old appeared to be a risk of getting infected with microsporidia. Further studies are needed to determine the genetic characteristics of these organisms in the study population. Leptospirosis is a neglected zoonosis in developing countries including Egypt where its burden is underestimated. A cross sectional study was carried out to estimate the seroprevalence and associated risk factors of Leptospira interrogans serovar Hardjo infection among cows and leptospirosis among human patients in Mid-Delta of Egypt. Out of 112 examined cows using ELISA, 3.6% were seropositive to L. interrogans serovar Hardjo infection. Seroconversion occurred in 5 animals (1 herd) of all examined animals in convalescent phase testing (5/112, 4.5%). Affected herd suffered acute outbreak with 43.3% within herd prevalence; signs of infection included abortions, bloody urine and sudden death of 2 cows. Highest risk for L. interrogans serovar Hardjo infection in cows was in animals drank from untreated surface water (6.7 times, p = 0.06). The seroprevalence of leptospirosis was 6.2% in all tested humans, 28.6% in nonspecific fever cases and 22.2% in non-viral hepatitis cases. The risk of leptospirosis among patients with nonspecific fever or non-viral hepatitis cases was 4 times higher than those with viral hepatitis (p = 0.01). Additionally, there was a significant association between leptospirosis and patients with livestock contact (Odds 8, p = 0.01). This is the first report of L. interrogans serovar Hardjo outbreak in cows in Egypt. The study also highlighted the role of leptospirosis as neglected cause of nonspecific fever/non-viral hepatitis in humans in study region.This is the first report of L. interrogans serovar Hardjo outbreak in cows in Egypt. The study also highlighted the role of leptospirosis as neglected cause of nonspecific fever/non-viral hepatitis in humans in study region. Carbapenem resistance is an emerging problem in Enterobactarales. We aimed to investigate the presence of carbapenemase genes blaNDM, blaKPC, blaVIM and blaOXA-48 and evaluate the phenotypic blue-carba method and carbapenem inactivation method (CIM) in Enterobacterales isolates. Total of 153 Enterobacterales isolates were tested in the study. Presence of blaNDM, blaKPC, blaVIM and blaOXA-48 genes was investigated by polymerase chain reaction (PCR) method. Carbapenemase production of the isolates was also tested by blue-carba method and CIM. The presence of blaOXA-48 gene was detected in 110 (71.4%) and blaNDM gene was detected in 2 (1.3%) of the Enterobacterales isolates by PCR method. None of the isolates were positive for blaKPC and blaVIM genes. The 121 (78.54%) of the isolates were found to be positive by blue-carba method and CIM. PF-4708671 cell line And 105 (68.18%) of the isolates were determined as positive by both PCR, blue-carba and CIM. In our study, 112 (72.7%) of the Enterobacterales isolates were found to be positive for carbapenemase genes (blaoxa-48 and blaNDM), and 121 (78.57%) of different isolates were found to be positive for blue-carba and CIM. However, 105 (68.18%) of the carbapenem resistance isolates found to be positive for all three methods.In our study, 112 (72.7%) of the Enterobacterales isolates were found to be positive for carbapenemase genes (blaoxa-48 and blaNDM), and 121 (78.57%) of different isolates were found to be positive for blue-carba and CIM. However, 105 (68.18%) of the carbapenem resistance isolates found to be positive for all three methods. Tuberculosis is the major global burden of disease contributing about 2% of the global challenges. Poor tuberculosis treatment increased risk of multi-drug resistance tuberculosis occurence. Thus, we aimed to identify determinants of mult-drug resistant tuberclosis in treatment centers of Eastern Amhara, Ethiopia. Facility based unmatched case-control study was employed in East Amhara, Ethiopia. Cases were tuberculosis patients confirmed for mult-drug resistant tuberclosis while controls were tuberculosis patients with confirmed tuberculosis but susceptible to first line drugs. Respondents were selected using simple random sampling technique. Bivariable and multivariable analysis was conducted to identify diterminants at level of statistical significance p < 0.05. We enrolled 450 tuberculosis patients. Rural residents (AOR = 3, 95% CI 1.4-6.0; p = 0.024), family size greater than five (AOR = 3.7, 95% CI 1.6-8.6; p = 0.0098), having single room (AOR = 4.1, 95% CI1.8-9.0; p = 0.027), room without windocontact with known tuberculosis patient) variables were the identified determinants for increased multi-drug resistance tuberculosis. Quantitative analysis of Mycobacterium tuberculosis using microscope is very critical for diagnosing tuberculosis diseases. Microbiologist encounter several challenges which can lead to misdiagnosis. However, there are 3 main challenges (1) The size of Mycobacterium tuberculosis is very small and difficult to identify as a result of low contrast background, heterogenous shape, irregular appearance and faint boundaries (2) Mycobacterium tuberculosis overlapped with each other making it difficult to conduct accurate diagnosis (3) Large amount of slide can be time consuming and tedious to microbiologist and which can lead to misinterpretations. To solve these challenges and limitations, we proposed an automated-based detection method using pretrained AlexNet to trained the model in 3 sets of experiments A, B and C and adjust the protocols accordingly. We compared the detection of tuberculosis using AlexNet Models with the ground truth result provided by microbiologist and analyzed inconsistencies between network models and human. 98.15 % accuracy, 96.77% sensitivity and 100% specificity for experiment A, 98.09% accuracy, 98.59% sensitivity and 97.67% specificity for experiment B and 98.73% testing accuracy, 98.59 sensitivity, 98.84% specificity ofr experiment C which sound robust and promising. The results indicated that network performance was successful with high accuracies, sensitivities and specificities and it can be used to support microbiologist for diagnosis of tuberculosis.The results indicated that network performance was successful with high accuracies, sensitivities and specificities and it can be used to support microbiologist for diagnosis of tuberculosis.