Potential Roles of Large Language Models in the Production of Systematic Reviews and Meta-Analyses.

Large language models (LLMs) such as ChatGPT have become widely applied in the field of medical research. In the process of conducting systematic reviews, similar tools can be used to expedite various steps, including defining clinical questions, performing the literature search, document screening, information extraction, and language refinement, thereby conserving resources and enhancing efficiency. However, when using LLMs, attention should be paid to transparent reporting, distinguishing between genuine and false content, and avoiding academic misconduct. In this viewpoint, we highlight the potential roles of LLMs in the creation of systematic reviews and meta-analyses, elucidating their advantages, limitations, and future research directions, aiming to provide insights and guidance for authors planning systematic reviews and meta-analyses.

Intermittent aeration mitigating carbon emission from landfills with gas-water joint regulation.

Landfill is a significant source of atmospheric CH4 and CO2 emissions. In this study, four landfill reactor systems were constructed to investigate the effects of different ventilation methods, including continuous aeration (20 h d-1) and intermittent aeration (continuous aeration for 4 h d-1 and 2 h of aeration every 12 h, twice a day), on properties of landfilled waste and emissions of CH4 and CO2, in comparison to a traditional landfill. Compared with continuous aeration, intermittent aeration could reduce the potential global warming effect of the CH4 and CO2 emissions, especially multiple intermittent aeration. The CH4 and CO2 emissions could be predicted by the multiple linear regression model based on the contents of carbon, sulfur and/or pH during landfill stabilization. Both intermittent and continuous aeration could enhance the methane oxidation activity of landfilled waste. The aerobic methane oxidation activity of landfilled waste reached the maximums of 50.77-73.78 µg g-1 h-1 after aeration for 5 or 15 d, which was higher than the anaerobic methane oxidation activity (0.45-1.27 µg g-1 h-1). CO2 was the predominant form of organic carbon loss in the bioreactor landfills. Candidatus Methylomirabilis, Methylobacter, Methylomonas and Crenothrix were the main methane-oxidating microorganisms (MOM) in the landfills. Total, NO2--N, pH and Fe3+ were the main environmental variables influencing the MOM community, among which NO2--N and pH had the significant impact on the MOM community. Partial least squares path modelling indicated that aeration modes mainly influenced the emissions of CH4 and CO2 by affecting the degradation of landfilled waste, environmental variables and microbial activities. The results would be helpful for designing aeration systems to reduce the emissions of CH4 and CO2, and the cost during landfill stabilization.

A Novel Regulation Strategy of Solid Electrolyte Interphase Based on Anion-Solvent Coordination for Magnesium Metal Anode.

Magnesium (Mg) metal anode is a highly desirable candidate among various high energy density metal anodes, possessing higher volumetric capacity and better safety characteristic compared to lithium metal. However, most Mg salts in conventional Mg electrolytes easily react with Mg metal to form blocking layers, leading to inferior reversibility of Mg plating/stripping. Here, a stable Mg2+ -conducting solid electrolyte interphase (SEI) is successfully constructed on Mg metal anode by regulating the molecular-orbital-energy-level toward an aluminum(III)-centered anion Mg salt through anion-solvent coordination. Of which, the LUMO energy level of perfluorinated pinacolatoaluminate (Al(O2 C2 (CF3 )4 )2 - , abbreviated as FPA) anion has been adjusted by coordinating with solvent molecule (tetrahydrofuran) for facilitating the formation of advantageous SEI. The existence of SEI formed by FPA anion greatly improves the reversibility and long-term stability of Mg plating/stripping process. More importantly, based on this aluminum(III)-centered Mg electrolyte, the Mo6 S8 /Mg batteries can achieve a fantastic cycle performance of 9000 cycles, proving the beneficial effect of SEI on the cycling stability of Mg battery system. These findings open up a promising avenue to construct stable and compatible SEI on Mg metal anode, and lay significant foundations for the successful development of rechargeable Mg metal batteries.

The Internet Hospital Plus Drug Delivery Platform for Health Management During the COVID-19 Pandemic: Observational Study.

BACKGROUND: Widespread access to the internet has boosted the emergence of online hospitals. A new outpatient service called "internet hospital plus drug delivery" (IHDD) has been developed in China, but little is known about this platform. OBJECTIVE: The aim of this study is to investigate the characteristics, acceptance, and initial impact of IHDD during the outbreak of COVID-19 in a tertiary hospital in South China. METHODS: The total number of and detailed information on online prescriptions during the first 2 months after work resumption were obtained. Patients' gender, age, residence, associated prescription department, time of prescription, payment, and drug delivery region were included in the analysis. RESULTS: A total of 1380 prescriptions were picked up or delivered between March 2 and April 20, 2020. The largest group of patients were 36-59 years old (n=680, 49.3%), followed by the 18-35 years age category (n=573, 41.5%). In total, 39.4% (n=544) of the patients chose to get their medicine by self-pickup, while 60.6% (n=836) preferred to receive their medicine via drug delivery service. The top five online prescription departments were infectious diseases (n=572, 41.4%), nephrology (n=264, 19.1%), endocrinology (n=145, 10.5%), angiocardiopathy (n=107, 7.8%), and neurology (n=42, 3%). Of the 836 delivered prescriptions, 440 (52.6%) were sent to Guangdong Province (including 363 [43.4%] to Shenzhen), and 396 (47.4%) were sent to other provinces in China. CONCLUSIONS: The IHDD platform is efficient and convenient for various types of patients during the COVID-19 crisis. Although offline visits are essential for patients with severe conditions, IHDD can help to relieve pressure on hospitals by reducing an influx of patients with mild symptoms. Further efforts need to be made to improve the quality and acceptance of IHDD, as well as to regulate and standardize the management of this novel service.

Bacterial transport mediated by micro-nanobubbles in porous media.

Determining the role of micro-nanobubbles (MNBs) in controlling the risk posed by pathogens to soil and groundwater during reclaimed water irrigation requires clarification of the mechanism of how MNBs block pathogenic bacteria. In this study, real-time bioluminescence imaging was used to investigate the effects of MNBs on the transport and spatiotemporal distribution of bioluminescent Escherichia coli 652T7 strain in porous media. The presence of MNBs significantly increased the retention of bacteria in the porous media, decreasing the maximum relative effluent concentration (C/C0) by 78 % from 0.97 (without MNBs) to 0.21 (with MNBs). The results suggested that MNBs provided additional sites at the air-water interface (AWI) for bacterial attachment and acted as physical obstacles to reduce bacterial passage. These effects varied with environmental conditions such as solution ionic strength and pore water velocity. The results indicated that MNBs enhanced electrostatic attachment of bacteria at the AWI and their mechanical straining in pores. This study suggests that adding MNBs in pathogen-containing water is an effective measure for increasing filtration efficiency and reducing the risk of pathogenic contamination during agricultural irrigation.

Prediction of atrazine degradation in soil based on XGBoost model.

We established the optimal model by using the automatic machine learning method to predict the degradation efficiency of herbicide atrazine in soil, which could be used to assess the residual risk of atrazine in soil. We collected 494 pairs of data from 49 published articles, and selected seven factors as input features, including soil pH, organic matter content, saturated hydraulic conductivity, soil moisture, initial concentration of atrazine, incubation time, and inoculation dose. Using the first-order reaction rate constant of atrazine in soil as the output feature, we established six models to predict the degradation efficiency of atrazine in soil, and conducted comprehensive analysis of model performance through linear regression and related evaluation indicators. The results showed that the XGBoost model had the best performance in predicting the first-order reaction rate constant (k). Based on the prediction model, the feature importance ranking of each factor was in an order of soil moisture > incubation time > pH > organic matter > initial concentration of atrazine > saturated hydraulic conductivity > inoculation dose. We used SHAP to explain the potential relationship between each feature and the degradation ability of atrazine in soil, as well as the relative contribution of each feature. Results of SHAP showed that time had a negative contribution and saturated hydraulic conductivity had a positive contribution. High values of soil moisture, initial concentration of atrazine, pH, inoculation dose and organic matter content were generally distributed on both sides of SHAP=0, indicating their complex contributions to the degradation of atrazine in soil. The XGBoost model method combined with the SHAP method had high accuracy in predicting the performance and interpretability of the k model. By using machine learning method to fully explore the value of historical experimental data and predict the degradation efficiency of atrazine using environmental parameters, it is of great significance to set the threshold for atrazine application, reduce the residual and diffusion risks of atrazine in soil, and ensure the safety of soil environment.

Annual atrazine residue estimation in Chinese agricultural soils by integrated modeling of machine learning and mechanism-based models.

This study presents a comprehensive approach to estimating annual atrazine residues in China's agricultural soils, integrating machine learning algorithms and mechanism-based models. First, machine learning was used to predict essential parameters influencing atrazine's adsorption, degradation, and dispersivity of solute transport. The results demonstrated that soil organic matter was the most important input variable for predicting adsorption and degradation; clay content was the primary variable for predicting dispersivity. The SHapley Additive exPlanations (SHAP) contribution of various soil properties on target variables were also analyzed to reveal whether each input variable has a positive, negative, or complex effect. Subsequently, these parameters inform the construction of a detailed model across 23,692 subregions of China, with a 20 km × 20 km resolution. The model considered regional variations and soil layer heterogeneity, including rainfall, soil depth-specific properties, and parameters for adsorption, degradation, and dispersivity. Utilizing the convection-dispersion equations and the Phydrus, the model simulated atrazine's transport and degradation patterns across diverse soil environments after applying 250 mL of atrazine (40%) per Chinese mu. The outcomes provided a spatially explicit distribution of atrazine residues, specifying that the arid areas have the highest residual risk, followed by the Northeast, Southwest, and Southeast. Atrazine levels may exceed national drinking water standards at 50 cm depth in Inner Mongolia, the Qinghai-Tibet Plateau, and the Jungar Basin. This study's integrative approach may also offer valuable insights and tools for evaluating residues of various pesticides and herbicides in agricultural soils.

Assessing the risk of E. coli contamination from manure application in Chinese farmland by integrating machine learning and Phydrus.

This study aims to present a comprehensive study on the risks associated with the residual presence and transport of Escherichia coli (E. coli) in soil following the application of livestock manure in Chinese farmlands by integrating machine learning algorithms with mechanism-based models (Phydrus). We initially review 28 published papers to gather data on E. coli's die-off and attachment characteristics in soil. Machine learning models, including deep learning and gradient boosting machine, are employed to predict key parameters such as the die-off rate of E. coli and first-order attachment coefficient in soil. Then, Phydrus was used to simulate E. coli transport and survival in 23692 subregions in China. The model considered regional differences in E. coli residual risk and transport, influenced by soil properties, soil depths, precipitation, seasonal variations, and regional disparities. The findings indicate higher residual risks in regions such as the Northeast China, Eastern Qinghai-Tibet Plateau, and pronounced transport risks in the fringe of the Sichuan Basin fringe, the Loess Plateau, the North China Plain, the Northeast Plain, the Shigatse Basin, and the Shangri-La region. The study also demonstrates a significant reduction in both residual and transport risks one month after manure application, highlighting the importance of timing manure application and implementing region-specific standards. This research contributes to the broader understanding of pathogen behavior in agricultural soils and offers practical guidelines for managing the risks associated with manure use. This study's comprehensive method offers a potentially valuable tool for evaluating microbial contaminants in agricultural soils across the globe.

Phylogenetic indices and temporal and spatial scales shape the neighborhood effect on seedling survival in a mid-mountain moist evergreen broad-leaved forest, Gaoligong Mountains, Southwestern China.

Density dependence and habitat filtering have been proposed to aid in understanding community assembly and species coexistence. Phylogenetic relatedness between neighbors was used as a proxy for assessing the degree of ecological similarity among species. There are different conclusions regarding the neighborhood effect in previous studies with different phylogenetic indices or at different spatiotemporal scales. However, the effects of density dependence, neighbor phylogenetic relatedness, and habitat filtering on seedling survival with different phylogenetic indices or at different temporal and spatial scales are poorly understood. We monitored 916 seedlings representing 56 woody plant species within a 4-ha forest dynamics plot for 4 years (from 2020 to 2023) in a subtropical mid-mountain moist evergreen broad-leaved forest in the Gaoligong Mountains, Southwestern China. Using generalized linear mixed models, we tested whether and how four phylogenetic indices: total phylogenetic distance (TOTPd), average phylogenetic distance (AVEPd), relative average phylogenetic distance (APd'), and relative nearest taxon phylogenetic distance (NTPd'), three temporals (1, 2, and 3 years), and spatial scales (1, 2, and 4 ha) affect the effect of density dependence, phylogenetic density dependence, and habitat filtering on seedling survival. We found evidence of the effect of phylogenetic density dependence in the 4-ha forest dynamics plot. The effects of density dependence, phylogenetic density dependence, and habitat filtering on seedling survival were influenced by phylogenetic indices and temporal and spatial scales. The effects of phylogenetic density dependence and habitat filtering on seedling survival were more conspicuous only at 1-year intervals, compared with those at 2- and 3-year intervals. We did not detect any effects of neighborhood or habitat factors on seedling survival at small scales (1 and 2 ha), although these effects were more evident at the largest spatial scale (4 ha). These findings highlight that the effects of local neighborhoods and habitats on seedling survival are affected by phylogenetic indices as well as temporal and spatial scales. Our study suggested that phylogenetic index APd', shortest time scale (1 year), and largest spatial scales (4 ha) were suitable for neighborhood studies in a mid-mountain moist evergreen broad-leaved forest in Gaoligong Mountains. Phylogenetic indices and spatiotemporal scales have important impacts on the results of the neighborhood studies.

Phylogenetic relatedness and habitat affect seedling abundance of a mid-montane humid evergreen broad-leaved forest in the Gaoligong Mountains, Southwestern China.

The neighborhood effect has become an important framework with which to study the mechanisms that maintain the coexistence of tree species. Phylogenetic relatedness among neighboring plants directly affects species coexistence and the maintenance of tree diversity. And some studies have reported that seedling performance is negatively correlated with phylogenetic relatedness, which termed phylogenetic negative density dependence. Soil-borne fungal pathogens affected seedling performance of phylogenetically related host species, i.e., phylogenetic Janzen-Connell effect. Seedlings may be particularly vulnerable to habitat and neighbor characteristics. Although previous studies have demonstrated the influence of neighborhood effects, phylogenetic relatedness, and habitat filtering on seedling survival, growth, and mortality, the effect of variation in these factors on seedling abundance remains unclear. To address this question, we used a 4-ha (200 m × 200 m) and monitored four-year (2020-2023) seedling dataset from a mid-montane humid evergreen broad-leaved subtropical forest in the Gaoligong Mountains, Yunnan, Southwestern China, and which consisted of 916 seedlings belonging to 56 species. The results of generalized linear mixed models showed no significant effect of conspecific adult neighbors on seedling abundance at any of the intervals evaluated. In contrast, we found evidence of phylogenetic distance density dependence in the forests of the Gaoligong Mountains. Specifically, there was a significant positive effect of the relative average phylogenetic distance between heterospecific adult neighbors and focal seedlings on focal seedling abundance in 2020; however, the relative average phylogenetic distance between heterospecific seedling neighbors and focal seedlings had a significant negative effect on seedling abundance over the four-year period (2020-2023). Among the habitat factors, only light (canopy opening) had a negative effect on seedling abundance in all four years. Light resources may be a limiting factor for seedlings, and determine seedling dynamics in subtropical forests. Overall, our results demonstrated that phylogenetic density dependence and habitat filtering affected subtropical seedling abundance. Our findings provide new evidence of the impact of phylogenetic density dependence on seedling abundance in a subtropical mid-montane humid evergreen broad-leaved forest and highlight the need to incorporate the neighborhood effect, phylogenetic relatedness, and habitat factors in models assessing seedling abundance.

Association of synuclein alpha (SNCA) gene polymorphisms with spontaneous brain activity in patients with Parkinson's disease.

Background: The synuclein alpha (SNCA) gene responsible for encoding alpha-synuclein, is believed to play a crucial role in the pathogenesis of Parkinson's disease (PD). However, the specific impact of SNCA gene single-nucleotide polymorphisms (SNPs) on brain function in PD remains unclear. Therefore, this cross-sectional retrospective study, particularly through use of imaging analysis, aimed to characterize the relationship between SNCA gene SNPs and spontaneous brain activity in PD in order to enhance our understanding of the mechanisms underlying PD pathogenesis. Methods: A total of 63 patients with PD and 73 sex- and age-matched healthy control (HC) participants were recruited from outpatient and inpatient clinics at Fujian Medical University Union Hospital from August 2017 to November 2019, and all underwent a resting-state functional magnetic resonance imaging (rs-fMRI) scanning. All participants were also examined to determine the correlation of different genotypes with regional brain activity measured by rs-fMRI using amplitude of low-frequency fluctuation (ALFF) analysis. Multivariate regression analysis was used to calculate the correlation between the brain function data and clinical features. All rs-fMRI data were analyzed with the SPM12 software and adjusted according to the false discovery rate (FDR) at the cluster level. Results: This study included 63 patients with PD and 73 sex- and age-matched healthy participants were included in the study. The spontaneous brain activity in the right superior cerebellum (Cerebelum_Crus1_R), vermis (Vermis_7), and left supplementary motor area (Supp_Motor_Area_L) of patients in the PD group was weak compared to that in the HC group. The z-score ALFF of left central posterior gyrus was positively correlated with the Mini-Mental State Examination score (r=0.542; P<0.001) in the PD group. For rs11931074, the main genotypic effects were found in the left inferior cerebellum (Cerebellum_9_L) and right anterior cingulate and paracingulate gyri (Cingulum_Ant_R); for rs356219 and rs356165, the main genotypic effects were found in the left caudate nucleus (Caudate_L). An interaction effect of disease with genotype was found in the right inferior parietal gyrus (Parietal_Inf_R) only for rs356219. Conclusions: Our study found a correlation of the SNCA SNPs rs11931074, rs356219, and rs356165 with brain functional alterations in patients with PD. Furthermore, an interaction effect was found in the right inferior parietal gyrus only for rs356219. This study may contribute to furthering the understanding of the influence of SNCA gene SNPs on brain function in patients with PD.

Predicting bacterial transport through saturated porous media using an automated machine learning model.

Escherichia coli, as an indicator of fecal contamination, can move from manure-amended soil to groundwater under rainfall or irrigation events. Predicting its vertical transport in the subsurface is essential for the development of engineering solutions to reduce the risk of microbiological contamination. In this study, we collected 377 datasets from 61 published papers addressing E. coli transport through saturated porous media and trained six types of machine learning algorithms to predict bacterial transport. Eight variables, including bacterial concentration, porous medium type, median grain size, ionic strength, pore water velocity, column length, saturated hydraulic conductivity, and organic matter content were used as input variables while the first-order attachment coefficient and spatial removal rate were set as target variables. The eight input variables have low correlations with the target variables, namely, they cannot predict target variables independently. However, using the predictive models, input variables can effectively predict the target variables. For scenarios with higher bacterial retention, such as smaller median grain size, the predictive models showed better performance. Among six types of machine learning algorithms, Gradient Boosting Machine and Extreme Gradient Boosting outperformed other algorithms. In most predictive models, pore water velocity, ionic strength, median grain size, and column length showed higher importance than other input variables. This study provided a valuable tool to evaluate the transport risk of E.coli in the subsurface under saturated water flow conditions. It also proved the feasibility of data-driven methods that could be used for predicting other contaminants' transport in the environment.

Spectroscopic techniques for quantitative characterization of Cu (II) and Hg (II) complexation by dissolved organic matter from lake sediment in arid and semi-arid region.

Dissolved organic matter (DOM) was extracted from six sediment samples in arid and semi-arid region, which was characterized by fluorescence excitation-emission matrices (EEMs). The results showed that four fluorescent peak, fulvic-like (peak A), humic-like (peak C) and two tryptophan-like (peaks B and D), were identified in lake sediment DOM. Fluorescence quenching titration showed that peaks B and D were quenched gradually by adding additional Cu (II) and Hg (II), whereas humic-like substances had no systematic trend of the change of fluorescence intensity. Increasing fluorescence intensity value of humic-like substances can also be found. The modified Stern-Volmer model was used to calculate conditional stability constants (logK) and the percent of fluorophores (f %) which participate in the complexation between DOM and Cu (II), and Hg (II). The results showed that DOM-Cu (II) and DOM-Hg (II) complexes had higher logK values of 4.21-5.23 and the logK values of DOM-Cu (II) are much larger than the corresponding values for Hg (II). Peak B showed relatively low logK and high f % values than those of peak D. Different pollution sources which are mainly obtained from the upstream industrial wastewater, domestic sewage and return water of farmland irrigation tend to affect the stability constants and complexing capacities of Cu (II) and Hg (II).

Quantifying bacterial concentration in water and sand media during flow-through experiments using a non-invasive, real-time, and efficient method.

Monitoring the dynamics of bacteria in porous media is of great significance to understand the bacterial transport and the interplay between bacteria and environmental factors. In this study, we reported a non-invasive, real-time, and efficient method to quantify bioluminescent bacterial concentration in water and sand media during flow-through experiments. First, 27 column experiments were conducted, and the bacterial transport was monitored using a real-time bioluminescent imaging system. Next, we quantified the bacterial concentration in water and sand media using two methods-viable count and bioluminescent count. The principle of the bioluminescent count in sand media was, for a given bioluminescence image, the total number of bacteria was proportionally allocated to each segment according to its bioluminescence intensity. We then compared the bacterial concentration for the two methods and found a good linear correlation between the bioluminescent count and viable count. Finally, the effects of porous media surface coating, pore water velocity, and ionic strength on the bioluminescent count in sand media were investigated, and the results showed that the bioluminescence counting accuracy was most affected by surface coating, followed by ionic strength, and was hardly affected by pore water velocity. Overall, the study proved that the bioluminescent count was a reliable method to quantify bacterial concentration in water (106 to 2 × 108 cell mL-1) or sand media (5 × 106-5 × 108 cell cm-3). This approach also offers a new way of thinking for in situ bacterial enumeration in two-dimensional devices such as 2D flow cells, microfluidic devices, and rhizoboxes.

Bioremediation of Petroleum-Contaminated Soils with Biosurfactant-Producing Degraders Isolated from the Native Desert Soils.

A crude oil spill in 2014 resulted in extensive soil contamination of the hyper arid Evrona Nature Reserve in Israel's Negev Desert. The contaminated soils became highly hydrophobic, threatening the existence of plants in the habitat. We hypothesized that bioaugmenting the soil with indigenous biosurfactant-producing, hydrocarbon-degrading bacteria (HDB) would accelerate the reduction in the soil's hydrophobicity. We aimed to isolate and characterize biosurfactant-producing HDBs from the desert-contaminated soil and test if they can be used for augmenting the soil. Twelve hydrocarbon-degrading strains were isolated, identified as Pseudomonas, and classified as biosurfactants "producing" and "nonproducing". Inoculating 109 CFU/g of "producing" strains into the polluted soil resulted in a 99.2% reduction in soil hydrophobicity within seven days. At the same time, nonproducing strains reduced hydrophobicity by only 17%, while no change was observed in the untreated control. The microbial community in the inoculated soil was dominated by the introduced strains over 28 days, pointing to their persistence. Rhamnolipid biosynthesis gene rhlAB remained persistent in soil inoculated with biosurfactants, indicating in situ production. We propose that the success of the treatment is due to the use of inoculum enriched from the polluted soil.

Effects of co-inoculation with arbuscular mycorrhizal fungi and rhizobia on soybean growth as related to root architecture and availability of N and P.

Soybean plants can form tripartite symbiotic associations with rhizobia and arbuscular mycorrhizal (AM) fungi, but little is known about effects of co-inoculation with rhizobia and AM fungi on plant growth, or their relationships to root architecture as well as nitrogen (N) and phosphorus (P) availability. In the present study, two soybean genotypes contrasting in root architecture were grown in a field experiment to evaluate relationships among soybean root architecture, AMF colonization, and nodulation under natural conditions. Additionally, a soil pot experiment in greenhouse was conducted to investigate the effects of co-inoculation with rhizobia and AM fungi on soybean growth, and uptake of N and P. Our results indicated that there was a complementary relationship between root architecture and AMF colonization in the field. The deep root soybean genotype had greater AMF colonization at low P, but better nodulation with high P supply than the shallow root genotype. A synergistic relationship dependent on N and P status exists between rhizobia and AM fungi on soybean growth. Co-inoculation with rhizobia and AM fungi significantly increased soybean growth under low P and/or low N conditions as indicated by increased shoot dry weight, along with plant N and P content. There were no significant effects of inoculation under adequate N and P conditions. Furthermore, the effects of co-inoculation were related to root architecture. The deep root genotype, HN112, benefited more from co-inoculation than the shallow root genotype, HN89. Our results elucidate new insights into the relationship between rhizobia, AM fungi, and plant growth under limitation of multiple nutrients, and thereby provides a theoretical basis for application of co-inoculation in field-grown soybean.

Increased Salivary microRNAs That Regulate DJ-1 Gene Expression as Potential Markers for Parkinson's Disease.

Small molecule RNAs (microRNAs) are a kind of endogenous, stable, and noncoding RNA molecule that can regulate the expression of target genes such as DJ-1 at the posttranscriptional level. This study aimed to detect the expression of salivary microRNAs and to discover their value as a salivary potential biomarker for Parkinson's disease (PD). Through a case-control study, RT-qPCR technology was used to detect the expression of miR-874 and miR-145-3p in the saliva of 30 PD patients and 30 healthy volunteers. Then we compared the differences in the expression levels of salivary miR-874 and miR-145-3p between the PD group and the control group and analyzed the correlation between the expression of salivary miR-874 and miR-145-3p in terms of age, gender, disease condition, and disease course. We found that salivary miR-874 and miR-145-3p were both positively expressed in the PD group and control group, and their expression in the PD group was higher than that in the control group. The expression of salivary miRNA-874 and miR-145-3p had no clear correlation to age, gender, total RNA concentrations in saliva, the score of UPDRSII, UPDRSIII, olfactory test scale, MMSE, MoCA, Hohn-Yahr stage and disease course. In conclusion, in the PD group and the control group with positive expression, the expression levels of miR-874 and miR-145-3p in the PD group were higher than those in the control group. The detection of miR-874 and miR-145-3p expression in saliva can be used as an auxiliary biomarker for PD.