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311 results for “function dataset”
Dataset related to: "Effects of Motor Rehabilitation on Balance and Functional Activities in Elderly Patients with Peripheral Neuropathy and Recurrent Falls"
<p>We provide the raw data used for the following article:</p> <p><strong>Gialanella B, Comini L, Prometti P, Vanoglio F, Santoro R.</strong> Effects of Motor Rehabilitation on Balance and Functional Activities in Elderly Patients with Peripheral Neuropathy and Recurrent Falls. Life (Basel). 2023 Apr 20;13(4):1059. doi: 10.3390/life13041059. </p> <p><strong>Abstract:</strong> To date, little is known about the effects of motor rehabilitation in peripheral neuropathy (PN) patients with a history of recurrent falls (RFH). This study aimed to assess balance and the activities of daily living (ADLs) in elderly lower limb PN patients with and without RFH and to verify the effects of motor rehabilitation on balance and ADLs in these patients. We collected data from<br> 64 lower limb PN patients, who underwent a conventional motor rehabilitation program: 35 patients had a history of recurrent falls, and 29 did not. The Berg Balance Scale (BBS) and motor FIM, before and after rehabilitation, were the outcome measures. After rehabilitation, lower limb PN patients with RFH had significantly higher scores in BBS and motor FIM (p < 0.001, for both) than at entry. The final BBS score and effectiveness in the BBS score of lower limb PN patients with RFH were lower than those of patients without RFH (p < 0.05 and p = 0.009, respectively). The study shows that conventional motor rehabilitation improves both balance and ADLs in patients, but balance improvement is lower in those with RFH. Thus, motor rehabilitation can be a therapeutic option for<br> the management of these patients.</p> <p> </p>
Training and test dataset for Receiver functions for quality classification and inversion
<p> </p> <p>traindata_inv.dat file contains the 60,000 1D crust layered-model and synthetic receiver functions, as well as surface wave dispersion. </p> <p> </p> <p>First row: the name of synthetic models, </p> <p>Second row: Receiver functions with 300 sample points (-5~25 s)</p> <p>Third row: Surface wave dispersion data with 16 sample points (4~32 s with 2 s interval)</p> <p>Fourth row: Shear wave velocities of 60-layer-model, and the thickness from the first layer to the last layer are</p> <pre>0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0</pre> <p> </p>
Dataset related to the article "Feasibility and Accuracy of the Automated Software for Dynamic Quantification of Left Ventricular and Atrial Volumes and Function in a Large Unselected Population"
<p>This record contains raw data related to the article “Feasibility and Accuracy of the Automated Software for Dynamic Quantification of Left Ventricular and Atrial Volumes and Function in a Large Unselected Population”</p> <p>Abstract</p> <p>We aimed to evaluate the feasibility and accuracy of machine learning-based automated dynamic quantification of left ventricular (LV) and left atrial (LA) volumes in an unselected population. We enrolled 600 unselected patients (12% in atrial fibrillation) clinically referred for transthoracic echocardiography (2DTTE), who also underwent 3D echocardiography (3DE) imaging. LV ejection fraction (EF), LV, and LA volumes were obtained from 2D images; 3D images were analyzed using dynamic heart model (DHM) software (Philips) resulting in LV and LA volume-time curves. A subgroup of 140 patients also underwent cardiac magnetic resonance (CMR) imaging. Average time of analysis, feasibility, and image quality were recorded, and results were compared between 2DTTE, DHM, and CMR. The use of DHM was feasible in 522/600 cases (87%). When feasible, the boundary position was considered accurate in 335/522 patients (64%), while major (n = 38) or minor (n = 149) border corrections were needed. The overall time required for DHM datasets was approximately 40 seconds. As expected, DHM LV volumes were larger than 2D ones (end-diastolic volume: 173 ± 64 vs. 142 ± 58 mL, respectively), while no differences were found for LV EF and LA volumes (EF: 55% ± 12 vs. 56% ± 14; LA volume 89 ± 36 vs. 89 ± 38 mL, respectively). The comparison between DHM and CMR values showed a high correlation for LV volumes (r = 0.70 and r = 0.82, <em>p</em> < 0.001 for end-diastolic and end-systolic volume, respectively) and an excellent correlation for EF (r = 0.82, <em>p</em> < 0.001) and LA volumes. The DHM software is feasible, accurate, and quick in a large series of unselected patients, including those with suboptimal 2D images or in atrial fibrillation.</p>
Dataset for Mechanically Flexible Polymeric Nanoneedle Arrays for Promoting Differentiation and Functional Activity of Neural Progenitor Cells
Open the record for dataset details and reuse information.
Dataset to submitted manuscript "Imaging the blinking properties of a mitochondrial fluorophore marker turns it into a multi-functional sensing probe"
<p><strong>This folder contains all raw data underlying the results presented in a manuscript, submitted to Light: Science & Applications, and entitled:</strong></p> <p> </p> <p><strong><em>Imaging the blinking properties of a mitochondrial fluorophore marker turns it into a multi-functional sensing probe</em></strong></p> <p> </p> <p><strong>Authored by:</strong></p> <p>Zhixue Du<sup>a</sup><em>, </em>Joachim Piguet<sup>a,+</sup>, Gleb Baryshnikov<sup>b,+</sup>, Johan Tornmalm<sup>a</sup>, Baris Demirbay<sup>a</sup>, Hans Ågren<sup>b</sup>, Jerker Widengren<sup>a,*</sup></p> <p> </p> <p><sup>a</sup> Royal Institute of Technology (KTH), Experimental Biomolecular Physics, Dept. Applied Physics, Albanova Univ Center 106 91 Stockholm, Sweden</p> <p><sup>b</sup> Royal Institute of Technology (KTH), Dept Theroretical Chemistry and Biology, Albanova Univ Center 106 91 Stockholm, Sweden</p> <p><sup>+</sup> Contributed equally</p> <p>* Corresponding author: Email: <a href="mailto:jwideng@kth.se">jwideng@kth.se</a>, Phone: +46-8-7907813</p> <p> </p> <p><strong>The data files are grouped into the different techniques used to generate them, and refer to the figures/tables in the manuscript where the extracted results are presented. </strong></p> <p> </p> <p><strong>ABSTRACT</strong></p> <p>By transient state (TRAST) measurements, monitoring the average fluorescence intensity upon excitation modulation, we show that the mitochondrial localization fluorophore 10-Nonyl Acridine Orange (NAO) exhibits prominent singlet-triplet state transitions and can act as a light-induced Lewis acid forming a red-emissive doublet radical. The blinking properties resulting from these transitions can be monitored in a broadly applicable manner, and under biologically relevant conditions. By TRAST studies of NAO in small unilamellar vesicles (SUVs) we show that these blinking properties are highly environment sensitive, specifically reflecting local oxygen concentrations, redox conditions, membrane charge, fluidity and lipid compositions. In SUVs containing the phospholipid cardiolipin (CL) the NAO blinking properties depend not only on the concentration of the CL added, but also on the CL acyl chain composition. The blinking also reflects hydroxyl ion dependent transitions to and from the NAO doublet radical. This makes it possible to monitor local pH and buffering properties, from a 3D bulk buffer above the membrane, or from a 2D buffer at the membrane surface itself. Finally, by live cell TRAST imaging, we show that the fluorescence blinking properties of NAO can also be imaged in a spatially resolved manner, and that this commercially available location-specific fluorophore thereby can be turned into a multi-parametric intracellular sensing probe. This study demonstrates new possibilities for fundamental membrane studies in artificial vesicles and live cells, using existing fluorophore markers, monitoring parameters and conditions of large biological relevance, which are difficult to retrieve by other means.</p>
Datasets related to article "Frequency-dependent functional connectivity in resting state networks"
<p><strong>DATASET #1</strong></p> <p>Power measured for the six resting state networks (DMN, DAN, VAN, LN, SMN, VN) and frequency bands (delta, theta, alpha, beta, gamma) of each participant.</p> <p> </p> <p><strong>DATASET #2</strong></p> <p>Intra-network and inter-network connectivity measured for the six resting state networks (DMN, DAN, VAN, LN, SMN, VN) and frequency bands (delta, theta, alpha, beta, gamma) of each participant.</p>
Dataset related to article "Robot-assisted rehabilitation of hand function after stroke: Development of prediction models for reference to therapy"
<p>DATASET #1</p> <p>Il data set è composto da 174 osservazioni riferite ad un campione di n=174 pazienti.</p> <p>Le variabili prese in considerazione per lo studio del data set sono 21:</p> <ul> <li> <p>ID_Pazient: variabile quantitativa continua, indica il numero di identificazione del paziente</p> </li> <li> <p>Sex: variabile dicotomica, indica il sesso del paziente (Maschio=0, Femmina=1)</p> </li> <li> <p>Age: variabile quantitativa continua, indica l'età del paziente nel momento in cui è stata effettuata la valutazione</p> </li> <li> <p>EMG_Control: variabile dicotomica, indica la capacità (Si=1) o meno (No=0) del soggetto di controllare il dispositivo con i propri segnali elettromiografici</p> </li> <li> <p>Force_Control: variabile dicotomica, indica la capacità (Si=1) o meno (No=0) del paziente di controllare il dispositivo con la propria forza</p> </li> <li> <p>Month_Injury: variabile quantitativa continua, indica i mesi trascorsi dalla data in cui è avvenuto l'ictus</p> </li> <li> <p>Diagnosis: variabile dicotomica, indica la tipologia di ictus: (Ischemico=0, Emorragico =1)</p> </li> <li> <p>Hemisphere: variabile dicotomica, indica quale emisfero cerebrale è stato colpito dall'ictus (Destro=0, Sinistro=1)</p> </li> <li> <p>FM_UE: variabile quantitativa discreta, indica la misura della funzione motoria dell'arto superiore determinata somministrando la scala Fugl-Meyer Upper Extremity</p> </li> <li> <p>Sensitivity: variabile quantitativa discreta, indica la sezione per la misura della sensibilità della scala Fugl-Meyer</p> </li> <li> <p>Pain_ROM: variabile quantitativa discreta, indica la sezione per la misura di articolarità e dolore della scala Fugl-Meyer</p> </li> <li> <p>FIM: variabile quantitativa discreta, indica la misura di autonomia della persona nelle attività della vita quotidiana, determinata dalla somministrazione della scala Functional Independence Measure</p> </li> <li> <p>RPS: variabile quantitativa discreta, indica la misura della funzione di raggiungimento di un oggetto</p> </li> <li> <p>Peg_Sec: variabile quantitativa continua, indica la misura della destrezza manuale fine e coincide con il rapporto tra il numero di pioli e i secondi impiegati per inserirli in uno specifico supporto</p> </li> <li> <p>PectMaj: variabile qualitativa ordinata, indica la misura della spasticità del pettorale, secondo la Modified Ashworth Scale</p> </li> <li> <p>BicBrach: variabile qualitativa ordinata, indica la misura della spasticità del bicipite, secondo la Modified Ashworth Scale</p> </li> <li> <p>FlexCarp: variabile qualitativa ordinata, indica la misura della spasticità del flessore del carpo, secondo la Modified Ashworth Scale</p> </li> <li> <p>FlexProfDig: variabile qualitativa ordinata, indica la misura della spasticità del flessore profondo delle dita, secondo la Modified Ashworth Scale</p> </li> <li> <p>FlexSupDig: variabile qualitativa ordinata, indica la misura della spasticità del flessore superficiale delle dita, secondo la Modified Ashworth Scale</p> </li> <li> <p>Ashworth_TOT: variabile quantitativa discreta, indica la misura totale della Modified Ashworth Scale, data dalla somma delle 5 variabili precedenti</p> </li> <li> <p>BB_par: variabile quantitativa discreta, indica la misura della destrezza manuale grossolana dell'arto paretico</p> </li> </ul>
Dataset related to article: "Functional recovery in hip fracture patients: the role of pharmacotherapy"
<p>We provide the raw data used for the following article:</p> <ul> <li>Gialanella B, Santoro R, Prometti P, Giordano A, Monguzzi V, Comini L, Olivares A, Grioni G.<br> <em>Functional recovery in hip fracture patients: the role of pharmacotherapy</em>.<br> "Aging Clin Exp Res." 2020 Jan;32(1):49-57.<br> doi: 10.1007/s40520-019-01175-w. Epub 2019 Mar 21. PMID: 30900212.</li> </ul> <p><strong>Background and aim: </strong>The aim of this prospective observational cohort study was to verify the relationship between number of drugs used and functional outcome in hip fracture patients undergoing rehabilitation.</p> <p><strong>Methods: </strong>This study was conducted on 139 patients with hip fracture who underwent a rehabilitation program. Efficiency rate in the Functional Independence Measure (FIM) and Berg Balance Scale (BBS), and length of stay (LOS) were the outcome measures.</p> <p><strong>Results: </strong>At the end of rehabilitation, 66.1% of patients showed an increase in number of drugs used, while 33.9% used the same or lower number of drugs than at admission. At the end of rehabilitation patients with increased pharmacotherapy took a higher total number of drug classes (p = 0.001), had longer LOS (p = 0.009) and lower Berg efficiency (p = 0.048) than patients with the same or lower pharmacotherapy. The number of drugs used at discharge was an independent determinant of LOS (beta = 0.19, p = 0.022) and FIM efficiency (beta = - 0.20, p = 0.025). Age was a determinant of LOS (beta = 0.17, p = 0.044) and BBS efficiency (beta = - 0.23, p = 0.009), while CIRS severity was a determinant of BBS efficiency only (beta = - 0.22, p = 0.016).</p> <p><strong>Discussion: </strong>Findings of study indicate that in hip fracture patients, the number of drugs prescribed at discharge is an important indicator of LOS and rehabilitation efficiency.</p> <p><strong>Conclusions: </strong>These findings can help the physician to better plan the rehabilitation of hip fracture patients who require polypharmacy.</p>
Dataset related to article "Functional changes in the lower extremity after non-immersive virtual reality and physiotherapy following stroke"
<p>DATASET #1</p> <p>Variable description of the dataset from publication DOI: 10.2340/16501977-2763<br> ID_PATIENT = unique identifier of subject in the original clinical database<br> ID_EVO4 = unique identifier of subject in the original device database for kinematic analysis<br> AGE = given age of the subject at the time of assessment<br> GENDER = given gender of the subject at the time of assessment<br> ID_LESION = unique identifier of aetiology of the brain lesion (1 = ischemic lesion; 2 = haemorrhagic lesion)<br> LESION_SIDE = unique identifier of hemisphere affected by the brain lesion (1 = right; 2 = left)<br> MONTHS FROM LESION = time distance between time of assessment and stroke onset<br> FM LE_PRE = Fugl-Meyer Assessment scale, lower limb section detected before treatment. This outcome measure assesses the impairment of motor function at the lower limb after stroke.<br> MAS_PRE = FM LE_PRE = Modified Ashworth Scale at the ankle, knee and hip, detected before treatment. This outcome measure assesses the reaction to fast elongation of muscles.<br> FIM_pre = Functional Independence Measure detected before treatment. This outcome measure assesses independence of the subject in the activities of daily living.<br> 10MTWT_PRE = 10 meters walking test detected before treatment. This outcome measure assesses the time needed to cover 10 meters by walking.<br> BERG_PRE = Berg Balance Scale detected before treatment. This outcome measure assesses impairment of balance function.<br> FAC_PRE = BERG_PRE = Functional Ambulation Category detected before treatment. This outcome measure classifies autonomy in ambulation function.<br> FM LE_POST = Fugl-Meyer Assessment scale, lower limb section detected after treatment. This outcome measure assesses the impairment of motor function at the lower limb after stroke.<br> MAS_ POST = FM LE_PRE = Modified Ashworth Scale at the ankle, knee and hip, detected after treatment. This outcome measure assesses the reaction to fast elongation of muscles.<br> FIM_ POST = Functional Independence Measure detected after treatment. This outcome measure assesses independence of the subject in the activities of daily living.<br> 10MTWT_ POST = 10 meters walking test detected after treatment. This outcome measure assesses the time needed to cover 10 meters by walking.<br> BERG_ POST = Berg Balance Scale detected after treatment. This outcome measure assesses impairment of balance function.<br> FAC_ POST = BERG_PRE = Functional Ambulation Category detected after treatment. This outcome measure classifies autonomy in ambulation function.<br> spatial error_PRE = this kinematic measure is the difference (computed as an area, mm2) between the trajectory performed by the patient in executing a standard motor task and the ideal one that should be performed.<br> Time_PRE = this kinematic measure is the overall time (s) needed by the patient to perform a standard motor task, before the treatment.<br> Lenght_PRE = this kinematic measure is the overall distance (mm) covered by the patient to perform a standard motor task, before the treatment.<br> speed_PRE = this kinematic measure is the mean velocity (m/s) expressed by the patient to perform a standard motor task, before the treatment.<br> peak_PRE = this kinematic measure is the jerk (m/s2) expressed by the patient to perform a standard motor task, before the treatment.<br> spatial error_POST = this kinematic measure is the difference (computed as an area, mm2) between the trajectory performed by the patient in executing a standard motor task and the ideal one that should be performed.<br> Time_ POST = this kinematic measure is the overall time (s) needed by the patient to perform a standard motor task, before the treatment.<br> Lenght_ POST = this kinematic measure is the overall distance (mm) covered by the patient to perform a standard motor task, before the treatment.<br> speed_ POST = this kinematic measure is the mean velocity (m/s) expressed by the patient to perform a standard motor task, before the treatment.<br> peak_ POST = this kinematic measure is the jerk (m/s2) expressed by the patient to perform a standard motor task, before the treatment.</p>
Dataset related to article: "Nocturnal Hypoxemia Impacts Right Ventricle Diastolic Function in Obstructive Sleep Apnea: A Retrospective Observational Study"
<p>We provide the raw data used for the following article:</p> <ul> <li>Scotti C, Porta R, Olivares A, Comini L, Cinelli A, Scalvini S, Vitacca M.<br> <em>Nocturnal Hypoxemia Impacts Right Ventricle Diastolic Function in Obstructive Sleep Apnea: A Retrospective Observational Study.</em><br> "J Clin Med." 2020 Jan 7;9(1):162. doi: 10.3390/jcm9010162. PMID: 31936097; PMCID: PMC7019852.</li> </ul> <p> </p> <p><strong>Abstract</strong><br> Obstructive sleep apnea (OSA), although a growing healthcare problem and documented risk factor for cardiovascular diseases, is still under-diagnosed in cardiac patients. To investigate the correlation between OSA and echocardiographic parameters of right ventricle diastolic (RVD) dysfunction, in particular trans-tricuspid E-wave deceleration time (EDT), we retrospectively analyzed data of 103 pure (comorbidity-free) OSA patients with comprehensive echocardiographic examination (ETT). Apnea/hypopnea index (AHI), oxygen desaturation index (ODI), mean nighttime oxyhemoglobin saturation (SpO2), time elapsed with SpO2 < 90% (T90) and mean peak desaturation of nocturnal events (Mdes, graded as mild, medium or severe) were compared with echocardiographic parameters. We found RVD dysfunction present in 58.3% of patients. Altered EDT correlated significantly with mean SpO2, T90, and Mdes (p < 0.01, all). Nocturnal desaturators had a significantly worse EDT than non-desaturators (p = 0.027) and a higher risk of prolonged EDT (odds ratio, OR = 2.86). EDT differed significantly according to Mdes severity (p = 0.005) with a higher risk of prolonged EDT in medium/severe vs. mild Mdes (OR = 3.44). EDT detected the presence of RVD dysfunction in 58.3% of our pure OSA patients. It correlated poorly with AHI severity but strongly with nocturnal desaturation severity, independently of age. This ETT marker may be useful for deciding appropriate diagnostic and therapeutic strategies.</p>
Dataset (II) related to publication: A cancer-associated K-RAS mutant protein that preferentially binds GDP displays gain-of-function and atypical signaling
<p>Dataset (II) related to publication:</p> <blockquote> <p>A cancer-associated K-RAS mutant protein that preferentially binds GDP displays gain-of-function and atypical signaling</p> </blockquote> <p>Includes raw trajectories of MD-simulations: K-RAS(M67L)+GDP replicas 11–20 (each replica 10 micros.)</p> <ul> <li>Individual .zip files contain raw-desmond trajectories (-out.cms files and trj-files)</li> </ul> <p>Other datasets related to this publication: 10.5281/zenodo.5506845 and 10.5281/zenodo.5507131</p>
Dataset (III) related to publication: A cancer-associated K-RAS mutant protein that preferentially binds GDP displays gain-of-function and atypical signaling
<p>Dataset (III) related to publication:</p> <blockquote> <p>A cancer-associated K-RAS mutant protein that preferentially binds GDP displays gain-of-function and atypical signaling</p> </blockquote> <p>Includes raw trajectories of MD-simulations: K-RAS(WT)+GDP replicas 1–10 (each replica 10 micros.)</p> <ul> <li>Individual .zip files contain raw-desmond trajectories (-out.cms files and trj-files)</li> </ul> <p>Other datasets related to this publication: 10.5281/zenodo.5506845 and 10.5281/zenodo.5507212</p>
Dataset (I) related to publication: A cancer-associated K-RAS mutant protein that preferentially binds GDP displays gain-of-function and atypical signaling
<p>Dataset (I) related to publication:</p> <blockquote> <p>A cancer-associated K-RAS mutant protein that preferentially binds GDP displays gain-of-function and atypical signaling</p> </blockquote> <p>Includes raw trajectories of MD-simulations: K-RAS(M67L)+GDP replicas 1–10 (each replica 10 micros.) and K-RAS(M67L)+GDP in complex with RALGDS (1 micros.; SI Movie trajectory)</p> <ul> <li>Individual .zip files contain raw-desmond trajectories (-out.cms files and trj-files)</li> </ul> <p>Other datasets related to this publication: 10.5281/zenodo.5507131 and 10.5281/zenodo.5507212</p>
Receiver Function Open-Access Dataset for the European Alps
<p>This dataset comprises of the receiver function traces calculated using seismic waveform data from the <a href="http://www.alparray.ethz.ch/en/home/">AlpArray Seismic Network</a>, three other temporary seismic networks, and permanent stations in the broader European Alpine region. We use this dataset to compile a new Moho depth map for the broader European Alps.</p> <p>In particular, we include:</p> <ol> <li>the radial receiver function traces in .SAC format (RRF.zip). One SAC trace per station and teleseismic earthquake. </li> <li>the transverse (TRF.zip) traces in .SAC format. </li> <li>the receiver function stacks from all the seismic stations plotted versus back-azimuth in .png format (RRF_plots.zip and TRF_plots.zip)</li> </ol> <p>For details on how to read and process the receiver function traces have a look at the README file of the repository.</p>
Dataset related to the article: "N-acetylcysteine inhibits platelet function through the regeneration of the non-oxidative form of albumin"
<p>This record contains raw data related to the article: "N-acetylcysteine inhibits platelet function through the regeneration of the non-oxidative form of albumin"</p> <p><strong>Abstract: </strong>N-acetylcysteine (NAC), is able to break down protein disulfides generating free thiols. This mechanism occurs on mixed disulfides of albumin (HSA) to form mercaptoalbumin (HMA), the main antioxidant species in the plasma. Circulating HSA exists in two main forms: the reduced form (HMA), and the oxidized forms, whose predominant modification is cystenylation (HSA-Cys). Increased levels of oxidized HSA were detected in several diseases associated with oxidative stress. This study showed that NAC inhibits platelet aggregation by restoring HMA. In addition, the regeneration of HMA by NAC inhibits platelet functions, such as intracellular calcium mobilization, reactive oxygen species generation, arachidonic acid metabolites synthesis, and adhesion to the collagen matrix. In our conditions, the exposure of platelet to NAC did not increased GSH levels. However, the inhibition of platelet aggregation was also detected following treatment of platelet-rich plasma with GSH, which, similarly to NAC, reduced HSA-Cys levels. Furthermore, this study shows that cysteine, another compound able to restore HMA by reducing the HSA-Cys content, inhibited platelet aggregation to a similar extent as NAC. The results obtained in this study suggest a new mechanism by which NAC can modulate platelet activation and suggest its possible use as an antiplatelet drug in conditions associated with oxidative stress.</p>
DATASET RELATED TO ARTICLE "Preoperative nonmedical predictors of functional impairment after brain tumor surgery"
<p>The .xls datasheet contains anonymized demographic, clinical e neuropsychological evaluation data of patients involved in the study reported at title.</p> <p>Variables list: </p> <table> <tbody> <tr> <td>gender</td> </tr> <tr> <td>age</td> </tr> <tr> <td>Tumor location</td> </tr> <tr> <td>Location groups</td> </tr> <tr> <td>Eloquent area</td> </tr> <tr> <td>Tumor side</td> </tr> <tr> <td>Dimension</td> </tr> <tr> <td>KPS preoperatory</td> </tr> <tr> <td>complication</td> </tr> <tr> <td>Landriel classification</td> </tr> <tr> <td>KPS discharge</td> </tr> <tr> <td>Histology</td> </tr> <tr> <td>diagnosis</td> </tr> <tr> <td>WHO grade</td> </tr> <tr> <td>KPS follow up</td> </tr> <tr> <td>T0 Datacompilazione</td> </tr> <tr> <td>educational level</td> </tr> <tr> <td>YEARS STUDY</td> </tr> <tr> <td>MARITAL STATUS</td> </tr> <tr> <td>living situation</td> </tr> <tr> <td>T0 Personenucleofamiliare</td> </tr> <tr> <td>WORK</td> </tr> <tr> <td>T0 WHODAS12 Totalscore</td> </tr> <tr> <td>T0 WHODAS12 H1</td> </tr> <tr> <td>T0 WHODAS12 H2</td> </tr> <tr> <td>T0 WHODAS12 H3</td> </tr> <tr> <td>T0 WHODAS12 H4</td> </tr> <tr> <td>T0 WHODAS12 H5</td> </tr> <tr> <td>T0 HADS anxiety</td> </tr> <tr> <td>T0 HADS depression</td> </tr> <tr> <td>T0 HADS emotional distress</td> </tr> <tr> <td>T0 OSLO</td> </tr> <tr> <td>T0 TIPIEXTRAVERSION</td> </tr> <tr> <td>T0 TIPIAGREEABLENESS</td> </tr> <tr> <td>TO TIPICOSCIENZIOUSNESS</td> </tr> <tr> <td>TO TIPINEUROTICISM</td> </tr> <tr> <td>T0 TIPIOPENESS</td> </tr> <tr> <td>T0 RS14</td> </tr> <tr> <td>T0 RS14 Livello</td> </tr> <tr> <td>T0 EORTC global health</td> </tr> <tr> <td>T0 EORTC physical functioning</td> </tr> <tr> <td>T0 EORTC role functioning</td> </tr> <tr> <td>T0 EORTC emotional functioning</td> </tr> <tr> <td>T0 EORTC cognitive functioning</td> </tr> <tr> <td>T0 EORTC social functioning</td> </tr> <tr> <td>T0 EORTC fatigue</td> </tr> <tr> <td>T0 EORTC NV</td> </tr> <tr> <td>T0 EORTC PA</td> </tr> <tr> <td>T0 EORTC DY</td> </tr> <tr> <td>T0 EORTC SL</td> </tr> <tr> <td>T0 EORTC AP</td> </tr> <tr> <td>T0 EORTC CO</td> </tr> <tr> <td>T0 EORTC DI</td> </tr> <tr> <td>T0 EORTC FI</td> </tr> <tr> <td>T0 BN20 BNFU</td> </tr> <tr> <td>T0 BN20 BNVD</td> </tr> <tr> <td>T0 BN20 BNMD</td> </tr> <tr> <td>T0 BN20 BNCD</td> </tr> <tr> <td>T0 BN20 BNHA</td> </tr> <tr> <td>T0 BN20 BNSE</td> </tr> <tr> <td>T0 BN20 BNDR</td> </tr> <tr> <td>T0 BN20 BNIS</td> </tr> <tr> <td>T0 BN20 BNHL</td> </tr> <tr> <td>T0 BN20 BNWL</td> </tr> <tr> <td>T0 BN20 BNBC</td> </tr> <tr> <td>T0 FAS raw score</td> </tr> <tr> <td>T0 FAS standard score</td> </tr> <tr> <td>T0 FAS equivalent scores</td> </tr> <tr> <td>T0 ROWL IR raw score</td> </tr> <tr> <td>T0 ROWL IR standard score</td> </tr> <tr> <td>T0 ROWL IR equivalent score</td> </tr> <tr> <td>T0 ROWL DR raw score</td> </tr> <tr> <td>T0 ROWL DR standard score</td> </tr> <tr> <td>T0 ROWL DR equivalent score</td> </tr> <tr> <td>T0 TMTA raw score</td> </tr> <tr> <td>T0 TMTA standard score</td> </tr> <tr> <td>T0 TMTA equivalent score</td> </tr> <tr> <td>T0 TMTB raw score</td> </tr> <tr> <td>T0 TMTB standard score</td> </tr> <tr> <td>T0 TMTB equivalent score</td> </tr> <tr> <td>T0 TMTBA PG</td> </tr> <tr> <td>T0 TMTBA PC</td> </tr> <tr> <td>T0 TMTBA PE</td> </tr> <tr> <td>T0 MOCATOT raw score</td> </tr> <tr> <td>T0 MOCATOT standard score</td> </tr> <tr> <td>T0 MOCATOT equivalent score</td> </tr> <tr> <td>T0 MOCAVisuospatial PG</td> </tr> <tr> <td>T0 MOCAExecutive PG</td> </tr> <tr> <td>T0 MOCALanguage PG</td> </tr> <tr> <td>T0 MOCAOrientation PG</td> </tr> <tr> <td>T0 MOCAAttention PG</td> </tr> <tr> <td>T0 MOCAMemory PG</td> </tr> <tr> <td>T0 MOCAVisuospatial PC</td> </tr> <tr> <td>T0 MOCAExecutive PC</td> </tr> <tr> <td>T0 MOCALanguage PC</td> </tr> <tr> <td>T0 MOCAOrientation PC</td> </tr> <tr> <td>T0 MOCAAttention PC</td> </tr> <tr> <td>T0 MOCAMemory PC</td> </tr> <tr> <td>T0 MOCAVisuospatial PE</td> </tr> <tr> <td>T0 MOCAExecutive PE</td> </tr> <tr> <td>T0 MOCALanguage PE</td> </tr> <tr> <td>T0 MOCAOrientation PE</td> </tr> <tr> <td>T0 MOCAAttention PE</td> </tr> <tr> <td>T0 MOCAMemory PE</td> </tr> <tr> <td>FU Datacompilazione</td> </tr> <tr> <td>FU WHODAS12 Totalscore</td> </tr> <tr> <td>FU WHODAS12 H1</td> </tr> <tr> <td>FU WHODAS12 H2</td> </tr> <tr> <td>FU WHODAS12 H3</td> </tr> <tr> <td>FU WHODAS12 H4</td> </tr> <tr> <td>FU WHODAS12 H5</td> </tr> <tr> <td>FU HADS anxiety</td> </tr> <tr> <td>FU HADS depression</td> </tr> <tr> <td>FU HADS emotional distress</td> </tr> <tr> <td>FU EORTC global health</td> </tr> <tr> <td>FU EORTC physical functioning</td> </tr> <tr> <td>FU EORTC role functioning</td> </tr> <tr> <td>FU EORTC emotional functioning</td> </tr> <tr> <td>FU EORTC cognitive functioning</td> </tr> <tr> <td>FU EORTC social functioning</td> </tr> <tr> <td>FU EORTC fatigue</td> </tr> <tr> <td>FU EORTC NV</td> </tr> <tr> <td>FU EORTC PA</td> </tr> <tr> <td>FU EORTC DY</td> </tr> <tr> <td>FU EORTC SL</td> </tr> <tr> <td>FU EORTC AP</td> </tr> <tr> <td>FU EORTC CO</td> </tr> <tr> <td>FU EORTC DI</td> </tr> <tr> <td>FU EORTC FI</td> </tr> <tr> <td>FU BN20 BNFU</td> </tr> <tr> <td>FU BN20 BNVD</td> </tr> <tr> <td>FU BN20 BNMD</td> </tr> <tr> <td>FU BN20 BNCD</td> </tr> <tr> <td>FU BN20 BNHA</td> </tr> <tr> <td>FU BN20 BNSE</td> </tr> <tr> <td>FU BN20 BNDR</td> </tr> <tr> <td>FU BN20 BNIS</td> </tr> <tr> <td>FU BN20 BNHL</td> </tr> <tr> <td>FU BN20 BNWL</td> </tr> <tr> <td>FU BN20 BNBC</td> </tr> <tr> <td>FU FAS raw score</td> </tr> <tr> <td>FU FAS standard score</td> </tr> <tr> <td>FU FAS equivalent score</td> </tr> <tr> <td>FU ROWl IR raw score</td> </tr> <tr> <td>FU ROWL IR standard score</td> </tr> <tr> <td>FU ROWL IR equivalent score</td> </tr> <tr> <td>FU ROWL DR raw score</td> </tr> <tr> <td>FU ROWL DR standard score</td> </tr> <tr> <td>FU ROWL DR equivalent score</td> </tr> <tr> <td>FU TMTA raw score</td> </tr> <tr> <td>FU TMTA standard score</td> </tr> <tr> <td>FU TMTA equivalent score</td> </tr> <tr> <td>FU TMTB raw score</td> </tr> <tr> <td>FU TMTB standard score</td> </tr> <tr> <td>FU TMTB equivalent score</td> </tr> <tr> <td>FU TMTBA PG</td> </tr> <tr> <td>FU TMTBA PC</td> </tr> <tr> <td>FU TMTBA PE</td> </tr> <tr> <td>FU MOCATOT raw score</td> </tr> <tr> <td>FU MOCATOT standard score</td> </tr> <tr> <td>FU MOCATOT equivalent score</td> </tr> <tr> <td>FU MOCAVisuospatial PG</td> </tr> <tr> <td>FU MOCAExecutive PG</td> </tr> <tr> <td>FU MOCALanguage PG</td> </tr> <tr> <td>FU MOCAOrientation PG</td> </tr> <tr> <td>FU MOCAAttention PG</td> </tr> <tr> <td>FU MOCAMemory PG</td> </tr> <tr> <td>FU MOCAVisuospatial PC</td> </tr> <tr> <td>FU MOCAExecutive PC</td> </tr> <tr> <td>FU MOCALanguage PC</td> </tr> <tr> <td>FU MOCAOrientation PC</td> </tr> <tr> <td>FU MOCAAttention PC</td> </tr> <tr> <td>FU MOCAMemory PC</td> </tr> <tr> <td>FU MOCAVisuospatial PC A</td> </tr> <tr> <td>FU MOCAExecutive PE</td> </tr> <tr> <td>FU MOCALanguage PE</td> </tr> <tr> <td>FU MOCAOrientation PE</td> </tr> <tr> <td>FU MOCAAttention PE</td> </tr> <tr> <td>FU MOCAMemory PE</td> </tr> <tr> <td>GliomaEMeningioma</td> </tr> <tr> <td>employment status</td> </tr> <tr> <td>DeltaKPS</td> </tr> </tbody> </table> <p> </p>
Dataset related to article "Germline loss-of-function PAM variants are enriched in subjects with pituitary hypersecretion"
<p>This record contains raw data related to article "Germline loss-of-function PAM variants are enriched in subjects with pituitary hypersecretion"</p> <p><strong>Abstract</strong></p> <p>Pituitary adenomas (PAs) are common, usually benign tumors of the anterior pituitary gland which, for the most part, have no known genetic cause. PAs are associated with major clinical effects due to hormonal dysregulation and tumoral impingement on vital brain structures.</p> <p>Following the identification of a loss-of-function variant (p.Arg703Gln) in the <em>PAM </em>gene in a family with pituitary gigantism, we investigated 299 individuals with sporadic PAs and 17 familial isolated pituitary adenomas kindreds for <em>PAM </em>variants. <em>PAM </em>encodes a multifunctional protein responsible for the essential C-terminal amidation of secreted peptides.</p> <p>Genetic screening was performed by germline and tumor sequencing and germline copy number variation (CNV) analysis. No germline CNVs or somatic single nucleotide variants (SNVs) were identified. We detected seven likely pathogenic heterozygous missense, truncating, and regulatory SNVs. These SNVs were found in sporadic subjects with GH excess (p.Gly552Arg and p.Phe759Ser), pediatric Cushing disease (c.-133T>C and p.His778fs), or with different types of PAs (c.-361G>A, p.Ser539Trp, and p.Asp563Gly). The SNVs were functionally tested <em>in vitro </em>for protein expression and trafficking by Western blotting, for splicing by minigene assays, and for amidation activity in cell lysates and serum samples. These analyses confirmed a deleterious effect on protein expression and/or function. By interrogating 200,000 exomes from the UK Biobank, we confirmed a significant association of the <em>PAM</em> gene and rare <em>PAM </em>SNVs to diagnoses linked to pituitary gland hyperfunction.</p> <p>Identification of <em>PAM </em>as a candidate gene associated with pituitary hypersecretion opens the possibility of developing novel therapeutics based on altering PAM function.</p>
Dataset for "Spatiotemporal impacts of land conversion on avian functional and phylogenetic diversities''
<p>The dataset was obtained with information from:</p> <ol> <li> <p>The National Land Cover Database (NLCD), free online available, and released by Dewitz and U.S. Geological Survey (2019) (<a href="https://www.usgs.gov/centers/eros/science/national-land-cover-database">https://www.usgs.gov/centers/eros/science/national-land-cover-database</a>)</p> </li> <li> <p>The North American Breeding Bird Survey Dataset, free online available, and released by Ziolkowski Jr. DJ, Lutmerding M, Aponte VI, and Hudson M-A R (2022) North American Breeding Bird Survey Dataset 1966 - 2021: U.S. Geological Survey data release (<a href="https://www.usgs.gov/centers/eesc/science/north-american-breeding-bird-survey">https://www.usgs.gov/centers/eesc/science/north-american-breeding-bird-survey</a>)</p> </li> <li> <p>The AVONET Dataset, published by Tobias JA, Sheard C, Pigot AL, Devenish AJM, Yang J, Sayol F et al. (2022) AVONET: morphological, ecological and geographical data or all birds. Ecology Letters, 25, 581– 597 (<a href="https://onlinelibrary.wiley.com/doi/full/10.1111/ele.13898">https://onlinelibrary.wiley.com/doi/full/10.1111/ele.13898</a>)</p> </li> <li> <p>Data on life history traits of birds, assembled by the hosting lab members (including myself)</p> </li> <li> <p>Maximum clade credibility phylogenies (CCP) based on the pseudo-posterior distribution of trees available at www.birdtree.org, developed by the hosting lab.</p> </li> </ol> <p>Data was extracted and structured to be used to reach the results from the manuscript "<strong>Spatiotemporal impacts of land conversion on avian functional and phylogenetic diversities</strong>". </p> <p>This manuscript is part of the second and fifth Working Packages (WP2 and WP5) of the project ENVIRON_CHANGE, financed by the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 101030199. The aim of the WP2 was to estimate changes in phylogenetic and functional diversities across land use change and describe its temporal dynamics. The WP5 was dedicated to disseminate and communicate the research.</p>
Dataset_Urinary sC5b-9 is independently associated with kidney function decline in diabetic kidney disease.
<p>The dataset is provided with all variables studied and described in the research letter. In particular, clinical, demographic, 24h blood pressure and urinary complement levels are listed. A stata do file describes the statistical analysis of the data set. </p>
Dataset related to article "Important functional role of the protein osteopontin in the progression of malignant pleural mesothelioma"
<p>This record contains raw data related to article "Important functional role of the protein osteopontin in the progression of malignant pleural mesothelioma"</p><p><strong>Background: </strong>Malignant Pleural Mesothelioma (MPM) is an aggressive cancer of the mesothelial lining associated with exposure to airborne non-degradable asbestos fibers. Its poor response to currently available treatments prompted us to explore the biological mechanisms involved in its progression. MPM is characterized by chronic non-resolving inflammation; in this study we investigated which inflammatory mediators are mostly expressed in biological tumor samples from MPM patients, with a focus on inflammatory cytokines, chemokines and matrix components.</p><p><strong>Methods: </strong>Expression and quantification of Osteopontin (OPN) was detected in tumor and plasma samples of MPM patients by mRNA, immunohistochemistry and ELISA. The functional role of OPN was investigated in mouse MPM cell lines <i>in vivo</i> using an orthotopic syngeneic mouse model.</p><p><strong>Results: </strong>In patients with MPM, the protein OPN was significantly more expressed in tumors than in normal pleural tissues and predominantly produced by mesothelioma cells; plasma levels were elevated in patients and associated with poor prognosis. However, modulation of OPN levels was not significantly different in a series of 18 MPM patients receiving immunotherapy with durvalumab alone or with pembrolizumab in combination with chemotherapy, some of whom achieved a partial clinical response. Two established murine mesothelioma cell lines: AB1 and AB22 of sarcomatoid and epithelioid histology, respectively, spontaneously produced high levels of OPN. Silencing of the OPN gene (<i>Spp1</i>) dramatically inhibited tumor growth <i>in vivo</i> in an orthotopic model, indicating that OPN has an important promoting role in the proliferation of MPM cells. Treatment of mice with anti-CD44 mAb, blocking a major OPN receptor, significantly reduced tumor growth <i>in vivo</i>.</p><p><strong>Conclusion: </strong>These results demonstrate that OPN is an endogenous growth factor for mesothelial cells and inhibition of its signaling may be helpful to restrain tumor progression <i>in vivo</i>. These findings have translational potential to improve the therapeutic response of human MPM.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.