Search Results (743)

Antibody–drug conjugates (ADCs) comprise a monoclonal antibody covalently bound to a cytotoxic payload by a linker. ADCs minimize off-target effects on healthy tissues, leveraging the specificity of monoclonal antibodies to deliver cytotoxic drugs to the intended tumor site. ADCs can be prone to poor behavior, including aggregation and misfolding, leading to poor efficacy, impaired pharmacokinetics, and immunogenicity. It is advantageous to understand the developability and potential liabilities of a protein candidate prior to costly in vivo studies or clinical trials. This review summarizes biophysical and structural techniques used to characterize ADCs and introduces emerging techniques aimed at accurately assessing the developability of protein candidates. Stability is commonly assayed using techniques like differential scanning calorimetry (DSC), differential scanning fluorimetry (DSF), or spectroscopic probes such as circular dichroism and intrinsic fluorescence. Drug-to-antibody ratio (DAR) is a critical parameter that can be measured using absorbance spectroscopy or chromatographic analysis. Aggregation and self-association can be probed using scattering techniques such as dynamic light scattering (DLS), static light scattering (SLS), and size exclusion chromatography–multi-angle light scattering (SEC-MALS), as well as more specialized approaches such as fluorescence correlation spectroscopy (FCS) and analytical ultracentrifugation (AUC). Mass spectrometry (MS) provides extremely valuable insight into stability, covalent modifications, and, through approaches like hydrogen–deuterium exchange (HDX-MS), structural dynamics of ADCs. Looking forward, the use of biophysical assays in ex vivo matrices and strategic use of artificial intelligence/machine learning (AI/ML) approaches are likely to advance the efficient and rapid development of ADCs and other next-generation protein therapeutics. Full article

Trichocentrum ascendens, commonly known as “cola de rata”, is an orchid traditionally used by Indigenous communities to remove warts and heal wounds and in cultural cleansing rituals (“limpia”). However, additional medicinal uses preserved by the Chinantec people of Oaxaca, Mexico, remain largely undocumented and are at risk of disappearing, as this knowledge is now held by only a few individuals. This study gathered information on the therapeutic applications of T. ascendens in the Chinantla region through semi-structured interviews with key collaborators. Information was collected regarding the socio-demographic profiles of the collaborators, as well as the conditions treated, plant parts used, preparation modes, and doses. The species is used to alleviate headaches, toothaches, stomach pains, menstrual pains, body aches, kidney diseases, and inflammation, as well as to treat cultural diseases known as “mal de orin” and “sangre sucia”. Infusions made from mature or developing leaves, along with topical application of crushed leaves, are the main forms of administration. The traditional knowledge documented here underscores the importance of further research to identify the bioactive compounds in T. ascendens and to evaluate their potential inflammatory and analgesic effect. Such studies could lead to the discovery of new pharmacologically active molecules while preserving valuable traditional knowledge. Full article

The existing research on Android malware detection using graph neural networks (GNNs) has largely focused on architectural improvements, while input node feature representations have received less systematic attention. This study adopts a representation-centric approach to enhance function call graph (FCG)-based malware classification through interpretability-driven feature engineering. We propose a dual-level structural feature framework integrating local topological patterns with global graph-level properties. The initial feature set comprises 13 dimensions: five local degree profile (LDP) features and eight global structural features capturing community structure, execution flow, and connectivity patterns. To mitigate the curse of dimensionality, we apply an interpretability-driven selection using integrated gradients (IG), gradient-weighted class activation mapping (GradCAM), and Shapley additive explanations (SHAP), yielding an optimized seven-dimensional subset. Experiments on the MalNet-Tiny benchmark demonstrate that the proposed approach achieves 94.47 ± 0.25% accuracy with jumping knowledge GraphSAGE (JK-GraphSAGE), improving the LDP-only baseline by 0.32 percentage points while reducing feature dimensionality by 46%. The selected features exhibit consistent importance across four GNN architectures and multiple message-passing layers, demonstrating model-agnostic effectiveness. The results reveal that aggregation mechanisms critically influence feature utility, highlighting the necessity of interpretability-guided design for robust malware detection. This work provides a systematic methodology for feature engineering in graph-based security applications. Full article

Background: Traditionally, wild jujube (Ziziphus jujuba Mill. var. spinosa (Bunge) Hu ex H. F. Chou) has been used to nourish the heart, calm the spirit, and arrest spontaneous sweating. Modern research confirms its broad pharmacological activities, including antioxidant, anti-inflammatory, neuroprotective, and cognitive-enhancing effects. This study aims to isolate and characterize the structure of jujube polysaccharides and evaluate their protective effects against oxidative stress damage in neural stem cells (NSCs). Methods: We successfully isolated and purified a novel pectin polysaccharide (ZJP-2) from wild jujube. Its structure was characterized in detail using high-performance liquid chromatography coupled with multi-angle laser light scattering and refractive index detection (HPLC-MALS-RI), high-performance anion exchange chromatography (HPAEC), gas chromatography–mass spectrometry (GC-MS), and nuclear magnetic resonance (NMR) spectroscopy. Results: Structural analysis revealed that ZJP-2 is a pectin heteropolysaccharide with a molecular weight of approximately 67.93 kDa. Its monosaccharide composition primarily includes galac-turonic acid (GalA), arabinose (Ara), rhamnose (Rha), galactose (Gal), and glucose (Glc). The backbone consists of α-GalA and rhamnose-galacturonic acid-I (RG-I) domains linked by (1→4)-glycosidic bonds. NMR spectroscopy further confirmed its glycosidic bond types. In activity assessment, our study demonstrated that ZJP-2 significantly alleviated DMNQ-induced oxidative stress damage in C17.2 neural stem cells. Its protective effect was achieved by reducing intracellular reactive oxygen species (ROS) levels and upregulating the mRNA expression of antioxidant genes associated with the signaling axis (p < 0.05). Moreover, ZJP-2 suppressed DMNQ-induced overexpression of Nestin and NeuN (p < 0.05), contributing to the maintenance of NSCs’ undifferentiated state and functional homeostasis. Conclusions: In conclusion, ZJP-2 possesses distinct structural characteristics and significant neuroprotective potential, supporting its development as a natural functional food or dietary supplement for preventing oxidative stress-related neural damage. Full article

Hedera helix (HE) contains diverse bioactive constituents, including triterpenoid saponins, flavonoids, and phenolic acids, which exhibit various pharmacological activities. In this study, ultrasound-assisted extraction (UAE) combined with natural deep eutectic solvent (NADES) was employed to enhance the extraction efficiency and elucidate the underlying mechanisms. Among the tested formulations, a ternary system composed of malonic acid (Mal), N,N′-dimethylurea (DMU), and 1,4-butanediol (1,4-BDO) achieved the highest efficiency for extracting eight target compounds from the HE leaves. In addition, the key interactions among NADES components were confirmed by Fourier-transform infrared (FT-IR) spectroscopy, providing valuable insights into the extraction mechanism. The UAE process was systematically optimized through single-factor experiments. Subsequently, response surface methodology (RSM) identified the optimal conditions as ultrasonic time of 45 min, solid/liquid ratio of 1:54 g/mL, and ultrasonic temperature of 42 °C. Scanning electron microscopy (SEM) elucidated the microstructural alterations in plant cell walls induced by NADES-UAE, alongside the enhanced penetration and disruption mechanisms. In vitro bioactivity revealed that the NADES-extracted HE exerted strong inhibitory effect on HT-29 colon cancer cells. Overall, these findings demonstrate the high effectiveness and sustainability of NADES-UAE for extracting HE bioactive compounds and provide valuable implications for the industrial-scale production of plant-based functional products. Full article

Low back pain (LBP) remains a leading cause of disability worldwide, imposing substantial socioeconomic burdens. Among its many causes, facetogenic pain accounts for a significant proportion of cases and is generally attributed to irritation of the richly innervated facet joint capsule, mediated by the medial branches of the dorsal rami. This narrative, hypothesis-driven review synthesises the current anatomical, biomechanical, neurophysiological, and clinical literature and advances a conceptual framework proposing a novel anatomical mechanism that may contribute to LBP. We hypothesise that ossification of the mamillo-accessory ligament (MAL) may be a plausible but under-recognised anatomical variant that may influence lumbar biomechanics and neural interfaces. The MAL connects the mammillary and accessory processes of lumbar vertebrae, serving as a stabilising anchor for deep paraspinal muscles and forming a conduit for the medial branch of the dorsal ramus (MBDR). Ossification of the MAL, resulting in a mamillo-accessory foramen, may theoretically impair spinal biomechanics via three principal mechanistic domains: (1) disruption of muscle attachment and segmental stabilisation, (2) potential compression of the MBDR causing denervation and muscle atrophy, and (3) chronic nerve entrapment leading to asymmetrical postural adaptations and persistent pain. Collectively, these pathways may contribute to spinal instability, facet degeneration, and variable response to standard interventional treatments such as radiofrequency ablation. Recognition of MAL ossification may have potential implications for clinical assessment, targeted imaging strategies, and treatment stratification in patients with chronic, non-specific LBP. Full article

Safe and reliable railway operation forms an integral part of autonomous transport systems and depends on accurate knowledge of the adhesion conditions. Both the underestimation and overestimation of adhesion can compromise real-time decision-making in traction and braking control, leading to accidents or excessive wear at the wheel–rail interface. Although limited research has explored the estimation of adhesion forces using data-driven algorithms, most existing approaches lack self-reliance and fail to adequately capture low adhesion levels, which are critical to identify. Moreover, obtaining labelled experimental data remains a significant challenge in adopting data-driven solutions for domain-specific problems. This study implements self-reliant deep learning (DL) models as perception modules for intelligent railway systems, enabling low adhesion identification by training on raw time sequences. In the second phase, to address the challenge of label acquisition, a semi-supervised generative adversarial network (SGAN) is developed. Compared to the supervised algorithms, the SGAN achieved superior performance, with 98.38% accuracy, 98.42% precision, and 98.28% F1-score in identifying seven different adhesion conditions. In contrast, the MLP and 1D-CNN models achieved accuracy of 91% and 93.88%, respectively. These findings demonstrate the potential of SGAN-based data-driven perception for enhancing autonomy, adaptability, and fault diagnosis in intelligent rail and robotic mobility systems. The proposed approach offers an efficient and scalable solution for real-time railway condition monitoring and fault identification, eliminating the overhead associated with manual data labelling. Full article

Background: Reverse-oblique femoral fractures are regarded as highly unstable and are still associated with high complication and failure rates. A new classification system is said to facilitate risk assessment and decision-making. Methods: Over ten years, 7804 patients with per/subtrochanteric fractures were screened in this retrospective analysis. A total of 552 patients with a reverse-oblique fracture pattern were included. The fractures were classified according to the new classification system. The choice of implants, complication rates, revision surgery, and time of surgery were recorded. Radiological outcome parameters and dislocation were measured. Results: For the classification, a good intra-rater reliability (r = 0.77) and inter-rater reliability (k = 0.64) were calculated. The complication rate was overall 19% (n = 105). More than 60% of complications needed revision surgery. The most common complications were cut-out and implant failure (3%); only Parker’s ratio, as a radiological parameter, had prognostic value. Malreduction had a negative impact on mal- or non-unions (p < 0.01), and a trend towards higher overall complications (p = 0.52). Prolonged time of surgery increased the overall complication rate (r = 0.2, p < 0.001). The same was found after open reduction (p = 0.005, OR 2.00). The use of cerclage wires had no positive or negative effects. The use of short or long implants did not influence the outcome. Conclusions: Reverse-oblique femoral fractures are associated with a high complication rate. Short implants can be safely used in cases without severe dislocation if a sufficient working length is considered. Anatomical reduction benefits the outcome as long as it can be performed closed. The classification system presents good inter- and intra-rater reliability. Full article

In arid and semi-arid regions, the hydro-sedimentary processes responsible for reservoir siltation remain insufficiently studied. This study focuses on the Taskourt Dam, one of the major reservoirs in the Marrakech-Safi region in central Morocco. A 450 cm thick sediment core was collected from the reservoir to assess the impact of extreme flood variability on sediment dynamic. A multi-approach analysis was conducted, including sequence analysis, grain-size and bulk and clay mineralogy of the sediments. In addition, hydrological parameters, instantaneous discharge, historical variations in daily water volumes in the reservoir, spillway discharge volumes, and siltation rates were determined through bathymetric surveys. The aim is to identify and evaluate the dynamics of sedimentation evolution within the reservoir. The results highlight two major phases in the siltation history of the Taskourt reservoir. (1) From 2011 to 2016, the siltation rate experienced rapid growth, marked by several major flood events. This intense sedimentary dynamic is illustrated by an accumulation of 418 cm of sediments. The floods of 2014 and 2016 strongly contributed to the intensification of flow energy and to a significant sediment load during this period. (2) From 2017 to 2023, the siltation significantly slowed down, associated with a prolonged drought period. This trend is recorded by a limited sedimentary deposit of 32 cm in thickness. This study provides valuable insights for the development of integrated sediment management strategies, supporting sustainable reservoir operation and effective planning, particularly in similar contexts worldwide. Full article

Background and Objectives: Median arcuate ligament syndrome (MALS), also known as Dunbar syndrome, is a vascular compression disorder. Over time, laparoscopy has become increasingly important in the treatment of MALS, gradually replacing open surgical reconstruction as the preferred first-line approach in most cases. We present nine years of experience managing patients with MALS, aiming to contribute to the evidence supporting this long-debated condition. Materials and Methods: A single-center prospective observational cohort study analysis was conducted. CT angiography was used to confirm the diagnosis of MALS in all patients. All surgical patients completed the Gastrointestinal Quality of Life Index (GIQLI) and Gastrointestinal Symptom Rating Scale (GSRS) questionnaires preoperatively and postoperatively. All the patients underwent laparoscopic decompression. Postoperative follow-up visits were scheduled at one month and three years postoperatively. Results: During the study period, 37 patients were diagnosed with MALS, of whom 11 (29.7%) were symptomatic and underwent laparoscopic decompression. The operated cohort consisted of nine women and two men (mean age 56.7 ± 10.7 years). All patients reported postprandial epigastric pain, and 63.6% experienced weight loss. Laparoscopic decompression was successfully completed in all cases without intraoperative complications. Two patients had stenting after surgery, and in two, prior to surgery. The mean operative time was 103 ± 54 min, and the mean hospital stay was 4.2 ± 2.2 days. At 3-year follow-up, 36.4% of patients reported recurrent symptoms associated with recurrent celiac trunk stenosis on CT angiography. Overall, the patients had less indigestion and less pain; however, the differences did not reach a statistically significant level. Conclusions: Laparoscopic decompression of the median arcuate ligament is a feasible and safe treatment for carefully selected patients with symptomatic MALS, offering durable symptom relief in most cases with minimal morbidity. Full article

Modern malware presents significant challenges to traditional detection methods, often leveraging fileless techniques, in-memory execution, and process injection to evade antivirus and signature-based systems. To address these challenges, alert-driven memory forensics has emerged as a critical capability for uncovering stealthy, persistent, and zero-day threats. This study presents a two-stage host-based malware detection framework, that integrates memory forensics, explainable machine learning, and ensemble classification, designed as a post-alert asynchronous SOC workflow balancing forensic depth and operational efficiency. Utilizing the MemMal-D2024 dataset—comprising rich memory forensic artifacts from Windows systems infected with malware samples whose creation metadata spans 2006–2021—the system performs malware detection, using features extracted from volatile memory. In the first stage, an Attentive and Interpretable Learning for structured Tabular data (TabNet) model is used for binary classification (benign vs. malware), leveraging its sequential attention mechanism and built-in explainability. In the second stage, a Voting Classifier ensemble, composed of Light Gradient Boosting Machine (LGBM), eXtreme Gradient Boosting (XGB), and Histogram Gradient Boosting (HGB) models, is used to identify the specific malware family (Trojan, Ransomware, Spyware). To reduce memory dump extraction and analysis time without compromising detection performance, only a curated subset of 24 memory features—operationally selected to reduce acquisition/extraction time and validated via redundancy inspection, model explainability (SHAP/TabNet), and training data correlation analysis —was used during training and runtime, identifying the best trade-off between memory analysis and detection accuracy. The pipeline, which is triggered from host-based Wazuh Security Information and Event Management (SIEM) alerts, achieved 99.97% accuracy in binary detection and 70.17% multiclass accuracy, resulting in an overall performance of 87.02%, including both global and local explainability, ensuring operational transparency and forensic interpretability. This approach provides an efficient and interpretable detection solution used in combination with conventional security tools as an extra layer of defense suitable for modern threat landscapes. Full article

Two cobalt alloys and one nickel alloy, containing Ta and C in similar atomic contents and either 5 or 10 wt.% Al, were cast. Their microstructures and their oxidation behaviors in air at 1200 °C over 50 h were investigated. All contained eutectic script-like TaC carbides and a dendritic matrix which was either single-phased (FCC) or double-phased (FCC + Co₃Al). The cobalt sample with 5 wt.% oxidized catastrophically, became thinner, lost all its TaC, and was covered by a thick oxide shell (outer CoO and inner mixture of CoO, CoAl₂O₄ and Ta-rich oxides). The two other alloys, Ni-based with 5 wt.% Al and Co-based with 10 wt.% Al, oxidized more slowly, with a mass gain kinetic slightly lower than that for chromia-forming alloys at 1200 °C and a continuous duplex oxide scale made of an outer MAl₂O₄ spinel and inner Al₂O₃ scales. This evidences the existence of two Al content thresholds, depending on the base element, that must be exceeded to obtain acceptable oxidation behavior. Full article

In on-site rapid detection, the electrochemical method boasts high sensitivity and rapid response capabilities, while the colorimetric method can provide intuitive visual readings suitable for on-site screening. Therefore, this study developed an innovative dual-mode electrochemical/colorimetric aptasensor for the accurate detection of malathion (MAL) in vegetables. The sensor combines magnetic Fe₃O₄@ZIF-8-DNA composites and CuZr-MOF-cDNA probes, enabling simultaneous detection of the target through electrochemical reactions and colorimetric changes. The introduction of CuZr-MOF not only enhances the sensor’s conductivity but also significantly amplifies the electrochemical signal through its catalytic properties. The magnetic Fe₃O₄@ZIF-8-DNA composite facilitates solid–liquid separation under an external magnetic field. When the target MAL is present, the aptamer binds to the target, causing the CuZr-MOF-cDNA probes to release from the composite, altering the number of free probes in the supernatant and generating varying intensities of colorimetric signals. Meanwhile, the MAL captured in the precipitate by the aptamer is quantitatively detected through electrochemical methods. Experimental results demonstrate that as the target concentration increases, the colorimetric signal intensifies while the electrochemical signal weakens, showing a good linear relationship between the two. The aptasensor’s limit of detection (LOD) for colorimetric and electrochemical modes was 1.57 × 10⁻¹¹ M and 4.76 × 10⁻¹¹ M, respectively, with recoveries ranging from 87.71% to 107.68% and relative standard deviations between 3.23% and 10.75%. This method exhibits high sensitivity, excellent selectivity, and strong reliability, providing a novel technique for the accurate quantification of MAL in vegetables, particularly suited for on-site rapid detection. Full article

This study aimed to investigate the biological characteristics of Corynebacterium hindlerae, a pathogen discovered in bovine hosts, analyze its genomic features, clarify genetic relationships and differences among strains, and provide a scientific basis for comprehensive clinical prevention and control. The environmental tolerance, biofilm formation capability, and motility of the isolated strain LSKT01 were analyzed. A total of 12 Corynebacterium strains were selected for phylogenetic analysis based on core genes, evaluation of Average Nucleotide Identity (ANI), and comparative genomic analysis covering gene families, synteny, SNPs, InDels, and structural variations (SVs). The isolate exhibited optimal growth at 37 °C, pH 5.5, and 1–2% NaCl concentration, demonstrated strong biofilm-forming ability, but showed weak or no motility. Phylogenetic analysis divided the five Corynebacterium hindlerae strains into two subgroups, with strain LSKT01 being most closely related to strain 1864 (ANI 95.80–98.70%). Comparative genomic analysis revealed highly conserved gene families among strains, while LSKT01 possessed 20 specific paralogs. Genome synteny analysis showed an average similarity >90%, and strains ISL_960a and MAL_1082b exhibited more complex structural variations. The successful isolation and purification of LSKT01 revealed its biological characteristics and genomic features, elucidating the genetic relationships and evolutionary divergence among C. hindlerae strains from diverse sources. This provides a theoretical basis for the rapid diagnosis and precise prevention and control of Corynebacterium hindlerae in Xinjiang. Full article