Application of computer-aided diagnosis to predict malignancy in BI-RADS 3 breast lesions.

Purpose: To evaluate the ability of computer-aided diagnosis (CAD) system (S-Detect) to identify malignancy in ultrasound (US) -detected BI-RADS 3 breast lesions. Materials and methods: 148 patients with 148 breast lesions categorized as BI-RADS 3 were included in the study between January 2021 and September 2022. The malignancy rate, accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the curve (AUC) were calculated. Results: In this study, 143 breast lesions were found to be benign, and 5 breast lesions were malignant (malignancy rate, 3.4 %, 95 % confidence interval (CI): 0.5-6.3). The malignancy rate rose significantly to 18.2 % (4/22, 95 % CI: 2.1-34.3) in the high-risk group with a "possibly malignant" CAD result (p = 0.017). With a "possibly benign" CAD result, the malignancy rate decreased to 0.8 % (1/126, 95 % CI: 0-2.2) in the low-risk group (p = 0.297). The AUC, sensitivity, specificity, accuracy, PPV, and NPV of the CAD system in BI-RADS 3 breast lesions were 0.837 (95 % CI: 77.7-89.6), 80.0 % (95 % CI: 73.6-86.4), 87.4 % (95 % CI: 82.0-92.7), 87.2 % (95 % CI: 81.8-92.6), 18.2 % (95 % CI: 2.1-34.3) and 99.2 % (95 % CI: 97.8-100.0), respectively. Conclusions: CAD system (S-Detect) enables radiologists to distinguish a high-risk group and a low-risk group among US-detected BI-RADS 3 breast lesions, so that patients in the low-risk group can receive follow-up without anxiety, while those in the high-risk group with a significantly increased malignancy rate should actively receive biopsy to avoid delayed diagnosis of breast cancer.

Clinical Application of Computer-Aided Diagnosis System in Breast Ultrasound: A Prospective Multicenter Study.

OBJECTIVES: Ultrasound tends to present very high sensitivity but relatively low specificity and positive predictive value (PPV), which would result in unnecessary breast biopsies. The purpose of this study is to analyze the diagnostic performance of computer-aided diagnosis (CAD) (S-Detect) system in differentiating breast lesions and reducing unnecessary biopsies in non-university hospitals in less-developed regions of China. METHODS: The study was a prospective multicenter study from 8 hospitals. The ultrasound images, and cine, CAD analysis, and BI-RADS were recorded. The accuracy, sensitivity, specificity, PPV, negative predictive value (NPV), and area under the curve (AUC) were analyzed and compared between CAD and radiologists. The Youden Index (YI) was used to determine optimal cut-off for the number of planes to downgrade. RESULTS: A total of 491 breast lesions were included in the study. Less-experienced radiologists combined CAD was superior to less-experienced radiologists alone in AUC (0.878 vs 0.712, p < 0.001), and specificity (81.3% vs 44.6%, p < 0.001). There was no statistical difference in AUC (0.891 vs 0.878, p = 0.346), and specificity (82.3% vs 81.3%, p = 0.791) between experienced radiologists and less-experienced radiologists combined CAD. With CAD assistance, the biopsy rate of less-experienced radiologists was significantly decreased (100.0% vs 25.6%, p < 0.001), and malignant rate of biopsy was significantly increased (15.0% vs 43.9%, p < 0.001). CONCLUSIONS: CAD system can be an effective auxiliary tool in differentiating breast lesions and reducing unnecessary biopsies for radiologists from non-university hospitals in less-developed regions of China.

Association between new plasma inflammatory markers and risk of colorectal neoplasms in individuals over 50 years old.

OBJECTIVE(S): The prognostic value of systemic cytokine profiles and inflammatory markers in colorectal cancer were explored by several studies. We want to know more about inflammatory biomarkers in colorectal adenoma and early cancer. METHOD: The level of 38 inflammatory markers in the plasma of 112 adenoma patients, 72 Tis-T1 staging of colorectal carcinoma patients, 34 T2-T4 staging of colorectal carcinoma patients and 53 normal subjects were detected and compared. RESULT(S): Eight inflammatory biomarkers (Eotaxin, GCSF, IL-4, IL-5, IL-17E, MCP-1, TNF-α and VEGF-A) have higher plasma concentrations in colorectal adenoma and cancer patients compared with normal participants over 50 years old. CONCLUSION(S): Inflammatory markers may have the prognostic value for colorectal adenoma and early-stage carcinoma.

Deep Learning-Based Computer-Aided Diagnosis for Breast Lesion Classification on Ultrasound: A Prospective Multicenter Study of Radiologists Without Breast Ultrasound Expertise.

BACKGROUND. Computer-aided diagnosis (CAD) systems for breast ultrasound interpretation have been primarily evaluated at tertiary and/or urban medical centers by radiologists with breast ultrasound expertise. OBJECTIVE. The purpose of this study was to evaluate the usefulness of deep learning-based CAD software on the diagnostic performance of radiologists without breast ultrasound expertise at secondary or rural hospitals in the differentiation of benign and malignant breast lesions measuring up to 2.0 cm on ultrasound. METHODS. This prospective study included patients scheduled to undergo biopsy or surgical resection at any of eight participating secondary or rural hospitals in China of a breast lesion classified as BI-RADS category 3-5 on prior breast ultrasound from November 2021 to September 2022. Patients underwent an additional investigational breast ultrasound, performed and interpreted by a radiologist without breast ultrasound expertise (hybrid body/breast radiologists, either who lacked breast imaging subspecialty training or for whom the number of breast ultrasounds performed annually accounted for less than 10% of all ultrasounds performed annually by the radiologist), who assigned a BI-RADS category. CAD results were used to upgrade reader-assigned BI-RADS category 3 lesions to category 4A and to downgrade reader-assigned BI-RADS category 4A lesions to category 3. Histologic results of biopsy or resection served as the reference standard. RESULTS. The study included 313 patients (mean age, 47.0 ± 14.0 years) with 313 breast lesions (102 malignant, 211 benign). Of BI-RADS category 3 lesions, 6.0% (6/100) were upgraded by CAD to category 4A, of which 16.7% (1/6) were malignant. Of category 4A lesions, 79.1% (87/110) were downgraded by CAD to category 3, of which 4.6% (4/87) were malignant. Diagnostic performance was significantly better after application of CAD, in comparison with before application of CAD, in terms of accuracy (86.6% vs 62.6%, p < .001), specificity (82.9% vs 46.0%, p < .001), and PPV (72.7% vs 46.5%, p < .001) but not significantly different in terms of sensitivity (94.1% vs 97.1%, p = .38) or NPV (96.7% vs 97.0%, p > .99). CONCLUSION. CAD significantly improved radiologists' diagnostic performance, showing particular potential to reduce the frequency of benign breast biopsies. CLINICAL IMPACT. The findings indicate the ability of CAD to improve patient care in settings with incomplete access to breast imaging expertise.

Group 3 innate lymphoid cells secret neutrophil chemoattractants and are insensitive to glucocorticoid via aberrant GR phosphorylation.

BACKGROUND: Patients with neutrophil-mediated asthma have poor response to glucocorticoids. The roles and mechanisms of group 3 innate lymphoid cells (ILC3s) in inducing neutrophilic airway inflammation and glucocorticoid resistance in asthma have not been fully clarified. METHODS: ILC3s in peripheral blood were measured by flow cytometry in patients with eosinophilic asthma (EA) and non-eosinophilic asthma (NEA). ILC3s were sorted and cultured in vitro for RNA sequencing. Cytokines production and signaling pathways in ILC3s after IL-1ß stimulation and dexamethasone treatment were determined by real-time PCR, flow cytometry, ELISA and western blot. RESULTS: The percentage and numbers of ILC3s in peripheral blood was higher in patients with NEA compared with EA, and negatively correlated with blood eosinophils. IL-1ß stimulation significantly enhanced CXCL8 and CXCL1 production in ILC3s via activation of p65 NF-κB and p38/JNK MAPK signaling pathways. The expression of neutrophil chemoattractants from ILC3s was insensitive to dexamethasone treatment. Dexamethasone significantly increased phosphorylation of glucocorticoid receptor (GR) at Ser226 but only with a weak induction at Ser211 residues in ILC3s. Compared to human bronchial epithelial cell line (16HBE cells), the ratio of p-GR S226 to p-GR S211 (p-GR S226/S211) was significantly higher in ILC3s at baseline and after dexamethasone treatment. In addition, IL-1ß could induce Ser226 phosphorylation and had a crosstalk effect to dexamethasone via NF-κB pathway. CONCLUSIONS: ILC3s were elevated in patients with NEA, and associated with neutrophil inflammation by release of neutrophil chemoattractants and were glucocorticoid (GC) resistant. This paper provides a novel cellular and molecular mechanisms of neutrophil inflammation and GC-resistance in asthma. Trial registration The study has been prospectively registered in the World Health Organization International Clinical Trials Registry Platform (ChiCTR1900027125).

Discovery of novel tubulin inhibitors targeting taxanes site by virtual screening, molecular dynamic simulation, and biological evaluation.

Microtubules play crucial role in process of mitosis and cell proliferation, which have been considered as attractive drug targets for anticancer therapy. The aim of this study was to discover novel and chemically diverse tubulin inhibitors for treatment of cancer. In this investigation, the multilayer virtual screening methods, including common feature pharmacophore model, structure-based pharmacophore model and molecular docking, were developed to screen BioDiversity database with 30,000 compounds. A total of 102 compounds were obtained by the virtual screening, and further filtered by diverse chemical clusters with desired properties and PAINS analysis. Finally, 50 compounds were selected and submitted to the biological evaluation. Among these hits, hits 8 and 30 with novel scaffolds displayed stronger antiproliferative activity on four human tumor cells including Hela, A549, MCF-7, and HepG2. Moreover, the two hits were subsequently submitted to molecular dynamic simulations of 90 ns with the aim of exploring the stability of ligand-protein interactions into the binding pocket, and further probing the mechanism of the interaction between tubulin and hits. The molecular dynamic simulation results revealed there had stronger interactions between tubulin and hits in equilibrium state. Therefore, the hits 8 and 30 have been well characterized as lead compounds for developing new tubulin inhibitors with potential anticancer activity.

Developing novel computational prediction models for assessing chemical-induced neurotoxicity using naïve Bayes classifier technique.

Development of reliable and efficient alternative in vivo methods for evaluation of the chemicals with potential neurotoxicity is an urgent need in the early stages of drug design. In this investigation, the computational prediction models for drug-induced neurotoxicity were developed by using the classical naïve Bayes classifier. Eight molecular properties closely relevant to neurotoxicity were selected. Then, 110 classification models were developed with using the eight important molecular descriptors and 10 types of fingerprints with 11 different maximum diameters. Among these 110 prediction models, the prediction model (NB-03) based on eight molecular descriptors combined with ECFP_10 fingerprints showed the best prediction performance, which gave 90.5% overall prediction accuracy for the training set and 82.1% concordance for the external test set. In addition, compared to naïve Bayes classifier, the recursive partitioning classifier displayed worse predictive performance for neurotoxicity. Therefore, the established NB-03 prediction model can be used as a reliable virtual screening tool to predict neurotoxicity in the early stages of drug design. Moreover, some structure alerts for characterizing neurotoxicity were identified in this research, which could give an important guidance for the chemists in structural modification and optimization to reduce the chemicals with potential neurotoxicity.

Developing novel in silico prediction models for assessing chemical reproductive toxicity using the naïve Bayes classifier method.

Assessment of reproductive toxicity is one of the important safety considerations in drug development. Thus, in the present research, the naïve Bayes (NB)-classifier method was applied to develop binary classification models. Six important molecular descriptors for reproductive toxicity were selected by the genetic algorithm. Then, 110 classification models were developed using six molecular descriptors and10 types of fingerprints with 11 different maximum diameters. Among these established models, the model based on six molecular descriptors and the SciTegic extended-connectivity fingerprints with 20 maximum diameters (LCFC_20) displayed the best prediction performance for reproductive toxicity (NB-1), which gave a 0.884 receiver operating characteristic (ROC) score and 91.8% overall prediction accuracy for the Training Set, and produced a 0.888 ROC score and 83.0% overall accuracy for the external Test Set I. In addition, for the external rat multi-generation reproductive toxicity dataset (Test Set II), the NB-1 model generated a 0.806 ROC score and 85.1% concordance. The generated prediction results indicated that the NB-1 model could give robust and reliable predictions for a reproductive toxicity potential of chemicals. Thus, the established model could be applied to filter early-stage molecules for potential reproductive adverse effects. In addition, six important molecular descriptors and new structural alerts for reproductive toxicity were identified, which could help medicinal chemists rationally guide the optimization of lead compounds and select chemicals with the best prospects of being safe and effective.

Development of novel in silico prediction model for drug-induced ototoxicity by using naïve Bayes classifier approach.

Some drugs have the potential to cause cellular degeneration of cochlear and/or vestibular system, leading to temporary or permanent hearing loss, innitus, ataxia, dizziness, ear infections, hyperacusis, vertigo, nystagmus and other ear problems. Thus, precise assessment of ototoxicity has become a strong urge task for the toxicologist. In this research, the in silico prediction model of ototoxicity was developed based on 2612 diverse chemicals by using naïve Bayes classifier approach. A set of 7 molecular descriptors considered as important for ototoxicity was selected by genetic algorithm method, and some structural alerts for ototoxicity were identified. The established naïve Bayes prediction model produced 90.2% overall prediction accuracy for the training set and 88.7% for the external test set. We hope the established naïve Bayes prediction model should be employed as precise and convenient computational tool for assessing and screening the chemical-induced ototoxicity in drug development, and these important information of ototoxic chemical structures could provide theoretical guidance for hit and lead optimization in drug design.

Retraction Note: ß2-adrenoreceptor Inverse Agonist Down-regulates Muscarine Cholinergic Subtype-3 Receptor and Its Downstream Signal Pathways in Airway Smooth Muscle Cells in vitro.

This paper has been retracted at the request of the authors.

shRNA­mediated knockdown of KNTC1 suppresses cell viability and induces apoptosis in esophageal squamous cell carcinoma.

Kinetochore­associated proteins are critical components of mitotic checkpoints, which are essential for faithful chromosomal segregation and spindle assembly during cell division. Recent advances have demonstrated that kinetochore­associated proteins are upregulated and serve significant roles in the carcinogenesis of numerous types of cancer. However, the effects of kinetochore­associated protein 1 (KNTC1) on human cancer, particularly on esophageal squamous cell carcinoma (ESCC), remain unclear. The present study revealed that KNTC1 was highly expressed in ESCC cell lines. Subsequently, lentivirus­mediated short hairpin RNAs were used to knockdown KNTC1 expression in human ESCC cell lines. Cell growth and viability were measured using multiparametric high­content screening and the MTT assay, respectively. Cell apoptosis was assessed by staining cells with Annexin V­allophycocyanin and was detected using FACScan flow cytometry. The results demonstrated that knockdown of KNTC1 effectively inhibited cell viability and increased apoptosis. In addition, a gene set enrichment analysis of online ESCC datasets indicated that KNTC1 overexpression was associated with increases in the mitotic spindle and hypoxia pathways, and decreases in the DNA repair and mismatch repair pathways. The findings of the present study suggested that KNTC1 may have an essential role in mediating cell viability and apoptosis in human ESCC cells and may serve as a novel therapeutic target for ESCC.

Development and evaluation of in silico prediction model for drug-induced respiratory toxicity by using naïve Bayes classifier method.

Respiratory toxicity is considered as main cause of drug withdrawal, which could seriously injure human health or even lead to death. The objective of this investigation was to develop an in silico prediction model of drug-induced respiratory toxicity by using naïve Bayes classifier. The genetic algorithm was used to select important molecular descriptors related to respiratory toxicity, and the ECFP_6 fingerprint descriptors were applied to the respiratory toxic/non-toxic fragments production. The established prediction model was validated by the internal 5-fold cross validation and external test set. The naïve Bayes classifier generated overall prediction accuracy of 91.8% for the training set and 84.3% for the external test set. Furthermore, six molecular descriptors (e.g., number of O atoms, number of N atoms, molecular weight, Apol, number of H acceptors and molecular polar surface area) considered as important for the drug-induced respiratory toxicity were identified, and some critical fragments related to the respiratory toxicity were achieved. We hope the established naïve Bayes prediction model could be used as a toxicological screening of chemicals for respiratory sensitization potential in drug development, and these obtained important information of respiratory toxic chemical structures could offer theoretical guidance for hit and lead optimization.

Efficacy and safety of phosphodiesterase 4 inhibitors in patients with asthma: A systematic review and meta-analysis.

Phosphodiesterase 4 (PDE4) inhibitors are a novel medication approved for airway inflammatory diseases including chronic obstructive pulmonary disease. Their role and application in asthma are controversial and not defined. A comprehensive search was performed in major databases (1946-2016) using the keywords: 'phosphodiesterase 4 inhibitor' or 'roflumilast' and 'asthma'. Placebo-controlled trials reporting lung function, airway hyperresponsiveness by direct challenge, asthma control and exacerbations, and adverse events were included. Random or fixed-effects models were used to calculate odds ratios (OR) and mean differences between the two treatment groups. Statistical analyses were conducted using Mann-Whitney U-tests and Cochrane systematic review software, Review Manager. Seventeen studies were included in the systematic review, of which 14 studies were included in the meta-analysis. Except for significant statistical heterogeneity in pre- and post-challenge predicted percentage of forced expiratory volume in 1 s (FEV1 %; I2 = 72%, χ2 = 3.35, P = 0.06), there was no heterogeneity in outcome measures. Roflumilast (500 µg) significantly improved FEV1 (mean difference: 0.05, 95% CI: 0.01-0.09, Z = 2.50, P = 0.01), peak expiratory flow, asthma control and exacerbations, but showed variable effects on airway responsiveness to methacholine and a 20% fall in FEV1 .Of note, PDE4 inhibitors were accompanied with significantly higher adverse events such as headache (OR: 3.99, 95% CI: 1.65-9.66, Z = 3.07, P = 0.002) and nausea (OR: 5.53, 95% CI: 1.38-22.17, Z = 2.41, P = 0.02). In patients with mild asthma, oral PDE4 inhibitors can be considered as an alternative treatment to regular bronchodilators and inhaled controllers.

ß2-adrenoreceptor Inverse Agonist Down-regulates Muscarine Cholinergic Subtype-3 Receptor and Its Downstream Signal Pathways in Airway Smooth Muscle Cells in vitro.

Mechanisms underlying ß2-adrenoreceptor (ß2AR) inverse agonist mediated bronchoprotectiveness remain unknown. We incubated ICI118,551, formoterol, budesonide, and formoterol plus budesonide, as well as ICI118,551 or pindolol plus formoterol, ICI118,551 plus forskolin, SQ22,536 or H89 plus formoterol in ASMCs to detect expressions of M3R, PLCß1 and IP3. The level of M3R in the presence of 10-5 mmol/L ICI118,551 were significantly decreased at 12 h, 24 h and 48 h (P < 0.05), and at 24 h were significantly reduced in ICI118,551 with concentration of 10-5 mmol/L, 10-6 mmol/L, 10-7 mmol/L, and 10-8 mmol/L (P < 0.05). The level of IP3 in 10-5 mmol/L ICI118,551 was significantly diminished at 24 h (P < 0.01), except for that at 1 h, neither was in the level of PLCß1. A concentration of 10-5 mmol/L ICI118,551 at 24 h showed a significant reduction of M3R level compared to formoterol (P < 0.01), budesonide (P < 0.01), and formoterol + budesonide (P < 0.05), but significant reduction of PLCß1 and IP3 was only found between 10-5 mmol/L ICI118,551 and formoterol at 24 h, but not in the comparison of budesonide or formoterol + budesonide. Pindolol and H89 could not inhibit the formoterol-induced expression of M3R (P > 0.05), but SQ22,536 significantly antagonized the formoterol-induced M3R expression (P < 0.05). In conclusions, ß2AR inverse agonist, ICI118,551, exerts similar bronchoprotective effects to corticosteroids via decreasing the expression of M3R and inhibiting the production of IP3.

A morphologic study of the airway structure abnormalities in patients with asthma by high-resolution computed tomography.

BACKGROUND: Airway structure changes, termed as airway remodeling, are common in asthma patients due to chronic inflammation, which can be assessed by high-resolution computed tomography (HRCT). Considering the controversial conclusions in the correlation of morphologic abnormalities with clinical feature and outcome, we aimed to further specify and evaluate the structural abnormalities of Chinese asthmatics by HRCT. METHODS: From August 2012 to February 2015, outpatients with asthma were recruited consecutively in the Asthma Center of West China Hospital, Sichuan University. Standard HRCT and pulmonary function test (PFT) were performed to collect information of bronchial wall thickening, bronchial dilatation, mucus impaction, emphysema, mosaic perfusion, atelectasis, and spirometric parameters. We reported the incidence of each structural abnormality in HRCT and compared it among different asthmatic severities. RESULTS: A total of 123 asthmatics were enrolled, among which 84 (68.3%) were female and 39 (31.7%) were male. At least one structural abnormality was detected by HRCT in 85.4% asthmatics, and the incidence of bronchial wall thickening, bronchial dilatation, mucus impaction, emphysema, mosaic perfusion, and atelectasis was 57.7%, 51.2%, 22%, 24.4%, 5.7% and 1.6%, respectively. The incidences of bronchial wall thickening, bronchial dilation and emphysema were significantly increased by asthma severity (P<0.05), while incidences of mucus impaction (26/27, 96.30%), mosaic perfusion (6/7, 85.71%) and atelectasis (2/2, 100%) were mainly found in severe asthma. We found a longer asthma history (28.13±18.55 years, P<0.001, P=0.003), older age (51.30±10.70 years, P=0.022, P=0.006) and lower predicted percentage of forced expiratory volume in one second (FEV1%) (41.97±15.19, P<0.001, P<0.001) and ratio of forced expiratory volume to forced vital capacity (FEV1/FVC) (48.01±9.55, P<0.001, P<0.001) in patients with severe bronchial dilation compared with those in none and mild bronchial dilation. A negative correlation was also found between the extent of bronchial dilation and FEV1% as well as FEV1/FVC (r=-0.359, P=0.004; r=-0.266, P=0.035, respectively). CONCLUSIONS: The incidences of structural abnormalities detected by HRCT are fairly high in Chinese asthma populations, especially the bronchial wall thickening and bronchial dilation, which are significantly increased in severe asthma, and are potential risk factors of pulmonary function decline in asthmatics.