bluuebunny/crossref_metadata_2025_split huggingface dataset:
Zig Maneuver in Total Extraperitoneal Inguinal Hernia Repair
Authors: Kurtulus Idris , Culcu Osman D. , Degerli Mahmut S. 
Published: March 2022

Background:
The difficulties in the hernia sac dissection in repairing large inguinal hernias with the endoscopic total extraperitoneal (TEP) technique prolong the operation and increase the risk of injury. This study investigates the effectiveness of the Zig maneuver (ligation of the hernia sac at the deep inguinal ring) in TEP in male patients with L3 inguinal hernia by European Hernia Society classification.

Materials and Methods:
A total of 224 male patients with L3 inguinal hernia who underwent laparoscopic TEP surgery in 2018 and 2019 were retrospectively screened and included in the study. The patients were divided into 2 groups as Zig group (n=99) and the non-Zig group (n=125), depending on whether the Zig maneuver was performed during TEP application.

Results:
The mean patient age was 45.49 for the non-Zig group and 47.12 for the Zig group. The median operative time was 50 minutes in the non-Zig group and 43 minutes in the Zig group (P<0.005). The median value of the postoperative first 24 hours pain score was 3 in the non-Zig group and 2 in the Zig group (P=0.033). Scrotal edema was 7.2% in the non-Zig group and 1% in the Zig group (P=0.023). According to logistic regression analysis, the Zig maneuver reduced the probability of scrotal edema by 87% in almost the entire population (odds ratio=0.130, 95% confidence interval: 0.016-1.047). There was no statistically significant difference between the groups in terms of early and late recurrence.

Conclusions:
Zig maneuver shortened the operative time and significantly reduced the feeling of pain in the first 24 hours postoperatively in male patients with indirect L3 hernia who underwent endoscopic TEP. It also significantly reduced the likelihood of scrotal edema in almost the entire population. As a result, the Zig maneuver is an effective method that could be applied during endoscopic TEP.
A free classified advertising website is a high-risk unregulated source of domestic wild boar, pot-bellied pigs, and their hybrids with domestic swine in Canada: Implications for African Swine Fever Risk Preparedness
Authors: MacDonald Amanda M. , Brook Ryan K. 
Published: March 2022

ABSTRACTInvasive wild pigs are a global problem and are established in large areas across Canada. Domestic European wild boar were first introduced to Canadian farms in the 1980s, and their escape and intentional release have since led to free-ranging populations, which have expanded rapidly in numbers and range. Wild pigs are associated with widespread damage to crops and natural environments, as well as risks of disease transmission. African Swine Fever (ASF) is a highly contagious viral disease of global concern, that can spread between domestic and free-ranging swine and cause catastrophic economic impacts. As ASF preparedness requires a detailed understanding of the distribution and movements of all pig types, this paper aimed to characterize an unregulated ‘grey’ swine market in Canada to understand risks to domestic swine production and potential contributions to free-ranging wild pig populations. Kijiji.ca is a free Canadian internet classified advertising service, almost exclusively cash-based with no receipts and few, if any, records kept of transactions. We monitored Kijiji for sales of domestic wild boar, pot-bellied pigs, and their hybrids across Canada over two months from April 28 - June 30, 2021. Data was collected from all advertisements, including how the seller labelled pigs’ breed, age, sex, number for sale, sexual intactness, presence of tattoos and/or ear tags, as well as the date and location of the posting. Locations were mapped and compared to the spatial distribution of existing free-ranging wild pigs in Canada to identify areas they were likely to facilitate new populations or potentially supplement and genetically diversify existing populations. We identified 151 advertisements on Kijiji, the most coming from Ontario (34.4%; n = 52) and Alberta (29.1%; n = 44), followed by Saskatchewan (11.3%; n = 17), British Columbia (10.6%; n = 16), Nova Scotia (7.3%; n = 11), New Brunswick (4.0%; n = 6), Manitoba (2.0%; n = 3), Quebec (0.7%; n = 1), and Prince Edward Island (0.7%; n = 1). No listings were observed in Newfoundland and Labrador, or Yukon, Nunavut and Northwest Territories. Overall, 62 (41%) of advertisements were within watersheds with existing wild pigs that may contribute to supplementing additional swine, and 89 (59%) were within watersheds where wild pigs have not yet been identified. African swine fever preparedness should include policies and action to address this unmonitored and unregulated sale of swine in Canada, including ear tags, tattoos, genetic analysis, and mandatory reporting.
MSclassifR: an R Package for Supervised Classification of Mass Spectra with Machine Learning Methods
Authors: Godmer Alexandre , Benzerara Yahia , Varon Emmanuelle , Veziris Nicolas , Druart Karen , Mozet Renaud , Matondo Mariette , Aubry Alexandra , Giai Gianetto Quentin 
Published: March 2022

AbstractMSclassifR is an R package that has been specifically designed to improve the classification of mass spectra obtained from MALDI-TOF mass spectrometry. It offers a comprehensive range of functions that are focused on processing mass spectra, identifying discriminantm/zvalues, and making accurate predictions. The package introduces innovative algorithms for selecting discriminatingm/zvalues and making predictions. To assess the effectiveness of these methods, extensive tests were conducted using challenging real datasets, including bacterial subspecies of theMycobacterium abscessuscomplex, virulent and avirulent phenotypes ofEscherichia coli, different species of Streptococci and nasal swabs from individuals infected and uninfected with SARS-CoV-2. Additionally, multiple datasets of varying sizes were created from these real datasets to evaluate the robustness of the algorithms. The results demonstrated that the Machine Learning-based pipelines in MSclassifR achieved high levels of accuracy and Kappa values. On an in-house dataset, some pipelines even achieved more than 95% mean accuracy, whereas commercial system only achieved 62% mean accuracy. Certain methods showed greater resilience to changes in dataset sizes when constructing Machine Learning-based pipelines. These simulations also helped determine the minimum sizes of training sets required to obtain reliable results. The package is freely available online, and its open-source nature encourages collaborative development, customization, and fosters innovation within the community focused on improving diagnosis based on MALDI-TOF spectra.Key pointsMSclassifR is a comprehensive R package enabling the construction of data analysis pipelines for the precise classification of mass spectra.Our R package contains an innovative method for variable selection from random forests, which delivered excellent results on real data.In-depth analysis of various machine learning-based pipelines using our package allowed us to make conclusions about the optimal m/z selection and prediction methods depending on the size of the training dataset.Using a publicly available dataset of mass spectra obtained from various MALDI-TOF instruments across different countries, MSclassifR is able to build robust pipelines capable of adapting to different instruments in an automatic way.When tested on an in-house dataset, MSclassifR pipelines consistently outperformed a commercial software in terms of prediction accuracy.
Adenosine A1R/A3R Agonist AST-004 Reduces Brain Infarction in Mouse and Rat Models of Acute Ischemic Stroke
Authors: Fisher Elizabeth S. , Chen Yanan , Sifuentes Mikaela M. , Stubblefield Jeremy J. , Lozano Damian , Holstein Deborah M. , Ren JingMei , DeRosa Nicholas , Chen Tsung-pei , Nickel Gerard , Liston Theodore E. , Lechleiter James D. 
Published: March 2022

AbstractAcute ischemic stroke (AIS) is the second leading cause of death globally. No Food and Drug Administration (FDA) approved therapies exist targeting cerebroprotection following stroke. Our group recently reported significant cerebroprotection with the adenosine A1/A3 receptor agonist, AST-004, in a transient stroke model in non-human primates (NHP) and in a preclinical mouse model of traumatic brain injury (TBI). However, the specific receptor pathway activated was only inferred based onin vitrobinding studies. The current study investigated the underlying mechanism of AST-004 cerebroprotection in two independent models of AIS: permanent photothrombotic stroke in mice and transient middle cerebral artery occlusion (MCAO) in rats. AST-004 treatments across a range of doses were cerebroprotective and efficacy could be blocked by A3R antagonism, indicating a mechanism of action that does not require A1R agonism. The high affinity A3R agonist MRS5698 was also cerebroprotective following stroke, but not the A3R agonist Cl-IB-MECA under our experimental conditions. AST-004 efficacy was blocked by the astrocyte specific mitochondrial toxin fluoroacetate, confirming an underlying mechanism of cerebroprotection dependent on astrocyte mitochondrial metabolism. An increase in A3R mRNA levels following stroke suggested an intrinsic cerebroprotective response that was mediated by A3R signaling. Together, these studies confirm certain A3R agonists, such as AST-004, are promising new therapeutics for the treatment of AIS.
Probabilistic Brain Extraction in MR Images via Conditional Generative Adversarial Networks
Authors: Moazami Saeed , Ray Deep , Pelletier Daniel , Oberai Assad A. 
Published: March 2022

AbstractBrain extraction, which refers to the task of segmenting brain tissue in an MR image of a subject, forms an essential first step for many quantitative neuroimaging applications. These include quantifying grey and white matter volumes, monitoring neurological diseases like multiple sclerosis (MS) and Alzheimer’s disease, and estimating brain atrophy. Over the years several algorithms that automate brain extraction have been proposed. More recently, novel image-to-image deep learning methods have been implemented for this task, and have demonstrated significant gains in accuracy and robustness. However, to our knowledge, none of these algorithms account for the uncertainty that is inherent in brain extraction. Motivated by this, we propose a novel, probabilistic deep learning algorithm for brain extraction that recasts this task as a Bayesian inference problem, and then utilizes a conditional generative adversarial network (cGAN) to solve it. The input to the generator network is an MR image of the head and the output is a collection of images of the brain that are drawn from a probability density conditioned on the input image. These images are used to generate a pixel-wise mean image, which serves as the best guess for an image of the brain, and a pixel-wise standard deviation image, which quantifies the uncertainty in the prediction. We test this algorithm on head MR images of fifty subjects and demonstrate that it is more accurate than a commonly used brain extraction tool, and that its performance compares well with the current state of the art in deep learning algorithms. We also demonstrate the utility of the estimates of uncertainty generated by the algorithm.
Detecting Patient Position Using Bed-Reaction Forces and Monitoring Skin-Bed Interface Forces for Pressure Injury Prevention and Management
Authors: Pupic Nikola , Gabison Sharon , Evans Gary , Fernie Geoff , Dolatabadi Elham , Dutta Tilak 
Published: March 2022

AbstractPressure injuries are largely preventable, yet they affect one in four Canadians across all healthcare settings. A key best practice to prevent and treat pressure injuries is to minimize prolonged tissue deformation by ensuring at-risk individuals are repositioned regularly (typically every 2 hours). However, adherence to repositioning is poor in clinical settings and expected to be even worse in homecare settings.Our team has designed a position detection system for home use that uses machine learning approaches to predict a patient’s position in bed using data from load cells under the bed legs. The system predicts the patient’s position as one of three position categories: left-side lying, right-side lying, or supine. The objectives of this project were to: i) determine if measuring ground truth patient position with an inertial measurement unit can improve our system accuracy (predicting left-side lying, right-side lying, or supine) ii) to determine the range of transverse pelvis angles (TPA) that fully offloaded each of the great trochanters and sacrum and iii) evaluate the potential benefit of being able to predict the individual’s position with higher precision (classifying position into more than three categories) by taking into account a potential drop in prediction accuracy as well as the range of TPA for which the greater trochanters and sacrum were fully offloaded.Data from 18 participants was combined with previous data sets to train and evaluate classifiers to predict the participants’ TPA using four different position bin sizes (∼70°, 45°, ∼30°, and 15°) and the effects of increasing precision on performance, where patients are left side-lying at -90°, right side-lying at 90° and supine at 0°). A leave-one-participant-out cross validation approach was used to select the best performing classifier, which was found to have an accuracy of 84.03% with an F1 score of 0.8399. Skin-bed interface forces were measured using force sensitive resistors placed on the greater trochanters and sacrum. Complete offloading for the sacrum was only achieved for the positions with TPA angles <-90° or >90°, indicating there was no benefit to predicting with greater precision than with three categories: left, right, and supine.
The Gut Microbiome and their Metabolites in Human Blood Pressure Variability
Authors: Dinakis Evany , Nakai Michael , Gill Paul , Ribeiro Rosilene , Yiallourou Stephanie , Sata Yusuke , Muir Jane , Carrington Melinda , Head Geoffrey A. , Kaye David M. , Marques Francine Z. 
Published: March 2022

AbstractBlood pressure (BP) variability is an independent risk factor for cardiovascular events. Recent evidence supports a role for the gut microbiota in BP regulation. However, whether the gut microbiome is associated with BP variability is yet to be determined. Here, we aimed to investigate the interplay between the gut microbiome and their metabolites in relation to BP variability. Ambulatory BP monitoring was performed in 69 participants from Australia (55.1% women; mean±SD 59.8±7.26-years old, 25.2±2.83 kg/m2). This data was used to determine night-time dipping, morning BP surge (MBPS) and BP variability as standard deviation (SD). The gut microbiome was determined by 16S rRNA sequencing, and metabolite levels by gas chromatography. We identified specific taxa associated with systolic BP variability, night-time dipping and MBPS. Notably,AlistipesfinegoldiiandLactobacillus spp. were only present in participants within the normal ranges of BP variability, MBPS and dipping, whilePrevotella spp. and variousClostridiumspp. were found to be present in extreme dippers and the highest quartiles of BP SD and MBPS. There was a negative association between MBPS and microbial α-diversity (r=-0.244,P=0.046). MBPS was also negatively associated with total levels of microbial metabolites called short-chain fatty acids (SCFAs) in the plasma (r=-0.305,P=0.020), particularly acetate (r=-0.311,P=0.017). In conclusion, gut microbiome diversity, levels of microbial metabolites, and the bacteriaAlistipesfinegoldiiandLactobacilluswere associated with lower BP variability, andClostridiumandPrevotellawith higher BP variability. Thus, our findings suggest the gut microbiome and metabolites may be involved in the regulation of BP variability.
Low Vision Rehabilitation Training and Referral Patterns Among Ophthalmologists
Authors: Robinson Joshua L. , Kunnath Ansley , Law Janice C. 
Published: March 2022

AbstractObjectiveVision impairment represents a growing burden to society and training protocols related to low vision rehabilitation (vision rehab) vary across ophthalmology residency programs. We surveyed practicing ophthalmologists regarding their vision rehab knowledge, confidence levels, and referral thresholds. We categorized subjects and compared response patterns between groups.DesignProspective observationalSubjects185 practicing ophthalmologistsMethodsWe created an Ophthalmology Low Vision Questionnaire and administered it to all enrolled subjects via email. We categorized subjects based on duration of practice, subspecialty area, and exposure to vision rehab during residency training. We drew conclusions by comparing responses between various subject categories.Main Outcome MeasuresThe primary outcome measure was comparison of confidence levels and thresholds for vision rehab referral across groups. We used statistical tests to look for associations between practice duration, subspecialty area, vision rehab exposure during residency, and referral patterns.ResultsOphthalmologists practicing for 6 or less years were more likely to have had a formal vision rehab rotation during their residency training compared to ophthalmologists practicing for 7 or more years (P = 0.03). Ophthalmologists who completed a formal vision rehab rotation during residency reported greater confidence in their ability to appropriately identify patients who could benefit from vision rehab referral (P = 0.04) and referred patients at earlier visual acuity thresholds (P = 0.04). Clinical subspecialty did not have a significant effect on vision rehab referral confidence or thresholds.ConclusionsVision rehab rotations during residency lead to improved referral confidence and earlier referral for these vital services. Standardization of vision rehab exposure across ophthalmology residency programs can help to improve outcomes for visually impaired patients.
Comprehensive interactome analysis for the sole adenylyl cyclase Cyr1 of Candida albicans
Authors: Zeng Guisheng , Neo Suat Peng , Pang Li Mei , Gao Jiaxin , Gunaratne Jayantha , Wang Yue 
Published: March 2022

AbstractCyr1, the sole adenylyl cyclase of the fungal pathogen Candida albicans, is a central component of the cAMP/PKA signaling pathway that controls the yeast-to-hyphal morphogenesis. Cyr1 functions as a multivalent senor and integrator of various external and internal signals. To better understand how these signals are relayed to Cyr1 and how Cyr1 activity is regulated, we sought to identify global interacting partners of Cyr1 using stable isotope labeling by amino acids in cell culture (SILAC)-based quantitative proteomics. From the proteins co-immunoprecipitated with Myc-tagged Cyr1, 38 were assigned as authentic Cyr1-interacting partners, including two previously characterized Cyr1-binding proteins, Cap1 and Act1. Many of the identified proteins remain uncharacterized, but some can be classified into several functional groups, such as actin regulatory proteins, cell wall components, and mitochondrial proteins. We used biochemical and genetic methods to further characterize the interaction of Cyr1 with Mp65, a cell surface mannoprotein previously reported to have a role in cell wall integrity and possess several virulence-related traits. Taken together, our comprehensive analysis of the Cyr1-interacting proteins provides important information for establishing the Cyr1 interactome and uncovers a potential role for cell wall proteins in Cyr1-mediated cAMP signaling.
Hypothalamic-pituitary-adrenal axis dysfunction worsens epilepsy outcomes and increases SUDEP risk
Authors: Basu Trina , Antonoudiou Pantelis , Weiss Grant L. , Friedman Daniel , Laze Juliana , Devinsky Orrin , Maguire Jamie 
Published: March 2022

SummaryEpilepsy is often comorbid with psychiatric illnesses, including anxiety and depression. Despite the high incidence of psychiatric comorbidities in people with epilepsy, few studies address the underlying mechanisms. Stress can trigger both epilepsy and depression and significant accumulating evidence, from both human and animal studies, support that hypothalamic-pituitary-adrenal (HPA) axis dysfunction may contribute to the comorbidity of these two disorders1. Here we directly investigate the contribution of HPA axis dysfunction to epilepsy outcomes and psychiatric comorbidities. We generated a novel mouse model (KCC2/Crh mice) that lacks the K+/Cl- co-transporter, KCC2, in corticotropin-releasing hormone (CRH) neurons, which exhibit stress- and seizure-induced hyperactivation of the HPA axis.2,3 Here we demonstrate that HPA axis dysfunction in KCC2/Crh mice is associated with increased vulnerability to behavioral deficits in chronically epileptic mice and more severe neuropathological features associated with chronic epilepsy (e.g. increased mossy fiber sprouting). Despite equivalent seizure burden, HPA axis dysfunction in these chronically epileptic mice increased the incidence of sudden unexpected death in epilepsy (SUDEP). Suppressing HPA axis hyperexcitability in this model using a pharmacological or chemogenetic approach decreased SUDEP incidence, supporting HPA axis dysfunction in SUDEP vulnerability. We also observed changes in neuroendocrine markers in individuals that died of SUDEP compared to people with epilepsy or individuals without a history of epilepsy. Together, these findings describe a novel, nongenetic mouse model of SUDEP which will benefit preclinical SUDEP research to gain a better understanding of the underlying pathological mechanisms contributing to SUDEP. HPA axis dysfunction increases vulnerability to psychiatric comorbidities in epilepsy and may be a risk factor for SUDEP.
Targeting an evolutionarily conserved “E-L-L” motif in the spike protein to develop a small molecule fusion inhibitor against SARS-CoV-2
Authors: Das Jana Indrani , Bhattacharya Prabuddha , Mayilsamy Karthick , Banerjee Saptarshi , Bhattacharje Gourab , Das Sayan , Aditya Seemanti , Ghosh Anandita , McGill Andrew R. , Srikrishnan Syamanthak , Das Amit Kumar , Basak Amit , Mohapatra Shyam S. , Chandran Bala , Bhimsaria Devesh , Mohapatra Subhra , Roy Arunava , Mondal Arindam 
Published: March 2022

AbstractAs newer variants of SARS-CoV-2 continue to pose major threats to global human health and economy, identifying novel druggable antiviral targets is the key towards sustenance. Here, we identify an evolutionary conserved “E-L-L” motif present within the HR2 domain of all human and non-human coronavirus spike (S) proteins that play a crucial role in stabilizing the post-fusion six-helix bundle (6-HB) structure and thus, fusion-mediated viral entry. Mutations within this motif reduce the fusogenicity of the S protein without affecting its stability or membrane localization. We found that posaconazole, an FDA-approved drug, binds to this “E-L-L” motif resulting in effective inhibition of SARS-CoV-2 infection in cells. While posaconazole exhibits high efficacy towards blocking S protein-mediated viral entry, mutations within the “E-L-L” motif rendered the protein completely resistant to the drug, establishing its specificity towards this motif. Our data demonstrate that posaconazole restricts early stages of infection through specific inhibition of membrane fusion and viral genome release into the host cell and is equally effective towards all major variants of concerns of SARS-CoV-2 including beta, kappa, delta, and omicron. Together, we show that this conserved essential “E-L-L” motif is an ideal target for the development of prophylactic and therapeutic interventions against SARS-CoV-2.