bluuebunny/crossref_metadata_2025 huggingface dataset:
Precise Monitoring of Returning Sensation in Digital Nerve Lesions by 3-D Imaging: A Proof-of-Concept Study
Authors: Ruewe Marc , Eigenberger Andreas , Klein Silvan M. , von Riedheim Antonia , Gugg Christine , Prantl Lukas , Palm Christoph , Weiherer Maximilian , Zeman Florian , Anker Alexandra M.
Published: March 2023

Digital nerve lesions result in a loss of tactile sensation reflected by an anesthetic area (AA) at the radial or ulnar aspect of the respective digit. Yet, available tools to monitor the recovery of tactile sense have been criticized for their lack of validity. However, the precise quantification of AA dynamics by three-dimensional (3-D) imaging could serve as an accurate surrogate to monitor recovery following digital nerve repair.

For validation, AAs were marked on digits of healthy volunteers to simulate the AA of an impaired cutaneous innervation. Three dimensional models were composed from raw images that had been acquired with a 3-D camera (Vectra H2) to precisely quantify relative AA for each digit (3-D models, n= 80). Operator properties varied regarding individual experience in 3-D imaging and image processing. Additionally, the concept was applied in a clinical case study.

Images taken by experienced photographers were rated better quality (p< 0.001) and needed less processing time (p= 0.020). Quantification of the relative AA was neither altered significantly by experience levels of the photographer (p= 0.425) nor the image assembler (p= 0.749).

The proposed concept allows precise and reliable surface quantification of digits and can be performed consistently without relevant distortion by lack of examiner experience. Routine 3-D imaging of the AA has the great potential to provide visual evidence of various returning states of sensation and to convert sensory nerve recovery into a metric variable with high responsiveness to temporal progress.
Correlation Between Apparent Diffusion Coefficient and the Ki-67 Proliferation Index in Grading Pediatric Glioma
Authors: Yao Rong , Cheng Ailan , Zhang Zhengwei , Jin Biao , Yu Hong
Published: March 2023

Objective

This study aimed to investigate the correlation between apparent diffusion coefficient (ADC) and the Ki-67 proliferation index with the pathologic grades of pediatric glioma and to compare their diagnostic performance in differentiating grades of pediatric glioma.

Patients and Methods

Magnetic resonance imaging examinations and histopathologies of 121 surgically treated pediatric gliomas (87 low-grade gliomas [LGGs; grades 1 and 2] and 34 high-grade gliomas [HGGs; grades 3 and 4]) were retrospectively reviewed. The mean tumor ADC (ADCmean), minimum tumor ADC (ADCmin), tumor/normal brain ADC ratio (ADC ratio), and value of the Ki-67 proliferation index of LGGs and HGGs were compared. Correlation coefficients were calculated for ADC parameters and Ki-67 values. The receiver operating characteristic curve was used to determine the diagnostic value of ADCmean, ADCmin, ADC ratio, and Ki-67 proliferation index for differentiating LGGs and HGGs.

Results

The ADC values were significantly negatively correlated with glioma grade, and the Ki-67 proliferation index had a significant positive correlation with glioma grade. A significant negative correlation was observed between ADCmean and Ki-67 proliferation index, between ADCmin and Ki-67 proliferation index, and between ADC ratio and Ki-67 proliferation index. The receiver operating characteristic analysis demonstrated moderate to good accuracy for ADCmean in discriminating LGGs from HGGs (area under the curve [AUC], 0.875; sensitivity, 79.3%; specificity, 82.4%; accuracy, 80.2%; positive predictive value [PPV], 92.0%; and negative predictive value [NPV], 60.9% [cutoff value, 1.187] [×10−3 mm2/s]). Minimum tumor ADC showed very good to excellent accuracy with AUC of 0.946, sensitivity of 86.2%, specificity of 94.1%, accuracy of 88.4%, PPV of 97.4%, and NPV of 72.7% (cutoff value, 0.970) (×10−3 mm2/s). The ADC ratio showed moderate to good accuracy with AUC of 0.854, sensitivity of 72.4%, specificity of 88.2%, accuracy of 76.9%, PPV of 94.0%, and NPV of 55.6% (cutoff value, 1.426). For the parameter of the Ki-67 proliferation index, in discriminating LGGs from HGGs, very good to excellent diagnostic accuracy was observed (AUC, 0.962; sensitivity, 94.1%; specificity, 89.7%; accuracy, 90.9%; PPV, 97.5%; and NPV, 78.0% [cutoff value, 7]).

Conclusions

Apparent diffusion coefficient parameters and the Ki-67 proliferation index were significantly correlated with histological grade in pediatric gliomas. Apparent diffusion coefficient was closely correlated with the proliferative potential of pediatric gliomas. In addition, ADCmin showed superior performance compared with ADCmean and ADC ratio in differentiating pediatric glioma grade, with a close diagnostic efficacy to the Ki-67 proliferation index.
The risk of pig and chicken farming for carriage and transmission of Escherichia coli containing extended-spectrum beta-lactamase (ESBL) and mobile colistin resistance (mcr) genes in Thailand
Authors: Sudatip Duangdao , Mostacci Nadezda , Tiengrim Surapee , Thamlikitkul Visanu , Chasiri Kittipong , Kritiyakan Anamika , Phanprasit Wantanee , Thinphovong Chuanphot , Abdallah Rim , Baron Sophie Alexandra , Rolain Jean-Marc , Morand Serge , Oppliger Anne , Hilty Markus
Published: March 2023

South-East Asian countries report a high prevalence of extended-spectrum cephalosporin- (ESC-) and colistin-resistant

Escherichia coli

(Col-R-Ec). However, there are still few studies describing the molecular mechanisms and transmission dynamics of ESC-R-Ec and, especially, Col-R-Ec. This study aimed to evaluate the prevalence and transmission dynamics of Ec containing extended spectrum beta-lactamases (ESBL) and mobile colistin resistance (mcr) genes using a 'One Health' design in Thailand. The ESC-R-Ec and Col-R-Ec isolates of human stool samples (69 pig farmers, 155 chicken farmers, and 61 non-farmers), rectal swabs from animals (269 pigs and 318 chickens), and the intestinal contents of 196 rodents were investigated. Resistance mechanisms and transmission dynamics of Ec isolates (n=638) were studied using short and long read sequencing. We found higher rates of ESBL-Ec isolates among pig farmers (n=36; 52.2%) than among chicken farmers (n=58; 37.4 %; P<0.05) and the control group (n=61; 31.1 %; P<0.05). Ec with co-occurring ESBL and mcr genes were found in 17 (6.0 %), 50 (18.6 %) and 15 (4.7 %) samples from humans, pigs and chickens, respectively. We also identified 39 (13.7 %) human samples with non-identical Ec containing ESBL and mcr. We found higher rates of ESBL-Ec, in particular CTX-M-55, isolates among pig farmers than among non-pig farmers (P<0.01). 'Clonal' animal-human transmission of ESBL-Ec and Ec with mcr genes was identified but rare as we overall found a heterogenous population structure of Ec. The Col-R-Ec from human and animal samples often carried mcr-1.1 on conjugative IncX4 plasmids. The latter has been identified in Ec of many different clonal backgrounds.
KIR2DL1gene is a surrogate marker of protection against infection-related hospitalisation among HIV-1 unexposed versus exposed uninfected infants in Cameroon
Authors: Simeni Luc-Aimé Kagoué , Yengo Clauvis Kunkeng , Wouambo Rodrigue Kamga , Fischer Janett , A Bessong Oumarou M’rikam , Fokam Joseph , Assob Nguedia Jules Clément
Published: March 2023

Background

HIV-exposed uninfected infants (HEU) experience appear more vulnerable to infections compared to their HIV-unexposed uninfected (HUU) peers, generally attributed to poor passive immunity acquired from the mother. This may be due to some genetic factors that could alter the immune system. We thus sought to determine the distribution of Killer Cells Immunoglobulin-Like Receptor (KIR) genes in HEU versus HUU, and study the association between KIR profiling and occurrence of infection-related hospitalization.

Methods

A cohort-study was conducted from May 2019 to April 2020 among HEU and HUU, followed-up at birth, week 6, 12, 24 and 48, in reference pediatric centers in Yaounde, Cameroon. Infant HIV status was determined, types of infections were analyzed, and 15 KIR genes were investigated using the sequence specific primer polymerase chain reaction (PCR-SSP) method. Rate of KIR genes and infection-related hospitalizations were compared in HEU versus HUU, with p<0.05 considered statistically significant.

Results

In this cohort, a total of 19 infection-related hospitalizations occurred in 66 infants (14.81%, 04/27 HUU and 38.46%, 15/39 HEU, p=0.037), the majority occurring during the first 24 weeks of life: 10 (25.64%) HEU and 03 (11.11%) HUU, p=0.14. At week 48 (39 HEU and 27 HUU), the relative risk (RR) for infection-related hospitalizations was 2.42 (95% CI: 1.028-5.823) for HEU versus HUU, with aOR 3.59 (95% CI: 1.037-12.448). Incidence of hospitalization was 3.2 (95% CI: 1.63–7.14) per 100 infant-months among HEU versus 1.2 (95% CI: 0.57–3.60) in HUU, and RR was 2.22 (95% CI: 0.50–9.39).KIR2DL1gene was significantly higher in HUU versus HEU (OR= 0.183, 95%CI: 0.053-0.629; p=0.003), and the absence ofKIR2DL1was significantly associated with infection-related hospitalization (p<0.001; OR=0.063; 95%CI: 0.017-0.229).

Conclusion

Compared to HUU, the vulnerability of HEU is driving byKIR2DL1, indicating the protective role of this KIR against infection and hospitalizations.
A standardised differential privacy framework for epidemiological modelling with mobile phone data
Authors: Savi Merveille Koissi , Yadav Akash , Zhang Wanrong , Vembar Navin , Schroeder Andrew , Balsari Satchit , Buckee Caroline O. , Vadhan Salil , Kishore Nishant
Published: March 2023

During the COVID-19 pandemic, the use of mobile phone data for monitoring human mobility patterns has become increasingly common, both to study the impact of travel restrictions on population movement and epidemiological modelling. Despite the importance of these data, the use of location information to guide public policy can raise issues of privacy and ethical use. Studies have shown that simple aggregation does not protect the privacy of an individual, and there are no universal standards for aggregation that guarantee anonymity. Newer methods, such as differential privacy, can provide statistically verifiable protection against identifiability but have been largely untested as inputs for compartment models used in infectious disease epidemiology. Our study examines the application of differential privacy as an anonymisation tool in epidemiological models, studying the impact of adding quantifiable statistical noise to mobile phone-based location data on the bias of ten common epidemiological metrics. We find that many epidemiological metrics are preserved and remain close to their non-private values when the true noise state is less than 20, in a count transition matrix, which corresponds to a privacy-less parameter∈ = 0.05per release. We show that differential privacy offers a robust approach to preserving individual privacy in mobility data while providing useful population-level insights for public health. Importantly, we have built a modular software pipeline to facilitate the replication and expansion of our framework.

Author Summary

Human mobility data has been used broadly in epidemiological population models to better understand the transmission dynamics of an epidemic, predict its future trajectory, and evaluate potential interventions. The availability and use of these data inherently raises the question of how we can balance individual privacy and the statistical utility of these data. Unfortunately, there are few existing frameworks that allow us to quantify this trade-off. Here, we have developed a framework to implement a differential privacy layer on top of human mobility data which can guarantee a minimum level of privacy protection and evaluate their effects on the statistical utility of model outputs. We show that this set of models and their outputs are resilient to high levels of privacy-preserving noise and suggest a standard privacy threshold with an epsilon of 0.05. Finally, we provide a reproducible framework for public health researchers and data providers to evaluate varying levels of privacy-preserving noise in human mobility data inputs, models, and epidemiological outputs.
Carnosine supplementation improves glucose control in adults with pre-diabetes and type 2 diabetes: a randomised controlled trial
Authors: Hariharan Rohit , Cameron James , Menon Kirthi , Mesinovic Jakub , Jansons Paul , Scott David , Lu Zhong X , de Courten Maximilian , Feehan Jack , de Courten Barbora
Published: March 2023

Type 2 diabetes (T2DM) is a major cause of morbidity and mortality globally. Carnosine, a naturally occurring dipeptide, has anti-inflammatory, antioxidant, and anti-glycating effects, with preliminary evidence suggesting it may improve important chronic disease risk factors in adults with cardiometabolic conditions. In this randomized controlled trial, 43 adults (30%F) living with prediabetes or T2DM consumed carnosine (2 grams) or a matching placebo daily for 14 weeks to evaluate its effect on glucose metabolism assessed via 2 hr, 75g oral glucose tolerance test. Secondary outcomes included body composition analysis by dual energy x-ray absorptiometry (DEXA), calf muscle density by pQCT and anthropometry. Carnosine supplementation decreased blood glucose at 90 minutes (−1.31mmol/L; p=0.02) and 120 minutes (−1.60mmol/L, p=0.02) and total glucose area under the curve (−3.30mmol/L; p=0.04) following an oral glucose tolerance test. There were no additional changes in secondary outcomes. The carnosine group results remained significant before and after adjustment for age, sex, and change in weight (all>0.05), and in further sensitivity analyses accounting for missing data. There were no significant changes in insulin levels. Likely mechanisms may include changes to hepatic glucose output explaining the observed reduction in blood glucose without changes in insulin secretion following carnosine supplementation. This study provides preliminary support for larger trials evaluating carnosine as a potential treatment for prediabetes and the early stages of T2DM.
escheR: Unified multi-dimensional visualizations with Gestalt principles
Authors: Guo Boyi , Huuki-Myers Louise A. , Grant-Peters Melissa , Collado-Torres Leonardo , Hicks Stephanie C.
Published: March 2023

The creation of effective visualizations is a fundamental component of data analysis. In biomedical research, new challenges are emerging to visualize multi-dimensional data in a 2D space, but current data visualization tools have limited capabilities. To address this problem, we leverage Gestalt principles to improve the design and interpretability of multi-dimensional data in 2D data visualizations, layering aesthetics to display multiple variables. The proposed visualization can be applied to spatially-resolved transcriptomics data, but also broadly to data visualized in 2D space, such as embedding visualizations. We provide an open source R packageescheR, which is built off of the state-of-the-artggplot2visualization framework and can be seamlessly integrated into genomics toolboxes and workflows.

Availability and implementation

The open source R packageescheRis freely available on Bioconductor (bioconductor.org/packages/escheR).
Analysis of phage therapy failure in a patient with aPseudomonas aeruginosaprosthetic vascular graft infection
Authors: Blasco Lucia , López-Hernández Inmaculada , Rodríguez-Fernández Miguel , Pérez-Florido Javier , Casimiro-Soriguer Carlos S. , Djebara Sarah , Merabishvili Maya , Pirnay Jean-Paul , Rodríguez-Baño Jesús , Tomás María , López Cortés Luis Eduardo
Published: March 2023

Clinical case of a patient with aPseudomonas aeruginosamultidrug-resistant prosthetic vascular graft infection which was treated with a cocktail of phages (PT07, 14/01 and PNM) in combination with ceftazidime-avibactam (CAZ/AVI). After the application of the phage treatment and in absence of antimicrobial therapy, a newP. aeruginosabloodstream infection (BSI) with a septic residual limb metastasis occurred, now involving a wild-type strain being susceptible to ß-lactams and quinolones. Clinical strains were analyzed by microbiology and whole genome sequencing techniques. In relation with phage administration, the clinical isolates ofP. aeruginosabefore phage therapy (HE2011471) and post phage therapy (HE2105886) showed a clonal relationship but with important genomic changes which could be involved in the resistance to this therapy. Finally, phenotypic studies showed a decreased in Minimum Inhibitory Concentration (MIC) to ß-lactams and quinolones as well as an increase of the biofilm production and phage resistant mutants in the clinical isolate ofP. aeruginosapost phage therapy.

Importance

Phage therapy is a promising new treatment against infections produced by multi-drug resistant pathogens. For that, it would be necessary to know more about the clinical response and host-phage interactions by massive sequencing techniques to improve phage therapy application. In this work, we analyzed the clinical, microbiological and molecular features of theP. aeruginosaisolates in prosthetic vascular graft infection after the phages administration failure against this infection. This knowledge could allow to develop strategies of improvement of the use of phage therapy as treatment of multiple clinical infections.
An Ensemble Learning Approach to perform Link Prediction on Large Scale Biomedical Knowledge Graphs for Drug Repurposing and Discovery
Authors: Prabhakar Vignesh , Vu Chau , Crawford Jennifer , Waite Joseph , Liu Kai
Published: March 2023

Generating knowledge graph embeddings (KGEs) to represent entities (nodes) and relations (edges) in large scale knowledge graph datasets has been a challenging problem in representation learning. This is primarily because the embeddings / vector representations that are required to encode the full scope of data in a large heterogeneous graph needs to have a high dimensionality. The orientation of a large number of vectors requires a lot of space which is achieved by projecting the embeddings to higher dimensions. This is not a scalable solution especially when we expect the knowledge graph to grow in size in order to incorporate more data. Any efforts to constrain the embeddings to lower number of dimensions could be problematic as insufficient space to spatially orient the large number of embeddings / vector representations within limited number of dimensions could lead to poor inferencing on downstream tasks such as link prediction which leverage these embeddings to predict the likelihood of existence of a link between two or more entities in a knowledge graph. This is especially the case with large biomedical knowledge graphs which relate several diverse entities such as genes, diseases, signaling pathways, biological functions etc. that are clinically relevant for the application of KGs to drug discovery. The size of the biomedical knowledge graphs are therefore much larger compared to typical benchmark knowledge graph datasets. This poses a huge challenge in generating embeddings / vector representations of good quality to represent the latent semantic structure of the graph. Attempts to circumvent this challenge by increasing the dimensionality of the embeddings often render hardware limitations as generating high dimensional embeddings is computationally expensive and often times infeasible. To practically deal with representing the latent structure of such large scale knowledge graphs (KGs), our work proposes an ensemble learning model in which the full knowledge graph is sampled into several smaller subgraphs and KGE models generate embeddings for each individual subgraph. The results of link prediction from the KGE models trained on each subgraph are then aggregated to generate a consolidated set of link predictions across the full knowledge graph. The experimental results demonstrated significant improvement in rank-based evaluation metrics on task specific link predictions as well as general link predictions on four open-sourced biomedical knowledge graph datasets.
Genomic clustering by geography not species in taxonomically complex British and Irish eyebrights (Euphrasia)
Authors: Ding Yanqian , Metherell Chris , Huang Wu , Hollingsworth Peter M. , Twyford Alex D.
Published: March 2023

Genomic studies of incipient speciation are fundamental to understand the origin and establishment of species. However, a wide range of evolutionary processes and complex evolutionary interactions remain to be explored outside of genetically tractable evolutionary and ecological model systems. Here, we study taxonomically complex British and Irish eyebrights (Euphrasia), as a test case for how different evolutionary factors influence species boundaries across geographic space.Euphrasiais a plant genus that has remarkable diversity in ploidy, mating system and ecology. There are 21 British and IrishEuphrasiaspecies, but with species that are exceptionally difficult to identify based on morphology or DNA barcoding. Here, we test the hypothesis that species boundaries are highly permeable, and taxa experience extensive gene flow despite potential barriers such as ploidy and contrasting mating systems. To understand geographic genetic structure and the nature of species differences, we applied genotyping-by-sequencing (GBS) and spatial-aware clustering methods to 378 population samples from 18 British and Irish species. We find the selfing heathland specialistE. micranthademonstrates genome-wide divergence in Northern Scotland, indicative of a distinct post-glacial colonisation history and the role of a highly selfing mating system in divergence. In contrast, all other genetic clusters correspond to geographic regions, with extensive gene flow between species and a complete absence of species-specific SNPs. Our results reveal the highly permeable species boundaries present in a recently diverging group, with an overriding signal of geographic genetic structure over and above genetic clustering by species.
Induced pluripotent stem cell-derived extracellular vesicles promote wound repair in a diabetic mouse model via an anti-inflammatory immunomodulatory mechanism
Authors: Levy Daniel , Abadchi Sanaz Nourmohammadi , Shababi Niloufar , Ravari Mohsen Rouhani , Pirolli Nicholas H. , Bergeron Cade , Obiorah Angel , Mokhtari-Esbuie Farzad , Gheshlaghi Shayan , Abraham John M. , Smith Ian M. , Powsner Emily , Solomon Talia , Harmon John W. , Jay Steven M.
Published: March 2023

Extracellular vesicles (EVs) derived from mesenchymal stem/stromal cells (MSCs) have recently been widely explored in clinical trials for treatment of diseases with complex pathophysiology. However, production of MSC EVs is currently hampered by donor-specific characteristics and limitedex vivoexpansion capabilities before decreased potency, thus restricting their potential as a scalable and reproducible therapeutic. Induced pluripotent stem cells (iPSCs) represent a self-renewing source for obtaining differentiated iPSC-derived MSCs (iMSCs), circumventing both scalability and donor variability concerns for therapeutic EV production. Thus, we initially sought to evaluate the therapeutic potential of iMSC EVs. Interestingly, while utilizing undifferentiated iPSC EVs as a control, we found that their vascularization bioactivity was similar and their anti-inflammatory bioactivity was superior to donor-matched iMSC EVs in cell-based assays. To supplement this initialin vitrobioactivity screen, we employed a diabetic wound healing mouse model where both the pro-vascularization and anti-inflammatory activity of these EVs would be beneficial. In thisin vivomodel, iPSC EVs more effectively mediated inflammation resolution within the wound bed. Combined with the lack of additional differentiation steps required for iMSC generation, these results support the use of undifferentiated iPSCs as a source for therapeutic EV production with respect to both scalability and efficacy.
Bayesian Inference for Brain Source Imaging with Joint Estimation of Structured Low-rank Noise
Authors: Ghosh Sanjay , Cai Chang , Gao Yijing , Hashemi Ali , Haufe Stefan , Sekihara Kensuke , Raj Ashish , Nagarajan Srikantan S.
Published: March 2023

The inverse problem in brain source imaging is the reconstruction of brain activity from non-invasive recordings of electroencephalography (EEG) and magnetoencephalography (MEG). One key challenge is the efficient recovery of sparse brain activity when the data is corrupted by structured noise that is low-rank noise. This is often the case when there are a few active sources of environmental noise and the MEG/EEG sensor noise is highly correlated. In this paper, we propose a novel robust empirical Bayesian framework which provides us a tractable algorithm for jointly estimating a low-rank noise covariance and brain source activity. Specifically, we use a factor analysis model for the structured noise, and infer a sparse set of variance parameters for source activity, while performing Variational Bayesian inference for the noise. One key aspect of this algorithm is that it does not require any additional baseline measurements to estimate the noise covariance from the sensor data. We perform exhaustive experiments on both simulated and real datasets. Our algorithm achieves superior performance as compared to several existing benchmark algorithms.
Depression: an adaptive disease?
Authors: Yang Shiyu , Zheng Chenqing , Xia Canwei , Gu Langyu
Published: March 2023

Whether depression can be adaptive has long been discussed, but systematically investigation of the adaptive evolution of its genetic basis at the genomic level is sparse. Here, we conducted genome-wide analyses on 320 depression genes at two levels, i.e., across the primate phylogeny (large timescale selection), and in modern human populations (recent selection). We identified seven genes under positive selection in the human lineage, and 46 genes under positive selection in modern human populations. Most positively selected variants in modern human populations were at UTR regions and non-coding exons, indicating the importance of gene expression regulation in the adaptive evolution of depression gene networks. Positively selected genes are not only related to immune response, but also function in reproduction and dietary adaptation, extending the pathogen host defense hypothesis. Notably, the proportion of positively selected depression genes was significantly larger than the proportion of positively selected genes at the genomic level in certain modern human populations. We also identified two positively selected variants that happened to be depression-associated variants. We thus propose that recent selection plays important roles on the adaptive evolution of depression genes. Depression can be adaptive, or is a by-product of evolution, which is still an open question.
Accurate Flow Decomposition via Robust Integer Linear Programming
Authors: Dias Fernando H. C. , Tomescu Alexandru I.
Published: March 2023

Minimum flow decomposition (MFD) is a common problem across various fields of Computer Science, where a flow is decomposed into a minimum set of weighted paths. However, in Bioinformatics applications, such as RNA transcript or quasi-species assembly, the flow is erroneous, since is obtained from noisy read coverages. Typical generalizations of the MFD problem to handle errors are based on least-squares formulations, or on modeling the erroneous flow values as ranges. All of these are thus focused on error-handling at the level of individual edges.

Interpreting the flow decomposition problem as a robust optimization problem, we lift error-handling from individual edges tosolution paths. As such, we introduce a newminimum path-error flow decompositionproblem, for which we give an efficient Integer Linear Programming formulation. Our experimental results reveal that our formulation can account for errors with an accuracy significantly surpassing that of previous error-handling formulations, with computational requirements that remain practical.
The first genome assembly of the amphibian nematode parasite (Aplectana chamaeleonis)
Authors: Han Lei , Liu Tianlu , He Fengping , Hou Zhijun
Published: March 2023

Cosmocercoid nematodes are common parasites of the digestive tract of amphibians. Genomic resources are important for understanding the evolution of a species and the molecular mechanisms of parasite adaptation. So far, no genome resource of Cosmocercoid has been reported. In 2020, a massive Cosmocercoid infection was found in the small intestine of a toad, causing severe intestinal blockage. We morphologically identified this parasite asA. chamaeleonis. Here, we report the firstA. chamaeleonisgenome with a genome size of 1.04 Gb. The repeat content of thisA. chamaeleonisgenome is 72.45 %, and the total length is 751 Mb. This resource is fundamental for understanding the evolution of Cosmocercoid and provides the molecular basis for Cosmocercoid infection and control.
Amorphous calcium phosphate-coated surfaces as a model for bone microenvironment in prostate cancer
Authors: Martin Rebeca San , Das Priyojit , Xue Tianchun , Brown Morgan Rose , Reis Marques Renata Dos , Essington Michael , Gonzalez Adrian , McCord Rachel Patton
Published: March 2023

Bone metastasis remains one of the biggest challenges in the treatment of prostate cancer, and other solid tumors such as breast, lung, and colon. Modeling a complex microenvironment in-vitro, such as the bone niche, requires interrogation of cell-cell interactions, specific extracellular matrix proteins and a high calcium environment. Here, we present a fast and cost-effective system in which commercially available, non-adhesive, cell culture vessels are coated with amorphous calcium phosphate (ACP) as a surrogate for bone matrix. We further present modified protocols for subculturing cells, as well as nucleic acid and protein collection in high calcium samples. We find that prostate epithelial cell lines show increased adhesion and proliferation when cultured in these surfaces, as well as independence from androgen starvation. We observe gene expression changes on ACP surfaces in early adenocarcinoma cell lines which may reflect alterations relevant to prostate cancer progression.

Summary statement

To model the role of calcium in the microenvironment of the metastatic bone niche, we developed a cost-effective way to coat cell culture vessels in bioavailable calcium, and show that it has an effect on prostate cancer cell survival
Compromised beta-cell identity in type 2 diabetes
Authors: Dutta Pritha , Merabet Nadège , Quax Rick , Carlotti Françoise , Sloot Peter M.A.
Published: March 2023

Compromised beta-cell identity is emerging as an important contributor of beta-cell dysfunction in type 2 diabetes (T2D). Several studies suggest that hyperglycemia induces the inactivation of transcription factors involved in mature beta-cell identity. More specifically, chronic hyperglycemia leads to the downregulation of PDX1 and MAFA, two important beta-cell identity transcription factors and regulators of insulin promoter activity. Regulation of these transcription factors depends on interactions between multiple signaling cascades and processes driven by complex non-linear dynamics and taking place in different cellular compartments. To better understand these non-linear dynamics, we developed an integrated mathematical model of the underlying mechanisms regulating these key transcription factors. Our model was able to reproduce experimentally measured variations in the levels of PDX1, MAFA and insulin mRNA under different glucose concentrations. We used this model to simulate scenarios that could allow to restore PDX1 and MAFA levels and therefore insulin gene expression. From these simulations, FOXO1 emerged as an important target for the restoration of beta-cell identity.

Author summary

Glucose regulation depends on the secretion of insulin by beta-cells and uptake of glucose by the peripheral cells mediated by the action of insulin. In type 2 diabetes both aspects can be compromised. Defective insulin secretion results from compromised beta-cell function. One of the reasons behind compromised beta-cell function is that beta-cells fail to express one or more of the genes involved in insulin production and secretion and thus maintenance of beta-cell identity. The processes involved in the regulation of insulin production and secretion are complex. In this work, we are particularly interested in the role and downregulation of transcription factors, PDX1 and MAFA, which are critical regulators of insulin production, in relation with compromised beta-cell identity and function in the presence of chronic hyperglycemia. To understand better these complex processes, we use mathematical modelling which enables to generate hypotheses and simulate scenarios to extend our understanding of the mechanisms leading to compromised beta-cell function in the presence of chronic hyperglycemia. Our model and similar models can serve to identify therapeutical targets in beta-cells in order to restore their function.
ProRafts: A machine-learning predictor forraftophilicity, the protein affinity for biomembrane rafts
Authors: Yurtsever Deniz , Keşmir Can , Sperotto Maria Maddalena
Published: March 2023

Background

Protein raftophilicity refers to the affinity of proteins for cell biomembrane lipid domains, called ‘rafts’. Rafts are fluctuating nanoscale platforms that are enriched in cholesterol and sphingolipids, and that are considered relevant for cell signalling, viral function, and biomembrane trafficking. The dynamic partitioning of proteins into rafts depends on the physical and physico-chemical properties of the biomembranes where such proteins are embedded or attached; however it also depends on specific protein “features”, such as acylation, glypidation, specific amino acid sequence motifs, transmembrane hydrophobic length, and surface accessible area to solvent. In this paper we present a method, and the resulting “ProRafts” predictor, that can be used to predict if a given mammal protein may be “raftophilic” or “non-raftophilic”, without having an a priori knowledge of the physical and physico-chemical properties of the biomembranes where such protein is embedded or attached. ProRafts is based on a machine-learning algorithm, XGBoost, where data regarding the features of known raftophilic human-proteins fed the algorithm.

Results

ProRafts enabled to predict correctly more than 80% of human proteins that area prioriknown to be raftophilic; this is a promising result considering the limited size of the training dataset that we could build with data retrieved from protein databases. In addition, although we used protein features of known human raftophilic proteins, it was possible to identify accurately raft-proteins from othermammalsthan humans, such as mouse and rats. This finding suggests that certain protein features are sufficient to predict raftophilicity of proteins from different species. Moreover, our results indicated that phosphorylation may play a more relevant role for protein raftophilicity than indicated by previous studies.

Conclusion

Raftophilic proteins can be used as biomarkers in medical research, or can serve as targeting sites for therapeutics. In this respect, the machine learning method presented in this paper is a useful tool to guide experimental validations of raftophilicity of proteins in biomembranes, and facilitate the choice of proteins that can be used for experiments on biomimetic membranes.
Shotgun proteomic analysis of the caterpillarLonomia obliqua(Lepitoptera, Saturniidae) hemolymph and effects in rat hippocampal neurons culture
Authors: Maggi Silviane , Pinto Antônio Frederico Michel , Udo Mariana Sayuri Berto , Alencar da Silva Mariana Aguilera , Garcia Raphael Caio Tamborelli , Minetto Luciane , Tasso Leandro , Machado Pablo , Santos Diógenes Santiago , Barcellos Thiago , da Silva Junior Pedro Ismael , Mendonça Ronaldo Zucatelli , Marcourakis Tania , Moura Sidnei
Published: March 2023

Study of substances with potentially neuroprotective has been one of the research focus on drugs development. Toxic proteins ofLonomia obliquacaterpillars, which have caused several accidents in southern Brazil, were identified in the hemolymph with anti-apoptotic activity. This study aims the evaluation of the protein profile and the hemolymph effect on cell viability of rats’ primary cultured hippocampal neurons after apoptosis induction. Semi-quantitative shotgun proteomics approach was used to evaluate the protein profile of 3 caterpillars lots of different origin. Were identified a total of 76 proteins, 71 in hemolymph and 40 in fractions. Antiviral protein predominated in crude hemolymph, following by serine proteases, hemolins and protease inhibitors. In fractions were identified hemolins, serine proteases and protease inhibitors. The treatment of rats’ primary cultured hippocampal neurons with the chromatographic fraction at concentration of 0.05 and 0.10% (v/v) for 24 hours, with subsequently apoptosis induction was able to maintain cell viability significantly higher than positive control. Hemolymph protein composition can show qualitative and quantitative variations intra species when compared different origins animals and consequently exposed to various environmental factors. The results shown on this study may contribute to the identification of proteins with potential use as neuroprotective in degenerative conditions.
Computational design of novel Cas9 PAM-interacting domains using evolution-based modelling and structural quality assessment
Authors: Malbranke Cyril , Rostain William , Depardieu Florence , Cocco Simona , Monasson Rémi , Bikard David
Published: March 2023

We present here an approach to protein design that enables to leverage (i) scarce functional information such as experimental data (ii) evolutionary information learned from a natural sequence variants and (iii) physics-grounded modeling. Using a Restricted Boltzmann Machine (RBM), we learned a sequence model of a protein family. We use semi-supervision to leverage available functional information during the RBM training. We then propose a strategy to explore the protein representation space that can be informed by external models such as an empirical force field method (FoldX). This method was applied to a domain of the Cas9 protein responsible for recognition of a short DNA motif. We experimentally assessed the functionality of 71 variants that were generated to explore a range of RBM and FoldX energies. We show how a combination of functional, structural and evolutionary information can identify functional variants with high accuracy. Sequences with as many as 50 differences (20% of the protein domain) to the wild-type retained functionality. Overall, 21/71 sequences designed with our method were functional. Interestingly, 6/71 sequences showed an improved activity in comparison with the original wild-type protein sequence. These results demonstrate the interest in further exploring the synergies between machine-learning of protein sequence representations and physics grounded modeling strategies informed by structural information.
Inter-rater reliability of the Infectious Disease Modeling Reproducibility Checklist (IDMRC) as applied to COVID-19 computational modeling research
Authors: Pokutnaya Darya , Van Panhuis Willem G , Childers Bruce , Hawkins Marquis S , Arcury-Quandt Alice E , Matlack Meghan , Carpio Kharlya , Hochheiser Harry
Published: March 2023

Background

Infectious disease computational modeling studies have been widely published during the coronavirus disease 2019 (COVID-19) pandemic, yet they have limited reproducibility. Developed through an iterative testing process with multiple reviewers, the Infectious Disease Modeling Reproducibility Checklist (IDMRC) enumerates the minimal elements necessary to support reproducible infectious disease computational modeling publications. The primary objective of this study was to assess the reliability of the IDMRC and to identify which reproducibility elements were unreported in a sample of COVID-19 computational modeling publications.

Methods

Four reviewers used the IDMRC to assess 46 preprint and peer reviewed COVID-19 modeling studies published between March 13th, 2020, and July 31st, 2020. The inter-rater reliability was evaluated by mean percent agreement and Fleiss’ kappa coefficients (κ). Papers were ranked based on the average number of reported reproducibility elements, and average proportion of papers that reported each checklist item were tabulated.

Results

Questions related to the computational environment (mean κ = 0.90, range = 0.90–0.90), analytical software (mean κ = 0.74, range = 0.68–0.82), model description (mean κ = 0.71, range = 0.58–0.84), model implementation (mean κ = 0.68, range = 0.39–0.86), and experimental protocol (mean κ = 0.63, range = 0.58–0.69) had moderate or greater (κ > 0.41) inter-rater reliability. Questions related to data had the lowest values (mean κ = 0.37, range = 0.23–0.59). Reviewers ranked similar papers in the upper and lower quartiles based on the proportion of reproducibility elements each paper reported. While over 70% of the publications provided data used in their models, less than 30% provided the model implementation.

Conclusions

The IDMRC is the first comprehensive, quality-assessed tool for guiding researchers in reporting reproducible infectious disease computational modeling studies. The inter-rater reliability assessment found that most scores were characterized by moderate or greater agreement. These results suggests that the IDMRC might be used to provide reliable assessments of the potential for reproducibility of published infectious disease modeling publications. Results of this evaluation identified opportunities for improvement to the model implementation and data questions that can further improve the reliability of the checklist.
How influenza vaccination changed over the COVID-19 pandemic
Authors: Yang Yong
Published: March 2023

Background

Vaccination for seasonal influenzas is particularly important during the COVID-19 pandemic, but the influenza vaccination coverage in the U.S. was far lower than the targeted rate.

Objective

To examine how people’s actual uptake of the influenza vaccine and the disparity of the vaccination changed during the pandemic.

Methods

A survey was conducted online in November 2022. Respondents were asked for influenza vaccination during each of the three latest seasons, prior influenza vaccination history, and COVID-19 vaccination. A linear regression model was used to estimate how the respondents’ change in influenza vaccination was associated with their demographics, COVID-19 vaccination status, and other related variables.

Results

Nearly 70% of US adults had influenza vaccine each season during past the three seasons of the COVID-19 pandemic. The prevalence of influenza vaccination varied markedly across demographics. Non-Hispanic Black, Hispanic, and people with low educational attainment were more likely to see relatively negative changes in their level of influenza vaccination. Respondents who uptook their COVID-19 vaccine in 2022 increased their level of influenza vaccine more than those who uptook the vaccine in 2021.

Conclusions

Our study indicated that influenza vaccination increased during the pandemic compared with before the pandemic. The disparity of influenza vaccination by race/ethnicity and socioeconomic status may enlarge during the pandemic. Tailored interventions were needed to target some groups to promote their vaccination uptake.
Comparison of Vaccination and Booster Rates and Their Impact on Excess Mortality During the COVID-19 Pandemic in European Countries
Authors: Matveeva Olga , Shabalina Svetlana A.
Published: March 2023

Aim To evaluate the effect of vaccination/booster administration dynamics on the reduction of excess mortality during COVID-19 infection waves in European countries. Methods We selected twenty-nine countries from the OurWorldInData project database according to their population size of more than one million and the availability of information on dominant SARS-CoV-2 variants during COVID-19 infection waves. After selection, we categorized countries according to their ″faster″ or ″slower″ vaccination rates. The first category included countries that reached 60% of vaccinated residents by October 2021 and 70% by January 2022. The second or ″slower″ category included all other countries. In the first or ″faster″ category, two groups, ″boosters faster'' and ″boosters slower″ were created. Pearson correlation analysis, linear regression, and chi-square test for categorical data were used to identify the association between vaccination rate and excess mortality. We chose time intervals corresponding to the dominance of viral variants: Wuhan, Alpha, Delta, and Omicron BA.1/2. Results The ″faster″ countries, as opposed to the ″slower″ ones, did better in protecting their residents from mortality during all periods of the SARS-CoV-2 pandemic and even before vaccination. Perhaps higher GDP per capita contributed to their better performance throughout the pandemic. During mass vaccination, when the Delta variant prevailed, the contrast in mortality rates between the ″faster″ and ″slower″ categories was strongest. The average excess mortality in the ″slower″ countries was nearly 5 times higher than in the ″faster″ countries, and the odds ratio (OR) was 4.9 (95% CI 4.4 to 5.4). Slower booster rates were associated with significantly higher mortality during periods dominated by Omicron BA.1 and BA.2, with an OR of 2.6 (CI 95%. 2.1 to 3.3). Among the European countries we analyzed, Denmark, Norway, and Ireland did best, with a pandemic mortality rate of 0.1% of the population or less. By comparison, Bulgaria, Serbia, and Russia had a much higher mortality rate of up to 1% of the population. Thus, slow vaccination and booster administration was a major factor contributing to an order of magnitude higher excess mortality in ″slower″ European countries compared to more rapidly immunized countries.
Early left ventricular systolic function is a more sensitive predictor of adverse events after heart transplant
Authors: Sun Zhenxing , Cai Yu , Yang Yujia , Huang Lei , Xie Yuji , Zhu Shuangshuang , Wu Chun , Sun Wei , Zhang Ziming , Li Yuman , Wang Jing , Fang Lingyun , Yang Yali , Lv Qing , Dong Nianguo , Zhang Li , Gu Haotian , Xie Mingxing
Published: March 2023

Background

First-phase ejection fraction (EF1) is a novel measure of early systolic function. This study was to investigate the prognostic value of EF1 in heart transplant recipients.

Methods

Heart transplant recipients were prospectively recruited at the Union Hospital, Wuhan, China between January 2015 and December 2019. All patients underwent clinical examination, biochemistry measures [brain natriuretic peptide (BNP) and creatinine] and transthoracic echocardiography. The primary endpoint was a combined event of all-cause mortality and graft rejection.

Results

In 277 patients (aged 48.6±12.5 years) followed for a median of 38.7 (interquartile range: 18.3) months, there were 35 (12.6%) patients had adverse events including 20 deaths and 15 rejections. EF1 was negatively associated with BNP (β=-0.220, p<0.001) and was significantly lower in patients with events compared to those without. EF1 had the largest area under the curve in ROC analysis compared to other measures. An optimal cut-off value of 25.8% for EF1 had a sensitivity of 96.3% and a specificity of 97.1% for prediction of events. EF1 was the most powerful predictor of events with hazard ratio per 1% change in EF1: 0.628 (95%CI: 0.555-0.710, p<0.001) after adjustment for left ventricular ejection fraction and global longitudinal strain.

Conclusions

Early left ventricular systolic function as measured by EF1 is a powerful predictor of adverse outcomes after heart transplant. EF1 may be useful in risk stratification and management of heart transplant recipients.