-
GeoHealth Jun 2024Tourist volcanic caves are in high demand for ecotourism and geotourism lovers, as well as by sun and beach tourists as a complementary activity during their holidays....
Tourist volcanic caves are in high demand for ecotourism and geotourism lovers, as well as by sun and beach tourists as a complementary activity during their holidays. There are six tourist volcanic caves in the Canary Islands, all of them managed by the local administration of the island. The managers of these caves must ensure the safety of visitors and workers, who are exposed to natural hazards, such as radon, inherent to the environment in which the activity takes place. The methodology for analyzing natural radon radiation is based on the latest studies published by experts in this field and on previous experiences in tourist caves. This article proposes a protocol for the correct management of radon in tourist caves in the Canary Islands, adapted to current regulations, to mitigate effects on the health of visitors and workers.
PubMed: 38884068
DOI: 10.1029/2024GH001067 -
American Journal of Translational... 2024Liquid biopsy is an innovative approach that provides a more complete understanding of treatment response and prognosis in monitoring metastatic prostate cancer. It... (Review)
Review
Liquid biopsy is an innovative approach that provides a more complete understanding of treatment response and prognosis in monitoring metastatic prostate cancer. It complements invasive tissue biopsy and involves the assessment of various biomarkers in body fluids such as blood, semen, and urine. Liquid biopsy analyzes circulating tumor cells, extracellular vesicles, circulating tumor DNA, and the secretome. This is particularly important given the heterogeneity of prostate cancer and the need for better prognostic biomarkers. Liquid biopsy can personalize the treatment of homonosensitive and castration-resistant metastatic prostate cancer by acting as a predictive and prognostic tool. This review discusses various biomarkers, assay techniques, and potential applications in daily clinical practice, highlighting the exciting possibilities that this emerging field holds for improving patient outcomes.
PubMed: 38883349
DOI: 10.62347/DICU9510 -
Data in Brief Jun 2024To advance high-energy atmospheric physics, studying atmospheric electric fields (AEF) and cosmic ray fluxes as an interconnected system is crucial. At Mt. Argats,...
To advance high-energy atmospheric physics, studying atmospheric electric fields (AEF) and cosmic ray fluxes as an interconnected system is crucial. At Mt. Argats, simultaneous measurements of particle fluxes, electric fields, weather conditions, and lightning locations have significantly enhanced the validation of models that describe the charge structures of thunderclouds and the mechanics of internal electron accelerators. In 2023, observations of the five largest thunderstorm ground enhancements (TGEs) revealed electric fields exceeding 2.0 kV/cm at elevations just tens of meters above ground-potentially hazardous to rockets and aircraft during launch and charging operations. Utilizing simple yet effective monitoring equipment developed at Aragats, we can mitigate the risks posed by these high-intensity fields. The Mendeley dataset, comprising various measured parameters during thunderstorm activities, enables researchers to perform advanced correlation analysis and uncover complex relationships between these atmospheric phenomena. This study underscores the critical importance of integrated atmospheric studies for ensuring the safety of high-altitude operations and advancing atmospheric science.
PubMed: 38882191
DOI: 10.1016/j.dib.2024.110554 -
Environment International Jun 2024To inform radiofrequency electromagnetic field (RF-EMF) exposure guidelines the World Health Organization (WHO) is bringing together evidence on RF-EMF in relation to...
BACKGROUND
To inform radiofrequency electromagnetic field (RF-EMF) exposure guidelines the World Health Organization (WHO) is bringing together evidence on RF-EMF in relation to health outcomes prioritised for evaluation by experts in this field. Given this, a network of topic experts and methodologists have conducted a series of systematic reviews collecting, assessing, and synthesising data of relevance to these guidelines. Here we present a systematic review of the effect of RF-EMF exposure on adverse pregnancy outcomes in human observational studies which follows the WHO handbook for guideline development and the COSTER conduct guidelines.
METHODS
We conducted a broad, sensitive search for potentially relevant records within the following bibliographic databases: MEDLINE; Embase; and the EMF Portal. Grey literature searches were also conducted through relevant databases (including OpenGrey), organisational websites and via consultation of RF-EMF experts. We included quantitative human observational studies on the effect of RF-EMF exposure in adults' preconception or pregnant women on pre-term birth, small for gestational age (SGA; associated with intrauterine growth restriction), miscarriage, stillbirth, low birth weight (LBW) and congenital anomalies. In blinded duplicate, titles and abstracts then full texts were screened against eligibility criteria. A third reviewer gave input when consensus was not reached. Citation chaining of included studies was completed. Two reviewers' data extracted and assessed included studies for risk of bias using the Office of Health Assessment and Translation (OHAT) tool. Random effects meta-analyses of the highest versus the lowest exposures and dose-response meta-analysis were conducted as appropriate and plausible. Two reviewers assessed the certainty in each body of evidence using the OHAT GRADE tool.
RESULTS
We identified 18 studies in this review; eight were general public studies (with the general public as the population of interest) and 10 were occupational studies (with the population of interest specific workers/workforces). General public studies. From pairwise meta-analyses of general public studies, the evidence is very uncertain about the effects of RF-EMF from mobile phone exposure on preterm birth risk (relative risk (RR) 1.14, 95% confidence interval (CI): 0.97-1.34, 95% prediction interval (PI): 0.83-1.57; 4 studies), LBW (RR 1.14, 95% CI: 0.96-1.36, 95% PI: 0.84-1.57; 4 studies) or SGA (RR 1.13, 95% CI: 1.02-1.24, 95% PI: 0.99-1.28; 2 studies) due to very low-certainty evidence. It was not feasible to meta-analyse studies reporting on the effect of RF-EMF from mobile phone exposure on congenital anomalies or miscarriage risk. The reported effects from the studies assessing these outcomes varied and the studies were at some risk of bias. No studies of the general public assessed the impact of RF-EMF exposure on stillbirth. Occupational studies. In occupational studies, based on dose-response meta-analyses, the evidence is very uncertain about the effects of RF-EMF amongst female physiotherapists using shortwave diathermy on miscarriage due to very low-certainty evidence (OR 1.02 95% CI 0.94-1.1; 2 studies). Amongst offspring of female physiotherapists using shortwave diathermy, the evidence is very uncertain about the effects of RF-EMF on the risk of congenital malformations due to very low-certainty evidence (OR 1.4, 95% CI 0.85 to 2.32; 2 studies). From pairwise meta-analyses, the evidence is very uncertain about the effects of RF-EMF on the risk of miscarriage (RR 1.06, 95% CI 0.96 to 1.18; very low-certainty evidence), pre-term births (RR 1.19, 95% CI 0.32 to 4.37; 3 studies; very low-certainty evidence), and low birth weight (RR 2.90, 95% CI: 0.69 to 12.23; 3 studies; very low-certainty evidence). Results for stillbirth and SGA could not be pooled in meta-analyses. The results from the studies reporting these outcomes were inconsistent and the studies were at some risk of bias.
DISCUSSION
Most of the evidence identified in this review was from general public studies assessing localised RF-EMF exposure from mobile phone use on female reproductive outcomes. In occupational settings, each study was of heterogenous whole-body RF-EMF exposure from radar, short or microwave diathermy, surveillance and welding equipment and its effect on female reproductive outcomes. Overall, the body of evidence is very uncertain about the effect of RF-EMF exposure on female reproductive outcomes. Further prospective studies conducted with greater rigour (particularly improved accuracy of exposure measurement and using appropriate statistical methods) are required to identify any potential effects of RF-EMF exposure on female reproductive outcomes of interest.
PubMed: 38880062
DOI: 10.1016/j.envint.2024.108816 -
Environment International Jun 2024The World Health Organization (WHO) is bringing together evidence on radiofrequency electromagnetic field (RF-EMF) exposure in relation to health outcomes, previously...
BACKGROUND
The World Health Organization (WHO) is bringing together evidence on radiofrequency electromagnetic field (RF-EMF) exposure in relation to health outcomes, previously identified as priorities for research and evaluation by experts in the field, to inform exposure guidelines. A suite of systematic reviews have been undertaken by a network of topic experts and methodologists to collect, assess and synthesise data relevant to these guidelines. Following the WHO handbook for guideline development and the COSTER conduct guidelines, we systematically reviewed the evidence on the potential effects of RF-EMF exposure on male fertility in human observational studies.
METHODS
We conducted a broad and sensitive search for potentially relevant records within the following bibliographic databases: MEDLINE; Embase; Web of Science and EMF Portal. We also conducted searches of grey literature through relevant databases including OpenGrey, and organisational websites and consulted RF-EMF experts. We hand searched reference lists of included study records and for citations of these studies. We included quantitative human observational studies on the effect of RF-EMF exposure in adult male participants on infertility: sperm concentration; sperm morphology; sperm total motility; sperm progressive motility; total sperm count; and time to pregnancy. Titles and abstracts followed by full texts were screened in blinded duplicate against pre-set eligibility criteria with consensus input from a third reviewer as required. Data extraction from included studies was completed by two reviewers, as was risk of bias assessment using the Office of Health Assessment and Translation (OHAT) tool. We conducted a dose-response meta-analysis as possible and appropriate. Certainty of the evidence was assessed by two reviewers using the OHAT GRADE tool with input from a third reviewer as required.
RESULTS
We identified nine studies in this review; seven were general public studies (with the general public as the population of interest) and two were occupational studies (with specific workers/workforces as the population of interest). General public studies. Duration of phone use: The evidence is very uncertain surrounding the effects of RF-EMF on sperm concentration (10/6 mL) (MD (mean difference) per hour of daily phone use 1.6 10/mL, 95 % CI -1.7 to 4.9; 3 studies), sperm morphology (MD 0.15 percentage points of deviation of normal forms per hour, 95 % CI -0.21 to 0.51; 3 studies), sperm progressive motility (MD -0.46 percentage points per hour, 95 % CI -1.04 to 0.13; 2 studies) and total sperm count (MD per hour -0.44 10/ejaculate, 95 % CI -2.59 to 1.7; 2 studies) due to very low-certainty evidence. Four additional studies reported on the effect of mobile phone use on sperm motility but were unsuitable for pooling; only one of these studies identified a statistically significant effect. All four studies were at risk of exposure characterisation and selection bias; two of confounding, selective reporting and attrition bias; three of outcome assessment bias and one used an inappropriate statistical method. Position of phone: There may be no or little effect of carrying a mobile phone in the front pocket on sperm concentration, total count, morphology, progressive motility or on time to pregnancy. Of three studies reporting on the effect of mobile phone location on sperm total motility and, or, total motile count, one showed a statistically significant effect. All three studies were at risk of exposure characterisation and selection bias; two of confounding, selective reporting and attrition bias; three of outcome assessment bias and one used inappropriate statistical method. RF-EMF Source: One study indicates there may be little or no effect of computer or other electric device use on sperm concentration, total motility or total count. This study is at probably high risk of exposure characterisation bias and outcome assessment bias. Occupational studies. With only two studies of occupational exposure to RF-EMF and heterogeneity in the population and exposure source (technicians exposed to microwaves or seamen exposed to radar equipment), it was not plausible to statistically pool findings. One study was at probably or definitely high risk of bias across all domains, the other across domains for exposure characterisation bias, outcome assessment bias and confounding.
DISCUSSION
The majority of evidence identified was assessing localised RF-EMF exposure from mobile phone use on male fertility with few studies assessing the impact of phone position. Overall, the evidence identified is very uncertain about the effect of RF-EMF exposure from mobile phones on sperm outcomes. One study assessed the impact of other RF-EMF sources on male fertility amongst the general public and two studies assessed the impact of RF-EMF exposure in occupational cohorts from different sources (radar or microwave) on male fertility. Further prospective studies conducted with greater rigour (in particular, improved accuracy of exposure measurement and appropriate statistical method use) would build the existing evidence base and are required to have greater certainty in any potential effects of RF-EMF on male reproductive outcomes. Prospero Registration: CRD42021265401 (SR3A).
PubMed: 38880061
DOI: 10.1016/j.envint.2024.108817 -
Computerized Medical Imaging and... Jun 2024Bio-medical image segmentation models typically attempt to predict one segmentation that resembles a ground-truth structure as closely as possible. However, as medical...
BACKGROUND AND OBJECTIVES
Bio-medical image segmentation models typically attempt to predict one segmentation that resembles a ground-truth structure as closely as possible. However, as medical images are not perfect representations of anatomy, obtaining this ground truth is not possible. A surrogate commonly used is to have multiple expert observers define the same structure for a dataset. When multiple observers define the same structure on the same image there can be significant differences depending on the structure, image quality/modality and the region being defined. It is often desirable to estimate this type of aleatoric uncertainty in a segmentation model to help understand the region in which the true structure is likely to be positioned. Furthermore, obtaining these datasets is resource intensive so training such models using limited data may be required. With a small dataset size, differing patient anatomy is likely not well represented causing epistemic uncertainty which should also be estimated so it can be determined for which cases the model is effective or not.
METHODS
We use a 3D probabilistic U-Net to train a model from which several segmentations can be sampled to estimate the range of uncertainty seen between multiple observers. To ensure that regions where observers disagree most are emphasised in model training, we expand the Generalised Evidence Lower Bound (ELBO) with a Constrained Optimisation (GECO) loss function with an additional contour loss term to give attention to this region. Ensemble and Monte-Carlo dropout (MCDO) uncertainty quantification methods are used during inference to estimate model confidence on an unseen case. We apply our methodology to two radiotherapy clinical trial datasets, a gastric cancer trial (TOPGEAR, TROG 08.08) and a post-prostatectomy prostate cancer trial (RAVES, TROG 08.03). Each dataset contains only 10 cases each for model development to segment the clinical target volume (CTV) which was defined by multiple observers on each case. An additional 50 cases are available as a hold-out dataset for each trial which had only one observer define the CTV structure on each case. Up to 50 samples were generated using the probabilistic model for each case in the hold-out dataset. To assess performance, each manually defined structure was matched to the closest matching sampled segmentation based on commonly used metrics.
RESULTS
The TOPGEAR CTV model achieved a Dice Similarity Coefficient (DSC) and Surface DSC (sDSC) of 0.7 and 0.43 respectively with the RAVES model achieving 0.75 and 0.71 respectively. Segmentation quality across cases in the hold-out datasets was variable however both the ensemble and MCDO uncertainty estimation approaches were able to accurately estimate model confidence with a p-value < 0.001 for both TOPGEAR and RAVES when comparing the DSC using the Pearson correlation coefficient.
CONCLUSIONS
We demonstrated that training auto-segmentation models which can estimate aleatoric and epistemic uncertainty using limited datasets is possible. Having the model estimate prediction confidence is important to understand for which unseen cases a model is likely to be useful.
PubMed: 38878632
DOI: 10.1016/j.compmedimag.2024.102403 -
Journal of Environmental Management Jul 2024The war in Ukraine has had a devastating impact on the environment. Military actions have caused the release of hazardous substances into the environment, such as...
The war in Ukraine has had a devastating impact on the environment. Military actions have caused the release of hazardous substances into the environment, such as pollutants and toxic chemicals, that have contaminated the water, soil, and air, posing a threat to both human health and the environment. This has resulted in widespread destruction and contamination of natural habitats and resources and has disrupted wildlife populations and ecosystems. The impacts of military activity on the soils of protected areas are particularly critical, as they are the basis of biotic and landscape diversity and require special management and scientifically based monitoring measures even in peaceful conditions. In this context, this communication paper aims to provide an overview of the impacts of the war on the soils in four Ukrainian protected areas, namely Chornobyl Radiation and Ecological Biosphere Reserve; Desniansko-Starohutskyi National Nature Park; Holosiivskyi National Nature Park, and Hetmanskyi National Nature Park. To address these aspects, this paper combined GIS analysis and secondary data including soil samples obtained during field expeditions, to provide evidence of how ground battles, occupation, terrestrial land mines, and explosions can severely impact the soils. Practical and theoretical implications of the military actions are also discussed.
Topics: Ukraine; Conservation of Natural Resources; Ecosystem; Soil; Humans; Warfare; Environment
PubMed: 38878570
DOI: 10.1016/j.jenvman.2024.121399 -
Environment International Jul 2024
Topics: Brain Neoplasms; Humans; Prospective Studies; Cell Phone; Cell Phone Use
PubMed: 38870580
DOI: 10.1016/j.envint.2024.108808 -
PloS One 2024Among many types of cancers, to date, lung cancer remains one of the deadliest cancers around the world. Many researchers, scientists, doctors, and people from other...
Among many types of cancers, to date, lung cancer remains one of the deadliest cancers around the world. Many researchers, scientists, doctors, and people from other fields continuously contribute to this subject regarding early prediction and diagnosis. One of the significant problems in prediction is the black-box nature of machine learning models. Though the detection rate is comparatively satisfactory, people have yet to learn how a model came to that decision, causing trust issues among patients and healthcare workers. This work uses multiple machine learning models on a numerical dataset of lung cancer-relevant parameters and compares performance and accuracy. After comparison, each model has been explained using different methods. The main contribution of this research is to give logical explanations of why the model reached a particular decision to achieve trust. This research has also been compared with a previous study that worked with a similar dataset and took expert opinions regarding their proposed model. We also showed that our research achieved better results than their proposed model and specialist opinion using hyperparameter tuning, having an improved accuracy of almost 100% in all four models.
Topics: Humans; Lung Neoplasms; Machine Learning; Risk Assessment
PubMed: 38870229
DOI: 10.1371/journal.pone.0305035 -
PloS One 2024Infrared target detection is widely used in industrial fields, such as environmental monitoring, automatic driving, etc., and the detection of weak targets is one of the...
Infrared target detection is widely used in industrial fields, such as environmental monitoring, automatic driving, etc., and the detection of weak targets is one of the most challenging research topics in this field. Due to the small size of these targets, limited information and less surrounding contextual information, it increases the difficulty of target detection and recognition. To address these issues, this paper proposes YOLO-ISTD, an improved method for infrared small target detection based on the YOLOv5-S framework. Firstly, we propose a feature extraction module called SACSP, which incorporates the Shuffle Attention mechanism and makes certain adjustments to the CSP structure, enhancing the feature extraction capability and improving the performance of the detector. Secondly, we introduce a feature fusion module called NL-SPPF. By introducing an NL-Block, the network is able to capture richer long-range features, better capturing the correlation between background information and targets, thereby enhancing the detection capability for small targets. Lastly, we propose a modified K-means clustering algorithm based on Distance-IoU (DIoU), called K-means_DIOU, to improve the accuracy of clustering and generate anchors suitable for the task. Additionally, modifications are made to the detection heads in YOLOv5-S. The original 8, 16, and 32 times downsampling detection heads are replaced with 4, 8, and 16 times downsampling detection heads, capturing more informative coarse-grained features. This enables better understanding of the overall characteristics and structure of the targets, resulting in improved representation and localization of small targets. Experimental results demonstrate significant achievements of YOLO-ISTD on the NUST-SIRST dataset, with an improvement of 8.568% in [email protected] and 8.618% in [email protected]. Compared to the comparative models, the proposed approach effectively addresses issues of missed detections and false alarms in the detection results, leading to substantial improvements in precision, recall, and model convergence speed.
Topics: Algorithms; Infrared Rays; Cluster Analysis; Pattern Recognition, Automated
PubMed: 38870195
DOI: 10.1371/journal.pone.0303451