RESUMEN
Treatment of resistant obsessive-compulsive disorder (OCD) typically results in insufficient symptom alleviation, and even long-term medication often fails to have the intended effect. Ketamine is a potent non-competitive antagonist of the N-methyl-D-aspartate (NMDA) receptor. Studies have shown that low-dose ketamine infusion results in a considerable reduction in obsessive-compulsive symptoms and a rapid resolution of suicidal ideation. This is a case report on the effect of intravenous ketamine infusion on a patient with resistant OCD and severe suicidal ideation. Intravenous (IV) ketamine was given once a week over consecutive three weeks with necessary precautions. Psychometric tools such as the Yale-Brown Obsessive Compulsive Scale (Y-BOCS), the Clinical Global Impressions Scale (CGI-S), the Beck Scale for Suicidal Ideations (BSSI), and Depression Anxiety and Stress Scale 21 (DASS-21) were applied before and after infusions. Obsessive-compulsive symptoms and suicidal severity started to decrease rapidly after the first infusion. However, after a transient improvement, these symptoms again began to increase after a stressful incident on the second day of the first infusion. All the symptoms measured by validated rating scales showed continued improvement after the following two infusions. The improvement was sustained until discharge (one week after the last infusion) and subsequent follow-up in the sixth and 12th weeks. The role of ketamine in reducing suicidal thoughts and behavior is already established. Very few studies emphasized its effectiveness in improving severe/resistant obsessive-compulsive symptoms. This pioneering work may offer scope for similar research in the relevant field.
RESUMEN
The urea oxidation reaction (UOR) with low required oxidation potential is not only an energy-saving strategy for efficient hydrogen production but also offers an effective way to treat wastewater by decomposing urea. An amorphous cobalt oxyborate with optimum vanadium doping has been identified as an efficient electrocatalyst for UOR for the first time with great stability. The electrocatalyst requires only 1.37 V potential to achieve a current density of 20 mA cm-2. Impressively, the developed electrocatalyst exhibited very active and long stability in alkaline raw bovine urine as extreme urine sewage media coupled with efficient hydrogen production at the cathode.
RESUMEN
Electrocatalytic direct seawater splitting is considered to be one of the most desirable and necessary approach to produce substantial amount of green hydrogen to meet the energy demand. However, practical seawater splitting remains far-fetched due to the electrochemical interference of multiple elements present in seawater, among which chlorine chemistry is the most aggravating one, causing severe damages to electrodes. To overcome such limitations, apart from robust electrocatalyst design, electrolyte engineering along with in depth corrosion engineering are essential aspects, which needs to be thoroughly judged and explored. Indeed, extensive studies and various approaches including smart electrolyzer design have been attempted in the last couple of years on this matter. The present review offers a comprehensive discussion on various strategies to achieve effective and sustainable direct seawater splitting, avoiding chlorine electrochemistry to achieve industry-level performances.
RESUMEN
According to a recent study, around 99% of hospitals across the US now use electronic health record systems (EHRs). One of the most common types of EHR is the unstructured textual data, and unlocking hidden details from this data is critical for improving current medical practices and research endeavors. However, these textual data contain sensitive information, which could compromise our privacy. Therefore, medical textual data cannot be released publicly without undergoing any privacy-protective measures. De-identification is a process of detecting and removing all sensitive information present in EHRs, and it is a necessary step towards privacy-preserving EHR data sharing. Over the last decade, there have been several proposals to de-identify textual data using manual, rule-based, and machine learning methods. In this article, we propose new methods to de-identify textual data based on the self-attention mechanism and stacked Recurrent Neural Network. To the best of our knowledge, we are the first to employ these techniques. Experimental results on three different datasets show that our model performs better than all state-of-the-art mechanism irrespective of the dataset. Additionally, our proposed method is significantly faster than the existing techniques. Finally, we introduced three utility metrics to judge the quality of the de-identified data.
