Supramolecular assemblies with spatio-temporal sequential drug delivery capability treat spinal cord injury via neuroprotection and immunoregulation.

Spinal cord injury (SCI) can result in irreversible motor and sensory deficits. However, up to data, clinical first-line drugs have ambiguous benefits and debilitating side effects, mainly due to the insufficient accumulation, poor physiological barrier penetration, and lack of spatio-temporal controlled release at lesion tissue. Herein, we proposed a supramolecular assemblies composed of hyperbranched polymer-formed core/shell structure through host-guest interactions. Such HPAA-BM@CD-HPG-C assemblies co-loaded with p38 inhibitor (SB203580) and insulin-like growth factor 1(IGF-1) are able to achieve time- and space-programmed sequential delivery benefiting from their cascaded responsiveness. The core-shell disassembly of HPAA-BM@CD-HPG-C occurs in acidic micro-environment around lesion, achieving preferentially the burst release of IGF-1 to protect survival neurons. Subsequently, the HPAA-BM cores containing SB203580 are endocytosed by the recruited macrophages and degraded by intracellular GSH, accelerating the release of SB203580 to promote the conversion from M1 to M2 macrophage. Hence, the successive synergy of neuroprotection and immunoregulation effects contribute to subsequent nerve repair and locomotor recovery as demonstrated in vitro and in vivo studies. Thus, our fabrication provides a strategy that multiple drugs co-delivery in a spatio-temporal selective manner adapting to the disease progression through self-cascaded disintegration, are expected to realize multidimensional precise treatment of SCI.

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.

Development and Validation of a CT-Based Radiomics Nomogram in Patients With Anterior Mediastinal Mass: Individualized Options for Preoperative Patients.

Background: To improve the preoperative diagnostic accuracy and reduce the non-therapeutic thymectomy rate, we established a comprehensive predictive nomogram based on radiomics data and computed tomography (CT) features and further explored its potential use in clinical decision-making for anterior mediastinal masses (AMMs). Methods: A total of 280 patients, including 280 with unenhanced CT (UECT) and 241 with contrast-enhanced CT (CECT) scans, all of whom had undergone thymectomy for AMM with confirmed histopathology, were enrolled in this study. A total of 1,288 radiomics features were extracted from each labeled mass. The least absolute shrinkage and selection operator model was used to select the optimal radiomics features in the training set to construct the radscore. Multivariate logistic regression analysis was conducted to establish a combined clinical radiographic radscore model, and an individualized prediction nomogram was developed. Results: In the UECT dataset, radscore and the UECT ratio were selected for the nomogram. The combined model achieved higher accuracy (AUC: 0.870) than the clinical model (AUC: 0.752) for the prediction of therapeutic thymectomy probability. In the CECT dataset, the clinical and combined models achieved higher accuracy (AUC: 0.851 and 0.836, respectively) than the radscore model (AUC: 0.618) for the prediction of therapeutic thymectomy probability. Conclusions: In patients who underwent UECT only, a nomogram integrating the radscore and the UECT ratio achieved good accuracy in predicting therapeutic thymectomy in AMMs. However, the use of radiomics in patients with CECT scans did not improve prediction performance; therefore, a clinical model is recommended.

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.

Clinical, radiological, and laboratory characteristics and risk factors for severity and mortality of 289 hospitalized COVID-19 patients.

BACKGROUND: The coronavirus disease 2019 (COVID-19) has become a global pandemic, with 10%-20% of severe cases and over 508 000 deaths worldwide. OBJECTIVE: This study aims to address the risk factors associated with the severity of COVID-19 patients and the mortality of severe patients. METHODS: 289 hospitalized laboratory-confirmed COVID-19 patients were included in this study. Electronic medical records, including patient demographics, clinical manifestation, comorbidities, laboratory tests results, and radiological materials, were collected and analyzed. According to the severity and outcomes of the patients, they were divided into three groups: nonsurvived (n = 49), survived severe (n = 78), and nonsevere (n = 162) groups. Clinical, laboratory, and radiological data were compared among these groups. Principal component analysis (PCA) was applied to reduce the dimensionality and visualize the patients on a low-dimensional space. Correlations between clinical, radiological, and laboratory parameters were investigated. Univariate and multivariate logistic regression methods were used to determine the risk factors associated with mortality in severe patients. Longitudinal changes of laboratory findings of survived severe cases and nonsurvived cases during hospital stay were also collected. RESULTS: Of the 289 patients, the median age was 57 years (range, 22-88) and 155 (53.4%) patients were male. As of the final follow-up date of this study, 240 (83.0%) patients were discharged from the hospital and 49 (17.0%) patients died. Elder age, underlying comorbidities, and increased laboratory variables, such as leukocyte count, neutrophil count, neutrophil-to-lymphocyte ratio (NLR), C-reactive protein (CRP), procalcitonin (PCT), D-dimer, alanine aminotransferase (ALT), aspartate aminotransferase (AST), and blood urea nitrogen (BUN) on admission, were found in survived severe cases compared to nonsevere cases. According to the multivariate logistic regression analysis, elder age, a higher number of affected lobes, elevated CRP levels on admission, increased prevalence of chest tightness/dyspnea, and smoking history were independent risk factors for death of severe patients. A trajectory in PCA was observed from "nonsevere" toward "nonsurvived" via "severe and survived" patients. Strong correlations between the age of patients, the affected lobe numbers, and laboratory variables were identified. Dynamic changes of laboratory findings of survived severe cases and nonsurvived cases during hospital stay showed that continuing increase of leukocytes and neutrophil count, sustained lymphopenia and eosinopenia, progressing decrease in platelet count, as well as high levels of NLR, CRP, PCT, AST, BUN, and serum creatinine were associated with in-hospital death. CONCLUSIONS: Survived severe and nonsurvived COVID-19 patients had distinct clinical and laboratory characteristics, which were separated by principle component analysis. Elder age, increased number of affected lobes, higher levels of serum CRP, chest tightness/dyspnea, and smoking history were risk factors for mortality of severe COVID-19 patients. Longitudinal changes of laboratory findings may be helpful in predicting disease progression and clinical outcome of severe patients.