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PurposeTo develop a method of biologically guided deep learning for post-radiation 18FDG-PET image outcome prediction based on pre-radiation images and radiotherapy dose information.MethodsBased on the classic reaction-diffusion mechanism, a novel biological model was proposed using a partial differential equation that incorporates spatial radiation dose distribution as a patient-specific treatment information variable. A 7-layer encoder-decoder-based convolutional neural network (CNN) was designed and trained to learn the proposed biological model. As such, the model could generate post-radiation 18FDG-PET image outcome predictions with breakdown biological components for enhanced explainability. The proposed method was developed using 64 oropharyngeal patients with paired 18FDG-PET studies before and after 20-Gy delivery (2 Gy/day fraction) by intensity-modulated radiotherapy (IMRT). In a two-branch deep learning execution, the proposed CNN learns specific terms in the biological model from paired 18FDG-PET images and spatial dose distribution in one branch, and the biological model generates post-20-Gy 18FDG-PET image prediction in the other branch. As in 2D execution, 718/233/230 axial slices from 38/13/13 patients were used for training/validation/independent test. The prediction image results in test cases were compared with the ground-truth results quantitatively.ResultsThe proposed method successfully generated post-20-Gy 18FDG-PET image outcome prediction with breakdown illustrations of biological model components. Standardized uptake value (SUV) mean values in 18FDG high-uptake regions of predicted images (2.45 ± 0.25) were similar to ground-truth results (2.51 ± 0.33). In 2D-based Gamma analysis, the median/mean Gamma Index (

Background: Over the last decade, studies have shown an increased incidence of colorectal cancer (CRC), particularly early onset colorectal cancer (EOCRC). Researchers have demonstrated that dietary behavior, especially among young adults, influences alterations in the gut microbial community, leading to an increased accumulation of pathogenic gut microbiota and a decrease in beneficial ones. Unfortunately, CRC is likely to be diagnosed at a late stage, increasing CRC-related mortality. However, this alteration in the gut microbiota (gut dysbiosis) can be harnessed as a biomarker for non-invasive diagnosis, prognosis, prevention, and treatment of CRC in an effort to prevent late diagnosis and poor prognosis associated with CRC. Aim of review: This review discusses identification of potential biomarkers by targeting diet-mediated gut dysbiosis for the stage-specific diagnosis, prognosis, treatment, and prevention of CRC. Our findings provide a comprehensive insight into the potential of protumorigenic bacteria (e.g. pathogenic Escherichia coli, enterotoxigenic Bacteroides fragilis and Fusobacterium nucleatum) and their metabolites (e.g., colibactin and B. fragilis toxin) from gut dysbiosis as biomarkers for the diagnosis of CRC. Key scientific concepts of review: Collectively, a detailed understanding of the available data from current studies suggests that, further research on quantification of metabolites and stage-specific pathogenic microbial abundance is required for the diagnosis and treatment of CRC based on microbial dysbiosis. Specifically, future studies on faecal samples, from patient with CRC, should be conducted for F. nucleatum among different opportunistic bacteria, given its repeated occurrence in faecal samples and CRC biopsies in numerous studies. Finally, we discuss the potential of faecal microbial transplantation (FMT) as an intervention to restore damaged gut microbiota during CRC treatment and management.

Pseudomonas aeruginosa is pathogenic in immunocompromised individuals. It has several complex mechanisms for evading human immunity. The objective of the study was to examine the secretory immunoglobulin A (sIgA) mediated immune response in saliva to detect P. aeruginosa in pulmonary tuberculosis.

To prevent in a short time the rapid increase of tourists and corresponding traffic restriction measures’ lack in scenic areas, this study established a prediction model based on an improved convolutional neural network (CNN) and long- and short-term memory (LSTM) combined neural network. The study used this to predict the inflow and outflow of tourists in scenic areas. The model uses a residual unit, batch normalization, and principal component analysis to improve the CNN. The experimental results show that the model works best when batches’ quantity is 10, neurons’ quantity in the LSTM layer is 50, and the number of iterations is 50 on a workday; on non-working days, it is best to choose 10, 100, or 50. Using root mean square error (RMSE), normalized root mean square error (NRMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) as evaluation indicators, the inflow and outflow RMSEs of this study model are 82.51 and 89.80, MAEs are 26.92 and 30.91, NRMSEs are 3.99 and 3.94, and MAPEs are 1.55 and 1.53. Among the various models, this research model possesses the best prediction function. This provides a more accurate prediction method for the prediction of visitors’ flow rate in scenic spots. Meanwhile, the research model is also conducive to making corresponding flow-limiting measures to protect the ecology of the scenic area.

Limited evidence is available regarding the impact of ambient inhalable particulate matter (PM) on mental disorder (MD) or dementia-related deaths, particularly PM1, PM1–2.5, and coarse particles (PM2.5–10). Moreover, individual confounders have rarely been considered. In addition, evidence from low-pollution areas is needed but is inadequate. Using death records from the Death Registration System during 2015–2021 in Ningde, a coastal city in southeast China, we combined a conditional quasi-Poisson model with a distributed lag nonlinear model to estimate the nonlinear and lagged associations of PM exposure with MD or dementia-related deaths in Ningde, China, comprehensively controlling for individual time-invariant confounders using a time-stratified case-crossover design. The attributable fraction and number were calculated to quantify the burden of MD or dementia-related deaths that were related to PMs. We found J-shaped relationships between MD or dementia-related deaths and PMs, with different thresholds of 13, 9, 19, 33 and 12 μg/m3 for PM1, PM1–2.5, PM2.5, PM10 and PM2.5–10. An inter-quartile range increase for PM1, PM1–2.5, PM2.5, PM10 and PM2.5–10 above the thresholds led to an increase of 31.8% (95% confidence interval, 14.3–51.9%), 53.7% (22.4–93.1%), 32.6% (15.0–53.0%), 35.1% (17.7–55.0%) and 25.9% (13.0–40.3%) in MD-related deaths at lag 0–3 days, respectively. The associations were significant in the cool season rather than in the warm season and were significantly greater among people aged 75–84 years than in others. The fractions of MD-related deaths attributable to PM1, PM1–2.5, PM2.5, PM10 and PM2.5–10 were 5.55%, 6.49%, 7.68%, 10.66%, and 15.11%, respectively; however, only some of them could be protected by the concentrations recommended by the World Health Organisation or China grade I standard. Smaller associations and similar patterns were observed between PMs and dementia-related death. These findings suggest stricter standards, and provide evidence for the development of relevant policies and measures.

Abstract Background Non-invasive risk stratification contributes to the precise treatment of prostate cancer (PCa). In previous studies, lymphocyte subsets were used to differentiate between low-/intermediate-risk and high-risk PCa, with limited clinical value and poor interpretability. Based on functional subsets of peripheral lymphocyte with the largest sample size to date, this study aims to construct an easy-to-use and robust nomogram to guide the tripartite risk stratifications for PCa. Methods We retrospectively collected data from 2039 PCa and benign prostate disease (BPD) patients with 42 clinical characteristics on functional subsets of peripheral lymphocyte. After quality control and feature selection, clinical data with the optimal feature subset were utilized for the 10-fold cross-validation of five Machine Learning (ML) models for the task of predicting low-, intermediate- and high-risk stratification of PCa. Then, a novel clinic-ML nomogram was constructed using probabilistic predictions of the trained ML models via the combination of a multivariable Ordinal Logistic Regression analysis and the proposed feature mapping algorithm. Results 197 PCa patients, including 56 BPD, were enrolled in the study. An optimal subset with nine clinical features was selected. Compared with the best ML model and the clinic nomogram, the clinic-ML nomogram achieved the superior performance with a sensitivity of 0.713 (95% CI 0.573–0.853), specificity of 0.869 (95% CI 0.764–0.974), F1 of 0.699 (95% CI 0.557–0.841), and AUC of 0.864 (95% CI 0.794–0.935). The calibration curve and Decision Curve Analysis (DCA) indicated the predictive capacity and net benefits of the clinic-ML nomogram were improved. Conclusion Combining the interpretability and simplicity of a nomogram with the efficacy and robustness of ML models, the proposed clinic-ML nomogram can serve as an insight tool for preoperative assessment of PCa risk stratifications, and could provide essential information for the individual diagnosis and treatment in PCa patients.

Abstract A two-dimensional phononic crystal sensor model with high-quality factor and excellent sensitivity for sensing acetone solutions and operating at 25–45 kHz is proposed. The model for filling solution cavities is based on reference designs of quasi-crystal and gradient cavity structures. The transmission spectrum of sensor is simulated by the finite element method. High-quality factor of 45,793.06 and sensitivity of 80,166.67 Hz are obtained for the acetone concentration with 1–9.1%, and quality factor of 61,438.09 and sensitivity of 24,400.00 Hz are obtained for the acetone concentration range of 10–100%, which indicated the sensor could still achieve high sensitivity and quality factor at operating frequencies from 25 to 45 kHz. To verify the application of the sensor to sensing other solutions, the sensitivity for sound velocity and density is calculated as 24.61 m-1 and 0.7764 m3/(kg × s), respectively. It indicates the sensor is sensitive to acoustic impedance changes of the solution and equally suitable for sensing other solutions. The simulation results reveal the phononic crystal sensor possessed high-performance in composition capture in pharmaceutical production and petrochemical industry, which can provide theoretical reference for the design of new biochemical sensors for reliable detection of solution concentration.

Abstract Tissues are highly complicated with spatial heterogeneity in gene expression. However, the cutting-edge single-cell RNA-seq technology eliminates the spatial information of individual cells, which contributes to the characterization of cell identities. Herein, we propose single-cell spatial position associated co-embeddings (scSpace), an integrative method to identify spatially variable cell subpopulations by reconstructing cells onto a pseudo-space with spatial transcriptome references (Visium, STARmap, Slide-seq, etc.). We benchmark scSpace with both simulated and biological datasets, and demonstrate that scSpace can accurately and robustly identify spatially variated cell subpopulations. When employed to reconstruct the spatial architectures of complex tissue such as the brain cortex, the small intestinal villus, the liver lobule, the kidney, the embryonic heart, and others, scSpace shows promising performance on revealing the pairwise cellular spatial association within single-cell data. The application of scSpace in melanoma and COVID-19 exhibits a broad prospect in the discovery of spatial therapeutic markers.

为绿色高效制备生物柴油，利用L-天冬氨酸离子液体（\[Asp\]HSO4）联合猪胰脂肪酶催化油酸制备油酸甲酯，采用单因素实验探究了反应时间、醇酸物质的量比、\[Asp\]HSO4用量、猪胰脂肪酶用量和反应温度对转化率的影响，在此基础上，采用正交实验进行优化，得到\[Asp\]HSO4联合猪胰脂肪酶催化油酸酯化反应的最优工艺条件为\[Asp\]HSO4用量6%、醇酸物质的量比5.5∶ 1、猪胰脂肪酶用量4%、反应时间21 h、反应温度45 ℃，在此条件下转化率可达91.91%。\[Asp\]HSO4可降低甲醇和温度对猪胰脂肪酶催化活性的影响，对猪胰脂肪酶催化油酸酯化具有协同效应。 In order to prepare biodiesel greenly and efficiently，L-Aspartic acid ionic liquid (\[Asp\]HSO4) combined with porcine pancreatic lipase was used to catalyze the esterification of oleic acid to prepare methyl oleate. The effects of reaction time, molar ratio of methanol to oleic acid, amount of \[Asp\]HSO4, amount of porcine pancreatic lipase, and the reaction temperature on the conversion rate were investigated by single factor experiment.Then the esterification process conditions were optimized by orthogonal experiment as follows: amount of \[Asp\]HSO4 6%, molar ratio of methanol to oleic acid 5.5∶ 1, amount of porcine pancreatic lipase 4%, reaction time 21 h, and reaction temperature 45 ℃. The conversion rate could reach 91.91% under the optimal conditions.\[Asp\]HSO4can reduce the effects of methanol and temperature on the catalytic activity of porcine pancreatic lipase,and has synergistic effect on the esterification of oleic acid catalyzed by porcine pancreatic lipase.

Inflammatory bowel disease (IBD) is an inflammatory condition affecting the colon and small intestine, with Crohn’s disease and ulcerative colitis being the major types. Individuals with long-term IBD are at an increased risk of developing colorectal cancer. Early growth response protein 1 (Egr1) is a nuclear protein that functions as a transcriptional regulator. Egr1 is known to control the expression of numerous genes and play a role in cell growth, proliferation, and differentiation. While IBD has been associated with severe inflammation, the precise mechanisms underlying its pathogenesis remain unclear. This study aimed to investigate the role of Egr1 in the development of IBD. High levels of Egr1 expression were observed in a mouse model of colitis induced by dextran sulfate sodium (DSS), as determined by immunohistochemical (IHC) staining. Chronic DSS treatment showed that Egr1 knockout (KO) mice exhibited resistance to the development of IBD, as determined by changes in their body weight and disease scores. Additionally, enzyme-linked immunosorbent assay (ELISA) and IHC staining demonstrated decreased expression levels of proinflammatory cytokines such as IL-1β, IL-6, and TNF-α, as well as matrix metalloproteinase 12 (MMP12). Putative Egr1 binding sites were identified within the MMP12 promoter region. Through reporter assays and chromatin immunoprecipitation (ChIP) analysis, it was shown that Egr1 binds to the MMP12 promoter and regulates MMP12 expression. In conclusion, we found that Egr1 plays a role in the inflammation process of IBD through transcriptionally activating MMP12.