Study on the effects of rapamycin and the mTORC1/2 dual inhibitor OSI-027 on the metabolism of colon cancer cells based on UPLC-MS/MS metabolomics.

mTORC1/2 dual inhibitors may be more effective than mTORC1 inhibitor rapamycin. However, their metabolic impacts on colon cancer cells remain unexplored. We conducted a comparative analysis of the anti-proliferative effects of rapamycin and the novel OSI-027 in colon cancer cells HCT-116, evaluating their metabolic influences through ultra-high performance liquid chromatography-mass spectrometry (UPLC-MS/MS). Our results demonstrate that OSI-027 more effectively inhibits colon cancer cell proliferation than rapamycin. Additionally, we identified nearly 600 metabolites from the spectra, revealing significant differences in metabolic patterns between cells treated with OSI-027 and rapamycin. Through VIP value screening, we pinpointed crucial metabolites contributing to these distinctions. For inhibiting glycolysis and reducing glucose consumption, OSI-027 was likely to be more potent than rapamycin. For amino acids metabolism, although OSI-027 has a broad effect as rapamycin, their effects in degrees were not exactly the same. These findings address the knowledge gap regarding mTORC1/2 dual inhibitors and lay a foundation for their further development and research.

Identification of a novel de novo mutation in SOX4 for syndromic tooth agenesis.

OBJECTIVES: Coffin-Siris Syndrome (CSS) is a congenital disorder characterized by delayed growth, dysmorphic facial features, hypoplastic nails and phalanges of the fifth digit, and dental abnormalities. Tooth agenesis has been reported in CSS patients, but the mechanisms regulating this syndromic tooth agenesis remain largely unknown. This study aims to identify the pathogenic mutation of CSS presenting tooth genesis and explore potential regulatory mechanisms. MATERIALS AND METHODS: We utilized whole-exome sequencing to identify variants in a CSS patient, followed by Sanger validation. In silico analysis including conservation analysis, pathogenicity predictions, and 3D structural assessments were carried out. Additionally, single-cell RNA sequencing and fluorescence in situ hybridization (FISH) were applied to explore the spatio-temporal expression of Sox4 expression during murine tooth development. Weighted Gene Co-expression Network Analysis (WGCNA) was employed to examine the functional role of SOX4. RESULTS: A novel de novo SOX4 missense mutation (c.1255C > G, p.Leu419Val) was identified in a Chinese CSS patient exhibiting tooth agenesis. Single-cell RNA sequencing and FISH further verified high expression of Sox4 during murine tooth development, and WGCNA confirmed its central role in tooth development pathways. Enriched functions included cell-substrate junctions, focal adhesion, and RNA splicing. CONCLUSIONS: Our findings link a novel SOX4 mutation to syndromic tooth agenesis in CSS. This is the first report of SOX4 missense mutation causing syndromic tooth agenesis. CLINICAL RELEVANCE: This study not only enhances our understanding of the pathogenic mutation for syndromic tooth agenesis but also provides genetic diagnosis and potential therapeutic insights for syndromic tooth agenesis.

Automatic prediction of hepatic arterial infusion chemotherapy response in advanced hepatocellular carcinoma with deep learning radiomic nomogram.

OBJECTIVES: Hepatic arterial infusion chemotherapy (HAIC) using the FOLFOX regimen (oxaliplatin plus fluorouracil and leucovorin) is a promising option for advanced hepatocellular carcinoma (Ad-HCC). As identifying patients with Ad-HCC who would obtain objective response (OR) to HAIC preoperatively remains a challenge, we aimed to develop an automatic and non-invasive model for predicting HAIC response. METHODS: A total of 458 patients with Ad-HCC who underwent HAIC were retrospectively included from three hospitals (310 for training, 77 for internal validation, and 71 for external validation). The deep learning and radiomic features were extracted from the automatically segmented liver region on contrast-enhanced computed tomography images. Then, a deep learning radiomic nomogram (DLRN) was constructed by integrating deep learning scores, radiomic scores, and significant clinical variables with multivariate logistic regression. Model performance was assessed by AUC and Kaplan-Meier estimator. RESULTS: After automatic segmentation, only a few modifications were needed (less than 30 min for 458 patients). The DLRN achieved an AUC of 0.988 in the training cohort, 0.915 in the internal validation cohort, and 0.896 in the external validation cohort, respectively, outperforming other models in HAIC response prediction. Moreover, survival risk stratification was also successfully performed by the DLRN. The overall survival (OS) of the predictive OR group was significantly longer than that of the predictive non-OR group (median OS: 26.0 vs. 12.3 months, p < 0.001). CONCLUSIONS: The DLRN provided a satisfactory performance for predicting HAIC response, which is essential to identify Ad-HCC patients for HAIC and may potentially benefit personalized pre-treatment decision-making. CLINICAL RELEVANCE STATEMENT: This study presents an accurate and automatic method for predicting response to hepatic arterial infusion chemotherapy in patients with advanced hepatocellular carcinoma, and therefore help in defining the best candidates for this treatment. KEY POINTS: • Deep learning radiomic nomogram (DLRN) based on automatic segmentation of CECT can accurately predict hepatic arterial infusion chemotherapy (HAIC) response of advanced HCC patients. • The proposed prediction model can perform survival risk stratification and is an easy-to-use tool for personalized pre-treatment decision-making for advanced HCC patients.

Expanding the genetic spectrum of tooth agenesis using whole-exome sequencing.

Tooth agenesis is a high genetic heterogeneous disorder with more than 80 genes identified as associated molecular causes. The present study aimed to detect the possible pathogenic variants in a cohort of well-characterized probands with a clinical diagnosis of tooth agenesis. We performed whole-exome sequencing (WES) in 131 tooth agenesis patients with no previously identified molecular diagnosis. All the potential pathogenic variants were verified by Sanger sequencing in patients and their family members. Seventy-three patients were genetically diagnosed in 131 unrelated Chinese patients with tooth agenesis, providing a positive molecular diagnostic rate of 55.7%, including 53.8% (49/91) in the non-syndromic tooth agenesis (NSTA) group, and 60.0% (24/40) in syndromic tooth agenesis (STA) group. A total of 75 variants from 13 different genes were identified, including 33 novel variants, and WNT10A and EDA are the most common causative genes associated with non-syndromic and syndromic tooth agenesis, respectively. This study further extends the variant spectrum and clinical profiles of tooth agenesis, which has a positive significance for clinical practice, genetic diagnosis, prenatal counseling and future treatment.

Optimization of the Contrast Agent Injection Protocol for Carotid Artery Dynamic Contrast-Enhanced Magnetic Resonance Imaging.

BACKGROUND: The injection protocol used in previous carotid artery dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) studies varied. PURPOSE: To investigate the effect of contrast injection protocol and optimize this protocol for carotid artery DCE-MRI. STUDY TYPE: Prospective. SUBJECTS: Digital phantom and seven patients with carotid atherosclerosis. FIELD STRENGTH/SEQUENCE: 3 T, spoiled gradient recalled echo sequence. ASSESSMENT: Different injection doses (0.01-0.3 mmol/kg) and effective injection rates (0.01-1 mmol/sec) were tested using a digital carotid plaque phantom considering the contrast pharmacokinetics, DCE-MRI imaging, contrast variation and flow-related imaging artifacts, random time delay between the contrast injection and image acquisition, and pharmacokinetic analysis process. For each injection protocol, combining the root mean square relative error (RMSRE) of the measured K trans and v P maps within the adventitial vasa vasorum from 10 tested time delays by the root mean square produced RMSREoverall-vv which was used to measure the overall accuracy of the pharmacokinetic parameters. In vivo validation was performed on seven patients with carotid atherosclerosis by imaging them twice using the traditional commonly used protocol and the recommended protocol found by simulation. STATISTICAL TEST: Student's t-test, chi-square test, and paired t-test, P < 0.05 was considered statistically significant. RESULTS: A low region of RMSREoverall-vv with the combination of medium injection dose and low effective injection rate was found. The protocol with injection dose of 0.07 mmol/kg and effective injection rate of 0.06 mmol/sec achieved the minimal RMSREoverall-vv (4.29%), thus was recommended, which showed more accurate arterial input function. Coinciding with the simulation results, this recommended protocol in in vivo experiments produced significantly fewer image artifacts, lower K trans and v P (P all <0.05) than traditional protocol which overestimated these parameters in simulation. DATA CONCLUSION: The contrast injection protocol influenced the accuracy of the pharmacokinetics parameter estimation in carotid artery DCE-MRI. The injection protocol with injection dose of 0.07 mmol/kg and effective injection rate of 0.06 mmol/sec was recommended. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY STAGE: 1.

Fourier Convolution Block with global receptive field for MRI reconstruction.

Reconstructing images from under-sampled Magnetic Resonance Imaging (MRI) signals significantly reduces scan time and improves clinical practice. However, Convolutional Neural Network (CNN)-based methods, while demonstrating great performance in MRI reconstruction, may face limitations due to their restricted receptive field (RF), hindering the capture of global features. This is particularly crucial for reconstruction, as aliasing artifacts are distributed globally. Recent advancements in Vision Transformers have further emphasized the significance of a large RF. In this study, we proposed a novel global Fourier Convolution Block (FCB) with whole image RF and low computational complexity by transforming the regular spatial domain convolutions into frequency domain. Visualizations of the effective RF and trained kernels demonstrated that FCB improves the RF of reconstruction models in practice. The proposed FCB was evaluated on four popular CNN architectures using brain and knee MRI datasets. Models with FCB achieved superior PSNR and SSIM than baseline models and exhibited more details and texture recovery. The code is publicly available at https://github.com/Haozhoong/FCB.

Novel 3D morphological characteristics for congenital biliary dilatation diagnosis: a case-control study.

BACKGROUND: Congenital biliary dilatation (CBD) necessitates the timely removal of dilated bile ducts. Accurate differentiation between CBD and secondary biliary dilatation (SBD) is crucial for treatment decisions, and identification of CBD with intrahepatic involvement is vital for surgical planning and supportive care. This study aimed to develop quantitative models based on bile duct morphology to distinguish CBD from SBD and further identify CBD with intrahepatic involvement. MATERIALS AND METHODS: The retrospective study included 131 CBD and 209 SBD patients between December 2014 and December 2021 for model development, internal validation, and testing. A separate cohort of 15 CBD and 34 SBD patients between January 2022 and December 2022 was recruited for temporally-independent validation. Quantitative shape-based (Shape) and diameter-based (Diam) morphological characteristics of bile ducts were extracted to build a CBD diagnosis model to distinguish CBD from SBD and an intrahepatic involvement identification model to classify CBD with/without intrahepatic involvement. The diagnostic performance of the models was compared with that of experienced hepatobiliary surgeons. RESULTS: The CBD diagnosis model using clinical, Shape, and Diam characteristics showed good performance with an AUROC of 0.942 (95% CI: 0.890-0.994), AUPRC of 0.917 (0.855-0.979), accuracy of 0.891, sensitivity of 0.950, and F1-score of 0.864. The model outperformed two experienced surgeons in accuracy, sensitivity, and F1-score. The intrahepatic involvement identification model using clinical, Shape, and Diam characteristics yielded outstanding performance with an AUROC of 0.944 (0.879-1.000), AUPRC of 0.982 (0.947-1.000), accuracy of 0.932, sensitivity of 0.971, and F1-score of 0.957. The models demonstrated generalizable performance on the temporally-independent validation cohort. CONCLUSIONS: This study developed two robust quantitative models for distinguishing CBD from SBD and identifying CBD with intrahepatic involvement, respectively, based on morphological characteristics of the bile ducts, showing great potential in risk stratification and surgical planning of CBD.

A deep learning model for personalized intra-arterial therapy planning in unresectable hepatocellular carcinoma: a multicenter retrospective study.

Background: Unresectable Hepatocellular Carcinoma (uHCC) poses a substantial global health challenge, demanding innovative prognostic and therapeutic planning tools for improved patient management. The predominant treatment strategies include Transarterial chemoembolization (TACE) and hepatic arterial infusion chemotherapy (HAIC). Methods: Between January 2014 and November 2021, a total of 1725 uHCC patients [mean age, 52.8 ± 11.5 years; 1529 males] received preoperative CECT scans and were eligible for TACE or HAIC. Patients were assigned to one of the four cohorts according to their treatment, four transformer models (SELECTION) were trained and validated on each cohort; AUC was used to determine the prognostic performance of the trained models. Patients were stratified into high and low-risk groups based on the survival scores computed by SELECTION. The proposed AI-based treatment decision model (ATOM) utilizes survival scores to further inform final therapeutic recommendation. Findings: In this study, the training and validation sets included 1448 patients, with an additional 277 patients allocated to the external validation sets. The SELECTION model outperformed both clinical models and the ResNet approach in terms of AUC. Specifically, SELECTION-TACE and SELECTION-HAIC achieved AUCs of 0.761 (95% CI, 0.693-0.820) and 0.805 (95% CI, 0.707-0.881) respectively, in predicting ORR in their external validation cohorts. In predicting OS, SELECTION-TC and SELECTION-HC demonstrated AUCs of 0.736 (95% CI, 0.608-0.841) and 0.748 (95% CI, 0.599-0.865) respectively, in their external validation sets. SELECTION-derived survival scores effectively stratified patients into high and low-risk groups, showing significant differences in survival probabilities (P < 0.05 across all four cohorts). Additionally, the concordance between ATOM and clinician recommendations was associated with significantly higher response/survival rates in cases of agreement, particularly within the TACE, HAIC, and TC cohorts in the external validation sets (P < 0.05). Interpretation: ATOM was proposed based on SELECTION-derived survival scores, emerges as a promising tool to inform the selection among different intra-arterial interventional therapy techniques. Funding: This study received funding from the Beijing Municipal Natural Science Foundation, China (Z190024); the Key Program of the National Natural Science Foundation of China, China (81930119); The Science and Technology Planning Program of Beijing Municipal Science & Technology Commission and Administrative Commission of Zhongguancun Science Park, China (Z231100004823012); Tsinghua University Initiative Scientific Research Program of Precision Medicine, China (10001020108); and Institute for Intelligent Healthcare, Tsinghua University, China (041531001).

Effects of low nitrogen on seedling growth, photosynthetic characteristics and antioxidant system of rice varieties with different nitrogen efficiencies.

Nitrogen plays a significant role in influencing various physiological processes in plants, thereby impacting their ability to withstand abiotic stresses. This study used hydroponics to compare the effects of three nitrogen supply levels (1N, 1/2N and 1/4N) on the antioxidant capacity of rice varieties JJ88 (nitrogen efficient) and XN999 (nitrogen inefficient) with different nitrogen use efficiencies. The results show that compared with the XN999 variety, the JJ88 variety has stronger adaptability to low-nitrogen conditions, which is mainly reflected in the relatively small decrease in dry weight and net photosynthetic rate (Pn); In the early stage of low-nitrogen treatment (0-7 d), the [Formula: see text] production rate, hydrogen peroxide (H2O2) and malondialdehyde (MDA) content of JJ88 variety increased relatively slightly, but the superoxide dismutase (SOD), peroxide The activity of enzyme (POD) and catalase (CAT) increased significantly; After low-nitrogen treatment, the ASA-GSH cycle enzyme activity of JJ88 variety was relatively high, and the dehydroascorbate reductase (DHAR) activity after 14 days of low-nitrogen treatment was higher than that of 1N treatment; The content of reduced ascorbic acid (ASA) in non-enzymatic antioxidants was lower than that of 1N treatment after 14 days of low nitrogen treatment; The contents of oxidized dehydroascorbic acid (DHA) and carotenoids (Car) were higher than those of 1N treatment after 21d and 14d of low nitrogen treatment respectively; The contents of reduced glutathione (GSH), oxidized glutathione (GSSG) and proline (Pro) showed a larger upward trend during the entire low-nitrogen treatment period. In summary, the JJ88 rice variety has a strong ability to regulate oxidative stress and osmotic damage under low nitrogen conditions. It can slow down plant damage by regulating antioxidant enzyme activity and antioxidant content. This provides a basis for achieving nitrogen reduction and efficiency improvement in rice and the breeding of nitrogen-efficient varieties.

Plaque components segmentation in carotid artery on simultaneous non-contrast angiography and intraplaque hemorrhage imaging using machine learning.

PURPOSE: This study sought to determine the feasibility of using Simultaneous Non-contrast Angiography and intraPlaque Hemorrhage (SNAP) to detect the lipid-rich/necrotic core (LRNC), and develop a machine learning based algorithm to segment plaque components on SNAP images. METHODS: Sixty-eight patients (age: 58±9 years, 24 males) with carotid artery atherosclerotic plaque were imaged on a 3 T MR scanner with both traditional multi-contrast vessel wall MR sequences (TOF, T1W, and T2W) and 3D SNAP sequence. The manual segmentations of carotid plaque components including LRNC, intraplaque hemorrhage (IPH), calcification (CA) and fibrous tissue (FT) on traditional multi-contrast images were used as reference. By utilizing the intensity and morphological information from SNAP, a machine learning based two steps algorithm was developed to firstly identify LRNC (with or without IPH), CA and FT, and then segmented IPH from LRNC. Ten-fold cross-validation was used to evaluate the performance of proposed method. The overall pixel-wise accuracy, the slice-wise sensitivity & specificity & Youden's index, and the Pearson's correlation coefficient of the component area between the proposed method and the manual segmentation were reported. RESULTS: In the first step, all tested classifiers (Naive Bayes (NB), Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Decision Tree (GBDT) and Artificial Neural Network (ANN)) had overall pixel-wise accuracy higher than 0.88. For RF, GBDT and ANN classifiers, the correlation coefficients of areas were all higher than 0.82 (p < 0.001) for LRNC and 0.79 for CA (p < 0.001), and the Youden's indexes were all higher than 0.79 for LRNC and 0.76 for CA, which were better than that of NB and SVM. In the second step, the overall pixel-wise accuracy was higher than 0.78 for the five classifiers, and RF achieved the highest Youden's index (0.69) with the correlation coefficients as 0.63 (p < 0.001). CONCLUSIONS: The RF is the overall best classifier for our proposed method, and the feasibility of using SNAP to identify plaque components, including LRNC, IPH, CA, and FT has been validated. The proposed segmentation method using a single SNAP sequence might be a promising tool for atherosclerotic plaque components assessment.