Efficient Screening Approach Research Articles

Single Question for Screening Suicide Risk in Patients With Cancer in Nursing Routine Work: A Retrospective Cross-Sectional Study.

To examine the efficacy of the Single Suicide Question, a tactful single-item tool, in assessing suicide risk among cancer patients. Suicide risk screening in cancer patients faces challenges such as cultural, the degree of attention given and the complexity and diversity of assessment methods. An efficient and simple suicide screening tool is needed within the nursing and treatment process. A retrospective cross-sectional study. All data utilised in this study were obtained from assessments conducted by researchers at our institution between March 2018 and August 2023. Retrospectively analysed Single Suicide Question scores from 9703 patients (March 2018-August 2023), with 229 undergoing additional using other established tools (the Patient Health Questionnaire-9, Generalised Anxiety Disorder-7, Distress Thermometer) alongside the Mini International Neuropsychiatric Interview Suicidal Subscale (MINI Suicidal Subscale). These 229 patients were divided into the cut-off determination dataset (174/229) and the validation dataset (55/229) to analyse, validate and compare the screening efficacy, consistency and optimal cut-off points of the tools relative to the MINI Suicidal subscale (golden standard). K-fold cross-validation was employed to assess the stability of the Single Suicide Question and to revalidate its screening efficacy. In cut-off determination dataset, the Single Suicide Question exhibited good screening efficacy and substantial agreement relative to the MINI Suicidal subscale, as validated in the validation dataset, outperforming other tools. The optimal cut-off points of Single Suicide Question for screening moderate or higher and high suicide risk as 2 and 3 respectively. Its stability in K-fold cross-validation further supported its screening efficacy. The Single Suicide Question shows potential as a preliminary screening tool for suicide risk. Furthermore, it has also demonstrated the potential of a simple, tactful, culturally fitting question in assessing suicide risk, providing insight for the development of relevant tools in different cultural and linguistic backgrounds. Our study demonstrates the potential of the Single Suicide Question as a screening tool for identifying suicide risk among cancer patients, which may facilitate efficient suicide screening. Furthermore, it reveals that tactful, culturally fitting, yet simple tools can achieve good screening efficacy, providing insights for the development of similar tools in different cultural and linguistic contexts. This study investigated the effectiveness of using a culturally contextualised, specific single question to screen for suicide risk in Chinese cancer patients, demonstrating high screening efficacy and stability. With further research, this method may provide a tactful, culturally informed and efficient suicide screening approach to address the critical issue of suicide risk in cancer care, and it also serves as a reference for developing similar tools in diverse cultural and linguistic contexts. The study followed the STROBE checklist guidelines for cross-sectional studies. The data provided by patients were the foundation of this study. Nurses played a central role in conducting the majority of assessments and contributing to the research design in this study.

Our journey toward implementation of digital breast tomosynthesis in breast cancer screening: the Malmö Breast Tomosynthesis Screening Project.

The purpose is to describe the Malmö Breast Tomosynthesis Screening Project from the beginning to where we are now, and thoughts for the future. In two acts, we describe the efforts made by our research group to improve breast cancer screening by introducing digital breast tomosynthesis (DBT), all the way from initial studies to a large prospective population-based screening trial and beyond. Our studies have shown that DBT has significant advantages over digital mammography (DM), the current gold standard method for breast cancer screening in Europe, in many aspects except a major one-the increased radiologist workload introduced with DBT compared with DM. It is foreseen that AI could be a viable solution to overcome this problem. We have proved that one-view DBT is a highly efficient screening approach with respect to diagnostic performance.

Exploration of novel boron nitride polymorphs: High-throughput screening combined with multi-task orbital crystal graph convolutional neural network (MT-OCGCN)

In response to the limitations of traditional material property prediction methods, we have developed a Multi-task Orbital Crystal Graph Convolutional Neural Network (MT-OCGCN). This innovative model allows for sharing parameters and computational resources, significantly improving prediction efficiency and accuracy. Experimental comparisons show that the model can achieve multi-task prediction with high accuracy for both strong and weak correlation properties, this offers an efficient approach for computational materials screening and design. Combined with high-throughput screening has resulted in the discovery of 13 new boron nitride polymorphs in the P21/c phase, among which 7 are characterized as wide direct band gap semiconductors. Furthermore, we have comprehensively analyzed the mechanical, thermal, and electronic properties of these new boron nitride polymorphs. It can be asserted that MT-OCGCN has the capability to precisely forecast the physical properties of novel structures that are absent from current databases.

Analysis of chemosensitivity of tumor spheroids exposed to two-dimensional gradient of combination drugs in a hydrogel-based diffusion microfluidic platform

BackgroundLiver cancer stands as a leading cause of cancer-related deaths globally, challenging conventional treatments due to resistance to chemotherapy and targeted therapy. Although frontline medications show initial efficacy, prolonged use often leads to resistance and harm. Current clinical strategies rely on combination therapies, but evaluating their effectiveness remains challenging. ResultsTo address this, we developed a hydrogel-based diffusion microfluidic platform for assessing chemosensitivity. This platform features a hydrogel-filled diffusion layer linked to liquid wells, allowing the creation of drug gradients. Tumor spheroids, cultured on the non-adhesive hydrogel surface, were exposed to single or combination drug gradients. Analysis revealed that drug efficacy, quantified by IC50 values, could be determined from responses to single drug gradients. A novel method was introduced to assess spheroid circularity as a time-invariant index of drug efficacy. Furthermore, exposing spheroids to 2D combination drug gradients allowed intuitive visualization of their responses via a color map. This analysis identified optimal drug combinations, exhibiting superior efficacy to monotherapy. SignificanceThe microfluidic platform enables assessment of synergistic effects and replicates in vivo conditions, enhancing the relevance of test results. By offering a streamlined, fast, and efficient drug screening approach, this platform aims to provide insight into tumor spheroid responses to varying drug combinations, facilitating more effective clinical applications.

Adsorbate-induced adatom formation on Au-Cu bimetallic alloys and its possible consequences for CO2 electroreduction

The adsorbate-induced formation of sub-nanometer clusters on transition-metal single crystals observed in previous high-pressure microscopic studies hinted at the in-situ formation of unique active sites even on large nanoparticle catalysts. We propose that the adatom formation energy can be used as an energetic descriptor for the initial step toward the adsorbate-induced metal-cluster formation process. This descriptor can be efficiently computed using density functional theory (DFT) calculations and applied for screening and identification of metal catalysts where this phenomenon may play an important role in generating active sites in-situ. As a proof of concept, here, we construct an adatom formation energy database for three AuxCuy alloys (x:y = 3:1, 1:1, or 1:3) and eighteen adsorbates (H, C, N, O, F, S, Cl, Br, I, CHx, NHx (x = 1 – 3), CO, NO, and OH) commonly involved in catalytic reactions. The energetics of adatom formation were examined in all cases where the (111) terrace, (211) step-edge, and (874) kink were the sources of the adatom. We demonstrate that the presence of an adsorbate could alter not only the energetics for adatom formation but also the elemental nature of the preferred adatom being formed. Using our database, we identified promising systems which favor adsorbate-induced adatom formation under near-ambient conditions. Specifically, CO-induced adatom formation on all three Au-Cu alloy surfaces could occur under CO2 electroreduction (CO2RR) conditions. This phenomenon offers a qualitative explanation for the experimentally observed CO2RR activity on Au-Cu alloy catalysts. Our methodology offers an easily expandable and efficient approach for large-scale catalyst screening with regards to adatom/cluster formation under reaction conditions and provides insight into the possible nature of active sites on alloy catalysts from a novel perspective.

Characterization of human aquaporin ion channels in a yeast expression system as a tool for novel ion channel discovery.

Aquaporin (AQP) channels found in all domains of life are transmembrane proteins which mediate passive transport of water, glycerol, signaling molecules, metabolites, and charged solutes. Discovery of new classes of ion-conducting AQP channels has been slow, likely reflecting time- and labor-intensive methods required for traditional electrophysiology. Work here defines a sensitive mass-throughput system for detecting AQP ion channels, identified by rescue of cell growth in the K+-transport-defective yeast strain CY162 following genetic complementation with heterologously expressed cation-permeable channels, using the well characterized human AQP1 channel for proof of concept. Results showed AQP1 conferred transmembrane permeability to cations which rescued survival in CY162 yeast. Comprehensive testing showed that growth response properties fully recapitulated AQP1 pharmacological agonist and antagonist profiles for activation, inhibition, dose-dependence, and structure-function relationships, demonstrating validity of the yeast screening tool for AQP channel identification and drug discovery efforts. This method also provided new information on divalent cation blockers of AQP1, pH sensitivity of antagonists, and ion permeability of human AQP6. Site-directed mutagenesis of AQP1 channel regulatory domains confirmed that yeast growth rescue was mediated by the introduced channels. Optical monitoring with a lithium-sensitive photoswitchable probe in living cells independently demonstrated monovalent cation permeability of AQP1 channels in yeast plasma membrane. Ion channel properties of AQP1 expressed in yeast were consistent with those of AQP1 expressed in Xenopus laevis oocyte and K+-transport defective Escherichia coli. Outcomes here establish a powerful new approach for efficient screening of phylogenetically diverse AQPs for yet untested functions as cation channels.

Machine learning accelerated the prediction of mechanical properties of copper modified by TMDs based on molecular dynamics simulation

In this study, we constructed a dataset of elastic modulus and ultimate stress for copper material enhanced by Transition Metal Dichalcogenides (TMDs) through Molecular Dynamics (MD) simulations. Subsequently, leveraging chemical insights, we selected appropriate descriptors and established machine learning prediction models for elastic modulus and ultimate stress, respectively. Finally, the performance of the machine learning models was evaluated using a test set. The results demonstrate excellent performance of the machine learning models in predicting material properties. This work presents a novel approach for efficient material screening, demonstrating the synergy between MD simulations and machine learning in advancing materials research and intelligent material selection platforms.

Superimposed Electric Field Enhanced Electrospray for High-Throughput and Consistent Cell Encapsulation.

Cell encapsulation technology, crucial for advanced biomedical applications, faces challenges in existing microfluidic and electrospray methods. Microfluidic techniques, while precise, can damage vulnerable cells, and conventional electrospray methods often encounter instability and capsule breakage during high-throughput encapsulation. Inspired by the transformation of the working state from unstable dripping to stable jetting triggered by local electric potential, this study introduces a superimposed electric field (SEF)-enhanced electrospray method for cell encapsulation, with improved stability and biocompatibility. Utilizing stiffness theory, the stability of the electrospray, whose stiffness is five times stronger under conical confinement, is quantitatively analyzed. The SEF technique enables rapid, continuous production of ≈300 core-shell capsules per second in an aqueous environment, significantly improving cell encapsulation efficiency. This method demonstrates remarkable potential as exemplified in two key applications: (1) a 92-fold increase in human-derived induced pluripotent stem cells (iPSCs) expansion over 10 d, outperforming traditional 2D cultures in both growth rate and pluripotency maintenance, and (2) the development of liver capsules for steatosis modeling, exhibiting normal function and biomimetic lipid accumulation. The SEF-enhanced electrospray method presents a significant advancement in cell encapsulation technology. It offers a more efficient, stable, and biocompatible approach for clinical transplantation, drug screening, and cell therapy.

A High-Throughput Screening Strategy for Synthesizing Molecularly Imprinted Polymer Nanoparticles Selectively Targeting Tumors.

Molecularly imprinted polymers (MIPs) show significant promise as effective alternatives to antibodies in disease diagnosis and therapy. However, the challenging process of screening extensive libraries of monomer combinations and synthesis conditions to identify formulations with enhanced selectivity and affinity presents a notable time constraint. The need for expedient methods becomes clear in accelerating the strategic development of MIPs tailored for precise molecular recognition purposes. In this study, an innovative high-throughput screening methodology designed to identify the optimal MIP formulation for targeting tumors is presented. Employing a microtiter plate format, over 100 polymer syntheses are conducted, incorporating diverse combinations of functional monomers. Evaluation of binding performance utilizes fluorescence-based assays, focusing on an epitope of the epidermal growth factor receptor (EGFR). Through this meticulously structured screening process, synthesis conditions that produced MIP nanoparticles exhibiting substantial specificity for EGFR targeting (KD = 10-12m) are identified. These "bionic antibodies" demonstrate selective recognition of cancer cells in whole blood samples, even at concentrations as low as 5cellsmL-1. Further validation through fluorescent imaging confirms the tumor-specific localization of the MIPs in vivo. This highly efficient screening approach facilitates the strategic synthesis of imprinted polymers functioning as precision bioprobes.

Interpretable Data‐Driven Descriptors for Establishing the Structure‐Activity Relationship of Metal‐Organic Frameworks Toward Oxygen Evolution Reaction

The development of readily accessible and interpretable descriptors is pivotal yet challenging in the rational design of metal‐organic framework (MOF) catalysts. This study presents a straightforward and physically interpretable activity descriptor for the oxygen evolution reaction (OER), derived from a dataset of bimetallic Ni‐based MOFs. Through an artificial‐intelligence (AI) data‐mining subgroup discovery (SGD) approach, a combination of the d‐band center and number of missing electrons in eg states of Ni, as well as the first ionization energy and number of electrons in eg states of the substituents, is revealed as a gene of a superior OER catalyst. The found descriptor, obtained from the AI analysis of a dataset of MOFs containing 3‐5d transition metals and 13 organic linkers, has been demonstrated to facilitate in‐depth understanding of structure–activity relationship at the molecular orbital level. The descriptor is validated experimentally for 11 Ni‐based MOFs. Combining SGD with physical insights and experimental verification, our work offers a highly efficient approach for screening MOF‐based OER catalysts, simultaneously providing comprehensive understanding of the catalytic mechanism.

Development and characterization of Zn[sbnd]xCu[sbnd]yTi[sbnd]zMo alloys for biomedical applications: A high-throughput gradient continuous casting approach

The limited mechanical properties of pure Zn, such as its low strength and ductility, hinder its application as a material for biodegradable implants. Addressing this challenge, the current study focuses on the development of biodegradable Zn-based alloys, employing innovative alloy design and processing strategies. Here, alloys with compositions ranging from 0.02 to 0.10 weight percent (wt%) Cu, 1.22 to 1.80 wt% Ti, and 0.04 to 0.06 wt% Mo were produced utilizing a high-throughput gradient continuous casting process. This study highlights three specific alloys: Zn1.82Cu0.10Ti0.05Mo (HR8), Zn0.08Cu1.86Ti0Mo (HR7), and Zn1.26Cu0.13Ti0.06Mo (HR6), which were extensively evaluated for their microstructure, mechanical properties, electrochemical performance, potential as bioimplants, and cytotoxicity. These alloys were found to exhibit enhanced mechanical strength, optimal degradation rates, and superior biocompatibility, evidenced by in-vivo experiments with SD rats, positioning them as promising candidates for medical implants. This research not only introduces a significant advancement in biodegradable alloy development but also proposes an efficient method for their production, marking a pivotal step forward in biomedical engineering. Statement of significanceThe limited mechanical properties of pure Zn have hindered its application in biodegradable implants. Our research primarily focuses on the alloy design and process strategies of biodegradable Zn-based alloys. We explore the ZnCuxTixMox alloys. This study introduces a high-throughput experimental approach for efficient screening of multi-component alloy systems with optimal properties. The ZnCuxTixMox alloys were designed and processed through gradient continuous casting, followed by homogenization and hot rolling. Our findings indicate that the Zn1.82Cu0.10Ti0.05Mo alloy demonstrates superior tensile, mechanical, and corrosion properties post hot rolling. The study suggests that Zn0.13Cu1.26Ti0.06Mo, Zn0.08Cu1.86Ti0Mo, and Zn1.82Cu0.10Ti0.05Mo alloys hold significant potential as biodegradable materials.

A cost-benefit analysis of mass prostate cancer screening

BackgroundProstate cancer (PCa) causes a substantial health and financial burden worldwide, underscoring the need for efficient mass screening approaches. This study attempts to evaluate the Net Cost-Benefit Index (NCBI) of PCa screening in Iran to offer insights for informed decision-making and resource allocation.MethodThe Net Cost-Benefit Index (NCBI) was calculated for four age groups (40 years and above) using a decision-analysis model. Two screening strategies, prostate-specific antigen (PSA) solely and PSA with Digital Rectal Examination (DRE), were evaluated from the health system perspective. A retrospective assessment of 1402 prostate cancer (PCa) patients’ profiles were conducted, and direct medical and non-medical costs were calculated based on the 2021 official tariff rates, patient records, and interviews. The monetary value of mass screening was determined through Willingness to Pay (WTP) assessments, which served as a measure for the benefit aspect.ResultThe combined PSA and DRE strategy of screening is cost-effective, yields up to $3 saving in costs per case and emerges as the dominant strategy over PSA alone. Screening for men aged 70 and above does not meet economic justification, indicated by a negative Net Cost-Benefit Index (NCBI). The 40–49 age group exhibits the highest net benefit, $13.81 based on basic information and $13.54 based on comprehensive information. Sensitivity analysis strongly supports the cost-effectiveness of the combined screening approach.ConclusionThis study advocates prostate cancer screening with PSA and DRE, is economically justified for men aged 40–69. The results of the study recommend that policymakers prioritize resource allocation for PCa screening programs based on age and budget constraints. Men’s willingness to pay, especially for the 40–49 age group which had the highest net benefit, leverages their financial participation in screening services. Additionally, screening services for other age groups, such as 50–54 or 55–59, can be provided either for free or at a reduced cost.

Fully automated deep learning system for osteoporosis screening using chest computed tomography images.

Osteoporosis, a disease stemming from bone metabolism irregularities, affects approximately 200 million people worldwide. Timely detection of osteoporosis is pivotal in grappling with this public health challenge. Deep learning (DL), emerging as a promising methodology in the field of medical imaging, holds considerable potential for the assessment of bone mineral density (BMD). This study aimed to propose an automated DL framework for BMD assessment that integrates localization, segmentation, and ternary classification using various dominant convolutional neural networks (CNNs). In this retrospective study, a cohort of 2,274 patients underwent chest computed tomography (CT) was enrolled from January 2022 to June 2023 for the development of the integrated DL system. The study unfolded in 2 phases. Initially, 1,025 patients were selected based on specific criteria to develop an automated segmentation model, utilizing 2 VB-Net networks. Subsequently, a distinct cohort of 902 patients was employed for the development and testing of classification models for BMD assessment. Then, 3 distinct DL network architectures, specifically DenseNet, ResNet-18, and ResNet-50, were applied to formulate the 3-classification BMD assessment model. The performance of both phases was evaluated using an independent test set consisting of 347 individuals. Segmentation performance was evaluated using the Dice similarity coefficient; classification performance was appraised using the receiver operating characteristic (ROC) curve. Furthermore, metrics such as the area under the curve (AUC), accuracy, and precision were meticulously calculated. In the first stage, the automatic segmentation model demonstrated excellent segmentation performance, with mean Dice surpassing 0.93 in the independent test set. In the second stage, both the DenseNet and ResNet-18 demonstrated excellent diagnostic performance in detecting bone status. For osteoporosis, and osteopenia, the AUCs were as follows: DenseNet achieved 0.94 [95% confidence interval (CI): 0.91-0.97], and 0.91 (95% CI: 0.87-0.94), respectively; ResNet-18 attained 0.96 (95% CI: 0.92-0.98), and 0.91 (95% CI: 0.87-0.94), respectively. However, the ResNet-50 model exhibited suboptimal diagnostic performance for osteopenia, with an AUC value of only 0.76 (95% CI: 0.69-0.80). Alterations in tube voltage had a more pronounced impact on the performance of the DenseNet. In the independent test set with tube voltage at 100 kVp images, the accuracy and precision of DenseNet decreased on average by approximately 14.29% and 18.82%, respectively, whereas the accuracy and precision of ResNet-18 decreased by about 8.33% and 7.14%, respectively. The state-of-the-art DL framework model offers an effective and efficient approach for opportunistic osteoporosis screening using chest CT, without incurring additional costs or radiation exposure.

Implementation of pooled saliva tests for universal screening of cCMV infection.

Congenital cytomegalovirus (cCMV) is the most common intrauterine infection, leading to neurodevelopmental disabilities. Universal newborn infant screening of cCMV has been increasingly advocated. In the absence of a high-throughput screening test, which can identify all infected newborn infants, the development of an accurate and efficient testing strategy has remained an ongoing challenge. Here we assessed the implementation of pooled saliva polymerase chain reaction (PCR) tests for universal screening of cCMV, in two hospitals of Jerusalem from April 2022 through April 2023. During the 13-month study period, 15,805 infants (93.6% of all live newborn infants) were screened for cCMV using the pooled approach that has since become our routine screening method. The empirical efficiency of the pooling was six (number of tested newborn infants per test), thereby sparing 83% of the saliva tests. Only a minor 3.05 PCR cycle loss of sensitivity was observed for the pooled testing, in accordance with the theoretical prediction for an eight-sample pool. cCMV was identified in 54 newborn infants, with a birth prevalence of 3.4 per 1,000; 55.6% of infants identified with cCMV were asymptomatic at birth and would not have been otherwise targeted for screening. The study demonstrates the wide feasibility and benefits of pooled saliva testing as an efficient, cost-sparing and sensitive approach for universal screening of cCMV.

A new approach based on VIKOR and Monte-Carlo algorithms for determining the most efficient enhanced oil recovery methods: EOR screening

Most of oil reservoirs in the world have faced decrease in production and they are in the second half of their life cycle. Therefore, tertiary and enhanced oil recovery (EOR) techniques are needed for continuous production from these reservoirs. As choosing the most appropriate EOR methods for a reservoir is a challenging task for reservoir engineers, screening of EOR approaches is of high importance before any full field simulation and experiments. Enhanced oil recovery screening is a multiple criteria decision-making (MCDM) problem and hence, a systematic statistical algorithm based on MCDM can be used for this purpose. In this study, for the first time, a new EOR screening method is proposed by using VIKOR and Monte-Carlo algorithms. The approach used a large database of successful EOR projects around the world and was applied to 12 various EOR methods including a wide range of conditions and properties. Pre-processing was performed on the gathered database and then based on reservoir engineering analyses and using a pairwise comparison matrix, initial weights were considered for the parameters in each EOR method. Afterward, these weights were used in the proposed VIKOR MCDM calculation algorithm and the corresponding numerical values of EOR techniques for each reservoir were obtained. Finally, the EOR method with the highest corresponding value was selected as the most suitable method. Results demonstrated that by using the presented approach, a high classification accuracy of 98% was obtained for different cases, which shows the proficiency and robustness of the developed screening algorithm. In addition, the reliability of the developed method was validated using data obtained from 11 oil reservoirs in the southwest of Iran. Also, the results were compared with the results of previous studies and they were in a very good match. The developed approach is less expensive and faster than full field simulation method and can be used as an efficient EOR screening approach for reservoirs with different properties in the world.

Artificial intelligence-guided approach for efficient virtual screening of hits against Schistosoma mansoni.

Background: The impact of schistosomiasis, which affects over 230million people, emphasizes the urgency of developing new antischistosomal drugs. Artificial intelligence is vital in accelerating the drug discovery process. Methodology & results: We developed classification and regression machine learning modelsto predict the schistosomicidal activity of compounds not experimentally tested. The prioritized compounds were tested on schistosomula and adult stages of Schistosoma mansoni. Four compounds demonstrated significant activity against schistosomula, with 50% effective concentration values ranging from 9.8 to 32.5μM, while exhibiting no toxicity in animal and human cell lines. Conclusion: These findings represent a significant step forward in the discovery of antischistosomal drugs. Further optimization of these active compounds can pave the way for their progression into preclinical studies.

Uncovering the magnitude of African pangolin poaching with extensive nanopore DNA genotyping of seized scales.

Trade in pangolins is illegal, and yet tons of their scales and products are seized at various ports. These large seizures are challenging to process and comprehensively genotype for upstream provenance tracing and species identification for prosecution. We implemented a scalable DNA barcoding pipeline in which rapid DNA extraction and MinION sequencing were used to genotype a substantial proportion of pangolin scales subsampled from 2 record shipments seized in Singapore in 2019 (37.5t). We used reference sequences to match the scales to phylogeographical regions of origin. In total, we identified 2346 cytochrome b (cytb) barcodes of white-bellied (Phataginus tricuspis) (from 1091 scales), black-bellied (Phataginus tetradactyla) (227 scales), and giant (Smutsia gigantea) (1028 scales) pangolins. Haplotype diversity was higher for P. tricuspis scales (121 haplotypes, 66 novel) than that for P. tetradactyla (22 haplotypes, 15 novel) and S. gigantea (25 haplotypes, 21 novel) scales. Of the novel haplotypes, 74.2% were likely from western and west-central Africa, suggesting potential resurgence of poaching and newly exploited populations in these regions. Our results illustrate the utility of extensively subsampling large seizures and outline an efficient molecular approach for rapid genetic screening that should be accessible to most forensic laboratories and enforcement agencies.

Comparative analysis of three immunochromatographic test strips for rapid detection of cadmium ion in Asparagus

SummaryCurrently, there is increasing concern regarding the presence of cadmium ion (Cd2+) residues in Asparagus, necessitating the development of a rapid, sensitive and effective detection method. Immunochromatographic detection techniques offer a swift and efficient primary screening approach, significantly reducing labour and material resources compared to traditional detection methods. In this study, we aimed to construct and optimise an immunochromatographic test strip method for precise determination of Cd2+ content in Asparagus. To achieve this, monoclonal cadmium antibody (4A9) was prepared and conjugated with colloidal gold nanoparticles (AuNPs), latex microspheres (LM) and polydopamine (PDA) to create immunoprobes for test strip construction. We optimised key factors, such as probe parameters, strip assembly conditions and sample pretreatment methods. The sensitivity and detection capabilities of the three test strips were evaluated using actual Asparagus samples. The results demonstrated that all three immunochromatographic test strips exhibited high sensitivity, efficiency and rapidity in detecting Cd2+ levels in Asparagus. Notably, the LM‐based and PDA‐based test strips showed outstanding performance, with a visual limit of detection (vLOD) of 0.1 ng mL−1 and an elimination value of 2.0 ng mL−1. These findings underscore the promising potential of these test strips for practical cadmium detection in Asparagus samples.

High-Throughput Small Molecule Drug Screening For Age-Related Sleep Disorders Using Drosophila melanogaster.

Sleep, an essential component of health and overall well-being, often presents challenges for older individuals who frequently experience sleep disorders characterized by shortened sleep duration and fragmented patterns. These sleep disruptions also correlate with an increased risk of various illnesses in the elderly, including diabetes, cardiovascular diseases, and psychological disorders. Unfortunately, existing drugs for sleep disorders are associated with significant side effects such as cognitive impairment and addiction. Consequently, the development of new, safer, and more effective sleep disorder medications is urgently needed. However, the high cost and lengthy experimental duration of current drug screening methods remain limiting factors. This protocol describes a cost-effective and high-throughput screening method that utilizes Drosophila melanogaster, a species with a highly conserved sleep regulation mechanism compared to mammals, making it an ideal model for studying sleep disorders in the elderly. By administering various small compounds to aged flies, we can assess their effects on sleep disorders. The sleep behaviors of these flies are recorded using an infrared monitoring device and analyzed with the open-source data package Sleep and Circadian Analysis MATLAB Program 2020 (SCAMP2020). This protocol offers a low-cost, reproducible, and efficient screening approach for sleep regulation. Fruit flies, due to their short life cycle, low husbandry cost, and ease of handling, serve as excellent subjects for this method. As an illustration, Reserpine, one of the tested drugs, demonstrated the ability to promote sleep duration in elderly flies, highlighting the effectiveness of this protocol.

Calcium-Alginate-Chitosan Nanoparticle as a Potential Solution for Pesticide Removal, a Computational Approach.

Pesticides have a significant negative impact on the environment, non-target organisms, and human health. To address these issues, sustainable pest management practices and government regulations are necessary. However, biotechnology can provide additional solutions, such as the use of polyelectrolyte complexes to encapsulate and remove pesticides from water sources. We introduce a computational methodology to evaluate the capture capabilities of Calcium-Alginate-Chitosan (CAC) nanoparticles for a broad range of pesticides. By employing ensemble-docking and molecular dynamics simulations, we investigate the intermolecular interactions and absorption/adsorption characteristics between the CAC nanoparticles and selected pesticides. Our findings reveal that charged pesticide molecules exhibit more than double capture rates compared to neutral counterparts, owing to their stronger affinity for the CAC nanoparticles. Non-covalent interactions, such as van der Waals forces, π-π stacking, and hydrogen bonds, are identified as key factors which stabilized the capture and physisorption of pesticides. Density profile analysis confirms the localization of pesticides adsorbed onto the surface or absorbed into the polymer matrix, depending on their chemical nature. The mobility and diffusion behavior of captured compounds within the nanoparticle matrix is assessed using mean square displacement and diffusion coefficients. Compounds with high capture levels exhibit limited mobility, indicative of effective absorption and adsorption. Intermolecular interaction analysis highlights the significance of hydrogen bonds and electrostatic interactions in the pesticide-polymer association. Notably, two promising candidates, an antibiotic derived from tetracycline and a rodenticide, demonstrate a strong affinity for CAC nanoparticles. This computational methodology offers a reliable and efficient screening approach for identifying effective pesticide capture agents, contributing to the development of eco-friendly strategies for pesticide removal.