Firefighters’ occupational activity causes cancer, and the characterization of exposure during firefighting activities remains limited. This work characterizes, for the first time, firefighters’ exposure to (coarse/fine/ultrafine) particulate matter (PM) bound polycyclic aromatic hydrocarbons (PAHs) and metal(loid)s during prescribed fires, Fire 1 and Fire 2 (210 min). An impactor collected 14 PM fractions, the PM levels were determined by gravimetry, and the PM-bound PAHs and metal(loid)s were determined by chromatographic and spectroscopic methodologies, respectively. Firefighters were exposed to a total PM level of 1408.3 and 342.5 µg/m³ in Fire 1 and Fire 2, respectively; fine/ultrafine PM represented more than 90% of total PM. Total PM-bound PAHs (3260.2 ng/m³ in Fire 1; 412.1 ng/m³ in Fire 2) and metal(loid)s (660.8 ng/m³ versus 262.2 ng/m³), distributed between fine/ultrafine PM, contained 4.57–24.5% and 11.7–12.6% of (possible/probable) carcinogenic PAHs and metal(loid)s, respectively. Firefighters’ exposure to PM, PAHs, and metal(loid)s were below available occupational limits. The estimated carcinogenic risks associated with the inhalation of PM-bound PAHs (3.78 × 10⁻⁹ − 1.74 × 10⁻⁶) and metal(loid)s (1.50 × 10⁻² − 2.37 × 10⁻²) were, respectively, below and 150–237 times higher than the acceptable risk level defined by the USEPA during 210 min of firefighting activity and assuming a 40-year career as a firefighter. Additional studies need to (1) explore exposure to (coarse/fine/ultrafine) PM, (2) assess health risks, (3) identify intervention needs, and (4) support regulatory agencies recommending mitigation procedures to reduce the impact of fire effluents on firefighters. Full article

Groundwater contaminated by benzene and toluene is a common issue, posing a threat to the ecosystems and human health. The removal of benzene and toluene under sulfate-reducing condition is well known, but how the bacterial community shifts during this process remains unclear. This study aims to evaluate the shift in bacterial community structure during the biodegradation of benzene and toluene under sulfate-reducing condition. In this study, groundwater contaminated with benzene and toluene were collected from the field and used to construct three artificial samples: Control (benzene 50 mg/L, toluene 1.24 mg/L, sulfate 470 mg/L, and HgCl₂ 250 mg/L), S1 (benzene 50 mg/L, toluene 1.24 mg/L, sulfate 470 mg/L), and S2 (benzene 100 mg/L, toluene 2.5 mg/L, sulfate 940 mg/L). The contaminants (benzene and toluene), geochemical parameters (sulfate, ORP, and pH), and bacterial community structure in the artificial samples were monitored over time. By the end of this study (day 90), approximately 99% of benzene and 96% of toluene could be eliminated in both S1 and S2 artificial samples, while in the Control artificial sample the contaminant levels remained unchanged due to microbial inactivation. The richness of bacterial communities initially decreased but subsequently increased over time in both S1 and S2 artificial samples. Under sulfate-reducing condition, key players in benzene and toluene degradation were identified as Pseudomonas, Janthinobacterium, Novosphingobium, Staphylococcus, and Bradyrhizobium. The results could provide scientific basis for remediation and risk management strategies at the benzene and toluene contaminated sites Full article

Spacetime wormholes are evidently an essential component of the construction of a time machine. Within the context of general relativity, such objects require, for their formation, exotic matter—matter that violates at least one of the standard energy conditions. Here, we explore the possibility that higher-curvature gravity theories might permit the construction of a wormhole without any matter at all. In particular, we consider the simplest form of a generalized quasi topological theory in four spacetime dimensions, known as Einsteinian Cubic Gravity. This theory has a number of promising features that make it an interesting phenomenological competitor to general relativity, including having non-hairy generalizations of the Schwarzschild black hole and linearized equations of second order around maximally symmetric backgrounds. By matching series solutions near the horizon and at large distances, we find evidence that strong asymptotically AdS wormhole solutions can be constructed, with strong curvature effects ensuring that the wormhole throat can exist. Full article

This study explored the relationship between technology acceptance and learning satisfaction within a virtual learning environment (VLE) with cognitive presence, cognitive engagement, social presence, and emotional engagement as mediators. A total of 237 university students participated and completed a questionnaire after studying in the Virbela VLE. The results revealed direct and indirect links between technology acceptance and virtual learning satisfaction. The mediation analysis showed the critical mediating roles of cognitive presence and emotional engagement in fostering satisfaction. There also appeared to be a sequential mediating pathway from technology acceptance to learning satisfaction through social presence and emotional engagement. Notably, cognitive engagement and social presence did not have a significant mediating effect on satisfaction. These results provide a supplementary perspective on how technological, cognitive, and emotional factors can enhance student satisfaction in VLEs. The study concludes with several implications for future research and practice of VLEs in higher education. Full article

The current usage of federated learning in applications relies on the existence of servers. To address the inability to conduct federated learning for IoV (Internet of Vehicles) applications in serverless areas, a P2P (peer-to-peer) architecture for federated learning is proposed in this paper. Following node segmentation based on limited subgraph diameters, an edge aggregation mode is employed to propagate models inwardly, and a mode for propagating the model inward to the C-node (center node) while aggregating is proposed. Simultaneously, a personalized differential privacy scheme was designed under this architecture. Through experimentation and verification, the approach proposed in this paper demonstrates the combination of both security and usability. Full article

This paper focuses on converting complex 3D maps created by LiDAR and SLAM technology into simple 2D maps to make them easier to understand. While 3D maps provide a lot of useful details for robots and computer programs, they can be difficult to read for humans who are used to flat maps. We developed a new system to clean up these 3D maps and convert them into intuitive and accurate 2D maps. The system uses three steps designed to correct different kinds of errors found in 3D LiDAR scan data: clustering-based denoising, height-based denoising, and Statistical Outlier Removal. In particular, height-based denoising is the method we propose in this paper, an algorithm that leaves only indoor structures such as walls. The paper proposes an algorithm that considers the entire range of the point cloud, rather than just the points near the ceiling, as is the case with existing methods, to make denoising more effective. This makes the final 2D map easy to understand and useful for building planning or emergency preparedness. Our main goal is to map the interior of buildings faster and more effectively, creating 2D drawings that reflect accurate and current information. We want to make it easier to use LiDAR and SLAM data in our daily work and increase productivity. Full article

This research introduces a novel framework utilizing a sequential gated graph convolutional neural network (GGCN) designed specifically for botnet detection within Internet of Things (IoT) network environments. By capitalizing on the strengths of graph neural networks (GNNs) to represent network traffic as complex graph structures, our approach adeptly handles the temporal dynamics inherent to botnet attacks. Key to our approach is the development of a time-stamped multi-edge graph structure that uncovers subtle temporal patterns and hidden relationships in network flows, critical for recognizing botnet behaviors. Moreover, our sequential graph learning framework incorporates time-sequenced edges and multi-edged structures into a two-layered gated graph model, which is optimized with specialized message-passing layers and aggregation functions to address the challenges of time-series traffic data effectively. Our comparative analysis with the state of the art reveals that our sequential gated graph convolutional neural network achieves substantial improvements in detecting IoT botnets. The proposed GGCN model consistently outperforms the conventional model, achieving improvements in accuracy ranging from marginal to substantial—0.01% for BoT IoT and up to 25% for Mirai. Moreover, our empirical analysis underscores the GGCN’s enhanced capabilities, particularly in binary classification tasks, on imbalanced datasets. These findings highlight the model’s ability to effectively navigate and manage the varying complexity and characteristics of IoT security threats across different datasets. Full article

Researchers have proposed different obfuscation transformations supported by numerous smartphone protection tools (obfuscators and deobfuscators). However, there is a need for a comprehensive study to empirically characterize these tools that belong to different categories of transformations. We propose a property-based framework to systematically classify twenty cutting-edge tools according to their features, analysis type, programming language support, licensing, applied obfuscation transformations, and general technical drawbacks. Our analysis predominantly reveals that very few tools work at the dynamic level, and most tools (which are static-based) work for Java or Java-based ecosystems (e.g., Android). The findings also show that the widespread adoption of renaming transformations is followed by formatting and code injection. In addition, this paper pinpoints the technical shortcomings of each tool; some of these drawbacks are common in static-based analyzers (e.g., resource consumption), and other drawbacks have negative effects on the experiment conducted by students (e.g., a third-party library involved). According to these critical limitations, we provide some timely recommendations for further research. This study can assist not only Android developers and researchers to improve the overall health of their apps but also the managers of computer science and cybersecurity academic programs to embed suitable obfuscation tools in their curricula. Full article

Medical Visual Question Answering (Med-VQA) faces significant limitations in application development due to sparse and challenging data acquisition. Existing approaches focus on multi-modal learning to equip models with medical image inference and natural language understanding, but this worsens data scarcity in Med-VQA, hindering clinical application and advancement. This paper proposes the ITLTA framework for Med-VQA, designed based on field requirements. ITLTA combines multi-label learning of medical images with the language understanding and reasoning capabilities of large language models (LLMs) to achieve zero-shot learning, meeting natural language module needs without end-to-end training. This approach reduces deployment costs and training data requirements, allowing LLMs to function as flexible, plug-and-play modules. To enhance multi-label classification accuracy, the framework uses external medical image data for pretraining, integrated with a joint feature and label attention mechanism. This configuration ensures robust performance and applicability, even with limited data. Additionally, the framework clarifies the decision-making process for visual labels and question prompts, enhancing the interpretability of Med-VQA. Validated on the VQA-Med 2019 dataset, our method demonstrates superior effectiveness compared to existing methods, confirming its outstanding performance for enhanced clinical applications. Full article

One technique, especially in chaos-based cryptographic applications, is to include the message in the evolution of the dynamical system. This paper aims to find out if and to what extent the statistical behavior of the chaotic system is affected by the message inclusion in its dynamic evolution. The study is illustrated by the dynamical system described by the logistic map in cryptographic applications based on images. The evaluation of the statistical behavior was performed on an original scheme proposed. The Monte Carlo analysis of the applied Kolmogorov–Smirnov statistical test revealed that the dynamical system in the processing scheme with message inclusion does not modify its proper statistical behavior (revealed by definition relation). This was possible due to the proposed scheme designed. Namely, this scheme contains a decision switch which, supported by an appropriate choice of the magnitude of the scaling factor, ensures that the values of the dynamical system are maintained in the definition domain. The proposed framework for analyzing the statistical properties and for preserving the dynamical system behavior is one main contribution of this research. The message inclusion scheme also provides an enhancement with cryptographic mixing functions applied internally; the statistical behavior of the dynamical system is also analyzed in this case. Thus, the paper contributes to the theoretical complex characterization of the dynamical system considering also the message inclusion case. Full article

Automatic detection of safety helmet wearing is significant in ensuring safe production. However, the accuracy of safety helmet detection can be challenged by various factors, such as complex environments, poor lighting conditions and small-sized targets. This paper presents a novel and efficient deep learning framework named High-Resolution You Only Look Once (HR-YOLO) for safety helmet wearing detection. The proposed framework synthesizes safety helmet wearing information from the features of helmet objects and human pose. HR-YOLO can use features from two branches to make the bounding box of suppression predictions more accurate for small targets. Then, to further improve the iterative efficiency and accuracy of the model, we design an optimized residual network structure by using Optimized Powered Stochastic Gradient Descent (OP-SGD). Moreover, a Laplace-Aware Attention Model (LAAM) is designed to make the YOLOv5 decoder pay more attention to the feature information from human pose and suppress interference from irrelevant features, which enhances network representation. Finally, non-maximum suppression voting (PA-NMS voting) is proposed to improve detection accuracy for occluded targets, using pose information to constrain the confidence of bounding boxes and select optimal bounding boxes through a modified voting process. Experimental results demonstrate that the presented safety helmet detection network outperforms other approaches and has practical value in application scenarios. Compared with the other algorithms, the proposed algorithm improves the precision, recall and mAP by 7.27%, 5.46% and 7.3%, on average, respectively. Full article

Fog, as a common weather condition, severely affects the visual quality of images. Polarization-based dehazing techniques can effectively produce clear results by utilizing the atmospheric polarization transmission model. However, current polarization-based dehazing methods are only suitable for scenes with strong illumination, such as daytime scenes, and cannot be applied to low-light scenes. Due to the insufficient illumination at night and the differences in polarization characteristics between it and sunlight, polarization images captured in a low-light environment can suffer from loss of polarization and intensity information. Therefore, this paper proposes a two-stage low-light image dehazing method based on polarization. We firstly construct a polarization-based low-light enhancement module to remove noise interference in polarization images and improve image brightness. Then, we design a low-light polarization dehazing module, which combines the polarization characteristics of the scene and objects to remove fog, thereby restoring the intensity and polarization information of the scene and improving image contrast. For network training, we generate a simulation dataset for low-light polarization dehazing. We also collect a low-light polarization hazy dataset to test the performance of our method. Experimental results indicate that our proposed method can achieve the best dehazing effect. Full article

Ulcerative colitis (UC) is an inflammatory bowel disease that causes chronic inflammation in the large intestine. The etiology of UC is complex and incompletely understood, with potential contributing factors including genetic susceptibility, environmental influences, immune dysregulation, and gut barrier dysfunction. Despite available therapeutic drugs, the suboptimal cure rate for UC emphasizes the necessity of developing novel therapeutics. Traditional Chinese Medicine (TCM) has attracted great interest in the treatment of such chronic inflammatory diseases due to its advantages, such as multi-targets and low side effects. In this study, a mouse model of Dextran Sulfate Sodium (DSS)-induced acute colitis was established and the efficacy of Zhenqi Granule, a TCM preparation composed of the extractives from Astragali Radix and Fructus Ligustri Lucidi, was evaluated. The results showed that treatment with Zhenqi Granule prior to or post-DSS induction could alleviate the symptoms of colitis, including weight loss, diarrhea, hematochezia, colon length shortening, and pathological damage of colon tissues of the DSS-treated mice. Further, network pharmacology analysis showed that there were 98 common targets between the active components of Zhenqi Granule and the targets of UC, and the common targets were involved in the regulation of inflammatory signaling pathways. Our results showed that Zhenqi Granule had preventive and therapeutic effects on acute colitis in mice, and the mechanism may be that the active components of Zhenqi Granule participated in the regulation of inflammatory response. This study provided data reference for further exploring the mechanism of Zhenqi Granule and also provided potential treatment strategies for UC. Full article

Biocide resistance poses a significant challenge in industrial processes, with bacteria like Pseudomonas oleovorans exhibiting intrinsic resistance to traditional antimicrobial agents. In this study, the impact of biocide exposure on the metabolome of two P. oleovorans strains, namely, P. oleovorans P4A, isolated from contaminated coating material, and P. oleovorans 1045 reference strain, were investigated. The strains were exposed to 2-Methylisothiazol-3(2H)-one (MI) MIT, 1,2-Benzisothiazol-3(2H)-one (BIT), and 5-chloro-2-methyl-isothiazol-3-one (CMIT) at two different sub-inhibitory concentrations and the lipids and polar and semipolar metabolites were analyzed by ultra-high performance liquid chromatography quadrupole time-of-flight mass spectrometry UPLC–Q–TOF/MS. Exposure to the BIT biocide induced significant metabolic modifications in P. oleovorans. Notable changes were observed in lipid and metabolite profiles, particularly in phospholipids, amino acid metabolism, and pathways related to stress response and adaptation. The 1045 strain showed more pronounced metabolic alterations than the P4A strain, suggesting potential implications for lipid, amino acid metabolism, energy metabolism, and stress adaptation. Improving our understanding of how different substances interact with bacteria is crucial for making antimicrobial chemicals more effective and addressing the challenges of resistance. We observed that different biocides trigged significantly different metabolic responses in these strains. Our study shows that metabolomics can be used as a tool for the investigation of metabolic mechanisms underlying biocide resistance, and thus in the development of targeted biocides. This in turn can have implications in combating biocide resistance in bacteria such as P. oleovorans. Full article

The detrimental impact of toxic chemicals, gas, and oil spills in aquatic environments poses a severe threat to plants, animals, and human life. Regions such as Cancer Alley exemplify the profound consequences of inadequately controlled chemical spills, significantly affecting the local community. Given the far-reaching effects of these spills, it has become imperative to devise an efficient method for early monitoring, estimation, and cleanup, utilizing affordable and effective techniques. In this research, we explore the application of U-shaped neural Network (UNET) and U-shaped neural network transformer (UNETR) neural network models designed for the image segmentation of chemical and oil spills. Our models undergo training using the Commonwealth Scientific and Industrial Research Organization (CSIRO) dataset and the Oil Spill Detection dataset, employing a specialized filtering technique to enhance detection accuracy. We achieved training accuracies of 95.35% and 91% by applying UNET on the Oil Spill and the CSIRO datasets after 50 epochs of training, respectively. We also achieved a training accuracy of 75% by applying UNETR to the Oil Spill dataset. Additionally, we integrated mixed precision to expedite the model training process, thus maximizing data throughput. To further accelerate our implementation, we propose the utilization of the Field Programmable Gate Array (FPGA) architecture. The results obtained from our study demonstrate improvements in inference latency on FPGA. Full article

Agricultural products are vitally important for sustaining life on earth and their production has notably grown over the years worldwide in general and in Brazil particularly. Elevating agricultural practices consequently leads to a proportionate increase in the usage of pesticides that are crucially important for enhanced crop yield and protection. These compounds have been employed excessively in alarming concentrations, causing the contamination of soil, water, and air. Additionally, they pose serious threats to human health. The current study introduces an innovative tool for producing appropriate materials coupled with an electrochemical sensor designed to measure carbendazim levels. The sensor is developed using a molecularly imprinted polymer (MIP) mounted on a glassy carbon electrode. This electrode is equipped with multi-walled carbon nanotubes (MWCNTs) for improved performance. The combined system demonstrates promising potential for accurately quantifying carbendazim. The morphological characteristics of the synthesized materials were investigated using field emission scanning electron microscopy (FESEM) and the Fourier-transform infrared (FTIR) technique. The analytical curve was drawn using the electrochemical method in the range of 2 to 20 ppm while for HPLC 2–12 ppm; the results are presented as the maximum adsorption capacity of the MIP (82.4%) when compared with NIP (41%) using the HPLC method. The analysis conducted using differential pulse voltammetry (DPV) yielded a limit of detection (LOD) of 1.0 ppm and a repeatability of 5.08% (n = 10). The results obtained from the analysis of selectivity demonstrated that the proposed electrochemical sensor is remarkably efficient for the quantitative assessment of carbendazim, even in the presence of another interferent. The sensor was successfully tested for river water samples for carbendazim detection, and recovery rates ranging from 94 to 101% were obtained for HPLC and 94 to 104% for the electrochemical method. The results obtained show that the proposed electrochemical technique is viable for the application and quantitative determination of carbendazim in any medium. Full article

Background/Objectives: To assess the frequency, extent, localization and potential progression of optic disc drusen (ODD) and the correlation with the angioid streak (AS) length and retinal atrophy in patients with pseudoxanthoma elasticum (PXE). Methods: This retrospective study included patient data from a dedicated PXE clinic at the Department of Ophthalmology, University of Bonn, Germany (observation period from February 2008 to July 2023). Two readers evaluated the presence, localization, and the extent of the ODD on fundus autofluorescence (FAF) imaging at baseline and the follow-up assessments. Additionally, we measured the length of the longest AS visible at baseline and follow-up and the area of atrophy at baseline, both on FAF. Results: A total of 150 eyes of 75 PXE patients (median age at baseline 51.8 years, IRQ 46.3; 57.5 years, 49 female) underwent retrospective analysis. At baseline, 23 of 75 patients exhibited ODD in a minimum of one eye, resulting in an ODD prevalence of 30.7% in our cohort of PXE patients. Among these, 14 patients showed monocular and 9 binocular ODD that were localized predominantly nasally (46.9%). During the observational period (mean 97.5 ± 44.7 months), only one patient developed de novo ODD in one eye and one other patient showed a progression in the size of the existing ODD. The group of patients with ODD had significantly longer ASs (median 7020 µm, IQR 4604; 9183, vs. AS length without ODD: median 4404 µm, IQR 3512; 5965, p < 0.001). No association with the size of the atrophy was found at baseline (p = 0.27). Conclusions: This study demonstrates a prevalence of ODD of 30.7%. ODD presence is associated with longer ASs (an indicator of the severity and extent of ocular Bruch’s membrane calcification), suggesting that ODD formation is tightly related to ectopic calcification—possibly secondary to calcification of the lamina cribrosa. Prospective studies investigating the impact of ODD (in conjunction with intraocular pressure) on visual function in PXE warrant consideration. Full article

Hereditary fructose intolerance is a rare genetic disorder that is inherited in an autosomal recessive manner, with mutations sometimes occurring spontaneously. Consuming fructose triggers biochemical abnormalities, disrupting liver processes like glycogenolysis and gluconeogenesis. Recent studies have revealed elevated intrahepatic fat levels in affected individuals. Symptoms include aversion to fructose-containing foods, hypoglycemia, liver and kidney dysfunction, and growth delays, with severe cases leading to liver enlargement, fatty liver disease, kidney failure, and life-threatening hypoglycemia. In this case study, we present a 20-month-old child with symptoms including difficulty passing stool, abdominal rigidity, abdominal pain with bloating and hypoglycemia. Initial clinical findings revealed elevated liver enzymes, a mildly enlarged hyperechoic liver, hypercholesterolemia, and borderline alpha-fetoprotein values. Diagnostic assessments identified hereditary fructose intolerance (HFI) with pathogenic variants in the ALDOB gene, along with a diagnosis of celiac disease. Genetic testing of the parents revealed carrier status for pathological aldolase B genes. This case underscores the importance of comprehensive clinical evaluation and genetic testing in pediatric patients with complex metabolic presentations. Full article

Objectives: Congenital diaphragmatic hernia (CDH) is defined as organ protrusion from the abdominal to the thoracic cavity. The Hadlock formula is the most commonly used tool for calculating estimated fetal weight (EFW). The anatomical nature of CDH usually leads to underestimation of the abdominal circumference, resulting in underestimation of fetal weight. Accurate weight estimation is essential before birth for counselling, preparation before surgery and ECMO. The research is made to compare the accuracy of Hadlock’s formula and Faschingbauer’s formula for fetal weight estimation in CDH fetuses population. Methods: In our study, we investigated differences between EFW and actual birthweight in 42 fetuses with CDH as compared to 80 healthy matched controls. EFW was calculated using the Hadlock formula and a recently introduced formula described by Faschingbauer et al., which was tailored for fetuses with CDH. Additionally, both of the formulas were adjusted for the interval between the ultrasound and delivery for both of the groups. Results: The majority of hernias were left-sided (92.8% vs. 7.2%). EFW adjusted for the interval between the ultrasound and delivery had the highest correlation with the actual birthweight in both, study group and controls. We compared the results for both tools and found the Hadlock formula to predict birthweight in CDH children with a 7.8 ± 5.5% error as compared to 7.9 ± 6.5% error for the Faschingbauer’s formula. Conclusions: The Hadlock formula adjusted for the interval between the ultrasound and delivery is a more precise method of calculating EFW in fetuses with CDH. Routine biometry scan using Hadlock’s formula remains reliable for predicting birthweight. Full article

Background: The intensive care unit (ICU) is a scarce resource in all health care systems, necessitating a well-defined utilization. Therefore, benchmarks are essential; and yet, they are limited due to heterogenous definitions of what an ICU is. This study analyzed the case distribution, patient characteristics, and hospital course and outcomes of 6,204,093 patients in the German Helios Hospital Group according to 10 derived ICU definitions. We aimed to set a baseline for the development of a nationwide, uniform ICU definition. Methods: We analyzed ten different ICU definitions: seven derived from the German administrative data set of claims data according to the German Hospital Remuneration Act, three definitions were taken from the Helios Hospital Group’s own bed classification. For each ICU definition, the size of the respective ICU population was analyzed. Due to similar patient characteristics for all ten definitions, we selected three indicator definitions to additionally test statistically against IQM. Results: We analyzed a total of 5,980,702 completed hospital cases, out of which 913,402 referred to an ICU criterion (14.7% of all cases). A key finding is the significant variability in ICU population size, depending on definitions. The most restrictive definition of only mechanical ventilation (DOV definition) resulted in 111,966 (1.9%) cases; mechanical ventilation plus typical intensive care procedure codes (IQM definition) resulted in 210,147 (3.5%) cases; defining each single bed individually as ICU or IMC (ICUᴧIMC definition) resulted in 411,681 (6.9%) cases; and defining any coded length of stay at ICU (LOSi definition) resulted in 721,293 (12.1%) cases. Further testing results for indicator definitions are reported. Conclusions: The size of the population, utilization rates, outcomes, and capacity assumptions clearly depend on the definition of ICU. Therefore, the underlying ICU definition should be stated when making any comparisons. From previous studies, we anticipated that 25–30% of all ICU patients should be mechanically ventilated, and therefore, we conclude that the ICUᴧIMC definition is the most plausible approximation. We suggest a mandatory application of a clearly defined ICU term for all hospitals nationwide for improved benchmarking and data analysis. Full article

Background/Objectives: Polycythemia vera (PV) is a chronic hematologic neoplasm commonly treated with hydroxyurea (HU). We utilized the advanced digitalized database of Maccabi Healthcare Services to retrospectively investigate the clinical and economic implications of HU intolerance in the routine clinical care of PV patients in Israel. Methods: We collected data on demographics, physician visits, hospitalizations, laboratory results, medication purchases, cardiovascular and thrombotic events, mental health, economic outcomes, and mortality. Outcomes included cardiovascular and other thrombotic events, disease progression, mental health events, economic outcomes, and overall mortality. Results: Of the 830 patients studied, 3 (0.4%) were resistant to HU treatment, 318 (38.3%) were intolerant to HU treatment, and 509 (61.3%) were stable on HU treatment. The venous thrombosis rate was significantly higher among HU-intolerant compared to HU-stable patients (1.58 vs. 0.47 per 100 person-years [PY], respectively; p < 0.001). The rate of progression to myelofibrosis was 6 vs. 0.9 per 100 PY in HU-intolerant patients vs. HU-stable patients, respectively (p < 0.001), and the rate of progression to acute myeloid leukemia (AML) was 1.16 vs. 0.2 per 100 PY in HU-intolerant patients vs. HU-stable patients, respectively (p < 0.001). The phlebotomy requirement, mortality rate, and total hospitalization days among HU-intolerant patients were significantly higher than in HU-stable patients (p = 0.049, p < 0.001, p < 0.001, respectively). More mental health-related events were noted in HU-intolerant patients vs. HU-stable patients (p = 0.007), and the total healthcare cost ratio was 2.65 for the HU-intolerant patients compared with HU-stable patients. Conclusions: This study suggests that HU-intolerant patients are more likely to have worse outcomes than HU-stable patients, highlighting the need for the close monitoring of these patients for disease-related complications or progression. Full article

Background: The improvement of oral-health-related quality of life (OHRQoL) with different types of prosthesis for completely edentulous jaws in the elderly population is a critical factor in clinical decision making for these vulnerable patients. This review aims to evaluate the changes in OHRQoL after treatment with different types of full-arch prostheses in the elderly edentulous population to determine the prostheses that result in the greatest improvement in OHRQoL. Materials and Methods: Clinical studies of different types of full-arch prostheses that measured the OHRQoL in edentulous patients 60 years or older were searched for in the PubMed, Embase and Scopus electronic databases, with additional hand searching to summarize the outcomes of the selected studies. Result: Among the 302 identified studies, 10 studies were selected. A total of 504 patients wearing 133 complete dentures, 372 implant overdentures and 39 fixed prostheses were assessed among the selected studies. The overall OHIP and GOHAI scores were evaluated at baseline and in the 3rd, 6th, 12th and 18th months of treatment with the respective prostheses. The improved OHRQoL with overall OHIP scores associated with conventional dentures were 9.21–12.5% from the 3rd month to 1 year after treatment, whereas those associated with implant overdentures and full-arch fixed prosthesis were 9–25.26% at 1 year and 18.53–26.79 at the 18th-month follow-up, respectively. The increased overall GOHAI scores were 21.3–25.43% for conventional dentures, 36.82–41.32% for implant overdentures and 39.48–42.83% for full-arch fixed prosthesis from the 3rd month to the 6th-month follow-up. Conclusion: In general, the improvement in OHRQoL after rehabilitation with implant overdentures declined at one year, and that with full-arch fixed prosthesis declined at the 18th-month follow-up; meanwhile, the OHRQoL associated with conventional dentures improved stably up to one year, but the implant-supported prostheses resulted in an obviously greater improvement in the OHRQoL than that obtained with conventional dentures. However, studies with longer follow-up periods are still required to evaluate the long-term clinical effectiveness. Full article