Perspective Chapter: Podological Deformities and Its Management

The ankle and foot complex plays on important role in gait and weight bearing of the body weight. The deformity of the ankle and foot affects and alters the biomechanics of the body and normal gait pattern, and this consequently affects the other parts and joints of the lower limb and also trunk

Cancer Diagnosis through Contour Visualization of Gene Expression Leveraging Deep Learning Techniques

Prompt diagnostics and appropriate cancer therapy necessitate the use of gene expression databases. The integration of analytical methods can enhance detection precision by capturing intricate patterns and subtle connections in the data. This study proposes a diagnostic-integrated approach combining Empirical Bayes Harmonization (EBS), Jensen–Shannon Divergence (JSD), deep learning, and contour mathematics for cancer detection using gene expression data. EBS preprocesses the gene expression data, while JSD measures the distributional differences between cancerous and non-cancerous samples, providing invaluable insights into gene expression patterns. Deep learning (DL) models are employed for automatic deep feature extraction and to discern complex patterns from the data. Contour mathematics is applied to visualize decision boundaries and regions in the high-dimensional feature space. JSD imparts significant information to the deep learning model, directing it to concentrate on pertinent features associated with cancerous samples. Contour visualization elucidates the model’s decision-making process, bolstering interpretability. The amalgamation of JSD, deep learning, and contour mathematics in gene expression dataset analysis diagnostics presents a promising pathway for precise cancer detection. This method taps into the prowess of deep learning for feature extraction while employing JSD to pinpoint distributional differences and contour mathematics for visual elucidation. The outcomes underscore its potential as a formidable instrument for cancer detection, furnishing crucial insights for timely diagnostics and tailor-made treatment strategies

Cancer Diagnosis through Contour Visualization of Gene Expression Leveraging Deep Learning Techniques

Prompt diagnostics and appropriate cancer therapy necessitate the use of gene expression databases. The integration of analytical methods can enhance detection precision by capturing intricate patterns and subtle connections in the data. This study proposes a diagnostic-integrated approach combining Empirical Bayes Harmonization (EBS), Jensen–Shannon Divergence (JSD), deep learning, and contour mathematics for cancer detection using gene expression data. EBS preprocesses the gene expression data, while JSD measures the distributional differences between cancerous and non-cancerous samples, providing invaluable insights into gene expression patterns. Deep learning (DL) models are employed for automatic deep feature extraction and to discern complex patterns from the data. Contour mathematics is applied to visualize decision boundaries and regions in the high-dimensional feature space. JSD imparts significant information to the deep learning model, directing it to concentrate on pertinent features associated with cancerous samples. Contour visualization elucidates the model’s decision-making process, bolstering interpretability. The amalgamation of JSD, deep learning, and contour mathematics in gene expression dataset analysis diagnostics presents a promising pathway for precise cancer detection. This method taps into the prowess of deep learning for feature extraction while employing JSD to pinpoint distributional differences and contour mathematics for visual elucidation. The outcomes underscore its potential as a formidable instrument for cancer detection, furnishing crucial insights for timely diagnostics and tailor-made treatment strategies

Synthesis of novel 1,3-thiazin-4-ones by acetylene diester cyclization and their anticancer activities

<p>A novel series of 1,3-thiazin-4-one derivatives containing a piperidyl ring (<b>7</b>–<b>16</b>) were designed and synthesized efficiently by thioamide and acetylene diester cyclization reaction <i>via</i> aminolysis of the ester group and the elimination of an alcohol molecule. The structures of all the novel compounds were established by their FTIR, Mass, ¹H NMR, and ¹³C NMR spectral techniques. The newly synthesized compounds (<b>7</b>–<b>16</b>) were studied for their <i>in vitro</i> anticancer activity against human liver cancer cell lines Hep G2 using MTT assay. The IC₅₀ values of the tested compounds were ranging in between 7.48 ± 0.71 and 56.57 ± 1.37 µM. Further, the apoptosis evaluation by the mitochondrial membrane potential using JC-1 dye was carried out and the results are in good agreement with the cytotoxicity studies.</p

In Vitro and In Silico Toxicological Properties of Natural Antioxidant Therapeutic Agent Azima tetracantha. LAM

Plant-derived antioxidants are a large group of natural products with the capacity to reduce radical-scavenging. Due to their potent therapeutic and preventive actions, these compounds receive a lot of attention from scientists, particularly pharmacologists. The pharmacological activities of the Azima tetracantha Lam. (AT) plant, belonging to the Salvadoraceae family, reported here justifies its traditional use in treating several diseases or disorders. This study aims to look at the propensity of certain plant compounds found in natural AT plant extracts that might play a critical role as a secondary metabolite in cervical cancer treatment. There is a shortage of information on the plant’s phytochemical and biological characteristics. Methanol (MeOH) solvent extracts of the dried AT plant were screened phytochemically. Its aqueous extract was tested for antioxidant, antiseptic, anti-inflammatory, and anticancerous properties. Absorption Distribution Metabolism and Excretion (ADME/T), Docking, and HPLC were also performed. In clinical treatment, the plant shown no adverse effects. The antioxidant activity was evaluated and showed the highest concentration at 150 µg/mL (63.50%). MeOH leaf extract of AT exhibited the highest and best inhibitory activity against Staphylococcus aureus (15.3 mm/1000) and displayed a high antiseptic potential. At a 200 µg/mL concentration, MeOH leaves-extract inhibited red blood cells (RBC) hemolysis by 66.56 ± 0.40, compared with 62.33 ± 0.40 from the standard. Albumin’s ability to suppress protein denaturation ranged from 16.75 ± 0.65 to 62.35 ± 0.20 inhibitions in this test, providing even more support for its favorable anti-inflammatory properties. The ADME/T studies were considered for a potential cancer drug molecule, and one of our compounds from MeOH extract fills the ADME and toxicity parameters. The forms of compound 4 showed a strong hydrogen-bonding interaction with the vital amino acids (ASN923, THR410, LEU840TRY927, PHE921, and GLY922). A total of 90% of cell inhibition was observed when HeLa cell lines were treated with 300 µg/mL of compound 4 (7-acetyl-3a1-methyl- 4,14-dioxo-1,2,3a,3a1,4,5,5a,6,8a,9b,10,11,11a-tetradecahydro-2,5a epoxy5,6a (methanooxymethano)phenaleno[1′,9′:5,6,7]indeno[1,7a-b]oxiren-2-yl acetate). The polyphenol compounds demonstrated significant advances in anticancer drug properties, and it could lead to activation of cancer cell apoptosis