bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effective Local Anesthetic Use in Nasal Surgery: A Systematic Review and Meta-analysis of Randomized Controlled Studies
Authors: Alaniz Leonardo , Vu Cindy , Arora Jagmeet , Stulginski Avril , Zhu Xiao , Cordero Justin , Vyas Raj M. , Pfaff Miles J. 
Published: August 2023

Background:
Intraoperative nerve blocks have shown promise in managing pain after nasal surgery. The purpose of this systematic review and meta-analysis was to analyze existing level I and II evidence on intraoperative nerve blocks in nasal surgery to optimize postoperative recovery.

Methods:
The primary outcome of this systematic review and meta-analysis was postoperative pain scores; secondary outcomes included perioperative opioid requirements, patient satisfaction scores, and time to first analgesic requirement. PubMed, Embase, and MEDLINE databases were searched, and two independent reviewers conducted article screening. Methodological quality assessment of studies utilized the Jadad instrument, and interrater reliability was assessed using Cohen kappa. An inverse-variance, fixed-effects model was used for meta-analysis with Cohen d used to normalize effect size between studies. I
2 and Q statistics were used to assess interstudy variability.

Results:
Four studies were included for meta-analysis, totaling 265 randomized patients. The nerve blocks assessed included infraorbital nerve, sphenopalatine ganglion, external nasal nerve, central facial nerve blocks, and total nerve blocks. All demonstrated significantly reduced postoperative pain compared with controls, with a large effect size (P < 0.001). Opioid requirements were lower in the nerve block groups (P < 0.001), and patient satisfaction scores were higher (P < 0.001). Supplemental meta-analyses showed a longer time to first analgesic requirement for patients who received a nerve block (P < 0.001).

Conclusions:
These findings support the efficacy of nerve blocks in providing postoperative pain relief and enhancing patient satisfaction with pain management. Perioperative nerve blocks, in combination with general anesthesia, should be considered for postoperative pain control.
Histologic and molecular features of antibody-mediated rejection
Authors: Rosales Ivy A. , Smith Rex Neal , Colvin Robert B. 
Published: August 2023

Purpose of review
This review aims to summarize the highlights from recent research that involved pathological and molecular analysis of kidney allografts.

Recent findings
As the research on antibody-mediated rejection (AMR) continues to evolve, studies are focused on identification through transcript studies of pathogenetic pathways involved in the development of AMR as well as refinement of diagnostic methods either by correlating Banff pathologic lesions with clinical and molecular data or by machine learning. Of note, the past year has generated high impact research that underscore the importance of pathologic and molecular correlations and detection of transcripts or gene sets that would aid prognostication. The studies involving refinement of pathologic criteria also highlight the continuous efforts to achieve diagnostic accuracy and standardization.

Summary
Research involving histologic and molecular characteristics that define AMR are central to identification and understanding of pathogenetic pathways and remain critical in the development of diagnostic criteria.
Speech Outcomes and Velopharyngeal Function in Children Undergoing Submucous Cleft Palate Repair
Authors: Álvarez Carvajal Drina C. , Inostroza-Allende Felipe , Geldres Meneses María B. , Giugliano Villarroel Carlos 
Published: August 2023

Objective:
Describe and compare the results of speech and velopharyngeal function in children with classic and occult submucous cleft palate undergoing interdisciplinary treatment at the Gantz Foundation.

Methods:
The clinical history of all patients born between 2012 and 2017 with a diagnosis of classic or occult submucous cleft palate was retrospectively reviewed. Preoperative and postoperative medical, surgical, and speech and language history were collected.

Results:
Twenty-eight cases diagnosed at the age of 44.8±23.9 months were included. Of these, 71.4% presented classic submucous cleft, and 28.6% occult. Before primary surgery, 7.1% had a diagnosis of the syndrome, and 21.4% were under study. A total of 39.3% had hearing difficulties and 21.4% used tympanic ventilation tubes. A total of 60.7% had language problems, 39.3% had compensatory articulation, 17.9% had absent hypernasality, and 21.4% had absent nasal emission. The team indicated primary palate surgery in 71.4%, of which 85% performed the surgery at the mean age of 61.7±24.7 months. The surgical technique was Furlow in 88.2% of the cases and intravelar veloplasty in the remaining 11.8%. Then, 3 cases underwent velopharyngeal insufficiency surgery; 2 of them eliminated hypernasality and reduced nasal emission. The age of diagnosis (P=0.021) and the performance of velopharyngeal insufficiency surgery (P=0029) of the occult submucous cleft palate group was significatively later than the classic cleft palate group.

Conclusions:
Language, hearing, compensatory articulation, hypernasality, and nasal emission problems were recorded. A high percentage required primary surgery. Of these, a low proportion also required a velopharyngeal insufficiency surgery, which improved the velopharyngeal function of the children but did not completely adapt it. In this regard, early diagnosis is essential, as well as an analysis of each center primary closure protocol.
A Head-to-head Comparison of De Novo Sirolimus or Everolimus Plus Reduced-dose Tacrolimus in Kidney Transplant Recipients: A Prospective and Randomized Trial
Authors: Toniato de Rezende Freschi Juliana , Cristelli Marina Pontello , Viana Laila Almeida , Ficher Klaus Nunes , Nakamura Monica Rika , Proença Henrique , Dreige Yasmim Cardoso , de Marco Renato , de Lima Maria Gerbase , Foresto Renato Demarchi , Aguiar Wilson Ferreira , Medina-Pestana Jose , Tedesco-Silva Helio 
Published: August 2023

Background.
Mammalian target of rapamycin inhibitors (mTORi), sirolimus (SRL) and everolimus (EVR), have distinct pharmacokinetic/pharmacodynamics properties. There are no studies comparing the efficacy and safety of de novo use of SRL versus EVR in combination with reduced-dose calcineurin inhibitor.

Methods.
This single-center prospective, randomized study included first kidney transplant recipients receiving a single 3 mg/kg antithymocyte globulin dose, tacrolimus, and prednisone, without cytomegalovirus (CMV) pharmacological prophylaxis. Patients were randomized into 3 groups: SRL, EVR, or mycophenolate sodium (MPS). Doses of SRL and EVR were adjusted to maintain whole blood concentrations between 4 and 8 ng/mL. The primary endpoint was the 12-mo incidence of the first CMV infection/disease.

Results.
There were 266 patients (SRL, n = 86; EVR, n = 90; MPS, n = 90). The incidence of the first CMV event was lower in the mTORi versus MPS groups (10.5% versus 7.8% versus 43.3%, P < 0.0001). There were no differences in the incidence of BK polyomavirus viremia (8.2% versus 10.1% versus 15.1%, P = 0.360). There were no differences in survival-free from treatment failure (87.8% versus 88.8% versus 93.3%, P = 0.421) and incidence of donor-specific antibodies. At 12 mo, there were no differences in kidney function (75 ± 23 versus 78 ± 24 versus 77 ± 24 mL/min/1.73 m2, P = 0.736), proteinuria, and histology in protocol biopsies. Treatment discontinuation was higher among patients receiving SRL or EVR (18.6% versus 15.6% versus 6.7%, P = 0.054).

Conclusions.
De novo use of SRL or EVR, targeting similar therapeutic blood concentrations, shows comparable efficacy and safety. The reduced incidence of CMV infection/disease and distinct safety profile of mTORi versus mycophenolate were confirmed in this study.
DeepMap: A deep learning-based model with a four-line code for prediction-based breeding in crops
Authors: Kumar Ajay , Sundaram Krishna T. , Gnanapragasam Niranjani , Singh Uma Maheshwar , Pranesh K. J. , Venkateshwarlu Challa , Paul Pronob J. , Hussain Waseem , Bhosale Sankalp , Kohli Ajay , Miro Berta , Singh Vikas Kumar , Sinha Pallavi 
Published: July 2023

ABSTRACTPrediction of phenotype through genotyping data using the emerging machine or deep learning technology has been proven successful in genomic prediction. We present here a graphical processing unit (GPU) enabled DeepMap configurable deep learning-based python package for the genomic prediction of quantitative phenotype traits. We found that deep learning captures non-linear patterns more efficiently than conventional statistical methods. Furthermore, we suggest an additional module inclusion of epistasis interactions and training of the model on Graphical Processing Units (GPUs) in addition to Central Processing Unit (CPU) to enhance efficiency and increase the model’s performance. We developed and demonstrated the application of DeepMap using a 3K rice genome panel and 1K-Rice Custom Amplicon (1kRiCA) data for several phenotypic traits including days to 50% flowering (DTF), number of productive tillers (NPT), panicle length (PL), plant height (PH), and plot yield (PY). We have found that DeepMap outperformed the best existing state-of-the-art models by giving higher predictive correlation and low mean squared error for the datasets studied. This prediction performance was higher than other compared models in the range of 13-31%. Similarly for Dataset-2, significantly higher predictions were observed than the compared models (16-20% higher prediction ability). On Dataset-3, we have also shown the better and versatile performance of our model across crops (wheat, maize, and soybean) for yield and yield-related traits. This demonstrates the potentiality of the framework and ease of use for future research in crop improvement. The DeepMap is accessible athttps://test.pypi.org/project/DeepMap-1.0/.Short SummaryDeepMap is a deep learning-based breeder-friendly python package to perform genomic prediction. It utilizes epistatic interactions for data augmentation and outperforms the existing state-of-the-art machine/deep learning models such as Bayesian LASSO, GBLUP, DeepGS, and dualCNN. DeepMap developed for rice and tested across crops such as maize, wheat, soybean etc.
getphylo: rapid and automatic generation of multi-locus phylogenetic trees
Authors: Booth T. J. , Shaw Simon , Weber T. 
Published: July 2023

AbstractMotivationPhylogenetic trees are the primary tool for visualising evolutionary relationships. Traditionally, phylogenies are inferred from manually curated sets of marker genes. As available genomic data increases, there is increasing demand for tools to automatically build phylogenies from assembled genomes. Existing tools rely on reference databases of preselected marker genes, limiting their taxonomic scope. We sought to develop a tool that could quickly build phylogeny from input genomes alone.ResultsWe developedgetphylo, a tool to automatically generate multi-locus phylogenetic trees from GenBank files. It has a low barrier to entry with minimal dependencies.getphylouses a parallelised, heuristic workflow to keep runtime and system requirements as low as possible.getphyloconsistently produces trees with topologies comparable to other tools in less time. Furthermore, asgetphylodoes not rely on reference databases, it has a virtually unlimited scope in terms of taxonomy (e.g., not limited to bacteria) and genetic scale (e.g., can analyse plasmids, prophage, and gene clusters). This combination of speed and flexibility makesgetphyloa valuable addition to the phylogenetics toolkit.Availabilitygetphylois freely available and is downloadable through the Python Package Index (pip install getphylo;https://pypi.org/project/getphylo/) and GitHub (https://github.com/drboothtj/getphylo).
Exploring congruent diversification histories with flexibility and parsimony
Authors: Andréoletti Jérémy , Morlon Hélène 
Published: July 2023

AbstractUsing phylogenies of present-day species to estimate diversification rate trajectories – speciation and extinction rates over time – is a challenging task due to non-identifiability issues. Given a phylogeny, there exists an infinite set of trajectories that result in the same likelihood; this set has been coined a congruence class. Previous work has developed approaches for sampling trajectories within a given congruence class, with the aim to assess the extent to which congruent scenarios can vary from one another. Based on this sampling approach, it has been suggested that rapid changes in speciation or extinction rates are conserved across the class. Reaching such conclusions requires to sample the broadest possible set of distinct trajectories.We introduce a new method for exploring congruence classes, that we implement in the R package CRABS. Whereas existing methods constrain either the speciation rate or the extinction rate trajectory, ours provides more flexibility by sampling congruent speciation and extinction rate trajectories simultaneously. This allows covering a more representative set of distinct diversification rate trajectories. We also implement a filtering step that allows selecting the most parsimonious trajectories within a class.We demonstrate the utility of our new sampling strategy using a simulated scenario. Next, we apply our approach to the study of mammalian diversification history. We show that rapid changes in speciation and extinction rates need not be conserved across a congruence class, but that selecting the most parsimonious trajectories shrinks the class to concordant scenarios.Our approach opens new avenues both to truly explore the myriad of potential diversification histories consistent with a given phylogeny, embracing the uncertainty inherent to phylogenetic diversification models, and to select among these different histories. This should help refining our inference of diversification trajectories from extant data.
More than just density: the role of egg number, food volume and container dimensions in mediating larval competition inDrosophila melanogaster
Authors: Venkitachalam Srikant , Sajith V. S. , Joshi Amitabh 
Published: July 2023

AbstractFlies of the genusDrosophilahave been extensively used as model systems to study competition. Experiments using larval crowding in these species have furthered our understanding of competition ecology, stress-adaptation, density-dependent selection and population dynamics. Historically, larval competition has been induced by crowding the larvae in high density cultures, compared to low-density controls. However, recent studies have shown that two larval cultures having the same total eggs per mL food density, with different absolute quantities of eggs and food, can differ greatly in their density-specific fitness functions. Similarly, populations adapted to two types of cultures achieving the same density through different means, can also evolve different traits. Thus, it is clear that there is more to larval crowding than just eggs/food density, which has until now been the benchmark variable for quantifying larval crowding inDrosophilastudies. In the current study, we explore the consequences of implementing crowding in different ways, using a three-way factorial experiment with egg number and food volume, cast into different food column heights or cylindrical vials with different diameters. We find that not just the same density, but cultures having the same egg number and food volume combination but experienced in food columns of varying height and diameter can have very different pre-adult survivorship and development times. We further propose a new variable for quantifying larval crowding – effective density, which is the density within the larval feeding band, a volume of food close to the surface, with access to air, wherein a majority of the larvae feed. We show that effective density is a much better predictor of the outcomes of competition than the popularly used total eggs/food density.
Deep Batch Active Learning for Drug Discovery
Authors: Bailey Michael , Moayedpour Saeed , Li Ruijiang , Corrochano-Navarro Alejandro , Kötter Alexander , Kogler-Anele Lorenzo , Riahi Saleh , Grebner Christoph , Hessler Gerhard , Matter Hans , Bianciotto Marc , Mas Pablo , Bar-Joseph Ziv , Jager Sven 
Published: July 2023

AbstractA key challenge in drug discovery is to optimize, in silico, various absorption and affinity properties of small molecules. One strategy that was proposed for such optimization process is active learning. In active learning molecules are selected for testing based on their likelihood of improving model performance. To enable the use of active learning with advanced neural network models we developed two novel active learning batch selection methods. These methods were tested on several public datasets for different optimization goals and with different sizes. We have also curated new affinity datasets that provide chronological information on state-of-the-art experimental strategy. As we show, for all datasets the new active learning methods greatly improved on existing and current batch selection methods leading to significant potential saving in the number of experiments needed to reach the same model performance. Our methods are general and can be used with any package including the popularDeepChemlibrary.
In vivoexpansion of gene-targeted hepatocytes through transient inhibition of an essential gene
Authors: De Giorgi Marco , Park So Hyun , Castoreno Adam , Cao Mingming , Hurley Ayrea , Saxena Lavanya , Chuecos Marcel A. , Walkey Christopher J. , Doerfler Alexandria M. , Furgurson Mia N. , Ljungberg M. Cecilia , Patel Kalyani R. , Hyde Sarah , Chickering Tyler , Lefebvre Stephanie , Wassarman Kelly , Miller Patrick , Qin June , Schlegel Mark K. , Zlatev Ivan , Li Rich Gang , Kim Jong , Martin James F. , Bissig Karl-Dimiter , Jadhav Vasant , Bao Gang , Lagor William R. 
Published: July 2023

AbstractHomology Directed Repair (HDR)-based genome editing is an approach that could permanently correct a broad range of genetic diseases. However, its utility is limited by inefficient and imprecise DNA repair mechanisms in terminally differentiated tissues. Here, we tested “Repair Drive”, a novel method for improving targeted gene insertion in the liver by selectively expanding correctly repaired hepatocytesin vivo. Our system consists of transient conditioning of the liver by knocking down an essential gene, and delivery of an untargetable version of the essential genein ciswith a therapeutic transgene. We show that Repair Drive dramatically increases the percentage of correctly targeted hepatocytes, up to 25%. This resulted in a five-fold increased expression of a therapeutic transgene. Repair Drive was well-tolerated and did not induce toxicity or tumorigenesis in long term follow up. This approach will broaden the range of liver diseases that can be treated with somatic genome editing.
TEMPTED: time-informed dimensionality reduction for longitudinal microbiome studies
Authors: Shi Pixu , Martino Cameron , Han Rungang , Janssen Stefan , Buck Gregory , Serrano Myrna , Owzar Kouros , Knight Rob , Shenhav Liat , Zhang Anru R. 
Published: July 2023

AbstractLongitudinal studies are crucial for understanding complex microbiome dynamics and their link to health. We introduce TEMPoral TEnsor Decomposition (TEMPTED), a time-informed dimensionality reduction method for high-dimensional longitudinal data that treats time as a continuous variable, effectively characterizing temporal information and handling varying temporal sampling. TEMPTED captures key microbial dynamics, facilitates beta-diversity analysis, and enhances reproducibility by transferring learned representations to new data. In simulations, it achieves 90% accuracy in phenotype classification, significantly outperforming existing methods. In real data, TEMPTED identifies vaginal microbial markers linked to term and preterm births, demonstrating robust performance across datasets and sequencing platforms.
Diabetes Detection from Diabetic Retinopathy-Absent Images Using Deep Learning Methodology
Authors: Rom Yovel , Aviv Rachelle , Cohen Gal Yaakov , Friedman Yehudit Eden , Dvey-Aharon Zack 
Published: August 2023

AbstractAimsDiabetes is one of the leading causes of morbidity and mortality in the United States and worldwide. This research aimed to develop an artificial intelligence (AI) machine learning model which can detect the presence of diabetes from fundus imagery of eyes without diabetic eye disease.MethodsOur researchers trained a machine learning algorithm on the EyePACS dataset, consisting of 47,076 images. Patients were also divided into cohorts based on disease duration, each cohort consisting of patients diagnosed within the timeframe in question (e.g., 15 years) and healthy patients.ResultsThe algorithm achieved 0.83 area under receiver operating curve (AUC) in detecting diabetes per image, and AUC 0.86 on the task of detecting diabetes per patient.ConclusionOur results suggest that diabetes may be diagnosed non-invasively using fundus imagery alone. This may enable diabetes diagnosis at point of care, as well as other, accessible venues, facilitating the diagnosis of many undiagnosed people with diabetes.
KoNA: Korean Nucleotide Archive as a New Data Repository for Nucleotide Sequence Data
Authors: Ko Gunhwan , Lee Jae Ho , Sim Young Mi , Song Wangho , Yoon Byung-Ha , Byeon Iksu , Lee Bang Hyuck , Kim Sang-Ok , Choi Jinhyuk , Jang Insoo , Kim Hyerin , Yang Jin Ok , Jang Kiwon , Kim Sora , Kim Jong-Hwan , Jeon Jongbum , Jung Jaeeun , Hwang Seungwoo , Park Ji-Hwan , Kim Pan-Gyu , Kim Seon-Young , Lee Byungwook 
Published: July 2023

AbstractDuring the last decade, generation and accumulation of petabase-scale high-throughput sequencing data have resulted in ethical and technical challenges, including access to human data, and transfer, storage, and sharing of enormous amount of data. To promote data-driven research in biology, the Korean government announced that all the biological data generated from government-funded research projects should be deposited in the Korea BioData Station (K-BDS), which consists of multiple databases for individual data types. We introduce the Korean Nucleotide Archive (KoNA), a repository for nucleotide sequence data. As of July 2022, the Korean Read Archive in KoNA has collected over 477 TB of raw next generation sequencing data from several national genome projects. To ensure data quality and prepare for international alignment, a standard operating procedure (SOP) was adopted, which is similar to the International Nucleotide Sequence Database Collaboration. The SOP includes quality control processes for submitted data and metadata using an automated pipeline followed by manual examination. To ensure fast and stable data transfer, a high-speed transmission system called GBox is used in KoNA. Furthermore, the data uploaded to or downloaded from KoNA through GBox can be readily processed in a cloud-computing service for genomic data analysis called Bio-Express. This seamless coupling of KoNA, GBox, and Bio-Express enhances data experience including submission, access, and analysis of raw nucleotide sequences. KoNA not only satisfies the unmet needs for a national sequence repository in Korea, but also provides datasets to researchers globally and contribute to advances in genomics. KoNA is available athttps://www.kobic.re.kr/kona/.