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Indian Journal of Dental Research :... Jan 2024During orthodontic treatment, temporary anchoring devices (TADs) are used to restrain tooth movement. They are a relatively recent addition to the dental toolkit.
INTRODUCTION
During orthodontic treatment, temporary anchoring devices (TADs) are used to restrain tooth movement. They are a relatively recent addition to the dental toolkit.
AIM
As TADs have limitations, Dr. Eric Lieu of Taiwan developed Infra Zygomatic Crest (IZC) screws which are placed between the maxillary second premolar at the bony crest.
TREATMENT PLANNING
The goal of this case study is to emphasize the value of anatomy, site selection, and IZC retrieval in the event of an accident. Cone beam computed tomography was used as a diagnostic tool for the precise location of the displaced IZC and immediate surgical retrieval was done under local anesthesia from the infratemporal space to prevent further complications.
TAKEAWAY LESSONS
Orthodontists knowledge of soft tissue and hard tissue anatomy and precise positioning is crucial for successful TAD implantation.
Topics: Humans; Orthodontic Anchorage Procedures; Cone-Beam Computed Tomography; Zygoma; Bone Screws; Male; Female; Bicuspid; Maxilla; Tooth Movement Techniques
PubMed: 38934757
DOI: 10.4103/ijdr.ijdr_143_23 -
Archivio Italiano Di Urologia,... Jun 2024The retention of foreign bodies inside the body during ludic/sexual procedures or for traumatism represents one of the causes of visits to accident and emergency...
BACKGROUND
The retention of foreign bodies inside the body during ludic/sexual procedures or for traumatism represents one of the causes of visits to accident and emergency departments that often requires surgical removal of the foreign body. However, there are cases where the discovery of such foreign bodies takes place after many years, as in patients that are slightly compromised from a neuro-sociological point of view.
CASE PRESENTATION
A 76-year-old male presented to an outpatient urological examination due to an increase in scrotal volume. At the ultrasound check, an acoustic interference from a solid object was detected, for which computed tomography was requested. The computed tomography scan revealed the presence of an elongated metal body in the perineum. The removal of the foreign body in the operating theatre was then scheduled. A 10 cm long stainless-steel nail located within an abscessed foreign body granuloma was identified and removed via a scrotal access. Four days later, a new surgical toilet was performed due to minimal necrosis of the skin flaps. The patient then performed three more dressings in the operating theatre during the following week. Healing took place by secondary intention until a perfect healing of the surgical wound was obtained.
CONCLUSIONS
Removal of foreign bodies from the perineum in case of infection can be challenging. Careful attention and postoperative dressings are crucial for the success of the case.
Topics: Humans; Male; Aged; Foreign Bodies; Scrotum; Stainless Steel; Nails; Tomography, X-Ray Computed
PubMed: 38934526
DOI: 10.4081/aiua.2024.12363 -
Cardiorenal Medicine Jun 2024Studies exploring the effectiveness and safety of percutaneous left atrial appendage occlusion (pLAAO) in patients with chronic kidney disease (CKD) are limited.
BACKGROUND
Studies exploring the effectiveness and safety of percutaneous left atrial appendage occlusion (pLAAO) in patients with chronic kidney disease (CKD) are limited.
OBJECTIVES
We aimed to analyze trends and outcomes following pLAAO in patients with CKD.
METHODS
We utilized the National Inpatient Sample (NIS) to identify hospitalizations for pLAAO from 2016-2020 and further identified cases with concomitant CKD. The primary outcome was mortality, and secondary outcomes were cerebrovascular accidents, major bleeding, vasopressor requirements, percutaneous coronary intervention, cardiac arrest, acute respiratory failure, transfusion, length of stay (LOS), and total hospital charges. Multivariable logistic regression was performed to further adjust for covariates.
RESULTS
A total of 89,309 pLAAO procedures from 2016 to 2020 were identified, of which 21,559 (24.1%) reported concomitant CKD, with males comprising the majority (62.2%). An increasing trend in pLAAO procedures was seen from 2.24 to 13.9 per 10,000 patients from 2016 to 2020. Despite patients with CKD having a higher rate of most comorbidities, there was no difference in mortality (non-CKD vs. CKD, 0.07% vs. 0.42%; aOR: 1.3, 95% CI: 0.4 - 4.4, p=0.686) and complications for CKD and non-CKD patients, while CKD patients had longer LOS and higher total hospital charge. No significant sex differences in outcomes among CKD patients were observed except for a longer LOS in females.
CONCLUSION
Despite generally having more comorbidities, outcomes of patients with CKD following pLAAO are similar to those without CKD, suggesting that pLAAO can be offered as a safe option for the treatment of AF in eligible patients with CKD.
PubMed: 38934134
DOI: 10.1159/000539953 -
Heliyon Jun 2024Ergonomic risk factors are a prominent cause of fatality and severe injuries in building constructions. Hence, this study applies a Structural Equation Modeling (SEM)...
Structural equation modeling approach for the analysis of ergonomics risk factors and occupational injuries among building construction workers in Bahir Dar City-Ethiopia.
Ergonomic risk factors are a prominent cause of fatality and severe injuries in building constructions. Hence, this study applies a Structural Equation Modeling (SEM) approach to analyze ergonomics risk factors and occupational injuries among building construction workers in Bahir Dar City, Ethiopia. The results indicate significant relationships between ergonomics risk factors and the prevalence of occupational injuries. This study's findings contribute to the understanding of occupational health and safety in the construction industry, highlighting the need for targeted interventions. A cross-sectional study has been carried out, where data was collected through direct observations and standardized pretested questionnaires. The study recruited 220 participants in the construction industry. The data was analyzed using AMOS to study the direct and indirect effects of the identified variable. SEM has shown that the magnitude of the prevalence of occupational injury was 65.2 %. The results also revealed that the mostly affected body parts were lower arm, lower leg, hand, toe, and knee. Carpenter & roofers, plasterer and daily labors & other helpers were highly injury subjected occupations in respective order. The six leading significant risk factors were, do not tie belt at scaffold, falling stairways & ladders, exposure hazardous substances, tools & machinery, electricity (electric power accidents), repetitive tasks, the layout didn't consider health & safety aspects in the site, and do not provide safety orientation for new workers engaging the job. Employees are mostly affected on their lower body parts which needs more focus to prevent it, especially carpenter & roofers, plasterer and daily labors. Also the findings show that 50 % of respondents agree that the higher priority for safety and health management practices should be given to finishing part of the construction followed by excavation and earth work, masonry, and electrical installations. Therefore, it is recommended that the contractors must focus more on the finishing phase.
PubMed: 38933936
DOI: 10.1016/j.heliyon.2024.e32234 -
Frontiers in Public Health 2024Uncertainty and inconsistency in terminology regarding the risk factors (RFs) for in-hospital falls are present in the literature. (Meta-Analysis)
Meta-Analysis
BACKGROUND
Uncertainty and inconsistency in terminology regarding the risk factors (RFs) for in-hospital falls are present in the literature.
OBJECTIVE
(1) To perform a literature review to identify the fall RFs among hospitalized adults; (2) to link the found RFs to the corresponding categories of international health classifications to reduce the heterogeneity of their definitions; (3) to perform a meta-analysis on the risk categories to identify the significant RFs; (4) to refine the final list of significant categories to avoid redundancies.
METHODS
Four databases were investigated. We included observational studies assessing patients who had experienced in-hospital falls. Two independent reviewers performed the inclusion and extrapolation process and evaluated the methodological quality of the included studies. RFs were grouped into categories according to three health classifications (ICF, ICD-10, and ATC). Meta-analyses were performed to obtain an overall pooled odds ratio for each RF. Finally, protective RFs or redundant RFs across different classifications were excluded.
RESULTS
Thirty-six articles were included in the meta-analysis. One thousand one hundred and eleven RFs were identified; 616 were linked to ICF classification, 450 to ICD-10, and 260 to ATC. The meta-analyses and subsequent refinement of the categories yielded 53 significant RFs. Overall, the initial number of RFs was reduced by about 21 times.
CONCLUSION
We identified 53 significant RF categories for in-hospital falls. These results provide proof of concept of the feasibility and validity of the proposed methodology. The list of significant RFs can be used as a template to build more accurate measurement instruments to predict in-hospital falls.
Topics: Accidental Falls; Humans; Risk Factors; Proof of Concept Study; Hospitalization
PubMed: 38932769
DOI: 10.3389/fpubh.2024.1390185 -
Sensors (Basel, Switzerland) Jun 2024The aim of the study is to compare the head displacement of the KPSIT C50 dummy during a frontal collision at a speed of 20 km/h, along with the change in the angle of...
The aim of the study is to compare the head displacement of the KPSIT C50 dummy during a frontal collision at a speed of 20 km/h, along with the change in the angle of the car seat backrest. Passenger car manufacturers recommend setting the backrest angle of the car seat between 100 and 125 degrees. It should be noted that the driver's position is of great importance in the event of a collision injury. In the event of a rear-end collision, the position of the headrest of the car seat is an element that affects the degree of the driver's injuries. In extreme cases, incorrect positioning of the headrest, even at low speed, can lead to serious injuries to the cervical spine and even death. The article is part of a large-scale study on low-speed crash testing. The research problem concerned the influence of the seat backrest angle on the head displacement during a low-speed collision. The article compares the displacement of the head of the KPSIT C50 dummy during a series of crash tests, where the angle of the car seat backrest was changed. On the basis of the research, it was found that the optimal angle of the car seat backrest is 110 degrees. In addition, a preliminary analysis of the displacements of the dummy's head showed a high risk of whiplash injury in people sitting in a fully reclined seat.
Topics: Humans; Accidents, Traffic; Head; Male; Automobiles; Manikins; Automobile Driving; Equipment Design
PubMed: 38931652
DOI: 10.3390/s24123868 -
Sensors (Basel, Switzerland) Jun 2024The latest survey results show an increase in accidents on the roads involving pedestrians and cyclists. The reasons for such situations are many, the fault actually...
The latest survey results show an increase in accidents on the roads involving pedestrians and cyclists. The reasons for such situations are many, the fault actually lies on both sides. Equipping vehicles, especially autonomous vehicles, with frequency-modulated continuous-wave (FMCW) radar and dedicated algorithms for analyzing signals in the time-frequency domain as well as algorithms for recognizing objects in radar imaging through deep neural networks can positively affect safety. This paper presents a method for recognizing and distinguishing a group of objects based on radar signatures of objects and a special convolutional neural network structure. The proposed approach is based on a database of radar signatures generated on pedestrian, cyclist, and car models in a Matlab environment. The obtained results of simulations and positive tests provide a basis for the application of the system in many sectors and areas of the economy. Innovative aspects of the work include the method of discriminating between multiple objects on a single radar signature, the dedicated architecture of the convolutional neural network, and the use of a method of generating a custom input database.
PubMed: 38931616
DOI: 10.3390/s24123832 -
SDC-Net++: End-to-End Crash Detection and Action Control for Self-Driving Car Deep-IoT-Based System.Sensors (Basel, Switzerland) Jun 2024Few prior works study self-driving cars by deep learning with IoT collaboration. SDC-Net, which is an end-to-end multitask self-driving car camera cocoon IoT-based...
Few prior works study self-driving cars by deep learning with IoT collaboration. SDC-Net, which is an end-to-end multitask self-driving car camera cocoon IoT-based system, is one of the research areas that tackles this direction. However, by design, SDC-Net is not able to identify the accident locations; it only classifies whether a scene is a crash scene or not. In this work, we introduce an enhanced design for the SDC-Net system by (1) replacing the classification network with a detection one, (2) adapting our benchmark dataset labels built on the CARLA simulator to include the vehicles' bounding boxes while keeping the same training, validation, and testing samples, and (3) modifying the shared information via IoT to include the accident location. We keep the same path planning and automatic emergency braking network, the digital automation platform, and the input representations to formulate the comparative study. The SDC-Net++ system is proposed to (1) output the relevant control actions, especially in case of accidents: accelerate, decelerate, maneuver, and brake, and (2) share the most critical information to the connected vehicles via IoT, especially the accident locations. A comparative study is also conducted between SDC-Net and SDC-Net++ with the same input representations: front camera only, panorama and bird's eye views, and with single-task networks, crash avoidance only, and multitask networks. The multitask network with a BEV input representation outperforms the nearest representation in precision, recall, f1-score, and accuracy by more than 15.134%, 12.046%, 13.593%, and 5%, respectively. The SDC-Net++ multitask network with BEV outperforms SDC-Net multitask with BEV in precision, recall, f1-score, accuracy, and average MSE by more than 2.201%, 2.8%, 2.505%, 2%, and 18.677%, respectively.
PubMed: 38931589
DOI: 10.3390/s24123805 -
Sensors (Basel, Switzerland) Jun 2024Driving while drowsy poses significant risks, including reduced cognitive function and the potential for accidents, which can lead to severe consequences such as trauma,...
Driving while drowsy poses significant risks, including reduced cognitive function and the potential for accidents, which can lead to severe consequences such as trauma, economic losses, injuries, or death. The use of artificial intelligence can enable effective detection of driver drowsiness, helping to prevent accidents and enhance driver performance. This research aims to address the crucial need for real-time and accurate drowsiness detection to mitigate the impact of fatigue-related accidents. Leveraging ultra-wideband radar data collected over five minutes, the dataset was segmented into one-minute chunks and transformed into grayscale images. Spatial features are retrieved from the images using a two-dimensional Convolutional Neural Network. Following that, these features were used to train and test multiple machine learning classifiers. The ensemble classifier RF-XGB-SVM, which combines Random Forest, XGBoost, and Support Vector Machine using a hard voting criterion, performed admirably with an accuracy of 96.6%. Additionally, the proposed approach was validated with a robust k-fold score of 97% and a standard deviation of 0.018, demonstrating significant results. The dataset is augmented using Generative Adversarial Networks, resulting in improved accuracies for all models. Among them, the RF-XGB-SVM model outperformed the rest with an accuracy score of 99.58%.
Topics: Humans; Radar; Artificial Intelligence; Neural Networks, Computer; Automobile Driving; Support Vector Machine; Algorithms; Machine Learning
PubMed: 38931541
DOI: 10.3390/s24123754 -
Sensors (Basel, Switzerland) Jun 2024Aircraft failures can result in the leakage of fuel, hydraulic oil, or other lubricants onto the runway during landing or taxiing. Damage to fuel tanks or oil lines...
Aircraft failures can result in the leakage of fuel, hydraulic oil, or other lubricants onto the runway during landing or taxiing. Damage to fuel tanks or oil lines during hard landings or accidents can also contribute to these spills. Further, improper maintenance or operational errors may leave oil traces on the runway before take-off or after landing. Identifying oil spills in airport runway videos is crucial to flight safety and accident investigation. Advanced image processing techniques can overcome the limitations of conventional RGB-based detection, which struggles to differentiate between oil spills and sewage due to similar coloration; given that oil and sewage have distinct spectral absorption patterns, precise detection can be performed based on multispectral images. In this study, we developed a method for spectrally enhancing RGB images of oil spills on airport runways to generate HSI images, facilitating oil spill detection in conventional RGB imagery. To this end, we employed the MST++ spectral reconstruction network model to effectively reconstruct RGB images into multispectral images, yielding improved accuracy in oil detection compared with other models. Additionally, we utilized the Fast R-CNN oil spill detection model, resulting in a 5% increase in Intersection over Union (IOU) for HSI images. Moreover, compared with RGB images, this approach significantly enhanced detection accuracy and completeness by 25.3% and 26.5%, respectively. These findings clearly demonstrate the superior precision and accuracy of HSI images based on spectral reconstruction in oil spill detection compared with traditional RGB images. With the spectral reconstruction technique, we can effectively make use of the spectral information inherent in oil spills, thereby enhancing detection accuracy. Future research could delve deeper into optimization techniques and conduct extensive validation in real airport environments. In conclusion, this spectral reconstruction-based technique for detecting oil spills on airport runways offers a novel and efficient approach that upholds both efficacy and accuracy. Its wide-scale implementation in airport operations holds great potential for improving aviation safety and environmental protection.
PubMed: 38931499
DOI: 10.3390/s24123716