Identification of Methamphetamine Abusers Can Be Supported by EEG-Based Wavelet Transform and BiLSTM Networks.

Methamphetamine (MA) is a neurological drug, which is harmful to the overall brain cognitive function when abused. Based on this property of MA, people can be divided into those with MA abuse and healthy people. However, few studies to date have investigated automatic detection of MA abusers based on the neural activity. For this reason, the purpose of this research was to investigate the difference in the neural activity between MA abusers and healthy persons and accordingly discriminate MA abusers. First, we performed event-related potential (ERP) analysis to determine the time range of P300. Then, the wavelet coefficients of the P300 component were extracted as the main features, along with the time and frequency domain features within the selected P300 range to classify. To optimize the feature set, F_score was used to remove features below the average score. Finally, a Bidirectional Long Short-term Memory (BiLSTM) network was performed for classification. The experimental result showed that the detection accuracy of BiLSTM could reach 83.85%. In conclusion, the P300 component of EEG signals of MA abusers is different from that in normal persons. Based on this difference, this study proposes a novel way for the prevention and diagnosis of MA abuse.

Cuproptosis-related classification and personalized treatment in lower-grade gliomas to prompt precise oncology.

BACKGROUND: Cuproptosis is implicated in regulating tricarboxylic acid cycle and associated with tumor therapeutic sensitivity, patient outcomes and tumorigenesis. However, the classification and prognostic effect of cuproptosis-associated genes (CAGs), the relationship between cuproptosis and tumor microenvironment (TME) and the treatment of lower-grade glioma (LrGG) remain enigmatic. METHODS: The genetic and transcriptional alterations, prognostic value and classification related to cuproptosis were systematically analyzed. Subtypes of cuproptosis and cuproptosis score (Cuscore) were constructed and further confirmed by two external cohorts. The relationships between cuproptosis and TME, prognosis, and treatment response were also evaluated. RESULTS: Four clusters were identified based on cuproptosis-associated genes. The associations between cuproptosis-associated clusters and clinical features, prognosis, immune cell infiltration, and chemotherapy sensitivity were observed. The Cuscore is an independent prognostic indicator in LrGG patients. The nomogram is constructed according to Cuscore and clinical characteristics, and has good predictive ability and calibration. Patients with high Cuscore had a worse prognosis and advanced performance. A higher Cuscore also indicated a higher stromal score, abundant immune infiltration, and increased tumor mutation burden. A high Cuscore was remarkably related to immune checkpoint inhibitors, immunotherapy response and immune phenotype. CONCLUSIONS: This study demonstrates the clinical effect of CAGs, and suggests that cuproptosis could be a potential therapeutic target in LrGG.

A glycosylation signature for predicting the progression and immunotherapeutic response of prostate cancer.

BACKGROUND: Glycosylation has been proposed as a new cancer hallmark. However, focusing on specific glycans or glycoproteins may lose much data relevant to glycosylation alterations. The present study aimed to first comprehensively investigate the expression and mutation profiles of glycosylation-related genes (GRgenes) in prostate cancer (PCa) and then develop a glycosylation signature and explore its role in predicting the progression and immunotherapeutic response of PCa. METHODS: Based on The Cancer Genome Atlas database, we comprehensively screened potential prognostic GRgenes and analyzed their expression and mutation profiles in PCa. Through consensus clustering analysis, the study cohort was classified to investigate the effect of glycosylation patterns on the prognosis of PCa. Next, we developed a glycosylation signature (i.e., the glycosylation score [Gly_score]) using the differentially expressed genes between glycosylation pattern groups and evaluated its role in predicting the progression and immunotherapeutic response of PCa. RESULTS: We identified two distinct glycosylation patterns in PCa and found that GRgene expression patterns rather than mutations are associated with the prognosis of PCa. The high Gly_score group had significantly shorter progression-free survival, lower PD-L1 levels, less infiltration of immune cells and lower immunophenoscores than the low Gly_score group. When the patients were grouped according to both the Gly_score and PD-L1 level, patients with a combination of low Gly_score and low PD-L1 expression had the best survival outcomes. CONCLUSIONS: In the present study, for the first time, we developed a glycosylation signature and demonstrated that the proposed glycosylation signature is a promising tool for predicting the prognosis and immunotherapeutic response of PCa.

Refractory lower urinary tract symptoms in patients with lumbar disc hernia relieved by non-surgical treatment.

PURPOSE: Refractory lower urinary tract symptoms (LUTS) coexisting with lumbar disc hernia (LDH) have been shown to resolve following LDH surgery, implying that LDH causes these LUTS. The purpose of this study was to report outcomes in patients with refractory LUTS and LDH following non-surgical treatment targeting LDH. METHODS: A retrospective cohort study was conducted using outpatient data collected at Tongji Hospital, China, between 2016 and 2018. This study included 131 adult patients with refractory LUTS and LDH. Patients were stratified into two groups. Group A underwent non-surgical treatment for LDH plus pharmacological treatment for LUTS. Group B underwent only pharmacological treatment for LUTS. The International Prostate Symptom Score (IPSS), the IPSS quality of life (QoL) score, and uroflowmetry were used to evaluate outcomes. RESULTS: In group A, following treatment, the maximum flow rate (Qmax) increased by 3.92 ml/s (p < 0.001), the IPSS reduced by 5.99 points (p < 0.001), and the QoL score decreased by 1.51 points (p < 0.001). In group B, the Qmax increased by 0.09 ml/s (p = 0.833), the IPSS reduced by 0.72 points (p = 0.163), and the QoL score decreased by 0.07 points (p = 0.784). CONCLUSIONS: LUTS can be relieved by a combination of pharmacological treatment for LUTS and non-surgical treatment for LDH in some refractory LUTS patients with LDH. MRI is recommended for these patients.

Abbreviated Biparametric Versus Standard Multiparametric MRI for Diagnosis of Prostate Cancer: A Systematic Review and Meta-Analysis.

OBJECTIVE: The objective of our study was to evaluate the diagnostic accuracy of abbreviated biparametric MRI (bpMRI) versus standard multiparametric MRI (mpMRI) for prostate cancer (PCa) using guided biopsy or prostatectomy histopathology results as the reference standard. MATERIALS AND METHODS: A comprehensive literature search of PubMed, Web of Science, and Cochrane Library databases was performed by two researchers independently and the relevant references were assessed. Original research studies comparing bpMRI with mpMRI in diagnosing PCa were included. The methodologic quality of eligible studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. Data necessary to complete 2 × 2 contingency tables were obtained to calculate the diagnostic performance of bpMRI and mpMRI using Stata (version 14). RESULTS: Ten studies were included, and a total of 1705 patients and 3419 lesions were analyzed. Sensitivity, specificity, positive likelihood ratio (LR), negative LR, and diagnostic odds ratio (DOR) of mpMRI in diagnosing PCa were 0.79 (95% CI, 0.69-0.87), 0.89 (95% CI, 0.70-0.96), 6.9 (95% CI, 2.5-18.8), 0.24 (95% CI, 0.16-0.35), and 29 (95% CI, 10-83). Sensitivity, specificity, positive LR, negative LR, and DOR of bpMRI in diagnosing PCa were 0.79 (95% CI, 0.69-0.87), 0.88 (95% CI, 0.73-0.95), 6.4 (95% CI, 2.9-14.5), 0.24 (95% CI, 0.16-0.35), and 27 (95% CI, 11-67). Meta-analysis showed no statistically significant difference between bpMRI and mpMRI for the diagnosis of PCa, and the areas under the summary ROC (SROC) curves were 0.89 and 0.88, respectively (p = 0.9944). Results of the sensitivity analysis were consistent, and the area under the SROC curve for bpMRI and mpMRI was 0.89 for both (p = 0.9349). CONCLUSION: The available evidence indicates that bpMRI and mpMRI have similar diagnostic efficacy in diagnosing PCa.

Benign prostatic hyperplasia after prostatic arterial embolization in a canine model: A 3T multiparametric MRI and whole-mount step-section pathology correlated longitudinal study.

PURPOSE: To explore the morphological and functional characteristics of prostatic arterial embolization (PAE) in a canine model of benign prostatic hyperplasia (BPH) with 3T multiparametric magnetic resonance imaging (mp-MRI) and whole-mount step-section pathology correlation. MATERIALS AND METHODS: Eight adult male beagle dogs with hormone-induced BPH underwent 3T mp-MRI before and 1, 3, and 6 months after PAE, with subsequent whole-mount step-section pathologic assessment. Images were acquired using T1 -weighted images (T1 WI), T2 WI, 3D-SPACE, diffusion-weighted imaging (DWI), susceptibility-weighted imaging (SWI), T2 -mapping, and dynamic contrast-enhanced (DCE) sequences. Variance analysis was performed to assess statistical differences in prostatic volume (PV), apparent diffusion coefficient (ADC), and T2 values. Pearson correlation analysis was performed to correlate ADC, T2 , and PV. RESULTS: The PV decreased from baseline to 1, 3, and 6 months after PAE from (25.88 ± 7.09) cm3 to (6.48 ± 2.08) cm3 , (6.48 ± 3.39) cm3 , (6.20 ± 2.88) cm3 . The ADC values sequentially decreased from baseline to 1, 3, and 6 months after PAE from (1497.06 ± 222.72) × 10-6 mm2 /s to (1056.00 ± 189.46) × 10-6 mm2 /s, (950.48 ± 77.85) × 10-6 mm2 /s, (980.98 ± 107.78) × 10-6 mm2 /s. The T2 values decreased from baseline to 1, 3, and 6 months after PAE were (83.74 ± 5.29) msec, (68.72 ± 5.66) msec, (53.96 ± 15.04) msec, (49.81 ± 13.34) msec, respectively. ADC and T2 values were positively correlated with PV (r = 0.823 and 0.744, respectively). Microhemorrhages and hemosiderin were found on SWI after PAE. CONCLUSION: 3T mp-MRI may facilitate noninvasive assessment of morphological and functional changes of BPH after PAE. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2017;46:1220-1229.

Correlation of gleason scores with magnetic resonance diffusion tensor imaging in peripheral zone prostate cancer.

BACKGROUND: To investigate tumor aggressiveness in peripheral zone prostate cancer (PCa) by correlating Gleason score (GS) with diffusion tensor imaging (DTI) from multiparametric magnetic resonance imaging (MRI) at 3.0 Tesla (T). METHODS: Eighty-three patients with pathological proven peripheral zone PCa whose GS in at least one core biopsy met the criteria(GS ≤3+3, GS 3+4, GS 4+3, or GS ≥4+4) were included in this study. DTI was performed using b values of 0 and 800 s/mm(2) with 32 directions in all patients on a 3.0T MRI scanner. Fractional anisotropy (FA) and apparent diffusion coefficient (ADC) values were calculated from the DTI data of patients with the previously mentioned four categories of Gleason scores. An association between DTI measurements(FA, ADC) and GS was tested using the Spearman rank correlation analysis. RESULTS: FA values in the sextants found to harbor cancer were positively correlated with the GS(r = 0.48; P < 0.001), while the ADC values were negatively correlated with GS(r = -0.54; P < 0.001). Statistical significance(P < 0.05) was found for FA values among different GS groups, with the exception of GS 3+4 versus GS 4+3 (P = 0.105). The differences between the ADC values were statistically significant for all four different scores(all P < 0.05). CONCLUSION: Quantitative DTI at 3.0T MRI shows a significant association with GS in the evaluation of tumor aggressiveness in peripheral zone PCa, which may be useful to ensure concordance of biopsy results and therefore make the appropriate decision in the management of patients with PCa.

Identification and validation of a novel anoikis-related prognostic model for prostate cancer.

BACKGROUND: Anoikis resistance is a hallmark characteristic of oncogenic transformation, which is crucial for tumor progression and metastasis. The aim of this study was to identify and validate a novel anoikis-related prognostic model for prostate cancer (PCa). METHODS: We collected a gene expression profile, single nucleotide polymorphism mutation and copy number variation (CNV) data of 495 PCa patients from the TCGA database and 140 PCa samples from the MSKCC dataset. We extracted 434 anoikis-related genes and unsupervised consensus cluster analysis was used to identify molecular subtypes. The immune infiltration, molecular function, and genome alteration of subtypes were evaluated. A risk signature was developed using Cox regression analysis and validated with the MSKCC dataset. We also identify potential drugs for high-risk group patients. RESULTS: Two subtypes were identified. C1 exhibited a higher level of CNV amplification, immune score, stromal score, aneuploidy score, homologous recombination deficiency, intratumor heterogeneity, single-nucleotide variant neoantigens, and tumor mutational burden compared to C2. C2 showed a better survival outcome and had a high level of gamma delta T cell and activated B cell infiltration. The risk signature consisting of four genes (HELLS, ZWINT, ABCC5, and TPSB2) was developed (area under the curve = 0.780) and was found to be an independent prognostic factor for overall survival in PCa patients. Four CTRP-derived and four PRISM-derived compounds were identified for high-risk patients. CONCLUSIONS: The anoikis-related prognostic model developed in this study could be a useful tool for clinical decision-making. This study may provide a new perspective for the treatment of anoikis-related PCa.

Comparison and analysis of multiple machine learning models for discriminating benign and malignant testicular lesions based on magnetic resonance imaging radiomics.

Objective: Accurate identification of testicular tumors through better lesion characterization can optimize the radical surgical procedures. Here, we compared the performance of different machine learning approaches for discriminating benign testicular lesions from malignant ones, using a radiomics score derived from magnetic resonance imaging (MRI). Methods: One hundred fifteen lesions from 108 patients who underwent MRI between February 2014 and July 2022 were enrolled in this study. Based on regions-of-interest, radiomics features extraction can be realized through PyRadiomics. For measuring feature reproducibility, we considered both intraclass and interclass correlation coefficients. We calculated the correlation between each feature and the predicted target, removing redundant features. In our radiomics-based analysis, we trained classifiers on 70% of the lesions and compared different models, including linear discrimination, gradient boosting, and decision trees. We applied each classification algorithm to the training set using different random seeds, repeating this process 10 times and recording performance. The highest-performing model was then tested on the remaining 30% of the lesions. We used widely accepted metrics, such as the area under the curve (AUC), to evaluate model performance. Results: We acquired 1,781 radiomic features from the T2-weighted maps of each lesion. Subsequently, we constructed classification models using the top 10 most significant features. The 10 machine-learning algorithms we utilized were capable of diagnosing testicular lesions. Of these, the XGBoost classification emerged as the most superior, achieving the highest AUC value of 0.905 (95% confidence interval: 0.886-0.925) on the testing set and outstripping the other models that typically scored AUC values between 0.697-0.898. Conclusion: Preoperative MRI radiomics offers potential for distinguishing between benign and malignant testicular lesions. An ensemble model like the boosting algorithm embodied by XGBoost may outperform other models.

Noninvasive Prediction of Sperm Retrieval Using Diffusion Tensor Imaging in Patients with Nonobstructive Azoospermia.

Microdissection testicular sperm extraction (mTESE) is the first-line treatment plan for nonobstructive azoospermia (NOA). However, studies reported that the overall sperm retrieval rate (SRR) was 43% to 63% among men with NOA, implying that nearly half of the patients fail sperm retrieval. This study aimed to evaluate the diagnostic performance of parameters derived from diffusion tensor imaging (DTI) in predicting SRR in patients with NOA. Seventy patients diagnosed with NOA were enrolled and classified into two groups based on the outcome of sperm retrieval during mTESE: success (29 patients) and failure (41 patients). Scrotal magnetic resonance imaging was performed, and the DTI parameters, including mean diffusivity and fractional anisotropy, were analyzed between groups. The results showed that there was a significant difference in mean diffusivity values between the two groups, and the area under the curve for mean diffusivity was calculated as 0.865, with a sensitivity of 72.2% and a specificity of 97.5%. No statistically significant difference was observed in fractional anisotropy values and sex hormone levels between the two groups. This study demonstrated that the mean diffusivity value might serve as a useful noninvasive imaging marker for predicting the SRR of NOA patients undergoing mTESE.

Magnetic resonance imaging based deep-learning model: a rapid, high-performance, automated tool for testicular volume measurements.

Background: Testicular volume (TV) is an essential parameter for monitoring testicular functions and pathologies. Nevertheless, current measurement tools, including orchidometers and ultrasonography, encounter challenges in obtaining accurate and personalized TV measurements. Purpose: Based on magnetic resonance imaging (MRI), this study aimed to establish a deep learning model and evaluate its efficacy in segmenting the testes and measuring TV. Materials and methods: The study cohort consisted of retrospectively collected patient data (N = 200) and a prospectively collected dataset comprising 10 healthy volunteers. The retrospective dataset was divided into training and independent validation sets, with an 8:2 random distribution. Each of the 10 healthy volunteers underwent 5 scans (forming the testing dataset) to evaluate the measurement reproducibility. A ResUNet algorithm was applied to segment the testes. Volume of each testis was calculated by multiplying the voxel volume by the number of voxels. Manually determined masks by experts were used as ground truth to assess the performance of the deep learning model. Results: The deep learning model achieved a mean Dice score of 0.926 ± 0.034 (0.921 ± 0.026 for the left testis and 0.926 ± 0.034 for the right testis) in the validation cohort and a mean Dice score of 0.922 ± 0.02 (0.931 ± 0.019 for the left testis and 0.932 ± 0.022 for the right testis) in the testing cohort. There was strong correlation between the manual and automated TV (R2 ranging from 0.974 to 0.987 in the validation cohort; R2 ranging from 0.936 to 0.973 in the testing cohort). The volume differences between the manual and automated measurements were 0.838 ± 0.991 (0.209 ± 0.665 for LTV and 0.630 ± 0.728 for RTV) in the validation cohort and 0.815 ± 0.824 (0.303 ± 0.664 for LTV and 0.511 ± 0.444 for RTV) in the testing cohort. Additionally, the deep-learning model exhibited excellent reproducibility (intraclass correlation >0.9) in determining TV. Conclusion: The MRI-based deep learning model is an accurate and reliable tool for measuring TV.

InterNet: Detection of Active Abdominal Arterial Bleeding Using Emergency Digital Subtraction Angiography Imaging With Two-Stage Deep Learning.

Objective: Active abdominal arterial bleeding is an emergency medical condition. Herein, we present our use of this two-stage InterNet model for detection of active abdominal arterial bleeding using emergency DSA imaging. Methods: Firstly, 450 patients who underwent abdominal DSA procedures were randomly selected for development of the region localization stage (RLS). Secondly, 160 consecutive patients with active abdominal arterial bleeding were included for development of the bleeding site detection stage (BSDS) and InterNet (cascade network of RLS and BSDS). Another 50 patients that ruled out active abdominal arterial bleeding were used as negative samples to evaluate InterNet performance. We evaluated the mode's efficacy using the precision-recall (PR) curve. The classification performance of a doctor with and without InterNet was evaluated using a receiver operating characteristic (ROC) curve analysis. Results: The AP, precision, and recall of the RLS were 0.99, 0.95, and 0.99 in the validation dataset, respectively. Our InterNet reached a recall of 0.7, the precision for detection of bleeding sites was 53% in the evaluation set. The AUCs of doctors with and without InterNet were 0.803 and 0.759, respectively. In addition, the doctor with InterNet assistant could significantly reduce the elapsed time for the interpretation of each DSA sequence from 84.88 to 43.78 s. Conclusion: Our InterNet system could assist interventional radiologists in identifying bleeding foci quickly and may improve the workflow of the DSA operation to a more real-time procedure.

Development of a Deep Learning System to Detect Esophageal Cancer by Barium Esophagram.

Background: Implementation of deep learning systems (DLSs) for analysis of barium esophagram, a cost-effective diagnostic test for esophageal cancer detection, is expected to reduce the burden to radiologists while ensuring the accuracy of diagnosis. Objective: To develop an automated DLS to detect esophageal cancer on barium esophagram. Methods: This was a retrospective study using deep learning for esophageal cancer detection. A two-stage DLS (including a Selection network and a Classification network) was developed. Five datasets based on barium esophagram were used for stepwise training, validation, and testing of the DLS. Datasets 1 and 2 were used to respectively train and test the Selection network, while Datasets 3, 4, and 5 were respectively used to train, validate, and test the Classification network. Finally, a positioning box with a probability value was outputted by the DLS. A region of interest delineated by experienced radiologists was selected as the ground truth to evaluate the detection and classification efficiency of the DLS. Standard machine learning metrics (accuracy, recall, precision, sensitivity, and specificity) were calculated. A comparison with the conventional visual inspection approach was also conducted. Results: The accuracy, sensitivity, and specificity of our DLS in detecting esophageal cancer were 90.3%, 92.5%, and 88.7%, respectively. With the aid of DLS, the radiologists' interpretation time was significantly shortened (Reader1, 45.7 s vs. 72.2 s without DLS aid; Reader2, 54.1 s vs. 108.7 s without DLS aid). Respective diagnostic efficiencies for Reader1 with and without DLS aid were 96.8% vs. 89.3% for accuracy, 97.5% vs. 87.5% for sensitivity, 96.2% vs. 90.6% for specificity, and 0.969 vs. 0.890 for AUC. Respective diagnostic efficiencies for Reader2 with and without DLS aid were 95.7% vs. 88.2% for accuracy, 92.5% vs. 77.5% for sensitivity, 98.1% vs. 96.2% for specificity, and 0.953 vs. 0.869 for AUC. Of note, the positioning boxes outputted by the DLS almost overlapped with those manually labeled by the radiologists on Dataset 5. Conclusions: The proposed two-stage DLS for detecting esophageal cancer on barium esophagram could effectively shorten the interpretation time with an excellent diagnostic performance. It may well assist radiologists in clinical practice to reduce their burden.

Brain Fingerprinting and Lie Detection: A Study of Dynamic Functional Connectivity Patterns of Deception Using EEG Phase Synchrony Analysis.

This study investigated the brain functional connectivity (FC) patterns related to lie detection (LD) tasks with the purpose of analyzing the underlying cognitive processes and mechanisms in deception. Using the guilty knowledge test protocol, 30 subjects were divided randomly into guilty and innocent groups, and their electroencephalogram (EEG) signals were recorded on 32 electrodes. Phase synchrony of EEG was analyzed between different brain regions. A few-trials-based relative phase synchrony (FTRPS) measure was proposed to avoid the false synchronization that occurs due to volume conduction. FTRPS values with a significantly statistical difference between two groups were employed to construct FC patterns of deception, and the FTRPS values from the FC networks were extracted as the features for the training and testing of the support vector machine. Finally, four more intuitive brain fingerprinting graphs (BFG) on delta, theta, alpha and beta bands were respectively proposed. The experimental results reveal that deceptive responses elicited greater oscillatory synchronization than truthful responses between different brain regions, which plays an important role in executing lying tasks. The functional connectivity in the BFG is mainly implicated in the visuo-spatial imagery, bottom-top attention and memory systems, work memory and episodic encoding, and top-down attention and inhibition processing. These may, in part, underlie the mechanism of communication between different brain cortices during lying. High classification accuracy demonstrates the validation of BFG to identify deception behavior, and suggests that the proposed FTRPS could be a sensitive measure for LD in the real application.

Effective Connectivity in Cortical Networks During Deception: A Lie Detection Study Based on EEG.

Thus far, when deception behaviors occur, the connectivity patterns and the communication between different brain areas remain largely unclear. In this study, the most important information flows (MIIFs) between different brain cortices during deception were explored. First, the guilty knowledge test protocol was employed, and 64 electrodes' electroencephalogram (EEG) signals were recorded from 30 subjects (15 guilty and 15 innocent). Cortical current density waveforms were then estimated on the 24 regions of interest (ROIs). Next, partial directed coherence (PDC), an effective connectivity (EC) analysis was applied in the cortical waveforms to obtain the brain EC networks for four bands: delta (1-4 Hz), theta (4-8 Hz), alpha (8-13 Hz) and beta (13-30 Hz). Furthermore, using the graph theoretical analysis, the network parameters with significant differences in the EC network were extracted as features to identify the two groups. The high classification accuracy of the four bands demonstrated that the proposed method was suitable for lie detection. In addition, based on the optimal features in the classification mode, the brain "hub" regions were identified, and the MIIFs were significantly different between the guilty and innocent groups. Moreover, the fronto-parietal network was found to be most prominent among all MIIFs at the four bands. Furthermore, combining the neurophysiology significance of the four frequency bands, the roles of all MIIFs were analyzed, which could help us to uncover the underlying cognitive processes and mechanisms of deception.

A Graph Convolutional Multiple Instance Learning on a Hypersphere Manifold Approach for Diagnosing Chronic Obstructive Pulmonary Disease in CT Images.

Chronic obstructive pulmonary disease (COPD) is a prevalent chronic disease with high morbidity and mortality. The early diagnosis of COPD is vital for clinical treatment, which helps patients to have a better quality of life. Because COPD can be ascribed to chronic bronchitis and emphysema, lesions in a computed tomography (CT) image can present anywhere inside the lung with different types, shapes and sizes. Multiple instance learning (MIL) is an effective tool for solving COPD discrimination. In this study, a novel graph convolutional MIL with the adaptive additive margin loss (GCMIL-AAMS) approach is proposed to diagnose COPD by CT. Specifically, for those early stage patients, the selected instance-level features can be more discriminative if they were learned by our proposed graph convolution and pooling with self-attention mechanism. The AAMS loss can utilize the information of COPD severity on a hypersphere manifold by adaptively setting the angular margins to improve the performance, as the severity can be quantified as four grades by pulmonary function test. The results show that our proposed GCMIL-AAMS method provides superior discrimination and generalization abilities in COPD discrimination, with areas under a receiver operating characteristic curve (AUCs) of 0.960 ± 0.014 and 0.862 ± 0.010 in the test set and external testing set, respectively, in 5-fold stratified cross validation; moreover, it demonstrates that graph learning is applicable to MIL and suggests that MIL may be adaptable to graph learning.

p53 positively regulates the proliferation of hepatic progenitor cells promoted by laminin-521.

Hepatic progenitor cells (HPCs) hold tremendous potential for liver regeneration, but their well-known limitation of proliferation hampers their broader use. There is evidence that laminin is required for the proliferation of HPCs, but the laminin isoform that plays the dominant role and the key intracellular downstream targets that mediate the regulation of HPC proliferation have yet to be determined. Here we showed that p53 expression increased gradually and reached maximal levels around 8 days when laminin α4, α5, ß2, ß1, and γ1 subunit levels also reached a maximum during HPC activation and expansion. Laminin-521 (LN-521) promoted greater proliferation of HPCs than do laminin, matrigel or other laminin isoforms. Inactivation of p53 by PFT-α or Ad-p53V143A inhibited the promotion of proliferation by LN-521. Further complementary MRI and bioluminescence imaging analysis showed that p53 inactivation decreased the proliferation of transplanted HPCs in vivo. p53 was activated by LN-521 through the Integrin α6ß1/FAK-Src-Paxillin/Akt axis. Activated p53 was involved in the nuclear translocation of CDK4 and inactivation of Rb by inducing p27Kip1. Taken together, this study identifies LN-521 as an ideal candidate substrate for HPC culture and uncovers an unexpected positive role for p53 in regulating proliferation of HPCs, which makes it a potential target for HPC-based regenerative medicine.

Discriminating malignant from benign testicular masses using machine-learning based radiomics signature of appearance diffusion coefficient maps: Comparing with conventional mean and minimum ADC values.

PURPOSE: To develop a machine-learning-based radiomics signature of ADC for discriminating between benign and malignant testicular masses and compare its classification performance with that of minimum and mean ADC. METHODS: A total of ninety-seven patients with 101 histopathologically confirmed testicular masses (70 malignancies, 31 benignities) were evaluated in this retrospective study. Eight hundred fifty-one radiomics features were extracted from the preoperative ADC map of each lesion. The mean and minimum ADC values are part of the radiomics features. Thirty lesions were randomly selected to estimate the reliability of the features. The redundant features were eliminated using univariate analysis (independent t test and Mann-Whitney U test, where appropriate) and Spearman's rank correlation. The least absolute shrinkage and selection operator (LASSO) algorithm was employed for feature selection and radiomics signature generation. The classification performance of the radiomics signature and minimum and mean ADC values were evaluated by receiver operating characteristic (ROC) curve analysis and compared by DeLong's test. RESULTS: The whole lesion-based mean ADC showed no difference between benign and malignant testicular masses (P = 0.070, training cohort; P = 0.418, validation cohort). Compared with the minimum ADC, the ADC-based radiomics signature yielded a higher area under the curve (AUC) in both the training (AUC: 0.904, 95% confidence interval [CI]: 0.832-0.975) and validation cohorts (AUC: 0.868, 95% CI: 0.728-1.00). CONCLUSIONS: Conventional mean ADC values are not always helpful in discriminating between testicular benignities and malignancies. The minimum ADC and radiomics signature might be better alternatives, with the radiomics signature performing better than the minimum ADC.

Long-term chest CT follow-up in COVID-19 Survivors: 102-361 days after onset.

BACKGROUND: The aim of this study was to evaluate long-term longitudinal changes in chest computed tomography (CT) findings in coronavirus disease 2019 (COVID-19) survivors and their correlations with dyspnea after discharge. METHODS: A total of 337 COVID-19 survivors who underwent CT scan during hospitalization and between 102 and 361 days after onset were retrospectively included. Subjective CT findings, lesion volume, therapeutic measures and laboratory parameters were collected. The severity of the survivors' dyspnea was determined by follow-up questionnaire. The evolution of the CT findings from the peak period to discharge and throughout follow-up and the abilities of CT findings and clinical parameters to predict survival with and without dyspnea were analyzed. RESULTS: Ninety-one COVID-19 survivors still had dyspnea at follow-up. The age, comorbidity score, duration of hospital stays, receipt of hormone administration, receipt of immunoglobulin injections, intensive care unit (ICU) admission, receipt of mechanical ventilation, laboratory parameters, clinical classifications and parameters associated with lesion volume of the survivors with dyspnea were significantly different from those of survivors without dyspnea. Among the clinical parameters and CT parameters used to identify dyspnea, parameters associated with lesion volume showed the largest area under the curve (AUC) values, with lesion volume at discharge showing the largest AUC (0.820). Lesion volume decreased gradually from the peak period to discharge and through follow-up, with a notable decrease observed after discharge. Absorption of lesions continued 6 months after discharge. CONCLUSIONS: Among the clinical parameters and subjective CT findings, CT findings associated with lesion volume were the best predictors of post-discharge dyspnea in COVID-19 survivors.