The discovery of modern medicine relies on the sustainable development of synthetic methodologies to meet the needs associated with drug molecular design. Heterocycles containing difluoromethyl ...groups are an emerging but scarcely investigated class of organofluoro molecules with potential applications in pharmaceutical, agricultural and material science. Herein, we developed an organophotocatalytic direct difluoromethylation of heterocycles using O
as a green oxidant. The C-H oxidative difluoromethylation obviates the need for pre-functionalization of the substrates, metals and additives. The operationally straightforward method enriches the efficient synthesis of many difluoromethylated heterocycles in moderate to excellent yields. The direct difluoromethylation of pharmaceutical moleculars demonstrates the practicability of this methodology to late-stage drug development. Moreover, 2'-deoxy-5-difluoromethyluridine (F
TDR) exhibits promising activity against some cancer cell lines, indicating that the difluoromethylation methodology might provide assistance for drug discovery.
When a firm is controlled by a large shareholder, the principal agency problem arises from expropriation by controlling shareholders of other shareholders. Using a sample of 3776 publicly traded ...firms in the Chinese A-share market over the period 2007–2020, this study investigates whether stock liquidity impedes or enhances the expropriation behavior of controlling shareholders. I demonstrate that (1) a liquid stock market generally encourages expropriation behavior by controlling shareholders, (2) the positive effect of stock liquidity on expropriation by controlling shareholders is mitigated when active investors hold a large stake in the firm, and (3) active investors’ monitoring of expropriation by controlling shareholders is mitigated at state-owned enterprises and firms that have a large gap between ownership and control. However, higher competition among blockholders strengthens the discipline of active investors with respect to expropriation. This study builds a new link between a market factor (stock liquidity) and a governance problem (expropriation by controlling shareholders) and reveals some new characteristics in the relationship between stock liquidity and corporate governance in an emerging market.
•High stock liquidity facilitates the entry of investors.•Active investors monitor the expropriation behavior by controlling shareholders.•Competition among blockholders enhances active investors’ monitoring.
The cobalt‐catalyzed alkoxylation of C(sp2)H bonds in aromatic and olefinic carboxamides has been developed. The reaction proceeded under mild conditions in the presence of Co(OAc)2⋅4H2O as the ...catalyst and tolerates a wide range of both alcohols and benzamide substrates, including even olefinic carboxamides. In addition, this reaction is the first example of the direct alkoxylation of alkenes through CH bond activation.
Alcohols in action: A wide range of alcohols and benzamide substrates functionalized with electron‐rich or electron‐poor substituents are tolerated in the title reaction. This practical reaction occurs under mild conditions.
Patients who had any of the following features at the time of, or after, admission were classified as severe cases: (1) respiratory distress (≥30 breaths per min); (2) oxygen saturation at rest ≤93%; ...(3) ratio of partial pressure of arterial oxygen to fractional concentration of oxygen inspired air ≤300 mm Hg; or (4) severe disease complications (eg, respiratory failure, requirement of mechanical ventilation, septic shock, or non-respiratory organ failure). Parameters did not differ significantly between the groups, except that patients in the severe group were significantly older than those in the mild group, as expected.4 No patient died from the infection. 23 (77%) of 30 severe cases received intensive care unit (ICU) treatment, whereas none of the mild cases required ICU treatment. All samples were collected according to WHO guidelines.5 The mean viral load of severe cases was around 60 times higher than that of mild cases, suggesting that higher viral loads might be associated with severe clinical outcomes.
Prior studies have not reasonably explained why executive stock options (ESOs) encourage innovation through vega rather than delta. This study re-examines the relationship among vega, delta and ...innovation performance when stock prices are informative. The findings indicate that informative stock prices amplify the delta effect on encouraging executives to improve innovation performance but that informative prices alleviate the traditional positive effect of vega on innovation. Moreover, when stock prices are informative, deep-in-the-money options reduce the positive effect of delta on innovation, whereas the state control, independent directors and manager and director shareholding enhance (reduce) the positive effect of delta (vega) on innovation. The results hold after conducting robustness tests.
Prior literature indicates that the stock market is not simply a sideshow but also a factor that impacts corporate operations and decisions. This study examines the effect of a noise factor in the ...stock market, investor sentiment, on the relationship between the firm's R&D spending and firm performance. Using a sample of publicly traded firms in the Chinese A-share market between 2006 and 2019, the study demonstrates that R&D spending generally enhances (reduces) firm performance during optimism (pessimism) periods. Concerning the channels through which investor sentiment impacts the R&D spending-firm performance relationship, market-timing effects indicate that firms that time equity issuance during optimism periods experience a positive R&D spending-firm performance relationship, whereas firms that initiate equity repurchase during pessimism periods have a negative R&D spending-firm performance relationship. For catering effects, when firms cater to short-horizon investors, R&D spending reduces firm performance. The results contribute to R&D and behavioural finance literature.
Background
Dynamic contrast‐enhanced (DCE) MRI commonly outperforms diffusion‐weighted (DW) MRI in breast cancer discrimination. However, the side effects of contrast agents limit the use of DCE‐MRI, ...particularly in patients with chronic kidney disease.
Purpose
To develop a novel deep learning model to fully exploit the potential of overall b‐value DW‐MRI without the need for a contrast agent in predicting breast cancer molecular subtypes and to evaluate its performance in comparison with DCE‐MRI.
Study Type
Prospective.
Subjects
486 female breast cancer patients (training/validation/test: 64%/16%/20%).
Field Strength/Sequence
3.0 T/DW‐MRI (13 b‐values) and DCE‐MRI (one precontrast and five postcontrast phases).
Assessment
The breast cancers were divided into four subtypes: luminal A, luminal B, HER2+, and triple negative. A channel‐dimensional feature‐reconstructed (CDFR) deep neural network (DNN) was proposed to predict these subtypes using pathological diagnosis as the reference standard. Additionally, a non‐CDFR DNN (NCDFR‐DNN) was built for comparative purposes. A mixture ensemble DNN (ME‐DNN) integrating two CDFR‐DNNs was constructed to identify subtypes on multiparametric MRI (MP‐MRI) combing DW‐MRI and DCE‐MRI.
Statistical Tests
Model performance was evaluated using accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Model comparisons were performed using the one‐way analysis of variance with least significant difference post hoc test and the DeLong test. P < 0.05 was considered significant.
Results
The CDFR‐DNN (accuracies, 0.79 ~ 0.80; AUCs, 0.93 ~ 0.94) demonstrated significantly improved predictive performance than the NCDFR‐DNN (accuracies, 0.76 ~ 0.78; AUCs, 0.92 ~ 0.93) on DW‐MRI. Utilizing the CDFR‐DNN, DW‐MRI attained the predictive performance equal (P = 0.065 ~ 1.000) to DCE‐MRI (accuracies, 0.79 ~ 0.80; AUCs, 0.93 ~ 0.95). The predictive performance of the ME‐DNN on MP‐MRI (accuracies, 0.85 ~ 0.87; AUCs, 0.96 ~ 0.97) was superior to those of both the CDFR‐DNN and NCDFR‐DNN on either DW‐MRI or DCE‐MRI.
Data Conclusion
The CDFR‐DNN enabled overall b‐value DW‐MRI to achieve the predictive performance comparable to DCE‐MRI. MP‐MRI outperformed DW‐MRI and DCE‐MRI in subtype prediction.
Level of Evidence
2
Technical Efficacy Stage
1
Mex-3 RNA Binding Family Member A (MEX3A) is an RNA-binding protein that plays complex and diverse roles in the development of various malignancies. However, its role and mechanism in nasopharyngeal ...carcinoma (NPC) remain undefined and were therefore evaluated in this study. By analyzing Gene Expression Omnibus data and using tissue microarrays, we found that MEX3A is significantly upregulated in NPC and negatively associated with prognosis. Notably, MEX3A depletion led to decreased cell proliferation, invasion, and migration, but increased apoptosis in NPC cells in vitro, while inhibiting tumor growth in vivo. Using whole-transcript expression arrays and bioinformatic analysis, we identified scinderin (SCIN) and miR-3163 as potential downstream targets of MEX3A in NPC. The regulatory mechanisms of MEX3A, SCIN and miR-3163 were further investigated using rescue experiments. Importantly, SCIN depletion and miR-3163 inhibition reversed and rescued the oncogenic effects of MEX3A, respectively. Moreover, NF-κB signaling inhibition reversed the oncogenic effects of both SCIN and MEX3A. In summary, our results demonstrate that MEX3A may promote NPC development and progression via the miR-3163/SCIN axis by regulating NF-κB signaling, thus providing a potential target for NPC treatment.
Dynamic contrast-enhanced (DCE) MRI and non-mono-exponential model-based diffusion-weighted imaging (NME-DWI) that does not require contrast agent can both characterize breast cancer. However, which ...technique is superior remains unclear.
To compare the performances of DCE-MRI, NME-DWI and their combination as multiparametric MRI (MP-MRI) in the prediction of breast cancer prognostic biomarkers and molecular subtypes based on radiomics.
Prospective.
A total of 477 female patients with 483 breast cancers (5-fold cross-validation: training/validation, 80%/20%).
A 3.0 T/DCE-MRI (6 dynamic frames) and NME-DWI (13 b values).
After data preprocessing, high-throughput features were extracted from each tumor volume of interest, and optimal features were selected using recursive feature elimination method. To identify ER+ vs. ER-, PR+ vs. PR-, HER2+ vs. HER2-, Ki-67+ vs. Ki-67-, luminal A/B vs. nonluminal A/B, and triple negative (TN) vs. non-TN, the following models were implemented: random forest, adaptive boosting, support vector machine, linear discriminant analysis, and logistic regression.
Student's t, chi-square, and Fisher's exact tests were applied on clinical characteristics to confirm whether significant differences exist between different statuses (±) of prognostic biomarkers or molecular subtypes. The model performances were compared between the DCE-MRI, NME-DWI, and MP-MRI datasets using the area under the receiver-operating characteristic curve (AUC) and the DeLong test. P < 0.05 was considered significant.
With few exceptions, no significant differences (P = 0.062-0.984) were observed in the AUCs of models for six classification tasks between the DCE-MRI (AUC = 0.62-0.87) and NME-DWI (AUC = 0.62-0.91) datasets, while the model performances on the two imaging datasets were significantly poorer than on the MP-MRI dataset (AUC = 0.68-0.93). Additionally, the random forest and adaptive boosting models (AUC = 0.62-0.93) outperformed other three models (AUC = 0.62-0.90).
NME-DWI was comparable with DCE-MRI in predictive performance and could be used as an alternative technique. Besides, MP-MRI demonstrated significantly higher AUCs than either DCE-MRI or NME-DWI.
2.
Stage 2.
Background
Dynamic‐exponential intravoxel incoherent motion (IVIM) imaging is a potential technique for prediction, monitoring, and differential diagnosis of hepatic diseases, especially liver ...tumors. However, the use of such technique at voxel level is still limited.
Purpose
To develop an unsupervised deep learning approach for voxel‐wise dynamic‐exponential IVIM modeling and parameter estimation in the liver.
Study Type
Prospective.
Population
Ten healthy subjects (4 males; age 28 ± 6 years).
Field Strength/Sequence
Single‐shot spin‐echo echo planar imaging (SE‐EPI) sequence with monopolar diffusion‐encoding gradients (12 b‐values, 0–800 seconds/mm2) at 3.0 T.
Assessment
The proposed deep neural network (DNN) was separately trained on simulated and in vivo hepatic IVIM datasets. The trained networks were compared to the approach combining least squares with Akaike information criterion (LSQ‐AIC) in terms of dynamic‐exponential modeling accuracy, inter‐subject coefficients of variation (CVs), and fitting residuals on the simulated subsets and regions of interest (ROIs) in the left and right liver lobes. The ROIs were delineated by a radiologist (H.‐X.Z.) with 7 years of experience in MRI reading.
Statistical Tests
Comparisons between approaches were performed with a paired t‐test (normality) or a Wilcoxon rank‐sum test (nonnormality). P < 0.05 was considered statistically significant.
Results
In simulations, DNN gave significantly higher accuracy (91.6%–95.5%) for identification of bi‐exponential decays with respect to LSQ‐AIC (79.7%–86.8%). For tri‐exponential identification, DNN was also superior to LSQ‐AIC despite not reaching a significant level (P = 0.08). Additionally, DNN always yielded comparatively low root‐mean‐square error for estimated parameters. For the in vivo IVIM measurements, inter‐subject CVs (0.011–0.150) of DNN were significantly smaller than those (0.049–0.573) of LSQ‐AIC. Concerning fitting residuals, there was no significant difference between the two approaches (P = 0.56 and 0.76) in both the simulated and in vivo studies.
Data Conclusion
The proposed DNN is recommended for accurate and robust dynamic‐exponential modeling and parameter estimation in hepatic IVIM imaging.
Level of Evidence
2
Technical Efficacy
Stage 1
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