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Dermatological conditions are primarily prevalent in humans and are primarily caused by environmental and climatic fluctuations, as well as various other reasons. Timely identification is the most effective remedy to avert minor ailments from escalating into severe conditions. Diagnosing skin illnesses is consistently challenging for health practitioners. Presently, they rely on conventional methods, such as examining the condition of the skin. State-of-the-art technologies can enhance the accuracy of skin disease diagnosis by utilizing data-driven approaches. This paper presents a Computer Assisted Diagnosis (CAD) framework that has been developed to detect skin illnesses at an early stage. We suggest a computationally efficient and lightweight deep learning model that utilizes a CNN architecture. We then do thorough experiments to compare the performance of shallow and deep learning models. The CNN model under consideration consists of seven convolutional layers and has obtained an accuracy of 87.64% when applied to three distinct disease categories. The studies were conducted using the International Skin Imaging Collaboration (ISIC) dataset, which exclusively consists of dermoscopic images. This study enhances the field of skin disease diagnostics by utilizing state-of-the-art technology, attaining exceptional levels of accuracy, and striving for efficiency improvements. The unique features and future considerations of this technology create opportunities for additional advancements in the automated diagnosis of skin diseases and tailored treatment. [ABSTRACT FROM AUTHOR]

The application of magnetic resonance imaging (MRI) in the classification of brain tumors is constrained by the complex and time-consuming characteristics of traditional diagnostics procedures, mainly because of the need for a thorough assessment across several regions. Nevertheless, advancements in deep learning (DL) have facilitated the development of an automated system that improves the identification and assessment of medical images, effectively addressing these difficulties. Convolutional neural networks (CNNs) have emerged as steadfast tools for image classification and visual perception. This study introduces an innovative approach that combines CNNs with a hybrid attention mechanism to classify primary brain tumors, including glioma, meningioma, pituitary, and no-tumor cases. The proposed algorithm was rigorously tested with benchmark data from well-documented sources in the literature. It was evaluated alongside established pre-trained models such as Xception, ResNet50V2, Densenet201, ResNet101V2, and DenseNet169. The performance metrics of the proposed method were remarkable, demonstrating classification accuracy of 98.33%, precision and recall of 98.30%, and F1-score of 98.20%. The experimental finding highlights the superior performance of the new approach in identifying the most frequent types of brain tumors. Furthermore, the method shows excellent generalization capabilities, making it an invaluable tool for healthcare in diagnosing brain conditions accurately and efficiently. [ABSTRACT FROM AUTHOR]

Akt, a serine‐threonine protein kinase, is regulated by class‐I PI3K signaling. Akt regulates a wide variety of cell processes including cell proliferation, survival, and angiogenesis through serine/threonine phosphorylation of downstream targets including mTOR and glycogen‐synthase‐kinase‐3‐beta (GSK3β). Targeting cancer‐specific overexpression of Akt protein could be an efficient way to control cancer‐cell proliferation. However, the ATP‐competitive inhibitors are challenged by the highly conserved ATP binding site, and by competition with high cellular concentrations of ATP. We previously developed an allosteric inhibitor, 2‐arylidene‐4, 7‐dimethyl indan‐1‐one (FXY‐1) that showed promising activity against several lung cancer models. In this work, we designed a congeneric series of molecules based on FXY‐1 and optimized lead based on computational, in vitro assays. Computational screening followed by enzyme‐inhibition and cell‐proliferation assays identified a derivative (FCX‐146) as a new lead molecule with threefold greater potency than the parent compound. FCX‐146 increased apoptosis in HL‐60 cells, mediated in part through decreased expression of antiapoptotic Bcl‐2 protein and increased levels of Bax‐2 and Caspase‐3. Molecular‐dynamic simulations showed stable binding of FCX‐146 to an allosteric (i.e., noncatalytic) pocket in Akt. Together, we propose FCX‐146 as a potent second‐generation arylidene indanone compound that binds to the allosteric pocket of Akt and potently inhibits its activation. [ABSTRACT FROM AUTHOR]

Coded within Wien2K, we carry out DFT-based calculations for investigations of the structural, elastic, optoelectronic, and thermoelectric properties of BaXF 3 (X = Co, Ir) fluoro-perovskites. The Birch-Murnaghan fit to the energy-vs-volume data and formation energy shows that these fluoro-perovskites are structurally stable. The phonon calculation confirms the thermodynamic stability, while the relation between elastic constants such as C 11 - C 12 > 0, C 11 > 0, C 11 + 2 C 12 > 0, and B > 0 validates the mechanical stability of the compounds. BaIrF 3 exhibits a strong ability to endure compressive and shear stresses. BaCoF 3 shows a weaker capacity of withstanding changes in volume, attributed to a lower bulk modulus. Demonstrating a higher G -modulus of rigidity than the BaIrF 3 , BaCoF 3 demonstrates stronger resistance to change the shape and both compounds are found to be anisotropic and brittle. The determined band structure profiles reveal that both BaCoF 3 and BaIrF 3 demonstrate a metallic nature. In addition, the metallic nature of BaCoF 3 and BaIrF 3 is reinforced by the density-of-states (DOS) study, where Co and F atoms contribute significantly to the total DOS in the valence band in the case of BaCoF 3 , while that of BaIrF 3 is predominated by the Ba and F atoms. The computed values of ε 1 (0) for BaCoF 3 and BaIrF 3 are approximately 30 and 19, respectively, which are in line with Penn's model. The researched materials are confirmed to be strong contenders for optoelectronics by the lack of absorption in the visible range. For their potential use in thermoelectric device applications, thermoelectric parameters such as temperature-dependent Seebeck coefficient, specific heat capacity, thermal conductivity, power factor, and figure of merit are also investigated, which show that these materials are thermally stable and promising for applications in thermoelectric devices., Competing Interests: The authors declare no competing financial interest., (© 2023 The Authors. Published by American Chemical Society.)