Small extracellular vesicles (sEVs) have emerged as promising therapeutic agents and drug delivery vehicles. Targeted modification of sEVs and their contents using genetic modification strategies is one of the most popular methods. This study investigated the effects of p53 fusion with arrestin domain-containing protein 1 (ARRDC1) and CD63 on the generation of sEVs, p53 loading efficiency, and therapeutic efficacy. Overexpression of either ARRDC1–p53 (ARP) or CD63–p53 (CDP) significantly elevated p53 mRNA and protein levels. The incorporation of ARRDC1 and CD63 significantly enhanced HEK293T-sEV biogenesis, evidenced by significant increases in sEV-associated proteins TSG101 and LAMP1, resulting in a boost in sEV production. Importantly, fusion with ARRDC1 or CD63 substantially increased the efficiency of loading both p53 fusion proteins and its mRNA into sEVs. sEVs equipped with ARP or CDP significantly enhanced the enrichment of p53 fusion proteins and mRNA in p53-null H1299 cells, resulting in a marked increase in apoptosis and a reduction in cell proliferation, with ARP-sEVs demonstrating greater effectiveness than CDP-sEVs. These findings underscore the enhanced functionality of ARRDC1- and CD63-modified sEVs, emphasizing the potential of genetic modifications in sEV-based therapies for targeted cancer treatment. Full article

The present paper addresses a comprehensive historical assessment of water consumption, demand, and supply in Saudi Arabia, along with future projections regarding water balance, in terms of demand and supply by source in various sectors. Being an arid region, Saudi Arabia experiences scorching heat, low precipitation, a high rate of potential evaporation, and the absence of permanent water bodies over the territory. Groundwater contributes almost 61% of total available water, while the recharge rate is negligible. However, few widyan (ephemeral streams) systems exists to satisfy water demand, which could contribute to approximately one year of domestic water consumption if managed efficiently. The study also predicts water consumption scenarios for the next three consecutive development plans, i.e., the 10th plan (2015–2019), 11th plan (2020–2024), and 12th plan (2025–2029). The analysis shows that water consumption may decline significantly in the future, if the present rate of decline continues. Scenario I, if the current rate is assumed, provides a decrease in consumption of 14.36, 12.66, and 11.15 BCM for 10th, 11th, and 12th plans, respectively. Moreover, the domestic and industrial sectors will consume more water in the future. In the same way, scenarios II and III represent a decline in total water consumption, along with that of agriculture, while domestic and industrial water usage would increase, thus improving environmental sustainability. Full article

To achieve sustainable development goals and meet the demand for clean and efficient energy utilization, it is imperative to advance the penetration of renewable energy in various sectors. Energy storage systems can mitigate the intermittent issues of renewable energy and enhance the efficiency and economic viability of existing energy facilities. Among various energy storage technologies, thermocline heat storage (THS) has garnered widespread attention from researchers due to its stability and economic advantages. Currently, there are only a few review articles focusing on THS, and there is a gap in the literature regarding the optimization design of THS systems. Therefore, this paper provides a comprehensive review of the recent research progress in THS, elucidating its principles, thermal storage materials, applications, and optimization designs. The novelty of this work lies in the detailed classification and analysis of various optimization designs for THS, including tank shape, aspect ratio, inlet/outlet configuration, thermal energy storage materials arrangement, operating strategies, and numerical model optimization approaches. The limitations of existing research are also identified, and future perspectives are proposed, aiming to provide recommendations for THS research and contribute to the development and promotion of THS technology. Full article

As a new vegetation monitoring index, the KNDVI has certain advantages in characterizing the evolutionary process of regional desertification. However, there are few reports on desertification monitoring based on KNDVI and feature space models. In this study, seven feature parameters, including the kernel normalized difference vegetation index (KNDVI) and Albedo, were introduced to construct different models for desertification remote-sensing monitoring. The optimal desertification remote-sensing monitoring index model was determined with the measured data; then, the spatiotemporal evolution pattern of desertification in Gulang County from 2013 to 2023 was analyzed and revealed. The main conclusions were as follows: (1) Compared with the NDVI and MSAVI, the KNDVI showed more advantages in the characterization of the desertification evolution process. (2) The point–line pattern KNDVI-Albedo remote-sensing index model had the highest monitoring accuracy, reaching 94.93%, while the point–line pattern NDVI-TGSI remote-sensing monitoring index had the lowest accuracy of 54.38%. (3) From 2013 to 2023, the overall desertification situation in Gulang County showed a trend of improvement with a pattern of “firstly aggravation and then alleviation.” Additionally, the gravity center of desertification in Gulang County first shifted to the southeast and then to the northeast, indicating that the northeast’s aggravating rate of desertification was higher than in the southwest during the period. (4) From 2013 to 2023, the area of stable desertification in Gulang County was the largest, followed by the slightly weakened zone, and the most significant transition area was that of extreme desertification to severe desertification. The research results provide important decision support for the precise monitoring and governance of regional desertification. Full article

In the present study, we have explored the involvement of Toll-like Receptor 4 (TLR4) in atrial fibrillation (AF), by using a meta-analysis of publicly available human transcriptomic data. The meta-analysis revealed 565 upregulated and 267 downregulated differentially expressed genes associated with AF. Pathway enrichment analysis highlighted a significant overrepresentation in immune-related pathways for the upregulated genes. A significant overlap between AF differentially expressed genes and TLR4-modulated genes was also identified, suggesting the potential role of TLR4 in AF-related transcriptional changes. Additionally, the analysis of other Toll-like receptors (TLRs) revealed a significant association with TLR2 and TLR3 in AF-related gene expression patterns. The examination of MYD88 and TICAM1, genes associated with TLR4 signalling pathways, indicated a significant yet nonspecific enrichment of AF differentially expressed genes. In summary, this study offers novel insights into the molecular aspects of AF, suggesting a pathophysiological role of TLR4 and other TLRs. By targeting these specific receptors, new treatments might be designed to better manage AF, offering hope for improved outcomes in affected patients. Full article

The use of deep convolutional neural networks has significantly improved the performance of super-resolution. Employing deeper networks to enhance the non-linear mapping capability from low-resolution (LR) to high-resolution (HR) images has inadvertently weakened the information flow and disrupted long-term memory. Moreover, overly deep networks are challenging to train, thus failing to exhibit the expressive capability commensurate with their depth. High-frequency and low-frequency features in images play different roles in image super-resolution. Networks based on CNNs, which should focus more on high-frequency features, treat these two types of features equally. This results in redundant computations when processing low-frequency features and causes complex and detailed parts of the reconstructed images to appear as smooth as the background. To maintain long-term memory and focus more on the restoration of image details in networks with strong representational capabilities, we propose the Frequency-Separated Attention Network (FSANet), where dense connections ensure the full utilization of multi-level features. In the Feature Extraction Module (FEM), the use of the Res ASPP Module expands the network’s receptive field without increasing its depth. To differentiate between high-frequency and low-frequency features within the network, we introduce the Feature-Separated Attention Block (FSAB). Furthermore, to enhance the quality of the restored images using heuristic features, we incorporate attention mechanisms into the Low-Frequency Attention Block (LFAB) and the High-Frequency Attention Block (HFAB) for processing low-frequency and high-frequency features, respectively. The proposed network outperforms the current state-of-the-art methods in tests on benchmark datasets. Full article

Peptide pools consist of short amino acid sequences and have proven to be versatile tools in various research areas in immunology and clinical applications. They are commercially available in many different compositions and variants. However, unlike other reagents that consist of only one or a few compounds, peptide pools are highly complex products which makes their quality control a major challenge. Quantitative peptide analysis usually requires sophisticated methods, in most cases isotope-labeled standards and reference materials. Usually, this would be prohibitively laborious and expensive. Therefore, an approach is needed to provide a practical and feasible method for quality control of peptide pools. With insufficient quality control, the use of such products could lead to incorrect experimental results, worsening the well-known reproducibility crisis in the biomedical sciences. Here we propose the use of ultra-high performance liquid chromatography (UHPLC) with two detectors, a standard UV detector at 214 nm for quantitative analysis and a high-resolution mass spectrometer (HRMS) for identity confirmation. To be cost-efficient and fast, quantification and identification are performed in one chromatographic run. An optimized protocol is shown, and different peak integration methods are compared and discussed. This work was performed using a peptide pool known as CEF advanced, which consists of 32 peptides derived from cytomegalovirus (CMV), Epstein–Barr virus (EBV) and influenza virus, ranging from 8 to 12 amino acids in length. Full article

Mounting evidence indicates a complex link between circulating saturated fatty acids (SFAs) and cardiovascular disease (CVD) risk factors, but research on erythrocyte membrane SFA associations with metabolic markers remains limited. Our study sought to investigate the correlations between erythrocyte membrane SFAs and key metabolic markers within glycemic and lipid metabolism in a Chinese population of 798 residents aged 41 to 71 from Guangzhou. Using gas chromatography–mass spectrometry, we assessed the erythrocyte membrane saturated fatty acid profile and performed multiple linear regression to evaluate the relationship between different SFA subtypes and metabolic markers. Our findings revealed that the odd-chain SFA group (C15:0 + C17:0) exhibited negative associations with fasting blood glucose (FBG), homeostatic model assessment for insulin resistance (HOMA-IR), and triglycerides (TG). Conversely, the very-long-chain SFA group (C20:0 + C22:0 + C23:0 + C24:0) exhibited positive associations with fasting insulins (FINS), HOMA-IR, total cholesterol (TC), and low-density lipoprotein cholesterol (LDL-C). Furthermore, there was no evidence supporting an association between the even-chain group (C14:0 + C16:0+ C18:0) and metabolic markers. Our findings suggest that different subtypes of SFAs have diverse effects on glycemic and lipid metabolic markers, with odd-chain SFAs associated with a lower metabolic risk. However, the results concerning the correlations between even-chain SFAs and very-long-chain SFAs with markers of glycemic and lipid metabolism pathways are confusing, highlighting the necessity for further exploration and investigation. Full article

Advances in deep learning and computer vision have overcome many challenges inherent in the field of autonomous intelligent vehicles. To improve the detection accuracy and efficiency of EdgeBoard intelligent vehicles, we proposed an optimized design of EdgeBoard based on our PP-YOLOE+ model. This model innovatively introduces a composite backbone network, incorporating deep residual networks, feature pyramid networks, and RepResBlock structures to enrich environmental perception capabilities through the advanced analysis of sensor data. The incorporation of an efficient task-aligned head (ET-head) in the PP-YOLOE+ framework marks a pivotal innovation for precise interpretation of sensor information, addressing the interplay between classification and localization tasks with high effectiveness. Subsequent refinement of target regions by detection head units significantly sharpens the system’s ability to navigate and adapt to diverse driving scenarios. Our innovative hardware design, featuring a custom-designed mainboard and drive board, is specifically tailored to enhance the computational speed and data processing capabilities of intelligent vehicles. Furthermore, the optimization of our Pos-PID control algorithm allows the system to dynamically adjust to complex driving scenarios, significantly enhancing vehicle safety and reliability. Besides, our methodology leverages the latest technologies in edge computing and dynamic label assignment, enhancing intelligent vehicles’ operations through seamless sensor integration. Our custom dataset, specifically designed for this study, includes 4777 images captured by intelligent vehicles under a variety of environmental and lighting conditions. The dataset features diverse scenarios and objects pertinent to autonomous driving, such as pedestrian crossings and traffic signs, ensuring a comprehensive evaluation of the model’s performance. We conducted extensive testing of our model on this dataset to thoroughly assess sensor performance. Evaluated against metrics including accuracy, error rate, precision, recall, mean average precision (mAP), and F1-score, our findings reveal that the model achieves a remarkable accuracy rate of 99.113%, an mAP of 54.9%, and a real-time detection frame rate of 192 FPS, all within a compact parameter footprint of just 81 MB. These results demonstrate the superior capability of our PP-YOLOE+ model to integrate sensor data, achieving an optimal balance between detection accuracy and computational speed compared with existing algorithms. Full article

Astrocytes play a pivotal role in maintaining brain homeostasis. Recent research has highlighted the significance of palmitic acid (PA) in triggering pro-inflammatory pathways contributing to neurotoxicity. Furthermore, Genomic-scale metabolic models and control theory have revealed that metabolic switches (MSs) are metabolic pathway regulators by potentially exacerbating neurotoxicity, thereby offering promising therapeutic targets. Herein, we characterized these enzymatic MSs in silico as potential therapeutic targets, employing protein–protein and drug–protein interaction networks alongside structural characterization techniques. Our findings indicate that five MSs (P00558, P04406, Q08426, P09110, and O76062) were functionally linked to nervous system drug targets and may be indirectly regulated by specific neurological drugs, some of which exhibit polypharmacological potential (e.g., Trifluperidol, Trifluoperazine, Disulfiram, and Haloperidol). Furthermore, four MSs (P00558, P04406, Q08426, and P09110) feature ligand-binding or allosteric cavities with druggable potential. Our results advocate for a focused exploration of P00558 (phosphoglycerate kinase 1), P04406 (glyceraldehyde-3-phosphate dehydrogenase), Q08426 (peroxisomal bifunctional enzyme, enoyl-CoA hydratase, and 3-hydroxyacyl CoA dehydrogenase), P09110 (peroxisomal 3-ketoacyl-CoA thiolase), and O76062 (Delta(14)-sterol reductase) as promising targets for the development or repurposing of pharmacological compounds, which could have the potential to modulate lipotoxic-altered metabolic pathways, offering new avenues for the treatment of related human diseases such as neurological diseases. Full article

Environmental contamination by heavy metals (HMs) has emerged as a significant global issue in recent decades. Among natural substances, food-deriving polyphenols have found a valuable application in chelating therapy, partially limited by their low water solubility. Thus, three different hydroalcoholic extracts titrated in quercetin (QE), ellagic acid (EA), and curcumin (CUR) were formulated using maltodextrins as carriers, achieving a powder with a valuable water solubility (MQE 91.3 ± 1.2%, MEA 93.4 ± 2.1, and MCUR 89.3 ± 2%). Overcoming the problem of water solubility, such formulations were tested in an in vitro simulated gastrointestinal digestion experiment conducted on a water sample with standardized concentrations of the principal HMs. Our results indicate that regarding the nonessential HMs investigated (Pb, Cd, As, Sb, and Hg), MQE has been shown to be the most effective in increasing the HMs’ non-bioaccessible concentration, resulting in concentration increases in Cd of 68.3%, in As of 51.9%, in Hg of 58.9%, in Pb of 271.4, and in Sb of 111.2% (vs control, p < 0.001) in non-bioaccessible fractions. Regarding the essential HMs, MEA has shown the greatest capability to increase their intestinal bioaccessibility, resulting in +68.5%, +61.1, and +22.3% (vs control, p < 0.001) increases in Cu, Zn, and Fe, respectively. Finally, considering the strong relation between the antiradical and chelating activities, the radical scavenging potentials of the formulations was assayed in DPPH and ABTS assays. Full article

Gait analysis plays important roles in various applications such as exercise therapy, biometrics, and robot control. It can also be used to prevent and improve movement disorders and monitor health conditions. We implemented a wearable module equipped with an MPU-9250 IMU sensor, and Bluetooth modules were implemented on an Arduino Uno R3 board for gait analysis. Gait cycles were identified based on roll values measured by the accelerometer embedded in the IMU sensor. By superimposing the gait cycles that occurred during the walking period, they could be analyzed using statistical methods. We found that the subjects could be identified using the gait feature points extracted through the statistical modeling process. To validate the feasibility of feature-based gait pattern identification, we constructed various machine learning models and compared the accuracy of their gait pattern identification. Based on this, we also investigated whether there was a significant difference between the gait patterns of people who used cell phones while walking and those who did not. Full article

This work investigated the fermentation kinetics and characteristics of goat yogurt supplemented with bovine whey protein isolate (WPI) (0%, 2.5% and 5.0%) subjected to high shear dispersion (HSD) assisted by ultrasound (US). Protein supplementation and the physical processes increased the electronegativity of the zeta potential (≤60%), whereas particle size reduction was observed only with physical processes (≤42%). The addition of 2.5% WPI reduced yogurt fermentation time by 30 min. After 24 h of storage at 7 °C, lactic acid bacteria counts did not differ between samples (≥8 log CFU/mL), and the supplementation was sufficient to increase the apparent viscosity (≤5.65 times) and water-holding capacity (WHC) of the yogurt (≤35% increase). However, supplementation combined with physical processes promoted greater improvements in these parameters (6.41 times in apparent viscosity and 48% in WHC) (p < 0.05), as confirmed by the denser and better-organized protein clusters observed in microscopic evaluation. Thus, both approaches proved to be promising alternatives to improve goat yogurt quality. Therefore, the decision to adopt these strategies, either independently or in combination, should consider cost implications, the product quality, and market demand. Full article

Most advances in the remote sensing of floating marine plastic litter have been made using passive remote-sensing techniques in the visible (VIS) to short-wave-infrared (SWIR) parts of the electromagnetic spectrum based on the spectral absorption features of plastic surfaces. In this paper, we present developments of new and emerging remote-sensing technologies of marine plastic litter such as passive techniques: fluid lensing, multi-angle polarimetry, and thermal infrared sensing (TIS); and active techniques: light detection and ranging (LiDAR), multispectral imaging detection and active reflectance (MiDAR), and radio detection and ranging (RADAR). Our review of the detection capabilities and limitations of the different sensing technologies shows that each has their own weaknesses and strengths, and that there is not one single sensing technique that applies to all kinds of marine litter under every different condition in the aquatic environment. Rather, we should focus on the synergy between different technologies to detect marine plastic litter and potentially the use of proxies to estimate its presence. Therefore, in addition to further developing remote-sensing techniques, more research is needed in the composition of marine litter and the relationships between marine plastic litter and their proxies. In this paper, we propose a common vocabulary to help the community to translate concepts among different disciplines and techniques. Full article

This paper undertakes a revisit of the sunk cost fallacy, which refers to the tendency of people to persist investing resources into something, even if it is destined to have no good outcome. We emphasize that the utilities associated with different alternatives are not static for decision makers, which is exactly opposite to the traditional perspective. This paper argues that the utility of an option may change due to the choice of another option, suggesting that decisions considered irrational by the traditional analytical method, i.e., sunk cost fallacy, may be rational. We propose a novel analytical method for decision making with sunk cost when considering the utility change and validate the effectiveness of this method through mathematical modeling and computational experiments. This paper mathematically describes such decision-making problems, analyzing the impact of changes in the utilities across different alternatives on decision making with a real-world example. Furthermore, we develop a two-stage stochastic optimization model for such decision-making problems and employ the sample average approximation (SAA) method to solve them. The results from computational experiments indicate that some decisions traditionally considered irrational are, in fact, rational when the utility of an option changes as a result of choosing another option. This paper, therefore, highlights the significance of incorporating utility changes into the decision-making process and stands as a valuable addition to the literature, offering a refreshed and effective decision-making method for improved decision making. Full article

Pithiness is one of the physiological diseases of radishes, which is accompanied by the accumulation of reactive oxygen species (ROS) during the sponging of parenchyma tissue in the fleshy roots. A respiratory burst oxidase homolog (Rboh, also known as NADPH oxidase) is a key enzyme that catalyzes the production of ROS in plants. To understand the role of Rboh genes in radish pithiness, herein, 10 RsRboh gene families were identified in the genome of Raphanus sativus using Blastp and Hmmer searching methods and were subjected to basic functional analyses such as phylogenetic tree construction, chromosomal localization, conserved structural domain analysis, and promoter element prediction. The expression profiles of RsRbohs in five stages (Pithiness grade = 0, 1, 2, 3, 4, respectively) of radish pithiness were analyzed. The results showed that 10 RsRbohs expressed different levels during the development of radish pithiness. Except for RsRbohB and RsRbohE, the expression of other members increased and reached the peak at the P2 (Pithiness grade = 2) stage, among which RsRbohD1 showed the highest transcripts. Then, the expression of 40 genes related to RsRbohD1 and pithiness were analyzed. These results can provide a theoretical basis for improving pithiness tolerance in radishes. Full article

Fractures through the physis account for 18–30% of all paediatric fractures, leading to growth arrest in up to 5.5% of cases. We have limited knowledge to predict which physeal fractures result in growth arrest and subsequent deformity or limb length discrepancy. The purpose of this study is to identify factors associated with physeal growth arrest to improve patient outcomes. Methods: This prospective cohort study was designed to develop a clinical prediction model for growth arrest after physeal injury. Patients ≤ 18 years old presenting within four weeks of injury were enrolled if they had open physes and sustained a physeal fracture of the humerus, radius, ulna, femur, tibia or fibula. Patients with prior history of same-site fracture or a condition known to alter bone growth or healing were excluded. Demographic data, potential prognostic indicators, and radiographic data were collected at baseline, during healing, and at one- and two-years post-injury. Results: A total of 332 patients had at least six months of follow-up or a diagnosis of growth arrest within six months of injury. In a comparison analysis, patients who developed growth arrest were more likely to be older (12.8 years vs. 9.4 years) and injured on the right side (53.0% vs. 45.7%). Initial displacement and angulation rates were higher in the growth arrest group (59.0% vs. 47.8% and 47.0% vs. 38.8%, respectively), but the amount of angulation was similar (27.0° vs. 28.4°). Rates of growth arrest were highest in distal femoral fractures (86%). Conclusion: The incidence of growth arrest in this patient population appears higher than the past literature reports at 30.1%. However, there may be variances in diagnostic criteria for growth arrest, and the true incidence may be lower. A number of patients were approaching skeletal maturity, and any growth arrest is likely to have less clinical significance in these cases. Further prospective long-term follow-up is required to determine risk factors, incidence, and true clinical impact of growth arrest when it does occur. Full article

Childhood obesity has become a global epidemic, with significant increases in prevalence over recent decades. While excessive calorie consumption and physical inactivity are known factors, emerging research highlights the role of endocrine-disrupting chemicals (EDCs), particularly obesogens, in obesity’s pathogenesis. This review explores the historical context of the environmental obesogens hypothesis, their sources, mechanism of action, impact on prenatal and postnatal development, and epigenetics. Additionally, it discusses the long-term consequences of childhood obesity and proposes prevention strategies that will mitigate negative health effects. Obesogens were found to disrupt hormonal balance and metabolic processes through various mechanisms such as altering gene expression, hormonal interference, and inflammation. Especially significant was exposure during critical windows of development, which correlates with an increased risk of obesity in childhood or adolescence. Long-term effects of childhood obesity include chronic health conditions and psychosocial issues. A comprehensive approach is necessary to address childhood obesity encompassing genetic, environmental, and lifestyle factors. Prevention strategies should focus on reducing obesogen exposure, promoting healthy lifestyles, and implementing regulatory policies. Future research should investigate obesogens–diet interactions, microbiome impacts, and combined obesogens effects. Long-term human studies are also crucial for validating findings from animal models and allowing for informed decision-making to combat the obesity pandemic. Full article

Background: Staphylococcus aureus is a common pathogenic microorganism in humans and animals. Type II NADH oxidoreductase (NDH-2) is the only NADH:quinone oxidoreductase present in this organism and represents a promising target for the development of anti-staphylococcal drugs. Recently, myricetin, a natural flavonoid from vegetables and fruits, was found to be a potential inhibitor of NDH-2 of S. aureus. The objective of this study was to evaluate the inhibitory properties of myricetin against NDH-2 and its impact on the growth and expression of virulence factors in S. aureus. Results: A screening method was established to identify effective inhibitors of NDH-2, based on heterologously expressed S. aureus NDH-2. Myricetin was found to be an effective inhibitor of NDH-2 with a half maximal inhibitory concentration (IC₅₀) of 2 μM. In silico predictions and enzyme inhibition kinetics further characterized myricetin as a competitive inhibitor of NDH-2 with respect to the substrate menadione (MK). The minimum inhibitory concentrations (MICs) of myricetin against S. aureus strains ranged from 64 to 128 μg/mL. Time–kill assays showed that myricetin was a bactericidal agent against S. aureus. In line with being a competitive inhibitor of the NDH-2 substrate MK, the anti-staphylococcal activity of myricetin was antagonized by MK-4. In addition, myricetin was found to inhibit the gene expression of enterotoxin SeA and reduce the hemolytic activity induced by S. aureus culture on rabbit erythrocytes in a dose-dependent manner. Conclusions: Myricetin was newly discovered to be a competitive inhibitor of S. aureus NDH-2 in relation to the substrate MK. This discovery offers a fresh perspective on the anti-staphylococcal activity of myricetin. Full article

The risk of aberrant growth of induced pluripotent stem cell (iPSC)-derived cells in response to DNA damage is a potential concern as the tumor suppressor genes TP53 and CDKN2A are transiently inactivated during reprogramming. Herein, we evaluate the integrity of cellular senescence pathways and DNA double-strand break (DSB) repair in Sendai virus reprogrammed iPSC-derived human fibroblasts (i-HF) compared to their parental skin fibroblasts (HF). Using transcriptomics analysis and a variety of functional assays, we show that the capacity of i-HF to enter senescence and repair DSB is not compromised after damage induced by ionizing radiation (IR) or the overexpression of H-RAS^V12. Still, i-HF lines are transcriptionally different from their parental lines, showing enhanced metabolic activity and higher expression of p53-related effector genes. As a result, i-HF lines generally exhibit increased sensitivity to various stresses, have an elevated senescence-associated secretory phenotype (SASP), and cannot be immortalized unless p53 expression is knocked down. In conclusion, while our results suggest that i-HF are not at a greater risk of transformation, their overall hyperactivation of senescence pathways may impede their function as a cell therapy product. Full article

Based on the Hamilton canonical equations for ocean surface waves with four-five-six-wave resonance conditions , the determinate dynamical equation of four-five-six-wave resonances for ocean surface gravity waves in water with a finite depth is established, thus leading to the elimination of the nonresonant second-, third-, fourth-, and fifth-order nonlinear terms though a suitable canonical transformation. The four kernels of the equation and the 18 coefficients of the transformation are expressed in explicit form in terms of the expansion coefficients of the gravity wave Hamiltonian in integral-power series in normal variables. The possibilities of the existence of integrals of motion for the wave momentum and the wave action are discussed, particularly the special integrals for the latter. For ocean surface capillary–gravity waves on a fluid with a finite depth, the sixth-order expansion coefficients of the Hamiltonian in integral-power series in normal variables are concretely provided, thus naturally including the classical fifth-order kinetic energy expansion coefficients given by Krasitskii. Full article

Psoriasis is a chronic recurrent inflammatory autoimmune pathology with a significant genetic component and several interferences of immunological cells and their cytokines. The complex orchestration of psoriasis pathogenesis is related to the synergic effect of immune cells, polygenic alterations, autoantigens, and several other external factors. The major act of the IL-23/IL-17 axis, strongly influencing the inflammatory pattern established during the disease activity, is visible as a continuous perpetuation of the pro-inflammatory response and keratinocyte activation and proliferation, leading to the development of psoriatic lesions. Genome-wide association studies (GWASs) offer a better view of psoriasis pathogenic pathways, with approximately one-third of psoriasis’s genetic impact on psoriasis development associated with the MHC region, with genetic loci located on chromosome 6. The most eloquent genetic factor of psoriasis, PSORS1, was identified in the MHC I site. Among the several factors involved in its complex etiology, dysbiosis, due to genetic or external stimulus, induces a burst of pro-inflammatory consequences; both the cutaneous and gut microbiome get involved in the psoriasis pathogenic process. Cutting-edge research studies and comprehensive insights into psoriasis pathogenesis, fostering novel genetic, epigenetic, and immunological factors, have generated a spectacular improvement over the past decades, securing the path toward a specific and targeted immunotherapeutic approach and delayed progression to inflammatory arthritis. This review aimed to offer insight into various domains that underline the pathogenesis of psoriasis and how they influence disease development and evolution. The pathogenesis mechanism of psoriasis is multifaceted and involves an interplay of cellular and humoral immunity, which affects susceptible microbiota and the genetic background. An in-depth understanding of the role of pathogenic factors forms the basis for developing novel and individualized therapeutic targets that can improve disease management. Full article