To develop a deep learning system based on 3D convolutional neural networks (CNNs), and to automatically predict EGFR‐mutant pulmonary adenocarcinoma in CT images. A dataset of 579 nodules with EGFR mutation status labels of mutant (Mut) or wild‐type (WT) was retrospectively analyzed. A deep learning system, namely 3D DenseNets, was developed to process 3D patches of nodules from CT data, and learn strong representations with supervised end‐to‐end training. The 3D DenseNets were trained with a training subset of 348 nodules and tuned with a development subset of 116 nodules. A strong data augmentation technique, mixup , was used for better generalization. We evaluated our model on a holdout subset of 115 nodules. An independent public dataset of 37 nodules from the cancer imaging archive (TCIA) was also used to test the generalization of our method. Conventional radiomics analysis was also performed for comparison. Our method achieved promising performance on predicting EGFR mutation status, with AUCs of 75.8% and 75.0% for our holdout test set and public test set, respectively. Moreover, strong relations were found between deep learning feature and conventional radiomics, while deep learning worked through an enhanced radiomics manner, that is, deep learned radiomics (DLR), in terms of robustness, compactness and expressiveness. The proposed deep learning system predicts EGFR‐mutant of lung adenocarcinomas in CT images noninvasively and automatically, indicating its potential to help clinical decision‐making by identifying eligible patients of pulmonary adenocarcinoma for EGFR‐targeted therapy.

The aim of the present study was to develop and validate a radiomics-based nomogram for differentiation of preinvasive lesions from invasive lesions that appearing as ground-glass opacity nodules (GGNs) ≤10 mm (subcentimeter) in diameter at CT. A total of 542 consecutive patients with 626 pathologically confirmed pulmonary subcentimeter GGNs were retrospectively studied from October 2011 to September 2017. All the GGNs were divided into a training set (n = 334) and a validation set (n = 292). Researchers extracted 475 radiomics features from the plain CT images; a radiomics signature was constructed with the least absolute shrinkage and selection operator (LASSO) based on multivariable regression in the training set. Based on the multivariable logistic regression model, a radiomics nomogram was developed in the training set. The performance of the nomogram was evaluated with respect to its calibration, discrimination, and clinical-utility and this was assessed in the validation set. The constructed radiomics signature, which consisted of 15 radiomics features, was significantly associated with the invasiveness of subcentimeter GGNs (P < 0.0001 for both training set and validation set). To build the nomogram model, radiomics signature and mean CT value were used. The nomogram model demonstrated good discrimination and calibration in both training set (C-index, 0.716 [95% CI, 0.632 to 0.801]) and validation set (C-index, 0.707 [95% CI, 0.625 to 0.788]). Decision curve analysis (DCA) indicated that radiomics-based nomogram was clinically useful. A radiomics-based nomogram that incorporates both radiomics signature and mean CT value is constructed in the study, which can be conveniently used to facilitate the preoperative individualized prediction of the invasiveness in patients with subcentimeter GGNs.

SARS-CoV-2 attaches to its host receptor,angiotensin-converting enzyme 2( ACE2), via the receptor-binding domain (RBD) of the spike protein. The RBD glycoprotein is acritical target for the development of neutralizing antibodies and vaccines against SARS-CoV-2. However,t he high heterogeneity of RBD glycoforms may lead to an incomplete neutralization effect and impact the immunogenic integrity of RBD-based vaccines.Investigating the role of different carbohydrate domains is of paramount importance.U nfortunately, there is no viable method for preparing RBD glycoproteins with structurally defined glycans.H erein we describe ah ighly efficient and scalable strategy for the preparation of six glycosylated RBDs bearing defined structure glycoforms at T323, N331, and N343. Ac ombination of modern oligosaccharide,peptide synthesis and recombinant protein engineering provides ar obust route to decipher carbohydrate structurefunction relationships.

It remains challenging to precisely decipher the structural and functional characteristics of protein coronas. To overcome the drawbacks frequently occurring in the traditional separation methods, an anti-PEG single-chain variable fragment (PEG-scFv) based affinity chromatography (AfC) was developed to achieve precise and efficient separation of protein coronas on PEGylated liposomes (sLip). His-tagged PEG-scFv could readily capture sLip without affecting protein corona compositions, and separate sLip/protein complex from plasma protein aggregates and endogenous vesicles through the Ni-NTA column. AfC demonstrated 43-fold higher protein corona collecting efficiency than centrifugation, which was extremely crucial for separation of in vivo protein coronas due to the limitation of sample size. AfC evaded contamination by endogenous vesicles and protein aggregates occurring in centrifugation, and reserved the loosely bound proteins, providing an unprecedented approach to deeply decipher protein coronas. The scFv-based AfC also paves new avenues for the separation of protein coronas formed on other nanomedicines.

Controversy and challenges remain regarding the cognition of lung adenocarcinomas presented as subcentimeter ground glass nodules (GGNs). Postoperative lymphatic involvement or intrapulmonary metastasis is found in approximately 15% to 20% of these cases. This study aimed to develop and validate a radiomics signature to identify the invasiveness of lung adenocarcinoma appearing as subcentimeter ground glass nodules. We retrospectively enrolled 318 subcentimeter GGNs with histopathology-confirmed adenocarcinomas in situ (AIS), minimally invasive adenocarcinomas (MIA) and invasive adenocarcinomas (IAC). The radiomics features were extracted from manual segmentation based on contrast-enhanced CT (CECT) and non-contrast enhanced CT (NCECT) images after imaging preprocessing. The Lasso algorithm was applied to construct radiomics signatures. The predictive performance of radiomics models was evaluated by receiver operating characteristic (ROC) analysis. A radiographic-radiomics combined nomogram was developed to evaluate its clinical utility. The radiomics signature on CECT (AUC: 0.896 [95% CI 0.815–0.977]) performed better than the radiomics signature on NCECT data (AUC: 0.851[95% CI 0.712–0.989]) in the validation set. An individualized prediction nomogram was developed using radiomics model on CECT and radiographic model including type, shape and vascular change. The C index of the nomogram was 0.915 in the training set and 0.881 in the validation set, demonstrating good discrimination. Decision curve analysis (DCA) revealed that the proposed model was clinically useful. The radiomics signature built on CECT could provide additional benefit to promote the preoperative prediction of invasiveness in patients with subcentimeter lung adenocarcinomas.

BackgroundTraumatic sternoclavicular joint dislocations are rare; closed reduction is the primary treatment. The failure of closed reduction or a prominent insult to the skin may require surgery to ensure the best possible outcome.MethodsThe records of 5 patients operated at our institution for sternoclavicular joint dislocation were reviewed. All patients were treated with open reduction and single 3.5-mm locking plate was used for fixation. Outcomes were evaluated with the Constant Shoulder Score (CSS) and Disability of the Arm, Shoulder, and Hand (DASH) questionnaire. Intraoperative and postoperative complications were recorded.ResultsAll the patients had an average follow-up of 14 months (range, 11–16 months). At the final follow-up, the mean CSS score was 89.5 (range, 78–98) and the mean DASH score was 9.0 (range, 4–16). There were no early complications, including wound infection or neurologic or vascular deficits; there were also no broken or loosened screws or plates. No case of redislocation or arthrosis was observed.ConclusionOur study indicates that open reduction and fixation with a single locking plate for the treatment of traumatic sternoclavicular joint dislocation is a safe, relatively simple surgical procedure that can lead to satisfactory outcomes.

PEGylated nanocarriers have gained increasing attention due to reduced toxicity and enhanced circulation compared with free drugs. According to guidances of drug regulatory departments worldwide, it is crucial to determine free and liposomal drug concentrations; however, the conventional used separation methods including dialysis, ultrafiltration, and solid-phase extraction (SPE) have drawbacks of time-consuming, drug leakage, environmental pollution or error bias of trace level drug. Here we developed a facile PEG-scFv-based separation method combined with HPLC to quantify free doxorubicin (DOX) and liposomal DOX in plasma. Anti-PEG single chain variable fragment antibody (PEG-scFv) was adopted to sediment PEGylated liposomes by simple incubation and low speed centrifugation. Compared to SPE, it demonstrated sufficient accuracy and sensitivity to evaluate free and liposomal DOX with intact liposomes. Therefore, it can serve as an alternative approach of SPE, which is suitable for quality assessment and pharmacokinetics evaluation of PEGylated liposomal drugs and possible other PEGylated nanocarriers.

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