Comorbidity of Depression and Diabetes: A Literature Review on Systemic Flaws in Healthcare and the Benefits of Collaborative Diagnosis and Treatment in Primary Care Settings.

BACKGROUND: The increasing specialization and dispersion of healthcare systems have led to a shortage of resources to address comorbidities. Patients with coexisting mental and physical conditions are disadvantaged, as medical providers often only focus on the patient's mental illness while neglecting their physical needs, resulting in poorer health outcomes. OBJECTIVE: This study aimed to shed light on the systemic flaws in healthcare systems that contribute to suboptimal health outcomes in individuals with comorbid diseases, including depression and diabetes. This paper also discusses the clinical and economic benefits of collaborative methods for diagnosing and treating depressive disorders in primary care settings. METHODS: A comprehensive literature review of the relationship between depression and diabetes was conducted. The outcomes of the literature review were carefully analyzed. Several databases were searched using keywords such as "diabetes," "depression," "comorbidity," "prevalence," "epidemiology," and "risk factors" using Google Scholar and PubMed as search engines. The review and research papers written between 1961 and 2023 were our main focus. RESULTS: This study revealed improved depressive symptoms and better blood sugar and blood pressure control. Additionally, individuals with comorbid depression and diabetes have higher direct and secondary medical costs. Antidepressants and psychological interventions are equally effective in treating depressive symptoms in patients with diabetes, although they have conflicting effects on glycemic control. For individuals with comorbid diabetes and depression, clear care pathways, including a multidisciplinary team, are essential for achieving the best medical and mental health outcomes. CONCLUSION: Coordinated healthcare solutions are necessary to reduce the burden of illness and improve therapeutic outcomes. Numerous pathophysiological mechanisms interact with one another and may support the comorbidities of T2DM, and depressive disorders could exacerbate the course of both diseases.

Therapeutic potential of vasopressin in the treatment of neurological disorders.

Vasopressin (VP) is a nonapeptide made of nine amino acids synthesized by the hypothalamus and released by the pituitary gland. VP acts as a neurohormone, neuropeptide and neuromodulator and plays an important role in the regulation of water balance, osmolarity, blood pressure, body temperature, stress response, emotional challenges, etc. Traditionally VP is known to regulate the osmolarity and tonicity. VP and its receptors are widely expressed in the various region of the brain including cortex, hippocampus, basal forebrain, amygdala, etc. VP has been shown to modulate the behavior, stress response, circadian rhythm, cerebral blood flow, learning and memory, etc. The potential role of VP in the regulation of these neurological functions have suggested the therapeutic importance of VP and its analogues in the management of neurological disorders. Further, different VP analogues have been developed across the world with different pharmacotherapeutic potential. In the present work authors highlighted the therapeutic potential of VP and its analogues in the treatment and management of various neurological disorders.

Unsupervised landmark detection and classification of lung infection using transporter neural networks.

Supervised deep learning techniques have been very popular in medical imaging for various tasks of classification, segmentation, and object detection. However, they require a large number of labelled data which is expensive and requires many hours of careful annotation by experts. In this paper, an unsupervised transporter neural network framework with an attention mechanism is proposed to automatically identify relevant landmarks with applications in lung ultrasound (LUS) imaging. The proposed framework identifies key points that provide a concise geometric representation highlighting regions with high structural variation in the LUS videos. In order for the landmarks to be clinically relevant, we have employed acoustic propagation physics driven feature maps and angle-controlled Radon Transformed frames at the input instead of directly employing the gray scale LUS frames. Once the landmarks are identified, the presence of these landmarks can be employed for classification of the given frame into various classes of severity of infection in lung. The proposed framework has been trained on 130 LUS videos and validated on 100 LUS videos acquired from multiple centres at Spain and India. Frames were independently assessed by experts to identify clinically relevant features such as A-lines, B-lines, and pleura in LUS videos. The key points detected showed high sensitivity of 99% in detecting the image landmarks identified by experts. Also, on employing for classification of the given lung image into normal and abnormal classes, the proposed approach, even with no prior training, achieved an average accuracy of 97% and an average F1-score of 95% respectively on the task of co-classification with 3-fold cross-validation.

Domain-aware contrastive learning for ultrasound hip image analysis.

Early diagnosis of Developmental Dysplasia of Hip (DDH) using ultrasound can result in simpler and more effective treatment options. Handheld ultrasound probes are ideally suited for such screening due to their low cost and portability. However, images from the pocket-sized probes are of lower quality than conventional probes. Image quality can be enhanced by image translation techniques that generate a pseudo-image mimicking the image quality of conventional probes. This can also help in generalizing the performance of AI-based automatic interpretation techniques to multiple probes. We develop a new domain-aware contrastive unpaired translation (D-CUT) technique for translating between images acquired from different ultrasound probes. Our approach embeds a Bone Probability Map (BPM) as part of the loss function which enforces higher structural similarity around bony regions in the image. Using the D-CUT model we translated 575 images acquired from a Philips Lumify handheld probe to generate pseudo-3D ultrasound (3DUS) images similar (Fréchet Inception Distance = 92) to those acquired from a conventional ultrasound probe (Philips iU22). The pseudo-3DUS images showed high structural similarity (SSIM = 0.68, Cosine Similarity = 0.65) with the original images and improved the contrast around the bony regions. This study establishes the feasibility of using D-CUT to improve the quality of data acquired from handheld ultrasound probes. Among other potential applications, clinical use of this tool could result in wider use of ultrasound for DDH screening programs.