The Relationship Between Gestational Diabetes and Postpartum Depression: A Systematic Review.

This study aims to examine the relationship between gestational diabetes mellitus (GDM) and the likelihood of postpartum depression (PPD) symptoms. PubMed, Scopus, Web of Science, ScienceDirect, and the Wiley Online Library were systematically searched for relevant literature. Our results included eight studies with a total of 4,209 women diagnosed with GDM and/or PPD. The prevalence of PPD in women diagnosed with GDM ranged from 6.5% to 48.4%. The included studies demonstrated that PPD was more likely to strike women with GDM. One study reported that the most severe type of GDM is more likely to occur in those with a history of depression. Perinatal depression during pregnancy can be strongly predicted by age, BMI, and a personal history of depression. The findings imply that GDM and the likelihood of depression during the postpartum phase are related. It was also found that there was a positive correlation between depression and the chance of having GDM. This emphasizes how the association between GDM and depression appears to be reciprocal. However, the association does not imply causation, and the data at hand do not allow for the establishment of causality. Subsequent studies ought to endeavor to show causative connections between GDM and depression as well as pinpoint shared underlying endocrine variables that may play a role in the genesis of both conditions. The available information that is now available is limited due to the complexity of the etiology of both GD and depression in pregnant women; nonetheless, prevention of both conditions depends on a better understanding of the link between GD and depression. The risk of bias in the included studies was moderate to high.

Knowledge, attitudes, and practices of adults in the Kingdom of Saudi Arabia regarding Helicobacter pylori-induced gastric ulcers, cancers, and treatment.

Helicobacter pylori (HP) infection presents a significant threat to global health with serious associated morbidities. This study aimed to assess awareness, attitudes, and practices related to HP in the Kingdom of Saudi Arabia (KSA) through a survey-based cross-sectional study involving 2,541 respondents. We used a structured online questionnaire to gather data on personal and sociodemographic characteristics, as well as HP-related knowledge, attitudes, and practices. The survey was distributed through various social media platforms. The results revealed that 59.4% of respondents demonstrated good knowledge about HP, with a mean knowledge score of 3.7 ± 1.0 out of 5. Knowledge gaps were particularly evident regarding the contagiousness and transmission modes of HP. The mean attitude score was 12.2 ± 2.2 out of a maximum score of 15. In total, 37.6% of respondents reported ever being tested for HP, with 54.2% testing positive. Among those treated for HP, only 79% received antibiotic therapy and 37.8% received acid-reducing medications. Knowledge levels were significantly higher among younger and highly educated respondents (P < 0.001), and respondents with higher knowledge scores also had higher attitude scores than those with lower knowledge scores (12.6 ± 2.0 vs. 11.6 ± 2.0, P < 0.001). Individuals who had undergone HP testing had significantly higher knowledge levels than those who did not (62.3 vs. 57.8, P = 0.024). These findings underscore the urgent need for raising the population's awareness regarding the risks, prevention, and management of HP infection through targeted educational strategies.

Impact of Obesity on Echinocandin Effectiveness in Treating Candida Infections: A Retrospective Observational Cohort Study.

Introduction: Echinocandins are used to treat invasive candidiasis (IC), with FDA-approved doses indicated for both obese and non-obese patients. Pharmacokinetic (PK) studies have identified subtherapeutic exposure in obese patients receiving standard doses (SDs) of echinocandins. However, research on clinical outcome differences of echinocandins' SDs between obese and non-obese patients is lacking. Therefore, this study aimed to evaluate the effectiveness of echinocandins' SDs in obese compared to normal-weight patients with IC. Patients and Methods: This retrospective cohort study was conducted at King Saud University Medical City (KSUMC) from Jan 2017 to Feb 2023. The study included adult patients diagnosed with Candida infections who received ≥ 4 doses of echinocandins. Patients with body mass index (BMI) less than 18 kg/m2 were excluded from the study. The primary and secondary outcomes included the total length of stay (LOS), IC duration, frequency of clinical resolution and all-cause mortality. Results: This study included 132 patients (47 obese; 85 non-obese) with a median age of 61 years. The median BMI and weight were different between the obese (34.5 kg/m2, 88 kg) and non-obese (24 kg/m2, 65 kg) groups (P= 0.01). Micafungin and caspofungin were used in 63.6% and 36.4% of patients, respectively. The total LOS and length of IC infections were similar between both groups, with median values of 29.5 days (P= 0.896) and 18 days (P = 0.160), respectively. The clinical improvement percentages were 68.1% for obese and 65.9% for non-obese patients (P= 0.797), with all-cause mortality rates at 44.7% and 42.4%, respectively (P= 0.796). Conclusion: The study found no clinical outcome differences between obese and non-obese patients, with Similar effectiveness of the echinocandins' SDs in both groups. Further research in multi-centre settings is recommended to detect any potential differences between the two groups.

Quality of Life Outcomes Following Aortofemoral and Iliofemoral Bypass Surgery in Patients With Peripheral Arterial Disease: A Two-Year Follow-Up Study.

INTRODUCTION: Peripheral arterial disease is a circulatory disorder characterized by reduced blood flow to the extremities, predominantly affecting the lower limbs. This study aims to evaluate the impact of aortofemoral and iliofemoral bypass surgeries on patients' quality of life two years post operation and identify predictors of quality-of-life improvements. METHODS: This cross-sectional study included adult patients with aortoiliac disease who underwent bypass surgery (aortofemoral or iliofemoral) at East Jeddah General Hospital from January 2020 to December 2022. Quality of life was assessed using the Arabic version of the Short Form Health Survey 12 (SF-12) preoperatively and two years postoperatively. Data on sociodemographic factors (age, sex, education, income) and medical factors (smoking, BMI, comorbidities) were collected. Statistical analyses included descriptive statistics, t-tests, one-way ANOVA, and regression analyses using IBM SPSS version 25.0 (IBM Corp., Armonk, NY). RESULTS: The study included 275 patients. Significant improvements in both physical and mental SF-12 scores were observed postoperatively across all patient groups (P < 0.001). Older age, unemployment, and lower income were associated with lower SF-12 scores. Males had higher postoperative mental scores (P = 0.036). Higher BMI and smoking pack-years negatively correlated with SF-12 scores. Patients with comorbidities had significantly lower preoperative and postoperative SF-12 scores (P < 0.05) but showed significant improvements postoperatively (P < 0.001). CONCLUSION: Aortofemoral and iliofemoral bypass surgeries significantly improve the quality of life in peripheral arterial disease patients two years post operation. Key predictors of lower quality of life include older age, unemployment, lower income, high BMI, smoking, and comorbidities. Targeted interventions, such as smoking cessation programs, weight management, and comprehensive medical care, are essential for optimizing postoperative outcomes and enhancing patients' physical and mental well-being.

Brain Tumor MRI Classification Using a Novel Deep Residual and Regional CNN.

Brain tumor classification is essential for clinical diagnosis and treatment planning. Deep learning models have shown great promise in this task, but they are often challenged by the complex and diverse nature of brain tumors. To address this challenge, we propose a novel deep residual and region-based convolutional neural network (CNN) architecture, called Res-BRNet, for brain tumor classification using magnetic resonance imaging (MRI) scans. Res-BRNet employs a systematic combination of regional and boundary-based operations within modified spatial and residual blocks. The spatial blocks extract homogeneity, heterogeneity, and boundary-related features of brain tumors, while the residual blocks significantly capture local and global texture variations. We evaluated the performance of Res-BRNet on a challenging dataset collected from Kaggle repositories, Br35H, and figshare, containing various tumor categories, including meningioma, glioma, pituitary, and healthy images. Res-BRNet outperformed standard CNN models, achieving excellent accuracy (98.22%), sensitivity (0.9811), F1-score (0.9841), and precision (0.9822). Our results suggest that Res-BRNet is a promising tool for brain tumor classification, with the potential to improve the accuracy and efficiency of clinical diagnosis and treatment planning.

Optimizing Prostate Imaging Practices in Saudi Arabian Hospitals: A Comprehensive Analysis of PI-RADS Compliance in Multiparametric MRI.

BACKGROUND: Prostate cancer, a significant contributor to male cancer mortality globally, demands improved diagnostic strategies. In Saudi Arabia, where the incidence is expected to double, this study assessed the compliance of multiparametric MRI (mpMRI) practices with Prostate Imaging-Reporting and Data System version 2 (PI-RADS v2) guidelines across diverse healthcare institutions. METHODS: A survey was distributed to the radiology departments of all tertiary referral hospitals in Saudi Arabia (n=60) to assess their compliance with the technical specifications outlined in PI-RADS v2. Statistical analysis included chi-square, Fisher exact, ANOVA, and Student t-tests to examine the collected data. RESULTS: The study revealed an overall commendable compliance rate of 95.23%. However, significant variations were observed in technical parameters, particularly between 1.5 Tesla and 3 Tesla scanners and tertiary versus non-tertiary hospitals. Notable adherence in certain sequences contrasted with discrepancies in T2-weighted and diffusion-weighted imaging parameters. CONCLUSION: These findings underscore the need for nuanced approaches to optimize prostate imaging protocols, considering field strength and institutional differences. The study contributes to the ongoing refinement of standardized mpMRI practices, aiming to enhance diagnostic accuracy and improve clinical outcomes in prostate cancer.

Leveraging multivariate analysis and adjusted mutual information to improve stroke prediction and interpretability.

OBJECTIVES: To develop a machine learning model to accurately predict stroke risk based on demographic and clinical data. It also sought to identify the most significant stroke risk factors and determine the optimal machine learning algorithm for stroke prediction. METHODS: This cross-sectional study analyzed data on 438,693 adults from the 2021 Behavioral Risk Factor Surveillance System. Features encompassed demographics and clinical factors. Descriptive analysis profiled the dataset. Logistic regression quantified risk relationships. Adjusted mutual information evaluated feature importance. Multiple machine learning models were built and evaluated on metrics like accuracy, AUC ROC, and F1 score. RESULTS: Key factors significantly associated with higher stroke odds included older age, diabetes, hypertension, high cholesterol, and history of myocardial infarction or angina. Random forest model achieved the best performance with accuracy of 72.46%, AUC ROC of 0.72, and F1 score of 0.74. Cross-validation confirmed its reliability. Top features were hypertension, myocardial infarction history, angina, age, diabetes status, and cholesterol. CONCLUSION: The random forest model robustly predicted stroke risk using demographic and clinical variables. Feature importance highlighted priorities like hypertension and diabetes for clinical monitoring and intervention. This could help enable data-driven stroke prevention strategies.

Vagal Nerve Stimulation in the Pediatric Population and Correlation Between Family and Treatment Team Perspectives: Single-center Experience.

BACKGROUND: Vagal nerve stimulation (VNS) is an adjunctive therapy to pharmacological treatment in patients with drug-resistant epilepsy. This study aimed to assess the efficacy of VNS therapy for seizure frequency reduction and improving quality of life (QOL) measures in children with refractory epilepsy and to evaluate the correlation between the perspectives of families and those of the treating team. METHODS: This was a prospective cohort study conducted at Abha Maternity and Children's Hospital, Saudi Arabia, from 2018 to 2022. A total of 21 pediatric patients who completed one year of follow-up after VNS implantation were included. Patients were aged between 2 and 14 years, with a mean age of 8.14 ± 3.92; 11 (52.4%) patients were female. Family and physician assessments were collected blinded to each other using Clinical Global Impression of Improvement (CGI-I) scores and QOL assessments to evaluate the correlation between the families' and treating team's perspectives on VNS outcomes. RESULTS: In this study involving 21 patients with intractable epilepsy, VNS showed significant efficacy in reducing the frequency of seizures. VNS significantly reduced the number of seizures per week from a baseline median of 35 to a median of 0.25 at the end of the follow-up period, representing a dramatic reduction of 99.3% (p < 0.001). The number of emergency department visits per year decreased from a baseline median of 12 to a median of 2, a reduction of 83.3% (p < 0.001), whereas the number of hospital admissions per year decreased from a baseline median of 3 to a median of 1, a 66.7% decrease (p < 0.001). The number of antiepileptic medications taken decreased from a median of 4 to 3 (p < 0.001). Notably, 28.57% of the patients achieved complete seizure freedom, and 38% exhibited significant improvement, with at least 50% reduction in seizure frequency. Importantly, none of the patients experienced an escalation in seizure frequency following VNS treatment. The family and physician assessments showed varying degrees of alignment in perceptions, with "concentration" exhibiting a significant positive correlation (r = 0.498, p = 0.022), indicating noteworthy agreement, whereas verbal communication did not show a substantial correlation (r = -0.062, p = 0.791), indicating a divergence of views. CONCLUSION: VNS is a promising and well-tolerated therapy for individuals with intractable seizures, offering clinical benefits and potential enhancements in various aspects of QOL. The varying perceptions between family and physician assessments highlight the importance of considering multiple perspectives when evaluating treatment outcomes.

The Impact of Repeated Steam Sterilization Cycles on the Efficacy of Chairside Adjustment Kits for Polishing Monolithic Multi-Layered Zirconia Dental Restoration Material.

BACKGROUND Before insertion, chairside adjustment kits are heat sterilized for positioning and polishing dental restorations. This study aimed to evaluate the effects of 2 steam sterilization cycles on the efficacy of polishing highly translucent monolithic zirconia (HTMLZ) dental restoration material. MATERIAL AND METHODS 100 HTMLZ disc-shaped specimens were adjusted (grinding, finishing, polishing) with EVE Diacera kit. Two steam sterilization techniques [standard (Gp S), immediate/flash (Gp (F)] of CAK were further subgrouped based on number of sterilization cycles [cycle 1 (control), cycle 5, 10, 15, and 20 (experimental)] (n=10 each). Each subgroup accordingly was evaluated for average surface roughness (Ra) and root mean square roughness (Rq) using a profilometer. Mean and standard deviation of 5 subgroups were statistically analyzed using one-way ANOVA/post hoc Tukey's test. Scanning electron microscopy complemented Ra, Rq measurements. Statistical differences of P≤0.05 were considered significant. RESULTS HTMLZ specimens in both groups showed increased (Ra/Rq) values after repeated sterilization of EVE Diacera kit, with Gp F showing lesser increase than Gp S (20 cycles). Gp F at 10 cycles and Gp S at 15 cycles showed clinically unacceptable roughness threshold (0.25 µm). Differences between subgroups for Ra and Rq values were significant (P≤0.05) with less differences within groups observed in early cycles (1, 10). Results validate the manufacturer's recommendations of using flash sterilization/10 cycles for EVE Diacera kit. CONCLUSIONS Repeated sterilization reduces efficacy of chairside adjustment kit to produce smooth surfaces on HTMLZ. This study recommends flash sterilization to a maximum of 10 times to get the clinically acceptable results of Ra and Rq.

Photoacoustic imaging in prostate cancer: A new paradigm for diagnosis and management.

The global health issue of prostate cancer (PCa) requires better diagnosis and treatment. Photoacoustic imaging (PAI) may change PCa management. This review examines PAI's principles, diagnostic role, and therapeutic guidance. PAI uses optical light excitation and ultrasonic detection for high-resolution functional and molecular imaging. PAI uses endogenous and exogenous contrast agents to distinguish cancerous and benign prostate tissues with greater sensitivity and specificity than PSA testing and TRUS-guided biopsy. In addition to diagnosing, PAI can guide and monitor PCa therapy. Its real-time imaging allows precise biopsies and brachytherapy seed placement. Photoacoustic temperature imaging allows non-invasive monitoring of thermal therapies like cryotherapy, improving treatment precision and success. Transurethral illumination probes, innovative contrast agents, integration with other imaging modalities, and machine learning analysis are being developed to overcome depth and data complexity restrictions. PAI could become an essential tool for PCa diagnosis and therapeutic guidance as the field advances.

Assessment of dimensional stability of novel VPES impression material at different time intervals with standard disinfectants.

BACKGROUND: Vinyl polyether silicone (VPES) is a novel impression biomaterial made of a combination of vinyl polysiloxane (VPS) and polyether (PE). Thus, it is significant to assess its properties and behaviour under varied disinfectant test conditions. This study aimed to assess the dimensional stability of novel VPES impression material after immersion in standard disinfectants for different time intervals. METHODS: Elastomeric impression material used -medium body regular set (Monophase) [Exa'lence GC America]. A total of 84 Specimens were fabricated using stainless steel die and ring (ADA specification 19). These samples were distributed into a control group (n=12) and a test group (n=72). The test group was divided into 3 groups, based on the type of disinfectant used - Group-A- 2% Glutaraldehyde, Group-B- 0. 5% Sodium hypochlorite and Group-C- 2% Chlorhexidine each test group was further divided into 2 subgroups (n=12/subgroup) based on time intervals for which each sample was immersed in the disinfectants - subgroup-1- 10 mins and Subgroup 2- 30 mins. After the impression material was set, it was removed from the ring and then it was washed in water for 15 seconds. Control group measurements were made immediately on a stereomicroscope and other samples were immersed in the three disinfection solutions for 10 mins and 30 mins to check the dimensional stability by measuring the distance between the lines generated by the stainless steel die on the samples using a stereomicroscope at x40 magnification. RESULTS: The distance measured in the control group was 4397.2078 µm and 4396.1571 µm; for the test group Group-A- 2% Glutaraldehyde was 4396.4075 µm and 4394.5992 µm; Group-B- 0. 5% Sodium hypochlorite was 4394.5453 µm and 4389.4711 µm Group-C- 2% Chlorhexidine was 4395.2953 µm and 4387.1703 µm respectively for 10 mins and 30 mins. Percentage dimensional change was in the range of 0.02 - 0.25 for all the groups for 10 mins and 30 mins. CONCLUSIONS: 2 % Glutaraldehyde is the most suitable disinfectant for VPES elastomeric impression material in terms of dimensional stability and shows minimum dimensional changes as compared to that of 2% Chlorhexidine and 0.5% Sodium hypochlorite.

Applications and Performance of Machine Learning Algorithms in Emergency Medical Services: A Scoping Review.

OBJECTIVE: The aim of this study was to summarize the literature on the applications of machine learning (ML) and their performance in Emergency Medical Services (EMS). METHODS: Four relevant electronic databases were searched (from inception through January 2024) for all original studies that employed EMS-guided ML algorithms to enhance the clinical and operational performance of EMS. Two reviewers screened the retrieved studies and extracted relevant data from the included studies. The characteristics of included studies, employed ML algorithms, and their performance were quantitively described across primary domains and subdomains. RESULTS: This review included a total of 164 studies published from 2005 through 2024. Of those, 125 were clinical domain focused and 39 were operational. The characteristics of ML algorithms such as sample size, number and type of input features, and performance varied between and within domains and subdomains of applications. Clinical applications of ML algorithms involved triage or diagnosis classification (n = 62), treatment prediction (n = 12), or clinical outcome prediction (n = 50), mainly for out-of-hospital cardiac arrest/OHCA (n = 62), cardiovascular diseases/CVDs (n = 19), and trauma (n = 24). The performance of these ML algorithms varied, with a median area under the receiver operating characteristic curve (AUC) of 85.6%, accuracy of 88.1%, sensitivity of 86.05%, and specificity of 86.5%. Within the operational studies, the operational task of most ML algorithms was ambulance allocation (n = 21), followed by ambulance detection (n = 5), ambulance deployment (n = 5), route optimization (n = 5), and quality assurance (n = 3). The performance of all operational ML algorithms varied and had a median AUC of 96.1%, accuracy of 90.0%, sensitivity of 94.4%, and specificity of 87.7%. Generally, neural network and ensemble algorithms, to some degree, out-performed other ML algorithms. CONCLUSION: Triaging and managing different prehospital medical conditions and augmenting ambulance performance can be improved by ML algorithms. Future reports should focus on a specific clinical condition or operational task to improve the precision of the performance metrics of ML models.

Enhancing brain tumor diagnosis: an optimized CNN hyperparameter model for improved accuracy and reliability.

Hyperparameter tuning plays a pivotal role in the accuracy and reliability of convolutional neural network (CNN) models used in brain tumor diagnosis. These hyperparameters exert control over various aspects of the neural network, encompassing feature extraction, spatial resolution, non-linear mapping, convergence speed, and model complexity. We propose a meticulously refined CNN hyperparameter model designed to optimize critical parameters, including filter number and size, stride padding, pooling techniques, activation functions, learning rate, batch size, and the number of layers. Our approach leverages two publicly available brain tumor MRI datasets for research purposes. The first dataset comprises a total of 7,023 human brain images, categorized into four classes: glioma, meningioma, no tumor, and pituitary. The second dataset contains 253 images classified as "yes" and "no." Our approach delivers exceptional results, demonstrating an average 94.25% precision, recall, and F1-score with 96% accuracy for dataset 1, while an average 87.5% precision, recall, and F1-score, with accuracy of 88% for dataset 2. To affirm the robustness of our findings, we perform a comprehensive comparison with existing techniques, revealing that our method consistently outperforms these approaches. By systematically fine-tuning these critical hyperparameters, our model not only enhances its performance but also bolsters its generalization capabilities. This optimized CNN model provides medical experts with a more precise and efficient tool for supporting their decision-making processes in brain tumor diagnosis.

The Effect of Chitosan Incorporation on Physico-Mechanical and Biological Characteristics of a Calcium Silicate Filling Material.

OBJECTIVES: A tricalcium silicate-based cement, Biodentine™, has displayed antibiofilm activity when mixed with chitosan powder. This study aimed to assess the effect of chitosan incorporation on the physico-mechanical and biological properties of Biodentine™. METHODS: In this study, medium molecular weight chitosan powder was incorporated into Biodentine™ in varying proportions (2.5 wt%, 5 wt%, 10 wt%, and 20 wt%). The setting time was determined using a Vicat apparatus, solubility was assessed by calculating weight variation after water immersion, radiopacity was evaluated and expressed in millimeters of aluminum, the compressive strength was evaluated using an Instron testing machine, and the microhardness was measured with a Vickers microhardness tester. In addition, surface topography of specimens was analyzed using scanning electron microscopy, and the effect of chitosan on the viability of human embryonic kidney (HEK 293) cells was measured by a colorimetric MTT assay. RESULTS: Incorporation of 2.5 wt% and 5 wt% chitosan powder delivered an advantage by speeding up the setting time of Biodentine material. However, the incorporation of chitosan compromised all other material properties and the crystalline structure in a dose-dependent manner. The chitosan-modified material also showed significant decreases in the proliferation of the HEK 293 cells, signifying decreased biocompatibility. SIGNIFICANCE: Chitosan incorporation into calcium silicate materials adversely affects the physical and biological properties of the material. Despite the increased antimicrobial activity of the modified material, the diminution in these properties is likely to reduce its clinical value.

Open Surgical Conversion After Endovascular Aortic Aneurysm Repair: A Systematic Review and Meta-Analysis.

Endovascular aneurysm repair (EVAR) is a preferred treatment for abdominal aortic aneurysms, though it comes with complications such as endoleaks and graft infections that may necessitate late open conversion (LOC). This systematic review and meta-analysis, drawing on studies from PubMed/MEDLINE, Embase, and the Cochrane Database of Systematic Reviews, aimed to evaluate the incidence, outcomes, and factors leading to LOC after EVAR. The analysis of 11 selected studies revealed a 5.3% incidence of LOC, with a patient cohort predominantly male (79%) and an average age of 73.5 years. The interval between initial EVAR and LOC was 35.1 months on average, with the Excluder device most frequently necessitating LOC. A notable 68% of endovascular salvage attempts before LOC failed. The study highlighted rupture and type I endoleak as the primary reasons for urgent LOC, which exhibited a 10-fold higher mortality rate compared to elective LOC. Elective LOC procedures had a 30-day mortality rate similar to primary elective open aneurysm repairs. These findings underscore the importance of vigilant post-EVAR patient monitoring and suggest that the methodological quality of underlying research should be considered in interpreting these results.

Comparative Accuracy of Intraoral and Extraoral Digital Workflows for Short Span Implant Supported Fixed Partial Denture Fabrication: An In Vitro Study.

BACKGROUND The advent of digital impressions using computer-aided design and manufacturing technology (CAD/CAM) has simplified and improved the fabrication of implant prostheses in dentistry. The conventional impression has several drawbacks, including tray selection, material type, impression technique, impression disinfection, and cast model storage. The inaccuracies caused by distortion and contraction of impression material can be minimized with digital impressions. This study aimed to compare digital dental impressions of 10 working casts made using the Pindex laser removable die system to fabricate parallel drill channels vs 10 working casts made using the Di-Lok plastic tray removable die system. MATERIAL AND METHODS An implant master die with 2 dental implant analogs was fabricated. Ten working casts using the Pindex laser removable die system with parallel drill channels and 10 working casts using the Di-Lok plastic tray removable die system were fabricated. The working casts were scanned using an extra-oral laboratory scanner and the implant master model was scanned with an intra-oral scanner. RESULTS The properties of the casts made using the 2 systems were evaluated and analyzed with ANOVA and post hoc Tukey test. The mean horizontal linear distances between A1B1 (P<0.021), A2B2 (P<0.018), C1D1 (P<0.026), C2D2 (P<0.03), B1C1 (P<0.01), and mean vertical distances between B1A2 (P<0.015), C1D2 (P<0.001), B1B2 (P<0.028), and C1C2 (P<0.001) were significantly different between the Pindex system and Di-Lok tray system as compared to intra-oral scans. CONCLUSIONS Complete digital workflow with intra-oral scans were more than the partial digital workflow with extra-oral scans for the Pindex system and Di-Lok tray systems.

Advancing brain tumor detection: harnessing the Swin Transformer's power for accurate classification and performance analysis.

The accurate detection of brain tumors through medical imaging is paramount for precise diagnoses and effective treatment strategies. In this study, we introduce an innovative and robust methodology that capitalizes on the transformative potential of the Swin Transformer architecture for meticulous brain tumor image classification. Our approach handles the classification of brain tumors across four distinct categories: glioma, meningioma, non-tumor, and pituitary, leveraging a dataset comprising 2,870 images. Employing the Swin Transformer architecture, our method intricately integrates a multifaceted pipeline encompassing sophisticated preprocessing, intricate feature extraction mechanisms, and a highly nuanced classification framework. Utilizing 21 matrices for performance evaluation across all four classes, these matrices provide a detailed insight into the model's behavior throughout the learning process, furthermore showcasing a graphical representation of confusion matrix, training and validation loss and accuracy. The standout performance parameter, accuracy, stands at an impressive 97%. This achievement outperforms established models like CNN, DCNN, ViT, and their variants in brain tumor classification. Our methodology's robustness and exceptional accuracy showcase its potential as a pioneering model in this domain, promising substantial advancements in accurate tumor identification and classification, thereby contributing significantly to the landscape of medical image analysis.

Impact of Auxiliary Features on Retention of Short Dental Crowns: An In-Vitro Analysis of Box and Groove Preparations.

BACKGROUND Several auxiliary features have been proposed to achieve sustainable retention for short-prepared dental crowns; however, achieving retention is challenging. This study aimed to assess the impact of increased total occlusal convergence and auxiliary preparation factors like box and groove on the retention form of short tooth preparations. MATERIAL AND METHODS Eighty resin machine-milled dies with a height of 3 mm and a deep chamfer margin of 1 mm were prepared to mimic the short-prepared molar. Initially, 2 teeth were prepared following the guidelines, and the total occlusal convergence was kept at 10° and 20°, respectively. Auxiliary features such as the proximal box and buccal groove were prepared on separate 20° dies. Eighty dies were prepared with 10 samples each for 10°, 20°, 20° with proximal box and 20° with buccal groove for zirconia (n=40) and metal crowns (n=40). Cementation was done with glass ionomer luting cement, and a pull-off test was conducted. Data were analyzed using one-way analysis of variance and post hoc fisher least significant difference test (P<0.05). RESULTS The highest mean was observed in the proximal box group with the metal crown (14.59), and the lowest in the group with 20° zirconia crowns (9.12). Within groups, the highest retentive values were found for the 20° taper with proximal box preparation; the lowest was for the 20° taper group. CONCLUSIONS Within the study limitations, it could be concluded that incorporating a proximal box or buccal groove in short tooth preparations with an increased total occlusal convergence improved retentive values.

Predicting immunogenicity of COVID-19 vaccines in hemodialysis patients with renal disease.

Individuals who are diagnosed with chronic kidney disease, particularly those receiving maintenance hemodialysis treatment, face a greater likelihood of suffering from severe symptoms and fatality due to COVID-19. This study aimed to explore the optimal vaccination approach for these individuals. The study used data analysis tasks such as data preprocessing, cleaning, and exploration, and machine learning models including linear regression, random forest, XGBoost, gradient boosting, AdaBoost, decision trees, Lasso, and ridge regression were used to construct the predictive model. The study found that the Lasso model performed the best overall in predicting anti-S IgG antibodies levels in response to COVID-19 vaccines for people with kidney failure with MAE of 8.81, RMSE of 19.59, and R2 value of 0.93. The adjusted R2 value for the Lasso model was also 0.93, indicating that the model's ability to explain the variance in the data was not affected by the number of predictors in the model. The Random Forest model best predicted the duration of immunogenicity, with R2 and adjusted R2 values of 0.71 and 0.69, respectively. The ensemble model that includes all eight models, i.e., Ridge, Lasso, Linear Regression, Random Forest, AdaBoost, Gradient Boosting, XGBoost, and Decision Tree, has the best performance with the lowest MAE, the lowest RMSE, the highest R2, and the highest adjusted R2 values of 3.91, 5.00, 0.73, and 0.72, respectively. However, further research is required to validate these models and extend their application to different populations and vaccine types, as well as considering other factors that may affect immune response to COVID-19 vaccines. These findings can be helpful in improving vaccination strategies and promoting public health.

Stroke and high-risk TIA outcomes with reduction of treatment duration when treatment initiated in emergency rooms (SHORTER-study).

BACKGROUND: Following transient ischemic attack (TIA) and minor stroke, the risk of recurrent stroke can be significantly reduced with short-duration dual antiplatelet therapy (DAPT). We wish to investigate whether 10 days of DAPT is as effective as 21 days' treatment. STUDY DESIGN: This is an open-label, randomized, parallel-group study comparing whether 10 days of DAPT treatment (ASA + clopidogrel) is non-inferior to 21 days of DAPT in patients with acute ischemic stroke (AIS) or high-risk TIA. In both groups, DAPT is started within 24 hours of symptom onset. This study is being conducted in approximately 15 study sites in the Kingdom of Saudi Arabia. The planned sample size is 1932. OUTCOMES: Non-inferiority of 10 days compared to 21 days of DAPT in the prevention of the composite endpoint of stroke and death at 90 days in AIS/TIA patients. The primary safety outcome is major intra-cranial and systemic hemorrhage. STUDY PERIOD: Enrolment started in the second quarter of 2023, and the completion of the study is expected in the fourth quarter of 2025. DISCUSSION: The trial is expected to show that 10 days of DAPT is non-inferior for the prevention of early recurrence of vascular events in patients with high-risk TIAs and minor strokes.