Prediction of human epidermal growth factor receptor 2 (HER2) status in breast cancer by mammographic radiomics features and clinical characteristics: a multicenter study.

OBJECTIVES: To preoperatively evaluate the human epidermal growth factor 2 (HER2) status in breast cancer using mammographic radiomics features and clinical characteristics on a multi-vendor and multi-center basis. METHODS: This multi-center study included a cohort of 1512 Chinese female with invasive ductal carcinoma of no special type (IDC-NST) from two different hospitals and five devices (1332 from Institution A, used for training and testing the models, and 180 women from Institution B, as the external validation cohort). The Gradient Boosting Machine (GBM) was employed to establish radiomics and multiomics models. Model efficacy was evaluated by the area under the curve (AUC). RESULTS: The number of HER2-positive patients in the training, testing, and external validation cohort were 245(26.3%), 105 (26.3.8%), and 51(28.3%), respectively, with no statistical differences among the three cohorts (p = 0.842, chi-square test). The radiomics model, based solely on the radiomics features, achieved an AUC of 0.814 (95% CI, 0.784-0.844) in the training cohort, 0.776 (95% CI, 0.727-0.825) in the testing cohort, and 0.702 (95% CI, 0.614-0.790) in the external validation cohort. The multiomics model, incorporated radiomics features with clinical characteristics, consistently outperformed the radiomics model with AUC values of 0.838 (95% CI, 0.810-0.866) in the training cohort, 0.788 (95% CI, 0.741-0.835) in the testing cohort, and 0.722 (95% CI, 0.637-0.811) in the external validation cohort. CONCLUSIONS: Our study demonstrates that a model based on radiomics features and clinical characteristics has the potential to accurately predict HER2 status of breast cancer patients across multiple devices and centers. CLINICAL RELEVANCE STATEMENT: By predicting the HER2 status of breast cancer reliably, the presented model built upon radiomics features and clinical characteristics on a multi-vendor and multi-center basis can help in bolstering the model's applicability and generalizability in real-world clinical scenarios. KEY POINTS: • The mammographic presentation of breast cancer is closely associated with the status of human epidermal growth factor receptor 2 (HER2). • The radiomics model, based solely on radiomics features, exhibits sub-optimal performance in the external validation cohort. • By combining radiomics features and clinical characteristics, the multiomics model can improve the prediction ability in external data.

Evaluating the mutagenicity of N-nitrosodimethylamine in 2D and 3D HepaRG cell cultures using error-corrected next generation sequencing.

Human liver-derived metabolically competent HepaRG cells have been successfully employed in both two-dimensional (2D) and 3D spheroid formats for performing the comet assay and micronucleus (MN) assay. In the present study, we have investigated expanding the genotoxicity endpoints evaluated in HepaRG cells by detecting mutagenesis using two error-corrected next generation sequencing (ecNGS) technologies, Duplex Sequencing (DS) and High-Fidelity (HiFi) Sequencing. Both HepaRG 2D cells and 3D spheroids were exposed for 72 h to N-nitrosodimethylamine (NDMA), followed by an additional incubation for the fixation of induced mutations. NDMA-induced DNA damage, chromosomal damage, and mutagenesis were determined using the comet assay, MN assay, and ecNGS, respectively. The 72-h treatment with NDMA resulted in concentration-dependent increases in cytotoxicity, DNA damage, MN formation, and mutation frequency in both 2D and 3D cultures, with greater responses observed in the 3D spheroids compared to 2D cells. The mutational spectrum analysis showed that NDMA induced predominantly A:T → G:C transitions, along with a lower frequency of G:C → A:T transitions, and exhibited a different trinucleotide signature relative to the negative control. These results demonstrate that the HepaRG 2D cells and 3D spheroid models can be used for mutagenesis assessment using both DS and HiFi Sequencing, with the caveat that severe cytotoxic concentrations should be avoided when conducting DS. With further validation, the HepaRG 2D/3D system may become a powerful human-based metabolically competent platform for genotoxicity testing.

Distinct MRI characteristics of spinal cord diffuse midline glioma, H3 K27-altered in comparison to spinal cord glioma without H3 K27-alteration and demyelination disorder.

BACKGROUND: An applicable magnetic resonance imaging (MRI) biomarker for diffuse midline glioma (DMG), H3 K27-altered of the spinal cord is important for non-invasive diagnosis. PURPOSE: To evaluate the efficacy of conventional MRI (cMRI) in distinguishing between DMGs, H3 K27-altered, gliomas without H3 K27-alteration, and demyelinating lesions in the spinal cord. MATERIAL AND METHODS: Between January 2017 and February 2023, patients with pathology-confirmed spinal cord gliomas (including ependymomas) with definite H3 K27 status and demyelinating diseases diagnosed by recognized criteria were recruited as the training set for this retrospective study. Morphologic parameter assessment was performed by two neuroradiologists on T1-weighted, T2-weighted, and contrast-enhanced T1-weighted imaging. Variables with high inter- and intra-observer agreement were included in univariable correlation analysis and multivariable logistic regression. The performance of the final model was verified by internal and external testing sets. RESULTS: The training cohort included 21 patients with DMGs (13 men; mean age = 34.57 ± 13.489 years), 21 with wild-type gliomas (10 men; mean age = 46.76 ± 17.017 years), and 20 with demyelinating diseases (5 men; mean age = 49.50 ± 18.872 years). A significant difference was observed in MRI features, including cyst(s), hemorrhage, pial thickening with enhancement, and the maximum anteroposterior diameter of the spinal cord. The prediction model, integrating age, age2, and morphological characteristics, demonstrated good performance in the internal and external testing cohort (accuracy: 0.810 and 0.800, specificity: 0.810 and 0.720, sensitivity: 0.872 and 0.849, respectively). CONCLUSION: Based on cMRI, we developed a model with good performance for differentiating among DMGs, H3 K27-altered, wild-type glioma, and demyelinating lesions in the spinal cord.

Glioblastomas with and without peritumoral fluid-attenuated inversion recovery (FLAIR) hyperintensity present morphological and microstructural differences on conventional MR images.

OBJECTIVES: Glioblastoma (GB) without peritumoral fluid-attenuated inversion recovery (FLAIR) hyperintensity is atypical and its characteristics are barely known. The aim of this study was to explore the differences in pathological and MRI-based intrinsic features (including morphologic and first-order features) between GBs with peritumoral FLAIR hyperintensity (PFH-bearing GBs) and GBs without peritumoral FLAIR hyperintensity (PFH-free GBs). METHODS: In total, 155 patients with pathologically diagnosed GBs were retrospectively collected, which included 110 PFH-bearing GBs and 45 PFH-free GBs. The pathological and imaging data were collected. The Visually AcceSAble Rembrandt Images (VASARI) features were carefully evaluated. The first-order radiomics features from the tumor region were extracted from FLAIR, apparent diffusion coefficient (ADC), and T1CE (T1-contrast enhanced) images. All parameters were compared between the two groups of GBs. RESULTS: The pathological data showed more alpha thalassemia/mental retardation syndrome X-linked (ATRX)-loss in PFH-free GBs compared to PFH-bearing ones (p < 0.001). Based on VASARI evaluation, PFH-free GBs had larger intra-tumoral enhancing proportion and smaller necrotic proportion (both, p < 0.001), more common non-enhancing tumor (p < 0.001), mild/minimal enhancement (p = 0.003), expansive T1/FLAIR ratio (p < 0.001) and solid enhancement (p = 0.009), and less pial invasion (p = 0.010). Moreover, multiple ADC- and T1CE-based first-order radiomics features demonstrated differences, especially the lower intensity heterogeneity in PFH-free GBs (for all, adjusted p < 0.05). CONCLUSIONS: Compared to PFH-bearing GBs, PFH-free ones demonstrated less immature neovascularization and lower intra-tumoral heterogeneity, which would be helpful in clinical treatment stratification. CLINICAL RELEVANCE STATEMENT: Glioblastomas without peritumoral FLAIR hyperintensity show less immature neovascularization and lower heterogeneity leading to potential higher treatment benefits due to less drug resistance and treatment failure. KEY POINTS: • The study explored the differences between glioblastomas with and without peritumoral FLAIR hyperintensity. • Glioblastomas without peritumoral FLAIR hyperintensity showed less necrosis and contrast enhancement and lower intensity heterogeneity. • Glioblastomas without peritumoral FLAIR hyperintensity had less immature neovascularization and lower tumor heterogeneity.

Predicting peritumoral edema development after gamma knife radiosurgery of meningiomas using machine learning methods: a multicenter study.

OBJECTIVES: Edema is a complication of gamma knife radiosurgery (GKS) in meningioma patients that leads to a variety of consequences. The aim of this study is to construct radiomics-based machine learning models to predict post-GKS edema development. METHODS: In total, 445 meningioma patients who underwent GKS in our institution were enrolled and partitioned into training and internal validation datasets (8:2). A total of 150 cases from multicenter data were included as the external validation dataset. In each case, 1132 radiomics features were extracted from each pre-treatment MRI sequence (contrast-enhanced T1WI, T2WI, and ADC maps). Nine clinical features and eight semantic features were also generated. Nineteen random survival forest (RSF) and nineteen neural network (DeepSurv) models with different combinations of radiomics, clinical, and semantic features were developed with the training dataset, and evaluated with internal and external validation. A nomogram was derived from the model achieving the highest C-index in external validation. RESULTS: All the models were successfully validated on both validation datasets. The RSF model incorporating clinical, semantic, and ADC radiomics features achieved the best performance with a C-index of 0.861 (95% CI: 0.748-0.975) in internal validation, and 0.780 (95% CI: 0.673-0.887) in external validation. It stratifies high-risk and low-risk cases effectively. The nomogram based on the predicted risks provided personalized prediction with a C-index of 0.962 (95%CI: 0.951-0.973) and satisfactory calibration. CONCLUSION: This RSF model with a nomogram could represent a non-invasive and cost-effective tool to predict post-GKS edema risk, thus facilitating personalized decision-making in meningioma treatment. CLINICAL RELEVANCE STATEMENT: The RSF model with a nomogram built in this study represents a handy, non-invasive, and cost-effective tool for meningioma patients to assist in better counselling on the risks, appropriate individual treatment decisions, and customized follow-up plans. KEY POINTS: • Machine learning models were built to predict post-GKS edema in meningioma. The random survival forest model with clinical, semantic, and ADC radiomics features achieved excellent performance. • The nomogram based on the predicted risks provides personalized prediction with a C-index of 0.962 (95%CI: 0.951-0.973) and satisfactory calibration and shows the potential to assist in better counselling, appropriate treatment decisions, and customized follow-up plans. • Given the excellent performance and convenient acquisition of the conventional sequence, we envision that this non-invasive and cost-effective tool will facilitate personalized medicine in meningioma treatment.

Oligodendrocyte Transcription Factor 2 as a Potential Prognostic Biomarker of Glioblastoma: Kaplan-Meier Analysis and the Development of a Binary Predictive Model Based on Visually Accessible Rembrandt Image and Magnetic Resonance Imaging Radiomic Features.

OBJECTIVE: Oligodendrocyte transcription factor 2 (OLIG2) is universally expressed in human glioblastoma (GB). Our study explores whether OLIG2 expression impacts GB patients' overall survival and establishes a machine learning model for OLIG2 level prediction in patients with GB based on clinical, semantic, and magnetic resonance imaging radiomic features. METHODS: Kaplan-Meier analysis was used to determine the optimal cutoff value of the OLIG2 in 168 GB patients. Three hundred thirteen patients enrolled in the OLIG2 prediction model were randomly divided into training and testing sets in a ratio of 7:3. The radiomic, semantic, and clinical features were collected for each patient. Recursive feature elimination (RFE) was used for feature selection. The random forest (RF) model was built and fine-tuned, and the area under the curve was calculated to evaluate the performance. Finally, a new testing set excluding IDH-mutant patients was built and tested in a predictive model using the fifth edition of the central nervous system tumor classification criteria. RESULTS: One hundred nineteen patients were included in the survival analysis. Oligodendrocyte transcription factor 2 was positively associated with GB survival, with an optimal cutoff of 10% ( P = 0.00093). One hundred thirty-four patients were eligible for the OLIG2 prediction model. An RFE-RF model based on 2 semantic and 21 radiomic signatures achieved areas under the curve of 0.854 in the training set, 0.819 in the testing set, and 0.825 in the new testing set. CONCLUSIONS: Glioblastoma patients with ≤10% OLIG2 expression tended to have worse overall survival. An RFE-RF model integrating 23 features can predict the OLIG2 level of GB patients preoperatively, irrespective of the central nervous system classification criteria, further guiding individualized treatment.

Multicenter Study of the Utility of Convolutional Neural Network and Transformer Models for the Detection and Segmentation of Meningiomas.

PURPOSE: This study aimed to investigate the effectiveness and practicality of using models like convolutional neural network and transformer in detecting and precise segmenting meningioma from magnetic resonance images. METHODS: The retrospective study on T1-weighted and contrast-enhanced images of 523 meningioma patients from 3 centers between 2010 and 2020. A total of 373 cases split 8:2 for training and validation. Three independent test sets were built based on the remaining 150 cases. Six convolutional neural network detection models trained via transfer learning were evaluated using 4 metrics and receiver operating characteristic analysis. Detected images were used for segmentation. Three segmentation models were trained for meningioma segmentation and were evaluated via 4 metrics. In 3 test sets, intraclass consistency values were used to evaluate the consistency of detection and segmentation models with manually annotated results from 3 different levels of radiologists. RESULTS: The average accuracies of the detection model in the 3 test sets were 97.3%, 93.5%, and 96.0%, respectively. The model of segmentation showed mean Dice similarity coefficient values of 0.884, 0.834, and 0.892, respectively. Intraclass consistency values showed that the results of detection and segmentation models were highly consistent with those of intermediate and senior radiologists and lowly consistent with those of junior radiologists. CONCLUSIONS: The proposed deep learning system exhibits advanced performance comparable with intermediate and senior radiologists in meningioma detection and segmentation. This system could potentially significantly improve the efficiency of the detection and segmentation of meningiomas.

High-throughput micronucleus assay using three-dimensional HepaRG spheroids for in vitro genotoxicity testing.

The in vitro micronucleus (MN) assay is a component of most test batteries used in assessing potential genotoxicity. Our previous study adapted metabolically competent HepaRG cells to the high-throughput (HT) flow-cytometry-based MN assay for genotoxicity assessment (Guo et al. in J Toxicol Environ Health A 83:702-717, 2020b, https://doi.org/10.1080/15287394.2020.1822972 ). We also demonstrated that, compared to HepaRG cells grown as two-dimensional (2D) cultures, 3D HepaRG spheroids have increased metabolic capacity and improved sensitivity in detecting DNA damage induced by genotoxicants using the comet assay (Seo et al. in ALTEX 39:583-604, 2022, https://doi.org/10.14573/altex.22011212022 ). In the present study, we have compared the performance of the HT flow-cytometry-based MN assay in HepaRG spheroids and 2D HepaRG cells by testing 34 compounds, including 19 genotoxicants or carcinogens and 15 compounds that show different genotoxic responses in vitro and in vivo. 2D HepaRG cells and spheroids were exposed to the test compounds for 24 h, followed by an additional 3- or 6-day incubation with human epidermal growth factor to stimulate cell division. The results demonstrated that HepaRG spheroids showed generally higher sensitivity in detecting several indirect-acting genotoxicants (require metabolic activation) compared to 2D cultures, with 7,12-dimethylbenzanthracene and N-nitrosodimethylamine inducing higher % MN formation along with having significantly lower benchmark dose values for MN induction in 3D spheroids. These data suggest that 3D HepaRG spheroids can be adapted to the HT flow-cytometry-based MN assay for genotoxicity testing. Our findings also indicate that integration of the MN and comet assays improved the sensitivity for detecting genotoxicants that require metabolic activation. These results suggest that HepaRG spheroids may contribute to New Approach Methodologies for genotoxicity assessment.

Genotoxicity assessment of eight nitrosamines using 2D and 3D HepaRG cell models.

N-nitrosamine impurities have been increasingly detected in human drugs. This is a safety concern as many nitrosamines are mutagenic in bacteria and carcinogenic in rodent models. Typically, the mutagenic and carcinogenic activity of nitrosamines requires metabolic activation by cytochromes P450 enzymes (CYPs), which in many in vitro models are supplied exogenously using rodent liver homogenates. There are only limited data on the genotoxicity of nitrosamines in human cell systems. In this study, we used metabolically competent human HepaRG cells, whose metabolic capability is comparable to that of primary human hepatocytes, to evaluate the genotoxicity of eight nitrosamines [N-cyclopentyl-4-nitrosopiperazine (CPNP), N-nitrosodibutylamine (NDBA), N-nitrosodiethylamine (NDEA), N-nitrosodimethylamine (NDMA), N-nitrosodiisopropylamine (NDIPA), N-nitrosoethylisopropylamine (NEIPA), N-nitroso-N-methyl-4-aminobutyric acid (NMBA), and N-nitrosomethylphenylamine (NMPA)]. Under the conditions we used to culture HepaRG cells, three-dimensional (3D) spheroids possessed higher levels of CYP activity compared to 2D monolayer cells; thus the genotoxicity of the eight nitrosamines was investigated using 3D HepaRG spheroids in addition to more conventional 2D cultures. Genotoxicity was assessed as DNA damage using the high-throughput CometChip assay and as aneugenicity/clastogenicity in the flow-cytometry-based micronucleus (MN) assay. Following a 24-h treatment, all the nitrosamines induced DNA damage in 3D spheroids, while only three nitrosamines, NDBA, NDEA, and NDMA, produced positive responses in 2D HepaRG cells. In addition, these three nitrosamines also caused significant increases in MN frequency in both 2D and 3D HepaRG models, while NMBA and NMPA were positive only in the 3D HepaRG MN assay. Overall, our results indicate that HepaRG spheroids may provide a sensitive, human-based cell system for evaluating the genotoxicity of nitrosamines.

Revisiting the mutagenicity and genotoxicity of N-nitroso propranolol in bacterial and human in vitro assays.

Propranolol is a widely used ß-blocker that can generate a nitrosated derivative, N-nitroso propranolol (NNP). NNP has been reported to be negative in the bacterial reverse mutation test (the Ames test) but genotoxic in other in vitro assays. In the current study, we systematically examined the in vitro mutagenicity and genotoxicity of NNP using several modifications of the Ames test known to affect the mutagenicity of nitrosamines, as well as a battery of genotoxicity tests using human cells. We found that NNP induced concentration-dependent mutations in the Ames test, both in two tester strains that detect base pair substitutions, TA1535 and TA100, as well as in the TA98 frameshift-detector strain. Although positive results were seen with rat liver S9, the hamster liver S9 fraction was more effective in bio-transforming NNP into a reactive mutagen. NNP also induced micronuclei and gene mutations in human lymphoblastoid TK6 cells in the presence of hamster liver S9. Using a panel of TK6 cell lines that each expresses a different human cytochrome P450 (CYP), CYP2C19 was identified as the most active enzyme in the bioactivation of NNP to a genotoxicant among those tested. NNP also induced concentration-dependent DNA strand breakage in metabolically competent 2-dimensional (2D) and 3D cultures of human HepaRG cells. This study indicates that NNP is genotoxic in a variety of bacterial and mammalian systems. Thus, NNP is a mutagenic and genotoxic nitrosamine and a potential human carcinogen.

[Chemical reaction mechanism of decoction of traditional Chinese medicines: a review].

Complicated chemical reactions occur in the decoction of traditional Chinese medicines(TCMs) which features complex components, influencing the safety, efficacy, and quality controllability of TCMs. Therefore, it is particularly important to clarify the chemical reaction mechanism of TCMs in the decoction. This study summarized eight typical chemical reactions in the decoction of TCMs, such as substitution reaction, redox reaction, isomerization/stereoselective reaction, complexation, and supramolecular reaction. With the "toxicity attenuation and efficiency enhancement" of aconitines and other examples, this study reviewed the reactions in decoction of TCMs, which was expected to clarify the variation mechanisms of key chemical components in this process and to help guide medicine preparation and safe and rational use of medicine in clinical settings. The current main research methods for chemical reaction mechanisms of decoction of TCMs were also summed up and compared. The novel real-time analysis device of decoction system for TCMs was found to be efficient and simple without the pre-treatment of samples. This device provides a promising solution, which has great potential in quantity evaluation and control of TCMs. Moreover, it is expected to become a foundational and exemplary research tool, which can advance the research in this field.

Methanobacterium alkalithermotolerans sp. nov., a novel alkaliphilic and hydrogen-utilizing methanogen isolated from an alkaline geothermal spring (La Crouen, New Caledonia).

An anaerobic, hydrogenotrophic methane-producing archaeon was isolated from an alkaline thermal spring (42 °C, pH 9.0) in New Caledonia. This methanogen, designated strain CANT, is alkaliphilic, thermotolerant, with Gram-positive staining non-motile cells. Strain CANT grows autotrophically using hydrogen exclusively as an energy source and carbon dioxide as the sole carbon source (without the requirement of yeast extract or other organic compounds). It grows at 20-45 °C (optimum, 45 °C) and pH 7.3-9.7 (optimum, pH 9.0). NaCl is not required for growth (optimum 0 %) but is tolerated up to 1.5 %. It resists novobiocin, streptomycin and vancomycin but is inhibited by ampicillin and penicillin, among other antibiotics. The genome consists of a circular chromosome (2.2 Mb) containing 2126 predicted protein-encoding genes with a G+C content of 36.4 mol%. Phylogenetic analysis based on the 16S rRNA gene sequence indicated that strain CANT is a member of the genus Methanobacterium, most closely related to the alkaliphilic Methanobacterium alcaliphilum WeN4T with 98.5 % 16S rRNA gene sequence identity. The genomes of strain CANT and M. alcaliphilum DSM 3459, sequenced in this study, share 71.6 % average nucleotide identity and 14.0 % digital DNA-DNA hybridization. Therefore, phylogenetic and physiological results indicate that strain CANT represents a novel species, for which the name Methanobacterium alkalithermotolerans sp. nov. is proposed, and strain CANT (=DSM 102889T= JCM 31304T) is assigned as the type strain.

A Comparative and Summative Study of Radiomics-based Overall Survival Prediction in Glioblastoma Patients.

PURPOSE: This study aimed to assess different machine learning models based on radiomic features, Visually Accessible Rembrandt Images features and clinical characteristics in overall survival prediction of glioblastoma and to identify the reproducible features. MATERIALS AND METHODS: Patients with preoperative magnetic resonance scans were allocated into 3 data sets. The Least Absolute Shrinkage and Selection Operator was used for feature selection. The prediction models were built by random survival forest (RSF) and Cox regression. C-index and integrated Brier scores were calculated to compare model performances. RESULTS: Patients with cortical involvement had shorter survival times in the training set (P = 0.006). Random survival forest showed higher C-index than Cox, and the RSF model based on the radiomic features was the best one (testing set: C-index = 0.935 ± 0.023). Ten reproducible radiomic features were summarized. CONCLUSIONS: The RSF model based on radiomic features had promising potential in predicting overall survival of glioblastoma. Ten reproducible features were identified.

Genotoxicity evaluation of nitrosamine impurities using human TK6 cells transduced with cytochrome P450s.

Many nitrosamines are recognized as mutagens and potent rodent carcinogens. Over the past few years, nitrosamine impurities have been detected in various drugs leading to drug recalls. Although nitrosamines are included in a 'cohort of concern' because of their potential human health risks, most of this concern is based on rodent cancer and bacterial mutagenicity data, and there are little data on their genotoxicity in human-based systems. In this study, we employed human lymphoblastoid TK6 cells transduced with human cytochrome P450 (CYP) 2A6 to evaluate the genotoxicity of six nitrosamines that have been identified as impurities in drug products: N-nitrosodiethylamine (NDEA), N-nitrosoethylisopropylamine (NEIPA), N-nitroso-N-methyl-4-aminobutanoic acid (NMBA), N-nitrosomethylphenylamine (NMPA), N-nitrosodiisopropylamine (NDIPA), and N-nitrosodibutylamine (NDBA). Using flow cytometry-based assays, we found that 24-h treatment with NDEA, NEIPA, NMBA, and NMPA caused concentration-dependent increases in the phosphorylation of histone H2A.X (γH2A.X) in CYP2A6-expressing TK6 cells. Metabolism of these four nitrosamines by CYP2A6 also caused significant increases in micronucleus frequency as well as G2/M phase cell-cycle arrest. In addition, nuclear P53 activation was found in CYP2A6-expressing TK6 cells exposed to NDEA, NEIPA, and NMPA. Overall, the genotoxic potency of the six nitrosamine impurities in our test system was NMPA > NDEA ≈ NEIPA > NMBA > NDBA ≈ NDIPA. This study provides new information on the genotoxic potential of nitrosamines in human cells, complementing test results generated from traditional assays and partially addressing the issue of the relevance of nitrosamine genotoxicity for humans. The metabolically competent human cell system reported here may be a useful model for risk assessment of nitrosamine impurities found in drugs.

Performances of clinical characteristics and radiological findings in identifying COVID-19 from suspected cases.

BACKGROUND: To identify effective factors and establish a model to distinguish COVID-19 patients from suspected cases. METHODS: The clinical characteristics, laboratory results and initial chest CT findings of suspected COVID-19 patients in 3 institutions were retrospectively reviewed. Univariate and multivariate logistic regression were performed to identify significant features. A nomogram was constructed, with calibration validated internally and externally. RESULTS: 239 patients from 2 institutions were enrolled in the primary cohort including 157 COVID-19 and 82 non-COVID-19 patients. 11 features were selected by LASSO selection, and 8 features were found significant using multivariate logistic regression analysis. We found that the COVID-19 group are more likely to have fever (OR 4.22), contact history (OR 284.73), lower WBC count (OR 0.63), left lower lobe involvement (OR 9.42), multifocal lesions (OR 8.98), pleural thickening (OR 5.59), peripheral distribution (OR 0.09), and less mediastinal lymphadenopathy (OR 0.037). The nomogram developed accordingly for clinical practice showed satisfactory internal and external validation. CONCLUSIONS: In conclusion, fever, contact history, decreased WBC count, left lower lobe involvement, pleural thickening, multifocal lesions, peripheral distribution, and absence of mediastinal lymphadenopathy are able to distinguish COVID-19 patients from other suspected patients. The corresponding nomogram is a useful tool in clinical practice.

Actein contributes to black cohosh extract-induced genotoxicity in human TK6 cells.

Black cohosh extract (BCE) is one of the most popular botanical products for relieving menopausal symptoms. However, recent studies indicate that BCE is not only ineffective for menopausal therapy but also induces genotoxicity through an aneugenic mode of action (MoA). In this study, the cytotoxicity of five constituents of BCE was evaluated in human lymphoblastoid TK6 cells. Among the five constituents, actein (up to 50 µM) showed the highest cytotoxicity and was thus selected for further genotoxicity evaluations. Actein caused DNA damage proportionally to concentration as evidenced by the phosphorylation of the histone protein H2A.X (γH2A.X) and resulted in chromosomal damage as measured by the increased percentage of micronuclei (%MN) in cells. In addition, actein activated DNA damage response (DDR) pathway through induction of p-ATM, p-Chk1, and p-Chk2, which subsequently induced cell cycle changes and apoptosis. Moreover, both BCE and actein increased intracellular reactive oxygen species (ROS) production, decreased glutathione levels, and activated the mitogen-activated protein kinases (MAPK) signaling pathway. N-acetylcysteine, a ROS scavenger, attenuated BCE- and actein-induced ROS production, apoptosis, and DNA damage. These findings indicate that BCE- and actein-induced genotoxicity is mediated, at least partially, through oxidative stress. Taken together, our data show that actein is likely one of the major contributors to BCE-induced genotoxicity.

Comparative potency analysis of whole smoke solutions in the bacterial reverse mutation test.

Short-term in vitro genotoxicity assays are useful tools to assess whether new and emerging tobacco products potentially have reduced toxicity. We previously demonstrated that potency ranking by benchmark dose (BMD) analysis quantitatively identifies differences among several known carcinogens and toxic chemicals representing different chemical classes found in cigarette smoke. In this study, six whole smoke solution (WSS) samples containing both the particulate and gas phases of tobacco smoke were generated from two commercial cigarette brands under different smoking-machine regimens. Sixty test cigarettes of each brand were machine-smoked according to the International Organization for Standardization (ISO) puffing protocol. In addition, either 60 or 20 test cigarettes of each brand were machine-smoked with the Canadian Intense (CI) puffing protocol. All six WSSs were evaluated in the bacterial reverse mutation (Ames) test using Salmonella typhimurium strains, in the presence or absence of S9 metabolic activation. The resulting S9-mediated mutagenic concentration-responses for the four WSSs from 60 cigarettes were then compared using BMD modelling analysis and the mutagenic potency expressed as number of revertants per µl of the WSS. The quantitative approaches resulted in a similar rank order of mutagenic potency for the Ames test in both TA98 and TA100. Under the conditions of this study, these results indicate that quantitative analysis of the Ames test data can discriminate between the mutagenic potencies of WSSs on the basis of smoking-machine regimen (ISO vs. CI), and cigarette product (differences in smoke chemistry).

The usage of deep neural network improves distinguishing COVID-19 from other suspected viral pneumonia by clinicians on chest CT: a real-world study.

OBJECTIVES: Based on the current clinical routine, we aimed to develop a novel deep learning model to distinguish coronavirus disease 2019 (COVID-19) pneumonia from other types of pneumonia and validate it with a real-world dataset (RWD). METHODS: A total of 563 chest CT scans of 380 patients (227/380 were diagnosed with COVID-19 pneumonia) from 5 hospitals were collected to train our deep learning (DL) model. Lung regions were extracted by U-net, then transformed and fed to pre-trained ResNet-50-based IDANNet (Identification and Analysis of New covid-19 Net) to produce a diagnostic probability. Fivefold cross-validation was employed to validate the application of our model. Another 318 scans of 316 patients (243/316 were diagnosed with COVID-19 pneumonia) from 2 other hospitals were enrolled prospectively as the RWDs to testify our DL model's performance and compared it with that from 3 experienced radiologists. RESULTS: A three-dimensional DL model was successfully established. The diagnostic threshold to differentiate COVID-19 and non-COVID-19 pneumonia was 0.685 with an AUC of 0.906 (95% CI: 0.886-0.913) in the internal validation group. In the RWD cohort, our model achieved an AUC of 0.868 (95% CI: 0.851-0.876) with the sensitivity of 0.811 and the specificity of 0.822, non-inferior to the performance of 3 experienced radiologists, suggesting promising clinical practical usage. CONCLUSIONS: The established DL model was able to achieve accurate identification of COVID-19 pneumonia from other suspected ones in the real-world situation, which could become a reliable tool in clinical routine. KEY POINTS: • In an internal validation set, our DL model achieved the best performance to differentiate COVID-19 from non-COVID-19 pneumonia with a sensitivity of 0.836, a specificity of 0.800, and an AUC of 0.906 (95% CI: 0.886-0.913) when the threshold was set at 0.685. • In the prospective RWD cohort, our DL diagnostic model achieved a sensitivity of 0.811, a specificity of 0.822, and AUC of 0.868 (95% CI: 0.851-0.876), non-inferior to the performance of 3 experienced radiologists. • The attention heatmaps were fully generated by the model without additional manual annotation and the attention regions were highly aligned with the ROIs acquired by human radiologists for diagnosis.

The different toxic effects of metalaxyl and metalaxyl-M on Tubifex tubifex.

Metalaxyl and Metalaxyl-M are the fungicides that widely used in many countries. In this study, the environmental behaviors between metalaxyl and metalaxyl-M in Tubifex tubifex (T. tubifex) were quantitative analyzed by using a high performance liquid chromatography with photo-diode-array-detector (HPLC-DAD). Results demonstrated that there was no significant difference (p > 0.05) in the concentration of metalaxyl and metalaxyl-M in T. tubifex during the exposure process. However, the dissipation behaviors of metalaxyl and metalaxyl-M in T. tubifex were different (p < 0.05) during the non-exposure culture process. Meanwhile, the toxic effects were also evaluated by comparing the different influences of these two compounds on related physiological indicators, and functional enzyme activities. The survival rates of T. tubifex were 63.33 ± 15.28% (20 mg L-1), 63.33 ± 5.77% (200 mg L-1) treated with metalaxyl and were 50.00 ± 10.00% (20 mg L-1), 46.67 ± 11.55 (200 mg L-1) treated with metalaxyl-M at the non-exposure culture process. The autotomy rates were increased significantly compared with the initial in all treatments. Besides, the activities of CAT, SOD, and GST in T. tubifex were also inhibited by metalaxyl and metalaxyl-M treatments. Finally, the high-throughput transcriptome sequencing technology was applied to investigate the metabolic pathways of target analytes in T. tubifex, and results proved that the metabolic pathways associated with human diseases (such as viral myocarditis) were up-regulated expression for metalaxyl and metalaxyl-M treatments, and metalaxyl-M up-regulated more significantly. All the results demonstrated that metalaxyl-M had a higher toxicity than metalaxyl on T. tubifex.

Genetic toxicity assessment using liver cell models: past, present, and future.

Genotoxic compounds may be detoxified to non-genotoxic metabolites while many pro-carcinogens require metabolic activation to exert their genotoxicity in vivo. Standard genotoxicity assays were developed and utilized for risk assessment for over 40 years. Most of these assays are conducted in metabolically incompetent rodent or human cell lines. Deficient in normal metabolism and relying on exogenous metabolic activation systems, the current in vitro genotoxicity assays often have yielded high false positive rates, which trigger unnecessary and costly in vivo studies. Metabolically active cells such as hepatocytes have been recognized as a promising cell model in predicting genotoxicity of carcinogens in vivo. In recent years, significant advances in tissue culture and biological technologies provided new opportunities for using hepatocytes in genetic toxicology. This review encompasses published studies (both in vitro and in vivo) using hepatocytes for genotoxicity assessment. Findings from both standard and newly developed genotoxicity assays are summarized. Various liver cell models used for genotoxicity assessment are described, including the potential application of advanced liver cell models such as 3D spheroids, organoids, and engineered hepatocytes. An integrated strategy, that includes the use of human-based cells with enhanced biological relevance and throughput, and applying the quantitative analysis of data, may provide an approach for future genotoxicity risk assessment.