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This paper studies gravitational waves in a dark matter model composed of three types of particles with distinct spins, along with a scalar field ϕ that mediates interactions between Standard Model particles and dark matter. It discusses the electroweak phase transition following the Big Bang, during which all particles are initially massless due to the inactive Higgs mechanism. As temperature decreases, the effective potential reaches zero at two points, leading to two minima at the critical temperature ( T c ), and eventually to a true vacuum state. The formation of new vacuum bubbles, where electroweak symmetry is broken and particles acquire mass, generates gravitational waves as these bubbles interact with the fabric of space-time. The paper derives the gravitational wave frequency and detection range based on the model’s parameters, aligning with observational data from the Planck satellite and detection thresholds from PandaX-4T and XENONnT for some parameter points. It concludes by comparing the predicted background gravitational wave density with the sensitivities of LISA, BBO and μ -Ares detectors.

In recent years, the use of ceramic base zirconia is considered in dentistry for all ceramic restorations because of its chemical stability, biocompatibility, and good compressive as well as flexural strength. However, due to its chemical stability, there is a challenge with dental bonding. Several studies have been done to improve zirconia bonding but they are not reliable. The purpose of this research is to study the effect of plasma treatment on bonding strength of zirconia. In this in vitro study, 180 zirconia discs' (thickness was 0.85-0.9 mm) surfaces were processed with plasma of oxygen, argon, air and oxygen-argon combination with 90-10 and 80-20 ratio (n=30 for each group) after being polished by sandblast. Surface modifications were assessed by measuring the contact angle, surface roughness, and topographical evaluations. Cylindrical Panavia f2 resin-cement and Diafill were used for microshear strength bond measurements. The data analysis was performed by SPSS 20.0 software and one-way analysis of variance (ANOVA) and Tukey test as the post hoc. Plasma treatment in all groups significantly reduces contact angle compare with control ( =0.001). Topographic evaluations revealed coarseness promotion occurred in all plasma treated groups which was significant when compared to control ( <0.05), except argon plasma treated group that significantly decreased surface roughness ( <0.05). In all treated groups, microshear bond strength increased, except oxygen treated plasma group which decreased this strength. Air and argon-oxygen combination (both groups) significantly increased microshear bond strength ( <0.05). According to this research, plasmatic processing with dielectric barrier method in atmospheric pressure can increase zirconia bonding strength.

Accumulating evidence suggests positive effects of branched-chain amino acids (BCAAs) on moderate muscle damage. However, findings vary substantially across studies. The aim of this review was to examine the effect of BCAAs on recovery following exercise-induced muscle damage. Controlled trials were identified through a computerized literature search and tracking of citations performed up to November 2015. To pool data, either a fixed-effects or a random-effects model was used; for assessing heterogeneity, Cochran's Q and I2 tests were used. Eight trials met the inclusion criteria. Pooled data from the eight studies showed that BCAAs significantly reduced creatine kinase at two follow-up times (<24 and 24 h) in comparison with placebo recovery (<24 h: mean difference, –71.55 U/L, 95% confidence interval, –93.49 to –49.60, P < 0.000, n = 5 trials; 24 h: mean difference, –145.04 U/L, 95% confidence interval, –253.66 to –36.43, P = 0.009, n = 8 trials). In contrast, effects were not significant in any of the follow-up times for muscle soreness or lactate dehydrogenase. The current evidence-based information indicates that use of BCAAs is better than passive recovery or rest after various forms of exhaustive and damaging exercise. The advantages relate to a reduction in muscle soreness and ameliorated muscle function because of an attenuation of muscle strength and muscle power loss after exercise. •Branched-chain amino acids (BCAAs) significantly reduced creatine kinase for up to 24 h.•BCAAs demonstrated no beneficial effects in any of the follow-up times for muscle soreness and lactate dehydrogenase.•BCAA is better than passive recovery or rest after various forms of exhaustive and damaging exercise.

This study was performed to compare the effects of single and multiple irradiations of low-level laser therapy (LLLT) on bone regeneration in a mid-palatal suture following rapid palatal expansion (RPE). In this animal study, 40 male Wistar rats underwent RPE for 7 days and were divided into 4 groups including A: single LLLT on day 7, B: Multiple LLLT on days 7, 9, 11, 13 and 15, C: control (no LLLT), and D: sacrificed on day 7. Animals in group D were used to determine the amount of suture expansion. LLLT was done by a diode laser set at an 808 nm wavelength with a useful power output of 100 mW and duration of 0.1 ms. LLLT was applied to three points. After three weeks of retention, the rats were sacrificed and beheaded and the maxilla was evaluated by occlusal radiography, µ-CT, and histomorphometric analyses. A comparison of the mean measurements between the groups was performed using ANOVA and the Tukey post hoc test. Based on occlusal radiography and µCT, bone density in group B was significantly higher than group A and group C ( <0.05). There was no significant difference in bone density between group A and group C ( >0.05). Mean suture width (MSW) in group B was significantly lesser than the control group ( =0.027) while there was no significant difference between MSWnin groups A and B ( =0.116) and groups A and C ( =0.317). It may be concluded that multiple low-power laser irradiation improves bone regeneration after RPE while single irradiation does not have a positive effect.

Digital images allow for the objective evaluation of facial appearance and abnormalities as well as treatment outcomes and stability. With the advancement of technology, manual clinical measurements can be replaced with fully automatic photographic assessments. However, obtaining millimetric measurements on photographs does not provide clinicians with their actual value due to different image magnification ratios. A deep learning tool was developed to estimate linear measurements on images with unknown magnification using the iris diameter. A framework was designed to segment the eyes’ iris and calculate the horizontal visible iris diameter (HVID) in pixels. A constant value of 12.2 mm was assigned as the HVID value in all the photographs. A vertical and a horizontal distance were measured in pixels on photographs of 94 subjects and were estimated in millimeters by calculating the magnification ratio using HVID. Manual measurement of the distances was conducted on the subjects and the actual and estimated amounts were compared using Bland–Altman analysis. The obtained error was calculated as mean absolute percentage error (MAPE) of 2.9% and 4.3% in horizontal and vertical measurements. Our study shows that due to the consistent size and narrow range of HVID values, the iris diameter can be used as a reliable scale to calibrate the magnification of the images to obtain precise measurements in further research.

The artificial neural network (ANN) is a sort of machine learning method which has been used in determination of risk of human disorders. In the current investigation, we have created an ANN and trained it based on the genetic data of 401 multiple sclerosis (MS) patients and 390 healthy subjects. Single nucleotide polymorphisms (SNPs) within ANRIL (rs1333045, rs1333048, rs4977574 and rs10757278), EVI5 (rs6680578, rs10735781 and rs11810217), ACE (rs4359 and rs1799752), MALAT1 (rs619586 and rs3200401), GAS5 (rs2067079 and rs6790), H19 (rs2839698 and rs217727), NINJ2 (rs11833579 and rs3809263), GRM7 (rs6782011 and rs779867), VLA4 (rs1143676), CBLB (rs12487066) and VEGFA (rs3025039 and rs2071559) had been genotyped in all individuals. We used “Keras” package for generation and training the ANN model. The k-folds cross-validation strategy was applied to confirm model generalization and overfit prevention. The locally interpretable model-agnostic explanation (LIME) was applied to explain model predictions at the data sample level. The TT genotype of the GAS5 rs2067079 had the most protective effect against MS, while the DD genotype of the ACE rs1799752 was the most prominent risk genotype. The accuracy, sensitivity and specificity values of the model were 64.73%, 64.95% and 64.49% respectively. The ROC AUC value was 69.67%. The current study is a preliminary study to appraise the application of ANN for prediction of risk of MS based on genomic data.

Background Aging results in many changes in health status, body composition, muscle strength, and, ultimately, functional capacity. These changes coincide with significant alterations in the endocrine system, such as insulin-like growth factor-1 (IGF-1) and IGF-binding proteins (IGFBPs), and may be associated with many symptoms of aging. The objectives of this study is to investigate the potential influence of different types of exercise, such as resistance training and aerobic training, on IGF-1 and IGFBP-3 levels in postmenopausal women. Methods Medline, Scopus, and Google Scholar databases were systematically searched up to November 2023. The Cochrane Collaboration tool was used to assess the risk of bias and the quality of the studies. The random-effects model, weighted mean difference (WMD), and 95% confidence interval (CI) were used to estimate the overall effect. Between-study heterogeneity was assessed using the chi-squared and I 2 tests. Results Seventeen studies were included in the present systematic review and 16 studies were included in the meta-analysis. The pooled results from 16 studies (21 trials) with 1170 participants examining the impact of exercise on IGF-1 concentration showed a significant increase in IGF-1, and the pooled results among six studies (trials) showed a significant decrease in IGFBP-3 concentration (730 participants). In addition, resistance training and aerobic training had a significant effect on increasing IGF-1 concentration post-exercise compared with placebo. Conclusion Based on this meta-analysis, Women who have completed menopause and followed an exercise routine showed changes in IGF-1 and IGFBP-3 levels that can indirectly be associated with risk of chronic age-related conditions.

Objectives Canine-induced root resorption (CIRR) is caused by impacted canines and CBCT images have shown to be more accurate in diagnosing CIRR than panoramic and periapical radiographs with the reported AUCs being 0.95, 0.49, and 0.57, respectively. The aim of this study was to use deep learning to automatically evaluate the diagnosis of CIRR in maxillary incisors using CBCT images. Methods A total of 50 cone beam computed tomography (CBCT) images and 176 incisors were selected for the present study. The maxillary incisors were manually segmented and labeled from the CBCT images by two independent radiologists as either healthy or affected by root resorption induced by the impacted canines. We used five different strategies for training the model: (A) classification using 3D ResNet50 (Baseline), (B) classification of the segmented masks using the outcome of a 3D U-Net pretrained on the 3D MNIST, (C) training a 3D U-Net for the segmentation task and use its outputs for classification, (D) pretraining a 3D U-Net for the segmentation and transfer of the model, and (E) pretraining a 3D U-Net for the segmentation and fine-tuning the model with only the model encoder. The segmentation models were evaluated using the mean intersection over union (mIoU) and Dice coefficient (DSC). The classification models were evaluated in terms of classification accuracy, precision, recall, and F1 score. Results The segmentation model achieved a mean intersection over union (mIoU) of 0.641 and a DSC of 0.901, indicating good performance in segmenting the tooth structures from the CBCT images. For the main classification task of detecting CIRR, Model C (classification of the segmented masks using 3D ResNet) and Model E (pretraining on segmentation followed by fine-tuning for classification) performed the best, both achieving 82% classification accuracy and 0.62 F1-scores on the test set. These results demonstrate the effectiveness of the proposed hierarchical, data-efficient deep learning approaches in improving the accuracy of automated CIRR diagnosis from limited CBCT data compared to the 3D ResNet baseline model. Conclusion The proposed approaches are effective at improving the accuracy of classification tasks and are helpful when the diagnosis is based on the volume and boundaries of an object. While the study demonstrated promising results, future studies with larger sample size are required to validate the effectiveness of the proposed method in enhancing the medical image classification tasks.

Objectives This study aimed to assess the accuracy of a two-stage deep learning (DL) model for (1) detecting mandibular third molars (MTMs) and the mandibular canal (MC), and (2) classifying the anatomical relationship between these structures (contact/no contact) on panoramic radiographs. Method MTMs and MCs were labeled on panoramic radiographs by a calibrated examiner using bounding boxes. Each bounding box contained MTM and MC on one side. The relationship of MTMs with the MC was assessed on CBCT scans by two independent examiners without the knowledge of the condition of MTM and MC on the corresponding panoramic image, and dichotomized as contact/no contact. Data were split into training, validation, and testing sets with a ratio of 80:10:10. Faster R-CNN was used for detecting MTMs and MCs and ResNeXt for classifying their relationship. AP50 and AP75 were used as outcomes for detecting MTMs and MCs, and accuracy, precision, recall, F1-score, and the area-under-the-receiver-operating-characteristics curve (AUROC) were used to assess classification performance. The training and validation of the models were conducted using the Python programming language with the PyTorch framework. Results Three hundred eighty-seven panoramic radiographs were evaluated. MTMs were present bilaterally on 232 and unilaterally on 155 radiographs. In total, 619 images were collected which included MTMs and MCs. AP50 and AP75 indicating accuracy for detecting MTMs and MCs were 0.99 and 0.90 respectively. Classification accuracy, recall, specificity, F1-score, precision, and AUROC values were 0.85, 0.85, 0.93, 0.84, 0.86, and 0.91, respectively. Conclusion DL can detect MTMs and MCs and accurately assess their anatomical relationship on panoramic radiographs.

Using super-resolution (SR) algorithms, an image with a low resolution can be converted into a high-quality image. Our objective was to compare deep learning-based SR models to a conventional approach for improving the resolution of dental panoramic radiographs. A total of 888 dental panoramic radiographs were obtained. Our study involved five state-of-the-art deep learning-based SR approaches, including SR convolutional neural networks (SRCNN), SR generative adversarial network (SRGAN), U-Net, Swin for image restoration (SwinIr), and local texture estimator (LTE). Their results were compared with one another and with conventional bicubic interpolation. The performance of each model was evaluated using the metrics of mean squared error (MSE), peak signal-to-noise ratio (PNSR), structural similarity index (SSIM), and mean opinion score by four experts (MOS). Among all the models evaluated, the LTE model presented the highest performance, with MSE, SSIM, PSNR, and MOS results of 7.42 ± 0.44, 39.74 ± 0.17, 0.919 ± 0.003, and 3.59 ± 0.54, respectively. Additionally, compared with low-resolution images, the output of all the used approaches showed significant improvements in MOS evaluation. A significant enhancement in the quality of panoramic radiographs can be achieved by SR. The LTE model outperformed the other models.