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Introduction This retrospective study evaluated the imaging dose of 4D-CBCT in patients treated with thoracic radiotherapy. Methods A geometric model of a 4D-CBCT system was built for Monte Carlo (MC) calculations. Percentage depth dose (PDD) and profiles of the system were measured and compared with MC calculations to verify the model. Eight lung cancer patients were retrospectively selected for 4D-CBCT imaging dose assessment. For each patient, the imaging dose was calculated using the CBCT log file, simulating CT and the MC model. Organ at risk (OAR) dose parameters from the imaging dose were calculated, and the effect of the imaging dose on OAR dose parameters when the imaging dose was added to the treatment dose was analyzed. Results Deviations between MC calculations and measurements for PDD and profiles were within 1.1% and 5%, respectively. For a pre-treatment 4D-CBCT scan, the mean lung and heart doses were 9.5 ± 1.4 mGy (mean value ± standard deviation, hereafter the same) and 9.7 ± 1.4 mGy, and the maximum spinal cord, esophagus and ribs doses were 17.6 ± 4.2 mGy, 17.1 ± 3.3 mGy and 37.8 ± 5.3 mGy, respectively. For an in-treatment 4D-CBCT scan, the dose parameters were 5.6 ± 1.8 mGy, 5.6 ± 1.5 mGy, 10.3 ± 3.5 mGy, 10.3 ± 4.3 mGy and 22.4 ± 5.3 mGy, respectively. The OAR dose parameters for in-treatment 4D-CBCT scans showed a relatively strong linear relationship with beam MU and number of frames. For patients receiving 5-fraction treatments and one pre-treatment and one in-treatment 4D-CBCT for each fraction, the increases in lung V20, lung V5, mean lung and heart doses, maximum spinal cord, esophagus and ribs doses were 0.04 ± 0.03%, 0.29 ± 0.23%, 76.3 ± 14.4 mGy, 76.5 ± 12.0 mGy, 76.3 ± 20.2 mGy, 70.5 ± 13.1 mGy and 101.2 ± 20.5 mGy, respectively. The increase in OAR dose parameters was proportional to the number of fractions. Conclusion The imaging dose of pre-treatment and in-treatment 4D-CBCT was calculated using a validated MC model. For 5-fraction treatments, the imaging dose will likely have minimal clinical impact on OAR dose parameters.

Commensal bacteria generate immensely diverse active metabolites to maintain gut homeostasis, however their fundamental role in establishing an immunotolerogenic microenvironment in the intestinal tract remains obscure. Here, we demonstrate that an understudied murine commensal bacterium, Dubosiella newyorkensis , and its human homologue Clostridium innocuum , have a probiotic immunomodulatory effect on dextran sulfate sodium-induced colitis using conventional, antibiotic-treated and germ-free mouse models. We identify an important role for the D. newyorkensis in rebalancing Treg/Th17 responses and ameliorating mucosal barrier injury by producing short-chain fatty acids, especially propionate and L-Lysine (Lys). We further show that Lys induces the immune tolerance ability of dendritic cells (DCs) by enhancing Trp catabolism towards the kynurenine (Kyn) pathway through activation of the metabolic enzyme indoleamine-2,3-dioxygenase 1 (IDO1) in an aryl hydrocarbon receptor (AhR)-dependent manner. This study identifies a previously unrecognized metabolic communication by which Lys-producing commensal bacteria exert their immunoregulatory capacity to establish a Treg-mediated immunosuppressive microenvironment by activating AhR-IDO1-Kyn metabolic circuitry in DCs. This metabolic circuit represents a potential therapeutic target for the treatment of inflammatory bowel diseases. Here, Zhang et al . identify a metabolic axis by which Lys-producing commensal bacterium Dubosiella newyorkensis mediates a Treg-mediated immunosuppressive microenvironment by activating AhR-IDO1-Kyn metabolic circuitry in dendritic cells.

Images, as a crucial information carrier in the era of big data, are constantly generated, stored, and transmitted. Determining how to guarantee the security of images is a hot topic in the information security community. Image encryption is a simple and direct approach for this purpose. In order to cope with this issue, we propose a novel scheme based on eight-base DNA-level permutation and diffusion, termed as EDPD, for color image encryption in this paper. The proposed EDPD integrates secure hash algorithm-512 (SHA-512), a four-dimensional hyperchaotic system, and eight-base DNA-level permutation and diffusion that conducts on one-dimensional sequences and three-dimensional cubes. To be more specific, the EDPD has four main stages. First, four initial values for the proposed chaotic system are generated from plaintext color images using SHA-512, and a four-dimensional hyperchaotic system is constructed using the initial values and control parameters. Second, a hyperchaotic sequence is generated from the four-dimensional hyperchaotic system for consequent encryption operations. Third, multiple permutation and diffusion operations are conducted on different dimensions with dynamic eight-base DNA-level encoding and algebraic operation rules determined via the hyperchaotic sequence. Finally, DNA decoding is performed in order to obtain the cipher images. Experimental results from some common testing images verify that the EDPD has excellent performance in color image encryption and can resist various attacks.

As a novel natural compound delivery system, liposomes are capable of incorporating lipophilic bioactive compounds with enhanced compound solubility, stability and bioavailability, and have been successfully translated into real-time clinical applications. To construct the soy phosphatidylcholine (SPC)–cholesterol (Chol) liposome system, the optimal formulation was investigated as 3:1 of SPC to Chol, 10% mannosylerythritol lipid-A (MEL-A) and 1% betulinic acid. Results show that liposomes with or without betulinic acid or MEL-A are able to inhibit the proliferation of HepG2 cells with a dose-effect relation remarkably. In addition, the modification of MEL-A in liposomes can significantly promote cell apoptosis and strengthen the destruction of mitochondrial membrane potential in HepG2 cells. Liposomes containing MEL-A and betulinic acid have exhibited excellent anticancer activity, which provide factual basis for the development of MEL-A in the anti-cancer applications. These results provide a design thought to develop delivery liposome systems carrying betulinic acid with enhanced functional and pharmaceutical attributes.

With the widespread adoption of Internet of Things (IoT) technologies in healthcare systems, security issues related to user privacy during data transmission and sharing have become increasingly prominent. To address these challenges, this paper proposes a medical privacy protection and secure sharing scheme based on Quantum Key Distribution (QKD). The scheme integrates multiple technologies, including blockchain, smart contracts, zero-knowledge proofs, and Chebyshev chaotic mapping, to ensure secure data sharing and access control among multiple communication entities. Compared with existing solutions, our approach enhances key management security through quantum keys and improves communication resilience against attacks by leveraging chaotic systems. User identity privacy is protected via zero-knowledge proofs. Under the random oracle model, the security of the proposed scheme is formally proven. Moreover, comparative experiments with existing protocols demonstrate the scheme’s comprehensive advantages in terms of security and performance, evaluated across throughput, computational overhead, communication overhead, and storage overhead.

Oral cancer can occur in different parts of the mouth, including the lips, palate, gums, and inside the cheeks. If not treated in time, it can be life-threatening. Incidentally, using CAD-based diagnosis systems can be so helpful for early detection of this disease and curing it. In this study, a new deep learning-based methodology has been proposed for optimal oral cancer diagnosis from the images. In this method, after some preprocessing steps, a new deep belief network (DBN) has been proposed as the main part of the diagnosis system. The main contribution of the proposed DBN is its combination with a developed version of a metaheuristic technique, known as the Combined Group Teaching Optimization algorithm to provide an efficient system of diagnosis. The presented method is then implemented in the “Oral Cancer (Lips and Tongue) images dataset” and a comparison is done between the results and other methods, including ANN, Bayesian, CNN, GSO-NN, and End-to-End NN to show the efficacy of the techniques. The results showed that the DBN-CGTO method achieved a precision rate of 97.71%, sensitivity rate of 92.37%, the Matthews Correlation Coefficient of 94.65%, and 94.65% F1 score, which signifies its ability as the highest efficiency among the others to accurately classify positive samples while remaining the independent correct classification of negative samples.

Ghost imaging is a technique for indirectly reconstructing images by utilizing the second-order or higher-order correlation properties of the light field, which exhibits a robust ability to resist interference. On the premise of ensuring the quality of the image, effectively broadening the imaging range can improve the practicality of the technology. In this paper, a dual-path computational ghost imaging method based on convolutional neural networks is proposed. By using the dual-path detection structure, a wider range of target image information can be obtained, and the imaging range can be expanded. In this paper, for the first time, we try to use the two-channel probe as the input of the convolutional neural network and successfully reconstruct the target image. In addition, the network model incorporates a self-attention mechanism, which can dynamically adjust the network focus and further improve the reconstruction efficiency. Simulation results show that the method is effective. The method in this paper can effectively broaden the imaging range and provide a new idea for the practical application of ghost imaging technology.

In order to effectively reduce the total CO2 emissions of coal-fired power plants and reduce greenhouse gas emissions, the relevant data of a typical 600MW coal-fired power plant in the past five years was collected and investigated, and CO2 emissions and emission intensity were calculated. And the results were used to measure the CO2 emission level of coal-fired power plants. By comparing and analyzing the CO2 emission intensity and emission trend of 600MW coal-fired units with different unit types and different fuel types, the CO2 emission characteristics of typical 600MW coal-fired power plants are obtained.