Supernumerary teeth are common clinical dental anomalies. Although various studies have provided abundant information regarding genes and signaling pathways involved in tooth morphogenesis, which include Wnt, FGF, BMP, and Shh, the molecular mechanism of tooth formation, especially for supernumerary teeth, is still unclear. In the population, some cases of supernumerary teeth are sporadic, while others are syndrome-related with familial hereditary. The prompt and accurate diagnosis of syndrome related supernumerary teeth is quite important for some distinctive disorders. Mice are the most commonly used model system for investigating supernumerary teeth. The upregulation of Wnt and Shh signaling in the dental epithelium results in the formation of multiple supernumerary teeth in mice. Understanding the molecular mechanism of supernumerary teeth is also a component of understanding tooth formation in general and provides clinical guidance for early diagnosis and treatment in the future.

Background Acute kidney injury (AKI) is the most common and serious complication of sepsis, accompanied by high mortality and disease burden. The early prediction of AKI is critical for timely intervention and ultimately improves prognosis. This study aims to establish and validate predictive models based on novel machine learning (ML) algorithms for AKI in critically ill patients with sepsis. Methods Data of patients with sepsis were extracted from the Medical Information Mart for Intensive Care III (MIMIC- III) database. Feature selection was performed using a Boruta algorithm. ML algorithms such as logistic regression (LR), k-nearest neighbors (KNN), support vector machine (SVM), decision tree, random forest, Extreme Gradient Boosting (XGBoost), and artificial neural network (ANN) were applied for model construction by utilizing tenfold cross-validation. The performances of these models were assessed in terms of discrimination, calibration, and clinical application. Moreover, the discrimination of ML-based models was compared with those of Sequential Organ Failure Assessment (SOFA) and the customized Simplified Acute Physiology Score (SAPS) II model. Results A total of 3176 critically ill patients with sepsis were included for analysis, of which 2397 cases (75.5%) developed AKI during hospitalization. A total of 36 variables were selected for model construction. The models of LR, KNN, SVM, decision tree, random forest, ANN, XGBoost, SOFA and SAPS II score were established and obtained area under the receiver operating characteristic curves of 0.7365, 0.6637, 0.7353, 0.7492, 0.7787, 0.7547, 0.821, 0.6457 and 0.7015, respectively. The XGBoost model had the best predictive performance in terms of discrimination, calibration, and clinical application among all models. Conclusion The ML models can be reliable tools for predicting AKI in septic patients. The XGBoost model has the best predictive performance, which can be used to assist clinicians in identifying high-risk patients and implementing early interventions to reduce mortality.

Background. Inflammation plays a key role in the pathophysiology and progression of acute kidney injury (AKI). Red cell distribution width (RDW) to platelet ratio (RPR) is a novel inflammatory index, and its prognostic effect on critically ill patients with AKI is rarely investigated. This work is aimed at investigating the association between RPR and in-hospital mortality in these patients. Methods. Data were extracted from the Medical Information Mart for Intensive Care III database. All-cause death during hospitalization was selected as the primary outcome. Receiver operating characteristic (ROC) curve was used to determine the optimal cut-off value, and the area under the curve (AUC) was applied to compare predictive ability among different indices. Cox proportional hazard models were utilized to assess the association between RPR and in-hospital mortality. Restricted cubic spline analysis for multivariate Cox model was performed to explore the shape of the relationship between RPR and mortality. Results. A total of 24,166 critically ill patients with AKI were included. The relationship of RPR and in-hospital mortality was nonlinear with a trend to rise rapidly and then gradually. For mortality prediction, RPR had the optimal cut-off value of 0.093, of which the AUC was 0.791 (95% confidence interval (CI): 0.773–0.810), which was higher than those of RDW, platelet, sequential organ failure assessment score, simplified acute physiology score II, neutrophil to lymphocyte ratio, and platelet to lymphocytes ratio. After adjustments for various confounders, high RPR showed a significant association with increased mortality with hazard ratios of 1.46 (95% CI: 1.40–1.55) for categorical variable and 1.88 (95% CI: 1.80–1.97) for continuous variables in the fully adjusted model. Conclusions. Elevated RPR on admission is substantially associated with high risk of in-hospital mortality in critically ill patients with AKI and thus may serve as a novel predictor of prognosis for these patients.

ObjectivesThis study aimed to investigate the functional effects of microRNA (miR)-214-5p on osteoblastic cells, which might provide a potential role of miR-214-5p in bone fracture healing.MethodsBlood samples were obtained from patients with hand fracture or intra-articular calcaneal fracture and from healthy controls (HCs). Expression of miR-214-5p was monitored by qRT-PCR at day 7, 14 and 21 post-surgery. Mouse osteoblastic MC3T3-E1 cells were transfected with antisense oligonucleotides (ASO)-miR-214-5p, collagen type IV alpha 1 (COL4A1) vector or their controls; thereafter, cell viability, apoptotic rate, and the expression of collagen type I alpha 1 (COL1A1), type II collagen (COL-II), and type X collagen (COL-X) were determined. Luciferase reporter assay, qRT-PCR, and Western blot were performed to ascertain whether COL4A1 was a target of miR-214-5p.ResultsPlasma miR-214-5p was highly expressed in patients with bone fracture compared with HCs after fracture (p < 0.05 or p < 0.01). Inhibition of miR-214-5p increased the viability of MC3T3-E1 cells and the expressions of COL1A1 and COL-X, but decreased the apoptotic rate and COL-II expression (p < 0.05 or p < 0.01). COL4A1 was a target of miR-214-5p, and was negatively regulated by miR-214-5p (p < 0.05 or p < 0.01). Overexpression of COL4A1 showed a similar impact on cell viability, apoptotic rate, and COL1A1, COL-II, and COL-X expressions inhibiting miR-214-5p (p < 0.01).ConclusionInhibition of miR-214-5p promotes cell survival and extracellular matrix (ECM) formation of osteoblastic MC3T3-E1 cells by targeting COL4A1.Cite this article: Q. S. Li, F. Y. Meng, Y. H. Zhao, C. L. Jin, J. Tian, X. J. Yi. Inhibition of microRNA-214-5p promotes cell survival and extracellular matrix formation by targeting collagen type IV alpha 1 in osteoblastic MC3T3-E1 cells. Bone Joint Res 2017;6:464–471. DOI: 10.1302/2046-3758.68.BJR-2016-0208.R2

The aquathermolysis of Liaohe heavy crude oil during steam stimulation was studied by using molybdenum oleate as oil-soluble catalyst for the reaction in this paper. The laboratory experiment shows that viscosity-reduction ratio of heavy oil is over 90% at 240°C, 24hr, with 0.5wt% catalyst solution. The field test that applied aquathermolytic technology in puff-and-huff operation was carried out in Qi-40 and Qi-108 blocks of Liaohe oilfield. As a result, significant viscosity reduction and production increase were obtained. Further filed test indicated that the cycle decline rate of heavy oil production in puff-and-huff operation was improved by aquathermolytic technology. Introduction The researches continue to be aimed at developing alternative methods for heavy oils production because of the anticipated shortfall in supplies of high quality crude oil in China. Heavy oil accounts for a large proportion in the proved oil reservoirs. Heavy oil reservoirs account for 15% of the total oil reservoirs in China[1]. It is difficulty to produce heavy oils by conventional methods because of their high viscosity. Nowadays, steam stimulation is the most effective way to produce heavy oil in the world. Steam can reduce the viscosity of heavy oil and enable the heavy oil to flow through the porous media in reservoir. The oil production can start after injection of steam and soaking periods. Many researched results show that the injected steam can not only reduce the viscosity of heavy oil, but also react with some components in the heavy oil and the reservoir minerals, thereby, leading to the heavy oil properties and compositions changed [2,3].Hyne et al. described all of the reaction between steam, heavy oil and minerals as "aquathermalysis".[4] The catalysts usually applied in aquathermalysis are not oil-soluble. It is mean that the catalysts and the heavy oil cannot fully mix up, so the catalytic effect is relatively low. In this study, a new oil-soluble catalyst -Molybdenum Oleate - was prepared, which is more effective than inorganic catalyst for the aquathermalysis reaction of heavy crude oil. This advantage was confirmed by laboratory and field experiments. Laboratory Experiment Preparation of oil-soluble catalyst. MoO3 was put into distilled water firstly. Then put quantitative oileic acid into the solution and keep the mixture boiling for half an hour. Finally, separating the organic and aqueous phase after the mixture is cooled. The molybdenum oleate exists in the organic phase. The Mo content in the organic metal compounds is 24.9%. Properties of heavy oil samples. The heavy oil samples used in this study were taken from Qi40 block (1#) and Qi108 block (2#) in Liaohe oil field. The properties and composition of the samples are given in table 1. Experiment process. In the aquathermolytic process, 75g heavy oil sample, 0.4g catalyst and 25g water were put into the autoclave (internal volume 125mL), and then the system was heated to 240°C kept for 24h. At the end of heating period, the autoclave was cooled slowly to room temperature. Element analysis. The elements in heavy oil were determined by Carle Erba EA1108 model element analysis instrument, 3–4 mg sample was used for auto sampling. Oil compositions analysis. The compositions of oil samples were determined by high performance liquid chromatographic (HPLC) analysis of deasphaltened oil. The asphaltene were removed by precipitation with addition of 40-volume excess dry hexane to the solution of oil in dichlormethane. The HPLC analysis was carried out on a semi-preparative basis using a Whatman Magnum-9, 10µsilica column and ultraviolet (UV) and refractive index (RI) detectors in series. The silica column was activated previously by overnight flushing (2 ml/min) with dry hexane. The saturate and aromatic fractions were obtained by elution with hexane, and the resins were obtained by back-flushing the column with tetrahydrofuran (distilled from LIAlH4) after the collection of the aromatic fraction. These fractions were quantified gravimetrically after removal of solvent. Viscosity Determinations. Viscosities were determined using a rotary viscometer (Haake RV-550, made in Germen). For the measurement a SV2 senor system was used. Approximately 2g oil sample was placed in the sample cup and allow to equilibrate to 50°C over 20 min. then the measurement was made according to procedures specified by the manufacturer.

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The transcription factor MYC, which is dysregulated in the majority of gliomas, is difficult to target directly. Deubiquitinase ubiquitin-specific protease 28 (USP28) stabilizes oncogenic factors, including MYC. However, the contribution of USP28 in tumorigenesis, particularly in glioma, is unknown. Here, we determined the expression of USP28 and assessed its clinical significance in human glioma. We found that USP28 is overexpressed in human glioma but not in normal brain tissue. The level of USP28 protein expression in human glioma tissues was directly correlated with glioma grade. Meanwhile, the level of USP28 protein expression in human glioblastoma tissues was inversely correlated with patient survival. Enforced USP28 expression promotes SW1783 glioma cell proliferation. Moreover, gliomas that arose from USP28-transfected SW1783 cells displayed tumorigenicity in nude mouse model systems. Inhibition of USP28 expression in glioblastoma U373 cells suppressed anchorage-independent growth in vitro and tumorigenicity in vivo. Furthermore, USP28 regulates the expression of MYC protein, which is essential in USP28-induced cell growth in glioma cells. These results showed that USP28 is overexpressed in human glioblastomas and it contributes to glioma tumorigenicity. Therefore, USP28 could be a new target of therapy for human malignant glioma.

Background The early death of patients is a global cancer issue. We aimed to identify the risk factors for early death in stage IV breast cancer. Predictive nomograms for early death evaluation were generated based on the risk factors. Material/Methods Based on the Surveillance, Epidemiology, and End Results (SEER) database, patients diagnosed with IV breast cancer were selected. The risk factors for early death (survival time ≤1 year) were identified using logistic regression model analysis. Predictive nomograms were constructed and internal validation was performed. Results A total of 5998 (32.6%) breast cancer patients were diagnosed as early death in the construction cohort. Age older than 50 years, unmarried status, black race, uninsured status, triple-negative type, grade (II and III), tumor size >5 cm, and metastasis to lung, liver, and brain were risk factors for total early death, while Luminal B subtype, N1 stage, and surgical interventions were associated with lower risk of early death. As for cancer-specific and non-cancer-specific early death, several factors were not consistent between the 2 groups. Nomograms for all-cause, cancer-specific, and non-cancer-specific early death were constructed. The calibration curve showed satisfactory agreement. The areas under the ROC curve (AUC) were 78.3% (95% CI: 77.7–78.9%), 75.8% (75.1–76.4%), and 72.3% (71.6–72.9%), respectively. In the validation cohort, a total of 689 (19.3%) patients were diagnosed as early death and the calibration curve showed satisfactory agreement. The AUCs of the all-cause, cancer-specific, and non-cancer-specific early death prediction were 74.0% (95% CI: 72.5–75.4%), 73.5% (72.0–74.9%), and 68.6% (67.0–70.1%), respectively. Conclusions Nomograms were generated to predict early death, with good calibration and discrimination. The predictive model can provide a reference for identifying cases with high risk of early death among stage IV breast cancer patients and play an auxiliary role in guiding individual treatment.