diseases with asymptomatic infections such as COVID-19, has the advantage of supplementing the limitations of existing infectious disease prediction models that use symptoms and other information.
Remote monitoring of depression severity: A machine learning approach
Authors: Holstein Vincent , Rahimi-Eichi Habiballah , Emden Daniel , Gutfleisch Lara , Refisch Alexander , Goltermann Janik , Leenings Ramona , Winter Nils , Kircher Tilo , Nenadić Igor , Redlich Ronny , Leehr Elisabeth Johanna , Dohm Katharina , Baker Justin , Dannlowski Udo , Opel Nils , Hahn Tim 
Published: August 2023

Depression is a widely prevalent psychiatric illness with variable levels of severity that necessitate different approaches to treatment. To enhance the management of this condition, there is a growing interest in utilizing mobile devices, especially smartphones, for remote monitoring of patients. This study aims to build prediction models for depression severity based on active and passive features collected from patients with major depressive disorder (MDD) and healthy controls to assess the feasibility of remote monitoring of depression severity.Using data from 142 participants (85 healthy controls, 67 MDD) we extracted features such as GPS-derived mobility markers, ecological momentary assessments (EMA), age, and sex to develop machine learning models of depression severity on the different diagnostic subgroups in this cohort.Our results indicate that the employed models outperformed baseline estimators in random split scenarios. However, the improvement was marginal in user-split scenarios, highlighting the need for larger and more diverse samples for clinical utility. Among the features, mood EMA emerged as the most influential predictor, followed by GPS-derived mobility features. Models also showed a significant association between depression severity and average reported mood, as well as GPS-derived mobility markers such as number of places visited and percent home.While predicting composite depression scores is important, future studies could explore predicting individual symptom items or symptom groups for a more comprehensive assessment of depression severity. Challenges for clinical utility include participant dropout, which could be addressed through more engaging app design to promote user adherence. Harmonization of phone-derived measures is also crucial to facilitate model transfer across studies.In conclusion, this study contributes valuable evidence supporting the potential utility of smartphone data for mood state monitoring and predicting depression severity. Future research should focus on predicting depression further ahead in time and addressing the challenges identified to create more robust and effective depression monitoring solutions using smartphone-based data.
Heat Inactivation of Aqueous Viable Norovirus and MS2 Bacteriophage
Authors: Shaffer Marlee , Huynh Kimberly , Costantini Verónica , Bibby Kyle , Vinjé Jan 
Published: August 2023

ABSTRACTHuman norovirus is a leading cause of acute gastroenteritis often associated with contaminated food or water exposure. Many studies have used morphologically similar viruses, such as MS2 bacteriophage, and molecular detection methods to study the environmental fate and inactivation characteristics, given the historical challenges to culture human norovirus. In this study, we used human intestinal enteroids (HIEs) to analyze the heat inactivation kinetics of viable human norovirus compared to the surrogate MS2 bacteriophage. Norovirus decay rates were 0.22 min-1, 0.68 min-1, and 1.11 min-1for 50°C, 60°C, and 70°C, respectively, and MS2 bacteriophage decay rates were 0.0065 min-1, 0.045 min-1, and 0.16 min-1for 50°C, 60°C, and 70°C, respectively. Norovirus and MS2 bacteriophage had significantly different decay rates at all tested temperatures (p = 0.002 – 0.007). No decrease of RNA titers as measured by realtime RT-PCR for both human norovirus and MS2 bacteriophage over time was observed, indicating molecular methods do not accurately depict viable human norovirus after heat inactivation. Overall, our data demonstrate that MS2 bacteriophage is a conservative surrogate to measure heat inactivation and potentially overestimates the infectious risk of norovirus.IMPORTANCENorovirus is the leading cause of epidemic and endemic acute gastroenteritis worldwide. Treatments to inactivate norovirus are critical to reducing the risk associated with contaminated food and water. The recent developments to replicate human norovirus in human intestinal enteroids (HIE) enables the evaluation of heat inactivation kinetics of viable norovirus. Historically, cultivable surrogate viruses, such as bacteriophage MS2, have been used to measure the environmental fate of human norovirus. Our findings indicate that compared to human norovirus, MS2 bacteriophage is a conservative surrogate to measure the effect of heat inactivation. Furthermore, this study corroborates that measuring viral RNA titers, as evaluated by PCR methods, does not correlate with persistence of viable norovirus.
Perceptions and experiences of different Long COVID community rehabilitation service models from the perspectives of people living with Long COVID and healthcare professionals
Authors: Duncan Edward , Alexander Lyndsay , Cowie Julie , Love Alison , Morris Jacqui , Moss Rachel , Ormerod Jane , Preston Jenny , Shim Joanna , Stage Emma , Tooman Tricia , Cooper Kay 
Published: August 2023

ABSTRACTObjectivesTo explore the perceptions and experiences of barriers and facilitators to accessing Long COVID community rehabilitation.DesignWe used a qualitative descriptive [1] design over two rounds of data collection with three participant groups: i) people with experience of rehabilitation for Long COVID (PwLC); ii) NHS staff delivering and/or managing community rehabilitation services (allied health professionals (AHPs)), and iii) NHS staff involved in strategic planning around Long COVID in their health board (Long COVID leads).SettingFour NHS Scotland territorial health boards.Participants51 interviews: eight Long COVID leads (11 interviews); 15 AHPs (25 interviews) and 15 PwLC (15 interviews).ResultsThree key themes were identified: i) Accessing care for PwLC, ii) Understanding Long COVID and its management; and iii) Strengths and limitations of existing Long COVID rehabilitation services.ConclusionsOrganisational delivery of Long COVID community rehabilitation is complex and presents multiple challenges. In addition, access to Long COVID community rehabilitation can be challenging. When accessed, these services are valued by PwLC but require adequate planning, publicity, and resource. The findings presented here can be used by those developing and delivering services for people with Long COVID.Strengths and limitations of this studyThis is the first study to explore perceptions and experiences of different community rehabilitation models for Long COVID in the Scottish contextPerspectives of people with Long COVID, staff delivering services, and staff leading on Long COVID in four geographically diverse Scottish health boards are includedThe strengths and limitations of different Long COVID rehabilitation service model components are highlightedThe rapidly evolving nature of Long COVID and its management resulted in fewer distinct differences between the four health boards than originally anticipated
Characterising the neurobiological mechanisms of action of exercise and cognitive behavioural interventions for rheumatoid arthritis fatigue: an MRI brain study
Authors: Dehsarvi Amir , Al-Wasity Salim , Stefanov Kristian , Wiseman Stewart , Ralston Stuart , Wardlaw Joanna , Emsley Richard , Bachmair Eva-Maria , Cavanagh Jonathan , Waiter Gordon D. , Basu Neil 
Published: August 2023

AbstractBackgroundChronic Fatigue is a major clinical unmet need among patients with Rheumatoid Arthritis (RA). Current therapies are limited to non-pharmacological interventions, such as personalised exercise programmes (PEP) and cognitive behavioural approaches (CBA), however, still most patients continue to report severe fatigue. To inform more effective therapies, we conducted an MRI brain study of PEP and CBA, nested within a randomised controlled trial (RCT), to identify their neurobiological mechanisms of fatigue reduction in RA.MethodsA sub-group of RA subjects (n=90), participating in a RCT of PEP/CBA for fatigue, undertook a multi-modal MRI brain scan following randomisation to either usual care (UC) alone or in addition to PEP/CBA, and again after the intervention (6 months). Brain regional volumetric, functional, and structural connectivity indices were curated and then computed employing a causal analysis framework. The primary outcome was fatigue improvement (Chalder Fatigue Scale).FindingsSeveral structural and functional connections were identified as mediators of fatigue improvement in both PEP and CBA compared to UC. PEP had a more pronounced effect on functional connectivity than CBA, however, structural connectivity between the left isthmus cingulate cortex (L-ICC) and left paracentral lobule (L-PCL) was shared and the size of mediation effect ranked highly for both PEP/CBA (ßAverage=-0·46, SD 0·61; ßAverage=-0·32, SD 0·47, respectively).InterpretationThe structural connection between the L-ICC and L-PCL appears to be a dominant mechanism for how both PEP/CBA reduces fatigue among RA patients. This supports its potential as a substrate of fatigue neurobiology and a putative candidate for future targeting.
The role of social network structure and function in moderate and severe social and emotional loneliness: the Dutch SaNAE study in older adults
Authors: Steijvers Lisanne CJ , Brinkhues Stephanie , Suanet Bianca , Stijnen Mandy MN , Hoebe Christian JPA , Dukers-Muijrers Nicole HTM 
Published: August 2023

AbstractBackgroundLoneliness is a serious public health problem and became even more visible during the COVID-19 pandemic. Yet it is unknown which aspects of social networks are most important. Here, we evaluated social network structure and function and associations with moderate and severe social and emotional loneliness in older adults.MethodsThis cross-sectional study includes online questionnaire data (SaNAE cohort, August-November 2020), in independently living Dutch adults aged 40 years and older. For the separate outcomes social and emotional loneliness, associations with structural network aspects (e.g., network diversity - having various types of relationships, and density - having network members who know each other), and functional network aspects (informational, emotional, and practical social support) were assessed and risk estimates were adjusted for the number of contacts, age, educational level, level of urbanization and chronic conditions. Multivariable logistic regression analyses were stratified by sex.ResultsOf 3,396 participants (55% men; mean age 65 years), 18% were socially lonely which was associated with a less diverse and less dense network, living alone, feeling less connected to friends, not having a club membership, and fewer emotional supporters (men only) or informational supporters (women only). 28% were emotionally lonely, which was associated with being socially lonely, and more exclusively online (versus in-person) contacts (men only), and fewer emotional supporters (women only).ConclusionNetwork structure and function beyond the mere number of contacts is key in loneliness, and in particular, the types of relationships are important. Public health strategies should be sex-tailored and promote network diversity and density, club membership, informational and emotional support, and in-person contact.
Circulating metabolites and physical performance are predictors of overall survival in metastatic lung cancer patients
Authors: Neves Willian das , Alves Christiano R. R. , Santos Gabriela dos , Alves Maria J. N. , Deik Amy , Pierce Kerry , Dennis Courtney , Buckley Lily , Clish Clary B. , Swoboda Kathryn J. , Brum Patricia C. , de Castro Gilberto 
Published: August 2023

AbstractBackground:Skeletal muscle atrophy and low physical performance are associated with disease progression and higher mortality rates in multiple pathological conditions. Here, we determined whether body composition and physical performance would predict mortality in metastatic non-small cell lung cancer (NSCLC) patients. In addition, we defined whether plasma samples from NSCLC patients would directly affect the homeostasis of skeletal muscle cells.Methods:The prospective cohort included 55 metastatic NSCLC patients and seven age-matched control subjects. We assessed clinical characteristics, body composition, cancer cachexia, and quality of life (QoL). We determined physical performance with a series of functional tests. We analyzed skeletal muscle and adipose tissue areas. Finally, we evaluated the overall survival rate, and additional blood samples were collected from a subcohort of eighteen patients for further studies in cell culture and metabolomic analysis.Results:We found that physical performance, not body composition, was associated with overall survival in this cohort. Moreover, incubation with plasma derived from NSCLC patients with low physical performance impaired the metabolism and proliferation of primary human myotubes. Unbiased metabolomics revealed several metabolites differentially expressed in the plasma of NSCLC patients with low physical performance compared to healthy control subjects, with serine and N2,N2-dimethylguanosine (M22G) being the most reduced and increased metabolites, respectively.Conclusion:These novel findings confirm physical performance as a significant predictor of overall survival in metastatic NSCLC patients and provide insights into cancer-induced circulating factors that can directly affect skeletal muscle homeostasis and prognosis.
Re-emergent Tremor during stable posture in Parkinson’s Disease: Evidence of Pathological Beta and Prokinetic Gamma Activity
Authors: Ding Hao , Nasseroleslami Bahman , Mirzac Daniela , Volkmann Jens , Deuschl Gunter , Groppa Sergiu , Muthuraman Muthuraman 
Published: August 2023

AbstractRe-emergent tremor (RET) during stable posture in Parkinson’s disease (PD) is characterized as a continuation of resting tremor (RT) and is often highly therapy refractory. The pathophysiology of both RT and RET is linked to deficits in cerebello-cortical circuits and dopaminergic depletion. However, it remains unclear how these two types of tremors differ functionally. The aim of this study is to examine the differences in brain activity between RT and RET in PD, as well as to investigate the relationship between neuronal oscillatory activity, in PD tremor phenotype patients.Forty PD patients (25 males, mean age 66.78 ± 5.03 years) and 40 age- and sex-matched healthy controls were assessed. 256 channel HD-EEG and EMG signals were recorded while the participants extended their hands against gravity. Tremor was recorded in both L-dopa ON and OFF for PD patients and mimicked by healthy controls. Coherent sources of EEG-EMG were located using beamforming technique, and information flow between different sources was estimated using time-resolved partial-directed coherence. Cross-frequency coupling (CFC) was then used to analyze the association between tremor frequency and neuronal oscillatory activity.Under L-dopa administration, coherent sources referenced to the tremor frequency revealed differences in brain activity between RT and RET in the premotor cortex and cerebellum of PD patients, which were similar to those observed in healthy controls. However, PD patients exhibited an additional source location in the primary sensorimotor cortex. Withdrawal of L-dopa led to coherent sources being observed in the supplementary motor area and subthalamic nucleus. Furthermore, L-dopa was found to suppress the strength of connections between these coherent sources and modulate the tremor-associated beta and gamma frequency, leading to a decrease in beta power and an increase in gamma power.The findings of this study reveal discernible neural activity patterns during resting and re-emergent tremors. In patients with PD, the primary sensorimotor cortex plays a primordial role as the exclusive source of activity involved in the generation of RET. It shows a significant amount of co-activation within the network involving the premotor areas and cerebellum, and its activation accounts for the discrepancy in tremor phenotypes. Moreover, the oscillatory neuronal responses involve pathological beta and prokinetic gamma activity alterations that are highly specific to tremor phenotypes. These peculiarities play an essential role in our understanding of tremor phenomena and its therapeutic modulation by dopamine medication or deep brain stimulation, which could specifically target the tremor- and motor-control-related pathological beta and prokinetic gamma oscillations.