Relevant Model Research Articles

Developing Clinically Relevant Decision Tree Models Using Machine-Learning to Predict Psychotherapy Success in Late Life Depression

Developing Clinically Relevant Decision Tree Models Using Machine-Learning to Predict Psychotherapy Success in Late Life Depression

Image dermoscopy skin lesion classification using deep learning method: systematic literature review

Classifying skin lesions poses a significant challenge due to the distinctive characteristics and diverse shapes they can exhibit, particularly in identifying early-stage melanoma. To address the shortcomings of the prior method, a neural network-driven strategy was introduced to differentiate between two types of skin lesions based on dermoscopic images. This new approach comprises four key stages: i) initial image processing, ii) skin lesion segmentation, iii) feature extraction, and iv) classification using deep neural networks (DNNs). Computers can also provide more accurate diagnosis results. In the review process, the articles are analyzed and summarized to contribute to developing methods or application development in skin lesion diagnosis. The stages include defining the relevant theory, input data, methods used (architecture and modules), training process, and model evaluation. This review also explores information based on trends and users, emphasizing the skin lesion segmentation process, skin lesion classification process, and minimal datasets as recommendations for future research.

Raman spectroscopy and one-dimensional convolutional neural network modeling as a real-time monitoring tool for in vitro transaminase-catalyzed synthesis of a pharmaceutically relevant amine precursor.

Raman spectroscopy has been used to measure the concentration of a pharmaceutically relevant model amine intermediate for positive allosteric modulators of nicotinic acetylcholine receptor in a ω-transaminase-catalyzed conversion. A model based on a one-dimensional convolutional neural network was developed to translate raw data augmented Raman spectra directly into substrate concentrations, with which the conversion from ketone to amine by ω-transaminase could be determined over time. The model showed very good predictive capabilities, with R2 values higher than 0.99 for the spectra included in the modeling and 0.964 for an independent dataset. However, the model could not extrapolate outside the concentrations specified by the model. The presented work shows the potential of Raman spectroscopy as a real-time monitoring tool for biocatalytic reactions.

AT4CTR: Auxiliary Match Tasks for Enhancing Click-Through Rate Prediction

Click-through rate (CTR) prediction is a vital task in industrial recommendation systems. Most existing methods focus on the network architecture design of the CTR model for better accuracy and suffer from the data sparsity problem. Especially in industrial recommendation systems, the widely applied negative sample down-sampling technique due to resource limitation worsens the problem, resulting in a decline in performance. In this paper, we propose Auxiliary Match Tasks for enhancing Click-Through Rate (AT4CTR) prediction accuracy by alleviating the data sparsity problem. Specifically, we design two match tasks inspired by collaborative filtering to enhance the relevance modeling between user and item. As the "click" action is a strong signal which indicates the user's preference towards the item directly, we make the first match task aim at pulling closer the representation between the user and the item regarding the positive samples. Since the user's past click behaviors can also be treated as the user him/herself, we apply the next item prediction as the second match task. For both the match tasks, we choose the InfoNCE as their loss function. The two match tasks can provide meaningful training signals to speed up the model's convergence and alleviate the data sparsity. We conduct extensive experiments on one public dataset and one large-scale industrial recommendation dataset. The result demonstrates the effectiveness of the proposed auxiliary match tasks. AT4CTR has been deployed in the real industrial advertising system and has gained remarkable revenue.

Abstract 1514: A novel workflow to assess the T-cell and patient-derived organoid interaction

Abstract Immunotherapy is increasingly popular as a type of cancer treatment. These therapies include the use of Chimeric Antigen Receptor-engineered T-cells (CAR T-cells), tumor-infiltrating lymphocytes (TIL), and other genetically modified T-cells to specifically target the cancer cells. Although much success has been achieved with immunotherapy for treatment of blood cancers, its efficacy remains limited in solid tumors. One of the reasons for the low success rate is attributed to the solid tumor microenvironment (TME) where suppressive cytokines limit the tumor killing ability of T-cells. Thus, understanding the role the TME plays in T-cell responses is essential for the development of effective cancer therapies. The benefits of using 3-dimensional (3D) PDOs lie in the physical and chemical cues present within the TME that cannot be mimicked in traditional 2D monolayer cultures. Studies show that PDOs show similar responses to drugs as original tumors, suggesting the value of using PDOs to improve therapeutic outcomes. Thus, PDOs can provide more relevant physiological and pathological cancer models that recapitulate the basic features of primary tumors and is more suited for assessing the effectiveness of T-cell killing than 2D cell models. Despite the benefits associated with the use of PDOs, there are significant barriers to widespread adoption of PDOs in drug discovery. Organoid production is a costly and highly labor-intensive process. Moreover, organoid culture is a skilled manual process, and thus there can be significant variability between operators. To address the challenges associated with the use of PDOs in large scale applications, a semi-automated bioprocess has been developed for the large-scale expansion of assay ready organoids. Here, we developed a method to assess the effectiveness of T-cell invasion in solid tumors using patient derive organoids (PDOs). Using bioreactor expanded patient-derived colorectal cancer organoids (CRCs), activated PBMCs (human peripheral blood mononuclear cells) stained with CellTracker were added to CRCs (stained with MitoTracker) in a 96well microtiter plate and monitored on every 4 hours for 3 days using high content imager. To quantify T-cell invasion, we developed an image analysis method to measure the distance of each T-cell to the nearest organoid (interaction distance). We find that stimulated T-cell resulted in smaller interaction distance than non-stimulated T-cells. The results demonstrate the utility of the bioreactor-expanded organoids in large scale T-cell based screens. Citation Format: Zhisong Tong, Angeline Lim, Oksana Sirenko. A novel workflow to assess the T-cell and patient-derived organoid interaction [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1514.

Abstract 1813: Discovery, biomarker validation, and pre-clinical profiling of a novel PKMYT1 inhibitor

Abstract Protein kinase membrane-associated tyrosine/threonine 1 (PKMYT1) is a negative regulator of CDK1 activity and has been identified as a putative synthetic lethal target in cancers which upregulate cyclin E (CCNE1). We used high-throughput virtual screening to identify inhibitors of PKMYT1 with high lipophilic ligand efficiency (LLE), which were rapidly optimized to potent leads with exceptional selectivity over a key off target, WEE1. Machine learning-driven optimization rapidly reduced mouse hepatocyte clearance to acceptable levels, delivering a compound with best-in-class potency, selectivity, and pharmacokinetic profiles in relevant animal models. Tool compounds from this series were used to validate a differentiated biomarker for sensitivity to PKMYT1 inhibition in cellular models, which could significantly expand the potential patient population. In vivo proof-of-concept studies are ongoing in a variety of cell line- and patient-derived xenograft models which represent the novel biomarker previously identified, to further validate our therapeutic strategy. Citation Format: Alfie Brennan, Jan Lanz, Daniel S. Miller, Oliver Vipond, Noah Harrison, James Aaronson, Anna Hercot. Discovery, biomarker validation, and pre-clinical profiling of a novel PKMYT1 inhibitor [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1813.

Abstract 2839: The mouse models of human cancer database (MMHCdb)

Abstract The laboratory mouse plays a pivotal role as an experimental system for investigations into the genetic and genomic basis of cancer susceptibility, initiation, and progression and for preclinical research on novel treatment strategies for cancer. Advances in mouse models include humanized mice, transplant compliant mouse strains, and panels of genetically diverse mice (e.g., Diversity Outbred and Collaborative Cross). The distributed and heterogenous nature of information about these model systems presents a significant challenge to finding and aggregating information about mouse models and to identifying the most relevant models for a particular research question. The Mouse Models of Human Cancer database (http://tumor.informatics.jax.org) is an expertly curated FAIR-compliant knowledgebase that applies community annotation standards and nomenclature standards for genes, alleles, and strains to support accurate and comprehensive search results for mouse models of human cancer. The data in MMHCdb are curated from peer-reviewed scientific publications and direct data submissions from individual investigators and large-scale programs. A primary focus of the resource is to reflect the impact that genetic background has on the cancer-related characteristics of mouse models. The database includes information on spontaneous and genetically engineered cancer models as well as Patient Derived Xenografts and diversity panels. The database supports faceted searching for models by multiple criteria including, tumor type, genetic background of the mouse model, somatic mutations, and publication. Model descriptions include summaries of observed frequencies of tumors observed in the model and histopathology images. Information in MMHCdb is linked to cancer model data from other bioinformatics resources including the Patient Derived Cancer Models database (PDCM), PathBase, the Gene Expression Omnibus (GEO), and ArrayExpress. MMHCdb is supported by NCI R01 CA089713 Citation Format: Dale A. Begley, Debra M. Krupke, Emily Jocoy, Steven B. Neuhauser, Carol J. Bult. The mouse models of human cancer database (MMHCdb) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2839.

Abstract 4259: A novel human model of lung squamous cell carcinoma to deconvolve the cell-intrinsic effects of key dysregulated pathways

Abstract Tractable, patient relevant models are needed to investigate the essential components of normal cell transformation. We developed a unique in vitro model of early lung squamous cell carcinoma (LUSC) genetic alterations and driver pathway dysregulation using genetically modified primary human bronchial basal cells (hBCs) from three independent donors. The co-operation of ubiquitous and early LUSC alterations (TP53 and CDKN2A loss) and commonly deregulated pathways including squamous differentiation (SOX2), PI3K signaling (PTEN) and the oxidative stress response (KEAP1) was investigated using a combinatorial approach, culminating in eight unique hBC mutants harboring cumulative pathway alterations in a TP53-/-/CDKN2A-/- background. Phenotypes indicative of mutant hBC transformation were investigated using proliferation assays, organotypic cultures and anchorage independent growth assays. Organotypic cultures revealed LUSC pathway alterations to drive dramatic changes in airway histology, ranging from a normal epithelium to severe dysplasia. Furthermore, these analyses confirmed the role of SOX2-overexpression in initiating transformation by inducing squamous cell commitment and anchorage independent growth. We also identified a co-operation of SOX2-overexpression with the oxidative stress response and PI3K pathways to drive more advanced lesions in-vivo and in-vitro. Importantly, we were able to map changes in the expression of immunomodulatory factors with the pathway dysregulation. We demonstrated that this strategy constitute an alternative and patient-relevant system to model LUSC and identify genotype-phenotype correlations of clinical relevance. Citation Format: Julia Ogden, Robert Sellers, Anthony Oojageer, Sudhakar Sahoo, Anshuman Chaturvedi, Carlos Lopez-Garcia. A novel human model of lung squamous cell carcinoma to deconvolve the cell-intrinsic effects of key dysregulated pathways [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4259.

Abstract 6897: Defining critical features for successful preclinical use of glioblastoma patient-derived xenografts (PDX) models for therapeutic assessment and clinical trial development

Abstract Mayo Clinic has developed 111 GBM PDXs from newly diagnosed (72 PDX) and recurrent (39 PDX) patients. This large collection of PDXs provides a broad representation of genetic heterogeneity seen in patients. GBM PDXs that perform well in both in vitro and in vivo settings can be extensively utilized in the preclinical setting for downstream analysis. Based on 20 years of experience, we have defined the attributes that best enable preclinical use of our GBM PDX models. Among them are short-term in vitro culturing performance, large scale genomic evaluation data, benchmarked responses to standards of care, and median time to moribund for untreated orthotopic tumors. PDXs with prolonged survival timelines delay study readouts and, in some situations, confound efficacy results due to age-related animal decline. Setting median survival thresholds as part of the upfront experimental design allows for more timely study readouts that are unaffected by these confounding variables. Our preferred cutoff for the Mayo GBM PDX collection is 70 days median survival (n=64). Using this 64 PDX line panel, regimens expected to double survival would produce results within 4-5 months. This timeframe is well within the range of robust health for athymic nude mice. This 64-line collection features both newly diagnosed (nGBM; n=42) and recurrent (rGBM; n=22) tumors. MGMT promoter methylation status was determined by qMS-PCR (25 methylated, 37 unmethylated, 2 indeterminate). Available WES (n=62) and RNAseq (n=42) data, along with our tissue microarray (n=42), are critical resources for biomarker-based evaluations aimed at defining patient populations that are most likely to benefit from a given therapeutic. From these data, the molecular subtypes available include 26 classical, 14 proneural, and 3 mesenchymal. Twenty-seven of these 64 PDX models have also been successfully cultured from fresh as well as cryopreserved cell stocks to further expand their experimental value. Correlative clinical data including diagnosis and treatments are also available (n=61). Current standards of care for GBM include radiation, temozolomide, and bevacizumab. Defining sensitivity to these therapies is an important first step for evaluating the integration of novel therapeutics with these standards. Since the creation of our GBM PDX collection, efficacy of these therapies has been evaluated as follows: RT 26 PDXs, TMZ 20 PDXs, and Avastin 23 PDXs. The Mayo Clinic GBM PDX collection provides an important platform for preclinical evaluation of novel therapeutics. Through careful selection of PDX lines with favorable growth characteristics and known genetic features, researchers can select the most relevant models for their study. This approach will enable more meaningful preclinical work in the hopes of providing more tangible clinical benefits for GBM patients. Citation Format: Danielle M. Burgenske, Ann C. Mladek, Katrina K. Bakken, Zeng Hu, Brett L. Carlson, Paul A. Decker, Jeanette E. Eckel-Passow, Jann N. Sarkaria. Defining critical features for successful preclinical use of glioblastoma patient-derived xenografts (PDX) models for therapeutic assessment and clinical trial development [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6897.

Nanoparticle mediated photodynamic therapy as a method to ablate oral cavity squamous cell carcinoma in preclinical models.

Photodynamic therapy (PDT) is a tissue ablation technique able to selectively target tumour cells by activating the cytotoxicity of photosensitiser dyes with light. PDT is nonsurgical and tissue sparing, two advantages for treatments in anatomically complex disease sites such as the oral cavity. We have previously developed PORPHYSOME (PS) nanoparticles assembled from chlorin photosensitiser-containing building blocks (~94,000 photosensitisers per particle) and capable of potent PDT. In this study, we demonstrate the selective uptake and curative tumour ablation of PS-enabled PDT in three preclinical models of oral cavity squamous cell carcinoma: biologically relevant subcutaneous Cal-33 (cell-line) and MOC22 (syngeneic) mouse models, and an anatomically relevant orthotopic VX-2 rabbit model. Tumours selectively uptake PS (10 mg/kg, IV) with 6~40-fold greater concentration versus muscle 24 hours post-injection. Single PS-PDT treatment (100 J/cm2, 100 mW/cm2) of Cal-33 tumours yielded significant apoptosis in 65.7% of tumour cells. Survival studies following PS-PDT treatments demonstrated 90% (36/40) overall response rate across all three tumour models. Complete tumour response was achieved in 65% of Cal-33 and 91% of MOC22 tumour mouse models 14 days after PS-PDT, and partial responses obtained in 25% and 9% of Cal-33 and MOC22 tumours, respectively. In buccal VX-2 rabbit tumours, combined surface and interstitial PS-PDT (200 J total) yielded complete responses in only 60% of rabbits 6 weeks after a single treatment, whereas three repeated weekly treatments with PS-PDT (200 J/week) achieved complete ablation in 100% of tumours. PS-PDT treatments were well tolerated by animals with no treatment-associated toxicities and excellent cosmetic outcomes.

Singlet Fission in a New Series of Systematically Designed Through-space Coupled Tetracene Oligomers.

Singlet fission (SF) holds great promise for current photovoltaic technologies, where tetracenes, with their relatively high triplet energies, play a major role for application in silicon-based solar cells. However, the SF efficiencies in tetracene dimers are low due to the unfavorable energetics of their singlet and triplet energy levels. In the solid state, tetracene exhibits high yields of triplet formation through SF, raising great interest about the underlying mechanisms. To address this discrepancy, we designed and prepared a novel molecular system based on a hexaphenylbenzene core decorated with 2 to 6 tetracene chromophores. The spatial arrangement of tetracene units, induced by steric hindrance in the central part, dictates through-space coupling, making it a relevant model for solid-state chromophore organization. We then revealed a remarkable increase in SF quantum yield with the number of tetracenes, reaching quantitative (196 %) triplet pair formation in hexamer. We observed a short-lived correlated triplet pair and limited magnetic effects, indicating ineffective triplet dissociation in these through-space coupled systems. These findings emphasize the crucial role of the number of chromophores involved and the interchromophore arrangement for the SF efficiency. The insights gained from this study will aid designing more efficient and technology-compatible SF systems for applications in photovoltaics.

Neutrophil ALDH2 is a new therapeutic target for the effective treatment of sepsis-induced ARDS.

Acetaldehyde dehydrogenase 2 (ALDH2) mutations are commonly found in a subgroup of the Asian population. However, the role of ALDH2 in septic acute respiratory distress syndrome (ARDS) remains unknown. Here, we showed that human subjects carrying the ALDH2rs671 mutation were highly susceptible to developing septic ARDS. Intriguingly, ALDH2rs671-ARDS patients showed higher levels of blood cell-free DNA(cfDNA) and myeloperoxidase (MPO)-DNA than ALDH2WT-ARDS patients. To investigate the mechanisms underlying ALDH2 deficiency in the development of septic ARDS, we utilized Aldh2 gene knockout mice and Aldh2rs671 gene knock-in mice. In clinically relevant mouse sepsis models, Aldh2-/- mice and Aldh2rs671 mice exhibited pulmonary and circulating NETosis, a specific process that releases neutrophil extracellular traps (NETs) from neutrophils. Furthermore, we discovered that NETosis strongly promoted endothelial destruction, accelerated vascular leakage, and exacerbated septic ARDS. At the molecular level, ALDH2 increased K48-linked polyubiquitination and degradation of peptidylarginine deiminase 4 (PAD4) to inhibit NETosis, which was achieved by promoting PAD4 binding to the E3 ubiquitin ligase CHIP. Pharmacological administration of the ALDH2-specific activator Alda-1 substantially alleviated septic ARDS by inhibiting NETosis. Together, our data reveal a novel ALDH2-based protective mechanism against septic ARDS, and the activation of ALDH2 may be an effective treatment strategy for sepsis.

Jailing is failing: psychiatry can help.

There is accumulating evidence that imprisonment is expensive but does little to address the underlying drivers of offending. At the same time, it is now recognised that a large proportion of prisoners are diagnosable with significant psychiatric disorders. In this piece we explore the potential role of psychiatry in addressing the societal challenge of a failing prison system. We argue that core psychiatric skills of engaging in balanced, values-based thinking and implementing sound clinical processes can play an important role in reducing reoffending risk. We briefly discuss some of the key challenges involved and outline several relevant service models.

Using latent class analysis to operationalize a wholistic assessment of Inuit health and well-being

ABSTRACT Many Indigenous cultures conceptualize health wholistically, whereby physical, mental, spiritual and relational dimensions of health are interconnected. Yet, quantitative approaches to studying Indigenous health remain anchored in western perspectives, that separate the dimensions of health. This paper aims to operationalize a wholistic indicator of health based on the IQI model of Inuit health. Variables from the 2017 Nunavik Health Survey (N = 1196) were selected based on their representativeness of IQI model. Exploratory Latent Class Analysis (LCA) was used to identify wholistic health profiles. Once participants assigned to their health profile, sociodemographic characteristics were compared across profiles, and multinomial regression models were used to examine the relationship between community-level social determinants of health and the profiles. The LCA revealed three health profiles, labelled as “excellent”, “good” and “fair” based on the distribution of answers to the indicators. Nunavimmiut in “excellent” and “good” health were more likely to: rate their health positively; be over 30 years old; be in a relationship; and have participated or volunteered in community events. Nunavimmiut in ”fair” health tended to report lower levels of community cohesion, family relationships, and emotional support. Intergrating culturally relevant models of health can support improved health status assessments and identify opportunities for health promotion.

SFFNet: Staged Feature Fusion Network of Connecting Convolutional Neural Networks and Graph Convolutional Neural Networks for Hyperspectral Image Classification

The immense representation power of deep learning frameworks has kept them in the spotlight in hyperspectral image (HSI) classification. Graph Convolutional Neural Networks (GCNs) can be used to compensate for the lack of spatial information in Convolutional Neural Networks (CNNs). However, most GCNs construct graph data structures based on pixel points, which requires the construction of neighborhood matrices on all data. Meanwhile, the setting of GCNs to construct similarity relations based on spatial structure is not fully applicable to HSIs. To make the network more compatible with HSIs, we propose a staged feature fusion model called SFFNet, a neural network framework connecting CNN and GCN models. The CNN performs the first stage of feature extraction, assisted by adding neighboring features and overcoming the defects of local convolution; then, the GCN performs the second stage for classification, and the graph data structure is constructed based on spectral similarity, optimizing the original connectivity relationships. In addition, the framework enables the batch training of the GCN by using the extracted spectral features as nodes, which greatly reduces the hardware requirements. The experimental results on three publicly available benchmark hyperspectral datasets show that our proposed framework outperforms other relevant deep learning models, with an overall classification accuracy of over 97%.

[Characteristics and Risk Assessment of Antibiotic Contamination in Oujiang River Basin in Southern Zhejiang Province].

Antibiotic pollution in the environment has a negative impact on ecosystem security. Taking the Oujiang River Basin as an example,high-performance liquid chromatography mass spectrometry(LC-MS)was used to detect the concentration of six classes of 35 antibiotics in the surface water of the southern Zhejiang River Basin. The concentration level and spatial distribution of antibiotics were analyzed,the risk of antibiotics to ecology and human health were assessed using relevant models,and the sources of antibiotics were discussed. The results showed that in 20 sampling sites,a total of four classes of 12 antibiotics were detected,including sulfonamides,quinolones,tetracyclines,and lincosamides. The total concentration was ND-1 018 ng·L-1. The highest detection rate was that of Lincomycin(90.48%),followed by that of sulfapyridine(38.10%). The three antibiotics with the highest average concentrations were ofloxacin(12.49 ng·L-1),Lincomycin(11.08 ng·L-1),and difloxacin(7.38 ng·L-1). Antibiotics in the basin showed mainly spotty pollution,which had large spatial differentiation. The average concentration of antibiotics in the upstream(54.39 ng·L-1)was higher than that mid-downstream(46.64 ng·L-1). The degree of antibiotic pollution from upstream to downstream showed a characteristic of being "sparse in the upstream and dense in the downstream. " This indicated that the concentration of antibiotics in the upstream was significantly different,whereas the pollution degree of antibiotics in the downstream was uniform. The upstream was mainly polluted by health,livestock,and poultry breeding wastewater emissions,and downstream pollution was mainly caused by densely populated activities and the rapid development of economy,trade,and industry. The ecological risk assessment results showed that the upstream site H6 had the highest risk quotient,ofloxacin and enrofloxacin had high risk levels, and lincomycin had a moderate risk level. Health risk assessment results showed that the Oujiang River surface water antibiotics posed no risk to human health.

Mechanical impairment characteristics and a novel constitutive model for rocks subjected to uniaxial loading process

The study of constitutive relationship and damage degradation is crucial in solving the stability challenges faced in the rock engineering. In this work, the stress-strain relationships of different type of rocks subjected to uniaxial loading processes are investigated in details. Experimental results demonstrate measurements, such as uniaxial compressive strength (UCS), tangent deformation modulus, peak strain, and Poisson’s ratio ([Formula: see text]). A novel piecewise constitutive model is proposed that utilizes both a constitutive model during compaction and a conventional damage model using the strain equivalence assumption and logistic growth theory to represent the characteristics of stress-deformation curves during both compaction and post-compaction stages. The performance of the proposed constitutive models in capturing deformation characteristics of damaged rocks is demonstrated to be more outstanding in comparison to existing models. In all experimental cases discussed in this study, the proposed model outperforms existing reference models in terms of the coefficients of determination ([Formula: see text]), with the former having coefficients of determination greater than 0.95. Furthermore, physical meanings of relevant model parameters are found to be closely associated with properties of experimental stress-strain curves.

Mkk7 Protects Against Cardiac Dysfunction in Heart Failure with Preserved Ejection Fraction

Article Mkk7 Protects Against Cardiac Dysfunction in Heart Failure with Preserved Ejection Fraction Tayyiba Azam 1, * , Hongyuan Zhang 1, Susanne S. Hille 2, Elizabeth J. Cartwright 1, Oliver J. Müller 2, and Xin Wang 1, * 1 Faculty of Biology, Medicine, and Health, University of Manchester, Oxford Road, M13 9PT, Manchester, UK 2 Department of Internal Medicine III, University of Kiel, Germany; German Centre for Cardiovascular Research (DZHK), 24105 Partner Site Hamburg/Kiel/Lübeck, Germany * Correspondence: xin.wang@manchester.ac.uk (Xin Wang); tayyiba.azam@manchester.ac.uk (Tayyiba Azam)     Received: 12 June 2023 Accepted: 25 September 2023 Published: 6 March 2024   Abstract: Shifts in epidemiological patterns foretell a rapid increase in the number of patients with heart failure (HF) globally, representing a significant health and economic burden. Heart failure with preserved ejection (HFpEF) is now considered the prevailing subtype of HF, with no effective treatment available to combat this syndrome. Previous studies have highlighted the cardioprotective role of MKK7 during cardiac pathology, however, no extensive research has been performed to examine MKK7 in the context of HFpEF. This study aimed to address this shortcoming by using adeno-associated virus (AAV) 9 to overexpress MKK7 in the two-hit clinically relevant HFpEF mouse model. We report that cardiomyocyte-specific overexpression of MKK7 improved the HFpEF phenotype in mice, by impeding cardiac diastolic dysfunction and myocardial fibrosis. Mechanistically, it was found that MKK7 ameliorated ER stress by maintaining IRE1-XBP1 signalling and blunted CHOP increase in the myocardium. To summarise, MKK7 overexpression holds the ability to protect the myocardium from HFpEF associated pathologies.

Hyper ensembled extreme gradient boosting techniques via novel triple algorithms to appraise the hardened properties of modern ternary admixed concrete

Mechanical parameters used in many design codes can be achieved by expensive and time-consuming experiments or by non-destructive approaches such as estimative modelling. This investigation proposed Extreme Gradient Boost (XGB) for estimating the slump (SL) and compressive strength (CS) of high-performance concrete (HPC). In addition, to bring the results of the models closer to the experimental data and increase the accuracy, algorithms were combined with the model, including Sunflower Optimizer (SFO) and Jellyfish Search Optimize (JSO). The relevant models have been examined in three frameworks: individual, hybrid, and ensemble-hybrid. For this purpose, several evaluators were provided to determine the errors, compare, and accuracy of the presented models. The XGFJ model has demonstrated exceptional performance, achieving remarkable results in terms of RMSE (Root Mean Square Error) and R2 (R-squared) values. Specifically, it has attained an exceptionally small RMSE value of 1.785 for CS and 5.183 for SL, indicating the model’s high precision in predicting these parameters. Additionally, it has achieved the biggest R2 values of 0.9960 for CS and 0.9949 for SL. Additionally, it is worth noting that the XGSF model closely matches the performance of the ensemble form of XGFJ, as evident from its R2 values of 0.9956 for CS and 0.9934 for SL. Based on the study, it was observed that using machine learning to anticipate the mechanical characteristics of concrete is valuable and efficient and can be considered an alternative method instead of time-consuming laboratory methods. This research addresses challenges in predicting HPC properties fueled by the need to overcome drawbacks in traditional methods. Costly and time-intensive laboratory experiments prompted the exploration of alternatives, leading to the proposal of XGB combined with optimization algorithms (SFO and JSO). The study aims to enhance prediction accuracy while tackling broader concerns such as construction costs, material efficiency, and environmental impact. The resource-intensive nature of conventional methods, along with inaccuracies due to material variations, serves as a primary challenge. The proposed resolution advocates for a paradigm shift to machine learning, exemplified by the XGFJ model, showcasing exceptional precision and efficiency in predicting HPC properties.

Break Down of the Complexity and Inconsistency Between Levels of Matriglycan and Disease Phenotype in FKRP-Related Dystroglycanopathies: A Review and Model of Interpretation.

Dystroglycanopathies are a group of muscle degenerative diseases characterized with significant reduction in matriglycan expression critical in disease pathogenesis. Missense point mutations in the Fukutin-related protein (FKRP) gene cause variable reduction in the synthesis of matriglycan on alpha-dystroglycan (α-DG) and a wide range of disease severity. Data analyses of muscle biopsies from patients fail to show consistent correlation between the levels of matriglycan and clinical phenotypes. By reviewing clinical reports in conjunction with analysis of clinically relevant mouse models, we identify likely causes for the confusion. Nearly all missense FKRP mutations retain variable, but sufficient function for the synthesis of matriglycan during the later stage of muscle development and periods of muscle regeneration. These factors lead to a highly heterogenous pattern of matriglycan expression in diseased muscles, depending on age and stages of muscle regeneration. The limited size in clinical biopsy samples from different parts of even a single muscle tissue at different time points of disease progression may well mis-represent the residual function (base-levels) of the mutated FKRPs and phenotypes. We propose to use a simple Multi Point tool from ImageJ to more accurately measure the signal intensity of matriglycan expression on fiber membrane for assessing mutant FKRP function and therapeutic efficacy. A robust and sensitive immunohistochemical protocol would further improve reliability and comparability for the detection of matriglycan.