Integrative Analysis of Lactylome and Proteome of Hypertrophic Scar To Identify Pathways or Proteins Associated with Disease Development.

Lactylation (Kla), a recently discovered post-translational modification derived from lactate, plays crucial roles in various cellular processes. However, the specific influence of lactylation on the biological processes underlying hypertrophic scar formation remains unclear. In this study, we present a comprehensive profiling of the lactylome and proteome in both hypertrophic scars and adjacent normal skin tissues. A total of 1023 Kla sites originating from 338 nonhistone proteins were identified based on lactylome analysis. Proteome analysis in hypertrophic scar and adjacent skin samples revealed the identification of 2008 proteins. It is worth noting that Kla exhibits a preference for genes associated with ribosome function as well as glycolysis/gluconeogenesis in both normal skin and hypertrophic scar tissues. Furthermore, the functional enrichment analysis demonstrated that differentially lactyled proteins are primarily involved in proteoglycans, HIF-1, and AMPK signaling pathways. The combined analysis of the lactylome and proteome data highlighted a significant upregulation of 14 lactylation sites in hypertrophic scar tissues. Overall, our investigation unveiled the significant involvement of protein lactylation in the regulation of ribosome function as well as glycolysis/gluconeogenesis, potentially contributing to the formation of hypertrophic scars.

Spatial metabolomics to discover hypertrophic scar relevant metabolic alterations and potential therapeutic strategies: A preliminary study.

Spatially mapping the metabolic remodeling of hypertrophic scar and surrounding normal skin tissues has the potential to enhance our comprehension of scar formation and aid in the advancement of therapeutic interventions. In this study, we employed matrix-assisted laser desorption/ionization (MALDI), a mass spectrometry imaging technique, to visualize the hierarchical distribution of metabolites within sections of hypertrophic scar and surrounding normal skin tissues. A comprehensive analysis identified a total of 1631 metabolites in these tissues. The top four classes that were identified included benzene and substituted derivatives, heterocyclic compounds, amino acids and its metabolites, and glycerophospholipids. In hypertrophic scar tissues, 22 metabolites were upregulated and 66 metabolites were downregulated. MetaboAnalyst pathway analysis indicated that glycerophospholipid metabolism was primarily associated with these altered 88 metabolites. Subsequently, six metabolites were selected, their spatial characteristics were analyzed, and they were individually added to the cell culture medium of primary hypertrophic scar fibroblasts. The preliminary findings of this study demonstrate that specific concentrations of 1-pyrrolidinecarboxamide, 2-benzylideneheptanal, glycerol trioleate, Lyso-PAF C-16, and moxonidine effectively inhibited the expressions of COL1A1, COL1A2, COL3A1, and ACTA2. These bioactive metabolites exhibit mild and non-toxic properties, along with favorable pharmacokinetics and pharmacodynamics, making them promising candidates for drug development. Consequently, this research offers novel therapeutic insights for hypertrophic scar treatment.

Removal of tetracycline in the water by a kind of S/N co-doped tea residue biochar.

Tetracycline (TC) is widely present in the environment, and adsorption technology is a potential remediation method. S/N co-doped tea residue biochar (SNBC) was successfully prepared by hydrothermal carbonization method using tea residue as raw material. S was doped by Na2S2O3·5H2O, and N was doped by N in tea residue. The adsorption efficiency of SNBC could reach 94.16% when the concentration of TC was 100 mg L-1. The adsorption effect of SNBC on TC was 9.38 times more than that of unmodified biochar. Tea biochar had good adsorption effect at pH 4-9. The maximum adsorption capacity of 271 mg g-1 was calculated by the Langmuir isotherm model. The adsorption mechanism involved many mechanisms such as pore filling, π-π interaction and hydrogen bonding. The adsorbent prepared in this study could be used as an effective adsorbent in the treatment of TC wastewater.

Probing fluctuations in sulfur dioxide and viscosity levels during mitochondrial dysfunction using a dual-response fluorescent probe with good water solubility.

Mitochondrial dysfunction is associated with increased viscosity and reactive oxygen species (ROS) levels. As an effective antioxidant, sulfur dioxide (SO2) can actively scavenge excess ROS to regulate the redox state and protect cells from oxidative stress. However, few studies have evaluated the connection between viscosity and SO2 during mitochondrial dysfunction. Herein, a water-soluble fluorescent probe (MBI) is designed and synthesized for dual-detecting SO2 and viscosity. The probe rapidly detects SO2 within 12 s based on Michael's addition reaction. Meanwhile, increasing viscosity further inhibits the intramolecular rotation, causing the probe to show a greatly enhanced fluorescence. Probe MBI possesses mitochondria targeting capability due to its quaternary ammonium salt. More importantly, probe MBI successfully supports SO2 and viscosity imaging in living cells and can effectively monitor them during mitochondrial dysfunction and cell apoptosis.

Automatic Annotation Diagnostic Framework for Nasopharyngeal Carcinoma via Pathology-Fidelity GAN and Prior-Driven Classification.

Non-keratinizing carcinoma is the most common subtype of nasopharyngeal carcinoma (NPC). Its poorly differentiated tumor cells and complex microenvironment present challenges to pathological diagnosis. AI-based pathological models have demonstrated potential in diagnosing NPC, but the reliance on costly manual annotation hinders development. To address the challenges, this paper proposes a deep learning-based framework for diagnosing NPC without manual annotation. The framework includes a novel unpaired generative network and a prior-driven image classification system. With pathology-fidelity constraints, the generative network achieves accurate digital staining from H&E to EBER images. The classification system leverages staining specificity and pathological prior knowledge to annotate training data automatically and to classify images for NPC diagnosis. This work used 232 cases for study. The experimental results show that the classification system reached a 99.59% accuracy in classifying EBER images, which closely matched the diagnostic results of pathologists. Utilizing PF-GAN as the backbone of the framework, the system attained a specificity of 0.8826 in generating EBER images, markedly outperforming that of other GANs (0.6137, 0.5815). Furthermore, the F1-Score of the framework for patch level diagnosis was 0.9143, exceeding those of fully supervised models (0.9103, 0.8777). To further validate its clinical efficacy, the framework was compared with experienced pathologists at the WSI level, showing comparable NPC diagnosis performance. This low-cost and precise diagnostic framework optimizes the early pathological diagnosis method for NPC and provides an innovative strategic direction for AI-based cancer diagnosis.

Unsupervised stain augmentation enhanced glomerular instance segmentation on pathology images.

PURPOSE: In pathology images, different stains highlight different glomerular structures, so a supervised deep learning-based glomerular instance segmentation model trained on individual stains performs poorly on other stains. However, it is difficult to obtain a training set with multiple stains because the labeling of pathology images is very time-consuming and tedious. Therefore, in this paper, we proposed an unsupervised stain augmentation-based method for segmentation of glomerular instances. METHODS: In this study, we successfully realized the conversion between different staining methods such as PAS, MT and PASM by contrastive unpaired translation (CUT), thus improving the staining diversity of the training set. Moreover, we replaced the backbone of mask R-CNN with swin transformer to further improve the efficiency of feature extraction and thus achieve better performance in instance segmentation task. RESULTS: To validate the method presented in this paper, we constructed a dataset from 216 WSIs of the three stains in this study. After conducting in-depth experiments, we verified that the instance segmentation method based on stain augmentation outperforms existing methods across all metrics for PAS, PASM, and MT stains. Furthermore, ablation experiments are performed in this paper to further demonstrate the effectiveness of the proposed module. CONCLUSION: This study successfully demonstrated the potential of unsupervised stain augmentation to improve glomerular segmentation in pathology analysis. Future research could extend this approach to other complex segmentation tasks in the pathology image domain to further explore the potential of applying stain augmentation techniques in different domains of pathology image analysis.

Global attention based GNN with Bayesian collaborative learning for glomerular lesion recognition.

BACKGROUND: Glomerular lesions reflect the onset and progression of renal disease. Pathological diagnoses are widely regarded as the definitive method for recognizing these lesions, as the deviations in histopathological structures closely correlate with impairments in renal function. METHODS: Deep learning plays a crucial role in streamlining the laborious, challenging, and subjective task of recognizing glomerular lesions by pathologists. However, the current methods treat pathology images as data in regular Euclidean space, limiting their ability to efficiently represent the complex local features and global connections. In response to this challenge, this paper proposes a graph neural network (GNN) that utilizes global attention pooling (GAP) to more effectively extract high-level semantic features from glomerular images. The model incorporates Bayesian collaborative learning (BCL), enhancing node feature fine-tuning and fusion during training. In addition, this paper adds a soft classification head to mitigate the semantic ambiguity associated with a purely hard classification. RESULTS: This paper conducted extensive experiments on four glomerular datasets, comprising a total of 491 whole slide images (WSIs) and 9030 images. The results demonstrate that the proposed model achieves impressive F1 scores of 81.37%, 90.12%, 87.72%, and 98.68% on four private datasets for glomerular lesion recognition. These scores surpass the performance of the other models used for comparison. Furthermore, this paper employed a publicly available BReAst Carcinoma Subtyping (BRACS) dataset with an 85.61% F1 score to further prove the superiority of the proposed model. CONCLUSION: The proposed model not only facilitates precise recognition of glomerular lesions but also serves as a potent tool for diagnosing kidney diseases effectively. Furthermore, the framework and training methodology of the GNN can be adeptly applied to address various pathology image classification challenges.

Factors affecting consumer attitudes towards using digital media platforms on health knowledge communication: Findings of cognition-affect-conation pattern.

As the world enters the second year of the pandemic, many posts are marketed to promote products under the guise of spreading knowledge to fulfill the users' requirement for health knowledge. Current research, however, has primarily focused on methods to improve health literacy rather than the impact of this new form. To remedy this deficiency, this research use Cognition-Affect-Conation Pattern (CAC) to investigate digital media users and to determine attitudes towards this new form and influential. Data was collected in December 2021 from 314 users in Guangzhou, China, via a Likert-type scale developed by the researchers. Regression analyzes were also performed. Research has shown that consumer attitudes in health knowledge marketing fall within the standard learning hierarchy, with consumer self-cognition and information quality cognitions showing a positive relationship with their affect and conation. At the same time, affect did play a mediating role in this model. The results of our study provide constructive solutions for companies to optimize the communication environment of health spread in society. The findings not only provide researchers with a new perspective to study the impact of interactive digital media communication on health knowledge, but also help identify users' information needs and formulate effective strategies to increase consumers' understanding and application of health knowledge and products. Providing health knowledge content and leveraging digital media to develop well-established communication channels is important to foster relationships with customers.

Removal ibuprofen from aqueous solution by a noval Al-modified biochar.

With the increase of organic emissions in production and human life, the pollution control of organic is now an urgent problem in the environmental field. In this study, hydrothermal carbonization rice husk-loaded Al-modified biochar (Al-BC) was synthesized, and the results of scanning electron microscopy could be used to determine that Al oxide composite was loaded on the surface of the material. The specific surface area was 57.049 m2 g-1, pore volume was 0.254 cm3 g-1, and average pore diameter was 8.922 nm for BC and 109.617 m2 g-1, 0.215 cm3 g-1, and 3.969 nm for Al-BC, respectively. The control effects of these two adsorption materials on organic pollutant ibuprofen (IBU) under different pH conditions were also investigated. The research results show that the adsorption capacity of Al-BC (30.24-1.48 mg g-1) is better than BC (19.98-0.92 mg g-1) at pH from 2 to 11. Solution pH plays a crucial role in IBU adsorption from organic solution. The Langmuir fitting results show that at pH = 7, the saturated adsorption capacity of IBU on BC could reach up to 18.68 mg g-1; the adsorption capacity on Al-BC was 60.49 mg g-1. The thermodynamic parameters indicate that the adsorption is spontaneous, endothermic, and increased disorder. The adsorption material prepared in this study could provide a reference for organic pollution control in water.

Estimating the self-thinning boundary line for oak mixed forests in central China by using stochastic frontier analysis and a proposed variable density model.

A suitable self-thinning model is fundamental to effective density control and management. Using data from 265 plot measurements in oak mixed forests in central China, we demonstrated how to estimate a suitable self-thinning line for oak mixed forests from three aspects, i.e., self-thinning models (Reineke's model and the variable density model), statistical methods (quantile regression and stochastic frontier analysis), and the variables affecting stands (topography and stand structure factors). The proposed variable density model, which is based on the quadratic mean diameter and dominant height, exhibited a better goodness of fit and biological relevance than Reineke's model for modeling the self-thinning line for mixed oak forests. In addition, the normal-truncated normal stochastic frontier model was superior to quantile regression for modeling the self-thinning line. The altitude, Simpson index, and dominant height-diameter ratio ( H d /D) also had significant effects on the density of mixed forests. Overall, a variable density self-thinning model may be constructed using stochastic frontier analysis for oak mixed forests while considering the effects of site quality and stand structure on density. The findings may contribute to a more accurate density management map for mixed forests.

Emerging Theranostic Nanomaterials in Diabetes and Its Complications.

Diabetes mellitus (DM) refers to a group of metabolic disorders that are characterized by hyperglycemia. Oral subcutaneously administered antidiabetic drugs such as insulin, glipalamide, and metformin can temporarily balance blood sugar levels, however, long-term administration of these therapies is associated with undesirable side effects on the kidney and liver. In addition, due to overproduction of reactive oxygen species and hyperglycemia-induced macrovascular system damage, diabetics have an increased risk of complications. Fortunately, recent advances in nanomaterials have provided new opportunities for diabetes therapy and diagnosis. This review provides a panoramic overview of the current nanomaterials for the detection of diabetic biomarkers and diabetes treatment. Apart from diabetic sensing mechanisms and antidiabetic activities, the applications of these bioengineered nanoparticles for preventing several diabetic complications are elucidated. This review provides an overall perspective in this field, including current challenges and future trends, which may be helpful in informing the development of novel nanomaterials with new functions and properties for diabetes diagnosis and therapy.

Influence of Plasma Ultraviolet/Vacuum Ultraviolet Irradiation Damage on Silicon Metal-Oxide-Semiconductor Capacitor During Etching.

In this study, we evaluate the defects and charges caused by the ultraviolet (UV)/vacuum ultraviolet (VUV) irradiation in the high-k/metal gate stack structure, especially in HfO2 layer and at Si/HfO2 interface. First, we measured the photons irradiating to the surface in the neutral beam etching (NBE) system and in the conventional inductively coupled plasma (ICP) system through optical emission spectroscopy (OES), respectively. By using this method, we evaluate the ability of reducing UV/VUV irradiation damage in the NBE system. As a result, photon intensity detected in the ICP system shows larger magnitude as compared to the NBE system, which indicates the UV/VUV irradiation is more severe in the ICP system. Moreover, in order to understand the twisting of electrical characteristics caused by UV/VUV irradiation, we set the prefabricated metal-oxide-semiconductor (MOS) capacitors in both systems to absorb the irradiation of UV/VUV photons respectively. The electrical characteristics of the etched MOS capacitors and its related plasma-induced damage model are discussed. The result of the devices exposed in the ICP system reveals a greater electrical characteristics shift compared to the devices in NBE such as the interface trap density (Dit) in case of NBE is 3.55621×1012 cm-2eV-1 and in case of ICP is higher i.e., 4.19961×1012 cm-2eV-1.

Synthesis and antidiabetic activity of selenium nanoparticles in the presence of polysaccharides from Catathelasma ventricosum.

Selenium nanoparticles (SeNPs) were prepared by adding Catathelasma ventricosum polysaccharides (CVPs) to the redox system of selenite and ascorbic acid. Taking particle size as an investigation index, the optimal synthesis conditions of CVPs-SeNPs were obtained by orthogonal test. Herein, the diameter, morphology, and stability of the CVPs-SeNPs were characterized by dynamic light scattering (DLS) and transmission electron microscopy (TEM). Moreover, the antidiabetic activities of CVPs-SeNPs were evaluated by STZ (streptozocin)-induced diabetic mice. The obtained results showed that, optimum synthesis conditions of CVPs-SeNPs were: ultrasonic time 60min, concentration of Vc 0.04M, reaction time 2h, pH7.0. Under these conditions, mean diameter of the synthesized CVPs-SeNPs was around 49.73nm. TEM of CVPs-SeNPs prepared in optimal conditions showed individual and spherical nanostructure. CVPs-SeNPs (particle size of about 50nm) could be stable for approximately 3months at 4°C, but only 1month at 25°C. The results on serum profiles and antioxidant enzymes levels revealed that CVPs-SeNPs had a potential antidiabetic effect. In addition, CVPs-SeNPs showed significantly higher antidiabetic activity (p<0.05) than other selenium preparations such as SeNPs, selenocysteine, sodium selenite.

Synthesis and antidiabetic properties of chitosan-stabilized selenium nanoparticles.

Chitosan-stabilized selenium nanoparticles (CTS-SeNPs) were prepared through the reduction of selenite acid with ascorbic acid. The optimal synthesis conditions of CTS-SeNPs were obtained by orthogonal experiments. Besides, the size, morphology and stability of CTS-SeNPs were characterized by transmission electron microscopy (TEM), scanning electron microscope (SEM) and dynamic light scattering (DLS). The results showed that, the CTS-SeNPs with diameter of about 54 nm could be obtained under the optimal conditions (temperature of 25 °C, reaction time of 2 h, Vc concentration of 0.04 M and the CTS concentration of 1.0 mg/mL). CTS-SeNPs were uniform spherical and could be stable for approximately 60 days at 4 °C. Further, the antidiabetic activities of CTS-SeNPs in streptozotocin (STZ)-induced diabetic mice were investigated as well. The results revealed that CTS-SeNPs at a dose of 2.0 mg Se/kg bw exhibited higher antidiabetic activity than other doses of CTS-SeNPs and other selenium compounds with the same selenium content.

Nutritional composition of boletus mushrooms from Southwest China and their antihyperglycemic and antioxidant activities.

Thirteen samples representing five species were collected from different provinces of Southwest China, and their chemical composition, antihyperglycemic activity, and antioxidant activity were evaluated. These mushrooms had high crude protein (21.72-30.59g/100g dw) and total carbohydrate (49.18-62.58g/100g dw) contents, but low crude fat contents (1.96-7.87g/100g dw). They also accumulated notable quantities of potassium, zinc, sodium, magnesium and copper from the soil. The potassium content, in particular, was 18.75-39.21 times that found in the soil at the collection site. The natural habitat of these mushrooms, especially the mineral content of the soil, seems to have more influence on the mineral content of these mushrooms than their species. Most of the samples possessed antihyperglycemic and antioxidant activities. Suillellus luridus showed the highest antioxidant activity and antihyperglycemic activities, suggesting that S. luridus shows potential for development as a dietary nutritional supplement.

Structural characterization and antidiabetic activity of a glucopyranose-rich heteropolysaccharide from Catathelasma ventricosum.

Polysaccharides, which are the main bioactive constituents of edible mushrooms, have been shown to have a variety of useful biological activities. In this study, a polysaccharide fraction from the edible mushroom Catathelasma ventricosum was purified using anion exchange and size exclusion chromatographies. The structure of the resulting polysaccharide, named CVP-1S, was characterized on the basis of partial acid hydrolysis, periodic acid oxidation, methylation analysis, gas chromatography-mass spectrometry, Fourier-transform infrared spectroscopy, 1D/2D NMR spectroscopy, scanning electron microscopy, and atomic force spectroscopy. The results showed that CVP-1S is a heteropolysaccharide consisting of glucose (94.2%), galactose (3.51%) and fucose (1.3%) with a molecular weight of 1.5×10(4)Da. Its backbone is mainly linked by (1→6)-ß-d-Glcp glycosidic bonds, and branches are attached to the backbone through 1,3-linked glycosidic bonds. CVP-1S was also found to have antioxidant, hypoglycemic, and hypolipidemic activities in the streptozoicin-induced diabetic mouse model. From these results, we conclude that CVP-1S should receive further attention as a potential agent for the treatment and prevention of diabetes.