The potential of major ion chemistry to assess groundwater vulnerability of a regional aquifer in southern Quebec (Canada)

Groundwater vulnerability mapping provides useful but limited information for developing protection plans of the resource. Classical vulnerability ranking methods often do not take into account complex hydrostratigraphy and never consider groundwater flow dynamics. The objective of this work was to test the potential of major ion chemistry to assess regional-scale intrinsic groundwater vulnerability. Because it reflects water–sediment and water–rock interactions, the new vulnerability index reflects both infiltration processes and groundwater hydrodynamics. The method was applied on a regional fractured bedrock aquifer located in the Becancour region of southern Quebec (Canada). In this region, hydrogeochemistry shows that freshly recharged groundwater evolves from (Ca, Mg)–HCO3 and Ca–SO4 to Na–HCO3 type with gradually increasing confinement conditions in the fractured aquifer and tends to Na–Cl type locally by mixing with trapped marine pore-water. The new method identified recharge areas as those of the highest vulnerability and gradually decreasing vulnerability as confinement of the aquifer increased. It also highlights local discontinuities in confinement that differ from the regional pattern. Results showed a good correlation between groundwater vulnerability estimated with the new method and nitrate occurrence in groundwater. Eighty-two per cent of all samples presenting detectable nitrate concentrations were characterized by a Hydrogeochemical Vulnerability Index greater than 9 (maximum is 10). The ability of the new vulnerability method to identify areas vulnerable to detectable nitrate concentrations was much higher than that deriving from the DRASTIC method. This work confirms that major ions chemistry contains significant information about groundwater vulnerability and could be used to improve groundwater resource management

Warping-based co-registration of thermal infrared images: Study of factors influencing its applicability

A relevant issue for processing biomedical thermal imaging data is the availability of tools for objective and quantitative comparison of images across different conditions or subjects. To this goal, a solution can be offered by projecting the thermal distribution data onto a fictitious template to obtain a common reference for comparison across cases or subjects. In this preliminary study, we tested the feasibility of applying a warping procedure on infrared thermal images. Fifteen thermal images of checkerboard were recorded at three different distances and five different angles in order to evaluate which factor mostly influences the warping accuracy. The accuracy of three different warping transformation models (local weighted mean (LWM), polynomial, affine) was tested by comparing the positioning error between users’ selected fiduciary points on each thermal image and their corresponding reference position assigned on the template image. Fifteen users, divided into three groups upon on their experience in thermal imaging processing, participated in this study in order to evaluate the effect of experience in applying a warping procedure to the analysis of thermal infrared images. The most relevant factor influencing the positioning and thermal errors is the acquisition distance, while the users’ level of experience and the inclination angle do not seem to play the same importance. Comparing the three transformations, the LWM seems to be the best in terms of minimizing the two categories of errors. This preliminary work helps to understand the limits and the possibilities of applying warping techniques for objective, quantitative and automatic thermal image comparisons

LonP1 Differently Modulates Mitochondrial Function and Bioenergetics of Primary Versus Metastatic Colon Cancer Cells

Mitochondrial Lon protease (LonP1) is a multi-function enzyme that regulates mitochondrial functions in several human malignancies, including colorectal cancer (CRC). The mechanism(s) by which LonP1 contributes to colorectal carcinogenesis is not fully understood. We found that silencing LonP1 leads to severe mitochondrial impairment and apoptosis in colon cancer cells. Here, we investigate the role of LonP1 in mitochondrial functions, metabolism, and epithelial-mesenchymal transition (EMT) in colon tumor cells and in metastasis. LonP1 was almost absent in normal mucosa, gradually increased from aberrant crypt foci to adenoma, and was most abundant in CRC. Moreover, LonP1 was preferentially upregulated in colorectal samples with mutated p53 or nuclear \u3b2-catenin, and its overexpression led to increased levels of \u3b2-catenin and decreased levels of E-cadherin, key proteins in EMT, in vitro. LonP1 upregulation also induced opposite changes in oxidative phosphorylation, glycolysis, and pentose pathway in SW480 primary colon tumor cells when compared to SW620 metastatic colon cancer cells. In conclusion, basal LonP1 expression is essential for normal mitochondrial function, and increased LonP1 levels in SW480 and SW620 cells induce a metabolic shift toward glycolysis, leading to EMT

Increased plasma levels of mitochondrial DNA and pro-inflammatory cytokines in patients with progressive multiple sclerosis

The role of damage-associated molecular patterns in multiple sclerosis (MS) is under investigation. Here, we studied the contribution of circulating high mobility group box protein 1 (HMGB1) and mitochondrial DNA (mtDNA) to neuroinflammation in progressive MS. We measured plasmatic mtDNA, HMGB1 and pro-inflammatory cytokines in 38 secondary progressive (SP) patients, 35 primary progressive (PP) patients and 42 controls. Free mtDNA was higher in SP than PP. Pro-inflammatory cytokines were increased in progressive patients. In PP, tumor necrosis factor-α correlated with MS Severity Score. Thus, in progressive patients, plasmatic mtDNA and pro-inflammatory cytokines likely contribute to the systemic inflammatory status

Investigational chemotherapy and novel pharmacokinetic mechanisms for the treatment of breast cancer brain metastases

In women, breast cancer is the most common cancer diagnosis and second most common cause of cancer death. More than half of breast cancer patients will develop metastases to the bone, liver, lung, or brain. Breast cancer brain metastases (BCBM) confers a poor prognosis, as current therapeutic options of surgery, radiation, and chemotherapy rarely significantly extend life and are considered palliative. Within the realm of chemotherapy, the last decade has seen an explosion of novel chemotherapeutics involving targeting agents and unique dosage forms. We provide a historical overview of BCBM chemotherapy, review the mechanisms of new agents such as poly-ADP ribose polymerase inhibitors, cyclin-dependent kinase 4/6 inhibitors, phosphatidyl inositol 3-kinaseinhibitors, estrogen pathway antagonists for hormone-receptor positive BCBM; tyrosine kinase inhibitors, antibodies, and conjugates for HER2+ BCBM; repurposed cytotoxic chemotherapy for triple negative BCBM; and the utilization of these new agents and formulations in ongoing clinical trials. The mechanisms of novel dosage formulations such as nanoparticles, liposomes, pegylation, the concepts of enhanced permeation and retention, and drugs utilizing these concepts involved in clinical trials are also discussed. These new treatments provide a promising outlook in the treatment of BCBM

Castel di Sangro-Scontrone field camp – structural and applied geomorphology

The Geomorphological Field Camp 2014 in the Castel di Sangro-Scontrone area is the result of geological and geomorphological teaching field work activities carried out in Central Italy by a group of 23 students attending the Structural Geomorphology and Applied Geomorphology courses (Master's Degree in Geological Science and Technology of the Università degli Studi ‘G. d'Annunzio’ Chieti-Pescara, Italy, Department of Engineering and Geology). The Field Camp 2014 was organized in May 2014, following regular classes held during the fall term. General activities for the field camp were developed over four main stages: (1) preliminary analysis of the regional geological and geomorphological setting of the area; (2) preliminary activities for the analysis of the local area (orography, hydrography and photogeology investigations, and geographical information system processing); (3) field work, focused on the analysis of a specific issue concerning structural geomorphology or applied geomorphology (e.g. landscape evolution, river channel change, landslide distribution, and flood hazard); and (4) post-field work production of the map. Finally, the fundamental role of field work in the analysis of landscape and in land management was outlined: indeed, the overall field camp enhanced the crucial role of field-based learning for young geomorphologists in order to acquire a strong sensitivity to geomorphological processes and landscape evolution

Innate immunity changes in soccer players after whole-body cryotherapy

Whole-body cryotherapy (WBC) consists of short exposure (up to 2–3 min) to dry air at cryogenic temperatures (up to -190 °C) and has recently been applied for muscle recovery after injury to reduce the inflammation process. We aimed to determine the impact of cryotherapy on immunological, hormonal, and metabolic responses in non-professional soccer players (NPSPs). Nine male NPSPs (age: 20 ± 2 years) who trained regularly over 5 consecutive days, immediately before and after each training session, were subjected to WBC treatment (WBC-t). Blood samples were collected for the evaluation of fifty analytes including hematologic parameters, serum chemistry, and hormone profiles. Monocytes phenotyping (Mo) was performed and plasmatic markers, usually increased during inflammation [CCL2, IL-18, free mitochondrial (mt)DNA] or with anti-inflammatory effects (IL2RA, IL1RN), were quantified. After WBC-t, we observed reduced levels of ferritin, mean corpuscular hemoglobin, mean platelet volume, testosterone, and estradiol, which however remain within the normal ranges. The percentage of the total, intermediates and non-classical Mo increased, while classical Mo decreased. CXCR4 expression decreased in each Mo subset. Plasma IL18 and IL2RA levels decreased, while IL1RN only exhibited a tendency to decrease and CCL2 showed a tendency to increase. Circulating mtDNA levels were not altered following WBC-t. The differences observed in monocyte subsets after WBC-t may be attributable to their redistribution into the surrounding tissue. Moreover, the decrease of CXCR4 in Mo subpopulations could be coherent with their differentiation process. Thus, WBC through yet unknown mechanisms could promote their differentiation having a role in tissue repair

Circulating mucosal-associated invariant T cells identify patients responding to anti-PD-1 therapy

Immune checkpoint inhibitors are used for treating patients with metastatic melanoma. Since the response to treatment is variable, biomarkers are urgently needed to identify patients who may benefit from such therapy. Here, we combine single-cell RNA-sequencing and multiparameter flow cytometry to assess changes in circulating CD8+ T cells in 28 patients with metastatic melanoma starting anti-PD-1 therapy, followed for 6 months: 17 responded to therapy, whilst 11 did not. Proportions of activated and proliferating CD8+ T cells and of mucosal-associated invariant T (MAIT) cells are significantly higher in responders, prior to and throughout therapy duration. MAIT cells from responders express higher level of CXCR4 and produce more granzyme B. In silico analysis support MAIT presence in the tumor microenvironment. Finally, patients with >1.7% of MAIT among peripheral CD8+ population show a better response to treatment. Our results thus suggest that MAIT cells may be considered a biomarker for patients responding to anti-PD-1 therapy

Rapamycin treatment for amyotrophic lateral sclerosis protocol for a phase II randomized, double-blind, placebo-controlled, multicenter, clinical trial (RAP-ALS trial)

Introduction: Misfolded aggregated proteins and neuroinflammation significantly contribute to amyotrophic lateral sclerosis (ALS) pathogenesis, hence representing therapeutic targets to modify disease expression. Rapamycin inhibits mechanistic target of Rapamycin (mTOR) pathway and enhances autophagy with demonstrated beneficial effects in neurodegeneration in cell line and animal models, improving phenotype in SQSTM1 zebrafish, in Drosophila model of ALS-TDP, and in the TDP43 mouse model, in which it reduced neuronal loss and TDP43 inclusions. Rapamycin also expands regulatory T lymphocytes (Treg) and increased Treg levels are associated with slow progression in ALS patients. Therefore, we planned a randomized clinical trial testing Rapamycin treatment in ALS patients. Methods: RAP-ALS is a phase II randomized, double-blind, placebo-controlled, multicenter (8 ALS centers in Italy), clinical trial. The primary aim is to assess whether Rapamycin administration increases Tregs number in treated patients compared with control arm. Secondary aims include the assessment of safety and tolerability of Rapamycin in patients with ALS; the minimum dosage to have Rapamycin in cerebrospinal fluid; changes in immunological (activation and homing of T, B, NK cell subpopulations) and inflammatory markers, and on mTOR downstream pathway (S6RP phosphorylation); clinical activity (ALS Functional Rating Scale-Revised, survival, forced vital capacity); and quality of life (ALSAQ40 scale). Discussion: Rapamycin potentially targets mechanisms at play in ALS (i.e., autophagy and neuroinflammation), with promising preclinical studies. It is an already approved drug, with known pharmacokinetics, already available and therefore with significant possibility of rapid translation to daily clinics. Findings will provide reliable data for further potential trials. Ethics and dissemination: The study protocol was approved by the Ethics Committee of Azienda Ospedaliero Universitaria of Modena and by the Ethics Committees of participating centers (Eudract n. 2016-002399-28) based on the Helsinki declaration

Machine-learning to Stratify Diabetic Patients Using Novel Cardiac Biomarkers and Integrative Genomics

Background: Diabetes mellitus is a chronic disease that impacts an increasing percentage of people each year. Among its comorbidities, diabetics are two to four times more likely to develop cardiovascular diseases. While HbA1c remains the primary diagnostic for diabetics, its ability to predict long-term, health outcomes across diverse demographics, ethnic groups, and at a personalized level are limited. The purpose of this study was to provide a model for precision medicine through the implementation of machine-learning algorithms using multiple cardiac biomarkers as a means for predicting diabetes mellitus development. Methods: Right atrial appendages from 50 patients, 30 non-diabetic and 20 type 2 diabetic, were procured from the WVU Ruby Memorial Hospital. Machine-learning was applied to physiological, biochemical, and sequencing data for each patient. Supervised learning implementing SHapley Additive exPlanations (SHAP) allowed binary (no diabetes or type 2 diabetes) and multiple classifcation (no diabetes, prediabetes, and type 2 diabetes) of the patient cohort with and without the inclusion of HbA1c levels. Findings were validated through Logistic Regression (LR), Linear Discriminant Analysis (LDA), Gaussian Naïve Bayes (NB), Support Vector Machine (SVM), and Classifcation and Regression Tree (CART) models with tenfold cross validation. Results: Total nuclear methylation and hydroxymethylation were highly correlated to diabetic status, with nuclear methylation and mitochondrial electron transport chain (ETC) activities achieving superior testing accuracies in the predictive model (~84% testing, binary). Mitochondrial DNA SNPs found in the D-Loop region (SNP-73G, -16126C, and -16362C) were highly associated with diabetes mellitus. The CpG island of transcription factor A, mitochondrial (TFAM) revealed CpG24 (chr10:58385262, P=0.003) and CpG29 (chr10:58385324, P=0.001) as markers correlating with diabetic progression. When combining the most predictive factors from each set, total nuclear methylation and CpG24 methylation were the best diagnostic measures in both binary and multiple classifcation sets. Conclusions: Using machine-learning, we were able to identify novel as well as the most relevant biomarkers associated with type 2 diabetes mellitus by integrating physiological, biochemical, and sequencing datasets. Ultimately, this approach may be used as a guideline for future investigations into disease pathogenesis and novel biomarker discover