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pACP-HybDeep: predicting anticancer peptides using binary tree growth based transformer and structural feature encoding with deep-hybrid learning
A nomogram predicting the risk of venous thromboembolism in patients following urologic surgeries
CHST3, PGBD5, and SLIT2 can be identified as potential genes for the diagnosis and treatment of osteoporosis and sarcopenia
Research and application of multi-frequency electromagnetic technology in real-time online characterization of steel microstructures and mechanical properties
The appropriate nutrient conditions for methicillin-resistant Staphylococcus aureus and Candida albicans dual-species biofilm formation in vitro
AbstractPolymicrobial biofilms, the reason for most chronic wound infections, play a significant role in increasing antibiotic resistance. The in vivo effectiveness of the new anti-biofilm therapy is conditioned by the profound evaluation using appropriate in vitro biofilm models. Since nutrient availability is crucial for in vitro biofilm formation, this study is focused on the impact of four selected cultivation media on the properties of methicillin-resistant Staphylococcus aureus and Candida albicans dual-species biofilms. To reflect the wound environment, Tryptic soy broth, RPMI 1640 with and without glucose, and Lubbock medium were supplemented with different amounts of host effector molecules present in human plasma or sheep red blood cells. The study demonstrates that the Lubbock medium provided the most appropriate amount of nutrients regarding the biomass structure and the highest degree of tolerance to selected antimicrobials with the evident contribution of the biofilm matrix. Our results allow the rational employment of nutrition conditions within methicillin-resistant Staphylococcus aureus and Candida albicans dual-species biofilm formation in vitro for preclinical research. Additionally, one of the potential targets of a complex antibiofilm strategy, carbohydrates, was revealed since they are prevailing molecules in the matrices regardless of the cultivation media.
Application of the Lasso regularisation technique in mitigating overfitting in air quality prediction models
2 × 2 MIMO dual-wideband ground radiation antenna with a T-shaped isolator for Wi-Fi 6/6E/7 applications
Investigating the effect of parameters in the thermodynamic analysis of the solid oxide fuel cell cycle using response surface methodology
Study on dynamic compression characteristics of coal containing gas under different strain rates
Identification of ferroptosis-related signature predicting prognosis and therapeutic responses in pancreatic cancer
Application of response surface methodology (RSM) for experimental optimization in biogenic silica extraction from rice husk and straw ash
A robust and interpretable ensemble machine learning model for predicting healthcare insurance fraud
A quality by design HPLC method for cephalosporin analysis in pharmaceuticals and water samples with environmental impact assessment
AbstractThe present study applied a combined analytical quality-by-design and green analytical chemistry approach to develop an HPLC method for the determination of four cephalosporin pharmaceuticals in both their formulations and water samples. These drugs include ceftriaxone, cefotaxime, ceftazidime and cefoperazone. A Box–Behnken experimental design was employed to optimize three chromatographic parameters: mobile phase composition, flow rate and buffer pH. The predicted optimal conditions involved using a mobile phase of acetonitrile and 0.04 M phosphate buffer at pH 6 in a 7:93 (v/v) ratio, pumped at 1.3 mL/min through a Nucleosil C18 (4.6 × 250 mm, 5 μm) column with UV detection at 240 nm. Under these optimum conditions, the developed HPLC method successfully separated the four drugs with good resolution in less than 6 min. Linearity was established across the concentration ranges of 5–300 µg/mL for ceftriaxone and cefotaxime, 5–400 µg/mL for ceftazidime and 5–100 µg/mL for cefoperazone. Furthermore, full validation of the method in terms of accuracy, precision, specificity and robustness was carried out as per ICH guidelines. The greenness profile of the optimized HPLC method was also evaluated using the Analytical GREEnness (AGREE) tool and found to be environmentally friendly with AGREE score of 0.75, making it a greener alternative for quality control and routine analysis of the investigated cephalosporins in their pharmaceutical formulations and tap water samples. Furthermore, the blueness assessment of the proposed HPLC method using the blue applicability grade index (BAGI) tool yielded a value of 77.5, indicating its high analytical practicality and substantial potential for routine analysis applications.
Gut microbiota and blood biomarkers in IBD-Related arthritis: insights from mendelian randomization
Theoretical analysis of bearing mechanism and engineering application of pipe roof in a highway tunnel
Sustainable leachate treatment by integrating electrolysis with palm-shell activated carbon contactor for environmental protection
Design and numerical simulation of CuBi2O4 solar cells with graphene quantum dots as hole transport layer under ideal and non-ideal conditions
Abstract The simulation of ideal and non-ideal conditions using the SCAPS-1D simulator for novel structure Ag/FTO/CuBi 2 O 4 /GQD/Au was done for the first time. The recombination of charge carriers in CuBi 2 O 4 is an inherent problem due to very low hole mobility and polaron transport in the valence band. The in-depth analysis of the simulation result revealed that Graphene Quantum Dots (GQDs) can act as an appropriate hole transport layer (HTL) and can enhance hole transportation. The simulation was done under ideal and nonideal conditions. The non-ideal conditions include parasitic resistances, reflection losses, radiative, and Auger recombination whereas the ideal condition was studied without the inclusion of any losses. Under ideal conditions, the cell Ag/FTO/CuBi 2 O 4 /GQD/Au exhibited a photovoltaic (PV) parameter such as open circuit voltage (V oc ), short circuit current (J sc ), fill factor (FF), photo conversion efficiency (PCE) are 1.39 V, 25.898 mA/cm 2 , 90.92%, and 32.79%, respectively. The effect of various cell parameters such as the thickness of the absorber layer, HTL layer, and FTO, acceptor and defect density, the bandgap of the absorber and HTL layer, series and shunt resistance, back and front contact materials, radiation and Auger recombination of the absorber layer, reflection losses on the efficiency of the proposed cell is analysed. The drastic reduction in all PV parameters was observed under non-ideal conditions and the PV parameters are V oc (1.22 V), J sc (2.904 mA/cm 2 ), FF (86.3), and PCE of 3.06%. The charge kinetics such as impedance, conductivity, and capacitance plots, and possible reasons for reductions in PV parameters are discussed in detail.
The development of an efficient artificial intelligence-based classification approach for colorectal cancer response to radiochemotherapy: deep learning vs. machine learning
Brain-model neural similarity reveals abstractive summarization performance
AbstractDeep language models (DLMs) have exhibited remarkable language understanding and generation capabilities, prompting researchers to explore the similarities between their internal mechanisms and human language cognitive processing. This study investigated the representational similarity (RS) between the abstractive summarization (ABS) models and the human brain and its correlation to the performance of ABS tasks. Specifically, representational similarity analysis (RSA) was used to measure the similarity between the representational patterns (RPs) of the BART, PEGASUS, and T5 models’ hidden layers and the human brain’s language RPs under different spatiotemporal conditions. Layer-wise ablation manipulation, including attention ablation and noise addition was employed to examine the hidden layers’ effect on model performance. The results demonstrate that as the depth of hidden layers increases, the models’ text encoding becomes increasingly similar to the human brain’s language RPs. Manipulating deeper layers leads to more substantial decline in summarization performance compared to shallower layers, highlighting the crucial role of deeper layers in integrating essential information. Notably, the study confirms the hypothesis that the hidden layers exhibiting higher similarity to human brain activity play a more critical role in model performance, with their correlations reaching statistical significance even after controlling for perplexity. These findings deepen our understanding of the cognitive mechanisms underlying language representations in DLMs and their neural correlates, potentially providing insights for optimizing and improving language models by aligning them with the human brain’s language-processing mechanisms.