Degree of conversion and interfacial adaptation of touch-cure resin cement polymerized by self-curing or dual-curing with reduced light.

OBJECTIVES: The first aim of this study was to determine whether there is a difference in degree of conversion (DC) of touch-cure cements polymerized by self-curing with adhesive or dual-curing under reduced light. The second aim was to compare interfacial adaptation of zirconia restoration cemented using touch-cure cements self-cured or dual-cured by reduced light. METHODS: The DC of touch-cure resin cements with adhesive was measured continuously using Fourier transform infrared spectrometry. Experimental groups differed depending on touch-cure cement. Each group had three subgroups of polymerization method. For subgroup 1, the DC was measured by self-curing. For subgroups 2 and 3, the DCs were measured by dual-curing with reduced light penetrating 3 mm and 1 mm zirconia blocks, respectively. For interfacial adaptation evaluation, Class I cavity was prepared on an extracted third molar, and zirconia restoration was fabricated. The restoration was cemented using the same cement. Groups and subgroups for interfacial adaptation were the same as those of the DC measurement. After thermo-cycling, interfacial adaptation at the tooth-restoration interface was evaluated using swept-source optical coherence tomography imaging. RESULTS: The DC of touch-cure cement differed depending on the measurement time, resin cement, and polymerization method (p < 0.05). Interfacial adaptation was different depending on the resin cement and polymerization method (p < 0.05). CONCLUSION: For touch-cure cement, light-curing with higher irradiance presented a higher DC and superior interfacial adaptation than light-curing with lower irradiance or self-curing. CLINICAL RELEVANCE: Although some adhesives accelerate the self-curing of touch-cure cement, light-curing for touch-cure cement is necessary for zirconia cementation.

Many Models, Little Adoption-What Accounts for Low Uptake of Machine Learning Models for Atrial Fibrillation Prediction and Detection?

(1) Background: Atrial fibrillation (AF) is a major risk factor for stroke and is often underdiagnosed, despite being present in 13-26% of ischemic stroke patients. Recently, a significant number of machine learning (ML)-based models have been proposed for AF prediction and detection for primary and secondary stroke prevention. However, clinical translation of these technological innovations to close the AF care gap has been scant. Herein, we sought to systematically examine studies, employing ML models to predict incident AF in a population without prior AF or to detect paroxysmal AF in stroke cohorts to identify key reasons for the lack of translation into the clinical workflow. We conclude with a set of recommendations to improve the clinical translatability of ML-based models for AF. (2) Methods: MEDLINE, Embase, Web of Science, Clinicaltrials.gov, and ICTRP databases were searched for relevant articles from the inception of the databases up to September 2022 to identify peer-reviewed articles in English that used ML methods to predict incident AF or detect AF after stroke and reported adequate performance metrics. The search yielded 2815 articles, of which 16 studies using ML models to predict incident AF and three studies focusing on ML models to detect AF post-stroke were included. (3) Conclusions: This study highlights that (1) many models utilized only a limited subset of variables available from patients' health records; (2) only 37% of models were externally validated, and stratified analysis was often lacking; (3) 0% of models and 53% of datasets were explicitly made available, limiting reproducibility and transparency; and (4) data pre-processing did not include bias mitigation and sufficient details, leading to potential selection bias. Low generalizability, high false alarm rate, and lack of interpretability were identified as additional factors to be addressed before ML models can be widely deployed in the clinical care setting. Given these limitations, our recommendations to improve the uptake of ML models for better AF outcomes include improving generalizability, reducing potential systemic biases, and investing in external validation studies whilst developing a transparent modeling pipeline to ensure reproducibility.

Enhancing Sepsis prognosis: Integrating social determinants and demographic variables into a comprehensive model for critically ill patients.

BACKGROUND: The Sequential Organ Failure Assessment (SOFA) score monitors organ failure and defines sepsis but may not fully capture factors influencing sepsis mortality. Socioeconomic and demographic impacts on sepsis outcomes have been highlighted recently. OBJECTIVE: To evaluate the prognostic value of SOFA scores against demographic and social health determinants for predicting sepsis mortality in critically ill patients, and to assess if a combined model increases predictive accuracy. METHODS: The study utilized retrospective data from the MIMIC-IV database and prospective external validation from the Penn State Health cohort. A Random Forest model incorporating SOFA scores, demographic/social data, and the Charlson Comorbidity Index was trained and validated. FINDINGS: In the MIMIC-IV dataset of 32,970 sepsis patients, 6,824 (20.7%) died within 30 days. A model including demographic, socioeconomic, and comorbidity data with SOFA scores improved predictive accuracy beyond SOFA scores alone. Day 2 SOFA, age, weight, and comorbidities were significant predictors. External validation showed consistent performance, highlighting the importance of delta SOFA between days 1 and 3. CONCLUSION: Adding patient-specific demographic and socioeconomic information to clinical metrics significantly improves sepsis mortality prediction. This suggests a more comprehensive, multidimensional prognostic approach is needed for accurate sepsis outcome predictions.

Prevalence and Determinants of Long-Term Post-COVID Conditions in the United States: 2022 Behavioral Risk Factor Surveillance System.

BACKGROUND: A significant proportion of COVID survivors experience lingering and debilitating symptoms following acute COVID-19 infection. According to the national research plan on long COVID, it is a national priority to identify the prevalence of post-COVID conditions and their associated factors. METHOD: We performed a cross-sectional analysis of the Prevention Behavioral Risk Factor Surveillance System (BRFSS) 2022, the largest continuously gathered health survey dataset worldwide by the Centers for Disease Control. After identifying individuals with a positive history of COVID-19, we grouped COVID-19 survivors based on whether they experienced long-term post-COVID conditions. Using survey-specific R packages, we compared the two groups' socio-demographics, comorbidities, and lifestyle-related factors. A logistic regression model was used to identify factors associated with post-COVID conditions. RESULTS: The overall estimated prevalence of long-term post-COVID conditions among COVID survivors was 21.7%. Fatigue (5.7%), dyspnea (4.2%), and anosmia/ageusia (3.8%) were the most frequent symptoms. Based on multivariate logistic regression analysis, female sex, body mass index (BMI)≥25, lack of insurance, history of pulmonary disease, depression, and arthritis, being a former smoker, and sleep duration <7 h/d were associated with higher odds of post-COVID conditions. On the other hand, age >64 y/o, Black race, and annual household income ≥$100k were associated with lower odds of post-COVID conditions. CONCLUSION: Our findings indicate a notable prevalence of post-COVID conditions, particularly among middle-aged women and individuals with comorbidities or adverse lifestyles. This high-risk demographic may require long-term follow-up and support. Further investigations are essential to facilitate the development of specified healthcare and therapeutic strategies for those suffering from post-COVID conditions.

Prospective Comparison of Urinary Measured Creatinine Clearance With eGFR and Cystatin C Based Cis-eGFR, Including Kinetic eGFR in the Immediate Post-transplant Period With Prompt Allograft Function.

BACKGROUND: The estimated glomerular filtration rate (eGFR) and kinetic estimated glomerular filtration rate (KeGFR) have not been compared, with urinary measured creatinine clearance (mCrCl) or serum cystatin C (CysC) eGFR, soon after kidney transplantation (KTx) with prompt primary function. This study aims to compare post-KTx, urinary mCrCl, and eGFR CysC with eGFR and KeGFR. METHODS: Post-KTx, urine was collected every 12 hours from 25 of the 34 consenting subjects to calculate mCrCl and compare with Modification of Diet in Renal Disease (MDRD)-4, Jelliffe eGFR, Cockcroft-Gault creatinine clearance (CrCl), and KeGFR by Chen and Brater formulae. Serum CysC levels were also measured in the last 14 subjects to compare with creatinine, mCrCl, and eGFR CysC. RESULTS: At 12 to 96 hours post-KTx (n = 25), mCrCl was 55.8% to 13.6% higher than MDRD-4 eGFR. The mean CysC level (n = 14) was 58% to 14% lower than creatinine for up to 3.0 days post-KTx, with higher MDRD-4 eGFR CysC. Chen and Brater KeGFR were significantly lower than mCrCl and eGFR (Fig 1B, Table 1). Within 3 days post-KTx, a 50% decrease in creatinine provided ≥ 50 mL/min CrCl in 90% of cases (mean mCrCl 61.7 ± 22.8). This difference was greater when the initial creatinine was higher with the rapid decrease in creatinine. CONCLUSIONS: (1) Post-KTx eGFR/KeGFR formulae underestimated mCrCl. (2) Serum CysC levels were lower than creatinine, corresponding with higher eGFR CysC. (3) A 50% decrease from initial serum creatinine; mean mCrCl was 61.7 ± 22.8 mL/min, and 90% of them have mCrCl > 50 mL/min. Post-KTx, until creatinine is stabilized, recipients are often receiving subtherapeutic dosing of renally adjusted medications. More prospective studies are necessary, including radioisotope clearance.

Self-Management among Stroke Survivors in the United States, 2016 to 2021.

Background: Self-management among stroke survivors is effective in mitigating the risk of a recurrent stroke. This study aims to determine the prevalence of self-management and its associated factors among stroke survivors in the United States. Methods: We analyzed the Behavioral Risk Factor Surveillance System (BRFSS) data from 2016 to 2021, a nationally representative health survey. A new outcome variable, stroke self-management (SSM = low or SSM = high), was defined based on five AHA guideline-recommended self-management practices, including regular physical activity, maintaining body mass index, regular doctor checkups, smoking cessation, and limiting alcohol consumption. A low level of self-management was defined as adherence to three or fewer practices. Results: Among 95,645 American stroke survivors, 46.7% have low self-management. Stroke survivors aged less than 65 are less likely to self-manage (low SSM: 56.8% vs. 42.3%; p < 0.0001). Blacks are less likely to self-manage than non-Hispanic Whites (low SSM: 52.0% vs. 48.6%; p < 0.0001); however, when adjusted for demographic and clinical factors, the difference was dissipated. Higher education and income levels are associated with better self-management (OR: 2.49, [95%CI: 2.16-2.88] and OR: 1.45, [95%CI: 1.26-1.67], respectively). Further sub-analysis revealed that women are less likely to be physically active (OR: 0.88, [95%CI: 0.81-0.95]) but more likely to manage their alcohol consumption (OR: 1.57, [95%CI: 1.29-1.92]). Stroke survivors residing in the Stroke Belt did not self-manage as well as their counterparts (low-SSM: 53.1% vs. 48.0%; p < 0.001). Conclusions: The substantial diversity in self-management practices emphasizes the need for tailored interventions. Particularly, multi-modal interventions should be targeted toward specific populations, including younger stroke survivors with lower education and income.

Differential privacy preserved federated learning for prognostic modeling in COVID-19 patients using large multi-institutional chest CT dataset.

BACKGROUND: Notwithstanding the encouraging results of previous studies reporting on the efficiency of deep learning (DL) in COVID-19 prognostication, clinical adoption of the developed methodology still needs to be improved. To overcome this limitation, we set out to predict the prognosis of a large multi-institutional cohort of patients with COVID-19 using a DL-based model. PURPOSE: This study aimed to evaluate the performance of deep privacy-preserving federated learning (DPFL) in predicting COVID-19 outcomes using chest CT images. METHODS: After applying inclusion and exclusion criteria, 3055 patients from 19 centers, including 1599 alive and 1456 deceased, were enrolled in this study. Data from all centers were split (randomly with stratification respective to each center and class) into a training/validation set (70%/10%) and a hold-out test set (20%). For the DL model, feature extraction was performed on 2D slices, and averaging was performed at the final layer to construct a 3D model for each scan. The DensNet model was used for feature extraction. The model was developed using centralized and FL approaches. For FL, we employed DPFL approaches. Membership inference attack was also evaluated in the FL strategy. For model evaluation, different metrics were reported in the hold-out test sets. In addition, models trained in two scenarios, centralized and FL, were compared using the DeLong test for statistical differences. RESULTS: The centralized model achieved an accuracy of 0.76, while the DPFL model had an accuracy of 0.75. Both the centralized and DPFL models achieved a specificity of 0.77. The centralized model achieved a sensitivity of 0.74, while the DPFL model had a sensitivity of 0.73. A mean AUC of 0.82 and 0.81 with 95% confidence intervals of (95% CI: 0.79-0.85) and (95% CI: 0.77-0.84) were achieved by the centralized model and the DPFL model, respectively. The DeLong test did not prove statistically significant differences between the two models (p-value = 0.98). The AUC values for the inference attacks fluctuate between 0.49 and 0.51, with an average of 0.50 ± 0.003 and 95% CI for the mean AUC of 0.500 to 0.501. CONCLUSION: The performance of the proposed model was comparable to centralized models while operating on large and heterogeneous multi-institutional datasets. In addition, the model was resistant to inference attacks, ensuring the privacy of shared data during the training process.

Evaluation of bulk-fill systems: microtensile bond strength and non-destructive imaging of marginal adaptation

Abstract The objective of this study was to investigate microtensile bond strength (MTBS) and interfacial adaptation (IA) of bulk-fill restorative systems bonded to dentin in Class-I-preparations. Box-shaped preparations (4-mm-long, 3-mm-wide, 2-mm-high) made in extracted molars, and Teflon matrix with the same dimensions positioned over the occlusal surface were restored, providing a total of 4-mm composite depth using three bulk-fill restorative systems: Tetric EvoCeram Bulk Fill with Tetric N-Bond (TEC/TNB), SureFil SDR Flow with XP Bond (SDR/XPB) and Filtek Bulk Fill Flowable Restorative with Scotchbond Universal (FBF/SBU); or incrementally restored with a conventional restorative system: Herculite Classic with OptiBond FL (HER/OBF). The specimens were sectioned into beams and the MTBS measured after 24-hours or one-year storage. For evaluation of IA, round-tapered tooth preparations (3-mm-diameter, 1.5-mm-deep) were made, restored with each material and their cross-sectional images were obtained after 24-hours using optical coherence tomography (OCT). The gap percentage for each restoration system was calculated using image analysis software. MTBS for both storage periods: HER/OBF=TEC/TNB=SDR/XPB>FBF/SBU (ANOVA, Tukey's post-hoc, P<0.05) differed significantly among groups, which values were significantly reduced after one-year. SDR/XPB showed comparatively lesser gap formation at the tooth-interface after 24 hours (ANOVA, Dunnett's T3 post-hoc, P<0.05). For deeper restorations, bond strength of TEC/TNB and SDR/XPB can be equal to that of HER/OBF after 24-hours and one-year; however, in a shallower preparation, SDR/XPB showed greater initial interfacial adaptation.