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Nevin Manimala Statistics

Antibacterial effects of bioactive restorative dental materials on Streptococcus mutans: An in vitro study using the direct contact test

Saudi Dent J. 2025 Oct 15;37(7-9):64. doi: 10.1007/s44445-025-00073-4.

ABSTRACT

This in vitro study aimed to evaluate the antibacterial activity of various restorative dental materials against Streptococcus mutans, a major cariogenic pathogen. The materials tested included a resin composite (Estelite Sigma Quick), conventional glass ionomer cement (Fuji IX), resin-modified glass ionomer cement (Fuji II LC), a bioactive resin-based material (Activa BioACTIVE Restorative), and a calcium silicate-based material (Biodentine). Antibacterial activity was assessed using the direct contact test (DCT). Each material was tested against S. mutans at 3, 6, 16, and 24-h intervals. Colony-forming units (CFU) were quantified following serial dilution and culture on BHI agar. Statistical comparisons were conducted using the Kruskal-Wallis test. All materials except Estelite demonstrated significant antibacterial effects. Biodentine exhibited the greatest inhibition (P ≤ 0.001), followed by Fuji IX (P ≤ 0.001), and Fuji II LC (P ≤ 0.01). Activa BioACTIVE showed significant bacterial reduction at 16 and 24 h (P ≤ 0.05). Estelite showed no significant antibacterial effect (P > 0.05). Biodentine displayed sustained and pronounced antibacterial effects, suggesting its suitability for patients at high risk of caries. Fuji IX and Fuji II LC also exhibited antibacterial properties, though to a lesser extent. The findings support the use of bioactive restorative materials in managing bacterial presence and enhancing restoration longevity. The superior antibacterial performance of Biodentine highlights its potential role in preventing secondary caries, particularly in high-risk populations. Clinicians are encouraged to consider bioactive materials as part of comprehensive caries management strategies.

PMID:41091362 | DOI:10.1007/s44445-025-00073-4

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A Cross-Sectional Analysis of the Social Determinants of Health (SDOH) Needs in Early Childhood Among Children at Risk and Diagnosed With Autism Spectrum Disorder

J Autism Dev Disord. 2025 Oct 15. doi: 10.1007/s10803-025-07085-3. Online ahead of print.

ABSTRACT

PURPOSE: Unmet social determinants of health (SDOH) play a significant role in the reported barriers to healthcare access and support experienced by children with autism spectrum disorder (ASD). Limited studies have explored how unmet SDOH needs impact children with ASD during early childhood. This study examined SDOH needs in children with a positive Modified Checklist for Autism in Toddlers (MCHAT) or ASD diagnosis and whether specific needs are more strongly associated with these outcomes.

METHODS: Using cross-sectional data from pediatric patients’ electronic health records, descriptive statistics were used to examine the prevalence of SDOH needs and associations were examined using multinomial logistic regression among children (< 5 years of age) with developmental delays. Patients were stratified into 3 groups: (1) no positive MCHAT screen or no ASD diagnosis, (2) positive MCHAT screen and no diagnosis, and (3) ASD diagnosis (ICD-10 code: F84.0). Patients and/or their caregivers were screened for five SDOH factors: food insecurity, housing stability, financial strain, health literacy, and transportation issues.

RESULTS: Children diagnosed with ASD had 49% higher odds of reporting at least one SDOH (AOR:1.49, p = 0.045), compared to those with no positive MCHAT screen or diagnosis. Although not significant, a positive MCHAT screen and/or ASD diagnosis trended toward higher odds of financial strain, housing instability, and low health literacy.

CONCLUSIONS: The findings emphasize the need to screen and address SDOH, particularly financial risk and health literacy, during early childhood among autistic children. Pediatric primary care SDOH programs should be tailored to better support families’ unique needs.

PMID:41091358 | DOI:10.1007/s10803-025-07085-3

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Nevin Manimala Statistics

Key factors in mandibular third molar surgery: a comprehensive view of their prevalence and interrelationships

Saudi Dent J. 2025 Oct 15;37(7-9):53. doi: 10.1007/s44445-025-00061-8.

ABSTRACT

This study comprehensively assessed the factors affecting the complexity of wisdom tooth surgery, the prevalence of each factor class, and the relationships between them. This retrospective cross-sectional study analyzed 526 Cone Beam Computed Tomography (CBCT) scans of wisdom teeth. Variables examined included impaction positions using Winter’s and Pell and Gregory’s classifications, root counts, and frequency of contact with the inferior alveolar canal. The statistical analysis was performed using Pearson’s chi-square test. The vertical type was the most prevalent impaction position (n = 189, 35.9%), followed by the mesioangular type. According to Pell and Gregory’s classification, the most frequent impactions were in positions A and 1. Of the cases studied, 234 involved mesial root contact, 239 had distal root contact, and 158 showed contact with both roots. In total, 345 cases (65.6%) exhibited contact between the third molar and the inferior alveolar nerve, most frequently in the apical third of the root (62.7%). The canal was most often positioned apical to the third molar (n = 415, 78.9%), and the highest ridge position was lingual (N = 234, 44.5%). These findings underscore the high prevalence of contact between impacted mandibular third molars and the inferior alveolar nerve, emphasizing the importance of meticulous preoperative planning to minimize the risk of nerve injury.

PMID:41091355 | DOI:10.1007/s44445-025-00061-8

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Nevin Manimala Statistics

Assessment of PAH and heavy metal contamination in the pars special economic energy zone surface sediments: ecological implications and source identification

Environ Sci Pollut Res Int. 2025 Oct 15. doi: 10.1007/s11356-025-37035-6. Online ahead of print.

ABSTRACT

This study provides a comprehensive assessment of polycyclic aromatic hydrocarbons (PAHs) and heavy metal concentrations in the surface sediments of the Pars Special Economic Energy Zone (PSEEZ), aiming to identify potential contamination sources and evaluate their ecological impacts. Sediment samples were collected from 14 stations during winter 2023 and analyzed for 16 priority PAHs and a suite of heavy metals. PAH concentrations ranged from below detection limits to 25.21 ng/g, with localized hotspots near industrial discharge zones. Heavy metals, including cadmium (Cd), lead (Pb), and arsenic (As), were detected at varying levels, with some exceeding sediment quality thresholds such as the Effects Range Low (ERL). Statistical source identification revealed petrogenic inputs from oil spills. The predominance of coarse-grained sediments in the PSEEZ, with minimal fine silt and clay content, suggests a lower overall retention capacity for PAHs and heavy metals. While fine particles were present only in limited areas, their potential for adsorbing contaminants remains a concern. Although most PAH and heavy metal concentration indicate below harmful thresholds, the detection of carcinogenic PAHs and elevated metal levels at specific stations indicates localized ecological risks, particularly for benthic organisms and potential trophic transfer within the marine food web.

PMID:41091349 | DOI:10.1007/s11356-025-37035-6

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Integrated Chemical Array and SERS Profiling of Plasma Small Extracellular Vesicles for Breast Cancer Diagnosis

Nano Lett. 2025 Oct 14. doi: 10.1021/acs.nanolett.5c04034. Online ahead of print.

ABSTRACT

Small extracellular vesicles (sEVs) are nanoscale vesicles carrying biomolecules reflective of their cellular origin, making them attractive biomarkers for cancer diagnosis. In this study, we present a high-throughput strategy integrating amphiphile-dendrimer supramolecular probe (ADSP)-based sEV capture with surface-enhanced Raman scattering (SERS) for sensitive, multiplexed detection of breast cancer (BC)-related surface proteins. Plasma-derived sEVs from BC patients at different clinical stages were analyzed, focusing on CD9, EpCAM, and HER2 as key proteins linked to vesicle identity and tumor progression. Gold nanoparticle-based SERS nanotags conjugated with specific antibodies enabled precise detection. Statistical and machine learning analyses of protein profiles allowed accurate discrimination among healthy donors, ductal carcinoma in situ (Stage 0), early stage BC (Stage I-II), and metastatic-stage BC (Stage IV). This integrated platform provides a powerful tool for BC diagnosis and highlights the potential of sEV-based liquid biopsy strategies for clinical application.

PMID:41084822 | DOI:10.1021/acs.nanolett.5c04034

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Nevin Manimala Statistics

Harnessing data and control with AI/ML-driven polymerization and copolymerization

Faraday Discuss. 2025 Oct 14. doi: 10.1039/d5fd00066a. Online ahead of print.

ABSTRACT

Creating and curating new data to augment heuristics is a forthcoming approach to materials science in the future. Highly improved properties are advantageous even with “commodity polymers” that do not need to undergo new synthesis, high-temperature processes, or extensive reformulation. With artificial intelligence and machine learning (AI/ML), optimizing synthesis and manufacturing methods will enable higher throughput and innovative directed experiments. Simulation and modeling to create digital twins with statistical and logic-derived design, such as the design of experiments (DOE), will be superior to trial-and-error approaches when working with polymer materials. This paper describes and demonstrates protocols for understanding hierarchical approaches in optimizing the polymerization and copolymerization process via AI/ML to target specific properties, using model monomers such as styrene and acrylate. The key is self-driving continuous flow chemistry reactors with sensors (instruments) and real-time ML with an online monitoring set-up that allows a feedback loop mechanism. We provide initial results using ML refinement of the classical Mayo-Lewis equation (MLE), time-series data, and an autonomous flow reactor system build-up as a future data-generating station. More importantly, it lays the ground for precision control of the copolymerization process. In the future, it should be possible to undertake collaborative human-AI-guided protocols for the autonomous fabrication of new polymers guided by literature and available data sources targeting new properties.

PMID:41084813 | DOI:10.1039/d5fd00066a

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Explainable Machine Learning Framework for Dynamic Monitoring of Disease Prognostic Risk: Retrospective Cohort Study

JMIR Form Res. 2025 Aug 7;9:e65585. doi: 10.2196/65585.

ABSTRACT

BACKGROUND: Patients’ clinical status often evolves rapidly after an initial diagnosis, with each patient exhibiting a distinct disease trajectory. As a result, static risk scores fall short in supporting timely interventions-an issue highlighted by COVID-19, where deaths have stemmed from heterogeneous pathways such as pneumonia, multiorgan failure, or exacerbation of preexisting conditions.

OBJECTIVE: This study aims to propose a dynamic prognostic risk assessment framework based on longitudinal data collected during hospitalization, using COVID-19 as an example. Our aim was to develop and validate an interpretable framework that (1) screens prognosis at admission and (2) dynamically updates mortality risk throughout hospitalization, thereby providing clinicians with early, explainable warnings while minimizing additional cognitive load.

METHODS: In this retrospective study, we extracted electronic medical records of 382 COVID-19 cases treated at Tokyo Shinagawa Hospital between January 27 and September 30, 2020. At admission, gradient boosting decision trees (Light Gradient Boosting Machine) were used to predict the maximum clinical deterioration, including death, based on data available at initial diagnosis. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC). For in-hospital monitoring, random survival forests (RSF) were trained on a longitudinal dataset that combined static demographic characteristics with serially measured vital signs and laboratory results. The model dynamically assessed daily mortality risk by calculating a 7-day cumulative hazard function, with risk scores recalculated each day during hospitalization. RSF accuracy was evaluated in an independent one-third test set using the concordance index (C-index), an integrated Brier score (1-50 days), and mean time-dependent AUC. SurvSHAP(t), an extension of Shapley Additive Explanations, was applied to provide time-dependent explanations of each variable’s contribution to the prediction.

RESULTS: The prediction at initial diagnosis showed good agreement with the actual severity outcomes (AUC of 0.717 for predicting hospitalization/severity ≥2; 0.878 for severity ≥3; 0.951 for severity ≥4; 0.952 for severity ≥5; and 0.970 for death/severity=6), although some cases exhibited discrepancies between the predicted and actual prognoses. The dynamic mortality risk assessment during hospitalization using the RSF achieved a test-set C-index of 0.941, an integrated Brier score of 0.315, and a mean time-dependent AUC of 0.936. This dynamic assessment was able to distinguish between dead and surviving patients as early as 1-2 weeks before the outcome. Early in hospitalization, C-reactive protein was an important risk factor for mortality; during the middle period, peripheral oxygen saturation (SpO2) gained importance; and immediately before death, platelets and β-D-glucan were the primary risk factors.

CONCLUSIONS: Integrating static admission triage with daily, explainable RSF predictions enables early identification of patients with COVID-19 at high risk of deterioration. By surfacing phase-specific, actionable predictors, the framework supports timely interventions and more efficient resource allocation. Prospective, multicenter studies are warranted to validate its generalizability and clinical impact.

PMID:41084807 | DOI:10.2196/65585

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Cryopreservation increases sperm DNA fragmentation in normozoospermic Libyan men: the role of oxidative stress and the protective effect of melatonin

Libyan J Med. 2025 Dec 31;20(1):2569151. doi: 10.1080/19932820.2025.2569151. Epub 2025 Oct 14.

ABSTRACT

Cryopreservation of sperm is routinely used in assisted reproduction technology (ART) for male fertility preservation. However, this method has been associated with oxidative stress and DNA fragmentation that may impair sperm quality. Additionally, antioxidant interventions such as melatonin supplementation have not been thoroughly explored in this setting. Although Libya is reported to have one of the highest global prevalence rates of male infertility, Libya-specific data remain limited. This study aimed to determine the effect of a single freeze-thaw cycle on sperm DNA fragmentation and oxidative stress markers, and to evaluate whether melatonin has an impact on post-thaw oxidation profiles. This prospective cohort study was conducted at the Fertility and Reproductive Medicine Center, Beirut Hospital, Benghazi. Semen samples of 104 normozoospermic Libyans were evaluated before and after freezing. DNA fragmentation index (DFI) was measured by sperm chromatin dispersion (SCD) test, and reactive oxygen species (ROS) were quantified by using luminol-enhanced chemiluminescence. In a subset of ejaculates, aliquots were supplemented with 2 mM of melatonin prior to cryopreservation. Cryopreservation was associated with a statistically significant increase in DFI (46.3 ± 18.3% to 60.0 ± 23.0%; p < 0.001) and ROS levels (3.2 × 10³ to 14.7 × 10³ RLU/s; p < 0.001). Smokers presented significantly higher DFI at both pre-freeze and post-thaw evaluations (p < 0.001). We detected a positive correlation between ROS and post-thaw DFI (r = 0.68; p < 0.001). Melatonin-treated samples exhibited moderate but significant differences in ROS (12%, p = 0.045) and DFI (11%, p = 0.004) compared to untreated aliquots. These findings suggested that the freeze-thaw process may contribute to oxidative and genomic stress in spermatozoa, while melatonin supplementation appears to provide limited protection. Larger, multicenter studies incorporating ART endpoints are required to determine the potential translational relevance of these findings.

PMID:41084793 | DOI:10.1080/19932820.2025.2569151

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Results from the SCIENCE and Danish ASC trials using allogeneic mesenchymal stromal cells to treat ischemic heart failure patients

Regen Med. 2025 Oct 14:1-12. doi: 10.1080/17460751.2025.2574194. Online ahead of print.

ABSTRACT

BACKGROUND AND AIMS: Allogeneic mesenchymal stromal cell (ASC) therapy is a potential treatment option in patients with ischemic heart failure (HF). We aimed to investigate the effect of allogeneic Cardiology Stem Cell Center Adipose tissue derived mesenchymal Stromal Cell product (CSCC_ASC) by joining data from the international SCIENCE and the Danish ASC trial.

METHODS: Data from two double-blinded placebo-controlled phase II studies including patients with same inclusion and exclusion criteria and identical endpoints were combined. Patients had left ventricular ejection fraction (LVEF) < 45% and New York Heart Association (NYHA) II-III without further treatment options.

RESULTS: Two hundred and fourteen patients were randomly assigned to receive CSCC_ASCs or placebo injections. There was no difference in baseline characteristics between groups.No significant differences from baseline to 6-month follow-up were detected between the intervention and placebo-group in left ventricular end-systolic volume, end-diastolic volume or LVEF (p = 0.973, p = 0.601, p = 0.152). The 6-min walking test, NYHA-classification and quality of life score were unchanged in both groups. The difference between groups in N-terminal pro B-type natriuretic peptide and C-reactive protein at 6 months was statistically significant (p = 0.045, p = 0.021).

CONCLUSIONS: Post-hoc analysis demonstrated that a single intramyocardial CSCC_ASC injection in patients with chronic ischemic no-option HF did not improve cardiac function.

PMID:41084786 | DOI:10.1080/17460751.2025.2574194

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Whole 16S rRNA gene sequencing reveals differences in the oral microbiota of healthy individuals infected with oral HPV

Future Microbiol. 2025 Oct 14:1-10. doi: 10.1080/17460913.2025.2572259. Online ahead of print.

ABSTRACT

AIM: To explore the oral microbiota in healthy individuals with and without oral human papillomavirus (HPV) infection and identify any features or changes in the oral microbiota associated with intermediate HPV infection.

MATERIALS AND METHODS: PacBio HiFi sequencing of the whole 16S rRNA gene was performed on 128 saliva samples from a previously characterized cohort (64 HPV-positive and 64 HPV-negative). Statistical analysis of alpha- and beta-diversity, as well as taxonomic composition (differential abundance testing), were used to determine differences between-subject groups.

RESULTS: We demonstrated (1) significant differences in the abundance of Streptococcus salivarius in oral HPV-negative males; (2) males with low-risk HPV had an increased abundance of several species and (3) decreased abundance of Streptococcus salivarius and Streptococcus parasanguinis. For women, (4) oral microbiota Shannon diversity was significantly lower in HPV-positive subjects compared to HPV-negative subjects and (5) oral community structure (beta-diversity) was significantly different when stratified by HPV status.

CONCLUSIONS: Intermediate oral HPV infection is associated with several perturbations in the abundance of some bacterial taxa in the oral microbiota, which differ between males and females. Further research is required to determine whether these changes contribute to oral carcinogenesis.

PMID:41084770 | DOI:10.1080/17460913.2025.2572259