An atlas of brain-bone sympathetic neural circuits in mice.

There is clear evidence that the sympathetic nervous system (SNS) mediates bone metabolism. Histological studies show abundant SNS innervation of the periosteum and bone marrow-these nerves consist of noradrenergic fibers that immunostain for tyrosine hydroxylase, dopamine beta-hydroxylase, or neuropeptide Y. Nonetheless, the brain sites that send efferent SNS outflow to the bone have not yet been characterized. Using pseudorabies (PRV) viral transneuronal tracing, we report, for the first time, the identification of central SNS outflow sites that innervate bone. We find that the central SNS outflow to bone originates from 87 brain nuclei, sub-nuclei, and regions of six brain divisions, namely the midbrain and pons, hypothalamus, hindbrain medulla, forebrain, cerebral cortex, and thalamus. We also find that certain sites, such as the raphe magnus (RMg) of the medulla and periaqueductal gray (PAG) of the midbrain, display greater degrees of PRV152 infection, suggesting that there is considerable site-specific variation in the levels of central SNS outflow to the bone. This comprehensive compendium illustrating the central coding and control of SNS efferent signals to bone should allow for a greater understanding of the neural regulation of bone metabolism, and importantly and of clinical relevance, mechanisms for central bone pain.

Spectrum-based deep learning framework for dermatological pigment analysis and simulation.

BACKGROUND: Deep learning in dermatology presents promising tools for automated diagnosis but faces challenges, including labor-intensive ground truth preparation and a primary focus on visually identifiable features. Spectrum-based approaches offer professional-level information like pigment distribution maps, but encounter practical limitations such as complex system requirements. METHODS: This study introduces a spectrum-based framework for training a deep learning model to generate melanin and hemoglobin distribution maps from skin images. This approach eliminates the need for manually prepared ground truth by synthesizing output maps into skin images for regression analysis. The framework is applied to acquire spectral data, create pigment distribution maps, and simulate pigment variations. RESULTS: Our model generated reflectance spectra and spectral images that accurately reflect pigment absorption properties, outperforming spectral upsampling methods. It produced pigment distribution maps with correlation coefficients of 0.913 for melanin and 0.941 for hemoglobin compared to the VISIA system. Additionally, the model's simulated images of pigment variations exhibited a proportional correlation with adjustments made to pigment levels. These evaluations are based on pigment absorption properties, the Individual Typology Angle (ITA), and pigment indices. CONCLUSION: The model produces pigment distribution maps comparable to those from specialized clinical equipment and simulated images with numerically adjusted pigment variations. This approach demonstrates significant promise for developing professional-level diagnostic tools for future clinical applications.

Semi-Supervised Facial Acne Segmentation Using Bidirectional Copy-Paste.

Facial acne is a prevalent dermatological condition regularly observed in the general population. However, it is important to detect acne early as the condition can worsen if not treated. For this purpose, deep-learning-based methods have been proposed to automate detection, but acquiring acne training data is not easy. Therefore, this study proposes a novel deep learning model for facial acne segmentation utilizing a semi-supervised learning method known as bidirectional copy-paste, which synthesizes images by interchanging foreground and background parts between labeled and unlabeled images during the training phase. To overcome the lower performance observed in the labeled image training part compared to the previous methods, a new framework was devised to directly compute the training loss based on labeled images. The effectiveness of the proposed method was evaluated against previous semi-supervised learning methods using images cropped from facial images at acne sites. The proposed method achieved a Dice score of 0.5205 in experiments utilizing only 3% of labels, marking an improvement of 0.0151 to 0.0473 in Dice score over previous methods. The proposed semi-supervised learning approach for facial acne segmentation demonstrated an improvement in performance, offering a novel direction for future acne analysis.

First report of molecular epidemiology and phylogenetic characteristics of feline herpesvirus (FHV-1) from naturally infected cats in Kunshan, China.

BACKGROUND: Feline herpesvirus type 1 (FHV-1) is a life threatening highly contagious virus in cats and typically causes upper respiratory tract infections as well as conjunctival and corneal ulcers. Genetic variability could alter the severity of diseases and clinical signs. Despite regular vaccine practices against FHV-1 in China, new FHV-1 cases still commonly occur. The genetic and phylogenetic characteristics of FHV-1 in Kunshan city of China has not been studied yet. Therefore, this study was planned to investigate the prevalence, molecular characteristics of circulating strains, and phylogenetic analyses of FHV-1. This is the first report of molecular epidemiology and phylogenetic characteristics of FHV-1 from naturally infected cats in Kunshan, China. METHODS: The occulo-nasal swabs were collected from diseased cats showing respiratory distress, conjunctivitis, and corneal ulcers at different veterinary clinics in Kunshan from 2022 to 2023. Clinical data and general information were recorded. Swab samples were processed for preliminary detection of FHV-1. Thymidine kinase (TK), glycoprotein B (gB) and glycoprotein D (gD) genes were sequenced and analyzed to investigate genetic diversity and evolution of FHV-1. RESULTS: The FHV-1 genome was detected in 43 (43/200, 21.5%) samples using RT-PCR targeting the TK gene. Statistical analysis showed a significant correlation between age, vaccination status and living environment (p < 0.05) with FHV-1 positivity, while a non-significant correlation was observed for FHV-1 positivity and sex of cats (p > 0.05). Additionally, eight FHV-1 positive cats were co-infected with feline calicivirus (8/43,18.6%). FHV-1 identified in the present study was confirmed as FHV-1 based on phylogenetic analyses. The sequence analyses revealed that 43 FHV-1 strains identified in the present study did not differ much with reference strains within China and worldwide. A nucleotide homology of 99-100% was determined among gB, TK and gD genes nucleotide sequences when compared with standard strain C-27 and vaccine strains. Amino acid analysis showed some amino acid substitutions in TK, gB and gD protein sequences. A potential N-linked glycosylation site was observed in all TK protein sequences. Phylogenetic analyses revealed minor variations and short evolutionary distance among FHV-1 strains detected in this study. CONCLUSIONS: Our findings indicate that genomes of 43 FHV-1 strains are highly homogenous and antigenically similar, and the degree of variation in major envelope proteins between strains is low. This study demonstrated some useful data about prevalence, genetic characteristics, and evolution of FHV-1 in Kunshan, which may aid in future vaccine development.

Next-generation all-organic composites: A sustainable successor to organic-inorganic hybrid materials.

This Review presents an overview of all-organic nanocomposites, a sustainable alternative to organic-inorganic hybrids. All-organic nanocomposites contain nanocellulose, nanochitin, and aramid nanofibers as highly rigid reinforcing fillers. They offer superior mechanical properties and lightweight characteristics suitable for diverse applications. The Review discusses various methods for preparing the organic nanofillers, including top-down and bottom-up approaches. It highlights in situ polymerization as the preferred method for incorporating these nanomaterials into polymer matrices to achieve homogeneous filler dispersion, a crucial factor for realizing desired performance. Furthermore, the Review explores several applications of all-organic nanocomposites in diverse fields including food packaging, performance-advantaged plastics, and electronic materials. Future research directions-developing sustainable production methods, expanding biomedical applications, and enhancing resistance against heat, chemicals, and radiation of all-organic nanocomposites to permit their use in extreme environments-are explored. This Review offers insights into the potential of all-organic nanocomposites to drive sustainable growth while meeting the demand for high-performance materials across various industries.

FSH, bone, belly and brain.

The pituitary gland orchestrates multiple endocrine organs by secreting tropic hormones, and therefore plays a significant role in a myriad of physiological processes, including skeletal modeling and remodeling, fat and glucose metabolism, and cognition. Expression of receptors for each pituitary hormone and the hormone itself in the skeleton, fat, immune cells, and the brain suggest that their role is much broader than the traditionally attributed functions. FSH, believed solely to regulate gonadal function is also involved in fat and bone metabolism, as well as in cognition. Our emerging understanding of nonreproductive functions of FSH, thus, opens potential therapeutic opportunities to address detrimental health consequences during and after menopause, namely, osteoporosis, obesity, and dementia. In this review, we outline current understanding of the cross-talk between the pituitary, bone, adipose tissue, and brain through FSH. Preclinical evidence from genetic and pharmacologic interventions in rodent models, and human data from population-based observations, genetic studies, and a small number of interventional studies provide compelling evidence for independent functions of FSH in bone loss, fat gain, and congnitive impairment.

Practical Applications of Self-Healing Polymers Beyond Mechanical and Electrical Recovery.

Self-healing polymeric materials, which can repair physical damage, offer promising prospects for protective applications across various industries. Although prolonged durability and resource conservation are key advantages, focusing solely on mechanical recovery may limit the market potential of these materials. The unique physical properties of self-healing polymers, such as interfacial reduction, seamless connection lines, temperature/pressure responses, and phase transitions, enable a multitude of innovative applications. In this perspective, the diverse applications of self-healing polymers beyond their traditional mechanical strength are emphasized and their potential in various sectors such as food packaging, damage-reporting, radiation shielding, acoustic conservation, biomedical monitoring, and tissue regeneration is explored. With regards to the commercialization challenges, including scalability, robustness, and performance degradation under extreme conditions, strategies to overcome these limitations and promote successful industrialization are discussed. Furthermore, the potential impacts of self-healing materials on future research directions, encompassing environmental sustainability, advanced computational techniques, integration with emerging technologies, and tailoring materials for specific applications are examined. This perspective aims to inspire interdisciplinary approaches and foster the adoption of self-healing materials in various real-life settings, ultimately contributing to the development of next-generation materials.

Molecular epidemiology and phylogenetic analysis of feline calicivirus in Kunshan, China.

Feline calicivirus (FCV) is a highly contagious virus in cats, which typically causes respiratory tract and oral infections. Despite vaccination against FCV being a regular practice in China, new FCV cases still occur. Antigenic diversity of FCV hinders the effective control by vaccination. This is first report which aims to investigate the molecular epidemiology and molecular characteristics of FCV in Kunshan, China. The nasopharyngeal swabs were collected from cats showing variable clinical signs from different animal clinics in Kunshan from 2022 to 2023. Preliminary detection and sequencing of the FCV capsid gene were performed to study genetic diversity and evolutionary characteristics. FCV-RNA was identified in 52 (26%) of the samples using RT-PCR. A significant association was found between FCV-positive detection rate, age, gender, vaccination status and living environment, while a non-significant association was found with breed of cats. Nucleotide analysis revealed two genotypes, GI and GII. GII predominated in Kunshan, with diverse strains and amino acid variations potentially affecting vaccination efficacy and FCV detection. Notably, analysis pinpointed certain strains' association with FCV-virulent systemic disease pathotypes. This investigation sheds light on FCV dynamics, which may aid in developing better prevention strategies and future vaccine designs against circulating FCV genotypes.

Deep learning-based skin care product recommendation: A focus on cosmetic ingredient analysis and facial skin conditions.

BACKGROUND: Recommendations for cosmetics are gaining popularity, but they are not being made with consideration of the analysis of cosmetic ingredients, which customers consider important when selecting cosmetics. AIMS: This article aims to propose a method for estimating the efficacy of cosmetics based on their ingredients and introduces a system that recommends personalized products for consumers, combined with AI skin analysis. METHODS: We constructed a deep neural network architecture to analyze sequentially arranged cosmetic ingredients in the product and incorporated skin analysis models to get the precise skin status of users from frontal face images. Our recommendation system makes decisions based on the results optimized for the individual. RESULTS: Our cosmetic recommendation system has shown its effectiveness through reliable evaluation metrics, and numerous examples have demonstrated its ability to make reasonable recommendations for various skin problems. CONCLUSION: The result shows that deep learning methods can be used to predict the effects of products based on their cosmetic ingredients and are available for use in personalized cosmetic recommendations.

An Atlas of Brain-Bone Sympathetic Neural Circuits.

There is clear evidence that the sympathetic nervous system (SNS) mediates bone metabolism. Histological studies show abundant SNS innervation of the periosteum and bone marrow--these nerves consist of noradrenergic fibers that immunostain for tyrosine hydroxylase, dopamine beta hydroxylase, or neuropeptide Y. Nonetheless, the brain sites that send efferent SNS outflow to bone have not yet been characterized. Using pseudorabies (PRV) viral transneuronal tracing, we report, for the first time, the identification of central SNS outflow sites that innervate bone. We find that the central SNS outflow to bone originates from 87 brain nuclei, sub-nuclei and regions of six brain divisions, namely the midbrain and pons, hypothalamus, hindbrain medulla, forebrain, cerebral cortex, and thalamus. We also find that certain sites, such as the raphe magnus (RMg) of the medulla and periaqueductal gray (PAG) of the midbrain, display greater degrees of PRV152 infection, suggesting that there is considerable site-specific variation in the levels of central SNS outflow to bone. This comprehensive compendium illustrating the central coding and control of SNS efferent signals to bone should allow for a greater understanding of the neural regulation of bone metabolism, and importantly and of clinical relevance, mechanisms for central bone pain.

Facial wrinkle segmentation using weighted deep supervision and semi-automatic labeling.

Facial wrinkles are important indicators of human aging. Recently, a method using deep learning and a semi-automatic labeling was proposed to segment facial wrinkles, which showed much better performance than conventional image-processing-based methods. However, the difficulty of wrinkle segmentation remains challenging due to the thinness of wrinkles and their small proportion in the entire image. Therefore, performance improvement in wrinkle segmentation is still necessary. To address this issue, we propose a novel loss function that takes into account the thickness of wrinkles based on the semi-automatic labeling approach. First, considering the different spatial dimensions of the decoder in the U-Net architecture, we generated weighted wrinkle maps from ground truth. These weighted wrinkle maps were used to calculate the training losses more accurately than the existing deep supervision approach. This new loss computation approach is defined as weighted deep supervision in our study. The proposed method was evaluated using an image dataset obtained from a professional skin analysis device and labeled using semi-automatic labeling. In our experiment, the proposed weighted deep supervision showed higher Jaccard Similarity Index (JSI) performance for wrinkle segmentation compared to conventional deep supervision and traditional image processing methods. Additionally, we conducted experiments on the labeling using a semi-automatic labeling approach, which had not been explored in previous research, and compared it with human labeling. The semi-automatic labeling technology showed more consistent wrinkle labels than human-made labels. Furthermore, to assess the scalability of the proposed method to other domains, we applied it to retinal vessel segmentation. The results demonstrated superior performance of the proposed method compared to existing retinal vessel segmentation approaches. In conclusion, the proposed method offers high performance and can be easily applied to various biomedical domains and U-Net-based architectures. Therefore, the proposed approach will be beneficial for various biomedical imaging approaches. To facilitate this, we have made the source code of the proposed method publicly available at: https://github.com/resemin/WeightedDeepSupervision.

A review of nucleic acid-based detection methods for foodborne viruses: Sample pretreatment and detection techniques.

Viruses are major pathogens that cause food poisoning when ingested via contaminated food and water. Therefore, the development of foodborne virus detection technologies that can be applied throughout the food distribution chain is essential for food safety. A common nucleic acid-based detection method is polymerase chain reaction (PCR), which has become the gold standard for monitoring food contamination by viruses due to its high sensitivity, and availability of commercial kits. However, PCR-based methods are labor intensive and time consuming, and are vulnerable to inhibitors that may be present in food samples. In addition, the methods are restricted with regard to site of analysis due to the requirement of expensive and large equipment for sophisticated temperature regulation and signal analysis procedures. To overcome these limitations, optical and electrical readout biosensors based on nucleic acid isothermal amplification technology and nanomaterials have emerged as alternatives for nucleic acid-based detection of foodborne viruses. Biosensors are promising portable detection tools owing to their easy integration into compact platforms and ability to be operated on-site. However, the complexity of food components necessitates the inclusion of tedious preprocessing steps, and the lack of stability studies on residual food components further restricts the practical application of biosensors as a universal detection method. Here, we summarize the latest advances in nucleic acid-based strategies for the detection of foodborne viruses, including PCR-based and isothermal amplification-based methods, gene amplification-free methods, as well as food pretreatment methods. The principles, strengths/disadvantages, and performance of each method, problems to be solved, and future prospects for the development of a universal detection method are discussed.

Generation of skin tone and pigmented region-modified images using a pigment discrimination model trained with an optical approach.

BACKGROUND: Skin tone and pigmented regions, associated with melanin and hemoglobin, are critical indicators of skin condition. While most prior research focuses on pigment analysis, the capability to simulate diverse pigmentation conditions could greatly broaden the range of applications. However, current methodologies have limitations in terms of numerical control and versatility. METHODS: We introduce a hybrid technique that integrates optical methods with deep learning to produce skin tone and pigmented region-modified images with numerical control. The pigment discrimination model produces melanin, hemoglobin, and shading maps from skin images. The outputs are reconstructed into skin images using a forward problem-solving approach, with model training aimed at minimizing the discrepancy between the reconstructed and input images. By adjusting the melanin and hemoglobin maps, we create pigment-modified images, allowing precise control over changes in melanin and hemoglobin levels. Changes in pigmentation are quantified using the individual typology angle (ITA) for skin tone and melanin and erythema indices for pigmented regions, validating the intended modifications. RESULTS: The pigment discrimination model achieved correlation coefficients with clinical equipment of 0.915 for melanin and 0.931 for hemoglobin. The alterations in the melanin and hemoglobin maps exhibit a proportional correlation with the ITA and pigment indices in both quantitative and qualitative assessments. Additionally, regions overlaying melanin and hemoglobin are demonstrated to verify independent adjustments. CONCLUSION: The proposed method offers an approach to generate modified images of skin tone and pigmented regions. Potential applications include visualizing alterations for clinical assessments, simulating the effects of skincare products, and generating datasets for deep learning.

Recent Advances in Biosensor Development for the Detection of Viral Particles in Foods: A Comprehensive Review.

Viral foodborne diseases cause serious harm to human health and the economy. Rapid, accurate, and convenient approaches for detecting foodborne viruses are crucial for preventing diseases. Biosensors integrating electrochemical and optical properties of nanomaterials have emerged as effective tools for the detection of viruses in foods. However, they still face several challenges, including substantial sample preparation and relatively poor sensitivity due to complex food matrices, which limit their field applications. Hence, the purpose of this review is to provide an overview of recent advances in biosensing techniques, including electrochemical, SERS-based, and colorimetric biosensors, for detecting viral particles in food samples, with emerging techniques for extraction/concentration of virus particles from food samples. Moreover, the principle, design, and advantages/disadvantages of each biosensing method are comprehensively described. This review covers the recent development of rapid and sensitive biosensors that can be used as new standards for monitoring food safety and food quality in the food industry.

Extracellular matrix-degrading enzymes as a biofilm control strategy for food-related microorganisms.

Biofilm is one of the major problems in food industries and is difficult to be removed or prevented by conventional sanitizers. In this review, we discussed the extracellular matrix-degrading enzymes as a strategy to control biofilms of foodborne pathogenic and food-contaminating bacteria. The biofilms can be degraded by using the enzymes targeting proteins, polysaccharides, extracellular DNA, or lipids which mainly constitute the extracellular polymeric substances of biofilms. However, the efficacy of enzymes varies by the growth medium, bacterial species, strains, or counterpart microorganisms due to a high variation in the composition of extracellular polymeric substances. Several studies demonstrated that the combined treatment using conventional sanitizers or multiple enzymes can synergistically enhance the biofilm removal efficacies. In this review, the application of the immobilized enzymes on solid substrates is also discussed as a potential strategy to prevent biofilm formation on food contact surfaces.

Pituitary crosstalk with bone, adipose tissue and brain.

Traditional textbook physiology has ascribed unitary functions to hormones from the anterior and posterior pituitary gland, mainly in the regulation of effector hormone secretion from endocrine organs. However, the evolutionary biology of pituitary hormones and their receptors provides evidence for a broad range of functions in vertebrate physiology. Over the past decade, we and others have discovered that thyroid-stimulating hormone, follicle-stimulating hormone, adrenocorticotropic hormone, prolactin, oxytocin and arginine vasopressin act directly on somatic organs, including bone, adipose tissue and liver. New evidence also indicates that pituitary hormone receptors are expressed in brain regions, nuclei and subnuclei. These studies have prompted us to attribute the pathophysiology of certain human diseases, including osteoporosis, obesity and neurodegeneration, at least in part, to changes in pituitary hormone levels. This new information has identified actionable therapeutic targets for drug discovery.

Deep learning-based pigment analysis model trained with optical approach and ground truth assistance.

This study introduces an integrated training method combining the optical approach with ground truth for skin pigment analysis. Deep learning is increasingly applied to skin pigment analysis, primarily melanin and hemoglobin. While regression analysis is a widely used training method to predict ground truth-like outputs, the input image resolution is restricted by computational resources. The optical approach-based regression method can alleviate this problem, but compromises performance. We propose a strategy to overcome the limitation of image resolution while preserving performance by incorporating ground truth within the optical approach-based learning structure. The proposed model decomposes skin images into melanin, hemoglobin, and shading maps, reconstructing them by solving the forward problem with reference to the ground truth for pigments. Evaluation against the VISIA system, a professional diagnostic equipment, yields correlation coefficients of 0.978 for melanin and 0.975 for hemoglobin. Furthermore, our model can produce pigment-modified images for applications like simulating treatment effects.

Improvement of phoswich detector-based ß+/γ-ray discrimination algorithm with deep learning.

BACKGROUND: Positron probes can accurately localize malignant tumors by directly detecting positrons emitted from positron-emitting radiopharmaceuticals that accumulate in malignant tumors. In the conventional method for direct positron detection, multilayer scintillator detection and pulse shape discrimination techniques are used. However, some γ-rays cannot be distinguished by conventional methods. Accordingly, these γ-rays are misidentified as positrons, which may increase the error rate of positron detection. PURPOSE: To analyze the energy distribution in each scintillator of the multilayer scintillator detector to distinguish true positrons and γ-rays and to improve the positron detection algorithm by discriminating true and false positrons. METHODS: We used Autoencoder, an unsupervised deep learning architecture, to obtain the energy distribution data in each scintillator of the multilayer scintillator detector. The Autoencoder was trained to separate the combined signals generated from the multilayer scintillator detector into two signals of each scintillator. An energy window was then applied to the energy distribution obtained using the trained Autoencoder to distinguish true positrons from false positrons. Finally, the performance of the proposed method and conventional positron detection algorithm was evaluated in terms of the sensitivity and error rate for positron detection. RESULTS: The energy distribution map obtained using the trained Autoencoder was proven to be similar to that of the simulated results. Furthermore, the proposed method demonstrated a 29.79% (+0.42%p) increase in positron detection sensitivity compared to the conventional method, both having an equal error rate of 0.48%. However, when both methods were set to have the same sensitivity of 1.83%, the proposed method had an error rate that was 25.0% (-0.16%p) lower than that of the conventional method. CONCLUSIONS: We proposed and developed an Autoencoder-based positron detection algorithm that can discriminate between true and false positrons with a smaller error rate than conventional methods. We verified that the proposed method could increase the positron detection sensitivity while maintaining a low error rate compared to the conventional method. If the proposed algorithm is implemented in handheld positron detection probes or cameras, diseases such as cancers can be more accurately localized in a shorter time compared with using traditional methods.

Fast-Embeddable Grooved Microneedles by Shear Actuation for Accurate Transdermal Drug Delivery.

Percutaneous drug delivery using microneedles (MNs) has been extensively exploited to increase the transdermal permeability of therapeutic drugs. However, it is difficult to control the precise dosage with existing MNs and they need to be attached for a long time, so a more simple and scalable method is required for accurate transdermal drug delivery. In this study, we developed grooved MNs that can be embedded into the skin by mechanical fracture following simple shear actuation. Grooved MNs are prepared from hyaluronic acid (HA), which is a highly biocompatible and biodegradable biopolymer. By adjusting the aspect ratio (length:diameter) of the MN and the position of the groove, the MN tip inserted into the skin can be easily broken by shear force. In addition, it was demonstrated that it is possible to deliver the desired amount of triamcinolone acetonide (TCA) for alopecia areata by controlling the position of the groove structure and the concentration of TCA loaded in the MN. It was also confirmed that the tip of the TCA MN can be accurately delivered into the skin with a high probability (98% or more) by fabricating an easy-to-operate applicator to provide adequate shear force. The grooved MN platform has proven to be able to load the desired amount of a drug and deliver it at the correct dose.