RAS oncogenes are amongst the most commonly mutated proteins in human cancers. They regulate a wide range of effector pathways that control cell proliferation, survival, differentiation, migration and metabolic status. Including aberrations in these pathways, RAS dependent signaling is altered in more than half of human cancers. Targeting mutant RAS proteins and their downstream oncogenic signaling pathways has been elusive. However, recent results comprising detailed molecular studies, large scale omics studies and computational modeling have painted a new and more comprehensive portrait of RAS signaling that helps us to understand the intricacies of RAS, how its physiological and pathophysiological functions are regulated, and how we can target them. Here, we review these efforts particularly trying to relate the detailed mechanistic studies with global functional studies. We highlight the importance of computational modeling and data integration to derive an actionable understanding of RAS signaling that will allow us to design new mechanism based therapies for RAS mutated cancers.

The study of pain requires a balance between subjective methods that rely on self-reports and complementary objective biometrics that ascertain physical signals associated with subjective accounts. There are at present no objective scales that enable the personalized assessment of pain, as most work involving electrophysiology rely on summary statistics from a priori theoretical population assumptions. Along these lines, recent work has provided evidence of differences in pain sensations between participants with Sensory Over Responsivity (SOR) and controls. While these analyses are useful to understand pain across groups, there remains a need to quantify individual differences more precisely in a personalized manner. Here we offer new methods to characterize pain using the moment-by-moment standardized fluctuations in EEG brain activity centrally reflecting the person’s experiencing temperature-based stimulation at the periphery. This type of gross data is often disregarded as noise, yet here we show its utility to characterize the lingering sensation of discomfort raising to the level of pain, individually, for each participant. We show fundamental differences between the SOR group in relation to controls and provide an objective account of pain congruent with the subjective self-reported data. This offers the potential to build a standardized scale useful to profile pain levels in a personalized manner across the general population.

Cancer cells are adept at reprogramming energy metabolism and the precise manifestation of this metabolic reprogramming exhibits heterogeneity across individuals (and from cell to cell). In this study, we analyzed the metabolic differences between interpersonal heterogeneous cancer phenotypes. We used divergence analysis on gene expression data of 1156 breast normal and tumor samples from The Cancer Genome Atlas (TCGA) and integrated this information with a genome-scale reconstruction of human metabolism to generate personalized, context-specific metabolic networks. Using this approach, we classified the samples into four distinct groups based on their metabolic profiles. Enrichment analysis of the subsystems indicated that amino acid metabolism, fatty acid oxidation, citric acid cycle, androgen and estrogen metabolism and ROS detoxification distinguished these four groups. Additionally, we developed a workflow to identify potential drugs that can selectively target genes associated with the reactions of interest. MG-132 (a proteasome inhibitor) and OSU-03012 (a celecoxib derivative) were the top-ranking drugs identified from our analysis and known to have anti-tumor activity. Our approach has the potential to provide mechanistic insights into cancer-specific metabolic dependencies, ultimately enabling the identification of potential drug targets for each patient independently, contributing to a rational personalized medicine approach.

Neurodegenerative diseases are multifactorial, initiated by a series of the causative complex which develops into a certain clinical picture. The pathogenesis and disease course vary from patient to patient. Thus, it should be likewise to the treatment. Peripheral biomarkers are to play a central role for tailoring a personalized therapeutic plan for patients who suffered from neurodegenerative diseases such as Alzheimer’s diseases, Parkinson’s disease, and multiple sclerosis, among others. Nevertheless, the use of biomarkers in clinical practice is still underappreciated and data presented in biomarker research for clinical use is still uncompelling, compared to abundant data available for drug research and development. So is the case with kynurenines (KYNs) and the kynurenine pathway (KP) enzymes which have been associated with a wide range of diseases including cancer, autoimmune diseases, inflammatory diseases, neurologic diseases, and psychiatric disorders. This review article discusses current knowledge of the KP alteration observed in the central nervous system as well as the periphery, its involvement in pathogenesis and disease progression, and emerging evidence of roles of microbiota to the gut-brain axis, searching for practical peripheral biomarkers which ensure personalized treatment plans for neurodegenerative diseases.

Background: Developing patient-centric baseline standards that enable the detection of clinically significant outlier gene products on a genome-scale remains an unaddressed challenge required for advancing personalized medicine beyond the small pools of subjects implied by “precision medicine”. This manuscript proposes a novel approach for reference standard development to evaluate the accuracy of single-subject analyses of metabolomes, proteomes, or transcriptomes. Since distributional assumptions of statistical testing may inadequately model genome dynamics of gene products, the so-called significant results of previous studies may artefactually conflate with real signals. Model confirmation biases escalate when studies use the same analytical methods in the discovery sets and reference standards, as corroboration of results leads to an evaluation of reproducibility confounded with replicated biases rather than a measure of accuracy. We hypothesized that developing method-agnostic reference standards using effect-size and expression-level filtering of results, obtained from multiple discovery methods that are distinct from the one evaluated, would maximize the evaluation of clinical-transcriptomic signals and minimize statistical artefactual biases. We developed and released an R package “referenceNof1” to facilitate the construction of robust reference standards. Results: Since RNA-Seq data analysis methods often rely on binomial and negative binomial assumptions to non-parametric analyses, the differences create statistical noise and make the reference standards method dependent. In our experimental design, the accuracy of 30 distinct combinations of fold changes (FC) and expression levels (EL) were determined for five types of RNA analyses in two different datasets. This design was applied to two distinct datasets: breast cancer cell lines and a yeast study with isogenic biological replicates in two experimental conditions. In addition, the reference standard (RS) comprised all RNA analytical methods with the exception of the method testing accuracy. To mitigate for biased optimization of the RS parameters towards a specific analytical method, similarity between observed results of distinct analytical methods were calculated across all methods (Jaccard Concordance Index). The greatest differences were observed across diametric extremes. For example, filtering out differentially expressed genes (DEGs) using a fold change < 1.2 leads to a 50% increase in concordance between techniques when compared to results with FC > 1.2. Combining this FC cutoff with genes with mean expressions > 30 counts leads to a 65% increase in concordance in comparison to genes with expression levels < 30 counts and with FC < 1.2. Conclusions: We have demonstrated that comparing accuracies of different single-subject analysis methods for clinical optimization requires a new evaluation framework. Reliable and robust reference standards, independent of the evaluated method, can be obtained under a limited number of parameter combinations: fold change (FC) ranges thresholds, expression level cutoffs, and exclusion of the tested method from the RS development process. When applying anticonservative reference standard frameworks (e.g., using the same method for RS development and for prediction), a majority of the concordant signal between prediction and Gold Standard (GS) cannot be confirmed by other methods, which we conclude as biased results. Statistical tests to determine DEGs from a single-subject study generate many biased results that require subsequent filtering for increasing their reliability. Conventional single-subject studies pertain to one or a few measures in one patient over time [1]and need a substantial conceptual framework extension in order to address the tens of thousands of measures in genome-wide analyses of gene products. The proposed referenceNof1 framework addresses some of the inherent challenges in improving transcriptome scale single-subject analyses by providing a robust approach to constructing reference standards. Github: https://github.com/SamirRachidZaim/referenceNof1

Currently, the number of approved veterinary medicines are limited, and human medications are used off-label. These approved human medications are of too high potencies for a cat or a small dog breed. Therefore, there is a dire demand for smaller doses of veterinary medicines. This study aims to investigate the use of three semi-solid extrusion 3D printers in a pharmacy or animal clinic setting for extemporaneous manufacturing of prednisolone containing orodispersible films for veterinary use. Orodispersible films with adequate content uniformity and acceptance values defined by the European Pharmacopoeia was produced with one of the studied printers, namely, the Allevi 2 bioprinter. Smooth and flexible films, with high mechanical strength, neutral pH, and low moisture content were produced with high correlation between prepared design and obtained drug amount, indicating that the Allevi 2 printer could successfully be used to extemporaneously manufacture personalized doses for animals at the point-of-care.

Type 1 diabetes affects millions of people globally and requires careful management to avoid serious long-term complications, including heart and kidney disease, stroke, and loss of sight. The present standard-of-care for type 1 diabetes is exogenic insulin substitutional therapy. The most advanced stretegies in this area is the development of hybrid-closed loop system and the producing of long-acting insulins. Progresses in stem cell therapies have started to revolutionize the care of patients with type 1 diabetes; however, significant challenges remain including the limited islets availability, difficulties in maintaining the viability, the heterogeneity within a complex pathology and in patients’ responses to treatment. On the way, a considerable amount of efforts in maximizing the islet transplantation effectiveness by controlling the advantageous of different stem cell approaches. With the availability and the use of big data, the concept of precision medicine is gaining wide attention worldwide and could bring the dream of “presonlaized” therapies as a reality in the near future. Here we review the current range of treatments available as well as recent pre-clinical breakthroughs in the field of personlaized medicine for type 1 diabetes.

Artificial intelligence methods may help in unveliling information hidden in high-dimensional oncological data. Flow cytometry studies of haematological malignancies provide quantitative data with the potential to be used for the construction of response biomarkers. Many computational methods from the bioinformatics toolbox can be applied to these data but have not been exploited in their full potential in leukaemias, specifically for the case of childhood B-cell acute lymphoblastic leukemia. In this paper we analysed flow cytometry data obtained on diagnosis from 54 paediatric B-cell acute lymphoblastic leukemia patients from two local institutions. We constructed classifiers based on the Fisher’s Ratio to quantify differences in expression levels of immunophenotypical markers between patients with relapsing and non-relapsing disease. The distribution of the marker CD38 was found and validated to have a strong discriminating power between both patient cohorts, thus providing a classifier.

One of the rapidly developing research areas is the creation of systems. which are commonly referred to as cyberphysical complexes. In such systems, devices and complexes interact with a completely different physical nature. The role of a person in such systems usually consists in the formation of final tasks for “artificial intelligence” and executive mechanisms. The functioning of actuators is controlled by accurate information systems.

Worldwide, over 2.2 million people are suffered from multiple sclerosis (MS), a multifactorial demyelinating disease of the central nervous system. MS is characterized by a wide range of motor, autonomic, and psychobehavioral symptoms including depression, anxiety, and dementia. The blood, cerebrospinal fluid, and postmortem brain samples of MS patients evidenced the disturbance of reduction-oxidation (redox) homeostasis such as the alterations of oxidative and antioxidative enzyme activities and the presence of degradation products. This review article discussed the components of redox homeostasis including reactive chemical species, oxidative enzymes, antioxidative enzymes, and degradation products. The reactive chemical species covered frequently discussed reactive oxygen/nitrogen species, infrequently featured reactive chemicals such as sulfur, carbonyl, halogen, selenium, and nucleophilic species that potentially act as reductive as well as pro-oxidative stressors. The antioxidative enzyme systems covered the nuclear factor erythroid-2-related factor 2 (NRF2)-Kelch-like ECH-associated protein 1 (KEAP1) signaling pathway. The NRF2 and other transcriptional factors potentially become a biomarker sensitive to the initial phase of oxidative stress. Altered components of the redox homeostasis in MS were discussed in search of a diagnostic, prognostic, predictive, and/or therapeutic biomarker. Finally, monitoring a battery of reactive chemical species, oxidative enzymes, antioxidative enzymes and degradation products helps evaluate the redox status of MS patients to expedite building personalized treatment plans for the sake of better quality of life.

Parkinson’s disease (PD) is characterized by motor deficits and a wide variety of non-motor symptoms. The age of onset, rate of disease progression and the precise profile of motor and non-motor symptoms display considerable individual variation. Neuropathologically, the loss of substantia nigra dopaminergic neurons is a key feature of PD. The vast majority of PD patients exhibit alpha-synuclein aggregates in several brain regions, but there is also great variability in the neuropathology between individuals. While the dopamine replacement therapies can reduce motor symptoms, current therapies do not modify the disease progression. Numerous clinical trials using a wide variety of approaches have failed to achieve disease modification. It has been suggested that the heterogeneity of PD is a major contributing factor to the failure of disease modification trials, and that it is unlikely that a single treatment will be effective in all patients. Precision medicine, using drugs designed to target the pathophysiology in a manner that is specific to each individual with PD, has been suggested as a way forward. PD patients can be stratified according to whether they carry one of the risk variants associated with elevated PD risk. In this review we assess current clinical trials targeting two enzymes, leucine-rich repeat kinase 2 (LRRK2) and glucocerebrosidase (GBA), which are encoded by two most common PD risk genes. Because the details of the pathogenic processes coupled to the different LRRK2and GBA risk variants are not fully understood, we ask if these precision medicine-based intervention strategies will prove “precise“ or “personalized“ enough to modify the disease process in PD patients. We also consider at what phases of the disease that such strategies might be effective, in light of the genes being primarily associated with the risk of developing disease in the first place, and less clearly linked to the rate of disease progression. Finally, we critically evaluate the notion that therapies targeting LRRK2 and GBA might be relevant to a wider segment of PD patients, beyond those that actually carry risk variants of these genes.

The development of targeted therapeutics for cancer continues to receive intense research attention as laboratories and pharmaceutical companies seek to develop drugs and technologies that improve treatment efficacy and mitigate harmful side effects. In the aftermath of World War I, it was discovered that mustard gas destroys rapidly dividing cells and could be used to treat cancer. Since then, chemotherapy has remained a predominant treatment for cancer; however, the destruction of dividing cells throughout the body yields devastating side effects including off-target damage of the digestive tract, bone marrow, skin, and reproductive tract. Furthermore, the high mutation rate of cancerous cells often renders chemotherapy ineffective long-term. Therapies with improved specificity, localization, and efficacy are redefining cancer treatment. Herein, we define and summarize the principal advancements in targeted cancer treatment and briefly comment on the march towards personalized medicine in the treatment of human cancer.

Plants and plant-derived food products have been used for medicinal purposes since the ancient. Medicinal Plant-based functional foods or plant-based dietary compounds are a re-emerged interest for their therapeutic benefits and nutritive supports which has implicated in healthcare systems across the world. Neurological disorders are one of the greatest threats to public health and according to the World Health Organization, about 100 million people are affected globally by several neurological and mental ailments. In a traditional medication system, medicinal plants have been applied as both neuro-therapeutic purposes and micro-macro nutrients provider for the wellbeing of psychological states e.g. anti-depressant, anti-anxiety, anti-convulsions, anti-dementia, anti-psychotic, etc. Herein, it is a topic of great interest to present a conceptual aspect by reviewing relevant scientific literature about the plant-based functional foods or bioactive phytochemicals for the prevention and treatment of mental and neurological disorders. From the literature assessment, we have found that nutritional neuroscience is becoming an advanced research discipline and there has been a growing pile of evidence concerning the therapeutic use of plant-based functional foods and/or plant-derived food compounds for the management of neurologic health, evolving with promising impact over the time.

Imatinib became the standard treatment for chronic myeloid leukemia (CML) about 20 years ago, which was a major breakthrough in stabilizing the pathology and improving the quality of life of patients. However, the emergence of resistance to imatinib and other tyrosine kinase inhibitors leads researchers to characterize new therapeutic targets. Several studies have highlighted the role of histone deacetylase 6 (HDAC6) in various pathologies, including cancer. This protein effectively intervenes in cellular activities by its primarily cytoplasmic localization. In this review, we will discuss the molecular characteristics of the HDAC6 protein, as well as its overexpression in CML leukemic stem cells, which make it a promising therapeutic target for the treatment of CML.

With the development of information technology and the popularization of mobile devices, collecting various types of customer data such as purchase history or behavior patterns became possible. As the customer data being accumulated, there is a growing demand for personalized recommendation services that provide customized services to customers. Currently, global e-commerce companies offer personalized recommendation services to gain a sustainable competitive advantage. However, previous research on recommendation systems has consistently raised the issue that the accuracy of recommendation algorithms does not necessarily lead to the satisfaction of recommended service users. It also claims that customers are highly satisfied when the recommendation system recommends diverse items to them. In this study, we want to identify the factors that determine customer satisfaction when using the recommendation system which provides personalized services. To this end, we developed a recommendation system based on Deep Neural Networks (DNN) and measured the accuracy of recommendation service, the diversity of recommended items and customer satisfaction with the recommendation service. The experimental results of is the study showed that both recommendation system accuracy and diversity would have a positive effect on customer satisfaction. These results can further improve customer satisfaction with the recommendation system and promote the sustainable development of e-commerce.

The use of machine learning (ML) approaches to target clinical problems is called to revolutionize clinical decision-making. The success of these tools is subjected to the understanding of the intrinsic processes being used during the classical pathway by which clinicians make decisions. In a parallelism with this pathway, ML can have an impact at four levels: for data acquisition, predominantly by extracting standardized, high-quality information with the smallest possible learning curve; for feature extraction, by discharging healthcare practitioners from performing tedious measurements on raw data; for interpretation, by digesting complex, heterogeneous data in order to augment the understanding of the patient status; and for decision support, by leveraging the previous step to predict clinical outcomes, response to treatment or to recommend a specific intervention. This paper discusses the state-of-the-art, as well as the current clinical status and challenges associated with each of these tasks, together with the challenges related to the learning process, the auditability/traceability, the system infrastructure and the integration within clinical processes.

Within the framework of theoretical developments in so-called participatory culture and the context of funds of identity, incorporated within what is known as the funds of knowledge approach, an innovative teaching methodology is implemented that allows students to actively participate in knowledge construction. The project translates into a proposal for educational contextualization and personalization based on the students’ funds of identity, that is, those resources (people, artifacts, places, activities, institutions) they consider to be most relevant and significant to define themselves. Once these have been identified through identity artifacts, such as collages or self-drawings, students link some of these funds of identity to curricular content of the subject and produce a video that shows the results of this academic work. The final product is shared on a YouTube channel containing the videos of all of the students in the class. The phases of the project are described and illustrated. We argue that the proposed teaching and learning project, which is cross-disciplinary in nature, allows for principles such as educational contextualization, funds of identity, and participatory culture to be incorporated into what we call here the funds of identity 2.0 approach, putting it into educational practice.

Background: Accurate prediction of patients’ response to therapy is clinically indispensable, howbeit challenging. With increased understanding of the human genome and malignancies, there is the renaissance of in silico pharmacogenomics with renewed interest in drug response predictability based on gene-drug interaction. Objective: Evidence-based transcript-proteome profiling is essential for synthesizing clinically applicable algorithms for predicting response to anticancer therapy, including immune checkpoint blockade (ICBT); thus, saving physicians’ time, reducing polypharmacy, and curtailing unnecessary treatment expense. In this study, we tested and validated the hypothesis that a selected proteomic signature in ICBT-naïve patients is sufficient for the prediction of response to ICBT. Methods: Using a multimodal approach consisting of computational pharmacogenomics, transcript-proteome analytics, mathematical modeling, and machine learning systems; we delineated therapy-sensitivity and stratified patients into graduated response groups based on their proteomic profile. Protein expression levels in our cohort tissue specimens were evaluated based on T cell- and non-T cell- inflamed phenotypes by immunohistochemistry. Results: We established β-catenin, PDL1, CD3 and CD8 expression-based ICBT response model. Statistical regression models validated the predictive association between our predefined algorithms and therapeutic outcome. Interestingly, our 4-gene prediction classifier was constitutively independent of tumor tissue origin, correctly stratified patients into high-, low-, and non- responders pre-treatment, with high prediction accuracy, and exhibited good association with patients’ performance status and prognosis (p < 0.01). Conclusion: Our findings demonstrate the possibility of accurate proteomics based ICBT response prediction and provide a putative basis for drug response prediction based on selective proteome profile in untreated cancer patients.

Background: We conducted research on the newly developed personalized recommendation service (PRS) of the global network of Korean scientists and engineers (KOSEN) in order to explore the information usage behavior and importance of the PRS used by Korean scientists and engineers. Methods: In order to understand information usage behavior, we gathered data from 513 survey results and analyzed them in terms of information usage behavior and the corresponding importance in each of the service quality areas. Results: We analyzed the 321 outcomes that indicated non-use of the PRS in order to understand the underlying reason(s); we employed 192 results that demonstrated the use of functionality to examine information usage behavior and importance. They found that the predominant motive for non-use of the service resulted from the respondents not knowing how to use it. According to demographic characteristics, the usage behavior of the PRS showed a difference regarding the purpose of using the service in the categories of gender and major field of study. Furthermore, users were concerned with various components of the PRS such as ease of use, design, relevance of content, user support, and interactivity. Conclusions: We suggest reinforcing user education degree and promotion to enhance the PRS. Since users were concerned with ease of use, design, relevance, user support, and interactivity, we recommend these as major points for improvement.

It is estimated that pancreatic cancer will be the 2^nd leading cause of cancer-related deaths globally by 2030, highlighting the ongoing lack of effective treatment options in this devastating condition. There is a lack of reliable prognostic or predictive markers in pancreatic cancer to guide management decisions, whether for systemic chemotherapy, molecularly targeted therapies, or immunotherapies. To date, the results for targeted agents and immunotherapies in unselected populations of chemo-refractory pancreatic cancer have not met expectations. The reasons for this lack of efficacy of immunotherapy in pancreatic cancer are incompletely understood. The challenges in pancreatic cancer include the physical barrier created by the dense desmoplastic stroma surrounding the tumor, chemokine-mediated exclusion of T cells, poor antigenicity, paucity of infiltrating T cells within the tumor, ultimately leading to an immunosuppressive microenvironment. A better understanding of the role of inflammation in pancreatic cancer, its tumor microenvironment and individualized patient-related features, be they molecular, clinical or histopathological would enable a more effective tailored approach to the management of pancreatic cancer. In this review, the role of inflammation, the immune tumor microenvironment and potential immune biomarkers in pancreatic cancer are explored.

In the context of food packaging design, customization enhances the value of a product by meeting the needs of the consumer. Personalization is also linked to adaptation. This makes it possible to improve the properties of the packaging from various points of view: functional, aesthetic, economic and ecological. Currently the functional and formal properties of packaging are not investigated among themselves, however the study of both properties are the basis for creating a new concept of personalized and sustainable product. In accordance with this approach, the conceptual design procedure of packaging with personalized and adapted geometries based on the digitization of fresh food is proposed in this work. This study is based on the application of advanced technologies for the design and development of food packaging, in this case apples, in order to improve the quality of the packaging. The results obtained show that it is possible to use advanced technologies in the early stages of product design in order to obtain competitive products adapted to new emerging needs.

The modification the gut microbiota in metabolic syndrome and associated chronic diseases is among leading tasks of microbiome research and needs for clinical use of probiotics. Evidence lack for the implications for microbiome modification to improve metabolic health in particular when applied impersonalized. Probiotics have tremendous potential in personalized nutrition and medicine to develop healthy diets. The aim was to to conduct comprehensive overview of recent updates of role of microbiota on human health and development of metabolic syndrome and efficacy of microbiota modulation considering specific properties of probiotic strain and particular aspects of metabolic syndrome and patient`s phenotype to fill the gap between probiotic product and individual to facilitate development of individualized / personalized probiotic and prebiotic treatments. We discuss the relevance of using host phenotype-associated biomarkers, those based on imaging and molecular and patrient`s history, reliable and accessible to facilitate person-specific appication of probiotics and prebiotic substances. Microbiome phenotypes can be parameters of predictive medicine to recognize patient`s predispositions and evaluate treatment responses; the number of phenotype markers can be effectively involved to monitor microbiome modulation. The studied strain-dependent properties of probiotic strains are potentially relevant for individualized treatment for gut and distant sites microbiome modulation. The evidence regarding probiotic strains properties can be taken to account via pathophysiology-based approach for most effective individualized treatment via gut, oral and vaginal and other sites microbiome modulation according to phenotype of the patient providing individualized and personalized medical approaches. Preventive potential of probiotics is strong and well-documented. Recommendations for individualized clinical use of probiotics, and for probiotic studies design have been suggested.

Coordinated sets of extremely numerous digital data, on a given social or economic event, are treated by Artificial Intelligence tools to obtain reasonably accurate, valuable predictions. The same approach, applied to biomedical issues, as how to choose the right drug to completely cure a given cancer patient, does not reach satisfactory results. It is the “organized biological complexity”, which requires a different systems approach, to integrate, in an Augmented Intelligence strategy, statistical computations of digital data, network construction of “omics” findings, well-designed mathematical models and new experiments in an iterative pathway to reconstruct the “logic” beneath the “organized complexity”, as shown here for Systems Metabolomics of cancer. On this basis new diagnostic approaches, able to identify precision drug treatments, as well as new discovery strategy for more effective anti-cancer drugs are described.

The value of landscape, as part of collective heritage, can be acquired by GIS due to the multilayer approach of the spatial configuration. Proficiency in geospatial technologies in order to collect, process, analyze, interpret, visualize and communicate geographic information is being increased by undergraduate and graduate students, but in particular by those who are training to become geography teachers at secondary education. This training can be carried out through personalized learning and distance learning methodology. Personalized GIS education aims to integrate students and enhance their understanding of landscape. Some teaching experiences are shown whereby opportunities offered by WebGIS will be described, through quantitative tools and techniques that will allow this modality of learning and improve its effectiveness. Results of this research show that students, through geospatial technologies, learn landscape as a diversity of elements but also the complexity of physical and human factors involved. Several conclusions will be highlighted: i) the contribution of geospatial training to education for sustainable development; ii) spatial analysis as a mean of skills acquisition about measures for landscape conservation; iii) expanding and applying acquired knowledge to other geographic spaces and different landscapes.

We wanted to investigate the current knowledge on the impact of diet on anti-TNF response in inflammatory bowel diseases (IBD), to identify dietary factors that warrant further investigations in relation to anti-TNF treatment response, and, finally, to discuss potential strategies for such investigations. PubMed was searched using specified search terms. One small prospective study on diet and anti-TNF treatment in 56 patients with CD found similar remission rates after 56 weeks among 32 patients with good compliance that received concomitant enteral nutrition and 24 with poor compliance that had no dietary restrictions (78% versus 67%, p = 0.51). A meta-analysis of 295 patients found higher odds of achieving clinical remission and remaining in clinical remission among patients on combination therapy with specialised enteral nutrition and Infliximab (IFX) compared with IFX monotherapy (OR 2.73; 95% CI: 1.73–4.31, p < 0.01, OR 2.93; 95% CI: 1.66–5.17, p < 0.01, respectively). In conclusion, evidence-based knowledge on impact of diet on anti-TNF treatment response for clinical use is scarce. Here we propose a mechanism by which Western style diet high in meat and low in fibre may promote colonic inflammation and potentially impact treatment response to anti-TNF drugs. Further studies using hypothesis-driven and data-driven strategies in observational, animal and interventional studies are warranted.

Although suicide is a major public health issue worldwide, we understand little of the onset and development of suicidal behavior. Suicidal behavior is argued to be the end result of the complex interaction between psychological, social and biological factors. Epidemiological studies resulted in a range of risk factors for suicidal behavior, but we do not yet understand how their interaction increases the risk for suicidal behavior. A new approach called network analysis can help us better understand this process as it allows to visualize and quantify complex association between many different symptoms or risk factors. A network analysis of data contain information on suicidal patients can help us understand how risk factors interact and how their interaction is related to suicidal thoughts and behaviour. A network perspective has been successfully applied to the field of depression and psychosis, but not yet to the field of suicidology. In this theoretical article, I will introduce the concept of network analysis to the field of suicide prevention, and offer directions for future applications and studies.

The importance of personalized medicine is growing, since there is an urged need to avoid unnecessary and expensive treatments. In nuclear medicine, the theranostic approach is an established tool for a specific molecular targeting in means of diagnostics and therapy. The visualisation of potential targets can help to predict if a patient would benefit from a particular treatment or not. Thanks to the quick development of radiopharmaceuticals and diagnostic techniques, the use of theranostic agents is constantly rising. In this article important milestones of nuclear therapies and diagnostics in the context of theranostics are highlighted. It begins with the well-known radioiodine therapy in patients with thyroid cancer and then guides through different approaches for the treatment of advanced cancer with targeted therapies. The aim of this review is to provide a summary of background knowledge, current applications and advantages of targeted therapies and imaging in nuclear medicine practice.

It has been declared repeatedly that cancer is a result of molecular genetic abnormalities. However, there has been no working model describing the specific functional consequences of the deranged genomic processes that result in the initiation and propagation of the cancer process during carcinogenesis. We no longer need to question whether or not cancer arises as a result of a molecular genetic defect within the cancer cell. The legitimate questions are: how and why? This article reviews the preeminent data on cancer molecular genetics and subsequently proposes that the sentinel event in cancer initiation is the aberrant production of fused transcription activators with new molecular properties within normal tissue stem cells. This results in the production of vital oncogenes with dysfunctional gene activation transcription properties, which leads to dysfunctional gene regulation, the aberrant activation of transduction pathways, chromosomal breakage, activation of driver oncogenes, reactivation of stem cell transduction pathways and the activation of genes that result in the hallmarks of cancer. Furthermore, a novel holistic molecular genetic model of cancer initiation and progression is presented along with a new paradigm for the approach to personalized targeted cancer therapy, clinical monitoring and cancer diagnosis.