Influence of body mass index on the clinicopathological features of 13,995 papillary thyroid tumors

PURPOSE

This retrospective study aimed to assess the association between obesity, gender, and specific clinicopathological features in patients with papillary thyroid cancer (PTC) and whether diagnostic ultrasonography (US) is adversely affected by obesity in these patients.

MATERIALS AND METHODS

This study retrospectively analyzed 13,995 adult patients with PTC from a single medical center in China. Data stratification was performed to assess the association of obesity with US features and aggressive clinicopathological features in different models according to the World Health Organization Body Mass Index (WHO-BMI) and Chinese BMI classification (CN-BMI). The odds ratio (OR) of malignant US features and aggressive clinicopathological features was calculated from binary logistic regression models.

RESULTS

The BMI, obesity ratio, malignant US features, and aggressive pathological characteristics of males were significantly higher than those of females. After adjusting for confounding factors, the association of obesity with malignant US features and aggressive pathological characteristics was found to be sex-dependent. Next, obesity (WHO-BMI) was found to have an "interfering effect" on the US assessment of PTC (OR = 0.754, 95% CI 0.609-0.932, P = 0.009) in women. Among both sexes, obesity (WHO-BMI) increased the risk of tumor size (OR_male = 1.539 and OR_female = 1.521) and multifocality (OR_male = 1.659 and OR_female = 1.449). However, obesity did not increase the risk of capsular invasion or tumor staging in males. The above results are consistent with the CN-BMI. In addition, age was found to have an "interfering effect" on the US evaluation of malignant nodules in both sexes.

CONCLUSION

The results of our study confirm that higher BMI is significantly associated with aggressive clinicopathological features of PTC. Gender differences were present with obesity ratios and aggressive clinicopathological features being significantly higher in men.

Job satisfaction among oncology nurse practitioners

BACKGROUND

One proposed solution to the predicted shortage of oncology nurse practitioners (NPs) is expanding the role of the oncology NP. However, role expansion may lead to an increase in work-related stress and a decrease in job satisfaction. It is important to understand oncology NPs' job satisfaction and stress and their intent to leave their job or profession in order to further develop and potentially expand the role.

PURPOSE

The purpose of this study is to determine the main factors that affect job satisfaction, especially the relationship with stress and the intent to leave the oncology specialty.

METHODS

A convenience sample of responses to a series of surveys administered by the Oncology Nursing Society and residing in the ONS database was used for this analysis. Exploratory data analysis, principal component analysis, and regression models were applied to explore characteristics of the questionnaires, assess the reliability of the Coping Skills Questionnaire, and find out main factors for their intent to leave.

RESULTS

Items in the Coping Skills Questionnaire were internally consistent, and stress had a positive effect on NPs' intent to leave. Satisfaction and coping skills were also significant in some models; higher levels of satisfaction and coping skills resulted in lower levels of intent to leave. Moreover, several demographic factors such as having children, schedule days off, and patient population also affected the response significantly.

IMPLICATIONS FOR PRACTICE

This study provides nursing leaders with information to guide retention of NPs.

Improved tracking of sevoflurane anesthetic states with drug-specific machine learning models

OBJECTIVE

The ability to monitor anesthetic states using automated approaches is expected to reduce inaccurate drug dosing and side-effects. Commercially available anesthetic state monitors perform poorly when ketamine is administered as an anesthetic-analgesic adjunct. Poor performance is likely because the models underlying these monitors are not optimized for the electroencephalogram (EEG) oscillations that are unique to the co-administration of ketamine.

APPROACH

In this work, we designed two k-nearest neighbors algorithms for anesthetic state prediction.

MAIN RESULTS

The first algorithm was trained only on sevoflurane EEG data, making it sevoflurane-specific. This algorithm enabled discrimination of the sevoflurane general anesthesia (GA) state from sedated and awake states (true positive rate = 0.87, [95% CI, 0.76, 0.97]). However, it did not enable discrimination of the sevoflurane-plus-ketamine GA state from sedated and awake states (true positive rate = 0.43, [0.19, 0.67]). In our second algorithm, we implemented a cross drug training paradigm by including both sevoflurane and sevoflurane-plus-ketamine EEG data in our training set. This algorithm enabled discrimination of the sevoflurane-plus-ketamine GA state from sedated and awake states (true positive rate = 0.91, [0.84, 0.98]).

SIGNIFICANCE

Instead of a one-algorithm-fits-all-drugs approach to anesthetic state monitoring, our results suggest that drug-specific models are necessary to improve the performance of automated anesthetic state monitors.

Contribution of single-gene defects to congenital cardiac left-sided lesions in the prenatal setting

OBJECTIVES

To explore the contribution of single-gene defects to the genetic cause of cardiac left-sided lesions (LSLs), and to evaluate the incremental diagnostic yield of whole-exome sequencing (WES) for single-gene defects in fetuses with LSLs without aneuploidy or a pathogenic copy-number variant (pCNV).

METHODS

Between 10 April 2015 and 30 October 2018, we recruited 80 pregnant women diagnosed with a LSL who had termination of pregnancy and genetic testing. Eligible LSLs were aortic valve atresia or stenosis, coarctation of the aorta, mitral atresia or stenosis and hypoplastic left heart syndrome (HLHS). CNV sequencing (CNV-seq) and WES were performed sequentially on specimens from these fetuses and their parents. CNV-seq was used to identify aneuploidies and pCNVs, while WES was used to identify diagnostic genetic variants in cases without aneuploidy or pCNV.

RESULTS

Of 80 pregnancies included in the study, 27 (33.8%) had a genetic diagnosis. CNV-seq analysis identified six (7.5%) fetuses with aneuploidy and eight (10.0%) with pCNVs. WES analysis of the remaining 66 cases revealed diagnostic genetic variants in 13 (19.7%) cases, indicating that the diagnostic yield of WES for the entire cohort was 16.3% (13/80). KMT2D was the most frequently mutated gene (7/66 (10.6%)) in fetuses with LSL without aneuploidy or pCNVs, followed by NOTCH1 (4/66 (6.1%)). HLHS was the most prevalent cardiac phenotype (4/7) in cases with a KMT2D mutation in this cohort. An additional six (9.1%) cases were found to have potentially deleterious variants in candidate genes.

CONCLUSIONS

Single-gene defects contribute substantially to the genetic etiology of fetal LSLs. KMT2D mutations accounted for approximately 10% of LSLs in our fetal cohort. WES has the potential to provide genetic diagnoses in fetuses with LSLs without aneuploidy or pCNVs. Copyright © 2019 ISUOG. Published by John Wiley & Sons Ltd.

[Clinical features and prognostic factors in adult acute myeloid leukemia patients with FLT3-ITD and CEBPA gene co-mutation]

目的

研究FLT3-ITD及CEBPA双等位基因突变(CEBPAdm)共突变成人急性髓系白血病(AML)患者的临床特征及预后。

方法

对2016年1月至2018年9月就诊于郑州大学第一附属医院的初治成人AML患者的临床资料进行回顾性研究，比较分析其临床特点及预后。基因突变检测采用直接测序法。

结果

①接受基因突变检测的非M₃且资料完整患者599例，检出FLT3-ITD基因突变阳性（FLT3-ITD⁺）且CEBPAdm阳性（CEBPAdm⁺）患者19例（A组），FLT3-ITD⁺且CEBPAdm⁻患者84例（B组），FLT3-ITD⁻且CEBPAdm⁺患者95例（C组），未检出任何已知基因突变患者70例（D组），共计268例。②A、B、C、D四组间性别、PLT、FAB分型、诱导治疗方案、融合基因突变情况差异均无统计学意义（P值均>0.05）；而发病年龄、初诊时WBC、HGB含量、外周血原始幼稚细胞比例、骨髓原始幼稚细胞比例差异均有统计学意义（P值均<0.05）。组间两两比较，A组较B、C、D组性别、年龄、HGB含量、PLT、FAB分型差异无统计学意义（P值均>0.05）。A组初诊时外周血WBC、外周血原始幼稚细胞比例、首疗程诱导治疗后微小残留病（MRD）水平高于B、C、D各组。③A、B、C、D组首疗程化疗后完全缓解（CR₁）率分别为50.0%、32.4%、59.8%、39.0%（P=0.003），复发率分别为55.6%、50.0%、21.1%、40.0%（P<0.001），中位总生存时间分别为6.25、3.0、15.5、10.5个月（P<0.001），中位无进展生存时间分别为5.0、4.0、10.0、6.7个月（P=0.032）。

结论

FLT3-ITD及CEBPAdm共突变成人AML患者初诊时外周血WBC高，外周血原始幼稚细胞比例高，首疗程诱导化疗后MRD水平高，CR₁率低，复发率高，中位总生存时间、中位无进展生存时间短，预后不佳。

MiR-92 overexpression suppresses immune cell function in ovarian cancer via LATS2/YAP1/PD-L1 pathway

PURPOSE

Increasing evidence suggested that microRNA plays an important role in ovarian cancer. In this study, the role of miR-92 in ovarian cancer was investigated.

METHODS

In this study, miR-92 expression in clinical sample was evaluated, role of miR-92 was investigated in vitro, and underlying mechanism was investigated using Chip, co-IP, and western blot.

RESULTS

In this study, we show that miR-92 is overexpressed in ovarian cancer tissue compared with normal cancer tissue. Transfection of miR-92 increased proliferation of ovarian cancer cell, and increased migration capacity and colony formation were observed after miR-92 transfection; we found that expression of LATS2 was decreased by miR-92, and this was further confirmed by luciferase assay, which proved that miR-92 is targeting 3' of the endogenous LATS2 gene. Downregulation of LATS2 resulted in increased translocation of YAP1 and upregulation of PD-L1, which subsequently suppressed NK cell function and promoted T cell apoptosis. Moreover, co-transfection of YAP1-targeted shRNA could relieve miR-92-induced immune suppression effect. Mechanically, immunoprecipitation (IP) was used to show that LATS2 interacted with YAP1 and subsequently limited nuclear translocation of YAP1; chromatin immunoprecipitation (ChIP) was used to confirm that YAP1 could bind to enhancer region of PD-L1 to enhance transcription activity of PD-L1.

CONCLUSIONS

Our data revealed a novel mechanism which finally resulted in immune suppression in ovarian cancer.

CDC6 is up-regulated and a poor prognostic signature in glioblastoma multiforme

PURPOSE

Glioblastoma multiforme (GBM) represents the most common and the most malignant type of brain tumor. Cell division cycle 6 (CDC6), a gene associated with DNA replication initiation, has been proven to be associated with the prognosis of multiple tumors. In this study, we aim to explore the association between CDC6 expression and GBM carcinogenesis and prognosis.

METHODS

CDC6 expression in normal cells and GBM cells was explored by analyzing TCGA dataset, as well as by RT-PCR and western blot methods. Survival analysis was performed by the Kaplan-Meier method. Multivariate Cox-regression analysis was adopted to estimate the independence of CDC6 as a GBM prognostic factor.

RESULTS AND CONCLUSIONS

Elevated CDC6 levels in GBM tumor tissues compared with those in normal brain tissues were illustrated by analyzing the gene expression profiles from TCGA dataset, and confirmed by RT-PCR and western blot assays in GBM tumor and normal human astrocyte cell lines. Kaplan-Meier analysis indicated the negative influence of high CDC6 expression on GBM overall survival (OS) probability and days to progression (D2P) after initial treatment, but not on days to recurrence (D2R) after initial treatment. Multivariate Cox regression analysis showed CDC6 as an independent signature marker gene for GBM prognosis. In addition, the combination of CDC6 mRNA expression and CpG island methylator phenotype (CIMP) could sensitively predict 3-year OS and D2P. In conclusion, our study uncovered the role of CDC6 in GBM carcinogenesis and prognosis for the first time, which could shed new light on GBM diagnosis and treatment.

Development and Validation of Forecasting Next Reported Seizure Using e-Diaries

OBJECTIVE

There are no validated methods for predicting the timing of seizures. Using machine learning, we sought to forecast 24-hour risk of self-reported seizure from e-diaries.

METHODS

Data from 5,419 patients on SeizureTracker.com (including seizure count, type, and duration) were split into training (3,806 patients/1,665,215 patient-days) and testing (1,613 patients/549,588 patient-days) sets with no overlapping patients. An artificial intelligence (AI) program, consisting of recurrent networks followed by a multilayer perceptron ("deep learning" model), was trained to produce risk forecasts. Forecasts were made from a sliding window of 3-month diary history for each day of each patient's diary. After training, the model parameters were held constant and the testing set was scored. A rate-matched random (RMR) forecast was compared to the AI. Comparisons were made using the area under the receiver operating characteristic curve (AUC), a measure of binary discrimination performance, and the Brier score, a measure of forecast calibration. The Brier skill score (BSS) measured the improvement of the AI Brier score compared to the benchmark RMR Brier score. Confidence intervals (CIs) on performance statistics were obtained via bootstrapping.

RESULTS

The AUC was 0.86 (95% CI = 0.85-0.88) for AI and 0.83 (95% CI = 0.81-0.85) for RMR, favoring AI (p < 0.001). Overall (all patients combined), BSS was 0.27 (95% CI = 0.23-0.31), also favoring AI (p < 0.001).

INTERPRETATION

The AI produced a valid forecast superior to a chance forecaster, and provided meaningful forecasts in the majority of patients. Future studies will be needed to quantify the clinical value of these forecasts for patients. ANN NEUROL 2020;88:588-595.

Photonic paper: Multiscale assembly of reflective cellulose sheets in Lunaria annua

Bright, iridescent colors observed in nature are often caused by light interference within nanoscale periodic lattices, inspiring numerous strategies for coloration devoid of inorganic pigments. Here, we describe and characterize the septum of the Lunaria annua plant that generates large (multicentimeter), freestanding iridescent sheets, with distinctive silvery-white reflective appearance. This originates from the thin-film assembly of cellulose fibers in the cells of the septum that induce thin-film interference-like colors at the microscale, thus accounting for the structure's overall silvery-white reflectance at the macroscale. These cells further assemble into two thin layers, resulting in a mechanically robust, iridescent septum, which is also significantly light due to its high air porosity (>70%) arising from the cells' hollow-core structure. This combination of hierarchical structure comprising mechanical and optical function can inspire technological classes of devices and interfaces based on robust, light, and spectrally responsive natural substrates.

[Analysis of drug - resistant gene polymorphisms in Plasmodium falciparum imported from Equatorial Guinea to Shandong Province in 2015 and 2016]

OBJECTIVE

To investigate the drug-resistant gene polymorphisms in Plasmodium falciparum imported from Equatorial Guinea to Shandong Province.

METHODS

From 2015 to 2016, blood samples were collected from imported P. falciparum malaria patients returning from Equatorial Guinea to Shandong Province, and genome DNA of the malaria parasite was extracted. The drug-resistant Pfcrt, Pfmdr1, Pfdhfr, Pfdhps, and K13 genes of P. falciparum were amplified using a PCR assay, followed by DNA sequencing, and the sequences were aligned.

RESULTS

The target fragments of all 5 drug-resistant genes of P. falciparum were successfully amplified and sequenced. There were 72.8%, 18.6%, and 8.6% of P. falciparum parasites carrying the wild-, mutant-, and mixed-type Pfcrt gene, respectively, and all mutant haplotypes were CVIET (the underline indicates the mutation site). There were 20.0%, 61.4% and 18.6% of P. falciparum parasites carrying the wild-, mutant-, and mixed-type Pfmdr1 gene, respectively, and the mutant haplotypes mainly included YF and NF (the underlines indicate the mutation sites). There were 1.4%, 98.6%, and 0 of P. falciparum parasites carrying the wild-, mutant-, and mixed-type Pfdhfr gene, respectively, and AIRNI was the predominant mutant haplotype (the underline indicates the mutation site). There were 1.4%, 94.3%, and 4.3% of P. falciparum parasites carrying the wild-, mutant-, and mixed-type Pfdhps gene, respectively, and SGKAA was the predominant mutant haplotype (the underline indicates the mutation site). The complete drug-resistant IRNGE genotype consisted of 8.6% of the Pfdhfr and Pfdhps genes, and the K13 gene A578S mutation occurred in 1.4% of the parasite samples.

CONCLUSIONS

There are mutations in the Pfcrt, Pfmdr1, Pfdhfr, Pfdhps, and K13 genes of P. falciparum imported from Equatorial Guinea to Shandong Province, with a low frequency in the Pfcrt gene mutation and a high frequency in the Pfmdr1, Pfdhfr, and Pfdhps gene mutations, and the K13 gene A578S mutation is detected in the parasite samples.

Assessing the impact of drinking water iodine concentrations on the iodine intake of Chinese pregnant women living in areas with restricted iodized salt supply

PURPOSE

The supply of non-iodized salt and the water improvement project have been conducted to reduce the iodine concentration in drinking water in areas with elevated water iodine. We aimed to assess the impact of water iodine concentration (WIC) on the iodine intake of pregnant women in areas with restricted iodized salt supply, and determine the cutoff values of WIC in areas with non-iodized salt supply.

METHODS

Overall, 534 pregnant women who attended routine antenatal outpatient visits in Zibo Maternal and Child Health Hospital in Gaoqing County were recruited. The 24-h urine iodine excretion (UIE) in 534 samples and the iodine concentration in 534 drinking water samples were estimated. Urinary iodine excretion, daily iodine intake, and daily iodine intake from drinking water (WII) were calculated. The relationship between WIC and daily iodine take was analyzed.

RESULTS

The median WIC, spot urine iodine concentration (UIC), and 24-h UIE were 17 (6, 226) μg/L, 145 (88, 267) μg/L, and 190 (110, 390) μg/day, respectively. A significant positive correlation was found between WIC and UIE (R² = 0.265, p < 0.001) and UIC (R² = 0.261, p < 0.001). The contribution rate of WII to total iodine intake increased from 3.0% in the group with WIC of < 10 μg/L to 45.7% in the group with WIC of 50-99 μg/L.

CONCLUSION

The iodine content in drinking water is the major iodine source in pregnant women living in high-water iodine areas where iodized salt supply is restricted. The contribution rate of daily iodine intake from drinking water increases with the increase in water iodine concentration.

COVID-19 Outpatient Screening: a Prediction Score for Adverse Events

BACKGROUND

We sought to develop an automatable score to predict hospitalization, critical illness, or death in patients at risk for COVID-19 presenting for urgent care during the Massachusetts outbreak.

METHODS

Single-center study of adult outpatients seen in respiratory illness clinics (RICs) or the emergency department (ED), including development (n = 9381, March 7-May 2) and prospective (n = 2205, May 3-14) cohorts. Data was queried from Partners Enterprise Data Warehouse. Outcomes were hospitalization, critical illness or death within 7 days. We developed the COVID-19 Acuity Score (CoVA) using automatically extracted data from the electronic medical record and learning-to-rank ordinal logistic regression modeling. Calibration was assessed using predicted-to-observed event ratio (E/O). Discrimination was assessed by C-statistics (AUC).

RESULTS

In the development cohort, 27.3%, 7.2%, and 1.1% of patients experienced hospitalization, critical illness, or death, respectively; and in the prospective cohort, 26.1%, 6.3%, and 0.5%. CoVA showed excellent performance in the development cohort (concurrent validation) for hospitalization (E/O: 1.00, AUC: 0.80); for critical illness (E/O: 1.00, AUC: 0.82); and for death (E/O: 1.00, AUC: 0.87). Performance in the prospective cohort (prospective validation) was similar for hospitalization (E/O: 1.01, AUC: 0.76); for critical illness (E/O 1.03, AUC: 0.79); and for death (E/O: 1.63, AUC=0.93). Among 30 predictors, the top five were age, diastolic blood pressure, blood oxygen saturation, COVID-19 testing status, and respiratory rate.

CONCLUSIONS

CoVA is a prospectively validated automatable score to assessing risk for adverse outcomes related to COVID-19 infection in the outpatient setting.

Burden of Epileptiform Activity Predicts Discharge Neurologic Outcomes in Severe Acute Ischemic Stroke

BACKGROUND/OBJECTIVES

Clinical seizures following acute ischemic stroke (AIS) appear to contribute to worse neurologic outcomes. However, the effect of electrographic epileptiform abnormalities (EAs) more broadly is less clear. Here, we evaluate the impact of EAs, including electrographic seizures and periodic and rhythmic patterns, on outcomes in patients with AIS.

METHODS

This is a retrospective study of all patients with AIS aged ≥ 18 years who underwent at least 18 h of continuous electroencephalogram (EEG) monitoring at a single center between 2012 and 2017. EAs were classified according to American Clinical Neurophysiology Society (ACNS) nomenclature and included seizures and periodic and rhythmic patterns. EA burden for each 24-h epoch was defined using the following cutoffs: EA presence, maximum daily burden < 10% versus > 10%, maximum daily burden < 50% versus > 50%, and maximum daily burden using categories from ACNS nomenclature ("rare" < 1%; "occasional" 1-9%; "frequent" 10-49%; "abundant" 50-89%; "continuous" > 90%). Maximum EA frequency for each epoch was dichotomized into ≥ 1.5 Hz versus < 1.5 Hz. Poor neurologic outcome was defined as a modified Rankin Scale score of 4-6 (vs. 0-3 as good outcome) at hospital discharge.

RESULTS

One hundred and forty-three patients met study inclusion criteria. Sixty-seven patients (46.9%) had EAs. One hundred and twenty-four patients (86.7%) had poor outcome. On univariate analysis, the presence of EAs (OR 3.87 [1.27-11.71], p = 0.024) and maximum daily burden > 10% (OR 12.34 [2.34-210], p = 0.001) and > 50% (OR 8.26 [1.34-122], p = 0.035) were associated with worse outcomes. On multivariate analysis, after adjusting for clinical covariates (age, gender, NIHSS, APACHE II, stroke location, stroke treatment, hemorrhagic transformation, Charlson comorbidity index, history of epilepsy), EA presence (OR 5.78 [1.36-24.56], p = 0.017), maximum daily burden > 10% (OR 23.69 [2.43-230.7], p = 0.006), and maximum daily burden > 50% (OR 9.34 [1.01-86.72], p = 0.049) were associated with worse outcomes. After adjusting for covariates, we also found a dose-dependent association between increasing EA burden and increasing probability of poor outcomes (OR 1.89 [1.18-3.03] p = 0.009). We did not find an independent association between EA frequency and outcomes (OR: 4.43 [.98-20.03] p = 0.053). However, the combined effect of increasing EA burden and frequency ≥ 1.5 Hz (EA burden * frequency) was significantly associated with worse outcomes (OR 1.64 [1.03-2.63] p = 0.039).

CONCLUSIONS

Electrographic seizures and periodic and rhythmic patterns in patients with AIS are associated with worse outcomes in a dose-dependent manner. Future studies are needed to assess whether treatment of this EEG activity can improve outcomes.

SAT0647-HPR DEVELOP A MACHINE LEARNING MODEL AND ALGORITHM BASED ON SMART SYSTEM OF DISEASE MANAGEMENT (SSDM) BIG DATA FOR RA FLARE PREDICTION

Background:

Flare, relapse from status of treat-to-target (T2T, DAS28<=3.2), is hard predicted. We try to make it predictable by applying machine learning to a database from smart system of disease management (SSDM). SSDM is an interactive mobile disease management APPs.

Objectives:

To develop and validate machine learning algorithms for flare prediction in RA.

Methods:

Patients were trained using SSDM and input their data, including demographic, comorbidities (COMBs), lab test, medications and monthly self-assessments, including DAS28, HAQ, SF-36, Hospital Anxiety and Depression Scale (HADS). The data was uploaded to cloud and synchronized to the mobile of authorized rheumatologists. The COMBs were by ICD-9, and medications were listed as cDMARDs, Bio (BioDMARDs), NSAIDs, Steroid, FS (food supplements), MC (medicine for COMBs), TCM (Traditional Chinese Medicine), and combinations.

Results:

From Jan of 2015 to Jan of 2020, 8811 RA patients, 85% female and 15% male, used to reach T2T. 4556 were flare-free and 4255 suffering at least one flare. The average 160 attributes were extracted from each flare-free patient at time of reaching T2T, and each flare patients at time of 3 months before the flare. Patients were randomly assigned as model setup (training) group (70%) and validation (testing) group30%.

For training, data were processed using Python with statistical analyses in R. In R, random forests were implemented. Logistic regression via glm in base R. The random forest comprises a set of decision trees. “Splits” in the decision trees reflect binary (i.e., yes/no) respect to attributors. Bootstrapping was used to assess, quantify, and adjust for model optimism. Model performance was evaluated using AUC, precision and recall metrics. Brier scores for accuracy of probabilistic predictions ranged from 0 to 1 (0 is perfect discrimination).

The testing showed model performance for prediction windows are 0.78 for AUC (95% CI), 0.71 for Recall (sensitivity), 0.195 for Brier score, and 0.68 for precision (true positive 893, false positive 417, false negative 367, true negative 966).

Based on weighing in the random forest, the top 10 pro-flare attributes were CRP, swollen joint count (SJC), tender joint count (TJC), HAQ, DAS28, morning stiffness, gout, MCTD, OA, duration; while top 10 anti-flare attributes were cDMARDs+Bio, cDMARDs+steroid+NSAIDs, stable on HAQ, on morning stiffness, on SJC, medicine on COMBs, cDMARDs+TCM, stable on TJC, on ESR, income at 100-200k (Fig.1). The top weighing COMBs for pro-flaring were gout (0.81), MRD (0.75), OA (0.56), AS (0.48). The monotherapies with either Bio or NSAIDs, or steroid, or TCM was pro-flare; while with cDMARDs was anti-flare (-0.21).

Figure 1.

Conclusion:

The attempt to develop a machine learning algorithm for RA flare prediction is successful. The discrimination was acceptable. The attributes of both pro-flare and anti-flare are identified, which may inspire the proactive intervention.

Acknowledgments:

SSDM was developed by Shanghai Gothic Internet Technology Co., Ltd.

Disclosure of Interests:

None declared

Adaptive Sedation Monitoring From EEG in ICU Patients With Online Learning

Sedative medications are routinely administered to provide comfort and facilitate clinical care in critically ill ICU patients. Prior work shows that brain monitoring using electroencephalography (EEG) to track sedation levels may help medical personnel to optimize drug dosing and avoid the adverse effects of oversedation and undersedation. However, the performance of sedation monitoring methods proposed to date deal poorly with individual variability across patients, leading to inconsistent performance. To address this challenge we develop an online learning approach based on Adaptive Regularization of Weight Vectors (AROW). Our approach adaptively updates a sedation level prediction algorithm under a continuously evolving data distribution. The prediction model is gradually calibrated for individual patients in response to EEG observations and routine clinical assessments over time. The evaluations are performed on a population of 172 sedated ICU patients whose sedation levels were assessed using the Richmond Agitation-Sedation Scale (scores between -5 = comatose and 0 = awake). The proposed adaptive model achieves better performance than the same model without adaptation (average accuracies with tolerance of one level difference: 68.76% vs. 61.10%). Moreover, our approach is shown to be robust to sudden changes caused by label noise. Medication administrations have different effects on model performance. We find that the model performs best in patients receiving only propofol, compared to patients receiving no sedation or multiple simultaneous sedative medications.

1008 Brain Age Based on Sleep Encephalography is Elevated in HIV+ Adults on ART

Introduction

Sleep EEG is a promising tool to measure brain aging in vulnerable populations such as people with HIV, who are high risk of brain aging due to co-morbidities, increased inflammation, and antiretroviral neurotoxicity. Our lab previously developed a machine learning model that estimates age from sleep EEG (brain age, BA), which reliably predicts chronological age (CA) in healthy adults. The difference between BA and CA, the brain age index (BAI), independently predicts mortality, and is increased by cardiovascular co-morbidities. Here, we assessed BAI in HIV+ compared to matched HIV- adults.

Methods

Sleep EEGs from 43 treated HIV+ adults were gathered and matched to controls (HIV-, n=284) by age, gender, race, alcoholism, smoking and substance use history. We compared BAI between groups and used additional causal interference methods to ensure robustness. Individual EEG features that underlie BA prediction were also compared. We performed a sub-analysis of BAI between HIV+ with or without a history of AIDS.

Results

After matching, mean CA of HIV+ vs HIV- adults were 49 and 48 years, respectively (n.s.). The mean HIV+ BAI was 3.04 years higher than HIV- (4.4 vs 1.4 yr; p=0.048). We found consistent and significant results with alternative causal inference methods. Several EEG features predictive of BA were different in the HIV+ and HIV- cohorts. Most notably, non-REM stage 2 sleep (N2) delta power (1-4Hz) was decreased in HIV+ vs. HIV- adults, while theta (4-8Hz) and alpha (8-12Hz) power were increased. Those with AIDS (n=19, BAI=4.40) did not have significantly different BAI than HIV+ without AIDS (n=23, BAI=5.22). HIV+ subjects had higher rates of insomnia (56% vs 29%, p&lt;0.001), obstructive apnea (47% vs 30%, p=0.03), depression (49% vs 23%, p&lt;0.001), and bipolar disorder (19% vs 4%, p&lt;0.001).

Conclusion

HIV+ individuals on ART have excess sleep-EEG based brain age compared to matched controls. This excess brain age is partially due to reduction in delta power during N2, suggesting decreased sleep depth. These results suggest sleep EEG could be a valuable brain aging biomarker for the HIV population.

Support

This research is supported by the Harvard Center for AIDS Research HU CFAR NIH/NIAID 5P30AI060354-16.

0348 Sleep EEG-Based Brain Age Index is Reduced Under Continuous Positive Airway Pressure Treatment

Introduction

Continuous positive airway pressure (CPAP) is a treatment for apnea. With long-term CPAP, changes in electroencephalogram (EEG) include increased delta power (1 - 4Hz) and sigma power (11 - 15Hz, spindle). However, the short-term EEG response to CPAP in a split-night study is less quantified. We recently developed a “brain age” model using sleep EEG features. The brain age index (BAI) is defined as the difference between chronological age and brain age (BA - CA). Here we first quantify how BAI changes during CPAP in the same patient, and then investigate how much brain age features during the diagnostic part can predict the reduction in apnea-hypopnea index (AHI) during CPAP.

Methods

The dataset consisted of 160 subjects. The average age was 59 years with 53% male, 24% female and 23% unknown. We extracted 480 features including band powers, and then computed the BAIs for both diagnostic and CPAP parts. To predict the reduction in AHI during CPAP, we fit a Bayesian regression model using the brain age features, demographics, and sleep parameters during the diagnostic part, and assessed the feature importance using dominance analysis.

Results

The BAI from the diagnostic part is significantly reduced compared to BAI during CPAP for the same subject (paired t-test, p &lt; 0.01). The diagnostic part has an average BAI 2.24 years; and the CPAP part -4.75 years. The brain age features that are increased during CPAP include sigma powers in N2 and N3. The prediction of AHI reduction has Pearson’s correlation 0.85. The features predictive of reduced AHI are the diagnostic AHI (explained variance 69%), followed by high/low waveforms during N2 (e.g. K-complex, measured by kurtosis) (8.6%), delta power during REM (4.5%) and N1 (2%). The feature predictive of increased AHI is frontal alpha power during quiet awake (2.6%).

Conclusion

The average BAI is reduced during CPAP. BAI provides a novel view of the acute response to CPAP in sleep EEG. Future study with more CPAP failure patients has the potential of predicting CPAP failure.

Support

MBW is supported by Glenn Foundation for Medical Research. RJT is supported by Category I AASM Foundation.

Study Frequency Shift Evaluation of Ultrasound in Fresh and Frozen-thawed Tissues of Cryosurgery by AR Model

BACKGROUND

Noninvasive monitoring of cryosurgery is important for performing precise monitoring of the freezing process in situ and evaluating postoperative effects after therapy. One potential approach is to monitor the normal and freeze-thawed tissues through ultrasonic backscattered signal processing.

OBJECTIVE

A noninvasive method for cryosurgery monitoring based on the analysis of microstructural characteristics of in vitro porcine liver tissues at different state including normal and freeze-thawed tissues by estimating the center frequency of scatterers (CFS) using the autoregressive (AR) cepstrum of ultrasonic backscattered signals.

MATERIALS AND METHODS

The method is based on the discrete scattering model described in the tissue characterization literature and the observation that most biological tissues are semi-regular scattering lattices. A total of ten in vitro porcine liver samples were used and freeze by water bath in the experiments.

RESULTS

Experimental results show that the CFS in porcine liver tissues decreases after pre-frozen and then thawed.

CONCLUSION

The CFS obtained using this method may be used as a characteristic parameter for tissue characterization in noninvasive monitoring the transition zone between frozen and unfrozen tissues during the surgical therapy, and evaluating postoperative effects.

Excess brain age in the sleep electroencephalogram predicts reduced life expectancy

The brain age index (BAI) measures the difference between an individual's apparent "brain age" (BA; estimated by comparing EEG features during sleep from an individual with age norms), and their chronological age (CA); that is BAI = BA-CA. Here, we evaluate whether BAI predicts life expectancy. Brain age was quantified using a previously published machine learning algorithm for a cohort of participants ≥40 years old who underwent an overnight sleep electroencephalogram (EEG) as part of the Sleep Heart Health Study (n = 4877). Excess brain age (BAI >0) was associated with reduced life expectancy (adjusted hazard ratio: 1.12, [1.03, 1.21], p = 0.002). Life expectancy decreased by -0.81 [-1.44, -0.24] years per standard-deviation increase in BAI. Our findings show that BAI, a sleep EEG-based biomarker of the deviation of sleep microstructure from patterns normal for age, is an independent predictor of life expectancy.

Automatic Detection of General Anesthetic-States using ECG-Derived Autonomic Nervous System Features

Electroencephalogram (EEG)-based prediction systems are used to target anesthetic-states in patients undergoing procedures with general anesthesia (GA). These systems are not widely employed in resource-limited settings because they are cost-prohibitive. Although anesthetic-drugs induce highly-structured, oscillatory neural dynamics that make EEG-based systems a principled approach for anesthetic-state monitoring, anesthetic-drugs also significantly modulate the autonomic nervous system (ANS). Because ANS dynamics can be inferred from electrocardiogram (ECG) features such as heart rate variability, it may be possible to develop an ECG-based system to infer anesthetic-states as a low-cost and practical alternative to EEG-based anesthetic-state prediction systems. In this work, we demonstrate that an ECG-based system using ANS features can be used to discriminate between non-GA and GA states in sevoflurane, with a GA F₁ score of 0.834, [95% CI, 0.776, 0.892], and in sevoflurane-plus-ketamine, with a GA F₁ score of 0.880 [0.815, 0.954]. With further refinement, ECG-based anesthetic-state systems could be developed as a fully automated system for anesthetic-state monitoring in resource-limited settings.

Do patient-specific cutting guides and plates improve the accuracy of maxillary repositioning in hemifacial microsomia?

The aim of this retrospective study was to use computer-aided design and manufacturing (CAD/CAM) patient-specific plates and cutting guides for the waferless positioning and fixation of the maxilla after bimaxillary osteotomies in cases of hemifacial microsomia with condylar dysplasia or absence of the temporomandibular joint (TMJ), and to compare the results with the CAD/CAM fabricated surgical wafer by 3-dimensional analysis. Eighteen patients were selected from the hospital database, preoperative surgical planning and simulation were done on 3-dimensional computed tomographic models for all patients, and they were divided into Group I - in which CAD/CAM patient-specific cutting guides and plates were used; and Group II - in which CAD/CAM fabricated surgical wafers were used. Finally, the outcome was evaluated by comparing planned with postoperative outcomes. The largest discrepancies of the Le Fort I segment were 0.50 (0.18) mm in the anteroposterior direction and 0.82 (0.60)° in the yaw orientation with Group I. The largest discrepancies of the Le Fort I segment were 1.32 (1.40) mm in superioinferior direction and 8.48 (7.73)° in the yaw orientation with Group II. The CAD/CAM patient-specific cutting guides and plates proved to be reliable and have great value in improving the accuracy in repositioning the Le Fort I segment and in the efficacy of orthognathic treatment of hemifacial microsomia with condylar dysplasia or no TMJ. The CAD/CAM patient-specific cutting guides and plates are therefore a useful alternative to the wafer technique.

Burst Suppression: Causes and Effects on Mortality in Critical Illness

BACKGROUND

Burst suppression in mechanically ventilated intensive care unit (ICU) patients is associated with increased mortality. However, the relative contributions of propofol use and critical illness itself to burst suppression; of burst suppression, propofol, and critical illness to mortality; and whether preventing burst suppression might reduce mortality, have not been quantified.

METHODS

The dataset contains 471 adults from seven ICUs, after excluding anoxic encephalopathy due to cardiac arrest or intentional burst suppression for therapeutic reasons. We used multiple prediction and causal inference methods to estimate the effects connecting burst suppression, propofol, critical illness, and in-hospital mortality in an observational retrospective study. We also estimated the effects mediated by burst suppression. Sensitivity analysis was used to assess for unmeasured confounding.

RESULTS

The expected outcomes in a "counterfactual" randomized controlled trial (cRCT) that assigned patients to mild versus severe illness are expected to show a difference in burst suppression burden of 39%, 95% CI [8-66]%, and in mortality of 35% [29-41]%. Assigning patients to maximal (100%) burst suppression burden is expected to increase mortality by 12% [7-17]% compared to 0% burden. Burst suppression mediates 10% [2-21]% of the effect of critical illness on mortality. A high cumulative propofol dose (1316 mg/kg) is expected to increase burst suppression burden by 6% [0.8-12]% compared to a low dose (284 mg/kg). Propofol exposure has no significant direct effect on mortality; its effect is entirely mediated through burst suppression.

CONCLUSIONS

Our analysis clarifies how important factors contribute to mortality in ICU patients. Burst suppression appears to contribute to mortality but is primarily an effect of critical illness rather than iatrogenic use of propofol.

[Clinical characteristics of metastasis-induced acute pancreatitis in patients with lung cancer]

Objective: To analyze the clinical features and prognosis of lung cancer patients with metastasis-induced acute pancreatitis (MIAP), and to provide clues for early diagnosis. Methods: The characteristics and prognosis of 8 patients with MIAP in lung cancer admitted to Peking Union Medical College Hospital from January 2002 to September 2019 were retrospectively analyzed and were compared with non-tumor-induced AP. Results: Sevencases(7/8) were Mild AP, one (1/8) was Severe AP. Four patients (4/8) presented with AP as the reporting sign and lung cancer was not diagnosed until (112±36) days after the onset of AP. Clinical manifestations included abdominal pain (8/8), weight loss (4/8), nausea and vomiting (2/8), and jaundice (1/8). Stages of lung cancer were all Ⅳ.Histopathology proved that seven cases (7/8) were small cell lung cancer, and one case (1/8) was poorly differentiated adenocarcinoma. The median survival time was 11 months. Compared with non-tumor-induced AP, lung cancer patients with MIAP were older[(62±9) vs (48±15), P=0.018], the incidence of primary pancreatic duct dilatation (37.5% vs 3.1%, P=0.004) and abdominal lymphadenopathy (37.5% vs 6.3%, P=0.017) were higher; the level of hemoglobin [105.3±15.6) g/L vs (147.9±24.8) g/L, P<0.001] and hematocrit [(31.4±5.3) vs (42.5±6.1), P<0.001] were lower. Conclusions: Patientswith MIAP in lung cancer had poor outcome and unspecific symptoms. Old age, anemia, main pancreatic duct dilatation and abdominal lymphadenopathy are diagnostic clues that merit clinical attention.

101 Dothideomycetes genomes: A test case for predicting lifestyles and emergence of pathogens

Dothideomycetes is the largest class of kingdom Fungi and comprises an incredible diversity of lifestyles, many of which have evolved multiple times. Plant pathogens represent a major ecological niche of the class Dothideomycetes and they are known to infect most major food crops and feedstocks for biomass and biofuel production. Studying the ecology and evolution of Dothideomycetes has significant implications for our fundamental understanding of fungal evolution, their adaptation to stress and host specificity, and practical implications with regard to the effects of climate change and on the food, feed, and livestock elements of the agro-economy. In this study, we present the first large-scale, whole-genome comparison of 101 Dothideomycetes introducing 55 newly sequenced species. The availability of whole-genome data produced a high-confidence phylogeny leading to reclassification of 25 organisms, provided a clearer picture of the relationships among the various families, and indicated that pathogenicity evolved multiple times within this class. We also identified gene family expansions and contractions across the Dothideomycetes phylogeny linked to ecological niches providing insights into genome evolution and adaptation across this group. Using machine-learning methods we classified fungi into lifestyle classes with >95 % accuracy and identified a small number of gene families that positively correlated with these distinctions. This can become a valuable tool for genome-based prediction of species lifestyle, especially for rarely seen and poorly studied species.

Mathematical modelling and analysis of gravitational collapse in curved geometry

BACKGROUND AND OBJECTIVES

In the visible universe, it is believed that mass and energy are interchangeable. However, the physical and chemical processes in the hidden world put the scientist into the thought of matter and energy contents that are responsible for these phenomena. These are regarded as dark matter and dark energy. In this article, we study the effects of spacetime curvature on the gravitational collapse of dark energy in modified gravity, considering the collapse of the spherically symmetric star, which is composed of perfect and homogeneous fluid. We studied the collapse for closed, flat and hyperbolic geometry.

METHOD

As a result of mathematical modeling, we achieved highly non-linear differential equations. For the solution, we needed the assumption of physical significance. Specifically, we have taken the dark energy collapse. Then we achieved a simple system and solved for the analytic solutions of the field equations.

RESULTS

It is shown that the possible collapse is visibly influenced by spatial curvature. The collapse time is advanced for closed spacetime, delayed for the hypersurface, and the flat space behaves intermediately. We have taken here the equation of state in linear form to discuss the exhibition of fluid profile and a specific necessary criterion for the occurrence of spacetime singularity.

CONCLUSION

In this paper, we study the mathematical model of gravitational collapse in modified gravity, which derives the field equations using the principle of least action. The significant outcomes are the influences of the spatial curvature on the collapsing process and the time of formation of spacetime singularity. The matching of boundary and the fundamental continuity of the 1-form and 2-form are discussed.

[Systematic evaluation of neuromuscular blocking agents on prognosis of patients with moderate to severe acute respiratory distress syndrome]

Objective: To evaluate the prognostic impact of neuromuscular blocking agents (NMBA) on patients with acute respiratory distress syndrome (ARDS). Method: Online search of MEDLINE, Embase, Web of Science, CNKI, CBM and other Chinese databases for randomized controlled trials (RCTs) of NMBA in patients with ARDS from January 1994 to June 2019 was done, and literature was selected according to inclusion and exclusion criteria. The patients were divided into NMBA group and non-NMBA group according to whether NMBA was adopted or not. The prognostic indicators (ICU mortality, 28 d mortality, 90 d mortality) and NMBA-related complications (ICU acquired muscle weakness, barometric injury, pneumothorax) of the patients in the two groups were mainly analyzed. Meta-analysis of the data was performed using RevMan 5.0 software. Results: A total of 6 RCTs were included, and 1 502 patients were enrolled, including 761 in the NMBA group and 741 in the no-NMBA group. The 90-day mortality in the NMBA group and no-NMBA group were 38.8% and 42.6%, OR=0.87 (95%CI: 0.70-1.07, P=0.190); the 28-day mortality rates were 32.5% and 36.5%, OR=0.71 (95%CI: 0.45-1.11, P=0.130); ICU mortality rates were 31.8% and 43.8%, OR=0.60 (95%CI: 0.41-0.88, P=0.009). Conclusion: NMBA can reduce the ICU mortality of moderate to severe ARDS patients, but not reduce 28-day and 90-day mortality.

Lymph node characteristics of 6279 N1 differentiated thyroid cancer patients

PURPOSE

This study examined the clinicopathological characteristics of 6279 N1 differentiated thyroid cancer (DTC) patients who underwent operations in our center.

METHODS

This was a retrospective longitudinal analysis. We categorized the DTC on the basis of various lymph node (LN) characteristics. Logistic regression models and multiple linear regression models were used for the correlation analysis.

RESULTS

A total of 3693 (58.8%) N1a patients and 2586 (41.1%) N1b patients were included. Patients with N1b disease had larger metastatic foci (0.5 vs. 0.15 cm), a greater number of metastatic LNs (5 vs. 2), a greater number of dissected LNs (25 vs. 7), and a smaller lymph node ratio (NR, number of positive LNs / number of sampled LNs) (23.1% vs. 28.6%) than patients in stage N1a. Comparing the clinicopathological features, we found that male, increased tumor size, multifocality, and thyroiditis increased the risk of stage N1b disease (P<0.05). Sex, multifocality, capsular infiltration, and tumor size were associated with the size of the metastatic LNs (P<0.05). Sex, capsular infiltration, and nodular goiter were associated with the NR (P<0.05). Male sex, tumor located in inferior lobe, maximal tumor diameter (MTD) <1cm, and nodular goiter were independent predictors for skip metastases (P<0.05). MTD <1cm, central neck metastasis and advanced age were independent predictors for bilateral lateral neck metastasis (BLNM) (P<0.05).

CONCLUSION

The LN characteristics of stage N1a and N1b disease were associated with significantly different features, such as sex, tumor size, multifocality, capsular infiltration, and nodular goiter.

The Prevalence and Clinical Characteristics of Essential Tremor in Elderly Chineses: A Population-Based Study

OBJECTIVES

To investigate the prevalence and the clinical feature of essential tremor (ET) in a community cohort in Beijing.

METHODS

Using a door-to-door, two-phase approach, we investigated 2,835 residents aged ≥55 years old from rural, urban, and mountain areas.

RESULTS

The prevalence rate of ET was 4.29%, 2.85%, and 2.29% in rural, urban, and mountain areas, respectively. The overall age- and sex-adjusted prevalence was 3.29%. Among those aged ≥75 years, the prevalence rate in the urban area was higher than those in the rural and mountain areas. The prevalence rate increased with age, and the prevalence was higher among men (6.0%) than among women (3.6%). There was a correlation of ET prevalence with age, sex, and habitation area, but not with alcohol, tea drinking, and occupation. Women (25%) with ET were more likely to have head tremor than men (16.9%).

CONCLUSIONS

The ET prevalence in the elderly of Beijing was 3.29% which is higher in the urban area and in men.

Improvement of mapping vegetation cover for arid and semiarid areas using a local nonlinear modelling method and landsat images

Accurately estimating and mapping vegetation cover for monitoring land degradation and desertification of arid and semiarid areas using remotely sensed images is promising but challenging in remote, sparsely vegetated and large areas. In this study, a novel method – geographically weighted logistic regression (GWLR – integrating geographically weighted regression (GWR) and a logistic model) was proposed to improve vegetation cover mapping of Kangbao County, Hebei of China using Landsat 8 image and field data. Additionally, a new method to determine the bandwidth of GWLR is presented. Using cross-validation, GWLR was compared with a globally linear stepwise regression (LSR), a local linear modelling method GWR and a nonparametric method, k-nearest neighbours (kNN) with varying numbers of nearest plots. Results demonstrated (1) the red and near infrared relevant band ratios and vegetation indices significantly improved mapping; (2) the GWLR, GWR and kNN methods led to more accurate predictions than LSR; (3) GWLR reduced overestimations and underestimations compared with LSR, kNN and GWR, and also eliminated negative and very large estimates caused by GWR and LSR; and (4) The maximum distance of spatial autocorrelation could be used to determine the bandwidth for GWLR. Overall, GWLR proved more promising for mapping vegetation cover of arid and semiarid areas.

Detection of mesial temporal lobe epileptiform discharges on intracranial electrodes using deep learning

OBJECTIVE

Develop a high-performing algorithm to detect mesial temporal lobe (mTL) epileptiform discharges on intracranial electrode recordings.

METHODS

An epileptologist annotated 13,959 epileptiform discharges from a dataset of intracranial EEG recordings from 46 epilepsy patients. Using this dataset, we trained a convolutional neural network (CNN) to recognize mTL epileptiform discharges from a single intracranial bipolar channel. The CNN outputs from multiple bipolar channel inputs were averaged to generate the final detector output. Algorithm performance was estimated using a nested 5-fold cross-validation.

RESULTS

On the receiver-operating characteristic curve, our algorithm achieved an area under the curve (AUC) of 0.996 and a partial AUC (for specificity > 0.9) of 0.981. AUC on a precision-recall curve was 0.807. A sensitivity of 84% was attained at a false positive rate of 1 per minute. 35.9% of the false positive detections corresponded to epileptiform discharges that were missed during expert annotation.

CONCLUSIONS

Using deep learning, we developed a high-performing, patient non-specific algorithm for detection of mTL epileptiform discharges on intracranial electrodes.

SIGNIFICANCE

Our algorithm has many potential applications for understanding the impact of mTL epileptiform discharges in epilepsy and on cognition, and for developing therapies to specifically reduce mTL epileptiform activity.

Dual roles of misshapen/NIK-related kinase (MINK1) in osteoarthritis subtypes through the activation of TGFβ signaling

OBJECTIVE

To identify the role of misshapen/NIK-related kinase (MINK1) in age-related Osteoarthritis (OA) and injury-induced OA, and the effects of enhanced TGFβ signaling in these progresses.

DESIGN

The effect of MINK1 was analyzed with MINK1 knock out (Mink1-/-) mice and C57BL/6J mice. OA progress was studied in age-related OA and instability-associated OA (destabilization of the medial meniscus, DMM) models. The murine knee joint was evaluated through histological staining, Osteoarthritis Research Society International (OARSI) scores, immunohistochemistry, and μCT analysis. Primary chondrocytes were isolated from wild type and Mink1-/- mice and subjected to osteogenic induction and Western blot analysis.

RESULTS

MINK1 is highly expressed during cartilage development and in normal cartilage. Mink1-/- mice displayed markedly lower OARSI scores, aggrecan degradation neoepitope positive cells and increased Safranin O and pSMAD2 staining in aging-related OA model. However, in injury-induced OA, loss of MINK1 accelerates extracellular matrix (ECM) destruction, osteophyte formation, and subchondral bone sclerosis. Accelerated subchondral bone remodeling in Mink1-/- mice was accompanied with increased numbers of nestin-positive mesenchymal stem cells (MSCs) and osterix-positive osteoprogenitors. pSMAD2 staining was increased in the subchondral bone marrow of Mink1-/- mice and overexpression of MINK1 inhibited SMAD2 phosphorylation in vitro.

CONCLUSIONS

This study shows for the first time that activation of TGFβ/SMAD2 by MINK1 deficiency plays opposite roles in aging-related and injury-induced OA. MINK1 deficiency protects cartilage from degeneration in aging joints through increased SMAD2 activation in chondrocytes, while accelerating OA progress in injury-induced model through enhanced osteogenesis of MSCs in the subchondral bone. These findings provide insights for developing precision OA therapeutics targeting TGFβ/SMAD2 signaling.

ETph: enhancers and their targets in pig and human database

Enhancers, as the genomic non-coding sequences, play a key role in the activation of gene expression. They have been widely identified in the human genome. Pig is an important biomedical model for human health. Few studies have been performed to explore the enhancers in the pig genome. The human enhancer information may be useful to identify enhancers in the pig genome. In addition, the genetic background of pig traits could be useful to annotate human enhancers and diseases. Thus, in order to further study enhancers and their potential roles in human and pig, we developed a public database, ETph (Enhancers and their Targets in pig and human). ETph integrates the information on human enhancers, pig putative enhancers, target genes, pig QTL terms, human diseases, GO terms and the KEGG pathway. A total of 25 182 enhancers were identified in the pig genome using the human homology sequence information. Among them, 6232 high-confidence enhancers were used to build the ETph. ETph provides a convenient platform to search, browse and download data. Moreover, a web-based analytical tool was designed to visualize networks and topology graphs among pig putative enhancers, target genes, pig QTL traits and human diseases. ETph might provide a useful tool for researchers to investigate the genetic background of pig traits and human diseases. ETph is freely accessible at http://klab.sjtu.edu.cn/enhancer/.

Has-MiR-196a-2 is up-regulated and acts as an independent unfavorable prognostic factor in thyroid carcinoma

OBJECTIVE

To identify the role of hsa-miR-196a-2 in thyroid cancer by bioinformatics analysis.

MATERIALS AND METHODS

The expression profiles of thyroid cancer was download from TCGA. The dysregulated microRNAs were obtained by edger R package. Then, the prognostic data were analyzed by K-M plot. The difference between different groups was analyzed by the t-test. At last, the biological processes of has-miR-196a-2 were obtained with GSEA.

RESULTS

In this study, we found that has-miR-196a-2 was upregulated in thyroid carcinoma by analyzing the TCGA database, which was inversely proportional to the prognosis of patients with thyroid carcinoma. Univariate and multivariate COX analysis showed that has-miR-196a-2 was an independent prognostic risk factor for thyroid carcinoma. Higher expressions of has-miR-196a-2 were found in patients with older age, advanced tumor stage, lymph node metastasis, and local infiltration through the t-test. We found that has-miR-196a-2 was enriched in adherent junction, focal adhesion, and actin cytoskeleton, which are closely related to the invasion and migration of the function pathway. Moreover, it is mainly enriched in tumor progression pathways, such as the PPAR pathway and WNT pathway.

CONCLUSIONS

Hsa-miR-196a-2 is overexpressed in thyroid tumors and is an independent prognostic risk factor for thyroid carcinoma.

Expert-level sleep scoring with deep neural networks

Objectives

Scoring laboratory polysomnography (PSG) data remains a manual task of visually annotating 3 primary categories: sleep stages, sleep disordered breathing, and limb movements. Attempts to automate this process have been hampered by the complexity of PSG signals and physiological heterogeneity between patients. Deep neural networks, which have recently achieved expert-level performance for other complex medical tasks, are ideally suited to PSG scoring, given sufficient training data.

Methods

We used a combination of deep recurrent and convolutional neural networks (RCNN) for supervised learning of clinical labels designating sleep stages, sleep apnea events, and limb movements. The data for testing and training were derived from 10 000 clinical PSGs and 5804 research PSGs.

Results

When trained on the clinical dataset, the RCNN reproduces PSG diagnostic scoring for sleep staging, sleep apnea, and limb movements with accuracies of 87.6%, 88.2% and 84.7% on held-out test data, a level of performance comparable to human experts. The RCNN model performs equally well when tested on the independent research PSG database. Only small reductions in accuracy were noted when training on limited channels to mimic at-home monitoring devices: frontal leads only for sleep staging, and thoracic belt signals only for the apnea-hypopnea index.

Conclusions

By creating accurate deep learning models for sleep scoring, our work opens the path toward broader and more timely access to sleep diagnostics. Accurate scoring automation can improve the utility and efficiency of in-lab and at-home approaches to sleep diagnostics, potentially extending the reach of sleep expertise beyond specialty clinics.

Assessment of expression and specific activities of transglutaminases TG1, TG2, and FXIII-A during osteoclastogenesis

Osteoclasts are large multinucleated bone-resorbing cells derived from monocyte/macrophage lineage. Macrophage-colony stimulating factor (M-CSF) and receptor activator of nuclear factor-κB ligand (RANKL) drive the multi-stage osteoclastogenesis. Transglutaminases (TGs) are Ca²⁺- and thiol-dependent acyl transferases and protein crosslinking enzymes. TG enzyme family contains eight catalytically active enzymes TG1-7 and Factor XIII-A (FXIII-A). Recent studies have shown that TG1, TG2, and FXIII-A are present in osteoclasts and that TG2 and FXIII-A regulate osteoclastogenesis. In this study, we examined gene and protein expression and specific activities of TG1, TG2, and FXIII-A during osteoclastogenesis using "Hitomi peptides" in a day-by-day manner. We report that TG activities are highest in the differentiation and early fusion phases and then decrease dramatically. TG activities were upregulated by M-CSF and downregulated by addition of RANKL. FXIII-A was dramatically downregulated by RANKL, suggesting its involvement in M-CSF-mediated precursor commitment phase. TG1 and TG2 proteins were present throughout osteoclastogenesis, suggesting that they may have functions in both differentiation and fusion. In summary, the three TGs likely exert distinct functions at different stages of osteoclastogenesis. Our work also demonstrates that the "Hitomi peptides" are highly specific tools for detection of distinct TGs in a system where multiple TGs are present.

MicroRNA-1266 suppresses papillary thyroid carcinoma cell metastasis and growth via targeting FGFR2

OBJECTIVE

To explore the effects of microRNA-1266 (miR-1266) on metastasis and growth in papillary thyroid carcinoma cells and to provide therapeutic targets for papillary thyroid carcinoma.

PATIENTS AND METHODS

By quantitative Real-time polymerase chain reaction (PCR), miR-1266 expression level in 38 pairs of papillary thyroid carcinoma tissues and three breast cancer-derived cell lines was examined. After transfection with miR-1266 mimics, the effects of miR-1266 over-expression on cell proliferation, invasion and migration were analyzed. Further, we employed several databases for the target gene prediction. Dual-luciferase activity assay was performed to verify whether FGFR2 was the direct target gene of miR-1266. Western blotting was conducted to detect protein levels.

RESULTS

MiR-1266 was significantly downregulated in papillary thyroid carcinoma tissue samples and cell lines. Over-expression of miR-1266 in papillary thyroid carcinoma cells significantly attenuated the cell proliferation, invasion, and migration. Dual-luciferase report assay and Western blotting confirmed that FGFR2 was a target gene of miR-1266. Furthermore, up-regulation of FGFR2 partially reversed the suppressive effects of miR-1266 over-expression on cell growth and progression.

CONCLUSIONS

miR-1266 could inhibit cell proliferation and progression of papillary thyroid carcinoma via targeting FGFR2. Our findings might provide a new target for the diagnosis and treatment of papillary thyroid carcinoma.

Oxidative stress induces B lymphocyte DNA damage and apoptosis by upregulating p66shc

OBJECTIVE

B lymphoma is a type of malignant tumor originating from the lymphatic hematopoietic system. The pathogenesis and treatment methods are not clear. The change of oxidative stress is closely related to the cell DNA damage and cell apoptosis, which may be served as a target for cancer treatment. This study aims to illustrate the role of oxidative stress in the regulation of B lymphocytoma.

PATIENTS AND METHODS

The tumor tissue was collected from patients with B lymphocytoma. The p66shc level was detected by Western blot. Hydrogen peroxide (H2O2) was assessed by the kit. The oxidative stress model of B lymphoma cell was established by H2O2 treatment. ROS inhibitor or RNAi was used to regulate ROS level. ROS level was determined by flow cytometry. 8-OHdG level (DNA damage product) was tested by the kit. Cell apoptosis was evaluated by annexin V-PI.

RESULTS

P66shc expression was significantly reduced, while H2O2 production was significantly decreased in the tumor tissue of B lymphoma compared with adjacent normal control. H2O2 stimulation markedly elevated ROS level and p66shc expression (p < 0.05), accompanied by the aggravation of DNA damage and increase of apoptosis. ROS inhibitor or p66shc RNAi treatment significantly attenuated DNA damage and declined cell apoptosis (p < 0.05).

CONCLUSIONS

ROS production promoted p66shc expression, induced DNA damage, and facilitated cell apoptosis. Upregulation of p66shc by oxidative stress could be treated as a new therapeutic target for B lymphoma.

Classifying pain in transoral endoscopic thyroidectomy

PURPOSES

Knowledge of visual analog scale (VAS) pain assessment for transoral endoscopic thyroidectomy vestibular approach (TOETVA) is limited. The purpose of this analysis was to classify the postoperative discomfort scores in patients undergoing TOETVA compared to open thyroidectomy.

METHODS

Observational clinical study of patients who underwent thyroidectomy by VAS pain assessment from September 2016 to March 2017. Patients were stratified into two groups: patients eligible for TOETVA (Group TOETVA) and non-candidates for endoscopic intervention (open thyroidectomy approach-OTA). VAS was recorded in the recovery room, at 24 h, + 2, + 5, + 15, + 30, + 90 days, and 6 months after surgery. Pain assessment was stratified in VAS-lower lip, VAS-chin, VAS-jaw, VAS-anterior neck, VAS-cervical/back, VAS-swallowing, VAS-brushing, VAS-speaking, and VAS-shaving. Secondary outcome assessed were analgesic rescue dose, morbidity, operative notes, hospital stay, and histopathology.

RESULTS

41 TOETVA and 45 OTA constituted the analysis. There were differences between the TOETVA and OTA for age, gland volume, mean nodule diameter, coexistence thyroiditis, bilateral procedures, and use of drain. Operative time was longer in TOETVA. Results indicated that TOETVA was associated with reduced neck, cervical back, and swallowing VAS scores in the 24 h after surgery. Conversely, jaw and brushing teeth resulted in higher VAS score in TOETVA group. OTA patients never experienced lower lip or chin pain. The use of rescue analgesics did not differ between the two groups.

CONCLUSIONS

VAS was used to measure treatment outcome in TOETVA. VAS scores achieved overall a minimal clinical importance difference from the two procedures. There appears to be both a short- and long-term different range of interpretations of pain between TOETVA and OTA.

A Mean Field Model of Acute Hepatic Encephalopathy

Acute hepatic encephalopathy (AHE) is a common form of delirium, a state of confusion, impaired attention, and decreased arousal due to acute liver failure. However, the neurophysiological mechanisms underlying AHE are poorly understood. In order to develop hypotheses for mechanisms of AHE, our work builds on an existing neural mean field model for similar EEG patterns in cerebral anoxia, the bursting Liley model. The model proposes that generalized periodic discharges, similar to the triphasic waves (TPWs) seen in severe AHE, arise through three types of processes a) increased neuronal excitability; b) defective brain energy metabolism leading to impaired synaptic transmission; c) and enhanced postsynaptic inhibition mediated by increased GABA-ergic and glycinergic transmission. We relate the model parameters to human EEG data using a particle-filter based optimization method that matches the TPW inter-event-interval distribution of the model with that observed in patients EEGs. In this way our model relates microscopic mechanisms to EEG patterns. Our model represents a starting point for exploring the underlying mechanisms of brain dynamics in delirium.