The prevalence of post-thyroidectomy chronic asthenia: a prospective cohort study

Purpose: Chronic asthenia (CA) is complained by some patients that have undergone thyroid surgery. We evaluate its impact in patients undergoing unilateral or bilateral thyroidectomy, the trend during a 1-year follow-up, and the possible risk factors. Methods: A prospective, cohort study was carried out on 263 patients scheduled for thyroidectomy from 2012 and 2014. Exclusion criteria were as follows: Gravesâ disease, malignancies requiring radioiodine therapy, post-surgical hypoparathyroidism, laryngeal nerve palsy, abnormal pre- and post-operative thyroid hormone levels, and BMI outside the normal range. Demographics; smoking and alcoholism addiction; cardiac, pulmonary, renal, and hepatic failure; diabetes; anxiety; and depression were recorded. The Brief Fatigue Inventory (BFI) was used to evaluate CA and its possible association with these comorbidities 6 and 12 months after thyroidectomy. Results: One hundred seventy-seven patients underwent total thyroidectomy (TT), 54 hemithyroidectomy (HT). Thirty-two patients were not recorded because of the onset of exclusion criteria. In the 6 months after thyroidectomy, in the TT group, 64 patients (36.16%) reported an impairment in the BFI score and only 1 in the TL group. The mean BFI score changed from 1.663(±1.191) to 2.16 (±11.148) in the TT group, from 1.584 (±1.371) to 1.171 (±1.093) in the TL group (pÂ&nbsp

Wastewater-based epidemiology for early warning of SARS-COV-2 circulation: A pilot study conducted in Sicily, Italy

There is increasing evidence of the use of wastewater-based epidemiology to integrate conventional monitoring assessing disease symptoms and signs of viruses in a specific territory. We present the results of SARS-CoV-2 environmental surveillance activity in wastewater samples collected between September 2020 and July 2021 in 9 wastewater treatment plants (WTPs) located in central and western Sicily, serving over 570,000 residents. The presence of SARS-CoV-2, determined in 206 wastewater samples using RT-qPCR assays, was correlated with the notified and geo-referenced cases on the areas served by the WTPs in the same study period. Overall, 51% of wastewater samples were positive. Samples were correlated with 33,807 SARS-CoV-2 cases, reported in 4 epidemic waves, with a cumulative prevalence of 5.9% among Sicilian residents. The results suggest that the daily prevalence of SARS-CoV-2 active cases was statistically significant and higher in areas with SARS-CoV-2 positive wastewater samples. According to these findings, the proposed method achieves a good sensitivity profile (78.3%) in areas with moderate or high viral circulation (≥133 cases/100,000 residents) and may represent a useful tool in the management of epidemics based on an environmental approach, although it is necessary to improve the accuracy of the process

Leonor Fini, tra Surrealismo e "ritorno all'ordine"

Leonor Fini is one of the most important female artists and personalities of the Twentieth Century. Painter, costume designer, illustrator, drawer, scenographer and, above all, rebellious and cosmopolitan woman. Without ever disconnecting her life from her art, she has crossed time and places with remarkable artistic autonomy, transposing her ideals onto the canvas with strenght and determination, imposing herself on the Twentieth century art scene as a powerful figure. Fini's pictorial attention has its roots in the study of ancient masters, in particular in those of the Fifteenth Century who are combined with Pre-Raphaelite and Symbolist influences. From these studies comes her ability to move and find a synthesis between two movements apparently at the antipodes: Surrealism and the “Return to Order”

Study on the Growth and Enterotoxin Production by <i>Staphylococcus aureus</i> in Canned Meat before Retorting

Possible contamination by Staphylococcus aureus of the production environment and of the meat of a canned meat production factory was analysed. A total of 108 samples were taken from nine critical control points, 13 of them were positive for S. aureus. None of the isolates produced enterotoxins. To determine how much time can elapse between can seaming and sterilisation in the autoclave without any risk of enterotoxin production by S. aureus, the growth and enterotoxin production of three enterotoxin A producing strains of S. aureus (one ATCC strain and two field strains) in canned meat before sterilisation was investigated at three different temperatures (37, 20 and 10 &#176;C). Two types of meat were used, one with and one without sodium nitrite. In the canned products, the spiked bacteria spread throughout the meat and reached high levels. Enterotoxin production was shown to start 10 hours after incubation at 37 &#176;C and after 48 h after incubation at 20 &#176;C; the production of enterotoxin was always detected in the transition between the exponential and the stationary growth phase. At 10 &#176;C, the enterotoxin was never detected. The statistical analysis of the data showed that the difference between the two different types of meat was not statistically significant (p value &gt; 0.05). Since it is well known that following heat treatment, staphylococcal enterotoxins, although still active (in in vivo assays), can be undetectable (loss of serological recognition) depending on the food matrix and pH, it is quite difficult to foresee the impact of heat treatment on enterotoxin activity. Therefore, although the bacteria are eliminated, the toxins may remain and cause food poisoning. The significance of the results of this study towards implementing good manufacturing practices and hazard analysis critical control points in a canned meat factory are discussed with reference to the management of pre-retorting steps after seaming

The role of Point of Care Ultrasound (POCUS) and focused echocardiography in optimization of non-invasive mechanical ventilation: from diaphragmatic functionality to hemodynamic monitoring

This case shows the use of ultrasound guidance to optimize non-invasive mechanical ventilation for a 62-year-old patient with a complex medical history. Point-of-care ultrasound (POCUS) was used to assess diaphragmatic function and hemodynamics, leading to adjustments in ventilator setting. The approach improved gas exchange, resolved respiratory acidosis, and enhanced hemodynamics, providing a promising strategy for ventilator management in complex clinical cases

Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2022, Grenoble, France, September 19-23, 2022, Proceedings, Part II

Workshop Papers ECML-PKDD 202

Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2022, Grenoble, France, September 19-23, 2022, Proceedings, Part I

Workshop Papers from ECML-PKDD 202

Error Variance, Fairness, and the Curse on Minorities

Machine learning systems can make more errors for certain populations and not others, and thus create discriminations. To assess such fairness issue, errors are typically compared across populations. We argue that we also need to account for the variability of errors in practice, as the errors measured in test data may not be exactly the same in real-life data (called target data). We first introduce statistical methods for estimating random error variance in machine learning problems. The methods estimate how often errors would exceed certain magnitudes, and how often the errors of a population would exceed that of another (e.g., by more than a certain range). The methods are based on well-established sampling theory, and the recently introduced Sample-to-Sample estimation. The latter shows that small target samples yield high error variance, even if the test sample is very large. We demonstrate that, in practice, minorities are bound to bear higher variance, thus amplified error and bias. This can occur even if the test and training sets are accurate, representative, and extremely large. We call this statistical phenomenon the curse on minorities, and we show examples of its impact with basic classification and regression problems. Finally, we outline potential approaches to protect minorities from such curse, and to develop variance-aware fairness assessments

This Looks Like That, Because.. Explaining Prototypes for Interpretable Image Recognition

Image recognition with prototypes is considered an interpretable alternative for black box deep learning models. Classification depends on the extent to which a test image “looks like” a prototype. However, perceptual similarity for humans can be different from the similarity learned by the classification model. Hence, only visualising prototypes can be insufficient for a user to understand what a prototype exactly represents, and why the model considers a prototype and an image to be similar. We address this ambiguity and argue that prototypes should be explained. We improve interpretability by automatically enhancing visual prototypes with quantitative information about visual characteristics deemed important by the classification model. Specifically, our method clarifies the meaning of a prototype by quantifying the influence of colour hue, shape, texture, contrast and saturation and can generate both global and local explanations. Because of the generality of our approach, it can improve the interpretability of any similarity-based method for prototypical image recognition. In our experiments, we apply our method to the existing Prototypical Part Network (ProtoPNet). Our analysis confirms that the global explanations are generalisable, and often correspond to the visually perceptible properties of a prototype. Our explanations are especially relevant for prototypes which might have been interpreted incorrectly otherwise. By explaining such ‘misleading’ prototypes, we improve the interpretability and simulatability of a prototype-based classification model. We also use our method to check whether visually similar prototypes have similar explanations, and are able to discover redundancy. Code is available at https://github.com/M-Nauta/Explaining_Prototypes