Therapy resistance is a major challenge in the management of ovarian cancer (OC). Advances in detection and new technology validation have led to the emergence of biomarkers that can predict responses to available therapies. It is important to identify predictive biomarkers to select resistant and sensitive patients in order to reduce important toxicities, to reduce costs and to increase survival. The discovery of predictive and prognostic biomarkers for monitoring therapy is a developing field and provides promising perspectives in the era of personalized medicine. This review article will discuss the biology of OC with a focus on targetable pathways; current therapies; mechanisms of resistance; predictive biomarkers for chemotherapy, antiangiogenic and DNA-targeted therapies, and optimal cytoreductive surgery; and the emergence of liquid biopsy using recent studies from the Medline database and ClinicalTrials.gov.

Thalassemia traits (TTs) and iron deficiency anemia (IDA) are the most common disorders of hypochromic microcytic anemia (HMA). The present study aimed to differentiate TTs from IDA by analyzing discrimination formulas and provides comprehensive data of hemoglobin disorders prevalent in Pakistan. Among 12 published discrimination formulas, 6 formulas—MI, EF, G&K, RDWI, R, and HHI—were the most reliable to discriminate TTs from IDA. The failure cutoff values were improved by the random forest (RF) decision-tree approach. Moreover, the Shine and Lal (S&L) formula, which completely failed to discriminate IDA from TTs with original cutoff value (<1530), improved with the use of new proposed cutoff value (<1016) and was found to successfully discriminate all cases of TTs from those with IDA. In addition, 2 newly proposed formulas discriminated TTs from IDA more reliably than the original 12 formulas assessed. The proposed formulas could play a crucial role for clinicians to discriminate between TTs and IDA.

Background: Dyslipidemia is one of the major forms of lipid disorder, characterized by increased triglycerides (TGs), increased low-density lipoprotein-cholesterol (LDL-C), and decreased high-density lipoprotein-cholesterol (HDL-C) levels in blood. Recently, MicroRNAs (miRNAs) have been reported to involve in various biological processes; their potential usage being a biomarkers and in diagnosis of various diseases. Computational approaches including text mining have been used recently to analyze abstracts from the public databases to observe the relationships/associations between the biological molecules, miRNAs, and disease phenotypes. Materials and Methods: In the present study, significance of text mined extracted pair associations (miRNA-lipid disease) were estimated by one-sided Fisher's exact test. The top 20 significant miRNA-disease associations were visualized on Cytoscape. The CyTargetLinker plug-in tool on Cytoscape was used to extend the network and predicts new miRNA target genes. The Biological Networks Gene Ontology (BiNGO) plug-in tool on Cytoscape was used to retrieve gene ontology (GO) annotations for the targeted genes. Results: We retrieved 227 miRNA-lipid disease associations including 148 miRNAs. The top 20 significant miRNAs analysis on CyTargetLinker provides defined, predicted and validated gene targets, further targeted genes analyzed by BiNGO showed targeted genes were significantly associated with lipid, cholesterol, apolipoprotein, and fatty acids GO terms. Conclusion: We are the first to provide a reliable miRNA-lipid disease association network based on text mining. This could help future experimental studies that aim to validate predicted gene targets.

Among microcytic hypochromic anemias, the most common disorders are iron deficiency anemia and co-pathological conditions such as α- or β-thalassemia (α- or β-thal) traits. The aim of the present study was to determine the frequency and prevalence of iron deficiency anemia and α- or β-thal traits based on clinical laboratory data across different ethnic groups in five districts of Sindh Province, Pakistan. The present retrospective study analyzed 3 years (2012-2015) of encoded and unlinked clinical laboratory data, and identified 3030 microcytic hypochromic anemia cases. The data contained complete blood counts (CBCs) with smear morphology examinations, serum ferritin levels, and hemoglobin (Hb) electrophoreses. After reviewing the data, 994 confirmed subjects (iron deficiency anemia and α- and β-thal traits) were then selected for the present study. The prevalence of α- and β-thal traits was highest in Badin district (35.27%), while the prevalence of iron deficiency anemia was highest in Larkana district (30.73%). According to the ethnic-wise distribution, higher numbers of α- and β-thal trait cases were seen in the Sindhi ethnic group [375 (64.21%) and 283 (69.02%), respectively] than in the other ethnic groups. In addition, a higher distribution of β-thal trait cases was observed in the Sindhi ethnic group [n = 327 (56%)] in α- and β-thal cases overall. Findings from the present study strongly suggested that screening is important not only for β-thal trait but also other traits as well. However, careful monitoring of CBC parameters, including red blood cell (RBC) indices and morphology, along with clinical findings are essential to diagnose carrier cases, especially in high prevalence areas.

Host defense peptides (HDPs) represents a class of ubiquitous and rapid responding immune molecules capable of direct inactivation of a wide range of pathogens. Recent research has shown HDPs to be promising candidates for development as a novel class of broad-spectrum chemotherapeutic agent that is effective against both pathogenic microbes and malignant neoplasm. This study aims to quantitatively explore the relationship between easy-to-interpret amino acid composition descriptors of HDPs with their respective bioactivities. Classification models were constructed using the C4.5 decision tree and random forest classifiers. Good predictive performance was achieved as deduced from the accuracy, sensitivity and specificity in excess of 90% and Matthews correlation coefficient in excess of 0.5 for all three evaluated data subsets (e.g. training, 10-fold cross-validation and external validation sets). The source code and data set used for the construction of classification models are available on GitHub at https://github.com/chaninn/pepbio/.