Recent Development of Heterocyclic Compounds with Indazole Moiety as Potential Antiparasitic Agents.

Indazole is a vital nitrogen-containing heterocyclic unit in organic and medicinal chemistry research and a helpful precursor molecule for the production of various types of encirclement heterocycles. Indazole analogues are diverse pharmacological agents that can be used to treat a variety of conditions, including cancer, inflammation, infectious diseases, and neurological problems. In fact, the indazole moiety containing inhibitors also showed excellent medicinal properties for the treatment of parasitic diseases. Therefore, the development of new inhibitors has immense promise for usage as key components for the next generation of antiparasitic medication. In this review, we have summarized the recent developments of indazole-containing antiparasitic inhibitors, specially anti-protozoal, anti-fungal, and antiamoebic inhibitors, as well as their structure-activity relationship (SAR) findings for medicinal chemists who are searching for new preclinical parasitic drug candidates.

Understanding stem cells and its pivotal role in regenerative medicine.

Stem cells (SCs) are clonogenic cells that develop into the specialized cells which later responsible for making up various types of tissue in the human body. SCs are not only the appropriate source of information for cell division, molecular and cellular processes, and tissue homeostasis but also one of the major putative biological aids to diagnose and cure various degenerative diseases. This study emphasises on various research outputs that occurred in the past two decades. This will give brief information on classification, differentiation, detection, and various isolation techniques of SCs. Here, the various signalling pathways which includes WNT, Sonic hedgehog, Notch, BMI1 and C-met pathways and how does it effect on the regeneration of various classes of SCs and factors that regulates the potency of the SCs are also been discussed. We also focused on the application of SCs in the area of regenerative medicine along with the cellular markers that are useful as salient diagnostic or curative tools or in both, by the process of reprogramming, which includes diabetes, cancer, cardiovascular disorders and neurological disorders. The biomarkers that are mentioned in various literatures and experiments include PDX1, FOXA2, HNF6, and NKX6-1 (for diabetes); CD33, CD24, CD133 (for cancer); c-Kit, SCA-1, Wilm's tumor 1 (for cardiovascular disorders); and OCT4, SOX2, c-MYC, EN1, DAT and VMAT2 (for neurological disorders). In this review, we come to know the advancements and scopes of potential SC-based therapies, its diverse applications in clinical fields that can be helpful in the near future.

Machine Learning Supports Automated Digital Image Scoring of Stool Consistency in Diapers.

BACKGROUND/AIMS: Accurate stool consistency classification of non-toilet-trained children remains challenging. This study evaluated the feasibility of automated classification of stool consistencies from diaper photos using machine learning (ML). METHODS: In total, 2687 usable smartphone photos of diapers with stool from 96 children younger than 24 months were obtained after independent ethical study approval. Stool consistency was assessed from each photo according to the original 7 types of the Brussels Infant and Toddler Stool Scale independently by study participants and 2 researchers. A health care professional assigned a final score in case of scoring disagreement between the researchers. A proof-of-concept ML model was built upon this collected photo database, using transfer learning to re-train the classification layer of a pretrained deep convolutional neural network model. The model was built on random training (n = 2478) and test (n = 209) subsets. RESULTS: Agreements between study participants and both researchers were 58.0% and 48.5%, respectively, and between researchers 77.5% (assessable n = 2366). The model classified 60.3% of the test photos in exact agreement with the final score. With respect to the 4-class grouping of the 7 Brussels Infant and Toddler Stool Scale types, the agreement between model-based and researcher classification was 77.0%. CONCLUSION: The automated and objective scoring of stool consistency from diaper photos by the ML model shows robust agreement with human raters and overcomes limitations of other methods relying on caregiver reporting. Integrated with a smartphone application, this new framework for photo database construction and ML classification has numerous potential applications in clinical studies and home assessment.

D-loop somatic mutations and ∼5 kb "common" deletion in mitochondrial DNA: important molecular markers to distinguish oral precancer and cancer.

Apart from genomic DNA, mutations at mitochondrial DNA (mtDNA) have been hypothesized to play vital roles in cancer development. In this study, ∼5 kb deletion and D-loop mutations in mtDNA and alteration in mtDNA content were investigated in buccal smears from 104 healthy controls and 74 leukoplakia and 117 cancer tissue samples using Taqman-based quantitative assay and re-sequencing. The ∼5 kb deletion in mtDNA was significantly less (9.8 and 10.5 folds, P < 0.0001) in cancer tissues compared to control and leukoplakia tissues, respectively. On the other hand, somatic mutations in D-loop, investigated in 54 controls, 50 leukoplakias and 56 cancer patients, were found to be significantly more in cancer tissues, but not in leukoplakia tissues, compared to control (Z-score = 5.4). MtDNA contents were observed to be significantly more in leukoplakia (2.1 folds, P = 0.004) and cancer (1.6 folds, P = 0.03) tissues compared to control tissues. So, D-loop somatic mutations and ∼5 kb deletion patterns could be used as distinguishing markers between precancer and cancer tissues. This observation further suggests that somatic mutations in D-loop may facilitate carcinogenesis and cancer cells with less ∼5 kb deletion, i.e., intact mtDNA, may become resistant to apoptosis.

Single nucleotide polymorphism network: a combinatorial paradigm for risk prediction.

Risk prediction for a particular disease in a population through SNP genotyping exploits tests whose primary goal is to rank the SNPs on the basis of their disease association. This manuscript reveals a different approach of predicting the risk through network representation by using combined genotypic data (instead of a single allele/haplotype). The aim of this study is to classify diseased group and prediction of disease risk by identifying the responsible genotype. Genotypic combination is chosen from five independent loci present on platelet receptor genes P2RY1 and P2RY12. Genotype-sets constructed from combinations of genotypes served as a network input, the network architecture constituting super-nodes (e.g., case and control) and nodes representing individuals, each individual is described by a set of genotypes containing M markers (M = number of SNP). The analysis becomes further enriched when we consider a set of networks derived from the parent network. By maintaining the super-nodes identical, each network is carrying an independent combination of M-1 markers taken from M markers. For each of the network, the ratio of case specific and control specific connections vary and the ratio of super-node specific connection shows variability. This method of network has also been applied in another case-control study which includes oral cancer, precancer and control individuals to check whether it improves presentation and interpretation of data. The analyses reveal a perfect segregation between super-nodes, only a fraction of mixed state being connected to both the super-nodes (i.e. common genotype set). This kind of approach is favorable for a population to classify whether an individual with a particular genotypic combination can be in a risk group to develop disease. In addition with that we can identify the most important polymorphism whose presence or absence in a population can make a large difference in the number of case and control individuals.

Variant haplotypes at XRCC1 and risk of oral leukoplakia in HPV non-infected samples.

BACKGROUND: One of the mechanisms in human papillomavirus (HPV)-related carcinogenesis is inhibition of DNA repair by HPV oncoprotein. In this study, we investigated whether polymorphisms at XRCC1, one of the DNA repair loci, could modulate the risk of tobacco-related leukoplakia and cancer in HPV-infected individuals. METHODS: Tissue DNA from 83 oral cancer, 91 leukoplakia and 100 healthy controls were screened for HPV 16/18 infection and polymorphisms at XRCC1 by PCR-RFLP to estimate the risk of diseases independently and jointly. RESULTS: Human papillomavirus infection was significantly associated with increased risk of leukoplakia and cancer (OR = 2.8, 95% CI = 1.2-6.5 and OR = 5.5, 95% CI = 1.6-19, respectively). Independently, genotypes at three polymorphic sites on XRCC1 did not modulate the risk of diseases but pooled variant haplotypes increased the risk of leukoplakia in overall and HPV non-infected (OR = 1.8, 95% CI = 1.2-2.8; OR = 2.2, 95% CI = 1.2-4.0, respectively) samples but not that of cancer. CONCLUSION: The association between variant haplotypes at XRCC1 and risk of leukoplakia is pronounced in non-infected individuals since HPV oncoprotein could inhibit directly the DNA repair activity of XRCC1. But more samples of leukoplakia and cancer are essential to validate these results.

Antiplatelet drug resistance in patients with recurrent acute coronary syndrome undergoing conservative management.

AIMS AND OBJECTIVES: Recurrent ischemic events continue to occur despite combination anti-platelet therapy. Currently aspirin, clopidogrel and dual resistance are increasingly recognized entities. The relationship of such resistance to recurrent ischemic events is largely unknown. In this study, we tried to gain an insight into the role of antiplatelet drug resistance with recurrent Acute Coronary Syndrome (ACS). MATERIALS AND METHODS: The antiplatelet effect of aspirin and clopidogrel was studied in 40 recurrent ACS patients and 170 patients with first episode of ACS, after > or = 7 days of dual antiplatelet therapy. Platelet aggregation study was done with optical aggregometer. Resistance to aspirin and clopidogrel was defined as > or = 50% aggregation with collagen and ADP respectively. RESULTS: Aspirin, clopidogrel and dual drug resistance were encountered respectively in 35%, 72.5% and 32.5% patients with recurrent ACS. The corresponding figures for the patients with first episode of ACS were 25.3%, 42.3% and 18.8% respectively. P values for the comparisons were 0.237 for aspirin, 0.0007 for clopidogrel and 0.084 for dual drugs. Patients with recurrent ACS were relatively younger and had a higher prevalence of conventional risk factors like hypertension, diabetes and elevated LDL. CONCLUSION: Antiplatelet drug resistance is likely to play an important role in recurrent ACS alongside other conventional risk factors. Further research is required in this field to have a definitive conclusion.

Pharmacogenomics of anti-TB drugs-related hepatotoxicity.

Anti-TB drug (ATD)-related hepatotoxicity is a worldwide serious medical problem among TB patients. Apart from acting on the bacteria, isoniazid, the principal ATD, is also metabolized by human enzymes to generate toxic chemicals that might cause hepatotoxicity. It has been proposed that the production and elimination of the toxic metabolites depends on the activities of several enzymes, such as N-acetyl transferase 2 (NAT2), cytochrome P450 oxidase (CYP2E1) and glutathione S-transferase (GSTM1). There is now evidence that DNA sequence variations or polymorphisms at these loci (NAT2, CYP2E1 and GSTM1) could modulate the activities of these enzymes and, hence, the risk of hepatotoxicity. Since the prevalence of polymorphisms is different in worldwide populations, the risk of ATD hepatotoxicity varies in the populations. Thus, the knowledge of polymorphisms at these loci, prior to medication, may be useful in evaluating risk and controlling ATD hepatotoxicity.