Automatic recognition of protein subcellular location patterns in single cells from immunofluorescence images based on deep learning.

With the improvement of single-cell measurement techniques, there is a growing awareness that individual differences exist among cells, and protein expression distribution can vary across cells in the same tissue or cell line. Pinpointing the protein subcellular locations in single cells is crucial for mapping functional specificity of proteins and studying related diseases. Currently, research about single-cell protein location is still in its infancy, and most studies and databases do not annotate proteins at the cell level. For example, in the human protein atlas database, an immunofluorescence image stained for a particular protein shows multiple cells, but the subcellular location annotation is for the whole image, ignoring intercellular difference. In this study, we used large-scale immunofluorescence images and image-level subcellular locations to develop a deep-learning-based pipeline that could accurately recognize protein localizations in single cells. The pipeline consisted of two deep learning models, i.e. an image-based model and a cell-based model. The former used a multi-instance learning framework to comprehensively model protein distribution in multiple cells in each image, and could give both image-level and cell-level predictions. The latter firstly used clustering and heuristics algorithms to assign pseudo-labels of subcellular locations to the segmented cell images, and then used the pseudo-labels to train a classification model. Finally, the image-based model was fused with the cell-based model at the decision level to obtain the final ensemble model for single-cell prediction. Our experimental results showed that the ensemble model could achieve higher accuracy and robustness on independent test sets than state-of-the-art methods.

Bone-Targeted Biomimetic Nanogels Re-Establish Osteoblast/Osteoclast Balance to Treat Postmenopausal Osteoporosis.

Insufficient bone formation and excessive bone resorption caused by estrogen deficiency are the major factors resulting in the incidence of postmenopausal osteoporosis (PMOP). The existing drugs usually fail to re-establish the osteoblast/osteoclast balance from both sides and generate side-effects owing to the lack of bone-targeting ability. Here, engineered cell-membrane-coated nanogels PNG@mR&C capable of scavenging receptor activator of nuclear factor-κB ligand (RANKL) and responsively releasing therapeutic PTH 1-34 in the bone microenvironment are prepared from RANK and CXCR4 overexpressed bone mesenchymal stem cell (BMSC) membrane-coated chitosan biopolymers. The CXCR4 on the coated-membranes confer bone-targeting ability, and abundant RANK effectively absorb RANKL to inhibit osteoclastogenesis. Meanwhile, the release of PTH 1-34 triggered by osteoclast-mediated acid microenvironment promote osteogenesis. In addition, the dose and frequency are greatly reduced due to the smart release property, prolonged circulation time, and bone-specific accumulation. Thus, PNG@mR&C exhibits satisfactory therapeutic effects in the ovariectomized (OVX) mouse model. This study provides a new paradigm re-establishing the bone metabolic homeostasis from multitargets and shows great promise for the treatment of PMOP.

Learning protein subcellular localization multi-view patterns from heterogeneous data of imaging, sequence and networks.

Location proteomics seeks to provide automated high-resolution descriptions of protein location patterns within cells. Many efforts have been undertaken in location proteomics over the past decades, thereby producing plenty of automated predictors for protein subcellular localization. However, most of these predictors are trained solely from high-throughput microscopic images or protein amino acid sequences alone. Unifying heterogeneous protein data sources has yet to be exploited. In this paper, we present a pipeline called sequence, image, network-based protein subcellular locator (SIN-Locator) that constructs a multi-view description of proteins by integrating multiple data types including images of protein expression in cells or tissues, amino acid sequences and protein-protein interaction networks, to classify the patterns of protein subcellular locations. Proteins were encoded by both handcrafted features and deep learning features, and multiple combining methods were implemented. Our experimental results indicated that optimal integrations can considerately enhance the classification accuracy, and the utility of SIN-Locator has been demonstrated through applying to new released proteins in the human protein atlas. Furthermore, we also investigate the contribution of different data sources and influence of partial absence of data. This work is anticipated to provide clues for reconciliation and combination of multi-source data for protein location analysis.

Fluoride impairs vascular smooth muscle A7R5 cell lines via disrupting amino acids metabolism.

Given the insidious and high-fatality nature of cardiovascular diseases (CVDs), the emergence of fluoride as a newly identified risk factor demands serious consideration alongside traditional risk factors. While vascular smooth muscle cells (VSMCs) play a pivotal role in the progression of CVDs, the toxicological impact of fluoride on VSMCs remains largely uncharted. In this study, we constructed fluorosis model in SD rats and A7R5 aortic smooth muscle cell lines to confirm fluoride impaired VSMCs. Fluoride aggravated the pathological damage of rat aorta in vivo. Then A7R5 were exposed to fluoride with concentration ranging from 0 to 1200 µmol/L over a 24-h period, revealing a dose-dependent inhibition of cell proliferation and migration. The further metabolomic analysis showed alterations in metabolite profiles induced by fluoride exposure, notably decreasing organic acids and lipid molecules level. Additionally, gene network analysis underscored the frequency of fluoride's interference with amino acids metabolism, potentially impacting the tricarboxylic acid (TCA) cycle. Our results also highlighted the ATP-binding cassette (ABC) transporters pathway as a central element in VSMC impairment. Moreover, we observed a dose-dependent increase in osteopontin (OPN) and α-smooth muscle actin (α-SMA) mRNA level and a dose-dependent decrease in ABC subfamily C member 1 (ABCC1) and bestrophin 1 (BEST1) mRNA level. These findings advance our understanding of fluoride as a CVD risk factor and its influence on VSMCs and metabolic pathways, warranting further investigation into this emerging risk factor.

Analysis of extreme ultraviolet radiation and hydrodynamics simulation at the core of laser-produced nickel plasmas.

The non-uniformity and transient nature of laser-produced plasma are critical factors that affect the analysis of the extreme ultraviolet spectra of highly charged ions and the diagnosis of plasma states. This paper systematically investigates the characteristics of extreme ultraviolet radiation and the hydrodynamic evolution of laser-produced nickel plasmas from two perspectives: high-spatio-temporal-resolution extreme-ultraviolet spectroscopic measurement and radiation hydrodynamics simulation. The consistency between the four-band experimental spectra and their theoretically simulated spectra confirms the accuracy of the atomic structure parameters and plasma state parameters. We also analyze the significant contribution of the 3d-4f double-excited state radiation to the spectral profile and discuss the influence of the self-absorption caused by plasma opacity on the characteristics of extreme ultraviolet radiation. The findings are crucial for accurately understanding the characteristics of extreme ultraviolet radiation, the hydrodynamic evolution, and the application of medium- and high-Z laser-produced plasma as a pulsed short-wavelength light source.

Reconstruction and analysis of transient evolution images of laser-produced plasma plumes.

We introduce a method for the analysis and simulation of transient images of laser-produced plasma (LPP) plumes. This method comprises three steps: (i) calculating the two-dimensional distribution of plasma parameters using a radiation hydrodynamics model, (ii) constructing radiation paths through ray tracing, and (iii) solving the radiation transport equation along these paths. In our simulations, we have meticulously considered factors that could influence the imaging results, including the quantum efficiency to different radiation wavelengths, the imaging lens' transmittance, the target surface's reflectivity, and the absorption, emission, and scattering quantum effect of the detector processes occurring in the plasma. We applied this method to analyze and simulate the transient images of aluminum plasma plumes in a background air environment at a pressure of 2000 Pa. The results demonstrate that our method not only produces simulated images that align with experimental results but also provides a reliable distribution of plasma state parameters and clearly identifies the ion species radiating in different bands. Given its capability in transient image reconstruction and its adaptability as a tool for spectral simulation and analysis of LPPs, we believe this method holds significant potential for spectral diagnostics in fields such as laser-induced breakdown spectroscopy, extreme ultraviolet lithography sources, and high-energy-density physics, among others.

Current understanding of functional peptides encoded by lncRNA in cancer.

Dysregulated gene expression and imbalance of transcriptional regulation are typical features of cancer. RNA always plays a key role in these processes. Human transcripts contain many RNAs without long open reading frames (ORF, > 100 aa) and that are more than 200 bp in length. They are usually regarded as long non-coding RNA (lncRNA) which play an important role in cancer regulation, including chromatin remodeling, transcriptional regulation, translational regulation and as miRNA sponges. With the advancement of ribosome profiling and sequencing technologies, increasing research evidence revealed that some ORFs in lncRNA can also encode peptides and participate in the regulation of multiple organ tumors, which undoubtedly opens a new chapter in the field of lncRNA and oncology research. In this review, we discuss the biological function of lncRNA in tumors, the current methods to evaluate their coding potential and the role of functional small peptides encoded by lncRNA in cancers. Investigating the small peptides encoded by lncRNA and understanding the regulatory mechanisms of these functional peptides may contribute to a deeper understanding of cancer and the development of new targeted anticancer therapies.

Decoding the epitranscriptome: a new frontier for cancer therapy and drug resistance.

As the role of RNA modification in gene expression regulation and human diseases, the "epitranscriptome" has been shown to be an important player in regulating many physiological and pathological processes. Meanwhile, the phenomenon of cancer drug resistance is becoming more and more frequent, especially in the case of cancer chemotherapy resistance. In recent years, research on relationship between post-transcriptional modification and cancer including drug resistance has become a hot topic, especially the methylation of the sixth nitrogen site of RNA adenosine-m6A (N6-methyladenosine). m6A modification is the most common post-transcriptional modification of eukaryotic mRNA, accounting for 80% of RNA methylation modifications. At the same time, several other modifications of RNA, such as N1-methyladenosine (m1A), 5-methylcytosine (m5C), 3-methylcytosine (m3C), pseudouridine (Ψ) and N7-methylguanosine (m7G) have also been demonstrated to be involved in cancer and drug resistance. This review mainly discusses the research progress of RNA modifications in the field of cancer and drug resistance and targeting of m6A regulators by small molecule modulators, providing reference for future study and development of combination therapy to reverse cancer drug resistance.

Medicare Payments and ACOs for Dementia Patients Across Race and Social Vulnerability.

OBJECTIVES: This study investigated variations in Medicare payments for Alzheimer's disease and related dementia (ADRD) by race, ethnicity, and neighborhood social vulnerability, together with cost variations by beneficiaries' enrollment in Accountable Care Organizations (ACOs). METHODS: We used merged datasets of longitudinal Medicare Beneficiary Summary File (2016-2020), the Social Vulnerability Index (SVI), and the Medicare Shared Savings Program (MSSP) ACO to measure beneficiary-level ACO enrollment at the diagnosis year of ADRD. We analyzed Medicare payments for patients newly diagnosed with ADRD for the year preceding the diagnosis and for the subsequent 3 years. The dataset included 742,175 Medicare fee-for-service (FFS) beneficiaries aged 65 and older with a new diagnosis of ADRD in 2017 who remained in the Medicare FFS plan from 2016 to 2020. RESULTS: Among those newly diagnosed, Black and Hispanic patients encountered higher total costs compared to White patients, and ADRD patients living in the most vulnerable areas experienced the highest total costs compared to patients living in other regions. These cost differences persisted over 3 years postdiagnosis. Patients enrolled in ACOs incurred lower costs across all racial and ethnic groups and SVI areas. For ADRD patients living in the areas with the highest vulnerability, the cost differences by ACO enrollment of the total Medicare costs ranged from $4,403.1 to $6,922.7, and beneficiaries' savings ranged from $114.5 to $726.6 over three years post-ADRD diagnosis by patient's race and ethnicity. CONCLUSIONS: Black and Hispanic ADRD patients and ADRD patients living in areas with higher social vulnerability would gain more from ACO enrollment compared to their counterparts.

Vanadium (V) reduction and the performance of electroactive biofilms in microbial fuel cells with Shewanella putrefaciens.

The electron transfer ability of biofilms significantly influences the electrochemical activity of microbial fuel cells (MFCs). However, there is limited understanding of pentavalent vanadium (V(V)) bioreduction and microbial response characteristics in MFCs. In this study, the effect of gradient concentrations of V(V) on the performance of EABs with Shewanella putrefaciens in MFCs was investigated. The results showed that as V(V) concentration increased (0-100 mg/L), the voltage output, power densities, polarization, and electrode potential decreased. V(V) was found to act as an electron acceptor and was reduced during MFCs operation, with a yield of 83.16% being observed at 25 mg/L V(V). Cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) indicated declining electrochemical performance of the MFCs with escalating V(V) concentration. The content of protein and polysaccharide from extracellular polymeric substances (EPS) in anodic biofilms increased to 66.75 and 49.15 mg/L at 75 mg/L V(V), respectively. Three-dimensional fluorescence spectroscopy confirmed increased humic substances in EPS extraction with V(V) exposure. The functional genes narG, nirK, and gor involved in V(V) reduction were upregulated with rising V(V) concentration through quantitative polymerase chain reaction (qPCR) analysis. Additionally, riboflavin, cytochrome c, nicotinamide adenine dinucleotide (NADH), and electron transport system activity (ETSA), key indicators for assessing electron transfer behavior, exhibited a negative correlation with various V(V) concentrations, decreasing by 31.81%, 57.14%, 67.39%, and 51.41%, respectively, at a concentration of 100 mg/L V(V) compared to the blank control. These findings contribute valuable insights into the response of EABs to V(V) exposure, presenting potential strategies for enhancing their effectiveness in the treatment of vanadium-contaminated wastewater.

Factors influencing patient-provider communication about subjective cognitive decline in people with COPD: Insights from a national survey.

Objective: While there is a growing body of evidence indicating a relationship between COPD and cognitive impairment, there is a gap in evidence regarding discussions of cognitive symptoms in healthcare settings. This study investigated the extent to which individuals with Chronic Obstructive Pulmonary Disease (COPD) and Subjective Cognitive Decline (SCD) self-reported confusion or memory loss with healthcare professionals. Methods: A secondary analysis of 2019 BRFSS data of US adults aged 45+ with COPD (N = 107,204), using logistic regression to explore associations between socio-demographic and health-related indicators with discussion of cognitive symptoms with healthcare professionals. Results: Less than half (45.88%) of individuals reporting SCD discussed their cognitive symptoms with their healthcare provider. In the adjusted model, unemployed (AOR = 2.92, 95% CI: 1.70-5.02, p < .005), retired (AOR = 3.16, 95% CI: 1.37-7.30, p < .01), and current smokers (AOR = 1.73, 95% CI: 1.02-2.93, p < .05) were more likely to discuss cognitive decline with a healthcare professional than their counterparts. In contrast, males (AOR = 0.53, 95% CI: 0.32-0.86, p < .05) and binge drinkers (AOR = 0.49, 95% CI: 0.30-0.79, p < .01) were significantly less likely to do so when compared to their counterparts. Discussion: The study highlighted significant disparities in the likelihood of individuals with COPD discussing cognitive symptoms based on socio-demographic and health risk behaviors. Conclusion: Addressing gender disparities, occupational status, and personal health risks is crucial for improving patient-provider communication about SCD among adults with COPD.

Protectin Conjugates in Tissue Regeneration 1 Inhibits Macrophage Pyroptosis by Restricting NLRP3 Inflammasome Assembly to Mitigate Sepsis via the cAMP-PKA Pathway.

Protectin conjugates in tissue regeneration 1 (PCTR1) is a novel anti-inflammatory and proresolving lipid mediator biosynthesized from docosahexaenoic acid. Excessive activation of NLR family pyrin domain containing 3 (NLRP3) inflammasome and consequent pyroptosis are involved in diverse inflammatory diseases. However, how PCTR1 affects NLRP3 inflammasome activation and pyroptosis are still unclear. Here, we demonstrated that PCTR1 inhibited NLRP3 inflammasome activation and pyroptosis. These results show that PCTR1 dose-dependently inhibited gasdermin D cleavage in lipopolysaccharide (LPS)-primed murine primary macrophages upon nigericin stimulation. Additionally, PCTR1 treatment after LPS priming inhibited caspase-1 activation and subsequent mature interleukin-1ß release independent of the nuclear factor-kappa B pathway. PCTR1 exerted its inhibitory effects by blocking NLRP3-apoptosis-associated speck-like protein containing a CARD (ASC) interaction and ASC oligomerization, thereby restricting NLRP3 inflammasome assembly. However, the inhibitory effect of PCTR1 could be reversed by KH7 and H89, which are the inhibitors of the cyclic adenosine monophosphate (cAMP)-protein kinase A (PKA) signaling pathway. Moreover, PCTR1 treatment alleviated lung tissue damage and improved mouse survival in LPS-induced sepsis. Our study unveils the molecular mechanism of negative regulation of NLRP3 inflammasome activation and pyroptosis by a novel lipid mediator and suggests that PCTR1 may serve as a potential treatment option for NLRP3-inflammasome driven diseases.

DULoc: quantitatively unmixing protein subcellular location patterns in immunofluorescence images based on deep learning features.

MOTIVATION: Knowledge of subcellular locations of proteins is of great significance for understanding their functions. The multi-label proteins that simultaneously reside in or move between more than one subcellular structure usually involve with complex cellular processes. Currently, the subcellular location annotations of proteins in most studies and databases are descriptive terms, which fail to capture the protein amount or fractions across different locations. This highly limits the understanding of complex spatial distribution and functional mechanism of multi-label proteins. Thus, quantitatively analyzing the multiplex location patterns of proteins is an urgent and challenging task. RESULTS: In this study, we developed a deep-learning-based pattern unmixing pipeline for protein subcellular localization (DULoc) to quantitatively estimate the fractions of proteins localizing in different subcellular compartments from immunofluorescence images. This model used a deep convolutional neural network to construct feature representations, and combined multiple nonlinear decomposing algorithms as the pattern unmixing method. Our experimental results showed that the DULoc can achieve over 0.93 correlation between estimated and true fractions on both real and synthetic datasets. In addition, we applied the DULoc method on the images in the human protein atlas database on a large scale, and showed that 70.52% of proteins can achieve consistent location orders with the database annotations. AVAILABILITY AND IMPLEMENTATION: The datasets and code are available at: https://github.com/PRBioimages/DULoc. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Real optical imaging simulation of laser-produced aluminum plasmas.

We developed a post-processing optical imaging model based on two-dimensional axisymmetric radiation hydrodynamics. Simulation and program benchmarks were performed using laser-produced Al plasma optical images obtained via transient imaging. The emission profiles of a laser-produced Al plasma plume in air at atmospheric pressure were reproduced, and the influence of plasma state parameters on radiation characteristics were clarified. In this model, the radiation transport equation is solved on the real optical path, which is mainly used to study the radiation of luminescent particles during plasma expansion. The model outputs consist of the electron temperature, particle density, charge distribution, absorption coefficient, and corresponding spatio-temporal evolution of the optical radiation profile. The model helps with understanding element detection and quantitative analysis of laser-induced breakdown spectroscopy.

Hospital-based Health Information Technology Infrastructure: Evidence of Reduced Medicare Payments and Racial Disparities Among Patients With ADRD.

BACKGROUND: Alzheimer disease and related dementia (ADRD) is one of the most expensive health conditions in the United States. Understanding the potential cost-savings or cost-enhancements of Health Information Technology (HIT) can help policymakers understand the capacity of HIT investment to promote population health and health equity for patients with ADRD. OBJECTIVES: This study examined access to hospital-based HIT infrastructure and its association with racial and ethnic disparities in Medicare payments for patients with ADRD. RESEARCH DESIGN: We used the 2017 Medicare Beneficiary Summary File, inpatient claims, and the American Hospital Association Annual Survey. Our study focused on community-dwelling Medicare fee-for-service beneficiaries who were diagnosed with ADRD. Our study focused on hospital-based telehealth-postdischarge (eg, remote patient monitoring) and telehealth-treatment (eg, psychiatric and addiction treatment) services. RESULTS: Results showed that hospital-based telehealth postdischarge services were associated with significantly higher total Medicare payment and acute inpatient Medicare payment per person per year among patients with ADRD on average. The associations between hospital-based telehealth-treatment services and payments were not significant. However, the association varied by patient's race and ethnicity. The reductions of the payments associated with telehealth postdischarge and treatment services were more pronounced among Black patients with ADRD. Telehealth-treatment services were associated with significant payment reductions among Hispanic patients with ADRD. CONCLUSION: Results showed that having hospital-based telehealth services might be cost-enhancing at the population level but cost-saving for Black and Hispanic patients with ADRD. Results suggested that personalized HIT services might be necessary to reduce the cost associated with ADRD treatment for racial and ethnic minority groups.

Examining HIV Diagnosis and Linkage to PrEP Prescription Among Members at An Integrated Health System in the Southeast United States.

Pre-Exposure Prophylaxis (PrEP) is a priority method for preventing HIV infection. This study's aims were threefold: (1) identify characteristics of members of a large health maintenance organization, Kaiser Permanente Georgia (KPGA), associated with HIV infection, (2) identify which member characteristics associated with HIV were also associated with PrEP prescription, and (3) identify which HIV-associated characteristics were associated with under- or over-prescribing of PrEP. Analysis of variables from the electronic medical record revealed that age, race, gender, mental health diagnosis, STI diagnosis, and sexual orientation were independently associated with HIV diagnosis. The same characteristics were independently associated with PrEP prescription except for race. Persons identifying as Black or unknown race, women, and/or heterosexual; and who had an STI diagnosis and/or illicit drug use had lower odds of being prescribed PrEP than of having an HIV diagnosis. The implications of these findings for improving physician identification of candidates for PrEP prescription are discussed.

A Comparative Trial of Improving Care for Underserved Asian Americans Infected with Hepatitis B Virus.

BACKGROUND: Hepatitis B virus (HBV) is the leading cause of hepatocellular carcinoma (HCC). Asian Americans have the highest incidence and mortality rates of HCC among all US racial/ethnic groups. Inadequate monitoring and treatment of chronic hepatitis B contribute to poor health outcomes and increased healthcare costs among Asian Americans. AIMS: The goal of this study is to assess the effect of a patient-led strategy on chronic hepatitis B monitoring and treatment adherence specifically among Asian Americans with culturally tailored Patient Navigator-led Intervention. METHODS: From 2015 to 2018, 532 eligible participants living with chronic hepatitis B in the greater Philadelphia and New York city metropolitan areas were randomly assigned to either the intervention group or the control group. Generalized linear mixed-effects models were used to estimate the odds ratio (OR) for rates of doctor visits for chronic hepatitis B and rates of alanine aminotransferase testing for evidence of liver damage. RESULTS: Intervention group had higher rates of doctor visits than the control group at both 6-month (77.22% vs. 45.75%) and 12-month assessments (90.73% vs. 60.61%). Significantly more intervention group participants received ALT testing than control group participants at 6-month (52.90% vs. 25.10%) and 12-month (75.40% vs. 46.75%) follow-up. CONCLUSIONS: Culturally and linguistically appropriate intervention has strong effects on adherence to follow-up care among Asian American hepatitis B patients experiencing challenges to medication adherence and follow up care. These findings further identify opportunities for practical implementation of evidence-based intervention that could lead to reductions in disparities in chronic liver disease and liver cancer among high-risk, underserved populations.

Reducing Rural and Urban Disparities in Potentially Preventable Hospitalizations: Evidence of Hospital-Based Health Information Technology and Enabling Services.

Background: Telehealth and enabling services are promising approaches to address the intersecting challenges of chronic disease burden, a growing aging population, and poor access to care disproportionately affecting rural areas. Using potentially preventable hospitalizations (PPHs) as an indicator for health system efficiency and quality, this study examined the relationship between health information technology and hospital-provided enabling services on PPHs across rural, micropolitan, and metropolitan areas. Methods: We constructed a patient-, hospital-, community-, and state-level data set using the Medicare fee-for-service claims file and the Medicare Master Beneficiary Summary File, and the American Hospital Association Annual Survey. Logistic regressions were applied to examine associations between PPH and telehealth post-discharge, telehealth treatment, and telehealth post-discharge and enabling services. Results: Approximately 50% of rural and micropolitan residents (vs. 36% of urban residents) were treated in hospitals providing neither telehealth post-discharge services nor enabling services, and 7% (vs. 11% of urban residents) were treated in hospitals with both services. Telehealth post-discharge services were associated with significantly lower odds ratio (OR) of having any PPH due to acute (OR = 0.91, p < 0.001) and chronic conditions (OR = 0.94, p < 0.001). The ORs of having any PPH due to acute and chronic conditions were the least among beneficiaries who were treated in hospitals with both telehealth post-discharge and enabling services (OR = 0.56, p < 0.001, for acute conditions, and OR = 0.73, p < 0.001, for chronic conditions). Conclusions: Hospital use of post-discharge telehealth alongside enabling services may help provide timely access to care, improve care coordination, and reduce PPHs for older rural residents.

Immunomodulatory Mechanism and Potential Application of Dental Pulp-Derived Stem Cells in Immune-Mediated Diseases.

Dental pulp stem cells (DPSCs) are mesenchymal stem cells (MSCs) derived from dental pulp tissue, which have high self-renewal ability and multi-lineage differentiation potential. With the discovery of the immunoregulatory ability of stem cells, DPSCs have attracted much attention because they have similar or even better immunomodulatory effects than MSCs from other sources. DPSCs and their exosomes can exert an immunomodulatory ability by acting on target immune cells to regulate cytokines. DPSCs can also migrate to the lesion site to differentiate into target cells to repair the injured tissue, and play an important role in tissue regeneration. The aim of this review is to summarize the molecular mechanism and target cells of the immunomodulatory effects of DPSCs, and the latest advances in preclinical research in the treatment of various immune-mediated diseases, providing new reflections for their clinical application. DPSCs may be a promising source of stem cells for the treatment of immune-mediated diseases.

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OBJECTIVES: To explore the molecular characteristics of Staphylococcus aureus (S. aureus) in children, and to compare the molecular characteristics of different types of strains (infection and colonization strains) so as to reveal pathogenic molecular markers of S. aureus. METHODS: A cross-sectional study design was used to conduct nasopharyngeal swab sampling from healthy children in the community and clinical samples from infected children in the hospital. Whole genome sequencing was used to detect antibiotic resistance genes and virulence genes. A random forest method to used to screen pathogenic markers. RESULTS: A total of 512 S. aureus strains were detected, including 272 infection strains and 240 colonization strains. For virulence genes, the carrying rates of enterotoxin genes (seb and sep), extracellular enzyme coding genes (splA, splB, splE and edinC), leukocytotoxin genes (lukD, lukE, lukF-PV and lukS-PV) and epidermal exfoliating genes (eta and etb) in infection strains were higher than those in colonization strains. But the carrying rates of enterotoxin genes (sec, sec3, seg, seh, sei, sel, sem, sen, seo and seu) were lower in infection strains than in colonization strains (P<0.05). For antibiotic resistance genes, the carrying rates of lnuA, lnuG, aadD, tetK and dfrG were significantly higher in infection strains than in colonization strains (P<0.05). The accuracy of cross-validation of the random forest model for screening pathogenic markers of S. aureus before and after screening was 69% and 68%, respectively, and the area under the curve was 0.75 and 0.70, respectively. The random forest model finally screened out 16 pathogenic markers (sem, etb, splE, sep, ser, mecA, lnuA, sea, blaZ, cat(pC233), blaTEm-1A, aph(3')-III, ermB, ermA, ant(9)-Ia and ant(6)-Ia). The top five variables in the variable importance ranking were sem (OR=0.40), etb (OR=3.95), splE (OR=1.68), sep (OR=3.97), and ser (OR=1.68). CONCLUSIONS: The random forest model can screen out pathogenic markers of S. aureus and exhibits a superior predictive performance, providing genetic evidence for tracing highly pathogenic S. aureus and conducting precise targeted interventions.