Children

This cross-sectional study evaluated the association of tooth loss and oral health-related quality of life (OHRQoL) with sexual function in adult women attending a primary dental care service. Methodology: Ninety-nine sexually active women aged 19-66 years were consecutively recruited from a primary dental care service between January and October 2023. Tooth loss was quantified by standardized oral examination. OHRQoL was assessed using the Oral Health Impact Profile-14 (OHIP-14), and sexual function was assessed using the Female Sexual Function Index (FSFI). Sexual dysfunction was defined as FSFI <=26.5. Spearman rank correlation was used for bivariate analyses. Multivariable logistic regression was used to evaluate factors associated with sexual dysfunction, including number of missing teeth, OHIP-14 score, age, and relationship status. Results: Tooth loss was present in 83.8% of participants, with a median of 4 missing teeth (interquartile range [IQR], 1-10). Sexual dysfunction was identified in 62.6% of women. FSFI scores were negatively correlated with number of missing teeth (rho = -0.407; p < 0.001), OHIP-14 score (rho = -0.279; p = 0.005), and age (rho = -0.334; p < 0.001). In multivariable logistic regression, OHIP-14 score was independently associated with sexual dysfunction (OR = 1.05; 95% CI, 1.01-1.10; p = 0.015), whereas number of missing teeth was not independently associated after adjustment. Conclusion: Worse OHRQoL was independently associated with sexual dysfunction, whereas tooth loss was associated with lower FSFI scores only in bivariate analysis. These findings are compatible with the hypothesis that the impact of tooth loss on sexual function may be partly explained by oral health-related quality of life, but longitudinal studies are required to test causal and mediational pathways. Keywords: tooth loss; oral health; quality of life; sexual dysfunction, physiological; women; cross-sectional studies

Background: Automatic tube compensation (ATC) was designed to compensate for the additional resistive load imposed by the endotracheal tube during spontaneous breathing. In ATC mode, the ventilator adds or subtracts the flow-dependent pressure drop across the tube during both inspiration and expiration so that tracheal pressure remains close to PEEP. Early prototype ventilators achieved true tracheal-pressure control and showed physiological and clinical benefits, but clinical studies with commercial systems have failed to confirm these earlier findings. A 2003 bench study found that commercial ventilators provided, at best, only partial tube compensation, unlikely to result in meaningful clinical benefit. We therefore tested whether this limitation has been remedied in contemporary ICU ventilators. Methods: We performed a bench comparison of five commercial ICU ventilators and an ATC prototype ventilator designed to accurately compensate for the flow-dependent resistance over a wide range of flow rates. An active lung simulator generated spontaneous breathing patterns with weak, moderate, and strong inspiratory efforts at different PEEP levels. We tested each breathing pattern through endotracheal tubes with inner diameters of 7 and 8 mm, and measured airway pressure, tracheal pressure, and flow during CPAP with and without ATC. Breathing through the tube against open atmosphere served as a zero-PEEP/T-piece reference. Results: In CPAP mode, the commercial ventilators showed flow-dependent airway-pressure deviations, amounting to a substantial added resistance of 1.5 - 6.5 mbar/(L/s), whereas the ATC prototype ventilator imposed an added resistance of only 0.6 mbar/(L/s). In ATC mode, the commercial ventilators reduced the resistive load by no more than by 25%, and large tracheal-pressure deviations remained, especially at higher inspiratory effort and during expiration. In some cases, the residual load during ATC was even greater than the load during unsupported breathing through the tube. By contrast, the ATC prototype ventilator maintained tracheal pressure close to PEEP throughout the breathing cycle and eliminated on average 79% of the tube-related resistive load. Conclusions: In the commercial ventilators evaluated in this study, the defining physiological objective of ATC was only partially achieved. Therefore, clinical benefits previously reported for tracheal-pressure control support should be interpreted with caution when applied to commercial ATC implementations, unless effective tube compensation has been demonstrated under relevant conditions. These findings suggest that more advanced control approaches, such as those implemented in the ATC prototype ventilator, may be required to achieve consistent and physiologically accurate tube compensation.

Objective: Breast milk is the optimal source of nutrition for preterm infants, however, low breast milk production is common following a preterm birth. This study aimed to determine if taking brewers yeast' or beta-glucan improves daily expressed breast milk volume. Design: Randomised, blinded, parallel, placebo-controlled trial. Setting: Three Australian tertiary level neonatal units. Patients: Mothers with a singleton or twin pregnancy who gave birth at less than 34 weeks' gestation. Interventions: Mothers were randomised within 72 hours of birth into three parallel groups in 1:1:1 ratio to receive either brewers' yeast, beta-glucan or placebo capsules for seven days. Main outcome measure: Total expressed breast milk volume over a 24-hour period on day seven of intervention. Results: A total of 105 mothers underwent randomisation between August 2022 and April 2024 (36 brewers' yeast, 35 beta-glucan, and 34 placebo). The adjusted mean difference in daily expressed breast milk volume was 94 mL/day (95% CI -51 to 239 mL/day) between the brewers' yeast and placebo groups, and -25 mL/day (95% CI -173 to 123 mL/day) between the beta-glucan and placebo groups. Maternal side effects were similar across groups. Conclusion: We found no clear effect of short-term administration of brewers' yeast or beta-glucan on breast-milk production following preterm birth; both interventions were well tolerated. Given the small sample size, these findings do not rule out the possibility of a clinically meaningful benefit of brewers' yeast and suggest further research with a larger sample size may be warranted to clarify the potential clinical impact. Trial registration number ACTRN12622000968774.

Orofacial clefts (OFCs), including cleft lip (CL), cleft palate (CP), and cleft lip with cleft palate (CLP), are among the most common craniofacial malformations in humans, with a birth prevalence of approximately 1 in 1,000 live births globally. Non-syndromic forms of OFC are predominantly genetic, with significant variability in prevalence across populations. Understanding the genetic underpinnings of OFCs remains a key public health priority, given the substantial medical and societal burden of these conditions. Recent genome-wide association studies (GWAS) have implicated numerous genetic loci, but challenges remain due to genetic heterogeneity and complex gene-environment interactions. This study aimed to identify sex-specific genetic risk factors for cleft lip with or without cleft palate (CL/P) through a meta-analysis of whole genome sequencing (WGS) data from 1,922 case-parent trios across eight diverse cohorts. Our approach revealed four SNPs in three distinct regions that showed genome-wide significant sex-specific effects. However, despite each of these SNPs passing standard quality control filters, follow-up analyses showed that these signals most likely were technical artifacts caused by sequencing errors, in particular mis-mapped reads due to sequence similarities with the sex chromosomes. These findings highlight the necessity for careful scrutiny when studying differences between the sexes in genetic association studies.

Objective: This study aims to identify which markers of tissue hypoperfusion - specifically lactate levels, central venous oxygen saturation (ScvO2), and venous arterial carbon dioxide gradient (CO2 gradient) - have the highest sensitivity and specificity in predicting the discharge of postoperative cardiac surgical patients from the ICU within 48 hours. This is an exploratory, hypothesis-generating investigation. Methods: Prospective observational study involving 100 patients in the Surgical ICU at InCor-HCFMUSP undergoing cardiac surgery with cardiopulmonary bypass. Perfusion markers were assessed at ICU admission and 24 hours post-admission. Results: ScvO2 at 24 hours was the only marker significantly associated with ICU discharge (OR=1.096; 95% CI=1.020-1.180; p=0.012). Formal DeLong's test confirmed ScvO2 had significantly superior discriminatory performance compared to lactate (AUC 0.661 vs. 0.428; p=0.004). Lactato and CO2 gap showed no significant associations. Conclusions: In this exploratory cohort, ScvO2 at 24 hours post-admission showed a statistically significant association with early ICU discharge and superior discriminatory performance compared to lactate. These findings are hypothesis-generating and require prospective validation before clinical recommendations can be made.

Background: In Canada, there is a lack of data relating sociodemographic characteristics to the likelihood of consent and clinical trial participation. Objective: The overall objective of this study is to examine the association of hospital-level sociodemographic variables with a priori informed consent rates for participation in the REVISE trial. Design: This study is a retrospective observational analysis of Canadian sites participating in the international REVISE trial. Methods: Sociodemographic characteristics for 42 hospitals participating in the REVISE trial will be supplemented by national data from the 2021 Canadian Census of Population Profile at the census tract level corresponding to the hospital's location. Hospital level information for Ontario sites will be derived from the Institute for Clinical Evaluate Sciences (ICES) database. Site clustering will be performed using latent class analysis, a flexible clustering technique that identifies meaningful subgroups based on sociodemographic variables purposively selected from data available through the Statistics Canada 2021 census profile, ICES, and hospital-reported data. Clustering analysis will be performed for all Ontario hospitals with available ICES data, followed by a separate analysis for all Canadian REVISE sites using Statistics Canada data. Concordance in the clustering of REVISE sites will be examined by comparing the assignment of hospitals to the latent classes separately identified using ICES and Statistics Canada data. If there is a high degree of agreement between the two datasets, sociodemographic predictors will be analyzed using the clusters identified through ICES for Ontario sites with the concordant classes based on Statistics Canada data for Canadian sites outsite Ontario. If there is disagreement in cluster assignment between the two datasets, separate analyses of sociodemographic factors will be conducted for Ontario sites using ICES data and for all Canadian sites using the 2021 Census Profile. Multivariate linear regression models will be used to analyze the association between hospital-level characteristics and the likelihood of a priori and deferred consent. Results: Results of this study will generate information about the relationship between informed consent to participate in a low-risk critical care clinical trial using different consent models, and socioeconomic patient characteristics at the hospital site level (e.g., educational attainment, knowledge of official languages, citizenship rates, family income, poverty, rurality and immigration patterns). Conclusions: This study will fill an evidence gap by generating information on the relationship between sociodemographic variables and the likelihood of informed consent to participate in a critical care clinical trial in Canada.

Objectives: Explore the perspectives of primary caregivers towards pediatric tissue-based research participation. Design: Cross-sectional. Setting: Two academic pediatric gastroenterology sites in the United States, including one serving a largely rural referral population and one urban clinic population. Participants: Primary caregivers of children who underwent endoscopy between 2017-2018 at UVA or were seen in the clinic setting between 2024-2025 at Tulane and referred by their child's gastroenterologist to complete an electronic survey. Measures and Analysis: Primary caregiver attitudes, motivations, and concerns toward pediatric tissue-based research were explored using descriptive-focused coding in NVivo and a large language model (LLM) processing pipeline based on OpenAI's GPT-4 for thematic, emotional, and sentiment analyses. Results: Data were analyzed from 92 primary caregivers. Overall, respondents were amenable to having their children provide specimens for research. Primary motivations included a desire to help others or advance science, and perceived medical benefits for their child so long as specimen collection did not cause additional distress. Discomfort with participation was often linked to prior traumatic clinical experiences, concerns about additional biopsies causing unnecessary discomfort, or privacy issues. A desire to help others and potentially their own child was the strongest motivator for participation, while scheduling constraints and perceived risks to the child's health were the main barriers. Conclusions: At both sites, primary caregivers expressed strong willingness to participate in pediatric research. Primary concerns included perceived invasiveness of biospecimen collection and potential for additional discomfort. Limitations of the study included the unstructured nature of the data making the analysis and interpretation challenging. Strengths included two demographically diverse sites, intentional enrollment of primary caregivers of children both with and without invasive diagnostic testing, and use of LLM based analyses.

Background: Antimicrobial resistance poses a major threat to global public health. Large language models (LLMs) offer new possibilities for optimizing antibiotic prescribing decisions, but the capabilities of general-purpose versus domain-specific medical LLMs under different prompting strategies remain to be clarified. Methods: This double-blind, randomized-sequence evaluation used a 2X2 factorial design comparing four AI conditions-the domain-specific model MedGo and the general-purpose model DeepSeek V3.5, each under standard direct prompting and chain-of-thought (CoT) prompting-alongside real physician prescriptions across 59 complex inpatient infection cases. Five parallel regimens were generated per case and independently evaluated by three senior clinicians (1-5 comprehensive score and five domain sub-scores). ChatGPT 5.2 was additionally assessed as an automated evaluation tool. Results: Score ranking: real physicians > MedGo-CoT > DeepSeek-CoT > MedGo> DeepSeek (Friedman test, p<0.001). In base mode, MedGo significantly outperformed DeepSeek (Holm-adjusted p=0.040). CoT improved both models (Holm-adjusted p<0.001 for DeepSeek; p=0.024 for MedGo) and reduced score dispersion. MedGo-CoT significantly outperformed DeepSeek-CoT in individualized adjustment (adjusted p<0.001) and dosing precision (adjusted p=0.005). ChatGPT-expert correlation was negligible (overall Kendall {tau}=0.153, p=0.003; subgroup {tau}=0.06-0.20, all p>0.05). Conclusions: Domain-specific medical LLMs enhanced by CoT approach the antibiotic decision-making level of real physicians, with advantages in individualization and dosing precision. However, notable deficiencies persist in antimicrobial stewardship ecological awareness and automated evaluation reliability, underscoring the continued indispensability of senior clinical expertise.

Background: Annually, 2.3 million newborns die, largely from preventable causes. Neonatal hypothermia is an important contributor to morbidity and mortality, particularly in low-resource settings. This study quantified the burden of hypothermia at and after admission in four NEST360-supported countries and examined associations between outside air temperature, ward temperature, and neonatal hypothermia. Methods: We conducted a retrospective analysis of newborn admissions (January 2021 to June 2025) across 66 neonatal units in Kenya, Malawi, Nigeria, and Tanzania. Hypothermia was defined using WHO thresholds (mild: 36.0-36.4{degrees}C, moderate: 32.0-35.9{degrees}C, severe: <32.0{degrees}C). Newborn admission and lowest after admission body temperatures were extracted from routine clinical records. Ward temperatures were captured using the Hadli Monitoring System, and environmental temperatures were obtained from Open-Meteo. Multivariate ordinal logistic regression assessed associations between air temperature, ward temperature, and hypothermia at admission and during admission. Results: Among 418,458 newborn admissions with recorded admission temperatures, 47.3% (n=220,684) were hypothermic at admission (country range: 22.8%-61.9%), while 63.5% (n=48,746) experienced hypothermia during hospital stay (country range: 18.5%-74.4%), based on 76,855 admissions (July 2024-June 2025) with temperature data. Based on admission and subsequent temperature, 28.5% had no documented hypothermia, 8.6% improved to non-hypothermic status, 29.4% developed hypothermia after admission, and 33.5% experienced hypothermia at admission and during hospital stay. Across 59 neonatal units, minimum ward temperatures >26{degrees}C were maintained on 92.6% of 365 days. At admission, ward temperatures of 30-33{degrees}C were associated with 9% lower odds of a lower thermal category versus 26-28{degrees}C (p<0.01). After admission, ward temperatures of 28-30{degrees}C reduced odds by 18% (p<0.05). Warmer outside temperatures (>24{degrees}C day, >21{degrees}C night) were protective, corresponding to 19% and 68% lower odds of a lower thermal category after admission, respectively, compared with 19-24{degrees}C and 15-21{degrees}C reference groups. Newborns had 3.6-fold higher odds of hypothermia at night than during the day. Each 1{degrees}C increase in post-admission temperature reduced odds of death by 6%. Conclusion: Neonatal hypothermia remains highly prevalent despite most units maintaining ward temperatures above WHO minimum standards (26{degrees}C). Strengthening all components of the warm chain, particularly at night and during colder seasons, is essential to reduce hypothermia and improve survival.

Purpose: To evaluate the latest open-weight vision-language models (VLMs) on the Japanese Diagnostic Radiology Board Examination (JDRBE), assessing overall accuracy and the effects of image input, reasoning, and language. Materials and Methods: In this retrospective study, 29 open-weight VLMs from 13 developers, released in or after January 2025, were evaluated on 327 image-bearing questions from four years of the JDRBE, a non-public benchmark with low risk of data leakage. Each question was answered by each model with and without the image(s), under three language conditions and with reasoning enabled and disabled. Accuracy was the primary outcome, and within-model differences were tested with paired bootstrap confidence intervals and sign-flip permutation tests with Benjamini-Hochberg correction. Results: In the Japanese condition with image input and reasoning, the leading models reached 73.7% (gemma-4-31B-it), 73.1% (Qwen3.5-397B-A17B), and 72.1% (Kimi-K2.6). On the 2025 subset, these three models (74.1%-75.5%) scored above the mean accuracy of five newly board-certified radiologists who passed the 2025 examination (72%; range, 65%-83%). Accuracy broadly scaled with model size, although compact gemma-4-31B-it matched larger models. Enabling reasoning improved accuracy in nearly all models and the contribution of image input was larger when reasoning was enabled, particularly in higher-performing models. English prompts generally outperformed Japanese prompts. Conclusion: Several open-weight VLMs, without medical adaptation, performed at or above the mean of newly board-certified radiologists on the JDRBE, with both model size and reasoning contributing. The highest Japanese-language accuracy came from a compact model suitable for parameter-efficient fine-tuning and serving on a single graphics processing unit.

The evolving epidemiology of COVID-19, driven by successive SARS-CoV-2 variants of concern (VOCs), has prompted ongoing evaluation of their impact on disease severity in children. In low- and middle-income countries (LMICs), children experience a higher burden of severe respiratory illness and pneumonia-related mortality due to factors such as malnutrition, incomplete immunisation, HIV exposure or infection, tuberculosis, and disparities in access to healthcare services. Hospital-based paediatric studies from LMICs are therefore needed to understand how the epidemiology and severity of COVID-19 have changed across pandemic waves. This study examined 354 hospitalised children with SARS-CoV-2 infection during the ancestral, pre-Omicron (Beta and Delta), and Omicron waves at Tygerberg Hospital in Cape Town, South Africa. We analysed data collected over an extended period, from March 2020 to June 2022. Statistical analyses were used to describe clinical characteristics across variant periods, and multivariable logistic regression models were applied to evaluate associations between potential risk factors and disease severity. Paediatric COVID-19 severity varied across VOC periods, with the highest burden observed during the pre-Omicron (Beta and Delta) waves. In multivariable analyses, younger age and circulating variants were associated with disease severity; CRP levels emerged as a marker associated with more severe illness, and corticosteroid treatment, while also associated with disease severity, reflects clinical response to more severe cases. These findings contribute to a better understanding of the epidemiology and clinical impact of COVID-19 in children and highlight the importance of context-specific surveillance and treatment strategies in resource-limited settings.

OBJECTIVE To examine parents perceptions regarding the introduction of a social media ban for children and to identify factors associated with these attitudes. METHOD A cross-sectional study was carried out in Greece in April 2026. Potential predictors of parents views on a social media ban included (a) sociodemographic variables (such as gender, age, educational attainment, and financial status), (b) social media usage patterns (number of accounts, daily usage duration, and posting frequency), and (c) level of political engagement (how often participants follow political news and discuss political issues). Outcome variables comprised parents agreement with the ban, level of awareness about its implementation, perceived necessity for additional measures, confidence in the ban effectiveness, perceived effects on children lives, and parents familiarity with digital parental control tools. RESULTS Overall, 68.0% of parents supported implementing a social media ban for children under 15. A large majority (91.8%) expressed the need for more governmental information regarding the ban. Additionally, 89.3% believed that further measures beyond the ban are required to effectively address the issue. Suggested measures included digital literacy courses in schools (86.1%), active parental involvement in digital literacy (74.6%), prohibition of inappropriate content (77.9%), reasonable parental limits on social media use (73.8%), and restriction of addictive platform features (73.0%). Older parents demonstrated greater confidence in the effectiveness of the ban. Furthermore, age, financial status, number of social media accounts, and time spent online were positively associated with perceived impacts of the ban. Younger age was linked to greater parental familiarity with digital control tools, while having more social media accounts was also positively associated with such familiarity. CONCLUSIONS There is a clear need for comprehensive, evidence-based policy approaches that combine regulation, education, and shared responsibility among stakeholders. Policymakers should leverage existing public support for child protection while investing in digital literacy initiatives, empowering parents, and strengthening regulatory oversight of social media platforms to achieve long-term and equitable results.

ABSTRACT IMPORTANCE Childhood obesity is a growing global health concern associated with adverse physical, psychiatric, and neurodevelopmental outcomes. Although previous neuroimaging studies have linked obesity to widespread alterations in brain structure and function, it remains unclear how well multimodal neuroimaging measures and genetic markers can predict future weight gain and inform early intervention strategies. OBJECTIVE To evaluate the predictive utility of multimodal MRI measures and polygenic risk scores for obesity in estimating proportional body weight at baseline and predicting weight gain over one year in preadolescent children. DESIGN, SETTING, AND PARTICIPANTS This study used data from the Adolescent Brain Cognitive Development (ABCD) Study, a large-scale, multisite longitudinal cohort of children aged 9 to 10 years (N = 11,880). Analyses included baseline data collected between 2016 and 2018, and one-year follow-up data collected between 2018 and 2020 across multiple imaging sites. MAIN OUTCOMES AND MEASURES Elastic net regression models were applied to structural MRI (including diffusion tensor imaging) and resting-state functional MRI data to predict baseline triponderal mass index (TMI), a weight-for-height measure that more accurately reflects adiposity in children than body-mass index (BMI). Longitudinal classification models were developed to predict excess weight gain relative to normative developmental trajectories at one-year follow-up. Models were evaluated with and without the inclusion of polygenic risk scores and other non-imaging covariates. Generalizability was assessed using leave-one-site-out cross-validation. RESULTS Structural MRI measures predicted baseline TMI with an R^2 of 0.21, whereas resting-state functional MRI measures predicted TMI with an R^2 of 0.08. Classification models predicted one-year weight gain with area under the receiver operating characteristic curve (AUC) values of 0.73 for structural MRI and 0.60 for resting-state functional MRI. Including polygenic risk scores and other covariates improved model performance (structural MRI: R^2 = 0.25, AUC = 0.75; resting-state functional MRI: R^2 = 0.15, AUC = 0.69). Leave-one-site-out cross-validation revealed reduced generalizability across imaging sites (structural MRI R^2 = 0.13-0.17; resting-state functional MRI R^2 = 0.02-0.09; structural MRI AUC = 0.73-0.74; resting-state functional MRI AUC = 0.60-0.67). CONCLUSIONS AND RELEVANCE Multimodal MRI measures were associated with proportional body weight and demonstrated modest predictive utility for future weight gain in preadolescent children, explaining up to one fifth of the variance in weight-related outcomes. The addition of genetic and non-imaging variables improved prediction accuracy, underscoring the multifactorial nature of childhood obesity. However, the observed decline in performance under site-wise cross-validation highlights the need to address site-related variability to enhance reproducibility and generalizability in neuroimaging-based predictive models of pediatric obesity.

Background: Human activities such as mining generally lead to increased heavy metal concentrations in the environment. While traditional remediation techniques are often costly, the use of fungi as bioremediators, known as mycoremediation, is increasingly gaining attention as a sustainable approach for removal of heavy metals. Here, we evaluated heavy metal levels inside the Kiirunavaara iron ore mine in Northern Sweden and analysed fungal responses to various metal concentrations by comparing growth and metal uptake in mine-derived isolates and closely related control isolates. Results: Sediments inside the mine were enriched in heavy metals compared to those from the outlet of the mine to natural lakes. Six Fusarium isolates were recovered from contaminated mining environments: five isolates from inside the mine were identified as Fusarium oxysporum, and one isolate from the outlet was identified as Fusarium tricinctum. Isolates from the mine and outlet showed overall higher survival and biomass production in presence of copper, iron, and zinc across a range of concentrations (up to 1000 mg/L) compared to control isolates. At the same time, these isolates often exhibited reduced relative metal uptake. As a result, mycoremediation potential, assessed as total uptake in the grown mycelium, was isolate-dependent. Conclusions: Based on these results, we conclude that Fusarium isolates from the Kiirunavaara mine show increased growth in media enriched with heavy metals compared to closely related control isolates. We additionally show that mycoremediation potential is not necessarily associated with environmental origin. Instead, mycoremediation potential should be evaluated on a case-by-case basis for each isolate and based on specific needs for mycoremediation.

BACKGROUND Nontuberculous mycobacterial lung disease (NTM-LD) is highly heterogenous, geographically and etiologically, hindering effective timely identification. Prior CT radiomics studies require manual segmentation of pathology. We developed a whole-lung CT radiomics-based machine learning approach and identified common features across two geographically distinct NTM-LD cohorts. STUDY DESIGN AND METHODS 1,300 chest CT scans from China (871 TB; 429 NTM, Dataset 1) and 173 independent NTM cohort from UAB, US. Whole-lung regions were automatically segmented on each scan, and 85 quantitative radiomic features were extracted using a standardized image-processing pipeline. We evaluated two frameworks to assess model performance and generalizability: (1) training on Dataset 1 with external validation on Dataset 2, and (2) training on the combined cohort. Linear discriminant analysis (LDA) was used as the primary classification method. Cross-cohort concordance analysis was performed to evaluate the reproducibility of radiomic features across datasets. RESULTS In Scenario 1, the LDA classifier trained on Dataset 1 achieved an AUC of 0.79 (95% CI, 0.73-0.84) with high specificity (0.91). On the external UAB cohort, the model achieved an AUC of 0.94 (95% CI, 0.90-0.97). In Scenario 2, the combined cohort model achieved an AUC of 0.81 (95% CI, 0.76-0.85) with improved sensitivity (0.61) and precision (0.82). Feature importance analysis identified 16 features consistently ranked among the top 20 in both scenarios, predominantly texture-based descriptors reflecting distinct parenchymal patterns between mycobacterial species. CONCLUSION Whole-lung CT radiomics enables interpretable NTM-LD classification across geographically distinct populations without manual annotation. Suggesting population-independent parenchymal signatures of NTM-LD.

Melanins are biological macromolecule with immense functionality synthesised by a wide spectrum of living organism. It is mainly synthesised by the oxidative polymerization of indolic and phenolic compounds through several enzymatic process. It has wide spread application in agriculture, cosmetic and therapeutic industry due to its various properties including antioxidation ability, UV protection efficiency and anticancer activity. Because of this wide range of application in different sectors, large scale production and commercialization attains enormous consideration. The present study deals with the effect of 12 different process parameters on melanin production viz., production media, incubation time, inoculum concentration, pH, temperature, agitation, carbon source, phosphate and magnesium source, CuSO4.5H2O, sodium chloride and L-tyrosine on melanin production by Pseudomonas stutzeri strain BTCZ 109 obtained from Arabian sea sediments was evaluated. After optimizing the important process parameters, the bacteria showed about ~4.65 fold increase in melanin production compared to unoptimized cultural conditions. The melanin optimized through this method was found to be nano sized. The Nano sized DOPA melanin in treating Skin cancer cell line SK ML28 which showed a dose-dependent activity with an IC50 value of 164 g/mL. All these results highlight the therapeutic efficiency of DOPA melanin Nano particle as promising bioactive molecule.

Abstract Background Neighborhood socioeconomic disadvantage may contribute to inequities in access to dental care by influencing the geographic distribution of providers. The Area Deprivation Index (ADI) is a validated measure of neighborhood deprivation, but its association with dental workforce availability has not been examined statewide in California. This study evaluated the relationship between neighborhood deprivation and dental provider density across California ZIP Code Tabulation Areas (ZCTAs). Methods We conducted a cross-sectional ecological study of California ZCTAs using publicly available data from the National Plan and Provider Enumeration System (April 2026), the Neighborhood Atlas 2023 ADI, and 2024 U.S. Census population estimates. Active dental providers were linked to ZCTAs and provider density was calculated per 10,000 residents. ADI was aggregated to the ZCTA level using the median ADI national percentile. Negative binomial regression was used to assess the association between ADI and dental provider density, with population included as an offset. Secondary analyses examined California-specific ADI quartiles, dental deserts, and specialist versus general dentist availability. Results The final analytic sample included 1,426 California ZCTAs representing 39,016,384 residents and 37,945 active dental providers. Greater neighborhood deprivation was significantly associated with lower dental provider density. Each one-percentile increase in ADI corresponded to a 1.8% reduction in provider density (incidence rate ratio [RR] 0.9823, 95% confidence interval [CI] 0.9799-0.9847; p < 0.001). Compared with the least deprived quartile, the most deprived quartile had 61% fewer dental providers (RR 0.39, 95% CI 0.34-0.45; p < 0.001). Overall, 15.9% of ZCTAs contained no active dental providers, increasing from 6.8% in the least deprived quartile to 31.1% in the most deprived quartile. Specialist availability demonstrated an even steeper deprivation gradient, with specialist density declining by 86% between the least and most deprived quartiles.

Objectives Increased risk of childhood obesity up to age six years has been linked to higher maternal allostatic load (AL), the physical manifestation of repeated stress exposure. However, associations are less evident when using psychological stress indicators, and data mainly come from higher income countries. Using psychological and physiological stress markers, this study evaluates maternal stress exposures and child risk of obesity in Peruvian women and their children, ages 5 to 15 years, living in a disadvantaged urban area. Methods Maternal stress exposures included mental distress (12-item General Health Questionnaire scores of 5+ for moderate/high and <5 for no/low distress) and AL (lower/moderate/higher AL assessed from Latent Profile Analysis of hair cortisol, BMI, waist circumference, systolic and diastolic blood pressure). Child outcomes included BMI-for-age and waist circumference-for-age z scores (BAZ and WCAZ). Linear regression analyses were conducted, adjusting for confounders and reported as coefficients and 95% confidence intervals (95% CI). Results Versus mothers with no/low distress, those with moderate/high distress had children with 0.40 (95% CI: -0.66,-0.13) and 0.32 lower (-0.53,-0.11) child BAZ and WCAZ respectively. Versus lower AL mothers, moderate AL mothers had children with 1.15 (0.41,1.88) and 0.74 (0.20,1.28) greater BAZ and WCAZ while higher AL mothers had children with 1.43 (0.95,1.92) and 0.91 (0.50,1.32) greater BAZ and WCAZ respectively. Conclusions Children of mothers with higher AL were at greater risk of overweight or obesity, which may add to the rising burdens of non-communicable diseases in resource-constrained settings as well as the related social, economic, and public health costs.

Health workforce performance is central to service quality, yet little empirical work has examined how performance management systems operate for physiotherapists in rehabilitation services in low- and middle-income settings. This cross-sectional study assessed the current state, perceived effectiveness, and process gaps of performance management systems among physiotherapists working in public rehabilitation centers in Dhaka, Bangladesh. A pretested semi-structured questionnaire was administered to 105 physiotherapists between September and October 2025. Descriptive statistics were used to summarize participant characteristics and performance management indicators. Wilson 95% confidence intervals were estimated for key proportions. A nine-item exploratory performance management system maturity score was constructed from process indicators. Fisher exact tests with Cramer's V were used to examine associations with perceived system effectiveness, and exploratory logistic regression estimated odds ratios for effective or moderately effective performance management. The mean age of respondents was 31.6 years, 56 of 105 were male, and 85 of 105 had graduate or postgraduate qualifications. Formal performance management systems were reported by 102 of 105 respondents (97.1%, 95% CI 91.9-99.0). Standardized appraisal timing and method, assessment form use, performance planning, and formal evaluation systems were each reported by about 60-70% of participants. Reward-performance linkage was perceived as motivating by 97 of 105 respondents (92.4%, 95% CI 85.7-96.1). Overall, 81 of 105 respondents (77.1%, 95% CI 68.2-84.1) rated the system as effective or moderately effective. Training recipient category was associated with perceived effectiveness (Fisher exact p=0.0035; Cramer's V=0.363), as was perceived appropriateness of the process (p=0.0323; Cramer's V=0.258). The maturity score was not independently associated with perceived effectiveness in exploratory regression. Public rehabilitation centers in Dhaka appear to have formal performance management systems, but the systems are only moderately developed. Strengthening training coverage, transparent evaluation criteria, routine feedback, and formal system review may improve staff confidence in performance management processes.

Background: Nuclear medicine and radiopharmaceutical development require coordinated radiochemistry, dosimetry, molecular imaging, radiation-safety and clinical decision processes. Current workflows remain fragmented, difficult to audit and poorly standardised for evaluating domain-specific AI support. Methods: We developed RadGuide AI, a nuclear medicine agent built around a traceable data-model-tool loop. Patent, literature and clinical-trial records were converted into 15,596 initial QA items; relevance screening, completeness checks, semantic deduplication and cross-validation retained 5,474 core QA items. MedGemma-27B-Instruct served as the foundation model and was adapted with LoRA. The system incorporated 55 MCP-wrapped tools covering radiopharmaceutical R&D, clinical decision support, imaging analysis and radiation-safety/dosimetry. Evaluation used a locked N=200 benchmark with predefined denominators, leakage control, expert scoring, statistical procedures, factuality audits and tool-execution metrics. Results: RadGuide-LLM achieved 88.5% answer accuracy (177/200; 95% CI, 83.3-92.2%) and a Macro-Average score of 21.5/25 (bootstrap 95% CI, 20.9-22.0), exceeding GPT-4o, DeepSeek-V3.2 and the base MedGemma model in this technical evaluation. Supplementary audits reported guideline compliance, terminology recall, knowledge coverage, tool-routing success and preclinical/phantom dosimetry agreement with explicit denominators and confidence intervals. Interpretation: RadGuide AI converts nuclear medicine queries into auditable retrieval, tool selection, calculation, verification and reporting workflows. The findings support technical feasibility, not definitive patient-level clinical validation; prospective multicentre studies and external benchmark release remain required before clinical deployment.