bluuebunny/crossref_metadata_2025_split huggingface dataset:
Perceptions of Experiences of Recovery After Pancreaticoduodenectomy—A Phenomenographic Interview Study
Authors: Andersson Thomas K. , Engström My , Bjerså Kristofer 
Published: January 2022

Background
Pancreatic surgery in the context of enhanced recovery has mainly been evaluated using clinical variables. However, there is limited knowledge about patients’ perceptions of recovery in this context. Hence, the aim of this study was to explore patients’ perceptions of recovery after pancreatic surgery within an enhanced recovery program.

Objective
To explore the variations in patients’ perceptions, a qualitatively designed study was undertaken.

Methods
Data in this phenomenographic study consisted of interviews with 19 patients at 4 to 6 weeks after surgery.

Results
Recovery after pancreatic surgery was classified into 5 categories, based on patients’ perceptions: to be as before, affected by symptoms, physical activity, understanding the process, and facilitated by other people.

Conclusion
The study focused on various areas of recovery, which took place between the in-hospital and after-discharge phases. Physical experiences were found to disturb recovery, in contrast to social and emotional experiences, which facilitated recovery. Variations in perceptions of recovery suggest that care may need to be more individualized, both in the preoperative and the postoperative phase. Working with realistic expectations and early patient education might better prepare patients to continue working on their own recovery after discharge from the hospital.

Implications for Practice
Major cancer surgery needs to be perceived as a personal journey for the patient, even in enhanced recovery program care. Preoperative education is good, but adding continuous and individually adopted education during recovery is better. Knowledge about both hindering and facilitating factors for recovery is important for the healthcare personal.
New Suturing Approaches for Patients with Nasal Hump and Septal Deviation
Authors: Öztürk Güncel 
Published: January 2022

Abstract
Rhinoplasty is one of the most common applied aesthetic surgery procedures worldwide. Dorsal hump deformity is common in western countries due to nasal profile of Whites. Septal deviation deformity is another common problem among patients who goes through aesthetic and plastic surgery. The estimated rate of septal deviation was reported to be 77% to 90% in the general population. The patients who had dorsal hump and septal deviation simultaneously are considered to be challenging for surgeons. In this research, the author presents new suturing approaches, which would be beneficial in dorsal preservation techniques in terms of fixing the septal deviation. One hundred and seventeen patients met the inclusion and exclusion criteria, and their records were assessed retrospectively. The patients who underwent either push-down and let-down techniques or septorhinoplasty were applied “8” shaped or loop suturing approaches. In addition, 33 patients with the same inclusion and exclusion criteria in whom classical loop suturing technique was used were included in the study. The Rhinoplasty Outcome Evaluation scores ranged between 80 and 100 points after 1 year. The initial median Rhinoplasty Outcome Evaluation score was 52.5, and it increased to 91.00 points at 12 months after surgery (P < 0.001). Patient satisfaction was excellent in 92.68% of the study group, and it was 72.72% in the control group. Excellent satisfaction rate was higher in the study group (P = 0.01). This study is the first to demonstrate the beneficial effects of loop and “8”-shaped sutures in patients who had nasal hump and septal deviation deformities. These suturing approaches will allow better long-term outcomes.
Tuftsin: a natural molecule against SARS-CoV-2 infection
Authors: Huang Jiahao , Wang Jing , Wang Ziyuan , Chu Ming , Wang Yuedan 
Published: January 2022

AbstractCoronavirus disease 2019 (COVID-19) continuously proceeds despite the application of a variety of vaccines. It is still urgent to find effective ways to treat COVID-19. Recent studies indicate that NRP1, an important receptor of the natural peptide tuftsin, facilitates SARS-CoV-2 infection. Importantly, tuftsin is a natural human molecule released from IgG. Here, we found 91 overlapping genes between tuftsin targets and COVID-19-associated genes. Bioinformatics analyses indicated that tuftsin could also target ACE2 and exert some immune-related functions to treat COVID-19. Using surface plasmon resonance (SPR) analysis, we confirmed that tuftsin can bind ACE2 and NRP1 directly. Moreover, tuftsin effectively impairs the binding of SARS-CoV-2 S1 to ACE2. Thus, tuftsin is an attractive drug against COVID-19. And tuftsin as natural immunostimulating peptide in human, we speculate that tuftsin may has crucial roles in asymptomatic carriers or mild cases of COVID-19.
Increased airborne transmission of COVID-19 with new variants, Implications for health policies
Authors: Rowe Bertrand.R. , Canosa André , Meslem Amina , Rowe Frantz 
Published: January 2022

AbstractNew COVID-19 variants, either of higher viral load such as delta or higher contagiousness like omicron, can lead to higher airborne transmission than historical strains. This paper highlights their implications for health policies, based on a clear analytical understanding and modeling of the airborne contamination paths, of the dose following exposure, and the importance of the counting unit for pathogens, itself linked to the dose-response law. Using the counting unit of Wells, i.e. the quantum of contagium, we develop the conservation equation of quanta which allows deriving the value of the quantum concentration at steady state for a well-mixed room. The link with the monitoring concentration of carbon dioxide is made and used for a risk analysis of a variety of situations for which we collected CO2time-series observations. The main conclusions of these observations are that 1) the present norms of ventilation, are both insufficient and not respected, especially in a variety of public premises, leading to high risk of contamination and that 2) air can often be considered well-mixed. Finally, we insist that public health policy in the field of airborne transmission should be based on a multi parameter analysis such as the time of exposure, the quantum production rate, mask wearing and the infector proportion in the population in order to evaluate the risk, considering the whole complexity of dose evaluation. Recognizing airborne transmission requires thinking in terms of time of exposure rather than in terms of proximal distance.HighlightsRelative airborne risk assessment following variant viral load and contagiousnessIndoor analytical risk assessment including absence of ventilationAdequacy of the present norms of ventilation to Covid-19 pandemicObservation of non-compliance to standards concerning CO2Indoor Air Quality
Sparse and Skew Hashing of K-Mers
Authors: Pibiri Giulio Ermanno 
Published: January 2022

AbstractMotivationA dictionary of k-mers is a data structure that stores a set of n distinct k-mers and supports membership queries. This data structure is at the hearth of many important tasks in computational biology. High-throughput sequencing of DNA can produce very large k-mer sets, in the size of billions of strings – in such cases, the memory consumption and query efficiency of the data structure is a concrete challenge.ResultsTo tackle this problem, we describe a compressed and associative dictionary for k-mers, that is: a data structure where strings are represented in compact form and each of them is associated to a unique integer identifier in the range [0, n). We show that some statistical properties of k-mer minimizers can be exploited by minimal perfect hashing to substantially improve the space/time trade-off of the dictionary compared to the best-known solutions.AvailabilityThe C++ implementation of the dictionary is available at https://github.com/jermp/sshash.Contactgiulio.ermanno.pibiri@isti.cnr.it
Hidden multiplicity in the analysis of variance (ANOVA): multiple contrast tests as an alternative
Authors: Hothorn Ludwig A. 
Published: January 2022

AbstractIn bio-medical studies, the p-values of the F-tests in ANOVA are usually interpreted independently as measures of the significance of the associated factors. This ’hidden multiplicity’ effect increases the false positive rate. Therefore, Cramer et al. (2016) proposed the Bonferroni adjustment of the p-values to control for familywise error rate for the experiment. Here, instead of using F-tests, it is alternatively suggested to use multiple contrast tests vs. total mean and to perform multiplicity adjustment by object merging in the interplay between the R-packages emmeans and multcomp. This new approach, denotes as multipleANOM, allows not only to interpret global factor effects but also local effects between factor levels as adjusted p-values or simultaneous confidence intervals for selected effect measures in generalized linear models. R-code is provided by means of selected data examples.
Dietary butyrate treatment enhances healthy metabolites by longitudinal untargeted metabolomic analysis in amyotrophic lateral sclerosis mice
Authors: Ogbu Destiny , Zhang Yongguo , Claud Katerina , Xia Yinglin , Sun Jun 
Published: January 2022

AbstractMicrobial metabolites affect the neuron system and muscle cell functions. Amyotrophic Lateral Sclerosis (ALS) is a multifactorial neuromuscular disease. Our previous study has demonstrated elevated intestinal inflammation and dysfunctional microbiome in ALS patients and an ALS mouse model (human-SOD1G93Atransgenic mice). However, the metabolites in ALS progression are unknown. Using an unbiased global metabolomic measurement and targeted measurement, we investigated the longitudinal changes of fecal metabolites in the SOD1G93Amice over the course of 13 weeks. We compared the changes of metabolites and inflammatory response in age-matched WT and SOD1G93Amice treated with bacterial product butyrate. We found changes in carbohydrate levels, amino acid metabolism, and formation of gamma-glutamyl amino acids. Shifts in several microbially-contributed catabolites of aromatic amino acids agree with butyrate-induced changes in composition of gut microbiome. Declines in gamma-glutamyl amino acids in feces may stem from differential expression of GGT in response to butyrate administration. Due to signaling nature of amino acid-derived metabolites, these changes indicate changes in inflammation (e.g. histamine) and contribute to differences in systemic levels of neurotransmitters (e.g. GABA, glutamate). Butyrate treatment was able to restore some of the healthy metabolites in ALS mice. Moreover, microglia in the spinal cord were measured by the IBA1 staining. Butyrate treatment significantly suppressed the IBA1 level in the SOD1G93Amice. The serum IL-17 and LPS were significantly reduced in the butyrate treated SOD1G93Amice. We have demonstrated an inter-organ communications link among metabolites, inflammation, and ALS progression, suggesting the potential to use metabolites as ALS hallmarks and for treatment.Graphic AbstractWe compared the changes of metabolites and inflammatory response in age-matched WT and SOD1G93Amice treated with bacterial product butyrate. Butyrate treatment was able to restore some of the healthy metabolites in ALS mice. Due to signaling nature of amino acid-derived metabolites, these changes indicate changes in inflammation and contribute to differences in systemic levels of neurotransmitters (e.g. GABA, glutamate). Moreover, butyrate treatment significantly suppressed the microglia IBA1 level and aggregated SOD1G93Ain the SOD1G93Amice. The inflammatory cytokine, e.g serum IL-17, was significantly reduced in the butyrate treated SOD1G93Amice. We have demonstrated an inter-organ communications link among metabolites, inflammation, and ALS progression, suggesting the potential to use metabolites as ALS hallmarks and for treatment.
Analog of the Hutchinson equation in biophysical neurodynamics: from the Morris-Lecar model to a delay differential equation
Authors: Paraskevov Alexander 
Published: January 2022

AbstractStarting with the classical biophysical Morris-Lecar model of neuronal excitability, we introduce a functional analog of the Hutchinson equation initially obtained for population dynamics with delayed negative feedback. It is shown that the resulting equation with a fixed time delay qualitatively reproduces the dynamics of the original model upon direct current stimulation, preserving both the initial type of neuronal excitability and biophysically realistic spike shape within a wide range of the delay values. If the delay becomes very small (2 ms or less), the simplified delay-based model exhibits a distinct transition from the 1st to the 2nd excitability type.
Single-sequence protein structure prediction using supervised transformer protein language models
Authors: Wang Wenkai , Peng Zhenling , Yang Jianyi 
Published: January 2022

AbstractIt remains challenging for single-sequence protein structure prediction with AlphaFold2 and other deep learning methods. In this work, we introduce trRosettaX-Single, a novel algorithm for singlesequence protein structure prediction. It is built on sequence embedding from s-ESM-1b, a supervised transformer protein language model optimized from the pre-trained model ESM-1b. The sequence embedding is fed into a multi-scale network with knowledge distillation to predict inter-residue 2D geometry, including distance and orientations. The predicted 2D geometry is then used to reconstruct 3D structure models based on energy minimization. Benchmark tests show that trRosettaX-Single outperforms AlphaFold2 and RoseTTAFold on natural proteins. For instance, with single-sequence input, trRosettaX-Single generates structure models with an average TM-score ~0.5 on 77 CASP14 domains, significantly higher than AlphaFold2 (0.35) and RoseTTAFold (0.34). Further test on 101 human-designed proteins indicates that trRosettaX-Single works very well, with accuracy (average TM-score 0.77) approaching AlphaFold2 and higher than RoseTTAFold, but using much less computing resource. On 2000 designed proteins from network hallucination, trRosettaX-Single generates structure models highly consistent to the hallucinated ones. These data suggest that trRosettaX-Single may find immediate applications in de novo protein design and related studies. trRosettaX-Single is available through the trRosetta server at: http://yanglab.nankai.edu.cn/trRosetta/.
Live-cell fluorescence lifetime multiplexing using synthetic fluorescent probes
Authors: Frei Michelle S. , Koch Birgit , Hiblot Julien , Johnsson Kai 
Published: January 2022

AbstractFluorescence lifetime multiplexing requires fluorescent probes with distinct fluorescence lifetimes but similar spectral properties. Even though synthetic probes for many cellular targets are available for multicolor live-cell fluorescence microscopy, few of them have been characterized for their use in fluorescence lifetime multiplexing. Here we demonstrate that from a panel of 18 synthetic probes, eight pairwise combinations are suitable for fluorescence lifetime multiplexing in living mammalian cell lines. Moreover, combining multiple pairs in different spectral channels enables us to image up to six different biological targets, effectively doubling the number of observable targets. The combination of synthetic probes with fluorescence lifetime multiplexing is thus a powerful approach for live-cell imaging.
Phylovar: Towards scalable phylogeny-aware inference of single-nucleotide variations from single-cell DNA sequencing data
Authors: Edrisi Mohammadamin , Valecha Monica V. , Chowdary Sunkara B. V. , Robledo Sergio , Ogilvie Huw A. , Posada David , Zafar Hamim , Nakhleh Luay 
Published: January 2022

AbstractSingle-nucleotide variants (SNVs) are the most common variations in the human genome. Recently developed methods for SNV detection from single-cell DNA sequencing (scDNAseq) data, such as SCIΦ and scVILP, leverage the evolutionary history of the cells to overcome the technical errors associated with single-cell sequencing protocols. Despite being accurate, these methods are not scalable to the extensive genomic breadth of single-cell whole-genome (scWGS) and whole-exome sequencing (scWES) data.Here we report on a new scalable method, Phylovar, which extends the phylogeny-guided variant calling approach to sequencing datasets containing millions of loci. Through benchmarking on simulated datasets under different settings, we show that, Phylovar outperforms SCIΦ in terms of running time while being more accurate than Monovar (which is not phylogeny-aware) in terms of SNV detection. Furthermore, we applied Phylovar to two real biological datasets: an scWES triple-negative breast cancer data consisting of 32 cells and 3375 loci as well as an scWGS data of neuron cells from a normal human brain containing 16 cells and approximately 2.5 million loci. For the cancer data, Phylovar detected somatic SNVs with high or moderate functional impact that were also supported by bulk sequencing dataset and for the neuron dataset, Phylovar identified 5745 SNVs with non-synonymous effects some of which were associated with neurodegenerative diseases. We implemented Phylovar and made it publicly available athttps://github.com/mae6/Phylovar.git.
RT-qPCR as a screening platform for mutational and small molecule impacts on structural stability of RNA tertiary structures
Authors: Zafferani Martina , Muralidharan Dhanasheel , Hargrove Amanda E. 
Published: January 2022

AbstractThe exponential increase in the discovery and characterization of RNA tertiary structures has highlighted their active role in a variety of human disease, yet often their interactome and specific function remain unknown. Small molecules offer opportunities to both decode these cellular roles and develop therapeutics, yet there are few examples of small molecules that target biologically relevant RNA tertiary structures. While RNA triple helices are a particular attractive target, discovery of triple helix modulators has been hindered by the lack of correlation between small molecule affinity and effect on structural modulation, thereby limiting the utility of affinity-based screening as a primary filtering method. To address this challenge, we developed a high-throughput RT-qPCR screening platform that reports on the effect of mutations and additives, such as small molecules, on the structuredness of triple helices. Using the 3’-end of the oncogenic non-coding RNA MALAT1 as an example, we demonstrated the applicability of both a two-step and a one-pot method to assess the impact of mutations and small molecules on the stability of the triple helix. Employment of a functional high-throughput assay as a primary screen will significantly expedite the discovery of probes that modulate RNA triple helices structural landscape and, consequently, help gain insight into the roles of these pervasive structures.
Conserved Untranslated Regions of Multipartite Viruses: Natural Markers of Novel Viral Genomic Components and Tags of Viral Evolution
Authors: Zhang Song , Yang Caixia , Wu Jiaxing , Qiu Yuanjian , Xuan Zhiyou , Yang Liu , Liao Ruiling , Liang Xiaofei , Yu Haodong , Ren Fang , Dong Yafeng , Xie Xiaoying , Han Yanhong , Wu Di , Ramos-González Pedro Luis , Freitas-Astúa Juliana , Zhou Changyong , Cao Mengji 
Published: January 2022

AbstractViruses with split genomes are categorized as being either segmented or multipartite according to whether their genomic segments occur in single or different virions. Some complexity will exist, in that inherited “core” vital segments viruses may renew the others once host and environmental alterations keep driving viral evolution. Despite this uncertainty, empirical observations have been made across the split genomes in the untranslated regions (UTRs) on the short or long stretches of conserved or identical sequences. In this study, we describe a methodology that combines RNA and small RNA sequencing, conventional BLASTx, and iterative BLASTn of UTRs to detect viral genomic components even if they encode orphan genes (ORFans). Within the phylum Kitrinoviricota, novel putative multipartite viruses and viral genomic components were annotated using data obtained from our sampling or publicly available sources. The novel viruses, as extensions or intermediate nodes, enriched the information of the evolutionary networks. Furthermore, the diversity of novel genomic components emphasized the evolutionary roles of reassortment and recombination, as well as genetic deletion, strongly supporting the genomic complexity. These data also suggest insufficient knowledge of these genomic components for categorizing some extant viral taxa. The relative conservation of UTRs at the genome level may explain the relationships between monopartite and multipartite viruses and how the multipartite viruses can have a life strategy involving multiple host cells.Author summaryThe current workflows for virus identification are largely based on high-throughput sequencing and coupled protein sequence homology-dependent analysis methods and tools. However, for viruses with split genomes, the identification of genomic components whose deduced protein sequences are not homologous to known sequences is inadequate. Furthermore, many plant-infecting multipartite viruses contain conserved UTRs across their genomic components. Based on this, we propose a practical method of UTR-backed iterative BLASTn (UTR-iBLASTn) to explore the components with ORFans and study virus evolution using the UTRs as signals. These shed light on viral “dark matter”—unknown/omitted genomic components of segmented/multipartite viruses from different kingdoms and hosts, and the origins of these components.
Comparative study of the unbinding process of some HTLV-1 protease inhibitors using Unbiased Molecular Dynamics simulation
Authors: Tiyoula Fereshteh Noroozi , Aryapour Hassan , Moghadam Mostafa Javaheri 
Published: January 2022

AbstractThe HTLV-1 protease is one of the major antiviral targets to overwhelm this virus. Several research groups have been developing protease inhibitors over the years, but none has been successful. In this regard, the development of new HTLV-1 protease inhibitors based on fixing the defects of previous inhibitors will overcome the absence of curative treatment for this oncovirus. Thus, we decided to study the unbinding pathways of the most potent (compound 10, Ki = 15 nM) and one of the weakest (compound 9, Ki = 7900 nM) protease inhibitors, which are very structurally similar, with the PDB IDs: 4YDG, 4YDF, using the Supervised Molecular Dynamics (SuMD) method. In this project, we had various short and long-time-scale simulations, that in total, we could have 12 successful unbindings (a total of 14.8 μs) for the two compounds in both mp forms. This comparative study measured all the essential factors simultaneously in two different inhibitors, which improved our results. This study revealed that Asp32 or Asp32′ in the two forms of mp state similarly exert super power effects on maintaining both potent and weak inhibitors in the binding pocket of HTLV-1 protease. In parallel with the important impact of these two residues, in the potent inhibitor’s unbinding process, His66′ was a great supporter, that was absent in the weak inhibitor’s unbinding pathway. In contrast, in the weak inhibitor’s unbinding process, Trp98/Trp98′ by pi-pi stacking interactions were unfavorable for the stability of the inhibitor in the binding site. In our opinion, these results will assist in designing more potent and effective inhibitors for the HTLV-1 protease.