Artificial intelligence-based non-invasive tumor segmentation, grade stratification and prognosis prediction for clear-cell renal-cell carcinoma

Due to the complicated histopathological characteristics of clear-cell renal-cell carcinoma (ccRCC), non-invasive prognosis before operative treatment is crucial in selecting the appropriate treatment. A total of 126 345 computerized tomography (CT) images from four independent patient cohorts were included for analysis in this study. We propose a V Bottleneck multi-resolution and focus-organ network (VB-MrFo-Net) using a cascade framework for deep learning analysis. The VB-MrFo-Net achieved better performance than VB-Net in tumor segmentation, with a Dice score of 0.87. The nuclear-grade prediction model performed best in the logistic regression classifier, with area under curve values from 0.782 to 0.746. Survival analysis revealed that our prediction model could significantly distinguish patients with high survival risk, with a hazard ratio (HR) of 2.49 [95% confidence interval (CI): 1.13-5.45, P = 0.023] in the General cohort. Excellent performance had also been verified in the Cancer Genome Atlas cohort, the Clinical Proteomic Tumor Analysis Consortium cohort, and the Kidney Tumor Segmentation Challenge cohort, with HRs of 2.77 (95%CI: 1.58-4.84, P = 0.0019), 3.83 (95%CI: 1.22-11.96, P = 0.029), and 2.80 (95%CI: 1.05-7.47, P = 0.025), respectively. In conclusion, we propose a novel VB-MrFo-Net for the renal tumor segmentation and automatic diagnosis of ccRCC. The risk stratification model could accurately distinguish patients with high tumor grade and high survival risk based on non-invasive CT images before surgical treatments, which could provide practical advice for deciding treatment options.</p

Identification of QTLs and candidate genes for rice seed germinability under low temperature using high‐density genetic mapping and RNA‐seq

Abstract Reduced temperatures during germination adversely affect rice (Oryza sativa L.) production. Little is known, however, of the genes or genetic loci involved. Here, QTLs were investigated in a recombinant inbred line (RIL) resulting from a 02428 (japonica)‐YZX (indica) cross. The phenotypes of the cultivars differ significantly when exposed to low temperatures during germination. Mapping with a high‐density bin map identified 11 loci associated with low‐temperature germination of which loci 2 and 4 were identified by multiple traits over two seasons. Locus 2 was a major genetic locus, explaining 22.36% of phenotypic variation. The haplotype results showed that the pyramiding of favorable alleles of these two loci was beneficial to improving the rice seeds' low‐temperature germinability. RNA‐seq analysis was performed on the second day of germination at low temperature for both parents. Three DGEs (Os03g0119800, Os03g0120900, and Os03g0121300) were obtained for locus 2 and were confirmed as the most likely candidates by qRT‐PCR verification, gene sequence alignment, and haplotype analysis. Collectively, these quantitative trait loci and candidate genes may be valuable for the breeding of cold‐tolerant rice lines as well as broadening our knowledge of the genetics underlying germination at low temperatures

Progress on the total synthesis of natural products in China: From 2006 to 2010

This paper summarizes the progress on the total syntheses of natural products accomplished in mainland China during the period from 2006 to 2010. The overview focuses on the first total synthesis of natural products of contemporary interest including alkaloids, cyclopeptides and cyclic depsipeptides, macrolides, terpenoids and steroids, saponins and glycosides. The development of novel synthetic strategies and methodologies, and application of new selective synthetic methods in the total syntheses of natural products are included as well.National Natural Science Foundation of China [20832005, 21072160, 20902075]; National Basic Research Program (973 Program) of China [2010CB833200]; Natural Science Foundation of Fujian Province of China [2009J05037]; Specialized Research Fund for the Doctoral Program of Higher Education [20090121120007]; Scientific Research Foundation for the Returned Overseas Chinese Scholars, Ministry of Education of Chin

STUDY OF CALCIPOTRIOL BETAMETHASONE OINTMENT IN THE TREATMENT OF PATIENTS WITH REFRACTORY CHRONIC ECZEMA

Chronic eczema is an inflammatory-immune disease of the skin, with the characteristics of skin thickening and varying degrees of lichenification, including severe itching, tendency of persistence and recurrence with serious impact on quality of life of patients. Objective: To assess the clinical efficacy and safety of calcipotriol betamethasone ointment in patients with chronic eczema. Methods: In this multi-center, randomized, single-blind, positive drug parallel controlled clinical study, patients were randomly divided into treatment and control groups, to receive calcipotriol betamethasone ointment or halometasone/triclosan cream, respectively, once daily in the evening over a 4-week period. The safety and efficacy of the two regimens were followed up on weeks 1, 2, 4 and at a 4-week treatment-free period. According to the degree of improvement, the total scores (0-4) before and after treatment and the efficacy index were calculated. The overall efficacy was assessed by four levels of evaluation model. Results: After 4 weeks of treatment, the cure rate was high (44.70%) in treatment group compared with control group (15.56%) (P&lt;0.001), and the effective rate was 83.33% and 55.56% in the respective groups (P&lt;0.001). At 2 and 4 weeks after treatment, there was significant difference (P&lt;0.05) between two groups, with a reduction in the intensity of pruritus, inflammation, infiltration/ hypertrophy, lichenification, and area of target lesions. The incidence of adverse events was more (1.52%) in treatment than control group (0.00%) (P&gt;0.05). Conclusion: Calcipotriol betamethasone ointment appears to be a safe and effective option for the treatment of chronic eczema

Video2_uRP: An integrated research platform for one-stop analysis of medical images.mp4

IntroductionMedical image analysis is of tremendous importance in serving clinical diagnosis, treatment planning, as well as prognosis assessment. However, the image analysis process usually involves multiple modality-specific software and relies on rigorous manual operations, which is time-consuming and potentially low reproducible.MethodsWe present an integrated platform - uAI Research Portal (uRP), to achieve one-stop analyses of multimodal images such as CT, MRI, and PET for clinical research applications. The proposed uRP adopts a modularized architecture to be multifunctional, extensible, and customizable.Results and DiscussionThe uRP shows 3 advantages, as it 1) spans a wealth of algorithms for image processing including semi-automatic delineation, automatic segmentation, registration, classification, quantitative analysis, and image visualization, to realize a one-stop analytic pipeline, 2) integrates a variety of functional modules, which can be directly applied, combined, or customized for specific application domains, such as brain, pneumonia, and knee joint analyses, 3) enables full-stack analysis of one disease, including diagnosis, treatment planning, and prognosis assessment, as well as full-spectrum coverage for multiple disease applications. With the continuous development and inclusion of advanced algorithms, we expect this platform to largely simplify the clinical scientific research process and promote more and better discoveries.</p

Video3_uRP: An integrated research platform for one-stop analysis of medical images.mp4

IntroductionMedical image analysis is of tremendous importance in serving clinical diagnosis, treatment planning, as well as prognosis assessment. However, the image analysis process usually involves multiple modality-specific software and relies on rigorous manual operations, which is time-consuming and potentially low reproducible.MethodsWe present an integrated platform - uAI Research Portal (uRP), to achieve one-stop analyses of multimodal images such as CT, MRI, and PET for clinical research applications. The proposed uRP adopts a modularized architecture to be multifunctional, extensible, and customizable.Results and DiscussionThe uRP shows 3 advantages, as it 1) spans a wealth of algorithms for image processing including semi-automatic delineation, automatic segmentation, registration, classification, quantitative analysis, and image visualization, to realize a one-stop analytic pipeline, 2) integrates a variety of functional modules, which can be directly applied, combined, or customized for specific application domains, such as brain, pneumonia, and knee joint analyses, 3) enables full-stack analysis of one disease, including diagnosis, treatment planning, and prognosis assessment, as well as full-spectrum coverage for multiple disease applications. With the continuous development and inclusion of advanced algorithms, we expect this platform to largely simplify the clinical scientific research process and promote more and better discoveries.</p

Video1_uRP: An integrated research platform for one-stop analysis of medical images.mp4

IntroductionMedical image analysis is of tremendous importance in serving clinical diagnosis, treatment planning, as well as prognosis assessment. However, the image analysis process usually involves multiple modality-specific software and relies on rigorous manual operations, which is time-consuming and potentially low reproducible.MethodsWe present an integrated platform - uAI Research Portal (uRP), to achieve one-stop analyses of multimodal images such as CT, MRI, and PET for clinical research applications. The proposed uRP adopts a modularized architecture to be multifunctional, extensible, and customizable.Results and DiscussionThe uRP shows 3 advantages, as it 1) spans a wealth of algorithms for image processing including semi-automatic delineation, automatic segmentation, registration, classification, quantitative analysis, and image visualization, to realize a one-stop analytic pipeline, 2) integrates a variety of functional modules, which can be directly applied, combined, or customized for specific application domains, such as brain, pneumonia, and knee joint analyses, 3) enables full-stack analysis of one disease, including diagnosis, treatment planning, and prognosis assessment, as well as full-spectrum coverage for multiple disease applications. With the continuous development and inclusion of advanced algorithms, we expect this platform to largely simplify the clinical scientific research process and promote more and better discoveries.</p

Table1_uRP: An integrated research platform for one-stop analysis of medical images.docx

IntroductionMedical image analysis is of tremendous importance in serving clinical diagnosis, treatment planning, as well as prognosis assessment. However, the image analysis process usually involves multiple modality-specific software and relies on rigorous manual operations, which is time-consuming and potentially low reproducible.MethodsWe present an integrated platform - uAI Research Portal (uRP), to achieve one-stop analyses of multimodal images such as CT, MRI, and PET for clinical research applications. The proposed uRP adopts a modularized architecture to be multifunctional, extensible, and customizable.Results and DiscussionThe uRP shows 3 advantages, as it 1) spans a wealth of algorithms for image processing including semi-automatic delineation, automatic segmentation, registration, classification, quantitative analysis, and image visualization, to realize a one-stop analytic pipeline, 2) integrates a variety of functional modules, which can be directly applied, combined, or customized for specific application domains, such as brain, pneumonia, and knee joint analyses, 3) enables full-stack analysis of one disease, including diagnosis, treatment planning, and prognosis assessment, as well as full-spectrum coverage for multiple disease applications. With the continuous development and inclusion of advanced algorithms, we expect this platform to largely simplify the clinical scientific research process and promote more and better discoveries.</p