Cost-effective Method Research Articles

Study of Sterically Crowded Alkanes: Assessment of Non-Empirical Density Functionals Including Double-Hybrid (Cost-Effective) Methods.

We theoretically study here the homolytic dissociation reactions of sterically crowded alkanes of increasing size, carrying three different (bulky) substituents such as tert-butyl, adamantyl, and [1.1.1]propellanyl, employing a family of parameter-free functionals ranging from semi-local, to hybrid and double-hybrid models. The study is complemented with the interaction between a pair of HC(CH3)3 molecules at repulsive and attractive regions, as an example of a system composed by a pair of weakly bound sterically crowded alkanes. We also assessed the effect of incorporating reliable dispersion corrections (i. e., D4 or NL) for all the functionals assessed, as well as the use of a tailored basis set (DH-SVPD) for non-covalent interactions, which provides the best trade-off between accuracy and computational cost for a seemingly extended applications to branched or crowded systems. Overall, the PBE-QIDH/DH-SVPD and r2SCAN-QIDH/DH-SVPD methods represent an excellent compromise providing relatively low, and thus very competitive, errors at a fraction of the cost of other quantum-chemical methods in use.

Quantifying the Tip Leakage Vortex Wandering in a Low-Speed Axial Flow Fan via URANS Simulation

The tip leakage vortex is a dominant noise and aerodynamic loss source of low-speed axial flow fans. The tip leakage vortex often has an unsteady behavior, like the periodic motion of the vortex core: the so-called vortex wandering. This periodic vortex wandering results in additional noise, aerodynamic loss, and an oscillation in the moment acting on the blading, which causes an unfavorable vibration of the rotor. In the research campaigns focusing on vortex wandering, complicated measurement techniques (PIV) or high-fidelity but computationally costly simulations are commonly used. The present paper aims to introduce a relatively cost-effective unsteady Reynolds-averaged Navier-Stokes simulation-based investigation method of vortex wandering. The capabilities of the proposed technique are presented through a low-speed axial flow fan case study.

Single Laboratory Evaluation of the Q20+ Nanopore Sequencing Kit for Bacterial Outbreak Investigations

Leafy greens are a significant source of produce-related Shiga toxin-producing Escherichia coli (STEC) outbreaks in the United States, with agricultural water often implicated as a potential source. Current FDA outbreak detection protocols are time-consuming and rely on sequencing methods performed in costly equipment. This study evaluated the potential of Oxford Nanopore Technologies (ONT) with Q20+ chemistry as a cost-effective, rapid, and accurate method for identifying and clustering foodborne pathogens. The study focuses on assessing whether ONT Q20+ technology could facilitate near real-time pathogen identification, including SNP differences, serotypes, and antimicrobial resistance genes. This pilot study evaluated different combinations of two DNA extraction methods (Maxwell RSC Cultured Cell DNA kit and Monarch high molecular weight extraction kits) and two ONT library preparation protocols (ligation and the rapid barcoding sequencing kit) using five well-characterized strains representing diverse foodborne pathogens. High-quality, closed bacterial genomes were obtained from all combinations of extraction and sequencing kits. However, variations in assembly length and genome completeness were observed, indicating the need for further optimization. In silico analyses demonstrated that Q20+ nanopore sequencing chemistry accurately identified species, genotype, and virulence factors, with comparable results to Illumina sequencing. Phylogenomic clustering showed that ONT assemblies clustered with reference genomes, though some indels and SNP differences were observed, likely due to sequencing and analysis methodologies rather than inherent genetic variation. Additionally, the study evaluated the impact of a change in the sampling rates from 4 kHz (260 bases pair second) to 5 kHz (400 bases pair second), finding no significant difference in sequencing accuracy. This evaluation workflow offers a framework for evaluating novel technologies for use in surveillance and foodborne outbreak investigations. Overall, the evaluation demonstrated the potential of ONT Q20+ nanopore sequencing chemistry to assist in identifying the correct strain during outbreak investigations. However, further research, validation studies, and optimization efforts are needed to address the observed limitations and fully realize the technology’s potential for improving public health outcomes and enabling more efficient responses to foodborne disease threats.

A Monte Carlo Approach for Simulating Electrical Conductivity in Highly Porous Ceramic Composites: Impact of Internal Structure.

Porous ceramic composites play an important role in several applications. This is due to their unique properties resulting from a combination of various materials. Determination of the composite properties and structure is crucial for their further development and optimization. However, composite analysis often requires complex, expensive, and time-demanding experimental work. Mathematical modeling represents an effective tool to substitute experimental approach. The present study employs a Monte Carlo 3D equivalent electronic circuit network model developed to analyze a highly porous composite on the basis of minimum easily obtainable input parameters. Solid oxide cell electrodes were used as a model example, and this study focuses primarily on materials with a porosity of 55% and higher, characterized by deviation of behavior from those of lower void fraction share. This task is approached by adding to the original Monte Carlo model an additional parameter defining the void phase coalescence phenomenon. The enhanced model accurately simulates electrical conductivity for experimental samples of up to 75% porosity. Using sample composition, single-phase properties, and experimentally determined conductivity, this model allows us to estimate data of the internal structure of the material. This approach offers a rapid and cost-effective method to study material microstructure, providing insights into properties, such as electrical conductivity and heat conductivity. The present research thus contributes to advancing predictive capabilities in understanding and optimizing the performance of composite materials with potential in various technological applications.

TATPat based explainable EEG model for neonatal seizure detection

The most cost-effective data collection method is electroencephalography (EEG) to obtain meaningful information about the brain. Therefore, EEG signal processing is very important for neuroscience and machine learning (ML). The primary objective of this research is to detect neonatal seizures and explain these seizures using the new version of Directed Lobish. This research uses a publicly available neonatal EEG signal dataset to get comparative results. In order to classify these EEG signals, an explainable feature engineering (EFE) model has been proposed. In this EFE model, there are four essential phases and these phases: (i) automaton and transformer-based feature extraction, (ii) feature selection deploying cumulative weight-based neighborhood component analysis (CWNCA), (iii) the Directed Lobish (DLob) and Causal Connectome Theory (CCT)-based explainable result generation and (iv) classification deploying t algorithm-based support vector machine (tSVM). In the first phase, we have used a channel transformer to get channel numbers and these values have been divided into three levels and these levels are named (1) high, (2) medium and (3) low. By utilizing these levels, we have created an automaton and this automaton has three nodes (each node defines each level). In the feature extraction phase, transition tables of these nodes has been extracted. Therefore, the proposed feature extraction function is termed Triple Nodes Automaton-based Transition table Pattern (TATPat). The used EEG signal dataset contains 19 channels and there are 9 (= 32) connection in the defined automaton. Thus, the presented TATPat extracts 3249 (= 19 × 19 × 9) features from each EEG segment. To choose the most informative features of these 3249 features, a new feature selector which is CWNCA has been applied. By cooperating findings of this feature selector and the presented DLob, the explainable results have been obtained. The last phase is the classification phase and to get high classification performance from this phase, an ensemble classifier (tSVM) has been presented and the classification results have been obtained using two validation techniques which are 10-fold cross-validation (CV) and leave-one subject-out (LOSO) CV. The proposed EFE model generates a DLob string and by using this string, the explainable results have been obtained. Moreover, the presented EFE model attained 99.15% and 76.37% classification accuracy deploying 10-fold and LOSO CVs respectively. According to the classification performances, the recommended TATPat-based EFE is a good model at EEG signal classification. Also, the presented TATPat-based EFE model is a good model for explainable artificial intelligence (XAI) since TTPat-based EFE is cooperating by the DLob.

Graphene quantum dots modified electrodes as electrochemical sensing tool towards the detection of codeine in biological fluids and soft drinks.

An electroanalytical method based on disposable screen-printed carbon electrodes modified with non-toxic carbonaceous nanodots is proposed as a reliable and effective device for codeine determination in biological fluids and soft drinks. Graphene quantum dots (GQDs), carbon quantum dots (CQDs) and carbon nanodots (CNDs) were evaluated as electrode modifiers for the determinationof the drug. The electroactive areas of the modified electrodes were assessed by cyclic voltammetry using potassium ferricyanide. Results demonstrated that GQDs provided the best analytical response for codeine, displaying an intense and well-defined anodic wave approximately 0.9V vs reference electrode. The method exhibits an acceptable linear dynamic range, low limits of detection and quantification (0.21 and 0.73µM, respectively), and satisfactory precision (below 3.9% expressed as relative standard deviation (RSD)) in saliva. Only the analysis of biofluids requires a simple extraction protocol. The feasibility and applicability of this novel approach were assessed by determining codeine in different matrices, with recoveries ranging from 69 to 112%. This cost-effective, simple, easily miniaturised and portable method was applied not only to biofluids but also for the direct detection of codeine in soft drinks combined with a codeine-enriched syrup, a medication that is being used to adulterate beverages, particularly at specific events (drinking and nightclub parties). There is no need forany sample treatment, demonstrating its versatility in analysing beverages for potential adulteration as well.

Comparative Study of Amino Acids in the Life Stages of Silkworm (Bombyx mori L.)

The silkworm (Bombyx mori L.) undergoes distinct life stages, including egg, larva (caterpillar), pupa (chrysalis), and adult moth(imago), each with specific physiological requirements. Amino acids are essential for the growth and development of the silkworm. In this study, we work in paper chromatography to compare the amino acid profiles in different life stages of the silkworm. Samples were collected from eggs, larvae, pupae, and adult moths. Amino acids were extracted and separated on chromatographic paper using a suitable solvent system. The developed chromatograms were analyzed to determine the relative concentrations of specific amino acids. Our results revealed variations in the amino acid composition across the life stages. The amino acids such as glycine, methionine, leucine, and aspartic acid were common in all life stages of silkworms. These differences may be attributed to the physiological changes associated with metamorphosis and reproduction in adult silkworms. Overall, this study provides valuable insights into the dynamic changes in amino acid profiles during the life stages of silkworms, highlighting the importance of amino acids in their growth, development, and silk production. And, this study demonstrates the utility of paper chromatography as a cost-effective and reliable method for analyzing amino acid composition in complex biological samples, providing valuable insights into the nutritional dynamics of the silkworm lifecycle.

Research on Flow Field Prediction in a Multi-Swirl Combustor Using Artificial Neural Network

In aero-engine combustion research, the pursuit of cost-effective and rapid methods for acquiring precise flow fields across various operating conditions remains a significant challenge. This study offers novel insights into the rapid modeling of complex multi-swirling flows, introducing flow-field-based analytical methods to evaluate flow topologies, spray dispersion, ignition dynamics, and flame propagation patterns. A data-driven model is proposed to predict the swirling velocity field inside a multi-swirl combustor, using spatial coordinates and air pressure drops as input features. Particle Image Velocimetry (PIV) experiments under different air pressure drops are performed to generate the necessary flow field dataset. A fully connected deep neural network is designed and optimized with a focus on prediction accuracy, training efficiency, and mitigation of over-fitting. The predicted flow characteristics, including swirling jets, shear layers, recirculation zones, and velocity profiles, align closely with the PIV experimental results. This demonstrates the model’s capability to effectively capture the intricate multi-swirling flow structures and the complex relationships between input parameters and the resulting flow field. Furthermore, the trained model shows excellent generalization capability, accurately predicting flow fields under previously unseen operating conditions. Finally, combustion-relevant characteristics, such as ignition and flame propagation, are successfully extracted and analyzed from the predicted flow fields using the proposed deep learning framework.

Active cardboard box with palm wood waste powder and orange oil to prevent browning and quality loss in cabbage: Mode of action and potential for reuse.

Browning, caused by enzymatic activity and storage conditions, affects cabbage during cold storage and is crucial for customer acceptance. This study investigated the effect of cardboard packaging containing low concentrations of nano-orange oil (ONE) at 0.006% in palm wood waste powder for anti-browning and extending the shelf life of cabbage. The incorporation of ONE into palm wood powder (PWP) using different methods (soaking, vapor, vapor with ultrasonic device, and control) was examined before using the active PWP to develop cardboard cabbage packaging. The reuse of the active cabbage box packaging was also investigated for up to three reuses. The results showed that a greater anti-browning effect was achieved with cardboard packaging made from active PWP with orange oil vapor and an ultrasonic device compared to other adsorption methods, with significantly higher inhibition of the key browning enzyme activities of polyphenol oxidase (PPO) and peroxidase (POD). Additionally, antioxidant activity and bioactive compounds were improved, maintaining the bright green color of cabbage after 21 days of storage. The shelf life of cabbage stored in active cardboard was extended to at least 21 days compared to 5 days for the control. The active cabbage box with PWP and ONE vapor with an ultrasonic device showed potential for reuse at least two times. Limonene was found on the surface of stored cabbage and may be a key factor in antimicrobial activity, helping to control microbial growth on the cabbage surface within standard limits during long-term storage. This finding provides valuable guidance for reducing cabbage waste during transportation and storage from farm to market. PRACTICAL APPLICATION: This research offers new insights into active cardboard packaging made from palm wood powder with a low concentration of orange oil vapor to prevent browning and microbial growth in storage boxes. The optimal method for producing this packaging uses nano-orange oil vapor at 0.006% with an ultrasonic device, which could be feasible for large-scale production. The packaging effectively reduced PPO and POD enzyme activity, delaying browning and extending cabbage shelf life by at least threefold compared to the control, while maintaining color and freshness. This cost-effective method promotes the sustainable use of agricultural waste in the fresh vegetable industry, as it can be reused at least twice, benefiting farmers and reducing cabbage waste.

AI-Driven Cardiomegaly Detection Using Cutting-Edge Chest X–ray imagery

This project aims to develop an innovative approach for detecting cardiomegaly, a condition characterized by an enlarged heart, using machine learning techniques, specifically Support Vector Machines (SVM), and state-of-the-art chest X-ray imagery. Cardiomegaly serves as a significant indicator of various cardiac diseases, and early detection through accurate imaging analysis can lead to timely intervention and improved patient outcomes. The proposed machine learning solution utilizes SVM algorithms trained on a diverse dataset of chest X-ray images to automatically identify signs of cardiomegaly with high precision and reliability. By harnessing the power of SVM, this system offers a non-invasive and cost-effective method for screening large populations for cardiac abnormalities, facilitating early diagnosis and appropriate medical management. Key features of the developed solution include robust feature extraction techniques, real-time analysis, and integration with existing healthcare infrastructure for seamless implementation in clinical settings. Moreover, the system prioritizes patient privacy and data security by adhering to relevant regulations and standards governing medical imaging and information management. Overall, this project represents a significant advancement in the field of cardiac health monitoring, providing healthcare professionals with a valuable tool for early detection and management of cardiomegaly, ultimately improving patient care and reducing the burden of cardio vascular disease

Modern-Day Green Strategies for the Removal of Chromium from Wastewater

Chromium is an essential element in various industrial processes, including stainless steel production, electroplating, metal finishing, leather tanning, photography, and textile manufacturing. However, it is also a well-documented contaminant of aquatic systems and agricultural land, posing significant economic and health challenges. The hexavalent form of chromium [Cr(VI)] is particularly toxic and carcinogenic, linked to severe health issues such as cancer, kidney disorders, liver failure, and environmental biomagnification. Due to the high risks associated with chromium contamination in potable water, researchers have focused on developing effective removal strategies. Among these strategies, biosorption has emerged as a promising, cost-effective, and energy-efficient method for eliminating toxic metals, especially chromium. This process utilizes agricultural waste, plants, algae, bacteria, fungi, and other biomass as adsorbents, demonstrating substantial potential for the remediation of heavy metals from contaminated environments at minimal cost. This review paper provides a comprehensive analysis of various strategies, materials, and mechanisms involved in the bioremediation of chromium, along with their commercial viability. It also highlights the advantages of biosorption over traditional chemical and physical methods, offering a thorough understanding of its applications and effectiveness.

Green synthesis of N-doped-carbon dots/ZnO for enhanced photocatalytic degradation of methylene blue dye: optimization of reaction parameters.

In this research work, nitrogen-doped carbon dots (N-CDs) adorned zinc oxide nanoparticles (N-CDs/ZnO) were successfully synthesized by a simple and cost-effective solution dispersion method and later on used as photocatalyst for decontamination of aqueous methylene blue (MB) dye on irradiation of UV light (300 W, 320-400nm) at room temperature. Both the N-CDs and ZnO were prepared through green technique utilizing non-toxic, inexpensive and eco-friendly precursors, namely Foeniculum vulgare and Psidium guajava leaf extract, respectively. All the synthesized samples exhibited crystalline nature with average diameter of particle 4.42nm, 12.38nm and 14.11nm corresponding to N-CDs, ZnO and N-CDs/ZnO, respectively. Further, band gap energy value (Eg) of 3.43, 2.76 and 2.49eV for N-CDs, ZnO and N-CDs/ZnO, respectively, were obtained by using Tauc's plot. The photocatalytic capability of the sample N-CDs/ZnO was compared with bare ZnO nanoparticles, utilizing identical experimental conditions. The results demonstrated that the composite exhibited notably higher photocatalytic degradation efficiency than bare ZnO nanoparticles up to 15.54%. Lower band gap value of N-CDs/ZnO was the major factor for exhibiting this behaviour, decreasing the recombination rate and thus enhancing the efficiency. Furthermore, N-CDs/ZnO exhibited 98.17% MB degradation under optimized conditions (0.03g, 5ppm, pH 10). The resultant N-CDs/ZnO exhibited good stability and decontamination efficiency up to five cycles with efficiency loss of only 7.89%. Along with, trapping experiments was conducted to analyze the role of active species involved for deep understanding of mechanism. The order of efficiency of active constituents was observed to be: •O2- > h+ > •OH. The study analyzed the non-toxic nature of treated water, revealing normal plant growth, suggesting its potential use in irrigation of parks and roadside areas. Overall, present research work obeys the green chemistry principles with the fabrication of highly efficient, eco-friendly, cost-effective photocatalyst N-CDs/ZnO by utilizing the green precursors for the whole research work.

Drainage Fluid Amylase as a Biomarker for the Detection of Anastomotic Leakage After Low Anterior Resection of Rectal Cancer: A Two-center Study.

This study aimed to investigate the utility of measuring amylase levels in drainage fluid (DFA) for early, non-invasive detection of anastomotic leakage (AL) in undergoing low anterior resection (LAR) for rectal cancer. This prospective observational cohort study analyzed drainage fluid samples from patients who underwent LAR for rectal cancer at two medical centers between February 2021 and December 2023. DFA levels were measured on postoperative days (PODs) 1, 3, and 5. AL was confirmed by clinical evidence and radiological imaging. Statistical analyses were performed to evaluate the diagnostic performance of DFA. Of 120 LAR cases, AL occurred in five (4.16%). DFA levels on POD 3 and 5 were significantly higher in the AL than in the non-AL group (p<0.0001). DFA on POD 5 had the highest diagnostic accuracy for early AL detection, with an area under the curve of 0.99, achieving 100% sensitivity and 99.5% specificity at a cutoff of 846 U/l. A DFA >846 U/l predicted AL with negative predictive and positive predictive values of 83.3% and 100%, respectively, on POD 5. Measuring DFA is a non-invasive, simple and cost-effective method for early AL detection in patients with rectal cancer undergoing LAR. Our findings also suggested that drain placement may be useful for the early detection of AL through DFA measurement.

Benchmarking miRNA reference genes in B-cell precursor acute lymphoblastic leukemia

MicroRNAs (miRNAs) play dual roles in acute lymphoblastic leukemia (ALL) as both tumor suppressors and oncogenes, and miRNA expression profiles can be used for patient risk stratification. Precise assessment of miRNA levels is crucial for understanding their role and function in gene regulation. Quantitative real-time polymerase chain reaction (qPCR) is a reliable, rapid, and cost-effective method for analyzing miRNA expression, assuming that appropriate normalization to stable references is performed to ensure valid data. In this study, we evaluated the stability of six commonly used miRNA references (5sRNA, SNORD44, RNU6, RNU1A1, miR-103a-3p, and miR-532-5p) across nine B-cell precursor (BCP) ALL cell lines, 22 patient-derived xenograft (PDX) BCP ALL samples from different organ compartments of leukemia bearing mice, and peripheral blood mononuclear cells (PBMCs) from six healthy donors. We used four different algorithms (Normfinder, ∆CT, geNorm, and BestKeeper) to assess the most stably expressed reference across all samples. Moreover, we validated our data in an additional set of 13 PDX ALL samples and six healthy controls, identifying miR-103a-3p and miR-532-5p as the most stable references for miRNA normalization in BCP ALL studies. Additionally, we demonstrated the critical importance of using a stable reference to accurately interpret miRNA data.

Locking organic solvents by crystallization-induced polymer network

Organogels that lock organic solvents within a polymer network shows promising applications in ice-phobicity, anti-biofouling, conserving cultural heritage etc. However, the synthesis of organogels by versatile and cost-effective methods still remain challenging. Here, we introduce a novel and economical approach to organogel preparation via crystallization-induced polymer precipitation, which involves the straightforward blending of carbonyl contained polymer, nano Ca(OH)2, and excess ethyl acetate (EA). During the formation, nano Ca(OH)2 reacts with EA to produce ethanol and calcium acetate, in which Ca2+ ionically bonds with carbonyl of polymer. Due to the low solubility in EA and ethanol, calcium acetate initiates crystallization. The interaction between Ca(CH3COO)2 and polymer induces the separation of polymer from the solvent, thereby forming a network that locks the surplus EA and ethanol. More importantly, the organogel is capable not only of encapsulating 12 organic solvents within its precursor but also of effectively removing various aged polymeric coatings from wall paintings without leaving residues. This innovative organogel system marks a significant forward in the realm of organogel with applications in conservation and surface engineering.

Risk-stratified hepatocellular carcinoma screening according to the degree of obesity and progression to cirrhosis for diabetic patients with metabolic dysfunction-associated steatotic liver disease (MASLD) in Japan: a cost-effectiveness study.

To evaluate the cost-effectiveness of risk-stratified hepatocellular carcinoma (HCC) screening in diabetic patients with metabolic dysfunction-associated steatotic liver disease (MASLD). A state-transition model from a healthcare payer perspective on a lifetime horizon. Japan. A hypothetical cohort of 50-year-old diabetic patients with MASLD risk-stratified according to degree of obesity and progression to cirrhosis. Metabolic dysfunction-associated steatotic liver (MASL), metabolic dysfunction-associated steatohepatitis (MASH) and MASH cirrhosis are progressive manifestations of this specific type of liver disease. Abdominal ultrasound (US), US with alpha-fetoprotein (AFP), US with AFP and lectin-reactive alpha-fetoprotein (AFP-L3), CT, extracellular contrast-media-enhanced MRI (ECCM-MRI), gadoxetic acid-enhanced MRI (EOB-MRI) and no screening. Costs, quality-adjusted life-years (QALYs), incremental cost-effectiveness ratios (ICERs), early-stage HCC cases, advanced-stage HCC cases and HCC-related deaths. EOB-MRI is the most cost-effective screening method for non-obese diabetic patients with MASH cirrhosis and for obese diabetic patients with MASH and MASH cirrhosis. Cost-effectiveness was sensitive to HCC incidence in non-obese diabetic patients with MASH cirrhosis and obese diabetic patients with MASH, and the adherence rate of HCC screening in obese diabetic patients with MASH. When the semiannual HCC incidence was between 0.008 and 0.0138 in non-obese diabetic patients with MASH cirrhosis, US with AFP was more cost-effective than EOB-MRI. Cost-effectiveness acceptability curves showed that EOB-MRI was 50.7%, 96.0% and 99.9% cost-effective in obese diabetic patients with MASH and non-obese diabetic patients with MASH cirrhosis, and obese diabetic patients with MASH cirrhosis at a willingness-to-pay level of $50 000 per QALY gained. Compared with no screening in 100 000 non-obese diabetic patients with MASH cirrhosis and obese diabetic patients with MASH cirrhosis, EOB-MRI reduced total costs by US$69 million and by US$142 million, increased lifetime effectiveness by 12 546 QALYs and by 15 815 QALYs, detected 17 873 and 21 014 early-stage HCC cases, and averted 2068 and 2471 HCC-related deaths, respectively. Of all HCC screening methods for diabetic patients with MASH cirrhosis, EOB-MRI yields the greatest cost-saving with the highest QALYs, detects the greatest number of early-stage HCC cases and averts the greatest number of advanced-stage HCC cases and HCC-related deaths. The findings provide important insights for the precise implementation of risk-stratified HCC surveillance to reduce morbidity and mortality and improve the quality of life in diabetic patients with MASLD.

Development of a LAMP assay for the rapid visual detection of the emerging tick-borne Songling virus

BackgroundSongling virus (SGLV) within the genus Orthonairovirus, family Nairoviridae, is an emerging tick-borne virus associated with human febrile illness. However, no rapid detection method for SGLV has been established.MethodsIn this study, four primer sets targeting the nucleocapsid protein gene of SGLV were designed for use in the LAMP assay and evaluated to identify the optimal primer set. Recombinant plasmids were constructed and utilized for assessing the sensitivity of the assay. Tacheng tick virus 1 (TcTV-1)-, Beiji nairovirus (BJNV)-, Yezo virus (YEZV)-, severe fever with thrombocytopenia syndrome virus (SFTSV)-, and tick-borne encephalitis virus (TBEV)-positive tick samples were utilized to assess the specificity. Field-collected ticks were also evaluated as biological specimens to validate the assay.ResultsA SGLV-specific LAMP assay was established with a detection limit of 1 × 10–2 copies/μl and could be visually confirmed by a color change from purple to blue in SGLV-positive samples. No cross-reactivity was observed in the detection of TcTV-1, BJNV, YEZV, SFTSV, and TBEV using the LAMP assay. In addition to the detection of the same seven high-copy numbers of SGLV as the SYBR Green quantitative RT-PCR assay within a reduced timeframe, the developed LAMP method also effectively identified an additional sample with a low copy number in the field-collected tick samples.ConclusionsWe successfully developed a sensitive, specific, and cost-effective visual method for the rapid detection of SGLV using the LAMP assay, which can be applied in pathogenesis and epidemiological surveillance studies of SGLV.Graphical

Validated Spectrofluorometric Assay for Folic Acid Determination in Pure and Pharmaceutical Forms via Basic Fuchsin Fluorescence Quenching.

A simple, specific, sensitive, rapid, and cost-effective spectrofluorometric method for quantifying folic acid has been developed using basic fuchsin as a fluorescence quenching agent. This method relies on the quenching effect of folic acid on the inherent fluorescence of basic fuchsin, facilitated by their interaction in Britton-Robinson buffer solution at pH 9, leading to the formation of an ion-associated complex. The decrease in fluorescence intensity of basic fuchsin was measured at 730 nm, with excitation at 365 nm. The method demonstrated a linear relationship (R2 = 0.9977) between the quenching fluorescence intensity (ΔF) and folic acid concentration over the range of 4-20 μg/mL, with a detection limit of 0.17 μg/mL. The method showed no significant interference from common excipients found in pharmaceutical tablets. The results obtained were in good agreement with those from high-performance liquid chromatography, with no significant differences in precision or accuracy. This spectrofluorimetric method was successfully applied to determine the folic acid content in various commercial pharmaceutical tablets.

Preliminary Exploration of T1ρ and T2 Mapping in Porcine Articular Cartilage Using Very-Low-Field Magnetic Resonance Imaging.

The high prevalence of osteoarthritis emphasizes the need for a cost-effective and accessible method for its early diagnosis. Recently, the portability and affordability of very-low-field (VLF) magnetic resonance imaging (MRI, 10-100 mT) have caused it to gain popularity. Nevertheless, there is insufficient evidence to quantify early degenerative changes in cartilage using VLF MRI. This study assessed the potential of T1ρ and T2 mapping for detecting degenerative changes in porcine cartilage specimens using a 50 mT MRI scanner. T2- and T1ρ-weighted images were acquired using a 50 mT MRI scanner with 2D spin-echo and triple-refocused T1ρ preparation sequences. MRI scans of porcine cartilage were also acquired using a 3 T MRI scanner for comparison. A mono-exponential algorithm was applied to fit a series of T2- and T1ρ-weighted images. T2 values for CuSO4·5H2O solutions measured via Carr-Purcell-Meiboom-Gill (CPMG) and spin-echo sequences were compared to verify the algorithm's reliability. The nonparametric Kruskal-Wallis statistical test was used to compare T2 and T1ρ values. Experimental repeatability was assessed using the root-mean-square of the coefficient of variation (rmsCV). T2 values of the CuSO4·5H2O solutions obtained using the spin-echo sequence showed differences within 2.3% of those obtained using the CPMG sequence, indicating the algorithm's reliability. The T1ρ values for varying concentrations of agarose gel solutions were higher than the T2 values. Furthermore, 50 mT and 3 T MRI results showed that both the T1ρ and T2 values were significantly higher for porcine cartilage degraded for 6 h vs intact cartilage, with p-values of 0.006 and 0.01, respectively. Our experimental results showed good reproducibility (rmsCV < 8%). We demonstrated the feasibility of quantitative cartilage imaging via T2 and T1ρ mapping at 50 mT MRI for the first time.

Biogenic silver nanoparticles synthesized using rose petals: Potential to inhibit multidrug-resistant bacterial pathogens in disabled patients

BackgroundDrug resistant bacterial infections can be fatal for disabled people needing multiple medications and new effective antibacterial drugs such as biosynthesized nanoparticles are needed. A cost-effective and green method was studied for the biosynthesis of silver nanoparticles using pink rose petals. Materials and methodsSilver nanoparticles (AgNP) were synthesized using the petals of pink rose flowers. The AgNPs were characterized using FITR, SEM, XRD, and EDS analyses. The antibacterial activity of the AgNPs against multidrug-resistant bacterial pathogens Staphylococcus aureus, Escherichia coli, Acinetobacter baumannii, and colistin-resistant Klebsiella pneumoniae were studied using the microplate method. ResultsTh AgNPs had a spherical shape and an average size of 26 nm. XRD analysis showed that the nanoparticles were Ag0. Antibacterial activity was the highest against the multidrug-resistant S. aureus the minimum inhibitory concentration (MIC) being 400 µg/ml AgNPs. The multidrug-resistant E. coli, multidrug-resistant A. baumannii, and colistin-resistant K. pneumoniae MICs were 800 µg/ml, 3200 µg/ml, and 1600 µg/ml, respectively. ConclusionThe green synthesized of AgNPS using pink rose petals and their antibacterial effects offer material for the treatments of common multidrug-resistant bacterial pathogens.