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Abstract Previous clinic models for patients with hepatocellular carcinoma (HCC) receiving transarterial chemoembolization (TACE) mainly focused on the overall survival, whereas a simple-to-use tool for predicting the response to the first TACE and the management of risk classification before TACE are lacking. Our aim was to develop a scoring system calculated manually for these patients. A total of 437 patients with hepatocellular carcinoma (HCC) who underwent TACE treatment were carefully selected for analysis. They were then randomly divided into two groups: a training group comprising 350 patients and a validation group comprising 77 patients. Furthermore, 45 HCC patients who had recently undergone TACE treatment been included in the study to validate the model’s efficacy and applicability. The factors selected for the predictive model were comprehensively based on the results of the LASSO, univariate and multivariate logistic regression analyses. The discrimination, calibration ability and clinic utility of models were evaluated in both the training and validation groups. A prediction model incorporated 3 objective imaging characteristics and 2 indicators of liver function. The model showed good discrimination, with AUROCs of 0.735, 0.706 and 0.884 and in the training group and validation groups, and good calibration. The model classified the patients into three groups based on the calculated score, including low risk, median risk and high-risk groups, with rates of no response to TACE of 26.3%, 40.2% and 76.8%, respectively. We derived and validated a model for predicting the response of patients with HCC before receiving the first TACE that had adequate performance and utility. This model may be a useful and layered management tool for patients with HCC undergoing TACE.

ObjectiveTo evaluate the prevalence of different types of temporomandibular disorders (TMD) symptoms in young adults and determine their associations with problematic smartphone use (PSU).MethodsThe data of the study were collected from local university students through an online questionnaire survey. Demographic information, Fonseca Anamnestic Index (FAI), Smartphone Addiction Scale-Short Version (SAS-SV), and Patient Health Questionnaire-4 (PHQ-4) responses were gathered electronically and analyzed using multiple logistic regression analysis.ResultsThere were 163 male and 307 female respondents were participated in this study. The prevalence of PSU and TMD were 83.6% and 66.4%, respectively. There was a moderate statistical correlation between PSU and TMD among young adults (r = 0.31, p < 0.01). The logistic regression model revealed that the risk of TMD was 1.77 times higher in people with PSU than in those without PSU (OR = 1.77; 95% CI 1.04–3.06). PSU is a risk factor for pain-related TMD (OR = 1.81; 95% CI 1.08–3.04) but not intra-articular TMD.ConclusionSubjects showed high prevalence of both TMD and PSU. People with PSU experienced more severe and frequent pain-related rather than intra-articular TMD symptoms than those without PSU. By reducing the problematic smartphone use, the risk factor of TMD might be avoided.

BackgroundAbnormal glucose metabolism was shown to be associated with the occurrence of remote diffusion-weighted imaging lesions (R-DWILs) after primary intracerebral hemorrhage (ICH) onset. Insulin resistance is a metabolic disorder that was regarded as an indicator of chronic systemic inflammation. In this study, we aimed to determine the effect of insulin resistance on the occurrence of R-DWILs in ICH.MethodsPatients with primary ICH within 14 days after onset were prospectively enrolled from November 2017 to October 2019. R-DWILs was defined as remote focal hyperintensity from the hematoma in DWI, with corresponding hypointensity in apparent diffusion coefficient. The homeostasis model assessment of insulin resistance (HOMA-IR) was used for insulin resistance estimation and calculated as fasting insulin (μU/ml) × fasting glucose (mmol/L)/22.5. Patients in our cohort were divided into four groups according to HOMA-IR index quartiles. Logistic regression analysis and smoothing plots were used to evaluate the association of HOMA-IR with R-DWIL occurrence. Sensitivity analysis was performed in non-diabetic patients, non-obese patients, hypertensive ICH patients, and patients 60 years and older separately. The association between HOMA-IR and systemic inflammatory immune indices neutrophil to lymphocyte ratio (NLR) and monocyte to lymphocyte ratio (MLR) was examined with multiple linear regression analysis.ResultsAmong the 345 patients, 54 (15.7%) had R-DWILs. Both the third and fourth quartiles of HOMA-IR index were robustly associated with an increased risk of R-DWIL occurrence (adjusted OR 3.58, 95% CI 1.33-9.65; adjusted OR 3.91, 95%CI 1.47-10.41) when compared with the first quartile. The association was consistent in non-diabetic, non-obese, hypertensive ICH patients, as well as in patients 60 years and older. Furthermore, both NLR and MLR were independently associated with HOMA-IR.ConclusionsOur study suggested that insulin resistance evaluated with HOMA-IR index was independently associated with the presence of R-DWILs in patients with acute and subacute primary ICH. It may provide new insights into the metabolism-related brain injury after ICH ictus.

CD200 is widely distributed in the central nervous system and plays an essential role in the immune response in neurological diseases. However, little is currently known about the effects of CD200 signaling on the blood–brain barrier (BBB) function after intracerebral hemorrhage (ICH). In this study, the role of CD200 during ICH in an autologous blood induced mouse ICH model was investigated. Following ICH, critical protein expression, BBB permeability, and neurological function were measured with or without CD200Fc administration. Our results showed that both the expression of CD200 and CD200R1 decreased after ICH and administration of CD200Fc attenuated BBB leakage and improved neurological functions. In conclusion, our work demonstrated that CD200Fc might be a potential treatment option for ICH by protecting the BBB and improving functional outcomes.

Background and purpose Early haematoma expansion is determinative in predicting outcome of intracerebral haemorrhage (ICH) patients. The aims of this study are to develop a novel prediction model for haematoma expansion by applying deep learning model and validate its prediction accuracy.Methods Data of this study were obtained from a prospectively enrolled cohort of patients with primary supratentorial ICH from our centre. We developed a deep learning model to predict haematoma expansion and compared its performance with conventional non-contrast CT (NCCT) markers. To evaluate the predictability of this model, it was also compared with a logistic regression model based on haematoma volume or the BAT score.Results A total of 266 patients were finally included for analysis, and 74 (27.8%) of them experienced early haematoma expansion. The deep learning model exhibited highest C statistic as 0.80, compared with 0.64, 0.65, 0.51, 0.58 and 0.55 for hypodensities, black hole sign, blend sign, fluid level and irregular shape, respectively. While the C statistics for swirl sign (0.70; p=0.211) and heterogenous density (0.70; p=0.141) were not significantly higher than that of the deep learning model. Moreover, the predictive value for the deep learning model was significantly superior to that of the logistic model of haematoma volume (0.62; p=0.042) and the BAT score (0.65; p=0.042).Conclusions Compared with the conventional NCCT markers and BAT predictive model, the deep learning algorithm showed superiority for predicting early haematoma expansion in ICH patients.