MicroRNA Expression Profiling-Potential Molecular Discrimination of Papillary Thyroid Carcinoma Subtypes.

Recent research has revealed the importance of miRNAs in the diagnosis and clinical evolution of papillary thyroid cancer (PTC). We aim to identify a specific miRNA profile that could differentiate between specific subtypes of PTC. METHODS: In this cross-sectional study, total RNA was extracted from paraffin-embedded tissues of 43 patients, 17 with an infiltrative follicular variant of PTC (iFVPTC) and 26 with a conventional variant of PTC (cPTC). Nine miRNAs were evaluated using qRT-PCR technology and specific miRNA assays. RESULTS: We found specific patterns for cPTC and iFVPTC, such as miRNA altered in both types of tumours (miR-146b-5p, miR-181a-5p, miR-221-3p, miR-21-5p and miR-222-3p) and two miRNAs significantly expressed only in cPTC (miR-20b-5p, miR-21-5p). The iFVPTC group presented strong and moderate correlations between miRNA expression and clinical data. miR-221-3p, miR-195-5p, miR-181-5p, miR-146b-5p and miR-222 were correlated with age, tumour size (TS) or lymph node metastases (N), while only miR-20b-5p, miR-195-5p and miR-181-5p were correlated with TS, N and age in the cPTC group. CONCLUSIONS: The present study allowed the identification of a signature of two miRNAs to confirm miRNA differences between the two histological subtypes of TC. Our results provide advances in the molecular diagnosis of TC and could help to improve the diagnostic performance of already existing molecular classifiers.

Fractional calculus in mathematical oncology.

Even though, nowadays, cancer is one of the leading causes of death, too little is known about the behavior of this disease due to its unpredictability from one patient to another. Classical mathematical models of tumor growth have shaped our understanding of cancer and have broad practical implications for treatment scheduling and dosage. However, improvements are still necessary on these models. The primary objective of the present research is to prove the efficiency of fractional order calculus in mathematical oncology, more specifically in tumor growth modeling. For this, a generalization of the four most used differential equation models in tumor volume measurements fitting is realized, using the corresponding fractional order equivalent. Are established the fractional order Exponential, Logistic, Gompertz, General Bertalanffy-Pütter and Classical Bertalanffy-Pütter models for a treated and untreated dataset. The obtained results are compared by Mean Squared Error (MSE) with the integer order correspondent of each model. The results prove the superiority of the fractional order models. The MSE of fractional order models are reduced at least at half in comparison with the MSE of the integer order equivalent. It is demonstrated in this way that fractional order deterministic models can offer a good starting point in finding a proper mathematical model for tumor evolution prediction. Fractional calculus is a suitable method in this case due to its memory property, aspect that particularly characterizes biological processes.

Mathematical Modeling and Optimization of Lactobacillus Species Single and Co-Culture Fermentation Processes in Wheat and Soy Dough Mixtures.

To improve food production via fermentation with co-cultures of microorganisms (e.g., multiple lactic acid bacteria-LAB strains), one must fully understand their metabolism and interaction patterns in various conditions. For example, LAB can bring added quality to bread by releasing several bioactive compounds when adding soy flour to wheat flour, thus revealing the great potential for functional food development. In the present work, the fermentation of three soy and wheat flour mixtures is studied using single cultures and co-cultures of Lactobacillus plantarum and Lactobacillus casei. Bio-chemical processes often require a significant amount of time to obtain the optimal amount of final product; creating a mathematical model can gain important information and aids in the optimization of the process. Consequently, mathematical modeling is used to optimize the fermentation process by following these LAB's growth kinetics and viability. The present work uses both multiple regression and artificial neural networks (ANN) to obtain the necessary mathematical model, useful in both prediction and process optimization. The main objective is to find a model with optimal performances, evaluated using an ANOVA test. To validate each obtained model, the simulation results are compared with the experimental data.

Automated detection of celiac disease using Machine Learning Algorithms.

Celiac disease is a disorder of the immune system that mainly affects the small intestine but can also affect the skeletal system. The diagnosis relies on histological assessment of duodenal biopsies acquired by upper digestive endoscopy. Immunological tests involve collecting a blood sample to detect if the antibodies have been produced in the body. Endoscopy is invasive and histology is time-consuming. In recent years there have been various algorithms that use artificial intelligence (AI) and neural convolutions (CNN, Convolutional Neural Network) to process images from capsule endoscopy, a non-invasive endoscopy approach, that provides magnified, high qualitative images of the small bowel mucosa, to quickly establish a diagnosis. The proposed innovative approach do not use complex learning algorithms, instead it find some artefacts in the endoscopies using kernels and use classified machine learning algorithms. Each used artefacts have a psychical meaning: atrophies of the mucosa with a visible submucosal vascular pattern; the presence of cracks (depressions) that have an appearance similar to that of dry land; reduction or complete loss of folds in the duodenum; the presence of a submerged appearance at the Kerckring folds and a low number of villi. The results obtained for video capsule endoscopy images processing reveal an accuracy of 94.1% and F1 score of 94%, which is competitive with other complex algorithms. The main goal of the present research was to demonstrate that computer-aided diagnosis of celiac disease is possible even without the use of very complex algorithms, which require expensive hardware and a lot of processing time. The use of the proposed automated images processing acquired noninvasively by capsule endoscopy would be assistive in detecting the subtle presence of villous atrophy not evident by visual inspection. It may also be useful to assess the degree of improvement of celiac. Patients on a gluten-free diet, the main treatment method for stopping the autoimmune process and improving the state of the small intestinal villi. The novelty of the work is that the algorithm uses two modified filters to properly analyse the intestine wall texture. It is proved that using the right filters, the proper diagnostic can be obtained by image processing, without the use of a complicated machine learning algorithm.

Organ-On-A-Chip: A Survey of Technical Results and Problems.

Organ-on-a-chip (OoC), also known as micro physiological systems or "tissue chips" have attracted substantial interest in recent years due to their numerous applications, especially in precision medicine, drug development and screening. Organ-on-a-chip devices can replicate key aspects of human physiology, providing insights into the studied organ function and disease pathophysiology. Moreover, these can accurately be used in drug discovery for personalized medicine. These devices present useful substitutes to traditional preclinical cell culture methods and can reduce the use of in vivo animal studies. In the last few years OoC design technology has seen dramatic advances, leading to a wide range of biomedical applications. These advances have also revealed not only new challenges but also new opportunities. There is a need for multidisciplinary knowledge from the biomedical and engineering fields to understand and realize OoCs. The present review provides a snapshot of this fast-evolving technology, discusses current applications and highlights advantages and disadvantages for biomedical approaches.

Automatic Detection of Colorectal Polyps Using Transfer Learning.

Colorectal cancer is the second leading cause of cancer death and ranks third worldwide in diagnosed malignant pathologies (1.36 million new cases annually). An increase in the diversity of treatment options as well as a rising population require novel diagnostic tools. Current diagnostics involve critical human thinking, but the decisional process loses accuracy due to the increased number of modulatory factors involved. The proposed computer-aided diagnosis system analyses each colonoscopy and provides predictions that will help the clinician to make the right decisions. Artificial intelligence is included in the system both offline and online image processing tools. Aiming to improve the diagnostic process of colon cancer patients, an application was built that allows the easiest and most intuitive interaction between medical staff and the proposed diagnosis system. The developed tool uses two networks. The first, a convolutional neural network, is capable of classifying eight classes of tissue with a sensitivity of 98.13% and an F1 score of 98.14%, while the second network, based on semantic segmentation, can identify the malignant areas with a Jaccard index of 75.18%. The results could have a direct impact on personalised medicine combining clinical knowledge with the computing power of intelligent algorithms.

A Review of Recent Advances in Fractional-Order Sensing and Filtering Techniques.

The present manuscript aims at raising awareness of the endless possibilities of fractional calculus applied not only to system identification and control engineering, but also into sensing and filtering domains. The creation of the fractance device has enabled the physical realization of a new array of sensors capable of gathering more information. The same fractional-order electronic component has led to the possibility of exploring analog filtering techniques from a practical perspective, enlarging the horizon to a wider frequency range, with increased robustness to component variation, stability and noise reduction. Furthermore, fractional-order digital filters have developed to provide an alternative solution to higher-order integer-order filters, with increased design flexibility and better performance. The present study is a comprehensive review of the latest advances in fractional-order sensors and filters, with a focus on design methodologies and their real-life applicability reported in the last decade. The potential enhancements brought by the use of fractional calculus have been exploited as well in sensing and filtering techniques. Several extensions of the classical sensing and filtering methods have been proposed to date. The basics of fractional-order filters are reviewed, with a focus on the popular fractional-order Kalman filter, as well as those related to sensing. A detailed presentation of fractional-order filters is included in applications such as data transmission and networking, electrical and chemical engineering, biomedicine and various industrial fields.

Artificial Intelligence in Colorectal Cancer Diagnosis Using Clinical Data: Non-Invasive Approach.

Colorectal cancer is the third most common and second most lethal tumor globally, causing 900,000 deaths annually. In this research, a computer aided diagnosis system was designed that detects colorectal cancer, using an innovative dataset composing of both numeric (blood and urine analysis) and qualitative data (living environment of the patient, tumor position, T, N, M, Dukes classification, associated pathology, technical approach, complications, incidents, ultrasonography-dimensions as well as localization). The intelligent computer aided colorectal cancer diagnosis system was designed using different machine learning techniques, such as classification and shallow and deep neural networks. The maximum accuracy obtained from solving the binary classification problem with traditional machine learning algorithms was 77.8%. However, the regression problem solved with deep neural networks yielded with significantly better performance in terms of mean squared error minimization, reaching the value of 0.0000529.

Sensors for Cryogenic Isotope-Separation Column.

Cryogenic isotope-separation equipment is special, encountered in relative few research centers in the world. In addition to the main equipment used in the operation column, a broad range of measuring devices and actuators are involved in the technological process. The proper sensors and transducers exhibit special features; therefore, common, industrial versions cannot be used. Three types of original sensors with electronic adapters are presented in the present study: a sensor for the liquid carbon monoxide level in the boiler, a sensor for the liquid nitrogen level in the condenser and a sensor for the electrical power dissipated in the boiler. The integration of these sensors in the pilot equipment is needed for comprehensive system monitoring and control. The sensors were tested on the experimental equipment from the National Institute for Research and Development of Isotopic and Molecular Technologies from Cluj-Napoca.

Connected Bike-smart IoT-based Cycling Training Solution.

The Connected Bike project combines several technologies, both hardware and software, to provide cycling enthusiasts with a modern alternative solution for training. Therefore, a trainer can monitor online through a Web Application some of the important parameters for training, more specifically the speed, cadence and power generated by the cyclist. Also, the trainer can see at every moment where the rider is with the aid of a GPS module. The system is built out of both hardware and software components. The hardware is in charge of collecting, scaling, converting and sending data from sensors. On the software side, there is the server, which consists of the Back-End and the MQTT (Message Queues Telemetry Transport) Broker, as well as the Front-End of the Web Application that displays and manages data as well as collaboration between cyclists and trainers. Finally, there is the Android Application that acts like a remote command for the hardware module on the bike, giving the rider control over how and when the ride is monitored.

Modeling tool using neural networks for L(+)-lactic acid production by pellet-form Rhizopus oryzae NRRL 395 on biodiesel crude glycerol.

Most chemical reactions produce unwanted by-products. In an effort to reduce environmental problems these by-products could be used to produce valuable organic chemicals. In biodiesel industry a huge amount of glycerol is generated, approximately 10% of the final product. The research group from University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca developed opportunities to produce L(+) lactic acid from the glycerol. The team is using the Rhizopus oryzae NRRL 395 bacteria for the fermentation of the glycerol. The purpose of the research is to improve the production of L(+) lactic acid in order to optimize the process. A predictive model obtained by neural networks is useful in this case. The main objective of the present work is to present the developed user-friendly application useful in modeling this fermentation process, in order to be used by people who are inexperienced with neural networks or specific software. Besides the interface for training of a new neural network in order to develop the model in some characteristic condition, the software also provides an interface for visualization of the results, useful in interpretation and as a tool for prediction.