Nucleolin protein expression in cutaneous melanocytic lesions

Background:  Nucleolin is a major nucleolar argyrophilic protein involved in carcinogenesis. There are only few studies on its tissue expression in human cancer and none in melanoma. We aimed at exploring this protein and its prognostic impact in cutaneous melanocytic lesions.
Methods:  We studied 193 cases including benign, dysplastic and malignant melanocytic lesions. Nuclear positivity was evaluated by immunohistochemistry and quantified by automated image analysis.
Results:  Most dysplastic and malignant lesions showed high percentages of cells with abnormal patterns of nuclear positivity (Abn+N) consisting in multiple, irregular, positive dots (ID+) and a coarse, irregularly positive nucleoplasm (CNpl+) or both (ID+CNpl+). The patterns CNpl+ and/or ID+CNpl+ were never observed in benign lesions, in which ID+ were also virtually absent. Abn+N% was significantly lower in dysplastic nevi than in primary melanomas and metastases and in primary melanomas than in metastases (p < 0.05). Furthermore, Abn+N was the second powerful prognostic discriminator, after melanoma thickness, and a significantly lower survival was observed in vertical growth phase melanoma patients showing Abn+N in more than 50% of melanoma cells.
Conclusion:  An altered nuclear nucleolin expression seems to accompany melanoma progression. Further investigation on nucleolin functionality and subcellular trafficking could add information on its altered role in melanoma.     

Applying spatial distribution analysis techniques to classification of 3D medical images

OBJECTIVE: The objective of this paper is to classify 3D medical images by analyzing spatial distributions to model and characterize the arrangement of the regions of interest (ROIs) in 3D space. METHODS AND MATERIAL: Two methods are proposed for facilitating such classification. The first method uses measures of similarity, such as the Mahalanobis distance and the Kullback-Leibler (KL) divergence, to compute the difference between spatial probability distributions of ROIs in an image of a new subject and each of the considered classes represented by historical data (e.g., normal versus disease class). A new subject is predicted to belong to the class corresponding to the most similar dataset. The second method employs the maximum likelihood (ML) principle to predict the class that most likely produced the dataset of the new subject. RESULTS: The proposed methods have been experimentally evaluated on three datasets: synthetic data (mixtures of Gaussian distributions), realistic lesion-deficit data (generated by a simulator conforming to a clinical study), and functional MRI activation data obtained from a study designed to explore neuroanatomical correlates of semantic processing in Alzheimer's disease (AD). CONCLUSION: Performed experiments demonstrated that the approaches based on the KL divergence and the ML method provide superior accuracy compared to the Mahalanobis distance. The later technique could still be a method of choice when the distributions differ significantly, since it is faster and less complex. The obtained classification accuracy with errors smaller than 1% supports that useful diagnosis assistance could be achieved assuming sufficiently informative historic data and sufficient information on the new subject.     

Fast Eating Is Associated with Increased BMI among High-School Students

Fast self-reported eating rate (SRER) has been associated with increased adiposity in children and adults. No studies have been conducted among high-school students, and SRER has not been validated vs. objective eating rate (OBER) in such populations. The objectives were to investigate (among high-school student populations) the association between OBER and BMI z-scores (BMIz), the validity of SRER vs. OBER, and potential differences in BMIz between SRER categories. Three studies were conducted. Study 1 included 116 Swedish students (mean ± SD age: 16.5 ± 0.8, 59% females) who were eating school lunch. Food intake and meal duration were objectively recorded, and OBER was calculated. Additionally, students provided SRER. Study 2 included students (n = 50, mean ± SD age: 16.7 ± 0.6, 58% females) from Study 1 who ate another objectively recorded school lunch. Study 3 included 1832 high-school students (mean ± SD age: 15.8 ± 0.9, 51% females) from Sweden (n = 748) and Greece (n = 1084) who provided SRER. In Study 1, students with BMIz ≥ 0 had faster OBER vs. students with BMIz < 0 (mean difference: +7.7 g/min or +27%, p = 0.012), while students with fast SRER had higher OBER vs. students with slow SRER (mean difference: +13.7 g/min or +56%, p = 0.001). However, there was “minimal” agreement between SRER and OBER categories (κ = 0.31, p < 0.001). In Study 2, OBER during lunch 1 had a “large” correlation with OBER during lunch 2 (r = 0.75, p < 0.001). In Study 3, fast SRER students had higher BMIz vs. slow SRER students (mean difference: 0.37, p < 0.001). Similar observations were found among both Swedish and Greek students. For the first time in high-school students, we confirm the association between fast eating and increased adiposity. Our validation analysis suggests that SRER could be used as a proxy for OBER in studies with large sample sizes on a group level. With smaller samples, OBER should be used instead. To assess eating rate on an individual level, OBER can be used while SRER should be avoided.     

PROTEIN AI Advisor: A Knowledge-Based Recommendation Framework Using Expert-Validated Meals for Healthy Diets

AI-based software applications for personalized nutrition have recently gained increasing attention to help users follow a healthy lifestyle. In this paper, we present a knowledge-based recommendation framework that exploits an explicit dataset of expert-validated meals to offer highly accurate diet plans spanning across ten user groups of both healthy subjects and participants with health conditions. The proposed advisor is built on a novel architecture that includes (a) a qualitative layer for verifying ingredient appropriateness, and (b) a quantitative layer for synthesizing meal plans. The first layer is implemented as an expert system for fuzzy inference relying on an ontology of rules acquired by experts in Nutrition, while the second layer as an optimization method for generating daily meal plans based on target nutrient values and ranges. The system’s effectiveness is evaluated through extensive experiments for establishing meal and meal plan appropriateness, meal variety, as well as system capacity for recommending meal plans. Evaluations involved synthetic data, including the generation of 3000 virtual user profiles and their weekly meal plans. Results reveal a high precision and recall for recommending appropriate ingredients in most user categories, while the meal plan generator achieved a total recommendation accuracy of 92% for all nutrient recommendations.     

Corifollitropin alfa compared with follitropin beta in GnRH-antagonist ovarian stimulation protocols in an unselected population undergoing IVF/ICSI

Recombinant DNA technologies have produced Corifollitropin alfa (CFa) used during IVF/ICSI in order to keep the circulating FSH levels above the threshold necessary to support multi-follicular growth for a week. In this prospective case-control study, we compared 70 participants treated with 150?μg CFa combined with 150?IU of follitropin beta (study group) with 70 subfertile participants with matching baseline characteristics, conforming with the same inclusion criteria and treated with an antagonist protocol using follitropin beta (control group). Live birth was the primary outcome, while secondary outcome measures were IVF/ICSI cycles characteristics, including adverse events and complications. Live birth was determined in reduced rates in the study compared to the control group, reaching statistical significance [6/70 versus 20/70, p?=?0.002], as also in the respective number of clinical pregnancies [9/70 versus 23/70, p?=?0.005], although the incidence of miscarriage was similar for both groups [6/70 versus 5/70, p?>?0.99]. Most of the secondary parameters examined were similar between groups. Logistic regression revealed that protocol and AFC had a direct impact on live birth. Ovarian stimulation with CFa does not seem to constitute an equally effective method as compared with follitropin beta to be offered in a general subfertile population seeking IVF/ICSI treatments.     

Catheter ablation of atrial fibrillation in the elderly

Background Atrial fibrillation （AF） catheter ablation has emerged as a promising treatment strategy for AF, but has not been widely adopted in the elderly population. The present study aimed to determine the safety and efficacy of AF catheter ablation in the elderly popula-tion. Methods and Results The study population consisted of 316 patients with paroxysmal AF who underwent left atrial ablation. Ninety-five patients were≥65 years （48 males, mean age 68.9 ± 3.0 years old） and 221 patients were〈65 years old （130 males, mean age 52.5 ± 10.4 years old）. After a mean follow-up period of 34.0 ± 15.1 months, 55 （57.9%） patients in the elderly group were free from ar-rhythmia recurrence compared with 149 （67.4%） patients in the younger group （P=0.169）. Procedural complications were uncommon in both study groups. In logistic regression analysis, left atrial diameter （P=0.003）, hypertension （P=0.001）, dyslipidemia （P=0.039）, and coronary artery disease （P=0.018） were independent predictors of AF recurrence in the elderly population. Conclusions Catheter ablation of AF is safe and effective in older patients. Invasive strategies should be considered as an alternative choice in symptomatic elderly patients with AF.