Habitat Imaging With Tumoral and Peritumoral Radiomics for Prediction of Lung Adenocarcinoma Invasiveness on Preoperative Chest CT: A Multicenter Study.

BACKGROUND. Tumor growth processes result in spatial heterogeneity, with the development of tumor subregions (i.e., habitats) having unique biologic characteristics. OBJECTIVE. The purpose of our study was to develop and validate a habitat model combining tumor and peritumoral radiomic features on chest CT for predicting invasiveness of lung adenocarcinoma. METHODS. This retrospective study included 1156 patients (mean age, 57.5 years; 464 men, 692 women), from three centers and a public dataset, who underwent chest CT before lung adenocarcinoma resection (variable date ranges across datasets). Patients from one center formed training (n = 500) and validation (n = 215) sets; patients from the other sources formed three external test sets (n = 249, 113, 79). For each patient, a single nodule was manually segmented on chest CT. The nodule segmentation was combined with an automatically generated 4-mm peritumoral region into a whole-volume volume of interest (VOI). A gaussian mixture model (GMM) identified voxel clusters with similar first-order energy across patients. GMM results were used to divide each patient's whole-volume VOI into multiple habitats, which were defined consistently across patients. Radiomic features were extracted from each habitat. After feature selection, a habitat model was developed for predicting invasiveness, with the use of pathologic assessment as a reference. An integrated model was constructed, combining features extracted from habitats and whole-volume VOIs. Model performance was evaluated, including in subgroups based on nodule density (pure ground-glass, part-solid, and solid). The code for habitat imaging and model construction is publicly available (https://github.com/Shangyoulan/Habitat/). RESULTS. Invasive cancer was diagnosed in 626 of 1156 patients. GMM identified four as the optimal number of voxel clusters and thus of per-patient tumor habitats. The habitat model had an AUC of 0.932 in the validation set and 0.881, 0.880, and 0.764 in the three external test sets. The integrated model had an AUC of 0.947 in the validation set and 0.936, 0.908, and 0.800 in the three external test sets. In the three external test sets combined, across nodule densities, AUCs for the habitat model were 0.836-0.869 and for the integrated model were 0.846-0.917. CONCLUSION. Habitat imaging combining tumoral and peritumoral radiomic features could help predict lung adenocarcinoma invasiveness. Prediction is improved when combining information on tumor subregions and the tumor overall. CLINICAL IMPACT. The findings may aid personalized preoperative assessments to guide clinical decision-making in lung adenocarcinoma.

Effect of electroacupuncture of "Zusanli" (ST36) combined with capeOX on apoptosis and ferroptosis in nude mice with colorectal cancer. / 电针"足三里"联合capeOXæ–¹æ¡ˆå¯¹ç»“ç›´è‚ ç™Œè£¸é¼ è‚¿ç˜¤ç»†èƒžå‡‹äº¡å’Œé“æ­»äº¡çš„å½±å“.

OBJECTIVES: To investigate the impact of combined treatment of colorectal cancer (CRC) with electroacupuncture (EA) and capeOX (combined administration of fluorouracil, oxaliplatin and capecitabine) on the tumor volume, weight, spleen coefficient, apoptosis and ferroptosis of tumor tissue, and liver and kidney functions in nude mice with CRC, so as to explore its mechanisms underlying inhibiting CRC and alleviating toxic reactions of capeOX. METHODS: Female Balb/c nude mice were randomly assigned to 3 groups：model, capeOX, and EA+capeOX, with 8 nude mice in each group. The CRC model was established by subcutaneous injection of colon cancer cells at the right inguinal region. Nude mice of the capeOX group received intraperitoneal injection of oxaliplatin for 1 day and gavage of capecitabine from day 2 to day 7. EA (1 mA, 2 Hz/100 Hz) was applied to bilateral "Zusanli" (ST36) for 20 min, once daily for 7 days. During the interven-tion, the tumor volume and weight were measured every day, and at the end of intervention, the weight of the tumor tissue and spleen were measured, with tumor volume difference and spleen coefficient calculated. The proportion of apoptotic cells was measured by flow cytometry, and the contents of serum malondialdehyde (MDA), alanine aninotransferase (ALT), aspartate aminotransferase (AST), blood urea nitrogen (BUN), and creatinine (Cr) were detected using ELISA. The expression level of glutathione peroxidase 4 (GPX4, a key regulator for ferroptosis) protein of the tumor tissue was determined using Western blot. RESULTS: Compared to the model group, both the capeOX group and EA+capeOX group showed a decrease in the tumor volume (on day 3 and 4 in the capeOX group, and from day 2 to 7 in the EA+capeOX group) and body weight (P<0.05, on day 3 to 7 in the EA+capeOX group and on day 2 to 7 in the capeOX group), being evidently lower in the tumor volume on day 7 in the EA+capeOX than in the capeOX group (P<0.05), and evidently higher in the body weight on day 6 and 7 in the EA+capeOX group than in the capeOX group (P<0.05). In comparison with the model group, the tumor volume difference, tumor weight and spleen coefficient in both capeOX and EA+capeOX groups were significantly decreased (P<0.05), and MDA content in EA+capeOX group was significantly decreased (P<0.05), while the contents of ALT, BUN and Cr in the capeOX group, the proportion of apoptotic cells in both capeOX and EA+capeOX groups, and the GPX4 expression level in the EA+capeOX group were all significantly increased (P<0.05). The tumor volume difference, tumor weight, and contents of MDA, ALT, AST, BUN and Cr in the EA+capeOX group were markedly lower than in the capeOX group (P<0.05), while the spleen coefficient, proportion of apoptotic cells and GPX4 expression level in the EA+capeOX group were markedly higher than those in the capeOX group (P<0.05). CONCLUSIONS: EA of ST36 can enhance the effect of capeOX in inhibiting colorectal cancer growth in nude mice with CRC, which may be related with its functions in promoting tumor cell apoptosis, inhibiting ferroptosis, and modulating immune tolerance. In addition, EA can lower the side effects of capeOX in hematopoietic and immune, liver, and kidney functions.

Computational Pathology for Prediction of Isocitrate Dehydrogenase Gene Mutation from Whole Slide Images in Adult Patients with Diffuse Glioma.

Isocitrate dehydrogenase gene (IDH) mutation is one of the most important molecular markers of glioma. Accurate detection of IDH status is a crucial step for integrated diagnosis of adult-type diffuse gliomas. Herein, a clustering-based hybrid of a convolutional neural network and a vision transformer deep learning model was developed to detect IDH mutation status from annotation-free hematoxylin and eosin-stained whole slide pathologic images of 2275 adult patients with diffuse gliomas. For comparison, a pure convolutional neural network, a pure vision transformer, and a classic multiple-instance learning model were also assessed. The hybrid model achieved an area under the receiver operating characteristic curve of 0.973 in the validation set and 0.953 in the external test set, outperforming the other models. The hybrid model's ability in IDH detection between difficult subgroups with different IDH status but shared histologic features, achieving areas under the receiver operating characteristic curve ranging from 0.850 to 0.985 in validation and test sets. These data suggest that the proposed hybrid model has a potential to be used as a computational pathology tool for preliminary rapid detection of IDH mutation from whole slide images in adult patients with diffuse gliomas.

Neighborhood evaluator for efficient super-resolution reconstruction of 2D medical images.

BACKGROUND: Deep learning-based super-resolution (SR) algorithms aim to reconstruct low-resolution (LR) images into high-fidelity high-resolution (HR) images by learning the low- and high-frequency information. Experts' diagnostic requirements are fulfilled in medical application scenarios through the high-quality reconstruction of LR digital medical images. PURPOSE: Medical image SR algorithms should satisfy the requirements of arbitrary resolution and high efficiency in applications. However, there is currently no relevant study available. Several SR research on natural images have accomplished the reconstruction of resolutions without limitations. However, these methodologies provide challenges in meeting medical applications due to the large scale of the model, which significantly limits efficiency. Hence, we suggest a highly effective method for reconstructing medical images at any desired resolution. METHODS: Statistical features of medical images exhibit greater continuity in the region of neighboring pixels than natural images. Hence, the process of reconstructing medical images is comparatively less challenging. Utilizing this property, we develop a neighborhood evaluator to represent the continuity of the neighborhood while controlling the network's depth. RESULTS: The suggested method has superior performance across seven scales of reconstruction, as evidenced by experiments conducted on panoramic radiographs and two external public datasets. Furthermore, the proposed network significantly decreases the parameter count by over 20× and the computational workload by over 10× compared to prior researches. On large-scale reconstruction, the inference speed can be enhanced by over 5×. CONCLUSION: The novel proposed SR strategy for medical images performs efficient reconstruction at arbitrary resolution, marking a significant breakthrough in the field. The given scheme facilitates the implementation of SR in mobile medical platforms.

Semi-supervised Medical Image Segmentation with Multiscale Contrastive Learning and Cross-Supervision.

We propose a semi-supervised segmentation method based on multiscale contrastive learning to solve the problem of shortage of annotations in medical image segmentation tasks. We apply perturbations to the input image and encoded features and make the output as consistent as possible by cross-supervision, which is a way to improve the generalizability of the model. Two scales of contrastive learning, patch-level and pixel-level, are employed to enhance the intra-class compactness and inter-class separability of the features. We evaluate the proposed model using three public datasets for brain tumor,left atrial, and cellular nuclei segmentation. The experiments showed that our model outperforms state-of-the-art methods.Clinical relevance- The proposed method can be used for medical image segmentation with limited annotated data and achieve comparable performance to the fully annotated situation. Such an approach can be easily extended to other clinical applications.

Imaging-proteomic analysis for prediction of neoadjuvant chemotherapy responses in patients with breast cancer.

BACKGROUND: Optimizing patient selection for neoadjuvant chemotherapy in patients with breast cancer remains an unmet clinical need. Quantitative features from medical imaging were reported to be predictive of treatment responses. However, the biologic meaning of these latent features is poorly understood, preventing the clinical use of such noninvasive imaging markers. The study aimed to develop a deep learning signature (DLS) from pretreatment magnetic resonance imaging (MRI) for predicting responses to neoadjuvant chemotherapy in patients with breast cancer and to further investigate the biologic meaning of the DLS by identifying its underlying pathways using paired MRI and proteomic sequencing data. METHODS: MRI-based DLS was constructed (radiogenomic training dataset, n = 105) and validated (radiogenomic validation dataset, n = 26) for the prediction of pathologic complete response (pCR) to neoadjuvant chemotherapy. Proteomic sequencing revealed biological functions facilitating pCR (n = 139). Their associations with DLS were uncovered by radiogenomic analysis. RESULTS: The DLS achieved a prediction accuracy of 0.923 with an AUC of 0.958, higher than the performance of the model trained by transfer learning. Cellular membrane formation, endocytosis, insulin-like growth factor binding, protein localization to membranes, and cytoskeleton-dependent trafficking were differentially regulated in patients showing pCR. Oncogenic signaling pathways, features correlated with human phenotypes, and features correlated with general biological processes were significantly correlated with DLS in both training and validation dataset (p.adj < 0.05). CONCLUSIONS: Our study offers a biologically interpretable DLS for the prediction of pCR to neoadjuvant chemotherapy in patients with breast cancer, which may guide personalized medication.

Neuropathologist-level integrated classification of adult-type diffuse gliomas using deep learning from whole-slide pathological images.

Current diagnosis of glioma types requires combining both histological features and molecular characteristics, which is an expensive and time-consuming procedure. Determining the tumor types directly from whole-slide images (WSIs) is of great value for glioma diagnosis. This study presents an integrated diagnosis model for automatic classification of diffuse gliomas from annotation-free standard WSIs. Our model is developed on a training cohort (n = 1362) and a validation cohort (n = 340), and tested on an internal testing cohort (n = 289) and two external cohorts (n = 305 and 328, respectively). The model can learn imaging features containing both pathological morphology and underlying biological clues to achieve the integrated diagnosis. Our model achieves high performance with area under receiver operator curve all above 0.90 in classifying major tumor types, in identifying tumor grades within type, and especially in distinguishing tumor genotypes with shared histological features. This integrated diagnosis model has the potential to be used in clinical scenarios for automated and unbiased classification of adult-type diffuse gliomas.

A radio-pathologic integrated model for prediction of lymph node metastasis stage in patients with gastric cancer.

BACKGROUND: Accurate prediction of lymph node metastasis stage (LNMs) facilitates precision therapy for gastric cancer. We aimed to develop and validate a deep learning-based radio-pathologic model to predict the LNM stage in patients with gastric cancer by integrating CT images and histopathological whole-slide images (WSIs). METHODS: A total of 252 patients were enrolled and randomly divided into a training set (n = 202) and a testing set (n = 50). Both pretreatment contrast-enhanced abdominal CT and WSI of biopsy specimens were collected for each patient. The deep radiologic and pathologic features were extracted from CT and WSI using ResNet-50 and Vision Transformer (ViT) network, respectively. By fusing both radiologic and pathologic features, a radio-pathologic integrated model was constructed to predict the five LNM stages. For comparison, four single-modality models using CT images or WSIs were also constructed, respectively. All models were trained on the training set and validated on the testing set. RESULTS: The radio-pathologic integrated mode achieved an overall accuracy of 84.0% and a kappa coefficient of 0.795 on the testing set. The areas under the curves (AUCs) of the integrated model in predicting the five LNM stages were 0.978 (95% Confidence Interval (CI 0.917-1.000), 0.946 (95% CI 0.867-1.000), 0.890 (95% CI 0.718-1.000), 0.971 (95% CI 0.920-1.000), and 0.982 (95% CI 0.911-1.000), respectively. Moreover, the integrated model achieved an AUC of 0.978 (95% CI 0.912-1.000) in predicting the binary status of nodal metastasis. CONCLUSION: Our study suggests that radio-pathologic integrated model that combined both macroscale radiologic image and microscale pathologic image can better predict lymph node metastasis stage in patients with gastric cancer.

Radiomic features from multiparametric magnetic resonance imaging predict molecular subgroups of pediatric low-grade gliomas.

BACKGROUND: We aimed to develop machine learning models for prediction of molecular subgroups (low-risk group and intermediate/high-risk group) and molecular marker (KIAA1549-BRAF fusion) of pediatric low-grade gliomas (PLGGs) based on radiomic features extracted from multiparametric MRI. METHODS: 61 patients with PLGGs were included in this retrospective study, which were divided into a training set and an internal validation set at a ratio of 2:1 based on the molecular subgroups or the molecular marker. The patients were classified into low-risk and intermediate/high-risk groups, BRAF fusion positive and negative groups, respectively. We extracted 5929 radiomic features from multiparametric MRI. Thereafter, we removed redundant features, trained random forest models on the training set for predicting the molecular subgroups or the molecular marker, and validated their performance on the internal validation set. The performance of the prediction model was verified by 3-fold cross-validation. RESULTS: We constructed the classification model differentiating low-risk PLGGs from intermediate/high-risk PLGGs using 4 relevant features, with an AUC of 0.833 and an accuracy of 76.2% in the internal validation set. In the prediction model for predicting KIAA1549-BRAF fusion using 4 relevant features, an AUC of 0.818 and an accuracy of 81.0% were achieved in the internal validation set. CONCLUSIONS: The current study demonstrates that MRI radiomics is able to predict molecular subgroups of PLGGs and KIAA1549-BRAF fusion with satisfying sensitivity. TRIAL REGISTRATION: This study was retrospectively registered at clinicaltrials.gov (NCT04217018).

Recognizing Pediatric Tuberous Sclerosis Complex Based on Multi-Contrast MRI and Deep Weighted Fusion Network.

Multi-contrast magnetic resonance imaging (MRI) is wildly applied to identify tuberous sclerosis complex (TSC) children in a clinic. In this work, a deep convolutional neural network with multi-contrast MRI is proposed to diagnose pediatric TSC. Firstly, by combining T2W and FLAIR images, a new synthesis modality named FLAIR3 was created to enhance the contrast between TSC lesions and normal brain tissues. After that, a deep weighted fusion network (DWF-net) using a late fusion strategy is proposed to diagnose TSC children. In experiments, a total of 680 children were enrolled, including 331 healthy children and 349 TSC children. The experimental results indicate that FLAIR3 successfully enhances the visibility of TSC lesions and improves the classification performance. Additionally, the proposed DWF-net delivers a superior classification performance compared to previous methods, achieving an AUC of 0.998 and an accuracy of 0.985. The proposed method has the potential to be a reliable computer-aided diagnostic tool for assisting radiologists in diagnosing TSC children.

N-Heterocyclic Carbene-Catalyzed Atroposelective Synthesis of Axially Chiral α-Carbolinones via Heterocycle Formation.

An NHC-catalyzed atroposelective synthesis of axially chiral α-carbolinones from α,ß-unsaturated iminoindole derivatives and α-chloroaldehydes was developed. The reaction proceeds through a cascade process including [4 + 2] annulation and then oxidative dehydrogenation with concomitant central-to-axial chirality conversion under mild conditions. The developed method opens a new avenue to efficiently access axially chiral α-carbolinones in moderate to good enantioselectivities.

Imaging phenotypes from MRI for the prediction of glioma immune subtypes from RNA sequencing: A multicenter study.

Tumor subtyping based on its immune landscape may guide precision immunotherapy. The aims of this study were to identify immune subtypes of adult diffuse gliomas with RNA sequencing data, and to noninvasively predict this subtype using a biologically interpretable radiomic signature from MRI. A subtype discovery dataset (n = 210) from a public database and two radiogenomic datasets (n = 130 and 55, respectively) from two local hospitals were included. Brain tumor microenvironment-specific signatures were constructed from RNA sequencing to identify the immune types. A radiomic signature was built from MRI to predict the identified immune subtypes. The pathways underlying the radiomic signature were identified to annotate their biological meanings. The reproducibility of the findings was verified externally in multicenter datasets. Three distinctive immune subtypes were identified, including an inflamed subtype marked by elevated hypoxia-induced immunosuppression, a "cold" subtype that exhibited scarce immune infiltration with downregulated antigen presentation, and an intermediate subtype that showed medium immune infiltration. A 10-feature radiomic signature was developed to predict immune subtypes, achieving an AUC of 0.924 in the validation dataset. The radiomic features correlated with biological functions underpinning immune suppression, which substantiated the hypothesis that molecular changes can be reflected by radiomic features. The immune subtypes, predictive radiomic signature, and radiomics-correlated biological pathways were validated externally. Our data suggest that adult-type diffuse gliomas harbor three distinctive immune subtypes that can be predicted by MRI radiomic features with clear biological significance. The immune subtypes, radiomic signature, and radiogenomic links can be replicated externally.

Image-based deep learning identifies glioblastoma risk groups with genomic and transcriptomic heterogeneity: a multi-center study.

OBJECTIVES: To develop and validate a deep learning imaging signature (DLIS) for risk stratification in patients with multiforme (GBM), and to investigate the biological pathways and genetic alterations underlying the DLIS. METHODS: The DLIS was developed from multi-parametric MRI based on a training set (n = 600) and validated on an internal validation set (n = 164), an external test set 1 (n = 100), an external test set 2 (n = 161), and a public TCIA set (n = 88). A co-profiling framework based on a radiogenomics analysis dataset (n = 127) using multiscale high-dimensional data, including imaging, transcriptome, and genome, was established to uncover the biological pathways and genetic alterations underpinning the DLIS. RESULTS: The DLIS was associated with survival (log-rank p < 0.001) and was an independent predictor (p < 0.001). The integrated nomogram incorporating the DLIS achieved improved C indices than the clinicomolecular nomogram (net reclassification improvement 0.39, p < 0.001). DLIS significantly correlated with core pathways of GBM (apoptosis and cell cycle-related P53 and RB pathways, and cell proliferation-related RTK pathway), as well as key genetic alterations (del_CDNK2A). The prognostic value of DLIS-correlated genes was externally confirmed on TCGA/CGGA sets (p < 0.01). CONCLUSIONS: Our study offers a biologically interpretable deep learning predictor of survival outcomes in patients with GBM, which is crucial for better understanding GBM patient's prognosis and guiding individualized treatment. KEY POINTS: • MRI-based deep learning imaging signature (DLIS) stratifies GBM into risk groups with distinct molecular characteristics. • DLIS is associated with P53, RB, and RTK pathways and del_CDNK2A mutation. • The prognostic value of DLIS-correlated pathway genes is externally demonstrated.

Glutathione metabolism in Cryptocaryon irritans involved in defense against oxidative stress induced by zinc ions.

BACKGROUND: Cryptocaryon irritans is a fatal parasite for marine teleosts and causes severe economic loss for aquaculture. Galvanized materials have shown efficacy in controlling this parasite infestation through the release of zinc ions to induce oxidative stress. METHODS: In this study, the resistance mechanism in C. irritans against oxidative stress induced by zinc ions was investigated. Untargeted metabolomics analysis was used to determine metabolic regulation in C. irritans in response to zinc ion treatment by the immersion of protomonts in ZnSO4 solution at a sublethal dose (20 µmol). Eight differential metabolites were selected to assess the efficacy of defense against zinc ion stimulation in protomonts of C. irritans. Furthermore, the mRNA relative levels of glutathione metabolism-associated enzymes were measured in protomonts following treatment with ZnSO4 solution at sublethal dose. RESULTS: The results showed that zinc ion exposure disrupted amino acid metabolism, carbohydrate metabolism, lipid metabolism, and nucleotide metabolism in C. irritans. Four antioxidants, namely ascorbate, S-hexyl-glutathione, syringic acid, and ubiquinone-1, were significantly increased in the Zn group (P < 0.01), while the glutathione metabolism pathway was enhanced. The encystment rate of C. irritans was significantly higher in the ascorbate and methionine treatment (P < 0.05) groups. Additionally, at 24 h post-zinc ion exposure, the relative mRNA level of glutathione reductase (GR) was increased significantly (P < 0.01). On the contrary, the relative mRNA levels of glutathione S-transferase (GT) and phospholipid-hydroperoxide glutathione peroxidase (GPx) were significantly decreased (P < 0.05), thus indicating that the generation of reduced glutathione was enhanced. CONCLUSIONS: These results revealed that glutathione metabolism in C. irritans contributes to oxidative stress resistance from zinc ions, and could be a potential drug target for controlling C. irritans infection.

Glioma survival prediction from whole-brain MRI without tumor segmentation using deep attention network: a multicenter study.

OBJECTIVES: To develop and validate a deep learning model for predicting overall survival from whole-brain MRI without tumor segmentation in patients with diffuse gliomas. METHODS: In this multicenter retrospective study, two deep learning models were built for survival prediction from MRI, including a DeepRisk model built from whole-brain MRI, and an original ResNet model built from expert-segmented tumor images. Both models were developed using a training dataset (n = 935) and an internal tuning dataset (n = 156) and tested on two external test datasets (n = 194 and 150) and a TCIA dataset (n = 121). C-index, integrated Brier score (IBS), prediction error curves, and calibration curves were used to assess the model performance. RESULTS: In total, 1556 patients were enrolled (age, 49.0 ± 13.1 years; 830 male). The DeepRisk score was an independent predictor and can stratify patients in each test dataset into three risk subgroups. The IBS and C-index for DeepRisk were 0.14 and 0.83 in external test dataset 1, 0.15 and 0.80 in external dataset 2, and 0.16 and 0.77 in TCIA dataset, respectively, which were comparable with those for original ResNet. The AUCs at 6, 12, 24, 26, and 48 months for DeepRisk ranged between 0.77 and 0.94. Combining DeepRisk score with clinicomolecular factors resulted in a nomogram with a better calibration and classification accuracy (net reclassification improvement 0.69, p < 0.001) than the clinical nomogram. CONCLUSIONS: DeepRisk that obviated the need of tumor segmentation can predict glioma survival from whole-brain MRI and offers incremental prognostic value. KEY POINTS: • DeepRisk can predict overall survival directly from whole-brain MRI without tumor segmentation. • DeepRisk achieves comparable accuracy in survival prediction with deep learning model built using expert-segmented tumor images. • DeepRisk has independent and incremental prognostic value over existing clinical parameters and IDH mutation status.

Quantitative detection of parasitic ciliate Cryptocaryon irritans in seawater with an optimized sample processing method.

The protozoan Cryptocaryon irritans is one of the most important ectoparasites of marine fish, causing 'white spot disease' and mass mortality in aquaculture. To accurately predict disease outbreaks and develop prevention strategies, improved detection methods are required that are sensitive, convenient and rapid. In this study, a pair of specific primers based on the C. irritans 18S rRNA gene was developed and used in a real-time PCR (qPCR) assay. This assay was able to detect five theronts in 1 L of natural seawater. Furthermore, a linear model was established to analyse the log of Ct value and parasite abundance in seawater (y = -2.9623x + 24.2930), and the coefficient of determination (R2 ) value was 0.979. A lysis buffer was optimized for theront DNA extraction and used for storage sample. This method was superior to the commercial water DNA kit, and there was no significant degradation of DNA at room temperature for 24-96 hr. A dilution method was developed to manage qPCR inhibitors and used to investigate natural seawater samples in a net cage farm with diseased fish, and the findings were consistent with the actual situation. This study provides a valuable tool for assisting in the early monitoring and control of cryptocaryoniasis in aquaculture.

Predicting 1p/19q co-deletion status from magnetic resonance imaging using deep learning in adult-type diffuse lower-grade gliomas: a discovery and validation study.

Determination of 1p/19q co-deletion status is important for the classification, prognostication, and personalized therapy in diffuse lower-grade gliomas (LGG). We developed and validated a deep learning imaging signature (DLIS) from preoperative magnetic resonance imaging (MRI) for predicting the 1p/19q status in patients with LGG. The DLIS was constructed on a training dataset (n = 330) and validated on both an internal validation dataset (n = 123) and a public TCIA dataset (n = 102). The receiver operating characteristic (ROC) analysis and precision recall curves (PRC) were used to measure the classification performance. The area under ROC curves (AUC) of the DLIS was 0.999 for training dataset, 0.986 for validation dataset, and 0.983 for testing dataset. The F1-score of the prediction model was 0.992 for training dataset, 0.940 for validation dataset, and 0.925 for testing dataset. Our data suggests that DLIS could be used to predict the 1p/19q status from preoperative imaging in patients with LGG. The imaging-based deep learning has the potential to be a noninvasive tool predictive of molecular markers in adult diffuse gliomas.

Fingerprinting and Determination of Hepatotoxic Constituents in Polygoni Multiflori Radix Praeparata of Different Producing Places by HPLC.

Polygoni Multiflori Radix Praeparata (PMRP) is used as Chinese herbal medicine with long history. However, reports about PMRP hepatotoxicity have increased recently, and producing area might be one reason. This article aims to figure out the relationship between producing area and hepatotoxic ingredients in PMRP. HPLC fingerprint for PMRP was established and the contents of gallic acid, trans-stilbene glycoside (TSG), emodin-8-O-ß-D-glucoside (EG), emodin and physcion were determined. Clustering heatmap was implemented by TCMNPAS software，and principal component analysis was implemented by SPSS and SIMCA-P software. Hepatotoxic constituents' contents of PMRP from separate producing area were different. PMRP from Guangxi had the highest content of gallic acid, TSG, EG, emodin and physcion, followed by Hubei, Guangdong, Guizhou, Yunnan. PMRP from Henan had the lowest contents of hepatotoxic components. Hepatotoxic components' contents of PMRP in southern were higher than central China. This study carried out a preliminary qualitative and quantitative investigation on the PMRP from different producing places, which provided a basis for safe medication of PMRP.