Molecular Targets of Plant-based Alkaloids and Polyphenolics in Liver and Breast Cancer- An Insight into Anticancer Drug Development.

Liver and Breast cancer are ranked as the most prevailing cancers that cause high cancer-related mortality. As cancer is a life-threatening disease that affects the human population globally, there is a need to develop novel therapies. Among the available treatment options include radiotherapy, chemotherapy, surgery, and immunotherapy. The most superlative modern method is the use of plant-derived anticancer drugs that target the cancerous cells and inhibit their proliferation. Plant-derived compounds are generally considered safer than synthetic drugs/traditional therapies and could serve as potential novel targets to treat liver and breast cancer to revolutionize cancer treatment. Alkaloids and Polyphenols have been shown to act as anticancer agents through molecular approaches. They disrupt various cellular mechanisms, inhibit the production of cyclins and CDKs to arrest the cell cycle, and activate the DNA repairing mechanism by upregulating p53, p21, and p38 expression. In severe cases, when no repair is possible, they induce apoptosis in liver and breast cancer cells by activating caspase-3, 8, and 9 and increasing the Bax/Bcl-2 ratio. They also deactivate several signaling pathways, such as PI3K/AKT/mTOR, STAT3, NF-kB, Shh, MAPK/ERK, and Wnt/ß-catenin pathways, to control cancer cell progression and metastasis. The highlights of this review are the regulation of specific protein expressions that are crucial in cancer, such as in HER2 over-expressing breast cancer cells; alkaloids and polyphenols have been reported to reduce HER2 as well as MMP expression. This study reviewed more than 40 of the plant-based alkaloids and polyphenols with specific molecular targets against liver and breast cancer. Among them, Oxymatrine, Hirsutine, Piperine, Solamargine, and Brucine are currently under clinical trials by qualifying as potent anticancer agents due to lesser side effects. As a lot of research is there on anticancer compounds, there is a desideratum to compile data to move towards clinical trials phase 4 and control the prevalence of liver and breast cancer.

Insights into AlphaFold's breakthrough in neurodegenerative diseases.

Neurodegenerative diseases (ND) are disorders of the central nervous system (CNS) characterized by impairment in neurons' functions, and complete loss, leading to memory loss, and difficulty in learning, language, and movement processes. The most common among these NDs are Alzheimer's disease (AD) and Parkinson's disease (PD), although several other disorders also exist. These are frontotemporal dementia (FTD), amyotrophic lateral syndrome (ALS), Huntington's disease (HD), and others; the major pathological hallmark of NDs is the proteinopathies, either of amyloid-ß (Aß), tauopathies, or synucleinopathies. Aggregation of proteins that do not undergo normal configuration, either due to mutations or through some disturbance in cellular pathway contributes to the diseases. Artificial Intelligence (AI) and deep learning (DL) have proven to be successful in the diagnosis and treatment of various congenital diseases. DL approaches like AlphaFold (AF) are a major leap towards success in CNS disorders. This 3D protein geometry modeling algorithm developed by DeepMind has the potential to revolutionize biology. AF has the potential to predict 3D-protein confirmation at an accuracy level comparable to experimentally predicted one, with the additional advantage of precisely estimating protein interactions. This breakthrough will be beneficial to identify diseases' advancement and the disturbance of signaling pathways stimulating impaired functions of proteins. Though AlphaFold has solved a major problem in structural biology, it cannot predict membrane proteins-a beneficial approach for drug designing.

Computational insights into missense mutations in HTT gene causing Huntington's disease and its interactome networks.

BACKGROUND: Huntington's disease is a rare neurodegenerative illness of the central nervous system that is inherited in an autosomal dominant pattern. Mutant huntingtin protein is produced as a result of enlargement of CAG repeat in the N-terminal of the polyglutamine tract. AIM OF THE STUDY: Herein, we aim to investigate the mutations and their effects on the HTT gene and its genetic variants. Additionally, the protein-protein interaction of HTT with other proteins and receptor-ligand interaction with the three-dimensional structure of huntingtin protein were identified. METHODS: A comprehensive analysis of the HTT interactome and protein-ligand interaction has been carried out to provide a global picture of structure-function analysis of huntingtin protein. Mutations were analyzed and mutation verification tools were used to check the effect of mutation on protein function. RESULTS: The results showed, mutations in a single gene are not only responsible for causing a particular disease but may also cause other hereditary disorders as well. Moreover, the modification at the nucleotide level also cause the change in the specific amino acid which may disrupt the function of HTT and its interacting proteins contributing in disease pathogenesis. Furthermore, the interaction between MECP2 and BDNF lowers the rate of transcriptional activity. Molecular docking further confirmed the strong interaction between MECP2 and BDNF with highest affinity. Amino acid residues of the HTT protein, involved in the interaction with tetrabenazine were N912, Y890, G2385, and V2320. These findings proved, tetrabenazine as one of the potential therapeutic agent for treatment of Huntington's disease. CONCLUSION: These results give further insights into the genetics of Huntington's disease for a better understanding of disease models which will be beneficial for the future therapeutic studies.

Insilico study of genes involved in Congenital Hypothyroidism.

OBJECTIVE: To study the orthologs of the five genes of congenital hypothyroidism NIS, PAX8, DUOX2, FOXE1, NKX2-1 that are involved in the development of the thyroid gland. METHODS: The study was conducted at INMOL Cancer Hospital, Lahore in September 2017 and comprised of finding gene orthologs, phylogenetic tree and domains of NIS, PAX8, DUOX2, FOXE1, NKX2-1 which were studied using different bioinformatics tools, including FASTA, BLAST, ENSEMBL, UniProt, MultiAlin, to find out the important domains involved in the mutations of these genes. RESULTS: Genes showed consensus sequence / motifs involved in congenital hypothyroidism. Phylogenetic results showed that these genes shared some common motifs. Phylogenetic trees revealed sub-clusters with high protein homology. CONCLUSIONS: Genes involved in congenital hypothyroidism were found to have a consensus sequence motifs.

Are we missing the diagnosis of depression in patients with rheumatoid arthritis at a tertiary care facility?

OBJECTIVES: To determine if we are missing clinical depression in patients with Rheumatoid Arthritis and its relationship with functional disability and level of formal education in such patients. METHODS: The data for this cross-sectional, analytical study was gathered from May 2015 till December 2015 and comprised of 128 with Rheumatoid arthritis diagnosed according to ACR/EULAR 2010 criteria. The study was conducted at Fauji Foundation Hospital Rawalpindi. Functional status was assessed with Modified Health Assessment Questionnaire (mHAQ) and Beck's Depression Inventory (BDI) was used for evaluation of symptoms of depression. The relation between depression, functional disability and educational status was established using Pearson correlation coefficient. RESULTS: The study included 128 patients with no previous diagnosis of depression. 122 (95.3%) were females and 6 (4.7%) were males. The mean age was 51.75 ± 9.25 years. Mean duration of disease was 8.95 ± 7.1 years. According to this study, the diagnosis of clinical depression was missed in 47.7% of patients with Rheumatoid Arthritis who had been under regular follow up at a tertiary care facility. About 18% were keen to seek professional help for depressive symptoms while 62.6% had functional disability (mild - severe). There is a positive correlation with BDI (Pearson's correlation +1) and functional disability. No correlation could be established between level of education and depression as out of 79 (61.7%) patients with no basic education, 45.5% had depression. In remaining 49 (38.2%) patients, with some formal education, 51.3% had clinical depression. CONCLUSION: Almost half of the patients with Rheumatoid Arthritis coming to a tertiary care set up had clinical depression but were never diagnosed or referred to a Psychiatrist. There is a positive correlation between depression and functional disability; however no statistically significant correlation could be established with the level of formal education. The study further emphasizes the importance of early recognition and swift referral of such patients to a psychiatrist since it is known to improve both treatment outcomes and functional status.

Semi-advised learning model for skin cancer diagnosis based on histopathalogical images.

Computer aided classification of skin cancer images is an active area of research and different classification methods has been proposed so far. However, the supervised classification models based on insufficient labeled training data can badly influence the diagnosis process. To deal with the problem of limited labeled data availability this paper presents a semi advised learning model for automated recognition of skin cancer using histopathalogical images. Deep belief architecture is constructed using unlabeled data by making efficient use of limited labeled data for fine tuning done the classification model. In parallel an advised SVM algorithm is used to enhance classification results by counteracting the effect of misclassified data using advised weights. To increase generalization capability of the model, advised SVM and Deep belief network are trained in parallel. Then the results are aggregated using least square estimation weighting. The proposed model is tested on a collection of 300 histopathalogical images taken from biopsy samples. The classification performance is compared with some popular methods and the proposed model outperformed most of the popular techniques including KNN, ANN, SVM and semi supervised algorithms like Expectation maximization algorithm and transductive SVM based classification model.

Differential evolution based advised SVM for histopathalogical image analysis for skin cancer detection.

Automated detection of cancerous tissue in histopathological images is a big challenge. This work proposed a new pattern recognition method for histopathological image analysis for identification of cancerous tissues. It comprised of feature extraction using a combination of wavelet and intensity based statistical features and autoregressive parameters. Moreover, differential evolution based feature selection is used for dimensionality reduction and an efficient self-advised version of support vector machine is used for evaluation of selected features and for the classification of images. The proposed system is trained and tested using a dataset of 150 histopathological images and showed promising comparative results with an average diagnostic accuracy of 89.1%.

Computer aided diagnostic support system for skin cancer: a review of techniques and algorithms.

Image-based computer aided diagnosis systems have significant potential for screening and early detection of malignant melanoma. We review the state of the art in these systems and examine current practices, problems, and prospects of image acquisition, pre-processing, segmentation, feature extraction and selection, and classification of dermoscopic images. This paper reports statistics and results from the most important implementations reported to date. We compared the performance of several classifiers specifically developed for skin lesion diagnosis and discussed the corresponding findings. Whenever available, indication of various conditions that affect the technique's performance is reported. We suggest a framework for comparative assessment of skin cancer diagnostic models and review the results based on these models. The deficiencies in some of the existing studies are highlighted and suggestions for future research are provided.