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Magnetite (Fe3O4) nanoparticles (NPs) are attractive nanomaterials in the field of material science, chemistry, and physics because of their valuable properties, such as soft ferromagnetism, half-metallicity, and biocompatibility. Various structures of Fe3O4 NPs with different sizes, geometries, and nanoarchitectures have been synthesized, and the related properties have been studied with targets in multiple fields of applications, including biomedical devices, electronic devices, environmental solutions, and energy applications. Tailoring the sizes, geometries, magnetic properties, and functionalities is an important task that determines the performance of Fe3O4 NPs in many applications. Therefore, this review focuses on the crucial aspects of Fe3O4 NPs, including structures, synthesis, magnetic properties, and strategies for functionalization, which jointly determine the application performance of various Fe3O4 NP-based systems. We first summarize the recent advances in the synthesis of magnetite NPs with different sizes, morphologies, and magnetic properties. We also highlight the importance of synthetic factors in controlling the structures and properties of NPs, such as the uniformity of sizes, morphology, surfaces, and magnetic properties. Moreover, emerging applications using Fe3O4 NPs and their functionalized nanostructures are also highlighted with a focus on applications in biomedical technologies, biosensing, environmental remedies for water treatment, and energy storage and conversion devices.

Due to their good magnetic properties, excellent biocompatibility, and low price, magnetic iron oxide nanoparticles (IONPs) are the most commonly used magnetic nanomaterials and have been extensively explored in biomedical applications. Although magnetic IONPs can be used for a variety of applications in biomedicine, most practical applications require IONP-based platforms that can perform several tasks in parallel. Thus, appropriate engineering and integration of magnetic IONPs with different classes of organic and inorganic materials can produce multifunctional nanoplatforms that can perform several functions simultaneously, allowing their application in a broad spectrum of biomedical fields. This review article summarizes the fabrication of current composite nanoplatforms based on integration of magnetic IONPs with organic dyes, biomolecules (e.g., lipids, DNAs, aptamers, and antibodies), quantum dots, noble metal NPs, and stimuli-responsive polymers. We also highlight the recent technological advances achieved from such integrated multifunctional platforms and their potential use in biomedical applications, including dual-mode imaging for biomolecule detection, targeted drug delivery, photodynamic therapy, chemotherapy, and magnetic hyperthermia therapy.

Despite their promise, circulating tumor DNA (ctDNA)-based assays for multi-cancer early detection face challenges in test performance, due mostly to the limited abundance of ctDNA and its inherent variability. To address these challenges, published assays to date demanded a very high-depth sequencing, resulting in an elevated price of test. Herein, we developed a multimodal assay called SPOT-MAS (screening for the presence of tumor by methylation and size) to simultaneously profile methylomics, fragmentomics, copy number, and end motifs in a single workflow using targeted and shallow genome-wide sequencing (~0.55×) of cell-free DNA. We applied SPOT-MAS to 738 non-metastatic patients with breast, colorectal, gastric, lung, and liver cancer, and 1550 healthy controls. We then employed machine learning to extract multiple cancer and tissue-specific signatures for detecting and locating cancer. SPOT-MAS successfully detected the five cancer types with a sensitivity of 72.4% at 97.0% specificity. The sensitivities for detecting early-stage cancers were 73.9% and 62.3% for stages I and II, respectively, increasing to 88.3% for non-metastatic stage IIIA. For tumor-of-origin, our assay achieved an accuracy of 0.7. Our study demonstrates comparable performance to other ctDNA-based assays while requiring significantly lower sequencing depth, making it economically feasible for population-wide screening.

The resistance of surfaces to biomaterial adsorption/adhesion is paramount for advancing marine and biomedical industries. A variety of approaches that involve bioinert materials have been developed to modify surfaces. Self-assembled monolayers (SAMs) are powerful platforms in which the surface composition is easily fabricated and a well-defined structure is provided; thus, the molecular-level interaction between biomolecules/biofoulants and the surface can be understood. In this review, we describe a wide variety of SAM structures on gold and silica surfaces for antifouling applications and the corresponding mechanism of nonfouling surfaces. Our analysis divides the surface properties of films into the following types: (1) hydrophilic, (2) hydrophobic, and (3) amphiphilic films.

In order to protect human lives and infrastructure, as well as to minimize the risk of damage, it is important to predict and respond to natural disasters in advance. However, currently, the standardized disaster response system in South Korea still needs further advancement, and the response phase systems need to be improved to ensure that they are properly equipped to cope with natural disasters. Existing studies on intelligent disaster management systems (IDSSs) in South Korea have focused only on storms, floods, and earthquakes, and they have not used past data. This research proposes a new conceptual framework of an IDSS for disaster management, with particular attention paid to wildfires and cold/heat waves. The IDSS uses big data collected from open application programming interface (API) and artificial intelligence (AI) algorithms to help decision-makers make faster and more accurate decisions. In addition, a simple example of the use of a convolutional neural network (CNN) to detect fire in surveillance video has been developed, which can be used for automatic fire detection and provide an appropriate response. The system will also consider connecting to open source intelligence (OSINT) to identify vulnerabilities, mitigate risks, and develop more robust security policies than those currently in place to prevent cyber-attacks.

This paper presents the design and implementation of a shipping container repurposed as an off-grid office space in Singapore. The system leverages renewable solar energy for its energy needs and incorporates phase change material (PCM)-based thermal energy storage to mitigate the intermittency of solar power. Experimental testing demonstrated the system's effectiveness in achieving both operational and thermal comfort requirements for the office space. The findings highlight the potential of PCM-based thermal energy storage as a viable solution for sustainable and energy-efficient off-grid buildings in tropical regions.

This study investigated the impact of synchronous computer-mediated communication (SCMC) mode and familiarity with partners on learner engagement in second-language task-based interaction, and whether learner engagement is linked to subsequent joint-written-text quality. Ninety-eight Vietnamese learners of English were assigned into (±) familiar groups and performed a picture-sequencing tasks in 2 SCMC modes (i.e., video and text chat). Scores of 3 types of learner engagement (cognitive, social, and emotional) were compared across the conditions. Results showed that scores of all engagement types in the video chat were significantly higher than in the text chat. Familiar dyads also showed higher engagement than unfamiliar peers during the interaction. Learners reported different reasons for their preferences of video chat over text chat. Language-related episodes, semantically engaged talk, and mutual help as measures of learner engagement were predictive of the subsequent text quality. The results contribute to the general understanding of the characteristics of video and text chat and their impact on learner engagement and text quality.

This paper presents a hybrid method of combining the Random Forest (RF) algorithm in machine learning (ML) and the Gaussian process (GP) to design microstrip patch antennas at any frequency from 0.6 to 6.5 GHz. Distinct from many published works, the proposed model is trained and tested with a high quality large dataset obtained from full-wave simulations in CST software, with experimental verification also performed. These factors are vital to assess the ML model’s efficacy, but they haven’t been thoroughly examined in other studies. In this paper, the GP is employed to seek the optimal hyper-parameters for the ML model using the RF algorithm, leading to superior predictive accuracy, as evaluated by the root mean square error (RMSE) of 0.0056. The advantage of this method is that antenna designers can directly use it to design antennas at any desired frequency within the 0.5–6 GHz range with high accuracy. The proposed method offers a transformative approach to antenna design by significantly reducing the optimization time by up to 99%, thereby improving the overall antenna design process.

Background Non-invasive multi-cancer early detection (MCED) tests have shown promise in enhancing early cancer detection. However, their clinical utility across diverse populations remains underexplored, limiting their routine implementation. This study aims to validate the clinical utility of a multimodal non-invasive circulating tumor DNA (ctDNA)-based MCED test, SPOT-MAS (Screening for the Presence Of Tumor by DNA Methylation And Size). Methods We conducted a multicenter prospective study, K-DETEK (ClinicalTrials.gov identifier: NCT05227261), involving 9057 asymptomatic individuals aged 40 years or older across 75 major hospitals and one research institute in Vietnam. Participants were followed for 12 months. Results Of the 9024 eligible participants, 43 (0.48%) tested positive for ctDNA. Among these, 17 were confirmed with malignant lesions in various primary organs through standard-of-care (SOC) imaging and biopsy, with 9 cases matching our tissue of origin (TOO) predictions. This resulted in a positive predictive value of 39.53% (95%CI 26.37–54.42) and a TOO accuracy of 52.94% (95%CI 30.96–73.83). Among the 8981 participants (99.52%) who tested negative, 8974 were confirmed cancer-free during a 12-month period after testing, yielding a negative predictive value of 99.92% (95% CI 99.84–99.96). The test demonstrated an overall sensitivity of 70.83% (95%CI 50.83–85.09) and a specificity of 99.71% (95% CI 99.58–99.80) for detecting various cancer types, including those without SOC screening options. Conclusions This study presents a prospective validation of a multi-cancer early detection (MCED) test conducted in a lower middle-income country, demonstrating the potential of SPOT-MAS for early cancer detection. Our findings indicate that MCED tests could be valuable additions to national cancer screening programs, particularly in regions where such initiatives are currently limited. Trial registration ClinicalTrials.gov ID: NCT05227261. Date of registration: 07/02/2022.

The identification and quantification of actionable mutations are of critical importance for effective genotype-directed therapies, prognosis and drug response monitoring in patients with non-small-cell lung cancer (NSCLC). Although tumor tissue biopsy remains the gold standard for diagnosis of NSCLC, the analysis of circulating tumor DNA (ctDNA) in plasma, known as liquid biopsy, has recently emerged as an alternative and noninvasive approach for exploring tumor genetic constitution. In this study, we developed a protocol for liquid biopsy using ultra-deep massively parallel sequencing (MPS) with unique molecular identifier tagging and evaluated its performance for the identification and quantification of tumor-derived mutations from plasma of patients with advanced NSCLC. Paired plasma and tumor tissue samples were used to evaluate mutation profiles detected by ultra-deep MPS, which showed 87.5% concordance. Cross-platform comparison with droplet digital PCR demonstrated comparable detection performance (91.4% concordance, Cohen's kappa coefficient of 0.85 with 95% CI = 0.72-0.97) and great reliability in quantification of mutation allele frequency (Intraclass correlation coefficient of 0.96 with 95% CI = 0.90-0.98). Our results highlight the potential application of liquid biopsy using ultra-deep MPS as a routine assay in clinical practice for both detection and quantification of actionable mutation landscape in NSCLC patients.