Object detection: A novel AI technology for the diagnosis of hepatocyte ballooning.

Metabolic dysfunction-associated fatty liver disease (MAFLD) has reached epidemic proportions worldwide and is the most frequent cause of chronic liver disease in developed countries. Within the spectrum of liver disease in MAFLD, steatohepatitis is a progressive form of liver disease and hepatocyte ballooning (HB) is a cardinal pathological feature of steatohepatitis. The accurate and reproducible diagnosis of HB is therefore critical for the early detection and treatment of steatohepatitis. Currently, a diagnosis of HB relies on pathological examination by expert pathologists, which may be a time-consuming and subjective process. Hence, there has been interest in developing automated methods for diagnosing HB. This narrative review briefly discusses the development of artificial intelligence (AI) technology for diagnosing fatty liver disease pathology over the last 30 years and provides an overview of the current research status of AI algorithms for the identification of HB, including published articles on traditional machine learning algorithms and deep learning algorithms. This narrative review also provides a summary of object detection algorithms, including the principles, historical developments, and applications in the medical image analysis. The potential benefits of object detection algorithms for HB diagnosis (specifically those combined with a transformer architecture) are discussed, along with the future directions of object detection algorithms in HB diagnosis and the potential applications of generative AI on transformer architecture in this field. In conclusion, object detection algorithms have huge potential for the identification of HB and could make the diagnosis of MAFLD more accurate and efficient in the near future.

Development and validation of a machine learning-based model for varices screening in compensated cirrhosis (CHESS2001): an international multicenter study.

BACKGROUND AND AIMS: The prevalence of high-risk varices (HRV) is low among compensated cirrhotic patients undergoing EGD. Our study aimed to identify a novel machine learning (ML)-based model, named ML EGD, for ruling out HRV and avoiding unnecessary EGDs in patients with compensated cirrhosis. METHODS: An international cohort from 17 institutions from China, Singapore, and India were enrolled (CHESS2001). The variables with the top 3 importance scores (liver stiffness, platelet count, and total bilirubin) were selected by the Shapley additive explanation and input into a light gradient-boosting machine algorithm to develop ML EGD for identification of HRV. Furthermore, we built a web-based calculator for ML EGD, which is free with open access (http://www.pan-chess.cn/calculator/MLEGD_score). Unnecessary EGDs that were not performed and the rates of missed HRV were used to assess the efficacy and safety for varices screening. RESULTS: Of 2794 enrolled patients, 1283 patients formed a real-world cohort from 1 university hospital in China used to develop and internally validate the performance of ML EGD for varices screening. They were randomly assigned into the training (n = 1154) and validation (n = 129) cohorts with a ratio of 9:1. In the training cohort, ML EGD spared 607 (52.6%) unnecessary EGDs with a missed HRV rate of 3.6%. In the validation cohort, ML EGD spared 75 (58.1%) EGDs with a missed HRV rate of 1.4%. To externally test the performance of ML EGD, 966 patients from 14 university hospitals in China (test cohort 1) and 545 from 2 hospitals in Singapore and India (test cohort 2) comprised the 2 test cohorts. In test cohort 1, ML EGD spared 506 (52.4%) EGDs with a missed HRV rate of 2.8%. In test cohort 2, ML EGD spared 224 (41.1%) EGDs with a missed HRV rate of 3.1%. When compared with the Baveno VI criteria, ML EGD spared more screening EGDs in all cohorts (training cohort, 52.6% vs 29.4%; validation cohort, 58.1% vs 44.2%; test cohort 1, 52.4% vs 26.5%; test cohort 2, 41.1% vs 21.1%, respectively; P < .001). CONCLUSIONS: We identified a novel model based on liver stiffness, platelet count, and total bilirubin, named ML EGD, as a free web-based calculator. ML EGD could efficiently help rule out HRV and avoid unnecessary EGDs in patients with compensated cirrhosis. (Clinical trial registration number: NCT04307264.).

Novel urinary protein panels for the non-invasive diagnosis of non-alcoholic fatty liver disease and fibrosis stages.

BACKGROUND & AIMS: There is an unmet clinical need for non-invasive tests to diagnose non-alcoholic fatty liver disease (NAFLD) and individual fibrosis stages. We aimed to test whether urine protein panels could be used to identify NAFLD, NAFLD with fibrosis (stage F ≥ 1) and NAFLD with significant fibrosis (stage F ≥ 2). METHODS: We collected urine samples from 100 patients with biopsy-confirmed NAFLD and 40 healthy volunteers, and proteomics and bioinformatics analyses were performed in this derivation cohort. Diagnostic models were developed for detecting NAFLD (UPNAFLD model), NAFLD with fibrosis (UPfibrosis model), or NAFLD with significant fibrosis (UPsignificant fibrosis model). Subsequently, the derivation cohort was divided into training and testing sets to evaluate the efficacy of these diagnostic models. Finally, in a separate independent validation cohort of 100 patients with biopsy-confirmed NAFLD and 45 healthy controls, urinary enzyme-linked immunosorbent assay analyses were undertaken to validate the accuracy of these new diagnostic models. RESULTS: The UPfibrosis model and the UPsignificant fibrosis model showed an AUROC of .863 (95% CI: .725-1.000) and 0.858 (95% CI: .712-1.000) in the training set; and .837 (95% CI: .711-.963) and .916 (95% CI: .825-1.000) in the testing set respectively. The UPNAFLD model showed an excellent diagnostic performance and the area under the receiver operator characteristic curve (AUROC) exceeded .90 in the derivation cohort. In the independent validation cohort, the AUROC for all three of the above diagnostic models exceeded .80. CONCLUSIONS: Our newly developed models constructed from urine protein biomarkers have good accuracy for non-invasively diagnosing liver fibrosis in NAFLD.

Segmentation of thyroid glands and nodules in ultrasound images using the improved U-Net architecture.

BACKGROUND: Identifying thyroid nodules' boundaries is crucial for making an accurate clinical assessment. However, manual segmentation is time-consuming. This paper utilized U-Net and its improved methods to automatically segment thyroid nodules and glands. METHODS: The 5822 ultrasound images used in the experiment came from two centers, 4658 images were used as the training dataset, and 1164 images were used as the independent mixed test dataset finally. Based on U-Net, deformable-pyramid split-attention residual U-Net (DSRU-Net) by introducing ResNeSt block, atrous spatial pyramid pooling, and deformable convolution v3 was proposed. This method combined context information and extracts features of interest better, and had advantages in segmenting nodules and glands of different shapes and sizes. RESULTS: DSRU-Net obtained 85.8% mean Intersection over Union, 92.5% mean dice coefficient and 94.1% nodule dice coefficient, which were increased by 1.8%, 1.3% and 1.9% compared with U-Net. CONCLUSIONS: Our method is more capable of identifying and segmenting glands and nodules than the original method, as shown by the results of correlational studies.

Preparation of fat replacer utilizing gluten and barley ß-glucan and the interaction between them.

BACKGROUND: Fat replacers prepared from polysaccharides and proteins possess functional properties of both polysaccharides and proteins. In this study, an aqueous system of barley ß-glucan (BBG) and gluten was prepared. The interactions between BBG and gluten (with/without extrusion modification) were studied. Triple analysis methods, including differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), and low-field nuclear magnetic resonance (LF-NMR), were utilized to analyze the freezing-thawing and thermal evaporation process, as well as the distribution state of water. Meanwhile, fluorescence microscopic analysis, dynamic rheological analysis and electrophoresis analysis were used to study the structure and rheological properties of the system. RESULTS: The results showed that BBG significantly increased the water-holding capacity of gluten, regardless of extrusion treatment, with the water absorption reaching about 4.8 to 6.4 times of its weight, which was 1 to 2.5 times higher than that without BBG. The triple analysis results suggested that BBG increased the binding capacity of the system to weakly bound water, hindered the aggregation of gluten and reduced the thermal decomposition temperature of the BBG and gluten composite system. After the gluten was extruded and homogenized with the BBG solution, the appearance of the composite system was more uniform and delicate. CONCLUSIONS: In conclusion, BBG increased the water-holding capacity of the BBG and gluten composite system. With these changes, the composite system presented great potential for the preparation of polysaccharide-gluten fat replacer. © 2023 Society of Chemical Industry.

Characterizing an Uncertainty Diagram and Kirkwood-Dirac Nonclassicality Based on Discrete Fourier Transform.

In this paper, we investigate an uncertainty diagram and Kirkwood-Dirac (KD) nonclassicality based on discrete Fourier transform (DFT) in a d-dimensional system. We first consider the uncertainty diagram of the DFT matrix, which is a transition matrix from basis A to basis B. Here, the bases A, B are not necessarily completely incompatible. We show that for the uncertainty diagram of the DFT matrix, there is no "hole" in the region of the (nA,nB) plane above and on the line nA+nB=d+1. Then, we present where the holes are in the region strictly below the line and above the hyperbola nAnB=d. Finally, we provide an alternative proof of the conjecture about KD nonclassicality based on DFT.

[An Improved Object Detection Algorithm for Thyroid Nodule Ultrasound Image Based on Faster R-CNN].

Objective: To propose an improved algorithm for thyroid nodule object detection based on Faster R-CNN so as to improve the detection precision of thyroid nodules in ultrasound images. Methods: The algorithm used ResNeSt50 combined with deformable convolution (DC) as the backbone network to improve the detection effect of irregularly shaped nodules. Feature pyramid networks (FPN) and Region of Interest (RoI) Align were introduced in the back of the trunk network. The former was used to reduce missed or mistaken detection of thyroid nodules, and the latter was used to improve the detection precision of small nodules. To improve the generalization ability of the model, parameters were updated during backpropagation with an optimizer improved by Sharpness-Aware Minimization (SAM). Results: In this experiment, 6 261 thyroid ultrasound images from the Affiliated Hospital of Xuzhou Medical University and the First Hospital of Nanjing were used to compare and evaluate the effectiveness of the improved algorithm. According to the findings, the algorithm showed optimization effect to a certain degree, with the AP50 of the final test set being as high as 97.4% and AP@50:5:95 also showing a 10.0% improvement compared with the original model. Compared with both the original model and the existing models, the improved algorithm had higher detection precision and improved capacity to detect thyroid nodules with better accuracy and precision. In particular, the improved algorithm had a higher recall rate under the requirement of lower detection frame precision. Conclusion: The improved method proposed in the study is an effective object detection algorithm for thyroid nodules and can be used to detect thyroid nodules with accuracy and precision.

Association between CD4+ T cell counts and gut microbiota and serum cytokines levels in HIV-infected immunological non-responders.

BACKGROUND: CD4+ T cell counts in certain human immunodeficiency virus (HIV)-infected patients called immunological non-responders (INRs) could not return to a normal level even with sustained antiretroviral therapy (ART) because of persistent immune activation, which is associated with pro-inflammatory cytokines production and an altered intestinal microbiome profile. Changes in gut bacterial composition have been linked to low CD4+ T cell counts in HIV-infected individuals. However, the association between CD4+ T cell counts and gut microbiota community composition and cytokines levels in INRs (CD4+ T cell counts < 500 cells/µL) from Yunnan Province, China, has not been previously investigated. METHODS: To address this issue, we carried out a cross-sectional study of 34 HIV-infected INRs. The patients were divided into CD4 count > 200 cells/µL group and CD4 count < 200 cells/µL group. The gut microbiota composition of each subject was analyzed by 16S rRNA gene sequencing. We also compared CD8+ T cell counts, pro-inflammatory cytokines levels, and nutritional status between the two groups. RESULTS: Compared to INRs with CD4 count > 200 cells/µL, those with CD4 count < 200 cells/µL had a lower CD4/CD8 ratio, lower nutritional status and higher serum levels of tumor necrosis factor (TNF)-α, interferon-γ-inducible protein (IP)-10 and interleukin (IL)-1α. Ruminococcaceae was less abundant in the CD4 count < 200 cells/µL group than in the CD4 count > 200 cells/µL group, and difference in alpha diversity was observed between the two groups. Moreover, CD4+ T cell counts were negatively associated with TNF-α and IL-1α levels and positively associated with the relative abundance of Ruminococcaceae. CONCLUSIONS: Our study demonstrated that lower CD4+ T cell counts in INRs are associated with a reduced abundance of Ruminococcaceae in the gut and elevated serum pro-inflammatory cytokines levels. Thus, interventions targeting gut microbiota to increase CD4+ T cell counts are a potential strategy for promoting immune reconstitution in HIV-infected INRs.

Genetic variation in the invasive weed Mikania micrantha (Asteraceae) suggests highways as corridors for its dispersal in southern China.

BACKGROUND AND AIMS: Roads as corridors of seed or fruit spatial dispersal have major impacts on the establishment and spread of invasive species, but their precise role in population genetic variation remains poorly understood. The South American weed Mikania micrantha has spread rapidly across southern China since its introduction to the Shenzhen area in 1984. This study investigated how its genetic diversity is distributed along highways, and whether highways have acted as corridors for the rapid expansion of M. micrantha METHODS: Twenty-seven roadside populations were sampled along four highways in southern China, and 787 samples were examined using 12 microsatellite markers. Variation in genetic diversity among populations was quantified and patterns of genetic differentiation were analysed. KEY RESULTS: A high level of genetic diversity was found at both the species and the population levels in this self-incompatible plant (expected heterozygosity = 0·497 and 0·477, respectively; allelic richness = 2·580 and 2·521, respectively). The Wright F-statistic value among populations (0·044, P < 0·01) and the analysis of molecular variance (91 % of genetic variation residing within populations, 9 % among populations within highways and 0 % among the four highways) showed a relatively low level of genetic differentiation among populations, while the principal coordinate and cluster analyses also indicated a lack of clear geographical genetic structure among populations. The calculated Nm value of 5·5 signifies strong gene flow. CONCLUSIONS: The pattern of genetic variation is consistent with facilitated dispersal along highways. The genetic admixtures among the roadside populations imply the occurrence of multiple population introductions during colonization. The long-distance dispersal of seeds associated with vehicular transportation on highways may have played important roles in shaping the genetic variation. This finding highlights the importance of highways as corridors for the spread of M. micrantha in southern China.

[Effect of Qishen Yiqi droplet on adenylate contents in myocardium of cor pulmonale rats].

Cor pulmonale rat models were induced by a single intraperitoneal injection of monocrotaline(MCT), and the sham group received a single intraperitioneal injection of normal saline. After the model rats received intragastric administration of Qishen Yiqi droplet(QS) for 6 weeks, the contents of adenylate(ATP, ADP and AMP) in right myocardial tissues were measured by HPLC, and then the metabolism changes in myocardium of cor pulmonale rats with QS were investigated. The results showed that ATP, ADP, and AMP were well separated, with a good linearity within a certain range of concentration; and the recovery rates were within the range of 90%-108%. As compared with model group, the level of ATP was significantly elevated in high-dose treatment group; ADP contents showed an increasing trend and AMP contents showed a decreasing trend, indicating that QS could significantly improve energy metabolism system in myocardium. By using the HPLC, a qualitative and quantitative analysis method was given for the determination of ATP, ADP and AMP contents in myocardium, providing a method for energy metabolism measurement in biological samples.

Development and application of a machine learning-based predictive model for obstructive sleep apnea screening.

Objective: To develop a robust machine learning prediction model for the automatic screening and diagnosis of obstructive sleep apnea (OSA) using five advanced algorithms, namely Extreme Gradient Boosting (XGBoost), Logistic Regression (LR), Support Vector Machine (SVM), Light Gradient Boosting Machine (LightGBM), and Random Forest (RF) to provide substantial support for early clinical diagnosis and intervention. Methods: We conducted a retrospective analysis of clinical data from 439 patients who underwent polysomnography at the Affiliated Hospital of Xuzhou Medical University between October 2019 and October 2022. Predictor variables such as demographic information [age, sex, height, weight, body mass index (BMI)], medical history, and Epworth Sleepiness Scale (ESS) were used. Univariate analysis was used to identify variables with significant differences, and the dataset was then divided into training and validation sets in a 4:1 ratio. The training set was established to predict OSA severity grading. The validation set was used to assess model performance using the area under the curve (AUC). Additionally, a separate analysis was conducted, categorizing the normal population as one group and patients with moderate-to-severe OSA as another. The same univariate analysis was applied, and the dataset was divided into training and validation sets in a 4:1 ratio. The training set was used to build a prediction model for screening moderate-to-severe OSA, while the validation set was used to verify the model's performance. Results: Among the four groups, the LightGBM model outperformed others, with the top five feature importance rankings of ESS total score, BMI, sex, hypertension, and gastroesophageal reflux (GERD), where Age, ESS total score and BMI played the most significant roles. In the dichotomous model, RF is the best performer of the five models respectively. The top five ranked feature importance of the best-performing RF models were ESS total score, BMI, GERD, age and Dry mouth, with ESS total score and BMI being particularly pivotal. Conclusion: Machine learning-based prediction models for OSA disease grading and screening prove instrumental in the early identification of patients with moderate-to-severe OSA, revealing pertinent risk factors and facilitating timely interventions to counter pathological changes induced by OSA. Notably, ESS total score and BMI emerge as the most critical features for predicting OSA, emphasizing their significance in clinical assessments. The dataset will be publicly available on my Github.

Whole-protein enteral nutrition formula supplementation reduces Escherichia and improves intestinal barrier function in HIV-infected immunological nonresponders.

People living with human immunodeficiency virus (PLWH) have persistent malnutrition, intestinal barrier dysfunction, and gut microbial imbalance. The interplay between gut microbiota and nutrients is involved in the immune reconstitution of PLWH. To evaluate the effects of whole-protein enteral nutrition formula supplementation on T-cell levels, intestinal barrier function, nutritional status, and gut microbiota composition in human immunodeficiency virus (HIV)-infected immunological nonresponders (INRs) who failed to normalize CD4+ T-cell counts, with a number <350 cells/µL, a pilot study was carried out in 13 HIV-infected INRs undergoing antiretroviral therapy who received a 3-month phase supplementation of 200 mL/200 kcal/45 g whole-protein enteral nutrition formula once daily. Our primary endpoint was increased CD4+ T-cell counts. Secondary outcome parameters were changes in intestinal barrier function, nutritional status, and gut microbiota composition. We showed that CD4+ T-cell counts of HIV-infected INRs increased significantly after the 3-month supplementation. Dietary supplementation for 3 months improved the intestinal barrier function and nutritional status of HIV-infected INRs. Furthermore, the enteral nutrition formula significantly decreased the relative abundance of Escherichia at the genus level and increased the alpha diversity of gut microbiota in HIV-infected INRs. The findings demonstrated that the whole-protein enteral nutrition formula aids in reducing Escherichia and improving intestinal barrier function in HIV-infected INRs. This study provides insight into the role of nutrients in the improvement of immune reconstitution in HIV-infected INRs. This study is registered in the Chinese Clinical Trial Registry (Document No. ChiCTR2000037839; http://www.chictr.org.cn/index.aspx).

The effects of ambient temperature and feeding regimens on cecum bacteria composition and circadian rhythm in growing rabbits.

Seasonal environmental shifts and improper eating habits are the important causes of diarrhea in children and growing animals. Whether adjusting feeding time at varying temperatures can modify cecal bacterial structure and improve diarrhea remains unknown. Three batches growing rabbits with two groups per batch were raised under different feeding regimens (fed at daytime vs. nighttime) in spring, summer and winter separately, and contents were collected at six time points in 1 day and used 16S rRNA sequencing to investigate the effects of feeding regimens and season on the composition and circadian rhythms of cecum bacteria. Randomized forest regression screened 12 genera that were significantly associated with seasonal ambient temperature changes. Nighttime feeding reduced the abundance of the conditionally pathogenic bacteria Desulfovibrio and Alistipes in summer and Campylobacter in winter. And also increases the circadian rhythmic Amplicon Sequence Variants in the cecum, enhancing the rhythm of bacterial metabolic activity. This rhythmic metabolic profile of cecum bacteria may be conducive to the digestion and absorption of nutrients in the host cecum. In addition, this study has identified 9 genera that were affected by the combination of seasons and feeding time. In general, we found that seasons and feeding time and their combinations affect cecum composition and circadian rhythms, and that daytime feeding during summer and winter disrupts the balance of cecum bacteria of growing rabbits, which may adversely affect cecum health and induce diarrhea risk.

Left Ventricular Segmentation, Warping, and Myocardial Registration for Automated Strain Measurement.

The left ventricular global longitudinal strain (LVGLS) is a crucial prognostic indicator. However, inconsistencies in measurements due to the speckle tracking algorithm and manual adjustments have hindered its standardization and democratization. To solve this issue, we proposed a fully automated strain measurement by artificial intelligence-assisted LV segmentation contours. The LV segmentation model was trained from echocardiograms of 368 adults (11,125 frames). We compared the registration-like effects of dynamic time warping (DTW) with speckle tracking on a synthetic echocardiographic dataset in experiment-1. In experiment-2, we enrolled 80 patients to compare the DTW method with commercially available software. In experiment-3, we combined the segmentation model and DTW method to create the artificial intelligence (AI)-DTW method, which was then tested on 40 patients with general LV morphology, 20 with dilated cardiomyopathy (DCMP), and 20 with transthyretin-associated cardiac amyloidosis (ATTR-CA), 20 with severe aortic stenosis (AS), and 20 with severe mitral regurgitation (MR). Experiments-1 and -2 revealed that the DTW method is consistent with dedicated software. In experiment-3, the AI-DTW strain method showed comparable results for general LV morphology (bias - 0.137 ± 0.398%), DCMP (- 0.397 ± 0.607%), ATTR-CA (0.095 ± 0.581%), AS (0.334 ± 0.358%), and MR (0.237 ± 0.490%). Moreover, the strain curves showed a high correlation in their characteristics, with R-squared values of 0.8879-0.9452 for those LV morphology in experiment-3. Measuring LVGLS through dynamic warping of segmentation contour is a feasible method compared to traditional tracking techniques. This approach has the potential to decrease the need for manual demarcation and make LVGLS measurements more efficient and user-friendly for daily practice.

Coordinate-Independent 3-D Ultrasound Principal Stretch and Direction Imaging.

OBJECTIVE: In clinical ultrasound, current 2-D strain imaging faces challenges in quantifying three orthogonal normal strain components. This requires separate image acquisitions based on the pixel-dependent cardiac coordinate system, leading to additional computations and estimation discrepancies due to probe orientation. Most systems lack shear strain information, as displaying all components is challenging to interpret. METHODS: This paper presents a 3-D high-spatial-resolution, coordinate-independent strain imaging approach based on principal stretch and axis estimation. All strain components are transformed into three principal stretches along three normal principal axes, enabling direct visualization of the primary deformation. We devised an efficient 3-D speckle tracking method with tilt filtering, incorporating randomized searching in a two-pass tracking framework and rotating the phase of the 3-D correlation function for robust filtering. The proposed speckle tracking approach significantly improves estimates of displacement gradients related to the axial displacement component. Non-axial displacement gradient estimates are enhanced using a correlation-weighted least-squares method constrained by tissue incompressibility. RESULTS: Simulated and in vivo canine cardiac datasets were evaluated to estimate Lagrangian strains from end-diastole to end-systole. The proposed speckle tracking method improves displacement estimation by a factor of 4.3 to 10.5 over conventional 1-pass processing. Maximum principal axis/direction imaging enables better detection of local disease regions than conventional strain imaging. CONCLUSION: Coordinate-independent tracking can identify myocardial abnormalities with high accuracy. SIGNIFICANCE: This study offers enhanced accuracy and robustness in strain imaging compared to current methods.

Fully connected neural network-based serum surface-enhanced Raman spectroscopy accurately identifies non-alcoholic steatohepatitis.

BACKGROUND/PURPOSE OF THE STUDY: There is a need to find a standardized and low-risk diagnostic tool that can non-invasively detect non-alcoholic steatohepatitis (NASH). Surface enhanced Raman spectroscopy (SERS), which is a technique combining Raman spectroscopy (RS) with nanotechnology, has recently received considerable attention due to its potential for improving medical diagnostics. We aimed to investigate combining SERS and neural network approaches, using a liver biopsy dataset to develop and validate a new diagnostic model for non-invasively identifying NASH. METHODS: Silver nanoparticles as the SERS-active nanostructures were mixed with blood serum to enhance the Raman scattering signals. The spectral data set was used to train the NASH classification model by a neural network primarily consisting of a fully connected residual module. RESULTS: Data on 261 Chinese individuals with biopsy-proven NAFLD were included and a prediction model for NASH was built based on SERS spectra and neural network approaches. The model yielded an AUROC of 0.83 (95% confidence interval [CI] 0.70-0.92) in the validation set, which was better than AUROCs of both serum CK-18-M30 levels (AUROC 0.63, 95% CI 0.48-0.76, p = 0.044) and the HAIR score (AUROC 0.65, 95% CI 0.51-0.77, p = 0.040). Subgroup analyses showed that the model performed well in different patient subgroups. CONCLUSIONS: Fully connected neural network-based serum SERS analysis is a rapid and practical tool for the non-invasive identification of NASH. The online calculator website for the estimated risk of NASH is freely available to healthcare providers and researchers ( http://www.pan-chess.cn/calculator/RAMAN_score ).

Intravenous Tenecteplase for Acute Ischemic Stroke Within 4.5-24 Hours of Onset (ROSE-TNK): A Phase 2, Randomized, Multicenter Study.

BACKGROUND AND PURPOSE: Intravenous tenecteplase (TNK) efficacy has not been well demonstrated in acute ischemic stroke (AIS) beyond 4.5 hours after onset. This study aimed to determine the effect of intravenous TNK for AIS within 4.5 to 24 hours of onset. METHODS: In this pilot trial, eligible AIS patients with diffusion-weighted imaging (DWI)-fluid attenuated inversion recovery (FLAIR) mismatch were randomly allocated to intravenous TNK (0.25 mg/kg) or standard care within 4.5-24 hours of onset. The primary endpoint was excellent functional outcome at 90 days (modified Rankin Scale [mRS] score of 0-1). The primary safety endpoint was symptomatic intracranial hemorrhage (sICH). RESULTS: Of the randomly assigned 80 patients, the primary endpoint occurred in 52.5% (21/40) of TNK group and 50.0% (20/40) of control group, with no significant difference (unadjusted odds ratio, 1.11; 95% confidence interval 0.46-2.66; P=0.82). More early neurological improvement occurred in TNK group than in control group (11 vs. 3, P=0.03), but no significant differences were found in other secondary endpoints, such as mRS 0-2 at 90 days, shift analysis of mRS at 90 days, and change in National Institutes of Health Stroke Scale score at 24 hours and 7 days. There were no cases of sICH in this trial; however, asymptomatic intracranial hemorrhage occurred in 3 of the 40 patients (7.5%) in the TNK group. CONCLUSION: This phase 2, randomized, multicenter study suggests that intravenous TNK within 4.5-24 hours of onset may be safe and feasible in AIS patients with a DWI-FLAIR mismatch.

Corrigendum: Machine learning prediction model for post- hepatectomy liver failure in hepatocellular carcinoma: a multicenter study.

[This corrects the article DOI: 10.3389/fonc.2022.986867.].

LEARN algorithm: a novel option for predicting non-alcoholic steatohepatitis.

Background: There is an unmet need for accurate non-invasive methods to diagnose non-alcoholic steatohepatitis (NASH). Since impedance-based measurements of body composition are simple, repeatable and have a strong association with non-alcoholic fatty liver disease (NAFLD) severity, we aimed to develop a novel and fully automatic machine learning algorithm, consisting of a deep neural network based on impedance-based measurements of body composition to identify NASH [the bioeLectrical impEdance Analysis foR Nash (LEARN) algorithm]. Methods: A total of 1,259 consecutive subjects with suspected NAFLD were screened from six medical centers across China, of which 766 patients with biopsy-proven NAFLD were included in final analysis. These patients were randomly subdivided into the training and validation groups, in a ratio of 4:1. The LEARN algorithm was developed in the training group to identify NASH, and subsequently, tested in the validation group. Results: The LEARN algorithm utilizing impedance-based measurements of body composition along with age, sex, pre-existing hypertension and diabetes, was able to predict the likelihood of having NASH. This algorithm showed good discriminatory ability for identifying NASH in both the training and validation groups [area under the receiver operating characteristics (AUROC): 0.81, 95% CI: 0.77-0.84 and AUROC: 0.80, 95% CI: 0.73-0.87, respectively]. This algorithm also performed better than serum cytokeratin-18 neoepitope M30 (CK-18 M30) level or other non-invasive NASH scores (including HAIR, ION, NICE) for identifying NASH (P value <0.001). Additionally, the LEARN algorithm performed well in identifying NASH in different patient subgroups, as well as in subjects with partial missing body composition data. Conclusions: The LEARN algorithm, utilizing simple easily obtained measures, provides a fully automated, simple, non-invasive method for identifying NASH.

Clinical efficacy and mechanism of mesenchymal stromal cells in treatment of COVID-19.

Coronavirus disease 2019 (COVID-19) is a highly infectious epidemic disease that has seriously affected human health worldwide. To date, however, there is still no definitive drug for the treatment of COVID-19. Cell-based therapies could represent a new breakthrough. Over the past several decades, mesenchymal stromal cells (MSCs) have proven to be ideal candidates for the treatment of many viral infectious diseases due to their immunomodulatory and tissue repair or regeneration promoting properties, and several relevant clinical trials for the treatment of COVID-19 have been registered internationally. Herein, we systematically summarize the clinical efficacy of MSCs in the treatment of COVID-19 based on published results, including mortality, time to symptom improvement, computed tomography (CT) imaging, cytokines, and safety, while elaborating on the possible mechanisms underpinning the effects of MSCs, to provide a reference for subsequent studies.