bluuebunny/crossref_metadata_2025_split huggingface dataset:
Negative Symptoms and Their Associations with Other Clinical Variables and Working Memory Across the Schizophrenia Spectrum and Bipolar Disorder
Authors: De Pieri Marco , Berg Xaver , Georgiadis Foivos , Brakowski Janis , Burrer Achim , Sabe Michel , Kaliuzhna Mariia , Vetter Stefan , Seifritz Erich , Homan Philipp , Kaiser Stefan , Kirschner Matthias 
Published: July 2023

ABSTRACTNegative symptoms (NS) of schizophrenia spectrum disorders (SSD) are also prevalent in bipolar-disorder-I (BD-I) and show associations with impaired working memory (WM). However, empirical work on their relationship to other clinical factors across SSD and BD-I are sparse. Here, we characterized the associations of NS with key clinical variables and WM capacity across a combined sample of SSD and BD. We included 50 outpatients with SSD and 49 with BD-I and assessed NS domains using SANS global scores for avolition-apathy, anhedonia-asociality, alogia and blunted affect. We assessed the transdiagnostic relationship between NS and other clinical variables including positive symptoms, disorganization, depressive symptoms, antipsychotic medication using multiple regressions. The strength of these associations was further determined through dominance analyses. Lastly, we used multiple regression to assess the relationship between NS domains and WM. To assess the generalizability of transdiagnostic associations, analyses were repeated in each diagnostic group separately. Across SSD and BD-I, disorganization was associated with avolition-apathy and anhedonia-asociality and depressive symptoms additionally predicted anhedonia-asociality. Antipsychotic dose was associated with blunted affect while group differences only predicted alogia. Higher avolition-apathy was related to impaired WM transdiagnostically, partially mediated by severity of disorganization, whereas only in BD-I higher anhedonia-asociality was associated with better WM capacity. This study demonstrated transdiagnostic associations of both avolition-apathy and anhedonia-asociality with disorganization, and identified avolition-apathy as potential transdiagnostic predictor of WM impairments. Overall, our findings highlight the importance of understanding the relationship between NS domains and other clinical factors with cognitive function across SSD and BD.
Artificial Intelligence and Machine Learning in Aneurysmal Subarachnoid Hemorrhage: Future Promises, Perils, and Practicalities
Authors: Salman Saif , Gu Qiangqiang , Sharma Rohan , Wei Yujia , Dherin Benoit , Reddy Sanjana , Tawk Rabih , Freeman W David 
Published: July 2023

AbstractIntroductionAneurysmal subarachnoid hemorrhage (SAH) is a subtype of hemorrhagic stroke with thirty-day mortality as high as 40%. Given the expansion of Machine Learning (ML) and Artificial intelligence (AI) methods in health care, SAH patients desperately need an integrated AI system that detects, segments, and supports clinical decisions based on presentation and severity.ObjectivesThis review aims to synthesize the current state of the art of AI and ML tools for the management of SAH patients alongside providing an up-to-date account of future horizons in patient care.MethodsWe performed a systematic review through various databases such as Cochrane Central Register of Controlled Trials, MEDLINE, Scopus, Cochrane Database of Systematic Reviews, and Embase.ResultsA total of 507 articles were identified. Following extensive revision, only 21 articles were relevant. Two studies reported improved mortality prediction using Glasgow Coma Scale and biomarkers such as Neutrophil to Lymphocyte Ratio and glucose. One study reported that ffANN is equal to the SAHIT and VASOGRADE scores. One study reported that metabolic biomarkers Ornithine, Symmetric Dimethylarginine, and Dimethylguanidine Valeric acid were associated with poor outcomes. Nine studies reported improved prediction of complications and reduction in latency until intervention using clinical scores and imaging. Four studies reported accurate prediction of aneurysmal rupture based on size, shape, and CNN. One study reported AI-assisted Robotic Transcranial Doppler as a substitute for clinicians.ConclusionAI/ML technologies possess tremendous potential in accelerating SAH systems-of- care. Keeping abreast of developments is vital in advancing timely interventions for critical diseases.
Evidence that intergenerational income mobility is the strongest predictor of drug overdose deaths in U. S. Heartland counties
Authors: Heyman Gene M. , Ryu Ehri , Brownell Hiram 
Published: July 2023

AbstractIn 2017, the Acting U. S. Secretary of Health and Human Services declared the “opioid crisis” a nation-wide health emergency. However, the crisis’s geography was not nation-wide. Many counties and towns had no overdose deaths, whereas others were home to hundreds. According to many influential research reports and news stories, geographic variation in overdose deaths was due to geographic variation in opioid prescription rates and/or geographic variation in socioeconomic factors, such as unemployment. Our goal was to test the degree to which prescription rates and socioeconomic correlates of income inequality predicted overdose deaths in the 1055 U.S. Midwest (“Heartland”) counties over the years 2006 to 2020. We used multilevel regression models to gauge the predictive strength of overdose rates and six socioeconomic measures that are correlated with income inequality. There were significant state-level and county-level differences. Intergenerational income mobility was the strongest predictor of overdose deaths, with regression coefficients that averaged about twice as large as the coefficients for opioid prescription rates. Every year, counties with greater upward intergenerational income mobility had lower overdose death rates. Social capital had the second largest regression coefficients, albeit by a small margin. Counties are the smallest demographic unit for which drug overdose rates are available; the results of this study link growing income inequality and drug overdose deaths at the county level.
Caregivers’ and nurses’ perceptions of the Smart Discharges Program for children with sepsis in Uganda: A descriptive qualitative study
Authors: Behan Justine , Kabajaasi Olive , Derksen Brooklyn , Sendegye George , Kugumikiriza Brenda , Komugisha Clare , Sundararajan Radhika , Jacob Shevin T. , Kenya-Mugisha Nathan , Wiens Matthew O. 
Published: July 2023

AbstractSepsis arises when the body’s response to infection results in organ dysfunction. Among children hospitalized with suspected sepsis in low-income country settings, mortality rates following discharge are similar to mortality rates in hospital. The Smart Discharges Program uses a mobile health (mHealth) platform to identify children at high risk of post-discharge mortality to receive enhanced post-discharge care. This study sought to explore the perceptions of the caregivers and nurses of children enrolled into the Smart Discharges Program. We conducted a descriptive qualitative study that used a phenomenological approach. We conducted in-person focus group discussions (FGDs) with 30 caregivers of pediatric patients enrolled in the Smart Discharges Program and individual, semi-structured interviews with eight Smart Discharges Program nurses. The study was carried out at four hospitals in Uganda in 2019.Following thematic analysis, three key themes pertaining to the Smart Discharges program were identified: (1) Facilitators and barriers to follow-up care after discharge; (2) Changed behavior following discharge; and (3) Increased involvement of male caregivers. Facilitators included telephone/text message reminders, positive nurse-patient relationship, and the complementary aspects of the program. Resource constraints and negative experiences during post-discharge care seeking were reported as the most prominent barriers to post-discharge follow up. When provided with relevant and well-timed information, caregivers reported increased knowledge about post-discharge care and improvements in their ability to care for their child. Enrolment in the Smart Discharges Program also increased male caregiver involvement, which was reported as improved engagement in care, increased provision of resources and improved communication within the family and with the healthcare system. The Smart Discharges approach is an impactful strategy to improve pediatric post-discharge care, and similar approaches should be considered to improve the hospital to home transition in similar low-income country settings.
Disrupted Limbic-Prefrontal Effective Connectivity in Response to Fearful Faces in Lifetime Depression
Authors: Stolicyn Aleks , Harris Mathew A. , de Nooij Laura , Shen Xueyi , Macfarlane Jennifer A. , Campbell Archie , McNeil Christopher J. , Sandu Anca-Larisa , Murray Alison D. , Waiter Gordon D. , Lawrie Stephen M. , Steele Douglas J. , McIntosh Andrew M. , Romaniuk Liana , Whalley Heather C. 
Published: July 2023

AbstractBackgroundMultiple brain imaging studies of negative emotional bias in major depressive disorder (MDD) have used images of fearful facial expressions and focused on the amygdala and the prefrontal cortex. The results have, however, been inconsistent, potentially due to small sample sizes (typicallyN< 50). It remains unclear if any alterations are a characteristic of current depression or of past experience of depression, and whether there are MDD-related changes in effective connectivity between the two brain regions.MethodsActivations and effective connectivity between the amygdala and dorsolateral prefrontal cortex (DLPFC) in response to fearful face stimuli were studied in a large population-based sample from Generation Scotland. Participants either had no history of MDD (N= 664 in activation analyses,N= 474 in connectivity analyses) or had a diagnosis of MDD during their lifetime (LMDD,N= 290 in activation analyses,N= 214 in connectivity analyses). The within-scanner task involved implicit facial emotion processing of neutral and fearful faces.ResultsCompared to controls, LMDD was associated with increased activations in left amygdala (PFWE= 0.031, kE= 4) and left DLPFC (PFWE= 0.002, kE= 33), increased mean bilateral amygdala activation (β= 0.0715, P= 0.0314), and increased inhibition from left amygdala to left DLPFC, all in response to fearful faces contrasted to baseline. Results did not appear to be attributable to depressive illness severity or antidepressant medication status at scan time.LimitationsMost studied participants had past rather than current depression, average severity of ongoing depression symptoms was low, and a substantial proportion of participants were receiving medication. The study was not longitudinal and the participants were only assessed a single time.ConclusionsLMDD is associated with hyperactivity of the amygdala and DLPFC, and with stronger amygdala to DLPFC inhibitory connectivity, all in response to fearful faces, unrelated to depression severity at scan time. These results help reduce inconsistency in past literature and suggest disruption of ‘bottom-up’ limbic-prefrontal effective connectivity in depression.
Association between oral anticoagulant adherence and serious clinical outcomes in patients with atrial fibrillation: A long-term retrospective cohort study
Authors: Safari Abdollah , Helisaz Hamed , Salmasi Shahrzad , Adelakun Adenike , De Vera Mary A. , Andrade Jason G. , Deyell Marc W. , Loewen Peter 
Published: July 2023

ABSTRACTBackgroundPatients with atrial fibrillation (AF) are frequently nonadherent to oral anticoagulants (OACs) prescribed for stroke and systemic embolism (SSE) prevention. We quantified the relationship between OAC adherence and AF clinical outcomes using methods not previously applied to this problem.MethodsRetrospective observational cohort study of incident cases of AF from population-based administrative data over 23 years. The exposure of interest was proportion of days covered (PDC) during 90 days before an event or end of follow-up. Cox proportional hazard models were used to evaluate time to first SSE and the composite of SSE, TIA, or death, and several secondary outcomes.Results44,172 patients were included with median follow-up of 6.7 years. For DOACs, each 10% decrease in adherence was associated with a 14% increased hazard of SSE and 5% increased hazard of SSE, TIA, or death. For VKA the corresponding increase in SSE hazard was 3%. Receiving DOAC or VKA was associated with primary outcome hazard reduction across most the PDC spectrum. Differences between VKA and DOAC were statistically significant for all efficacy outcomes and at most adherence levels.ConclusionsEven small reductions in OAC adherence in patients with AF were associated with significant increases in risk of stroke, with greater magnitudes for DOAC than VKA. DOAC recipients may be more vulnerable than VKA recipients to increased risk of stroke and death even with small reductions in adherence. The worsening efficacy outcomes associated with decreasing adherence occurred without the benefit of major bleeding reduction.
Risk of SARS-CoV-2 infection and hospitalization in individuals with natural, vaccine-induced and hybrid immunity: a retrospective population-based cohort study from Estonia
Authors: Uusküla Anneli , Pisarev Heti , Tisler Anna , Meister Tatjana , Suija Kadri , Huik Kristi , Abroi Aare , Kalda Ruth , Kolde Raivo , Fischer Krista 
Published: July 2023

AbstractA large proportion of the world’s population has some form of immunity against SARS-CoV- 2, through either infection (‘natural’), vaccination or both (‘hybrid’). This retrospective cohort study used data on SARS-CoV-2, vaccination, and hospitalization from national health system from February 2020 to June 2022 and Cox regression modelling to compare those with natural immunity to those with no (Cohort1, n=92917), hybrid (Cohort2, n=46813), and vaccine (Cohort3, n=252414) immunity. In Cohort 1, those with natural immunity were at lower risk for infection during the Delta (aHR 0.17, 95%CI 0.15-0.18) and higher risk (aHR 1.24, 95%CI 1.18-1.32) during the Omicron period than those with no immunity. Natural immunity conferred substantial protection against COVID-19-hospitalization. Cohort 2 - in comparison to natural immunity hybrid immunity offered strong protection during the Delta (aHR 0.61, 95%CI 0.46-0.80) but not the Omicron (aHR 1.05, 95%CI 0.93-1.1) period. COVID-19-hospitalization was extremely rare among individuals with hybrid immunity. In Cohort 3, individuals with vaccine-induced immunity were at higher risk than those with natural immunity for infection (Delta aHR 4.90, 95%CI 4.48-5.36; Omicron 1.13, 95%CI 1.06-1.21) and hospitalization (Delta aHR 7.19, 95%CI 4.02-12.84). These results show that risk of infection and severe COVID-19 are driven by personal immunity history and the variant of SARS-CoV-2 causing infection.
Identification of Multi-repeat Sequences using Genome Mining Approaches for Developing Highly Sensitive Molecular Diagnostic Assay for the Detection ofChlamydia trachomatis
Authors: Shiluli Clement , Kamath Shwetha , Kanoi Bernard N. , Kimani Rachael , Maina Michael , Waweru Harrison , Kamita Moses , Ndirangu Ibrahim , Abkallo Hussein M. , Oduor Bernard , Pamme Nicole , Dupaty Joshua , Klapperich Catherine M. , Lolabattu Srinivasa Raju , Gitaka Jesse 
Published: July 2023

1.SummaryChlamydia trachomatis (C. trachomatis)is a common sexually transmitted infection (STI). In 2019, the World Health Organization reported about 131 million infections. The majority of infected patients are asymptomatic with cases remaining undetected. It is likely that missedC. trachomatisinfections contribute to preventable adverse health outcomes in women and children. Consequently, there is an urgent need of developing efficient diagnostic methods. In this study, genome-mining approaches to identify identical multi-repeat sequences (IMRS) distributed throughout theC. trachomatisgenome were used to design a primer pair that would target regions in the genome. Genomic DNA was 10-fold serially diluted (100pg/μL to 1×10-3pg/μL) and used as DNA template for PCR reactions. The gold standard PCR using 16S rRNA primers was also run as a comparative test, and products were resolved on agarose gel. The novel assay,C. trachomatisIMRS-PCR, had an analytical sensitivity of 9.5 fg/μL, representing better sensitivity compared with 16S rRNA PCR (4.31 pg/μL). Our experimental data demonstrate the successful development of lateral flow and isothermal assays for detectingC. trachomatisDNA with potential use in field settings. There is a potential to implement this concept in miniaturized, isothermal, microfluidic platforms, and laboratory-on-a-chip diagnostic devices for reliable point-of-care testing.
A data-driven framework for clinical decision support applied to pneumonia management
Authors: Free Robert C. , Lozano Rojas Daniel , Richardson Matthew , Skeemer Julie , Small Leanne , Haldar Pranabashis , Woltmann Gerrit 
Published: July 2023

ABSTRACTDespite their long history, it can still be difficult to embed clinical decision support into existing health information systems, particularly if they utilise machine learning and artificial intelligence models. Moreover, when such tools are made available to healthcare workers, it is important that the users can understand and visualise the reasons for the decision support predictions. Plausibility can be hard to achieve for complex pathways and models and perceived ’black-box’ functionality often leads to a lack of trust. Here, we describe and evaluate a data-driven framework which moderates some of these issues and demonstrate its applicability to the in-hospital management of community acquired pneumonia, an acute respiratory disease which is a leading cause of in-hospital mortality world-wide. We use the framework to develop and test a clinical decision support tool based on local guideline aligned management of the disease and show how it could be used to effectively prioritise patients using retrospective analysis. Furthermore, we show how this tool can be embedded into a prototype clinical system for disease management by integrating metrics and visualisations for assisting decision makers examining complex patient journeys, risk scores and predictions from embedded machine learning and artificial intelligence models. Our results show the potential of this approach for developing, testing and evaluating workflow based clinical decision support tools which include complex models and embedding them into clinical systems.
The in-frame p.Lys254del-CAPN3 deletion is not sufficient to cause late-onset camptocormia in dominantly inherited calpainopathy
Authors: Valls Andrea , Gutiérrez-Gutiérrez Gerardo , Martínez Agustín , Ruiz-Roldán Cristina , Camaño Pilar , López de Munain Adolfo , Sáenz Amets 
Published: July 2023

AbstractIntroduction/AimsLimb-girdle muscular dystrophy R1 (LGMDR1) calpain 3-related is one of the most common forms of LGMD. It is typically recessively inherited and associated with progressive weakness of proximal limb-girdle muscles. Recently, several families with an autosomal dominant inheritance transmission pattern have been reported (LGMDD4). Camptocormia is a common clinical feature in these patients. In these families, different mutations inCAPN3have been identified, including in-frame deletions and missense mutations. In particular, two patients presenting with camptocormia carried the p.Lys254del missense mutation without a second identified mutation inCAPN3.MethodsAmong our patients, we identified two LGMDR1 families as carriers of the p.Lys254del mutation by DNA sequencing, both in homozygous and compound heterozygous states and clinically analyzed the family members carrying this mutation.ResultsInterestingly, these patients did not present the myopathic characteristics described in the so-called dominant patients. No camptocormia or any other severe clinical symptoms were observed.DiscussionAccordingly, we conclude that the p.Lys254del mutationper secannot be solely responsible for the camptocormia observed in dominant patients. Other additional undisclosed factors might regulate the phenotype associated to a dominant inheritance pattern inCAPN3mutation carriers.
Multi-omics analysis in primary T cells elucidates mechanisms behind disease associated genetic loci
Authors: Shi Chenfu , Zhao Danyun , Rossi Stefano , Frantzeskos Antonios , Ding James , Ferrazzano Carlo , Wynn Charlotte , Hum Ryan , Richards Ellie , Gupta Muskan , Yap Chuan Fu , Plant Darren , Grencis Richard , Martin Paul , Adamson Antony , Eyre Stephen , Bowes John , Barton Anne , Ho Pauline , Rattray Magnus , Orozco Gisela 
Published: July 2023

ABSTRACTIn this study, we present the most extensive dataset of chromatin conformation data with matching gene expression and chromatin accessibility from primary T cells to date. We use this data to enhance our understanding of the different mechanisms by which GWAS variants impact gene regulation and revealing how natural genetic variation alter chromatin accessibility and structure in primary cells at an unprecedented scale. Capitalizing on this vast dataset, we refine the mapping of GWAS loci to implicated regulatory elements, such as CTCF binding sites and other enhancer elements, aiding gene assignment. Importantly, we uncoverBCL2L11as the probable causal gene within the RA locus rs13396472, despite the GWAS variants’ intronic positioning relative toACOXLand we identify mechanisms involvingSESN3dysregulation in the RA locus rs4409785. Given these genes’ significant role in T cell development and maturation, our work is vital for deepening our comprehension of autoimmune disease pathogenesis and suggesting potential treatment targets.
Subset scanning for multi-trait analysis using GWAS summary statistics
Authors: Cao Rui , Olawsky Evan , McFowland Edward , Marcotte Erin , Spector Logan , Yang Tianzhong 
Published: July 2023

AbstractMulti-trait analysis has been shown to have greater statistical power than single-trait analysis. Most of the existing multi-trait analysis methods only work with a limited number of traits and usually prioritize high statistical power over identifying relevant traits, which heavily rely on domain knowledge. To handle diseases and traits with obscure etiology, we developed TraitScan, a powerful and fast algorithm that agnostically searches and tests a subset of traits from a moderate or large number of traits (e.g., dozens to thousands) based on either individual-level or summary-level genetic data. We evaluated TraitScan using extensive simulations and found that it outperformed existing methods in terms of both testing power and trait selection when sparsity was low or modest. We then applied it to search for traits associated with Ewing Sarcoma, a rare bone tumor with peak onset in adolescence, among 706 traits in UK Biobank. Our analysis revealed a few promising traits worthy of further investigation, highlighting the use of TraitScan for more effective multi-trait analysis as biobanks emerge. Our algorithm is implemented in an R package ‘TraitScan’ available athttps://github.com/RuiCao34/TraitScan.