Enhancing the Biodegradability, Water Solubility, and Thermal Properties of Polyvinyl Alcohol through Natural Polymer Blending: An Approach toward Sustainable Polymer Applications.

The escalating environmental crisis posed by single-use plastics underscores the urgent need for sustainable alternatives. This study provides an approach to introduce biodegradable polymer blends by blending synthetic polyvinyl alcohol (PVA) with natural polymers-corn starch (CS) and hydroxypropyl methylcellulose (HPMC)-to address this challenge. Through a comprehensive analysis, including of the structure, mechanical strength, water solubility, biodegradability, and thermal properties, we investigated the enhanced performance of PVA-CS and PVA-HPMC blends over conventional polymers. Scanning electron microscopy (SEM) findings of pure PVA and its blends were studied, and we found a complete homogeneity between the PVA and both types of natural polymers in the case of a high concentration of PVA, whereas at lower concentration of PVA, some granules of CS and HMPC appear in the SEM. Blending corn starch (CS) with PVA significantly boosts its biodegradability in soil environments, since adding starch of 50 w/w duplicates the rate of PVA biodegradation. Incorporating hydroxypropyl methylcellulose (HPMC) with PVA not only improves water solubility but also enhances biodegradation rates, as the addition of HPMC increases the biodegradation of pure PVA from 10 to 100% and raises the water solubility from 80 to 100%, highlighting the significant acceleration of the biodegradation process and water solubility caused by HPMC addition, making these blends suitable for a wide range of applications, from packaging and agricultural films to biomedical engineering. The thermal properties of pure PVA and its blends with natural were studied using diffraction scanning calorimetry (DSC). It is found that the glass transition temperature (Tg) increases after adding natural polymers to PVA, referring to an improvement in the molecular weight and intermolecular interactions between blend molecules. Moreover, the amorphous structure of natural polymers makes the melting temperature ™ lessen after adding natural polymer, so the blends require lower temperature to remelt and be recycled again. For the mechanical properties, both types of natural polymer decrease the tensile strength and elongation at break, which overall weakens the mechanical properties of PVA. Our findings offer a promising pathway for the development of environmentally friendly polymers that do not compromise on performance, marking a significant step forward in polymer science's contribution to sustainability. This work presents detailed experimental and theoretical insights into novel polymerization methods and the utilization of biological strategies for advanced material design.

Artificial Intelligence and Machine Learning in Cancer Pain: A Systematic Review.

BACKGROUND/OBJECTIVES: Pain is a challenging multifaceted symptom reported by most cancer patients. This systematic review aims to explore applications of artificial intelligence/machine learning (AI/ML) in predicting pain-related outcomes and pain management in cancer. METHODS: A comprehensive search of Ovid MEDLINE, EMBASE and Web of Science databases was conducted using terms: "Cancer", "Pain", "Pain Management", "Analgesics", "Artificial Intelligence", "Machine Learning", and "Neural Networks" published up to September 7, 2023. AI/ML models, their validation and performance were summarized. Quality assessment was conducted using PROBAST risk-of-bias andadherence to TRIPOD guidelines. RESULTS: Forty four studies from 2006-2023 were included. Nineteen studies used AI/ML for classifying pain after cancer therapy [median AUC 0.80 (range 0.76-0.94)]. Eighteen studies focused on cancer pain research [median AUC 0.86 (range 0.50-0.99)], and 7 focused on applying AI/ML for cancer pain management, [median AUC 0.71 (range 0.47-0.89)]. Median AUC (0.77) of models across all studies. Random forest models demonstrated the highest performance (median AUC 0.81), lasso models had the highest median sensitivity (1), while Support Vector Machine had the highest median specificity (0.74). Overall adherence to TRIPOD guidelines was 70.7%. Overall, high risk-of-bias (77.3%), lack of external validation (14%) and clinical application (23%) was detected. Reporting of model calibration was also missing (5%). CONCLUSION: Implementation of AI/ML tools promises significant advances in the classification, risk stratification, and management decisions for cancer pain. Further research focusing on quality improvement, model calibration, rigorous external clinical validation in real healthcare settings is imperative for ensuring its practical and reliable application in clinical practice.

Influenza vaccination-associated cryoglobulinemic vasculitis.

Mixed cryoglobulinemia is a small vessel vasculitis associated with viral infections, mainly hepatitis C virus, however, other important causes include lymphoproliferative and autoimmune disorders. Influenza vaccine-induced cryoglobulinemia has been rarely reported. A 68-year-old male presented on three occasions following influenza vaccination with purpuric rash and lower extremities swelling. His lab work showed mixed cryoglobulins. On his most recent presentation, in addition to the purpura, he presented with thrombocytopenia and nephritic syndrome. A kidney biopsy showed endocapillary proliferative glomerulonephritis with organized deposits, consistent with mixed type cryoglobulinemic glomerulonephritis. The patient was treated with rituximab infusion with progressive improvement of the acute kidney injury (AKI) and complete recovery. It is unclear why cryoglobulins are produced as a response to a vaccination, but this association is important to be aware of for prompt monitoring and treatment.

A Visualization and Radiation Treatment Plan Quality Scoring Method for Triage in a Population-Based Context.

Purpose: Our purpose was to develop a clinically intuitive and easily understandable scoring method using statistical metrics to visually determine the quality of a radiation treatment plan. Methods and Materials: Data from 111 patients with head and neck cancer were used to establish a percentile-based scoring system for treatment plan quality evaluation on both a plan-by-plan and objective-by-objective basis. The percentile scores for each clinical objective and the overall treatment plan score were then visualized using a daisy plot. To validate our scoring method, 6 physicians were recruited to assess 60 plans, each using a scoring table consisting of a 5-point Likert scale (with scores ≥3 considered passing). Spearman correlation analysis was conducted to assess the association between increasing treatment plan percentile rank and physician rating, with Likert scores of 1 and 2 representing clinically unacceptable plans, scores of 3 and 4 representing plans needing minor edits, and a score of 5 representing clinically acceptable plans. Receiver operating characteristic curve analysis was used to assess the scoring system's ability to quantify plan quality. Results: Of the 60 plans scored by the physicians, 8 were deemed as clinically acceptable; these plans had an 89.0th ± 14.5 percentile value using our scoring system. The plans needing minor edits or deemed unacceptable had more variation, with scores falling in the 62.6nd ± 25.1 percentile and 35.6th ± 25.7 percentile, respectively. The estimated Spearman correlation coefficient between the physician score and treatment plan percentile was 0.53 (P < .001), indicating a moderate but statistically significant correlation. Receiver operating characteristic curve analysis demonstrated discernment between acceptable and unacceptable plan quality, with an area under the curve of 0.76. Conclusions: Our scoring system correlates with physician ratings while providing intuitive visual feedback for identifying good treatment plan quality, thereby indicating its utility in the quality assurance process.

Dietary Chitosan Attenuates High-Fat Diet-Induced Oxidative Stress, Apoptosis, and Inflammation in Nile Tilapia (Oreochromis niloticus) through Regulation of Nrf2/Kaep1 and Bcl-2/Bax Pathways.

Fatty liver injury is a prevalent condition in most farmed fish, yet the molecular mechanisms underpinning this pathology remain largely elusive. A comprehensive feeding trial spanning eight weeks was conducted to discern the potential of dietary chitosan in mitigating the deleterious effects of a high-fat diet (HFD) while concurrently exploring the underlying mechanism. Growth performance, haemato-biochemical capacity, antioxidant capacity, apoptotic/anti-apoptotic gene expression, inflammatory gene expression, and histopathological changes in the liver, kidney, and intestine were meticulously assessed in Nile tilapia. Six experimental diets were formulated with varying concentrations of chitosan. The first three groups were administered a diet comprising 6% fat with chitosan concentrations of 0%, 5%, and 10% and were designated as F6Ch0, F6Ch5, and F6Ch10, respectively. Conversely, the fourth, fifth, and sixth groups were fed a diet containing 12% fat with chitosan concentrations of 0%, 5%, and 10%, respectively, for 60 days and were termed F12Ch0, F12Ch5, and F12Ch10. The results showed that fish fed an HFD demonstrated enhanced growth rates and a significant accumulation of fat in the perivisceral tissue, accompanied by markedly elevated serum hepatic injury biomarkers and serum lipid levels, along with upregulation of pro-apoptotic and inflammatory markers. In stark contrast, the expression levels of nrf2, sod, gpx, and bcl-2 were notably decreased when compared with the control normal fat group. These observations were accompanied by marked diffuse hepatic steatosis, diffuse tubular damage, and shortened intestinal villi. Intriguingly, chitosan supplementation effectively mitigated the aforementioned findings and alleviated intestinal injury by upregulating the expression of tight junction-related genes. It could be concluded that dietary chitosan alleviates the adverse impacts of an HFD on the liver, kidney, and intestine by modulating the impaired antioxidant defense system, inflammation, and apoptosis through the variation in nrf2 and cox2 signaling pathways.

Interleukin-17 Genotypes in Egyptian Asthmatic Children at Zagazig University Hospitals.

Bronchial asthma (BA) is increasing among Egyptian children. It is affected by multiple factors including genetic ones. In the current study, we assessed the relationship between interleukin-17 (IL-17) genotypes and the occurrence of BA among Egyptian children. This case-control study included 100 participants. Group I (the control group) comprised 50 healthy subjects. Group II (the asthmatic group) comprised 50 subjects diagnosed with atopic asthma according to the Global Initiative for Asthma. Measurement of serum Ig E and eosinophilic count was performed. Detection of single nucleotide polymorphism rs2275913 of IL-17 gene by restriction fragment length polymorphism-polymerase chain reaction was conducted. GA and AA genotypes were more frequent in the asthmatic group compared to the control group (P = 0.03 and 0.01, respectively). Subjects carrying GA and AA genotypes were more susceptible to have asthma [odds ratio (OR) = 2.21, 95% confidence interval (CI) = 1.14-9.94, P = 0.03; OR = 7.78, 95% CI = 1.59-38.3, P = 0.01, respectively]. The A allele was higher in the asthmatic group (33%) compared to the control group (10%). A allele carriers were more susceptible to have asthma (OR = 4.43, 95% CI = 2.04-9.82 and P < 0.001). Immunoglobulin E (IgE) levels and eosinophil percentages were higher among the carriers of GA and AA genotypes when compared with the GG genotype. All pulmonary function tests were significantly lower among carriers of AA genotype compared with GG genotype. An A allele carrier, AA genotype, increased IgE level, and eosinophil level were significant predictors for occurrence of asthma (P = 0.01, 0.02, 0.004, and 0.01). In conclusion, AA genotype carriers and A allele carriers of the IL-17 gene are more likely to have asthma compared with controls.

Associations Between Radiation Oncologist Demographic Factors and Segmentation Similarity Benchmarks: Insights From a Crowd-Sourced Challenge Using Bayesian Estimation.

PURPOSE: The quality of radiotherapy auto-segmentation training data, primarily derived from clinician observers, is of utmost importance. However, the factors influencing the quality of clinician-derived segmentations are poorly understood; our study aims to quantify these factors. METHODS: Organ at risk (OAR) and tumor-related segmentations provided by radiation oncologists from the Contouring Collaborative for Consensus in Radiation Oncology data set were used. Segmentations were derived from five disease sites: breast, sarcoma, head and neck (H&N), gynecologic (GYN), and GI. Segmentation quality was determined on a structure-by-structure basis by comparing the observer segmentations with an expert-derived consensus, which served as a reference standard benchmark. The Dice similarity coefficient (DSC) was primarily used as a metric for the comparisons. DSC was stratified into binary groups on the basis of structure-specific expert-derived interobserver variability (IOV) cutoffs. Generalized linear mixed-effects models using Bayesian estimation were used to investigate the association between demographic variables and the binarized DSC for each disease site. Variables with a highest density interval excluding zero were considered to substantially affect the outcome measure. RESULTS: Five hundred seventy-four, 110, 452, 112, and 48 segmentations were used for the breast, sarcoma, H&N, GYN, and GI cases, respectively. The median percentage of segmentations that crossed the expert DSC IOV cutoff when stratified by structure type was 55% and 31% for OARs and tumors, respectively. Regression analysis revealed that the structure being tumor-related had a substantial negative impact on binarized DSC for the breast, sarcoma, H&N, and GI cases. There were no recurring relationships between segmentation quality and demographic variables across the cases, with most variables demonstrating large standard deviations. CONCLUSION: Our study highlights substantial uncertainty surrounding conventionally presumed factors influencing segmentation quality relative to benchmarks.

Development of Penicillin-Based Carbonic Anhydrase Inhibitors Targeting Multidrug-Resistant Neisseria gonorrhoeae.

The development of antibacterial drugs with new mechanisms of action is crucial in combating the rise of antibiotic-resistant infections. Bacterial carbonic anhydrases (CAs, EC 4.2.1.1) have been validated as promising antibacterial targets against pathogens such as Helicobacter pylori, Neisseria gonorrhoeae, and vancomycin-resistant enterococci. A multitarget strategy is proposed to design penicillin-based CA inhibitor hybrids for tackling resistance by targeting multiple bacterial pathways, thereby resensitizing drug-resistant strains to clinical antibiotics. The sulfonamide derivatives potently inhibited the CAs from N. gonorrhoeae and Escherichia coli with KI values in the range of 7.1-617.2 nM. Computational simulations with the main penicillin-binding protein (PBP) of N. gonorrhoeae indicated that these hybrid derivatives maintained the mechanism of action of the lead ß-lactams. A subset of derivatives showed potent PBP-related antigonococcal effects against multidrug-resistant N. gonorrhoeae strains, with several compounds significantly outperforming both the lead ß-lactam and CA inhibitor drugs (MIC values in the range 0.25 to 0.5 µg/mL).

Synthetic Degradable Polyvinyl Alcohol Polymer and Its Blends with Starch and Cellulose-A Comprehensive Overview.

Approximately 50% of global plastic wastes are produced from plastic packaging, a substantial amount of which is disposed of within a few minutes of its use. Although many plastic types are designed for single use, they are not always disposable. It is now widely acknowledged that the production and disposal of plastics have led to a plethora of negative consequences, including the contamination of both groundwater and soil resources and the deterioration of human health. The undeniable impact of excessive plastic manufacturing and waste generation on the global plastic pollution crisis has been well documented. Therefore, degradable polymers are a crucial solution to the problem of the non-degradation of plastic wastes. The disadvantage of degradable polymers is their high cost, so blending them with natural polymers will reduce the cost of final products and maximize their degradation rate, making degradable polymers competitive with industrial polymers that are currently in use daily. In this work, we will delineate various degradable polymers, including polycaprolactone, starch, and cellulose. Furthermore, we will elucidate several aspects of polyvinyl alcohol (PVA) and its blends with natural polymers to show the effects of adding natural polymers on PVA properties. This paper will study cost-effective and ecologically acceptable polymers by combining inexpensive natural polymers with readily accessible biodegradable polymers such as polyvinyl alcohol (PVA).

Cluster-Based Toxicity Estimation of Osteoradionecrosis Via Unsupervised Machine Learning: Moving Beyond Single Dose-Parameter Normal Tissue Complication Probability by Using Whole Dose-Volume Histograms for Cohort Risk Stratification.

PURPOSE: Given the limitations of extant models for normal tissue complication probability estimation for osteoradionecrosis (ORN) of the mandible, the purpose of this study was to enrich statistical inference by exploiting structural properties of data and provide a clinically reliable model for ORN risk evaluation through an unsupervised-learning analysis that incorporates the whole radiation dose distribution on the mandible. METHODS AND MATERIALS: The analysis was conducted on retrospective data of 1259 patients with head and neck cancer treated at The University of Texas MD Anderson Cancer Center between 2005 and 2015. During a minimum 12-month posttherapy follow-up period, 173 patients in this cohort (13.7%) developed ORN (grades I to IV). The (structural) clusters of mandibular dose-volume histograms (DVHs) for these patients were identified using the K-means clustering method. A soft-margin support vector machine was used to determine the cluster borders and partition the dose-volume space. The risk of ORN for each dose-volume region was calculated based on incidence rates and other clinical risk factors. RESULTS: The K-means clustering method identified 6 clusters among the DVHs. Based on the first 5 clusters, the dose-volume space was partitioned by the soft-margin support vector machine into distinct regions with different risk indices. The sixth cluster entirely overlapped with the others; the region of this cluster was determined by its envelopes. For each region, the ORN incidence rate per preradiation dental extraction status (a statistically significant, nondose related risk factor for ORN) was reported as the corresponding risk index. CONCLUSIONS: This study presents an unsupervised-learning analysis of a large-scale data set to evaluate the risk of mandibular ORN among patients with head and neck cancer. The results provide a visual risk-assessment tool for ORN (based on the whole DVH and preradiation dental extraction status) as well as a range of constraints for dose optimization under different risk levels.

Seasonal screening of pesticide residues in beehive products collected from different districts in Egypt.

Pesticides are of immense importance in agriculture, but they might contaminate bees' products. In this study, samples of honey, pollen, and beeswax were collected, seasonally, from apiaries in Toshka (Aswan), El-Noubariya (El-Beheira), and Ismailia (Ismailia) cities in Egypt. The pesticide residues were analyzed using the GC-MS after being extracted and cleaned using the QuEChERS method. Results showed that samples from El-Noubariya had great content of residues followed by Ismailia, and finally Toshka. Samples collected during fall and winter had the highest pesticide residue contents. Specifically, the phenylconazole fungicide group was repeatedly detected in all the examined samples along with organophosphate insecticides. Beeswax samples had the greatest amounts of pesticide residues followed by pollen and then honey samples. Chlorpyrifos (0.07-39.16 ng/g) and profenofos (1.94-17.00 ng/g) were detected in honey samples and their products. Pyriproxyfen (57.12 ng/g) and chlorpyrifos-methyl (39.16 ng/g) were detected in great amounts in beeswax samples from Ismailia and El-Noubariya, respectively. Yet, according to health hazard and quotient studies, the amounts of pesticides detected in honey do not pose any health threats to humans.

GEMIN4 Variants: Risk Profiling, Bioinformatics, and Dynamic Simulations Uncover Susceptibility to Bladder Carcinoma.

BACKGROUND: The relationship between GEMIN4 genetic variants and cancer, especially bladder carcinoma (BLCA), has been explored without conclusive results. This study aims to elucidate the link between GEMIN4 polymorphisms and BLCA susceptibility through genetic analyses, bioinformatics, and molecular dynamics (MD) simulations. METHODS: A cohort of 249 participants (121 BLCA patients and 128 unrelated controls) was enrolled. PCR was employed for allelic discrimination of GEMIN4 variants, followed by subgroup stratification, haplotype analyses, structural prediction using the AlphaFold2 prediction tool, subsequent MD simulations, structural analysis, and residue interaction mapping using Desmond, UCSF ChimeraX, and Cytoscape softwares. RESULTS: The rs.2740348*G variant demonstrated a protective role against BLCA in allelic (OR = 0.55, p = 0.002) and recessive (OR = 0.54, p = 0.017) models, whereas the rs.7813*T variant increased BLCA risk under the recessive model (OR = 1.90, p = 0.019). Haplotype analysis revealed a significant association between GEMIN4 haplotype (rs.2740348*C/rs.7813*T) with increased BLCA risk (OR = 2.01, p = 0.004). Univariate analysis revealed associations of the variants with albumin levels and absolute neutrophil count in BLCA patients. Pathogenicity evaluation categorized p.Gln450Glu as neutral and p.Arg1033Cys as deleterious. MD simulations revealed structural alterations and conformational shifts in the GEMIN4 protein induced by the Glu450 and Cys1033 mutations. CONCLUSIONS: The study highlights the dual role of GEMIN4 variants in BLCA susceptibility, with rs.2740348 conferring protection and rs.7813 increasing risk. The Glu450 residue positively impacted protein stability, while Cys1033 had a detrimental effect on protein function. These findings underscore the significance of GEMIN4 variants in BLCA susceptibility and pave the way for future diagnostic and therapeutic initiatives.

Comparison of Machine Leaning Models for Prediction of Acute Pain Severity and On-Treatment Opioid Utilization in Oral Cavity and Oropharyngeal Cancer Patients Receiving Radiation Therapy: Exploratory Analysis from a Large-Scale Retrospective Cohort.

Background: Acute pain is a common and debilitating symptom experienced by oral cavity and oropharyngeal cancer (OC/OPC) patients undergoing radiation therapy (RT). Uncontrolled pain can result in opioid overuse and increased risks of long-term opioid dependence. The specific aim of this exploratory analysis was the prediction of severe acute pain and opioid use in the acute on-treatment setting, to develop risk-stratification models for pragmatic clinical trials. Materials and Methods: A retrospective study was conducted on 900 OC/OPC patients treated with RT during 2017 to 2023. Clinical data including demographics, tumor data, pain scores and medication data were extracted from patient records. On-treatment pain intensity scores were assessed using a numeric rating scale (0-none, 10-worst) and total opioid doses were calculated using morphine equivalent daily dose (MEDD) conversion factors. Analgesics efficacy was assessed based on the combined pain intensity and the total required MEDD. ML models, including Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting Model (GBM) were developed and validated using ten-fold cross-validation. Performance of models were evaluated using discrimination and calibration metrics. Feature importance was investigated using bootstrap and permutation techniques. Results: For predicting acute pain intensity, the GBM demonstrated superior area under the receiver operating curve (AUC) (0.71), recall (0.39), and F1 score (0.48). For predicting the total MEDD, LR outperformed other models in the AUC (0.67). For predicting the analgesics efficacy, SVM achieved the highest specificity (0.97), and best calibration (ECE of 0.06), while RF and GBM achieved the same highest AUC, 0.68. RF model emerged as the best calibrated model with ECE of 0.02 for pain intensity prediction and 0.05 for MEDD prediction. Baseline pain scores and vital signs demonstrated the most contributed features for the different predictive models. Conclusion: These ML models are promising in predicting end-of-treatment acute pain and opioid requirements and analgesics efficacy in OC/OPC patients undergoing RT. Baseline pain score, vital sign changes were identified as crucial predictors. Implementation of these models in clinical practice could facilitate early risk stratification and personalized pain management. Prospective multicentric studies and external validation are essential for further refinement and generalizability.

Retraction: Hematocolpos: An Unusual Cause of Right Iliac Fossa Pain.

[This retracts the article DOI: 10.7759/cureus.18835.].

Characterization, antibacterial, and cytotoxic activities of silver nanoparticles using the whole biofilm layer as a macromolecule in biosynthesis.

Recently, multi-drug resistant (MDR) bacteria are responsible for a large number of infectious diseases that can be life-threatening. Globally, new approaches are targeted to solve this essential issue. This study aims to discover novel antibiotic alternatives by using the whole components of the biofilm layer as a macromolecule to synthesize silver nanoparticles (AgNPs) as a promising agent against MDR. In particular, the biosynthesized biofilm-AgNPs were characterized using UV-Vis spectroscopy, electron microscopes, Energy Dispersive X-ray (EDX), zeta sizer and potential while their effect on bacterial strains and normal cell lines was identified. Accordingly, biofilm-AgNPs have a lavender-colored solution, spherical shape, with a size range of 20-60 nm. Notably, they have inhibitory effects when used on various bacterial strains with concentrations ranging between 12.5 and 25 µg/mL. In addition, they have an effective synergistic effect when combined with phage ZCSE9 to inhibit and kill Salmonella enterica with a concentration of 3.1 µg/mL. In conclusion, this work presents a novel biosynthesis preparation of AgNPs using biofilm for antibacterial purposes to reduce the possible toxicity by reducing the MICs using phage ZCSE9.

Investigation of autosegmentation techniques on T2-weighted MRI for off-line dose reconstruction in MR-linac workflow for head and neck cancers.

BACKGROUND: In order to accurately accumulate delivered dose for head and neck cancer patients treated with the Adapt to Position workflow on the 1.5T magnetic resonance imaging (MRI)-linear accelerator (MR-linac), the low-resolution T2-weighted MRIs used for daily setup must be segmented to enable reconstruction of the delivered dose at each fraction. PURPOSE: In this pilot study, we evaluate various autosegmentation methods for head and neck organs at risk (OARs) on on-board setup MRIs from the MR-linac for off-line reconstruction of delivered dose. METHODS: Seven OARs (parotid glands, submandibular glands, mandible, spinal cord, and brainstem) were contoured on 43 images by seven observers each. Ground truth contours were generated using a simultaneous truth and performance level estimation (STAPLE) algorithm. Twenty total autosegmentation methods were evaluated in ADMIRE: 1-9) atlas-based autosegmentation using a population atlas library (PAL) of 5/10/15 patients with STAPLE, patch fusion (PF), random forest (RF) for label fusion; 10-19) autosegmentation using images from a patient's 1-4 prior fractions (individualized patient prior [IPP]) using STAPLE/PF/RF; 20) deep learning (DL) (3D ResUNet trained on 43 ground truth structure sets plus 45 contoured by one observer). Execution time was measured for each method. Autosegmented structures were compared to ground truth structures using the Dice similarity coefficient, mean surface distance (MSD), Hausdorff distance (HD), and Jaccard index (JI). For each metric and OAR, performance was compared to the inter-observer variability using Dunn's test with control. Methods were compared pairwise using the Steel-Dwass test for each metric pooled across all OARs. Further dosimetric analysis was performed on three high-performing autosegmentation methods (DL, IPP with RF and 4 fractions [IPP_RF_4], IPP with 1 fraction [IPP_1]), and one low-performing (PAL with STAPLE and 5 atlases [PAL_ST_5]). For five patients, delivered doses from clinical plans were recalculated on setup images with ground truth and autosegmented structure sets. Differences in maximum and mean dose to each structure between the ground truth and autosegmented structures were calculated and correlated with geometric metrics. RESULTS: DL and IPP methods performed best overall, all significantly outperforming inter-observer variability and with no significant difference between methods in pairwise comparison. PAL methods performed worst overall; most were not significantly different from the inter-observer variability or from each other. DL was the fastest method (33 s per case) and PAL methods the slowest (3.7-13.8 min per case). Execution time increased with a number of prior fractions/atlases for IPP and PAL. For DL, IPP_1, and IPP_RF_4, the majority (95%) of dose differences were within ± 250 cGy from ground truth, but outlier differences up to 785 cGy occurred. Dose differences were much higher for PAL_ST_5, with outlier differences up to 1920 cGy. Dose differences showed weak but significant correlations with all geometric metrics (R2 between 0.030 and 0.314). CONCLUSIONS: The autosegmentation methods offering the best combination of performance and execution time are DL and IPP_1. Dose reconstruction on on-board T2-weighted MRIs is feasible with autosegmented structures with minimal dosimetric variation from ground truth, but contours should be visually inspected prior to dose reconstruction in an end-to-end dose accumulation workflow.

Patterns of failure for recurrent head and neck squamous cell carcinoma treated with salvage surgery and postoperative IMRT reirradiation.

Purpose/Objectives: The purpose of this study was to evaluate patterns of locoregional recurrence (LRR) after surgical salvage and adjuvant reirradiation with IMRT for recurrent head and neck squamous cell cancer (HNSCC). Materials/Methods: Patterns of LRR for 61 patients treated consecutively between 2003 and 2014 who received post-operative IMRT reirradiation to ≥ 60 Gy for recurrent HNSCC were determined by 2 methods: 1) physician classification via visual comparison of post-radiotherapy imaging to reirradiation plans; and 2) using deformable image registration (DIR). Those without evaluable CT planning image data were excluded. All recurrences were verified by biopsy or radiological progression. Failures were defined as in-field, marginal, or out-of-field. Logistic regression analyses were performed to identify predictors for LRR. Results: A total of 55 patients were eligible for analysis and 23 (42 %) had documented LRR after reirradiation. Location of recurrent disease prior to salvage surgery (lymphatic vs. mucosal) was the most significant predictor of LRR after post-operative reirradiation with salvage rate of 67 % for lymphatic vs. 33 % for mucosal sites (p = 0.037). Physician classification of LRR yielded 14 (61 %) in-field failures, 3 (13 %) marginal failures, and 6 (26 %) out-of-field failures, while DIR yielded 10 (44 %) in-field failures, 4 (17 %) marginal failures, and 9 (39 %) out-of-field failures. Most failures (57 %) occurred within the original site of recurrence or first echelon lymphatic drainage. Of patients who had a free flap placed during salvage surgery, 56 % of failures occurred within 1 cm of the surgical flap. Conclusion: Our study highlights the role of DIR in enhancing the accuracy and consistency of POF analysis. Compared to traditional visual inspection, DIR reduces interobserver variability and provides more nuanced insights into dose-specific and spatial parameters of locoregional recurrences. Additionally, the study identifies the location of the initial recurrence as a key predictor of subsequent locoregional recurrence after salvage surgery and re-IMRT.

From ablation to operation: Unraveling the surgical outcomes and complications of thyroidectomy after radiofrequency ablation.

BACKGROUND: Radiofrequency ablation is a minimally invasive treatment for thyroid nodules; however, concerns exist regarding its impact on subsequent thyroid surgery. We compared surgical outcomes and complications between patients undergoing thyroidectomy after radiofrequency ablation (post-radiofrequency ablation thyroidectomy group) and those without prior radiofrequency ablation (non-radiofrequency ablation thyroidectomy group). METHODS: We retrospectively analyzed thyroidectomy patients, comparing post-radiofrequency ablation thyroidectomy and non-radiofrequency ablation thyroidectomy groups, examining demographics, nodule characteristics, surgical techniques, and complications. RESULTS: The study included 96 patients (73 in the non-radiofrequency ablation thyroidectomy group and 23 in the post-radiofrequency ablation thyroidectomy group). The mean age was 53.3 ± 14.4 years, with 78.1% female patients and 36.5% African American patients. Median operative time was similar between the post-radiofrequency ablation thyroidectomy (110 minutes) and the non-radiofrequency ablation thyroidectomy (92 minutes) cohorts (P = .40). Complications were reported in 13 patients, without significant differences between groups (P = .54). No permanent complications, including nerve injury or hypoparathyroidism, were reported in either cohort. Prior radiofrequency ablation treatment did not increase the risk of complications (odds ratio = 3.48, 95% confidence interval = 0.70-17.43, P = .16). CONCLUSION: Our work found no differences in outcomes or safety in patients undergoing thyroidectomy with or without previous radiofrequency ablation treatment, potentiating the post-radiofrequency ablation thyroidectomy group as a safe management option. Accordingly, this may reassure both clinicians and patients of the safety of radiofrequency ablation in treating patients with thyroid nodules.

Develop the artificial neural network approach to predict thermal transport analysis of nanofluid inside a porous enclosure.

This study explores the impacts of heat transportation on hybrid (Ag + MgO) nanofluid flow in a porous cavity using artificial neural networks (Bayesian regularization approach (BRT-ANN) neural networks technique). The cavity considered in this analysis is a semicircular shape with a heated and a cooled wall. The dynamics of flow and energy transmission in the cavity are influenced by various features such as the effect of magnetize field, porosity and volume fraction of nanoparticles. To explore the outcomes of these features on hybrid nanofluid thermal and flow transport, a BRT-ANN model is developed. The ANN model is trained using a dataset generated through numerical scheme. The trained ANN model is then used to predict the heat and flow transport characteristics for various input parameters. The accuracy of the ANN simulation is confirmed through comparison of the predicted results with the results obtained through numerical simulations. By maintaining the corrugated wall uniformly heated, we inspected the levels of isotherms, streamlines and heat transfer distribution. A graphical illustration highlights the characteristics of the Hartmann and Rayleigh numbers, permeability component in porous material, drag force and rate of energy transport. According to the percentage analysis, nanofluids (Ag + MgO/H2O) are prominent to enhance the thermal distribution of traditional fluids. The study demonstrates the potential of ANNs in predicting the impacts of various factors on hybrid nanofluid flow and heat transport, which can be useful in designing and optimizing heat transfer systems.