Yoxas/statistical_literacy huggingface dataset:
Sandstorm, which injects generous newly emerging microbes into the atmosphere covering cities, adversely affects the air quality in built environments. However, few studies have examined the change of airborne bacteria during severe sandstorm events. In this work, we analyzed the airborne bacteria during one of the strongest sandstorms in East Asia on March 15th, 2021, which affected large areas of China and Mongolia. The characteristics of the sandstorm were compared with those of the subsequent clean and haze days. The composition of the bacterial community of air samples was investigated using quantitative polymerase chain reaction (qPCR) and high-throughput sequencing technology. During the sandstorm, the particulate matter (PM) concentration and bacterial richness were extremely high (PM2.5: 207 µg/m3; PM10: 1630 µg/m3; 5700 amplicon sequence variants/m3). In addition, the sandstorm brought 10 pathogenic bacterial genera to the atmosphere, posing a grave hazard to human health. As the sandstorm subsided, small bioaerosols (0.65-1.1 µm) with a similar bacterial community remained suspended in the atmosphere, bringing possible long-lasting health risks.
Santos-Silva et al. demonstrated that, for Brazilian soccer players, pursuing higher education is still a distant reality. This is because, in relative numbers, only 2.5% of the athletes have completed elementary school, whereas 67% and 5.5% completed high school and higher education, respectively. The mean initial professionalization age is 17.5 years, indicating that soccer players take on a crucial responsibility in a very early stage of life. However, they are still immature in terms of dealing with money,contractual relations with the club, and decision-making. Only 5.5% of Brazilian soccer players surveyed completed higher education. Of these 179 athletes, 67% completed high school. The most educated were goalkeepers and defenders at 37% (66/179). Physical education for sports is different from educating the mind in terms of brain plasticity. As the soccer culture in Brazil is more popular than schooling, this study reflected on the formal education levels of soccer (football) players through descriptive and quantitative analyses. We evaluated 179 national soccer players playing various positions on different teams across six seasons (2012, 2013, 2014, 2015, 2016, and 2022). Data were collected using a questionnaire comprising the following variables: age, position in the pitch, age of professionalization, and education (years of study). The data were distributed according to the pitch position. The mean age, length of professional career, and professionalization age were 23±6 years, 7±5 years, and 17±2 years, respectively. In terms of education, 121 athletes (67%) completed high school, equivalent to 11 years of study. Only 5.5% completed higher education, with defensive players (goalkeepers and defenders) being the most educated at 37% (66/179). According to 2017 figures from the Pesquisa Nacional por Amostra de Domicílios Contínua of the Federal Government of Brazil, the schooling rate is 31.7%, wherein 46.1% of Brazilians aged 25 years or over have completed education. In this study of 179 athletes, 67% had completed high school. Thus, players outrank the Brazilian population in terms of achieving a high school education. There were marked disparities in the relative proportions of goalkeepers (85%), defensive players (68%), midfielders (63%), and forward players (64%) in terms of their education. Our survey revealed that attending higher education remains a distant reality in Brazil.
SaNuRN is a five-year project by the University of Rouen Normandy (URN) and the Côte d’Azur University (CAU) consortium to optimize digital health education for medical and paramedical students, professionals, and administrators. The project includes a skills framework, training modules, and teaching resources. In 2027, SaNuRN is expected to train a significant portion of the 400,000 health and paramedical professions students at the French national level. Our purpose is to give a synopsis of the SaNuRN initiative, emphasizing its novel educational methods and how they will enhance the delivery of digital health education. Our goals include showcasing SaNuRN as a comprehensive program consisting of a proficiency framework, instructional modules, and educational materials and explaining how SaNuRN is implemented in the participating academic institutions. SaNuRN is a project aimed at educating and training health-related and paramedics students in digital health. The project results from a cooperative effort between URN and CAU, covering four French departments. The project is based on the French National Referential on Digital Health (FNRDH), which defines the skills and competencies to be acquired and validated by every student in the health, paramedical, and social professions curricula. The SaNuRN team is currently adapting the existing URN and CAU syllabi to FNRDH and developing short-duration video capsules of 20 to 30 minutes to teach all the relevant material. The project aims to ensure that the largest student population earns the necessary skills, and it has developed a two-tier system involving facilitators who will enable the efficient expansion of the project’s educational outreach and support the students in learning the needed material efficiently. With a focus on real-world scenarios and innovative teaching activities integrating telemedicine devices and virtual professionals, SaNuRN is committed to enabling continuous learning for healthcare professionals in clinical practice. The SaNuRN team introduced new ways of evaluating healthcare professionals by shifting from a knowledge-based to a competencies-based evaluation, aligning with the Miller teaching pyramid and using the Objective Structured Clinical Examination and Script Concordance Test in digital health education. Drawing on the expertise of URN, CAU, and their public health and digital research laboratories and partners, the SaNuRN project represents a platform for continuous innovation, including telemedicine training and living labs with virtual and interactive professional activities. The SaNuRN project provides a comprehensive, personalized 30-hour training package for health and paramedical students, addressing all 70 FNRDH competencies. The program is enhanced using AI and NLP to create virtual patients and professionals for digital healthcare simulation. SaNuRN teaching materials are open-access. The project collaborates with academic institutions worldwide to develop educational material in digital health in English and multilingual formats. SaNuRN offers a practical and persuasive training approach to meet the current digital health education requirements.
Sarcopenia, frailty, and malnutrition in patients with liver cirrhosis are commonly observed and are associated with higher long-term mortality. Therefore, recognizing patients with increased nutritional risk and providing recommended interventions are essential in the long- and short-term management of cirrhosis, especially as alcoholic and non-alcoholic fatty liver disease continues to rise. Various assessment tools are available to gauge frailty and malnutrition but are infrequently used. Given the global burden of liver cirrhosis, periodic screening for malnutrition, sarcopenia, and frailty is desperately needed as it improves liver transplantation outcomes. Necessary steps include addressing knowledge gaps in professional healthcare workers and patients and using standardized assessment tools to counteract physical deconditioning as early as possible. One potential method for assessing sarcopenia involves using computed tomography to evaluate the skeletal muscle index. Regarding frailty, useful tools for longitudinal assessment include the liver frailty index and the Karnofsky performance status. Addressing educational requirements related to malnutrition involves seeking guidance from dieticians, who can provide counseling on achieving sufficient calorie and protein intake to combat the progression of malnutrition.
SARS-CoV-19 or popularly known as COVID-19 is recognized as a global public health emergency and a pandemic necessitating readiness of the health system and its operational work force to address the unprecedent challenge. To assess perceived stress and coping methods using novel brief COPE-4 factor structure and narrate thematic testimonials among health care professionals involved in COVID care in India. Health care settings and multi-state cross-sectional study. A cross sectional mixed-methods study, with multi-modal aids was undertaken during April-May, 2020 using a novel brief COPE-4 Factor structure. Oral informed consent was obtained to document narrative testimonials verbatim from interviews among doctors, nurse practitioners. The web-survey data from Google forms analyzed using STATA (Ver 12.0) statistical package. The interview content was subjected to manual inductive thematic coding, grouped into piles to identify broad themes as main categories. Among total 218 respondents, 75 (34.4%) were female and 143 (65.6%) male, mean age of 34.71 (SD = 8.9) years. Majority of the respondents were medical post-graduates on duty (38%) followed by nurses (25%) and public health professionals (14%). Slightly higher levels of stress seen among respondents involved in COVID-19 duty, compared to those not involved (p < 0.001) and positive thinking is the major coping method adopted. Appraising the coping mechanisms of health care professionals reveals positive outlook amidst medium levels of perceived stress while providing COVID-19 pandemic related health care services.
SARS-CoV-2 infection is considered as a relapsing inflammatory process with a dysregulation of IL-6 signalling. Classic IL-6 signalling is thought to represent a defence mechanism against pathogens. In contrast, IL-6 trans-signalling has pro-inflammatory effects. In severe COVID-19, therapeutic strategies have focused on global inhibition of IL-6, with controversial results. We hypothesized that specific blockade of IL-6 trans-signalling could inhibit inflammatory response preserving the host defence activity inherent to IL-6 classic signalling. To test the role of the specific IL-6 trans-signalling inhibition by sgp130Fc in short- and long-term consequences of COVID-19, we used the established K18-hACE2 transgenic mouse model. Histological as well as immunohistochemical analysis, and pro-inflammatory marker profiling were performed. To investigate IL-6 trans-signalling in human cells we used primary lung microvascular endothelial cells and fibroblasts in the presence/absence of sgp130Fc. We report that targeting IL-6 trans-signalling by sgp130Fc attenuated SARS-CoV-2-related clinical symptoms and mortality. In surviving mice, the treatment caused a significant decrease in lung damage. In vitro, IL-6 trans-signalling induced strong and persisting JAK1/STAT3 activation in endothelial cells and lung fibroblasts with proinflammatory effects, which were attenuated by sgp130Fc. Our data also suggest that in those cells with scant amounts of IL-6R, the induction of gp130 and IL-6 by IL-6:sIL-6R complex sustains IL-6 trans-signalling. IL-6 trans-signalling fosters progression of COVID-19, and suggests that specific blockade of this signalling mode could offer a promising alternative to mitigate both short- and long-term consequences without affecting the beneficial effects of IL-6 classic signalling. These results have implications for the development of new therapies of lung injury and endotheliopathy in COVID-19. The project was supported by ISCIII, Spain (COV-20/00792 to MB, PI23/01351 to MARH) and the European Commission-Next generation EU (European Union) (Regulation EU 2020/2094), through CSIC's Global Health Platform (PTI Salud Global, SGL2103029 to MB). PID2019-110587RB-I00 (MB) supported by MICIN/AEI/10.13039/501100011033/and PID2022-143034OB-I00 (MB) by MICIN/AEI/10.13039/501100011033/FEDER. MAR-H acknowledges support from ISCIII, Spain and the European Commission-Next generation EU (European Union), through CSIC's Global Health PTI.
Schistosomiasis is a parasitic infection that causes significant public health problems in tropical countries. Schistosoma haematobium species are blamable for causing urinary schistosomiasis. The infected person, specifically children, may be carrying the disease. This systematic review aimed to identify the current knowledge of urinary Schistosmiasis in children or USC on its epidemiology, risk factors, and challenges to spread the understanding of controlling the disease and reducing the complications. In November 2021, a systematic computer-aided literature review was conducted using PubMed, SCOPUS and Web of Science, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria. The results were updated in February 2022. We only used papers that have at least the abstract available in English. Relevant articles were screened, duplicates were deleted, eligibility criteria were applied, and studies that met the criteria were reviewed. The keywords Human Schistosoma infections, prevalence, risk factors and challenges were included. The protocol for the review was registered with PROSPERO (registration number CRD42022311609). Pooled prevalence rates were calculated using the programme R version 4.2.1. Heterogeneity was assessed using the I2 statistic and p-value. A narrative approach was used to describe risk factors and challenges. Studies were selected and finalised based on the review question to prioritise. The quality of the included studies was assessed using the Mixed-Method Appraisal Tool (MMAT). A total of 248 publications met the requirements for inclusion. Fifteen articles were included in this review, with the result showing high heterogeneity. The pooled prevalence of urinary schistosomiasis in children is 4% (95% confidence interval (CI)). Age, poor socioeconomic status, education, exposure to river water, and poor sanitation are the risk factors identified in this review. Challenges are faced due to limitations of clean water, lack of water resources, and poor hygiene. Modifiable risk factors such as poor knowledge and practices must be addressed immediately. Healthcare providers and schools could accomplish engaging in practical promotional activities. Communicating the intended messages to raise community awareness of urinary schistosomiasis is critical.
Schizophrenia (SCZ) is linked to a heightened risk of impulsive aggression and disturbances in sleep patterns. Cognitive and social cognitive impairments have been connected to aggression, with social cognitive deficits appearing to play a more immediate role. In this investigation, we conducted a retrospective analysis of the impact of cognitive training and sleep interventions on aggressive behavior and the quality of sleep among individuals with SCZ who were hospitalized. This study divided 80 hospitalized patients into two groups according to medical advice, namely the normal group and the study group. The control group received routine drug treatment and education; The research group implemented cognitive training and sleep intervention based on the normal group. Collect basic clinical data, aggressive behavior indicators, and sleep quality indicators. There is no difference in the basic information statistics between the two groups. Both groups can reduce aggressive behavior and improve sleep quality. In the study group, there was a notable decrease in aggressive behavior compared to the control group. Furthermore, the sleep quality in the study group exhibited significant improvement when compared to the control group. Cognitive training and sleep intervention have been proven to be effective nonpharmacological treatments, effectively reducing aggressive behavior and improving sleep quality.
Schizophrenia (SCZ) patients exhibit 30% higher prevalence of metabolic syndrome (MetS) compared to the general population with its suboptimal management contributing to increased mortality. Large-scale studies providing real-world evidence of the underlying causes remain limited. To address this gap, we used real-world health data from the Estonian Biobank, spanning a median follow-up of ten years, to investigate the impact of genetic predisposition and antipsychotic treatment on the development of MetS in SCZ patients. Specifically, we set out to characterize antipsychotic treatment patterns, genetic predisposition of MetS traits, MetS prognosis, and body mass index (BMI) trajectories, comparing SCZ cases (n = 677) to age- and sex-matched controls (n = 2708). SCZ cases exhibited higher genetic predisposition to SCZ (OR = 1.75, 95% CI 1.58-1.94), but lower polygenic burden for increased BMI (OR = 0.88, 95% CI 0.88-0.96) and C-reactive protein (OR = 0.88, 95% CI 0.81-0.97) compared to controls. While SCZ cases showed worse prognosis of MetS (HR 1.95, 95% CI 1.54-2.46), higher antipsychotic adherence within the first treatment year was associated with reduced long-term MetS incidence. Linear mixed modelling, incorporating multiple BMI timepoints, underscored the significant contribution of both, antipsychotic medication, and genetic predisposition to higher BMI, driving the substantially upward trajectory of BMI in SCZ cases. These findings contribute to refining clinical risk prediction and prevention strategies for MetS among SCZ patients and emphasize the significance of incorporating genetic information, long-term patient tracking, and employing diverse perspectives when analyzing real-world health data. EU Horizon 2020, Swedish Research Council, Estonian Research Council, Estonian Ministry of Education and Research, University of Tartu.
Schizophrenia (SZ) is a severe mental disorder characterized by failure of functional integration (aka dysconnection) across the brain. Recent functional connectivity (FC) studies have adopted functional parcellations to define subnetworks of large-scale networks, and to characterize the (dys)connection between them, in normal and clinical populations. While FC examines statistical dependencies between observations, model-based effective connectivity (EC) can disclose the causal influences that underwrite the observed dependencies. In this study, we investigated resting state EC within seven large-scale networks, in 66 SZ and 74 healthy subjects from a public dataset. The results showed that a remarkable 33% of the effective connections (among subnetworks) of the cognitive control network had been pathologically modulated in SZ. Further dysconnection was identified within the visual, default mode and sensorimotor networks of SZ subjects, with 24%, 20%, and 11% aberrant couplings. Overall, the proportion of discriminative connections was remarkably larger in EC (24%) than FC (1%) analysis. Subsequently, to study the neural correlates of impaired cognition in SZ, we conducted a canonical correlation analysis between the EC parameters and the cognitive scores of the patients. As such, the self-inhibitions of supplementary motor area and paracentral lobule (in the sensorimotor network) and the excitatory connection from parahippocampal gyrus to inferior temporal gyrus (in the cognitive control network) were significantly correlated with the social cognition, reasoning/problem solving and working memory capabilities of the patients. Future research can investigate the potential of whole-brain EC as a biomarker for diagnosis of brain disorders and for neuroimaging-based cognitive assessment.
Schizophrenia affects about 1% of the global population. In addition to the complex etiology, linking this illness to genetic, environmental, and neurobiological factors, the dynamic experiences associated with this disease, such as experiences of delusions, hallucinations, disorganized thinking, and abnormal behaviors, limit neurological consensuses regarding mechanisms underlying this disease. In this study, we recruited 72 patients with schizophrenia and 74 healthy individuals matched by age and sex to investigate the structural brain changes that may serve as prognostic biomarkers, indicating evidence of neural dysfunction underlying schizophrenia and subsequent cognitive and behavioral deficits. We used voxel-based morphometry (VBM) to determine these changes in the three tissue structures: the gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). For both image processing and statistical analysis, we used statistical parametric mapping (SPM). Our results show that patients with schizophrenia exhibited a significant volume reduction in both GM and WM. In particular, GM volume reductions were more evident in the frontal, temporal, limbic, and parietal lobe, similarly the WM volume reductions were predominantly in the frontal, temporal, and limbic lobe. In addition, patients with schizophrenia demonstrated a significant increase in the CSF volume in the left third and lateral ventricle regions. This VBM study supports existing research showing that schizophrenia is associated with alterations in brain structure, including gray and white matter, and cerebrospinal fluid volume. These findings provide insights into the neurobiology of schizophrenia and may inform the development of more effective diagnostic and therapeutic approaches.
Schizophrenia is a chronic mental disorder that impairs a person's thinking capacity, feelings and emotions, behavioural traits, etc., Emotional distortions, delusions, hallucinations, and incoherent speech are all some of the symptoms of schizophrenia, and cause disruption of routine activities. Computer-assisted diagnosis of schizophrenia is significantly needed to give its patients a higher quality of life. Hence, an improved adaptive neuro-fuzzy inference system based on the Hybrid Grey Wolf-Bat Algorithm for accurate prediction of schizophrenia from multi-channel EEG signals is presented in this study. The EEG signals are pre-processed using a Butterworth band pass filter and wICA initially, from which statistical, time-domain, frequency-domain, and spectral features are extracted. Discriminating features are selected using the ReliefF algorithm and are then forwarded to ANFIS for classification into either schizophrenic or normal. ANFIS is optimized by the Hybrid Grey Wolf-Bat Algorithm (HWBO) for better efficiency. The method is experimented on two separate EEG datasets-1 and 2, demonstrating an accuracy of 99.54% and 99.35%, respectively, with appreciable F1-score and MCC. Further experiments reveal the efficiency of the Hybrid Wolf-Bat algorithm in optimizing the ANFIS parameters when compared with traditional ANFIS model and other proven algorithms like genetic algorithm-ANFIS, particle optimization-ANFIS, crow search optimization algorithm-ANFIS and ant colony optimization algorithm-ANFIS, showing high R2 value and low RSME value. To provide a bias free classification, tenfold cross validation is performed which produced an accuracy of 97.8% and 98.5% on the two datasets respectively. Experimental outcomes demonstrate the superiority of the Hybrid Grey Wolf-Bat Algorithm over the similar techniques in predicting schizophrenia.