Multilingual Framework for Risk Assessment and Symptom Tracking (MRAST).

The importance and value of real-world data in healthcare cannot be overstated because it offers a valuable source of insights into patient experiences. Traditional patient-reported experience and outcomes measures (PREMs/PROMs) often fall short in addressing the complexities of these experiences due to subjectivity and their inability to precisely target the questions asked. In contrast, diary recordings offer a promising solution. They can provide a comprehensive picture of psychological well-being, encompassing both psychological and physiological symptoms. This study explores how using advanced digital technologies, i.e., automatic speech recognition and natural language processing, can efficiently capture patient insights in oncology settings. We introduce the MRAST framework, a simplified way to collect, structure, and understand patient data using questionnaires and diary recordings. The framework was validated in a prospective study with 81 colorectal and 85 breast cancer survivors, of whom 37 were male and 129 were female. Overall, the patients evaluated the solution as well made; they found it easy to use and integrate into their daily routine. The majority (75.3%) of the cancer survivors participating in the study were willing to engage in health monitoring activities using digital wearable devices daily for an extended period. Throughout the study, there was a noticeable increase in the number of participants who perceived the system as having excellent usability. Despite some negative feedback, 44.44% of patients still rated the app's usability as above satisfactory (i.e., 7.9 on 1-10 scale) and the experience with diary recording as above satisfactory (i.e., 7.0 on 1-10 scale). Overall, these findings also underscore the significance of user testing and continuous improvement in enhancing the usability and user acceptance of solutions like the MRAST framework. Overall, the automated extraction of information from diaries represents a pivotal step toward a more patient-centered approach, where healthcare decisions are based on real-world experiences and tailored to individual needs. The potential usefulness of such data is enormous, as it enables better measurement of everyday experiences and opens new avenues for patient-centered care.

Machine Learning Algorithms to Predict Breast Cancer Recurrence Using Structured and Unstructured Sources from Electronic Health Records.

Recurrence is a critical aspect of breast cancer (BC) that is inexorably tied to mortality. Reuse of healthcare data through Machine Learning (ML) algorithms offers great opportunities to improve the stratification of patients at risk of cancer recurrence. We hypothesized that combining features from structured and unstructured sources would provide better prediction results for 5-year cancer recurrence than either source alone. We collected and preprocessed clinical data from a cohort of BC patients, resulting in 823 valid subjects for analysis. We derived three sets of features: structured information, features from free text, and a combination of both. We evaluated the performance of five ML algorithms to predict 5-year cancer recurrence and selected the best-performing to test our hypothesis. The XGB (eXtreme Gradient Boosting) model yielded the best performance among the five evaluated algorithms, with precision = 0.900, recall = 0.907, F1-score = 0.897, and area under the receiver operating characteristic AUROC = 0.807. The best prediction results were achieved with the structured dataset, followed by the unstructured dataset, while the combined dataset achieved the poorest performance. ML algorithms for BC recurrence prediction are valuable tools to improve patient risk stratification, help with post-cancer monitoring, and plan more effective follow-up. Structured data provides the best results when fed to ML algorithms. However, an approach based on natural language processing offers comparable results while potentially requiring less mapping effort.

Breast cancer survival analysis agents for clinical decision support.

Personalized support and assistance are essential for cancer survivors, given the physical and psychological consequences they have to suffer after all the treatments and conditions associated with this illness. Digital assistive technologies have proved to be effective in enhancing the quality of life of cancer survivors, for instance, through physical exercise monitoring and recommendation or emotional support and prediction. To maximize the efficacy of these techniques, it is challenging to develop accurate models of patient trajectories, which are typically fed with information acquired from retrospective datasets. This paper presents a Machine Learning-based survival model embedded in a clinical decision system architecture for predicting cancer survivors' trajectories. The proposed architecture of the system, named PERSIST, integrates the enrichment and pre-processing of clinical datasets coming from different sources and the development of clinical decision support modules. Moreover, the model includes detecting high-risk markers, which have been evaluated in terms of performance using both a third-party dataset of breast cancer patients and a retrospective dataset collected in the context of the PERSIST clinical study.

Deeper insights into long-term survival heterogeneity of pancreatic ductal adenocarcinoma (PDAC) patients using integrative individual- and group-level transcriptome network analyses.

Pancreatic ductal adenocarcinoma (PDAC) is categorized as the leading cause of cancer mortality worldwide. However, its predictive markers for long-term survival are not well known. It is interesting to delineate individual-specific perturbed genes when comparing long-term (LT) and short-term (ST) PDAC survivors and integrate individual- and group-based transcriptome profiling. Using a discovery cohort of 19 PDAC patients from CHU-Liège (Belgium), we first performed differential gene expression analysis comparing LT to ST survivor. Second, we adopted systems biology approaches to obtain clinically relevant gene modules. Third, we created individual-specific perturbation profiles. Furthermore, we used Degree-Aware disease gene prioritizing (DADA) method to develop PDAC disease modules; Network-based Integration of Multi-omics Data (NetICS) to integrate group-based and individual-specific perturbed genes in relation to PDAC LT survival. We identified 173 differentially expressed genes (DEGs) in ST and LT survivors and five modules (including 38 DEGs) showing associations to clinical traits. Validation of DEGs in the molecular lab suggested a role of REG4 and TSPAN8 in PDAC survival. Via NetICS and DADA, we identified various known oncogenes such as CUL1 and TGFB1. Our proposed analytic workflow shows the advantages of combining clinical and omics data as well as individual- and group-level transcriptome profiling.

Tumor cells may circulate in medullary thyroid cancer patients independently of serum calcitonin.

Early detection of tumor relapse is a major issue in patients with medullary thyroid carcinoma. Calcitonin has been reported as a sensitive and accurate marker for recurrence of medullary thyroid carcinoma after thyroidectomy. Recent evidence nevertheless reveals pitfalls in calcitonin immunoassays due to the presence of heterophilic antibodies or macroaggregates. Calcitonin can also remain undetectable despite metastasis of rare tumor cells in thyroidectomized patients. In this context, we designed a sensitive and specific technique to identify calcitonin-positive circulating tumor cells (CTC) in medullary thyroid carcinoma. We demonstrate that calcitonin-positive CTCs are present in the peripheral blood of medullary carcinoma patients following complete thyroidectomy. Unexpectedly, the presence of CTCs could be identified up to 12 years after surgery even in the absence of detectable levels of serum calcitonin.

Use of cinacalcet and sunitinib to treat hypercalcaemia due to a pancreatic neuroendocrine tumor.

Neuroendocrine tumors (NETs) can secrete hormones, including ectopic secretions, but they have been rarely associated with malignant hypercalcemia. A 52-year-old man with a history of diabetes mellitus was diagnosed with a pancreatic tumor. A pancreatic biopsy confirmed a well-differentiated pancreatic NET (pNET). The patient subsequently developed liver metastasis and hypercalcemia with high 1,25 OH vitamin D and suppressed parathyroid hormone (PTH) levels. Hypercalcemia was refractory to chemotherapy, intravenous saline fluids, diuretics, calcitonin and zoledronate. Cinacalcet administration (120 mg/day) resulted in a significant calcium reduction. Hypocalcemia was observed when sunitinib was added three months later and cinacalcet was stopped. Subsequently, the calcium and PTH levels normalized. After six months, we observed 20% shrinkage of the pancreatic tumor and necrosis of a liver metastasis. Cinacalcet is an allosteric activator of the calcium receptor agonist, and it is used for severe hypercalcemia in patients with primary (benign and malignant) hyperparathyroidism. In this patient, cinacalcet demonstrated a calcium lowering effect, normalized hypophosphatemia, and improved the clinical condition of the patient. The mechanism through which cinacalcet improved PTH-rp mediated hypercalcemia is still unclear, but studies have suggested that a potential mechanism is the activation of calcitonin secretion. Sunitinib is an oral multi-targeted tyrosine kinase inhibitor used to treat advanced pNETs. The hypocalcemic effects of sunitinib have not been previously described in a patient with pNET. Here, we report for the first time the successful combination of cinacalcet and sunitinib in the treatment of a pNET patient presenting with malignant hypercalcemia.

1H NMR studies of molecular interaction of D-glucosamine and N-acetyl-D-glucosamine with capsaicin in aqueous and non-aqueous media.

Complex formation of D-glucosamine (Gl) and N-acetyl-D-glucosamine (AGl) with capsaicin (Cp) were studied by 1H NMR titrations in H2O-d2 and DMSO-d6; capsaicin is the major bioactive component of chili peppers. Every titration curve has been interpreted by formulating a suitable model for the reaction equilibrium, to elucidate intermolecular interactions. In DMSO, glucosamine cations associate with each other to yield linear aggregates, and undergo pseudo-1:1-complexation with capsaicin, the formation constant being ca. 30 M-1. N-Acetylglucosamine, without self-association, forms a 2:1-complex AGl2Cp with the stability of ca. 70 M-2. These complexations are achieved by intermolecular hydrogen bonds. In D2O, glucosamine undergoes reversible protonation equilibrium between Gl0 and GlH+ with the logarithmic protonation constants log KD = 8.63 for α-glucosamine and 8.20 for ß-isomer. Both anomeric isomers of deprotonated glucosamine form Gl0Cp-type complexes of capsaicin, in a competitive manner, with a formation constant of 1040 M-1 for the α-glucosamine complex and 830 M-1 for the ß-complex; the anomeric carbons result in the difference in thermodynamic stability. The reactant molecules are closed up by the solvent-exclusion effect and/or the van der Waals interaction; the resulting pair is stabilized by intermolecular hydrogen bonding within a local water-free space between the component molecules. By contrast, neither protonated glucosamine (GlH+) nor N-acetylglucosamine yields a capsaicin complex with the definite stoichiometry. The monosaccharides recognize capsaicin under only a controlled condition; the same phenomena are predicted for biological systems and nanocarriers based on polysaccharides such as chitosan.

Use of cinacalcet and sunitinib to treat hypercalcaemia due to a pancreatic neuroendocrine tumor

SUMMARY Neuroendocrine tumors (NETs) can secrete hormones, including ectopic secretions, but they have been rarely associated with malignant hypercalcemia. A 52-year-old man with a history of diabetes mellitus was diagnosed with a pancreatic tumor. A pancreatic biopsy confirmed a well-differentiated pancreatic NET (pNET). The patient subsequently developed liver metastasis and hypercalcemia with high 1,25 OH vitamin D and suppressed parathyroid hormone (PTH) levels. Hypercalcemia was refractory to chemotherapy, intravenous saline fluids, diuretics, calcitonin and zoledronate. Cinacalcet administration (120 mg/day) resulted in a significant calcium reduction. Hypocalcemia was observed when sunitinib was added three months later and cinacalcet was stopped. Subsequently, the calcium and PTH levels normalized. After six months, we observed 20% shrinkage of the pancreatic tumor and necrosis of a liver metastasis. Cinacalcet is an allosteric activator of the calcium receptor agonist, and it is used for severe hypercalcemia in patients with primary (benign and malignant) hyperparathyroidism. In this patient, cinacalcet demonstrated a calcium lowering effect, normalized hypophosphatemia, and improved the clinical condition of the patient. The mechanism through which cinacalcet improved PTH-rp mediated hypercalcemia is still unclear, but studies have suggested that a potential mechanism is the activation of calcitonin secretion. Sunitinib is an oral multi-targeted tyrosine kinase inhibitor used to treat advanced pNETs. The hypocalcemic effects of sunitinib have not been previously described in a patient with pNET. Here, we report for the first time the successful combination of cinacalcet and sunitinib in the treatment of a pNET patient presenting with malignant hypercalcemia.

How to Apply for and Secure EU Funding for Collaborative IBD Research Projects.

The European Union offers opportunities for high-level of funding of collaborative European research. Calls are regularly published: after the end of the FP7 funding programme the new round of Horizon 2020 calls started in 2015. Several topics are relevant to inflammatory bowel disease (IBD) challenges, including chronic disease management, biomarker discovery and new treatments developments. The aim of this Viewpoint article is to describe the new Horizon 2020 instrument and the project submission procedures, and to highlight these through the description of tips and tricks, taking advantage of four examples of successful projects in the field of IBD: the SADEL, IBD-BIOM, IBD Character and BIOCYCLE projects.

Repression of the Transactivating Capacity of the Oncoprotein PLAG1 by SUMOylation.

Human pleomorphic adenoma gene 1 (PLAG1), a developmentally regulated proto-oncogene, is consistently rearranged and overexpressed in pleomorphic salivary gland adenomas and lipoblastomas with 8q12 translocations. Together with PLAGL1 and PLAGL2, PLAG1 belongs to a subfamily of C(2)H(2) zinc finger transcription factors that activate transcription through binding to the bipartite consensus sequence GRGGC(N)(6-8)GGG. Ectopic expression of PLAG1 deregulates target genes and presumably results in uncontrolled cell proliferation. To gain insight into molecular mechanisms regulating PLAG transcriptional capacity, we searched for interaction partners using the yeast two-hybrid system and confirmed these by glutathione S-transferase pull-down. Ubiquitin-conjugating enzyme 9 (UBC9) and protein inhibitor of activated STAT (PIAS) proteins were first identified as genuine interacting partners of mouse PlagL2. Because UBC9 and PIAS are components of the small ubiquitin-related modifier (SUMO) modification pathway, we hypothesized that PLAG proteins could be SUMOylated. Here, we report results obtained for founding family member PLAG1. Its endogenous SUMOylation was demonstrated, and SUMOylation of PLAG1 was further investigated in cells co-transfected with PLAG1 and SUMO-1 DNA or a SUMO-1 mutant form and similarly examined in the presence or absence of DNA encoding the various PIAS proteins. Using anti-PLAG1 antibodies, we discovered single and double SUMO-1-modified forms of PLAG1. By mutating predicted SUMO consensus sites, we defined two important target lysines for SUMOylation in PLAG1, Lys-244 and Lys-263. Moreover, mutation of both SUMO consensus sequences, resulting in inhibition of SUMOylation, led to a significant increase of the transactivation capacity of PLAG1. Nuclear distribution of PLAG1 was not measurably influenced. Our results suggest a direct repression of the transactivating capacity of the oncoprotein PLAG1 by SUMOylation.

Microarray screening for target genes of the proto-oncogene PLAG1.

PLAG1 is a proto-oncogene whose ectopic expression can trigger the development of pleomorphic adenomas of the salivary glands and of lipoblastomas. As PLAG1 is a transcription factor, able to activate transcription through the binding to the consensus sequence GRGGC(N)(6-8)GGG, its ectopic expression presumably results in the deregulation of target genes, leading to uncontrolled cell proliferation. The identification of PLAG1 target genes is therefore a crucial step in understanding the molecular mechanisms involved in PLAG1-induced tumorigenesis. To this end, we analysed the changes in gene expression caused by the conditional induction of PLAG1 expression in fetal kidney 293 cell lines. Using oligonucleotide microarray analyses of about 12 000 genes, we consistently identified 47 genes induced and 12 genes repressed by PLAG1. One of the largest classes identified as upregulated PLAG1 targets consists of growth factors such as the insulin-like growth factor II and the cytokine-like factor 1. The in silico search for PLAG1 consensus sequences in the promoter of the upregulated genes reveals that a large proportion of them harbor several copies of the PLAG1-binding motif, suggesting that they represent direct PLAG1 targets. Our approach was complemented by the comparison of the expression profiles of pleomorphic adenomas induced by PLAG1 versus normal salivary glands. Concordance between these two sets of experiments pinpointed 12 genes that were significantly and consistently upregulated in pleomorphic adenomas and in PLAG1-expressing cells, identifying them as putative PLAG1 targets in these tumors.

PLAG proteins: how they influence apoptosis and cell proliferation.

Pleomorphic adenoma gene 1 product (PLAG1) a transcription factor with zinc finger domains, is upregulated in several tumors, such as pleomorphic adenomas and lipoblastomas. PLAGL2 is a second member of the same subfamily of zinc finger proteins. Cells overexpressing either of these two genes display the typical markers of neoplastic transformation. We investigated whether PLAG1 and PLAGL2 could prevent cells from undergoing apoptosis, leading them to grow uncontrollably.