Health complaints in individual visiting primary health care: population-based national electronic health records of Iran.

BACKGROUND: The mission of medical schools is a sustainable commitment to orient education, research, and services based on the priorities and expectations of society. The most common complaints of patients from comprehensive health service centers (CHSCs) based on the data from electronic health records were assessed in order to determine primary health care (PHC) priorities for the educational planning of medical students in Iran. METHODS: A population-based national study was designed to assess clinical complaints of patients in all age groups who were referred to CHSCs at least once to be visited by physicians. All the data in the census were extracted from electronic health records in PHC system during 2015-2020, classified by the International Classification of Primary Care 2nd edition (ICPC-2e-English), and statistically analyzed. The total number of complaints that were recorded in the system was 17,430,139. RESULTS: 59% of the referring patients were women. The highest number of referrals was related to the age group of 18-59 years (56.9%), while the lowest belonged to the elderly people (13.3%). In all age and sex groups, the first ten complaints of patients with three top priorities in each category included process (follow-up, consultation, and results exam), digestive (toothache and gum complaint, abdominal pain, and diarrhea), respiratory (cough, sore throat, and runny nose), general (fever, pain, and weakness and fatigue), musculoskeletal (back pain, leg complaint, and knee injuries), endocrine and nutritional (weight gain, Feeding problem, and weight loss), cardiovascular (hypertension, palpitations, and Postural hypotension), neurological (headache, dizziness, and paralysis), sexual dysfunction (vaginal complaint, discharge, and irregular menstruation), and dermatological (pruritus, rash, and inflammation) problems. CONCLUSION: High priorities in referring to PHC had a key role in assessing the country's health needs. Since this study was in line with the national pattern of complaints and patients' profile, the present findings can be helpful to amend policy-making, educational planning and curricula development in medical schools.

Effect of Topical Honey on Mandibular Bone Defect Healing in Rats.

AIM: In medicine, honey is known for its various biological or pharmacological effects, from wound dressing to anticancero-genic and from anti-inflammatory to antibacterial activities. The aim of the current study was to evaluate the effect of honey on healing of mandibular bone defects in a rat model. MATERIALS AND METHODS: This animal study was performed on 24 wild-type Wistar rats. Following shaving, disinfection, and extraoral incision, a 2 × 2 mm defect was created at mandibular angle. In the experimental group, the defect was filled with sterile honey, while it was left unfilled in the control group. The rats were sacrificed after 2 and 4 weeks and defects were assessed histologically. The results were compared using Mann-Whitney U-test (α = 0.05). RESULTS: After 2 weeks, five samples of the experimental group were in mineralization phase, while all samples of the control group were in the vascularization phase (p = 0.015). After 4 weeks, the defects were filled in four samples of the experimental group, while all samples of the control group were in the mineralization stage (p = 0.002). Histomorphometric assessment revealed that the mean new bone formation in the experimental group was significantly more than the control group, both after 2 and 4 weeks (p = 0.041). CONCLUSION: The results showed that honey could accentuate bone healing of mandibular small defects in rats. CLINICAL SIGNIFICANCE: Honey might have potential in repair of human alveolar bone defects.

Study of enzymatic degradation and water absorption of composites carboxymethyl cellulose and poly (ϵ-caprolactone) containing SiO2nanoparticle by cellulase.

This study investigates the effect of Poly (ϵ-Caprolactone (PCL) and Nano-SiO2content within the Carboxymethyl Cellulose (CMC) blends on the rate and extent of carboxymethyl cellulose enzymatic hydrolysis using the enzyme cellulase. The results reveal that blends with Nano-SiO2content at 5 wt% exhibit a significantly reduced rate and extent of CMC hydrolysis. This may be attributed to interactions between CMC and SiO2that prevent further enzymatic attack on the remaining CMC phases within the blend. The total solids that remained after 2880 min were 44.8 wt.% (CMC: PCL); 62.7 wt.% (CMC: PCL: 1% Nano-SiO2); 69.8 wt.% (CMC: PCL: 3% Nano-SiO2); 73.1 wt.% (CMC: PCL: 5% Nano-SiO2). Enzymatic degradation behaviour of CMC: PCL: Nano-SiO2was based on the determinations of water resistance, weight loss and the reducing sugars. The degraded residues have been examined by scanning electronic microscopy (SEM) and UV-Vis spectroscopy.

Deep learning for tooth identification and enumeration in panoramic radiographs.

Background: Dentists begin the diagnosis by identifying and enumerating teeth. Panoramic radiographs are widely used for tooth identification due to their large field of view and low exposure dose. The automatic numbering of teeth in panoramic radiographs can assist clinicians in avoiding errors. Deep learning has emerged as a promising tool for automating tasks. Our goal is to evaluate the accuracy of a two-step deep learning method for tooth identification and enumeration in panoramic radiographs. Materials and Methods: In this retrospective observational study, 1007 panoramic radiographs were labeled by three experienced dentists. It involved drawing bounding boxes in two distinct ways: one for teeth and one for quadrants. All images were preprocessed using the contrast-limited adaptive histogram equalization method. First, panoramic images were allocated to a quadrant detection model, and the outputs of this model were provided to the tooth numbering models. A faster region-based convolutional neural network model was used in each step. Results: Average precision (AP) was calculated in different intersection-over-union thresholds. The AP50 of quadrant detection and tooth enumeration was 100% and 95%, respectively. Conclusion: We have obtained promising results with a high level of AP using our two-step deep learning framework for automatic tooth enumeration on panoramic radiographs. Further research should be conducted on diverse datasets and real-life situations.

Characterization of the bioactive and mechanical behavior of dental ceramic/sol-gel derived bioactive glass mixtures.

Dental ceramics can be modified by bioactive glasses in order to develop apatite layer on their surface. One of the benefits of such modification is to prolong the lifetime of the fixed dental prosthesis by preventing the formation of secondary caries. Dental ceramic/sol-gel derived bioactive glass mixture is one of the options for this modification. In the current study, mixtures of dental ceramic/bioactive glass with different compositions were successfully produced. To evaluate their bioactive behavior, prepared samples were immersed in a simulated body fluid at various time intervals. The prepared and soaked specimens were characterized using Fourier transform infrared spectroscopy, X-ray diffractometry and scanning electron microscopy. Since bioactive glasses have deleterious effects on the mechanical properties of dental ceramics, 3-point bending tests were used to evaluate the flexural strength, flexural strain, tangent modulus of elasticity and Weibull modulus of the specimens in order to find the optimal relationship between mechanical and bioactive properties.