bluuebunny/crossref_metadata_2025_split huggingface dataset:
Potential Space in Hospitals: Insight From a Health Psychologist
Authors: Grignoli Nicola 
Published: December 2020

Objective: Humanization is a challenge for the future of healthcare. Architecture may play a major role in designing spaces that enhance communication and help the patient to maintain mental health during physical illness. Health psychologists struggle to find adequate space for taking care of their patients. There is an urgent need to better define how relational space, defined here as potential, can be guaranteed in everyday hospital psychological consultations. Background: The author relates to his work as a health psychologist and psychotherapist in a consultation-liaison psychiatry (CLP) service operating in a general hospital in Lugano (Switzerland). Methods: An autoethnographic method is applied through calling on childhood memories on architecture and analyzing insights regarding the healthcare space in everyday work as a psychologist. Photographs and drawings are employed as evocative material. Results: Autoethnographical data show that building interiors can be a metaphor for an inner dimension. Spaces can be perceived as depersonalized in hospital. Through psychoanalytical theory, it is argued that space becomes ideal for CLP if it can ensure the continuity of the patient’s self during hospitalization. Proximity, confidentiality, and privacy are healthcare design requirements to be considered for favoring potential space and psychological intervention. Conclusion: Fostering potential space represents an outstanding challenge for the hospital of tomorrow in order to humanize healthcare spaces and promote a person-centered approach.
A Comparison of Various Surgical Treatments for Degenerative Cervical Myelopathy: A Propensity Score Matched Analysis
Authors: Lee Nathan J. , Kim Jun S. , Park Paul , Riew K. Daniel 
Published: December 2020

Study Design: Retrospective Cohort. Objective: To compare the short-term outcomes for Laminoplasty, Laminectomy/fusion, and ACDF. Methods: We utilized a prospectively-collected, multi-center national database with a propensity score matching algorithm to compare the short-term outcomes for laminoplasty, laminectomy/fusion, and multi-level (>3) ACDF (with and without corpectomy). Bivariate analyses involved both chi-square/fisher exact test and t-test/ANOVA on perioperative factors. Multivariate analyses were performed to determined independent risk factors for short term outcomes. Results: 546 patients remained after propensity score matching, with 182 patients in each cohort. ACDF required the longest operative time 188 ± 79 versus laminectomy/fusion (169 ± 75, p = 0.017), and laminoplasty (167 ± 66, p = 0.004). ACDF required the shortest hospital stay (LOS ≥ 2: ACDF 56.6%, laminoplasty 89.6%, laminectomy/fusion 93.4%, p < 0.05). ACDF had lower overall complications (ACDF 3.9%, laminoplasty 7.7%, laminectomy/fusion 11.5%, p < 0.05), mortality (ACDF 0%, laminoplasty 0.55%, laminectomy/fusion 2.2%, p < 0.05), and unplanned readmissions (ACDF 4.4%, laminoplasty 4.4%, laminectomy/fusion 9.9%, p < 0.05). No significant differences were seen in the other outcomes including DVT/PT, acute renal failure, UTI, stroke, cardiac complications, or sepsis. In the multivariate analysis, laminectomy/fusion (OR 17, reference: ACDF) and laminoplasty (OR10, reference: ACDF) were strong independent risk factors for LOS ≥ 2 days. Laminectomy/fusion (OR 3.2, reference: ACDF) was an independent predictor for any adverse events 30-days after surgery. Conclusions: Laminectomy/fusion carries the highest risk for morbidity, mortality, and unplanned readmissions in the short-term postoperative period. Laminoplasty and ACDF cases carry similar short-term complications risks. ACDF is significantly associated with the longest operative duration and shortest LOS without an increase in individual or overall complications, readmissions, or reoperations
The Association Between the Rotator Cuff Status and the Severity and Recovery of Weakness in the Shoulder Abductor Strength in a Case of Proximal Type Cervical Spondylotic Amyotrophy
Authors: Ishiwata Sho , Iizuka Yoichi , Shitara Hitoshi , Mieda Tokue , Takasawa Eiji , Tsunoda Daisuke , Kakuta Yohei , Honda Akira , Ito Shunsuke , Inomata Kazuhiro , Sasaki Tsuyoshi , Hamano Noritaka , Tajika Tsuyoshi , Chikuda Hirotaka 
Published: December 2020

Study Design: Case series study. Objective: We aimed to clarify the prevalence of rotator cuff tear (RCT), and the association between RCT and the severity and prognosis in patients with proximal type cervical spondylotic amyotrophy (CSA). Methods: We retrospectively analyzed 35 proximal type CSA patients who were treated conservatively. The following data was collected: age, rotator cuff status on MRI, manual muscle test (MMT) score of shoulder abductor and biceps brachii muscles both at the first visit and final follow-up. We investigated the prevalence of RCT and the association between the rotator cuff status, and the severity and recovery of upper extremity weakness in patients with proximal type CSA. Results: Of the 35 patients, 21 had an RCT on MRI, indicating that the prevalence of RCT in patients with proximal type CSA was 60%. An age-adjusted analysis showed that the presence of RCT was significantly associated with the MMT score of the shoulder abductor muscles both at the first visit and at the final follow-up. The presence of RCT was significantly associated with the recovery of the shoulder abductor muscles. The size of the RCT was negatively correlated with the MMT score of the shoulder abductor muscles at the final follow-up. The size of the RCT was independently correlated with the change of the MMT score of the shoulder abductor muscles. Conclusion: RCT was detected in >50% in patients with proximal type CSA, and the presence and severity of RCT can be used as prognostic factors for proximal type CSA patients who are treated conservatively.
Profile of Marital and Sexual Life of Patients With Schizophrenia and Recurrent Depressive Disorder: A Comparative Study
Authors: Aggarwal Shivali , Grover Sandeep , Chakrabarti Subho 
Published: December 2020

Aim: To evaluate the marital profile, family size, fertility rates, and perception about various aspects of marriage among the couples, with one of the partner having schizophrenia and compare the same with a group of couples, with one of the partner having recurrent depressive disorder (RDD). Methodology: A total of 76 married patients with schizophrenia and 58 married patients with RDD were evaluated by using a semistructured interview, which covered information about the duration of marriage, number of children, different aspects of marital and sexual life like perception about marriage, mental illness, sexual compatibility, and other variables. Results: A significantly higher proportion of patients with schizophrenia had onset of illness prior to marriage, when compared to the patients with RDD. There was no significant difference between the 2 groups in terms of mean number of children, number of spontaneous abortions, and medical termination of pregnancies. The mean numbers of postnatal deaths were significantly higher in the group in which one of the partners was diagnosed with schizophrenia. Compared to spouses of patients with RDD, higher proportion of the spouses of patients with schizophrenia reported sexual incompatibility since the beginning of the marriage and lack of full satisfaction with sexual life. Compared to patients with RDD, higher proportion of patients with schizophrenia were worried about their children developing mental illness and believed that people with mental illness should not marry. Conclusion: To conclude, this study demonstrates that compared to the spouses of patients with RDD, spouses of patients with schizophrenia have higher negative perception about the marriage.
Sexual Behavior During the Times of COVID-19-Related Lockdown in India: Results From an Online Survey
Authors: Gondwal Rohit , Pal Arghya , Paul Sayanti , Bohra Richarani , Aulakh Arvinder Pal Singh , Bhat Ashwani 
Published: December 2020

Introduction: Novel coronavirus (COVID-19) pandemic is the worst health emergency the world has seen in the last 100 years. It has adversely affected human life both directly and indirectly. The pandemic has led various countries to opt for lockdown to control the outbreak. India had also enforced lockdown in the country from 25 March 2020 in various phases. Lay reports have been speculative about the effects of this lockdown on the sexual practices of people. Method: The current study was a cross-sectional, online study performed to evaluate change in sexual behavior during the period of lockdown using a structured pro forma that studied the sociodemographic parameters, Arizona sexual experience scale, and sexual behavior of respondents before and during the lockdown. Result: Overall, 290 respondents were analyzed. Our research showed that there has been a decrease in the practice of vaginal sex during the lockdown but, the practice of anal sex increased among respondents who preferred it. Sexual experiences were better in respondents who were females, married, and working in essential services. Conclusion: The effect of lockdown on sexual behavior has been extensive, with couples adopting newer activities in their repertoire. The lockdown has also affected sexual behavior by its indirect effect on family life like increased time spent together within the boundaries of walls, direct or indirect financial loss, and lack of recreational options. There is a considerable amount of heterogeneity in the studies in this field and more studies are warranted.
Male Perpetrators of Intimate Partner Violence: MCMI III Profiles, Grossman Facet Scales, Family of Origin Experiences and Attitudes Toward Women in a Small Cohort of Men in India
Authors: Nigudkar Advaita , Wandrekar Jagruti , Osrin David , Daruwalla Nayreen 
Published: December 2020

Background: Personality psychopathology, substance abuse, family of origin experiences, and patriarchal attitudes are variables associated with intimate partner violence. Aim: This pilot study aimed to explore clinical and personality profiles and psychosocial variables in a small cohort of male perpetrators of intimate partner violence. Methodology: 20 men from informal settlements presenting to crisis counseling centers in Mumbai were administered the Millon Clinical Mulitiaxial Inventory III, the Revised Conflict Tactics Scale Short Form, and the Attitudes toward Women scale. Results: Millon Clinical Mulitiaxial Inventory III profiles and scores on Grossman Facet scales suggest personality psychopathology in the profiles of 95% of the men, and 85% reported anxiety. Less than 20% reported substance abuse. The men reported mutuality of violence in the relationship and espoused moderately liberal attitudes toward women. 55% of them reported violence in the family of origin. Conclusion: Our findings are helpful in providing therapeutic pointers for working with male perpetrators of violence.
Screening for pulmonary arterial hypertension in adults carrying aBMPR2mutation
Authors: Montani David , Girerd Barbara , Jaïs Xavier , Laveneziana Pierantonio , Lau Edmund M.T. , Bouchachi Amir , Hascoët Sébastien , Günther Sven , Godinas Laurent , Parent Florence , Guignabert Christophe , Beurnier Antoine , Chemla Denis , Hervé Philippe , Eyries Mélanie , Soubrier Florent , Simonneau Gérald , Sitbon Olivier , Savale Laurent , Humbert Marc 
Published: December 2020

BackgroundHeritable pulmonary arterial hypertension (PAH) is most commonly due to heterozygous mutations of theBMPR2gene. Based on expert consensus, guidelines recommend annual screening echocardiography in asymptomaticBMPR2mutation carriers. The main objectives of this study were to evaluate the characteristics of asymptomaticBMPR2mutation carriers, assess their risk of occurrence of PAH and detect PAH at an early stage in this high-risk population.MethodsAsymptomaticBMPR2mutation carriers underwent screening at baseline and annually for a minimum of 2 years (DELPHI-2 study;ClinicalTrials.gov:NCT01600898). Annual screening included clinical assessment, ECG, pulmonary function tests, 6-min walk distance, cardiopulmonary exercise testing, chest radiography, echocardiography and brain natriuretic peptide (BNP) or N-terminal (NT)-proBNP level. Right heart catheterisation (RHC) was performed based on predefined criteria. An optional RHC at rest and exercise was proposed at baseline.Results55 subjects (26 males; median age 37 years) were included. At baseline, no PAH was suspected based on echocardiography and NT-proBNP levels. All subjects accepted RHC at inclusion, which identified two mild PAH cases (3.6%) and 12 subjects with exercise pulmonary hypertension (21.8%). At long-term follow-up (118.8 patient-years of follow-up), three additional cases were diagnosed, yielding a PAH incidence of 2.3% per year (0.99% per year in males and 3.5% per year in females). All PAH cases remained at low-risk status on oral therapy at last follow-up.ConclusionsAsymptomaticBMPR2mutation carriers have a significant risk of developing incident PAH. International multicentre studies are needed to confirm that refined multimodal screening programmes with regular follow-up allow early detection of PAH.
Transcriptome analysis and differential gene expression profiling of two contrasting quinoa genotypes in response to salt stress
Authors: Shi Pibiao , Gu Minfeng 
Published: December 2020

Abstract
Background
Soil salinity is one of the major abiotic stress factors that affect crop growth and yield, which seriously restricts the sustainable development of agriculture. Quinoa is considered as one of the most promising crops in the future for its high nutrition value and strong adaptability to extreme weather and soil conditions. However, the molecular mechanisms underlying the adaptive response to salinity stress of quinoa remain poorly understood. To identify candidate genes related to salt tolerance, we performed reference-guided assembly and compared the gene expression in roots treated with 300 mM NaCl for 0, 0.5, 2, and 24 h of two contrasting quinoa genotypes differing in salt tolerance.

Results
The salt-tolerant (ST) genotype displayed higher seed germination rate and plant survival rate, and stronger seedling growth potential as well than the salt-sensitive (SS) genotype under salt stress. An average of 38,510,203 high-quality clean reads were generated. Significant Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were identified to deeper understand the differential response. Transcriptome analysis indicated that salt-responsive genes in quinoa were mainly related to biosynthesis of secondary metabolites, alpha-Linolenic acid metabolism, plant hormone signal transduction, and metabolic pathways. Moreover, several pathways were significantly enriched amongst the differentially expressed genes (DEGs) in ST genotypes, such as phenylpropanoid biosynthesis, plant-pathogen interaction, isoquinoline alkaloid biosynthesis, and tyrosine metabolism. One hundred seventeen DEGs were common to various stages of both genotypes, identified as core salt-responsive genes, including some transcription factor members, like MYB, WRKY and NAC, and some plant hormone signal transduction related genes, like PYL, PP2C and TIFY10A, which play an important role in the adaptation to salt conditions of this species. The expression patterns of 21 DEGs were detected by quantitative real-time PCR (qRT-PCR) and confirmed the reliability of the RNA-Seq results.

Conclusions
We identified candidate genes involved in salt tolerance in quinoa, as well as some DEGs exclusively expressed in ST genotype. The DEGs common to both genotypes under salt stress may be the key genes for quinoa to adapt to salinity environment. These candidate genes regulate salt tolerance primarily by participating in reactive oxygen species (ROS) scavenging system, protein kinases biosynthesis, plant hormone signal transduction and other important biological processes. These findings provide theoretical basis for further understanding the regulation mechanism underlying salt tolerance network of quinoa, as well establish foundation for improving its tolerance to salinity in future breeding programs.
Sleep duration and all-cause mortality in the elderly in China: a population-based cohort study
Authors: Ren Yanfeng , Miao Maohua , Yuan Wei , Sun Jiangwei 
Published: December 2020

Abstract
Background
Although a U-shaped association between sleep duration and all-cause mortality has been found in general population, its association in the elderly adults, especially in the oldest-old, is rarely explored.

Methods
In present cohort study, we prospectively explore the association between sleep duration and all-cause mortality among 15,092 participants enrolled in the Chinese Longitudinal Healthy Longevity Survey (CLHLS) from 2005 to 2019. Sleep duration and death information was collected by using structured questionnaires. Cox regression model with sleep duration as a time-varying exposure was performed to calculate the hazard ratios (HRs) and 95% confidence intervals (CIs). The dose-response association between them was explored via a restricted cubic spline function.

Results
During an average follow-up of 4.51 (standard deviation, SD: 3.62) years, 10,768 participants died during the follow-up period. The mean (SD) age of the participants was 89.26 (11.56) years old. Compared to individuals with moderate sleep duration (7–8 hours), individuals with long sleep duration (> 8 hours) had a significantly higher risk of all-cause mortality (HR: 1.13, 95%CI: 1.09–1.18), but not among individuals with short sleep duration (≤ 6 hours) (HR: 1.02, 95%CI: 0.96–1.09). Similar results were observed in subgroup analyses based on age and gender. In the dose-response analysis, a J-shaped association was observed.

Conclusions
Sleep duration was associated with all-cause mortality in a J-shaped pattern in the elderly population in China.
Stress detection using deep neural networks
Authors: Li Russell , Liu Zhandong 
Published: December 2020

Abstract
Background
Over 70% of Americans regularly experience stress. Chronic stress results in cancer, cardiovascular disease, depression, and diabetes, and thus is deeply detrimental to physiological health and psychological wellbeing. Developing robust methods for the rapid and accurate detection of human stress is of paramount importance.

Methods
Prior research has shown that analyzing physiological signals is a reliable predictor of stress. Such signals are collected from sensors that are attached to the human body. Researchers have attempted to detect stress by using traditional machine learning methods to analyze physiological signals. Results, ranging between 50 and 90% accuracy, have been mixed. A limitation of traditional machine learning algorithms is the requirement for hand-crafted features. Accuracy decreases if features are misidentified. To address this deficiency, we developed two deep neural networks: a 1-dimensional (1D) convolutional neural network and a multilayer perceptron neural network. Deep neural networks do not require hand-crafted features but instead extract features from raw data through the layers of the neural networks. The deep neural networks analyzed physiological data collected from chest-worn and wrist-worn sensors to perform two tasks. We tailored each neural network to analyze data from either the chest-worn (1D convolutional neural network) or wrist-worn (multilayer perceptron neural network) sensors. The first task was binary classification for stress detection, in which the networks differentiated between stressed and non-stressed states. The second task was 3-class classification for emotion classification, in which the networks differentiated between baseline, stressed, and amused states. The networks were trained and tested on publicly available data collected in previous studies.

Results
The deep convolutional neural network achieved 99.80% and 99.55% accuracy rates for binary and 3-class classification, respectively. The deep multilayer perceptron neural network achieved 99.65% and 98.38% accuracy rates for binary and 3-class classification, respectively. The networks’ performance exhibited a significant improvement over past methods that analyzed physiological signals for both binary stress detection and 3-class emotion classification.

Conclusions
We demonstrated the potential of deep neural networks for developing robust, continuous, and noninvasive methods for stress detection and emotion classification, with the end goal of improving the quality of life.
Predicting mortality in critically ill patients with diabetes using machine learning and clinical notes
Authors: Ye Jiancheng , Yao Liang , Shen Jiahong , Janarthanam Rethavathi , Luo Yuan 
Published: December 2020

Abstract
Background
Diabetes mellitus is a prevalent metabolic disease characterized by chronic hyperglycemia. The avalanche of healthcare data is accelerating precision and personalized medicine. Artificial intelligence and algorithm-based approaches are becoming more and more vital to support clinical decision-making. These methods are able to augment health care providers by taking away some of their routine work and enabling them to focus on critical issues. However, few studies have used predictive modeling to uncover associations between comorbidities in ICU patients and diabetes. This study aimed to use Unified Medical Language System (UMLS) resources, involving machine learning and natural language processing (NLP) approaches to predict the risk of mortality.

Methods
We conducted a secondary analysis of Medical Information Mart for Intensive Care III (MIMIC-III) data. Different machine learning modeling and NLP approaches were applied. Domain knowledge in health care is built on the dictionaries created by experts who defined the clinical terminologies such as medications or clinical symptoms. This knowledge is valuable to identify information from text notes that assert a certain disease. Knowledge-guided models can automatically extract knowledge from clinical notes or biomedical literature that contains conceptual entities and relationships among these various concepts. Mortality classification was based on the combination of knowledge-guided features and rules. UMLS entity embedding and convolutional neural network (CNN) with word embeddings were applied. Concept Unique Identifiers (CUIs) with entity embeddings were utilized to build clinical text representations.

Results
The best configuration of the employed machine learning models yielded a competitive AUC of 0.97. Machine learning models along with NLP of clinical notes are promising to assist health care providers to predict the risk of mortality of critically ill patients.

Conclusion
UMLS resources and clinical notes are powerful and important tools to predict mortality in diabetic patients in the critical care setting. The knowledge-guided CNN model is effective (AUC = 0.97) for learning hidden features.