Vanillin-based flame retardant enables polylactic acid high-efficiency fireproof, anti-UV and oxygen barrier for food packaging.

Polylactic acid (PLA) is widely known for its biocompatibility, biodegradability, and high transparency. However, it still has varied limitations such as flammability, UV sensitivity, and poor oxygen barrier properties. To address these issues, a bio-based compound, hexasubstituted cyclotriphosphazene (HVP), was synthesized by using vanillin and hexachlorocyclotriphosphazene to enhance the overall performance of PLA. The resulting PLA/HVP composites demonstrated improved mechanical strength and UV resistance. Specifically, PLA/3HVP, with a 3 wt% HVP loading, achieved a UL-94 V-0 rating and a high limiting oxygen index of 26.5 %. Cone calorimeter tests revealed that PLA/3HVP possessed a significantly longer ignition time and a lower peak heat release rate compared to pure PLA. These burning testing results indicated the enhanced fire resistance. Additionally, the oxygen transmission rate of PLA/3HVP was reduced by 81.1 % compared to pure PLA. When used as food packaging, the weight loss of mangoes covered with PLA/3HVP film was 2.2 % after 7 days, compared to 2.5 % with pure PLA film, highlighting its potential for food preservation applications.

Tissue engineering strategies for spiral ganglion neuron protection and regeneration.

Cochlear implants can directly activate the auditory system's primary sensory neurons, the spiral ganglion neurons (SGNs), via circumvention of defective cochlear hair cells. This bypass restores auditory input to the brainstem. SGN loss etiologies are complex, with limited mammalian regeneration. Protecting and revitalizing SGN is critical. Tissue engineering offers a novel therapeutic strategy, utilizing seed cells, biomolecules, and scaffold materials to create a cellular environment and regulate molecular cues. This review encapsulates the spectrum of both human and animal research, collating the factors contributing to SGN loss, the latest advancements in the utilization of exogenous stem cells for auditory nerve repair and preservation, the taxonomy and mechanism of action of standard biomolecules, and the architectural components of scaffold materials tailored for the inner ear. Furthermore, we delineate the potential and benefits of the biohybrid neural interface, an incipient technology in the realm of implantable devices. Nonetheless, tissue engineering requires refined cell selection and differentiation protocols for consistent SGN quality. In addition, strategies to improve stem cell survival, scaffold biocompatibility, and molecular cue timing are essential for biohybrid neural interface integration.

Highly accurate carbohydrate-binding site prediction with DeepGlycanSite.

As the most abundant organic substances in nature, carbohydrates are essential for life. Understanding how carbohydrates regulate proteins in the physiological and pathological processes presents opportunities to address crucial biological problems and develop new therapeutics. However, the diversity and complexity of carbohydrates pose a challenge in experimentally identifying the sites where carbohydrates bind to and act on proteins. Here, we introduce a deep learning model, DeepGlycanSite, capable of accurately predicting carbohydrate-binding sites on a given protein structure. Incorporating geometric and evolutionary features of proteins into a deep equivariant graph neural network with the transformer architecture, DeepGlycanSite remarkably outperforms previous state-of-the-art methods and effectively predicts binding sites for diverse carbohydrates. Integrating with a mutagenesis study, DeepGlycanSite reveals the guanosine-5'-diphosphate-sugar-recognition site of an important G-protein coupled receptor. These findings demonstrate DeepGlycanSite is invaluable for carbohydrate-binding site prediction and could provide insights into molecular mechanisms underlying carbohydrate-regulation of therapeutically important proteins.

Identification of early coagulation changes associated with survival outcomes post severe burns from multiple perspectives.

Coagulation alterations manifest early after severe burns and are closely linked to mortality outcomes. Nevertheless, the precise characterization of coagulation changes associated with early mortality remains elusive. We examined alterations in indicators linked to mortality outcomes at both the transcriptomic and clinical characteristic levels. At the transcriptomic level, we pinpointed 28 differentially expressed coagulation-related genes (DECRGs) following burn injuries and endeavored to validate their causal relationships through Mendelian randomization. DECRGs tied to survival exhibit a significant association with neutrophil function, wherein the expression of CYP4F2 and P2RX1 serves as robust predictors of fatal outcomes. In terms of clinical indicators, early levels of D-dimer and alterations in serum calcium show a strong correlation with mortality outcomes. Coagulation depletion and fibrinolytic activation, stemming from the hyperactivation of coagulation pathways post-severe burns, are strongly linked to patient mortality. Monitoring these early coagulation markers with predictive value can effectively identify individuals necessitating priority critical care.

NMR methods to detect fluoride binding and transport by membrane proteins.

Solid-state nuclear magnetic resonance (NMR) methods can probe the motions of membrane proteins in liposomes at the atomic level, and propel the understanding of biomolecular processes for which static structures cannot provide a satisfactory description. High-resolution crystallography snapshots have provided a structural basis for fluoride channels. NMR is a powerful tool to build upon these snapshots and depict a dynamic picture of fluoride channels in native-like lipid bilayers. In this contribution, we discuss solid-state and solution NMR experiments to detect fluoride binding and transport by fluoride channels. Ongoing developments in membrane protein sample preparation and ssNMR methodology, particularly in using 1H, 19F and 13C-detection schemes, offer additional opportunities to study structure and functional aspects of fluoride channels.

The role of anger rumination as a mediator in the relationship between driver moral disengagement and driving angry.

Background: Road rage is a common phenomenon during driving, which not only affects the psychological health of drivers but also may increase the risk of traffic accidents. This article explores the impact of moral disengagement and anger rumination on road rage through two studies. Method: This research combined experimental studies with survey questionnaires. Study one used a driving simulator to investigate whether moral disengagement and anger rumination are psychological triggers of road rage in real-time driving, and whether there are differences in the main psychological triggers of road rage under different road scenarios. Building on the first study, study two employed a survey questionnaire to analyze the relationship between moral disengagement, anger rumination, and road rage. Participants in both studies were drivers with certain driving ages and experience. Data were processed and analyzed using descriptive statistics, factor analysis, reliability and validity tests, and multiple regression analysis. Results: The findings indicated: (1) There were significant differences in the anger induction rate across different road scenarios, χ2 = 35.73, p < 0.01, effect size = 0.29. Significant differences in average anger levels were observed in scenarios involving oncoming vehicles, lane-cutting, sudden stops by the vehicle ahead, pedestrians crossing the road, and traffic congestion (F = 20.41, p < 0.01, ηp2 = 0.36), with anger rumination playing a major role in the formation of road rage; (2) Moral disengagement significantly predicted road rage (ß = 0.25, t = 3.85, p < 0.01). The predictive effect of moral disengagement on anger rumination was significant (ß = 0.39, t = 6.17, p < 0.01), as was the predictive effect of anger rumination on road rage (ß = 0.43, t = 6.3, p < 0.01). The direct effect of moral disengagement on road rage included 0 in the bootstrap 95% confidence interval, while the mediating effect of anger rumination did not include 0 in the bootstrap 95% confidence interval, indicating that anger rumination fully mediated the relationship between moral disengagement and road rage.

Development of an obesity-related multi-gene prognostic model incorporating clinical characteristics in luminal breast cancer.

Despite adjuvant chemotherapy and endocrine therapy in luminal breast cancer (LBC), relapses are common. Addressing this, we aim to develop a prognostic model to refine adjuvant therapy strategies, particularly for patients at high recurrence risk. Notably, obesity profoundly affects the tumor microenvironment (TME) of LBC. However, it is unclear whether obesity-related biological features can effectively screen high-risk patients. Utilizing weighted gene coexpression network analysis (WGCNA) on RNA sequencing (RNAseq) data, we identified seven obese LBC genes (OLGs) closely associated with patient prognosis. Subsequently, we developed a luminal obesity-gene clinical prognostic index (LOG-CPI), combining a 7-gene signature, TNM staging, and age. Its predictive efficacy was confirmed across validation datasets and a clinical cohort (5-year accuracy = 0.828, 0.760, 0.751, and 0.792, respectively). LOG-CPI emerges as a promising predictor for clinical prognosis and treatment response, helping distinguish molecular and immunological features in LBC patients and guiding clinical practice by identifying varying prognoses.

Ameliorating Mitochondrial Dysfunction for the Therapy of Parkinson's Disease.

Parkinson's disease (PD) is currently the second most incurable central neurodegenerative disease resulting from various pathogenesis. As the "energy factory" of cells, mitochondria play an extremely important role in supporting neuronal signal transmission and other physiological activities. Mitochondrial dysfunction can cause and accelerate the occurrence and progression of PD. How to effectively prevent and suppress mitochondrial disorders is a key strategy for the treatment of PD from the root. Therefore, the emerging mitochondria-targeted therapy has attracted considerable interest. Herein, the relationship between mitochondrial dysfunction and PD, the causes and results of mitochondrial dysfunction, and major strategies for ameliorating mitochondrial dysfunction to treat PD are systematically reviewed. The study also prospects the main challenges for the treatment of PD.

Enhancing geometric representations for molecules with equivariant vector-scalar interactive message passing.

Geometric deep learning has been revolutionizing the molecular modeling field. Despite the state-of-the-art neural network models are approaching ab initio accuracy for molecular property prediction, their applications, such as drug discovery and molecular dynamics (MD) simulation, have been hindered by insufficient utilization of geometric information and high computational costs. Here we propose an equivariant geometry-enhanced graph neural network called ViSNet, which elegantly extracts geometric features and efficiently models molecular structures with low computational costs. Our proposed ViSNet outperforms state-of-the-art approaches on multiple MD benchmarks, including MD17, revised MD17 and MD22, and achieves excellent chemical property prediction on QM9 and Molecule3D datasets. Furthermore, through a series of simulations and case studies, ViSNet can efficiently explore the conformational space and provide reasonable interpretability to map geometric representations to molecular structures.

A Bibliometric Analysis of the Trends and Evolution on Inhalation Injury Research.

Inhalation injury is a common complication in burn patients and is also a factor that can affect the multiple prognoses of burn patients. Attention to inhalation injury began early globally, but few articles have systematically analyzed its development. We employed bibliometric methods to analyze articles on inhalation injury published in 3 medical databases. A total of 3056 relevant articles on inhalation injury were included in our analysis and divided into 3 distinct periods based on Price's law. Notably, a slowdown in publication growth was observed in period III. The majority of these articles were authored by a small group of individuals, with a significant proportion of them being American scholars. In fact, nearly half of the articles were published by American researchers. Applying Bradford's Law, we identified 4 major output sources in the field, namely Burns, Journal of Burn Care & Research, Journal of Trauma, and Critical Care Medicine. Recent research has focused on the clinical risks and outcomes associated with inhalation injury, while basic research in this area has been relatively neglected over the last decade. In conclusion, the growth of publications on inhalation injuries has largely followed standard scientific growth patterns, with a small number of countries and established research groups contributing the majority of articles. However, the recent slowdown in scientific output is a cause for concern, and the lack of emphasis on basic research and clinical trials in this field raises questions about the foundation for widespread clinical management of inhalation injuries.

Gut Microbiota and Respiratory Infections: Insights from Mendelian Randomization.

The role of the gut microbiota in modulating the risk of respiratory infections has garnered increasing attention. However, conventional clinical trials have faced challenges in establishing the precise relationship between the two. In this study, we conducted a Mendelian randomization analysis with single nucleotide polymorphisms employed as instrumental variables to assess the causal links between the gut microbiota and respiratory infections. Two categories of bacteria, family Lactobacillaceae and genus Family XIII AD3011, were causally associated with the occurrence of upper respiratory tract infections (URTIs). Four categories of gut microbiota existed that were causally associated with lower respiratory tract infections (LRTIs), with order Bacillales and genus Paraprevotella showing a positive association and genus Alistipes and genus Ruminococcaceae UCG009 showing a negative association. The metabolites and metabolic pathways only played a role in the development of LRTIs, with the metabolite deoxycholine acting negatively and menaquinol 8 biosynthesis acting positively. The identification of specific bacterial populations, metabolites, and pathways may provide new clues for mechanism research concerning therapeutic interventions for respiratory infections. Future research should focus on elucidating the potential mechanisms regulating the gut microbiota and developing effective strategies to reduce the incidence of respiratory infections. These findings have the potential to significantly improve global respiratory health.

[Correlation between blood pressure indexes and prognosis in sepsis patients: a cohort study based on MIMIC-III database].

OBJECTIVE: To investigate the correlation between early-stage blood pressure indexes and prognosis in sepsis patients. METHODS: A retrospective cohort study was conducted on the medical records of patients diagnosed with sepsis from 2001 to 2012 in the Medical Information Mart for Intensive Care-III (MIMIC-III) database. Patients were divided into survival group and death group according to the 28-day prognosis. General data of patients and heart rate (HR) and blood pressure at admission to ICU and within 24 hours after admission were collected. The blood pressure indexes including the maximum, median and mean value of systolic index, diastolic index and mean arterial pressure (MAP) index were calculated. The data were randomly divided into training set and validation set (4 : 1). Univariate Logistic regression analysis was used to screen covariates, and multivariate Logistic stepwise regression models were further developed. Model 1 (including HR, blood pressure, and blood pressure index related variables with P < 0.1 and other variables with P < 0.05) and Model 2 (including HR, blood pressure, and blood pressure index related variables with P < 0.1) were developed respectively. The receiver operator characteristic curve (ROC curve), precision recall curve (PRC) and decision curve analysis (DCA) curve were used to evaluate the quality of the two models, and the influencing factors of the prognosis of sepsis patients were analyzed. Finally, nomogram model was developed according to the better model and effectiveness of it was evaluated. RESULTS: A total of 11 559 sepsis patients were included in the study, with 10 012 patients in the survival group and 1 547 patients in the death group. There were significant differences in age, survival time, Elixhauser comorbidity score and other 46 variables between the two groups (all P < 0.05). Thirty-seven variables were preliminarily screened by univariate Logistic regression analysis. After multivariate Logistic stepwise regression model screening, among the indicators related to HR, blood pressure and blood pressure index, the HR at admission to ICU [odds ratio (OR) = 0.992, 95% confidence interval (95%CI) was 0.988-0.997] and the maximum HR (OR = 1.006, 95%CI was 1.001-1.011), maximum MAP index (OR = 1.620, 95%CI was 1.244-2.126), mean diastolic index (OR = 0.283, 95%CI was 0.091-0.856), median systolic index (OR = 2.149, 95%CI was 0.805-4.461), median diastolic index (OR = 3.986, 95%CI was 1.376-11.758) were selected (all P < 0.1). There were 14 other variables with P < 0.05, including age, Elixhauser comorbidity score, continuous renal replacement therapy (CRRT), use of ventilator, sedation and analgesia, norepinephrine, norepinephrine, highest serum creatinine (SCr), maximum blood urea nitrogen (BUN), highest prothrombin time (PT), highest activated partial thromboplastin time (APTT), lowest platelet count (PLT), highest white blood cell count (WBC), minimum hemoglobin (Hb). The ROC curve showed that the area under the curve (AUC) of Model 1 and Model 2 were 0.769 and 0.637, respectively, indicating that model 1 had higher prediction accuracy. The PRC curve showed that the AUC of Model 1 and Model 2 were 0.381 and 0.240, respectively, indicating that Model 1 had a better effect. The DCA curve showed that when the threshold was 0-0.8 (the probability of death was 0-80%), the net benefit rate of Model 1 was higher than that of Model 2. The calibration curve showed that the prediction effect of the nomogram model developed according to Model 1 was in good agreement with the actual outcome. The Bootstrap verification results showed that the nomogram model was consistent with the above results and had good prediction effects. CONCLUSIONS: The nomogram model constructed has good prediction effects on the 28-day prognosis in sepsis patients, and the blood pressure indexes are important predictors in the model.

Insect-Inspired Strategy for Conferring Reversible, High Responsivity on Microgels to Dilute-Source CO2.

We described an insect-inspired strategy for conferring reversible, high responsivity on polymer microgels to dilute-source CO2 (≤5000 ppm in gas mixtures). This is demonstrated on oligo(ethylene oxide)-based microgels that contain tertiary amines on the polymer chains with proper organic small molecular carbonates in the polymer-solvent system. Similar to the synergistic contribution of the CO2 receptor subunits in mosquitoes for CO2 response, laser light scattering and related studies indicated that the CO2-response of the microgels in terms of the volume changes works through the coordination of different functional moieties in the system, making it different from the conventional CO2-response mechanism. While this pushes the lower response threshold of CO2 concentration down to ca. 1000 ppm, this unique strategy can also satisfy the urge to achieve both effective CO2 capture and facile CO2 release, making it possible to couple the detection with the capture and utilization of indoor excess CO2.

Development of pseudo-targeted profiling of isotopic metabolomics using combined platform of high resolution mass spectrometry and triple quadrupole mass spectrometry with application of 13C6-glucose tracing in HepG2 cells.

Isotope tracing assisted metabolic analysis is becoming a unique tool to understand metabolic regulation in cell biology and biomedical research. Targeted mass spectrometry analysis based on selected reaction monitoring (SRM) has been widely applied in isotope tracing experiment with the advantages of high sensitivity and broad linearity. However, its application for new pathway discovery is largely restrained by molecular coverage. To overcome this limitation, we describe a strategy called pseudo-targeted profiling of isotopic metabolomics (PtPIM) to expand the analysis of isotope labeled metabolites beyond the limit of known pathways and chemical standards. Pseudo-targeted metabolomics was first established with ion transitions and retention times transformed from high resolution (orbitrap) mass spectrometry. Isotope labeled MRM transitions were then generated according to chemical formulas of fragments, which were derived from accurate ion masses acquired by HRMS. An in-house software "PseudoIsoMRM" was developed to simulate isotope labeled ion transitions in batch mode and correct the interference of natural isotopologues. This PtPIM strategy was successfully applied to study 13C6-glucose traced HepG2 cells. As 313 molecules determined as analysis targets, a total of 4104 ion transitions were simulated to monitor 13C labeled metabolites in positive-negative switching mode of QQQ mass spectrometer with minimum dwell time of 0.3 ms achieved. A total of 68 metabolites covering glycolysis, TCA cycle, nucleotide biosynthesis, one-carbon metabolism and related derivatives were found to be labeled (> 2%) in HepG2 cells. Active pentose phosphate pathway was observed with diverse labeling status of glycolysis intermediates. Meanwhile, our PtPIM strategy revealed that rotenone severely suppressed mitochondrial function e.g. oxidative phosphorylation and fatty acid beta-oxidation. In this case, anaerobic respiration became the major source of energy metabolism by producing abundant lactate. Conclusively, the simulation based PtPIM method demonstrates a strategy to broaden metabolite coverage in isotope tracing analysis independent of standard chemicals.

Multi-weight susceptible-infected model for predicting COVID-19 in China.

The mutant strains of COVID-19 caused a global explosion of infections, including many cities of China. In 2020, a hybrid AI model was proposed by Zheng et al., which accurately predicted the epidemic in Wuhan. As the main part of the hybrid AI model, ISI method makes two important assumptions to avoid over-fitting. However, the assumptions cannot be effectively applied to new mutant strains. In this paper, a more general method, named the multi-weight susceptible-infected model (MSI) is proposed to predict COVID-19 in Chinese Mainland. First, a Gaussian pre-processing method is proposed to solve the problem of data fluctuation based on the quantity consistency of cumulative infection number and the trend consistency of daily infection number. Then, we improve the model from two aspects: changing the grouped multi-parameter strategy to the multi-weight strategy, and removing the restriction of weight distribution of viral infectivity. Experiments on the outbreaks in many places in China from the end of 2021 to May 2022 show that, in China, an individual infected by Delta or Omicron strains of SARS-CoV-2 can infect others within 3-4 days after he/she got infected. Especially, the proposed method effectively predicts the trend of the epidemics in Xi'an, Tianjin, Henan, and Shanghai from December 2021 to May 2022.

A generalized non-linear model predicting efficacy of neoadjuvant therapy in HER2+ breast cancer.

Neoadjuvant therapy (NAT) is currently recommended to patients with human epidermal growth factor receptor 2-positive breast cancer (HER2+ BC) that typically exhibit a poor prognosis. The tumor immune microenvironment profoundly affects the efficacy of NAT. However, the correlation between tumor-infiltrating lymphocytes or their specific subpopulations and the response to NAT in HER2+ BC remains largely unknown. In our study, the immune infiltration status of 295 patients was classified as "immune-rich" or "immune-poor" phenotypes. The "immune-rich" phenotype was significantly positively related to pathological complete response (pCR). Ten genes were correlated with both pCR and the immune phenotype based on the results of spline and logistic regression. We constructed a generalized non-linear model combining linear and non-linear gene effects and successfully validated its predictive power using an internal and external validation set (AUC = 0.819, 0.797; respectively) and a clinical set (accuracy = 0.75).

Bright and stable perovskite light-emitting diodes in the near-infrared range.

Perovskite light-emitting diodes (LEDs) have attracted broad attention due to their rapidly increasing external quantum efficiencies (EQEs)1-15. However, most high EQEs of perovskite LEDs are reported at low current densities (<1 mA cm-2) and low brightness. Decrease in efficiency and rapid degradation at high brightness inhibit their practical applications. Here, we demonstrate perovskite LEDs with exceptional performance at high brightness, achieved by the introduction of a multifunctional molecule that simultaneously removes non-radiative regions in the perovskite films and suppresses luminescence quenching of perovskites at the interface with charge-transport layers. The resulting LEDs emit near-infrared light at 800 nm, show a peak EQE of 23.8% at 33 mA cm-2 and retain EQEs more than 10% at high current densities of up to 1,000 mA cm-2. In pulsed operation, they retain EQE of 16% at an ultrahigh current density of 4,000 mA cm-2, along with a high radiance of more than 3,200 W s-1 m-2. Notably, an operational half-lifetime of 32 h at an initial radiance of 107 W s-1 m-2 has been achieved, representing the best stability for perovskite LEDs having EQEs exceeding 20% at high brightness levels. The demonstration of efficient and stable perovskite LEDs at high brightness is an important step towards commercialization and opens up new opportunities beyond conventional LED technologies, such as perovskite electrically pumped lasers.

A machine learning model to predict efficacy of neoadjuvant therapy in breast cancer based on dynamic changes in systemic immunity.

OBJECTIVE: Neoadjuvant therapy (NAT) has been widely implemented as an essential treatment to improve therapeutic efficacy in patients with locally-advanced cancer to reduce tumor burden and prolong survival, particularly for human epidermal growth receptor 2-positive and triple-negative breast cancer. The role of peripheral immune components in predicting therapeutic responses has received limited attention. Herein we determined the relationship between dynamic changes in peripheral immune indices and therapeutic responses during NAT administration. METHODS: Peripheral immune index data were collected from 134 patients before and after NAT. Logistic regression and machine learning algorithms were applied to the feature selection and model construction processes, respectively. RESULTS: Peripheral immune status with a greater number of CD3+ T cells before and after NAT, and a greater number of CD8+ T cells, fewer CD4+ T cells, and fewer NK cells after NAT was significantly related to a pathological complete response (P < 0.05). The post-NAT NK cell-to-pre-NAT NK cell ratio was negatively correlated with the response to NAT (HR = 0.13, P = 0.008). Based on the results of logistic regression, 14 reliable features (P < 0.05) were selected to construct the machine learning model. The random forest model exhibited the best power to predict efficacy of NAT among 10 machine learning model approaches (AUC = 0.733). CONCLUSIONS: Statistically significant relationships between several specific immune indices and the efficacy of NAT were revealed. A random forest model based on dynamic changes in peripheral immune indices showed robust performance in predicting NAT efficacy.

OUTER RETINAL TUBULATION IN BIETTI CRYSTALLINE DYSTROPHY ASSOCIATED WITH THE RETINAL PIGMENT EPITHELIUM ATROPHY.

PURPOSE: To determine the prognostic value of outer retinal tubulation (ORT) in the eyes of a Chinese cohort with Bietti crystalline dystrophy (BCD). METHODS: This retrospective, multicenter cohort study enrolled 42 patients with clinically and genetically diagnosed BCD. Eighty eyes with good-quality images of spectral domain optical coherence tomography were included. Demographic details and clinical data were collected. The characteristics of ORT, including prevalence, location, and morphologic characteristics were analyzed. RESULTS: Forty-two patients with BCD harbored potentially CYP4V2 disease-causing mutations. The mutation spectrum comprised 17 unique variants, 9 of which were novel. Fifty-two of these 80 eyes demonstrated evidence of ORT. The incidence of ORT is significantly higher in Stage 2 than other stages ( P < 0.001). ORT was mainly bilateral and located at the margin of the atrophic area of retinal pigment epithelium (RPE), and dynamically changed with the progressive RPE atrophy. The process of RPE atrophy was slower in eyes with ORT ( P = 0.017), with significantly longer intact RPE width in Stage 3 ( P = 0.024). Eyes with ORT had slower vision loss than eyes without ORT ( P = 0.044). CONCLUSION: ORT may be a sign of the onset of RPE atrophy in early-stage BCD and may suggest less risk of rapid progression in late-stage BCD.

PFKFB3 knockdown attenuates Amyloid ß-Induced microglial activation and retinal pigment epithelium disorders in mice.

Age-related macular degeneration (AMD) is characterized by progressive accumulation of drusen deposits and retinal pigment epithelium (RPE) disorders. As the main component of drusen, amyloid ß (Aß) plays a critical role in activating microglia and causing neuroinflammation in AMD pathogenesis. However, the role of activated microglia-mediated neuroinflammation in RPE senescence remains unclear. Recent evidence indicates that inflammatory microglia are glycolytic and driven by an increase in 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 3 (PFKFB3), an enzyme described as the master regulator of glycolysis. In this study, we mimicked the retinal inflammatory microenvironment of AMD by intravitreal injection of oligomeric Aß1-40 in mice, which resulted in activation of microglia and upregulation of PFKFB3. RNA sequencing was performed to evaluate PFKFB3-mediated microglial activation. The effect of microglial activation on RPE disorders was assessed using gene knockout experiments, immunofluorescence, CCK-8 assay, and ß-galactosidase staining. Intravitreal Aß1-40 injection induced proinflammatory activation of microglia by upregulating PFKFB3 and resulted in RPE disorders, which was verified in heterozygous Pfkfb3-deficient mice (Pfkfb3+/-) mice, Aß1-40-activated microglial cell line BV2, and co-culture of RPE cell line ARPE19. RNA sequencing revealed that PFKFB3 mainly affected innate immune processes during Aß1-40-induced retinal inflammation. PFKFB3 knockdown inhibited RPE disorders and rescued the retinal structure and function. Overall, the modulation of PFKFB3-mediated microglial glycolysis and activation is a promising strategy for AMD treatment.