Rational Medium-Range Charge Transfer Strategy Toward Highly Efficient Violet-Blue Organic Light-Emitting Diodes with Narrowed Emission.

The spectral narrowing engineering of pure-organic emitters attracts great research interests in realizing high color purity. Here, the adjusted medium-range charge transfer (MCT) strategy of TIC-BO with rigid planar structure by fusing two typical UV-emitting multiple resonance (MR) fragments via the ingenious double-halide cyclized coupling reaction is reported. The resulting TIC-BO with MCT nature shows efficient violet-blue emission in dilute toluene and evaporated host-guest films, and desirably narrowed spectra are achieved by the suppression of structural relaxation and the shortened charge transfer states. The single-doped device with TIC-BO as emitter shows narrowed violet-blue electroluminescence peaked at 428 nm with full-width at half-maximum of 43 nm (0.28 eV), and the Commission Internationale de l'Éclairage coordinates of (0.160, 0.050). A maximum external quantum efficiency (EQEmax ) of 20.50% is achieved, which is among the best results of the corresponding violet-blue emitting region. Further introduction of a stronger electron-donating carbazole group makes TIC-BNO exhibit red-shifted sky-blue emission with MR-dominant properties, and good device performance is received with EQEmax of 34.58%. The outstanding performances of TIC-BO successfully demonstrate the significance and prospect of the proposed molecular design strategy.

Brain abscess of odontogenic origin: A case report and literature review.

BACKGROUND: The objective of this study is to investigate and understand the characteristics of odontogenic brain abscess. METHODS: A case of brain abscess suspected to be caused by odontogenic infection was documented, and a comprehensive analysis and summary of odontogenic brain abscess cases reported in various countries over the past 20 years was conducted. RESULTS: Based on the analysis and synthesis of both the present and previous reports, we have examined and consolidated the distinctive features of odontogenic brain abscess, the potential transmission pathway of pathogenic bacteria, diagnostic assertions, verification techniques, and crucial considerations during treatment. CONCLUSION: This investigation contributes to an enhanced comprehension and improved clinical identification of odontogenic brain abscess.

CT radiomics model combined with clinical and radiographic features for discriminating peripheral small cell lung cancer from peripheral lung adenocarcinoma.

Purpose: Exploring a non-invasive method to accurately differentiate peripheral small cell lung cancer (PSCLC) and peripheral lung adenocarcinoma (PADC) could improve clinical decision-making and prognosis. Methods: This retrospective study reviewed the clinicopathological and imaging data of lung cancer patients between October 2017 and March 2022. A total of 240 patients were enrolled in this study, including 80 cases diagnosed with PSCLC and 160 with PADC. All patients were randomized in a seven-to-three ratio into the training and validation datasets (170 vs. 70, respectively). The least absolute shrinkage and selection operator regression was employed to generate radiomics features and univariate analysis, followed by multivariate logistic regression to select significant clinical and radiographic factors to generate four models: clinical, radiomics, clinical-radiographic, and clinical-radiographic-radiomics (comprehensive). The Delong test was to compare areas under the receiver operating characteristic curves (AUCs) in the models. Results: Five clinical-radiographic features and twenty-three selected radiomics features differed significantly in the identification of PSCLC and PADC. The clinical, radiomics, clinical-radiographic and comprehensive models demonstrated AUCs of 0.8960, 0.8356, 0.9396, and 0.9671 in the validation set, with the comprehensive model having better discernment than the clinical model (P=0.036), the radiomics model (P=0.006) and the clinical-radiographic model (P=0.049). Conclusions: The proposed model combining clinical data, radiographic characteristics and radiomics features could accurately distinguish PSCLC from PADC, thus providing a potential non-invasive method to help clinicians improve treatment decisions.

Risk factors for air embolism following computed tomography-guided percutaneous transthoracic needle biopsy: a systematic review and meta-analysis

To quantitatively analyze the risk factors for air embolism following computed tomography (CT)-guided percutaneous transthoracic needle biopsy (PTNB) and qualitatively review their characteristics. The databases of PubMed, Embase, Web of Science, Wanfang Data, VIP information, and China National Knowledge Infrastructure were searched on January 4, 2021, for studies reporting the occurrence of air embolisms following CT-guided PTNB. After study selection, data extraction, and quality assessment, the characteristics of the included cases were qualitatively and quantitatively analyzed. A total of 154 cases of air embolism following CT-guided PTNB were reported. The reported incidence was 0.06% to 4.80%, and 35 (22.73%) patients were asymptomatic. An unconscious or unresponsive state was the most common symptom (29.87%). Air was most commonly found in the left ventricle (44.81%), and 104 (67.53%) patients recovered without sequelae. Air location (P < 0.001), emphysema (P = 0.061), and cough (P = 0.076) were associated with clinical symptoms. Air location (P = 0.015) and symptoms (P < 0.001) were significantly associated with prognosis. Lesion location [odds ratio (OR): 1.85, P = 0.017], lesion subtype (OR: 3.78, P = 0.01), pneumothorax (OR: 2.16, P = 0.003), hemorrhage (OR: 3.20, P < 0.001), and lesions located above the left atrium (OR: 4.35, P = 0.042) were significant risk factors for air embolism. Based on the current evidence, a subsolid lesion, being located in the lower lobe, the presence of pneumothorax or hemorrhage, and lesions located above the left atrium were significant risk factors for air embolism.

The diagnostic value of CT-guided percutaneous puncture biopsy of pulmonary ground-glass nodules: a meta-analysis.

BACKGROUND: More and more pulmonary ground-glass nodules (GGNs) are screened with the extensive usage of low-dose computed tomography (CT). The need of CT-guided percutaneous puncture biopsy of GGN remains controversial. PURPOSE: To explore the diagnostic accuracy of CT-guided percutaneous puncture biopsy of GGNs. MATERIAL AND METHODS: We searched PubMed, EMBASE, the Cochrane Library, and CNKI. Included studies reported the puncture biopsy results of pulmonary GGNs, including the number of true positive (TP), false positive (FP), true negative (TN), and false negative (FN) cases. After evaluating the studies, statistical analysis, and quality assessment, the pooled diagnostic sensitivity (SEN), specificity (SPE), and diagnostic odds ratio (DOR) were calculated. The summary receiver operating characteristic (SROC) curve was constructed and the area under the curve (AUC) was calculated. Subgroup analysis was performed according to whether spiral CT or fluoroscopy-guided CT was used in the study. RESULTS: This meta-analysis included 14 studies with a total of 759 patients (702 samples). The pooled SEN, SPE, and DOR of CT-guided puncture biopsy of pulmonary GGNs were 0.91 (95% confidence interval [CI] = 0.89-0.94), 0.99 (95% CI = 0.95-1.00), and 138.72 (95% CI = 57.98-331.89), respectively. The AUC was 0.97. CONCLUSION: Our results indicated that CT-guided puncture biopsy of GGNs has high SEN, SPE, and DOR, which proved that CT-guided puncture biopsy was a good way to determine the pathological nature of GGN.

Correction: Liu et al. A Predictive Model for the Risk of Posterior Circulation Stroke in Patients with Intracranial Atherosclerosis Based on High Resolution MRI. Diagnostics 2022, 12, 812.

Errors occurred in the number of patients in the posterior circulation ischemic stroke (PCIS) group and non-PCIS group described in the original publication [...].

A Predictive Model for the Risk of Posterior Circulation Stroke in Patients with Intracranial Atherosclerosis Based on High Resolution MRI.

Intracranial vertebrobasilar atherosclerosis is the main cause of posterior circulation ischemic stroke. We aimed to construct a predictive model for the risk of posterior circulation ischemic stroke in patients with posterior circulation atherosclerosis based on high-resolution MRI (HR-MRI). A total of 208 consecutive patients with posterior circulation atherosclerosis confirmed by HR-MRI, from January 2020 to July 2021, were retrospectively assessed. They were assigned to the posterior circulation stroke (49 patients) and non-posterior circulation stroke group (159 patients) based on clinical presentation and diffusion-weighted imaging (DWI). Demographic data, risk factors of atherosclerosis, laboratory findings, and imaging characteristics were extracted from electronic health records. Plaque features were investigated by HR-MRI. Fifty-three clinical or imaging features were used to derive the model. Multivariable logistic regression analysis was employed to construct the prediction model. The nomogram was evaluated for calibration, differentiation, and clinical usefulness. Plaque enhancement, plaque irregular surface morphology, artery location of plaque, and dorsal quadrant of plaque location were significant predictors for posterior circulation stroke in patients with intracranial atherosclerosis. Subsequently, these variables were selected to establish a nomogram. The model showed good distinction (C-index 0.830, 95% CI 0.766-0.895). The calibration curve also showed excellent consistency between the prediction of the nomogram and the observed curve. Decision curve analysis further demonstrated that the nomogram conferred significantly high clinical net benefit. The nomogram calculated from plaque characteristics in HR-MRI may accurately predict the posterior circulation stroke occurrence and be of great help for stratification of stroke decision making.

A nomogram predicting the severity of COVID-19 based on initial clinical and radiologic characteristics.

Aim: This study aimed to build an easy-to-use nomogram to predict the severity of COVID-19. Patients & methods: From December 2019 to January 2020, patients confirmed with COVID-19 in our hospital were enrolled. The initial clinical and radiological characteristics were extracted. Univariate and multivariate logistic regression were used to identify variables for the nomogram. Results: In total, 104 patients were included. Based on statistical analysis, age, levels of neutrophil count, creatinine, procalcitonin and numbers of involved lung segments were identified for nomogram. The area under the curve was 0.939 (95% CI: 0.893-0.984). The calibration curve showed good agreement between prediction of nomogram and observation in the primary cohort. Conclusion: An easy-to-use nomogram with great discrimination was built to predict the severity of COVID-19.

Development and Validation of a Concise Prediction Scoring System for Asian Lung Cancer Patients with EGFR Mutation Before Treatment.

Purpose We aimed to determine the epidermal growth factor receptor (EGFR) genetic profile of lung cancer in Asians, and develop and validate a non-invasive prediction scoring system for EGFR mutation before treatment. Methods This was a single-center retrospective cohort study using data of patients with lung cancer who underwent EGFR detection (n = 1450) from December 2014 to October 2020. Independent predictors were filtered using univariate and multivariate logistic regression analyses. According to the weight of each factor, a prediction scoring system for EGFR mutation was constructed. The model was internally validated using bootstrapping techniques and temporally validated using prospectively collected data (n = 210) between November 2020 and June 2021.Results In 1450 patients with lung cancer, 723 single mutations and 51 compound mutations were observed in EGFR. Thirty-nine cases had two or more synchronous gene mutations. We developed a scoring system according to the independent clinical predictors and stratified patients into risk groups according to their scores: low-risk (score <4), moderate-risk (score 4-8), and high-risk (score >8) groups. The C-statistics of the scoring system model was 0.754 (95% CI 0.729-0.778). The factors in the validation group were introduced into the prediction model to test the predictive power of the model. The results showed that the C-statistics was 0.710 (95% CI 0.638-0.782). The Hosmer-Lemeshow goodness-of-fit showed that χ2 = 6.733, P = 0.566. Conclusions The scoring system constructed in our study may be a non-invasive tool to initially predict the EGFR mutation status for those who are not available for gene detection in clinical practice.

Comparison of CT-Guided Core Needle Biopsy in Pulmonary Ground-Glass and Solid Nodules Based on Propensity Score Matching Analysis.

Purpose: To compare the diagnostic accuracy and safety of computed tomography (CT)-guided core needle biopsy (CNB) between pulmonary ground-glass and solid nodules using propensity score matching (PSM) method and determine the relevant risk factors. Methods: This was a single-center retrospective cohort study using data from 665 patients who underwent CT-guided CNB of pulmonary nodules in our hospital between May 2019 and May 2021, including 39 ground-glass nodules (GGNs) and 626 solid nodules. We used a 1:4 PSM analysis to compared the diagnostic yields and complications rates of CT-guided CNB between 2 groups. Results: After PSM, 170 cases involved in the comparison (34 GGNs vs 136 solid nodules) were randomly matched (1:4) by patient demographics, clinical history, lesion characteristics, and procedure-related factors. There was no statistically significant difference in the diagnostic yields and complications rates between 2 groups. Significant pneumothorax incidence increase was noted at small lesion size, deep lesion location, and traversing interlobar fissure (P < .05). Post-biopsy hemorrhage was a protective factor for pneumothorax (P < .05). The size/proportion of consolidation of GGN did not influence the diagnostic accuracy and complication incidence (P > .05). Conclusions: The accuracy and safety of CT-guided CNB were comparable for ground-glass and solid nodules and the size/proportion of consolidation of GGN may be not a relevant risk factor. The biopsy should avoid traversing interlobar fissure as far as possible. Smaller lesion size and deeper lesion location may lead to higher pneumothorax rate and post-biopsy hemorrhage may be a protective factor for pneumothorax.

A Predictive Model to Differentiate Between Second Primary Lung Cancers and Pulmonary Metastasis.

RATIONALE AND OBJECTIVES: To develop and validate a nomogram for differentiating second primary lung cancers (SPLCs) from pulmonary metastases (PMs). MATERIALS AND METHODS: A total of 261 lesions from 253 eligible patients were included in this study. Among them, 195 lesions (87 SPLCs and 108 PMs) were used in the training cohort to establish the diagnostic model. Twenty-one clinical or imaging features were used to derive the model. Sixty-six lesions (32 SPLCs and 34 PMs) were included in the validation set. RESULTS: After analysis, age, lesion distribution, type of lesion, air bronchogram, contour, spiculation, and vessel convergence sign were considered to be significant variables for distinguishing SPLCs from PMs. Subsequently, these variables were selected to establish a nomogram. The model showed good distinction in the training set (area under the curve = 0.97) and the validation set (area under the curve = 0.92). CONCLUSION: This study found that the nomogram calculated from clinical and radiological characteristics could accurately classify SPLCs and PMs.

Radiomics Study for Discriminating Second Primary Lung Cancers From Pulmonary Metastases in Pulmonary Solid Lesions.

BACKGROUND: The objective of this study was to assess the value of quantitative radiomics features in discriminating second primary lung cancers (SPLCs) from pulmonary metastases (PMs). METHODS: This retrospective study enrolled 252 malignant pulmonary nodules with histopathologically confirmed SPLCs or PMs and randomly assigned them to a training or validation cohort. Clinical data were collected from the electronic medical records system. The imaging and radiomics features of each nodule were extracted from CT images. RESULTS: A rad-score was generated from the training cohort using the least absolute shrinkage and selection operator regression. A clinical and radiographic model was constructed using the clinical and imaging features selected by univariate and multivariate regression. A nomogram composed of clinical-radiographic factors and a rad-score were developed to validate the discriminative ability. The rad-scores differed significantly between the SPLC and PM groups. Sixteen radiomics features and four clinical-radiographic features were selected to build the final model to differentiate between SPLCs and PMs. The comprehensive clinical radiographic-radiomics model demonstrated good discriminative capacity with an area under the curve of the receiver operating characteristic curve of 0.9421 and 0.9041 in the respective training and validation cohorts. The decision curve analysis demonstrated that the comprehensive model showed a higher clinical value than the model without the rad-score. CONCLUSION: The proposed model based on clinical data, imaging features, and radiomics features could accurately discriminate SPLCs from PMs. The model thus has the potential to support clinicians in improving decision-making in a noninvasive manner.

Risk Factor Analysis and Nomogram Construction for Non-Survivors among Critical Patients with COVID-19.

The outbreak of the coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2, occurred in China in December 2019. This disease has caused more than 70,000 deaths worldwide. We intend to analyze the risk factors of death and establish a prognosis nomogram for critical patients with COVID-19. We analyzed the clinical data of COVID-19 patients in Zhongnan Hospital of Wuhan University who were in the critical state before March 20, 2020. Data were collected on admission and compared between survivors and non-survivors and analyzed by univariable and multivariable logistic regression analyses. Finally, 104 patients were included, 50 of whom died. Age (odds ratio, OR 5.73 [95% confidence interval, CI, 1.14-28.81]), chest tightness (OR 5.50 [95% CI, 1.02-9.64]), AST (OR 6.57 [95% CI, 1.33-32.48]), and blood urea nitrogen (5.59 [95% CI, 1.05-29.74]) at admission were considered predictors of the risk of death in critical patients and were selected to construct the nomogram. Subsequently, we established a nomogram model and validated it. The sensitivity and specificity of the nomogram were 96.0% and 74.1%, respectively. The area under the curve was 0.893 (95% CI, 0.807-0.980).