BP4RNAseq: a babysitter package for retrospective and newly generated RNA-seq data analyses using both alignment-based and alignment-free quantification method

Sensing of Acetaminophen Drug Using Silicon-Doped Graphdiyne: a DFT Inspection

Using first-principles calculations, we studied the electronic properties of graphdiyne (GDY) nanosheet and its Si-doped counterpart, SiGDY. Both GDY and SiGDY sheet surfaces were examined for acetaminophen (AP) drug adsorption using adsorption energy, charge transfer, and change in electrical conductivity (as indicators). As shown in this study, pure GDY has little affinity for AP. In specific, only 7.83 percent of the GDY surface's bandwidth energy changed after AP adsorption. On SiGDY, AP has a gaseous energy value of - 18.75 kcal/mol, as well as an aqueous energy value of - 49.39 kcal/mol. The water-phase solubility of the prescribed medications is determined using their solvation energy value. These charges are transferred between AP and the SiGDY sheet, which is extremely positively charged, giving AP the necessary binding energy. After AP adsorption, the electrical conductivity of SiGDY was increased by approximately 19.01 percent.

MXene-based composites against antibiotic-resistant bacteria: current trends and future perspectives

Today, finding novel nanomaterial-based strategies to combat bacterial resistance is an important field of science. MXene-based composites have shown excellent antimicrobial potential owing to their fascinating properties such as excellent photothermal effects, highly active sites, large interlayer spacing, unique chemical structures, and hydrophilicity; they have great potential to damage the bacterial cells by rupturing the bacterial cell membranes, enhancing the permeability across the membrane, causing DNA damages, reducing the metabolic activity, and generating oxidative stress. After inserting into or attaching on the surface of pathogenic bacteria, these two-dimensional structures can cause bacterial membrane disruption and cell content leakage owing to their sharp edges. Remarkably, MXenes and their composites with excellent photothermal performance have been studied in photothermal antibacterial therapy to combat antibiotic-resistant bacteria and suppress chronic wound infections, thus providing new opportunities for multidrug-resistant bacteria-infected wound healing. But, details about the possible interactions between MXene-based nanosystems and bacterial cell membranes are rather scarce. Also, the mechanisms of photothermal antibacterial therapy as well as synergistic tactics including photothermal, photodynamic or chemo-photothermal therapy still need to be uncovered. This review endeavors to delineate critical issues pertaining to the application of MXene-based composites against antibiotic-resistant bacteria, focusing on their photocatalytic inactivation, physical damage, and photothermal antibacterial therapy.

This review endeavors to delineate critical issues pertaining to the application of MXene-based composites against antibiotic-resistant bacteria.

iPseU-TWSVM: Identification of RNA pseudouridine sites based on TWSVM

Biological sequence analysis is an important basic research work in the field of bioinformatics. With the explosive growth of data, machine learning methods play an increasingly important role in biological sequence analysis. By constructing a classifier for prediction, the input sequence feature vector is predicted and evaluated, and the knowledge of gene structure, function and evolution is obtained from a large amount of sequence information, which lays a foundation for researchers to carry out in-depth research. At present, many machine learning methods have been applied to biological sequence analysis such as RNA gene recognition and protein secondary structure prediction. As a biological sequence, RNA plays an important biological role in the encoding, decoding, regulation and expression of genes. The analysis of RNA data is currently carried out from the aspects of structure and function, including secondary structure prediction, non-coding RNA identification and functional site prediction. Pseudouridine (У) is the most widespread and rich RNA modification and has been discovered in a variety of RNAs. It is highly essential for the study of related functional mechanisms and disease diagnosis to accurately identify У sites in RNA sequences. At present, several computational approaches have been suggested as an alternative to experimental methods to detect У sites, but there is still potential for improvement in their performance. In this study, we present a model based on twin support vector machine (TWSVM) for У site identification. The model combines a variety of feature representation techniques and uses the max-relevance and min-redundancy methods to obtain the optimum feature subset for training. The independent testing accuracy is improved by 3.4% in comparison to current advanced У site predictors. The outcomes demonstrate that our model has better generalization performance and improves the accuracy of У site identification. iPseU-TWSVM can be a helpful tool to identify У sites.

A survey on protein–DNA-binding sites in computational biology

Transcription factors are important cellular components of the process of gene expression control. Transcription factor binding sites are locations where transcription factors specifically recognize DNA sequences, targeting gene-specific regions and recruiting transcription factors or chromatin regulators to fine-tune spatiotemporal gene regulation. As the common proteins, transcription factors play a meaningful role in life-related activities. In the face of the increase in the protein sequence, it is urgent how to predict the structure and function of the protein effectively. At present, protein–DNA-binding site prediction methods are based on traditional machine learning algorithms and deep learning algorithms. In the early stage, we usually used the development method based on traditional machine learning algorithm to predict protein–DNA-binding sites. In recent years, methods based on deep learning to predict protein–DNA-binding sites from sequence data have achieved remarkable success. Various statistical and machine learning methods used to predict the function of DNA-binding proteins have been proposed and continuously improved. Existing deep learning methods for predicting protein–DNA-binding sites can be roughly divided into three categories: convolutional neural network (CNN), recursive neural network (RNN) and hybrid neural network based on CNN–RNN. The purpose of this review is to provide an overview of the computational and experimental methods applied in the field of protein–DNA-binding site prediction today. This paper introduces the methods of traditional machine learning and deep learning in protein–DNA-binding site prediction from the aspects of data processing characteristics of existing learning frameworks and differences between basic learning model frameworks. Our existing methods are relatively simple compared with natural language processing, computational vision, computer graphics and other fields. Therefore, the summary of existing protein–DNA-binding site prediction methods will help researchers better understand this field.

FDCNet: Presentation of the Fuzzy CNN and Fractal Feature Extraction for Detection and Classification of Tumors

The detection of brain tumors using magnetic resonance imaging is currently one of the biggest challenges in artificial intelligence and medical engineering. It is important to identify these brain tumors as early as possible, as they can grow to death. Brain tumors can be classified as benign or malignant. Creating an intelligent medical diagnosis system for the diagnosis of brain tumors from MRI imaging is an integral part of medical engineering as it helps doctors detect brain tumors early and oversee treatment throughout recovery. In this study, a comprehensive approach to diagnosing benign and malignant brain tumors is proposed. The proposed method consists of four parts: image enhancement to reduce noise and unify image size, contrast, and brightness, image segmentation based on morphological operators, feature extraction operations including size reduction and selection of features based on the fractal model, and eventually, feature improvement according to segmentation and selection of optimal class with a fuzzy deep convolutional neural network. The BraTS data set is used as magnetic resonance imaging data in experimental results. A series of evaluation criteria is also compared with previous methods, where the accuracy of the proposed method is 98.68%, which has significant results.

Actin gamma 1 is a critical regulator of pancreatic ductal adenocarcinoma

Background

Pancreatic ductal adenocarcinoma (PDAC) accounts for about 90% of pancreatic cancers, which represents one of the most lethal malignancies with a 5-year overall survival less than 10%. Identifying molecular biomarkers is invaluable in helping to predict clinical outcomes and developing targeted chemotherapies. Actin gamma 1 (ACTG1) is a kind of actin isoform that exists in almost all cell types as a component of the cytoskeleton, thus mediating cell viability. Although there have been studies revealing the prognostic significance of ACTG1 in several malignancies such as glioblastoma and hepatocellular carcinoma, its involvement and function in pancreatic cancer needs to be elucidated.

Methods

We retrospectively enrolled a cohort of PDAC patients after surgical resection (n = 149) and conducted immunohistochemistry experiments to explore the expression profile of ACTG1. Univariate and multivariate analyses were performed to investigate the clinical relevance of ACTG1. The functional role of ACTG1 in PDAC progression was further validated via both in vitro and in vivo studies.

Results

ACTG1 presented a higher expression in PDAC tissues than in nontumorous pancreatic tissues. ACTG1 level positively correlated with tumor stage, implying its potential role as a tumor promoter. Univariate and multivariate analyses identified that patients with lower ACTG1 showed a better overall survival compared to those with higher ACTG1 expression. Cellular and xenograft experiments confirmed the role of ACTG1 on facilitating tumor proliferation both in vitro and in vivo.

Conclusions

Our study revealed a pro-oncogenic role of ACTG1 in PDAC, which may help predict prognosis and serve as a novel therapeutic target.

Elevated Expression of SATB1 Predicts Unfavorable Clinical Outcomes in Colon Adenocarcinoma

BACKGROUNDS

Special AT-rich sequence-binding protein 1 (SATB1) belongs to the chromatin-remodeling protein which regulates different genes expression. High expression of SATB1 was found to be associated with the development of certain carcinomas. However, the functions of SATB1 in colon adenocarcinoma (CAC) remains unclear yet. Our study aims to investigate the potential role of SATB1 in CAC and whether it is associated with the unfavorable symptoms of CAC patients.

METHODS

The expression pattern of SATB1 was measured in CAC samples and adjacent noncancerous samples through quantitative real-time polymerase chain reaction and immunohistochemistry staining. We performed univariate and multivariate analyses to evaluate the clinical role of SATB1 in enrolled patients. The Kaplan-Meier analyses and log-rank tests were carried out to assess the clinicopathologic characteristics. The effect of SATB1 in human colon cancer cells was examined through cellular experiments.

RESULTS

The expression level of SATB1 in CAC tissues was significantly elevated compared with adjacent control tissues. High expression of SATB1 in tumor tissue was found to be associated with lymph node metastasis and advanced TNM stage. Higher SATB1 level in CAC patients indicated a worse 5-year survival time. Moreover, high SATB1 was defined as an independent poor prognostic factor. Cellular experiments showed that inhibition of the SATB1 protein level in human colon cells could suppress the migration and invasion capabilities.

CONCLUSIONS

Our findings revealed that high expression of SATB1 was significantly correlated with the poor clinical features and prognosis of CAC patients. It indicated that SATB1 might serve as a potential prognostic predictor and novel drug target for CAC treatment.

Electronic and structural computing features of some chromene derivatives and evaluating their anticancer activities

Electronic and structural features of some of representative chromene derivatives were investigated in this work towards recognizing their anticancer roles. Density functional theory (DFT) calculations were performed to obtain five structures of chromene derivatives with the same skeleton of original structure. In addition to obtaining optimized structural geometries, electronic molecular orbital features were evaluated for the models. Energy levels of the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO) indicated effects of additional R group pf chromene derivatives on electronic features. Based on such results, it was predicted that one of derivatives, L5, could better participate in interactions with other substances in comparison with other ligand structures. This achievement was obtained based on availability of HOMO and LUMO levels in lower energies easily catchable for electron transferring. On the other hand, L5 was assumed to interact in the weakest mode with other substances. Indeed, the main goal of this work was to examine anticancer activity of the investigated chromene derivatives, in which each of L1–L5 chromene derivatives were analyzed first to recognized electronic and structural features. Next, molecular docking (MD) simulations were performed to examine anticancer role of L1–L5 against methyltransferase cancerous enzyme target. The results indicated that formations of ligand-target complexes could be occurred within different types of interactions and surrounding amino acids of central ligand. In agreement with the achievements of analyses of single-standing L1–L5 compounds, L4-Target was seen as the strongest complex among possible complex formations. Moreover, values of binding energies and inhibition constant indicated that all five chromene derivatives could work as inhibitors of methyltransferase cancerous enzyme by the most advantage for L4 ligand. And as a final remark, details of such anticancer activity were recognized by graphical representations of ligand-target complexes showing types of interactions and involving amino acids in interactions.

Underlying Causes and Co-existence of Malnutrition and Infections: An Exceedingly Common Death Risk in Cancer

In nutrition science, malnutrition is a state of imbalance between intake and the needs of the organism, leading to metabolic changes, impaired physiological functions, and weight loss. Regardless of the countless efforts being taken and researched for years, the burden of malnutrition is still alarming and considered a significant agent of mortality across the globe. Around 45% of 12 million children deaths (0–5 years old) annually are due to malnutrition, mostly from developing countries. Malnutrition develops associations with other infections and leads to substantial clinical outcomes, such as mortality, more visits to hospitals, poor quality of life and physical frailty, and socioeconomic issues. Here, in this review, we intend to provide an overview of the current burden, underlying risk factors, and co-existence of malnutrition and other infections, such as cancer. Following the rising concern of the vicious interplay of malnutrition and other medical illnesses, we believed that this narrative review would highlight the need to re-make and re-define the future strategies by giving comprehensive and sustainable programs to alleviate poverty and combat the rampant infectious diseases and those nutrition-related health problems. Furthermore, the study also raises the concern for hospitalized malnourished cancer patients as it is crucially important to knowledge the caregiver healthcare staff for early interventions of providing nutritional support to delay or prevent the onset of malnutrition.

Loss of ATP5A1 enhances proliferation and predicts poor prognosis of colon adenocarcinoma

BACKGROUND

ATP Synthase F1 Subunit Alpha (ATP5F1A), also named as ATP5A1, is a subunit of mitochondrial ATP synthase. Dysregulated expression of ATP5A1 has been reported in several malignancies, nevertheless it showed either oncogenic or tumor-suppressing roles in different cancer types. Here we aimed to initially investigate the expression and role of ATP5A1 in colon adenocarcinoma.

METHODS

We firstly evaluated the transcription and mRNA levels of ATP5A1 using data from The Cancer Genome Atlas (TCGA). Besides, we tested its mRNA and protein expression in our enrolled retrospective cohort (n = 115). Univariate and multivariate analyzes were conducted to assess its prognostic value. Cellular experiments and xenografts in mice model were performed to validate the role of ATP5A1 in colon cancer.

RESULTS

ATP5A1 showed a significant lower level in colon adenocarcinoma than in adjacent nontumorous tissue. Advanced tumor stage was characterized with lower ATP5A1 level. Lower ATP5A1 was associated with poor prognosis in both TCGA dataset (P = 0.041) and our cohort (P = 0.001). Furthermore, Cox regression analysis demonstrated that ATP5A1 was a novel independent prognostic factor for colon cancer patients (HR=0.43, P = 0.018). Finally, cellular and xenografts data confirmed that overexpressing ATP5A1 can remarkably attenuate colon cancer growth.

CONCLUSION

Low expression of ATP5A1 may be a potential molecular marker for poor prognosis in colon cancer.

DATA AVAILABILITY

Data will be available upon request.

Zinc Finger Protein 24 is a Prognostic Factor in Ovarian Serous Carcinoma

OBJECTIVE

As a member of the zinc finger protein family, zinc finger protein 24 (ZNF24) contains a Cys2His2 zinc finger domain and acts as a transcription factor. ZNF24 has been reported to be downregulated in gastric cancer and breast cancer. However, little is known about its expression and function in ovarian serous carcinoma (OSC).

PATIENTS AND METHODS

We collected 117 OSC patients during 2011 to 2017 and retrospectively retrieved their clinicopathologic characteristics as well as their survival data. Protein level was analyzed by immunohistochemistry, mRNA level was evaluated by RT-qPCR assay, and transcriptional data was obtained from TCGA data sets. The correlations between ZNF24 expression and patients' features were assessed using χ2 test. Univariate and multivariate analyses were used to identify the prognosis predicative potential of ZNF24 in OSC. The function of ZNF24 in the epithelial ovarian cancer cells was also verified by in vitro cellular experiments.

RESULTS

Among the 117 cases, ZNF24 was downregulated in 52 OSC samples (44.6%) and significantly correlated with tumor stages. According to univariate and multivariate analyses, ZNF24 can act as an independent prognostic indicator for the overall survival of OSC patients, whose lower expression was associated with poorer clinical outcomes. Ectopic overexpression and knockdown assays indicated that ZNF24 can negatively regulate the OSC cell viability.

CONCLUSIONS

OSC patients with low level of ZNF24 have worse overall survival compared with those possess high-ZNF24 expression. Downregulated ZNF24 may be involved in the proliferation of OSC, and ZNF24 expression can serve as an independent survival predictor.

Impact of Early Intravenous Haemostatic Drugs on Brain Haemorrhage Patients and Their Image Segmentation Based on RGB-D Images

Cerebral haemorrhage is a serious subtype of stroke, with most patients experiencing short-term haematoma enlargement leading to worsening neurological symptoms and death. The main hemostatic agents currently used for cerebral haemorrhage are antifibrinolytics and recombinant coagulation factor VIIa. However, there is no clinical evidence that patients with cerebral haemorrhage can benefit from hemostatic treatment. We provide an overview of the mechanisms of haematoma expansion in cerebral haemorrhage and the progress of research on commonly used hemostatic drugs. To improve the semantic segmentation accuracy of cerebral haemorrhage, a segmentation method based on RGB-D images is proposed. Firstly, the parallax map was obtained based on a semiglobal stereo matching algorithm and fused with RGB images to form a four-channel RGB-D image to build a sample library. Secondly, the networks were trained with 2 different learning rate adjustment strategies for 2 different structures of convolutional neural networks. Finally, the trained networks were tested and compared for analysis. The 146 head CT images from the Chinese intracranial haemorrhage image database were divided into a training set and a test set using the random number table method. The validation set was divided into four methods: manual segmentation, algorithmic segmentation, the exact Tada formula, and the traditional Tada formula to measure the haematoma volume. The manual segmentation was used as the "gold standard," and the other three algorithms were tested for consistency. The results showed that the algorithmic segmentation had the lowest percentage error of 15.54 (8.41, 23.18) % compared to the Tada formula method.

An effective model for predicting serum albumin level in hemodialysis patients

Patients on hemodialysis (HD) are known to be at an increased risk of mortality. Hypoalbuminemia is one of the most important risk factors of death in HD patients, and is an independent risk factor for all-cause mortality that is associated with cardiac death, infection, and Protein-Energy Wasting (PEW). It is a clinical challenge to elevate serum albumin level. In addition, predicting trends in serum albumin level is effective for personalized treatment of hypoalbuminemia. In this study, we analyzed a total of 3069 records collected from 314 HD patients using a machine learning method that is based on an improved binary mutant quantum grey wolf optimizer (MQGWO) combined with Fuzzy K-Nearest Neighbor (FKNN). The performance of the proposed MQGWO method was evaluated using a series of experiments including global optimization experiments, feature selection experiments on open data sets, and prediction experiments on an HD dataset. The experimental results showed that the most critical relevant indicators such as age, presence or absence of diabetes, dialysis vintage, and baseline albumin can be identified by feature selection. Remarkably, the accuracy and the specificity of the method were 98.39% and 96.77%, respectively, demonstrating that this model has great potential to be used for detecting serum albumin level trends in HD patients.

Vismodegib anticancer drug: Analyzing electronic and structural features and examining biological activities

Vismodegib (Vis) is an anticancer drug, in which its electronic and structural features were examined in this work. To this aim, the chlorine atoms of original Vis model were substituted by other fluorine, bromine, and iodine halogen atoms yielding F-Vis, Br-Vis, and I-Vis in addition to the original Cl-Vis model. The models were optimized by performing quantum chemical calculations and their interactions with the smoothened (SMO) target were examined by performing molecular docking simulations. The results indicated that the stabilized structures of halogenated Vis models were achievable and their features indicated the dominant role of halogen atoms for their participation in interactions with other substances. Based on the obtained results, Br-Vis model was seen suitable for participating in interaction with the SMO target even better than the original Vis model. The hypothesis of this work was affirmed by employing the in silico approach for analyzing the features of singular ligands and for evaluating their biological functions.

Synthesis of Al-Based Metal-Organic Framework in Water With Caffeic Acid Ligand and NaOH as Linker Sources With Highly Efficient Anticancer Treatment

In this study, novel nanostructures of aluminum base metal-organic framework (Al-MOF) samples were synthesized using a sustainable, non-toxic, and cost-effective green synthesis route. Satureja hortensis extract was used as an effective source of linker for the development of the Al-MOF structures. The Fourier-transformed infrared (FTIR) spectrum confirmed the presence of characterization bonds related to the Al-MOF nanostructures synthesized by the green synthesis route. The scanning electron microscopy (SEM) and transmission electron microscopy (TEM) analyses revealed that the sample synthesized by Na2-CA was composed of multilayers, although it was agglomerated, but it had dispersed and occurred in spherical particles, indicating active organic matter. N2 adsorption/desorption isotherms demonstrated the significant porosity of the Al-MOF samples that facilitate the high potential of these nanostructures in medical applications. The anticancer treatment of Al-MOF samples was performed with different concentrations using the MTT standard method with untreated cancer cells for 24 and 48 h periods. The results exhibited the significant anticancer properties of Al-MOF samples developed in this study when compared with other MOF samples. Thus, the development of a novel Al-MOF and its application as a natural linker can influence the anticancer treatment of the samples. According to the results, the products developed in this study can be used in more applications such as biosensors, catalysts, and novel adsorbents.

iAIPs: Identifying Anti-Inflammatory Peptides Using Random Forest

Recently, several anti-inflammatory peptides (AIPs) have been found in the process of the inflammatory response, and these peptides have been used to treat some inflammatory and autoimmune diseases. Therefore, identifying AIPs accurately from a given amino acid sequences is critical for the discovery of novel and efficient anti-inflammatory peptide-based therapeutics and the acceleration of their application in therapy. In this paper, a random forest-based model called iAIPs for identifying AIPs is proposed. First, the original samples were encoded with three feature extraction methods, including g-gap dipeptide composition (GDC), dipeptide deviation from the expected mean (DDE), and amino acid composition (AAC). Second, the optimal feature subset is generated by a two-step feature selection method, in which the feature is ranked by the analysis of variance (ANOVA) method, and the optimal feature subset is generated by the incremental feature selection strategy. Finally, the optimal feature subset is inputted into the random forest classifier, and the identification model is constructed. Experiment results showed that iAIPs achieved an AUC value of 0.822 on an independent test dataset, which indicated that our proposed model has better performance than the existing methods. Furthermore, the extraction of features for peptide sequences provides the basis for evolutionary analysis. The study of peptide identification is helpful to understand the diversity of species and analyze the evolutionary history of species.

Green synthesis of bimetallic ZnO-CuO nanoparticles and their cytotoxicity properties

In this study, a simple and green strategy was reported to prepare bimetallic nanoparticles (NPs) by the combination of zinc oxide (ZnO) and copper oxide (CuO) using Sambucus nigra L. extract. The physicochemical properties of these NPs such as crystal structure, size, and morphology were studied by X-ray diffraction (XRD), field emission gun scanning electron microscopy (FEG-SEM), and transmission electron microscopy (TEM). The results suggested that these NPs contained polygonal ZnO NPs with hexagonal phase and spherical CuO NPs with monoclinic phase. The anticancer activity of the prepared bimetallic NPs was evaluated against lung and human melanoma cell lines based on MTT assay. As a result, the bimetallic ZnO/CuO NPs exhibited high toxicity on melanoma cancer cells while their toxicity on lung cancer cells was low.

In vitro and in vivo anticancer effect of pH-responsive paclitaxel-loaded niosomes

In this study, paclitaxel (PTX)-loaded pH-responsive niosomes modified with ergosterol were developed. This new formulation was characterized in terms of size, morphology, encapsulation efficiency (EE), and in vitro release at pH 5.2 and 7.4. The in vitro efficacy of free PTX and niosome/PTX was assessed using MCF7, Hela, and HUVEC cell lines. In order to evaluate the in vivo efficacy of niosomal PTX in rats as compared to free PTX, the animals were intraperitoneally administered with 2.5 mg/kg and 5 mg/kg niosomal PTX for two weeks. Results showed that the pH-responsive niosomes had a nanometric size, spherical morphology, 77% EE, and pH-responsive release in pH 5.2 and 7.4. Compared with free PTX, we found markedly lower IC50s when cancer cells were treated for 48 h with niosomal PTX, which also showed high efficacy against human cancers derived from cervix and breast tumors. Moreover, niosomal PTX induced evident morphological changes in these cell lines. In vivo administration of free PTX at the dose of 2.5 mg/kg significantly increased serum biochemical parameters and liver lipid peroxidation in rats compared to the control rats. The situation was different when niosomal PTX was administered to the rats: the 5 mg/kg dosage of niosomal PTX significantly increased serum biochemical parameters, but the group treated with the 2.5 mg/kg dose of niosomal PTX showed fewer toxic effects than the group treated with free PTX at the same dosage. Overall, our results provide proof of concept for encapsulating PTX in niosomal formulation to enhance its therapeutic efficacy.

Tripartite Motif Containing 3 inhibits the aggressive behaviors of papillary thyroid carcinoma and indicates lower recurrence risk

BACKGROUND

Tripartite Motif Containing 3 (TRIM3) has been reported to be downregulated in several malignancies. However, its prognostic significance in thyroid cancer remains unknown.

OBJECTIVE

Here we aimed to investigate TRIM3's expression and its involvement in papillary thyroid carcinoma (PTC).

METHODS

Clinicopathological analyses were performed in patients with PTC. Expression of TRIM3 protein was evaluated by IHC. The prognostic role of TRIM3 in PTC patients was assessed by univariate and multivariate analyses. Cell proliferation and invasion were tested in two PTC cell lines following overexpression or knockdown.

RESULTS

TRIM3 was decreased in PTC tissues compared to adjacent thyroid tissues on both mRNA and protein levels. Additionally, low expression of TRIM3 was significantly related to tumor size, lymph node metastasis and TNM stage. Moreover, TRIM3 was identified as an independent prognosis factor by multivariate analysis. Cellular data revealed that TRIM3 can inhibit the proliferation and invasion of PTC cells. Consistently, TRIM3 can upregulate the expression level of E-cadherin, while downregulate N-cadherin, Vimentin, and cyclin D1 expression.

CONCLUSIONS

TRIM3 expression was downregulated in PTC tissues comparing with that in adjacent nontumorous thyroid tissues. Lower TRIM3 expression in PTC can contribute independently to a poorer prognosis by enhancing PTC proliferation and invasion, highlighting its potential as a novel therapeutic target and prognostic biomarker.

Taguchi-Assisted Optimization Technique and Density Functional Theory for Green Synthesis of a Novel Cu-MOF Derived From Caffeic Acid and Its Anticancerious Activities

In this paper, we have reported an innovative greener method for developing copper-metal organic frameworks (Cu-MOFs) using caffeic acid (CA) as a linker extracted from Satureja hortensis using ultrasonic bath. The density functional theory is used to discuss the Cu-MOF-binding reaction mechanism. In order to achieve a discrepancy between the energy levels of the interactive precursor orbitals, the molecules have been optimized using the B3LYP/6-31G method. The Taguchi method was used to optimize the key parameters for the synthesis of Cu-MOF. FT-IR, XRD, nitrogen adsorption, and SEM analyses are used to characterize it. The adsorption/desorption and SEM analyses suggested that Cu-MOF has a larger surface area of 284.94 m2/g with high porosity. Cu-MOF has shown anticancer activities against the human breast cancer (MDA-MB-468) cell lines, and it could be a potent candidate for clinical applications.

Biosynthesis of Silver Nanoparticles by Conyza canadensis and Their Antifungal Activity against Bipolaris maydis

Silver nanoparticles were biosynthesized from Conyzacanadensis leaf extract with the help of a microwave oven. The UV-vis spectrum showed the maximum absorption at 441 nm, corresponding to the surface plasmon resonance of silver nanoparticles. Transmission electron microscope and scanning electron microscope images showed that the synthesized silver nanoparticles were spherical or near-spherical with an average diameter of 43.9 nm. X-ray diffraction demonstrated nanoparticles with a single-phase cubic structure. As-synthesized silver nanoparticles displayed prominent antifungal activity against Bipolaris maydis. The colony inhibition rate reached 88.6% when the concentration of nanosilver colloid was 100 μL·mL−1 (v/v). At such a concentration, no colony formation was observed on the solid plate. The diameter of the inhibition zone was 13.20 ± 1.12 mm. These results lay the foundation for the comprehensive control of plant pathogens using an environmentally friendly approach.

Iron Chelation by thiocytosine: Investigating electronic and structural features for describing tautomerism and metal chelation processes

This work was performed regarding the importance of iron (Fe) chelation for biological systems. This goal was investigated by assistance of a model of thiocytosine (TC) for participating in Fe-chelation processes. First, formations of tautomeric conformations were investigated to explore existence of possible structures of TC. Next, Fe-chelation processes were examined for all four obtained tautomers of TC. The results indicated that thiol tautomers could be seen at higher stability than thio tautomers, in which one of such thiol tautomers yielded the strongest Fe-chelation process to build FeTC3 model. As a consequence, parallel to the results of original TC tautomers, Fe-chelated models were found to be achievable for meaningful chelation processes or sensing the existence of Fe in media. Examining molecular orbital features could help for sensing purposes. The results of this work were obtained by performing density functional theory (DFT) calculations proposing TC compounds suitable for Fe-chelation purposes.

Substitution effects via aromaticity, polarizability, APT, AIM, IR analysis, and hydrogen adsorption in C20-nTin nanostructures: a DFT survey

In this paper, the substitution effects of titanium heteroatom(s) on aromaticity, the average isotropic polarizability (< α >), the atomic polar tensor (APT) charge, the electrostatic potential (ESP) map, and the atoms in molecule (AIM) analysis along with infrared (IR) spectroscopy of C₂₀ fullerene and its C_20-nTi_n derivatives (n = 1-5) are probed via density functional theory (DFT). The nucleus-independent chemical shifts (NICS) specify that substitution effect causes more aromaticity character of the optimized heterofullerenes than benzene molecule and higher APT charge distribution upon surfaces of them than pure cage. The highest negative and positive APT charge distribution on carbons of C₁₅Ti₅ and titanium substitutions of C₁₆Ti_4-2 implies that these sites can be attacked more readily by electrophilic and nucleophilic regents, respectively. We are very pleased to state that isolating the Ti-Ti single bonds by intermediacy of one or two C atoms is an applicable strategy for attaining the highest APT charge distribution on Ti atoms of C₁₆Ti_4-2 as suitable hydrogen storage. AIM analysis of the studied C_20-nAl_n derivatives signifies the highest electron density (ρ (r)) of 0.294 a.u. and the highest positive Laplacian of electron density (∇²ρ (r)) of 0.190 a.u., at bond critical points (BCPs) of C-Ti bond in the most stable C₁₉Ti₁ species. This heterofullerene shows the lowest < α > between the studied structures. IR spectroscopy shows that exclusive of C₁₆Ti_4-1 (as transition state), the other optimized hollow cages (as global minima) have no imaginary frequency.

Exploring chemistry features of favipiravir in octanol/water solutions

Favipiravir (Fav) has become a well-known drug for medication of patients by appearance of COVID-19. Heterocyclic structure and connected peptide group could make changes for Fav yielding different features from those required features. Therefore, it is indeed a challenging task to prepare a Fav compound with specific features of desired function. In this work, existence of eight Fav structures by tautomeric formations and peptide group rotations were obtained using density functional theory (DFT) optimization calculations. Gas phase, octanol solution, and water solution were employed to show impact of solution on features of Fav besides obtaining partition coefficients (LogP) for Fav compounds. Significant impacts of solutions were seen on features of Fav with the obtained LogP order: Fav-7 >  Fav-8 >  Fav-4 >  Fav-3 >  Fav-2 >  Fav-5 >  Fav-1 >  Fav-6. As a consequence, internal changes yielded significant impacts on features of Fav affirming its carful medication of COVID-19 patients.

Cytotoxicity properties of plant-mediated synthesized K-doped ZnO nanostructures

In this study, potassium-doped zinc oxide nanoparticles (K-doped ZnO NPs) were green-synthesized using pine pollen extracts based on bioethics principles. The synthesized NPs were analyzed using X-ray diffraction (XRD), inductively coupled plasma atomic emission spectroscopy (ICP-AES), scanning electron microscopy (SEM), energy-dispersive X-ray analysis (EDXA), and transmission electron microscopy (TEM). The cytotoxicity of these nanoparticles (NPs) on normal macrophage cells and cancer cell lines was evaluated. In the same concentrations of K-doped ZnO and pure ZnO NPs, K-doped ZnO NPs demonstrated higher toxicity. The results confirmed that the doped potassium could increase cytotoxicity. The IC₅₀ of K-doped ZnO NPs, pure ZnO NPs, and the examined control drug were 497 ± 15, 769 ± 12, and 606 ± 19 µg/mL, respectively. Considering the obtained IC₅₀ of K-doped ZnO NPs, they were more toxic to the cancer cell lines and had less cytotoxicity on normal macrophage cells.

Nanomaterials in the Management of Gram-Negative Bacterial Infections

The exploration of multiplexed bacterial virulence factors is a major problem in the early stages of Escherichia coli infection therapy. Traditional methods for detecting Escherichia coli (E. coli), such as serological experiments, immunoassays, polymerase chain reaction, and isothermal microcalorimetry have some drawbacks. As a result, detecting E. coli in a timely, cost-effective, and sensitive manner is critical for various areas of human safety and health. Intelligent devices based on nanotechnology are paving the way for fast and early detection of E. coli at the point of care. Due to their specific optical, magnetic, and electrical capabilities, nanostructures can play an important role in bacterial sensors. Another one of the applications involved use of nanomaterials in fighting microbial infections, including E. coli mediated infections. Various types of nanomaterials, either used directly as an antibacterial agent such as metallic nanoparticles (NPs) (silver, gold, zinc, etc.), or as a nanocarrier to deliver and target the antibiotic to the E. coli and its infected area. Among different types, polymeric NPs, lipidic nanocarriers, metallic nanocarriers, nanomicelles, nanoemulsion/ nanosuspension, dendrimers, graphene, etc. proved to be effective vehicles to deliver the drug in a controlled fashion at the targeted site with lower off-site drug leakage and side effects.

Alkoxysulfenylation of alkenes: development and recent advances

Among the wide variety of synthetic transformations of inexpensive and abundant feedstock alkenes, vicinal difunctionalization of carbon-carbon double bonds represent one of the most powerful and effective strategies for the introduction of two distinct functional groups into target compounds in a one-pot process. In this context, the direct alkoxysulfenylation of alkenes has emerged as an elegant method to construct valuable β-alkoxy sulfides in an atom- and pot-economic manner utilizing readily accessible starting materials. Here, we review the available literature on this appealing research topic by hoping that it will be beneficial for eliciting further research and thinking in this domain.

Direct synthesis of sulfenamides, sulfinamides, and sulfonamides from thiols and amines

Needless to say that organosulfur compounds with sulfur–nitrogen bonds have found various applications in diverse fields such as pharmaceuticals, agrochemicals, polymers, and so forth. Three major groups of such compounds are sulfenamides, sulfinamides, and sulfonamides which have been widely applied as building blocks in medical chemistry. Owing to their significant role in drug design and discovery programs, the search for and development of efficient, environmentally friendly, and economic processes for the preparation of the title compounds is of great importance in the pharmaceutical industry. Recently, oxidative coupling of thiols and amines, two readily available low-cost commodity chemicals, has emerged as a highly useful method for synthesizing structurally diverse sulfenamides, sulfinamides, and sulfonamides in a single step. Since this strategy does not require additional pre-functionalization and de-functionalization steps, it considerably streamlines synthetic routes and substantially reduces waste generation. This review will focus on recent advances and achievements in this attractive research arena.

This review provides a concise overview of the synthesis of biologically and synthetically valuable sulfenamide, sulfinamide, and sulfonamide derivatives through the direct oxidative coupling of readily available low-cost thiols and amines.

Active Targeted Nanoparticles for Delivery of Poly(ADP-ribose) Polymerase (PARP) Inhibitors: A Preliminary Review

Nanotechnology has revolutionized novel drug delivery strategies through establishing nanoscale drug carriers, such as niosomes, liposomes, nanomicelles, dendrimers, polymeric micelles, and nanoparticles (NPs). Owing to their desirable cancer-targeting efficacy and controlled release, these nanotherapeutic modalities are broadly used in clinics to improve the efficacy of small-molecule inhibitors. Poly(ADP-ribose) polymerase (PARP) family members engage in various intracellular processes, including DNA repair, gene transcription, signal transduction, cell cycle regulation, cell division, and antioxidant response. PARP inhibitors are synthetic small-molecules that have emerged as one of the most successful innovative strategies for targeted therapy in cancer cells harboring mutations in DNA repair genes. Despite these advances, drug resistance and unwanted side effects are two significant drawbacks to using PARP inhibitors in the clinic. Recently, the development of practical nanotechnology-based drug delivery systems has tremendously improved the efficacy of PARP inhibitors. NPs can specifically accumulate in the leaky vasculature of the tumor and cancer cells and release the chemotherapeutic moiety in the tumor microenvironment. On the contrary, NPs are usually unable to permeate across the body's normal organs and tissues; hence the toxicity is zero to none. NPs can modify the release of encapsulated drugs based on the composition of the coating substance. Delivering PARP inhibitors without modulation often leads to the toxic effect; therefore, a delivery vehicle is essential to encapsulate them. Various nanocarriers have been exploited to deliver PARP inhibitors in different cancers. Through this review, we hope to cast light on the most innovative advances in applying PARP inhibitors for therapeutic purposes.

Quantum chemical study the on interaction between sulfanilamide drug and MgO nanocluster

The adsorption and interaction of sulfanilamide (SA) with a pristine magnesium oxide (MgO) nano-cage was scrutinized through density functional theory (DFT) calculations. All geometries were optimized at M06-2X/6-311G(d,p) level, and the single-point energy calculation was also carried out at the same level of theory. Also, natural bond orbital (NBO) analysis was carried out and the values related to Wiberg bond index (WBI), donor-acceptor interactions, and partial natural charges were inspected. The MgO nano-cage can adsorb SA more strongly with the adsorption energy (E_ads) of - 41.74 kcal/mol, corresponding to the stable configurations. In addition, NBO analysis showed that the donor-acceptor interactions with SA and the MgO nano-cage are stronger. Based on our computations, the HOMO-LUMO gap of the MgO nano-cage changed to a great extent following the adsorption of the SA molecule, which corresponds to the most stable configuration that leads to improved electrical conductivity of the MgO nano-cage. The change in the gap determines the sensing mechanism, which is associated with the change in the electrical conductivity. To calculate the recovery time, transition-state theory (TST) was employed. Based on our calculation, Mg₁₂O₁₂-SA complex composites possess a short recovery time for the desorption of SA. The results show that the MgO nano-cage is an ideal candidate to be employed for developing SA sensors with high efficiency.

Interactions between favipiravir and a BNC cage towards drug delivery applications

Electronic structure analysis of bimolecular formation of favipiravir (Fav) and a representative model of boron-nitrogen-carbon (BNC) cage was performed in this work for providing more insightful information regarding the drug delivery purposes by the importance of Fav drug for medication of COVID-19. To achieve the purpose of this work, density functional theory (DFT) calculations were carried out to obtain the stabilized structures and corresponding molecular and atomic scale descriptors. Six models of BNC-Fav complexes were obtained reading the participation of different atomic positions of Fav to interactions with the BNC cage surface. The results yielded BNC-Fav2 at the highest strength and BNC-Fav4 at the lowest strength of bimolecular formations. Molecular orbital–related features and atomic scale quadrupole coping constants all revealed that BNC-Fav2 complex could be proposed for employing in drug delivery process by managing the loaded Fav contribution to future interactions.

Prospects and challenges of cancer systems medicine: from genes to disease networks

It is becoming evident that holistic perspectives toward cancer are crucial in deciphering the overwhelming complexity of tumors. Single-layer analysis of genome-wide data has greatly contributed to our understanding of cellular systems and their perturbations. However, fundamental gaps in our knowledge persist and hamper the design of effective interventions. It is becoming more apparent than ever, that cancer should not only be viewed as a disease of the genome but as a disease of the cellular system. Integrative multilayer approaches are emerging as vigorous assets in our endeavors to achieve systemic views on cancer biology. Herein, we provide a comprehensive review of the approaches, methods and technologies that can serve to achieve systemic perspectives of cancer. We start with genome-wide single-layer approaches of omics analyses of cellular systems and move on to multilayer integrative approaches in which in-depth descriptions of proteogenomics and network-based data analysis are provided. Proteogenomics is a remarkable example of how the integration of multiple levels of information can reduce our blind spots and increase the accuracy and reliability of our interpretations and network-based data analysis is a major approach for data interpretation and a robust scaffold for data integration and modeling. Overall, this review aims to increase cross-field awareness of the approaches and challenges regarding the omics-based study of cancer and to facilitate the necessary shift toward holistic approaches.