Beni-Suef University Journal of Basic and Applied Sciences最新文献

Background

Within the manufacturing sector, assembly processes relying on mechanical fasteners such as nuts, washers, and bolts hold critical importance. Presently, these fasteners undergo manual inspection or are identified by human operators, a practice susceptible to errors that can adversely affect product efficiency and safety. Given considerations such as time constraints, escalating facility and labor expenses, and the imperative of seamless integration, the integration of machine vision into assembly operations has become imperative.

Results

This study endeavors to construct a robust system grounded in deep learning algorithms to autonomously identify commonly used fasteners and delineate their attributes (e.g., thread type, head type) with acceptable precision. A dataset comprising 6084 images featuring 150 distinct fasteners across various classes was assembled. The dataset was partitioned into training, validation, and testing sets at a ratio of 7.5:2:0.5, respectively. Two prominent object detection algorithms, Mask-RCNN (regional-based convolutional neural network) and You Look Only Once-v5 (YOLO v5), were evaluated for efficiency and accuracy in fastener identification. The findings revealed that YOLO v5 surpassed Mask-RCNN in processing speed and attained an mean average precision (MAP) of 99%. Additionally, YOLO v5 showcased superior performance conducive to real-time deployment.

Conclusions

The development of a resilient system employing deep learning algorithms for fastener identification within assembly processes signifies a significant stride in manufacturing technology. This study underscores the efficacy of YOLO v5 in achieving exceptional accuracy and efficiency, thereby augmenting the automation and dependability of assembly operations in manufacturing environments. Such advancements hold promise for streamlining production processes, mitigating errors, and enhancing overall productivity in the manufacturing sector.

Background

Acute leukemia may present with extramedullary (EM) tissues. Myeloid sarcoma (MS) and leukemia cutis (LC) are considered extramedullary diseases. This study aims to evaluate the incidence, clinical characteristics, and prognostic factors affecting the outcome of pediatric patients with myeloid sarcoma at the pediatric oncology center from July 2007 to December 2017. Radiological imaging was used to stage the tumor, biopsy was done for pathological diagnosis, and bone marrow aspirate for morphology, flow cytometry, cytogenetics, and molecular analysis. Patients received chemotherapy protocols based on those used by the Children's Cancer Group for acute myeloid leukemia (AML).

Result

The study included 91 (13.2%) of 687 pediatric patients with acute myeloid leukemia, with a male-to-female ratio of 1.9 to 1. Prognostic factors that improved the patient’s 5-year overall survival (OS) were age > 5 years old, molecular and cytogenetic detection of t (8; 21), inv 16, presence of a single and small size lesion < 5 cm, negative CNS lesion, and achieved radiological response in isolated disease or radiological and marrow complete remission in disseminated disease post induction 1 with significant P value. Relapse, particularly early relapse, worsens the OS and EFS by 10% and 7.7%, respectively.

Conclusion

Patients over five with low-risk disease based on cytogenetics, a small, single, negative CNS lesion, and a complete response post induction 1 had better outcomes, with no significant difference between those with isolated extramedullary disease and those with marrow dissemination. Early relapse harms the outcome. The study group’s 5-year OS and EFS rates are 51.4% and 49.4%, respectively.

Background

Distinguishing between middle ear cholesteatoma and chronic suppurative otitis media (CSOM) is an ongoing challenge. While temporal bone computed tomography (CT) scan is highly accurate for diagnosing middle ear conditions, its specificity in discerning between cholesteatoma and CSOM is only moderate. To address this issue, we utilized trained machine learning models to enhance the specificity of temporal bone CT scan in diagnosing middle ear cholesteatoma. Our database consisted of temporal bone CT scan native images from 122 patients diagnosed with middle ear cholesteatoma and a control group of 115 patients diagnosed with CSOM, with both groups labeled based on surgical findings. We preprocessed the native images to isolate the region of interest and then utilized the Inception V3 convolutional neural network for image embedding into data vectors. Classification was performed using machine learning models including support vector machine (SVM), k-nearest neighbors (k-NN), random forest, and neural network. Statistical metrics employed to interpret the results included classification accuracy, precision, recall, F1 score, confusion matrix, area under the receiver operating characteristic curve (AUC), and FreeViz diagram.

Results

Our training dataset comprised 5390 images, and the testing dataset included 125 different images. The neural network, k-NN, and SVM models demonstrated significantly higher relevance in terms of classification accuracy, precision, and recall compared to the random forest model. For instance, the F1 scores were 0.974, 0.987, and 0.897, respectively, for the former three models, in contrast to 0.661 for the random forest model.

Conclusion

The performance metrics of the presented trained machine learning models hold promising prospects as potentially clinically useful aids.

Background

The extracellular matrix (ECM) structural deficiencies in chronic wounds prevent the wounds from healing through natural physiological processes. Electrospun biocompatible polymers offer a platform to produce microstructure wound dressing materials that mimic the ECM containing various bioactives to address the deficiencies in the chronic wound healing process. Quantitative characterization of the electrospun fiber microstructure could provide valuable information on using fiber constructs to facilitate wound healing. This work employed a validated image analysis tool to quantitatively explain various parameters for the microstructure of six electrospun fiber constructs, D1{Polycaprolactone (PCL), Polyvinyl alcohol (PVA), Keratin}, D2{PCL, PVA, keratin, Co-enzyme Q10 (CoQ10)}, D3 (PCL, PVA, keratin, mupirocin), D4 (PCL, PVA, keratin, CoQ10, mupirocin, valsartan), D5 {PVA, Hyaluronic acid (HA)}, and D6 (PVA), using scanning electron microscopy imaging modality.

Results

The fiber intersection density (FID) parameter was quantified in the formulations, e.g., 0.272% for D5 and 0.416% for D4. Orientation histograms for D1 and D6 are characteristic of isotropic materials, while orientations for D2 and D3 indicate anisotropy with 2 preferred orientations in each formulation. D4 and D5 present orientations characteristic of transversely isotropic materials. The tortuosity for D2 and D4 indicates almost straight fiber segments, in contrast with undulated fiber segments in all other formulations. Furthermore, the mean fiber diameter was quantified, e.g., 1.414 and 1.630 mm for D3 and D4, respectively.

Conclusion

Co-electrospun PVA/PCL microfibers offer great potential for controlled delivery of bioactives needed to accelerate the healing of chronic wounds. This image-based analysis technology quantitatively characterized different formulations of electrospun fiber scaffolds. This analysis sets the stage for future study that utilizes microstructural information in finite element biomechanical modeling, to investigate possible influence of structure-based mechanical factors on the ECM restorative potential of wound dressings. Adjustment of electrospinning conditions could produce fabricated constructs like the native ECM structural components with a functional role in wound healing.

Background

In this research, a brewer’s yeast suspension was used to biotreat raw linen fibers under a range of different circumstances utilizing an ultrasonic cleaner device. In order to optimize circumstances for the treatment process, this extensive work is focused on examining the variables that could affect the biotreatment, such as the amount of brewer’s yeast used, the duration, the temperature of the treatment, and the pH throughout the treatment. After enzymatic treatment, the printing process utilizing turmeric natural dye was used. Variable assesses were conducted to determine the steaming time, thermofixation time, pH of the printing paste, types of dyes, and types of fabrics. How these elements affected the wettability and fabric color strength is investigated. To better comprehend, scanning electron microscope (SEM) was used to study the morphology of treated and untreated linen samples. The effects of treating the fibers with yeast enzyme on their multifunctional qualities, such as color and antibacterial activity against gram-positive bacteria like Staphylococcus aureus and gram-negative bacteria like Escherichia coli, were assessed.

Results

Results demonstrated that the enzyme extract, which predominantly contains lipase, amylase, and protease enzymes that develop the fabric printability, is responsible for the increase of color strength which increased by about 152.27% with good fastness properties compared by the untreated printed samples.

Conclusions

The overall findings showed that the treated fabrics have superior color fastness and antibacterial properties when compared to the untreated fabrics, demonstrating that the procedure of production used to create these multifunctional linen fabrics is environmentally friendly.

Background

Abiotic stresses, like drought, are the major cause of shrinking plant, growth crop yields and quality. Nanotechnology has provided a significant improvement in increasing plant growth and yield of crops under stress conditions. This work assessed the potential of silicon for mitigating the negative effects of drought against wheat. In completely randomized design with three replicates, wheat seedlings grown under three watering levels (100, 60 and 40% of water holding capacity) were treated by silicon dioxide (SiO₂) as a normal or bulk form (Si) and SiO₂ nanoparticles (SiNPs) with concentrations of 100 and 200 mg L⁻¹. SiNPs was extracted from rice husk.

Results

Si and SiNPs treatments are shown to improve the growth of plants and increase the shoots and root weight, relative water content, photosynthetic pigments, and proline in wheat. SiO₂ either normal or nanoparticles at 100 mg L⁻¹ decreased lipid peroxidation as malondialdehyde was reduced. Also, nano-silicon increased free amino acids, antioxidant enzymes while decreased soluble sugars. Cytotoxicity assay proved the safety of nano-silicon usage.

Conclusions

In conclusion, the present study documented the significance of rice husk-extracted nano-silicon at rate of 100 mg L⁻¹ for improving growth and increasing tolerance to drought in wheat grown under water deficit.

Background

It is crucial to improve cancer patients' quality of life by developing medications that can treat cancer with minimum adverse effects. This study aimed to evaluate the therapeutic effect of chitosan nanoparticles (CNPs) and camel milk exosomes (CMEs) alone or in combination with Sorafenib (SOR) on Ehrlich ascites carcinoma (EAC)-bearing mice and to assess whether EAC-associated liver injury would be ameliorated due to this combination. Liver function and oxidant/antioxidant status were determined spectrophotometrically, while the levels of inflammatory cytokines were estimated by enzyme-linked immunosorbent assay. Gene expression was detected using real-time polymerase chain reaction.

Results

The tumor burden in EAC-bearing mice was reduced after treatment with CNPs ± CMEs ± SOR as indicated by (1) reduced ascetic fluid volume and tumor-cell viability; (2) induction of apoptosis [high p53, BCL2 associated X (Bax), caspase 3, low B-cell leukemia/lymphoma 2 protein (Bcl2)]; (3) increased intracellular reactive oxygen species; (4) decreased migration [high matrix metalloproteinase 9 (MMP9) and low TIMP metallopeptidase inhibitor 1 (TIMP1)]; (5) declined angiogenesis [low vascular endothelial growth factor (VEGF). These treatments also reduced liver injury induced by EAC as noticed by (1) restored liver function indices [alanine transaminase (ALT), aspartate transaminase (AST), alkaline phosphatase (ALP), and albumin]; (2) restored redox balance [low malondialdehyde (MDA) levels and high superoxide dismutase (SOD), catalase (CAT) and glutathione peroxidase (GPx) activities]; (3) increased antioxidant gene expression [high nuclear factor erythroid 2-related factor 2 (Nrf2) and heme oxygenase-1 (HO-1)]; (4) declined inflammation [low interleukin-1β (IL1β) and tumor necrosis factor alpha (TNFα) levels), and (5) enhanced structure of liver. SOR + CNPs-treated mice showed the most improvement, followed by SOR + CMEs-treated animals.

Conclusions

Based on these findings, we determined that CNPs and CMEs enhanced SOR's anticancer efficacy and had an ameliorative role against EAC-induced liver injuries.

Graphic abstract

Background

The study’s relevance is determined by the current desire to reduce the negative environmental impact of the textile industry. The study aims to develop and optimise dyeing processes using natural dyes in the textile industry.

Results

The process of dye transfer from solution to Bombyx mori natural silk fibre can be divided into three successive kinetic stages. The first stage involves the adsorption of dye molecules on the active surface of the fibre, the second, their diffusion deep into the fibre towards its centre, and the third, the uniform distribution of dye molecules along the fibre starting from its centre. It is noticed that diffusion at the third stage slows down significantly, and the third stage lasts much longer than the first and second stages. The analysis of experimental data on dye concentration over time on dyed materials and their comparison with hypothetical data will make it possible to establish time intervals for each stage of the process and diffusion coefficients for each of them.

Conclusion

This study has practical implications as it may contribute to more efficient and sustainable dyeing of textile materials using natural dyes, helping to reduce the negative environmental impact of the textile industry, and contributing to our knowledge of diffusion and dyeing processes.

Background

This systematic review aims to review and assess the importance and relationship between host defence antimicrobial peptides with type 2 diabetes mellitus (T2DM) complications and the correlation of their expression with hyperglycaemic status.

Main body

The systematic search included three electronic databases (PMC, PubMed, and Google Scholar) that were searched from July to November 2023. After identifying and screening the research articles, eleven studies fulfilled the selection criteria and were included (six case–control and five cross-sectional studies). The Newcastle Ottawa Scale assessed the selected studies’ quality. Most studies indicated a correlation between certain types of AMPs and diabetic complications.

Conclusion

Hyperglycaemia in type 2 diabetes mellitus (T2DM) affects the expression of certain types of antimicrobial peptides (AMPs) which have a dual function (antibacterial and modulation of immune response) that may enhance inflammation which may correlate with the development of long-term complications, increased susceptibility to bacterial infection, and impaired wound healing.

Background

There are a variety of lethal infectious diseases that are seriously affecting people's lives worldwide, particularly in developing countries. Hepatitis B, a fatal liver disease, is a contagious disease spreading globally. In this paper, a new hybrid approach of feed forward neural networks is considered to investigate aspects of the SEACTR (susceptible, exposed, acutely infected, chronically infected, treated, and recovered) transmission model of hepatitis B virus disease (HBVD). The combination of genetic algorithms and sequential quadratic programming, namely CGASQP, is applied, where genetic algorithm (GA) is used as the main optimization algorithm and sequential quadratic programming (SQP) is used as a fast-searching algorithm to fine-tune the outcomes obtained by GA. Considering the nature of HBVD, the whole population is divided into six compartments. An activation function based on mean square errors (MSEs) is constructed for the best performance of CGASQP using proposed model.

Results

The solution's confidence is boosted through comparative analysis with reference to the Adam numerical approach. The results revealed that approximated results of CGASQP overlapped the reference approach up to 3–9 decimal places. The convergence, resilience, and stability characteristics are explored through mean absolute deviation (MAD), Theil’s coefficient (TIC), and root mean square error (RMSE), as well as minimum, semi-interquartile range, and median values with respect to time for the nonlinear proposed model. Most of these values lie around 10⁻¹⁰–10⁻⁴ for all classes of the model.

Conclusion

The results are extremely encouraging and indicate that the CGASQP framework is very effective and highly feasible for implementation. In addition to excellent reliability and level of precision, the developed CGASQP technique also stands out for its simplicity, wider applicability, and flexibility.