Articles

Advances in web-centric cloud computing have facilitated the establishment of an integrated cloud environment connecting a wide variety of clinical trial stakeholders. A web-centric cloud framework is proposed for real-time monitoring and risk prediction during clinical trials. The framework focuses on identifying relevant datasets, developing a data-management interface, and implementing machine-learning algorithms for data analysis. Detailed descriptions of the data-management interface and the machine-learning processes are provided, targeting active clinical trials with therapeutic uses in cancer. Demonstrations utilize publicly available clinical-trial data from the ClinicalTrials.gov repository. The real-time monitoring and risk prediction systems were assessed by developing five supervised-classification-machine-learning models for trial-status prediction and six unsupervised models for patient-safety-profile assessment, each representing a different phase of the clinical-trial process. All supervised models yielded high accuracy and area-under-the-curve values at the testing stage, while the unsupervised models demonstrated practical applicability. The results underscore the advantages of using the trial-status algorithm, the patient-safety-profile model, and the proposed framework for performing real-time monitoring and risk prediction of clinical trials.

Novel corona virus disease (COVID-19) pandemic, which started in China's Hubei province in 2019, has caused a significant loss of human lives globally. This study describes the epidemiologic and clinical profiles of COVID-19 related deaths among patients admitted to treatment centers in Sidama region, Ethiopia. A cross-sectional study of 186 in hospital COVID-19 related deaths that occurred from July 2020 to December 2021 in Sidama region were analyzed. Data was extracted from regional emergency operation center death report. Data was entered using Epidata v3.1 and analysis was done using SPSS v.20. Categorical data was summarized using frequency and percentage while continuous data was summarized using median and interquartile range. Association between variables was assessed using chi-square test. More than two-third of the deceased patients were male (135; 72.6%) and median age at death was 60. The majority of deaths (151; 81.1%) occurred in 2021, while April 2021 had the highest death records. Cough and shortness of breath were the main presenting symptoms occurring in 89.2% and 85.5% of deceased patients respectively. Most of the COVID-19 related deaths (64.5%) had associated comorbidities. Diabetes (50%) and Hypertension (39.2%) were the most prevalent comorbidities. Significant proportion of patients (74.73%) presented on severe end of disease spectrum (critical/ severe). Of the deceased patients, around two-third required Intensive care unit (ICU) admission and 111 of them were put on mechanical ventilator. Moreover, the median ICU stay was 4 days. Around half of the death (48.4%) occurred in the first 5 days. The median survival time from symptom onset was 11.5 days with most (43.5%) of the deaths occurring within the first 14 days of symptom onset. Age category was significantly associated with the number of days from onset to death (p=0.006). The case fatality rate was 1.87% which is lower than national and global reports. Unlike previous studies, the prevalence of asthma among deceased patients was low and there were no patients with documented COPD.

A qualitatively, interpretative design was adopted to evaluate type of laboratory practical Teacher Support Materials that could be used to teach practical skills of observation, listening and communication and manipulation of apparatus in titration. The population of the study consisted of three public Senior High Schools (SHS) in Komenda Edina Eguafo Abirem (KEEA) municipality of the Central Region of Ghana. Each school offers chemistry as a subject to students. Convenient, purposive and simple random sampling techniques were used to sample schools, teachers and students for the study. Six (6) teachers were purposively sampled out of a total of 8 chemistry teachers because they had qualification in Science Education in Chemistry. Five (5) students from each school were randomly selected from each of the three SHS for focal group discussion. The main instrument used for data collection were semi structured interview guide and Observation Checklist (OC). Teachers interview schedule was used to solicit information from the chemistry teachers on how they perceived the use of Practical Skills- based TSMs for teaching practical process skills in titration at the SHS levels. The curriculum profile was used to collect data to answer the research question. For each of the four Lesson Profiles (lesson introduction, skill development, application and closure) of the lesson plan, the total number of scores was divided by the maximum possible scores for the profile and expressed as percentage and qualitatively describing the trends of emerging themes. The study concluded that there has been some improvement in the cognitive experience and pedagogical content knowledge of the teachers as well as the students in the use of the PS-based teacher support material in the laboratory. It is recommended that teachers and educators should thoroughly read and assimilate the contents and the processes described in the PS-based TSMs before they are used in teaching titration so as to develop practical process skills of the students.

The objective of this work is to materialize the learning of geobotanical concepts, and a methodology for the interpretation of the landscape, which allows the student to acquire practical knowledge, to obtain sufficient autonomy that allows him to join the labor market. Regarding the methodology, 100 field samples are taken for 3 years, and the participation of 60 students. The evaluation is carried out through reports on the field study. The landscapes of various areas of southern Spain are studied. For this we rely on the methodology previously established by other researchers, through which a complete diagnosis of a territory is reached, since the series and geoseries of vegetation are revealed. Being a study of natural reality, the abstract character presented by concepts such as sigmetum, sinassociation, series, geoseries, climatophilous, edaphoxerophilous, chain; it is perfectly clarified to the student, which makes the student progress efficiently, coming to the fore the acquisition of practical knowledge compared to theoretical ones. For this reason, practical teaching acquires preponderance, since it not only provides knowledge, but also development of cognitive and psychomotor skills, which are essential in the acquisition of skills and development of the individual's personality.

This research is based on a retrospective analysis of medical records filed in the archives of the emergency departments of Kalembe-lembe Hospital in Kinshasa city in the Democratic Republic of the Congo. The study involved 324 records of patients aged 0-71 months admitted to the emergency departments and hospitalized for acute diarrhea from January 1 to December 31, 2015. The aim was to inventory the cases of rotavirus diarrhea and/or other germs (individually or in combination) to study their epidemiological and clinical aspects. Thus, the epidemiological and clinical parameters (age, sex, season, symptoms, frequency and physical aspects of stools, dehydration status and duration of hospitalization) of diarrheic children diagnosed as positive for rotavirus were compared with those infected with other germs (individually or in combination with rotavirus or other viruses). The search for the etiological agents of the diarrhea was performed in 56.48% of the cases. The results of this work allowed us to show: (i) a predominance of infections by viruses (69.94%) including rotavirus (48.08%), (ii) high rates of infections by etiological agents of diarrhea including rotavirus in children under 12 months, (iii) a high proportion of vomiting, fever, physical asthenia and restlessness or frequent and liquid stools or moderate dehydration in children infected with rotavirus, (iv) specific clinical pictures according to the etiological agents of diarrhea or their combinations.

Imagine a consumer financial services company with 20 million customers. Its sales and marketing organizations collaborate across product lines, deploying hundreds of marketing campaigns each quarter that aim to increase customer product usage and/or cross-buying of products. Each campaign is based on forecasts of customer responses derived from predictive models updated every quarter. The goals of these models are to achieve large return on investment ratios and to maximize contribution to local profit centers. What’s important is that their modeling is based only on data created, curated and maintained by these marketing organizations. The difference today is that the modeling is no longer based solely on a small number of response-determined variables that are constantly assessed in terms of importance. A quarterly campaign update generates hundreds of statistical models — involving campaign responses, purchase-lag time, the relative magnitude of the direct effect, and the cross-buying effects — using thousands of variables, including customer demographics, life stage, product transactions, household composition, and customer service history. It’s a network of models, not just a table of variable-by-residual importance values. But that’s only part of the story of data products. The predictive modeling utilized by these campaign plans is based on analytics and data preparation, which are data products in their most diminutive form. These products would be even more elementary were they not crafted quarterly by highly skilled, experienced modelers using advanced software and processes. Most companies have enough data to create models that contain not simply hundreds of variables, but thousands, so that the focus can return to information instead of data reduction. These models largely replace the internal econometric models previously used to produce advanced forecasts in the absence of campaign modeling. People used these forecasts to simulate ROI and contribution forecasts for the planned campaigns. In the old days, reliance on econometrically forecast ROI-guideline contribution values reduced the reliance on the marketing campaign modelers because of a lack of trust in their predictive ability.