Tag: nevin manimala

Eur Arch Otorhinolaryngol. 2025 Nov 16. doi: 10.1007/s00405-025-09836-5. Online ahead of print.

ABSTRACT

PURPOSE: This study aimed to evaluate the efficacy of preoperative embolization (EMB) and its impact on complication rates in patients undergoing surgery for carotid body tumors (CBT) at a tertiary referral center.

METHODS: A retrospective analysis was performed on 44 patients who underwent surgical resection of carotid body paragangliomas between January 2000 and June 2024. 13 patients with tumor size less than 3 cm, which is the recommended criterion for preoperative embolization, were excluded from the study. Patients who underwent preoperative embolization (EMB group) were retrospectively compared with patients who did not undergo preoperative embolization (NEMB group). The effects of embolization on cranial nerve injuries, internal carotid artery (ICA) repair, blood loss, operative time, transfusion requirements, and hospital stay duration were evaluated.

RESULTS: Preoperative EMB was not performed in 21 patients (67.7%), and EMB was performed in 10 patients (32.3%) with a CBT > 3 cm. No differences were observed between the two groups in terms of age, gender, tumor size, or Shamblin classification. Additionally, there was no significant difference regarding cranial nerve injury, ICA repair, external carotid artery ligation, or overall complications (p > 0.05). Hemoglobin decrease, operative time, and transfusion requirements were also comparable between the groups (p > 0.05). When the preoperative hospitalization period for embolization was excluded, the mean hospital stay was 4.19 ± 1.37 days in the NEMB group and 4.40 ± 2.46 days in the EMB group with no statistically significant difference (p = 0.761).

CONCLUSION: Preoperative EMB did not significantly alter overall complication rates in CBT surgery but may offer particular benefits for large tumors. Given the variability in tumor characteristics among patients, decisions regarding embolization should be individualized.

PMID:41243017 | DOI:10.1007/s00405-025-09836-5

Support Care Cancer. 2025 Nov 17;33(12):1070. doi: 10.1007/s00520-025-10118-3.

ABSTRACT

PURPOSE: This study aims to assess the effectiveness of psychological prehabilitation in reducing postoperative complications and length of hospital stay in patients undergoing cancer surgery.

METHODS: A comprehensive electronic search was conducted in CINAHL, Cochrane Library, Medline, PsycINFO, AMED and Embase databases from inception to December 2023. Randomised controlled trials assessing the effectiveness of psychological prehabilitation compared to control in patients undergoing abdominal, pelvic, and/or thoracic cancer surgery were included. The primary outcome measures were postoperative complications and length of hospital stay. Two independent reviewers extracted relevant information and assessed the risk of bias. Random-effect meta-analyses were used to pool outcomes, and the quality of evidence was assessed using GRADE.

RESULTS: A total of 18 trials were identified (N = 1612) and 11 (N = 923) analysed, including eight multimodal (N = 719), one bimodal (N = 90) and two unimodal (N = 189). There was high-quality evidence that trials including psychological prehabilitation significantly reduced the incidence of overall postoperative complications in all cancer types included in the studies (relative risk: 0.73; 95% CI: 0.60 to 0.89) and abdominal cancer subgroup (relative risk: 0.65; 95% CI: 0.48 to 0.88) compared to control. Psychological prehabilitation was not effective in reducing length of hospital stay (mean difference: – 0.78; 95% CI: – 1.72 to 0.17).

CONCLUSION: Psychological prehabilitation appears effective in reducing postoperative complications in cancer patients. Future studies should investigate the optimal preoperative psychological interventions according to individual cancer groups undergoing surgery.

PMID:41243006 | DOI:10.1007/s00520-025-10118-3

Clin Oral Investig. 2025 Nov 17;29(12):571. doi: 10.1007/s00784-025-06649-y.

ABSTRACT

OBJECTIVE: This study aimed to evaluate the efficacy of a single preoperative 15 mg sublingual dose of melatonin in reducing surgical discomfort, pain, edema, and trismus following mandibular third molar extraction.

MATERIALS AND METHODS: A randomized, double-blind, placebo-controlled trial was conducted with 46 patients allocated to receive melatonin (n = 22) or placebo (n = 24) 45 min before surgery. The primary outcomes were intraoperative pain and discomfort and postoperative pain. Secondary outcomes included patient-perceived edema (VAS-Edema, days 1-5), trismus (5-point scale, days 1-5; interincisal measurement, day 7), and rescue medication consumption.

RESULTS: No statistically significant differences were observed between the melatonin and placebo groups for any outcome. Intraoperative discomfort (QCirDental total score, p = 0.54) and pain (VAS, p = 0.67) were comparable. Similarly, postoperative pain levels across all time points (p = 0.67), edema over five days (p = 0.26), and trismus based on self-assessment (all days p > 0.50) and clinical measurement (p = 0.79) did not differ.

CONCLUSION: Within the limitations of this clinical trial, a single 15 mg preoperative dose of sublingual melatonin was not superior to placebo in alleviating surgical discomfort, pain, edema, or trismus after third molar extraction.

CLINICAL RELEVANCE: Clinically, these findings suggest that melatonin may have limited effectiveness in managing common complications in oral surgery.

PMID:41243001 | DOI:10.1007/s00784-025-06649-y

Int J Colorectal Dis. 2025 Nov 17;40(1):233. doi: 10.1007/s00384-025-05031-4.

ABSTRACT

PURPOSE: Current diagnostic modalities lack sufficient sensitivity for detecting omental metastasis (OM), often underestimating metastatic burden. Unlike traditional statistical model, machine learning (ML) model is designed to detect subtle variable interactions and model nonlinear patterns that traditional statistics overlook, enhancing the reliability of OM risk evaluation in clinical practice. The aim of the study was to build a ML model in preoperatively predicting OM in right-sided colon cancer (RCC) patients using a multicenter dataset.

METHODS: This retrospective multicenter study included 1798 RCC patients: 1206 from Zhejiang Cancer Hospital (training set n = 804, test set n = 402) and 592 from the Second Affiliated Hospital of Harbin Medical University (validation set). OM status, tumor location, preoperative CEA level, preoperative CA199 level, Grade, histology, tumor size and age of patients were recorded. Six ML models including extreme gradient boosting (XGB), artificial neural network (ANN), logistic regression (LR), random forest (RF), support vector machine (SVM) and decision tree (DT) were developed for the OM prediction in RCC. The area under the receiver operator characteristic (ROC) curve (AUC), accuracy, sensitivity, specificity, precision, F1 score and decision curve analysis (DCA) were analyzed for judging predictive performance.

RESULTS: The OM rates in training set, test set and validation set were 10.4%, 9.5% and 10.0%, respectively. The XGB model outperforming five other algorithms (ANN, RF, LR, SVM, and DT) across training set (AUC = 0.924, 0.096 gain vs LR), internal test (AUC = 0.868, 0.038 gain vs LR) and validation set (AUC = 0.766, 0.065 gain vs LR). The comparison of accuracy, sensitivity, specificity, precision and F1 score revealed the XGB model exhibited the best performance. The DCA curve also suggested that XGB had better clinical decision-making capability than the other five models. Feature importance analysis highlighted preoperative CEA level and tumor location as key predictors.

CONCLUSION: Our study developed and validated an XGB-based machine learning model that could accurately predict OM in RCC patients using routine preoperative variables. This model demonstrates strong discriminative ability and clinical utility, assisting personalized risk stratification and appropriate treatment decisions.

PMID:41242993 | DOI:10.1007/s00384-025-05031-4

Bull Environ Contam Toxicol. 2025 Nov 16;115(6):68. doi: 10.1007/s00128-025-04143-5.

ABSTRACT

Benzene is a highly volatile monoaromatic hydrocarbon and genotoxic carcinogen. In Brazil, it is considered a priority for the National Health System. However, although this compound is targeted by health surveillance in Brazil, scarce data are available on occupational or environmental exposure. This review contributes to an overview regarding benzene levels in the second-largest metropolis in Southeastern Brazil, Rio de Janeiro, and associated risks. A decreasing trend has been noted in the city of Rio de Janeiro, in the last decades, due to more stringent vehicular emission legislations and advances in vehicular technology, although adulterated gasoline is still a concern. Future actions regarding reductions of benzene emissions in the city include regulatory and surveillance programs concerning adulterated gasoline, substitution of raw materials to reduce benzene input to production processes, implementing changes in operating conditions to minimize benzene formation or volatilization and equipment modification to avoid benzene escaping into the environment. Finally, the increasing use and further construction of alternative transportation can significantly contribute to lowering benzene emissions in Rio de Janeiro and other metropolis worldwide and should be implemented as soon as possible.

PMID:41242992 | DOI:10.1007/s00128-025-04143-5

Bull Environ Contam Toxicol. 2025 Nov 16;115(6):69. doi: 10.1007/s00128-025-04144-4.

ABSTRACT

Nitrogen dioxide (NO₂) in the air at concentrations exceeding permissible levels impacts environmental quality and human health. In addition, NO₂ is also a precursor to ozone and an agent that creates acid rain that affects the habitat of organisms. NO₂ emission inventories are the first and most important step, especially in urban or industrial production areas. This study assesses NO₂ emissions in Ho Chi Minh City (HCMC), Vietnam-a large city with high traffic and industrial activity but limited published emission data. Using the Lifetime-Modified Accumulation Method (LMAM), we analyze tropospheric NO₂ column data from the OMI/Aura satellite (2019-2024) to estimate spatial and temporal emission trends. The results showed an average emission rate of 6.56 × 10¹⁵ molecules cm⁻² h⁻¹ in 2019, decreasing to 5.79 × 10¹⁵ molecules cm⁻² h⁻¹ in 2020 due to the COVID-19 lockdown. Emissions were highest in urban and industrial areas and lowest in suburban areas. The LMAM model demonstrated a strong correlation with TROPESS Chemical Reanalysis (TCR) NO_x data (Spearman’s r = 0.71 in 2019; r = 0.70 in 2020), confirming its reliability for trend analysis. Long-term trends reflect the socioeconomic impact: a sharp decline during the pandemic (2020-2021) followed by a recovery to 1.3 × 10¹⁶ molecules cm⁻² h⁻¹ in 2023-2024 when economic activities resume. This result can provide information on NO₂ emissions as a reference for future city emission control policies and inventory plans.

PMID:41242983 | DOI:10.1007/s00128-025-04144-4

Brain Dev. 2025 Nov 15;47(6):104485. doi: 10.1016/j.braindev.2025.104485. Online ahead of print.

ABSTRACT

INTRODUCTION: Epilepsy is a chronic brain disorder characterized by recurrent seizures. Limited data on childhood epilepsy in Cameroon prompted this study.

METHODS: We conducted a cross-sectional study with retrospective data collection over six months (December 2023-May 2024). Medical records of children aged 3 months to 15 years, diagnosed with epilepsy and followed at Douala General Hospital between January 2020 and December 2023, were analyzed. Statistical analysis used SPSS 26.0, with Fisher’s exact and chi-square tests for associations, and logistic regression for predictive factors (p < 0.05).

RESULTS: 142 patients were included (male-to-female ratio = 1.21). Epilepsy prevalence was 2.4 %. Generalized seizures predominated (65.7 %) with focal epileptic abnormalities in 50.7 % of cases. Idiopathic generalized epilepsy represented 57.7 % of cases. Sodium valproate was used in 52.8 % of cases. Main etiological factors included: neonatal convulsions (61; 43 %), febrile seizures (49; 34.5 %) and neonatal asphyxia (35; 24.6 %). Seizures persisted in 35 patients (24.6 %) under treatment. Predictive factors for poor seizure control included unknown seizure type (OR 14.25 [1.10-183.97]; p = 0.04), cryptogenic focal epilepsy (OR 12.55 [1.58-99.71]; p = 0.02), and use of prayers or traditional medicines (OR 7.45 [1.01-55.13]; p = 0.05). Memory disorders significantly impacted school performance (OR 4.95 [1.80-13.59]; p = 0.002), along with lack of concentration (OR 3.04 [1.04-8.84]; p = 0.04).

CONCLUSION: This study identified specific predictive factors for poor epileptic control and confirms cognitive impact on schooling, providing intervention targets to optimize neurological and educational management in Cameroon.

PMID:41242021 | DOI:10.1016/j.braindev.2025.104485

Mar Pollut Bull. 2025 Nov 15;223:118998. doi: 10.1016/j.marpolbul.2025.118998. Online ahead of print.

ABSTRACT

Brown and red macroalgae are promising biomonitors of inorganic pollutants in marine environments, due to quick growth, high biomass, ease of sampling and the production of a diverse array of chelating agents. The limited distribution and low population densities of most of the currently adopted species, however, prompt the need to search for suitable alternatives. To this end, the accumulation kinetics of ten metals (Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, V, Zn) and one metalloid (As) under field-conditions were studied in Dictyota spiralis and Laurencia microcladia, brown and red macroalgae, respectively. Specifically, element concentrations were measured at 0, 2, 4, 8, 16 and 24d on algae (both alive and devitalized) exposed in the field (through purposely developed bags) in 4 sites differing in anthropogenic impacts along the Tyrrhenian coast (southern Italy). Robust Bayesian analyses were adopted for model fitting and selection, as well as to derive posterior distributions for parameters with straightforward practical implications for biomonitoring of coastal waters. Kinetics followed pseudo-first order, pseudo-second order and two-phase intraparticle diffusion models, with saturation times varying among elements, between species and between devitalization treatments. The spatial discrimination capability of the species varies in relation to the element, with a distinct advantage of living L. microcladia for the biomonitoring of Co, Cr, Cu, Fe, Mn and V, and of devitalized D. spiralis for the biomonitoring of As, Cu, Ni, Pb and Zn. Overall, findings highlight the remarkable effectiveness of transplants of both the species for the active biomonitoring of marine coastal ecosystems, especially when leveraging over their complementary selectivity toward different elements. The specificity in accumulation behavior allows also diversifying the target use of the two species, suggesting shorter-term monitoring and even bioremediation applications in the case of D. spiralis.

PMID:41242013 | DOI:10.1016/j.marpolbul.2025.118998

Comput Med Imaging Graph. 2025 Nov 14;126:102667. doi: 10.1016/j.compmedimag.2025.102667. Online ahead of print.

ABSTRACT

BACKGROUND AND OBJECTIVES: Neurofibromatosis type 1 (NF1) is a genetic disorder characterized by the development of multiple neurofibromas (NFs) throughout the body. Accurate segmentation of these tumors in whole-body magnetic resonance imaging (WB-MRI) is critical for quantifying tumor burden and clinical decision-making. This study aims to develop a pipeline for NF segmentation in fat-suppressed T2-weighted WB-MRI that incorporates anatomical context and radiomics to improve accuracy and specificity.

METHODS: The proposed pipeline consists of three stages: (1) anatomy segmentation using MRSegmentator and refinement with a high-risk NF zone; (2) NF segmentation using an ensemble of 3D anisotropic anatomy-informed U-Nets; and (3) tumor candidate classification using radiomic features to filter false positives. The study used 109 WB-MRI scans from 74 NF1 patients, divided into training and three test sets representing in-domain (3T), domain-shifted (1.5T), and low tumor burden scenarios. Evaluation metrics included per-scan and per-tumor Dice Similarity Coefficient (DSC), Volume Overlap Error (VOE), Absolute Relative Volume Difference (ARVD), and per-scan F1 score. Statistical significance was assessed using Wilcoxon signed-rank tests with Bonferroni correction.

RESULTS: On the in-domain test set, the proposed ensemble of 3D anisotropic anatomy-informed U-Nets with tumor candidate classification achieved a per-scan DSC of 0.64, outperforming 2D nnU-Net (DSC: 0.52) and 3D full-resolution nnU-Net (DSC: 0.54). Performance was maintained on the domain-shift test set (DSC: 0.51) but declined on low tumor burden cases (DSC: 0.23). Preliminary inter-reader variability analysis showed model-to-expert agreement (DSC: 0.67-0.69) comparable to inter-expert agreement (DSC: 0.69).

CONCLUSIONS: The proposed pipeline achieves the highest performance among established methods for automated NF segmentation in WB-MRI and approaches expert-level consistency. The integration of anatomical context and radiomics enhances robustness. Nonetheless, segmentation performance decreases in low tumor burden scenarios, indicating a key area for future methodological improvements. Additionally, the limited inter-reader agreement observed among experts underscores the inherent complexity and ambiguity of the NF segmentation task.

PMID:41241992 | DOI:10.1016/j.compmedimag.2025.102667

Phys Med. 2025 Nov 15;140:105215. doi: 10.1016/j.ejmp.2025.105215. Online ahead of print.

ABSTRACT

PURPOSE: Converting radiotherapy (RT) data from DICOM-RT into datasets suitable for statistical modelling remains challenging. We developed the DICOM Extraction and Structuration Toolkit (DEST), an automated solution that streamlines data extraction and ensures compatibility with statistical software. The reliability of DEST was also assessed by comparing its outputs with those from treatment planning systems (TPS) in study of cardiopulmonary dose-volume histograms (DVH) after radiotherapy (RT) for localised breast cancer.

METHODS: DEST comprises two main modules: a data extraction module and a viewer/analysis module. It processes DICOM-RT objects, including RT structure sets, RT plans, and RT dose files. Extractions are performed per treatment for predefined patient lists, after which structured data is consolidated. A 3D visualisation module verifies dose distributions for selected regions of interest, ensuring consistency and accuracy.

RESULTS: DEST was successfully applied to 404 patients from the “CANcer TOxicities – Radiation Therapy” (CANTO-RT) cohort. In this initial implementation, DEST showed strong overall agreement with regard to TPS for heart and lung dose metrics, including mean doses and dose-volume measurements. Specifically, near-minimum dose, median dose, near-maximum dose and percentages of volume receiving at least 10 Gy (V_10Gy), 20 Gy (V_20Gy), 30 Gy (V_30Gy) and 40 Gy (V_40Gy) showed high consistency between DEST and TPS.

CONCLUSIONS: DEST enhances accessibility to dose-volume metrics and will facilitate advanced modelling of medical outcomes (efficiency and risk) at the voxel level. By providing streamlined access to voxel spatial coordinates and local dose information, DEST enables more sophisticated analyses, such as clustering and localized region selection, supporting deeper insights into dose-response relationships.

PMID:41241989 | DOI:10.1016/j.ejmp.2025.105215