Background: It is often felt that developing countries need to improve their quality of healthcare provision. This study hopes to generate data that can help managers and doctors to improve the standard of care they provide in line with the wishes of the patients.

Over the past decade, computer-aided diagnosis is rapidly growing due to the availability of patient data, sophisticated image acquisition tools and advancement in image processing and machine learning algorithms. Meningiomas are the tumors of brain and spinal cord. They account for 20% of all the brain tumors. Meningioma subtype classification involves the classification of benign meningioma into four major subtypes: meningothelial, fibroblastic, transitional, and psammomatous. Under the microscope, the histology images of these four subtypes show a variety of textural and structural characteristics. High intraclass and low interclass variabilities in meningioma subtypes make it an extremely complex classification problem. A number of techniques have been proposed for meningioma subtype classification with varying performances on different subtypes. Most of these techniques employed wavelet packet transforms for textural features extraction and analysis of meningioma histology images. In this article, a hybrid classification technique based on texture and shape characteristics is proposed for the classification of meningioma subtypes. Meningothelial and fibroblastic subtypes are classified on the basis of nuclei shapes while grey-level co-occurrence matrix textural features are used to train a multilayer perceptron for the classification of transitional and psammomatous subtypes. On the whole, average classification accuracy of 92.50% is achieved through the proposed hybrid classifier; which to the best of our knowledge is the highest.

Theoretical chemistry involves number of steps for drug designing, which are cost and time effective. In order to remove these barriers in drug designing, computational studies are helpful. Computer‐assisted molecular modeling is valuable in drug designing. Nowadays, molecular docking is routinely used for prediction of protein−ligand interactions and to help in selecting potent molecules as a part of virtual screening of large databases. In this piece of work, we have proposed eight amino‐based estearses (AChE and BChE) inhibitors (dithiocarbamates). The hypothetical structures were optimized via density functional theory (DFT) studies using B3LYP basis set and calculated their different physical properties, which stated that these compounds may be prepared in the wet lab. The energy gap between HOMO and LUMO was ranged from 0.1517 to 0.1789. The proposed molecules were also docked with MOE, and it was depicted from docking results that they are moderate inhibitors against targeted enzymes. ADMET studies were also done for these compounds in order to check their pharmacological parameters. All these results suggested that dithiocarbamates may be good inhibitors in future.

Background and objectives: Reports comparing the characteristics of patients and their clinical outcomes between community-acquired (CA) and hospital-acquired (HA) COVID-19 have not yet been reported in the literature. We aimed to characterise and compare clinical, biochemical and haematological features, in addition to clinical outcomes, between these patients. Methods: This multi-centre, retrospective, observational study enrolled 488 SARS-CoV-2 positive patients -339 with CA infection and 149 with HA infection. All patients were admitted to a hospital within the University Hospitals of Morecambe Bay NHS Foundation Trust between March 7th and May 18th , 2020. Results: The CA cohort comprised of a significantly younger population, median age 75 years, versus 80 years in the HA cohort (P = 0⋅0002). Significantly less patients in the HA group experienced fever (P = 0⋅03) and breathlessness (P < 0⋅0001). Furthermore, significantly more patients had anaemia and hypoalbuminaemia in the HA group, compared to the CA group (P < 0⋅0001 for both). Hypertension and a lower median BMI were also significantly more pronounced in the HA cohort (P = 0⋅03 and P = 0⋅0001, respectively). The mortality rate was not significantly different between the two cohorts (34% in the CA group and 32% in the HA group, P = 0⋅64). However, the CA group required significantly greater ICU care (10% versus 3% in the HA group, P = 0⋅009). Conclusion: Hospital-acquired and community-acquired COVID-19 display similar rates of mortality despite significant differences in baseline characteristics of the respective patient populations. Delineation of community-and hospital-acquired COVID-19 in future studies on COVID-19 may allow for more accurate interpretation of results.

Amygdalin is obtained from the pebbles of rosaceous fruits, like apricots, almond, cherries, peaches and plums. It is a plant glucoside which is traditionally used as antitumor drug. It produces synergistic effect if it combines with conditional chemotherapy drugs. Amygdalin also used to cure many other diseases like to control asthma, improve immune system, causes apoptosis of human renal fibroblast, inhibit hyperglycemia. Amygdalin is banning to use as antitumor drug by FDA due to lack evidences of cure in case of cancer but in ancient times it is used as antitumor drug by Chinese.