Nanochemistry of gold: from surface engineering to dental healthcare applications.

Advancements in nanochemistry have led to the development of engineered gold nanostructures (GNSs) with remarkable potential for a variety of dental healthcare applications. These innovative nanomaterials offer unique properties and functionalities that can significantly improve dental diagnostics, treatment, and overall oral healthcare applications. This review provides an overview of the latest advancements in the design, synthesis, and application of GNSs for dental healthcare applications. Engineered GNSs have emerged as versatile tools, demonstrating immense potential across different aspects of dentistry, including enhanced imaging and diagnosis, prevention, bioactive coatings, and targeted treatment of oral diseases. Key highlights encompass the precise control over GNSs' size, crystal structure, shape, and surface functionalization, enabling their integration into sensing, imaging diagnostics, drug delivery systems, and regenerative therapies. GNSs, with their exceptional biocompatibility and antimicrobial properties, have demonstrated efficacy in combating dental caries, periodontitis, peri-implantitis, and oral mucosal diseases. Additionally, they show great promise in the development of advanced sensing techniques for early diagnosis, such as nanobiosensor technology, while their role in targeted drug delivery, photothermal therapy, and immunomodulatory approaches has opened new avenues for oral cancer therapy. Challenges including long-term toxicity, biosafety, immune recognition, and personalized treatment are under rigorous investigation. As research at the intersection of nanotechnology and dentistry continues to thrive, this review highlights the transformative potential of engineered GNSs in revolutionizing dental healthcare, offering accurate, personalized, and minimally invasive solutions to address the oral health challenges of the modern era.

PENCIL imaging: A novel approach for neuromelanin sensitive MRI in Parkinson's disease.

BACKGROUND: Parkinson's disease (PD) is associated with the loss of neuromelanin (NM) and increased iron in the substantia nigra (SN). Magnetization transfer contrast (MTC) is widely used for NM visualization but has limitations in brain coverage and scan time. This study aimed to develop a new approach called Proton-density Enhanced Neuromelanin Contrast in Low flip angle gradient echo (PENCIL) imaging to visualize NM in the SN. METHODS: This study included 30 PD subjects and 50 healthy controls (HCs) scanned at 3T. PENCIL and MTC images were acquired. NM volume in the SN pars compacta (SNpc), normalized image contrast (Cnorm), and contrast-to-noise ratio (CNR) were calculated. The change of NM volume in the SNpc with age was analyzed using the HC data. A group analysis compared differences between PD subjects and HCs. Receiver operating characteristic (ROC) analysis and area under the curve (AUC) calculations were used to evaluate the diagnostic performance of NM volume and CNR in the SNpc. RESULTS: PENCIL provided similar visualization and structural information of NM compared to MTC. In HCs, PENCIL showed higher NM volume in the SNpc than MTC, but this difference was not observed in PD subjects. PENCIL had higher CNR, while MTC had higher Cnorm. Both methods revealed a similar pattern of NM volume in SNpc changes with age. There were no significant differences in AUCs between NM volume in SNpc measured by PENCIL and MTC. Both methods exhibited comparable diagnostic performance in this regard. CONCLUSIONS: PENCIL imaging provided improved CNR compared to MTC and showed similar diagnostic performance for differentiating PD subjects from HCs. The major advantage is PENCIL has rapid whole-brain coverage and, when using STAGE imaging, offers a one-stop quantitative assessment of tissue properties.

Diffusion-Weighted MRI-Based Virtual Elastography and Shear-Wave Elastography for the Assessment of Breast Lesions.

BACKGROUND: Diffusion-weighted imaging (DWI)-based virtual MR elastography (DWI-vMRE) in the assessment of breast lesions is still in the research stage. PURPOSE: To investigate the usefulness of elasticity values on DWI-vMRE in the evaluation of breast lesions, and the correlation with the values calculated from shear-wave elastography (SWE). STUDY TYPE: Prospective. POPULATION/SUBJECTS: 153 patients (mean age ± standard deviation: 55 ± 12 years) with 153 pathological confirmed breast lesions (24 benign and 129 malignant lesions). FIELD STRENGTH/SEQUENCE: 1.5-T MRI, multi-b readout segmented echo planar imaging (b-values of 0, 200, 800, and 1000 sec/mm2 ). ASSESSMENT: For DWI-vMRE assessment, lesions were manually segmented using apparent diffusion coefficient (ADC0-1000 ) map, then the region of interests were copied to the map of shifted-ADC (sADC200-800 , sADC 200-1500 ). For SWE assessment, the shear modulus of the lesions was measured by US elastic modulus (µUSE ). Intraclass/interclass kappa coefficients were calculated to measure the consistency. STATISTICAL TESTS: Pearson's correlation was used to assess the relationship between sADC and µUSE . A receiver operating characteristic analysis with the area under the curve (AUC) was performed to compare the diagnostic accuracy between benign and malignant breast lesions of sADC and µUSE . A P value <0.05 was considered statistically significant. RESULTS: There were significant differences between benign and malignant breast lesions of µUSE (24.17 ± 10.64 vs. 37.20 ± 12.61), sADC200-800 (1.38 ± 0.31 vs. 0.97 ± 0.18 × 10-3 mm2 /sec), and sADC200-1500 (1.14 ± 0.30 vs. 0.78 ± 0.13 × 10-3 mm2 /sec). In all breast lesions, a moderate but significant correlation was observed between µUSE and sADC200-800 /sADC200-1500 (r = -0.49/-0.44). AUC values to differentiate benign from malignant lesions were as follows: µUSE , 0.78; sADC200-800 , 0.89; sADC200-1500 , 0.89. DATA CONCLUSIONS: Both SWE and DWI-vMRE could be used for the differentiation of benign versus malignant breast lesions. Furthermore, DWI-vMRE with the use of sADC show relatively higher AUC values than SWE. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY: Stage 2.

A Comparative Study of Three Systems for Liver Magnetic Resonance Elastography.

BACKGROUND: Different MR elastography (MRE) systems may produce different stiffness measurements, making direct comparison difficult in multi-center investigations. PURPOSE: To assess the repeatability and reproducibility of liver stiffness measured by three typical MRE systems. STUDY TYPE: Prospective. POPULATION/PHANTOMS: Thirty volunteers without liver disease history (20 males, aged 21-28)/5 gel phantoms. FIELD STRENGTH/SEQUENCE: 3.0 T United Imaging Healthcare (UIH), 1.5 T Siemens Healthcare, 3.0 T General Electric Healthcare (GE)/Echo planar imaging-based MRE sequence. ASSESSMENT: Wave images of volunteers and phantoms were acquired by three MRE systems. Tissue stiffness was evaluated by two observers, while phantom stiffness was assessed automatically by code. The reproducibility across three MRE systems was quantified based on the mean stiffness of each volunteer and phantom. STATISTICAL TESTS: Intraclass correlation coefficients (ICC), coefficients of variation (CV), and Bland-Altman analyses were used to assess the interobserver reproducibility, the interscan repeatability, and the intersystem reproducibility. Paired t-tests were performed to assess the interobserver and interscan variation. Friedman tests with Dunn's multiple comparison correction were performed to assess the intersystem variation. P values less than 0.05 indicated significant difference. RESULTS: The reproducibility of stiffness measured by the two observers demonstrated consistency with ICC > 0.92, CV < 4.32%, Mean bias < 2.23%, and P > 0.06. The repeatability of measurements obtained using the electromagnetic system for the liver revealed ICC > 0.96, CV < 3.86%, Mean bias < 0.19%, P > 0.90. When considering the range of reproducibility across the three systems for liver evaluations, results ranged with ICCs from 0.70 to 0.87, CVs from 6.46% to 10.99%, and Mean biases between 1.89% and 6.30%. Phantom studies showed similar results. The values of measured stiffness differed across all three systems significantly. DATA CONCLUSION: Liver stiffness values measured from different MRE systems can be different, but the measurements across the three MRE systems produced consistent results with excellent reproducibility. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 2.

Enhancing Nigrosome-1 Sign Identification via Interpretable AI using True Susceptibility Weighted Imaging.

BACKGROUND: Nigrosome 1 (N1), the largest nigrosome region in the ventrolateral area of the substantia nigra pars compacta, is identifiable by the "N1 sign" in long echo time gradient echo MRI. The N1 sign's absence is a vital Parkinson's disease (PD) diagnostic marker. However, it is challenging to visualize and assess the N1 sign in clinical practice. PURPOSE: To automatically detect the presence or absence of the N1 sign from true susceptibility weighted imaging by using deep-learning method. STUDY TYPE: Prospective. POPULATION/SUBJECTS: 453 subjects, including 225 PD patients, 120 healthy controls (HCs), and 108 patients with other movement disorders, were prospectively recruited including 227 males and 226 females. They were divided into training, validation, and test cohorts of 289, 73, and 91 cases, respectively. FIELD STRENGTH/SEQUENCE: 3D gradient echo SWI sequence at 3T; 3D multiecho strategically acquired gradient echo imaging at 3T; NM-sensitive 3D gradient echo sequence with MTC pulse at 3T. ASSESSMENT: A neuroradiologist with 5 years of experience manually delineated substantia nigra regions. Two raters with 2 and 36 years of experience assessed the N1 sign on true susceptibility weighted imaging (tSWI), QSM with high-pass filter, and magnitude data combined with MTC data. We proposed NINet, a neural model, for automatic N1 sign identification in tSWI images. STATISTICAL TESTS: We compared the performance of NINet to the subjective reference standard using Receiver Operating Characteristic analyses, and a decision curve analysis assessed identification accuracy. RESULTS: NINet achieved an area under the curve (AUC) of 0.87 (CI: 0.76-0.89) in N1 sign identification, surpassing other models and neuroradiologists. NINet localized the putative N1 sign within tSWI images with 67.3% accuracy. DATA CONCLUSION: Our proposed NINet model's capability to determine the presence or absence of the N1 sign, along with its localization, holds promise for enhancing diagnostic accuracy when evaluating PD using MR images. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 1.

Computed Diffusion-Weighted Images of Rectal Cancer: Image Quality, Restaging, and Treatment Response after Neoadjuvant Therapy.

BACKGROUND: Computed diffusion-weighted images (cDWI) of random b value could be derived from acquired DWI (aDWI) with at least two different b values. However, its comparison between aDWI and cDWI images in locally advanced rectal cancer (LARC) patients after neoadjuvant therapy (NT) is needed. PURPOSE: To compare the cDWI and aDWI in image quality, restaging, and treatment response of LARC after NT. STUDY TYPE: Retrospective. POPULATION: Eighty-seven consecutive patients. FIELD STRENGTH/SEQUENCE: 3.0 T/DWI. ASSESSMENT: All patients underwent two DWI sequences, including conventional acquisition with b = 0 and 1000 s/mm2 (aDWIb1000 ) and another with b = 0 and 700 s/mm2 on a 3.0-T MR scanner. The images of the latter were used to compute the diffusion images with b = 1000 s/mm2 (cDWIb1000 ). Four radiologists with 3, 4, 14, and 25 years of experience evaluated the images to compare the image quality, TN restaging performance, and treatment response between aDWIb1000 and cDWIb1000 . STATISTICAL TESTS: Interclass correlation coefficients, weighted κ coefficient, paired Wilcoxon, and McNemar or Fisher test were used. A significance level of 0.05 was used. RESULTS: The cDWIb1000 images were superior to the aDWIb1000 ones in both subjective and objective image quality. In T restaging, the overall diagnostic accuracy of cDWIb1000 images was higher than that of aDWIb1000 images (57.47% vs. 49.43%, P = 0.289 for the inexperienced radiologist; 77.01% vs. 63.22%, significant for the experienced radiologist), with better sensitivity in determining ypT0-Tis tumors. Additionally, it increased the sensitivity in detecting ypT2 tumors for the inexperienced radiologist and ypT3 tumors for the experienced radiologist. N restaging and treatment response were found to be similar between two sequences for both radiologists. DATA CONCLUSION: Compared to aDWIb1000 images, the computed ones might serve as a wise approach, providing comparable or better image quality, restaging performance, and treatment response assessment for LARC after NT. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 2.

Three-Dimensional Multifrequency MR Elastography for Microvascular Invasion and Prognosis Assessment in Hepatocellular Carcinoma.

BACKGROUND: Pretreatment identification of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) is important when selecting treatment strategies. PURPOSE: To improve models for predicting MVI and recurrence-free survival (RFS) by developing nomograms containing three-dimensional (3D) MR elastography (MRE). STUDY TYPE: Prospective. POPULATION: 188 patients with HCC, divided into a training cohort (n = 150) and a validation cohort (n = 38). In the training cohort, 106/150 patients completed a 2-year follow-up. FIELD STRENGTH/SEQUENCE: 1.5T 3D multifrequency MRE with a single-shot spin-echo echo planar imaging sequence, and 3.0T multiparametric MRI (mp-MRI), consisting of diffusion-weighted echo planar imaging, T2-weighted fast spin echo, in-phase out-of-phase T1-weighted fast spoiled gradient-recalled dual-echo and dynamic contrast-enhanced gradient echo sequences. ASSESSMENT: Multivariable analysis was used to identify the independent predictors for MVI and RFS. Nomograms were constructed for visualization. Models for predicting MVI and RFS were built using mp-MRI parameters and a combination of mp-MRI and 3D MRE predictors. STATISTICAL TESTS: Student's t-test, Mann-Whitney U test, chi-squared or Fisher's exact tests, multivariable analysis, area under the receiver operating characteristic curve (AUC), DeLong test, Kaplan-Meier analysis and log rank tests. P < 0.05 was considered significant. RESULTS: Tumor c and liver c were independent predictors of MVI and RFS, respectively. Adding tumor c significantly improved the diagnostic performance of mp-MRI (AUC increased from 0.70 to 0.87) for MVI detection. Of the 106 patients in the training cohort who completed the 2-year follow up, 34 experienced recurrence. RFS was shorter for patients with MVI-positive histology than MVI-negative histology (27.1 months vs. >40 months). The MVI predicted by the 3D MRE model yielded similar results (26.9 months vs. >40 months). The MVI and RFS nomograms of the histologic-MVI and model-predicted MVI-positive showed good predictive performance. DATA CONCLUSION: Biomechanical properties of 3D MRE were biomarkers for MVI and RFS. MVI and RFS nomograms were established. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 2.

Improvement of coronary stent visualization using ultra-high-resolution photon-counting detector CT.

OBJECTIVES: This study aimed to compare the image quality and diagnostic performance of standard-resolution (SR) and ultra-high-resolution (UHR) coronary CT angiography (CCTA) based on photon-counting detector CT (PCD-CT) of coronary stents and explore the best reconstruction kernel for stent imaging. METHODS: From July 2023 to September 2023, patients were enrolled to undergo CCTA using a dual-source PCD-CT system after coronary angioplasty with stent placement. SR images with a slice thickness/increment of 0.6/0.4 mm were reconstructed using a vascular kernel (Bv48), while UHR images with a slice thickness/increment of 0.2/0.2 mm were reconstructed using vascular kernels of six sharpness levels (Bv48, Bv56, Bv60, Bv64, Bv72, and Bv76). The in-stent lumen diameters were evaluated. Subjective image quality was also evaluated by a 5-point Likert scale. Invasive coronary angiography was conducted in 12 patients (25 stents). RESULTS: Sixty-nine patients (68.0 [61.0, 73.0] years, 46 males) with 131 stents were included. All UHR images had significantly larger in-stent lumen diameter than SR images (p < 0.001). Specifically, UHR-Bv72 and UHR-Bv76 for in-stent lumen diameter (2.17 [1.93, 2.63] mm versus 2.20 [1.93, 2.59] mm) ranked the two best kernels. The subjective analysis demonstrated that UHR-Bv72 images had the most pronounced effect on reducing blooming artifacts, showcasing in-stent lumen and stent demonstration, and diagnostic confidence (p < 0.001). Furthermore, SR and UHR-Bv72 images showed a diagnostic accuracy of 78.3% (95% confidence interval [CI]: 56.3%-92.5%) and 88.0% (95%CI: 68.8%-97.5%), respectively. CONCLUSION: UHR CCTA by PCD-CT leads to significantly improved visualization and diagnostic performance of coronary stents, and Bv72 is the optimal reconstruction kernel showing the stent struts and in-stent lumen. CLINICAL RELEVANCE STATEMENT: The significantly improved visualization of coronary stents using ultra-high resolution CCTA could increase the diagnostic accuracy for in-stent restenosis and avoid unnecessary invasive quantitative coronary angiography, thus changing the clinical management for patients after percutaneous coronary intervention. KEY POINTS: Coronary stent imaging is challenging with energy-integrating detector CT due to "blooming artifacts." UHR images using a PCD-CT enhanced coronary stent visualization. UHR coronary stent imaging demonstrated improved diagnostic accuracy in clinical settings.

Consistency of Monoenergetic Attenuation Measurements for a Clinical Dual-Source Photon-Counting Detector CT System Across Scanning Paradigms: A Phantom Study.

BACKGROUND. Use of virtual monoenergetic images (VMIs) from multienergy CT scans can mitigate inconsistencies in traditional attenuation measurements that result from variation in scan-related factors. Photon-counting detector (PCD) CT systems produce VMIs as standard image output under flexible scanning conditions. OBJECTIVE. The purpose of this article was to evaluate the consistency of monoenergetic attenuation measurements obtained from a clinical PCD CT scanner across a spectrum of scanning paradigms. METHODS. A phantom with 10 tissue-simulating inserts was imaged using a clinical dual-source PCD CT scanner. Nine scanning paradigms were obtained across combinations of tube voltages (90, 120, and 140 kVp) and image quality (IQ) levels (80, 145, and 180). Images were reconstructed at VMI levels of 50, 60, 70, and 80 keV. Consistency of attenuation measurements was assessed, using the 120 kVp with IQ level of 145 scanning paradigm as the reference scan. RESULTS. For all scanning paradigms, attenuation measurements showed intra-class correlation of 0.999 and higher with respect to the reference scan. Across inserts, mean bias relative to the reference scan ranged from -14.9 to 13.6 HU, -2.7 to 1.7 HU, and -3.9 to 3.8 HU at tube voltages of 90, 120, and 140 kVp, respectively; and from -14.9 to 13.6 HU, -6.4 to 3.8 HU, -3.7 to 1.4 HU, and -7.2 to 4.3 HU at VMI levels of 50, 60, 70, and 80 keV, respectively. Thus, mean bias did not exceed 5 HU for any insert at tube potentials of 120 kVp and 140 kVp, nor for any insert at a VMI level of 70 keV. At a VMI level of 50 keV and tube potential of 90 kVp, mean bias exceeded 5 HU for 14 of 30 possible combinations of inserts and scanning paradigms and exceeded 10 HU for four of 30 such combinations. At VMI levels of both 60 and 80 keV, mean bias exceeded 5 HU for only two combinations of inserts and scanning paradigms, all at a tube potential of 90 kVp. CONCLUSION. PCD CT generally provided consistent attenuation measurements across combinations of scanning paradigms and VMI levels. CLINICAL IMPACT. PCD CT may facilitate quantitative applications of CT data in clinical practice.

Association between Mediterranean diet and periodontitis among US adults: The mediating roles of obesity indicators.

OBJECTIVE: This study aimed to assess the association between the Mediterranean diet (MedDiet) and periodontitis in US adults and to further explore the mediating roles of obesity indicators in this association. BACKGROUND DATA: The relationship between MedDiet and periodontitis is controversial. And it is unclear whether obesity indicators are potential mediators of this relationship. METHODS: Using data derived from the National Health and Nutrition Examination Survey (2009-2014). Weighted binary logistic regression and restricted cubic spline were used to assess the association between MedDiet and periodontitis. Weighted ordinal logistic regression was performed to evaluate the relationship between MedDiet and periodontitis severity. The mediating roles of body mass index (BMI) and waist circumference in the relationship between the MedDiet and periodontitis were explored. Association analyses were further performed using mean clinical attachment loss (CAL) or mean periodontal probing depth (PPD) as dependent variables. The false discovery rate method was used to correct the p-values in the regression analyses. RESULTS: A total of 8290 eligible participants (4159 participants with periodontitis and 4131 without periodontitis) were included. A negative association between the MedDiet adherence score and periodontitis was observed in the binary logistic regression model (adjusted odds ratio = 0.94, 95% confidence interval: 0.90-0.97, p = .001). Restricted cubic spline regression revealed a dose-response relationship between the MedDiet adherence score and periodontitis. BMI and waist circumference significantly mediate this association, with mediation proportions of 9.7% (p = .032) and 9.3% (p = .012), respectively. Multivariable ordinal logistic regression showed that the MedDiet adherence score was negatively associated with the severity of periodontitis (all p < .05). Additionally, the MedDiet adherence score was negatively associated with mean PPD or mean CAL (all p < .05). CONCLUSIONS: This study suggests a significant negative association between adherence to the MedDiet and periodontitis and a possible mediating role of obesity indicators in this association. Furthermore, studies are still warranted to confirm our findings.

Subthalamic stimulation modulates motor network in Parkinson's disease: recover, relieve and remodel.

Aberrant dynamic switches between internal brain states are believed to underlie motor dysfunction in Parkinson's disease. Deep brain stimulation of the subthalamic nucleus is a well-established treatment for the motor symptoms of Parkinson's disease, yet it remains poorly understood how subthalamic stimulation modulates the whole-brain intrinsic motor network state dynamics. To investigate this, we acquired resting-state functional magnetic resonance imaging time-series data from 27 medication-free patients with Parkinson's disease (mean age: 64.8 years, standard deviation: 7.6) who had deep brain stimulation electrodes implanted in the subthalamic nucleus, in both on and off stimulation states. Sixteen matched healthy individuals were included as a control group. We adopted a powerful data-driven modelling approach, known as a hidden Markov model, to disclose the emergence of recurring activation patterns of interacting motor regions (whole-brain intrinsic motor network states) via the blood oxygen level-dependent signal detected in the resting-state functional magnetic resonance imaging time-series data from all participants. The estimated hidden Markov model disclosed the dynamics of distinct whole-brain motor network states, including frequency of occurrence, state duration, fractional coverage and their transition probabilities. Notably, the data-driven decoding of whole-brain intrinsic motor network states revealed that subthalamic stimulation reshaped functional network expression and stabilized state transitions. Moreover, subthalamic stimulation improved motor symptoms by modulating key trajectories of state transition within whole-brain intrinsic motor network states. This modulation mechanism of subthalamic stimulation was manifested in three significant effects: recovery, relieving and remodelling effects. Significantly, recovery effects correlated with improvements in tremor and posture symptoms induced by subthalamic stimulation (P < 0.05). Furthermore, subthalamic stimulation was found to restore a relatively low level of fluctuation of functional connectivity in all motor regions to a level closer to that of healthy participants. Also, changes in the fluctuation of functional connectivity between motor regions were associated with improvements in tremor and gait symptoms (P < 0.05). These findings fill a gap in our knowledge of the role of subthalamic stimulation at the level of neural activity, revealing the regulatory effects of subthalamic stimulation on whole-brain inherent motor network states in Parkinson's disease. Our results provide mechanistic insight and explanation for how subthalamic stimulation modulates motor symptoms in Parkinson's disease.

Polarized M2 macrophages induced by glycosylated nano-hydroxyapatites activate bone regeneration in periodontitis therapy.

AIM: To investigate the association between periodontal macrophage polarization states and the alveolar bone levels, and to assess whether glycosylated nano-hydroxyapatites (GHANPs) could improve bone regeneration in periodontitis by inducing macrophage M2 polarization. MATERIALS AND METHODS: The change of macrophage polarization state in inflammatory periodontal tissues (with bone loss) was examined using clinical gingival samples. The relationship between macrophage phenotype and bone level in periodontal bone loss and repair was evaluated using a mouse periodontitis model. The effect of GHANPs on macrophage polarization was assessed by the in vitro model of lipopolysaccharide (LPS)-stimulated inflammation. The polarization-related markers were detected by immunofluorescence staining, real-time polymerase chain reaction and enzyme-linked immunosorbent assay analysis. The therapeutic effect of GHANPs on alveolar bone loss was explored in experimental periodontitis by histological staining and micro-CT analysis. RESULTS: A lower macrophage M2/M1 ratio was observed in periodontitis-affected human gingival tissues. The results of animal experiments demonstrated a positive correlation between a lower Arg-1/iNOS ratio and accelerated alveolar bone loss; also, the proportion of Arg-1-positive macrophages increased during bone repair and regeneration. The administration of GHANPs partially restored M2 macrophage polarization after LPS stimulation. GHANPs increased alveolar bone repair and regeneration in experimental periodontitis induced by ligation, potentially related to their macrophage M2 transition regulation. CONCLUSIONS: The findings of this study indicate that the induction of macrophage M2 polarization can be considered a viable approach for enhancing inflammatory bone repair. Additionally, GHANPs show potential in the clinical treatment of periodontitis.

Association between polycyclic aromatic hydrocarbons and periodontitis: Results from a large population-based study.

AIM: To explore the association between polycyclic aromatic hydrocarbons (PAHs) (measured using urinary metabolites) and periodontitis using data from the National Health and Nutrition Examination Survey 2009-2014. MATERIALS AND METHODS: Weighted binary logistic regression, Bayesian kernel machine regression (BKMR) and weighted quantile sum (WQS) regression were used to evaluate independent and joint associations between the six urinary monohydroxylated metabolites of PAHs (OH-PAHs) and periodontitis. RESULTS: In all, 3413 participants were included in this study. All six urinary OH-PAHs were present at higher levels in the periodontitis group compared with the non-periodontitis group (p < .001). Fully adjusted multivariable logistic regressions showed positive associations between the six urinary OH-PAHs and periodontitis (p < .05). Higher concentrations of OH-PAHs were also positively associated with attachment loss, periodontal pocket depth (PPD) and the number of tooth loss. BKMR and WQS regression yielded similar positive associations between OH-PAH mixtures and periodontitis. CONCLUSIONS: PAHs and their mixture are positively associated with periodontitis, which may provide novel insights into periodontitis prevention from an environmental exposure perspective.

Association of per- and polyfluoroalkyl substances (PFAS) with periodontitis: the mediating role of sex hormones.

OBJECTIVES: To investigate the association between serum per- and polyfluoroalkyl substances (PFAS) and periodontitis, and further explore the possible mediating role of sex hormones in this association. METHODS: We extracted data from National Health and Nutrition Examination Survey (NHANES) 2009-2014. Univariable and multivariable logistic regression models were performed to investigate the association between serum levels of seven PFASs and periodontitis. Bayesian kernel machine regression (BKMR) was conducted to assess the joint effect of PFASs in mixtures. Mediation analyses were used to explore the potential mediating role of sex hormones. RESULTS: Participants with periodontitis had higher concentrations of serum perfluorooctane sulfonate (PFOS) and perfluorononanoic acid (PFNA) than those without periodontitis (both P < 0.05). In fully adjusted models, high serum concentrations of PFOS and PFNA were positively associated with periodontitis (tertile 3 vs. tertile 1: prevalence ratio (PR) = 1.19 for PFOS, 95% CI: 1.01-1.39; PR = 1.17 for PFNA, 95% CI: 1.02-1.34). The results from the BKMR models consistently showed a positive association between PFAS mixtures and periodontitis. Of note, testosterone and the ratio of testosterone to estradiol significantly mediated the relationship between high level of PFOS and periodontitis, accounting for 16.5% and 31.7% of the total effect, respectively. Sensitivity analyses yielded similar results when using periodontal clinical indices (mean loss of attachment, mean periodontal probing depth, and the number of teeth) as dependent variables. CONCLUSIONS: These findings provide evidence to support a positive association between certain PFASs and periodontitis, which might be partially mediated by sex hormones.

Knockdown of Bach1 protects periodontal bone regeneration from inflammatory damage.

Periodontal bone regeneration is a major challenge in the treatment of periodontitis. However, the regenerative vitality of periodontal ligament cells (PDLCs) declines in the environment of periodontitis and accompanying oxidative stress. This study aimed to investigate the functional mechanisms of Bach1, a transcriptional suppressor involved in oxidative stress response, and its regulation of PDLC osteogenesis under inflammatory conditions. We observed a significant elevation in Bach1 expression in periodontal tissues with periodontitis and PDLCs under inflammatory conditions. Knockdown of Bach1 alleviated the inflammation-induced oxidative stress level and partly offset the inhibitory effect of inflammatory conditions on osteogenesis, as well as the expression of osteogenic genes BMP6, OPG and RUNX2. Similarly, knockdown of Bach1 protects PDLCs from inflammatory damage to periodontal bone regeneration in vivo. Furthermore, we found that Bach1 could bind to the histone methyltransferase EZH2, and the binding increased under inflammatory conditions. Bach1 enhanced the ability of EZH2 to catalyse H3K27me3 on the promoter region of RUNX2 and BMP6, thus repressing the expression of osteoblastic genes. In conclusion, our study revealed that knockdown of Bach1 effectively rescued the osteogenesis and oxidative stress of PDLCs with inflammation. Bach1 could be a promising target for enhancing periodontal tissue regeneration under periodontitis conditions.

Assessment of vibration modulated regional cerebral blood flow with MRI.

Human brain experiences vibration of certain magnitude and frequency during various physical activities such as vehicle transportation and machine operation, which may cause traumatic brain injury or other brain diseases. However, the mechanisms of brain pathogenesis due to vibration are not fully elucidated due to the lack of techniques to study brain functions while applying vibration to the brain at a specific magnitude and frequency. Here, this study reported a custom-built head-worn electromagnetic actuator that applied vibration to the brain in vivo at an accurate frequency inside a magnetic resonance imaging scanner while cerebral blood flow (CBF) was acquired. Using this technique, CBF values from 45 healthy volunteers were quantitatively measured immediately following vibration at 20, 30, 40 Hz, respectively. Results showed increasingly reduced CBF with increasing frequency at multiple regions of the brain, while the size of the regions expanded. Importantly, the vibration-induced CBF reduction regions largely fell inside the brain's default mode network (DMN), with about 58 or 46% overlap at 30 or 40 Hz, respectively. These findings demonstrate that vibration as a mechanical stimulus can change strain conditions, which may induce CBF reduction in the brain with regional differences in a frequency-dependent manner. Furthermore, the overlap between vibration-induced CBF reduction regions and DMN suggested a potential relationship between external mechanical stimuli and cognitive functions.

Diagnosing Parkinson's disease by combining neuromelanin and iron imaging features using an automated midbrain template approach.

BACKGROUND AND PURPOSE: Early diagnosis of Parkinson's disease (PD) is still a clinical challenge. Most previous studies using manual or semi-automated methods for segmenting the substantia nigra (SN) are time-consuming and, despite raters being well-trained, individual variation can be significant. In this study, we used a template-based, automatic, SN subregion segmentation pipeline to detect the neuromelanin (NM) and iron features in the SN and SN pars compacta (SNpc) derived from a single 3D magnetization transfer contrast (MTC) gradient echo (GRE) sequence in an attempt to develop a comprehensive imaging biomarker that could be used to diagnose PD. MATERIALS AND METHODS: A total of 100 PD patients and 100 age- and sex-matched healthy controls (HCs) were imaged on a 3T scanner. NM-based SN (SNNM) boundaries and iron-based SN (SNQSM) boundaries and their overlap region (representing the SNpc) were delineated automatically using a template-based SN subregion segmentation approach based on quantitative susceptibility mapping (QSM) and NM images derived from the same MTC-GRE sequence. All PD and HC subjects were evaluated for the nigrosome-1 (N1) sign by two raters independently. Receiver Operating Characteristic (ROC) analyses were performed to evaluate the utility of SNNM volume, SNQSM volume, SNpc volume and iron content with a variety of thresholds as well as the N1 sign in diagnosing PD. Correlation analyses were performed to study the relationship between these imaging measures and the clinical scales in PD. RESULTS: In this study, we verified the value of the fully automatic template based midbrain deep gray matter mapping approach in differentiating PD patients from HCs. The automatic segmentation of the SN in PD patients led to satisfactory DICE similarity coefficients and volume ratio (VR) values of 0.81 and 1.17 for the SNNM, and 0.87 and 1.05 for the SNQSM, respectively. For the HC group, the average DICE similarity coefficients and VR values were 0.85 and 0.94 for the SNNM, and 0.87 and 0.96 for the SNQSM, respectively. The SNQSM volume tended to decrease with age for both the PD and HC groups but was more severe for the PD group. For diagnosing PD, the N1 sign performed reasonably well by itself (Area Under the Curve (AUC) = 0.783). However, combining the N1 sign with the other quantitative measures (SNNM volume, SNQSM volume, SNpc volume and iron content) resulted in an improved diagnosis of PD with an AUC as high as 0.947 (using an SN threshold of 50ppb and an NM threshold of 0.15). Finally, the SNQSM volume showed a negative correlation with the MDS-UPDRS III (R2 = 0.1, p = 0.036) and the Hoehn and Yahr scale (R2 = 0.04, p = 0.013) in PD patients. CONCLUSION: In summary, this fully automatic template based deep gray matter mapping approach performs well in the segmentation of the SN and its subregions for not only HCs but also PD patients with SN degeneration. The combination of the N1 sign with other quantitative measures (SNNM volume, SNQSM volume, SNpc volume and iron content) resulted in an AUC of 0.947 and provided a comprehensive set of imaging biomarkers that, potentially, could be used to diagnose PD clinically.

Visualizing the deep cerebellar nuclei using quantitative susceptibility mapping: An application in healthy controls, Parkinson's disease patients and essential tremor patients.

The visualization and identification of the deep cerebellar nuclei (DCN) (dentate [DN], interposed [IN] and fastigial nuclei [FN]) are particularly challenging. We aimed to visualize the DCN using quantitative susceptibility mapping (QSM), predict the contrast differences between QSM and T2* weighted imaging, and compare the DCN volume and susceptibility in movement disorder populations and healthy controls (HCs). Seventy-one Parkinson's disease (PD) patients, 39 essential tremor patients, and 80 HCs were enrolled. The PD patients were subdivided into tremor dominant (TD) and postural instability/gait difficulty (PIGD) groups. A 3D strategically acquired gradient echo MR imaging protocol was used for each subject to obtain the QSM data. Regions of interest were drawn manually on the QSM data to calculate the volume and susceptibility. Correlation analysis between the susceptibility and either age or volume was performed and the intergroup differences of the volume and magnetic susceptibility in all the DCN structures were evaluated. For the most part, all the DCN structures were clearly visualized on the QSM data. The susceptibility increased as a function of volume for both the HC group and disease groups in the DN and IN (p < .001) but not the FN (p = .74). Only the volume of the FN in the TD-PD group was higher than that in the HCs (p = .012), otherwise, the volume and susceptibility among these four groups did not differ significantly. In conclusion, QSM provides clear visualization of the DCN structures. The results for the volume and susceptibility of the DCN can be used as baseline references in future studies of movement disorders.

An automatic interpretable deep learning pipeline for accurate Parkinson's disease diagnosis using quantitative susceptibility mapping and T1-weighted images.

Parkinson's disease (PD) diagnosis based on magnetic resonance imaging (MRI) is still challenging clinically. Quantitative susceptibility maps (QSM) can potentially provide underlying pathophysiological information by detecting the iron distribution in deep gray matter (DGM) nuclei. We hypothesized that deep learning (DL) could be used to automatically segment all DGM nuclei and use relevant features for a better differentiation between PD and healthy controls (HC). In this study, we proposed a DL-based pipeline for automatic PD diagnosis based on QSM and T1-weighted (T1W) images. This consists of (1) a convolutional neural network model integrated with multiple attention mechanisms which simultaneously segments caudate nucleus, globus pallidus, putamen, red nucleus, and substantia nigra from QSM and T1W images, and (2) an SE-ResNeXt50 model with an anatomical attention mechanism, which uses QSM data and the segmented nuclei to distinguish PD from HC. The mean dice values for segmentation of the five DGM nuclei are all >0.83 in the internal testing cohort, suggesting that the model could segment brain nuclei accurately. The proposed PD diagnosis model achieved area under the the receiver operating characteristic curve (AUCs) of 0.901 and 0.845 on independent internal and external testing cohorts, respectively. Gradient-weighted class activation mapping (Grad-CAM) heatmaps were used to identify contributing nuclei for PD diagnosis on patient level. In conclusion, the proposed approach can potentially be used as an automatic, explainable pipeline for PD diagnosis in a clinical setting.

TP53 Mutation Estimation Based on MRI Radiomics Analysis for Breast Cancer.

BACKGROUND: Noninvasive detection of TP53 mutations is useful for the molecular stratification of breast cancer. PURPOSE: To explore MRI radiomics features reflecting TP53 mutations in breast cancer and propose a classifier for detecting such mutations. STUDY TYPE: Retrospective. POPULATION/SUBJECTS: A total of 139 breast cancer patients with TP53 expression profiling (98 with TP53 mutations and 41 without TP53 mutations). FIELD STRENGTH/SEQUENCE: 1.5 T, T1-weighted (T1W) DCE-MRI. ASSESSMENT: Lesions were manually segmented using subtracted T1WI. A total of 944 radiomics features (including 744 wavelet-related features) and 7 clinicopathological features were extracted from each lesion. Principal component analysis and Pearson's correlation analysis were used to preprocess the features. Linear discriminant analysis, logistic regression (LR), support vector machine (SVM), and random forest (RF) were used as the classifiers. STATISTICAL TESTS: Analysis of variance, Kruskal-Wallis and recursive features elimination were used to select features. Receiver operating characteristic (ROC) analysis was performed to compare the diagnostic accuracy. RESULTS: For the radiomics model, the validation cohorts AUCs of the four classifiers ranged from 0.69 (RF) to 0.74 (LR), and LR (0.74) attained the highest AUCs. For the clinicopathological-radiomics combined model, the validation AUCs of the four classifiers ranged from 0.68 (RF) to 0.86 (SVM), and SVM (0.86) attained highest AUCs. In the subgroup analysis of triple-negative (TN) and luminal type breast cancer, RF achieved the highest AUCs (0.83 and 0.94). DATA CONCLUSION: Clinicopathological-radiomics combined model with SVM could be used as noninvasive biomarkers for predicting TP53 mutations. RF was recommended for the detection of TP53 mutations in TN and luminal type breast cancer. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.