Objective: To investigate the value of initial CT quantitative analysis of ground-glass opacity (GGO), consolidation, and total lesion volume and its relationship with clinical features for assessing the severity of coronavirus disease 2019 (COVID-19). Materials and Methods: A total of 84 patients with COVID-19 were retrospectively reviewed from January 23, 2020 to February 19, 2020. Patients were divided into two groups: severe group (n = 23) and non-severe group (n = 61). Clinical symptoms, laboratory data, and CT findings on admission were analyzed. CT quantitative parameters, including GGO, consolidation, total lesion score, percentage GGO, and percentage consolidation (both relative to total lesion volume) were calculated. Relationships between the CT findings and laboratory data were estimated. Finally, a discrimination model was established to assess the severity of COVID-19. Results: Patients in the severe group had higher baseline neutrophil percentage, increased high-sensitivity C-reactive protein (hs-CRP) and procalcitonin levels, and lower baseline lymphocyte count and lymphocyte percentage (p < 0.001). The severe group also had higher GGO score (p < 0.001), consolidation score (p < 0.001), total lesion score (p < 0.001), and percentage consolidation (p = 0.002), but had a lower percentage GGO (p = 0.008). These CT quantitative parameters were significantly correlated with laboratory inflammatory marker levels, including neutrophil percentage, lymphocyte count, lymphocyte percentage, hs-CRP level, and procalcitonin level (p < 0.05). The total lesion score demonstrated the best performance when the data cut-off was 8.2%. Furthermore, the area under the curve, sensitivity, and specificity were 93.8% (confidence interval [CI]: 86.8–100%), 91.3% (CI: 69.6–100%), and 91.8% (CI: 23.0–98.4%), respectively. Conclusion CT quantitative parameters showed strong correlations with laboratory inflammatory markers, suggesting that CT quantitative analysis might be an effective and important method for assessing the severity of COVID-19, and may provide additional guidance for planning clinical treatment strategies.

Objective: To improve the N biomarker in the amyloid/taueurodegeneration system by radiomics and study its value for predicting cognitive progression in individuals with mild cognitive impairment (MCI). Materials and Methods: A group of 147 healthy controls (HCs) (72 male; mean age ± standard deviation, 73.7 ± 6.3 years), 197 patients with MCI (114 male; 72.2 ± 7.1 years), and 128 patients with Alzheimer’s disease (AD) (74 male; 73.7 ± 8.4 years) were included. Optimal A, T, and N biomarkers for discriminating HC and AD were selected using receiver operating characteristic (ROC) curve analysis. A radiomics model containing comprehensive information of the whole cerebral cortex and deep nuclei was established to create a new N biomarker. Cerebrospinal fluid (CSF) biomarkers were evaluated to determine the optimal A or T biomarkers. All MCI patients were followed up until AD conversion or for at least 60 months. The predictive value of A, T, and the radiomics-based N biomarker for cognitive progression of MCI to AD were analyzed using Kaplan-Meier estimates and the log-rank test. Results: The radiomics-based N biomarker showed an ROC curve area of 0.998 for discriminating between AD and HC. CSF Aβ42 and p-tau proteins were identified as the optimal A and T biomarkers, respectively. For MCI patients on the Alzheimer’s continuum, isolated A+ was an indicator of cognitive stability, while abnormalities of T and N, separately or simultaneously, indicated a high risk of progression. For MCI patients with suspected non-Alzheimer’s disease pathophysiology, isolated T+ indicated cognitive stability, while the appearance of the radiomics-based N+ indicated a high risk of progression to AD. Conclusion We proposed a new radiomics-based improved N biomarker that could help identify patients with MCI who are at a higher risk for cognitive progression. In addition, we clarified the value of a single A/T/N biomarker for predicting the cognitive progression of MCI.

Glucose transporter 1(GLUT1)overexpression in tumor cells is a potential target for drug therapy,but few studies have reported screening GLUT1 inhibitors from natural or synthetic compounds.With cur-rent analysis techniques,it is difficult to accurately monitor the GLUT1 inhibitory effect of drug molecules in real-time.We developed a cell membrane-based glucose sensor(CMGS)that integrated a hydrogel electrode with tumor cell membranes to monitor GLUT1 transmembrane transport and screen for GLUT1 inhibitors in traditional Chinese medicines(TCMs).CMGS is compatible with cell membranes of various origins,including different types of tumors and cell lines with GLUT1 expression knocked down by small interfering RNA or small molecules.Based on CMGS continuous monitoring technique,we inves-tigated the glucose transport kinetics of cell membranes with varying levels of GLUT1 expression.We used CMGS to determine the GLUT1-inhibitory effects of drug monomers with similar structures from Scutellaria baicalensis and catechins families.Results were consistent with those of the cellular glucose uptake test and molecular-docking simulation.CMGS could accurately screen drug molecules in TCMs that inhibit GLUT1,providing a new strategy for studying transmembrane protein-receptor interactions.

Objective: To determine whether noncontrast computed tomography (NCCT) models based on multivariable, radiomics features, and machine learning (ML) algorithms could further improve the discrimination of early hematoma expansion (HE) in patients with spontaneous intracerebral hemorrhage (sICH). Materials and Methods: We retrospectively reviewed 261 patients with sICH who underwent initial NCCT within 6 hours of ictus and follow-up CT within 24 hours after initial NCCT, between April 2011 and March 2019. The clinical characteristics, imaging signs and radiomics features extracted from the initial NCCT images were used to construct models to discriminate early HE. A clinical-radiologic model was constructed using a multivariate logistic regression (LR) analysis. Radiomics models, a radiomics-radiologic model, and a combined model were constructed in the training cohort (n = 182) and independently verified in the validation cohort (n = 79). Receiver operating characteristic analysis and the area under the curve (AUC) were used to evaluate the discriminative power. Results: The AUC of the clinical-radiologic model for discriminating early HE was 0.766. The AUCs of the radiomics model for discriminating early HE built using the LR algorithm in the training and validation cohorts were 0.926 and 0.850, respectively.The AUCs of the radiomics-radiologic model in the training and validation cohorts were 0.946 and 0.867, respectively. The AUCs of the combined model in the training and validation cohorts were 0.960 and 0.867, respectively. Conclusion NCCT models based on multivariable, radiomics features and ML algorithm could improve the discrimination of early HE. The combined model was the best recommended model to identify sICH patients at risk of early HE.

In magnetic resonance examination,the interaction between implants and the radio frequency(RF)fields induces heating in human tissue and may cause tissue damage.To assess the RF-induced heating of implants,three steps should be executed,including electromagnetic model construction,electromagnetic model validation,and virtual human body simulations.The crucial step of assessing RF-induced heating involves the construction of a test environment for electromagnetic model validation.In this study,a hardware environment,comprised of a RF generation system,electromagnetic field measurement system,and a robotic arm positioning system,was established.Furthermore,an automated control software environment was developed using a Python-based software development platform to enable the creation of a high-precision automated integrated test environment.The results indicate that the electric field generated in this test environment aligns well with the simulated electric field,making it suitable for assessing the RF-induced heating effects of implants.

This study aimed to identify subtypes of genomic variants associated with the efficacy of immune checkpoint inhibitors (ICIs) by conducting systematic literature search in electronic databases up to May 31, 2021. The main outcomes including overall survival (OS), progression-free survival (PFS), objective response rate (ORR), and durable clinical benefit (DCB) were correlated with tumor genomic features. A total of 1546 lung cancer patients with available genomic variation data were included from 14 studies. The Kirsten rat sarcoma viral oncogene homolog G12C (KRASG12C) mutation combined with tumor protein P53 (TP53) mutation revealed the promising efficacy of ICI therapy in these patients. Furthermore, patients with epidermal growth factor receptor (EGFR) classical activating mutations (including EGFRL858R and EGFRΔ19) exhibited worse outcomes to ICIs in OS (adjusted hazard ratio (HR), 1.40; 95％ confidence interval (CI), 1.01?1.95; P=0.0411) and PFS (adjusted HR, 1.98; 95％ CI, 1.49?2.63; P<0.0001), while classical activating mutations with EGFRT790M showed no difference compared to classical activating mutations without EGFRT790M in OS (adjusted HR, 0.96; 95％ CI, 0.48?1.94; P=0.9157) or PFS (adjusted HR, 0.72; 95％ CI, 0.39?1.35; P=0.3050). Of note, for patients harboring the Usher syndrome type-2A (USH2A) missense mutation, correspondingly better outcomes were observed in OS (adjusted HR, 0.52; 95％ CI, 0.32?0.82; P=0.0077), PFS (adjusted HR, 0.51; 95％ CI, 0.38?0.69; P<0.0001), DCB (adjusted odds ratio (OR), 4.74; 95％ CI, 2.75?8.17; P<0.0001), and ORR (adjusted OR, 3.45; 95％ CI, 1.88?6.33; P<0.0001). Our findings indicated that, USH2A missense mutations and the KRASG12C mutation combined with TP53 mutation were associated with better efficacy and survival outcomes, but EGFR classical mutations irrespective of combination with EGFRT790M showed the opposite role in the ICI therapy among lung cancer patients. Our findings might guide the selection of precise targets for effective immunotherapy in the clinic.

Uridine diphosphate glycosyltransferase(UGT) is a highly conserved protein in plants, which usually functions in secondary metabolic pathways. This study used the Hidden Markov Model(HMM) to screen out members of UGT gene family in the whole genome of Dendrobium officinale, and 44 UGT genes were identified. Bioinformatics was used to analyze the structure, phylogeny, and promoter region components of D. officinale genes. The results showed that UGT gene family could be divided into four subfamilies, and UGT gene structure was relatively conserved in each subfamily, with nine conserved domains. The upstream promoter region of UGT gene contained a variety of cis-acting elements related to plant hormones and environmental factors, indicating that UGT gene expression may be induced by plant hormones and external environmental factors. UGT gene expression in different tissues of D. officinale was compared, and UGT gene expression was found in all parts of D. officinale. It was speculated that UGT gene played an important role in many tissues of D. officinale. Through transcriptome analysis of D. officinale mycorrhizal symbiosis environment, low temperature stress, and phosphorus deficiency stress, this study found that only one gene was up-regulated in all three conditions. The results of this study can help understand the functions of UGT gene family in Orchidaceae plants and provide a basis for further study on the molecular regulation mechanism of polysaccharide metabolism pathway in D. officinale.
Dendrobium/genetics* ; Plant Growth Regulators ; Glycosyltransferases/metabolism* ; Gene Expression Profiling ; Mycorrhizae ; Phylogeny ; Plant Proteins/metabolism*

This study aimed to identify subtypes of genomic variants associated with the efficacy of immune checkpoint inhibitors (ICIs) by conducting systematic literature search in electronic databases up to May 31, 2021. The main outcomes including overall survival (OS), progression-free survival (PFS), objective response rate (ORR), and durable clinical benefit (DCB) were correlated with tumor genomic features. A total of 1546 lung cancer patients with available genomic variation data were included from 14 studies. The Kirsten rat sarcoma viral oncogene homolog G12C (KRAS^G12C) mutation combined with tumor protein P53 (TP53) mutation revealed the promising efficacy of ICI therapy in these patients. Furthermore, patients with epidermal growth factor receptor (EGFR) classical activating mutations (including EGFR^L858R and EGFR^Δ19) exhibited worse outcomes to ICIs in OS (adjusted hazard ratio (HR), 1.40; 95% confidence interval (CI), 1.01‍‒‍1.95; P=0.0411) and PFS (adjusted HR, 1.98; 95% CI, 1.49‍‒‍2.63; P<0.0001), while classical activating mutations with EGFR^T790M showed no difference compared to classical activating mutations without EGFR^T790M in OS (adjusted HR, 0.96; 95% CI, 0.48‍‒‍1.94; P=0.9157) or PFS (adjusted HR, 0.72; 95% CI, 0.39‍‒‍1.35; P=0.3050). Of note, for patients harboring the Usher syndrome type-2A(USH2A) missense mutation, correspondingly better outcomes were observed in OS (adjusted HR, 0.52; 95% CI, 0.32‍‒‍0.82; P=0.0077), PFS (adjusted HR, 0.51; 95% CI, 0.38‍‒‍0.69; P<0.0001), DCB (adjusted odds ratio (OR), 4.74; 95% CI, 2.75‍‒‍8.17; P<0.0001), and ORR (adjusted OR, 3.45; 95% CI, 1.88‍‒‍6.33; P<0.0001). Our findings indicated that, USH2A missense mutations and the KRAS^G12Cmutation combined with TP53 mutation were associated with better efficacy and survival outcomes, but EGFR classical mutations irrespective of combination with EGFR^T790M showed the opposite role in the ICI therapy among lung cancer patients. Our findings might guide the selection of precise targets for effective immunotherapy in the clinic.
Humans ; Carcinoma, Non-Small-Cell Lung/genetics* ; ErbB Receptors/genetics* ; Extracellular Matrix Proteins/genetics* ; Immune Checkpoint Inhibitors/therapeutic use* ; Lung Neoplasms/genetics* ; Mutation ; Protein Kinase Inhibitors/therapeutic use* ; Proto-Oncogene Proteins p21(ras)/genetics* ; Treatment Outcome