A single nucleotide distinguishes the SARS-CoV-2 in the Wuhan outbreak in December 2019 from that in Beijing-Xinfadi in June 2020, China

Two major locally transmitted outbreaks of coronavirus disease 2019 occurred in China, one in Wuhan from December 2019 to April 2020, another in Beijing-Xinfadi in June 2020. Severe acute respiratory syndrome coronavirus 2 isolated from these two outbreaks can be distinguished by a conserved pyrimidine nucleotide located at nucleotide position 241 in the 5′-untranslated region of the virus genome.

Predictors of plaque erosion in current smokers and non-current smokers presented with ST-segment elevation myocardial infarction: an optical coherence tomography study

Background

Plaque erosion with subsequent coronary thrombosis is considered as an important cause of ST-segment elevation myocardial infarction (STEMI). Smoking is a major risk factor for acute coronary thrombosis. However, the relationship between current smoking status and plaque erosion has not been systematically investigated.

Purpose

The present study aimed to investigate predictors of plaque erosion in current smokers and non-current smokers with STEMI by using optical coherence tomography (OCT).

Methods

Between January 2015 to December 2017, a total of 1313 STEMI patients underwent pre-intervention OCT of culprit lesion were enrolled and divided into two groups based on current smoking status: current smoking group (n=713) and non-current smoking group (n=600). Using established criteria, quantitative and qualitative underlying plaque characteristics were assessed by OCT. Clinical, angiographic and OCT characteristics of all enrolled patients were recorded. Univariable and multivariable logistic regression analyses were used to identify predictors of plaque erosion in two groups.

Results

Plaque erosion were found in 30.9% (220/713) culprit lesions in current smoking group and 20.8% (125/600) of those in non-current smoking group detected by OCT. In multivariate regression analysis, the predictors that strongly related to plaque erosion in the current smoking group were nearby bifurcation (OR: 4.84; 95% CI:2.38–9.87; p<0.001); the minimum fiber cap thickness (FCT, OR:1.05; 95% CI:1.03–1.08; p<0.001); thin-cap fibroatheroma (TCFA, OR: 0.22; 95% CI: 0.07–0.67; p=0.007) and lipid core length (OR: 0.91; 95% CI: 0.84–0.97; p=0.007). The predictors in the non-current smoking group were nearby bifurcation (OR: 4.84; 95% CI: 2.38–9.87; p=0.006); the minimal FCT (OR: 1.09; 95% CI: 1.06–1.13; p<0.001); multi-vessel disease (MVD, OR: 0.43; 95% CI: 0.19–0.97; p=0.042) and dyslipidemia (OR: 0.34; 95% CI: 0.14–0.84; p=0.020).

Conclusions

Predictors of plaque erosion causing STEMI onset are different between current smokers and non-current smoker, with nearby bifurcation and thicker minimal FCT both predicting plaque erosion in two groups of patients.

Funding Acknowledgement

Type of funding source: Public grant(s) – National budget only. Main funding source(s): National Key Research and Development Program of China, National Natural Science Foundation of China.

Morphological characteristics of plaque erosion with noncritical coronary stenosis: an optical coherence tomography study

Background

Plaque erosion is a frequent and important mechanism of acute coronary thrombosis only secondary to plaque rupture. Recent studies suggested plaque erosion with noncritical stenosis could be treated conservatively that distinct from those with critical stenosis. However, characteristics of plaque erosions with different coronary stenosis remain unknown.

Purpose

The present study aimed to investigate morphological features of plaque erosions with different coronary stenosis using optical coherence tomography (OCT).

Methods

Consecutive ST-segment elevated myocardial infarction (STEMI) patients with OCT images of culprit lesion between August 2014 and December 2017 were enrolled and 348 cases presented with plaque erosion identified by OCT. Based on the severity of lumen area stenosis [calculated by (1-minimal lumen area/reference lumen area) * 100%], all culprit plaque erosions were divided into three groups: Group A (area stenosis<50%, n=50, 14.4%); Group B (50%≤area stenosis<75%, n=146, 42.0%); Group C (area stenosis≥75%, n=152, 43.7%). Clinical characteristics, lesion features detected by coronary angiography and OCT were compared among three groups.

Results

Of all 348 STEMI patients with plaque erosions, patients in Group A were youngest (p=0.008) and had the lowest frequency of hypertension (p=0.029) as compared with those in Group B and C. Angiographic analysis showed 72.0% of plaque erosions in Group A located in LAD, while 67.8% in Group B and 53.9% in Group C (p=0.039). OCT findings (Figure 1-A) showed the prevalence of fibrous plaque was significantly highest in Group A than those in Group B and C (82.0% vs. 54.8% vs. 34.9%, p<0.001), whereas lipid rich plaque was most frequent in Group C (16.0% vs. 43.8% vs. 62.5%, p<0.001). The prevalence of macrophage (p<0.001), microvessel (p=0.009) and cholesterol crystals (p<0.001) increased gradually from plaque erosion with lumen area stenosis <50% to 50–75% to ≥75%. Notably, compared with Group B and C, nearby bifurcation was most common in Group A (72.0% vs. 67.1% vs. 55.3%, p=0.036). Multivariable regression analyses (Figure 1-B) showed fibrous plaque and nearby bifurcation were independently associated with plaque erosion with noncritical stenosis (area stenosis<75%).

Conclusion

56.3% plaque erosion in STEMI patients presented with noncritical stenosis, having distinct morphological features from erosion with critical stenosis. Fibrous plaque and nearby bifurcation were independently associated with the presence of noncritically stenotic plaque erosion, remaining a desire to tailor treatment therapy to individual patients.

Figure 1

Funding Acknowledgement

Type of funding source: Foundation. Main funding source(s): National Key R&D Program of China

Literature mapping: association of microscopic skin microflora and biomarkers with macroscopic skin health

Associations between skin microbes or biomarkers and pathological conditions have been reported in the literature. However, there is a lack of clarity on the interaction between the coexistence of common skin microbes with skin physiology and subsequent development of clinical symptoms, and the role of biomarkers in mediating these changes before the development of skin disease. In this review, we aim to identify areas in which extensive research for the studied factors has already been conducted, and which research areas are under-represented. The SciFinder database was searched for articles containing key words including specific skin microbes, biomarkers, skin physiology and diseases from the beginning of the SciFinder data record to 26 April 2016, and we included an additional relevant recent publication from our group. Among the 8000 + articles selected, the frequency of keyword pairs between two roles [microscopic markers (microflora or biomarkers) and reactions (skin physiology or clinical symptoms, or skin disease)] was investigated. Associated research between the individual factors such as skin microflora or biomarkers (chosen based on our earlier publication) and specific biophysical parameters, symptoms or skin disease was identified. The present research heatmap emphasizes the significance of a structured review of research on concerned factor associations to identify early/subclinical clues that can be used to prevent progression to overt skin disease with the help of precise skin care or early intervention, as indicated by skin microflora, biomarkers and an interactive skin biophysics profile. The findings provide a novel approach to explore such associations and may guide future research directed towards predicting disease from early/subclinical symptoms.

Radiomics: A primer for the radiation oncologist

PURPOSE

Radiomics are a set of methods used to leverage medical imaging and extract quantitative features that can characterize a patient's phenotype. All modalities can be used with several different software packages. Specific informatics methods can then be used to create meaningful predictive models. In this review, we will explain the major steps of a radiomics analysis pipeline and then present the studies published in the context of radiation therapy.

METHODS

A literature review was performed on Medline using the search engine PubMed. The search strategy included the search terms "radiotherapy", "radiation oncology" and "radiomics". The search was conducted in July 2019 and reference lists of selected articles were hand searched for relevance to this review.

RESULTS

A typical radiomics workflow always includes five steps: imaging and segmenting, data curation and preparation, feature extraction, exploration and selection and finally modeling. In radiation oncology, radiomics studies have been published to explore different clinical outcome in lung (n=5), head and neck (n=5), esophageal (n=3), rectal (n=3), pancreatic (n=2) cancer and brain metastases (n=2). The quality of these retrospective studies is heterogeneous and their results have not been translated to the clinic.

CONCLUSION

Radiomics has a great potential to predict clinical outcome and better personalize treatment. But the field is still young and constantly evolving. Improvement in bias reduction techniques and multicenter studies will hopefully allow more robust and generalizable models.

Augmented decision-making for acral lentiginous melanoma detection using deep convolutional neural networks

BACKGROUND

Several studies have achieved high-level performance of melanoma detection using convolutional neural networks (CNNs). However, few have described the extent to which the implementation of CNNs improves the diagnostic performance of the physicians.

OBJECTIVE

This study is aimed at developing a CNN for detecting acral lentiginous melanoma (ALM) and investigating whether its implementation can improve the initial decision for ALM detection made by the physicians.

METHODS

A CNN was trained using 1072 dermoscopic images of acral benign nevi, ALM and intermediate tumours. To investigate whether the implementation of CNN can improve the initial decision for ALM detection, 60 physicians completed a three-stage survey. In Stage I, they were asked for their decisions solely on the basis of dermoscopic images provided to them. In Stage II, they were also provided with clinical information. In Stage III, they were provided with the additional diagnosis and probability predicted by the CNN.

RESULTS

The accuracy of ALM detection in the participants was 74.7% (95% confidence interval [CI], 72.6-76.8%) in Stage I and 79.0% (95% CI, 76.7-81.2%) in Stage II. In Stage III, it was 86.9% (95% CI, 85.3-88.4%), which exceeds the accuracy delivered in Stage I by 12.2%p (95% CI, 10.1-14.3%p) and Stage II by 7.9%p (95% CI, 6.0-9.9%p). Moreover, the concordance between the participants considerably increased (Fleiss-κ of 0.436 [95% CI, 0.437-0.573] in Stage I, 0.506 [95% CI, 0.621-0.749] in Stage II and 0.684 [95% CI, 0.621-0.749] in Stage III).

CONCLUSIONS

Augmented decision-making improved the performance of and concordance between the clinical decisions of a diverse group of experts. This study demonstrates the potential use of CNNs as an adjoining, decision-supporting system for physicians' decisions.