Hemogram indices of healthy lactovegetarian population from Tharparkar village.

Introduction: The evidence of micro nutrients deficiencies related mortalities are emerging all over the globe. Lake of the knowledge of dietary source of vitamin D complex and iron affects the haemogram indices. Aim of this study was to assess the haemogram values of healthy lactovegetarian population of Tharparkar village and to correlate these with WHO parameters.Methodology: This descriptive cross-sectional study was conducted in 2016-17 on 100 apparently healthy subjects of both genders with age 14 to 55 years. Peripheral smears were prepared using Leishman stain at the research field during sampling. Coagulated whole blood samples were collected and transported to the Dow university lab at Karachi under proper temperature.Result: The mean age of the subjects in this study was 30.5 (±8.3) and the male to female ratio was 2.1:1 the mean hemoglobin level was 13.5 (±1.6). Mean NCV level was83.6 (±9.9) mean MCH was found as 63.9 (±3.1).and mean Hematocrit was found as 40.4. (±5.7)Conclusion: The blood indices of lactovegetarian population of Tharparkar village fall within the specified range as set by WHO Parameters accept MCV which was found higher than normal. This may be attributed to the deficiency of vitamin B12 or Folate.Key word: Lacto-vegetarian, Hemogram indices, vitamin B12/Folate deficiency, Anemia

Dissemination and spread of New Delhi metallo-beta-lactamase-1 superbugs in hospital settings

Objective: To find out frequency of isolation of carbapenem-resistant enterobacteriaceae and the predominantly responsible metallo-beta-lactamasegene in a hospital setting. Methods: The descriptive, cross-sectional study was conducted from May 2009 to June 2012 at the Aga Khan University Hospital, Karachi, and comprised non-duplicate clinical carbapenem-resistant enterobacteriaceae isolates obtained from different collection units across Pakistan. Kirby-Bauer disk diffusion screening of carbapenem-resistant enterobacteriaceae was confirmed by minimum inhibitory concentration using E-test. Polymerase chain reaction assay was performed to detect blaKPC, blaNDM-1, blaIMP, and blaVIM genes. In addition variable number tandem repeat typing was performed on selected cluster of New Delhi metallo-beta-lactamase-1- positive Klebsiella pneumoniae. Results: Of the 114 carbapenem-resistant enterobacteriaceae isolates, 104(94%) tested positive for blaNDM-1 gene. At 68(66%), Klebsiella pneumoniae was the most frequent species isolated, followed by E.coli 33(31%). Moreover, 89(78%) of the blaNDM-1 gene positive Klebsiella pneumonia isolates were from the clinical samples of patients admitted to the critical care units and 75(66%) were from neonates and the elderly. Of the 65(67%) patientssuffering from bacteraemia and sepsis, 32(57%) had expired, of which 22(60%) were aged \u3c1 month. Variable number tandem repeat analysis of hospital-acquired New Delhi metallo-beta-lactamase-1-positive Klebsiella pneumoniae showed similarities between the isolates. Conclusion:New Delhi metallo-beta-lactamase-1-positive enterobacteriaceae was found widely disseminated in major hospitals across Pakistan. Patients at extreme ages and those in critical care units were found to be the most affected with fatal outcome

Mixed salmonella infection: a case series from Pakistan

Enteric fever remains a major health problem in the developing world, including Pakistan. Poor sanitation and hygienic conditions are the major predisposing factors. Salmonella infection with different strains in the same patient has rarely been reported previously. We are reporting two cases of bacteraemia with simultaneous detection of two strains of Salmonella in a single episode of infection. In both the cases, 2 different serotypes of Salmonella were causing bacteraemia leading to fever. In highly endemic area, one must be aware of mixed Salmonella infections as inappropriate diagnosis of such infections may lead to treatment failure

Infection of a ventricular septal defect patch with acremonium species

A ventricular septal defect (VSD) patch infection with Acremonium species isolated from vegetation and blood culture is described. Antifungal treatment was discontinued after 3 months and patient developed relapse. Surgery with prolonged oral voriconazole was instituted with recovery. We emphasize importance of surgery and prolonged therapy to treat such infections

Community-acquired meningoencephalitis due to concomitant infection caused by Naegleria fowleri and Streptococcus pneumoniae from Karachi, Pakistan: A Case Report

Naegleria fowleri causes acute fatal primary amoebic meningoencephalitis in adults and children with a history of exposure to aquatic activities. However, several cases of Primary Amoebic Meningoencephalitis (PAM) have been reported from Karachi with no history of aquatic recreational activities suggesting the presence of N. fowleri in domestic water. This study reports a case of co-infection of N. fowleri with Streptococcus pneumoniae in an elderly hypertensive mal

The Underemphasized Epidemiology of Non- Dermatophytes in Tinea Capitis: A Study from Tertiary Care Hospital, Karachi

Background: Tinea capitis (TC) is a fungal infection that victimizes every age group. The fundamental culprits of TC are dermatophytes and the role of non-dermatophytes (NDM) in pathogenesis is overshadowed. Therefore, the current study was designed to evaluate the epidemiology of non-dermatophytes as the etiologic agent of tinea capitis among local population. Methods: It was a cross sectional descriptive study, which was conducted at the Department of Microbiology, Jinnah Post Graduate Medical Centre Karachi Pakistan, from January 2019 to September 2019. A total of 207 patients diagnosed with tinea capitis were enrolled in the study. The scalp scrapings and hair were collected and processed for Potassium Hydroxide (KOH), Lactophenol Cotton Blue (LPCB) and Calcofluor White (CFW) staining. The specimens were cultured on Dermatophyte Test Medium (DTM) and Sabouraud Dextrose Agar (SDA). The species were identified by slide culture, LPCB staining and biochemical tests. The Chi squared test was used for determining the association between variables. The kappa index was utilized for determining the correlation between the efficacies of tests, provided that p-value lesser than 0.05 was considered statistically significant.   Results: Among isolated species, 61(29.5%) were dermatophytes and 45(21.7%) were non-dermatophytes. The most common isolated non-dermatophytes were Aspergillus spp. (n=16, 35.5%), followed by Penicillium spp. (n=7, 15.55%). A significant association was observed between the non-inflammatory type of lesions of TC and non-dermatophytes (p-value=0.000). CFW staining was found to be a better tool in detecting fungal components in the specimen compared to KOH mounts (p-value=0.000). Conclusion: The non-dermatophytes carry substantial importance in causing tinea capitis and related superficial scalp mycoses. Keywords: Aspergillus; Dermatophytes; Tinea Capitis; Epidemiology

A Novel Deep Learning-Based Mitosis Recognition Approach and Dataset for Uterine Leiomyosarcoma Histopathology

Uterine leiomyosarcoma (ULMS) is the most common sarcoma of the uterus, It is aggressive and has poor prognosis. Its diagnosis is sometimes challenging owing to its resemblance by benign smooth muscle neoplasms of the uterus. Pathologists diagnose and grade leiomyosarcoma based on three standard criteria (i.e., mitosis count, necrosis, and nuclear atypia). Among these, mitosis count is the most important and challenging biomarker. In general, pathologists use the traditional manual counting method for the detection and counting of mitosis. This procedure is very time-consuming, tedious, and subjective. To overcome these challenges, artificial intelligence (AI) based methods have been developed that automatically detect mitosis. In this paper, we propose a new ULMS dataset and an AI-based approach for mitosis detection. We collected our dataset from a local medical facility in collaboration with highly trained pathologists. Preprocessing and annotations are performed using standard procedures, and a deep learning-based method is applied to provide baseline accuracies. The experimental results showed 0.7462 precision, 0.8981 recall, and 0.8151 F1-score. For research and development, the code and dataset have been made publicly available