Essential Oils as Antimicrobials against : Experimental and Literature Data to Definite Predictive Quantitative Composition-Activity Relationship Models Using Machine Learning Algorithms.

Essential oils (EOs) exhibit a broad spectrum of biological activities; however, their clinical application is hindered by challenges, such as variability in chemical composition and chemical/physical instability. A critical limitation is the lack of chemical consistency across EO samples, which impedes standardization. Despite this, evidence suggests that EOs with differing chemical profiles often display similar (micro)biological activities, raising the possibility of standardizing EOs based on their biological effects rather than their chemical composition.

Deep Learning for Opportunistic Rain Estimation via Satellite Microwave Links.

Accurate precipitation measurement is critical for managing flood and drought risks. Traditional meteorological tools, such as rain gauges and remote sensors, have limitations in resolution, coverage, and cost-effectiveness. Recently, the opportunistic use of microwave communication signals has been explored to improve precipitation estimation.

Efficacy and Safety of Esmethadone (REL-1017) in Patients With Major Depressive Disorder and Inadequate Response to Standard Antidepressants: A Phase 3 Randomized Controlled Trial.

To test esmethadone (REL-1017) as adjunctive treatment in patients with major depressive disorder (MDD) and inadequate response to standard antidepressants. In this phase 3, double-blind, placebo-controlled trial, outpatients with MDD () were randomized to daily oral esmethadone (75 mg on day 1, followed by 25 mg daily on days 2 through 28) or placebo between December 2020 and December 2022. The primary efficacy measure was change from baseline (CFB) to day 28 in the Montgomery-Asberg Depression Rating Scale (MADRS) score.

Data formats and standards for opportunistic rainfall sensors.

Opportunistic sensors are increasingly used for rainfall measurement. However, their raw data are collected by a variety of systems that are often not primarily intended for rainfall monitoring, resulting in a plethora of different data formats and a lack of common standards. This hinders the sharing of opportunistic sensing (OS) data, their automated processing, and, at the end, their practical usage and integration into standard observation systems.