bluuebunny/crossref_metadata_2025 huggingface dataset:
Orchards and paddy differentially impact rock outcrop amphibians: Insights from community- and species-level responses
Authors: Jithin Vijayan , Rane Manali , Watve Aparna , Naniwadekar Rohit
Published: October 2023

With agricultural demands increasing globally, determining the nature of impacts of different forms of agriculture on biodiversity, especially for threatened vertebrates and habitats, is critical to inform land management. We determined the impacts of converting rock outcrops (a habitat more threatened than rainforests) to orchards and paddy on anurans in the Western Ghats biodiversity hotspot. We sampled 50 belt transects four times across four sites during the rainy season and recorded information on amphibians and their microhabitats. We determined community-level responses using Hill numbers, beta-diversity measures, and non-metric multidimensional scaling, and species-level responses using joint species distribution modelling. Converting rock outcrops to paddy and orchards significantly altered microhabitat availability. Conversion to paddy mostly had community-level impacts, i.e., lowered species richness and more nested communities, whereas conversion to orchards mostly had species-level impacts, i.e., lowered species occurrence, highlighting the differential impacts of different forms of agriculture on amphibians and the need to determine impacts of land-use change on communities and species concurrently. We show that large rock pools are critical microhabitats for anurans as they serve as a refuge and protect anurans from desiccation during dry spells, which may be prolonged by climate change. Since rock outcrop habitats in low elevations are rapidly being converted to orchards, efforts are needed to conserve them in partnership with local communities, the custodians of these habitats. Our findings demonstrate that different forms of agriculture can have divergent impacts on biodiversity, and determining their impacts may require assessments at multiple scales, from species to communities.
Estimating protein-ligand interactions with geometric deep learning and mixture density models
Authors: Kalakoti Yogesh , Gawande Swaraj , Sundar Durai
Published: October 2023

Understanding the interactions between a ligand and its molecular target is crucial in guiding the optimization of molecules for anyin-silicodrug-design workflow. Multiple experimental and computational methods have been developed to better understand these intermolecular interactions. With the availability of a large number of structural datasets, there is a need for developing statistical frameworks that improve upon existing physics-based solutions. Here, we report a method based on geometric deep learning that is capable of predicting the binding conformations of ligands to protein targets. A technique to generate graphical representations of protein was developed to exploit the topological and electrostatic properties of the binding region. The developed framework, based on graph neural networks, learns a statistical potential based on the distance likelihood, which is tailor-made for each ligand–target pair. This potential can be coupled with global optimization algorithms such as differential evolution to reproduce the experimental binding conformations of ligands. We show that the potential based on distance likelihood, described here, performs similarly or better than well-established scoring functions for docking and screening tasks. Overall, this method represents an example of how artificial intelligence can be used to improve structure-based drug design.

Significance statement

Current machine learning-based solutions to model protein-ligand interactions lack the level of interpretability that physics-based methods usually provide. Here, a workflow to embed protein binding surfaces as graphs was developed to serve as a viable data structure to be processed by geometric deep learning. The developed architecture based on mixture density models was employed to accurately estimate the position and conformation of the small molecule within the binding region. The likelihood-based scoring function was compared against existing physics-based alternatives, and significant performance improvements in terms of docking power, screening power and reverse screening power were observed. Taken together, the developed framework provides a platform for utilising geometric deep-learning models for interpretable prediction of protein-ligand interactions at a residue level.
The Role of Ryanodine Receptor 2 in Drug-Associated Learning
Authors: Barber Kara R. , Vizcarra Velia S. , Zilch Ashlyn , Majuta Lisa , Diezel Cody C. , Culver Oliver P. , Hughes Brandon W. , Taniguchi Makoto , Streicher John M. , Vanderah Todd W. , Riegel Arthur C.
Published: October 2023

Type-2 ryanodine receptor (RyR2) ion channels facilitate the release of Ca2+from stores and serve an important function in neuroplasticity. The role for RyR2 in hippocampal-dependent learning and memory is well established and chronic hyperphosphorylation of RyR2 (RyR2P) is associated with pathological calcium leakage and cognitive disorders, including Alzheimer’s disease. By comparison, little is known about the role of RyR2 in the ventral medial prefrontal cortex (vmPFC) circuitry important for working memory, decision making, and reward seeking. Here, we evaluated the basal expression and localization of RyR2 and RyR2P in the vmPFC. Next, we employed an operant model of sucrose, cocaine, or morphine self-administration (SA) followed by a (reward-free) recall test, to reengage vmPFC neurons and reactivate reward-seeking and re-evaluated the expression and localization of RyR2 and RyR2P in vmPFC. Under basal conditions, RyR2 was expressed in pyramidal cells but not regularly detected in PV/SST interneurons. On the contrary, RyR2P was rarely observed in PFC somata and was restricted to a different subcompartment of the same neuron - the apical dendrites of layer-5 pyramidal cells. Chronic SA of drug (cocaine or morphine) and nondrug (sucrose) rewards produced comparable increases in RyR2 protein expression. However, recalling either drug reward impaired the usual localization of RyR2P in dendrites and markedly increased its expression in somata immunoreactive for Fos, a marker of highly activated neurons. These effects could not be explained by chronic stress or drug withdrawal and instead appeared to require a recall experience associated with prior drug SA. In addition to showing the differential distribution of RyR2/RyR2P and affirming the general role of vmPFC in reward learning, this study provides information on the propensity of addictive drugs to redistribute RyR2P ion channels in a neuronal population engaged in drug-seeking. Hence, focusing on the early impact of addictive drugs on RyR2 function may serve as a promising approach to finding a treatment for substance use disorders.
Association of leaf spectral variation with functional genetic variants
Authors: Li Cheng , Czyż Ewa A. , Ray Rishav , Halitschke Rayko , Baldwin Ian T. , Schaepman Michael E. , Schuman Meredith C.
Published: October 2023

The application of in-field and aerial spectroscopy to assess functional and phylogenetic variation in plants has led to novel ecological insights and promises to support global assessments of plant biodiversity. Understanding the influence of plant genetic variation on reflectance spectra will help to harness this potential for biodiversity monitoring and improve our understanding of why plants differ in their functional responses to environmental change. Here, we use an unusually well-resolved genetic mapping population in a wild plant, the coyote tobaccoNicotiana attenuata, to associate genetic differences with differences in leaf spectra for plants in a field experiment in their natural environment. We analyzed the leaf reflectance spectra using FieldSpec 4 spectroradiometers on plants from 325 fully genotyped recombinant inbred lines (RILs) ofN. attenuatagrown in a blocked and randomized common garden experiment. We then tested three approaches to conducting Genome-Wide Association Studies (GWAS) on spectral variants. We introduce a new Hierarchical Spectral Clustering with Parallel Analysis (HSC-PA) method which efficiently captured the variation in our high-dimensional dataset and allowed us to discover a novel association, between a locus on Chromosome 1 and the 445-499 nm spectral range, which corresponds to the blue light absorption region of chlorophyll, indicating a genetic basis for variation in photosynthetic efficiency. These associations lie in close proximity to candidate genes known to be expressed in leaves and having annotated functions as methyltransferases, indicating possible underlying mechanisms governing these spectral differences. In contrast, an approach using well-established spectral indices related to photosynthesis, reducing complex spectra to a few dimensionless numbers, was not able to identify any robust associations, while an approach treating single wavelengths as phenotypes identified the same associations as HSC-PA but without the statistical power to pinpoint significant associations. The HSC-PA approach we describe here can support a comprehensive understanding of the genetic determinants of leaf spectral variation which is datadriven but human-interpretable, and lays a robust foundation for future research in plant genetics and remote sensing applications.
Interpreting Molecular Dynamics Forces as Deep Learning Gradients Improves Quality Of Predicted Protein Structures
Authors: King Jonathan Edward , Koes David Ryan
Published: October 2023

Protein structure predictions from deep learning models like AlphaFold2, despite their remarkable accuracy, are likely insufficient for direct use in downstream tasks like molecular docking. The functionality of such models could be improved with a combination of increased accuracy and physical intuition. We propose a new method to train deep learning protein structure prediction models using molecular dynamics force fields to work toward these goals. Our custom PyTorch loss function, OpenMM-Loss, represents the potential energy of a predicted structure. OpenMM-Loss can be applied to any all-atom representation of protein structure capable of mapping into our software package, SidechainNet. We demonstrate our method’s efficacy by finetuning OpenFold. We show that subsequently predicted protein structures, both before and after a relaxation procedure, exhibit comparable accuracy while displaying lower potential energy and improved structural quality as assessed by MolProbity metrics.

SIGNIFICANCE

We propose a novel framework to directly incorporate forces from molecular dynamics as gradients for training deep learning models like AlphaFold2. We implement our method as a PyTorch loss function, OpenMM-Loss, which frames the potential energy of predicted protein structures as a minimization objective. When applied to OpenFold, our method demonstrates improved structural quality, lower potential energy, and comparable accuracy relative to the OpenFold baseline. Our framework may enhance the ability of deep learning models to recapitulate fundamental biophysical principles, reducing the number of structural irregularities in their predictions and paving the way for more effective downstream applications.
The Bias of Using Cross-Validation in Genomic Predictions and Its Correction
Authors: Qian Yanzhao , Wang Dinghao , Ding Qi Xuan , Greenberg Matthew , Long Quan
Published: October 2023

Cross-validation (CV) is a widely used technique in statistical learning for model evaluation and selection. Meanwhile, various of statistical learning methods, such as Generalized Least Square (GLS), Linear Mixed-Effects Models (LMM), and regularization methods are commonly used in genomic predictions, a field that utilizes DNA polymorphisms to predict phenotypic traits. However, due to high dimensionality, relatively small sample sizes, and data sparsity in genomic data, CV in these scenarios may lead to an underestimation of the generalization error. In this work, we analyzed the bias of CV in eight methods: Ordinary Least Square (OLS), GLS, LMM, Lasso, Ridge, elastic-net (ENET), and two hybrid methods: one combining GLS with Ridge regularization (GLS+Ridge), and the other combining LMM with Ridge regularization (LMM+Ridge). Leveraging genomics data from the 1,000 Genomes Project and simulated phenotypes, our investigation revealed the presence of bias in all these methods. To address this bias, we adapted a variance-structure method known as Cross-Validation Correction (CVc). This approach aims to rectify the cross-validation error by providing a more accurate estimate of the generalization error. To quantify the performance of our adapted CVc towards all these methods, we applied the trained model to an independently generated dataset, which served as a gold standard for validating the models and calculating the generalization error. The outcomes show that, by leveraging CVc, we corrected the CV bias for most of the methods mentioned above, with two exceptions that are unrectifiable methods: ENET and Lasso. Our work revealed the substantial bias in the use of CV in genomics, a phenomenon under-appreciated by the field of statistical genomics and medicine. Additionally, we demonstrated that bias-corrected models may be formed by adapting CVc, although more work is needed to cover the full spectrum.
Key determinants of the dual clamp/activator function of Complexin
Authors: Makke Mazen , Ruiz Alejandro Pastor , Yarzagaray Antonio , Gaya Surya , Zimmer Michelle , Frisch Walentina , Bruns Dieter
Published: October 2023

Complexin determines magnitude and kinetics of synchronized secretion, but the underlying molecular mechanisms remained unclear. Here, we show that the hydrophobic face of the amphipathic helix at the C-terminus of Complexin II (CpxII, amino acids 115- 134) binds to fusion-promoting SNARE proteins, prevents premature secretion and allows vesicles to accumulate in a release-ready state. Specifically, we demonstrate that an unrelated amphipathic helix functionally substitutes for the CTD of CpxII and that amino acid substitutions on the hydrophobic side compromise the arrest of the prefusion intermediate. To facilitate synchronous vesicle fusion, the N-terminal domain (NTD) of CpxII (amino acids 1-27) specifically cooperates with synaptotagmin I (SytI), but not with synaptotagmin VII. Expression of CpxII rescues the slow release kinetics of the Ca2+- binding mutant Syt I R233Q, whereas the N-terminally truncated variant of CpxII further delays it. These results indicate that the CpxII NTD regulates mechanisms which are governed by the forward rate of Ca2+binding to Syt I. Overall, our results shed new light on key molecular properties of CpxII that hinder premature exocytosis and accelerate synchronous exocytosis.
Detecting Cerebral Ischemia from Electroencephalography During Carotid Endarterectomy Using Machine Learning
Authors: Mina Amir I. , Espino Jessi U. , Bradley Allison M. , Thirumala Parthasarathy D. , Batmanghelich Kayhan , Visweswaran Shyam
Published: October 2023

Intraoperative stroke is a major concern during high-risk surgical procedures such as carotid endarterectomy (CEA). Ischemia, a stroke precursor, can be detected using continuous electroencephalographic (cEEG) monitoring of electrical changes caused by changes in cerebral blood flow. However, monitoring by experts is currently resource-intensive and prone to error. We investigated if supervised machine learning (ML) could detect ischemia accurately using intraoperative cEEG. Using cEEG recordings from 802 patients, we trained six ML models, including naïve Bayes, logistic regression, support vector classifier, random forest (RF), light gradient-boosting machine (LGBM), and eXtreme Gradient Boosting with random forest (XGBoost RF), and tested them on a validation dataset of 30 patients. Each cEEG recording in the validation dataset was labeled independently by five expert neurophysiologists who regularly perform intraoperative neuromonitoring. We did not derive consensus labels but rather evaluated an ML model in a pairwise fashion using one expert as a reference at a time, due to the experts’ variability in label determination, which is typical for clinical tasks. The tree-based ML models, including RF, LGBM, and XGBoost RF, performed best, with AUROC values ranging from 0.92 to 0.93 and AUPRC values ranging from 0.79 to 0.83. Our findings suggest that ML models can serve as the foundation for a real-time intraoperative monitoring system that can assist the neurophysiologist in monitoring patients.
Exploring the potential of a saliva-based, RNA-extraction-free PCR test for the multiplexed detection of key respiratory pathogens
Authors: Allicock Orchid M. , Lin Tzu-Yi , Fajardo Katherine T. , Yolda-Carr Devyn , Hislop Maikel S. , Wang Jianhui , Zuniga Denora , Platt William , Tuohy Beth , Wyllie Anne L.
Published: October 2023

Introduction

Efforts to diagnose and monitor transmissible respiratory infections can be impaired by invasive or resource-intensive sample collection. Having extensively demonstrated the feasibility of saliva for SARS-CoV-2 detection, we sought to validate its potential for other common upper respiratory tract pathogens.

Methods

We modified our RNA-extraction-free SARS-CoV-2 PCR test for multiplexed detection of influenza A/B (IAV/IBV), respiratory syncytial virus (RSV) and human metapneumovirus (hMPV). Stability of virus detection in saliva from virus-positive patients was tested after storage at +4°C, room temperature (∼19°C), 30°C and 40°C for up to 7 days and through simulated shipping conditions. De-identified saliva samples were collected from individuals (≥18 years) with respiratory symptoms who were undergoing nasal-swab-based testing for SARS-CoV-2 (New Haven, CT). Saliva samples from SARS-CoV-2-negative individuals were tested with the multiplexed assay, with and without RNA extraction.

Results

The limit of assay detection ranged from 3-6 copies/μl, virus target depending. Detection remained stable after prolonged sample storage at elevated temperatures and through shipping conditions. From the symptomatic testing sites, 1,095 clinical specimens tested SARS-CoV-2-negative. Upon multiplexed testing of their paired saliva, 41 (3.7%) tested positive (IAV, n=20; RSV, n=5; hMPV, n=7). Additionally, upon screening samples in singleplex for pneumococcus, 29 (3%) samples tested positive.

Conclusion

Our findings emphasize the adaptability of a low-cost, open-source saliva-based PCR test for common respiratory pathogens, beyond SARS-CoV-2. We demonstrated its utility in symptomatic individuals, identifying viral infection missed when testing focused solely on a singular target, such as SARS-CoV-2.
Characterizing Potential Interaction Between Respiratory Syncytial Virus and Seasonal Influenza in the U.S.
Authors: Chen Jiani , Gokhale Deven V. , Liu Liang , Rohani Pejman , Bahl Justin
Published: October 2023

RSV and seasonal influenza are two of the most important causes of respiratory infection that consistently peak during winter months in the U.S. Here, we characterized the circulation of these viruses in the U.S. with weekly positive case reports and genetic surveillance and used a mathematical modeling approach to explore their potential interaction at an HHS regional level. Our analyses showed RSV and seasonal influenza co-circulate with various relatively epidemic sizes and seasonal overlaps across seasons and regions. We found RSV might have different evolutionary dynamics compared to seasonal influenza, with local persistence may play a role in underlying annual epidemics. Our analysis supports a competitive interaction between RSV and seasonal influenza in most HHS regions and we speculate that cross-immunity after infection might be the major driver of viral competition. Together, our work supports the competition between RSV and seasonal influenza across the U.S. at a population level. Our findings are important for the future development of protective strategies against these respiratory viruses.
Leprosy campaigns in Brazil: analysis of "Janeiro Roxo" impact using Google Trends.
Authors: VASCONCELLOS-SILVA PAULO ROBERTO , DE SOUSA ARTUR CUSTÓDIO MOREIRA
Published: October 2023

“Janeiro Roxo” (Purple January) is a Brazilian campaign to raise awareness about leprosy and its early symptoms. The negative influence of the COVID-19 pandemic on institutional campaigns was especially relevant for the control of leprosy, if considered its status of neglected disease by the media.  Google Trends (GT) is a free tool that tracks accesses to given terms on Google. Using GT we studied (i) impact of “Janeiro Roxo” in public interest on leprosy in the last 5 years; (ii) public interest during and after COVID-19 pandemic; (iii) patterns of public interest in self-identification of leprosy signs. Methods:  GT tracking follows the popularity of topics in time range in “Relative Search Volume” (RSV) – proxies of public interest in a particular subject on a scale from 0 to 100. We selected “HANSENÍASE” (HAN) and “HANSENÍASE SINTOMAS” (leprosy symptoms) (H.SIN) to estimate public interest in leprosy and “self-diagnosis” during 261 weeks (August 2018 to 2023). Polynomial trend lines estimate trends over the period. Weekly RSV, monthly and annual means were compared using Analysis of Variance (ANOVA). Results:  In 261 weeks: higher RSV to “HANSENÍASE” (HAN) compared to “HANSENÍASE SINTOMAS” (H.SIN); highest Monthly Means (MM) in January months (2019 to 2023) for both HAN and H.SIN. Pandemic effect in “Janeiro Roxo”: decrease in January MM 2021 compared to 2020 (24% to HAN, 25% to H.SIN). Both HAN/H.SIN reached pre-pandemic levels in January 2022/2023. Breakpoints (points of abrupt change which influences trend lines) in the 26th week (February 2019); in the 55th and 213th week (September 2019 and 2022). Trend line (HAN): upward curve between 33rd-45th week (April to June 2019); pandemic downward trend between 120th-136th week (December 2020 to March 2021); upward trend curve between 220th-240th week (November 2022 to March 2023). Conclusion:  “Janeiro Roxo” and in-person activities, among other media stimuli, have a relevant impact in terms of public interest in leprosy, even after their interruption due to pandemic. Considering their impacts on national searches, regional campaigns should be considered very successful. RSV to HAN may be due to “reactive searches” motivated by transient stimuli from campaigns, in an ephemeral and impersonal way. “Proactive searches” to H.SIN may be associated to those interested - in a personal way - in early symptoms and self-diagnosis. Internet-based campaign evaluation programs could be useful in integrating vulnerable regions and exposing their needs for assistance services.
The α2A-adrenergic receptor (ADRA2A) modulates susceptibility to Raynaud’s syndrome
Authors: Tervi Anniina , Ramste Markus , Abner Erik , Cheng Paul , Lane Jacqueline M. , Maher Matthew , Lammi Vilma , Strausz Satu , Nguyen Trieu , Docampo Mauro Lago , Gu Wenduo ,   FinnGen, Estonian biobank research team, Esko Tõnu , Saxena Richa , Palotie Aarno , Ripatti Samuli , Sinnott-Armstrong Nasa , Daly Mark , Rabinovitch Marlene , Heckman Caroline A. , Quertermous Thomas , Jones Samuel E. , Ollila Hanna M.
Published: October 2023

Raynaud’s syndrome is a common dysautonomia where exposure to cold increases the vascular tone of distal arteries causing vasoconstriction and hypoxia, particularly in the extremities. Current treatment options are limited and unspecific. Biological mechanisms leading to the phenotype remain uncharacterized. Using genetic and electronic health record data from the UK Biobank, the Mass-General Brigham Biobank, the Estonian Biobank, and the FinnGen study, we identified 11,358 individuals with a diagnosis of Raynaud’s syndrome and 1,106,871 population controls. We found eight loci including endothelial nitric oxide synthase (NOS3), HLA, and a notable association at the α2A-adrenergic receptor (ADRA2A)locus (rs7090046, P = 3.93×10-47), implicating adrenergic signaling as a major risk factor with Raynaud’s syndrome. We further investigate the role of the variants andADRA2Aexpression in functional and physiological models.In silicofollow-up analysis revealed an expression quantitative trait locus (eQTL) that co-localized and increasedADRA2Agene expression in a tissue-specific manner in the distal arteries. Staining with RNA scope further clarified the specificity ofADRA2Aexpression in small vessels. We show by CRISPR gene editing that the SNP region modifiesADRA2Agene expression in pulmonary artery smooth muscle cells. Finally, we performed a functional contraction assay on smooth muscle cells in cold conditions and showed lower contraction inADRA2A-deficient and higher contraction inADRA2A-overexpressing smooth muscle cells. Our results indicate that Raynaud’s syndrome is related to vascular function mediated by adrenergic signaling throughADRA2A. Our study highlights the power of genome-wide association testing as a discovery tool for poorly understood clinical endpoints and further clarifies the role of adrenergic signaling in Raynaud’s syndrome by fine-mapping, usingin vitrogenomic manipulations and functional validation in distal smooth muscle cell populations located in arterioles
Explainable artificial intelligence for cough-related quality of life impairment prediction in asthmatic patients
Authors: Narteni Sara , Baiardini Ilaria , Braido Fulvio , Mongelli Maurizio
Published: October 2023

Explainable Artificial Intelligence (XAI) is becoming a disruptive trend in healthcare, allowing for transparency and interpretability of autonomous decision-making. In this study, we present an innovative application of a rule-based classification model to identify the main causes of chronic cough-related quality of life (QoL) impairment in a cohort of asthmatic patients. The proposed approach first involves the design of a suitable symptoms questionnaire and the subsequent analyses via XAI. Specifically, feature ranking, derived from statistically validated decision rules, helped in automatically identifying the main factors influencing an impaired QoL: pharynx/larynx and upper airways when asthma is under control, and asthma itself and digestive trait when asthma is not controlled. Moreover, the obtainedif-thenrules identified specific thresholds on the symptoms associated to the impaired QoL. These results, by finding priorities among symptoms, may prove helpful in supporting physicians in the choice of the most adequate diagnostic/therapeutic plan.
ASSESSMENT OF THE BACTERIOLOGICAL QUALITIES OF MEAT AND CONTACT SURFACES IN MARKETS IN ABIA STATE, NIGERIA
Authors: Iwuagwu Uchechukwu Olive , Amadi Agwu Nkwa , Nworuh Blessed Okwuchi , Iwuala Chimezie Christian , Innocent David Chinaecherem , Ikeanumba Michael Okwudiri , Okorie Mary Onyinyechi
Published: October 2023

Background

Microbial contamination of meat comes from external sources during cutting, handling and processing of the meat. This study was carried out to assess the bacteriological qualities of meat and contact surfaces in markets in Abia State, Nigeria.

Methods

This research involved the use of a Hazard Analysis Critical Control Point (HACCP) checklist to investigate the sanitation and hygiene practices of meat sellers and a laboratory study of red and white meat, water and meat-contact surface samples. A total of 425 meat samples collected from 425 meat sellers from some randomly selected markets in Abia State were used for the study. There were also 20 water samples, 22 samples from table surfaces, 22 samples from knife surfaces and 14 samples from transport vehicles. The multistage simple random sampling technique through balloting was employed to determine communities/markets for the study. Samples for the study were collected and analyzed using standard microbiological techniques such as culturing and the bacteria were enumerated and identified using biochemical and chemical tests.

Results

The prevalent bacterial isolates includeStaphylococcussp (78.80%),Bacillussp (73.17%),Enterococcussp (64.00%),Escherichia coli(62.11%),Salmonellasp (62.11%),Klebsiellasp (51.29%),Micrococcus sp(44.94%) andCampylobactersp (43.52%). SPSS analysis using the one way ANOVA showed no significant difference (P>0.05) in bacteria isolated from markets in the three Senatorial Zones of the State.Staphylococcussp was isolated in 61.11% of the tables, 50.00% of vehicles, 41.67% of knives and 46.32% of water;Salmonellasp was isolated in 47.22% of the tables, 36.11% of vehicles, 30.56% of knives and 43.85% of water;Bacillussp was isolated in 41.67% of the tables, 44.44% of vehicles, 33.33% of knives and 23.70% of water;Campylobactersp was isolated in 27.78% of the tables, 25.00% of vehicles, 30.56% of knives and none in water. There was no significant difference (P>0.05) in bacteria isolated from the contact surfaces and water from the markets in the three zones of the State.

Conclusion

The bacteriological quality of meat in markets in Abia State could be said to be poor due to the isolation of Indicator bacteria such asE, coli, Salmonella and Campylobacterfrom the studied meat samples. The presence ofE. coliin the studied meat samples is an indicator of feacal contamination and a red alert for the Public health sector. It is recommended that meat sellers undergo proper training and regularly update their knowledge of meat safety.
Prevalence of polymyxin resistant bacterial strains in India: a systematic review and meta-analysis
Authors: Dwibedy Sambit K. , Padhy Indira , Panda Aditya K. , Mohapatra Saswat S.
Published: October 2023

Introduction

Polymyxins, the cationic lipopeptide antibiotics, are the last line of therapeutics against the MDR Gram-negative bacterial (GNB) pathogens. Unfortunately, the rising cases of polymyxin-resistant strains from across the globe have adversely impacted their utility. While the molecular mechanisms responsible for developing polymyxin resistance (PolR) are largely understood, the prevalence of PolRstrains in India has not been investigated systematically. The current study was undertaken to primarily determine the prevalence of PolRstrains in India. Moreover, the extent of the spread of mobile colistin resistance (mcr) genes among the GNB strains in India was also determined.

Method

A systematic search for articles using the relevant inclusion and exclusion criteria was performed in the applicable databases for the period January 2015 to December 2023. The included 41 studies were subjected to a meta-analysis using the Comprehensive Meta-Analysis software (V.4). Publication biases were assessed using funnel plots and Egger’s regression analysis.

Result

Considering a total of 41 studies including 24,589 bacterial isolates the present meta-analysis found the rate of PolRbacteria in India to be at 15.0% (95% CI: 11.2 to 19.8). Among the Indian States, Tamil Nadu topped with the highest prevalence of PolRat 28.3%. Investigating the contribution of themcrgenes, it was observed that among the PolRstrains, 8.4% (95% CI: 4.8 to 14.3) weremcrpositive.

Conclusion

The study determined the prevalence of PolRstrains in India at 15.0% which is higher than that of the global average at 10%. The study also determined that 8.4% of the PolRstrains carried themcrgenes. Themcr-positive strains reported from India could be an underestimation of the actual numbers due to the non-inclusion ofmcrscreening in many previous studies. This study provides insight into the state of the PolRsituation in India, which may be useful to develop a monitoring strategy to contain the spread of such strains and preserve the efficacy of the polymyxins.
Brain-targeted autoimmunity is strongly associated with Long COVID and its chronic fatigue syndrome as well as its affective symptoms
Authors: Almulla Abbas F. , Maes Michael , Zhou Bo , Al-Hakeim Hussein K. , Vojdani Aristo
Published: October 2023

Background

Autoimmune responses contribute to the pathophysiology of Long COVID, affective symptoms and myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS).

Objectives

To examine whether Long COVID, and its accompanying affective symptoms and CFS are associated with immunoglobulin (Ig)A/IgM/IgG directed at neuronal proteins including myelin basic protein (MBP), myelin oligodendrocyte glycoprotein (MOG), synapsin, α+β-tubulin, neurofilament protein (NFP), cerebellar protein-2 (CP2), and the blood-brain-barrier-brain-damage (BBD) proteins claudin-5 and S100B.

Methods

IgA/IgM/IgG to the above neuronal proteins, human herpes virus-6 (HHV-6) and Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) were measured in 90 Long COVID patients and 90 healthy controls, while C-reactive protein (CRP), and advanced oxidation protein products (AOPP) in association with affective and CFS ratings were additionally assessed in a subgroup thereof.

Results

Long COVID is associated with significant increases in IgG directed at tubulin (IgG-tubulin), MBP, MOG and synapsin; IgM-MBP, MOG, CP2, synapsin and BBD; and IgA-CP2 and synapsin. IgM-SARS-CoV-2 and IgM-HHV-6 antibody titers were significantly correlated with IgA/IgG/IgM-tubulin and -CP2, IgG/IgM-BBD, IgM-MOG, IgA/IgM-NFP, and IgG/IgM-synapsin. Binary logistic regression analysis shows that IgM-MBP and IgG-MBP are the best predictors of Long COVID. Multiple regression analysis shows that IgG-MOG, CRP and AOPP explain together 41.7% of the variance in the severity of CFS. Neural network analysis shows that IgM-synapsin, IgA-MBP, IgG-MOG, IgA-synapsin, IgA-CP2, IgG-MBP and CRP are the most important predictors of affective symptoms due to Long COVID with a predictive accuracy of r=0.801.

Conclusion

Brain-targeted autoimmunity contributes significantly to the pathogenesis of Long COVID and the severity of its physio-affective phenome.
Cerebrovascular Lesion Loads and Accelerated Brain Ageing: Insights into the Cognitive Spectrum
Authors: Beheshti Iman , Potvin Olivier , Dadar Mahsa , Duchesne Simon ,   the CCNA Group
Published: October 2023

INTRODUCTION

White matter hyperintensities (WMHs) and cerebral microbleeds are widespread among aging population and linked with cognitive deficits in mild cognitive impairment (MCI), vascular MCI (V-MCI), and Alzheimer’s disease without (AD) or with a vascular component (V-AD). In this study, we aimed to investigate the association between brain age, which reflects global brain health, and cerebrovascular lesion load in the context of pathological aging in diverse forms of clinically-defined neurodegenerative conditions.

METHODS

We computed brain-predicted age difference (brain-PAD: predicted brain age minus chronological age) in the Comprehensive Assessment of Neurodegeneration and Dementia cohort of the Canadian Consortium on Neurodegeneration in Aging including 70 cognitively intact elderly (CIE), 173 MCI, 88 V-MCI, 50 AD, and 47 V-AD using T1-weighted magnetic resonance imaging (MRI) scans. We used a well-established automated methodology that leveraged fluid attenuated inversion recovery MRIs for precise quantification of WMH burden. Additionally, cerebral microbleeds were detected utilizing a validated segmentation tool based on the ResNet50 network, utilizing routine T1-weighted, T2-weighted, and T2* MRI scans.

RESULTS

The mean brain-PAD in the CIE cohort was around zero, whereas the four categories showed a significantly higher mean brain-PAD compared to CIE. The brain-PAD was significantly correlated with WMHs in all groups.

DISCUSSION

WMHs were associated with faster brain ageing and should be considered as a risk factor which imperils brain health in aging and exacerbate brain abnormalities in the context of neurodegeneration of presumed AD origin. Our findings underscore the significance of novel research endeavors aimed at elucidating the etiology, prevention, and treatment of WMH in the area of brain ageing.

Highlights

We assessed the association between brain-PAD and cerebrovascular lesion loads in aging and AD.

There were noticeably links between brain-PAD and WMH loads.

The structure of the ageing brain is associated with WMHs.

WMH needs to be taken into account as a risk factor that increase the brain age in aging and AD.
Novel Alzheimer’s disease genes and epistasis identified using machine learning GWAS platform
Authors: Lundberg Mischa , Sng Letitia M.F. , Szul Piotr , Dunne Rob , Bayat Arash , Burnham Samantha C. , Bauer Denis C. , Twine Natalie A. ,   the Alzheimer’s Disease Neuroimaging Initiative
Published: October 2023

Alzheimer’s disease (AD) is a complex genetic disease, and variants identified through genome-wide association studies (GWAS) explain only part of its heritability. Epistasis has been proposed as a major contributor to this ‘missing heritability’, however, many current methods are limited to only modelling additive effects. We use VariantSpark, a machine learning (ML) approach to GWAS, and BitEpi, a tool for epistasis detection, to identify AD associated variants and interactions across two independent cohorts, ADNI and UK Biobank. By incorporating significant epistatic interactions, we captured 10.41% more phenotypic variance than logistic regression (LR). We validate the well-established AD loci,APOE, and identify two novel genome-wide significant AD associated loci in both cohorts,SH3BP4andSASH1, which are also in significant epistatic interactions withAPOE. We show that theSH3BP4SNP has a modulating effect on the known pathogenicAPOESNP, demonstrating a possible protective mechanism against AD.SASH1is involved in a triplet interaction with pathogenicAPOESNP andACOT11,where theSASH1SNP lowered the pathogenic interaction effect betweenACOT11andAPOE. Finally, we demonstrate that VariantSpark detects disease associations with 80% fewer controls than LR, unlocking discoveries in well annotated but smaller cohorts.
Egg Consumption and Bone Mass Density among the Elderly: A Scoping Review
Authors: Olagunju Mobolaji T. , Abodunrin Olunike R. , Omotoso Ifeoluwa O. , Adewole Ifeoluwa E. , Ola Oluwabukola M. , Abel Chukwuemeka , Akinsolu Folahanmi T.
Published: October 2023

Introduction

Eggs offer a range of essential nutrients that could potentially support skeletal health as individuals age. Maintaining bone density is crucial for reducing the risk of fractures and improving overall mobility and quality of life in later years. Understanding the potential benefits of habitual egg consumption on bone mass density among older people is essential, given that the natural decline in bone mass density occurs with age. This area of research has not garnered sufficient attention basically because of the mixed reactions and conflicting reports about the safety of egg consumption especially among the elderly hence the scoping review aims to systematically examine the existing literature to map the evidence regarding the association between habitual egg consumption and bone mass density in elderly individuals.

Methodology

The scoping review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines to ensure methodological rigor and transparency. Five electronic databases were searched for published pieces of literature. The review included letters, reviews, observational studies, and experimental studies, while the exclusion criteria were books, grey literature, and publications not in English.

Results

Out of an initial 315 studies found across five databases, 27 duplicates were removed. After screening, 283 studies were excluded as they did not meet the study eligibility criteria. Only two studies were finally included in the review, with two excluded due to focusing on eggshell powder’s impact on elderly bone mass and one involving children.

Conclusion

Limited research on the link between egg consumption and bone mass density in the elderly highlights the need for further investigation. Concerns about cholesterol have overshadowed potential benefits. Given aging populations and bone health challenges, exploring eggs’ role in preventing falls and fractures is essential for a proactive approach to elderly well-being.
Myocardial Characterization for Early Diagnosis, Treatment Response Monitoring, and Risk Assessment in Systemic Light-Chain Amyloidosis
Authors: Clerc Olivier F. , Cuddy Sarah A. M. , Jerosch-Herold Michael , Benz Dominik C , Katznelson Ethan , Canseco Neri Jocelyn , Taylor Alexandra , Kijewski Marie Foley , Bianchi Giada , Ruberg Frederick L. , Di Carli Marcelo F. , Liao Ronglih , Kwong Raymond Y. , Falk Rodney H. , Dorbala Sharmila
Published: October 2023

Abstract

Aims

In systemic light-chain (AL) amyloidosis, cardiac involvement portends poor prognosis. Using myocardial characteristics on magnetic resonance imaging (MRI), this study aimed to detect early myocardial alterations, to analyze temporal changes with plasma cell therapy, and to predict risk of major adverse cardiac events (MACE) in AL amyloidosis.

Methods and Results

Participants with recently diagnosed AL amyloidosis were prospectively enrolled. Presence of AL cardiomyopathy (AL-CMP vs. AL-non-CMP) was determined by abnormal cardiac biomarkers. MRI was performed at baseline and 6 months, with 12-month imaging in AL-CMP cohort. MACE was defined as all-cause death, heart failure hospitalization, or cardiac transplantation. Mayo AL stage was based on troponin T, NT-proBNP, and difference in free light chains. The study cohort included 80 participants (median age 62 years, 58% males). Median left ventricular extracellular volume (ECV) was significantly higher in AL-CMP (53% vs. 30%, p<0.001). ECV was abnormal (>32%) in all AL-CMP and in 47% of AL-non-CMP. ECV tended to increase at 6 months and decreased significantly from 6 to 12 months in AL-CMP (median -3%, p=0.011). ECV was strongly associated with MACE (p<0.001), and improved MACE prediction when added to Mayo AL stage (p=0.002). ECV≤32% identified a cohort without MACE, while ECV>48% identified a cohort with 74% MACE.

Conclusions

In AL amyloidosis, ECV detects subclinical cardiomyopathy. ECV tends to increase from baseline to 6 months and decreases significantly from 6 and 12 months of plasma cell therapy in AL-CMP. ECV provides excellent risk stratification and offers additional prognostic performance over Mayo AL stage.
Unveiling the antiviral capabilities of targeting Human Dihydroorotate Dehydrogenase against SARS-CoV-2
Authors: Purificação Aline D. , Mendonça Sabrina S. , Cruz Luiza V. , Sacramento Carolina Q. , Temerozo Jairo R. , Fintelman-Rodrigues Natalia , Souza de Freitas Caroline , Godoi Bruna F. , Vaidergorn Miguel M. , Leite Juliana Almeida , Salazar Alvarez Luis Carlos , Freitas Murillo V. , Silva Meryck F. B. , Martin Bianca A. , Lopez Renata F. V. , Neves Bruno J. , Costa Fabio T. M. , Souza Thiago M. L. , Emery Flavio da S. , Andrade Carolina Horta , Nonato M. Cristina
Published: October 2023

The urgent need for effective treatments against emerging viral diseases, driven by drug-resistant strains and new viral variants, remains critical. We focus on inhibiting the human dihydroorotate dehydrogenase (HsDHODH), one of the enzymes in charge of pyrimidine nucleotide synthesis. This strategy could impede viral replication without provoking resistance. We evaluated quinone-based compounds, discovering potentHsDHODH inhibition (low nanomolar IC50) and promising in vitro anti-SARS-CoV-2 activity (low micromolar EC50). These compounds exhibited low toxicity, indicating potential for further development. Additionally, we employed computational tools like molecular docking and QSAR models to analyze protein-ligand interactions. These findings represent a significant step forward in the search for effective antiviral treatments and have great potential to impact the development of new broad-spectrum antiviral drugs.
Telomerase RNA structural heterogeneity in living human cells detected by DMS-MaPseq
Authors: Forino Nicholas M. , Woo Jia Zheng , Zaug Arthur J. , Jimenez Arcelia Gonzalez , Edelson Eva , Cech Thomas R. , Rouskin Silvi , Stone Michael D.
Published: October 2023

Telomerase is a specialized reverse transcriptase that uses an intrinsic RNA subunit as the template for telomeric DNA synthesis. Biogenesis of human telomerase requires its RNA subunit (hTR) to fold into a multi-domain architecture that includes the template-containing pseudoknot (t/PK) and the three-way junction (CR4/5). These two hTR domains bind the telomerase reverse transcriptase (hTERT) protein and are thus essential for telomerase catalytic activity. Here, we probe the structure of hTR in living cells using dimethyl sulfate mutational profiling with sequencing (DMS-MaPseq) and ensemble deconvolution analysis. Unexpectedly, approximately 15% of the steady state population of hTR has a CR4/5 conformation lacking features thought to be required for hTERT binding. The proportion of hTR CR4/5 that is folded into the primary functional conformation does not require hTERT expression and the fraction of hTR that assumes a misfolded CR4/5 domain is not refolded by overexpression of its hTERT binding partner. This result suggests a functional role for an RNA folding cofactor other than hTERT during telomerase biogenesis. Mutagenesis demonstrates that stabilization of the alternative CR4/5 conformation is detrimental to telomerase assembly and activity. Moreover, the alternative CR4/5 conformation is not found in telomerase RNP complexes purified from cells via an epitope tag on hTERT, supporting the hypothesis that only the major CR4/5 conformer is active. We propose that this misfolded portion of the cellular hTR pool is either slowly refolded or degraded. Thus, kinetic traps for RNA folding that have been so well-studied in vitro may also present barriers for assembly of ribonucleoprotein complexes in vivo.
A Mathematical Model Simulating the Adaptive Immune Response in Various Vaccines and Vaccination Strategies
Authors: Xu Zhaobin , Song Jian , Zhang Hongmei , Wei Zhenlin , Wei Dongqing , Demongeot Jacques
Published: October 2023

Vaccination is the most effective measure for preventing infectious diseases. Developing an appropriate mathematical model facilitates quantitative research into the activation of adaptive immune responses in the human body by vaccines, thereby providing better guidance for vaccine development. In this study, we have constructed a novel mathematical model to simulate the dynamics of antibody levels following vaccination. Based on principles from immunology, our model provides a concise and accurate representation of the kinetics of antibody response. We have compared the antibody dynamics within the body after administering several common vaccines, including traditional inactivated vaccines, mRNA vaccines, and future attenuated vaccines based on defective interfering viral particles (DVG). Our model explains the crucial role of booster shots in enhancing IgG antibody levels and provides a detailed discussion on the advantages and disadvantages of different vaccine types. From a mathematical standpoint, our model systematically proposes four essential approaches to guide vaccine design: enhancing antigenic T-cell immunogenicity, directing the production of high-affinity antibodies, reducing the rate of IgG decay, and lowering the peak level of vaccine antigen-antibody complexes. Our model contributes to the understanding of vaccine design and its application by explaining various phenomena and providing positive guidance in comprehending the interactions between antibodies and antigenic substances during the immune process.