Evaluation of the impact of customized serum-free culture medium on the production of clinical-grade human umbilical cord mesenchymal stem cells: insights for future clinical applications.

BACKGROUND: The selection of suitable culture medium is critical for achieving good clinical outcomes in cell therapy. To support the commercial application of stem cell therapy, customized culture media not only need to promote stem cell proliferation, but also need to save costs and meet industrial requirements for inter-batch consistency, efficacy, and biosafety. In this study, we developed a series of serum-free media (SFM) and elucidated the effects between different SFM, as well as between SFM and serum-containing meida (SCM), on human umbilical cord mesenchymal stem cells (hUC-MSCs) phenotype and function. We analyze and emphasize from the perspectives of clinical and commercial application why research on customized culture media is critical for the success of enterprises developing novel cellular therapeutics. METHODS: We cultured hUC-MSCs with identical cell seeding densities in different formulations of SFM and SCM until passage 10 and examined the changes in cell phenotype and function. We analyzed the results with the commercial application requirments of the cellular therapy industry to assess the potential impact of customized culture media on inter-batch consistency, efficacy, stability, biosafety, and cost-effectiveness of industrial-scale cell production. RESULTS: hUC-MSCs cultured in SCM and SFM exhibit consistent cell morphology and surface molecule expression, but hUC-MSCs cultured in SFM demonstrate higher activity, superior proliferative capacity, and greater stability. Furthermore, hUC-MSCs cultured in different SFM exhibit differences in cell activity, proliferative capacity, senescent rate, and S/M ratio of cell cycle, while maintaining a normal karyotype after long-term in vitro cultivation. Moreover, we found that hUC-MSCs cultured in different media exhibit variations in paracrine capacity and in their support of hematopoietic stem cell (HSC) self-renewal. CONCLUSION: Considering the substantial funding and time required for cell-based drug development, our results underscore the importances of comprehensively optimizing the composition of medium for the specific disease prior to conducting clinical trials of cell-based therapies. The criteria for selecting culture medium should be based on the requirements of the target disease for cellular function. In addition, we provide a way to formulate different customized SFM, which is beneficial for the development of cell therapy industry.

Advancing quantitative PCR with color cycle multiplex amplification.

Quantitative PCR (qPCR) is the gold standard for detection and quantitation of known DNA targets, but the scarcity of spectrally distinct fluorophores and filter sets limits the number of detectable targets. Here, we introduce color cycle multiplex amplification (CCMA) to significantly increase the number of detectable DNA targets in a single qPCR reaction using standard instrumentation. In CCMA, presence of one DNA target species results in a pre-programmed pattern of fluorescence increases. This pattern is distinguished by cycle thresholds (Cts) through rationally designed delays in amplification. For example, we design an assay wherein Staphylococcus aureus sequentially induces FAM, then Cy5.5, then ROX fluorescence increases with more than 3 cycles between each signal. CCMA offers notably higher potential for multiplexing because it uses fluorescence permutation rather than combination. With 4 distinct fluorescence colors, CCMA theoretically allows the detection of up to 136 distinct DNA target sequences using fluorescence permutation. Experimentally, we demonstrated a single-tube qPCR assay screening 21 sepsis-related bacterial DNA targets in samples of blood, sputum, pleural effusion and bronchoalveolar lavage fluid, with 89% clinical sensitivity and 100% clinical specificity, showing its potential as a powerful tool for advanced quantitative screening in molecular diagnostics.

Genomic structural variations link multiple genes to bone mineral density in a multi-ethnic cohort study: Louisiana osteoporosis study.

Osteoporosis, characterized by low BMD, is a highly heritable metabolic bone disorder. Although single nucleotide variations (SNVs) have been extensively studied, they explain only a fraction of BMD heritability. Although genomic structural variations (SVs) are large-scale genomic alterations that contribute to genetic diversity in shaping phenotypic variations, the role of SVs in osteoporosis susceptibility remains poorly understood. This study aims to identify and prioritize genes that harbor BMD-related SVs. We performed whole genome sequencing on 4982 subjects from the Louisiana Osteoporosis Study. To obtain high-confidence SVs, the detection of SVs was performed using an ensemble approach. The SVs were tested for association with BMD variation at the hip (HIP), femoral neck (FNK), and lumbar spine (SPN), respectively. Additionally, we conducted co-occurrence analysis using multi-omics approaches to prioritize the identified genes based on their functional importance. Stratification was employed to explore the sex- and ethnicity-specific effects. We identified significant SV-BMD associations: 125 for FNK-BMD, 99 for SPN-BMD, and 83 for HIP-BMD. We observed SVs that were commonly associated with both FNK and HIP BMDs in our combined and stratified analyses. These SVs explain 13.3% to 19.1% of BMD variation. Novel bone-related genes emerged, including LINC02370, ZNF family genes, and ZDHHC family genes. Additionally, FMN2, carrying BMD-related deletions, showed associations with FNK or HIP BMDs, with sex-specific effects. The co-occurrence analysis prioritized an RNA gene LINC00494 and ZNF family genes positively associated with BMDs at different skeletal sites. Two potential causal genes, IBSP and SPP1, for osteoporosis were also identified. Our study uncovers new insights into genetic factors influencing BMD through SV analysis. We highlight BMD-related SVs, revealing a mix of shared and specific genetic influences across skeletal sites and gender or ethnicity. These findings suggest potential roles in osteoporosis pathophysiology, opening avenues for further research and therapeutic targets.

Mechanisms and treatment of pulmonary arterial hypertension.

Substantial progress has been made in the management of pulmonary arterial hypertension (PAH) in the past 25 years, but the disease remains life-limiting. Established therapies for PAH are mostly limited to symptomatic relief by correcting the imbalance of vasoactive factors. The tyrosine kinase inhibitor imatinib, the first predominantly non-vasodilatory drug to be tested in patients with PAH, improved exercise capacity and pulmonary haemodynamics compared with placebo but at the expense of adverse events such as subdural haematoma. Given that administration by inhalation might reduce the risk of systemic adverse effects, inhaled formulations of tyrosine kinase inhibitors are currently in clinical development. Other novel therapeutic approaches for PAH include suppression of activin receptor type IIA signalling with sotatercept, which has shown substantial efficacy in clinical trials and was approved for use in the USA in 2024, but the long-term safety of the drug remains unclear. Future advances in the management of PAH will focus on right ventricular function and involve deep phenotyping and the development of a personalized medicine approach. In this Review, we summarize the mechanisms underlying PAH, provide an overview of available PAH therapies and their limitations, describe the development of newer, predominantly non-vasodilatory drugs that are currently being tested in phase II or III clinical trials, and discuss future directions for PAH research.

Variability in performance of genetic-enhanced DXA-BMD prediction models across diverse ethnic and geographic populations: A risk prediction study.

BACKGROUND: Osteoporosis is a major global health issue, weakening bones and increasing fracture risk. Dual-energy X-ray absorptiometry (DXA) is the standard for measuring bone mineral density (BMD) and diagnosing osteoporosis, but its costliness and complexity impede widespread screening adoption. Predictive modeling using genetic and clinical data offers a cost-effective alternative for assessing osteoporosis and fracture risk. This study aims to develop BMD prediction models using data from the UK Biobank (UKBB) and test their performance across different ethnic and geographical populations. METHODS AND FINDINGS: We developed BMD prediction models for the femoral neck (FNK) and lumbar spine (SPN) using both genetic variants and clinical factors (such as sex, age, height, and weight), within 17,964 British white individuals from UKBB. Models based on regression with least absolute shrinkage and selection operator (LASSO), selected based on the coefficient of determination (R2) from a model selection subset of 5,973 individuals from British white population. These models were tested on 5 UKBB test sets and 12 independent cohorts of diverse ancestries, totaling over 15,000 individuals. Furthermore, we assessed the correlation of predicted BMDs with fragility fractures risk in 10 years in a case-control set of 287,183 European white participants without DXA-BMDs in the UKBB. With single-nucleotide polymorphism (SNP) inclusion thresholds at 5×10-6 and 5×10-7, the prediction models for FNK-BMD and SPN-BMD achieved the highest R2 of 27.70% with a 95% confidence interval (CI) of [27.56%, 27.84%] and 48.28% (95% CI [48.23%, 48.34%]), respectively. Adding genetic factors improved predictions slightly, explaining an additional 2.3% variation for FNK-BMD and 3% for SPN-BMD over clinical factors alone. Survival analysis revealed that the predicted FNK-BMD and SPN-BMD were significantly associated with fragility fracture risk in the European white population (P < 0.001). The hazard ratios (HRs) of the predicted FNK-BMD and SPN-BMD were 0.83 (95% CI [0.79, 0.88], corresponding to a 1.44% difference in 10-year absolute risk) and 0.72 (95% CI [0.68, 0.76], corresponding to a 1.64% difference in 10-year absolute risk), respectively, indicating that for every increase of one standard deviation in BMD, the fracture risk will decrease by 17% and 28%, respectively. However, the model's performance declined in other ethnic groups and independent cohorts. The limitations of this study include differences in clinical factors distribution and the use of only SNPs as genetic factors. CONCLUSIONS: In this study, we observed that combining genetic and clinical factors improves BMD prediction compared to clinical factors alone. Adjusting inclusion thresholds for genetic variants (e.g., 5×10-6 or 5×10-7) rather than solely considering genome-wide association study (GWAS)-significant variants can enhance the model's explanatory power. The study highlights the need for training models on diverse populations to improve predictive performance across various ethnic and geographical groups.

Multi-scale variational autoencoder for imputation of missing values in untargeted metabolomics using whole-genome sequencing data.

BACKGROUND: Missing data is a common challenge in mass spectrometry-based metabolomics, which can lead to biased and incomplete analyses. The integration of whole-genome sequencing (WGS) data with metabolomics data has emerged as a promising approach to enhance the accuracy of data imputation in metabolomics studies. METHOD: In this study, we propose a novel method that leverages the information from WGS data and reference metabolites to impute unknown metabolites. Our approach utilizes a multi-scale variational autoencoder to jointly model the burden score, polygenetic risk score (PGS), and linkage disequilibrium (LD) pruned single nucleotide polymorphisms (SNPs) for feature extraction and missing metabolomics data imputation. By learning the latent representations of both omics data, our method can effectively impute missing metabolomics values based on genomic information. RESULTS: We evaluate the performance of our method on empirical metabolomics datasets with missing values and demonstrate its superiority compared to conventional imputation techniques. Using 35 template metabolites derived burden scores, PGS and LD-pruned SNPs, the proposed methods achieved R2-scores > 0.01 for 71.55 % of metabolites. CONCLUSION: The integration of WGS data in metabolomics imputation not only improves data completeness but also enhances downstream analyses, paving the way for more comprehensive and accurate investigations of metabolic pathways and disease associations. Our findings offer valuable insights into the potential benefits of utilizing WGS data for metabolomics data imputation and underscore the importance of leveraging multi-modal data integration in precision medicine research.

Development and Validation of an Interpretable Machine Learning Model for Early Prognosis Prediction in ICU Patients with Malignant Tumors and Hyperkalemia.

This study aims to develop and validate a machine learning (ML) predictive model for assessing mortality in patients with malignant tumors and hyperkalemia (MTH). We extracted data on patients with MTH from the Medical Information Mart for Intensive Care-IV, version 2.2 (MIMIC-IV v2.2) database. The dataset was split into a training set (75%) and a validation set (25%). We used the Least Absolute Shrinkage and Selection Operator (LASSO) regression to identify potential predictors, which included clinical laboratory indicators and vital signs. Pearson correlation analysis tested the correlation between predictors. In-hospital death was the prediction target. The Area Under the Curve (AUC) and accuracy of the training and validation sets of 7 ML algorithms were compared, and the optimal 1 was selected to develop the model. The calibration curve was used to evaluate the prediction accuracy of the model further. SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) enhanced model interpretability. 496 patients with MTH in the Intensive Care Unit (ICU) were included. After screening, 17 clinical features were included in the construction of the ML model, and the Pearson correlation coefficient was <0.8, indicating that the correlation between the clinical features was small. eXtreme Gradient Boosting (XGBoost) outperformed other algorithms, achieving perfect scores in the training set (accuracy: 1.000, AUC: 1.000) and high scores in the validation set (accuracy: 0.734, AUC: 0.733). The calibration curves indicated good predictive calibration of the model. SHAP analysis identified the top 8 predictive factors: urine output, mean heart rate, maximum urea nitrogen, minimum oxygen saturation, minimum mean blood pressure, maximum total bilirubin, mean respiratory rate, and minimum pH. In addition, SHAP and LIME performed in-depth individual case analyses. This study demonstrates the effectiveness of ML methods in predicting mortality risk in ICU patients with MTH. It highlights the importance of predictors like urine output and mean heart rate. SHAP and LIME significantly enhanced the model's interpretability.

Deep phenotyping of unaffected carriers of pathogenic BMPR2 variants screened for pulmonary arterial hypertension.

INTRODUCTION: Pathogenic variants in the gene encoding for BMPR2 are a major genetic risk factor for heritable pulmonary arterial hypertension (PAH). Due to incomplete penetrance, deep-phenotyping of unaffected carriers (UCs) of a pathogenic BMPR2 variant through multi-modality screening may aid in early diagnosis and identify susceptibility traits for future development of PAH. METHODS: 28 UCs (44±16 years, 57% female) and 21 healthy controls (43±18 years, 48% female) underwent annual screening, including cardiac magnetic resonance imaging (cMRI), transthoracic echocardiography (TTE), cardiopulmonary exercise testing (CPET) and right heart catheterization (RHC). Right ventricular (RV) pressure-volume (PV) loops were constructed to assess load independent contractility and compared with a healthy control group. A transgenic Bmpr2Δ71Ex1/+ rat model was employed to validate findings in humans. RESULTS: UCs had lower indexed right ventricular end-diastolic (80±18 mL·m-2 versus 64±14 mL·m-2;p= 0.003), end-systolic (34±11 mL·m-2 versus 27±8 mL·m-2;p=0.024) and left end-diastolic volumes (69±14 mL·m-2 versus 60±11 mL·m-2;p=0.019) than control subjects. Bmpr2Δ71Ex1/+ rats were also observed to have smaller cardiac volumes than WT rats. PV loop analysis showed significantly higher afterload (Ea) (0.15±0.06 versus 0.27±0.08; p<0.001), and end-systolic elastance (Ees) 0.28±0.07 versus 0.35±0.10; p=0.047) in addition to lower RV-pulmonary artery coupling (Ees/Ea)(2.24±1.03 versus 1.36±0.37; p=0.006) in UCs. During the 4-year follow-up period, two UCs developed PAH with normal NT-proBNP and TTE indices at diagnosis. CONCLUSION: Unaffected BMPR2 mutation carriers have an altered cardiac phenotype mimicked in Bmpr2Δ71Ex1/+ transgenic rats. Future efforts in establishing an effective screening protocol for individuals at risk for developing PAH warrants longer follow-up periods.

CD11b maintains West Nile virus replication through modulation of immune response in human neuroblastoma cells.

BACKGROUND: West Nile virus (WNV) is a rapidly spreading mosquito-borne virus accounted for neuroinvasive diseases. An insight into WNV-host factors interaction is necessary for development of therapeutic approaches against WNV infection. CD11b has key biological functions and been identified as a therapeutic target for several human diseases. The purpose of this study was to determine whether CD11b was implicated in WNV infection. METHODS: SH-SY5Y cells with and without MEK1/2 inhibitor U0126 or AKT inhibitor MK-2206 treatment were infected with WNV. CD11b mRNA levels were assessed by real-time PCR. WNV replication and expression of stress (ATF6 and CHOP), pro-inflammatory (TNF-α), and antiviral (IFN-α, IFN-ß, and IFN-γ) factors were evaluated in WNV-infected SH-SY5Y cells with CD11b siRNA transfection. Cell viability was determined by MTS assay. RESULTS: CD11b mRNA expression was remarkably up-regulated by WNV in a time-dependent manner. U0126 but not MK-2206 treatment reduced the CD11b induction by WNV. CD11b knockdown significantly decreased WNV replication and protected the infected cells. CD11b knockdown markedly increased TNF-α, IFN-α, IFN-ß, and IFN-γ mRNA expression induced by WNV. ATF6 mRNA expression was reduced upon CD11b knockdown following WNV infection. CONCLUSION: These results demonstrate that CD11b is involved in maintaining WNV replication and modulating inflammatory as well as antiviral immune response, highlighting the potential of CD11b as a target for therapeutics for WNV infection.

Food-Pollen Cross-Reactivity and its Molecular Diagnosis in China.

PURPOSE OF REVIEW: Plant-derived foods are one of the most common causative sources of food allergy in China, with a significant relationship to pollinosis. This review aims to provide a comprehensive overview of this food-pollen allergy syndrome and its molecular allergen diagnosis to better understand the cross-reactive basis. RECENT FINDINGS: Food-pollen cross-reactivity has been mainly reported in Northern China, Artemisia pollen is the major related inhalant source, followed by tree pollen (Betula), while grass pollen plays a minor role. Pollen allergy is relatively low in Southern China, with allergies to grass pollen being more important than weed and tree pollens. Rosaceae fruits and legume seeds stand out as major related allergenic foods. Non-specific lipid transfer protein (nsLTP) has been found to be the most clinically relevant cross-reacting allergenic component, able to induce severe reactions. PR-10, profilin, defensin, chitinase, and gibberellin-regulated proteins are other important cross-reactive allergen molecules. Artemisia pollen can induce allergenic cross-reactions with a wide range of plant-derived foods in China, and spring tree pollens (Betula) are also important. nsLTP found in both pollen and plant-derived food is considered the most significant allergen in food pollen cross-reactivity. Component-resolved diagnosis with potential allergenic proteins is recommended to improve diagnostic accuracy and predict the potential risk of causing allergic symptoms.

Clinical predictors of severe radiation pneumonitis in patients undergoing thoracic radiotherapy for lung cancer.

Background: Severe radiation pneumonitis (RP), one of adverse events in patients with lung cancer receiving thoracic radiotherapy, is more likely to lead to more mortality and poor quality of life, which could be predicted by clinical information and treatment scheme. In this study, we aimed to explore the clinical predict model for severe RP. Methods: We collected information on lung cancer patients who received radiotherapy from August 2020 to August 2022. Clinical features were obtained from 690 patients, including baseline and treatment data as well as radiation dose measurement parameters, including lung volume exceeding 5 Gy (V5), lung volume exceeding 20 Gy (V20), lung volume exceeding 30 Gy (V30), mean lung dose (MLD), etc. Among them, 621 patients were in the training cohort, and 69 patients were in the test cohort. Three models were built using different screening methods, including multivariate logistics regression (MLR), backward stepwise regression (BSR), and random forest regression (RFR), to evaluate their predictive power. Overoptimism in the training cohorts was evaluated by four validation methods, including hold-out, 10-fold, leave-one-out, and bootstrap methods, and test cohort was used to evaluate the predictive performance of the model. Model calibration, decision curve analysis (DCA), and evaluation of the nomograms for the three models were completed. Results: Severe RP was up to 9.4%. The results of multivariate analysis of logistics regression in all patients showed that patients with subclinical (untreated and asymptomatic) interstitial lung disease (ILD) could increase the risk of severe RP, and patients with a better lung diffusion function and received standardized steroids treatment could decrease the risk of severe RP. The three models built by MLR, BSR, and RFR all had good accuracy (>0.850) and moderate κ value (>0.4), and the model 2 built by BSR had the highest area under the receiver operating characteristic (ROC) curve (AUC) in three models, which was 0.958 [95% confidence interval (CI): 0.932-0.985]. The calibration curve showed good agreement between the predicted and actual values, and the DCA showed a positive net benefit for the model 2 which drew the nomogram. The model 2 included subclinical ILD, diffusing capacity of the lung for carbon monoxide (DLCO), ipsilateral lung V20, and standardized steroid treatment, which could affect the incidence of severe RP. Conclusions: Subclinical ILD, DLCO, ipsilateral lung V20, and with or not standardized steroid treatment could affect the incidence of severe RP. Strict lung dose limitation and standardized steroid treatment could contribute to a decrease in severe RP.

omicsMIC: a comprehensive benchmarking platform for robust comparison of imputation methods in mass spectrometry-based omics data.

Mass spectrometry is a powerful and widely used tool for generating proteomics, lipidomics and metabolomics profiles, which is pivotal for elucidating biological processes and identifying biomarkers. However, missing values in mass spectrometry-based omics data may pose a critical challenge for the comprehensive identification of biomarkers and elucidation of the biological processes underlying human complex disorders. To alleviate this issue, various imputation methods for mass spectrometry-based omics data have been developed. However, a comprehensive comparison of these imputation methods is still lacking, and researchers are frequently confronted with a multitude of options without a clear rationale for method selection. To address this pressing need, we developed omicsMIC (mass spectrometry-based omics with Missing values Imputation methods Comparison platform), an interactive platform that provides researchers with a versatile framework to evaluate the performance of 28 diverse imputation methods. omicsMIC offers a nuanced perspective, acknowledging the inherent heterogeneity in biological data and the unique attributes of each dataset. Our platform empowers researchers to make data-driven decisions in imputation method selection based on real-time visualizations of the outcomes associated with different imputation strategies. The comprehensive benchmarking and versatility of omicsMIC make it a valuable tool for the scientific community engaged in mass spectrometry-based omics research. omicsMIC is freely available at https://github.com/WQLin8/omicsMIC.

CRISPR-mediated Sox9 activation and RelA inhibition enhance cell therapy for osteoarthritis.

Osteoarthritis (OA) is a painful and debilitating disease affecting over 500 million people worldwide. Intraarticular injection of mesenchymal stromal cells (MSCs) shows promise for the clinical treatment of OA, but the lack of consistency in MSC preparation and application makes it difficult to further optimize MSC therapy and to properly evaluate the clinical outcomes. In this study, we used Sox9 activation and RelA inhibition, both mediated by the CRISPR-dCas9 technology simultaneously, to engineer MSCs with enhanced chondrogenic potential and downregulated inflammatory responses. We found that both Sox9 and RelA could be fine-tuned to the desired levels, which enhances the chondrogenic and immunomodulatory potentials of the cells. Intraarticular injection of modified cells significantly attenuated cartilage degradation and palliated OA pain compared with the injection of cell culture medium or unmodified cells. Mechanistically, the modified cells promoted the expression of factors beneficial to cartilage integrity, inhibited the production of catabolic enzymes in osteoarthritic joints, and suppressed immune cells. Interestingly, a substantial number of modified cells could survive in the cartilaginous tissues including articular cartilage and meniscus. Together, our results suggest that CRISPR-dCas9-based gene regulation is useful for optimizing MSC therapy for OA.

Hydroformylation of Olefins with CO2/H2 and Hydrosilane by Copper/Cobalt Tandem Catalysis.

A Cu/Co tandem catalysis protocol was developed to conduct the hydroformylation of olefins using CO2/H2 and PMHS (polymethylhydrosiloxane) as a readily available and environmentally friendly hydride source. This methodology was performed via a two-step approach consisting of the copper-catalyzed reduction of CO2 by hydrosilane and subsequent cobalt-promoted hydroformylation with H2 and the in situ formed CO. The optimized triphos oxide ligand, which presumably facilitates the migratory insertion of CO gives moderate to excellent yields for both terminal and internal alkenes. This earth-abundant metal catalysis provides a reliable and efficient way to afford useful aldehydes in industry using silicon by-product PMHS as hydrogen source and renewable CO2 as carbonyl source.

Regulating NCOA4-Mediated Ferritinophagy for Therapeutic Intervention in Cerebral Ischemia-Reperfusion Injury.

Ischemic stroke presents a global health challenge, necessitating an in-depth comprehension of its pathophysiology and therapeutic strategies. While reperfusion therapy salvages brain tissue, it also triggers detrimental cerebral ischemia-reperfusion injury (CIRI). In our investigation, we observed the activation of nuclear receptor coactivator 4 (NCOA4)-mediated ferritinophagy in an oxygen-glucose deprivation/reoxygenation (OGD/R) model using HT22 cells (P < 0.05). This activation contributed to oxidative stress (P < 0.05), enhanced autophagy (P < 0.05) and cell death (P < 0.05) during CIRI. Silencing NCOA4 effectively mitigated OGD/R-induced damage (P < 0.05). These findings suggested that targeting NCOA4-mediated ferritinophagy held promise for preventing and treating CIRI. Subsequently, we substantiated the cyclic GMP-AMP synthase (cGAS)-stimulator of interferon genes (STING) pathway effectively regulated the NCOA4-mediated ferritinophagy, by applying the cGAS inhibitor RU.521 and performing NCOA4 overexpression (P < 0.05). Suppressing the cGAS-STING pathway efficiently curtailed ferritinophagy (P < 0.05), oxidative stress (P < 0.05), and cell damage (P < 0.05) of CIRI, while NCOA4 overexpression could alleviate this effect (P < 0.05). Finally, we elucidated the specific molecular mechanism underlying the protective effect of the iron chelator deferoxamine (DFO) on CIRI. Our findings revealed that DFO alleviated hypoxia-reoxygenation injury in HT22 cells through inhibiting NCOA4-mediated ferritinophagy and reducing ferrous ion levels (P < 0.05). However, the protective effects of DFO were counteracted by cGAS overexpression (P < 0.05). In summary, our results indicated that the activation of the cGAS-STING pathway intensified cerebral damage during CIRI by inducing NCOA4-mediated ferritinophagy. Administering the iron chelator DFO effectively attenuated NCOA4-induced ferritinophagy, thereby alleviating CIRI. Nevertheless, the role of the cGAS-STING pathway in CIRI regulation likely involves intricate mechanisms, necessitating further validation in subsequent investigations.

A natural hydrogel complex improves intervertebral disc degeneration by correcting fatty acid metabolism and inhibiting nucleus pulposus cell pyroptosis.

The degeneration of intervertebral discs is strongly associated with the occurrence of pyroptosis in nucleus pulposus (NP) cells. This pyroptosis is characterized by abnormal metabolism of fatty acids in the degenerative pathological state, which is further exacerbated by the inflammatory microenvironment and degradation of the extracellular matrix. In order to address this issue, we have developed a fibrin hydrogel complex (FG@PEV). This intricate formulation amalgamates the beneficial attributes of platelet extravasation vesicles, contributing to tissue repair and regeneration. Furthermore, this complex showcases exceptional stability, gradual-release capabilities, and a high degree of biocompatibility. In order to substantiate the biological significance of FG@PEV in intervertebral disc degeneration (IVDD), we conducted a comprehensive investigation into its potential mechanism of action through the integration of RNA-seq sequencing and metabolomics analysis. Furthermore, these findings were subsequently validated through experimentation in both in vivo and in vitro models. The experimental results revealed that the FG@PEV intervention possesses the capability to reshape the inflammatory microenvironment within the disc. It also addresses the irregularities in fatty acid metabolism of nucleus pulposus cells, consequently hindering cellular pyroptosis and slowing down disc degeneration through the regulation of extracellular matrix synthesis and degradation. As a result, this injectable gel system represents a promising and innovative therapeutic approach for mitigating disc degeneration.

Nerve growth factor receptor limits inflammation to promote remodeling and repair of osteoarthritic joints.

Osteoarthritis (OA) is a painful, incurable disease affecting over 500 million people. Recent clinical trials of the nerve growth factor (NGF) inhibitors in OA patients have suggested adverse effects of NGF inhibition on joint structure. Here we report that nerve growth factor receptor (NGFR) is upregulated in skeletal cells during OA and plays an essential role in the remodeling and repair of osteoarthritic joints. Specifically, NGFR is expressed in osteochondral cells but not in skeletal progenitor cells and induced by TNFα to attenuate NF-κB activation, maintaining proper BMP-SMAD1 signaling and suppressing RANKL expression in mice. NGFR deficiency hyper-activates NF-κB in murine osteoarthritic joints, which impairs bone formation and enhances bone resorption as exemplified by a reduction in subchondral bone and osteophytes. In human OA cartilage, NGFR is also negatively associated with NF-κB activation. Together, this study suggests a role of NGFR in limiting inflammation for repair of diseased skeletal tissues.

The application value of metagenomic next-generation sequencing technology in the diagnosis and treatment of neonatal infectious meningitis - a single center retrospective case-control study.

OBJECTIVE: This retrospective study was conducted to investigate the application value of metagenomics next generation sequencing (mNGS) technology in the diagnosis and treatment of neonatal infectious meningitis. METHODS: From 1 January 2020 to 31 December 2022, 73 newborns suspected of infectious meningitis were hospitalized. After screening by inclusion and exclusion criteria, 69 newborns were subsequently included in the study, containing 27 cases with positive mNGS result and 42 cases with negative mNGS result. Furthermore, according to the diagnosis of meningitis, mNGS positive group and mNGS negative group were further divided into infectious meningitis with mNGS (+) group (n = 27) and infectious meningitis with mNGS (-) group (n = 26), respectively. RESULTS: (1) Compared with cerebrospinal fluid (CSF) culture, mNGS has better diagnostic value [positive predictive value (PPV) = 100.00% (27/27), negative predictive value (NPV) = 38.10% (16/42), agreement rate = 62.32% (43/69), area under the curve (AUC) = 0.750, 95% confidence interval (CI): 0.636-0.864]. (2) There were significant differences in the onset age, age at first CSF test, CSF leukocyte count, CSF glucose, positive rate of CSF culture, blood leukocyte count, procalcitonin (PCT), C-reaction protein (CRP), age at first mNGS test and adjusting anti-infective medication in the comparison between infectious meningitis with mNGS (+) group and infectious meningitis with mNGS (-) group (p < 0.05). (3) mNGS could help improve the cure rate [crude odds ratio (OR) = 3.393, 95%CI: 1.072-10.737; adjusted OR = 15.580, 95%CI: 2.114-114.798]. CONCLUSION: Compared with classic meningitis detection methods, mNGS has better PPV, NPV, agreement rate, and AUC. mNGS could help improve the cure rate.

P75NTR+CD64+ neutrophils promote sepsis-induced acute lung injury.

Patients suffering from sepsis-induced acute lung injury (ALI) exhibit a high mortality rate, and their prognosis is closely associated with infiltration of neutrophils into the lungs. In this study, we found a significant elevation of CD64+ neutrophils, which highly expressed p75 neurotrophin receptor (p75NTR) in peripheral blood of mice and patients with sepsis-induced ALI. p75NTR+CD64+ neutrophils were also abundantly expressed in the lung of ALI mice induced by lipopolysaccharide. Conditional knock-out of the myeloid lineage's p75NTR gene improved the survival rates, attenuated lung tissue inflammation, reduced neutrophil infiltration and enhanced the phagocytic functions of CD64+ neutrophils. In vitro, p75NTR+CD64+ neutrophils exhibited an upregulation and compromised phagocytic activity in blood samples of ALI patients. Blocking p75NTR activity by soluble p75NTR extracellular domain peptide (p75ECD-Fc) boosted CD64+ neutrophils phagocytic activity and reduced inflammatory cytokine production via regulation of the NF-κB activity. The findings strongly indicate that p75NTR+CD64+ neutrophils are a novel pathogenic neutrophil subpopulation promoting sepsis-induced ALI.