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.

Simple fabrication of electrochemical sensor based on integration of dual signal amplification by the supporting electrode and modified nanochannel array for direct and sensitive detection of vitamin B2.

Development of simple and reliable sensor for detecting vitamin content is of great significance for guiding human nutrition metabolism, overseeing the quality of food or drugs, and advancing the treatment of related diseases. In this work, a simple electrochemical sensor was conveniently fabricated by modification a carbon electrode with vertically-ordered mesoporous silica film (VMSF), enabling highly sensitive electrochemical detection of vitamin B2 (VB2) based on the dual enrichment of the analyte by the supporting electrode and nanochannels. The widely used glassy carbon electrode (GCE), was preactivated using simple electrochemical polarization, The resulting preactivated GCE (p-GCE) exhibited improved potential resolution ability and enhanced peak current of VB2. Stable modification of VMSF on p-GCE (VMSF/p-GCE) was achieved without introducing another binding layer. The dual enrichment effect of the supporting p-GCE and nanochannels facilitated sensitive detection of VB2, with a linear concentration ranged from 20 nM to 7 µM and from 7 µM to 20 µM. The limit of detection (LOD), determined based on a signal-to-noise ratio of three (S/N = 3), was found to be 11 nM. The modification of ultra-small nanochannels of VMSF endowed VMSF/p-GCE with excellent anti-interference and anti-fouling performance, along with high stability. The constructed sensor demonstrated the capability to achieve direct electrochemical detection of VB2 in turbid samples including milk and leachate of compound vitamin B tablet without the need for complex sample pretreatment. The fabricated electrochemical can be easily regenerated and has high reusability. The advantages of simple preparation, high detection performance, and good regeneration endow the constructed electrochemical sensor with great potential for direct detection of small molecule in complex samples.

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 2934 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 for sarcopenia, especially for 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.

Associations of polycyclic aromatic hydrocarbons mixtures with cardiovascular diseases mortality and all-cause mortality and the mediation role of phenotypic ageing: A time-to-event analysis.

The associations of polycyclic aromatic hydrocarbons (PAHs) with cardiovascular diseases (CVDs) and all-cause mortality are unclear, especially the joint effects of PAHs exposure. Meanwhile, no studies have examined the effect of phenotypic ageing on the relationship between PAHs and mortality. Therefore, this study aimed to investigate the independent and joint associations between PAHs and CVDs, all-cause mortality, and assess whether phenotypic age acceleration (PhenoAgeAccel) mediate this relationship. We retrospectively collected data of 11,983 adults from the National Health and Nutrition Examination Survey database. Firstly, Cox proportional hazards regression and restricted cubic splines were applied to evaluate the independent association of single PAH on mortality. Further, time-dependent Probit extension of Bayesian Kernel Machine Regression and quantile-based g-computation models were conducted to test the joint effect of PAHs on mortality. Then, difference method was used to calculate the mediation proportion of PhenoAgeAccel in the association between PAHs and mortality. Our results revealed that joint exposure to PAHs showed positive association with CVDs and all-cause mortality. By controlling potential confounders, 1-Hydroxynapthalene (1-NAP) (HR = 1.24, P = 0.035) and 2-Hydroxyfluorene (2-FLU) (HR = 1.25, P < 0.001) showed positive association with CVDs mortality, and they were the top 2 predictors (weight: 0.82 for 1-NAP, 0.14 for 2-FLU) of CVDs mortality. 1-NAP (HR = 1.15, P < 0.001) and 2-FLU (HR = 1.13, P < 0.001) also showed positive association with all-cause mortality, and they were also the top 2 predictors of all-cause mortality (weight: 0.66 for 1-NAP, 0.34 for 2-FLU). PhenoAgeAccel mediated the relationship between 1-NAP, 2-FLU and CVDs, all-cause mortality, with a mediation proportion of 10.00 % to 24.90 % (P < 0.05). Specifically, the components of PhenoAgeAccel including C-reactive protein, lymphocyte percent, white blood cell count, red cell distribution width, and mean cell volume were the main contributors of mediation effects. Our study highlights the hazards of joint exposure of PAHs and the importance of phenotypic ageing on the relationship between PAHs and mortality.

LepR-expressing cells are a critical population in periodontal healing post periodontitis.

Identification of promising seed cells plays a pivotal role in achieving tissue regeneration. This study demonstrated that LepR-expressing cells (LepR+ cells) are required for maintaining periodontal homeostasis at the adult stage. We further investigated how LepR+ cells behave in periodontal healing using a ligature-induced periodontitis (PD) and a self-healing murine model with LepRCre/+; R26RtdTomato/+ mice. Lineage tracing experiments revealed that the largely suppressed osteogenic ability of LepR+ cells results from periodontal inflammation. Periodontal defects were partially recovered when the ligature was removed, in which the osteogenic differentiation of LepR+ cell lineage was promoted and contributed to the newly formed alveolar bone. A cell ablation model established with LepRCre/+; R26RtdTomato/+; R26RDTA/+ mice further proved that LepR+ cells are an important cell source of newly formed alveolar bone. Expressions of ß-catenin and LEF1 in LepR+ cells were upregulated when the inflammatory stimuli were removed, which are consistent with the functional changes observed during periodontal healing. Furthermore, the conditional upregulation of WNT signaling or the application of sclerostin neutralized antibody promoted the osteogenic function of LepR+ cells. In contrast, the specific knockdown of ß-catenin in LepR+ human periodontal ligament cells with small interfering RNA caused arrested osteogenic function. Our findings identified the LepR+ cell lineage as a critical cell population for endogenous periodontal healing post PD, which is regulated by the WNT signaling pathway, making it a promising seed cell population in periodontal tissue regeneration.

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.

CYP1B1 affects the integrity of the blood-brain barrier and oxidative stress in the striatum: An investigation of manganese-induced neurotoxicity.

AIMS: Excessive influx of manganese (Mn) into the brain across the blood-brain barrier induces neurodegeneration. CYP1B1 is involved in the metabolism of arachidonic acid (AA) that affects vascular homeostasis. We aimed to investigate the effect of brain CYP1B1 on Mn-induced neurotoxicity. METHOD: Brain Mn concentrations and α-synuclein accumulation were measured in wild-type and CYP1B1 knockout mice treated with MnCl2 (30 mg/kg) and biotin (0.2 g/kg) for 21 continuous days. Tight junctions and oxidative stress were analyzed in hCMEC/D3 and SH-SY5Y cells after the treatment with MnCl2 (200 µM) and CYP1B1-derived AA metabolites (HETEs and EETs). RESULTS: Mn exposure inhibited brain CYP1B1, and CYP1B1 deficiency increased brain Mn concentrations and accelerated α-synuclein deposition in the striatum. CYP1B1 deficiency disrupted the integrity of the blood-brain barrier (BBB) and increased the ratio of 3, 4-dihydroxyphenylacetic acid (DOPAC) to dopamine in the striatum. HETEs attenuated Mn-induced inhibition of tight junctions by activating PPARγ in endothelial cells. Additionally, EETs attenuated Mn-induced up-regulation of the KLF/MAO-B axis and down-regulation of NRF2 in neuronal cells. Biotin up-regulated brain CYP1B1 and reduced Mn-induced neurotoxicity in mice. CONCLUSIONS: Brain CYP1B1 plays a critical role in both cerebrovascular and dopamine homeostasis, which might serve as a novel therapeutic target for the prevention of Mn-induced neurotoxicity.

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.

Efficacy of photodynamic therapy as an adjunct to scaling and root planing on clinical parameters and microbial composition in subgingival plaque of periodontitis patients: A split-mouth randomized clinical trial.

BACKGROUND: The aim of this study was to assess the efficacy of photodynamic therapy (PDT) as an adjunct to scaling and root planing (SRP) on clinical parameters and microbial composition in subgingival plaque of periodontitis patients. METHODS: Seventeen patients were included in this split-mouth randomized clinical trial. Sites with probing pocket depth (PPD) ≥5 mm in combination with bleeding on probing in different quadrants were randomized into the control group, the group with a single PDT application right after SRP, and the group with three repeated PDT applications 1 week after SRP. The subgingival plaque was collected for 16S rRNA gene sequencing at baseline, Week 2, and Week 8. RESULTS: Seventeen patients with 60 sites completed this 8-week follow-up, and 157 subgingival plaques were successfully analyzed by sequencing. Significant improvements were observed in two primary outcomes: PPD at Week 8 and subgingival microbial composition. Compared to the control group, the repeated-PDT group showed a notable improvement in PPD, substantial alterations in the microbial profile, including a reduction in α-diversity and anaerobic bacteria, and an increase in aerobic bacteria at Week 2. Secondary outcomes, such as clinical attachment level and sulcus bleeding index, also showed improvement at Week 8. Furthermore, both the single- and repeated-PDT groups exhibited a decrease in periodontopathogens and an increase in beneficial bacteria compared with baseline. CONCLUSION: PDT promotes changes in the microbial composition of periodontitis patients' subgingival plaque in a direction favorable to periodontal health, and repeated PDT is a promising adjunctive therapy for periodontal treatment.

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.

α-Ketoglutarate Improves Ovarian Reserve Function in Primary Ovarian Insufficiency by Inhibiting NLRP3-Mediated Pyroptosis of Granulosa Cells.

SCOPE: Premature ovarian insufficiency (POI) is a common female infertility problem, with its pathogenesis remains unknown. The NOD-like receptor family pyrin domain-containing 3 (NLRP3)-mediated pyroptosis has been proposed as a possible mechanism in POI. This study investigates the therapeutic effect of α-ketoglutarate (AKG) on ovarian reserve function in POI rats and further explores the potential molecular mechanisms. METHODS AND RESULTS: POI rats are caused by administration of cyclophosphamide (CTX) to determine whether AKG has a protective effect. AKG treatment increases the ovarian index, maintains both serum hormone levels and follicle number, and improves the ovarian reserve function in POI rats, as evidence by increased the level of lactate and the expression of rate-limiting enzymes of glycolysis in the ovaries, additionally reduced the expression of NLRP3, Gasdermin D (GSDMD), Caspase-1, Interleukin-18 (IL-18), and Interleukin-1 beta (IL-1ß). In vitro, KGN cells are treated with LPS and nigericin to mimic pyroptosis, then treated with AKG and MCC950. AKG inhibits inflammatory and pyroptosis factors such as NLRP3, restores the glycolysis process in vitro, meanwhile inhibition of NLRP3 has the same effect. CONCLUSION: AKG ameliorates CTX-induced POI by inhibiting NLRP3-mediated pyroptosis, which provides a new therapeutic strategy and drug target for clinical POI patients.

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.

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.

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.

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.

Identifying and predicting physical limitation and cognitive decline trajectory group of older adults in China: A data-driven machine learning analysis.

OBJECTIVE: This study aimed to utilize data-driven machine learning methods to identify and predict potential physical and cognitive function trajectory groups of older adults and determine their crucial factors for promoting active ageing in China. METHODS: Longitudinal data on 3026 older adults from the Chinese Longitudinal Healthy Longevity and Happy Family Survey was used to identify potential physical and cognitive function trajectory groups using a group-based multi-trajectory model (GBMTM). Predictors were selected from sociodemographic characteristics, lifestyle factors, and physical and mental conditions. The trajectory groups were predicted using data-driven machine learning models and dynamic nomogram. Model performance was evaluated by area under the receiver operating characteristics curve (AUROC), area under the precision-recall curve (PRAUC), and confusion matrix. RESULTS: Two physical and cognitive function trajectory groups were determined, including a trajectory group with physical limitation and cognitive decline (14.18 %) and a normal trajectory group (85.82 %). Logistic regression performed well in predicting trajectory groups (AUROC = 0.881, PRAUC = 0.649). Older adults with lower baseline score of activities of daily living, older age, less frequent housework, and fewer actual teeth were more likely to experience physical limitation and cognitive decline trajectory group. LIMITATION: This study didn't carry out external validation. CONCLUSIONS: This study shows that GBMTM and machine learning models effectively identify and predict physical limitation and cognitive decline trajectory group. The identified predictors might be essential for developing targeted interventions to promote healthy ageing.

Predicting mild cognitive impairment in older adults: A machine learning analysis of the Alzheimer's Disease Neuroimaging Initiative.

AIM: Mild cognitive impairment (MCI) in older adults is potentially devastating, but an accurate prediction model is still lacking. We hypothesized that neuropsychological tests and MRI-related markers could predict the onset of MCI early. METHODS: We analyzed data from 306 older adults who were cognitive normal (CN) attending the Alzheimer's Disease Neuroimaging Initiative sequentially (474 pairs of visits) within 3 years. There were 231 pairs of MCI conversion (CN to MCI), and 242 pairs of CN maintenance (CN to CN). Variables on demographic, neuropsychological tests, genetic, and MRI-related markers were collected. Machine learning was used to construct MCI prediction models, comparing the area under the receiver operating characteristic curve (AUC) as the primary metric of performance. Important predictors were ranked for the optimal model. RESULTS: The baseline age of the study sample was 74.8 years old. The best-performing model (gradient boosting decision tree) with 13 variables predicted MCI with an AUC of 0.819, and the rank of variable importance showed that intracranial volume, hippocampal volume, and score from task 4 (word recognition) of the Alzheimer's Disease Assessment Scale were important predictors of MCI. CONCLUSIONS: With the help of machine learning, fewer neuropsychological tests and MRI-related markers are required to accurately predict MCI within 3 years, thereby facilitating targeted intervention. Geriatr Gerontol Int 2024; 24: 96-101.

Application status of risk assessment models for periodontal disease / 口腔疾病防治

@#Risk assessment models for periodontal disease provide dentists with a precise and consolidated evaluation of the prognosis of periodontitis, enabling the formulation of personalized treatment plans. Periodontal risk assessment systems have been widely applied in clinical practice and research. The application fields of periodontal risk assessment systems vary based on the distinctions between clinical periodontal parameters and risk factors. The assessment models listed below are commonly used in clinical practice, including the periodontal risk calculator (PRC), which is an individual-based periodontal risk assessment tool that collects both periodontal and systemic information for prediction; the periodontal assessment tool (PAT), which allows for quantitative differentiation of stages of periodontal disease; the periodontal risk assessment (PRA) and modified periodontal risk assessment (mPRA), which are easy to use; and the classification and regression trees (CART), which assess the periodontal prognosis based on a single affected tooth. Additionally, there are orthodontic-periodontal combined risk assessment systems and implant periapical risk assessment systems tailored for patients needing multidisciplinary treatment. This review focuses on the current application status of periodontal risk assessment systems.

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.

A feedback loop of PPP and PI3K/AKT signal pathway drives regorafenib-resistance in HCC.

BACKGROUND: Hepatocellular carcinoma (HCC) is a principal type of liver cancer with high incidence and mortality rates. Regorafenib is a novel oral multikinase inhibitor for second-line therapy for advanced HCC. However, resistance to regorafenib is gradually becoming a dilemma for HCC and the mechanism remains unclear. In this study, we aimed to reveal the metabolic profiles of regorafenib-resistant cells and the key role and mechanism of the most relevant metabolic pathway in regorafenib resistance. METHODS: Metabolomics was performed to detect the metabolic alteration between drug-sensitive and regorafenib-resistant cells. Colony formation assay, CCK-8 assay and flow cytometry were applied to observe cell colony formation, cell proliferation and apoptosis, respectively. The protein and mRNA levels were detected by western blot and RT-qPCR. Cell lines of Glucose-6-phosphate dehydrogenase(G6PD) knockdown in regorafenib-resistant cells or G6PD overexpression in HCC cell lines were stably established by lentivirus infection technique. G6PD activity, NADPH level, NADPH/NADP+ ratio, the ratio of ROS positive cells, GSH level, and GSH/GSSG ratio were detected to evaluate the anti-oxidative stress ability of cells. Phosphorylation levels of NADK were evaluated by immunoprecipitation. RESULTS: Metabonomics analysis revealed that pentose phosphate pathway (PPP) was the most relevant metabolic pathway in regorafenib resistance in HCC. Compared with drug-sensitive cells, G6PD enzyme activity, NADPH level and NADPH/NADP+ ratio were increased in regorafenib-resistant cells, but the ratio of ROS positive cells and the apoptosis rate under the conditions of oxidative stress were decreased. Furthermore, G6PD suppression using shRNA or an inhibitor, sensitized regorafenib-resistant cells to regorafenib. In contrast, G6PD overexpression blunted the effects of regorafenib to drug-sensitive cells. Mechanistically, G6PD, the rate-limiting enzyme of PPP, regulated the PI3K/AKT activation. Furthermore, PI3K/AKT inhibition decreased G6PD protein expression, G6PD enzymatic activity and the capacity of PPP to anti-oxidative stress possibly by inhibited the expression and phosphorylation of NADK. CONCLUSION: Taken together, a feedback loop of PPP and PI3K/AKT signal pathway drives regorafenib-resistance in HCC and targeting the feedback loop could be a promising approach to overcome drug resistance.