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Our emotional, psychological, and social well-being are all parts of our mental health, influencing our thoughts, emotions, and behaviors. Mental health also influences how we respond to stress, interact with others, and make good or bad decisions. There has been growing interest in the use of machine learning for the early detection of mental illness. This study reviews the machine learning models, algorithms, and applications for the early detection of mental disease, particularly emphasizing the data modalities. We further propose a comprehensive methodology for assessing mental health that synergistically combines social media monitoring, data analytics from wearable devices, verbal polls, and individualized support. We provide an overview of the field’s current state, highlight the potential benefits and challenges of using machine learning in mental health care, and a new taxonomy of mental disorders issues based on five domains of data types. We review existing research on using machine learning to detect and treat mental illness and discuss the implications for future research. Finally, the value of this work lies in its potential to provide a fast and accurate method for predicting the mental health status of a person, which may assist in the diagnosis and treatment of mental illness.

The Internet of Things (IoT) has positioned itself globally as a dominant force in the technology sector. IoT, a technology based on interconnected devices, has found applications in various research areas, including healthcare. Embedded devices and wearable technologies powered by IoT have been shown to be effective in patient monitoring and management systems, with a particular focus on pregnant women. This study provides a comprehensive systematic review of the literature on IoT architectures, systems, models and devices used to monitor and manage complications during pregnancy, postpartum and neonatal care. The study identifies emerging research trends and highlights existing research challenges and gaps, offering insights to improve the well-being of pregnant women at a critical moment in their lives. The literature review and discussions presented here serve as valuable resources for stakeholders in this field and pave the way for new and effective paradigms. Additionally, we outline a future research scope discussion for the benefit of researchers and healthcare professionals.

Psoriasis is a T-cell mediated chronic inflammatory diseases where pro-inflammatory mediators are involved in its pathogenesis. Alpha-2-macroglobulin (α-2M) is a panproteinase inhibitor having unique clearing role of different cytokines. This study was conducted on 30 patients with moderate to severe psoriasis to see the plasma level of α-2M and was compared with the normal healthy controls. Patients who were already selected for systemic treatment (methotrexate) and consented for routine blood test for monitoring at baseline and 12 weeks after treatment were enrolled along with 10 healthy controls. The venous blood (5 mL) was collected and the plasma alfa-2 macroglobulin was estimated using the enzyme-linked immunosorbent assay. The mean plasma α-2M level was 3.0 ± 0.4 g/L among the normal healthy persons, and 2.8 ± 0.7 g/L among the untreated patients of psoriasis (p>0.05). Its level among the patients with psoriasis after systemic antipsoriatic drugs was 2.8 ± 0.6 g/L which was not significantly different from the baseline level (p>0.05). The study shows that the plasma α-2M level in psoriasis is not different comparing with normal healthy persons.