Molecular investigation of Coxiella burnetii in aborted fetus of small ruminants in southeast Iran.

The domestic animal, known as a main reservoir of Coxiella burnetii, is susceptible to the occurrence of coxiellosis, which can lead to abortions in domestic animals, causing significant economic damage and posing risks to human health. Therefore, the purpose of this study is to investigate C. burnetii as the causative agent of Q fever in abortion samples of small ruminants in southeastern Iran. This study was conducted between 2020 and 2021 in Zarand city, located in Kerman province (southeast Iran). In this study, 50 abomasum swab samples of aborted sheep and goat fetuses were collected and analyzed using molecular methods to identify C. burnetii. The results revealed that 26% (n: 13) of the collected abortion samples were infected with C. burnetii. Among the positive samples, two (50%) belonged to goat abortion samples while 11 (23.9%) belonged to sheep abortion samples. This study demonstrates that C. burnetii is one of the causes of abortion in small ruminants in southeastern Iran. It is recommended to pay more attention to C. burnetii in domestic animals due to its significant economic impact on livestock and its potential implication for human health in Iran.

Artificial intelligence: A promising frontier in bladder cancer diagnosis and outcome prediction.

Bladder cancer (BCa) is the most common malignancy of the urinary tract and the most expensive malignancy to treat over the patients' lifetime. In recent years a number of studies have utilized Artificial Intelligence (AI) algorithms to perform certain clinical tasks involved in BCa diagnosis and outcome prediction. These tasks include automatic tumor detection, staging, and grading, bladder wall segmentation, as well as prediction of recurrence, response to chemotherapy, and overall survival. Despite the promising results reported, AI algorithms have not been fully integrated into the clinical workflow. In this article we (1) provide an accessible introduction to the fundamental nomenclature and concepts in AI, (2) review the literature to explore how AI is used for BCa diagnosis and outcome prediction, and (3) present our perspective on the obstacles that must be removed before AI algorithms can enter the mainstream of cancer management.

Cardiac magnetic resonance image-based classification of the risk of arrhythmias in post-myocardial infarction patients.

INTRODUCTION: Patients surviving myocardial infarction (MI) can be divided into high and low arrhythmic risk groups. Distinguishing between these two groups is of crucial importance since the high-risk group has been shown to benefit from implantable cardioverter defibrillator insertion; a costly surgical procedure with potential complications and no proven advantages for the low-risk group. Currently, markers such as left ventricular ejection fraction and myocardial scar size are used to evaluate arrhythmic risk. METHODS: In this paper, we propose quantitative discriminative features extracted from late gadolinium enhanced cardiac magnetic resonance images of post-MI patients, to distinguish between 20 high-risk and 34 low-risk patients. These features include size, location, and textural information concerning the scarred myocardium. To evaluate the discriminative power of the proposed features, we used several built-in classification schemes from matrix laboratory (MATLAB) and Waikato environment for knowledge analysis (WEKA) software, including k-nearest neighbor (k-NN), support vector machine (SVM), decision tree, and random forest. RESULTS: In Experiment 1, the leave-one-out cross-validation scheme is implemented in MATLAB to classify high- and low-risk groups with a classification accuracy of 94.44%, and an AUC of 0.965 for a feature combination that captures size, location and heterogeneity of the scar. In Experiment 2 with the help of WEKA, nested cross-validation is performed with k-NN, SVM, adjusting decision tree and random forest classifiers to differentiate high-risk and low-risk patients. SVM classifier provided average accuracy of 92.6%, and AUC of 0.921 for a feature combination capturing location and heterogeneity of the scar. Experiment 1 and Experiment 2 show that textural features from the scar are important for classification and that localization features provide an additional benefit. CONCLUSION: These promising results suggest that the discriminative features introduced in this paper can be used by medical professionals, or in automatic decision support systems, along with the recognized risk markers, to improve arrhythmic risk stratification in post-MI patients.