Determinants of Non-Adherence to Anti-Retroviral Therapy in HIV/AIDS Patients of HIV Centre Jinnah Hospital

Introduction: Human immunodeficiency virus is very harmful to human’s immune system. This virus can further cause specific contaminations and tumors in humans. The HIV-contaminated populace is at a higher danger of AIDS-characterizing tumors, for example, Kaposi sarcoma, non-Hodgkin lymphoma, and cervical malignant growth. Since the coming of exceptionally dynamic antiretroviral treatment (HAART) in 1996, the endurance of HIV-tainted populace in the United States has expanded significantly (Shiels et al., 2011).Methods: A descriptive study conducted at HIV centre Jinnah Hospital Allama Iqbal Medical College Lahore. Data was collected through questionnaire from patients of HIV clinic. Data was analysed by SPSS version 21.0, frequency, percentage and standard deviation was found with statistics of pie charts and histogram.Results: From one hundred participants 100(100%) shows forgetfulness , 29(29%) shows missing appointments, 33(33%) shows run out of medication, 11(11%) shows depression, anger, despair. 78(78%) don’t think that ART helps ,only 4(4%) shows side effects of ART and 1(1%) shows other reason for non-adherence. Conclusions:  The outcomes gave basic and valuable data that will help in decreasing the factors that are causing non-adherence to ART. Keywords: Antiretroviral therapy, Adherence, Determinants, Lahore, Pakistan DOI: 10.7176/JHMN/101-05 Publication date:June 30th 202

Entrepreneur as an authentic leader: A study of small and medium sized enterprises in Pakistan

The aim of this paper is to explore the authentic leadership styles of an entrepreneurs and its impact on employee’s commitment and satisfaction. By using the authentic leadership model, this study seeks to give a tentative test of the connection among employees’ awareness of the business creator as an authentic leader and the employees’ attitudes. Findings are that the opinion of employees’ about authentic leadership serves as the intoxicating analyst of employee job satisfaction and organizational commitment

Social media intention mining for sustainable information systems: categories, taxonomy, datasets and challenges

Intention mining is a promising research area of data mining that aims to determine end-users’ intentions from their past activities stored in the logs, which note users’ interaction with the system. Search engines are a major source to infer users’ past searching activities to predict their intention, facilitating the vendors and manufacturers to present their products to the user in a promising manner. This area has been consistently getting pertinence with an increasing trend for online purchasing. Noticeable research work has been accomplished in this area for the last two decades. There is no such systematic literature review available that provides a comprehensive review in intension mining domain to the best of our knowledge. This article presents a systematic literature review based on 109 high-quality research papers selected after rigorous screening. The analysis reveals that there exist eight prominent categories of intention. Furthermore, a taxonomy of the approaches and techniques used for intention mining have been discussed in this article. Similarly, six important types of data sets used for this purpose have also been discussed in this work. Lastly, future challenges and research gaps have also been presented for the researchers working in this domain

Impact of processing methods on the dissolution of artemether from two non-ordered mesoporous silicas

Poor aqueous solubility is often linked with a poor dissolution rate and ultimately, limited bioavailability of pharmaceutical compounds. This study describes the application of mesoporous materials (Syloid 244 and Syloid AL1) in improving the dissolution rate of a drug with poor aqueous solubility, namely artemether, utilising different processing methods including physical mixing, co-grinding and solid dispersions prepared by solvent evaporation and the lyophilisation technique. The prepared formulations were extensively characterised for their solid-state properties and the drug release attributes were studied. Differential scanning calorimetry and X-ray diffraction confirmed conversion of crystalline artemether into a disordered and amorphous form, whilst no intermolecular interactions were detected between artemether and silica. Both silica grades enhanced the dissolution rate of artemether in comparison with drug alone, for example from 17.43% (± 0.87%) to 71.55% (± 3.57%) after 120 mins with lyophilisation and Syloid 244 at a 1:3 ratio. This enhancement was also dependant on the choice of processing method, for example, co-ground and lyophilised formulations prepared with Syloid 244 at 1:3 ratio produced the most extensive dissolution, thus endorsing the importance of materials as well as choice of formulation method

Deep learning based enhanced secure emergency video streaming approach by leveraging blockchain technology for Vehicular AdHoc 5G Networks

VANET is a category of MANET that aims to provide wireless communication. It increases the safety of roads and passengers. Millions of people lose their precious lives in accidents yearly, millions are injured, and others incur disability daily. Emergency vehicles need clear roads to reach their destination faster to save lives. Video streaming can be more effective as compared to textual messages and warnings. To address this issue, we proposed a methodology to use visual sensors, cameras, and OBU to record emergency videos. Initially, the frames are detected. After re-recording, the frames detection algorithm detects the specific event from the video frames. Blockchain encrypts an emergency or specific event using hashing algorithms in the second layer of our proposed framework. In the third layer of the proposed methodology, encrypted video is broadcast with the help of 5G wireless technology to the connected nodes in the VANET. The dataset used in this research comprises up to 72 video sequences averaging about 120 seconds per video. All videos have different traffic conditions and vehicles. The ResNet-50 model is used for the feature extraction process of extracted frames. The model is trained using Tensorflow and Keras deep learning models. The Elbow method finds the optimal K number for the K Means model. This data is split into training and testing. 70% is reserved for training the support vector machine (SVM) model and test datasets, while 30%. 98% accuracy is achieved with 98% precision and 99% recall as results for the proposed methodology

Influence of polymer ratio and surfactants on controlled drug release from cellulosic microsponges

Microsponge refers to a highly cross-linked particle system with a capacity to adsorb (like a dry sponge) pharmaceutical materials. There are various methods available to prepare microsponge formulations, in this study we used quasi emulsion-solvent diffusion method with a combination of hydrophobic (ethyl cellulose) and hydrophilic polymers (hydroxypropyl methylcellulose) mediated via Tween 80 and polyvinyl alcohol. Various ratios and amounts of the polymers and surfactants were used to prepare microsponge formulations using ketoprofen as a model drug and extensively characterised. Our results, for the first time, indicate successful and optimised formulation with desired pharmaceutical characteristics using a combination of hydrophobic and hydrophilic polymers