Background: With the widespread use of gadolinium-based contrast agents (GBCAs), the incidence of allergic-like hypersensitivity reactions (HSRs) to GBCAs is increasing. Research on the incidence and risk factors for HSRs to GBCAs is needed for their safe use. Purpose:To determine the incidence of acute and delayed reactions to GBCAs and to discuss the risk factors and strategies for the prevention of HSRs to GBCAs. Materials and Methods:All cases of HSRs to contrast media that occurred at the Seoul National University Hospital from July 1, 2012, to June 30, 2020, were assessed. Information including age, sex, GBCA type, onset, and severity of HSRs was retrospectively analyzed.Results: Among the 331 070 cases of GBCA exposure in 154 539 patients, 1304 cases of HSRs (0.4%) were reported. Acute HSRs accounted for 1178 cases (0.4%), while 126 cases (0.04%) were delayed HSRs. While both premedication (odds ratio [OR] = 0.7, P = .041) and changing the type of GBCA (OR = 0.2, P , .001) showed preventative effects in patients with a history of acute HSRs, only premedication (OR = 0.2, P = .016) significantly reduced the incidence of HSRs in patients with a history of delayed reactions. The risk of an HSR to GBCA was higher in those with a history of an HSR to iodinated contrast media (OR = 4.6, P , .001). Conclusion:The rate of hypersensitivity reactions (HSRs) to gadolinium-based contrast agents (GBCAs) was 0.4%. The absence of premedication, repeated exposures to the culprit GBCA, and a history of HSRs to iodinated contrast media and GBCAs were risk factors for HSRs to GBCAs.

BackgroundDrug desensitization is helpful for patients who have experienced significant hypersensitivity reactions (HSRs) to antineoplastic agents. One-bag desensitization protocols, attracting attention in recent years, need to be validated on their safety and efficacy in a large number.MethodsOne-bag desensitization procedures conducted from 2018 to 2020 were analyzed; their outcomes and the risk factors for breakthrough reactions (BTRs) were assessed in desensitization procedures to major drug types (platins, taxanes, and monoclonal antibodies).ResultsA total of 1,143 procedures of one-bag desensitization were performed in 228 patients with 99% completion rate. BTRs occurred in 26% of the total desensitization procedures—34% in platins, 12% in taxanes, and 18% in mAbs. BTR occurrence rate decreased along the desensitization process with 80% of BTRs occurring within the 6th desensitization attempts. Severe BTR occurred more frequently with severe initial HSRs (1% in mild to moderate initial HSRs vs. 16% in severe). Severe initial HSR was also a significant risk factor for moderate to severe BTR in platins (odds ratio 1.56, 95% confidence interval [CI] 1.06–2.29, p = 0.025). The use of steroid was also associated with lower occurrence of moderate to severe BTR (odds ratio 0.50, 95% CI 0.35–0.72, p < 0.001).ConclusionMost patients with HSRs to antineoplastic agents can safely receive chemotherapy through a one-bag desensitization protocol. Further studies on each drug with larger sample size can help verify the risk factors of BTRs and evaluate the efficacy of steroid premedication in improving the safety of desensitization in high-risk patients.

Background Transcriptomic analysis has been used to elucidate the complex pathogenesis of heterogeneous disease and may also contribute to identify potential therapeutic targets by delineating the hub genes. This study aimed to investigate whether blood transcriptomic clustering can distinguish clinical and immune phenotypes of asthmatics, and microbiome in asthmatics. Methods Transcriptomic expression of peripheral blood mononuclear cells (PBMCs) from 47 asthmatics and 21 non-asthmatics was measured using RNA sequencing. A hierarchical clustering algorithm was used to classify asthmatics. Differentially expressed genes, clinical phenotypes, immune phenotypes, and microbiome of each transcriptomic cluster were assessed. Results In asthmatics, three distinct transcriptomic clusters with numerously different transcriptomic expressions were identified. The proportion of severe asthmatics was highest in cluster 3 as 73.3%, followed by cluster 2 (45.5%) and cluster 1 (28.6%). While cluster 1 represented clinically non-severe T2 asthma, cluster 3 tended to include severe non-T2 asthma. Cluster 2 had features of both T2 and non-T2 asthmatics characterized by the highest serum IgE level and neutrophil-dominant sputum cell population. Compared to non-asthmatics, cluster 1 showed higher CCL23 and IL1RL1 expression while the expression of TREML4 was suppressed in cluster 3. CTSD and ALDH2 showed a significant positive linear relationship across three clusters in the order of cluster 1 to 3. No significant differences in the diversities of lung and gut microbiomes were observed among transcriptomic clusters of asthmatics and non-asthmatics. However, our study has limitations in that small sample size data were analyzed with unmeasured confounding factors and causal relationships or function pathways were not verified. Conclusions Genetic clustering based on the blood transcriptome may provide novel immunological insight, which can be biomarkers of asthma immune phenotypes. Trial registration Retrospectively registered

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Background: As sleep disturbances are common in the intensive care unit (ICU), this study assessed the sleep quality in the ICU and identified barriers to sleep. Methods: Patients admitted to the ICUs of a tertiary hospital between June 2022 and December 2022 who were not mechanically ventilated at enrollment were included. The quality of sleep (QoS) at home was assessed on a visual analog scale as part of an eight-item survey, while the QoS in the ICU was evaluated using the Korean version of the Richards-Campbell Sleep Questionnaire (K-RCSQ). Good QoS was defined by a score of ≥50. Results: Of the 30 patients in the study, 19 reported a QoS score <50. The Spearman correlation coefficient showed no meaningful relationship between the QoS at home and overall K-RCSQ QoS score in the ICU (r=0.16, P=0.40). The most common barriers to sleep were physical discomfort (43%), being awoken for procedures (43%), and feeling unwell (37%); environmental factors including noise (30%) and light (13%) were also identified sources of sleep disruption. Physical discomfort (32 [28–38] vs. 69 [42–80]; P=0.004), patient care interactions (36 [20–48] vs. 54 [36–80]; P=0.044), and feeling unwell (31 [18–42] vs. 54 [40–76]; P=0.013) were associated with lower K-RCSQ scores.Conclusions: In the ICU, physical discomfort, patient care interactions, and feeling unwell were identified as barriers to sleep.