Key Points
Question
What patient characteristics are associated with benefit from treatment with COVID-19 convalescent plasma (CCP)?
Findings
This prognostic study of 2287 patients hospitalized with COVID-19 identified a combination of baseline characteristics that predict a gradation of benefit from CCP compared with treatment without CCP. Preexisting health conditions (diabetes, cardiovascular and pulmonary diseases), blood type A or AB, and earlier stage of COVID-19 were associated with a larger treatment benefit.
Meaning
These findings suggest that simple patient information collected at hospitalization can be used to guide CCP treatment decisions for patients with COVID-19.
The preponderance of research on the study of ethnocentrism has primarily attributed such attitudes to learned behavior. The research here advances the argument that both socialization and genetic inheritance contribute to the development of ethnocentric attitudes and behavior. This analysis employs the Minnesota Twins Political Survey data consisting of 596 complete twin pairs. Using the classical twin design, we employed structural equation modeling to model the covariance of twins in regards to additive genetic effects, shared environmental effects, and unique environmental effects (i.e., the classic ACE model). The findings reveal that genetic inheritance is significant in explaining the variance in genetic attitudes. Specifically, genetic inheritance explains 18% of the variance, with the overwhelming 82% being explained by the unique environment.
Background
Detecting bacteremia among surgical in-patients is more obscure than other patients due to the inflammatory condition caused by the surgery. The previous criteria such as systemic inflammatory response syndrome or Sepsis-3 are not available for use in general wards, and thus, many clinicians usually rely on practical senses to diagnose postoperative infection.
Objective
This study aims to evaluate the performance of continuous monitoring with a deep learning model for early detection of bacteremia for surgical in-patients in the general ward and the intensive care unit (ICU).
Methods
In this retrospective cohort study, we included 36,023 consecutive patients who underwent general surgery between October and December 2017 at a tertiary referral hospital in South Korea. The primary outcome was the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC) for detecting bacteremia by the deep learning model, and the secondary outcome was the feature explainability of the model by occlusion analysis.
Results
Out of the 36,023 patients in the data set, 720 cases of bacteremia were included. Our deep learning–based model showed an AUROC of 0.97 (95% CI 0.974-0.981) and an AUPRC of 0.17 (95% CI 0.147-0.203) for detecting bacteremia in surgical in-patients. For predicting bacteremia within the previous 24-hour period, the AUROC and AUPRC values were 0.93 and 0.15, respectively. Occlusion analysis showed that vital signs and laboratory measurements (eg, kidney function test and white blood cell group) were the most important variables for detecting bacteremia.
Conclusions
A deep learning model based on time series electronic health records data had a high detective ability for bacteremia for surgical in-patients in the general ward and the ICU. The model may be able to assist clinicians in evaluating infection among in-patients, ordering blood cultures, and prescribing antibiotics with real-time monitoring.
Acne vulgaris is a common skin problem affecting nearly 90% of adolescents and its development is associated with a colonization of Propionibacterium acnes (P. acnes). Although antibiotics have commonly been used to treat acne, antibiotic resistance of P. acnes is an emerging issue to be solved. In this study, a new way of photodynamic acne therapy is developed using P. acnes lipase-sensitive transfersome (DSPE-PEG-Pheo A (DPP) transfersome). For enhanced selectivity and skin penetration efficiency, DPP transfersomes are prepared from 1,2-distearoyl-sn-glycero-3-phosphoethanolamine-N-[amino(polyethylene glycol)-2000], pheophorbide A (Pheo A), cholesterol, and Tween-80. Incorporation of Tween-80 as an edge activator increases the deformability of DPP transfersomes, enhancing skin penetration efficiency to four times that of free Pheo A. The photoactivity of Pheo A quenched by DPP transfersomes is gradually recovered by selective cleavage of the ester linkage in DPP transfersomes by P. acnes lipases. In vitro P. acnes-specific photoactivity and subsequent selective antimicrobial effect exhibit a greater than 99% loss of P. acnes viability. In vivo antiacne therapeutic effect is confirmed by reduction of swelling volume and thickness of P. acnes-induced nude mice skin. These results demonstrate that DPP transfersome-mediated photodynamic therapy can be used as an alternative method to treat bacterial skin infections.
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