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https://www.selleckchem.com/ Image analysis in histopathology provides insights into the microscopic examination of tissue for disease diagnosis, prognosis, and biomarker discovery. Particularly for cancer research, precise classification of histopathological images is the ultimate objective of the image analysis. Here, the time-frequency time-space long short-term memory network (TF-TS LSTM) developed for classification of time series is applied for classifying histopathological images. The deep learning is empowered by the use of sequential time-frequency and time-space features extracted from the images. Furthermore, unlike conventional classification practice, a strategy for class modeling is designed to leverage the learning power of the TF-TS LSTM. Tests on several datasets of histopathological images of haematoxylin-and-eosin and immunohistochemistry stains demonstrate the strong capability of the artificial intelligence (AI)-based approach for producing very accurate classification results. The proposed approach has the potential to be an AI tool for robust classification of histopathological images.Female and male infertility have been associated to Chlamydia trachomatis, Ureaplasma spp. and Mycoplasma hominis urogenital infections. However, evidence from large studies assessing their prevalence and putative associations in patients with infertility is still scarce. The study design was a cross-sectional study including 5464 patients with a recent diagnosis of couple's primary infertility and 404 healthy control individuals from Cordoba, Argentina. Overall, the prevalence of C. trachomatis, Ureaplasma spp. and M. hominis urogenital infection was significantly higher in patients than in control individuals (5.3%, 22.8% and 7.4% vs. 2.0%, 17.8% and 1.7%, respectively). C. trachomatis and M. hominis infections were significantly more prevalent in male patients whereas Ureaplasma spp. and M. hominis infections were more prevalent in female patients. Of clinical

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