Multi-omics data integration and drug screening of AML cancer using Generative Adversarial Network.

In the era of precision medicine, accurate disease phenotype prediction for heterogeneous diseases, such as cancer, is emerging due to advanced technologies that link genotypes and phenotypes. However, it is difficult to integrate different types of biological data because they are so varied. In this study, we focused on predicting the traits of a blood cancer called Acute Myeloid Leukemia (AML) by combining different kinds of biological data. We used a recently developed method called Omics Generative Adversarial Network (GAN) to better classify cancer outcomes. The primary advantages of a GAN include its ability to create synthetic data that is nearly indistinguishable from real data, its high flexibility, and its wide range of applications, including multi-omics data analysis. In addition, the GAN was effective at combining two types of biological data. We created synthetic datasets for gene activity and DNA methylation. Our method was more accurate in predicting disease traits than using the original data alone. The experimental results provided evidence that the creation of synthetic data through interacting multi-omics data analysis using GANs improves the overall prediction quality. Furthermore, we identified the top-ranked significant genes through statistical methods and pinpointed potential candidate drug agents through in-silico studies. The proposed drugs, also supported by other independent studies, might play a crucial role in the treatment of AML cancer. The code is available on GitHub; https://github.com/SabrinAfroz/omicsGAN_codes?fbclid=IwAR1-/stuffmlE0hyWgSu2wlXo6dYlKUei3faLdlvpxTOOUPVlmYCloXf4Uk9ejK4I.

Reconstruction of Full-Length circRNA Sequences Using Chimeric Alignment Information.

Circular RNAs (circRNAs) are RNA molecules formed by joining a downstream 3 splice donor site and an upstream 5 splice acceptor site. Several recent studies have identified circRNAs as potential biomarker for different diseases. A number of methods are available for the identification of circRNAs. The circRNA identification methods cannot provide full-length sequences. Reconstruction of the full-length sequences is crucial for the downstream analyses of circRNA research including differential expression analysis, circRNA-miRNA interaction analysis and other functional studies of the circRNAs. However, a limited number of methods are available in the literature for the reconstruction of full-length circRNA sequences. We developed a new method, circRNA-full, for full-length circRNA sequence reconstruction utilizing chimeric alignment information from the STAR aligner. To evaluate our method, we used full-length circRNA sequences produced by isocirc and ciri-long using long-reads RNA-seq data. Our method achieved better reconstruction rate, precision, sensitivity and F1 score than the existing full-length circRNA sequence reconstruction tool ciri-full for both human and mouse data.

Bioinformatics Screening of Potential Biomarkers from mRNA Expression Profiles to Discover Drug Targets and Agents for Cervical Cancer.

Bioinformatics analysis has been playing a vital role in identifying potential genomic biomarkers more accurately from an enormous number of candidates by reducing time and cost compared to the wet-lab-based experimental procedures for disease diagnosis, prognosis, and therapies. Cervical cancer (CC) is one of the most malignant diseases seen in women worldwide. This study aimed at identifying potential key genes (KGs), highlighting their functions, signaling pathways, and candidate drugs for CC diagnosis and targeting therapies. Four publicly available microarray datasets of CC were analyzed for identifying differentially expressed genes (DEGs) by the LIMMA approach through GEO2R online tool. We identified 116 common DEGs (cDEGs) that were utilized to identify seven KGs (AURKA, BRCA1, CCNB1, CDK1, MCM2, NCAPG2, and TOP2A) by the protein-protein interaction (PPI) network analysis. The GO functional and KEGG pathway enrichment analyses of KGs revealed some important functions and signaling pathways that were significantly associated with CC infections. The interaction network analysis identified four TFs proteins and two miRNAs as the key transcriptional and post-transcriptional regulators of KGs. Considering seven KGs-based proteins, four key TFs proteins, and already published top-ranked seven KGs-based proteins (where five KGs were common with our proposed seven KGs) as drug target receptors, we performed their docking analysis with the 80 meta-drug agents that were already published by different reputed journals as CC drugs. We found Paclitaxel, Vinorelbine, Vincristine, Docetaxel, Everolimus, Temsirolimus, and Cabazitaxel as the top-ranked seven candidate drugs. Finally, we investigated the binding stability of the top-ranked three drugs (Paclitaxel, Vincristine, Vinorelbine) by using 100 ns MD-based MM-PBSA simulations with the three top-ranked proposed receptors (AURKA, CDK1, TOP2A) and observed their stable performance. Therefore, the proposed drugs might play a vital role in the treatment against CC.

Identification of Potential Long Non-Coding RNA Candidates that Contribute to Triple-Negative Breast Cancer in Humans through Computational Approach.

Breast cancer (BC) is the most frequent malignancy identified in adult females, resulting in enormous financial losses worldwide. Owing to the heterogeneity as well as various molecular subtypes, the molecular pathways underlying carcinogenesis in various forms of BC are distinct. Therefore, the advancement of alternative therapy is required to combat the ailment. Recent analyses propose that long non-coding RNAs (lncRNAs) perform an essential function in controlling immune response, and therefore, may provide essential information about the disorder. However, their function in patients with triple-negative BC (TNBC) has not been explored in detail. Here, we analyzed the changes in the genomic expression of messenger RNA (mRNA) and lncRNA in standard control in response to cancer metastasis using publicly available single-cell RNA-Seq data. We identified a total of 197 potentially novel lncRNAs in TNBC patients of which 86 were differentially upregulated and 111 were differentially downregulated. In addition, among the 909 candidate lncRNA transcripts, 19 were significantly differentially expressed (DE) of which three were upregulated and 16 were downregulated. On the other hand, 1901 mRNA transcripts were significantly DE of which 1110 were upregulated and 791 were downregulated by TNBCs subtypes. The Gene Ontology (GO) analyses showed that some of the host genes were enriched in various biological, molecular, and cellular functions. The Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis showed that some of the genes were involved in only one pathway of prostate cancer. The lncRNA-miRNA-gene network analysis showed that the lncRNAs TCONS_00076394 and TCONS_00051377 interacted with breast cancer-related micro RNAs (miRNAs) and the host genes of these lncRNAs were also functionally related to breast cancer. Thus, this study provides novel lncRNAs as potential biomarkers for the therapeutic intervention of this cancer subtype.

Soil pollution and elemental sources along Barapukuria coal mine, Bangladesh: Implications for eco-environmental and health risk assessment.

For the first time, different pollution indices and a receptor model have been used to quantify eco-environmental and health risk assessments as well as identify the sources of potentially toxic elements in soil along the Barapukuria Coal Mine (BCM). Individual indices include enrichment and contamination factors showing the soil samples are moderately to highly contaminated by arsenic, cobalt, chromium, copper, lead, and zinc and heavily contaminated by sulfur. According to the geo-accumulation index, there is significant pollution with arsenic (1.24 ± 0.38), lead (1.49 ± 0.58), cobalt (1.49 ± 0.58), and sulfur (1.63 ± 0.38). Modified hazard quotient and ecological risk factor values also suggest low to moderate environmental risk hazards from the same elements. The nemerow pollution index, pollution load index, nemerow risk index, ecological risk index, and toxic risk index of soil range from 1.65 to 3.03, 0.82-1.23, 11-26, 77-165, and 6.82-11.76 suggest low toxic risk and moderate pollution, among other synergistic indices. Health risk assessment indicates that iron poses lower cancer risk for children than adults, while both face unacceptable cancer risks from inhaling chromium, cobalt, or arsenic. Principal component and phylogenetic cluster analysis extracted by the multiple linear regression with the absolute principal component score (APCS-MLR) model refer to the fact that manganese, iron, titanium, and nickel have originated from geogenic sources, while coal mine effluents enrich elements like arsenic, chromium, zinc, lead, uranium, sulfur, thorium, and zinc and phosphorous sourced from agriculture. In addition, geogenic and anthropogenic sources, including mine and agriculture activities, could potentially pollute the soil and ecosystem. The findings are crucial for regional and national planners in devising strategies to mitigate potentially toxic element pollution in soil along coal mine areas.

Polyaromatic Hydrocarbon-Functionalized 2D MXene-Based 3D Porous Antifouling Nanocomposite with Long Shelf Life for High-Performance Electrochemical Immunosensor Applications.

Affinity-based electrochemical (AEC) biosensors have gained more attention in the field of point-of-care management. However, AEC sensing is hampered by biofouling of the electrode surface and degradation of the antifouling material. Therefore, a breakthrough in antifouling nanomaterials is crucial for the fabrication of reliable AEC biosensors. Herein, for the first time, we propose 1-pyrenebutyric acid-functionalized MXene to develop an antifouling nanocomposite to resist biofouling in the immunosensors. The nanocomposite consisted of a 3D porous network of bovine serum albumin cross-linked with glutaraldehyde with functionalized MXene as conductive nanofillers, where the inherited oxidation resistance property of functionalized MXene improved the electrochemical lifetime of the nanocomposite. On the other hand, the size-extruded porous structure of the nanocomposite inhibited the biofouling activity on the electrode surface for up to 90 days in real samples. As a proof of concept, the antifouling nanocomposite was utilized to fabricate a multiplexed immunosensor for the detection of C-reactive protein (CRP) and ferritin biomarkers. The fabricated sensor showed good selectivity over time and an excellent limit of detection for CRP and ferritin of 6.2 and 4.2 pg/mL, respectively. This research successfully demonstrated that functionalized MXene-based antifouling nanocomposites have great potential to develop high-performance and low-cost immunosensors.

Identification of most representative hub-genes for diagnosis, prognosis, and therapies of hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related deaths globally. To reduce HCC-related mortality, early diagnosis and therapeutic improvement are essential. Hub differentially expressed genes (HubGs) may serve as potential diagnostic and prognostic biomarkers, also offering therapeutic targets for precise therapies. Therefore, we aimed to identify top-ranked hub genes for the diagnosis, prognosis, and therapy of HCC. METHODS: Through a systematic literature review, 202 HCC-related HubGs were derived from 59 studies, yet consistent detection across these was lacking. Then, we identified top-ranked HubGs (tHubGs) by integrated bioinformatics analysis, highlighting their functions, pathways, and regulators that might be more representative of the diagnosis, prognosis, and therapies of HCC. RESULTS: In this study, eight HubGs (CDK1, AURKA, CDC20, CCNB2, TOP2A, PLK1, BUB1B, and BIRC5) were identified as the tHubGs through the protein-protein interaction (PPI) network and survival analysis. Their differential expression in different stages of HCC, validated using The Cancer Genome Atlas (TCGA) Program database, suggests their potential as early HCC markers. The enrichment analyses revealed some important roles in HCC-related biological processes (BPs), molecular functions (MFs), cellular components (CCs), and signaling pathways. Moreover, the gene regulatory network analysis highlighted key transcription factors (TFs) and microRNAs (miRNAs) that regulate these tHubGs at transcriptional and post-transcriptional. Finally, we selected three drugs (CD437, avrainvillamide, and LRRK2-IN-1) as candidate drugs for HCC treatment as they showed strong binding with all of our proposed and published protein receptors. CONCLUSIONS: The findings of this study may provide valuable resources for early diagnosis, prognosis, and therapies for HCC.

Reduced graphene oxide-functionalized polymer microneedle for continuous and wide-range monitoring of lactate in interstitial fluid.

Despite substantial developments in minimally invasive lactate monitoring microneedle electrodes, most such electrode developments have focused on either sensitivity or invasiveness while ignoring a wide range of detection, which is the most important factor in measuring the normal range of lactate in interstitial fluid (ISF). Herein, we present a polymer-based planar microneedle electrode fabrication using microelectromechanical and femtosecond laser technology for the continuous monitoring of lactate in ISF. The microneedle is functionalized with two-dimensional reduced graphene oxide (rGO) and electrochemically synthesized platinum nanoparticles (PtNPs). A particular quantity of Nafion (1.25 wt%) is applied on top of the lactate enzyme to create a diffusion-controlled membrane. Due to the combined effects of the planar structure of the microneedle, rGO, and membrane, the biosensor exhibited excellent linearity up to 10 mM lactate with a limit of detection of 2.04 µM, high sensitivity of 43.96 µA mM-1cm-2, a reaction time of 8 s and outstanding stability, selectivity, and repeatability. The feasibility of the microneedle is evaluated by using it to measure lactate concentrations in artificial ISF and human serum. The results demonstrate that the microneedle described here has great potential for use in real-time lactate monitoring for use in sports medicine and treatment.

Bioinformatics analysis to disclose shared molecular mechanisms between type-2 diabetes and clear-cell renal-cell carcinoma, and therapeutic indications.

Type 2 diabetes (T2D) and Clear-cell renal cell carcinoma (ccRCC) are both complicated diseases which incidence rates gradually increasing. Population based studies show that severity of ccRCC might be associated with T2D. However, so far, no researcher yet investigated about the molecular mechanisms of their association. This study explored T2D and ccRCC causing shared key genes (sKGs) from multiple transcriptomics profiles to investigate their common pathogenetic processes and associated drug molecules. We identified 259 shared differentially expressed genes (sDEGs) that can separate both T2D and ccRCC patients from control samples. Local correlation analysis based on the expressions of sDEGs indicated significant association between T2D and ccRCC. Then ten sDEGs (CDC42, SCARB1, GOT2, CXCL8, FN1, IL1B, JUN, TLR2, TLR4, and VIM) were selected as the sKGs through the protein-protein interaction (PPI) network analysis. These sKGs were found significantly associated with different CpG sites of DNA methylation that might be the cause of ccRCC. The sKGs-set enrichment analysis with Gene Ontology (GO) terms and KEGG pathways revealed some crucial shared molecular functions, biological process, cellular components and KEGG pathways that might be associated with development of both T2D and ccRCC. The regulatory network analysis of sKGs identified six post-transcriptional regulators (hsa-mir-93-5p, hsa-mir-203a-3p, hsa-mir-204-5p, hsa-mir-335-5p, hsa-mir-26b-5p, and hsa-mir-1-3p) and five transcriptional regulators (YY1, FOXL1, FOXC1, NR2F1 and GATA2) of sKGs. Finally, sKGs-guided top-ranked three repurposable drug molecules (Digoxin, Imatinib, and Dovitinib) were recommended as the common treatment for both T2D and ccRCC by molecular docking and ADME/T analysis. Therefore, the results of this study may be useful for diagnosis and therapies of ccRCC patients who are also suffering from T2D.

Design and Optimization of High-Performance Novel RbPbBr3-Based Solar Cells with Wide-Band-Gap S-Chalcogenide Electron Transport Layers (ETLs).

Inorganic cubic rubidium-lead-halide perovskites have attracted considerable attention owing to their structural, electronic, and unique optical properties. In this study, novel rubidium-lead-bromide (RbPbBr3)-based hybrid perovskite solar cells (HPSCs) with several high-band-gap chalcogenide electron transport layers (ETLs) of In2S3, WS2, and SnS2 were studied by density functional theory (DFT) and using the SCAPS-1D simulator. Initially, the band gap and optical performance were computed using DFT, and these results were utilized for the first time in the SCAPS-1D simulator. Furthermore, the impact of different major influencing parameters, that is, the thickness of the layer, bulk defect density, doping concentration, and defect density of interfaces, including the working temperature, were also investigated and unveiled. Further, a study on an optimized device with the most potential ETL (SnS2) layer was performed systematically. Finally, a comparative study of different reported heterostructures was performed to explore the benchmark of the most recent efficient RbPbBr3-based photovoltaics. The highest power conversion efficiency (PCE) was 29.75% for the SnS2 ETL with Voc of 0.9789 V, Jsc of 34.57863 mA cm-2, and fill factor (FF) of 87.91%, while the PCEs of 21.15 and 24.57% were obtained for In2S3 and WS2 ETLs, respectively. The electron-hole generation, recombination rates, and quantum efficiency (QE) characteristics were also investigated in detail. Thus, the SnS2 ETL shows strong potential for use in RbPbBr3-based hybrid perovskite high-performance photovoltaic devices.

Evaluation of Biological Variation of Different Clinical Laboratory Analytes in the Blood of Healthy Subjects.

Background Biological variation (BV) as a prognostic marker implies that each individual has a "subject mean" or central tendency, control level, or "set point" concentration for maintaining homeostasis regulation, which is influenced by factors such as genes, diet, exercise, and age. Uses for information on BV include determining the value of population-based reference intervals, assessing the importance of variation in serial findings, and establishing criteria for judging correct analysis. Aims We focused on the assessment of BV parameters for these elements as within-subject BV (CVW), between subject BV (CVG), the index of individuality (II), and the reference change value (RCV) of important biochemical analytes in the Bangladeshi adult population. Methodology This is a cross-sectional analytical study of a representative sample in the population of Bangladesh to determine BV in clinical laboratory analytes. For the study, 758 people were requested to take part; among those 730 (ages 18-65) apparently, healthy adults were blood donors, hospital staff, laboratory personnel, or any individuals who presented themselves for health screening at a tertiary hospital in Dhaka, Bangladesh. Results The CVW for blood sugar, creatinine, urea, uric acid, sodium, potassium, chloride, calcium, magnesium, and phosphate were calculated as 5.10 %, 4.64%, 10.72%, 5.71%, 0.69%, 4.35%, 0.75%, 3.69%, 4.57%, and 4.72%, respectively. The CVG for blood sugar, creatinine, urea, uric acid, sodium, potassium, chloride, calcium, magnesium, and phosphate was 10.70%, 21.46%, 31.47%, 23.52%, 1.95%, 9.74%, 2.56%, 4.64%, 9.96 %, and 17.45%, respectively. The index of individuality (II) for blood sugar, creatinine, urea, uric acid, sodium, potassium, chloride, calcium, magnesium, and phosphate were 0.48, 0.22, 0.34, 0.24, 0.35, 0.45, 0.29, 0.79, 0.46, and 0.27, respectively. The RCV for blood sugar, creatinine, urea, uric acid, sodium, potassium, chloride, calcium, magnesium, and phosphate was 14.75%, 14.10%, 30.58%, 16.13%, 2.82%, 12.58%, 3.54%, 10.62%, 13.62 %, and 15.80%, respectively. Conclusions Nine serum biochemistry analytes (blood sugar, creatinine, urea, uric acid, sodium, potassium, chloride, magnesium, and phosphate) had low individuality, indicating that subject-based reference intervals are appropriate, only one analyte (calcium) had high individuality and, therefore, population-based reference intervals are more appropriate.

Pharmacophore mapping approach to find anti-cancer phytochemicals with metformin-like activities against transforming growth factor (TGF)-beta receptor I kinase: An in silico study.

The most frequently prescribed first-line treatment for type II diabetes mellitus is metformin. Recent reports asserted that this diabetes medication can also shield users from cancer. Metformin induces cell cycle arrest in cancer cells. However, the exact mechanism by which this occurs in the cancer system is yet to be elucidated. Here, we investigated the impact of metformin on cell cycle arrest in cancer cells utilizing transforming growth factor (TGF)-beta pathway. TGF-ß pathway has significant effect on cell progression and growth. In order to gain an insight on the underlying molecular mechanism of metformin's effect on TGF beta receptor 1 kinase, molecular docking was performed. Metformin was predicted to interact with transforming growth factor (TGF)-beta receptor I kinase based on molecular docking and molecular dynamics simulations. Furthermore, pharmacophore was generated for metformin-TGF-ßR1 complex to hunt for novel compounds having similar pharmacophore as metformin with enhanced anti-cancer potentials. Virtual screening with 29,000 natural compounds from NPASS database was conducted separately for the generated pharmacophores in Ligandscout® software. Pharmacophore mapping showed 60 lead compounds for metformin-TGF-ßR1 complex. Molecular docking, molecular dynamics simulation for 100 ns and ADMET analysis were performed on these compounds. Compounds with CID 72473, 10316977 and 45140078 showed promising binding affinities and formed stable complexes during dynamics simulation with aforementioned protein and thus have potentiality to be developed into anti-cancer medicaments.

An oxygen-insensitive and minimally invasive polymeric microneedle sensor for continuous and wide-range transdermal glucose monitoring.

Despite significant advances in diabetes management, particularly with the introduction of the most recent continuous glucose monitoring devices (CGMDs) that can monitor glucose actively in the transdermal interstitial fluid (ISF) in vivo, CGMDs still have significant disadvantages in terms of accuracy, low interference effect, precision, and stability. This is mostly because they detect hydrogen peroxide at higher potentials and require an oxygen-rich environment. First in its class, we developed an oxygen-insensitive polymeric glucose microneedle (MN) that was functionalized using a new electron-transfer mediator, 3-(3'-phenylimino)-3H-phenothiazinesulfonic acid-based enzyme cocktail for the NAD-GDH system. The inclusion of reduced graphene oxide aided in the absorption of the cocktail via the π-π interaction and enhanced the conductivity and sensor performance. The MN exhibited a dynamic linear range (1-30 mM) with a low detection limit of 26 µM, high sensitivity (18.05 µAmM-1 cm-2), stability (up to 7 days), high selectivity (due to a low oxidation potential of 0.15 V), and a fast response time (∼3 s). In vivo, deployment of the MN in a rabbit model demonstrated that the ISF glucose concentrations measured with the MN for up to 24 h correlate very well with the blood glucose concentrations measured with a commercial glucometer.

Corrigendum: Identification of circRNA biomarker for gastric cancer through integrated analysis.

[This corrects the article DOI: 10.3389/fmolb.2022.857320.].

A hybridized nano-porous carbon reinforced 3D graphene-based epidermal patch for precise sweat glucose and lactate analysis.

Wearable electrochemical biosensors for perspiration analysis offer a promising non-invasive biomarker monitoring method. Herein, a functionalized hybridized nanoporous carbon (H-NPC)-encapsulated flexible 3D porous graphene-based epidermal patch was firstly fabricated for monitoring sweat glucose, lactate, pH, and temperature using simple, cost-effective, laser-engraved, and spray-coating techniques. The fabricated H-NPC-modified electrode significantly increased electrochemical surface area and electrocatalytic activity. Within the physiological sweat range (0-1.5 mM), the second-generation glucose sensor exhibited an excellent sensitivity of 82.7 µAmM-1cm-2 with 0.025 µM LOD. Moreover, the lactate biosensor exhibited an extraordinary linear range (0-56 mM) response owing to the incorporation of an outer diffusion limiting layer (DLL) that controls the lactate flux reaching the enzyme with comparable sensitivity (204 nAmM-1cm-2) and LOD (4 µM). Finally, we employed an analytical correction approach incorporating pH and temperature adjustments during on-body tests. In addition to connecting various carbon-based materials to limitless metal-organic frameworks as a transduction material, our research also paves the way for enabling these sensors to operate on pH and T correction independently while delivering accurate results.

Identification of Drug Targets and Agents Associated with Hepatocellular Carcinoma through Integrated Bioinformatics Analysis.

BACKGROUND: Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death globally. The mechanisms underlying the development of HCC are mostly unknown till now. OBJECTIVE: The main goal of this study was to identify potential drug target proteins and agents for the treatment of HCC. METHODS: The publicly available three independent mRNA expression profile datasets were downloaded from the NCBI-GEO database to explore common differentially expressed genes (cDEGs) between HCC and control samples using the Statistical LIMMA approach. Hub-cDEGs as drug targets highlighting their functions, pathways, and regulators were identified by using integrated bioinformatics tools and databases. Finally, Hub-cDEGs-guided top-ranked drug agents were identified by molecular docking study for HCC. RESULTS: We identified 160 common DEGs (cDEGs) from three independent mRNA expression datasets in which ten cDEGs (CDKN3, TK1, NCAPG, CDCA5, RACGAP1, AURKA, PRC1, UBE2T, MELK, and ASPM) were selected as Hub-cDEGs. The GO functional and KEGG pathway enrichment analysis of Hub-cDEGs revealed some crucial cancer-stimulating biological processes, molecular functions, cellular components, and signaling pathways. The interaction network analysis identified three TF proteins and five miRNAs as the key transcriptional and post-transcriptional regulators of HubcDEGs. Then, we detected the proposed Hub-cDEGs guided top-ranked three anti-HCC drug molecules (Dactinomycin, Vincristine, Sirolimus) that were also highly supported by the already published top-ranked HCC-causing Hub-DEGs mediated receptors. CONCLUSION: The findings of this study would be useful resources for diagnosis, prognosis, and therapies of HCC.

Metformin Hydrochloride Loaded Mucoadhesive Microspheres and Nanoparticles for Anti-Hyperglycemic and Anticancer Effects Using Factorial Experimental Design.

Background: Metformin hydrochloride (HCl) microspheres and nanoparticles were formulated to enhance bioavailability and minimize side effects through sustained action and optimized drug-release characteristics. Initially, the same formulation design with different ratios of metformin HCl and Eudragit RSPO was used to formulate four batches of microspheres and nanoparticles using solvent evaporation and nanoprecipitation methods, respectively. Methods: The produced formulations were evaluated based on particle size and shape (particle size distribution (PSD), scanning electron microscope (SEM)), incompatibility (differential scanning calorimetry (DSC), Fourier-transform infrared (FTIR)), drug release pattern, permeation behavior, in vivo hypoglycemic effects, and in vitro anticancer potential. Results: Compatibility studies concluded that there was minimal interaction between metformin HCl and the polymer, whereas SEM images revealed smoother, more spherical nanoparticles than microspheres. Drug release from the formulations was primarily controlled by the non-Fickian diffusion process, except for A1 and A4 by Fickian, and B3 by Super case II. Korsmeyer-Peppas was the best-fit model for the maximum formulations. The best formulations of microspheres and nanoparticles, based on greater drug release, drug entrapment, and compatibility characteristics, were attributed to the study of drug permeation by non-everted intestinal sacs, in vivo anti-hyperglycemic activity, and in vitro anticancer activity. Conclusion: This study suggests that the proposed metformin HCl formulation can dramatically reduce hyperglycemic conditions and may also have anticancer potential.

Robust identification of common genomic biomarkers from multiple gene expression profiles for the prognosis, diagnosis, and therapies of pancreatic cancer.

Pancreatic cancer (PC) is one of the leading causes of cancer-related death globally. So, identification of potential molecular signatures is required for diagnosis, prognosis, and therapies of PC. In this study, we detected 71 common differentially expressed genes (cDEGs) between PC and control samples from four microarray gene-expression datasets (GSE15471, GSE16515, GSE71989, and GSE22780) by using robust statistical and machine learning approaches, since microarray gene-expression datasets are often contaminated by outliers due to several steps involved in the data generating processes. Then we detected 8 cDEGs (ADAM10, COL1A2, FN1, P4HB, ITGB1, ITGB5, ANXA2, and MYOF) as the PC-causing key genes (KGs) by the protein-protein interaction (PPI) network analysis. We validated the expression patterns of KGs between case and control samples by box plot analysis with the TCGA and GTEx databases. The proposed KGs showed high prognostic power with the random forest (RF) based prediction model and Kaplan-Meier-based survival probability curve. The KGs regulatory network analysis detected few transcriptional and post-transcriptional regulators for KGs. The cDEGs-set enrichment analysis revealed some crucial PC-causing molecular functions, biological processes, cellular components, and pathways that are associated with KGs. Finally, we suggested KGs-guided five repurposable drug molecules (Linsitinib, CX5461, Irinotecan, Timosaponin AIII, and Olaparib) and a new molecule (NVP-BHG712) against PC by molecular docking. The stability of the top three protein-ligand complexes was confirmed by molecular dynamic (MD) simulation studies. The cross-validation and some literature reviews also supported our findings. Therefore, the finding of this study might be useful resources to the researchers and medical doctors for diagnosis, prognosis and therapies of PC by the wet-lab validation.

Bioinformatics-based investigation on the genetic influence between SARS-CoV-2 infections and idiopathic pulmonary fibrosis (IPF) diseases, and drug repurposing.

Some recent studies showed that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections and idiopathic pulmonary fibrosis (IPF) disease might stimulate each other through the shared genes. Therefore, in this study, an attempt was made to explore common genomic biomarkers for SARS-CoV-2 infections and IPF disease highlighting their functions, pathways, regulators and associated drug molecules. At first, we identified 32 statistically significant common differentially expressed genes (cDEGs) between disease (SARS-CoV-2 and IPF) and control samples of RNA-Seq profiles by using a statistical r-package (edgeR). Then we detected 10 cDEGs (CXCR4, TNFAIP3, VCAM1, NLRP3, TNFAIP6, SELE, MX2, IRF4, UBD and CH25H) out of 32 as the common hub genes (cHubGs) by the protein-protein interaction (PPI) network analysis. The cHubGs regulatory network analysis detected few key TFs-proteins and miRNAs as the transcriptional and post-transcriptional regulators of cHubGs. The cDEGs-set enrichment analysis identified some crucial SARS-CoV-2 and IPF causing common molecular mechanisms including biological processes, molecular functions, cellular components and signaling pathways. Then, we suggested the cHubGs-guided top-ranked 10 candidate drug molecules (Tegobuvir, Nilotinib, Digoxin, Proscillaridin, Simeprevir, Sorafenib, Torin 2, Rapamycin, Vancomycin and Hesperidin) for the treatment against SARS-CoV-2 infections with IFP diseases as comorbidity. Finally, we investigated the resistance performance of our proposed drug molecules compare to the already published molecules, against the state-of-the-art alternatives publicly available top-ranked independent receptors by molecular docking analysis. Molecular docking results suggested that our proposed drug molecules would be more effective compare to the already published drug molecules. Thus, the findings of this study might be played a vital role for diagnosis and therapies of SARS-CoV-2 infections with IPF disease as comorbidity risk.

Exploring Core Genes by Comparative Transcriptomics Analysis for Early Diagnosis, Prognosis, and Therapies of Colorectal Cancer.

Colorectal cancer (CRC) is one of the most common cancers with a high mortality rate. Early diagnosis and therapies for CRC may reduce the mortality rate. However, so far, no researchers have yet investigated core genes (CGs) rigorously for early diagnosis, prognosis, and therapies of CRC. Therefore, an attempt was made in this study to explore CRC-related CGs for early diagnosis, prognosis, and therapies. At first, we identified 252 common differentially expressed genes (cDEGs) between CRC and control samples based on three gene-expression datasets. Then, we identified ten cDEGs (AURKA, TOP2A, CDK1, PTTG1, CDKN3, CDC20, MAD2L1, CKS2, MELK, and TPX2) as the CGs, highlighting their mechanisms in CRC progression. The enrichment analysis of CGs with GO terms and KEGG pathways revealed some crucial biological processes, molecular functions, and signaling pathways that are associated with CRC progression. The survival probability curves and box-plot analyses with the expressions of CGs in different stages of CRC indicated their strong prognostic performance from the earlier stage of the disease. Then, we detected CGs-guided seven candidate drugs (Manzamine A, Cardidigin, Staurosporine, Sitosterol, Benzo[a]pyrene, Nocardiopsis sp., and Riccardin D) by molecular docking. Finally, the binding stability of four top-ranked complexes (TPX2 vs. Manzamine A, CDC20 vs. Cardidigin, MELK vs. Staurosporine, and CDK1 vs. Riccardin D) was investigated by using 100 ns molecular dynamics simulation studies, and their stable performance was observed. Therefore, the output of this study may play a vital role in developing a proper treatment plan at the earlier stages of CRC.