Relationship between using cell phone and the risk of accident with motor vehicles: An analytical cross-sectional study

Purpose: Traffic accidents are one of the major health problems in the world, being the first cause of burden of illness and the second leading cause of death in Iran. The Sistan-Baluchestan province is one of the most accidental provinces of Iran with the highest rate of accidents-caused deaths. This study was conducted to determine the risk factors associated with traffic accidents in Zahedan through 2013 to 2016. Methods: This analytical cross-sectional study was carried out on 223 drivers from Zahedan who were traumatized by traffic accident and sent to Zahedan hospitals. The data were obtained through interviews taken by the trained interviewers via refereeing to the medical records and collected in the researcher-made checklist. Census was obtained from the study subjects. For data analysis, independent t-test, one-way ANOVA, Chi-square and logistic regression were used with the Stata software version 11.0. Results: In this study, 223 male subjects with the mean age of (32.54 ± 12.95) years, 39.8% single and 60.2% married, entered for investigation. Most accidents (38.8%) occurred between 12:00 to 17:59. While driving, 47.1% of the study subjects were using cell phones, 89.1% had manual use of mobile phones, 21.9% had a habit of sending short message service (SMS) and 23.4% had sent SMS within 10 min before the accident. The one way analysis of variance showed that the mean age of individuals with marital status, driving experience, education and accident with motorcycle were significantly different (p < 0.05). Also, the multivariate logistic regression test indicated a significant relationship of smoking, ethnicity, insurance and SMS typing while driving with motorcycle accident (p < 0.05). Conclusion: In this study, SMS and smoking while driving had the highest risk among the variables studied in the motorcycle accidents. Therefore, effective education attempting to enhance people''s awareness about the consequences of using cell phone and smoking during driving to reduce traffic accidents seems necessary.     

Inhibition of cysteine proteinases by autolytic digestion is mediated by CBP2/Hsp47

CBP2/Hsp47 is a glycoprotein normally limited to the ER-Golgi where it is first associated with procollagen chains at a very early point during translation of nascent chains and later with properly folded procollagen. Although CBP2/Hsp47 is regarded as a molecular chaperone belonging to the serpin superfamily, this protein does not appear to inhibit serine proteinases. Here we demonstrate that CBP2/Hsp47 functions in a manner similar to other serpin superfamily members by cross class inhibiting cysteine proteinases. A CBP2/Hsp47 to cathepsin L inactivation stoichiometery of approximately 1.5 revealed concurrent cleavage of CBP2/Hsp47 with proteinase inactivation. Cleavage of the CBP2/Hsp47 was shown to occur outside the P1-P1' at the P16-P15 and P2'-P3' bonds. In addition, the proteinase bands in SDS/PAGE diminished on reaction of the enzyme with CBP2/Hsp47. These results sustain a mechanism advocated by Bjork et al. (1998), in which cysteine proteinases assault a peptide bond in the reactive site loop of serpins, (CBP2/Hsp47) adjacent to the P1-P1' bonds involved in serine proteinase inhibition. The reaction proceeds with the substrate pathway dominating in the cysteine proteinase reaction. In these complexes the cysteine proteinases, papain and cathepsin L, are rendered more susceptible to proteolysis and are degraded by active enzyme. These properties help explain the mechanism by which CBP2/Hsp47 increases the fidelity of collagen production. Moreover, if CBP2/Hsp47 is shown to involve the multiplexin subclass of collagens, it may further provide a mechanism by which the motogen and angiogenic properties during development and/or neoplasia are regulated.     

Cone Beam Computed Tomographic Measurement of Maxillary Central Incisors to Determine Prevalence of Facial Alveolar Bone Width ≥2 mm

Background: The initial thickness of maxillary bone has significant impact on the responding level of facial bone and soft tissue after extraction and immediate implant placement. A prevailing notion is that following implant placement in fresh extraction sites, at least 2 mm of facial bone is needed to prevent soft tissue recession, fenestration, and dehiscence. Purpose: The purpose of this study was to use cone beam computed tomography (CBCT) to measure horizontal width of facial alveolar bone overlying healthy maxillary central incisors and to determine prevalence of bone thickness ≥2 mm. Materials and Methods: Tomographic data from 101 randomly selected patients were evaluated by two independent observers. Assessments were made of facial bone width at levels 1.0 to 10.0 mm apical to the bone crest. Results: Healthy maxillary central incisors (n = 202) were measured from 101 patient scans. The percent of teeth with facial bone ≥2 mm at levels 1, 2, 3, 4, and 5 mm from the bone crest was 0, 1.5, 2.0, 3.0, and 2.5%, respectively. Overall mean thickness of the bone was 1.05 mm for right and left central incisors combined. The range of individual measurements for all levels was 0 to 5.1 mm. The occurrence of ≥2 mm thickness bone measurements increased with increasing depth. However, mean widths observed at levels 6 to 10 mm from the crest ranged only 1.0 to 1.3 mm because of apparent fenestration occurrence (0 mm bone) in approximately 12% of teeth. Overall, no significant differences in bone thickness were found between ethnic, gender, age, or scan groups. Conclusions: Using CBCT, occurrences of ≥2 mm maxillary facial alveolar bone were found on no more than 3% of root surfaces 1.0 to 5.0 mm apical to the bone crest in this sample of maxillary central incisors. The study evidenced prevalence of a thin facial alveolar bone (<2 mm) that may contribute to risk of facial bone fenestration, dehiscence, and soft tissue recession after immediate implant therapy.     

Tuberculosis Disease Diagnosis Using Artificial Immune Recognition System

Background: There is a high risk of tuberculosis (TB) disease diagnosis among conventional methods.Objectives:This study is aimed at diagnosing TB using hybrid machine learning approaches.Materials and Methods: Patient epicrisis reports obtained from the Pasteur Laboratory in the north of Iran were used. All 175 samples have twenty features. The features are classified based on incorporating a fuzzy logic controller and artificial immune recognition system. The features are normalized through a fuzzy rule based on a labeling system. The labeled features are categorized into normal and tuberculosis classes using the Artificial Immune Recognition Algorithm.Results:Overall, the highest classification accuracy reached was for the 0.8 learning rate (α) values. The artificial immune recognition system (AIRS) classification approaches using fuzzy logic also yielded better diagnosis results in terms of detection accuracy compared to other empirical methods. Classification accuracy was 99.14%, sensitivity 87.00%, and specificity 86.12%.