Explainable AI-Based Skin Cancer Detection Using CNN, Particle Swarm Optimization and Machine Learning.

Skin cancer is among the most prevalent cancers globally, emphasizing the need for early detection and accurate diagnosis to improve outcomes. Traditional diagnostic methods, based on visual examination, are subjective, time-intensive, and require specialized expertise. Current artificial intelligence (AI) approaches for skin cancer detection face challenges such as computational inefficiency, lack of interpretability, and reliance on standalone CNN architectures.

Exploring TAS2R46 biomechanics through molecular dynamics and network analysis.

Understanding the intricate interplay between structural features and signal-processing events is crucial for unravelling the mechanisms of biomolecular systems. G protein-coupled receptors (GPCRs), a pervasive protein family in humans, serve a wide spectrum of vital functions. TAS2Rs, a subfamily of GPCRs, play a primary role in recognizing bitter molecules and triggering events leading to the perception of bitterness, a crucial defence mechanism against spoiled or poisonous food.

Predicting multiple taste sensations with a multiobjective machine learning method.

Taste perception plays a pivotal role in guiding nutrient intake and aiding in the avoidance of potentially harmful substances through five basic tastes - sweet, bitter, umami, salty, and sour. Taste perception originates from molecular interactions in the oral cavity between taste receptors and chemical tastants. Hence, the recognition of taste receptors and the subsequent perception of taste heavily rely on the physicochemical properties of food ingredients.

AI can empower agriculture for global food security: challenges and prospects in developing nations.

Food and nutrition are a steadfast essential to all living organisms. With specific reference to humans, the sufficient and efficient supply of food is a challenge as the world population continues to grow. Artificial Intelligence (AI) could be identified as a plausible technology in this 5th industrial revolution in bringing us closer to achieving zero hunger by 2030-Goal 2 of the United Nations Sustainable Development Goals (UNSDG).

VirtuousPocketome: a computational tool for screening protein-ligand complexes to identify similar binding sites.

Protein residues within binding pockets play a critical role in determining the range of ligands that can interact with a protein, influencing its structure and function. Identifying structural similarities in proteins offers valuable insights into their function and activation mechanisms, aiding in predicting protein-ligand interactions, anticipating off-target effects, and facilitating the development of therapeutic agents. Numerous computational methods assessing global or local similarity in protein cavities have emerged, but their utilization is impeded by complexity, impractical automation for amino acid pattern searches, and an inability to evaluate the dynamics of scrutinized protein-ligand systems.

Informed classification of sweeteners/bitterants compounds via explainable machine learning.

Perception of taste is an emergent phenomenon arising from complex molecular interactions between chemical compounds and specific taste receptors. Among all the taste perceptions, the dichotomy of sweet and bitter tastes has been the subject of several machine learning studies for classification purposes. While previous studies have provided accurate sweeteners/bitterants classifiers, there is ample scope to enhance these models by enriching the understanding of the molecular basis of bitter-sweet tastes.

Fungi, P-Solubilization, and Plant Nutrition.

The application of plant beneficial microorganisms is widely accepted as an efficient alternative to chemical fertilizers and pesticides. It was shown that annually, mycorrhizal fungi and nitrogen-fixing bacteria are responsible for 5 to 80% of all nitrogen, and up to 75% of P plant acquisition. However, while bacteria are the most studied soil microorganisms and most frequently reported in the scientific literature, the role of fungi is relatively understudied, although they are the primary organic matter decomposers and govern soil carbon and other elements, including P-cycling.

A survey on computational taste predictors.

Unlabelled: Taste is a sensory modality crucial for nutrition and survival, since it allows the discrimination between healthy foods and toxic substances thanks to five tastes, i.e., sweet, bitter, umami, salty, and sour, associated with distinct nutritional or physiological needs.

Cardiovascular Risk Prediction in Ankylosing Spondylitis: From Traditional Scores to Machine Learning Assessment.

Introduction: The performance of seven cardiovascular (CV) risk algorithms is evaluated in a multicentric cohort of ankylosing spondylitis (AS) patients. Performance and calibration of traditional CV predictors have been compared with the novel paradigm of machine learning (ML).

Methods: A retrospective analysis of prospectively collected data from an AS cohort has been performed.

Predicting the metabolic condition after gestational diabetes mellitus from oral glucose tolerance test curves shape.

The objective of this feasibility study is to predict the metabolic condition in women with a history of gestational diabetes mellitus (GDM) from the shape of oral glucose tolerance test (OGTT) data. The rationale for this approach is that the evolution to a metabolic condition could be traceable in the shape of OGTT curves. 3-h OGTT data of 136 women with follow up, for a total of 401 OGTTs were analyzed.

A 4-h hyperglycaemic excursion induces rapid and slow changes in major electrolytes in blood in healthy human subjects.

Hyperglycaemia is well known to cause reductions in plasma Na(+) levels or even hyponatraemia due to an osmotically induced dilution of the interstitium and blood. It is, however, unclear whether this dilution is significantly counteracted by ion regulatory homeostatic mechanism(s) or not. Furthermore, the effects of moderate hyperglycaemia on other major ions are less well known.

Identification of a model of non-esterified fatty acids dynamics through genetic algorithms: the case of women with a history of gestational diabetes.

Elevation in non-esterified fatty acids (NEFA) has been shown to modulate insulin secretion and it is considered as a risk factor for the development of type 2 diabetes. Here we present a method that complements a mathematical model of NEFA kinetics with genetic algorithms for model identification. The complemented strategy allowed to assess parameters of NEFA kinetics and to get insight into their relationship with insulin during oral glucose tolerance tests in women with former gestational diabetes: (i) providing a reliable estimation of the model parameters, (ii) assuring the usability of the model, and (iii) promoting and facilitating its application in a clinical context.

Biomechanics of actin filaments: a computational multi-level study.

The actin microfilament (F-actin) is a structural and functional component of the cell cytoskeleton. Notwithstanding the primary role it plays for the mechanics of the cell, the mechanical behaviour of F-actin is still not totally explored. In particular, the relationship between the mechanics of F-actin and its molecular architecture is not completely understood.

A self-organizing map based morphological analysis of oral glucose tolerance test curves in women with gestational diabetes mellitus.

Gestational diabetes mellitus (GDM) makes women at risk of type 2 diabetes during their life. In order to predict this later abnormal glucose intolerance, several antepartum and postpartum predictors have been identified. In this study we conjecture that future evolution is predictable from morphology of the oral glucose tolerance test (OGTT) curves at baseline.

Decoding 3D search coil signals in a non-homogeneous magnetic field.

We present a method for recording eye-head movements with the magnetic search coil technique in a small external magnetic field. Since magnetic fields are typically non-linear, except in a relative small region in the center small field frames have not been used for head-unrestrained experiments in oculomotor studies. Here we present a method for recording 3D eye movements by accounting for the magnetic non-linearities using the Biot-Savart law.

Improved usability of the minimal model of insulin sensitivity based on an automated approach and genetic algorithms for parameter estimation.

Minimal model analysis of glucose and insulin data from an IVGTT (intravenous glucose tolerance test) is widely used to estimate insulin sensitivity; however, the use of the model often requires intervention by a trained operator and some problems can occur in the estimation of model parameters. In the present study, a new method for minimal model analysis, termed GAMMOD, was developed based on genetic algorithms for the estimation of model parameters. Such an algorithm does not require the fixing of initial values for the parameters (that may lead to unreliable estimates).

A novel formulation for blood trauma prediction by a modified power-law mathematical model.

With the increasing use of artificial organs, blood damage has been raising ever more clinical concern. Blood trauma is in fact a major complication resulting from the implantation of medical devices and the use of life support apparatuses. Red blood cells damage predictive models furnish critical information on both the design and the evaluation of artificial organs, because their correct usage and implementation are thought to provide clear and rational guidance for the improvement of safety and efficacy.

Evolutive algorithms for beat-by-beat estimation of left ventricular mechanics.

Traditional methods to evaluate the ventricular mechanics need intraventricular pressure and volume recordings for multiple variably loaded beats. To do this, a complex and invasive procedure must be applied, that may decrease the clinical use. To overcome this limitation, a method to estimate the ventricular mechanics beat-by-beat is presented, modeling the ventricular pressure-volume relationship with a time-varying elastance function.

The power-law mathematical model for blood damage prediction: analytical developments and physical inconsistencies.

Blood trauma caused by medical devices is a major concern. Complications following the implantation/application of devices such as prosthetic heart valves, cannulae, blood pumps, tubing, and throttles lead to sublethal and lethal damage to platelets and erythrocytes. This damage is provided by the alterations in fluid dynamics, providing a mechanical load on the blood corpuscle's membrane by means of the shear stress.