Ca 2+ ion channel blockers efficiency in supressing pentylenetetrazol-induced epileptiform seizures in kcnj10a morphant zebrafish
The following essay was part of my MSc in applied neuroscience. It suggests a unique model for the quick and multi-systemic spread of epileptiform seizures through astrocytic Ca2+ signaling rather than the commonly researched neuronal origin. The theory here pose a potential for alternative and cheap pharmacological intervention to contain or even prevent epileptic seizures.
Abnormalities in dopamine rewarding of episodic memory retrieval as a neurocognitive model for dysfunctional social mirroring in autism spectrum disorder
A new proposed model for ASD, suggesting that the abnormalities of rewarding signals for effective retrieval of episodic memory to facilitate social intervention may cause the reduced social interaction abilities.
Why attention deficit may be a clinician disorder rather than a patient disorder
The text discusses the dropping attention span in modern society due to the exponential growth in media outlets and stimuli processing. It introduces the concept of Heavy Media Multitaskers (HMM) who use multiple media devices extensively. Studies show HMM individuals perform worse in cognitive tests, raising concerns about overdiagnosing attention issues like ADHD.
A Machine Learning Approach for Continuous Mining of Nonidentifiable Smartphone Data to Create a Novel Digital Biomarker Detecting Generalized Anxiety Disorder: Prospective Cohort Study
Anxiety is one of the leading causes of mental health disability around the world. Currently, a majority of the population who experience anxiety go undiagnosed or untreated. New and innovative ways of diagnosing and monitoring anxiety have emerged using smartphone sensor–based monitoring as a metric for the management of anxiety. This is a novel study as it adds to the field of research through the use of nonidentifiable smartphone usage to help detect and monitor anxiety remotely and in a continuous and passive manner.
A Machine Learning Approach for Detecting Digital Behavioral Patterns of Depression Using Nonintrusive Smartphone Data (Complementary Path to Patient Health Questionnaire-9 Assessment): Prospective Observational Study
Depression is a major global cause of morbidity, an economic burden, and the greatest health challenge leading to chronic disability. Mobile monitoring of mental conditions has long been a sought-after metric to overcome the problems associated with the screening, diagnosis, and monitoring of depression and its heterogeneous presentation. The widespread availability of smartphones has made it possible to use their data to generate digital behavioral models that can be used for both clinical and remote screening and monitoring purposes. This study is novel as it adds to the field by conducting a trial using private and nonintrusive sensors that can help detect and monitor depression in a continuous, passive manner.
Bleach for Covid-19, SSRI for Depression?
Whilst writing this blog, the virus has infected 106,000,000 people and more than 2,000,000 died according to official numbers. The global economy has been stunted and highlighted to all of us how incredibly vulnerable we are — lockdown after lockdown. Moreover, we all got a hefty dose of experiencing the effects of mental health turbulence, whether it be depressed mood, anxiety, or stress.
Psychiatry HAS to lead AI adoption in Medicine
Artificial intelligence (AI) technology has the potential to revolutionize the field of psychiatry by improving the accuracy of diagnoses and therapeutic solutions, if only for its ability to observe, analyze and process huge amounts of data in a short time frame.
World Mental Health Day is a disorder by itself
Mental health and psychiatry are still relying mostly on disease classifications based on the early 20th century. It is based on observation of non-exclusive symptom groups, defining it with a name and avoiding any clear biomarkers (biological signs, brain mechanisms, protein expressions, genetics etc.).