Optimization of a mouse model of pancreatic cancer to simulate the human phenotypes of metastasis and cachexia.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) presents with a high mortality rate. Two important features of PDAC contribute to this poor outcome. The first is metastasis which occurs in ~ 80% of PDAC patients. The second is cachexia, which compromises treatment tolerance for patients and reduces their quality of life. Although various mouse models of PDAC exist, recapitulating both metastatic and cachectic features have been challenging. METHODS: Here, we optimize an orthotopic mouse model of PDAC by altering several conditions, including the subcloning of parental murine PDAC cells, implantation site, number of transplanted cells, and age of recipient mice. We perform spatial profiling to compare primary and metastatic immune microenvironments and RNA sequencing to gain insight into the mechanisms of muscle wasting in PDAC-induced cachexia, comparing non-metastatic to metastatic conditions. RESULTS: These modifications extend the time course of the disease and concurrently increase the rate of metastasis to approximately 70%. Furthermore, reliable cachexia endpoints are achieved in both PDAC mice with and without metastases, which is reminiscent of patients. We also find that cachectic muscles from PDAC mice with metastasis exhibit a similar transcriptional profile to muscles derived from mice and patients without metastasis. CONCLUSION: Together, this model is likely to be advantageous in both advancing our understanding of the mechanism of PDAC cachexia, as well as in the evaluation of novel therapeutics.

Reversibility in the Physical Properties of Agarose Gels following an Exchange in Solvent and Non-Solvent.

Agarose forms a homogeneous thermoreversible gel in an aqueous solvent above a critical polymer concentration. Contrary to the prevailing consensus, recent confirmations indicate that agarose gels are also stable in non-solvents like acetone and ethanol. A previous study compared gel characterisations and behaviours in water and ethanol, discussing the gelation mechanism. In the current work, the ethanol gel is exchanged with water to explore the potential reversibility of the displacement of water in agarose. Initially, the structure is characterised using 1H NMR in DMSO-d6 and D2O solvents. Subsequently, a very low yield (0.04) of methyl substitution per agarobiose unit is determined. The different gels after stabilisation are characterised using rheology, and their physical properties are compared based on the solvent used. The bound water molecules, acting as plasticizers in aqueous medium, are likely removed during the exchange process with ethanol, resulting in a stronger and more fragile gel. Next, the gel obtained after the second exchange from ethanol back to water is compared with the initial gel prepared in water. This is the first time where such gel has been characterised without undergoing a phase transition when switching from a good solvent to a non-solvent, and vice versa, thereby testing the reversibility of the solvent exchange. Reversibility of this behaviour is demonstrated through swelling and rheology experiments. This study extends the application of agarose in chromatography and electrophoresis.

Comprehensive experimental dataset on large-amplitude Rayleigh-Plateau instability in continuous InkJet printing regime.

The Rayleigh-Plateau instability, a phenomenon of paramount significance in fluid dynamics, finds widespread application in the Continuous InkJet (CIJ) printing process. This study presents a comprehensive dataset comprising experimental investigations of fluid jet breakup phenomena under large-amplitude stimulation conditions using an industrial CIJ print-head from Markem-Imaje. Unlike previous studies, this dataset encompasses a diverse range of experimental conditions, including nine different Newtonian fluids with meticulously measured rheological properties (viscosities, surface tensions and densities). The applied stimulation amplitudes vary from 5V to 45V, representing a substantial span of excitation levels. The experimental setup captures the intricate dynamics of fluid jets subjected to these varying conditions, producing a rich collection of over 5,000 high-resolution images depicting the breakup phenomena. Each amplitude of stimulation and fluid type yields more than 55 distinct images, providing detailed insights into the evolving jet morphologies. To ensure the accuracy and relevance of the dataset, all ejection parameters are rigorously documented and included. The dataset thus serves as a valuable resource for researchers seeking to explore the dynamics of large-amplitude Rayleigh-Plateau instability in CIJ printing. Its comprehensiveness and diversity make it particularly suitable for the application of novel machine learning and deep-learning approaches, enabling the study of jet morphological evolution beyond the confines of classical Rayleigh's theory. This dataset holds promise for advancing our understanding of fluid jet dynamics and enhancing the efficiency and quality of CIJ printing processes.

Data Visualization of CRISPR-Cas9 Guide RNA Design Tools.

With the advancement of genomic engineering and genetic modification techniques, the uptake of computational tools to design guide RNA increased drastically. Searching for genomic targets to design guides with maximum on-target activity (efficiency) and minimum off-target activity (specificity) is now an essential part of genome editing experiments. Today, a variety of tools exist that allow the search of genomic targets and let users customize their search parameters to better suit their experiments. Here we present an overview of different ways to visualize these searched CRISPR target sites along with specific downstream information like primer design, restriction enzyme activity and mutational outcome prediction after a double-stranded break. We discuss the importance of a good visualization summary to interpret information along with different ways to represent similar information effectively.

PEPS: Polygenic Epistatic Phenotype Simulation.

Genetic data is limited and generating new datasets is often an expensive, time-consuming process, involving countless moving parts to genotype and phenotype individuals. While sharing data is beneficial for quality control and software development, privacy and security are of utmost importance. Generating synthetic data is a practical solution to mitigate the cost, time and sensitivities that hamper developers and researchers in producing and validating novel biotechnological solutions to data intensive problems. Existing methods focus on mutation frequencies at specific loci while ignoring epistatic interactions. Alternatively, programs that do consider epistasis are limited to two-way interactions or apply genomic constraints that make synthetic data generation arduous or computationally intensive. To solve this, we developed Polygenic Epistatic Phenotype Simulator (PEPS). Our tool is a probabilistic model that can generate synthetic phenotypes with a controllable level of complexity.

An Approach for Generating Realistic Australian Synthetic Healthcare Data.

Healthcare data is a scarce resource and access is often cumbersome. While medical software development would benefit from real datasets, the privacy of the patients is held at a higher priority. Realistic synthetic healthcare data can fill this gap by providing a dataset for quality control while at the same time preserving the patient's anonymity and privacy. Existing methods focus on American or European patient healthcare data but none is exclusively focused on the Australian population. Australia is a highly diverse country that has a unique healthcare system. To overcome this problem, we used a popular publicly available tool, Synthea, to generate disease progressions based on the Australian population. With this approach, we were able to generate 100,000 patients following Queensland (Australia) demographics.

Prediction of Coronary Artery Disease Risk Using Genetic and Phenotypic Variables.

Coronary artery disease (CAD) has the highest disease burden worldwide. To manage this burden, predictive models are required to screen patients for preventative treatment. A range of variables have been explored for their capacity to predict disease, including phenotypic (age, sex, BMI and smoking status), medical imaging (carotid artery thickness) and genotypic. We use a machine learning models and the UK Biobank cohort to measure the prediction capacity of these 3 variable categories, both in combination and isolation. We demonstrate that phenotypic variables from the Framingham risk score have the best prediction capacity, although a combination of phenotypic, medical imaging and genotypic variables deliver the most specific models. Furthermore, we demonstrate that Variant Spark, a random forest based GWAS platform, performs effective feature selection for SNP-based genotype variables, identifying 115 significantly associated SNPs to the CAD phenotype.

The impact of inflammation and acute phase activation in cancer cachexia.

The development of cachexia in the setting of cancer or other chronic diseases is a significant detriment for patients. Cachexia is associated with a decreased ability to tolerate therapies, reduction in ambulation, reduced quality of life, and increased mortality. Cachexia appears intricately linked to the activation of the acute phase response and is a drain on metabolic resources. Work has begun to focus on the important inflammatory factors associated with the acute phase response and their role in the immune activation of cachexia. Furthermore, data supporting the liver, lung, skeletal muscle, and tumor as all playing a role in activation of the acute phase are emerging. Although the acute phase is increasingly being recognized as being involved in cachexia, work in understanding underlying mechanisms of cachexia associated with the acute phase response remains an active area of investigation and still lack a holistic understanding and a clear causal link. Studies to date are largely correlative in nature, nonetheless suggesting the possibility for a role for various acute phase reactants. Herein, we examine the current literature regarding the acute phase response proteins, the evidence these proteins play in the promotion and exacerbation of cachexia, and current evidence of a therapeutic potential for patients.

Predicting CRISPR-Cas12a guide efficiency for targeting using machine learning.

Genome editing through the development of CRISPR (Clustered Regularly Interspaced Short Palindromic Repeat)-Cas technology has revolutionized many fields in biology. Beyond Cas9 nucleases, Cas12a (formerly Cpf1) has emerged as a promising alternative to Cas9 for editing AT-rich genomes. Despite the promises, guide RNA efficiency prediction through computational tools search still lacks accuracy. Through a computational meta-analysis, here we report that Cas12a target and off-target cleavage behavior are a factor of nucleotide bias combined with nucleotide mismatches relative to the protospacer adjacent motif (PAM) site. These features helped to train a Random Forest machine learning model to improve the accuracy by at least 15% over existing algorithms to predict guide RNA efficiency for the Cas12a enzyme. Despite the progresses, our report underscores the need for more representative datasets and further benchmarking to reliably and accurately predict guide RNA efficiency and off-target effects for Cas12a enzymes.

Novel Alzheimer's disease genes and epistasis identified using machine learning GWAS platform.

Alzheimer's disease (AD) is a complex genetic disease, and variants identified through genome-wide association studies (GWAS) explain only part of its heritability. Epistasis has been proposed as a major contributor to this 'missing heritability', however, many current methods are limited to only modelling additive effects. We use VariantSpark, a machine learning approach to GWAS, and BitEpi, a tool for epistasis detection, to identify AD associated variants and interactions across two independent cohorts, ADNI and UK Biobank. By incorporating significant epistatic interactions, we captured 10.41% more phenotypic variance than logistic regression (LR). We validate the well-established AD loci, APOE, and identify two novel genome-wide significant AD associated loci in both cohorts, SH3BP4 and SASH1, which are also in significant epistatic interactions with APOE. We show that the SH3BP4 SNP has a modulating effect on the known pathogenic APOE SNP, demonstrating a possible protective mechanism against AD. SASH1 is involved in a triplet interaction with pathogenic APOE SNP and ACOT11, where the SASH1 SNP lowered the pathogenic interaction effect between ACOT11 and APOE. Finally, we demonstrate that VariantSpark detects disease associations with 80% fewer controls than LR, unlocking discoveries in well annotated but smaller cohorts.

Cell-Penetrating Milk-Derived Peptides with a Non-Inflammatory Profile.

Milk-derived peptides are known to confer anti-inflammatory effects. We hypothesised that milk-derived cell-penetrating peptides might modulate inflammation in useful ways. Using computational techniques, we identified and synthesised peptides from the milk protein Alpha-S1-casein that were predicted to be cell-penetrating using a machine learning predictor. We modified the interpretation of the prediction results to consider the effects of histidine. Peptides were then selected for testing to determine their cell penetrability and anti-inflammatory effects using HeLa cells and J774.2 mouse macrophage cell lines. The selected peptides all showed cell penetrating behaviour, as judged using confocal microscopy of fluorescently labelled peptides. None of the peptides had an effect on either the NF-κB transcription factor or TNFα and IL-1ß secretion. Thus, the identified milk-derived sequences have the ability to be internalised into the cell without affecting cell homeostatic mechanisms such as NF-κB activation. These peptides are worthy of further investigation for other potential bioactivities or as a naturally derived carrier to promote the cellular internalisation of other active peptides.

Thresholding Gini variable importance with a single-trained random forest: An empirical Bayes approach.

Random forests (RFs) are a widely used modelling tool capable of feature selection via a variable importance measure (VIM), however, a threshold is needed to control for false positives. In the absence of a good understanding of the characteristics of VIMs, many current approaches attempt to select features associated to the response by training multiple RFs to generate statistical power via a permutation null, by employing recursive feature elimination, or through a combination of both. However, for high-dimensional datasets these approaches become computationally infeasible. In this paper, we present RFlocalfdr, a statistical approach, built on the empirical Bayes argument of Efron, for thresholding mean decrease in impurity (MDI) importances. It identifies features significantly associated with the response while controlling the false positive rate. Using synthetic data and real-world data in health, we demonstrate that RFlocalfdr has equivalent accuracy to currently published approaches, while being orders of magnitude faster. We show that RFlocalfdr can successfully threshold a dataset of 106 datapoints, establishing its usability for large-scale datasets, like genomics. Furthermore, RFlocalfdr is compatible with any RF implementation that returns a VIM and counts, making it a versatile feature selection tool that reduces false discoveries.

Comparison Adsorption of Cd (II) onto Lignin and Polysaccharide-Based Polymers.

Given the predominantly negative impact of heavy metals on living organisms, the present study proposed to evaluate the adsorption performances under static conditions of Cd (II) from aqueous solutions on unmodified Sarkanda grass lignin compared to the adsorption performances of polysaccharide polymers chemically functionalized, obtained by synthesis and in their native state, but which, although effective, have a cost price that does not allow for large-scale expansion. To improve the retention of Cd (II) on this aromatic component of the biomass resulting from the processing of lignocellulosic materials, different experimental conditions (pH, concentration, dose and contact time) were followed. The Freundlich and Langmuir isotherms were used to describe the equilibrium conditions. Adsorption kinetics were assessed using the Lagergren I and Ho and McKay II kinetic models, furnishing informative insights into the process mechanism. Lignin adsorption capacity was also analyzed by performing biological tests on tomato seeds (Lypercosium esculentum), since heavy metals are known to be a stress factor for seeds by disturbing the osmotic equilibrium. Through the prism of the investigated parameters and under precisely established experimental conditions, unmodified Sarkanda grass lignin-an aromatic biopolymer-can be recommended as a promising adsorbent for the retention of Cd (II) from aqueous solutions, successfully replacing polysaccharide, especially cellulose-based polymers.

The multifaceted aspects of sleep and sleep-wake disorders following stroke.

Sleep-wake disorders (SWD) are acknowledged risk factors for both ischemic stroke and poor cardiovascular and functional outcome after stroke. SWD are frequent following stroke, with sleep apnea (SA) being the most frequent SWD affecting more than half of stroke survivors. While sleep disturbances and SWD are frequently reported in the acute phase, they may persist in the chronic phase after an ischemic stroke. Despite the frequency and risk associated with SWD following stroke, screening for SWD remains rare in the clinical setting, due to challenges in the assessment of post-stroke SWD, uncertainty regarding the optimal timing for their diagnosis, and a lack of clear treatment guidelines (i.e., when to treat and the optimal treatment strategy). However, little evidence support the feasibility of SWD treatment even in the acute phase of stroke and its favorable effect on long-term cardiovascular and functional outcomes. Thus, sleep health recommendations and SWD treatment should be systematically embedded in secondary stroke prevention strategy. We therefore propose that the management of SWD associated with stroke should rely on a multidisciplinary approach, with an integrated diagnostic, treatment, and follow-up strategy. The challenges in the field are to improve post-stroke SWD diagnosis, prognosis and treatment, through a better appraisal of their pathophysiology and temporal evolution.

Different time course recovery of muscle edema within the quadriceps femoris and functional performance after single- vs multi-joint exercises.

This study aimed to verify the time course recovery of muscle edema within the quadriceps femoris and functional performance after lower-body single- and multi-joint exercises. For this within-participant unilateral and contralateral experimental design, fourteen untrained young males performed a unilateral knee extension exercise (KE), and a unilateral leg press (LP) exercise in a counterbalanced order. At pre-, post-, 24 h, 48 h, 72 h, and 96 h after exercise, the peak torque (PT), unilateral countermovement jump (uCMJ) performance, and rectus femoris (RF) and vastus lateralis (VL) muscle thicknesses were recorded in both legs. The PT decreased immediately after (p = 0.01) both exercises (KE and LP) and was fully recovered 24 h after KE (p = 0.38) and 48 h after LP (p = 0.68). Jump height and power, in the uCMJ, followed the same PT recovery pattern after both exercises. However, vertical stiffness (Kvert) was not affected at any time point after both protocols. The RF thickness increased after both exercises (p = 0.01) and was fully restored 48 h after KE (p = 0.86) and 96 h after LP (p = 1.00). The VL thickness increased after both exercises (p = 0.01) and was fully restored 24 h after LP (p = 1.00) and 48 h after KE (p = 1.00). The LP exercise, compared to KE, induced more prolonged impairment of functional performance and delayed recovery of RF muscle edema. However, the VL edema-induced muscle swelling recovery was delayed after the KE exercise. The different recovery kinetics between functional performance and muscle damage should be taken into consideration depending on the objectives of the next training sessions.

Characterization of Agarose Gels in Solvent and Non-Solvent Media.

Agarose is known to form a homogeneous thermoreversible gel in an aqueous medium over a critical polymer concentration. The solid-liquid phase transitions are thermoreversible but depend on the molecular structure of the agarose sample tested. The literature has mentioned that agarose gels could remain stable in non-solvents such as acetone or ethanol. However, there has been no characterization of their behavior nor a comparison with the gels formed in a good solvent such as water. In the first step of this article, the structure was characterized using 1H and 13C NMR in both D2O and DMSO-d6 solvents. DMSO is a solvent that dissolves agarose regardless of the temperature. First, we have determined a low yield of methyl substitution on the D-galactose unit. Then, the evolution of the 1H NMR spectrum was monitored as a function of temperature during both increasing and decreasing temperature processes, ranging from 25 to 80 °C. A large thermal hysteresis was obtained and discussed, which aided in the interpretation of rheological behavior. The hysteresis of NMR signals is related to the mobility of the agarose chains, which follows the sol/gel transition depending on the chains' association with H-bonds between water and the -OH groups of agarose for tightly bound water and agarose/agarose in chain packing. In the second step of the study, the water in the agarose gel was exchanged with ethanol, which is a non-solvent for agarose. The resulting gel was stable, and its properties were characterized using rheology and compared to its behavior in aqueous media. The bound water molecules that act as plasticizers were likely removed during the exchange process, resulting in a stronger and more brittle gel in ethanol, with higher thermal stability compared to the aqueous gel. It is the first time that such gel is characterized without phase transition when passing from a good solvent to a non-solvent. This extends the domains of application of agarose.

Psychometric Evaluation of the Grit Psychological Resources Scale (GPRS).

Background: Psychological resources have been shown to play a prominent role in buffering against stress and are associated with various positive constructs, including grit, defined as having the disposition to pursue long-term goals with both passion and perseverance. Objective: The objective of this study was to validate a new scale developed to measure the psychological resources of grit. Methods: A quantitative research study was conducted online with an international sample of 277 adults from a range of professional backgrounds. The psychometric properties of the 20-item Grit Psychological Resources Scale (GPRS) were evaluated by performing tests of reliability and validity. Results: Reliability tests provided evidence of high internal consistency (α = 0.91) and test-retest reliability (r = 0.75). Demographic variables did not significantly predict scores or influence survey completion. Face, content and convergent validity provided additional psychometric support for the GPRS with this sample. Confirmatory factor analysis results supported a second-order model with four sub-scales. The first-order factors loaded highly onto the second-order factor, with correlations ranging from 0.80 to 0.97. Conclusion: The GPRS showed satisfactory psychometric properties, indicating that the scale is a reliable and valid instrument for measuring the psychological resources of grit. This scale can be used to identify more targeted developmental approaches for personal and professional growth. Further, the tool enables information to be gathered on changes pre- and post-improvement initiatives to assess their effectiveness in training and continuing education.

Relationship between Protein Digestibility and the Proteolysis of Legume Proteins during Seed Germination.

Legume seed protein is an important source of nutrition, but generally it is less digestible than animal protein. Poor protein digestibility in legume seeds and seedlings may partly reflect defenses against herbivores. Protein changes during germination typically increase proteolysis and digestibility, by lowering the levels of anti-nutrient protease inhibitors, activating proteases, and breaking down storage proteins (including allergens). Germinating legume sprouts also show striking increases in free amino acids (especially asparagine), but their roles in host defense or other processes are not known. While the net effect of germination is generally to increase the digestibility of legume seed proteins, the extent of improvement in digestibility is species- and strain-dependent. Further research is needed to highlight which changes contribute most to improved digestibility of sprouted seeds. Such knowledge could guide the selection of varieties that are more digestible and also guide the development of food preparations that are more digestible, potentially combining germination with other factors altering digestibility, such as heating and fermentation. Techniques to characterize the shifts in protein make-up, activity and degradation during germination need to draw on traditional analytical approaches, complemented by proteomic and peptidomic analysis of mass spectrometry-identified peptide breakdown products.

Targeting protein tyrosine phosphatases for CDK6-induced immunotherapy resistance.

Elucidating the mechanisms of resistance to immunotherapy and developing strategies to improve its efficacy are challenging goals. Bioinformatics analysis demonstrates that high CDK6 expression in melanoma is associated with poor progression-free survival of patients receiving single-agent immunotherapy. Depletion of CDK6 or cyclin D3 (but not of CDK4, cyclin D1, or D2) in cells of the tumor microenvironment inhibits tumor growth. CDK6 depletion reshapes the tumor immune microenvironment, and the host anti-tumor effect depends on cyclin D3/CDK6-expressing CD8+ and CD4+ T cells. This occurs by CDK6 phosphorylating and increasing the activities of PTP1B and T cell protein tyrosine phosphatase (TCPTP), which, in turn, decreases tyrosine phosphorylation of CD3ζ, reducing the signal transduction for T cell activation. Administration of a PTP1B and TCPTP inhibitor prove more efficacious than using a CDK6 degrader in enhancing T cell-mediated immunotherapy. Targeting protein tyrosine phosphatases (PTPs) might be an effective strategy for cancer patients who resist immunotherapy treatment.