Introduction: Since 2016, the province of Sindh is in the limelight because of its association with the emergence and spread of extensively drug-resistant Salmonella typhi (XDR S. typhi). Although its global spread has been proven in several studies, our information regarding its countrywide existence is still insufficient. In the last four years, few cases of XDR S. typhi were identified at the Shifa International Hospital (SIH), Islamabad, Pakistan. This article aims to report demographic patterns, clinical presentations, and treatment outcome of these cases. Materials and methods: This study was conducted at SIH, Islamabad, on blood culture-proven XDR S. typhi cases from January 2015 to December 2018. The data were retrieved from the hospital's record system. Patient demographic details, clinical presentations, management, and disease outcomes were evaluated and statistical analysis was performed through IBM SPSS Statistics for Windows, version 23.0 (IBM Corp., Armonk, NY). Results: A total of 30 blood culture-proven XDR S. typhi cases were identified and 80% (24) of them were reported in 2018. The mean age at presentation was 12.8±9.6 years. Twelve (40%) patients came from Islamabad, nine (30%) from Rawalpindi, and eight (26.6%) from Khyber Pakhtunkhwa (KPK). All patients, except one, were prescribed meropenem and azithromycin. Three patients developed complications but no mortality was documented. Over four years, these XDR S. typhi cases contributed 5.01% to the total S. typhi isolates. Conclusion: This study validates the existence of XDR S. typhi all over Pakistan. It stresses upon the fact that more stringent methods should be adopted for its identification and control.

Every year, around 28,100 journals publish 2.5 million research publications. Search engines, digital libraries, and citation indexes are used extensively to search these publications. When a user submits a query, it generates a large number of documents among which just a few are relevant. Due to inadequate indexing, the resultant documents are largely unstructured. Publicly known systems mostly index the research papers using keywords rather than using subject hierarchy. Numerous methods reported for performing single-label classification (SLC) or multi-label classification (MLC) are based on content and metadata features. Content-based techniques offer higher outcomes due to the extreme richness of features. But the drawback of content-based techniques is the unavailability of full text in most cases. The use of metadata-based parameters, such as title, keywords, and general terms, acts as an alternative to content. However, existing metadata-based techniques indicate low accuracy due to the use of traditional statistical measures to express textual properties in quantitative form, such as BOW, TF, and TFIDF. These measures may not establish the semantic context of the words. The existing MLC techniques require a specified threshold value to map articles into predetermined categories for which domain knowledge is necessary. The objective of this paper is to get over the limitations of SLC and MLC techniques. To capture the semantic and contextual information of words, the suggested approach leverages the Word2Vec paradigm for textual representation. The suggested model determines threshold values using rigorous data analysis, obviating the necessity for domain expertise. Experimentation is carried out on two datasets from the field of computer science (JUCS and ACM). In comparison to current state-of-the-art methodologies, the proposed model performed well. Experiments yielded average accuracy of 0.86 and 0.84 for JUCS and ACM for SLC, and 0.81 and 0.80 for JUCS and ACM for MLC. On both datasets, the proposed SLC model improved the accuracy up to 4%, while the proposed MLC model increased the accuracy up to 3%.

CAMPYLOBACTERIOSIS is an important foodborne zoonosis primarily associated with the consumption of undercooked poultry and poultry products (Mazick and others 2006). Other risk factors for human infection include the consumption of raw and unpasteurised milk or untreated water, handling and cooking contaminated food, having contact with infected food-producing animals and pets (Altekruse and others 1999, Gilpin and others 2008, Acke and others 2009), and swimming in natural bodies of water (Schonberg-Norio and others 2004). Campylobacter species have emerged as the most common cause of bacterial foodborne gastroenteritis in industrialised and developed countries, with the number of cases of campylobacteriosis often exceeding the number of cases of salmonellosis and shigellosis (Altekruse and others 1999, WHO 2001). Thermophilic Campylobacter species, namely Campylobacter jejuni, Campylobacter coli and Campylobacter lari, are the most common causes of the disease, with C jejuni accounting for over 80 per cent of cases in human beings in the UK (Gillespie and others 2002). A wide variety of avian species, including domestic and wild birds, harbour Campylobacter species and are important in the epidemiology of human campylobacteriosis (Oyarzabal and others 1995, Atanassova and Ring 1999). In Nigeria, few studies have been

Background: Viral outbreaks have always been a challenging task for clinicians and Influenza virus has been on top of the list. The history of influenza epidemic reveals its devastating effects in the form of multiple deaths and economic burden. Hence this study was planned to recognize the peak activity time span of Influenza infection and its frequency in our set-up at Shifa International Hospital, Islamabad Pakistan. Material and Methods: A cross sectional study was performed in Pathology Laboratory, Shifa international hospital Islamabad from April 2016 to March 2019. Nasopharyngeal swabs were collected from patients of all age groups, with clinically suspected influenza infection throughout the year, irrespective of gender, according to hospital’s standard policy. Samples were analysed on GeneXpert kit (Xpert Flu Assay). Data collected was entered and then analysed in SPSS version 17. Results: Of the total 591 samples included in study, 233 (39.4%) were positive for influenza (Flu A or Flu B), while 358 (60.6%) showed negative results. Total 172 (73.8%) were positive for Flu A while 61 (26.1%) were positive for Flu B. Among Flu A cases, 107 (62.2%) were positive for H1N1. Most of the positive cases (n=206; 88.4%) were reported in the months of January and February during the three-year period (2016-2019) of this study. Conclusions: Influenza virus has peak activity in the months of January and February. Both Influenza A and B are circulating in the environment but Flu A is predominant and H1N1 is more prevalent.