Discriminative detection of soman or VX exposure using europium chelated microparticle-based immunofluorescence microfluidic chip.

The retrospective detection of organophosphorus nerve agents (OPNAs) exposure has been achieved by the off-site analysis of OPNA-human serum albumin (HSA) adducts using mass spectrometry-based detection approaches. However, few specific methods are accessible for on-site detection. To address this, a novel immunofluorescence microfluidic chip (IFMC) testing system combining europium chelated microparticle (EuCM) with self-driven microfluidic chip assay has been established to unambiguously determine soman (GD) and VX exposure within 20 min, respectively. The detection system was based on the principle of indirect competitive enzyme-linked immunosorbent assay. The specific monoclonal antibodies that respectively recognized the phosphonylated tyrosine 411 of GD-HSA and VX-HSA adducts were labeled by EuCM to capture corresponding adducts in the exposed samples. The phosphonylated peptides in the test line and goat-anti-rabbit antibody in the control line were utilized to bind the EuCM-labeled antibodies for signal exhibition. The developed IFMC chip could discriminatively detect exposed HSA adducts with high specificity, demonstrating a low limit of detection at exposure concentrations of 0.5 × 10-6 mol/L VX and 1.0 × 10-6 mol/L GD. The exposed serum samples can be qualitatively detected following an additional pretreatment procedure. This is a novel rapid detection system capable of discriminating GD and VX exposure, providing an alternative method for rapidly identifying OPNA exposure.

Sonodynamic and sonomechanical effect on cellular stemness and extracellular physicochemical environment to potentiate chemotherapy.

BACKGROUND: Hypoxia-activated prodrug (HAP) is a promising candidate for highly tumor-specific chemotherapy. However, the oxygenation heterogeneity and dense extracellular matrix (ECM) of tumor, as well as the potential resistance to chemotherapy, have severely impeded the resulting overall efficacy of HAP. RESULTS: A HAP potentiating strategy is proposed based on ultrasound responsive nanodroplets (PTP@PLGA), which is composed of protoporphyrin (PpIX), perfluoropropane (PFP) and a typical HAP, tirapazamine (TPZ). The intense vaporization of PFP upon ultrasound irradiation can magnify the sonomechanical effect, which loosens the ECM to promote the penetration of TPZ into the deep hypoxic region. Meanwhile, the PpIX enabled sonodynamic effect can further reduce the oxygen level, thus activating the TPZ in the relatively normoxic region as well. Surprisingly, abovementioned ultrasound effect also results in the downregulation of the stemness of cancer cells, which is highly associated with drug-refractoriness. CONCLUSIONS: This work manifests an ideal example of ultrasound-based nanotechnology for potentiating HAP and also reveals the potential acoustic effect of intervening cancer stem-like cells.

Axillary lymph node metastasis in pure mucinous carcinoma of breast: clinicopathologic and ultrasonographic features.

BACKGROUND: The purpose of this research is to study the sonographic and clinicopathologic characteristics that associate with axillary lymph node metastasis (ALNM) for pure mucinous carcinoma of breast (PMBC). METHODS: A total of 176 patients diagnosed as PMBC after surgery were included. According to the status of axillary lymph nodes, all patients were classified into ALNM group (n = 15) and non-ALNM group (n = 161). The clinical factors (patient age, tumor size, location), molecular biomarkers (ER, PR, HER2 and Ki-67) and sonographic features (shape, orientation, margin, echo pattern, posterior acoustic pattern and vascularity) between two groups were analyzed to unclose the clinicopathologic and ultrasonographic characteristics in PMBC with ALNM. RESULTS: The incidence of axillary lymph node metastasis was 8.5% in this study. Tumors located in the outer side of the breast (upper outer quadrant and lower outer quadrant) were more likely to have lymphatic metastasis, and the difference between the two group was significantly (86.7% vs. 60.3%, P = 0.043). ALNM not associated with age (P = 0.437). Although tumor size not associated with ALNM(P = 0.418), the tumor size in ALNM group (32.3 ± 32.7 mm) was bigger than non-ALNM group (25.2 ± 12.8 mm). All the tumors expressed progesterone receptor (PR) positively, and 90% of all expressed estrogen receptor (ER) positively, human epidermal growth factor receptor 2 (HER2) were positive in two cases of non-ALNM group. Ki-67 high expression was observed in 36 tumors in our study (20.5%), and it was higher in ALNM group than non-ALNM group (33.3% vs. 19.3%), but the difference wasn't significantly (P = 0.338). CONCLUSIONS: Tumor location is a significant factor for ALNM in PMBC. Outer side location is more easily for ALNM. With the bigger size and/or Ki-67 higher expression status, the lymphatic metastasis seems more likely to present.

Ultrasound-based deep learning in the establishment of a breast lesion risk stratification system: a multicenter study.

OBJECTIVES: To establish a breast lesion risk stratification system using ultrasound images to predict breast malignancy and assess Breast Imaging Reporting and Data System (BI-RADS) categories simultaneously. METHODS: This multicenter study prospectively collected a dataset of ultrasound images for 5012 patients at thirty-two hospitals from December 2018 to December 2020. A deep learning (DL) model was developed to conduct binary categorization (benign and malignant) and BI-RADS categories (2, 3, 4a, 4b, 4c, and 5) simultaneously. The training set of 4212 patients and the internal test set of 416 patients were from thirty hospitals. The remaining two hospitals with 384 patients were used as an external test set. Three experienced radiologists performed a reader study on 324 patients randomly selected from the test sets. We compared the performance of the DL model with that of three radiologists and the consensus of the three radiologists. RESULTS: In the external test set, the DL model achieved areas under the receiver operating characteristic curve (AUCs) of 0.980 and 0.945 for the binary categorization and six-way categorizations, respectively. In the reader study set, the DL BI-RADS categories achieved a similar AUC (0.901 vs. 0.933, p = 0.0632), sensitivity (90.98% vs. 95.90%, p = 0.1094), and accuracy (83.33% vs. 79.01%, p = 0.0541), but higher specificity (78.71% vs. 68.81%, p = 0.0012) than those of the consensus of the three radiologists. CONCLUSIONS: The DL model performed well in distinguishing benign from malignant breast lesions and yielded outcomes similar to experienced radiologists. This indicates the potential applicability of the DL model in clinical diagnosis. KEY POINTS: • The DL model can achieve binary categorization for benign and malignant breast lesions and six-way BI-RADS categorizations for categories 2, 3, 4a, 4b, 4c, and 5, simultaneously. • The DL model showed acceptable agreement with radiologists for the classification of breast lesions. • The DL model performed well in distinguishing benign from malignant breast lesions and had promise in helping reduce unnecessary biopsies of BI-RADS 4a lesions.

Differentiation between Phyllodes Tumors and Fibroadenomas through Breast Ultrasound: Deep-Learning Model Outperforms Ultrasound Physicians.

The preoperative differentiation of breast phyllodes tumors (PTs) from fibroadenomas (FAs) plays a critical role in identifying an appropriate surgical treatment. Although several imaging modalities are available, reliable differentiation between PT and FA remains a great challenge for radiologists in clinical work. Artificial intelligence (AI)-assisted diagnosis has shown promise in distinguishing PT from FA. However, a very small sample size was adopted in previous studies. In this work, we retrospectively enrolled 656 breast tumors (372 FAs and 284 PTs) with 1945 ultrasound images in total. Two experienced ultrasound physicians independently evaluated the ultrasound images. Meanwhile, three deep-learning models (i.e., ResNet, VGG, and GoogLeNet) were applied to classify FAs and PTs. The robustness of the models was evaluated by fivefold cross validation. The performance of each model was assessed by using the receiver operating characteristic (ROC) curve. The area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were also calculated. Among the three models, the ResNet model yielded the highest AUC value, of 0.91, with an accuracy value of 95.3%, a sensitivity value of 96.2%, and a specificity value of 94.7% in the testing data set. In contrast, the two physicians yielded an average AUC value of 0.69, an accuracy value of 70.7%, a sensitivity value of 54.4%, and a specificity value of 53.2%. Our findings indicate that the diagnostic performance of deep learning is better than that of physicians in the distinction of PTs from FAs. This further suggests that AI is a valuable tool for aiding clinical diagnosis, thereby advancing precision therapy.

Survival outcome assessment for triple-negative breast cancer: a nomogram analysis based on integrated clinicopathological, sonographic, and mammographic characteristics.

OBJECTIVE: This study aimed to incorporate clinicopathological, sonographic, and mammographic characteristics to construct and validate a nomogram model for predicting disease-free survival (DFS) in patients with triple-negative breast cancer (TNBC). METHODS: Patients diagnosed with TNBC at our institution between 2011 and 2015 were retrospectively evaluated. A nomogram model was generated based on clinicopathological, sonographic, and mammographic variables that were associated with 1-, 3-, and 5-year DFS determined by multivariate logistic regression analysis in the training set. The nomogram model was validated according to the concordance index (C-index) and calibration curves in the validation set. RESULTS: A total of 636 TNBC patients were enrolled and divided into training cohort (n = 446) and validation cohort (n = 190). Clinical factors including tumor size > 2 cm, axillary dissection, presence of LVI, and sonographic features such as angular/spiculated margins, posterior acoustic shadows, and presence of suspicious lymph nodes on preoperative US showed a tendency towards worse DFS. The multivariate analysis showed that no adjuvant chemotherapy (HR = 6.7, 95% CI: 2.6, 17.5, p < 0.0005), higher axillary tumor burden (HR = 2.7, 95% CI: 1.0, 7.1, p = 0.045), and ≥ 3 malignant features on ultrasound (HR = 2.4, CI: 1.1, 5.0, p = 0.021) were identified as independent prognostic factors associated with poorer DFS outcomes. In the nomogram, the C-index was 0.693 for the training cohort and 0.694 for the validation cohort. The calibration plots also exhibited excellent consistency between the nomogram-predicted and actual survival probabilities in both the training and validation cohorts. CONCLUSIONS: Clinical variables and sonographic features were correlated with the prognosis of TNBCs. The nomogram model based on three variables including no adjuvant chemotherapy, higher axillary tumor load, and more malignant sonographic features showed good predictive performance for poor survival outcomes of TNBC. KEY POINTS: • The absence of adjuvant chemotherapy, heavy axillary tumor load, and malignant-like sonographic features can predict DFS in patients with TNBC. • Mammographic features of TNBC could not predict the survival outcomes of patients with TNBC. • The nomogram integrating clinicopathological and sonographic characteristics is a reliable predictive model for the prognostic outcome of TNBC.

Prediction for pathological and immunohistochemical characteristics of triple-negative invasive breast carcinomas: the performance comparison between quantitative and qualitative sonographic feature analysis.

OBJECTIVE: Sonographic features are associated with pathological and immunohistochemical characteristics of triple-negative breast cancer (TNBC). To predict the biological property of TNBC, the performance using quantitative high-throughput sonographic feature analysis was compared with that using qualitative feature assessment. METHODS: We retrospectively reviewed ultrasound images, clinical, pathological, and immunohistochemical (IHC) data of 252 female TNBC patients. All patients were subgrouped according to the histological grade, Ki67 expression level, and human epidermal growth factor receptor 2 (HER2) score. Qualitative sonographic feature assessment included shape, margin, posterior acoustic pattern, and calcification referring to the Breast Imaging Reporting and Data System (BI-RADS). Quantitative sonographic features were acquired based on the computer-aided radiomics analysis. Breast cancer masses were manually segmented from the surrounding breast tissues. For each ultrasound image, 1688 radiomics features of 7 feature classes were extracted. The principal component analysis (PCA), least absolute shrinkage and selection operator (LASSO), and support vector machine (SVM) were used to determine the high-throughput radiomics features that were highly correlated to biological properties. The performance using both quantitative and qualitative sonographic features to predict biological properties of TNBC was represented by the area under the receiver operating characteristic curve (AUC). RESULTS: In the qualitative assessment, regular tumor shape, no angular or spiculated margin, posterior acoustic enhancement, and no calcification were used as the independent sonographic features for TNBC. Using the combination of these four features to predict the histological grade, Ki67, HER2, axillary lymph node metastasis (ALNM), and lymphovascular invasion (LVI), the AUC was 0.673, 0.680, 0.651, 0.587, and 0.566, respectively. The number of high-throughput features that closely correlated with biological properties was 34 for histological grade (AUC 0.942), 27 for Ki67 (AUC 0.732), 25 for HER2 (AUC 0.730), 34 for ALNM (AUC 0.804), and 34 for LVI (AUC 0.795). CONCLUSION: High-throughput quantitative sonographic features are superior to traditional qualitative ultrasound features in predicting the biological behavior of TNBC. KEY POINTS: • Sonographic appearances of TNBCs showed a great variety in accordance with its biological and clinical characteristics. • Both qualitative and quantitative sonographic features of TNBCs are associated with tumor biological characteristics. • The quantitative high-throughput feature analysis is superior to two-dimensional sonographic feature assessment in predicting tumor biological property.

Screening of monoclonal antibodies against specific phosphonylation sites and analysis of serum samples exposed to soman and VX using an indirect competitive enzyme-linked immunosorbent assay.

Organophosphorus nerve agents (OPNAs) covalently bind to tyrosine 411 of human serum albumin (HSA) and the formed adducts are stable biomarkers of OPNA exposure. The detection of these adducts has been limited to mass spectrometry techniques combined with protein digestion. Here, we developed indirect competitive ELISA (icELISA) methods to verify OPNA exposure by the detection of OPNA-phosphonylated adducts at tyrosine 411 residue (OPNA-HSA adducts), in which monoclonal antibodies (mAbs) against phosphonylation sites at tyrosine 411 were introduced. The two mAbs were prepared by the fourth generation of rabbit mAb technology using the phosphonylated peptides of LVRY(GD or VX)TKKVPQC as the haptens. These mAbs were screened using our developed competitive ELISA method and then selected based on their individual affinity and selectivity. As a result, we obtained two mAbs that recognized the HSA Tyr 411 adduct of GD (mAb-5G2) or VX (mAb-12B9), respectively. They shared the highest affinity exhibiting a Kd value of about 10-6 mol/L of the OPNA exposure concentration. They also had remarkable selectivity, which could especially recognize their individual OPNA-HSA adducts in a native state but did not recognize other OPNA-HSAs and unadducted HSAs. Especially for mAb-12B9, it could clearly distinguish VX-HSA and GB-HSA between which there was only one alkyl difference in their phosphonyl portion of the adducted sites. The two mAbs were then used to build the icELISA method for analysis of the serum samples exposed to OPNA. It was found that the detectable lowest GD- and VX-exposed concentrations in serum samples were respectively 1.0 × 10-6 mol/L and 10.0 × 10-6 mol/L. This study provides one novel approach and strategy for the retrospective detection of OPNA exposure, and the two mAbs have great potential to be extended for point-of-care testing of OPNA intoxication.

Ultrasound-based radiomics analysis for preoperative prediction of central and lateral cervical lymph node metastasis in papillary thyroid carcinoma: a multi-institutional study.

BACKGROUND: An accurate preoperative assessment of cervical lymph node metastasis (LNM) is important for choosing an optimal therapeutic strategy for papillary thyroid carcinoma (PTC) patients. This study aimed to develop and validate two ultrasound (US) nomograms for the individual prediction of central and lateral compartment LNM in patients with PTC. METHODS: A total of 720 PTC patients from 3 institutions were enrolled in this study. They were categorized into a primary cohort, an internal validation, and two external validation cohorts. Radiomics features were extracted from conventional US images. LASSO regression was used to select optimized features to construct the radiomics signature. Two nomograms integrating independent clinical variables and radiomics signature were established with multivariate logistic regression. The performance of the nomograms was assessed with regard to discrimination, calibration, and clinical usefulness. RESULTS: The radiomics scores were significantly higher in patients with central/lateral LNM. A radiomics nomogram indicated good discrimination for central compartment LNM, with an area under the curve (AUC) of 0.875 in the training set, the corresponding value in the validation sets were 0.856, 0.870 and 0.870, respectively. Another nomogram for predicting lateral LNM also demonstrated good performance with an AUC of 0.938 and 0.905 in the training and internal validation cohorts, respectively. The AUC for the two external validation cohorts were 0.881 and 0.903, respectively. The clinical utility of the nomograms was confirmed by the decision curve analysis. CONCLUSION: The nomograms proposed here have favorable performance for preoperatively predicting cervical LNM, hold promise for optimizing the personalized treatment, and might greatly facilitate the decision-making in clinical practice.

Direct Acetonitrile-Assisted Trypsin Digestion Method Combined with LC-MS/MS-Targeted Peptide Analysis for Unambiguous Identification of Intact Ricin.

Ricin is a type II ribosome-inactivating protein toxin consisting of A and B chains linked by one interchain disulfide bond. Because of its high toxicity depending on both chains together, confirming the presence of both A and B chains of intact ricin is required during the investigation of the illegal production and application. Here, we report a novel and sensitive acetonitrile (ACN)-assisted trypsin digestion method for unambiguous identification of intact ricin by simultaneous detection of its marker peptides from A and B chains. Marker peptides were generated with a simple procedure by direct cleaving the native ricin at 45 °C for 4 h using Promega modified sequencing grade trypsin under the assistance of 10% ACN, and then directly analyzed by ultrahigh performance liquid chromatography tandem mass spectrometry. The type of trypsin was found to be one critical factor for cleavage of intact ricin based on a significant difference in the yields of specific peptides generated while using various types of trypsin. A low content of ACN in enzymatic buffer significantly reduced the digestion time from overnight to 4 h. There was commonly a better MS response of marker peptides when using the developed ACN-assisted trypsin digestion method than methanol-assisted trypsin digestion within the same 4 h. Totally, seven specific peptides with high sensitivity and specificity including three in the A-chain (TA7, TA11, and TA10) and four in the B-chain (TB6, TB14-ss-TB16, TB20, and TB18) were obtained as good marker peptides for unambiguous identification of intact ricin. The lowest concentration of native ricin for unambiguous identification was 20 ng/mL, in which three marker peptides from both the A-chain and B-chain could be measured with a minimum of three ion transitions. Combined with affinity enrichment, the developed approach was successfully applied for the measurement of intact ricin from the complicated matrix samples of the second, third, and fourth biotoxin exercises organized by the Organisation for the Prohibition of Chemical Weapons (OPCW). This study has provided a recommended detection method combined with one novel ACN-assisted trypsin digestion with MS for forensic unambiguous confirmation of trace ricin intact with high confidence.

Quantitative detection of ricin in beverages using trypsin/Glu-C tandem digestion coupled with ultra-high-pressure liquid chromatography-tandem mass spectrometry.

The toxic protein of ricin has drawn wide attention in recent years as a potential bioterrorism agent due to its high toxicity and wide availability. For the verification of the potential anti-terrorism activities, it is urgent for the quantification of ricin in food-related matrices. Here, a novel strategy of trypsin/Glu-C tandem digestion was introduced for quantitative detection of ricin marker peptides in several beverage matrices using isotope-labeled internal standard (IS)-mass spectrometry. The ricin in beverages was captured and enriched by biotinylated anti-ricin polyclonal antibodies conjugated to streptavidin magnetic beads. The purified ricin was cleaved using the developed trypsin/Glu-C tandem digestion method and then quantitatively detected by ultra-high-pressure liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) with isotope-labeled T7A and TG11B selected as IS. The use of trypsin/Glu-C digestion allows shorter peptides, which are more suitable for MS detection, to be obtained than the use of single trypsin digestion. Under the optimized tandem digestion condition, except for T7A in the A-chain, two resulting specific peptides of TG13A, TG28A from the A-chain and two of TG11B, TG33B from the B-chain were chosen as novel marker peptides with high MS response. The uniqueness of the selected marker peptides allows for unambiguous identification of ricin among its homologous proteins in a single run. The MS response of the four novel marker peptides is increased by more than 10 times compared with that of individual corresponding tryptic peptides. Both the marker peptides of A-chain T7A and B-chain TG11B were selected as quantitative peptides based on the highest MS response among the marker peptides from their individual chains. The limit of detection (LOD) of ricin is 0.1 ng/mL in PBS and 0.5 ng/mL in either milk or orange juice. The linear range of calibration curves for ricin were 0.5-300 ng/mL in PBS, 1.0-400 ng/mL in milk, and 1.0-250 ng/mL in orange juice. The method accuracy ranged between 82.6 and 101.8% for PBS, 88.9-105.2% for milk, and 95.3-118.7% for orange juice. The intra-day and inter-day precision had relative standard deviations (%RSD) of 0.3-9.4%, 0.7-8.9%, and 0.2-6.9% in the three matrices respectively. Furthermore, whether T7A or TG11B is used as a quantitative peptide, the quantitative results of ricin are consistent. This study provides not only a practical method for the absolute quantification of ricin in beverage matrices but also a new strategy for the investigation of illegal use of ricin in chemical weapon verification tasks such as OPCW biotoxin sample analysis exercises.

Co-delivery of nanoparticle and molecular drug by hollow mesoporous organosilica for tumor-activated and photothermal-augmented chemotherapy of breast cancer.

BACKGROUND: In comparison with traditional therapeutics, it is highly preferable to develop a combinatorial therapeutic modality for nanomedicine and photothermal hyperthermia to achieve safe, efficient, and localized delivery of chemotherapeutic drugs into tumor tissues and exert tumor-activated nanotherapy. Biocompatible organic-inorganic hybrid hollow mesoporous organosilica nanoparticles (HMONs) have shown high performance in molecular imaging and drug delivery as compared to other inorganic nanosystems. Disulfiram (DSF), an alcohol-abuse drug, can act as a chemotherapeutic agent according to its recently reported effectiveness for cancer chemotherapy, whose activity strongly depends on copper ions. RESULTS: In this work, a therapeutic construction with high biosafety and efficiency was proposed and developed for synergistic tumor-activated and photothermal-augmented chemotherapy in breast tumor eradication both in vitro and in vivo. The proposed strategy is based on the employment of HMONs to integrate ultrasmall photothermal CuS particles onto the surface of the organosilica and the molecular drug DSF inside the mesopores and hollow interior. The ultrasmall CuS acted as both photothermal agent under near-infrared (NIR) irradiation for photonic tumor hyperthermia and Cu2+ self-supplier in an acidic tumor microenvironment to activate the nontoxic DSF drug into a highly toxic diethyldithiocarbamate (DTC)-copper complex for enhanced DSF chemotherapy, which effectively achieved a remarkable synergistic in-situ anticancer outcome with minimal side effects. CONCLUSION: This work provides a representative paradigm on the engineering of combinatorial therapeutic nanomedicine with both exogenous response for photonic tumor ablation and endogenous tumor microenvironment-responsive in-situ toxicity activation of a molecular drug (DSF) for augmented tumor chemotherapy.

Application of preoperative ultrasound features combined with clinical factors in predicting HER2-positive subtype (non-luminal) breast cancer.

BACKGROUND: Human epidermal growth factor receptor2+ subtype breast cancer has a high degree of malignancy and a poor prognosis. The aim of this study is to develop a prediction model for the human epidermal growth factor receptor2+ subtype (non-luminal) of breast cancer based on the clinical and ultrasound features related with estrogen receptor, progesterone receptor, and human epidermal growth factor receptor2. METHODS: We collected clinical data and reviewed preoperative ultrasound images of enrolled breast cancers from September 2017 to August 2020. We divided the data into in three groups as follows. Group I: estrogen receptor ± , Group II: progesterone receptor ± and Group III: human epidermal growth factor receptor2 ± . Univariate and multivariate logistic regression analyses were used to analyze the clinical and ultrasound features related with biomarkers among these groups. A model to predict human epidermal growth factor receptor2+ subtype was then developed based on the results of multivariate regression analyses, and the efficacy was evaluated using the area under receiver operating characteristic curve, accuracy, sensitivity, specificity. RESULTS: The human epidermal growth factor receptor2+ subtype accounted for 138 cases (11.8%) in the training set and 51 cases (10.1%) in the test set. In the multivariate regression analysis, age ≤ 50 years was an independent predictor of progesterone receptor + (p = 0.007), and posterior enhancement was a negative predictor of progesterone receptor + (p = 0.013) in Group II; palpable axillary lymph node, round, irregular shape and calcifications were independent predictors of the positivity for human epidermal growth factor receptor-2 in Group III (p = 0.001, p = 0.007, p = 0.010, p < 0.001, respectively). In Group I, shape was the only factor related to estrogen receptor status in the univariate analysis (p < 0.05). The area under receiver operating characteristic curve, accuracy, sensitivity, specificity of the model to predict human epidermal growth factor receptor2+ subtype breast cancer was 0.697, 60.14%, 72.46%, 58.49% and 0.725, 72.06%, 64.71%, 72.89% in the training and test sets, respectively. CONCLUSIONS: Our study established a model to predict the human epidermal growth factor receptor2-positive subtype with moderate performance. And the results demonstrated that clinical and ultrasound features were significantly associated with biomarkers.

Differentiating Westlake Longjing tea from the first- and second-grade producing regions using ultra high performance liquid chromatography with quadrupole time-of-flight mass spectrometry-based untargeted metabolomics in combination with chemometrics.

There are numerous articles published for geographical discrimination of tea. However, few research works focused on the authentication and traceability of Westlake Longjing green tea from the first- and second-grade producing regions because the tea trees are planted in a limited growing zone with identical cultivate condition. In this work, a comprehensive analytical strategy was proposed by ultrahigh performance liquid chromatography-quadrupole time-of-flight mass spectrometry-based untargeted metabolomics coupled with chemometrics. The automatic untargeted data analysis strategy was introduced to screen metabolites that expressed significantly among different regions. Chromatographic features of metabolites can be automatically and efficiently extracted and registered. Meanwhile, those that were valuable for geographical origin discrimination were screened based on statistical analysis and contents in samples. Metabolite identification was performed based on high-resolution mass values and tandem mass spectra of screened peaks. Twenty metabolites were identified, based on which the two-way encoding partial least squares discrimination analysis was built for geographical origin prediction. Monte Caro simulation results indicated that prediction accuracy was up to 99%. Our strategy can be applicable for practical applications in the quality control of Westlake Longjing green tea.

Tumor Proliferation and Invasiveness Derived From Ultrasound Appearances of Invasive Breast Cancers: Moving Beyond the Routine Differential Diagnosis.

OBJECTIVES: To investigate the correlation between ultrasound (US) appearances of invasive breast cancers and tumor proliferation and invasiveness measured according to the histologic grade, Ki-67 expression, axillary lymph node metastasis (ALNM), and lymphovascular invasion (LVI). METHODS: This study evaluated 676 patients who underwent primary surgical treatment of invasive breast cancers. The preoperative US reports and postoperative pathologic and immunohistochemical results of the patients were retrospectively reviewed. Ultrasound characteristics were evaluated according to the American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) lexicon. Logistic regression analyses were used to identify independent predictive US features that were correlated with tumor proliferation and invasiveness of breast cancers. Odds ratios (ORs) were calculated. RESULTS: Posterior acoustic enhancement and calcifications on US images were independent predictive factors of a higher histologic grade and a higher Ki-67 level (OR, 1.69-6.54; P < .05). Meanwhile, a noncircumscribed margin (OR, 2.61; P < .05) and posterior acoustic shadow (OR, 1.62; P < .05) were independent predictors of ALNM. An irregular shape (OR, 2.13; P < .05) and calcifications (OR, 1.69; P < .05) were independent risk factors for LVI. Infiltrative breast cancers scored as BI-RADS category 5 had higher probability to be associated with ALNM (OR, 3.33; P < .0005) and LVI (OR, 2.87; P < .0005). CONCLUSIONS: Ultrasound features of invasieve breast cancers might have a predictive value for tumor proliferation and invasiveness. The US features correlated with a high cellular proliferation rate were different from those associated with ALNM. The tumor shape, margin, posterior acoustic pattern, and calcifications at US are suggested to be considered by clinicians when making clinical decisions.

The Association Between Ultrasound Features and Biological Properties of Invasive Breast Carcinoma Is Modified by Age, Tumor Size, and the Preoperative Axilla Status.

OBJECTIVES: To investigate the value of ultrasound (US) feature-based models in predicting the proliferation and invasiveness of invasive breast cancer (IBC) and to compare the performance of models based solely on US features with models that combined US features, patient age, tumor size, and axilla status from US. METHODS: With ethical approval, 746 patients with a pathologic diagnosis of IBC were reviewed for preoperative clinical, US, and postoperative pathologic data. The proliferation and invasiveness properties of the IBC included the histologic grade and Ki-67 status and lymphovascular invasion (LVI) and axillary lymph node metastasis (ALNM), respectively. Logistic regression analyses were used to identify independent risk factors for tumor proliferation and invasiveness. RESULTS: Posterior echo enhancement, calcification, a tumor size larger than 2 cm, and suspicion of ALNM from axillary US were independent risk factors for a high histologic grade and high Ki-67 expression of IBC (P < .05). A posterior echo shadow, patient age younger than 45 years, and suspicious findings on axillary US imaging were independent variables for predicting the presence of LVI and ALNM in IBC (P < .05). Calcification was the independent factor for predicting LVI (P = .013). The predictive performance of the combined models was improved compared with the US feature-based models, with a higher accuracy rate and negative predictive value. The area under curve of the combined models was also significantly higher than that of the single models (P < .05). CONCLUSIONS: Compared with the US feature-based models, the combined models yielded better predictive performance. This may provide a more robust model to predict the tumor biological properties of IBC before surgery.

Nodule Localization in Thyroid Ultrasound Images with a Joint-Training Convolutional Neural Network.

The accurate localization of nodules in ultrasound images can convey crucial information to support a reliable diagnosis. However, this is usually challenging due to low contrast and image artifacts, especially in thyroid ultrasound images where nodules are relatively small in most cases. To address these problems, in this paper, we propose a joint-training convolutional neural network (CNN) for thyroid nodule localization in ultrasound images. Considering the advantage of the faster region-based CNN (Faster R-CNN) in detecting natural targets, we adopt it as the basic framework. To boost the representative power and noise suppression capability of the network, the attention mechanism module is embedded for adaptive feature refinement along the channel and spatial dimensions. Furthermore, in the training process, we annotate the training set in a novel way, called joint-training annotation, by exploiting the fake foreground (FFG) area around the nodule as a spatial prior constraint to improve the sensitivity to small nodules. Ablation experiments are conducted to verify the effectiveness of our proposed method. The experimental results show that our method outperforms others by a mean average precision (mAP) of 0.93 and achieves an intersection over union (IoU) of 0.9, indicating that the localization results agree well with the ground truth. Furthermore, extended experiments on breast nodule datasets are also conducted to verify the generalizability of the proposed approach. Above all, the proposed algorithm is of considerable significance for accurate thyroid nodule localization in ultrasound images and can be generalized to other types of nodules, thereby providing trustworthy assistance for clinical diagnosis.

A sensitive quantification approach for detection of HETE-CP adduct after benzyl chloroformate derivatization using ultra-high-pressure liquid chromatography tandem mass spectrometry.

Sulfur mustard (HD) reacts with human serum albumin (HSA) at Cys34 and produces a long-term biomarker of HD exposure. Here, we present a novel, sensitive, and convenient method for quantification of HD exposure by detection of HD-HSA adducts using pronase digestion, benzyl chloroformate (Cbz-Cl) derivatization, and ultra-high-pressure liquid chromatography tandem mass spectrometry (UHPLC-MS/MS). The HSA in HD-exposed plasma in vitro was precipitated with acetone and digested (2 h, 50 °C) with pronase to form the alkylated dipeptide, S-hydroxyethylthioethyl-CysPro (HETE-CP). The HETE-CP adduct was derivatized with Cbz-Cl to generate N-carbobenzoxy HETE-CP (HETE-C(Cbz)P). The derivatized product was analyzed by UHPLC-MS/MS. HD surrogate, 2-chloroethyl ethyl sulfide (2-CEES), was introduced as a non-isotope internal standard (ISTD) instead of traditional d8-HD for quantification. The method was found to be linear between 1.00 and 200 ng/mL HD exposure (R2 > 0.998) with precision of ≤ 9.0% relative standard deviation (RSD) and accuracy ranged between 97.1 and 111%. The limit of detection (LOD) is 0.500 ng/mL (S/N~5), over 15 times lower than that of the previous method (7.95 ng/mL). Time-consuming affinity purification or solid phase extraction (SPE) is not needed in the experiment and the operation takes less than 5 h. This study provides a new strategy and useful tool for retrospective analysis of HD exposure by HETE-CP biomarker detection. Graphical abstract Flow diagram for quantification of sulfur mustard exposure by detection of HETE-CP dipeptide adduct after benzyl chloroformate derivatization using ultra-high-pressure liquid chromatography tandem mass spectrometry.

Clinicopathologic and Ultrasound Variables Associated With a Heavy Axillary Nodal Tumor Burden in Invasive Breast Carcinoma.

OBJECTIVES: To identify clinicopathologic and ultrasound (US) variables that were associated with a heavy nodal tumor burden, which was defined as 3 or more lymph nodes involved with metastasis to the axilla after invasive breast carcinoma. METHODS: With ethical approval, 621 patients with a pathologic diagnosis of invasive breast carcinoma were retrospectively analyzed for clinical, pathologic, and US data. Pathologic findings were ascertained by the final paraffin pathologic analysis. Ultrasound characteristics were evaluated on the basis of the American College of Radiology's Breast Imaging Reporting and Data System (BI-RADS). Univariate and multivariate logistic regression analyses were used to assess the clinicopathologic and US variables that were associated with a heavy nodal tumor burden at the axilla. RESULTS: There were 107 cases (17.2%) of invasive breast carcinoma with a heavy tumor burden at the axilla. The independent clinicopathologic variables for a heavy tumor burden at the axilla included a tumor size of 2 to 5 cm (odds ratio [OR], 1.86; P = .036), the presence of lymphovascular invasion (OR, 23.52; P < .001), the presence of papillary invasion (OR, 2.93; P = .043), and a non-triple-negative subtype (OR, 2.34; P = .04). The independent US features of breast tumors that were associated with a heavy tumor burden at the axilla included BI-RADS category 5 (OR, 5.50; P = .024) and a posterior acoustic shadow (OR, 1.94; P = .024). CONCLUSIONS: A large tumor size, lymphovascular invasion, papillary invasion, and a non-triple-negative subtype on the pathologic analysis as well as BI-RADS category 5 and a posterior acoustic shadow on a US assessment were associated with a heavy nodal tumor burden at the axilla. These US characteristics of the primary breast carcinoma might provide additional information to axillary US for the prediction of axillary nodal tumor loads.

Copper-Catalyzed and Air-Mediated Mild Cross-Dehydrogenative Coupling of Aryl Thioureas and Dialkyl H-Phosphonates: The Synthesis of Thiophosphonates.

A copper-catalyzed and air-mediated mild cross-dehydrogenative coupling of aryl thioureas and dialkyl H-phosphonates to produce thiophosphonates has been reported. Without addition of base or acid, air as an oxidant, the phosphorylation occurred smoothly to give the target products in moderate to excellent yields at room temperature. The protocol allows the direct formation of a sulfur-phosphorus bond, tolerates a series of functional groups, and shows potential synthetic value for the synthesis of a diversity of thiophosphonates.