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Periodontitis is a chronic inflammatory disease and leading cause of tooth loss among US adults. Beyond smoking and socioeconomic factors, modifiable lifestyle behaviors affecting systemic inflammation may influence periodontal health. Physical activity reduces inflammation, yet its association with periodontitis remains inconsistent. This study examined this relationship using nationally representative US data. We analyzed cross-sectional data from the National Health and Nutrition Examination Survey 2009 to 2014 among 6590 adults aged 20 years or older with complete data; pregnant individuals and those with diabetes were excluded. Physical activity was self-reported and categorized as none, moderate, or heavy. Periodontitis was defined using Centers for Disease Control and Prevention and American Academy of Periodontology criteria. Multivariable logistic regression examined associations, adjusting for demographic, socioeconomic, and behavioral factors. Overall, 31.2
Peri-implantitis is a major biological complication that compromises the longevity of dental implants, and accurate radiographic assessment is essential for early detection and intervention. Artificial intelligence (AI) has shown promising performance in dental imaging; however, the evidence supporting its diagnostic utility for peri-implant disease on two-dimensional radiographs remains fragmented. To systematically evaluate and synthesise the performance, methodological quality, and certainty of evidence of deep-learning models developed for the detection or assessment of peri-implantitis and peri-implant bone loss using two-dimensional dental radiographs. A systematic search of PubMed, Scopus (including Embase-indexed records), and the Cochrane Library was conducted from inception to January 2025. Studies using deep-learning models to detect peri-implantitis, quantify peri-implant bone loss, or classify peri-implant defect morphology on two-dimensional radiographs were eligible. Screening, data extraction, and risk-of-bias assessment (QUADAS-2) were performed in duplicate. Due to substantial heterogeneity in outcomes, diagnostic definitions, and reporting formats, a formal meta-analysis was not feasible; results were synthesised narratively, with descriptive pooling where appropriate. Certainty of evidence was evaluated using GRADE for diagnostic accuracy. Twenty-eight records were screened, and eight studies met the inclusion criteria. All were retrospective, single-centre investigations published between 2021 and 2025. Deep-learning models demonstrated high technical performance within internal datasets, with binary classifiers achieving sensitivities up to 0.90–0.98, specificities up to 0.95, and segmentation models yielding Dice coefficients exceeding 0.97. Multi-class and measurement-based systems also showed strong agreement with clinician assessments. However, all studies exhibited high risk of bias in patient selection and index-test domains, relied exclusively on internal validation, and used heterogeneous diagnostic targets and reference standards. Only two studies reported sufficient information to derive sensitivity and specificity. The overall certainty of evidence was judged to be very low. Deep-learning systems show strong technical promise for identifying peri-implant bone loss and peri-implantitis on two-dimensional radiographs, frequently matching or surpassing clinician-level performance within controlled settings. However, serious methodological limitations and the absence of external validation constrain the certainty and generalisability of current evidence. AI-based peri-implant diagnostic systems should therefore be considered investigational, and robust multicentre prospective studies are required before clinical implementation.
Cardiac disease is a well-established manifestation of myotonic dystrophy type 1 (DM1), characterized by progressive cardiac conduction slowing with increased risk of atrial and ventricular arrhythmias, heart block, and sudden cardiac death. Multiple disease modifying therapies are in clinical trials for DM1 and show promise in improving skeletal muscle weakness and myotonia. Testing the effects of these medicines, or others, in the heart is of critical importance, but requires identification of endpoints of cardiac function that accurately reflect the state of DM1 cardiac disease. To better define cardiac endpoints, the Myotonic Dystrophy Clinical Research Network (DMCRN) and the Myotonic Dystrophy Foundation (MDF) convened a workshop entitled ''Cardiac Endpoint Workshop'' in May 2025 at the Myotonic Dystrophy Foundation International Conference. Here, we summarize the discussion at the workshop and perform secondary analysis of cardiac outcomes in the published literature to evaluate cardiac endpoints for clinical impact and trial feasibility. This analysis demonstrates that major cardiac events are too infrequent (<1% annual incidence), and alternatives such as composite endpoints or progression of cardiac conduction prolongation would likely be underpowered in a conventional clinical trial. Given these limitations, we identify areas for further natural history study to better describe longitudinal cardiac structural and functional changes to inform specialized patient selection or identify alternative measures with sensitivity to detect therapeutic impact in a trial.
OBJECTIVE:To assess students' program-required and self-directed preparation for the North American Pharmacist Licensure Examination (NAPLEX), as well as determine any association between pass rates and timing of the NAPLEX after graduation, completion of a National Association of Boards of Pharmacy (NABP) practice exam, pharmacy grade average point (GPA), remedial course work, self-directed study hours, program-required preparation hours, and primary language. METHODS:A survey was administered to Class of 2024 graduates from 9 schools, which collected self-reported data on NAPLEX attempt (pass/fail, timing after graduation, study time, commercial products used), preparation (both program-required and self-directed), perception of their preparation, and advice they would give to others. Descriptive statistics were examined, and comparisons were conducted between those who passed and those who failed. RESULTS:A total of 717 Class of 2024 graduates from 9 schools took the NAPLEX, and 329 individuals completed the survey (response rate, 45.9%). Most respondents took the NAPLEX within 60 days of graduation and spent up to 3 months preparing. Most students reported feeling adequately prepared for the NAPLEX (80%), with the lowest level of agreement for preparedness in compounding, dispensing, and administering drugs (73.9%). Group comparisons demonstrated significant differences in NAPLEX pass rates based on the time after graduation that the NAPLEX was taken and pharmacy GPA. CONCLUSION:This study reinforces that characteristics, particularly the timing of the exam in relation to graduation and cumulative GPA, are significantly associated with NAPLEX outcomes. The findings underscore patterns in preparation strategies, timing, academic achievement, and perceived gaps in readiness that are valuable for both educators and students.
