Summary Background Most patients who have heart failure with a reduced ejection fraction, when left ventricular ejection fraction (LVEF) is 40% or lower, are diagnosed in hospital. This is despite previous presentations to primary care with symptoms. We aimed to test an artificial intelligence (AI) algorithm applied to a single-lead ECG, recorded during ECG-enabled stethoscope examination, to validate a potential point-of-care screening tool for LVEF of 40% or lower. Methods We conducted an observational, prospective, multicentre study of a convolutional neural network (known as AI-ECG) that was previously validated for the detection of reduced LVEF using 12-lead ECG as input. We used AI-ECG retrained to interpret single-lead ECG input alone. Patients (aged ≥18 years) attending for transthoracic echocardiogram in London (UK) were recruited. All participants had 15 s of supine, single-lead ECG recorded at the four standard anatomical positions for cardiac auscultation, plus one handheld position, using an ECG-enabled stethoscope. Transthoracic echocardiogram-derived percentage LVEF was used as ground truth. The primary outcome was performance of AI-ECG at classifying reduced LVEF (LVEF ≤40%), measured using metrics including the area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity, with two-sided 95% CIs. The primary outcome was reported for each position individually and with an optimal combination of AI-ECG outputs (interval range 0–1) from two positions using a rule-based approach and several classification models. This study is registered with ClinicalTrials.gov , NCT04601415 . Findings Between Feb 6 and May 27, 2021, we recruited 1050 patients (mean age 62 years [SD 17·4], 535 [51%] male, 432 [41%] non-White). 945 (90%) had an ejection fraction of at least 40%, and 105 (10%) had an ejection fraction of 40% or lower. Across all positions, ECGs were most frequently of adequate quality for AI-ECG interpretation at the pulmonary position (979 [93·3%] of 1050). Quality was lowest for the aortic position (846 [80·6%]). AI-ECG performed best at the pulmonary valve position (p=0·02), with an AUROC of 0·85 (95% CI 0·81–0·89), sensitivity of 84·8% (76·2–91·3), and specificity of 69·5% (66·4–72·6). Diagnostic odds ratios did not differ by age, sex, or non-White ethnicity. Taking the optimal combination of two positions (pulmonary and handheld positions), the rule-based approach resulted in an AUROC of 0·85 (0·81–0·89), sensitivity of 82·7% (72·7–90·2), and specificity of 79·9% (77·0–82·6). Using AI-ECG outputs from these two positions, a weighted logistic regression with l2 regularisation resulted in an AUROC of 0·91 (0·88–0·95), sensitivity of 91·9% (78·1–98·3), and specificity of 80·2% (75·5–84·3). Interpretation A deep learning system applied to single-lead ECGs acquired during a routine examination with an ECG...

Honey is considered the only food of animal origin that can be consumed without being processed. The literature presents several reasons why people consume honey, namely, it being a natural and healthy product known for its dietary, nutritional and medicinal characteristics. Moreover, other reasons for honey's purchase include the product quality; the region of origin; the information available on the product's label, the brand's reputation; and the variety, texture, taste, aroma, appearance, packaging and price of honey. Thus, we intend to identify determinant factors on which consumers base their purchasing decision. Therefore, we developed a cross-sectional study based on a non-probabilistic sample of 474 individuals, 399 of whom were honey consumers. We collected the data in the period from March to May 2016 using a questionnaire [1], which we applied directly to consumers in the city of Bragança. Later, we analysed the data with SPSS 23.0 software. The data analysis included a univariate descriptive analysis and a multivariate analysis that involved assessment of a binary logistic regression in order to identify the determinant factors for purchasing and consuming honey. The statistically significant parameters included taste, colour, origin, and certification label, at a significance level of 1 %. These characteristics explained 68.9 % of the consumer's decision to purchase honey. It is noteworthy that non-consumers considered the certification label important (when purchasing the product to offer to someone), while in the process of decision-making honey consumers valued taste, colour and country of origin.

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This research aimed to identify the most developed themes in the field of digital marketing from 2010 to 2019. A total of 898 publications were selected from the Scopus database from the Business, Management and Accounting and Economics, Econometrics, and Finance domains. A bibliometric analysis was carried out using VOSviewer software and the term co-occurrence technique was used. Three clusters were identified. The first cluster relates digital marketing to the changes and adaptations of society and the economy since the emergence of the internet. The second cluster relates digital marketing to information technologies, e-commerce, and consumer behavior. Finally, the third cluster relates digital marketing with markets, social media, users, tourism, and electronic word-of-mouth (e-WOM).