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Photothermal conversion material that generates thermal energy from sunlight is expected to be a promising technology for harvesting and conversion of clean solar energy. However, its photothermal conversion will be affected due to the surface contamination by dirt in outdoor applications, which greatly reduces the solar energy absorption. Herein, a superhydrophobic photothermal conversion fabric was fabricated through layer-by-lay self-assembly of carbon nanotubes (CNTs) on the surface of fibers, and followed by post-treatment with polydimethylsiloxane (PDMS). The CNTs not only impart to the fabric high photothermal conversion capability, but also construct roughening structures on the fiber surface. Consequently, the as-prepared fabric could be rapidly heated to 89.8 °C under one sun (1 kW/m2) irradiation, and showed excellent superhydrophobicity with contact angle 165° ± 0.9° and rolling angle 0.6° ± 0.2°. Meanwhile, the fabric possessed excellent stability to organic solvent, acid/alkali solutions, and UV irradiation. Importantly, the superhydrophobic self-cleaning property prevents the photothermal coating from contamination to guarantee the durability of the photothermal conversion efficiency.Graphic abstract

Formalized knowledge representation is the foundation of Big Data computing, mining and visualization. Current knowledge representations regard information as items linked to relevant objects or concepts by tree or graph structures. However, geographic knowledge differs from general knowledge, which is more focused on temporal, spatial, and changing knowledge. Thus, discrete knowledge items are difficult to represent geographic states, evolutions, and mechanisms, e.g., the processes of a storm “9:30-60 mm-precipitation-12:00-80 mm-precipitation-…”. The underlying problem is the constructors of the logic foundation (ALC description language) of current geographic knowledge representations, which cannot provide these descriptions. To address this issue, this study designed a formalized geographic knowledge representation called GeoKG and supplemented the constructors of the ALC description language. Then, an evolution case of administrative divisions of Nanjing was represented with the GeoKG. In order to evaluate the capabilities of our formalized model, two knowledge graphs were constructed by using the GeoKG and the YAGO by using the administrative division case. Then, a set of geographic questions were defined and translated into queries. The query results have shown that GeoKG results are more accurate and complete than the YAGO’s with the enhancing state information. Additionally, the user evaluation verified these improvements, which indicates it is a promising powerful model for geographic knowledge representation.

Background The relationship between periodontitis and cardiovascular disease (CVD) has been extensively studied, but the role of biological aging in this relationship remains poorly understood. This study is dedicated to investigating the effect of periodontitis on the incidence of CVD and to elucidating the potential mediating role of biological aging. Furthermore, this study will seek to elucidate the causal association between periodontitis, CVD, and biological aging. Methods We included 3269 participants from the National Health and Nutrition Examination Survey (2009–2014) with diagnostic information on periodontitis and composite CVD events. Biological aging was evaluated by utilizing both the Klemera–Doubal method’s calculated biological age (KDMAge) and phenotypic age (PhenoAge). Logistic regression, restricted cubic spline (RCS) analysis, and subgroup analysis were used for data analysis. Mediation analysis was employed to explore the mediating role of biological aging. Subsequently, Mendelian randomization (MR) analyses were performed using genome-wide association study databases to explore potential causal relationships between periodontitis, CVD, and biological aging. Results Periodontitis was associated with a higher risk of CVD. Participants with periodontitis were found to have increased levels of biological aging, and elevated levels of biological aging were associated with increased CVD risk. Mediation analyses showed a partial mediating effect of biological aging (PhenoAge: 44.6%; KDMAge: 22.9%) between periodontitis and CVD risk. MR analysis showed that periodontitis played a causal role in increasing the risk of small vessel stroke, while myocardial infarction was found to increase the risk of periodontitis. In addition, reverse MR analysis showed that phenotypic aging can increase the risk of periodontitis, and there is a two-way causal relationship between CVD and biological aging. Conclusions Periodontitis is associated with an increased CVD risk, partially mediated by biological aging, with a complex causal interrelationship. Targeted interventions for periodontal health may slow the biological aging processes and reduce CVD risk.

Objective To evaluate the antibacterial effectiveness of a combination of ε-poly-L-lysine (ε-PL), funme peptide (FP) as well as domiphen against oral pathogens, and assess the efficacy of a BOP® mouthwash supplemented with this combination in reducing halitosis and supragingival plaque in a clinical trial. Materials and methods The minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) of the compound against Fusobacterium nucleatum , Porphyromonas gingivalis , Streptococcus mutans , and Aggregatibacter actinomycetemcomitans were determined by the gradient dilution method. Subsequently, the CCK-8 assay was used to detect the toxicity of mouthwash on human gingival fibroblastst, and the effectiveness in reducing halitosis and supragingival plaque of the mouthwash supplemented with the combination was analyzed by a randomized, double-blind, parallel-controlled clinical trial. Results The combination exhibited significant inhibitory effects on tested oral pathogens with the MIC < 1.56% (v/v) and the MBC < 3.13% (v/v), and the mouthwash containing this combination did not inhibit the viability of human gingival fibroblasts at the test concentrations. The clinical trial showed that the test group displayed notably lower volatile sulfur compounds (VSCs) at 0, 10, 24 h, and 7 d post-mouthwash ( P  < 0.05), compared with the baseline. After 7 days, the VSC levels of the and control groups were reduced by 50.27% and 32.12%, respectively, and notably cutting severe halitosis by 57.03% in the test group. Additionally, the Plaque Index (PLI) of the test and control group decreased by 54.55% and 8.38%, respectively, and there was a significant difference in PLI between the two groups after 7 days ( P  < 0.01). Conclusions The combination of ε-PL, FP and domiphen demonstrated potent inhibitory and bactericidal effects against the tested oral pathogens, and the newly formulated mouthwash added with the combination exhibited anti-dental plaque and anti-halitosis properties in a clinical trial and was safe. Trial registration The randomized controlled clinical trial was registered on Chinese Clinical Trial Registry (No. ChiCTR2300073816, Date: 21/07/2023).

In Geographical Information Systems, geo-coding is used for the task of mapping from implicitly geo-referenced data to explicitly geo-referenced coordinates. At present, an enormous amount of implicitly geo-referenced information is hidden in unstructured text, e.g., Wikipedia, social data and news. Toponym recognition is the foundation of mining this useful geo-referenced information by identifying words as toponyms in text. In this paper, we propose an adapted toponym recognition approach based on deep belief network (DBN) by exploring two key issues: word representation and model interpretation. A Skip-Gram model is used in the word representation process to represent words with contextual information that are ignored by current word representation models. We then determine the core hyper-parameters of the DBN model by illustrating the relationship between the performance and the hyper-parameters, e.g., vector dimensionality, DBN structures and probability thresholds. The experiments evaluate the performance of the Skip-Gram model implemented by the Word2Vec open-source tool, determine stable hyper-parameters and compare our approach with a conditional random field (CRF) based approach. The experimental results show that the DBN model outperforms the CRF model with smaller corpus. When the corpus size is large enough, their statistical metrics become approaching. However, their recognition results express differences and complementarity on different kinds of toponyms. More importantly, combining their results can directly improve the performance of toponym recognition relative to their individual performances. It seems that the scale of the corpus has an obvious effect on the performance of toponym recognition. Generally, there is no adequate tagged corpus on specific toponym recognition tasks, especially in the era of Big Data. In conclusion, we believe that the DBN-based approach is a promising and powerful method to extract geo-referenced information from text in the future.

Sufficient bone volume is indispensable to achieve functional and aesthetic results in the fields of oral oncology, trauma, and implantology. Currently, guided bone regeneration (GBR) is widely used in reconstructing the alveolar ridge and repairing bone defects owing to its low technical sensitivity and considerable osteogenic effect. However, traditional barrier membranes such as collagen membranes or commercial titanium mesh cannot meet clinical requirements, such as lack of space-preserving ability, or may lead to more complications. With the development of digitalization and three-dimensional printing technology, the above problems can be addressed by employing customized barrier membranes to achieve space maintenance, precise predictability of bone graft, and optimization of patient-specific strategies. The article reviews the processes and advantages of three-dimensional computer-assisted surgery with GBR in maxillofacial reconstruction and alveolar bone augmentation; the properties of materials used in fabricating customized bone regeneration sheets; the promising bone regeneration potency of customized barrier membranes in clinical applications; and up-to-date achievements. This review aims to present a reference on the clinical aspects and future applications of customized barrier membranes.

The horseshoe crab Tachypleus tridentatus is a unique marine species and a potential model for marine invertebrate. Limited genomic and transcriptional data are currently available to understand the molecular mechanisms underlying the embryonic development of T. tridentatus. Here, we reported for the first time the de novo transcriptome assembly for T. tridentatus at embryonic developmental stage using Illumina RNA-seq platform. Approximate 38 million reads were obtained and further assembled into 133,212 unigenes. Sequence homology analysis against public databases revealed that 33,796 unigenes could be annotated with gene descriptions. Of the annotated unigenes, we identified a number of key components of several conserved metazoan signaling pathways (Hedgehog, Wnt, TGF-beta and Notch pathways) and other important regulatory genes involved in embryonic development. Targeted searching of Pax family genes which play critical roles in the formation of tissue and organ during embryonic development identified a complete set of Pax family genes. Moreover, the full length T. tridentatus Pax1/9a (TtPax1/9a) and Pax1/9b (TtPax1/9b) cDNA sequences were determined based on the transcriptome, demonstrating the immediate application of our database. Using quantitative real time PCR, we analyzed the expression patterns of TtPax1/9a and TtPax1/9b in different tissues of horseshoe crab. Taking advantage of Drosophila model, we further found that TtPax1/9b, but not TtPax1/9a, can partly rescue the Drosophila homolog Poxm dysfunction-caused lethality at the larval stage. Our study provides the embryonic transcriptome of T. tridentatus which could be immediately used for gene discovery and characterization, functional genomics studies in T. tridentatus. This transcriptome database will also facilitate the investigations of molecular mechanisms underlying embryonic development of T. tridentatus and other marine arthropods as well.

This study aims to demonstrate the use of the tree-based machine learning algorithms to predict the 3- and 5-year disease-specific survival of oral and pharyngeal cancers (OPCs) and compare their performance with the traditional Cox regression. A total of 21,154 individuals diagnosed with OPCs between 2004 and 2009 were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Three tree-based machine learning algorithms (survival tree (ST), random forest (RF) and conditional inference forest (CF)), together with a reference technique (Cox proportional hazard models (Cox)), were used to develop the survival prediction models. To handle the missing values in predictors, we applied the substantive model compatible version of the fully conditional specification imputation approach to the Cox model, whereas we used RF to impute missing data for the ST, RF and CF models. For internal validation, we used 10-fold cross-validation with 50 iterations in the model development datasets. Following this, model performance was evaluated using the C-index, integrated Brier score (IBS) and calibration curves in the test datasets. For predicting the 3-year survival of OPCs with the complete cases, the C-index in the development sets were 0.77 (0.77, 0.77), 0.70 (0.70, 0.70), 0.83 (0.83, 0.84) and 0.83 (0.83, 0.86) for Cox, ST, RF and CF, respectively. Similar results were observed in the 5-year survival prediction models, with C-index for Cox, ST, RF and CF being 0.76 (0.76, 0.76), 0.69 (0.69, 0.70), 0.83 (0.83, 0.83) and 0.85 (0.84, 0.86), respectively, in development datasets. The prediction error curves based on IBS showed a similar pattern for these models. The predictive performance remained unchanged in the analyses with imputed data. Additionally, a free web-based calculator was developed for potential clinical use. In conclusion, compared to Cox regression, ST had a lower and RF and CF had a higher predictive accuracy in predicting the 3- and 5-year OPCs survival using SEER data. The RF and CF algorithms provide non-parametric alternatives to Cox regression to be of clinical use for estimating the survival probability of OPCs patients.

Purpose This systematic review aims to assess the performance, methodological quality and reporting transparency in prediction models for the dental implant’s complications and survival rates. Methods A literature search was conducted in PubMed, Web of Science, and Embase databases. Peer-reviewed studies that developed prediction models for dental implant’s complications and survival rate were included. Two reviewers independently evaluated the risk of bias and reporting quality using the PROBAST and TRIPOD guidelines. The performance of the models were also compared in this study. The review followed the PRISMA guidelines and was registered with PROSPERO (CRD42019122274). Results The initial screening yielded 1769 publications, from which 14 studies featuring 43 models were selected. Four of the 14 studies predicted peri-implantitis as the most common outcome. Three studies predicted the marginal bone loss, two predicted suppuration of peri-implant tissue. The remaining five models predicted the implant loss, osseointergration or other complication. Common predictors included implant position, length, patient age, and a history of periodontitis. Sixteen models showed good to excellent discrimination (AUROC >0.8), but only three had undergone external validation. A significant number of models lacked model presentation. Most studies had a high or unclear risk of bias, primarily due to methodological limitation. The included studies conformed to 18–27 TRIPOD checklist items. Conclusions The current prediction models for dental implant complications and survival rate have limited methodological quality and external validity. There is a need for enhanced reliability, generalizability, and clinical applicability in future models.

To evaluate the antibacterial effectiveness of a combination of [epsilon]-poly-L-lysine ([epsilon]-PL), funme peptide (FP) as well as domiphen against oral pathogens, and assess the efficacy of a BOP[R] mouthwash supplemented with this combination in reducing halitosis and supragingival plaque in a clinical trial. The minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) of the compound against Fusobacterium nucleatum, Porphyromonas gingivalis, Streptococcus mutans, and Aggregatibacter actinomycetemcomitans were determined by the gradient dilution method. Subsequently, the CCK-8 assay was used to detect the toxicity of mouthwash on human gingival fibroblastst, and the effectiveness in reducing halitosis and supragingival plaque of the mouthwash supplemented with the combination was analyzed by a randomized, double-blind, parallel-controlled clinical trial. The combination exhibited significant inhibitory effects on tested oral pathogens with the MIC < 1.56% (v/v) and the MBC < 3.13% (v/v), and the mouthwash containing this combination did not inhibit the viability of human gingival fibroblasts at the test concentrations. The clinical trial showed that the test group displayed notably lower volatile sulfur compounds (VSCs) at 0, 10, 24 h, and 7 d post-mouthwash (P < 0.05), compared with the baseline. After 7 days, the VSC levels of the and control groups were reduced by 50.27% and 32.12%, respectively, and notably cutting severe halitosis by 57.03% in the test group. Additionally, the Plaque Index (PLI) of the test and control group decreased by 54.55% and 8.38%, respectively, and there was a significant difference in PLI between the two groups after 7 days (P < 0.01). The combination of [epsilon]-PL, FP and domiphen demonstrated potent inhibitory and bactericidal effects against the tested oral pathogens, and the newly formulated mouthwash added with the combination exhibited anti-dental plaque and anti-halitosis properties in a clinical trial and was safe.