Dynamic optical coherence elastography for skin burn assessment: A preliminary study on mice model.

Skin burns that include tissue coagulation necrosis imply variations in stiffness. Dynamic phase-sensitive optical coherence elastography (OCE) is used to evaluate the stiffness of burned skin nondestructively in this paper. The homemade dynamic OCE was initially verified through tissue-mimicking phantom experiments regarding Rayleigh wave speed. After being burned with a series of temperatures and durations, the corresponding structure and stiffness variations of mice skin were demonstrated by histological images, optical coherence tomography B-scans, and OCE elastic wave speed maps. The results clearly displayed the variation in elastic properties and stiffness of the scab edge extending in the lateral direction. Statistical analysis revealed that murine skin burned at temperatures exceeding 100°C typically exhibited greater stiffness than skin burned at temperatures below 100°C. The dynamic OCE technique shows potential application for incorporating elasticity properties as a biomechanical extension module to diagnose skin burn injuries.

Enhancement of OCTen faceimages by unsupervised deep learning.

Objective. The quality of optical coherence tomography (OCT)en faceimages is crucial for clinical visualization of early disease. As a three dimensional and coherent imaging, defocus and speckle noise are inevitable, which seriously affect evaluation of microstructure of bio-samples in OCT images. The deep learning has demonstrated great potential in OCT refocusing and denoising, but it is limited by the difficulty of sufficient paired training data. This work aims to develop an unsupervised method to enhance the quality of OCTen faceimages.Approach. We proposed an unsupervised deep learning-based pipeline. The unregistered defocused conventional OCT images and focused speckle-free OCT images were collected by a home-made speckle modulating OCT system to construct the dataset. The image enhancement model was trained with the cycle training strategy. Finally, the speckle noise and defocus were both effectively improved.Main results. The experimental results on complex bio-samples indicated that the proposed method is effective and generalized in enhancing the quality of OCTen faceimages.Significance. The proposed unsupervised deep learning method helps to reduce the complexity of data construction, which is conducive to practical applications in OCT bio-sample imaging.

Salinity decreases the contribution of microbial necromass to soil organic carbon pool in arid regions.

Saline soils are widely distributed in arid areas but there is a lack of mechanistic understanding on the effect of salinity on the formation and biochemical composition of soil organic carbon (SOC). We investigated the effects of salinity on the accumulation of microbial necromass under natural vegetation and in cropland in salt-affected arid areas stretching over a 1200-km transect in northwest China. Under both natural vegetation and cropland, microbial physiological activity (indicated by microbial biomass carbon normalized enzymatic activity) decreased sharply where the electrical conductivity approached 4 ds m-1 (a threshold to distinguish between saline and non-saline soils), but microbial biomass was only slightly affected by salinity. These indicated that a larger proportion of microbes could be inactive or dormant in saline soils. The contribution of fungal necromass C to SOC decreased but the contribution of bacterial necromass C to the SOC increased with increasing soil salinity. Adding fungal and bacterial necromass C together, the contribution of microbial necromass C to SOC in saline soils was 32-39 % smaller compared with non-saline soils. Fungal necromass C took up 85-86 % of microbial necromass C in non-saline soils but this proportion dropped to 60-66 % in saline soils. We suggested that the activity, growth, and turnover rate of microbes slowed by salinity was responsible for the decreased accumulation of fungal necromass in saline compared with non-saline soils, while the increased accumulation of bacterial residue in saline soils could be induced mainly by its slower decomposition. Soil microbial biomass was a poor predictor for the accumulation of microbial necromass in saline soils. We demonstrated a reduced contribution of microbial necromass to SOC and a shift in its composition towards the increase in bacterial origin in saline relative to non-saline soils. We concluded that salinity profoundly changes the biochemistry of SOC in arid regions.

Altered regional brain activity moderating the relationship between childhood trauma and depression severity.

OBJECTIVE: Childhood trauma (CT) is a major environmental risk factor for an adverse course and treatment outcome of major depressive disorder (MDD). Evidence suggests that an altered regional brain activity may play a crucial role in the relationship between CT and MDD. This study aimed to clarify the relationship between CT, regional brain activity, and depression severity. METHODS: In this study, 96 patients with MDD and 82 healthy controls (HCs) participated. Regional brain activity was measured using the fractional amplitude of low-frequency fluctuation (fALFF) and regional homogeneity (ReHo). These measures were compared between the MDD and HC groups, and the values of different brain regions were extracted as moderators. RESULTS: Increased fALFF and ReHo values were observed in the left middle temporal gyrus in the MDD group compared with the HC group (p < 0.001). Furthermore, the fALFF and ReHo values moderated the positive correlation between the Childhood Trauma Questionnaire (CTQ) score, 17-item Hamilton Depression Rating Scale (HAMD-17) total score, and retardation factor score in the MDD group (all, p < 0.05). Finally, as the fALFF and ReHo values increased, the positive correlations between CTQ, HAMD-17 total, and retardation dimension scores became stronger. CONCLUSION: Our study highlighted the crucial role of altered brain function in connecting childhood maltreatment with depressive symptoms. Our findings indicate that an altered regional brain activity could explain the potential neurobiological mechanisms of MDD symptoms, offering the opportunity to function as a powerful diagnostic biomarker.

Long-term quality of life after repeated ketamine infusions in anxious and nonanxious patients with depression.

BACKGROUND: There have been many studies on the benefits of repeated ketamine infusions on patients' depression but few on the impact of ketamine on patients' long-term quality of life (QoL). This study investigated long-term QoL in individuals with depression, both anxious and nonanxious. METHODS: A total of 107 individuals with a diagnosis of depression were included in the study. The patients were evaluated on Days 0, 13 and 26 and Months 6 and 9, and they received six ketamine infusions over the course of two weeks. The World Health Organization Quality of Life-BREF (WHOQOL-BREF) Scale and the Patient Health Questionnaire-9 (PHQ-9) Scale were used to measure depressive symptoms and QoL. Linear mixed models were used to evaluate depressive symptoms and QoL during ketamine treatment. RESULTS: A total of 67.2 % of patients were diagnosed with anxious depression. In the long term, there were no significant differences in the time-by-group interactions for general QoL (F = 0.510; P = 0.676), physical QoL (F = 2.092; P = 0.102), psychological QoL (F = 0.102; P = 0.959), social QoL (F = 2.180; P = 0.091), or environmental QoL (F = 1.849; P = 0.139) between the two groups. LIMITATIONS: The main limitation of this study is its open-label design. CONCLUSION: The improvement in depression symptoms and QoL following ketamine treatment was not impacted by the presence or absence of anxiety in patients who were depressed prior to treatment. Only occasionally did depressed individuals with anxiety experience a worsening of their quality of life compared to those without anxiety.

Self super-resolution of optical coherence tomography images based on deep learning.

As a medical imaging modality, many researches have been devoted to improving the resolution of optical coherence tomography (OCT). We developed a deep-learning based OCT self super-resolution (OCT-SSR) pipeline to improve the axial resolution of OCT images based on the high-resolution and low-resolution spectral data collected by the OCT system. In this pipeline, the enhanced super-resolution asymmetric generative adversarial networks were built to improve the network outputs without increasing the complexity. The feasibility and effectiveness of the approach were demonstrated by experimental results on the images of the biological samples collected by the home-made spectral-domain OCT and swept-source OCT systems. More importantly, we found the sidelobes in the original images can be obviously suppressed while improving the resolution based on the OCT-SSR method, which can help to reduce pseudo-signal in OCT imaging when non-Gaussian spectra light source is used. We believe that the OCT-SSR method has broad prospects in breaking the limitation of the source bandwidth on the axial resolution of the OCT system.

Information-Rich Multi-Functional OCT for Adult Zebrafish Intra- and Extracranial Imaging.

The zebrafish serves as a valuable animal model for both intra- and extracranial research, particularly in relation to the brain and skull. To effectively investigate the development and regeneration of adult zebrafish, a versatile in vivo imaging technique capable of showing both intra- and extracranial conditions is essential. In this paper, we utilized a high-resolution multi-functional optical coherence tomography (OCT) to obtain rich intra- and extracranial imaging outcomes of adult zebrafish, encompassing pigmentation distribution, tissue-specific information, cranial vascular imaging, and the monitoring of traumatic brain injury (TBI). Notably, it is the first that the channels through the zebrafish cranial suture, which may have a crucial function in maintaining the patency of the cranial sutures, have been observed. Rich imaging results demonstrated that a high-resolution multi-functional OCT system can provide a wealth of novel and interpretable biological information for intra- and extracranial studies of adult zebrafish.

Selectivity Quantification with the Fluorescent Quantitative Model-Assisted Semi-Selective Probe (Carbon Dots): Accurate Determination of Chlortetracycline in Aqueous Environments with Interference.

Probes such as carbon dots (C-dots) have extensive and important applications in the quantitative analysis of complex biological and environmental systems. However, the development of probes is often hindered by incomplete selectivity, i.e., a probe that responds to one substance is also prone to respond to coexisting structurally similar substances. Therefore, the above dilemma often leads to be developed as semi-selective probes, so that the development of probes is abandoned halfway. This work shows how a semi-selective probe can enhance selectivity by combining a proper multivariate calibration model. Primarily, we developed a semi-selective fluorescent probe that responded to tetracyclines (TCs) with discarded tobacco leaves. Then, we introduced the multivariate quantitative fluorescence model (QFM) to enhance its selectivity and solve the problem of fluorescence spectral shift. For the determination of chlortetracycline (CTC) with this semi-selective C-dots probe in mineral and lake water samples and compared to the traditional quantitative model, the introduced QFM resulted in an average relative predictive error (ARPE) in mineral water spiked samples decreased from 57.1 to 5.6%, which reduced the ARPE in the lake water spiked samples from 18.1 to 4.7%. The above results show that the QFM-assisted semi-selective probe C-dots strategy (QFMC-dots) can enhance selectivity, and QFMC-dots achieved high-selective and accurate determination of CTC in interfering mineral and lake water samples, with the limit of detection and limit of quantitation of 0.55 and 1.66 µM, respectively. The proposed strategy of enhancing selectivity by introducing a proper multivariate calibration model can reduce the difficulty and increase success rate of developing probes, which can be expected to provide an interesting alternative for the development of probes, especially when encountering semi-selective problems.

The role of Sirtuin 1 and its activators in age-related lung disease.

Aging is a major driving factor in lung diseases. Age-related lung disease is associated with downregulated expression of SIRT1, an NAD+-dependent deacetylase that regulates inflammation and stress resistance. SIRT1 acts by inducing the deacetylation of various substrates and regulates several mechanisms that relate to lung aging, such as genomic instability, lung stem cell exhaustion, mitochondrial dysfunction, telomere shortening, and immune senescence. Chinese herbal medicines have many biological activities, exerting anti-inflammatory, anti-oxidation, anti-tumor, and immune regulatory effects. Recent studies have confirmed that many Chinese herbs have the effect of activating SIRT1. Therefore, we reviewed the mechanism of SIRT1 in age-related lung disease and explored the potential roles of Chinese herbs as SIRT1 activators in the treatment of age-related lung disease.

Deep-Learning-Based Automated Identification and Visualization of Oral Cancer in Optical Coherence Tomography Images.

Early detection and diagnosis of oral cancer are critical for a better prognosis, but accurate and automatic identification is difficult using the available technologies. Optical coherence tomography (OCT) can be used as diagnostic aid due to the advantages of high resolution and non-invasion. We aim to evaluate deep-learning-based algorithms for OCT images to assist clinicians in oral cancer screening and diagnosis. An OCT data set was first established, including normal mucosa, precancerous lesion, and oral squamous cell carcinoma. Then, three kinds of convolutional neural networks (CNNs) were trained and evaluated by using four metrics (accuracy, precision, sensitivity, and specificity). Moreover, the CNN-based methods were compared against machine learning approaches through the same dataset. The results show the performance of CNNs, with a classification accuracy of up to 96.76%, is better than the machine-learning-based method with an accuracy of 92.52%. Moreover, visualization of lesions in OCT images was performed and the rationality and interpretability of the model for distinguishing different oral tissues were evaluated. It is proved that the automatic identification algorithm of OCT images based on deep learning has the potential to provide decision support for the effective screening and diagnosis of oral cancer.

Multiscale reconstruction of bronchus and cancer cells in human lung adenocarcinoma.

BACKGROUND: While previous studies primarily focused on the structure of the normal whole mouse lung, the whole bronchus and cytoarchitectural details of the mouse intact lung lobe have been discovered at single-cell resolution. Revealing the sophisticated lung adenocarcinoma structure at three-dimensional (3D) and single-cell level remains a fundamental and critical challenge for the pathological mechanism research of lung adenocarcinoma (LA). METHODS: Fluorescence micro-optical Sectioning Tomography (fMOST) combined with PI staining were used to obtain the 3D imaging of the human LA tissue at single-cell resolution. RESULTS: With a spatial resolution of 0.32 × 0.32 × 1.0 µm3, the dataset of human LA with single-cell precision consists of two channels, each of which contains information about the bronchi and the cytoarchitecture. The bronchial wall is thicker and the lumen is smaller in the cancer tissue, in which its original normal structure is vanished. More solid components, more clustered cancer cells with larger nucleoli, and more significant atypia are found in cancer tissue. In paracancerous tissue, the bronchial wall cells have a monolayer or bilayer structure, cluster along the wall, and are relatively dispersed. Few fibrous structures and occasional dissemination of spread through air spaces (STAS) are observed. CONCLUSIONS: Based on the human LA tissue dataset obtained by fMOST and PI staining, the bronchi and cells were reconstructed and visualized. This work provides a technical roadmap for studying the bronchus and cytoarchitectural structure and their spatial relationship in LA tissue, which may help with the understanding of the main histological structure of LA among pathologists.

Interference-free quantitation of aromatic amino acids in two complex systems by three-way calibration with ultraviolet-visible spectrophotometer: Exploration of trilinear decomposition of spectrum-pH data.

Aromatic amino acids play an extremely important role in life activities and participate in many biological processes. Their concentration levels are associated with a variety of diseases, such as phenylketonuria and colorectal cancer. Therefore, the quantification of aromatic amino acids is an important task. In the present work, a novel and rapid three-way analytical method was proposed to detect the levels of aromatic amino acids in prostate cancer cells (PC3 cells) and Dulbecco's modified minimal essential medium (DMEM cell culture), by using the affordable ultraviolet-visible spectrophotometer. First, spectrum-pH second-order data were designed per sample; Second, properties of the resulted spectrum-pH-sample three-way data were investigated by utilizing the parallel factor analysis (PARAFAC), alternating trilinear decomposition (ATLD), and constrained alternating trilinear decomposition (CATLD) algorithms, and a flexible scanning approach for determining the constraint parameters of CATLD was proposed; Third, a three-way calibration method based on the CATLD algorithm with the proposed scanning approach was developed for interference-free quantification of aromatic amino acids in these systems. The average relative predictive errors of validation (ARPEV) for phenylalanine, tyrosine, and tryptophan were 1.4%, 3.0%, and 0.7% in prostate cancer cells, and ARPEV for phenylalanine, tyrosine, and tryptophan were 4.1%, 1.2%, and 0.7% in DMEM cell culture. The predicted contents of tyrosine and tryptophan in DMEM cell culture were 64.2 ± 2.9 µg mL-1, 5.6 ± 0.3 µg mL-1, there are no significant differences in the concentrations between the developed analytical method and high performance liquid chromatography method. The proposed spectrum-pH-sample three-way calibration method based on CATLD algorithm can provide an interesting analytical strategy with high selectivity and accuracy for ultraviolet-visible spectrophotometer.

Rare Earth Doping Engineering Tailoring Advanced Oxygen-Vacancy Co3 O4 with Tunable Structures for High-Efficiency Energy Storage.

Co3 O4  with high theoretical capacitance is a promising electrode material for high-end energy applications, yet the unexcited bulk electrochemical activity, low conductivity, and poor kinetics of Co3 O4  lead to unsatisfactory charge storage capacity. For boosting its energy storage capability, rare earth (RE)-doped Co3 O4  nanostructures with abundant oxygen vacancies are constructed by simple, economical, and universal chemical precipitation. By changing different types of RE (RE = La, Yb, Y, Ce, Er, Ho, Nd, Eu) as dopants, the RE-doped Co3 O4  nanostructures can be well transformed from large nanosheets to coiled tiny nanosheets and finally to ultrafine nanoparticles, meanwhile, their specific surface area, pore distribution, the ratio of Co2+ /Co3+ , oxygen vacancy content, crystalline phase, microstrain parameter, and the capacitance performance are regularly affected. Notably, Eu-doped Co3 O4  nanoparticles with good cycle stability show a maximum specific capacitance of 1021.3 F g-1 (90.78 mAh g-1 ) at 2 A g-1 , higher than 388 F g-1 (34.49 mAh g-1 ) of pristine Co3 O4  nanosheets. The assembling asymmetric supercapacitor delivers a high energy density of 48.23 Wh kg-1  at high power density of 1.2 kW kg-1 . These findings denote the significance and great potential of RE-doped Co3 O4  in the development of high-efficiency energy storage.

Classification of neck tissues in OCT images by using convolutional neural network.

Identification and classification of surrounding neck tissues are very important in thyroid surgery. The advantages of optical coherence tomography (OCT), high resolution, non-invasion, and non-destruction make it have great potential in identifying different neck tissues during thyroidectomy. We studied the automatic classification for neck tissues in OCT images based on convolutional neural network in this paper. OCT images of five kinds of neck tissues were collected firstly by our home-made swept source (SS-OCT) system, and a dataset was built for neural network training. Three image classification neural networks: LeNet, VGGNet, and ResNet, were used to train and test the dataset. The impact of transfer learning on the classification of neck tissue OCT images was also studied. Through the comparison of accuracy, it was found that ResNet has the best classification accuracy among the three networks. In addition, transfer learning did not significantly improve the accuracy, but it can somewhat accelerate the convergence of the network and shorten the network training time.

Zebrafish brain and skull imaging based on polarization-sensitive optical coherence tomography.

Zebrafish brain imaging is very important for the study of brain disease and regeneration. We scanned the adult zebrafish brain before and after skull removal and monitored the recovery process of a head wound by polarization-sensitive optical coherence tomography (PS-OCT) in this paper. We analyzed the structure and polarization characteristics of the brain and skull in PS-OCT images, and found their internal microstructure can be clearly identified with the polarization information. Further, we estimated the pigment distribution of the skull area and found that the density of pigment in skull is a critical factor of affecting zebrafish brain in vivo polarization imaging. Our results demonstrated that more features of brain can be displayed by introducing the polarization information, and proved high-resolution PS-OCT will play a great potential role in studying the zebrafish brain and skull.

Digital refocusing based on deep learning in optical coherence tomography.

We present a deep learning-based digital refocusing approach to extend depth of focus for optical coherence tomography (OCT) in this paper. We built pixel-level registered pairs of en face low-resolution (LR) and high-resolution (HR) OCT images based on experimental data and introduced the receptive field block into the generative adversarial networks to learn the complex mapping relationship between LR-HR image pairs. It was demonstrated by results of phantom and biological samples that the lateral resolutions of OCT images were improved in a large imaging depth clearly. We firmly believe deep learning methods have broad prospects in optimizing OCT imaging.

Waste tobacco leaves derived carbon dots for tetracycline detection: Improving quantitative accuracy with the aid of chemometric model.

The recycling and reutilization of biomass wastes are significant for environmental protection and sustainable development. Recently, there have many studies on utilizing biomass wastes to produce carbon dots. Whereas, the spectrum shift effect that occurs in the quantitative application of carbon dots as fluorescent probes limits the accuracy of the quantitative analysis. In this work, waste tobacco leaves were used as the carbon source for synthesizing a novel carbon dots (CDs(WTL)) through a facile hydrothermal method. The CDs(WTL) possess a series of excellent properties, including good water solubility, well stability, and high fluorescence quantum yield. The fluorescent intensity of the CDs(WTL) can be quenched by tetracycline (TC) obviously, but there is a spectrum shift. In order to use the CDs(WTL) as fluorescent probes to quantify TC with higher accuracy, a quantification fluorescence model (QFM) was introduced to overcome this spectrum shift effect that often occurs. The coefficient of determination (R2) of traditional quantification model (TQ), partial least squares (PLS), and QFM are 0.9672, 0.9834, and 0.9991, respectively; the average relative predictive error (ARPE) of TQ, PLS, and QFM are 8.8%, 4.5%, and 3.9% for the spiked water samples, and 21.9%, 22.0%, and 2.9% for spiked tablet samples, respectively. The obtained results suggest that QFM is more accurate than PLS and TQ for the TC detection. By utilizing QFM, the spike recoveries (mean ± standard deviation) in three kinds of real tablet samples produced by different manufacturers are 98.9 ± 3.6%, 102.5 ± 6.2%, and 98.5 ± 2.7%, respectively; the spike recovery in river water samples is 99.4 ± 5.0%. In addition, high performance liquid chromatography (HPLC) was used as a reference method, the F and t tests suggest that there are no significant differences on the precision and accuracy between QFM and HPLC methods.

Identification of oral precancerous and cancerous tissue by swept source optical coherence tomography.

BACKGROUND AND OBJECTIVES: Distinguishing cancer from precancerous lesions is critical and challenging in oral medicine. As a noninvasive method, optical coherence tomography (OCT) has the advantages of real-time, in vivo, and large-depth imaging. Texture information hidden in OCT images can provide an important auxiliary effect for improving diagnostic accuracy. The aim of this study is to explore a reliable and accurate OCT-based method for the screening and diagnosis of human oral diseases, especially oral cancer. MATERIALS AND METHODS: Fresh ex vivo oral tissues including normal mucosa, leukoplakia with epithelial hyperplasia (LEH), and oral squamous cell carcinoma (OSCC) were imaged intraoperatively by a homemade OCT system, and 58 texture features were extracted to create computational models of these tissues. A principal component analysis algorithm was employed to optimize the combination of texture feature vectors. The identification based on artificial neural network (ANN) was proposed and the sensitivity/specificity was calculated statistically to evaluate the classification performance. RESULTS: A total of 71 sites of three types of oral tissues were measured, and 5176 OCT images of three types of oral tissues were used in this study. The superior classification result based on ANN was obtained with an average accuracy of 98.17%. The sensitivity and specificity of normal mucosa, LEH, and OSCC are 98.17% / 98.38%, 93.81% / 98.54%, and 98.11% / 99.04%, respectively. CONCLUSION: It is demonstrated from the high accuracies, sensitivities, and specificities that texture-based analysis can be used to identify oral precancerous and cancerous tissue in OCT images, and it has the potential to help surgeons in diseases screening and diagnosis effectively.

Classification of oral salivary gland tumors based on texture features in optical coherence tomography images.

Currently, the diagnoses of oral diseases primarily depend on the visual recognition of experienced clinicians. It has been proven that automatic recognition based on images can support clinical decision-making by extracting and analyzing objective hidden information. In recent years, optical coherence tomography (OCT) has become a powerful optical imaging technique with the advantages of high resolution and non-invasion. In our study, a dataset composed of four kinds of oral salivary gland tumors (SGTs) was obtained from a homemade swept-source OCT, including two benign and two malignant tumors. Seventy-six texture features were extracted from OCT images to create computational models of diseases. It was demonstrated that the artificial neural network (ANN) based on principal component analysis (PCA) can obtain high diagnostic sensitivity and specificity (higher than 99%) for these four kinds of tumors. The classification accuracy of each tumor is larger than 99%. In addition, the performances of two classifiers (ANN and support vector machine) were quantitatively evaluated based on SGTs. It was proven that the texture features in OCT images provided objective information to classify oral tumors.

Study on an artificial phenomenon observed in terahertz biological imaging.

Terahertz (THz) wave-based imaging of biological samples is an emerging but promising field. In the present work, we report an artificial phenomenon observed in imaging melanoma slices, which can lead to mistakenly interpretation of the experimental results. It was observed that a structure similar to but smaller than the sample contour appeared inside the melanoma slice image. The underlying mechanism of this phenomenon was then investigated both experimentally and theoretically. By imaging a regular standard sample (vinyl coverslip) with a THz time domain spectroscopy (THz-TDS) system and reconstructing its images at 0.8 and 1.2 THz, we can clearly observe the afore-mentioned artifacts. The experimental results are highly consistent with the simulations based on the Fresnel-Kirchhoff diffraction theory in which possible optical aberrations were incorporated. It can be concluded that this artifact was caused by the frequency-dependent diffraction of the sample edge. The work demonstrated here is essential for correct interpretation of the images obtained by the THz-TDS technique.