Find research datasets worth reusing

opencc-by-4.0Mar 2022View details →

A cross-disorder dosage sensitivity map of the human genome

This repository contains data from Collins et al., A cross-disorder dosage sensitivity map of the human genome (2022), including: 1. Collins_rCNV_2022.dosage_sensitivity_scores.tsv.gz: This file contains predicted probabilities of haploinsufficiency (pHaplo) and triplosensitivity (pTriplo) for 18,641 autosomal protein-coding genes as defined in Gencode v19. 2. Collins_rCNV_2022.sliding_window_sumstats.tar.gz: This compressed directory contains rCNV association summary statistics for 54 phenotypes from genome-wide sliding window meta-analyses. Please refer to the README file included in this compressed directory for more details. 3. Collins_rCNV_2022.gene_association_sumstats.tar.gz: This compressed directory contains rCNV association summary statistics for 54 phenotypes from exome-wide gene-based meta-analyses. Please refer to the README file included in this compressed directory for more details.  4. Collins_rCNV_2022.gene_features_matrix.tar.gz: This compressed directory contains gene-level feature annotations for 145 features and 18,641 autosomal protein-coding genes. Please refer to the README file included in this compressed directory for more details.  Smaller data files have been provided as supplemental tables alongside the publication online. Please also refer to the original publication for details on data sources, study design, methods, and other analyses.

opencc-by-4.0Sep 2019View details →

Data for manuscript Marmet, Studer, Lemoine, Grazioli, Bertholet & Gmel (2019). Reconsidering the associations between self-reported alcohol use disorder and mental health problems in the light of co-occurring addictions in young Swiss men. Plos One. DOI: 10.1371/journal.pone.0222806.

Dataset for the manuscript  Marmet, Studer, Lemoine, Grazioli, Bertholet & Gmel (2019). Reconsidering the associations between self-reported alcohol use disorder and mental health problems in the light of co-occurring addictions in young Swiss men. Plos One. DOI: 10.1371/journal.pone.0222806. The dataset contains all data needed to reproduce the results in the above cited manuscript. Variable description and labels can be found in the codebook. For further information on the instruments used please refer to the manuscript. The data was collected between April 2016 and March 2018 in Switzerland by the C-SURF study (<a href="http://www.c-surf.ch">www.c-surf.ch</a>). Participants were on average 25 years old when they answered the questionnaires. The final sample size used in the manuscript is 5516. Please note that the dataset contains 25 datasets created with multiple imputation, therefore there are no missing values in the dataset. The research protocol for this study was approved by the Human Research Ethics Committee of the Canton Vaud (Protocol No. 15/07). Data collection was funded by the Swiss National Science Foundation (FN 33CSC0-122679, FN 33CS30_139467, FN 33CS30_148493)

opencc-by-4.0Jul 2023View details →

HRV-ACC: a dataset with R-R intervals and accelerometer data for the diagnosis of psychotic disorders using a Polar H10 wearable sensor

ABSTRACT The issue of diagnosing psychotic diseases, including schizophrenia and bipolar disorder, in particular, the objectification of symptom severity assessment, is still a problem requiring the attention of researchers. Two measures that can be helpful in patient diagnosis are heart rate variability calculated based on electrocardiographic signal and accelerometer mobility data. The following dataset contains data from 30 psychiatric ward patients having schizophrenia or bipolar disorder and 30 healthy persons. The duration of the measurements for individuals was usually between 1.5 and 2 hours. R-R intervals necessary for heart rate variability calculation were collected simultaneously with accelerometer data using a wearable Polar H10 device. The Positive and Negative Syndrome Scale (PANSS) test was performed for each patient participating in the experiment, and its results were attached to the dataset. Furthermore, the code for loading and preprocessing data, as well as for statistical analysis, was included on the corresponding GitHub repository. BACKGROUND Heart rate variability (HRV), calculated based on electrocardiographic (ECG) recordings of R-R intervals stemming from the heart's electrical activity, may be used as a biomarker of mental illnesses, including schizophrenia and bipolar disorder (BD) [Benjamin et al]. The variations of R-R interval values correspond to the heart's autonomic regulation changes [Berntson et al, Stogios et al]. Moreover, the HRV measure reflects the activity of the sympathetic and parasympathetic parts of the autonomous nervous system (ANS) [Task Force of the European Society of Cardiology the North American Society of Pacing Electrophysiology, Matusik et al]. Patients with psychotic mental disorders show a tendency for a change in the centrally regulated ANS balance in the direction of less dynamic changes in the ANS activity in response to different environmental conditions [Stogios et al]. Larger sympathetic activity relative to the parasympathetic one leads to lower HRV, while, on the other hand, higher parasympathetic activity translates to higher HRV. This loss of dynamic response may be an indicator of mental health. Additional benefits may come from measuring the daily activity of patients using accelerometry. This may be used to register periods of physical activity and inactivity or withdrawal for further correlation with HRV values recorded at the same time. EXPERIMENTS In our experiment, the participants were 30 psychiatric ward patients with schizophrenia or BD and 30 healthy people. All measurements were performed using a Polar H10 wearable device. The sensor collects ECG recordings and accelerometer data and, additionally, prepares a detection of R wave peaks. Participants of the experiment had to wear the sensor for a given time. Basically, it was between 1.5 and 2 hours, but the shortest recording was 70 minutes. During this time, evaluated persons could perform any activity a few minutes after starting the measurement. Participants were encouraged to undertake physical activity and, more specifically, to take a walk. Due to patients being in the medical ward, they received instruction to take a walk in the corridors at the beginning of the experiment. They were to repeat the walk 30 minutes and 1 hour after the first walk. The subsequent walks were to be slightly longer (about 3, 5 and 7 minutes, respectively). We did not remind or supervise the command during the experiment, both in the treatment and the control group. Seven persons from the control group did not receive this order and their measurements correspond to freely selected activities with rest periods but at least three of them performed physical activities during this time. Nevertheless, at the start of the experiment, all participants were requested to rest in a sitting position for 5 minutes. Moreover, for each patient, the disease severity was assessed using the PANSS test and its scores are attached to the dataset. The data from sensors were collected using Polar Sensor Logger application [Happonen]. Such extracted measurements were then preprocessed and analyzed using the code prepared by the authors of the experiment. It is publicly available on the GitHub repository [Książek et al]. Firstly, we performed a manual artifact detection to remove abnormal heartbeats due to non-sinus beats and technical issues of the device (e.g. temporary disconnections and inappropriate electrode readings). We also performed anomaly detection using Daubechies wavelet transform. Nevertheless, the dataset includes raw data, while a full code necessary to reproduce our anomaly detection approach is available in the repository. Optionally, it is also possible to perform cubic spline data interpolation. After that step, rolling windows of a particular size and time intervals between them are created. Then, a statistical analysis is prepared, e.g. mean HRV calculation using the RMSSD (Root Mean Square of Successive Differences) approach, measuring a relationship between mean HRV and PANSS scores, mobility coefficient calculation based on accelerometer data and verification of dependencies between HRV and mobility scores. DATA DESCRIPTION The structure of the dataset is as follows. One folder, called HRV_anonymized_data contains values of R-R intervals together with timestamps for each experiment participant. The data was properly anonymized, i.e. the day of the measurement was removed to prevent person identification. Files concerned with patients have the name treatment_X.csv, where X is the number of the person, while files related to the healthy controls are named control_Y.csv, where Y is the identification number of the person. Furthermore, for visualization purposes, an image of the raw RR intervals for each participant is presented. Its name is raw_RR_{control,treatment}_N.png, where N is the number of the person from the control/treatment group. The collected data are raw, i.e. before the anomaly removal. The code enabling reproducing the anomaly detection stage and removing suspicious heartbeats is publicly available in the repository [Książek et al]. The structure of consecutive files collecting R-R intervals is following: <table> <tbody> <tr> <td>Phone timestamp</td> <td>RR-interval [ms]</td> </tr> <tr> <td>12:43:26.538000</td> <td>651</td> </tr> <tr> <td>12:43:27.189000</td> <td>632</td> </tr> <tr> <td>12:43:27.821000</td> <td>618</td> </tr> <tr> <td>12:43:28.439000</td> <td>621</td> </tr> <tr> <td>12:43:29.060000</td> <td>661</td> </tr> <tr> <td>...</td> <td>...</td> </tr> </tbody> </table> The first column contains the timestamp for which the distance between two consecutive R peaks was registered. The corresponding R-R interval is presented in the second column of the file and is expressed in milliseconds.   The second folder, called accelerometer_anonymized_data contains values of accelerometer data collected at the same time as R-R intervals. The naming convention is similar to that of the R-R interval data: treatment_X.csv and control_X.csv represent the data coming from the persons from the treatment and control group, respectively, while X is the identification number of the selected participant. The numbers are exactly the same as for R-R intervals. The structure of the files with accelerometer recordings is as follows: <table> <tbody> <tr> <td>Phone timestamp</td> <td>X [mg]</td> <td>Y [mg]</td> <td>Z [mg]</td> </tr> <tr> <td>13:00:17.196000</td> <td>-961</td> <td>-23</td> <td>182</td> </tr> <tr> <td>13:00:17.205000</td> <td>-965</td> <td>-21</td> <td>181</td> </tr> <tr> <td>13:00:17.215000</td> <td>-966</td> <td>-22</td> <td>187</td> </tr> <tr> <td>13:00:17.225000</td> <td>-967</td> <td>-26</td> <td>193</td> </tr> <tr> <td>13:00:17.235000</td> <td>-965</td> <td>-27</td> <td>191</td> </tr> <tr> <td>...</td> <td>...</td> <td>...</td> <td>...</td> </tr> </tbody> </table> The first column contains a timestamp, while the next three columns correspond to the currently registered acceleration in three axes: X, Y and Z, in milli-g unit. We also attached a file with the PANSS test scores (PANSS.csv) for all patients participating in the measurement. The structure of this file is as follows: <table> <tbody> <tr> <td>no_of_person</td> <td>PANSS_P</td> <td>PANSS_N</td> <td>PANSS_G</td> <td>PANSS_total</td> </tr> <tr> <td>1</td> <td>8</td> <td>13</td> <td>22</td> <td>43</td> </tr> <tr> <td>2</td> <td>11</td> <td>7</td> <td>18</td> <td>36</td> </tr> <tr> <td>3</td> <td>14</td> <td>30</td> <td>44</td> <td>88</td> </tr> <tr> <td>4</td> <td>18</td> <td>13</td> <td>27</td> <td>58</td> </tr> <tr> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>..</td> </tr> </tbody> </table> The first column contains the identification number of the patient, while the three following columns refer to the PANSS scores related to positive, negative and general symptoms, respectively. USAGE NOTES All the files necessary to run the HRV and/or accelerometer data analysis are available on the GitHub repository [Książek et al]. HRV data loading, preprocessing (i.e. anomaly detection and removal), as well as the calculation of mean HRV values in terms of the RMSSD, is performed in the main.py file. Also, Pearson's correlation coefficients between HRV values and PANSS scores and the statistical tests (Levene's and Mann-Whitney U tests) comparing the treatment and control groups are computed. By default, a sensitivity analysis is made, i.e. running the full pipeline for different settings of the window size for which the HRV is calculated and various time intervals between consecutive windows. Preparing the heatmaps of correlation coefficients and corresponding p-values can be done by running the utils_advanced_plots.py file after performing the sensitivity analysis. Furthermore, a detailed analysis for the one selected set of hyperparameters may be prepared (by setting sensitivity_analysis = False), i.e. for 15-minute window sizes, 1-minute time intervals between consecutive windows and without data interpolation method. Also, patients taking quetiapine may be excluded from further calculations by setting exclude_quetiapine = True because this medicine can have a strong impact on HRV [Hattori et al]. The accelerometer data processing may be performed using the utils_accelerometer.py file. In this case, accelerometer recordings are downsampled to ensure the same timestamps as for R-R intervals and, for each participant, the mobility coefficient is calculated. Then, a correlation coefficient between mean HRV values and mobility coefficient is computed. The plotting of the pure accelerometer signal may be done by running the utils_loading.py file. The comparison of age distribution between the tested groups can be made by the histogram plotted with the use of the utils_basic_plots.py file.

opencc-by-4.0Aug 2023View details →

Shared genetic factors between stress-related disorders and cardiovascular disease

The ultimate goal of this study is to advance our understanding of the biological mechanisms of stress-related disorders and CVD, through demonstrating pleiotropic genes and pathways underlying their comorbidity that are potentially testable as targets of future interventions in experimental investigations.

opencc-by-4.0Dec 2019View details →

Synthetic magnetic nanoparticles for remote-controlled stemcell therapies of neurodegenerative disorders

In the context of the MAGNEURON european project, we developed different types of magnetic nanoparticles that can act as nanoactuators to manipulate intracellular proteins involved in signaling pathways. Four types of particles are presented here. First, size-sorted maghemite cores of different diameter (8 to 20 nm) were synthesized. Then these cores were used to make Fe2O3@SiO2 core-shell nanoparticles that are colloidally stable and easy to functionalize, and poly(acrylic acid) coated nanoparticles. Both types of particles can be rendered fluorescent by the addition of a fluorophore. Finally, we also developed a way to synthesize micro-needles made of aligned maghemite cores encapsulated in a silica layer. In this dataset are presented some electron microscopy images of the optimized particles and their characterizations in terms of sizes and magnetic properties. These particles have then been used by the other members of the Magneuron consortium in order to manipulate different intracellular signalling pathways.

opencc-by-4.0Jan 2020View details →

Data for the paper Lemoine, Gmel, Foster, Marmet, Studer (2020). Multiple trajectories of alcohol use and the development of alcohol use disorder: do Swiss men mature-out of problematic alcohol use during emerging adulthood? Plos One. https://doi.org/10.1371/journal.pone.0220232

Data for the paper Lemoine, Gmel, Foster, Marmet, Studer (2020). Multiple trajectories of alcohol use and the development of alcohol use disorder: do Swiss men mature-out of problematic alcohol use during emerging adulthood? Plos One. <a href="https://doi.org/10.1371/journal.pone.0220232">https://doi.org/10.1371/journal.pone.0220232</a> Please refer to the paper for further information about the data.   The dataset contains all data needed to reproduce the results in the above cited paper. Variable description and labels can be found in the codebook. For further information on the instruments used please refer to the paper. The dataset contains data for three waves that was collected between September 2010 and March 2018 in Switzerland by the C-SURF study (<a href="http://www.c-surf.ch/">www.c-surf.ch</a>). Participants were on average 20  years old at wave 1, 21 at wave 2 and 25 at wave 3 when they answered the questionnaires. The final sample size used in the paper is 4746 after excluding those that did not reply to a questionnaire or to a variable of interest for the main analysis. The research protocol for this study was approved by the Human Research Ethics Committee of the Canton Vaud (Protocol No. 15/07). Data collection was funded by the Swiss National Science Foundation (FN 33CSC0-122679, FN 33CS30_139467, FN 33CS30_148493).

Datasets of sequences, alignments and structural models generated for the structural prediction of complexes mediated by intrinsically disordered regions.

This repository contains input and ouput files used and generated for the scanning of intrinsically disordered region and the prediction of their binding sites to receptor proteins using the <a href="https://github.com/i2bc/SCAN_IDR">SCAN_IDR</a> pipeline with AlphaFold2-Multimer.It contains two archives: <ol><li><a href="https://zenodo.org/api/records/10068949/draft/files/scanidr_data_repository_corr6J08.tar/content">scanidr_data_repository_corr6J08.tar</a> dedicated to the analysis of a dataset of 42 protein complexes non redundant with the dataset used for AlphaFold2 training,</li><li><a href="https://zenodo.org/api/records/10068949/draft/files/923_elm_cases_repository.tar.gz/content">923_elm_cases_repository.tar.gz</a> dedicated to the analysis of 923 complexes from the ELM database.</li></ol>These data can be used to rerun specific sections of the pipeline and scripts provided in: <a href="https://github.com/i2bc/SCAN_IDR">https://github.com/i2bc/SCAN_IDR</a><h4>Dataset of 42 non redundant complexes</h4>The first archive <a href="https://zenodo.org/api/records/10068949/draft/files/scanidr_data_repository_corr6J08.tar/content">scanidr_data_repository_corr6J08.tar</a> contains 3 compressed directories and a README file detailing their contents :<ul><li>the initial raw sequence and alignment data for every chain        -> DIRECTORY fasta_msa/</li><li>the input and output data of every Alphafold run for every complex   -> DIRECTORY af2_runs/</li><li>the native reference structures    -> DIRECTORY ref_capri_curated/</li></ul>The protein-peptide complex cases have been assigned a distinct index number, from 1 to 42, consistent across the several directories of the archive. Their corresponding directories are labelled as <index>_<pdbcode>.The models in this archive were generated using AlphaFold2-Multimer v2.2<h4>Dataset of 923 complexes selected from the ELM database</h4>The second archive <a href="https://zenodo.org/api/records/10068949/draft/files/923_elm_cases_repository.tar.gz/content">923_elm_cases_repository.tar.gz</a> contains input and ouput files used and generated for the analysis of 923 Eukaryotic Linear Motifs (ELM) database entries.Each ELM entry is indexed with specific integer id and is composed of a receptor and a ligand protein.  The archive contains a Table associating ELM indexes with the ELM entry information, 5 directories and a README file detailing their contents:<ul><li>the table describing ELM entries -> FILE Table_923ELM_uid_delimitations_info_for_archive.txt</li><li>the initial raw sequence and multiple sequence alignment (MSA) data for every chain        -> DIRECTORY fasta_msa/</li><li>the concatenated MSA model for every ELM complex and protocol used -> DIRECTORY af2_elm_coali_inputs/</li><li>the best model of every AF2 protocol for every complex according to the AF2   -> DIRECTORY af2_elm_models/</li><li>the best model cut in the ligand part to select only the ELM motifs as used for the evaluation of the models -> DIRECTORY elm_cut_models/</li><li>the reference structures used for the evaluation of the models   -> DIRECTORY ref_capri_curated/</li></ul>The models in this archive were generated using AlphaFold2-Multimer v2.3

Implications of Socio-Economic Conditions on Common Mental Disorders: A Case-study of Salt Pan Workers in Marakkanam Block of Tamil Nadu

Socio-economic indicators of Saltpan workers in Marakaanam, Tamil Nadu, India and their SRQ-20 scoring.Data regarding; Consumption, Wages, Debt, Social Group, Gender, Age, Ration Card, Education, Distance from work(saltpan), ownership of house and the SRQ-20 Questionanaire used for Screening of CMDs and Distress levels

Discrepancies between parents and teachers of students with autism spectrum disorder (ASD) in executive functioning according to the BRIEF

This database corresponds to the results of the paper:González Sala, F., Pastor-Cerezuela, G., Sanz-Cervera, P., & Tárraga-Mínguez, R. (2023). Discrepancies between parents and teachers of students with autism spectrum disorder (ASD) in executive functioning according to the BRIEF. Anales de Psicología / Annals of Psychology, 39(1), 81–87. https://doi.org/10.6018/analesps.481531

Implicit and explicit measurement of pre-service teachers' attitudes toward autism spectrum disorder

This database corresponds to the results of the paper:Lacruz-Pérez, I., Pastor-Cerezuela, G., Tárraga-Mínguez, R. & Lüke, T. (2023): Implicit and explicit measurement of pre-service teachers' attitudes toward autism spectrum disorder, European Journal of Special Needs Education. <a href="https://doi.org/10.1080/08856257.2023.2185858">https://doi.org/10.1080/08856257.2023.2185858</a>

opencc-by-4.0Sep 2023View details →

Improving causality perception judgments in schizophrenia spectrum disorder via transcranial direct current stimulation - Dataset

Raw data related to the publication: Schülke, R., Schmitter, C. V., & Straube, B. (2023). Improving causality perception judgments in schizophrenia spectrum disorder via transcranial direct current stimulation. Journal of Psychiatry and Neuroscience, 48(4), E245–E254. <a href="https://doi.org/10.1503/jpn.220184">https://doi.org/10.1503/jpn.220184</a> Variables: <ul> <li>Subject</li> <li>Condition – Stimulation condition; parietal (left parietal cathodal, right parietal anodal [LPC-RPA]), frontoparietal (left frontal cathodal, right parietal anodal [LFC-RPA]), frontal (left frontal cathodal, right frontal anodal [LFC-RFA])</li> <li>Timepoint – Before/After (stimulation)</li> <li>Angle – in degrees</li> <li>Angle_scaled – mean-centered and scaled Angle</li> <li>Delay_ms – in milliseconds</li> <li>Delay_ms_scaled – mean-centered and scaled Delayed_ms</li> <li>Causality – causal/non-causal (judgment)</li> <li>RT – reaction time in milliseconds</li> </ul> In the original version of the data, the data had been incorrectly labelled: The data actually corresponding to the LFC-RPA condition had been incorrectly labelled as LPC-RPA, and the data actually corresponding to the LPC-RPA condition had been incorrectly labelled as LFC-RPA. This has been corrected with the 04/2024 version of the dataset.

opencc-by-4.0Oct 2024View details →

Supplementary Data Files for the paper "Intrinsically disordered compositional bias in proteins: Sequence traits, region clustering, and generation of hypothetical functional associations"

<div> <div> <div> <div> Supplementary data files relating to <a href="https://doi.org/10.1177/11779322241287485">https://doi.org/10.1177/11779322241287485. </a> Suppl. File 1: Protein Family Clusters. Suppl. File 2: Cluster GO enrichments/depletions. Suppl. File 3: The raw ID-CBR data with annotations. Suppl. File 4: ID-CBR Cluster membership. Each file has an explanatory header.    </div> </div> </div> </div>

opencc-by-4.0Dec 2024View details →

Data associated with the article 'Intervention factors associated with efficacy, when targeting oral language comprehension of children with or at risk for (Developmental) Language Disorder: A meta-analysis'

The efficacy of oral language comprehension interventions varies, but the reasons for this variation have received little attention. A meta-analysis was conducted to examine intervention factors associated with the efficacy (as expressed with effect sizes) of oral language comprehension interventions in children under the age of 18 with or at risk for (Developmental) Language Disorder, (D)LD. The meta-analysis article together with this additional material comprise the content needed for a thorough understanding and replication of the results. This dataset is based on two systematic scoping reviews on oral language comprehension interventions (Tarvainen et al., 2020, 2021). Further information from the sourced articles was extracted for this study titled ‘Intervention factors associated with efficacy, when targeting oral language comprehension of children with or at risk for (Developmental) Language Disorder: A meta-analysis’.  In the future, we hope that this data is used with a growing body of oral language comprehension interventions to conduct further and more detailed examinations of intervention factors associated with efficacy. References: Tarvainen, S., Launonen, K., & Stolt, S. (2021). Oral language comprehension interventions in school-age children and adolescents with developmental language disorder: A systematic scoping review. Autism & Developmental Language Impairments, 6, 1–24. https://doi.org/10.1177/23969415211010423 Tarvainen, S., Stolt, S., & Launonen, K. (2020). Oral language comprehension interventions in 1–8-year-old children with language disorders or difficulties: A systematic scoping review. Autism & Developmental Language Impairments, 5, 1–24. https://doi.org/10.1177/2396941520946

opencc-by-4.0Oct 2021View details →

Intrinsic disorder, phase separation and fibrillation by the Henipavirus V and W proteins | Talk- I PhasAGE International Conference

The I PhasAGE international conference brought together members of the PhasAGE consortium as well as outstanding international speakers showcasing high impact achievements in the field of liquid-liquid phase separation in aging and late-onset diseases. For details on conference program please see: https://phasage.eu/phasage-conference-1/

opencc-by-4.0Oct 2021View details →

Investigating the effects of COVID‑19 lockdown on Italian children and adolescents with and without neurodevelopmental disorders: a cross‑sectional study - DATASET

Dataset to support the findings in the journal paper titled "Investigating the effects of COVID‑19 lockdown on Italian children and adolescents with and without neurodevelopmental disorders: a cross‑sectional study". Each row is a different subject. Each column represents an answer to the questionnaire. For single choice questions, the answer was reported as-is (Italian). For multiple choice questions, the alternatives where splitted in several columns and the answer was coded as 0/1 (one hot encoding). For the "school" column, 2=primary school, 3=middle school, 4=high school. For the "school.class" column, classes from 4 to 8 belong to primary school, from first to fifth grade; classes from 9 to 11 belong to middle school, from first to third grade; classes from 12 to 16 belong to high school, from first to fifth grade.

opencc-by-4.0Nov 2021View details →

Raw data for: "Postsynaptic autism spectrum disorder genes and synaptic dysfunction"

Schematic illustration representing postsynaptic proteins associated to ASD. These proteins are involved in different synaptic functions, either directly (ion channels and glutamate receptors), or indirectly, including transmembrane heterophilic (NLGNs) and homophilic (NrCAM) cell-adhesion molecules, and scaffolding proteins (PSD-95, Shank, Homer), that link transmembrane and membrane-associated protein complexes with the underlying actin cytoskeleton. Additional cellular functions may influence synaptic activity in ASD, such as alternative splicing (PTEN, RBFOX1, nSR100/SRRM4), RNA editing (FMR1, FXR1), transcription (FOXP1, FOXP2, TBR1, TSHZ3), translation (FMR1), degradation (UBE3A), and mitochondrial activity (AGC1).

opencc-by-4.0Jun 2022View details →

The top 100 most influential articles in olfactory disorder: a bibliometric analysis

Introduction: Studies on olfactory disorder have varied considerably in content and field over the past four decades. Influential publications of olfactory disorder have not been analyzed quantitatively. This study aimed to analyze the top 100 most influential articles in olfactory disorder through bibliometrics. Methods: The researchers searched the Web of Science Core Collection for publications from 1980 to August 31, 2021. The top 100 highly cited articles were screened in accordance with the inclusion and exclusion criteria. The journal impact factor and the SCImago Journal Rank indicator were searched for the journal in which the top-cited articles were published. The researchers analyzed the data via Microsoft Excel and SPSS 24.0 and used VOSviewer software for data visualization. Results: The total citations of the top 100 articles have increased exponentially, especially in the past 2 years. The journal LARYNGOSCOPE contributed the most influential papers (n=6). The United States published the most top 100 articles (n=52), followed by Germany and the United Kingdom. The University of Pennsylvania published the most influential studies, with a total of 3,852 citations. Richard L. Doty and Thomas Hummel contributed the most influential literature, and the top 100 studies also cited their research frequently. The most important articles in the field of OD mainly focused on COVID-19, Parkinson's disease and olfactory tests. Conclusion: Neurodegenerative diseases and COVID-19-related olfactory disorders have been considered main topics over the past 40 years. This study identified the most influential articles for olfactory disorder researchers, providing guidance for their research.