Development and validation of MRI-based scoring models for predicting placental invasiveness in high-risk women for placenta accreta spectrum.

OBJECTIVES: To develop and validate MRI-based scoring models for predicting placenta accreta spectrum (PAS) invasiveness. MATERIALS AND METHODS: This retrospective study comprised a derivation cohort and a validation cohort. The derivation cohort came from a systematic review of published studies evaluating the diagnostic performance of MRI signs for PAS and/or placenta percreta in high-risk women. The significant signs were identified and used to develop prediction models for PAS and placenta percreta. Between 2016 and 2021, consecutive high-risk pregnant women for PAS who underwent placental MRI constituted the validation cohort. Two radiologists independently evaluated the MRI signs. The reference standard was intraoperative and pathologic findings. The predictive ability of MRI-based models was evaluated using the area under the curve (AUC). RESULTS: The derivation cohort included 26 studies involving 2568 women and the validation cohort consisted of 294 women with PAS diagnosed in 258 women (88%). Quantitative meta-analysis revealed that T2-dark bands, placental/uterine bulge, loss of T2 hypointense interface, bladder wall interruption, placental heterogeneity, and abnormal intraplacental vascularity were associated with both PAS and placenta percreta, and myometrial thinning and focal exophytic mass were exclusively associated with PAS. The PAS model was validated with an AUC of 0.90 (95% CI: 0.86, 0.93) for predicting PAS and 0.85 (95% CI: 0.79, 0.90) for adverse peripartum outcome; the placenta percreta model showed an AUC of 0.92 (95% CI: 0.86, 0.98) for predicting placenta percreta. CONCLUSION: MRI-based scoring models established based on quantitative meta-analysis can accurately predict PAS, placenta percreta, and adverse peripartum outcome. CLINICAL RELEVANCE STATEMENT: These proposed MRI-based scoring models could help accurately predict PAS invasiveness and provide evidence-based risk stratification in the management of high-risk pregnant women for PAS. KEY POINTS: • Accurately identifying placenta accreta spectrum (PAS) and assessing its invasiveness depending solely on individual MRI signs remained challenging. • MRI-based scoring models, established through quantitative meta-analysis of multiple MRI signs, offered the potential to predict PAS invasiveness in high-risk pregnant women. • These MRI-based models allowed for evidence-based risk stratification in the management of pregnancies suspected of having PAS.

CT enterography-based radiomics combined with body composition to predict infliximab treatment failure in Crohn's disease.

PURPOSE: Accurately predicting the treatment response in patients with Crohn's disease (CD) receiving infliximab therapy is crucial for clinical decision-making. We aimed to construct a prediction model incorporating radiomics and body composition features derived from computed tomography (CT) enterography for identifying individuals at high risk for infliximab treatment failure. METHODS: This retrospective study included 137 patients with CD between 2015 and 2021, who were divided into a training cohort and a validation cohort with a ratio of 7:3. Patients underwent CT enterography examinations within 1 month before infliximab initiation. Radiomic features of the intestinal segments involved were extracted, and body composition features were measured at the level of the L3 lumbar vertebra. A model that combined radiomics with body composition was constructed. The primary outcome was the occurrence of infliximab treatment failure within 1 year. The model performance was evaluated using discrimination, calibration, and decision curves. RESULTS: Fifty-two patients (38.0%) showed infliximab treatment failure. Eight significant radiomic features were used to develop the radiomics model. The model incorporating radiomics model score, skeletal muscle index (SMI), and creeping fat showed good discrimination for predicting infliximab treatment failure, with an area under the curve (AUC) of 0.88 (95% CI 0.81, 0.95) in the training cohort and 0.83 (95% CI 0.66, 1.00) in the validation cohort. The favorable clinical application was observed using decision curve analysis. CONCLUSIONS: We constructed a comprehensive model incorporating radiomics and muscle volume, which could potentially be used to facilitate the individualized prediction of infliximab treatment response in patients with CD.

CT Radiomics to Predict Macrotrabecular-Massive Subtype and Immune Status in Hepatocellular Carcinoma.

Background Macrotrabecular-massive (MTM) subtype of hepatocellular carcinoma (HCC) is an aggressive variant associated with angiogenesis and immunosuppressive tumor microenvironment, which is expected to be noninvasively identified using radiomics approaches. Purpose To construct a CT radiomics model to predict the MTM subtype and to investigate the underlying immune infiltration patterns. Materials and Methods This study included five retrospective data sets and one prospective data set from three academic medical centers between January 2015 and December 2021. The preoperative liver contrast-enhanced CT studies of 365 adult patients with resected HCC were evaluated. The Third Xiangya Hospital of Central South University provided the training set and internal test set, while Yueyang Central Hospital and Hunan Cancer Hospital provided the external test sets. Radiomic features were extracted and used to develop a radiomics model with machine learning in the training set, and the performance was verified in the two test sets. The outcomes cohort, including 58 adult patients with advanced HCC undergoing transarterial chemoembolization and antiangiogenic therapy, was used to evaluate the predictive value of the radiomics model for progression-free survival (PFS). Bulk RNA sequencing of tumors from 41 patients in The Cancer Genome Atlas (TCGA) and single-cell RNA sequencing from seven prospectively enrolled participants were used to investigate the radiomics-related immune infiltration patterns. Area under the receiver operating characteristics curve of the radiomics model was calculated, and Cox proportional regression was performed to identify predictors of PFS. Results Among 365 patients (mean age, 55 years ± 10 [SD]; 319 men) used for radiomics modeling, 122 (33%) were confirmed to have the MTM subtype. The radiomics model included 11 radiomic features and showed good performance for predicting the MTM subtype, with AUCs of 0.84, 0.80, and 0.74 in the training set, internal test set, and external test set, respectively. A low radiomics model score relative to the median value in the outcomes cohort was independently associated with PFS (hazard ratio, 0.4; 95% CI: 0.2, 0.8; P = .01). The radiomics model was associated with dysregulated humoral immunity involving B-cell infiltration and immunoglobulin synthesis. Conclusion Accurate prediction of the macrotrabecular-massive subtype in patients with hepatocellular carcinoma was achieved using a CT radiomics model, which was also associated with defective humoral immunity. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Yoon and Kim in this issue.

Establishment and application of the BRP prognosis model for idiopathic pulmonary fibrosis.

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is the most common idiopathic interstitial lung disease. Clinical models to accurately evaluate the prognosis of IPF are currently lacking. This study aimed to construct an easy-to-use and robust prediction model for transplant-free survival (TFS) of IPF based on clinical and radiological information. METHODS: A multicenter prognostic study was conducted involving 166 IPF patients who were followed up for 3 years. The end point of follow-up was death or lung transplantation. Clinical information, lung function tests, and chest computed tomography (CT) scans were collected. Body composition quantification on CT was performed using 3D Slicer software. Risk factors in blood routine examination-radiology-pulmonary function (BRP) were identified by Cox regression and utilized to construct the "BRP Prognosis Model". The performance of the BRP model and the gender-age-physiology variables (GAP) model was compared using time-ROC curves, calibration curves, and decision curve analysis (DCA). Furthermore, histopathology fibrosis scores in clinical specimens were compared between the different risk stratifications identified by the BRP model. The correlations among body composition, lung function, serum inflammatory factors, and profibrotic factors were analyzed. RESULTS: Neutrophil percentage > 68.3%, pericardial adipose tissue (PAT) > 94.91 cm3, pectoralis muscle radiodensity (PMD) ≤ 36.24 HU, diffusing capacity of the lung for carbon monoxide/alveolar ventilation (DLCO/VA) ≤ 56.03%, and maximum vital capacity (VCmax) < 90.5% were identified as independent risk factors for poor TFS among patients with IPF. We constructed a BRP model, which showed superior accuracy, discrimination, and clinical practicability to the GAP model. Median TFS differed significantly among patients at different risk levels identified by the BRP model (low risk: TFS > 3 years; intermediate risk: TFS = 2-3 years; high risk: TFS ≈ 1 year). Patients with a high-risk stratification according to the BRP model had a higher fibrosis score on histopathology. Additionally, serum proinflammatory markers were positively correlated with visceral fat volume and infiltration. CONCLUSIONS: In this study, the BRP prognostic model of IPF was successfully constructed and validated. Compared with the commonly used GAP model, the BRP model had better performance and generalization with easily obtainable indicators. The BRP model is suitable for clinical promotion.

High-power, sub-100-fs, 1600-1700-nm all-fiber laser for deep multiphoton microscopy.

The 1600-1700-nm ultrafast fiber lasers attract great interests in the deep multiphoton microscopy, due to the reduced levels of the tissue scattering and absorption. Here, we report on the 86.7-MHz, 717-mW, 91.2-fs, all-fiber laser located in the spectral range from 1600 nm to 1700nm. The soliton self-frequency shift (SSFS) was introduced into the Er:Yb co-doped fiber amplifier (EYDFA) to generate the high-power, 1600-1700-nm Raman soliton. Detailed investigations of the nonlinear fiber amplification process were implemented in optimizing the generated Raman soliton pulses. The miniature multiphoton microscopy was further realized with this home-built laser source. The clearly imaging results can be achieved by collecting the generated harmonic signals from the mouse tail skin tissue with a penetration depth of ∼500 µm. The experimental results indicate the great potential in utilizing this 1600-1700-nm fiber laser in the deep multiphoton microscopy.

Eclampsia with hypothyroidism complicated with posterior reversible encephalopathy syndrome-a case report.

BACKGROUND: Posterior reversible encephalopathy syndrome (PRES) is a rare neurological disorder with complex physiopathological mechanisms that have not been fully understood. Early identification is of great prognostic significance, of which the symptoms and radiological abnormalities can be completely reversed. If the diagnosis and treatment are delayed, ischemia and massive infarction may be developed in some patients. Posterior reversible encephalopathy syndrome (PRES) has been reported mainly in association with postpartum eclampsia, which have been rarely reported, while the association with hypothyroidism has not been reported at home or abroad. CASE PRESENTATION: Here we report on a pregnant 29-year-old with multipara and a chief complication of hypothyroidism. She presented in the emergency department with frequent attacks of severe headache symptoms resulting from reversible cerebral vasoconstriction syndrome (RCVS), accompanied with prenatal eclampsia. PRES was determined by radiological examination. CONCLUSION: To the best of our knowledge, this is the first case of PRES complicated by hypothyroidism and prepartum eclampsia.Clinicians should be alert for the co-occurence of eclampsia, PRES, and RCVS when patients have convulsions after a typical throbbing headache. Moreover, regular monitoring of thyroid function during pregnancy should also occupy certain special attention.

Fault Diagnosis for Complex Equipment Based on Belief Rule Base with Adaptive Nonlinear Membership Function.

Fault diagnosis of complex equipment has become a hot field in recent years. Due to excellent uncertainty processing capability and small sample problem modeling capability, belief rule base (BRB) has been widely used in the fault diagnosis. However, previous BRB models almost did not consider the diverse distributions of observation data which may reduce diagnostic accuracy. In this paper, a new fault diagnosis model based on BRB is proposed. Considering that the previous triangular membership function cannot address the diverse distribution of observation data, a new nonlinear membership function is proposed to transform the input information. Then, since the model parameters initially determined by experts are inaccurate, a new parameter optimization model with the parameters of the nonlinear membership function is proposed and driven by the gradient descent method to prevent the expert knowledge from being destroyed. A fault diagnosis case of laser gyro is used to verify the validity of the proposed model. In the case study, the diagnosis accuracy of the new BRB-based fault diagnosis model reached 95.56%, which shows better fault diagnosis performance than other methods.

Endowing Polyetheretherketone Implants with Osseointegration Properties: In Situ Construction of Patterned Nanorod Arrays.

Polyetheretherketone (PEEK) is widely used in orthopedic and craniomaxillofacial surgeries as it exhibits excellent biocompatibility, mechanical property, and chemical stability. However, its clinical application is limited by the biological inertness of PEEK. Numerous efforts have been made to improve the bioactivity of this polymer over the years. However, modification methods that can not only promote osteogenesis but also maintain excellent properties are still limited. Hence, a facile hot die formation technique is developed for establishing patterned nanorod arrays on the PEEK surface in situ. This method can maintain the excellent properties of PEEK and can be used in implantation as it can facilitate osteogenic activity in the absence of any organic/inorganic differentiation-inducing factors. PEEK with 200-nm patterned nanorod arrays on the surface exhibits excellent osteogenic properties. This result is obtained by assessing the osteogenic differentiation properties of rat adipose-derived stem cells at the gene and protein levels in vitro. In vivo experimental results reveal that the surface-modified cylindrical PEEK 200 implants present with excellent osseointegration properties. Moreover, they can tightly bind with the surrounding bone tissue. A practical method for manufacturing single-component PEEK implants with excellent osseointegration properties is reported, and the materials can be possibly used as orthopedic implants.

Stem Cell Membrane-Encapsulated Zeolitic Imidazolate Framework-8: A Targeted Nano-Platform for Osteogenic Differentiation.

Mesenchymal stem cells (MSCs) have been recognized as one of the most promising pharmaceutical multipotent cells, and a key step for their wide application is to safely and efficiently regulate their activities. Various methods have been proposed to regulate the directional differentiation of MSCs during tissue regeneration, such as nanoparticles and metal ions. Herein, nanoscale zeolitic imidazolate framework-8 (ZIF-8), a Zn-based metal-organic framework, is modified to direct MSCs toward an osteoblast lineage. Specifically, ZIF-8 nanoparticles are encapsulated using stem cell membranes (SCMs) to mimic natural molecules and improve the biocompatibility and targeted ability toward MSCs. SCM/ZIF-8 nanoparticles adjust the sustained release of Zn2+ , and promote their specific internalization toward MSCs. The internalized SCM/ZIF-8 nanoparticles show excellent biocompatibility, and increase MSCs' osteogenic potentials. Moreover, RNA-sequencing results elucidate that the activated cyclic adenosine 3,5-monophosphate (cAMP)-PKA-CREB signaling pathway can be dominant in accelerating osteogenic differentiation. In vivo, SCM/ZIF-8 nanoparticles greatly promote the formation of new bone tissue in the femoral bone defect detected by 3D micro-CT, hematoxylin and eosin staining, and Masson staining after 4 weeks. Overall, the SCM-derived ZIF-8 nanostructures achieve the superior targeting ability, biocompatibility, and enhanced osteogenesis, providing a constructive design for tissue repair.

Stemness Maintenance and Massproduction of Neural Stem Cells on Poly L-Lactic Acid Nanofibrous Membrane Based on Piezoelectriceffect.

Neural stem cells (NSCs) therapy is promising for treating neurodegenerative disorders and neural injuries. However, the limited in vitro expansion, spontaneous differentiation, and decrease in stemness obstruct the acquisition of high quantities of NSCs, restricting the clinical application of cell-based therapies and tissue engineering. This article reports a facile method of promoting NSCs expansion and maintaining stemness using wireless electrical stimulation triggered by piezoelectric nanomaterials. A nanofibrous membrane of poly L-lactic acid (PLLA) is prepared by electrostatic spinning, and the favorable piezoelectric property of PLLA facilitates the freeing of electrons after transformation. These self-powered electric signals generated by PLLA significantly enhance NSCs proliferation. Further, an undifferentiated cellular state is maintained in the NSCs cultured on the surfaces of PLLA nanofibers exposed to ultrasonic vibration. In addition, the neural differentiation potencies and functions of NSCs expanded by piezoelectric-driven localized electricity are not attenuated. Moreover, cell stemness can be maintained by wireless electric stimulation. Taken together, the electronic signals mediated by PLLA nanofibers facilitate NSCs proliferation. This efficient and simple strategy can maintain the stemness of NSCs during proliferation, which is essential for their clinical application, and opens up opportunities for the mass production of NSCs for use in cell therapy.

"Trojan Horse" Salmonella Enabling Tumor Homing of Silver Nanoparticles via Neutrophil Infiltration for Synergistic Tumor Therapy and Enhanced Biosafety.

Salmonella selectively colonizes into the hypoxic tumor region and exerts antitumor effects via multiple mechanisms, while the tumor colonized Salmonella recruits host neutrophils into the tumor, presenting a key immunological restraint to compromise the Salmonella efficacy. Here, we develop a combinatorial strategy by employing silver nanoparticles (AgNPs) to improve the efficacy and biosafety of Salmonella. The AgNPs were decorated with sialic acid (SA) to allow selective recognition of L-selectin on neutrophil surfaces, based on which the tumor-homing of AgNPs was achieved by neutrophil infiltration in the Salmonella colonized tumor. The tumor-targeting AgNPs exert the functions of (1) local depletion of neutrophils in tumors to boost the efficacy of Salmonella, (2) direct killing tumor cells via L-selectin-mediated intracellular delivery, and (3) clearing the residual Salmonella after complete tumor eradication to minimize the side effects. With a single tail vein injection of such combination treatment, the tumor was eliminated with high biosafety, resulting in a superior therapeutic outcome.

A New Topology-Switching Strategy for Fault Diagnosis of Multi-Agent Systems Based on Belief Rule Base.

Effective fault-diagnosis strategies have been the focus of research on multi-agent systems (MASs). In this paper, the belief rule base (BRB)-based distributed fault-diagnosis problem for MASs is investigated, and a topology-switching strategy is developed to increase the reliability of fault-diagnosis model. Firstly, a BRB-based distributed fault-diagnosis model is constructed for the MAS with multiple faults, then expert knowledge is used to judge whether the agent is faulty. Then, considering that the system may be influenced by the fault or some other factors and thus leading to a decrease in the accuracy of the fault-diagnosis results, a topology-switching strategy based on the average distance of the output diagnosis accuracy is proposed to update the topology of the agent so that the fault-diagnosis results can be more reliable. Note that the topology-switching threshold is designed based on the average distance between the accuracy of the fault diagnosis of each agent. The method proposed in this paper can solve the problem when the fault-diagnosis accuracy of the model is affected by some common factors and thus decreases, and can improve the reliability of the fault-diagnosis model very well. Finally, the effectiveness of the BRB-based distributed fault-diagnosis model and the proposed topology-switching strategy to improve the fault-diagnosis accuracy is verified by simulation examples.

Influence of different region of interest sizes on CT-based radiomics model for microvascular invasion prediction in hepatocellular carcinoma. / æ„Ÿå ´è¶£åŒºèŒƒå›´å¯¹CT影像组学模型预测肝细胞癌微血管侵犯的影响.

OBJECTIVES: Microvascular invasion (MVI) is an important predictor of postoperative recurrence or poor outcomes of hepatocellular carcinoma (HCC). Radiomics is able to predict MVI in HCC preoperatively. This study aims to investigate the influence of different region of interest (ROI) sizes on CT-based radiomics model for MVI prediction in HCC. METHODS: Patients with HCC with or without MVI confirmed by pathology and those who underwent preoperative plain or enhanced abdominal CT scans in the Third Xiangya Hospital of Central South University from January 2010 to December 2020 were retrospectively and consecutively included. According to the ratio of 7 to 3, the patients were randomly assigned into a training set and a validation set. Clinical data were collected from medical records, and radiomics features were extracted from the arterial phase (AP) and portal venous phase (PVP) of preoperatively acquired CT in all patients. Six different ROI sizes were employed. The original ROI (OROI) was manually delineated along the visible borders of the tumor layer-by-layer. The OROI was expanded out by 1-5 mm. The OROI was combined with 5 different peritumoral regions to generate the other 5 ROIs, named Plus1-Plus5. Feature extraction, dimension reduction, and model development were conducted in 6 different ROIs separately. Supporter vector machine (SVM) was used for model construction. Model performance was assessed via receiver operating characteristic (ROC) curve. RESULTS: A total of 172 HCC patients were included, in which 83 (48.3%) were MVI positive, and 89 (51.7%) were MVI negative. Three hundred and ninety-six features based on AP or PVP images were extracted from each ROI. After feature selection and dimension reduction, 4, 5, 15, 11, 6, and 3 features of OROI, Plus1, Plus2, Plus 3, Plus4, and Plus5 were selected for model construction, respectively. In the training set, the sensitivity, specificity, and area under the curve (AUC) of OROI were 0.759, 0.806, and 0.855, respectively. The AUC values of Plus2 (0.979) and Plus3 (0.954) were higher than that of OROI. The AUC values of Plus1 (0.802), Plus4 (0.792), and Plus5 (0.774) were not significantly different from those of OROI. In the validation set, the sensitivity, specificity, and AUC value of OROI were 0.640, 0.630, and 0.664, respectively. The AUC value of Plus3 was 0.903, which was higher than that of OROI. The AUC values of Plus1 (0.679), Plus2 (0.536), Plus4 (0.708), and Plus5 (0.757) were not significantly different from that of OROI (P>0.05). CONCLUSIONS: The size of ROI significantly inflluences on the performance of CT-based radiomics model for MVI prediction in HCC. Including appropriate area around the tumor into ROI could improve the predictive performance of the model, and 3 mm might be appropriate distance.

Preoperative CT for Characterization of Aggressive Macrotrabecular-Massive Subtype and Vessels That Encapsulate Tumor Clusters Pattern in Hepatocellular Carcinoma.

Background Macrotrabecular-massive (MTM) subtype and vessels encapsulating tumor clusters (VETC) pattern of hepatocellular carcinoma (HCC) are associated with unfavorable prognosis. Purpose To estimate the potential of preoperative CT in the prediction of MTM subtype and VETC pattern. Materials and Methods Patients who underwent surgical resection or liver transplant and preoperative CT for HCC between January 2015 and June 2018 were retrospectively included in the primary cohort. CT imaging features were evaluated by two radiologists. Predictors associated with the MTM subtype or VETC pattern were determined by using logistic regression analyses and the performance was tested in a validation cohort. Prognostic factors associated with early recurrence after surgical resection were identified by using Cox regression analyses. Results The primary cohort included 170 patients (median age, 55 years; interquartile range, 48-63 years; 152 men). Serum α-fetoprotein level higher than 100 ng/mL (odds ratio [OR], 4.3; 95% CI: 2.1, 9.2; P < .001), intratumor necrosis (OR, 5.2; 95% CI: 2.5, 11.0; P < .001), and intratumor hemorrhage (OR, 5.4; 95% CI: 1.3, 23.3; P = .02) were independent predictors for MTM subtype, whereas tumor size greater than 5 cm (OR, 3.8; 95% CI: 1.7, 8.1; P = .001) and intratumor necrosis (OR, 2.1; 95% CI: 1.0, 4.4; P = .045) were independent predictors for VETC pattern. These features were used for the construction of ANH and SN scores (where A is α-fetoprotein level, N is necrosis, H is hemorrhage, and S is size), respectively, which showed comparable prediction performance in the primary and validation cohorts. Preoperative high ANH and high SN phenotype (hazard ratio, 1.9; 95% CI: 1.2, 3.0; P = .01) was independently associated with early recurrence after surgical resection. Conclusion Preoperative CT features could be used for the characterization of macrotrabecular-massive subtype and vessels that encapsulate tumor clusters pattern and were of prognostic significance for early recurrence in patients with hepatocellular carcinoma. Online supplemental material is available for this article. See also the editorial by Yoon and Kim in this issue. Published under a CC BY 4.0 license.

Diagnostic performance of MRI using extracellular contrast agents versus gadoxetic acid for hepatocellular carcinoma: A systematic review and meta-analysis.

BACKGROUND & AIMS: Magnetic resonance imaging (MRI) is the first-line tool for the noninvasive diagnosis of hepatocellular carcinoma (HCC) in patients with chronic liver diseases. We performed a meta-analysis to compare the performance of MRI using extracellular contrast agents (ECA-MRI) with that using gadoxetic acid (EOB-MRI) for diagnosing HCC. METHODS: We searched multiple databases for studies comparing the diagnostic performance of ECA-MRI with that of EOB-MRI in patients with suspected HCC until 31 May 2020. The bivariate random-effects model was used to pool the performance and further subgroup analysis was performed. RESULTS: Eight studies were included evaluating a total of 1002 patients. ECA-MRI revealed significantly higher per-lesion sensitivity in the diagnosis of HCC than EOB-MRI did (0.76 vs 0.63, P = .002). For modified EOB-MRI (mEOB-MRI) using extended washout to the transitional phase (TP) or hepatobiliary phase (HBP), the sensitivity increased compared with that of EOB-MRI using restrictive washout in the portal venous phase (PVP) (0.74 vs 0.63, P = .07). No significant difference among the specificities of ECA-MRI, EOB-MRI, and mEOB-MRI (0.96, 0.98, and 0.93, respectively) was found. The sensitivity for lesions < 20 mm was significantly lower than that for lesions ≥ 20mm (0.66 vs 0.87, P = .01) only for ECA-MRI, which achieved higher sensitivity in Asian patients or with a 3.0 T scanner. CONCLUSIONS: ECA-MRI outperforms EOB-MRI in per-lesion sensitivity for diagnosing HCC, whereas mEOB-MRI shows a trend towards improved sensitivity compared with EOB-MRI with slightly decreased specificity. Registration: Prospero CRD42020189680.

Machine learning based on clinical characteristics and chest CT quantitative measurements for prediction of adverse clinical outcomes in hospitalized patients with COVID-19.

OBJECTIVES: To develop and validate a machine learning model for the prediction of adverse outcomes in hospitalized patients with COVID-19. METHODS: We included 424 patients with non-severe COVID-19 on admission from January 17, 2020, to February 17, 2020, in the primary cohort of this retrospective multicenter study. The extent of lung involvement was quantified on chest CT images by a deep learning-based framework. The composite endpoint was the occurrence of severe or critical COVID-19 or death during hospitalization. The optimal machine learning classifier and feature subset were selected for model construction. The performance was further tested in an external validation cohort consisting of 98 patients. RESULTS: There was no significant difference in the prevalence of adverse outcomes (8.7% vs. 8.2%, p = 0.858) between the primary and validation cohorts. The machine learning method extreme gradient boosting (XGBoost) and optimal feature subset including lactic dehydrogenase (LDH), presence of comorbidity, CT lesion ratio (lesion%), and hypersensitive cardiac troponin I (hs-cTnI) were selected for model construction. The XGBoost classifier based on the optimal feature subset performed well for the prediction of developing adverse outcomes in the primary and validation cohorts, with AUCs of 0.959 (95% confidence interval [CI]: 0.936-0.976) and 0.953 (95% CI: 0.891-0.986), respectively. Furthermore, the XGBoost classifier also showed clinical usefulness. CONCLUSIONS: We presented a machine learning model that could be effectively used as a predictor of adverse outcomes in hospitalized patients with COVID-19, opening up the possibility for patient stratification and treatment allocation. KEY POINTS: • Developing an individually prognostic model for COVID-19 has the potential to allow efficient allocation of medical resources. • We proposed a deep learning-based framework for accurate lung involvement quantification on chest CT images. • Machine learning based on clinical and CT variables can facilitate the prediction of adverse outcomes of COVID-19.

First CT characteristic appearance of patients with coronavirus disease 2019. / 2019å† çŠ¶ç— æ¯’ç— æ‚£è€ é¦–æ¬¡CT影像学特征.

OBJECTIVES: To investigate imaging features of the coronavirus disease 2019 (COVID-19), and to provide concrete evidences for diagnosis of COVID-19. METHODS: Imaging data of the first chest CT examination and clinical data (age, sex, clinical history, epidemiological history, and laboratory tests) of 163 patients with COVID-19 from 2 hospitals were collected for retrospective analysis. Imaging features of the first chest CT examination and the correspondence between CT manifestations and the nucleic acid test results of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) were analyzed. RESULTS: The first chest CT images of 163 COVID-19 patients showed that 92.02% of lesions were ground-glass opacity (GGO), 76.69% were consolidation, and 73.62% were GGO together with consolidation. Multiple lesions were found in 71.17% patients and multiple lobules in 86.50% patients. Lesions in 53.37% patients were found with bronchial inflation signs and those in 36.20% patients presented with "crazy paving" pattern, while only 7.36% were found with hilar node enlargement and pleural effusion. First CT findings of 18 patients were found to be inconsistent with the results of pathogen examination. CONCLUSIONS: COVID-19 patients showed specific features in the first chest CT examination. The combination of the first chest CT imaging features and SARS-CoV-2 nucleic acid test results as well as reexamination if necessary can help to make the diagnosis of SARS-CoV-2 infection accurately.

Volume changes of subcortical structures in patients with post-hepatitis B cirrhosis: A magnetic resonance imaging study. / ä¹™åž‹è‚ç‚ŽåŽè‚ç¡¬åŒ–æ‚£è€ è„‘çš®å±‚ä¸‹ç»“æž„ä½“ç§¯æ”¹å˜çš„MRIç ”ç©¶.

OBJECTIVES: To investigate volume changes of subcortical structures in patients with post-hepatitis B cirrhosis. METHODS: Thirty patients with post-hepatitis B cirrhosis (the cirrhosis group) and 24 age- and sex-matched healthy controls (the control group) were enrolled in this prospective study. All subjects underwent neuropsychological tests, blood biochemical determinations, and cerebral MRI. Volumes of 18 selected subcortical structures were automatically segmented and analyzed by the FreeSurfer. In the cirrhosis group, the relationships between abnormal subcortical volumes and clinical index or neurocognitive performance were investigated. The relationships between globus pallidus volumes and pallidal hyperintensity were also examined. RESULTS: Compared with the healthy controls, patients with post-hepatitis B cirrhosis displayed smaller bilateral putamen, amygdala, and nucleus accumbens volumes and larger bilateral globus pallidus volumes (P<0.001 or P=0.001). In the cirrhosis group, the volumes of left putamen and amygdala were negatively correlated with the number connection test-A (NCT-A)(left putamen r=-0.410, P=0.034; left amygdala r=-0.439, P=0.022), and the volumes of bilateral globus pallidus were positively correlated with pallidal index (PI) (left globus pallidus r=0.889, P<0.001; right globus pallidus r=0.900, P<0.001). CONCLUSIONS: Abnormalities of subcortical volumes appear bilaterally symmetrical in patients with post-hepatitis B cirrhosis. Atrophy of left putamen and amygdala might contribute to poor neurocognitive performance, and the manganese deposition might contribute to the increased globus pallidus volumes in patients with post-hepatitis B cirrhosis.

Combination of 3.0T magnetic resonance imaging T2 mapping with texture analysis for evaluating the degeneration of lumbar facet joints. / 3.0Tç£å ±æŒ¯æˆåƒT2 mappingè”åˆçº¹ç†åˆ†æžè¯„ä¼°è °æ¤Žå°å ³èŠ‚é€€å˜ç¨‹åº¦.

OBJECTIVES: Quantitative magnetic resonance imaging has been successfully applied to assess the status of cartilage biochemical components. This study aimed to investigate the performance of 3.0T magnetic resonance imaging T2 mapping combined with texture analysis for evaluating the early degeneration of lumbar facet joints. METHODS: A total of 38 patients (20 in the asymptomatic group and 18 in the symptomatic group) were enrolled. All patients underwent 3.0T magnetic resonance imaging conventional sequences, water excitation three-dimensional spoiled gradient echo sequence (3D-WATSc), and T2 mapping scans. The bilateral L4/5 and L5/S1 lumbar facet joints were morphological graded using the Weishaupt criteria, T2 values, and texture parameters derived from T2 mapping of cartilage. The Kruskal-Wallis H test was used to compare the differences of parameters among different groups. Multivariate logistic regression analysis was used to obtain the independent predictive factors for evaluating the early degeneration of lumbar facet joints. Receiver operating characteristic (ROC) curve was performed and the area under curve (AUC) was calculated. Spearman correlation analysis was used to evaluate the correlation of the independent predictors of cartilage T2 value and texture parameters with the subjects' Japanese Orthopedic Association (JOA) score or Visual Analogue Scale (VAS) score. RESULTS: A total of 148 facet joints were selected, including 70 in Weishaupt 0 (normal) group, 58 in Weishaupt 1 group, and 20 in Weishaupt 2-3 group. T2 value, entropy, and contrast increased significantly as the exacerbation of facet joint degeneration (all P<0.05), while the inverse difference moment, energy, and correlation decreased (all P<0.05). Entropy among different groups was significantly different (all P<0.05), and the differences of T2 value, contrast, inverse difference moment, and energy between Weishaupt 0 and Weishaupt 1 groups, or Weishaupt 0 and Weishaupt 2-3 groups were statistically significant (all P<0.05). Multivariate logistic regression analysis suggested that T2 value and inverse difference moment were the independent predictors for evaluating early degeneration of facet joints. The combination of T2 value with inverse difference moment achieved the best performance in distinguishing Weishaupt 0 from Weishaupt 1 (AUC=0.85), with sensitivity and specificity at 92.7% and 76.5%, respectively. In the symptom group, the cartilage T2 value combined inverse difference moment was positively correlated with JOA score (r=0.475, P<0.05) and VAS score (r=0.452, P<0.05). CONCLUSIONS: 3.0T magnetic resonance imaging T2 mapping combined with texture analysis is helpful to quantitatively evaluate the early degeneration of lumbar facet joints, in which the T2 value and inverse difference moment show an indicative significance．.

Long-term c-Kit overexpression in beta cells compromises their function in ageing mice.

AIMS/HYPOTHESIS: c-Kit signalling regulates intracellular pathways that enhance beta cell proliferation, insulin secretion and islet vascularisation in mice up to 28 weeks of age and on short-term high-fat diet. However, long-term c-Kit activation in ageing mouse islets has yet to be examined. This study utilises beta cell-specific c-Kit-overexpressing transgenic (c-KitßTg) ageing mice (~60 weeks) to determine the effect of its activation on beta cell dysfunction and insulin secretion. METHODS: Wild-type and c-KitßTg mice, aged 60 weeks, were examined using metabolic tests to determine glucose tolerance and insulin secretion. Pancreas histology and proteins in isolated islets were examined to determine the expression of beta cell transcription factors, proliferation and intracellular signalling. To determine the role of insulin receptor signalling in ageing c-KitßTg mice, we generated beta cell-specific inducible insulin receptor knockout in ageing c-KitßTg mice (c-KitßTg;ßIRKO mice) and examined the ageing mice for glucose tolerance and islet histology. RESULTS: Ageing c-KitßTg mice progressively developed glucose intolerance, compared with age-matched wild-type littermates, due to impaired insulin secretion. Increased beta cell mass, proliferation and nuclear forkhead box transcription factor O1 (FOXO1) expression and reduced exocytotic protein levels were detected in ageing c-KitßTg mouse islets. Protein analyses of isolated islets showed increased insulin receptor, phosphorylated IRS-1Ser612 and cleaved poly(ADP-ribose) polymerase levels in ageing c-KitßTg mice. Ageing c-KitßTg mouse islets treated ex vivo with insulin demonstrated reduced Akt phosphorylation, indicating that prolonged c-Kit induced beta cell insulin insensitivity. Ageing c-KitßTg;ßIRKO mice displayed improved glucose tolerance and beta cell function compared with ageing c-KitßTg mice. CONCLUSIONS/INTERPRETATION: These findings indicate that long-term c-Kit overexpression in beta cells has a negative impact on insulin exocytosis and that temporally dependent regulation of c-Kit-insulin receptor signalling is important for optimal beta cell function.