bluuebunny/crossref_metadata_2025 huggingface dataset:
GIL: A Python package for designing custom indexing primers
Authors: Mateyko Nicholas , Tariq Omar , Chen Xinyi E. , Cheney Will , Salaudeen Asfar Lathif , Luthra Ishika , Nikpour Najmeh , Rafi Abdul Muntakim , Deghan Hadis Kamali , Jensen Cassandra , de Boer Carl
Published: November 2022

Next-generation sequencing allows samples to be multiplexed by adding a unique DNA index to each sample. Multiplexing greatly reduces the price of sequencing large numbers of samples, yet the minimum cost per sample remains high when using commercially available indexing kits and designing custom indexes is challenging. To address these issues, we created GIL (Generate Indexes for Libraries), a software tool that designs indexing primers for producing multiplexed sequencing libraries. GIL can be customized in numerous ways to meet user specifications, including index length, sequencing modality, color balancing, and compatibility with existing primers, and produces ordering and demultiplexing-ready outputs. GIL is written in Python and is freely available on GitHub athttps://github.com/de-Boer-Lab/GIL. It can also be accessed as a web-application implemented in Streamlit athttps://dbl-gil.streamlitapp.com.
Genomic Structural Equation Modeling Reveals Latent Phenotypes in the Human Cortex with Distinct Genetic Architecture
Authors: Morey Rajendra A. , Zheng Yuanchao , Sun Delin , Garrett Melanie E. , Gasperi Marianna , Maihofer Adam X. , Baird Lexi , Grasby Katrina L. , Huggins Ashley , Haswell Courtney C. , Thompson C. Paul M. , Medland Sarah , Gustavson Daniel E. , Panizzon Matthew S. , Kremen William S. , Nievergelt Caroline M. , Ashley-Koch Allison E. , Logue Mark W.
Published: November 2022

Genetic contributions to human cortical structure manifest pervasive pleiotropy. This pleiotropy may be harnessed to identify unique genetically-informed parcellations of the cortex that are neurobiologically distinct from anatomical, functional, cytoarchitectural, or other cortical parcellation schemes. We investigated genetic pleiotropy by applying genomic structural equation modeling (SEM) to model the genetic architecture of cortical surface area (SA) and cortical thickness (CT) of 34 brain regions recently reported in the ENIGMA cortical GWAS. Genomic SEM uses the empirical genetic covariance estimated from GWAS summary statistics with LD score regression (LDSC) to discover factors underlying genetic covariance. Genomic SEM can fit a multivariate GWAS from summary statistics, which can subsequently be used for LD score regression (LDSC). We found the best-fitting model of cortical SA was explained by 6 latent factors and CT was explained by 4 latent factors. The multivariate GWAS of these latent factors identified 74 genome-wide significant (GWS) loci (p<5×10−8), including many previously implicated in neuroimaging phenotypes, behavioral traits, and psychiatric conditions. LDSC of latent factor GWAS results found that SA-derived factors had a positive genetic correlation with bipolar disorder (BPD), and major depressive disorder (MDD), and a negative genetic correlation with attention deficit hyperactivity disorder (ADHD), MDD, and insomnia, while CT factors displayed a negative genetic correlation with alcohol dependence. Jointly modeling the genetic architecture of complex traits and investigating multivariate genetic links across phenotypes offers a new vantage point for mapping genetically informed cortical networks.

HIGHLIGHTS

Genomic SEM can examine genetic correlation across cortical regions.

We inferred regional genetic networks of cortical thickness and surface area.

Network-associated variants have been implicated in multiple traits.

These networks are genetically correlated with several psychiatric disorders including MDD, bipolar, ADHD, and alcohol dependence.
Systematic review of the prevalence of Long Covid
Authors: Woodrow Mirembe , Carey Charles , Ziauddeen Nida , Thomas Rebecca , Akrami Athena , Lutje Vittoria , Greenwood Darren C , Alwan Nisreen A
Published: November 2022

Background

Long Covid occurs in those infected with SARSCoV2 whose symptoms persist or develop beyond the acute phase. We conducted a systematic review to determine the prevalence of persistent symptoms, functional disability or pathological changes in adults or children at least 12 weeks post- infection.

Methods

We searched key registers and databases from 1stJanuary 2020 to 2ndr 2021, limited to publications in English and studies with at least 100 participants. Studies where all participants were critically ill were excluded. Long Covid was extracted as prevalence of at least one symptom or pathology, or prevalence of the most common symptom or pathology, at 12 weeks or later. Heterogeneity was quantified in absolute terms and as a proportion of total variation and explored across pre-defined subgroups (PROSPERO ID CRD42020218351).

Results

120 studies in 130 publications were included. Length of follow-up varied between 12 weeks - 12 months. Few studies had low risk of bias. All complete and subgroup analyses except one had I2≥ 90%, with prevalence of persistent symptoms range of 0% - 93% (pooled estimate 42.1%, 95% prediction interval : 6.8% to 87.9%). Studies using routine healthcare records tended to report lower prevalence of persistent symptoms/pathology than self-report. However, studies systematically investigating pathology in all participants at follow up tended to report the highest estimates of all three. Studies of hospitalised cases had generally higher estimates than community- based studies.

Conclusions

The way in which Long Covid is defined and measured affects prevalence estimation. Given the widespread nature of SARSCoV2 infection globally, the burden of chronic illness is likely to be substantial even using the most conservative estimates.

Funding this systematic review received no specific funding.

Key points

In a systematic review of 130 publications, prevalence estimates of Long Covid (>12 weeks) after SARSCoV2 infection differed according to how persistent symptoms/pathology were identified and measured, and ranged between 0% - 93% (pooled estimate 42.1%, 95% prediction interval: 6.8% to 87.9%).
Too much is too much: influence of former stress levels on food craving and weight gain during the COVID-19 period
Authors: Granger Rachel , Kubis Hans P.
Published: November 2022

The COVID-19 pandemic and associated social restrictions had an extensive effect on peoples’ lives. Increased rates of weight gain were widely reported, as were declines in the general populations’ mental health, including increases in perceived stress. This study investigated whether higher perceived levels of stress during the pandemic were associated with greater levels of weight gain, and whether poor prior levels of mental health were a factor in higher levels of both stress and weight gain during the pandemic. Underlying changes in eating behaviours and dietary consumption were also investigated. During January-February 2021, UK adults (n=179) completed a self-report online questionnaire to measure perceived levels of stress and changes (current versus pre-COVID-19 restrictions) in weight, eating behaviours, dietary consumption, and physical activity. Participants also reported on how COVID-19 had impacted their lives and their level of mental health prior to the pandemic. Participants with higher levels of stress were significantly more likely to report weight gain and twice as likely to report increased food cravings and comfort food consumption (OR=2.3 and 1.9-2.5, respectively). Participants reporting an increase in food cravings were 6-11 times more likely to snack and to have increased consumption of high sugar or processed foods (OR=6.3, 11.2 and 6.3, respectively). Females reported a far greater number of COVID-19 enforced lifestyle changes and both female gender and having poor mental health prior to the pandemic were significant predictors of higher stress and weight gain during the pandemic. Although COVID-19 and the pandemic restrictions were unprecedented, this study suggests that understanding and addressing the disparity of higher perceived stress in females and individuals’ previous levels of mental health, as well as the key role of food cravings, is key for successfully addressing the continuing societal issue of weight gain and obesity.
Neonatal Paenibacilliosis:Paenibacillus thiaminolyticusas a Novel Cause of Neonatal Sepsis with High Risk of Sequelae in Uganda
Authors: Ericson Jessica E. , Burgoine Kathy , Kumbakumba Elias , Ochora Moses , Hehnly Christine , Bajunirwe Francis , Bazira Joel , Fronterre Claudio , Hagmann Cornelia , Kulkarni Abhaya V. , Kumar M. Senthil , Magombe Joshua , Mbabazi-Kabachelor Edith , Morton Sarah U. , Movassagh Mercedeh , Mugamba John , Mulondo Ronald , Natukwatsa Davis , Kaaya Brian Nsubuga , Olupot-Olupot Peter , Onen Justin , Sheldon Kathryn , Smith Jasmine , Ssentongo Paddy , Ssenyonga Peter , Warf Benjamin , Wegoye Emmanuel , Zhang Lijun , Kiwanuka Julius , Paulson Joseph N. , Broach James R. , Schiff Steven J.
Published: November 2022

Paenibacillus thiaminolyticusmay be an underdiagnosed cause of neonatal sepsis. We prospectively enrolled a cohort of 800 neonates presenting with a clinical diagnosis of sepsis at two Ugandan hospitals. Quantitative polymerase chain reaction specific toP. thiaminolyticusand to thePaenibacillusgenus were performed on the blood and cerebrospinal fluid (CSF) of 631 neonates who had both specimen types available. Neonates with virus detected in either specimen type were considered to potentially have paenibacilliosis, (37/631, 6%). We described antenatal, perinatal, and neonatal characteristics, presenting signs, and 12-month developmental outcomes for neonates with paenibacillosis vs. clinical sepsis. Median age at presentation was 3 (interquartile range 1, 7) days. Fever (92%), irritability (84%) and seizures (51%) were common. Eleven (30%) had an adverse outcome: 5 (14%) neonates died during the first year of life; 5 of 32 (16%) survivors developed postinfectious hydrocephalus and one (3%) additional survivor had neurodevelopmental impairment without hydrocephalus. These results highlight the need to consider local pathogen prevalence and the possibility of unusual pathogens when determining antibiotic choice for neonatal sepsis.
Comparison of causal forest and regression-based approaches to evaluate treatment effect heterogeneity: An application for type 2 diabetes precision medicine
Authors: Venkatasubramaniam Ashwini , Mateen Bilal A. , Shields Beverley M , Hattersley Andrew T , Jones Angus G , Vollmer Sebastian J. , Dennis John M.
Published: November 2022

Objective

To compare individualized treatment selection strategies based on predicted individual-level treatment effects from a causal forest machine learning algorithm and a penalized regression model.

Study Design and Setting

Cohort study characterizing individual-level glucose-lowering response (6 month reduction in HbA1c) in people with type 2 diabetes initiating SGLT2-inhibitor or DPP4-inhibitor therapy. Model development set comprised 1,428 participants in the CANTATA-D and CANTATA-D2 trials (SGLT2-inhibitor versus DPP4-inhibitor). For external validation, calibration of observed versus predicted differences in HbA1c in patient strata defined by size of predicted HbA1c benefit was evaluated in 18,741 UK primary care patients (Clinical Practice Research Datalink).

Results

Heterogeneity in treatment effects was detected in trial participants with both approaches (causal forest: 98.6% & penalized regression: 81.7% predicted to have a benefit on SGLT2-inhibitor therapy over DPP4-inhibitor therapy). In validation, calibration was good with penalized regression but sub-optimal with causal forest. A strata with an HbA1c benefit >10 mmol/mol with SGLT2-inhibitors (3.7% of patients, observed benefit 11.0 mmol/mol [95%CI 8.0-14.0]) was identified using penalized regression but not causal forest, and a much larger strata with an HbA1c benefit 5-10 mmol with SGLT2-inhibitors was identified with penalized regression (regression: 20.9% of patients, observed benefit 7.8 mmol/mol (95%CI 6.7-8.9); causal forest 11.6%, observed benefit 8.7 mmol/mol (95%CI 7.4-10.1).

Conclusion

When evaluating treatment effect heterogeneity researchers should not rely on causal forest (or other similar machine learning algorithms) alone, and must compare outputs with standard regression.

What is new?

Question

What is the comparative utility of machine learning compared to standard regression for identifying variation in patient-level outcomes (treatment effect heterogeneity) due to different treatments?

Findings

Causal forest and penalized regression models were developed using trial data to predict glycated hemoglobin [HbA1c]) outcomes with SGLT2-inhibitor and DPP4-inhibitor therapy in 1,428 individuals with type 2 diabetes. In external validation (18,741 patients), penalized regression outperformed causal forest at identifying population strata with a superior glycemic response to SGLT2-inhibitors compared to DPP4-inhibitors.

Implications

Studies estimating treatment effect heterogeneity should not solely rely on machine learning and should compare results with standard regression.
PSKR1 balances the plant growth-defense trade-off in the rhizosphere microbiome
Authors: Song Siyu , Moreira Zayda Morales , Zhang Xuecheng , Diener Andrew C. , Haney Cara H.
Published: November 2022

Microbiota provide benefits to their hosts including nutrient uptake and protection against pathogens. How hosts balance an appropriate immune response to avoid microbiota overgrowth while avoiding autoimmunity is not well understood. Here we show thatArabidopsis pskr1(phytosulfokine receptor 1) loss-of-function mutants display autoimmunity and reduced rhizosphere bacterial growth when inoculated with normally growth-promotingPseudomonas fluorescens. Transcriptional profiling demonstrated that PSKR1 regulates the plant growth-defense trade-off during plant-microbiome interactions: PSKR1 upregulates plant photosynthesis and root growth but suppresses salicylic acid (SA)-mediated defense responses. Genetic epistasis experiments showed thatPSRK1inhibition of microbiota-induced autoimmunity is fully dependent on SA signaling. Finally, using a transgenic reporter, we showed thatP. fluorescensinducesPSKR1expression in roots, suggestingP. fluorescensmight manipulate plant signaling to promote its colonization. Our data demonstrate a genetic mechanism to coordinate beneficial functions of the microbiome while preventing autoimmunity.
GDF3 is an obesity-induced regulator of TGFβ superfamily signaling
Authors: Ramachandran Deepti , Kotikalapudi Nagasuryaprasad , Gipson Gregory R. , Troncone Luca , Vestal Kylie , Maridas David E. , Gulko Anton , Tsai Linus T. , Rosen Vicki , Yu Paul , Thompson Thomas B. , Banks Alexander S.
Published: November 2022

Growth differentiation factor 3 (GDF3) is a relatively understudied member of the TGFβ superfamily that is highly expressed during development. However, the function of GDF3 in adult biology is contentious. We usein vivoapproaches to show that GDF3 loss-of-function in adipose tissue of obese adult mice causes reduced body weight and improved whole-body insulin sensitivity. These effects are accompanied by altered regulation of genes targeted by the TGFβ superfamilyin vivo. Usingin vitroapproaches, we show that GDF3 can influence both arms of the TGFβ superfamily: GDF3 simultaneously inhibits BMP signaling and activates activin-like SMAD 2/3 signaling. We identify the type II receptors mediating this activity. GDF3 binds to the type II receptors BMPR2, ACTRIIA and ACTRIIB and achieves dose-dependent inhibition of multiple BMP proteins including BMP2, BMP7, BMP9, BMP10, and BMP15in vitro. We also find that GDF3 activates TGFβ/activin-like SMAD2/3 signaling. Unbiased expression profiling confirms that GDF3 both attenuates BMP2-regulated gene expression and drives TGFβ/activin-like gene expression. Together these results provide much needed clarity to both the molecular pathways involved in GDF3 signaling and the physiological effects of GDF3 loss of function.
mEnrich-seq: Methylation-guided enrichment sequencing of bacterial taxa of interest from microbiome
Authors: Cao Lei , Kong Yimeng , Fan Yu , Ni Mi , Tourancheau Alan , Ksiezarek Magdalena , Mead Edward A. , Koo Tonny , Gitman Melissa , Zhang Xue-Song , Fang Gang
Published: November 2022

Metagenomics has enabled the comprehensive study of microbiomes. However, many applications would benefit from a method that can sequence specific bacterial taxa of interest (pathogens, beneficial microbes, or low-abundance taxa), but not the vast background of other taxa in a microbiome sample. To address this need, we developed mEnrich-seq, a method that can enrich taxa of interest from metagenomic DNA before sequencing. The core idea is to exploit the self vs. non-self genome differentiation provided by natural bacterial DNA methylation and rationally choose methylation-sensitive restriction enzymes (REs), individually or in combination, to deplete host DNA and most background microbial DNA while enriching bacterial taxa of interest. This core idea is integrated with library preparation procedures in a way that only non-digested DNA libraries are sequenced. We performed in-depth evaluations of mEnrich-seq and demonstrated its use in several applications to enrich (up to 117-fold) genomic DNA of pathogenic or beneficial bacteria from human urine and fecal samples, including several species that are hard to culture or of low abundance. We also assessed the broad applicability of mEnrich-seq and found that 3130 (68.03%) of the 4601 strains with mapped methylomes to date can be targeted by at least one commercially available RE, representing 54.78% of the species examined in this analysis. mEnrich-seq provides microbiome researchers with a versatile and cost-effective approach for selective sequencing of diverse taxa of interest directly from the microbiome.
Functional and structural asymmetry suggest a unifying principle for catalysis in integral membrane-bound pyrophosphatases
Authors: Strauss Jannik , Wilkinson Craig , Vidilaseris Keni , Ribeiro Orquidea , Liu Jianing , Hillier James , Malinen Anssi , Gehl Bernadette , Jeuken Lars J.C. , Pearson Arwen R. , Goldman Adrian
Published: November 2022

Membrane-bound pyrophosphatases (M-PPases) are homodimeric primary ion pumps that couple the transport of Na+- and/or H+across membranes to the hydrolysis of pyrophosphate. Their role in the virulence of protist pathogens likePlasmodium falciparummakes them an intriguing target for structural and functional studies. Here, we show the first structure of a K+-independent M-PPase, asymmetric and time-dependent substrate binding in time-resolved structures of a K+-dependent M-PPase, and demonstrate pumping-before-hydrolysis by electrometric studies. We suggest how key residues in helix 12, 13, and the exit channel loops affect ion selectivity and K+-activation due to a complex interplay of residues that are involved in subunit-subunit communication. Our findings not only explain ion selectivity in M-PPases but also why they display half-of-the-sites reactivity. Based on this we propose, for the first time, a unified and testable model for ion pumping, hydrolysis, and energy-coupling inallM-PPases, including those that pump both Na+and H+.
A rapid platform for 3D patient-derived cutaneous neurofibroma organoid establishment and screening
Authors: Nguyen Huyen Thi Lam , Kohl Emily , Bade Jessica , Eng Stefan E. , Tosevska Anela , Al Shihabi Ahmad , Hong Jenny J. , Dry Sarah , Boutros Paul C. , Panossian Andre , Gosline Sara , Soragni Alice
Published: November 2022

Localized cutaneous neurofibromas (cNFs) are benign tumors that arise in the dermis of patients affected by Neurofibromatosis Type 1 syndrome (NF1). cNFs are fundamentally benign lesions: they do not undergo malignant transformation or metastasize. Nevertheless, in NF1 patients, they can cover a significant proportion of the body, with some individuals developing hundreds to thousands of lesions. cNFs can cause pain, itching, and disfigurement with substantial socio-emotional repercussions. To date, surgical removal or laser desiccation are the only treatment options, but can result in scarring and the leave a potential for regrowth.

To support drug discovery efforts focused on identifying effective systemic therapies for cNF, we introduce an approach to routinely establish and screen cNF tumor organoids. We optimized conditions to support ex vivo growth of genomically-diverse cNFs. Patient-derived cNF organoids closely recapitulate the molecular and cellular heterogeneity of these tumors as measured by immunohistopathology, DNA methylation, RNA-seq and flow cytometry. Our tractable patient-derived cNF organoid platform enables rapid screening of hundreds of compounds in a patient- and tumor-specific manner.
Structured Joint Decomposition (SJD) identifies conserved molecular dynamics across collections of biologically related multi-omics data matrices
Authors: Chen Huan , Liu Jinrui , Sonthalia Shreyash , Stein-O’Brien Genevieve , Xiao Luo , Caffo Brian , Colantuoni Carlo
Published: November 2022

Motivation

It is necessary to develop exploratory tools to learn from the unprecedented volume of high-dimensional multi-omic data currently being produced. We have developed an R package, SJD, which identifies components of variation that are shared across multiple matrices. The approach focuses specifically on variation across the samples/cells within each dataset while incorporating biologist-defined hierarchical structure among input experiments that can spanin vivoandin vitrosystems, multi-omic data modalities, and species.

Results

SJD enables the definition of molecular variation that is conserved across systems, those that are shared within subsets of studies, and elements unique to individual matrices. We have included functions to simplify the construction and visualization of highly complexin silicoexperiments involving many diverse multi-omic matrices from multiple species. Here we apply SJD to decompose four RNA-seq experiments focused on neurogenesis in the neocortex. The public datasets used in this analysis and the conserved transcriptomic dynamics in mammalian neurogenesis that we define here can view viewed and explored together athttps://nemoanalytics.org/p?l=ChenEtAlSJD2022&g=DCX.

Availability and Implementation

The SJD package can be found athttps://chuansite.github.io/SJD.

Contact

hzchenhuan@gmail.com;ccolant1@jhmi.edu

Supplementary information

Supplementary data are available at Bioinformatics online.
SEEK to Identify Super Enhancer-Expression Correlated Pairs using Single-cell Multi-omic Data
Authors: Cai Guoshuai
Published: November 2022

Super enhancers (SEs) drive cell identity and disease related genes. However, current methods for studying associations between SE and gene expression are time consuming, costly and with poor scalability.

This study formulated a computational approach for screening genome-wide SE-expression associations by analyzing single-cell multi-omic data of transcriptome and H3K27ac histone modification. A pipeline was also constructed for an easy workflow application. Further our application study identified expression correlated SEs (eSEs) in brain and found they mark cell types. Moreover, our analysis provided new insights into the functional role of SEs close toKcnip4andNifb1in frontal cortex neurons and CGE derived inhibitory neurons, linking to neuron development and neurological diseases.

Collectively, this study provides a new tool for studying SE-expression associations and identifying significant expression associated SEs, which pave the way for understanding the regulatory role of SEs in gene expression and related cellular and disease development.
Reliable estimation of tree branch lengths using deep neural networks
Authors: Suvorov Anton , Schrider Daniel R.
Published: November 2022

A phylogenetic tree represents hypothesized evolutionary history for a set of taxa. Besides the branching patterns (i.e., tree topology), phylogenies contain information about the evolutionary distances (i.e. branch lengths) between all taxa in the tree, which include extant taxa (external nodes) and their last common ancestors (internal nodes). During phylogenetic tree inference, the branch lengths are typically co-estimated along with other phylogenetic parameters during tree topology space exploration. There are well-known regions of the branch length parameter space where accurate estimation of phylogenetic trees is especially difficult. Several novel studies have recently demonstrated that machine learning approaches have the potential to help solve phylogenetic problems with greater accuracy and computational efficiency. In this study, as a proof of concept, we sought to explore the possibility of machine learning models to predict branch lengths. To that end, we designed several deep learning frameworks to estimate branch lengths on fixed tree topologies from multiple sequence alignments or its representations. Our results show that deep learning methods can exhibit superior performance in some difficult regions of branch length parameter space. For example, in contrast to maximum likelihood inference, which is typically used for estimating branch lengths, deep learning methods are more efficient and accurate when inferring long branches that are associated with distantly related taxa and perform well in the aforementioned challenging regions of the parameter space. Together, our findings represent a next step toward accurate, fast, and reliable phylogenetic inference with machine learning approaches.
The gut microbiome has sexually dimorphic effects on bone tissue energy metabolism and multiscale bone quality in C57BL/6J mice
Authors: Vahidi G. , Moody M. , Welhaven H. , Davidson L. , Walk S. T. , Martin S. , June R. K. , Heveran C. M.
Published: November 2022

The gut microbiome impacts bone mass, implying a disruption to bone homeostasis, yet significant uncertainty remains regarding the impacts of the gut microbiome on remodeling bone cells. The gut microbiome is thought to be essential for normal biomineralization, but the specific consequences of the absent gut microbiome on tissue mineralization and multiscale bone quality are not determined. We hypothesized that bone homeostasis and tissue-scale metabolism, tissue mineralization, and whole-bone biomechanics are altered in germ-free (GF) C57BL/6J mice. Further, because many characteristics of the gut microbiome are sexually dimorphic, we hypothesized that the gut microbiome would show important sex differences with regards to its impact on bone quality. Differences between GF and conventional mouse bone extended from bone tissue metabolism to whole bone biomechanics. Cortical bone tissue from male mice had a greater signature of amino acid metabolism whereas female cells had a greater signature of lipid metabolism. These sex differences were also present in GF mice and were indeed even more stark. GF increased cortical femur bone formation for both sexes and decreased bone resorption and osteoclast density only in females. GF similarly increased cortical femur tissue mineralization and altered collagen structure for both sexes but led to greater gains in distal femur trabecular microarchitecture for males. Whole femur strength was similarly increased with GF for both sexes, but males had a greater increase in modulus. GF did not impact fracture toughness for either sex. The altered bone quality with GF is multifactorial and is likely contributed to by differences in tissue-scale composition as well as lower cortical porosity. Together, these data demonstrate that the gut microbiome influences bone cells and multiscale bone quality, but that the specific relationships that underlie these effects to bone are different for females and males.
Technical Skill Assessment in Minimally Invasive Surgery Using Artificial Intelligence: A Systematic Review
Authors: Pedrett Romina , Mascagni Pietro , Beldi Guido , Padoy Nicolas , Lavanchy Joël L.
Published: November 2022

Objective

To review artificial intelligence (AI) based applications for the assessment of technical skills in minimally invasive surgery.

Background

As technical skill assessment in surgery relies on expert opinion, it is time-consuming, costly, and often lacks objectivity. Analysis of routinely generated data by AI methods has the potential for automatic technical skill assessment in minimally invasive surgery.

Methods

A systematic search of Medline, Embase, Web of Science and IEEE Xplore was performed to identify original articles reporting the use of AI in the assessment of technical skill in minimally invasive surgery. Risk of bias (RoB) and quality of included studies were analyzed according to Quality Assessment of Diagnostic Accuracy Studies criteria and the modified Joanna Briggs Institute checklists, respectively. Findings were reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement.

Results

In total, 1467 articles were identified, 37 articles met eligibility criteria and were analyzed. Motion data extracted from surgical videos (49%) or kinematic data from robotic systems or sensors (46%) were the most frequent input data for AI. Most studies used deep learning (73%) and predicted technical skills using an ordinal assessment scale (73%) with good accuracies in simulated settings. However, all proposed models were in development stage, only 8% were externally validated and 16% showed a low RoB.

Conclusion

AI is promising to automate technical skill assessment in minimally invasive surgery. However, models should be benchmarked on representative datasets using predefined performance metrics and tested in clinical implementation studies.

Mini Abstract

Technical skill assessment in minimally invasive surgery is time consuming and costly. Artificial intelligence is a promising technology to facilitate and automate technical skill assessment. Therefore, this article systematically reviews artificial intelligence applications for the assessment of technical skills in minimally invasive surgery.
Coronary artery disease and prostate cancer share a common genetic risk mediated through Lipoprotein(a)
Authors: Baylis Richard A. , Wang Fudi , Gao Hua , Bell Caitlin , Luo Lingfeng , Nead Kevin T. , Neilan Tomas G. , Ross Elsie Gyang , Klarin Derek , Leeper Nicholas J.
Published: November 2022

Brief unreferenced abstract

The genetics underlying cancer and cardiovascular disease have been studied for decades. However, despite sharing numerous risk factors and pathologic features, the contribution of shared genetics in these diseases remains poorly defined. Our study sought to identify common genetic factors between these two diseases. We found a unique relationship between coronary artery disease and prostate cancer that appears to be mediated through germline genetic variation in LPA.
An investigation of patent-intensive industries and government development strategy: evidence from China
Authors: Chang Xuhua , Jiang Nan , Liu Hairui
Published: November 2022

Purpose

The number of patents in China has grown rapidly in recent years. The purpose of this paper is to investigate how patents impact economic development in China.

Design/methodology/approach

This paper developed an empirical model by using panel data of 42 China's patent-intensive industries to investigate the economic contribution made by Chinese patent-intensive manufacturing industries.

Findings

This paper found that the intensity of valid patents is strongly positively related to economic growth. The intensity of yearly added patents presented an inverse U-shaped and a U-shaped curve with the economy made by China’s patent-intensive industries. The correlativity mainly depended on whether the patent intensity converges near the economic indicators. Meanwhile, from the perspective of input–output efficiency, for China’s patent-intensive industries, R&D institutes were overinvested, followed by R&D intensity and R&D staff.

Originality/value

Investigating patent influence on economic development is quite complex research. Existing studies have mainly focused on patent protection in legal systems, but have not provided a definitive answer to what the real influence is. This study sought to narrow this gap from the patent economy perspective.
Cross-border acquisitions and technological spillover: evidence from European regional clusters
Authors: Sabbadin Elisa , De Noni Ivan , Belussi Fiorenza
Published: November 2022

Purpose

Relying on mergers and acquisition transaction-level data set and adopting a more region-specific approach with a focus on industry-region pairs, this paper aims to examine how cross-border acquisitions (CBAs) have an effect, in terms of technological spillover and collaboration, on European regional clusters.

Design/methodology/approach

Adopting an industry-region pair approach, this study is based on a quantitative analysis of regional clusters belonging to 262 European regions and 25 patenting industries. Different thresholds of industrial specialization are used to identify clustering industries within a region. Invention performance at the regional cluster level is defined through two sets of different measurements to assess the impact of CBAs on invention quantity performance and internal and external technological collaboration.

Findings

The results reveal that CBAs have a positive and significant impact on the number of patents as well as the number of internal and external technological collaborations and that this effect is persistent over time. Furthermore, through exploring the interindustry technological spillover effect of CBAs registered in the same region of a cluster but outside the cluster itself, the authors found that CBAs in a regional cluster are inclined to produce technological spillovers within the cluster but no significant effects in the other industries of the region.

Originality/value

This paper is an attempt to empirically explore CBAs and technological spillover in European regional clusters. Therefore, it contributes to the debate, thanks to the use of an industry-region pair approach.
Consumer attitudes toward ordering from cloud kitchens: a gender and marital status perspective
Authors: Pookulangara Sanjukta , Wen Han , Bharath Josiam
Published: November 2022

Purpose

The purpose of this study was to examine consumer’s perceptions of cloud kitchens, including the influence of perceived innovativeness (PI), utility motivations (price and food varieties), and food safety risk perceptions (FSRP) on trust; the impact of hedonic motivations and trust on attitude; and the moderating effect of FSRP on the relationship between utility motivations (food varieties) and trust. The relationships were examined with respect to gender and marital status.

Design/methodology/approach

The Theory of Consumption Value served as the theoretical underpinning of this study. Data was collected with an online survey (n = 316) using the Qualtrics panel. The partial least squares–structural equation modeling method was used to analyze the survey data.

Findings

PI, utilitarian motivations (price) and hedonic motivations (food varieties) positively influenced trust, whereas trust positively influenced attitude. FSRP negatively impacted trust and moderated the relationship between utility motivations (food varieties) and trust. Hedonic motivations positively influenced consumers’ attitudes. Multi-group analyses highlighted the differences attributed to gender and marital status.

Research limitations/implications

PI and utility motivations influenced trust, supporting the notion that consumers are open to new and convenient ways to order food. Additionally, the negative influence of FSRP on trust and its moderating role on the path between food varieties and trust highlights the importance of FSRP on food consumption. Finally, this study provided insight into the influence of gender and marital status on perceptions of ordering from cloud kitchens.

Originality/value

The cloud kitchen business model is undergoing exponential growth, and this study provides an understanding of cloud kitchens from a consumer’s perspective.
Resourcing, sensemaking and legitimizing: blockchain technology-enhanced market practices
Authors: Russo-Spena Tiziana , Mele Cristina , Pels Jaqueline
Published: November 2022

Purpose

This paper aims to focus on how the use of new technologies disrupts markets. To date, marketing literature has lacked studies investigating the link between market practices and new technologies. The study adopts the blockchain technology (BcT) context to elicit novel technology-enhanced market practices.

Design/methodology/approach

The authors adopt a qualitative multimethod research design to engage in interpretative theorizing. They investigated 77 companies and used the Gioia method for the data coding and analysis.

Findings

The study of the adoption of blockchain prompts three technology-enhanced market practices. The latter offers new ways of resourcing by removing constraints and expanding actors’ network and knowledge to integrate resources; sensemaking by expressing new language and assigning novel meaning to represent markets; and legitimizing, by structuring new rules and trusting new mechanisms to institutionalize markets.

Research limitations/implications

The technology-enhanced market practices are distinct from extant market practices as well as related, thus, enriching and complementing them. Therefore, this work expands the understanding of the mechanisms of how markets work.

Originality/value

This study is the first, to the best of the authors’ knowledge, to focus on how BcT features affect market practices. BcT market practices entail how actors perform, share and interpret symbols and objects and set rules for how markets should work.
Development of a reporter gene assay for antibody dependent cellular cytotoxicity activity determination of anti‐rabies virus glycoprotein antibodies
Authors: Wang Wenbo , Yu Chuanfei , Cui Yongfei , Liu Chunyu , Yang Yalan , Xu Gangling , Wu Gang , Du Jialiang , Fu Zhihao , Guo Luyong , Long Caifeng , Xia Xijie , Li Yuhua , Wang Lan , Wang Youchun
Published: November 2022

Rabies is a viral disease that is nearly 100% fatal once clinical signs and symptoms develop. Post‐exposure prophylaxis can efficiently prevent rabies, and antibody (Ab) induction by vaccination or passive immunization of human rabies immunoglobulin (HRIG) or monoclonal antibodies (mAbs) play an integral role in prevention against rabies. In addition to their capacity to neutralize viruses, antibodies exert their antiviral effects by antibody‐dependent cellular cytotoxicity (ADCC), which plays an important role in antiviral immunity and clearance of viral infections. For antibodies against rabies virus (RABV), evaluation of ADCC activity was neglected. Here, we developed a robust cell‐based reporter gene assay (RGA) for the determination of the ADCC activity of anti‐RABV antibodies using CVS‐N2c‐293 cells, which stably express the glycoprotein (G) of RABV strain CVS‐N2c as target cells, and Jurkat cells, which stably express FcγRⅢa and nuclear factor of activated T cells (NFAT) reporter gene as effector cells (Jurkat/NFAT‐luc/FcγRⅢa cells). The experimental parameters were carefully optimized, and the established ADCC assay was systematically validated according to the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) Q2 guideline. We also evaluated the ADCC activity of anti‐RABV antibodies, including mAbs, HRIG, and vaccine induced antisera, and found that all test antibodies exhibited ADCC activity with varied strengths. The established RGA provides a novel method for evaluating the ADCC of anti‐RABV antibodies.
Imagining and assessing future risks: A dynamic scenario‐based social vulnerability analysis framework for disaster planning and response
Authors: Orru Kati , Klaos Margo , Nero Kristi , Gabel Friedrich , Hansson Sten , Nævestad Tor‐Olav
Published: November 2022

While social vulnerability assessments should play a crucial part in disaster management, there is a lack of assessment tools that retain sensitivity to the situation‐specific dynamics of vulnerabilities emerging in particular hazard scenarios. We developed a novel scenario‐based vulnerability assessment framework together with practitioners in crisis management and assessed the suitability of its components in three past crises and their scenario‐based derivations: a large‐scale power outage, the COVID‐19 pandemic, and a cyber‐attack. Rather than deterministically concluding about vulnerability based on prefixed factors, the framework guides relevant stakeholders to systematically think through categories of vulnerability pertinent to a scenario. We used a table‐top exercise, interviews, and focus groups to demonstrate how the framework broadens the crisis managers’ understanding of the scope of factors that may cause vulnerability, the related sources of information and enables to identify individuals burdened by certain vulnerability mixes. The new framework could be applied to different types of crises to enhance preparedness, demand‐driven relief and rescue during critical events.
Recognising risk, influencing reality and mental healthcare in a regional emergency department
Authors: Craythorn Scott , O'Sullivan Margaret
Published: November 2022

Mental health presentations to EDs continue to rise with associated challenges increasingly understood and well documented. Despite growing evidence of current and ongoing systemic issues contributing to this increase, there continues to be a lack of administrative engagement to address ED attendance, with avoidance and diversion strategies initiated during the COVID‐19 pandemic no longer prioritised, despite growing need. Clear and well‐documented reports of ED overcrowding and bed boarding is evidence that things need to change. Moreover, if innovative action is not taken, the risk and reality is that long waits, poor outcomes and exposure to unsafe conditions will continue. This is broadly unacceptable to consumers, their carers and the broader ED network. Delayed action in seeking alternative solutions only reinforces the risk and reality that people experiencing mental health concerns have ‘Nowhere Else To Go’.
Silencing of V‐ATPase‐E gene causes midgut apoptosis of Diaphorina citri and affects its acquisition of Huanglongbing pathogen
Authors: Guo Chang‐Fei , Qiu Jun‐Hong , Hu Yu‐Wei , Xu Pei‐Ping , Deng Ying‐Qi , Tian Ling , Wei Yi‐Yun , Sang Wen , Liu Yu‐Tao , Qiu Bao‐Li
Published: November 2022

The Asian citrus psyllid, Diaphorina citri Kuwayama, is among the most important pests of citrus. It is the main vector of the Huanglongbing (HLB) pathogen Candidatus Liberibacter asiaticus (CLas), which causes severe losses in citrus crops. Control of D. citri is therefore of paramount importance to reduce the spread of HLB. In this regard, using RNA interference (RNAi) to silence target genes is a useful strategy to control psyllids. In this study, using RNAi, we examined the biological functions of the V‐ATPase subunit E (V‐ATP‐E) gene of D. citri, including its effect on acquisition of CLas. The amino acid sequence of V‐ATP‐E from D. citri had high homology with proteins from other insects. V‐ATP‐E was expressed at all D. citri life stages analyzed, and the expression level in mature adults was higher than that of teneral adults. Silencing of V‐ATP‐E resulted in a significant increase in mortality, reduced body weight, and induced cell apoptosis of the D. citri midgut. The reduced expression of V‐ATP‐E was indicated to inhibit CLas passing through the midgut and into the hemolymph, leading to a majority of CLas being confined to the midgut. In addition, double‐stranded RNA of D. citri V‐ATP‐E was safe to non‐target parasitic wasps. These results suggest that V‐ATP‐E is an effective RNAi target that can be used in D. citri control to block CLas infection.
Assessment of Triozoida limbata (Hemiptera: Triozidae) attacks: morphological and biochemical changes on Psidium guajava plants
Authors: Picanço Mayara M. , Silva Ricardo S. , Azevedo Aristea A. , Lima Lucas L. , Ramos Humberto J. O. , Souza Og F. F. , Carmo Flávia M. S. , Picanço Marcelo C.
Published: November 2022

The relationship between herbivorous insects and plants is an example of an ‘evolutionary arms race’. Understanding the relationship between insects and their host plants makes it possible to determine critical points in the bioecology of these herbivores. In this relationship, insects and plants use strategies to overcome, avoid and mitigate the actions of the other. Insect‐induced galls are one of the great examples of this ‘arms race’. Psyllids (Hemiptera: Psylloidea) are among the most important gall‐inducing insects. The psyllid Triozoida limbata (Triozidae) is an important pest of guava (Psidium guajava) in the Neotropics. Thus, this work aimed to determine the morphological, anatomical, and biochemical changes in guava leaves caused by T. limbata to study the ‘arms race’ between these two organisms. Therefore, the process of gall formation on guava leaves during the psyllid life cycle was described. A comparative study of the anatomy and levels of phytohormones in leaves attacked and not attacked by the psyllid was carried out. It was found that the psyllid attack triggered the plant's defence by increasing the jasmonic acid content in the leaves. The psyllid attack caused anatomical changes in the leaves (such as cell non‐differentiation), allowing the insect to survive feeding on the plant. In addition, the psyllid‐induced galls provided shelter for the psyllid.
Morphometric analysis of the spinal cord of the Sus scrofa (large white and landrace crossbreed)
Authors: Femi‐Akinlosotu Omowumi , Olopade Funmilayo Eniola , Mustapha Oluwaseun , Adekanmbi Adejoke , Olopade James O.
Published: November 2022

The incidence of spinal cord (SC) injury in developed and undeveloped countries is alarming. The pig (Sus scrofa) has been recommended as a suitable research model for translational studies because of its morphophysiological similarities of organ systems with humans. There is a dearth of information on the SC anatomy of the large white and landrace crossbreed (LW‐LC) pigs. We therefore aim to describe the gross morphology and morphometry of its SC. Twelve juvenile LW‐LC pigs (six males and six females) were used. The skin and epaxial muscles were dissected to expose the vertebral column. The SC was carefully harvested by laminectomy, and 13 gross SC morphometric parameters were evaluated. Thirty‐three spinal nerves were seen emanating from either side of the SC by means of dorsal and ventral spinal roots. The overall average of SC length and weight was 36.23 ± 1.01 cm and 16.60 ± 0.58 g, respectively. However, the mean SC length and weight were higher in females compared with males, with SC weight being statistically significant. A positive relationship between SC length and weight was significant for males (p = 0.0435) but not for females (p = 0.42). Likewise, the strength of the relationship between SC length and weight was significant in males (r = 0.82) but not significant in females (r = 0.41). Baseline data for the morphometric features of the spinal cord in the LW‐LC pigs were generated, which will contribute to the knowledge of this species anatomy and useful information on regional anaesthesia that should further strengthen the drive in adopting the pig as a suitable research model for biomedical research.