Background: The prevalence of obesity has increased in patients with type 1 diabetes (T1D) and latent autoimmune diabetes of the adult (LADA), limiting the use of clinical features such as the body mass index for its differentiation with type 2 diabetes (T2D). Additionally, some patients with maturity-onset diabetes of the young (MODY) or LADA are misdiagnosed as having T2D. The evaluation of autoantibodies and genetic testing are not fully available. We aimed to evaluate the utility of a widely available and less expensive diagnostic tool such as C-peptide to differentiate between T1D, T2D, MODY, and LADA. Methods: Our study included 38 patients with T1D, 49 with T2D, 13 with MODY, and 61 with LADA. We recorded anthropometric measurements, biochemical profiles, and antidiabetic treatment and determined C-peptide, anti-GAD65, and anti-IA2 antibodies. Results: C-peptide concentration differed significantly among populations (T1D: 0.2 ng/mL; T2D: 2.4 ng/mL; MODY: 1.14 ng/mL; LADA: 1.87 ng/mL). Through a ROC curve, we observed that the C-peptide cut-off point of 0.95 ng/mL allows differentiation between T1D and T2D (sensitivity 82%, specificity 77%); 0.82 ng/mL between T1D and LADA (sensitivity 82%, specificity 77%); and 1.65 ng/mL between T2D and MODY (sensitivity 72%, specificity 72%). Conclusions: C-peptide is useful for the diagnostic differentiation of patients with type 1, type 2 diabetes, MODY, and LADA. Full article

Recording large amounts of users’ sessions performed through 3D applications may provide crucial insights into interaction patterns. Such data can be captured from interactive experiences in public exhibits, remote motion tracking equipment, immersive XR devices, lab installations or online web applications. Immersive analytics (IA) deals with the benefits and challenges of using immersive environments for data analysis and related design solutions to improve the quality and efficiency of the analysis process. Today, web technologies allow us to craft complex applications accessible through common browsers, and APIs like WebXR allow us to interact with and explore virtual 3D environments using immersive devices. These technologies can be used to access rich, immersive spaces but present new challenges related to performance, network bottlenecks and interface design. WebXR IA tools are still quite new in the literature: they present several challenges and leave quite unexplored the possibility of synchronous collaborative inspection. The opportunity to share the virtual space with remote analysts in fact improves sense-making tasks and offers new ways to discuss interaction patterns together, while inspecting captured records or data aggregates. Furthermore, with proper collaborative approaches, analysts are able to share machine learning (ML) pipelines and constructively discuss the outcomes and insights through tailored data visualization, directly inside immersive 3D spaces, using a web browser. Under the H2IOSC project, we present the first results of an open-source pipeline involving tools and services aimed at capturing, processing and inspecting interactive sessions collaboratively in WebXR with other analysts. The modular pipeline can be easily deployed in research infrastructures (RIs), remote dedicated hubs or local scenarios. The developed WebXR immersive analytics tool specifically offers advanced features for volumetric data inspection, query, annotation and discovery, alongside spatial interfaces. We assess the pipeline through users’ sessions captured during two remote public exhibits, by a WebXR application presenting generative AI content to visitors. We deployed the pipeline to assess the different services and to better understand how people interact with generative AI environments. The obtained results can be easily adopted for a multitude of case studies, interactive applications, remote equipment or online applications, to support or accelerate the detection of interaction patterns among remote analysts collaborating in the same 3D space. Full article

The emergence of electric vehicles has brought new issues such as the problem of rolling element bearings (REBs) operating at high speeds. Losses due to these components in mechanical transmissions are a key issue and must therefore be taken into account right from the design stage of these systems. Among these losses, the one induced by the motion of rolling elements, known as drag loss, becomes predominant in high-speed REBs. Although an experimental approach is still possible, it is difficult to isolate this loss in order to study it properly. A numerical approach based on CFD is therefore a possible way forward, even if other issues arise. The aim of this article is to study the ability of such an approach to correctly estimate the drag coefficient associated with the motion of rolling elements. The influence of the numerical domain extension, the mesh refinement, the simplification of the ring shape, and the presence of the cage on the values of the drag coefficient is presented. While it seems possible to compromise on the calculation domain and mesh size, it appears that the other parameters must be taken into account as much as possible to obtain realistic results. Full article

With the continuous expansion of the transport network, the safe operation of high-speed railway rails has become a crucial issue. Defect detection on the surface of rails is a key part of ensuring the safe operation of trains. Despite the progress of deep learning techniques in defect detection on the rails’ surface, there are still challenges related to various problems, such as small datasets and the varying scales of defects. Based on this, this paper proposes an improved encoder–decoder architecture based on Swin Transformer network, named Rail-STrans, which is specifically designed for intelligent segmentation of high-speed rail surface defects. The problem of a small and black-and-white rail dataset is solved using self-made large and multiple rail surface defect datasets through field shooting, data labelling, and data expansion. In this paper, two Local Perception Modules (LPMs) are added to the encoding network, which helps to obtain local context information and improve the accuracy of detection. Then, the Multiscale Feature Fusion Module (MFFM) is added to the decoding network, which helps to effectively fuse the feature information of defects at different scales in the decoding process and improves the accuracy of defect detection at multiple scales. Meanwhile, the Spatial Detail Extraction Module (SDEM) is added to the decoding network, which helps to retain the spatial detail information in the decoding process and further improves the detection accuracy of small-scale defects. The experimental results show that the mean accuracy of the semantic segmentation of the method proposed in this paper can reach 90.1%, the mean dice coefficient can reach 89.5%, and the segmentation speed can reach 37.83 FPS, which is higher than other networks’ segmentation accuracy. And, at the same time, it can achieve higher efficiency. Full article

Adipose tissue is a multifunctional organ that regulates many physiological processes such as energy homeostasis, nutrition, the regulation of insulin sensitivity, body temperature, and immune response. In this review, we highlight the relevance of the different mediators that control adipose tissue activity through a systematic review of the main players present in white and brown adipose tissues. Among them, inflammatory mediators secreted by the adipose tissue, such as classical adipokines and more recent ones, elements of the immune system infiltrated into the adipose tissue (certain cell types and interleukins), as well as the role of intestinal microbiota and derived metabolites, have been reviewed. Furthermore, anti-obesity mediators that promote the activation of beige adipose tissue, e.g., myokines, thyroid hormones, amino acids, and both long and micro RNAs, are exhaustively examined. Finally, we also analyze therapeutic strategies based on those mediators that have been described to date. In conclusion, novel regulators of obesity, such as microRNAs or microbiota, are being characterized and are promising tools to treat obesity in the future. Full article

Since the onset of the coronavirus disease 2019 (COVID-19) pandemic, SARS-CoV-2 variants capable of breakthrough infections have attracted global attention. These variants have significant mutations in the receptor-binding domain (RBD) of the spike protein and the membrane (M) protein, which may imply an enhanced ability to evade immune responses. In this study, an examination of co-mutations within the spike RBD and their potential correlation with mutations in the M protein was conducted. The EVmutation method was utilized to analyze the distribution of the mutations to elucidate the relationship between the mutations in the spike RBD and the alterations in the M protein. Additionally, the Sequence-to-Sequence Transformer Model (S2STM) was employed to establish mapping between the amino acid sequences of the spike RBD and M proteins, offering a novel and efficient approach for streamlined sequence analysis and the exploration of their interrelationship. Certain mutations in the spike RBD, G339D-S373P-S375F and Q493R-Q498R-Y505, are associated with a heightened propensity for inducing mutations at specific sites within the M protein, especially sites 3 and 19/63. These results shed light on the concept of mutational synergy between the spike RBD and M proteins, illuminating a potential mechanism that could be driving the evolution of SARS-CoV-2. Full article

The aim of this study was to determine the peroxide values, acid numbers, oxidative stability (Rancimat method), antioxidant activity (DPPH method), antimicrobial activity (disc diffusion method), sensory properties (9-point hedonic scale), and fatty acid profiles (FAME) of five edible oils purchased from local Slovakian producers—grape seed oil, flax seed oil, walnut oil, poppy seed oil, and milk thistle seed oil. The peroxide value ranged from 2.27 (milk thistle oil) to 8.51 (flax seed oil) mmol O₂/kg. All these values were in accordance with regulations (upper limit of 20 mmol O₂/kg). The values of the acid number ranged from 0.11 (walnut oil) to 2.49 (milk thistle oil) mg KOH/g, and were in accordance with regulations as they did not exceed the value of 4 mg KOH/g. The oxidation stability was the lowest in flax seed oil (0.18 h) and the highest in grape seed oil (2.05 h). In milk thistle oil, the highest amounts of oleic and behenic acids, in flax seed oil, the highest amount of α-linolenic acid, and in grape seed oil, the highest amount of linolic acid were determined. Antioxidant activity was the strongest in the sample of grape seed oil—65.53 mg TEAC/L (Trolox equivalent antioxidant capacity). Samples of flax seed oil showed the strongest inhibition of Candida albicans CCM 8186 (4.58 mm) and Bacillus subtilis CCM 2010 (0.31 mm). Poppy seed oil was determined to be the most inhibiting towards Klebsiella pneumoniae CCM 2318 (3.68 mm). Milk thistle oil showed the strongest inhibition of Clostridium perfringens CCM 4435 (6.31 mm). Grape seed oil was the most inhibitory towards Staphylococcus aureus subs. aureus CCM 2461 (5.32 mm). Walnut oil showed the strongest activity towards Yersinia enterocolitica CCM 5671 (6.33 mm). The sensory analysis resulted in the samples of walnut and grape seed oil being awarded the highest scores for smell, taste, and overall acceptability. The tested edible oils are rich in biologically active compounds with antioxidant and antimicrobial activities. Their consumption can have a positive effect on the functioning of the human body and its health. Proper storage conditions are, however, necessary because of the susceptibility of these oils to oxidation. Full article

Objectives: This study aimed to identify predictors of remission or low disease activity (LDA) in patients with rheumatoid arthritis (RA) and low-ultrasound inflammation. Methods: A total of 80 patients with RA who fulfilled the 1987 ACR criteria for RA with a disease activity score of 28 joints (DAS28) > 3.2 were recruited. Over 1 year of therapy, we conducted blood tests every 6 months to examine erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), monocyte chemotactic protein-1 (MCP-1), neuraminidase 3 (Neu3), and α-2,3-sialyltrasnferse I (ST3Gal-1) levels in B cells and monocytes. Additionally, we evaluated physical function by using the Health Assessment Questionnaire–Disability Index (HAQ-DI). Data on demographic and clinical parameters were collected, and musculoskeletal ultrasonography was performed twice a year on 12 specific joints to assess synovial changes. One year later, we compared all collected data and laboratory or ultrasound results between patients achieving remission or LDA and those who did not in order to determine the predictors. Results: Age, the presence or absence of rheumatoid factor, and the number of conventional disease-modifying anti-rheumatic drugs used were not correlated with remission or LDA for DAS28 or Simplified Disease Activity Index formulas. However, male sex, low CRP levels, low ESR levels, and low HAQ-DI scores were associated with a higher likelihood of achieving remission or LDA for DAS28-ESR. Negative anticyclic citrullinated peptide (CCP) and low HAQ-DI scores were predictors of remission or LDA for DAS28-MCP-1. Interestingly, having less than two comorbidities is a good predictor of a combined remission/low disease activity state for SDAI and DAS28-MCP-1. Furthermore, Neu3 and ST3Gal-1 levels and ST3Gal-1/Neu3 ratios in B cells and monocytes had no significant correlation with total ultrasound scores. Nevertheless, monocyte ST3Gal-1 and Neu3 correlated significantly with DAS28-ESR >5.1 and DAS-MCP-1 >4.8 (both categories belong to high disease activity), respectively (rho = 0.609 with p = 0.012, and rho = 0.727 with p = 0.011, respectively). Monocyte ST3Gal-1/Neu3 ratios connected with DAS28-ESR >5.1 and 3.3 < SDAI ≦ 11 (low disease activity), respectively (rho = 0.662 with p = 0.005, and rho = 0.342 with p = 0.048, respectively). Conclusions: In patients with RA in Taiwan, male sex, low CRP levels, low ESR levels, and low HAQ-DI scores are predictors of remission or LDA for DAS28-ESR, which differ from the predictors for DAS28-MCP-1. Moreover, monocyte ST3Gal-1, Neu3, and their ratios correlated with different disease activity categories of DAS28-ESR, DAS28-MCP-1, and SDAI scores. Full article

Background and Objectives: The guidelines for chronic urticaria in children contain recommendations that are often based on adult studies. The diagnostic pathway has not been standardized and the effectiveness of anti-H1, omalizumab, montelukast, and systemic glucocorticoids is rarely reported in the pediatric population. There is a wide variation in the rate of remission of chronic urticaria between studies. The aim of this study is to enhance our understanding of pediatric chronic urticaria. Materials and Methods: This study enrolled 37 children with chronic urticaria aged from 0 to 18 years. Demographic parameters, medical history, clinical features, laboratory data and treatment information were collected. Children were treated with the recommended dosage of second-generation H1-antihistamines, which was increased by up to twofold. Omalizumab was added for refractory anti-H1 patients. A three-day course with systemic glucocorticoids was administered for severe exacerbations. Montelukast was administered to some children. Results: Wheals without angioedema were common. Chronic urticaria was spontaneous in 32 children (86.48%), inducible in 2 (5.41%), induced by a parasite in 1 and vasculitic in 2. Treatment of the potential causes of chronic urticaria was of no benefit, except for eradication of Dientamoeba fragilis. Chronic urticaria was resolved within three years in 45.9% of cases. Allergic diseases were present in nine children (24.32%) and autoimmune diseases were present in three (8.11%). All children were treated with anti-H1 at the licensed dose or at a higher dose. A partial or complete response to anti-H1 was observed in 29 (78.38%) patients. Montelukast showed no benefit. All children treated with omalizumab responded. Systemic glucocorticoids were successfully used to treat exacerbations. Conclusions: Our findings indicate that laboratory tests should not be routinely performed in children with chronic urticaria without clinical suspicion. However, comorbidities such as thyroid autoimmune disease and coeliac disease are suggested to be monitored over the chronic urticaria course. These clinical conditions could be diagnosed from the diagnostic framework of chronic urticaria. Increasing the dosage of anti-H1 and omalizumab was effective in children resistant to standard treatment but we still need further studies to generate a standard patient-centered treatment. Full article

To enable the regulation and utilization of electric vehicle (EV) load resources by the power grid in the electricity market environment, a third-party electric vehicle aggregator (EVA) must be introduced. The strategy of EVA participation in the electricity market must be studied. During operation, the EVA faces a double uncertainty in the market, namely, electricity demand and electricity price, and must optimize its market behavior to protect its own interests. To achieve this goal, we propose a robust pricing strategy for the EVA that takes into account the coordination of two-stage market behavior to enhance operational efficiency and risk resistance. A two-stage robust pricing strategy that takes into account uncertainty was established by first considering day-ahead pricing, day-ahead electricity purchases, real-time electricity management, and EV customer demand response for the EVA, and further considering the uncertainty in electricity demand and electricity prices. The two-stage robust pricing model was transformed into a two-stage mixed integer programming by linearization method and solved iteratively by the columns and constraints generation (CCG) algorithm. Simulation verification was carried out, and the results show that the proposed strategy fully considers the influence of price uncertainty factors, effectively avoids market risks, and improves the adaptability and economy of the EVA’s business strategy. Full article

Pixel-level classification of very-high-resolution images is a crucial yet challenging task in remote sensing. While transformers have demonstrated effectiveness in capturing dependencies, their tendency to partition images into patches may restrict their applicability to highly detailed remote sensing images. To extract latent contextual semantic information from high-resolution remote sensing images, we proposed a gaze–saccade transformer (GSV-Trans) with visual perceptual attention. GSV-Trans incorporates a visual perceptual attention (VPA) mechanism that dynamically allocates computational resources based on the semantic complexity of the image. The VPA mechanism includes both gaze attention and eye movement attention, enabling the model to focus on the most critical parts of the image and acquire competitive semantic information. Additionally, to capture contextual semantic information across different levels in the image, we designed an inter-layer short-term visual memory module with bidirectional affinity propagation to guide attention allocation. Furthermore, we introduced a dual-branch pseudo-label module (DBPL) that imposes pixel-level and category-level semantic constraints on both gaze and saccade branches. DBPL encourages the model to extract domain-invariant features and align semantic information across different domains in the feature space. Extensive experiments on multiple pixel-level classification benchmarks confirm the effectiveness and superiority of our method over the state of the art. Full article

Next-generation wireless systems require increased spectral and energy efficiency in terminals. This can only be achieved if the nonlinear characteristics of the radio front-end, primarily the power amplifier (PA), are known and considered while designing algorithms or optimizing radio links. PA datasheets contain limited information, and wideband PA measurements typically require advanced equipment and software. This paper proposes a measurement and signal processing framework that enables the acquisition of nonlinear PA characteristics using generic complex sample transmitters and receivers, e.g., software-defined radio (SDR). This paper also proposes a multistage signal calibration and synchronization procedure that allows obtaining nonlinear PA characteristics that are non-distorted by other phenomena. The correctness and usefulness of the proposed framework are demonstrated by measuring three PAs, each under varying supply voltage and carrier frequency. At the same time, the high variability of the obtained nonlinear characteristic justifies the need for PA measurements to obtain awareness of nonlinear characteristics. Full article

Emerging data support associations between the depletion of the healthy gut microbiome and aging-related physiological decline and disease. In humans, fecal microbiota transplantation (FMT) has been used successfully to restore gut microbiome structure and function and to treat C. difficile infections, but its application to healthy aging has been scarcely investigated. The marmoset is an excellent model for evaluating microbiome-mediated changes with age and interventional treatments due to their relatively shorter lifespan and many social, behavioral, and physiological functions that mimic human aging. Prior work indicates that FMT is safe in marmosets and may successfully mediate gut microbiome function and host health. This narrative review (1) provides an overview of the rationale for FMT to support healthy aging using the marmoset as a translational geroscience model, (2) summarizes the prior use of FMT in marmosets, (3) outlines a protocol synthesized from prior literature for studying FMT in aging marmosets, and (4) describes limitations, knowledge gaps, and future research needs in this field. Full article

For decades, wavelet theory has attracted interest in several fields in dealing with signals. Nowadays, it is acknowledged that it is not very suitable to face aspects of multidimensional data like singularities and this has led to the development of other mathematical tools. A recent application of wavelet theory is in radiomics, an emerging field aiming to improve diagnostic, prognostic and predictive analysis of various cancer types through the analysis of features extracted from medical images. In this paper, for a radiomics study of prostate cancer with magnetic resonance (MR) images, we apply a similar but more sophisticated tool, namely the shearlet transform which, in contrast to the wavelet transform, allows us to examine variations along more orientations. In particular, we conduct a parallel radiomics analysis based on the two different transformations and highlight a better performance (evaluated in terms of statistical measures) in the use of the shearlet transform (in absolute value). The results achieved suggest taking the shearlet transform into consideration for radiomics studies in other contexts. Full article

The multi-frequency noisy vibration of an autonomous underwater vehicle (AUV) is a significant factor affecting the performance of shear probes mounted on the head of AUVs. Many efforts have been made to suppress mechanical radiation noise; however, conventional noise reduction methods have their limitations, such as mode mixing. In order to extract thorough information from the aliasing modes and achieve multi-frequency mode targeted correction, a multi-frequency noise reduction method is proposed, based on secondary decomposition and the multi-mode coherence correction algorithm. Weak impulses in aliasing shear mode are enhanced, and mixing frequencies are isolated for thorough decomposition. Noisy mechanical vibrations in the shear modes are eliminated with the use of the acceleration modes along the identical central frequency series. The denoised modes are used to reconstruct the cleaned shear signal, and the updated spectra are aligned with the standard Nasmyth spectrum. Compared with the raw profiles, the variation in the dissipation rate estimated from the corrected shear is reduced by more than an order of magnitude. Full article

The introduction and subsequent range expansion of the American bullfrog (Lithobates catesbeianus) is part of a rising trend of troublesome biological invasions happening in China. This detrimental amphibious invasive species has strong adaptability. After its introduction and spread, it established its own ecological niche in many provinces of China, and its range has continued to expand to more areas. Previous studies recorded the introduction time of bullfrogs and calculated the changes in their genetic diversity in China using mitochondria, but the specific introduction route in China is still unknown. Expanding upon previous research, we employed whole-genome scans (utilizing 2b-RAD genomic sequencing) to examine single nucleotide polymorphisms (SNPs) and microsatellites within Lithobates catesbeianus to screen the genomes of these invasive amphibian species from eight Chinese provinces and two U.S. states, including Kansas, where bullfrogs originate. A total of 1,336,475 single nucleotide polymorphic loci and 17 microsatellite loci were used to calculate the genetic diversity of bullfrogs and their migration pathways. Our results suggest that the population in Hunan was the first to be introduced and to spread, and there may have been multiple introductions of subpopulations. Additionally, the genetic diversity of both the SNP and microsatellite loci in the Chinese bullfrog population was lower than that of the US population due to bottleneck effects, but the bullfrogs can adapt and spread rapidly. This study will offer crucial insights for preventing and controlling future introductions into the natural habitats in China. Additionally, it will assist in devising more precise strategies to manage the existing populations and curtail their continued expansion, as well as aim to improve clarity and originality while mitigating plagiarism risk. Full article

β-lactoglobulin (BLG) forms amyloid-like aggregates at high temperatures, low pH, and low ionic strengths. At a pH below 2, BLG undergoes hydrolysis into peptides, with N-terminal peptides 1–33 and 1–52 being prone to fibrillization, forming amyloid-like fibrils. Due to their good mechanical properties, BLG amyloids demonstrate great potential for diverse applications, including biosensors, nanocomposites, and catalysts. Consequently, further studies are essential to comprehensively understand the factors governing the formation of BLG amyloid-like morphologies. In this study, all-atom molecular dynamics simulations were employed to explore the aggregation of N-terminal 1–33 and 1–52 BLG peptides under conditions of pH 2 and at 10 mM NaCl concentration. The simulations revealed that the peptides spontaneously assembled into aggregates of varying sizes. The aggregation process was enabled by the low charge of peptides and the presence of hydrophobic residues within them. As the peptides associated into aggregates, there was a concurrent increase in β-sheet structures and the establishment of hydrogen bonds, enhancing the stability of the aggregates. Notably, on average, 1–33 peptides formed larger aggregates compared to their 1–52 counterparts, while the latter exhibited a slightly higher content of β-sheets and higher cluster orderliness. The applied approach facilitated insights into the early stages of amyloid-like aggregation and molecular-level insight into the formation of β-sheets, which serve as nucleation points for further fibril growth. Full article

Data clumps, groups of variables that repeatedly appear together across different parts of a software system, are indicative of poor code structure and can lead to potential issues such as maintenance challenges, testing complexity, and scalability concerns, among others. Addressing this, our study introduces an innovative AI-driven pipeline specifically designed for the refactoring of data clumps in software repositories. This pipeline leverages the capabilities of Large Language Models (LLM), such as ChatGPT, to automate the detection and resolution of data clumps, thereby enhancing code quality and maintainability. In developing this pipeline, we have taken into consideration the new European Union (EU)-Artificial Intelligence (AI) Act, ensuring that our pipeline complies with the latest regulatory requirements and ethical standards for use of AI in software development by outsourcing decisions to a human in the loop. Preliminary experiments utilizing ChatGPT were conducted to validate the effectiveness and efficiency of our approach. These tests demonstrate promising results in identifying and refactoring data clumps, but also the challenges using LLMs. Full article

Southwestern China is a critical biodiversity hotspot area, and many large hydroelectric projects have been established in the valleys in the region. Tree growth in the valleys will be affected by both regional climate and reservoir impoundment. However, it remains unknown whether the radial growth of trees in the valleys has a common response pattern to the regional climate, and it is also unclear whether the response of radial growth to reservoir impoundment can be detected. In this study, we developed tree-ring width chronologies of Pinus yunnanensis Franch. collected at 11 sites with vertical and horizontal gradients to three hydroelectric reservoirs in three riverine valleys in southwestern China. We analyzed the radial growth responses to the regional climate from 1986 to 2017 by correlation with instrumental meteorological data. Tree growth responses to reservoir impoundment were investigated through spatial and temporal comparisons using the change in the Euclidean distance and difference test. We also distinguished their responses at tree-ring sites without influenced by reservoir impoundment including two sites in the valleys and seven sites at high elevations. The results showed that the climate conditions in May and the dry season before the growth season significantly limit the radial growth in the valleys, which is different to that at high-elevation areas in southwestern China. Growth variations in the valleys are related to elevations and the trees in similar slopes positions exhibit similar responses. For trees in the low slope positions, both variance and mean values of radial growth are affected by reservoir impoundment. Trees at relatively low sites (i.e., sites M2, R2, L2), rather than the trees close to the reservoirs (i.e., sites M1, R1, L1), respond more sensitively to reservoir impoundment. Full article

Trastuzumab (T) and tyrosine kinase inhibitors (TKIs) are among the first-line treatments recommended for HER2-positive breast cancer. More recently, antibody-drug conjugates (ADCs) such as trastuzumab deruxtecan (T-DXd) and trastuzumab emtansine (T-DM1) have been authorized, and they represent the second-line therapy in this type of cancer. The present study aimed to evaluate adverse drug reactions (ADRs) associated with T-based ADCs that were spontaneously reported in EudraVigilance—the European pharmacovigilance database. Out of 42,272 ADRs reported for currently approved ADCs on the market, 24% of ADRs were related to T-DM1, while 12% of ADRs were related to T-DXd. T-DM1 had a higher probability of reporting eye, ear and labyrinth, and cardiac and hepatobiliary ADRs, while T-DXd had a higher probability of reporting respiratory, thoracic and mediastinal, blood and lymphatic system, metabolism and nutrition, and gastrointestinal ADRs. The present research found that in terms of hematological disorders, T-DM1 and T-DXd had a higher probability of reporting ADRs than TKIs. Moreover, the data showed that T-DM1 seemed to have a higher risk of cardiotoxicity than T-DXd, while T-DXd had a higher probability of reporting metabolism and nutrition disorders than T-DM1. Full article

During our search for natural resources that can inhibit lipid droplet accumulation (LDA) and potentially prevent metabolic dysfunction-associated fatty liver disease (MAFLD) and its progressive stages, such as metabolic dysfunction-associated steatohepatitis (MASH), eight bean extracts (BE1–BE8) were tested for their ability to inhibit lipid accumulation and oxidation in hepatocytes. Substantial inhibitory effects on LDA with bean extracts (BEs) BE2, BE4, BE5, and BE8 were demonstrated. An advanced lipidomic approach was used to quantify the accumulation and inhibition of intracellular triacylglycerol (TAG) and its oxidized species, TAG hydroperoxide (TGOOH), in hepatocytes under fatty acid-loading conditions. The results show that the antioxidants BE2 and BE8 are potential candidates for regulating TAG and TGOOH accumulation in fatty acid-induced lipid droplets (LDs). This study suggests that bean-based foods inhibit LDs formation by decreasing intracellular lipids and lipid hydroperoxides in the hepatocytes. The metabolic profiling of BEs revealed that BE2 and BE8 contained polyphenolic compounds. These may be potential resources for the development of functional foods and drug discovery targeting MAFLD/MASH. Full article