ABSTRACT
As major depressive disorder (MDD) is such a diverse condition, there are currently no clear ways for determining its severity, endophenotype, or therapy response. The distinctive nature of depression, the variability of analysis in literature and the large number of conceptually complicated biomarkers are some of the many reasons for the lack of progress. Markers are involved in the process of neurotrophic, metabolic, and inflammation as well as neuroendocrine and neurotransmitter systems' components. Some clinical indicators are strong enough so that can be measured using assessments of proteomic, genetic, metabolomics, neuroimaging, epigenetic and transcriptomic. Markers of oxidative stress, endocrine, inflammatory, proteomic, and growth indicators are currently among the promising biologic systems/markers identified in this analysis. This narrative review examines succinct studies which investigated cytokines of inflammatory factors, peripheral factors of development, metabolic and endocrine markers as pathophysiological biomarkers of MDD, and treatment responses. Endocrine and metabolic alterations have also been linked to MDD in various studies. So, this study summarizes all of the numerous biomarkers that are significant in the detection or treatment of MDD patients. The paper also provides an overview of various biomarkers which are important for the regulation and its effects on MDD.
ABSTRACT
Background and objective Accurate identification and categorization of injuries from medical records can be challenging, yet it is important for injury epidemiology and prevention efforts. Coding systems such as the International Classification of Diseases (ICD) have well-known limitations. Utilizing computer-based techniques such as natural language processing (NLP) can help augment the identification and categorization of diseases in electronic health records. We used a Python program to search the text to identify cases of scooter injuries that presented to our emergency department (ED). Materials and methods This retrospective chart review was conducted between March 2017 and June 2019 in a single, urban academic ED with approximately 80,000 annual visits. The physician documentation was stored as combined PDF files by date. A Python program was developed to search the text from 186,987 encounters to find the string "scoot" and to extract the 100 characters before and after the phrase to facilitate a manual review of this subset of charts. Results A total of 890 charts were identified using the Python program, of which 235 (26.4%) were confirmed as e-scooter cases. Patients had an average age of 36 years and 53% were male. In 81.7% of cases, the patients reported a fall from the scooter and only 1.7% reported wearing a helmet during the event. The most commonly injured body areas were the upper extremity (57.9%), head (42.1%), and lower extremity (36.2%). The most frequently consulted specialists were orthopedic and trauma surgeons with 28% of cases requiring a consult. In our population, 9.4% of patients required admission to the hospital. Conclusions The number of results and data returned by the Python program was easy to manage and made it easier to identify charts for abstraction. The charts obtained allowed us to understand the nature and demographics of e-scooter injuries in our ED. E-scooters continue to be a popular mode of transportation, and understanding injury patterns related to them may inform and guide opportunities for policy and prevention.
ABSTRACT
INTRODUCTION: Advances in technology have revolutionized the medical field and changed the way healthcare is delivered. Unmanned aerial vehicles (UAVs) are the next wave of technological advancements that have the potential to make a huge splash in clinical medicine. UAVs, originally developed for military use, are making their way into the public and private sector. Because they can be flown autonomously and can reach almost any geographical location, the significance of UAVs are becoming increasingly apparent in the medical field. MATERIALS AND METHODS: We conducted a comprehensive review of the English language literature via the PubMed and Google Scholar databases using search terms "unmanned aerial vehicles," "UAVs," and "drone." Preference was given to clinical trials and review articles that addressed the keywords and clinical medicine. RESULTS: Potential applications of UAVs in medicine are broad. Based on articles identified, we grouped UAV application in medicine into three categories: (1) Prehospital Emergency Care; (2) Expediting Laboratory Diagnostic Testing; and (3) Surveillance. Currently, UAVs have been shown to deliver vaccines, automated external defibrillators, and hematological products. In addition, they are also being studied in the identification of mosquito habitats as well as drowning victims at beaches as a public health surveillance modality. CONCLUSIONS: These preliminary studies shine light on the possibility that UAVs may help to increase access to healthcare for patients who may be otherwise restricted from proper care due to cost, distance, or infrastructure. As with any emerging technology and due to the highly regulated healthcare environment, the safety and effectiveness of this technology need to be thoroughly discussed. Despite the many questions that need to be answered, the application of drones in medicine appears to be promising and can both increase the quality and accessibility of healthcare.
