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Objective: To compare the awareness regarding the mode of transmission in educated and uneducated patients presenting to OPDs of five hospitals in Sindh province. Study Design: Cross-sectional survey. Place and Duration of Study: Five Combined Military Hospitals, of Sindh province; Badin, Chhor, Hyderabad, Malir and Pano Aqil, Pakistan, from April to July 2022. Methodology: Patients presenting to the five Combined Military Hospitals were included through non probability consecutive sampling technique. The questionnaire was reviewed by two medical education experts for content validity and piloted among ten patients before being tested. Results: A total of 121 participants were included in the study. Participants had better knowledge regarding sharing of shaving equipment (p=0.008), unsafe blood transfusions (p

In the present work, twelve machine learning (ML) models are developed for assessment of monthly average diffuse solar radiation (DSR) with solitary input forecaster as clearness index. Two categories of ML models were demarcated (i.e. diffusion coefficient and diffuse fraction) with six models for each group. The correctness of models was examined as a function of some frequently used statistical pointers. A comparision was also done between developed ML models and some well-recognised models available from previous works. The results show that ML models perform very well in comparision to models available in the literature. The top-performing models in category 1 are the k-nearest neighbours (KNN) model for both training and testing data. In category 2, for training data random forest (RF) model perform well while for testing data support vector regression (SVR) models perform well. The performance can be slightly improved by using two or more input parameters such as temperature difference, relative humidity and relative sunshine along with clearness index as input. Thus, ML models can be used to estimate DSR in the humid-subtropical climate of India.

For the various climatic zones of India, machine learning (ML) models are created in the current work to forecast monthly-average diffuse solar radiation (DSR). The long-term solar radiation data are taken from Indian Meteorological Department (IMD), Pune, provided for 21 cities that span all of India’s climatic zones. The diffusion coefficient and diffuse fraction are the two groups of ML models with dual input parameters (sunshine ratio and clearness index) that are built and compared (each category has seven models). To create ML models, two well-known ML techniques, random forest (RF) and k-nearest neighbours (KNN), are used. The proposed ML models are compared with well-known models that are found in the literature. The ML models are ranked according to their overall and within predictive power using the Global Performance Indicator (GPI). It is discovered that KNN models generally outperform RF models. The results reveal that in diffusion coefficient models perform well than diffuse fraction models. Moreover, functional form 2 is the best followed by form 6. The ML models created here can be effectively used to accurately forecast DSR in various climates.

BackgroundColorectal cancer (CRC) incidence is increasing among young adults below screening age, despite the effectiveness of screening in older populations. Individuals with diabetes mellitus are at increased risk of early-onset CRC. We aimed to determine how many years earlier than the general population patients with diabetes with/without family history of CRC reach the threshold risk at which CRC screening is recommended to the general population.Methods and findingsA nationwide cohort study (follow-up:1964-2015) involving all Swedish residents born after 1931 and their parents was carried out using record linkage of Swedish Population Register, Cancer Registry, National Patient Register, and Multi-Generation Register. Of 12,614,256 individuals who were followed between 1964 and 2015 (51% men; age range at baseline 0-107 years), 162,226 developed CRC, and 559,375 developed diabetes. Age-specific 10-year cumulative risk curves were used to draw conclusions about how many years earlier patients with diabetes reach the 10-year cumulative risks of CRC in 50-year-old men and women (most common age of first screening), which were 0.44% and 0.41%, respectively. Diabetic patients attained the screening level of CRC risk earlier than the general Swedish population. Men with diabetes reached 0.44% risk at age 45 (5 years earlier than the recommended age of screening). In women with diabetes, the risk advancement was 4 years. Risk was more pronounced for those with additional family history of CRC (12-21 years earlier depending on sex and benchmark starting age of screening). The study limitations include lack of detailed information on diabetes type, lifestyle factors, and colonoscopy data.ConclusionsUsing high-quality registers, this study is, to our knowledge, the first one that provides novel evidence-based information for risk-adapted starting ages of CRC screening for patients with diabetes, who are at higher risk of early-onset CRC than the general population.