bluuebunny/crossref_metadata_2025_split huggingface dataset:
Connectivity patterns of task-specific brain networks allow individual prediction of cognitive symptom dimension of schizophrenia and link to molecular architecture
Authors: Chen Ji , Müller Veronika I. , Dukart Juergen , Hoffstaedter Felix , Baker Justin T. , Holmes Avram J. , Vatansever Deniz , Nickl-Jockschat Thomas , Liu Xiaojin , Derntl Birgit , Kogler Lydia , Jardri Renaud , Gruber Oliver , Aleman André , Sommer Iris E. , Eickhoff Simon B. , Patil Kaustubh R. 
Published: July 2020

AbstractBackgroundDespite the marked inter-individual variability in the clinical presentation of schizophrenia, it remains unclear the extent to which individual dimensions of psychopathology may be reflected in variability across the collective set of functional brain connections. Here, we address this question using network-based predictive modeling of individual psychopathology along four data-driven symptom dimensions. Follow-up analyses assess the molecular underpinnings of predictive networks by relating them to neurotransmitter-receptor distribution patterns.MethodsWe investigated resting-state fMRI data from 147 schizophrenia patients recruited at seven sites. Individual expression along negative, positive, affective, and cognitive symptom dimensions was predicted using relevance vector machine based on functional connectivity within 17 meta-analytic task-networks following a repeated 10-fold cross-validation and leave-one-site-out analyses. Results were validated in an independent sample. Networks robustly predicting individual symptom dimensions were spatially correlated with density maps of nine receptors/transporters from prior molecular imaging in healthy populations.ResultsTen-fold and leave-one-site-out analyses revealed five predictive network-symptom associations. Connectivity within theory-of-mind, cognitive reappraisal, and mirror neuron networks predicted negative, positive, and affective symptom dimensions, respectively. Cognitive dimension was predicted by theory-of-mind and socio-affective-default networks. Importantly, these predictions generalized to the independent sample. Intriguingly, these two networks were positively associated with D1dopamine receptor and serotonin reuptake transporter densities as well as dopamine-synthesis-capacity.ConclusionsWe revealed a robust association between intrinsic functional connectivity within networks for socio-affective processes and the cognitive dimension of psychopathology. By investigating the molecular architecture, the present work links dopaminergic and serotonergic systems with the functional topography of brain networks underlying cognitive symptoms in schizophrenia.
An Automatic Computer-Based Method for Fast and Accurate Covid-19 Diagnosis
Authors: Jaid Jim Abdullah Al , Rafi Ibrahim , Chowdhury Md. Sanaullah , Sikder Niloy , Mahmud M. A. Parvez , Rubaiee Saeed , Masud Mehedi , Bairagi Anupam Kumar , Bhakta Kangkan , Nahid Abdullah-Al 
Published: July 2020

AbstractAt present, the whole world is witnessing a horrifying outbreak caused by the Coronavirus Disease 2019 (COVID-19). The virus responsible for this disease is called SARS-CoV-2. It affects its victims’ respiratory system and causes severe lung inflammation, making it harder for them to breathe. The virus is airborne, and so has a high infection rate. Originated in China last December, the virus has spread across seven continents, affecting the population of over 210 countries, making it one of the fiercest pandemics ever recorded. Despite multiple independent and collaborative attempts to develop a vaccine or a cure, an effective solution is yet to come out. While the disease has put the world in a standstill, detecting the positive subjects and isolating them from the others as soon as possible is the only way to minimize its spread. However, many countries are currently experiencing a massive shortage of diagnostic equipment and medical personals. This insufficiency inspired us to work on a computer-based automatic method for the diagnosis of COVID-19. In this paper, we proposed a sequential Convolutional Neural Network (CNN)-based model to detect COVID-19 through analyzing Computed Tomography (CT) scan images. The model is capable of identifying the disease with almost 92.5% accuracy. We believe the implementation of this model will help the physicians and pathologists all over the world to single out the victims quickly and thus reduce the prevalence of COVID-19.
Is nasopharyngeal swab comparable with nasopharyngeal aspirate to detect SARS-CoV-2 in children?
Authors: Capecchi Ester , Di Pietro Giada Maria , Luconi Ester 
Published: July 2020

AbstractThe tests currently used for the direct identification of SARS-CoV-2 include specimens taken from the upper and the lower respiratory tract.In our paediatric department all children undergo both nasopharyngeal swab and nasopharyngeal aspirate, performed from both nostrils, on admission and after 24 hours.We decided to compare these two methods of detection of SARS-CoV-2. Considering nasopharyngeal aspirate as the gold standard, we calculated sensitivities and specificities of nasopharyngeal swab.Based on our results, we suggest to prefer the collection of aspirates whenever possible.
Characterization of Microbial Co-infections in the Respiratory Tract of hospitalized COVID-19 patients
Authors: Zhong Huanzi , Wang Yanqun , Shi Zhun , Zhang Lu , Ren Huahui , He Weiqun , Zhang Zhaoyong , Zhu Airu , Zhao Jingxian , Xiao Fei , Yang Fangming , Liang Tianzhu , Ye Feng , Zhong Bei , Ruan Shicong , Gan Mian , Zhu Jiahui , Li Fang , Li Fuqiang , Wang Daxi , Li Jiandong , Ren Peidi , Zhu Shida , Yang Huanming , Wang Jian , Kristiansen Karsten , Tun Hein Min , Chen Weijun , Zhong Nanshan , Xu Xun , Li Yi-min , Li Junhua , Zhao Jincun 
Published: July 2020

SummaryBackgroundSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a global pandemic of Coronavirus disease 2019 (COVID-19). However, microbial composition of the respiratory tract and other infected tissues, as well as their possible pathogenic contributions to varying degrees of disease severity in COVID-19 patients remain unclear.MethodBetween January 27 and February 26, 2020, serial clinical specimens (sputum, nasal and throat swab, anal swab and feces) were collected from a cohort of hospitalized COVID-19 patients, including 8 mildly and 15 severely ill patients (requiring ICU admission and mechanical ventilation), in the Guangdong province, China. Total RNA was extracted and ultra-deep metatranscriptomic sequencing was performed in combination with laboratory diagnostic assays. Co-infection rates, the prevalence and abundance of microbial communities in these COVID-19 patients were determined.FindingsNotably, respiratory microbial co-infections were exclusively found in 84.6% of severely ill patients (11/13), among which viral and bacterial co-infections were detected by sequencing in 30.8% (4/13) and 69.2% (9/13) of the patients, respectively. In addition, for 23.1% (3/13) of the patients, bacterial co-infections with Burkholderia cepacia complex (BCC) and Staphylococcus epidermidis were also confirmed by bacterial culture. Further, a time-dependent, secondary infection of B. cenocepacia with expressions of multiple virulence genes in one severely ill patient was demonstrated, which might be the primary cause of his disease deterioration and death one month after ICU admission.InterpretationOur findings identified distinct patterns of co-infections with SARS-CoV-2 and various respiratory pathogenic microbes in hospitalized COVID-19 patients in relation to disease severity. Detection and tracking of BCC-associated nosocomial infections are recommended to improve the pre-emptive treatment regimen and reduce fatal outcomes of hospitalized patients infected with SARS-CoV-2.FundingNational Science and Technology Major Project of China, National Major Project for Control and Prevention of Infectious Disease in China, the emergency grants for prevention and control of SARS-CoV-2 of Ministry of Science and Technology and Guangdong province, Guangdong Provincial Key Laboratory of Genome Read and Write, Guangdong Provincial Academician Workstation of BGI Synthetic Genomics, and Shenzhen Engineering Laboratory for Innovative Molecular Diagnostics.
Covid-19 Risk Among Airline Passengers: Should the Middle Seat Stay Empty?
Authors: Barnett Arnold 
Published: July 2020

AbstractWe use recent data and research results to approximate the probability that an air traveler in coach will contract Covid-19 on a US domestic flight two hours long, both when all coach seats are full and when all but middle seats are full. The point estimates we reach based on data from late June 2020 are 1 in 4,300 for full flights and 1 in 7,700 when middle seats are kept empty. These estimates are subject to both quantifiable and nonquantifiable sources of uncertainty, and sustain known margins of error of a factor about 2.5. However, because uncertainties in key parameters affect both risk estimates the same way, they leave the relative risk ratio for “fill all seats” compared to “middle seat open” close to 1.8 (i.e., close to 1/4,300)/(1/7,700). We estimate the mortality risks caused by Covid-19 infections contracted on airplanes, taking into account that infected passengers can in turn infect others. The point estimates—which use 2019 data about the percentage of seats actually occupied on US flights--range from one death per 400,000 passengers to one death per 600,000. These death-risk levels are considerably higher than those associated with plane crashes but comparable to those arising from two hours of everyday activities during the pandemic.
Convolutional neural networks on eye tracking trajectories classify patients with spatial neglect
Authors: Franceschiello Benedetta , Noto Tommaso Di , Bourgeois Alexia , Murray Micah M. , Minier Astrid , Pouget Pierre , Richiardi Jonas , Bartolomeo Paolo , Anselmi Fabio 
Published: July 2020

AbstractBackground and ObjectiveEye-movement trajectories are rich behavioral data, providing a window on how the brain processes information. We address the challenge of characterizing signs of visuo-spatial neglect from saccadic eye trajectories recorded in brain-damaged patients with spatial neglect as well as in healthy controls during a visual search task.MethodsWe establish a standardized preprocessing pipeline adaptable to other task-based eye-tracker measurements. We use a deep convolutional network, a very successful type of neural network architecture in many computer vision applications, including medical diagnosis systems, to automatically analyze eye trajectories.ResultsOur algorithm can classify brain-damaged patients vs. healthy individuals with an accuracy of 86±5%. Moreover, the algorithm scores correlate with the degree of severity of neglect signs estimated with standardized paper-and-pencil test and with white matter tracts impairment via Diffusion Tensor Imaging (DTI). Interestingly, the latter showed a clear correlation with the third branch of the superior longitudinal fasciculus (SLF), especially damaged in neglect.ConclusionsThe study introduces a new classification method to analyze eyes trajectories in patients with neglect syndrome. The method can likely be applied to other types of neurological diseases opening to the possibility of new computer-aided, precise, sensitive and non-invasive diagnosing tools.HighlightsWe identify signs of visuo-spatial neglect through an automated analysis of saccadic eye trajectories using deep convolutional neural networks (CNNs).We provide a standardized pre-processing pipeline adaptable to other task-based eye-tracker measurements.Patient-wise, we benchmark the algorithm prediction with standardized paper-and-pencil test results.We evaluate white matter tracts by using Diffusion Tensor Imaging (DTI) and find a clear correlation with the microstructure of the third branch of the superior longitudinal fasciculus.Deep CNNs can efficiently and non-invasively characterize left spatial neglect.
Revealing antibiotic cross-resistance patterns in hospitalized patients through Bayesian network modelling
Authors: Cherny Stacey S , Nevo Daniel , Baraz Avi , Baruch Shoham , Lewin-Epstein Ohad , Stein Gideon Y , Obolski Uri 
Published: July 2020

AbstractObjectivesMicrobial resistance exhibits dependency patterns between different antibiotics, termed cross-resistance and collateral sensitivity. These patterns differ between experimental and clinical settings. It is unclear whether the differences result from biological reasons or from confounding, biasing results found in clinical settings. We set out to elucidate the underlying dependency patterns between resistance to different antibiotics from clinical data, while accounting for patient characteristics and previous antibiotic usage.MethodsAdditive Bayesian network modelling was employed to simultaneously estimate relationships between variables in a dataset of bacterial cultures derived from hospitalized patients and tested for resistance to multiple antibiotics. Data contained resistance results, patient demographics, and previous antibiotic usage, for five bacterial species:E. coli(n=1054),K. pneumoniae(n=664),P. aeruginosa(n=571), CoNS (n=495), andP. mirabilis(n=415).ResultsAll links between resistance to the various antibiotics were positive. Multiple direct links between resistance of antibiotics from different classes were observed across bacterial species. For example, resistance to gentamicin inE.coliwas directly linked with resistance to ciprofloxacin (OR = 8.39, 95%CI[5.58, 13.30]) and sulfamethoxazole-trimethoprim (OR = 2.95, 95%CI[1,97, 4.51]). In addition, resistance to various antibiotics was directly linked with previous antibiotic usage.ConclusionsRobust relationships among resistance to antibiotics belonging to different classes, as well as resistance being linked to having taken antibiotics of a different class, exist even when taking into account multiple covariate dependencies. These relationships could help inform choices of antibiotic treatment in clinical settings.
Manual ability in hand surgery patients: validation of the ABILHAND scale in four diagnostic groups
Authors: Khoury Ghady El , Barbier Olivier , Libouton Xavier , Thonnard Jean-Louis , Lefevre Philippe , Penta Massimo 
Published: July 2020

AbstractBackgroundPatients treated in hand surgery (HS) belong to different demographic groups and have varying impairments related to different pathologies. HS outcomes are measured to assess treatment results, complication risks and intervention reliability. Quantitative analysis of functional recovery requires a unidimensional, linear scale.ObjectiveTo adapt the ABILHAND questionnaire through Rasch analysis for specific use in HS patients and to examine its validity.MethodsA preliminary 90-item questionnaire was presented to 216 patients representing the diagnoses most frequently encountered in HS, including distal radius fracture (n=74), basal thumb arthritis (n=66), carpal tunnel syndrome (n=53), and heavy wrist surgery (n=23). Patients were assessed during the early recovery and in the late follow-up period (0-3 months, 3–6 months and >6 months), leading to a total of 305 assessments. They rated their perceived difficulty with queried activities as impossible, difficult, or easy. Responses were analyzed using the RUMM2030 software. Items were refined based on item-patient targeting, fit statistics, differential item functioning, local independence and item redundancy. Patients also completed the QuickDASH, 12-item Short Form Survey (SF-12) and a numerical pain scale.ResultsThe rating scale Rasch model was used to select 23 mostly bimanual items on a 3-level scale, which constitute a unidimensional, linear measure of manual ability with good reliability across all included diagnostic groups (Person-Separation Index = 0.90). The resulting scale was found to be invariant across demographic and clinical subgroups and over time. ABILHAND-HS patient measures correlated significantly (p<0.001) with the QuickDASH (r=-0.77), SF-12 Physical Component Summary (r=0.56), SF-12 Mental Component Summary (r=0.31), and pain scale (r=-0.49).ConclusionABILHAND-HS is a robust person-centered measure of manual ability in HS patients.
Transfer RNA fragments replace microRNA regulators of the cholinergic post-stroke immune blockade
Authors: Winek Katarzyna , Lobentanzer Sebastian , Nadorp Bettina , Dubnov Serafima , Dames Claudia , Moshitzky Gilli , Hotter Benjamin , Meisel Christian , Greenberg David S , Shifman Sagiv , Klein Jochen , Shenhar-Tsarfaty Shani , Meisel Andreas , Soreq Hermona 
Published: July 2020

AbstractStroke is a leading cause of death and disability. Recovery depends on balance between inflammatory response and immune suppression, which can be CNS-protective but may worsen prognosis by increasing patients’ susceptibility to infections. Peripheral cholinergic blockade of immune reactions fine-tunes this immune response, but its molecular regulators are unknown. Therefore, we sought small RNA balancers of the cholinergic anti-inflammatory pathway in peripheral blood from ischemic stroke patients. Using RNA-sequencing and RT-qPCR, we discovered in patients’ blood on day 2 after stroke a “change of guards” reflected in massive decreases in microRNAs (miRs) and increases in transfer RNA fragments (tRFs) targeting cholinergic transcripts. Electrophoresis-based size-selection followed by RT-qPCR validated the top 6 upregulated tRFs in a separate cohort of stroke patients, and independent small RNA-sequencing datasets presented post-stroke enriched tRFs as originating from lymphocytes and monocytes. In these immune compartments, we found CD14+ monocytes to express the highest amounts of cholinergic transcripts. In-depth analysis of CD14+ regulatory circuits revealed minimally overlapping subsets of transcription factors carrying complementary motifs to miRs or tRFs, indicating different roles for the stroke-perturbed members of these small RNA species. Furthermore, LPS-stimulated murine RAW264.7 cells presented dexamethasone-suppressible upregulation of the top 6 tRFs identified in human patients, indicating an evolutionarily conserved and pharmaceutically treatable tRF response to inflammatory cues. Our findings identify tRF/miR subgroups which may co-modulate the homeostatic response to stroke in patients’ blood and open novel venues for establishing RNA-targeted concepts for post-stroke diagnosis and therapeutics.
Metabolic indicators associated with non-communicable diseases deteriorated in COVID-19 outbreak: evidence from a two-center, retrospective study
Authors: Xue Ting , Xu Lizhen , Mao Yaqian , Lin Wei , Liang Jixing , Huang Huibin , Li Liantao , Wen Junping , Chen Gang 
Published: July 2020

AbstractObjectiveOur study aimed to investigate whether the metabolic indicators associated with non-communicable diseases (NCDs) in the general population have changed during the COVID-19 outbreak.METHODSThis retrospective self-controlled study enrolled adult participants with metabolic indicators relate to NCDs followed at Fujian Provincial Hospital and Fujian Provincial Hospital South Branch. The metabolic indicators followed during January 1, 2020 and April 30, 2020, the peak period of the COVID-19 epidemic in China, were compared with the baseline value in the same period last year. Pared-samples T-test and Wilcoxon signed-rank test were performed to analyze the differences between paired data.ResultsThe follow-up total cholesterol was significantly increased than that of the baseline (4.73 (4.05, 5.46) mmol/L vs 4.71 (4.05, 5.43) mmol/L, p=0.019; n=3379). Similar results were observed in triglyceride (1.29 (0.91, 1.88) vs 1.25 (0.87, 1.81) mmol/L, p<0.001; n=3381), uric acid (330.0 (272.0, 397.0) vs 327.0 (271.0, 389.0) umol/L, p<0.001; n=3364), and glycosylated hemoglobin (6.50 (6.10, 7.30) vs 6.50 (6.10, 7.20) %, p=0.013; n=532). No significant difference was observed in low density lipoprotein, body mass index and blood pressure.ConclusionsMetabolic indicators associated with NCDs deteriorated in the COVID-19 outbreak. We should take action to prevent and control NCDs without delay.
Highly predictive regression model of active cases of COVID-19 in a population by screening wastewater viral load
Authors: Vallejo Juan A. , Rumbo-Feal Soraya , Conde-Pérez Kelly , López-Oriona Ángel , Tarrío Javier , Reif Rubén , Ladra Susana , Rodiño-Janeiro Bruno K. , Nasser Mohammed , Cid Ángeles , Veiga María C , Acevedo Antón , Lamora Carlos , Bou Germán , Cao Ricardo , Poza Margarita 
Published: July 2020

ABSTRACTThe quantification of the SARS-CoV-2 load in wastewater has emerged as a useful method to monitor COVID-19 outbreaks in the community. This approach was implemented in the metropolitan area of A Coruña (NW Spain), where wastewater from the treatment plant of Bens was analyzed to track the epidemic’s dynamic in a population of 369,098 inhabitants. We developed statistical regression models that allowed us to estimate the number of infected people from the viral load detected in the wastewater with a reliability close to 90%. This is the first wastewater-based epidemiological model that could potentially be adapted to track the evolution of the COVID-19 epidemic anywhere in the world, monitoring both symptomatic and asymptomatic individuals. It can help to understand with a high degree of reliability the true magnitude of the epidemic in a place at any given time and can be used as an effective early warning tool for predicting outbreaks.
Antibiotics resistance and mitigation strategies in healthcare settings: A scoping review
Authors: Okeah O.O. , Morrison V. , Huws J. 
Published: July 2020

AbstractBackgroundAccording to the European Center for Disease Prevention and Control (ECDC), the EU records an estimated 3.2 million healthcare associated infections (HAIs) and an associated 37,000 deaths annually. A significant proportion of the HAIs burden is attributable to multi-drug resistant organisms (MDROs). Infectious diseases remain top on the list of the leading causes of death globally with MDROs playing a significant role. Key amongst these organisms isClostridium difficileandKlebsiella pneumoniaewhich belong to the broader group of ESKAPE pathogens.AimsThis review aimed at identifying literature on interventions targetingClostridium difficileandKlebsiella pneumoniae, their key outcomes, and the extent to which behavioural theory has been applied in such interventions.MethodsThis scoping review was undertaken and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analysis Extension for Scoping Reviews (PRISMA-ScR) guidelines. The specific databases searched included MEDLINE, PubMed, Web of Science Core Collection, and CINAHL. The process for screening articles and data extraction was undertaken in duplicate by two reviewers. A narrative synthesis of the results is provided.ResultsThe review included 34 studies (16 studies onClostridium difficileand 18 articles focussed onKlebsiella pneumoniae). The specific antimicrobial stewardship interventions identified includeEducation,Surveillance andScreening,Consultations,Audits,Policies andProtocols,Environmental measures,Bundles of care,Isolation precautions, as well asNotifications and alerts systems (ESCAPE-BIN). The identified outcomes include antimicrobial use, resistance rates, risk reduction, adherence to contact precautions, hospital stay, and time savings. Only one study incorporated Kotter’s stages of behaviour change and recorded the second largest (75%) sustained reduction in antimicrobials use whereas the remainder of the studies were devoid of behavioural approaches. The highest improvement (95%) in adherence isolation precautions was reported by an intervention involving the use of an IPC bundle and an environmental cleaning protocol.ConclusionThis scoping review identified the available evidence on antimicrobial the mitigation strategies forClostridium difficileandKlebsiella pneumoniaein healthcare settings as well as the key outcomes. There is need for further investigations on the feasibility of behaviour-based approaches in improving adherence of health workers to interventions targetingClostridium difficileandKlebsiella pneumoniae.