Exosomes are lipid bilayer vesicles released by cells and serve as natural carriers for cell–cell communication. Exosomes provide a promising approach to the diagnosis and treatment of diseases and are considered as an alternative to cell therapy. However, one main restriction in their clinical application is that the current understanding of these vesicles, especially their in vivo behaviors and distributions, remains inadequate. Here, we reviewed the current and emerging methods for in vivo imaging and tracking of exosomes, including fluorescence imaging, bioluminescence imaging, nuclear imaging, X-ray imaging, magnetic resonance imaging, photoacoustic imaging, and multimodal imaging. In vivo imaging and tracking of exosomes by these methods can help researchers further understand their uptake mechanism, biodistribution, migration, function, and therapeutic performance. The pioneering studies in this field can elucidate many unknown exosomal behaviors at different levels. We discussed the advantages and limitations of each labeling and imaging strategy. The advances in labeling and in vivo imaging will expand our understanding of exosomes and promote their clinical application. We finally provide a perspective and discuss several important issues that need to be explored in future research. This review highlights the values of efficient, sensitive, and biocompatible exosome labeling and imaging techniques in disease theranostics.

Stroke is caused by an acute focal disruption of the vasculature in the central nervous system. Neurological-related functional deficits are the most devastating consequences for stroke survivors. Neural signals from stroke patients can reflect the functional statuses of patients and provide insights into the neuronal recovery mechanism for functioning, which could be used as the basis for designing optimal treatment strategies. Near-infrared spectroscopy (NIRS) is a low-cost, noninvasive, easily operated neuroimage method and it is compatible with various rehabilitative programs. These advantages make NIRS an excellent candidate in research for stroke recovery. Here, we focused on the brain functions and recovery for stroke patients at stable status, conducted a systematic literature review about NIRS applications in stroke since 2000 and identified a total of 72 references through ScienceDirect and PubMed database retrieval. The NIRS studies in stroke include resting-state function and its recovery, motor function and its recovery, motor and cognition interference, cognitive function and its recovery, language function and its recovery, emotional function and its recovery and other applications. Based on the results of the quality assessment, we identified some study gaps from the previous research and provided suggestions for some methodological improvement in the future. The trend of NIRS gives a boost to its application in stroke, and the potential research directions for NIRS in stroke are prospected, including multi-center clinical research, treatment efficacy prediction research and brain– muscle coupling research. Finally, limitations of NIRS are discussed.

The design of a handheld capnography device is in great demand because of its effective and practical uses in all cardiac arrest resuscitations, according to the recommendation of the American Heart Association. Herein, a handheld capnography device that can be used in clinical settings and the home environment is reported. The proposed device was developed by using an infrared CO_{2 sensor, Arduino Mega2560, and a high-resolution display (2.8"). Furthermore, two rechargeable batteries (7.6 V, 0.99 A) and a secure digital card with a capacity of 16GB were incorporated to increase the portability and usability of the device. Algorithms were implemented to measure standard features, namely, inspired CO2 (ICO2), end-tidal CO2 (EtCO2), and respiratory rate (RR). The features of 15 healthy subjects were recorded by using the developed prototype and the standard capnography device (CapnostreamTM20 Model CS08798). Validation was performed with Bland–Altman plots. Findings revealed that mean differences ± standard deviations for the set limits of ICO2, EtCO2 and RR were 0.29 ± 1.30 millimeters of mercury (mmHg), 0.15±1.77mmHg and 0.40 ± 0.97 breaths per minute (bpm), respectively. Most of the differences among device measurements across all features fell within the 95% limits of agreement. Thus, the developed device may help manage respiratory distress conditions in and outside of a hospital setting.}

To date, the clinical use of functional near-infrared spectroscopy (NIRS) to detect cerebral ischemia has been largely limited to surgical settings, where motion artifacts are minimal. In this study, we present novel techniques to address the challenges of using NIRS to monitor ambulatory patients with kidney disease during approximately eight hours of hemodialysis (HD) treatment. People with end-stage kidney disease who require HD are at higher risk for cognitive impairment and dementia than age-matched controls. Recent studies have suggested that HDrelated declines in cerebral blood flow might explain some of the adverse outcomes of HD treatment. However, there are currently no established paradigms for monitoring cerebral perfusion in real-time during HD treatment. In this study, we used NIRS to assess cerebral hemodynamic responses among 95 prevalent HD patients during two consecutive HD treatments. We observed substantial signal attenuation in our predominantly Black patient cohort that required probe modifications. We also observed consistent motion artifacts that we addressed by developing a novel NIRS methodology, called the HD cerebral oxygen demand algorithm (HDCODA), to identify episodes when cerebral oxygen demand might be outpacing supply during HD treatment. We then examined the association between a summary measure of time spent in cerebral deoxygenation, derived using the HD-CODA, and hemodynamic and treatment-related variables. We found that this summary measure was associated with intradialytic mean arterial pressure, heart rate, and volume removal. Future studies should use the HD-CODA to implement studies of real-time NIRS monitoring for incident dialysis patients, over longer time frames, and in other dialysis modalities.

Clathrin- and caveolae-mediated endocytosis are the most commonly used pathways for the internalization of cell membrane receptors. However, due to their dimensions are within the diffraction limit, traditional fluorescence microscopy cannot distinguish them and little is known about their interactions underneath cell membrane. In this study, we proposed the line-switching scanning imaging mode for dual-color triplet-state relaxation (T-Rex) stimulated emission depletion (STED) super-resolution microscopy. With this line-switching mode, the cross-talk between the two channels, the side effects from pulse picker and image drift in frame scanning mode can be effectively eliminated. The dual-color super-resolution imaging results in mixed fluorescent beads validated the excellent performance. With this super-resolution microscope, not only the ring-shaped structure of clathrin and caveolae endocytic vesicles, but also their semifused structures underneath the cell membrane were distinguished clearly. The resultant information will greatly facilitate the study of clathrin- and caveolae-mediated receptor endocytosis and signaling process and also our home-built dual-color T-Rex STED microscope with this lineswitching imaging mode provides a precise and convenient way to study subcellular-scale protein interactions.

Recently, research has been conducted to assist in the processing and analysis of histopathological images using machine learning algorithms. In this study, we established machine learning-based algorithms to detect photothrombotic lesions in histological images of photothrombosis-induced rabbit brains. Six machine learning-based algorithms for binary classification were applied, and the accuracies were compared to classify normal tissues and photothrombotic lesions. The lesion classification model consisting of a 3-layered neural network with a rectified linear unit (ReLU) activation function, Xavier initialization, and Adam optimization using datasets with a unit size of 128 × 128 pixels yielded the highest accuracy (0.975). In the validation using the tested histological images, it was confirmed that the model could identify regions where brain damage occurred due to photochemical ischemic stroke. Through the development of machine learning-based photothrombotic lesion classi- fication models and performance comparisons, we confirmed that machine learning algorithms have the potential to be utilized in histopathology and various medical diagnostic techniques.

Radiation therapy (RT) is a treatment option for head and neck cancer (HNC), but 2% of RT patients may experience damage to the jawbone, resulting in osteoradionecrosis (ORN). The ORN can manifest years after RT exposure. Changes in the local microchemical bone quality prior to the clinical manifestation of ORN could play a key role in ORN pathogenesis. Chemical bone quality can be analyzed using Fourier transform infrared spectroscopy (FTIR), that is applied to examine the effects of cancer, chemotherapy, and RT on the quality of human mandibular bone. Cortical mandibular bone samples were harvested from dental implant beds of 23 individuals, i.e., patients with surgically and radiotherapeutically treated HNC (RT-HNC, n = 7), surgically and radiochemotherapeutically treated HNC (CH-RT-HNC, n = 3), only surgically treated HNC (SRG-HNC, n = 4), and healthy controls (n = 9). Infrared spectra were acquired from two representative regions of interest in cortical mandibular bone. Spectral parameters, i.e., mineral-to-matrix ratio (MM), carbonate-to-matrix ratio (CM), carbonate-tophosphate ratio (CP), collagen maturity (cross-linking), crystallinity, acid phosphate substitution (APS), and advanced glycation end products (AGEs), were analyzed for each sample. Amide I region of the CH-RT-HNC group differed from the control group in cluster analysis (p = 0.02). Apart from a minor variation trend in collagen maturity (p = 0.07), there were no other significant differences between the groups. Thus, the effect of radiochemotherapy on mandibular bone composition should be further investigated. In future trials, this study design is potential when the effects of the cancer burden and different HNC treatment modalities on jawbone composition are studied, in order to reveal ORN pathogenesis.

This paper discusses and studies the composition and characteristics of biospeckle on the surface of bone tissues. We used a laser speckle device to capture biospeckle patterns from fresh pig bone tissue. Traditional speckle activity metrics were used to measure the speckle activity of ex vivo bone tissue over time. Both Gaussian and Lorentzian correlation functions were used to characterize the ordered and disordered motion of the bone surface, together with volume scattering, to construct the model. Using the established mathematical model of the spatio-temporal evolution of the biospeckle pattern, it is possible to account for the presence of volume scattering from the biospeckle of bones, quantify the ordered or disordered motions in the biological speckle activity at the current time, and assess the ability of laser speckle correlation technique to determine biological activity.

To overcome the low efficiency of conventional confocal Raman spectroscopy, many efforts have been devoted to parallelizing the Raman excitation and acquisition, in which the scattering from multiple foci is projected onto different locations on a spectrometer's CCD, along either its vertical, horizontal dimension, or even both. While the latter projection scheme relieves the limitation on the row numbers of the CCD, the spectra of multiple foci are recorded in one spectral channel, resulting in spectral overlapping. Here, we developed a method under a compressive sensing framework to demultiplex the superimposed spectra of multiple cells during their dynamic processes. Unlike the previous methods which ignore the information connection between the spectra of the cells recorded at different time, the proposed method utilizes a prior that a cell's spectra acquired at different time have the same sparsity structure in their principal components. Rather than independently demultiplexing the mixed spectra at the individual time intervals, the method demultiplexes the whole spectral sequence acquired continuously during the dynamic process. By penalizing the sparsity combined from all time intervals, the collaborative optimization of the inversion problem gave more accurate recovery results. The performances of the method were substantiated by a 1D Raman tweezers array, which monitored the germination of multiple bacterial spores. The method can be extended to the monitoring of many living cells randomly scattering on a coverslip, and has a potential to improve the throughput by a few orders.

Zebrafish is an important animal model, which is used to study development, pathology, and genetic research. The zebrafish skin model is widely used in cutaneous research, and angiogenesis is critical for cutaneous wound healing. However, limited by the penetration depth, the available optical methods are difficult to describe the internal skin structure and the connection of blood vessels between the skin and subcutaneous tissue. By a homemade high-resolution polarization-sensitive optical coherence tomography (PS-OCT) system, we imaged the polarization contrast of zebrafish skin and the zebrafish skin vasculature with optical coherence tomography angiography (OCTA). Based on these OCT images, the spatial distribution of the zebrafish skin vasculature was described. Furthermore, we monitored the healing process of zebrafish cutaneous wounds. We think the highresolution PS-OCT system will be a promising tool in studying cutaneous models of zebrafish.