Search Results (110)

Background: Delay in diagnosing metastatic epidural spinal cord compression (MESCC) adversely impacts clinical outcomes. High-grade MESCC is frequently overlooked on routine staging CT scans. We aim to assess the potential of our deep learning model (DLM) in detecting high-grade MESCC and reducing diagnostic delays. Methods: This retrospective review analyzed 140 patients with surgically treated MESCC between C7 and L2 during 2015–2022. An experienced radiologist (serving as the reference standard), a consultant spine surgeon, and the DLM independently classified staging CT scans into high-grade MESCC or not. The findings were compared to original radiologist (OR) reports; inter-rater agreement was assessed. Diagnostic delay referred to the number of days elapsed from CT to diagnostic MRI scan. Results: Overall, 95/140 (67.8%) patients had preoperative CT scans. High-grade MESCC was identified in 84/95 (88.4%) of the scans by the radiologist (reference standard), but in only 32/95 (33.7%) of the preoperative scans reported by the OR. There was almost perfect agreement between the radiologist and the surgeon (kappa = 0.947, 95% CI = 0.893–1.000) (p < 0.001), and between the radiologist and the DLM (kappa = 0.891, 95% CI = 0.816–0.967) (p < 0.001). In contrast, inter-observer agreement between the OR and all other readers was slight (kappa range = 0.022–0.125). Diagnostic delay was potentially reduced by 20 ± 28 (range = 1–131) days. Conclusions: The original radiologist reports frequently missed high-grade MESCC in staging CT. Our DLM for CT diagnosis of high-grade MESCC showed almost perfect inter-rater agreement with two experienced reviewers. This study is the first to demonstrate that the DLM could help reduce diagnostic delays. Further prospective research is required to understand its precise role in improving the early diagnosis/treatment of MESCC. Full article

This study examines how the effective dielectric characteristics of the human torso affect the carrier-link-margin (CLM) and data-link-margin (DLM) of a biocompatible gelatin-encapsulated implantable medical device (IMD) that consists of a small implantable antenna, battery, printed circuit board (PCB), camera, and sensor operating at 2.5 GHz. The specific absorption rate (SAR) and the radio frequency (RF) link performances of the IMD are tested for ±20% changes in reference to the mean values of the effective relative permittivity, ɛ_eff, and the effective conductivity, σ_eff, of the human body model. An artificial neural network (ANN) with two inputs (ɛ_eff, σ_eff) and five outputs (SAR_1 g, SAR_10 g, fractional bandwidth, CLM, and DLM) is trained by 80% of the total scenarios and tested by 20% of them in order to provide reliable dependent analyses. The highest changes in 1 g SAR value, 10 g SAR value, fractional bandwidth, CLM, and DLM at a 4 m distance for 100 Kbps are 63%, 41.6%, 17.97%, 26.79%, and 5.89%, respectively, when compared to the reference effective electrical properties of the homogeneous human body model. This work is the first to accurately depend on the electrical analyses of the human body for the link margins of an implantable antenna system. Furthermore, the work’s uniqueness is distinguished by the application of the CLM and DLM principles in the sphere of IMD communication. Full article

Given the increasing digitalization of education and the variety of available digital learning materials (dLMs) of differing quality, (pre-service) teachers must develop the ability to select appropriate dLMs. Objective, reliable, and valid assessment instruments are necessary to evaluate the effectiveness of that development. This study conceptualized and designed an economical four-item instrument for assessing “selecting dLMs” based on accepted frameworks and competence models. The scientific quality of the instrument was evaluated in Study 1 (n = 164) with four dLMs and empirically investigated in a subsequent Study 2 (n = 395) with pre-service mathematics teachers from two universities. The empirical results indicate that the instrument could objectively and reliably gauge different levels of “selecting dLMs”. Furthermore, the results are consistent with the widely accepted notion that the competence of “selecting dLMs” depends on (content) knowledge; however, that relation was not strong. In addition, the results for objectively assessing “selecting dLMs” paralleled the results of self-assessed TPACK in terms of the academic progression of participants. The proposed approach allows for variations and integration of diverse dLMs, and it has the potential to be adapted in other subject areas and contexts. Full article

In this study, we examine the relevance of the coexistence of structural change and long memory to model and forecast the volatility of Tunisian stock returns and to deliver a more accurate measure of risk along the lines of VaR and expected shortfall. To this end, we propose three time-series models that incorporate long-term dependence on the level and volatility of returns. In addition, we introduce structural change points using the iterated cumulative sums of squares (ICSS) and the modified ICSS algorithms, synonymous with stock market turbulence, into the conditional variance equations of the models studied. We choose a conditional innovation density function other than the normal distribution, that is, a Student distribution, to account for excess kurtosis. The empirical results show that the inclusion of structural breakpoints in the conditional variance equation and Dual LM provides better short- and long-term predictability. Within such a framework, the ICSS-ARFIMA-HYGARCH model with Student’s t distribution was able to account for the long-term dependence in the level and volatility of TUNINDEX index returns, excess kurtosis, and structural changes, delivering an accurate estimator of VaR and expected shortfall. Full article

Background/Object: This study investigates the in vitro antiviral potential of D-limonene (DLM), monolaurin (ML), and cetylpyridinium chloride (CPC) in formulations targeting SARS-CoV-2 and influenza viruses. The aim was to develop oral and nasal formulations with optimized concentrations of these active ingredients to evaluate their efficacy, safety, and stability. Methods: Oral (formulation D) and nasal (formulation E) products were developed using specific concentrations of DLM (0.2–0.3% w/w), ML (0.1–0.2% w/w), and CPC (0.05–0.075% w/w). In vitro virucidal activity assays were conducted to assess the antiviral efficacy of the formulations against SARS-CoV-2 and influenza viruses. Stability testing was also performed under various storage conditions. Results: Formulation D (0.3% w/w DLM, 0.2% w/w ML, 0.05% w/w CPC, and 1.5% w/w Cremophor RH40) demonstrated a 3.875 ± 0.1021 log reduction and 99.99 ± 0.0032% efficacy against SARS-CoV-2 within 120 s. Formulation E (0.2% w/w DLM, 0.05% w/w CPC, and 0.75% w/w Cremophor RH40) showed a 2.9063 ± 0.1197 log reduction and 99.87 ± 0.0369% efficacy against SARS-CoV-2. Both formulations achieved >99.99% efficacy and log reductions exceeding 4.000 against various influenza strains. Stability testing confirmed optimal performance at 4 °C with no microbial contamination. Conclusions: The findings suggest that both formulations exhibit broad-spectrum antiviral activity against SARS-CoV-2 and influenza viruses in vitro. These results support their potential for further clinical evaluations and therapeutic applications, particularly in oral and nasal spray formulations. Full article

MicroRNAs function as post-transcriptional regulators in gene expression and control a broad range of biological processes in metazoans. The formation of multinucleated muscles is essential for locomotion, growth, and muscle repair. microRNAs have also emerged as important regulators for muscle development and function. In order to identify new microRNAs required for muscle formation, we have performed a large microRNA overexpression screen. We screened for defects during embryonic and adult muscle formation. Here, we describe the identification of mir-276a as a regulator for muscle migration during testis formation. The mir-276a overexpression phenotype in testis muscles resembles the loss-of-function phenotype of heartless. A GFP sensor assay reveals that the 3′UTR of heartless is a target of mir-276a. Furthermore, we found that mir-276a is essential for the proper development of indirect flight muscles and describe a method for determining the number of nuclei for each of the six longitudinal muscle fibers (DLMs), which are part of the indirect flight muscles. Full article

Background/Objectives: Recently, the prevalence of diseases such as diabetes, arthritis, and inflammatory diseases, along with their complications, has become a significant health problem. This is in addition to the various biomedical applications of pyrazole, isatin, and indole derivatives. Accordingly, cooperation will continue between chemistry scientists, pharmaceutical scientists, and human doctors to produce hybrid compounds from pyrazole with isatin or indole possessing biological activities as anti-diabetic, anti-arthritic, and anti-inflammatory agents. Methods: The two series of pyrazole–isatin conjugates 12a–h and pyrazole–indole conjugates 14a–d were prepared from our previous works via the direct reaction of 5-amino-pyrazoles 10a–d with N-alkyl isatin 11a,b, and 1H-indole-3-carbaldehyde (13), respectively, using the previously reported procedure. The potential biological activities of 12a–h and 14a–d as anti-diabetic, anti-arthritic, and anti-inflammatory agents were assessed through estimated inhibition percentage (%) and the median inhibitory concentrations (IC₅₀) using methods described in the literature. Further, the computational assessments of 12a–h and 14a–d such as toxic doses (the median lethal dose, LD₅₀), toxicity classes, drug-likeness model scores (DLMS), molecular lipophilicity potential (MLP) maps, polar surface area (PSA) maps, and topological polar surface area (TPSA) values were predicted using available free websites. Results: The in vitro enzymatic assessment results showed that pyrazole–indole conjugate 14b possesses powerful activities against (i) α-amylase (% = 65.74 ± 0.23, IC₅₀ = 4.21 ± 0.03 µg/mL) and α-glucosidase (% = 55.49 ± 0.23, IC₅₀ = 2.76 ± 0.01 µg/mL); (ii) the protein denaturation enzyme (% = 49.30 ± 0.17) and against the proteinase enzyme (% = 46.55 ± 0.17) with an IC₅₀ value of 6.77 ± 0.01 µg/mL; (iii) the COX-1, COX-2, and 5-LOX enzymes with an IC₅₀ of 5.44 ± 0.03, 5.37 ± 0.04, and 7.52 ± 0.04, respectively, which is almost close to the IC₅₀ of the indomethacin and zileuton drugs. Also, the computational assessment results showed (i) the conjugate 14b possesses lipophilic surface properties thus can cross cell membranes, and is effective for treatment; (ii) all the conjugates possess a TPSA value of more than 140 Ų thus possess good intestinal absorption. Conclusions: The two series of pyrazole–isatin conjugates 12a–h and pyrazole–indole conjugates 14a–d were synthesized from our previous works. The results of these in vitro enzymatic and computational assessments concluded that the pyrazole–indole conjugate 14b possesses powerful activities against various studied enzymes and possesses good computational results. In the future, our research team will present in vitro, in vivo biological, and computational assessments to hopefully obtain effectual agents such as anti-diabetic, anti-arthritic, and anti-inflammatory. Full article

Background: Colorectal cancer is the second most common type of cancer and a leading cause of cancer-related deaths worldwide. Approximately 15% of the patients with colorectal cancer will already have liver metastases (CRLMs) at diagnosis. Luckily, the advances in chemotherapy regimens during the past few decades have led to increased rates of disease regression that could even render an originally unresectable disease resectable. In certain patients with CRLMs, the hepatic lesions are missing on preoperative imaging after neoadjuvant chemotherapy. These patients can undergo surgery with or without resection of the sites of the disappearing liver metastases (DLMs). In this systematic review, we assess the recurrence rate of the DLMs that were left unresected as well as the complete pathologic response of those resected. Methods: A literature search was conducted in PubMed for studies including patients with CRLMs who received neoadjuvant chemotherapy and had DLMs in preoperative imaging. Two independent reviewers completed the search according to the PRISMA checklist. Results: Three hundred and twenty-six patients with 1134 DLMs were included in our review. A total of 47 out of 480 DLMs (72.29%) that were removed had viable tumor cells in postoperative histology. One hundred and forty-five tumors could not be identified intraoperatively and were removed based on previous imaging, with thirty (20.69%) of them presenting viable cancer cells. Four hundred and sixty-five lesions could not be identified and were left in place. Of them, 152 (32.69%) developed local recurrence within 5 years. Of note, 34 DLMs could not be categorized as viable or non-viable tumors. Finally, DLMs that were identifiable intraoperatively had a higher possibility of viable tumors compared to non-identifiable ones (72.29% vs. 20.69%, respectively). Conclusions: Disappearing liver metastases that are left unresected have an increased possibility of recurrence. Patients receiving neoadjuvant treatment for CRLMs may have better survival chances after resecting all the DLM sites, either identifiable intraoperatively or not. Full article

Objectives: Tacrolimus, a Biopharmaceutics Classification System (BCS) class II drug, is widely used for transplant patients to prevent graft rejection. To enhance its bioavailability, amorphous solid dispersion (ASD) formulations were developed and evaluated. The release properties of several ASD-based tacrolimus formulations were studied using an in-house USP IV dissolution method. Methods: The pharmacokinetics of a promising test product were compared with the commercially available Advagraf^® in a pilot clinical bioequivalence study with 12 healthy subjects. A previously published PBPK model for tacrolimus was validated using in vivo data and then applied to predict the human pharmacokinetics of several ASD-based tacrolimus formulations. Results: This study compares the pharmacokinetic (PK) parameters—AUC, Cmax, and Tmax—of Advagraf^® and a test formulation using two methodologies: one incorporating the dissolution profile directly into the PBPK model and the other utilizing the DLM approach. The results show that both methods provided accurate predictions for Cmax and Tmax, with the dissolution profile approach underestimating AUC slightly, while the DLM method predicted AUC adequately. Sensitivity analysis refining the DLM scalars in the Ileum and Colon led to optimized predictions of PK parameters. Furthermore, this study explores the use of PBPK modeling to predict in vivo behavior for additional tacrolimus formulations, highlighting the influence of formulation composition, such as the inclusion of Eudragit-S100, on dissolution profiles and bioavailability. Conclusions: This study evaluates formulations with different compositions and manufacturing characteristics; key factors that could influence their performance in the body were identified. These insights—spanning qualitative, quantitative, and manufacturing aspects—can greatly simplify the development of generic drugs, offering strong evidence of the critical role that physiologically based pharmacokinetic (PBPK) modeling can play in the early phases of generic drug development, especially in designing and assessing biopredictive dissolution methods. Full article

The paper presents the original results of cyclic testing of materials that are identical in chemical composition but produced by two different technologies: conventional metallurgy and additive manufacturing. For the aluminium alloy AlSi10Mg and the austenitic steel 316L, tensile curves, tension–compression and torsion alternating fatigue curves are experimentally obtained and presented. The experimental results are compared for two fabrication technologies—conventional metallurgy and additive DLMS technology. The results indicate a significant effect of anisotropy on the fatigue performance of the AM materials and a different slope of the fatigue life curves in the cyclic torsion versus cyclic tension–compression. The static and, in particular, the fatigue properties of both materials are discussed in relation to the microstructure of the materials after conventional production and after additive manufacturing. This comparison allowed us to explain both the causes of the anisotropy of the AM materials and the different slope of the curves for normal and shear stresses under cyclic loading. Using the example of the strength assessment of bicycle frames, the possibility of progressively wider use of additive manufacturing for load-bearing structures is presented. Full article

Background/Objectives: To assess the impact of a vendor-agnostic deep learning model (DLM) on image quality parameters and noise reduction in dental cone-beam computed tomography (CBCT) reconstructions. Methods: This retrospective study was conducted on CBCT scans of 93 patients (41 males and 52 females, mean age 41.2 years, SD 15.8 years) from a single center using the inclusion criteria of standard radiation dose protocol images. Objective and subjective image quality was assessed in three predefined landmarks through contrast-to-noise ratio (CNR) measurements and visual assessment using a 5-point scale by three experienced readers. The inter-reader reliability and repeatability were calculated. Results: Eighty patients (30 males and 50 females; mean age 41.5 years, SD 15.94 years) were included in this study. The CNR in DLM reconstructions was significantly greater than in native reconstructions, and the mean CNR in regions of interest 1-3 (ROI1-3) in DLM images was 11.12 ± 9.29, while in the case of native reconstructions, it was 7.64 ± 4.33 (p < 0.001). The noise level in native reconstructions was significantly higher than in the DLM reconstructions, and the mean noise level in ROI1-3 in native images was 45.83 ± 25.89, while in the case of DLM reconstructions, it was 35.61 ± 24.28 (p < 0.05). Subjective image quality assessment revealed no statistically significant differences between native and DLM reconstructions. Conclusions: The use of deep learning-based image reconstruction algorithms for CBCT imaging of the oral cavity can improve image quality by enhancing the CNR and lowering the noise. Full article

Remote Sensing (RS) and Geographic Information Science (GIS) techniques are powerful tools for spatial data collection, analysis, management, and digitization within cultural heritage frameworks. Despite their capabilities, challenges remain in automating data semantic classification for conservation purposes. To address this, leveraging airborne Light Detection And Ranging (LiDAR) point clouds, complex spatial analyses, and automated data structuring is crucial for supporting heritage preservation and knowledge processes. In this context, the present contribution investigates the latest Artificial Intelligence (AI) technologies for automating existing LiDAR data structuring, focusing on the case study of Sardinia coastlines. Moreover, the study preliminary addresses automation challenges in the perspective of historical defensive landscapes mapping. Since historical defensive architectures and landscapes are characterized by several challenging complexities—including their association with dark periods in recent history and chronological stratification—their digitization and preservation are highly multidisciplinary issues. This research aims to improve data structuring automation in these large heritage contexts with a multiscale approach by applying Machine Learning (ML) techniques to low-scale 3D Airborne Laser Scanning (ALS) point clouds. The study thus develops a predictive Deep Learning Model (DLM) for the semantic segmentation of sparse point clouds (<10 pts/m²), adaptable to large landscape heritage contexts and heterogeneous data scales. Additionally, a preliminary investigation into object-detection methods has been conducted to map specific fortification artifacts efficiently. Full article

The German government aims to convert its energy system to renewable energy by 2045. This requires a comprehensive understanding of land eligibility for renewables, particularly land-intensive ground-mounted photovoltaics (GM-PV). Federal states must set aside at least 2% of their land for renewable energy. This target value was derived using a top-down energy demand approach. Georeferenced land use data can be used to make bottom-up estimates. This study investigates how the choice of data source influences the bottom-up evaluation of land eligibility for GM-PV installations in Germany. This study evaluates the quality of data sources and their applicability for GM-PV scenario assessment by comparing the official data source Basis-DLM as the reference with the open-access data sources OpenStreetMap (OSM), Corine Land Cover (CLC), and Copernicus Emergency Management Service (CEMS). The intersection over union (IoU) and Matthews correlation coefficient (MCC) methods were used to analyse the differences in land use and eligibility due to the quality of the data sources and to compare their accuracy. The study’s results show the crucial role of data source selection in estimating the potential for GM-PV in Germany. The results indicate that open-access data overestimate land eligibility by 4.0% to 4.5% compared to the official Basis-DLM data. Spatial similarities and discrepancies between the OSM, CEMS CLC, and Basis-DLM land uses were identified. The CLC data exhibit higher consistency with Basis-DLM. These findings emphasise the importance of selecting the appropriate data source depending on the purpose and the use of official data sources for accurate and spatially differentiated decision-making and project planning at different scales. Open-access data sources can be applied for initial orientation and large-scale rough assessment as they balance data accuracy and accessibility. Full article

Underwater imaging presents unique challenges, notably color distortions and reduced contrast due to light attenuation and scattering. Most underwater image enhancement methods first use linear transformations for color compensation and then enhance the image. We observed that linear transformation for color compensation is not suitable for certain images. For such images, non-linear mapping is a better choice. This paper introduces a unique underwater image restoration approach leveraging a streamlined convolutional neural network (CNN) for dynamic weight learning for linear and non-linear mapping. In the first phase, a classifier is applied that classifies the input images as Type I or Type II. In the second phase, we use the Deep Line Model (DLM) for Type-I images and the Deep Curve Model (DCM) for Type-II images. For mapping an input image to an output image, the DLM creatively combines color compensation and contrast adjustment in a single step and uses deep lines for transformation, whereas the DCM employs higher-order curves. Both models utilize lightweight neural networks that learn per-pixel dynamic weights based on the input image’s characteristics. Comprehensive evaluations on benchmark datasets using metrics like peak signal-to-noise ratio (PSNR) and root mean square error (RMSE) affirm our method’s effectiveness in accurately restoring underwater images, outperforming existing techniques. Full article

This study evaluates land cover classification accuracy through adjustments to the deep learning model (DLM) training process, including variations in loss function, the learning rate scheduler, and the optimizer, along with diverse input dataset compositions. DLM datasets were created by integrating surface reflectance (SR) spectral data from satellite imagery with textural information derived from the gray-level co-occurrence matrix, yielding four distinct datasets. The U-Net model served as the baseline, with models A and B configured by adjusting the training parameters. Eight land cover classifications were generated from four datasets and two deep learning training conditions. Model B, utilizing a dataset comprising spectral, textural, and terrain information, achieved the highest overall accuracy of 90.3% and a kappa coefficient of 0.78. Comparing different dataset compositions, incorporating textural and terrain data alongside SR from satellite imagery significantly enhanced classification accuracy. Furthermore, using a combination of multiple loss functions or dynamically adjusting the learning rate effectively mitigated overfitting issues, enhancing land cover classification accuracy compared to using a single loss function. Full article