Toxic epidermal necrolysis induced by acetaminophen: a case report

Acetaminophen is a very commonly used analgesic and antipyretic drug across various age groups. Although mild to moderate cutaneous reactions have been reported quite frequently, serious reactions like Stevens –Johnson syndrome and Toxic epidermal necrolysis (TEN) are very rare. We report the case of a 10 year old child who had TEN after ingestion of tablet acetaminophen. This case report highlights the need to be critically aware of this rare and serious adverse effect of this commonly used over the counter drug

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Neurosteroid allopregnanolone mediates anxiolytic effect of etifoxine in rats

Etifoxine (6-chloro-2-ethylamino-4-methyl-4-phenyl-4 H-3,1-benzoxazine hydrochloride), a nonbenzodiazepine anxiolytic drug, potentiates GABA A receptor function perhaps through stimulation of neurosteroid biosynthesis. However, the exact mechanism of etifoxine action is not fully understood. In this study, we have assessed the possible role of GABAergic neurosteroid like allopregnanolone (ALLO) in the anxiolytic-like effect of etifoxine in rats using elevated plus maze test. Selective GABA A receptor agonist, muscimol, ALLO or neurosteroidogenic agents like progesterone, metyrapone or mitochondrial diazepam binding inhibitor receptor (MDR) agonist, FGIN 1–27 significantly heightened the etifoxine-induced anxiolysis. On the other hand, GABA A receptor antagonist, bicuculline or neurosteroid biosynthesis inhibitors like finasteride, indomethacin, trilostane or PBR antagonist, PK11195 significantly blocked the effect of etifoxine. Bilateral adrenalectomy did not influence anti-anxiety effect of etifoxine thereby ruling out contribution of adrenal steroids. Thus, our results provide behavioral evidence for the role of neurosteroids like ALLO in the anti-anxiety effect of etifoxine

Hybrid KNN-SVM machine learning approach for solar power forecasting

Predictions about solar power will have a significant impact on large-scale renewable energy plants. Photovoltaic (PV) power generation forecasting is particularly sensitive to measuring the uncertainty in weather conditions. Although several conventional techniques like long short-term memory (LSTM), support vector machine (SVM), etc. are available, but due to some restrictions, their application is limited. To enhance the precision of forecasting solar power from solar farms, a hybrid machine learning model that includes blends of the K-Nearest Neighbor (KNN) machine learning technique with the SVM to increase reliability for power system operators is proposed in this investigation. The conventional LSTM technique is also implemented to compare the performance of the proposed hybrid technique. The suggested hybrid model is improved by the use of structural diversity and data diversity in KNN and SVM, respectively. For the solar power predictions, the suggested method was tested on the Jodhpur real-time series dataset obtained from the data centers of weather stations using Meteonorm. The data set includes metrics such as Hourly Average Temperature (HAT), Hourly Total Sunlight Duration (HTSD), Hourly Total Global Solar Radiation (HTGSR), and Hourly Total Photovoltaic Energy Generation (HTPEG). The collated data has been segmented into training data, validation data, and testing data. Furthermore, the proposed technique performed better when evaluated on the three performance indices, viz., accuracy, sensitivity, and specificity. Compared with the conventional LSTM technique, the hybrid technique improved the prediction with 98 % accuracy