bluuebunny/crossref_metadata_2025_split huggingface dataset:
Viral genome sequence datasets display pervasive evidence of strand-specific substitution biases that are best described using non-reversible nucleotide substitution models
Authors: Sianga-Mete Rita , Hartnady Penelope , Mandikumba Wimbai Caroline , Rutherford Kayleigh , Currin Christopher Brian , Phelanyane Florence , Stefan Sabina , Pond Sergei L Kosakovsky , Martin Darren Patrick 
Published: December 2022

Abstract

Background
The vast majority of phylogenetic trees are inferred from molecular sequence data (nucleotides or amino acids) using time-reversible evolutionary models which assume that, for any pair of nucleotide or amino acid characters, the relative rate of X to Y substitution is the same as the relative rate of Y to X substitution. However, this reversibility assumption is unlikely to accurately reflect the actual underlying biochemical and/or evolutionary processes that lead to the fixation of substitutions. Here, we use empirical viral genome sequence data to reveal that evolutionary non-reversibility is pervasive among most groups of viruses. Specifically, we consider two non-reversible nucleotide substitution models: (1) a 6-rate non-reversible model (NREV6) in which Watson-Crick complementary substitutions occur at identical relative rates and which might therefor be most applicable to analyzing the evolution of genomes where both complementary strands are subject to the same mutational processes (such as might be expected for double-stranded (ds) RNA or dsDNA genomes); and (2) a 12-rate non-reversible model (NREV12) in which all relative substitution types are free to occur at different rates and which might therefore be applicable to analyzing the evolution of genomes where the complementary genome strands are subject to different mutational processes (such as might be expected for viruses with single-stranded (ss) RNA or ssDNA genomes).
Results
Using likelihood ratio and Akaike Information Criterion-based model tests, we show that, surprisingly, NREV12 provided a significantly better fit to 21/31 dsRNA and 20/30 dsDNA datasets than did the general time reversible (GTR) and NREV6 models with NREV6 providing a better fit than NREV12 and GTR in only 5/30 dsDNA and 2/31 dsRNA datasets. As expected, NREV12 provided a significantly better fit to 24/33 ssDNA and 40/47 ssRNA datasets. Next, we used simulations to show that increasing degrees of strand-specific substitution bias decrease the accuracy of phylogenetic inference irrespective of whether GTR or NREV12 is used to describe mutational processes. However, in cases where strand-specific substitution biases are extreme (such as in SARS-CoV-2 and Torque teno sus virus datasets) NREV12 tends to yield more accurate phylogenetic trees than those obtained using GTR.
Conclusion
We show that NREV12 should, be seriously considered during the model selection phase of phylogenetic analyses involving viral genomic sequences.
Antibacterial, antioxidant potency and chemical composition of essential oils from dried powdered leaves and flowers of Hypericum revolutum subsp. keniense (Schweinf.)
Authors: Sengera Geoffrey Ogeto , Kenanda Evans Okemwa , Onyancha Jared Misonge 
Published: December 2022

Abstract

Hypericum revolutum subsp. keniense is a plant mainly used to treat diarrhoea, rheumatism, nervous disorders and wounds in African traditional medicine. The objective of the current work was to establish antibacterial, antioxidant potency and chemical composition of essential oil from the leaves and flowers of Hypericum revolutum subsp. keniense. The oils were isolated by steam distillation. Antibacterial activity against methicillin-resistant Staphylococcus aureus, Staphylococcus aureus (ATCC 12393), Escherichia coli (ATCC 25922), Acinetobacter baumannii (ATTC 19606), Salmonella Enteritidis (NCTC12023), Salmonella Typhimurium (ATCC 14028), Pseudomonas aeruginosa (ATCC 15442), and Haemophilus influenzae (ATCC 49766) were done by Agar Disk Diffusion and Microtiter Broth Dilution methods. Antioxidant activities of the essentail oils were examined by different methods, DPPH, FRAP and H2O2 assays. Chemical characterization was done using Gas Chromatography interfaced with Mass Spectrometry, Fourier-Transform Infra-red (FTIR) spectroscopy and the quantification of phenolics and flavonoids by Folin-Ciocalteu reagent and Aluminium nitrate respectively. The oils showed potential antibacterial activity with mean zone of inhibition ranging from 20.67 ± 0.33 to 32.00 ± 1.00 mm at 100 % oil concentration against the tested bacteria. Furthermore, the minimum inhibitory concentrations (MICs) in all the tested microorganisms were in the range of 250 to 15.6250 µg/ml. The essential oils derived from th leaves revealed varied antioxidant activity levels with the different methods of assay. The IC50 of values obtained from the three methods, DPPH, FRAP and H2O2 were ˃1000 µg/ml, 0.31 µg/ml and 12.33 µg/ml respectively. Caryophyllene (22.1 %) and 2,3,4-trimethylhexane were the major chemical components of the essential oils derived from the leaves and flowers respectively. FTIR spectroscopy of the essential oils from the leaves and flowers showed similarity at peaks for hydroxyl, unsaturated olefinic and amine functional groups. Further findings indicated that the total phenolic and flavonoid contents essential oils derived from leaves were 130.4 6 ± 10.5 mg GAE/g dry weight and 0.911 ± 0.04 mg CE/g dry weight respectively. It was therefore concluded that essential oils from the leaves and flowers of H. revolutum subsp. keniense have compounds that have antibacterial and antioxidant potency.
Role of endoscopic ultrasound in primary staging and vascular assessment of pancreatic cancer
Authors: El-Nady Mohammed Ali Ahmed , Haridy Mustafa Ahmed , Hassan Amro Metwally , Abdelwahid Saad Rizk , Mohammed Mohammed Fakhry , Ead Khalid Abdelazeem 
Published: December 2022

Abstract
Background: computed tomography (CT) of the abdomen with contrast is the gold standard method for primary evaluation of pancreatic cancer as regard to staging and vascular assessment. however, not all patients can be evaluated by contrast enhanced CT abdomen as those with allergy to the contrast agent, pregnancy, renal impairment, in addition to hazards of radiation and non-availability of tissue sampling in CECT abdomen .so this study was designed to evaluate the diagnostic ability of Endoscopic Ultrasound for staging and vascular assessment of pancreatic cancer in comparison to CT abdomen with contrast.
Method: fifty patients with cancer pancreas were evaluated by EUS as regard staging and vascular invasion. Vascular assessment was done by dividing the EUS findings into three types in accordance with the relationship between tumors and major vessels, type 1, clear invasion, encasement of vessel by a tumor or tumor that contact a vessel wall more than 180o; type2, Abutment, a tumor that contacts a vessel wall but less than 180o and type 3, clear non-invasion, existence of distance between a tumor and a vessel. We regarded types 1 and type 2 as signs of vascular invasion and types 3 as sign of vascular non-invasion, these findings were compared with the findings of CT abdomen with contrast. The endoscopist was blind to CT result before EUS examination.
Result: as regard to staging of pancreatic cancer EUS showed sensitivity, specificity and accuracy of 100% for all. and as regard vascular invasion EUS showed sensitivity, specificity and accuracy of 100% ,95.93% and 96% respectively for venous invasion and 95.65% ,100% and 99.5% for arterial invasion.
Conclusion: EUS can evaluate staging and vascular invasion of pancreatic cancer with very high sensitivity, specificity and accuracy to the extend it can replace CT in primary evaluation, with superiority of EUS due to tissue sampling.
Consistency as a Data Quality Measure for German Corona Consensus items mapped from National Pandemic Cohort Network data collections
Authors: Yusuf Khalid O. , Miljukov Olga , Schoneberg Anne , Hanß Sabine , Wiesenfeldt Martin , Stecher Melanie , Mitrov Lazar , Hopff Sina Marie , Steinbrecher Sarah , Kurth Florian , Bahmer Thomas , Schreiber Stefan , Pape Daniel , Hoffman Anna-Lena , Kohls Mirjam , Störk Stefan , Stubbe Hans Christian , Tebbe Johannes J. , Hellmuth Johannes C. , Erber Johanna , Krist Lilian , Rieg Siegbert , Pilgram Lisa , Vehreschild Jörg J. , Reese Jens-Peter , Krefting Dagmar 
Published: December 2022

Abstract
Background
As a national effort to better understand the current pandemic, three cohorts collect sociodemographic and clinical data from COVID-19 patients from different target populations within the German National Pandemic Cohort Network (NAPKON). Furthermore, the German Corona Consensus Dataset (GECCO) was introduced as a harmonized basic information model for COVID-19 patients in clinical routine. To compare the cohort data with other GECCO-based studies, data items are mapped to GECCO. As mapping from one information model to another is complex, an additional consistency evaluation of the mapped items is recommended to detect possible mapping issues or source data inconsistencies.
Objectives
The goal of this work is to assure high consistency of research data mapped to the GECCO data model. In particular, it aims at identifying contradictions within interdependent GECCO data items of the German national COVID-19 cohorts to allow investigation of possible reasons for identified contradictions. We furthermore aim at enabling other researchers to easily perform data quality evaluation on GECCO-based datasets and adapt to similar data models.
Methods
All suitable data items from each of the three NAPKON cohorts are mapped to the GECCO items. A consistency assessment tool (dqGecco) is implemented, following the design of an existing quality assessment framework, retaining their-defined consistency taxonomies, including logical and empirical contradictions. Results of the assessment are verified independently on the primary data source.
Results
Our consistency assessment tool helped in correcting the mapping procedure and reveals remaining contradictory value combinations within COVID-19 symptoms, vital-signs, and COVID-19 severity. Consistency rates differ between the different indicators and cohorts ranging from 95.84% up to 100%.
Conclusion
An efficient and portable tool capable to discover inconsistencies in the COVID-19 domain has been developed and applied to three different cohorts. As the GECCO dataset is employed in different platforms and studies, the tool can be directly applied there or adapted to similar information models.
A highly selective fluorescent probe for imaging hydrogen sulfide in living HeLa cells
Authors: Zhang Wensheng , Xun Qining , Xing Wenfang , Xu Feng , Liu Xia 
Published: December 2022

Abstract
As an important endogenous gasotransmitter, hydrogen sulfide (H2S) has been implicated with a variety of biological processes and has attracted more and more attention for its key role in a wide range of pathological processes. However, lacking tools for H2S-specific in situ detection, the changes of endogenous H2S levels in the pathological progression of diseases are still unclear. In this work, a turn-on fluorescent probe (BF2-DBS) has been designed and synthesized by two-step reactions using 4-diethylaminosalicylaldehyde and 1,4-dimethylpyridinium iodide as raw materials. Probe BF2-DBS displays high selectivity and sensitivity to H2S with a large Stokes shift and good anti-interference ability. The practical application of probe BF2-DBS to detect endogenous H2S were evaluated in living HeLa cells.
Large-scale deep learning analysis for the early diagnosis of primary immunodeficiencies
Authors: Papanastasiou Giorgos , Yang Guang , Fotiadis Dimitris , Dikaios Nikolaos , Wang Chengjia , Huda Ahsan , Sobolevsky Luba , Sidhu Gurinder , Palumbo Donna 
Published: December 2022

Abstract
Primary immunodeficiency (PID) is a group of heterogeneous disorders resulting from immune system defects. The early PID diagnosis is compromised by the heterogeneous manifestations along with low clinical awareness. Most PID cases are significantly underdiagnosed leading to increased mortality, co-morbidities and healthcare visits and costs. Among PID, combined immunodeficiencies (CID) are characterized by complex immune defects. Common variable immunodeficiency (CVID) is among the most common types of PID. In light of available treatments for CID and CVID, it is critical to systematize their early diagnosis. Our study objectives were two-fold. First, we developed and evaluated an accurate deep learning model to analyze administrative medical claims data from EHRs towards systematizing screening and early identification of CID and CVID. Second, we revealed the most important CID- and CVID-associated clinical phenotypes and their combinations, demonstrating a systematic methodology to improve early identification of these PID. All data were composed of medical claims derived from the Optum® de-identified electronic health record (EHR) database. Four large cohorts were generated: 797, 797, 2,312, and 19,924 CID/CVID cases and equal control sizes in Cohorts 1–4, respectively (a total of 47,660 cases and controls). Two deep learning models were developed (TabMLPNet and TabResNet) and compared against baseline models. Univariate logistic regression was used to calculate odds ratios across all clinical phenotypes and their combinations. The TabMLPNet model showed the highest diagnostic performance across cohorts with sensitivity, specificity, and overall accuracy ranging from 0.82–0.88, 0.82–0.85, and 0.80–0.87, respectively. For the first time, we identified distinctive combinations of antecedent phenotypes associated with CID/CVID per cohort, being consisted of respiratory infections/conditions, genetic anomalies, cardiac defects, autoimmune diseases, blood disorders and malignancies. Most phenotypes emerged were well described in the literature, which validated our findings. Moreover, several less well documented individual phenotypes (i.e., asthma, coagulation defects complicating pregnancy, cancer of lymphoid histiocytic tissue, lymphoid leukemia chronic) were also identified, which can lead to better clinical surveillance of PID. We demonstrated a generalized and accurate method evaluated on a large EHR-derived cohort of CID/CVID cases and controls. Our methodology can lead to the development of new clinical guidelines and pathways for earlier identification of the most important antecedent phenotypes and their combinations, enhance clinical awareness and be used to improve PID diagnosis and outcomes on a population level.
Various forms of programmed cell death, including apoptosis and regulated necrosis, are concurrently activated in retinal ganglion cells after ischemia and reperfusion
Authors: Dvoriantchikova Galina , Adis Emily Victoria , Lypka Karin Rose , Ivanov Dmitry 
Published: December 2022

Abstract

Retinal ischemia–reperfusion (IR) injury – the ultimate consequence of which is retinal ganglion cell (RGC) death – is a common cause of visual impairment and blindness worldwide, largely due to rather ineffective treatments. A special role here belongs to various forms of programmed cell death (PCD), since they can be prevented by inhibiting the activity of the corresponding signaling cascades. To study the PCD pathways in ischemic RGCs, we used a mouse model of retinal IR and a variety of approaches including high-throughput expression profiling (RNA-seq), animals with reduced expression of target genes (FAS and TNFR1/Tnfrsf1a deficient mice), and animals treated with the oral iron chelator deferiprone (1 mg/ml in the drinking water). In our RNA-seq analysis, we utilized RGCs isolated from ischemic and control retinas 24 hours after reperfusion using the two-step immunopanning protocol. Our RNA-seq analysis has shown increased expression in ischemic RGCs compared to control RGCs of many genes that regulate (e.g., Tlr4, Ticam1, Zbp1, Tnfrsf1a, Tnfrsf10b, Fas) or are directly involved in such types of PCD as apoptosis (e.g., Bid, Bcl2l11, Bak1, Hrk, Casp8), necroptosis (Ripk1, Ripk3, Mlkl), pyroptosis (e.g., Pycard, and Casp1), oxytosis/ferroptosis (e.g., Acsl5, Ftl1, Hmox1, Lpcat3, Slc39a14, Steap3), and parthanatos (Parp1). These data indicate that multiple types of PCD are active simultaneously in ischemic RGCs. We found that genetic ablation of death receptors (TNFR1 and FAS) protects RGCs from retinal IR. Our data indicate that the signaling cascades that regulate ferrous iron (Fe2+) metabolism undergo significant changes in ischemic RGCs, leading to retinal damage after IR. All this data set allows us to assume that activation of death receptors and toll-like receptors on the surface of ischemic RGCs, increased ferrous iron (Fe2+) production in these neurons may be responsible for the simultaneous triggering of apoptosis, necroptosis, pyroptosis, oxytosis/ferroptosis, and parthanatos. Thus, therapy is needed that could concurrently regulate the activity of the multiple PCD pathways to significantly reduce RGC death after IR.
Narody Zjednoczone, Stolica Apostolska i „globalny reset”
Authors: Adolphe Jane 
Published: December 2022

Celem niniejszego opracowania jest przyjrzenie się stanowisku Stolicy Apostolskiej w sprawie „globalnego resetu” poprzez analizę jej kluczowych wypowiedzi i działań w odniesieniu do Światowego Forum Ekonomicznego (WEF), będącego organizacją pozarządową (NGO), oraz Organizacji Narodów Zjednoczonych (ONZ), będącej organizacją międzyrządową. Artykuł został podzielony na trzy części. W części pierwszej zdefiniowano pojęcia i przedstawiono tło dyskusji. W części drugiej omówiono i poddano krytyce prace Klausa Schwaba, przewodniczącego Światowego Forum Ekonomicznego (WEF), który postrzega COVID-19 jako impuls do rozpoczęcia procesu określanego przez niego mianem „globalnego resetu”. Część trzecia zawiera przegląd obecnych działań Stolicy Apostolskiej w ramach systemu ONZ, które wydają się ściśle powiązane z inicjatywami WEF dotyczącymi globalnego resetu. Rozważane są zarówno uzasadnienia stanowiska Stolicy Apostolskiej, jak i głosy krytyczne wobec niego. Artykuł stwierdza, że Stolica Apostolska przyjęła pewne inicjatywy globalnego resetu i celowo używa języka podobnego do tego, którym posługuje się WEF, jednocześnie utrzymując, że jej podejście jest ugruntowane w Piśmie Świętym i stanowi roztropne zastosowanie społecznej doktryny Kościoła. Nie wszyscy zgadzają się z taką oceną.
Palladium-catalyzed C-N Coupling Reactions in the Synthesis of Dibenzodiazepines
Authors: Grewal Pooja , Kaur Navjeet 
Published: December 2022

Abstract:
In today’s world, aryl halides have attracted our attention toward the amination
reactions catalyzed by palladium. In this review article, there are some selected developments
in the field of catalysis, and the Buchwald-Hartwig amination reaction is one of them. It is a
common and highly efficient method reported for forming dibenzodiazepines. The readily
accessible precursors and ammonia undergo cross-coupling reactions in the presence of a palladium
catalyst; after that, the intermediate immediately undergoes intramolecular condensation
to give the desired dibenzodiazepines in one step. Due to the structural characteristics of
these compounds, the potential for diversification - principally for functional group incorporation
- is immense. New and advanced technologies are also employed to form these medicinally
important molecules and are reviewed here. Our purpose is to inform the researchers about
recent advances in this protocol for the C-N bond formation, especially used for synthesizing dibenzodiazepines.
Machine Learning in Drug Metabolism Study
Authors: Sinha Krishnendu , Ghosh Jyotirmoy , Sil Parames Chandra 
Published: December 2022

Abstract:Metabolic reactions in the body transform the administered drug into metabolites. These metabolites exhibit diverse biological activities. Drug metabolism is the major underlying cause of drug overdose-related toxicity, adversative drug effects and the drug’s reduced efficacy. Though metabolic reactions deactivate a drug, drug metabolites are often considered pivotal agents for off-target effects or toxicity. On the other side, in combination drug therapy, one drug may influence another drug’s metabolism and clearance and is thus considered one of the primary causes of drug-drug interactions. Today with the advancement of machine learning, the metabolic fate of a drug candidate can be comprehensively studied throughout the drug development procedure. Naïve Bayes, Logistic Regression, k-Nearest Neighbours, Decision Trees, different Boosting and Ensemble methods, Support Vector Machines and Artificial Neural Network boosted Deep Learning are some machine learning algorithms which are being extensively used in such studies. Such tools are covering several attributes of drug metabolism, with an emphasis on the prediction of drug-drug interactions, drug-target-interactions, clinical drug responses, metabolite predictions, sites of metabolism, etc. These reports are crucial for evaluating metabolic stability and predicting prospective drug-drug interactions, and can help pharmaceutical companies accelerate the drug development process in a less resourcedemanding manner than what in vitro studies offer. It could also help medical practitioners to use combinatorial drug therapy in a more resourceful manner. Also, with the help of the enormous growth of deep learning, traditional fields of computational drug development like molecular interaction fields, molecular docking, quantitative structure-toactivity relationship (QSAR) studies and quantum mechanical simulations are producing results which were unimaginable couple of years back. This review provides a glimpse of a few contextually relevant machine learning algorithms and then focuses on their outcomes in different studies.
The Gut Microbiome and Alzheimer’s Disease: A Growing Relationship
Authors: Bou Zerdan Maroun , Hebbo Elsa , Hijazi Ali , El Gemayel Maria , Nasr Janane , Nasr Dayana , Yaghi Marita , Bouferraa Youssef , Nagarajan Arun 
Published: December 2022

Abstract:
Evidence that the gut microbiota plays a key role in the pathogenesis of Alzheimer’s disease is already un-ravelling. The microbiota-gut-brain axis is a bidirectional communication system that is not fully understood but includes neural, immune, endocrine, and metabolic pathways. The progression of Alzheimer’s disease is supported by mechanisms related to the imbalance in the gut microbiota and the development of amyloid plaques in the brain, which are at the origin of Alzheimer's disease. Alterations in the composition of the gut microbiome led to dysregulation in the pathways governing this system. This leads to neurodegeneration through neuroinflammation and neurotransmitter dysregulation. Neurodegeneration and disruption of the blood-brain barrier are frontiers at the origin of Alzheimer’s disease. Furthermore, bacteria populating the gut microbiota can secrete large amounts of amyloid proteins and lipopolysaccharides, which modulate signaling pathways and alter the production of proinflammatory cytokines associated with the pathogenesis of Alz-heimer's disease. Importantly, through molecular mimicry, bacterial amyloids may elicit cross-seeding of misfolding and induce microglial priming at different levels of the brain-gut-microbiota axis. The potential mechanisms of amyloid spreading include neuron-to-neuron or distal neuron spreading, direct blood-brain barrier crossing, or via other cells such as astrocytes, fibroblasts, microglia, and immune system cells. Gut microbiota metabolites, including short-chain fatty acids, pro-inflammatory factors, and neurotransmitters may also affect AD pathogenesis and associated cognitive decline. The purpose of this review is to summarize and discuss the current findings that may elucidate the role of gut microbiota in the development of Alzheimer's disease. Understanding the underlying mechanisms may provide new insights into novel therapeutic strategies for Alzheimer's disease, such as probiotics and targeted oligosaccharides.