bluuebunny/crossref_metadata_2025_split huggingface dataset:
Depression, anxiety and stress among Swedish university students during the second and third waves of COVID-19: A cohort study
Authors: Johansson Fred , Côté Pierre , Hogg-Johnson Sheilah , Skillgate Eva 
Published: July 2021

Our research group recently reported that symptom levels of depression, anxiety, and stress among Swedish university students were relatively stable during the first three months of COVID-19 and decreased slightly during the following three summer months. Since then, Sweden has faced a second and third wave of COVID-19. Aims: This study aims to describe the mean trajectories of depression, anxiety and stress symptoms among Swedish university students before and during the second and third waves of the COVID-19 pandemic. Methods: We recruited 1835 participants in September 2020, of whom 81% provided follow-ups in December 2020–January 2021 and 77% provided follow-ups in March–April 2021. The short-form Depression, Anxiety and Stress Scale was used to measure mental health symptoms. Generalized estimating equations were used to estimate the mean differences in symptom levels over the three time periods. Results: Compared with September, mean depression was 0.91 points of 21 higher (95% confidence interval (CI) 0.70–1.13) in December 2020–January 2021 and 0.66 points higher (95% CI 0.43–.88) in March–April 2021. Anxiety levels were 0.20 points higher (95% CI 0.05–0.34) in December 2020–January 2021 and 0.17 points higher (95% CI 0.02–0.33) in March–April 2021. Stress levels were 0.21 points higher (95% CI 0.00–0.41) in December 2020–January 2021 and 0.16 points lower (95% CI −0.38 to 0.05) in March–April 2021. Conclusions: Our results indicate relatively stable levels of mental health among Swedish university students during the second and third waves of COVID-19 compared with before the second wave. Mean depression symptom scores increased slightly, but the importance of this small increase is uncertain.
Effect of Periodized Resistance Training on Skeletal Muscle During Androgen Deprivation Therapy for Prostate Cancer: A Pilot Randomized Trial
Authors: Dawson Jacqueline K. , Dorff Tanya B. , Tuzon Creighton , Rice Judd C. , Schroeder Edward Todd , Lane Christianne J. , Gross Mitchell E. , Dieli-Conwright Christina M. 
Published: July 2021

Purpose: Prostate cancer survivors (PCS) receive androgen deprivation therapy (ADT) as treatment for recurrent cancer, yet ADT is associated with loss of skeletal muscle and physical function. Resistance training can counter both muscle and physical function loss; however, an understanding of the molecular responses of skeletal muscle to resistance training during ADT is still undefined. This sub-analysis of the original randomized, controlled pilot trial investigated effects of 12 weeks of periodized resistance training on mRNA expression of the anabolic genes IGF-1, myogenin, PGC-1α4 and the catabolic genes myostatin and MuRF-1 in skeletal muscle of PCS on ADT. Secondary aims investigated if changes in lean mass and physical function correlated with changes in mRNA expression. Methods: PCS on ADT (n = 17) were randomized to 12 weeks of supervised resistance training (EXE, n = 9) or home-based stretching (STRETCH, n = 8) 3 days per week. Outcomes were assessed at baseline and post-intervention. Muscle biopsies were analyzed by RT-PCR for mRNA expression. Body composition was assessed through dual-energy X-ray absorptiometry, and physical function through muscular strength, timed up and go, stair climb, and 400 m walk. Results: MuRF-1 mRNA expression was significantly greater in EXE compared to STRETCH post-intervention ( P = .005). Change in MuRF-1 mRNA expression significantly correlated with improvements in strength and physical function ( P < .05), while change in IGF-1 expression correlated with change in lean mass ( P = .015). Conclusion: Twelve weeks of resistance training increased mRNA expression of MuRF-1 in skeletal muscle of PCS on ADT. Elevations in resting mRNA expression of IGF-1, myogenin and PGC-1α4, and reduction in mRNA expression of myostatin that are typically expected following resistance training were not observed.
A Qualitative Examination Detailing Medical Student Experiences of a Novel Competency-Based Neuroanatomy eLearning Intervention Designed to Bridge a Gap Within an Integrated Medical Curriculum
Authors: Hart Elizabeth Paige , Brueckner-Collins Jennifer , Bergden Jessica S 
Published: July 2021

Background: A 1-year time-gap between first- and second-year neuroanatomy courses was created at our institution as a result of restructuring the curriculum from systems-based to an integrated format. Additionally, neuroanatomy hours decreased significantly (48.8%) when transitioning to an integrated curriculum, similar to other medical schools. Competency-based eLearning in medical education has shown promising results with decreasing overall learning time and improving accuracy. To date, competency-based eLearning has not yet been explored in neuroanatomy education. Objective: The purpose of this study is to develop and assess a novel competency-based neuroanatomy eLearning intervention for second-year medical students designed to bridge a 1-year time-gap, without adding significant instructional hours, in an integrated curriculum. Methods: A competency-based eLearning intervention encompassing the major tracts, brainstem anatomy, and an interactive case featuring a simulated patient experience was developed in the Articulate Storyline® platform. Student usage data, single-session course evaluations, and a focus group were used to evaluate the module’s effectiveness. Results: Student usage data showed an average completion time of M = 2:59:25 hours which fit within the scheduled 3-hour timeframe. Students rated the module’s overall effectiveness as M = 3.65 (out of 4) on a single-session evaluation. A focus group provided qualitative feedback suggesting improvements to the eLearning module in the domains of content, mechanics, and timing. Conclusion: A competency-based neuroanatomy eLearning intervention shows promising initial results to bridge a 1-year educational gap within an integrated curriculum. Overall, students described this educational tool as helpful and outlined ways in which to improve this resource.
Risky Sexual Behavior and Its Association With Attention-Deficit Hyperactivity Disorder, Stressful Life Events, and Other Psychiatric Correlates Among Women in Sex Work
Authors: Pandiyan Kasthuri , Kumar Shankar , Kota Sushmitha 
Published: July 2021

Introduction: Numerous psychiatric disorders are known to be associated with risky sexual behavior (RSB). Depression or substance use is known to be higher in those who are in sex work. Recent literature has also described the association between attention-deficit hyperactivity disorder (ADHD) and RSB. However, the association between ADHD and RSB among women in sex work (WSW) is relatively unknown. In this background, this study was conducted to study the prevalence of ADHD and associated psychiatric correlates among WSW and also to study the prevalence of RSB in this population. Methodology: A sample of 96 women who were in sex work and consented to be the part of the study were recruited. They were later administered Adult ADHD Self-Report Scale to screen for adult ADHD symptoms, Wender Utah Rating Scale to retrospectively look into a childhood diagnosis of ADHD, MINI neuropsychiatric interview for DSM 4-TR psychiatric diagnoses, HIV Risk Behavior Scale to study RSB, Beck’s Depression Inventory to quantify depression, and Presumptive Stressful Life Events Scale to quantify the presence of stressful life events. Results: The prevalence of Adult ADHD in the sample was 32.20 % (n = 31). Individuals with ADHD had a higher prevalence of RSB ( P = .02), comorbid depression ( P = .04), presence of stressful life events ( P = .001), and a higher prevalence of sexually transmitted infections ( P = .03). Conclusion: The prevalence of ADHD is high among WSW (32%). Risky sexual behavior in this population could be probably due to higher prevalence of associated psychiatric conditions including depression, higher presence of stressful life events, and ADHD, the association which is less understood. There is a need to replicate these findings in larger samples to study ADHD and its relationship with risky sexual behavior among WSW.
Sociodemographic and Clinical Profile of Patients Presenting With Erectile Dysfunction: Experience From a Tertiary Care Centre of North India
Authors: Tripathi Adarsh , Kumar Dhirendra , Kar Sujita Kumar , Dalal P. K. , Nischal Anil 
Published: July 2021

Background: Erectile dysfunction (ED) is one of the most common psychosexual disorders in clinical practice, and it results in significant distress, interpersonal impairments, poor quality of life, and marital disharmony. However, there is limited research on ED in India. Therefore, this study aimed to assess the sociodemographic and clinical profile of patients presenting with ED. Method: Cross-sectional evaluation of patients with ED presenting to the psychosexual outpatient department (OPD) of psychiatry department in a tertiary care hospital was done on structured clinical pro forma, Mini-International Neuropsychiatric Interview, International Index of Erectile Function-5, Arizona Sexual Experience, Hamilton rating scale for depression, and Hamilton rating scale for anxiety. Results: The sample included 102 patients. The mean age was 33.38 years. The majority of the patients were married (81.4%), Hindu (82.4%), residing in a rural area (60.8%), and belonging to a nuclear family (62.7%). The majority of the patients had a moderate level of ED (50%) followed by mild-to-moderate ED (26.5%) and severe ED (23.5%). Premature ejaculation (46.1%) and depression (28.4%) were the most common sexual and psychiatric comorbidities. Obesity was common (62.7%), and only a minority had other metabolic dysfunction, namely dyslipidemia (7.8%), diabetes (5.9%), and hypertension (4.9%). Tobacco dependence and alcohol dependence were present in 37.3% and 6.9% cases, respectively. Conclusion: Young adults with moderate-to-severe ED were present for treatment at a tertiary center. Comorbidities of other sexual disorders, psychiatric disorders, and substance use are commonly encountered in such patients. Promotion of early help-seeking should be encouraged. Clinicians should thoroughly assess even the young patients for other sexual, psychiatric, and medical comorbidities.
A neural network-based method for polypharmacy side effects prediction
Authors: Masumshah Raziyeh , Aghdam Rosa , Eslahchi Changiz 
Published: July 2021

Abstract
Background
Polypharmacy is a type of treatment that involves the concurrent use of multiple medications. Drugs may interact when they are used simultaneously. So, understanding and mitigating polypharmacy side effects are critical for patient safety and health. Since the known polypharmacy side effects are rare and they are not detected in clinical trials, computational methods are developed to model polypharmacy side effects.

Results
We propose a neural network-based method for polypharmacy side effects prediction (NNPS) by using novel feature vectors based on mono side effects, and drug–protein interaction information. The proposed method is fast and efficient which allows the investigation of large numbers of polypharmacy side effects. Our novelty is defining new feature vectors for drugs and combining them with a neural network architecture to apply for the context of polypharmacy side effects prediction. We compare NNPS on a benchmark dataset to predict 964 polypharmacy side effects against 5 well-established methods and show that NNPS achieves better results than the results of all 5 methods in terms of accuracy, complexity, and running time speed. NNPS outperforms about 9.2% in Area Under the Receiver-Operating Characteristic, 12.8% in Area Under the Precision–Recall Curve, 8.6% in F-score, 10.3% in Accuracy, and 18.7% in Matthews Correlation Coefficient with 5-fold cross-validation against the best algorithm among other well-established methods (Decagon method). Also, the running time of the Decagon method which is 15 days for one fold of cross-validation is reduced to 8 h by the NNPS method.

Conclusions
The performance of NNPS is benchmarked against 5 well-known methods, Decagon, Concatenated drug features, Deep Walk, DEDICOM, and RESCAL, for 964 polypharmacy side effects. We adopt the 5-fold cross-validation for 50 iterations and use the average of the results to assess the performance of the NNPS method. The evaluation of the NNPS against five well-known methods, in terms of accuracy, complexity, and running time speed shows the performance of the presented method for an essential and challenging problem in pharmacology. Datasets and code for NNPS algorithm are freely accessible at https://github.com/raziyehmasumshah/NNPS.
A consensus and saturated genetic map provides insight into genome anchoring, synteny of Solanaceae and leaf- and fruit-related QTLs in wolfberry (Lycium Linn.)
Authors: Zhao Jianhua , Li Haoxia , Xu Yuhui , Yin Yue , Huang Ting , Zhang Bo , Wang Yajun , Li Yanlong , Cao Youlong , An Wei 
Published: July 2021

Abstract
Background
Lycium Linn. (Solanaceae) is a genus of economically important plants producing fruits and leaves with high nutritional value and medicinal benefits. However, genetic analysis of this plant and molecular breeding for quality improvement are limited by the lack of sufficient molecular markers.

Results
In this study, two parental strains, ‘Ningqi No. 1’ (Lycium barbarum L.) and ‘Yunnan Gouqi’ (Lycium yunnanense Kuang et A.M. Lu), and 200 F1 hybrid individuals were resequenced for genetic analysis. In total, 8,507 well-selected SNPs were developed, and a high-density genetic map (NY map) was constructed with a total genetic distance of 2,122.24 cM. A consensus genetic map was established by integrating the NY map and a previously published genetic map (NC map) containing 15,240 SNPs, with a total genetic distance of 3,058.19 cM and an average map distance of 0.21 cM. The 12 pseudochromosomes of the Lycium reference genome were anchored using this consensus genetic map, with an anchoring rate of 64.3%. Moreover, weak collinearities between the consensus map and the pepper, potato, and tomato genomes were observed. Twenty-five stable QTLs were identified for leaf- and fruit-related phenotypes, including fruit weight, fruit longitude, leaf length, the fruit index, and the leaf index; these stable QTLs were mapped to four different linkage groups, with LOD scores ranging from 2.51 to 19.37 and amounts of phenotypic variance explained from 6.2% to 51.9%. Finally, 82 out of 188 predicted genes underlying stable QTLs for fruit-related traits were differentially expressed according to RNA-seq analysis.

Conclusions
A chromosome-level assembly can provide a foundation for further functional genomics research for wolfberry. The genomic regions of these stably expressed QTLs could be used as targets for further fine mapping and development of molecular markers for marker-assisted selection (MAS). The present study provided valuable information on saturated SNP markers and reliable QTLs for map-based cloning of functional genes related to yield and morphological traits in Lycium spp.
Metabolome and transcriptome profiling provide insights into green apple peel reveals light- and UV-B-responsive pathway in anthocyanins accumulation
Authors: Ding Ruirui , Che Xingkai , Shen Zhen , Zhang Yuanhu 
Published: July 2021

Abstract
Background
In nature, green apple are associated with the accumulation of chlorophyll, while red apple varieties are associated with anthocyanins accumulation. Notably, in this study, the green skin color apple variety ‘white winter pearmain’ treated with ultraviolet-B (UV-B) exhibited red skins and marked anthocyanin accumulation, while visible light could not. But there are few reports on the biosynthesis difference of anthocyanins in green apple by visible light and UV-B-treatment. Here, we explored the difference of metabolites and genes expression level in green apple by transcriptomic and metabolic.

Results
The metabolic analysis revealed that there were 152 and 178 significantly changed metabolites in the visible light and UV-B-treated green apple, respectively, compared to the control, and flavone, flavonol, and anthocyanin were the most significantly increased; and transcriptomic analysis showed that 37,110 and 37,709 differentially expressed genes, including 382 and 475 transcription factors (TFs) were detected in light and UV-B-treatment fruit, respectively. Quantitative reverse transcription PCR (qRT-PCR) results confirmed changes in the expression levels of genes encoding metabolites involved in the flavonoid synthesis pathways. The flavonoid metabolic flux in the UV-B treatment increased the accumulation of cyanidin 3-glucoside and cyanidin 3, 5-diglucoside compared to under the light-treatment. Furthermore, we performed qRT-PCR analysis of anthocyanin biosynthesis genes and predicted the gene of MD00G1134400 (a UDP glucose-flavonoid 3–0-glucosyltransferase) may be a candidate gene for anthocyanins accumulation and highly expressed in UV-B-treatment fruit. Expression profiles of several transcription factors of the families MYB, bHLH, NAC were highly correlated with the content of the anthocyanin.

Conclusions
The composition and contents of anthocyanins in green apple in UV-B-treatment very greatly. A series of metabolites and candidate genes were revealed through combined analysis of metabolome and transcriptome. These results provide an important data for dissecting candidate genes and molecular basis governing green apple color formation in response to visible light and UV-B light.
Comparative metabolomics analysis of different sesame (Sesamum indicum L.) tissues reveals a tissue-specific accumulation of metabolites
Authors: Dossou Senouwa Segla Koffi , Xu Fangtao , Cui Xianghua , Sheng Chen , Zhou Rong , You Jun , Tozo Koffi , Wang Linhai 
Published: July 2021

Abstract
Background
Sesame (Sesamum indicum L.) leaves, flowers, especially seeds are used in traditional medicine to prevent or cure various diseases. Its seed’s market is expanding. However, the other tissues are still underexploited due to the lack of information related to metabolites distribution and variability in the plant. Herein, the metabolite profiles of five sesame tissues (leaves, fresh seeds, white and purple flowers, and fresh carpels) have been investigated using ultra-high-performance liquid chromatography-mass spectrometry (UPLC-MS/MS)-based widely targeted metabolomics analysis platform.

Results
In total, 776 metabolites belonging to diverse classes were qualitatively and quantitatively identified. The different tissues exhibited obvious differences in metabolites composition. The majority of flavonoids predominantly accumulated in flowers. Amino acids and derivatives, and lipids were identified predominantly in fresh seeds followed by flowers. Many metabolites, including quinones, coumarins, tannins, vitamins, terpenoids and some bioactive phenolic acids (acteoside, isoacteoside, verbascoside, plantamajoside, etc.) accumulated mostly in leaves. Lignans were principally detected in seeds. 238 key significantly differential metabolites were filtered out. KEGG annotation and enrichment analyses of the differential metabolites revealed that flavonoid biosynthesis, amino acids biosynthesis, and phenylpropanoid biosynthesis were the main differently regulated pathways. In addition to the tissue-specific accumulation of metabolites, we noticed a cooperative relationship between leaves, fresh carpels, and developing seeds in terms of metabolites transfer. Delphinidin-3-O-(6ʺ-O-p-coumaroyl)glucoside and most of the flavonols were up-regulated in the purple flowers indicating they might be responsible for the purple coloration.

Conclusion
This study revealed that the metabolic processes in the sesame tissues are differently regulated. It offers valuable resources for investigating gene-metabolites interactions in sesame tissues and examining metabolic transports during seed development in sesame. Furthermore, our findings provide crucial knowledge that will facilitate sesame biomass valorization.
High performance implementation of the hierarchical likelihood for generalized linear mixed models: an application to estimate the potassium reference range in massive electronic health records datasets
Authors: Bologa Cristian G. , Pankratz Vernon Shane , Unruh Mark L. , Roumelioti Maria Eleni , Shah Vallabh , Shaffi Saeed Kamran , Arzhan Soraya , Cook John , Argyropoulos Christos 
Published: July 2021

AbstractBackgroundConverting electronic health record (EHR) entries to useful clinical inferences requires one to address the poor scalability of existing implementations of Generalized Linear Mixed Models (GLMM) for repeated measures. The major computational bottleneck concerns the numerical evaluation of multivariable integrals, which even for the simplest EHR analyses may involve millions of dimensions (one for each patient). The hierarchical likelihood (h-lik) approach to GLMMs is a methodologically rigorous framework for the estimation of GLMMs that is based on the Laplace Approximation (LA), which replaces integration with numerical optimization, and thus scales very well with dimensionality.MethodsWe present a high-performance, direct implementation of the h-lik for GLMMs in the R package TMB. Using this approach, we examined the relation of repeated serum potassium measurements and survival in the Cerner Real World Data (CRWD) EHR database. Analyzing this data requires the evaluation of an integral in over 3 million dimensions, putting this problem beyond the reach of conventional approaches. We also assessed the scalability and accuracy of LA in smaller samples of 1 and 10% size of the full dataset that were analyzed via the a) original, interconnected Generalized Linear Models (iGLM), approach to h-lik, b) Adaptive Gaussian Hermite (AGH) and c) the gold standard for multivariate integration Markov Chain Monte Carlo (MCMC).ResultsRandom effects estimates generated by the LA were within 10% of the values obtained by the iGLMs, AGH and MCMC techniques. The H-lik approach was 4–30 times faster than AGH and nearly 800 times faster than MCMC. The major clinical inferences in this problem are the establishment of the non-linear relationship between the potassium level and the risk of mortality, as well as estimates of the individual and health care facility sources of variations for mortality risk in CRWD.ConclusionsWe found that the direct implementation of the h-lik offers a computationally efficient, numerically accurate approach for the analysis of extremely large, real world repeated measures data via theh-likapproach to GLMMs. The clinical inference from our analysis may guide choices of treatment thresholds for treating potassium disorders in the clinic.