Tag: nevin manimala

Circ Genom Precis Med. 2026 Jul 9:e005666. doi: 10.1161/CIRCGEN.125.005666. Online ahead of print.

ABSTRACT

BACKGROUND: Genetic differences may contribute to interindividual variability in LDL-C (low-density lipoprotein cholesterol) lowering with statin therapy. Polygenic risk scores may help identify individuals unlikely to achieve guideline-concordant LDL-C targets on statins, enabling earlier therapy intensification.

METHODS: We developed a multiancestry polygenic risk score for statin nongoal response (PRS-NGR) and evaluated its association with failure to achieve an on-statin LDL-C level of ≤70 mg/dL and with percent LDL-C reduction. This longitudinal cohort study used genotyping and electronic health record-linked data from the All of Us Research Program (2018-2025), the UK Biobank (2014-2023), and the Biobank Japan (2003-2008). Participants were statin users with at least 1 prestatin and 1 on-statin LDL-C measurement. Associations were assessed overall and by genetic ancestry, with replication in the UK Biobank and Biobank Japan.

RESULTS: The study included 46 564 participants from All of Us, 37 009 from the UK Biobank, and 3613 from Biobank Japan. In All of Us, higher PRS-NGR was associated with increased odds of nongoal response (odds ratio per SD, 1.43 [95% CI, 1.37-1.49]). Compared with the middle quintile, individuals in the top 1% had a higher risk, whereas those in the bottom 1% had a lower risk of nongoal response. Associations of PRS-NGR were consistent across African, European, and Latin American ancestry groups. Each SD increase in PRS-NGR corresponded to a 1.2-percentage-point smaller LDL-C reduction. Findings were replicated in the UK Biobank (odds ratio per SD, 2.39 [95% CI, 2.23-2.57]) and in Biobank Japan (odds ratio per SD, 1.31 [95% CI, 1.17-1.46]). Integration of PRS-NGR with guideline-based criteria identified individuals who derived a higher LDL-C% change and increased identification of statin-eligible individuals.

CONCLUSIONS: We developed and validated a multiancestry polygenic risk score that estimates the risk of nongoal LDL-C response to statin therapy. Incorporation of polygenic risk into lipid-lowering treatment paradigms may improve risk stratification and support more tailored therapy intensification strategies.

PMID:42422963 | DOI:10.1161/CIRCGEN.125.005666

J Med Eng Technol. 2026 Jul 9:1-16. doi: 10.1080/03091902.2026.2698512. Online ahead of print.

ABSTRACT

Reliable differentiation between cancer and non-cancer states using minimally invasive approaches remains a significant challenge. Most currently available biomarkers are cancer-type specific and may not capture systemic alterations associated with malignancy. This study evaluates the feasibility of microwave-assisted thermal profiling of blood as a method to distinguish cancer from non-cancer conditions. A cross-sectional analytical study was conducted involving 232 participants (87 cancer patients and 145 non-cancer individuals). A custom-built device was used to perform microwave heating of 0.5 mL venous blood samples. Temperature rise patterns were recorded using an infra-red thermal imager. Key parameters, including the time taken to reach the maximum temperature and maximum gradient time, temperature increase and rate of temperature increase, were extracted and statistically analysed. Significant differences in thermal profiles between cancer and non-cancer groups were observed. Cancer patients exhibited a higher Gradient time ratio (GTR) and lower Z_blood value, which indicates an alteration in thermal profile of blood due to cancer. The non-probabilistic prediction rule achieved 96.10 accuracy, with a sensitivity of 96.54 and specificity of 95.86. The microwave-based thermal profiling method demonstrated high diagnostic accuracy and has the potential to serve as a reliable cancer biomarker. Further validation in larger cohorts is required.

PMID:42422931 | DOI:10.1080/03091902.2026.2698512

Stat Med. 2026 Jul;45(15-17):e70649. doi: 10.1002/sim.70649.

ABSTRACT

Panel count data occurs in a wide variety of applications, ranging from biomedical research to business, such as the number of accidents, product defects, and insurance claims. For such data under the FDA investigation, millions of reported adverse events (AEs) associated with thousands of drugs are monitored in the post-market drug safety surveillance systems worldwide. Evaluating the AEs of the associated drugs is an important public health concern and motivates our method. One statistical challenge in such systems is handling the excessive number of zero AE counts. Most existing methods utilize Poisson count models that cannot incorporate covariates nor account for the excessive zero counts adequately. This article proposes a novel semiparametric nonhomogeneous panel count model to detect AE signals by accounting for covariates, background AE occurrences, and excessive zero counts. The model is estimated using the Expectation-Maximization (EM) algorithm iteratively, where in each M-step, the maximization of the nonparametric component is reformulated as an optimization problem, as in the isotonic regression. The strong consistency and the asymptotic distributions of the estimators are formally derived. We conduct extensive simulation studies to evaluate the finite sample performance of the proposed method and to demonstrate the apparent advantage of the proposed method in signal detection with high power, high specificity, and sensitivity. We apply the method to a VigiBase dataset to detect the AE signals as an application of the proposed method.

PMID:42422929 | DOI:10.1002/sim.70649

J Cachexia Sarcopenia Muscle. 2026 Aug;17(4):e70342. doi: 10.1002/jcsm.70342.

ABSTRACT

BACKGROUND: Venous thromboembolism (VTE) is a major acute cardiovascular condition with high mortality, affecting nearly 10 million individuals worldwide each year. Identifying novel and modifiable risk factors is crucial for advancing prevention strategies. Intramuscular fat infiltration (IMFI), a modifiable condition linked to inflammation and muscle weakness, both established contributors to VTE risk, has not been previously studied in relation to incident VTE. We aimed to examine the association between thigh IMFI and incident VTE, including pulmonary embolism (PE) and deep vein thrombosis (DVT).

METHODS: This population-based cohort study included 24 529 UK Biobank participants with baseline IMFI assessed using magnetic resonance imaging of the thigh muscles. The primary outcome was incident VTE. Secondary outcomes included incident PE and DVT. Cox proportional hazards models estimated hazard ratios (HRs) and 95% confidence intervals (CIs) for the associations of IMFI with incident VTE, PE and DVT, adjusting for potential confounders. Dose-response relationships were evaluated using restricted cubic spline regression models.

RESULTS: Over a mean follow-up of 4.92 years, 227 participants developed incident VTE. The incidence rates across increasing age- and sex-specific anterior thigh IMFI quartiles were 1.34, 1.28, 1.58 and 3.34 per 1000 person-years, respectively. Compared with the lowest anterior thigh IMFI quartile, adjusted HRs for incident VTE were 0.88 (95% CI: 0.57, 1.37), 1.02 (95% CI: 0.67, 1.55) and 1.88 (95% CI: 1.26, 2.80) for the second, third and fourth quartiles, respectively. Similar associations were observed for incident PE and DVT. Restricted spline regression models revealed that VTE risk increased progressively across higher IMFI levels. PE and DVT risk showed similar patterns. Analyses using posterior thigh IMFI showed generally consistent associations with incident VTE, PE and DVT.

CONCLUSIONS: Elevated thigh IMFI was associated with higher risks of VTE, PE and DVT. These findings identify thigh IMFI as a potential modifiable risk factor for VTE and support further investigation of strategies targeting muscle fat infiltration for thrombotic disease prevention.

PMID:42422920 | DOI:10.1002/jcsm.70342

Trends Hear. 2026 Jan-Dec;30:23312165261465030. doi: 10.1177/23312165261465030. Epub 2026 Jul 9.

ABSTRACT

The dynamic interplay between source-specific spectral features and spatial cues is central to auditory inference. While sagittal-plane localization relies on direction-dependent spectral cues shaped by the listener’s anatomy, sound sources themselves introduce spectral patterns that can obscure these cues, creating an ill-posed inference problem. We tested whether listeners can mitigate that problem by statistically learning a source’s spectral shape over the short term. In a free-field localization task, participants localized ripple-spectrum sounds under two conditions: within a block, source spectra were either fixed (predictable) or randomized (unpredictable). Predictability reduced large-scale localization errors – such as front-back reversals and quadrant confusions – by up to 5% within minutes. These findings demonstrate that listeners exploit spectral consistency across stimulus history to adapt spatial decoding, providing empirical evidence for short-term updating of spectral priors and underscoring the adaptive nature of auditory inference.

PMID:42422899 | DOI:10.1177/23312165261465030

Med Pr. 2026 Jul 3:224513. doi: 10.13075/mp.5893.01657. Online ahead of print.

ABSTRACT

BACKGROUND: Contemporary healthcare facility management operates under conditions of increasing instability, requiring high leadership competencies from executives. Crises, such as the COVID-19 pandemic, have underscored the key importance of skills in coping with pressure and stress, as well as efficient work reorganization (adaptive capabilities). The study utilized the Healthcare Leadership Alliance (HLA) model to assess these competencies.

MATERIAL AND METHODS: A computer-assisted web interview survey was conducted among 71 representatives of healthcare management staff in Poland. An original questionnaire based on the 5 domains of the HLA model was used. Statistical analyses were performed using Fisher’s exact test and Spearman’s rank correlation.

RESULTS: In the self-assessment according to the Dreyfus model, the competent level prevailed (nearly 50%), and team management was the highest-rated area. Significant deficits were identified regarding the reorganization of one’s own and the team’s work (difficulties reported by 1/3 of respondents) and the identification of stress in employees (deficiencies perceived by 25% of managers). Women more frequently indicated the mobilizing effect of stress (p = 0.048), while men rated their active listening skills higher (p = 0.02). Higher self-assessment correlated with better internal communication (p = 0.006) and operational continuity planning (p = 0.002). Contract-based managers were significantly more capable of planning business continuity (p = 0.009). Data-driven management is significantly more frequent in inpatient care than in outpatient care (p = 0.03).

CONCLUSIONS: The identified deficits in the ability to cope with stress and data analytics limit managers; ability to adapt effectively to the volatile and unpredictable environment described by the volatility, uncertainty, complexity, ambiguity – VUCA and brittle, anxious, non-linear, incomprehensible – BANI concepts. It is necessary to modify management training programs by integrating psychological knowledge (coping with pressure) with the practical application of data in management, which will enable the building of sustainable organizational resilience in healthcare facilities. Med Pr Work Health Saf. 2026;77(3).

PMID:42422888 | DOI:10.13075/mp.5893.01657

Front Mol Biosci. 2026 Jun 24;13:1878152. doi: 10.3389/fmolb.2026.1878152. eCollection 2026.

ABSTRACT

BACKGROUND: Osimertinib is an approved first-line therapy for epidermal growth factor receptor (EGFR)-mutated non-small cell lung cancer (NSCLC). However, beyond the identification of common EGFR mutations, additional pathological or molecular factors that predict treatment response, survival outcomes, or toxicity remain limited.

MATERIALS AND METHODS: This retrospective study analyzed data from a registry of NSCLC patients with EGFR mutations treated with first-line osimertinib between March 2017 and December 2024. Variant allele frequency (VAF) was evaluated as a potential predictive factor for overall survival (OS), progression-free survival (PFS), and adverse events (AEs).

RESULTS: Among 147 eligible patients, the mean OS was 25.5 months and the mean PFS was 21.4 months. Patients with VAF ≥30% exhibited improved outcomes compared to those with VAF <30%, with mean OS of 31.4 months versus 19.7 months (p = 0.022), and mean PFS of 25.0 months versus 18.2 months (p = 0.234). Similar trends were observed across EGFR exon 19 deletion and exon 21 L858R subgroups (p = 0.056). When comparing toxicity profiles, the overall AE rates were similar between high-VAF and low-VAF patients. However, several statistically significant differences were noted: diarrhea (21.3% vs. 5.7%, p = 0.005) and dyspnea (16.4% vs. 3.4%, p = 0.0085) were more frequent in the high-VAF group, while anemia (9.2% vs. 3.3%, p = 0.03) and creatinine elevation (5.7% vs. 1.6%, p = 0.01) occurred more commonly in the low-VAF group.

CONCLUSION: Higher EGFR VAF was significantly associated with improved overall survival in patients with EGFR-mutated NSCLC treated with first-line osimertinib and showed a numerical trend toward longer progression-free survival. Similar patterns were observed across key molecular subgroups. Additionally, this study is the first to report potential VAF-related differences in adverse event patterns, suggesting that VAF may have relevance not only for efficacy but also for toxicity characterization. These findings support the potential role of VAF as a prognostic biomarker in EGFR-mutant NSCLC and warrant further prospective validation.

PMID:42422880 | PMC:PMC13342395 | DOI:10.3389/fmolb.2026.1878152

Atmos Pollut Res. 2026 May;17(5):102904. doi: 10.1016/j.apr.2026.102904. Epub 2026 Apr 19.

ABSTRACT

The United States Environmental Protection Agency (USEPA) established regulatory frameworks (Pre-Tier 2, Tier 2, and Tier 3) to address vehicle emissions. Simultaneously, a technological shift from port fuel injection (PFI) to gasoline direct injection (GDI) engines (began in 2007), has been underway. GDI has greater fuel efficiency, but potentially produces more secondary organic aerosol (SOA) under Tier 2 emissions than Tier 3. This study examined light-duty vehicle fleet transitions in New York State from 2013-2025, specifically the shift between engine technology and fleet turnover across emission tiers. Registration data from the New York State Department of Motor Vehicles were analyzed and vehicles classified into Pre-Tier 2, Tier 2, and Tier 3 based on Model Year, Vehicle Identification Number (VIN), and regulatory phase-in schedules. Manufacturer-reported statistics and VIN were used to categorize vehicles. New vehicles sold after 2007 had to meet Tier 2 standards, but only 53% of the total fleet were Tier 2 vehicles in 2013 suggesting lag time in fleet-wide penetration. Similarly, penetration of Tier 3 vehicles (introduced in 2017) was slower than expected. By 2025 only 36% of the fleet consisted of Tier 3 vehicles, and ~6% were plug-in electric or hybrid vehicles. Meanwhile, GDI technology adoption increased rapidly and grew from 5% in 2013 to 36% in 2025. The slow fleet turnover highlights substantial lag between regulatory implementation and fleet composition changes. This lag resulted in continuing higher emissions and SOA formation indicating that air quality benefits from Tier 3 implementation will take longer to be realized.

PMID:42422862 | PMC:PMC13345421 | DOI:10.1016/j.apr.2026.102904

Front Med (Lausanne). 2026 Jun 24;13:1884234. doi: 10.3389/fmed.2026.1884234. eCollection 2026.

ABSTRACT

BACKGROUND: Social media has become an important component of healthcare practice, offering opportunities for communication, education, and professional engagement while introducing risks related to patient confidentiality, professional conduct, and institutional reputation. Evidence on social media practices and e-professionalism among dental practitioners in Saudi Arabia remains limited.

OBJECTIVE: This study aimed to assess social media practices, risk perceptions, and policy awareness among dental practitioners and trainees in Saudi Arabia and to identify factors associated with variations in awareness and behavior.

METHODS: A cross-sectional online survey was conducted among 326 dental practitioners and trainees across Saudi Arabia. The questionnaire assessed social media use, risk perception using five Likert-scale items, and awareness of institutional and international guidelines. Responses were measured using a 5-point Likert agreement scale. The risk perception scale demonstrated acceptable internal consistency (Cronbach’s α = 0.73). Data were analyzed using descriptive statistics, chi-square tests, and binary logistic regression.

RESULTS: Instagram was the most commonly used platform (70.6%), and one-third of participants reported daily professional use. Risk perception was generally low, with a mean composite score of 2.88 (SD = 0.44); only 2.5% of participants had high risk perception. Awareness of guidelines was limited, with 38.0% reporting awareness of WHO guidelines, 25.2% UNESCO guidelines, and 35.9% Ministry of Health policies. Only 20.6% reported having a workplace social media policy, and 4.6% had received formal training. Although age and years of professional experience were associated with policy awareness in bivariate analyses (p < 0.001), only years of professional experience remained independently associated with policy awareness after adjustment. Practitioners with ≥5 years of experience were significantly less likely to have low policy awareness (AOR = 0.41; 95% CI, 0.22-0.76; p = 0.004).

CONCLUSION: Social media use is widespread among dental practitioners in Saudi Arabia; however, awareness of formal guidelines and institutional policies remains limited. Greater professional experience was associated with higher policy awareness, suggesting that early-career practitioners may benefit from targeted training. Strengthening education on e-professionalism and implementing clear institutional policies are essential to support the safe and ethical use of social media in dental practice.

PMID:42422858 | PMC:PMC13341825 | DOI:10.3389/fmed.2026.1884234

Front Med (Lausanne). 2026 Jun 24;13:1830356. doi: 10.3389/fmed.2026.1830356. eCollection 2026.

ABSTRACT

BACKGROUND: Advances in digital health technologies have transformed how individuals with chronic diseases seek and use health information. Patients increasingly rely on online sources, including search engines, social media, and messaging applications, to understand symptoms and manage chronic conditions. However, these digital environments can also expose users to misinformation or conflicting advice. Artificial intelligence (AI) enabled tools and mobile health (mHealth) services have emerged to assist patients in identifying symptoms, verifying health information, and supporting timely health decisions. Despite these developments, limited conceptual work has examined how individuals with chronic diseases integrate such tools into their health information-seeking and decision-making processes.

OBJECTIVE: This study aimed to develop and empirically illustrate a model explaining how individuals with chronic diseases seek and verify digital health information using AI-enabled tools and how these processes influence trust and health-related decision-making.

METHODS: A cross-sectional survey was conducted among adults aged ≥ 18 years diagnosed with diabetes or hypertension. Participants were recruited through chronic disease support groups on Facebook and WhatsApp. The survey assessed digital health information-seeking behavior, verification practices, trust in AI-enabled physician chatbots and national mHealth services, and their role in health-related decision-making. Descriptive statistics and visualization analyses were conducted using R.

RESULTS: Health information seeking occurred across multiple digital platforms, with considerable overlap between messaging applications, social media, and web-based sources. Participants reported using AI physician chatbots and national mHealth services mainly to verify health information encountered online. Trust in AI diagnostic support tools was moderate, indicating cautious but active engagement. Most participants used these tools to support clinical consultations rather than replace professional medical advice.

CONCLUSION: Verification behavior and trust play key roles in how individuals with chronic diseases engage with digital health information. AI-enabled mHealth tools may function as complementary decision-support resources that help patients verify information and interpret symptoms while supporting informed health decisions alongside traditional healthcare services.

PMID:42422856 | PMC:PMC13341845 | DOI:10.3389/fmed.2026.1830356