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Purpose This study aimed to identify patients with pathological complete response (pCR) and make better clinical decisions by constructing a preoperative predictive model based on tumoral and peritumoral volumes of multiparametric magnetic resonance imaging (MRI) obtained before neoadjuvant chemotherapy (NAC). Methods This study investigated MRI before NAC in 448 patients with nonmetastatic invasive ductal breast cancer (Sun Yat-sen Memorial Hospital, Sun Yat-sen University, n = 362, training cohort; and Sun Yat-sen University Cancer Center, n = 86, validation cohort). The tumoral and peritumoral volumes of interest (VOIs) were segmented and MRI features were extracted. The radiomic features were filtered via a random forest algorithm, and a supporting vector machine was used for modeling. The receiver operator characteristic curve and area under the curve (AUC) were calculated to assess the performance of the radiomics-based classifiers. Results For each MRI sequence, a total of 863 radiomic features were extracted and the top 30 features were selected for model construction. The radiomic classifiers of tumoral VOI and peritumoral VOI were both promising for predicting pCR, with AUCs of 0.96 and 0.97 in the training cohort and 0.89 and 0.78 in the validation cohort, respectively. The tumoral + peritumoral VOI radiomic model could further improve the predictive accuracy, with AUCs of 0.98 and 0.92 in the training and validation cohorts. Conclusions The tumoral and peritumoral multiparametric MRI radiomics model can promisingly predict pCR in breast cancer using MRI images before surgery. Our results highlighted the potential value of the tumoral and peritumoral radiomic model in cancer management.

Chlorogenic acid (CGA) is a kind of traditional Chinese medicine, abundant in honeysuckle and eucommia, and has a wide range of biological activities, and pharmacological effects. Previous studies have shown that CGA can regulate learning, memory, cognitive ability, coupled with improvement to anxiety, depression, and other post-traumatic stress disorder (PTSD)-like symptoms. This article explores the protective effects of CGA on neurons through its anti-apoptotic effect, inhibition of neuroinflammation and oxidative stress, which may be the mechanisms of its improvement of PTSD-like symptoms. It may provide a new therapeutic strategy for the treatment of PTSD and its comorbidities.

Objectives: To determine the values of quantitative metrics derived from synthetic MRI (SyMRI) and apparent diffusion coefficient (ADC) in evaluating the prognostic factors of cervical carcinoma (CC). Methods: In this prospective study, 74 patients with pathologically confirmed CC were enrolled. Pretreatment quantitative metrics including T1, T2 and ADC values were obtained from SyMRI and diffusion-weighted imaging (DWI) sequences. The values of all metrics were compared for different prognostic features using Student’s t-test or Mann-Whitney U-test. The receiver operating characteristic (ROC) curve and multivariate logistic regression analysis were utilized to evaluate the diagnostic performance of quantitative variables. Results: T1 and T2 values of parametrial involvement (PMI)-negative were significantly higher than those of PMI-positive (p = 0.002 and < 0.001), while ADC values did not show a significant difference. The area under curve (AUC) of T1 and T2 values for identifying PMI were 0.743 and 0.831. Only the T2 values showed a significant difference between the lymphovascular space involvement (LVSI)-negative and LVSI-positive (p < 0.001), and the AUC of T2 values for discriminating LVSI was 0.814. The differences of T1, T2, and ADC values between the well/moderately and the poorly differentiated CC were significant (all p < 0.001). The AUCs of T1, T2 and ADC values for predicting differentiation grades were 0.762, 0.830, and 0.808. The combined model of all metrics proved to achieve good diagnostic performance with the AUC of 0.866. Conclusion: SyMRI may be a potential noninvasive tool for assessing the prognostic factors such as PMI, LVSI, and differentiation grades in CC. Moreover, the overall diagnostic performances of synthetic quantitative metrics were superior to the ADC values, especially in identifying PMI and LVSI. Advances in knowledge: This is the first study to assess the utility of SyMRI-derived parameters and ADC value in evaluating the prognostic factors in CC.

Purpose The prognosis of nasopharyngeal carcinoma (NPC) patients with parotid lymph node (PLN) metastasis remains unclear. This study was performed to investigate the prognostic significance and optimal staging category of PLN metastasis and develop a nomogram for estimating individual risk.Materials and MethodsClinical data of 7,084 non-metastatic NPC patients were retrospectively reviewed. Overall survival (OS) was the primary endpoint. A nomogram was established based on the Cox proportional hazards regression model. The accuracy and calibration ability of this nomogram was evaluated by C-index and calibration curves with bootstrap validation.ResultTotally, 164/7,084 NPC patients (2.3%) presented with PLNs. Multivariate analyses showed that PLN metastasis was a negative prognostic factor for OS, progression-free survival (PFS), distant metastasis-free survival (DMFS), and locoregional relapse-free survival (LRFS). Patients with PLN metastasis had a worse prognosis than N3 disease. Five independent prognostic factors were included in the nomogram, which showed a C-index of 0.743. The calibration curves for probability of 3- and 5-year OS indicated satisfactory agreement between nomogram-based prediction and actual observation. All results were confirmed in the validation cohort.ConclusionNPC patient with PLN metastasis had poorer survival outcome (OS, PFS, DMFS, and LRFS) than N3 disease. We developed a nomogram to provide individual prediction of OS for patients with PLN metastasis.

IMPORTANCE: Capecitabine maintenance therapy improves survival outcomes in various cancer types, but data are limited on the efficacy and safety of capecitabine maintenance therapy in metastatic nasopharyngeal carcinoma (NPC). OBJECTIVE: To investigate the efficacy and safety of capecitabine maintenance therapy in metastatic NPC. DESIGN, SETTING, AND PARTICIPANTS: This randomized phase 3 clinical trial was conducted at Sun Yat-sen University Cancer Center from May 16, 2015, to January 9, 2020, among 104 patients with newly diagnosed metastatic NPC who had achieved disease control after 4 to 6 cycles of induction chemotherapy with paclitaxel, cisplatin, and capecitabine. The final follow-up date was May 30, 2021. All efficacy analyses were conducted in the intention-to-treat population. INTERVENTIONS: Eligible patients were randomly assigned (1:1) to receive either capecitabine maintenance therapy (1000 mg/m(2) orally twice daily on days 1-14) every 3 weeks plus best supportive care (BSC) (capecitabine maintenance group) or BSC alone after 4 to 6 cycles of induction chemotherapy. MAIN OUTCOMES AND MEASURES: Progression-free survival (PFS). Secondary end points were objective response rate, duration of response, overall survival, and safety. RESULTS: This study included 104 patients (84 men [80.8%]; median age, 47 years [IQR, 38-54 years]), with 52 assigned to the capecitabine maintenance group and 52 assigned to the BSC group. After a median follow-up of 33.8 months (IQR, 22.9-50.7 months), there were 23 events (44.2%) of progression or death in the capecitabine maintenance group and 37 events (71.2%) of progression or death in the BSC group. Median PFS survival was significantly higher in the capecitabine maintenance group (35.9 months [95% CI, 20.5 months-not reached]) than in the BSC group (8.2 months [95% CI, 6.4-10.0 months]), with a hazard ratio of 0.44 (95% CI, 0.26-0.74; P = .002). Higher objective response rates and longer median duration of response were observed in the capecitabine maintenance group (25.0%; 40.0 months) compared with the BSC group (objective response rate, 25.0% [n = 13] vs 11.5% [n = 6]; and median duration of response, 40.0 months [95% CI, not reached-not reached] vs 13.2 months [95% CI, 9.9-16.5 months]). The most common grade 3 or 4 adverse events during maintenance therapy were anemia (6 of 50 [12.0%]), hand-foot syndrome (5 of 50 [10.0%]), nausea and vomiting (3 of 50 [6.0%]), fatigue (2 of 50 [4.0%]), and mucositis (2 of 50 [4.0%]). No deaths in the maintenance group were deemed treatment-related. CONCLUSIONS AND RELEVANCE: In this phase 3 randomized clinical trial, capecitabine maintenance therapy significantly improved PFS for patients with newly diagnosed metastatic NPC who achieved disease control after capecitabine-containing induction chemotherapy. Capecitabine exhibited manageable toxic effects. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02460419

Background To explore prognostic value of the pre-treatment primary lesion apparent diffusion coefficient (ADC) in locoregionally advanced nasopharyngeal carcinoma (LA-NPC). Methods A total of 843 patients with newly diagnosed LA-NPC were enrolled from January 2011 to April 2014 and divided into two groups based on ADC values: the low-ADC group and high-ADC group. The 3-year local relapse-free survival (LRFS), distant metastasis free survival (DMFS), disease-free survival (DFS) and overall survival (OS) rates between two groups were compared using Kaplan-Meier curve, and Cox regression analyses were performed to test prognostic value of the pretreatment ADC in LA-NPC. Results The cut-off value of the pretreatment ADC for predicting local relapse was 784.5 × 10− 6 mm2/s (AUC [area under curve] = 0.604; sensitivity = 0.640; specificity = 0.574), thus patients were divided into low-ADC (

The selective catalytic hydrogenation of acetophenone (AP)to 1-phenylethanol (PhE) has been studied in water, ethanol and n-hexanesolvent over Co/mordenite. It was shown that the highest yield of PhE (99.9%) was obtained in water solvent. The adsorption of AP hindered the adsorption of PhE in water, which in turn reduced the rate of hydrogenolysis of PhE to ethylbenzene (EB).By increasing the reaction temperature the hydrogenolysis of PhE to EB is promoted. The water phase was collected after reaction and reused successfully. The yield of PhE was 96.1% when the AP concentration in solution and the catalyst loading increased to three times. The neutral and alkaline conditions were beneficial to the hydrogenation of AP to PhE in water.

Wang-Zhong Li,1,2,* Xin Hua,3,* Shu-Hui Lv,1,2,* Hu Liang,1,2 Guo-Ying Liu,1,2 Nian Lu,1,2 Wei-Xin Bei,1,2 Wei-Xiong Xia,1,2 Yan-Qun Xiang1,2 1State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, 510060, People’s Republic of China; 2Department of Nasopharyngeal Carcinoma, Sun Yat-sen University Cancer Center, Guangzhou, 510060, People’s Republic of China; 3Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, 510060, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yan-Qun Xiang; Wei-Xiong XiaDepartment of Nasopharyngeal Carcinoma, Sun Yat-sen University Cancer Center, Guangzhou, People’s Republic of ChinaTel +86-1866-6096-623Email xiangyq@sysucc.org.cn; xiawx@sysucc.org.cnPurpose: We aimed to develop a simple scoring system based on baseline inflammatory and nutritional parameters to predict the efficacy of first-line chemotherapy and survival outcomes for de novo metastatic nasopharyngeal carcinoma (mNPC).Patients and Methods: We retrospectively collected ten candidate inflammatory and nutritional parameters from de novo mNPC patients who received platinum-based first-line chemotherapy treatment. We examined the effects of these ten candidate variables on progression-free survival (PFS) using the Cox regression model. We built a risk-scoring system based on the regression coefficients associated with the identified independent prognostic factors. The predictive accuracy of the scoring system was evaluated and independently validated.Results: A total of 460 patients were analyzed. Four independent prognostic factors were identified in a training cohort and were used to construct the scoring system, including nutritional risk index, C-reactive protein level, alkaline phosphatase level, and lactate dehydrogenase level. Based on the score obtained from the scoring system, we stratified patients into three prognostic subgroups (low: 0– 1 point, intermediate: 2– 3 points, and high: 4 points) associated with significantly different disease control rates (94.7% vs. 92.5% vs. 66.0%, respectively) and survival outcomes (3-year PFS: 55.8% vs. 29.1% vs. 11.9%, respectively). The scoring system had a good performance for the prediction of short-term disease control (area under the receiver operating characteristic curve [AUC]: 0.701) and long-term survival outcomes (time-dependent AUC for 5-year PFS: 0.713). The results were internally validated using an independent cohort (AUC for predicting disease control: 0.697; time-dependent AUC for 5-year PFS: 0.713).Conclusion: We developed and validated a clinically useful risk-scoring system that could predict the efficacy of first-line chemotherapy and survival outcomes in de novo mNPC patients. This system may help clinicians to design personalized treatment strategies.Keywords: metastatic nasopharyngeal carcinoma, nutritional status, cancer-related inflammation, chemotherapy efficacy, survival outcomes

ObjectiveWe aimed to identify imaging biomarkers to assess predictive capacity of radiomics nomogram regarding treatment response status (responder/non-responder) in patients with advanced NSCLC undergoing anti-PD1 immunotherapy.Methods197 eligible patients with histologically confirmed NSCLC were retrospectively enrolled from nine hospitals. We carried out a radiomics characterization from target lesions (TL) approach and largest target lesion (LL) approach on baseline and first follow-up (TP1) CT imaging data. Delta-radiomics feature was calculated as the relative net change in radiomics feature between baseline and TP1. Minimum Redundancy Maximum Relevance (mRMR) and Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression were applied for feature selection and radiomics signature construction.ResultsRadiomics signature at baseline did not show significant predictive value regarding response status for LL approach (P = 0.10), nor in terms of TL approach (P = 0.27). A combined Delta-radiomics nomogram incorporating Delta-radiomics signature with clinical factor of distant metastasis for target lesions had satisfactory performance in distinguishing responders from non-responders with AUCs of 0.83 (95% CI: 0.75–0.91) and 0.81 (95% CI: 0.68–0.95) in the training and test sets respectively, which was comparable with that from LL approach (P = 0.92, P = 0.97). Among a subset of those patients with available pretreatment PD-L1 expression status (n = 66), models that incorporating Delta-radiomics features showed superior predictive accuracy than that of PD-L1 expression status alone (P <0.001).ConclusionEarly response assessment using combined Delta-radiomics nomograms have potential advantages to identify patients that were more likely to benefit from immunotherapy, and help oncologists modify treatments tailored individually to each patient under therapy.

Induction chemotherapy added to concurrent chemoradiotherapy significantly improves survival for patients with locoregionally advanced nasopharyngeal carcinoma, but the optimal induction regimen remains unclear.To determine whether induction chemotherapy with paclitaxel, cisplatin, and capecitabine (TPC) improves survival vs cisplatin and fluorouracil (PF) prior to chemoradiotherapy for patients with stage IVA to IVB nasopharyngeal carcinoma.This randomized, open-label, phase 3 clinical trial recruited 238 patients at 4 hospitals in China from October 20, 2016, to August 29, 2019. Patients were 18 to 65 years of age with treatment-naive, nonkeratinizing stage IVA to IVB nasopharyngeal carcinoma and an Eastern Cooperative Oncology Group performance status of 0 to 1.Patients were randomly assigned (1:1) to receive induction chemotherapy with two 21-day cycles of TPC (intravenous paclitaxel [150 mg/m2, day 1], intravenous cisplatin [60 mg/m2, day 1], and oral capecitabine [1000 mg/m2 orally twice daily, days 1-14]) or PF (intravenous cisplatin [100 mg/m2, day 1] and fluorouracil [800 mg/m2 daily, days 1-5]), followed by chemoradiotherapy.The primary end point was failure-free survival in the intention-to-treat population. Secondary end points included distant metastasis-free survival, locoregional relapse-free survival, overall survival, tumor response, and safety.Overall, 238 eligible patients (187 men [78.6%]; median age, 45 years [range, 18-65 years]) were randomly assigned to receive TPC (n = 118) or PF (n = 120). The median follow-up duration was 48.4 months (IQR, 39.6-53.3 months). Failure-free survival at 3 years was 83.5% (95% CI, 77.0%-90.6%) in the TPC group and 68.9% (95% CI, 61.1%-77.8%) in the PF group (stratified hazard ratio [HR] for recurrence or death, 0.47; 95% CI, 0.28-0.79; P = .004). Induction with the TPC regimen resulted in a significant reduction in the risk of distant metastases (stratified HR, 0.49 [95% CI, 0.24-0.98]; P = .04) and locoregional recurrence (stratified HR, 0.40 [95% CI, 0.18-0.93]; P = .03) compared with the PF regimen. However, there was no effect on early overall survival (stratified HR, 0.45 [95% CI, 0.17-1.18]; P = .10). The incidences of grade 3 to 4 acute adverse events and late-onset toxicities were 57.6% (n = 68) and 13.6% (16 of 118), respectively, in the TPC group and 65.8% (n = 79) and 17.9% (21 of 117), respectively, in the PF group. One treatment-related death occurred in the PF group.This randomized clinical trial found that induction chemotherapy with 2 cycles of TPC for patients with stage IVA to IVB nasopharyngeal carcinoma improved failure-free survival compared with 2 cycles of PF, with no increase in the toxicity profile.ClinicalTrials.gov Identifier: NCT02940925.

In current clinical practice, the standard evaluation for axillary lymph node (ALN) status in breast cancer is based on the invasive procedure and many patients will suffer from operative associated complications. Hence, a novel signature incorporated tumor and lymph node magnetic resonance imaging (MRI) radiomics, clinical and pathological characteristics, and molecular subtypes based on the machine learning approach was established to accurately identify ALN metastasis in early-stage invasive breast cancer patients. Although the misjudgment of ALN status by clinicians according to preoperative MRI are common during clinical practice and even the senior radiologists make mistakes sometimes, this multiomic radiomic signature showed the superiority over clinicians and could precisely discriminate ALN metastasis among different molecular subtype patients. Furthermore, the association between MRI radiomic features and tumor-microenvironment features including immune cells, long non-coding RNAs, and types of methylated sites were found, which revealed the potential biological underpinning of MRI radiomics.

There are no satisfying approaches to identify high- and low-risk recurrence patients with early-stage breast cancer in current clinical practice. Patients might be overtreated or undertreated due to the inaccurate prediction of recurrence risk. Herein, machine learning magnetic resonance imaging radiomic-based signature that integrates the intratumoral and peritumoral radiomic signatures, and clinicopathological characteristics was developed to classify high- and low-risk recurrence patients and predict recurrence within multicentre cohorts. The radiomic-clinical signature could also discriminate high- from low-risk recurrence patients among different breast cancer molecular subtype, and HR+/Her2-, T1N0M0 stage patients. Furthermore, it was observed that the neoadjuvant chemotherapy improved survival in high-risk Luminal subtype patients compared with the adjuvant chemotherapy. The survival-associated radiomic features also showed the correlation with the immune microenvironment. The radiomic-clinical signature presented the feasibility of predicting recurrence risk and assisting clinical decision-making in early-stage invasive breast cancer patients.

High-level ab initio computations have been performed to investigate molecular structures, potential energy curves, vibrational energy levels and spectroscopic constants for twelve Λ-S states of the first four dissociation limits of MgBi. Characterizations of seven Ω states, corresponding to the first and the second Λ-S dissociation limits, have been explored for the first time. The spin–orbit coupling effect is revealed to have introduced a significant impact on the pattern of these electronic states and interactions among them. Our predictions for molecular structures and spectroscopic constants of MgBi are compared with available data of other magnesium-group 18 family species. Regular tendencies of these parameters are clearly exhibited when the group 18 atom is replaced by another one in the group. Information associated with transition dipole moments, Franck-Condon factors, vibrational branching ratios and radiative lifetimes between the Ω states are obtained and their transitional properties are analyzed and discussed. The results and data determined in this work are expected to guide and assist laboratorial detections of MgBi and to extend our understanding for the magnesium-group 18 species.

In this work, we investigate the low-lying electronic states correlated to the first and the second dissociation channels of MgGa molecule, neglecting and including the spin–orbit coupling effect. High-level ab initio calculations have been performed by using the icMRCI + Q method. Potential energy curves, spectroscopic constants, electron configurations and dipole moments are derived and discussed. Molecular structures of several magnesium-group 13 diatomics have been probed and analyzed. Information associated with transition dipole moments, Franck-Condon factors, vibrational branching ratios and radiative lifetimes between the Ω states are also well characterized. It is anticipated this work will provide some inspiration for further studies on MgGa.

Key Points Question Can incorporating Epstein-Barr virus DNA status into analysis of the tumor-node-metastasis framework improve cancer staging quality in nasopharyngeal carcinoma? Findings In this multicenter prognostic study of 2354 patients in China, a recursive partitioning analysis (RPA)–based staging system that divided nonmetastatic nasopharyngeal carcinoma cases into 3 groups with distinctly different prognoses was developed and validated. The RPA stage system outperformed the current TNM staging and 2 reported RPA staging schemes. Meaning These results suggest that an RPA-based staging system can outperform conventional TMN staging groups for predicting survival rates., This prognostic study describes the development and validation of a novel staging system for nonmetastatic nasopharyngeal carcinoma based on recursive partitioning analysis of Epstein-Barr virus status., Importance Nonanatomic prognostic factors complement the traditional anatomic staging system and could be incorporated into the tumor-node-metastasis (TNM) framework. Several diseases have incorporated nonanatomic prognostic factors into the determination of TNM staging groups. Objective To refine TNM staging groups for Epstein-Barr virus (EBV)–related nonmetastatic nasopharyngeal carcinoma (NPC) by incorporating EBV DNA status. Design, Setting, and Participants This multicenter prognostic study included patients with NPC treated with radiotherapy at 2 hospitals in China from January 2008 to December 2016. Progression-free survival and overall survival according to EBV DNA status and the TNM staging system were compared. Recursive partitioning analysis (RPA) combined with supervised clustering was applied to derive prognostic groupings, and then a refined RPA staging schema was developed, validated, and compared with existing staging schemes. Statistical analyses were conducted from October 1, 2020, to June 15, 2021. Exposures Curative intensity-modulated radiotherapy with or without platinum-based chemotherapy. Main Outcomes and Measures The primary end point was progression-free survival. The performance of the staging system was assessed using the time-dependent area under the receiver operating characteristic curves and the TNM stage system’s evaluation methodology. Results A total of 2354 patients (1709 men [72.6%]; median [interquartile range] age, 45 [38-53] years) were split into training (1372 [58.3%]), internal validation (672 [28.5%]), and external validation (310 [13.2%]) cohorts. Pretreatment EBV DNA was detected in 1338 (56.8%) patients. EBV DNA status was an independent prognostic factor: lower survival probability by higher TNM stage was evident in EBV DNA–positive patients but not in those with EBV DNA–negative disease. After integrating EBV DNA status and TNM stage, nonmetastatic NPC cases were categorized into RPA-I (T1-3N0 or EBV DNA–negative T1-3N1 cancers), RPA-II (EBV DNA–positive T1-3N1-2 or EBV DNA–negative T1-3N2-3/T4N0-3 cancers), and RPA-III (EBV DNA–positive T4N0-3/T1-3N3 cancers) groups, each with distinctly different prognosis. This system of RPA staging outperformed the current TNM stage system and 2 reported RPA staging schemes. These results were internally and externally validated. Conclusions and Relevance An RPA-based staging system for EBV-related NPC cases was associated with improved outcomes. This staging system may facilitate prognostic stratification and clinical trial designs.

Background: in current clinical practice, the standard evaluation for axillary lymph node (ALN) status in breast cancer has a low efficiency and is based on an invasive procedure that causes operative-associated complications in many patients. Therefore, we aimed to use machine learning techniques to develop an efficient preoperative magnetic resonance imaging (MRI) radiomics evaluation approach of ALN status and explore the association between radiomics and the tumor microenvironment in patients with early-stage invasive breast cancer. Methods: in this retrospective multicenter study, three independent cohorts of patients with breast cancer (n = 1,088) were used to develop and validate signatures predictive of ALN status. After applying the machine learning random forest algorithm to select the key preoperative MRI radiomic features, we used ALN and tumor radiomic features to develop the ALN-tumor radiomic signature for ALN status prediction by the support vector machine algorithm in 803 patients with breast cancer from Sun Yat-sen Memorial Hospital and Sun Yat-sen University Cancer Center (training cohort). By combining ALN and tumor radiomic features with corresponding clinicopathologic information, the multiomic signature was constructed in the training cohort. Next, the external validation cohort (n = 179) of patients from Shunde Hospital of Southern Medical University and Tungwah Hospital of Sun Yat-Sen University, and the prospective-retrospective validation cohort (n = 106) of patients treated with neoadjuvant chemotherapy in prospective phase 3 trials [{"type":"clinical-trial","attrs":{"text":"NCT01503905","term_id":"NCT01503905"}}NCT01503905], were included to evaluate the predictive value of the two signatures, and their predictive performance was assessed by the area under operating characteristic curve (AUC). This study was registered with ClinicalTrials.gov, number {"type":"clinical-trial","attrs":{"text":"NCT04003558","term_id":"NCT04003558"}}NCT04003558. Findings: the ALN-tumor radiomic signature for ALN status prediction comprising ALN and tumor radiomic features showed a high prediction quality with AUC of 0·88 in the training cohort, 0·87 in the external validation cohort, and 0·87 in the prospective-retrospective validation cohort. The multiomic signature incorporating tumor and lymph node MRI radiomics, clinical and pathologic characteristics, and molecular subtypes achieved better performance for ALN status prediction with AUCs of 0·90, 0·91, and 0·93 in the training cohort, the external validation cohort, and the prospective-retrospective validation cohort, respectively. Among patients who underwent neoadjuvant chemotherapy in the prospective-retrospective validation cohort, there were significant differences in the key radiomic features before and after neoadjuvant chemotherapy, especially in the gray-level dependence matrix features. Furthermore, there was an association between MRI radiomics and tumor microenvironment features including immune cells, long non-coding RNAs, and types of methylated sites. Interpretation this study presented a multiomic signature that could be preoperatively and conveniently used for identifying patients with ALN metastasis in early-stage invasive breast cancer. The multiomic signature exhibited powerful predictive ability and showed the prospect of extended application to tailor surgical management. Besides, significant changes in key radiomic features after neoadjuvant chemotherapy may be explained by changes in the tumor microenvironment, and the association between MRI radiomic features and tumor microenvironment features may reveal the potential biological underpinning of MRI radiomics.

In this study, a novel sandwiched electrochemiluminescence (ECL) immunosensor for the detection of carcinoembryonic antigen (CEA) was developed. The nanocomposite of polydopamine and Ag nanoparticles (PDA-AgNPs) was prepared by the redox reaction between Ag+ and dopamine. This nanocomposite not only provided an effective matrix for the immobilization of primary antibody (Ab1) but also enhanced the conductivity of the electrode. Carbon quantum dots (CQDs) were immobilized on the poly(ethylenimine) functionalized graphene oxide (PEI-GO) through amido-bond. Then Au nanoparticles were decorated on the CQDs modified PEI-GO matrix, and the resulted complex AuNPs/CQDs-PEI-GO was introduced to link secondary antibody (Ab2). The CQDs can be connected to the electrode surface through the combination of CEA with Ab1 and Ab2, and then the amplified electrochemiluminescence signal of CQDs was obtained with the synergistic effect of AgNPs, polydopamine, AuNPs and PEI-GO. Under the optimal conditions, the ECL intensity was proportional to the logarithm value of CEA concentration in the linear range from 5pgmL-1 to 500ngmL-1 with a detection limit of 1.67pgmL-1 for CEA detection. The immunosensor was applied for the CEA detection in real samples with satisfactory results. The proposed ECL immunosensor showed good performance with high sensitivity, specificity, reproducibility, stability and will be potential in clinical detection.

The twelve Λ-S electronic states of the first four dissociation limits of the MgSb molecule have been examined at the icMRCI+Q level employing basis sets of quintuple-ζ quality. The potential energy curves, vibrational levels and spectroscopic constants of the species have been investigated. The permanent dipole moments of the interested states are derived, and the transition dipole moments, Einstein emission coefficients, radiation lifetimes and Franck-Condon factors between selected states are also determined. Four Λ-S states of the first two dissociation limits split into seven Ω states under the effect of spin-orbit coupling. Characterizations of the MgSb low-lying Ω states are performed for the first time. In addition, the results and relevant data provided in this work on MgSb are compared with the antimony-IIA group and magnesium-VA group diatomic species. It is anticipated that this work will shed some light on further investigations of MgSb and other antimony-IIA group systems.

6044 Background: Capecitabine maintenance therapy improves outcomes in various tumor types, but minimal data are available on the effect of capecitabine maintenance therapy in metastatic nasopharyngeal carcinoma (NPC). We aimed to investigate whether capecitabine maintenance therapy would prolong the progression-free survival (PFS) of newly diagnosed metastatic NPC, in comparison to best supportive care (BSC). Methods: This was an open-label, randomized, controlled, phase trial. Eligible patients for maintenance randomisation were aged 18-65 years old with newly diagnosed metastatic NPC at the Sun Yat-Sun University Cancer Center (SYSUCC), had completed 4 to 6 cycles of induction chemotherapy as per protocol and had achieved disease control to protocol treatment, including capecitabine. Patients were randomly assigned 1:1 to capecitabine maintenance (oral 1,250 mg/m2/day on days 1-14 every 21 days) for up to 24 months with BSC or BSC alone. The primary endpoint was PFS. The secondary endpoints included overall survival, duration of response, objective response rate and adverse effects. Analyses were done by intention to treat. This trial is registered with ClinicalTrials.gov, number NCT02460419 and is ongoing and no longer recruiting new patients. Results: Between May 16th, 2015, and January 9th, 2020, 140 metastatic NPC patients were screened, and 104 eligible patients were randomly assigned to capecitabine maintenance plus BSC (n = 52) or BSC alone (n = 52). After a median follow-up of 33.1 months (IQR, 21.5-50.7 months), median PFS was 35.2 months in the capecitabine maintenance group and 9.1 months in the BSC group (HR: 0.426; 95%CI: 0.248-0.731, P = 0.001). The most commongrade 3 or 4 adverse events during maintenance therapy were hand-foot syndrome (10.0%), nausea/vomiting (6.0%), fatigue (4.0%), and mucositis (4.0%). Totally 37 deaths occurred during follow-up, 14 (26.9%) in the capecitabine maintenance group and 23 (44.2%) in the BSC group. Overall survival data was immature. No deaths in the capecitabine maintenance group were deemed treatment related. Conclusions: Capecitabine maintenance significantly improved PFS in patients with newly diagnosed metastatic NPC who achieved disease control after induction chemotherapy compared to BSC and exhibited low grade and manageable toxicities. Clinical trial information: NCT02460419.

Carbon quantum dots (CQDs) decorated Ag nanoparticles (Ag@CQDs) were successfully synthesized and characterized by UV-Vis spectroscopy, fluorescence, FT-IR, and transmission electron microscopy (TEM), then utilized for the sensitive detection of chloride ions (Cl−). Compared to CQDs, the Ag@CQDs showed stronger electrochemiluminescence (ECL) in 0.1 M PBS (pH = 7.4) containing 0.1 M K2S2O8. In the presence of chloride (Cl−), a significant reduction in the ECL intensity of Ag@CQDs was noticed, which was used to develop a sensitive detection method of Cl−. The ECL quenching of Ag@CQDs was utilized to establish the method, of which the mechanism might be caused by the formation of AgCl on the Ag@CQDs composite surface. The prepared Cl− sensor showed a wide linear range (0.99–450 nM) and low detection limit (0.33 nM). Furthermore, the prepared sensor exhibited high sensitivity and good selectivity towards several ion inferences.

Background: Axillary lymph node status has a guiding significance in the operative schemes. However, sensitivity and noninvasive for prediction of lymph node metastasis and survival of currently evidences is limited. This study aims to establish a deep learning algorithms of multi-parametric MRI radiomics for identifying lymph node metastasis and prognostic prediction, and investigate the relationship between the radiomics and tumor microenvironment in early stage breast cancer. Methods: RBC-01 is a multicentre, ambispective cohort study aims to assess multi-parametric MRI radiomics-based prediction model for identifying metastasis lymph nodes and prognostic prediction in early stage breast cancer. Patients are recruited from Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Sun Yat-sen University Cancer Center, Tungwah hospital of Sun Yat-sen University, and Shunde Hospital of Southern Medical University in China. Patients from Sun Yat-sen Memorial Hospital of Sun Yat-sen University are training cohort, and the independent validation cohort were built by patients from other centers. Eligible women could be aged from 18 to 75 years old with pathological diagnosis of early stage invasive breast cancer, without distant organ metastasis, and completed the breast MRI examination before therapy. PyRadiomics is used to extract patients’ breast cancer region, paratumor region, and axillary lymph nodes in multi-parametric MRI. To avoid MRI data heterogeneity bias, all MRI data are subjected to imaging normalization and resampled to the same resolution before feature extraction. The least absolute shrinkage and selection operator, random forest and supporting vector machine models are used to select the most useful radiomics characteristics from the training cohort, and then test by validation cohorts. The correlation between radiomics features and tumor microenvironment is also planned to investigate. Combined with independent risk factors, a radiomics nomogram was built with a multivariate logistic regression model. Decision curve analysis was performed with the combined training and validation set to estimate the clinical usefulness of the nomogram. The primary endpoint is disease-free survival. Secondary endpoints are lymph node metastasis prediction and overall survival. The trial is registered with ClinicalTrials.gov, number NCT04003558, and Chinese Clinical Trail Registry, number ChiCTR1900024020. Citation Format: Herui Yao, Yujie Tan, Yunfang Yu, Chuanmiao Xie, Qiugen Hu, Jie Ouyang, Chenchen Li, Kai Chen, Nian Lu, Xiaohong Li, Rong Zhang, Jiafan Ma, Ying Wang, Jianli Zhao. Radiomics of multi-parametric MRI associated with axillary lymph node metastasis and prognostic in patients with breast cancer: A multicenter RBC-001 study [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr OT3-02-02.

Background: Axillary lymph node metastasis is a vital factor of prognosis in patients with invasive breast cancer. However, sensitivity and noninvasive for prediction of lymph node metastasis and survival after neoadjuvant chemotherapy is limited. This study aims to develop radiomics combining multi-parametric MRI before and after neoadjuvant chemotherapy to predict axillary lymph node metastasis and prognosis, as well as investigate the relationship between the radiomics and tumor microenvironment in invasive breast- cancer. Methods: RBC-02 is a multicenter, ambispective cohort study proposes to build a clinical predictive model to predict axillary lymph node metastasis and prognosis in invasive-breast- cancer patients who received neoadjuvant chemotherapy. Patients are recruited from Sun Yat-sen Memorial Hospital of Sun Yat-sen University (training cohort), Sun Yat-sen University Cancer Center (validation cohort), and Tungwah hospital of Sun Yat-sen University (validation cohort) in China. Invasive breast cancer patients undergo multi-parametric MRI at baseline, then undergo multi-parametric MRI after received neoadjuvant chemotherapy for at least 4 cycles as planned. After the surgery, responses to neoadjuvant chemotherapy are determined according to the histopathologically examination of the surgically resected specimens. The model is built based on breast MRI signatures extracted and analyzed via deep machine- learning algorithm methods. The correlation between radiomics features and tumor microenvironment is also planned to investigate. Decision curve analysis was performed with the combined training and validation set to estimate the clinical usefulness of the radiomics nomogram, which is built with a multivariate logistic regression model. The primary endpoint is disease-free survival. Secondary endpoints include pathological complete response, pathological axillary lymph node status, and overall survival. The trial is registered with ClinicalTrials.gov, number NCT04004559, and Chinese Clinical Trail Registry, number ChiCTR1900024113. Citation Format: Herui Yao, ChenChen Li, Yunfang Yu, Chuanmiao Xie, Jie Ouyang, Yujie Tan, Nian Lu, Ying Wang, Jianli Zhao, Kai Chen, Jiafan Ma. Radiomics multi-parametric MRI of before and after neoadjuvant chemotherapy associated with axillary lymph node metastasis and prognostic in patients with breast cancer: A multicenter RBC-002 study [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr OT3-02-03.

High-level ab initio computations have been performed on SnH+. The potential energy curves and spectroscopic constants of the low-lying Λ-S electronic states, as well as their associated Ω states, are derived at the icMRCI + Q level employing basis sets of quintuple-ζ quality. The transition dipole moments, Einstein coefficients, radiative lifetimes and Franck-Condon factors of three spin-forbidden transition bands ( 1 3 Π 1 − X 1 Σ 0 + + , 1 3 Π 0 + − X 1 Σ 0 + + and 1 1 Π 1 − X 1 Σ 0 + + ) are determined. Comparisons between our predictions and available experimental results indicate reasonable agreement. The spin-orbit coupling effect has been proved to affect these low-lying electronic states significantly.

An investigation was carried out on thermal stability of α-amylase. The influence of various additives (calcium acetate, sodium lactate, L-histidine, and water-soluble chitosan) on the stability of α-amylase was studied. Results showed the inactivation behavior of α-amylase with or without additives all followed the first-order kinetics. All additives (Ca2+, sodium lactate, L-histidine, and water-soluble chitosan) displayed good stabilizing effect on α-amylase lower than 80 °C, and only water-soluble chitosan had an efficient stabilizing effect on α-amylase when the treatment temperature exceeds 80 °C. All additives improved the catalytic activity of α-amylase at 70–90 °C, and the appearance of water-soluble chitosan increased the catalytic activity of α-amylase at 90 °C sharply. A desizing ratio of 68.42% was obtained by treating the cotton fabrics in the buffer solution at 100 °C without α-amylase. To obtain a desizing ratio exceed 95% when fabrics were treated at 100 °C for 10 min, the addition of wate...

A novel solid-state electrochemiluminescence (ECL) quenching sensor was constructed for determination of brilliant blue FCF (BB FCF). Under a simple electropolymerization step, poly(sulfosalicylic acid) (PSSA) film attached luminophore Ru(bpy)