Biological cells classification using bio-inspired descriptor in a boosting k-NN framework

International audienceHigh-content imaging is an emerging technology for the analysis and quantification of biological phenomena. Thus, classifying a huge number of cells or quantifying markers from large sets of images by experts is a very time-consuming and poorly reproducible task. In order to overcome such limitations, we propose a supervised method for automatic cell classification. Our approach consists of two steps: the first one is an indexing stage based on specific bio-inspired features relying on the distribution of contrast information on segmented cells. The second one is a supervised learning stage that selects the prototypical samples best representing the cell categories. These prototypes are used in a leveraged k-NN framework to predict the class of unlabeled cells. In this paper we have tested our new learning algorithm on cellular images acquired for the analysis of pathologies. In order to evaluate the automatic classification performances, we tested our algorithm on the HEp2 Cells dataset of (Foggia et al, CBMS 2010). Results are very promising, showing classification precision larger than 96% on average, thus suggesting our method as a valuable decision-support tool in such cellular imaging applications

Classification of biological cells using bio-inspired descriptors

International audienceThis paper proposes a novel automated approach for the categorization of cells in fluorescence microscopy images. Our supervised classification method aims at recognizing patterns of unlabeled cells based on an annotated dataset. First, the cell images need to be indexed by encoding them in a feature space. For this purpose, we propose tailored bio-inspired features relying on the distribution of contrast information. Then, a supervised learning algorithm is proposed for classifying the cells. We carried out experiments on cellular images related to the diagnosis of autoimmune diseases, testing our classification method on the HEp-2 Cells dataset of Foggia et al (CBMS 2010). Results show classification precision larger than 96% on average, thus confirming promising application of our approach to the challenging application of cellular image classification for computer-aided diagnosis

A Bio-inspired Learning and Classification Method for Subcellular Localization of a Plasma Membrane Protein

International audienceHigh-content cellular imaging is an emerging technology for studying many biological phenomena. statistical analyses on large populations (more than thousands) of cells are required. Hence classifying cells by experts is a very time-consuming task and poorly reproducible. In order to overcome such limitations, we propose an automatic supervised classification method. Our new cell classification method consists of two steps: The first one is an indexing process based on specific bio-inspired features using contrast information distributions on cell sub-regions. The second is a supervised learning process to select prototypical samples (that best represent the cells categories) which are used in a leveraged k-NN framework to predict the class of unlabeled cells. In this paper we have tested our new learning algorithm on cellular images acquired for the analysis of changes in the subcellular localization of a membrane protein (the sodium iodide symporter). In order to evaluate the automatic classification performances, we tested our algorithm on a significantly large database of cellular images annotated by experts of our group. Results in term of Mean Avarage Precision (MAP) are very promising, providing precision upper than 87% on average, thus suggesting our method as a valuable decision-support tool in such cellular imaging applications. Such supervised classification method has many other applications in cell imaging in the areas of research in basic biology and medicine but also in clinical histology

The Efficacy of Flogofilm® in the Treatment of Chronic Bacterial Prostatitis as an Adjuvant to Antibiotic Therapy: A Randomized Prospective Trial

Introduction: Bacterial prostatitis (BP) is a common prostatic infection characterized by a bimodal distribution in young and older men, with a prevalence between 5-10% among all cases of prostatitis and a high impact on quality of life. Although the management of bacterial prostatitis involves the use of appropriate spectrum antibiotics, which represent the first choice of treatment, a multimodal approach encompassing antibiotics and nutraceutical products in order to improve the efficacy of chosen antimicrobial regimen is often required. Objective: To evaluate the efficacy of Flogofilm® in association with fluoroquinolones in patients with chronic bacterial prostatitis (CBP). Methods: Patients diagnosed with prostatitis (positivity to Meares-Stamey Test and symptoms duration > 3 months) at the University of Naples "Federico II", Italy, from July 2021 to December 2021, were included in this study. All patients underwent bacterial cultures and trans-rectal ultrasounds. Patients were randomized into two groups (A and B) receiving antibiotic alone or an association of antibiotics plus Flogofilm® tablets containing Flogomicina® for one month, respectively. The NIH-CPSI and IPSS questionnaires were administered at baseline, four weeks, twelve and twenty-four weeks. Results: A total of 96 (Group A = 47, Group B = 49) patients concluded the study protocol. The mean age was comparable, with a mean age of 34.62 ± 9.04 years for Group A and 35.29 ± 10.32 years for Group B (p = 0.755), and IPSS at the baseline was 8.28 ± 6.33 and 9.88 ± 6.89 (p = 0.256), respectively, while NIH-CPSI at baseline was 21.70 ± 4.38 and 21.67 ± 6.06 (p = 0.959), respectively. At 1, 3 and 6 months, the IPSS score was 6.45 ± 4.8 versus 4.31 ± 4.35 (p = 0.020), 5.32 ± 4.63 versus 3.20 ± 3.05 (p = 0.042) and 4.91 ± 4.47 versus 2.63 ± 3.28 (p = 0.005) for Groups A and B, respectively. Similarly, the NIH-CPSI total score at 1, 3 and 6 months was 16.15 ± 3.31 versus 13.10 ± 5.03 (p < 0.0001), 13.47 ± 3.07 versus 9.65 ± 4.23 (p < 0.0001) and 9.83 ± 2.53 versus 5.51 ± 2.84 (p < 0.0001), respectively. Conclusions: Flogofilm®, associated with fluoroquinolones, demonstrate a significant improvement in pain, urinary symptoms and quality of life in patients affected by chronic bacterial prostatitis in both IPSS and NIH-CPSI scores compared with fluoroquinolones alone