White blood cells classification using multi-fold pre-processing and optimized CNN model

Aqueous Vernonia amygdalina leaf extract in drinking water mitigates aflatoxin B1 toxicity in broilers: effects on performance, biomarker analysis, and liver histology

This study evaluated aqueous Vernonia amygdalina leaf extract in drinking water as a mitigation strategy against Aflatoxin B1-induced toxicity in broilers, focusing on performance, haematology, serum biochemistry, pro-inflammatory cytokines, cellular stress markers, and liver histology. Two hundred and forty (240) day-old chicks (mixed sex), of the Cobb 500 breed were divided into four groups: control (CONT), AFB1-exposed (AFLB1), and two treatment groups (VE1AF and VE2AF) receiving 0.5 mg/kg AFB1 and Vernonia amygdalina aqueous extract at 1 g/L and 2 g/L, respectively. At 42 days, VE1AF and VE2AF chickens showed higher (P < 0.05) final weights and weight gains than CONT and AFLB1 groups. The red blood cells, packed cell volume, haemoglobin, and white blood cell counts were higher (P < 0.05) in CONT, VE1AF, and VE2AF groups compared to AFLB1. Mean cell volume, and mean cell haemaoglobin were higher (P < 0.05) in AFLB1 and VE2AF. Serum analysis revealed lower (P < 0.05) total protein, globulin, and albumin in AFLB1, which were restored by the extract. The tumor necrosis factor-α, interleukin-6, interleukin-1β, and interferon-γ, were elevated (P < 0.05) in AFLB1 but reduced in VE1AF and VE2AF. The heat shock protein 70, 8-hydroxy-2'-deoxyguanosine and adiponectin levels were higher (P < 0.05) in AFLB1, but were normalized by the extract in VE1AF and VE2AF. Leptin and triiodothyronine levels were significantly (P < 0.05) better in VE1AF and VE2AF, compared to AFLB1. Liver histology showed reduced inflammation in VE1AF and VE2AF, with near-normal hepatic architecture. In conclusion, Vernonia amygdalina leaf extract effectively counteracts AFB1 toxicity, enhancing overall health and performance in broiler chickens.

Computer-aided diagnosis of Haematologic disorders detection based on spatial feature learning networks using blood cell images

Analyzing biomedical images is vital in permitting the highest-performing imaging and numerous medical applications. Determining the analysis of the disease is an essential stage in handling the patients. Similarly, the statistical value of blood tests, the personal data of patients, and an expert estimation are necessary to diagnose a disease. With the growth of technology, patient-related information is attained rapidly and in big sizes. Currently, numerous physical methods exist to evaluate and forecast blood cancer utilizing the microscopic health information of white blood cell (WBC) images that are stable for prediction and cause many deaths. Machine learning (ML) and deep learning (DL) have aided the classification and collection of patterns in data, foremost in the growth of AI methods employed in numerous haematology fields. This study presents a novel Computer-Aided Diagnosis of Haematologic Disorders Detection Based on Spatial Feature Learning Networks with Hybrid Model (CADHDD-SFLNHM) approach using Blood Cell Images. The main aim of the CADHDD-SFLNHM approach is to enhance the detection and classification of haematologic disorders. At first, the Sobel filter (SF) technique is utilized for preprocessing to improve the quality of blood cell images. Additionally, the modified LeNet-5 model is used in the feature extractor process to capture the essential characteristics of blood cells relevant to disorder classification. The convolutional neural network and bi-directional gated recurrent unit with attention (CNN-BiGRU-A) method is employed to classify and detect haematologic disorders. Finally, the CADHDD-SFLNHM model implements the pelican optimization algorithm (POA) method to fine-tune the hyperparameters involved in the CNN-BiGRU-A method. The experimental result analysis of the CADHDD-SFLNHM model was accomplished using a benchmark database. The performance validation of the CADHDD-SFLNHM model portrayed a superior accuracy value of 97.91% over other techniques.

Integration of nanobiosensors into organ-on-chip systems for monitoring viral infections

The integration of nanobiosensors into organ-on-chip (OoC) models offers a promising advancement in the study of viral infections and therapeutic development. Conventional research methods for studying viral infection, such as two-dimensional cell cultures and animal models, face challenges in replicating the complex and dynamic nature of human tissues. In contrast, OoC systems provide more accurate, physiologically relevant models for investigating viral infections, disease mechanisms, and host responses. Nanobiosensors, with their miniaturized designs and enhanced sensitivity, enable real-time, continuous, in situ monitoring of key biomarkers, such as cytokines and proteins within these systems. This review highlights the need for integrating nanobiosensors into OoC systems to advance virological research and improve therapeutic outcomes. Although there is extensive literature on biosensors for viral infection detection and OoC models for replicating infections, real integration of biosensors into OoCs for continuous monitoring remains unachieved. We discuss the advantages of nanobiosensor integration for real-time tracking of critical biomarkers within OoC models, key biosensor technologies, and current OoC systems relevant to viral infection studies. Additionally, we address the main technical challenges and propose solutions for successful integration. This review aims to guide the development of biosensor-integrated OoCs, paving the way for precise diagnostics and personalized treatments in virological research.

An Innovative Hybrid Model for Automatic Detection of White Blood Cells in Clinical Laboratories

Background: Microscopic examination of peripheral blood is a standard practice in clinical medicine. Although manual examination is considered the gold standard, it presents several disadvantages, such as interobserver variability, being quite time-consuming, and requiring well-trained professionals. New automatic digital algorithms have been developed to eliminate the disadvantages of manual examination and improve the workload of clinical laboratories. Objectives: Regular analysis of peripheral blood cells and careful interpretation of their results are critical for protecting individual health and early diagnosis of diseases. Because many diseases can occur due to this, this study aims to detect white blood cells automatically. Methods: A hybrid model has been developed for this purpose. In the developed model, feature extraction has been performed with MobileNetV2 and EfficientNetb0 architectures. In the next step, the neighborhood component analysis (NCA) method eliminated unnecessary features in the feature maps so that the model could work faster. Then, different features of the same image were combined, and the extracted features were combined to increase the model's performance. Results: The optimized feature map was classified into different classifiers in the last step. The proposed model obtained a competitive accuracy value of 95.6%. Conclusions: The results obtained in the proposed model show that the proposed model can be used in the detection of white blood cells.

Classification of white blood cells (leucocytes) from blood smear imagery using machine and deep learning models: A global scoping review

Machine learning (ML) and deep learning (DL) models are being increasingly employed for medical imagery analyses, with both approaches used to enhance the accuracy of classification/prediction in the diagnoses of various cancers, tumors and bloodborne diseases. To date however, no review of these techniques and their application(s) within the domain of white blood cell (WBC) classification in blood smear images has been undertaken, representing a notable knowledge gap with respect to model selection and comparison. Accordingly, the current study sought to comprehensively identify, explore and contrast ML and DL methods for classifying WBCs. Following development and implementation of a formalized review protocol, a cohort of 136 primary studies published between January 2006 and May 2023 were identified from the global literature, with the most widely used techniques and best-performing WBC classification methods subsequently ascertained. Studies derived from 26 countries, with highest numbers from high-income countries including the United States (n = 32) and The Netherlands (n = 26). While WBC classification was originally rooted in conventional ML, there has been a notable shift toward the use of DL, and particularly convolutional neural networks (CNN), with 54.4% of identified studies (n = 74) including the use of CNNs, and particularly in concurrence with larger datasets and bespoke features e.g., parallel data pre-processing, feature selection, and extraction. While some conventional ML models achieved up to 99% accuracy, accuracy was shown to decrease in concurrence with decreasing dataset size. Deep learning models exhibited improved performance for more extensive datasets and exhibited higher levels of accuracy in concurrence with increasingly large datasets. Availability of appropriate datasets remains a primary challenge, potentially resolvable using data augmentation techniques. Moreover, medical training of computer science researchers is recommended to improve current understanding of leucocyte structure and subsequent selection of appropriate classification models. Likewise, it is critical that future health professionals be made aware of the power, efficacy, precision and applicability of computer science, soft computing and artificial intelligence contributions to medicine, and particularly in areas like medical imaging.