Machine learning applications to essential oils and natural extracts

Machine Learning (ML) is a branch of Artificial Intelligence (AI) that allow computers to learn without being explicitly programmed. Various are the applications of ML in pharmaceutical sciences, especially for the prediction of chemical bioactivity and physical properties, becoming an integral component of the drug discovery process. ML is characterized by three learning paradigms that differ in the type of task or problem that an algorithm is intended to solve: supervised, unsupervised, and reinforcement learning. In chapter 2, supervised learning methods were applied to extracts of Lycium barbarum L. fruits for the development of a QSPR model to predict zeaxanthin and carotenoids content based on routinely colorimetric analyses performed on homogenized samples, developing a useful tool that could be used in the food industry. In chapters 3 and 4, ML was applied to the chemical composition of essential oils and correlated to the experimentally determined associated biofilm modulation influence that was either positive or negative. In these two studies, it was demonstrated that biofilm growth is influenced by the presence of essential oils extracted from different plants harvested in different seasons. ML classification techniques were used to develop a Quantitative Activity-Composition Relationship (QCAR) to discover the chemical components mainly responsible for the anti-biofilm activity. The derived models demonstrated that machine learning is a valuable tool to investigate complex chemical mixtures, enabling scientists to understand each component's contribution to the activity. Therefore, these classification models can describe and predict the activity of chemical mixtures and guide the composition of artificial essential oils with desired biological activity. In chapter 5, unsupervised learning models were developed and applied to clinical strains of bacteria that cause cystic fibrosis. The most severe infections reoccurring in cystic fibrosis are due to S. aureus and P. aeruginosa. Intensive use of antimicrobial drugs to fight lung infections leads to the development of antibiotic-resistant bacterial strains. New antimicrobial compounds should be identified to overcome antibiotic resistance in patients. Sixty-one essential oils were studied against a panel of 40 clinical strains of S. aureus and P. aeruginosa isolated from cystic fibrosis patients, and unsupervised machine learning algorithms were applied to pick-up a small number of representative strains (clusters of strains) among the panel of 40. Thus, rapidly identifying three essential oils that strongly inhibit antibiotic-resistant bacterial growth

Antimicrobial and antibiofilm activity and machine learning classification analysis of essential oils from different mediterranean plants against pseudomonas aeruginosa

Pseudomonas aeruginosa is a ubiquitous organism and opportunistic pathogen that can cause persistent infections due to its peculiar antibiotic resistance mechanisms and to its ability to adhere and form biofilm. The interest in the development of new approaches for the prevention and treatment of biofilm formation has recently increased. The aim of this study was to seek new non-biocidal agents able to inhibit biofilm formation, in order to counteract virulence rather than bacterial growth and avoid the selection of escape mutants. Herein, different essential oils extracted from Mediterranean plants were analyzed for their activity againstP. aeruginosa. Results show that they were able to destabilize biofilm at very low concentration without impairing bacterial viability. Since the action is not related to a bacteriostatic/bactericidal activity onP. aeruginosa, the biofilm change of growth in presence of the essential oils was possibly due to a modulation of the phenotype. To this aim, application of machine learning algorithms led to the development of quantitative activity-composition relationships classification models that allowed to direct point out those essential oil chemical components more involved in the inhibition of biofilm production. The action of selected essential oils on sessile phenotype make them particularly interesting for possible applications such as prevention of bacterial contamination in the community and in healthcare environments in order to prevent human infections. We assayed 89 samples of different essential oils asP. aeruginosaanti-biofilm. Many samples inhibitedP. aeruginosabiofilm at concentrations as low as 48.8 µg/mL. Classification of the models was developed through machine learning algorithms

Node-Max-Cut and the Complexity of Equilibrium in Linear Weighted Congestion Games

In this work, we seek a more refined understanding of the complexity of local optimum computation for Max-Cut and pure Nash equilibrium (PNE) computation for congestion games with weighted players and linear latency functions. We show that computing a PNE of linear weighted congestion games is PLS-complete either for very restricted strategy spaces, namely when player strategies are paths on a series-parallel network with a single origin and destination, or for very restricted latency functions, namely when the latency on each resource is equal to the congestion. Our results reveal a remarkable gap regarding the complexity of PNE in congestion games with weighted and unweighted players, since in case of unweighted players, a PNE can be easily computed by either a simple greedy algorithm (for series-parallel networks) or any better response dynamics (when the latency is equal to the congestion). For the latter of the results above, we need to show first that computing a local optimum of a natural restriction of Max-Cut, which we call Node-Max-Cut, is PLS-complete. In Node-Max-Cut, the input graph is vertex-weighted and the weight of each edge is equal to the product of the weights of its endpoints. Due to the very restricted nature of Node-Max-Cut, the reduction requires a careful combination of new gadgets with ideas and techniques from previous work. We also show how to compute efficiently a (1+?)-approximate equilibrium for Node-Max-Cut, if the number of different vertex weights is constant

Pteridine-2,4-diamine derivatives as radical scavengers and inhibitors of lipoxygenase that can possess anti-inflammatory properties

BACKGROUND: Reactive oxygen species are associated with inflammation implicated in cancer, atherosclerosis and autoimmune diseases. The complex nature of inflammation and of oxidative stress suggests that dual-target agents may be effective in combating diseases involving reactive oxygen species. RESULTS: A novel series of N-substituted 2,4-diaminopteridines has been synthesized and evaluated as antioxidants in several assays. Many exhibited potent lipid antioxidant properties, and some are inhibitors of soybean lipoxygenase, IC50 values extending down to 100 nM for both targets. Several pteridine derivatives showed efficacy at 0.01 mmol/kg with little tissue damage in a rat model of colitis. 2-(4-methylpiperazin-1-yl)-N-(thiophen-2-ylmethyl)pteridin-4-amine (18f) at 0.01 mmol/kg exhibited potent anti-inflammatory activity (reduction by 41%). CONCLUSION: The 2,4-diaminopteridine core represents a new scaffold for lipoxygenase inhibition as well as sustaining anti-inflammatory properties

Shmt2: a stat3 signaling new player in prostate cancer energy metabolism

Prostate cancer (PCa) is a multifactorial disease characterized by the aberrant activity of different regulatory pathways. STAT3 protein mediates some of these pathways and its activation is implicated in the modulation of several metabolic enzymes. A bioinformatic analysis indicated a STAT3 binding site in the upstream region of SHMT2 gene. We demonstrated that in LNCaP, PCa cells' SHMT2 expression is upregulated by the JAK2/STAT3 canonical pathway upon IL-6 stimulation. Activation of SHTM2 leads to a decrease in serine levels, pushing PKM2 towards the nuclear compartment where it can activate STAT3 in a non-canonical fashion that in turn promotes a transient shift toward anaerobic metabolism. These results were also confirmed on FFPE prostate tissue sections at different Gleason scores. STAT3/SHMT2/PKM2 loop in LNCaP cells can modulate a metabolic shift in response to inflammation at early stages of cancer progression, whereas a non-canonical STAT3 activation involving the STAT3/HIF-1α/PKM2 loop is responsible for the maintenance of Warburg effect distinctive of more aggressive PCa cells. Chronic inflammation might thus prime the transition of PCa cells towards more advanced stages, and SHMT2 could represent a missing factor to further understand the molecular mechanisms responsible for the transition of prostate cancer towards a more aggressive phenotyp

Essential oils against bacterial isolates from cystic fibrosis patients by means of antimicrobial and unsupervised machine learning approaches

Recurrent and chronic respiratory tract infections in cystic fibrosis (CF) patients result in progressive lung damage and represent the primary cause of morbidity and mortality. Staphylococcus aureus (S. aureus) is one of the earliest bacteria in CF infants and children. Starting from early adolescence, patients become chronically infected with Gram-negative non-fermenting bacteria, and Pseudomonas aeruginosa (P. aeruginosa) is the most relevant and recurring. Intensive use of antimicrobial drugs to fight lung infections inevitably leads to the onset of antibiotic resistant bacterial strains. New antimicrobial compounds should be identified to overcome antibiotic resistance in these patients. Recently interesting data were reported in literature on the use of natural derived compounds that inhibited in vitro S. aureus and P. aeruginosa bacterial growth. Essential oils, among these, seemed to be the most promising. In this work is reported an extensive study on 61 essential oils (EOs) against a panel of 40 clinical strains isolated from CF patients. To reduce the in vitro procedure and render the investigation as convergent as possible, machine learning clusterization algorithms were firstly applied to pick-up a fewer number of representative strains among the panel of 40. This approach allowed us to easily identify three EOs able to strongly inhibit bacterial growth of all bacterial strains. Interestingly, the EOs antibacterial activity is completely unrelated to the antibiotic resistance profile of each strain. Taking into account the results obtained, a clinical use of EOs could be suggested

Recognition and Management of Incomplete Stent Expansion Facilitated by StentBoost and Guideliner Tools

Suboptimal or incomplete coronary stent expansion is associated with increased restenosis rate and target vessel revascularization. Stent visualisation with plain fluoroscopy has become challenging due to reduction of stent strut thickness. Inability of stent or balloon delivery is a frequent cause of procedural failures in percutaneous coronary interventions. This case report highlights the role of a novel stent enhancing technique, StentBoost, in recognition and management of incomplete stent expansion and of the Guideliner catheter, which is an essential assist device in complex and challenging coronary interventions, especially via the radial access

Shrimp Farming Practices in the Puttallam District of Sri Lanka: Implications for Disease Control, Industry Sustainability, and Rural Development

Shrimp farming has great potential to diversify and secure income in rural Sri Lanka, but production has significantly declined in recent years due to civil conflicts, some unsustainable practices and devastating outbreaks of disease. We examined management practices affecting disease prevention and control in the Puttalam district to identify extension services outputs that could support sustainable development of Sri Lankan shrimp farming. A survey on 621 shrimp farms (603 operational and 18 nonoperational) was conducted within the Puttalam district over 42 weeks comprising a series of three-day field visits from August 2008 to October 2009, covering two consecutive shrimp crops. Fundamental deficits in disease control, management, and biosecurity practices were found. Farmers had knowledge of biosecurity but the lack of financial resources was a major impediment to improved disease control. Smallholder farmers were disproportionately constrained in their ability to enact basic biosecurity practices due to their economic status. Basic breaches in biosecurity will keep disease as the rate limiting step in this industry. Plans to support this industry must recognize the socioeconomic reality of rural Sri Lankan aquaculture