Search Results (201)

The overexpression of atypical protein kinase C-iota (PKC-ι) is a biomarker for carcinogenesis in various cell types, such as glioma, ovarian, renal, etc., manifesting as a potential drug target. In previous in vitro studies, ICA-1S and ICA-1T, experimental candidates for inhibiting PKC-ι, have demonstrated their specificity and promising efficacy against various cancers. Moreover, the in vivo studies have demonstrated low toxicity levels in acute and chronic murine models. Despite these prior developments, the binding affinities of the inhibitors were never thoroughly explored from a biophysical perspective. Here, we present the biophysical characterizations of PKC-ι in combination with ICA-1S/1T. Various methods based on molecular docking, light scattering, intrinsic fluorescence, thermal denaturation, and heat exchange were applied. The biophysical characteristics including particle sizing, thermal unfolding, aggregation profiles, enthalpy, entropy, free energy changes, and binding affinity (K_d) of the PKC-ι in the presence of ICA-1S were observed. The studies indicate the presence of domain-specific stabilities in the protein–ligand complex. Moreover, the results indicate a spontaneous reaction with an entropic gain, resulting in a possible entropy-driven hydrophobic interaction and hydrogen bonds in the binding pocket. Altogether, these biophysical studies reveal important insights into the binding interactions of PKC-ι and its inhibitors ICA-1S/1T. Full article

SARS-CoV-2 undergoes frequent mutations that drive viral evolution and genomic diversity, influencing transmissibility, immune escape, and disease severity. In this study, we performed whole-genome sequencing on SARS-CoV-2 isolates from patients in New York City and identified several globally rare mutations across multiple viral lineages. The isolates analyzed for rare mutations belonged to three lineages: B.1.1.7 (Alpha), B.1.526 (Iota), and B.1.623. We identified 16 rare mutations (global incidence <1000) in non-structural protein genes, including nsp2, nsp3, nsp4, nsp6, nsp8, nsp13, nsp14, ORF7a, and ORF8. Three of these mutations—located in nsp2, nsp13, and ORF8—have been reported in fewer than 100 individuals worldwide. We also detected five rare mutations in structural proteins (S, M, and N), including two—one in M and one in N—previously reported in fewer than 100 cases globally. We present clinical profiles of three patients, each infected with genetically distinct viral isolates from the three lineages studied. Furthermore, we illustrate a local transmission chain inferred from unique mutation patterns identified in the Omicron genome. These findings underscore the importance of whole-genome sequencing for detecting rare mutations, tracking community spread, and identifying emerging variants with clinical and public health significance. Full article

The integration of functional ingredients into 3D food printing formulations presents both opportunities and challenges, particularly regarding the printability and structural integrity of the final product. This study investigates the effect of incorporating omega-3 fatty acids encapsulated in pea protein into a model food gel composed of gelatin and iota-carrageenan. Four formulations with varying concentrations of encapsulated omega-3 (0%, 3%, 3.75%, and 6%) were evaluated for their rheological, textural, and printability properties. Rheological analysis revealed a progressive increase in storage modulus (G′) from 1200 Pa (0%) to 2000 Pa (6%), indicating enhanced elastic behavior. Extrusion analysis showed a reduction in maximum extrusion force from 325 N (0%) to 250 N (6%), and an increase in buffer time from 390 s to 500 s. Print fidelity at time 0 showed minimal deviation in the checkerboard geometry (area deviation: −12%), while the concentric cylinder showed the highest stability over 60 min (height deviation: 9%). These findings highlight the potential of using encapsulated bioactive compounds in 3D food printing to develop functional foods with tailored nutritional and mechanical properties. Full article

Trust evaluation has become a major challenge in the quickly developing Internet of Things (IoT) environment because of the vulnerabilities and security hazards associated with networked devices. To overcome these obstacles, this study offers a novel approach for evaluating trust that uses IOTA Tangle technology. By decentralizing the trust evaluation process, our approach reduces the risks related to centralized solutions, including privacy violations and single points of failure. To offer a thorough and reliable trust evaluation, this study combines direct and indirect trust measures. Moreover, we incorporate IOTA-based trust metrics to evaluate a node’s trust based on its activity in creating and validating IOTA transactions. The proposed framework ensures data integrity and secrecy by implementing immutable, secure storage for trust scores on IOTA. This ensures that no node transmits a wrong trust score for itself. The results show that the proposed scheme is efficient compared to recent literature, achieving up to +3.5% higher malicious node detection accuracy, up to 93% improvement in throughput, 40% reduction in energy consumption, and up to 24% lower end-to-end delay across various network sizes and adversarial conditions. Our contributions improve the scalability, security, and dependability of trust assessment processes in Internet of Things networks, providing a strong solution to the prevailing issues in current centralized trust models. Full article

Breast cancer is the second most common cancer in the United States and consists of 30% of all new female cancer each year. PKC iota (PKC-$\iota$) is a bonafide human oncogene and is overexpressed in many types of cancer, including breast cancer. This study explores the role of PKC-$\iota$ in regulating the transcription factor Jun proto-oncogene (c-Jun), pro-inflammatory cytokine Tumor Necrosis Factor-alpha (TNF-$\alpha$), and the Mitogen-Activated Protein Kinase/Jun N-terminal kinase (MAPK/JNK) pathway, which also exhibits an oncogenic role in breast cancer. ICA-1S, a PKC-$\iota$ specific inhibitor, was used to inhibit PKC-$\iota$ to observe the subsequent effect on the levels of c-Jun, TNF-$\alpha$, and the MAPK/JNK signaling pathway. To obtain the results, cell proliferation assay, Western blotting, co-immunoprecipitation, small interfering RNA (siRNA), immunofluorescence, flow cytometry, cycloheximide (CHX) chase assay, and reverse transcription quantitative PCR (RT-qPCR) techniques were implemented. ICA-1S significantly inhibited cell proliferation and induced apoptosis in both breast cancer cell lines. Treatment with ICA-1S and siRNA also reduced the expression levels of the MAPK/JNK pathway protein, c-Jun, and TNF-$\alpha$ in both cell lines. PKC-$\iota$ was also found to be strongly associated with c-Jun, via which it regulated the MAPK/JNK pathway. Additionally, ICA-1S was found to promote the degradation of c-Jun and decrease the mRNA levels of c-Jun. We concluded that PKC-$\iota$ plays a crucial role in regulating breast cancer, and the inhibition of PKC-$\iota$ by ICA-1S reduces breast cancer cell proliferation and induces apoptosis. Therefore, targeting PKC-$\iota$ as a potential therapeutic target in breast cancer could be a significant approach in breast cancer research. Full article

The aim of this study was to evaluate the effect of the iota, kappa and lambda carrageenan fractions on the physical properties and crystal structure of a fruit sorbet prepared from frozen mango fruits. During this study, physical properties such as density, cryoscopic temperature, osmotic pressure, overrun and melting time were analyzed. In order to assess the crystal structure and its changes, microscope images were taken of each sample after 1, 30 and 90 days of storage. The stabilizers showed no significant effect on the physical properties of the ice cream mixture; however, the sample with iota carrageenan stood out for having the highest overrun (58.7%) and the sample with kappa carrageenan took the longest to melt of all tested samples (almost 21 min). This study shows a significant effect of carrageenans in reducing the initial size of ice crystals as well as reducing recrystallization during storage. The stabilizing blend using ι-carrageenan provided the most effective cryoprotective properties, with an ice crystal diameter of 9 µm. Full article

This research aims to synthesize a novel hydrogel bio-composite based on natural clay, sodium alginate (Na-AL), and iota-carrageenan as adsorbents to remove phosphate ions from aqueous solutions. The adsorbents were characterized by a variety of techniques, such as Fourier-transform infrared (FTIR) spectroscopy, scanning electron microscopy coupled with energy dispersive X-rays (SEM-EDX), and the determination of point zero charge (PZC). This research investigated how the adsorption process is influenced by parameters such as adsorbent dose, contact time, solution pH, and temperature. In this study, we used four isotherms and four kinetic models to investigate phosphate ion removal on the prepared bio-composite. The results showed that the second-order kinetic (PSO) model is the best model for describing the adsorption process. The findings demonstrate that the R² values are highly significant in both the Langmuir and Freundlich models (very close to 1). This suggests that Langmuir and Freundlich models, with a diversity of adsorption sites, promote the adsorption of phosphate ions. The maximum adsorbed amounts of phosphate ions by the bio-composite used were 140.84 mg/g for H₂PO₄⁻ ions and 105.26 mg/g for HPO₄²⁻ ions from the batch system. The positive ∆H° confirms the endothermic and physical nature of adsorption, in agreement with experimental results. Negative ∆G° values indicate spontaneity, while the positive ∆S° reflects increased disorder at the solid–liquid interface during phosphate uptake. The main parameters, including adsorbent dosage (mg), contact time (min), and initial concentration (mg/L), were tuned using the Box–Behnken design of the response surface methodology (BBD-RSM) to achieve the optimum conditions. The reliability of the constructed models is demonstrated by their high correlation coefficients (R²). An R² value of 0.9714 suggests that the model explains 97.14% of the variability in adsorption efficiency (%), which reflects its strong predictive capability and reliability. Finally, the adsorption behavior of phosphate ions on the prepared bio-composite beads was analyzed using an artificial neural network (ANN) to predict the process efficiency. The ANN model accurately predicted the adsorption of phosphate ions onto the bio-composite, with a strong correlation (R² = 0.974) between the predicted and experimental results. Full article

The Internet of Things (IoT) demands scalable, secure, and feeless distributed ledger technologies (DLTs) to enable seamless machine-to-machine transactions. The IOTA DLT was developed to fulfill this vision through its feeless Directed Acyclic Graph (DAG) named the Tangle, whose announced upgrade to IOTA 2.0 promised feeless microtransactions and coordinator-free (Coordicide) decentralization via a Nakamoto Consensus mechanism and a Mana anti-spam system. However, its delayed decentralization and scalability limitations hindered ecosystem growth and practical IoT adoption, leading to a new ledger architecture named IOTA Rebased. This paper critically analyzes this architectural pivot and its implications for IoT applications, contrasting the abandoned IOTA 2.0 protocol—a leaderless, feeless DAG designed for the IoT—with the adoption of a Move Virtual Machine-based, object-oriented ledger secured by a Delegated Proof-of-Stake consensus via the Mysticeti protocol in IOTA Rebased. We evaluate IOTA Rebased trade-offs: enhanced programmability and speed versus compromised IoT suitability due to fees, and explore mitigation strategies such as sponsored transactions, lightweight clients, and hierarchical tiered transaction architecture to align IOTA Rebased with IoT environments where microtransactions are prevalent. A use case analysis is provided for the integration of IOTA Rebased in IoT scenarios. This study underscores the tension between technological innovation and decentralization, offering insights for balancing scalability with the unique demands of the IoT. Full article

Adsorption has been found to be highly effective for removing heavy metals from polluted industrial wastewater. Adsorbents of biological origin, such as negatively charged polysaccharides, e.g., alginate and carrageenan, have attracted a lot of attention recently. In this study, these three polysaccharides were used to adsorb different heavy metal ions from aqueous solutions. The results showed that the sorption yields of various lanthanides with the kappa and iota carrageenan were similar, though the sorption yields of the iota beads were higher. Also, the iota and the kappa beads had higher sorption yields for Ru³⁺ and Rh³⁺ than they did for the lanthanides. In general, the presence of light metal ions in the solution affected the sorption yields of the heavy metal ions, depending on the type and concentration of the light metal ions. All three polysaccharides were also capable of adsorbing mixtures of lanthanides and heavy metal ions. In binary solutions that contained both lanthanide ions (Ce³⁺ or Eu³⁺) and transition heavy metal ions (Ru³⁺ or Rh³⁺), differences in sorption yields were observed, with all polysaccharides exhibiting higher selectivity for Ru³⁺ and Rh³⁺. Finally, FTIR, SEM/EDS, and TGA analyses confirmed that all metal ions were adsorbed onto both types of carrageenan. Full article

In response to the Cybersecurity Law, organizations face numerous management and technical requirements. Detection techniques such as vulnerability scanning and penetration testing are employed to identify risks. Addressing these vulnerabilities demands substantial manpower, time, and financial resources. Security concerns also arise during digital file transmission and remediation efforts. This study proposes a security detection platform with step-by-step implementation guidelines, enabling resource-limited units to replicate the setup and address security gaps. It compares detection results between open-source and commercial tools, highlighting key differences and offering remediation strategies. Numerous digital files (e.g., test reports) are generated during testing. To ensure secure storage and sharing, the system integrates IOTA’s distributed ledger and IPFS, generating HASH values and uploading files on-chain to preserve integrity and authenticity. The objective is to deliver a scalable, cost-effective security detection framework that enhances system resilience while minimizing resource consumption. Full article

Background: Our goal was to assess the diagnostic performance of the IOTA 3-step strategy for discriminating benign from malignant adnexal masses. Methods: Systematic review and meta-analysis design. A systematic search across three databases (Medline [PubMed], SCOPUS, and Web of Science) was conducted to identify primary studies reporting on the use of the IOTA three-step strategy from January 2012 to July 2024. Prospective cohort studies utilizing the three-step strategy, with histologic diagnosis or conservative management confirming spontaneous resolution or persistence in cases of benign-appearing masses for at least one year of follow-up, were used as the reference standard. Studies unrelated to the topic, those not addressing the IOTA three-step strategy, studies focusing on other prediction models, letters to the editor, commentaries, narrative reviews, consensus documents, and studies lacking data for constructing a 2 × 2 table were excluded. Quantitative synthesis was done, calculating the pooled sensitivity, specificity, and positive and negative likelihood ratios. Qualitative synthesis was done using QUADAS-2. Results: A total of 448 citations were initially identified, with 7 studies meeting inclusion criteria, comprising 5722 patients. The mean prevalence of ovarian malignancy was 28%. The quality of the studies was considered good. IOTA 3-step strategy showed a pooled sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio of the three-step strategy for adnexal mass classification were 94% (95% CI = 91–95%), 94% (95% CI = 91–97%), 17.0 (95% CI = 10–28.8), and 0.07 (95% CI = 0.05–0.1), respectively. Heterogeneity for sensitivity was moderate, and for specificity it was high. Conclusions: We conclude that the three-step strategy has good diagnostic performance, reducing the need for expert examiner evaluation. Full article

Objective: The aim of the study was evaluate the diagnostic performance of the ADNEX model performed by non-expert examiners in differentiating between benign and malignant adnexal masses and to compare its performance with the IOTA Simple Rules. Methods: This diagnostic study was a secondary analysis based on a prospectively collected database of gynecological patients scheduled for elective surgery for adnexal masses. Preoperative ultrasound was performed within 24 h before surgery by second- and third-year gynecology residents who had completed a 20 h training course on adnexal mass ultrasound. Ultrasound data, CA-125 levels, and demographic information were reviewed and validated. Malignancy risk assessment using the IOTA Simple Rules was based on M-rules and B-rules, while risk calculations with the ADNEX model were conducted using an online application. Results: The area under the curve (AUC) for the ADNEX model was 0.958 (95% CI: 0.936–0.980), which was significantly higher than that of the IOTA Simple Rules at 0.886 (95% CI: 0.840–0.931; p < 0.001). Using a 10% cutoff, the ADNEX model demonstrated a sensitivity of 93.9% and a specificity of 81.0%, while the IOTA Simple Rules had a sensitivity of 84.0% and a specificity of 93.1%. The AUCs of the ADNEX model did not significantly differ when center status was considered (oncologic vs. non-oncologic centers). Conclusions: The ADNEX model, using a 10% cutoff, exhibited superior efficacy in differentiating benign from malignant adnexal masses compared with the IOTA Simple Rules. Furthermore, the sensitivity and specificity of the ADNEX model did not significantly differ between oncologic and non-oncologic centers. Both methods demonstrated high diagnostic accuracy, even when performed by non-expert examiners, suggesting that their diagnostic performance is reproducible and applicable in general clinical practice by general practitioners and gynecologists. Full article

Ovarian cancer is the most lethal gynecological cancer, with a 5-year survival rate of approximately 50%. Mutation in the p53 gene and overexpression of the atypical protein kinase C iota (PKC-ι) are two phenomena widely manifested in ovarian cancer. This study investigated the role of PKC-ι-specific inhibitor ICA-1S and proteasome inhibitor MG-132 in ovarian cancer cell lines. To discern the result, cell proliferation assays, cytotoxicity assays, Western blotting, immunofluorescence, flow cytometry, small interfering RNA, and co-immunoprecipitation techniques were applied. ICA-1S and MG-132 were found to inhibit the proliferation of ovarian cancer cell lines significantly. ICA-1S reduced the level of oncogenic PKC-ι as expected. In addition, ICA-1S and MG-132 both were able to decrease the level of mutated p53 in the ES-2 cell line through separate pathways. On the contrary, MG-132 increased the level of wild-type p53 in the HEY-T30 cell line by inhibiting proteasomal degradation. MG-132 also induced apoptosis and autophagy in the ovarian cancer cell lines. We concluded that ICA-1S alone or in combination with MG-132 could be a potential treatment for mutated p53-containing and PKC-ι-overexpressing ovarian cancers. Full article

The fairness problem in the IOTA (Internet of Things Application) Tangle network has significant implications for transaction efficiency, scalability, and security, particularly concerning orphan transactions and lazy tips. Traditional tip selection algorithms (TSAs) struggle to ensure fair tip selection, leading to inefficient transaction confirmations and network congestion. This research proposes a novel partially observable Markov decision process (POMDP)-based TSA, which dynamically prioritizes tips with lower confirmation likelihood, reducing orphan transactions and enhancing network throughput. By leveraging probabilistic decision making and the Monte Carlo tree search, the proposed TSA efficiently selects tips based on long-term impact rather than immediate transaction weight. The algorithm is rigorously evaluated against seven existing TSAs, including Random Walk, Unweighted TSA, Weighted TSA, Hybrid TSA-1, Hybrid TSA-2, E-IOTA, and G-IOTA, under various network conditions. The experimental results demonstrate that the POMDP-based TSA achieves a confirmation rate of 89–94%, reduces the orphan tip rate to 1–5%, and completely eliminates lazy tips (0%). Additionally, the proposed method ensures stable scalability and high security resilience, making it a robust and efficient solution for decentralized ledger networks. These findings highlight the potential of reinforcement learning-driven TSAs to enhance fairness, efficiency, and robustness in DAG-based blockchain systems. This work paves the way for future research into adaptive and scalable consensus mechanisms for the IOTA Tangle. Full article

Sustainable agriculture faces major issues with resource efficiency, nutrient distribution, and plant health. Traditional soil-based and soilless farming systems encounter issues including excessive water use, insufficient nutrient uptake, nitrogen deficiency, and restricted plant development. According to the previous literature, aeroponic systems accelerate plant growth rates, improve root oxygenation, and significantly enhance water use efficiency, particularly when paired with both low- and high-pressure misting systems. However, despite these advantages, they also present certain challenges. A major drawback is the inefficiency of nitrogen fixation, resulting in insufficient nutrient availability and heightened plant stress from uncontrolled misting, which ultimately reduces yield. Many studies have investigated plasma uses in both soil-based and soilless plant cultures; nevertheless, however, its function in aeroponics remains unexplored. Therefore, the present work aims to thoroughly investigate and review the integration of plasma-activated water (PAW) and plasma-activated mist (PAM) in aeroponics systems to solve important problems. A review of the current literature discloses that PAW and PAM expand nitrogen fixation, promote nutrient efficiency, and modulate microbial populations, resulting in elevated crop yields and enhanced plant health, akin to soil-based and other soilless systems. Reactive oxygen and nitrogen species (RONS) produced by plasma treatments improve nutrient bioavailability, root development, and microbial equilibrium, alleviating critical challenges in aeroponics, especially within fine-mist settings. This review further examines artificial intelligence (AI) and the Internet of Things (IoT) in aeroponics. Models driven by AI enable the accurate regulation of fertilizer concentrations, misting cycles, temperature, and humidity, as well as real-time monitoring and predictive analytics. IoT-enabled smart farming systems employ sensors for continuous nutrient monitoring and gas detection (e.g., NO₂, O₃, NH₃), providing automated modifications to enhance aeroponic efficiency. Based on a brief review of the current literature, this study concludes that the future integration of plasma technology with AI and IoT may address the limitations of aeroponics. The integration of plasma technology with intelligent misting and data-driven control systems can enhance aeroponic systems for sustainable and efficient agricultural production. This research supports the existing body of research that advocates for plasma-based innovations and intelligent agricultural solutions in precision farming. Full article