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In recent times, there has been a surge of interest in the unique characteristics and possible applications of nanoparticles in various fields, including environmental clean-up. The contamination of water sources with pharmaceutical and organic substances has emerged as a significant environmental concern. Semiconductor nanoparticles, particularly Bi2S3 nanoparticles, have garnered considerable attention due to their efficacy and environmentally friendly nature in advanced oxidation processes (AOPs). These nanoparticles have shown remarkable photocatalytic abilities in the destruction of pollutants when exposed to UV–visible light. This study covers the production, characterization, and degradation of Bi2S3 nanoparticles in relation to organic and pharmaceutical contaminants. By utilizing co-precipitate methods, it becomes possible to precisely manipulate the size, shape, and composition of the nanoparticles, which are crucial factors in determining their catalytic effectiveness. The created nanoparticles are then examined using various analytical techniques, such as Powder X-ray Diffraction (P-XRD), scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), UV-visible spectroscopy, Photoluminescence study (PL), colour chromaticity coordinates (CIE), and Dynamic light scattering study (DLS). The catalytic capability of the synthesised compounds was compared to the degradation of Picric acid and paracetamol as model pollutants. The observed photocatalytic activity of these nanoparticles indicates their potential in water purification and environmental clean-up. The findings suggest that incorporating these nanoparticles into practical systems for pollutant degradation is feasible and can be applied in diverse scenarios. The results shed light on the efficient utilization of Bi2S3 nanoparticles as photocatalysts for the oxidation of drugs and organic contaminants. By utilizing Bi2S3 nanoparticles, the elimination of pharmaceutical and organic pollutants from water sources becomes achievable, thereby contributing to the preservation and safeguarding of the environment and public health.

Abstract Background Percutaneous thermal ablation has become the preferred therapeutic treatment option for liver cancers that cannot be resected. Since ablative zone tissue changes over time, it becomes challenging to determine therapy effectiveness over an extended period. Thus, an immediate post‐procedural evaluation of the ablation zone is crucial, as it could influence the need for a second‐look treatment or follow‐up plan. Assessing treatment response immediately after ablation is essential to attain favorable outcomes. This study examines the efficacy of image fusion strategies immediately post‐ablation in liver neoplasms to determine therapeutic response. Methodology A comprehensive systematic search using PRISMA methodology was conducted using EMBASE, MEDLINE (via PUBMED), and Cochrane Library Central Registry electronic databases to identify articles that assessed the immediate post‐ablation response in malignant hepatic tumors with fusion imaging (FI) systems. The data were retrieved on relevant clinical characteristics, including population demographics, pre‐intervention clinical history, lesion characteristics, and intervention type. For the outcome metrics, variables such as average fusion time, intervention metrics, technical success rate, ablative safety margin, supplementary ablation rate, technical efficacy rate, LTP rates, and reported complications were extracted. Results Twenty‐two studies were included for review after fulfilling the study eligibility criteria. FI's immediate technical success rate ranged from 81.3% to 100% in 17/22 studies. In 16/22 studies, the ablative safety margin was assessed immediately after ablation. Supplementary ablation was performed in 9 studies following immediate evaluation by FI. In 15/22 studies, the technical effectiveness rates during the first follow‐up varied from 89.3% to 100%. Conclusion Based on the studies included, we found that FI can accurately determine the immediate therapeutic response in liver cancer ablation image fusion and could be a feasible intraprocedural tool for determining short‐term post‐ablation outcomes in unresectable liver neoplasms. There are some technical challenges that limit the widespread adoption of FI techniques. Large‐scale randomized trials are warranted to improve on existing protocols. Future research should emphasize improving FI's technological capabilities and clinical applicability to a broader range of tumor types and ablation procedures.

Computational drug repurposing is an efficient method to utilize existing knowledge for understanding and predicting their effect on neurological diseases. The ability of a molecule to cross the blood-brain barrier is a primary criteria for effective therapy. Thus, accurate predictions by employing Machine learning models can effectively identify the drug candidates that could be repurposed for neurological conditions. This study comprehensively analyzes the performance of the well-known machine learning models on two different datasets to overcome dataset-related biases. We found that random forest and extratrees (i.e., tree-based ensembled models) have the highest accuracy with mol2vec fingerprint for BBB permeability prediction, attaining AUC_ROC of 0.9453 and 0.9601 on BBB and B3DB dataset, respectively. Additionally, we have analyzed the impact of the data balancing technique (i.e., SMOTE) to improve the specificity of the models. Finally, we have explored the impact of different fingerprint combinations on accuracy. By employing SMOTE and fingerprint combination, SVC attains the highest AUC_ROC of 0.9511 on BBB dataset. Finally, we used the best-performing models of the B3DB dataset to evaluate the BBB permeability for drugs intended to be used for repurposing. Model validation for repurposing predicted the non-passage for most antihypertensive drugs and passage for CYP17A1 cancer drugs.

Owing to limited drug testing possibilities in pregnant population, the development of computational algorithms is crucial to predict the fate of drugs in the placental barrier; it could serve as an alternative to animal testing. The ability of a molecule to effectively cross the placental barrier and reach the fetus determines the drug’s toxicological effects on the fetus. In this regard, our study aims to predict the permeability of molecules across the placental barrier. Based on publicly available datasets, several machine learning models are comprehensively analysed across different fingerprints and toolkits to find the best suitable models. Several dataset analysis models are utilised to study the data diversity. Further, this study demonstrates the application of neural network-based models to effectively predict the permeability. K-nearest neighbour (KNN), standard vector classifier (SVC) and Multi-layer perceptron (MLP) are found to be the best-performing models with a prediction percentage of 82%, 86.4% and 90.8%, respectively. Different models are compared to predict the chosen set of drugs, drugs like Aliskiren, some insulin secretagogues and glucocorticoids are found to be negative while predicting the permeability.

Automatic food recognition systems have been receiving increasing attention in the research community with the advancements in inductive learning (e.g., classification in computer vision) due to their applicability in the healthcare and hospitality industry. However, food recognition is challenging due to its fine-grained nature and its high correlation with culture, geo-location, and language. To make food recognition systems feasible for the Middle Eastern region, we present a large-scale dataset (MEFood) of commonly consumed food items in the Middle East, thereby providing a dataset for current development and establishing a benchmark for future research. We have also thoroughly examined the MEFood dataset highlighting its challenging aspects and its real-world nature. Additionally, we have conducted a thorough experimental study benchmarking the mainstream computer vision and mobile networks on classification, runtime, and resource utilization metrics. Our results highlight that EfficientNet-V2 achieves performance closer to the best-performing individual model on the MEFood dataset while having the least resource utilization and minimal inference times. Finally, we have performed a thorough error analysis study to glean additional insights about the networks and MEFood dataset.

Abstract Segmentation of abdominal Computed Tomography (CT) scan is essential for analyzing, diagnosing, and treating visceral organ diseases (e.g., hepatocellular carcinoma). This paper proposes a novel neural network (Res-PAC-UNet) that employs a fixed-width residual UNet backbone and Pyramid Atrous Convolutions, providing a low disk utilization method for precise liver CT segmentation. The proposed network is trained on medical segmentation decathlon dataset using a modified surface loss function. Additionally, we evaluate its quantitative and qualitative performance; the Res16-PAC-UNet achieves a Dice coefficient of 0.950 ± 0.019 with less than half a million parameters. Alternatively, the Res32-PAC-UNet obtains a Dice coefficient of 0.958 ± 0.015 with an acceptable parameter count of approximately 1.2 million.

Abstract Clinical imaging (e.g., magnetic resonance imaging and computed tomography) is a crucial adjunct for clinicians, aiding in the diagnosis of diseases and planning of appropriate interventions. This is especially true in malignant conditions such as hepatocellular carcinoma (HCC), where image segmentation (such as accurate delineation of liver and tumor) is the preliminary step taken by the clinicians to optimize diagnosis, staging, and treatment planning and intervention (e.g., transplantation, surgical resection, radiotherapy, PVE, embolization, etc). Thus, segmentation methods could potentially impact the diagnosis and treatment outcomes. This paper comprehensively reviews the literature (during the year 2012–2021) for relevant segmentation methods and proposes a broad categorization based on their clinical utility (i.e., surgical and radiological interventions) in HCC. The categorization is based on the parameters such as precision, accuracy, and automation.

Electrocardiogram (ECG) is a non-invasive approach to capture the overall electrical activity produced by the contraction and relaxation of the cardiac muscles. It has been established in the literature that the difference between ECG-derived age and chronological age represents a general measure of cardiovascular health. Elevated ECG-derived age strongly correlates with cardiovascular conditions (e.g., atherosclerotic cardiovascular disease). However, the neural networks for ECG age estimation are yet to be thoroughly evaluated from the perspective of ECG acquisition parameters. Additionally, deep learning systems for ECG analysis encounter challenges in generalizing across diverse ECG morphologies in various ethnic groups and are susceptible to errors with signals that exhibit random or systematic distortions To address these challenges, we perform a comprehensive empirical study to determine the threshold for the sampling rate and duration of ECG signals while considering their impact on the computational cost of the neural networks. To tackle the concern of ECG waveform variability in different populations, we evaluate the feasibility of utilizing pre-trained and fine-tuned networks to estimate ECG age in different ethnic groups. Additionally, we empirically demonstrate that finetuning is an environmentally sustainable way to train neural networks, and it significantly decreases the ECG instances required (by more than 100×) for attaining performance similar to the networks trained from random weight initialization on a complete dataset. Finally, we systematically evaluate augmentation schemes for ECG signals in the context of age estimation and introduce a random cropping scheme that provides best-in-class performance while using shorter-duration ECG signals. The results also show that random cropping enables the networks to perform well with systematic and random ECG signal corruptions.

In our research, we address the problem of coordination and planning in heterogeneous multi-robot systems for missions that consist of spatially localized tasks. Conventionally, this problem has been framed as a task allocation problem that maps tasks to robots. However, all previous work assumes that tasks are atomic procedures. In this work, we relax this assumption and adopt a non-atomic model of tasks that enables robots to accomplish mission tasks incrementally over disjoint periods, precisely to account for the possibility of having a task serviced by numerous individual contributions over time. We propose a cooperative, load-balancing task allocation and scheduling algorithm based on sequential single-item auctions (CoLoSSI) that explicitly considers the non-atomicity of tasks, promotes synergies between agents, and enables cooperation while maintaining computational tractability. We also propose a fully distributed variant of CoLoSSI that tackles sparse, communication-restricted scenarios. Computational and simulation results confirm the efficacy of the proposed approaches for generating good-quality mission plans with low computational effort.

Copper oxide nanoparticles (CuO NPs) were produced through an environmentally friendly green synthesis. The characteristics of these green synthesized CuO NPs, including their structural, optical, morphological, and electrochemical properties, were examined using various characterization techniques. X-ray diffraction analysis revealed that the CuO NPs have a monoclinic structure with a C2/c space group. Electrochemical detection of glucose was carried out using cyclic voltammetry. The green synthesized CuO NPs exhibited excellent catalytic properties for both electrochemical sensing and photocatalysis. Significantly, these CuO NPs exhibited excellent selectivity and sensitivity in glucose detection, with a sensitivity of 370 μA mM−1 cm−2 and a detection limit of 1.0 μM. Furthermore, the CuO NPs demonstrated a substantial 84 % degradation of dyes within 150 min. These results underscore the potential of the green synthesized CuO NPs as a promising material for applications in both sensing and dye degradation.

Elastography Ultrasound provides elasticity information of the tissues, which is crucial for understanding the density and texture, allowing for the diagnosis of different medical conditions such as fibrosis and cancer. In the current medical imaging scenario, elastograms for B-mode Ultrasound are restricted to well-equipped hospitals, making the modality unavailable for pocket ultrasound. To highlight the recent progress in elastogram synthesis, this article performs a critical review of generative adversarial network (GAN) methodology for elastogram generation from B-mode Ultrasound images. Along with a brief overview of cutting-edge medical image synthesis, the article highlights the contribution of the GAN framework in light of its impact and thoroughly analyzes the results to validate whether the existing challenges have been effectively addressed. Specifically, This article highlights that GANs can successfully generate accurate elastograms for deep-seated breast tumors (without having artifacts) and improve diagnostic effectiveness for pocket US. Furthermore, the results of the GAN framework are thoroughly analyzed by considering the quantitative metrics, visual evaluations, and cancer diagnostic accuracy. Finally, essential unaddressed challenges that lie at the intersection of elastography and GANs are presented, and a few future directions are shared for the elastogram synthesis research.