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Periodontitis is associated with abnormal purine metabolism, which is manifested by increased uric acid in host blood and increased expression of the purine-degrading enzyme, xanthine oxidoreductase (XOR), in periodontal tissues. Both XOR and uric acid are pro-oxidative and pro-inflammatory mediators under pathological conditions. Animal studies have found that injection of uric acid promotes the progression of periodontitis and that febuxostat (an XOR inhibitor) improves tissue destruction in periodontitis. Therefore, blocking the source of uric acid may be a therapeutic strategy to control the progression of periodontitis. In this article, the rationality of XOR inhibitors as potential therapeutic drugs for periodontitis is reviewed. The literature review results suggest that XOR inhibitors show antioxidative, anti-inflammatory, and anti-osteoclastic effects, and XOR inhibitors show clinical efficacy in the treatment of infectious, inflammatory and osteolytic diseases. Although there is no direct evidence to support the finding that XOR inhibitors can ameliorate periodontal microecological dysbiosis, these drugs can modulate intestinal microflora dysbiosis, and there is indirect evidence to support a beneficial effect of XOR inhibitors on periodontal microecological dysbiosis. In conclusion, XOR inhibitors may be used as immunomodulators for the adjuvant treatment of periodontitis by inhibiting inflammation, oxidative stress and anti-osteoclast effects.

Objective To describe the occurrence of general surgical complications and establish prediction models for different types of complications based on a multicenter cohort study. Methods Based on Modern Surgery and Anesthesia Safety Management System Construction and Promotion(MSCP), patients who underwent general surgery in 4 hospitals from January to June 2015 and from January to June 2016 were selected as participants, and perioperative data of patients were collected. Logistic regression was used to identify risk factors and predict complications. Results Among 19 223 patients, 830(4.32%) had complications. Among participants who had complications, 371(44.70%) had incision complications, 190(22.89%) had fistula complications, 310(37.35%) had infection complications, 161(19.40%) had failure complications, and 104(12.5%) died. There were significant differences in the risk factors of different types of complications. The risk factors of incision,infection and failure covered the whole perioperative period, while fistula complications mainly focused on the difficulty of surgery and postoperative treatment, and for death outcomes, postoperative risk factors were more severe than preoperative risk factors. The areas under the curves of prediction were between 0.80-0.93. Conclusions In general surgery, different types of complications have different risk factors. The targeted prediction model can avoid the rough simplification of complications and fuzzy influence of each factor, and can provide reference for the prevention of complications.

Background: The current guidelines for managing screen‐detected pulmonary nodules offer rule‐based recommendations for immediate diagnostic work‐up or follow‐up at intervals of 3, 6, or 12 months. Customized visit plans are lacking. Purpose: To develop individualized screening schedules using reinforcement learning (RL) and evaluate the effectiveness of RL‐based policy models. Methods: Using a nested case–control design, we retrospectively identified 308 patients with cancer who had positive screening results in at least two screening rounds in the National Lung Screening Trial. We established a control group that included cancer‐free patients with nodules, matched (1:1) according to the year of cancer diagnosis. By generating 10,164 sequence decision episodes, we trained RL‐based policy models, incorporating nodule diameter alone, combined with nodule appearance (attenuation and margin) and/or patient information (age, sex, smoking status, pack‐years, and family history). We calculated rates of misdiagnosis, missed diagnosis, and delayed diagnosis, and compared the performance of RL‐based policy models with rule‐based follow‐up protocols (National Comprehensive Cancer Network guideline; China Guideline for the Screening and Early Detection of Lung Cancer). Results: We identified significant interactions between certain variables (e.g., nodule shape and patient smoking pack‐years, beyond those considered in guideline protocols) and the selection of follow‐up testing intervals, thereby impacting the quality of the decision sequence. In validation, one RL‐based policy model achieved rates of 12.3% for misdiagnosis, 9.7% for missed diagnosis, and 11.7% for delayed diagnosis. Compared with the two rule‐based protocols, the three best‐performing RL‐based policy models consistently demonstrated optimal performance for specific patient subgroups based on disease characteristics (benign or malignant), nodule phenotypes (size, shape, and attenuation), and individual attributes. Conclusions: This study highlights the potential of using an RL‐based approach that is both clinically interpretable and performance‐robust to develop personalized lung cancer screening schedules. Our findings present opportunities for enhancing the current cancer screening system. [ABSTRACT FROM AUTHOR]

(Cell 173, 371–385.e1–e9; April 5, 2018) It has come to our attention that we made two errors in preparation of this manuscript. First, in the STAR Methods, under the subheading of “Hypermutators and Immune Infiltrates” within the “Quantification and Statistical Analysis” section, we inadvertently referred to Figures S7A–S7C for data corresponding to sample stratification by hypermutator status alone in the last sentence. It should have referred to Figure S6A–S6C. Second, the lists of highly frequent missense mutations for COAD (colon adenocarcinoma) and READ (rectum adenocarcinoma) displayed in Figure S7 were incorrect because when we ordered the mutations in the initial analysis, we mistakenly combined the two cancer types COAD and READ for analysis, despite the fact that they were listed as two separate cancer types in the x-axis of the figure. After re-ordering the mutations by frequency for COAD and READ independently, information on highly frequent missense mutations for each of these cancer types is different and updated now in the revised Figure S7. These errors don't change the major conclusions of the paper and have been corrected online. We apologize for any confusion they may have caused. [Figure-presented]

In this work, flexible Cu2−xS films on nylon membranes are prepared by combining a simple hydrothermal synthesis and vacuum filtration followed by hot pressing. The films consist of Cu2S and Cu1.96S two phases with grain sizes from nano to submicron. Doping Se on the S site not only increases the Cu1.96S content in the Cu2−xS to increase carrier concentration but also modifies electronic structure, thereby greatly improves the electrical properties of the Cu2−xS. Specifically, an optimal composite film with a nominal composition of Cu2−xS0.98Se0.02 exhibits a high power factor of ~150.1 μW m−1 K−2 at 300 K, which increases by ~138% compared to that of the pristine Cu2−xS film. Meanwhile, the composite film shows outstanding flexibility (~97.2% of the original electrical conductivity is maintained after 1500 bending cycles with a bending radius of 4 mm). A four-leg flexible thermoelectric (TE) generator assembled with the optimal film generates a maximum power of 329.6 nW (corresponding power density of 1.70 W m−2) at a temperature difference of 31.1 K. This work provides a simple route to the preparation of high TE performance Cu2−xS-based films. [ABSTRACT FROM AUTHOR]

With the advent of cancer immunotherapy, some special features including delayed treatment effect, cure rate, diminishing treatment effect and crossing survival are often observed in survival analysis. They violate the proportional hazard model assumption and pose a unique challenge for the conventional trial design and analysis strategies. Many methods like cure rate model have been developed based on mixture model to incorporate some of these features. In this work, we extend the mixture model to deal with multiple non‐proportional patterns and develop its geometric average hazard ratio (gAHR) to quantify the treatment effect. We further derive a sample size and power formula based on the non‐centrality parameter of the log‐rank test and conduct a thorough analysis of the impact of each parameter on performance. Simulation studies showed a clear advantage of our new method over the proportional hazard based calculation across different non‐proportional hazard scenarios. Moreover, the mixture modeling of two real trials demonstrates how to use the prior information on the survival distribution among patients with different biomarker and early efficacy results in practice. By comparison with a simulation‐based design, the new method provided a more efficient way to compute the power and sample size with high accuracy of estimation. Overall, both theoretical derivation and empirical studies demonstrate the promise of the proposed method in powering future innovative trial designs. [ABSTRACT FROM AUTHOR]