Tempeh flour as a substitute for soybean flour in coconut cookies.

The objective of this study was to prepare roasted and lyophilized tempeh flour with soybean cultivar BRS 267 to apply them in the formulation of coconut biscuits. The cookies produced with whole soy flour and mixed flour of soybean and tempeh were evaluated for proximate composition, fatty acid profile, and isoflavone aglycones in order to verify the effects of inoculation with the fungus Rhizopus oligosporus and those of the drying processes of roasting and lyophilization on the chemical characteristics of the final product. Sensory acceptance and purchase intention of the formulated products were also evaluated. The results indicate the maintenance of linolenic acid, which is important in the prevention of coronary diseases, and an increase in the aglycones levels when the tempeh flour was used. Lipids and proteins showed differences, and the sensory analyses demonstrated similarity between the cookies with satisfactory scores for aroma, flavor, texture, and overall acceptability for both samples. when compared to the control. Purchase intent was also positive for the lyophilized and toasted tempeh flours, thus enabling the use of the roasting process as a simple drying method, for processing tempeh and obtaining a flour rich in proteins and aglycones that can be used as a partial substitute for soy flour in cookies and other bakery products

A fully automated pipeline for a robust conjunctival hyperemia estimation

Purpose: Many semi-automated and fully-automated approaches have been proposed in literature to improve the objectivity of the estimation of conjunctival hyperemia, based on image processing analysis of eyes’ photographs. The purpose is to improve its evaluation using faster fully-automated systems and independent by the human subjectivity. Methods: In this work, we introduce a fully-automated analysis of the redness grading scales able to completely automatize the clinical procedure, starting from the acquired image to the redness estimation. In particular, we introduce a neural network model for the conjunctival segmentation followed by an image processing pipeline for the vessels network segmentation. From these steps, we extract some features already known in literature and whose correlation with the conjunctival redness has already been proved. Lastly, we implemented a predictive model for the conjunctival hyperemia using these features. Results: In this work, we used a dataset of images acquired during clinical practice.We trained a neural network model for the conjunctival segmentation, obtaining an average accuracy of 0.94 and a corresponding IoU score of 0.88 on a test set of images. The set of features extracted on these ROIs is able to correctly predict the Efron scale values with a Spearman’s correlation coefficient of 0.701 on a set of not previously used samples. Conclusions: The robustness of our pipeline confirms its possible usage in a clinical practice as a viable decision support system for the ophthalmologists

Prediction of vascular aging based on smartphone acquired PPG signals

Photoplethysmography (PPG) measured by smartphone has the potential for a large scale, non-invasive, and easy-to-use screening tool. Vascular aging is linked to increased arterial stiffness, which can be measured by PPG. We investigate the feasibility of using PPG to predict healthy vascular aging (HVA) based on two approaches: machine learning (ML) and deep learning (DL). We performed data preprocessing, including detrending, demodulating, and denoising on the raw PPG signals. For ML, ridge penalized regression has been applied to 38 features extracted from PPG, whereas for DL several convolutional neural networks (CNNs) have been applied to the whole PPG signals as input. The analysis has been conducted using the crowd-sourced Heart for Heart data. The prediction performance of ML using two features (AUC of 94.7%) \u2013 the a wave of the second derivative PPG and tpr, including four covariates, sex, height, weight, and smoking \u2013 was similar to that of the best performing CNN, 12-layer ResNet (AUC of 95.3%). Without having the heavy computational cost of DL, ML might be advantageous in finding potential biomarkers for HVA prediction. The whole workflow of the procedure is clearly described, and open software has been made available to facilitate replication of the results

Development of a Rotation Device for Microvascular Endothelial Cell Seeding

A rotation device (RD) specifically designed to achieve sterile endothelial cell (EC) seeding of vascular grafts has been developed. The basic characteristics of the RD include: small dimensions, fully autoclavable components, and perfectly sealed graft holders. These features make it possible to maintain sterility during all the steps of EC seeding. This was documented by negativity of all bacteriological assays performed . Moreover, the RD can simultaneously support three vascular grafts with different lengths (20, 40, and 60 cm) and diameters (4-8 mm). EC seeding is performed in the climatized chamber (37 °C; 5 % C02) with constant rotation (0.1 -3 rpm). The rotation cycle can be completed automatically. The practical efficacy of the RD was investigated by seeding 2 x 105/cm2 of human microvascular EC on 20 cm length, 4 mm internal diameter (ID) fibronectin- coated polytetrafluoroethylene (ePTFE) grafts for 24 and 48 hours respectively . Further, the effect of a highly viscous plasma expander, i.e., haemagel, on cell retention was also evaluated. Results were not as favorable as expected. However, it should be emphasized that after 48 hours of eel! incubation by using the RD, 42 % of the initially seeded EC were still present and approximately 15 % were fully spread over the graft surface. Moreover, the 10 minute perfusion with haemagel did not decrease the number of adherent microvascular EC

Prediction of vascular aging based on smartphone acquired PPG signals

Photoplethysmography (PPG) measured by smartphone has the potential for a large scale, non-invasive, and easy-to-use screening tool. Vascular aging is linked to increased arterial stiffness, which can be measured by PPG. We investigate the feasibility of using PPG to predict healthy vascular aging (HVA) based on two approaches: machine learning (ML) and deep learning (DL). We performed data preprocessing, including detrending, demodulating, and denoising on the raw PPG signals. For ML, ridge penalized regression has been applied to 38 features extracted from PPG, whereas for DL several convolutional neural networks (CNNs) have been applied to the whole PPG signals as input. The analysis has been conducted using the crowd-sourced Heart for Heart data. The prediction performance of ML using two features (AUC of 94.7%) – the a wave of the second derivative PPG and tpr, including four covariates, sex, height, weight, and smoking – was similar to that of the best performing CNN, 12-layer ResNet (AUC of 95.3%). Without having the heavy computational cost of DL, ML might be advantageous in finding potential biomarkers for HVA prediction. The whole workflow of the procedure is clearly described, and open software has been made available to facilitate replication of the results

Chondroitin sulfate immobilization at the surface of electrospun nanofiber meshes for cartilage regeneration

Aiming at improving the biocompatibility of biomaterial scaffolds, surface modification presents a way to preserve their mechanical properties and to improve the surface bioactivity. In this work, chondroitin sulfate (CS) was immobilized at the surface of electrospun poly(caprolactone) nanofiber meshes (PCL NFMs). The immobilization was performed with 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide (EDC)/N-hydroxysuccinimide (NHS). Contact Angle, SEM, Optical Profilometry, FTIR, X-ray photoelectron spectroscopy techniques confirmed the CS-immobilization in PCL NFMs. Furthermore, CS-immobilized PCL NFMs showed lower roughness and higher hydrophilicity than the samples without CS. Human articular chondrocytes (HACs) were cultured on electrospun PCL NFMs with or without CS immobilization. It was observed that HACs proliferated through the entire time course of the experiment in both types of scaffolds. SEM observations revealed that HACs maintained their typical morphology and produced extracellular matrix. Glycosaminoglycans quantification showed increased values over time. Quantitative-PCR of cartilage-related genes revealed over-expression of Aggrecan, Collagen type II, COMP and Sox9 on both types of NFMs tested, with higher values for PCL. In conclusion, CS immobilization in PCL NFM was achieved successfully and provides a valid platform enabling further surface functionalization methods in scaffolds to be developed for cartilage tissue engineering

Advantages of manual and automatic computer-aided compared to traditional histopathological diagnosis of melanoma: A pilot study

Background: Cutaneous malignant melanoma (CMM) accounts for the highest mortality rate among all skin cancers. Traditional histopathologic diagnosis may be limited by the pathologists’ subjectivity. Second-opinion strategies and multidisciplinary consultations are usually performed to overcome this issue. An available solution in the future could be the use of automated solutions based on a computational algorithm that could help the pathologist in everyday practice. The aim of this pilot study was to investigate the potential diagnostic aid of a machine-based algorithm in the histopathologic diagnosis of CMM. Methods: We retrospectively examined excisional biopsies of 50 CMM and 20 benign congenital compound nevi. Hematoxylin and eosin (H&E) stained WSI were reviewed independently by two expert dermatopathologists. A fully automated pipeline for WSI processing to support the estimation and prioritization of the melanoma areas was developed. Results: The spatial distribution of the nuclei in the sample provided a multi-scale overview of the tumor. A global overview of the lesion's silhouette was achieved and, by increasing the magnification, the topological distribution of the nuclei and the most informative areas of interest for the CMM diagnosis were identified and highlighted. These silhouettes allow the histopathologist to discriminate between nevus and CMM with an accuracy of 96% without any extra information. Conclusion: In this study we proposed an easy-to-use model that produces segmentations of CMM silhouettes at fine detail level

Combinatorial Discriminant Analysis Applied to RNAseq Data Reveals a Set of 10 Transcripts as Signatures of Exposure of Cattle to Mycobacterium avium subsp. paratuberculosis

Paratuberculosis or Johne's disease in cattle is a chronic granulomatous gastroenteritis caused by infection with Mycobacterium avium subspecies paratuberculosis (MAP). Paratuberculosis is not treatable; therefore, the early identification and isolation of infected animals is a key point to reduce its incidence. In this paper, we analyse RNAseq experimental data of 5 ELISA-negative cattle exposed to MAP in a positive herd, compared to 5 negative-unexposed controls. The purpose was to find a small set of differentially expressed genes able to discriminate between exposed animals in a preclinical phase from non-exposed controls. Our results identified 10 transcripts that differentiate between ELISA-negative, clinically healthy, and exposed animals belonging to paratuberculosis-positive herds and negative-unexposed animals. Of the 10 transcripts, five (TRPV4, RIC8B, IL5RA, ERF, CDC40) showed significant differential expression between the three groups while the remaining 5 (RDM1, EPHX1, STAU1, TLE1, ASB8) did not show a significant difference in at least one of the pairwise comparisons. When tested in a larger cohort, these findings may contribute to the development of a new diagnostic test for paratuberculosis based on a gene expression signature. Such a diagnostic tool could allow early interventions to reduce the risk of the infection spreading

Prediction of Overall Survival in Cervical Cancer Patients Using PET/CT Radiomic Features

Background: Radiomics is a field of research medicine and data science in which quantitative imaging features are extracted from medical images and successively analyzed to develop models for providing diagnostic, prognostic, and predictive information. The purpose of this work was to develop a machine learning model to predict the survival probability of 85 cervical cancer patients using PET and CT radiomic features as predictors. Methods: Initially, the patients were divided into two mutually exclusive sets: a training set containing 80% of the data and a testing set containing the remaining 20%. The entire analysis was separately conducted for CT and PET features. Genetic algorithms and LASSO regression were used to perform feature selection on the initial PET and CT feature sets. Two different survival models were employed: the Cox proportional hazard model and random survival forest. The Cox model was built using the subset of features obtained with the feature selection process, while all the available features were used for the random survival forest model. The models were trained on the training set; cross-validation was used to fine-tune the models and to obtain a preliminary measurement of the performance. The models were then validated on the test set, using the concordance index as the metric. In addition, alternative versions of the models were developed using tumor recurrence as an adjunct feature to evaluate its impact on predictive performance. Finally, the selected CT and PET features were combined to build a further Cox model. Results: The genetic algorithm was superior to the LASSO regression for feature selection. The best performing model was the Cox model, which was built using the selected CT features; it achieved a concordance index score of 0.707. With the addition of tumor recurrence as a predictive feature, the Cox CT model reached a concordance index score of 0.776. PET features, however, proved to be inadequate for survival prediction. The CT model performed better than the model with combined PET and CT features. Conclusions: The results showed that radiomic features can be used to successfully predict survival probability in cervical cancer patients. In particular, CT radiomic features proved to be better predictors than PET radiomic features in this specific case