A Mendelian randomization investigation of the causal association between the gut microbiota and sleep disorders.

Background: Increasing numbers of people are suffering from sleep disorders. The gut microbiota of these individuals differs significantly. However, no reports are available on the causal associations between specific gut microbiota and sleep disorders. Methods: Data on gut genera were obtained from the MiBioGen consortium. Twenty-four cohorts with 18,340 individuals of European origin were included. Sleep disorder data, which included 216,454 European individuals, were retrieved from the FinnGen Biobank. Subsequently, two-sample Mendelian randomization was performed to analyze associations between sleep disorders and specific components of the gut microbiota. Results: Inverse variance weighting (IVW) revealed a negative correlation between Coprobacter and sleep disorders (OR = 0.797, 95% CI = 0.66-0.96, and p = 0.016), a positive correlation between Lachnospiraceae and sleep disorders (OR = 1.429, 95% CI = 1.03-1.98, and p = 0.032), a negative association between Oscillospira and sleep disorders (OR = 0.745, 95% CI = 0.56-0.98, and p = 0.038), and a negative association between Peptococcus and sleep disorders (OR = 0.858, 95% CI = 0.74-0.99, p = 0.039). Conclusion: A significant causal relationship was found between four specific gut microbiota and sleep disorders. One family, Lachnospiraceae, was observed to increase the risk of sleep disorders, while three genera, namely, Coprobacter, Oscillospira, and Peptococcus, could reduce the risk of sleep disorders. However, further investigations are needed to confirm the specific mechanisms by which the gut microbiota affects sleep.

Identification and quantification of nanoplastics in different crops using pyrolysis gas chromatography-mass spectrometry.

Quantitative studies of nanoplastics (NPs) abundance on agricultural crops are crucial for understanding the environmental impact and potential health risks of NPs. However, the actual extent of NP contamination in different crops remains unclear, and therefore insufficient quantitative data are available for adequate exposure assessments. Herein, a method with nitric acid digestion, multiple organic extraction combined with pyrolysis gas chromatography-mass spectrometry (Py-GC/MS) quantification was used to determine the chemical composition and mass concentration of NPs in different crops (cowpea, flowering cabbage, rutabagas, and chieh-qua). Recoveries of 74.2-109.3% were obtained for different NPs in standard products (N = 6, RSD <9.6%). The limit of detection (LOD) and the limit of quantitation (LOQ) were 0.02-0.5 µg and 0.06-1.5 µg, respectively. The detection method for NPs exhibited good external calibration curves and linearity with 0.99. The results showed that poly (vinylchloride) (PVC), poly (ethylene terephthalate) (PET), polyethylene (PE), and polyadiohexylenediamine (PA66) NPs could be detected in crop samples, although the accumulation levels associated with the various crops varied significantly. PVC (N.D.-954.3 mg kg-1, dry weight (DW)) and PE (101.3-462.9 mg kg-1, DW) NPs were the dominant components in the samples of all four crop species, while high levels of PET (414.3-1430.1 mg kg-1, DW) NPs were detected in cowpea samples. Furthermore, there were notable differences in the accumulation levels of various edible crop parts, such as stems (60.2%) > leaves (39.8%) in flowering cabbage samples and peas (58.8%) > pods (41.2%) in cowpea samples. This study revealed the actual extent of NP contamination in different types of crops and provided crucial reference data for future research.

Characterization of radial artery perforation patterns using optical coherence tomography.

BACKGROUND: The characterization of radial artery perforation (RAP) patterns using optical coherence tomography (OCT) has not been well established. This study aimed to identify the characteristic RAP patterns in patients diagnosed through post-procedural OCT examination. METHODS: This retrospective study included 1936 consecutive patients who underwent radial artery (RA) OCT following OCT-guided transradial coronary intervention (TRI) from January 2016 to July 2022. Data regarding RAP characteristics were collected through OCT, including the perforation site as well as dimensions such as the length, width, and arc. Furthermore, RAP types were classified as small or large perforations, with a cut-off arc value of ≤90°. RESULTS: RAP, as identified by RA angiography (RAA) during TRI and on post-procedural OCT, was found in 16 out of 1936 patients (0.83 %). RA OCT imaging showed that the median distance between the RA ostium and the perforation site, the perforation length, width, and arc were 30.6 (14.4-42.2) mm, 1.55 (1.03-1.92) mm, 0.74 (0.60-1.14) mm, and 42.5 (25.0-58.1) °, respectively. Small perforations (arc ≤90°) were observed in 14 out of the 16 (87.5 %) patients with RAP. Post-procedural RAA revealed that 15 out of the 16 (93.7 %) patients with RAP had sealed perforations, with the remaining patient requiring external compression. CONCLUSIONS: Our findings demonstrated that RAP is uncommon during TRI, with clearly defined characteristic patterns on OCT. Most RAPs are small and tend to spontaneous seal through catheter tamponade.

Development and validation of a nomogram for predicting the prognosis in children with spinal cord injuries.

AIMS: This research aims to construct and verify an accurate nomogram for forecasting the 3-, 5-, and 7-year outcomes in pediatric patients afflicted with spinal cord injury (SCI). METHODS: Pediatric patients with SCI from multiple hospitals in China, diagnosed between Jan 2005 and Jan 2020, were incorporated into this research. Half of these patients were arbitrarily chosen for training sets, and the other half were designated for external validation sets. The Cox hazard model was employed to pinpoint potential prognosis determinants related to the American Spinal Injury Association (ASIA) and Functional Independence Assessment (FIM) index. These determinants were then employed to formulate the prognostic nomogram. Subsequently, the bootstrap technique was applied to validate the derived model internally. RESULTS: In total, 224 children with SCI were considered for the final evaluation, having a median monitoring duration of 68.0 months. The predictive nomogram showcased superior differentiation capabilities, yielding a refined C-index of 0.924 (95% CI: 0.883-0.965) for the training cohort and a C-index of 0.863 (95% CI: 0.735-0.933) for the external verification group. Additionally, when applying the aforementioned model to prognostic predictions as classified by the FIM, it demonstrated a high predictive value with a C-index of 0.908 (95% CI: 0.863-0.953). Moreover, the calibration diagrams indicated a consistent match between the projected and genuine ASIA outcomes across both sets. CONCLUSION: The crafted and verified prognostic nomogram emerges as a dependable instrument to foresee the 3-, 5-, and 7-year ASIA and FIM outcomes for children suffering from SCI.

Novel solid self-emulsifying drug delivery system to enhance oral bioavailability of cabazitaxel.

In this study, a novel cabazitaxel solid self-emulsifying drug delivery system (CTX S-SEDDS) was developed by solvent evaporation and liquid-solid compression technology, which overcame the limitations of the traditional SEDDS and improved the oral bioavailability. From the results of solubility, pseudo-ternary phase diagram, and single-factor analysis, Tween 80 (surfactant), Tricaprylin (oil), and Glyceryl monooleate (oil) with the ratio of 30:55:15 showed optimized particle size (140.87 nm), short emulsification and high cabazitaxel (CTX) loading capacity (50 mg·g-1). Based on the liquid-solid compression mathematical model, Syloid XDP3050 was determined as carrier material and Syloid 244FP as coating material. The prepared CTX S-SEDDS showed excellent flowability, tabletability, and reconstitution property. In vivo pharmacokinetics in rats demonstrated the absolute bioavailability of CTX S-SEDDS (17.27 %) was significantly enhanced compared with CTX solution (1.69 %), which was close to that of CTX-SEDSS (20.48 %). Lymphatic absorption was verified by in vitro imaging to be an important absorption route for self-emulsifying preparations. These results suggested that CTX S-SEDDS could enhance oral bioavailability of poorly water-soluble drug cabazitaxel while avoiding SEDDS limitations and harnessing the dual advantages of solid and liquid preparations.

A nomogram for predicting the hospital-acquired infections in children with spinal cord injuries: a retrospective, multicenter, observational study.

STUDY DESIGN: Retrospective cohort study. OBJECTIVES: Hospital-acquired infections (HAIs) pose a significant risk for pediatric patients with spinal cord injuries (SCIs), potentially leading to extended hospital stays and increased morbidity. This study aims to identify patients at higher risk for HAIs. SETTING: Hospitals from multiple areas in China. METHODS: This retrospective study included 220 pediatric SCI patients from Jan 2005 to Dec 2023, divided into a training set (n = 154) and a validation set (n = 66). We conducted a multivariate logistic regression analysis to identify risk factors associated with HAIs and constructed a predictive nomogram. The model's performance was assessed using receiver operating characteristic (ROC) curves, area under the ROC curve (AUC) and calibration plots, while decision curve analysis was utilized to determine clinical utility. RESULTS: The nomogram incorporated age, time from injury to the hospital, history of urinary and pulmonary infections, urobilinogen positivity, damaged segments, and admission American Spinal Injury Association (ASIA) scores. The model demonstrated excellent discrimination in the training set (AUC = 0.957) and good discrimination in the validation set (AUC = 0.919). Calibration plots indicated an acceptable fit between predicted probabilities and observed outcomes. Decision curve analysis confirmed the model's net benefit over clinical decision thresholds in both sets. CONCLUSION: We developed and validated a predictive nomogram for HAIs in pediatric SCI patients that shows promise for clinical application. The model can assist healthcare providers in identifying patients at higher risk for HAIs, potentially facilitating early interventions and improved patient care strategies.

Automated machine learning-based model for the prediction of pedicle screw loosening after degenerative lumbar fusion surgery.

The adequacy of screw anchorage is a critical factor in achieving successful spinal fusion. This study aimed to use machine learning algorithms to identify critical variables and predict pedicle screw loosening after degenerative lumbar fusion surgery. A total of 552 patients who underwent primary transpedicular lumbar fixation for lumbar degenerative disease were included. The LASSO method identified key features associated with pedicle screw loosening. Patient clinical characteristics, intraoperative variables, and radiographic parameters were collected and used to construct eight machine learning models, including a training set (80% of participants) and a test set (20% of participants). The XGBoost model exhibited the best performance, with an AUC of 0.884 (95% CI: 0.825-0.944) in the test set, along with the lowest Brier score. Ten crucial variables, including age, disease diagnosis: degenerative scoliosis, number of fused levels, fixation to S1, HU value, preoperative PT, preoperative PI-LL, postoperative LL, postoperative PT, and postoperative PI-LL were selected. In the prospective cohort, the XGBoost model demonstrated substantial performance with an accuracy of 83.32%. This study identified crucial variables associated with pedicle screw loosening after degenerative lumbar fusion surgery and successfully developed a machine learning model to predict pedicle screw loosening. The findings of this study may provide valuable information for clinical decision-making.

M6A methylation-regulated autophagy may be a new therapeutic target for intervertebral disc degeneration.

Degeneration of intervertebral discs is considered one of the most important causes of low back pain and disability. The intervertebral disc (IVD) is characterized by its susceptibility to various stressors that accelerate the senescence and apoptosis of nucleus pulposus cells, resulting in the loss of these cells and dysfunction of the intervertebral disc. Therefore, how to reduce the loss of nucleus pulposus cells under stress environment is the main problem in treating intervertebral disc degeneration. Autophagy is a kind of programmed cell death, which can provide energy by recycling substances in cells. It is considered to be an effective method to reduce the senescence and apoptosis of nucleus pulposus cells under stress. However, further research is needed on the mechanisms by which autophagy of nucleus pulposus cells is regulated under stress environments. M6A methylation, as the most extensive RNA modification in eukaryotic cells, participates in various cellular biological functions and is believed to be related to the regulation of autophagy under stress environments, may play a significant role in nucleus pulposus responding to stress. This article first summarizes the effects of various stressors on the death and autophagy of nucleus pulposus cells. Then, it summarizes the regulatory mechanism of m6A methylation on autophagy-related genes under stress and the role of these autophagy genes in nucleus pulposus cells. Finally, it proposes that the methylation modification of autophagy-related genes regulated by m6A may become a new treatment approach for intervertebral disc degeneration, providing new insights and ideas for the clinical treatment of intervertebral disc degeneration.

Identification of pollution source and prediction of water quality based on deep learning techniques.

Semi-arid rivers are particularly vulnerable and responsive to the impacts of industrial contamination. Prompt identification and projection of pollutant dynamics are crucial in the accidental pollution incidents, therefore required the timely informed and effective management strategies. In this study, we collected water quality monitoring data from a typical semi-arid river. By water quality inter-correlation mapping, we identified the regularity and abnormal fluctuations of pollutant discharges. Combining the association rule method (Apriori) and characterized pollutants of different industries, we tracked major industrial pollution sources in the Dahei River Basin. Meanwhile, we deployed the integrated multivariate long and short-term memory network (LSTM) to forecast principal contaminants. Our findings revealed that (1) biological oxygen demand (BOD), chemical oxygen demand (COD), total nitrogen, total phosphorus, and ammonia nitrogen exhibited high inter-correlations in water quality mapping, with lead and cadmium also demonstrating a strong association; (2) The main point sources of contaminant were coking, metal mining, and smelting industries. The government should strengthen the regulation and control of these industries and prevent further pollution of the river; (3) We confirmed 4 key pollutants: COD, ammonia nitrogen, total nitrogen, and total phosphorus. Our study accurately predicted the future changes in this water quality index. The best results were obtained when the prediction period was 1 day. The prediction accuracies reached 85.85%, 47.15%, 85.66%, and 89.07%, respectively. In essence, this research developed effective water quality traceability and predictive analysis methods in semi-arid river basins. It provided an effective tool for water quality surveillance in semi-arid river basins and imparts a scientific scaffold for the environmental stewardship endeavors of pertinent authorities.

Differences in Dissolved Organic Matter Molecular Composition along Two Plume Trajectories from the Yangtze River Estuary to the East China Sea.

Analyzing the molecular composition change of dissolved organic matter (DOM) during transportation in estuaries can enhance our understanding of the fate of DOM. However, the impact of hydrologic conditions resulting from large river plumes on the DOM cycle are less explored, and previous studies were insufficient to capture the molecular fate that occur during the transportation process. In this study, we used a range of bulk and optical techniques, as well as Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS), to determine the concentration and characteristics of DOM along two trajectories of downstream plumes of diluted water of the Yangtze (Changjiang) River estuary (YRE) during the high discharge season. These two plumes situated along the route of the summer Changjiang diluted water (CDW) have been identified and named CDW-North (CDW-N) and CDW-South (CDW-S), respectively. Despite having the same riverine end-member origin, the turbidity zone in YRE significantly modifies the molecular characteristics and composition of DOM. The results of FT-ICR MS indicated a spatial variation of DOM composition in the coastal zone of the two plumes. The relative intensities of the CHO, CHOS, and CHONS compounds are negatively correlated with salinity. In addition, the coastal zones of both CDW-N and CDW-S are characterized by more autochthonous DOM sources. More CHON compounds in CDW-N are probably due to the production of autochthonous DOM in offshore waters. The activity of phytoplankton increased the surface dissolved oxygen level of CDW-N in the coastal zone. However, the hypoxic zone formed at the bottom of the CDW-N due to microbial degradation of organic matter and may further benefit the preservation of CHON compounds. Our study emphasizes that the characteristics and composition of the estuarine DOM can be significantly shaped by distinct large river plumes. Furthermore, using FT-ICR MS in combination with complementary techniques can better assist in identifying the sources and transformation mechanisms of estuarine DOM in large river plume-affected systems and provide more valuable insights into the role of DOM in the estuarine biogeochemical cycle.

Dissolved organic matter composition and characteristics during extreme flood events in the Yangtze River Estuary.

Understanding the molecular composition and fate of dissolved organic matter (DOM) during transport in estuaries is essential for gaining a comprehensive understanding of its role within the global biogeochemical cycle. In 2020, a catastrophic flood occurred in the Yangtze River basin. It is currently unknown whether differences in hydrologic conditions due to extreme flooding will significantly impact the estuarine to oceanic DOM cycle. We determined the DOM composition in the Yangtze River estuary (YRE) to the East China Sea by using Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) during the high discharge and the flood period (monthly average discharge was 1.2 times higher) on the same trajectory. Our study found that the composition of DOM is more diverse, and more DOM molecules were introduced to the YRE during the flood, especially in the freshwater end member. The result revealed that the DOM was significantly labile and unstable during the flood period. A total of 1840 unique molecular formulas were identified during the flood period, most of which were CHON, CHONS, and CHOS compounds, most likely resulting from anthropogenic inputs from upstream. Only 194 of these molecules were detected in the seawater end member after transporting to the sea, suggesting that the YRE served as a 'filter' of DOM. However, the flood enhances the transport of a group of terrigenous DOM, that is resistant to photodegradation and biodegradation. As a result, YRE experienced ~1.6 times higher terrigenous DOC flux than high discharge period. Considering the increased frequency of future floods, our study provides a preliminary basis for further research on how floods affect the composition and characteristics of estuarine DOM. With the help of the FT-ICR MS technique, we can now better understand the dynamic of DOM composition and characteristics in large river estuaries.

Quantifying anthropogenic emission of iron in marine aerosol in the Northwest Pacific with shipborne online measurements.

Anthropogenic emissions are recognized as significant contributors to atmospheric soluble iron (Fe) in recent years, which may affect marine primary productivity, especially in Fe-limited areas. However, the contribution of different emission sources to Fe in marine aerosol has been primarily estimated by modeling approaches. Quantifying anthropogenic Fe based on field measurements remains a great challenge. In this study, online multi-element measurements and Positive Matrix Factorization (PMF) were combined for the first time to quantify sources of atmospheric Fe and soluble Fe in the Northwest Pacific during a cruise in spring 2015. Fe concentration in 624 atmospheric PM2.5 samples measured online was 74.58 ± 90.87 ng/m3. The PMF results showed anthropogenic activities, including industrial coal combustion, biomass burning, and maritime transport, were important in this region, contributing 31.4 % of atmospheric Fe on average. In addition, anthropogenic Fe concentration resolved by PMF was comparable to the simulation results of the CMAQ (Community Multiscale Air Quality) and GEOS-Chem (Goddard Earth Observing System-Chemical transport) models, with better correlation to CMAQ (r = 0.76) than GEOS-Chem (r = 0.26). This study developed a new method to estimate atmospheric soluble Fe, which integrates Fe source apportionment results and Fe solubility from different sources. Soluble Fe concentration was estimated as 3.93 ± 5.14 ng/m3, of which 87.0 % was attributed to anthropogenic emissions. Notably, ship emission alone contributed 27.5 % of soluble Fe, though its contribution to total Fe was only 2.2 %. Finally, the total deposition fluxes of atmospheric Fe (37.11 ± 38.43 µg/m2/day) and soluble Fe (1.85 ± 2.13 µg/m2/day) were estimated. This study developed a new methodology for quantifying contribution of anthropogenic emissions to Fe in marine aerosol, which could greatly help the assessment of impacts of human activities on marine environment.

Roles of Chemokines in Intervertebral Disk Degeneration.

PURPOSE OF REVIEW: Intervertebral disc degeneration is the primary etiology of low back pain and radicular pain. This review examines the roles of crucial chemokines in different stages of degenerative disc disease, along with interventions targeting chemokine function to mitigate disc degeneration. RECENT FINDINGS: The release of chemokines from degenerated discs facilitates the infiltration and activation of immune cells, thereby intensifying the inflammatory cascade response. The migration of immune cells into the venous lumen is concomitant with the emergence of microvascular tissue and nerve fibers. Furthermore, the presence of neurogenic factors secreted by disc cells and immune cells stimulates the activation of pain-related cation channels in the dorsal root ganglion, potentially exacerbating discogenic and neurogenic pain and intensifying the degenerative cascade response mediated by chemokines. Gaining a deeper comprehension of the functions of chemokines and immune cells in these processes involving catabolism, angiogenesis, and injury detection could offer novel therapeutic avenues for managing symptomatic disc disease.

Evolution of dissolved organic nitrogen chemistry during transportation to the marginal sea: Insights from nitrogen isotope and molecular composition analyses.

Estuaries are hotspots where terrestrially originated dissolved organic matter (DOM) is modified in molecular composition before entering marine environments. However, very few research has considered nitrogen (N) modifications of DOM molecules in estuaries, limiting our understanding of dissolved organic nitrogen (DON) cycling and the associated carbon cycling in estuaries. This study integrated optical, stable isotopes (δ15N and δ13C) and molecular composition (FT-ICR MS) to characterize the transformation of DOM in the Yangtze River Estuary. Both concentration of dissolved organic carbon (DOC) and DON decreased with increasing salinity, while their δ13C and δ15N increased with the increasing salinity. A significant positive correlation was found between δ15N and δ13C during the transportation of DOM to marginal seas, indicating that the behavior of both DOC and DON are primarily controlled by the mixing of freshwater and the seawater in the YRE. During the mixing process, the DON addition was observed using the conservative mixing curves. In the view of molecular composition, DOM molecules became more aromatic as the number of N atoms increased. Spearman correlations reveal that DOM molecules with fewer N atoms exhibited a higher enrichment in protein-like components, while those with more N atoms were more enriched in humic-like components. In addition, the δ15N and δ13C tended to increase as the N content of DOM decreased. Therefore, DON molecules with fewer N atoms were likely to be transformed into those with more N atoms based on the isotopic fractionation theory. This study establishes a linkage between the molecular composition and the δ15N of DOM, and discovers the N transformation pattern within DOM molecules during the transportation to marginal seas.

Study on remote sensing inversion and temporal-spatial variation of Hulun lake water quality based on machine learning.

Hulun Lake is facing significant water quality degradation, necessitating effective monitoring for safety. Traditional methods lack the necessary spatial and temporal coverage, underscoring the need for a remote sensing model. In this study, we utilized the Landsat 8 OLI dataset, incorporating cross-section monitoring and field sampling data comprehensively. Employing the random forest algorithm, we constructed a remote sensing inversion model for six water quality parameters in Hulun Lake: chlorophyll-a (Chl-a), total nitrogen (TN), total phosphorus (TP), ammonia nitrogen (NH3-N), chemical oxygen demand (COD), and dissolved oxygen (DO). The model was applied to the non-freezing period of Hulun Lake from 2016 to 2021, exhibiting commendable performance and generating high-resolution maps. Time series analysis revealed that during the study period, the pollution levels of TN, TP, and COD in Hulun Lake were extremely serious, exceeding the Class V water standard of China's surface water environmental quality standard. Regional analysis indicated lower pollutant concentrations in the central lake area compared to the lake inlet. The inflowing rivers with high pollution adversely impacted Hulun Lake's water quality. To ensure the continued health of Hulun Lake's water quality, it is imperative to monitor lake water quality attentively and implement necessary measures to prevent further deterioration. This study holds crucial importance for shaping and executing ecological protection and restoration strategies for Hulun Lake.

MMPD: Multi-Domain Mobile Video Physiology Dataset.

Remote photoplethysmography (rPPG) is an attractive method for noninvasive, convenient and concomitant measurement of physiological vital signals. Public benchmark datasets have served a valuable role in the development of this technology and improvements in accuracy over recent years. However, there remain gaps in the public datasets. First, despite the ubiquity of cameras on mobile devices, there are few datasets recorded specifically with mobile phone cameras. Second, most datasets are relatively small and therefore are limited in diversity, both in appearance (e.g., skin tone), behaviors (e.g., motion) and environment (e.g., lighting conditions). In an effort to help the field advance, we present the Multi-domain Mobile Video Physiology Dataset (MMPD), comprising 11 hours of recordings from mobile phones of 33 subjects. The dataset is designed to capture videos with greater representation across skin tone, body motion, and lighting conditions. MMPD is comprehensive with eight descriptive labels and can be used in conjunction with the rPPG-toolbox [1]. The reliability of the dataset is verified by mainstream unsupervised methods and neural methods. The GitHub repository of our dataset: https://github.com/THU-CS-PI/MMPD_rPPG_dataset.

Bioinformatics Analysis and Identification of Ferroptosis-Related Hub Genes in Intervertebral Disc Degeneration.

Approximately 80% of individuals encounter lower back pain (LBP), a prevalent clinical issue largely attributed to intervertebral disc degeneration (IDD). Ferroptosis is an iron-dependent lipid peroxidation-driven cell death, and there is growing evidence that ferroptosis plays an important role in various human diseases. However, the underlying mechanism of ferroptosis in IDD remains unclear. This study aims to reveal the potential hub genes and related pathways of ferroptosis in the pathogenesis and progression of IDD. In this study, we analyzed three microarray datasets from the GEO database. Additionally, we downloaded ferroptosis-related genes from FerrDb-V2 and extracted apoptosis-related genes from UniProt as a control to show the specificity of ferroptosis. Weighted gene co-expression network analysis (WGCNA) was performed to identify the IDD-related module genes. Then, ferroptosis-related genes and apoptosis-related genes were separately overlapped with the IDD-related module genes, resulting in the identification of 35 ferroptosis-related module genes (FRMG) and 142 apoptosis-related module genes (ARMG). Furthermore, we performed functional enrichment analysis and protein-protein interaction network, and Cytoscape along with CytoHubba was used to identify the hub genes. Finally, logistic regression models were constructed and identified two hub FRMGs (PTEN and EGFR) and one hub ARMG (CTNNB1), which could distinguish IDD patients from controls (P < 0.05). The areas under the ROC curves were 0.792 and 0.730, respectively, suggesting that ferroptosis is more specific than apoptosis in IDD. In conclusion, this study provided fresh perspectives on ferroptosis in the pathogenesis and progression of IDD that can be used to evaluate potential biomarker genes and therapeutic targets.

Enhancement of Chondrogenic Markers by Exosomes Derived from Cultured Human Synovial Fluid-Derived Cells: A Comparative Analysis of 2D and 3D Conditions.

OBJECTIVE: The goal of this pilot study was to investigate the effects of exosomes derived from synovial fluid-derived cells (SFDCs) cultured under normoxic conditions in a two-dimensional (2D) monolayer or encapsulated within a three-dimensional (3D) matrix for chondrogenic differentiation in vitro and cartilage defect repair in vivo. DESIGN: Synovial fluid samples were obtained from three patients, and SFDCs were isolated and expanded either in a 2D monolayer culture or seeded within a transglutaminase cross-linked gelatin (Col-Tgel) to create a 3D gel culture. Exosomes derived from each environment were isolated and characterized. Then, their effects on cartilage-cell proliferation and chondrogenic differentiation were assessed using an in vitro organoid model, and their potential for enhancing cartilage repair was evaluated using a rat cartilage defect model. RESULTS: SFDCs obtained from different donors reached a state of senescence after four passages in 2D culture. However, transferring these cells to a 3D culture environment mitigated the senescence and improved cell viability. The 3D-cultured exosomes exhibited enhanced potency in promoting chondrogenic differentiation, as evidenced by the increased expression of chondrogenic genes and greater deposition of cartilage-specific extracellular matrix. Furthermore, the 3D-cultured exosomes demonstrated superior effectiveness in enhancing cartilage repair and exhibited better healing properties compared to exosomes derived from a 2D culture. CONCLUSIONS: The optimized 3D culture provided a more favorable environment for the proliferation of human synovial cells and the secretion of exosomes compared to the 2D culture. The 3D-cultured exosomes exhibited greater potential for promoting chondrogenic gene expression in vitro and demonstrated improved healing properties in repairing cartilage defects compared to exosomes derived from the 2D culture.

[Accumulation Characteristics, Sources, and Health Risks of Soil Lead of Urban Parks in Beijing].

Urban parks have multiple functions such as social culture, economy, and environmental services during urban development. The rapid development of cities and economy may lead to the accumulation of heavy metals in the soil of urban parks, which may threaten human health. A total of 140 soil samples were collected in 32 typical parks in Beijing. The accumulation characteristics of Pb in the soil of urban parks were analyzed using the single-factor pollution and geo-accumulation indices. The sources of Pb pollution in soils were quantitatively analyzed using the stable isotope of Pb, and the health risk was assessed using the probabilistic risk assessment method based on Monte Carlo simulation. The results showed that the geometric mean of Pb in soils of urban parks in Beijing was 38.63 mg·kg-1, which was 1.48 times the background value. However, it did not exceed the risk screening value(GB 36600-2018). The accumulation of soil Pb in urban parks increased with the increase in the proximity between the park and the central urban area and the increase in the establishment time. The soil Pb pollution index of 2 ring, 2-4 ring, and 4-6 ring parks were 0.16, 0.10, and 0.09, which did not reach the pollution level, and the geo-accumulation indices were 0.80, 0.07, and -0.31, respectively. Except for the no-moderate pollution level in ring 2 and ring 2 to ring 4, the other rings did not reach the pollution level. The sources of Pb pollution in urban parks were coal combustion, road dust, and paint, with the contributions of 45.4%, 19.6%, and 13.9%, respectively. The 95% quantiles of hazard index(HI) of soil Pb in the park for different age groups were 1.11E-01, 8.57E-02, 6.39E-02, 1.64E-02, 1.36E-02, 1.26E-02, 1.64E-02, and 1.78E-02, respectively, which indicated that there was no potential non-carcinogenic risk(HI<1). Exposure duration was the most sensitive to non-carcinogenic risks in people aged 0-18 years, and soil Pb concentration was the most sensitive to non-carcinogenic risks in people aged 18-80 years. The increase in body weight often reduced the non-carcinogenic risks. These results can provide theoretical basis for soil environmental risk control in urban parks.

Construction and evaluation of a nomogram prediction model for aspiration pneumonia in patients with acute ischemic stroke.

Background: Aspiration Pneumonia (AP) is a leading cause of death in patients with Acute Ischemic Stroke (AIS). Early detection, diagnosis and effective prevention measures are crucial for improving patient prognosis. However, there is a lack of research predicting AP occurrence after AIS. This study aimed to identify risk factors and develop a nomogram model to determine the probability of developing AP after AIS. Method: A total of 3258 AIS patients admitted to Jinshan Hospital of Fudan University between January 1, 2016, and August 20, 2022, were included. Among them, 307 patients were diagnosed with AP (AP group), while 2951 patients formed the control group (NAP group). Univariate and multivariate logistic regression analyses were conducted to identify relevant risk factors for AP after AIS. These factors were used to establish a scoring system and develop a nomogram model using R software. Results: Univariate analysis revealed 20 factors significantly associated (P < 0.05) with the development of AP after AIS. These factors underwent multivariate logistic regression analysis, which identified age (elderly), National Institute of Health Stroke Scale (NIHSS) score, dysphagia, atrial fibrillation, cardiac insufficiency, renal insufficiency, hepatic insufficiency, elevated Fasting Blood Glucose (FBG), elevated C-Reactive Protein (CRP), elevated Neutrophil percentage (NEUT%), and decreased prealbumin as independent risk factors. A nomogram model incorporating these 11 risk factors was constructed, with a C-index of 0.872 (95 % CI: 0.845-0.899), indicating high accuracy. Calibration and clinical decision analyses demonstrated the model's reliability and clinical value. Conclusion: A nomogram model incorporating age, NIHSS score, dysphagia, atrial fibrillation, cardiac insufficiency, renal insufficiency, hepatic insufficiency, FBG, CRP, NEUT%, and prealbumin effectively predicts AP risk in AIS patients. This model provides guidance for early intervention strategies, enabling the identification of high-risk individuals for timely preventive measures.