bluuebunny/crossref_metadata_2025_split huggingface dataset:
Deeply supervised vestibule segmentation network for CT images with global context‐aware pyramid feature extraction
Authors: Chen Meijuan , Zhuo Li , Zhu Ziyao , Yin Hongxia , Li Xiaoguang , Wang Zhenchang 
Published: December 2022

AbstractAccurate vestibule segmentation for CT images is of great significance for the clinical diagnosis of congenital ear malformations and cochlear implant. However, it is still a challenging task due to extremely small size and irregular shape of vestibule. Here, a vestibule segmentation network for CT images is proposed under the basic encoder‐decoder framework. Firstly, a residual block based on channel attention mechanism, named Res‐CA block, is designed to guide the network to enhance the important features for the segmentation tasks while suppressing the irrelevant ones. And then, a global context‐aware pyramid feature extraction (GCPFE) module is proposed to capture multi‐receptive‐field global context information. Finally, active contour with elastic (ACE) loss function is adopted to guide network learning more detailed information of the boundary. Furthermore, deep supervision (DS) mechanism is employed to locate the boundaries finely, improving the robustness of the network. The experiments are conducted on the self‐established VestibuleDataset and UHRCT‐Dataset, as well as publicly available retinal dataset, namely DRIVE, to comprehensively verify the robustness and generalization capability of the proposed segmentation network. The experimental results show that the proposed network can achieve a superior performance.
Is Maximal Lactate Accumulation Rate Promising for Improving 5000-m
Prediction in Running?
Authors: Quittmann Oliver Jan , Foitschik Tina , Vafa Ramin , Freitag Finn Jannis , Sparmann Nordin , Nolte Simon , Abel Thomas 
Published: December 2022

AbstractEndurance running performance can be predicted by maximal oxygen uptake
(V̇O2max), the fractional utilisation of oxygen uptake
(%V̇O2max) and running economy at lactate
threshold (REOBLA). This study aims to assess maximal lactate
accumulation rate (ċLamax) in terms of improving running
performance prediction in trained athletes. Forty-four competitive female and
male runners/triathletes performed an incremental step test, a 100-m
sprint test and a ramp test to determine their metabolic profile. Stepwise
linear regression was used to predict 5000-m time trial performance. Split times
were recorded every 200-m to examine the ‘finishing kick’.
Females had a slower t5k and a lower V̇O2max,
ċLamax, ‘finishing kick’ and
REOBLA. Augmenting Joyner’s model by means of
ċLamax explained an additional 4.4% of variance
in performance. When performing the same analysis exclusively for males,
ċLamax was not included. ċLamax
significantly correlated with %V̇O2max
(r=-0.439, p=0.003) and the ‘finishing kick’
(r=0.389, p=0.010). ċLamax allows for
significant (yet minor) improvements in 5000-m performance prediction in a
mixed-sex group. This margin of improvement might differ in middle-distance
events. Due to the relationship to the ‘finishing kick’,
ċLamax might be related to individual pacing strategies,
which should be assessed in future research.
Arthroscopic Repair Versus Conservative Treatment for First-time
Anterior Shoulder Dislocation
Authors: Lei Dengqiang , Tang Bo , Li Yongjuan , Fan Cheng 
Published: December 2022

AbstractTo systematically compare the clinical efficacy of conservative treatment and
arthroscopic one-stage repair for patients with first-time anterior dislocation
of the shoulder. A retrieval and systematic review of randomized controlled
studies of primary anterior shoulder dislocation with conservative versus
arthroscopic repair was conducted and further pooled analysis was performed to
compare the overall difference in clinical outcomes, including redislocation and
revision surgery rates, shoulder function scores and related complications.
Preliminary meta-analysis showed that the risk of anterior shoulder
redislocation was 4.73-fold higher in patients treated conservatively than those
undergoing arthroscopic repair (RR:4.73 [2.95–7.58],
p<0.000; I2=0%,
p=0.920),and that the revision surgery rate was 5.91 times higher
in the conservative group than in the arthroscopic repair group (RR:5.91
[2.82–12.39], p<0.000; I2=0%,
p=0.560).Early arthroscopic repair can significantly reduce
the redislocation and revision surgery rates and also improve the shoulder joint
function score, without serious complications. However, it is necessary to
recognize the subset of patients with a higher risk of redislocation in young
patients through a high quality, blinded, prospective cohort study when early
arthroscopic repair intervention in this subset of high-risk patients will be of
significant clinical value.
Delaying Total Knee Arthroplasty More than 4 Weeks after Intra-Articular Knee Injection Does Not Further Decrease Risk of Septic Revision
Authors: Bains Sandeep S. , Chen Zhongming , Sax Oliver C. , Naziri Qais , Nace James , Delanois Ronald E. 
Published: December 2022

AbstractHyaluronic acid (HA) and corticosteroid (CS) injections are utilized in symptom management for patients with osteoarthritis. However, contamination of the joint may increase the risk of infection following total knee arthroplasty (TKA). Therefore, the purpose of this study was to examine 90-day surgical site infection (SSI) and manipulation under anesthesia (MUA) as well as up to 2-year prosthetic joint infection (PJI) in intra-articular knee injection recipients prior to TKA compared with patients who did not have knee injections. We specifically assessed (1) timing of HA and CS prior to TKA; (2) type of intra-articular knee injection; as well as (3) associated risk factors. We queried a national database to identify patients who underwent primary TKA from September 2015 to October 2020 (n = 1.5 million). Patients with prior knee injections were stratified to five cohorts: HA within 4 weeks (n = 140), HA 4 to 6 weeks prior (n = 337), CS within 4 weeks (n = 2,344), CS 4 to 6 weeks (n = 2,422), and a no injection, control, cohort prior to TKA (n = 5,000). Bivariate chi-square analyses of outcomes were conducted, and multivariate regressions were used to adjust for comorbidities and assess associated risk factors. The adjusted analysis showed a significant risk in infection for patients receiving an injection within 4 weeks of TKA (p < 0.023) and showed no difference in type of injection (p > 0.050). Additionally, SSIs were increased 1.58 times in the CS within 4 weeks of TKA cohort (p = 0.023). However, no difference in MUA risk was shown at 90-day postoperative (p > 0.212). Furthermore, tobacco use was identified as a risk factor that further increased likelihood of PJI. Intra-articular knee injection less than 4 weeks before TKA increased the risk for PJI; however, past 4 weeks did not confer infection risk. Tobacco use was identified as an associated risk factor that further increased likelihood for PJI. These results highlight the need for surgeons to wait 4 weeks between knee injection and TKA to decrease risk of septic revision.
Maternal Origins of Neonatal Infections: What Do Obstetrician–Gynecologist Should/Could Do?
Authors: Germano Chiara , Messina Alessandro , Massaro Alessio , Attini Rossella , Leo Livio , Manzoni Paolo , Masturzo Bianca 
Published: December 2022

Neonatal infections are responsible for 20% of neonatal deaths yearly. In this review, we focused on the origins of the commoner neonatal infections, and we define the role of obstetricians. Regarding group B Streptococcus, a key measure for the prevention of neonatal infection is the vaginal–rectal culture screening at term pregnancy. Intravenous penicillin is the first-line prophylaxis at the start of labor, with intravenous ampicillin as an alternative. First-generation cephalosporins or clindamycin are recommended in case of penicillin allergy. Concerning urinary tract infections (UTIs), guidelines recommend complete urinalysis and urine culture in the first trimester of pregnancy for the screening of asymptomatic bacteriuria. For lower UTIs, guidelines recommend nitrofurantoin as first-choice antibiotic. Amoxicillin or cefalexin are second-line antibiotics. For upper UTIs, guidelines recommend cephalexin per os as first line. Candida spp. colonization affects 20% of pregnant women; however, congenital fetal candidosis and Candida amnionitis are rare. First-line treatment in case of symptomatic vaginitis during pregnancy or asymptomatic colonization during the third trimester is vaginal clotrimazole. Fluconazole is not approved in pregnancy, especially during the first trimester. Genital mycoplasmas colonization during pregnancy is usually asymptomatic and associated with bacterial vaginosis. Colonization is related to neonatal respiratory distress syndrome (RDS), bronchopulmonary dysplasia (BPD), pneumonia, chorioamnionitis, and sepsis. Macrolides are the first-line treatment along with lactobacillus supplementation. In cases of preterm premature rupture of membranes or preterm labor, ceftriaxone, clarithromycin, and metronidazole are required to prevent intra-amniotic infection. Intra-amniotic infection affects 1 to 5% of deliveries at term and one-third of preterm ones and is associated with perinatal death, early-onset neonatal sepsis, RDS, BPD, pneumonia, meningitis, and prematurity-related diseases. Guidelines recommend a combination of ampicillin and gentamicin, and in case of caesarean section, an additional dose of clindamycin or metronidazole is required. In conclusion, obstetricians should be aware that the treatment of maternal infection during pregnancy can prevent potentially lethal infections in the newborn.
Key Points
Effects of maternal dietary selenium sources on growth performance, meat quality, antioxidant status and the immune response of broiler progeny
Authors: Ashraf Shahzad , Bhatti Shaukat Ali , Yousaf Muhammad , Sahar Najam Us , Shoaib Muhammad 
Published: December 2022

Context Selenium is an important trace mineral and plays an important role in protection against oxidative stress and improving productivity of poultry. Aim This study aimed to evaluate the effects of different maternal selenium sources on growth performance, antioxidant status, immune responses, and meat quality of broiler offspring. Methods In total, 600 female and 180 male 50-week-old Ross-308 broiler breeder birds were randomly allocated to four treatments with five replicates, each having 30 females and nine males. The birds were given 2-week adaptation followed by 7-weeks of feeding of one of four experimental diets. In one dietary treatment, birds were fed a basal corn–soybean-based diet supplemented with inorganic selenium (sodium selenite, SS), while in the other three dietary treatments, birds were given either of the following organic selenium sources: selenium enriched yeast (SY), hydroxy-selenomethionine (OH-Se-Met) or l-selenomethionine (l-Se-Met), each included at the rate of 0.3 mg/kg in their experimental diets. During the 7th week of experiment, 350 eggs were collected from each dietary treatment. After incubation, 250 healthy chicks per parental dietary treatment were divided into five replicates with 50 birds each. Offspring from all parental dietary treatments were fed the same basal diet supplemented with 0.15 mg/kg of selenium from inorganic source during the starter (1–21 days) and finisher (22–35 days) phases. Key results Maternal selenium sources had no effect on feed intake, weight gain, feed conversion ratio, nutrient efficiency, carcass parameters, and antibody titre against Newcastle disease virus in broiler offspring. Drip loss and oxidative stability of breast meat, plasma total antioxidant and glutathione peroxidase status were improved in the offspring at Day 21, by inclusion of organic selenium in parent broiler breeders’ diet; however, these parameters were not affected at Day 35. Conclusions Maternal dietary organic selenium improved antioxidant status and meat quality in broiler offspring at the end of starter phase; however, dietary maternal selenium sources had no effect on productivity, immune responses, and carcass traits in broiler offspring. Implications This may provide consideration for more effective sources of selenium for improving meat quality of poultry birds especially in early stage of grow-out period.
Development of an integrated microperfusion-EEG electrode for unbiased multimodal sampling of brain interstitial fluid and concurrent neural activity
Authors: Stangler Luke A , Nicolai Evan N , Mivalt Filip , Chang Su-Youne , Kim Inyong , Kouzani Abbas Z , Bennet Kevin , Berk Michael , Uthamaraj Susheil , Burns Terry C , Worrell Gregory A , Howe Charles L 
Published: December 2022

Abstract

Objective. To modify off-the-shelf components to build a device for collecting electroencephalography (EEG) from macroelectrodes surrounded by large fluid access ports sampled by an integrated microperfusion system in order to establish a method for sampling brain interstitial fluid (ISF) at the site of stimulation or seizure activity with no bias for molecular size. Approach. Twenty-four 560 µm diameter holes were ablated through the sheath surrounding one platinum–iridium macroelectrode of a standard Spencer depth electrode using a femtosecond UV laser. A syringe pump was converted to push–pull configuration and connected to the fluidics catheter of a commercially available microdialysis system. The fluidics were inserted into the lumen of the modified Spencer electrode with the microdialysis membrane removed, converting the system to open flow microperfusion. Electrical performance and analyte recovery were measured and parameters were systematically altered to improve performance. An optimized device was tested in the pig brain and unbiased quantitative mass spectrometry was used to characterize the perfusate collected from the peri-electrode brain in response to stimulation. Main results. Optimized parameters resulted in >70% recovery of 70 kDa dextran from a tissue analog. The optimized device was implanted in the cortex of a pig and perfusate was collected during four 60 min epochs. Following a baseline epoch, the macroelectrode surrounded by microperfusion ports was stimulated at 2 Hz (0.7 mA, 200 µs pulse width). Following a post-stimulation epoch, the cortex near the electrode was stimulated with benzylpenicillin to induce epileptiform activity. Proteomic analysis of the perfusates revealed a unique inflammatory signature induced by electrical stimulation. This signature was not detected in bulk tissue ISF. Significance. A modified dual-sensing electrode that permits coincident detection of EEG and ISF at the site of epileptiform neural activity may reveal novel pathogenic mechanisms and therapeutic targets that are otherwise undetectable at the bulk tissue level.
COPPER: an ensemble deep-learning approach for identifying exclusive virus-derived small interfering RNAs in plants
Authors: Bu Yuanyuan , Jia Cangzhi , Guo Xudong , Li Fuyi , Song Jiangning 
Published: December 2022

Abstract
Antiviral defenses are one of the significant roles of RNA interference (RNAi) in plants. It has been reported that the host RNAi mechanism machinery can target viral RNAs for destruction because virus-derived small interfering RNAs (vsiRNAs) are found in infected host cells. Therefore, the recognition of plant vsiRNAs is the key to understanding the functional mechanisms of vsiRNAs and developing antiviral plants. In this work, we introduce a deep learning-based stacking ensemble approach, named computational prediction of plant exclusive virus-derived small interfering RNAs (COPPER), for plant vsiRNA prediction. COPPER used word2vec and fastText to generate sequence features and a hybrid deep learning framework, including a convolutional neural network, multiscale residual network and bidirectional long short-term memory network with a self-attention mechanism to enable precise predictions of plant vsiRNAs. Extensive benchmarking experiments with different sequence homology thresholds and ablation studies illustrated the comparative predictive performance of COPPER. In addition, the performance comparison with PVsiRNAPred conducted on an independent test dataset showed that COPPER significantly improved the predictive performance for plant vsiRNAs compared with other state-of-the-art methods. The datasets and source codes are publicly available at https://github.com/yuanyuanbu/COPPER.
Genomic islands and their role in fitness traits of two key sepsis-causing bacterial pathogens
Authors: Ilyas Mohd , Purkait Dyuti , Atmakuri Krishnamohan 
Published: December 2022

Abstract
To survive and establish a niche for themselves, bacteria constantly evolve. Toward that, they not only insert point mutations and promote illegitimate recombinations within their genomes but also insert pieces of ‘foreign’ deoxyribonucleic acid, which are commonly referred to as ‘genomic islands’ (GEIs). The GEIs come in several forms, structures and types, often providing a fitness advantage to the harboring bacterium. In pathogenic bacteria, some GEIs may enhance virulence, thus altering disease burden, morbidity and mortality. Hence, delineating (i) the GEIs framework, (ii) their encoded functions, (iii) the triggers that help them move, (iv) the mechanisms they exploit to move among bacteria and (v) identification of their natural reservoirs will aid in superior tackling of several bacterial diseases, including sepsis. Given the vast array of comparative genomics data, in this short review, we provide an overview of the GEIs, their types and the compositions therein, especially highlighting GEIs harbored by two important pathogens, viz. Acinetobacter baumannii and Klebsiella pneumoniae, which prominently trigger sepsis in low- and middle-income countries. Our efforts help shed some light on the challenges these pathogens pose when equipped with GEIs. We hope that this review will provoke intense research into understanding GEIs, the cues that drive their mobility across bacteria and the ways and means to prevent their transfer, especially across pathogenic bacteria.
A comprehensive review on knowledge graphs for complex diseases
Authors: Yang Yang , Lu Yuwei , Yan Wenying 
Published: December 2022

AbstractIn recent years, knowledge graphs (KGs) have gained a great deal of popularity as a tool for storing relationships between entities and for performing higher level reasoning. KGs in biomedicine and clinical practice aim to provide an elegant solution for diagnosing and treating complex diseases more efficiently and flexibly. Here, we provide a systematic review to characterize the state-of-the-art of KGs in the area of complex disease research. We cover the following topics: (1) knowledge sources, (2) entity extraction methods, (3) relation extraction methods and (4) the application of KGs in complex diseases. As a result, we offer a complete picture of the domain. Finally, we discuss the challenges in the field by identifying gaps and opportunities for further research and propose potential research directions of KGs for complex disease diagnosis and treatment.
DNA-MP: a generalized DNA modifications predictor for multiple species based on powerful sequence encoding method
Authors: Nabeel Asim Muhammad , Ali Ibrahim Muhammad , Fazeel Ahtisham , Dengel Andreas , Ahmed Sheraz 
Published: December 2022

AbstractAccurate prediction of deoxyribonucleic acid (DNA) modifications is essential to explore and discern the process of cell differentiation, gene expression and epigenetic regulation. Several computational approaches have been proposed for particular type-specific DNA modification prediction. Two recent generalized computational predictors are capable of detecting three different types of DNA modifications; however, type-specific and generalized modifications predictors produce limited performance across multiple species mainly due to the use of ineffective sequence encoding methods. The paper in hand presents a generalized computational approach “DNA-MP” that is competent to more precisely predict three different DNA modifications across multiple species. Proposed DNA-MP approach makes use of a powerful encoding method “position specific nucleotides occurrence based 117 on modification and non-modification class densities normalized difference” (POCD-ND) to generate the statistical representations of DNA sequences and a deep forest classifier for modifications prediction. POCD-ND encoder generates statistical representations by extracting position specific distributional information of nucleotides in the DNA sequences. We perform a comprehensive intrinsic and extrinsic evaluation of the proposed encoder and compare its performance with 32 most widely used encoding methods on $17$ benchmark DNA modifications prediction datasets of $12$ different species using $10$ different machine learning classifiers. Overall, with all classifiers, the proposed POCD-ND encoder outperforms existing $32$ different encoders. Furthermore, combinedly over 5-fold cross validation benchmark datasets and independent test sets, proposed DNA-MP predictor outperforms state-of-the-art type-specific and generalized modifications predictors by an average accuracy of 7% across 4mc datasets, 1.35% across 5hmc datasets and 10% for 6ma datasets. To facilitate the scientific community, the DNA-MP web application is available at https://sds_genetic_analysis.opendfki.de/DNA_Modifications/.
Multi-view contrastive heterogeneous graph attention network for lncRNA–disease association prediction
Authors: Zhao Xiaosa , Wu Jun , Zhao Xiaowei , Yin Minghao 
Published: December 2022

AbstractMotivation: Exploring the potential long noncoding RNA (lncRNA)-disease associations (LDAs) plays a critical role for understanding disease etiology and pathogenesis. Given the high cost of biological experiments, developing a computational method is a practical necessity to effectively accelerate experimental screening process of candidate LDAs. However, under the high sparsity of LDA dataset, many computational models hardly exploit enough knowledge to learn comprehensive patterns of node representations. Moreover, although the metapath-based GNN has been recently introduced into LDA prediction, it discards intermediate nodes along the meta-path and results in information loss. Results: This paper presents a new multi-view contrastive heterogeneous graph attention network (GAT) for lncRNA-disease association prediction, MCHNLDA for brevity. Specifically, MCHNLDA firstly leverages rich biological data sources of lncRNA, gene and disease to construct two-view graphs, feature structural graph of feature schema view and lncRNA-gene-disease heterogeneous graph of network topology view. Then, we design a cross-contrastive learning task to collaboratively guide graph embeddings of the two views without relying on any labels. In this way, we can pull closer the nodes of similar features and network topology, and push other nodes away. Furthermore, we propose a heterogeneous contextual GAT, where long short-term memory network is incorporated into attention mechanism to effectively capture sequential structure information along the meta-path. Extensive experimental comparisons against several state-of-the-art methods show the effectiveness of proposed framework.The code and data of proposed framework is freely available at https://github.com/zhaoxs686/MCHNLDA.