Label-free and highly sensitive detection of aflatoxin B1 by Ag IANPs via surface-enhanced Raman spectroscopy.

Aflatoxin B1 (AFB1), a pernicious constituent of the aflatoxin family, predominantly contaminates cereals, oils, and their derivatives. Acknowledged as a Class I carcinogen by the World Health Organization (WHO), the expeditious and quantitative discernment of AFB1 remains imperative. This investigation delineates that aluminum ions can precipitate the coalescence of iodine-modified silver nanoparticles, thereby engendering hot spots conducive for label-free AFB1 identification via Surface-Enhanced Raman Spectroscopy (SERS). This methodology manifests a remarkable limit of detection (LOD) at 0.47 fg/mL, surpassing the sensitivity thresholds of conventional survey techniques. Moreover, this method has good anti-interference ability, with a relative error of less than 10% and a relative standard deviation of less than 6% in quantitative results. Collectively, these findings illuminate the substantial application potential and viability of this approach in the quantitative analysis of AFB1, underpinning a significant advancement in food safety diagnostics.

Anthrahydroquinone­2,6­disulfonate attenuates PQ­induced acute lung injury through decreasing pulmonary microvascular permeability via inhibition of the PI3K/AKT/eNOS pathway.

In paraquat (PQ)­induced acute lung injury (ALI)/ acute respiratory distress syndrome, PQ disrupts endothelial cell function and vascular integrity, which leads to increased pulmonary leakage. Anthrahydroquinone­2,6­disulfonate (AH2QDS) is a reducing agent that attenuates the extent of renal injury and improves survival in PQ­intoxicated Sprague­Dawley (SD) rats. The present study aimed to explore the beneficial role of AH2QDS in PQ­induced ALI and its related mechanisms. A PQ­intoxicated ALI model was established using PQ gavage in SD rats. Human pulmonary microvascular endothelial cells (HPMECs) were challenged with PQ. Superoxide dismutase, malondialdehyde, reactive oxygen species and nitric oxide (NO) fluorescence were examined to detect the level of oxidative stress in HPMECs. The levels of TNF­α, IL­1ß and IL­6 were assessed using an ELISA. Transwell and Cell Counting Kit­8 assays were performed to detect the migration and proliferation of the cells. The pathological changes in lung tissues and blood vessels were examined by haematoxylin and eosin staining. Evans blue staining was used to detect pulmonary microvascular permeability. Western blotting was performed to detect target protein levels. Immunofluorescence and immunohistochemical staining were used to detect the expression levels of target proteins in HPMECs and lung tissues. AH2QDS inhibited inflammatory responses in lung tissues and HPMECs, and promoted the proliferation and migration of HPMECs. In addition, AH2QDS reduced pulmonary microvascular permeability by upregulating the levels of vascular endothelial­cadherin, zonula occludens­1 and CD31, thereby attenuating pathological changes in the lungs in rats. Finally, these effects may be related to the suppression of the phosphatidylinositol­3­kinase (PI3K)/protein kinase B (AKT)/endothelial­type NO synthase (eNOS) signalling pathway in endothelial cells. In conclusion, AH2QDS ameliorated PQ­induced ALI by improving alveolar endothelial barrier disruption via modulation of the PI3K/AKT/eNOS signalling pathway, which may be an effective candidate for the treatment of PQ­induced ALI.

A potential-controlled electrochromic visual biosensor based on distance readout for zearalenone detection.

In this work, a potential-controlled electrochromic visual biosensor was developed for detecting zearalenone (ZEN) using a distance readout strategy. The sensor chip includes a square detection area and a folded signal output area created with laser etching technology. The detection area is modified with graphene oxide and ZEN aptamer, while Prussian blue (PB) is electrodeposited onto the signal output channel. When an appropriate voltage is applied, PB in the signal output area is reduced to colorless Prussian white (PW). The target ZEN molecules have the capability to release aptamers from graphene oxide (GO) surface in the detection area, resulting in a subsequent change in the potential of the visual signal output channel. This change determines the length of the channel that changes from blue to colorless, with the color change distance being proportional to the ZEN concentration. Using this distance readout strategy, ZEN detection within the range of 1 ng/mL to 300 ng/mL was achieved, with a detection limit of 0.29 ng/mL.

The impact of hollow core-shell nanozymes in biosensing: A case study of p-Fe3O4@PDA@ZIF-67.

BACKGROUND: Nanozymes, a new class of nanomaterials, have emerged as promising substitutes for enzymes in biosensor design due to their exceptional stability, affordability, and ready availability. While nanozymes address many limitations of natural enzymes, they still face challenges, particularly in achieving the catalytic activity levels of their natural counterparts. This indicates the need for enhancing the sensitivity of biosensors based on nanozymes. The catalytic activity of nanozyme can be significantly improved by regulating its size, morphology, and surface composition of nanomaterial. RESULTS: In this work, a kind of hollow core-shell structure was designed to enhance the catalytic activity of nanozymes. The hollow core-shell structure material consists of a nanozymes core layer, a hollow layer, and a MOF shell layer. Taking the classic peroxidase like Fe3O4 as an example, the development of a novel nanozyme@MOF, specifically p-Fe3O4@PDA@ZIF-67, is detailed, showcasing its application in enhancing the sensitivity of sensors based on Fe3O4 nanozymes. This innovative nanocomposite, featuring that MOF layer was designed to adsorb the signal molecules of the sensor to improve the utilization rate of reactive oxygen species generated by the nanozymes catalyzed reactions and the hollow layer was designed to prevent the active sites of nanozymes from being cover by the MOF layer. The manuscript emphasizes the nanocomposite's remarkable sensitivity in detecting hydrogen peroxide (H2O2), coupled with high specificity and reproducibility, even in complex environments like milk samples. SIGNIFICANCE AND NOVELTY: This work firstly proposed and proved that Fe3O4 nanozyme@MOF with hollow layer structure was designed to improve the catalytic activity of the Fe3O4 nanozyme and the sensitivity of the sensors based on Fe3O4 nanozyme. This research marks a significant advancement in nanozyme technology, demonstrating the potential of structural innovation in creating high-performance, sensitive, and stable biosensors for various applications.

Bulked segregant RNA-seq reveals complex resistance expression profile to powdery mildew in wild emmer wheat W762.

Powdery mildew, caused by Blumeria graminis f. sp. tritici (Bgt), is one of the most destructive fungal diseases threatening global wheat production. Exploring powdery mildew resistance (Pm) gene(s) and dissecting the molecular mechanism of the host resistance are critical to effectively and reasonably control this disease. Durum wheat (Triticum turgidum L. var. durumDesf.) is an important gene donor for wheat improvement against powdery mildew. In this study, a resistant durum wheat accession W762 was used to investigate its potential resistance component(s) and profile its expression pattern in responding to Bgt invasion using bulked segregant RNA-Seq (BSR-Seq) and further qRT-PCR verification. Genetic analysis showed that the powdery mildew resistance in W762 did not meet monogenic inheritance and complex genetic model might exist within the population of W762 × Langdon (susceptible durum wheat). After BSR-Seq, 6,196 consistently different single nucleotide polymorphisms (SNPs) were called between resistant and susceptible parents and bulks, and among them, 763 SNPs were assigned to the chromosome arm 7B. Subsequently, 3,653 differentially expressed genes (DEGs) between resistant and susceptible parents and bulks were annotated and analyzed by Gene Ontology (GO), Cluster of Orthologous Groups (COG), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. The potential regulated genes were selected and analyzed their temporal expression patterns following Bgt inoculation. As a result, nine disease-related genes showed distinctive expression profile after Bgt invasion and might serve as potential targets to regulate the resistance against powdery mildew in W762. Our study could lay a foundation for analysis of the molecular mechanism and also provide potential targets for the improvement of durable resistance against powdery mildew.

Detection of ion cyclotron emission by using an ion cyclotron range of frequency antennas-based diagnostic system in experimental advanced superconducting tokamak.

In the experimental advanced superconducting tokamak (EAST), a novel ion cyclotron range of frequency (ICRF) antenna-based diagnostic system is designed to measure ion cyclotron emission (ICE) driven by high-energy ions. The diagnostic system includes ICRF antenna straps, a three-tune impedance matching system, a coaxial switching system, a direct current block, and a data acquisition and storage system. Using the coaxial switching system, the ICRF antenna can be switched from the heating mode to the coupling mode between two discharges. In the 2023 EAST experiment campaign, core ICE was observed using the ICRF antenna-based diagnostic system during neutron beam injection heating, and the obtained results agreed well with the signal detected by the previous high-frequency B-dot probe-based diagnostic system.

Colonoscopy-assisted removal of an impaction foreign body at the rectosigmoid junction: A case report.

BACKGROUND: When an anorectal foreign body is found, its composition and shape should be evaluated, and a timely and effective treatment plan should be developed based on the patient's symptoms to avoid serious complications such as intestinal perforation caused by displacement of the foreign body. CASE SUMMARY: A 54-year-old male was admitted to our outpatient clinic on June 3, 2023, due to a rectal foreign body that had been embedded for more than 24 h. The patient reported using a glass electrode tube to assist in the recovery of prolapsed hemorrhoids, however, the electrode tube was inadvertently inserted into the anus and could not be removed by the patient. During hospitalization, the patient underwent surgery, and the foreign body was dragged into the rectum with the aid of colonoscopy. The anus was dilated with a comb-type pulling hook and an anal fistula pulling hook to widen the anus and remove the foreign body, and the local anal symptoms were then relieved with topical drugs. The patient was allowed to eat and drink, and an entire abdominal Computed tomography (CT) and colonoscopy were reviewed 3 d after surgery. CT revealed no foreign body residue and colonoscopy showed no metal or other residues in the colon and rectum, and no apparent intestinal tract damage. CONCLUSION: The timeliness and rationality of the surgical and therapeutic options for this patient were based on a literature review of the clinical signs and conceivable conditions in such cases. The type, material and the potential risks of rectal foreign bodies should be considered.

Characterization of the powdery mildew resistance locus in wheat breeding line Jimai 809 and its breeding application.

Powdery mildew, caused by Blumeria graminis f. sp. tritici (Bgt), is a severe disease that affects the yield and quality of wheat. Popularization of resistant cultivars in production is the preferred strategy to control this disease. In the present study, the Chinese wheat breeding line Jimai 809 showed excellent agronomic performance and high resistance to powdery mildew at the whole growth stage. To dissect the genetic basis for this resistance, Jimai 809 was crossed with the susceptible wheat cultivar Junda 159 to produce segregation populations. Genetic analysis showed that a single dominant gene, temporarily designated PmJM809, conferred the resistance to different Bgt isolates. PmJM809 was then mapped on the chromosome arm 2BL and flanked by the markers CISSR02g-1 and CIT02g-13 with genetic distances 0.4 and 0.8 cM, respectively, corresponding to a physical interval of 704.12-708.24 Mb. PmJM809 differed from the reported Pm genes on chromosome arm 2BL in origin, resistance spectrum, physical position and/or genetic diversity of the mapping interval, also suggesting PmJM809 was located on a complex interval with multiple resistance genes. To analyze and screen the candidate gene(s) of PmJM809, six genes related to disease resistance in the candidate interval were evaluated their expression patterns using an additional set of wheat samples and time-course analysis post-inoculation of the Bgt isolate E09. As a result, four genes were speculated as the key candidate or regulatory genes. Considering its comprehensive agronomic traits and resistance findings, PmJM809 was expected to be a valuable gene resource in wheat disease resistance breeding. To efficiently transfer PmJM809 into different genetic backgrounds, 13 of 19 closely linked markers were confirmed to be suitable for marker-assisted selection. Using these markers, a series of wheat breeding lines with harmonious disease resistance and agronomic performance were selected from the crosses of Jimai 809 and several susceptible cultivars. Supplementary Information: The online version contains supplementary material available at 10.1007/s11032-024-01467-8.

Multi-task reconstruction network for synthetic diffusion kurtosis imaging: Predicting neoadjuvant chemoradiotherapy response in locally advanced rectal cancer.

PURPOSE: To assess the feasibility and clinical value of synthetic diffusion kurtosis imaging (DKI) generated from diffusion weighted imaging (DWI) through multi-task reconstruction network (MTR-Net) for tumor response prediction in patients with locally advanced rectal cancer (LARC). METHODS: In this retrospective study, 120 eligible patients with LARC were enrolled and randomly divided into training and testing datasets with a 7:3 ratio. The MTR-Net was developed for reconstructing Dapp and Kapp images from apparent diffusion coefficient (ADC) images. Tumor regions were manually segmented on both true and synthetic DKI images. The synthetic image quality and manual segmentation agreement were quantitatively assessed. The support vector machine (SVM) classifier was used to construct radiomics models based on the true and synthetic DKI images for pathological complete response (pCR) prediction. The prediction performance for the models was evaluated by the receiver operating characteristic (ROC) curve analysis. RESULTS: The mean squared error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM) for tumor regions were 0.212, 24.278, and 0.853, respectively, for the synthetic Dapp images and 0.516, 24.883, and 0.804, respectively, for the synthetic Kapp images. The Dice similarity coefficient (DSC), positive predictive value (PPV), sensitivity (SEN), and Hausdorff distance (HD) for the manually segmented tumor regions were 0.786, 0.844, 0.755, and 0.582, respectively. For predicting pCR, the true and synthetic DKI-based radiomics models achieved area under the curve (AUC) values of 0.825 and 0.807 in the testing datasets, respectively. CONCLUSIONS: Generating synthetic DKI images from DWI images using MTR-Net is feasible, and the efficiency of synthetic DKI images in predicting pCR is comparable to that of true DKI images.

Evaluation and identification of powdery mildew-resistant genes in 137 wheat relatives.

Powdery mildew is one of the most severe diseases affecting wheat yield and quality and is caused by Blumeria graminis f. sp. tritici (Bgt). Host resistance is the preferred strategy to prevent this disease. However, the narrow genetic basis of common wheat has increased the demand for diversified germplasm resources against powdery mildew. Wheat relatives, especially the secondary gene pool of common wheat, are important gene donors in the genetic improvement of common wheat because of its abundant genetic variation and close kinship with wheat. In this study, a series of 137 wheat relatives, including 53 Triticum monococcum L. (2n = 2x = 14, AA), 6 T. urartu Thumanjan ex Gandilyan (2n = 2x = 14, AA), 9 T. timopheevii Zhuk. (2n = 4x = 28, AAGG), 66 T. aestivum subsp. spelta (2n = 6x = 42, AABBDD), and 3 Aegilops speltoides (2n = 2x = 14, SS) were systematically evaluated for their powdery mildew resistance and composition of Pm genes. Out of 137 (60.58%) accessions, 83 were resistant to Bgt isolate E09 at the seedling stage, and 116 of 137 (84.67%) wheat relatives were resistant to the mixture of Bgt isolates at the adult stage. This indicates that these accessions show a high level of resistance to powdery mildew. Some 31 markers for 23 known Pm genes were used to test these 137 accessions, and, in the results, only Pm2, Pm4, Pm6, Pm58, and Pm68 were detected. Among them, three Pm4 alleles (Pm4a, Pm4b, and Pm4f) were identified in 4 T. subsp. spelta accessions. q-RT PCR further confirmed that Pm4 alleles played a role in disease resistance in these four accessions. The phylogenetic tree showed that the kinship of Pm4 was close to Pm24 and Sr62. This study not only provides reference information and valuable germplasm resources for breeding new wheat varieties with disease resistance but also lays a foundation for enriching the genetic basis of wheat resistance to powdery mildew.

Modality-Specific Information Disentanglement From Multi-Parametric MRI for Breast Tumor Segmentation and Computer-Aided Diagnosis.

Breast cancer is becoming a significant global health challenge, with millions of fatalities annually. Magnetic Resonance Imaging (MRI) can provide various sequences for characterizing tumor morphology and internal patterns, and becomes an effective tool for detection and diagnosis of breast tumors. However, previous deep-learning based tumor segmentation methods from multi-parametric MRI still have limitations in exploring inter-modality information and focusing task-informative modality/modalities. To address these shortcomings, we propose a Modality-Specific Information Disentanglement (MoSID) framework to extract both inter- and intra-modality attention maps as prior knowledge for guiding tumor segmentation. Specifically, by disentangling modality-specific information, the MoSID framework provides complementary clues for the segmentation task, by generating modality-specific attention maps to guide modality selection and inter-modality evaluation. Our experiments on two 3D breast datasets and one 2D prostate dataset demonstrate that the MoSID framework outperforms other state-of-the-art multi-modality segmentation methods, even in the cases of missing modalities. Based on the segmented lesions, we further train a classifier to predict the patients' response to radiotherapy. The prediction accuracy is comparable to the case of using manually-segmented tumors for treatment outcome prediction, indicating the robustness and effectiveness of the proposed segmentation method. The code is available at https://github.com/Qianqian-Chen/MoSID.

Beam-wise dose composition learning for head and neck cancer dose prediction in radiotherapy.

Automatic and accurate dose distribution prediction plays an important role in radiotherapy plan. Although previous methods can provide promising performance, most methods did not consider beam-shaped radiation of treatment delivery in clinical practice. This leads to inaccurate prediction, especially on beam paths. To solve this problem, we propose a beam-wise dose composition learning (BDCL) method for dose prediction in the context of head and neck (H&N) radiotherapy plan. Specifically, a global dose network is first utilized to predict coarse dose values in the whole-image space. Then, we propose to generate individual beam masks to decompose the coarse dose distribution into multiple field doses, called beam voters, which are further refined by a subsequent beam dose network and reassembled to form the final dose distribution. In particular, we design an overlap consistency module to keep the similarity of high-level features in overlapping regions between different beam voters. To make the predicted dose distribution more consistent with the real radiotherapy plan, we also propose a dose-volume histogram (DVH) calibration process to facilitate feature learning in some clinically concerned regions. We further apply an edge enhancement procedure to enhance the learning of the extracted feature from the dose falloff regions. Experimental results on a public H&N cancer dataset from the AAPM OpenKBP challenge show that our method achieves superior performance over other state-of-the-art approaches by significant margins. Source code is released at https://github.com/TL9792/BDCLDosePrediction.

Uncertainty-aware refinement framework for ovarian tumor segmentation in CECT volume.

BACKGROUND: Ovarian cancer is a highly lethal gynecological disease. Accurate and automated segmentation of ovarian tumors in contrast-enhanced computed tomography (CECT) images is crucial in the radiotherapy treatment of ovarian cancer, enabling radiologists to evaluate cancer progression and develop timely therapeutic plans. However, automatic ovarian tumor segmentation is challenging due to factors such as inhomogeneous background, ambiguous tumor boundaries, and imbalanced foreground-background, all of which contribute to high predictive uncertainty for a segmentation model. PURPOSE: To tackle these challenges, we propose an uncertainty-aware refinement framework that aims to estimate and refine regions with high predictive uncertainty for accurate ovarian tumor segmentation in CECT images. METHODS: To this end, we first employ an approximate Bayesian network to detect coarse regions of interest (ROIs) of both ovarian tumors and uncertain regions. These ROIs allow a subsequent segmentation network to narrow down the search area for tumors and prioritize uncertain regions, resulting in precise segmentation of ovarian tumors. Meanwhile, the framework integrates two guidance modules that learn two implicit functions capable of mapping query features sampled according to their uncertainty to organ or boundary manifolds, guiding the segmentation network to facilitate information encoding of uncertain regions. RESULTS: Firstly, 367 CECT images are collected from the same hospital for experiments. Dice score, Jaccard, Recall, Positive predictive value (PPV), 95% Hausdorff distance (HD95) and Average symmetric surface distance (ASSD) for the testing group of 77 cases are 86.31%, 73.93%, 83.95%, 86.03%, 15.17  mm and 2.57  mm, all of which are significantly better than that of the other state-of-the-art models. And results of visual comparison shows that the compared methods have more mis-segmentation than our method. Furthermore, our method achieves a Dice score that is at least 20% higher than the Dice scores of other compared methods when tumor volumes are less than 20 cm 3 $^3$ , indicating better recognition ability to small regions by our method. And then, 38 CECT images are collected from another hospital to form an external testing group. Our approach consistently outperform the compared methods significantly, with the external testing group exhibiting substantial improvements across key evaluation metrics: Dice score (83.74%), Jaccard (69.55%), Recall (82.12%), PPV (81.61%), HD95 (12.31 mm), and ASSD (2.32 mm), robustly establishing its superior performance. CONCLUSIONS: Experimental results demonstrate that the framework significantly outperforms the compared state-of-the-art methods, with decreased under- or over-segmentation and better small tumor identification. It has the potential for clinical application.

S-Wave Accelerates Optimization-based Photoacoustic Image Reconstruction in vivo.

OBJECTIVE: Photoacoustic imaging has undergone rapid development in recent years. To simulate photoacoustic imaging on a computer, the most popular MATLAB toolbox currently used for the forward projection process is k-Wave. However, k-Wave suffers from significant computation time. Here we propose a straightforward simulation approach based on superposed Wave (s-Wave) to accelerate photoacoustic simulation. METHODS: In this study, we consider the initial pressure distribution as a collection of individual pixels. By obtaining standard sensor data from a single pixel beforehand, we can easily manipulate the phase and amplitude of the sensor data for specific pixels using loop and multiplication operators. The effectiveness of this approach is validated through an optimization-based reconstruction algorithm. RESULTS: The results reveal significantly reduced computation time compared with k-Wave. Particularly in a sparse 3-D configuration, s-Wave exhibits a speed improvement >2000 times compared with k-Wave. In terms of optimization-based image reconstruction, in vivo imaging results reveal that using the s-Wave method yields images highly similar to those obtained using k-Wave, while reducing the reconstruction time by approximately 50 times. CONCLUSION: Proposed here is an accelerated optimization-based algorithm for photoacoustic image reconstruction, using the fast s-Wave forward projection simulation. Our method achieves substantial time savings, particularly in sparse system configurations. Future work will focus on further optimizing the algorithm and expanding its applicability to a broader range of photoacoustic imaging scenarios.

VP-SOM: View-Planning Method for Indoor Active Sparse Object Mapping Based on Information Abundance and Observation Continuity.

Active mapping is an important technique for mobile robots to autonomously explore and recognize indoor environments. View planning, as the core of active mapping, determines the quality of the map and the efficiency of exploration. However, most current view-planning methods focus on low-level geometric information like point clouds and neglect the indoor objects that are important for human-robot interaction. We propose a novel View-Planning method for indoor active Sparse Object Mapping (VP-SOM). VP-SOM takes into account for the first time the properties of object clusters in the coexisting human-robot environment. We categorized the views into global views and local views based on the object cluster, to balance the efficiency of exploration and the mapping accuracy. We developed a new view-evaluation function based on objects' information abundance and observation continuity, to select the Next-Best View (NBV). Especially for calculating the uncertainty of the sparse object model, we built the object surface occupancy probability map. Our experimental results demonstrated that our view-planning method can explore the indoor environments and build object maps more accurately, efficiently, and robustly.

Dual-mode sensing chip for photoelectrochemical and electrochromic visual determination of deoxynivalenol mycotoxin.

The successful development of a dual-mode sensing chip for deoxynivalenol (DON) detection using photoelectrochemical (PEC) and electrochromic visualization techniques is reported. By laser etching technology, different functional areas, including the photoanode, the cathode, and the electrochromic area, are fabricated on indium tin oxide (ITO) glass. Then, these three areas are further respectively modified with PEC active materials, platinum nanoparticles, and Prussian blue. Under light illumination, photocurrents generate between the photoanode and the cathode due to the separation of photo-induced electrons and holes in the TiO2/3DNGH material. Meanwhile, the photo-induced electrons are transferred to Prussian blue on the visualization area, which will be reduced to colorless Prussian white. The binding of DON molecules and aptamers can promote electron transfer and reduce the recombination of electrons and holes, allowing for simultaneous quantitative detection of DON using either the photocurrent or color change. The sensor chip has a broad detection range of DON concentrations of 1 fg⋅mL-1 to 100 pg⋅mL-1 in the PEC mode with the limit of detection of 0.37 fg⋅mL-1, and 1 to 250 ng⋅mL-1 in the visualization mode with the limit of detection of 0.51 ng⋅mL-1. This portable dual-mode sensor chip can be used in both laboratory and field settings without the need for specialized instruments, making it a powerful tool for ensuring food safety. At the same time, the analysis of the standard addition method of the actual sample by using the sensor chip shows that, in the PEC mode, the recoveries of the dual-mode aptasensor chip were 91.3 to 99.0% with RSD values of 1.73~2.55%, and in visualization mode, the recoveries of the dual-mode aptasensor chip were 99.2 to 102.0% with RSD values of 1.00~6.21%, which indicate good accuracy and reproducibility.

A double-quenching paperclip ECL biosensing platform for ultrasensitive detection of antibiotic resistance genes (mecA) based on Ti3C2 MXene-Au NPs as a coreactant accelerator.

The global spread of environmental biological pollutants, such as antibiotic-resistant bacteria and their antibiotic resistance genes (ARGs), has emerged as a critical public health concern. It is imperative to address this pressing issue due to its potential implications for public health. Herein, a DNA paperclip probe with double-quenching function of target cyclic cleavage was proposed, and an electrochemiluminescence (ECL) biosensing platform was constructed using Ti3C2 MXene in-situ reduction growth of Au NPs (TCM-Au) as a coreactant accelerator, and applied to the sensitive detection of ARGs. Thanks to the excellent catalytic performance, large surface area and Au-S affinity of TCM-Au, the ECL performance of CdS QDs have been significantly improved. By cleverly utilizing the negative charge of the paperclip nucleic acid probe and its modification group, double-quenching of the ECL signal was achieved. This innovative approach, combined with target cyclic amplification, facilitated specific and sensitive detection of the mecA gene. This biosensing platform manifested highly selective and sensitive determination of mecA genes in the range of 10 fM to 100 nM and a low detection limit of 2.7 fM. The credible detectability and anti-interference were demonstrated in Yangtze river and Aeration tank outlet, indicating its promising application toward pollution monitoring of ARGs.

Recent advancements in artificial intelligence for breast cancer: Image augmentation, segmentation, diagnosis, and prognosis approaches.

Breast cancer is a significant global health burden, with increasing morbidity and mortality worldwide. Early screening and accurate diagnosis are crucial for improving prognosis. Radiographic imaging modalities such as digital mammography (DM), digital breast tomosynthesis (DBT), magnetic resonance imaging (MRI), ultrasound (US), and nuclear medicine techniques, are commonly used for breast cancer assessment. And histopathology (HP) serves as the gold standard for confirming malignancy. Artificial intelligence (AI) technologies show great potential for quantitative representation of medical images to effectively assist in segmentation, diagnosis, and prognosis of breast cancer. In this review, we overview the recent advancements of AI technologies for breast cancer, including 1) improving image quality by data augmentation, 2) fast detection and segmentation of breast lesions and diagnosis of malignancy, 3) biological characterization of the cancer such as staging and subtyping by AI-based classification technologies, 4) prediction of clinical outcomes such as metastasis, treatment response, and survival by integrating multi-omics data. Then, we then summarize large-scale databases available to help train robust, generalizable, and reproducible deep learning models. Furthermore, we conclude the challenges faced by AI in real-world applications, including data curating, model interpretability, and practice regulations. Besides, we expect that clinical implementation of AI will provide important guidance for the patient-tailored management.

Breast Fibroglandular Tissue Segmentation for Automated BPE Quantification With Iterative Cycle-Consistent Semi-Supervised Learning.

Background Parenchymal Enhancement (BPE) quantification in Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) plays a pivotal role in clinical breast cancer diagnosis and prognosis. However, the emerging deep learning-based breast fibroglandular tissue segmentation, a crucial step in automated BPE quantification, often suffers from limited training samples with accurate annotations. To address this challenge, we propose a novel iterative cycle-consistent semi-supervised framework to leverage segmentation performance by using a large amount of paired pre-/post-contrast images without annotations. Specifically, we design the reconstruction network, cascaded with the segmentation network, to learn a mapping from the pre-contrast images and segmentation predictions to the post-contrast images. Thus, we can implicitly use the reconstruction task to explore the inter-relationship between these two-phase images, which in return guides the segmentation task. Moreover, the reconstructed post-contrast images across multiple auto-context modeling-based iterations can be viewed as new augmentations, facilitating cycle-consistent constraints across each segmentation output. Extensive experiments on two datasets with various data distributions show great segmentation and BPE quantification accuracy compared with other state-of-the-art semi-supervised methods. Importantly, our method achieves 11.80 times of quantification accuracy improvement along with 10 times faster, compared with clinical physicians, demonstrating its potential for automated BPE quantification. The code is available at https://github.com/ZhangJD-ong/Iterative-Cycle-consistent-Semi-supervised-Learning-for-fibroglandular-tissue-segmentation.