Deep-learning model for screening sepsis using electrocardiography.

BACKGROUND: Sepsis is a life-threatening organ dysfunction and a major healthcare burden worldwide. Although sepsis is a medical emergency that requires immediate management, screening for the occurrence of sepsis is difficult. Herein, we propose a deep learning-based model (DLM) for screening sepsis using electrocardiography (ECG). METHODS: This retrospective cohort study included 46,017 patients who were admitted to two hospitals. A total of 1,548 and 639 patients had sepsis and septic shock, respectively. The DLM was developed using 73,727 ECGs from 18,142 patients, and internal validation was conducted using 7774 ECGs from 7,774 patients. Furthermore, we conducted an external validation with 20,101 ECGs from 20,101 patients from another hospital to verify the applicability of the DLM across centers. RESULTS: During the internal and external validations, the area under the receiver operating characteristic curve (AUC) of the DLM using 12-lead ECG was 0.901 (95% confidence interval, 0.882-0.920) and 0.863 (0.846-0.879), respectively, for screening sepsis and 0.906 (95% confidence interval (CI), 0.877-0.936) and 0.899 (95% CI, 0.872-0.925), respectively, for detecting septic shock. The AUC of the DLM for detecting sepsis using 6-lead and single-lead ECGs was 0.845-0.882. A sensitivity map revealed that the QRS complex and T waves were associated with sepsis. Subgroup analysis was conducted using ECGs from 4,609 patients who were admitted with an infectious disease, and the AUC of the DLM for predicting in-hospital mortality was 0.817 (0.793-0.840). There was a significant difference in the prediction score of DLM using ECG according to the presence of infection in the validation dataset (0.277 vs. 0.574, p < 0.001), including severe acute respiratory syndrome coronavirus 2 (0.260 vs. 0.725, p = 0.018). CONCLUSIONS: The DLM delivered reasonable performance for sepsis screening using 12-, 6-, and single-lead ECGs. The results suggest that sepsis can be screened using not only conventional ECG devices but also diverse life-type ECG machines employing the DLM, thereby preventing irreversible disease progression and mortality.

The Differential Consequences of Fear, Anger, and Depression in Response to COVID-19 in South Korea.

Studies on previous outbreaks of contagious diseases suggest that the impact of the emotions associated with an epidemic can be greater than that of the epidemic in terms of the number of people affected. This study explores the relationships between the three most commonly expressed emotional responses to the COVID-19 pandemic (fear, anger, and depression) and two outcome variables (compliance with the social-distancing policy and the stigmatization of those infected by COVID-19). A large online, public opinion survey was conducted in South Korea (n = 1000) between 4 and 11 June 2020, which was between the first and the second waves of COVID-19. A series of regression analyses suggest that the emotional response was accompanied by differential behavioral and perceptual consequences. Fear was consistently positively related to all indicators of compliance with social-distancing policies (the voluntary practice of social distancing, support for the "routine-life-distancing" policy, and support for stronger social-distancing policies). Anger was positively related to both stigmatization indicators (responsibility attribution and stigmatizing attitude toward people infected with COVID-19). Finally, depression showed negative relationships with support for the "routine-life-distancing" policy and for stronger social-distancing policies but a positive relationship with the voluntary practice of social distancing. By examining whether and how certain types of emotional responses are more or less related to compliance with social distancing and stigmatization, the present study provides practical implications for effective public communication during an epidemic such as COVID-19.

One-Component Multifunctional Sequence-Defined Ionizable Amphiphilic Janus Dendrimer Delivery Systems for mRNA.

Efficient viral or nonviral delivery of nucleic acids is the key step of genetic nanomedicine. Both viral and synthetic vectors have been successfully employed for genetic delivery with recent examples being DNA, adenoviral, and mRNA-based Covid-19 vaccines. Viral vectors can be target specific and very efficient but can also mediate severe immune response, cell toxicity, and mutations. Four-component lipid nanoparticles (LNPs) containing ionizable lipids, phospholipids, cholesterol for mechanical properties, and PEG-conjugated lipid for stability represent the current leading nonviral vectors for mRNA. However, the segregation of the neutral ionizable lipid as droplets in the core of the LNP, the "PEG dilemma", and the stability at only very low temperatures limit their efficiency. Here, we report the development of a one-component multifunctional ionizable amphiphilic Janus dendrimer (IAJD) delivery system for mRNA that exhibits high activity at a low concentration of ionizable amines organized in a sequence-defined arrangement. Six libraries containing 54 sequence-defined IAJDs were synthesized by an accelerated modular-orthogonal methodology and coassembled with mRNA into dendrimersome nanoparticles (DNPs) by a simple injection method rather than by the complex microfluidic technology often used for LNPs. Forty four (81%) showed activity in vitro and 31 (57%) in vivo. Some, exhibiting organ specificity, are stable at 5 °C and demonstrated higher transfection efficiency than positive control experiments in vitro and in vivo. Aside from practical applications, this proof of concept will help elucidate the mechanisms of packaging and release of mRNA from DNPs as a function of ionizable amine concentration, their sequence, and constitutional isomerism of IAJDs.

Trends in Nursing Research on Infections: Semantic Network Analysis and Topic Modeling.

BACKGROUND: Many countries around the world are currently threatened by the COVID-19 pandemic, and nurses are facing increasing responsibilities and work demands related to infection control. To establish a developmental strategy for infection control, it is important to analyze, understand, or visualize the accumulated data gathered from research in the field of nursing. METHODS: A total of 4854 articles published between 1978 and 2017 were retrieved from the Web of Science. Abstracts from these articles were extracted, and network analysis was conducted using the semantic network module. RESULTS: 'wound', 'injury', 'breast', "dressing", 'temperature', 'drainage', 'diabetes', 'abscess', and 'cleaning' were identified as the keywords with high values of degree centrality, betweenness centrality, and closeness centrality; hence, they were determined to be influential in the network. The major topics were 'PLWH' (people living with HIV), 'pregnancy', and 'STI' (sexually transmitted infection). CONCLUSIONS: Diverse infection research has been conducted on the topics of blood-borne infections, sexually transmitted infections, respiratory infections, urinary tract infections, and bacterial infections. STIs (including HIV), pregnancy, and bacterial infections have been the focus of particularly intense research by nursing researchers. More research on viral infections, urinary tract infections, immune topic, and hospital-acquired infections will be needed.

Clinical Features of COVID-19 in Uzbekistan.

BACKGROUND: As of April 30, 2020, a total of 2,039 cases of the novel coronavirus disease 2019 (COVID-19) were confirmed in the Republic of Uzbekistan after the first detection on March 15. Reports on symptoms of COVID-19 are non-specific and known to vary from asymptomatic, mild to severe, or fatal. This study aimed to analyze the symptomatic and clinical characteristics of study participants based on the medical records of participants hospitalized with COVID-19 in Uzbekistan. METHODS: We collected all data from medical records of COVID-19 confirmed patients in 19 hospitals from 13 regions of Uzbekistan between March 15 and April 30. We selected 1,030 patients discharged from the hospitals after COVID-19 treatment as study participants, excluding those with missing data. Further, we collected demographics, symptoms, clinical outcomes, and treatment data through medical records. RESULTS: More than half (57.6%) of confirmed cases of COVID-19 were males, and the median age was 36.0 years. The most frequent symptoms at the first inspection on hospital admission of all patients were fatigue (59.7%), dry cough (54.1%), pharyngalgia (31.6%), headache (20.6%), and anorexia (12.5%). Compared to the oldest group, the youngest group showed a lower frequency of symptoms. About half of the group aged 18-49 years reported that they came from abroad. One-fifth of patients in group 50-84 received oxygen support, while no patients in group aged 0-17 years received oxygen support. About two-thirds of the participants from intensive care unit (ICU) came from abroad, whereas 42.1% of the non-ICU group returned from other countries. Regarding symptoms, 16.9% of the patients in the ICU group were asymptomatic, while 5.8% in the non-ICU group were asymptomatic. CONCLUSION: This study suggests that the medical delivery system and resource distribution need to be implemented based on clinical characteristics by age and severity to delay and effectively respond to the spread of infections in the future. This study analyzed symptoms of COVID-19 patients across Uzbekistan, which is useful as primary data for policies on COVID-19 in Uzbekistan.