Myokines and exosomes, originating from skeletal muscle, are shown to play a significant role in maintaining brain homeostasis. While exercise has been reported to promote muscle secretion, little is known about the effects of neuronal innervation and activity on the yield and molecular composition of biologically active molecules from muscle. As neuromuscular diseases and disabilities associated with denervation impact muscle metabolism, we hypothesize that neuronal innervation and firing may play a pivotal role in regulating secretion activities of skeletal muscles. We examined this hypothesis using an engineered neuromuscular tissue model consisting of skeletal muscles innervated by motor neurons. The innervated muscles displayed elevated expression of mRNAs encoding neurotrophic myokines, such as interleukin-6, brain-derived neurotrophic factor, and FDNC5, as well as the mRNA of peroxisome-proliferator-activated receptor γ coactivator 1α, a key regulator of muscle metabolism. Upon glutamate stimulation, the innervated muscles secreted higher levels of irisin and exosomes containing more diverse neurotrophic microRNAs than neuron-free muscles. Consequently, biological factors secreted by innervated muscles enhanced branching, axonal transport, and, ultimately, spontaneous network activities of primary hippocampal neurons in vitro. Overall, these results reveal the importance of neuronal innervation in modulating muscle-derived factors that promote neuronal function and suggest that the engineered neuromuscular tissue model holds significant promise as a platform for producing neurotrophic molecules.

Background: Approximately 90% of global cervical cancer (CC) is mostly found in low- and middle-income countries. In most cases, CC can be detected early through routine screening programs, including a cytology-based test. However, it is logistically difficult to offer this program in low-resource settings due to limited resources and infrastructure, and few trained experts. A visual inspection following the application of acetic acid (VIA) has been widely promoted and is routinely recommended as a viable form of CC screening in resource-constrained countries. Digital images of the cervix have been acquired during VIA procedure with better quality assurance and visualization, leading to higher diagnostic accuracy and reduction of the variability of detection rate. However, a colposcope is bulky, expensive, electricity-dependent, and needs routine maintenance, and to confirm the grade of abnormality through its images, a specialist must be present. Recently, smartphone-based imaging systems have made a significant impact on the practice of medicine by offering a cost-effective, rapid, and noninvasive method of evaluation. Furthermore, computer-aided analyses, including image processing–based methods and machine learning techniques, have also shown great potential for a high impact on medicinal evaluations.Objective: In this study, we demonstrate a new quantitative CC screening technique and implement a machine learning algorithm for smartphone-based endoscopic VIA. We also evaluated the diagnostic performance and practicability of the approach based on the results compared to the gold standard and from physicians’ interpretation.Methods: A smartphone-based endoscope system was developed and applied to the VIA screening. A total of 20 patients were recruited for this study to evaluate the system. Overall, five were healthy, and 15 were patients who had shown a low to high grade of cervical intraepithelial neoplasia (CIN) from both colposcopy and cytology tests. Endoscopic VIA images were obtained before a loop electrosurgical excision procedure for patients with abnormal tissues, and their histology tissues were collected. Endoscopic VIA images were assessed by four expert physicians relative to the gold standard of histopathology. Also, VIA features were extracted from multiple steps of image processing techniques to find the differences between abnormal (CIN2+) and normal (≤CIN1). By using the extracted features, the performance of different machine learning classifiers, such as k-nearest neighbors (KNN), support vector machine, and decision tree (DT), were compared to find the best algorithm for VIA. After determining the best performing classifying model, it was used to evaluate the screening performance of VIA.Results: An average accuracy of 78%, with a Cohen kappa of 0.571, was observed for the evaluation of the system by four physicians. Through image processing, 240 sliced images were obtained from the cervicogram at each clock position, and five features of VIA were extracted. Among the three models, KNN showed the best performance for finding VIA within holdout 10-fold cross-validation, with an accuracy of 78.3%, area under the curve of 0.807, a specificity of 80.3%, and a sensitivity of 75.0%, respectively. The trained model performed using an unprovided data set resulted in an accuracy of 80.8%, specificity of 84.1%, and sensitivity of 71.9%. Predictions were visualized with intuitive color labels, indicating the normal/abnormal tissue using a circular clock-type segmentation. Calculating the overlapped abnormal tissues between the gold standard and predicted value, the KNN model overperformed the average assessments of physicians for finding VIA.Conclusions: We explored the potential of the smartphone-based endoscopic VIA as an evaluation technique and used the cervicogram to evaluate normal/abnormal tissue using machine learning techniques. The results of this study demonstrate its potential as a screening tool in low-resource settings.

The quantitative monitoring of skin topography is important in the field of cosmetics and dermatology. The most widespread method for determining skin roughness in vivo is to use skin microrelief, PRIMOS device, which allows a noninvasive, fast and direct measurement of the skin surface. However, it has drawbacks, such as the interference of backscattering from volumetric skin and motion artifacts. In this study, we demonstrate the potential of OCT for providing reliable and quantitative skin surface roughness. In order to evaluate the performance of OCT for skin surface analysis, different types of skin phantoms were fabricated and measured. We utilized OCT to identify the effect of cosmetics as well as human skin topology for various aging groups and different skin regions. Skin surface roughness parameters based on ISO 25178 part 2 standard definitions were then derived from home-built image processing software and compared with one acquired from PRIMOS. Our results showed that skin surface geometry acquired from 3D OCT images was robust to motion artifacts and the angle to the subject. Since OCT enables to present quantitative skin topology and volumetric skin anatomy simultaneously, it would be a useful tool to deliver comprehensive and intuitive information in dynamic skin observations.

Approaches to regenerating bone often rely on integrating biomaterials and biological signals in the form of cells or cytokines. However, from a translational point of view, these approaches are challenging due to the sourcing and quality of the biologic, unpredictable immune responses, complex regulatory paths, and high costs. We describe a simple manufacturing process and a material-centric 3D-printed composite scaffold system (CSS) that offers distinct advantages for clinical translation. The CSS comprises a 3D-printed porous polydiolcitrate-hydroxyapatite composite elastomer infused with a polydiolcitrate-graphene oxide hydrogel composite. Using a micro-continuous liquid interface production 3D printer, we fabricate a precise porous ceramic scaffold with 60 wt% hydroxyapatite resembling natural bone. The resulting scaffold integrates with a thermoresponsive hydrogel composite in situ to fit the defect, which is expected to enhance surface contact with surrounding tissue and facilitate biointegration. The antioxidative properties of citrate polymers prevent long-term inflammatory responses. The CSS stimulates osteogenesis in vitro and in vivo. Within 4 weeks in a calvarial critical-sized bone defect model, the CSS accelerated ECM deposition (8-fold) and mineralized osteoid (69-fold) compared to the untreated. Through spatial transcriptomics, we demonstrated the comprehensive biological processes of CSS for prompt osseointegration. Our material-centric approach delivers impressive osteogenic properties and streamlined manufacturing advantages, potentially expediting clinical application for bone reconstruction surgeries.

The outer epidermal skin is a primary barrier that protects the body from extrinsic factors, such as ultraviolet (UV) radiation, chemicals and pollutants. The complete epithelialization of a wound by keratinocytes is essential for restoring the barrier function of the skin. However, age-related alterations predispose the elderly to impaired wound healing. Therefore, wound-healing efficacy could be also considered as a potent function of an anti-aging reagent. Here, we examine the epidermal wound-healing efficacy of the fourth-generation retinoid, seletinoid G, using HaCaT keratinocytes and skin tissues. We found that seletinoid G promoted the proliferation and migration of keratinocytes in scratch assays and time-lapse imaging. It also increased the gene expression levels of several keratinocyte proliferation-regulating factors. In human skin equivalents, seletinoid G accelerated epidermal wound closure, as assessed using optical coherence tomography (OCT) imaging. Moreover, second harmonic generation (SHG) imaging revealed that seletinoid G recovered the reduced dermal collagen deposition seen in ultraviolet B (UVB)-irradiated human skin equivalents. Taken together, these results indicate that seletinoid G protects the skin barrier by accelerating wound healing in the epidermis and by repairing collagen deficiency in the dermis. Thus, seletinoid G could be a potent anti-aging agent for protecting the skin barrier.

Since glaucoma is a progressive and irreversible optic neuropathy, accurate screening and/or early diagnosis is critical in preventing permanent vision loss. Recently, optical coherence tomography (OCT) has become an accurate diagnostic tool to observe and extract the thickness of the retinal nerve fiber layer (RNFL), which closely reflects the nerve damage caused by glaucoma. However, OCT is less accessible than fundus photography due to higher cost and expertise required for operation. Though widely used, fundus photography is effective for early glaucoma detection only when used by experts with extensive training. Here, we introduce a deep learning-based approach to predict the RNFL thickness around optic disc regions in fundus photography for glaucoma screening. The proposed deep learning model is based on a convolutional neural network (CNN) and utilizes images taken with fundus photography and with RNFL thickness measured with OCT for model training and validation. Using a dataset acquired from normal tension glaucoma (NTG) patients, the trained model can estimate RNFL thicknesses in 12 optic disc regions from fundus photos. Using intuitive thickness labels to identify localized damage of the optic nerve head and then estimating regional RNFL thicknesses from fundus images, we determine that screening for glaucoma could achieve 92% sensitivity and 86.9% specificity. Receiver operating characteristic (ROC) analysis results for specificity of 80% demonstrate that use of the localized mean over superior and inferior regions reaches 90.7% sensitivity, whereas 71.2% sensitivity is reached using the global RNFL thicknesses for specificity at 80%. This demonstrates that the new approach of using regional RNFL thicknesses in fundus images holds good promise as a potential screening technique for early stage of glaucoma.