Signal enhancement ratio of multi-phase contrast-enhanced MRI: an imaging biomarker for survival in pancreatic adenocarcinoma.

OBJECTIVES: To evaluate signal enhancement ratio (SER) for tissue characterization and prognosis stratification in pancreatic adenocarcinoma (PDAC), with quantitative histopathological analysis (QHA) as the reference standard. METHODS: This retrospective study included 277 PDAC patients who underwent multi-phase contrast-enhanced (CE) MRI and whole-slide imaging (WSI) from three centers (2015-2021). SER is defined as (SIlt - SIpre)/(SIea - SIpre), where SIpre, SIea, and SIlt represent the signal intensity of the tumor in pre-contrast, early-, and late post-contrast images, respectively. Deep-learning algorithms were implemented to quantify the stroma, epithelium, and lumen of PDAC on WSIs. Correlation, regression, and Bland-Altman analyses were utilized to investigate the associations between SER and QHA. The prognostic significance of SER on overall survival (OS) was evaluated using Cox regression analysis and Kaplan-Meier curves. RESULTS: The internal dataset comprised 159 patients, which was further divided into training, validation, and internal test datasets (n = 60, 41, and 58, respectively). Sixty-five and 53 patients were included in two external test datasets. Excluding lumen, SER demonstrated significant correlations with stroma (r = 0.29-0.74, all p < 0.001) and epithelium (r = -0.23 to -0.71, all p < 0.001) across a wide post-injection time window (range, 25-300 s). Bland-Altman analysis revealed a small bias between SER and QHA for quantifying stroma/epithelium in individual training, validation (all within ± 2%), and three test datasets (all within ± 4%). Moreover, SER-predicted low stromal proportion was independently associated with worse OS (HR = 1.84 (1.17-2.91), p = 0.009) in training and validation datasets, which remained significant across three combined test datasets (HR = 1.73 (1.25-2.41), p = 0.001). CONCLUSION: SER of multi-phase CE-MRI allows for tissue characterization and prognosis stratification in PDAC. CLINICAL RELEVANCE STATEMENT: The signal enhancement ratio of multi-phase CE-MRI can serve as a novel imaging biomarker for characterizing tissue composition and holds the potential for improving patient stratification and therapy in PDAC. KEY POINTS: Imaging biomarkers are needed to better characterize tumor tissue in pancreatic adenocarcinoma. Signal enhancement ratio (SER)-predicted stromal/epithelial proportion showed good agreement with histopathology measurements across three distinct centers. Signal enhancement ratio (SER)-predicted stromal proportion was demonstrated to be an independent prognostic factor for OS in PDAC.

Primary lymphoma of the female genital tract masquerading as gynecological malignancy.

BACKGROUND: Primary lymphoma of the female genital tract (PLFGT) is a rare malignant tumor in the female reproductive system, with a low incidence and few clinical reports. The aim of this study is to report our institutional experience with this rare malignancy and emphasize the need for increasing the awareness about PLFGT presenting with gynecologic symptoms. METHODS: The medical records of patients diagnosed with PLFGT from March 2014 to November 2022 in the First Affiliated Hospital of Wannan Medical College were reviewed. Histological classification and staging were based on the World Health Organization and Ann Arbor systems, respectively. RESULTS: There were 13 patients with diagnosis of PLFGT and the median length of follow-up was 31 months (0-102 months). The main clinical symptoms included postmenopausal vaginal bleeding, pelvic mass and abdominal pain. Serum LDH increased in 10 patients and serum CA125 elevated in 2 patients. The tumor of ovarian or uterine presented as solid masses in CT or MRI, and ascites was rare. The histological subtypes were diffuse large B-cell (n = 12) and follicular (n = 1) lymphoma. Tumors were located in ovary (n = 8), uterus (n = 3), and cervix (n = 2). According to the Ann Arbor staging system, 6 cases were classified as stage II and 7 cases were classified as stage IV, respectively. A total of 10 patients underwent surgery. Combination chemotherapy was used in 10 patients. Eight patients had tumor-free survival, 1 patient had recurrent disease, 3 patients died and 1 patient lost to follow-up. The median survival time was 32 months (1-102 months). CONCLUSION: PLFGT usually presents as gynecological symptoms and solid masses in pelvis. Surgery or biopsy was the way to obtain the pathologic diagnosis, and combination chemotherapy is the efficient method for PLFGT. Making an accurate preoperative diagnosis is of paramount importance to avoid radical gynecologic surgery.

Nonalcoholic Fatty Liver Disease: Changes in Gut Microbiota and Blood Lipids.

Background and Aims: The global prevalence of nonalcoholic fatty liver disease (NAFLD) is 25%. This study aimed to explore differences in the gut microbial community and blood lipids between normal livers and those affected by NAFLD using 16S ribosomal deoxyribonucleic acid sequencing. Methods: Gut microbiome profiles of 40 NAFLD and 20 non-NAFLD controls were analyzed. Information about four blood lipids and 13 other clinical features was collected. Patients were divided into three groups by ultrasound and FibroScan, those with a normal liver, mild FL (FL1), and moderate-to-severe FL (FL2). FL1 and FL2 patients were divided into two groups, those with either hyperlipidemia or non-hyperlipidemia based on their blood lipids. Potential keystone species within the groups were identified using univariate analysis and a specificity-occupancy plot. Significant difference in biochemical parameters ion NAFLD patients and healthy individuals were identified by detrended correspondence analysis and canonical correspondence analysis. Results: Decreased gut bacterial diversity was found in patients with NAFLD. Firmicutes/Bacteroidetes decreased as NAFLD progressed. Faecalibacterium and Ruminococcus 2 were the most representative fatty-related bacteria. Glutamate pyruvic transaminase, aspartate aminotransferase, and white blood cell count were selected as the most significant biochemical indexes. Calculation of areas under the curve identified two microbiomes combined with the three biochemical indexes that identified normal liver and FL2 very well but performed poorly in diagnosing FL1. Conclusions: Faecalibacterium and Ruminococcus 2, combined with glutamate pyruvic transaminase, aspartate aminotransferase, and white blood cell count distinguished NAFLD. We speculate that regulating the health of gut microbiota may release NAFLD, in addition to providing new targets for clinicians to treat NAFLD.

Knockdown of ACOT4 alleviates gluconeogenesis and lipid accumulation in hepatocytes.

Acyl-CoA thioesterase 4 (ACOT4) has been reported to be related to acetyl-CoA carboxylase activity regulation; However, its exact functions in liver lipid and glucose metabolism are still unclear. Here, we discovered explored the regulatory roles of ACOT4 in hepatic lipid and glucose metabolism in vitro. We found that the expression level of ACOT4 was significantly increased in the hepatic of db/db and ob/ob mice as well as obese mice fed a high fat diet. Adenovirus-mediated overexpression of ACOT4 promoted gluconeogenesis and high-glucose/high-insulin-induced lipid accumulation and impaired insulin sensitivity in primary mouse hepatocytes, whereas ACOT4 knockdown notably suppressed gluconeogenesis and decreased the triglycerides accumulation in hepatocytes. Furthermore, ACOT4 knockdown increased insulin-induced phosphorylation of AKT and GSK-3ß in primary mouse hepatocytes. Mechanistically, we found that upregulation of ACOT4 expression inhibited AMP-activated protein kinase (AMPK) activity, and its knockdown had the opposite effect. However, activator A769662 and inhibitor compound C of AMPK suppressed the impact of the change in ACOT4 expression on AMPK activity. Our data indicated that ACOT4 is related to hepatic glucose and lipid metabolism, primarily via the regulation of AMPK activity. In conclusion, ACOT4 is a potential target for the therapy of non-alcoholic fatty liver (NAFLD) and type 2 diabetes.

Photoacoustic Imaging Radiomics to Identify Breast Cancer in BI-RADS 4 or 5 Lesions.

OBJECTIVES: To develop a nomogram based on photoacoustic imaging (PAI) radiomics and BI-RADs to identify breast cancer (BC) in BI-RADS 4 or 5 lesions detected by ultrasound (US). METHODS: In this retrospective study, 119 females with 119 breast lesions at US and PAI examination were included (January 2022 to December 2022). Patients were divided into the training set (n = 83) or testing set (n = 36) to develop a nomogram to identify BC in BI-RADS 4 or 5 lesions. Relevant factors at clinic, BI-RADS category, and PAI were reviewed. Univariate and multivariate regression was used to evaluate factors for associations with BC. To evaluate the diagnostic performance of nomogram, the area under the curve (AUC) of receiver operating characteristic curve, accuracy, specificity and sensitivity was employed. RESULTS: The nomogram that included BI-RADS category and PAI radiomics score demonstrated a high AUC of 0.925 (95%CI: 0.8467-0.9712) in the training set and 0.926 (95%CI: 0.846-1.000) in the test set. The nomogram also showed significantly better discrimination than the radiomics score (P = .048) or BI-RADS category (P = .009) in the training set. These significant differences were demonstrated in the testing set, outperform the radiomics score (P = .038) and BI-RADS category (P = .013). CONCLUSIONS: The nomogram developed with BI-RADS and PAI radiomics score can effectively identify BC in BI-RADS 4 or 5 lesions. This technique has the potential to further improve early diagnostic accuracy for BC.

Comparative study of ultrasound attenuation analysis and controlled attenuation parameter in the diagnosis and grading of liver steatosis in non-alcoholic fatty liver disease patients.

PURPOSE: To assess the diagnostic performance of Ultrasound Attenuation Analysis (USAT) in the diagnosis and grading of hepatic steatosis in patients with non-alcoholic fatty liver disease (NAFLD) using Controlled Attenuation Parameters (CAP) as a reference. MATERIALS AND METHODS: From February 13, 2023, to September 26, 2023, participants underwent CAP and USAT examinations on the same day. We used manufacturer-recommended CAP thresholds to categorize the stages of hepatic steatosis: stage 1 (mild) - 240 dB/m, stage 2 (moderate) - 265 dB/m, stage 3 (severe) - 295 dB/m. Receiver Operating Characteristic curves were employed to evaluate the diagnostic accuracy of USAT and determine the thresholds for different levels of hepatic steatosis. RESULTS: Using CAP as the reference, we observed that the average USAT value increased with the severity of hepatic steatosis, and the differences in USAT values among the different hepatic steatosis groups were statistically significant (p < 0.05). There was a strong positive correlation between USAT and CAP (r = 0.674, p < 0.0001). When using CAP as the reference, the optimal cut-off values for diagnosing and predicting different levels of hepatic steatosis with USAT were as follows: the cut-off value for excluding the presence of hepatic steatosis was 0.54 dB/cm/MHz (AUC 0.96); for mild hepatic steatosis, it was 0.59 dB/cm/MHz (AUC 0.86); for moderate hepatic steatosis, it was 0.73 dB/cm/MHz (AUC 0.81); and for severe hepatic steatosis, it was 0.87 dB/cm/MHz (AUC 0.87). CONCLUSION: USAT exhibits strong diagnostic performance for hepatic steatosis and shows a high correlation with CAP values.

ViromeFlowX: a Comprehensive Nextflow-based Automated Workflow for Mining Viral Genomes from Metagenomic Sequencing Data.

Understanding the link between the human gut virome and diseases has garnered significant interest in the research community. Extracting virus-related information from metagenomic sequencing data is crucial for unravelling virus composition, host interactions, and disease associations. However, current metagenomic analysis workflows for viral genomes vary in effectiveness, posing challenges for researchers seeking the most up-to-date tools. To address this, we present ViromeFlowX, a user-friendly Nextflow workflow that automates viral genome assembly, identification, classification, and annotation. This streamlined workflow integrates cutting-edge tools for processing raw sequencing data for taxonomic annotation and functional analysis. Application to a dataset of 200 metagenomic samples yielded high-quality viral genomes. ViromeFlowX enables efficient mining of viral genomic data, offering a valuable resource to investigate the gut virome's role in virus-host interactions and virus-related diseases.

Assessment of Oxygen Saturation in Breast Lesions Using Photoacoustic Imaging: Correlation With Benign and Malignant Disease.

BACKGROUND: Hypoxia is a hallmark of breast cancer (BC). Photoacoustic (PA) imaging, based on the use of laser-generated ultrasound (US), can detect oxygen saturation (So2) in the tissues of breast lesion patients. PURPOSE: To measure the oxygenation status of tissue in and on both sides of the lesion in breast lesion participants using a multimodal Photoacoustic/ultrasound (PA/US) imaging system and to determine the correlation between So2 measured by PA imaging and benign or malignant disease. MATERIALS AND METHODS: Multimodal PA/US imaging and gray-scale US (GSUS) of breast lesion was performed in consecutive breast lesion participants imaged in the US Outpatient Clinic between 2022 and 2023. Dual-wavelength PA imaging was used to measure the So2 value inside the lesion and on both sides of the tissue, and to distinguish benign from malignant lesions based on the So2 value. The ability of So2 to distinguish benign from malignant breast lesions was evaluated by the receiver operating characteristic curve (ROC) and the De-Long test. RESULTS: A total of 120 breast lesion participants (median age, 42.5 years) were included in the study. The malignant lesions exhibited lower So2 levels compared to benign lesions (malignant: 71.30%; benign: 83.81%; P < .01). Moreover, PA/US imaging demonstrates superior diagnostic results compared to GSUS, with an area under the curve (AUC) of 0.89 versus 0.70, sensitivity of 89.58% versus 85.42%, and specificity of 86.11% versus 55.56% at the So2 cut-off value of 78.85 (P < .001). The false positive rate in GSUS reduced by 30.75%, and the false negative rate diminished by 4.16% with PA /US diagnosis. Finally, the So2 on both sides tissues of malignant lesions are lower than that of benign lesions (P < .01). CONCLUSION: PA imaging allows for the assessment of So2 within the lesions of breast lesion patients, thereby facilitating a superior distinction between benign and malignant lesions.

Sonography-based multimodal information platform for identifying the surgical pathology of ductal carcinoma in situ.

BACKGROUND: The risk of ductal carcinoma in situ (DCIS) identified by biopsy often increases during surgery. Therefore, confirming the DCIS grade preoperatively is necessary for clinical decision-making. PURPOSE: To train a three-classification deep learning (DL) model based on ultrasound (US), combining clinical data, mammography (MG), US, and core needle biopsy (CNB) pathology to predict low-grade DCIS, intermediate-to-high-grade DCIS, and upstaged DCIS. MATERIALS AND METHODS: Data of 733 patients with 754 DCIS cases confirmed by biopsy were retrospectively collected from May 2013 to June 2022 (N1), and other data (N2) were confirmed by biopsy as low-grade DCIS. The lesions were randomly divided into training (n=471), validation (n=142), and test (n = 141) sets to establish the DCIS-Net. Information on the DCIS-Net, clinical (age and sign), US (size, calcifications, type, breast imaging reporting and data system [BI-RADS]), MG (microcalcifications, BI-RADS), and CNB pathology (nuclear grade, architectural features, and immunohistochemistry) were collected. Logistic regression and random forest analyses were conducted to develop Multimodal DCIS-Net to calculate the specificity, sensitivity, accuracy, receiver operating characteristic curve, and area under the curve (AUC). RESULTS: In the test set of N1, the accuracy and AUC of the multimodal DCIS-Net were 0.752-0.766 and 0.859-0.907 in the three-classification task, respectively. The accuracy and AUC for discriminating DCIS from upstaged DCIS were 0.751-0.780 and 0.829-0.861, respectively. In the test set of N2, the accuracy and AUC of discriminating low-grade DCIS from upstaged low-grade DCIS were 0.769-0.987 and 0.818-0.939, respectively. DL was ranked from one to five in the importance of features in the multimodal-DCIS-Net. CONCLUSION: By developing the DCIS-Net and integrating it with multimodal information, diagnosing low-grade DCIS, intermediate-to high-grade DCIS, and upstaged DCIS is possible. It can also be used to distinguish DCIS from upstaged DCIS and low-grade DCIS from upstaged low-grade DCIS, which could pave the way for the DCIS clinical workflow.

Development and validation of nomograms using photoacoustic imaging and 2D ultrasound to predict breast nodule benignity and malignancy.

BACKGROUND: The application of photoacoustic imaging (PAI), utilizing laser-induced ultrasound, shows potential in assessing blood oxygenation in breast nodules. However, its effectiveness in distinguishing between malignant and benign nodules remains insufficiently explored. PURPOSE: This study aims to develop nomogram models for predicting the benign or malignant nature of breast nodules using PAI. METHOD: A prospective cohort study enrolled 369 breast nodules, subjecting them to PAI and ultrasound examination. The training and testing cohorts were randomly divided into two cohorts in a ratio of 3:1. Based on the source of the variables, three models were developed, Model 1: photoacoustic-BIRADS+BMI + blood oxygenation, Model 2: BIRADS+Shape+Intranodal blood (Doppler) + BMI, Model 3: photoacoustic-BIRADS+BIRADS+ Shape+Intranodal blood (Doppler) + BMI + blood oxygenation. Risk factors were identified through logistic regression, resulting in the creation of three predictive models. These models were evaluated using calibration curves, subject receiver operating characteristic (ROC), and decision curve analysis. RESULTS: The area under the ROC curve for the training cohort was 0.91 (95% confidence interval, 95% CI: 0.88-0.95), 0.92 (95% CI: 0.89-0.95), and 0.97 (95% CI: 0.96-0.99) for Models 1-3, and the ROC curve for the testing cohort was 0.95 (95% CI: 0.91-0.98), 0.89 (95% CI: 0.83-0.96), and 0.97 (95% CI: 0.95-0.99) for Models 1-3. CONCLUSIONS: The calibration curves demonstrate that the model's predictions agree with the actual values. Decision curve analysis suggests a good clinical application.

Development of a radiomics-based model to predict occult liver metastases of pancreatic ductal adenocarcinoma: a multicenter study.

BACKGROUND: Undetectable occult liver metastases block the long-term survival of pancreatic ductal adenocarcinoma (PDAC). This study aimed to develop a radiomics-based model to predict occult liver metastases and assess its prognostic capacity for survival. MATERIALS AND METHODS: Patients who underwent surgical resection and were pathologically proven with PDAC were recruited retrospectively from five tertiary hospitals between January 2015 and December 2020. Radiomics features were extracted from tumors, and the radiomics-based model was developed in the training cohort using LASSO-logistic regression. The model's performance was assessed in the internal and external validation cohorts using the area under the receiver operating curve (AUC). Subsequently, the association of the model's risk stratification with progression-free survival (PFS) and overall survival (OS) was then statistically examined using Cox regression analysis and the log-rank test. RESULTS: A total of 438 patients [mean (SD) age, 62.0 (10.0) years; 255 (58.2%) male] were divided into the training cohort ( n =235), internal validation cohort ( n =100), and external validation cohort ( n =103). The radiomics-based model yielded an AUC of 0.73 (95% CI: 0.66-0.80), 0.72 (95% CI: 0.62-0.80), and 0.71 (95% CI: 0.61-0.80) in the training, internal validation, and external validation cohorts, respectively, which were higher than the preoperative clinical model. The model's risk stratification was an independent predictor of PFS (all P <0.05) and OS (all P <0.05). Furthermore, patients in the high-risk group stratified by the model consistently had a significantly shorter PFS and OS at each TNM stage (all P <0.05). CONCLUSION: The proposed radiomics-based model provided a promising tool to predict occult liver metastases and had a great significance in prognosis.

Delta Radiomics Model for the Prediction of Overall Survival and Local Recurrence in Small Cell Lung Cancer Patients After Chemotherapy.

RATIONALE AND OBJECTIVES: To evaluate the validity of CT-based delta radiomics signatures in predicting overall survival (OS) and local recurrence (LR) in small cell lung cancer (SCLC) patients after chemotherapy. MATERIALS AND METHODS: Retrospectively enrolled 136 SCLC patients were split into training and testing cohorts. Radiomics features were extracted from CT images before, after the second, and the fourth cycle of chemotherapy. Delta radiomics features were obtained by calculating the net changes of features. Three radiomics signatures (R1, R2, and R3) and three delta radiomics signatures (R21, R31, and R32) were developed. The best signature was defined as the radiomics risk signature (RRS). The significant clinicoradiological factors and RRS of OS or LR were applied to build the combined model. RRS was also investigated in the subgroups based on stage and treatment regimens, respectively. RESULTS: Delta radiomics models presented improved performance. R32 signature demonstrated the highest C-indices in the training and testing cohorts, with C-indices of 0.850 and 0.834 in the OS arm, and 0.723 and 0.737 in the LR arm, respectively. The incremental performance was observed after the clinicoradiological characteristics integrated into the RRSOS, with C-indexes of 0.857 and 0.836, respectively. Furthermore, the stratified analysis also confirmed the ability of RRS based on the stage and treatment regimen subgroups in the OS and LR arms, respectively. CONCLUSION: Delta radiomics signatures could improve the personalized prediction of OS and LR at the early stage of chemotherapy in SCLC patients. R32 signature performed the highest performance.

An artificial intelligence model based on transrectal ultrasound images of biopsy needle tract tissues to differentiate prostate cancer.

PURPOSE: We aimed to develop an artificial intelligence (AI) model based on transrectal ultrasonography (TRUS) images of biopsy needle tract (BNT) tissues for predicting prostate cancer (PCa) and to compare the PCa diagnostic performance of the radiologist model and clinical model. METHODS: A total of 1696 2D prostate TRUS images were involved from 142 patients between July 2021 and May 2022. The ResNet50 network model was utilized to train classification models with different input methods: original image (Whole model), BNT (Needle model), and combined image [Feature Pyramid Networks (FPN) model]. The training set, validation set, and test set were randomly assigned, then randomized 5-fold cross-validation between the training set and validation set was performed. The diagnostic effectiveness of AI models and image combination was accessed by an independent testing set. Then, the optimal AI model and image combination were selected to compare the diagnostic efficacy with that of senior radiologists and the clinical model. RESULTS: In the test set, the area under the curve, specificity, and sensitivity of the FPN model were 0.934, 0.966, and 0.829, respectively; the diagnostic efficacy was improved compared with the Whole and Needle models, with statistically significant differences (P < 0.05), and was better than that of senior radiologists (area under the curve: 0.667). The FPN model detected more PCa compared with senior physicians (82.9% vs. 55.8%), with a 61.3% decrease in the false-positive rate and a 23.2% increase in overall accuracy (0.887 vs. 0.655). CONCLUSION: The proposed FPN model can offer a new method for prostate tissue classification, improve the diagnostic performance, and may be a helpful tool to guide prostate biopsy.

Electroacupuncture Ameliorates Depression-Like Behaviors in Post-Stroke Rats via Activating AMPK-Mediated Mitochondrial Function.

Background: Post-stroke depression (PSD) is one of the most common complications of stroke. Electroacupuncture (EA) is an effective traditional Chinese medicine treatment for PSD, which is widely used in clinical settings. EA has a significant therapeutic effect against PSD, but the mechanism is still unclear. This study aimed to determine whether EA ameliorates depression-like behaviors in PSD rats by regulating the adenosine 5'-monophosphate (AMP)-activated protein kinase (AMPK)-mediated mitochondrial function. Methods: Middle cerebral artery occlusion (MCAO) and chronic unpredictable mild stress (CUMS) were used to develop a PSD rat model. To elucidate the role of AMPK in EA treatment, compound C, a selective inhibitor of AMPK, was injected into the lateral ventricle of rats before EA treatment. EA treatment was performed for 14 consecutive days for 30 min per day after PSD modeling. A modified Zea-Longa five-point scale scoring system was used to determine neurologic function in MCAO rats. Behavioral tests were conducted to evaluate depression-like phenotypes in rats. Depression-like behaviors were tested by sucrose preference test (SPT), novelty suppressed feeding test (NSFT), and open-field test (OFT). The structure and morphology of the prefrontal cortex were observed by histopathological hematoxylin-eosin (HE) and Nissl staining. The mitochondrial morphology and function were analyzed by colorimetry, chemiluminescence, Western blotting, and quantitative real-time polymerase chain reaction (qRT-PCR). Results: EA treatment successfully ameliorated depression-like behaviors, upregulated AMPK expression, and improved mitochondrial function. However, AMPK inhibition by Compound C exacerbated depression-like behaviors and aggravated neuronal and mitochondrial injury in PSD rats. Conclusion: EA treatment improved depression-like behaviors in PSD rats and promoted mitochondrial function by activating AMPK.

Re-evaluation of Mirror Visual Feedback Therapy for a Systematic Evaluation/Meta-analysis of Physical Function Re-education After Stroke.

OBJECTIVE: The aim of the study is to re-evaluate the methodological quality and quality of evidence for a systematic evaluation/meta-analysis of the effect of mirror visual feedback therapy on physical function re-education after stroke. METHODS: Systematic evaluations/meta-analyses of mirror visual feedback therapy on physical function re-education after stroke were searched in the China Knowledge Network database, Wanfang database, Vipers database, China Biomedical Literature database, PubMed, Web of Science, and Embase using a computer, and the search time frame was up to January 2022. Methodological quality and quality of evidence ratings of the included studies were determined using the AMSTAR2 scale and GRADE classification by two authors. RESULTS: Seventeen publications were included. The evaluation with the AMSTAR2 scale showed that one study had an intermediate quality rating, five had a low-quality rating, and the remaining 11 were all very low quality. The GRADE scale showed 93 outcome indicators, of which 6 were intermediate, 23 were low grades, and the rest were very low grades, with low overall quality. CONCLUSIONS: Mirror visual feedback therapy is efficacious for physical function re-education after stroke and promotes recovery from physical dysfunction, but the methodological quality and quality of evidence from the related systematic evaluations/meta-analyses are low.

Simulation of the Internal Temperature Field and Flow Field in a Double-Layer Sintering Bed.

To meet the requirements of Angang's blast furnace smelting for sintering output, improve the double-layer sintering process, and determine the appropriate parameters for the double-layer sintering process, this article established a mathematical model and simulated the temperature field in the burden bed and the changing trends of O2 and CO2 concentrations in the sintering tail gas during the single-layer and double-layer sintering processes of the sintering machine. The simulation results show that (1) compared with the sintering time of single-layer sintering in the baseline period, the error of the single-sintering model is only about 2.5%, and the model's accuracy is high. (2) Two combustion zones of double-layer sintering increase O2 consumption, and the O2 concentration in the tail gas decreases significantly. (3) The thickness of the upper and lower feeding layers of double-layer oxygen supplement sintering is 650 + 300 mm better than that of 600 + 350 mm. (4) The optimal secondary ignition time is 15 min.

Spleen volume-based non-invasive tool for predicting hepatic decompensation in people with compensated cirrhosis (CHESS1701).

Background & Aims: Non-invasive stratification of the liver decompensation risk remains unmet in people with compensated cirrhosis. This study aimed to develop a non-invasive tool (NIT) to predict hepatic decompensation. Methods: This retrospective study recruited 689 people with compensated cirrhosis (median age, 54 years; 441 men) from 5 centres from January 2016 to June 2020. Baseline abdominal computed tomography (CT), clinical features, and liver stiffness were collected, and then the first decompensation was registered during the follow-up. The spleen-based model was designed for predicting decompensation based on a deep learning segmentation network to generate the spleen volume and least absolute shrinkage and selection operator (LASSO)-Cox. The spleen-based model was trained on the training cohort of 282 individuals (Institutions I-III) and was validated in 2 external validation cohorts (97 and 310 individuals from Institutions IV and V, respectively) and compared with the conventional serum-based models and the Baveno VII criteria. Results: The decompensation rate at 3 years was 23%, with a 37.6-month median (IQR 21.1-52.1 months) follow-up. The proposed model showed good performance in predicting decompensation (C-index ≥0.84) and outperformed the serum-based models (C-index comparison test p <0.05) in both the training and validation cohorts. The hazard ratio (HR) for decompensation in individuals with high risk was 7.3 (95% CI 4.2-12.8) in the training and 5.8 (95% CI 3.9-8.6) in the validation (log-rank test, p <0.05) cohorts. The low-risk group had a negligible 3-year decompensation risk (≤1%), and the model had a competitive performance compared with the Baveno VII criteria. Conclusions: This spleen-based model provides a non-invasive and user-friendly method to help predict decompensation in people with compensated cirrhosis in diverse healthcare settings where liver stiffness is not available. Lay summary: People with compensated cirrhosis with larger spleen volume would have a higher risk of decompensation. We developed a spleen-based model and validated it in external validation cohorts. The proposed model might help predict hepatic decompensation in people with compensated cirrhosis when invasive tools are unavailable.

Dysregulated autophagic activity induced in response to chronic intermittent hypoxia contributes to the pathogenesis of NAFLD.

Chronic intermittent hypoxia (CIH) is a pathological characteristic of obstructive sleep apnea (OSA) that has been linked to the pathogenesis of nonalcoholic fatty liver disease (NAFLD). The specific link between CIH, autophagic activity, and NAFLD, however, has not previously been characterized. The goal of this study was to assess the relationship between CIH-induced autophagy and the pathogenesis of OSA-associated NAFLD. Western blotting was used to assess the expression of proteins associated with lipid synthesis, endoplasmic reticulum (ER) stress, and autophagic activity. To establish an in vivo model system, C57BL/6 mice were subjected to CIH conditions for 8 h per day over a 12-week period, and were administered chloroquine (CQ) for 1 week prior to euthanization. Levels of serum and liver triglycerides in these animals were assessed, as were proteins related to hepatic autophagy, ER stress, and lipogenesis. qPCR was additionally used to assess hepatic inflammation-related gene expression, while transmission electron microscopy was used to monitor lipid droplet (LD) accumulation and ER morphology. OSA patients and CIH model mice exhibited increases in the expression of proteins associated with hepatic autophagy, ER stress, and lipogenesis. CIH was also associated with more pronounced LD accumulation, hepatic inflammation, and hepatic steatosis in these mice. While serum and hepatic TG and TC levels and serum ALT/AST were increased in response to CIH treatment, the administration of CQ to these mice led to reductions in ER stress-related proteins (XBP1, IRE1α, EIF2α) and lipogenesis-related proteins (ACC, SCD1, FASn), in addition to significantly reducing hepatic inflammation, steatosis, and LD accumulation in these animals. These results suggest that persistent CIH can drive dysregulated hepatic autophagic activity, hepatic steatosis, and ER stress, highlighting potential targets for therapeutic intervention aimed at preventing or treating OSA-associated NAFLD.

Assessment of elastographic Q-analysis score combined with Prostate Imaging-Reporting and Data System (PI-RADS) based on transrectal ultrasound (TRUS)/multi-parameter magnetic resonance imaging (MP-MRI) fusion-guided biopsy in differentiating benign and malignant prostate.

Background: Magnetic resonance imaging (MRI) has advantages in the diagnosis of prostate diseases, but there is also overdiagnosis. We compensate for this with fusion imaging and elastography. In this study, we want to evaluate Elastographic Q-analysis score (EQS) combined with Prostate Imaging Reporting and Data System (PI-RADS), based on transrectal ultrasound (TRUS)/multi-parameter magnetic resonance imaging (MP-MRI) fusion biopsy in differentiating benign and malignant prostate lesions. Methods: A total of 296 patients with 318 prostate lesions who underwent TRUS/MP-MRI fusion biopsy between October 2017 and October 2019 were retrospectively analysed. The performance of the EQS was evaluated on the sites of the suspicious areas of MP-MRI. The cut-off value of EQS was obtained according to receiver operating characteristic (ROC) curve, which was used to upgrade and downgrade the PI-RADS scores. The area under the curve (AUC), integrated discrimination improvement, and decision curve analysis were used to assess the new PI-RADS performance. Results: In total, 318 MP-MRI suspicious prostate lesions (94 malignant vs. 224 benign lesions). The EQS optimal threshold was 1.85, and the AUC was 0.816. All cases were constructed three models by using 1.85 as the cut-off value: upgrade-PI-RADS, downgrade-PI-RADS and complex-PI-RADS. The AUC of PI-RADS, upgrade-PI-RADS, downgrade-PI-RADS and complex-PI-RADS were 0.869, 0.867, 0.872 and 0.873 respectively. The diagnostic coincidence rate of PI-RADS was increased from 0.667 to 0.874 by using strain elastography, among which the diagnostic rate of prostate cancer was increased from 0.557 to 0.806, and the diagnostic rate of non-prostate cancer was increased from 0.775 to 0.967. The integrated discrimination improvement indicated that downgrade-PI-RADS had a better diagnostic capability (P<0.05). The net benefit of all models, which downgrade-PI-RADS can maximize the net benefit value of patients by decision curve analysis. Conclusions: The combination of PI-RADS and EQS with TRUS/MP-MRI fusion, particularly downgrade-PI-RADS, can reduce unnecessary biopsy procedures and prevent overdiagnosis.

Evaluation of the Transverse Carpal Ligament in Carpal Tunnel Syndrome by Shear Wave Elastography: A Non-Invasive Approach of Diagnosis and Management.

Objectives: The goal of this work is to determine the clinical value of the transverse carpal ligament (TCL) in carpal tunnel syndrome (CTS) for guiding subsequent treatment. Methods: This study analyzed patients who underwent median nerve (MN) ultrasound (US) examination of the wrist from April 2020 to April 2021. The cross-sectional area and anteroposterior diameter of the MN, as well as the TCL thickness and stiffness, were measured from images. The intra-group and intra-patient subgroup differences were compared using a t-test and a rank test. We also utilized receiver operating characteristic (ROC) curves to diagnose CTS and evaluate the severity. Results: The final cohort consisted of 120 wrists (bilateral) from 60 samples, evenly balanced across the patient and control groups according to their CTS diagnosis. In the unilateral positive patient subgroup, the MN and TCL of the positive hand were significantly thicker and stiffer than the negative counterparts (both, p < 0.05). The values from the right were also thicker and stiffer than the left (both, p < 0.05) in patients with bilateral CTS. The MN and TCL of the patient group were also significantly thicker and stiffer than those of the control group (both, p < 0.001). For diagnosing CTS, the area under the curve (AUC) of TCL thickness and stiffness at the distal carpal tunnel (DCT) ranged between 0.925 and 0.967. For evaluating CTS severity, we found that the optimal TCL stiffness is sufficient for diagnosing mild and non-mild patient cases (AUC: Emean = 0.757, Emax = 0.779). Conclusions: Shear wave elastography is therefore an effective method for CTS diagnosis and management.