Exploring the large chemical space in search of thermodynamically stable and mechanically robust MXenes via machine learning.

To effectively utilize MXenes, a family of two-dimensional materials, in various applications that include thermoelectric devices, semiconductors, and transistors, their thermodynamic and mechanical properties, which are closely related to their stability, must be understood. However, exploring the large chemical space of MXenes and verifying their stability using first-principles calculations are computationally expensive and inefficient. Therefore, this study proposes a machine learning (ML)-based high-throughput MXene screening framework to identify thermodynamically stable MXenes and determine their mechanical properties. A dataset of 23 857 MXenes with various compositions was used to validate this framework, and 48 MXenes were predicted to be stable by ML models in terms of heat of formation and energy above the convex hull. Among them, 45 MXenes were validated using density functional theory calculations, of which 23 MXenes, including Ti2CClBr and Zr2NCl2, have not been previously known for their stability, confirming the effectiveness of this framework. The in-plane stiffness, shear moduli, and Poisson's ratio of the 45 MXenes were observed to vary widely according to their constituent elements, ranging from 90.11 to 198.02 N m-1, 64.00 to 163.40 N m-1, and 0.19 to 0.58, respectively. MXenes with Group-4 transition metals and halogen surface terminations were shown to be both thermodynamically stable and mechanically robust, highlighting the importance of electronegativity difference between constituent elements. Structurally, a smaller volume per atom and minimum bond length were determined to be preferable for obtaining mechanically robust MXenes. The proposed framework, along with an analysis of these two properties of MXenes, demonstrates immense potential for expediting the discovery of stable and robust MXenes.

Active Learning Framework for Expediting the Search of Thermodynamically Stable MXenes in the Extensive Chemical Space.

MXenes possess a wide range of materials properties owing to their compositional and stoichiometric diversities, facilitating their utilization in various technological applications such as electrodes, catalysts, and supercapacitors. To explore their applicability, identification of thermodynamically stable and synthesizable MXenes should precede. The energy above the convex hull (Ehull) calculated using the density functional theory (DFT) is a powerful scale to probe the thermodynamic stability. However, the high calculation cost of DFT limits the search space of unknown chemistry. To address this challenge, this study proposes an active learning (AL) framework consisting of a surrogate model and utility function for expeditious identification of thermodynamically stable MXenes in the extensive chemical space of 23,857 MXenes with compositional and stoichiometric diversity. Exploiting the fast inference speed and the capability of the AL framework to accurately identify stable MXenes, only 480 DFT calculations were required to identify 126 thermodynamically stable MXenes; among these, the stabilities of 89 MXenes have not been previously reported. In contrast, only two stable MXenes were identified among randomly selected 1693 MXenes, demonstrating the inefficiency of using only DFT calculations in exploring a large chemical space. The AL framework successfully minimized the number of DFT calculations while maximizing that of thermodynamically stable MXenes identified and can contribute to future studies in finding stable MXenes expeditiously.

mTOR potentiates senescent phenotypes and primary cilia formation after cisplatin-induced G2 arrest in retinal pigment epithelial cells.

Cisplatin, a platinum-based anticancer drug, is used to treat several types of cancer. Despite its effectiveness, cisplatin-induced side effects have often been reported. Although cisplatin-induced toxicities, such as apoptosis and/or necrosis, have been well studied, the fate of cells after exposure to sublethal doses of cisplatin needs further elucidation. Treatment with a sublethal dose of cisplatin induced cell cycle arrest at the G2 phase in retinal pigment epithelial cells. Following cisplatin withdrawal, the cells irreversibly exited the cell cycle and became senescent. Notably, the progression from the G2 to the G1 phase occurred without mitotic entry, a phenomenon referred to as mitotic bypass, resulting in the accumulation of cells containing 4N DNA content. Cisplatin-exposed cells exhibited morphological changes associated with senescence, including an enlarged size of cell and nucleus and increased granularity. In addition, the senescent cells possessed primary cilia and persistent DNA lesions. Senescence induced by transient exposure to cisplatin involves mTOR activation. Although transient co-exposure with an mTORC1 inhibitor rapamycin did not prevent mitotic bypass and entry into senescence, it delayed the progression of senescence and attenuated senescent phenotypes, resulting in shorter primary cilia formation. Conclusively, cisplatin induces senescence in retinal pigment epithelial cells by promoting mTOR activation.

Uncovering the Relationship between Metal Elements and Mechanical Stability for Metal-Organic Frameworks.

Assessing the mechanical robustness of metal-organic frameworks (MOFs) is crucial to enhance their applicability in various fields. Although considerable research has been conducted on the relationship between the mechanical properties of MOFs and their structural features (such as pore size, surface area, and topology), the insufficient exploration of metal elements has prevented researchers from fully understanding their mechanical behavior. To plug this knowledge gap, we constructed a database of mechanical properties for 20,342 MOFs included in the QMOF database using molecular simulations to investigate the impact of metal elements on mechanical stability. Through Shapley additive explanations (SHAP) analysis, we found that Co and Ln could enhance the structural stability of MOFs. We validated these findings using newly generated hypothetical MOFs. Notably, we adopted an interpretable machine learning technique to analyze the contribution of remarkably diverse metal elements in the 20,342 MOFs to the mechanical properties of each MOF. We anticipate that this research will serve as a valuable tool for future studies on identifying mechanically robust MOFs suitable for various industrial applications.

Deep Learning Model for Predicting Airway Organoid Differentiation.

BACKGROUND: Organoids are self-organized three-dimensional culture systems and have the advantages of both in vitro and in vivo experiments. However, each organoid has a different degree of self-organization, and methods such as immunofluorescence staining are required for confirmation. Therefore, we established a system to select organoids with high tissue-specific similarity using deep learning without relying on staining by acquiring bright-field images in a non-destructive manner. METHODS: We identified four biomarkers in RNA extracted from airway organoids. We also predicted biomarker expression by image-based analysis of organoids by convolution neural network, a deep learning method. RESULTS: We predicted airway organoid-specific marker expression from bright-field images of organoids. Organoid differentiation was verified by immunofluorescence staining of the same organoid after predicting biomarker expression in bright-field images. CONCLUSION: Our study demonstrates the potential of imaging and deep learning to distinguish organoids with high human tissue similarity in disease research and drug screening.

Morphological Analysis of PSMA/PEI Core-Shell Nanoparticles Synthesized by Soap-Free Emulsion Polymerization.

Emulsion polymerization presents the disadvantage that the physical properties of polymer particles are altered by surfactant adsorption. Therefore, in the soap-free emulsion polymerization method, a hydrophilic initiator is utilized while inducing repulsion among particles on the polymer particle surface, resulting in stable polymer particle production. In this study, we developed a methodology wherein spherical and uniform poly(styrene-co-maleic anhydride) (PSMA)/polyethyleneimine (PEI) core-shell nanoparticles were prepared. Further, their morphology was analyzed. During PSMA polymerization, the addition of up to 30% maleic anhydride (MA) resulted in stable polymerization. In PSMA/PEI nanoparticle fabrication, the number of reactants increased with increased initial monomer feed amounts; consequently, the particle size increased, and as the complete monomer consumption time increased, the particle distribution widened. The styrene (St) copolymer acted as a stabilizer, reducing particle size and narrowing particle distribution. Furthermore, the monomers were more rapidly consumed at high initiator concentrations, irrespective of the initiator used, resulting in increased particle stability and narrowed particle distribution. The shell thickness and particle size were PEI feed ratio dependent, with 0.08 being the optimal PEI-to-MA ratio. The fabricated nanoparticles possess immense potential for application in environmental science and in chemical and health care industries.

Quality Characteristics and Antioxidant Potential of Lemon (Citrus limon Burm. f.) Seed Oil Extracted by Different Methods.

Lemon (Citrus limon Burm. f.) is one of the most widely produced and consumed fruits in the world. The seeds of lemon are generally discarded as waste. The purpose of this study was to investigate the quality characteristics and antioxidant potential of lemon seed oil obtained by four extraction methods (roasted-pressing at 170°C, RP-170; roasted-pressing at 100°C, RP-100; cold-pressing, CP; and supercritical fluid, SF). No significant differences in the viscosity, density, and refractive index were observed in the oil obtained from different methods. In the case of Hunter's value, L (lightness) and b (yellowness) values of SF were higher than those of the others. The oil obtained by the CP method exhibited higher levels of Ca (252.17 mg/kg), Cu (2.38 mg/kg), K (225.98 mg/kg), and Mo (0.47 mg/kg) than that of other methods. The highest contents of total phenols (165.90 mg/mL) and flavonoids (21.69 mg/mL) were significantly high in oil obtained by the SF method. Oleic and linoleic acids consisted of principal fatty acids, which were significantly higher in oil obtained by RP-170. Higher amounts of volatile flavor compounds, such as γ-terpinene, sabinene, and limonene, were observed in CP compared to those observed for the other methods. This study elucidates the effects of different methods of oil extraction on the composition of lemon seed oil and highlights potential applications of these benefits in the food, cosmetic, pharmaceutical, and/or fragrance industries.

Acute fibrinous and organizing pneumonia following hematopoietic stem cell transplantation.

A 60-year-old man presented with cough, sputum, and dyspnea. He had a history of acute myeloid leukemia and hematopoietic stem cell transplantation with chronic renal failure. Chest CT scans showed miliary nodules and patchy consolidations. Histological examination revealed numerous fibrin balls within the alveoli and thickening of the alveolar septum, both of which are typical pathological features of acute fibrinous and organizing pneumonia (AFOP). We report the first case of AFOP following allogeneic hematopoietic stem cell transplantation.