Background: Myopia is a public health problem, with estimations that 50% of the world population will be myopic by 2050. Some environmental factors, such as time spent outdoors, doing near work, and using digital devices, influence the development of myopia in children. Home confinement in Spain has increased these risk factors, so this study aims to investigate the impact of home confinement during the COVID-19 outbreak in the vision of school-aged children; Methods: A cross-sectional study in children between 5 and 7 years old that completed a visual screening and a questionnaire about their lifestyles at opticians in Spain in September and October of 2019 and 2020. Statistical analysis to compare lifestyles pre and post confinement, and vision in 2020 versus a similar cohort examined at the same opticians in 2019, was conducted; Results: Spanish children spent less time outdoors and more time doing near work in 2020 than in 2019 (p ≤ 0.001). There was a significant decrease of the spherical equivalent (mean ± standard deviation; 0.66 ± 2.03 D in 2019 vs. 0.48 ± 1.81 D in 2020; p ≤ 0.001); Conclusions: Lifestyles of Spanish children changed during the home confinement at the beginning of 2020. Together with changes in their lifestyles, spherical equivalents have decreased, which implies higher figures of myopia for children aged between 5 and 7.

A systematic review was conducted to assess the cost of ketosis in dairy cattle, and to elucidate how ketosis cost is estimated in each of the studies. Scientific papers addressing the economic impact of ketosis in dairy cows were identified through a search in 4 databases (Medline, ISI Web of Science, CAB Abstracts, and Agricola). The literature search was conducted with no restrictions on the date of study publication, publication type, or language. The methodological quality of the studies was assessed regarding study design, data collection, and analysis and interpretation of the study results. Of 531 identified records, 10 were selected, of which 9 were published from 2015 onward. Of the 10 studies reviewed, 9 report cost of a case of ketosis, and the estimates vary widely, with values ranging from €19 to €812. Two studies report ketosis cost at a farm level (€3.6-€29/cow per year). Among the studies, we observed great variation not only in the estimation models and inputs used (costs and losses associated with the disease) but also in the definition of ketosis and its prevalence or incidence figures. Moreover, the cost of ketosis was estimated for dairy farms in the United States, Canada, the Netherlands, Denmark, France, Germany, Spain, Sweden, Norway, and India. Consequently, there was great heterogeneity regarding herd characteristics, milk production, milk prices, culled cows' value, feed prices, and costs of veterinary services. Ketosis cost estimates vary as a consequence of all these aspects. Therefore, although most of the studies were well-designed and used high-quality data, the systematic approach review does not allow combination of the cost estimates of into a single figure. In conclusion, our review highlights an overall considerable economic impact of ketosis in dairy cattle. Economic prevention and mitigation strategies should be taken according to herd- and country-specific conditions. Ketosis cost figures reported in economic studies should always be considered carefully and interpreted with appropriate consideration of the inputs of the estimation, country context, and herd parameters.

Background: Academic performance at different educational levels has become a very important subject of study in local, national and international institutions. A visual system working properly is critical to improving academic performance. It is important to check children’s vision during the different stages of elementary school. Methods: A visual screening was carried out in elementary school children, aged between 6 and 12, across Spain. The screening included: the collection of demographic data, surveys of children and families about their vision habits and a basic optometric screening to detect visual problems. Results: Children with bad academic performance had worse visual health than those with good academic performance. Conclusions: It would be highly recommendable to introduce policies that ensure the early detection of visual disorders in schools and primary care in order to improve the academic performance of elementary students.

Background Different methods have been proposed to study skeletal muscle mass in sarcopenia diagnosis, although all have inherent drawbacks. The aim of this study was to evaluate the utility of muscle ultrasound in muscle assessment by studying its correlation with dual-energy x-ray absorptiometry (DXA) and calf circumference (CC), cut-off values for ultrasound-based detection of low muscle mass, and the correlation with muscle performance. Methods Fifty-seven participants older than 70 years, underwent a muscle ultrasound study, DXA, calf circumference (CC) and functional assessment. Ultrasound measurements were taken in the femoral quadriceps (transverse plane) and in the medial gastrocnemius (transverse and longitudinal planes). Muscle function was assessed by gait speed, Short Physical Performance Battery (SPPB) and grip strength. Results Median age was 78.9 years (IQR 74.9–81.9), and 33 were women (57.9%). We found good correlation between muscle thickness of gastrocnemius muscle in transverse and longitudinal plane and appendicular lean mass measured by DXA (r=0.546 and r=0.689 respectively) and good correlations between muscle thickness of gastrocnemius in transverse and longitudinal plane with CC (r=0.651 and r=0.447 respectively). The thickness of gastrocnemius medialis optimal cut-off points for low muscle mass were 18,5mm in the transverse plane (Sensitivity: 77,8%, Specificity: 77,1%), and 17.3mm in the longitudinal plane (Sensitivity: 100%, Specificity: 68.8%). Muscle thickness was also significantly correlated with gait speed, SPPB and grip strength. Conclusions Measures of gastrocnemius medialis thickness obtained by ultrasound are reliable and correlate well with DXA and CC values and muscle performance.

Background: The increasing integration of AI in chest X-ray evaluation holds promise for enhancing diagnostic accuracy and optimizing clinical workflows. However, understanding its performance in real-world clinical settings is essential. Objectives: In this study, we evaluated the sensitivity (Se) and specificity (Sp) of an AI-based software (Arterys MICA v29.4.0) alongside a radiology resident in interpreting chest X-rays referred from the emergency department (ED), using a senior radiologist’s assessment as the gold standard (GS). We assessed the concordance between the AI system and the resident, noted the frequency of doubtful cases for each category, identified how many were considered positive by the GS, and assessed variables that AI was not trained to detect. Methods: We conducted a retrospective observational study analyzing chest X-rays from a sample of 784 patients referred from the ED at our hospital. The AI system was trained to detect five categorical variables—pulmonary nodule, pulmonary opacity, pleural effusion, pneumothorax, and fracture—and assign each a confidence label (“positive”, “doubtful”, or “negative”). Results: Sensitivity in detecting fractures and pneumothorax was high (100%) for both AI and the resident, moderate for pulmonary opacity (AI = 76%, resident = 71%), and acceptable for pleural effusion (AI = 60%, resident = 67%), with negative predictive values (NPV) above 95% and areas under the curve (AUC) exceeding 0.8. The resident showed moderate sensitivity (75%) for pulmonary nodules, while AI’s sensitivity was low (33%). AI assigned a “doubtful” label to some diagnoses, most of which were deemed negative by the GS; the resident expressed doubt less frequently. The Kappa coefficient between the resident and AI was fair (0.3) across most categories, except for pleural effusion, where concordance was moderate (0.5). Our study highlighted additional findings not detected by AI, including 16% prevalence of mediastinal abnormalities, 20% surgical materials, and 20% other pulmonary findings. Conclusions: Although AI demonstrated utility in identifying most primary findings—except for pulmonary nodules—its high NPV suggests it may be valuable for screening. Further training of the AI software and broadening its scope to identify additional findings could enhance its detection capabilities and increase its applicability in clinical practice.