Biomedical Engineering: Applications, Basis and Communications

Organometallic complexes with ruthenium have shown promise as potential anticancer agents. These complexes represent a class of compounds that can exhibit unique mechanisms of action, and they may offer advantages or overcome some limitations associated with platinum-based drugs, such as cisplatin. Ruthenium-based complexes can have diverse mechanisms of action. They may interact with DNA, proteins, or other cellular components, leading to cytotoxic effects on cancer cells. The versatility of ruthenium coordination chemistry allows for the design of complexes with specific properties. Some cancers that become resistant to platinum-based drugs may still respond to ruthenium-based compounds. This reduced cross-resistance is attributed to differences in the mechanisms of action between platinum and ruthenium complexes. Researchers can design ruthenium complexes to target specific cellular pathways or processes associated with cancer. This targeted approach aims to enhance the selectivity of the treatment for cancer cells while minimizing the impact on normal cells. Ruthenium complexes often have unique structural features that contribute to their stability and reactivity. These features can be tailored to optimize the pharmacological properties of the compounds. Ruthenium-based complexes may be explored in combination with other anticancer agents to achieve synergistic effects and enhance overall treatment efficacy. Ongoing research continues to explore the potential of ruthenium-based organometallic complexes, and preclinical studies have demonstrated promising results. As with any emerging field, further investigations and clinical trials are needed to fully understand the therapeutic potential and safety profiles of these compounds for various types of cancers.

Objectives: Photoplethysmogram (PPG) signals have become a crucial tool in the non-invasive monitoring of oxygen saturation levels (SpO2). The main purpose of the present review is to perform a meta-analysis of the involvement and consideration of critical SpO2 levels (<90%) in the research papers where SpO2 levels are calculated/predicted from PPG and to elaborate on the impact of the critical levels when presenting the evaluation results. Data sources: PubMed, Science Direct, and Scopus were searched for papers published between January 1, 2016, and September 10, 2022. Results: This study produced several results, concerning the main objective as well as other important issues for improving the SpO2 estimation/calculation. We discovered that only 21 out of 75 papers considered SpO2 values that are in the critical domain. Many papers do not provide access to their databases or disclose the software/models used. Additionally, some studies lack sufficient testing subjects and fail to make their results reproducible. The findings reveal a preference for SpO2 calculation over prediction, limited data availability, undisclosed methodologies, and diverse evaluation metrics hinder replication and direct comparisons between studies. Also, a scoring table is offered that scores higher the papers that are more valuable for SpO2 calculation/prediction. Conclusion: Employing PRISMA guidelines, a comprehensive search across PubMed, Science Direct, and Scopus databases initially extracted 6173 potential papers. Following rigorous screening, 75 papers were selected for detailed analysis, of which only 21 included data from critical SpO2 levels. Furthermore, this research provided information for the filtered 21 paper about the sample size of the study participants, the models utilized to derive the results, the availability of databases, the specific devices employed in the research, the methodologies employed for PPG signal measurement, and the collaborative efforts among authors from different institutions. This information is sublimed in the scoring table which gives higher scoring to those papers that are more valuable for SpO2 calculation/prediction. This study offers references to all these findings that can be used as concrete guidelines for prospective researchers and developers of new sensors for SpO2 estimation/calculation utilizing PPG signals.

Electroencephalogram (EEG) recordings typically capture the integrals of active brain potentials, which vary in latencies and populations. Anomalies in EEG data, often associated with epilepsy, play a crucial role in identifying conditions such as brain death, encephalopathy, coma, depth of anesthesia, and sleep disturbances. To get early warnings for these diseases, this work intends to propose a novel approach for brain activity detection from EEG signals. A new Hybrid Classification of Combined Coot Blue Monkey Optimization (HC-CCBO) method is proposed in this work. Initially, improved $Z$-score normalization was used to preprocess the EEG signal. Further, Discrete Wavelet Transform (DWT), improved correlation and statistical features were extracted. After that, we set up hybrid classification, which exploited Bi-Directional Gated Recurrent Unit (Bi-GRU) and Deep Max Out (DMO) models. Further, weights of BI-GRU and DMO were optimized via Combined Coot Blue Monkey Optimization (CCBO) optimization. Finally, we obtain the output scores of the rest, left fist, both fists, right fist, and both feet from the suggested hybrid brain activity recognition model. The effectiveness of the suggested HC-CCBO model is compared to conventional techniques using a variety of metrics. Compared to the existing models, the suggested model obtains a high maximum accuracy of 0.93 at the 90th learning percentage.

Clinical manifestations and standard psychological tests have been widely used to diagnose autism spectrum disorder (ASD) patients and evaluate their severity level. The gold-standard criterion to diagnose ASD patients is the childhood autism rating scale (CARS), which is a qualitative questionnaire that is filled out through a systematic interview while no physiological test/record is performed to determine this score. To make the diagnosis process quantitative, electroencephalography (EEG) signals have been repeatedly analyzed to differentiate healthy subjects from ASD patients. However, the precise relationship between the abnormal behavior of EEG signals and different ASD severity levels is not well investigated. Here, we use CARS to qualitatively determine the severity level of 14 autistic children, who voluntarily enrolled in our study. We recorded their EEG signals from 19 scalp channels when they were awake in the idle state and elicited three informative features including approximation entropy, multiscale entropy and sample entropy in successive time frames. Among the three measures of entropy, the last one exhibits the highest sensitivity, where its correlation coefficient (CC) exceeds 0.7, on the electrode positions T6 (CC $\approx$ 0.74), P4 (CC $\approx$ 0.76) and Cz (CC $\approx$ 0.75). Results of sample entropy in channels Cz-versus-Pz and P4-versus-FP2 show that a simple K-nearest neighbor classifier can provide 93% classification accuracy among patients with mild, moderate and severe ASD levels. Comparing the proposed method to the conventional ones, we also extracted power spectral density features from the channels but they failed to identify the ASD severity level with an acceptable accuracy.

Hospitals are overcrowded in proportion to the massive increase in population, making it difficult for hospitals to manage or arrange or shorten patient wait time during medical care. However, it is difficult to overstate the significance of patient waiting time management and predictability. In most of the hospitals, the clinical workflow is defined and driven by the patient queues. Predicting the incoming patients and waiting times has thus emerged as one of the most crucial clinical management techniques. Our proposed work aims to address the limitations of current waiting time prediction models by providing a more sophisticated, data-driven approach that can adapt to the complexities of healthcare environments. This work focuses on developing a new model for predicting the patient’s waiting time. The primary aim of this research is to create a reliable predictive model capable of accurately anticipating patient waiting times. This innovation aims to enhance both patient satisfaction and operational efficiency within healthcare settings. By offering healthcare administrators a tool to better manage patient flow, allocate resources efficiently, and minimize waiting times, this model seeks to optimize overall healthcare service delivery. The initial process carried out is pre-processing which includes data acquisition and improved $z$-score normalization. Subsequently, entropy features, correlation features, and improved mutual information (MI) features are extracted. Then, prediction is carried out using hybrid classifier (HC) that involves a convolutional neural network (CNN) and bidirectional gated recurrent unit (Bi-GRU). Finally, improved score level fusion (ISLF) is introduced to get absolute outcomes on WTP. The analysis outcomes on varied error metrics show the efficacy of the proposed WTP model.

This work addresses the issues of noise and tissue appearance fluctuations in histopathology image classification by using a novel deep ensemble method. The experiment’s images were inherently noisy; however, the proposed approach includes features that allow for noise to be effectively encountered while classification tasks are being completed. This integration streamlines the categorization process by eliminating the requirement for a separate denoising phase. This approach encompasses studies on two types of noise, namely Gaussian and Rician, both commonly encountered in histopathological images. Remarkably, our proposed model demonstrated effectiveness in handling both types of noise, yielding satisfactory performance across diverse noise conditions. The proposed ensemble model achieves an accuracy of 83.74%, an $F$1-score of 81.72%, an $F$2-score of 81.04%, and an MCC of 83.99% for the highest level of rician noise. The proposed approach improves classification resilience and accuracy by combining the output of several deep-learning models. It does this by increasing the $F$2-score for malignant classes by 3–5%, which helps to reduce False Negatives. This approach differs from current technology and has promising implications for the diagnosis and treatment of breast cancer. Compared to other approaches, our suggested model performs better at higher noise levels. LIME and saliency map integration improve the interpretability of model decisions, which in turn improves classification accuracy and decision clarity. These features emphasize the adaptability and resilience of the suggested method, highlighting it as a potential instrument for enhancing the results of breast cancer diagnosis and therapy in clinical settings. The workload for pathologists is lessened, and diagnostic consistency and accuracy are improved through automation of the classification process.

Lung cancer is a malicious disorder that affects millions of people around the globe. Premature detection and classification of malignant cells can gradually improve the rate of survival of the patients. The study proposed an innovative strategy for lung cancer identification and classification by utilizing the hybrid deep learning (DL) technique. The presented strategy contains two major parts, segmentation and classification. Attention gated (AG) network is used for segmenting the affected regions. AGs guide a method’s attention to significant regions while repressing the activation of features in non-related fields. This augments the model’s representative influence without significantly increasing computing cost or the number of model parameters. For the lung cancer classification, by combining the architectures of LeNet and Dense Convolutional Network (DenseNet), the proposed LeNet–DenseNet was developed. LeNet and DenseNet are two popular CNN architectures known for the classification of images. LeNet comprises alternating and convolutional layers, followed by fully connected layers. As per the idea of densely connected layers, each layer in DenseNet, which is a recent CNN architecture, is associated with other layers in a feed-forward fashion. This allows for better feature reuse and gradient flow and lessens the parameter count. The findings stated that the proposed method outperforms when compared to other traditional strategies corresponding to specificity, sensitivity and accuracy. The presented approach assists in early-stage detection of high-accuracy lung cancer, which is considered crucial for the treatment procedures in time. The findings stipulated that the presented LeNet–DenseNet model procured accuracy of 94.8%, sensitivity of 96.8% and specificity of 93.5%.

Four keypoints corresponding to upper and lower eyelid positions may be localized in the scan videos of a blink reflexometer, an edge device. Images from prior reflexometer scans are tagged in order to train a residual neural network, dubbed BlinkResNet. In order to provide adequate demographic coverage, comprehensive data collection procedures are implemented. The architecture is thoroughly parameterized, allowing for a multidimensional space of compatible architectures to be swept through using a random search. Experimental results on a dataset of annotated reflexometer frames demonstrate significant improvement over a previous algorithm for eyelid localization. A random search identified variants of BlinkResNet best-suited for reflexometer frame annotation, with BlinkResNet-Default achieving a $R$² score of 0.966 on the validation dataset. Requirements for deployment on an edge device are met: training ease, generalizable inference performance, and low computational cost. A sweep may be conducted on any combination of hyperparameters and datasets, making it an adaptable tool for the continual development of customized machine learning architectures. The analysis of sweep results provides valuable insights into optimal model organization for specific tasks. Thus, methodical data annotation and sweeps over a parameterized machine learning routine are investigated as measures to achieve rapid, accurate inference results.