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.

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.

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.

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.

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.

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.

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.

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 ).

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.).

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.

Effects of Probiotics on Diarrhea and CD4 Cell Count in People Living With HIV: A Systematic Review and Meta-Analysis.

Gastrointestinal probiotics play an important role in maintaining intestinal bacteria homeostasis. They might benefit people with human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS), which remains a global health challenge. However, there is a controversy regarding the efficacy of probiotics for the treatment of AIDS. This study systematically reviewed the evidence of the effects of existing probiotic interventions on AIDS and sought to provide information on the role of probiotics in the treatment of HIV/AIDS patients. A meta-analysis of studies identified by screening multiple databases was performed using a fixed-effects model in Review Manager 5.2 software. The meta-analysis showed that probiotics could reduce the incidence of AIDS-related diarrhea (RR = 0.60 (95% CI: 0.44-0.82), p = 0.001). The short-term use of probiotics (supplementation duration shorter than 30 days) did not reduce the incidence of diarrhea (RR = 0.76 (95% CI: 0.51-1.14), p = 0.19), while the long-term use of probiotics (supplementation duration longer than 30 days) reduced diarrhea (RR = 0.47 (95% CI: 0.29-0.76), p = 0.002). Probiotics had no effect on CD4 cell counts in HIV/AIDS patients (MD = 21.24 (95% CI: -12.95-55.39), p = 0.22). Our data support that probiotics were associated with an obvious reduction in AIDS-related diarrhea, which indicates the need for additional research on this potential preventive strategy for AIDS.

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.

Learning-Based Regularization for Cardiac Strain Analysis via Domain Adaptation.

Reliable motion estimation and strain analysis using 3D+ time echocardiography (4DE) for localization and characterization of myocardial injury is valuable for early detection and targeted interventions. However, motion estimation is difficult due to the low-SNR that stems from the inherent image properties of 4DE, and intelligent regularization is critical for producing reliable motion estimates. In this work, we incorporated the notion of domain adaptation into a supervised neural network regularization framework. We first propose a semi-supervised Multi-Layered Perceptron (MLP) network with biomechanical constraints for learning a latent representation that is shown to have more physiologically plausible displacements. We extended this framework to include a supervised loss term on synthetic data and showed the effects of biomechanical constraints on the network's ability for domain adaptation. We validated the semi-supervised regularization method on in vivo data with implanted sonomicrometers. Finally, we showed the ability of our semi-supervised learning regularization approach to identify infarct regions using estimated regional strain maps with good agreement to manually traced infarct regions from postmortem excised hearts.

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.