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BACKGROUND: Bladder cancer (BC) is very common among cancers of urinary system. It was usually categorized into two types: non-muscle invasive bladder cancer (NMIBC) and muscle invasive bladder cancer (MIBC). NMIBC and MIBC groupings are heterogeneous and have different characteristics. OBJECTIVES: The study was aimed to find some hub genes and related signal pathways which might be engaged in the progression of BC and to investigate the relationship with clinical stages and its prognostic significance. METHODS: GSE37317 datasets were acquired from Gene Expression Omnibus (GEO) database. GEO2R on-line tool was selected to screen the differentially expressed genes (DEGs) of the two different types of BC. Then, Gene Ontology (GO) enrichment and KOBAS-Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of these DEGs were conducted. A protein-protein interaction (PPI) network was employed to help us screen hub genes and find significant modules. Finally, we made analysis of gene expression and survival curve by GEPIA and Kaplan-Meier plotter database. RESULTS: 224 DEGs were screened in total, with 110 showing increased expression and 114 demonstrating decreased expression. GO and KEGG pathway enrichment analysis showed that DEGs were mostly involved in collagen fibril organization, extracellular matrix (ECM) structural constituent, bHLH transcription factor binding, AGE-RAGE signaling pathway and TGF-beta signaling pathway. Only 3 hub genes (DCN, JUN, THBS1) displayed significantly higher expression compared to those in the healthy controls. These hub genes were also strongly related to clinical stages as well as overall survival (OS) of BC patients. CONCLUSIONS: Taken together, most of hub genes involved in the progression of BC were related to ECM and EMT. In addition, 3 hub genes (DCN, JUN, THBS1) were strongly related with clinical stages and OS of BC patients. This study can enhance our comprehension of the progression of NMIBC and identify novel potential targets for MIBC.

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BACKGROUND: Schizophrenia is a disorder associated with neurocognitive deficits that adversely affect daily functioning and impose an economic burden. Cognitive rehabilitation interventions, particularly during the early phases of illness, have been shown to improve cognition, functionality, and quality of life. The Feuerstein Instrumental Enrichment (FIE) program, based on the Mediated Learning Experience and the Structural Cognitive Modifiability theory, has been applied in various disorders, but its applicability in schizophrenia has not yet been clarified. OBJECTIVE: This study aims to investigate the effects of the FIE program on the functionality of patients with first-episode schizophrenia. METHODS: In total, 17 patients will be recruited for an open-label intervention consisting of twice-weekly sessions for 10 weeks. The primary outcome measure will be changes in the Goal Achievement Scale score. Maze task performance from the Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) battery will serve as a secondary outcome measure. At the same time, changes in Positive and Negative Syndrome Scale scores and other MATRICS domains will be analyzed as exploratory outcomes. Assessments will be administered before and after the intervention, with a follow-up period of 6 months. RESULTS: This trial was preregistered in The Brazilian Registry of Clinical Trials (RBR-4gzhy4s). By February 2024, 11 participants were enrolled in the training. Recruitment is expected to be completed by May 2024. Data analysis will be conducted between May and September 2024. The results are expected to be published in January 2025. CONCLUSIONS: This study may establish a protocol for the FIE program that uses mediation techniques for individuals in the early stages of schizophrenia. The results will add to the knowledge about strategies to promote cognitive skills and functional impairment in daily life. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/57031.

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AIM: The study aimed to investigate a lipid profile in people with normal glucose tolerance (NGT), NGT and 1hrOGTT > 8.6 mmol/l, and impaired glucose tolerance (IGT); and to assess its association with some cardio-metabolic parameters. MATERIAL AND METHODS: A total of 90 subjects, of mean age 46.7 ± 10.5 years and mean BMI of 32.0 ± 6.3 kg/m2 were enrolled. They were divided into 3 groups: 19 with NGT, 22 with NGT and 1hrOGTT > 8.6 mmol/l, and 49 with IGT; and subdivided into 2 subgroups according to HOMA-IR: 40 with HOMA-IR < 2.5 and 50 with HOMA-IR ≥ 2.5. Body composition (Inbody 720) and advanced glycation end products (AGE Reader) were assessed. Two functional tests (OGTT; MMTT) were performed and AUC for glucose, insulin and triglycerides were calculated. RESULTS: There was no difference across the glucose tolerance groups for all evaluated lipids. The results showed higher AUCinsulin during OGTT (p = 0.037 and 0.020), AUCtriglycerides during MMTT (p = 0.048) and triglycerides/HDL ratio (p = 0.064 and 0.016) in the 1hrOGTT and IGT subgroups with HOMA-IR ≥ 2.5 in comparison to those with HOMA-IR < 2.5. AUCtriglycerides during OGTT is independently related to body composition, b-cell function and insulin sensitivity; and AUCtriglycerides during MMTT is independently related to blood pressure and hsCRP in prediabetes. Triglycerides/HDL-C ratio emerged as an independent contributor to glycaemia and insulinemia. CONCLUSION: Our results demonstrate a similar lipid profile in subjects with 1hrOGTT > 8.6 mmol/l and IGT, whereas increased AUCtriglycerides during OGTT, AUCtriglycerides during MMTT and triglycerides/HDL-C ratio have been found in the subgroups with insulin resistance. The triglycerides/HDL-C ratio outlined as an independent predictor of insulin secretion and action, and postload triglycerides appear to be independently related to most of the metabolic parameters.

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Past studies in animal models have extensively investigated the impact of early life experiences on behavioral development, yet relatively few have specifically examined the implications of peripubertal experiences on the evolution of competitive behavior across distinct stages of adulthood. In the current research, we probed potential differences in competitive behavior during emerging adulthood (3 months old) and middle adulthood (12 months old) in 81 Sprague-Dawley male rats exposed to three different peripubertal (postnatal Days 37-60) environments: an enriched environment (EE), a chronic unpredictable mild stress (CUMS) condition, and a control condition. Anxiety-like behavior served as a positive control in our study. Results revealed significant variations in competitive behavior among the groups during emerging adulthood. The EE group displayed the least anxiety and outperformed their peers in food-reward-oriented competition, whereas the CUMS group excelled in status-driven, agonistic competition. However, these behavioral differentiations gradually attenuated by middle adulthood, at which point the control group began to show an advantage. Our findings suggest that although peripubertal experiences significantly shape competitive behavior in the emerging adulthood stage, this effect diminishes over time and is nearly non-detectable during mid-adulthood, underscoring the fluidity of behavioral development and demonstrating that the effects of peripubertal experiences can be modulated by subsequent life experiences.

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This study examines tuberculosis (TB) incidence among students in Jilin Province, China, focusing on spatial, temporal, and demographic dynamics in areas of social inequality. Variation in incidence rate of TB was analyzed using the joinpoint regression method. Spatial analyses techniques included the global and local Moran indices and Getis-Ord Gi* analysis. Demographic changes in new cases were analyzed descriptively, and the Geodetector method measured the influence of risk factors on student TB incidence. The analysis revealed a declining trend in TB cases, particularly among male students. TB incidence showed geographical heterogeneity, with lower rates in underdeveloped rural areas compared to urban regions. Significant spatial correlations were observed, with high-high clusters forming in central Jilin Province. Hotspots of student TB transmission were primarily concentrated in the southwestern and central regions from 2008 to 2018. Socio-economic factors exhibited nonlinear enhancement effects on incidence rates, with a dominant bifactor effect. High-risk zones were predominantly located in urban centers, with university and high school students showing higher incidences than other educational stages. The study revealed economic determinants as being especially important in affecting TB incidence among students, with these factors having nonlinear interacting effects on student TB incidence.

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BACKGROUND: The Cardiogenic Shock Working Group-modified Society for Cardiovascular Angiography and Interventions (CSWG-SCAI) staging was developed to risk stratify cardiogenic shock (CS) severity. Data showing progressive changes in SCAI stages and outcomes are limited. OBJECTIVES: We investigated serial changes in CSWG-SCAI stages and outcomes of patients presenting with cardiogenic shock complicating acute myocardial infarction (MI-CS) and heart failure-related CS (HF-CS). METHODS: The multicenter CSWG registry was queried. CSWG-SCAI stages were computed at CS diagnosis and 24, 48, and 72 hours. RESULTS: A total of 3,268 patients (57% HF-CS; 27% MI-CS) were included. At CS diagnosis, CSWG-SCAI stage breakdown was 593 (18.1%) stage B, 528 (16.2%) stage C, 1,659 (50.8%) stage D, and 488 (14.9%) noncardiac arrest stage E. At 24 hours, >50% of stages B and C patients worsened, but 86% of stage D patients stayed at stage D. Among stage E patients, 54% improved to stage D and 36% stayed at stage E by 24 hours. Minimal SCAI stage changes occurred beyond 24 hours. SCAI stage trajectories were similar between MI-CS and HF-CS groups. Within 24 hours, unadjusted mortality rates of patients with any SCAI stage worsening or improving were 44.6% and 34.2%, respectively. Patients who presented in or progressed to stage E by 24 hours had the worst prognosis. Survivors had lower lactate than nonsurvivors. CONCLUSIONS: Most patients with CS changed SCAI stages within 24 hours from CS diagnosis. Stage B patients were at high risk of worsening shock severity by 24 hours, associated with excess mortality. Early CS recognition and serial assessment may improve risk stratification.

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AIM: To investigate the diagnostic accuracy of parent-completed Ages and Stages Questionnaire, Third Edition (ASQ-3) to identify abnormal or delayed gross motor development in infants born less than 1000 g or less than 28 weeks gestation. METHODS: Prospective cohort study of high-risk infants comparing ASQ-3 as the index test with concurrent score on Alberta Infant Motor Scale (AIMS) as the reference standard, at 4-, 8- and 12-month corrected (post-term) age. Reference standard positivity cut-offs were 'Abnormal motor development' (AIMS Clinical Range) and 'Motor delay' (AIMS score >1 SD below mean, not captured in Clinical Range). RESULTS: Participating infants (n = 191) had mean gestational age (95% confidence interval (CI)) 26.8 weeks (26.6-27.1) and mean birthweight (95% CI) 870 g (844-896). AIMS rated 51%, 31% and 23% of infants as having 'Abnormal motor development' and 12%, 28% and 13% with 'Motor delay', at 4, 8 and 12 months, respectively. Diagnostic accuracy of ASQ-3 to identify abnormal motor development was acceptable for older infants only if 'Monitor' cut-off was used: sensitivity (95% CI) 33% (23-44), 86% (73-95) and 80% (63-92) and specificity (95% CI) 84% (74-92), 76% (66-84), and 76% (67-83) at 4, 8 and 12 months, respectively. ASQ-3 sensitivity to identify motor delay was low. CONCLUSIONS: ASQ-3 has poor sensitivity to identify abnormal or delayed motor development at 4 months. Using the 'Monitor' cut-off improves the diagnostic accuracy of ASQ-3 for identification of older infants with abnormal motor development who are at high risk of motor disability. However, ASQ-3 has poor sensitivity to identify motor delay. Clinical motor assessment of high-risk infants is recommended, particularly in early infancy.

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BACKGROUND: This study aims to evaluate the performance of a deep learning system for the evaluation of tooth development stages on images obtained from panoramic radiographs from child patients. METHODS: The study collected a total of 1500 images obtained from panoramic radiographs from child patients between the ages of 5 and 14 years. YOLOv5, a convolutional neural network (CNN)-based object detection model, was used to automatically detect the calcification states of teeth. Images obtained from panoramic radiographs from child patients were trained and tested in the YOLOv5 algorithm. True-positive (TP), false-positive (FP), and false-negative (FN) ratios were calculated. A confusion matrix was used to evaluate the performance of the model. RESULTS: Among the 146 test group images with 1022 labels, there were 828 TPs, 308 FPs, and 1 FN. The sensitivity, precision, and F1-score values of the detection model of the tooth stage development model were 0.99, 0.72, and 0.84, respectively. CONCLUSIONS: In conclusion, utilizing a deep learning-based approach for the detection of dental development on pediatric panoramic radiographs may facilitate a precise evaluation of the chronological correlation between tooth development stages and age. This can help clinicians make treatment decisions and aid dentists in finding more accurate treatment options.

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The analysis of utilising unmanned aerial vehicles (UAVs) to form flying networks in obstacle conditions and various algorithms for obstacle avoidance is conducted. A planning scheme for deploying a flying LiFi network based on UAVs in a production facility with obstacles is developed and described. Such networks are necessary to ensure reliable data transmission from sensors or other sources of information located in dangerous or hard-to-reach places to the crisis centre. Based on the planning scheme, the following stages are described: (1) laying the LiFi signal propagation route in conditions of interference, (2) placement of the UAV at the specified points of the laid route for the deployment of the LiFi network, and (3) ensuring the reliability of the deployed LiFi network. Strategies for deploying UAVs from a stationary depot to form a flying LiFi network in a room with obstacles are considered, namely the strategy of the first point for the route, the strategy of radial movement, and the strategy of the middle point for the route. Methods for ensuring the uninterrupted functioning of the flying LiFi network with the required level of reliability within a given time are developed and discussed. To implement the planning stages for deploying the UAV flying LiFi network in a production facility with obstacles, the "Simulation Way" and "Reliability Level" software tools are developed and described. Examples of utilising the proposed software tools are given.

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Background: In light of the global effort to eradicate stunting in childhood, the objective of this research endeavor was to assess the prevalence of stunting and associated factors, simultaneously construct and validate a risk prediction model for stunting among children under the age of three in Shenzhen, China. Methods: Using the stratified random sampling method, we selected 9,581 children under the age of three for research and analysis. The dataset underwent a random allocation into training and validation sets, adhering to a 8:2 split ratio. Within the training set, a combined approach of LASSO regression analysis and binary logistic regression analysis was implemented to identify and select the predictive variables for the model. Subsequently, model construction was conducted in the training set, encompassing model evaluation, visualization, and internal validation procedures. Finally, to assess the model's generalizability, external validation was performed using the validation set. Results: A total of 684 (7.14%) had phenotypes of stunt. Utilizing a combined approach of LASSO regression and logistic regression, key predictors of stunting among children under three years of age were identified, including sex, age in months, mother's education, father's age, birth order, feeding patterns, delivery mode, average daily parent-child reading time, average time spent in child-parent interactions, and average daily outdoor time. These variables were subsequently employed to develop a comprehensive prediction model for childhood stunting. A nomogram model was constructed based on these factors, demonstrating excellent consistency and accuracy. Calibration curves validated the agreement between the nomogram predictions and actual observations. Furthermore, ROC and DCA analyses indicated the strong predictive performance of the nomograms. Conclusions: The developed model for forecasting stunt risk, which integrates a spectrum of variables. This analytical framework presents actionable intelligence to medical professionals, laying down a foundational framework and a pivot for the conception and execution of preemptive strategies and therapeutic interventions.

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Microglia, the resident immune cells in the brain, dynamically adapt their morphology based on their functional state. This study explored the relationship between microglial morphology and sleep-wake cycles in mice. Using Iba1 immunostaining to identify microglia, we quantified morphological changes in microglia at different timepoints in multiple brain regions (cortex, hippocampus, basal forebrain, hindbrain, and cerebellum) in B6 male mice using semi-automated 3D structural analysis. Simultaneously, in a separate group, we monitored wake and sleep stage-specific brain activity using EEG/EMG recordings. During natural sleep-wake cycles, we observed increased microglial complexity (enlarged volume, territorial coverage, and ramification) during wakefulness, characterized by high-frequency theta (8-12 Hz) and gamma activity (30-80 Hz). Conversely, during NREM sleep, which is dominated by delta activity (0.5-4 Hz), microglia displayed reduced complexity. Notably, this pattern was absent in brain regions lacking direct functional connections to areas generating vigilance stage-dependent thalamocortical oscillations. We then extended wakefulness to decouple circadian influence from sleep-wake-specific neuronal activity. This procedure attenuated the decrease in microglial complexity observed during natural sleep, suggesting a crucial role for neuronal activity. Subsequent recovery sleep restored microglial features, independent of the time of day (zeitgeber time). These findings reveal a dynamic interplay between vigilance stage-specific thalamocortical activity and microglial morphology across various brain regions. This suggests a potential role for microglia in sleep regulation and warrants further investigation to understand the underlying mechanisms.

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OBJECTIVE: The purpose of this study was to investigate the correlation between different subtypes of acute kidney injury (AKI) and clinical outcomes following lung transplantation (LTx) and to identify a reliable indicator for predicting poor prognosis in the LTx population. METHODS: We retrospectively analyzed the clinical data of 279 LTx patients from August 2016 to March 2023. The AKI subtypes included AKI, persistent AKI on Day 7 (P7-AKI) and Day 14 (P14-AKI) after LTx, and AKI stages. The correlations of these factors with respiratory outcomes, mortality at 90 days, mortality at 1 year and data finalization were assessed, and the risk factors for the selected AKI subtypes were evaluated. RESULTS: AKI occurred in 215 patients (77.1%), with 129 (46.2%) experiencing P7-AKI and 95 (34.1%) experiencing P14-AKI. P7-AKI was associated with more respiratory and mortality outcomes than were AKI and AKI stages, and P7-AKI surpassed P14-AKI in terms of a shorter diagnostic time. After adjusting for age, sex, BMI, type of transplant, transplant diagnosis and comorbidities, P7-AKI independently correlated with increased mortality risk at 90 days [HR 12.312 (95% CI: 2.839-53.402)], 1 year [HR 3.847 (95% CI: 1.840-8.044)], and data finalization [HR 2.010 (95% CI: 1.331-3.033)]. Five variables were identified as independent predictors for P7-AKI, including preoperative body mass index, prothrombin activity, hemoglobin and serum creatinine, and intraoperative colloid administration. CONCLUSION: P7-AKI has been identified as a reliable indicator for predicting adverse outcomes in LTx patients, which may assist healthcare professionals in identifying high-risk individuals.

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STUDY OBJECTIVES: To assess whether the frequency content of electroencephalography (EEG) and electrooculography (EOG) during nocturnal polysomnography (PSG) can predict all-cause mortality. METHODS: Power spectra from PSGs of 8,716 participants, included from the MrOS Sleep Study and the Sleep Heart Health Study (SHHS), were analyzed in deep learning-based survival models. The best-performing model was further examined using SHapley Additive Explanation (SHAP) for data-driven sleep-stage specific definitions of power bands, which were evaluated in predicting mortality using Cox Proportional Hazards models. RESULTS: Survival analyses, adjusted for known covariates, identified multiple EEG frequency bands across all sleep stages predicting all-cause mortality. For EEG, we found an all-cause mortality hazard ratio (HR) of 0.90 (CI95% 0.85-0.96) for 12-15 Hz in N2, 0.86 (CI95% 0.82-0.91) for 0.75-1.5 Hz in N3, and 0.87 (CI95% 0.83-0.92) for 14.75-33.5 Hz in REM sleep. For EOG, we found several low-frequency effects including an all-cause mortality HR of 1.19 (CI95% 1.11-1.28) for 0.25 Hz in N3, 1.11 (CI95% 1.03-1.21) for 0.75 Hz in N1, and 1.11 (CI95% 1.03-1.20) for 1.25-1.75 Hz in Wake. The gain in the concordance index (C-index) for all-cause mortality is minimal, with only a 0.24% increase: The best single mortality predictor was EEG N3 (0-0.5 Hz) with C-index of 77.78% compared to 77.54% for confounders alone. CONCLUSION: Spectral power features, possibly reflecting abnormal sleep microstructure, are associated with mortality risk. These findings add to a growing literature suggesting that sleep contains incipient predictors of health and mortality.

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Arabidopsis thaliana is more susceptible to certain viruses during its later developmental stages. The differential responses and the mechanisms behind this development-dependent susceptibility to infection are still not fully understood. Here we explored the outcome of a viral infection at different host developmental stages by studying the response of A. thaliana to infection with turnip mosaic virus at three developmental stages: juvenile vegetative, bolting, and mature flowering plants. We found that infected plants at later stages downregulate cell wall biosynthetic genes and that this downregulation may be one factor facilitating viral spread and systemic infection. We also found that, despite being more susceptible to infection, infected mature flowering plants were more fertile (i.e. produce more viable seeds) than juvenile vegetative and bolting infected plants; that is, plants infected at the reproductive stage have greater fitness than plants infected at earlier developmental stages. Moreover, treatment of mature plants with salicylic acid increased resistance to infection at the cost of significantly reducing fertility. Together, these observations support a negative trade-off between viral susceptibility and plant fertility. Our findings point towards a development-dependent tolerance to infection.

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Numerous cultivars of chili are grown in Bangladesh for their nutritional and sensory attributes, serving as both spices and food items. Among many, indigenous chili cultivars in Bangladesh include Sada Akshi, Kajini, Dhani, and Naga are the important ones. The functional qualities of chili peppers are attributed to the plentiful presence of bioactive substances. Consequently, this study aimed to determine the variations in bioactive compounds, antioxidant activities, and hotness among the pre-mature, mature, pre-ripening, and ripening stages of four distinct chili cultivars. Four different cultivars of chilis at four different maturity stages were collected and analyzed for their antioxidant and bioactive profiles. The findings of the research revealed that all chili varieties exhibited a notable range of vitamin C concentration, ranging from 1.67 to 8.45 mg/g FW during the maturity stages. The values of TPC, TFC, total carotenoids, and chlorophyll a and b ranged from 16.68 to 46.76 mg GAE/g, 2.80-8.53 mg QE/g, 4.31-85.79 µg/g DW, 2.83-15.54 and 0.74-5.66 µg/g DW on a dry weight basis, respectively. The antioxidant activity was assessed using the FRAP and the DPPH scavenging assay and the values ranged from 142.62 to 311.03 mM Fe (II) Equivalent/100g DW and 216.36-329.52 µM Trolox Equivalent/g DW, respectively. The content of vitamin C, TPC, total carotenoids, and chlorophyll b was increased with the stages of development. The hotness of chili also increased with the development stages. However, the antioxidant activity fluctuated during the development stages of chili. Furthermore, the study incorporated the evaluation of physical parameters, such as height, weight, and color attributes concerning chilies. The Naga variety of chili demonstrated the highest level of efficacy when compared to other varieties. The nutritional and physicochemical information of the different cultivars of chili in this study might be useful to the breeders, spice processors, and consumers for desired size, taste, and hotness with health-promoting bioactive compounds, eventually for determining the harvest time.

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Introduction: Symbiotic bacteria play key roles in a variety of important life processes of insects such as development, reproduction and environmental adaptation, and the elucidation of symbiont population structure and dynamics is crucial for revealing the underlying regulatory mechanisms. The marmalade hoverfly (Episyrphus balteatus) is not only a remarkable aphid predator, but also a worldwide pollinator second to honeybees. However, its symbiont composition and dynamics remain unclear. Methods: Herein, we investigate the symbiotic bacterial dynamics in marmalade hoverfly throughout whole life cycle, across two sexes, and in its prey Megoura crassicauda by 16S rRNA sequencing. Results: In general, the dominant phyla were Proteobacteria and Firmicutes, and the dominant genera were Serratia and Wolbachia. Serratia mainly existed in the larval stage of hoverfly with the highest relative abundance of 86.24% in the 1st instar larvae. Wolbachia was found in adults and eggs with the highest relative abundance of 62.80% in eggs. Significant difference in species diversity was observed between the adults feeding on pollen and larvae feeding on M. crassicauda, in which the dominant symbiotic bacteria were Asaia and Serratia, respectively. However, between two sexes, the symbionts exhibited high similarity in species composition. In addition, our results suggested that E. balteatus obtainded Serratia mainly through horizontal transmission by feeding on prey aphids, whereas it acquired Wolbachia mainly through intergeneration vertical transmission. Taken together, our study revealed the effects of development stages, diet types and genders of E. balteatus on symbionts, and explored transmission modes of dominant bacteria Serratia and Wolbachia. Discussion: Our findings lay a foundation for further studying the roles of symbiotic bacteria in E. balteatus life cycle, which will benefit for revealing the co-adaptation mechanisms of insects and symbiotic bacteria.

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OBJECTIVES: This study aimed to assess the accuracy of machine learning (ML) models with feature selection technique in classifying cervical vertebral maturation stages (CVMS). Consensus-based datasets were used for models training and evaluation for their model generalization capabilities on unseen datasets. METHODS: Three clinicians independently rated CVMS on 1380 lateral cephalograms, resulting in the creation of five datasets: two consensus-based datasets (Complete Agreement and Majority Voting), and three datasets based on a single rater's evaluations. Additionally, landmarks annotation of the second to fourth cervical vertebrae and patients' information underwent a feature selection process. These datasets were used to train various ML models and identify the top-performing model for each dataset. These models were subsequently tested on their generalization capabilities. RESULTS: Features that considered significant in the consensus-based datasets were consistent with a CVMS guideline. The Support Vector Machine model on the Complete Agreement dataset achieved the highest accuracy (77.4%), followed by the Multi-Layer Perceptron model on the Majority Voting dataset (69.6%). Models from individual ratings showed lower accuracies (60.4-67.9%). The consensus-based training models also exhibited lower coefficient of variation (CV), indicating superior generalization capability compared to models from single raters. CONCLUSION: ML models trained on consensus-based datasets for CVMS classification exhibited the highest accuracy, with significant features consistent with the original CVMS guidelines. These models also showed robust generalization capabilities, underscoring the importance of dataset quality.

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Type 1 diabetes is rising in the pediatric population, affecting approximately 1.2 million children and adolescents globally. Its complex pathogenesis involves the interaction between genetic predisposition and environmental factors, leading to T cell-mediated destruction of insulin-producing pancreatic beta-cells. This destruction results in insulin insufficiency and hyperglycemia. Hence, managing type 1 diabetes requires a comprehensive approach that includes various aspects such as blood glucose monitoring, insulin therapy, carbohydrate counting, caloric intake monitoring, considering family habits and food preferences, planning daily schedules, and incorporating physical activity. Children with type 1 diabetes encounter age-specific challenges in disease management that may exacerbate the risk of metabolic complications and adverse health outcomes. These risk factors may be neurological, physiological, behavioral, psychological, or social, complicate management and necessitate tailored approaches for effective care. Regardless of the age group, primary caregivers have a high responsibility to maintain optimal glycemic control, including monitoring diet, daily activity, and administering insulin. By reviewing research on the challenges faced by pediatric patients with type 1 diabetes, we summarized key insights aimed at developing targeted interventions and support systems that enhance diabetes management and improve health outcomes in this vulnerable population.

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Understanding the correlation between topography, species biomass and species life stage would allow forest managers to better foster carbon storage in forests. Using census data from a 60-ha plot in south China, we first quantified aboveground biomass (AGB) and how much it varied among different topographies. Next, the specific contribution of 42 dominant species to total aboveground biomass was analyzed for each of the different topographies. We also explored whether these species-topography associations changed, in terms of species' AGB distribution, during each of the three life stages (sapling, juvenile, adult) for these 42 species. Our results showed that the average AGB was 368.79 Mg ha-1 and that it varied noticeably among the four topographies (Low valley, Slope, High valley and Ridge, which were classified by using fuzzy C-mean clustering based on elevation, convexity, and slope). AGB was significantly lower in the two valleys than in the two other topographies. Of the 42 species, 88.1% showed topographic preferences, and 78.6% appeared to exhibit topographic preferences that changed with life stage. Our work highlights the importance of topography and life stage in species biomass distribution and suggests that different combinations of species and life stages, based on species topographic preferences across life stages, may be better suited in different tropical rainforest topographies to maximize carbon storage overall.