Tag: nevin manimala

Front Vet Sci. 2026 Jul 31;13:1895478. doi: 10.3389/fvets.2026.1895478. eCollection 2026.

ABSTRACT

BACKGROUND: Toxocariasis caused by Toxocara canis is a common parasitic disease in dogs with important zoonotic implications. Although fecal examination is traditionally used for diagnosis, it has limitations in detecting prepatent, low-intensity, or intermittent infections. Therefore, serological testing was selected in this study as a more sensitive and time-efficient approach for assessing exposure at the population level. This study aimed to determine the seroprevalence of T. canis infection and identify associated risk factors among dogs in four Egyptian governorates.

METHODS: A total of 409 dogs from Cairo, Qalyubia, Kafr El-Sheikh, and Gharbia were sampled. Serum samples were tested for anti-T. canis IgG antibodies using a sensitive immunoassay. Epidemiological and management-related risk factors, including age, sex, feeding practices, housing conditions, cohabitation with cats, coprophagy, and deworming history, were recorded and analyzed using univariate and multivariate logistic regression models.

RESULTS: The overall seroprevalence was 25.9% (106/409; 95% CI: 21.91-30.38%), with the highest prevalence in Cairo (32.2%) and the lowest in Qalyubia (19%). Dogs older than 4 years showed the highest seropositivity (37.4%). Seroprevalence was significantly higher among dogs fed homemade food (36%), living outdoors (41.5%), cohabiting with cats (32.4%), exhibiting coprophagy (30.4%), and those without regular deworming (33%). No statistically significant differences were observed between male and female dogs. Multivariate logistic regression identified older age, outdoor living, cohabitation with cats, coprophagy, homemade diet, and irregular deworming as significant risk factors for infection.

CONCLUSION: The findings demonstrate substantial exposure of dogs to T. canis in the studied regions and highlight the cumulative impact of environmental and management-related risk factors. The use of serology provided a broader estimate of exposure than fecal examination alone. Regular deworming, improved feeding practices, and environmental hygiene are essential to reduce infection risk and minimize zoonotic transmission.

PMID:42601940 | PMC:PMC13472903 | DOI:10.3389/fvets.2026.1895478

Front Aging Neurosci. 2026 Jul 31;18:1830477. doi: 10.3389/fnagi.2026.1830477. eCollection 2026.

ABSTRACT

BACKGROUND: Dementia with Lewy bodies (DLB) and Alzheimer’s disease (AD) are common types of dementia, however, diagnosis is challenging. An innovative MR method called neurite orientation dispersion and density imaging (NODDI) is used to assess different microstructural alterations between the conditions.

OBJECTIVE: To assess microstructural white matter (WM) changes in DLB and AD using NODDI.

METHODS: Diffusion images were acquired from 26 DLB patients, 36 AD patients, and 37 normal controls (NCs). The diffusion tensor imaging (DTI) was used to generate fractional anisotropy (FA), mean diffusivity (MD), and NODDI was used to generate neurite density index (NDI), orientation dispersion index (ODI), and volume fraction of isotropic water molecules (Viso), these parameters were compared among groups applying Tract-based spatial statistics (TBSS). We also analyzed the correlations between altered parameters and cognitive scores and investigated the diagnostic efficacy of different parameters using k-nearest neighbor (KNN).

RESULTS: Compared with NC, FA, ODI, and NDI significantly decreased, while MD and Viso significantly increased in both DLB and AD. Compared with DLB, ODI and NDI significantly decreased in AD. The diffusion parameters of multiple fibers were strongly correlated with cognition. NDI exhibited a relatively larger AUC value in differentiating between groups.

CONCLUSION: Widespread disruption of white matter microstructure is evident in both DLB and AD, with differences in these alterations between the two conditions. NDI holds promise as a neuroimaging biomarker for distinguishing DLB from AD.

PMID:42601937 | PMC:PMC13472807 | DOI:10.3389/fnagi.2026.1830477

Front Endocrinol (Lausanne). 2026 Jul 31;17:1861158. doi: 10.3389/fendo.2026.1861158. eCollection 2026.

ABSTRACT

AIMS: Metabolic syndrome (MetS) is diagnosed using fixed thresholds that may not fully reflect the continuous accumulation of cardio-metabolic risk. We investigated whether whole blood viscosity (WBV) increases progressively with increasing MetS burden and explored the relative contribution of individual MetS components to hemorheologic alterations.

METHODS: We analyzed 38,574 adults (21,914 men and 16,660 women) who underwent comprehensive health examinations between January and April 2021. Whole blood viscosity was measured by cone-plate viscometry at shear rates of 300 s^-1 (systolic blood viscosity [SBV]) and 5 s^-1 (diastolic blood viscosity [DBV]). MetS was defined according to NCEP-ATP III criteria. Associations between WBV and MetS burden were evaluated using logistic regression, ANCOVA, regression tree analyses, ROC analyses, and sex-by-viscosity interaction testing.

RESULTS: WBV increased progressively with each additional MetS component in both sexes (p for trend <0.001). After full adjustment for hematocrit, age, smoking status, eGFR, white blood cell count, and AST/ALT, higher SBV was associated with MetS in both women (OR 1.45, 95% CI 1.32-1.58, per 1-SD increase) and men (OR 1.27, 95% CI 1.22-1.33, per 1-SD increase). Regression tree analyses identified triglycerides and waist circumference as metabolic factors associated with elevated WBV. Sex-stratified analyses demonstrated a stronger association between WBV and MetS in women than in men.

CONCLUSIONS: WBV increased in parallel with accumulating cardio-metabolic risk burden, including among individuals who did not meet diagnostic criteria for MetS. WBV may serve as a complementary marker associated with metabolic risk burden, particularly among women and individuals with hypertriglyceridemia or central adiposity.

PMID:42601926 | PMC:PMC13472914 | DOI:10.3389/fendo.2026.1861158

Front Oncol. 2026 Jul 31;16:1834400. doi: 10.3389/fonc.2026.1834400. eCollection 2026.

ABSTRACT

Accurate and timely identification of brain tumours from Magnetic Resonance Imaging (MRI) scans remains one of the most clinically consequential problems in medical image analysis, because the four principal tumour categories – glioma, meningioma, pituitary adenoma, and the tumour-free condition – share overlapping intensity profiles and highly variable morphological presentations. This research introduces BrainFusionNet, a novel deep learning pipeline built on an ImageNet-pre-trained EfficientNet-B4 backbone augmented with a Convolutional Block Attention Module (CBAM) and trained with a hybrid loss combining label-smoothing cross-entropy and focal loss. A structured two-phase fine-tuning strategy – frozen early layers during warm-up, followed by full-network unfreezing under a Cosine Annealing with Warm Restarts (CAWR) schedule – maximises generalisation from a moderately sized dataset. At inference time, a five-fold Test-Time Augmentation (TTA) ensemble further sharpens predictions. Evaluated on the publicly available Kaggle Brain Tumour MRI dataset comprising 7-200 annotated scans across four classes, BrainFusionNet achieves a test accuracy of 99.81%, a macro-averaged F1-score of 99.78%, and a mean AUC of 0.9994 – surpassing every compared baseline, including ResNet-50, DenseNet-201, EfficientNet-B4 (standalone), and ViT-B/16, with statistical superiority confirmed by McNemar tests (p< 0.0001 for all comparisons). Cross-institutional validation on the BraTS 2020 dataset (19 institutions, three scanner vendors, zero-shot transfer) yields 98.25% accuracy and AUC = 0.9962, demonstrating robust generalisation to unseen multi-scanner clinical data. Grad-CAM visualisations confirm that the model attends to diagnostically meaningful anatomical regions, and t-SNE embeddings reveal clearly separable inter-class clusters in the learned feature space.

PMID:42601923 | PMC:PMC13472811 | DOI:10.3389/fonc.2026.1834400

Front Oncol. 2026 Jul 31;16:1876274. doi: 10.3389/fonc.2026.1876274. eCollection 2026.

ABSTRACT

BACKGROUND: Immune checkpoint inhibitors (ICIs) have improved outcomes in advanced non-small cell lung cancer (NSCLC), but clinical benefit remains heterogeneous and is not fully explained by programmed death-ligand 1 (PD-L1) expression or tumour mutational burden (TMB). We explored whether whole-exome sequencing (WES) of circulating tumour DNA (ctDNA) could identify mutational and copy-number (CN) signatures associated with outcome in advanced NSCLC treated with first-line ICI.

METHODS: Baseline plasma ctDNA from 37 patients with advanced NSCLC treated with first-line ICI or chemo-ICI was analysed by WES. Single-base substitution (SBS), indel (InD) and CN signatures were inferred using COSMIC-based signature frameworks. SBS signatures reflect point mutation patterns, including clock-like processes that accumulate with age; indel signatures reflect small insertions and deletions; and CN signatures summarise structural changes such as chromosomal gains and loss of heterozygosity. Signatures were integrated with PD-L1 status, TMB, progression-free survival (PFS) and overall survival (OS). Patients were classified into four clinicogenomic groups according to PD-L1 expression and PFS.

RESULTS: Median PFS and OS were 9 and 20 months, respectively. Single-nucleotide-variant-derived TMB (TMB-SNV) was similar in patients with adverse and favourable outcomes, and patients with TMB-SNV >10 mutations/Mb were evenly distributed between both groups. The SBS profile was mainly represented by the clock-like signatures SBS1, SBS5 and by the tobacco-like signature SBS92, whereas indel signatures were mostly represented by InD4a and InD10. CN signatures showed the most apparent separation between outcome groups. CN9 exposure, a marker of structurally unstable genomes, was higher in adverse-outcome patients and most prominent in Group 1 (PD-L1 ≥50% and adverse outcome), whilst CN21 exposure was highest in Group 4 (PD-L1 ≥50% and favourable outcome). No signature retained statistical significance after correction for multiple testing.

CONCLUSIONS: In this exploratory cohort, ctDNA-derived CN signatures may capture structural genomic phenotypes associated with ICI outcome beyond PD-L1 and TMB. CN9 emerged as a candidate resistance-associated signature, whereas CN21 was associated with more favourable disease control. These findings are hypothesis-generating and require validation in larger prospective cohorts.

PMID:42601918 | PMC:PMC13472933 | DOI:10.3389/fonc.2026.1876274

J Prosthet Dent. 2026 Aug 15:S0022-3913(26)00516-0. doi: 10.1016/j.prosdent.2026.07.012. Online ahead of print.

ABSTRACT

STATEMENT OF PROBLEM: The design outcomes of dental crowns generated by artificial intelligence (AI)-based computer-aided design (CAD) systems have not been sufficiently evaluated in comparison with those produced by conventional CAD software programs.

PURPOSE: The purpose of this study was to compare the precision, morphology, and structural stability of dental crowns designed using AI-based software programs and a conventional CAD software program.

MATERIAL AND METHODS: A digital cast of the maxillary first molar was created from a typodont. Crowns were designed with 3 CAD software programs: a conventional software program (3Shape Dental Designer; 3Shape A/S) (CC group) and 2 AI-based software programs (3Shape Automate; 3Shape A/S) (AA group) and (Dentbird; Imagoworks Inc) (AD group) using the same prepared tooth standard tessellation language (STL) file. Sample size had been determined using an a priori power analysis with α=.05, 1-β=.80, and 10 specimens per group were fabricated using a 5-axis milling machine (BX5 PLUS; Dental Plus). The designs were evaluated for precision using root mean square (RMS) deviation, and the cusp angle of the functional cusp was analyzed for morphological comparison. Finite element analysis (FEA) was performed to compare the stress distribution among the groups. Statistical significance was assessed using the Kruskal-Wallis test and 1-way analysis of variance (ANOVA) (α=.05).

RESULTS: The CC group showed the highest precision, presenting the lowest RMS deviation (13.9 ±3.8 µm), followed by the AD and AA groups, with significant intergroup differences (P<.001). For morphological evaluation, the AA group exhibited the lowest functional cusp angle (64.4 ±3.5 degrees), which was significantly smaller than those of the CC and AD groups (P<.001). FEA demonstrated that all crowns experienced maximum stress under 0-degree loading and minimum stress under 90-degree loading conditions. The AA group showed the highest von Mises stress (839.8 MPa), whereas the AD group exhibited the smallest displacement and the most homogeneous stress distribution.

CONCLUSIONS: Conventional CAD exhibited higher precision than AI-based crown design systems, while AI-based designs showed altered cusp angle and occlusal morphology. FEA demonstrated design-dependent differences in stress distribution and displacement.

PMID:42601314 | DOI:10.1016/j.prosdent.2026.07.012

Oral Surg Oral Med Oral Pathol Oral Radiol. 2026 Jun 25:S2212-4403(26)00256-7. doi: 10.1016/j.oooo.2026.06.009. Online ahead of print.

ABSTRACT

BACKGROUND: There is increasing interest in MRI for diagnostic purposes in dentistry, resulting in development of a novel dental-dedicated MRI (ddMRI) system. This study compared 2D and 3D acquisitions of the mandibular third molar (MTM) region using ddMRI.

METHODS: Twenty MTMs of 20 patients were randomly selected from participants in clinical trials scanned using ddMRI (MAGNETOM Free. Max, 0.55 T, Siemens Healthineers AG, Forchheim, Germany). The 2D and 3D acquisitions were obtained for each patient. Three observers evaluated both 2D and 3D image datasets of each patient for overall quality and visibility of relevant anatomic structures. Results focused on descriptive statistics, inter- and intraobserver reproducibility and inferential statistics for comparison of 2D and 3D images within each observer.

RESULTS: Most anatomic structures were recorded as visible in every dataset for both 2D and 3D acquisitions. The 2D images showed superior quality and lingual nerve visibility across all observers compared to 3D volumes. Inter- and intraobserver reproducibility ranged vastly, from -0.167 to 1 and -0.667 to 1.

CONCLUSIONS: Most anatomic features in the MTM region are adequately depicted with both 2D and 3D acquisitions. The 2D acquisition quality was sufficient for diagnostic purposes of the MTM region, while diagnostic capability of the 3D volumes remains uncertain.

PMID:42601287 | DOI:10.1016/j.oooo.2026.06.009

Int J Oral Maxillofac Surg. 2026 Aug 14:S0901-5027(26)00338-3. doi: 10.1016/j.ijom.2026.07.017. Online ahead of print.

ABSTRACT

Surgical navigation can improve orbital implant positioning, yet current commercial systems are limited by the feedback interpretation and its timing after implant positioning. This study evaluated how different visualization methods impact the efficiency and interpretability of real-time feedback during orbital reconstruction. A standardized in-vitro setup was used to simulate orbital reconstructions by six surgeons of varying experience levels. Each surgeon performed implant positioning tasks using six different visualization modes, starting from three predefined misaligned positions. Interpretation accuracy and time to correct implant positioning were recorded. Visualization methods varied in the presence of computed tomography (CT) images, visualization of three-dimensional (3D) models, and use of implant-to-target distance visualization (distance mapping). Across a total of 108 reconstructions, no statistically significant differences in positioning time or interpretation accuracy were found between visualization methods. However, surgeon feedback revealed strong preferences: anatomical CT data and additional coronal view at the ledge improved spatial understanding, while views relying solely on 3D models or instrument orientation were seen as less intuitive. Notably, surgeons reported a consistent two-step strategy: initially 3D views were used for initial spatial alignment, followed by two-dimensional slices for precise adjustments. These findings suggest that while performance metrics may not differ across visualization modes, surgeon usability and interpretability are critical for clinical implementation. Future development of a clinically viable navigation workflow should include real-time feedback with both anatomical context and tailored visual support to promote more intuitive navigation in orbital surgery.

PMID:42601279 | DOI:10.1016/j.ijom.2026.07.017

Dig Liver Dis. 2026 Aug 14:S1590-8658(26)00881-9. doi: 10.1016/j.dld.2026.07.084. Online ahead of print.

ABSTRACT

BACKGROUND: Gastrointestinal stromal tumors (GISTs) are tumors with malignant potential. This research aims to develop an artificial intelligence (AI)-based system for analyzing endoscopic ultrasonography (EUS) visuals and generating risk scores.

AIMS: This system is designed to better predict GIST risk levels by identifying risk factors.

METHODS: An internal dataset comprising 504 EUS images from 226 patients with pathologically confirmed GISTs was collected from Yuzhong Hospital and Jiangnan Hospital of the Second Affiliated Hospital of Chongqing Medical University, Sichuan Provincial People’s Hospital between 2018 and 2024. Multiple AI-based machine learning models were developed and tested. Six machine learning models were compared, and the optimal diagnostic model was selected based on statistical results.

RESULTS: For the best-performing model, XGBoost, the internal test set results were as follows: overall accuracy 83.17%, sensitivity 75.68%, specificity 93.72%, positive predictive value (PPV) 73.21%, negative predictive value (NPV) 93.52%, F1 score 0.74, and area under the curve (AUC) 0.97. The external validation set results were: overall accuracy 80.68%, sensitivity 71.53%, specificity 92.72%, PPV 70.44%, NPV 92.78%, F1 score 0.70, and AUC 0.94.

CONCLUSIONS: This model utilizes machine learning algorithms on EUS images to accurately predict GIST risk stratification.

PMID:42601260 | DOI:10.1016/j.dld.2026.07.084

Sci Total Environ. 2026 Aug 14:182202. doi: 10.1016/j.scitotenv.2026.182202. Online ahead of print.

NO ABSTRACT

PMID:42601255 | DOI:10.1016/j.scitotenv.2026.182202