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Background: It is of great importance to predict the early recurrence (ER) of hepatocellular carcinoma (HCC) after hepatectomy using preoperative imaging modalities. Nevertheless, no comparative studies have been conducted to determine which modality, CT or MRI with radiomics analysis, is more effective. Methods: We retrospectively enrolled 119 HCC patients who underwent preoperative CT and MRI. A total of 3776 CT features and 4720 MRI features were extracted from the whole tumor. The minimum redundancy and maximum relevance algorithm (MRMR) and least absolute shrinkage and selection operator (LASSO) regression were applied for feature selection, then support vector machines (SVMs) were applied for model construction. Multivariable logistic regression analysis was employed to construct combined models that integrate clinical–radiological–pathological (CRP) traits and radscore. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were used to compare the efficacy of CT, MRI, and CT and MRI models in the test cohort. Results: The CT model and MRI model showed no significant difference in the prediction of ER in HCC patients (p = 0.911). RadiomicsCT&MRI demonstrated a superior predictive performance than either RadiomicsCT or RadiomicsMRI alone (p = 0.032, 0.039). The combined CT and MRI model can significantly stratify patients at high risk of ER (area under the curve (AUC) of 0.951 in the training set and 0.955 in the test set) than the CT model (AUC of 0.894 and 0.784) and the MRI model (AUC of 0.856 and 0.787). DCA demonstrated that the CT and MRI model provided a greater net benefit than the models without radiomics analysis. Conclusions: No significant difference was found in predicting the ER of HCC between CT models and MRI models. However, the multimodal radiomics model derived from CT and MRI can significantly improve the prediction of ER in HCC patients after resection.

Mental health issues and needs have increased substantially since the onset of the COVID-19 pandemic. However, health policy and decision-makers do not have adequate data and tools to predict population-level mental health demand, especially amid a crisis. This study investigates whether situational indicators and social media emotions can be effectively used to predict public mental health needs. We collected time-series data from multiple sources in Singapore between 1 July 2020 and 31 December 2021, including daily-level records of situation indicators, emotions expressed on social media, and mental health needs measured by the number of public visits to the emergency room of the country's largest psychiatric hospital, and use of government-initiated online mental health self-help portal. Compared to mental health needs data alone, social media emotions were found to have significant Granger-causality effects with as early as four to five days lag length. Each resulted in a statistically significant enhancement in predicting the public's visits to the emergency room and the online self-help portal (e.g., Facebook Anger Count on emergency room visits, χ2 = 13·7, P = ·0085**). In contrast, situational indicators such as daily new cases had Granger-causality effects (χ2 = 10·3, P = ·016*) with a moderate lag length of three days. The findings indicate that emotions algorithmically extracted from social media platforms can provide new indicators for tracking and forecasting population-level mental health states and needs.

Background Severe ocular surface disorders are one of the major blinding diseases, and a paucity of original tissue obscures successful reconstruction. We developed a new surgical technique of direct oral mucosal epithelial transplantation (OMET) to reconstruct severely damaged ocular surfaces in 2011. This study elaborates on the clinical efficacy of OMET. Methods A retrospective review of patients with severe ocular surface disorders who underwent OMET from 2011 to 2021 at the Department of Ophthalmology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine was conducted. Patients who were followed up for at least 3 months postoperatively and had sufficient pre or postoperative records were included. Surgical efficacy was evaluated by comparing the best-corrected visual acuity (BCVA), corneal transparency, neovascularization grade, and symblepharon grade. Additionally, postoperative ocular surface impression cytology was used to study the morphology of the newborn epithelial cells. Results Forty-eight patients (49 eyes; mean age: 42.55 ± 12.40 years, range:12–66 years) were enrolled in the study. The etiology included chemical burns (30 eyes), thermal burns (16 eyes), explosive injuries (1 eye), Stevens-Johnson syndrome (1 eye), and multiple pterygiums (1 eye). The mean follow-up period was 25.97 ± 22.99 months. Postoperatively, 29 eyes (59.18%) showed improved corneal transparency, 26 eyes (53.06%) had improved BCVA, 47 eyes (95.92%) had a stable epithelium until the final follow-up, 44 eyes (89.80%) had a reduced neovascularization grade. Of the 20 eyes with preoperative symblepharon, 15 (75%) were completely resolved, and five (25%) were partially resolved. Impression cytological studies showed no postoperative conjunctival invasion onto the corneal surface. Conclusions OMET is a safe and effective surgical technique for reconstruction in severe ocular surface disorder by maintaining a stable epithelium and reducing the neovascularization and symblepharon grade.

Background Several previous studies demonstrated that the combination of self-expandable metallic stents (SEMS) and 125I seed implantation might prolong stent patency and obtain survival benefits for malignant obstructive jaundice (MOJ) patients. However, these studies rarely mentioned a comparison between CT-guided intratumoral 125I seed implantation and intraluminal 125I seed strand insertion combined with stenting for the management of MOJ. This study aimed to further evaluate the safety and efficacy of SEMS combined with 125I brachytherapy in the management of unresectable MOJ. Methods Fifty-nine patients with unresectable MOJ were retrospectively included from March 2018 to June 2021. The main therapeutic outcomes were evaluated in terms of stent patency, and overall survival. Cumulative stent patency and overall survival rates were calculated by Kaplan–Meier survival analysis. Both clinical and treatment factors associated with survival were analyzed. Results Technical success was achieved in all patients. The clinical success rate was 94% (32/34) in the seeds group and 92% (23/25) in the control group, no significant difference was found (p =1.000). The median duration of stent patency was significantly longer in the 125I brachytherapy group compared with the control group (289 days vs. 88 days, respectively, p =0.001). The 125I brachytherapy group demonstrated a significantly better median overall survival rate than the control group (221 days vs. 78 days, respectively, p =0.001). In multivariate analysis, stents with 125I brachytherapy (p =0.004) was a significant favorable prognostic factor that affected patient survival. No significant difference was observed between CT-guided 125I seed implantation and 125I seed strand insertion in stent patency (p =0.268), and overall survival (p =0.483). Conclusion SEMS combined with 125I brachytherapy is safe and effective for treating MOJ. 125I brachytherapy may help to maintain stent patency and prolong overall survival. There was no significant difference between CT-guided 125I seed implantation with SEMS and 125I seed strand insertion with SEMS in stent patency and overall survival.

Metal-oxide-semiconductor capacitors with single and multi-layer high-K gate dielectrics on Si (0001) face of n-type 4H-SiC substrates have been investigated. Multi-layered nanolaminated gate-stack comprises alternating ultrathin (6nm) Al2O3 and HfO2. A 5nm thick interfacial silicon oxynitride is deposited prior to laminated films to investigate interface trap properties and tuning of flat band voltage. Total thickness of gate-stack films including interfacial layer is 55nm. The thermal stability of multi-layered nanolaminated film is investigated using XTEM. Localized crystallization of HfO2 is visible after RTA at 900°C while Al2O3 remains fully amorphous. Some of HfO2 grains have extended into Al2O3 layer but was not able to crossover. The measured accumulation capacitance of 55nm thick gate dielectric gives an effective dielectric constant value of 9.6 and an equivalent oxide thickness of 22nm from high-frequency capacitance-voltage measurements. A positive flat band voltage ( of 12.2V and 10.6V are observed from both single layer HfO2 and Al2O3 dielectrics, respectively due to presence of negatively charged oxygen interstitial defects generated during atomic layer deposition process. However, VFB shifted towards negative voltage-7.6V for multi-layered Al2O3/HfO2 stacks probably associated with positive Al and Hf interstitials at interface of Al2O3/HfO2. Ultrathin interfacial oxynitride films is effective to reduce Dit to 3×1011/eVcm2 and tuning of VFB. The breakdown field of stacked gate dielectric on 4H-SiC is 10.0 MV/cm.

The application of machine learning (ML)-based methods to the study of thermoelectric (TE) materials is promising. Although conventional ML algorithms can achieve high prediction performance, their lack of interpretability severely obstructs researchers from extracting material-oriented insights from ML models. In this work, high ML-based prediction performance was achieved with respect to TE power factors (PFs), and the results were well understood by the SHapley Additive exPlanations (SHAP), a method to identify the correlations between targets and descriptors. We designed a robust PF prediction model for diamond-like compounds via a stacking technique, and the model achieved a coefficient of determination value above 0.95 on the test set. From the SHAP analysis, the PFs were negatively correlated with electronegativity and positively correlated with the descriptor “volume per atom” based on the previously reported dataset. TE domain knowledge was adopted to understand these correlations. This work shows that ML models can achieve high accuracy while exhibiting good interpretability, making them useful for materials scientists.