Therapeutic Approaches to Tackle the Challenge of Depression That Is Resistant to Treatment-A Narrative Review.

Background And Aims: The lack of therapeutic response characterizes treatment-resistant depression despite undergoing at least two adequate monotherapy trials with medications from distinct pharmacologic classes. The inability to attain remission in patients diagnosed with major depressive disorder (MDD) is a significant issue of concern within public health. Therefore, the management of treatment-resistant depression (TRD) poses significant obstacles for both patients and healthcare professionals.

Serum Brain-Derived Neurotrophic Factor: An Emerging Marker for Diabetic Retinopathy.

Background:  Diabetes mellitus (DM) is a global disease that is strongly associated with both microvascular and macrovascular complications. A significant proportion of individuals with diabetes develop diabetic retinopathy (DR), a microvascular complication that can lead to blindness, particularly in working-age adults. Diabetes adversely affects the entire neurosensory retina, with accelerated neuronal apoptosis and activation or altered metabolism of neuroretinal supporting cells.

A Comprehensive Overview of the Current Status and Advancements in Various Treatment Strategies against Epilepsy.

Epilepsy affects more than 70 million individuals of all ages worldwide and remains one of the most severe chronic noncommunicable neurological diseases globally. Several neurotransmitters, membrane protein channels, receptors, enzymes, and, more recently noted, various pathways, such as inflammatory and mTORC complexes, play significant roles in the initiation and propagation of seizures. Over the past two decades, significant developments have been made in the diagnosis and treatment of epilepsy.

Random survival forest model for early prediction of Alzheimer's disease conversion in early and late Mild cognitive impairment stages.

With a clinical trial failure rate of 99.6% for Alzheimer's Disease (AD), early diagnosis is critical. Machine learning (ML) models have shown promising results in early AD prediction, with survival ML models outperforming typical classifiers by providing probabilities of disease progression over time.