Inhibition of RhoGEF/RhoA alleviates regorafenib resistance and cancer stemness via Hippo signaling pathway in hepatocellular carcinoma.

Patients with hepatocellular carcinoma (HCC) are vulnerable to drug resistance. Although drug resistance has been taken much attention to HCC therapy, little is known of regorafenib and regorafenib resistance (RR). This study aimed to determine the drug resistance pattern and the role of RhoA in RR. Two regorafenib-resistant cell lines were constructed based on Huh7 and Hep3B cell lines. In vitro and in vivo assays were conducted to study RhoA expression, the activity of Hippo signaling pathway and cancer stem cell (CSC) traits. The data showed that RhoA was highly expressed, Hippo signaling was hypoactivated and CSC traits were more prominent in RR cells. Inhibiting RhoA could reverse RR, and the alliance of RhoA inhibition and regorafenib synergistically attenuated CSC phenotype. Furthermore, inhibiting LARG/RhoA increased Kibra/NF2 complex formation, prevented YAP from shuttling into the nucleus and repressed CD44 mRNA expression. Clinically, the high expression of RhoA correlated with poor prognosis. LARG, RhoA, YAP1 and CD44 show positive correlation with each other. Thus, inhibition of RhoGEF/RhoA has the potential to reverse RR and repress CSC phenotype in HCC.

Garnet secondary ion mass spectrometry oxygen isotopes reveal crucial roles of pulsed magmatic fluid and its mixing with meteoric water in lode gold genesis.

SignificanceThere is a common consensus that lode gold deposits mostly precipitated from metamorphic fluids via fluid boiling and/or fluid-rock interaction, but whether magmatic hydrothermal fluids and the mixing of such fluids with an external component have played a vital role in the formation of lode gold deposits remains elusive. We use garnet secondary ion mass spectrometry oxygen isotope analysis to demonstrate that the world-class Dongping lode gold deposit has been formed by multiple pulses of magmatic hydrothermal fluids and their mixing with large volumes of meteoric water. This study opens an opportunity to tightly constrain the origin of lode gold deposits worldwide and other hydrothermal systems that may have generated giant ore deposits in the Earth's crust.

Long-term PM2.5 exposure and peak expiratory flow in middle-aged and older people in China: A quasi-experimental study.

China's Clean Air Act (CCAA) has been demonstrated to reduce the public health burden of ambient air pollution. Few studies have assessed the health effects of CCAA on lung function. We aimed to investigate the effects of CCAA and PM2.5 exposures on peak expiratory flow (PEF) in middle-aged and older people in China. Three waves (2011, 2013, and 2015) of the China Health and Retirement Longitudinal Study (CHARLS) were included in this study. We performed a difference-in-difference (DID) model and mixed effect method to assess the association between CCAA, PM2.5, and PEF. To increase the reliability, multiple environmental factors were considered, and spline function was utilized to fit the spatial autocorrelations. We found that the risk of decreased PEF in the policy intervention group was reduced by 46% (95% CI: 23%~62%). The estimate showed a 10µg/m3 increase in PM2.5 would increase the risk of decreased PEF by 10% (95% CI: 3%~18%). The results of the mixed effect model showed a 10 µg/m3 increase in PM2.5 concentration was associated with a 2.23% (95% CI: 1.35%~3.06%) decrease in the PEF. These results contributed to the limited epidemiology evidence on demonstrating the effect of PM2.5 on lung function.

Identifying vital sign trajectories to predict 28-day mortality of critically ill elderly patients with acute respiratory distress syndrome.

BACKGROUND: The mortality rate of acute respiratory distress syndrome (ARDS) increases with age (≥ 65 years old) in critically ill patients, and it is necessary to prevent mortality in elderly patients with ARDS in the intensive care unit (ICU). Among the potential risk factors, dynamic subphenotypes of respiratory rate (RR), heart rate (HR), and respiratory rate-oxygenation (ROX) and their associations with 28-day mortality have not been clearly explored. METHODS: Based on the eICU Collaborative Research Database (eICU-CRD), this study used a group-based trajectory model to identify longitudinal subphenotypes of RR, HR, and ROX during the first 72 h of ICU stays. A logistic model was used to evaluate the associations of trajectories with 28-day mortality considering the group with the lowest rate of mortality as a reference. Restricted cubic spline was used to quantify linear and nonlinear effects of static RR-related factors during the first 72 h of ICU stays on 28-day mortality. Receiver operating characteristic (ROC) curves were used to assess the prediction models with the Delong test. RESULTS: A total of 938 critically ill elderly patients with ARDS were involved with five and 5 trajectories of RR and HR, respectively. A total of 204 patients fit 4 ROX trajectories. In the subphenotypes of RR, when compared with group 4, the odds ratios (ORs) and 95% confidence intervals (CIs) of group 3 were 2.74 (1.48-5.07) (P = 0.001). Regarding the HR subphenotypes, in comparison to group 1, the ORs and 95% CIs were 2.20 (1.19-4.08) (P = 0.012) for group 2, 2.70 (1.40-5.23) (P = 0.003) for group 3, 2.16 (1.04-4.49) (P = 0.040) for group 5. Low last ROX had a higher mortality risk (P linear = 0.023, P nonlinear = 0.010). Trajectories of RR and HR improved the predictive ability for 28-day mortality (AUC increased by 2.5%, P = 0.020). CONCLUSIONS: For RR and HR, longitudinal subphenotypes are risk factors for 28-day mortality and have additional predictive enrichment, whereas the last ROX during the first 72 h of ICU stays is associated with 28-day mortality. These findings indicate that maintaining the health dynamic subphenotypes of RR and HR in the ICU and elevating static ROX after initial critical care may have potentially beneficial effects on prognosis in critically ill elderly patients with ARDS.

Association of Performance on Multiple Cognitive Domains with Sarcopenia among Middle-Aged and Older Adults.

INTRODUCTION: The relationship between cognitive function and subsequent sarcopenia remains unclear. Therefore, this study aimed to examine the associations of performance on multiple cognitive domains with sarcopenia in the middle-aged and older adults. METHODS: This longitudinal analysis (wave 2011-2013) included 2,934 participants from the CHARLS study. Sarcopenia was defined by the Asian Sarcopenia Working Group 2019 criteria. Cognitive function was measured by the Chinese version of the Mini-Mental State Examination (MMSE). Three interpretable techniques, namely SHapley Additive exPlanations (SHAP) and two built-in methods (coefficients of logistic regression and Gini importance of random forest), were used to assess the relationship between MMSE, its components (orientation, attention, episodic memory, and visuospatial ability) and sarcopenia. In addition, the association of MMSE score and its components with sarcopenia was further validated using stepwise regression. RESULTS: All interpretable methods showed that MMSE score was important predictors of sarcopenia, especially the SHAP (MMSE score ranked top one). For its components, episodic memory, visuospatial ability, and attention showed high predictive value compared with orientation. Stepwise regression analyses showed that MMSE score and its components of episodic memory and visuospatial ability were correlated with sarcopenia, with their odds ratios of 0.93 (95% CI: 0.91-0.96, p < 0.001), 0.87 (95% CI: 0.82-0.93, p < 0.001), and 1.32 (95% CI: 1.05-1.65, p = 0.016), respectively. CONCLUSIONS: Better cognitive function especially episodic memory and visuospatial ability was negatively associated with incident sarcopenia among community middle-aged and older adults.

The survival of periodontally treated molars in long-term maintenance: A systematic review and meta-analysis.

AIM: This systematic review and meta-analysis aimed to determine the survival of periodontally treated molars during maintenance care and identify the risk factors associated with molar loss among patients with periodontitis who received professional periodontal therapy and maintenance. MATERIALS AND METHODS: Longitudinal studies with a minimum follow-up duration of 5 years published until 28 August 2023 were retrieved from the following databases: the Cochrane Library, Embase, MEDLINE and Web of Science. All included studies reported data on molar retention. Meta-analysis was performed using Review Manager 5.4. A modified version of the Newcastle-Ottawa Scale was used to evaluate the study quality. Statistical results of analyses of the overall survival rate and molar loss are presented as estimated standardized mean differences, whereas the results of the analyses of risk factors are presented as risk ratios with 95% confidence intervals (95% CIs). RESULTS: From among the 1323 potentially eligible reports, 41 studies (5584 patients, 29,908 molars retained at the beginning of maintenance therapy, mean follow-up duration of 14.7 years) were included. The pooled survival rate of the molars during maintenance therapy was 82% (95% CI: 80%-84%). The average loss of molars was 0.05 per patient per year (95% CI: 0.04-0.06) among the patients receiving long-term periodontal maintenance (PM) therapy. Fifteen factors were examined in this meta-analysis. Six patient-related factors (older age, lack of compliance, smoking, bruxism, diabetes and lack of private insurance) and five tooth-related factors (maxillary location, high probing pocket depth, furcation involvement, higher mobility and lack of pulpal vitality) were identified as risk factors for molar loss during maintenance therapy. CONCLUSIONS: The findings of the present study suggest that the long-term retention of periodontally compromised molars can be achieved. The average number of molars lost per decade was <1 among the patients receiving long-term PM therapy. Older age, noncompliance, smoking, bruxism, diabetes, lack of private insurance coverage, maxillary location, furcation involvement, higher mobility, increase in the probing pocket depth and loss of pulpal vitality are strong risk factors for the long-term prognosis of molars.

Associations Between Metabolic Obesity Phenotypes and Pathological Characteristics of Papillary Thyroid Carcinoma.

OBJECTIVE: The association between obesity, metabolic dysregulation, and the aggressive pathological traits of papillary thyroid carcinoma (PTC) continues to be a contentious issue. To date, no investigations have examined the impact of metabolic status on the malignant pathological features of PTC in relation to obesity. METHODS: This research involved 855 adult patients with PTC from Shandong Provincial Hospital, classified into 4 groups based on metabolic and obesity status: metabolically healthy nonobese, metabolically unhealthy nonobese (MUNO), metabolically healthy obese, and metabolically unhealthy obese. We employed logistic regression to investigate the relationship between these metabolic obesity phenotypes and PTC's pathological characteristics. Mediation analysis was also performed to determine metabolic abnormalities' mediating role in the nexus between obesity and these characteristics. RESULTS: Relative to metabolically healthy nonobese individuals, the metabolically unhealthy obese group was significantly associated with an elevated risk of larger tumor sizes and a greater number of tumor foci in PTC. Mediation analysis indicated that obesity directly influences tumor size, whereas its effect on tumor multifocality is mediated through metabolic dysfunctions. Specifically, high-density lipoprotein cholesterol levels were notably associated with tumor multifocality within obese subjects, serving as a mediator in obesity's impact on this trait. CONCLUSION: The concurrent presence of obesity and metabolic dysregulation is often connected to more aggressive pathological features in PTC. The mediation analysis suggests obesity directly affects tumor size and indirectly influences tumor multifocality via low high-density lipoprotein cholesterol levels.

Photodynamic therapy in periodontitis: A narrative review.

BACKGROUND: Periodontitis, a chronic infectious disease, is primarily caused by a dysbiotic microbiome, leading to the destruction of tooth-supporting tissues and tooth loss. Photodynamic therapy (PDT), which combines excitation light with photosensitizers (PS) and oxygen to produce antibacterial reactive oxygen species, is emerging as a promising adjuvant treatment for periodontitis. METHODS: This review focuses on studies examining the antibacterial effects of PDT against periodontal pathogens. It also explores the impact of PDT on various aspects of periodontal health, including periodontal immune cells, human gingival fibroblasts, gingival collagen, inflammatory mediators, cytokines in the periodontium, vascular oxidative stress, vascular behavior, and alveolar bone health. Clinical trials assessing the types of PSs and light sources used in PDT, as well as its effects on clinical and immune factors in gingival sulcus fluid and the bacterial composition of dental plaque, are discussed. RESULTS: The findings indicate that PDT is effective in reducing periodontal pathogens and improving markers of periodontal health. It has shown positive impacts on periodontal immune response, tissue integrity, and alveolar bone preservation. Clinical trials have demonstrated improvements in periodontal health and alterations in the microbial composition of dental plaque when PDT is used alongside conventional treatments. CONCLUSIONS: PDT offers a promising adjunctive treatment for periodontitis, with benefits in bacterial reduction, tissue healing, and immune modulation. This article highlights the potential of PDT in periodontal therapy and emphasizes the need for further research to refine its clinical application and efficacy.

Development of interpretable machine learning models associated with environmental chemicals to predict all-cause and specific-cause mortality：A longitudinal study based on NHANES.

Limited information is available on potential predictive value of environmental chemicals for mortality. Our study aimed to investigate the associations between 43 of 8 classes representative environmental chemicals in serum/urine and mortality, and further develop the interpretable machine learning models associated with environmental chemicals to predict mortality. A total of 1602 participants were included from the National Health and Nutrition Examination Survey (NHANES). During 154,646 person-months of follow-up, 127 deaths occurred. We found that machine learning showed promise in predicting mortality. CoxPH was selected as the optimal model for predicting all-cause mortality with time-dependent AUROC of 0.953 (95%CI: 0.951-0.955). Coxnet was the best model for predicting cardiovascular disease (CVD) and cancer mortality with time-dependent AUROCs of 0.935 (95%CI: 0.933-0.936) and 0.850 (95%CI: 0.844-0.857). Based on clinical variables, adding environmental chemicals could enhance the predictive ability of cancer mortality (P < 0.05). Some environmental chemicals contributed more to the models than traditional clinical variables. Combined the results of association and prediction models by interpretable machine learning analyses, we found urinary methyl paraben (MP) and urinary 2-napthol (2-NAP) were negatively associated with all-cause mortality, while serum cadmium (Cd) was positively associated with all-cause mortality. Urinary bisphenol A (BPA) was positively associated with CVD mortality.

An Optimized Instance Segmentation of Underlying Surface in Low-Altitude TIR Sensing Images for Enhancing the Calculation of LSTs.

The calculation of land surface temperatures (LSTs) via low-altitude thermal infrared remote (TIR) sensing images at a block scale is gaining attention. However, the accurate calculation of LSTs requires a precise determination of the range of various underlying surfaces in the TIR images, and existing approaches face challenges in effectively segmenting the underlying surfaces in the TIR images. To address this challenge, this study proposes a deep learning (DL) methodology to complete the instance segmentation and quantification of underlying surfaces through the low-altitude TIR image dataset. Mask region-based convolutional neural networks were utilized for pixel-level classification and segmentation with an image dataset of 1350 annotated TIR images of an urban rail transit hub with a complex distribution of underlying surfaces. Subsequently, the hyper-parameters and architecture were optimized for the precise classification of the underlying surfaces. The algorithms were validated using 150 new TIR images, and four evaluation indictors demonstrated that the optimized algorithm outperformed the other algorithms. High-quality segmented masks of the underlying surfaces were generated, and the area of each instance was obtained by counting the true-positive pixels with values of 1. This research promotes the accurate calculation of LSTs based on the low-altitude TIR sensing images.

Characterization and application of in situ curcumin/ZNP hydrogels for periodontitis treatment.

BACKGROUND: Periodontitis is a chronic inflammatory disease that occurs in tooth-supporting tissues. Controlling inflammation and alleviating periodontal tissue destruction are key factors in periodontal therapy. This study aimed to develop an in situ curcumin/zinc oxide (Cur/ZNP) hydrogel and investigate its characteristics and effectiveness in the treatment of periodontitis. METHODS: Antibacterial activity and cytotoxicity assays were performed in vitro. To evaluate the effect of the in situ Cur/ZNP hydrogel on periodontitis in vivo, an experimental periodontitis model was established in Sprague‒Dawley rats via silk ligature and inoculation of the maxillary first molar with Porphyromonas gingivalis. After one month of in situ treatment with the hydrogel, we examined the transcriptional responses of the gingiva to the Cur/ZNP hydrogel treatment and detected the alveolar bone level as well as the expression of osteocalcin (OCN) and osteoprotegerin (OPG) in the periodontal tissues of the rats. RESULTS: Cur/ZNPs had synergistic inhibitory effects on P. gingivalis and good biocompatibility. RNA sequencing of the gingiva showed that immune effector process-related genes were significantly induced by experimental periodontitis. Carcinoembryonic antigen-related cell adhesion molecule 1 (Ceacam1), which is involved in the negative regulation of bone resorption, was differentially regulated by the Cur/ZNP hydrogel but not by the Cur hydrogel or ZNP hydrogel. The Cur/ZNP hydrogel also had a stronger protective effect on alveolar bone resorption than both the Cur hydrogel and the ZNP hydrogel. CONCLUSION: The Cur/ZNP hydrogel effectively inhibited periodontal pathogenic bacteria and alleviated alveolar bone destruction while exhibiting favorable biocompatibility.

Interpretable prediction models for disability in older adults with hypertension: the Chinese Longitudinal Healthy Longevity and Happy Family Study.

BACKGROUND: Older adults with hypertension have a high risk of disability, while an accurate risk prediction model is still lacking. This study aimed to construct interpretable disability prediction models for older Chinese with hypertension based on multiple time intervals. METHODS: Data were collected from the Chinese Longitudinal Healthy Longevity and Happy Family Study for 2008-2018. A total of 1602, 1108, and 537 older adults were included for the periods of 2008-2012, 2008-2014, and 2008-2018, respectively. Disability was measured by basic activities of daily living. Least absolute shrinkage and selection operator (LASSO) was applied for feature selection. Five machine learning algorithms combined with LASSO set and full-variable set were used to predict 4-, 6-, and 10-year disability risk, respectively. Area under the receiver operating characteristic curve was used as the main metric for selection of the optimal model. SHapley Additive exPlanations (SHAP) was used to explore important predictors of the optimal model. RESULTS: Random forest in full-variable set and XGBoost in LASSO set were the optimal models for 4-year prediction. Support vector machine was the optimal model for 6-year prediction on both sets. For 10-year prediction, deep neural network in full variable set and logistic regression in LASSO set were optimal models. Age ranked the most important predictor. Marital status, body mass index, score of Mini-Mental State Examination, and psychological well-being score were also important predictors. CONCLUSIONS: Machine learning shows promise in screening out older adults at high risk of disability. Disability prevention strategies should specifically focus on older patients with unfortunate marriage, high BMI, and poor cognitive and psychological conditions.

Pairing d-Band Center of Metal Sites with π-Orbital of Alkynes for Efficient Electrocatalytic Alkyne Semi-Hydrogenation.

Electrocatalytic alkyne semi-hydrogenation has attracted ever-growing attention as a promising alternative to traditional thermocatalytic hydrogenation. However, the correlation between the structure of active sites and electrocatalytic performance still remains elusive. Herein, the energy difference (∆ε) between the d-band center of metal sites and π orbital of alkynes as a key descriptor for correlating the intrinsic electrocatalytic activity is reported. With two-dimensional conductive metal organic frameworks as the model electrocatalysts, theoretical and experimental investigations reveal that the decreased ∆ε induces the strengthened d-π orbitals interaction, which thus enhances acetylene π-adsorption and accelerates subsequent hydrogenation kinetics. As a result, Cu3 (HITP)2 featuring the smallest ∆ε (0.10 eV) delivers the highest turnover frequency of 0.36 s-1 , which is about 124 times higher than 2.9 × 10-3  s-1 for Co3 (HITP)2 with the largest ∆ε of 2.71 eV. Meanwhile, Cu3 (HITP)2 presents a high ethylene partial current density of -124 mA cm-2 and a large ethylene Faradaic efficiency of 99.3% at -0.9 V versus RHE. This work will spark the rapid exploration of high-activity alkyne semi-hydrogenation catalysts.

Predicting Alzheimer's Disease with Interpretable Machine Learning.

INTRODUCTION: This study aimed to develop novel machine learning models for predicting Alzheimer's disease (AD) and identify key factors for targeted prevention. METHODS: We included 1,219, 863, and 482 participants aged 60+ years with only sociodemographic, both sociodemographic and self-reported health, both the former two and blood biomarkers information from Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Machine learning models were constructed for predicting the risk of AD for the above three populations. Model performance was evaluated by discrimination, calibration, and clinical usefulness. SHapley Additive exPlanation (SHAP) was applied to identify key predictors of optimal models. RESULTS: The mean age was 73.49, 74.52, and 74.29 years for the three populations, respectively. Models with sociodemographic information and models with both sociodemographic and self-reported health information showed modest performance. For models with sociodemographic, self-reported health, and blood biomarker information, their overall performance improved substantially, specifically, logistic regression performed best, with an AUC value of 0.818. Blood biomarkers of ptau protein and plasma neurofilament light, age, blood tau protein, and education level were top five significant predictors. In addition, taurine, inosine, xanthine, marital status, and L.Glutamine also showed importance to AD prediction. CONCLUSION: Interpretable machine learning showed promise in screening high-risk AD individual and could further identify key predictors for targeted prevention.

Palladium-catalyzed [4 + 2] cycloaddition of 2-methylidenetrimethylene carbonate or methylene cyclic carbamate with sulfamate-derived cyclic imines.

A palladium-catalyzed [4 + 2] cycloaddition of 2-methylidenetrimethylene carbonate or methylene cyclic carbamate with sulfamate-derived cyclic imines has been successfully developed under mild reaction conditions, affording pharmacologically interesting oxazine or hydropyrimidine derivatives in high yields (up to 99% yield). Furthermore, the cycloaddition reactions could be efficiently scaled up and several synthetic transformations were accomplished for the construction of other useful 1,3-oxazine and hydropyrimidinone derivatives.

Loss of ß-catenin causes cementum hypoplasia by hampering cementogenic differentiation of Axin2-expressing cells.

BACKGROUND AND OBJECTIVE: Although cementum plays an essential role in tooth attachment and adaptation to occlusal force, the regulatory mechanisms of cementogenesis remain largely unknown. We have previously reported that Axin2-expressing (Axin2+ ) mesenchymal cells in periodontal ligament (PDL) are the main cell source for cementum growth, and constitutive activation of Wnt/ß-catenin signaling in Axin2+ cells results in hypercementosis. Therefore, the aim of the present study was to further evaluate the effects of ß-catenin deletion in Axin2+ cells on cementogenesis. MATERIALS AND METHODS: We generated triple transgenic mice to conditionally delete ß-catenin in Axin2-lineage cells by crossing Axin2CreERT2/+ ; R26RtdTomato/+ mice with ß-cateninflox/flox mice. Multiple approaches, including X-ray analysis, micro-CT, histological stainings, and immunostaining assays, were used to analyze cementum phenotypes and molecular mechanisms. RESULTS: Our data revealed that loss of ß-catenin in Axin2+ cells led to a cementum hypoplasia phenotype characterized by a sharp reduction in the formation of both acellular and cellular cementum. Mechanistically, we found that conditional removal of ß-catenin in Axin2+ cells severely impaired the secretion of cementum matrix proteins, for example, bone sialoprotein (BSP), dentin matrix protein 1 (DMP1) and osteopontin (OPN), and markedly inhibited the differentiation of Axin2+ mesenchymal cells into osterix+ cementoblasts. CONCLUSIONS: Our findings confirm the vital role of Axin2+ mesenchymal PDL cells in cementum growth and demonstrate that Wnt/ß-catenin signaling shows a positive correlation with cementogenic differentiation of Axin2+ cells.

Expenditure projections for community home-based care services for older adults with functional decline in China.

INTRODUCTION: Difficulty in identifying the functional status of older adults creates an imbalance between the supply and demand for community home-based care. Using a multi-level functional classification system to guide care cost measurement may optimize care resources and meet diverse eldercare demands. METHODS: The Markov model was used to project the older population size in different functional decline (FD) statuses. The project cost and the man-hour costing method were combined to forecast the cost of community home-based care for older adults with FD. RESULTS: The projected cost of eldercare increased from 1668.623 billion yuan in 2020 to 2836.754 billion yuan in 2035. By 2035, the total cost for community-based home care for those in pathological development of FD statuses such as "viability disorder," "acute disease," "somatic functional disorder," and "sub-disorder" was projected to be 1094.591 billion, 433.855 billion, 1256.236 billion, and 52.072 billion yuan, respectively, which is 1.24, 1.58, 1.78, and 0.49 times higher than the results by the man-hour costing method. Family caregiving costs are about three times those of professional caregivers. CONCLUSION: The escalating cost of providing graded care for older adults, particularly by family caregivers, presenting a significant evidence for the need to optimize resource allocation and develop a robust human resources plan for community home-based care.

Investigating predictors of progression from mild cognitive impairment to Alzheimer's disease based on different time intervals.

BACKGROUND: Mild cognitive impairment (MCI) is the early stage of AD, and about 10-12% of MCI patients will progress to AD every year. At present, there are no effective markers for the early diagnosis of whether MCI patients will progress to AD. This study aimed to develop machine learning-based models for predicting the progression from MCI to AD within 3 years, to assist in screening and prevention of high-risk populations. METHODS: Data were collected from the Alzheimer's Disease Neuroimaging Initiative, a representative sample of cognitive impairment population. Machine learning models were applied to predict the progression from MCI to AD, using demographic, neuropsychological test and MRI-related biomarkers. Data were divided into training (56%), validation (14%) and test sets (30%). AUC (area under ROC curve) was used as the main evaluation metric. Key predictors were ranked utilising their importance. RESULTS: The AdaBoost model based on logistic regression achieved the best performance (AUC: 0.98) in 0-6 month prediction. Scores from the Functional Activities Questionnaire, Modified Preclinical Alzheimer Cognitive Composite with Trails test and ADAS11 (Unweighted sum of 11 items from The Alzheimer's Disease Assessment Scale-Cognitive Subscale) were key predictors. CONCLUSION: Through machine learning, neuropsychological tests and MRI-related markers could accurately predict the progression from MCI to AD, especially in a short period time. This is of great significance for clinical staff to screen and diagnose AD, and to intervene and treat high-risk MCI patients early.

Effect of lipoxin A4 on the osteogenic differentiation of periodontal ligament stem cells under lipopolysaccharide-induced inflammatory conditions.

Lipoxin A4 (LXA4) has been identified as the braking signal of inflammation, but the specific role of LXA4 in regulating the regenerative potential of periodontal ligament stem cells (PDLSCs) remains unclear. The aim of this study was to investigate whether and, if so, how LXA4 improves the osteogenic differentiation of PDLSCs in a lipopolysaccharide (LPS)-induced inflammatory environment. We detected the effects of LXA4 on the osteogenic differentiation of PDLSCs in vitro and explored the bone regenerative potential of LXA4-treated inflammatory PDLSCs in vivo using a calvarial critical sized defect model in male rats. RNA sequencing, real-time PCR and western blot were performed to elucidate the relevant potential mechanisms. Results showed that LXA4 promoted the proliferation, migration and osteogenic differentiation of PDLSCs in vitro, and effectively improved the impaired osteogenic capacity of PDLSCs induced by LPS both in vitro and in vivo. Mechanistically, LXA4 significantly promoted the PI3K/AKT phosphorylation under inflammatory conditions. Additionally, LY294002 (a PI3K inhibitor) blocked the effect of LXA4, suggesting that the PI3K/AKT pathway is a key signaling pathway that mediates the effect of LXA4 on the osteogenesis of inflammatory PDLSCs. These findings indicate LXA4 may be a promising strategy for periodontal regeneration using inflammatory PDLSCs.

Activation of Wnt signaling in Axin2+ cells leads to osteodentin formation and cementum overgrowth.

OBJECTIVES: In this study, we used the mouse incisor model to investigate the regulatory mechanisms of Wnt/ß-catenin signaling on Axin2+ cells in tooth development. MATERIALS AND METHODS: Axin2lacZ/+ reporter mice were used to define the expression pattern of Axin2 in mouse incisors. We traced the fate of Axin2+ cells from postnatal Day 21 (P21) to P56 using Axin2CreERT2/+ and R26RtdTomato/+ reporter mice. For constitutive activation of Wnt signaling, Axin2CreERT2/+ , ß-cateninflox(Ex3)/+ , and R26RtdTomato/+ (CA-ß-cat) mice were generated to investigate the gain of function (GOF) of ß-catenin in mouse incisor growth. RESULTS: The X-gal staining of Axin2lacZ/+ reporter mice and lineage tracing showed that Axin2 was widely expressed in dental mesenchyme of mouse incisors, and Axin2+ cells were essential cell sources for odontoblasts, pulp cells, and periodontal ligament cells. The constitutive activation of Wnt signaling in Axin2+ cells resulted in the formation of osteodentin featured with increased DMP1 and dispersed DSP expression and overgrowth of cementum. CONCLUSION: Wnt signaling plays a key role in the differentiation and maturation of Axin2+ cells in mouse incisors.