METHODS: An extracted four-rooted mandibular first premolar tooth sample was subjected to 2D radiographic imaging in two views and micro-computed tomography (micro-CT) scanning with a resolution of 25 μm. Subsequently, 3D- reconstruction of the tooth sample was performed using Mimics software (Materialise, Leuven, Belgium). 3D (volume and surface area) and 2D measurements (distances between orifices, area, perimeter, maximum and minimum diameter, roundness, aspect ratio and form factor) were obtained. In addition, endodontic access was prepared, and the canals were explored under the DOM. Location of the canals were confirmed by periapical radiographs with the aid of hand files.
RESULTS: The 2D imaging showed the presence of four canals. Micro-CT analysis showed a complex canal anatomy which was classified using Ahmed et al. coding system as 444 MB1-2-1-2 DB2-1-2-1-3-2-3 1(ML1 DL1). Quantitative analysis showed that the MB root had the highest canal volume and surface area compared to other canals. The 2D measurements showed wide variations among canals, which reflects the complexity of the canals in terms of size and geometry. The MB and DB canals tend to have more aspect ratio values (more oval/flattened) than other canals. After access cavity preparation and exploration, six root canals were identified [mesio-buccal one (MB1), mesio-buccal two (MB2), disto-buccal (DB), mesio-lingual (ML), disto-lingual (DL) and lingual disto-buccal (LDB)]. The canals in the MB root showed two locations of splitting.
CONCLUSIONS: Mandibular first premolars may have complex variations in the number of roots and canal configurations. The different anatomical presentations demonstrated in the 2D and 3D measurements of the six canals presented in this report signifies the anatomical variabilities, which could complicate the detection and negotiation of canals during root canal treatment. Ahmed et al. coding system is useful in classifying teeth with complex root and canal anatomy.
METHODS: Systematic searching was applied (by August 10, 2024) in databases of PubMed, Scopus, WoS, ScienceDirect, Embase, and the Google Scholar search engine. Selected investigations were imported to the EndNote Citation Management Software and duplicate papers were merged. Following consideration of inclusion and exclusion criteria (during primary and secondary screening) relevant papers were selected and underwent validation. Finally, eligible papers were selected for data extraction and meta-analysis (CMA v.2). The I2 index was used for heterogeneity assessment, and the Random Effect Model was used for meta-analysis. The results were categorized based on hematocrit and hemoglobin levels, and study type, and meta-regression was also applied for sample size and year of paper publication.
RESULTS: In the review of 9 eligible studies, the global prevalence of anemia in anorexia nervosa patient was found to be 44.8% (95%CI:25.7-65.7). Also, this value was detected in 48% (95%CI:19.9-77.4) and 43.4% (95%CI:18.6-72) based on hematocrit and hemoglobin levels, respectively. Meta-regression analysis showed that following the increase in sample size and year of paper publication, the global prevalence of anemia in Anorexia nervosa patient decreased and increased, respectively.
CONCLUSION: A relatively high prevalence of anemia in individuals with anorexia nervosa requires proper attention to the regular blood monitoring and laboratory evaluations of the patients.
OBJECTIVE: The study aimed to develop a noninvasive approach for the early detection of PD by analyzing model-based gait features. The primary focus is on identifying subtle gait abnormalities associated with PD using kinematic characteristics.
METHODS: Data were collected through controlled video recordings of participants performing the timed up and go (TUG) assessment, with particular emphasis on the turning phase. The kinematic features analyzed include shoulder distance, step length, stride length, knee and hip angles, leg and arm symmetry, and trunk angles. These features were processed using advanced filtering techniques and analyzed through machine learning methods to distinguish between normal and PD-affected gait patterns.
RESULTS: The analysis of kinematic features during the turning phase of the TUG assessment revealed that individuals with PD exhibited subtle gait abnormalities, such as freezing of gait, reduced step length, and asymmetrical movements. The model-based features proved effective in differentiating between normal and PD-affected gait, demonstrating the potential of this approach in early detection.
CONCLUSIONS: This study presents a promising noninvasive method for the early detection of PD by analyzing specific gait features during the turning phase of the TUG assessment. The findings suggest that this approach could serve as a sensitive and accurate tool for diagnosing and monitoring PD, potentially leading to earlier intervention and improved patient outcomes.
METHODS: A systematic search was conducted on electronic databases, namely ScienceDirect, PubMed, and Taylor and Francis. Data analysis was conducted using PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). Study results were mapped based on the following criteria: 1) conceptual analysis; 2) predictor factors; and 3) research progress. A total of 43 articles met the inclusion and eligibility criteria for further review in this scoping literature review.
RESULTS: The results showed that it is difficult to imagine how a conceptual model of compassion fatigue could be equally relevant and applicable to various helping professions. Factors that can influence compassion fatigue are divided into personal factors (professional factors and sociodemographic factors), such as resilience, burnout, moral courage, emotional control, mindfulness, work experience, professional competence, and professional efficacy, and work-related factors such as traumatic experiences, life disorders, number of patients treated, job satisfaction, emotional support, social support, and fluctuations in interactions with suffering patients. Research on compassion fatigue has developed a lot, especially in the health sector, especially nursing using experimental, cross-sectional, and literature review research methods.
CONCLUSION: Further analysis is needed in developing a conceptual analysis of compassion fatigue that focuses on other fields of work more specifically and comprehensively by paying attention to, aspects, determinants, and validity of compassion fatigue symptoms.