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.

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).

[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.

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.

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.

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.

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 ).

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

Introduction: Post-hepatectomy liver failure (PHLF) is one of the most serious complications and causes of death in patients with hepatocellular carcinoma (HCC) after hepatectomy. This study aimed to develop a novel machine learning (ML) model based on the light gradient boosting machines (LightGBM) algorithm for predicting PHLF. Methods: A total of 875 patients with HCC who underwent hepatectomy were randomized into a training cohort (n=612), a validation cohort (n=88), and a testing cohort (n=175). Shapley additive explanation (SHAP) was performed to determine the importance of individual variables. By combining these independent risk factors, an ML model for predicting PHLF was established. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value, negative predictive value, and decision curve analyses (DCA) were used to evaluate the accuracy of the ML model and compare it to that of other noninvasive models. Results: The AUCs of the ML model for predicting PHLF in the training cohort, validation cohort, and testing cohort were 0.944, 0.870, and 0.822, respectively. The ML model had a higher AUC for predicting PHLF than did other non-invasive models. The ML model for predicting PHLF was found to be more valuable than other noninvasive models. Conclusion: A novel ML model for the prediction of PHLF using common clinical parameters was constructed and validated. The novel ML model performed better than did existing noninvasive models for the prediction of PHLF.

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.

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.

Laser-Generated Leaky Acoustic Wave Imaging for Interventional Guidewire Guidance.

Ultrasound (US) is widely used to visualize both tissue and the positions of surgical instruments in real time during surgery. Previously we proposed a new method to exploit US imaging and laser-generated leaky acoustic waves (LAWs) for needle visualization. Although successful, that method only detects the position of a needle tip, with the location of the entire needle deduced from knowing that the needle is straight. The purpose of the current study was to develop a beamforming-based method for the direct visualization of objects. The approach can be applied to objects with arbitrary shapes, such as the guidewires that are commonly used in interventional guidance. With this method, illumination by a short laser pulse generates photoacoustic waves at the top of the guidewire that propagate down its metal surface. These waves then leak into the surrounding tissue, which can be detected by a US array transducer. The time of flight consists of two parts: 1) the propagation time of the guided waves on the guidewire and 2) the propagation time of the US that leaks into the tissue. In principle, an image of the guidewire can be formed based on array beamforming by taking the propagation time on the metal into consideration. Furthermore, we introduced directional filtering and a matched filter to compress the dispersion signal associated with long propagation times. The results showed that guidewires could be detected at depths of at least 70 mm. The maximum detectable angle was 56.3°. LAW imaging with a 1268-mm-long guidewire was also demonstrated. The proposed method has considerable potential in new clinical applications.

[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.

Intelligent diagnosis of jaundice with dynamic uncertain causality graph model.

Jaundice is a common and complex clinical symptom potentially occurring in hepatology, general surgery, pediatrics, infectious diseases, gynecology, and obstetrics, and it is fairly difficult to distinguish the cause of jaundice in clinical practice, especially for general practitioners in less developed regions. With collaboration between physicians and artificial intelligence engineers, a comprehensive knowledge base relevant to jaundice was created based on demographic information, symptoms, physical signs, laboratory tests, imaging diagnosis, medical histories, and risk factors. Then a diagnostic modeling and reasoning system using the dynamic uncertain causality graph was proposed. A modularized modeling scheme was presented to reduce the complexity of model construction, providing multiple perspectives and arbitrary granularity for disease causality representations. A "chaining" inference algorithm and weighted logic operation mechanism were employed to guarantee the exactness and efficiency of diagnostic reasoning under situations of incomplete and uncertain information. Moreover, the causal interactions among diseases and symptoms intuitively demonstrated the reasoning process in a graphical manner. Verification was performed using 203 randomly pooled clinical cases, and the accuracy was 99.01% and 84.73%, respectively, with or without laboratory tests in the model. The solutions were more explicable and convincing than common methods such as Bayesian Networks, further increasing the objectivity of clinical decision-making. The promising results indicated that our model could be potentially used in intelligent diagnosis and help decrease public health expenditure.

Kanglaite attenuates UVB-induced down-regulation of aquaporin-3 in cultured human skin keratinocytes.

Ultraviolet (UV) radiation plays an important role in the pathogenesis of skin photoaging. Depending on the wavelength of UV, the epidermis is affected primarily by UVB. One major characteristic of photoaging is the dehydration of the skin. Membrane-inserted water channels (aquaporins) are involved in this process. In this study we demonstrated that UVB radiation induced aquaporin-3 (AQP3) down-regulation in cultured human skin keratinocytes. Kanglaite is a mixture consisting of extractions of Coix Seed, which is an effective anti-neoplastic agent and can inhibit the activities of protein kinase C and NF-κB. We demonstrated that Kanglaite inhibited UVB-induced AQP3 down-regulation of cultured human skin keratinocytes. Our findings provide a potential new agent for anti-photoaging. The related molecular mechanisms remain to be further elucidated.

Expression of survivin and human telomerase reverse transcriptase in extramammary Paget's disease.

BACKGROUND: Extramammary Paget's disease (EMPD) is a rare neoplasm of apocrine gland-bearing skin. It is known that over-expression of survivin and human telomerase reverse transcriptase (hTERT) correlates with malignancies. We investigated the expression of hTERT and survivin by Paget's cells and their role in the tissue invasion and recurrence of EMPD. METHOD: Forty-two patients were enrolled into the study. Expression of survivin and hTERT were analyzed by immunohistochemistry and in situ hybridization techniques. The variables including the expression level of survivin and hTERT, gender, age, lesion location, invasion level and number of surgeries were statistically analyzed using Fisher's exact test. RESULTS: Survivin was positively stained in 18 of 22 cases (81.8%), and hTERT in 18 of 29 cases (62.1%). Significantly higher level of survivin expression was detected in patients with multiple surgeries than those with single one (p = 0.0458). Expression of hTERT was significantly higher in the patients with micro-invasive and invasive lesions than those with non-invasive lesions (p = 0.0478). CONCLUSIONS: Over-expression of survivin and hTERT correlated strongly with recurrence and local invasion of EMPD lesions. EMPD has male gender predominance in Oriental population.