bluuebunny/crossref_metadata_2025_split huggingface dataset:
An assessment of physician assistant student diversity in the United States: a snapshot for the healthcare workforce
Authors: Bradley-Guidry Carolyn , Burwell Nicole , Dorough Ramona , Bester Vanessa , Kayingo Gerald , Suzuki Sumihiro 
Published: September 2022

Abstract
Background
The Physician Assistant (PA) workforce falls short of mirroring national demographics mainly due to a lack of diversity in student enrollment. Few studies have systematically examined diversity across PA programs at the national level, and little is known about best practices for consistently graduating a diverse group of students. We descriptively characterized the extent to which PA programs are graduating a diverse group of students and identified top performing PA programs.

Methods
Data from the Integrated Postsecondary Education Data System (IPEDS) were used to calculate the number and proportion of racial or ethnically diverse graduates. The study sample included 139 accredited PA programs that had graduated a minimum of five cohorts from 2014–2018. Within each of the United States Census Divisions, programs were ranked according to the number and proportion of graduates who were underrepresented minority (URM) race, Hispanic ethnicity, and of non-white (URM race, Hispanic, and Asian).

Results
Amongst PA programs in the United States, a large disparity in the number and proportion of racial and ethnic graduates was observed. Of 34,625 PA graduates, only 2,207 (6.4%) were Hispanic ethnicity and 1,220 (3.5%) were URM race. Furthermore, a large number of diverse graduates came from a small number of top performing programs.

Conclusion
Despite the abundance of evidence for the need to diversify the healthcare workforce, PA programs have had difficulty recruiting and graduating a diverse group of students. This study provides empirical evidence that PA programs have not been able to attain the level of diversity necessary to shift the lack of diversity in the PA workforce. Based upon this study's findings, the top performing PA programs can be used as role models to establish benchmarks for other programs. The results of this descriptive study are currently being used to guide a qualitative study to identify the top performers’ strategies for success.
Entrepreneurial intention, expectations of success and self-efficacy in undergraduate students of health sciences
Authors: Romero-Galisteo Rita-Pilar , González-Sánchez Manuel , Gálvez-Ruiz Pablo , Palomo-Carrión Rocío , Casuso-Holgado Maria Jesus , Pinero-Pinto Elena 
Published: September 2022

Abstract
Background
Entrepreneurial intention is considered to be the best predictor of entrepreneurial behaviour. The Theory of Planned Behaviour (TPB) explains the degree of correlation between variables such as entrepreneurial intention, perceived feasibility and perceived desirability. Knowing the entrepreneurial intention of students of Health Sciences will help to guide and promote effective university policies to support entrepreneurship. The authors aimed to analyse the entrepreneurial intention of university students in the field of Health Sciences.

Methods
A cross-sectional study was conducted in the Faculties of Health Sciences of two public universities of Southern Spain. 1518 students of different degrees of Health Sciences (Physiotherapy, Podiatry, Dentistry, Nursing and Occupational Therapy), from first to fourth year. An online structured questionnaire was used, the Entrepreneurial Event Model (EEM) adapted to the Spanish context. This measurement model was completed with 8 items from the Motivated Strategies for Learning Questionnaire-MSLQ.

Results
The hypothetical model showed that perceived desirability and perceived feasibility were positive and significant predictors of entrepreneurial intention. Perceived desirability showed an indirect effect on entrepreneurial intention through perceived feasibility. Expectation of success and self-efficacy had no direct effect on entrepreneurial intention.

Conclusions
Perceived desirability and perceived feasibility are related to entrepreneurial intention in Health Sciences students.
The role of machine learning in developing non-magnetic resonance imaging based biomarkers for multiple sclerosis: a systematic review
Authors: Hossain Md Zakir , Daskalaki Elena , Brüstle Anne , Desborough Jane , Lueck Christian J. , Suominen Hanna 
Published: September 2022

Abstract
Background
Multiple sclerosis (MS) is a neurological condition whose symptoms, severity, and progression over time vary enormously among individuals. Ideally, each person living with MS should be provided with an accurate prognosis at the time of diagnosis, precision in initial and subsequent treatment decisions, and improved timeliness in detecting the need to reassess treatment regimens. To manage these three components, discovering an accurate, objective measure of overall disease severity is essential. Machine learning (ML) algorithms can contribute to finding such a clinically useful biomarker of MS through their ability to search and analyze datasets about potential biomarkers at scale. Our aim was to conduct a systematic review to determine how, and in what way, ML has been applied to the study of MS biomarkers on data from sources other than magnetic resonance imaging.

Methods
Systematic searches through eight databases were conducted for literature published in 2014–2020 on MS and specified ML algorithms.

Results
Of the 1, 052 returned papers, 66 met the inclusion criteria. All included papers addressed developing classifiers for MS identification or measuring its progression, typically, using hold-out evaluation on subsets of fewer than 200 participants with MS. These classifiers focused on biomarkers of MS, ranging from those derived from omics and phenotypical data (34.5% clinical, 33.3% biological, 23.0% physiological, and 9.2% drug response). Algorithmic choices were dependent on both the amount of data available for supervised ML (91.5%; 49.2% classification and 42.3% regression) and the requirement to be able to justify the resulting decision-making principles in healthcare settings. Therefore, algorithms based on decision trees and support vector machines were commonly used, and the maximum average performance of 89.9% AUC was found in random forests comparing with other ML algorithms.

Conclusions
ML is applicable to determining how candidate biomarkers perform in the assessment of disease severity. However, applying ML research to develop decision aids to help clinicians optimize treatment strategies and analyze treatment responses in individual patients calls for creating appropriate data resources and shared experimental protocols. They should target proceeding from segregated classification of signals or natural language to both holistic analyses across data modalities and clinically-meaningful differentiation of disease.
Single-cell analysis of menstrual endometrial tissues defines phenotypes associated with endometriosis
Authors: Shih Andrew J. , Adelson Robert P. , Vashistha Himanshu , Khalili Houman , Nayyar Ashima , Puran Radha , Herrera Rixsi , Chatterjee Prodyot K. , Lee Annette T. , Truskinovsky Alexander M. , Elmaliki Kristine , DeFranco Margaret , Metz Christine N. , Gregersen Peter K. 
Published: September 2022

AbstractBackgroundEndometriosis is a common, complex disorder which is underrecognized and subject to prolonged delays in diagnosis. It is accompanied by significant changes in the eutopic endometrial lining.MethodsWe have undertaken the first single-cell RNA-sequencing (scRNA-Seq) comparison of endometrial tissues in freshly collected menstrual effluent (ME) from 33 subjects, including confirmed endometriosis patients (cases) and controls as well as symptomatic subjects (who have chronic symptoms suggestive of endometriosis but have not been diagnosed).ResultsWe identify a unique subcluster of proliferating uterine natural killer (uNK) cells in ME-tissues from controls that is almost absent from endometriosis cases, along with a striking reduction of total uNK cells in the ME of cases (p < 10−16). In addition, an IGFBP1+ decidualized subset of endometrial stromal cells are abundant in the shed endometrium of controls when compared to cases (p < 10−16) confirming findings of compromised decidualization of cultured stromal cells from cases. By contrast, endometrial stromal cells from cases are enriched in cells expressing pro-inflammatory and senescent phenotypes. An enrichment of B cells in the cases (p = 5.8 × 10−6) raises the possibility that some may have chronic endometritis, a disorder which predisposes to endometriosis.ConclusionsWe propose that characterization of endometrial tissues in ME will provide an effective screening tool for identifying endometriosis in patients with chronic symptoms suggestive of this disorder. This constitutes a major advance, since delayed diagnosis for many years is a major clinical problem in the evaluation of these patients. Comprehensive analysis of ME is expected to lead to new diagnostic and therapeutic approaches to endometriosis and other associated reproductive disorders such as female infertility.
CCL7 as a novel inflammatory mediator in cardiovascular disease, diabetes mellitus, and kidney disease
Authors: Chang Ting-Ting , Chen Ching , Chen Jaw-Wen 
Published: September 2022

AbstractChemokines are key components in the pathology of chronic diseases. Chemokine CC motif ligand 7 (CCL7) is believed to be associated with cardiovascular disease, diabetes mellitus, and kidney disease. CCL7 may play a role in inflammatory events by attracting macrophages and monocytes to further amplify inflammatory processes and contribute to disease progression. However, CCL7-specific pathological signaling pathways need to be further confirmed in these chronic diseases. Given the multiple redundancy system among chemokines and their receptors, further experimental and clinical studies are needed to clarify whether direct CCL7 inhibition mechanisms could be a promising therapeutic approach to attenuating the development of cardiovascular disease, diabetes mellitus, and kidney disease.
Recent advances and limitations of mTOR inhibitors in the treatment of cancer
Authors: Ali Eunus S. , Mitra Kangkana , Akter Shamima , Ramproshad Sarker , Mondal Banani , Khan Ishaq N. , Islam Muhammad Torequl , Sharifi-Rad Javad , Calina Daniela , Cho William C. 
Published: September 2022

AbstractThe PI3K-Akt-mechanistic (formerly mammalian) target of the rapamycin (mTOR) signaling pathway is important in a variety of biological activities, including cellular proliferation, survival, metabolism, autophagy, and immunity. Abnormal PI3K-Akt-mTOR signalling activation can promote transformation by creating a cellular environment conducive to it. Deregulation of such a system in terms of genetic mutations and amplification has been related to several human cancers. Consequently, mTOR has been recognized as a key target for the treatment of cancer, especially for treating cancers with elevated mTOR signaling due to genetic or metabolic disorders. In vitro and in vivo, rapamycin which is an immunosuppressant agent actively suppresses the activity of mTOR and reduces cancer cell growth. As a result, various sirolimus-derived compounds have now been established as therapies for cancer, and now these medications are being investigated in clinical studies. In this updated review, we discuss the usage of sirolimus-derived compounds and other drugs in several preclinical or clinical studies as well as explain some of the challenges involved in targeting mTOR for treating various human cancers.
Targeting therapy and tumor microenvironment remodeling of triple-negative breast cancer by ginsenoside Rg3 based liposomes
Authors: Xia Jiaxuan , Zhang Shuya , Zhang Ru , Wang Anni , Zhu Ying , Dong Meichen , Ma Shaojie , Hong Chao , Liu Shengyao , Wang Dan , Wang Jianxin 
Published: September 2022

AbstractThe chemotherapy effect of docetaxel (DTX) against triple-negative breast cancer (TNBC) remains mediocre and limited when encapsulated in conventional cholesterol liposomes, mainly ascribed to poor penetration and immunosuppressive tumor microenvironment (TME) caused by tumor stroma cells, especially cancer-associated fibroblasts (CAFs). Many studies have attempted to address these problems but trapped into the common dilemma of excessively complicated formulation strategies at the expense of druggability as well as clinical translational feasibility. To better address the discrepancy, ginsenoside Rg3 was utilized to substitute cholesterol to develop a multifunctional DTX-loaded Rg3 liposome (Rg3-Lp/DTX). The obtained Rg3-Lp/DTX was proved to be preferentially uptake by 4T1 cells and accumulate more at tumor site via the interaction between the glycosyl moiety of Rg3 exposed on liposome surface and glucose transporter1 (Glut1) overexpressed on tumor cells. After reaching tumor site, Rg3 was shown to reverse the activated CAFs to the resting stage and attenuate the dense stroma barrier by suppressing secretion of TGF-β from tumor cells and regulating TGF-β/Smad signaling. Therefore, reduced levels of CAFs and collagens were found in TME after incorporation of Rg3, inducing enhanced penetration of Rg3-Lp/DTX in the tumor and reversed immune system which can detect and neutralize tumor cells. Compared with wooden cholesterol liposomes, the smart and versatile Rg3-Lp/DTX could significantly improve the anti-tumor effect of DTX, providing a promising approach for TNBC therapy with excellent therapeutic efficacy and simple preparation process.
Is bone marrow oedema in patients with labral tear an indicator of hip pain?
Authors: Koyama Tomohisa , Fukushima Kensuke , Uchida Kentaro , Ohashi Yoshihisa , Uchiyama Katsufumi , Takahira Naonobu , Takaso Masashi 
Published: September 2022

Abstract
Background
Hip labral tear (LT) causes various degrees of hip pain, for which there are few objective measures. Bone marrow oedema (BME), characterized by a diffuse, widely spreading change in the bone marrow, is observed in some patients with LT. However, its pathological role has not been fully understood. The purpose of this study was to investigate the prevalence of BME on hip magnetic resonance imaging (MRI) in patients with LT and to determine whether BME was an objective indicator of hip pain.

Methods
In total, 84 patients with LT who underwent MRI scanning under the same conditions were included. We determined the presence or absence of BME and its size on MRI and evaluated the relationships between BME and sex, age, and pain and total scores on the modified Harris hip score (MHHS). In addition, we collected data on surgical treatments such as hip arthroscopy within a one-year follow-up period and examined whether the presence of BME affected the course of therapy.

Results
BME was found in 34.5% of patients. MHHS pain and total scores were significantly lower in patients with BME (MHHS pain score: non-BME vs. BME ≤ 1 cm: p = 0.022, non-BME vs. BME > 1 cm: p < 0.001; MHHS total score: non-BME vs. BME ≤ 1 cm: p = 0.131, non-BME vs. BME > 1 cm: p = 0.027). The presence of BME did not differ between patients who did and did not undergo surgery during follow-up (p = 0.563).

Conclusion
BME on MRI in patients with LT might be an indicator of hip pain and hip joint dysfunction.
Increased behavioral problems in children with sleep-disordered breathing
Authors: Csábi Eszter , Gaál Veronika , Hallgató Emese , Schulcz Rebeka Anna , Katona Gábor , Benedek Pálma 
Published: September 2022

Abstract
Background
Healthy sleep is essential for the cognitive, behavioral and emotional development of children. Therefore, this study aimed to assess the behavioral consequences of sleep disturbances by examining children with sleep-disordered breathing compared with control participants.

Methods
Seventy-eight children with SDB (average age: 6.7 years (SD = 1.83); 61 had OSA and 17 had primary snoring) and 156 control subjects (average age: 6.57 years (SD = 1.46) participated in the study. We matched the groups in age (t(232) = 0.578, p = 0.564) and gender (χ2(1) = 2.192, p = 0.139). In the SDB group, the average Apnea–Hypopnea Index was 3.44 event/h (SD = 4.00), the average desaturation level was 87.37% (SD = 6.91). Parent-report rating scales were used to measure the children’s daytime behavior including Attention Deficit Hyperactivity Disorder Rating Scale, Strengths and Difficulties Questionnaire, and Child Behavior Checklist.

Results
Our results showed that children with SDB exhibited a higher level of inattentiveness and hyperactive behavior. Furthermore, the SDB group demonstrated more internalizing (anxiety, depression, somatic complaints, social problems) (p < 0.001) and externalizing (aggressive and rule-breaking behavior) problems compared with children without SDB, irrespective of severity.

Conclusions
Based on our findings we supposed that snoring and mild OSA had a risk for developing behavioral and emotional dysfunctions as much as moderate-severe OSA. Therefore, clinical research and practice need to focus more on the accurate assessment and treatment of sleep disturbances in childhood, particularly primary snoring, and mild obstructive sleep apnea.
Comparative study of left atrium epicardial fat tissue pattern using persistent homology approach
Authors: Deepa Deepa , Singh Yashbir , Mansoor Wathiq , Hu Weichih , Paul Rahul , Carlsson Gunnar E. 
Published: September 2022

Abstract
Objective
Atrial Fibrillation (A-fib) is an abnormal heartbeat condition in which the heart races and beats in an uncontrollable way. It is observed that the presence of increased epicardial fat/fatty tissue in the atrium can lead to A-fib. Persistent homology using topological features can be used to recapitulate enormous amounts of spatially complicated medical data into a visual code to identify a specific pattern of epicardial fat tissue with non-fat tissue. Our aim is to evaluate the topological pattern of left atrium epicardial fat tissue with non-fat tissue.

Results
A topological data analysis approach was acquired to study the imaging pattern between the left atrium epicardial fat tissue and non-fat tissue patches. The patches of eight patients from CT images of the left atrium heart were used and categorized into “left atrium epicardial fat tissue” and “non-fat tissue” groups. The features that distinguish the “epicardial fat tissue” and “non-fat tissue” groups are extracted using persistent homology (PH). Our result reveals that our proposed research can discriminate between left atrium epicardial fat tissue and non-fat tissue. Specifically, the range of Betti numbers in the epicardial tissue is smaller (0–30) than the non-fat tissue (0–100), indicating that non-fat tissue has good topology.
RETRACTED ARTICLE: Second-order impulsive differential systems of mixed type: oscillation theorems
Authors: Santra Shyam Sundar , Scapellato Andrea , Moaaz Osama 
Published: September 2022

AbstractIn this paper, we obtain necessary and sufficient conditions for the oscillation of solutions to a second-order neutral differential equation with impulses. Two examples are provided to show the effectiveness and feasibility of the main results. Our main tool is Lebesgue’s dominated convergence theorem.
Exclusion of tourist species from assemblages in ecological studies: a methodological approach using spiders
Authors: Nadal María Florencia , González Alda , Avalos Gilberto 
Published: September 2022

Abstract
Background
The exclusion of tourist species from samples is important to avoid bias in community analyses. However, in practice, this is a very difficult task. The commonly used methods by researchers, when the habitat of the species is not known, have several shortcomings: first, they exclude not only pseudo-rare species but also genuinely rare species; second, the results obtained with those methods depend on the abundance of the sampling; and third, they follow very subjective rules. The aims of this study were: (i) to propose a methodology to detect and exclude habitat-tourist species from the database used to carry out analyses in community ecology studies, (ii) to evaluate how the presence of habitat-tourist species affects the richness estimates, and (iii) to assess the effect of including juvenile spiders in the detection of tourist species and the effect of removing them from the richness estimates.

Results
When the adult + juvenile dataset was considered, twenty-one habitat-tourist species were detected: 8 in forest foliage, 11 in forest leaf litter, and 2 in grassland. When habitat-tourist species were considered with this dataset, richness overestimation was significant in foliage and in leaf litter, and the final slopes of the richness estimation curves were significantly steeper in leaf litter. When only the adult dataset was considered, eight habitat-tourist species were detected: 3 in forest foliage, 4 in forest leaf litter, and just one in grassland. The inclusion of habitat-tourist species in this dataset showed an overestimation of richness, but this was not significant.

Conclusions
The proposed methodology contributes to solving the problem of tourist species, which was recognized as one of the great problems in biodiversity studies. This study showed that common estimators overestimate species richness when habitat-tourist species are included, leading to erroneous conclusions. Besides, this research showed that the inclusion of juveniles (e.g. spiders) could improve the analysis outputs because it allowed the detection of more habitat-tourist species.
A review of brain imaging biomarker genomics in Alzheimer’s disease: implementation and perspectives
Authors: Li Lanlan , Yu Xianfeng , Sheng Can , Jiang Xueyan , Zhang Qi , Han Ying , Jiang Jiehui 
Published: September 2022

AbstractAlzheimer’s disease (AD) is a progressive neurodegenerative disease with phenotypic changes closely associated with both genetic variants and imaging pathology. Brain imaging biomarker genomics has been developed in recent years to reveal potential AD pathological mechanisms and provide early diagnoses. This technique integrates multimodal imaging phenotypes with genetic data in a noninvasive and high-throughput manner. In this review, we summarize the basic analytical framework of brain imaging biomarker genomics and elucidate two main implementation scenarios of this technique in AD studies: (1) exploring novel biomarkers and seeking mutual interpretability and (2) providing a diagnosis and prognosis for AD with combined use of machine learning methods and brain imaging biomarker genomics. Importantly, we highlight the necessity of brain imaging biomarker genomics, discuss the strengths and limitations of current methods, and propose directions for development of this research field.