bluuebunny/crossref_metadata_2025_split huggingface dataset:
Robotics vs Laparoscopy in Foregut Surgery: Systematic Review and Meta-Analysis Analyzing Hiatal Hernia Repair and Heller Myotomy
Authors: Awshah Sabrina , Mhaskar Rahul , Diab Abdul-Rahman Fadi , Read Meagan , Coughlin Emily , Ganam Samer , Saad Adham R , Sujka Joseph , DuCoin Christopher 
Published: March 2024

BACKGROUND:
Laparoscopic surgery remains the mainstay of treating foregut pathologies. Several studies have shown improved outcomes with the robotic approach. A systematic review and meta-analysis comparing outcomes of robotic and laparoscopic hiatal hernia repairs (HHR) and Heller myotomy (HM) repairs is needed.

STUDY DESIGN:
PubMed, EMBASE, and SCOPUS databases were searched for studies published between January 2010 and November 2022. The risk of bias was assessed using the Cochrane Risk of Bias in Non-Randomized Studies of Interventions tool. Assessed outcomes included intra- and postoperative outcomes. We pooled the dichotomous data using the Mantel-Haenszel random-effects model to report odds ratio (OR) and 95% CIs and continuous data to report mean difference and 95% CIs.

RESULTS:
Twenty-two comparative studies enrolling 196,339 patients were included. Thirteen (13,426 robotic and 168,335 laparoscopic patients) studies assessed HHR outcomes, whereas 9 (2,384 robotic and 12,225 laparoscopic patients) assessed HM outcomes. Robotic HHR had a nonsignificantly shorter length of hospital stay (LOS) (mean difference −0.41, 95% CI −0.87 to −0.05), fewer conversions to open (OR 0.22, 95% CI 0.03 to 1.49), and lower morbidity rates (OR 0.76, 95% CI 0.47 to 1.23). Robotic HM led to significantly fewer esophageal perforations (OR 0.36, 95% CI 0.15 to 0.83), reinterventions (OR 0.18, 95% CI 0.07 to 0.47) a nonsignificantly shorter LOS (mean difference −0.31, 95% CI −0.62 to 0.00). Both robotic HM and HHR had significantly longer operative times.

CONCLUSIONS:
Laparoscopic and robotic HHR and HM repairs have similar safety profiles and perioperative outcomes. Randomized controlled trials are warranted to compare the 2 methods, given the low-to-moderate quality of included studies.
More than a colour; how pigment influences colourblind microbes
Authors: Wessel Gary M. , Xing Lili , Oulhen Nathalie 
Published: March 2024

Many animals have pigments when they themselves cannot see colour. Perhaps those pigments enable the animal to avoid predators, or to attract mates. Maybe even those pigmented surfaces are hosts for microbes, even when the microbes do not see colour. Do some pigments then serve as a chemical signal for a good or bad microbial substrate? Maybe pigments attract or repel various microbe types? Echinoderms serve as an important model to test the mechanisms of pigment-based microbial interactions. Echinoderms are marine benthic organisms, ranging from intertidal habitats to depths of thousands of metres and are exposed to large varieties of microbes. They are also highly pigmented, with a diverse variety of colours between and even within species. Here we focus on one type of pigment (naphthoquinones) made by polyketide synthase, modified by flavin-dependent monoxygenases, and on one type of function, microbial interaction. Recent successes in targeted gene inactivation by CRISPR/Cas9 in sea urchins supports the contention that colour is more than it seems. Here we dissect the players, and their interactions to better understand how such host factors influence a microbial colonization.
This article is part of the theme issue ‘Sculpting the microbiome: how host factors determine and respond to microbial colonization’.
Integrated Analysis Reveals Immunogenic Cell Death in Sepsis-induced Cardiomyopathy
Authors: wang qinxue , huang haobin 
Published: March 2024

AbstractBackgroundSepsis-induced cardiomyopathy (SIC) poses a significant challenge in critical care, necessitating comprehensive understanding and innovative diagnostic approaches. This study explores the immune-related molecular intricacies underlying SIC, employing bioinformatics analyses and machine learning techniques.MethodsRNA-seq and scRNA-seq datasets (GSE79962 and GSE190856) were obtained from the Gene Expression Omnibus (GEO). After initial quality control and preprocessing, scRNA-seq data (GSE190856) were analyzed using the Seurat package, including cell clustering and annotation. The CellChat package was then used to analyze immune cell interactions. Unsupervised clustering of SIC patients was performed based on differentially expressed ICD-related genes (GSE79962). Immune cell infiltration and gene set variation analysis were conducted, and weighted gene co-expression network analysis identified co-expression modules. A predictive signature for SIC was constructed through machine learning methods.ResultsTrough analyzing the GSE190856 scRNA-seq dataset, the communication between macrophages/monocytes and lymphocytes was found to be enhanced in mouse myocardial tissue during the early onset of SIC. Meanwhile, the expression level of ICD-related genes was upregulated in the monocytes infiltrating to the heart. These results suggestted that ICD may play a crucial role in the pathogenesis of SIC, which had been verified by the upregulated expression of ICD-related genes in the hearts of SIC patients in the GSE79962 dataset. The SIC patients were classified to 2 clusters, with cluster 1 exhibited an upregulation of the renin-angiotensin system, while cluster 2 displayed heightened activity in the RIG-I-like receptor signaling pathway. After comparing four machine learning models, the support vector machine (SVM) model exhibited better discrimination for SIC patients. By correlating the expression levels of the five crucial genes contained in this model with the clinical features of SIC patients, we found that JARID2 was negatively related to the Left Ventricular Ejection Fractions, while TNIP2 was negatively related to the variety of inotropes and vasopressors used in the SIC patients.ConclusionThis research unveils the correlation between ICD and SIC, offering insights into immune activity in the hearts during sepsis. The constructed SVM model with selected genes provides a promising molecular strategy for SIC diagnosis.
Economic Evaluation: Costing participatory learning and action cycles with women’s groups to improve feeding, care and dental hygiene for South Asian infants in London
Authors: Zhang Yeqing , Patil Priyanka , Lakhanpaul Monica , Heys Michelle , Chakraborty Subarna , Dwardzweska Joanna , Llewellyn Clare H. , Webb-Martin Kelley , Irish Carol , Archibong Mfon , Gilmour Jenny , Kalungi Phoebe , Skordis Prof Jolene , Manikam Logan , Batura Neha 
Published: March 2024

AbstractBackgroundThe Nurture Early for Optimal Nutrition (NEON) programme was designed to promote equitable early childhood development by educating mothers of South Asian origin in east London on optimal feeding, care, and dental hygiene practices. This study conducts a cost analysis of the NEON programme and evaluates its financial sustainability.MethodsWe conducted an economic costing from the provider perspective and followed a stepdown procedure to identify all costs incurred from December 2019, the initiation of the trial, to May 2023, the completion of final evaluation and dissemination. Costs associated with start-up, implementation, and monitoring and evaluation activities are differentiated. Affordability analysis was conducted with respect to the budget of the local authorities.ResultsThe total cost of NEON design and delivery in Newham and Towe Hamlets was £75,992 ($INT 114,445), with 45% for staff salaries, 50% for material, and 5% for capital investment. The start-up stage cost 57% while the implementation stage cost 43%. The average cost per mother participating in the programme was £409($INT 615). The total cost of trial delivery in Newham accounted for around 0.053% of the borough’s annual child development expenditure, while the total trial cost in Tower Hamlets was equivalent to 0.003% of its’ spending on children’s development.ConclusionThe delivery of NEON is largely within local authorities’ budget for childhood development. The unit cost is expected to decrease when sharing costs are spread across more participants and implementing systems are validated and well developed.
Interventions to reduce opioid use for patients with chronic non-cancer pain in primary care settings: a systematic review and meta-analysis
Authors: Cai Qian , Grigoroglou Christos , Allen Thomas , Chen Teng-Chou , Chen Li-Chia , Kontopantelis Evangelos 
Published: March 2024

AbstractObjectiveThis systematic review and meta-analysis aimed to assess interventions to reduce opioid use for patients with chronic non-cancer pain (CNCP) versus usual care or active controls in primary care settings.MethodsIn this registered study (PROSPERO: CRD42022338458), we searched MEDLINE, Embase PsycInfo, CINAHL, and Cochrane Library from inception to December 28th2021, and updated on Dec 14th2023 for randomized controlled trials (RCTs) and cohort studies with no restrictions. Methodological quality was assessed using the Cochrane Risk of Bias tool for RCTs and Newcastle Ottawa Scale for cohort studies. Primary outcomes included mean reduction in morphine equivalent daily dose (reported as mean differences [MDs] mg/day; 95% confidence intervals [95%CIs]) and/or opioid cessation proportion. Secondary outcomes were mean changes in pain severity (reported as standardized mean difference [SMDs]; 95%CIs) and (serious) adverse events. Meta-analyses were performed using random-effects models.ResultsWe identified 3,826 records, of which five RCTs (953 participants) and six cohort studies (967 participants) were included. Overall, opioid dosage was significantly reduced in intervention groups compared to controls (MD: -24.88 mg/day, 95%CI: -36.40 to -13.36; I2=59.41%; nine studies). Subgroup analyses revealed significant opioid dose reductions with mindfulness (MD: -29.36 mg/day 95%CI: - 40.55 to -18.17; I2=0.0%; two trials) and CBT-based multimodalities (MD: -41.68 mg/day; 95%CI: -58.47 to -24.89; I2=0.0%; two cohort studies), respectively, compared to usual care. No significant differences were observed in opioid cessation (Odds ratio: 1.55, 95%CI: 0.3 to 2.81, I2=50.79%; three studies) or pain severity (SMD: -0.13, 95%CI: -0.37 to 0.11; I2=33.51%; three trials). Adverse events were infrequently examined, with withdrawal symptoms commonly reported.ConclusionsThe studied interventions were effective in reducing opioid dosage for people with CNCP in primary care. They highlighted the importance of multidisciplinary collaboration. Large-scale RCTs measuring the long-term effects and cost of these interventions are needed before their implementation.
Validation of Enhancer Regions in Primary Human Neural Progenitor Cells using Capture STARR-seq
Authors: Gaynor-Gillett Sophia C. , Cheng Lijun , Shi Manman , Liu Jason , Wang Gaoyuan , Spector Megan , Flaherty Mary , Wall Martha , Hwang Ahyeon , Gu Mengting , Chen Zhanlin , Chen Yuhang ,   PsychENCODE Consortium, Moran Jennifer R. , Zhang Jing , Lee Donghoon , Gerstein Mark , Geschwind Daniel , White Kevin P. 
Published: March 2024

AbstractGenome-wide association studies (GWAS) and expression analyses implicate noncoding regulatory regions as harboring risk factors for psychiatric disease, but functional characterization of these regions remains limited. We performed capture STARR-sequencing of over 78,000 candidate regions to identify active enhancers in primary human neural progenitor cells (phNPCs). We selected candidate regions by integrating data from NPCs, prefrontal cortex, developmental timepoints, and GWAS. Over 8,000 regions demonstrated enhancer activity in the phNPCs, and we linked these regions to over 2,200 predicted target genes. These genes are involved in neuronal and psychiatric disease-associated pathways, including dopaminergic synapse, axon guidance, and schizophrenia. We functionally validated a subset of these enhancers using mutation STARR-sequencing and CRISPR deletions, demonstrating the effects of genetic variation on enhancer activity and enhancer deletion on gene expression. Overall, we identified thousands of highly active enhancers and functionally validated a subset of these enhancers, improving our understanding of regulatory networks underlying brain function and disease.
Chloroplastic ascorbate acts as a regulatory hub in plant metabolism regardless of oxidative stress
Authors: Tóth Dávid , Tengölics Roland , Aarabi Fayezeh , Karlsson Anna , Vidal-Meireles André , Kovács László , Kuntam Soujanya , Körmöczi Tímea , Fernie Alisdair R. , Hudson Elton P. , Papp Balázs , Tóth Szilvia Z. 
Published: March 2024

AbstractAscorbate is a major plant metabolite that plays crucial roles in various processes, from reactive oxygen scavenging to epigenetic regulation. However, to what extent and how ascorbate modulates metabolism is largely unknown. To address this, we investigated the consequences of chloroplastic and total cellular ascorbate-deficiencies by studying chloroplastic ascorbate-transporterpht4;4mutant lines, and the ascorbate-deficientvtc2-4mutant ofArabidopsis thaliana. Under regular growth conditions, both ascorbate-deficiencies caused minor alterations in photosynthesis, with no apparent signs of oxidative damage. In contrast, metabolomics analysis revealed a global and largely overlapping metabolome rewiring in both ascorbate-deficiencies, suggesting that chloroplastic ascorbate modulates plant metabolism. We observed significant alterations in amino acid metabolism, particularly in arginine metabolism, activation of nucleotide salvage pathways, and changes in secondary metabolism. In addition, proteome-wide analysis of thermostability revealed that ascorbate may interact with enzymes involved in arginine metabolism, the Calvin-Benson cycle, and several photosynthetic electron transport components. Overall, our results suggest that, independently of oxidative stress, chloroplastic ascorbate interconnects and coordinates diverse metabolic pathways in vascular plants and thus acts as a regulatory hub.
Empowering future scientists: Mentors employ various strategies to engage students in professional science disciplinary literacy practices
Authors: Minocha Trisha , Bhagatwala Tanya , Mirzoyan Gwendolyn , McDowell Gary S , Fankhauser Sarah C 
Published: March 2024

AbstractPeer-review and publication are important parts of the scientific enterprise, and research has shown that engaging students in such scholarly practices helps build their sense of belonging and scientific identity. Yet, these disciplinary literacy skills and professional practices are often part of the hidden curriculum of science research, thus excluding students and others from fully understanding ways in which scientific knowledge is constructed, refined, and disseminated even though students are participating in such activities. Secondary students are increasingly involved in scientific research projects that include authentic disciplinary literacy components such as research proposals, posters, videos, and scientific research papers. More and more, students are also engaging in professional practice of publishing their scientific research papers through dedicated secondary science journals. How teachers and other mentors support the development of professional disciplinary literacies in students is critical to understand as part of supporting more student participation in research. To this end, we used a mixed- methods study of interviews and surveys to examine the experience and conceptions of the mentors (teachers and professional scientists) who guided pre-college students through the writing and publication of their scientific research projects. Analyzing our data from a lens of cognitive apprenticeship, we find that mentors encourage independence by primarily employing the method of “exploration”. We also find that mentors have divergent views on the value of publication within science, versus for student scientists specifically. Our findings suggest that mentors could work to explicitly reveal their own thinking within science writing to provide more sequenced support for student scientists.
Estimation of spatial demographic maps from polymorphism data using a neural network
Authors: Smith Chris C. R. , Patterson Gilia , Ralph Peter L. , Kern Andrew D. 
Published: March 2024

AbstractA fundamental goal in population genetics is to understand how variation is arrayed over natural landscapes. From first principles we know that common features such as heterogeneous population densities and barriers to dispersal should shape genetic variation over space, however there are few tools currently available that can deal with these ubiquitous complexities. Geographically referenced single nucleotide polymorphism (SNP) data are increasingly accessible, presenting an opportunity to study genetic variation across geographic space in myriad species. We present a new inference method that uses geo-referenced SNPs and a deep neural network to estimate spatially heterogeneous maps of population density and dispersal rate. Our neural network trains on simulated input and output pairings, where the input consists of genotypes and sampling locations generated from a continuous space population genetic simulator, and the output is a map of the true demographic parameters. We benchmark our tool against existing methods and discuss qualitative differences between the different approaches; in particular, our program is unique because it infers the magnitude of both dispersal and density as well as their variation over the landscape, and it does so using SNP data. Similar methods are constrained to estimating relative migration rates, or require identity by descent blocks as input. We applied our tool to empirical data from North American grey wolves, for which it estimated mostly reasonable demographic parameters, but was affected by incomplete spatial sampling. Genetic based methods like ours complement other, direct methods for estimating past and present demography, and we believe will serve as valuable tools for applications in conservation, ecology, and evolutionary biology. An open source software package implementing our method is available fromhttps://github.com/kr-colab/mapNN.
An integrative systems-biology approach defines mechanisms of Alzheimer’s disease neurodegeneration
Authors: Leventhal Matthew J , Zanella Camila A , Kang Byunguk , Peng Jiajie , Gritsch David , Liao Zhixiang , Bukhari Hassan , Wang Tao , Pao Ping-Chieh , Danquah Serwah , Benetatos Joseph , Nehme Ralda , Farhi Samouil , Tsai Li-Huei , Dong Xianjun , Scherzer Clemens R , Feany Mel B , Fraenkel Ernest 
Published: March 2024

AbstractDespite years of intense investigation, the mechanisms underlying neuronal death in Alzheimer’s disease, the most common neurodegenerative disorder, remain incompletely understood. To define relevant pathways, we integrated the results of an unbiased, genome-scale forward genetic screen for age-associated neurodegeneration inDrosophilawith human andDrosophilaAlzheimer’s disease-associated multi-omics. We measured proteomics, phosphoproteomics, and metabolomics inDrosophilamodels of Alzheimer’s disease and identified Alzheimer’s disease human genetic variants that modify expression in disease-vulnerable neurons. We used a network optimization approach to integrate these data with previously published Alzheimer’s disease multi-omic data. We computationally predicted and experimentally demonstrated howHNRNPA2B1andMEPCEenhance tau-mediated neurotoxicity. Furthermore, we demonstrated that the screen hitsCSNK2A1andNOTCH1regulate DNA damage inDrosophilaand human iPSC-derived neural progenitor cells. Our work identifies candidate pathways that could be targeted to ameliorate neurodegeneration in Alzheimer’s disease.
Mutational analysis of the F plasmid partitioning protein ParA reveals novel residues required for oligomerisation and plasmid maintenance
Authors: Mitra Nivedita , Mishra Dipika , Puthethu Irene Aniyan , Srinivasan Ramanujam 
Published: March 2024

AbstractMobile genetic elements such as plasmids play a crucial role in the emergence of antimicrobial resistance. Hence, plasmid maintenance proteins like ParA of the Walker A type cytoskeletal ATPases/ ParA superfamily are potential targets for novel antibiotics. Plasmid partitioning by ParA relies upon ATP-dependent dimerisation and formation of chemophoretic gradients of ParA-ATP on bacterial nucleoids. Though polymerisation of ParA has been reported in many instances, the need for polymerisation in plasmid maintenance remains unclear. In this study, we provide novel insights into the polymerisation of ParA and the effect of polymerisation on plasmid maintenance. We first characterise two mutations, Q351H and W362E, in ParA from F plasmid (ParAF) that form cytoplasmic filaments independent of the ParBSFpartitioning complex. Both mutants fail to partition plasmids, do not bind non-specific DNA and act as super-repressors to suppress transcription from the ParA promoter. Further, we show that the polymerisation of ParAFrequires the conformational switch to the ParA-ATP* state. We identify two mutations, R320A in the C-terminal helix-14 and E375A helix-16 of ParAF,that abolish filament assembly and affect plasmid partitioning. Our results thus suggest a role for higher-order structures or polymerisation of ParA in plasmid maintenance.
A deep profile of gene expression across 18 human cancers
Authors: Qiu Wei , Dincer Ayse B. , Janizek Joseph D. , Celik Safiye , Pittet Mikael , Naxerova Kamila , Lee Su-In 
Published: March 2024

AbstractClinically and biologically valuable information may reside untapped in large cancer gene expression data sets. Deep unsupervised learning has the potential to extract this information with unprecedented efficacy but has thus far been hampered by a lack of biological interpretability and robustness. Here, we present DeepProfile, a comprehensive framework that addresses current challenges in applying unsupervised deep learning to gene expression profiles. We use DeepProfile to learn low-dimensional latent spaces for 18 human cancers from 50,211 transcriptomes. DeepProfile outperforms existing dimensionality reduction methods with respect to biological interpretability. Using DeepProfile interpretability methods, we show that genes that are universally important in defining the latent spaces across all cancer types control immune cell activation, while cancer type-specific genes and pathways define molecular disease subtypes. By linking DeepProfile latent variables to secondary tumor characteristics, we discover that tumor mutation burden is closely associated with the expression of cell cycle-related genes. DNA mismatch repair and MHC class II antigen presentation pathway expression, on the other hand, are consistently associated with patient survival. We validate these results through Kaplan-Meier analyses and nominate tumor-associated macrophages as an important source of survival-correlated MHC class II transcripts. Our results illustrate the power of unsupervised deep learning for discovery of cancer biology from existing gene expression data.