Mechanical analysis of the improved superelastic Ni-Ti alloy wire using the orthodontic simulator with high-precision sensors.

PURPOSE: The authors have been using improved superelastic Nickel-Titanium alloy wire (ISW) to close and align extraction spaces simultaneously, instead of separately using rigid wires for closing extraction spaces and Ni-Ti alloy wires for leveling and aligning. ISW has a low stiffness, which makes it challenging to generate sufficient moments. This study aimed to demonstrate the forces and moments exerted on adjacent brackets using an orthodontic simulator (OSIM) attached to a high-precision 6-axis sensor. MATERIALS AND METHODS: In experiment 1, a 0.016 × 0.022-inch ISW, stainless steel (SS) wire, and ß-titanium wires were ligatured into the two brackets. The 0.018 × 0.025-inch slot self-ligating brackets were bonded to two simulated teeth at the same height, and the experiment was conducted using the high-precision OSIM. The distance between the brackets was 10 mm, the V-bend angles of the installed wires were 10°, 20°, 30°, and 40°, and the apex position was set at the center of the bracket. In experiment 2, 6.0- and 9.0-mm long elastomeric chains were placed on the same brackets as in Experiment 1 to measure forces and moments. The distance between the brackets was increased by 1.0 mm from 6.0 to 15.0 mm. Both experiments were conducted in a 37 °C thermostatic chamber similar to the oral environment. RESULTS AND DISCUSSION: In experiment 1, we measured moments on both sides for all the wires. As the V-bend angle increased, the absolute values of the moments also increased. With a V-bend angle of 10°, there was a significant (p < 0.05) difference in the moment generated in the left and right brackets among the three wire types. In the ISW, -1.67 ± 0.38 N・mm was generated in the left bracket, while 0.38 ± 0.26 N・mm was generated in the right bracket at 10°. At 20°, -1.77 ± 0.69 N・mm was generated in the left bracket, while 2.37 ± 0.94 N・mm was generated in the right bracket. At 30°, -2.98 ± 0.49 N・mm was generated in the left bracket, while 3.25 ± 0.32 N・mm was generated in the right bracket. Moreover, at 40°, -3.96 ± 0.58 N・mm was generated in the left bracket, while 3.55 ± 0.53 N・mm was generated in the right bracket. Furthermore, in experiment 2, the moments increased in proportion to the increase in distance between the centers of the two brackets. Absolute values of the moments were approximately equal for the left and right brackets. The 6.0-mm elastomeric chain generated a minimum force of -0.09 ± 0.05 N in the left direction when the distance between brackets was 6.0 mm, while a maximum of 1.24 ± 0.3 N when the distance between brackets was 12 mm in the right bracket. In the left bracket, minimum and maximum forces of -0.09 ± 0.07 and 1.3 ± 0.4 N were generated in the right direction, respectively. The 9.0-mm elastomeric chain generated a minimum force of 0.03 ± 0.07 N in the left direction when the distance between brackets was 9.0 mm, while a maximum of 1.3 ± 0.1 N when the distance between brackets was 15 mm in the right bracket. In the left bracket, minimum and maximum forces of 0.05 ± 0.06 and 0.98 ± 0.2 N were generated in the right direction, respectively. CONCLUSION: Mechanical data of the ISW have been collected in the study, which was previously difficult to perform owing to the low stiffness of the wire. It is suggested that the ISW can provide sufficient moments with the addition of V-bends to close the space by bodily movement.

Influence of different ligation methods on force and moment generation in a simulated condition of the maxillary crowded anterior dentition with linguo-version and rotation.

BACKGROUND: Most orthodontic cases consist of varying degrees of crowding. To manage crowded dentitions, nickel-titanium archwires with various ligation methods are often used. OBJECTIVE: We aimed to investigate the effect of different ligation methods with respect to force and moment and suggest the efficient ligation method for treating rotation and displacement simultaneously. METHODS: We built a model that simulated the three anterior teeth of the maxilla. The teeth on the two ends were fixed, and the middle tooth was set in several different positions by manipulating the amount of displacement in bucco-lingual direction and rotation angle. The measurements were taken with three different ligation methods of self-ligation (SL), elastomeric o-ring ligation on both side wings (EB), and on one side wings (EO). RESULTS: The magnitude of linguo-buccal force exceeded the standard optimal force in each condition examined and was significantly larger in EB than in other ligation methods. Moreover, the magnitude of moment generation with SL was suitable in the 0.0 mm linguo-version, whereas it was suitable with EO in the linguo-version ranging 1.0-3.0 mm. CONCLUSIONS: The ligation method significantly affected the force and moment. SL and EO are recommended in dentitions with light and deep lingual displacements, respectively.

Machine Learning Approach to Predict Positive Screening of Methicillin-Resistant Staphylococcus aureus During Mechanical Ventilation Using Synthetic Dataset From MIMIC-IV Database.

Background: Mechanically ventilated patients are susceptible to nosocomial infections such as ventilator-associated pneumonia. To treat ventilated patients with suspected infection, clinicians select appropriate antibiotics. However, decision-making regarding the use of antibiotics for methicillin-resistant Staphylococcus aureus (MRSA) is challenging, because of the lack of evidence-supported criteria. This study aims to derive a machine learning model to predict MRSA as a possible pathogen responsible for infection in mechanically ventilated patients. Methods: Data were collected from the Medical Information Mart for Intensive Care (MIMIC)-IV database (an openly available database of patients treated at the Beth Israel Deaconess Medical Center in the period 2008-2019). Of 26,409 mechanically ventilated patients, 809 were screened for MRSA during the mechanical ventilation period and included in the study. The outcome was positivity to MRSA on screening, which was highly imbalanced in the dataset, with 93.9% positive outcomes. Therefore, after dividing the dataset into a training set (n = 566) and a test set (n = 243) for validation by stratified random sampling with a 7:3 allocation ratio, synthetic datasets with 50% positive outcomes were created by synthetic minority over-sampling for both sets individually (synthetic training set: n = 1,064; synthetic test set: n = 456). Using these synthetic datasets, we trained and validated an XGBoost machine learning model using 28 predictor variables for outcome prediction. Model performance was evaluated by area under the receiver operating characteristic (AUROC), sensitivity, specificity, and other statistical measurements. Feature importance was computed by the Gini method. Results: In validation, the XGBoost model demonstrated reliable outcome prediction with an AUROC value of 0.89 [95% confidence interval (CI): 0.83-0.95]. The model showed a high sensitivity of 0.98 [CI: 0.95-0.99], but a low specificity of 0.47 [CI: 0.41-0.54] and a positive predictive value of 0.65 [CI: 0.62-0.68]. Important predictor variables included admission from the emergency department, insertion of arterial lines, prior quinolone use, hemodialysis, and admission to a surgical intensive care unit. Conclusions: We were able to develop an effective machine learning model to predict positive MRSA screening during mechanical ventilation using synthetic datasets, thus encouraging further research to develop a clinically relevant machine learning model for antibiotics stewardship.

Development of 6-Axis Orthodontic Force and Moment Sensing Device for Decreasing Accident of Orthodontic Treatment.

The purpose of this study is to develop the sensing device which measures three-axis force and three-axis moment for reducing the number of accident in orthodontic treatment. The device is necessary for adequate quantitative evaluation of orthodontic forces during orthodontics. The developed sensing device is composed of six-axis force sensors, tooth models, and arms for connecting sensors and tooth models. The developed device simulates rows of teeth in orthodontic operation and measures $14 \times 6$ axes force and moment from tooth models simultaneously. The averages of the difference of force and moment to theoretical values in each direction are 1.78 % (0.043 N) and 2.72 % (0.60 Nmm) respectively. The average moment applying couple forces is 17.1 % (0.81 Nmm). Then the device is able to measure more accurately as the value of the moment was larger. Therefore using our proposed device, we can conduct the orthodontic treatment which dentition moves large for attaching the rail of wire to the teeth.

A new orthodontic force system for moment control utilizing the flexibility of common wires: Evaluation of the effect of contractile force and hook length.

BACKGROUND/PURPOSE: The application of an appropriate force system is indispensable for successful orthodontic treatments. Second-order moment control is especially important in many clinical situations, so we developed a new force system composed of a straight orthodontic wire and two crimpable hooks of different lengths to produce the second-order moment. The objective of this study was to evaluate this new force system and determine an optimum condition that could be used in clinics. METHODS: We built a premolar extraction model with two teeth according to the concept of a modified orthodontic simulator. This system was activated by applying contractile force from two hooks that generated second-order moment and force. The experimental device incorporated two sensors, and forces and moments were measured along six axes. We changed the contractile force and hook length to elucidate their effects. Three types of commercial wires were tested. RESULTS: The second-order moment was greater on the longer hook side of the model. Vertical force balanced the difference in moments between the two teeth. Greater contractile force generated a greater second-order moment, which reached a limit of 150 g. Excessive contractile force induced more undesired reactions in the other direction. Longer hooks induced greater moment generation, reaching their limit at 10 mm in length. CONCLUSION: The system acted similar to an off-center V-bend and can be applied in clinical practice as an unconventional loop design. We suggest that this force system has the potential for second-order moment control in clinical applications.

Development and modification of a device for three-dimensional measurement of orthodontic force system: The V-bend system re-visited.

We developed a device to evaluate the orthodontic force applied by systems requiring high operability. A life-sized, two-tooth model was designed, and the measurements were performed using a custom-made jointed attachment, referred to as an "action stick", to allow clearance for the oversized six-axis sensors. This tooth-sensor apparatus was accurately calibrated, and the error was limited. Vector analysis and rotating coordinate transformation were required to derive the force and moment at the tooth from the sensor readings. The device was then used to obtain measurements of the force and moment generated by the V-bend system. Our device was effective, providing results that were consistent with those of previous studies. This measurement device can be manufactured with force sensors of any size, and it can also be expanded to models with any number of teeth.

Six-axis orthodontic force and moment sensing system for dentist technique training.

The purpose of this study is to develop a sensing system device that measures three-axis orthodontic forces and three-axis orthodontic moments for dentist training. The developed sensing system is composed of six-axis force sensors, action sticks, sliders, and tooth models. The developed system also simulates various types of tooth row shape patterns in orthodontic operations, and measures a 14 × 6 axis orthodontic force and moment from tooth models simultaneously. The average force and moment error per loaded axis were 2.06 % and 2.00 %, respectively.