Browse Articles

To Study the Incidence, Risk Factors, Antibiotic Susceptibility, Resistant Mechanism and Treatment Outcome in Critically Ill Patients with Carbapenem Resistant Acinetobacter Baumanii (CRAB) Infection in a Tertiery Care Setup: A Prospective Observational Study

Clinical, psychological, and hematological factors predicting sleep bruxism in patients with temporomandibular disorders

Abstract This cross-sectional observational study aimed to identify the predictors of sleep bruxism (SB) in patients with temporomandibular disorder (TMD) and to comprehensively investigate its association with clinical, sleep-related, psychological, and hematological factors. Seventy-nine patients with TMD (69 females and 10 males; mean age 45.46 ± 14.46 years) were divided into two groups based on the presence or absence of SB: TMD_nonbruxer and TMD_bruxer. Descriptive statistics, correlation analyses, and multivariate stepwise logistic regression were conducted; p < 0.05 was considered statistically significant. In Cramer’s V, SB was correlated with several clinical and sleep-related factors, including TMJ noise (r = 0.52), TMD pain (r = 0.48), craniomandibular index (r = 0.32), limited mouth opening (r = 0.29), tinnitus (r = 0.29), an increase in the Pittsburgh sleep quality index (PSQI) global score (r = 0.24), and poor sleep quality, defined as a PSQI global score ≥ 5 (r = 0.19) (all p < 0.05). SB was also associated with psychological distress. Regarding hematological factors, elevated levels of cortisol (r = 0.30), adrenocorticotropic hormone (ACTH) (r = 0.34), and cortisol/ACTH ratio (r = 0.35) were also associated with SB (all p < 0.05). The factors associated with an increased likelihood of SB ranked in terms of the odds ratio (OR) were: craniomandibular index (OR = 18.400, p = 0.006), poor sleep quality with a PSQI global score ≥ 5 (OR = 11.425, p = 0.027), depression (OR = 1.189, p = 0.014), cortisol/ACTH ratio (OR = 1.151, p = 0.007), anxiety (OR = 1.081, p = 0.040), and adrenocorticotropic hormone (OR = 1.073, p = 0.019). Notably, an increase in age was associated with a decreased likelihood of SB (OR = 0.905, p = 0.006), with a cut-off value of 50 years (AUC = 0.259, 95% CI: 0.149–0.368, p = 0.024), indicating a significant decrease in bruxism occurrence in individuals aged ≥ 50 years. Further analysis revealed complex interconnections between SB and its predictors. In conclusion, SB in TMD patients was associated with age < 50 years, various clinical factors, such as TMD pain and TMJ noise, poor sleep quality, psychological deterioration, and elevated cortisol and ACTH levels.

Does Hypoalbumenemia at Presentation Increases Cumulative Fluid Balance and Vasopressors Requirement in Critically Ill Patients? A Prospective Randomised Study

Sex differences in the association between total energy intake and all-cause mortality among patients with metabolic dysfunction-associated steatotic liver disease

Escherichia Coli Urinary Tract Infection Related Hyperammonemia: Clinical Spectrum and Outcome

Predicting lncRNA and disease associations with graph autoencoder and noise robust gradient boosting

Surveillance Report (Atlas) on Antimicrobial Activity of Aztreonam-avibactam and Comparator Agents Tested against MBL-positive Carbapenem-resistant Klebsiella Spp. Isolated From India (2022)

Exploring the influence of symbiosis between arbuscular mycorrhizal fungi and beans on potassium uptake and the activity of AKT and HKT genes

The Correlation of Hypokalemia and Hypomagnesemia in ICU Patients: A Cross Sectional Study

c-Myc-dependent LAMP3 regulates the proliferation, metastasis and metabolic reprogramming of tongue squamous cell carcinoma

Vasopressor Dependency as A Predictor of Mortality in Septic Shock

Applying machine learning with MobileNetV2 model for rapid screening of vaginal discharge samples in vaginitis diagnosis

Temporal Analysis of Diastolic Shock Index in Patients with Septic Shock and its Correlation with Clinical Outcomes in Indian Setting – A Prospective Observational Study

The comparative study of machine learning agent models in flood forecasting for tidal river reaches

Short Term Outcome of Veno Venous Extracorporeal Membrane Oxygenation in Patients of ARDS Presenting at Tertiary Care Center – An Observational Study

Health literacy as the most important covariate of self-rated health in adolescents

Abo Blood Types and Mortality Following Critical Illness: A Single Centre Retrospective Observational Study

Utilization of ornamental rock waste as a catalytic support for α-MoO₃ in biodiesel production

Comparison of Rox Index and Hacor Score for Predicting Failure of Non-invasive Respiratory Therapy in Acute Hypoxemic Respiratory Failure

Generative inpainting of incomplete Euclidean distance matrices of trajectories generated by a fractional Brownian motion

Abstract Fractional Brownian motion (fBm) exhibits both randomness and strong scale-free correlations, posing a challenge for generative artificial intelligence to replicate the underlying stochastic process. In this study, we evaluate the performance of diffusion-based inpainting methods on a specific dataset of corrupted images, which represent incomplete Euclidean distance matrices (EDMs) of fBm across various memory exponents (H). Our dataset reveals that, in the regime of low missing ratios, data imputation is unique, as the remaining partial graph is rigid, thus providing a reliable ground truth for inpainting. We find that conditional diffusion generation effectively reproduces the inherent correlations of fBm paths across different memory regimes, including sub-diffusion, Brownian motion, and super-diffusion trajectories, making it a robust tool for statistical imputation in cases with high missing ratios. Moreover, while recent studies have suggested that diffusion models memorize samples from the training dataset, our findings indicate that diffusion behaves qualitatively differently from simple database searches, allowing for generalization rather than mere memorization of the training data. As a biological application, we utilize our fBm-trained diffusion model to impute microscopy-derived distance matrices of chromosomal segments (FISH data), which are incomplete due to experimental imperfections. We demonstrate that our inpainting method outperforms standard bioinformatic methods, suggesting a novel physics-informed generative approach for the enrichment of high-throughput biological datasets.