ABSTRACT
Introduction Ventilator-associated pneumonia (VAP) is the most common infectious complication related to admission to an Intensive Treatment Unit (ITU). Ventilator-associated lower respiratory tract infection (VA-LRTI) is a broader diagnosis than VAP. By disregarding radiological criteria, it will include both VAP and ventilator-associated tracheobronchitis. This study, conducted in the setting of a Portuguese ITU, aims to study the incidence, microbiology and clinical outcome of VA-LRTI and its association with COVID-19. Methods A retrospective cohort study included patients admitted to a Portuguese ITU who underwent invasive mechanical ventilation (IMV) for over 48 hours between 01/01/2021 and 31/12/2021. The Hospitals in Europe Link for Infection Control through Surveillance (HELICS) criteria were applied, disregarding the radiological criteria, for the diagnosis of VA-LRTI. Results The group of patients with COVID-19 had 46.38 episodes of VA-LRTI/1000 days of ventilation, while patients without COVID-19 had 16.35 episodes/1000 days of ventilation (RR 2.78, p < 0.001). Of the 85 microorganisms isolated, 82% were gram-negative microorganisms, with species of the genus Klebsiella being the most prevalent (22.4%). There was a lower prevalence of beta-lactam-resistant organisms in patients with COVID-19 (RR 0.35, p = 0.031). The development of VA-LRTI is associated with longer times of IMV (difference in medians 10 days, p < 0.001), but with no significant differences in mortality (RR 1.21, p = 0.14). Discussion Patients with COVID-19 seem more predisposed to developing VA-LRTI, possibly due to intrinsic characteristics of the disease. Although no increase in mortality has been demonstrated, VA-LRTI can entail important costs related to morbidity, antibiotic pressure and economic costs that must be considered.
ABSTRACT
Frequently, the vineyards in the Douro Region present multiple grape varieties per parcel and even per row. An automatic algorithm for grape variety identification as an integrated software component was proposed that can be applied, for example, to a robotic harvesting system. However, some issues and constraints in its development were highlighted, namely, the images captured in natural environment, low volume of images, high similarity of the images among different grape varieties, leaf senescence, and significant changes on the grapevine leaf and bunch images in the harvest seasons, mainly due to adverse climatic conditions, diseases, and the presence of pesticides. In this paper, the performance of the transfer learning and fine-tuning techniques based on AlexNet architecture were evaluated when applied to the identification of grape varieties. Two natural vineyard image datasets were captured in different geographical locations and harvest seasons. To generate different datasets for training and classification, some image processing methods, including a proposed four-corners-in-one image warping algorithm, were used. The experimental results, obtained from the application of an AlexNet-based transfer learning scheme and trained on the image dataset pre-processed through the four-corners-in-one method, achieved a test accuracy score of 77.30%. Applying this classifier model, an accuracy of 89.75% on the popular Flavia leaf dataset was reached. The results obtained by the proposed approach are promising and encouraging in helping Douro wine growers in the automatic task of identifying grape varieties.
ABSTRACT
People can experience different emotions when listening to music. A growing number of studies have investigated the brain structures and neural connectivities associated with perceived emotions. However, very little is known about the effect of an explicit act of judgment on the neural processing of emotionally-valenced music. In this study, we adopted the novel consensus clustering paradigm, called binarisation of consensus partition matrices (Bi-CoPaM), to study whether and how the conscious aesthetic evaluation of the music would modulate brain connectivity networks related to emotion and reward processing. Participants listened to music under three conditions - one involving a non-evaluative judgment, one involving an explicit evaluative aesthetic judgment, and one involving no judgment at all (passive listening only). During non-evaluative attentive listening we obtained auditory-limbic connectivity whereas when participants were asked to decide explicitly whether they liked or disliked the music excerpt, only two clusters of intercommunicating brain regions were found: one including areas related to auditory processing and action observation, and the other comprising higher-order structures involved with visual processing. Results indicate that explicit evaluative judgment has an impact on the neural auditory-limbic connectivity during affective processing of music.
