Age-group associations of schistosomiasis prevalence from trial data, Côte d'Ivoire, Kenya and the United Republic of Tanzania.

Objective: To determine if the prevalence of schistosomiasis in children aged 9-12 years is associated with the prevalence in 5-8-year-olds and adults after preventive chemotherapy in schools or the community. Methods: We combined data from four community-randomized, preventive chemotherapy trials in treatment-naïve populations in Côte d'Ivoire, Kenya and the United Republic of Tanzania during 2010-2016 according to the number of praziquantel treatments and the delivery method. Schistosoma mansoni infection was sought on two slides prepared from each participant's first stool using the Kato-Katz technique. We assessed associations between S. mansoni prevalence in 9-12-year-olds and 5-8-year-olds and adults in the community before and after treatment using Bayesian regression models. Findings: Stool samples from 47 985 5-8-year-olds, 81 077 9-12-year-olds and 20 492 adults were analysed. We found associations between the prevalence in 9-12-year-olds and that in 5-8-year-olds and adults after preventive treatment, even when only school-age children were treated. When the prevalence in 9-12-year-olds was under 10%, the prevalence in 5-8-year-olds was consistently under 10%. When the prevalence in 9-12-year-olds was under 50%, the prevalence in adults after two or four rounds of preventive chemotherapy was 10%-15% lower than before chemotherapy. Post-chemotherapy age-group associations were consistent with pre-chemotherapy associations in this analysis and previous studies. Conclusion: The prevalence of S. mansoni infection in 9-12-year-olds was associated with the prevalence in other age groups and could be used to guide community treatment decisions.

Bayesian estimation of the measurement of interactions in epidemiological studies.

Background: Interaction identification is important in epidemiological studies and can be detected by including a product term in the model. However, as Rothman noted, a product term in exponential models may be regarded as multiplicative rather than additive to better reflect biological interactions. Currently, the additive interaction is largely measured by the relative excess risk due to interaction (RERI), the attributable proportion due to interaction (AP), and the synergy index (S), and confidence intervals are developed via frequentist approaches. However, few studies have focused on the same issue from a Bayesian perspective. The present study aims to provide a Bayesian view of the estimation and credible intervals of the additive interaction measures. Methods: Bayesian logistic regression was employed, and estimates and credible intervals were calculated from posterior samples of the RERI, AP and S. Since Bayesian inference depends only on posterior samples, it is very easy to apply this method to preventive factors. The validity of the proposed method was verified by comparing the Bayesian method with the delta and bootstrap approaches in simulation studies with example data. Results: In all the simulation studies, the Bayesian estimates were very close to the corresponding true values. Due to the skewness of the interaction measures, compared with the confidence intervals of the delta method, the credible intervals of the Bayesian approach were more balanced and matched the nominal 95% level. Compared with the bootstrap method, the Bayesian method appeared to be a competitive alternative and fared better when small sample sizes were used. Conclusions: The proposed Bayesian method is a competitive alternative to other methods. This approach can assist epidemiologists in detecting additive-scale interactions.

Widespread habitat loss leads to ecosystem-scale decrease in trophic function.

Natural and anthropogenic disturbances have led to rapid declines in the amount and quality of available habitat in many ecosystems. Many studies have focused on how habitat loss has affected the composition and configuration of habitats, but there have been fewer studies that investigate how this loss affects ecosystem function. We investigated how a large-scale seagrass die-off altered the distribution of energetic resources of three seagrass-associated consumers with varied resource use patterns. Using long-term benthic habitat monitoring data and resource use data from Bayesian stable isotope mixing models, we generated energetic resource landscapes (E-scapes) annually between 2007 and 2019. E-scapes link the resources being used by a consumer to the habitats that produce those resources to calculate a habitat resource index as a measurement of energetic quality of the landscape. Overall, our results revealed that following the die-off there was a reduction in trophic function across all species in areas affected by the die-off event, but the response was species-specific and dependent on resource use and recovery patterns. This study highlights how habitat loss can lead to changes in ecosystem function. Incorporating changes in ecosystem function into models of habitat loss could improve understanding of how species will respond to future change.

Climate warming shifts riverine macroinvertebrate communities to be more sensitive to chemical pollutants.

Freshwaters are highly threatened ecosystems that are vulnerable to chemical pollution and climate change. Freshwater taxa vary in their sensitivity to chemicals and changes in species composition can potentially affect the sensitivity of assemblages to chemical exposure. Here we explore the potential consequences of future climate change on the composition and sensitivity of freshwater macroinvertebrate assemblages to chemical stressors using the UK as a case study. Macroinvertebrate assemblages under end of century (2080-2100) and baseline (1980-2000) climate conditions were predicted for 608 UK sites for four climate scenarios corresponding to mean temperature changes of 1.28 to 3.78°C. Freshwater macroinvertebrate toxicity data were collated for 19 chemicals and the hierarchical species sensitivity distribution model was used to predict the sensitivity of untested taxa using relatedness within a Bayesian approach. All four future climate scenarios shifted assemblage compositions, increasing the prevalence of Mollusca, Crustacea and Oligochaeta species, and the insect taxa of Odonata, Chironomidae, and Baetidae species. Contrastingly, decreases were projected for Plecoptera, Ephemeroptera (except for Baetidae) and Coleoptera species. Shifts in taxonomic composition were associated with changes in the percentage of species at risk from chemical exposure. For the 3.78°C climate scenario, 76% of all assemblages became more sensitive to chemicals and for 18 of the 19 chemicals, the percentage of species at risk increased. Climate warming-induced increases in sensitivity were greatest for assemblages exposed to metals and were dependent on baseline assemblage composition, which varied spatially. Climate warming is predicted to result in changes in the use, environmental exposure and toxicity of chemicals. Here we show that, even in the absence of these climate-chemical interactions, shifts in species composition due to climate warming will increase chemical risk and that the impact of chemical pollution on freshwater macroinvertebrate biodiversity may double or quadruple by the end of the 21st century.

Pulmonary Vein Isolation With or Without Left Atrial Appendage Ligation in Atrial Fibrillation: The aMAZE Randomized Clinical Trial.

Importance: Left atrial appendage elimination may improve catheter ablation outcomes for atrial fibrillation. Objective: To assess the safety and effectiveness of percutaneous left atrial appendage ligation adjunctive to catheter pulmonary vein isolation for nonparoxysmal atrial fibrillation. Design, Setting, and Participants: This multicenter, prospective, open-label, randomized clinical trial evaluated the safety and effectiveness of percutaneous left atrial appendage ligation adjunctive to planned pulmonary vein isolation for nonparoxysmal atrial fibrillation present for less than 3 years. Eligible patients were randomized in a 2:1 ratio to undergo left atrial appendage ligation and pulmonary vein isolation or pulmonary vein isolation alone. Use of a 2:1 randomization ratio was intended to provide more device experience and safety data. Patients were enrolled from October 2015 to December 2019 at 53 US sites, with the final follow-up visit on April 21, 2021. Interventions: Left atrial appendage ligation plus pulmonary vein isolation compared with pulmonary vein isolation alone. Main Outcomes and Measures: A bayesian adaptive analysis was used for primary end points. Primary effectiveness was freedom from documented atrial arrythmias of greater than 30 seconds duration 12 months after undergoing pulmonary vein isolation. Rhythm was assessed by Holter monitoring at 6 and 12 months after pulmonary vein isolation, symptomatic event monitoring, or any electrocardiographic tracing obtained through 12 months after pulmonary vein isolation. Primary safety was a composite of predefined serious adverse events compared with a prespecified 10% performance goal 30 days after the procedure. Left atrial appendage closure was evaluated through 12 months after pulmonary vein isolation. Results: Overall, 404 patients were randomized to undergo left atrial appendage ligation plus pulmonary vein isolation and 206 were randomized to undergo pulmonary vein isolation alone. Primary effectiveness was 64.3% with left atrial appendage ligation and pulmonary vein isolation and 59.9% with pulmonary vein isolation only (difference, 4.3% [bayesian 95% credible interval, -4.2% to 13.2%]; posterior superiority probability, 0.835), which did not meet the statistical criterion to establish superiority (0.977). Primary safety was met, with a 30-day serious adverse event rate of 3.4% (bayesian 95% credible interval, 2.0% to 5.0%; posterior probability, 1.0) which was less than the prespecified threshold of 10%. At 12 months after pulmonary vein isolation, complete left atrial appendage closure (0 mm residual communication) was observed in 84% of patients and less than or equal to 5 mm residual communication was observed in 99% of patients. Conclusions and Relevance: Percutaneous left atrial appendage ligation adjunctive to pulmonary vein isolation did not meet prespecified efficacy criteria for freedom from atrial arrhythmias at 12 months compared with pulmonary vein isolation alone for patients with nonparoxysmal atrial fibrillation, but met prespecified safety criteria and demonstrated high rates of closure at 12 months. Trial Registration: ClinicalTrials.gov Identifier: NCT02513797.

Efficacy of immune checkpoint inhibitors in advanced non-small cell lung cancer patients with KRAS mutations: A network meta-analysis.

OBJECTIVE: Previous studies have shown that immune checkpoint inhibitors can improve the survival of patients with advanced non-small cell lung cancer with KRAS mutations; however, there is a lack of comparisons between treatment regimens associated with immune checkpoint inhibitors, and our study aims to compare several treatment parties to find a more effective treatment regimen. METHOD: A comprehensive literature search was conducted across multiple databases, namely PubMed, Web of Science, Embase, and Cochrane Library, to identify relevant studies. The screened studies were thoroughly examined, and data were collected to establish a Bayesian framework. The study focused on two primary endpoints: overall survival (OS) and progression-free survival (PFS). Data analysis and graphical plotting using R software and Revman (version 5.3). It is worth mentioning that the study protocol was registered with the International Prospective Registry for Systematic Reviews, ensuring transparency and adherence to predetermined protocols (CRD42022379595). RESULT: In total, our analysis included six RCTs involving 469 patients with KRAS mutations. Among these patients, 224 received chemotherapy, while 245 were treated with immune checkpoint inhibitors. Meta-analysis results showed that the addition of ICIs could significantly improve OS and PFS (0.69, 95% CI 0.55, 0.86; 0.57, 95% CI 0.42, 0.77). The results of the network meta-analysis showed that Pembrolizumab could improve OS (HR 0.42, 95% CI 0.22-0.80) and Pembrolizumab emerged as the most effective treatment option for enhancing OS in patients (SUCRA 65.03%). Additionally, pembrolizumab in combination with chemotherapy showed improvement in PFS (HR 0.47, 95% CI 0.29-0.76). CONCLUSION: Our analysis found that among advanced NSCLC patients with KRAS gene mutations, first-line treatment with pembrolizumab alone demonstrated greater efficacy. Similarly, second-line treatment with nivolumab alone was found to be more effective in this patient population. However, the sample size of this study was limited, Therefore, additional clinical data is necessary to validate this finding in subsequent research.

Six complete mitochondrial genomes of ground beetles from the Harpalinae and Carabinae (Coleoptera, Carabidae) with phylogenetic analysis based on mitogenomic data.

In this study, we employed high-throughput sequencing technology to determine the complete mitochondrial genomes of six ground beetles, encompassing five Harpalinae species and one Carabinae species. The sizes of mitochondrial genomes ranged from 15,334 to 16,972 bp, encompassing 37 genes, including 13 protein-coding genes, 22 transfer RNA genes, and 2 ribosomal RNA genes. Furthermore, each species was found to possess a putative control region. Combining with 65 published mitochondrial genome sequences of Carabidae as ingroups and four species from Trachypachidae, Gyrinidae and Dytiscidae as outgroups, we conducted phylogenetic analyses utilizing Maximum likelihood and Bayesian inference methods. Moreover, we reconstructed a species tree of Carabidae based on mitochondrial genome data using the coalescent-based species tree method (ASTRAL). The results revealed that the family Carabidae was not a monophyletic group. The subfamily Harpalinae was supported to be a monophyletic group in Maximum likelihood analysis. Although the subfamily Carabinae was found to be nonmonophyletic in the concatenation analyses under both Maximum likelihood and Bayesian inference criteria, it was identified as a monophyletic group in the species tree analysis.

Assessing the Time Evolution of COVID-19 Effective Reproduction Number in Brazil.

In this paper, we use a Bayesian method to estimate the effective reproduction number ( R ( t ) ), in the context of monitoring the time evolution of the COVID-19 pandemic in Brazil at different geographic levels. The focus of this study is to investigate the similarities between the trends in the evolution of such indicators at different subnational levels with the trends observed nationally. The underlying question addressed is whether national surveillance of such variables is enough to provide a picture of the epidemic evolution in the country or if it may hide important localized trends. This is particularly relevant in the scenario where health authorities use information obtained from such indicators in the design of non-pharmaceutical intervention policies to control the epidemic. A comparison between R ( t ) estimates and the moving average (MA) of daily reported infections is also presented, which is another commonly monitored variable. The analysis carried out in this paper is based on the data of confirmed infected cases provided by a public repository. The correlations between the time series of R ( t ) and MA in different geographic levels are assessed. Comparing national with subnational trends, higher degrees of correlation are found for the time series of R ( t ) estimates, compared to the MA time series. Nevertheless, differences between national and subnational trends are observed for both indicators, suggesting that local epidemiological surveillance would be more suitable as an input to the design of non-pharmaceutical intervention policies in Brazil, particularly for the least populated states.

The effective sample size in Bayesian information criterion for level-specific fixed and random-effect selection in a two-level nested model.

Popular statistical software provides the Bayesian information criterion (BIC) for multi-level models or linear mixed models. However, it has been observed that the combination of statistical literature and software documentation has led to discrepancies in the formulas of the BIC and uncertainties as to the proper use of the BIC in selecting a multi-level model with respect to level-specific fixed and random effects. These discrepancies and uncertainties result from different specifications of sample size in the BIC's penalty term for multi-level models. In this study, we derive the BIC's penalty term for level-specific fixed- and random-effect selection in a two-level nested design. In this new version of BIC, called BIC E 1 , this penalty term is decomposed into two parts if the random-effect variance-covariance matrix has full rank: (a) a term with the log of average sample size per cluster and (b) the total number of parameters times the log of the total number of clusters. Furthermore, we derive the new version of BIC, called BIC E 2 , in the presence of redundant random effects. We show that the derived formulae, BIC E 1 and BIC E 2 , adhere to empirical values via numerical demonstration and that BIC E ( E indicating either E 1 or E 2 ) is the best global selection criterion, as it performs at least as well as BIC with the total sample size and BIC with the number of clusters across various multi-level conditions through a simulation study. In addition, the use of BIC E 1 is illustrated with a textbook example dataset.

Groundwater hydrochemical signatures, nitrate sources, and potential health risks in a typical karst catchment of North China using hydrochemistry and multiple stable isotopes.

Nitrate pollution in aquatic ecosystems has received growing concern, particularly in fragile karst basins. In this study, hydrochemical compositions, multiple stable isotopes (δ2H-H2O, δ18Ο-Η2Ο, δ15Ν-ΝΟ3-, and δ18Ο-ΝΟ3-), and Bayesian stable isotope mixing model (MixSIAR) were applied to elucidate nitrate pollution sources in groundwater of the Yangzhuang Basin. The Durov diagram identified the dominant groundwater chemical face as Ca-HCO3 type. The NO3- concentration ranged from 10.89 to 90.45 mg/L (average 47.34 mg/L), showing an increasing trend from the upstream forest and grassland to the downstream agricultural dominant area. It is worth noting that 47.2% of groundwater samples exceeded the NO3- threshold value of 50 mg/L for drinking water recommended by the World Health Organization. The relationship between NO3-/Cl- and Cl- ratios suggested that most groundwater samples were located in nitrate mixed endmember from agricultural input, soil organic nitrogen, and manure & sewage. The Self-Organizing Map (SOM) and Pearson correlations analysis further indicated that the application of calcium fertilizer, sodium fertilizer, and livestock and poultry excrement in farmland elevated NO3- level in groundwater. The output results of the MixSIAR model showed that the primary sources of NO3- in groundwater were soil organic nitrogen (55.3%), followed by chemical fertilizers (28.5%), sewage & manure (12.7%), and atmospheric deposition (3.4%). Microbial nitrification was a dominant nitrogen conversion pathway elevating NO3- levels in groundwater, while the denitrification can be neglectable across the study area. The human health risk assessment (HHRA) model identified that about 88.9%, 77.8%, 72.2%, and 50.0% of groundwater samples posing nitrate's non-carcinogenic health hazards (HQ > 1) through oral intake for infants, children, females, and males, respectively. The findings of this study can offer useful biogeochemical information on nitrogen pollution in karst groundwater to support sustainable groundwater management in similar human-affected karst regions.

Trend and projection of non-communicable diseases risk factors in Iran from 2001 to 2030.

This study aims to investigate the trends and project the major risk factors of Non-communicable Diseases (NCDs) in Iran. We obtained the trend of prevalence of main risk factors related to NCDs in 30 to 70-year-old-individuals. The data were extracted from WHO STEP wise approach to NCDs risk factor surveillance (STEPS) survey. Also,the previous studies conducted at national and subnational levels from 2001 to 2016 were employed. The prevalence of risk factors was projected by 2030 using Bayesian Model Averaging (BMA) and Spatio-temporal model stratified by sex and province. The percent change for the age-standardized prevalence of smoking in men between 2001 and 2016 was calculated to be - 27.0. Also, the corresponding values for the risk factors of diabetes, hypertension, obesity and overweight, physical inactivity (PI), and mean of salt intake were - 26.1, 29.0, 70.0, 96.8, 116.6, and 7.5, respectively. It is predicted that smoking and these risk factors will undergo a change to show values of - 1.26, 38.7, 43.7, 2.36, and 15.3 by 2030, respectively. The corresponding values in women for the time interval of 2001-2016 were - 27.3, 26.3, 82.8, 1.88, 75.2, and 4.2, respectively. Plus, projections indicate that the 2030 variation values are expected to be - 25.0, 16.7, 37.5, 28.7, 26.7, and 10.9 respectively. This study showed that the prevalence of four risk factors of PI, overweight and obesity, hypertension, and diabetes is increasing in Iran. Therefor, it is necessary to carry out effective interventions to adopt a healthy lifestyle and reduce the risk factors.

Identification of a novel HIV-1 third-generation circulating recombinant form (CRF126_0755) in Guangdong, China.

The genetic recombination patterns and genetic distribution of HIV-1 are valuable for elucidating the epidemic and genetic diversity of HIV. Numerous HIV-1 circulating recombinant forms (CRFs) have recently emerged and disseminated rapidly. In China, at least 32 CRFs have been reported to account for more than 80% of all HIV infections. However, CRFs derived from the CRF07_BC and CRF55_01B lineages have never been recorded. Here, a novel third-generation CRF involving HIV-1 was identified in four HIV-1-infected patients in Guangdong, China, who had no epidemiological association with each other. Phylogenetic and recombinant analyses confirmed that these strains shared highly similar recombination patterns, with the CRF07_BC backbone substituted by a CRF55_01B segment; therefore, these strains were classified as CRF126_0755. This is the first study of a CRF derived from CRF07_BC and CRF55_01B. Bayesian phylogenetic inference suggested that CRF126_0755 originated in approximately 2005-2007. The present findings reveal that the genotype composition of HIV-1 has become more complex than that of other viruses and highlight the urgent need for continuous molecular screening and epidemic surveillance within HIV-1-infected populations to advance our understanding of viral transmission mechanisms.

Evaluation of sensitivity and specificity of Kato-Katz and circulating cathodic antigen in terms of Schistosoma japonicum using latent class analysis.

Schistosoma japonicum is endemic in the Philippines. The Kato-Katz (KK) method was used to diagnose S. japonicum. This is impractical, particularly when the sample size is limited. Knowledge on point-of-care circulating cathodic antigen (CCA) test performance for S. japonicum is limited. Determining the sensitivity and specificity of new diagnostics is difficult when the gold standard test is less effective or absent. Latent class analysis (LCA) can address some limitations. A total of 484 children and 572 adults from the Philippines were screened for S. japonicum. We performed Bayesian LCA to estimate the infection prevalence, sensitivity and specificity of each test by stratifying them into two age groups. Observed prevalence assessed by KK was 50.2% and 31.8%, and by CCA was 89.9% and 66.8%, respectively. Using Bayesian LCA, among children, the sensitivity and specificity of CCA were 94.8% (88.7-99.4) and 21.5% (10.5-36.1) while those of KK were 66.0% (54.2-83.3) and 78.1% (61.1-91.3). Among adults, the sensitivity and specificity of CCA were 86.4% (76.6-96.9) and 62.8% (49.1-81.1) while those of KK were 43.6% (35.1-53.9) and 85.5% (75.8-94.6). Overall, CCA was more sensitive than KK, regardless of the age group at diagnosis, as KK was more specific. KK and CCA have different diagnostic performance, which should inform their use in the planning and implementation of S. japonicum control programs.

Mesoscale activity drives the habitat suitability of yellowfin tuna in the Gulf of Mexico.

Yellowfin tuna, Thunnus albacares, represents an important component of commercial and recreational fisheries in the Gulf of Mexico (GoM). We investigated the influence of environmental conditions on the spatiotemporal distribution of yellowfin tuna using fisheries' catch data spanning 2012-2019 within Mexican waters. We implemented hierarchical Bayesian regression models with spatial and temporal random effects and fixed effects of several environmental covariates to predict habitat suitability (HS) for the species. The best model included spatial and interannual anomalies of the absolute dynamic topography of the ocean surface (ADTSA and ADTIA, respectively), bottom depth, and a seasonal cyclical random effect. High catches occurred mainly towards anticyclonic features at bottom depths > 1000 m. The spatial extent of HS was higher in years with positive ADTIA, which implies more anticyclonic activity. The highest values of HS (> 0.7) generally occurred at positive ADTSA in oceanic waters of the central and northern GoM. However, high HS values (> 0.6) were observed in the southern GoM, in waters with cyclonic activity during summer. Our results highlight the importance of mesoscale features for the spatiotemporal distribution of yellowfin tunas and could help to develop dynamic fisheries management strategies in Mexico and the U.S. for this valuable resource.

Spatial-temporal Bayesian accelerated failure time models for survival endpoints with applications to prostate cancer registry data.

Prostate cancer is the most common cancer after non-melanoma skin cancer and the second leading cause of cancer deaths in US men. Its incidence and mortality rates vary substantially across geographical regions and over time, with large disparities by race, geographic regions (i.e., Appalachia), among others. The widely used Cox proportional hazards model is usually not applicable in such scenarios owing to the violation of the proportional hazards assumption. In this paper, we fit Bayesian accelerated failure time models for the analysis of prostate cancer survival and take dependent spatial structures and temporal information into account by incorporating random effects with multivariate conditional autoregressive priors. In particular, we relax the proportional hazards assumption, consider flexible frailty structures in space and time, and also explore strategies for handling the temporal variable. The parameter estimation and inference are based on a Monte Carlo Markov chain technique under a Bayesian framework. The deviance information criterion is used to check goodness of fit and to select the best candidate model. Extensive simulations are performed to examine and compare the performances of models in different contexts. Finally, we illustrate our approach by using the 2004-2014 Pennsylvania Prostate Cancer Registry data to explore spatial-temporal heterogeneity in overall survival and identify significant risk factors.

Utilization of machine learning models in predicting caries risk groups and oral health-related risk factors in adults.

BACKGROUND: The aim of this study was to analyse the risk factors that affect oral health in adults and to evaluate the success of different machine learning algorithms in predicting these risk factors. METHODS: This study included 2000 patients aged 18 years and older who were admitted to the Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Gaziantep University, between September and December 2023. In this study, patients completed a 30-item questionnaire designed to assess the factors that affect the decayed, missing, and filled teeth (DMFT). Clinical and radiological examinations were performed, and DMFT scores were calculated after completion of the questionnaire. The obtained data were randomly divided into a 75% training group and a 25% test group. The preprocessed dataset was analysed using various machine learning algorithms, including naive Bayes, logistic regression, support vector machine, decision tree, random forest and Multilayer Perceptron algorithms. Pearson's correlation test was also conducted to assess the correlation between participants' DMFT scores and oral health risk factors. The performance of each algorithm was evaluated to determine the most appropriate algorithm, and model performance was assessed using accuracy, precision, recall and F1 score on the test dataset. RESULTS: A statistically significant difference was found between various factors and DMFT-based risk groups (p < 0.05), including age, sex, body mass index, tooth brushing frequency, socioeconomic status, employment status, education level, marital status, hypertension, diabetes status, renal disease status, consumption of sugary snacks, dry mouth status and screen time. When considering machine learning algorithms for risk group assessments, the Multilayer Perceptron model demonstrated the highest level of success, achieving an accuracy of 95.8%, an F1-score of 96%, and precision and recall rates of 96%. CONCLUSIONS: Caries risk assessment using a simple questionnaire can identify individuals at risk of dental caries, determine the key risk factors, provide information to help reduce the risk of dental caries over time and ensure follow-up. In addition, it is extremely important to apply effective preventive treatments and to prevent the general health problems that are caused by the deterioration of oral health. The results of this study show the potential of machine learning algorithms for predicting caries risk groups, and these algorithms are promising for future studies.

Comparative analysis of glymphatic system alterations in multiple sclerosis and neuromyelitis optica spectrum disorder using MRI indices from diffusion tensor imaging.

OBJECTIVE: The glymphatic system is a glial-based perivascular network that promotes brain metabolic waste clearance. Glymphatic system dysfunction has been observed in both multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD), indicating the role of neuroinflammation in the glymphatic system. However, little is known about how the two diseases differently affect the human glymphatic system. The present study aims to evaluate the diffusion MRI-based measures of the glymphatic system by contrasting MS and NMOSD. METHODS: This prospective study included 63 patients with NMOSD (n = 21) and MS (n = 42) who underwent DTI. The fractional volume of extracellular-free water (FW) and an index of diffusion tensor imaging (DTI) along the perivascular space (DTI-ALPS) were used as indirect indicators of water diffusivity in the interstitial extracellular and perivenous spaces of white matter, respectively. Age and EDSS scores were adjusted. RESULTS: Using Bayesian hypothesis testing, we show that the present data substantially favor the null model of no differences between MS and NMOSD for the diffusion MRI-based measures of the glymphatic system. The inclusion Bayes factor (BF10) of model-averaged probabilities of the group (MS, NMOSD) was 0.280 for FW and 0.236 for the ALPS index. CONCLUSION: Together, these findings suggest that glymphatic alteration associated with MS and NMOSD might be similar and common as an eventual result, albeit the disease etiologies differ. PRACTITIONER POINTS: Previous literature indicates important glymphatic system alteration in MS and NMOSD. We explore the difference between MS and NMOSD using diffusion MRI-based measures of the glymphatic system. We show support for the null hypothesis of no difference between MS and NMOSD. This suggests that glymphatic alteration associated with MS and NMOSD might be similar and common etiology.

VSSI-GGD: A Variation Sparse EEG Source Imaging Approach Based on Generalized Gaussian Distribution.

Electroencephalographic (EEG) source imaging (ESI) is a powerful method for studying brain functions and surgical resection of epileptic foci. However, accurately estimating the location and extent of brain sources remains challenging due to noise and background interference in EEG signals. To reconstruct extended brain sources, we propose a new ESI method called Variation Sparse Source Imaging based on Generalized Gaussian Distribution (VSSI-GGD). VSSI-GGD uses the generalized Gaussian prior as a sparse constraint on the spatial variation domain and embeds it into the Bayesian framework for source estimation. Using a variational technique, we approximate the intractable true posterior with a Gaussian density. Through convex analysis, the Bayesian inference problem is transformed entirely into a series of regularized L2p -norm ( ) optimization problems, which are efficiently solved with the ADMM algorithm. Imaging results of numerical simulations and human experimental dataset analysis reveal the superior performance of VSSI-GGD, which provides higher spatial resolution with clear boundaries compared to benchmark algorithms. VSSI-GGD can potentially serve as an effective and robust spatiotemporal EEG source imaging method. The source code of VSSI-GGD is available at https://github.com/Mashirops/VSSI-GGD.git.

Genetic Analysis Reveals Dispersal Patterns of Japanese Serow in Two Different Habitats of a Mountainous Region.

Dispersal increases the costs of feeding and predation risk in the new environment and is reported to be biased toward habitats similar to the natal region in some mammals. The benefits and costs of dispersal often differ between sexes, and most mammals show male-biased dispersal in relation to a polygamous mating system. Japanese serow is generally a solitary and monogamous species. However, recent studies have shown that the sociality of serows on Mt. Asama differs between habitat types. In the mountain forests with low forage availability, solitary habits and social monogamy were observed, while, in alpine grasslands, female grouping and social polygyny were observed, which is probably due to abundant forage availability. We investigated the effects of habitat characteristics and sociality on the dispersal of serows using fecal and tissue samples from two different habitats on Mt. Asama. The Fst value between the two areas was significantly positive, and the mean relatedness within areas was significantly higher than that between areas, which suggests limited gene flow and natal habitat-biased dispersal. Bayesian clustering analysis showed unidirectional gene flow from forest to grassland, which was probably due to the high forage availability of the grassland. Analyses of the assignment index and mean relatedness did not show male-biased dispersal, even in the grassland, where serows were polygynous. Thus, polygyny in the grassland is not linked to male-biased dispersal. In summary, our study suggests that dispersal patterns in Japanese serows are affected by habitat rather than social differences.

Diagnostic accuracy of tests for tuberculous pericarditis: A network meta-analysis.

Tuberculous pericarditis (TBP) is a relatively uncommon but potentially fatal extrapulmonary manifestation of tuberculosis. Despite its severity, there is no universally accepted gold standard diagnostic test for TBP currently. The objective of this study is to compare the diagnostic accuracy of the most commonly used tests in terms of specificity, sensitivity, negative predictive value (NPV), and positive predictive value (PPV), and provide a summary of their diagnostic accuracies. A comprehensive literature review was performed using Scopus, MEDLINE, and Cochrane central register of controlled trials, encompassing studies published from start to April 2022. Studies that compared Interferon Gamma Release Assay (IGRA), Xpert MTB/RIF, Adenosine Deaminase levels (ADA), and Smear Microscopy (SM) were included in the analysis. Bayesian random-effects model was used for statistical analysis and mean and standard deviation (SD) with 95% confidence intervals were calculated using the absolute risk (AR) and odds ratio (OR). Rank probability and heterogeneity were determined using risk difference and Cochran Q test, respectively. Sensitivity and specificity were evaluated using true negative, true positive, false positive, and false negative rates. Area under the receiver operating characteristic (AUROC) was calculated for mean and standard error. A total of seven studies comprising 16 arms and 618 patients were included in the analysis. IGRA exhibited the highest mean (SD) sensitivity of 0.934 (0.049), with a high rank probability of 87.5% for being the best diagnostic test, and the AUROC was found to be 94.8 (0.36). On the other hand, SM demonstrated the highest mean (SD) specificity of 0.999 (0.011), with a rank probability of 99.5%, but a leave-one-out analysis excluding SM studies revealed that Xpert MTB/RIF ranked highest for specificity, with a mean (SD) of 0.962 (0.064). The diagnostic tests compared in our study exhibited similar high NPV, while ADA was found to have the lowest PPV among the evaluated methods. Further research, including comparative studies, should be conducted using a standardized cutoff value for both ADA levels and IGRA to mitigate the risk of threshold effect and minimize bias and heterogeneity in data analysis.