Multiple sclerosis lesions show a propensity for frontal and parietal white matter. Lesion burden in these areas was strongly associated with performance on tasks requiring sustained complex attention and working verbal memory. This relationship was consistent over a 4-year period, suggesting that disruption of frontoparietal subcortical networks may underlie the pattern of neuropsychological impairment seen in many patients with MS.
Headache disorders are considered the second leading cause of years lived with disability worldwide, and 90% of people have a headache episode at least once a year, thus representing a relevant public health priority. As the pharmacist is often the first and only point of reference for people complaining of headache, we carried out a survey in a nationwide sample of Italian pharmacies, in order to describe the distribution of migraine or non-migraine type headaches and medicines overuse among people entering pharmacies seeking for self-medication; and to evaluate the association, in particular of migraine, with socio-demographic and clinical characteristics, and with the pathway of care followed by the patients. A 14-item questionnaire, including socio-demographic and clinical factors, was administered by trained pharmacists to subjects who entered a pharmacy requesting self-medication for a headache attack. The ID Migraine™ Screener was used to classify headache sufferers in four classes. From June 2016 to January 2017, 4424 people have been interviewed. The prevalence of definite migraines was 40%, significantly higher among women and less educated people. About half of all headache sufferers and a third of migraineurs do not consider their condition as a disease and are not cared by any doctor. Among people seeking self-medication in pharmacies for acute headache attacks, the rate of definite or probable migraine is high, and a large percentage of them is not correctly diagnosed and treated. The pharmacy can be a valuable observatory for the study of headaches, and the first important step to improve the quality of care delivered to these patients.
Pharmacists in the community and the essential requirement to safeguard their own health have become fundamental since the spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The aims of this paper were (I) to analyze the directives provided to pharmacists in 2020 regarding preventative safety measures to be adopted; (II) to determine the number of pharmacists who came into contact with SARS-CoV-2 in North-West Italy and relate this to the adopted preventative measures. The first aim was pursued by conducting a bibliographic research, consulting the principal regulatory sources. The second one was achieved with an observational study by administering a questionnaire and performing a serological test. The various protection measures imposed by national and regional legislation were analyzed. Two hundred and eighty-six pharmacists (about 8% of the invited ones) responded to the survey. Ten pharmacists reported a positive result to the serological test. Of the subjects who presented a positive result, three declared that they had not used a hand sanitizer, while two stated that they had not scheduled the cleaning and decontamination of surfaces. Two interviewees had not set up a system of quota restrictions on admissions. In four cases, a certified cleaning company had decontaminated the premises. The results of our study show that during the coronavirus disease 2019 (COVID-19) pandemic, the most pressing challenge for community pharmacists has been the protection of staff and clients inside the pharmacy; the challenge to be faced in the near future will probably be the management of new responsibilities.
Background and study aims Colon capsule endoscopy (CCE) is a minimally invasive alternative to conventional colonoscopy. However, CCE produces long videos, making its analysis time-consuming and prone to errors. Convolutional neural networks (CNN) are artificial intelligence (AI) algorithms with high performance levels in image analysis. We aimed to develop a deep learning model for automatic identification and differentiation of significant colonic mucosal lesions and blood in CCE images. Patients and methods A retrospective multicenter study including 124 CCE examinations was conducted for development of a CNN model, using a database of CCE images including anonymized images of patients with normal colon mucosa, several mucosal lesions (erosions, ulcers, vascular lesions and protruding lesions) and luminal blood. For CNN development, 9005 images (3,075 normal mucosa, 3,115 blood and 2,815 mucosal lesions) were ultimately extracted. Two image datasets were created and used for CNN training and validation. Results The mean (standard deviation) sensitivity and specificity of the CNN were 96.3 % (3.9 %) and 98.2 % (1.8 %) Mucosal lesions were detected with a sensitivity of 92.0 % and a specificity of 98.5 %. Blood was detected with a sensitivity and specificity of 97.2 % and 99.9 %, respectively. The algorithm was 99.2 % sensitive and 99.6 % specific in distinguishing blood from mucosal lesions. The CNN processed 65 frames per second. Conclusions This is the first CNN-based algorithm to accurately detect and distinguish colonic mucosal lesions and luminal blood in CCE images. AI may improve diagnostic and time efficiency of CCE exams, thus facilitating CCE adoption to routine clinical practice.
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