Search Results (143)

Feathers are commonly used to monitor trace elements in birds, including heavy metals. Typically, a single feather is analyzed to avoid harming living birds, assuming it reflects the organism’s overall contamination. To verify this assumption, we analyzed mercury concentrations in 12 flight and contour feathers from 25 barn swallows Hirundo rustica (16 adults and nine juveniles) that had died accidentally in a colony of the Po Plain (northern Italy). The median concentration in all feathers examined was 1.03 µg g⁻¹ in adults (range 0.76 µg g⁻¹–1.30 µg g⁻¹) and 0.39 µg g⁻¹ in juveniles (range 0.28 µg g⁻¹–0.71 µg g⁻¹), which is consistent with the results of similar research carried out on other world regions. No significant differences were observed between sexes, whereas marked differences were observed between adults and juveniles. In adults, mercury concentration was similar across remiges, rectrices, and contour feathers while in juveniles it was higher in contour feathers than in flight feathers. Mercury accumulation was highest in primary remiges and contour feathers, accounting for 67.6% of total mercury in adults and 77.5% in juveniles. However, primary remiges cannot be collected from live adults due to their importance in flight. In juveniles, contour feathers carry about 50% of total mercury, suggesting ventral and dorsal plumage may be useful for assessing mercury burden. Our findings are consistent with the hypothesis that mercury accumulation in feathers aids detoxification, with early-molted feathers (primary remiges and contour feathers) containing higher mercury levels than those replaced later (rectrices and secondary remiges). Full article

Background/Objectives: The prevalence of people with incurable and progressive diseases in primary health care is high. Family doctors and nurses must be active agents in the early identification of palliative needs and the implementation of palliative approaches in cases of low to intermediate complexity. While there is a need for early referral of more complex palliative care (PC) cases to specialized teams, primary health care (PHC) professionals lack the confidence or skill to describe their role. This study sought to explore and describe (a) the practices of PHC professionals regarding their PC provision; (b) the barriers regarding access and referral of patients to specialized PC services; and (c) the strategies used or recommended to mitigate difficulties in accessing and referring to specialized PC. Methods: A descriptive qualitative study was carried out, using five focus groups conducted with nursing and medical staff at three local health units in the central region of Portugal. Semi-structured interviews were conducted, and then recorded, transcribed, and analyzed through a thematic analysis approach. The reporting of this research follows the COREQ checklist. Results: In total, 34 PHC professionals participated in this study. The majority of participants were women (n = 26) and family doctors (n = 24). Their mean age was 43.8 ± 11.9 (range: 29 to 65 years). The findings were organized into three core themes: (1) the contours of palliative action developed by PHC teams; (2) barriers to access and safe transition between PHC and specialized PC; and (3) ways to mitigate difficulties in accessing and referring to specialized PC. Conclusions: Our findings highlight the fundamental role of PHC professionals in providing primary PC, and in identifying PC needs and referring patients to PC early on, while exposing the systemic and interpersonal challenges that hinder these processes. To overcome these challenges, it is essential to invest in the development of integrated care models that promote practical, low-bureaucratic referral processes and capture the human resources necessary for the adequate follow-up of users. Full article

Background: Efforts have been joined to set the parameters for the reliability of glucometers, yet once they are on the market, they are not further tested for the maintenance of accuracy, specificity, or precision. Methods: This comparative analytical study investigated the precision of commonly used glucometers in Saudi Arabia, namely Accu-Chek Instant^®, On-Call Sharp^®, and ConTour^®, as well as the effects of vitamin C, acetaminophen, and maltose on glucose readings. Ten milliliters of blood was drawn in lithium heparin from healthy volunteers (n = 9). Six samples were divided into two groups of three. One group was designed for normal glucose levels. The second group was designed for high glucose levels by adding a dextrose solution. The last three samples were designed for low glucose levels by leaving the sample for 24 h at room temperature and then following with centrifuge and plasma extraction. Results: This study showed that only Accu-Chek Instant met the International Organization for Standardization (ISO) standard for precision across all dextrose concentrations, along with intra-class correlation values ranging from 0.95–1 (p < 0.001). By spiking the plasma samples with sub-therapeutic, therapeutic, and overdose concentrations of the metabolites, we found that vitamin C had a more evident interference on glucose readings compared to acetaminophen and maltose. Conclusions: The ascertainment of the precision of glucometers and the effects of interferences on them are vital in preventing the improper administration of insulin, which can lead to serious complications. Full article

Background: Radiotherapy (RT) is a mainstay treatment for prostate cancer (PC). Accurate delineation of organs at risk (OARs) is crucial for optimizing the therapeutic window by minimizing side effects. Manual segmentation is time-consuming and prone to inter-operator variability. This study investigates the performance of Limbus^® Contour^® (LC), a deep learning-based auto-contouring software, in delineating pelvic structures in PC patients. Methods: We evaluated LC’s performance on key structures (bowel bag, bladder, rectum, sigmoid colon, and pelvic lymph nodes) in 52 patients. We compared auto-contoured structures with those manually delineated by radiation oncologists using different metrics. Results: LC achieved good agreement for the bladder (median Dice: 0.95) and rectum (median Dice: 0.83). However, limitations were observed for the bowel bag (median Dice: 0.64) and sigmoid colon (median Dice: 0.6), with inclusion of irrelevant structures. While the median Dice for pelvic lymph nodes was acceptable (0.73), the software lacked sub-regional differentiation, limiting its applicability in certain other oncologic settings. Conclusions: LC shows promise for automating OAR delineation in prostate radiotherapy, particularly for the bladder and rectum. Improvements are needed for bowel bag, sigmoid colon, and lymph node sub-regionalization. Further validation with a broader and larger patient cohort is recommended to assess generalizability. Full article

Abdominal organ segmentation in CT images is crucial for accurate diagnosis, treatment planning, and condition monitoring. However, the annotation process is often hindered by challenges such as low contrast, artifacts, and complex organ structures. While unsupervised domain adaptation (UDA) has shown promise in addressing these issues by transferring knowledge from a different modality (source domain), its reliance on both source and target data during training presents a practical challenge in many clinical settings due to data privacy concerns. This study aims to develop a cross-modality abdominal multi-organ segmentation model for label-free CT (target domain) data, leveraging knowledge solely from a pre-trained source domain (MRI) model without accessing the source data. To achieve this, we generate source-like images from target-domain images using a one-way image translation approach with the pre-trained model. These synthesized images preserve the anatomical structure of the target, enabling segmentation predictions from the pre-trained model. To further enhance segmentation accuracy, particularly for organ boundaries and small contours, we introduce an auxiliary translation module with an image decoder and multi-level discriminator. The results demonstrate significant improvements across several performance metrics, including the Dice similarity coefficient (DSC) and average symmetric surface distance (ASSD), highlighting the effectiveness of the proposed method. Full article

The purpose of infrared and visible image fusion is to generate a composite image that can contain both the thermal radiation profile information of the infrared image and the texture details of the visible image. This kind of composite image can be used to detect targets under various lighting conditions and offer high scene spatial resolution. However, the existing image fusion algorithms rarely consider light factor in the modeling process. The study presents a novel image fusion approach (AIF) that can adaptively fuse infrared and visible images under various lighting conditions. Specifically, the infrared image and the visible image are extracted by the AdC feature extractor, respectively, and both of them are adaptively fused under the guidance of the illumination perception subnetwork. The image fusion model is trained in an unsupervised manner with a customized loss function. The AdC feature extractor adopts an ascending–descending feature extraction mechanism to organize convolutional layers and combines these convolutional layers with cross-modal interactive differential modules to achieve the effective extraction of hierarchical complementary and differential information. The illumination perception subnetwork obtains the scene lighting condition based on the visible image, which determines the contribution weights of the visible image and the infrared image in the composite image. The customized loss function consists of illumination loss, gradient loss, and intensity loss. It is more targeted and can effectively improve the fusion effect of visible images and infrared images under different lighting conditions. Ablation experiments demonstrate the effectiveness of the loss function. We compare our method with nine other methods on public datasets, including four traditional methods and five deep-learning-based methods. Qualitative and quantitative experiments show that our method performs better in terms of indicators such as SD, and the fused image has more prominent contour information and richer detail information. Full article

Background/Objectives: Stereotactic body radiotherapy is frequently used in patients with adrenal metastases. Motion of adherent radiosensitive organs at risk (OARs) and tumors influence OAR toxicity and tumor control. Online-adaptive Magnetic Resonance-guided radiotherapy (MRgRT) can address and mitigate interfractional changes. However, the impact of intrafractional variations in adrenal MRgRT is unknown. Methods: A total of 23 patients with 24 adrenal metastases were treated with MRgRT. After daily plan adaptation and before beam application, an additional (preRT) 3d MRI was acquired. PreRT target volumes and OARs were retrospectively recontoured in 200 fractions. The delivered, online-adapted treatment plans, as well as non-adapted baseline plans, were calculated on these re-contoured structures to quantify the dosimetric impact of intrafractional variations on target volume coverage and OAR doses with and without online adaptation. Normal tissue complication probabilities (NTCPs) were calculated. Results: The median time between the two MRIs was 56.4 min. GTV and PTV coverage (dose to 95% of the PTV, D95%, and volume covered by 100% of the prescription dose, V100%) were significantly inferior in the preRT plans. GTV D_mean was significantly impaired in left-sided metastases, but not in right-sided metastases. Compared to non-adapted preRT plans, adapted preRT plans were still significantly superior for all GTV and PTV metrics. Intrafractional violations of OAR constraints were frequent. D0.5cc and the volume exposed to the near-maximum dose constraint were significantly higher in the preRT plans. The volume exposed to the D0.5cc constraints in single fractions escalated up to 1.5 cc for the esophagus, 3.2 cc for the stomach, 5.3 cc for the duodenum and 7.3 cc for the bowel. This led to significantly elevated NTCPs for the stomach, bowel and duodenum. Neither PTV D95%, nor gastrointestinal OAR maximum doses were significantly impaired by longer fraction duration. Conclusions: Intrafractional motion in adrenal MRgRT caused significant impairment of target volume coverage (D95% and V100%), potentially undermining local control. Frequent violation of gastrointestinal OAR constraints led to elevated NTCP. Compared to non-adaptive treatment, online adaptation still highly improved GTV and PTV coverage. Full article

Dechlorane Plus (DP), an emerging type of persistent organic pollutant (POP), poses potential harmful effects on plants, animals, and humans alike, garnering increasing attention. Urban surface soil is easily accessible to urban residents, and its environmental conditions have a more significant impact on urban residents. However, there are few studies on related DP contamination. In this study, the contamination of DP in surface soil from Shenyang City, Liaoning Province, China, was investigated. Soil samples were collected from 33 different locations in May and June 2023. The total DP (∑DP), anti-DP, and syn-DP were determined by gas chromatography and ranged from not detected (ND) to 77.80 ng/g, from ND to 61.50 ng/g, and from ND to 16.30 ng/g, respectively. The mean values were 33.60 ± 18.93 ng/g, 27.01 ± 14.32 ng/g, and 8.57 ± 4.55 ng/g. The findings indicate that anti-DP is more readily detectable than syn-DP, attributable to the lower proportion of syn-DP in the overall DP production and the distinct physicochemical properties of DP isomers. The fsyn [syn-DP/(anti-DP + syn-DP)] is 0.14–0.40, with a mean value of 0.22. This aligns closely with the values observed in commercial DP formulations, suggesting that the primary sources are derived from commercial DP products. Contour maps show that DP concentrations are influenced by urban land use and DP production. Based on the Tyson polygon method, the DP inventory was calculated at approximately 1.18 tons, with the unit area load exceeding previously reported values. The results also show that the health risks of DP are minimal, but children are more susceptible to the impacts of DP than adults, and oral ingestion is a more critical exposure pathway. Full article

Acrylated silicone elastomers for UV-curing 3D printing have gathered considerable attention in biomedical applications due to their exceptional mechanical and thermal stability. However, traditional manufacturing methods for these resins often face challenges such as stringent conditions and self-polymerization. In this study, various acrylate silicone resins (LMDT-AE) and silicone oils (PDMS-AE) were synthesized through ring-opening hydrolysis-polycondensation. The structures of LMDT-AE and PDMS-AE, with varying AE contents (molar ratio of organic groups to silicon atoms), were characterized using FTIR, ¹H NMR, ¹³C NMR, and GPC. Additionally, their physical properties, including viscosity, density, refractive index, and transparency, were thoroughly examined. The 3D-AE silicone resin composed of LMDT-AE-2.0 and PDMS-AE-20/1, in a mass ratio of 2:1, demonstrated superior mechanical properties, thermal stability, and curing shrinkage rate compared to other formulations. This curing silicone resin is capable of producing 3D physical entities with smooth surfaces and well-defined contours. It is shown that the successful preparation of transparent and high-strength UV-cured silicone resin based on free radical polymerization can provide a potential path for high-precision biological 3D printing. Full article

As yet, there is no systematic review focusing on benefits and issues of commercial deep learning-based auto-segmentation (DLAS) software for prostate cancer (PCa) radiation therapy (RT) planning despite that NRG Oncology has underscored such necessity. This article’s purpose is to systematically review commercial DLAS software product performances for PCa RT planning and their associated evaluation methodology. A literature search was performed with the use of electronic databases on 7 November 2024. Thirty-two articles were included as per the selection criteria. They evaluated 12 products (Carina Medical LLC INTContour (Lexington, KY, USA), Elekta AB ADMIRE (Stockholm, Sweden), Limbus AI Inc. Contour (Regina, SK, Canada), Manteia Medical Technologies Co. AccuContour (Jian Sheng, China), MIM Software Inc. Contour ProtégéAI (Cleveland, OH, USA), Mirada Medical Ltd. DLCExpert (Oxford, UK), MVision.ai Contour+ (Helsinki, Finland), Radformation Inc. AutoContour (New York, NY, USA), RaySearch Laboratories AB RayStation (Stockholm, Sweden), Siemens Healthineers AG AI-Rad Companion Organs RT, syngo.via RT Image Suite and DirectORGANS (Erlangen, Germany), Therapanacea Annotate (Paris, France), and Varian Medical Systems, Inc. Ethos (Palo Alto, CA, USA)). Their results illustrate that the DLAS products can delineate 12 organs at risk (abdominopelvic cavity, anal canal, bladder, body, cauda equina, left (L) and right (R) femurs, L and R pelvis, L and R proximal femurs, and sacrum) and four clinical target volumes (prostate, lymph nodes, prostate bed, and seminal vesicle bed) with clinically acceptable outcomes, resulting in delineation time reduction, 5.7–81.1%. Although NRG Oncology has recommended each clinical centre to perform its own DLAS product evaluation prior to clinical implementation, such evaluation seems more important for AccuContour and Ethos due to the methodological issues of the respective single studies, e.g., small dataset used, etc. Full article

This study introduces and examines the principle of accentuation as a novel mechanism in perceptual organization, analyzing its effects through the framework of Grossberg’s Adaptive Resonance Theory (ART). We demonstrate that localized accentuators, manifesting as minimal dissimilarities or discontinuities, can significantly modulate global perceptions, inducing illusions of geometric distortion, orientation shifts, and apparent motion. Through a series of phenomenological experiments, we establish that accentuation can supersede classical Gestalt principles, influencing figure-ground segregation, shape perception, and lexical processing. Our findings suggest that accentuation functions as an autonomous organizing principle, leveraging salience-driven attentional capture to generate perceptual effects. We then apply the ART model to elucidate these phenomena, focusing on its core constructs of complementary computing, boundary–surface interactions, and resonant states. Specifically, we show how accentuation-induced asymmetries in boundary signals within the boundary contour system (BCS) can propagate through laminar cortical circuits, biasing figure-ground assignments and shape representations. The interaction between these biased signals and top–down expectations, as modeled by ART’s resonance mechanisms, provides a neurally plausible account for the observed illusions. This integration of accentuation effects with ART offers novel insights into the neural substrates of visual perception and presents a unifying theoretical framework for a diverse array of perceptual phenomena, bridging low-level feature processing with high-level cognitive representations. Full article

Water erosion has severe impacts on soil and the carbon cycle. In tropical regions, it is significantly influenced by rainfall, soil erodibility, rapid changes in land use and land cover (LULC), and agricultural management practices. Understanding the dynamics of water erosion is essential for implementing precise land degradation control. This study aimed to estimate soil and soil organic carbon (SOC) losses due to water erosion over five years in a coffee-producing area in Brazil using the revised universal soil loss equation (RUSLE). The results revealed that average soil losses in coffee plantation areas ranged from 1.77 to 1.80 Mg ha⁻¹ yr⁻¹, classified as very low. Total and potential soil loss ranged from 2184.60 to 6657.14 Mg ha⁻¹, a 305% difference, demonstrating the efficiency of vegetative cover (C factor) and conservation practices (P factor) in reducing soil loss rates. SOC losses were less than 200 kg ha⁻¹ yr⁻¹, with averages of 17.67 and 13.00 kg ha⁻¹ yr⁻¹ in coffee areas. In conclusion, agricultural management practices, such as the presence of native vegetation, maintaining vegetative cover in coffee rows, contour planting, and improving agronomic techniques, are essential for reducing soil and SOC losses, even in scenarios of biennial alternation in coffee production. Thus, sustainable agricultural management plays a crucial role in mitigating water erosion, maintaining productivity, and addressing climate change. Full article

As the fundamental unit of soil structure, soil aggregates play a crucial role in enhancing soil carbon and nitrogen storage, thereby supporting soil fertility and overall health, particularly in fragile karst regions. This study aims to quantify the effects of various ecological construction measures on soil aggregate stability, including focusing on geometric mean diameter (GMD), mean weight diameter (MWD), and K values, as well as aggregate-related organic carbon (SOC) and total nitrogen (TN), soil mechanical composition, and aggregate content. The ecological construction measures examined include plantation forests (Y7th–rgl), restored forests (Y6th–zr), fruit forests (Y6th–jgl), and contour reverse slope terraces (Y1th–crt). Compared to sloping farmland, contour reverse slope terraces, with their distinctive priority induction function, significantly increased the content of medium-fine particle aggregates, greater than 87%. Among the ecological construction measures, plantation forests exhibited the highest aggregate stability, with an average increase ranging from 8% to 157%. Notably, microaggregates, regardless of size, possessed the highest carbon and nitrogen contents, contributing significantly to soil carbon and nitrogen pools. Furthermore, both plantation and contour reverse slope terrace treatments demonstrated an equal contribution of carbon and nitrogen across all aggregate sizes. The partial least squares path modeling (PLS-PM) analysis indicates that land use type and the content of carbon and nitrogen pools are the primary factors influencing soil aggregate stability. These findings suggest that plantations are particularly effective in enhancing soil and water conservation in fragile karst areas, while the contour reverse slope terrace method shows potential for stabilizing soil structure over extended time scales due to its unique “preferential entrainment” function. Full article

As yet, no systematic review on commercial deep learning-based auto-segmentation (DLAS) software for breast cancer radiation therapy (RT) planning has been published, although NRG Oncology has highlighted the necessity for such. The purpose of this systematic review is to investigate the performances of commercial DLAS software packages for breast cancer RT planning and methods for their performance evaluation. A literature search was conducted with the use of electronic databases. Fifteen papers met the selection criteria and were included. The included studies evaluated eight software packages (Limbus Contour, Manteia AccuLearning, Mirada DLCExpert, MVision.ai Contour+, Radformation AutoContour, RaySearch RayStation, Siemens syngo.via RT Image Suite/AI-Rad Companion Organs RT, and Therapanacea Annotate). Their findings show that the DLAS software could contour ten organs at risk (body, contralateral breast, esophagus-overlapping area, heart, ipsilateral humeral head, left and right lungs, liver, and sternum and trachea) and three clinical target volumes (CTVp_breast, CTVp_chestwall, and CTVn_L1) up to the clinically acceptable standard. This can contribute to 45.4%–93.7% contouring time reduction per patient. Although NRO Oncology has suggested that every clinical center should conduct its own DLAS software evaluation before clinical implementation, such testing appears particularly crucial for Manteia AccuLearning, Mirada DLCExpert, and MVision.ai Contour+ as a result of the methodological weaknesses of the corresponding studies such as the use of small datasets collected retrospectively from single centers for the evaluation. Full article

Background/Objectives: We assessed the influence of local patients and clinical characteristics on the performance of commercial deep learning (DL) segmentation models for head-and-neck (HN), breast, and prostate cancers. Methods: Clinical computed tomography (CT) scans and clinically approved contours of 210 patients (53 HN, 49 left breast, 55 right breast, and 53 prostate cancer) were used to train and validate segmentation models integrated within a vendor-supplied DL training toolkit and to assess the performance of both vendor-pretrained and custom-trained models. Four custom models (HN, left breast, right breast, and prostate) were trained and validated with 30 (training)/5 (validation) HN, 34/5 left breast, 39/5 right breast, and 30/5 prostate patients to auto-segment a total of 24 organs at risk (OARs). Subsequently, both vendor-pretrained and custom-trained models were tested on the remaining patients from each group. Auto-segmented contours were evaluated by comparing them with clinically approved contours via the Dice similarity coefficient (DSC) and mean surface distance (MSD). The performance of the left and right breast models was assessed jointly according to ipsilateral/contralateral locations. Results: The average DSCs for all structures in vendor-pretrained and custom-trained models were as follows: 0.81 ± 0.12 and 0.86 ± 0.11 in HN; 0.67 ± 0.16 and 0.80 ± 0.11 in the breast; and 0.87 ± 0.09 and 0.92 ± 0.06 in the prostate. The corresponding average MSDs were 0.81 ± 0.76 mm and 0.76 ± 0.56 mm (HN), 4.85 ± 2.44 mm and 2.42 ± 1.49 mm (breast), and 2.17 ± 1.39 mm and 1.21 ± 1.00 mm (prostate). Notably, custom-trained models showed significant improvements over vendor-pretrained models for 14 of 24 OARs, reflecting the influence of data/contouring variations in segmentation performance. Conclusions: These findings underscore the substantial impact of institutional preferences and clinical practices on the implementation of vendor-pretrained models. We also found that a relatively small amount of institutional data was sufficient to train customized segmentation models with sufficient accuracy. Full article