Bioimpacts最新文献

Introduction: Multi-target anti-Alzheimer's disease (AD) compounds are promising leads for the development of AD modifying agents. Ionic compounds containing quaternary ammonium moiety were synthesized, and their multi-targeted anti-AD effects were examined.

Methods: Imidazole derivatives containing a quaternary ammonium moiety were synthesized and evaluated for their potential anti-Alzheimer properties using computational (in silico), cellular (in vitro), and animal (in vivo) models. The inhibition kinetics of both human acetylcholinesterase (hAChE) and butyrylcholinesterase (hBuChE) were assessed. Neuroprotective effects in amyloid-beta (Aβ)-exposed PC12 cells were also examined. Furthermore, the compounds' impact on Aβ-induced memory impairment in Wistar rats was evaluated, with a detailed analysis of the underlying mechanisms.

Results: Compound 5g demonstrated acceptable cytotoxicity against human cells. This compound exhibited non-competitive dual inhibition of both hAChE and hBuChE. Additionally, compound 5g mitigated the morphological changes induced by amyloid-beta (Aβ) in PC12 cells and decreased cell mortality. It exhibited anti-oxidative stress properties, evident by reduction in reactive oxygen species (ROS) production, and inhibition of lipid peroxidation. The compound also down regulated the expression of pro-inflammatory genes IL-1β and TNF-α. In vitro studies validated compound 5g's ability to inhibit lactate dehydrogenase (LDH), attenuate neuroinflammation, and prevent the autophagy-apoptosis cascade. When administered to rats with Aβ-induced memory dysfunction, compound 5g enhanced cognitive function and improved spatial memory. In the hippocampi of treated rats, there was a noted downregulation of TNF-α and NF-kB. Furthermore, compound 5g counteracted the elevated activity of AChE. Molecular modeling validated the binding of compound 5g to both steric and catalytic sites of cholinesterase enzymes.

Conclusion: The novel quaternary ammonium derivative, compound 5g, demonstrated multi-target anti-AD properties, as evidenced by in silico, in vitro and in vivo studies. Behavioral assessments and molecular analyses further confirmed its therapeutic efficacy in amyloid-beta (Aβ)-challenged rats.

Introduction: The tumor microenvironment (TME) plays a pivotal role in cancer progression, influencing tumor initiation, growth, invasion, metastasis, and response to therapies. This study explores the dynamic interactions within the TME, particularly focusing on self-organization-a process by which tumor cells and their microenvironment reciprocally shape one another, leading to cancer progression and resistance. Understanding these interactions can reveal new prognostic markers and therapeutic targets within the TME, such as extracellular matrix (ECM) components, immune cells, and cytokine signaling pathways.

Methods: A comprehensive search method was employed to investigate the current academic literature on TME, particularly focusing on self-organization in the context of cancer progression and resistance across the PubMed, Google Scholar, and Science Direct databases.

Results: Recent studies suggest that therapies that disrupt TME self-organization could improve patient outcomes by defeating drug resistance and increasing the effectiveness of conventional therapy. Additionally, this research highlights the essential of understanding the biophysical properties of the TME, like cytoskeletal alterations, in the development of more effective malignancy therapy.

Conclusion: This review indicated that targeting the ECM and immune cells within the TME can improve therapy effectiveness. Also, by focusing on TME self-organization, we can recognize new therapeutic plans to defeat drug resistance.

Introduction: In recent years, advancements in information and communication technology (ICT) and the internet of things (IoT) have revolutionized the healthcare industry, enabling the collection, analysis, and utilization of medical data to improved patient care. One critical area of focus is the development of predictive care systems for early diagnosis and treatment of cancer and disease.

Methods: Leveraging medical IoT data, this study proposes a novel approach based on transformer model for disease diagnosis. In this paper, features are first extracted from IoT images using a transformer network. The network utilizes a convolutional neural network (CNN) in the encoder part to extract suitable features and employs decoder layers along with attention mechanisms in the decoder part. In the next step, considering that the extracted features have high dimensions and many of these features are irrelevant and redundant, relevant features are selected using the Harris hawk optimization algorithm.

Results: Various classifiers are used to label the input data. The proposed method is evaluated using a dataset consisting of 5 classes for testing and evaluation, and all results are provided into tables and plots.

Conclusion: The experimental results demonstrate that the proposed method acceptable performance compared to other methods.

Introduction: Colorectal cancer (CRC) is among the lethal cancers, indicating the need for the identification of novel biomarkers for the detection of patients in earlier stages. RNA and microRNA sequencing were analyzed using bioinformatics and machine learning algorithms to identify differentially expressed genes (DEGs), followed by validation in CRC patients.

Methods: The genome-wide RNA sequencing of 631 samples, comprising 398 patients and 233 normal cases was extracted from the Cancer Genome Atlas (TCGA). The DEGs were identified using DESeq package in R. Survival analysis was evaluated using Kaplan-Meier analysis to identify prognostic biomarkers. Predictive biomarkers were determined by machine learning algorithms such as Deep learning, Decision Tree, and Support Vector Machine. The biological pathways, protein-protein interaction (PPI), the co-expression of DEGs, and the correlation between DEGs and clinical data were evaluated. Additionally, the diagnostic markers were assessed with a combioROC package. Finally, the candidate tope score gene was validated by Real-time PCR in CRC patients.

Results: The survival analysis revealed five novel prognostic genes, including KCNK13, C1orf174, CLEC18A, SRRM5, and GPR89A. Thirty-nine upregulated, 40 downregulated genes, and 20 miRNAs were detected by SVM with high accuracy and AUC. The upregulation of KRT20 and FAM118A genes and the downregulation of LRAT and PROZ genes had the highest coefficient in the advanced stage. Furthermore, our findings showed that three miRNAs (mir-19b-1, mir-326, and mir-330) upregulated in the advanced stage. C1orf174, as a novel gene, was validated using RT-PCR in CRC patients. The combineROC curve analysis indicated that the combination of C1orf174-AKAP4-DIRC1-SKIL-Scan29A4 can be considered as diagnostic markers with sensitivity, specificity, and AUC values of 0.90, 0.94, and 0.92, respectively.

Conclusion: Machine learning algorithms can be used to Identify key dysregulated genes/miRNAs involved in the pathogenesis of diseases, leading to the detection of patients in earlier stages. Our data also demonstrated the prognostic value of C1orf174 in colorectal cancer.

Introduction: The SARS-CoV-2 pandemic necessitates effective therapeutic solutions. The receptor-binding motif (RBM) is a subdomain of the spike protein's receptor-binding domain (RBD) and is critical for facilitating the binding of SARS-CoV-2 to the human ACE2 receptor. This study investigates the use of the receptor-binding motif (RBM) domain as an immunogen to produce potent neutralizing antibodies against SARS-CoV-2.

Methods: The RBM gene was codon-optimized and cloned into the pET17b vector for expression in E. coli BL21 (DE3) cells, induced with 1 mM IPTG. The recombinant RBM protein was purified using Ni-NTA affinity chromatography. After validating the recombinant RBM by Western blotting with anti-His tag antibodies, BALB/c mice were immunized with 20 µg of the purified RBM protein. Anti-RBM IgG was subsequently purified using protein G resin, and its neutralizing capacity was assessed using the Pishtaz Teb Zaman Neutralization Assay Kit.

Results: The recombinant RBM protein, with a molecular weight of 10 kDa, was expressed as inclusion bodies. the typical yield of purification was 27 mg/L of bacterial culture. The neutralization test demonstrated a concentration of 36 µg/mL of neutralizing antibodies in the immunized serum, preventing the spike protein from binding to ACE2.

Conclusion: Our study demonstrated that anti-RBM antibodies exhibited neutralization effects on SARS-CoV-2. These findings provide evidence for the development of a vaccine candidate through the induction of antibodies against the RBM, necessitating further studies with adjuvants suitable for human use to evaluate its potential for human vaccination.

Introduction: Mesenchymal stem cells of human umbilical cord origin (hucMSCs) appear to be an attractive candidate for cell-based therapies. However, their efficacy requires improvement as poor survival and specific homing to the site of injury are the major barriers to their effective implementation in cell therapy. As Wnt signaling pathway is involved in the development and repair of organs, we adopted a preconditioning strategy of stem cells by using CHIR99021 compound (a Wnt pathway agonist) to potentiate hucMSCs beneficial effects and circumvent their therapeutic limitations.

Methods: We treated hucMSCs with 5 µM of CHIR99021 and evaluated the expression levels of genes involved in different biological processes through qRT-PCR. Subsequently, we examined the effectiveness of preconditioned cells (CHIR99021-hucMSCs) in a cisplatin-induced rat acute kidney injury model. Amelioration in tissue injury was evaluated by histopathology, immunohistochemistry and renal functional assessment.

Results: In treated groups, we observed preserved renal tissue architecture in terms of lesser epithelial cells necrosis (P ≤ 0.001) and cast formation ( ≤ 0.05). Accelerated proliferation of injured tubular cells (P ≤ 0.001) and low serum creatinine values (P ≤ 0.01) were observed in preconditioned hucMSCs group compared to untreated AKI rats. In addition, administration of preconditioned hucMSCs in kidney injury model offered better restoration of tubular cell membrane β-catenin molecules. Our findings showed that CHIR99021-modified hucMSCs may exhibit better capacity for cell migration and proliferation.

Conclusion: The results showed that preconditioning of stem cells with Wnt pathway activators could provide advanced benefits for organ repair, which may contribute to design a more effective therapeutic approach for renal regeneration.

By integrating the cutting-edge principles of nanotechnology with medical science, nanomedicine offers unprecedented opportunities to develop advanced drug delivery systems that surpass the limitations of conventional therapies. These nanoscale systems are designed to enhance treatments' efficacy, specificity, and safety by optimizing pharmacokinetics and biodistribution, ensuring that therapeutic agents reach their intended targets with minimal side effects. The article provides an in-depth analysis of nanomaterials' pivotal role in overcoming challenges related to drug delivery, including the ability to bypass biological barriers, improve bioavailability, and achieve controlled release of drugs. Despite these promising advancements, the transition of nanomedicine from research to clinical practice faces significant hurdles. The review highlights key obstacles such as patient heterogeneity, physiological variability, and the complex ADME (Absorption, Distribution, Metabolism, Excretion) profiles of nanocarriers, which complicate treatment predictability and effectiveness. Moreover, the article addresses the issues of limited tissue penetration, variable patient responses, and the need for standardized protocols in nanomaterial characterization, all of which hinder the widespread clinical adoption of nanomedicine. Nevertheless, the potential of nanomedicine in revolutionizing personalized cancer therapy remains immense. The article advocates for increased translational research and international collaboration to overcome these challenges, paving the way for fully realizing nanomedicine's capabilities in precision oncology and beyond.

Introduction: Drowsy driving is a significant contributor to accidents, accounting for 35 to 45% of all crashes. Implementation of an internet of things (IoT) system capable of alerting fatigued drivers has the potential to substantially reduce road fatalities and associated issues. Often referred to as the internet of medical things (IoMT), this system leverages a combination of biosensors, actuators, detectors, cloud-based and edge computing, machine intelligence, and communication networks to deliver reliable performance and enhance quality of life in smart societies.

Methods: Electroencephalogram (EEG) signals offer potential insights into fatigue detection. However, accurately identifying fatigue from brain signals is challenging due to inter-individual EEG variability and the difficulty of collecting sufficient data during periods of exhaustion. To address these challenges, a novel evolutionary optimization method combining convolutional neural networks (CNNs) and XGBoost, termed CNN-XGBoost Evolutionary Learning, was proposed to improve fatigue identification accuracy. The research explored various subbands of decomposed EEG data and introduced an innovative approach of transforming EEG recordings into RGB scalograms. These scalogram images were processed using a 2D Convolutional Neural Network (2DCNN) to extract essential features, which were subsequently fed into a dense layer for training.

Results: The resulting model achieved a noteworthy accuracy of 99.80% on a substantial driver fatigue dataset, surpassing existing methods.

Conclusion: By integrating this approach into an IoT framework, researchers effectively addressed previous challenges and established an artificial intelligence of things (AIoT) infrastructure for critical driving conditions. This IoT-based system optimizes data processing, reduces computational complexity, and enhances overall system performance, enabling accurate and timely detection of fatigue in extreme driving environments.

Introduction: The current understanding highlights the intricate relationship between leukemic cells and their microenvironment, emphasizing the significant impact of environmental factors on chemotherapy resistance or sensitivity. Platelet-derived microparticles (PMPs) play a crucial role in facilitating intercellular communication, significantly contributing to the complex dynamics of cancer pathology and treatment outcomes. This study aims to investigate the cytotoxic and apoptotic effects of PMP, Ara-C, and their combinations on cancer cells, as well as their influence on the expression of critical genes like Bax, Bcl-2, P21, and h-TERT in the context of Acute Lymphoblastic Leukemia (ALL) cell line (Nalm-6).

Methods: PMPs were isolated through centrifugation at varying speeds, and their concentration was determined using the BCA assay. The size and immunophenotypic characteristics of PMPs were analyzed using dynamic light scattering (DLS) and flow cytometry. The cytotoxic and apoptotic effects of PMP, Ara-C, and their combinations on Nalm-6 cells were assessed using the MTT assay, the trypan blue exclusion assay, and flow cytometry. Gene expression levels were analyzed using real-time PCR.

Results: According to our research findings, PMPs did not independently impact the viability and apoptosis of Nalm-6 cells; however, they synergistically augmented Ara-C's suppressive impact on viability and apoptosis. The MTT assay showed that both PMPs and Ara-C, whether administered alone or in combination, had a cytotoxic effect on the Nalm-6 cells. Furthermore, the combined treatment significantly affected the expression of Bax, Bcl-2, P21, and h-TERT genes.

Conclusion: Our study demonstrates that PMPs have the potential to improve the effectiveness of Ara-C chemotherapy in treating ALL. These findings contribute to a deeper understanding of the interplay between PMP and chemotherapy agents, offering potential insights for optimizing treatment strategies and improving patient outcomes in ALL.

Introduction: Nanocomposite scaffolds comprising mesoporous bioactive glass (MBG) were able to increase the viability, proliferation, and growth of stem cells in vitro, rendering them promising candidates for dental root tissue regeneration.

Methods: The Sol-Gel process was utilized for the synthesis of MBG and zinc-doped MBG (Zn-MBG), the latter being integrated into alginate/chitosan scaffolds which in turn were cross-linked to strengthen mechanical properties, followed by freeze-drying. The scaffold's physicochemical characterizations were evaluated, followed by investigations of its antioxidant properties, swelling behavior, mechanical properties, and porosity. The capacity of these biomaterials to increase cell viability and growth of apical papilla stem cells (SCAPs) and hemocompatibility was assessed as a final step.

Results: All fabricated scaffolds demonstrated proper porosity, biocompatibility, and hemocompatibility. Nanocomposite scaffolds with Zn-MBG presented a significant enhancement in cell viability for SCAPs compared to alginate/chitosan scaffolds. DPPH tests indicated that the Zn-MBG-alginate/chitosan scaffold showed the highest antioxidant properties.

Conclusion: Zn-MBG-alginate/chitosan nanocomposite scaffolds demonstrated great physicochemical characteristics and biological and mechanical properties, marking them as suitable candidates for dental root tissue engineering.