Search Results (307)

Leishmaniasis remains an important neglected tropical disease, and current treatments are limited by toxicity, resistance, and low bioavailability. In this study, novel guanidine derivatives were evaluated through an integrated approach, combining in silico physicochemical profiling with in vitro biological assays using Leishmania (Viannia) braziliensis, the etiological agent of American Tegumentary Leishmaniasis (ATL). Most compounds exhibited favorable drug-like properties, though variations in lipophilicity and solubility influenced biological performance. Among the tested molecules, FURL-G5 emerged as the most promising candidate, showing potent activity against promastigote forms and low cytotoxicity in murine macrophages, resulting in high selectivity indices (SI > 10), comparable to those of LQOF-G1, a compound with previously established leishmanicidal effects. These compounds were also tested on intracellular amastigotes, drastically reducing the infection rate of macrophages. The integration of an in silico approach and biological validation enabled rational compound prioritization and supports the early-stage development of these scaffolds. Overall, this study reinforces the potential of guanidine-based compounds as leads for innovative ATL drug discovery and demonstrates the value of multidisciplinary strategies for identifying selective and safe therapeutic candidates. Full article

Building height is a critical parameter for urban analysis, yet accurately estimating it from ICESat-2 photon-counting LiDAR data remains challenging due to pervasive noise photons and uneven noise distribution. To address the limitations of fixed-threshold denoising methods and improve adaptability across varying density conditions, this study proposes a dual-stage denoising framework for ICESat-2 ATL03 photon data. In the first stage, local photon densities are estimated within a reliable radius, log-transformed, and stratified into multiple levels. Adaptive thresholds are then applied at each level to suppress low-density noise while minimizing over-filtering in sparse regions. In the second stage, residual feedback-driven adaptive fitting strategy is applied along the ground track, where polynomial fitting was performed in sliding windows, with the window size dynamically adjusted based on residuals to refine local structures and eliminate outliers. The experiment was conducted in South Holland and Friesland, across 84 ICESat-2 tracks, where quantitative evaluations under varying day/night and beam conditions confirmed the effectiveness of the proposed framework. For denoising, the proposed method achieved high denoising accuracy, with F1-scores exceeding 0.97 in most cases, outperforming previous methods. Furthermore, building heights derived from footprint buffering and elevation differencing are validated against airborne LiDAR, yielding coefficient of determination (R²) values of 0.7235 and 0.9487 for the two regions, with root mean square error (RMSE) values of 1.5045 m and 1.8849 m, respectively. This study confirms the effectiveness and robustness of the proposed dual-stage framework, demonstrating its strong capability for both noise suppression in ICESat-2 ATL03 photon data and the subsequent accurate estimation of building heights. Full article

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), equipped with the Advanced Topographic Laser Altimeter System (ATLAS), is capable of acquiring large-scale terrain and forest structural information through photon-counting LiDAR. However, photon point clouds exhibit significant noise variability due to diurnal changes and variations in beam intensity, which undermines the accuracy and stability of terrain and canopy height retrievals in forested regions. To address the limited adaptability of existing methods under daytime/nighttime and strong/weak beam conditions, this study proposes a multi-stage processing framework integrating photon denoising, classification, and quasi-full-waveform reconstruction. First, local statistical features combined with adaptive parameter optimization were employed, applying Gaussian and exponential fitting to denoise daytime strong and weak beams and enhance the signal-to-noise ratio (SNR). Subsequently, an improved random sample consensus (RANSAC) algorithm was introduced to remove residual noise and classify photons under both diurnal and beam-intensity variations. Finally, a radial basis function (RBF) interpolation was used to reconstruct quasi-full-waveform curves for terrain and canopy heights. Compared with the ATL08 product (terrain root mean square error (RMSE): 2.65 m for daytime strong beams and 5.77 m for daytime weak beams), the proposed method reduced RMSE by 0.53 m and 1.30 m, respectively, demonstrating enhanced stability and robustness under low-SNR conditions. For canopy height estimation, all beam types showed high consistency with airborne LiDAR measurements, with the highest correlation achieved for nighttime strong beams (R = 0.90), accompanied by the lowest RMSE (4.82 m) and mean absolute error (MAE = 2.97 m). In comparison, ATL08 canopy height errors for nighttime strong beams were higher (RMSE = 5.67 m; MAE = 4.16 m). Notably, significant improvements were observed for weak beams relative to ATL08. These results indicate that the proposed framework effectively denoises and classifies photon point clouds under diverse daytime/nighttime and strong/weak beam conditions, providing a robust methodological reference for high-precision terrain and forest canopy height estimation in forested regions. Full article

Background/Objectives: Repetitive Transcranial Magnetic Stimulation (rTMS) can enhance post-stroke aphasia recovery. The right Inferior Frontal Gyrus is the most common target in rTMS studies for inhibitory stimulation. However, lexicosemantic processes involve a large brain network including the Anterior Temporal Lobe (ATL). We hypothesize that rTMS targeting the ATL will improve lexicosemantic impairments in people with post-stroke aphasia. Methods: In a Single-Case Experimental Design, three people with post-stroke aphasia with lexicosemantic impairments performed Picture and Auditory Naming and Semantic Decision tasks five times a week for one or two weeks to establish baseline scores. Then, each participant received continuous inhibitory Theta Burst Stimulation targeting the right ATL, five times a week for two weeks. After each rTMS session, participants performed all linguistic tasks. A follow-up measurement was performed one month after the end of the study. Results: All participants showed significant improvement in the Picture Naming task, while only P1 improved in Auditory Naming accuracy. In the Semantic Decision task, only P2 showed improvement in both accuracy and RT, while P1 showed improvement in RT alone and P3 showed no improvement. Conclusions: The results suggest that ATL could be a potential target for future brain stimulation studies in aphasia involving lexicosemantic impairments. RTMS targeting the ATL may modulate the connected ventral semantic stream, leading to improvements in lexical access. This preliminary study highlights the possibility of selecting the cortical target for rTMS based on the clinical profile of the participant, an approach that will need further investigation in larger sham-controlled studies. Full article

HTLV-1 and HIV-1 represent biologically significant, structurally close, and equally problematic yet divergent human retroviruses. Although both infect CD4+ T cells and share similar structural elements, they differ markedly in genomic stability, transmission dynamics, clinical progression, and, most importantly, their transcriptional regulatory mechanisms. HTLV-1, an ancient virus with a limited global burden, often remains asymptomatic for decades before potentially causing ATL or HAM/TSP. Conversely, HIV-1, a relatively recent zoonotic transmission, undergoes rapid replication, exhibits high genetic diversity, and causes progressive immunodeficiency unless controlled by antiretroviral therapy (ART). At the molecular level, HTLV-1 maintains proviral latency through a balanced bidirectional transcription of regulatory genes (e.g., Tax and HBZ) that manipulate host transcription and immune evasion pathways, facilitating persistence and oncogenesis. HBZ and Tax were shown to contribute to driving the progressive acquisition of Treg-like and HLA class II phenotype in chronically activated CD4+ T-cells, promoting tolerogenic antigen presentation and immune evasion in ATL cells. This well-controlled differential expression of HTLV-1 regulatory genes is attributed to multiple intragenic virus regulatory mechanisms, which will be discussed in this review. In contrast, HIV-1 transcription is driven by a tightly regulated 5′ LTR promoter involving host factors such as NF-κB, Sp1, AP-1, and NFAT, among others, with strong influence imposed by the landscape of the provirus integration site, playing a pivotal role in latency and reactivation. The distinct regulatory circuitry of each virus suggests a key difference in their essential regulation, with HTLV-1 primarily relying on intragenic mechanisms, while HIV-1 relies more heavily on interactions with the surrounding host environment to control its expression. This difference underscores unique therapeutic challenges in managing viral latency, persistence, and pathogenesis. Full article

American tegumentary leishmaniasis (ATL) and other infectious granulomatous diseases (IGDs) may present with oral/oropharyngeal mucosal lesions (OOPML). IGD-OOPML can result from fungal, parasitic, or bacterial infections, and squamous cell carcinoma (SCC) represents the main differential diagnosis. ATL, other IGD, and SCC share overlapping clinical and epidemiological features, making diagnostic suspicion challenging. This study compared sociodemographic and clinical characteristics among ATL, other IGD, and SCC. Descriptive, comparative, and multivariable logistic regression analyses were performed. Among 7551 patients, 213 met inclusion criteria (83-SCC and 130-IGD). Except for smoking, which differed only between ATL and SCC, most IGD parameters were similar. Male patients predominated in all groups. SCC patients were significantly older (p < 0.001) and had a shorter median disease duration (p = 0.007). The presence of pain increased the odds of SCC-OOPML by 3.96 times (95% CI 1.97–12.51). SCC patients were more likely to present lesions in a single subsite, either the oral cavity or oropharynx. Painful, ulcerated, or exophytic lesions favored SCC diagnosis, whereas infiltrative, granular, or mulberry-like lesions, involvement of multiple subsites, or associated nasal and laryngeal lesions suggested IGDs. Although clinical differentiation remains difficult, these findings may support early diagnostic suspicion, prompt treatment, and reduced sequelae. Full article

This in vitro study evaluated the shear bond strength (SBS) of four CAD/CAM (Computer aided design/Computer aided manufacturing) polymer-based indirect composites bonded to dentin and microhybrid composite substrates using two resin cements. Gradia Plus (GP), Ceramage (Ce), Tescera ATL (TA), and Lava Ultimate (LA) were fabricated into cylindrical specimens (3 × 3 mm). Dentin substrates were obtained from extracted molars, while composite substrates were prepared from Filtek Z250 (4 mm × 2 mm). Bonding was performed using either a self-adhesive resin cement (RelyX U200; RU200) or a dual-cure adhesive resin cement (RelyX Ultimate; RU), resulting in 16 experimental groups (n = 12 per group). SBS was measured using a universal testing machine (1 mm/min), and failure modes were assessed under stereomicroscopy. Bond strength was significantly higher on composite substrates than on dentin (p < 0.001), primarily due to favorable polymer–polymer compatibility and matrix interdiffusion, which improved stress accommodation at the adhesive interface. TA and Ce showed superior adhesion when combined with RU, while LA exhibited the lowest values, particularly on dentin bonded with RU200. Overall, the dual-cure adhesive system provided stronger bonding than the self-adhesive system (p < 0.05). These findings highlight the influence of substrate type, composite architecture, and cement chemistry on interfacial performance in indirect polymer-based restorations. Full article

This research focusses on three satellite-derived bathymetry methods and optical satellite instruments: (1) a stereo photogrammetry bathymetry module (SaTSeaD) developed for the NASA Ames stereo pipeline open-source software (version 3.6.0) using stereo WorldView data; (2) physics-based radiative transfer equations (PBSDB) using Landsat data; and (3) a modified composite band-ratio method for Sentinel-2 (SatBathy) with an initial simplified calibration, followed by a more rigorous linear regression against in situ bathymetry data. All methods were tested in three different areas with different geological and environmental conditions, Cabo Rojo, Puerto Rico; Key West, Florida; and Cocos Lagoon and Achang Flat Reef Preserve, Guam. It is demonstrated that all satellite derived bathymetry (SDB) methods have increased accuracy when the results are aligned with higher-accuracy ICESat-2 ATL24 track bathymetry data using the iterative closest point (ICP). SDB vertical accuracy depends more on location characteristics than the method or optical satellite instrument used. All error metrics considered (mean absolute error, median absolute deviation, and root mean square error) can be less than 5% of the maximum bathymetry depth penetration for at least one method, although not necessarily for the same method for all sites. The SDB error distribution tends to be bimodal irrespective of method, satellite instrument, alignment, site, or maximum bathymetry depth, leading to the potential ineffectiveness of traditional error metrics, such as the root mean square error. However, our analysis demonstrates that performing detrending where possible can achieve an error distribution as close to normality as possible for which error metrics are more diagnostic. Full article

Beyond their traditional role as short-lived antimicrobial cells, neutrophils are increasingly recognized as key regulators of adaptive immunity and tumor progression. This AI-assisted integrative review investigated the neutrophil–T-cell axis, particularly the role of Galectin-9 (Gal-9), across adult T-cell leukemia/lymphoma (ATL), Sézary syndrome (SS), coronavirus disease 2019 (COVID-19), and psoriasis. Leveraging AI tools (GPT-5 and Adobe Acrobat AI Assistant) for literature synthesis (2000–2025) and expert validation, we aimed to identify common immunological mechanisms. Across all conditions, neutrophils displayed persistent activation, elevated Gal-9 expression, and modulated T-cell interactions. In ATL and SS, neutrophilia correlated with poor survival and TCR signaling dysregulation, suggesting Gal-9-mediated immune modulation. In COVID-19 and psoriasis, neutrophil-derived Gal-9-linked innate hyperactivation to T-cell exhaustion and IL-17-driven inflammation. These findings define a recurring neutrophil–Gal-9 regulatory module connecting innate and adaptive immune responses. This study underscores the feasibility of combining AI-driven literature synthesis with expert review to identify unifying immunological mechanisms and therapeutic targets across malignancy and inflammation. Full article

NASA’s ICESat-2 mission offers potential for coastal monitoring by combining its land/vegetation (ATL08) and nearshore bathymetry (ATL24) products. However, the combined performance of these products in environments where both canopy and bathymetry are present, such as mangroves, has not been explored. This work assesses ATL08 and ATL24 over mangroves using a dual approach: (1) a detailed regional validation in Everglades National Park against high-resolution airborne lidar (ALS), and (2) a global analysis characterizing mangrove structure. The regional validation found strong accuracies, with a root mean square error (RMSE) of 1.63 m for ATL08 canopy height and 0.25 m for ATL24 bathymetry for 10 m segments. In this comparison, using 30 m segments, ICESat-2 (RMSE 1.37 m) demonstrated superior performance to GEDI (RMSE 1.51 m) when measuring the same mangrove canopies. The global analysis confirmed that the majority of mangroves are short-stature (<10 m), a structural range where ICESat-2 demonstrates optimal performance. Despite these strengths, disagreements in photon labels between the ATL08 and ATL24 algorithms limit the ability to identify differences between topography, bathymetry, and water surface in these intertidal areas. While ICESat-2 has potential to accurately measure canopy height and bathymetry in mangroves, the integrated mapping beneath dense canopies is not yet feasible with standard products. Full article

Antarctica’s aerodynamic roughness length (z_0m) is crucial for surface energy exchange and atmospheric modeling, but its remote sensing estimation remains challenging due to complex ice-surface conditions and limited observations. To address these challenges, this study establishes a z_0m retrieval framework derived from the Raupach model using Unmanned Aerial Vehicle (UAV), Reference Elevation Model of Antarctica (REMA), and Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) datasets at three representative Antarctic sites. The results show that UAV benchmarks yield mean z_0m values of 0.009795, 0.011597, and 0.005203 m at Zhongshan Station, Great Wall Station, and Qinling Station, respectively. In experiments with ICESat-2 data, z_0m derived from ATL06 demonstrates accuracy comparable to that from ATL03 (RMSE = 7.45 × 10⁻⁶ m), with the best performance obtained at a 2 km window. Spatially, the agreement with UAV-derived z_0m decreases in the order: REMA > ICESat-2 (IDW-interpolated). The accuracy of REMA and ICESat-2 decreased with terrain complexity, from ice-free zones to the ice-shelf front and finally to the steep ice sheet margin. The elevation and slope variations emerge as dominant controls of z_0m spatial patterns. This study demonstrates the complementary strengths of UAV, REMA, and ICESat-2 datasets in Antarctic aerodynamic roughness estimation, providing practical guidance for data selection and methodology optimization. This study develops an improved z_0m retrieval method for Antarctica, clarifies the applicability and limitations of UAV, REMA, and ICESat-2 data, and provides methodological and data support for simulations of near-surface atmospheric parameters in Antarctica region. Full article

Laser-based remote sensing (lidar) is a proven technique for detecting atmospheric features such as clouds and aerosols as well as for determining their vertical distribution with high accuracy. Even simple elastic backscatter lidars can distinguish clouds from aerosols, and accurate knowledge of their vertical location is essential for air quality assessment, hazard avoidance, and operational decision-making. However, daytime lidar measurements suffer from reduced signal-to-noise ratio (SNR) due to solar background contamination. Conventional processing approaches mitigate this by applying horizontal and vertical averaging, which improves SNR at the expense of spatial resolution and feature detectability. This work presents a deep learning-based framework that enhances lidar SNR at native resolution and performs fast layer detection and cloud–aerosol discrimination. We apply this approach to ICESat-2 532 nm photon-counting data, using artificially noised nighttime profiles to generate simulated daytime observations for training and evaluation. Relative to the simulated daytime data, our method improves peak SNR by more than a factor of three while preserving structural similarity with true nighttime profiles. After recalibration, the denoised photon counts yield an order-of-magnitude reduction in mean absolute percentage error in calibrated attenuated backscatter compared with the simulated daytime data, when validated against real nighttime measurements. We further apply the trained model to a full month of real daytime ICESat-2 observations (April 2023) and demonstrate effective layer detection and cloud–aerosol discrimination, maintaining high recall for both clouds and aerosols and showing qualitative improvement relative to the standard ATL09 data products. As an alternative to traditional averaging-based workflows, this deep learning approach offers accurate, near real-time data processing at native resolution. A key implication is the potential to enable smaller, lower-power spaceborne lidar systems that perform as well as larger instruments. Full article

Heat extremes or heatwave events have significantly impacted socioeconomic activities and ecological systems, causing serious health issues and increased mortality rates in Pakistan over the past few decades. This study investigates the relationship between heat extremes in the northern Indian Ocean’s sea surface temperature (SST) and atmospheric temperature over Land (ATL) in Pakistan, and their connection to the Niño 3.4 Index, for monthly (March–August) and seasonal (spring and summer) basis from 1979 to 2015. Results show that SST has a higher frequency of heat extreme anomalies over different stretches of days than ATL. On a seasonal scale, heat extremes in ATL showed a significant correlation with SST, while the relationship was insignificant on a monthly basis. Both ATL and SST exhibited strong associations with the Niño 3.4 Index for land and ocean. These findings suggest that large-scale ocean-atmosphere interactions, particularly El Niño Southern Oscillation (ENSO), play a key role in modulating heat extremes in the region. The results of this study support SDGs by improving adaptive capacity and resilience on health, hunger, and climate by guiding policymakers in mitigating heat extremes. Integrating the findings of this study into national and provincial heat extreme plans may facilitate timely resource allocation and adaptation strategies in one of the world’s most climate-vulnerable regions. Full article

Introduction: Mass casualty incident (MCI) simulation scenarios are developed based on detailed review and planning by multidisciplinary trauma teams. This study aimed to assess the feasibility of using generative artificial intelligence (AI) in developing mass casualty trauma simulation scenarios. The study evaluated a range of mass casualty trauma simulation scenarios generated from a public generative artificial intelligence platform based on publicly available data with a validated objective simulation scoring tool. Methods: Using a large language model (LLM) platform (ChatGPT4, OpenAI, San Francisco, CA, USA), 10 complex MCI trauma simulation scenarios were generated based on publicly available US reported trauma data. Each scenario was evaluated by two Advanced Trauma Life Support (ATLS) certified raters based on the Simulation Scenario Evaluation Tool (SSET), a validated scoring tool out of 100 points. The tool scoring is based on learning objectives, tasks for performance, clinical progression, debriefing criteria, and resources. Two publicly available mass casualty trauma scenarios were similarly evaluated as controls. Revision and recommended feedback was provided for the scenarios, with review time recorded. Post-revision scenarios were evaluated. Interrater reliability was calculated based on Intraclass Correlation Coefficients (2, k) (ICCs). For the scenarios, scores and review times were reported as medians with interquartile range (IQR) as 25th and 75th percentiles. Results: Ten mass casualty trauma simulation scenarios were generated by an LLM, producing a total of 62 simulated patients. The initial LLM-generated scenarios demonstrated a median SSET score of 78.5 (IQR 74–82), substantially lower than the median score of 94 (IQR 93–95) observed in publicly available scenarios. The interrater reliability ICC for the LLM-generated scenarios was 0.965 and 1.00 for publicly available scenarios. Following secondary human revision and iterative refinement, the LLM-generated scenarios improved, achieving a median SSET score of 94 (IQR 93–96) with an interrater reliability ICC of 0.7425. Conclusions: The feasibility study suggests that a structured, collaborative workflow combining LLM-based generation with expert human review may enable a new approach to mass casualty trauma simulation scenario creation. LLMs hold promise as a scalable tool for the development of MCI training materials. However, consistent human oversight, quality assurance processes, and governance frameworks remain essential to ensure clinical accuracy, safety, and educational value. Full article

Clarifying the function of approximately 20,000 proteins encoded by the human genome is a key challenge in the fields of medicine and biology. However, many proteins remain uncharacterized. In this review, we introduce a challenge that uses adult T-cell leukemia/lymphoma (ATL) and proteomics to study human proteins of unknown function (PUFs). The characteristic properties of ATL cells are as follows: ATL cells (1) are infected with virus, (2) are derived from CD4⁺ T cells, (3) are generated via multi-stage carcinogenesis, (4) have flower-like nuclei, and (5) are highly infiltrative in the aggressive type. Given that ATL cells have contributed to impressive basic research, such as the discovery of HTLV-1 as a human cancer virus and interleukin-2 (IL-2) receptor α chain (IL-2Rα)/CD25, which is used for identifying regulatory T (Treg) cells, ATL cell lines could still be considered an attractive research tool. Furthermore, the “Unknome database” is useful for examining function-unknown degrees of proteins of interest using known scores based on Gene Ontology (GO) annotations and protein analysis through evolutionary relationships (PANTHER). Combining ATL proteomic data obtained by us with the “Unknome database” is expected to contribute not only to investigating the pathogenetic mechanism of ATL but also to clarifying the functions of PUFs. Full article