Fine-Tuning Electron Transfer for Nanozyme Design.

Nanozymes, with their versatile composition and structural adaptability, present distinct advantages over natural enzymes including heightened stability, customizable catalytic activity, cost-effectiveness, and simplified synthesis process, making them as promising alternatives in various applications. Recent advancements in nanozyme research have shifted focus from serendipitous discovery toward a more systematic approach, leveraging machine learning, theoretical calculations, and mechanistic explorations to engineer nanomaterial structures with tailored catalytic functions. Despite its pivotal role, electron transfer, a fundamental process in catalysis, has often been overlooked in previous reviews. This review comprehensively summarizes recent strategies for modulating electron transfer processes to fine-tune the catalytic activity and specificity of nanozymes, including electron-hole separation and carrier transfer. Furthermore, the bioapplications of these engineered nanozymes, including antimicrobial treatments, cancer therapy, and biosensing are also introduced. Ultimately, this review aims to offer invaluable insights for the design and synthesis of nanozymes with enhanced performance, thereby advancing the field of nanozyme research.

Epigallocatechin gallate (EGCG) alleviates inflammation and endothelial dysfunction and improves pregnancy outcomes in preeclampsia (PE)-like rats via eNOS/Nrf2/HO-1 pathway.

BACKGROUND AND PURPOSE: Epigallocatechin gallate (EGCG), a natural antioxidant, has shown protective effect in many diseases. We explore the effect and potential regulatory mechanisms of EGCG in preeclampsia (PE)-like rats. METHODS AND MATERIALS: PE was mimicked in pregnant rats. EGCG was orally administered at a dosage of 25(Low, L) or 50 mg/kg (High, H) from gestational day (GD) 6-17. The blood pressure signatures, heart rates were monitored. The 24-h proteinuria and serum were analyzed. On GD 18, rats were sacrificed, and pups and placentas were weighed. Kidneys and placentas were analyzed using immunohistochemistry (IHC) and hematoxylin-eosin staining (H&E). Placentas were examined using western blot for sFlt1, eNOS, Nrf2, HO-1, SLC7A11. MDA, GSH, GPx and Fe2+ were measured. RESULTS: EGCG inhibits systolic blood pressure, BUN, CREA, ALT, AST, UA and proteinuria levels in PE-like rats. EGCG enhances the pup weight and crown-rump length and reduces the rate of fetus growth restriction in PE group. Endothelial dysfunction and infiltration of inflammatory cells were found in kidney cortex and placenta tissues in PE group and were inhibited by EGCG treatment. sFlt1 was activated in placentas in PE group and inhibited by EGCG while eNOS/Nrf2/HO-1 were inhibited in PE group and restored by EGCG. MDA and Fe concentrations were elevated in PE group and reduced by EGCG while the GSH level, SLC7A11 and the GPx activity were inhibited in PE group and restored by EGCG. CONCLUSION: EGCG alleviates inflammation, endothelial dysfunction and placental ferroptosis, improves pregnancy outcomes in PE-like rats via eNOS/Nrf2/HO-1.

Prediction of severe preeclampsia and intrauterine growth restriction based on serum placental exosome miR-520a-5p levels during the first-trimester.

BACKGROUND: To assess the predictive capabilities of serum exosomal levels of micro-RNA-520a-5p (miR-520a-5p) concerning the occurrence of severe preeclampsia (sPE) and fetal growth restriction (FGR) during the first trimester of pregnancy. METHODS: During the period spanning from October 2020 to October 2021, serum samples were procured from the first trimester and subsequently preserved by freezing at -80 ℃. These samples were obtained from 105 pregnant women in a nested case-control study. This cohort consisted of individuals who later developed sPE (sPE group, n = 35) and FGR (FGR group, n = 35) during the third trimester. Additionally, 35 women with normal blood pressure were denoted as normal pregnancy group. Serum samples from the first trimester were retrieved from all groups for further analysis after thawing. Exosomes were extracted from the serum samples collected during the first trimester and examined using transmission electron microscopy, western blot, and nanoparticle tracking analysis. Additionally, the determination of their placental origin was also established during the course of the study. Exosome miR-520a-5p levels were measured using real-time quantitative polymerase chain reaction assays, primarily involving quantitative reverse transcription polymerase chain reactions. Fetal placental tissues from the 3 groups were collected shortly after birth, and miR-520a-5p expression was measured using real-time quantitative polymerase chain reaction. Serum placental exosomes and fetal placental tissues were compared for miR-520a-5p levels. Placental trophoblasts were identified as the source of serum exosomes in all 3 groups. RESULTS: It was found that serum placental exosomes exhibited lower levels of miR-520a-5p in both the sPE and FGR groups when compared to the normal pregnancy group. This finding was consistent with observations made in postpartum placental tissues. The predictive accuracy for sPE using miR-520a-5p levels in serum placental exosomes during the first trimester was notably higher (area under the receiver operating characteristic curve = 0.806, P <.05) compared to the prediction of FGR (area under the receiver operating characteristic curve = 0.628, P <.05). CONCLUSION: Placenta-derived exosomes can be extracted from maternal serum during the first trimester of pregnancy and miR-520a-5p detected from the exosomes. The downregulation of miR-520a-5p serves as a more predictive indicator for the subsequent development of sPE compared to predicting FGR.

Modification and completion of geological structure knowledge graph based on pattern matching.

As a knowledge representation method, knowledge graph is widely used in intelligent question answering systems and recommendation systems. At present, the research on knowledge graph mainly focuses on information query and retrieval based on knowledge graph. In some domain knowledge graphs, specific subgraph structures (patterns) have specific physical meanings. Aiming at this problem, this paper proposes a method and framework of knowledge graph pattern mining based on gat. Firstly, the patterns with specific physical meaning were transformed into subgraph structures containing topological structures and entity attributes. Secondly, the subgraph structure of the pattern is regarded as the query graph, and the knowledge graph is regarded as the data graph, so that the problem is transformed into an approximate subgraph matching problem. Then, the improved relational graph attention network is used to fuse the adaptive edge deletion mechanism to realize the approximate subgraph matching of subgraph structure and attribute, so as to obtain the best matching subgraph. The proposed method is trained in an end-to-end manner. The approximate subgraph matching is realized on the existing data set, and the research work of key pattern mining of complex geological structure knowledge graph is carried out.

Interventions to reduce Salmonella and Campylobacter during chilling and post-chilling stages of poultry processing: a systematic review and meta-analysis.

Salmonella and Campylobacter are common bacterial hazards causing foodborne illnesses worldwide. A large proportion of Salmonella and Campylobacter illnesses are attributed to contaminated poultry products that are mishandled or under cooked. Processing interventions such as chilling and post-chill dip are critical to reducing microbial contamination of poultry. A comprehensive search of the literature published between 2000 and 2021 was conducted in the databases Web of Science, Academic Search Complete, and Academic OneFile. Studies were included if they were in English and investigated the effects of interventions against Salmonella and/or Campylobacter on whole carcasses and/or parts during the chilling or post-chill stages of poultry processing. Random-effects meta-analyses were performed using the "meta" package in the R programming language. Subgroup analyses were assessed according to outcome measure reported, microorganism tested, processing stage assessed, and chemical treatment used. The results included 41 eligible studies. Eighteen studies reported results of 28 separate interventions against Salmonella and 31 reported results of 50 separate interventions against Campylobacter. No significant difference (P> 0.05) was observed when comparing the combined mean difference of all interventions targeting Salmonella to the combined mean difference of all interventions targeting Campylobacter or when comparing chilling times within each pathogen subgroup. For analyses examining antimicrobial additives, peroxyacetic acid (PAA) had the largest reduction against Salmonella population regardless of chilling time (P< 0.05). PAA also had the largest reduction against Campylobacter population and prevalence during primary chilling (P< 0.01). Air chilling showed a lower reduction for Campylobacter than any immersion chilling intervention (P< 0.05). Chilling time and antimicrobial used during poultry processing had varying effects depending on the pathogen and outcome measure investigated (concentration or prevalence). High heterogeneity and low sample numbers in most analyses suggest that more high-quality research that is well-designed and has transparent reporting of methodology and results is needed to corroborate the results.

Construction of knowledge constraints: a case study of 3D structural modeling.

The uncertainty of structural interpretation complicates the practical production and application of data-driven complex geological structure modeling technology. Intelligent structural modeling excavates and extracts structural knowledge from structural interpretation through human-machine collaboration and combines structural interpretation to form a new model of complex structural modeling guided by knowledge. Specifically, we focus on utilizing knowledge rule reasoning technology to extract topological semantic knowledge from interpretive data and employ knowledge inference to derive structural constraint information from complex geological structure models, thus effectively constraining the 3D geological structure modeling process. To achieve this, we develop a rule-based knowledge inference system that derives theoretical models consistent with expert cognition from interpretive data and prior knowledge. Additionally, we represent the extracted knowledge as a topological semantic knowledge graph, which facilitates computer recognition and allows estimation of intersection lines during 3D geological modeling, resulting in the creation of accurate models. The applicability of our proposed method to various complex geological structures is validated through application tests using real-world data. Furthermore, our method effectively supports the realization of intelligent structure modeling in real working area.

Metal-Organic Framework-Based Nanomedicines for Ferroptotic Cancer Therapy.

As an iron-dependent, non-apoptosis, regulated cell death (RCD) modality, ferroptosis has gained growing attention for cancer therapy. With the development of nanomaterials in the biomedical field, ferroptotic cancer nanomedicine is extensively investigated. Amongst various nanomaterials, metal-organic frameworks (MOFs) are hybridized porous materials consisting of metal ions or clusters bridged by organic linkers. The superior properties of MOFs, such as high porosity and cargo loading, ease of surface modification, and good biocompatibility, make them appealing in inducing or sensitizing ferroptotic cell death. There are remarkable achievements in the field of MOF-based ferroptosis cancer therapy. However, this topic is not reviewed. This review will introduce the fundamentals of MOF and ferroptosis machinery, summarize the recent progress of MOF-based ferroptotic anticancer drug delivery, discuss the benefits and problems of MOFs as vehicles and sensitizers for cancer ferroptosis, and provide the perspective on future research direction on this promising field.

Effects of amoxicillin and metronidazole as an adjunct to scaling and root planing on glycemic control in patients with periodontitis and type 2 diabetes: A short-term randomized controlled trial.

OBJECTIVE: To assess the effects of amoxicillin and metronidazole with scaling and root planing (SRP) on periodontal parameters and glycemic control in patients with severe periodontitis and diabetes mellitus. BACKGROUND: Adjunctive antibiotics use is advantageous for treating periodontitis in patients with severe periodontitis and diabetes. However, the effects of adjunctive antibiotic use on hemoglobin A1c (HbA1c) levels remain unclear. METHODS: This short-term, randomized controlled trial enrolled patients with severe periodontitis and type 2 diabetes. The patients were randomly allocated to SPR only (i.e., control) or SPR + antibiotics (500 mg of amoxicillin and 200 mg of metronidazole, three times daily for 7 days) groups. Periodontal and hematological parameters were assessed at baseline and 3 months after treatment. Inter- and intra-group analyses were performed using Student's t-tests, Mann-Whitney U tests, and the binary logistic regression models. p-values of <.05 were considered statistically significant. RESULTS: This study enrolled 49 patients, with 23 and 26 patients in the SRP-only and SRP + antibiotics groups, respectively. The periodontal parameters improved significantly and similarly in both groups after treatment (p < .05). The SRP + antibiotics group had more sites of improvement than the SRP-only group when the initial probing depth was >6 mm. (698 [78.96%] vs. 545 [73.35%], p = .008). The HbA1c levels decreased in the SRP-only and SRP + antibiotics groups after treatment (0.39% and 0.53%, respectively). The multivariable binary logistic regression model demonstrated that antibiotics administration and a high baseline HbA1c level were associated with a greater reduction in the HbA1c level (odds ratio = 4.551, 95% confidence interval: 1.012-20.463; odds ratio = 7.162, 95% confidence interval: 1.359-37.753, respectively). CONCLUSIONS: SRP and SRP plus systemic antibiotics were beneficial for glycemic control. Adjunctive antibiotic use slightly improved the outcome for patients with severe periodontitis and poorly controlled diabetes.

Deep Learning Based Feature Selection and Ensemble Learning for Sintering State Recognition.

Sintering is a commonly used agglomeration process to prepare iron ore fines for blast furnace. The quality of sinter significantly impacts the blast furnace ironmaking process. In the vast majority of sintering plants, the judgment of sintering quality still relies on the intuitive observation of the cross section at sintering machine tail by operators, which is susceptible to the external environment and the experience of operators. In this paper, we propose a new sintering state recognition method using deep learning based feature selection and ensemble learning. First, features from the infrared thermal images of sinter cross section at the tail of the sinterer are extracted based on ResNeXt. Then, to eliminate the irrelevant, redundant and noisy features, an efficient feature selection method based on binary state transition algorithm (BSTA) is proposed to find the truly useful features. Subsequently, an ensemble learning (EL) method based on group decision making (GDM) is proposed to recognize the sintering states. Novel combination strategies considering the varying performance of the base learners are designed to further improve recognition accuracy. Industrial experiments conducted at a steel plant verify the effectiveness and superiority of the proposed method.

Reorganization of the Flagellum Scaffolding Induces a Sperm Standstill During Fertilization.

Mammalian sperm delve into the female reproductive tract to fertilize the female gamete. The available information about how sperm regulate their motility during the final journey to the fertilization site is extremely limited. In this work, we investigated the structural and functional changes in the sperm flagellum after acrosomal exocytosis and during the interaction with the eggs. The evidence demonstrates that the double helix actin network surrounding the mitochondrial sheath of the midpiece undergoes structural changes prior to the motility cessation. This structural modification is accompanied by a decrease in diameter of the midpiece and is driven by intracellular calcium changes that occur concomitant with a reorganization of the actin helicoidal cortex. Although midpiece contraction may occur in a subset of cells that undergo acrosomal exocytosis, live-cell imaging during in vitro fertilization showed that the midpiece contraction is required for motility cessation after fusion is initiated. These findings provide the first evidence of the F-actin network's role in regulating sperm motility, adapting its function to meet specific cellular requirements during fertilization, and highlighting the broader significance of understanding sperm motility. Significant statement: In this work, we demonstrate that the helical structure of polymerized actin in the flagellum undergoes a rearrangement at the time of sperm-egg fusion. This process is driven by intracellular calcium and promotes a decrease in the sperm midpiece diameter as well as the arrest in motility, which is observed after the fusion process is initiated.

Relationship of the Poultry Microbiome to Pathogen Colonization, Farm Management, Poultry Production, and Foodborne Illness Risk Assessment.

Despite the continuous progress in food science and technology, the global burden of foodborne illnesses remains substantial, with pathogens in food causing millions of infections each year. Traditional microbiological culture methods are inadequate in detecting the full spectrum of these microorganisms, highlighting the need for more comprehensive detection strategies. This review paper aims to elucidate the relationship between foodborne pathogen colonization and the composition of the poultry microbiome, and how this knowledge can be used for improved food safety. Our review highlights that the relationship between pathogen colonization varies across different sections of the poultry microbiome. Further, our review suggests that the microbiome profile of poultry litter, farm soil, and farm dust may serve as potential indicators of the farm environment's food safety issues. We also agree that the microbiome of processed chicken samples may reveal potential pathogen contamination and food quality issues. In addition, utilizing predictive modeling techniques on the collected microbiome data, we suggest establishing correlations between particular taxonomic groups and the colonization of pathogens, thus providing insights into food safety, and offering a comprehensive overview of the microbial community. In conclusion, this review underscores the potential of microbiome analysis as a powerful tool in food safety, pathogen detection, and risk assessment.

Assessing the Risk of Seasonal Effects of Campylobacter Contaminated Broiler Meat Prepared In-Home in the United States.

Campylobacter has consistently posed a food safety issue in broiler meat. This study aimed to create a quantitative microbial risk assessment model from retail to consumption, designed to evaluate the seasonal risk of campylobacteriosis associated with broiler meat consumption in the United States. To achieve this, data was gathered to build distributions that would enable us to predict the growth of Campylobacter during various stages such as retail storage, transit, and home storage. The model also included potential fluctuations in concentration during food preparation and potential cross-contamination scenarios. A Monte Carlo simulation with 100,000 iterations was used to estimate the risk of infection per serving and the number of infections in the United States by season. In the summer, chicken meat was estimated to have a median risk of infection per serving of 9.22 × 10-7 and cause an average of about 27,058,680 infections. During the winter months, the median risk of infection per serving was estimated to be 4.06 × 10-7 and cause an average of about 12,085,638 infections. The risk assessment model provides information about the risk of broiler meat to public health by season. These results will help understand the most important steps to reduce the food safety risks from contaminated chicken products.

Comparison of the inflammatory states of serum and gingival crevicular fluid in periodontitis patients with or without type 2 diabetes mellitus.

Background/purpose:There is a two-way relationship between periodontitis and type 2 diabetes mellitus. This study aimed to compare the inflammatory states in serum and gingival crevicular fluid (GCF) in periodontitis patients with or without type 2 diabetes mellitus (T2DM) and healthy subjects. Materials and methods: 20 subjects were systematic and periodontal healthy (H group), 40 subjects were with periodontitis (CP group), and other 40 were with periodontitis and type 2 diabetes mellitus (DC group). Fasting blood glucose (FBG) and HbA1c was tested. GCF and serum level of interleukin (IL) -17, visfatin, receptor activator of nuclear factor-kappa B (NF-κB) ligand (RANKL)/osteoprotegerin (OPG) ratio were measured. Results: The GCF volume, total amount of IL-17, vastatin, RANKL/OPG ratio in GCF and their concentrations in serum were higher (P < 0.05) in CP and DC groups than in H group, which were also higher (P < 0.05) in DC group than in CP group except for visfatin in GCF and IL-17 in serum. At sample sites of PD ≤ 3 mm, GCF volume, IL-17, visfatin and RANKL/OPG ratio in DC and CP groups were higher (P < 0.05) than that in H group, which were also higher in DC group than in CP group either with PD ≤ 3 mm or PD > 3 mm. Inflammatory state in GCF was positively correlated to systemic inflammation, and both of them were positively correlated to FBG. Conclusion: Moderate and severe periodontitis aggravated systemic inflammation. T2DM together with periodontitis resulted in more severe systemic inflammation. The positive correlation between the periodontal and systemic inflammation and their association with FBG indicated an inflammatory link between periodontitis and T2DM.

Integration of metabolomics and peptidomics reveals distinct molecular landscape of human diabetic kidney disease.

Diabetic kidney disease (DKD) is the most common microvascular complication of diabetes, and there is an urgent need to discover reliable biomarkers for early diagnosis. Here, we established an effective urine multi-omics platform and integrated metabolomics and peptidomics to investigate the biological changes during DKD pathogenesis. Methods: Totally 766 volunteers (221 HC, 198 T2DM, 175 early DKD, 125 overt DKD, and 47 grey-zone T2DM patients with abnormal urinary mALB concentration) were included in this study. Non-targeted metabolic fingerprints of urine samples were acquired on matrix-free LDI-MS platform by the tip-contact extraction method using fluorinated ethylene propylene coated silicon nanowires chips (FEP@SiNWs), while peptide profiles hidden in urine samples were uncovered by MALDI-TOF MS after capturing urine peptides by porous silicon microparticles. Results: After multivariate analysis, ten metabolites and six peptides were verified to be stepwise regulated in different DKD stages. The altered metabolic pathways and biological processes associated with the DKD pathogenesis were concentrated in amino acid metabolism and cellular protein metabolic process, which were supported by renal transcriptomics. Interestingly, multi-omics significantly increased the diagnostic accuracy for both early DKD diagnosis and DKD status discrimination. Combined with machine learning, a stepwise prediction model was constructed and 89.9% of HC, 75.5% of T2DM, 69.6% of early DKD and 75.7% of overt DKD subjects in the external validation cohort were correctly classified. In addition, 87.5% of grey-zone patients were successfully distinguished from T2DM patients. Conclusion: This multi-omics platform displayed a satisfactory ability to explore molecular information and provided a new insight for establishing effective DKD management.

Noncoding SNP at rs1663689 represses ADGRG6 via interchromosomal interaction and reduces lung cancer progression.

A previous genome-wide association study (GWAS) revealed an association of the noncoding SNP rs1663689 with susceptibility to lung cancer in the Chinese population. However, the underlying mechanism is unknown. In this study, using allele-specific 4C-seq in heterozygous lung cancer cells combined with epigenetic information from CRISPR/Cas9-edited cell lines, we show that the rs1663689 C/C variant represses the expression of ADGRG6, a gene located on a separate chromosome, through an interchromosomal interaction of the rs1663689 bearing region with the ADGRG6 promoter. This reduces downstream cAMP-PKA signaling and subsequently tumor growth both in vitro and in xenograft models. Using patient-derived organoids, we show that rs1663689 T/T-but not C/C-bearing lung tumors are sensitive to the PKA inhibitor H89, potentially informing therapeutic strategies. Our study identifies a genetic variant-mediated interchromosomal interaction underlying ADGRG6 regulation and suggests that targeting the cAMP-PKA signaling pathway may be beneficial in lung cancer patients bearing the homozygous risk genotype at rs1663689.

Platinum-based anticancer drugs-induced downregulation of myosin heavy chain isoforms in skeletal muscle of mouse.

Cisplatin, a platinum-based anticancer drug used frequently in cancer treatment, causes skeletal muscle atrophy. It was predicted that the proteolytic pathway is enhanced as the mechanism of this atrophy. Therefore, we investigated whether a platinum-based anticancer drug affects the expression of the major proteins of skeletal muscle, myosin heavy chain (MyHC). Mice were injected with cisplatin or oxaliplatin for four consecutive days. C2C12 myotubes were treated using cisplatin and oxaliplatin. Administration of platinum-based anticancer drug reduced quadriceps mass and muscle strength compared to the control group. Protein levels of all MyHC isoforms were reduced in the platinum-based anticancer drug groups. However, only Myh2 (MyHC-IIa) gene expression in skeletal muscle of mice treated with platinum-based anticancer drugs was found to be reduced. Treatment of C2C12 myotubes with platinum-based anticancer drugs reduced the protein levels of all MyHCs, and treatment with the proteasome inhibitor MG-132 restored this reduction. The expression of Mef2c, which was predicted to act upstream of Myh2, was reduced in the skeletal muscle of mice treated systemically with platinum-based anticancer drug. Degradation of skeletal muscle MyHCs by proteasomes may be a factor that plays an important role in muscle mass loss in platinum-based anticancer drug-induced muscle atrophy.

Impact of chlorine or peracetic acid on inactivation of Salmonella, Escherichia coli, and Listeria monocytogenes in agricultural water.

Preharvest agricultural water has been recognized as one of the routes of contamination for foodborne pathogens during fruit and vegetable production. Several strategies have been proposed to reduce the risk of pathogens, including preharvest water chemigation, but literature is lacking with regards to microbiological inactivation of common bacterial foodborne pathogens associated with fresh produce contamination, Salmonella enterica, Shiga-toxigenic Escherichia coli (STEC), and Listeria monocytogenes, in surface irrigation water after exposure to chlorine and peracetic acid (PAA). Surface water supplied by a local irrigation district was collected over the summer of 2019. Water was autoclaved, divided into 100 mL samples, and inoculated with a cocktail of five Salmonella, STEC, or Listeria monocytogenes strains or a single strain non-pathogenic E. coli. Samples were then treated with 3, 5, or 7 ppm of free chlorine or PAA, and surviving populations were evaluated using a time-kill assay. A first-order kinetic model was used to fit the inactivation data and obtain the D-values. A secondary model was used to explain the changes due to the type of water, treatment, and microorganism. At a concentration of 3 ppm, the observed and predicted D-values of free chlorine treatments were higher than that of PAA treatments for ground and surface water. Results indicated that PAA was more effective inactivating bacteria than sodium hypochlorite at concentrations of 3 and 5 ppm for both water sources (surface and ground). However, at 7 ppm, the effectiveness of PAA and sodium hypochlorite showed no statistically significant difference for both surface and groundwater. Findings will provide information regarding efficacy of chemical sanitizers like chlorine and PAA for inactivation of Salmonella, Listeria, and STEC in surface water from which treatments can be derived. Ultimately benefitting growers in the selection of an appropriate method for in-field treatment of irrigation water if deemed necessary.

Biasing the neurocognitive processing of videos with the presence of a real cultural other.

In the digital age, while short videos present vital events with powerful information, the presence of cultural cues may bias our processing of videos of foreign cultures. However, the underlying neurocognitive processes remain unclear. In this study, we hypothesized that cultural cues might bias video processing by either enhancing cultural perspective-taking or shifting cultural self-schema. To test these hypotheses, we used a novel paradigm in which the cultural cue was a real cultural other (the priming participants) who watched American/Chinese videos together with the primed participants. The results showed that when the cue was present, the right temporoparietal junction (rTPJ) response to videos with other cultural content was shifted, showing a priming effect. Moreover, the activity pattern in the rTPJ was more congruent with the primed culture than with the original culture, reflecting a neural biasing effect. Finally, intersubject representational similarity analysis indicated that the neural biasing effect in the rTPJ was more closely associated with cultural perspective-taking than with cultural self-schema. In summary, these findings support the perspective-taking hypothesis, suggesting that cultural cues can significantly bias our cultural mindset by altering cultural perspective-taking when we are exposed to culture-relevant naturalistic stimuli.

Glucose and lipid metabolism indexes and blood inflammatory biomarkers of patients with severe periodontitis: A cross-sectional study.

BACKGROUND: To investigate the relation of established glucose and lipid metabolism indexes and blood inflammatory biomarkers with severe periodontitis in systemically healthy patients. METHODS: Systemically healthy Stage III/IV periodontitis patients (case group) (n = 397), Stage II periodontitis patients (n = 36), and periodontally healthy subjects (control group) (n = 285) were recruited. A periodontal examination, complete blood cell examination, and blood biochemical examination were conducted for all participants. Full-mouth apical films were taken for the case group. Both the case and control groups were divided by age into younger (≤ 35 years) and elder subjects. Multiple logistic regression analysis and Pearson correlation analysis were conducted. A logistic least absolute shrinkage and selection operator (LASSO) model was constructed for the younger subgroups. RESULTS: Various glucose and lipid metabolism indexes and blood inflammatory biomarkers significantly differed between severe periodontitis patients and healthy controls, and the younger subgroups presented a greater degree of statistical differences than the elder ones. More pairs of periodontal parameters and blood indexes with significantly fair linear correlations were found in the younger patient subgroup. A logistic LASSO regression model containing eight blood indexes to assess a severe periodontitis outcome in younger subgroups showed satisfactory predictive ability. CONCLUSION: The present study revealed various glucose and lipid metabolism indexes and blood inflammatory biomarkers significantly differ between severe periodontitis patients and healthy controls, especially in the younger subgroups. A LASSO regression model could be a viable option to assess severe periodontitis risk for younger patients.

Comparison between LASSO and RT methods for prediction of generic E. coli concentration in pastured poultry farms.

Though most strains of E. coli are non-pathogenic components of the intestinal microbiome, certain pathogenic E. coli strains are the cause of diseases and outbreaks. Poultry is identified as a common reservoir for pathogenic E. coli. It is important to identify farm practice factors associated with E. coli in the pastured poultry environment. The objective of this study is to develop models that can predict E. coli levels and to select farm practice factors contributing to E. coli concentration in pastured poultry farms. Five kinds of samples: feces, soil, whole carcass rinse after processing (WCR-P), final product after chilling and storage (WCR-F), and ceca samples were collected for E. coli counts from 11 pastured poultry farms in the southeastern US. The regression tree (RT) and least absolute shrinkage and selection operator (LASSO) methods were applied to data from each sample type. The farm management practices and processing factors such as source of eggs, type of feed used, appearance of other animals on farm, chilling method, and storage time and temperature were all considered as possible explanatory factors in the models. Models were developed to predict the levels of E. coli and to select the most important factors used in predicting E. coli population. Model performances were compared using root mean square error (RMSE). For feces samples, average number of birds and animal source were the two most important variables affecting E. coli population by LASSO. The RT selected animal source, brood feed, day of year, flock age in days, and flock size as the most important variables in predicting E. coli concentration. The RMSE (in log10 scale) under LASSO was 0.974, while under RT it was 1.032 for feces samples. The predictive models provide practical and effective tools to predict E. coli population and to identify farm practice factors that affect E. coli levels.