Tag: pubmed

Niger J Physiol Sci. 2026 Jun 30;41(1):17-27.

ABSTRACT

Hypertension is a prevalent disease globally and its prevalence is rising in sub-Saharan Africa. Urban-rural differences in the prevalence of hypertension are under-reported in Nigeria. Though hypertension is known to be related to obesity, the nature of the relationship is still unclear. This study sought to interrogate these and thus enrolled a total of 1,755 subjects (59% females) from five states in the old Eastern Region of Nigeria. Standard protocols and definitions were used to take measurements and diagnose relevant disease states. Appropriate statistical tools were used for data analysis. The results indicate that prehypertension was found in 40.0% of males and 27.5% of females while hypertension was found in 19.1% of males and 18.6% of females. The prevalence of (pre)hypertension among urban and rural males were similar (19%). However, prehypertension was more prevalent among urban females (29.1% Vs 24.3%); while hypertension was more prevalent among rural females (21.9% Vs 17.1%). SBP was found to be positively and significantly correlated with weight, waist circumference, hip circumference, waist-to-height ratio and body mass index in the general population. When disaggregated based on blood pressure phenotypes, the correlations were found to be consistent mostly among normotensive subjects and among females. Among subjects with (pre)hypertension, the correlations varied in strength and direction depending on sex and measure of adiposity used. The urban-rural difference in the prevalence of hypertension and the relationship between hypertension and obesity are not linear.

PMID:42537033

JMIR Cardio. 2026 Jul 31;10:e92930. doi: 10.2196/92930.

ABSTRACT

Synthetic data offer significant potential for cardiology research by enabling data sharing, preserving privacy, and supporting machine learning model development. By generating artificial patient records that reflect real-world distributions, synthetic data can accelerate clinical research, improve model performance for rare cardiovascular conditions, and facilitate transnational collaborations that would otherwise be restricted by data-sharing barriers. Despite these advantages, the increasing use of synthetic data raises important ethical, regulatory, and methodological concerns that remain insufficiently addressed. Key challenges include assessing the validity and generalizability of synthetic datasets, understanding their limitations in representing complex and heterogeneous patient populations, and preventing the amplification of existing biases in cardiovascular care. Current regulatory frameworks, including the General Data Protection Regulation (GDPR) and Health Insurance Portability and Accountability Act (HIPAA), do not fully address emerging risks such as reidentification and data leakage, and there is no harmonized guidance to govern the use of synthetic data as stand-alone evidence for medical device evaluation or therapeutic research. In this viewpoint, we argue that responsible integration of synthetic data in cardiology requires, first, clear differentiation between synthetic data as a privacy-preserving distributional substitute and synthetic data as a counterfactual simulation tool, and, second, fit-for-purpose governance frameworks that pair rigorous utility and fidelity testing with explicit, adversary-aware privacy evaluation before synthetic cohorts are accepted as evidence in research or product evaluation. A prerequisite for that governance is conceptual clarity about what synthetic data are being used for. Synthetic data in health care serve 2 fundamentally distinct roles that carry entirely different validity requirements, failure modes, and regulatory implications, yet they are routinely conflated. The first role is as a privacy-preserving distributional substitute: the goal is statistical fidelity to the real data distribution, so that analyses of the synthetic dataset yield results equivalent to those of the original. The second role is as a tool for counterfactual simulation: the goal is to generate data that could not have been observed, such as rare conditions, hypothetical interventions, or extrapolations to new populations. These 2 roles are methodologically distinct. A dataset that accurately reflects real-world distributions may be inadequate for extrapolating findings to underrepresented subgroups. Conversely, a simulator optimized for novel scenario generation may systematically diverge from real-world distributions. This distinction informs every subsequent discussion of validity, bias, and regulation in this viewpoint and our proposed 4 concrete actions for the cardiology research community, including mandatory 3-layer (fidelity, utility, and privacy) validation, systematic subgroup reporting, explicit intended-use scoping, and domain-specific acceptability thresholds for synthetic data-based evidence.

PMID:42537019 | DOI:10.2196/92930

Int J Oral Maxillofac Implants. 2026 Jul 31;0(0):1-25. doi: 10.11607/jomi.11696. Online ahead of print.

ABSTRACT

PURPOSE: Healthy peri-implant soft tissues are essential for implant longevity. Low-level laser therapy (LLLT) has emerged as a promising additional approach for enhancing wound healing after implant surgery, but evidence remains inconsistent. This study evaluates the clinical, radiographic, and inflammatory effects of LLLT on peri-implant soft tissue healing, pain, inflammatory biomarkers, and marginal bone preservation following implant placement.

MATERIALS AND METHODS: This randomized, controlled, single-blinded trial included 272 adults requiring single-tooth implants. Participants were randomized to two equal groups, LLLT (n=136) or control (n=136). The intervention involved daily 810 nm diode laser irradiation for five days post-surgery. Outcomes assessed included pain (VAS), healing clinical indices, oral health-related quality of life (OHIP-14), marginal bone loss (MBL), and peri-implant crevicular fluid biomarkers. Statistical analysis was conducted using SPSS software with mixed-effects models adjusted for baseline imbalances.

RESULTS: Among 272 participants, baseline demographics and comorbidities were comparable (all p>0.23 and p≥0.658, respectively). LLLT significantly reduced pain scores across all follow-ups (0.35-1.21 points lower, partial η²=0.783) and improved healing indices, gingival inflammation, and bleeding scores compared to controls. The LLLT group demonstrated persistently lower peri-implant inflammatory markers (TNF-α) and higher angiogenic response (VEGF). Radiographic evaluation revealed less MBL in the LLLT group with 0.07-0.22 mm less than control after day 7. Additionally, patients reported improved oral-health-related quality of life.

CONCLUSION: LLLT enhances peri-implant wound healing, reduces pain and inflammation, and preserves marginal bone, suggesting its value as a minimally invasive adjunct to implant therapy.

PMID:42537018 | DOI:10.11607/jomi.11696

Int J Oral Maxillofac Implants. 2026 Jul 31;0(0):1-21. doi: 10.11607/jomi.11841. Online ahead of print.

ABSTRACT

PURPOSE: This study compared the effects of local and systemic calcitriol administration on bone regeneration in a xenograft-based rabbit maxillary sinus augmentation model.

MATERIALS AND METHODS: Eighteen New Zealand rabbits were randomly assigned to three groups (n = 6 per group at baseline): control (xenograft only), local calcitriol (xenograft hydrated with 0.1 µg/kg calcitriol per sinus), and systemic calcitriol (single intravenous dose of 0.2 µg/kg). After 10 weeks, histomorphological scoring, RT-qPCR quantification of VEGF and BMP-2, and serum 25(OH)D measurements were performed.

RESULTS: Osteoblast activity was significantly higher in the local calcitriol group than in the control group (p = 0.024). Ossification scores showed a numerical trend toward higher values in the local group but did not reach statistical significance (p = 0.061). VEGF and BMP-2 expression were approximately 17.2-fold and 29.3-fold higher in the local group compared with controls (Bonferroni-corrected p < 0.001 for both); the systemic group showed intermediate expression without reaching corrected significance. Serum 25(OH)D levels showed no significant between-group differences at any time point.

CONCLUSIONS: Local calcitriol delivery was associated with significantly greater osteoblast activity and VEGF and BMP-2 expression, along with a non-significant trend toward higher ossification scores. Local application via routine graft hydration may represent a practical, low-cost strategy to enhance bone regeneration without systemic exposure, warranting confirmation in larger clinical studies.

PMID:42537017 | DOI:10.11607/jomi.11841

J Med Internet Res. 2026 Jul 31;28:e93378. doi: 10.2196/93378.

ABSTRACT

BACKGROUND: Sleep apnea (SA) is a serious sleep disorder, and its diagnostic gold standard, polysomnography, is costly and time-consuming. Electroencephalogram (EEG) signals, due to their direct correlation with neural activity and ease of extraction, represent a promising tool. Despite increasing research on machine learning (ML) and deep learning for EEG-based SA detection, model performance has not been consistently evaluated.

OBJECTIVE: This systematic review evaluated the accuracy of ML in detecting SA from EEG data and provided an evidence base for further clinical application and future research.

METHODS: Following the PRISMA-DTA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses of Diagnostic Test Accuracy) and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 expanded checklists, we systematically searched PubMed, Embase, Web of Science, Cochrane Library (CENTRAL), Scopus, IEEE Xplore, and ClinicalTrials.gov databases from inception to April 2026. Studies evaluating the value of ML algorithms for detecting SA based only on EEG data were included. The Quality Assessment of Diagnostic Accuracy Studies-2 and Prediction Model Risk of Bias Assessment Tool for Artificial Intelligence tools were used to assess the risk of bias in each study. Statistical analysis was performed using the mada and metafor packages in R (version 4.6.0; R Foundation for Statistical Computing) and the Meta-DiSc (version 1.4; Hospital Ramón y Cajal) software. We used GRADE (Grading of Recommendations Assessment, Development and Evaluation) to evaluate the certainty of evidence.

RESULTS: A total of 27 retrospective studies were included. Segment-level analyses showed high diagnostic performance, with a pooled sensitivity of 0.90 (95% CI 0.85-0.94; 95% prediction interval 0.43-0.99) and specificity of 0.92 (95% CI 0.87-0.95; 95% prediction interval 0.46-0.99). The pooled area under the summary receiver operating characteristic curve was 0.95 (95% CI 0.92-0.99). Meta-regression identified EEG channel configuration, region, and validation strategy as significant sources of heterogeneity (P=.004, P=.003, and P=.046, respectively). Multichannel EEG, deep learning approaches, and hold-out validation strategies generally demonstrated better diagnostic performance. Only 2 studies evaluated patient-level diagnostic performance, which was summarized qualitatively.

CONCLUSIONS: To our knowledge, this is the first systematic review and meta-analysis specifically focused on the diagnostic accuracy of EEG-based ML models in the detection of SA. This meta-analysis indicates that ML models based on EEG demonstrate good diagnostic accuracy in detecting SA at the segment level and show promise as tools for SA screening and clinical decision support. However, most current studies are retrospective segment-level analyses, which may overestimate the practical value of this technology in real-world clinical settings. To reliably integrate EEG-based ML models into clinical diagnostic workflows, further prospective studies incorporating full-night monitoring and patient-level validation are needed.

PMID:42537009 | DOI:10.2196/93378

J Med Entomol. 2026 Jul 1;63(4):tjag127. doi: 10.1093/jme/tjag127.

ABSTRACT

Tunga penetrans (Linnaeus, 1758) (Siphonaptera: Tungidae) is an ectoparasitic flea whose adult females cause tungiasis by embedding into the host’s skin. Environmental, social and financial challenges in tungiasis endemic areas have left this disease neglected and lacking locally available control measures. One possible approach for controlling T. penetrans targets its off-host stages; however, surveillance tools for adult fleas are still lacking. Moreover, host-seeking cues and behaviour of adult T. penetrans remain largely unknown. In this study, we show that visual cues are attractive to adult T. penetrans. Newly emerged T. penetrans responded to light stimuli across a broad spectrum, ranging from 375 nm to 690 nm. Within this range, significantly stronger attraction was observed at 430 nm over 375 nm, at 490 nm over 610 nm, and at 610 nm over 690 nm. Attraction did not differ significantly between 430 nm and 490 nm or between 535 nm and 490 nm. Overall, wavelengths between 430-535 nm induced stronger attraction in both sexes under constant electrical current conditions. In contrast, although host odor and body temperature attracted more than half of the tested adult fleas on average, the observed responses were not statistically significant. These findings suggest that visual cues in this spectral range could serve as promising attractants for capturing adult fleas. Incorporating these insights into trap strategies could support improved monitoring, surveillance, and potentially contribute to control efforts.

PMID:42537008 | DOI:10.1093/jme/tjag127

Arch Clin Neuropsychol. 2026 Jul 31;41(6):acag057. doi: 10.1093/arclin/acag057.

ABSTRACT

OBJECTIVE: This cross-sectional study investigated the prevalence of adult Attention Deficit/Hyperactivity Disorder (ADHD) evaluations in an outpatient neuropsychology clinic and the sensitivity and specificity of neuropsychological assessments in differentiating ADHD with comorbid psychiatric conditions from psychiatric-related cognitive deficits.

METHOD: Logistic regression analyses were run on neuropsychological testing results extracted from medical records to evaluate each test’s ability to predict ADHD diagnosis. Receiver Operating Characteristic curves were plotted, and an Area Under the Curve (AUC) >0.7 was considered acceptable.

RESULTS: Sixty-seven of 252 reviewed charts were included (n = 30 with ADHD, n = 37 without ADHD). Overall, 54% of individuals presenting to the clinic were referred for ADHD, and 58% of those diagnosed with ADHD had a comorbid psychiatric disorder. No cognitive testing was predictive of ADHD diagnosis (AUC range: 0.503-0.636). The Generalized Anxiety Disorder-7 was the strongest predictive scale of ADHD (AUC = 0.800; χ2(28) = 8.216, p = .004, Nagelkerke R2 = 0.333). Adults who endorsed higher levels of anxiety were 28% more likely to be diagnosed with ADHD (OR = 1.28, 95%CI [0.05, 0.45]). Subtests of ADHD scales performed worse, but still within the adequate range (AUC range: 0.703-0.751). Sample demographics were notable for over-representation of women (n = 46, 69%), and they skewed White (n = 43, 64%) and highly educated (M years of education = 15.79, SD = 1.94), which may limit generalizability.

CONCLUSIONS: Cognitive testing profiles and screeners of current inattention and hyperactivity were indiscriminate between individuals with ADHD and comorbid psychiatric conditions versus psychiatric conditions alone. Current anxiety was the strongest predictor of adult ADHD diagnosis.

PMID:42537007 | DOI:10.1093/arclin/acag057

Brief Bioinform. 2026 May 4;27(4):bbag406. doi: 10.1093/bib/bbag406.

ABSTRACT

The European Union (EU) enforces strict regulations on the traceability and labeling of genetically modified organisms (GMOs), including genome-edited (GE) lines produced through new genomic techniques (NGTs). Identifying GE organisms created by single nucleotide variations (SNVs) is however challenging, as a single SNV alone cannot unambiguously define a GE line. Recently, we introduced the concept of generating a genetic fingerprint to distinguish a specific GE rice line. This proof-of-concept approach integrated whole-genome sequencing (WGS)-based characterization with the Illumina technology, the public 3 K Rice Genomes (3KRG) database, and statistical feature-selection tools, to select and combine key genetic elements, including GE on-target site(s) and cultivar-specific 2-SNV barcodes, into a unique genetic fingerprint. In the present study, we expand this concept into a generalized data-driven framework allowing identification of multiple rice lines. Supported by newly developed bioinformatics and statistical feature-selection-based pipelines, this optimized strategy enables the generation of genetic fingerprints irrespective of a rice cultivar’s inclusion in publicly available databases like 3KRG. In addition, this refined strategy can leverage WGS data generated from both Illumina and Oxford Nanopore Technologies (ONT) platforms for fingerprint generation and GE line identification. Using two distinct in-house GE rice lines from different cultivars, along with various publicly available WGS datasets, we demonstrated the robustness, scalability, and specificity of this approach for reliable GE rice line identification. Our findings provide a methodological foundation for data-driven traceability of GE rice lines, reinforcing regulatory compliance, supporting intellectual property (IP) protection, and contributing to the responsible implementation of EU GMO/NGT legislation.

PMID:42537001 | DOI:10.1093/bib/bbag406

Ultrasound Obstet Gynecol. 2026 Jul 31. doi: 10.1002/uog.70289. Online ahead of print.

ABSTRACT

OBJECTIVE: To investigate, using transvaginal ultrasound imaging, the overlap between polycystic ovarian morphology (PCOM) and endometriosis, and to explore demographic and clinical factors associated with their coexistence across endometriosis phenotypes.

METHODS: This was a retrospective cohort study of consecutive patients undergoing advanced transvaginal ultrasound examination for suspected or previously diagnosed endometriosis at a tertiary gynecological ultrasound clinic between February 2023 and June 2023. Ultrasound was performed following the International Deep Endometriosis Analysis consensus and the International Ovarian Tumor Analysis framework guided lesion characterization, with PCOM defined as per the 2018 and the modified 2023 International Polycystic Ovary Syndrome Guideline (≥ 20 follicles measuring 2-9 mm in diameter and/or ovarian volume > 10 mL in the absence of a dominant follicle, cyst or corpus luteum). Endometriosis phenotypes were classified sonographically as superficial (SE), ovarian (OE) or deep (DE) endometriosis. Demographic and clinical variables were compared between participants with endometriosis alone and those with concurrent endometriosis and PCOM. Logistic regression analysis was performed within the endometriosis-positive subgroup using PCOM as the dependent variable in a univariable model including age and a multivariable model including age and body mass index (BMI). An age-restricted (25-35 years) sensitivity analysis compared the frequencies of any endometriosis and of individual endometriosis phenotypes between participants with and those without PCOM. A secondary analysis within this subgroup compared clinical and demographic characteristics between endometriosis participants with and those without concurrent PCOM.

RESULTS: Among 165 included patients, 62.4% (n = 103) were diagnosed with endometriosis, 37.0% (n = 61) demonstrated PCOM and 35.0% (36/103) of those with endometriosis had concurrent PCOM. In the whole cohort, the frequency of any endometriosis did not differ significantly between participants with and those without PCOM. DE was less frequent among those with PCOM, whereas differences in the frequency of OE and SE between participants with and those without PCOM were not statistically significant. Compared to individuals with endometriosis alone, those with concurrent PCOM were significantly younger and had lower body weight. The proportions of nulligravidae and of nulliparae were also higher. In age-restricted analyses limited to participants aged 25-35 years, no significant differences were observed between those with and those without PCOM for the presence of any endometriosis or for any individual endometriosis phenotype. However, among endometriosis-positive participants, increasing age was associated with lower odds of concurrent PCOM in both the univariable model including age (odds ratio (OR), 0.83 (95% CI, 0.77-0.90) per 1-year increase; P < 0.001) and the multivariable model including age and BMI (OR, 0.84 (95% CI, 0.77-0.92) per 1-year increase; P < 0.001), whereas BMI was not independently associated with PCOM (OR, 0.99 (95% CI, 0.91-1.09) per 1 kg/m² increase; P = 0.874). In the sensitivity analysis restricted to participants aged 25-35 years, the differences observed previously in age, weight, gravidity and parity were no longer statistically significant.

CONCLUSIONS: Endometriosis and PCOM can coexist on ultrasound in a tertiary referral cohort. However, the observed between-group differences were strongly influenced by age, and our findings should be interpreted as exploratory and hypothesis-generating rather than confirmatory. Prospective studies incorporating endocrine characterization, standardized cycle-phase assessment and side-specific ovarian assessment are needed to clarify the clinical and biological significance of this coexistence.

PMID:42536999 | DOI:10.1002/uog.70289

JMIR Med Inform. 2026 Jul 31;14:e84260. doi: 10.2196/84260.

ABSTRACT

BACKGROUND: Postoperative nausea and vomiting are common complications after anesthesia. However, vomiting represents a clinically distinct and objectively measurable endpoint.

OBJECTIVE: This study aimed to develop and internally validate predictive models for postoperative vomiting within 24 hours using structured perioperative data and unstructured clinical text, while introducing a structured framework that separates feature construction from interpretability using large language models (LLMs).

METHODS: We analyzed 33,460 anesthesia records from a single center (2019-2022). Two temporally defined prediction tasks were constructed to reflect real-world clinical decision-making and prevent information leakage: a preoperative model using variables available before anesthesia induction, and a perioperative model using variables available up to the end of surgery. Structured data were modeled using machine learning algorithms (logistic regression, Extreme Gradient Boosting, Light Gradient Boosting Machine [LightGBM]). Unstructured clinical text was incorporated through a deterministic, concept-driven preprocessing pipeline, where LLMs were used solely for normalization (temperature=0) without feature generation, followed by rule-based concept mapping and feature encoding. Post hoc interpretability was further supported using an LLM-based Question Answering Chain module. Model performance was evaluated using receiver operating characteristic-area under the curve (AUC), precision-recall AUC, calibration metrics, and threshold-based operating characteristics. Classification thresholds were selected using the Youden J statistic, and all metrics were reported with 95% CIs derived from bootstrap resampling. Decision curve analysis was performed to assess clinical utility.

RESULTS: A total of 33,460 surgical procedures were included, of which 3607 (10.8%) experienced postoperative vomiting within 24 hours. In the preoperative task, LightGBM achieved an AUC of 0.729 (95% CI 0.706-0.749), compared with 0.610 (95% CI 0.588-0.632) for the Apfel score. In the end-of-surgery task, LightGBM achieved an AUC of 0.735 (95% CI 0.714-0.757). At the Youden-optimal threshold, the negative predictive value exceeded 0.95 across all models. Decision curve analysis demonstrated positive net benefit across clinically relevant threshold probabilities. Incorporating text-derived features provided modest improvements, while LLM-based explanation modules generated structured, natural-language explanations intended to enhance interpretability without substantially improving predictive performance.

CONCLUSIONS: Machine learning models can effectively predict postoperative vomiting within 24 hours using perioperative data. The proposed framework demonstrates that LLMs can be integrated in a controlled and reproducible manner-restricted to deterministic normalization and post hoc reasoning-to generate natural-language explanations intended to enhance the interpretability of model predictions, without introducing information leakage or altering predictive modeling. As no formal clinician-based evaluation was conducted, this interpretability benefit cannot yet be objectively confirmed, and the generated explanations should be regarded as a useful interpretability aid to be validated in future clinician-centered studies. External, multicenter validation is required before broader clinical applicability can be assumed.

PMID:42536998 | DOI:10.2196/84260