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M87's Wobbling Jet: A Spin on Black Hole Mysteries

Researchers confirm the spin of the M87 galaxy's supermassive black hole by observing the wobble in its jet, using two decades of data from global radio telescopes. This discovery marks a significant advancement in black hole studies. The supermassive black hole at the heart of galaxy M87, made f

Japan
Motoki
Iwate
Italy
Hideyuki-kobayashi
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Jintao-yu
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Artificial intelligence used in male infertility scoring

Artificial intelligence used in male infertility scoring Appeared in BioNews 1095 Artificial intelligence (AI) has been used to evaluate tissue samples in men who produce low levels of, or no, sperm. Researchers at Toho University School of Medicine in Japan tested whether or not the Google Cloud Automated Machine Learning (AutoML) Vision platform could be used to carry out the traditional Johnson scoring method, in place of pathologists. The Johnson scoring method is used to classify the ability of a male patient to create viable sperm, based on examination of tissue samples taken from the testes, and is often the first stage in the treatment of azoospermia (a condition with no sperm in semen). The researchers findings have been published in Scientific Reports in Nature Communication.

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Japan
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Automated machine learning predicts male infertility based on Johnsen score

Automated machine learning predicts male infertility based on Johnsen score Infertility affects females and males equally. In male infertility, azoospermia (a medical condition with no sperm in semen) is a major problem that prevents a couple from having a child. For the treatment of patients with azoospermia, testicular sperm extraction (TESE) is required to obtain mature sperms. When examined, histological specimens are typically given a score, called the Johnsen score, on a scale of 1 to 10, based on the histopathological features of the testis. The Johnsen score has been widely used in urology since it was first reported 50 years ago. However, histopathological evaluation of the testis is not an easy task and takes much time due to the complexity of testicular tissue arising from the multiple, highly specialized steps in spermatogenesis.

Hideyuki-kobayashi
Emily-henderson
Google
Urology-department
Toho-university-school-of-medicine
Professor-of-urology-department
Associate-professor
Toho-university-school
Google-cloud
Microsoft-powerpoint
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கூகிள்

Male infertility scoring using AI-assisted image classification requiring no programming

 E-Mail IMAGE: All images are X400 magnification. Algorithm performance using Google Cloud AutoML Vision, Average precision recall curve for image dataset, magnification X400. view more  Credit: Hideyuki Kobayashi Infertility affects females and males equally. In male infertility, azoospermia (a medical condition with no sperm in semen) is a major problem that prevents a couple from having a child. For the treatment of patients with azoospermia, testicular sperm extraction (TESE) is required to obtain mature sperms. When examined, histological specimens are typically given a score, called the Johnsen score, on a scale of 1 to 10, based on the histopathological features of the testis.

Hideyuki-kobayashi
Google
Toho-university-school-of-medicine-fig
Professor-of-urology-department
Associate-professor
Toho-university-school
Google-cloud
Microsoft-powerpoint
Scientific-reports
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