Introduction: Diabetes mellitus (DM), particularly Type 2 DM is now recognized as a major chronic public health problem throughout the world. Different anti-diabetic agents, oral or injectable are used to regulate patients’ glycemic status as monotherapy or in combination. Objectives: To observe the prescribingpattern ofanti-diabetic agents and to collect demographic and disease details of type 2 DM patients. Materials and Methods: This descriptive cross-sectional study was conducted from January to August 2017 in the outpatient department of Border Guard Hospital, Pilkhana; a tertiary level hospital in Dhaka. The study enrolled 172 type 2 diabetic patients purposively on specified criteria. Demographic data,drug prescribing pattern, disease pattern were collected by an interview and from patients’ prescriptions. Results: Among 172 respondents,70 (40.70%) were male and 102 (59.30%) were female. The mean age of patients was 54.21±10.09 years. Eighty seven (50.58%) patients were either overweight or obese. Eighty two (47.67%) patients had family history of DM. Majority of patients (84.89%) had duration of diabetes >5 years and 84 (48.84%) patients had co-morbidities. Majority of the patients 135(78.49%) were prescribed oral drugs either alone or in combination. Among them 83 (63.48%) patients were prescribed oral monotherapy and 52(38.52%) patients were prescribed oral combination therapy. Metformin was the most prescribed oral anti-diabetic drug as monothearpy (71.08%). Among combined anti-diabetic drugsbased on class, metformin+Dipeptidyl peptidase 4 inhibitors(DPP4i) (36.11%), combination was the most commonly prescribed combination. The findings can lead to select the formulation and combination of anti-diabetic drugs in this part of the world for developing & marketing a new anti-diabetic drug. Conclusion: Metformin was the most commonly prescribed drug both as monotherapy as well as combination therapy and monotherapy was predominant over combination therapy. Journal of Armed Forces Medical College Bangladesh Vol.14 (2) 2018: 139-143
Introduction: Oxidative stress has been assumed to contribute to the pathophysiology of schizophrenia. Increased oxidative stress is the result of either an increased production of free radicals or a depletion of the endogenous antioxidants. Objective: To assess the levels of oxidative stress and antioxidant status in schizophrenia. Materials and Methods: This observational study was carried out in the department of Pharmacology, Bangabandhu Sheikh Mujib Medical University from September 2013 to January 2015. Ninety three schizophrenia patients were enrolled as study group and 30 healthy indivuduals were taken as control group. The peripheral levels of several molecules associated with oxidative stress namely malondialdehyde (MDA), glutathione (GSH) and anti-oxidant status like plasma levels of ascorbic acid (vitamin C) and α-tocopherol (vitamin E) in 93 patients with schizophrenia and 30 healthy participants were assessed. Results: Study found that the schizophrenia group presented substantially higher levels of oxidative stress than the control group, as revealed by elevated quantities of the pro-oxidant MDA (6.3±0.5μmol/L in study group and 2.1±0.5μmol/L in control group), decreased levels of the antioxidants GSH (0.6±0.2mg/gm of Hb in study group and 2.1±0.5mg/gm of Hb in control group), plasma α-tocopherol and ascorbic acid. Results found were highly significant (p=0.001). Conclusion: In schizophrenia there are increased level of oxidative stress and decreased level of the antioxidants. Journal of Armed Forces Medical College Bangladesh Vol.12(2) 2016: 40-43
The OMNeT++ simulator is well-suited for the simulation of randomized user behavior in communication networks. However, there are scenarios, where such a random model is unsuited to evaluate a communication system, and this paper attempts to highlight such a case. Using this example of ad-hoc communication between aircraft mid-flight, a tutorialstyle description is attempted that shall show how the OMNeT++ simulator can be used when a wealth of real-world trace data is available. In particular, it is described how mobility trace files can be directly used within OMNeT++, and how to link the generation of data messages to this mobility data. This is explained via an example simulation that evaluates a communication network in which an aircraft notifies the ground control when it enters or leaves a specific geographic region. Additionally, a novel trace-based application has been developed to achieve this link between mobility and message generation. Furthermore, a new TDMA-based medium access protocol for decentralized communication networks is presented, which is oracle-based and thus allows a TDMA-like behavior of medium access without causing any overhead; it can be useful when upper-layer protocols should be evaluated under the assumption of TDMA-like behavior, but isolated from the effects of a full-fledged TDMA protocol. Finally, physical layer behavior is often either overly simplistic or overly computationally expensive. For the latter case, when a detailed channel model is available but its evaluation requires prohibitive computational effort, then averaging its behavior into trace data can find a middle ground between efficient evaluation and realistic representation. Hence, a novel trace-based radio model has been developed that makes use of an SNR to PER mapping. In the spirit of open science, all implementations have been made available under open licenses -please see the conclusion.
Plant illness recognition is a gigantic issue and frequently need proficient support to distinguish the sickness. Plants are susceptive to different illnesses in their developing stages. Early discovery of sicknesses in plants is one of the most difficult issues in farming. On the off chance that the sicknesses are not distinguished in the beginning phases, then they may unfavorably influence the all out yield, bringing about a diminishing in the ranchers' benefits. To conquer this issue, numerous scientists have introduced different cutting edge frameworks in light of Deep Learning and Machine Learning draws near. Nonetheless, the greater part of these frameworks either use a large number of preparing boundaries or have low characterization correctnesses. The proposed cross breed model requires lesser number of preparing boundaries when contrasted with different methodologies existing, we are attempting to. Our venture is pointed on making Application that distinguishes the sort of infection that impacted the plant from the pictures of the leaves of the plants. We are utilizing novel cross breed model in view of Convolution Autoencoder (CAE) organization and Convolutional Neural Network (CNN) for programmed plant sickness recognition. In this work, the proposed half and half model is applied to distinguish Bacterial Spot illness present in plants utilizing their leaf pictures; notwithstanding, it very well may be utilized for any plant sickness discovery involving any Image as info.
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