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Phthalates are widespread and commonly used plasticizers that lead to adverse health effects. Several natural products provide a protective effect against phthalates. Moreover, microRNAs (miRNAs) are regulated by natural products and phthalates. Therefore, miRNAs' impacts and potential targets may underlie the mechanism of phthalates. However, the relationship between phthalate-modulated miRNAs and phthalate protectors derived from natural products is poorly understood and requires further supporting information. In this paper, we review the adverse effects and potential targets of phthalates on reproductive systems as well as cancer and non-cancer responses. Information on natural products that attenuate the adverse effects of phthalates is retrieved through a search of Google Scholar and the miRDB database. Moreover, information on miRNAs that are upregulated or downregulated in response to phthalates is collected, along with their potential targets. The interplay between phthalate-modulated miRNAs and natural products is established. Overall, this review proposes a straightforward pathway showing how phthalates modulate different miRNAs and targets and cause adverse effects, which are partly attenuated by several natural products, thereby providing a direction for investigating the natural product–miRNA–target axis against phthalate-induced effects.

Ferroptosis, which comprises iron-dependent cell death, is crucial in cancer and non-cancer treatments. Exosomes, the extracellular vesicles, may deliver biomolecules to regulate disease progression. The interplay between ferroptosis and exosomes may modulate cancer development but is rarely investigated in natural product treatments and their modulating miRNAs. This review focuses on the ferroptosis-modulating effects of natural products and miRNAs concerning their participation in ferroptosis and exosome biogenesis (secretion and assembly)-related targets in cancer and non-cancer cells. Natural products and miRNAs with ferroptosis-modulating effects were retrieved and organized. Next, a literature search established the connection of a panel of ferroptosis-modulating genes to these ferroptosis-associated natural products. Moreover, ferroptosis-associated miRNAs were inputted into the miRNA database (miRDB) to bioinformatically search the potential targets for the modulation of ferroptosis and exosome biogenesis. Finally, the literature search provided a connection between ferroptosis-modulating miRNAs and natural products. Consequently, the connections from ferroptosis–miRNA–exosome biogenesis to natural product-based anticancer treatments are well-organized. This review sheds light on the research directions for integrating miRNAs and exosome biogenesis into the ferroptosis-modulating therapeutic effects of natural products on cancer and non-cancer diseases.

Intradialytic hypotension is a common problem during hemodialysis treatment. Despite several clinical variables have been authenticated for associations during dialysis session, the interaction effects between variables has not yet been presented. Our study aimed to investigate clinical factors associated with intradialytic hypotension by deep learning. A total of 279 participants with 780 hemodialysis sessions on an outpatient in a hospital-facilitated hemodialysis center were enrolled in March 2018. Associations between clinical factors and intradialytic hypotension were determined using linear regression method and deep neural network. A full-adjusted model indicated that intradialytic hypotension is positively associated with body mass index (Beta = 0.17, p = 0.028), hypertension comorbidity (Beta = 0.17, p = 0.008), and ultrafiltration amount (Beta = 0.31, p

Abstract Background The function of a protein is determined by its native protein structure. Among many protein prediction methods, the Hydrophobic-Polar (HP) model, an ab initio method, simplifies the protein folding prediction process in order to reduce the prediction complexity. Results In this study, the ions motion optimization (IMO) algorithm was combined with the greedy algorithm (namely IMOG) and implemented to the HP model for the protein folding prediction based on the 2D-triangular-lattice model. Prediction results showed that the integration method IMOG provided a better prediction efficiency in a HP model. Compared to others, our proposed method turned out as superior in its prediction ability and resilience for most of the test sequences. The efficiency of the proposed method was verified by the prediction results. The global search capability and the ability to escape from the local best solution of IMO combined with a local search (greedy algorithm) to the new algorithm IMOG greatly improve the search for the best solution with reliable protein folding prediction. Conclusion Overall, the HP model integrated with IMO and a greedy algorithm as IMOG provides an improved way of protein structure prediction of high stability, high efficiency, and outstanding performance.

The mitochondrial gene cytochrome c oxidase I (COI) is commonly used for DNA barcoding in animals. However, most of the COI barcode nucleotides are conserved and sequences longer than about 650 base pairs increase the computational burden for species identification. To solve this problem, we propose a decision theory-based COI SNP tagging (DCST) approach that focuses on the discrimination of species using single nucleotide polymorphisms (SNPs) as the variable nucleotides of the sequences of a group of species. Using the example of 126 teleost mackerel fish species (order: Scombriformes), we identified 281 SNPs by alignment and trimming of their COI sequences. After decision rule making, 49 SNPs in 126 fish species were determined using the scoring system of the DCST approach. These COI-SNP barcodes were finally transformed into one-dimensional barcode images. Our proposed DCST approach simplifies the computational complexity and identifies the most effective and fewest SNPs to resolve or discriminate species for species tagging.

IntroductionGenetic polymorphisms and social factors (alcohol consumption, betel quid (BQ) usage, and cigarette consumption), both separately or jointly, play a crucial role in the occurrence of oral malignant disorders such as oral and pharyngeal cancers and oral potentially malignant disorders (OPMD).Material and methodsSimultaneous analyses of multiple single nucleotide polymorphisms (SNPs) and environmental effects on oral malignant disorders are essential to examine, albeit challenging. Thus, we conducted a case-control study (N = 576) to analyze the risk of occurrence of oral malignant disorders by using binary particle swarm optimization (BPSO) with an odds ratio (OR)-based method.ResultsWe demonstrated that a combination of SNPs (CYP26B1 rs887844 and CYP26C1 rs12256889) and socio-demographic factors (age, ethnicity, and BQ chewing), referred to as the combined effects of SNP-environment, correlated with maximal risk diversity of occurrence observed between the oral malignant disorder group and the control group. The risks were more prominent in the oral and pharyngeal cancers group (OR = 10.30; 95% confidence interval (CI) = 4.58-23.15) than in the OPMD group (OR = 5.42; 95% CI = 1.94-15.12).ConclusionsSimulation-based "SNP-environment barcodes" may be used to predict the risk of occurrence of oral malignant disorders. Applying simulation-based "SNP-environment barcodes" may provide insight into the importance of screening tests in preventing oral and pharyngeal cancers and OPMD.

DNA barcode sequences are accumulating in large data sets. A barcode is generally a sequence larger than 1000 base pairs and generates a computational burden. Although the DNA barcode was originally envisioned as straightforward species tags, the identification usage of barcode sequences is rarely emphasized currently. Single-nucleotide polymorphism (SNP) association studies provide us an idea that the SNPs may be the ideal target of feature selection to discriminate between different species. We hypothesize that SNP-based barcodes may be more effective than the full length of DNA barcode sequences for species discrimination. To address this issue, we tested a r ibulose diphosphate carboxylase ( rbcL ) S NP b arcoding (RSB) strategy using a decision tree algorithm. After alignment and trimming, 31 SNPs were discovered in the rbcL sequences from 38 Brassicaceae plant species. In the decision tree construction, these SNPs were computed to set up the decision rule to assign the sequences into 2 groups level by level. After algorithm processing, 37 nodes and 31 loci were required for discriminating 38 species. Finally, the sequence tags consisting of 31 rbcL SNP barcodes were identified for discriminating 38 Brassicaceae species based on the decision tree–selected SNP pattern using RSB method. Taken together, this study provides the rational that the SNP aspect of DNA barcode for rbcL gene is a useful and effective sequence for tagging 38 Brassicaceae species.