bluuebunny/crossref_metadata_2025_split huggingface dataset:
Parent Support for Physical Activity and Motor Skills During Early Childhood: A Mixed-Methods Application of the Multi-process Action Control Framework
Authors: James Maeghan E , Rhodes Ryan E , Cairney John , Sabiston Catherine M , Finlay-Watson Tracia , Arbour-Nicitopoulos Kelly P 
Published: February 2024

Abstract

Background
Promoting physical activity (PA) and fundamental movement skills (FMS) in early childhood is necessary to address the high rates of inactivity in children. Parent support is a determinant of PA in children, however, parental intentions to support are not always translated into behavior resulting in an intention–behavior gap.

Purpose
Positioned within the multi-process action control (M-PAC) framework, this study used an explanatory concurrent mixed-methods design to explore parents’ intentions and support of PA and FMS during early childhood.

Methods
Parents (N=124) of children aged 3–4 years completed an online survey consisting of items assessing reflective (e.g., attitudes), regulatory (e.g., planning), and reflexive (e.g., habit) processes of M-PAC and intentions and support for child PA and FMS. A subset of parents (n=20) completed a semi-structured online interview guided by the M-PAC framework.

Results
Significantly more parents intended to support PA (71%) compared with FMS (44%; p&lt;0.001) and successfully translated intentions into action for PA (57%) compared with FMS (27%; p&lt;0.001). Discriminant function analysis showed parent behavior profiles for PA and FMS support were associated with a combination of reflective, regulatory, and reflexive processes, however, these differed based on support behavior. Qualitative findings highlighted parents’ differential views on PA and FMS support and contextualized the psychological processes associated with each.

Conclusions
Parent PA support interventions during early childhood should include strategies for supporting FMS. Interventions should prioritize fostering a combination of reflective, regulatory, and reflexive behaviors to translate intentions to support PA and FMS into behavior among parents of young children.
Banff scoring of kidney allograft biopsies: “Manual” application vs software-assisted sign-out
Authors: Demetris Anthony J , Lesniak Andrew J , Popp Benjamin A , Frencho Ronald J , Minervini Marta I , Nalesnik Michael A , El Hag Mohamed I , Hariharan Sundaram , Randhawa Parmjeet S 
Published: February 2024

Abstract

Objectives
Pathologists interpreting kidney allograft biopsies using the Banff system usually start by recording component scores (eg, i, t, cg) using histopathologic criteria committed to memory. Component scores are then melded into diagnoses using the same manual/mental processes. This approach to complex Banff rules during routine sign-out produces a lack of fidelity and needs improvement.

Methods
We constructed a web-based “smart template” (software-assisted sign-out) system that uniquely starts with upstream Banff-defined additional diagnostic parameters (eg, infection) and histopathologic criteria (eg, percent interstitial inflammation) collectively referred to as feeder data that is then translated into component scores and integrated into final diagnoses using software-encoded decision trees.

Results
Software-assisted sign-out enables pathologists to (1) accurately and uniformly apply Banff rules, thereby eliminating human inconsistencies (present in 25% of the cohort); (2) document areas of improvement; (3) show improved correlation with function; (4) examine t-Distributed Stochastic Neighbor Embedding clustering for diagnosis stratification; and (5) ready upstream incorporation of artificial intelligence–assisted scoring of biopsies.

Conclusions
Compared with the legacy approach, software-assisted sign-out improves Banff accuracy and fidelity, more closely correlates with kidney function, is practical for routine clinical work and translational research studies, facilitates downstream integration with nonpathology data, and readies biopsy scoring for artificial intelligence algorithms.
Cefdinir vs cephalexin for the treatment of urinary tract infections: A retrospective evaluation
Authors: Lloyd Andie , Grey Jonathan , Fronczek Christopher , Durkin Heather , Marr Kerry 
Published: February 2024

Abstract

Purpose
Cefdinir and cephalexin are cephalosporin antibiotics commonly used in the treatment of urinary tract infections (UTIs). Their efficacy depends on achieving sufficient time with concentrations exceeding the minimum inhibitory concentration (MIC). Despite being frequently prescribed for UTIs, cefdinir has markedly lower urine penetration compared to cephalexin. It is possible that differences in pharmacokinetics could result in dissimilar efficacy between these agents; however, comparative studies of cephalosporins in UTIs are lacking.

Methods
This was a retrospective comparative study of patients discharged from emergency departments within a community health system with a diagnosis of acute cystitis who were prescribed cefdinir or cephalexin. Treatment failure rates at 7 and 14 days were compared between the 2 agents using a χ2 or Fisher’s exact test, as appropriate.

Results
There were no differences in overall treatment failure between the cefdinir and cephalexin groups. Treatment failure at 7 days occurred in 11.6% (n = 14) of patients in the cefdinir group and 8.3% (n = 10) of patients in the cephalexin group (P = 0.389). Treatment failure at 14 days was higher for cefdinir at 20.7% (n = 25) than for cephalexin at 11.8% (n = 14), but this difference was not statistically significant (P = 0.053). There were no differences in the rate of treatment failure in subgroup analyses of uncomplicated or complicated UTIs.

Conclusion
The results of this study suggest that cefdinir and cephalexin have comparable efficacy for the treatment of lower UTIs. While there was a numerically higher rate of treatment failure with cefdinir, there were no significant differences in treatment failure between the agents.
Randomized, Single-Blind, Comparative Study of Remimazolam Besylate vs Propofol for Facial Plastic Surgery
Authors: Huang Yunping , Zheng Di , Xu Kai , Li Jun , Gu Yimei , Yin Zhiwei , Pan Junli , Shen Jian , Lu Xiaoyun , Zhong Feifei , Qiu Yanfen 
Published: February 2024

Abstract

Background
Use of local anesthesia and conscious sedation with a combination of a sedative and anesthetic drug during a surgical procedure is an approach designed to avoid intubation, which produces fewer adverse events compared to general anesthesia. In the present study, a comparison was made between the efficacy and safety of remimazolam besylate and propofol for facial plastic surgery.

Objectives
The objective was to evaluate the clinical efficacy, comfort, and incidence of adverse events of remimazolam compared with propofol combined with alfentanil in outpatient facial plastic surgery.

Methods
In this randomized, single-blind, single-center, comparative study, facial plastic surgery patients were randomly divided into remimazolam-alfentanil (n = 50) and propofol-alfentanil (n = 50) groups for sedation and analgesia. The primary endpoint was the incidence of hypoxemia, while secondary endpoints included efficacy and safety evaluations.

Results
There were no significant differences regarding the surgical procedure, sedation and induction times, pain and comfort scores, muscle strength recovery, heart rate, respiratory rate, and blood pressure, but the dosage of alfentanil administered to the remimazolam group (387.5 μg) was lower than that for the propofol group (600 μg). The incidence of hypoxemia (P = .046) and towing of the mandibular (P = .028), as well as wake-up (P = .027) and injection pain (P = .008), were significantly higher in the propofol group than the remimazolam group.

Conclusions
Remimazolam and propofol had similar efficacies for sedation and analgesia during facial plastic surgery, but especially the incidence of respiratory depression was significantly lower in patients given remimazolam.

Level of Evidence: 2
Introducing π-HelixNovo for practical large-scale de novo peptide sequencing
Authors: Yang Tingpeng , Ling Tianze , Sun Boyan , Liang Zhendong , Xu Fan , Huang Xiansong , Xie Linhai , He Yonghong , Li Leyuan , He Fuchu , Wang Yu , Chang Cheng 
Published: February 2024

Abstract
De novo peptide sequencing is a promising approach for novel peptide discovery, highlighting the performance improvements for the state-of-the-art models. The quality of mass spectra often varies due to unexpected missing of certain ions, presenting a significant challenge in de novo peptide sequencing. Here, we use a novel concept of complementary spectra to enhance ion information of the experimental spectrum and demonstrate it through conceptual and practical analyses. Afterward, we design suitable encoders to encode the experimental spectrum and the corresponding complementary spectrum and propose a de novo sequencing model $\pi$-HelixNovo based on the Transformer architecture. We first demonstrated that $\pi$-HelixNovo outperforms other state-of-the-art models using a series of comparative experiments. Then, we utilized $\pi$-HelixNovo to de novo gut metaproteome peptides for the first time. The results show $\pi$-HelixNovo increases the identification coverage and accuracy of gut metaproteome and enhances the taxonomic resolution of gut metaproteome. We finally trained a powerful $\pi$-HelixNovo utilizing a larger training dataset, and as expected, $\pi$-HelixNovo achieves unprecedented performance, even for peptide-spectrum matches with never-before-seen peptide sequences. We also use the powerful $\pi$-HelixNovo to identify antibody peptides and multi-enzyme cleavage peptides, and $\pi$-HelixNovo is highly robust in these applications. Our results demonstrate the effectivity of the complementary spectrum and take a significant step forward in de novo peptide sequencing.
Should we really use graph neural networks for transcriptomic prediction?
Authors: Brouard Céline , Mourad Raphaël , Vialaneix Nathalie 
Published: February 2024

Abstract
The recent development of deep learning methods have undoubtedly led to great improvement in various machine learning tasks, especially in prediction tasks. This type of methods have also been adapted to answer various problems in bioinformatics, including automatic genome annotation, artificial genome generation or phenotype prediction. In particular, a specific type of deep learning method, called graph neural network (GNN) has repeatedly been reported as a good candidate to predict phenotypes from gene expression because its ability to embed information on gene regulation or co-expression through the use of a gene network. However, up to date, no complete and reproducible benchmark has ever been performed to analyze the trade-off between cost and benefit of this approach compared to more standard (and simpler) machine learning methods. In this article, we provide such a benchmark, based on clear and comparable policies to evaluate the different methods on several datasets. Our conclusion is that GNN rarely provides a real improvement in prediction performance, especially when compared to the computation effort required by the methods. Our findings on a limited but controlled simulated dataset shows that this could be explained by the limited quality or predictive power of the input biological gene network itself.
KGE-UNIT: toward the unification of molecular interactions prediction based on knowledge graph and multi-task learning on drug discovery
Authors: Zhang Chengcheng , Zang Tianyi , Zhao Tianyi 
Published: February 2024

Abstract
The prediction of molecular interactions is vital for drug discovery. Existing methods often focus on individual prediction tasks and overlook the relationships between them. Additionally, certain tasks encounter limitations due to insufficient data availability, resulting in limited performance. To overcome these limitations, we propose KGE-UNIT, a unified framework that combines knowledge graph embedding (KGE) and multi-task learning, for simultaneous prediction of drug–target interactions (DTIs) and drug–drug interactions (DDIs) and enhancing the performance of each task, even when data availability is limited. Via KGE, we extract heterogeneous features from the drug knowledge graph to enhance the structural features of drug and protein nodes, thereby improving the quality of features. Additionally, employing multi-task learning, we introduce an innovative predictor that comprises the task-aware Convolutional Neural Network-based (CNN-based) encoder and the task-aware attention decoder which can fuse better multimodal features, capture the contextual interactions of molecular tasks and enhance task awareness, leading to improved performance. Experiments on two imbalanced datasets for DTIs and DDIs demonstrate the superiority of KGE-UNIT, achieving high area under the receiver operating characteristics curves (AUROCs) (0.942, 0.987) and area under the precision-recall curve ( AUPRs) (0.930, 0.980) for DTIs and high AUROCs (0.975, 0.989) and AUPRs (0.966, 0.988) for DDIs. Notably, on the LUO dataset where the data were more limited, KGE-UNIT exhibited a more pronounced improvement, with increases of 4.32$\%$ in AUROC and 3.56$\%$ in AUPR for DTIs and 6.56$\%$ in AUROC and 8.17$\%$ in AUPR for DDIs. The scalability of KGE-UNIT is demonstrated through its extension to protein–protein interactions prediction, ablation studies and case studies further validate its effectiveness.
A novel nomogram for prediction of intrahepatic recurrence-free survival in patients with HCC followed by radiofrequency ablation
Authors: Wang Bing , Wu Hao , Wang Song , Zhang Zhong-yi , Wu Wei , Yan Kun , Yang Wei 
Published: February 2024

Abstract

Objective
Although radiofrequency ablation (RFA) has been considered as the favourable treatment option for hepatocellular carcinoma (HCC), there still exist some challenges for new recurrence after RFA. The present study aims to determine the factors affecting recurrence and develop an effective model to predict intrahepatic recurrence-free survival (RFS).

Methods
Patients with HCC followed by RFA between 2000 and 2021 were included in this study. Multivariable Cox regression analysis was used to determine the independent prognostic factors and establish the nomogram predicting intrahepatic RFS after RFA. The predictive performance of the nomogram was assessed according to the C-index, calibration plots, and Kaplan-Meier curves stratified by the tertiles.

Results
A total of 801 sessions in 660 patients (including 1155 lesions) were enrolled into this study. Intrahepatic new recurrence was observed in all patients during the follow-up, and the mean intrahepatic RFS was 21.9 months in the present cohort. According to multivariate COX regression analysis, five independent prognostic factors affecting intrahepatic RFS were determined, including age, Child-Pugh class, tumour distribution, number of tumours, and a-fetoprotein (AFP). Based on all independent prognostic factors, the nomogram model was developed and evaluated, which achieved favourable discrimination and calibration.

Conclusion
This study established five independent prognostic factors and constructed a nomogram model to predict intrahepatic RFS for HCC patients followed by RFA. It could better help clinicians select RFA candidates, as well as offering the important information about whether patients need receive comprehensive treatment to prevent new recurrence after RFA.

Advances in knowledge
(1) In this study, 5 preoperative clinic-pathological variables were determined as the independent prognostic factors affecting RFS after RFA in the current largest sample size. (2) Based on these independent prognostic factors, a prognostic nomogram predicting RFS after RFA was established, which may be used to select patients who benefit from RFA and could help both surgeons and patients provide useful information for choosing the personalized treatment.
Surgical management for massive rotator cuff tears: a network meta-analysis of randomized controlled trials
Authors: Jin Hongfu , Deng Zhenhan , Sun Jianfeng , Vithran Djandan Tadum Arthur , Xiao Wenfeng , Li Yusheng 
Published: February 2024

Abstract

Background
Multiple surgical strategies have been developed for treating massive rotator cuff tears (mRCTs). However, there is still no consensus on the best surgical option for mRCTs. Through a network meta-analysis, we aimed to comprehensively and systematically analyse the evidence in randomized controlled trials to help clinicians make evidence-based clinical decisions for patients with mRCTs.

Methods
Our study was a network meta-analysis of the surgical management of mRCTs (PROSPERO Registration ID: CRD42023397971). We searched PubMed, EMBASE, Cochrane and Web of Science for randomized controlled trials that examined the efficacy of surgical management for mRCTs up to 3 November 2022. A three-step method was employed for the study process. Study selection, data extraction and risk of bias evaluation were conducted by two independent reviewers. R software (version 4.2.1) and Stata (version 15.1) were used for the data analysis.

Results
From 10,633 publications, we included 15 randomized controlled trials (996 participants) for the quantitative analysis. In terms of both long-term and short-term surgical effects, there were no statistically significant differences among surgical interventions such as patch-augmented rotator cuff repair (RCR), RCR with platelet-rich plasma, arthroscopic decompression, bridging reconstruction, arthroscopic RCR with platelet-leukocyte membrane, open RCR, mini-open RCR, arthroscopic debridement, superior capsular reconstruction, arthroscopic suture-spanning augmented repair, subacromial balloon spacer and latissimus dorsi tendon transfer. Based on algorithms, the probability ranking suggests that patch augmentation is the most highly ranked surgical intervention for achieving better short-term surgical outcomes. Furthermore, arthroscopic-associated mini-open RCR was ranked as the highest surgical intervention for achieving better long-term surgical effects.

Conclusions
Based on the available data from the included studies, similar surgical efficacies were observed among the reported intervention measures for mRCTs. The patch augmentation technique was found to potentially achieve better short-term surgical outcomes, which is consistent with previous reports. However, the best surgical interventions for achieving long-term surgical effects remain unknown. More high-quality research is needed to evaluate the efficacy and safety of these interventions and to guide clinical practice.
“Atom and Dream”: Meatspace and Cyberspace in Jeanette Winterson’s The PowerBook
Authors: Luburić-Cvijanović Arijana , Krombholc Viktorija 
Published: February 2024

Abstract
With an air of twenty-first-century cosmopolitanism, Jeanette Winterson’s The PowerBook (2000) spans countries and centuries in an endlessly shifting narrative that formally mirrors the settings’ and characters’ kaleidoscopic identities. Immersed in the Internet’s opportunities for transformation and switching between cities and the Internet as sites of global circulations and cosmopolitan encounters, The PowerBook takes globalization’s redefinition of location further to juxtapose meatspace and cyberspace and establish an imagined transterritorial, transtemporal community. Using the rapidly growing body of cosmopolitan theory with a tinge of geocriticism, this essay aims at analyzing the novel’s conceptions of meatspace and cyberspace, its independence of location, its focus on community as being-in-common, and the ever-changing cellular narrative structure typical of what Berthold Schoene calls a new cosmopolitan modus operandi for British fiction (97). The article wishes to show that, despite Winterson’s exploration of privileged forms of existence, The PowerBook is a political cosmopolitan fiction on place, space, identity, community, and love as a recurrent trope of “cosmopolitan empathy” (McCulloch 10).
A new distribution and host record for the rare moth, Callioratis millari (Lepidoptera: Geometridae), and some ecological observations
Authors: Janse van Rensburg Paul Duvel , Bezuidenhout Hugo , Steyn Tommie , van den Berg Johnnie 
Published: February 2024

Abstract
Callioratis millari Hampson (Lepidoptera: Geometridae) is a Critically Endangered moth endemic to South Africa. Despite extensive searches, it was previously known only from the Entumeni Nature Reserve in KwaZulu-Natal, where its larvae exclusively feed on the cycad Stangeria eriopus (Kunze) Baill (Cycadales: Stangeriaceae). In July 2022, a new population of C. millari was discovered in the Kabouga section of Addo Elephant National Park in the Eastern Cape. Larvae of C. millari were feeding on the cycad Encephalartos caffer (Thunb.) Lehm (Cycadales: Zamiaceae), which also constitutes a new host record. In June 2023, we determined larval incidence and herbivory at this new locality, offering insights into the ecological requirements of C. millari. Known C. millari localities, although ecologically different, share low altitudes (700–950 m a.s.l.), moderate to high rainfall, and grassy habitats with sparse woody cover. A total of 59 larvae were counted in Kabouga, mostly in the fifth and sixth (final) instars. Herbivory incidence was lower on smaller plants and those covered by other vegetation. The flight period of adult C. millari likely occurs between mid-March and April in Kabouga, but further investigation is needed to clarify this. The peak period of larval occurrence in Kabouga occurs during the driest and coldest months of the year (May–July). Considering limited habitat availability, host plant poaching, and the risk of untimely fires, the species should be considered highly threatened. This study adds to our understanding of the biology of C. millari and provides information on its ecological requirements and may contribute to making informed management decisions.
Muscle eosinophilia is a hallmark of chronic disease in facioscapulohumeral muscular dystrophy
Authors: Nunes Andreia M , Ramirez Monique M , Garcia-Collazo Enrique , Jones Takako Iida , Jones Peter L 
Published: February 2024

Abstract
Facioscapulohumeral muscular dystrophy (FSHD) is a progressive myopathy caused by the aberrant increased expression of the DUX4 retrogene in skeletal muscle cells. The DUX4 gene encodes a transcription factor that functions in zygotic genome activation and then is silenced in most adult somatic tissues. DUX4 expression in FSHD disrupts normal muscle cell function; however, the downstream pathogenic mechanisms are still unclear. Histologically, FSHD affected muscles show a characteristic dystrophic phenotype that is often accompanied by a pronounced immune cell infiltration, but the role of the immune system in FSHD is not understood. Previously, we used ACTA1;FLExDUX4 FSHD-like mouse models varying in severity as discovery tools to identify increased Interleukin 6 and microRNA-206 levels as serum biomarkers for FSHD disease severity. In this study, we use the ACTA1;FLExDUX4 chronic FSHD-like mouse model to provide insight into the immune response to DUX4 expression in skeletal muscles. We demonstrate that these FSHD-like muscles are enriched with the chemoattractant eotaxin and the cytotoxic eosinophil peroxidase, and exhibit muscle eosinophilia. We further identified muscle fibers with positive staining for eosinophil peroxidase in human FSHD muscle. Our data supports that skeletal muscle eosinophilia is a hallmark of FSHD pathology.