Performance of PLS regression coefficients in selecting variables for each response of a multivariate PLS for omics-type data

Multivariate partial least square (PLS) regression allows the modeling of complex biological events, by considering different factors at the same time. It is unaffected by data collinearity, representing a valuable method for modeling high-dimensional biological data (as derived from genomics, proteomics and peptidomics). In presence of multiple responses, it is of particular interest how to appropriately “dissect” the model, to reveal the importance of single attributes with regard to individual responses (for example, variable selection). In this paper, performances of multivariate PLS regression coefficients, in selecting relevant predictors for different responses in omics-type of data, were investigated by means of a receiver operating characteristic (ROC) analysis. For this purpose, simulated data, mimicking the covariance structures of microarray and liquid chromatography mass spectrometric data, were used to generate matrices of predictors and responses. The relevant predictors were set a priori. The influences of noise, the source of data with different covariance structure and the size of relevant predictors were investigated. Results demonstrate the applicability of PLS regression coefficients in selecting variables for each response of a multivariate PLS, in omics-type of data. Comparisons with other feature selection methods, such as variable importance in the projection scores, principal component regression, and least absolute shrinkage and selection operator regression were also provided

Adverse events associated with intraocular injection of anti-VEGF(bevacizumab) in retinal vein ccclusion: a case report

Introduction: Antiangiogenic agents are often administered for treatment of Branch Retinal Vein Occlusion (BRVO). Among them, Bevacizumab has noticeable antiangiogenic and antiedemigenic properties and possesses great capacity to penetrate the retinal tissue, particularly in pathological circumstances characterized by altered external or internal blood-retinal barrier.Bevacizumab has an optimal bio-efficacy based on inhibition of the activity of Vascular Endothelial Growth Factor (VEGF). Nonetheless, despite its efficacy, here we describe the adverse effects associated with intraocular injection of bevacizumab in a patient affected by retinal vein occlusion. Case presentation: We present a case report of an 11-year old Caucasian malesubject affected by BRVO in his left eye. The patient underwent an intra-vitreal (i.v.) injection of bevacizumab 100 (1.25 mg/0.05ml). After that, the patient was monitored over time through a series of analyses including Ocular Coherence Tomography, Fluorangiography, Bulbar Ultrasound, Angio MRI BCVA scores and Intra Ocular Pressure. Results: Immediately after the i.v. injection, the patient experienced a strong and relentless pain radiating from the left ocular orbit, caused by a serious and unexpected malignant glaucoma and phthisis bulbi. Furthermore, the patient did not show any sign of improvement in visual function in the follow-up and at last required an ophthalmic prosthesisas a result of a subatrophic and hypotonic eyeball. Conclusion: This case report suggests that i.v. injections of anti-VEGFs should be considered wit

Use of dairy and non-dairy Lactobacillus plantarum, Lactobacillus paraplantarum and Lactobacillus pentosus strains as adjuncts in cheddar cheese

Lactobacilli have been used as adjunct cultures in the manufacture of different cheeses with the objective of accelerating ripening and/or improving cheese quality, but no studies have been conducted with strains from non-dairy origins. A miniature cheddar-type cheese model was used to screen ten dairy and non-dairy Lactobacillus plantarum, Lactobacillus paraplantarum and Lactobacillus pentosus strains for their performances as adjuncts in cheese manufacture. All strains were able to grow and survive in the cheese environment and produced only minor, although statistically significant, changes in gross cheese composition. Adjuncts affected secondary proteolysis causing differences in the levels of free amino groups, total free amino acids and reversed-phase HPLC (RP-HPLC) profiles of pH 4.6-soluble extract. Three strains were selected on the basis of differences in proteolysis pattern and used in a pilot-plant production of cheddar cheese, which was ripened for 180 days. The results confirmed that use of L. plantarum adjuncts significantly affected secondary proteolysis as measured by free amino acid production with minor impact on gross composition and primary starter performance, but the impact on RP- HPLC profiles of pH 4.6-soluble extracts was not statistically significant. The use of a strain originally isolated from olive brine fermentation, L. plantarum P1.5, resulted in significantly improved preference scores over the control

Trace Metals do not Accumulate over Time in The Edible Mediterranean Jellyfish Rhizostoma pulmo (Cnidaria, Scyphozoa) from Urban Coastal Waters

Jellyfish as food represent a millennial tradition in Asia. Recently, jellyfish have also been proposed as a valuable source of protein in Western countries. To identify health risks associated with the potential human consumption of jellyfish as food, trace element accumulation was assessed in the gonads and umbrella tissues of the Mediterranean Rhizostoma pulmo (Macri, 1778), sampled over a period of 16 months along the shallow coastal waters a short distance from the city of Taranto, an area affected by metallurgic and oil refinery sources of pollution. Higher tissue concentrations of trace elements were usually detected in gonads than in umbrella tissue. In particular, significant differences in the toxic metalloid As, and in the metals Mn, Mo, and Zn, were observed among different tissues. The concentrations of vanadium were slightly higher in umbrella tissues than in gonads. No positive correlation was observed between element concentration and jellyfish size, suggesting the lack of bioaccumulation processes. Moreover, toxic element concentrations in R. pulmo were found below the threshold levels for human consumption allowed by Australian, USA, and EU Food Regulations. These results corroborate the hypothesis that R. pulmo is a safe, potentially novel food source, even when jellyfish are harvested from coastal areas affected by anthropogenic impacts

The first record of the white-spotted Australian jellyfish Phyllorhiza punctata von Lendenfeld, 1884 from Maltese waters (Western Mediterranean) and from the Ionian coast of Italy

The occurrence of the white-spotted Australian jellyfish Phyllorhiza punctata Lendenfeld, 1884, an Indo-Pacific scyphozoan species mainly restricted to the Levantine Basin, is hereby reported for the first time from Maltese waters (western Mediterranean) and from the Ionian coast of Italy. Considerations on possible vectors of introduction of the jellyfish species to this part of the Mediterranean are made.peer-reviewe

Walking-related digital mobility outcomes as clinical trial endpoint measures: protocol for a scoping review

Introduction Advances in wearable sensor technology now enable frequent, objective monitoring of real-world walking. Walking-related digital mobility outcomes (DMOs), such as real-world walking speed, have the potential to be more sensitive to mobility changes than traditional clinical assessments. However, it is not yet clear which DMOs are most suitable for formal validation. In this review, we will explore the evidence on discriminant ability, construct validity, prognostic value and responsiveness of walking-related DMOs in four disease areas: Parkinson’s disease, multiple sclerosis, chronic obstructive pulmonary disease and proximal femoral fracture. Methods and analysis Arksey and O’Malley’s methodological framework for scoping reviews will guide study conduct. We will search seven databases (Medline, CINAHL, Scopus, Web of Science, EMBASE, IEEE Digital Library and Cochrane Library) and grey literature for studies which (1) measure differences in DMOs between healthy and pathological walking, (2) assess relationships between DMOs and traditional clinical measures, (3) assess the prognostic value of DMOs and (4) use DMOs as endpoints in interventional clinical trials. Two reviewers will screen each abstract and full-text manuscript according to predefined eligibility criteria. We will then chart extracted data, map the literature, perform a narrative synthesis and identify gaps. Ethics and dissemination As this review is limited to publicly available materials, it does not require ethical approval. This work is part of Mobilise-D, an Innovative Medicines Initiative Joint Undertaking which aims to deliver, validate and obtain regulatory approval for DMOs. Results will be shared with the scientific community and general public in cooperation with the Mobilise-D communication team. Registration Study materials and updates will be made available through the Center for Open Science’s OSFRegistry (https://osf.io/k7395)

Connecting real-world digital mobility assessment to clinical outcomes for regulatory and clinical endorsement–the Mobilise-D study protocol

Background: The development of optimal strategies to treat impaired mobility related to ageing and chronic disease requires better ways to detect and measure it. Digital health technology, including body worn sensors, has the potential to directly and accurately capture real-world mobility. Mobilise-D consists of 34 partners from 13 countries who are working together to jointly develop and implement a digital mobility assessment solution to demonstrate that real-world digital mobility outcomes have the potential to provide a better, safer, and quicker way to assess, monitor, and predict the efficacy of new interventions on impaired mobility. The overarching objective of the study is to establish the clinical validity of digital outcomes in patient populations impacted by mobility challenges, and to support engagement with regulatory and health technology agencies towards acceptance of digital mobility assessment in regulatory and health technology assessment decisions. Methods/design: The Mobilise-D clinical validation study is a longitudinal observational cohort study that will recruit 2400 participants from four clinical cohorts. The populations of the Innovative Medicine Initiative-Joint Undertaking represent neurodegenerative conditions (Parkinson’s Disease), respiratory disease (Chronic Obstructive Pulmonary Disease), neuro-inflammatory disorder (Multiple Sclerosis), fall-related injuries, osteoporosis, sarcopenia, and frailty (Proximal Femoral Fracture). In total, 17 clinical sites in ten countries will recruit participants who will be evaluated every six months over a period of two years. A wide range of core and cohort specific outcome measures will be collected, spanning patient-reported, observer-reported, and clinician-reported outcomes as well as performance-based outcomes (physical measures and cognitive/mental measures). Daily-living mobility and physical capacity will be assessed directly using a wearable device. These four clinical cohorts were chosen to obtain generalizable clinical findings, including diverse clinical, cultural, geographical, and age representation. The disease cohorts include a broad and heterogeneous range of subject characteristics with varying chronic care needs, and represent different trajectories of mobility disability. Discussion: The results of Mobilise-D will provide longitudinal data on the use of digital mobility outcomes to identify, stratify, and monitor disability. This will support the development of widespread, cost-effective access to optimal clinical mobility management through personalised healthcare. Further, Mobilise-D will provide evidence-based, direct measures which can be endorsed by regulatory agencies and health technology assessment bodies to quantify the impact of disease-modifying interventions on mobility. Trial registration: ISRCTN12051706

Estimating intergenerational income mobility on sub-optimal data: a machine learning approach

Much of the global evidence on intergenerational income mobility is based on sub-optimal data. In particular, two-stage techniques are widely used to impute parental incomes for analyses of lower-income countries and for estimating long-run trends across multiple generations and historical periods. We propose applying machine learning methods to improve the reliability and comparability of such estimates. Supervised learning algorithms minimize the out-of-sample prediction error in the parental income imputation and provide an objective criterion for choosing across different specifications of the first-stage equation. We use our approach on data from the United States and South Africa to show that under common conditions it can limit the bias generally associated to mobility estimates based on imputed parental income