Browse Articles

Design and development of a portable multiwavelength LED-based diffuse reflectance spectroscopy tool for rapid breast cancer identification

Abstract Breast cancer is the most prevalent cancer among women worldwide, emphasizing the need for rapid and accurate diagnostic tools to improve patient outcomes and survival rates. In this study, we developed a diagnostic tool—a multispectral pen based on diffuse reflectance spectroscopy (DRS)—to enable real-time ex vivo differentiation between malignant and adjacent normal human breast tissues, primarily based on lipid and collagen absorbers. Diffuse reflectance was observed to be higher, while reduced absorbance was lower for malignant tissue compared to adjacent normal tissue across 62 samples (50 formalin-fixed and 12 fresh tissues). Clinical data from 31 patients, with paired adjacent normal and malignant samples per patient, revealed significantly lower mean reduced absorbance values for malignant tissue at three wavelengths: 850 nm (0.13 ± 0.02 vs. 0.28 ± 0.02), 940 nm (0.19 ± 0.01 vs. 0.37 ± 0.02), and 1050 nm (0.25 ± 0.03 vs. 0.43 ± 0.03) with  p  < 0.0001 across formalin-fixed tissue samples. Using a CatBoost machine learning model, the tool achieved an accuracy of 90%, a sensitivity of 80%, and a specificity of 100% in distinguishing malignant from normal tissues. Limitations include a small sample size and limited patient diversity. Future work aims to integrate the DRS technology with machine learning algorithms to provide real-time intraoperative margin assessment, and testing in a larger study.

Simulated heatwave alters intertidal estuary greenhouse gas fluxes

Evaluation of antibacterial and cytotoxic effects of silver oxide nanoparticles synthesized from Psidium Guajava

Abstract Growing concerns over the toxicity and environmental impact of traditional nanoparticle synthesis methods have driven the search for safer, more sustainable alternatives. At the same time, the rising prevalence of antibiotic-resistant bacteria and the ongoing challenges in effective cancer treatment emphasize the urgent need for new antimicrobial and anticancer solutions. The manufacture of silver oxide nanoparticles (Ag₂O-NPs) is investigated in this work using Psidium guajava (guava) leaves extract, focusing on their dual biological potential. The use of dangerous chemicals is reduced by this green synthesis technique by employing natural phytochemicals from guava leaves as stabilizing and reducing agents, making it more environmentally friendly compared to traditional chemical approaches. At 435 nm, the biosynthesized Ag₂O-NPs displayed a distinctive surface plasmon resonance (SPR) band. With a polydispersity index (PDI) of 0.368. Nanocrystalline, mostly spherical Ag₂O-NPs with an average size of 25 to 30 nm were successfully formed, according to thorough characterization utilizing FTIR, XRD, SEM, TEM, and EDX. These nanoparticles demonstrated strong antibacterial activity against four clinically relevant, drug-resistant bacterial strains: Pseudomonas aeruginosa ATCC 27853, Salmonella typhimurium ATCC 13311, Escherichia coli ATCC 25922, and Staphylococcus aureus ATCC 29213. Minimum inhibitory concentration (MIC) values ranged from 31.2 to 250 µg/mL, indicating a dose-dependent antibacterial action. Notably, P. aeruginosa and S. aureus showed the highest sensitivity. Cytotoxicity testing further revealed selective anticancer activity, with the Ag₂O-NPs significantly reducing the viability of HepG2 liver cancer cells (IC₅₀ = 73.93 ± 0.49 µg/mL), while displaying lower toxicity toward normal Vero cells (IC₅₀ = 158.1 ± 0.41 µg/mL). These findings suggest that green-synthesized Ag₂O-NPs hold considerable promise as both potent antibacterial agents and effective anticancer therapeutics.

MedNet: a lightweight attention-augmented CNN for medical image classification

SVNC-Net: An optimized U-Net variant with 2D convolutions for lightweight 3D spleen segmentation

Accurate measurement of spleen volume is essential for the diagnosis of splenomegaly. While Computed Tomography (CT) is among the most reliable imaging modalities for this task, manual segmentation of the spleen is labor-intensive and impractical for routine clinical workflows. Automatic segmentation methods provide a more viable alternative for clinical deployment. In recent years, 3D Convolutional Neural Network (CNN) models have been widely used for this purpose due to their high segmentation accuracy. However, their computational and memory demands make them less suitable for real-time applications on edge devices with limited processing capabilities. To address these limitations, we introduce SVNC-Net (Spleen Volume and Neighborhood Convolutional Network) for efficient 3D spleen segmentation from CT scans. Rather than developing an entirely new architecture from scratch, SVNC-Net builds upon the U-Net framework with targeted architectural optimizations for efficiency. In SVNC-Net, each CT slice is processed independently using 2D convolutions. In its architecture, depthwise separable convolution is used to significantly reduce computational complexity and memory usage. To evaluate its performance and efficiency, a comparative analysis was conducted against well-known CNN-based models, including UPerNet, EMANet, CCNet, SegNet, and ShuffleNet. This evaluation was performed on two publicly available datasets used together for the first time in the literature. The promising results achieved from the comparative analysis verified that SVNC-Net is highly suitable for real-time applications and resource-constrained environments. Additionally, we explore post-training compression techniques such as pruning and quantization, which further enhance the model’s compactness and inference speed. These findings contribute to the ongoing efforts to develop efficient 2D deep learning models for 3D organ segmentation, particularly in resource-constrained clinical scenarios.

mop1 affects maize recombination landscapes by modulating methylation of MITEs near genes in open chromatin

Stromal transcriptomics uncover LIF as a key effector in high tumor budding triple-negative breast cancer

Lung function assessment by electrical impedance tomography among obese patients

Correction: Mechanisms of synergy creation for social-ecological transformation: Leverage point analysis of the emergence of autonomous innovations

Single-cell and spatial transcriptomics implicate a prognostic function of tertiary lymphoid structures in gastric cancer

Multi-response optimization of PETG FDM parameters using taguchi–grey relational analysis and perdition by regression modeling

Biodegradable magnesium-calcium mineralized collagen metal materials inhibits invasion and metastasis of osteosarcoma

Systematic transformation of urban cold chain networks: From cross-regional dependencies to sustainable local excellence

Urban agglomerations in developing regions face cascading inefficiencies in cold chain logistics, driven by structural dependencies on cross-regional distribution that generate excessive costs, carbon emissions, and quality deterioration. This study develops and empirically validates a systematic transformation framework that utilizes hierarchical optimization to reconfigure these inefficient networks into integrated, sustainable local systems. Our approach coordinates strategic facility location with operational vehicle routing, enabling emergent, system-level improvements that transcend conventional optimization. Empirical validation using 35 supermarket stores in the Hohhot-Baotou-Ordos-Ulanqab (HBOU) urban agglomeration demonstrates substantial, concurrent outcomes under practical conditions: a 44.1% reduction in both cost and carbon emissions, and a 21.9% enhancement in product freshness. Statistical analysis confirms high significance (p < 0.001), with a resulting Transformation Effectiveness Coefficient of 1.34, signifying a paradigm-level improvement. The framework reveals that apparent trade-offs between economic, environmental, and service objectives can be systematically resolved through strategic network reconfiguration. These findings advance urban logistics transformation theory by providing a reproducible, data-driven framework for designing sustainable distribution systems, offering significant policy and practical implications for comparable urban contexts globally.

Unraveling the mechanism behind the probable extinction of the B/Yamagata lineage of influenza B viruses

Molecular dynamics and COSMO-RS model of menthol–fatty acid deep eutectic solvents: thermodynamic, structural, and dynamics insights

Estimating the realised economic value of a historic Mediterranean fruit fly eradication

Guard dog behaviour (Canis lupus familiaris) towards various animal species and humans on farms in Germany

Over recent decades, several predator species have returned to human-dominated landscapes in Europe, with wolves ( Canis lupus) causing the most damage to livestock. In Germany, some ‘pioneer’ farmers started keeping guard dogs ( Canis lupus familiaris) to protect their livestock, but these ‘pioneers’ faced opposition from a general public unfamiliar with methods of protecting against predators. To evaluate the use and management of guard dogs to protect various farm animal species against predators in rural areas frequently used by the public in Germany, we studied the behaviour of 113 guard dogs on farms across Germany that have frequent public contact. Two approaches were used: I) we observed guard dog proximity to and behaviour towards goats and horses with direct field observation, and II) we asked equine science and agriculture students trained in behaviour observations and official herd management commissioners to report their experiences of guard dogs during their initial visits to farms keeping various livestock species. These reports included observations of the dogs’ behaviour and information about the farm and dog management practices, and showed that guard dogs preferentially stay within 1 farm-animal body lengths of goats and horses. They adapted to a large variety of tasks and could protect various species. They displayed friendly behaviour towards the owners of the farms and known persons, and all behaviour categories towards farm animals and unfamiliar persons in the presence of the owner. They were dominant and watchful towards unknown persons and external dogs. The farmers’ training and socialising of their guard dogs appear to be successful, as older dogs, and large mixed-sex guard dog groups were consistently watchful against external individuals, but friendly towards farm personnel. In conclusion, guard dogs adapt well to guarding various species on German farms.

Exploring a bimetallic catalyst family for hydrogen oxidation with insights into superior activity and durability

Impaired use of function words in European French-speaking children with developmental language disorder

PDualNet: a deep learning framework for joint prediction of Parkinson’s disease progression subtype and MDS-UPDRS scores

Abstract Parkinson’s disease is one of the most common and complex neurodegenerative diseases, characterized by remarkable motor and cognitive decline. As it is a highly heterogeneous disorder, i.e., the specific symptoms, their severity, and their progression rate manifest significant interpersonal variability, multiple progression subtypes can be defined. The identification and prediction of these subtypes is crucial for understanding the disease’s state and future trajectory, advancing prognostic accuracy and personalized treatment planning. At the same time, the ability to predict future MDS-UPDRS scores, provides an objective assessment of symptoms, supporting clinicians in tracking disease progression and evaluating treatment efficacy. To address both critical objectives, we introduce PDualNet, a novel dual-task framework that jointly models and predicts the disease progression and severity based on longitudinal clinical patient data. Our approach involves two key components: (i) an unsupervised module that maps the single-visit data of each patient, onto a “Single-Visit Embedding (SiVE) space”, and (ii) a supervised part, that utilizes the pre-trained SiVE embeddings to learn a compact representation of the longitudinal data of each patient, representing the “Disease State Embeddings (DiSE)”. These embeddings drive two parallel decoders: one predicting the progression subtype, and the other forecasting the future MDS-UPDRS I–III scores. After analysing patient visit data from up to six years after baseline, each consisting of 89 clinical features, we trained and evaluated PDualNet on 579 participants from the Parkinson’s Progression Markers Initiative. The resulting model, demonstrated remarkable performance on both classification and regression tasks, while additional validation on 490 participants from the Parkinson’s Disease Biomarkers Program cohort, confirmed its robust performance and strong generalization capabilities.