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.

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.

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.

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.

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.

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

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

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.

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

Background/objective: Pain is a challenging multifaceted symptom reported by most cancer patients, resulting in a substantial burden on both patients and healthcare systems. This systematic review aims to explore applications of artificial intelligence/machine learning (AI/ML) in predicting pain-related outcomes and supporting decision-making processes in pain management in cancer. Methods: A comprehensive search of Ovid MEDLINE, EMBASE and Web of Science databases was conducted using terms including "Cancer", "Pain", "Pain Management", "Analgesics", "Opioids", "Artificial Intelligence", "Machine Learning", "Deep Learning", and "Neural Networks" published up to September 7, 2023. The screening process was performed using the Covidence screening tool. Only original studies conducted in human cohorts were included. AI/ML models, their validation and performance and adherence to TRIPOD guidelines were summarized from the final included studies. Results: This systematic review included 44 studies from 2006-2023. Most studies were prospective and uni-institutional. There was an increase in the trend of AI/ML studies in cancer pain in the last 4 years. Nineteen studies used AI/ML for classifying cancer patients' pain development after cancer therapy, with median AUC 0.80 (range 0.76-0.94). Eighteen studies focused on cancer pain research with median AUC 0.86 (range 0.50-0.99), and 7 focused on applying AI/ML for cancer pain management decisions with median AUC 0.71 (range 0.47-0.89). Multiple ML models were investigated with. median AUC across all models in all studies (0.77). 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 of included studies to TRIPOD guidelines was 70.7%. Lack of external validation (14%) and clinical application (23%) of most included studies was detected. Reporting of model calibration was also missing in the majority of studies (5%). Conclusion: Implementation of various novel AI/ML tools promises significant advances in the classification, risk stratification, and management decisions for cancer pain. These advanced tools will integrate big health-related data for personalized pain management in cancer patients. Further research focusing on model calibration and rigorous external clinical validation in real healthcare settings is imperative for ensuring its practical and reliable application in clinical practice.

CFD microscale modelling of flow behavior in different parts of a rotating packed bed.

Process intensification (PI) is playing a key role in alleviating the challenge of reducing carbon footprint of many chemical processes and bringing down their development costs. Over the years, many PI technologies have been investigated with rotating packed bed (RPB) technology receiving much of the attention for its potential of significant intensification in terms of capital expenditure, operating costs, and hardware size. In this study, microscale CFD simulations of a rotating packed bed were conducted, and the results were validated with experimental data. The results show the strong relation between the reverse flow at the packing outer periphery and the gas maldistribution factor. The latter is mainly caused by the accelerating flow in the outer cavity. Inside the wire mesh packing, the gas flow is found to be almost fully uniform for nearly half of the total packing depth. Also, turbulent kinetic energy (TKE) levels at the packing outer edge are strongly linked to the slip tangential velocity component, while at its inner edge, they depend mainly on the radial packing velocity. The so-called gas end effect zone is detected by observing the TKE profiles near the packing outer edge. The latter accounts for less than 10% of the total packing depth. The validity of the widely used porous media model in RPBs' packing for both radial and tangential directions is confirmed by the obtained results, but this excludes the packing inner and outer edges. In the inner cavity region, gas exhibits two distinctive behaviors and transits from free vortex flow to swirling flow as the flow becomes close to the vortex core. As a result of this transition, the increase in shear stress accelerates the decrease in the gas tangential velocity in the vortex core and help speed up the favorable pressure gradient and flow establishment beyond the vortex core.

Development and implementation of optimized endogenous contrast sequences for delineation in adaptive radiotherapy on a 1.5T MR-linear-accelerator: a prospective R-IDEAL stage 0-2a quantitative/qualitative evaluation of in vivo site-specific quality-assurance using a 3D T2 fat-suppressed platform for head and neck cancer.

Purpose: To improve segmentation accuracy in head and neck cancer (HNC) radiotherapy treatment planning for the 1.5T hybrid magnetic resonance imaging/linear accelerator (MR-Linac), three-dimensional (3D), T2-weighted, fat-suppressed magnetic resonance imaging sequences were developed and optimized. Approach: After initial testing, spectral attenuated inversion recovery (SPAIR) was chosen as the fat suppression technique. Five candidate SPAIR sequences and a nonsuppressed, T2-weighted sequence were acquired for five HNC patients using a 1.5T MR-Linac. MR physicists identified persistent artifacts in two of the SPAIR sequences, so the remaining three SPAIR sequences were further analyzed. The gross primary tumor volume, metastatic lymph nodes, parotid glands, and pterygoid muscles were delineated using five segmentors. A robust image quality analysis platform was developed to objectively score the SPAIR sequences on the basis of qualitative and quantitative metrics. Results: Sequences were analyzed for the signal-to-noise ratio and the contrast-to-noise ratio and compared with fat and muscle, conspicuity, pairwise distance metrics, and segmentor assessments. In this analysis, the nonsuppressed sequence was inferior to each of the SPAIR sequences for the primary tumor, lymph nodes, and parotid glands, but it was superior for the pterygoid muscles. The SPAIR sequence that received the highest combined score among the analysis categories was recommended to Unity MR-Linac users for HNC radiotherapy treatment planning. Conclusions: Our study led to two developments: an optimized, 3D, T2-weighted, fat-suppressed sequence that can be disseminated to Unity MR-Linac users and a robust image quality analysis pathway that can be used to objectively score SPAIR sequences and can be customized and generalized to any image quality optimization protocol. Improved segmentation accuracy with the proposed SPAIR sequence will potentially lead to improved treatment outcomes and reduced toxicity for patients by maximizing the target coverage and minimizing the radiation exposure of organs at risk.

Advent in proteins, nucleic acids, and biological cell membranes functionalized nanocarriers to accomplish active or homologous tumor targeting for smart amalgamated chemotherapy/photo-therapy: A review.

Conventional cancer mono-therapeutic approaches including radiotherapy, surgery, and chemotherapy don't always achieve satisfactory outcomes and are frequently associated with significant limitations. Although chemotherapy is a vital intervention, its effectiveness is frequently inadequate and is associated with metastasis, multidrug resistance, off-target effect, and normal cells toxicity. Phototherapies are employed in cancer therapy, encompassing photo-dynamic and photo-thermal therapies which under favorable NIR laser light irradiation initiate the included photosensitizers and photo-thermal agents to generate ROS or thermal heat respectively for cancer cells destruction. Photo-therapy is considered noninvasive, posing no resistance, but it still suffers from several pitfalls like low penetration depth and excessive heat generation affecting neighboring tissues. Improved selectivity and tumor-homing capacity could be attained through surface modulation of nanoparticles with targeting ligands that bind to receptors, which are exclusively overexpressed on cancerous cells. Developing novel modified targeted nanoparticulate platforms integrating different therapeutic modalities like photo-therapy and chemotherapy is a topic of active research. This review aimed to highlight recent advances in proteins, nucleic acids, and biological cell membranes functionalized nanocarriers for smart combinatorial chemotherapy/photo-therapy. Nanocarriers decorated with precise targeting ligands, like aptamers, antibody, and lactoferrin, to achieve active tumor-targeting or camouflaging using various biological cell membrane coating are designed to achieve homologous tumor-targeting.

Isolation, screening and characterization of phage.

Bacterial resistance threatens public health due to a lack of novel antibacterial classes since the 21st century. Bacteriophages, the most ubiquitous microorganism on Earth and natural predators of bacteria, have the potential to save the world from the post-antibiotic era. Therefore, phage isolation and characterization are in high demand to find suitable phages for therapeutic and bacterial control applications. The chapter presents brief guidance supported by recommendations on the isolation of phages, and initial screening of phage antimicrobial efficacy, in addition to, conducting comprehensive characterization addressing morphological, biological, genomic, and taxonomic features.

Determining The Role Of Radiation Oncologist Demographic Factors On Segmentation Quality: Insights From A Crowd-Sourced Challenge Using Bayesian Estimation.

BACKGROUND: Medical image auto-segmentation is poised to revolutionize radiotherapy workflows. The quality of auto-segmentation training data, primarily derived from clinician observers, is of utmost importance. However, the factors influencing the quality of these clinician-derived segmentations have yet to be fully understood or quantified. Therefore, the purpose of this study was to determine the role of common observer demographic variables on quantitative segmentation performance. METHODS: Organ at risk (OAR) and tumor volume segmentations provided by radiation oncologist observers from the Contouring Collaborative for Consensus in Radiation Oncology public dataset were utilized for this study. Segmentations were derived from five separate disease sites comprised of one patient case each: breast, sarcoma, head and neck (H&N), gynecologic (GYN), and gastrointestinal (GI). Segmentation quality was determined on a structure-by-structure basis by comparing the observer segmentations with an expert-derived consensus gold standard primarily using the Dice Similarity Coefficient (DSC); surface DSC was investigated as a secondary metric. Metrics were stratified into binary groups based on previously established structure-specific expert-derived interobserver variability (IOV) cutoffs. Generalized linear mixed-effects models using Markov chain Monte Carlo Bayesian estimation were used to investigate the association between demographic variables and the binarized segmentation quality for each disease site separately. Variables with a highest density interval excluding zero - loosely analogous to frequentist significance - were considered to substantially impact the outcome measure. RESULTS: After filtering by practicing radiation oncologists, 574, 110, 452, 112, and 48 structure observations remained for the breast, sarcoma, H&N, GYN, and GI cases, respectively. The median percentage of observations that crossed the expert DSC IOV cutoff when stratified by structure type was 55% and 31% for OARs and tumor volumes, respectively. Bayesian regression analysis revealed tumor category had a substantial negative impact on binarized DSC for the breast (coefficient mean ± standard deviation: -0.97 ± 0.20), sarcoma (-1.04 ± 0.54), H&N (-1.00 ± 0.24), and GI (-2.95 ± 0.98) cases. There were no clear recurring relationships between segmentation quality and demographic variables across the cases, with most variables demonstrating large standard deviations and wide highest density intervals. CONCLUSION: Our study highlights substantial uncertainty surrounding conventionally presumed factors influencing segmentation quality. Future studies should investigate additional demographic variables, more patients and imaging modalities, and alternative metrics of segmentation acceptability.

Substituted salicylic acid analogs offer improved potency against multidrug-resistant Neisseria gonorrhoeae and good selectivity against commensal vaginal bacteria.

Drug-resistant Neisseria gonorrhoeae represents a major threat to public health; without new effective antibiotics, untreatable gonococcal infections loom as a real possibility. In a previous drug-repurposing study, we reported that salicylic acid had good potency against azithromycin-resistant N. gonorrhoeae. We now report that the anti-gonococcal activity in this scaffold is easily lost by inopportune substitution, but that select substituted naphthyl analogs (3b, 3o and 3p) have superior activity to salicylic acid itself. Furthermore, these compounds retained potency against multiple ceftriaxone- and azithromycin-resistant strains, exhibited rapid bactericidal activity against N. gonorrhoeae, and showed high tolerability to mammalian cells (CC50 > 128 µg/mL). Promisingly, these compounds also show very weak growth inhibition of commensal vaginal bacteria.

Multi-organ spatial stratification of 3-D dose distributions improves risk prediction of long-term self-reported severe symptoms in oropharyngeal cancer patients receiving radiotherapy: development of a pre-treatment decision support tool.

Purpose: Identify Oropharyngeal cancer (OPC) patients at high-risk of developing long-term severe radiation-associated symptoms using dose volume histograms for organs-at-risk, via unsupervised clustering. Material and methods: All patients were treated using radiation therapy for OPC. Dose-volume histograms of organs-at-risk were extracted from patients' treatment plans. Symptom ratings were collected via the MD Anderson Symptom Inventory (MDASI) given weekly during, and 6 months post-treatment. Drymouth, trouble swallowing, mucus, and vocal dysfunction were selected for analysis in this study. Patient stratifications were obtained by applying Bayesian Mixture Models with three components to patient's dose histograms for relevant organs. The clusters with the highest total mean doses were translated into dose thresholds using rule mining. Patient stratifications were compared against Tumor staging information using multivariate likelihood ratio tests. Model performance for prediction of moderate/severe symptoms at 6 months was compared against normal tissue complication probability (NTCP) models using cross-validation. Results: A total of 349 patients were included for long-term symptom prediction. High-risk clusters were significantly correlated with outcomes for severe late drymouth (p <.0001, OR = 2.94), swallow (p = .002, OR = 5.13), mucus (p = .001, OR = 3.18), and voice (p = .009, OR = 8.99). Simplified clusters were also correlated with late severe symptoms for drymouth (p <.001, OR = 2.77), swallow (p = .01, OR = 3.63), mucus (p = .01, OR = 2.37), and voice (p <.001, OR = 19.75). Proposed cluster stratifications show better performance than NTCP models for severe drymouth (AUC.598 vs.559, MCC.143 vs.062), swallow (AUC.631 vs.561, MCC.20 vs -.030), mucus (AUC.596 vs.492, MCC.164 vs -.041), and voice (AUC.681 vs.555, MCC.181 vs -.019). Simplified dose thresholds also show better performance than baseline models for predicting late severe ratings for all symptoms. Conclusion: Our results show that leveraging the 3-D dose histograms from radiation therapy plan improves stratification of patients according to their risk of experiencing long-term severe radiation associated symptoms, beyond existing NTPC models. Our rule-based method can approximate our stratifications with minimal loss of accuracy and can proactively identify risk factors for radiation-associated toxicity.

Unsteady mix convectional stagnation point flow of nanofluid over a movable electro-magnetohydrodynamics Riga plate numerical approach.

The flow at a time-independent separable stagnation point on a Riga plate under thermal radiation and electro-magnetohydrodynamic settings is examined in this research. Two distinct base fluids-H2O and C2H6O2 and TiO2 nanostructures develop the nanocomposites. The flow problem incorporates the equations of motion and energy along with a unique model for viscosity and thermal conductivity. Similarity components are then used to reduce these model problem calculations. The Runge Kutta (RK-4) function yields the simulation result, which is displayed in graphical and tabular form. For both involved base fluid theories, the nanofluids flow and thermal profiles relating to the relevant aspects are computed and analyzed. According to the findings of this research, the C2H6O2 model heat exchange rate is significantly higher than the H2O model. As the volume percentage of nanoparticles rises, the velocity field degrades while the temperature distribution improves. Moreover, for greater acceleration parameters, TiO2/ C2H6O2has the highest thermal coefficient whereas TiO2/ H2O has the highest skin friction coefficient. The key observation is that C2H6O2 base nanofluid has a little higher performance than H2O nanofluid.

Dietary Olive Leaf Extract Differentially Modulates Antioxidant Defense of Normal and Aeromonas hydrophila-Infected Common Carp (Cyprinus carpio) via Keap1/Nrf2 Pathway Signaling: A Phytochemical and Biological Link.

Olive leaves are an immense source of antioxidant and antimicrobial bioactive constituents. This study investigated the effects of dietary incorporation of olive leaf extract (OLE) on the growth performance, hematobiochemical parameters, immune response, antioxidant defense, histopathological changes, and some growth- and immune-related genes in the common carp (Cyprinus carpio). A total of 180 fish were allocated into four groups with triplicate each. The control group received the basal diet without OLE, while the other three groups were fed a basal diet with the OLE at 0.1, 0.2, and 0.3%, respectively. The feeding study lasted for 8 weeks, then fish were challenged with Aeromonas hydrophila. The results revealed that the group supplied with the 0.1% OLE significantly exhibited a higher final body weight (FBW), weight gain (WG%), and specific growth rate (SGR) with a decreased feed conversion ratio (FCR) compared to the other groups (p < 0.05). An increase in immune response was also observed in the fish from this group, with higher lysosome activity, immunoglobulin (IgM), and respiratory burst than nonsupplemented fish, both before and after the A. hydrophila challenge (p < 0.05). Similarly, the supplementation of the 0.1% OLE also promoted the C. carpio's digestive capacity pre- and post-challenge, presenting the highest activity of protease and alkaline phosphatase (p < 0.05). In addition, this dose of the OLE enhanced fish antioxidant capacity through an increase in the activity of superoxide dismutase (SOD) and glutathione peroxidase (GPx) and decreased hepatic lipid peroxidation end products (malondialdehyde-MDA), when compared to the control group, both pre- and post-infection (p < 0.05). Concomitantly with the superior immune response and antioxidant capacity, the fish fed the 0.1% OLE revealed the highest survival rate after the challenge with A. hydrophila (p < 0.05). A significant remarkable upregulation of the hepatic sod, nrf2, and protein kinase C transcription levels was detected as a vital approach for the prevention of both oxidative stress and inflammation compared to the infected unsupplied control group (p < 0.05). Interestingly, HPLC and UPLC-ESI-MS/MS analyses recognized that oleuropein is the main constituent (20.4%) with other 45 compounds in addition to tentative identification of two new compounds, namely oleuroside-10-carboxylic acid (I) and demethyl oleuroside-10-carboxylic acid (II). These constituents may be responsible for the OLE exerted potential effects. To conclude, the OLE at a dose range of 0.66-0.83 g/kg w/w can be included in the C. carpio diet to improve the growth, antioxidant capacity, and immune response under normal health conditions along with regulating the infection-associated pro-inflammatory gene expressions, thus enhancing resistance against A. hydrophila.