Deep Learning-Based Spermatogenic Staging in Tissue Sections of Cynomolgus Macaque Testes.

The indirect assessment of adverse effects on fertility in cynomolgus monkeys requires that tissue sections of the testis be microscopically evaluated with awareness of the stage of spermatogenesis that a particular cross-section of a seminiferous tubule is in. This difficult and subjective task could very much benefit from automation. Using digital whole slide images (WSIs) from tissue sections of testis, we have developed a deep learning model that can annotate the stage of each tubule with high sensitivity, precision, and accuracy. The model was validated on six WSI using a six-stage spermatogenic classification system. Whole slide images contained an average number of 4938 seminiferous tubule cross-sections. On average, 78% of these tubules were staged with 29% in stage I-IV, 12% in stage V-VI, 4% in stage VII, 19% in stage VIII-IX, 18% in stage X-XI, and 17% in stage XII. The deep learning model supports pathologists in conducting a stage-aware evaluation of the testis. It also allows derivation of a stage-frequency map. The diagnostic value of this stage-frequency map is still unclear, as further data on its variability and relevance need to be generated for testes with spermatogenic disturbances.

Deep learning-based image-analysis algorithm for classification and quantification of multiple histopathological lesions in rat liver.

Artificial intelligence (AI)-based image analysis is increasingly being used for preclinical safety-assessment studies in the pharmaceutical industry. In this paper, we present an AI-based solution for preclinical toxicology studies. We trained a set of algorithms to learn and quantify multiple typical histopathological findings in whole slide images (WSIs) of the livers of young Sprague Dawley rats by using a U-Net-based deep learning network. The trained algorithms were validated using 255 liver WSIs to detect, classify, and quantify seven types of histopathological findings (including vacuolation, bile duct hyperplasia, and single-cell necrosis) in the liver. The algorithms showed consistently good performance in detecting abnormal areas. Approximately 75% of all specimens could be classified as true positive or true negative. In general, findings with clear boundaries with the surrounding normal structures, such as vacuolation and single-cell necrosis, were accurately detected with high statistical scores. The results of quantitative analyses and classification of the diagnosis based on the threshold values between "no findings" and "abnormal findings" correlated well with diagnoses made by professional pathologists. However, the scores for findings ambiguous boundaries, such as hepatocellular hypertrophy, were poor. These results suggest that deep learning-based algorithms can detect, classify, and quantify multiple findings simultaneously on rat liver WSIs. Thus, it can be a useful supportive tool for a histopathological evaluation, especially for primary screening in rat toxicity studies.

Arterial and venous strokes in the setting of COVID-19.

BACKGROUND AND PURPOSE: Coronavirus disease 2019 (COVID-19) is a global pandemic that causes flu-like symptoms. There is a growing body of evidence suggesting that both the central and peripheral nervous systems can be affected by SARS-CoV-2, including stroke. We present three cases of arterial ischemic strokes and one venous infarction from a cerebral venous sinus thrombosis in the setting of COVID-19 infection who otherwise had low risk factors for stroke. METHODS: We retrospectively reviewed patients presenting to a large tertiary care academic US hospital with stroke and who tested positive for COVID-19. Medical records were reviewed for demographics, imaging results and lab findings. RESULTS: There were 3 cases of arterial ischemic strokes and 1 case of venous stroke: 3 males and 1 female. The mean age was 55 (48-70) years. All arterial strokes presented with large vessel occlusions and had mechanical thrombectomy performed. Two cases presented with stroke despite being on full anticoagulation. CONCLUSIONS: It is important to recognize the neurological manifestations of COVID-19, especially ischemic stroke, either arterial or venous in nature. Hypercoagulability and the cytokine surge are perhaps the cause of ischemic stroke in these patients. Further studies are needed to understand the role of anticoagulation in these patients.

Prevalence of restless legs syndrome and quality of sleep in type 2 diabetics.

INTRODUCTION: Restless legs syndrome (RLS) is characterized by an irresistible urge to move, with or without paraesthesia occurring or worsening at rest and relieved by activity. Only a few reports of prevalence of RLS with type 2 diabetes are available in India. AIMS: To estimate the occurrence and risk factors of RLS among Indian patients with type 2 diabetes mellitus. METHOD: This cross-sectional study was done in consecutive adult patients with type 2 diabetes. Demographic and comorbidity profile were collected. RLS diagnosis was made based on revised international RLS study group (IRLSSG) criteria. RESULTS: Two hundred and ten diabetic patients were interviewed. Mean age was 56 ±â€¯13.5 years. Male-female ratio was 139: 71. Mean duration of diabetes was 8.3 years. Treatment received for diabetes included oral hypoglycaemic agents (153 patients) and insulin (85 patients). Forty-five patients had polyneuropathy, 18 had retinopathy and 22 had nephropathy. Majority (103) of subjects reported their bedtime as 9-10 pm. Average sleep duration was 8.4 h per night. RLS was diagnosed in 17 (8%) subjects. Mean sleep onset in subjects with RLS was 56 min versus 29 min in diabetics without RLS (p-0.01). The mean Pittsburgh Sleep Quality Index score was 5 in RLS and 3.3 in non-RLS patients (p-0.01). DISCUSSION AND CONCLUSIONS: RLS resulted in poor sleep quality and affected overall quality of life in diabetics. As poor sleep is a known risk factor for uncontrolled diabetes, early identification and treatment of RLS would help improve glycaemic control and quality of life in these patients.