Deep learning-assisted LI-RADS grading and distinguishing hepatocellular carcinoma (HCC) from non-HCC based on multiphase CT: a two-center study.

OBJECTIVES: To develop a deep learning (DL) method that can determine the Liver Imaging Reporting and Data System (LI-RADS) grading of high-risk liver lesions and distinguish hepatocellular carcinoma (HCC) from non-HCC based on multiphase CT. METHODS: This retrospective study included 1049 patients with 1082 lesions from two independent hospitals that were pathologically confirmed as HCC or non-HCC. All patients underwent a four-phase CT imaging protocol. All lesions were graded (LR 4/5/M) by radiologists and divided into an internal (n = 886) and external cohort (n = 196) based on the examination date. In the internal cohort, Swin-Transformer based on different CT protocols were trained and tested for their ability to LI-RADS grading and distinguish HCC from non-HCC, and then validated in the external cohort. We further developed a combined model with the optimal protocol and clinical information for distinguishing HCC from non-HCC. RESULTS: In the test and external validation cohorts, the three-phase protocol without pre-contrast showed κ values of 0.6094 and 0.4845 for LI-RADS grading, and its accuracy was 0.8371 and 0.8061, while the accuracy of the radiologist was 0.8596 and 0.8622, respectively. The AUCs in distinguishing HCC from non-HCC were 0.865 and 0.715 in the test and external validation cohorts, while those of the combined model were 0.887 and 0.808. CONCLUSION: The Swin-Transformer based on three-phase CT protocol without pre-contrast could feasibly simplify LI-RADS grading and distinguish HCC from non-HCC. Furthermore, the DL model have the potential in accurately distinguishing HCC from non-HCC using imaging and highly characteristic clinical data as inputs. CLINICAL RELEVANCE STATEMENT: The application of deep learning model for multiphase CT has proven to improve the clinical applicability of the Liver Imaging Reporting and Data System and provide support to optimize the management of patients with liver diseases. KEY POINTS: • Deep learning (DL) simplifies LI-RADS grading and helps distinguish hepatocellular carcinoma (HCC) from non-HCC. • The Swin-Transformer based on the three-phase CT protocol without pre-contrast outperformed other CT protocols. • The Swin-Transformer provide help in distinguishing HCC from non-HCC by using CT and characteristic clinical information as inputs.

Individualized Prediction of Early Alzheimer's Disease Based on Magnetic Resonance Imaging Radiomics, Clinical, and Laboratory Examinations: A 60-Month Follow-Up Study.

BACKGROUND: Accurately predicting whether and when mild cognitive impairment (MCI) will progress to Alzheimer's disease (AD) is of vital importance to help developing individualized treatment plans to defer the occurrence of irreversible dementia. PURPOSE: To develop and validate radiomics models and multipredictor nomogram for predicting the time to progression (TTP) from MCI to AD. STUDY TYPE: Retrospective. POPULATION: One hundred sixty-two MCI patients (96 men and 66 women [median age, 72; age range, 56-88 years]) were included from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. FIELD STRENGTH/SEQUENCE: T1 -weighted imaging and T2 -weighted fluid-attenuation inversion recovery imaging acquired at 3.0 T. ASSESSMENT: During the 5-year follow-up, 68 patients converted to AD and 94 remained stable. Patients were randomly divided into the training (n = 112) and validation datasets (n = 50). Radiomic features were extracted from the whole cerebral cortex and subcortical nucleus of MR images. A radiomics model was established using least absolute shrinkage and selection operator (LASSO) Cox regression. The clinical-laboratory model and radiomics-clinical-laboratory model were developed by multivariate Cox proportional hazard model. The performance of each model was assessed by the concordance index (C-index). A multipredictor nomogram derived from the radiomics-clinical-laboratory model was constructed for individualized TTP estimation. STATISTICAL TESTS: LASSO cox regression, univariate and multivariate Cox regression, Kaplan-Meier analysis and Student's t test were performed. RESULTS: The C-index of the radiomics, clinical-laboratory and radiomics-clinical-laboratory models were 0.924 (95% confidence interval [CI]: 0.894-0.952), 0.903 (0.868-0.938), 0.950 (0.929-0.971) in the training cohort and 0.811 (0.707-0.914), 0.901 (0824-0.977), 0.907 (0.836-0.979) in the validation cohort, respectively. A multipredictor nomogram with 15 predictors was established, which had high accuracy for individual TTP prediction with the C-index of 0.950 (0.929-0.971). DATA CONCLUSION: The prediction of individual TTP from MCI to AD could be accurately conducted using the radiomics-clinical-laboratory model and multipredictor nomogram. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: 2.

Discovery of novel indolin-2-one compounds as potent inhibitors of HsClpP for cancer treatment.

Human caseinolytic protease proteolytic subunit (HsClpP) is a highly conserved serine protease that plays an essential role in cell homeostasis through removal of the damaged and/or misfolded proteins. Recently, due to its critical role in cancer proliferation and metastasis, HsClpP has been considered as a promising target for the cancer treatment. In this paper, through a random screening toward a library of 2086 bioactive chemicals, a novel compound I, 3-(3,5-dibromo-4-hydroxybenzylidene) -5-iodoindolin-2-one, was identified as a potent suppressor of HsClpP. Herein, a series of compound I derivatives were synthesized, and evaluated for their anti-cancer activities on a variety of cancers cells. Through the preliminary biological assay in vitro, including MTT assay and proteolytic activity assay, compound I was identified as the most potent inhibitor. Treatment with compound I impaired the migration of Hela cells. In addition, compound I disrupted the mitochondrial function, and reduced the level of the SDHB and induced the production of the ATF4. In general, compound I is a promising probe of HsClpP for cancer treatment, and is a good lead compound for the development of novel anti-cancer agent.

The CRISPR-Cas system in molecular diagnostics.

Robust, sensitive, and rapid molecular detection tools are essential prerequisites for disease diagnosis and epidemiological control. However, the current mainstream tests necessitate expensive equipment and specialized operators, impeding the advancement of molecular diagnostics. The CRISPR-Cas system is an integral component of the bacterial adaptive immune system, wherein Cas proteins recognize PAM sequences by binding to CRISPR RNA, subsequently triggering DNA or RNA cleavage. The discovery of the CRISPR-Cas system has invigorated molecular diagnostics. With further in-depth research on this system, its application in molecular diagnosis is flourishing. In this review, we provide a comprehensive overview of recent research progress on the CRISPR-Cas system, specifically focusing on its application in molecular diagnosis.

Prediction of microvascular invasion and pathological differentiation of hepatocellular carcinoma based on a deep learning model.

PURPOSE: To develop a deep learning (DL) model based on preoperative contrast-enhanced computed tomography (CECT) images to predict microvascular invasion (MVI) and pathological differentiation of hepatocellular carcinoma (HCC). METHODS: This retrospective study included 640 consecutive patients who underwent surgical resection and were pathologically diagnosed with HCC at two medical institutions from April 2017 to May 2022. CECT images and relevant clinical parameters were collected. All the data were divided into 368 training sets, 138 test sets and 134 validation sets. Through DL, a segmentation model was used to obtain a region of interest (ROI) of the liver, and a classification model was established to predict the pathological status of HCC. RESULTS: The liver segmentation model based on the 3D U-Network had a mean intersection over union (mIoU) score of 0.9120 and a Dice score of 0.9473. Among all the classification prediction models based on the Swin transformer, the fusion models combining image information and clinical parameters exhibited the best performance. The area under the curve (AUC) of the fusion model for predicting the MVI status was 0.941, its accuracy was 0.917, and its specificity was 0.908. The AUC values of the fusion model for predicting poorly differentiated, moderately differentiated and highly differentiated HCC based on the test set were 0.962, 0.957 and 0.996, respectively. CONCLUSION: The established DL models established can be used to noninvasively and effectively predict the MVI status and the degree of pathological differentiation of HCC, and aid in clinical diagnosis and treatment.

Prediction of Cognitive Progression in Individuals with Mild Cognitive Impairment Using Radiomics as an Improvement of the ATN System: A Five-Year Follow-Up Study.

OBJECTIVE: To improve the N biomarker in the amyloid/tau/neurodegeneration system by radiomics and study its value for predicting cognitive progression in individuals with mild cognitive impairment (MCI). MATERIALS AND METHODS: A group of 147 healthy controls (HCs) (72 male; mean age ± standard deviation, 73.7 ± 6.3 years), 197 patients with MCI (114 male; 72.2 ± 7.1 years), and 128 patients with Alzheimer's disease (AD) (74 male; 73.7 ± 8.4 years) were included. Optimal A, T, and N biomarkers for discriminating HC and AD were selected using receiver operating characteristic (ROC) curve analysis. A radiomics model containing comprehensive information of the whole cerebral cortex and deep nuclei was established to create a new N biomarker. Cerebrospinal fluid (CSF) biomarkers were evaluated to determine the optimal A or T biomarkers. All MCI patients were followed up until AD conversion or for at least 60 months. The predictive value of A, T, and the radiomics-based N biomarker for cognitive progression of MCI to AD were analyzed using Kaplan-Meier estimates and the log-rank test. RESULTS: The radiomics-based N biomarker showed an ROC curve area of 0.998 for discriminating between AD and HC. CSF Aß42 and p-tau proteins were identified as the optimal A and T biomarkers, respectively. For MCI patients on the Alzheimer's continuum, isolated A+ was an indicator of cognitive stability, while abnormalities of T and N, separately or simultaneously, indicated a high risk of progression. For MCI patients with suspected non-Alzheimer's disease pathophysiology, isolated T+ indicated cognitive stability, while the appearance of the radiomics-based N+ indicated a high risk of progression to AD. CONCLUSION: We proposed a new radiomics-based improved N biomarker that could help identify patients with MCI who are at a higher risk for cognitive progression. In addition, we clarified the value of a single A/T/N biomarker for predicting the cognitive progression of MCI.

Discovery of a Novel Series of Imipridone Compounds as Homo sapiens Caseinolytic Protease P Agonists with Potent Antitumor Activities In Vitro and In Vivo.

Homo sapiens caseinolytic protease P (HsClpP) plays an important role in maintaining mitochondrial proteostasis. Activating HsClpP has been proved to be a potential strategy for cancer therapy. In this paper, a novel class of HsClpP agonists is designed and synthesized using a position shift strategy based on the imipridone ONC201. Among these newly synthesized imipridone derivatives, compound 16z exhibits remarkably enhanced antitumor activity (IC50 = 0.04 µM against HCT116 cells). It can improve HsClpP thermal stability and induce mitochondrial dysfunction, reactive oxygen species production, cell cycle arrest in the G0/G1 phase, and apoptosis of HCT116 cells. Moreover, compound 16z possesses excellent pharmacokinetic profiles and significantly inhibits tumor growth in HCT116 cell-inoculated xenograft nude mouse models. Our study demonstrates that 16z has potential to be an antitumor drug candidate for further development and provides insights for the design of the next generation of HsClpP agonists for cancer treatment.

Proteases and Their Modulators in Cancer Therapy: Challenges and Opportunities.

Proteostasis is the process of regulating intracellular proteins to maintain the balance of the cell proteome, which is crucial for cancer cell survival. Several proteases located in the cytoplasm, mitochondria, lysosome, and extracellular environment have been identified as potential antitumor targets because of their involvement in proteostasis. Although the discovery of small-molecule inhibitors targeting proteases faces particular challenges, rapid advances in chemical biology and structural biology, and the new technology of drug discovery have facilitated the development of promising protease modulators. In this review, the protein structure and function of important tumor-related proteases and their inhibitors are presented. We also provide a prospective on advances and the outlook of new drug strategies that target these proteases.

Efficacy and safety of direct balloon angioplasty in the treatment of large atherosclerotic stroke.

OBJECTIVE: Based on the unclear safety and effectiveness of direct balloon angioplasty as a first-line treatment for patients with acute ischemic stroke caused by large artery atherosclerosis (LAA), our paper would center on investigating the safety and effectiveness of this novel strategy. PATIENTS AND METHODS: A consecutive series of acute ischemic stroke patients due to intracranial atherosclerosis and short thrombus who underwent thorough direct balloon angioplasty from October 2019 to March 2021 were enrolled. The primary end point included arterial recanalization (modified Thrombolysis in Cerebral Infarction [mTICI]: 2b-3), and 90-day functional independence (modified Rankin Scale[mRS]: 0-2). The secondary end point was symptomatic intracerebral hemorrhage (sICH) and perioperative restenosis and re-occlusion of offending vessel. RESULTS: 68 patients were included. Mean time from onset to groin puncture was 342.5 min and 50 min for groin puncture to successful recanalization. 61 (89.7%) patients achieved successful recanalization and 41 (60.3%) acquired functional independence. 11 (16.0%) patients experienced ICH and only 3 (4.4%) for sICH. 8 (11.8%) patients developed symptomatic restenosis or re-occlusion within seven days after the operation. In addition, 16 (23.5%) patients received rescue stenting and 3 (18.8%) of this subgroup appeared immediate intra-stent thrombosis. CONCLUSION: Direct balloon angioplasty may be a safe and effective method for the treatment of stroke caused by intracranial large arteriosclerosis occlusion.

Scaffold hopping of agomelatine leads to enhanced antidepressant effects by modulation of gut microbiota and host immune responses.

The mechanisms underlying the pathophysiology of depression remain elusive, and the development of novel, effective antidepressant drugs remains necessary. A dihydroquinoline analog of agomelatine (AGO), N-(2-(7-methoxy-3,4-dihydroisoquinolin-1-yl)ethyl)acetamide hydrochloride (NMDEA), was synthesized by employing a scaffold-hopping strategy in our previous study. In this study, NMDEA was demonstrated to attenuate depression-related behaviors in mice models of chronic unpredictable mild stress (CUMS), using a sucrose preference test, a forced swimming test, and a tail suspension test. However, the antidepressant mechanism of NMDEA appears to differ from that for AGO. Based on the analysis of fecal microbiota from mice, stress can alter the richness of the gut bacterial community, increasing the expression of immune-modulating microbiota, such as Clostridia, and decreasing the expression of probiotic bacteria, such as Lactobacillus. Treatment with NMDEA was able to recover the richness and to regulate the dysbiosis among bacterial species. Several studies have demonstrated that the gut microbiota population can induce inflammatory processes. To explore the effects of NMDEA on the suppression of pro-inflammatory factors, we used Western blotting to analyze the levels of interleukin 1 beta (IL-1ß), interleukin 6 (IL-6), p65, and inducible nitric oxide synthase (iNOS). NMDEA suppressed the activation of IL-1ß and IL-6, in the hippocampus, and IL-1ß, IL-6, p65, and iNOS, in lipopolysaccharide (LPS)-induced BV-2 cells. These results suggested that NMDEA may affect the microbiota-inflammasome-brain axis, regulating relevant neuro-inflammatory markers and gut microbiota. Our data also suggested that using small molecules to modify the gut microbiota population or alter inflammasome signaling may represent a new therapeutic opportunity for the mitigation of depression.

Discovery of Novel Peptidomimetic Boronate ClpP Inhibitors with Noncanonical Enzyme Mechanism as Potent Virulence Blockers in Vitro and in Vivo.

Caseinolytic protease P (ClpP) is considered as a promising target for the treatment of Staphylococcus aureus infections. In an unbiased screen of 2632 molecules, a peptidomimetic boronate, MLN9708, was found to be a potent suppressor of SaClpP function. A time-saving and cost-efficient strategy integrating in silico position scanning, multistep miniaturized synthesis, and bioactivity testing was deployed for optimization of this hit compound and led to fast exploration of structure-activity relationships. Five of 150 compounds from the miniaturized synthesis exhibited improved inhibitory activity. Compound 43Hf was the most active inhibitor and showed reversible covalent binding to SaClpP while did not destabilize the tetradecameric structure of SaClpP. The crystal structure of 43Hf-SaClpP complex provided mechanistic insight into the covalent binding mode of peptidomimetic boronate and SaClpP. Furthermore, 43Hf could bind endogenous ClpP in S. aureus cells and exhibited significant efficacy in attenuating S. aureus virulence in vitro and in vivo.