bluuebunny/crossref_metadata_2025_split huggingface dataset:
Rare disorders have many faces: in silico characterization of rare disorder spectrum
Authors: Frederiksen Simona D. , Avramović Vladimir , Maroilley Tatiana , Lehman Anna , Arbour Laura , Tarailo-Graovac Maja 
Published: February 2022

AbstractBackgroundThe diagnostic journey for many rare disease patients remains challenging despite use of latest genetic technological advancements. We hypothesize that some patients remain undiagnosed due to more complex diagnostic scenarios that are currently not considered in genome analysis pipelines. To better understand this, we characterized the rare disorder (RD) spectrum using various bioinformatics resources (e.g., Orphanet/Orphadata, Human Phenotype Ontology, Reactome pathways) combined with custom-made R scripts.ResultsOur in silico characterization led to identification of 145 borderline-common, 412 rare and 2967 ultra-rare disorders. Based on these findings and point prevalence, we would expect that approximately 6.53%, 0.34%, and 0.30% of individuals in a randomly selected population have a borderline-common, rare, and ultra-rare disorder, respectively (equaling to 1 RD patient in 14 people). Importantly, our analyses revealed that (1) a higher proportion of borderline-common disorders were caused by multiple gene defects and/or other factors compared with the rare and ultra-rare disorders, (2) the phenotypic expressivity was more variable for the borderline-common disorders than for the rarer disorders, and (3) unique clinical characteristics were observed across the disorder categories forming the spectrum.ConclusionsRecognizing that RD patients who remain unsolved even after genome sequencing might belong to the more common end of the RD spectrum support the usage of computational pipelines that account for more complex genetic and phenotypic scenarios.
Artificial intelligence in mammographic phenotyping of breast cancer risk: a narrative review
Authors: Gastounioti Aimilia , Desai Shyam , Ahluwalia Vinayak S. , Conant Emily F. , Kontos Despina 
Published: February 2022

AbstractBackgroundImproved breast cancer risk assessment models are needed to enable personalized screening strategies that achieve better harm-to-benefit ratio based on earlier detection and better breast cancer outcomes than existing screening guidelines. Computational mammographic phenotypes have demonstrated a promising role in breast cancer risk prediction. With the recent exponential growth of computational efficiency, the artificial intelligence (AI) revolution, driven by the introduction of deep learning, has expanded the utility of imaging in predictive models. Consequently, AI-based imaging-derived data has led to some of the most promising tools for precision breast cancer screening.Main bodyThis review aims to synthesize the current state-of-the-art applications of AI in mammographic phenotyping of breast cancer risk. We discuss the fundamentals of AI and explore the computing advancements that have made AI-based image analysis essential in refining breast cancer risk assessment. Specifically, we discuss the use of data derived from digital mammography as well as digital breast tomosynthesis. Different aspects of breast cancer risk assessment are targeted including (a) robust and reproducible evaluations of breast density, a well-established breast cancer risk factor, (b) assessment of a woman’s inherent breast cancer risk, and (c) identification of women who are likely to be diagnosed with breast cancers after a negative or routine screen due to masking or the rapid and aggressive growth of a tumor. Lastly, we discuss AI challenges unique to the computational analysis of mammographic imaging as well as future directions for this promising research field.ConclusionsWe provide a useful reference for AI researchers investigating image-based breast cancer risk assessment while indicating key priorities and challenges that, if properly addressed, could accelerate the implementation of AI-assisted risk stratification to future refine and individualize breast cancer screening strategies.
Efficacy and safety of Tengfu Jiangya tablet combined with valsartan/amlodipine in the treatment of stage 2 hypertension: study protocol for a randomized controlled trial
Authors: Wang Yu , Hua Zhen , Chen Wenjing , Zhu Yushuo , Li Yunlun 
Published: February 2022

AbstractBackgroundThe prevalence rate of hypertension in the Chinese population is on the rise, and the control rate of hypertension is low. International guidelines, including the 2018 Chinese Guidelines for the Management of Hypertension, recommend optimized drug selection and combination therapy for patients with stage 2 hypertension and blood pressure ≥ 160/100 mmHg, including valsartan/amlodipine (Val/Aml). The traditional Chinese medicine (TCM) compound Tengfu Jiangya tablet (TJT; No. Z20110021, Shandong Provincial Food and Drug Administration) is prepared in the medical institution of Affiliated Hospital of Shandong University of Traditional Chinese Medicine. It is an effective compound preparation of TCM for the treatment of hypertension in the national clinical research base of TCM. The aim of this study was to evaluate the efficacy and safety of TJT combined with Val/Aml in the treatment of stage 2 hypertension with hyperactivity of liver yang.MethodsThis randomized double-blind, placebo-controlled, multicenter trial will be conducted with a total of 288 participants with stage 2 hypertension at seven clinical trial centers. The stratified random method will be used, and the subcenter will be taken as the stratification factor. Eligible patients will be randomly assigned (1:1) into groups receiving either TJT or placebo three times daily for 28 days, both combined with Val/Aml 80/5 mg. The primary efficacy endpoint is the reduction in the mean sitting systolic blood pressure (msSBP) and the mean sitting diastolic blood pressure (msDBP) from baseline to week 4. Adverse events and laboratory test results will be monitored throughout the trial.DiscussionThis is the first placebo-controlled randomized trial conducted to evaluate the efficacy and safety of a Chinese herbal extract combined with Val/Aml in patients with stage 2 hypertension. Our study may help to provide evidence-based recommendations of a complementary preventive measure for stage 2 hypertension.Trial registrationChinese Clinical Trial RegistryChiCTR2000030611. Registered on 8 March 2020
Comparing the efficacy and safety of minimally invasive biportal endoscopic spine surgery versus conventional microscopic discectomy in single-level lumbar herniated intervertebral disc (ENDO-BH Trial): a multicenter, prospective, randomized controlled equivalence trial study protocol
Authors: Park Sang-Min , Song Kwang-Sup , Kim Ho-Joong , Park Si-Young , Kang Taewook , Kang Min-Seok , Heo Dong Hwa , Park Choon Keun , Lee Dong-Geun , Hwang Jin Sub , Jang Jae-Won , Kim Jun Young , Kim Jin-Sung , Lee Hong-Jae , You Ki-Han , Park Hyun-Jin 
Published: February 2022

Abstract
Background
Biportal endoscopic surgery has recently been performed in lumbar discectomy, with advantages over conventional surgery, such as less skin scarring and muscle damage. However, the clinical results have not been established. Although previous studies reported no difference between the biportal endoscopic and microscopic discectomy clinical results, the evidence was weak. Therefore, this study aims to evaluate the efficacy and safety of the biportal endoscopic discectomy versus the microscopic discectomy.

Methods
This prospective multicenter randomized controlled equivalence trial is designed to compare the efficacy and safety outcomes of patients who underwent lumbar discectomy using biportal endoscopy or microscopy. We will include 100 participants (50 per group) with a lumbar herniated disc. The primary outcome will be the Oswestry Disability Index (ODI) score 12 months after surgery based on a modified intention-to-treat strategy. The secondary outcomes will include the visual analog scale score for low back and lower extremity radiating pain, the ODI score, the Euro-Qol-5-Dimensions score, surgery satisfaction, walking time, postoperative return to daily life period, postoperative surgical scar, and surgery-related variables, such as postoperative drainage, operation time, admission duration, postoperative creatine kinase, and implementation status of conversion to open surgery. Radiographic outcomes will also be analyzed using magnetic resonance imaging (MRI) or computed tomography (CT) and simple radiographs. Safety will be assessed by evaluating all adverse and severe adverse events and surgery-related effects. The participants will be assessed by a blinded assessor before surgery (baseline) and 2 weeks and 3, 6, and 12 months after surgery.

Discussion
This trial will be the first prospective, multicenter, randomized controlled trial to analyze the efficacy and safety of biportal endoscopic discectomy in lumbar herniated disc.
This trial is designed for evaluating the equivalence of the results between biportal endoscopic and microscopic discectomy including adequate sample size, blinded analyses, and prospective registration to reduce bias. This trial will provide enough data on the effectiveness and safety of biportal endoscopic surgery and will be an important study that allows clear conclusions.

Trial registration
Clinical Research Information Service (cris.nih.go.kr.) (KCT0006191). Registered on 27 March 2021
Sympatric occurrence of Ixodes ricinus with Dermacentor reticulatus and Haemaphysalis concinna and the associated tick-borne pathogens near the German Baltic coast
Authors: Răileanu Cristian , Tauchmann Oliver , Silaghi Cornelia 
Published: February 2022

Abstract
Background
Ixodid ticks from the Northern Hemisphere have registered a northward expansion in recent years, and Dermacentor reticulatus is such an example in Europe, its expansion being considered a result of climate change alongside other factors. The aim of this study was to identify the composition of questing tick species and the associated pathogens at different sites near the German Baltic coast.

Methods
Questing ticks were collected monthly at four sites (May–November, 2020), mainly grasslands, and in October and November 2020 at a fifth site. Molecular screening of ticks for pathogens included RT-qPCR for the tick-borne encephalitis virus (TBEV), qPCR for Anaplasma phagocytophilum, PCR for Babesia species and Rickettsia species, and nested PCR for Borrelia species.

Results
Altogether 1174 questing ticks were collected: 760 Ixodes ricinus, 326 D. reticulatus and 88 Haemaphysalis concinna. The highest activity peak of I. ricinus and D. reticulatus was in May, in June for H. concinna while a second peak was observed only for I. ricinus and D. reticulatus in September and October, respectively. All samples tested negative for TBEV. For A. phagocytophilum, 1.5% of I. ricinus adults tested positive while the minimum infection rate (MIR) in nymphs was 1.3%. This pathogen was found in 0.6% of D. reticulatus. Babesia spp. were detected in I. ricinus (18.2% adults, 2.1% MIR in nymphs) and H. concinna (13.3% adults, 9.7% MIR in nymphs). Borrelia spp. were present only in I. ricinus (49.1% adults, 11.9% MIR in nymphs), while Rickettsia spp. were detected in I. ricinus (14% adults, 8.9% MIR in nymphs) and D. reticulatus (82%). Co-detection of pathogens was observed in 26.6% and 54.8% of positive I. ricinus adults and nymph pools, respectively, while one D. reticulatus tested positive for A. phagocytophilum and Rickettsia spp. The most common co-infection in I. ricinus adults was Babesia microti and Borrelia afzelii (12.3% of positive ticks).

Conclusions
The results of this study confirm the northern expansion of D. reticulatus and H. concinna in Germany. The detailed data of the infection levels at each location could be useful in assessing the risk of pathogen acquisition following a tick bite.

Graphical Abstract
DevKidCC allows for robust classification and direct comparisons of kidney organoid datasets
Authors: Wilson Sean B. , Howden Sara E. , Vanslambrouck Jessica M. , Dorison Aude , Alquicira-Hernandez Jose , Powell Joseph E. , Little Melissa H. 
Published: February 2022

Abstract
Background
While single-cell transcriptional profiling has greatly increased our capacity to interrogate biology, accurate cell classification within and between datasets is a key challenge. This is particularly so in pluripotent stem cell-derived organoids which represent a model of a developmental system. Here, clustering algorithms and selected marker genes can fail to accurately classify cellular identity while variation in analyses makes it difficult to meaningfully compare datasets. Kidney organoids provide a valuable resource to understand kidney development and disease. However, direct comparison of relative cellular composition between protocols has proved challenging. Hence, an unbiased approach for classifying cell identity is required.

Methods
The R package, scPred, was trained on multiple single cell RNA-seq datasets of human fetal kidney. A hierarchical model classified cellular subtypes into nephron, stroma and ureteric epithelial elements. This model, provided in the R package DevKidCC (github.com/KidneyRegeneration/DevKidCC), was then used to predict relative cell identity within published kidney organoid datasets generated using distinct cell lines and differentiation protocols, interrogating the impact of such variations. The package contains custom functions for the display of differential gene expression within cellular subtypes.

Results
DevKidCC was used to directly compare between distinct kidney organoid protocols, identifying differences in relative proportions of cell types at all hierarchical levels of the model and highlighting variations in stromal and unassigned cell types, nephron progenitor prevalence and relative maturation of individual epithelial segments. Of note, DevKidCC was able to distinguish distal nephron from ureteric epithelium, cell types with overlapping profiles that have previously confounded analyses. When applied to a variation in protocol via the addition of retinoic acid, DevKidCC identified a consequential depletion of nephron progenitors.

Conclusions
The application of DevKidCC to kidney organoids reproducibly classifies component cellular identity within distinct single-cell datasets. The application of the tool is summarised in an interactive Shiny application, as are examples of the utility of in-built functions for data presentation. This tool will enable the consistent and rapid comparison of kidney organoid protocols, driving improvements in patterning to kidney endpoints and validating new approaches.
Mobile applications for the sport and exercise nutritionist: a narrative review
Authors: Peart Daniel J. , Briggs Marc A. , Shaw Matthew P. 
Published: February 2022

AbstractMobile technology is widespread in modern society, and the applications (apps) that they run can serve various purposes. Features such as portability, ease of communication, storage, and relative low cost may make such technology attractive to practitioners in several fields. This review provides a critical narrative on the existing literature for apps relevant to the field of sport and exercise nutrition. Three main areas are discussed: (1) dietary analysis of athletes, (2) nutrition education for athletes, (3) estimating body composition. The key purpose of the review was to identify what literature is available, in what areas apps may have a benefit over traditional methods, and considerations that practitioners should make before they implement apps into their practice or recommend their use to coaches and athletes.
Relationship of sex differences in cortical thickness and memory among cognitively healthy subjects and individuals with mild cognitive impairment and Alzheimer disease
Authors: Cieri Filippo , Zhuang Xiaowei , Cordes Dietmar , Kaplan Nikki , Cummings Jeffery , Caldwell Jessica ,   for the Alzheimer’s Disease Neuroimaging Initiative (ADNI)
Published: February 2022

Abstract
Background
An aging society has increased rates of late onset Alzheimer disease dementia (ADD), the most common form of age-related dementia. This neurodegenerative disease disproportionately affects women.

Methods
We use data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) to examine sex differences in cortical thickness (CT) and memory performance. Analyses of covariance (ANCOVA) models were used to examine effects of sex and diagnosis (DX) on CT and verbal memory. For regions demonstrating significant interaction effects of sex and DX, we tested whether sex moderated cognition-thickness relationships. We used machine learning as a complementary method to explore multivariate CT differences between women and men.

Results
Women demonstrated greater CT in many brain regions. More specifically, men showed relatively consistent CT declines in all stages, from normal control (NC) to ADD in the bilateral cingulate cortex, bilateral temporal regions, and left precuneus; women had more stable CT in these regions between NC and mild cognitive impairment (MCI) stages, but sharper declines from MCI to ADD. Similarly, for the Rey Auditory Verbal Learning Test (RAVLT), ANCOVA analyses showed that women had significantly better immediate and delayed recall scores than men, at NC and MCI stages, but greater differences, cross-sectionally, from MCI to ADD than men. We found significant sex moderation effects between RAVLT-immediate scores and CT of right isthmus-cingulate for all subjects across DX. Partial correlation analyses revealed that increased CT of right isthmus-cingulate was associated with better verbal learning in women, driven by positron emission tomography defined amyloid positive (Aβ+) subjects. Significant sex-moderation effects in cognition-thickness relationships were further found in the right middle-temporal, left precuneus, and left superior temporal regions in Aβ+ subjects. Using a machine learning approach, we investigated multivariate CT differences between women and men, showing an accuracy in classification of 75% for Aβ+ cognitively NC participants.

Conclusions
Sex differences in memory and CT can play a key role in the different vulnerability and progression of ADD in women compared to men. Machine learning indicates sex differences in CT are most relevant early in the ADD neurodegeneration.
A multipurpose TNM stage ontology for cancer registries
Authors: Nicholson Nicholas Charles , Giusti Francesco , Bettio Manola , Carvalho Raquel Negrao , Dimitrova Nadya , Dyba Tadeusz , Flego Manuela , Neamtiu Luciana , Randi Giorgia , Martos Carmen 
Published: February 2022

Abstract
Background
Population-based cancer registries are a critical reference source for the surveillance and control of cancer. Cancer registries work extensively with the internationally recognised TNM classification system used to stage solid tumours, but the system is complex and compounded by the different TNM editions in concurrent use. TNM ontologies exist but the design requirements are different for the needs of the clinical and cancer-registry domains. Two TNM ontologies developed specifically for cancer registries were designed for different purposes and have limitations for serving wider application. A unified ontology is proposed to serve the various cancer registry TNM-related tasks and reduce the multiplication effects of different ontologies serving specific tasks. The ontology is comprehensive of the rules for TNM edition 7 as required by cancer registries and designed on a modular basis to allow extension to other TNM editions.

Results
A unified ontology was developed building on the experience and design of the existing ontologies. It follows a modular approach allowing plug in of components dependent upon any particular TNM edition. A Java front-end was developed to interface with the ontology via the Web Ontology Language application programme interface and enables batch validation or classification of cancer registry records. The programme also allows the means of automated error correction in some instances. Initial tests verified the design concept by correctly inferring TNM stage and successfully handling the TNM-related validation checks on a number of cancer case records, with a performance similar to that of an existing ontology dedicated to the task.

Conclusions
The unified ontology provides a multi-purpose tool for TNM-related tasks in a cancer registry and is scalable for different editions of TNM. It offers a convenient way of quickly checking validity of cancer case stage information and for batch processing of multi-record data via a dedicated front-end programme. The ontology is adaptable to many uses, either as a standalone TNM module or as a component in applications of wider focus. It provides a first step towards a single, unified TNM ontology for cancer registries.
Multiple species animal movements: network properties, disease dynamics and the impact of targeted control actions
Authors: Cardenas Nicolas C. , Sykes Abagael L. , Lopes Francisco P. N. , Machado Gustavo 
Published: February 2022

AbstractInfectious diseases in livestock are well-known to infect multiple hosts and persist through a combination of within- and between-host transmission pathways. Uncertainty remains about the epidemic dynamics of diseases being introduced on farms with more than one susceptible host species. Here, we describe multi-host contact networks and elucidate the potential of disease spread through farms with multiple hosts. Four years of between-farm animal movement among all farms of a Brazilian state were described through a static and monthly snapshot of network representations. We developed a stochastic multilevel model to simulate scenarios in which infection was seeded into single host and multi-host farms to quantify disease spread potential, and simulate network-based control actions used to evaluate the reduction of secondarily infected farms. We showed that the swine network was more connected than cattle and small ruminants in both the static and monthly snapshots. The small ruminant network was highly fragmented, however, contributed to interconnecting farms, with other hosts acting as intermediaries throughout the networks. When a single host was initially infected, secondary infections were observed across farms with all other species. Our stochastic multi-host model demonstrated that targeting the top 3.25% of the farms ranked by degree reduced the number of secondarily infected farms. The results of the simulation highlight the importance of considering multi-host dynamics and contact networks while designing surveillance and preparedness control strategies against pathogens known to infect multiple species.