Feasibility of using NF1-GRD and AAV for gene replacement therapy in NF1-associated tumors

Neurofibromatosis type 1, including the highly aggressive malignant peripheral nerve sheath tumors (MPNSTs), is featured by the loss of functional neurofibromin 1 (NF1) protein resulting from genetic alterations. A major function of NF1 is suppressing Ras activities, which is conveyed by an intrinsic GTPase-activating protein-related domain (GRD). In this study, we explored the feasibility of restoring Ras GTPase via exogenous expression of various GRD constructs, via gene delivery using a panel of adeno-associated virus (AAV) vectors in MPNST and human Schwann cells (HSCs). We demonstrated that several AAV serotypes achieved favorable transduction efficacies in those cells and a membrane-targeting GRD fused with an H-Ras C-terminal motif (C10) dramatically inhibited the Ras pathway and MPNST cells in a NF1-specific manner. Our results opened up a venue of gene replacement therapy in NF1-related tumors

The predominant learning approaches of medical students

Background By identifying medical students’ learning approaches and the factors that influence students’ learning approaches, medical schools and health care institutions are better equipped to intervene and optimize their learning experience. The aims of our study is to determine the predominant learning approach amongst medical students on a clinical posting in a hospital in Singapore and to examine the demographic factors that affect their learning approach. Methods The Approaches and Study Skills Inventory for Students (ASSIST) questionnaire was administered to 250 medical students from various medical schools on clinical attachment to the Obstetrics and Gynaecology (O&G) department of KK Women’s and Children’s Hospital (KKH) Singapore between March 2013 and May 2015 to determine students’ predominant learning approaches. Multinomial logistic regression was used to examine the association between demographic factors (age, gender and highest education qualification) and predominant learning approach. A cut-off of p \u3c 0.05 was used for statistical significance. Results Amongst 238 students with one predominant learning approach, 96 (40.3%) and 121 students (50.8%) adopted the deep and strategic approach respectively, whilst only 21 (8.8%) adopted the surface approach. Male students appeared less likely to adopt the strategic learning approach than female students (p value = 0.06). Predominant learning approaches were not influenced by demographic characteristics such as age, gender and highest educational qualifications. Conclusions This study provided insight into the learning approaches of a heterogeneous group of medical students in Singapore. While it is encouraging that the majority of students predominantly utilised the deep and strategic learning approach, there was a significant proportion of students who utilised the surface approach. Interventions can be explored to promote deeper learning amongst these students

Transfer learning in a biomaterial fibrosis model identifies in vivo senescence heterogeneity and contributions to vascularization and matrix production across species and diverse pathologies

Cellular senescence is a state of permanent growth arrest that plays an important role in wound healing, tissue fibrosis, and tumor suppression. Despite senescent cells’ (SnCs) pathological role and therapeutic interest, their phenotype in vivo remains poorly defined. Here, we developed an in vivo–derived senescence signature (SenSig) using a foreign body response–driven fibrosis model in a p16-CreERT2;Ai14 reporter mouse. We identified pericytes and “cartilage-like” fibroblasts as senescent and defined cell type–specific senescence-associated secretory phenotypes (SASPs). Transfer learning and senescence scoring identified these two SnC populations along with endothelial and epithelial SnCs in new and publicly available murine and human data single-cell RNA sequencing (scRNAseq) datasets from diverse pathologies. Signaling analysis uncovered crosstalk between SnCs and myeloid cells via an IL34–CSF1R–TGFβR signaling axis, contributing to tissue balance of vascularization and matrix production. Overall, our study provides a senescence signature and a computational approach that may be broadly applied to identify SnC transcriptional profiles and SASP factors in wound healing, aging, and other pathologies.</p

Transfer learning in a biomaterial fibrosis model identifies in vivo senescence heterogeneity and contributions to vascularization and matrix production across species and diverse pathologies

Cellular senescence is a state of permanent growth arrest that plays an important role in wound healing, tissue fibrosis, and tumor suppression. Despite senescent cells’ (SnCs) pathological role and therapeutic interest, their phenotype in vivo remains poorly defined. Here, we developed an in vivo–derived senescence signature (SenSig) using a foreign body response–driven fibrosis model in a p16-CreERT2;Ai14 reporter mouse. We identified pericytes and “cartilage-like” fibroblasts as senescent and defined cell type–specific senescence-associated secretory phenotypes (SASPs). Transfer learning and senescence scoring identified these two SnC populations along with endothelial and epithelial SnCs in new and publicly available murine and human data single-cell RNA sequencing (scRNAseq) datasets from diverse pathologies. Signaling analysis uncovered crosstalk between SnCs and myeloid cells via an IL34–CSF1R–TGFβR signaling axis, contributing to tissue balance of vascularization and matrix production. Overall, our study provides a senescence signature and a computational approach that may be broadly applied to identify SnC transcriptional profiles and SASP factors in wound healing, aging, and other pathologies.</p

Transfer learning in a biomaterial fibrosis model identifies in vivo senescence heterogeneity and contributions to vascularization and matrix production across species and diverse pathologies

Cellular senescence is a state of permanent growth arrest that plays an important role in wound healing, tissue fibrosis, and tumor suppression. Despite senescent cells’ (SnCs) pathological role and therapeutic interest, their phenotype in vivo remains poorly defined. Here, we developed an in vivo–derived senescence signature (SenSig) using a foreign body response–driven fibrosis model in a p16-CreERT2;Ai14 reporter mouse. We identified pericytes and “cartilage-like” fibroblasts as senescent and defined cell type–specific senescence-associated secretory phenotypes (SASPs). Transfer learning and senescence scoring identified these two SnC populations along with endothelial and epithelial SnCs in new and publicly available murine and human data single-cell RNA sequencing (scRNAseq) datasets from diverse pathologies. Signaling analysis uncovered crosstalk between SnCs and myeloid cells via an IL34–CSF1R–TGFβR signaling axis, contributing to tissue balance of vascularization and matrix production. Overall, our study provides a senescence signature and a computational approach that may be broadly applied to identify SnC transcriptional profiles and SASP factors in wound healing, aging, and other pathologies.</p

Transfer learning in a biomaterial fibrosis model identifies in vivo senescence heterogeneity and contributions to vascularization and matrix production across species and diverse pathologies

Cellular senescence is a state of permanent growth arrest that plays an important role in wound healing, tissue fibrosis, and tumor suppression. Despite senescent cells’ (SnCs) pathological role and therapeutic interest, their phenotype in vivo remains poorly defined. Here, we developed an in vivo–derived senescence signature (SenSig) using a foreign body response–driven fibrosis model in a p16-CreERT2;Ai14 reporter mouse. We identified pericytes and “cartilage-like” fibroblasts as senescent and defined cell type–specific senescence-associated secretory phenotypes (SASPs). Transfer learning and senescence scoring identified these two SnC populations along with endothelial and epithelial SnCs in new and publicly available murine and human data single-cell RNA sequencing (scRNAseq) datasets from diverse pathologies. Signaling analysis uncovered crosstalk between SnCs and myeloid cells via an IL34–CSF1R–TGFβR signaling axis, contributing to tissue balance of vascularization and matrix production. Overall, our study provides a senescence signature and a computational approach that may be broadly applied to identify SnC transcriptional profiles and SASP factors in wound healing, aging, and other pathologies.</p

Phenotypic and Functional Properties of Helios+ Regulatory T Cells

Helios, an Ikaros family transcription factor, is preferentially expressed at the mRNA and protein level in regulatory T cells. Helios expression previously appeared to be restricted to thymic-derived Treg. Consistent with recent data, we show here that Helios expression is inducible in vitro under certain conditions. To understand phenotypic and functional differences between Helios+ and Helios− Treg, we profiled cell-surface markers of FoxP3+ Treg using unmanipulated splenocytes. We found that CD103 and GITR are expressed at high levels on a subset of Helios+ Treg and that a Helios+ Treg population could be significantly enriched by FACS sorting using these two markers. Quantitative real-time PCR (qPCR) analysis revealed increased TGF-β message in Helios+ Treg, consistent with the possibility that this population possesses enhanced regulatory potential. In tumor-bearing mice, we found that Helios+ Treg were relatively over-represented in the tumor-mass, and BrdU studies showed that, in vivo, Helios+ Treg proliferated more than Helios− Treg. We hypothesized that Helios-enriched Treg might exert increased suppressive effects. Using in vitro suppression assays, we show that Treg function correlates with the absolute number of Helios+ cells in culture. Taken together, these data show that Helios+ Treg represent a functional subset with associated CD103 and GITR expression

Breakingtheice: A protocol for a randomised controlled trial of an internet-based intervention addressing amphetamine-type stimulant use

Background: The prevalence of amphetamine-type stimulant use is greater than that of opioids and cocaine combined. Currently, there are no approved pharmacotherapy treatments for amphetamine-type stimulant problems, but some face-to-face psychotherapies are of demonstrated effectiveness. However, most treatment services focus on alcohol or opioid disorders, have limited reach and may not appeal to users of amphetamine-type stimulants. Internet interventions have proven to be effective for some substance use problems but none has specifically targeted users of amphetamine-type stimulants. Design/method: The study will use a randomized controlled trial design to evaluate the effect of an internet intervention for amphetamine-type stimulant problems compared with a waitlist control group. The primary outcome will be assessed as amphetamine-type stimulant use (baseline, 3 and 6 months). Other outcomes measures will include ‘readiness to change’, quality of life, psychological distress (K-10 score), days out of role, poly-drug use, help-seeking intention and help-seeking behavior. The intervention consists of three modules requiring an estimated total completion time of 90 minutes. The content of the modules was adapted from face-to-face clinical techniques based on cognitive behavior therapy and motivation enhancement. The target sample is 160 men and women aged 18 and over who have used amphetamine-type stimulants in the last 3 months. Discussion: To our knowledge this will be the first randomized controlled trial of an internet intervention specifically developed for users of amphetamine-type stimulants. If successful, the intervention will offer greater reach than conventional therapies and may engage clients who do not generally seek treatment from existing service providers

Differential cross section measurements for the production of a W boson in association with jets in proton–proton collisions at √s = 7 TeV

Measurements are reported of differential cross sections for the production of a W boson, which decays into a muon and a neutrino, in association with jets, as a function of several variables, including the transverse momenta (pT) and pseudorapidities of the four leading jets, the scalar sum of jet transverse momenta (HT), and the difference in azimuthal angle between the directions of each jet and the muon. The data sample of pp collisions at a centre-of-mass energy of 7 TeV was collected with the CMS detector at the LHC and corresponds to an integrated luminosity of 5.0 fb[superscript −1]. The measured cross sections are compared to predictions from Monte Carlo generators, MadGraph + pythia and sherpa, and to next-to-leading-order calculations from BlackHat + sherpa. The differential cross sections are found to be in agreement with the predictions, apart from the pT distributions of the leading jets at high pT values, the distributions of the HT at high-HT and low jet multiplicity, and the distribution of the difference in azimuthal angle between the leading jet and the muon at low values.United States. Dept. of EnergyNational Science Foundation (U.S.)Alfred P. Sloan Foundatio

Optimasi Portofolio Resiko Menggunakan Model Markowitz MVO Dikaitkan dengan Keterbatasan Manusia dalam Memprediksi Masa Depan dalam Perspektif Al-Qur`an

Risk portfolio on modern finance has become increasingly technical, requiring the use of sophisticated mathematical tools in both research and practice. Since companies cannot insure themselves completely against risk, as human incompetence in predicting the future precisely that written in Al-Quran surah Luqman verse 34, they have to manage it to yield an optimal portfolio. The objective here is to minimize the variance among all portfolios, or alternatively, to maximize expected return among all portfolios that has at least a certain expected return. Furthermore, this study focuses on optimizing risk portfolio so called Markowitz MVO (Mean-Variance Optimization). Some theoretical frameworks for analysis are arithmetic mean, geometric mean, variance, covariance, linear programming, and quadratic programming. Moreover, finding a minimum variance portfolio produces a convex quadratic programming, that is minimizing the objective function ðð¥with constraintsð ð 𥠥 ðandð´ð¥ = ð. The outcome of this research is the solution of optimal risk portofolio in some investments that could be finished smoothly using MATLAB R2007b software together with its graphic analysis