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4,954 results for “hospitals”
Genome assemblies of 382 carbapenem-resistant Pseudomonas aeruginosa isolates collected from Japanese hospitals in 2019−2020
<p>This dataset provides genome assemblies used in the study of "Nationwide genome surveillance of carbapenem-resistant Pseudomonas aeruginosa in Japan".</p> <p> </p>
Intersectionality Spectrum - triage like a hospital
<p>alt-text:</p> <p>Intersectionality spectrum with different categories of intersectionality along the x-axis and the degree of difficulty shown as a bar graph on the y-axis. It shows 3 arrows pointing down on the bars that have the highest degree of difficulty to signify that we need to prioritise support to those who need it most because they have been discriinated the most. It has one green arrow pointing down to those with smaller degrees of difficulty to signify we still need to help those people as well, but with less intensity or frequency. This is similar to how a hospital should triage patients, in that we need to look after the sickest people first.</p>
Number of public and private hospitals and number of beds available in the spanish sanitary system in 1986
<p>Number of public and private hospitals and number of beds avaiable in the spanish sanitary system in 1986.</p> <p>Information about funding sources or sponsorship that supported the collection of the data: Financial support from the European Union, the European Regional Development Fund (ERDF), and Spain’s Ministry of Science and Innovation -State Research Agency- for the project entitled "The historical keys of hospital development in Spain and its international comparison during the twentieth century", Ref. RTI2018-094676-B-I00.</p>
Number of public and private hospitals and number of beds available in the spanish sanitary system in 1970
<p>Number of public and private hospitals and number of beds avaiable in the spanish sanitary system in 1970.</p> <p>Information about funding sources or sponsorship that supported the collection of the data: Financial support from the European Union, the European Regional Development Fund (ERDF), and Spain’s Ministry of Science and Innovation -State Research Agency- for the project entitled "The historical keys of hospital development in Spain and its international comparison during the twentieth century", Ref. RTI2018-094676-B-I00.</p>
Hand Washing Video Dataset Annotated According to the World Health Organization's Handwashing Guidelines - Jurmala Hospital Subset
<p><strong>Overview:</strong> This is a large-scale real-world dataset with videos recording medical staff washing their hands as part of their normal job duties in the Jurmala Hospital located in Jurmala, Latvia. There are 2427 hand washing episodes in total, almost all of which are annotated by two persons. The annotations classify the washing movements according to the World Health Organization's (WHO) guidelines by marking each frame in each video with a certain movement code.</p> <p>This dataset is part on three dataset series all following the same format:</p> <ul> <li><a href="https://zenodo.org/record/4537209">https://zenodo.org/record/4537209</a> - data collected in Pauls Stradins Clinical University Hospital</li> <li><a href="https://zenodo.org/record/5808764">https://zenodo.org/record/5808764</a> - data collected in Jurmala Hospital</li> <li><a href="https://zenodo.org/record/5808789">https://zenodo.org/record/5808789</a> - data collected in the Medical Education Technology Center (METC) of Riga Stradins University</li> </ul> <p><strong>Applications: </strong>The intention of this dataset is twofold: to serve as a basis for training machine learning classifiers for automated hand washing movement recognition and quality control, and to allow to investigate the real-world quality of washing performed by working medical staff.</p> <p><strong>Statistics:</strong></p> <ul> <li>Frame rate: 30 FPS</li> <li>Resolution: 320x240 and 640x480</li> <li>Number of videos: 2427</li> <li>Number of annotation files: 4818</li> </ul> <p>Movement codes (both in CSV and JSON files):</p> <ul> <li>1: Hand washing movement — Palm to palm</li> <li>2: Hand washing movement — Palm over dorsum, fingers interlaced</li> <li>3: Hand washing movement — Palm to palm, fingers interlaced</li> <li>4: Hand washing movement — Backs of fingers to opposing palm, fingers interlocked</li> <li>5: Hand washing movement — Rotational rubbing of the thumb</li> <li>6: Hand washing movement — Fingertips to palm</li> <li>7: Turning off the faucet with a paper towel</li> <li>0: Other hand washing movement</li> </ul> <p><strong>Acknowledgments: </strong>The dataset collection was funded by the Latvian Council of Science project: "Automated hand washing quality control and quality evaluation system with real-time feedback", No: lzp - Nr. 2020/2-0309.</p> <p><strong>References: </strong>For more detailed information, see this article, describing a similar dataset collected in a different project:</p> <ul> <li> <p>M. Lulla, A. Rutkovskis, A. Slavinska, A. Vilde, A. Gromova, M. Ivanovs, A. Skadins, R. Kadikis, A. Elsts. <em>Hand-Washing Video Dataset Annotated According to the World Health Organization’s Hand-Washing Guidelines</em>. Data. 2021; 6(4):38. <a href="https://doi.org/10.3390/data6040038">https://doi.org/10.3390/data6040038</a></p> </li> </ul> <p><strong>Contact information: </strong>atis.elsts@edi.lv</p>
Comparative analysis of surface sanitization protocols on the bacterial community structures in the hospital environment
<p>In this study, we used 16S rRNA gene sequencing approaches to characterize the bacterial microbiota on different surfaces of the hospital environment. The longitudinal data was then subjected to comprehensive comparisons between different sanitation strategies (disinfectants, detergents and probiotics) to measure their potential effect on the microbial community structures in the hospital environment.</p> <p>This archive contains results and data of the 16S rRNA amplicon sequencing performed on 1019 environmental and 271 patient DNA samples collected over the time course of 40 weeks in a newly opened ward in the neurological station at the Charité Hospital (Berlin). The files include a study information and sample metadata sheets, BIOM-tables and information about the taxonomy results and diversity metrics.</p>
Factors associated with length to recover adequate nutrition and length of stay in children hospitalized for bronchiolitis: a retrospective study.
<p><strong>Context:</strong> Inadequate feeding is a frequent reason for hospital referring in children with bronchiolitis and leads to prolonged hospitalization in 26% of the cases. The main objective was to identify the factors associated with the time to recover adequate nutrition in infants hospitalized for bronchiolitis.</p> <p><strong>Method:</strong> We conducted a single-center retrospective study including infants less than 12 months hospitalized for bronchiolitis at Le Havre Hospital (France) between September 2018 and February 2021. A multivariate logistic regression model was computed to investigate the factors associated with (1) the time to recover adequate feeding (LOFR), and (2) the hospital length of stay (LOS).</p> <p><strong>Results:</strong> 268 infants were included to assess the LOFR and 478 infants to assess the LOS. The median age was 3.2 months (1.6-5.4) and the sex ratio M/F was 11/20. The use of accessory muscles, nutritional support, and RR ≥ 70/min or < 30/min or apnea are associated (OR=1.5), from virtually no association (OR=1.0) to a significant positive association (OR=2.6) with the LOFR. Intense use of accessory muscles (OR=3.9; 95%CI 1.6-10.4) and "severe" clinical condition (OR=2.8; 95%CI 1.7-4. 8) at admission, O2 supplementation (OR=2.0; 95%CI 1.3-3.1) were significantly related to prolonged LOS in the multivariate analysis.</p> <p><strong>Conclusion:</strong> The clinical severity on admission may be related to the LOFR, ranging from none to significant. Other known factors such as oxygen therapy and the new clinical severity scale proposed by the latest French guidelines appeared to be related to the LOS in this work. Further studies are needed to highlight these factors.</p> <p> </p> <p>DATA.zip contain all statistics documents</p> <p>Supporting_documents contain ethic statement and reference methodology MR004 n° 2221599.</p>
Hospitality and Tourism Industry from TR-HT
<p>Set of data used in the paper <em>The impact of ESG dimensions on firm risk of hospitality and tourism industry.</em></p> <p>The data were obtained from Thomson Reuters Eikon database (TR_Eikon), we selected those companies whose activity sector was H&T regardless of their country of origin. The data are due in "xls" format and structured in two sheets.</p> <p>- the first sheet contains the selected companies, with the next information: </p> <ol> <li>Company Name</li> <li>NAICS Sector Code</li> <li>NAICS Subsector Code</li> <li>NAICS Industry Code</li> <li>NAICS National Industry Name</li> <li>Muestra Final: an indicator variable used to remark that the respective company is used in the study.</li> </ol> <p>- The second sheet contains the next information:</p> <ol> <li>Company Id: The Company Identificator. A number differentiating each company from the rest.</li> <li>Year: The year of the correspondig register.</li> <li>P_Crisis: Pandemic Crisis. Dummy variable coded 1 if year is 2020, 0 otherwise.</li> <li>F_Crisis Financial Crisis: Dummy variable coded 1 if year is from 2008 to 2012, 0 otherwise</li> <li>Firm_Size: Firm size. Logarithm of total asses</li> <li>Leverage: Leverage. Total debt to total assets</li> <li>ROA: Return on assets. EBITDA divided by total assets</li> <li>B_Gender: Gender diversity. Number of women directors as a percentage of total directors on the board</li> <li>B_Independence: Independence. number of independent directors as a percentage of total directors on the board</li> <li>B_Size: Board size. Number of directors on the board</li> <li>Duality: CEO duality. Dummy variable taking the value 1 if the chairperson of the board is the CEO and 0 otherwise</li> <li>ESG_Score: Evironmental Social and Governance Score. A weighted average relative rating based on reported environmental, social and governance information and ranges between 0 (worst) and 100 (best). It is provided by Eikon Thomson Reuters</li> <li>SOC_Score: Social Pillar Score. A weighted average relative rating based on reported social information and ranges between 0 (worst) and 100 (best). It is provided by Eikon Thomson Reuters</li> <li>GOV_Score: Governance Pillar Score. A weighted average relative rating based on reported governance information and ranges between 0 (worst) and 100 (best). It is provided by Eikon Thomson Reuters</li> <li>ENV_Score: Environmental Pillar Score. A weighted average relative rating based on reported environmental information and ranges between 0 (worst) and 100 (best). It is provided by Eikon Thomson Reuters</li> <li>Dependent variables</li> <li>D2D: Distance of Default. Merton’s distance to default</li> <li>SD_R: Volatility of the stock returns. Standard deviation of daily stock returns</li> </ol>
Hyperglycemia and steroid use increase the risk of rhino-orbito-cerebral mucormycosis regardless of COVID-19 hospitalization: Case-control study, India
<p><strong>Abstract</strong></p> <p><strong><em>BACKGROUND</em></strong></p> <p>In the context of the ongoing COVID-19 pandemic increased incidence of ROCM was noted in India, among those infected with COVID. We determined risk factors for rhino-orbito-cerebral mucormycosis (ROCM) post Coronavirus disease 2019 (COVID-19) among those never and ever hospitalized for COVID-19 separately through a multi-centric, hospital-based, unmatched case-control study across India.</p> <p><strong><em>METHODS</em></strong></p> <p>We defined cases and controls as those with and without post-COVID ROCM, respectively. We compared their socio-demographics, comorbidities, steroid use, glycaemic status, and practices. We calculated crude and adjusted odds ratio (AOR) with 95% confidence intervals (CI) through logistic regression. The covariates with p-value for crude OR of less 0·20 were considered for the regression model.</p> <p><strong><em>RESULTS</em></strong></p> <p>Among hospitalised, we recruited 267 cases and 256 controls and 116 cases and 231 controls among never hospitalised. Risk factors (AOR; 95% CI) for post-COVID ROCM among the hospitalised were age 45-59 years (2·1; 1·4 to 3·1), having diabetes mellitus (4·9; 3·4 to 7·1), elevated plasma glucose (6·4; 2·4 to 17·2), steroid use (3·2; 2 to 5·2) and frequent nasal washing (4·8; 1·4 to 17). Among those never hospitalised, age ≥ 60 years (6·6; 3·3 to 13·3), having diabetes mellitus (6·7; 3·8 to 11·6), elevated plasma glucose (13·7; 2·2 to 84), steroid use (9·8; 5·8 to 16·6), and cloth facemask use (2·6; 1·5 to 4·5) were associated with increased risk of post-COVID ROCM.</p> <p><strong><em>CONCLUSIONS</em></strong></p> <p>Hyperglycemia irrespective of having diabetes mellitus and steroid use was associated with increased risk of ROCM independent of COVID-19 hospitalisation. Rational steroid usage and glucose monitoring may reduce the risk of post-COVID.</p>
Fake Medical Data representing examinations from different hospital deprtments
<p>This data set includes examinations for a number of hypothetical patients in different hospital departments. They are organized in folders, one for each department. In each of these folders, a number of subfolders can be found, one for each patient. The files for the actual examinations can be found in the corresponding folder of each patient. </p>
Hospital Instances
<p><strong>Hospital Instances </strong><br> [as described in Jungwirth et al., "The vehicle routing problem with time windows, flexible service locations and time-dependent location capacity, Technical Report, 2020]</p> <p><br> <strong>Network Layout:</strong></p> <p> </p><p>We distinguish three layouts, which are defined by the number of buildings B ∈ {1, 2, 6} and the number of floors per building F ∈ {1, 3, 6}. Each floor has a certain number of rooms (locations) R ∈ {6, 7, . . . , 10} drawn from a discrete uniform distribution. One of the buildings contains the therapy centers, which has a capacity between 2 and 6. The capacity at the ward rooms is always unlimited since patients cannot be scheduled to other patients’ ward room. The travel time between two buildings is drawn at random from the set {10, 15, 20} minutes. The travel time between neighboring floors is assumed to be 5 minutes, and the travel time between two rooms on the same floor is either 5 or 10 minutes.</p> <p></p> <p><strong>Demand Scenarios: </strong><br> We distinguish six demand scenarios having 20, 40, 60, 80, 100 and 120 treatments. A 10% probability exists that the patient is an outpatient, i.e. he/she can only be treated in a therapy center and the start time for the treatment is fixed. Ten percent of the patients are bedridden, i.e. the patient must not be moved and can only be treated in his/her room at the ward. However, these latter patients have a rather wide start time window of 90 minutes. The remaining patients are regular inpatients, of which 50% have location flexibility, i.e. the patient can be treated at the ward and in the therapy centers; however, a preference for one location exists, generally the ward room. The start time window length of these patients varies between 30 and 45 minutes. Thirty percent of the patients receive multiple treatments (2 or 3) in one day. Every treatment job has a duration of 10 to 45 minutes and requires a certain skill level. We assume hierarchical skills ranging from 1 (lowest) to 3 (highest). The probabilities that a job requires a certain skill are 60%, 30% and 10% for skills 1, 2 and 3, respectively.<br> <br> <strong>Vehicles:</strong><br> We use a heterogeneous fleet, since therapists differ in their skills as well as their shift patterns. The skills are the same as for the jobs; however, the probabilities of having skill 1, 2 and 3 are 10%, 60% and 30%. A therapist has a regular (long) shift with 80% probability. Otherwise, the therapist has a short shift with 50% probability of being a morning or evening shift. We assume that therapists start and end their shifts in the break room (depot), which is 5 minutes away from the therapy centers.</p>
A dataset of anonymised hospitalised COVID-19 patient data: outcomes, demographics and biomarker measurements for two New York hospitals
<p>These datasets are for a cohort of n=1540 anonymised hospitalised COVID-19 patients, and the data provide information on outcomes (i.e. patient death or discharge), demographics and biomarker measurements for two New York hospitals: State<br> University of New York (SUNY) Downstate Health Sciences University and Maimonides<br> Medical Center.</p> <p>The file "demographics_both_hospitals.csv" contains the ultimate outcomes of hospitalisation (whether a patient was discharged or died), demographic information and known comorbidities for each of the patients.</p> <p>The file "dynamics_clean_both_hospitals.csv" contains cleaned dynamic biomarker measurements for the n=1233 patients where this information was available and the data passed our various checks (see https://doi.org/10.1101/2021.11.12.21266248 for information of these checks and the cleaning process). Patients can be matched to demographic data via the "id" column.</p> <p><strong>Study approval and data collection</strong></p> <p>Study approval was obtained from the State University of New York (SUNY) Downstate Health Sciences University Institutional Review Board (IRB\#1595271-1) and Maimonides Medical Center Institutional Review Board/Research Committee (IRB\#2020-05-07). A retrospective query was performed among the patients who were admitted to SUNY Downstate Medical Center and Maimonides Medical Center with COVID-19-related symptoms, which was subsequently confirmed by RT PCR, from the beginning of February 2020 until the end of May 2020. Stratified randomization was used to select at least 500 patients who were discharged and 500 patients who died due to the complications of COVID-19. Patient outcome was recorded as a binary choice of “discharged” versus “COVID-19 related mortality”. Patients whose outcome was unknown were excluded. Demographic, clinical history and laboratory data was extracted from the hospital’s electronic health records.</p>
Incidence and predictor of diabetic foot ulcer and its association with change in fasting blood sugar among diabetes mellitus patients at referral hospitals in Northwest Ethiopia, 2021
<p>Abstract</p> <p> </p> <p><strong>Background</strong></p> <p>Diabetes mellitus is one of the global public health problems and fasting blood sugar is an important indicator of diabetes management. Uncontrolled diabetes can lead to diabetic foot ulcers, which is a common and disabling complication. The association between fasting blood glucose level and the incidence of diabetic foot ulcers is rarely considered, and knowing its predictors is good for clinical decision-making. Therefore, the aim of this study was to determine the incidence and predictors of diabetic foot ulcers and its association with changes in fasting blood sugar among diabetes mellitus patients at referral hospitals in Northwest Ethiopia.</p> <p><strong>Methods</strong></p> <p>A multicenter retrospective follow-up study was conducted at a referral hospital in Northwest Ethiopia. A total of 539 newly diagnosed DM patients who had follow-up from 2010 to 2020 were selected using a computer-generated simple random sampling technique. Data was entered using Epi-Data 4.6 and analyzed in R software version 4.1. A Cox proportional hazard with a linear mixed effect model was jointly modeled and 95% Cl was used to select significant variables. AIC and BIC were used for model comparison.</p> <p><strong>Result</strong></p> <p>A total of 539 diabetes patients were followed for a total of 28727.53 person-month observations. Overall, 65 (12.1%) patients developed diabetic foot ulcers with incidence rate of 2.26/1000-person month observation with a 95% CI of [1.77, 2.88]. Being rural (AHR= 2.30, 95%CI: [1.23, 4.29]), being a DM patient with Diabetic Neuropathy (AHR= 2.61, 95%CI: [1.12, 6.06]), and having peripheral arterial disease(PAD) (AHR= 2.96, 95%CI: [1.37, 6.40]) were significant predictors of DFU. The time-dependent lagged value of fasting blood sugar change was significantly associated to the incident of DFU (α = 1.85, AHR=6.35, 95%CI [2.40, 16.79]).</p> <p><strong>Conclusion and recommendation</strong></p> <p>In this study, the incidence of DFU was higher than in previous studies and was influenced by multiple factors like rural residence, having neuropathy, and PAD were significant predictors of the incidence of DFU. In addition, longitudinal changes in fasting blood sugar were associated with an increased risk of DFU. Health professionals and DM patients should give greater attention to the identified risk factors for DFU were recommended.</p>
Transforming the UK's diagnostics agenda after COVID-19 and grand challenges – Future Blood Testing Landscape report - Prof Dimitris Grammatopoulos (University Hospitals Coventry & Warwickshire, University of Warwick)
<p>This video is the second talk from our two day Future Blood Testing: Challenges & Opportunities Event that took place on the 13/09/2022.</p> <p>Transforming the UK’s diagnostics agenda after COVID-19 and grand challenges – Future Blood Testing Landscape report - Prof Dimitris Grammatopoulos (University Hospitals Coventry & Warwickshire, University of Warwick)</p> <p>Bio: Dimitris Grammatopoulos, PhD, FRCPath, is Professor of Molecular Medicine at Warwick Medical School and Consultant in Clinical Biochemistry and Molecular Diagnostics at the University Hospitals of Coventry and Warwickshire, NHS Trust, United Kingdom. He also leads the Novel Biomarkers theme of the Institute of Precision Diagnostics and Translational Medicine, Pathology-UHCW NHS Trust. where he combines clinical expertise in diagnostic laboratory medicine with a research track-record in application of cutting edge multidiscipline methodologies in routine clinical diagnostics. He received academic and clinical training in Newcastle, Bristol, Johns Hopkins-Baltimore and Warwick. He has expertise in biochemical/molecular diagnosis of many endocrine and metabolic disorders. His translational research interest is focused on stress hormones and homeostatic adaptations of fetal development to maternal disease as well as development of novel -omics based biomarker approaches suitable for precision medicine and better characterisation of patient phenotype. He has experience around use of AI and ML for development and refinement of clinical and diagnostic pathways for complex chronic conditions that are considered as national priorities. Dimitris is the Lead in Diagnostics, Global Health Priorities in Health, University of Warwick.</p> <p>Further details on this event can be found at: https://futurebloodtesting.org/event/13-14-09-2022/</p> <p>This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1</p> <p>YouTube Link: https://youtu.be/HiOlRzJPR7Q</p>
Remote Immune Monitoring: Need, Opportunities and Challenges - Professor Kourosh Saeb-Parsy (University of Cambridge & Cambridge University Hospitals NHS Foundation Trust)
<p>This video is the seventh talk from our Future Blood Testing Network Plus Launch that took place on the 23/11/2021.</p> <p>Remote Immune Monitoring: Need, Opportunities and Challenges - Professor Kourosh Saeb-Parsy (University of Cambridge & Cambridge University Hospitals NHS Foundation Trust)</p> <p>Bio: Professor Kelvin Tsoi is an Epidemiologist specialized in Digital Health. His research interests focus on digital innovation in chronic disease management, including mobile and telecare application for hypertension management, technological implementation and social engagement for cognitive screening, artificial intelligent application on electronic health records. He also works as the traditional epidemiologist on evidence-based medicine and population cohort studies. He obtained his Bachler Degree from Department of Statistics and Doctor of Philosophy from School of Public Health in the Chinese University of Hong Kong. He further received post-doctoral training in the Division of Gastroenterology and Hepatology, Department of Medicine and Therapeutics. He was also appointed as a Director of CUHK JC Bowel Cancer Education Centre to promote colorectal cancer screening. In 2011, he worked as a research scientist in Hospital Authority. He led projects covering a wide range of service areas on chronic diseases, such as service demand projection for schizophrenia and dementia. The experience of database management enhanced his understanding of the HA database structures. In 2013, he was invited to join the interdisciplinary team for Big Data research and worked closely with a team of engineers and data scientists. Currently, Professor Tsoi is an Associate Professor in JC School of Public Health and Primary Care, SH big Data Decision Analytics Research Centre and JC Institute of Ageing. I matriculated as a medical student at Fitzwilliam College in 1993. My interest in biomedical research was developed during my Part II year studying Anatomy A (neurosciences and developmental biology) and I subsequently enrolled on the MB-PhD programme. I completed my doctoral thesis in neurophysiology of circadian rhythms in 2000 and qualified as a medical doctor in 2001. While studying for my PhD, I pursued an active interest in teaching and started supervising undergraduates at Fitzwilliam (and other colleges) in 1998. I served as MCR President in 1999, became a Fellow in 2003 and Director of Studies in Clinical Medicine in 2004. I pursued a career in surgery after graduation and was appointed as a University Lecturer in Transplant Surgery in 2012.</p> <p>Further details on this event can be found at: https://futurebloodtesting.org/event/23-11-21-future-blood-testing-network-launch/</p> <p>This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1</p> <p>YouTube Link: https://youtu.be/ANZKGxj87E0</p>
Surgical case mixes and distributions of perioperative surgical process durations for German hospitals
<p>The data set consists of parameters of distributions of perioperative surgical process durations in German hospitals subdivided by the level of care, the surgical specialty, patient type (in- or outpatient), and the procedure’s main OPS code. We consider the following processes: anesthesia induction time, anesthesia emergence time, surgical lead-in, incision-to-closure time, surgical lead-out, and closure-to-incision time (as described in the German Perioperative Procedural Time Glossary). In addition, we provide the number of cases (classified by the procedure’s main OPS code) treated in one year per level of care, surgical specialty, and patient type (in- or outpatient).</p> <p>The supplied data set is the result of processing the 2019 surgical process data set from the Operating Room benchmarking program of German-speaking countries provided by the company digmed GmbH. In total, we considered 2,035,126 recorded surgeries from 212 different hospitals. The data set was created and published to facilitate and promote research on Operating Room planning in the field of Operations Research. It can be used for generating specific problem instances or benchmark sets for Operating Room planning problems.</p> <p> </p> <p> </p>
Factors associated with child abuse in children and adolescents in a Peruvian public hospital
<p>The study's objective was to determine the factors associated with the type of child and adolescent abuse of the MAMIS program, Hipólito Unanue hospital, Tacna-Peru region, 2019-2021. It had a quantitative, retrospective, cross-sectional, correlational approach in which 174 child abuse cases in children and adolescents were examined. It was found that the predominant personal factors were located in the ages of 12-17 years (57.4%), secondary level (51.15%), female sex (56.9%), and do not consume alcohol and/or drugs (88.5%). The family factors that stood out were the type of single parenteral family (48.28%), the age of both parents was 30-59 years (58.5%), the divorced, marital status (37.3%), secondary level (68.9%), the occupation of the independent parents (64.9%), there is no history of the parents having suffered violence (91.3%), there are no problems of addiction or alcohol consumption and/or drugs (95.40%), and at the same time there are no psychiatric disorders (95.4%). The types of child abuse that prevails is the psychological type (93.68%), followed by the type of neglect or abandonment (38.51%), the physical type (37.93%), and sexual abuse (27.0%). It is concluded that there is a significant relationship, with a confidence level of 95%, between personal factors such as the age of the minor with psychological abuse and sexual abuse<strong>; </strong>the sex of the minor with physical abuse and sexual abuse; the consumption of alcohol and/or drugs with the physical abuse of children and adolescents.</p>
Targeted Gene Panel Sequencing Data of RELN paper - Meyer Children's Hospital IRCCS
<h3>Dataset description</h3> <p> </p> <p>The dataset has been prepared according the Minimal Information about a high throughput SEQuencing Experiment (MINSEQE) as reported in: <a href="https://doi.org/10.5281/zenodo.5706412">https://doi.org/10.5281/zenodo.5706412</a></p> <p>This dataset includes:</p> <ul> <li>The Targeted Gene Panel Sequencing Raw Data (FASTQ files) from two individuals harbouring RELN variants</li> <li>The ‘final’ processed data, submitted both as VCF and TXT files, and obtained from the ANNOVAR annotations of the two patients</li> </ul> <p>The gene panel list used in the targeted capture and the essential experimental and data processing protocols has been reported in the RELN paper.</p> <h3>Identifiers</h3> <p>The 444D indentifier correspond to <strong>DN1 patient</strong> in the RELN paper.</p> <p>Tissue: peripheral blood sample</p> <p>Sex: female</p> <p>Age at sequencing: 21 years</p> <p>The 528T indentifier correspond to <strong>DN2 patient </strong>in the RELN paper.</p> <p>Tissue: peripheral blood sample</p> <p>Sex: female</p> <p>Age at sequencing: 1.5 years</p>
Figure 1 in Tertiary hospital sewage as reservoir of bacteria expressing MDR phenotype in Brazil
Figure 1. Distribution of antibiotic resistance (%) of bacteria isolated in sewage from a tertiary hospital in Ribeirão Preto, São Paulo, Brazil. AMI: Amikacin; AMO: amoxicillin; AMC: amoxicillin clavulanate; AMP: ampicillin; ASB: ampicillin sulbactam; CPM: cefepime; CTX: cefotaxime; CFO: cefoxitin; CAZ:ceftazidime; CRO: ceftriaxone; CLO:chloramphenicol; COL: colistin; CRX: cefuroxime; CIP:ciprofloxacin; CLI: clindamycin; ERI: erythromycin; ERT: ertapenem; GEN: gentamycin; IPM: imipenem; LNZ: linezolid; MER: meropenem; NIT: nitrofurantoin; TZP:piperacillin tazobactam; SXT:trimethoprim-sulfamethoxazole; TCP: teicoplanin; TET:tetracycline; VAN:vancomycin.
Dataset: Target Hospitality Corp. (TH) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.