Mycobacterium spp. and other pathogens were investigated in 258 swine lymph nodes (129 with and 129 without apparent lesions), and 120 lymph nodes (60 with and 60 without lesions) from wild boars (Sus scrofa). A total of lymph nodes from swine and wild boars were collected of different animals. Submaxillar and mesenteric lymph nodes were submitted to microbiological examination and colonies suggestive of Mycobacterium spp. (alcohol-acid bacilli) were submitted to PCR Restriction Assay (PRA). In swine with lymphadenitis, Mycobacterium spp. (24.1%) and Rhodococcus equi (13.2%) were the most prevalent microorganisms, while in lymph nodes without lesions were identified a complex of microorganisms, including of environmental mycobacteria. In wild boars with lymphadenitis, ß-haemolytic Streptococcus (10.0%), Mycobacterium spp (8.4%) and R. equi (6.6%) were the most frequent. Among mycobacterias were identified predominantly Mycobacterium avium subspecies type 1 (48.3%) and M. avium subspecies type 2 (16.1%), followed by Mycobacterium intracellulare, Mycobacterium szulgai,Mycobacterium fortuitum, Mycobacterium gordonae, Mycobacterium simiae, Mycobacterium nonchromogenicum and Mycobacterium intracellulare type 2.
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http://dx.doi.org/10.1016/j.rvsc.2010.06.009 | DOI Listing |
Cureus
December 2024
Department of Radiology, University of Medicine and Pharmacy of Craiova, Craiova, ROU.
Background: Cervical cancer is considered one of the most common gynecological malignancies with an increased incidence in developing countries. Magnetic resonance imaging (MRI) plays a valuable role in staging cervical cancer and providing valuable information necessary for selecting the appropriate treatment plan, while closely correlating with the prognosis of the patient.
Objective: The aim of this study is to assess the diagnostic value of diffusion-weighted imaging (DWI) in the preoperative loco-regional staging of cervical carcinoma.
Vet Res Forum
November 2024
Department of Veterinary Medicine, College of Veterinary and Animal Sciences, Sardar Vallabhbhai Patel University of Agriculture and Technology, Meerut, India.
African swine fever (ASF) is considered as one of the most threatening diseases for the pig farming industry all over the world. Due to the lack of an effective vaccine, organized farms and backyard rearing must strictly enforce control measures in order to combat the disease. The present report describes the ASF epidemic in a piggery in Uttar Pradesh state, India.
View Article and Find Full Text PDFBMJ Open
December 2024
Department of Gastroenterology, The Tenth Affiliated Hospital, Southern Medical University (Dongguan People's Hospital), Dongguan, China
Objectives: This study investigated the prognostic role of log odds of negative lymph node/T stage (LONT) and established a nomogram based on LONT to predict the prognosis in colorectal cancer (CRC) patients.
Design: A retrospective cohort study.
Setting And Participants: We enrolled 80 518 CRC patients from the Surveillance, Epidemiology and End Results database between 2010 and 2015.
BMC Med Imaging
January 2025
Cancer Center, Department of Ultrasound Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, No. 158 Shang tang Road, Hangzhou, Zhejiang, 310011, China.
Background: This study aims to evaluate the predictive usefulness of a habitat radiomics model based on ultrasound images for anticipating lateral neck lymph node metastasis (LLNM) in differentiated thyroid cancer (DTC), and for pinpointing high-risk habitat regions and significant radiomics traits.
Methods: A group of 214 patients diagnosed with differentiated thyroid carcinoma (DTC) between August 2021 and August 2023 were included, consisting of 107 patients with confirmed postoperative lateral lymph node metastasis (LLNM) and 107 patients without metastasis or lateral cervical lymph node involvement. An additional cohort of 43 patients was recruited to serve as an independent external testing group for this study.
BMC Cancer
January 2025
The Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China.
Background And Objectives: Accurate classification of lymphadenopathy is essential for determining the pathological nature of lymph nodes (LNs), which plays a crucial role in treatment selection. The biopsy method is invasive and carries the risk of sampling failure, while the utilization of non-invasive approaches such as ultrasound can minimize the probability of iatrogenic injury and infection. With the advancement of artificial intelligence (AI) and machine learning, the diagnostic efficiency of LNs is further enhanced.
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