Pattern of benzodiazepine use in psychiatric outpatients in Pakistan: a cross-sectional survey.

Background: Benzodiazepines (BDZ) are the largest-selling drug group in the world. The potential of dependence with BDZ has been known for almost three decades now. In countries like Pakistan where laws against unlicensed sale of BDZ are not implemented vigorously the risk of misuse of and dependence on these drugs is even higher. Previous studies have shown that BDZ prevalence among Patients/visitors to general outPatient clinics in Pakistan may be as high as 30%. However, no research has been carried out on the prevalence of BDZ use in psychiatric Patients in Pakistan. Methods: We carried out a cross-sectional survey over 3 months in psychiatry outPatient clinics of two tertiary care hospitals in Karachi and Lahore. Besides basic socio-demographic data the participants were asked if they were taking a BDZ at present and if yes, the frequency, route and dosage of the drug, who had initiated the drug and why it had been prescribed. We used chi-square test and t-test to find out which socio-demographic or clinical factors were associated with an increased risk of BDZ use. We used Logistic Regression to find out which variable(s) best predicted the increased likelihood of BDZ use. Results: Out of a total of 419 participants 187 (45%) of the participants had been currently using at least one BDZ. Seventy-three percent of the users had been using the drug for 4 weeks or longer and 87% were taking it every day. In 90% of cases the BDZ had been initiated by a doctor, who was a psychiatrist in 70% of the cases. Female gender, increasing age, living in Lahore, and having seen a psychiatrist before, were associated with an increased likelihood of using BDZ. Conclusion: The study shows how high BDZ use is in psychiatric outPatients in Pakistan. Most of the users were taking it for a duration and with a frequency which puts them at risk of becoming dependent on BDZ. In most of the cases it had been initiated by a doctor. Both Patients and doctors need to be made aware of the risk of dependence associated with the use of BDZ

The Role of Machine Learning and Deep Learning Approaches for the Detection of Skin Cancer

Machine learning (ML) can enhance a dermatologist’s work, from diagnosis to customized care. The development of ML algorithms in dermatology has been supported lately regarding links to digital data processing (e.g., electronic medical records, Image Archives, omics), quicker computing and cheaper data storage. This article describes the fundamentals of ML-based implementations, as well as future limits and concerns for the production of skin cancer detection and classification systems. We also explored five fields of dermatology using deep learning applications: (1) the classification of diseases by clinical photos, (2) der moto pathology visual classification of cancer, and (3) the measurement of skin diseases by smartphone applications and personal tracking systems. This analysis aims to provide dermatologists with a guide that helps demystify the basics of ML and its different applications to identify their possible challenges correctly. This paper surveyed studies on skin cancer detection using deep learning to assess the features and advantages of other techniques. Moreover, this paper also defined the basic requirements for creating a skin cancer detection application, which revolves around two main issues: the full segmentation image and the tracking of the lesion on the skin using deep learning. Most of the techniques found in this survey address these two problems. Some of the methods also categorize the type of cancer too

Isolation of buffalo poxvirus from clinical case and variations in the genetics of the B5R gene over fifty passages

Outbreaks of buffalopox affect udder and teats, which may ultimately lead to mastitis in dairy buffalo and can significantly compromise the production. In this study, we report isolation of buffalo poxvirus and sequence analysis of the B5R gene collected from the buffalo clinically suspected to be poxvirus infected. The virus was isolated on BHK-21 cell line and was passaged for 50 times, B5R gene was amplified and sequenced using gene-specific primers, and analyzed at both nucleotide and amino acid levels. Phylogenetically, the isolate can be classified close to the previously reported Pakistani and Indian isolates with certain level of differential clustering patterns. Three significant putative mutations (I2K, N64D, and K111E) were observed in the B5R protein. The K111E was common with previous human isolate from Karachi, Pakistan in 2005. These mutations differed from poxviruses reported from the neighboring countries. Some deletion mutations were observed which were recovered in upcoming passages. The K111E mutation suggests potential to cause zoonotic infection in human all over the country