Phosphorus fertilization enhanced overwintering, root system and forage yield of late-seeded alfalfa in sodic soils.

Sowing date and soil fertility are very important factors in the overwintering and production performance of alfalfa (Medicago sativa L.), yet there's a knowledge gap in knowledge on how late-seeded alfalfa responds to phosphorus (P) fertilization. A field study was conducted in Inner Mongolia from 2020 to 2022 using a split-plot design. The main plots consisted of five sowing dates (31 July, 8, 16, and 24 August, and 1 September), while the subplots involved five P application rates (0, 40, 70, 100, and 130 kg P2O5 ha-1). Throughout the growing seasons, the overwintering rate, root traits, forage yield, and yield components were measured. The results revealed a consistent decrease in overwintering ability and productivity with the delayed sowing. This reduction in overwintering rate was mainly due to diminished root traits, while the decrease in forage yield was largely associated with a reduction in plants per square meter. However, P fertilizer application to late-seeded alfalfa demonstrated potential in enhancing the diameter of both the crown and taproot, thus strengthening the root system and improving the overwintering rate, the rate of increase ranges from 11.6 to 49%. This adjustment could also improve the shoots per square meter and mass per shoot, increasing by 9.4-31.3% and 15.0-27.1% respectively in 2 years, which can offset the decline in forage yield caused by late sowing and might even increase the forage yield. Regression and path analysis indicated that alfalfa forage yield is primarily affected by mass per shoot rather than shoots per square meter. This study recommended that the sowing of alfalfa in similar regions of Inner Mongolia should not be later than mid-August. Moreover, applying P fertilizer (P2O5) at 70.6-85.9 kg ha-1 can enhance the forage yield and persistence of late-seeded alfalfa. Therefore, appropriate late sowing combined with the application of P fertilizer can be used as an efficient cultivation strategy for alfalfa cultivation after a short-season crop harvest in arid and cold regions.

Unraveling the impact of Spartina alterniflora invasion on greenhouse gas production and emissions in coastal saltmarshes: New insights from dissolved organic matter characteristics and surface-porewater interactions.

Saltmarshes along the Chinese coast are threatened by the invasion of Spartina alterniflora (S. alterniflora). This study was carried out in the Andong Shoal, Hangzhou Bay, China, with the aim of comprehending the intricate impacts of S. alterniflora invasion on greenhouse gases (GHG) production and emissions. To address this issue, we thoroughly examined the chemistry of dissolved organic matter (DOM) and the rate of surface water-porewater interaction. Porewater and surface water samples were collected from farm land, S. alterniflora invaded areas, and Scirpus mariqueter (S. mariqueter) dominated areas. The findings indicated that the invasion of S. alterniflora impeded the interaction between surface water and porewater, resulting in reduced porewater exchange rates within its affected region (0.015-0.440 cm d-1), in contrast to areas dominated by S. mariqueter (9.635-18.232 cm d-1). The invasion also increased dissolved organic carbon concentration in porewater and created a stable and closed soil environment that resulted in DOM with smaller molecule sizes and higher humification levels. The presence of high tryptophan-like fluorescent DOM caused an increase in the production of methane and carbon dioxide in S. alterniflora invaded area. However, both limited surface-porewater exchange and significant differences in GHG concentrations between porewater and surface water suggested that the aerenchyma tissues of S. alterniflora may play an important role in transporting GHG from soil to the atmosphere.

Machine learning-based antibiotic resistance prediction models: An updated systematic review and meta-analysis.

BACKGROUND: The widespread use of antibiotics has led to a gradual adaptation of bacteria to these drugs, diminishing the effectiveness of treatments. OBJECTIVE: To comprehensively assess the research progress of antibiotic resistance prediction models based on machine learning (ML) algorithms, providing the latest quantitative analysis and methodological evaluation. METHODS: Relevant literature was systematically retrieved from databases, including PubMed, Embase and the Cochrane Library, from inception up to December 2023. Studies meeting predefined criteria were selected for inclusion. The prediction model risk of bias assessment tool was employed for methodological quality assessment, and a random-effects model was utilised for meta-analysis. RESULTS: The systematic review included a total of 22 studies with a combined sample size of 43,628; 10 studies were ultimately included in the meta-analysis. Commonly used ML algorithms included random forest, decision trees and neural networks. Frequently utilised predictive variables encompassed demographics, drug use history and underlying diseases. The overall sensitivity was 0.57 (95% CI: 0.42-0.70; p< 0.001; I2= 99.7%), the specificity was 0.95 (95% CI: 0.79-0.99; p< 0.001; I2 = 99.9%), the positive likelihood ratio was 10.7 (95% CI: 2.9-39.5), the negative likelihood ratio was 0.46 (95% CI: 0.34-0.61), the diagnostic odds ratio was 23 (95% CI: 7-81) and the area under the receiver operating characteristic curve was 0.78 (95% CI: 0.74-0.81; p< 0.001), indicating a good discriminative ability of ML models for antibiotic resistance. However, methodological assessment and funnel plots suggested a high risk of bias and publication bias in the included studies. CONCLUSION: This meta-analysis provides a current and comprehensive evaluation of ML models for predicting antibiotic resistance, emphasising their potential application in clinical practice. Nevertheless, stringent research design and reporting are warranted to enhance the quality and credibility of future studies. Future research should focus on methodological innovation and incorporate more high-quality studies to further advance this field.

Polyphenols as Potential Antioxidants for the Treatment of Intervertebral Disc Degeneration.

Intervertebral disc degeneration (IDD) is a common musculoskeletal system disease, which is one of the most important causes of low back pain. Despite the high prevalence of IDD, current treatments are limited to relieving symptoms, and there are no effective therapeutic agents that can block or reverse the progression of IDD. Oxidative stress, the result of an imbalance between the production of reactive oxygen species (ROS) and clearance by the antioxidant defense system, plays an important role in the progression of IDD. Polyphenols are antioxidant compounds that can inhibit ROS production, which can scavenge free radicals, reduce hydrogen peroxide production, and inhibit lipid oxidation in nucleus pulposus (NP) cells and IDD animal models. In this review, we discussed the antioxidant effects of polyphenols and their regulatory role in different molecular pathways associated with the pathogenesis of IDD, as well as the limitations and future prospects of polyphenols as a potential treatment of IDD.

Natural Language Processing-Driven Artificial Intelligence Models for the Diagnosis of Lumbar Disc Herniation with L5 and S1 Radiculopathy: A Preliminary Evaluation.

OBJECTIVE: To develop and validate natural language processing-driven artificial intelligence (AI) models for the diagnosis of lumbar disc herniation (LDH) with L5 and S1 radiculopathy using electronic health records (EHRs). METHODS: EHRs of patients undergoing single-level percutaneous endoscopic lumbar discectomy for the treatment of LDH at the L4/5 or L5/S1 level between June 1, 2013, and December 31, 2021, were collected. The primary outcome was LDH with L5 and S1 radiculopathy, which was defined as nerve root compression recorded in the operative notes. Datasets were created using the history of present illness text and positive symptom text with radiculopathy (L5 or S1), respectively. The datasets were randomly split into a training set and a testing set in a 7:3 ratio. Two machine learning models, the long short-term memory network and Extreme Gradient Boosting, were developed using the training set. Performance evaluation of the models on the testing set was done using measures such as the receiver operating characteristic curve, area under the curve, accuracy, recall, F1-score, and precision. RESULTS: The study included a total of 1681 patients, with 590 patients having L5 radiculopathy and 1091 patients having S1 radiculopathy. Among the 4 models developed, the long short-term memory model based on positive symptom text showed the best discrimination in the testing set, with precision (0.9054), recall (0.9405), accuracy (0.8950), F1-score (0.9226), and area under the curve (0.9485). CONCLUSIONS: This study provides preliminary validation of the concept that natural language processing-driven AI models can be used for the diagnosis of lumbar disease using EHRs. This study could pave the way for future research that may develop more comprehensive and clinically impactful AI-driven diagnostic systems.

The Effects of Assisted Freezing with Different Ultrasound Power Rates on the Quality and Flavor of Braised Beef.

This study investigated the effects of ultrasound-assisted immersion freezing (UIF) at different power rates (0, 200, 400, and 600 W) on the changes in beef quality and flavor after braising. The results demonstrated that UIF treatment at 400 W significantly reduced the juice loss (cooking loss decreased from 49.04% to 39.74%) and fat oxidation (TBARS value decreased from 0.32 mg/kg to 0.20 mg/kg) of braised beef. In addition, the tenderness (hardness value decreased from 5601.50 g to 2849.46 g) and color stability of braised beef were improved after UIF treatment. The flavor characteristics of braised beef were characterized using an electronic nose and an electronic tongue. The PCA analysis data showed that the cumulative contribution rates of the first and second principal components were 85% and 93.2%, respectively, with the first principal component accounting for a higher proportion. The UIF-400 W group had the highest concentration for the first principal component, and the differentiation was not significant compared to the control group. The total amino acid values of different power UIF treatment groups were improved compared to the AF treatment group, indicating that UIF can effectively reduce the losses caused by freezing. The results demonstrate that ultrasound-assisted freezing treatment is beneficial in enhancing the tenderness and flavor attributes of beef after braising, providing new insights into the processing of meat products with desirable quality characteristics.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.