Chapter one

1.1 Introduction  :-

         Leprosy, also called Hansens disease, results from infection with Mycobacterium leprae (M.leprae) or Mycobacterium lepromatosis (M.  lepromatosis)  morbidity is low because a large  portion of the population is naturally resistant to this  It ranges from a localized to a systemic infection. .(peter ,2018) .  Mycobacterium leprae is one of the important pathogens of the genus Mycobacterium, since it is responsible for causing leprosy in humans. Leprosy is caused by a chronic granulomatous infection of the skin and peripheral nerves by bacteria .  (Ujjini ,et,.al 2006) .occurs in a wide spectrum of clinical forms depending on the host cell-mediated immune (CMI) response to the pathogen from the tuberculoid pole, through to borderline cases ending at the lepromatous pole .  High CMI response is associated with a low number of bacilli (paucibacillary leprosy) and, inversely, low CMI response is connected with the presence of a high number of bacilli (multibacillary leprosy). If untreated, the chronic infection results in a progressive and permanent .(Anna Beltrame,2020) .Transmission pathways of M. leprae are not fully understood. Solid evidence exists of an increased risk for individuals living in close contact with leprosy patients, most likely through infectious aerosols, created by coughing and sneezing, but possibly also through skin to skin  contact.(Thomas et.al,.2001 ) .Has been classified as a neglected tropical disease (NTD) by the World Health Organization (WHO) .(Tan,.et,al.2019) .  The chronic infection results in a progressive and permanent damage of the skin, peripheral nerves, and eyes, leading to physical deformities and disabilities .Hansen (1874) was the first to report rod-shaped bodies resembling bacteria in cells from leprosy patients using a light microscope. Before that, leprosy was thought to be of environmental or hereditary nature. After the discovery by Hansen, attempts were made to grow the pathogen on an array of artificial media and in numerous animals with the purpose of studying its characteristics, and potential treatments.( Anna Beltrame ,et,.al .2002) .  Various classifications of leprosy based on clinical and bacterial observations have been used. The commonly used Madrid classification is based on clinical observations. It divides cases into 4 types:- indeterminate (I), tuberculoid (T), mid-borderline (B), and lepromatous (L).  Each of the four classes includes subclasses according to the clinical presentations. In 1966 for the purpose of research, Ridley and Jopling proposed a 5-group classification system based on the host immune response and the histological and clinical manifestations. This classification has now been adopted all over the world.  

Chapter one

1.1 Introduction  :-

         Leprosy, also called Hansens disease, results from infection with Mycobacterium leprae (M.leprae) or Mycobacterium lepromatosis (M.  lepromatosis)  morbidity is low because a large  portion of the population is naturally resistant to this  It ranges from a localized to a systemic infection. .(peter ,2018) .  Mycobacterium leprae is one of the important pathogens of the genus Mycobacterium, since it is responsible for causing leprosy in humans. Leprosy is caused by a chronic granulomatous infection of the skin and peripheral nerves by bacteria .  (Ujjini ,et,.al 2006) .occurs in a wide spectrum of clinical forms depending on the host cell-mediated immune (CMI) response to the pathogen from the tuberculoid pole, through to borderline cases ending at the lepromatous pole .  High CMI response is associated with a low number of bacilli (paucibacillary leprosy) and, inversely, low CMI response is connected with the presence of a high number of bacilli (multibacillary leprosy). If untreated, the chronic infection results in a progressive and permanent .(Anna Beltrame,2020) .Transmission pathways of M. leprae are not fully understood. Solid evidence exists of an increased risk for individuals living in close contact with leprosy patients, most likely through infectious aerosols, created by coughing and sneezing, but possibly also through skin to skin  contact.(Thomas et.al,.2001 ) .Has been classified as a neglected tropical disease (NTD) by the World Health Organization (WHO) .(Tan,.et,al.2019) .  The chronic infection results in a progressive and permanent damage of the skin, peripheral nerves, and eyes, leading to physical deformities and disabilities .Hansen (1874) was the first to report rod-shaped bodies resembling bacteria in cells from leprosy patients using a light microscope. Before that, leprosy was thought to be of environmental or hereditary nature. After the discovery by Hansen, attempts were made to grow the pathogen on an array of artificial media and in numerous animals with the purpose of studying its characteristics, and potential treatments.( Anna Beltrame ,et,.al .2002) .  Various classifications of leprosy based on clinical and bacterial observations have been used. The commonly used Madrid classification is based on clinical observations. It divides cases into 4 types:- indeterminate (I), tuberculoid (T), mid-borderline (B), and lepromatous (L).  Each of the four classes includes subclasses according to the clinical presentations. In 1966 for the purpose of research, Ridley and Jopling proposed a 5-group classification system based on the host immune response and the histological and clinical manifestations. This classification has now been adopted all over the world.  

Heart attack is mainly caused due to atherosclerosis. It is a coronary artery disease (CAD) and it is a leading cause of death worldwide. It occurs when the coronary artery that supplies blood and oxygen to the heart and different parts of the body becomes blocked or narrowed due to deposition of proteins, cholesterol and other fatty deposits in the inner wall of the coronary artery. This results in a heart attack or damage to the heart tissue. Existing techniques for detection of plaque are Magnetic Resonance Imaging (MRI), Electron Beam Computed Tomography (EBCT) and Angiography, among these existing techniques Angiography is widely used at present to detect and cure heart attack. The currently available techniques are expensive, beyond the reach of normal people, not easily available in remote areas and cannot detect deeply embedded plaque with accuracy at early stages. The plaque is only detected by these techniques when blockage in artery is more than 70%. In order to remove the limitations of existing techniques Intravascular Ultrasound (IVUS) technique is proposed in our research work. This technique is based on application of image processing by using bio-markers and Marker Controlled Watershed Modified Image Segmentation Algorithm which better detects the Plaque deposits embedded deep inside the Coronary Artery wall that are undetectable by Angiography or other Cardiac test that are prone to rupture without warning sign. In addition to this technique Artificial Intelligence (Deep Learning) computing technique will also be used for image classification. The proposed technique has tendency to detect inner layer (foreground) i.e. object and outer layer (background) of the artery for better detection of Region of Interest (ROI), which is deeper region of soft noncalcified plaque. So, the result obtained using the proposed techniques is expected to play a  crucial role in the visualization of inner view of Coronary Artery containing Plaque with more accuracy and also expected to show the exact measurement of plaque location, size and quantification for pre-detection of heart attack.

Microbiology is the study of micro-organisms like archaea, bacteria, fungi, protozoa and viruses. People studied micro-biology through existing methodologies like cultivation but this methods are very expensive and time-consuming.  With help of Due to the invention of this high thorough-put sequencing technology, a large amount of microbial data is being generated thus, machine learning is branch of artificial intelligence has steadily won its way into the area of microbial analysis as the go-to method to solve classification and interaction problems. Literature reviews have shown that ML can be used in many aspects of microbiology research, especially classification problems, and for exploring the interaction between microorganisms and the surrounding environment. In this talk , We will discuss about various machine learning algorithms with business use cases , open source tools covering all areas of microbiology

 

Background: The increase in antibiotic-resistant staphylococci among pets and its transfer to humans threaten veterinary medicine and public health. This study was designed to determine the antibiotic resistance patterns and the prevalence of virulence genes among S. pseudintermedius obtained from dogs and dog owners in Abakaliki, Nigeria.

Methods: Exactly 112 swab samples (perineum, nares, and mouth) were obtained from shelter dogs while nasal swabs of 97 dog owners and 150 non-dog owners were collected. Swab samples were processed and isolates were identified using standard microbiological procedures. MIC was determined by broth micro-dilution using the sensititre system. Isolates were screened for sec, siet, exi, and lukD virulence genes by PCR.

Results: A total of 99 S. pseudintermedius isolates [86 (76.8 %) from dogs and 13 (13.4 %) from dog owners] were obtained, out of which 52 (52.5 %) were identified as MRSP strains. No S. pseudintermedius isolate was recovered from non-dog owners. Isolates were highly resistant to penicillin (100 %) and ampicillin (94.2 %). Equal resistance frequency (51.2 %) was each observed for fluoroquinolones, clindamycin, trimethoprim/sulfamethoxazole, and erythromycin. Isolates also exhibited resistance to gentamycin (46.5 %), chloramphenicol (23.1 %), tetracycline (19.8 %), and tigecycline (8.1 %). Isolates harboured sec (73.7 %), exi (2 %), siet (62.6 %), and lukD (55.6 %) virulence genes. 

Conclusion: S. pseudintermedius isolates, including MRSP strains in this study were multi-drug resistant and notably more resistant than those reported in literature. Sec, exi, siet, and lukD virulence genes were haboured by the isolates. There was phenotypic homogeneity in the antibiogram of isolates from dogs and their owners, thus depicting a possible zoonotic transmission. The ability of S. pseudintermedius to cause human infections highlights its lack of host specificity and the importance of considering inter-species transmission.

Introduction: Proteus species are opportunistic pathogens of the Enterobacteriaceae family that cause various diseases such as complicated urinary tract infections, wound infections, burns, abscesses, respiratory tract infections and bacteremia.

Materials and Methods: In this study, Proteus mirabilis isolates were collected from four UTI patients with cefexime treatment failure. Antibiotic susceptibility of P. mirabilis isolates were evaluated by Vitek2. To identify the clonality relatedness, REP-PCR was used. Whole genome sequencing (WGS) Illumina platform, pair-end, for one of isolates was used. The sequence was assembled, annotated and then embedded in Comprehensive Antibiotic Resistance Database and Center for Genomic Epidemiology to find the resistance determinants as well as pathogenicity genes. The respective plasmids were also fully determined.

Results: Based on the results of Vitek2 device, all of four isolates were susceptible to all tested antibiotics. REP-PCR revealed that they were a clone. The WGS contigs submitted and accession number NZ_JABVMA000000000.1 was accepted. Four plasmids were identified. Four resistance genes including TEM-171 (monobactam resistance, cephalosporin), APH(3')-IIa and APH(3')-Ia  (aminoglycoside resistance) and sul1 (sulfonamide resistance) as well as various efflux pumps were identified in the bacterium.

Conclusion: According to the MIC results, aforementioned genes did not cause resistance to the tested antibiotics in-vitro. However, outpatient treatment with cefexime was not successful. Patients were treated by ciprofloxacin. Different reactions of TEM-171 under antibiotic stress, i.e. in-vitro and in-vivo, shows the effect of human barriers in front of effective dose of antibiotics in target organ.