A clinical prediction model for predicting the risk of liver metastasis from renal cell carcinoma based on machine learning.

Background: Renal cell carcinoma (RCC) is a highly metastatic urological cancer. RCC with liver metastasis (LM) carries a dismal prognosis. The objective of this study is to develop a machine learning (ML) model that predicts the risk of RCC with LM, which is used to assist clinical treatment. Methods: The retrospective study data of 42,547 patients with RCC were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. ML includes algorithmic methods and is a fast-rising field that has been widely used in the biomedical field. Logistic regression (LR), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGB), random forest (RF), decision tree (DT), and naive Bayesian model [Naive Bayes Classifier (NBC)] were applied to develop prediction models to predict the risk of RCC with LM. The six models were 10-fold cross-validated, and the best-performing model was selected based on the area under the curve (AUC) value. A web online calculator was constructed based on the best ML model. Results: Bone metastasis, lung metastasis, grade, T stage, N stage, and tumor size were independent risk factors for the development of RCC with LM by multivariate regression analysis. In addition, the correlation of the relative proportions of the six clinical variables was shown by a heat map. In the prediction models of RCC with LM, the mean AUC of the XGB model among the six ML algorithms was 0.947. Based on the XGB model, the web calculator (https://share.streamlit.io/liuwencai4/renal_liver/main/renal_liver.py) was developed to evaluate the risk of RCC with LM. Conclusions: This XGB model has the best predictive effect on RCC with LM. The web calculator constructed based on the XGB model has great potential for clinicians to make clinical decisions and improve the prognosis of RCC patients with LM.

Deep-learning models for the detection and incidence prediction of chronic kidney disease and type 2 diabetes from retinal fundus images.

Regular screening for the early detection of common chronic diseases might benefit from the use of deep-learning approaches, particularly in resource-poor or remote settings. Here we show that deep-learning models can be used to identify chronic kidney disease and type 2 diabetes solely from fundus images or in combination with clinical metadata (age, sex, height, weight, body-mass index and blood pressure) with areas under the receiver operating characteristic curve of 0.85-0.93. The models were trained and validated with a total of 115,344 retinal fundus photographs from 57,672 patients and can also be used to predict estimated glomerulal filtration rates and blood-glucose levels, with mean absolute errors of 11.1-13.4 ml min-1 per 1.73 m2 and 0.65-1.1 mmol l-1, and to stratify patients according to disease-progression risk. We evaluated the generalizability of the models for the identification of chronic kidney disease and type 2 diabetes with population-based external validation cohorts and via a prospective study with fundus images captured with smartphones, and assessed the feasibility of predicting disease progression in a longitudinal cohort.

DNA methylation markers in the diagnosis and prognosis of common leukemias.

The ability to identify a specific type of leukemia using minimally invasive biopsies holds great promise to improve the diagnosis, treatment selection, and prognosis prediction of patients. Using genome-wide methylation profiling and machine learning methods, we investigated the utility of CpG methylation status to differentiate blood from patients with acute lymphocytic leukemia (ALL) or acute myelogenous leukemia (AML) from normal blood. We established a CpG methylation panel that can distinguish ALL and AML blood from normal blood as well as ALL blood from AML blood with high sensitivity and specificity. We then developed a methylation-based survival classifier with 23 CpGs for ALL and 20 CpGs for AML that could successfully divide patients into high-risk and low-risk groups, with significant differences in clinical outcome in each leukemia type. Together, these findings demonstrate that methylation profiles can be highly sensitive and specific in the accurate diagnosis of ALL and AML, with implications for the prediction of prognosis and treatment selection.

Impaired lipid metabolism by age-dependent DNA methylation alterations accelerates aging.

Epigenetic alterations and metabolic dysfunction are two hallmarks of aging. However, the mechanism of how their interaction regulates aging, particularly in mammals, remains largely unknown. Here we show ELOVL fatty acid elongase 2 (Elovl2), a gene whose epigenetic alterations are most highly correlated with age prediction, contributes to aging by regulating lipid metabolism. Impaired Elovl2 function disturbs lipid synthesis with increased endoplasmic reticulum stress and mitochondrial dysfunction, leading to key accelerated aging phenotypes. Restoration of mitochondrial activity can rescue age-related macular degeneration (AMD) phenotypes induced by Elovl2 deficiency in human retinal pigmental epithelial (RPE) cells. We revealed an epigenetic-metabolism axis contributing to aging and potentially to antiaging therapy.

Mutation spectrum in GNAQ and GNA11 in Chinese uveal melanoma.

Uveal melanoma is the most common intraocular cancer in the adult eye. R183 and Q209 were found to be mutational hotspots in exon 4 and exon 5 of GNAQ and GNA11 in Caucasians. However, only a few studies have reported somatic mutations in GNAQ or GNA11 in uveal melanoma in Chinese. We extracted somatic DNA from paraffin-embedded biopsies of 63 Chinese uveal melanoma samples and sequenced the entire coding regions of exons 4 and 5 in GNAQ and GNA11. The results showed that 33% of Chinese uveal melanoma samples carried Q209 mutations while none had R183 mutation in GNAQ or GNA11. In addition, seven novel missense somatic mutations in GNAQ (Y192C, F194L, P170S, D236N, L232F, V230A, and M227I) and four novel missense somatic mutations in GNA11 (R166C, I200T, S225F, and V206M) were found in our study. The high mutation frequency of Q209 and the novel missense mutations detected in this study suggest that GNAQ and GNA11 are common targets for somatic mutations in Chinese uveal melanoma.

p53-mediated autophagy adjustment is involved in the protection of silibinin against murine dermal inflammation and epidermal apoptosis induced by UVB irradiation.

Apoptosis in murine dermal cells is retarded by ultraviolet B (UVB) irradiation-induced autophagic intervention while simultaneously epidermal cells commit apoptosis, during which inflammatory cytokines released from the lost epidermal cells promote immune responses of dermal inflammatory cells, forming morphological symptoms of acute cutaneous diseases. Autophagy is involved in prevention or provocation of apoptosis of dermal or epidermal cells of UVB-irradiated mice via modulation of intracellular metabolism, intervening the balance between cell death and survival in dermis and epidermis. p53 expressed in immune system affects autophagy function through activating or inactivating genes encoding apoptotic factors and inflammatory cytokines. Silibinin protects dermal and epidermal cells of UVB irradiated skin against abnormally autophagy-mediated apoptosis adjustments. In this study, how UVB irradiation intervenes autophagy in dermal and epidermal cells as well as how silibinin protects UVB irradiated skin through physiological recovering of autophagy function in dermis and epidermis are focused and elucidated preliminarily. Silibinin treatment (50 mg/kg/day for 4 days) reversed dermal and epidermal autophagy levels from UVB irradiation-induced improper autophagy intervention, repaired the balance between cell survival and death in dermis and epidermis, and protected skin against damage through mediation of p53 activation in dermal and epidermal cells.

Repeated injection of PEGylated solid lipid nanoparticles induces accelerated blood clearance in mice and beagles.

Surface modification of nanocarriers with amphiphilic polymer polyethylene glycol (PEG), known as PEGylation, is regarded as a major breakthrough in the application of nanocarriers. However, PEGylated nanocarriers (including liposomes and polymeric nanoparticles) induce what is referred to as the "accelerated blood clearance (ABC) phenomenon" upon repeated injection and consequently they lose their sustained circulation characteristics. Despite this, the present authors are not aware of any reports of accelerated clearance due to repeated injection for PEGylated solid lipid nanoparticles (SLNs), another promising nanocarrier. This study investigated the pharmacokinetics of PEGylated SLNs upon repeated administration in mice; moreover, the impact of circulation time on the induction of the ABC phenomenon was studied in beagles for the first time. The ABC index, selected as the ratio of the area under the concentration-time curve from time 0 to the last measured concentration of a second injection to that of the first injection, was used to evaluate the extent of this phenomenon. Results showed that the PEGylated SLNs exhibited accelerated clearance from systemic circulation upon repeated injection, both in mice and in beagles, and the ratio for the different time intervals, which showed that the ABC index exhibited significant difference within 30 minutes following the second injection, was good enough to evaluate the magnitude of ABC. This ABC index indicated that the 10 mol% PEG SLNs with a suitable prolonged circulation time induced the most marked ABC phenomenon in this research. This study demonstrated that, like PEGylated nanocarriers such as liposomes and polymeric nanoparticles, PEGylated SLNs induced the ABC phenomenon upon repeated injection--the beagle was a valuable experimental animal for this research. Furthermore, the authors considered that a relatively extended circulation time of the initial dose may be the underlying major factor determining the induction of the ABC phenomenon.