Search Results (3,784)

Outcomes after total hip arthroplasty (THA) remain limited by wear and wear particle reaction. Although highly cross-linked polyethylene (HXLPE) mitigated this issue, there are major differences among the various brands available. This study quantified, for the first time, the volumetric wear on the retrievals from various HXLPE brands, using an optical coordinate measuring machine. Patient- and implant-related factors and component orientation were considered. As of 30.06.2025, 104 HXLPE sockets were available, and 79 could be analysed, excluding those too severely deformed/damaged. The median (range) time in situ was 8.7 (2.1–22.1) years. Wear volume and volumetric wear rate were 91 (1–674) mm³ and 9 (0–130) mm³/year, respectively. Highcross (n = 56, Medacta) experienced approximately five times more wear (p < 0.001) than Durasul (n = 17, Zimmer Biomet). The other brands (n = 6) were too small in group size at this stage for statistical analysis. The correlation (R² = 0.94) between linear and volumetric wear was excellent, but high wearing retrievals had a strong impact on this analysis, whereas low wear retrievals showed high variability. Differences in wear were mostly driven by the brand, while other parameters had only a minor impact. This is the first retrieval study comparing volumetric wear among various brands of retrieved HXLPE bearings. Not all HXLPE bearings should be considered equal. As the cohort grows, further parameters and brands may be integrated. Full article

Background: Lifestyle behaviours tend to cluster, with health-promoting and risk-related behaviours forming structured patterns. However, evidence from Kosovo remains scarce, particularly regarding the role of sustained physical activity. Objective: This study aimed to examine associations between physical activity practice and multiple lifestyle behaviours, including the association between activity duration and lifestyle indicators in a predominantly young community sample. Methods: A cross-sectional study was conducted with 229 participants. Lifestyle behaviours were assessed using the EVS-KOS. Descriptive statistics, Spearman correlations, group comparisons (ANOVA and Kruskal–Wallis), and quantile regression analyses (q = 0.50) were performed. Physical activity practice was categorized as no practice, <6 months, or ≥6 months. Results: Health-promoting behaviours were positively associated, whereas risk-related behaviours were positively associated with each other and generally negatively associated with health-promoting behaviours. Significant between-group differences were observed for balanced diet, meal timing, and physical activity habits, with higher scores among participants practicing for ≥6 months (η² = 0.028–0.344). Quantile regression showed significant associations between physical activity practice and physical activity habits, both for <6 months (B = 2.000, p < 0.001) and ≥6 months (B = 2.333, p < 0.001), compared with no practice. No significant associations between physical activity practice and the remaining lifestyle indicators were observed. Conclusions: Physical activity practice was most strongly associated with physical activity habits, particularly among those with longer practice duration. Although between-group differences were also observed for dietary behaviours, associations across lifestyle domains were limited. Full article

Solar-induced chlorophyll fluorescence (SIF) provides a new pathway for the accurate estimation of gross primary productivity (GPP) by directly probing vegetation photosynthetic functioning, but mechanistic estimation is still constrained by the difficulty of obtaining the seasonal dynamics of key physiological parameters. Using maize, a representative C4 crop, as an experimental case, this study integrated long-term tower-based SIF observations with the SCOPE model to propose a cross-variable constraint strategy, in which key vegetation parameters were optimized against in situ SIF observations and independently validated against eddy covariance-derived GPP. Specifically, key sensitive parameters were first identified via Sobol global sensitivity analysis. A double-baseline asymmetric Gaussian phenology model was then constructed to characterize the seasonal dynamics of V_cmax, while C_ab was derived from V_cmax using a smoothed piecewise-linear empirical relationship. A two-stage Bayesian optimization scheme was applied to optimize the parameters, which were subsequently used to run the SCOPE model and reconstruct continuous SIF and GPP time series. The results showed that the estimated daily SIF and GPP agreed well with ground observations, yielding R² values of 0.86 and 0.74, respectively. Furthermore, the upscaled results correlated strongly with satellite products (MODIS: R = 0.94, R² = −2.69; PML: R = 0.95, R² = 0.81). This study demonstrates the feasibility of using SIF observations to constrain SCOPE vegetation parameters for improved GPP estimation, providing a parameterization basis and methodological support for mechanistic remote sensing monitoring of vegetation productivity. Full article

Bulk density (BD) is a key quality parameter of wood pellets, influencing handling, storage and volumetric energy density. The standard procedure (ISO 17828) is accurate but time-consuming and subject to operator variability. This preliminary study introduces the mass flow rate (MFR) as a new variable, not included in the standard, and presents a prototype built for its controlled measurement. Thirty commercial wood-pellet samples were characterized for MFR, pellet length distribution metrics and BD. After multivariate analysis, the relationships between MFR, dimensional descriptors and BD were explored through correlation analysis. As a first exploratory test, Support Vector Regression, Extreme Gradient Boosting and a weighted ensemble were evaluated by nested repeated cross-validation, with Gaussian noise-based augmentation applied to training data only. The mean out-of-fold MAE was 17.5 kg/m³ for the ensemble, 17.8 kg/m³ for SVR and 18.3 kg/m³ for XGB, with overlapping confidence intervals and considerable variability across data partitions. Nevertheless, the errors are contained and comparable to those attributable to operator variability and lower than the features standard deviation (24.3 kg/m³). The results suggest that MFR, combined with dimensional descriptors, can support BD estimation, although larger datasets are needed to establish a robust relationship. Full article

Deep learning has achieved high accuracy in machining condition monitoring, yet accuracy alone cannot reveal whether a model relies on physically meaningful evidence or on dataset shortcuts. The difficulty is that common tasks such as tool-wear and surface-roughness estimation lack a known ground-truth basis for discrimination, so attribution outputs cannot be validated. This study therefore constructs a benchmark task—three-class cutting-speed classification (Vc = 60, 80, 100 m/min)—in which, for a fixed tool diameter, the spindle-speed-dependent spectral content is an a priori known correct basis, enabling the reliability of explainable AI (XAI) methods to be examined under controlled conditions; the task is deliberately simple and serves as a validation framework for attribution methods rather than as a demanding monitoring problem. Cutting force signals were acquired from 3 × 3 full-factorial milling experiments on 17-4PH stainless steel using a Kistler 9170A rotating dynamometer; the torque Mz and resultant force Fa were segmented by sliding windows into 2970 single-channel samples (15 records × 99 windows × 2 channels), each represented by 50 time segments × 6 amplitude-independent statistics. A 1D-CNN, an LSTM, and a fully connected network (NN) were evaluated under five-fold StratifiedGroupKFold cross-validation. The 1D-CNN achieved 93.97% accuracy with roughly one-seventh of the parameters (NN 90.24%, LSTM 89.12%), the smallest fold-to-fold standard deviation, and 84.5% on the most difficult fold; as a single random seed was used, performance conclusions are descriptive only. Holding out whole cutting conditions reduced the mean accuracy to 83–86% (one feed level: 54–57%), so the accuracies describe generalization across records of the studied conditions, not to unseen conditions. Fold-wise application of LRP, LIME, and SHAP showed that the 1D-CNN and NN distributed importance across the entire signal (normalized entropy ≥ 0.97), whereas for the LSTM the gradient × input attribution used as the LRP implementation placed 9.4 times the uniform share on the final time segment, consistently across all five folds, a concentration that SHAP (expected gradients referenced to background training samples) and the perturbation-based LIME did not reproduce. Gradient-free occlusion and resampling reproduced it, and retraining showed that it follows the last input position while removing the final segments did not reduce accuracy; it therefore reflects positional sensitivity rather than reliance on the sequence end. In this run, cross-fold Spearman correlations of LIME rankings lay within −0.05 to 0.05, versus 0.20–0.70 for LRP and SHAP; because exploratory recalculations with alternative aggregation choices gave markedly different LIME correlations, this is reported as a run-specific observation rather than as a property of LIME. All three architectures relied predominantly on frequency-domain features; because the tooth-passing frequencies (159.2–265.3 Hz) exceed the 100 Hz Nyquist frequency of the 200 Hz sampling rate yet lie below the 300 Hz low-pass cutoff of the signal conditioner, they are aliased into the analyzed band, and the dominant-frequency feature reflects the spindle rotation frequency and aliased tooth-passing components, both proportional to spindle speed and hence consistent with the physical definition of cutting speed. The contributions are: a controlled validation framework that cross-validates attribution credibility through importance concentration, inter-method agreement, and cross-fold reproducibility; and the observation that the end-of-sequence concentration of the LSTM, revealed by gradient × input but not by SHAP or LIME, reflects positional sensitivity rather than the basis of classification, highlighting the need to verify attribution concentration with independent analyses rather than relying on a single attribution method. Full article

Background/Objectives: Frailty and postoperative symptom burden are common in older adults undergoing colorectal cancer surgery, but their temporal associations remain unclear. This study examined longitudinal associations between frailty and two symptom dimensions, symptom severity and symptom interference, using the Random Intercept Cross-Lagged Panel Model (RI-CLPM). Methods: This prospective longitudinal study enrolled 242 older adults (≥60 years) undergoing CRC surgery. Frailty and symptoms were assessed before surgery and at 1 week, 1 month, 3 months, 6 months, and 12 months postoperatively. Separate RI-CLPMs were estimated for frailty with symptom severity and symptom interference. Sensitivity analyses compared results with conventional CLPMs, adjustment for time-varying albumin and body mass index, and a worst-case missing-data scenario. Results: Frailty and both symptom dimensions increased at 1 week after surgery, then generally declined. At the between-person level, frailty was strongly correlated with symptom severity and symptom interference. At the within-person level, higher symptom severity was associated with higher subsequent frailty from 1 to 3 months (β = 0.27, p = 0.033) and from 3 to 6 months (β = 0.38, p = 0.004), with the latter association remaining relatively stable across sensitivity analyses. Higher frailty was associated with higher subsequent symptom interference from before surgery to 1 week after surgery (β = 0.34), which was also relatively consistent across model specifications. Several other interval-specific paths changed after covariate adjustment or under the worst-case missing-data assumption. Conclusions: Frailty and postoperative symptom burden were closely associated at the between-person level, whereas within-person longitudinal associations were phase-specific and symptom-dimension-dependent. Full article

Outpatient influenza-like illness (ILI) and hospitalized severe acute respiratory infection (SARI) surveillance measure respiratory-pathogen activity in distinct clinical settings. We analyzed 95 epidemiological weeks (2024-W46 to 2026-W36) of national China CDC positivity data for ten respiratory viruses and Mycoplasma pneumoniae. For each pathogen, weekly change in SARI positivity was regressed on ILI changes at lags 1–4, with adjustment for four SARI autoregressive lags and annual seasonality. Inference used Newey–West covariance, joint lag tests, and Benjamini–Hochberg false-discovery-rate correction; reverse-direction, specification, recursive block-bootstrap, prewhitened cross-correlation, rolling-coefficient, and rolling-origin forecast analyses were secondary. Asymptotic forward lag blocks were FDR-significant for ten pathogens, whereas recursive block-bootstrap inference supported six: influenza virus, respiratory syncytial virus, human metapneumovirus, parainfluenza virus, common human coronavirus, and rhinovirus. No augmented model showed FDR-significant evidence of improved forecast error at 1-, 2-, or 4-week horizons; with only 35–38 forecast origins, modest incremental value could not be excluded. National ILI and SARI positivity series showed pathogen-specific temporal coupling, but this 95-week record did not establish fixed lead times or incremental prospective forecasting value in this dataset. Full article

Viral infections continue to pose a significant global health challenge. Antiviral peptides (AVPs) have emerged as promising therapeutic agents owing to their high specificity, favorable safety profiles, and broad-spectrum antiviral activity. However, experimental AVP identification is time-consuming and costly, while existing computational predictors often have limitations in predictive accuracy and feature representation. Here, we propose DeepAVPred, a deep learning-based framework for AVP identification. DeepAVPred integrates complementary sequence descriptors to capture physicochemical properties and pairwise residue interactions. We present a novel feature representation, the Residue Energy Content Matrix augmented with a Discrete Wavelet Transform (RECM-DWT). RECM-DWT characterizes multi-scale patterns in residue-energy information and complements conventional sequence descriptors by providing wavelet-derived features across multiple resolution levels. The integrated features are then modeled using an attention-based Bidirectional Gated Recurrent Unit (BiGRU) network to learn contextual dependencies and informative sequence patterns. On the AVP_train dataset, DeepAVPred achieved strong performance in 10-fold cross-validation, with an accuracy of 0.990, a Matthews Correlation Coefficient (MCC) of 0.980, and an area under the receiver operating characteristic curve (AUC) of 0.993. On the independent test dataset, AVP_test, DeepAVPred achieved an accuracy of 0.942, an MCC of 0.886, and an AUC of 0.949. These results demonstrate the predictive performance of DeepAVPred and support its potential as a computational framework for prioritizing candidate antiviral peptides for further experimental validation. Full article

Wheat is one of the most important staple crops worldwide, and soil moisture plays a critical role in regulating crop growth, irrigation management, and agricultural water-use efficiency. Accurate soil moisture prediction is therefore essential for precision agriculture and intelligent irrigation scheduling. However, soil moisture variations are driven by complex interactions among meteorological conditions, soil layers, and temporal dynamics, resulting in strong nonlinear characteristics and long-term dependencies that remain challenging for conventional prediction approaches. To address these challenges, this study proposes a Multi-Task Collaborative Temporal Convolutional Network with Attention (MTC-TCN-Attn) model for multi-variable wheat soil moisture prediction. The proposed framework integrates a Temporal Convolutional Network (TCN) with a multi-head self-attention mechanism, where causal and dilated convolutions are employed to capture multi-scale temporal dependencies, while the attention module adaptively identifies informative time steps and enhances global temporal feature representation. Furthermore, a multi-task collaborative learning strategy based on shared feature representations is developed to exploit the intrinsic correlations among multiple soil-moisture-related variables and improve model generalization. Experiments conducted on wheat field data demonstrate that the proposed MTC-TCN-Attn model outperforms representative benchmark models, including SVR, LSTM, Transformer, and Multi-Task Long Short-Term Memory (MT-LSTM), across all five prediction targets in terms of $R^2$, RMSE, and MAE. For the three principal soil-moisture-state variables, the proposed model achieved $R^2$ values of 0.9147, 0.9651, and 0.9622 for surface soil moisture (SSM), subsurface soil moisture (SUSM), and soil moisture profile (SMP), respectively. Compared with single-task and multi-task TCN baselines, the proposed MTC-TCN-Attn model consistently reduces prediction errors and improves robustness by effectively integrating temporal dependency modeling and cross-task information sharing. The results indicate that the proposed framework provides an effective approach for multi-variable soil moisture forecasting and has promising potential for intelligent irrigation management and precision agricultural applications. Full article

Vegetation phenology derived from satellite vegetation indices is sensitive to spatial scale, yet how spatial aggregation affects phenological timing and temporal dynamics across broad geographical extents remains insufficiently understood. In this study, the 250 m MOD13Q1 Enhanced Vegetation Index (EVI) time series from 2000 to 2024 was used as a common data source, and 1 km and 5 km datasets were generated directly through spatial aggregation. By maintaining the same sensor, vegetation index, temporal sampling, and phenology retrieval method across scales, this controlled within-source framework minimizes confounding effects associated with cross-sensor differences. The start of the growing season (SOS) and end of the growing season (EOS) were retrieved using asymmetric Gaussian fitting combined with a dynamic threshold method. Their spatiotemporal patterns, aggregation-induced cross-scale consistency, and bias were systematically analyzed. The broad spatial patterns of multi-year mean SOS and EOS remained generally consistent across the three scales. SOS showed an overall advancing tendency and EOS an overall delaying tendency, while the proportion of pixels with relatively large trend magnitudes decreased at coarser spatial scales. Cross-scale correlations remained high across all scale pairs and generally increased with latitude, with SOS exhibiting higher cross-scale consistency than EOS. Annual SOS correlations declined during 2000–2024, whereas EOS correlations increased during the early part of the record and declined thereafter, with a breakpoint around 2010. The bias showed clear differences in direction and magnitude between SOS and EOS and among vegetation types, and its absolute magnitude generally increased with increasing scale disparity. These results indicate that aggregation-induced phenological responses are not spatially uniform but vary systematically with geographical context, vegetation type, and phenological phase. These findings provide a scientific reference for the cross-scale comparison and scale transformation of multi-source remotely sensed phenology products, as well as for studies of regional ecological processes. Full article

Background/Objectives: University students are at a critical stage for establishing healthy lifestyle habits, yet little is known about their adherence to integrated 24 h movement guidelines that include physical activity, sedentary behavior, and sleep. We examined the proportion of students meeting the Canadian 24 h movement guidelines and associated factors among first-year students at a single Spanish university (UNILIFE-M). Methods: This cross-sectional analysis included 671 first-year students from Universidad Loyola Andalucía (Spain) participating in the UNIversity Students’ LIFEstyle Behaviours and Mental Health (UNILIFE-M) study. Combined guideline adherence used complete cases (n = 646). Behaviors were assessed with the Spanish Physical Activity Scale 2.1 (PAS-2.1S). Adherence required ≥150 min/week of moderate-to-vigorous physical activity, ≤8 h/day of total sedentary time including ≤3 h/day of recreational screen time (both sedentary criteria required), and 7–9 h/night of sleep. Multivariable logistic regression estimated adjusted odds ratios (complete cases on covariates, n = 644). Results: Overall, 137/646 students (21.2%) met all three guidelines, while 54 (8.4%) met none. Male sex was associated with higher odds of adherence (adjusted odds ratio [aOR] = 1.65; 95% confidence interval [95% CI] 1.10 to 2.47), and a lifetime mental health diagnosis was associated with lower odds (aOR = 0.28; 95% CI 0.08 to 0.72). Age > 18 years (aOR = 0.64; 95% CI 0.39 to 1.03) and non-health sciences enrollment (aOR = 0.69; 95% CI 0.46 to 1.03) showed borderline associations that were not robust across sensitivity analyses. Conclusions: In this single-university first-year sample, adherence to the 24 h movement guidelines was relatively low. Associations should be interpreted as cross-sectional correlates, not causal determinants. Full article

Existing studies on the thermal effect of building-integrated photovoltaic (BIPV) curtain walls are mostly numerical and mainly use absolute PV temperature; few have compared the relative roles of ambient temperature and solar radiation across seasons, or their temporal expression, in cold regions. This study conducted continuous field measurements on a south-facing ventilated CIGS PV curtain wall of a free-running demonstration building in Jinan, China, over 62 summer and 60 winter days at 10-min intervals. Single and two-factor regressions, standardized coefficients and partial correlations were used to compare the two drivers, including an incidence-angle-corrected radiation input; diurnal profiles, cross-correlation and positive/negative temperature-excess integrals characterized the temporal response and day-night asymmetry. Results show that: (1) the daytime mean PV surface temperature was higher in summer (35.2 vs. 13.8 °C), whereas the daytime temperature rise above ambient (ΔT) was 2.4 times higher in winter (+10.7 vs. +4.5 °C); (2) within the two-factor model, the dominant driver of ΔT shifted from ambient temperature in summer (75.5% relative statistical contribution) to solar radiation in winter (62.4%), a ranking unchanged with the corrected radiation input; (3) in winter, the warming rate was 2.26 times that in summer and the peak occurred 1.3 h earlier, while normalized diurnal profiles were similar in shape; (4) the PV surface was a daily net heat source relative to ambient air, with positive-to-negative integral ratios of 6.16 in summer and 2.48 in winter. Scientifically, the seasonal variation reflects a shift in the relative importance of the two boundary conditions rather than a change in heat-transfer mechanism. Theoretically, absolute temperature and temperature rise should be chosen according to the analysis purpose. In practice, summer operation should limit daytime heat gain, winter operation should limit nocturnal heat loss, and heating-season analyses should allow for the apparent response lag. Full article

We present a short-timescale multiwavelength timing study of the symbiotic recurrent nova T Coronae Borealis (T CrB), based on Swift/X-Ray Telescope (XRT), Swift/UltraViolet Optical Telescope (UVOT), and Transiting Exoplanet Survey Satellite (TESS) observations obtained across the high state, the 2023–2024 optical/UV dip, and the subsequent recovery. We use high-cadence event-mode and TESS data to search specifically for sub-kilosecond variability and simultaneous UV–optical correlations. The UVM2 event-mode data show a candidate variability timescale near 445 s during MJD 59,954.1–60,276.9.The folded extrema correspond to a descriptive fractional modulation amplitude of about 9.9%. The feature remains at approximately the same timescale when 20 s, 10 s, and 5 s event-mode bins are used, while no corresponding 445 s peak is found in the spacecraft-pointing diagnostic, the background light curve, or a nearby comparison star. Nevertheless, we do not establish phase coherence across observations separated by days to weeks, and no UVW1 or UVW2 event-mode data are available for an equivalent high-cadence test; the feature is therefore treated as a tentative QPO-like timescale rather than a secure coherent period. No significant 445 s peak is present in the XRT or TESS Lomb–Scargle periodograms. Injection–recovery tests give a 90% coherent-sinusoid fractional semi-amplitude sensitivity of about 34.3% for the 0.3–10 keV XRT light curve and cadence-corrected limits of about 0.054%, 0.056%, 0.022%, and 0.012% for TESS Sectors 24, 25, 51, and 78, respectively. The only strictly contemporaneous UVM2–TESS Sector 78 overlap spans JD 2,460,442.1159722–2,460,442.1260764 (873 s); after excluding the final incomplete UVM2 bin, 840 s is used for the cross-correlation function (CCF). A phenomenological Gaussian fit to the CCF trough gives a dimensionless coefficient of $-0.41\pm 0.09$ and a lag of $84\pm 31$ s (90% confidence; $\Delta {\chi }^2=2.7$). The sign of the anti-correlation persists after simple linear and quadratic detrending, although the inferred lag is sensitive to detrending and edge effects. We therefore regard both the 445 s feature and the UV–optical lag as tentative. The present data do not uniquely identify the emitting regions or establish a propagation mechanism. These results highlight T CrB as a valuable target for simultaneous high-cadence ultraviolet, optical, and X-ray monitoring before and during its next nova eruption. Full article

Background/Objectives: Sleep health contributes to glucose homeostasis and metabolic regulation. We explored associations between subjective sleep characteristics, glycemic control, and diabetes-related clinical factors in adults with type 1 diabetes mellitus (T1DM). Methods: This exploratory cross-sectional, pilot investigation included 30 adults with T1DM at the Emergency Clinical County Hospital of Târgu Mureș between April 2025 and March 2026. We analyzed subjective sleep parameters, 90- and 14-day continuous glucose monitoring (CGM) data, clinical and laboratory parameters, diabetes distress and depression symptom scores. Results: Mean age was 39.0 ± 12.87 years, and 56.7% were male. Mean HbA1c was 7.28 ± 1.24%, 90-day time in range (TIR) was 61.47 ± 17.90%, and the coefficient of variation (CV) was 35.91 ± 6.71%. Mean nocturnal sleep duration was 7.15 ± 0.89 h, which was negatively correlated with mean afternoon nap duration on both weekdays (Pearson’s r = −0.492, 95% CI −0.739 to −0.124, n = 30, p = 0.009) and weekends (Pearson’s r = −0.481, 95% CI −0.736 to −0.119, n = 30, p = 0.012). Moreover, weekend sleep duration (p = 0.032, r = −0.401) was negatively correlated only with TIR assessed over 90 days. Social jetlag, self-reported sleep quality, the number of self-reported nocturnal awakenings, altered sleep duration on weekdays, and daytime napping were not significantly correlated with HbA1c or CGM-derived metrics (all p > 0.05). Mean subjective sleep quality score was 3.22 ± 0.87 on a five-point Likert scale. Restful sleep was reported only on half of the monitored nights; 20% had high risk for obstructive sleep apnea (OSA). Physical activity was positively correlated with TIR assessed over both 90 days (Pearson’s r = 0.446, 95% CI 0.144 to 0.606, n = 30, p = 0.025) and 14 days (Pearson’s r = 0.464, 95% CI 0.345 to 0.704, n = 30 p = 0.037). Conclusions: Among the assessed sleep characteristics, only weekend sleep duration was significantly associated with historical 90-day TIR, whereas physical activity was positively associated with TIR over both monitoring periods. Given the exploratory nature and small sample size of this study, larger longitudinal studies are needed to clarify the relationships between sleep health and glycemic control in adults with type 1 diabetes. Full article

Background/Objectives: Children who experience high levels of dental anxiety tend to exhibit poor cooperation during dental visits, which compromises treatment outcomes and leads to a vicious cycle with poor oral health and caries prevention outcomes. The purpose of this study is to analyze the prevalence of dental anxiety in a sample of clinical subjects with children and possible correlations with other factors, as well as the longitudinal nature of anxiety. Methods: This cross-sectional and longitudinal study was conducted on a sample of 150 children aged 12 to 18 years in the city of Zagreb and Zagreb County. The MDAS (Modified Dental Anxiety Scale) was used to collect data related to dental anxiety at three time intervals: before the clinical examination (T1), after the clinical examination (T2), and after a period of 3 months (T3). Results: Significant differences between the MDAS before and after the clinical examination were found, suggesting that dental anxiety decreased in most children (p < 0.001). The MDAS test results were 9.79 and 8.03 for the T1 and T2 periods, respectively, indicating mild anxiety. There are statistically significant differences between individual time points T1, T2, and T3 (p < 0.001), and these differences are significant between T1 and T2 (p = 0.045) and between T1 and T3 (p = 0.012), while between T2 and T3, the differences are not statistically significant (p = 0.616). At T1, children mostly exhibited mild dental anxiety (55.3%), followed by moderate (41.3%) and severe dental anxiety (3.3%). At T2, children mostly exhibited mild dental anxiety (77.2%), followed by moderate anxiety (22.8%) and no severe dental anxiety. Financial influence on the use of dental services (p = 0.014), the type of dental service [public/private/semi-private] (p = 0.008), and experience with the dentist (p = 0.004) were statistically significant factors. The initial MDAS and cariogenic diet scores (p < 0.012) have a negative correlation with the change in the MDAS score; greater fear before the clinical examination (T1) is associated with a greater reduction in fear after the clinical examination (T2). A low-cariogenic diet is associated with a greater reduction in fear after the clinical examination (T2). Conclusions: Most of the children in this study had mild to moderate anxiety, which decreased after a visit to the dentist. Financial influence on the use of dental services, the type of dental service, and experience with the dentist may be associated with dental anxiety. No statistically significant difference was found between dental anxiety and gender or socioeconomic status. Furthermore, a cariogenic diet may also be associated with higher dental anxiety. Better oral hygiene and oral status, higher socioeconomic status, and a low-cariogenic diet may influence the dental anxiety levels in children. Full article