Search Results (57)

Monoclonal antibodies (mAbs) have revolutionized the landscape of cancer therapy, offering unprecedented specificity and diverse mechanisms to combat malignant cells. These biologic agents have emerged as a cornerstone in targeted cancer treatment, binding to specific antigens on cancer cells and exerting their therapeutic effects through various mechanisms, including inhibition of signaling pathways, antibody-dependent cellular cytotoxicity (ADCC), complement-dependent cytotoxicity (CDC), and antibody-dependent cellular phagocytosis (ADCP). The unique ability of mAbs to engage the immune system and directly interfere with cancer cell function has significantly enhanced the therapeutic armamentarium against a broad spectrum of malignancies. mAbs were initially studied in oncology; however, today, treatments have been developed for eye diseases. This review discusses the current applications of mAbs for the treatment of ocular diseases, discussing the specificity and the variety of mechanisms by which these molecules exhibit their therapeutic effects. The benefits, drawbacks, effectiveness, and risks associated with using mAbs in ophthalmology are highlighted, focusing on the most relevant ocular diseases and mAbs currently in use. Technological advances have led to in vitro production methods and recombinant engineering techniques, allowing the development of chimeric, humanized, and fully human mAbs. Nowadays, many humanized mAbs have several applications, e.g., for the treatment of age-related macular disease, diabetic retinopathy, and uveitis, while studies about new applications of mAbs, such as for SARS-CoV-2 infection, are also currently ongoing to seek more efficient and safe approaches to treat this new ocular disease. Full article

Background and Objectives: The study aims to explore the potential for transplacental transmission of SARS-CoV-2, focusing on its pathophysiology, placental defense mechanisms, and the clinical implications for maternal and neonatal health. Materials and Methods: A comprehensive review of the current literature was conducted, analyzing studies on SARS-CoV-2 infection in pregnancy, the expression of key viral receptors (ACE2 and TMPRSS2) in placental cells, and the immune responses involved in placental defense. The review also examined the clinical outcomes related to maternal and neonatal health, including adverse pregnancy outcomes and neonatal infection. Results: The expression of ACE2 and TMPRSS2 in the placenta supports the biological plausibility of SARS-CoV-2 transplacental transmission. Histopathological findings from the infected placentas reveal inflammation, vascular changes, and the evidence of viral particles in placental tissues. Clinical reports indicate an increased risk of preterm birth, intrauterine growth restriction, and neonatal infection in pregnancies affected by COVID-19. However, the frequency and mechanisms of vertical transmission remain variable across studies, highlighting the need for standardized research protocols. Conclusions: SARS-CoV-2 can potentially infect placental cells, leading to adverse pregnancy outcomes and neonatal infection. While evidence of transplacental transmission has been documented, the risk and mechanisms are not fully understood. Ongoing research is essential to clarify these aspects and inform obstetric care practices to improve maternal and neonatal outcomes during the COVID-19 pandemic. Full article

The utilization of Synthetic Aperture Radar (SAR) for real-time ship detection proves highly advantageous in the supervision and monitoring of maritime activities. Ship detection in the range-compressed domain of SAR rather than in fully focused SAR imagery can significantly reduce the time and computational resources required for complete SAR imaging, enabling lightweight real-time ship detection methods to be implemented on an airborne or spaceborne SAR platform. However, there is a lack of lightweight ship detection methods specifically designed for the SAR range-compressed domain. In this paper, we propose Fast Range-Compressed Detection (FastRCDet), a novel lightweight network for ship detection in the SAR range-compressed domain. Firstly, to address the distinctive geometric characteristics of the SAR range-compressed domain, we propose a Lightweight Adaptive Network (LANet) as the backbone of the network. We introduce Arbitrary Kernel Convolution (AKConv) as a fundamental component, which enables the flexible adjustment of the receptive field shape and better adaptation to the large scale and aspect ratio characteristics of ships in the range-compressed domain. Secondly, to enhance the efficiency and simplicity of the network model further, we propose an innovative Multi-Scale Fusion Head (MSFH) module directly integrated after the backbone, eliminating the need for a neck module. This module effectively integrates features at various scales to more accurately capture detailed information about the target. Thirdly, to further enhance the network’s adaptability to ships in the range-compressed domain, we propose a novel Direction IoU (DIoU) loss function that leverages angle cost to control the convergence direction of predicted bounding boxes, thereby improving detection accuracy. Experimental results on a publicly available dataset demonstrate that FastRCDet achieves significant reductions in parameters and computational complexity compared to mainstream networks without compromising detection performance in SAR range-compressed images. FastRCDet achieves a low parameter of 2.49 M and a high detection speed of 38.02 frames per second (FPS), surpassing existing lightweight detection methods in terms of both model size and processing rate. Simultaneously, it attains an average accuracy (AP) of 77.12% in terms of its detection performance. This method provides a baseline in lightweight network design for SAR ship detection in the range-compressed domain and offers practical implications for resource-constrained embedded platforms. Full article

Background: This retrospective study contrasts the impact of the SARS-CoV-2 pandemic in Lombardy (Italy) and Israel, focusing on mortality, healthcare response, public health measures, and demographics. Methods: We analyzed SARS-CoV-2 data from Lombardy and Israel covering four viral waves. Data included infection rates, hospitalizations, and mortality. In Lombardy, healthcare data were collected from the administrative database of the Lombardy Welfare Directorate; in Israel, they were collected from Clalit Health Services and the Israeli Ministry of Health’s COVID-19 database. Statistical analyses compared trends in infection rates, demographics, and mortality rates across the four viral waves by using logistic and linear regression models and adjusting for age, sex, and comorbidities. Results: Lombardy exhibited significantly higher SARS-CoV-2 infections and COVID-19 hospitalization rates during the first wave than Israel, with 71,558 cases over a population sample of ~10 million versus 5741 over a population sample of ~4.7 million in Israel. The majority of cases in Israel were managed at home, with 18 cases only (0.3%) requiring intensive care unit (ICU) hospitalization during the first wave, compared to 4104 (5.7%) cases in Lombardy. Israel’s vaccination campaign began earlier, so that by the fourth wave, 439,545 (42.2%) people in Israel were fully vaccinated with three doses, compared to 214,542 (22.9%) in Lombardy. Mortality decreased over time in both sites, dropping from 103 cases (1.8%) to 1550 (0.1%) in Israel and from 13,372 (18.7%) to 4388 (0.3%) in Lombardy. Conclusions: Early public health interventions and vaccination were crucial in managing the SARS-CoV-2 impact. Full article

In recent years, with the miniaturization of high-precision position and orientation systems (POS), precise motion errors during SAR data collection can be calculated based on high-precision POS. However, compensating for these errors remains a significant challenge for multi-rotor UAV-borne SAR systems. Compared with large aircrafts, multi-rotor UAVs are lighter, slower, have more complex flight trajectories, and have larger squint angles, which result in significant differences in motion errors between building targets and ground targets. If the motion compensation is based on ground elevation, the motion error of the ground target will be fully compensated, but the building target will still have a large residual error; as a result, although the ground targets can be well-focused, the building targets may be severely defocused. Therefore, it is necessary to further compensate for the residual motion error of building targets based on the actual elevation on the SAR image. However, uncompensated errors will affect the time–frequency relationship; furthermore, the ω-k algorithm will further change these errors, resulting in errors in SAR images becoming even more complex and difficult to compensate for. To solve this problem, this paper proposes a novel improved precise topography and aperture-dependent (PTA) method that can precisely compensate for motion errors in the UAV-borne SAR system. After motion compensation and imaging processing based on ground elevation, a secondary focus is applied to defocused buildings. The improved PTA fully considers the coupling of the residual error with the time–frequency relationship and ω-k algorithm, and the precise errors in the two-dimensional frequency domain are determined through numerical calculations without any approximations. Simulation and actual data processing verify the effectiveness of the method, and the experimental results show that the proposed method in this paper is better than the traditional method. Full article

Lake ice thickness (LIT) is a sensitive indicator of climate change, identified as a thematic variable of Lakes as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS). Here, we present a novel and efficient analytically based retracking approach for estimating LIT from high-resolution Ku-band (13.6 GHz) synthetic-aperture radar (SAR) altimetry data. The retracker method is based on the analytical modeling of the SAR radar echoes over ice-covered lakes that show a characteristic double-peak feature attributed to the reflection of the Ku-band radar waves at the snow–ice and ice–water interfaces. The method is applied to Sentinel-6 Unfocused SAR (UFSAR) and Fully Focused SAR (FFSAR) data, with their corresponding tailored waveform model, referred to as the SAR_LIT and FFSAR_LIT retracker, respectively. We found that LIT retrievals from Sentinel-6 high-resolution SAR data at different posting rates are fully consistent with the LIT estimations obtained from thermodynamic lake ice model simulations and from low-resolution mode (LRM) Sentinel-6 and Jason-3 data over two ice seasons during the tandem phase of the two satellites, demonstrating the continuity between LRM and SAR LIT retrievals. By comparing the Sentinel-6 SAR LIT estimates to optical/radar images, we found that the Sentinel-6 LIT measurements are fully consistent with the evolution of the lake surface conditions, accurately capturing the seasonal transitions of ice formation and melt. The uncertainty in the LIT estimates obtained with Sentinel-6 UFSAR data at 20 Hz is in the order of 5 cm, meeting the GCOS requirements for LIT measurements. This uncertainty is significantly smaller, by a factor of 2 to 3 times, than the uncertainty obtained with LRM data. The FFSAR processing at 140 Hz provides even better LIT estimates, with 20% smaller uncertainties. The LIT retracker analysis performed on data at the higher posting rate (140 Hz) shows increased performance in comparison to the 20 Hz data, especially during the melt transition period, due to the increased statistics. The LIT analysis has been performed over two representative lakes, Great Slave Lake and Baker Lake (Canada), demonstrating that the results are robust and hold for lake targets that differ in terms of size, bathymetry, snow/ice properties, and seasonal evolution of LIT. The SAR LIT retrackers presented are promising tools for monitoring the inter-annual variability and trends in LIT from current and future SAR altimetry missions. Full article

High-velocity reentry objects suffer from plasma sheath during reentry through the atmosphere, which affects the propagation characteristics of radar signals. The existing research mainly focuses on the time-frequency characteristics of radar signals, neglecting the polarization within the geomagnetic environment. In this article, the distortion of polarization characteristics for L-band fully polarimetric radar is analyzed, and the influence of the geomagnetic field is evaluated. Based on the Appleton–Hartree formula, the refractive index of the plasma sheath considering the geomagnetic field is derived and analyzed. The error model for the polarization deflection (PD) of radar waves is then established based on the phase screen model. The magnetized plasma sheath causes the deflection of the polarization plane for the radar signal, leading to distortion in the polarization characteristics and the attenuation of the echo amplitude. Considering the typical parameters of the plasma sheath, the influences of the electron density, collision frequency, the geomagnetic field and the radar frequency are analyzed quantitatively. Specifically, the PD anomaly phenomenon is analyzed and the corresponding analytical result of radar frequency is also derived. The relationship between the geomagnetic field and the PD, as well as the attenuation, is considered to be approximately linear. The absorption attenuation is primarily influenced by collision frequency and is immune to the geomagnetic field. In addition, the increasing electron density expands them, whereas the radar frequency and the collision frequency have the opposite effect. Simulations with real SAR data from ALOS-2 demonstrate the distortions resulting from the magnetized plasma sheath on the radar echoes in an L-band fully polarimetric radar system. Full article

This study presents the first azimuth cutoff analysis in Synthetic Aperture Radar (SAR) altimetry, aiming to assess its applicability in characterizing sea-state dynamics. In SAR imaging, the azimuth cutoff serves as a proxy for the shortest waves, in terms of wavelength, that can be detected by the satellite under certain wind and wave conditions. The magnitude of this parameter is closely related to the wave orbital velocity variance, a key parameter for characterizing wind-wave systems. We exploit wave modulations exhibited in the tail of fully-focused SAR waveforms and extract the azimuth cutoff from the radar signal through the analysis of its along-track autocorrelation function. We showcase the capability of Sentinel-6A in deriving these two parameters based on analyses in the spatial and wavenumber domains, accompanied by a discussion of the limitations. We use Level-1A high-resolution Sentinel-6A data from one repeat cycle (10 days) globally to verify our findings against wave modeled data. In the spatial domain analysis, the estimation of azimuth cutoff involves fitting a Gaussian function to the along-track autocorrelation function. Results reveal pronounced dependencies on wind speed and significant wave height, factors primarily determining the magnitude of the velocity variance. In extreme sea states, the parameters are underestimated by the altimeter, while in relatively calm sea states and in the presence of swells, a substantial overestimation trend is observed. We introduce an alternative approach to extract the azimuth cutoff by identifying the fall-off wavenumber in the wavenumber domain. Results indicate effective mitigation of swell-induced errors, with some additional sensitivity to extreme sea states compared to the spatial domain approach. Full article

In this study, the full-focusing (FF) algorithm is reviewed with the objective of optimizing it for processing data from different types of surfaces probed in altimetry. In particular, this work aims to provide a set of optimal FF processing parameters for the Sentinel-6 Michael Freilich (S6-MF) mission. The S6-MF satellite carries an advanced radar altimeter offering a wide range of potential FF-based applications which are just beginning to be explored and require prior optimization of this processing. In S6-MF, the Synthetic Aperture Radar (SAR) altimeter acquisitions are known to be aliased in the along-track direction. Depending on the target, aliasing can be tolerated or may be a severe impairment to provide the level of performance expected from FF processing. Another key aspect to consider in this optimization study is the unprecedented resolution of the FF processing, which results in a higher posting rate than the standard SAR processing. This work investigates the relationship between posting rate and noise levels and provides recommendations for optimal algorithm configurations in various scenarios, including transponder, open ocean, and specular targets like sea-ice and inland water scenes. The Omega–Kappa (WK) algorithm, which has demonstrated superior CPU efficiency compared to the back-projection (BP) algorithm, is considered for this study. But, unlike BP, it operates in the Doppler frequency domain, necessitating further precise spectral and time domain settings. Based on the results of this work, real case studies using S6-MF acquisitions are presented. We first compare S6-MF FF radargrams with Sentinel-1 (S1) images to showcase the potential of optimally configured FF processing. For highly specular surfaces such as sea-ice, distinct techniques are employed for lead signature identification. S1 relies on image-based lineic reconstruction, while S6-MF utilizes phase coherency of focalized pulses for lead detection. The study also delves into two-dimensional wave spectra derived from the amplitude modulation of image/radargrams, with a focus on a coastal example. This case is especially intriguing, as it vividly illustrates different sea states characterized by varying spectral peak positions over time. Full article

With the development of deep learning in the field of computer vision, convolutional neural network models and attention mechanisms have been widely applied in SAR image target recognition. The improvement of convolutional neural network attention in existing SAR image target recognition focuses on spatial and channel information but lacks research on the relationship and recognition mechanism between spatial and channel information. In response to this issue, this article proposes a hybrid attention module and introduces a Mixed Attention (MA) mechanism module in the MobileNetV2 network. The proposed MA mechanism fully considers the comprehensive calculation of spatial attention (SPA), channel attention (CHA), and coordinated attention (CA). It can input feature maps for comprehensive weighting to enhance the features of the regions of interest, in order to improve the recognition rate of vehicle targets in SAR images.The superiority of our algorithm was verified through experiments on the MSTAR dataset. Full article

The unique imaging modality of synthetic aperture radar (SAR) has posed significant challenges for object detection, making it more complex to acquire and interpret than optical images. Recently, numerous studies have proposed cross-domain adaptive methods based on convolutional neural networks (CNNs) to promote SAR object detection using optical data. However, existing cross-domain methods focus on image features, lack improvement on input data, and ignore the valuable supervision provided by few labeled SAR images. Therefore, we propose a semi-supervised cross-domain object detection framework that uses optical data and few SAR data to achieve knowledge transfer for SAR object detection. Our method focuses on the data processing aspects to gradually reduce the domain shift at the image, instance, and feature levels. First, we propose a data augmentation method of image mixing and instance swapping to generate a mixed domain that is more similar to the SAR domain. This method fully utilizes few SAR annotation information to reduce domain shift at image and instance levels. Second, at the feature level, we propose an adaptive optimization strategy to filter out mixed domain samples that significantly deviate from the SAR feature distribution to train feature extractor. In addition, we employ Vision Transformer (ViT) as feature extractor to handle the global feature extraction of mixed images. We propose a detection head based on normalized Wasserstein distance (NWD) to enhance objects with smaller effective regions in SAR images. The effectiveness of our proposed method is evaluated on public SAR ship and oil tank datasets. Full article

COVID-19, caused by SARS-CoV-2, has emerged as the most destructive emerging infectious disease of the 21st century. Vaccination is an effective method to combat viral diseases. However, due to the constant mutation of the virus, new variants may weaken the efficacy of vaccines. In the current field of new coronavirus research, viral protease inhibitors have emerged as a highly regarded therapeutic strategy. Nevertheless, existing viral protease inhibitors do not fully meet the therapeutic needs. Therefore, this paper turned to traditional Chinese medicine to explore new active compounds. This study focused on 24 isolated compounds from Acorus calamus L. and identified 8 active components that exhibited significant inhibitory effects on SARS-CoV-2 PLpro. Among these, the compound 1R,5R,7S-guaiane-4R,10R-diol-6-one demonstrated the best inhibitory activity with IC₅₀ values of 0.386 ± 0.118 μM. Additionally, menecubebane B and neo-acorane A exhibited inhibitory activity against both Mpro and PLpro proteases, indicating their potential as dual-target inhibitors. The molecular docking results confirmed the stable conformations of these compounds with the key targets and their good activity. ADMET and Lipinski’s rule analyses revealed that all the small molecule ligands possessed excellent oral absorption properties. This study provides an experimental foundation for the discovery of promising antiviral lead compounds. Full article

SARS-CoV-2 virulence is known to increase with lowering of environmental temperature and solar ultraviolet radiation; therefore, we have focused our real-world nationwide study concerning with COVID-19 trend and dynamics on the coldest seasons of the year in Italy, the Western country hardest hit at the onset of the pandemic, comparing the autumn–winter of 2020 (before mass vaccination but when the emergency machinery was fully operative in terms of tracing and swabs) with the autumn–winter of 2021 (after mass vaccination), and analyzing the mortality burden by age groups and life stages in the years 2019 (pre-COVID-19), 2020 (before mass vaccination), and 2021 (after mass vaccination). Methods: During the state of national health emergency, the Civil Defense Department released the aggregate data coming from the Higher Institute of Health, the Ministry of Health, the Italian Regions, and the Independent Provinces, to inform the population about the pandemic situation, daily. Among these data, there were the number of contagions, performed swabs, hospitalizations in Intensive Care Units (ICU), non-ICU patients, and deaths. By means of a team effort, we have collected and elaborated all these data, comparing the COVID-19 pandemic in Italy during the autumn–winter of 2020 with the autumn–winter of 2021. Moreover, we have extracted from the database of the National Institute of Statistics the total number of annual deaths in Italy during the years 2019, 2020, and 2021, comparing them to each other in order to evaluate the mortality burden attributable to COVID-19. Results: From the autumn–winter of 2020 to the autumn–winter of 2021, the contagions increased by ≈285%, against a ≈290% increase in the performed swabs; therefore, the mean positivity rate passed from 8.74% before mass vaccination to 8.59% after mass vaccination. The unprecedent vaccination campaign allowed a ≈251% abatement in COVID-19 deaths, and a reduction of ≈224% and ≈228% in daily ICU and non-ICU hospitalizations due to COVID-19, respectively. Regarding COVID-19 deaths, in 2020, there was a mortality excess of ≈14.3% quantifiable in 105,900 more deaths compared to 2019, the pre-COVID-19 year; 103,183 out of 105,900 deaths occurred in older adults (≥60 years), which is equivalent to ≈97.4%, while in adults over 50, the segment of population just below older adults, in 2020, there were 2807 more deaths than in 2019. Surprisingly, from the analysis of our data, it is emerged that in people under the age of 40 in the years 2019, 2020, and 2021, there were 7103, 6808, and 7165 deaths, respectively. This means that in subjects under 40 during 2020, there were 295 fewer deaths than in 2019, while during 2021, there were 357 more deaths than in 2020, equivalent to ≈5.2% more. Conclusions: COVID-19 is a potential life-threatening disease mainly in older adults, as they are the most vulnerable due to inherent immunosenescence and inflammaging. Extensive vaccination in this segment of population with up-to-date vaccines is the means to reduce deaths, hospitalizations, and ICU pressure in the public interest. In the event of future threats, a new mass vaccination campaign should not be implemented without taking into account the individual age; it should primarily be aimed at people over 60 and at patients of any age with immune deficits, and secondly at people over 50. COVID-19 vaccination shows a favorable benefit–risk ratio in older adults, while the balance steps down under the age of 40; this younger segment of the population should be therefore exempt from any mandatory vaccination. Full article

This systematic review critically evaluated the impact of a pre-infection COVID-19 vaccination on the incidence and severity of post-COVID-19 syndrome and aimed to assess the potential protective effect across different vaccines and patient demographics. This study hypothesized that vaccination before infection substantially reduces the risk and severity of post-COVID-19 syndrome. In October 2023, a comprehensive literature search was conducted across three databases, PubMed, Embase, and Scopus, focusing on studies published up to that date. Utilizing a wide array of keywords, the search strategy adhered to the PRISMA guidelines and was registered in the Open Science Framework. The inclusion criteria comprised studies focusing on patients with a breakthrough SARS-CoV-2 infection who developed post-COVID-19 syndrome. We included a total of 13 articles that met the inclusion criteria, analyzing more than 10 million patients with a mean age of 50.6 years, showing that the incidence of intensive care unit (ICU) admissions post-vaccination was as low as 2.4%, with a significant reduction in mortality risk (OR 0.66, 95% CI 0.58–0.74). The prevalence of post-COVID-19 syndrome symptoms was lower in vaccinated individuals (9.5%) compared to unvaccinated (14.6%), with a notable decrease in activity-limiting symptoms (adjusted OR 0.59, 95% CI 0.48–0.73). Vaccinated patients also showed a quicker recovery and return to work (HR 1.37, 95% CI 1.04–1.79). The pooled odds ratio of 0.77 indicates that vaccination is associated with a 23% reduction in the risk of developing post-COVID-19 syndrome (95% CI 0.75–0.79). Despite the protective effects observed, a substantial heterogeneity among the studies was noted. In conclusion, a pre-infection COVID-19 vaccination is associated with a significant reduction in the risk and severity of post-COVID-19 syndrome. However, the observed heterogeneity across studies suggests a need for further research with standardized methods to fully comprehend vaccine efficacy against long COVID. Full article

The pandemic period due to coronavirus disease 2019 (COVID-19) revolutionized all possible areas of global health. Significant consequences were also related to diverse extrapulmonary manifestations of this pathology. The liver was found to be a relatively common organ, beyond the respiratory tract, affected by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Multiple studies revealed the essential role of chronic liver disease (CLD) in the general outcome of coronavirus infection. Present concerns in this field are related to the direct hepatic consequences caused by COVID-19 and pre-existing liver disorders as risk factors for the severe course of the infection. Which mechanism has a key role in this phenomenon—previously existing hepatic disorder or acute liver failure due to SARS-CoV-2—is still not fully clarified. Alcoholic liver disease (ALD) constitutes another not fully elucidated context of coronavirus infection. Should the toxic effects of ethanol or already developed liver cirrhosis and its consequences be perceived as a causative or triggering factor of hepatic impairment in COVID-19 patients? In the face of these discrepancies, we decided to summarize the role of the liver in the whole picture of coronavirus infection, paying special attention to ALD and focusing on the pathological pathways related to COVID-19, ethanol toxicity and liver cirrhosis. Full article