Purpose Nonsteroidal anti-inflammatory drugs (NSAIDs) are common cause of severe cutaneous adverse reactions (SCARs). The present study aimed to investigate the characteristics of SCARs induced by NSAIDs in the Korean SCAR registry. Methods A retrospective survey of NSAID-induced SCARs recorded between 2010 and 2015 at 27 university hospitals in Korea was conducted. Clinical phenotypes of SCARs were classified into Stevens-Johnson syndrome (SJS), toxic epidermal necrolysis (TEN), SJS-TEN overlap syndrome and drug reaction with eosinophilia and systemic symptoms (DRESS). Causative NSAIDs were classified into 7 groups according to their chemical properties: acetaminophen, and propionic, acetic, salicylic, fenamic and enolic acids. Results A total of 170 SCARs, consisting of 85 SJS, 32 TEN, 17 SJS-TEN overlap syndrome and 36 DRESS reactions, were induced by NSAIDs: propionic acids (n=68), acetaminophen (n=38), acetic acids (n=23), salicylic acids (n=16), coxibs (n=8), fenamic acids (n=7), enolic acids (n=5) and unclassified (n=5). Acetic acids (22%) and coxibs (14%) accounted for higher portions of DRESS than other SCARs. The phenotypes of SCARs induced by both propionic and salicylic acids were similar (SJS, TEN and DRESS, in order). Acetaminophen was primarily associated with SJS (27%) and was less involved in TEN (10%). DRESS occurred more readily among subjects experiencing coxib-induced SCARs than other NSAID-induced SCARs (62.5% vs. 19.7%, P = 0.013). The mean time to symptom onset was longer in DRESS than in SJS or TEN (19.1 ± 4.1 vs. 6.8 ±1.5 vs. 12.1 ± 3.8 days). SCARs caused by propionic salicylic acids showed longer latency, whereas acetaminophen- and acetic acid-induced SCARs appeared within shorter intervals. Conclusions The present study indicates that the phenotypes of SCARs may differ according to the chemical classifications of NSAIDs. To establish the mechanisms and incidences of NSAID-induced SCARs, further prospective studies are needed.
Besides improving charge transfer, there are two key factors, such as increasing active sites and promoting water dissociation, to be deeply investigated to realize high-performance MoS 2 -based electrocatalysts in alkaline hydrogen evolution reaction (HER). Herein, we have demonstrated the synergistic engineering to realize rich unsaturated sulfur atoms and activated O−H bonds toward the water for Ni-doped MoS 2 /CoS 2 hierarchical structures by an approach to Ni doping coupled with in situ sulfurizing for excellent alkaline HER. In this work, the Ni-doped atoms are evolved into Ni(OH) 2 during alkaline HER. Interestingly, the extra unsaturated sulfur atoms will be modulated into MoS 2 nanosheets by breaking Ni−S bonds during the formation of Ni(OH) 2 . On the other hand, the higher the mass of the Ni precursor (m Ni ) for the fabrication of our samples, the more Ni(OH) 2 is evolved, indicating a stronger ability for water dissociation of our samples during alkaline HER. Our results further reveal that regulating m Ni is crucial to the HER activity of the assynthesized samples. By regulating m Ni to 0.300 g, a balance between increasing active sites and promoting water dissociation is achieved for the Ni-doped MoS 2 /CoS 2 samples to boost alkaline HER. Consequently, the optimal samples present the highest HER activity among all counterparts, accompanied by reliable long-term stability. This work will promise important applications in the field of electrocatalytic hydrogen evolution in alkaline environments.
RATIONALE Cough is one of the most frequently encountered symptoms for many physicians. However, it is difficult to objectively measure cough in real time. We developed an artificial intelligence (AI) algorithm-based on a smartphone application that measures cough sound in real time. We did a preliminary analysis to evaluate the performance of this system. METHODS We recruited 53 participants who visited outpatient clinic for sub-acute or chronic cough at 8 academic medical centers in Korea. The participants were asked to record 1-3 hours of ambient sounds during daytime and at least 5 hours during nighttime sleep for 2 days using smartphone. In addition, visual analogue scales (VAS) for cough were measured at the time of enrollment. The recorded files were analyzed independently by two trained researchers to count the number of coughs. The number of coughs by the researchers was compared with the number of coughs measured using an AI algorithm. Two deep learning algorithms were developed for this, one for analyzing daytime ambient sounds and the other for nighttime sleep sounds. The deep learning algorithm counted the number of coughs 3 times from the same data and the average error rate was obtained. RESULTS There were 37 (69.8%) females and 16 (30.2%) males. About majority (73.6%) of the patients were less than 50 years old. The mean VAS score was 54.3 ± 21.4. From 255.04 hours of daytime recordings and 614.56 hours of nighttime sleep recordings, 15,050 daytime coughs and 3,442 nighttime sleep coughs were collected. The cough frequency was median 34.2 (0 to 433.7) and 1.6 (0 to 58.3) during days and nights, respectively. The AI algorithm analyzed test sets including manually counted 2,941 daytime coughs and 684 nighttime coughs and AI algorithm counted 2,998 and 730 in average. The average error rate was calculated as 6.0% and 9.1%, respectively, which was better than expected error rate of 10%. CONCLUSION Our AI algorithm could monitor cough sounds in real time with an accuracy of more than 90%. Further development and external validation with larger participants would be conducted to guarantee reliability and robustness in daily clinical and home setting.
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