Search Results (3,552)

The Internet of Medical Things (IoMT) is revolutionizing healthcare by enabling continuous patient monitoring, early diagnosis, and personalized treatments. However, the het-erogeneity of IoMT devices and the lack of standardized protocols introduce serious security vulnerabilities. To address these challenges, we propose a hybrid BiLSTM-DNN intrusion detection system, named HBiLD-IDS, that combines Bidirectional Long Short-Term Memory (BiLSTM) networks with Deep Neural Networks (DNNs), leveraging both temporal dependencies in network traffic and hierarchical feature extraction. The model is trained and evaluated on the CICIoMT2024 dataset, which accurately reflects the diversity of devices and attack vectors encountered in connected healthcare environments. The dataset undergoes rigorous preprocessing, including data cleaning, feature selection through correlation analysis and recursive elimination, and feature normalization. Compared to existing IDS models, our approach significantly enhances detection accuracy and generalization capacity in the face of complex and evolving attack patterns. Experimental results show that the proposed IDS model achieves a classification accuracy of 98.81% across 19 attack types confirming its robustness and scalability. This approach represents a promising solution for strengthening the security posture of IoMT networks against emerging cyber threats. Full article

The dairy industry generates significant amounts of wastewater, including microfiltration (MF) retentate, a byproduct thickened with organic and inorganic pollutants. This study focuses on the treatment of two times concentrated MF retentate using a hybrid system based on biological treatment in a sequential batch reactor (SBR) and adsorption on activated carbon. The first stage involved cross-flow microfiltration using a 0.2 µm PVDF membrane at 0.5 bar, resulting in reductions of 99% in turbidity and 79% in chemical oxygen demand (COD), as well as a partial reduction in conductivity. The second stage involved 24-h biological treatment in a sequential batch reactor (SBR) with activated sludge (activated sludge index: 80 cm³/g, MLSS 2500 mg/dm³), resulting in further reductions in COD (62%) and TOC (30%), as well as the removal of 46% of total phosphorus (TP) and 35% of total nitrogen (TN). In the third stage, the decantate underwent adsorption in a column containing powdered activated carbon (PAC; 1 g; S_(BET) = 969 m² g⁻¹), reducing the concentrations of key indicators to the following levels: COD 84%, TOC 70%, TN 77%, TP 87% and suspended solids 97%. Total pollutant retention ranged from 24.6% to 97.0%. These results confirm that the MF–SBR–PAC system is an effective, compact solution that significantly reduces the load of organic and biogenic pollutants in MF retentates, paving the way for their reuse or safe discharge into the environment. Full article

Breast cancer is the leading cause of cancer-related mortality among women world-wide, and early screening is critical for improving patient survival. Medical imaging plays a central role in breast cancer screening, diagnosis, and treatment monitoring. However, conventional imaging modalities—including mammography, ultrasound, and magnetic resonance imaging—face limitations such as low diagnostic specificity, relatively slow imaging speed, ionizing radiation exposure, and dependence on exogenous contrast agents. Photoacoustic imaging (PAI), a novel hybrid imaging technique that combines optical contrast with ultrasonic spatial resolution, has shown great promise in addressing these challenges. By revealing anatomical, functional, and molecular features of the breast tumor microenvironment, PAI offers high spatial resolution, rapid imaging, and minimal operator dependence. This review outlines the fundamental principles of PAI and systematically examines recent advances in its application to breast cancer screening, diagnosis, and therapeutic evaluation. Furthermore, we discuss the translational potential of PAI as an emerging breast imaging modality, complementing existing clinical techniques. Full article

The increasing use of pharmaceutically active compounds (PhACs), endocrine-disrupting compounds (EDCs), and personal care products (PCPs) has led to the widespread presence of organic micropollutants (OMPs) in aquatic environments, posing a significant global challenge for environmental conservation. In recent years, advanced materials-based nanofiltration (NF) technologies have emerged as a promising solution for water and wastewater treatment. This review begins by examining the sources of OMPs, as well as the risk of OMPs. Subsequently, the key criteria of NF membranes for OMPs are discussed, with a focus on the roles of pore size, charge property, molecular interaction, and hydrophilicity in the separation performance. Against that background, this review summarizes and analyzes recent advancements in materials such as metal organic frameworks (MOFs), covalent organic frameworks (COFs), graphene oxide (GO), MXenes, hybrid materials, and environmentally friendly materials. It highlights the porous nature and structural diversity of organic framework materials, the advantage of inorganic layered materials in forming controllable nanochannels through stacking, the synergistic effects of hybrid materials, and the importance of green materials. Finally, the challenges related to the performance optimization, scalable fabrication, environmental sustainability, and complex separation of advanced materials-based membranes for OMP removal are discussed, along with future research directions and potential breakthroughs. Full article

This study investigates Bimi^® (Brassica oleracea Italica × Alboglabra), a hybrid between kailan and conventional broccoli, to evaluate its compositional, functional, and sensory properties in relation to industrial sous-vide processing and refrigerated storage. Proximate composition, amino acid and fatty acid profiles, and mineral content were determined in raw samples. Color, chlorophyll content, total polyphenols, and antioxidant capacity (FRAP, ABTS, DPPH) were analyzed before and after sous-vide treatment and following 60 days of storage. Microbiological and physicochemical stability was monitored over 90 days under standard (4 °C) and mildly abusive (6–10 °C) storage conditions. Sensory profiling of Bimi^® and conventional broccoli was performed on sous-vide samples. The results showed an increase in total polyphenols and antioxidant activity after processing, while chlorophylls decreased. Microbiological safety was maintained under all conditions, with stable water activity and only moderate acidification. Bimi^® provided a valuable source of protein (4.32 g/100 g FW, 8.63% RDA), appreciable amounts of dietary fiber (2.96 g/100 g FW, 11.85% RDA), and essential minerals such as potassium (15.59% RDA), phosphorus (14.05% RDA), and calcium (8.09% RDA). Sensory evaluation revealed a milder flavor profile than that of conventional broccoli, accompanied by an asparagus-like aroma. These findings support the suitability of Bimi^® for industrial sous-vide processing and its potential as a nutritious convenience food. Full article

Gradual climate warming is favoring viticulture in Poland. At the same time, there is a lack of information about the suitability of grape varieties grown in Poland for processing. The primary aim of the study was to determine the effect of pre-treatment on the pressing yield of grape juice and its qualitative assessment. The study applied pre-treatment of raw material, involving either enzymatic liquefaction of the pulp in the first case or freezing and thawing of the pulp prior to pressing in the second case. There was considerable variation among the grape varieties studied in terms of the characteristics under analysis. The varietal characteristics had a significant effect on the pressing yield and the quality of the juice obtained. Pre-treatment had different effects on the pressing yield of the individual grape varieties and the quality of the obtained juices. The research carried out may improve the efficiency and quality of agricultural production with the rational use of locally grown grape hybrids. Full article

Cystic fibrosis produces viscous mucus in the lung that increases bacterial invasion, causing persistent infections and subsequent inflammation. Pseudomonas aeruginosa and Staphylococcus aureus are two of the most common infections in cystic fibrosis patients that are resistant to antibiotics. One antibiotic approved to treat these infections is levofloxacin (LVX), which functions to inhibit bacterial replication but can be further developed into tailorable particles. Nanoparticles are an emerging inhaled therapy due to enhanced targeting and delivery. The extracellular matrix (ECM) has been shown to possess pro-regenerative and non-toxic properties in vitro, making it a promising delivery agent. The combination of LVX and ECM formed into nanoparticles may overcome barriers to lung delivery to effectively treat cystic fibrosis bacterial infections. Our goal is to advance CF care by providing a combined treatment option that has the potential to address both bacterial infections and lung damage. Two hybrid formulations of a 10:1 and 1:1 ratio of LVX to ECM have shown neutral surface charges and an average size of ~525 nm and ~300 nm, respectively. The neutral charge and size of the particles may suggest their ability to attract toward and penetrate through the mucus barrier in order to target the bacteria. The NPs have also been shown to slow the drug dissolution, are non-toxic to human airway epithelial cells, and are effective in inhibiting Pseudomonas aeruginosa and Staphylococcus aureus. LVX-ECM NPs may be an effective treatment for pulmonary CF bacterial treatments. Full article

Electroless nickel deposition (ELD) is an autocatalytic technique extensively used to impart conductive, protective, and mechanical functionalities to inherently non-conductive synthetic substrates. This review systematically explores the fundamental mechanisms of electroless nickel deposition, emphasising recent advancements in surface activation methods, solvent systems, and microstructural control. Critical analysis reveals that bio-inspired activation methods, such as polydopamine (PDA) and tannic acid (TA), significantly enhance coating adhesion and durability compared to traditional chemical etching and plasma treatments. Additionally, solvent engineering, particularly using polar aprotic solvents like dimethyl sulfoxide (DMSO) and ethanol-based systems, emerges as a key strategy for achieving uniform, dense, and flexible coatings, overcoming limitations associated with traditional aqueous baths. The review also highlights that microstructural tailoring, specifically the development of amorphous-nanocrystalline hybrid nickel coatings, effectively balances mechanical robustness (hardness exceeding 800 HV), flexibility, and corrosion resistance, making these coatings particularly suitable for wearable electronic textiles and smart materials. Furthermore, commercial examples demonstrate the real-world applicability and market readiness of nickel-coated synthetic fibres. Despite significant progress, persistent challenges remain, including reliable long-term adhesion, internal stress management, and environmental sustainability. Future research should prioritise environmentally benign plating baths, standardised surface activation protocols, and scalable deposition processes to fully realise the industrial potential of electroless nickel coatings. Full article

Flocculants are indispensable in water and wastewater treatment, enabling the aggregation and removal of suspended particles, colloids, and emulsions. However, the conventional development and application of flocculants rely heavily on empirical methods, which are time-consuming, resource-intensive, and environmentally problematic due to issues such as sludge production and chemical residues. Recent advances in machine learning (ML) have opened transformative avenues for the design, optimization, and intelligent application of flocculants. This review systematically examines the integration of ML into flocculant research, covering algorithmic approaches, data-driven structure–property modeling, high-throughput formulation screening, and smart process control. ML models—including random forests, neural networks, and Gaussian processes—have successfully predicted flocculation performance, guided synthesis optimization, and enabled real-time dosing control. Applications extend to both synthetic and bioflocculants, with ML facilitating strain engineering, fermentation yield prediction, and polymer degradability assessments. Furthermore, the convergence of ML with IoT, digital twins, and life cycle assessment tools has accelerated the transition toward sustainable, adaptive, and low-impact treatment technologies. Despite its potential, challenges remain in data standardization, model interpretability, and real-world implementation. This review concludes by outlining strategic pathways for future research, including the development of open datasets, hybrid physics–ML frameworks, and interdisciplinary collaborations. By leveraging ML, the next generation of flocculant systems can be more effective, environmentally benign, and intelligently controlled, contributing to global water sustainability goals. Full article

Current work investigates the fabrication and performance of nanocomposite membranes, modified with varying concentrations of hybrid nanostructures comprising titanium nanowires coated with iron nanoparticles (TiO₂ NWs-Fe₂O₃), for the removal of Naphthol Blue Black (NBB) dye from industrial wastewater. A series of analytical tools were employed to confirm the successful modification including scanning electron microscopy and EDX analysis, porosity and hydrophilicity measurements, Fourier-transform infrared spectroscopy, and X-Ray Diffraction. The incorporation of TiO₂ NWs-Fe₂O₃ has enhanced membrane performance significantly by increasing the PWF and improving dye retention rates of nanocomposite membranes. At 0.7 g of nanostructure content, the modified membrane (M8) achieved a PWF of 93 L/m²·h and NBB dye rejection of over 98%. The flux recovery ratio (FRR) analysis disclosed improved antifouling properties, with the M8 membrane demonstrating a 73.4% FRR. This study confirms the potential of TiO₂ NWs-Fe₂O₃-modified membranes in enhancing water treatment processes, offering a promising solution for industrial wastewater treatment. These outstanding results highlight the potential of the novel PES-TiO₂ NWs-Fe₂O₃ membranes for dye removal and present adequate guidance for the modification of membrane physical properties in the field of wastewater treatment. Full article

The accurate dosing of polyaluminum chloride (PAC) is essential for achieving effective coagulation in drinking water treatment, yet conventional methods such as jar tests are limited in their responsiveness and operational efficiency. This study proposes a hybrid modeling framework that integrates artificial neural networks (ANN) with genetic algorithms (GA) to optimize PAC dosage under variable raw water conditions. Operational data from 400 jar test experiments, collected between 2022 and 2024 at the Yanahurco water treatment plant (Ecuador), were used to train an ANN model capable of predicting six post-treatment water quality indicators, including turbidity, color, and pH. The ANN achieved excellent predictive accuracy (R² > 0.95 for turbidity and color), supporting its use as a surrogate model within a GA-based optimization scheme. The genetic algorithm evaluated dosage strategies by minimizing treatment costs while enforcing compliance with national water quality standards. The results revealed a bimodal dosing pattern, favoring low PAC dosages (~4 ppm) during routine conditions and higher dosages (~12 ppm) when influent quality declined. Optimization yielded a 49% reduction in median chemical costs and improved color compliance from 52% to 63%, while maintaining pH compliance above 97%. Turbidity remained a challenge under some conditions, indicating the potential benefit of complementary coagulants. The proposed ANN–GA approach offers a scalable and adaptive solution for enhancing chemical dosing efficiency in water treatment operations. Full article

Recent advancements in deep learning have significantly enhanced brain tumor segmentation from MRI data, providing valuable support for clinical diagnosis and treatment planning. However, challenges persist in effectively integrating prior medical knowledge, capturing global multimodal features, and accurately delineating tumor boundaries. To address these challenges, the Hybrid Network for Multimodal Brain Tumor Segmentation (HN-MBTS) is proposed, which incorporates prior medical knowledge to refine feature extraction and boundary precision. Key innovations include the Two-Branch, Two-Model Attention (TB-TMA) module for efficient multimodal feature fusion, the Linear Attention Mamba (LAM) module for robust multi-scale feature modeling, and the Residual Attention (RA) module for enhanced boundary refinement. Experimental results demonstrate that this method significantly outperforms existing approaches. On the BraT2020 and BraT2023 datasets, the method achieved average Dice scores of 87.66% and 88.07%, respectively. These results confirm the superior segmentation accuracy and efficiency of the approach, highlighting its potential to provide valuable assistance in clinical settings. Full article

Electrochemical water splitting represents a sustainable approach for hydrogen production, yet efficient hydrogen evolution reaction (HER) catalysts operating in alkaline environments remain critically needed. Herein, we report the fabrication of Co₃O₄–NiS₂ nanocomposites synthesized through a facile coprecipitation and subsequent thermal treatment method. Detailed characterization via physicochemical techniques confirmed the successful formation of a hybrid Co₃O₄–NiS₂ heterostructure with tunable compositional and morphological characteristics. Among the synthesized catalysts (Co–Ni–1, Co–Ni–2, and Co–Ni–3), the Co–Ni–2 sample demonstrated optimal structural integration, displaying interconnected nanosheet morphologies and balanced elemental distribution. Remarkably, Co–Ni–2 achieved exceptional HER performance in 1 M KOH electrolyte, requiring an ultralow overpotential of only 84 mV at 10 mA cm⁻² and exhibiting a favorable Tafel slope of 67.5 mV dec⁻¹. Electrochemical impedance spectroscopy and electrochemical surface area measurements further substantiated the superior electrocatalytic kinetics, rapid charge transport, and abundant active site accessibility in the optimized Co–Ni–2 composite. Additionally, Co–Ni–2 demonstrated outstanding durability with negligible activity decay over 5000 cycles. This study not only highlights the strategic synthesis of Co₃O₄–NiS₂ nanostructures but also provides valuable insights for designing advanced, stable, and efficient non-noble electrocatalysts for sustainable hydrogen generation. Full article

DNA damage repair is a hallmark of any cancer growth, eventually leading to drug resistance and death. The poly ADP-ribose polymerase (PARP) enzyme is vital in repairing damaged DNA in normal and cancer cells with mutated DNA damage response (DDR) genes. Inhibitors of the PARP enzyme aid in chemotherapy, as shown by drug combinations such as Olaparib and Irinotecan in breast cancer treatment. However, the effect of Olaparib in colon cancer has not been studied extensively. Synthetic drugs have a significant limitation in cancer treatment due to drug resistance, leading to colon cancer relapse. Bioavailability of Olaparib and other PARP inhibitors is limited due to their hydrophobicity, which poses a significant challenge. These limitations and challenges can be addressed by encapsulating Olaparib in nanoparticles that could possibly increase the bioavailability of the drug at the site of action. New age nanoparticles, such as hybrid nanoparticles, provide superior quality in terms of design and circulatory time of the drug in the plasma. The side effects of Olaparib as a chemotherapeutic pave the way for exploring phytochemicals that may have similar effects. The combined impact of Olaparib and phytochemicals such as genistein, resveratrol and others in nano-encapsulated form can be explored in the treatment of colon cancer. Full article

Massively parallel or next-generation sequencing (NGS) has enabled the genetic characterization of cancer patients, allowing the identification of somatic and germline variants associated with their diagnosis, tumor classification, and therapy response. Despite its benefits, NGS testing is not yet available in the Chilean public health system, rendering it both costly and time-consuming for patients and clinicians. Using a retrospective cohort of 67 formalin-fixed, paraffin-embedded (FFPE) colorectal cancer (CRC) samples, we aimed to implement the identification, annotation, and prioritization of relevant actionable tumor somatic variants in our laboratory, as part of the public health system. We compared two different library preparation methodologies (amplicon-based and capture-based) and different bioinformatics pipelines for sequencing analysis to assess advantages and disadvantages of each one. We obtained 80.5% concordance between actionable variants detected in our analysis and those obtained in the Cancer Genomics Laboratory from the Universidad de Chile (62 out of 77 variants), a validated laboratory for this methodology. Notably, 98.4% (61 out of 62) of variants detected previously by the validated laboratory were also identified in our analysis. Then, comparing the hybridization capture-based library preparation methodology with the amplicon-based strategy, we found ~94% concordance between identified actionable variants across the 15 shared genes, analyzed by the TumorSec^TM bioinformatics pipeline, developed by the Cancer Genomics Laboratory. Our results demonstrate that it is entirely viable to implement an NGS-based analysis of actionable variant identification and prioritization in cancer samples in our laboratory, being part of the Chilean public health system and paving the way to improve the access to such analyses. Considering the economic realities of most Latin American countries, using a small NGS panel, such as TumorSec^TM, focused on relevant variants of the Chilean and Latin American population is a cost-effective approach to extensive global NGS panels. Furthermore, the incorporation of automated bioinformatics analysis in this streamlined assay holds the potential of facilitating the implementation of precision medicine in this geographic region, which aims to greatly support personalized treatment of cancer patients in Chile. Full article