As an alternative to modern western medicine, Traditional Chinese Medicine (TCM) is receiving increasingly attention worldwide. Great efforts have been paid to TCM’s modernization, which tries to bridge the gap between TCM and modern western medicine. As TCM and modern western medicine share a common aspect at molecular level that the compound(s) perturb human’s dysfunction network and restore human normal physiological condition, the relationship between compounds (in herb, refer to ingredients) and their targets (proteins) should be the key factor to connect TCM and modern medicine. Accordingly, we construct this Traditional Chinese Medicine Integrated Database (TCMID, http://www.megabionet.org/tcmid/), which records TCM-related information collected from different resources and through text-mining method. To enlarge the scope of the TCMID, the data have been linked to common drug and disease databases, including Drugbank, OMIM and PubChem. Currently, our TCMID contains ∼47 000 prescriptions, 8159 herbs, 25 210 compounds, 6828 drugs, 3791 diseases and 17 521 related targets, which is the largest data set for related field. Our web-based software displays a network for integrative relationships between herbs and their treated diseases, the active ingredients and their targets, which will facilitate the study of combination therapy and understanding of the underlying mechanisms for TCM at molecular level.

Long-term allograft survival generally requires lifelong immunosuppression (IS). Rarely, recipients display spontaneous ''operational tolerance'' with stable graft function in the absence of IS. The lack of biological markers of this phenomenon precludes identification of potentially tolerant patients in which IS could be tapered and hinders the development of new tolerance-inducing strategies. The objective of this study was to identify minimally invasive blood biomarkers for operational tolerance and use these biomarkers to determine the frequency of this state in immunosuppressed patients with stable graft function. Blood gene expression profiles from 75 renal-transplant patient cohorts (operational tolerance/acute and chronic rejection/stable graft function on IS) and 16 healthy individuals were analyzed. A subset of samples was used for microarray analysis where three-class comparison of the different groups of patients identified a ''tolerant footprint'' of 49 genes. These biomarkers were applied for prediction of operational tolerance by microarray and real-time PCR in independent test groups. Thirty-three of 49 genes correctly segregated tolerance and chronic rejection phenotypes with 99% and 86% specificity. The signature is shared with 1 of 12 and 5 of 10 stable patients on triple IS and low-dose steroid monotherapy, respectively. The gene signature suggests a pattern of reduced costimulatory signaling, immune quiescence, apoptosis, and memory T cell responses. This study identifies in the blood of kidney recipients a set of genes associated with operational tolerance that may have utility as a minimally invasive monitoring tool for guiding IS titration. Further validation of this tool for safe IS minimization in prospective clinical trials is warranted.kidney transplantation ͉ microarray ͉ tolerant ͉ genomics ͉ immunosuppression titration D espite continuous improvement in renal allograft survival over the last decade, the half-life of renal allografts has increased marginally because of accrual of chronic graft nephropathy from drug-related nephrotoxicity and chronic rejection (1, 2). Patients facing life-long immunosuppression (IS) have increased risk of infection and malignancy (3), whereas insufficient immunosuppressive drug exposure or interruption usually increases rejection risk (4). However, spontaneous and long-term graft acceptance is observed in a small number of patients after solid-organ transplantation (5, 6), years after total withdrawal of immunosuppressive drugs, confirming that a clinical operational state of tolerance to a mismatched graft, described as ''a state of quiescence of the transplanted organ, functioning without a destructive immune response'' (7), can indeed occur and exist in humans. However, the frequency of this observation in the kidney transplant population is unknown and, currently, we cannot identify patients primed to develop this immune adaptation or monitor for the stability of this status of ''operational tolerance.'' The operationally tolerant kidney transpla...

The surface of the cornea consists of a unique type of non-keratinized epithelial cells arranged in an orderly fashion, and this is essential for vision by maintaining transparency for light transmission. Cornea epithelial cells (CECs) undergo continuous renewal from limbal stem or progenitor cells (LSCs)1,2, and deficiency in LSCs or corneal epithelium—which turns cornea into a non-transparent, keratinized skin-like epithelium—causes corneal surface disease that leads to blindness in millions of people worldwide3. How LSCs are maintained and differentiated into corneal epithelium in healthy individuals and which key molecular events are defective in patients have been largely unknown. Here we report establishment of an in vitro feeder-cell-free LSC expansion and three-dimensional corneal differentiation protocol in which we found that the transcription factors p63 (tumour protein 63) and PAX6 (paired box protein PAX6) act together to specify LSCs, and WNT7A controls corneal epithelium differentiation through PAX6. Loss of WNT7A or PAX6 induces LSCs into skin-like epithelium, a critical defect tightly linked to common human corneal diseases. Notably, transduction of PAX6 in skin epithelial stem cells is sufficient to convert them to LSC-like cells, and upon transplantation onto eyes in a rabbit corneal injury model, these reprogrammed cells are able to replenish CECs and repair damaged corneal surface. These findings suggest a central role of the WNT7A–PAX6 axis in corneal epithelial cell fate determination, and point to a new strategy for treating corneal surface diseases.

Regular health screening plays a crucial role in the early detection of common chronic diseases and prevention of their progression. An AI system capable of recapitulating early disease detection, staging and incidence prediction would help to improve healthcare access and delivery, particularly in resource-poor or remote settings. Using a total of 115,344 retinal fundus photographs from 57,672 patients (with data split into mutually exclusive training, internal testing, and external validation sets), we first developed AI models capable of identifying chronic kidney disease (CKD) and type 2 diabetes mellitus (T2DM) based on fundus images. The AI system was shown to be capable of predicting the clinical indicators of CKD and T2DM (including eGFR and blood glucose levels), which indicates its potential for extracting quantitative clinical metrics embedded subtly within retinal fundus images. We further developed an AI system to predict the risk of disease progression using baseline images of 10,269 patients for whom longitudinal clinical data were available for up to 6 years, which demonstrated potential utility in optimizing health screening intervals and clinical management. The generalizability of the AI system in identifying and predicting the progression of CKD and T2DM was evaluated using population-based external validation cohorts. Moreover, a prospective pilot study with 3,081 patients was also conducted to demonstrate the broader applicability of the AI system at the 'point-of-care' using fundus images captured with smartphones. The results provide proof-of-concept for a reliable and non-invasive AI-based clinical screening tool based on fundus photographs for the early detection and incidence prediction of two common systemic diseases.