The influence of adverse events on inpatient outcomes in a tertiary hospital using a diagnosis-related group database.

Adverse events (AEs) are a significant concern for healthcare systems. However, it is difficult to evaluate their influence because of the complexity of various medical services. This study aimed to assess the influence of AEs on the outcomes of hospitalized patients using a diagnosis-related group (DRG) database. We conducted a case-control study of hospitalized patients at a multi-district tertiary hospital with 2200 beds in China, using data from a DRG database. An AE refers to an unintended physical injury caused or contributed to by medical care that requires additional hospitalization, monitoring, treatment, or even death. Relative weight (RW), a specific indicator of DRG, was used to measure the difficulty of diagnosis and treatment, disease severity, and medical resources utilized. The primary outcomes were hospital length of stay (LOS) and hospitalization costs. The secondary outcome was discharge to home. This study applied DRG-based matching, Hodges-Lehmann estimate, regression analysis, and subgroup analysis to evaluate the influence of AEs on outcomes. Two sensitivity analyses by excluding short LOS and changing adjustment factors were performed to assess the robustness of the results. We identified 2690 hospitalized patients who had been divided into 329 DRGs, including 1345 patients who experienced AEs (case group) and 1345 DRG-matched normal controls. The Hodges-Lehmann estimate and generalized linear regression analysis showed AEs led to prolonged LOS (unadjusted difference, 7 days, 95% confidence interval [CI] 6-8 days; adjusted difference, 8.31 days, 95% CI 7.16-9.52 days) and excess hospitalization costs (unadjusted difference, $2186.40, 95% CI: $1836.87-$2559.16; adjusted difference, $2822.67, 95% CI: $2351.25-$3334.88). Logistic regression analysis showed AEs were associated with lower odds of discharge to home (unadjusted odds ratio [OR] 0.66, 95% CI 0.54-0.82; adjusted OR 0.75, 95% CI 0.61-0.93). The subgroup analyses showed that the results for each subgroup were largely consistent. LOS and hospitalization costs increased significantly after AEs in complex diseases (RW ≥ 2) and in relation to high degrees of harm subgroups (moderate harm and above groups). Similar results were obtained in sensitivity analyses. The burden of AEs, especially those related to complex diseases and severe harm, is significant in China. The DRG database serves as a valuable source of information that can be utilized for the evaluation and management of AEs.

Identification of the risk factors for insomnia in nurses with long COVID-19.

PURPOSE: To investigate the prevalence of insomnia among nurses with long COVID-19, analyze the potential risk factors and establish a nomogram model. METHODS: Nurses in Ningbo, China, were recruited for this study. General demographic information and insomnia, burnout, and stress assessment scores were collected through a face-to face questionnaire survey administered at a single center from March to May 2023. We used LASSO regression to identify potential factors contributing to insomnia. Then, a nomogram was plotted based on the model chosen to visualize the results and evaluated by receiver operating characteristic curves and calibration curves. RESULTS: A total of 437 nurses were recruited. 54% of the nurses had insomnia according to the Insomnia Severity Index (ISI) score. Eleven variables, including family structure, years of work experience, relaxation time, respiratory system sequelae, nervous system sequelae, others sequelae, attitudes toward COVID-19, sleep duration before infection, previous sleep problems, stress, and job burnout, were independently associated with insomnia. The R-squared value was 0.464, and the area under the curve was 0.866. The derived nomogram showed that neurological sequelae, stress, job burnout, sleep duration before infection, and previous sleep problems contributed the most to insomnia. The calibration curves showed significant agreement between the nomogram models and actual observations. CONCLUSION: This study focused on insomnia among nurses with long COVID-19 and identified eleven risk factors related to nurses' insomnia. A nomogram model was established to illustrate and visualize these factors, which will be instrumental in future research for identifying nurses with insomnia amid pandemic normalization and may increase awareness of the health status of healthcare workers with long COVID-19.

Mogroside V and mogrol: unveiling the neuroprotective and metabolic regulatory roles of Siraitia grosvenorii in Parkinson's disease.

Introduction: Siraitia grosvenorii (Swingle) C. Jeffrey, is an edible and traditional medicine widely used in China. Mogroside V (MGV) and mogrol (MG) are its main active ingredients, which have been found to be effective in the treatment of neurodegenerative diseases recently. However, whether they can effectively treat Parkinson's disease (PD) and their underlying mechanisms have not been sufficiently explored. In this study, we investigated the neuroprotective and metabolic regulatory effects of MGV and MG on PD. Materials and methods: Using SH-SY5Y cell models and an MPTP-induced mouse model of PD, we evaluated the compounds' efficacy in mitigating MPP+-induced neurotoxicity and ameliorating motor deficits and dopaminergic neuron loss. Employing widely targeted metabolomics and bioinformatics analysis to investigate the Metabolic imbalance rectification caused by MGV and MG treatment. The vivo experimental protocol encompassed a 14-day drug administration regimen with mice randomly allocated into six groups (n = 9) receiving distinct compound dosages including a control group, a model group, MGV-H (30 mg/kg/day), MGV-L (10 mg/kg/day), MG-H (15 mg/kg/day), and MG-L (3 mg/kg/day). Results: Our findings revealed that pre-treatment with MGV and MG significantly enhanced cell viability in SH-SY5Y cells exposed to MPP+, demonstrating a potent protective effect against neurotoxicity. In the MPTP mouse model, MGV-H, MGV-L, and MG-H significantly enhanced motor coordination as assessed by the rotarod test (p < 0.05); MGV-L and MG-H evidently inhibited dopaminergic neuronal loss in the substantia nigra pars compacta (p < 0.05). Furthermore, metabolomic analysis of the substantia nigra highlighted the restoration of metabolic balance, with MGV-L and MG-H impacting 160 differential metabolites and modulating key pathways disrupted in PD, including sphingolipid metabolism, fatty acid metabolism, and amino acid metabolism. Notably, treatment with MGV-L and MG-H led to the regulation of 106 metabolites, showing a recovery trend towards normal levels, which constitutes approximately 17.5% of the identified metabolites. Key metabolites such as n-acetyl-l-glutamate, hexadecanoic acid, and 9-octadecenal were significantly altered (p < 0.05), underscoring their broad-spectrum metabolic regulatory capacity. Conclusion: This study underscores the potential of natural compounds in developing comprehensive treatment strategies for neurodegenerative diseases, paving the way for future clinical research to validate the therapeutic efficacy of mogrosides in PD.

The Identification of Potential Anti-Depression/Anxiety Drug Targets by Stress-Induced Rat Brain Regional Proteome and Network Analyses.

Depression and anxiety disorders are prevalent stress-related neuropsychiatric disorders and involve multiple molecular changes and dysfunctions across various brain regions. However, the specific and shared pathophysiological mechanisms occurring in these regions remain unclear. Previous research used a rat model of chronic mild stress (CMS) to segregate and identify depression-susceptible, anxiety-susceptible, and insusceptible groups; then the proteomes of six distinct brain regions (the hippocampus, prefrontal cortex, hypothalamus, pituitary, olfactory bulb, and striatum) were separately and quantitatively analyzed. To gain a comprehensive and systematic understanding of the molecular abnormalities, this study aimed to investigate and compare differential proteomics data from the six regions. Differentially expressed proteins (DEPs) were identified in between specific regions and across all regions and subjected to a series of bioinformatics analyses. Regional comparisons showed that stress-induced proteomic changes and corresponding gene ontology and pathway enrichments were largely distinct, attributable to differences in cell populations, protein compositions, and brain functions of these areas. Additionally, a notable degree of overlap in the significantly enriched terms was identified, potentially suggesting strong connections in the enrichment across different regions. Furthermore, intra-regional and inter-regional protein-protein interaction networks and drug-target-DEP networks were constructed. Integrated analysis of the three association networks in the six regions, along with the DisGeNET database, identified ten DEPs as potential targets for anti-depression/anxiety drugs. Collectively, these findings revealed commonalities and differences across different brain regions at the protein level induced by CMS, and identified several novel protein targets for the development of new therapeutics for depression and anxiety.

Role of oxylipins in ovarian function and disease: A comprehensive review.

Ovaries are essential for healthy female reproduction, with the follicles as their fundamental functional units, which consist of an oocyte and surrounding granulosa cells. The development and formation of follicles in the ovaries are closely linked to reproductive health. Oxylipins refer to oxidative metabolites produced from the oxidation of polyunsaturated fatty acids, either through automatic oxidation or with the help of specific enzymes. They play crucial regulatory roles in the immune system, oxidative stress, and inflammatory reactions and are intimately linked to the development of numerous illnesses, such as diabetes, heart disease, asthma, and Alzheimer's disease. Furthermore, oxylipins have a complex relationship with ovarian function, and both prostaglandins and leukotrienes produced by arachidonic acid affect processes such as follicle growth and development, ovulation, and hormone regulation. The synthesis and metabolism of oxylipins in the ovaries are finely regulated. Oxylipin dysregulation has been linked to various ovarian diseases, including endometriosis, polycystic ovary syndrome, ovarian cancer, and premature ovarian insufficiency. In addition, potential therapeutic targets and interventions targeting the oxylipin pathway for the treatment of ovarian diseases have become a prominent research focus, including regulating the enzymes responsible for oxylipin synthesis, using anti-inflammatory agents, and regulating lipid metabolism. Recent research has been directed towards improving the reproductive outcomes of women with ovarian diseases through this series of interventions. An overview of the role of oxylipins in ovarian function and disease is provided in this article, which will aid researchers in understanding the current state of the field and in identifying future directions.

Diagnostic performance of EfficientNetV2-S method for staging liver fibrosis based on multiparametric MRI.

Problem: Previous studies had confirmed that some deep learning models had high diagnostic performance in staging liver fibrosis. However, training efficiency of models predicting liver fibrosis need to be improved to achieve rapid diagnosis and precision medicine. Aim: The deep learning framework of EfficientNetV2-S was noted because of its faster training speed and better parameter efficiency compared with other models. Our study sought to develop noninvasive predictive models based on EfficientNetV2-S framework for staging liver fibrosis. Methods: Patients with chronic liver disease who underwent multi-parametric abdominal MRI were included in the retrospective study. Data augmentation methods including horizontal flip, vertical flip, perspective transformation and edge enhancement were applied to multi-parametric MR images to solve the data imbalance between different liver fibrosis groups. The EfficientNetV2-S models were used for the prediction of liver fibrosis stages F1-2, F1-3, F3, F4 and F3-4. We evaluated the diagnostic performance of our models in training, validation, and test sets by using receiver operating characteristic curve (ROC) analysis. Results: The total training time of EfficientNetV2-S was about 6 h. For differentiating of F1-2 vs F3, the accuracy, sensitivity and specificity of EfficientNetV2-S model were 96.2 %, 96.4 % and 96.0 % in the test set. The AUC in test set was 0.559. The accuracy, sensitivity and specificity were 82.1 %, 74.5 % and 89.6 % in the test set by using EfficientNetV2-S model to differentiate F1-2 vs F3-4, and the AUC in test set were 0.763. For differentiating F1-3 vs F4, the accuracy, sensitivity and specificity of EfficientNetV2-S model were 71.5 %, 73.4 % and 69.5 % in the test set. The AUC was 0.553 in test set. For differentiating F1-2 vs F4, the accuracy, sensitivity and specificity of our model were 84.3 %, 80.2 % and 88.3 % in the test set, and the AUC was 0.715, respectively. For differentiating F3 vs F4, the accuracy, sensitivity and specificity of EfficientNetV2-S model were 92.5 %, 89.1 % and 95.6 % in the test set, and the AUC was 0.696 in the test set. Conclusions: The EfficientNetV2-S models based on multi-parametric MRI had the feasibility for staging of liver fibrosis because they showed high training speed and diagnostic performance in our study.

Axial Length Shortening after Combined Repeated Low-Level Red-Light Therapy in Poor Responders of Orthokeratology in Myopic Children.

Purpose: To investigate the efficacy and safety of orthokeratology (ortho-k) and repeated low-level red-light (RLRL) therapy in treating poor responders of ortho-k in myopic children. Methods: Study participants were 100 myopic children who completed two years of ortho-k treatment in a retrospective study. In the first year of ortho-k treatment (phase one), they experienced axial elongation of 0.30 mm or greater (defined as poor responders to ortho-k). Children were divided into two groups: the orthokeratology group (OK, n = 45) continued to receive ortho-k monotherapy and the combination group (OK-RLRL, n = 55) received RLRL in addition to ortho-k for the next year (phase two). Axial elongation over time between the groups was compared. Results: The mean age, male-to-female ratio, axial length (AL), and axial elongation in phase one were comparable between OK and OK-RLRL groups (all P > 0.05). During phase two, significant AL shortening was observed in the OK-RLRL group compared with children in the OK group (-0.10 ± 0.16 mm vs 0.30 ± 0.19 mm, P < 0.001). Among these 55 myopic children in the OK-RLRL group, 35 (63.6%), 25 (45.4%), 11 (20%), 6 (10.9%), and 3 (5.4%) of them had AL shortening over 0.05 mm/year, 0.10 mm/year, and 0.20 mm/year, 0.3 mm/year, and 0.4 mm/year, respectively. Older baseline age (ß = -0.02), higher treatment compliance (ß = -0.462), and AL change at 1 month (ß = 1.263) were significantly associated with less AL elongation (all P < 0.05). Conclusions: For poor responders of orthokeratology, RLRL could slow axial elongation in addition to the ortho-k treatment effect. Those who respond poorly to ortho-k with elder age might benefit more from combined therapy.

Molecular regulation of DNA damage and repair in female infertility: a systematic review.

DNA damage is a key factor affecting gametogenesis and embryo development. The integrity and stability of DNA are fundamental to a woman's successful conception, embryonic development, pregnancy and the production of healthy offspring. Aging, reactive oxygen species, radiation therapy, and chemotherapy often induce oocyte DNA damage, diminished ovarian reserve, and infertility in women. With the increase of infertility population, there is an increasing need to study the relationship between infertility related diseases and DNA damage and repair. Researchers have tried various methods to reduce DNA damage in oocytes and enhance their DNA repair capabilities in an attempt to protect oocytes. In this review, we summarize recent advances in the DNA damage response mechanisms in infertility diseases such as PCOS, endometriosis, diminished ovarian reserve and hydrosalpinx, which has important implications for fertility preservation.

MGA deletion leads to Richter's transformation by modulating mitochondrial OXPHOS.

Richter's transformation (RT) is a progression of chronic lymphocytic leukemia (CLL) to aggressive lymphoma. MGA (Max gene associated), a functional MYC suppressor, is mutated at 3% in CLL and 36% in RT. However, genetic models and molecular mechanisms of MGA deletion that drive CLL to RT remain elusive. We established an RT mouse model by knockout of Mga in the Sf3b1/Mdr CLL model using CRISPR-Cas9 to determine the role of Mga in RT. Murine RT cells exhibited mitochondrial aberrations with elevated oxidative phosphorylation (OXPHOS). Through RNA sequencing and functional characterization, we identified Nme1 (nucleoside diphosphate kinase) as an Mga target, which drives RT by modulating OXPHOS. Given that NME1 is also a known MYC target without targetable compounds, we found that concurrent inhibition of MYC and electron transport chain complex II substantially prolongs the survival of RT mice in vivo. Our results suggest that the Mga-Nme1 axis drives murine CLL-to-RT transition via modulating OXPHOS, highlighting a potential therapeutic avenue for RT.

Semi-supervised urban haze pollution prediction based on multi-source heterogeneous data.

Particulate matter (PM) is defined by the Texas Commission on Environmental Quality (TCEQ) as "a mixture of solid particles and liquid droplets found in the air". These particles vary widely in size. Those particles that are less than 2.5 µm in aerodynamic diameter are known as Particulate Matter 2.5 or PM2.5. Urban haze pollution represented by PM2.5 is becoming serious, so air pollution monitoring is very important. However, due to high cost, the number of air monitoring stations is limited. Our work focuses on integrating multi-source heterogeneous data of Nanchang, China, which includes Taxi track, human mobility, Road networks, Points of Interest (POIs), Meteorology (e.g., temperature, dew point, humidity, wind speed, wind direction, atmospheric pressure, weather activity, weather conditions) and PM2.5 forecast data of air monitoring stations. This research presents an innovative approach to air quality prediction by integrating the above data sets from various sources and utilizing diverse architectures in Nanchang City, China. So for that, semi-supervised learning techniques will be used, namely collaborative training algorithm Co-Training (Co-T), who further adjusting algorithm Tri-Training (Tri-T). The objective is to accurately estimate haze pollution by integrating and using these multi-source heterogeneous data. We achieved this for the first time by employing a semi-supervised co-training strategy to accurately estimate pollution levels after applying the U-air system to environmental data. In particular, the algorithm of U-Air system is reproduced on these highly diverse heterogeneous data of Nanchang City, and the semi-supervised learning Co-T and Tri-T are used to conduct more detailed urban haze pollution prediction. Compared with Co-T, which train time classifier (TC) and subspace classifier (SC) respectively from the separated spatio-temporal perspective, the Tri-T is more accurate with a and faster because of its testing accuracy up to 85.62 %. The forecast results also present the potential of the city multi-source heterogeneous data and the effectiveness of the semi-supervised learning. We hope that this synthesis will motivate atmospheric environmental officials, scientists, and environmentalists in China to explore machine learning technology for controlling the discharge of pollutants and environmental management.

The influence of psychological change factors of tennis training strategy using optimized recurrent neural network and artificial intelligence.

Due to the specialization of tennis technical training, the teaching focus of tennis teaching has gradually shifted to the psychological skills training of tennis players. This work addresses the impact of psychological factors on tennis players' insufficient concentration in teaching and training on match results. It discusses the psychological changes' influencing factors in tennis training strategies and analyzes the current psychological changes that are easy to occur in tennis teaching. The Recurrent Neural Network (RNN) can simulate the human brain's information processing and learning process to establish models to study human psychological changes. To explore the influence of psychological changes on tennis training, artificial intelligence technology is combined to optimize the performance of RNN, and a prediction model of psychological distress in tennis training is constructed. Additionally, a questionnaire is applied to compare the sports state of tennis players before and after the psychological regulation intervention. The findings demonstrate that following psychological regulation, 73 % of players perform as usual, 20 % present exceptionally well, and 7 % do not perform as well as usual. These results indicate an improvement compared to previous performances, highlighting the efficacy of psychological regulation supported by optimized RNN under AI assistance. This study aims to foster a consistently positive psychological state among tennis players during daily training and competitions, ensuring that their competitive performance levels remain normal or even exceed their usual standards.

Research progresses of imaging studies on preoperative prediction of microvascular invasion of hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is the predominant form of primary liver cancer, accounting for approximately 90% of liver cancer cases. It currently ranks as the fifth most prevalent cancer worldwide and represents the third leading cause of cancer-related mortality. As a malignant disease with surgical resection and ablative therapy being the sole curative options available, it is disheartening that most HCC patients who undergo liver resection experience relapse within five years. Microvascular invasion (MVI), defined as the presence of micrometastatic HCC emboli within liver vessels, serves as an important histopathological feature and indicative factor for both disease-free survival and overall survival in HCC patients. Therefore, achieving accurate preoperative noninvasive prediction of MVI holds vital significance in selecting appropriate clinical treatments and improving patient prognosis. Currently, there are no universally recognized criteria for preoperative diagnosis of MVI in clinical practice. Consequently, extensive research efforts have been directed towards preoperative imaging prediction of MVI to address this problem and the relative research progresses were reviewed in this article to summarize its current limitations and future research prospects.

Infectious viruses and neurodegenerative diseases: The mitochondrial defect hypothesis.

Global attention is riveted on neurodegenerative diseases due to their unresolved aetiologies and lack of efficacious therapies. Two key factors implicated include mitochondrial impairment and microglial ageing. Several viral infections, including Herpes simplex virus-1 (HSV-1), human immunodeficiency virus (HIV) and Epstein-Barr virus, are linked to heightened risk of these disorders. Surprisingly, numerous studies indicate viruses induce these aforementioned precipitating events. Epstein-Barr virus, Hepatitis C Virus, HIV, respiratory syncytial virus, HSV-1, Japanese Encephalitis Virus, Zika virus and Enterovirus 71 specifically impact mitochondrial function, leading to mitochondrial malfunction. These vital organelles govern various cell activities and, under specific circumstances, trigger microglial ageing. This article explores the role of viral infections in elucidating the pathogenesis of neurodegenerative ailments. Various viruses instigate microglial ageing via mitochondrial destruction, causing senescent microglia to exhibit activated behaviour, thereby inducing neuroinflammation and contributing to neurodegeneration.

A novel Saclayvirus Acinetobacter baumannii phage genomic analysis and effectiveness in preventing pneumonia.

Acinetobacter baumannii, which is resistant to multiple drugs, is an opportunistic pathogen responsible for severe nosocomial infections. With no antibiotics available, phages have obtained clinical attention. However, since immunocompromised patients are often susceptible to infection, the appropriate timing of administration is particularly important. During this research, we obtained a lytic phage vB_AbaM_P1 that specifically targets A. baumannii. We then assessed its potential as a prophylactic treatment for lung infections caused by clinical strains. The virus experiences a period of inactivity lasting 30 min and produces approximately 788 particles during an outbreak. Transmission electron microscopy shows that vB_AbaM_P1 was similar to the Saclayvirus. Based on the analysis of high-throughput sequencing and bioinformatics, vB_AbaM_P1 consists of 107537 bases with a G + C content of 37.68%. It contains a total of 177 open reading frames and 14 tRNAs. No antibiotic genes were detected. In vivo experiments, using a cyclophosphamide-induced neutrophil deficiency model, tested the protective effect of phage on neutrophil-deficient rats by prophylactic application of phage. The use of phages resulted in a decrease in rat mortality caused by A. baumannii and a reduction in the bacterial burden in the lungs. Histologic examination of lung tissue revealed a decrease in the presence of immune cells. The presence of phage vB_AbaM_P1 had a notable impact on preventing A. baumannii infection, as evidenced by the decrease in oxidative stress in lung tissue and cytokine levels in serum. Our research offers more robust evidence for the early utilization of bacteriophages to mitigate A. baumannii infection. KEY POINTS: •A novel Saclayvirus phage infecting A. baumannii was isolated from sewage. •The whole genome was determined, analyzed, and compared to other phages. •Assaying the effect of phage in preventing infection in neutrophil-deficient models.

Substantial Energy Band Modulation of Semiconducting Hexagonal GaTe Quantum Wells by Layer Thickness and Mirror Twin Boundaries.

Exploring emerging two-dimensional (2D) van der Waals (vdW) semiconducting materials and precisely tuning their electronic properties at the atomic level have long been recognized as crucial issues for developing their high-end electronic and optoelectronic applications. As a III-VI semiconductor, ultrathin layered hexagonal GaTe (h-GaTe) remains unexplored in terms of its intrinsic electronic properties and band engineering strategies. Herein, we report the successful synthesis of ultrathin h-GaTe layers on a selected graphene/SiC(0001) substrate, via molecular beam epitaxy (MBE). The widely tunable quasiparticle band gaps (∼2.60-1.55 eV), as well as the vdW quantum well states (QWSs) that can be strictly counted by the layer numbers, are well characterized by onsite scanning tunneling microscopy/spectroscopy (STM/STS), and their origins are clearly addressed by density functional theory (DFT) calculations. More intriguingly, distinctive 8|8E and 4|4P (Ga) mirror twin boundaries (MTBs) are identified in the ultrathin h-GaTe flakes, which can induce decreased band gaps and prominent p-doping effects. This work should deepen our understanding on the electronic tunability of 2D III-VI semiconductors by thickness control and line defect engineering, which may hold promise for fabricating atomic-scale vertical and lateral homojunctions toward ultrascaled electronics and optoelectronics.

Cluster analysis-based classification of fatigue in stable maintenance hemodialysis patients: a prospective cohort study.

CLINICAL TRIAL REGISTRATION: This study has been registered (ChiCTR2100045493) 2021/04/03.

Controlled Release of Ceria and Ferric Oxide Nanoparticles via Collagen Hydrogel for Enhanced Osteoarthritis Therapy.

Osteoarthritis (OA), characterized by chronic inflammation and cartilage degeneration, significantly affects over 500 million people globally. Nanoparticles have emerged as promising treatments for OA; however, current strategies often employ a single type of nanoparticle targeting specific disease stages, limiting sustained therapeutic efficacy. In this study, a novel collagen hydrogel is introduced, thiol crosslinked collagen-cerium oxide-poly(D,L-lactic-co-glycolic acid) microspheres encapsulating nanoparticles (CSH-CeO2-pFe2O3), designed for the controlled release of cerium oxide (CeO2) and ferric oxide (Fe2O3) nanoparticles for comprehensive OA management. The sulfhydryl cross-linked collagen matrix embeds CeO2 nanoparticles and poly(D,L-lactic-co-glycolic acid) (PLGA) microspheres encapsulating Fe2O3 nanoparticles. The CSH-CeO2-pFe2O3 hydrogel exhibits enhanced mechanical strength and remarkable injectability, along with a significant promotion of cell adhesion, proliferation, and chondrogenic differentiation. Notably, the hydrogel demonstrates intelligent responsiveness to high levels of reactive oxygen species, initiating the rapid release of CeO2 nanoparticles to address the intense inflammatory responses of early-stage OA, followed by the sustained release of Fe2O3 nanoparticles to facilitate cartilage regeneration during the proliferative phase. In a rat model with cartilage defects, the hydrogel significantly alleviates inflammation and enhances cartilage regeneration, holding substantial potential for effectively managing the pathologically complex OA.

Organofluorosilicon Modified Polyacrylate with the Unidirectional Migration Promotion of Disperse Dyes toward Polyester Fabric for Wash-Free Digital Inkjet Dyeing.

The printing and dyeing industry is currently accelerating toward a direction of high efficiency, energy conservation, environmental protection, and integration with digitalization. Disperse dye wash-free digital inkjet dyeing is a revolutionary breakthrough for cleaning and coloring polyester fabric. Based on the solubility parameters and the hot-melt dyeing characteristics of disperse dyes, soft, hard, and functional monomers of acrylate were used as the main body. Moreover, single-vinyl fluorinated polysiloxane and divinyl polysiloxane with low solubility parameters were used as modified monomers. A modified polyacrylate (PFSMA) adhesive containing silicon in the main chain and fluorine silicon in the side chain was prepared via miniemulsion polymerization. Using disperse digital inkjet dyeing of polyester fabric without washing can realize energy saving, emission reduction, and carbon reduction. Results showed that the optimum preparation conditions of PFSMA were as follows: DVFS molecular weight of 957 g/mol and DVFS content of 2.5 wt %. Compared with that of polyacrylate (PA), the glass-transition temperature of PFSMA film decreased, and its water resistance, toughness, and adhesion enhanced. When the PFSMA content in the wash-free disperse red ink was 8 wt %, the color yields of the front and back of the PFSMA jet-dyed polyester fabric were 18.86 and 13.28, respectively. Moreover, the color yield of the front of PFSMA jet-dyed polyester fabric was 39.9% higher than that of the pure liquid disperse red jet-dyed fabric. The simulated fixation rate was 87.9%, approximately 2.9 times higher than that of the PA wash-free jet-dyed fabric. The color fastness to dry rubbing reached level 4 and the color fastness to wet rubbing reached level 3-4, which was one level higher than that of pure liquid disperse red jet-dyed fabrics. The color fastness to soaping reached grade 5 and the color fastness to heat compression reached grades 4-5 and above. The fabric was a little firmer but smoother. The color properties, color fastness, and hand feeling of the PFSMA wash-free jet-dyed polyester fabric exceeded the levels of commercially available adhesives.

Clinical features and multiomics profiles indicate coagulation and platelet dysfunction in COVID-19 viral sepsis.

Increased cases of sepsis during COVID-19 in the absence of known bacterial pathogens highlighted role of viruses as causative agents of sepsis. In this study, we investigated clinical, laboratory, proteomic, and metabolomic characteristics of viral sepsis patients (n = 45) and compared them to non-sepsis patients with COVID-19 (n = 186) to identify molecular mechanisms underlying the pathology of viral sepsis in COVID-19. We identified unique metabolomic and proteomic signatures that suggest a substantial perturbation in the coagulation, complement, and platelet activation pathways in viral sepsis. Our proteomic data indicated elevated coagulation pathway protein (fibrinogen), whereas a decrease in many of the complement proteins was observed. These alterations were associated with the functional consequences such as susceptibility to secondary bacterial infections and potentially contributing to both local and systemic disease phenotypes. Our data provide novel aspect of COVID-19 pathology that is centered around presence of sepsis phenotype in COVID-19.