Yanan Wang

Background and Purpose- Malignant brain edema after ischemic stroke has high mortality but limited treatment. Therefore, early prediction is important, and we systematically reviewed predictors and predictive models to identify reliable markers for the development of malignant edema. Methods- We searched Medline and Embase from inception to March 2018 and included studies assessing predictors or predictive models for malignant brain edema after ischemic stroke. Study quality was assessed by a 17-item tool. Odds ratios, mean differences, or standardized mean differences were pooled in random-effects modeling. Predictive models were descriptively analyzed. Results- We included 38 studies (3278 patients) with 24 clinical factors, 7 domains of imaging markers, 13 serum biomarkers, and 4 models. Generally, the included studies were small and showed potential publication bias. Malignant edema was associated with younger age (n=2075; mean difference, -4.42; 95% CI, -6.63 to -2.22), higher admission National Institutes of Health Stroke Scale scores (n=807, median 17-20 versus 5.5-15), and parenchymal hypoattenuation >50% of the middle cerebral artery territory on initial computed tomography (n=420; odds ratio, 5.33; 95% CI, 2.93-9.68). Revascularization (n=1600, odds ratio, 0.37; 95% CI, 0.24-0.57) were associated with a lower risk for malignant edema. Four predictive models all showed an overall C statistic >0.70, with a risk of overfitting. Conclusions- Younger age, higher National Institutes of Health Stroke Scale, and larger parenchymal hypoattenuation on computed tomography are reliable early predictors for malignant edema. Revascularization reduces the risk of malignant edema. Future studies with robust design are needed to explore optimal cutoff age and National Institutes of Health Stroke Scale scores and to validate and improve existing models.

Background: Tongue images (the colour, size and shape of the tongue and the colour, thickness and moisture content of the tongue coating), reflecting the health state of the whole body according to the theory of traditional Chinese medicine (TCM), have been widely used in China for thousands of years. Herein, we investigated the value of tongue images and the tongue coating microbiome in the diagnosis of gastric cancer (GC). Methods: From May 2020 to January 2021, we simultaneously collected tongue images and tongue coating samples from 328 patients with GC (all newly diagnosed with GC) and 304 non-gastric cancer (NGC) participants in China, and 16 S rDNA was used to characterize the microbiome of the tongue coating samples. Then, artificial intelligence (AI) deep learning models were established to evaluate the value of tongue images and the tongue coating microbiome in the diagnosis of GC. Considering that tongue imaging is more convenient and economical as a diagnostic tool, we further conducted a prospective multicentre clinical study from May 2020 to March 2022 in China and recruited 937 patients with GC and 1911 participants with NGC from 10 centres across China to further evaluate the role of tongue images in the diagnosis of GC. Moreover, we verified this approach in another independent external validation cohort that included 294 patients with GC and 521 participants with NGC from 7 centres. This study is registered at ClinicalTrials.gov, NCT01090362. Findings: For the first time, we found that both tongue images and the tongue coating microbiome can be used as tools for the diagnosis of GC, and the area under the curve (AUC) value of the tongue image-based diagnostic model was 0.89. The AUC values of the tongue coating microbiome-based model reached 0.94 using genus data and 0.95 using species data. The results of the prospective multicentre clinical study showed that the AUC values of the three tongue image-based models for GCs reached 0.88-0.92 in the internal verification and 0.83-0.88 in the independent external verification, which were significantly superior to the combination of eight blood biomarkers. Interpretation: Our results suggest that tongue images can be used as a stable method for GC diagnosis and are significantly superior to conventional blood biomarkers. The three kinds of tongue image-based AI deep learning diagnostic models that we developed can be used to adequately distinguish patients with GC from participants with NGC, even early GC and precancerous lesions, such as atrophic gastritis (AG). Funding: The National Key R&D Program of China (2021YFA0910100), Program of Zhejiang Provincial TCM Sci-tech Plan (2018ZY006), Medical Science and Technology Project of Zhejiang Province (2022KY114, WKJ-ZJ-2104), Zhejiang Provincial Research Center for Upper Gastrointestinal Tract Cancer (JBZX-202006), Natural Science Foundation of Zhejiang Province (HDMY22H160008), Science and Technology Projects of Zhejiang Province (2019C03049), National Natural Science Foundation of China (82074245, 81973634, 82204828), and Chinese Postdoctoral Science Foundation (2022M713203).

BACKGROUND: Lipid accumulation by oleaginous microorganisms is of great scientific interest and biotechnological potential. While nitrogen limitation has been routinely employed, low-cost raw materials usually contain rich nitrogenous components, thus preventing from efficient lipid production. Inorganic phosphate (Pi) limitation has been found sufficient to promote conversion of sugars into lipids, yet the molecular basis of cellular response to Pi limitation and concurrent lipid accumulation remains elusive. RESULTS: Here, we performed multi-omic analyses of the oleaginous yeast Rhodosporidium toruloides to shield lights on Pi-limitation-induced lipid accumulation. Samples were prepared under Pi-limited as well as Pi-repleted chemostat conditions, and subjected to analysis at the transcriptomic, proteomic, and metabolomic levels. In total, 7970 genes, 4212 proteins, and 123 metabolites were identified. Results showed that Pi limitation facilitates up-regulation of Pi-associated metabolism, RNA degradation, and triacylglycerol biosynthesis while down-regulation of ribosome biosynthesis and tricarboxylic acid cycle. Pi limitation leads to dephosphorylation of adenosine monophosphate and the allosteric activator of isocitrate dehydrogenase key to lipid biosynthesis. It was found that NADPH, the key cofactor for fatty acid biosynthesis, is limited due to reduced flux through the pentose phosphate pathway and transhydrogenation cycle and that this can be overcome by over-expression of an endogenous malic enzyme. These phenomena are found distinctive from those under nitrogen limitation. CONCLUSIONS: Our data suggest that Pi limitation activates Pi-related metabolism, RNA degradation, and TAG biosynthesis while inhibits ribosome biosynthesis and TCA cycle, leading to enhanced carbon fluxes into lipids. The information greatly enriches our understanding on microbial oleaginicity and Pi-related metabolism. Importantly, systems data may facilitate designing advanced cell factories for production of lipids and related oleochemicals.