cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5819139
title: Efficacy of bio- and neurofeedback for depression: a meta-analysis

description: Efficacy of bio- and neurofeedback for<br> depression: a meta-analysis Background. For many years, biofeedback and neurofeedback have been implemented in the<br> treatment of depression. However, the effectiveness of these techniques on depressive symptomatology<br> is still controversial. Hence, we conducted a meta-analysis of studies extracted<br> from PubMed, Scopus, Web of Science and Embase.<br> Methods. Two different strings were considered for each of the two objectives of the study:<br> A first group comprising studies patients with major depressive disorder (MDD) and a second<br> group including studies targeting depressive symptomatology reduction in other mental or<br> medical conditions.<br> Results. In the first group of studies including patients with MDD, the within-group analyses<br> yielded an effect size of Hedges’ g = 0.717, while the between-group analysis an effect size of<br> Hedges’ g = 1.050. Moderator analyses indicate that treatment efficacy is only significant when<br> accounting for experimental design, in favor of randomized controlled trials (RCTs) in comparison<br> to non RCTs, whereas the type of neurofeedback, trial design, year of publication,<br> number of sessions, age, sex and quality of study did not influence treatment efficacy. In<br> the second group of studies, a small but significant effect between groups was found<br> (Hedges’ g = 0.303) in favor of bio- and neurofeedback against control groups. Moderator analyses<br> revealed that treatment efficacy was not moderated by any of the sociodemographic and<br> clinical variables.<br> Conclusions. Heart rate variability (HRV) biofeedback and neurofeedback are associated with<br> a reduction in self-reported depression. Despite the fact that the field has still a large room for<br> improvement in terms of research quality, the results presented in this study suggests that both<br> modalities may become relevant complementary strategies for the treatment of MDD and<br> depressive symptomatology in the coming years.
DOI: 10.5281/zenodo.5819146
title: Prediction of illness remission in patients with Obsessive-Compulsive Disorder with supervised machine learning

description: Prediction of illness remission in patients with Obsessive-Compulsive<br> Disorder with supervised machine learning Introduction: The course of OCD differs widely among OCD patients, varying from chronic symptoms to full<br> remission. No tools for individual prediction of OCD remission are currently available. This study aimed to<br> develop a machine learning algorithm to predict OCD remission after two years, using solely predictors easily<br> accessible in the daily clinical routine.<br> Methods: Subjects were recruited in a longitudinal multi-center study (NOCDA). Gradient boosted decision<br> trees were used as supervised machine learning technique. The training of the algorithm was performed with 227<br> predictors and 213 observations collected in a single clinical center. Hyper-parameter optimization was performed<br> with cross-validation and a Bayesian optimization strategy. The predictive performance of the algorithm<br> was subsequently tested in an independent sample of 215 observations collected in five different centers.<br> Between-center differences were investigated with a bootstrap resampling approach.<br> Results: The average predictive performance of the algorithm in the test centers resulted in an AUROC of<br> 0.7820, a sensitivity of 73.42%, and a specificity of 71.45%. Results also showed a significant between-center<br> variation in the predictive performance. The most important predictors resulted related to OCD severity, OCD<br> chronic course, use of psychotropic medications, and better global functioning.<br> Limitations: All recruiting centers followed the same assessment protocol and are in The Netherlands. Moreover,<br> the sample of the data recruited in some of the test centers was limited in size.<br> Discussion: The algorithm demonstrated a moderate average predictive performance, and future studies will<br> focus on increasing the stability of the predictive performance across clinical settings.
DOI: 10.5281/zenodo.5819145
title: Prediction of illness remission in patients with Obsessive-Compulsive Disorder with supervised machine learning

description: Prediction of illness remission in patients with Obsessive-Compulsive<br> Disorder with supervised machine learning Introduction: The course of OCD differs widely among OCD patients, varying from chronic symptoms to full<br> remission. No tools for individual prediction of OCD remission are currently available. This study aimed to<br> develop a machine learning algorithm to predict OCD remission after two years, using solely predictors easily<br> accessible in the daily clinical routine.<br> Methods: Subjects were recruited in a longitudinal multi-center study (NOCDA). Gradient boosted decision<br> trees were used as supervised machine learning technique. The training of the algorithm was performed with 227<br> predictors and 213 observations collected in a single clinical center. Hyper-parameter optimization was performed<br> with cross-validation and a Bayesian optimization strategy. The predictive performance of the algorithm<br> was subsequently tested in an independent sample of 215 observations collected in five different centers.<br> Between-center differences were investigated with a bootstrap resampling approach.<br> Results: The average predictive performance of the algorithm in the test centers resulted in an AUROC of<br> 0.7820, a sensitivity of 73.42%, and a specificity of 71.45%. Results also showed a significant between-center<br> variation in the predictive performance. The most important predictors resulted related to OCD severity, OCD<br> chronic course, use of psychotropic medications, and better global functioning.<br> Limitations: All recruiting centers followed the same assessment protocol and are in The Netherlands. Moreover,<br> the sample of the data recruited in some of the test centers was limited in size.<br> Discussion: The algorithm demonstrated a moderate average predictive performance, and future studies will<br> focus on increasing the stability of the predictive performance across clinical settings.
DOI: 10.5517/ccdc.csd.cc23hmlv
title: CCDC 1951437: Experimental Crystal Structure Determination

description: Related Article: Fernando P. Cossío, Abel de Cózar, Miguel A. Sierra, Luis Casarrubios, Jaime G. Muntaner, Bimal K. Banik, Debasish Bandyopadhyay|2022|RSC Advances|12|104|doi:10.1039/D1RA06114C
DOI: 10.5517/ccdc.csd.cc23hmmw
title: CCDC 1951438: Experimental Crystal Structure Determination

description: Related Article: Fernando P. Cossío, Abel de Cózar, Miguel A. Sierra, Luis Casarrubios, Jaime G. Muntaner, Bimal K. Banik, Debasish Bandyopadhyay|2022|RSC Advances|12|104|doi:10.1039/D1RA06114C
DOI: 10.5281/zenodo.5819150
title: Psychopharmacological treatment in borderline personality disorder: A pilot observational study in a real-world setting

description: Psychopharmacological treatment in borderline personality disorder: A<br> pilot observational study in a real-world setting Psychotherapy is the cornerstone of treatment for borderline personality disorder (BPD) while pharmacotherapy<br> should be considered only as an adjunctive intervention. In clinical practice, however, most of BPD patients only<br> receive medication. The aim of the study is to first describe pharmacological treatment in BPD patients in Italy<br> and secondly to evaluate if comorbidity or illness severity are associated with the prescription of different class<br> compounds.<br> Data on pharmacological treatment and clinical evaluation of 75 BPD patients were collected in 5 clinical<br> settings. The association between comorbidity and medication was assessed. Moreover, we evaluated the association<br> between pharmacotherapy and severity, defined by a cluster analysis aimed at detecting different groups<br> of patients.<br> Most of the participants (82.7%) were characterized by polypharmacy, with a mean of 2.4 medications per<br> person. Interestingly, the prescription didn’t seem to depend on/be based on the severity of the disorder and was<br> only partially determined by the presence of comorbidity.<br> In conclusion, our findings are similar to what described in other clinical studies, supporting the idea that<br> medication management for BPD is only partially coherent with international guidelines. This pilot study confirms<br> the need for more rigorous studies to gain greater understanding of this topic and diminish the gap between<br> guidelines and the real clinical world.
DOI: 10.5281/zenodo.5819149
title: Psychopharmacological treatment in borderline personality disorder: A pilot observational study in a real-world setting

description: Psychopharmacological treatment in borderline personality disorder: A<br> pilot observational study in a real-world setting Psychotherapy is the cornerstone of treatment for borderline personality disorder (BPD) while pharmacotherapy<br> should be considered only as an adjunctive intervention. In clinical practice, however, most of BPD patients only<br> receive medication. The aim of the study is to first describe pharmacological treatment in BPD patients in Italy<br> and secondly to evaluate if comorbidity or illness severity are associated with the prescription of different class<br> compounds.<br> Data on pharmacological treatment and clinical evaluation of 75 BPD patients were collected in 5 clinical<br> settings. The association between comorbidity and medication was assessed. Moreover, we evaluated the association<br> between pharmacotherapy and severity, defined by a cluster analysis aimed at detecting different groups<br> of patients.<br> Most of the participants (82.7%) were characterized by polypharmacy, with a mean of 2.4 medications per<br> person. Interestingly, the prescription didn’t seem to depend on/be based on the severity of the disorder and was<br> only partially determined by the presence of comorbidity.<br> In conclusion, our findings are similar to what described in other clinical studies, supporting the idea that<br> medication management for BPD is only partially coherent with international guidelines. This pilot study confirms<br> the need for more rigorous studies to gain greater understanding of this topic and diminish the gap between<br> guidelines and the real clinical world.
DOI: 10.5281/zenodo.5819154
title: Impact of respiratory protective devices on respiration: Implications for panic vulnerability during the COVID-19 pandemic

description: Impact of respiratory protective devices on respiration: Implications for<br> panic vulnerability during the COVID-19 pandemic Background: The wearing of respiratory protective devices (RPDs) correctly and continually in situations where<br> people are at risk of respiratory infections is crucial for infection prevention. Certain people are poorly compliant<br> with RPDs due to RPD-related annoyance, including respiratory discomfort. We hypothesized that individuals<br> vulnerable to panic attacks are included in this group. No published studies on this topic are available. The<br> evidence for our hypothesis was reviewed in this study as a starting point for future research.<br> Methods: We selected a set of experimental studies that measured the respiratory physiological burden in RPD<br> wearers through objective and validated methods. We conducted a bibliographic search of publications in the<br> PubMed database (January 2000–May 2020) to identify representative studies that may be of interest for panic<br> respiratory pathophysiology.<br> Results: Five studies were included. Wearing RPDs exerted significant respiratory effects, including increased<br> breathing resistance, CO2 rebreathing due to CO2 accumulation in the RPD cavity, and decreased inhaled O2<br> concentration. We discussed the implications of these effects on the respiratory pathophysiology of panic.<br> Limitations: Most studies had a small sample size, with a preponderance of young participants. Different<br> methodologies were used across the studies. Furthermore, differences in physical responses between wearing<br> RPDs in experimental settings or daily life cannot be excluded.<br> Conclusions: This research supports the idea that panic-prone individuals may be at higher risk of respiratory<br> discomfort when wearing RPDs, thereby reducing their tolerance for these devices. Strategies to decrease discomfort<br> should be identified to overcome the risk of poor compliance.
DOI: 10.5281/zenodo.5819153
title: Impact of respiratory protective devices on respiration: Implications for panic vulnerability during the COVID-19 pandemic

description: Impact of respiratory protective devices on respiration: Implications for<br> panic vulnerability during the COVID-19 pandemic Background: The wearing of respiratory protective devices (RPDs) correctly and continually in situations where<br> people are at risk of respiratory infections is crucial for infection prevention. Certain people are poorly compliant<br> with RPDs due to RPD-related annoyance, including respiratory discomfort. We hypothesized that individuals<br> vulnerable to panic attacks are included in this group. No published studies on this topic are available. The<br> evidence for our hypothesis was reviewed in this study as a starting point for future research.<br> Methods: We selected a set of experimental studies that measured the respiratory physiological burden in RPD<br> wearers through objective and validated methods. We conducted a bibliographic search of publications in the<br> PubMed database (January 2000–May 2020) to identify representative studies that may be of interest for panic<br> respiratory pathophysiology.<br> Results: Five studies were included. Wearing RPDs exerted significant respiratory effects, including increased<br> breathing resistance, CO2 rebreathing due to CO2 accumulation in the RPD cavity, and decreased inhaled O2<br> concentration. We discussed the implications of these effects on the respiratory pathophysiology of panic.<br> Limitations: Most studies had a small sample size, with a preponderance of young participants. Different<br> methodologies were used across the studies. Furthermore, differences in physical responses between wearing<br> RPDs in experimental settings or daily life cannot be excluded.<br> Conclusions: This research supports the idea that panic-prone individuals may be at higher risk of respiratory<br> discomfort when wearing RPDs, thereby reducing their tolerance for these devices. Strategies to decrease discomfort<br> should be identified to overcome the risk of poor compliance.
DOI: 10.5281/zenodo.5819160
title: Heart rate variability: Can it serve as a marker of mental health resilience?

description: Heart rate variability: Can it serve as a marker of mental health resilience? Background: Stress resilience influences mental well-being and vulnerability to psychiatric disorders. Usually, measurement of resilience is based on subjective<br> reports, susceptible to biases. It justifies the need for objective biological/physiological biomarkers of resilience. One promising candidate as biomarker of mental<br> health resilience (MHR) is heart rate variability (HRV). The evidence for its use was reviewed in this study.<br> Methods: We focused on the relationship between HRV (as measured through decomposition of RR intervals from electrocardiogram) and responses to laboratory<br> stressors in individuals without medical and psychiatric diseases. We conducted a bibliographic search of publications in the PubMed for January 2010–September<br> 2018.<br> Results: Eight studies were included. High vagally mediated HRV before and/or during stressful laboratory tasks was associated with enhanced cognitive resilience to<br> competitive/self-control challenges, appropriate emotional regulation during emotional tasks, and better modulation of cortisol, cardiovascular and inflammatory<br> responses during psychosocial/mental tasks.<br> Limitations: All studies were cross-sectional, restricting conclusions that can be made. Most studies included only young participants, with some samples of only<br> males or females, and a limited array of HRV indexes. Ecological validity of stressful laboratory tasks remains unclear.<br> Conclusions: Vagally mediated HRV may serve as a global index of an individual's flexibility and adaptability to stressors. This supports the idea of HRV as a plausible,<br> noninvasive, and easily applicable biomarker of MHR. In future longitudinal studies, the implementation of wearable health devices, able to record HRV in naturalistic<br> contexts of real-life, may be a valuable strategy to gain more reliable insight into this topic.
DOI: 10.5281/zenodo.5819159
title: Heart rate variability: Can it serve as a marker of mental health resilience?

description: Heart rate variability: Can it serve as a marker of mental health resilience? Background: Stress resilience influences mental well-being and vulnerability to psychiatric disorders. Usually, measurement of resilience is based on subjective<br> reports, susceptible to biases. It justifies the need for objective biological/physiological biomarkers of resilience. One promising candidate as biomarker of mental<br> health resilience (MHR) is heart rate variability (HRV). The evidence for its use was reviewed in this study.<br> Methods: We focused on the relationship between HRV (as measured through decomposition of RR intervals from electrocardiogram) and responses to laboratory<br> stressors in individuals without medical and psychiatric diseases. We conducted a bibliographic search of publications in the PubMed for January 2010–September<br> 2018.<br> Results: Eight studies were included. High vagally mediated HRV before and/or during stressful laboratory tasks was associated with enhanced cognitive resilience to<br> competitive/self-control challenges, appropriate emotional regulation during emotional tasks, and better modulation of cortisol, cardiovascular and inflammatory<br> responses during psychosocial/mental tasks.<br> Limitations: All studies were cross-sectional, restricting conclusions that can be made. Most studies included only young participants, with some samples of only<br> males or females, and a limited array of HRV indexes. Ecological validity of stressful laboratory tasks remains unclear.<br> Conclusions: Vagally mediated HRV may serve as a global index of an individual's flexibility and adaptability to stressors. This supports the idea of HRV as a plausible,<br> noninvasive, and easily applicable biomarker of MHR. In future longitudinal studies, the implementation of wearable health devices, able to record HRV in naturalistic<br> contexts of real-life, may be a valuable strategy to gain more reliable insight into this topic.
DOI: 10.17632/49b3p7wtgm.1
title: Does audio and video recorded feedback improve surgical performance: a randomized controlled trial

description: Deidentified dataset of OB/GYN residents. 
Residents underwent initial assessment of simulated laparoscopic vaginal cuff closure scored with GOALS+. Residents then received single, proctored session with expert MIGS surgeon. Residents randomized to receive access to audio/video recording of proctored session or video only recording. Learners were then assessed approximately 1 week after proctored session with same vaginal cuff closure model (GOALS+).
DOI: 10.17632/mkfhndrthf.1
title: Morphometric and morphometry geometric Triatoma vitticipes populations

description: These data are part of the master's thesis presented as part of the requirements for obtaining a Master's degree in Biological Sciences (Zoology) with the Science Graduate Program Biological Sciences (Zoology) of the
Botucatu Biosciences, University Paulista State University “Júlio de Mesquita Filho”, Botucatu campus. With name "Triatoma vitticeps (Stål, 1859) (Hemiptera, Triatominae): um  vetor da doença de Chagas ou um complexo de vetores?". 
The entire methodological procedure for obtaining the data was carried out by the researcher Tiago Belintani of the Institute of Biology at the State University of Campinas IB-Unicamp.
DOI: 10.17632/9mmbmzjwxp.1
title: Spatial and temporal sampling biases and spatial scale affect species distribution models and applicability to conservation management

description: We used Maxent to model the distribution of Cypripedium acaule in North Carolina using records from 1) publicly available databases (GBIF and iNaturalist) and 2) herbaria. We compared distribution models made with the different sets of occurrence records to evaluate how spatial and temporal biases in records affect model results.

The data provided here include the original iNaturalist dataset (prior to cleaning as described in our methods) and the code for the evaluation of models based on ground-truthed populations. We cannot provide the original herbaria dataset because location records are kept in confidence due to poaching concerns.
DOI: 10.5281/zenodo.5819162
title: Personalized Clinical Approaches To Anxiety Disorders

description: Personalized Clinical Approaches To Anxiety Disorders
DOI: 10.5281/zenodo.5819161
title: Personalized Clinical Approaches To Anxiety Disorders

description: Personalized Clinical Approaches To Anxiety Disorders
DOI: 10.5281/zenodo.5819170
title: Personalized Psychiatry and Depression: The Role of Sociodemographic and Clinical Variables

description: Personalized Psychiatry and Depression:<br> The Role of Sociodemographic and Clinical Variables Despite several pharmacological options, the clinical outcomes of major depressive disorder (MDD) are often unsatisfactory. Personalized<br> psychiatry attempts to tailor therapeutic interventions according to each patient’s unique profile and characteristics. This approach<br> can be a crucial strategy in improving pharmacological outcomes in MDD and overcoming trial-and-error treatment choices. In this<br> narrative review, we evaluate whether sociodemographic (i.e., gender, age, race/ethnicity, and socioeconomic status) and clinical [i.e.,<br> body mass index (BMI), severity of depressive symptoms, and symptom profiles] variables that are easily assessable in clinical practice<br> may help clinicians to optimize the selection of antidepressant treatment for each patient with MDD at the early stages of the disorder.<br> We found that several variables were associated with poorer outcomes for all antidepressants. However, only preliminary associations<br> were found between some clinical variables (i.e., BMI, anhedonia, and MDD with melancholic/atypical features) and possible benefits<br> with some specific antidepressants. Finally, in clinical practice, the assessment of sociodemographic and clinical variables considered in<br> our review can be valuable for early identification of depressed individuals at high risk for poor responses to antidepressants, but there<br> are not enough data on which to ground any reliable selection of specific antidepressant class or compounds. Recent advances in computational<br> resources, such as machine learning techniques, which are able to integrate multiple potential predictors, such as individual/<br> clinical variables, biomarkers, and genetic factors, may offer future reliable tools to guide personalized antidepressant choice for each patient<br> with MDD.