bluuebunny/crossref_metadata_2025_split huggingface dataset:
Pseudoexfoliation syndrome and glaucoma: from genes to disease mechanisms
Authors: Schlötzer-Schrehardt Ursula , Khor Chiea Chuen 
Published: December 2020

Purpose of review
The genetic basis of pseudoexfoliation (PEX) syndrome, the most common identifiable cause of open-angle glaucoma, is steadily being elucidated. This review summarizes the recent advances on genetic risk factors for PEX syndrome/glaucoma and their potential functional implications in PEX pathophysiology.

Recent findings
As of today, seven loci associated with the risk of PEX surpassing genome-wide significance have been identified by well-powered genome-wide association studies and sequencing efforts. LOXL1 (lysyl oxidase-like 1) represents the major genetic effect locus, although the biological role of common risk variants and their reversed effect in different ethnicities remain an unresolved problem. Rare protein-coding variants at LOXL1 and a single noncoding variant downstream of LOXL1 showed no allele effect reversal and suggested potential roles for elastin homeostasis and vitamin A metabolism in PEX pathogenesis. Other PEX-associated genetic variants provided biological insights into additional disease processes and pathways, including ubiquitin-proteasome function, calcium signaling, and lipid biosynthesis. Gene-environment interactions, epigenetic alterations, and integration of multiomics data have further contributed to our knowledge of the complex etiology underlying PEX syndrome and glaucoma.

Summary
PEX-associated genes are beginning to reveal relevant biological pathways and processes involved in disease development. To understand the functional consequences and molecular mechanisms of these loci and to translate them into novel therapeutic approaches are the major challenges for the future.
Beta-blockers in chronic obstructive pulmonary disease: the good, the bad and the ugly
Authors: Leitao Filho Fernando Sergio , Choi Lauren , Sin Don D. 
Published: December 2020

Purpose of review
Several observational studies have suggested that β-blockers, especially cardioselective ones, are well tolerated and associated with a lower risk of acute exacerbations and death in patients with chronic obstructive pulmonary disease (COPD). However, there are dissenting studies. This review provides an update on the use of β-blockers in COPD, focusing on results of recent prospective studies and randomized controlled trials.

Recent findings
In totality, cohort studies indicate that β-blockers are generally well tolerated and effective in COPD patients who also have a clear cardiovascular indication for these medications. Although β-blockers on average reduce lung function acutely in COPD patients, the absolute decrease is relatively small, especially if cardioselective β-blockers are used. The results of two large randomized controlled trials suggest that β-blocker use does not reduce the therapeutic benefits of inhaled bronchodilators in COPD patients. The use of β-blockers in COPD patients, who do not have overt cardiovascular disease, does not prevent COPD exacerbations and may paradoxically increase the risk of COPD-related hospitalization and mortality.

Summary
The use of β-blockers is generally well tolerated and effective in COPD patients, who also have a clear cardiovascular indication for these drugs. However, they should not be used in patients who do not have overt cardiovascular disease as β-blockers can reduce lung function, worsen health status and increase the risk of COPD-related hospitalization.
Genetically increased circulating 25(OH)D level reduces the risk of type 2 diabetes in subjects with deficiency of vitamin D
Authors: Xu Yingying , Zhou Yuan , Liu Jingjing , Wang Chenfang , Qu Zhongjie , Wei Zhili , Zhou Dan 
Published: December 2020

Abstract
Observational studies have reported that Vitamin D deficiency and the risk type 2 diabetes are associated, but the causation is unclear. Mendelian randomization (MR) involving genetic variants as instrument variables (IVs) overcomes the reverse-casualty and unmeasured confounding. However, with limited sample size and IVs, previous MR studies showed inconsistent results. Leveraging by a largely increased sample size for both stages, we aim to provide an updated and precise estimate for the causality between Vitamin D and type 2 diabetes.
A 2-sample multi-IVs MR was performed. IVs for circulating 25-hydroxyvitamin D (25(OH)D) were obtained from a genome-wide association study from UK biobank involving 329,247 subjects of European ancestry. The causal effect of 25(OH)D and type 2 diabetes was estimated using traditional inverse variance weighting and MR pleiotropy residual sum and outlier (MR-PRESSO) framework which provides a robust estimate by systematically filtering out IVs identified with potential pleiotropy effects.
A higher genetically instrumented 25(OH)D was causally linked to reduced risk of type 2 diabetes risk by MR-PRESSO [odds ratio (OR) per standard deviation (SD) = 0.950, 95% confidence interval (CI) = 0.913–0.988, P = .010] after removing 13 (13/193) invalid IVs. In addition, we confirmed the causal role Vitamin D using 2 synthesis-related single-nucleotide polymorphisms (SNPs) which are consistent with previous MR studies [OR per SD = 0.894, 95% CI = 0.816–0.979, P = .016].
With a largely improved sample size, our results confirmed that genetically increased 25(OH)D concentration reduced the risk of type 2 diabetes and provided a more precise estimate for the effect size. The updated result empowers the role of Vitamin D and provides nontrivial evidence for interventional studies.
Effect of acupuncture treatment on post-stroke cognitive impairment
Authors: Du Yuzheng , Zhang Lili , Liu Wei , Rao Chang , Li Boxuan , Nan Xi , Li Zefang , Jiang Hailun 
Published: December 2020

Abstract

Introduction:
Post-stroke cognitive impairment (PSCI), which has a high morbidity, is closely associated with the recurrence and rehabilitation of ischemic stroke. There are 2 different stages of PSCI, including post-stroke cognitive impairment with no dementia (PSCIND) and post-stroke dementia (PSD). The latter has a significantly higher mortality rate than the previous one. Therefore, preventing the onset of PSD is of vital importance. However, there is no unequivocally effective prevention or treatment for PSCI, except intensive secondary prevention of stroke. The primary aim of this protocol is to explore whether acupuncture can improve cognitive function of patients with PSCIND and reduce the chances of developing PSD. On this bias, we also want to explore its possible mechanisms.

Methods and analysis:
A prospective, multicenter, large sample, randomized controlled trial will be conducted. A total of 360 eligible patients will be recruited from 5 different hospitals and randomly allocated into the acupuncture group (AG), sham acupuncture group (NAG), and waiting-list group (WLG) in a 1:1:1 ratio. The intervention period of NAG and AG will last 3 months (30 minutes per day, 3 times per week). Primary and secondary outcomes will be measured at baseline, 12 weeks (at the end of the intervention), 24 weeks (after the 12-week follow-up period), and 36 weeks (after the 24-week follow-up period). Resting-state and task-state functional MRI will be conducted at baseline and 12 weeks.

Ethics and dissemination:
The ethic committee of First Teaching Hospital of University of Traditional Chinese Medicine approved the study. Study results will be first informed to each participant and later disseminated to researchers, and the general public through courses, presentations and the internet, regardless of the magnitude or direction of effect. The results will also be documented in a published peer-reviewed academic journal.

Registration:
We have registered at ClinicalTrials.gov(ChiCTR2000033801).
Outcomes of dissolution therapy and monitoring for stone disease: should we do better?
Authors: Tzelves Lazaros , Mourmouris Panagiotis , Skolarikos Andreas 
Published: December 2020

Purpose of review
Surgical management is the cornerstone of urolithiasis treatment, but high recurrence rates and associated complications necessitate the existence of medical treatment options, aiming at dissolution of kidney stones and prevention of recurrence. The purpose of this review is to present the most recent knowledge existing in scientific literature, regarding dissolution therapy and ways of monitoring nephrolithiasis patients.

Recent findings
A number of laboratory studies have been performed testing experimental treatments (tolvaptan, chlorthalidone, atorvastatin, a-lipoic acid, glucosaminoglycans, plant extracts), to reduce kidney stone formation and cellular damage and showed encouraging results. Uric acid stones is the main target of dissolution therapy, but until last years, existing level of evidence was low. A number of prospective and randomized studies, proved the efficacy and safety of oral chemolysis for radiolucent stones and associated prognostic features of success. Tolvaptan and a-Lipoic acid were also tested in patients with cystinuria and resulted in reduction of recurrence. Finally, some new diagnostic markers are suggested as tests for the monitoring of urolithiasis patients, with satisfying accuracy and discriminative ability.

Summary
Medical dissolution therapy for nephrolithiasis is an effective option, especially for patients with radiolucent/uric acid stones. In the future, we need clinical trials to confirm the encouraging results of pilot studies testing several agents for patients with cystinuria.
Uterine-Artery Embolization or Myomectomy for Uterine Fibroids
Authors: Manyonda Isaac , Belli Anna-Maria , Lumsden Mary-Ann , Moss Jonathan , McKinnon William , Middleton Lee J. , Cheed Versha , Wu Olivia , Sirkeci Fusun , Daniels Jane P. , McPherson Klim 
Published: December 2020

(Abstracted from N Engl J Med 2020;383:440–451)
The most common type of tumor among women of reproductive age is uterine fibroids. The prevalence of these tumors increases with age.
Micronized Vaginal Progesterone to Prevent Miscarriage: A Critical Evaluation of Randomized Evidence
Authors: Coomarasamy Arri , Devall Adam J. , Brosens Jan J. , Quenby Siobhan , Stephenson Mary D. , Sierra Sony , Christiansen Ole B. , Small Rachel , Brewin Jane , Roberts Tracy E. , Dhillon-Smith Rima , Harb Hoda , Noordali Hannah , Papadopoulou Argyro , Eapen Abey , Prior Matt , Di Renzo Gian Carlo , Hinshaw Kim , Mol Ben W. , Lumsden Mary Ann , Khalaf Yacoub , Shennan Andrew , Goddijn Mariette , van Wely Madelon , Al-Memar Maya , Bennett Phil , Bourne Tom , Rai Raj , Regan Lesley , Gallos Ioannis D. 
Published: December 2020

(Abstracted from Am J Obstet Gynecol 2020;223(2):167–176)
Historically, a lack of methodologically strong and generalizable studies has limited policy makers from recommending the use of progesterone supplementation to improve outcomes in women at high risk of miscarriage. The PROMISE and PRISM trials were carried out to rectify this and generate robust evidence on the role of progesterone supplementation to prevent miscarriage.
Smartphone-Based Financial Incentives to Promote Smoking Cessation During Pregnancy: A Pilot Study
Authors: Kurti Allison N. , Tang Katherine , Bolivar Hypatia A. , Evemy Carolyn , Medina Norman , Skelly Joan , Nighbor Tyler , Higgins Stephen T. 
Published: December 2020

(Abstracted from Prev Med 2020;9:106201)
Smoking during pregnancy is a leading cause of preventable perinatal complications. Yet, its prevalence among pregnant women in the United States has remained about the same over the last decade—approximately 13%.
Interleukin 1 beta is associated with depression in Multiple Sclerosis patients
Authors: Aguilar-Talamantes Hugo , Islas-Hernandez Azul , Rodriguez-Castañenda Aleida , Bertado-Cortés Brenda , Corlay-Noriega Irma , Garcia-delaTorre Paola 
Published: December 2020

AbstractDepressive and anxiety symptoms occur more frequently in chronic encephalomyelitis. Inflammatory diseases are highly associated with psychiatric comorbidities, which has been well established in Multiple Sclerosis. However, no biomarkers have been found with the capacity to discern between MS and depression. Thirty-six individuals with a diagnosis of MS according to the revised McDonald criteria, were recruited from an outpatient Neurology and Psychiatry from the medical unit of high specialty in Mexico. We measured the association between BDNF, IL-1β, and TNFα in serum with the presence of depression and anxiety using the semi-structured psychiatric interview, the Beck Depression Inventory (IDB), and Hospital Anxiety and Depression Scale (HADS). This was a cross-sectional study. The Logistic Regression was used for the multivariate analysis adjusted by the Multiple Sclerosis Severity Score (MSSS). With a power of 0.75 in the final model, patients with multiple sclerosis, depression, and anxiety obtained the highest values of IL-1 β in our study.
Identifying Synergistic Interventions to Address COVID-19 Using a Large Scale Agent-Based Model
Authors: Li Junjiang , Giabbanelli Philippe J. 
Published: December 2020

AbstractThere is a range of public health tools and interventions to address the global pandemic of COVID-19. Although it is essential for public health efforts to comprehensively identifywhichinterventions have the largest impact on preventing new cases, most of the modeling studies that support such decision-making efforts have only considered a very small set of interventions. In addition, previous studies predominantly considered interventions as independent or examined a single scenario in which every possible intervention was applied. Reality has been more nuanced, as a subset of all possible interventions may be in effect for a given time period, in a given place. In this paper, we use cloud-based simulations and a previously published Agent-Based Model of COVID-19 (Covasim) to measure the individual and interacting contribution of interventions on reducing new infections in the US over 6 months. Simulated interventions include face masks, working remotely, stay-at-home orders, testing, contact tracing, and quarantining. Through a factorial design of experiments, we find that mask wearing together with transitioning to remote work/schooling has the largest impact. Having sufficient capacity to immediately and effectively perform contact tracing has a smaller contribution, primarily via interacting effects.
CD177, a specific marker of neutrophil activation, is a hallmark of COVID-19 severity and death
Authors: Lévy Yves , Wiedemann Aurélie , Hejblum Boris P. , Durand Mélany , Lefebvre Cécile , Surénaud Mathieu , Lacabaratz Christine , Perreau Matthieu , Foucat Emile , Déchenaud Marie , Tisserand Pascaline , Blengio Fabiola , Hivert Benjamin , Gautier Marine , Cervantes-Gonzalez Minerva , Bachelet Delphine , Laouénan Cédric , Bouadma Lila , Timsit Jean-François , Yazdanpanah Yazdan , Pantaleo Giuseppe , Hocini Hakim , Thiébaut Rodolphe ,   the French COVID cohort study group
Published: December 2020

AbstractCOVID-19 SARS-CoV-2 infection exhibits wide inter-individual clinical variability, from silent infection to severe disease and death. The identification of high-risk patients is a continuing challenge in routine care. We aimed to identify factors that influence clinical worsening. We analyzed 52 cell populations, 71 analytes, and RNA-seq gene expression in the blood of severe patients from the French COVID cohort upon hospitalization (n = 61). COVID-19 patients showed severe abnormalities of 27 cell populations relative to healthy donors (HDs). Forty-two cytokines, neutrophil chemo-attractants, and inflammatory components were elevated in COVID-19 patients. Supervised gene expression analyses showed differential expression of genes for neutrophil activation, interferon signaling, T- and B-cell receptors, EIF2 signaling, and ICOS-ICOSL pathways in COVID-19 patients. Unsupervised analysis confirmed the prominent role of neutrophil activation, with a high abundance of CD177, a specific neutrophil activation marker. CD177 was the most highly differentially-expressed gene contributing to the clustering of severe patients and its abundance correlated with CD177 protein serum levels. CD177 levels were higher in COVID-19 patients from both the French and “confirmatory” Swiss cohort (n = 203) than in HDs (P< 0.01) and in ICU than non-ICU patients (P< 0.001), correlating with the time to symptoms onset (P = 0.002). Longitudinal measurements showed sustained levels of serum CD177 to discriminate between patients with the worst prognosis, leading to death, and those who recovered (P = 0.01). These results highlight neutrophil activation as a hallmark of severe disease and CD177 assessment as a reliable prognostic marker for routine care.
Effectiveness and Cost-Effectiveness of TeleStroke Consultations to Support the Care of Stroke Patients Presenting to Regional Emergency Departments in Western Australia: An Economic Evaluation Case Study Protocol
Authors: Tsou Christina , Robinson Suzanne , Boyd James , Kamath Shruthi , Yeung Justin , Waters Stephanie , Gifford Karen , Jamieson Andrew , Hendrie Delia 
Published: December 2020

ABSTRACTIntroductionThe Western Australia Acute TeleStroke Programme commenced incrementally across regional Western Australia (WA) during 2016-2017. Since the introduction of the TeleStroke Programme, there has been monitoring of service outputs including regional patient access to tertiary stroke specialist advice and reperfusion treatment, however, the impact of consultation with a stroke specialist via telehealth (videoconferencing or telephone) on the effectiveness and cost-effectiveness of stroke care, and the drivers of cost-effectiveness has not been systematically evaluated.Methods and AnalysisThe aim of the case study is to examine the impact of consultation with a stroke specialist via telehealth on the effectiveness and cost-effectiveness of stroke and TIA care using a mixed methods approach. A categorical decision tree model will be constructed in collaboration with clinicians and programme managers. A before and after comparison using State-wide administrative datasets will be used to run the base model. If sample size and statistical power permits, the cases and comparators will be matched by stroke type and presence of CT scan at the initial site of presentation, age category and presenting hospital. The drivers of cost-effectiveness will be explored through stakeholder interviews. Data from the qualitative analysis will be cross-referenced with trends emerging from the quantitative dataset and used to guide the factors to be involved in sub-group and sensitivity analysis.Ethics and DisseminationEthics approval for this case study has been granted from the WACHS Human Research and Ethics Committee (RGS3076). Reciprocal approval has been granted from Curtin University Human Research Ethics Office (HRE2019-0740). Findings will be disseminated publicly through conference presentation and peer-review publications. Interim findings will be released as internal reports to inform the service development.Strengths and Limitations of This StudyComparison of the impact of stroke specialist consultation via telehealth in regional Australia in supporting the management of different stroke subtypesThe decision tree model will be constructed in collaboration with clinicians and programme administrators directly involved in the delivery of the TeleStroke ProgrammeUse of local administrative data as model inputs enables the base model to reflect the reality of the regional WA Health service deliveryCollaboration with WA Health stakeholders involved in TeleStroke Programme design and implementation to optimise utility of the case study to inform service development and expansionConversion of the functional outcome modified Rankin Scale score (mRS) to quality adjusted life years (QALY) relies on national or international averages
Predicting mortality in SARS-COV-2 (COVID-19) positive patients in the inpatient setting using a Novel Deep Neural Network
Authors: Naseem Maleeha , Arshad Hajra , Hashmi Syeda Amrah , Irfan Furqan , Ahmed Fahad Shabbir 
Published: December 2020

ABSTRACTBackgroundThe second wave of COVID-19 pandemic is anticipated to be worse than the initial one and will strain the healthcare systems even more during the winter months. Our aim was to develop a machine learning-based model to predict mortality using the deep learning Neo-V framework. We hypothesized this novel machine learning approach could be applied to COVID-19 patients to predict mortality successfully with high accuracy.MethodsThe current Deep-Neo-V model is built on our previously statistically rigorous machine learning framework [Fahad-Liaqat-Ahmad Intensive Machine (FLAIM) framework] that evaluated statistically significant risk factors, generated new combined variables and then supply these risk factors to deep neural network to predict mortality in RT-PCR positive COVID-19 patients in the inpatient setting. We analyzed adult patients (≥18 years) admitted to the Aga Khan University Hospital, Pakistan with a working diagnosis of COVID-19 infection (n=1228). We excluded patients that were negative on COVID-19 on RT-PCR, had incomplete or missing health records. The first phase selection of risk factor was done using Cox-regression univariate and multivariate analyses. In the second phase, we generated new variables and tested those statistically significant for mortality and in the third and final phase we applied deep neural networks and other traditional machine learning models like Decision Tree Model, k-nearest neighbor models and others.ResultsA total of 1228 cases were diagnosed as COVID-19 infection, we excluded 14 patients after the exclusion criteria and (n=)1214 patients were analyzed. We observed that several clinical and laboratory-based variables were statistically significant for both univariate and multivariate analyses while others were not. With most significant being septic shock (hazard ratio [HR], 4.30; 95% confidence interval [CI], 2.91-6.37), supportive treatment (HR, 3.51; 95% CI, 2.01-6.14), abnormal international normalized ratio (INR) (HR, 3.24; 95% CI, 2.28-4.63), admission to the intensive care unit (ICU) (HR, 3.24; 95% CI, 2.22-4.74), treatment with invasive ventilation (HR, 3.21; 95% CI, 2.15-4.79) and laboratory lymphocytic derangement (HR, 2.79; 95% CI, 1.6-4.86). Machine learning results showed our DNN (Neo-V) model outperformed all conventional machine learning models with test set accuracy of 99.53%, sensitivity of 89.87%, and specificity of 95.63%; positive predictive value, 50.00%; negative predictive value, 91.05%; and area under the curve of the receiver-operator curve of 88.5.ConclusionOur novel Deep-Neo-V model outperformed all other machine learning models. The model is easy to implement, user friendly and with high accuracy.