Using time-series chest radiographs and laboratory data by machine learning for identifying pulmonary infection and colonization of Acinetobacter baumannii.

BACKGROUND: Accurately distinguishing between pulmonary infection and colonization in patients with Acinetobacter baumannii is of utmost importance to optimize treatment and prevent antibiotic abuse or inadequate therapy. An efficient automated sorting tool could prompt individualized interventions and enhance overall patient outcomes. This study aims to develop a robust machine learning classification model using a combination of time-series chest radiographs and laboratory data to accurately classify pulmonary status caused by Acinetobacter baumannii. METHODS: We proposed nested logistic regression models based on different time-series data to automatically classify the pulmonary status of patients with Acinetobacter baumannii. Advanced features were extracted from the time-series data of hospitalized patients, encompassing dynamic pneumonia indicators observed on chest radiographs and laboratory indicator values recorded at three specific time points. RESULTS: Data of 152 patients with Acinetobacter baumannii cultured from sputum or alveolar lavage fluid were retrospectively analyzed. Our model with multiple time-series data demonstrated a higher performance of AUC (0.850, with a 95% confidence interval of [0.638-0.873]), an accuracy of 0.761, a sensitivity of 0.833. The model, which only incorporated a single time point feature, achieved an AUC of 0.741. The influential model variables included difference in the chest radiograph pneumonia score. CONCLUSION: Dynamic assessment of time-series chest radiographs and laboratory data using machine learning allowed for accurate classification of colonization and infection with Acinetobacter baumannii. This demonstrates the potential to help clinicians provide individualized treatment through early detection.

Targeting the ZNF-148/miR-335/SOD2 signaling cascade triggers oxidative stress-mediated pyroptosis and suppresses breast cancer progression.

BACKGROUND: The implication of zinc finger protein 148 (ZNF-148) in pathophysiology of most human cancers has been reported; however, the biological functions of ZNF-148 in breast cancer remain unclear. This study sought to elucidate the potential molecular mechanism of ZNF-148 on breast cancer pathology. METHODS: ZNF148 expression was tested in breast cancer tissues and cells. Then, cells were transfected with ZNF-148 overexpression or downregulation vector, and the cell proliferation, pyroptosis, apoptosis, and reactive oxygen species (ROS) production were analyzed by MTT, western blot, flow cytometry, and immunofluorescence staining, respectively. Tumor-bearing nude mouse was used to evaluate tumorigenesis of ZNF-148. Mechanisms underpinning ZNF-148 were examined using bioinformatics and luciferase assays. RESULTS: We found that ZNF-148 was upregulated in breast cancer tissues and cell lines. Knockdown of ZNF-148 suppressed malignant phenotypes, including cell proliferation, epithelial-mesenchymal transition, and tumorigenesis in vitro and in vivo, while ZNF-148 overexpression had the opposite effects. Further experiments showed that ZNF-148 deficiency promoted ROS production and triggered both apoptotic and pyroptotic cell death, which were restored by cotreating cells with ROS scavengers. A luciferase reporter assay revealed that miR-335 was the downstream target of ZNF-148 and that overexpressed ZNF-148 increased superoxide dismutase 2 (SOD2) expression by sponging miR-335. In parallel, both miR-335 downregulation and SOD2 overexpression abrogated the antitumor effects of ZNF-148 deficiency on proliferation and pyroptosis in breast cancer cells. CONCLUSIONS: Our findings indicated that ZNF-148 promotes breast cancer progression by triggering miR-335/SOD2/ROS-mediated pyroptotic cell death and aid the identification of potential therapeutic targets for breast cancer.

The effect of cyberbullying victimization and traditional bullying victimization on suicidal ideation among Chinese female college students: The role of rumination and insomnia.

BACKGROUND: The issue of college student suicide has emerged as a significant global public health concern. To date, there has been a lack of extensive research on the effects of distinct forms of bullying victimization (traditional bullying victimization and cyberbullying victimization) on suicidal ideation, as well as the differences between them. The present study aimed to investigate the relationship between two forms of bullying victimization and suicidal ideation among female college students, while also considering the potential mediating effects of rumination and insomnia. METHODS: A total of 2106 female college students from Southern China participated in this study. The participants had a mean age of 19.83 years (SD = 1.04 years). Participants completed the MINI-C questionnaire, School Bullying Behavior Questionnaire, Cyberbullying Inventory (CBI), Ruminative Responses Scale (RRS), and Insomnia Severity Index (ISI). The mediation models were conducted using Model 4 and Model 6 of the Process macro program in SPSS. RESULTS: The results showed that (1) the mediating effect of rumination in the relationship between different forms of bullying victimization and suicidal ideation was significant; (2) The mediating effect of insomnia in the relationship between traditional bullying victimization and suicidal ideation was not significant; the mediating effect between cyberbullying victimization and suicidal ideation was significant. (3) The chain mediating effect of rumination and insomnia in the relationship between different forms of bullying victimization and suicidal ideation were both significant. CONCLUSION: This study endeavor represents the first attempt to investigate the relationship between two forms of bullying victimization and suicidal ideation.

Atypical architectural distortion detection in digital breast tomosynthesis: a computer-aided detection model with adaptive receptive field.

Objective. In digital breast tomosynthesis (DBT), architectural distortion (AD) is a breast lesion that is difficult to detect. Compared with typical ADs, which have radial patterns, identifying a typical ADs is more difficult. Most existing computer-aided detection (CADe) models focus on the detection of typical ADs. This study focuses on atypical ADs and develops a deep learning-based CADe model with an adaptive receptive field in DBT.Approach. Our proposed model uses a Gabor filter and convergence measure to depict the distribution of fibroglandular tissues in DBT slices. Subsequently, two-dimensional (2D) detection is implemented using a deformable-convolution-based deep learning framework, in which an adaptive receptive field is introduced to extract global features in slices. Finally, 2D candidates are aggregated to form the three-dimensional AD detection results. The model is trained on 99 positive cases with ADs and evaluated on 120 AD-positive cases and 100 AD-negative cases.Main results. A convergence-measure-based model and deep-learning model without an adaptive receptive field are reproduced as controls. Their mean true positive fractions (MTPF) ranging from 0.05 to 4 false positives per volume are 0.3846 ± 0.0352 and 0.6501 ± 0.0380, respectively. Our proposed model achieves an MTPF of 0.7148 ± 0.0322, which is a significant improvement (p< 0.05) compared with the other two methods. In particular, our model detects more atypical ADs, primarily contributing to the performance improvement.Significance. The adaptive receptive field helps the model improve the atypical AD detection performance. It can help radiologists identify more ADs in breast cancer screening.

Multi-scale cascaded networks for synthesis of mammogram to decrease intensity distortion and increase model-based perceptual similarity.

PURPOSE: Synthetic digital mammogram (SDM) is a 2D image generated from digital breast tomosynthesis (DBT) and used as a substitute for a full-field digital mammogram (FFDM) to reduce the radiation dose for breast cancer screening. The previous deep learning-based method used FFDM images as the ground truth, and trained a single neural network to directly generate SDM images with similar appearances (e.g., intensity distribution, textures) to the FFDM images. However, the FFDM image has a different texture pattern from DBT. The difference in texture pattern might make the training of the neural network unstable and result in high-intensity distortion, which makes it hard to decrease intensity distortion and increase perceptual similarity (e.g., generate similar textures) at the same time. Clinically, radiologists want to have a 2D synthesized image that feels like an FFDM image in vision and preserves local structures such as both mass and microcalcifications (MCs) in DBT because radiologists have been trained on reading FFDM images for a long time, while local structures are important for diagnosis. In this study, we proposed to use a deep convolutional neural network to learn the transformation to generate SDM from DBT. METHOD: To decrease intensity distortion and increase perceptual similarity, a multi-scale cascaded network (MSCN) is proposed to generate low-frequency structures (e.g., intensity distribution) and high-frequency structures (e.g., textures) separately. The MSCN consist of two cascaded sub-networks: the first sub-network is used to predict the low-frequency part of the FFDM image; the second sub-network is used to generate a full SDM image with textures similar to the FFDM image based on the prediction of the first sub-network. The mean-squared error (MSE) objective function is used to train the first sub-network, termed low-frequency network, to generate a low-frequency SDM image. The gradient-guided generative adversarial network's objective function is to train the second sub-network, termed high-frequency network, to generate a full SDM image with textures similar to the FFDM image. RESULTS: 1646 cases with FFDM and DBT were retrospectively collected from the Hologic Selenia system for training and validation dataset, and 145 cases with masses or MC clusters were independently collected from the Hologic Selenia system for testing dataset. For comparison, the baseline network has the same architecture as the high-frequency network and directly generates a full SDM image. Compared to the baseline method, the proposed MSCN improves the peak-to-noise ratio from 25.3 to 27.9 dB and improves the structural similarity from 0.703 to 0.724, and significantly increases the perceptual similarity. CONCLUSIONS: The proposed method can stabilize the training and generate SDM images with lower intensity distortion and higher perceptual similarity.

CircRNAs in Tumor Radioresistance.

Circular RNAs (circRNAs) are endogenous, non-coding RNAs, which are derived from host genes that are present in several species and can be involved in the progression of various diseases. circRNAs' leading role is to act as RNA sponges. In recent years, the other roles of circRNAs have been discovered, such as regulating transcription and translation, regulating host genes, and even being translated into proteins. As some tumor cells are no longer radiosensitive, tumor radioresistance has since become a challenge in treating tumors. In recent years, circRNAs are differentially expressed in tumor cells and can be used as biological markers of tumors. In addition, circRNAs can regulate the radiosensitivity of tumors. Here, we list the mechanisms of circRNAs in glioma, nasopharyngeal carcinoma, and non-small cell lung cancer; further, these studies also provide new ideas for the purposes of eliminating radioresistance in tumors.

Architectural distortion detection based on superior-inferior directional context and anatomic prior knowledge in digital breast tomosynthesis.

BACKGROUND: In 2020, breast cancer becomes the most leading diagnosed cancer all over the world. The burden is increasing in the prevention and treatment of breast cancer. Accurately detecting breast lesions in screening images is important for early detection of cancer. Architectural distortion (AD) is one of the breast lesions that need to be detected. PURPOSE: To develop a deep-learning-based computer-aided detection (CADe) model for AD in digital breast tomosynthesis (DBT). This model uses the superior-inferior directional context of DBT and anatomic prior knowledge to reduce false positive (FP). It can identify some negative samples that cannot be distinguished by deep learning features. METHODS: The proposed CADe model consists of three steps. In the first step, a deep learning detection network detects two-dimensional (2D) candidates of ADs in DBT slices with the inputs preprocessed by Gabor filters and convergence measure. In the second step, three-dimensional (3D) candidates are obtained by stacking 2D candidates along superior-inferior direction. In the last step, FP reduction for 3D candidates is implemented based on superior-inferior directional context and anatomic prior knowledge of breast. DBT data from 99 cases with AD were used as the training set to train the CADe model, and data from 208 cases were used as an independent test set (including 108 cases with AD and 100 cases without AD as the control group). The free-response receiver operating characteristic and mean true positive fraction (MTPF) in the range of 0.05-2.0 FPs per volume are used to evaluate the model. RESULTS: Compared with the baseline model based on convergence measure, our proposed method demonstrates significant improvement (MTPF: 0.2826 ± 0.0321 vs. 0.6640 ± 0.0399). Results of an ablation study show that our proposed context- and anatomy-based FP reduction methods improve the detection performance. The number of FPs per DBT volume reduces from 2.47 to 1.66 at 80% sensitivity after employing these two schemes. CONCLUSIONS: The deep learning model demonstrates practical value for AD detection. The results indicate that introducing superior-inferior directional context and anatomic prior knowledge into model can indeed reduce FPs and improve the performance of CADe model.

Predicting the molecular subtype of breast cancer and identifying interpretable imaging features using machine learning algorithms.

OBJECTIVES: To evaluate the performance of interpretable machine learning models in predicting breast cancer molecular subtypes. METHODS: We retrospectively enrolled 600 patients with invasive breast carcinoma between 2012 and 2019. The patients were randomly divided into a training (n = 450) and a testing (n = 150) set. The five constructed models were trained based on clinical characteristics and imaging features (mammography and ultrasonography). The model classification performances were evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), accuracy, sensitivity, and specificity. Shapley additive explanation (SHAP) technique was used to interpret the optimal model output. Then we choose the optimal model as the assisted model to evaluate the performance of another four radiologists in predicting the molecular subtype of breast cancer with or without model assistance, according to mammography and ultrasound images. RESULTS: The decision tree (DT) model performed the best in distinguishing triple-negative breast cancer (TNBC) from other breast cancer subtypes, yielding an AUC of 0.971; accuracy, 0.947; sensitivity, 0.905; and specificity, 0.941. The accuracy, sensitivity, and specificity of all radiologists in distinguishing TNBC from other molecular subtypes and Luminal breast cancer from other molecular subtypes have significantly improved with the assistance of DT model. In the diagnosis of TNBC versus other subtypes, the average sensitivity, average specificity, and average accuracy of less experienced and more experienced radiologists increased by 0.090, 0.125, 0.114, and 0.060, 0.090, 0.083, respectively. In the diagnosis of Luminal versus other subtypes, the average sensitivity, average specificity, and average accuracy of less experienced and more experienced radiologists increased by 0.084, 0.152, 0.159, and 0.020, 0.100, 0.048. CONCLUSIONS: This study established an interpretable machine learning model to differentiate between breast cancer molecular subtypes, providing additional values for radiologists. KEY POINTS: • Interpretable machine learning model (MLM) could help clinicians and radiologists differentiate between breast cancer molecular subtypes. • The Shapley additive explanations (SHAP) technique can select important features for predicting the molecular subtypes of breast cancer from a large number of imaging signs. • Machine learning model can assist radiologists to evaluate the molecular subtype of breast cancer to some extent.

Improved detection of solitary pulmonary nodules on radiographs compared with deep bone suppression imaging.

BACKGROUND: The present study aimed to investigate whether deep bone suppression imaging (BSI) could increase the diagnostic performance for solitary pulmonary nodule detection compared with digital tomosynthesis (DTS), dual-energy subtraction (DES) radiography, and conventional chest radiography (CCR). METHODS: A total of 256 patients (123 with a solitary pulmonary nodule, 133 with normal findings) were included in the study. The confidence score of 6 observers determined the presence or absence of pulmonary nodules in each patient. These were first analyzed using a CCR image, then with CCR plus deep BSI, then with CCR plus DES radiography, and finally with DTS images. Receiver-operating characteristic curves were used to evaluate the performance of the 6 observers in the detection of pulmonary nodules. RESULTS: For the 6 observers, the average area under the curve improved significantly from 0.717 with CCR to 0.848 with CCR plus deep BSI (P<0.01), 0.834 with CCR plus DES radiography (P<0.01), and 0.939 with DTS (P<0.01). Comparisons between CCR and CCR plus deep BSI found that the sensitivities of the assessments by the 3 residents increased from 53.2% to 69.5% (P=0.014) for nodules located in the upper lung field, from 30.6% to 44.6% (P=0.015) for nodules that were partially/completely obscured by the bone, and from 33.2% to 45.8% (P=0.006) for nodules <10 mm. CONCLUSIONS: The deep BSI technique can significantly increase the sensitivity of radiology residents for solitary pulmonary nodules compared with CCR. Increased detection was seen mainly for smaller nodules, nodules with partial/complete obscuration, and nodules located in the upper lung field.

Synthesis of Mammogram From Digital Breast Tomosynthesis Using Deep Convolutional Neural Network With Gradient Guided cGANs.

Synthetic digital mammography (SDM), a 2D image generated from digital breast tomosynthesis (DBT), is used as a potential substitute for full-field digital mammography (FFDM) in clinic to reduce the radiation dose for breast cancer screening. Previous studies exploited projection geometry and fused projection data and DBT volume, with different post-processing techniques applied on re-projection data which may generate different image appearance compared to FFDM. To alleviate this issue, one possible solution to generate an SDM image is using a learning-based method to model the transformation from the DBT volume to the FFDM image using current DBT/FFDM combo images. In this study, we proposed to use a deep convolutional neural network (DCNN) to learn the transformation to generate SDM using current DBT/FFDM combo images. Gradient guided conditional generative adversarial networks (GGGAN) objective function was designed to preserve subtle MCs and the perceptual loss was exploited to improve the performance of the proposed DCNN on perceptual quality. We used various image quality criteria for evaluation, including preserving masses and MCs which are important in mammogram. Experiment results demonstrated progressive performance improvement of network using different objective functions in terms of those image quality criteria. The methodology we exploited in the SDM generation task to analyze and progressively improve image quality by designing objective functions may be helpful to other image generation tasks.

Can a Computer-Aided Mass Diagnosis Model Based on Perceptive Features Learned From Quantitative Mammography Radiology Reports Improve Junior Radiologists' Diagnosis Performance? An Observer Study.

Radiologists' diagnostic capabilities for breast mass lesions depend on their experience. Junior radiologists may underestimate or overestimate Breast Imaging Reporting and Data System (BI-RADS) categories of mass lesions owing to a lack of diagnostic experience. The computer-aided diagnosis (CAD) method assists in improving diagnostic performance by providing a breast mass classification reference to radiologists. This study aims to evaluate the impact of a CAD method based on perceptive features learned from quantitative BI-RADS descriptions on breast mass diagnosis performance. We conducted a retrospective multi-reader multi-case (MRMC) study to assess the perceptive feature-based CAD method. A total of 416 digital mammograms of patients with breast masses were obtained from 2014 through 2017, including 231 benign and 185 malignant masses, from which we randomly selected 214 cases (109 benign, 105 malignant) to train the CAD model for perceptive feature extraction and classification. The remaining 202 cases were enrolled as the test set for evaluation, of which 51 patients (29 benign and 22 malignant) participated in the MRMC study. In the MRMC study, we categorized six radiologists into three groups: junior, middle-senior, and senior. They diagnosed 51 patients with and without support from the CAD model. The BI-RADS category, benign or malignant diagnosis, malignancy probability, and diagnosis time during the two evaluation sessions were recorded. In the MRMC evaluation, the average area under the curve (AUC) of the six radiologists with CAD support was slightly higher than that without support (0.896 vs. 0.850, p = 0.0209). Both average sensitivity and specificity increased (p = 0.0253). Under CAD assistance, junior and middle-senior radiologists adjusted the assessment categories of more BI-RADS 4 cases. The diagnosis time with and without CAD support was comparable for five radiologists. The CAD model improved the radiologists' diagnostic performance for breast masses without prolonging the diagnosis time and assisted in a better BI-RADS assessment, especially for junior radiologists.

The Effect of Transjugular Intrahepatic Portosystemic Shunt Plus Partial Splenic Embolization for the Treatment of Patients with Recurrent Variceal Bleeding.

RATIONALE AND OBJECTIVES: Transjugular intrahepatic portosystemic shunt (TIPS) and partial splenic embolization (PSE) were two interventional therapies effective for the management of variceal bleeding with cirrhosis. This study aimed to investigate the effect of TIPS plus PSE for the treatment of patients with cirrhosis and recurrent variceal bleeding. MATERIAL AND METHODS: This is a single-center, nonrandomized and retrospective study that included 32 patients undergoing TIPS alone (the TIPS group) and 16 patients undergoing TIPS plus PSE (the TIPS+PSE group). RESULTS: The 5-year cumulative rates of variceal rebleeding (20.0% vs. 37.9%, p = 0.027) and shunt stenosis (35.1% vs. 55.9%, p = 0.036) in the TIPS+PSE group were significantly lower than in the TIPS group, whereas the 5-year cumulative rates of shunt blockage (12.5% vs. 25.8%, p = 0.388), and all-cause mortality (37.5% vs. 69.3%, p = 0.414) were not statistically different between the two groups. The 2-year cumulative rate of remaining free of hepatic encephalopathy was also similar between the two groups (75.0% vs. 81.3%, p = 0.704). Cox-regression analyses showed that group and reduction of portal venous pressure before and after TIPS creation were associated with both variceal rebleeding and shunt stenosis, whereas only reduction of portal venous pressure (hazard ratio 0.648, 95% confidence interval: 0.444-0.946, p = 0.025) was associated with shunt blockage. No severe adverse event was observed in the two groups. CONCLUSION: TIPS+PSE is superior to TIPS alone in control of variceal rebleeding and shunt stenosis. Further prospective studies are warranted to confirm our findings.

Prednisone is not beneficial for the treatment of severe drug-induced liver injury: An observational study (STROBE compliant).

There is limited information about the effects of corticosteroids on severe drug-induced liver injury (DILI). This study aimed to investigate the efficacy and safety of prednisone in severe DILI.Ninety patients with severe DILI were enrolled and studied retrospectively. They were divided into prednisone (n = 66) and control groups (n = 24), undergoing the same treatment regimen except that patients in the prednisone group received a median daily dose of 40 mg prednisone. The primary endpoint was severity reduction (serum total bilirubin [TBIL] <86 µmol/L).During the study, the cumulative rates of severity reduction at 4-, 8-, and 12 days were comparable between the 2 groups (prednisone versus control: 7.6%, 33.3%, and 60.6% versus 12.5%, 37.5%, and 66.7%, P = .331), and were markedly lower in the high-dose group than in the low-dose group (0%, 28.6%, and 35.7% versus 9.6%, 34.6%, and 67.3%, P = .012) or in the control group (0%, 28.6%, and 35.7% versus 12.5%, 37.5%, and 66.7%, P = .023). The 30-day overall survival rate in the prednisone group was significantly higher than in the control group (100% versus 91.7%, P = .018). Serum bilirubin and transaminase values gradually decreased in both groups, which were not significantly different mostly. Cox-regression models revealed that baseline TBIL (hazard ratio: 0.235; 95% confidence interval: 0.084-0.665; P = .006) was the only predictor for severity reduction. No severe adverse event was noted in both groups.Prednisone therapy is safe but not beneficial, and even detrimental at a daily dose > 40 mg for the treatment of severe DILI.

Cloning and expression analysis of cDNAs for ABA 8'-hydroxylase during sweet cherry fruit maturation and under stress conditions.

Abscisic acid (ABA) plays a key role in various aspects of plant growth and development, including adaptation to environmental stress and fruit maturation in sweet cherry fruit. In higher plants, the level of ABA is determined by synthesis and catabolism. In order to gain insight into ABA synthesis and catabolism in sweet cherry fruit during maturation and under stress conditions, four cDNAs of PacCYP707A1 -PacCYP707A4 for 8'-hydroxylase, a key enzyme in the oxidative catabolism of ABA, and one cDNA of PacNCED1 for 9-cis-epoxycarotenoid dioxygenase, a key enzyme in the ABA biosynthetic pathway, were isolated from sweet cherry fruit (Prunus avium L.). The timing and pattern of PacNCED1 expression was coincident with that of ABA accumulation, which was correlated to maturation of sweet cherry fruit. All four PacCYP707As were expressed at varying intensities throughout fruit development and appeared to play overlapping roles in ABA catabolism throughout sweet cherry fruit development. The application of ABA enhanced the expression of PacCYP707A1 -PacCYP707A3 as well as PacNCED1, but downregulated the PacCYP707A4 transcript level. Expressions of PacCYP707A1, PacCYP707A3 and PacNCED1 were strongly increased by water stress. No significant differences in PacCYP707A2 and PacCYP707A4 expression were observed between dehydrated and control fruits. The results suggest that endogenous ABA content is modulated by a dynamic balance between biosynthesis and catabolism, which are regulated by PacNCED1 and PacCYP707As transcripts, respectively, during fruit maturation and under stress conditions.