Head and neck tumor segmentation from [18F]F-FDG PET/CT images based on 3D diffusion model.

Objective.Head and neck (H&N) cancers are among the most prevalent types of cancer worldwide, and [18F]F-FDG PET/CT is widely used for H&N cancer management. Recently, the diffusion model has demonstrated remarkable performance in various image-generation tasks. In this work, we proposed a 3D diffusion model to accurately perform H&N tumor segmentation from 3D PET and CT volumes.Approach.The 3D diffusion model was developed considering the 3D nature of PET and CT images acquired. During the reverse process, the model utilized a 3D UNet structure and took the concatenation of 3D PET, CT, and Gaussian noise volumes as the network input to generate the tumor mask. Experiments based on the HECKTOR challenge dataset were conducted to evaluate the effectiveness of the proposed diffusion model. Several state-of-the-art techniques based on U-Net and Transformer structures were adopted as the reference methods. Benefits of employing both PET and CT as the network input, as well as further extending the diffusion model from 2D to 3D, were investigated based on various quantitative metrics and qualitative results.Main results.Results showed that the proposed 3D diffusion model could generate more accurate segmentation results compared with other methods (mean Dice of 0.739 compared to less than 0.726 for other methods). Compared to the diffusion model in 2D form, the proposed 3D model yielded superior results (mean Dice of 0.739 compared to 0.669). Our experiments also highlighted the advantage of utilizing dual-modality PET and CT data over only single-modality data for H&N tumor segmentation (with mean Dice less than 0.570).Significance.This work demonstrated the effectiveness of the proposed 3D diffusion model in generating more accurate H&N tumor segmentation masks compared to the other reference methods.

Head and Neck Tumor Segmentation from [18F]F-FDG PET/CT Images Based on 3D Diffusion Model.

Head and neck (H&N) cancers are among the most prevalent types of cancer worldwide, and [18F]F-FDG PET/CT is widely used for H&N cancer management. Recently, the diffusion model has demonstrated remarkable performance in various image-generation tasks. In this work, we proposed a 3D diffusion model to accurately perform H&N tumor segmentation from 3D PET and CT volumes. The 3D diffusion model was developed considering the 3D nature of PET and CT images acquired. During the reverse process, the model utilized a 3D UNet structure and took the concatenation of PET, CT, and Gaussian noise volumes as the network input to generate the tumor mask. Experiments based on the HECKTOR challenge dataset were conducted to evaluate the effectiveness of the proposed diffusion model. Several state-of-the-art techniques based on U-Net and Transformer structures were adopted as the reference methods. Benefits of employing both PET and CT as the network input as well as further extending the diffusion model from 2D to 3D were investigated based on various quantitative metrics and the uncertainty maps generated. Results showed that the proposed 3D diffusion model could generate more accurate segmentation results compared with other methods. Compared to the diffusion model in 2D format, the proposed 3D model yielded superior results. Our experiments also highlighted the advantage of utilizing dual-modality PET and CT data over only single-modality data for H&N tumor segmentation.

A label free fluorescent aptamer sensor based on the combined action of Graphene oxide and SYBR Green I for the detection of Aflatoxin B1.

Here, based on the characteristics of Graphene oxide(GO) and SYBR Green I(SGI) dye, an enzyme-free and label-free fluorescent biosensor with signal amplification through DNA strand reaction is proposed for the detection of Aflatoxin B1(AFB1) in food safety. Firstly, without the addition of AFB1, the substrate in the system includes a double stranded Apt-S with a long sticky end and two hairpins H1 and H2. Although the complementary pairing of bases may exhibit fluorescence due to the insertion of SGI dyes, the use of GO, which is highly capable of adsorbing single stranded parts and quenching fluorescence, cleverly reduces the background fluorescence. Adding the target AFB1 triggers DNA inter chain reactions, generating a large amount of long double stranded DNA H1-H2, thereby generating strong fluorescence signals under the action of SGI. More importantly, logical theory verification and computer simulation were conducted before biological experiments, providing a theoretical basis for the implementation of the biosensor. After analysis, the fluorescence biosensor exhibits a good linear relationship with AFB1 concentration in the range of 5-50nM, with a detection limit of 0.76nM. It also has good specificity, anti-interference ability, and practical application ability, and has broad application prospects in the field of food safety.

A enzyme-free fluorescence quenching sensor for amplified detection of kanamycin in milk based on competitive triggering strategies.

In this work, we constructed a FAM fluorescence quenching biosensor based on an aptamer competition recognition and enzyme-free amplification strategy. We design a competing unit consisting of an aptamer chain and a complementary chain, and a catalytic hairpin self-assembly (CHA) unit consisting of two hairpins in which the complementary chain can trigger the catalytic hairpin self-assembly. In the initial state, the aptamer chain is combined with the complementary chain, the catalytic hairpin self-assembly unit is inhibited, the FAM fluorescence group was far away from the BHQ1 quenching group, and the fluorescence is turn-on. In the presence of kanamycin, the aptamer chain recognizes kanamycin and doesn't form double chains, resulting in the free complementary chain triggering hairpin 1 (H1), and then H1 triggering hairpin 2 (H2), FAM fluorophore is close to the BHQ1 quenching group, and the fluorescence is off-on. When H1 and H2 form a cyclic reaction, enzyme-free amplification is achieved and there is significant output of the fluorescence signal. Therefore, the biosensor has good performance in detecting kanamycin, the detection line is 54 nM, the linear range is 54 nM-0.9 µM, and it can achieve highly selective detection of kanamycin. Kanamycin residue may cause serious harm to human health. The high sensitivity detection of kanamycin is urgent, so this project has a great application potential for food detection.