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Oxidative stress triggered by testicular torsion and detorsion in young males could negatively impact future fertility. Using a rat animal model for testicular IRI (tIRI), we aim to study the induction of autophagy (ATG) during testicular ischemia and tIRI and the role of oxidative-stress-induced c-Jun N-terminal Kinase (JNK) as a cytoprotective mechanism. Sixty male Sprague-Dawley rats were divided into five groups: sham, ischemia only, ischemia+SP600125 (a JNK inhibitor), tIRI only, and tIRI+SP600125. The tIRI rats underwent an ischemic injury for 1 h followed by 4 h of reperfusion, while ischemic rats were subjected to 1 h of ischemia only without reperfusion. Testicular-ischemia-induced Beclin 1 and LC3B expression was associated with decreased p62/SQSTM1 expression, increased ATP and alkaline phosphatase (AP) activity, and slightly impaired spermatogenesis. SP600125 treatment improved p62 expression and reduced the levels of Beclin 1 and LC3B but did not affect ATP or AP levels. The tIRI-induced apoptosis lowered the expression of the three ATG proteins and AP activity, activated caspase 3, and caused spermatogenic arrest. SP600125-inhibited JNK during tIRI restored sham levels to all investigated parameters. This study emphasizes the regulatory role of JNK in balancing autophagy and apoptosis during testicular oxidative injuries.

Cancer seems to have a vast number of deaths due to its heterogeneity, aggressiveness, and significant propensity for metastasis. The predominant categories of cancer that may affect males and females and occur worldwide are colon and lung cancer. A precise and on-time analysis of this cancer can increase the survival rate and improve the appropriate treatment characteristics. An efficient and effective method for the speedy and accurate recognition of tumours in the colon and lung areas is provided as an alternative to cancer recognition methods. Earlier diagnosis of the disease on the front drastically reduces the chance of death. Machine learning (ML) and deep learning (DL) approaches can accelerate this cancer diagnosis, facilitating researcher workers to study a vast majority of patients in a limited period and at a low cost. This research presents Histopathological Imaging for the Early Detection of Lung and Colon Cancer via Ensemble DL (HIELCC-EDL) model. The HIELCC-EDL technique utilizes histopathological images to identify lung and colon cancer (LCC). To achieve this, the HIELCC-EDL technique uses the Wiener filtering (WF) method for noise elimination. In addition, the HIELCC-EDL model uses the channel attention Residual Network (CA-ResNet50) model for learning complex feature patterns. Moreover, the hyperparameter selection of the CA-ResNet50 model is performed using the tuna swarm optimization (TSO) technique. Finally, the detection of LCC is achieved by using the ensemble of three classifiers such as extreme learning machine (ELM), competitive neural networks (CNNs), and long short-term memory (LSTM). To illustrate the promising performance of the HIELCC-EDL model, a complete set of experimentations was performed on a benchmark dataset. The experimental validation of the HIELCC-EDL model portrayed a superior accuracy value of 99.60% over recent approaches., (© 2024. The Author(s).)

Mobile health (m-health) is the term of monitoring the health using mobile phones and patient monitoring devices etc. It has been often deemed as the substantial breakthrough in technology in this modern era. Recently, artificial intelligence (AI) and big data analytics have been applied within the m-health for providing an effective healthcare system. Various types of data such as electronic health records (EHRs), medical images, and complicated text which are diversified, poorly interpreted, and extensively unorganized have been used in the modern medical research. This is an important reason for the cause of various unorganized and unstructured datasets due to emergence of mobile applications along with the healthcare systems. In this paper, a systematic review is carried out on application of AI and the big data analytics to improve the m-health system. Various AI-based algorithms and frameworks of big data with respect to the source of data, techniques used, and the area of application are also discussed. This paper explores the applications of AI and big data analytics for providing insights to the users and enabling them to plan, using the resources especially for the specific challenges in m-health, and proposes a model based on the AI and big data analytics for m-health. Findings of this paper will guide the development of techniques using the combination of AI and the big data as source for handling m-health data more effectively., Competing Interests: The authors declare that there are no conflicts of interest regarding the publication of this paper., (Copyright © 2020 Z. Faizal khan and Sultan Refa Alotaibi.)