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Summary: Background: Early prediction of lymph node status after neoadjuvant chemotherapy (NAC) facilitates promptly optimization of treatment strategies. This study aimed to develop and validate a deep learning network (DLN) using baseline computed tomography images to predict lymph node metastasis (LNM) after NAC in patients with locally advanced gastric cancer (LAGC). Methods: A total of 1205 LAGC patients were retrospectively recruited from three hospitals between January 2013 and March 2023, constituting a training cohort, an internal validation cohort, and two external validation cohorts. A transformer-based DLN was developed using 3D tumor images to predict LNM after NAC. A clinical model was constructed through multivariate logistic regression analysis as a baseline for subsequent comparisons. The performance of the models was evaluated through discrimination, calibration, and clinical applicability. Furthermore, Kaplan–Meier survival analysis was conducted to assess overall survival (OS) of LAGC patients at two follow-up centers. Findings: The DLN outperformed the clinical model and demonstrated a robust performance for predicting LNM in the training and validation cohorts, with areas under the curve (AUCs) of 0.804 (95% confidence interval [CI], 0.752–0.849), 0.748 (95% CI, 0.660–0.830), 0.788 (95% CI, 0.735–0.835), and 0.766 (95% CI, 0.717–0.814), respectively. Decision curve analysis exhibited a high net clinical benefit of the DLN. Moreover, the DLN was significantly associated with the OS of LAGC patients [Center 1: hazard ratio (HR), 1.789, P

BackgroundPancreatic hamartoma, a rare benign non-neoplastic condition, presents challenges in differentiating from other pancreatic diseases due to its atypical imaging and unreliable biopsy results. In this study, we present a case of pancreatic hamartoma and conduct a comprehensive review of relevant literature to outline its characteristic features, aiming to underscore its clinical relevance and implications.Case presentationA 63-year-old man presented with a pancreatic mass, discovered during evaluation of abdominal pain and distension. Laboratory tests were largely unremarkable. Ultrasound revealed a hypoechoic mass in the head of the pancreas. Subsequent computed tomography and magnetic resonance imaging demonstrated an inhomogeneous mass with a clear boundary in the uncinate process of the pancreas. Furthermore, a distinct delayed enhancement pattern was noted on imaging. Histopathological examination confirmed the diagnosis of pancreatic hamartoma.ConclusionsPreoperative diagnosis of pancreatic hamartoma remains challenging. Imaging modalities can play a crucial role in facilitating accurate diagnosis and potentially avoiding unnecessary surgical intervention in patients with this condition.

Problem: Previous studies had confirmed that some deep learning models had high diagnostic performance in staging liver fibrosis. However, training efficiency of models predicting liver fibrosis need to be improved to achieve rapid diagnosis and precision medicine. Aim: The deep learning framework of EfficientNetV2-S was noted because of its faster training speed and better parameter efficiency compared with other models. Our study sought to develop noninvasive predictive models based on EfficientNetV2-S framework for staging liver fibrosis. Methods: Patients with chronic liver disease who underwent multi-parametric abdominal MRI were included in the retrospective study. Data augmentation methods including horizontal flip, vertical flip, perspective transformation and edge enhancement were applied to multi-parametric MR images to solve the data imbalance between different liver fibrosis groups. The EfficientNetV2-S models were used for the prediction of liver fibrosis stages F1-2, F1-3, F3, F4 and F3-4. We evaluated the diagnostic performance of our models in training, validation, and test sets by using receiver operating characteristic curve (ROC) analysis. Results: The total training time of EfficientNetV2-S was about 6 h. For differentiating of F1-2 vs F3, the accuracy, sensitivity and specificity of EfficientNetV2-S model were 96.2 %, 96.4 % and 96.0 % in the test set. The AUC in test set was 0.559. The accuracy, sensitivity and specificity were 82.1 %, 74.5 % and 89.6 % in the test set by using EfficientNetV2-S model to differentiate F1-2 vs F3-4, and the AUC in test set were 0.763. For differentiating F1-3 vs F4, the accuracy, sensitivity and specificity of EfficientNetV2-S model were 71.5 %, 73.4 % and 69.5 % in the test set. The AUC was 0.553 in test set. For differentiating F1-2 vs F4, the accuracy, sensitivity and specificity of our model were 84.3 %, 80.2 % and 88.3 % in the test set, and the AUC was 0.715, respectively. For differentiating F3 vs F4, the accuracy, sensitivity and specificity of EfficientNetV2-S model were 92.5 %, 89.1 % and 95.6 % in the test set, and the AUC was 0.696 in the test set. Conclusions: The EfficientNetV2-S models based on multi-parametric MRI had the feasibility for staging of liver fibrosis because they showed high training speed and diagnostic performance in our study.

Summary: Background: Contrast-enhanced CT scans provide a means to detect unsuspected colorectal cancer. However, colorectal cancers in contrast-enhanced CT without bowel preparation may elude detection by radiologists. We aimed to develop a deep learning (DL) model for accurate detection of colorectal cancer, and evaluate whether it could improve the detection performance of radiologists. Methods: We developed a DL model using a manually annotated dataset (1196 cancer vs 1034 normal). The DL model was tested using an internal test set (98 vs 115), two external test sets (202 vs 265 in 1, and 252 vs 481 in 2), and a real-world test set (53 vs 1524). We compared the detection performance of the DL model with radiologists, and evaluated its capacity to enhance radiologists’ detection performance. Findings: In the four test sets, the DL model had the area under the receiver operating characteristic curves (AUCs) ranging between 0.957 and 0.994. In both the internal test set and external test set 1, the DL model yielded higher accuracy than that of radiologists (97.2% vs 86.0%, p 0.99), and it detected 2 cases that had been missed by radiologists. Interpretation: The developed DL model can accurately detect colorectal cancer and improve radiologists’ detection performance, showing its potential as an effective computer-aided detection tool. Funding: This study was supported by National Science Fund for Distinguished Young Scholars of China (No. 81925023); Regional Innovation and Development Joint Fund of National Natural Science Foundation of China (No. U22A20345); National Natural Science Foundation of China (No. 82072090 and No. 82371954); Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application (No. 2022B1212010011); High-level Hospital Construction Project (No. DFJHBF202105).

Background: We created and validated a computed tomography (CT)-based radiomic model using both clinical factors and the radiomic signature for assessing the strangulation risk of acute intestinal obstruction. This would assist surgeons in accurately predicting intestinal ischemia and strangulation in patients with intestinal obstruction. Methods: We recruited 289 patients with acute intestinal obstruction admitted in the Affiliated Hospital of Qingdao University from January 2019 to February 2022. The patients were allocated to a training (n = 226) and validation cohort (n = 63). Radiomic features were collected from CT images, and the radiomic signature was extracted and used to calculate a radiomic score (Rad-score). A nomogram was constructed using the clinical features and the Rad-score, and the performance of the clinical, radiomics, and nomogram models was assessed in the two cohorts. Results: Six robust features were used to construct the radiomic signature. The nomogram incorporating hemoglobin levels, C-reactive protein levels, American Society of Anesthesiologists score, time of obstruction, CT image of mesenteric fluid (P < 0.05), and the signature demonstrated good predictive ability for intestinal ischemia in patients with acute intestinal obstruction, with areas under the curve of 0.892 (95% confidence interval, 0.837–0.947) and 0.781 (95% confidence interval, 0.619–0.944) for the training and validation sets, respectively. The decision curve analysis showed that this model outperformed the clinical and radiomic signature models. Conclusion: The radiomic nomogram may effectively predict intestinal ischemia in patients with acute intestinal disease and may assist clinical decision-making.

Pigments derived from red pepper fruits are widely used in food and cosmetics as natural colorants. Nitrogen (N) is a key nutrient affecting plant growth and metabolism; however, its regulation of color-related metabolites in pepper fruit has not been fully elucidated. This study analyzed the effects of N supply (0, 250, and 400 kg N ha-1) on the growth, fruit skin color, and targeted and non-target secondary metabolites of field-grown pepper fruits at the mature red stage. Overall, 16 carotenoids were detected, of which capsanthin, zeaxanthin, and capsorubin were the dominant ones. N application at 250 kg ha-1 dramatically increased contents of red pigment capsanthin, yellow-orange zeaxanthin and β-carotene, with optimum fruit yield. A total of 290 secondary metabolites were detected and identified. The relative content of most flavonoids and phenolic acids was decreased with increasing N supply. Correlation analysis showed that color parameters were highly correlated with N application rates, carotenoids, flavonoids, phenolic acids, lignans, and coumarins. Collectively, N promoted carotenoid biosynthesis but downregulated phenylpropanoid and flavonoid biosynthesis, which together determined the spectrum of red color expression in pepper fruit. Our results provide a better understanding of the impact of N nutrition on pepper fruit color formation and related physiology, and identification of target metabolites for enhancement of nutritional quality and consumer appeal.

Objective: To develop novel multiparametric models based on computed tomography enterography (CTE) scores to identify endoscopic activity and surgical risk in patients with Crohn's disease (CD). Methods: We analyzed 171 patients from 3 hospitals. Correlations between CTE outcomes and endoscopic scores were assessed using Spearman's rank correlation analysis. Predictive models for moderate to severe CD were developed, and receiver operating characteristic (ROC) curves were constructed to determine the area under the ROC curve (AUC). A combined nomogram based on CTE scores and clinical variables was also developed for predicting moderate to severe CD and surgery. Results: CTE scores were significantly correlated with endoscopy scores at the segment level. The global CTE score was an independent predictor of severe (HR = 1.231, 95% CI: 1.048–1.446, p = 0.012) and moderate-to-severe Simplified Endoscopic Scores for Crohn's Disease (SES-CD) (HR = 1.202, 95% CI: 1.090–1.325, p

Utilizing the Marangoni effect, miniaturized machineries can be endowed with self-propulsion. Here, the authors report high-performance Marangoni hydrogel rotors with asymmetric porosity and drag reduction profile and demonstrate multiple potential functionalities.

Modern research has proved that the main medicinal component of Rhodiola crenulata, which has a wide range of medicinal value, is its secondary metabolite salidroside. The MYB transcription factor family is widely involved in biosynthesis of second metabolism and other roles in the stress response in plants, so a genome-wide identification and analysis for this family in R. crenulata is worth conducting. In this research, genome-wide analysis identified 139 MYB genes based on conserved domains in the R. crenulata genome, and 137 genes were used to construct a phylogenetic tree and modified with expression files to reveal evolutionary characteristics. Physical and chemical characteristics, gene structure, and conserved motif analysis were also used to further analyze RcMYBs. Additionally, cis-acting elements related to transcription, hormone, and MYB binding were found in the promoter region of the selected RcMYBs. Four RcMYBs were cloned, sequenced, and their gene expression pattern was analyzed for further analysis of their functions. The research results lay the foundation for further research on the function of RcMYB and R. crenulata.

Inspired by origami/kirigami, three-dimensional (3D) mesostructures assembled via a mechanics-guided approach, with reversible and maneuverable shape-morphing capabilities, have attracted great interest with regard to a broad range of applications. Despite intensive studies, the development of morphable 3D mesostructures with high-order (multi-degree-of-freedom) deformation and untethered high-frequency actuation remains challenging. This work introduces a scheme for a magnetically encoded transferable 3D mesostructure, with polyethylene terephthalate (PET) film as the skeleton and discrete magnetic domains as actuation units, to address this challenge. The high-order deformation, including hierarchical, multidirectional and blending shape morphing, is realized by encoding 3D discrete magnetization profiles on the architecture through ultraviolet curing. Reconfigurable 3D mesostructures with a modest structural modulus (∼3 GPa) enable both high-frequency (∼55 Hz) and large-deformation (∼66.8%) actuation under an alternating magnetic field. Additionally, combined with the shape-retention and adhesion property of PET, these 3D mesostructures can be readily transferred and attached to many solid substrates. On this basis, diverse functional devices, including a switchable colour letter display, liquid mixer, sequential flashlight and biomimetic sliding robot, are demonstrated to offer new perspectives for robotics and microelectronics. [ABSTRACT FROM AUTHOR]