Unlocking the Potential of the CA2, CA7, and ITM2C Gene Signatures for the Early Detection of Colorectal Cancer: A Comprehensive Analysis of RNA-Seq Data by Utilizing Machine Learning Algorithms.

Colorectal cancer affects the colon or rectum and is a common global health issue, with 1.1 million new cases occurring yearly. The study aimed to identify gene signatures for the early detection of CRC using machine learning (ML) algorithms utilizing gene expression data. The TCGA-CRC and GSE50760 datasets were pre-processed and subjected to feature selection using the LASSO method in combination with five ML algorithms: Adaboost, Random Forest (RF), Logistic Regression (LR), Gaussian Naive Bayes (GNB), and Support Vector Machine (SVM). The important features were further analyzed for gene expression, correlation, and survival analyses. Validation of the external dataset GSE142279 was also performed. The RF model had the best classification accuracy for both datasets. A feature selection process resulted in the identification of 12 candidate genes, which were subsequently reduced to 3 (CA2, CA7, and ITM2C) through gene expression and correlation analyses. These three genes achieved 100% accuracy in an external dataset. The AUC values for these genes were 99.24%, 100%, and 99.5%, respectively. The survival analysis showed a significant logrank p-value of 0.044 for the final gene signatures. The analysis of tumor immunocyte infiltration showed a weak correlation with the expression of the gene signatures. CA2, CA7, and ITM2C can serve as gene signatures for the early detection of CRC and may provide valuable information for prognostic and therapeutic decision making. Further research is needed to fully understand the potential of these genes in the context of CRC.

Navigating Molecular Pathways: An Update on Drugs in Colorectal Cancer Treatment.

Colorectal cancer (CRC) is a multifaceted and heterogeneous ailment that affects the colon or rectum of the digestive system. It is the second most commonly occurring form of cancer and ranks third in terms of mortality rate. The progression of CRC does not occur due to a single mutational event; rather, it is the result of the sequential and cumulative accumulation of mutations in key driver genes of signaling pathways. The most significant signaling pathways, which have oncogenic potential due to their deregulation, include Wnt/ß-catenin, Notch, TGF-ß, EGFR/MAPK, and PI3K/AKT pathways. Numerous drug target therapies have been developed to treat CRC using small molecule inhibitors, antibodies, or peptides. Although drug-targeted therapy is effective in most cases, the development of resistance mechanisms in CRC has raised questions about their efficacy. To overcome this issue, a novel approach to drug repurposing has come to light, which utilizes already FDA-approved drugs to treat CRC. This approach has shown some promising experimental results, making it a crucial avenue of research in the treatment of CRC.

Prognostic model development for classification of colorectal adenocarcinoma by using machine learning model based on feature selection technique boruta.

Colorectal cancer (CRC) is the third most prevalent cancer type and accounts for nearly one million deaths worldwide. The CRC mRNA gene expression datasets from TCGA and GEO (GSE144259, GSE50760, and GSE87096) were analyzed to find the significant differentially expressed genes (DEGs). These significant genes were further processed for feature selection through boruta and the confirmed features of importance (genes) were subsequently used for ML-based prognostic classification model development. These genes were analyzed for survival and correlation analysis between final genes and infiltrated immunocytes. A total of 770 CRC samples were included having 78 normal and 692 tumor tissue samples. 170 significant DEGs were identified after DESeq2 analysis along with the topconfects R package. The 33 confirmed features of importance-based RF prognostic classification model have given accuracy, precision, recall, and f1-score of 100% with 0% standard deviation. The overall survival analysis had finalized GLP2R and VSTM2A genes that were significantly downregulated in tumor samples and had a strong correlation with immunocyte infiltration. The involvement of these genes in CRC prognosis was further confirmed on the basis of their biological function and literature analysis. The current findings indicate that GLP2R and VSTM2A may play a significant role in CRC progression and immune response suppression.

Herbal Therapeutics for Alzheimer's Disease: Ancient Indian Medicine System from the Modern Viewpoint.

Alzheimer's is a chronic neurodegenerative disease where amyloid-beta (Aß) plaques and neurofibrillary tangles are formed inside the brain. It is also characterized by progressive memory loss, depression, neuroinflammation, and derangement of other neurotransmitters. Due to its complex etiopathology, current drugs have failed to completely cure the disease. Natural compounds have been investigated as an alternative therapy for their ability to treat Alzheimer's disease (AD). Traditional herbs and formulations which are used in the Indian ayurvedic system are rich sources of antioxidant, anti-amyloidogenic, neuroprotective, and anti-inflammatory compounds. They promote quality of life by improving cognitive memory and rejuvenating brain functioning through neurogenesis. A rich knowledge base of traditional herbal plants (Turmeric, Gingko, Ashwagandha, Shankhpushpi, Giloy, Gotu kola, Garlic, Tulsi, Ginger, and Cinnamon) combined with modern science could suggest new functional leads for Alzheimer's drug discovery. In this article Ayurveda, the ancient Indian herbal medicine system based on multiple clinical and experimental, evidence have been reviewed for treating AD and improving brain functioning. This article presents a modern perspective on the herbs available in the ancient Indian medicine system as well as their possible mechanisms of action for AD treatment. The main objective of this research is to provide a systematic review of herbal drugs that are easily accessible and effective for the treatment of AD.

Screening of potential antiplasmodial agents targeting cysteine protease-Falcipain 2: a computational pipeline.

The spread of antimalarial drug resistance is a substantial challenge in achieving global malaria elimination. Consequently, the identification of novel therapeutic candidates is a global health priority. Malaria parasite necessitates hemoglobin degradation for its survival, which is mediated by Falcipain 2 (FP2), a promising antimalarial target. In particular, FP2 is a key enzyme in the erythrocytic stage of the parasite's life cycle. Here, we report the screening of approved drugs listed in DrugBank using a computational pipeline that includes drug-likeness, toxicity assessments, oral toxicity evaluation, oral bioavailability, docking analysis, maximum common substructure (MCS) and molecular dynamics (MD) Simulations analysis to identify capable FP2 inhibitors, which are hence potential antiplasmodial agents. A total of 45 drugs were identified, which have positive drug-likeness, no toxic features and good bioavailability. Among these, six drugs showed good binding affinity towards FP2 compared to E64, an epoxide known to inhibit FP2. Notably, two of them, Cefalotin and Cefoxitin, shared the highest MCS with E64, which suggests that they possess similar biological activity as E64. In an investigation using MD for 100 ns, Cefalotin and Cefoxitin showed adequate protein compactness as well as satisfactory complex stability. Overall, these computational approach findings can be applied for designing and developing specific inhibitors or new antimalarial agents for the treatment of malaria infections.Communicated by Ramaswamy H. Sarma.

Identification of Potential Antimalarial Drug Candidates Targeting Falcipain-2 Protein of Malaria Parasite-A Computational Strategy.

Falcipain-2 (FP-2) is one of the main haemoglobinase of P. falciparum which is an important molecular target for the treatment of malaria. In this study, we have screened alkaloids to identify potential inhibitors against FP-2 since alkaloids possess great potential as anti-malarial agents. A total of 340 alkaloids were considered for the study using a series of computational pipelines. Initially, pharmacokinetics and toxicity risk assessment parameters were applied to screen compounds. Subsequently, molecular docking algorithms were utilised to understand the binding efficiency of alkaloids against FP-2. Further, oral toxicity prediction was done using the pkCSM tool, and 3D pharmacophore features were analysed using the PharmaGist server. Finally, MD simulation was performed for Artemisinin and the top 3 drug candidates (Noscapine, Reticuline, Aclidinium) based on docking scores to understand the functional impact of the complexes, followed by a binding site interaction residues study. Overall analysis suggests that Noscapine conceded good pharmacokinetics and oral bioavailability properties. Also, it showed better binding efficiency with FP-2 when compared to Artemisinin. Interestingly, structure alignment analysis with artemisinin revealed that Noscapine, Reticuline, and Aclidinium might possess similar biological action. Molecular dynamics and free energy calculations revealed that Noscapine could be a potent antimalarial agent targeting FP-2 that can be used for the treatment of malaria and need to be studied experimentally in the future.

Transcriptome profiling by combined machine learning and statistical R analysis identifies TMEM236 as a potential novel diagnostic biomarker for colorectal cancer.

Colorectal cancer (CRC) is a common cause of cancer-related deaths worldwide. The CRC mRNA gene expression dataset containing 644 CRC tumor and 51 normal samples from the cancer genome atlas (TCGA) was pre-processed to identify the significant differentially expressed genes (DEGs). Feature selection techniques Least absolute shrinkage and selection operator (LASSO) and Relief were used along with class balancing for obtaining features (genes) of high importance. The classification of the CRC dataset was done by ML algorithms namely, random forest (RF), K-nearest neighbour (KNN), and artificial neural networks (ANN). The significant DEGs were 2933, having 1832 upregulated and 1101 downregulated genes. The CRC gene expression dataset had 23,186 features. LASSO had performed better than Relief for classifying tumor and normal samples through ML algorithms namely RF, KNN, and ANN with an accuracy of 100%, while Relief had given 79.5%, 85.05%, and 100% respectively. Common features between LASSO and DEGs were 38, from them only 5 common genes namely, VSTM2A, NR5A2, TMEM236, GDLN, and ETFDH had shown statistically significant survival analysis. Functional review and analysis of the selected genes helped in downsizing the 5 genes to 2, which are VSTM2A and TMEM236. Differential expression of TMEM236 was statistically significant and was markedly reduced in the dataset which solicits appreciation for assessment as a novel biomarker for CRC diagnosis.

Determinants of socio-demographic and environmental barriers in prevention of COVID-19 pandemic.

Aim and Objectives: This study assessed the knowledge, attitude, and practice of patients with ESIC, who are insured patients with the public health sector about COVID-19. Materials and Method: This was a cross-sectional observational study that used a questionnaire developed for the purpose of the survey. The questionnaire was divided into five parts including the demographic variables and environmental variables that are enablers for positive preventive practices of COVID-19, knowledge, attitude, and preventive practices followed with respect to COVID-19. Results: The mean age of the study population was 36.96 ± 13.05 (18-73) years with almost an equal proportion of males and females. The knowledge about measures to be taken for prevention of corona infection such as Balanced diet, Lockdown, Social distance, frequently wash hands often, using face mask and regularly cleaning with disinfectant surfaces was significantly better among females. Majority of the subjects displaying good attitude toward the corona disease prevention. Preventive practices were found to be satisfactory among the study population. Use of soap and handwashing was more than the use of sanitizer. Hand washing, use of mouth mask, and taking bath after returning home from outside was significantly better among males. Conclusion: There are increased worries and apprehensions among the public regarding acquiring the COVID-19 infection. People have higher perceived needs to deal with their anxiety towards acquiring the infection.

Comparative binding analysis of noscapine and piperine with tRNA: A structural perturbation and energetic study.

In this study, we have exploring the binding mechanisms of the two anticancer alkaloid noscapine (NOS) and piperine (PIP) with tRNA using different spectroscopy and computational method. Absorbance and emission spectra revealed that both the drugs show strong binding with tRNA, where NOS intercalate between the base pairs of tRNA and PIP binds in the groove of tRNA. Competitive binding study and steady state anisotropy further confirms the intercalative mode of binding between NOS and tRNA and groove binding in PIP-tRNA complex. The observed thermodynamic parameters suggested that NOS-tRNA complex formation is endothermic and entropy driven, however it was exothermic, and enthalpy driven in case of PIP-tRNA complex. CD and time resolved fluorescence studies show the structural perturbations and conformational change in tRNA structure with NOS as well as PIP. Molecular docking studies are comparable with experimental results and further confirmed that the hydrophobic interactions involved in the NOS-tRNA binding, whereas hydrogen binding and van der Waals interactions play important role in the PIP-tRNA complex formation. This study can be useful to understand the potential binding and resultant tRNA damage by alkaloids and deigned new target specific anticancer drug.

Machine learning method using position-specific mutation based classification outperforms one hot coding for disease severity prediction in haemophilia 'A'.

Haemophilia is an X-linked genetic disorder in which A and B types are the most common that occur due to absence or lack of protein factors VIII and IX, respectively. Severity of the disease depends on mutation. Available Machine Learning (ML) methods that predict the mutational severity by using traditional encoding approaches, generally have high time complexity and compromised accuracy. In this study, Haemophilia 'A' patient mutation dataset containing 7784 mutations was processed by the proposed Position-Specific Mutation (PSM) and One-Hot Encoding (OHE) technique to predict the disease severity. The dataset processed by PSM and OHE methods was analyzed and trained for classification of mutation severity level using various ML algorithms. Surprisingly, PSM outperformed OHE, both in terms of time efficiency and accuracy, with training and prediction time improvement in the range of approximately 91 to 98% and 80 to 99% respectively. The severity prediction accuracy also improved by using PSM with different ML algorithms.

Comparative in vitro cytotoxicity and binding investigation of artemisinin and its biogenetic precursors with ctDNA.

Artemisinin (ART) and its biogenetic precursors artemisinic acid (AA) and dihydroartemisinic acid (DHAA) are important traditional medicinal herb compounds with tumor growth inhibition properties. Herein, we have studied the cytotoxicity of ART, AA, and DHAA on different cancer cell lines (H1299, A431, and HCT 116) and investigated in detail their binding mechanisms with ctDNA by using spectroscopy, cyclic voltammetry, and computational methods. The UV absorbance, cyclic voltammetry, DNA helix melting, competition binding, and circular dichroism studies suggested that the complex formation of ART-ctDNA and AA-ctDNA occurs through groove binding. However, in the case of DHAA-ctDNA interaction, electrostatic interaction plays a major role. The thermodynamic parameters, viz., ΔG 0, ΔH 0, and ΔS 0 were calculated, which showed the involvement of hydrogen bonds and van der Waals interactions for drug-ctDNA interaction. FTIR and molecular docking results suggested that ART, AA, and DHAA were bound to the A-T rich region in the minor groove of ctDNA.

Ionic Liquid Green Assembly-Mediated Migration of Piperine from Calf-Thymus DNA: A New Possibility of the Tunable Drug Delivery System.

Biocompatible surface-active ionic liquid (SAIL) was used first to study the deintercalation process of a well-known natural compound piperine (PIP) as an anticancer drug, obtained from PIP-calf thymus DNA (ctDNA) complex under controlled experimental conditions. In this study, we have been exploring the interaction of PIP in SAIL (1-butyl-3-methylimidazolium octyl sulfate ionic liquid ([C4mim][C8OSO3])), ctDNA, and deintercalation of PIP from the PIP-ctDNA complex through SAIL micelle using various spectroscopic techniques. Absorption, emission, and lifetime decay measurements provide strong evidence of the relocation of PIP molecules from ctDNA to SAIL micelle. Fluorescence quenching and steady-state fluorescence anisotropy were employed to examine the exact location of PIP in different media. Moreover, the surface tension technique was also employed to confirm the release of PIP molecules from the PIP-ctDNA complex in the presence of SAIL. Circular dichroism analysis suggested that SAIL micelle does not perturb the ctDNA structure, which supported the fact that SAIL micelle can be used as a safe vehicle for PIP. Overall, the study highlighted a novel strategy for deintercalation of drug using SAIL because the release of the drug can be controlled over a period by varying the concentration and composition of the SAIL.

Probing the Intercalation of Noscapine from Sodium Dodecyl Sulfate Micelles to Calf Thymus Deoxyribose Nucleic Acid: A Mechanistic Approach.

Noscapine (NOS) is efficient in inhibiting cellular proliferation and induces apoptosis in nonsmall cell, lung, breast, lymphatic, and prostate cancers. The micelle-assisted drug delivery is a well-known phenomenon; however, the proper mechanism is still unclear. Therefore, in the present study, we have shown a mechanistic approach for the delivery of NOS from sodium dodecyl sulfate (SDS) micelles to calf thymus deoxyribose nucleic acid (ctDNA) base-pairs using various spectroscopic techniques. The absorption and emission spectroscopy results revealed that NOS interacts with the SDS micelle and resides in its hydrophobic core. Further, the intercalation of NOS from SDS micelles to ctDNA was also shown by these techniques. The anisotropy and quenching results further confirmed the relocation of NOS from SDS micelles to ctDNA. The CD analysis suggested that SDS micelles do not perturb the structure of ctDNA, which supported that SDS micelles can be used as a safe delivery vehicle for NOS. This work may be helpful for the invention of advanced micelle-based vehicles for the delivery of an anticancer drug to their specific target site.

Recent Advances and Computational Approaches in Peptide Drug Discovery.

BACKGROUND: Drug design and development is a vast field that requires huge investment along with a long duration for providing approval to suitable drug candidates. With the advancement in the field of genomics, the information about druggable targets is being updated at a fast rate which is helpful in finding a cure for various diseases. METHODS: There are certain biochemicals as well as physiological advantages of using peptide-based therapeutics. Additionally, the limitations of peptide-based drugs can be overcome by modulating the properties of peptide molecules through various biomolecular engineering techniques. Recent advances in computational approaches have been helpful in studying the effect of peptide drugs on the biomolecular targets. Receptor - ligand-based molecular docking studies have made it easy to screen compatible inhibitors against a target.Furthermore, there are simulation tools available to evaluate stability of complexes at the molecular level. Machine learning methods have added a new edge by enabling accurate prediction of therapeutic peptides. RESULTS: Peptide-based drugs are expected to take over many popular drugs in the near future due to their biosafety, lower off-target binding chances and multifunctional properties. CONCLUSION: This article summarises the latest developments in the field of peptide-based therapeutics related to their usage, tools, and databases.

Coumarin tethered cyclic imides as efficacious glucose uptake agents and investigation of hit candidate to probe its binding mechanism with human serum albumin.

A series of novel coumarin-cyclic imide conjugates (1a-1j) were designed and synthesized to evaluate their glucose uptake activity by insulin resistant liver hepatocyte carcinoma (HepG2) cells through 2-NBDG uptake assay. Compounds (1a-1j) were characterised using various analytical methods such as 1H NMR, 13C NMR, IR, GC-MS, elemental and single-crystal X-ray diffraction techniques. Compounds (1a-1j) exhibited 85.21 - 65.80% of glucose uptake and showed low level of cytotoxicity towards human embryonic kidney cells (HEK-293) indicating good selectivity and safety profile. Compound 1f was identified as a hit candidate exhibiting 85.21% of glucose uptake which was comparable with standard antidiabetic drug Metformin (93.25% glucose uptake). Solution stability study under physiological pH conditions ≈ (3.4 - 8.7), indicates that compound 1f is sufficiently stable at varied pH conditions and thereby compatible with bio-physiological environments. Interaction of 1f with human serum albumin (HSA) were also studied which quantifies that compound 1f binds with HSA efficiently through facile binding reaction in solution. Fluorescence, UV-vis spectrophotometry and molecular modeling methodologies were employed for studying the interaction mechanism of compound 1f with protein.

In Vitro Cytotoxicity and Interaction of Noscapine with Human Serum Albumin: Effect on Structure and Esterase Activity of HSA.

Noscapine is effective to inhibit cellular proliferation and induced apoptosis in nonsmall cell, lung, breast, lymphoma, and prostate cancer. It also shows good efficiency to skin cancer cells. In the current work, we studied the mechanism of interaction between the anticancer drug noscapine (NOS) and carrier protein human serum albumin (HSA) by using a variety of spectroscopic techniques (fluorescence spectroscopy, time-resolved fluorescence, UV-visible, fluorescence resonance energy transfer (FRET), Fourier transform infrared (FTIR), and circular dichroism (CD) spectroscopy), electrochemistry (cyclic voltammetry), and computational methods (molecular docking and molecular dynamic simulation). The steady-state fluorescence results showed that fluorescence intensity of HSA decreased in the presence of NOS via a static quenching mechanism, which involves ground state complex formation between NOS and HSA. UV-visible and FRET results also supported the fluorescence result. The corresponding thermodynamic result shows that binding of NOS with HSA is exothermic in nature, involving electrostatic interactions as major binding forces. The binding results were further confirmed through a cyclic voltammetry approach. The FRET result signifies the energy transfer from Trp214 of HSA to the NOS. Molecular site marker, molecular docking, and MD simulation results indicated that the principal binding site of HSA for NOS is site I. Synchronous fluorescence spectra, FTIR, 3D fluorescence, CD spectra, and MD simulation results reveal that NOS induced the structural change in HSA. In addition, the MTT assay study on a human skin cancer cell line (A-431) was also performed for NOS, which shows that NOS induced 80% cell death of the population at a 320 µM concentration. Moreover, the esterase-like activity of HSA with NOS was also done to determine the variation in protein functionality after binding with NOS.

Esterase activity and conformational changes of bovine serum albumin toward interaction with mephedrone: Spectroscopic and computational studies.

The present work is a brief overview of the effect of psychostimulant drug mephedrone hydrochloride (4MMC) on a transport protein, bovine serum albumin (BSA). The binding effect of 4MMC on BSA has been investigated by using UV-visible, steady-state fluorescence, time-resolved fluorescence, fluorescence resonance energy transfer, Fourier transform infrared, circular dichroism, and molecular docking method. 4-Methylmephedrone quenched the intrinsic fluorescence of BSA by static quenching mechanism, which was further confirmed by time-resolved fluorescence. The absorption spectrum of BSA in the presence of various concentrations of 4MMC reveals the change in the absorption bands of BSA-4MMC complex. The binding constant between 4MMC and BSA was calculated to be of the order of 104  Lmol-1 . The thermodynamic parameters such as molar enthalpy change (∆H), molar Gibbs free energy change (∆G), and molar entropy contribution (∆S) were obtained by the van't Hoff equation. The values obtained suggested that the binding mechanism was entropic driven and the major forces involved are hydrophobic in nature. The fluorescence resonance energy transfer result indicates the high probability of energy transfer from Trp residue of BSA to the 4MMC (r = 2.01 nm). Fourier transform infrared and CD results showed that 4MMC induced secondary structural changes in BSA. The esterase-like activity of BSA in presence of 4MMC further validated our CD results, confirming the distortion and change in functionality of protein upon binding with 4MMC. Molecular docking analysis showed that 4MMC principally bind at the II site (subdomain IIIA) of BSA.

Interaction of promethazine and adiphenine to human hemoglobin: A comparative spectroscopic and computational analysis.

The binding nature of amphiphilic drugs viz. promethazine hydrochloride (PMT) and adiphenine hydrochloride (ADP), with human hemoglobin (Hb) was unraveled by fluorescence, absorbance, time resolved fluorescence, fluorescence resonance energy transfer (FRET) and circular dichroism (CD) spectral techniques in combination with molecular docking and molecular dynamic simulation methods. The steady state fluorescence spectra indicated that both PMT and ADP quenches the fluorescence of Hb through static quenching mechanism which was further confirmed by time resolved fluorescence spectra. The UV-Vis spectroscopy suggested ground state complex formation. The activation energy (Ea) was observed more in the case of Hb-ADP than Hb-PMT interaction system. The FRET result indicates the high probability of energy transfer from ß Trp37 residue of Hb to the PMT (r=2.02nm) and ADP (r=2.33nm). The thermodynamic data reveal that binding of PMT with Hb are exothermic in nature involving hydrogen bonding and van der Waal interaction whereas in the case of ADP hydrophobic forces play the major role and binding process is endothermic in nature. The CD results show that both PMT and ADP, induced secondary structural changes of Hb and unfold the protein by losing a large helical content while the effect is more pronounced with ADP. Additionally, we also utilized computational approaches for deep insight into the binding of these drugs with Hb and the results are well matched with our experimental results.