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Probing the role of Coniferin and Tetrahydrocurcumin from Traditional Chinese medicine against PSAT1 in early-stage ovarian cancer: An in silico study

Ovarian cancer, a formidable gynaecological malignancy, poses a significant global health challenge, and it is characterised by late-stage diagnosis and a high mortality rate. Even in its early stages, when treatment choices are scarce, ovarian cancer is still a complicated cancer to treat. In this work, we used computational approaches to find putative Traditional Chinese Medicine (TCM) inhibitors that target Phosphoserine Aminotransferase 1 (PSAT1), a crucial enzyme linked to the development of early-stage ovarian cancer. Using a methodical screening approach, we chose a panel of TCM compounds and prepared them, expected to interact with PSAT1. We next evaluated the binding affinities using molecular docking, which helped to identify Coniferin and Tetrahydrocurcumin compounds as potent inhibitors with the docking score of -8.8 kcal/mol and -8.9 Kcal/mol, respectively, and compared to the native ligand. The inhibitory effects of Coniferin and Tetrahydrocurcumin compounds were evaluated with the Pharmacokinetic studies and compared with the standard values, which resulted in an utter performance against each descriptor of the QikProp and performed the Molecular Interaction Fingerprints that resulted in the most interaction residues with counts were 4GLY, 4ASN, 4HIE, 4SER, 4THR, 3ARG and many more. Further, 100ns MD Simulation was performed in neutralised water, resulting in entirely stable deviations, fluctuations, and many intermolecular interactions, and the MM\GBSA studies on all 1000 trajectories have supported the complex’s stability. The computational studies have shown a completely stable performance that supports Coniferin, and Tetrahydrocurcumin can be a potent inhibitor of PSAT1. However, further experimental works are needed to confirm this study.

The effect of pomegranate peel extract and apple cider vinegar as an irrigants in disinfecting the necrotic primary root canal

Behavioral intention to use electronic cigarettes in the Philippines: The role of social influence, knowledge, price and health impact

Electronic cigarettes or e-cigarettes have gained significant popularity as an alternative to traditional cigarettes, yet limited research has examined the factors influencing their adoption, particularly in developing nations like the Philippines, where usage is rising. This study investigates the behavioral drivers of e-cigarette use, with a particular focus on the role of knowledge, alongside social influence, perceived price impact, perceived health impact, and perceived usefulness. Using purposive sampling, 310 valid responses were collected from current e-cigarette users, traditional cigarette users, or individuals with prior experience with these products. A structured questionnaire with 21 indicators was administered, and data were analyzed using Partial Least Square-Structural Equation Modeling (PLS-SEM). The findings reveal that knowledge is the strongest predictor of behavioral intention, highlighting the critical role of informed awareness about the risks and impacts of e-cigarettes in shaping user decisions. Social influence, perceived price impact, and perceived health impact also significantly influence behavioral intention, demonstrating the interconnectedness of cognitive, social, and economic factors. Interestingly, perceived usefulness did not have a significant effect, challenging assumptions about the importance of functional benefits in driving e-cigarette adoption. These results underscore the importance of education and awareness campaigns in addressing misconceptions about e-cigarettes. Policymakers, regulators, and health professionals should prioritize knowledge-driven interventions to empower individuals to make informed decisions and mitigate e-cigarette use, particularly among younger and economically vulnerable populations.

Purpurin suppresses Salmonella invasion of host cells by reducing the secretion of T3SS-1 effector proteins

Efficacy of probiotic supplements in the treatment of sarcopenia: A systematic review and meta-analysis

Although some studies have confirmed the efficacy of probiotics in the treatment of sarcopenia, the intervention of sarcopenia is a comprehensive consideration of many factors, and the efficacy of probiotics is still controversial. Therefore, this study systematically evaluated the efficacy of probiotics in the intervention of sarcopenia via high—quality meta—analysis, providing a basis for the clinical diagnosis and treatment of sarcopenia. Randomized controlled trials related to probiotics in the treatment of sarcopenia were searched in PubMed, Embase, the Cochrane Library and Web of Science. The search time was from inception to 2024-07-17. Two investigators independently screened the articles, extracted data, and assessed the risk of bias of the included studies. Meta-analysis was performed using RevMan 5.4 and Stata 14.0 software. A total of 22 eligible studies were included. The results showed that there was no statistically significant difference between probiotics and placebo in improving muscle mass and Lean body mass in sarcopenia patients; MD: 0.66, 95%CI: - 0.01–1.33; Z = 1.93, P > 0.05; MD: - 0.13, 95%CI: -0.81–0.55; Z = 0.38, P = 0.71 > 0.05. However, probiotics were found to significantly improve overall muscle strength compared with the placebo group. MD: 2.99, 95%CI: 2.14–3.85; Z = 6.86, P < 0.001. Probiotics can significantly improve global muscle strength in patients with sarcopenia. It is suggested that probiotics have certain clinical value in the clinical treatment of sarcopenia, but the results may be limited by the number and quality of included studies. The above conclusions need to be verified by more high-quality studies.

A hybrid swarm intelligent optimization algorithm for antenna design problems

Saturated Transposon Analysis in Yeast as a one-step method to quantify the fitness effects of gene disruptions on a genome-wide scale

Transposon insertion site sequencing (TIS) is a powerful tool that has significantly advanced our knowledge of functional genomics. For example, TIS has been used to identify essential genes of Saccharomyces cerevisiae , screen for antibiotic resistance genes in Klebsiella pneumoniae and determine the set of genes required for virulence of Mycobacterium tuberculosis . While providing valuable insights, these applications of TIS focus on (conditional) gene essentiality and neglect possibly interesting but subtle differences in the importance of genes for fitness. Notably, it has been demonstrated that data obtained from TIS experiments can be used for fitness quantification and the construction of genetic interaction maps, but this potential is only sporadically exploited. Here, we present a method to quantify the fitness of gene disruption mutants using data obtained from a TIS screen developed for the yeast Saccharomyces cerevisiae called SATAY. We show that the mean read count per transposon insertion site provides a metric for fitness that is robust across biological and technical replicate experiments. Importantly, the ability to resolve differences between gene disruption mutants with low fitness depends crucially on the inclusion of insertion sites that are not observed in the sequencing data to estimate the mean. While our method provides reproducible results between replicate SATAY datasets, the obtained fitness distribution differs substantially from those obtained using other techniques. It is currently unclear whether these inconsistencies are due to biological or technical differences between the methods. We end with suggestions for modifications of the SATAY procedure that could improve the resolution of the fitness estimates. Our analysis indicates that increasing the sequencing depth does very little to reduce the uncertainty in the estimates, while replacing the PCR amplification with methods that avoid or reduce the number of amplification cycles will likely be most effective in reducing noise.

Size distribution of extracellular vesicles in pretreatment ascites and plasma is correlated with primary treatment outcome in advanced high-grade serous carcinoma

Abstract To improve the treatment outcome and survival of patients with advanced high-grade serous carcinoma (HGSC), prognostic biomarkers for assessing the feasibility of complete (R0) or optimal (R1) primary cytoreductive surgery are needed. Additionally, biomarkers for predicting the response to neoadjuvant chemotherapy (NACT) in patients with primary inoperable disease could help stratify patients for tailored therapy and improve personalised approach. Such promising biomarkers are extracellular vesicles (EVs), which are present in ascites and plasma and are available for minimally invasive liquid biopsy. EV concentration and EV molecular profile have been at the forefront of research in the field of biomarkers for many years now, but recent studies have highlighted the importance of EV size distribution. Our study aimed to evaluate the potential of the EV concentration and size distribution in pretreatment ascites and plasma samples from patients with advanced HGSC as prognostic biomarkers. In our prospective cohort study, nanoparticle tracking analysis (NTA) was used to determine EV characteristics in paired pretreatment ascites and plasma samples from 37 patients with advanced HGSC. Patients were treated with primary cytoreductive surgery followed by adjuvant chemotherapy (ACT) (N = 15) or NACT followed by interval debulking surgery (IDS) when optimal cytoreduction was not feasible (N = 22). The correlations of the EV concentration and size distribution in ascites and plasma with treatment outcome, progression-free survival (PFS) and overall survival (OS) were analysed. We found a significant correlation between the EV size distribution in ascites and residual disease after primary cytoreductive surgery. Larger EVs in ascites correlated with worse resection success after primary cytoreductive surgery. A significant correlation between the D10 value of EVs in plasma and the chemotherapy response score (CRS) after NACT was observed. A smaller D10 value of plasma EVs was correlated with a better chemotherapy response. Receiver operating characteristic (ROC) curve analysis revealed excellent performance for D10 value in ascites for the prediction of suboptimal (R2) resection at primary debulking surgery and excellent performance for D10 value in plasma for the prediction of complete or near-complete chemotherapy response score (CRS 3) at interval debulking surgery. There was a significant correlation between the mean diameter, D90 value and proportion of medium/large (> 200 nm) EVs in ascites and those in plasma. On the other hand, there was no correlation of the EV concentration or D10 and D50 values between the ascites fluid and plasma samples. Our results indicate that the EV size distribution in ascites has the potential to predict resection success after primary cytoreductive surgery and that the EV size distribution of the smallest EVs in plasma might help predict the chemotherapy response of patients treated with NACT. In the future, molecular analyses of size-dependent EV cargo could provide more insight into their biological functions and potential as predictive biomarkers.

Major factors influencing student engagement in Ethiopian higher education institutions: Evidence from one institution

This study investigates factors influencing student engagement in Ethiopian higher education, focusing on self-efficacy, teacher support, and technology support at Mattu University. A cross-sectional survey design was employed; involving 620 undergraduate students selected using stratified sampling. Data were collected through structured questionnaires and analyzed using Structural Equation Modeling (SEM) to assess the impact of these variables on student engagement. The findings reveal that teacher support, technology support, and self-efficacy collectively explain 69% of the variance in student engagement, with teacher support identified as the strongest predictor. The results highlight the importance of supportive teacher-student relationships, accessible digital resources, and students’ confidence in their academic abilities for fostering engagement. The study underscores the need for improved teacher preparation, diverse instructional strategies, and enhanced access to digital resources. These findings offer practical recommendations for policymakers and educational institutions in Ethiopia to create more engaging learning environments, ultimately enhancing academic success and retention in higher education.

Publisher Correction: Proposal of a quantum version of active particles via a nonunitary quantum walk

Development of a novel evaluation method by mathematical anatomy for foot bone alignment displacement using x-ray radiographs

The major methods of evaluating the foot arch in clinical practice in patients with foot deformities are medial longitudinal arch measurement using body surface somatometry and radiographic morphometry. Although these methods are widely used, they are considered problematic in terms of differences in scores between the methods. In this study, we developed a new geometric shape analysis method for the bony arrangement of the foot using the two-dimensional fast Fourier transform (2D-FFT), which incorporates mathematical anatomy using x-ray radiographs. Lateral radiographs of the foot bones were obtained using ImageJ2 provided by the National Institutes of Health. The 2D-FFT images show the characteristic directional power spectrum extending from low to high frequencies in the first and third quadrants on the normal, low and high arched foot respectively. The current method of reflecting the bone arrangement status of the metatarsal and tarsal bones may have the potential to establish a new radiographic evaluation method for assessing abnormal foot–bone alignment. As a result, the foot bone 2D-FFT method can be useful in assessing the medial longitudinal arch and predicting the prognosis of these patients.

Thrust-efficiency limits of a swimming tail with variable chordwise flexural rigidity

Abstract Emulating oscillations performed by natural swimmers can provide different functionalities than those of propeller-based underwater robots. Yet, to successfully accomplish specific missions under limited power, there is a need to design efficient bio-inspired robots. Adding an appropriate level of flexibility to flapping caudal fins (tails) of robots emulating the thunniform swimming mode has been shown to enhance the thrust generation over a finite range of the flapping frequency. Still, in many cases, adding flexibility to increase thrust generation may require increased input power, which may cause a significant reduction in the efficiency. These observations lead to the concept of enhanced performance by varying the stiffness of the tail as in the case of natural swimmers. This study is concerned with assessing the impact of varying the chordwise stiffness on the tail deflection and flow dynamics, including contributions of added mass and circulation forces to thrust generation and their impact on efficiency. The simulation data are used to identify specific flow dynamics and tail deflections associated with the enhanced thrust generation and/or efficiency, and to define a performance limit expressed as the maximum efficiency as a function of the thrust coefficient.

HPRNA: Predicting synergistic drug combinations for angina pectoris based on human pathway relationship network algorithm

Over the years, synergistic drug combinations therapies have attracted widespread attention due to its advantages of overcoming drug resistance, increasing treatment efficacy and decreasing toxicity. Compared to lengthy medical drugs experimental screening, mathematical models and algorithms show great potential in synergistic drug combinations prediction. In this paper, we introduce a novel mathematical algorithm, the Human Pathway Relationship Network Algorithm (HPRNA), which is designed to predict synergistic drug combinations for angina pectoris. We first reconstruct a novel angina pectoris drug dataset, which include drug name, drug metabolism, chemical formula, targets and pathways, then construct a comprehensive human pathway network based on the genetic similarity of the pathways which contain information about the targets. Finally, we introduce a novel indicator to calculate drug pair scores which measure the likelihood of forming synergistic drug combination. Experimental results on angina pectoris drug datasets convincingly demonstrate that the HPRNA makes efficient use of target and pathway information and is superior to previous algorithms.

Modulatory effects of cinnamomi cortex and its components epicatechin and linalool on skin circadian rhythms

Correction: In vitro and in vivo burn healing study of standardized propolis: Unveiling its antibacterial, antioxidant and anti-inflammatory actions in relation to its phytochemical profiling

Influence of seed moisture content and storage period on germination and biochemical indices: Lallemantia iberica and Lallemantia royleana

The Ronne Ice Shelf survived the last interglacial

Abstract The fate of the West Antarctic Ice Sheet (WAIS)1 is the largest cause of uncertainty in long-term sea-level projections. In the last interglacial (LIG) around 125,000 years ago, data suggest that sea level was several metres higher than today2–4, and required a significant contribution from Antarctic ice loss, with WAIS usually implicated. Antarctica and the Southern Ocean were warmer than today5–8, by amounts comparable to those expected by 2100 under moderate to high future warming scenarios. However, direct evidence about the size of WAIS in the LIG is sparse. Here we use sea salt data from an ice core from Skytrain Ice Rise, adjacent to WAIS, to show that, during most of the LIG, the Ronne Ice Shelf was still in place, and close to its current extent. Water isotope data are consistent with a retreat of WAIS9, but seem inconsistent with more dramatic model realizations10 in which both WAIS and the large Antarctic ice shelves were lost. This new constraint calls for a reappraisal of other elements of the LIG sea-level budget. It also weakens the observational basis that motivated model simulations projecting the highest end of projections for future rates of sea-level rise to 2300 and beyond.

Machine learning-based analyzing earthquake-induced slope displacement

Accurately evaluating earthquake-induced slope displacement is a key factor for designing slopes that can effectively respond to seismic activity. This study evaluates the capabilities of various machine learning models, including artificial neural network (ANN), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost) in analyzing earthquake-induced slope displacement. A dataset of 45 samples was used, with 70% allocated for training and 30% for testing. To improve model robustness, repeated 5-fold cross-validation was applied. Among the models, XGBoost demonstrated superior predictive accuracy, with an R2 value of 0.99 on both the train and test data, outperforming ANN, SVM, and RF, which had R2 values of 0.63 and 0.80, 0.87 and 0.86, 0.94 and 0.87 on the train and test data, respectively. Sensitivity analysis identified maximum horizontal acceleration (kmax = 0.714) as the most influential factor in slope displacement. The findings suggest that the XGBoost model developed in this study is highly effective in predicting earthquake-induced slope displacement, offering valuable insights for early warning systems and slope stability management.

Author Correction: Combination gemcitabine and PD-L1xCD3 bispecific T cell engager (BiTE) enhances T lymphocyte cytotoxicity against cholangiocarcinoma cells

A novel inverter control strategy for maximum hosting capacity photovoltaic systems in distribution networks using power factor

The conventional inverter is undergoing a transformation into a smart inverter, driven by the expanding penetration of Photovoltaic (PV) power production in Low Voltage (LV) systems. The adoption of smart inverters is on the rise. Power companies are keen on integrating them into their networks to acquire essential frequency and voltage support as required. These inverters actively exchange actual and reactive power in connection with the grid, altering the system’s operational state. This dynamic behavior within the distribution level of power networks might give rise to unprecedented issues. This paper investigates the influence of diverse connection prerequisites that explore the methods for determining the Hosting Capacity (HC) of PV solar systems and their applicability within the low-voltage utility grid. Smart inverters provided with different Volt-VAr and Power Factor (PF) regulation capabilities are analyzed using MATLAB SIMULINK. The outcomes reveal a notable augmentation in the network’s HC. This progress improves the grid’s attributes, and the incorporation of smart inverter functionalities stands to considerably facilitate incorporating PV solar installations into electrical networks.