Gynecologic malignant neoplasms are a severe health problem among female patients, of which cervical cancer (CC), in particular, is a common disease leading to high mortality rates. Despite extensive attempts by researchers to solve the molecular mystery of CC, the mechanisms of its pathogenesis remain unclear. Tumor markers used in the clinical laboratory, such as squamous cell carcinoma (SCC), cancer antigen (CA)–125, and CA19-9, provide some help in diagnosing patients with CC. However, finding new molecular markers with high sensitivity and specificity is necessary. This review focuses on the role of epigenetic changes, particularly microRNAs (miRNAs), to CC. Several miRNAs that associated with CC potentially have the advantage of being early biomarkers. Moreover, altered serum miRNAs or single nucleotide polymorphisms in miRNA patterns may predict disease progression.

Background: The hypochromic microcytic anemia (HMA) commonly found in Thailand are iron deficiency anemia (IDA) and thalassemia trait (TT). Accurate discrimination between IDA and TT is an important issue and better methods are urgently needed. Although considerable RBC formulas and indices with various optimal cut-off values have been developed, distinguishing between IDA and TT is still a challenging problem due to the diversity of various anemic populations. To address this problem, it is desirable to develop an improved and automated prediction model for discriminating IDA from TT.Methods: We retrospectively collected laboratory data of HMA found in Thai adults. Five machine learnings, includingk-nearest neighbor (k-NN), decision tree, random forest (RF), artificial neural network (ANN) and support vector machine (SVM), were applied to construct a discriminant model. Performance was assessed and compared with thirteen existing discriminant formulas and indices.Results: The data of 186 patients (146 patients with TT and 40 with IDA) were enrolled. The interpretable rules derived from the RF model were proposed to demonstrate the combination of RBC indices for discriminating IDA from TT. A web-based tool ‘ThalPred’ was implemented using an SVM model based on seven RBC parameters. ThalPred achieved prediction results with an external accuracy, MCC and AUC of 95.59, 0.87 and 0.98, respectively.Conclusion: ThalPred and an interpretable rule were provided for distinguishing IDA from TT. For the convenience of health care team experimental scientists, a web-based tool has been established athttp://codes.bio/thalpred/by which users can easily get their desired screening test result without the need to go through the underlying mathematical and computational details.

Cancer remains one of the major causes of death worldwide. Angiogenesis is crucial for the pathogenesis of various human diseases, especially solid tumors. The discovery of anti-angiogenic peptides is a promising therapeutic route for cancer treatment. Thus, reliably identifying anti-angiogenic peptides is extremely important for understanding their biophysical and biochemical properties that serve as the basis for the discovery of new anti-cancer drugs. This study aims to develop an efficient and interpretable computational model called TargetAntiAngio for predicting and characterizing anti-angiogenic peptides. TargetAntiAngio was developed using the random forest classifier in conjunction with various classes of peptide features. It was observed via an independent validation test that TargetAntiAngio can identify anti-angiogenic peptides with an average accuracy of 77.50% on an objective benchmark dataset. Comparisons demonstrated that TargetAntiAngio is superior to other existing methods. In addition, results revealed the following important characteristics of anti-angiogenic peptides: (i) disulfide bond forming Cys residues play an important role for inhibiting blood vessel proliferation; (ii) Cys located at the C-terminal domain can decrease endothelial formatting activity and suppress tumor growth; and (iii) Cyclic disulfide-rich peptides contribute to the inhibition of angiogenesis and cell migration, selectivity and stability. Finally, for the convenience of experimental scientists, the TargetAntiAngio web server was established and made freely available online.

Background: Molecular genetics of the Rh system has been extensively studied in Caucasians, Black Africans, East Asians, and Indians more recently. In this work, we sought to investigate the molecular basis of variant D expression in the Thai population, which remains unknown. Materials and Methods: Blood samples from 450 Thai donors showing the variant D phenotype were collected. The RHD gene was analyzed by quantitative multiplex polymerase chain reaction of short fluorescent fragments and/or Sanger sequencing. Results: The most frequent alleles in 200 D-negative and 121 DEL samples were the whole RHD gene deletion and the Asian DEL alleles, respectively. In 129 weak/partial D samples, 36 variant alleles were identified, including eight novel alleles. RHD*06.03, which is common in variant D samples from South China, is the most prevalent variant allele, followed by the recently reported Indian RHD*01W.150 allele. Discussion: For the first time, a comprehensive overview of the nature and distribution of variant RHD alleles in Thailand is reported. It is a milestone to pave the way towards improvement of the current screening strategy to identify DEL donors accurately. The next step will be the design and implementation of a simple molecular test for screening the most frequent alleles, specifically in this population.

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.