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Epidermal growth factor receptor (EGFR) is estimated to be overexpressed in 60~80% of colorectal cancer (CRC), which is associated with a poor prognosis. Anti-EGFR targeted monoclonal antibodies (cetuximab and panitumumab) have played an important role in the treatment of metastatic CRC. However, the therapeutic response of anti-EGFR monoclonal antibodies is limited due to multiple resistance mechanisms. With the discovery of new functions for gold nanoparticles (AuNPs), we hypothesize that cetuximab-conjugated AuNPs (cetuximab-AuNPs) will not only improve the cytotoxicity for cancer cells, but also introduce expression change of the related biomarkers on cancer cell surface. In this contribution, we investigated the size-dependent cytotoxicity of cetuximab-AuNPs to CRC cell line (HT-29), while also monitored the expression of cell surface biomarkers in response to treatment with cetuximab and cetuximab-AuNPs. AuNPs with the size of 60 nm showed the highest impact for cell cytotoxicity, which was tested by cell counting kit-8 (CCK-8) assay. Three cell surface biomarkers including epithelial cell adhesion molecule (EpCAM), melanoma cell adhesion molecule (MCAM), and human epidermal growth factor receptor-3 (HER-3) were found to be expressed at higher heterogeneity when cetuximab was conjugated to AuNPs. Both surface-enhanced Raman scattering/spectroscopy (SERS) and flow cytometry demonstrated the correlation of cell surface biomarkers in response to the drug treatment. We thus believe this study provides powerful potential for drug-conjugated AuNPs to enhance cancer prognosis and therapy.

DNA methylation has been widely studied with the goal of correlating the genome profiles of various diseases with epigenetic mechanisms. Multiple approaches have been developed that employ extensive steps, such as bisulfite treatments, polymerase chain reactions (PCR), restriction digestion, sequencing, mass analysis, etc., to identify DNA methylation. In this article, we report a facile label-free surface-enhanced Raman scattering (SERS) spectroscopy system that utilizes gold nanoparticles (AuNPs) for signal enhancement of methylated DNA. The key innovation of this work is to use anionic nanoparticles at a high ionic strength to introduce the aggregation of AuNPs with anionic DNA. When target methylated DNA is present, the presence of a methyl group in the cytosine C5 position of CpG sites induces a Raman peak at 1350 cm−1. Our amplification-free system has a limit of detection (LOD) of 5% and a limit of quantification (LOQ) of 16% with good specificity. The method was applied to determine the hypermethylated levels of the germline of colorectal cancer cell lines SW48 and SW480. Our simple label-free method holds the potential to read the disease-associated methylation of genomic DNA.

Liquid biopsy has emerged as a promising non-invasive strategy for cancer diagnosis, enabling the detection of various circulating biomarkers, including circulating tumor cells (CTCs), circulating tumor nucleic acids (ctNAs), circulating tumor-derived small extracellular vesicles (sEVs), and circulating proteins. Surface-enhanced Raman scattering (SERS) biosensors have revolutionized liquid biopsy by offering sensitive and specific detection methodologies for these biomarkers. This review comprehensively examines the application of SERS-based biosensors for identification and analysis of various circulating biomarkers including CTCs, ctNAs, sEVs and proteins in liquid biopsy for cancer diagnosis. The discussion encompasses a diverse range of SERS biosensor platforms, including label-free SERS assay, magnetic bead-based SERS assay, microfluidic device-based SERS system, and paper-based SERS assay, each demonstrating unique capabilities in enhancing the sensitivity and specificity for detection of liquid biopsy cancer biomarkers. This review critically assesses the strengths, limitations, and future directions of SERS biosensors in liquid biopsy for cancer diagnosis.

Lanthanide‐doped upconversion nanoparticles (UCNPs) exhibit unique luminescence properties, making them promising for applications in displays, sensors, security labels, and solar cells. Embedding UCNPs in polymer films can enhance their functionality; however, the properties of the polymer matrix significantly influence the dispersion and loading capacity of UCNPs, ultimately affecting optical performance. In this study, we investigate the incorporation of UCNPs into two distinct polymer matrices, poly(3‐hexylthiophene) (P3HT) and poly(methyl methacrylate) (PMMA), via spin coating at different speeds. Our findings demonstrate that UCNP dispersion and monodispersity are governed by polymer polarity, viscosity, and UCNP concentration in the suspension. To enhance UCNP loading, multiple spin coatings were explored. In UCNP−P3HT films, the volume fraction of UCNPs increased from 26.1% to 51.4% after three consecutive spin coatings, while maintaining a uniform distribution. In contrast, the lower miscibility and higher viscosity of PMMA restricted UCNP loading to 12.0% before significant clustering occurred. Although multiple spin coatings increased the total UCNP content in PMMA films, the volume fraction decreased to 8.0% due to film thickening. This comparative analysis highlights the critical role of polymer matrix properties in UCNP embedding and provides valuable insights for optimizing UCNP−polymer composites for advanced optical applications. We study the fabrication of nanocomposite thin films incorporating upconversion nanoparticles into two types of polymer matrices, demonstrating the key roles of nanoparticle surface–polymer compatibility and processing conditions in determining particle dispersity and loading capacity.

Colorectal cancer (CRC) is the third most diagnosed and the second lethal cancer worldwide. Approximately 30–50% of CRC are driven by mutations in the KRAS oncogene, which is a strong negative predictor for response to anti‐epidermal growth factor receptor (anti‐EGFR) therapy. Examining the phenotype of KRAS mutant and wild‐type (WT) CRC cells in response to anti‐EGFR treatment may provide significant insights into drug response and resistance. Herein, surface‐enhanced Raman spectroscopy (SERS) assay was applied to phenotype four cell surface proteins (EpCAM, EGFR, HER2, HER3) in KRAS mutant (SW480) and WT (SW48) cells over a 24‐day time course of anti‐EGFR treatment with cetuximab. Cell phenotypes were obtained using Raman reporter‐coated and antibody‐conjugated gold nanoparticles (SERS nanotags), where a characteristic Raman spectrum was generated upon single laser excitation, reflecting the presence of the targeted surface marker proteins. Compared to the KRAS mutant cells, KRAS WT cells were more sensitive to anti‐EGFR treatment and displayed a significant decrease in HER2 and HER3 expression. The SERS results were validated with flow cytometry, confirming the SERS assay is promising as an alternative method for multiplexed characterization of cell surface biomarkers using a single laser excitation system. Examining the phenotypes of KRAS mutant and wild‐type colorectal cancer cells during anti‐EGFR treatment may provide significant insights into drug resistance. Herein, we applied surface‐enhanced Raman spectroscopy (SERS) assay to detect four cell surface proteins on KRAS mutant and wild‐type cells over a 24‐day treatment, where SERS assay achieved multiplexed characterization of cell surface markers using a single laser excitation.

Deserts are important landscapes on the earth and their variations have impacts on global climate through feedback processes. However, there is a limited understanding of the climatic controls on the spatial and temporal variations of global deserts. Here, we use climate reanalysis datasets, global land use/land cover (LULC) products and the CMIP6 (Coupled Model Intercomparison Project) model outputs to calculate the changing of global deserts during 1982–2020, and estimate future spatial trends of global deserts. Our results show that mean annual global desert area over this period is 17.64×10 6 km 2 , accounting for 12% of the terrestrial land. Desert areas decreased rapidly from the end of the 1980s to the 1990s in North Africa and Australia, followed by a slow expansion in the early 21st century globally. Spatio-temporal variations of areas of arid climate are characterized by interdecadal fluctuations, and there are clear regional differences in dynamics of the aridity index (AI, used here as a proxy for the area of drylands) and desert areas. Statistical analyses reveal that increased vegetation cover is directly related to the reduction of desert area, while potential evaporation, surface temperature and humidity are also significantly correlated with the desert area. The relationship between wind speed and desert dynamics varies regionally. The results of the CMIP6 simulations suggest that global deserts will expand in the 21st century, albeit at different rates under the ssp245 and ssp585 scenarios. Desert expansions are modelled to be greatest in Asia, Africa and Australia, while those of southern North Africa may reduce as their southern borders migrate northwards.

Objectives To assess image quality and liver metastasis detection of reduced-dose dual-energy CT (DECT) with deep learning image reconstruction (DLIR) compared to standard-dose single-energy CT (SECT) with DLIR or iterative reconstruction (IR). Methods In this prospective study, two groups of 40 participants each underwent abdominal contrast-enhanced scans with full-dose SECT (120-kVp images, DLIR and IR algorithms) or reduced-dose DECT (40- to 60-keV virtual monochromatic images [VMIs], DLIR algorithm), with 122 and 106 metastases, respectively. Groups were matched by age, sex ratio, body mass index, and cross-sectional area. Noise power spectrum of liver images and task-based transfer function of metastases were calculated to assess the noise texture and low-contrast resolution. The image noise, signal-to-noise ratios (SNR) of liver and portal vein, liver-to-lesion contrast-to-noise ratio (LLR), lesion conspicuity, lesion detection rate, and the subjective image quality metrics were compared between groups on 1.25-mm reconstructed images. Results Compared to 120-kVp images with IR, 40- and 50-keV VMIs with DLIR showed similar noise texture and LLR, similar or higher image noise and low-contrast resolution, improved SNR and lesion conspicuity, and similar or better perceptual image quality. When compared to 120-kVp images with DLIR, 50-keV VMIs with DLIR had similar low-contrast resolution, SNR, LLR, lesion conspicuity, and perceptual image quality but lower frequency noise texture and higher image noise. For the detection of hepatic metastases, reduced-dose DECT by 34% maintained observer lesion detection rates. Conclusion DECT assisted with DLIR enables a 34% dose reduction for detecting hepatic metastases while maintaining comparable perceptual image quality to full-dose SECT. Clinical relevance statement Reduced-dose dual-energy CT with deep learning image reconstruction is as accurate as standard-dose single-energy CT for the detection of liver metastases and saves more than 30% of the radiation dose. Key Points • The 40- and 50-keV virtual monochromatic images (VMIs) with deep learning image reconstruction (DLIR) improved lesion conspicuity compared with 120-kVp images with iterative reconstruction while providing similar or better perceptual image quality. • The 50-keV VMIs with DLIR provided comparable perceptual image quality and lesion conspicuity to 120-kVp images with DLIR. • The reduction of radiation by 34% by DLIR in low-keV VMIs is clinically sufficient for detecting low-contrast hepatic metastases.

Background Previous research has found that inter-item binding in working memory requires more executive attention than single items, and since intra-item binding in working memory requires more object-based attention to maintain than its constituent elements, it is possible that inter-item binding may also need more object-based attention than single items. Additionally, studies have found that semantic relationships between items in working memory help to facilitate working memory, but the mechanisms by which these semantic relationships enhance working memory are not clear. This study examines whether the semantic relationships between items in working memory can reduce the attentional resources required for inter-item binding. Methods During working memory task, participants were presented with memory items that either had or lacked semantic associations, and were required to complete an executive attention-consuming backward counting task or an object-based attention-consuming Duncan task while performing the working memory task. Results Experiment 1 revealed that for non-semantic pairs, the high-load backward counting task caused significantly greater impairment to inter-item binding than to single items ( t (23) = 3.47, p  =.002, d  = 0.71, BF₁₀ = 15.39). For semantically related pairs, Bayesian evidence strongly supported the null hypothesis of no differential executive attention cost between binding and single-item conditions ( p  =.787, d  = 0.06, BF₁₀ = 0.22). Experiment 2 showed a parallel but weaker pattern for object-based attention: non-semantic pairs exhibited higher binding costs under the Duncan task ( t (23) = 3.41, p  =.002, d  = 0.70, BF₁₀ = 17.27), whereas semantic pairs showed only partial attenuation of these costs ( p  =.123, d  = 0.33, BF₁₀ = 0.59). Conclusion Semantic relationships between items primarily alleviate executive attention required for inter-item binding maintenance and moderately reduced but did not fully eliminate the object-based attention demands.

Objective This study aimed to evaluate the image quality and lesion conspicuity of the deep learning image reconstruction (DLIR) algorithm compared with standard image reconstruction algorithms on abdominal enhanced computed tomography (CT) scanning with a wide range of body mass indexes (BMIs). Methods A total of 112 participants who underwent contrast-enhanced abdominal CT scans were divided into three groups according to BMIs: the 80-kVp group (BMI ≤ 23.9 kg/m 2 ), 100-kVp group (BMI 24–28.9 kg/m 2 ), and 120-kVp group (BMI ≥ 29 kg/m 2 ). All images were reconstructed using filtered back projection (FBP), adaptive statistical iterative reconstruction-V of 50% level (IR), and DLIR at low, medium, and high levels (DL, DM, and DH, respectively). Subjective noise, artifact, overall image quality, and low- and high-contrast hepatic lesion conspicuity were all graded on a 5-point scale. The CT attenuation value (in HU), image noise, and contrast-to-noise ratio (CNR) were quantified and compared. Results DM and DH improved the qualitative and quantitative parameters compared with FBP and IR for all three BMI groups. DH had the lowest image noise and highest CNR value, while DM had the highest subjective overall image quality and low- and high-contrast lesion conspicuity scores for the three BMI groups. Based on the FBP, the improvement in image quality and lesion conspicuity of DM and DH images was greater in the 80-kVp group than in the 100-kVp and 120-kVp groups. Conclusion For all BMIs, DLIR improves both image quality and hepatic lesion conspicuity, of which DM would be the best choice to balance both. Clinical relevance statement The study suggests that utilizing DLIR, particularly at the medium level, can significantly enhance image quality and lesion visibility on abdominal CT scans across a wide range of BMIs. Key Points • DLIR improved the image quality and lesion conspicuity across a wide range of BMIs. • DLIR at medium level had the highest subjective parameters and lesion conspicuity scores among all reconstruction levels. • On the basis of the FBP, the 80-kVp group had improved image quality and lesion conspicuity more than the 100-kVp and 120-kVp groups.

Objectives To compare the image quality and hepatic metastasis detection of low-dose deep learning image reconstruction (DLIR) with full-dose filtered back projection (FBP)/iterative reconstruction (IR). Methods A contrast-detail phantom consisting of low-contrast objects was scanned at five CT dose index levels (10, 6, 3, 2, and 1 mGy). A total of 154 participants with 305 hepatic lesions who underwent abdominal CT were enrolled in a prospective non-inferiority trial with a three-arm design based on phantom results. Data sets with full dosage (13.6 mGy) and low dosages (9.5, 6.8, or 4.1 mGy) were acquired from two consecutive portal venous acquisitions, respectively. All images were reconstructed with FBP (reference), IR (control), and DLIR (test). Eleven readers evaluated phantom data sets for object detectability using a two-alternative forced-choice approach. Non-inferiority analyses were performed to interpret the differences in image quality and metastasis detection of low-dose DLIR relative to full-dose FBP/IR. Results The phantom experiment showed the dose reduction potential from DLIR was up to 57% based on the reference FBP dose index. Radiation decreases of 30% and 50% resulted in non-inferior image quality and hepatic metastasis detection with DLIR compared to full-dose FBP/IR. Radiation reduction of 70% by DLIR performed inferiorly in detecting small metastases (< 1 cm) compared to full-dose FBP (difference: −0.112; 95% confidence interval [CI]: −0.178 to 0.047) and full-dose IR (difference: −0.123; 95% CI: −0.182 to 0.053) ( p < 0.001). Conclusion DLIR enables a 50% dose reduction for detecting low-contrast hepatic metastases while maintaining comparable image quality to full-dose FBP and IR. Key Points • Non-inferiority study showed that deep learning image reconstruction (DLIR) can reduce the dose to oncological patients with low-contrast lesions without compromising the diagnostic information. • Radiation dose levels for DLIR can be reduced to 50% of full-dose FBP and IR for detecting low-contrast hepatic metastases, while maintaining comparable image quality. • The reduction of radiation by 70% by DLIR is clinically acceptable but insufficient for detecting small low-contrast hepatic metastases (< 1 cm).