Introduction The overdiagnosing of papillary thyroid carcinoma (PTC) in China necessitates the development of an evidence-based diagnosis and prognosis strategy in line with precision medicine. A landscape of PTC in Chinese cohorts is needed to provide comprehensiveness. Methods 6 paired PTC samples were employed for whole-exome sequencing, RNA sequencing, and data-dependent acquisition mass spectrum analysis. Weighted gene co-expression network analysis and protein-protein interactions networks were used to screen for hub genes. Moreover, we verified the hub genes' diagnostic and prognostic potential using online databases. Logistic regression was employed to construct a diagnostic model, and we evaluated its efficacy and specificity based on TCGA-THCA and GEO datasets. Results The basic multiomics landscape of PTC among local patients were drawn. The similarities and differences were compared between the Chinese cohort and TCGA-THCA cohorts, including the identification of PNPLA5 as a driver gene in addition to BRAF mutation. Besides, we found 572 differentially expressed genes and 79 differentially expressed proteins. Through integrative analysis, we identified 17 hub genes for prognosis and diagnosis of PTC. Four of these genes, ABR, AHNAK2, GPX1, and TPO, were used to construct a diagnostic model with high accuracy, explicitly targeting PTC (AUC=0.969/0.959 in training/test sets). Discussion Multiomics analysis of the Chinese cohort demonstrated significant distinctions compared to TCGA-THCA cohorts, highlighting the unique genetic characteristics of Chinese individuals with PTC. The novel biomarkers, holding potential for diagnosis and prognosis of PTC, were identified. Furthermore, these biomarkers provide a valuable tool for precise medicine, especially for immunotherapeutic or nanomedicine based cancer therapy.

PurposeFDG PET imaging is often recommended for the diagnosis of pulmonary nodules after indeterminate low dose CT lung cancer screening. Lowering FDG injecting is desirable for PET imaging. In this work, we aimed to investigate the performance of a deep learning framework in the automatic diagnoses of pulmonary nodules at different count levels of PET imaging.Materials and methodsTwenty patients with 18F-FDG-avid pulmonary nodules were included and divided into independent training (60%), validation (20%), and test (20%) subsets. We trained a convolutional neural network (ResNet-50) on original DICOM images and used ImageNet pre-trained weight to fine-tune the model. Simulated low-dose PET images at the 9 count levels (20 × 106, 15 × 106, 10 × 106, 7.5 × 106, 5 × 106, 2 × 106, 1 × 106, 0.5 × 106, and 0.25 × 106 counts) were obtained by randomly discarding events in the PET list mode data for each subject. For the test dataset with 4 patients at the 9 count levels, 3,307 and 3,384 image patches were produced for lesion and background, respectively. The receiver-operator characteristic (ROC) curve of the proposed model under the different count levels with different lesion size groups were assessed and the areas under the ROC curve (AUC) were compared.ResultsThe AUC values were >0.98 for all count levels except for 0.5 and 0.25 million true counts (0.975 (CL 95%, 0.953–0.992) and 0.963 (CL 95%, 0.941–0.982), respectively). The AUC values were 0.941(CL 95%, 0.923–0.956), 0.993(CL 95%, 0.990–0.996) and 0.998(CL 95%, 0.996-0.999) for different groups of lesion size with effective diameter (R) <10 mm, 10–20 mm, and >20 mm, respectively. The count limit for achieving high AUC (≥0.96) for lesions with size R < 10 mm and R > 10 mm were 2 million (equivalent to an effective dose of 0.08 mSv) and 0.25 million true counts (equivalent to an effective dose of 0.01 mSv), respectively.ConclusionAll of the above results suggest that the proposed deep learning based method may detect small lesions <10 mm at an effective radiation dose <0.1 mSv.Advances in knowledgeWe investigated the advantages and limitations of a fully automated lung cancer detection method based on deep learning models for data with different lesion sizes and different count levels, and gave guidance for clinical application.

BackgroundTryptophol (TOL) is a metabolic derivative of tryptophan (Trp) and shows pleiotropic effects in humans, plants and microbes. The mechanisms of TOL biosynthesis were first explored several decades ago. Nonetheless, a systematic interpretation of TOL over-accumulation is still lacking.ResultsBased on TOL yield, a suitable transformation medium (TM1) was used to culture Saccharomyces cerevisiae strain KMLY1-2. The dynamics of TOL production, cell growth, and gene transcription revealed that TOL production was dependent on cell density and the expression of key genes. Additionally, the effects of Trp and phenylalanine (Phe) on TOL production were tested, and the results showed that Trp can significantly facilitate TOL accumulation, but output plateaued (231.02−266.31 mg/L) at Trp concentrations ≥0.6 g/L. In contrast, Phe reduced the stimulatory effect of Trp, which strongly depended on the Phe concentration. To elucidate the molecular basis and regulatory mechanism of TOL overproduction, an integrated analysis of metabolomics, genomics, and transcriptomics was performed. The results revealed that 1) both the Ehrlich pathway and tryptamine-dependent pathway were involved in S. cerevisiae TOL biosynthesis; 2) Trp increased TOL production by enhancing the Ehrlich pathway, in which the steps of transamination (including aminotransferase genes aro9 , aat1 , bat2 and his5 ) and decarboxylation (including decarboxylase genes aro10 and pdc5 ) played important roles. Of course, this process was assisted by amino acid permease genes agp1 and tat2 , dihydrolipoyl dehydrogenase gene lpd1 , and transcriptional activator gene aro80 , etc.; 3) Phe restricted TOL biosynthesis by repressing the transcript levels of genes such as aat1 , his5 , aro10 , pdc5 and aro80 , thus interfering with the transamination and decarboxylation reactions; and 4) under sufficient Trp conditions, the de novo Trp biosynthetic pathway and central carbon metabolism (glycolysis, pentose phosphate pathway, and citrate cycle) of S. cerevisiae were weakened, while the content of some amino acids increased, which may be related to the promotion of yeast cell growth by Trp.ConclusionsIn this study, TOL production of S. cerevisiae was significantly improved, and our integrated multi-omics analyses have provided insights into the understanding of TOL over-accumulation, which will be useful for future production of TOL using metabolic engineering strategies.

Over the past decade or so, Chinese government has been strategically luring back overseas Chinese high-fliers to strengthen science, technology, and higher education. One of the major initiatives is the Thousand Youth Talents Scheme (TYTS) launched in 2011. By 2017, 2980 Thousand Youth Talents Scheme scholars (TYTSs) had been recruited into China’s universities, research institutes, and enterprises (Retrieved from http://www.1000plan.org ). Enjoying favorable policies and possessing their unique capital, they are well positioned to transfer the knowledge, skills, and experiences obtained overseas to their home institutions, while at the same time face challenges and difficulties in their professional development at home. While China’s overseas talent policies, at both governmental and institutional levels, have been well documented, the lived experiences of such elite scholars have been little understood. Employing a qualitative method of semi-structure interviews, this article examines how they mobilize domestic and international resources and networks to construct their professional development spaces and navigate their careers in the Chinese academic environment. It reports that TYTSs have established advantageous conditions for their professional development in both national and global environment and participated in transnational knowledge production. They have made significant contributions to the development of their affiliated institutions by producing compelling publications, extending new research directions, and uplifting domestic academic communities.