This study was conducted to verify the results of a preceding retrospective pilot study by means of a prospective controlled investigation including a larger sample size. Therefore, the aim of this clinical investigation was to analyze the relationship between sleep bruxism and several functional and occlusal parameters. The null hypothesis of this study was that there would be no differences among sleep bruxism subjects and non-sleep bruxism controls regarding several functional and occlusal parameters. Fifty-eight sleep bruxism subjects and 31 controls participated in this study. The diagnosis sleep bruxism was based on clinical criteria of the American Academy of Sleep Medicine. Sixteen functional and occlusal parameters were recorded clinically or from dental study casts. Similar to the recently published retrospective pilot study, with a mean slide of 0.77 mm (s.d., 0.69 mm) in the sleep bruxism group and a mean slide of 0.4 mm (s.d., 0.57 mm) in the control group, the evaluation of the mean comparison between the two groups demonstrated a larger slide from centric occlusion to maximum intercuspation in sleep bruxism subjects (Mann-Whitney U-test; P=0.008). However, following Bonferroni adjustment, none of the 16 occlusal and functional variables differed significantly between the sleep bruxism subjects and the non-sleep bruxism controls. The present study shows that the occlusal and functional parameters evaluated do not differ between sleep bruxism subjects and non-sleep bruxism subjects. However, as the literature reveals a possible association between bruxism and certain subgroups of temporomandibular disorders, it appears advisable to incorporate the individual adaptive capacity of the stomatognathic system into future investigations.
Adult ; Case-Control Studies ; Dental Occlusion ; Female ; Humans ; Hypertrophy ; Male ; Masseter Muscle ; pathology ; Prospective Studies ; Sleep Bruxism ; complications ; physiopathology ; Statistics, Nonparametric ; Temporomandibular Joint ; physiopathology ; Tooth Wear ; etiology ; Young Adult

BACKGROUNDHigh positive end-expiratory pressure (PEEP) and low tidal volume (VT) ventilation is thought to be a protective ventilation strategy. It is hypothesized that the stabilization of collapsible alveoli during expiration contributes to lung protection. However, this hypothesis came from analysis of indirect indices like the analysis of the pressure-volume curve of the lung. The purpose of this study was to investigate isolated healthy and injured rat lungs by means of alveolar microscopy, in which combination of PEEP and VT is beneficial with respect to alveolar stability (I-E%).

METHODSAlveolar stability was investigated in isolated, non-perfused mechanically ventilated rat lungs. Injured lungs were compared with normal lungs. For both groups three PEEP settings (5, 10, 20 cmH2O) were combined with three VT settings (6, 10, 15 ml/kg) resulting in nine PEEP-VT combinations per group. Analysis was performed by alveolar microscopy.

RESULTSIn normal lungs alveolar stability persisted in all PEEP-VT combinations (I-E% (3.2 +/- 11.0)%). There was no significant difference using different settings (P > 0.01). In contrast, alveoli in injured lungs were extremely instable at PEEP levels of 5 cmH2O (mean I-E% 100%) and 10 cmH2O (mean I-E% (30.7 +/- 16.8)%); only at a PEEP of 20 cmH2O were alveoli stabilized (mean I-E% of (0.2 +/- 9.3)%).

CONCLUSIONSIn isolated healthy lungs alveolar stability is almost unaffected by different settings of PEEP and VT. In isolated injured lungs only a high PEEP level of 20 cmH2O resulted in stabilized alveoli whereas lower PEEP levels are associated with alveolar instability.

Animals ; Female ; Lung ; pathology ; Lung Injury ; pathology ; Microscopy ; Pulmonary Alveoli ; pathology ; Rats ; Rats, Wistar ; Tidal Volume ; physiology

Objective: The aim of this study was to compare image quality features and lesion characteristics between a faster deep learning (DL) reconstructed T2-weighted (T2-w) fast spin-echo (FSE) Dixon sequence with super-resolution (T2DL) and a conventional T2-w FSE Dixon sequence (T2STD) for breast magnetic resonance imaging (MRI). Materials and Methods: This prospective study was conducted between November 2022 and April 2023 using a 3T scanner.Both T2DL and T2STD sequences were acquired for each patient. Quantitative analysis was based on region-of-interest (ROI) measurements of signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Qualitative analysis was performed independently by two radiologists using Likert scales to evaluate various image quality features, morphology, and diagnostic confidence for cysts and breast cancers. Reader preference between T2DL and T2STD was assessed via side-by-side comparison, and inter-reader reliability was also analyzed. Results: Total of 151 women were enrolled, with 140 women (mean age: 52 ± 14 years; 85 cysts and 31 breast cancers) included in the final analysis. The acquisition time was 110 s ± 0 for T2DL compared to 266 s ± 0 for T2STD. SNR and CNR were significantly higher in T2DL (P < 0.001). T2DL was associated with higher image quality scores, reduced noise, and fewer artifacts (P < 0.001). All evaluated anatomical regions (breast and axilla), breast implants, and bone margins were rated higher in T2DL (P ≤ 0.008), except for bone marrow, which scored higher in T2STD (P < 0.001). Scores for conspicuity, sharpness/ margins, and microstructure of cysts and breast cancers were higher in T2DL (P ≤ 0.002). Diagnostic confidence for cysts was improved with T2DL (P < 0.001). Readers significantly preferred T2DL over T2STD in side-by-side comparisons (P < 0.001). Conclusion T2DL effectively corrected for SNR loss caused by accelerated image acquisition and provided a 58% reduction in acquisition time compared to T2STD. This led to fewer artifacts and improved overall image quality. Thus, T2DL is feasible and has the potential to replace conventional T2-w sequences for breast MRI examinations.

Objective: The aim of this study was to compare image quality features and lesion characteristics between a faster deep learning (DL) reconstructed T2-weighted (T2-w) fast spin-echo (FSE) Dixon sequence with super-resolution (T2DL) and a conventional T2-w FSE Dixon sequence (T2STD) for breast magnetic resonance imaging (MRI). Materials and Methods: This prospective study was conducted between November 2022 and April 2023 using a 3T scanner.Both T2DL and T2STD sequences were acquired for each patient. Quantitative analysis was based on region-of-interest (ROI) measurements of signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Qualitative analysis was performed independently by two radiologists using Likert scales to evaluate various image quality features, morphology, and diagnostic confidence for cysts and breast cancers. Reader preference between T2DL and T2STD was assessed via side-by-side comparison, and inter-reader reliability was also analyzed. Results: Total of 151 women were enrolled, with 140 women (mean age: 52 ± 14 years; 85 cysts and 31 breast cancers) included in the final analysis. The acquisition time was 110 s ± 0 for T2DL compared to 266 s ± 0 for T2STD. SNR and CNR were significantly higher in T2DL (P < 0.001). T2DL was associated with higher image quality scores, reduced noise, and fewer artifacts (P < 0.001). All evaluated anatomical regions (breast and axilla), breast implants, and bone margins were rated higher in T2DL (P ≤ 0.008), except for bone marrow, which scored higher in T2STD (P < 0.001). Scores for conspicuity, sharpness/ margins, and microstructure of cysts and breast cancers were higher in T2DL (P ≤ 0.002). Diagnostic confidence for cysts was improved with T2DL (P < 0.001). Readers significantly preferred T2DL over T2STD in side-by-side comparisons (P < 0.001). Conclusion T2DL effectively corrected for SNR loss caused by accelerated image acquisition and provided a 58% reduction in acquisition time compared to T2STD. This led to fewer artifacts and improved overall image quality. Thus, T2DL is feasible and has the potential to replace conventional T2-w sequences for breast MRI examinations.

Objective: The aim of this study was to compare image quality features and lesion characteristics between a faster deep learning (DL) reconstructed T2-weighted (T2-w) fast spin-echo (FSE) Dixon sequence with super-resolution (T2DL) and a conventional T2-w FSE Dixon sequence (T2STD) for breast magnetic resonance imaging (MRI). Materials and Methods: This prospective study was conducted between November 2022 and April 2023 using a 3T scanner.Both T2DL and T2STD sequences were acquired for each patient. Quantitative analysis was based on region-of-interest (ROI) measurements of signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Qualitative analysis was performed independently by two radiologists using Likert scales to evaluate various image quality features, morphology, and diagnostic confidence for cysts and breast cancers. Reader preference between T2DL and T2STD was assessed via side-by-side comparison, and inter-reader reliability was also analyzed. Results: Total of 151 women were enrolled, with 140 women (mean age: 52 ± 14 years; 85 cysts and 31 breast cancers) included in the final analysis. The acquisition time was 110 s ± 0 for T2DL compared to 266 s ± 0 for T2STD. SNR and CNR were significantly higher in T2DL (P < 0.001). T2DL was associated with higher image quality scores, reduced noise, and fewer artifacts (P < 0.001). All evaluated anatomical regions (breast and axilla), breast implants, and bone margins were rated higher in T2DL (P ≤ 0.008), except for bone marrow, which scored higher in T2STD (P < 0.001). Scores for conspicuity, sharpness/ margins, and microstructure of cysts and breast cancers were higher in T2DL (P ≤ 0.002). Diagnostic confidence for cysts was improved with T2DL (P < 0.001). Readers significantly preferred T2DL over T2STD in side-by-side comparisons (P < 0.001). Conclusion T2DL effectively corrected for SNR loss caused by accelerated image acquisition and provided a 58% reduction in acquisition time compared to T2STD. This led to fewer artifacts and improved overall image quality. Thus, T2DL is feasible and has the potential to replace conventional T2-w sequences for breast MRI examinations.

Objective: The aim of this study was to compare image quality features and lesion characteristics between a faster deep learning (DL) reconstructed T2-weighted (T2-w) fast spin-echo (FSE) Dixon sequence with super-resolution (T2DL) and a conventional T2-w FSE Dixon sequence (T2STD) for breast magnetic resonance imaging (MRI). Materials and Methods: This prospective study was conducted between November 2022 and April 2023 using a 3T scanner.Both T2DL and T2STD sequences were acquired for each patient. Quantitative analysis was based on region-of-interest (ROI) measurements of signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Qualitative analysis was performed independently by two radiologists using Likert scales to evaluate various image quality features, morphology, and diagnostic confidence for cysts and breast cancers. Reader preference between T2DL and T2STD was assessed via side-by-side comparison, and inter-reader reliability was also analyzed. Results: Total of 151 women were enrolled, with 140 women (mean age: 52 ± 14 years; 85 cysts and 31 breast cancers) included in the final analysis. The acquisition time was 110 s ± 0 for T2DL compared to 266 s ± 0 for T2STD. SNR and CNR were significantly higher in T2DL (P < 0.001). T2DL was associated with higher image quality scores, reduced noise, and fewer artifacts (P < 0.001). All evaluated anatomical regions (breast and axilla), breast implants, and bone margins were rated higher in T2DL (P ≤ 0.008), except for bone marrow, which scored higher in T2STD (P < 0.001). Scores for conspicuity, sharpness/ margins, and microstructure of cysts and breast cancers were higher in T2DL (P ≤ 0.002). Diagnostic confidence for cysts was improved with T2DL (P < 0.001). Readers significantly preferred T2DL over T2STD in side-by-side comparisons (P < 0.001). Conclusion T2DL effectively corrected for SNR loss caused by accelerated image acquisition and provided a 58% reduction in acquisition time compared to T2STD. This led to fewer artifacts and improved overall image quality. Thus, T2DL is feasible and has the potential to replace conventional T2-w sequences for breast MRI examinations.

Objective: The aim of this study was to compare image quality features and lesion characteristics between a faster deep learning (DL) reconstructed T2-weighted (T2-w) fast spin-echo (FSE) Dixon sequence with super-resolution (T2DL) and a conventional T2-w FSE Dixon sequence (T2STD) for breast magnetic resonance imaging (MRI). Materials and Methods: This prospective study was conducted between November 2022 and April 2023 using a 3T scanner.Both T2DL and T2STD sequences were acquired for each patient. Quantitative analysis was based on region-of-interest (ROI) measurements of signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Qualitative analysis was performed independently by two radiologists using Likert scales to evaluate various image quality features, morphology, and diagnostic confidence for cysts and breast cancers. Reader preference between T2DL and T2STD was assessed via side-by-side comparison, and inter-reader reliability was also analyzed. Results: Total of 151 women were enrolled, with 140 women (mean age: 52 ± 14 years; 85 cysts and 31 breast cancers) included in the final analysis. The acquisition time was 110 s ± 0 for T2DL compared to 266 s ± 0 for T2STD. SNR and CNR were significantly higher in T2DL (P < 0.001). T2DL was associated with higher image quality scores, reduced noise, and fewer artifacts (P < 0.001). All evaluated anatomical regions (breast and axilla), breast implants, and bone margins were rated higher in T2DL (P ≤ 0.008), except for bone marrow, which scored higher in T2STD (P < 0.001). Scores for conspicuity, sharpness/ margins, and microstructure of cysts and breast cancers were higher in T2DL (P ≤ 0.002). Diagnostic confidence for cysts was improved with T2DL (P < 0.001). Readers significantly preferred T2DL over T2STD in side-by-side comparisons (P < 0.001). Conclusion T2DL effectively corrected for SNR loss caused by accelerated image acquisition and provided a 58% reduction in acquisition time compared to T2STD. This led to fewer artifacts and improved overall image quality. Thus, T2DL is feasible and has the potential to replace conventional T2-w sequences for breast MRI examinations.