Category: Nevin Manimala

Int Dent J. 2026 May 27;76(4):109643. doi: 10.1016/j.identj.2026.109643. Online ahead of print.

ABSTRACT

INTRODUCTION AND AIMS: The aims of this study were to evaluate the clinical and radiological characteristics of patients diagnosed with mandibular medication-related osteonecrosis of the jaw (MRONJ) and to examine its associations with neurosensory disturbances (NSD) related to the inferior alveolar nerve.

METHODS: A retrospective review was conducted of all patients diagnosed with MRONJ who had undergone cone-beam computed tomography (CBCT) or computed tomography (CT) imaging in the Department of Oral and Maxillofacial Surgery at the Prince Philip Dental Hospital between January 2013 and August 2024. Clinical characteristics and the radiological manifestations of the mandibular canal were extracted. Statistical analyses were carried out with the significance level set at 5%.

RESULTS: A total of 61 patients with 65 MRONJ lesions were included, of which 21 lesions (32.3%) exhibited neurosensory disturbances. The most common radiological manifestation of the MC was osteosclerotic (60%) and osteolytic changes (60%), followed by sequestration (46.2%). Multivariate logistic regression analysis identified active infection (adjusted OR 4.91, 95% CI: 1.14-21.2, p = .033) and sequestrum impingement (adjusted OR: 6.17, 95% CI: 1.76-21.7, p = .005) as significant associations of NSD. Four out of 15 patients with active infection and preintervention NSD presented with complete resolution of NSD following the elimination of the infection.

CONCLUSION: Approximately one-third of mandibular MRONJ lesions presented with NSD. Sequestration impinging on the MC and active infection are associated with the presence of NSD.

CLINICAL RELEVANCE: The removal of sequestrum and treatment of infection may lead to an improvement in pre-intervention NSD; therefore, the inferior alveolar nerve should not be sacrificed routinely during the operation.

PMID:42202388 | DOI:10.1016/j.identj.2026.109643

Int Dent J. 2026 May 27;76(4):109641. doi: 10.1016/j.identj.2026.109641. Online ahead of print.

ABSTRACT

BACKGROUND: Oral cancer constitutes a significant public health burden globally and has long been associated with substantial socioeconomic, racial, and regional disparities worldwide. Population-level mortality surveillance in high-income countries such as the United States can provide crucial epidemiological evidence and methodological references for global cancer control and prevention strategies. However, national-level updated evidence on long-term trends, subgroup heterogeneity, and pre- and post-COVID-19 pandemic trends in oral cancer mortality in the United States remains limited. This study assesses the temporal trends and differences in oral cancer mortality in the United States from 1999 to 2024.

METHODS: Mortality data were extracted from the CDC WONDER Underlying Cause of Death database. Overall oral cavity cancer was defined using ICD-10 codes C00-C06, while subsite analyses focused on C02-C06. Adults aged 25 years and older were included. Joinpoint regression was used to analyse temporal trends in age-adjusted mortality rates (AAMRs, per 100,000 population) and to estimate annual percent change (APC) and average annual percent change (AAPC), with stratification by sex, region, and subsite. In addition, custom time intervals of 1999-2019 and 2020-2024 were specified to assess trend changes before and during the pandemic period. A supplementary age-stratified analysis comparing adults aged 25 to 44 years and those aged ≥45 years was also performed.

RESULTS: Overall oral cavity cancer mortality exhibited one joinpoint in 2009. Mortality declined significantly from 1999 to 2009 (APC = -1.81%, 95% CI: -2.48% to -1.28%, P < .001), but increased significantly from 2009 to 2024 (APC=1.29%, 95% CI: 0.99% to 1.66%, P < .001), whereas the AAPC for the full study period was not statistically significant (0.04%, 95% CI:-0.11% to 0.19%, P = .624). Mortality rates were consistently higher in males than in females, and pairwise comparison showed that sex-specific trends were neither parallel nor coincident (parallelism P = .012; coincidence P = .001). All 4 U.S. Census regions demonstrated an initial decline followed by a later rebound, although only the Midwest showed a significant net increase over the full study period. Marked heterogeneity was observed across subsites: tongue cancer (C02) was the only subsite with a significantly increased AAPC over the full study period, whereas floor-of-mouth cancer (C04) and palate cancer (C05) showed sustained net declines. Custom interval analyses further demonstrated a declining trend before the pandemic (1999-2019; AAPC=-0.2709%, 95% CI:-0.4281 to -0.1087, P < .001) and an increasing trend during the pandemic period (2020-2024; AAPC=1.2879%, 95% CI: 0.9922 to 1.6558, P < .001). Significant sex-based differences were observed, with additional heterogeneity across regions and subsites. In supplementary age-stratified analyses, no significant joinpoint was identified among adults aged 25-44 years, whereas adults aged ≥45 years showed a significant reversal in mortality trend, suggesting that the overall mortality rebound was driven predominantly by the older age group.

CONCLUSIONS: In the United States, oral cavity cancer mortality shifted from a declining trend to a sustained increase around 2009 and accelerated further during 2020-2024, indicating a biphasic rather than a simple linear pattern. Substantial heterogeneity was observed across sex, region, and subsite. Tongue cancer may have been a major contributor to the recent rebound in mortality, while the post-2020 trend reversal underscores the need to further examine the potential effects of diagnostic delay, disparities in healthcare access, and post-pandemic health system recovery on oral cavity cancer outcomes. Supplementary analyses further suggest that this mortality reversal was concentrated mainly in adults aged ≥45 years rather than in younger adults.

PMID:42202385 | DOI:10.1016/j.identj.2026.109641

N Engl J Med. 2026 May 28;394(20):1991-2001. doi: 10.1056/NEJMoa2513525.

ABSTRACT

BACKGROUND: Biologic ventricular assist tissue (BioVAT) is formulated from engineered heart muscle composed of cardiomyocytes and stromal cells derived from allogeneic induced pluripotent stem cells for cardiac remuscularization in patients with heart failure and a reduced left ventricular ejection fraction.

METHODS: We conducted an open-label, phase 1-2 study of tissue-engineered heart repair by means of BioVAT transplantation. Patients with heart failure and a left ventricular ejection fraction of 35% or less and at least one hypokinetic or dyskinetic left ventricular segment were treated with BioVAT allografts, which consisted of 5, 10, or 20 engineered-heart-muscle units. All the patients received immunosuppression. Safety was assessed as adverse events related to the procedure. The primary efficacy end points were the change from baseline in the target heart-wall thickness, the left ventricular ejection fraction, and the Kansas City Cardiomyopathy Questionnaire-Overall Summary Score (KCCQ-OSS).

RESULTS: A total of 20 patients were treated in the study. Three patients died during the study (1 each from vasoplegia, coronavirus disease 2019, and aortic dissection). One patient underwent heart transplantation. Immunosuppression was discontinued in 4 patients because of implantation of a left ventricular assist device (in 2 patients), renal failure (in 1 patient), and urothelial carcinoma (in 1 patient). Of the 16 patients who were treated with the safe maximal dose (20 engineered-heart-muscle units), 12 patients completed the prespecified 3-month interim follow-up. The least-squares mean increase in the target-wall thickness was 4.5 mm (90% confidence interval [CI], 3.7 to 5.4; P<0.001), the increase in the left ventricular ejection fraction was 3.9 percentage points (90% CI, 0.9 to 6.8; P = 0.04), and the increase in the KCCQ-OSS was 6.7 points (90% CI, 1.0 to 12.5; P = 0.06). All the patients had at least one adverse event.

CONCLUSIONS: In this interim analysis, cardiac remuscularization with BioVAT was associated with an increase in the target heart-wall thickness, left ventricular ejection fraction, and KCCQ-OSS at 3 months; all the patients had at least one adverse event. Longer-term follow-up and further clinical investigation are warranted. (Funded by the German Center for Cardiovascular Research and Repairon; BioVAT-HF ClinicalTrials.gov number, NCT04396899.).

PMID:42202318 | DOI:10.1056/NEJMoa2513525

Afr J Reprod Health. 2026 May 27;30(10):49-59. doi: 10.29063/ajrh2026/v30i10.4.

ABSTRACT

This study explores dance education as an informal health pathway linked to movement, gender, and reproductive wellbeing among Chinese women. A quantitative cross-sectional design surveyed women of reproductive age using a structured questionnaire measuring dance education participation, psychosocial mediators, and reproductive wellbeing. Data were analyzed with descriptive statistics, reliability analysis, correlation, regression, and mediation models. Results showed that higher levels of dance education participation were associated with greater reproductive wellbeing. Dance education also positively correlated with emotional regulation, body awareness, and social connection, and negatively with perceived stress. Mediation analysis confirmed that these psychosocial mechanisms partially explain the relationship between dance education and reproductive wellbeing. These findings position dance education as a cultural, gender-responsive, non-clinical intervention for women’s health promotion. The study provides empirical support for the proposed conceptual framework and aligns with Sustainable Development Goals 3 and 5, demonstrating how informal, community-based practices can support women’s reproductive wellbeing in China.

PMID:42202313 | DOI:10.29063/ajrh2026/v30i10.4

Int J Occup Saf Ergon. 2026 May 27:1-10. doi: 10.1080/10803548.2026.2655008. Online ahead of print.

ABSTRACT

Objectives. Empirical data on how different workspace clearance dimensions affect manual task outcomes and worker posture remain scarce, despite the acknowledged importance of spatial design in ergonomics. This study investigates the effects of horizontal and vertical clearances on cycle time, error count and postural behaviors. Methods. A within-subject repeated-measures design was employed, where participants (n = 12, mean age 23 ± 3.4 years) performed a simulated assembly task under varying horizontal and vertical clearance conditions. Results. Horizontal clearance has a statistically significant effect on cycle time (F = 36.15, p < 0.001, η² = 0.475), with reduced horizontal clearance leading to longer cycle times. In contrast, vertical clearance did not significantly influence cycle time. Analysis of error counts using a Poisson generalized linear model showed no significant impact of either horizontal or vertical clearance on assembly errors (p = 0.628), suggesting that error rates were not strongly affected by experimental parameters. Postural observations revealed that reduced horizontal clearance resulted in adoption of compensatory postures such as increased shoulder elevation and arm adduction, especially under limited vertical clearance. Conclusion. These findings highlight the importance of optimizing horizontal clearance (minimum of 450 mm) to improve efficiency while managing vertical clearance to reduce ergonomic risk in constrained workspaces.

PMID:42202309 | DOI:10.1080/10803548.2026.2655008

JMIR Form Res. 2026 May 27;10:e80769. doi: 10.2196/80769.

ABSTRACT

BACKGROUND: A prevalent and serious complication of diabetes mellitus is the development of diabetic foot ulcer (DFU). There is a need for effective solutions that help prevent DFU to support our increasingly stressed health care systems. The use of mobile health (mHealth) tools has been shown to improve awareness and effective self-care management skills in people at risk of developing diabetic foot ulceration.

OBJECTIVE: In this study, we aimed to investigate the perceived usefulness, engagement, and overall user experience of the Feetchecker app, a self-monitoring mHealth app for people at risk of DFU.

METHODS: A total of 24 patients (mean age 71, SD 8.6 years) with type 2 diabetes mellitus at risk of developing diabetic foot ulceration completed a 3-month evaluation period (70 recruited, 36 included, 12 dropped out) of a self-monitoring mobile app called Feetchecker app. A mixed methods approach was used to combine insights from app data with qualitative data from a pre- and postsurvey as well as interviews with patients and involved podiatrists. Data were analyzed using descriptive statistics and thematic analysis. We evaluated overall use of the app, patient engagement, and user experiences.

RESULTS: Patients who fully completed the study conducted 393 feetchecks. In total, 7 patients sent in 9 pictures; all 7 were called for follow-up by a podiatrist. Overall, patients had a positive experience with the app and perceived the Feetchecker app as a valuable tool to monitor their feet for potential risk factors of DFU. Ease of use in performing a feetcheck and sending the podiatrist a picture was described as an important feature. Three main types of engagement with the Feetchecker app emerged: continuous, frequent, and no to little engagement. These patterns highlight enablers for self-monitoring such as ease-of-use, easy access to a podiatrist, and social support, as well as barriers such as digital skills and sustained engagement. Podiatrists highlighted the benefits of having patients report potential issues quicker and the ability to monitor their patients remotely. Challenges remain in integrating the promotion of the Feetchecker app into their consultations.

CONCLUSIONS: The Feetchecker app supported patients in self-monitoring risk factors associated with DFU through routine checks and quick contact with a health care professional in case of a potential issue. Overall, patients described a positive user experience and considered the app helpful. While mHealth tools are not for everyone, user engagement for many patients was high and shows that such apps can offer support for people able to use them. Future research should focus on improving usability and engagement with the app as well as extend the way patients can communicate with health care professionals beyond a picture.

PMID:42202301 | DOI:10.2196/80769

Int J Prosthodont. 2026 May 27;39(3):387-396. doi: 10.11607/ijp.9281.

ABSTRACT

PURPOSE: To investigate the effects of build angle and position in the build platform on the dimensional accuracy of 3D-printed molar crowns using digital light processing technology (DLP).

MATERIALS AND METHODS: A mandibular right first molar crown was designed digitally and printed using DLP at nine standardized positions in the build platform at 90-, 120-, 135-, 150-, 180-, 210-, 225-, 240-, and 270-degree build angles. The experiment was repeated three times per build angle. Specimens were scanned with a TRIOS 4 scanner (3Shape). STL files of each specimen were compared to the original file using Geomagic Control X. Accuracy was evaluated by root mean square (RMS), 2D Compare, and simulated coordinate measuring machine (CMM) measurements. Results were analyzed using two-way and one-way ANOVA. Statistical significance was set at P < .05.

RESULTS: Build angle influenced the dimensional accuracy of DLP, with the lowest RMS values recorded at 210 degrees and C2 position. Crowns oriented toward 90 degrees (152 ± 46.6 μm) and 270 degrees (209 ± 25.7 μm) exhibited the greatest amount of deviation at mesial and distal internal axial surfaces and the greatest amount of deviation at external finish lines, which ranged from -81.9 to 79.9 μm.

CONCLUSIONS: Molar crowns can be placed in any position of the build platform of a DLP printer. However, crowns should be oriented at build angles that reduce the effects of resin pooling and minimize the number of layers at the finish line to maximize accuracy. A build angle of 210 degrees is recommended for optimal results.

PMID:42202298 | DOI:10.11607/ijp.9281

JMIR Med Inform. 2026 May 27;14:e80527. doi: 10.2196/80527.

ABSTRACT

BACKGROUND: The successful implementation of decision support systems promises to enhance high-quality care. However, the successful implementation of a clinical decision support system (CDSS) depends on user acceptance and adoption. A machine learning (ML)-based CDSS to assist primary care professionals treating urinary tract infections (UTIs) was implemented, and usability and usefulness were assessed through a questionnaire.

OBJECTIVE: This study aimed to assess the system’s usability by examining users’ experiences with the software. A secondary goal was to assess users’ attitudes toward evidence-based practice and innovation in health care.

METHODS: In collaboration with the Netherlands Institute for Health Services Research (NIVEL) and Leiden University Medical Center (LUMC), Pacmed Ltd developed the CDSS. The cohort was mostly recruited at the care group level; practices within participating care groups were required to participate. Health insurers partly funded the research. Practitioners participated in the implementation study for 4 months. A survey based on the Unified Theory of Acceptance and Use of Technology (UTAUT) was sent to 263 general practitioners and assistants shortly after the implementation period. Furthermore, usage data were analyzed.

RESULTS: Of the 34 participating practices that used the software, 30 (88%) submitted at least one survey response, with a mean of 2.23 responses per practice (SD 1.43). The CDSS was used throughout the pilot period, and 31 practices continued using the tool, with 9% dropping out during the first 8 weeks. Sixty-seven percent of respondents trusted the tool’s output, and 73% found it understandable how the algorithm came to predictions. Sixty-five percent of respondents indicated that the information provided was useful in addition to the available guidelines, and 52% agreed that it supported their decision-making. However, many respondents were uncertain whether the tool improved patient care (46%) or patient outcomes (66%). Forty-eight percent of respondents found the software easy to integrate into their clinical workflow.

CONCLUSIONS: The CDSS was perceived as trustworthy and easy to use. However, users were unable to determine whether the CDSS improved patient outcomes. In addition, the CDSS development could have benefited from including assistants as well as general practitioners more in the design phase of the software. Because assistants play an important role in UTI care, designing the software to better fit existing workflows may reduce the perceived time investment associated with using the tool. Finally, respondents reported strong motivation to contribute to further research in this field and indicated willingness to embrace change in health care delivery, which may also reflect selection bias in our sample.

PMID:42202295 | DOI:10.2196/80527

JMIR Med Inform. 2026 May 27;14:e84396. doi: 10.2196/84396.

ABSTRACT

BACKGROUND: Accurate preoperative prediction of renal tumor malignancy is critical for guiding decisions and reducing overtreatment, as a substantial proportion of renal masses prove benign. Although radiology assessments and structured electronic health record (EHR) data are routinely used, many tumor-specific descriptors remain embedded in free-text radiology reports and are underused due to extraction challenges.

OBJECTIVE: This study aimed to develop and evaluate a multimodal pipeline that integrates structured EHR variables with natural language processing features from computed tomography (CT) radiology reports, including large language model (LLM)-extracted abnormality characteristics and transformer-based report embeddings, to improve malignancy prediction.

METHODS: We conducted a retrospective cohort study using University of Florida Health Integrated Data Repository Observational Medical Outcomes Partnership-mapped EHR data from December 2011 to August 2024. Adults with renal tumors were included if they had longitudinal diagnostic documentation consistent with a renal mass and at least 1 preoperative renal CT report; final benign or malignant status served as the outcome. Structured features included demographics, comorbidities, medications, vital signs, and laboratory measurements. From the recent preindex CT report, an on-premises LLM isolated kidney-specific findings and extracted abnormality characteristics. Four locally deployed LLMs were evaluated against manual annotations of 500 reports. Kidney-specific text was encoded using pretrained biomedical transformer models, including radiology Bidirectional Encoder Representations from Transformers (BERT) variants. We evaluated unimodal baselines and multimodal early, middle, and late fusion strategies. Model development used 5-fold cross-validation within the 80% training partition; each fold-specific model was evaluated on the same independent 20% held-out test set, with performance reported as mean and SD across the 5 held-out test evaluations. The primary metric was area under the receiver operating characteristic curve (AUC).

RESULTS: The final cohort included 967 patients (n=712, 73.6% malignant). In extraction evaluation, Qwen2.5-32B achieved 88.3% overall accuracy with a 100% extraction success rate and was selected for downstream feature generation. Among unimodal models, the structured clinical variable model achieved an AUC of 0.758 (SD 0.012), kidney-specific text with radiology BERT achieved an AUC of 0.746 (SD 0.058), and abnormality characteristics alone achieved an AUC of 0.716 (SD 0.015). Multimodal fusion models achieved higher descriptive performance than unimodal models. Early fusion achieved the highest AUC (mean 0.813, SD 0.008), and F₁-score (mean 0.809, SD 0.030), while late fusion achieved an AUC of 0.805 (SD 0.016). Ablation and interpretability analyses suggested complementary predictive information from structured clinical variables and kidney-specific text embeddings.

CONCLUSIONS: Integrating unstructured radiology report text with structured EHR variables achieved higher mean predictive performance than unimodal approaches in descriptive comparisons. Multimodal fusion, particularly early fusion incorporating radiology BERT-derived kidney-specific text embeddings, achieved the strongest discrimination, suggesting potential value of natural language processing-enabled multimodal EHR pipelines for informing preoperative risk stratification.

PMID:42202288 | DOI:10.2196/84396

JMIR Aging. 2026 May 27;9:e82867. doi: 10.2196/82867.

ABSTRACT

BACKGROUND: Integrated Care for Older People (ICOPE), focused on monitoring and optimizing the intrinsic capacity (IC) of older adults, is a new model of geriatric care that is currently being accelerated globally. Digital health technologies are recommended for longitudinal IC monitoring to provide precise and timely interventions. However, little is known about the psychological intentions of engaging in digital monitoring of IC according to the profile heterogeneity of IC among older adults.

OBJECTIVE: This study aims to map a set of customized personas to capture the profiles of IC and match psychological intentions that support personalized digital IC monitoring.

METHODS: An explanatory sequential mixed methods study was conducted at 16 sites in Beijing, China. Older adults aged ≥60 years (n=481) were selected to complete the quantitative survey. Latent profile analysis, descriptive statistics, and logistic regression analyses were performed to cluster subgroups using Mplus (Muthén & Muthén) and SPSS (IBM Corp). A subsample of participants from each profile (n=25) was purposively sampled for semistructured interviews. An inductive-deductive content analysis was used to identify similar attributes and to affirm the personas gradually. A joint statistical and thematic visualization method was used to integrate the customized personas.

RESULTS: Three profiles of IC patterns emerged: “multisubdomain decline-IC imbalance group,” “multisubdomain moderate-sensory deficit group,” and “multisubdomain robust-whole balance group.” The distribution of latent profiles was influenced by age, education, monthly per capita household income, self-rated health, and number of chronic diseases, while positively impacting older adults’ functional ability. The following customized personas were captured regarding established themes: “affects my mood-anxious evader,” characterized by avoidance and anxiety, low digital interest, and perceived social isolation; “capitalize on what comes-accommodative adopter,” pragmatically oriented toward disease detection, with moderate digital openness but limited self-efficacy; and “more autonomy-active improver,” who exhibited proactive engagement, high digital literacy, and motivation rooted in self-management and social participation.

CONCLUSIONS: This study is the first to integrate latent profile analysis with customized qualitative personas to link the heterogeneity of IC with the psychological intentions underlying digital monitoring. The resulting personas model provides an actionable framework for tailoring digital IC monitoring strategies in community-based integrated care. The findings emphasize the need to align monitoring approaches with older adults’ IC characteristics, psychological readiness, digital literacy, and social support to enhance engagement in digital IC monitoring.

PMID:42202287 | DOI:10.2196/82867