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Abstract In this paper, the quantum effects of fine scaling on the buckling behavior of carbon nanotubes (CNTs) under axial loading are investigated. Molecular mechanics and quantum mechanics are respectively utilized to study the buckling behavior and to obtain the molecular mechanics coefficients of fine-scale nanotubes. The results of buckling behavior of CNTs with different chiralities with finite and infinite dimensions are given, and a comparison study is presented on them. The differences between finite and infinite nanotubes reflect the quantum effects of fine scaling on the buckling behavior. In addition, the results show that the dimensional changes highly affect the mechanical properties and the buckling behavior of CNTs to certain dimensions. Moreover, dimensional changes have a significant effect on the critical buckling strain. Beside, in addition to the structure dimensions, the arrangement of structural and boundary atoms have a major influence on the buckling behavior.

Abstract A three-dimensional finite element model is used to investigate the vibrational properties of double-walled silicon carbide nano-cones with various dimensions. The dependence of the vibrational properties of double-walled silicon carbide nano-cones on their length, apex angles and boundary conditions are evaluated. Current model consists a combination of beam and spring elements that simulates the interatomic interactions of bonding and nonbonding. The Lennard–Jones potential is employed to model the interactions between two non-bonding atoms. The fundamental frequency and mode shape of the double-walled silicon carbide nano-cones are calculated.

Accurate classification of tooth development stages from orthopantomograms (OPG) is crucial for dental diagnosis, treatment planning, age assessment, and forensic applications. This study aims to develop an automated method for classifying third molar development stages using OPGs. Initially, our data consisted of 3422 OPG images, each classified and curated by expert evaluators. The dataset includes images from both Q3 (lower jaw left side) and Q4 (lower right side) regions extracted from panoramic images, resulting in a total of 6624 images for analysis. Following data collection, the methodology employs region of interest extraction, pre-filtering, and extensive data augmentation techniques to enhance classification accuracy. The deep neural network model, including architectures such as EfficientNet, EfficientNetV2, MobileNet Large, MobileNet Small, ResNet18, and ShuffleNet, is optimized for this task. Our findings indicate that EfficientNet achieved the highest classification accuracy at 83.7%. Other architectures achieved accuracies ranging from 71.57 to 82.03%. The variation in performance across architectures highlights the influence of model complexity and task-specific features on classification accuracy. This research introduces a novel machine learning model designed to accurately estimate the development stages of lower wisdom teeth in OPG images, contributing to the fields of dental diagnostics and treatment planning., (© 2024. The Author(s).)

In this paper, the quantum effects of fine scaling on the buckling behavior of carbon nanotubes (CNTs) under axial loading are investigated. Molecular mechanics and quantum mechanics are respectively utilized to study the buckling behavior and to obtain the molecular mechanics coefficients of fine-scale nanotubes. The results of buckling behavior of CNTs with different chiralities with finite and infinite dimensions are given, and a comparison study is presented on them. The differences between finite and infinite nanotubes reflect the quantum effects of fine scaling on the buckling behavior. In addition, the results show that the dimensional changes highly affect the mechanical properties and the buckling behavior of CNTs to certain dimensions. Moreover, dimensional changes have a significant effect on the critical buckling strain. Beside, in addition to the structure dimensions, the arrangement of structural and boundary atoms have a major influence on the buckling behavior., (© 2024. The Author(s).)

A three-dimensional finite element model is used to investigate the vibrational properties of double-walled silicon carbide nano-cones with various dimensions. The dependence of the vibrational properties of double-walled silicon carbide nano-cones on their length, apex angles and boundary conditions are evaluated. Current model consists a combination of beam and spring elements that simulates the interatomic interactions of bonding and nonbonding. The Lennard-Jones potential is employed to model the interactions between two non-bonding atoms. The fundamental frequency and mode shape of the double-walled silicon carbide nano-cones are calculated., (© 2024. The Author(s).)

Accumulation of aggregated and misfolded proteins, leading to endoplasmic reticulum stress and activation of the unfolded protein response, is a hallmark of several neurodegenerative disorders, including Alzheimer's and Parkinson's disease. Genetic screens are powerful tools that are proving invaluable in identifying novel modulators of disease associated processes. Here, we performed a loss-of-function genetic screen using a human druggable genome library, followed by an arrayed-screen validation, in human iPSC-derived cortical neurons. We identified and genetically validated 13 genes, whose knockout was neuroprotective against Tunicamycin, a glycoprotein synthesis inhibitor widely used to induce endoplasmic reticulum stress. We also demonstrated that pharmacological inhibition of KAT2B, a lysine acetyltransferase identified by our genetic screens, by L-Moses, attenuates Tunicamycin-mediated neuronal cell death and activation of CHOP, a key pro-apoptotic member of the unfolded protein response in both cortical and dopaminergic neurons. Follow-up transcriptional analysis suggested that L-Moses provided neuroprotection by partly reversing the transcriptional changes caused by Tunicamycin. Finally, L-Moses treatment attenuated total protein levels affected by Tunicamycin, without affecting their acetylation profile. In summary, using an unbiased approach, we identified KAT2B and its inhibitor, L-Moses, as potential therapeutic targets for neurodegenerative diseases., (© 2023. The Author(s).)

Organoid cell culture methodologies are enabling the generation of cell models from healthy and diseased tissue. Patient-derived cancer organoids that recapitulate the genetic and histopathological diversity of patient tumours are being systematically generated, providing an opportunity to investigate new cancer biology and therapeutic approaches. The use of organoid cultures for many applications, including genetic and chemical perturbation screens, is limited due to the technical demands and cost associated with their handling and propagation. Here we report and benchmark a suspension culture technique for cancer organoids which allows for the expansion of models to tens of millions of cells with increased efficiency in comparison to standard organoid culturing protocols. Using whole-genome DNA and RNA sequencing analyses, as well as medium-throughput drug sensitivity testing and genome-wide CRISPR-Cas9 screening, we demonstrate that cancer organoids grown as a suspension culture are genetically and phenotypically similar to their counterparts grown in standard conditions. This culture technique simplifies organoid cell culture and extends the range of organoid applications, including for routine use in large-scale perturbation screens., (© 2022. The Author(s).)