Browse Articles

Gut–brain axis and neuropsychiatric health: recent advances

Alu-Sc-mediated exonization generated a mitochondrial LKB1 gene variant found only in higher order primates

Sensitivity of coronary hemodynamics to vascular structure variations in health and disease

Nanosilicon application changes the morphological attributes and essential oil compositions of hemp (Cannabis sativa L.) under water deficit stress

A bioinformatic approach to characterize the vitellogenin receptor and the low density lipoprotein receptor superfamily in the newt Cynops orientalis

Abstract The Low Density Lipoprotein receptors (LDLRs) gene family includes 15 receptors: very low-density lipoprotein receptor (VLDLR), LDLR, Sorting-related receptor with A-type repeats (SORLA), and 12 LDL receptor-related proteins (LRPs): LRP1, LRP1B, LRP2, LRP3, LRP4, LRP5, LRP6, LRP8, LRP10, LRP11, LRP12, LRP13. Most of these are involved in the transduction of key signals during embryonic development and in the regulation of cholesterol homeostasis. In oviparous animals, the VLDL receptor is also known as VTGR since it facilitates the uptake of vitellogenin in ovary. In tetrapods, information concerning genes encoding these proteins is limited to a few taxa. Here, we report the characterization of VTGR in the amphibian Cynops orientalis. The secondary structure analyses and the expression profiles obtained from hepatic and gonadal tissues of C. orientalis supported the role of VTGR as vitellogenin oocyte membrane receptor in this species. Moreover, to get a holistic view of the evolutionary history of this gene superfamily, we extended our investigation to all 15 genes belonging to the LDLR superfamily analyzing through a phylogenetic analysis a total of 161 sequences belonging to 11 genera of vertebrates. The position of LRP8 in the tree and its expression findings in C. orientalis ovary allowed us to suggest that other proteins of the LDLR superfamily could act as receptors during vitellogenesis.

Optimizing photovoltaic power plant forecasting with dynamic neural network structure refinement

Sustainability metrics targeted optimization and electric discharge process modelling by neural networks

Effect of external infusion connection devices replacement frequency on catheter related bloodstream infection

Metabolomic analysis of murine tissues infected with Brucella melitensis

Brucella is a gram negative, facultative intracellular bacterial pathogen that constitutes a substantial threat to human and animal health. Brucella can replicate in a variety of tissues and can induce immune responses that alter host metabolite availability. Here, mice were infected with B. melitensis and murine spleens, livers, and female reproductive tracts were analyzed by GC-MS to determine tissue-specific metabolic changes at one-, two- and four- weeks post infection. The most remarkable changes were observed at two-weeks post-infection when relative to uninfected tissues, 42 of 329 detected metabolites in reproductive tracts were significantly altered by Brucella infection, while in spleens and livers, 68/205 and 139/330 metabolites were significantly changed, respectively. Several of the altered metabolites in host tissues were linked to the GABA shunt and glutaminolysis. Treatment of macrophages with GABA did not alter control of B. melitensis infection, and deletion of the putative GABA transporter BMEI0265 did not alter B. melitensis virulence. While glutaminolysis inhibition did not affect control of B. melitensis in macrophages, glutaminolysis was required for macrophage IL-1β production in response to B. melitensis. In summary, these results indicate that Brucella infection alters host tissue metabolism and that these changes could have effects on inflammation and the outcome of infection.

Optimizing key parameters for grinding energy efficiency and modeling of particle size distribution in a stirred ball mill

Abstract Fine grinding using a stirred ball mill can enhance ore liberation but incurs high energy consumption, which can be minimized by optimizing operating conditions. This study explores the impact of key operational parameters—grinding time, stirrer tip speed, solid concentration, and feed size—on grinding efficiency, evaluated using specific energy inputs, in stirred milling of Egyptian copper ore. The particle size distribution (PSD) of ground products was simulated using the Gates–Gaudin-Schuhmann model (GGS) and the Rosin-Rammler-Benne (RRB) function. Taking minimum energy consumption into account, the finest particles (100% ~1 μm) were achieved at the maximum stirrer speed of 500 rpm and a moderate solid concentration of 33.3% after 17 h of grinding, consuming approximately 1225 kWh/t. Experimental data demonstrated a linear correlation between the natural logarithm of the cumulative retained fraction and particle size (µm). The proposed model accurately describes PSDs across different solid concentrations and grinding durations.

Site-selective photo-crosslinking for the characterisation of transient ubiquitin-like protein-protein interactions

Non-covalent protein-protein interactions are one of the most fundamental building blocks in cellular signalling pathways. Despite this, they have been historically hard to identify using conventional methods due to their often weak and transient nature. Using genetic code expansion and incorporation of commercially available unnatural amino acids, we have developed a highly accessible method whereby interactions between biotinylated ubiquitin-like protein (UBL) probes and their binding partners can be stabilised using ultraviolet (UV) light-induced crosslinks. The stabilised protein complexes can be purified using affinity purification and identified by mass spectrometry. The resultant covalent bonds can withstand even the harshest washing conditions, allowing for the removal of indirect binders whilst retaining and capturing weak and transient interactors that are commonly lost during wash steps. This technique is widely applicable and highly effective for identifying site-selective non-covalent interactors. Members of our team have previously demonstrated the benefit of this method using the small ubiquitin-like modifier (SUMO). Here, we provide further proof-of-principle validation of the method and highlight its generality by applying an optimised workflow to a lesser studied UBL, interferon stimulated gene 15 (ISG15). We show that this method is able to capture known ISG15 interactors from a complex protein mixture in a site-selective manner, only capturing proteins that specifically interact with the region of ISG15 where the unnatural amino acid was incorporated. This exquisite degree of sensitivity and specificity greatly improves upon previous screens aimed at identifying downstream non-covalent binders, or readers, of ISG15. Taken together, the approach opens the possibility of characterising previously undetected protein-protein interactions, with the potential of elucidating molecular mechanisms behind the most complex and poorly understood processes in the cell.

Comparison of aesthetic perception and acceptability of silver diamine fluoride staining between Spanish and Italian parents

Distributed Denial of Services (DDoS) attack detection in SDN using Optimizer-equipped CNN-MLP

Software-Defined Networks (SDN) provides more control and network operation over a network infrastructure as an emerging and revolutionary paradigm in networking. Operating the many network applications and preserving the network services and functions, the SDN controller is regarded as the operating system of the SDN-based network architecture. The SDN has several security problems because of its intricate design, even with all its amazing features. Denial-of-service (DoS) attacks continuously impact users and Internet service providers (ISPs). Because of its centralized design, distributed denial of service (DDoS) attacks on SDN are frequent and may have a widespread effect on the network, particularly at the control layer. We propose to implement both MLP (Multilayer Perceptron) and CNN (Convolutional Neural Networks) based on conventional methods to detect the Denial of Services (DDoS) attack. These models have got a complex optimizer installed on them to decrease the false positive or DDoS case detection efficiency. We use the SHAP feature selection technique to improve the detection procedure. By assisting in the identification of which features are most essential to spot the incidents, the approach aids in the process of enhancing precision and flammability. Fine-tuning the hyperparameters with the help of Bayesian optimization to obtain the best model performance is another important thing that we do in our model. Two datasets, InSDN and CICDDoS-2019, are utilized to assess the effectiveness of the proposed method, 99.95% for the true positive (TP) of the CICDDoS-2019 dataset and 99.98% for the InSDN dataset, the results show that the model is highly accurate.

Thermophysical analysis of time-dependent magnetized Casson hybrid nanofluid flow (Cu + GO/Kerosene Oil) using Darcy-Forchheimer and thermal radiative models for industrial cooling applications

Abstract This paper presents an in-depth analytical investigation into the time-dependent flow of a Casson hybrid nanofluid over a radially stretching sheet. The study introduces the effects of magnetic fields and thermal radiation, along with velocity and thermal slip, to model real-world systems for enhancing heat transfer in critical industrial applications. The hybrid nanofluid consists of three nanoparticles—Copper and Graphene Oxide—suspended in Kerosene Oil, selected for their stable and superior thermal properties. The theory of Darcy-Forchheimer, along with the suction and injection effect, is applied to refine the flow behaviour and enhance heat transfer efficiency. The governing nonlinear equations are solved using the Homotopy Analysis Method to provide a robust framework for solution accuracy. The graphical and tabulated results demonstrated that hybrid nanofluid outperforms mono and Casson hybrid nanofluids. The result shows that, at a nanoparticle volume concentration of 0.03, the Casson hybrid nanofluid showed a remarkable 19.99% increase in heat transfer, compared to 14.80% for simple nanofluid. The magnetic parameter and thermal radiation parameter further amplify thermal conductivity. This research provided a critical insight into optimizing thermal management systems for advanced engineering applications, positioning hybrid nanofluid as highly effective solutions for next-generation cooling technologies.

The customer satisfaction model in the mobile telecommunications sector after Covid-19 pandemic

The subject of this paper is modeling customer satisfaction in the mobile telecommunication industry following the Covid-19 pandemic. Based on standard customer satisfaction models, a specialized model tailored for the mobile telecommunication industry has been developed to account for its unique characteristics, including market concentration. This model was created within the Slovakian context using the Structural Equation Modelling method. The respondents were customers of all mobile operators in this market. The model revealed a positive relationship between image and perceived service quality and a negative relationship between customer expectations and perceived service value. However, it was not possible to demonstrate a relationship between image and customer loyalty or between customer expectations and customer satisfaction. Therefore, it seems that the factors influencing customer satisfaction in the telecommunications sector of an emerging EU economy differ from those in other sectors and economies in the post-Covid-19 context.

Spatiotemporal dynamics and driving factors of energy-related carbon emissions in the Yangtze River Delta region based on nighttime light data

Impact of nutritional status and pulmonary function on short- and long-term overall survival in hemodialysis patients

Background End-stage renal disease (ESRD) patients frequently experience protein-energy wasting (PEW), which increases their morbidity and mortality rates. Objective This study explores the effects of nutritional status and pulmonary function on the short- and long-term mortality of ESRD patients undergoing hemodialysis. Materials and methods 67 consecutive ESRD patients on maintenance hemodialysis were included in the study. The primary outcomes were all-cause one-year and five-year mortality. Data on demographic characteristics, comorbidities, and laboratory findings were collected. Pulmonary function tests were conducted along with body composition measurements using bioelectrical impedance analysis (BIA). Malnutrition was assessed using the Prognostic Nutritional Index (PNI). Results The median age of the patients was 60.9 ±  12.4 years, with 58.3% being male. Pulmonary function parameters (FEV1 and FVC) were significantly associated with short-term mortality. The PNI was a significant predictor of both short-term and long-term mortality. A PNI score ≤  39.01 was associated with increased short-term mortality (HR: 0.65, 95% CI: 0.48–0.88, p =  0.006), while a score ≤  40 was linked to increased long-term mortality (HR: 0.80, 95% CI: 0.67–0.95, p =  0.015). Additionally, older age (HR: 1.06, 95% CI: 1.01–1.12, p =  0.021) and higher glomerular filtration rate (GFR) (HR: 1.23, 95% CI: 1.02–1.42, p =  0.024) were related to increased long-term mortality risk. Conclusion The study demonstrates that PNI, age, and pulmonary function are critical factors influencing the survival of hemodialysis patients. These findings underscore the importance of comprehensive nutritional and pulmonary assessment to improve clinical outcomes in this population.

Enhancing trace DNA profile recovery in forensic casework using the amplicon RX post-PCR clean-up kit

Distinguishing benign and malignant myxoid soft tissue tumors: Performance of radiomics vs. radiologists

Introduction Benign and malignant myxoid soft tissue tumors have shared clinical, imaging, and histologic features that can make diagnosis challenging. The purpose of this study is comparison of the diagnostic performance of a radiomic based machine learning (ML) model to musculoskeletal radiologists. Methods Manual segmentation of 90 myxoid soft tissue tumors (45 myxomas and 45 myxofibrosarcomas) was performed on axial T1, and T2FS or STIR magnetic resonance imaging sequences. Eighty-seven radiomic features from each modality were extracted. Five ML models were trained to classify tumors as benign or malignant in 40 tumors and then tested with an additional 50 tumors using cross validation. The accuracy of the best ML model based on area under the receiver operating characteristic curve (AUC) was compared to the consensus diagnosis of three musculoskeletal radiologists. Correlation between radiologist confidence (equivocal, probably, consistent with) and accuracy was tested. Results The best ML classifier was a logistic regression model (AUC 0.792). Using T1 + T2/STIR images, the ML model classified 78% (39/50) of tumors correctly at a similar rate compared to 74% (37/50) by radiologists. When radiologists disagreed, the consensus diagnosis classified 50% of tumors (7/14) correctly compared to 86% (12/14) by the ML model, though this did not reach statistical significance. Radiologists had a cumulative accuracy of 91% (30/33) when they rated their confidence ‘consistent with’ compared to 61% (31/51) when they rated their confidence ‘equivocal/probably’ (P = 0.006). For cases when radiologists rated their confidence ‘equivocal/probably’, the ML model had 76% accuracy (39/51). Conclusions A radiomic based ML model predicted benign or malignant diagnosis in myxoid soft tissue tumors similarly to the consensus diagnosis by three musculoskeletal radiologists. Radiologist confidence in the diagnosis strongly correlated with their diagnostic accuracy. Though radiomics and radiologists perform similarly overall, radiomics may provide novel diagnostic utility when radiologist confidence is low, or when radiologists disagree.

Antihypertensive drug-related problems in Vietnamese ambulatory care