Bioimpacts最新文献

Introduction: Osteoarthritis is one of the most common orthopedic diseases that gradually causes wear and damage to the articular Subchondral bone due to the destruction of articular cartilage. One of the basic challenges in cartilage tissue engineering is the choice of scaffold. In the design of the cartilage scaffold, it is useful to consider parameters such as porosity, water absorption, high mechanical resistance, biocompatibility, and biodegradability. Therefore, in this study, demineralized bone matrix (DBM), which inherently has these characteristics to some extent, was chosen as the basic scaffold.

Methods: The gelatin/DBM (G/DBM) and the chondroitin sulfate-gelatin/DBM (GCS/DBM) scaffolds were prepared, respectively, by incorporating gelatin or chondroitin sulfate/gelatin solution inside DBM pores, freeze-drying and crosslinking with EDC/NHS. The physicochemical, biological characteristics and chondrogenic potential of scaffolds were studied.

Results: According to the SEM results, the size of the DBM pores in the G/DBM and GCS/DBM scaffolds decreased (from almost 100-1500 µm to less than 200 µm), which reduced cell escape compared to the DBM scaffold. Also, crosslinking the scaffolds has greatly increased their compressive E-modulus (more than 8 times). The cytocompatibility and non- toxicity of all scaffolds were confirmed by acridine orange/ethidium bromide (AO/EB) staining. The evaluation results of chondrogenic differentiation of placenta-derived mesenchymal stem cells (PMSCs) on modified scaffolds, using the real-time PCR method, showed that the presence of CS in the GCS/DBM scaffold improved the expression of chondrogenesis markers such as Aggrecan (AGC) (~4 times) and collagen 2 (COL-2) (~2.2 times) compared to the DBM scaffold. Also, Alcian blue staining and immunohistochemical analyses of the scaffolds showed denser and more coherent GAGs and COL-2 protein synthesis on the GCS/DBM than the G/DBM and DBM scaffolds.

Conclusion: According to the results, the GCS/DBM scaffold can be a suitable scaffold for cartilage tissue engineering.

Introduction: Pancreatic cancer, which results from the uncontrolled growth of pancreatic cells, is the fourth most frequent cause of cancer-related mortality in the United States. About 90% of instances of pancreatic cancer are pancreatic adenocarcinomas, and occasionally "pancreatic cancer" is used exclusively to describe this subtype. Nab-paclitaxel, gemcitabine, and FOLFIRINOX are examples of modern chemotherapeutic drugs that have the ability to quickly confer resistance in pancreatic tumor cells. Therefore, in order to treat this dreadful condition, it is essential to develop more effective medicines. Inhibition of the ubiquitin-proteasome system (UPS) causes pancreatic cancer cells to die apoptotically. In eukaryotes, UPS is an essential mechanism for protein breakdown. Pancreatic cancer cells are more susceptible to endoplasmic reticulum stress (endoplasmic reticulum [ER] stress) and apoptosis when treated with bortezomib, a proteasome inhibitor that is the first in this group of drugs approved for the treatment of cancer, especially multiple myeloma.

Methods: Searching through PubMed and Google Scholar and gathering data.

Results: UPS is still a popular target for pancreatic cancer treatment among researchers. However, despite the favorable results of UPS-based therapies in vitro and in vivo, the clinical results are not as promising as expected.

Conclusion: A deep understanding of it, is essential to achieving the maximum results. In this review, we aim to look into the UPS along with searching for the novelist therapies for pancreatic adenocarcinoma based on manipulating it.

Drug self-delivery systems are nanostructures composed of a drug as the main structural unit, having the ability of intracellular trafficking with no additional carrier. In these systems, the drug itself undertakes the functional and structural roles; thereby, the ancillary role of excipients and carrier-related limitations are circumvented and therapeutic effect is achieved at a much lower dose. Such advantages -which are mainly but not exclusively beneficial in cancer treatment- have recently led to an upsurge of research on these systems. Subsequently, various terminologies were utilized to describe them, referring to the same concept with different words. However, not all the systems developed based on the self-delivery approach are introduced using one of these keywords. Using a scoping strategy, this review aims to encompass the systems that have been developed as yet -inspired by the concept of self-delivery- and classify them in a coherent taxonomy. Two main groups are introduced based on the type of building blocks: small molecule-based nanomedicines and self-assembling hybrid prodrugs. Due to the diversity, covering the whole gamut of topics is beyond the scope of a single article, and, inevitably, the latter is just briefly introduced here, whereas the features of the former group are meticulously presented. Depending on whether the drug is merely a carrier for itself or carries a second drug as cargo, two classes of small molecule-based nanomedicines are defined (i.e., pure nanodrugs and carrier-mimicking systems, respectively), each having sub-branches. After introducing each branch and giving some examples, possible strategies for designing each particular system are visually displayed. The resultant mind map can create a macro view of the taken path and its prospects, give a profound insight into opportunities, spark new ideas, and facilitate overcoming obstacles. Taken together, one can foresee a brilliant future for self-delivery systems as a pioneering candidate for the next generation of drug delivery systems.

Introduction: Despite advances in orthopedic surgery, the lack of effective conventional treatment for cartilage defects has led to research in cartilage tissue engineering. One of the interesting topics is the use of decellularized extracellular matrix (ECM) as a suitable natural scaffold that supports the growth and function of cells cultured in it. A concern with decellularization protocols, especially those with high detergent concentrations, is the disruption of native ECM, which has deleterious effects on subsequent scaffold recellularization. Therefore, this study focused on optimizing cartilage decellularization by physical methods without the use of ionic detergents.

Methods: The bovine tracheal cartilage fragments were decellularized by a combination of 8 cycles of freeze-thaw and ultrasound techniques. Then, the tissues were immersed and shaken in 0.25% trypsin for 24 hours. Efficient cell removal and preservation of ECM were confirmed by histological and cytocompatibility assessments. The in-vivo studies were performed to evaluate the biocompatibility and bioactivity of the scaffold.

Results: The histological assessments indicated the appropriate cytocompatibility and the fibroblast cell culture study demonstrated that cells were able to proliferate and migrate on the decellularized cartilage. In-vivo evaluation also showed a reduced adverse immune response, including leukocyte infiltration into the ECM.

Conclusion: These results suggest that a cartilage scaffold created using a physical decellularization protocol that efficiently removes cells while preserving the native ECM can be a suitable scaffold for cartilage reconstruction. The main advantage of this protocol is the absence of potentially toxic chemicals in the tissues.

Introduction: The number of citations received by articles is a key indicator of a journal's quality and effectiveness within its field. This, in turn, influences the ability to secure funding for ongoing publication, establishing a suitable policy for its promotion, and helping researchers selecting an appropriate journal to publish their findings or find information they require. Meanwhile, one way to measure a journal's technological impact is to examine the patent citations attached to its articles. This study aims to evaluate the technological impact of BioImpacts (BI) by examining the patents' citations to its articles.

Methods: This descriptive study was conducted using the bibliometric method with a technological impact approach. The published BI belonged to the years 2011-2023. Data were extracted using the Lens and Espacenet databases. Descriptive statistics and Excel software were applied for data analysis.

Results: The results revealed that BI had a 10% technological impact. Original research contributed to most of the cited articles (27). American patents were the most impacted by BI's articles. Moreover, human necessities (A) and chemistry and metallurgy (C) were two main technological sections influenced by BI's articles. The most active and influential country was Iran and the most active and influential organization was Tabriz University of Medical Sciences.

Conclusion: Based on the present research findings, the technological impact of BI is considerable; so, it is important to present information about this issue on the journal's website and adopt appropriate policies for further development of BI in this field.

Introduction: Given the well-established association between metabolic syndrome (MetS) and obesity, this study elucidates the influences of sleep duration and weight on MetS risk and explores the potential role of miRNAs as underlying mechanisms.

Methods: According to sleep logs and biochemistry tests, this study investigated the association between MetS and its components, sleep duration, and weight in four subgroups: A: normal sleepers with normal weight (N = 145), B: normal sleepers with obesity (N = 140), C: short sleepers with normal weight (N = 130), and D: short sleepers with obesity (N = 142). Chi-square, one-way ANOVA, and Tukey's post hoc tests were used for statistical analysis. Furthermore, following total RNA isolation by TRIzol from blood samples, cDNA was synthesized using stem-loop technique. Quantitative real-time polymerase chain reaction (qRT-PCR) was then employed to evaluate the expression levels of miR-33a, miR-378a, miR-132-3p, and miR-181d. The data were analyzed using one-way ANOVA.

Results: Our findings revealed the strongest association between MetS prevalence and individuals in group D (short sleepers with obesity; Cramer's V = 0.649, P < 0.001). This observation underscores the synergistic effect of short sleep and obesity on MetS risk. Furthermore, there was an independent association between short sleep duration and elevated triglyceride levels (P < 0.05). MicroRNA expression analysis revealed downregulation of miR-33a and miR-181d in B, C, and D groups compared to the normal group. Conversely, miR-132-3p expression was upregulated in the B, C, and D groups.

Conclusion: Short sleep and obesity synergistically elevate MetS risk, potentially via miR-33a and miR-181d downregulation and miR-132-3p upregulation, impacting triglyceride metabolism.

Introduction: High-dimensional datasets often contain an abundance of features, many of which are irrelevant to the subject of interest. This issue is compounded by the frequently low number of samples and imbalanced class samples. These factors can negatively impact the performance of classification algorithms, necessitating feature selection before classification. The primary objective of feature selection algorithms is to identify a minimal subset of features that enables accurate classification.

Methods: In this paper, we propose a two-stage hybrid method for the optimal selection of relevant features. In the first stage, a filter method is employed to assign weights to the features, facilitating the removal of redundant and irrelevant features and reducing the computational cost of classification algorithms. A subset of high-weight features is retained for further processing in the second stage. In this stage, an enhanced Harris Hawks Optimization algorithm and GRASP, augmented with crossover and mutation operators from genetic algorithms, are utilized based on the weights calculated in the first stage to identify the optimal feature set.

Results: Experimental results demonstrate that the proposed algorithm successfully identifies the optimal subset of features.

Conclusion: The two-stage hybrid method effectively selects the optimal subset of features, improving the performance of classification algorithms on high-dimensional datasets. This approach addresses the challenges posed by the abundance of features, low number of samples, and imbalanced class samples, demonstrating its potential for application in various fields.

Introduction: Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal disease with a poor prognosis. Kinase proteins are essential regulators of cellular processes and potential targets for drug development.

Methods: Integration of multiple microarray datasets was screened to find differentially expressed kinases (DE-Kinases) across adjacent normal and tumor tissue samples in PDAC. The most effective kinase for drug design and docking in this study was selected by investigating biological mechanisms and survival analyses. Forty phytochemicals were extracted from the yellow sweet clover, Melilotus officinalis (Linn.) Pall, and were then subjected to in silico screening and molecular docking studies against a specific potent kinase.

Results: MET, PAK3, and PDK4 were identified as the DE-Kinases. After examining the pathways and biological processes, up-regulated MET had the most significant survival analysis and became our primary kinase for drug design and docking in this study. Four of the extracted phytocompounds of Melilotus officinalis (Linn.) Pall that exhibited high binding affinities with MET and were selected for toxicity analysis. Finally, the stability and mobility of the two nontoxic compounds that passed the toxicity test (dicumarol PubChem CID: 54676038 and melilotigenin PubChem CID: 14059499) were studied by molecular dynamics simulation.

Conclusion: This study's results identified two phytochemicals in yellow sweet clover that could be used to develop an anticancer drug, but experimental evaluation is necessary to confirm their efficacy.

Introduction: Extracellular vesicles (EVs) are crucial in intercellular signaling pathways. Since cancer has had a significant impact on global health as the second leading cause of death, this study aimed to systematically review the literature on the efficacy and safety of EVs in this setting.

Methods: A systematic literature review was performed on MEDLINE, Embase, the Cochrane Library, and ClinicalTrials.gov from database inception until August 10th, 2023. Based on PICOS, the inclusion criteria were: individuals with cancer treated with EVs compared to control among clinical studies.

Results: EVs administered to 46 individuals with cancer. Most studies revealed significant clinical benefits after treatment. Results also demonstrated that EVs are safe without major adverse events (AEs).

Conclusion: The use of EVs may provide potential therapeutic benefits for treating cancer. Further, well-designed randomized clinical trials (RCTs) are needed to provide robust evidence for supporting the clinical use of EVs in this setting.

Introduction: Giardiasis is a common intestinal infection caused by the Giardia lamblia parasite, and its rapid and accurate diagnosis is crucial for effective treatment. The automatic classification of Giardia infection from stool microscopic images plays a vital role in this diagnosis process. In this study, we applied deep learning-based models to automatically classify stool microscopic images into three categories, namely, normal, cyst, and trophozoite.

Methods: Unlike previous studies focused on images acquired from drinking water samples, we specifically targeted stool samples. In this regard, we collected a dataset of 1610 microscopic digital images captured by a smartphone with a resolution of 2340 × 1080 pixels from stool samples under the Nikon YS100 microscope. First, we applied CLAHE (Contrast Limited Adaptive Histogram Equalization) histogram equalization a method to enhance the image quality and contrast. We employed three deep learning models, namely Xception, ResNet-50, and EfficientNet-B0, to evaluate their classification performance. Each model was trained on the dataset of microscopic images and fine-tuned using transfer learning techniques.

Results: Among the three deep learning models, EfficientNet-B0 demonstrated superior performance in classifying Giardia lamblia parasites from stool microscopic images. The model achieved precision, accuracy, recall, specificity, and F1-score values of 0.9599, 0.9629, 0.9619, 0.9821, and 0.9607, respectively.

Conclusion: The EfficientNet-B0 showed promising results in accurately identifying normal, cyst, and trophozoite forms of Giardia lamblia parasites. This automated classification approach can provide valuable assistance to laboratory science experts and parasitologists in the rapid and accurate diagnosis of giardiasis, ultimately improving patient care and treatment outcomes.