Fine tuning deep learning models for breast tumor classification.

This paper proposes an approach to enhance the differentiation task between benign and malignant Breast Tumors (BT) using histopathology images from the BreakHis dataset. The main stages involve preprocessing, which encompasses image resizing, data partitioning (training and testing sets), followed by data augmentation techniques. Both feature extraction and classification tasks are employed by a Custom CNN. The experimental results show that the proposed approach using the Custom CNN model exhibits better performance with an accuracy of 84% than applying the same approach using other pretrained models, including MobileNetV3, EfficientNetB0, Vgg16, and ResNet50V2, that present relatively lower accuracies, ranging from 74 to 82%; these four models are used as both feature extractors and classifiers. To increase the accuracy and other performance metrics, Grey Wolf Optimization (GWO), and Modified Gorilla Troops Optimization (MGTO) metaheuristic optimizers are applied to each model separately for hyperparameter tuning. In this case, the experimental results show that the Custom CNN model, refined with MGTO optimization, reaches an exceptional accuracy of 93.13% in just 10 iterations, outperforming the other state-of-the-art methods, and the other four used pretrained models based on the BreakHis dataset.

Assessing the awareness, knowledge, and attitudes of adults in the general population in Saudi Arabia towards testicular torsion: A cross-sectional study.

Introduction: Testicular torsion (TT) is a rare but urgent condition; it leads to severe pain and swelling that must be diagnosed and treated immediately, where there is an expectant period of 6-8 h. It is an important prognostic factor in the arrival time to the emergency department. Raising awareness about TT is essential to ensure early diagnosis and timely treatment, which can prevent irreversible damage and loss of fertility. Aim: Our study aimed to measure the awareness, knowledge, and attitude toward testicular torsion (TT) among the general population in Saudi Arabia. Subject and Methods: This is a cross-sectional study conducted among the general population living in Saudi Arabia. A self-administered questionnaire was distributed among the population using an online survey. The questionnaire includes socio-demographic characteristics (i.e., age, gender, region, etc.), a questionnaire to assess the knowledge of testicular torsion, and questionnaires to assess the prevalence, management, and attitude toward testicular torsion. Results: Of the 473 participants, 56% were males, and 61% were aged between 18 to 30 years old. Unfortunately, One quarter of our population has a good knowledge level about testicular torsion. The overall mean knowledge about Testicular Torsion score was below average (3 out of 8 points). Factors associated with increased knowledge include younger age, male gender, living in the Central Region, working in the medical field, having heard of testicular torsion, and having a personal or family history of testicular torsion. Conclusion: In conclusion, there was a lack of testicular torsion knowledge among the general population. Further studies and social campaigns should be done to encourage the general population's understanding and increase their knowledge level.

Impact of carbimazole combined with vitamin E on testicular injury induced by experimental hyperthyroidism in adult albino rats: oxidative/inflammatory/apoptotic pathways.

Thyroid hormones play essential roles in spermatogenesis, but their effects on infertile males remain poorly understood. This study aimed to evaluate the impact of combining carbimazole (CBZ) with vitamin E (VE) on testicular injury induced by experimental hyperthyroidism in adult albino rats, focusing on oxidative, inflammatory, and apoptotic pathways. In this experimental study, 64 adult male albino Wistar rats were divided into eight groups: Group I (control-untreated), Group II (CBZ-control), Group III (VE-control), Group IV (CBZ + VE-control), Group V (levothyroxine-induced testicular injury), Group VI (levothyroxine + CBZ-treated), Group VII (levothyroxine + VE-treated), and Group VIII (levothyroxine + CBZ + VE-treated). The study was conducted in the Faculty of Medicine, Suez Canal University (Ismailia, Egypt). After cervical decapitation, both testes and epididymis were examined histopathologically and immunohistochemically. Significant differences were observed among groups concerning malondialdehyde (MDA), glutathione (GSH), superoxide dismutase (SOD), and catalase (CAT; all P < 0.001). Polymerase chain reaction analysis showed significant differences in tumor necrosis factor-α (TNF- α ), interleukin-10 (IL-10), Bcl-2-associated X protein (BAX), B-cell lymphoma 2 protein (Bcl2), p53, Caspase-3, Caspase-8, Caspase-9, and nuclear factor-kappa B (NF- κ B) mRNA levels (all P < 0.001). Hyperthyroid group treated with CBZ alone (Group VI) exhibited testicular side effects, affecting seminiferous tubules and spermatogenesis. However, the Group VIII showed improved spermatogenesis and a decrease in testicular side effects. The addition of VE to the treatment of hyperthyroid rats with CBZ reduced testicular side effects and seminiferous tubular affection when potentially improving spermatogenesis. Further research is needed to elucidate the underlying mechanisms fully.

MAC-ErrorReads: machine learning-assisted classifier for filtering erroneous NGS reads.

BACKGROUND: The rapid advancement of next-generation sequencing (NGS) machines in terms of speed and affordability has led to the generation of a massive amount of biological data at the expense of data quality as errors become more prevalent. This introduces the need to utilize different approaches to detect and filtrate errors, and data quality assurance is moved from the hardware space to the software preprocessing stages. RESULTS: We introduce MAC-ErrorReads, a novel Machine learning-Assisted Classifier designed for filtering Erroneous NGS Reads. MAC-ErrorReads transforms the erroneous NGS read filtration process into a robust binary classification task, employing five supervised machine learning algorithms. These models are trained on features extracted through the computation of Term Frequency-Inverse Document Frequency (TF_IDF) values from various datasets such as E. coli, GAGE S. aureus, H. Chr14, Arabidopsis thaliana Chr1 and Metriaclima zebra. Notably, Naive Bayes demonstrated robust performance across various datasets, displaying high accuracy, precision, recall, F1-score, MCC, and ROC values. The MAC-ErrorReads NB model accurately classified S. aureus reads, surpassing most error correction tools with a 38.69% alignment rate. For H. Chr14, tools like Lighter, Karect, CARE, Pollux, and MAC-ErrorReads showed rates above 99%. BFC and RECKONER exceeded 98%, while Fiona had 95.78%. For the Arabidopsis thaliana Chr1, Pollux, Karect, RECKONER, and MAC-ErrorReads demonstrated good alignment rates of 92.62%, 91.80%, 91.78%, and 90.87%, respectively. For the Metriaclima zebra, Pollux achieved a high alignment rate of 91.23%, despite having the lowest number of mapped reads. MAC-ErrorReads, Karect, and RECKONER demonstrated good alignment rates of 83.76%, 83.71%, and 83.67%, respectively, while also producing reasonable numbers of mapped reads to the reference genome. CONCLUSIONS: This study demonstrates that machine learning approaches for filtering NGS reads effectively identify and retain the most accurate reads, significantly enhancing assembly quality and genomic coverage. The integration of genomics and artificial intelligence through machine learning algorithms holds promise for enhancing NGS data quality, advancing downstream data analysis accuracy, and opening new opportunities in genetics, genomics, and personalized medicine research.

Quantitative assessment of dosimetric effect of using alternative OAR delineations in treatment planning as functions of delineations, setup uncertainty and planning techniques using an alternative truth assessment method.

PURPOSE: This study aims to quantify the variation in dose-volume histogram (DVH) and normal tissue complication probability(NTCP) metrics for head-and-neck (HN) cancer patients when alternative organ-at-risk(OAR) delineations are used for treatment planning and for treatment plan evaluation. We particularly focus on the effects of daily patient positioning/setup variations(SV) in relation to treatment technique and delineation variability. MATERIALS AND METHODS: We generated two-arc VMAT, 5-beam IMRT, and 9-beam IMRT treatment plans for a cohort of 209 HN patients. These plans incorporated five different OAR delineation sets, including manual and four automated algorithms. Each treatment plan was assessed under various simulated per-fraction patient setup uncertainties, evaluating the potential clinical impacts through DVH and NTCP metrics. RESULTS: The study demonstrates that increasing SV generally reduces differences in DVH metrics between alternative delineations. However, in contrast, differences in NTCP metrics tend to increase with higher setup variability. This pattern is observed consistently across different treatment plans and delineator combinations, illustrating the intricate relationship between SV and delineation accuracy. Additionally, the need for delineation accuracy in treatment planning is shown to be case-specific and dependent on factors beyond geometric variations. CONCLUSIONS: The findings highlight the necessity for comprehensive Quality Assurance programs in radiotherapy, incorporating both dosimetric impact analysis and geometric variation assessment to ensure optimal delineation quality. The study emphasizes the complex dynamics of treatment planning in radiotherapy, advocating for personalized, case-specific strategies in clinical practice to enhance patient care quality and efficacy in the face of varying SV and delineation accuracies.

IL-33 Expression Is Lower in Current Smokers at both Transcriptomic and Protein Levels.

Rationale: IL-33 is a proinflammatory cytokine thought to play a role in the pathogenesis of asthma and chronic obstructive pulmonary disease (COPD). A recent clinical trial using an anti-IL-33 antibody showed a reduction in exacerbation and improved lung function in ex-smokers but not current smokers with COPD. Objectives: This study aimed to understand the effects of smoking status on IL-33. Methods: We investigated the association of smoking status with the level of gene expression of IL-33 in the airways in eight independent transcriptomic studies of lung airways. Additionally, we performed Western blot analysis and immunohistochemistry for IL-33 in lung tissue to assess protein levels. Measurements and Main Results: Across the bulk RNA-sequencing datasets, IL-33 gene expression and its signaling pathway were significantly lower in current versus former or never-smokers and increased upon smoking cessation (P < 0.05). Single-cell sequencing showed that IL-33 is predominantly expressed in resting basal epithelial cells and decreases during the differentiation process triggered by smoke exposure. We also found a higher transitioning of this cellular subpopulation into a more differentiated cell type during chronic smoking, potentially driving the reduction of IL-33. Protein analysis demonstrated lower IL-33 levels in lung tissue from current versus former smokers with COPD and a lower proportion of IL-33-positive basal cells in current versus ex-smoking controls. Conclusions: We provide strong evidence that cigarette smoke leads to an overall reduction in IL-33 expression in transcriptomic and protein level, and this may be due to the decrease in resting basal cells. Together, these findings may explain the clinical observation that a recent antibody-based anti-IL-33 treatment is more effective in former than current smokers with COPD.

Adapting blockchain's proof-of-work mechanism for multiple traveling salesmen problem optimization.

The blockchain network uses a Proof-of-Work (PoW) mechanism to validate transactions and keep the blockchain growth safe against tampering, but it is hugely energy-consuming with no benefit to the peer-to-peer network participants. In this paper, we proposed a blockchain network for distributing products to different locations based on the use of the Proof of Useful Work mechanism, in which miners use computing resources to optimize the traveling salesman problem (TSP) as an alternative to solving mathematical problems that represent the basis of the traditional PoW mechanism to get a new block. According to this proposed blockchain, it not only receives and securely stores the distribution locations but also improves the paths for salesmen when traveling between different locations during the transportation process. This strategy aims to take advantage of the miners' efforts to minimize the traveled distance by applying the clustering technique and computing the shortest path by Guided Local Search (GLS) for each cluster at the same time. According to the tested results on TSP-LIB instances, the used strategy works efficiently with an average of 0.08 compared to the rest of the meta-heuristics, and the proposed architecture reduced total distances with an average of 0.025%. In addition, the block generation time in the blockchain decreased by 11.11% compared to other works.

Design and Optimization of Omeprazole-Curcumin-Loaded Hydrogel Beads Coated with Chitosan for Treating Peptic Ulcers.

This study aimed to formulate a pharmaceutical dosage form containing omeprazole (OMP) and curcumin (CURC) to treat experimental peptic ulcers. OMP and CURC were preliminarily complexed with hydroxypropyl-ß-cyclodextrin for enhancing their solubilization. After that, the combined complex (CURC/OMP) was loaded to alginate beads to sustain their release and then coated with chitosan. Finally, we tested the anti-ulcerogenic impact of the best formula versus free OMP or OMP-only-loaded beads. The formulated spherical beads' diameter ranged from a minimum value of 1.5 ± 0.08 mm to 2.6 ± 0.24 mm; the swelling results ranged from 400.00 ± 8.5% to 800.00 ± 6.2%. The entrapment efficiency was in a range from 60.85 ± 1.01% to 87.44 ± 1.88%. The optimized formula (F8) showed a maximum EE% (87.44 ± 1.88%), swelling (800.00 ± 6.2%), and diameter in the range of 2.60 ± 0.24, with a desirability of 0.941. In the first hour following the administration of the free drug complex, 95% of OMP and 98% of CURC were released. This is unacceptable for medications that require a delayed release in the stomach. The initial drug release from hydrogel beads was 23.19% for CURC and 17.19% for OMP after 2 h and 73.09% for CURC and 58.26% for OMP after 12 h; however, after 24 h, 87.81% of CURC and 81.67% of OMP had been released. The OMP/CURC beads showed a more stable particle size (0.52 ± 0.01 mm) after 6 weeks. In conclusion, the OMP/CURC hydrogel beads give stronger anti-ulcer effectiveness compared to free OMP, CURC-only beads, and OMP-only-loaded beads, indicating a prospective application for managing peptic ulcers.

CRISPR single base-editing: in silico predictions to variant clonal cell lines.

Engineering single base edits using CRISPR technology including specific deaminases and single-guide RNA (sgRNA) is a rapidly evolving field. Different types of base edits can be constructed, with cytidine base editors (CBEs) facilitating transition of C-to-T variants, adenine base editors (ABEs) enabling transition of A-to-G variants, C-to-G transversion base editors (CGBEs) and recently adenine transversion editors (AYBE) that create A-to-C and A-to-T variants. The base-editing machine learning algorithm BE-Hive predicts which sgRNA and base editor combinations have the strongest likelihood of achieving desired base edits. We have used BE-Hive and TP53 mutation data from The Cancer Genome Atlas (TCGA) ovarian cancer cohort to predict which mutations can be engineered, or reverted to wild-type (WT) sequence, using CBEs, ABEs or CGBEs. We have developed and automated a ranking system to assist in selecting optimally designed sgRNA that considers the presence of a suitable protospacer adjacent motif (PAM), the frequency of predicted bystander edits, editing efficiency and target base change. We have generated single constructs containing ABE or CBE editing machinery, an sgRNA cloning backbone and an enhanced green fluorescent protein tag (EGFP), removing the need for co-transfection of multiple plasmids. We have tested our ranking system and new plasmid constructs to engineer the p53 mutants Y220C, R282W and R248Q into WT p53 cells and shown that these mutants cannot activate four p53 target genes, mimicking the behaviour of endogenous p53 mutations. This field will continue to rapidly progress, requiring new strategies such as we propose to ensure desired base-editing outcomes.

Efficacious elimination of crystal violet pollutant via photo-Fenton process based on Gd(2-x)La(x)Zr2O7 nanoparticles.

The photo-Fenton process is an appropriate method of the Advanced Oxidation Process that is used in the photocatalysis of organic dyes like crystal violet (CV). La3+ ion substituted gadolinium zirconium oxide Gd(2-x)La(x)Zr2O7 nanopowders (x = 0.1, 0.2, 0.3, and 0.5) have been successfully prepared by using sol-gel auto-combustion method to be used for the efficient photocatalysis of CV with photo-Fenton process. The well-crystallized defect-fluorite, structured with space group: Fm-3m, was detected using X-ray diffraction analysis. The lattice parameters were found to increase with the evaluated La3+ ion concentration. The grain size of the synthesized powders increased with the increase in La3+ ion content. The SAED patterns depicted fluorite structured fluorite. UV/Vis. spectrophotometer was used for the determination of band gap energy of Gd(2-x)La(x)Zr2O7 nanopowders which increased with increasing La3+ ion content. It was found to enhance from 4 to 3.6 eV. The visible spectrophotometer was used for determining unknown concentrations during the photocatalysis process to assure the effectiveness of the process. Overall, results illustrate that the photo-Fenton reaction on Gd(2-x)La(x)Zr2O7 performed excellently in removing crystal violet (CV). The photo-remediation ratio of CV reached 90% within 1 h.

Development of Novel pH-Sensitive Eudragit Coated Beads Containing Curcumin-Mesalamine Combination for Colon-Specific Drug Delivery.

This research aims to develop a drug delivery system that effectively treats colitis while administering curcumin/mesalamine by coating alginate/chitosan beads with Eudragit® S-100 to target the colon. Beads were tested to determine their physicochemical characteristics. Coating with Eudragit® S-100 prevents drug release at a pH of less than 7; this was demonstrated by in-vitro release conducted in a medium with gradually varying pH to mimic circumstances in various regions of the gastrointestinal tract. This study examined the efficacy of the coated beads in treating acetic acid-induced colitis in rats. Results showed that spherical beads were formed with an average diameter of 1.6-2.8 mm, and the obtained swelling ranged from 409.80% to 890.19%. The calculated entrapment efficiency ranged from 87.49% to 97.89%. The optimized formula F13 (which was composed of mesalamine-curcumin active ingredients, Sodium alginate as a gelling agent, chitosan as a controlled release agent, CaCl2 as a crosslinking agent, and Eudragit S-100 as a pH-sensitive coating agent) demonstrated the best entrapment efficiency (97.89% ± 1.66), swelling (890.19% ± 60.1), and bead size (2.7 ± 0.62 mm). In formulation #13, which was coated with Eudragit S 100, curcumin (6.01 ± 0.04%) and mesalamine (8.64 ± 0.7%), were released after 2 h at pH 1.2; 6.36 ± 0.11% and 10.45 ± 1.52% of curcumin and mesalamine, respectively, were then released after 4 h and at pH 6.8. Meanwhile, at pH 7.4, after 24 h, approximately 85.34 ± 2.3% (curcumin) and 91.5 ± 1.2% (mesalamine) were released. Formula #13 significantly reduced the colitis, and this suggests that the developed hydrogel beads can be used for delivering curcumin-mesalamine combinations to treat ulcerative colitis after adequate research.

Early transcriptional responses of bronchial epithelial cells to whole cigarette smoke mirror those of in-vivo exposed human bronchial mucosa.

BACKGROUND: Despite the well-known detrimental effects of cigarette smoke (CS), little is known about the complex gene expression dynamics in the early stages after exposure. This study aims to investigate early transcriptomic responses following CS exposure of airway epithelial cells in culture and compare these to those found in human CS exposure studies. METHODS: Primary bronchial epithelial cells (PBEC) were differentiated at the air-liquid interface (ALI) and exposed to whole CS. Bulk RNA-sequencing was performed at 1 h, 4 h, and 24 h hereafter, followed by differential gene expression analysis. Results were additionally compared to data retrieved from human CS studies. RESULTS: ALI-PBEC gene expression in response to CS was most significantly changed at 4 h after exposure. Early transcriptomic changes (1 h, 4 h post CS exposure) were related to oxidative stress, xenobiotic metabolism, higher expression of immediate early genes and pro-inflammatory pathways (i.e., Nrf2, AP-1, AhR). At 24 h, ferroptosis-associated genes were significantly increased, whereas PRKN, involved in removing dysfunctional mitochondria, was downregulated. Importantly, the transcriptome dynamics of the current study mirrored in-vivo human studies of acute CS exposure, chronic smokers, and inversely mirrored smoking cessation. CONCLUSION: These findings show that early after CS exposure xenobiotic metabolism and pro-inflammatory pathways were activated, followed by activation of the ferroptosis-related cell death pathway. Moreover, significant overlap between these transcriptomic responses in the in-vitro model and human in-vivo studies was found, with an early response of ciliated cells. These results provide validation for the use of ALI-PBEC cultures to study the human lung epithelial response to inhaled toxicants.

Deeply Virtual Compton Scattering Cross Section at High Bjorken x_{B}.

We report high-precision measurements of the deeply virtual Compton scattering (DVCS) cross section at high values of the Bjorken variable x_{B}. DVCS is sensitive to the generalized parton distributions of the nucleon, which provide a three-dimensional description of its internal constituents. Using the exact analytic expression of the DVCS cross section for all possible polarization states of the initial and final electron and nucleon, and final state photon, we present the first experimental extraction of all four helicity-conserving Compton form factors (CFFs) of the nucleon as a function of x_{B}, while systematically including helicity flip amplitudes. In particular, the high accuracy of the present data demonstrates sensitivity to some very poorly known CFFs.

One- and Two-Sample Predictions Based on Progressively Type-II Censored Carbon Fibres Data Utilizing a Probability Model.

New Weibull-Pareto distribution is a significant and practical continuous lifetime distribution, which plays an important role in reliability engineering and analysis of some physical properties of chemical compounds such as polymers and carbon fibres. In this paper, we construct the predictive interval of unobserved units in the same sample (one sample prediction) and the future sample based on the current sample (two-sample prediction). The used samples are generated from new Weibull-Pareto distribution due to a progressive type-II censoring scheme. Bayesian and maximum likelihood approaches are implemented to the prediction problems. In the Bayesian approach, it is not easy to simplify the predictive posterior density function in a closed form, so we use the generated Markov chain Monte Carlo samples from the Metropolis-Hastings technique with Gibbs sampling. Moreover, the predictive interval of future upper-order statistics is reported. Finally, to demonstrate the proposed methodology, both simulated data and real-life data of carbon fibres examples are considered to show the applicabilities of the proposed methods.

Increased SARS-CoV-2 Infection, Protease, and Inflammatory Responses in Chronic Obstructive Pulmonary Disease Primary Bronchial Epithelial Cells Defined with Single-Cell RNA Sequencing.

Rationale: Patients with chronic obstructive pulmonary disease (COPD) develop more severe coronavirus disease (COVID-19); however, it is unclear whether they are more susceptible to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and what mechanisms are responsible for severe disease. Objectives: To determine whether SARS-CoV-2 inoculated primary bronchial epithelial cells (pBECs) from patients with COPD support greater infection and elucidate the effects and mechanisms involved. Methods: We performed single-cell RNA sequencing analysis on differentiated pBECs from healthy subjects and patients with COPD 7 days after SARS-CoV-2 inoculation. We correlated changes with viral titers, proinflammatory responses, and IFN production. Measurements and Main Results: Single-cell RNA sequencing revealed that COPD pBECs had 24-fold greater infection than healthy cells, which was supported by plaque assays. Club/goblet and basal cells were the predominant populations infected and expressed mRNAs involved in viral replication. Proteases involved in SARS-CoV-2 entry/infection (TMPRSS2 and CTSB) were increased, and protease inhibitors (serpins) were downregulated more so in COPD. Inflammatory cytokines linked to COPD exacerbations and severe COVID-19 were increased, whereas IFN responses were blunted. Coexpression analysis revealed a prominent population of club/goblet cells with high type 1/2 IFN responses that were important drivers of immune responses to infection in both healthy and COPD pBECs. Therapeutic inhibition of proteases and inflammatory imbalances reduced viral titers and cytokine responses, particularly in COPD pBECs. Conclusions: COPD pBECs are more susceptible to SARS-CoV-2 infection because of increases in coreceptor expression and protease imbalances and have greater inflammatory responses. A prominent cluster of IFN-responsive club/goblet cells emerges during infection, which may be important drivers of immunity. Therapeutic interventions suppress SARS-CoV-2 replication and consequent inflammation.

Evaluation of the diagnostic accuracy of CBCT-based interpretations of maxillary impacted canines compared to those of conventional radiography: An in vitro study.

BACKGROUND: Cone-beam computed tomography (CBCT) imaging has become widespread in diagnosing impacted teeth in the context of orthodontic treatment. However, the diagnostic accuracy of this 3D imaging tool has not been comprehensively evaluated yet. The objectives of this study were: (1) to investigate the diagnostic accuracy of employing CBCT imaging in the three-dimensional (3D) localization of maxillary impacted canines compared to the use of traditional two-dimensional (2D) imaging, and (2) to calculate sensitivity, specificity and diagnostic accuracy values for specific variables assessed in 3D and 2D. MATERIAL AND METHODS: An adult cadaver skull with permanent dentition was employed and 15 simulations of maxillary canine impactions were created. Two sets of 2D and 3D radiographic images were obtained. The two sets of images were evaluated by eleven postgraduate orthodontic students. Cochran's Q tests, Friedman's tests, and then McNemar's, McNemar-Bowker's and Wilcoxon tests were used to compare Observers' responses in 3D and 2D with the gold standard values. RESULTS: Insignificant differences were found between the CBCT-based interpretations and the gold standard (GS) concerning the labiopalatal position (P=0.34), labiopalatal and mesiodistal inclination (P=0.66 and P=0.194, respectively), whereas the mesiodistal and vertical positioning showed significant differences (P=0.002 and P<0.001, respectively). The percentages of agreement between the 2D- and 3D-based interpretations were acceptable in terms of labiopalatal and mesiodistal inclinations (71.5% and 77.5%, respectively). Remarkably higher values of sensitivity, specificity and diagnostic accuracy for the CBCT-based interpretations were observed. CONCLUSIONS: The diagnostic accuracy of CBCT outweighed that of 2D radiography in the labiopalatal localization of maxillary impacted canines, contact relationship with adjacent teeth and resorption diagnosis. 3D-based assessments and the gold standard had high percentages of agreement especially for the labiopalatal position evaluation and the proximity diagnosis.

Asymmetric Power Hazard Distribution for COVID-19 Mortality Rate under Adaptive Type-II Progressive Censoring: Theory and Inferences.

This article investigates the estimation of the parameters for power hazard function distribution and some lifetime indices such as reliability function, hazard rate function, and coefficient of variation based on adaptive Type-II progressive censoring. From the perspective of frequentism, we derive the point estimations through the method of maximum likelihood estimation. Besides, delta method is implemented to construct the variances of the reliability characteristics. Markov chain Monte Carlo techniques are proposed to construct the Bayes estimates. To this end, the results of the Bayes estimates are obtained under squared error and linear exponential loss functions. Also, the corresponding credible intervals are constructed. A simulation study is utilized to assay the performance of the proposed methods. Finally, a real data set of COVID-19 mortality rate is analyzed to validate the introduced inference methods.

Evaluation of the Levels of Pain, Discomfort, Functional Impairments and Satisfaction With the Periodontally Accelerated Osteogenic Orthodontics (PAOO) When Leveling and Aligning Crowded Teeth: A Prospective Cohort Study.

Background Only a few studies have reported levels of pain, discomfort, functional impairments, and patients' satisfaction after undergoing periodontally accelerated osteogenic orthodontics (PAOO). Therefore, the objectives were to evaluate postoperative pain, discomfort, functional impairments, and satisfaction following this surgical intervention. Methods A prospective cohort study was undertaken at the Department of Orthodontics, University of Damascus Dental School, between August 2018 and November 2020. The sample consisted of 16 patients (4 males, 12 females, mean age: 21±3.05 years) with moderately crowded lower anterior teeth who underwent full-thickness vestibular flap elevation followed by cortical cuts using piezosurgery. Then a bovine xenograft was applied before reflecting the flap and suturing. Fixed orthodontic therapy was then initiated. Questionnaires were administered to assess the levels of pain, discomfort, and oral functional problems using a visual analog scale (VAS) at one day, seven days, 14 days, and 28 days after surgery. The level of satisfaction was also assessed at 28 days postoperatively. Friedman's test was employed, and Wilcoxon signed-rank tests were used for post-hoc tests with an adjusted alpha level. Results On the first day following the PAOO, the mean values of perceived pain, discomfort, swelling, mastication difficulties, swallowing problems, limitation in jaw movements were 80.00, 80.63, 68.13, 78.13, 55.00, and 64.38, respectively. These mean values dropped significantly in the following assessment times (P<0.001). At 28 days following the PAOO, the mean satisfaction score was 84.94±22.46. All patients mentioned that they took painkillers after the surgical intervention. Conclusions On the first day following surgery, patients perceived high levels of pain and discomfort, moderate to severe levels of swelling and chewing difficulties, and suffered from restricted jaw movement. These levels decreased significantly in the following assessment times. Patients' satisfaction with the PAOO procedure was high.

Inferences for Stress-Strength Reliability Model in the Presence of Partially Accelerated Life Test to Its Strength Variable.

We focus on estimating the stress-strength reliability model when the strength variable is subjected to the step-stress partially accelerated life test. Based on the assumption that both stress and strength random variables follow Weibull distribution with a common first shape parameter, the inferences for this reliability system are constructed. The maximum likelihood, two parametric bootstraps, and Bayes estimates are obtained. Moreover, approximate confidence intervals, asymptotic variance-covariance matrix, and highest posterior density credible intervals are derived. A simulation study and application to real-life data are conducted to compare the proposed estimation methods developed here and also check the accuracy of the results.

The Diagnostic Accuracy of Cone-Beam Computed Tomography (CBCT) Imaging in Detecting and Measuring Dehiscence and Fenestration in Patients With Class I Malocclusion: A Surgical-Exposure-Based Validation Study.

Background No study has evaluated the diagnostic accuracy of cone-beam computed tomography (CBCT) imaging in detecting bone defects in orthodontic patients with Class I malocclusions. This study aimed to evaluate the accuracy of CBCT in detecting dehiscences and fenestrations before orthodontic treatment compared to the gold standard i.e., the actual clinical detection of bone defects on surgical exposure. Methods A validation study was undertaken at the Department of Orthodontics, University of Damascus between 29 August 2018 and 1 November 2020. The sample included 16 patients who had Class I malocclusion with moderate crowding on the lower anterior teeth. Results The proportion of dehiscence diagnosed on CBCT images was approximately two-and-a-half times greater than that found on direct examination i.e., 42.7% versus 17.7%, respectively. The proportion of fenestrations was almost three times greater when diagnosed on CBCT images compared to the gold standard i.e., 39.5% versus 13.5%, respectively. The sensitivity of CBCT imaging in detecting dehiscence and fenestration was 100%. The specificity of CBCT imaging ranged from 45.5% to 86.7% and from 50% to 86.7% for dehiscence and fenestration detection, respectively. Also, the diagnostic accuracy of CBCT imaging ranged from 44% to 87.5% and from 56% to 87.5% for dehiscence and fenestration detection, respectively. Conclusions The proportion of dehiscence diagnosed on CBCT images was approximately two-and-a-half times greater than that found on direct examination, and the proportion of fenestrations was almost three times greater when diagnosed on CBCT images compared to the gold standard. The CBCT overestimates the dimensions of the linear measurements compared to those measured by the gold standard.