bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Utility of Facial Nerve Ultrasonography in Bell’s Palsy
Authors: Baek Seol‐Hee , Kim Yoo Hwan , Kwon Ye‐Ji , Sung Joo Hye , Son Myeong Hun , Lee Jung Hun , Kim Byung‐Jo 
Published: December 2019

ObjectiveThis study aimed to investigate the utility of facial nerve ultrasonography in the functional and structural assessment of early‐stage Bell’s palsy and the prognostic value of facial nerve ultrasonography in Bell’s palsy.Study DesignProspective longitudinal study.SettingSingle center, a university‐affiliated neurology clinic.Subjects and MethodsPatients with unilateral Bell’s palsy who had visited our clinic within 3 days of symptom onset were enrolled in this study. Demographic information and House‐Brackmann grade were collected. Electrophysiologic studies and facial nerve ultrasonography were then performed. The facial nerves on each side were scanned longitudinally with a 5‐ to 12‐MHz probe. The diameter of the facial nerves with and without the sheath was measured at the proximal and distal portions. Follow‐up examinations, including House‐Brackmann grade analysis, electrophysiologic studies, and facial nerve ultrasonography, were performed after 2 months.ResultsFifty‐four patients with unilateral Bell’s palsy were enrolled, and 22 underwent the follow‐up examinations. The diameters of the facial nerves were larger on the affected side than on the unaffected side at the proximal and distal portions (P <. 01). On the affected side, the enlarged facial nerve at the proximal portion had decreased in size after 2 months (P <. 05). The initial ultrasonography findings were positively correlated with the initial severity of Bell’s palsy, but they did not predict prognosis.ConclusionUltrasonography could be a useful tool for evaluating the facial nerve in Bell’s palsy. Nevertheless, further studies are needed to demonstrate its prognostic value.
Effects of human umbilical cord mesenchymal stem cells on inflammatory factors and miR-181a in T lymphocytes from patients with systemic lupus erythematosus
Authors: Zheng B , Zhang P , Yuan L , Chhetri R K , Guo Y , Deng D 
Published: December 2019

Objectives The present study aimed to explore the effect of umbilical cord mesenchymal stem cells (UC-MSCs) on the modulation of T lymphocytes from system lupus erythematosus (SLE) patients and the possible mechanism. Methods A total of 24 hospitalized SLE patients and 28 healthy individuals were enrolled. T lymphocytes were sorted using Miltenyi magnetic beads. After the addition of recombinant human interleukin (IL)-2 and CD3CD28 T-cell activator, cells were loaded onto six-well plates pre-inoculated or not with UC-MSCs for 1 week of culture. The supernatants were collected for testing inflammatory factors by enzyme-linked immunosorbent assay. Meanwhile, T lymphocytes were collected to assess the expression levels of genes, proteins in relation to SLE and miR-181a by polymerase chain reaction and Western blot. Results Compared with T lymphocytes cultured alone, interferon-γ, IL-4, IL-6 and IL-10 levels were significantly decreased in T lymphocytes from SLE patients co-cultured with UC-MSCs. In addition, the gene and protein expression levels of TNF alpha, osteopontin and nuclear factor-kappa B in T lymphocytes were significantly decreased, while miR-181a expression was markedly elevated ( p < 0.05 or 0.008). Conclusion UC-MSCs have showed certain immunomodulatory and inhibitory effects in vitro on T lymphocytes from SLE patients, which could potentially be a beneficial treatment of the disease. UC-MSCs may up-regulate miR-181a and down-regulate inflammation-related gene expression.
Correlation of Placental Magnetic Resonance Imaging With Histopathologic Diagnosis: Detection of Aberrations in Structure and Water Diffusivity
Authors: Bockoven Crystal , Gastfield Roland D , Victor Thomas , Venkatasubramanian Palamadai N , Wyrwicz Alice M , Ernst Linda M 
Published: December 2019

Objective Noninvasive methods to identify placental pathologic conditions are being sought in order to recognize these conditions at an earlier stage leading to improved clinical interventions and perinatal outcomes. The objective of this study was to examine fixed tissue slices of placenta by T2- and diffusion-weighted magnetic resonance imaging (MRI) and correlate the images with placental pathologic findings defined by routine gross and histologic examination. Methods Four formalin-fixed placentas with significant placental pathology (maternal vascular malperfusion, chronic villitis of unknown etiology, and massive perivillous fibrin deposition) and 2 histologically normal placentas were evaluated by high-resolution MRI. Representative placental slices were selected (2 cm long and 10 mm wide) and rehydrated. Imaging was performed on a Bruker Avance 14.1 T microimager. Diffusion-weighted images were acquired from 16 slices using slice thickness 0.5 mm and in-plane resolution approximately 100 µm × 100 µm. T2 maps were obtained from the same slices. T2 relaxation time and apparent diffusion coefficient (ADC) were acquired from representative regions of interest and compared between normal and diseased placentas. Results In T2- and diffusion-weighted images, the placental microstructure differed subjectively between diseased and normal placentas. Furthermore, diseased placentas showed statistically significantly longer mean T2 relaxation times and generally higher mean ADC. Conclusion Diffusion- and T2-weighted MRI can potentially be used to detect significant placental pathology by using T2 relaxation time and ADC as markers of altered placental microstructure.
Blood Biomarkers of Parenchymal Damage in Ischemic Stroke Patients Treated With Revascularization Therapies
Authors: Piccardi Benedetta , Biagini Silvia , Iovene Veronica , Palumbo Vanessa 
Published: December 2019

Purpose: Postischemic reperfusion injury may exacerbate cerebral damage and capillary dysfunction, leading to brain edema (BE), hemorrhagic transformation (HT), necrosis, and injury from free radicals with subsequent infarct growth (IG). Several plasmatic biomarkers involved in the ischemic cascade have been studied in relation to radiological and clinical outcomes of reperfusion injury in ischemic stroke with heterogeneous results. This article provides a brief overview of the contribution of circulating biomarkers to the pathophysiology of parenchymal damage in ischemic stroke patients treated with revascularization therapies. Methods: We included full reports with measurements of plasma markers in patients with acute ischemic stroke treated with revascularization therapies. Findings: Our research included a large number of observational studies investigating a possible role of circulating biomarkers in the development of parenchymal damage after acute stroke treatments. To make the results clearer, we divided the review in 4 sections, exploring the relation of different biomarkers with each of the indicators of parenchymal damage (HT, BE, IG, recanalization). Discussion and conclusion: Definite conclusions are difficult to draw because of heterogeneity across studies. However, our review seems to confirm an association between some circulating biomarkers (particularly matrix metalloproteinase-9) and occurrence of parenchymal damage in ischemic stroke patients treated with revascularization therapies.
Association of Single-Nucleotide Polymorphisms With Chronic Rhinosinusitis in a Southwestern Han Chinese Population: A Replication Study
Authors: Xu Yang , Zheng Yongbo , Cao Min , Yang Wen , Ren Jianjun , Song Yao , Cheng Danni , Wang Jing , Huang Ligao , Xu Wei , Zhao Yu , Liu Geoffrey 
Published: December 2019

Background Chronic rhinosinusitis (CRS) is a multifactorial inflammatory disease. The role of genetic variations of related genes in the development of CRS and severity of symptoms is unknown in Southwestern Chinese populations. Objective We selected candidate CRS-related genetic polymorphisms and evaluated the associations that were different according to the presence of nasal polyp, asthma, and allergic rhinitis (AR) in a Southwestern Chinese population. Detailed phenotypes were compared among different genotypes. Methods In 452 CRS patients and 591 healthy controls, clinico-epidemiological information was collected and 23 previously reported CRS-related single-nucleotide polymorphisms (SNPs) were genotyped. Genotypes were determined using a Sequenom MassARRAY SNP genotyping system. Clinical disease measures including the sinonasal outcome test, visual analogue scale (VAS), and symptom severity VAS were evaluated for each patient. The association between CRS, genotypes, asthma, AR, and symptoms was analyzed. The effect of sex, age, body mass index, and status of smoking was considered. Results Statistically significant genotypic association with CRS was observed with an IL1RL1 genetic polymorphism (rs13431828; odds ratio [OR] = 1.45; 95% confidence interval [CI], 1.06–1.99; P = .02). Similar association was observed with rs13431828 in subgroups of CRS with nasal polyps (OR = 1.53; 95% CI, 1.03–2.29; P = .04), asthma (OR = 2.08; 95% CI, 1.14–3.79; P = .02), and AR (OR = 1.59; 95% CI, 1.06–2.39; P = .02). No significant association with other SNPs was observed. The evaluated genetic polymorphisms were not associated with clinical symptom scores. Conclusion This study replicated rs13431828 as being associated with CRS in Southwestern Chinese. rs13431828 was also significantly associated with CRS patients who have concurrent allergic nasal diseases.
Successful and unsuccessful recruitment and retainment strategies in a UK multicentre drug trial for a rare chronic pain condition which performed above target
Authors: Bisla Jatinder , Ambler Gareth , Frank Bernhard , Gulati Sumit , Hocken Poppy , James Mairi , Kelly Joanna , Keshet-Price Jocelyn , McCabe Candy , McGylnn Deborah , Padfield Nick , Pang David , Pout Gill , Sanders Mark , Serpell Mick , Shenker Nicholas , Shoukrey Karim , Wesley Samuel , Weston Margaret , White-Alao Beverly , Wyatt Lynne , Murphy Caroline , Goebel Andreas 
Published: December 2019

Introduction: Recruitment into trials in rare chronic pain conditions can be challenging, so such trials consequently are underpowered or fail. Methods: Drawing from our experience in conducting, to date, the largest academic trial in a rare chronic pain condition, complex regional pain syndrome, we have identified recruitment and retention strategies for successful trial conduct. Results: We present 13 strategies grouped across the categories of ‘setting the recruitment rate’, ‘networking’, ‘patient information’, ‘trial management’ and ‘patient retention’. Moreover, six recruitment risks are also discussed. A conservative recruitment estimate, based on audits of newly referred patients to the trial centres without taking into account availability of ‘old’ patients or recruitment from outside centres, and assuming a 55% patient refusal rate yielded accurate numbers. Conclusion: Appreciation of these identified recruitment challenges and opportunities may contribute to supporting prospective investigators when they design clinical trials for chronic pain patient population groups where it has been historically difficult to conduct high-quality and robust clinical trials.
Is It All About the Fascia? A Systematic Review and Meta-analysis of the Prevalence of Extramuscular Connective Tissue Lesions in Muscle Strain Injury
Authors: Wilke Jan , Hespanhol Luiz , Behrens Martin 
Published: December 2019

Background: The fascia has been demonstrated to represent a potential force transmitter intimately connected to the underlying skeletal muscle. Sports-related soft tissue strains may therefore result in damage to both structures. Purpose: To elucidate the prevalence of connective tissue lesions in muscle strain injury and their potential impact on return-to-play (RTP) duration. Study Design: Systematic review; Level of evidence, 3. Methods: Imaging studies describing frequency, location, and extent of soft tissue lesions in lower limb muscle strain injuries were identified by 2 independent investigators. Weighted proportions (random effects) were pooled for the occurrence of (1) myofascial or fascial lesions, (2) myotendinous lesions, and (3) purely muscular lesions. Study quality was evaluated by means of an adapted Downs and Black checklist, which evaluates reporting, risk of bias, and external validity. Results: A total of 16 studies (fair to good methodological quality) were identified. Prevalence of strain injury on imaging studies was 32.1% (95% CI, 24.2%-40.4%) for myofascial lesions, 68.4% (95% CI, 59.6%-76.6%) for myotendinous lesions, and 12.7% (95% CI, 3.0%-27.7%) for isolated muscular lesions. Evidence regarding associations between fascial damage and RTP duration was mixed. Conclusion: Lesions of the collagenous connective tissue, namely the fascia and the tendinous junction, are highly prevalent in athletic muscle strain injuries. However, at present, their impact on RTP duration is unclear and requires further investigation.
Learning misclassification costs for imbalanced classification on gene expression data
Authors: Lu Huijuan , Xu Yige , Ye Minchao , Yan Ke , Gao Zhigang , Jin Qun 
Published: December 2019

Abstract
Background
Cost-sensitive algorithm is an effective strategy to solve imbalanced classification problem. However, the misclassification costs are usually determined empirically based on user expertise, which leads to unstable performance of cost-sensitive classification. Therefore, an efficient and accurate method is needed to calculate the optimal cost weights.

Results
In this paper, two approaches are proposed to search for the optimal cost weights, targeting at the highest weighted classification accuracy (WCA). One is the optimal cost weights grid searching and the other is the function fitting. Comparisons are made between these between the two algorithms above. In experiments, we classify imbalanced gene expression data using extreme learning machine to test the cost weights obtained by the two approaches.

Conclusions
Comprehensive experimental results show that the function fitting method is generally more efficient, which can well find the optimal cost weights with acceptable WCA.
Detecting overlapping protein complexes in weighted PPI network based on overlay network chain in quotient space
Authors: Zhao Jie , Lei Xiujuan 
Published: December 2019

Abstract
Background
Protein complexes are the cornerstones of many biological processes and gather them to form various types of molecular machinery that perform a vast array of biological functions. In fact, a protein may belong to multiple protein complexes. Most existing protein complex detection algorithms cannot reflect overlapping protein complexes. To solve this problem, a novel overlapping protein complexes identification algorithm is proposed.

Results
In this paper, a new clustering algorithm based on overlay network chain in quotient space, marked as ONCQS, was proposed to detect overlapping protein complexes in weighted PPI networks. In the quotient space, a multilevel overlay network is constructed by using the maximal complete subgraph to mine overlapping protein complexes. The GO annotation data is used to weight the PPI network. According to the compatibility relation, the overlay network chain in quotient space was calculated. The protein complexes are contained in the last level of the overlay network. The experiments were carried out on four PPI databases, and compared ONCQS with five other state-of-the-art methods in the identification of protein complexes.

Conclusions
We have applied ONCQS to four PPI databases DIP, Gavin, Krogan and MIPS, the results show that it is superior to other five existing algorithms MCODE, MCL, CORE, ClusterONE and COACH in detecting overlapping protein complexes.
Ranking near-native candidate protein structures via random forest classification
Authors: Wu Hongjie , Huang Hongmei , Lu Weizhong , Fu Qiming , Ding Yijie , Qiu Jing , Li Haiou 
Published: December 2019

Abstract
Background
In ab initio protein-structure predictions, a large set of structural decoys are often generated, with the requirement to select best five or three candidates from the decoys. The clustered central structures with the most number of neighbors are frequently regarded as the near-native protein structures with the lowest free energy; however, limitations in clustering methods and three-dimensional structural-distance assessments make identifying exact order of the best five or three near-native candidate structures difficult.

Results
To address this issue, we propose a method that re-ranks the candidate structures via random forest classification using intra- and inter-cluster features from the results of the clustering. Comparative analysis indicated that our method was better able to identify the order of the candidate structures as comparing with current methods SPICKR, Calibur, and Durandal. The results confirmed that the identification of the first model were closer to the native structure in 12 of 43 cases versus four for SPICKER, and the same as the native structure in up to 27 of 43 cases versus 14 for Calibur and up to eight of 43 cases versus two for Durandal.

Conclusions
In this study, we presented an improved method based on random forest classification to transform the problem of re-ranking the candidate structures by an binary classification. Our results indicate that this method is a powerful method for the problem and the effect of this method is better than other methods.
Predicting drug-target interactions from drug structure and protein sequence using novel convolutional neural networks
Authors: Hu ShanShan , Zhang Chenglin , Chen Peng , Gu Pengying , Zhang Jun , Wang Bing 
Published: December 2019

Abstract
Background
Accurate identification of potential interactions between drugs and protein targets is a critical step to accelerate drug discovery. Despite many relative experimental researches have been done in the past decades, detecting drug-target interactions (DTIs) remains to be extremely resource-intensive and time-consuming. Therefore, many computational approaches have been developed for predicting drug-target associations on a large scale.

Results
In this paper, we proposed an deep learning-based method to predict DTIs only using the information of drug structures and protein sequences. The final results showed that our method can achieve good performance with the accuracies up to 92.0%, 90.0%, 92.0% and 90.7% for the target families of enzymes, ion channels, GPCRs and nuclear receptors of our created dataset, respectively. Another dataset derived from DrugBank was used to further assess the generalization of the model, which yielded an accuracy of 0.9015 and an AUC value of 0.9557.

Conclusion
It was elucidated that our model shows improved performance in comparison with other state-of-the-art computational methods on the common benchmark datasets. Experimental results demonstrated that our model successfully extracted more nuanced yet useful features, and therefore can be used as a practical tool to discover new drugs.

Availability
http://deeplearner.ahu.edu.cn/web/CnnDTI.htm.
NmSEER V2.0: a prediction tool for 2′-O-methylation sites based on random forest and multi-encoding combination
Authors: Zhou Yiran , Cui Qinghua , Zhou Yuan 
Published: December 2019

Abstract
Background
2′-O-methylation (2′-O-me or Nm) is a post-transcriptional RNA methylation modified at 2′-hydroxy, which is common in mRNAs and various non-coding RNAs. Previous studies revealed the significance of Nm in multiple biological processes. With Nm getting more and more attention, a revolutionary technique termed Nm-seq, was developed to profile Nm sites mainly in mRNA with single nucleotide resolution and high sensitivity. In a recent work, supported by the Nm-seq data, we have reported a method in silico for predicting Nm sites, which relies on nucleotide sequence information, and established an online server named NmSEER. More recently, a more confident dataset produced by refined Nm-seq was available. Therefore, in this work, we redesigned the prediction model to achieve a more robust performance on the new data.

Results
We redesigned the prediction model from two perspectives, including machine learning algorithm and multi-encoding scheme combination. With optimization by 5-fold cross-validation tests and evaluation by independent test respectively, random forest was selected as the most robust algorithm. Meanwhile, one-hot encoding, together with position-specific dinucleotide sequence profile and K-nucleotide frequency encoding were collectively applied to build the final predictor.

Conclusions
The predictor of updated version, named NmSEER V2.0, achieves an accurate prediction performance (AUROC = 0.862) and has been settled into a brand-new server, which is available at http://www.rnanut.net/nmseer-v2/ for free.
A systematic evaluation of copy number alterations detection methods on real SNP array and deep sequencing data
Authors: Luo Fei 
Published: December 2019

Abstract
Background
The Copy Number Alterations (CNAs) are discovered to be tightly associated with cancers, so accurately detecting them is one of the most important tasks in the cancer genomics. A series of CNAs detection methods have been proposed and new ones are still being developed. Due to the complexity of CNAs in cancers, no CNAs detection method has been accepted as the gold standard caller. Several evaluation works have made attempts to reveal typical CNAs detection methods’ performance. Limited by the scale of evaluation data, these different comparison works don’t reach a consensus and the researchers are still confused on how to choose one proper CNAs caller for their analysis. Therefore, it needs a more comprehensive evaluation of typical CNAs detection methods’ performance.

Results
In this work, we use a large-scale real dataset from CAGEKID consortium to evaluate total 12 typical CNAs detection methods. These methods are most widely used in cancer researches and always used as benchmark for the newly proposed CNAs detection methods. This large-scale dataset comprises of SNP array data on 94 samples and the whole genome sequencing data on 10 samples. Evaluations are comprehensively implemented in current scenarios of CNAs detection, which include that detect CNAs on SNP array data, on sequencing data with tumor and normal matched samples and on sequencing data with single tumor sample. Three SNP based methods are firstly ranked. Subsequently, the best SNP based method’s results are used as benchmark to compare six matched samples based methods and three single tumor sample based methods in terms of the preprocessing, recall rate, Jaccard index and segmentation characteristics.

Conclusions
Our survey thoroughly reveals 12 typical methods’ superiority and inferiority. We explain why methods show specific characteristics from a methodological standpoint. Finally, we present the guiding principle for choosing one proper CNAs detection method under specific conditions. Some unsolved problems and expectations are also addressed for upcoming CNAs detection methods.