Recurrent respiratory infections and vitamin A levels: a link? It is cross-sectional.

Respiratory tract infections are common illnesses in children, causing significant morbidity and negatively affecting their health. Vitamin A protects against infections and maintains epithelial integrity. The goal of this study was to determine the correlation between vitamin A deficiency and recurrent respiratory tract infections (RRTIs). Participants in this cross-sectional study were divided into 3 groups: RRTIs (including patients with history of RRTIs presenting with respiratory tract infection symptoms), RTI (including patients without history of RRTIs presenting with respiratory tract infection symptoms), and control (including children who came for a routine health checkup without a history of RRTIs or respiratory tract infection symptoms). The vitamin A assay was performed using high-performance liquid chromatography. The study included 550 children aged 6.64 ±â€…2.61 years. The RRTIs group included 150 children (27.3%), the RTI group included 300 children (54.5%), and the control group included 100 children (18.2%). Subclinical vitamin A deficiency and vitamin A deficiency affected 3.1% and 1.3% of subjects, respectively. Subclinical vitamin A deficiency and vitamin A deficiency were higher in children with RRTIs than in those with RTI (8% vs 1.3%, P = .001 and 4% vs 0.3%, P = .006). Additionally, children with RRTIs had significantly higher rates of subclinical vitamin A deficiency and vitamin A deficiency than those in the control group, which had 1% subclinical vitamin A deficiency (P = .017) and no cases of vitamin A deficiency (P = .043). The RRTIs group had higher rates of otitis media (27.3%), sinusitis (20%), and pneumonia (4.7%) than the RTI group (P = .002). Vitamin A insufficiency was associated with RRTIs in children.

Intratympanic injection of dexamethasone for management of labyrinthitis associated with COVID-19 disease resistant cases.

Objective: To evaluate the efficacy of mixed oral prednisolone and intratympanic dexamethasone (ITID) injection in labyrinthitis, due to COVID 19. Methods: Seventy-five post-COVID-19 labyrinthitis patients were included. Those patients were treated with systemic oral prednisolone for two weeks and ITID. Patients who refuse ITID were ordered to continue oral prednisolone treatment. Assessment of outcome and audiometry for hearing evaluation was done 1, 2 and 4 weeks as well as 3 months post-treatment. Results: Patients in oral steroid only group were 26 patients, while patients in oral steroid and ITID group were 49 patients. In oral steroid group; 11/26 patients showed complete recovery, 3/26 had partial recovery and 12/26 not recovered. In other group; 38/46 patients had complete recovery, 6 had partial recovery and 5/49 patients not recovered. Conclusion: Combined systemic corticosteroid with ITID showed a marked improvement of post-COVID vestibular disorder and hearing loss than only using oral corticosteroid therapy.

Breast Tumor Diagnosis Using Finite-Element Modeling Based on Clinical in vivo Elastographic Data.

OBJECTIVES: This study exploited finite-element modeling (FEM) to simulate breast tissue multicompression during ultrasound elastography to classify breast tumors based on their nonlinear biomechanical properties. METHODS: Numeric simulations were first calculated by using 3-dimensional (3D) virtual models with an assumed tumor's geometric dimensions but with actual material properties to test and validate the FEM. Further numeric simulations were used to construct 3D models based on in vivo experimental data to verify our models. The models were designed for each individual in vivo case, emphasizing the geometry, position, and biomechanical properties of the breast tissue. At different compression levels, tissue strains were analyzed between the tumors and the background normal tissues to explore their nonlinearity and classify the tumor type. Tumor classification parameters were deduced by using a power-law relationship between the applied compressive forces and strain differences. RESULTS: Classification parameters were compared between benign and malignant tumors, for which they were found to be statistically significant in classifying the tumor types (P < .05) by both the validation and verification of FEM. We compared the classification parameters between the in vivo and FEM classifications, for which they were found to be strongly correlated (R = 0.875; P < .001), with no statistical differences between their outcomes (P = .909). CONCLUSIONS: Good agreement between the model outcomes and the in vivo diagnostics was reported. The implemented models were validated and verified. The introduced 3D modeling method may augment elastographic methods to preliminary classify breast tumors at an early stage.