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Sleep apnea (SA) is a pervasive and highly prevalent sleep disorder identified by recurrent breathing-related problems such as respiratory pauses for almost 10 s (called apnea events) during sleep. It is a strongly underdiagnosed problem because the person suffering from this disease is not aware of this situation. It may cause serious health issues and badly affect the quality of life. Therefore, the diagnosis of sleep is crucial to cure disease. Polysomnography (PSG) is a golden technique for diagnosing sleep disorders. In this technique, multiple sensors are used to collect specific physiological signals such as electroencephalogram (EEG), electromyogram (EMG), electrooculogram (EOG), and many more. In regular clinical practice, medical experts need to manually analyze the signals of sleep hours which is a tedious process. Therefore, the automatic diagnosis tool is needed to simplify this process. Recently, many research groups have proposed deep learning models for the automatic diagnosis of SA using physiological signals with good accuracy. However, all these models require a large amount of annotated data in the supervised training process, which limits the use of those models in real-time scenarios. However, annotating a huge amount of biomedical signals is challenging and requires lots of time and domain expertise. This study proposes a self-supervised representation learning (SSRL) method for detecting hypopnea events from single-channel electrocardiography (ECG) signals. The proposed model is trained in two phases. In the first training phase, an encoder is trained to learn signal representation from the unlabeled data. In the second training phase, the classifier and the encoder are fine-tuned for the classification. Our proposed model performed well on the test dataset with a per-segment classification accuracy of 85%, 89%, and 92% using only 1%, 10%, and 100% of the training data with labels, respectively, for fine-tuning encoder along with the classifier. Also, our proposed model can identify a person suffering from the obstructive SA (OSA) with the accuracy of 100%, even when the encoder and classifier are fine-tuned using only 1% of the training data with the label. The proposed model outperformed the state-of-the-art techniques and can be implemented offline or online for rapid and accurate diagnosis of the problem.

Mortalities were observed in several Pangasianodon hypophthalmusfarms (n = 52) of Uttar Pradesh, North India during December 2020–January 2021. The affected fish were lethargic, swimming near the water surface and had white or grayish cotton-like patches over the body surface. Wet mount examination of the patches revealed presence of non-septate hyphae suggestive of oomycete infection. However, no parasites were observed in skin and gill scrapings. Histopathological examination of the affected skin tissue indicated severe destruction of the epidermis, degenerative changes and penetration of the oomycete hyphae in the dermis and underlying musculature. From the affected skin lesions, oomycete could be isolated which was identified as Saprolegnia parasiticabased on morphological characteristics, and amplification and sequencing of the internal transcribed spacer region. Importantly, from the diseased P. hypophthalmus, no bacteria and viruses could be isolated from kidney, and pooled kidney and spleen tissues, respectively. Further, following experimental infection with S. parasiticazoospores by immersion method, 100% mortality was observed in P. hypophthalmus, indicating that the isolate was virulent. From the experimentally-infected striped catfish, S. parasiticacould be reisolated and the histopathological lesions were similar to those observed in naturally infected fish, thereby fulfilling Koch’s postulates. Based on the gross and histopathological lesions, isolation of oomycete, molecular identification and bioassay, it was concluded that S. parasiticawas responsible for mortalities in P. hypophthalmusfarms. This forms the first report of mortalities of striped catfish due to oomycete S. parasiticainfection and the findings will pave way for developing control measures for reducing losses in striped catfish farms due to saprolegniasis.