Multiple scattering suppression for in vivo optical coherence tomography measurement using the B-scan-wise multi-focus averaging method.

We demonstrate a method that reduces the noise caused by multi-scattering (MS) photons in an in vivo optical coherence tomography image. This method combines a specially designed image acquisition (i.e., optical coherence tomography scan) scheme and subsequent complex signal processing. For the acquisition, multiple cross-sectional images (frames) are sequentially acquired while the depth position of the focus is altered for each frame by an electrically tunable lens. In the signal processing, the frames are numerically defocus-corrected, and complex averaged. Because of the inconsistency in the MS-photon trajectories among the different electrically tunable lens-induced defocus, this averaging reduces the MS signal. Unlike the previously demonstrated volume-wise multi-focus averaging method, our approach requires the sample to remain stable for only a brief period, approximately 70 ms, thus making it compatible with in vivo imaging. This method was validated using a scattering phantom and in vivo unanesthetized small fish samples, and was found to reduce MS noise even for unanesthetized in vivo measurement.

Optical-coherence-tomography-based deep-learning scatterer-density estimator using physically accurate noise model.

We demonstrate a deep-learning-based scatterer density estimator (SDE) that processes local speckle patterns of optical coherence tomography (OCT) images and estimates the scatterer density behind each speckle pattern. The SDE is trained using large quantities of numerically simulated OCT images and their associated scatterer densities. The numerical simulation uses a noise model that incorporates the spatial properties of three types of noise, i.e., shot noise, relative-intensity noise, and non-optical noise. The SDE's performance was evaluated numerically and experimentally using two types of scattering phantom and in vitro tumor spheroids. The results confirmed that the SDE estimates scatterer densities accurately. The estimation accuracy improved significantly when compared with our previous deep-learning-based SDE, which was trained using numerical speckle patterns generated from a noise model that did not account for the spatial properties of noise.

A Network Enhancement Method to Identify Spurious Drug-Drug Interactions.

As medical safety and drug regulation gain heightened attention, the detection of spurious drug-drug interactions (DDI) has become key in healthcare. Although current research using graph neural networks (GNNs) to predict DDI has shown impressive results, it often fails to account for false DDI in the constructed DDI networks. Such inaccuracies caused by data errors, false alarms, or incorrect drug details can skew the network's structure and hinder the accuracy of GNN-based predictions. To tackle this challenge, we propose ANSM, a network-enhancement method specifically designed to identify and attenuate spurious links between drugs for ensuring the accuracy of DDI networks. ANSM integrates three key components: the feature extractor, the network optimizer, and the discriminative classifier. The feature extractor captures local structural features from drug node pairs, while the network optimizer leverages network information to improve feature extraction and reduce the impact of spurious DDI links. The discriminative classifier then identifies potential spurious links. Experimental results demonstrate that ANSM outperforms state-of-the-art methods in identifying spurious DDI.

Erratum: Deep convolutional neural networks-based scatterer density and resolution estimators in optical coherence tomography: erratum.

[This corrects the article on p. 168 in vol. 13, PMID: 35154862.].

Polarization-artifact reduction and accuracy improvement of Jones-matrix polarization-sensitive optical coherence tomography by multi-focus-averaging based multiple scattering reduction.

Polarization-sensitive optical coherence tomography (PS-OCT) is a promising biomedical imaging tool for the differentiation of various tissue properties. However, the presence of multiple-scattering (MS) signals can degrade the quantitative polarization measurement accuracy. We demonstrate a method to reduce MS signals and increase the measurement accuracy of Jones matrix PS-OCT. This method suppresses MS signals by averaging multiple Jones matrix volumes measured using different focal positions. The MS signals are decorrelated among the volumes by focus position modulation and are thus reduced by averaging. However, the single scattering signals are kept consistent among the focus-modulated volumes by computational refocusing. We validated the proposed method using a scattering phantom and a postmortem medaka fish. The results showed reduced artifacts in birefringence and degree-of-polarization uniformity measurements, particularly in deeper regions in the samples. This method offers a practical solution to mitigate MS-induced artifacts in PS-OCT imaging and improves quantitative polarization measurement accuracy.

Multi-focus averaging for multiple scattering suppression in optical coherence tomography.

Multiple scattering is one of the main factors that limits the penetration depth of optical coherence tomography (OCT) in scattering samples. We propose a method termed multi-focus averaging (MFA) to suppress the multiple-scattering signals and improve the image contrast of OCT in deep regions. The MFA method captures multiple OCT volumes with various focal positions and averages them in complex form after correcting the varying defocus through computational refocusing. Because the multiple-scattering takes different trajectories among the different focal position configurations, this averaging suppresses the multiple-scattering signal. Meanwhile, the single-scattering takes a consistent trajectory regardless of the focal position configuration and is not suppressed. Hence, the MFA method improves the ratio between the single-scattering signal and multiple-scattering signal, resulting in an enhancement in the image contrast. A scattering phantom and a postmortem zebrafish were measured to validate the proposed method. The results showed that the contrast of intensity images of both the phantom and zebrafish were improved using the MFA method, such that they were better than the contrast provided by the standard single focus averaging method. The MFA method provides a cost-effective solution for contrast enhancement through multiple-scattering reduction in tissue imaging using OCT systems.

Older-aged C57BL/6 mice fed a diet high in saturated fat and sucrose for ten months show decreased resilience to aging.

The ability to respond to physical stress that disrupts normal physiological homeostasis at an older age embraces the concept of resilience to aging. A physical stressor could be used to induce physiological responses that are age-related, since resilience declines with increasing age. Increased fat and sugar intake is a nutritional stress with a high prevalence of obesity in older people. In order to determine the effect of this type of diet on resilience to aging, 18-month-old C57BL/6J male mice were fed a diet high in saturated fat (lard) and sucrose (HFS) for ten months. At the end of the 10-month study, mice fed the HFS diet showed increased cognitive impairment, decreased cardiac function, decreased strength and agility, and increased severity of renal pathology compared to mice fed a rodent chow diet low in saturated fat and sucrose (LFS). The degree of response aligned with decreased resilience to the long-term adverse effects of the diet with characteristics of accelerated aging. This observation suggests additional studies could be conducted to investigate the relationship between an accelerated decline in resilience to aging and enhanced resilience to aging under different dietary conditions.

Long-term treatment with Elamipretide enhances healthy aging phenotypes in mice.

Background: Disruption of metabolic and bioenergetic homeostasis related to mitochondrial dysfunction is a key driver of aging biology. Therefore, targeting mitochondrial function would be a rational approach to slowing aging. Elamipretide (Elam, a.k.a. SS-31) is a peptide known to target mitochondria and suppress mammalian signs of aging. The present study was designed to examine the phenotypic effects of long-term Elam treatment on aging in C57BL/6 mice starting at 18 months of age. Methods: Mice were fed regular chow (RC diet) or a diet high in fat and sugar (HF diet) and treated with 3 mg/kg of Elam or saline subcutaneously 5 days per week for 10 months. Physiological performance assessments were conducted at 28 months of age. Results: Elam improved the physical performance of males but not females, while in females Elam improved cognitive performance and enhanced the maintenance of body weight and fat mass. It also improved diastolic function in both males and females, but to a greater extent in males. The HF diet over 10 months had a negative effect on health span, as it increased body fat and decreased muscle strength and heart function, especially in females. Conclusions: Elam enhanced healthy aging and cardiac function in both male and female mice, although the specific effects on function differed between sexes. In females, the treatment led to better cognitive performance and maintenance of body composition, while in males, performance on a rotating rod was preserved. These overall observations have translational implications for considering additional studies using Elam in therapeutic or preventive approaches for aging and age-related diseases.

Vision-Sensor-Assisted Probabilistic Localization Method for Indoor Environment.

Among the numerous indoor localization methods, Light-Detection-and-Ranging (LiDAR)-based probabilistic algorithms have been extensively applied to indoor localization due to their real-time performance and high accuracy. Nevertheless, these methods are challenged in symmetrical environments when tackling global localization and the robot kidnapping problem. In this paper, a novel hybrid method that combines visual and probabilistic localization results is proposed. Augmented Monte Carlo Localization (AMCL) is improved for position tracking continually. LiDAR-based measurements' uncertainty is evaluated to incorporate discrete visual-based results; therefore, a better diversity of the particle can be maintained. The robot kidnapping problem can be detected and solved by preventing premature convergence of the particle filter. Extensive experiments were implemented to validate the robustness and accuracy performance. Meanwhile, the localization error was reduced from 30 mm to 9 mm during a 600 m tour.

Label-free metabolic imaging of non-alcoholic-fatty-liver-disease (NAFLD) liver by volumetric dynamic optical coherence tomography.

Label-free metabolic imaging of non-alcoholic fatty liver disease (NAFLD) mouse liver is demonstrated ex vivo by dynamic optical coherence tomography (OCT). The NAFLD mouse is a methionine choline-deficient (MCD)-diet model, and two mice fed the MCD diet for 1 and 2 weeks are involved in addition to a normal-diet mouse. The dynamic OCT is based on repeating raster scan and logarithmic intensity variance (LIV) analysis that enables volumetric metabolic imaging with a standard-speed (50,000 A-lines/s) OCT system. Metabolic domains associated with lipid droplet accumulation and inflammation are clearly visualized three-dimensionally. Particularly, the normal-diet liver exhibits highly metabolic vessel-like structures of peri-vascular hepatic zones. The 1-week MCD-diet liver shows ring-shaped highly metabolic structures formed with lipid droplets. The 2-week MCD-diet liver exhibits fragmented vessel-like structures associated with inflammation. These results imply that volumetric LIV imaging is useful for visualizing and assessing NAFLD abnormalities.

Erratum: Computational refocusing of Jones matrix polarization-sensitive optical coherence tomography and investigation of defocus-induced polarization artifacts: publisher's note.

[This corrects the article on p. 2975 in vol. 13.].

Computational refocusing of Jones matrix polarization-sensitive optical coherence tomography and investigation of defocus-induced polarization artifacts.

Here we demonstrate a long-depth-of-focus imaging method using polarization sensitive optical coherence tomography (PS-OCT). This method involves a combination of Fresnel-diffraction-model-based phase sensitive computational refocusing and Jones-matrix based PS-OCT (JM-OCT). JM-OCT measures four complex OCT images corresponding to four polarization channels. These OCT images are computationally refocused as preserving the mutual phase consistency. This method is validated using a static phantom, postmortem zebrafish, and ex vivo porcine muscle samples. All the samples demonstrated successful computationally-refocused birefringence and degree-of-polarization-uniformity (DOPU) images. We found that defocusing induces polarization artifacts, i.e., incorrectly high birefringence values and low DOPU values, which are substantially mitigated by computational refocusing.

DSEATM: drug set enrichment analysis uncovering disease mechanisms by biomedical text mining.

Disease pathogenesis is always a major topic in biomedical research. With the exponential growth of biomedical information, drug effect analysis for specific phenotypes has shown great promise in uncovering disease-associated pathways. However, this method has only been applied to a limited number of drugs. Here, we extracted the data of 4634 diseases, 3671 drugs, 112 809 disease-drug associations and 81 527 drug-gene associations by text mining of 29 168 919 publications. On this basis, we proposed a 'Drug Set Enrichment Analysis by Text Mining (DSEATM)' pipeline and applied it to 3250 diseases, which outperformed the state-of-the-art method. Furthermore, diseases pathways enriched by DSEATM were similar to those obtained using the TCGA cancer RNA-seq differentially expressed genes. In addition, the drug number, which showed a remarkable positive correlation of 0.73 with the AUC, plays a determining role in the performance of DSEATM. Taken together, DSEATM is an auspicious and accurate disease research tool that offers fresh insights.

Non-destructive characterization of adult zebrafish models using Jones matrix optical coherence tomography.

The zebrafish is a valuable vertebrate animal model in pre-clinical cancer research. A Jones matrix optical coherence tomography (JM-OCT) prototype operating at 1310 nm and an intensity-based spectral-domain OCT setup at 840 nm were utilized to investigate adult wildtype and a tumor-developing zebrafish model. Various anatomical features were characterized based on their inherent scattering and polarization signature. A motorized translation stage in combination with the JM-OCT prototype enabled large field-of-view imaging to investigate adult zebrafish in a non-destructive way. The diseased animals exhibited tumor-related abnormalities in the brain and near the eye region. The scatter intensity, the attenuation coefficients and local polarization parameters such as the birefringence and the degree of polarization uniformity were analyzed to quantify differences in tumor versus control regions. The proof-of-concept study in a limited number of animals revealed a significant decrease in birefringence in tumors found in the brain and near the eye compared to control regions. The presented work showed the potential of OCT and JM-OCT as non-destructive, high-resolution, and real-time imaging modalities for pre-clinical research based on zebrafish.

Short term treatment with a cocktail of rapamycin, acarbose and phenylbutyrate delays aging phenotypes in mice.

Pharmaceutical intervention of aging requires targeting multiple pathways, thus there is rationale to test combinations of drugs targeting different but overlapping processes. In order to determine if combining drugs shown to extend lifespan and healthy aging in mice would have greater impact than any individual drug, a cocktail diet containing 14 ppm rapamycin, 1000 ppm acarbose, and 1000 ppm phenylbutyrate was fed to 20-month-old C57BL/6 and HET3 4-way cross mice of both sexes for three months. Mice treated with the cocktail showed a sex and strain-dependent phenotype consistent with healthy aging including decreased body fat, improved cognition, increased strength and endurance, and decreased age-related pathology compared to mice treated with individual drugs or control. The severity of age-related lesions in heart, lungs, liver, and kidney was consistently decreased in mice treated with the cocktail compared to mice treated with individual drugs or control, suggesting an interactive advantage of the three drugs. This study shows that a combination of three drugs, each previously shown to enhance lifespan and health span in mice, is able to delay aging phenotypes in middle-aged mice more effectively than any individual drug in the cocktail over a 3-month treatment period.

Deep convolutional neural network-based scatterer density and resolution estimators in optical coherence tomography.

We present deep convolutional neural network (DCNN)-based estimators of the tissue scatterer density (SD), lateral and axial resolutions, signal-to-noise ratio (SNR), and effective number of scatterers (ENS, the number of scatterers within a resolution volume). The estimators analyze the speckle pattern of an optical coherence tomography (OCT) image in estimating these parameters. The DCNN is trained by a large number (1,280,000) of image patches that are fully numerically generated in OCT imaging simulation. Numerical and experimental validations were performed. The numerical validation shows good estimation accuracy as the root mean square errors were 0.23%, 3.65%, 3.58%, 3.79%, and 6.15% for SD, lateral and axial resolutions, SNR, and ENS, respectively. The experimental validation using scattering phantoms (Intralipid emulsion) shows reasonable estimations. Namely, the estimated SDs were proportional to the Intralipid concentrations, and the average estimation errors of lateral and axial resolutions were 1.36% and 0.68%, respectively. The scatterer density estimator was also applied to an in vitro tumor cell spheroid, and a reduction in the scatterer density during cell necrosis was found.

Multicontrast investigation of in vivo wildtype zebrafish in three development stages using polarization-sensitive optical coherence tomography.

SIGNIFICANCE: The scattering and polarization characteristics of various organs of in vivo wildtype zebrafish in three development stages were investigated using a non-destructive and label-free approach. The presented results showed a promising first step for the usability of Jones-matrix optical coherence tomography (JM-OCT) in zebrafish-based research. AIM: We aim to visualize and quantify the scatter and polarization signatures of various zebrafish organs for larvae, juvenile, and young adult animals in vivo in a non-invasive and label-free way. APPROACH: A custom-built polarization-sensitive JM-OCT setup in combination with a motorized translation stage was utilized to investigate live zebrafish. Depth-resolved scattering (intensity and attenuation coefficient) and polarization (birefringence and degree of polarization uniformity) properties were analyzed. OCT angiography (OCT-A) was utilized to investigate the vasculature label-free and non-destructively. RESULTS: The scatter and polarization signatures of the zebrafish organs such as the eye, gills, and muscles were investigated. The attenuation coefficient and birefringence changes between 1- and 2-month-old animals were evaluated in selected organs. OCT-A revealed the vasculature of in vivo larvae and juvenile zebrafish in a label-free manner. CONCLUSIONS: JM-OCT offers a rapid, label-free, non-invasive, tissue specific, and three-dimensional imaging tool to investigate in vivo processes in zebrafish in various development stages.

Label-free functional and structural imaging of liver microvascular complex in mice by Jones matrix optical coherence tomography.

We demonstrate label-free imaging of the functional and structural properties of microvascular complex in mice liver. The imaging was performed by a custom-built Jones-matrix based polarization sensitive optical coherence tomography (JM-OCT), which is capable of measuring tissue's attenuation coefficient, birefringence, and tiny tissue dynamics. Two longitudinal studies comprising a healthy liver and an early fibrotic liver model were performed. In the healthy liver, we observed distinctive high dynamics beneath the vessel at the initial time point (0 h) and reappearance of high dynamics at 32-h time point. In the early fibrotic liver model, we observed high dynamics signal that reveals a clear network vascular structure by volume rendering. Longitudinal time-course imaging showed that these high dynamics signals faded and decreased over time.

A Machine Learning Method for Drug Combination Prediction.

Drug combination is now a hot research topic in the pharmaceutical industry, but experiment-based methodologies are extremely costly in time and money. Many computational methods have been proposed to address these problems by starting from existing drug combinations. However, in most cases, only molecular structure information is included, which covers too limited a set of drug characteristics to efficiently screen drug combinations. Here, we integrated similarity-based multifeature drug data to improve the prediction accuracy by using the neighbor recommender method combined with ensemble learning algorithms. By conducting feature assessment analysis, we selected the most useful drug features and achieved 0.964 AUC in the ensemble models. The comparison results showed that the ensemble models outperform traditional machine learning algorithms such as support vector machine (SVM), naïve Bayes (NB), and logistic regression (GLM). Furthermore, we predicted 7 candidate drug combinations for a specific drug, paclitaxel, and successfully verified that the two of the predicted combinations have promising effects.

Effect of Laser Remelting on Cladding Layer of Inconel 718 Superalloy Formed by Laser Metal Deposition.

The brittle phase (Laves) of Inconel 718 parts formed by laser metal deposition (LMD) represents a bottleneck of the engineering applications. In order to investigate effectiveness of laser remelting (LR) technology on suppressing the formation of Laves phase, different laser scanning speeds of the LR process were adopted to build and remelt the single-pass cladding layers. The evolution of phase composition, microstructural morphology, and hardness of the LMD and LMD + LR specimens were analyzed. The experimental results show that different laser scanning speeds can obviously change the microstructural evolutions, Laves phase, and hardness. A low laser scanning speed (360 mm/min) made columnar dendrite uninterruptedly grow from the bottom to the top of the cladding layer. A high laser scanning speed (1320 mm/min) has a significant effect on refining Laves phase and reducing Nb segregation. When the laser scanning speed of LR process is equal to that of LMD, the cladding layers can be completely remelted and the content of Laves phase of the LMD + LR layer is 22.4% lower than that of the LMD layer. As the laser scanning speed increases from 360 to 1320 mm/min, the mean primary dendrite arm spacing (PDAS) values of the remelting area decrease from 6.35 to 3.28 µm gradually. In addition, the low content of Laves phase and porosity contribute to the growth of average hardness. However, the laser scanning speed has a little effect on the average hardness and the maximum average hardness difference of the LMD and LMD + LR layers is only 12.4 HV.