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The prevalence of viral infections, cancer, and diabetes is increasing at an alarming rate around the world, and these diseases are now considered to be the most serious risks to human well-being in the modern period. There is a widespread practice in Asian countries of using papaya leaves ( C. papaya L.) as herbal medicine, either alone or in combination with prescribed medications, to treat a variety of ailments. The importance of conducting the necessary descriptive studies in order to determine the safety of papaya leaf consumption is also emphasized in the context of their application in the healthcare sector. Electronic databases such as Google Scholar, Scopus, and PubMed were used to gather information on papaya leaves, their therapeutic potential, and clinical evidence-based studies. The literature was gathered from publications on papaya leaves, their therapeutic potential, and clinical evidence-based studies. The antidengue, anticancer, antidiabetic, neuroprotective, and anti-inflammatory effects of papaya leaves discussed in this article are supported by evidence from preclinical, in vivo , in vitro , and clinical trial studies, as well as from other sources. Leaves have been investigated for their mechanism of action as well as their potential to be used in the development of novel herbal products for the health business. According to the reports gathered, only a small number of research demonstrated that leaf extract at high concentrations was hazardous to certain organs. The collective literature reviewed in this review provides insights into the use of papaya leaves as a cure for epidemic diseases, highlighting the phytochemical composition and pharmacological attributes of papaya leaves, as well as the results of various preclinical and clinical studies that have been conducted so far on the subject. The review clearly demonstrates the successful medical evidence for the use of papaya leaf extracts in the healthcare system as a supplemental herbal medication in a variety of clinical settings., Competing Interests: The authors declare no conflict of interest., (Copyright © 2022 Anshu Sharma et al.)

Objective: Barrett's esophagus (BE) is transition from squamous to columnar mucosa as a result of gastroesophageal reflux disease (GERD). The role of microRNA during this transition has not been systematically studied., Design: For initial screening, total RNA from 5 GERD and 6 BE patients was size fractionated. RNA <70 nucleotides was subjected to SOLiD 3 library preparation and next generation sequencing (NGS). Bioinformatics analysis was performed using R package "DEseq". A p value<0.05 adjusted for a false discovery rate of 5% was considered significant. NGS-identified miRNA were validated using qRT-PCR in an independent group of 40 GERD and 27 BE patients. MicroRNA expression of human BE tissues was also compared with three BE cell lines., Results: NGS detected 19.6 million raw reads per sample. 53.1% of filtered reads mapped to miRBase version 18. NGS analysis followed by qRT-PCR validation found 10 differentially expressed miRNA; several are novel (-708-5p, -944, -224-5p and -3065-5p). Up- or down- regulation predicted by NGS was matched by qRT-PCR in every case. Human BE tissues and BE cell lines showed a high degree of concordance (70-80%) in miRNA expression. Prediction analysis identified targets that mapped to developmental signaling pathways such as TGFβ and Notch and inflammatory pathways such as toll-like receptor signaling and TGFβ. Cluster analysis found similarly regulated (up or down) miRNA to share common targets suggesting coordination between miRNA., Conclusion: Using highly sensitive next-generation sequencing, we have performed a comprehensive genome wide analysis of microRNA in BE and GERD patients. Differentially expressed miRNA between BE and GERD have been further validated. Expression of miRNA between BE human tissues and BE cell lines are highly correlated. These miRNA should be studied in biological models to further understand BE development.

Background: Lung cancer is the leading cause of death from cancer in the world and its treatment is dependant on the type and stage of cancer detected in the patient. Molecular biomarkers that can characterize the cancer phenotype are thus a key tool in planning a therapeutic response. A common protocol for identifying such biomarkers is to employ genomic microarray analysis to find genes that show differential expression according to disease state or type. Data-mining techniques such as feature selection are often used to isolate, from among a large manifold of genes with differential expression, those specific genes whose differential expression patterns are of optimal value in phenotypic differentiation. One such technique, Biomarker Identifier (BMI), has been developed to identify features with the ability to distinguish between two data groups of interest, which is thus highly applicable for such studies., Results: Microarray data with validated genes was used to evaluate the utility of BMI in identifying markers for lung cancer. This data set contains a set of 129 gene expression profiles from large-airway epithelial cells (60 samples from smokers with lung cancer and 69 from smokers without lung cancer) and 7 genes from this data have been confirmed to be differentially expressed by quantitative PCR. Using this data set, BMI was compared with various well-known feature selection methods and was found to be more successful than other methods in finding useful genes to classify cancerous samples. Also it is evident that genes selected by BMI (given the same number of genes and classification algorithms) showed better discriminative power than those from the original study. After pathway analysis on the selected genes by BMI, we have been able to correlate the selected genes with well-known cancer-related pathways., Conclusions: Our results show that BMI can be used to analyze microarray data and to find useful genes for classifying samples. Pathway analysis suggests that BMI is successful in identifying biomarker-quality cancer-related genes from the data.

Glycomics is a discipline of biology that deals with the structure and function of glycans (or carbohydrates). Analytical techniques such as mass spectrometry (MS) and nuclear magnetic resonance (NMR) are having a significant impact on the field of glycomics. However, effective progress in glycomics research requires collaboration between laboratories to share experimental data, structural information of glycans, and simulation results. Herein we report the development of a web-based data management system that can incorporate large volumes of data from disparate sources and organize them into a uniform format for users to store and access. This system enables participating laboratories to set up a shared data repository which members of interdisciplinary teams can access. The system is able to manage and share raw MS data and structural information of glycans.The database is available at http://www.glycomics.bcf.ku.edu.