Rapid Screening Research Articles

Rapid screening of prednisolone acetate adulterants in health foods using colloidal gold immunochromatographic assay

In this study, a rapid detection method utilizing colloidal gold immunochromatography (CG-ICA) was developed for the detection of illegally added prednisone acetate in health foods. Initially, the preparation conditions of colloidal gold solution were optimized. The optimal potassium carbonate dosage, antibody diluent type, antibody dosage, probe labeling time, blocking time and BSA dosage were determined. Technical analysis was performed to ensure that the established CG-ICA exhibited satisfactory color development and inhibition rates. Under optimized conditions, the cut-off value of CG-ICA was 250 μg/kg. The assay demonstrated a sensitivity of 100 %, a false positive rate of 8 %, and a false negative rate of 0, indicating high specificity for prednisone acetate. The results obtained from testing actual samples were consistent with those obtained using LC-MS/MS, thereby verifying the reliability of the developed method. This method offers robust support for the rapid detection of illegally added prednisone acetate in health foods.

Machine learning-based rapid visual screening for building damage assessment: study of Joshimath town of Garhwal Himalaya, India

Machine learning-based rapid visual screening for building damage assessment: study of Joshimath town of Garhwal Himalaya, India

Uncertainty Quantification of Linear Scaling, Machine Learning, and Density Functional Theory Derived Thermodynamics for the Catalytic Partial Oxidation of Methane on Rhodium.

Accurate and complete microkinetic models (MKMs) are powerful for anticipating the behavior of complex chemical systems at different operating conditions. In heterogeneous catalysis, they can be further used for the rapid development and screening of new catalysts. Density functional theory (DFT) is often used to calculate the parameters used in MKMs with relatively high fidelity. However, given the high cost of DFT calculations for adsorbates in heterogeneous catalysis, linear scaling relations (LSRs) and machine learning (ML) models were developed to give rapid estimates of the parameters in MKM. Regardless of the method, few studies have attempted to quantify the uncertainty in catalytic MKMs, as the uncertainties are often orders of magnitude larger than those for gas phase models. This study explores uncertainty quantification and Bayesian Parameter Estimation for thermodynamic parameters calculated by DFT, LSRs, and GemNet-OC, a ML model developed under the Open Catalyst Project. A model for catalytic partial oxidation of methane (CPOX) on Rhodium was chosen as a case study, in which the model's thermodynamic parameters and their associated uncertainties were determined using DFT, LSR, and GemNet-OC. Markov Chain Monte Carlo coupled with Ensemble Slice Sampling was used to sample the highest probability density (HPD) region of the posterior and determine the maximum of the a posteriori (MAP) for each thermodynamic parameter included. The optimized microkinetic models for each of the three estimation methods had quite similar mechanisms and agreed well with the experimental data for gas phase mole fractions. Exploration of the HPD region of the posterior further revealed that adsorbed hydroxide and oxygen likely bind on facets other than Rhodium 111. The demonstrated workflow addresses the issue of inaccuracies arising from the integration of data from multiple sources by considering both experimental and computational uncertainties, and further reveals information about the active site that would not have been discovered without considering the posterior.

B-152 Analysis of carbonyl compounds in exhaled breath for detection of COVID-19

Abstract Background COVID-19 pandemic has caused enormous loss of lives and economic disruption. The global crisis has resulted in more than 700 million infections and 7 million deaths worldwide. Rapid screening and detection of highly infectious respiratory diseases is a key for preventing and curbing future epidemic and pandemic. The objective of this work is to develop a novel breath analysis technique for detection of COVID-19 using chemoselective capture of carbonyl compounds in exhaled breath for analysis by gas chromatography-mass spectrometry (GC-MS). Respiratory viral infection causes oxidative stress and inflammation in its host body which leads to production of higher levels of some carbonyl compounds through oxidation of lipids and could be detected in exhaled breath. This breath analysis technique can be rapidly adapted for screening infectious respiratory diseases for future epidemic and potential pandemic. Methods A total of 237 subjects were enrolled in this study. Of these, 142 were COVID-19 positive and 95 were negative. One liter exhaled breath in Tedlar bags from both COVID-19 positive and negative subjects confirmed by reverse transcriptase polymerase chain reaction between April 2022 and September 2023. The collected one liter breath samples were completely evacuated through a cartridge packed with silica particles in 2-3 minutes. Silica particles were coated with a reactive aminooxy agent for capture of carbonyl compounds in the breath samples through oximation reactions. The captured carbonyl compounds were eluted by methanol and the eluted solutions were analyzed by GC-MS for calculation of each compound concentration in the exhaled breath samples. Biostatistics was used to identify significant carbonyl compounds (defined as p value < 0.05) as markers of CPOVID-19 infection. A machine learning algorithm was implemented with the captured carbonyl compounds as input features for differentiation of COVID-19 positive from negative subjects. Results A total of more than 30 carbonyl compounds were detected in exhaled breath samples of all subjects. 10 features including 3 individual carbonyl compounds and 7 compound ratios show significant differences (p value <0.05) between COVID-19 positive and negative groups. The developed machine learning and artificial intelligence (AI) algorithms successfully separated COVOD-19 positive from negative with an accuracy of 95.4%, sensitivity of 96.5%, specificity of 93.7%, and AUROC=0.974. Conclusions The analysis of carbonyl compounds in exhaled breath by GC-MS has great potential for non-invasive detection of COVID-19. The method could be rapidly adapted for detection of other respiratory infectious diseases with a small sample size and data set for training of the machine learning and AI algorithms.

Three birds with one stone: A nano zeolitic imidazolate framework-luciferase-antimicrobial peptide biocomposite-based biosensing platform for improving enzyme stability, detection sensitivity, and sterilization effect

Adenosine triphosphate-driven bioluminescence (ATP-BL) biosensors were widely employed for on-site screening of bacteria. However, unstable luciferase with strict storage conditions limited its field detection. Moreover, the traditional ATP-BL biosensors lacked the sterilization effect. In this study, a robust and multifunctional zeolitic imidazolate framework-90 decorated with luciferase and polyethylene glycol–antimicrobial peptides (ZIF-90@Luc-AMP) was prepared. It could not only enhance the activity and stability of luciferase at ambient temperatures (≥1 month) but also sensitively detect target bacteria via the bioluminescence (BL) method and kill them (three birds with one stone). During the detection process, the target pathogen Escherichia coli O157:H7 (E. coli O157:H7) was specifically captured on a phage-modified stir bar and formed a sandwich complex with ZIF-90@Luc-AMP. With the synergistic bacteriolysis of ZIF-90@Luc-AMP and the phage, the captured E. coli O157:H7 was decomposed and emitted adenosine triphosphate (ATP), achieving the sterilization effect. Meanwhile, the introduced luciferase catalyzed ATP to produce a BL signal, which was proportional to the concentration of E. coli O157:H7. Combining ZIF-90@Luc-AMP and a phage-labeled stir bar for target recognition and enrichment, the entire analytical process could be easily manipulated within 30 min with a low limit of detection (20 CFU/mL) through a portable ATP bioluminescent meter. The multifunctional ZIF-90@Luc-AMP biosensor with high stability, specificity, and sensitivity was also appropriate for rapid pathogen screening and antibacterial application in the wild.

B-273 Clinical Validation of a Novel Lateral Flow Assay for Rapid and Sensitive Urine Fentanyl Screening

Abstract Background The urgent need for rapid fentanyl detection at the point-of-care originates from its involvement in a high number of overdose deaths, primarily due to illicit use. Given fentanyl's potency compared to traditional opioids, accurate and sensitive detection is critical. Current lateral flow assays (LFA) cannot detect single-digit concentrations and may yield false negatives due to their lower sensitivity. Herein, we evaluate the clinical performance of a novel LFA (InstaStrip Fentanyl Rapid Test (urine)) with 1 ng/mL cutoff for fentanyl and no cross-reactivity for norfentanyl, offering enhanced sensitivity for effective overdose prevention at the ED. Methods A total of 172 urine samples submitted for UDS analysis were used for comparison studies. Fentanyl concentrations were categorized in 5 groups to cover various concentration categories around the cutoff of the LFA (1 ng/mL). These categories are: drug-free (<0.1 ng/mL, n=68), 0.1-0.5 ng/mL (n=14), 0.5-0.9 ng/mL (n=15), 1-1.5 ng/mL (n=9) and > 1.5 ng/mL (n= 66). Concentrations were determined by in-house LC-MS/MS methods validated according to CLIA standards, with fentanyl concentrations quantitated in the ABSciex software. For LFA testing, 150 μL of urine was added to a reagent tube containing dried rabbit anti-fentanyl antibody conjugated gold nanoparticles. After 20 sec of brief shaking, a test strip was inserted into the test tube. After 5 min, qualitative results were read as follow: both the control and test lines were visible in negative results, whereas only the control lines were visible in positive results. Method comparison study of the LFA was conducted by three operators who were blinded to the LC-MS/MS results. Result comparisons were made between the LFA strip and LC-MS/MS results, as well as among different operators for the same samples. Results Of the 75 urine samples with LC-MS/MS fentanyl concentrations ≥ 1 ng/mL, 74 produced consistently positive results using the InstaStrip Fentanyl Rapid Test. Of the 97 urine samples with LC-MS/MS fentanyl concentrations < 1 ng/mL, 96 produced consistently negative results using the InstaStrip Fentanyl Rapid Test. Using the LC-MS/MS as the gold standard method and 1 ng/mL as a cutoff, the clinical sensitivity of InstaStrip Fentanyl Rapid Test was calculated to be 98.67% (95% confidence interval (CI) 92.79-99.97%), and the clinical specificity was 98.97% (95% CI 94.39-99.97%). Consistent results among three operators were obtained for 100% of the “drug free”, “0.1- 0.5 ng/mL” and > 1.5 ng/mL groups, whereas 93.3 % (14 out of 15) and 88.9% (7 out of 8) were obtained for the “0.5-0.9 ng/mL” and “1-1.5 ng/mL” groups, respectively. The two samples with a singlet discrepant result (1 false positive and 1 false negative) in the triplicate experiments contained fentanyl concentrations of 0.9 and 1.0 ng/mL respectively, which are very close to the cutoff value. Conclusions In this study, the InstaStrip Fentanyl Rapid Test (urine) demonstrated high clinical sensitivity (98.67%), high specificity (98.97%) and high reproducibility showing that it may be useful in screening and identifying patients with fentanyl overdose in minutes in an emergency setting. This would facilitate appropriate patient triaging for optimal clinical care.

Deep learning-based analysis of EGFR mutation prevalence in lung adenocarcinoma H&E whole slide images.

EGFR mutations are a major prognostic factor in lung adenocarcinoma. However, current detection methods require sufficient samples and are costly. Deep learning is promising for mutation prediction in histopathological image analysis but has limitations in that it does not sufficiently reflect tumor heterogeneity and lacks interpretability. In this study, we developed a deep learning model to predict the presence of EGFR mutations by analyzing histopathological patterns in whole slide images (WSIs). We also introduced the EGFR mutation prevalence (EMP) score, which quantifies EGFR prevalence in WSIs based on patch-level predictions, and evaluated its interpretability and utility. Our model estimates the probability of EGFR prevalence in each patch by partitioning the WSI based on multiple-instance learning and predicts the presence of EGFR mutations at the slide level. We utilized a patch-masking scheduler training strategy to enable the model to learn various histopathological patterns of EGFR. This study included 868 WSI samples from lung adenocarcinoma patients collected from three medical institutions: Hallym University Medical Center, Inha University Hospital, and Chungnam National University Hospital. For the test dataset, 197 WSIs were collected from Ajou University Medical Center to evaluate the presence of EGFR mutations. Our model demonstrated prediction performance with an area under the receiver operating characteristic curve of 0.7680 (0.7607-0.7720) and an area under the precision-recall curve of 0.8391 (0.8326-0.8430). The EMP score showed Spearman correlation coefficients of 0.4705 (p = 0.0087) for p.L858R and 0.5918 (p = 0.0037) for exon 19 deletions in 64 samples subjected to next-generation sequencing analysis. Additionally, high EMP scores were associated with papillary and acinar patterns (p = 0.0038 and p = 0.0255, respectively), whereas low EMP scores were associated with solid patterns (p = 0.0001). These results validate the reliability of our model and suggest that it can provide crucial information for rapid screening and treatment plans.

A-326 Testing for Multidrug-Resistant Acinetobacter baumannii with a Microfluidic Device

Abstract Background Multidrug-resistant (MDR) Acinetobacter baumannii in hospitals contribute to high mortality rates and entail expensive infection control procedures. Conventionally, species and resistance determination of MDR bacteria is performed by mass spectrometry and labor-intensive phenotypical assays in clinical microbiology laboratories. Some institutions also apply time-consuming and expensive whole genome sequencing for in-depth resistance and transmission analysis. In contrast, faster and more cost-efficient MDR typing can be achieved by on-site application of real-time PCR-based point-of-care tests (POCTs) that target species- and resistance-specific genes and enable rapid screening of large sample sizes. Methods To construct a real-time PCR-based POCT system for species and resistance determination of MDR A. baumannii, we selected two species-specific genes (rpoB, OXA-51-like) and 18 resistance genes found in MDR A. baumannii (OXA-23-like, OXA-24/40-like, OXA-51-like, OXA-58-like, OXA-143-like, mcr, ADC, NDM, GIM, VIM, IMP, GES, PER, TEM, SHV, VEB, CTX-M, KPC) as targets. For each gene, one to six sets of primers and fluorophore-labeled probes that amplify conserved genetic regions were designed and their functionality was tested in vitro. Parallel, a fully automated system comprising microfluidic chips and a bench-top analyzer able to perform DNA extraction and real-time PCR starting from swab eluate was developed. Results All oligonucleotides tested so far reliably detected 100 to 100,000 copies of DNA extracted from MDR bacteria in single- and multiplex experiments in vitro. Tests with the microfluidic system showed that lysis of different bacterial species could be accomplished via sonication on a membrane located on the microfluidic chip. In addition, chip implementation of all PCR reagents like lyophilized master mix as well as primers and probes was successful and can be stored on chip at least one year. So far, the system delivered detectable fluorescence signals within one hour when performing test runs with bacteria eluted from swabs. Conclusions A real-time PCR-based Point-of-Care screening platform was developed for the detection of antibiotic-resistant A. baumannii from various patient sample materials starting directly from swabs, which detects colonization/infection and at the same time the relevant resistance genes within about an hour.

Establishment of a platform based on dual RPA combined with CRISPR/Cas12a for the detection of Klebsiella pneumoniae and its KPC resistance gene.

Carbapenem resistant Klebsiella pneumoniae (CRKP) can cause serious hospital- and community-acquired infections. Treatment for CRKP infection is limited, resulting in prolonged hospitalization and high consultation costs. The KPC genotype has the highest detection rate of CRKP, and its mortality rate is higher than the overall mortality rate of CRKP. However, traditional testing methods have disadvantages such as long time and reliance on complex and sophisticated instruments, which are not conducive to rapid screening for CRKP. Therefore, this study aimed to establish a detection platform for early screening of CRKP so that effective antimicrobial therapy could be administered promptly to prevent the widespread spread of CRKP. We integrated dual RPA with CRISPR/Cas12a to establish a dual platform for the detection of K. pneumoniae (Kp) rcsA-specific gene and KPC resistance gene. Four result reading methods were established, including fluorescence detection (FD), blue light irradiation detection (BLID), ultraviolet irradiation detection (UID), and lateral flow test strips (LFTS). For the rcsA gene, the LOD of FD was 1 × 10pg/μL, and the other three methods could detect 1 × 101pg/μL of bacterial DNA. As for the KPC gene, four resultant readout methods were able to detect 1 × 102pg/μL of bacterial DNA. In 59 clinical strains tested, the dual RPA-CRISPR/Cas12a detection of the rcsA had 100% sensitivity, specificity, and accuracy compared to the culture method. Compared with the drug sensitivity test, the sensitivity of dual RPA-CRISPR/Cas12a detection for the KPC was 85.71%, the specificity was 100%, and the accuracy was 94.92%. In summary, our dual RPA-CRISPR/Cas12a platform proved to be rapid, precise, and convenient for the efficient detection of Kp with KPC in the laboratory or at the point of care.

A-251 Simplification of Molecular Diagnostics for Sexually Transmitted Infection Point-of-Care Testing

Abstract Background HPV-caused cancers contribute to a large disease burden, accounting for 5% of all cancers. It is estimated that over 700,000 people develop HPV-related cancers each year with the majority being cervical cancer. The burden of this disease is disproportionately carried by low-income countries with the highest rates of HPV cervical infections primarily found in sub-Saharan Africa, Latin America, Eastern Europe, and South-East Asia. Many regions in low-resource countries lack adequate access to sensitive point-of-care screening tools, preventing timely diagnosis and treatment. Current low-cost methods, such as Pap smears and visual inspection using acetic acid suffer from low sensitivity, specificity, and reproducibility which limit their effectiveness as a screening modality. Many countries use nucleic acid amplification tests (NAATs) to identify key HPV infections which may progress towards cervical cancer. However, the expenditure for NAAT-capable equipment and reagents are cost-prohibitive for rural and underserved communities. To reduce screening barriers, we developed an isothermal HPV molecular test that can detect all 14 cancer-causing HPV strains using a novel amplification methodology combined with G-quadruplex DNAzyme colorimetric detection. Methods We developed a novel isothermal amplification technique that is related to loop-mediated isothermal amplification (LAMP) but uses modified looped primers to enable exponential amplification at 60°C for 1 hour. The modified primers only require two binding sites compared to 4-6 in LAMP. To detect the amplified products, we used G-quadruplex peroxidase-like DNAzyme probes which reduce ABTS to produce a visually interpreted result. Analytical validation evaluated the method’s limit of detection, specificity, and accuracy. As proof of concept in a relevant matrix, clinical cervical brushings collected ThinPrep Pap media were tested with the new method and compared to an FDA-approved reference qPCR test. Results Repeated experiments demonstrate that the lowest detectable HPV DNA concentration was approximately 1000 genomic copies/µL with no amplification with other pathogens. As proof of concept, the comparison to the reference standard demonstrated high accuracy. Conclusions The application of our assay is intended as a near-patient screening tool with further evaluation by a clinician for confirmation. With an accurate and rapid screening tool, we envision low-resource regions across the world will have the tools to rapidly detect and treat cervical cancers in their early stages.

Rapid determination of organophosphorus insecticides residue in fruit juice by a fluorescence sensor based on carbon quantum dots

Nitrogen doped carbon quantum dots (N-CQDs) were synthesized by a hydrothermal method. A fluorescence sensor for rapid determination of organophosphorus pesticides residue was developed using the N-CQDs based on acetylcholine enzyme inhibition. Cu2+ effectively quenched the strong fluorescence of N-CQDs, which could be recovered due to the complexation between copper ions and thiocholine, the hydrolysis product of acetylthiocholine catalyzed by acetylcholinesterase. Organophosphorus pesticides inhibited the activity of acetylcholinesterase, resulting in fluorescence quenching of N-CQDs. Fluorescence inhibiting efficiency depends on the concentration of the analytes, and therefore, the sensor could be used for quantitative analysis of organophosphorus pesticides residue. The average recoveries of the analytes ranged from 85.33 % to 110.67 % at the spiked concentrations of 0.015 mg/L, 0.04 mg/L and 0.1 mg/L, and the limits of detection were 2.89 ∼6.40 ng/mL. The developed method had the advantages of simple operation, fast speed, low cost, expensive apparatus-free, and environmental friendliness, which could be used for rapid screening of organophosphorus pesticides residue in real samples.

Analyzing high-throughput assay data to advance the rapid screening of environmental chemicals for human reproductive toxicity.

While high-throughput (HTP) assays have been proposed as platforms to rapidly assess reproductive toxicity, there is currently a lack of established assays that specifically address germline development/function and fertility. We assessed the applicability domains of yeast (S. cerevisiae) and nematode (C. elegans) HTP assays in toxicity screening of 124 environmental chemicals, determining their agreement in identifying toxicants and their concordance with reproductive toxicity in vivo. We integrated data generated in the two models and compared results using a streamlined, semi-automated benchmark dose (BMD) modeling approach. We then extracted and modeled relevant mammalian in vivo data available for the matching chemicals included in the Toxicological Reference Database (ToxRefDB). We ranked potencies of common compounds using the BMD and evaluated correlation between the datasets using Pearson and Spearman correlation coefficients. We found moderate to good correlation across the three data sets, with r = 0.48 (95 % CI: 0.28-1.00, p<0.001) and rs = 0.40 (p=0.002) for the parametric and rank order correlations between the HTP BMDs; r = 0.95 (95 % CI: 0.76-1.00, p=0.0005) and rs = 0.89 (p=0.006) between the yeast assay and ToxRefDB BMDs; and r = 0.81 (95 % CI: 0.28-1.00, p=0.014) and rs = 0.75 (p=0.033) between the worm assay and ToxRefDB BMDs. Our findings underscore the potential of these HTP assays to identify environmental chemicals that exhibit reproductive toxicity. Integrating these HTP datasets into mammalian in vivo prediction models using machine learning methods could further enhance their predictive value in future rapid screening efforts.

Rapid screening and identification strategy of lactic acid bacteria and yeasts based on Ramanomes technology and its application in fermented food

There is an urgent need for quick, sensitive, thorough, and low-cost preliminary screening and rapid identification method for probiotic resource development. Here, we constructed a culture-free, accurate and sensitive “separation-enrichment-detection” rapid evaluation and identification method of probiotics in fermented food based on Ramanomes technology, and established a Ramanome reference of lactic acid bacteria and yeasts by AgNPs nanostructure array (AgNPs NSA) chips with uniform “hot spots” and high signal enhancement ability as SERS substrates. Systematic cluster analysis could clearly distinguish among different genera of lactic acid bacteria and yeast, and could indicate the affinity and difference between lactic acid bacteria of the same genus and yeast of the same genus. The recognition accuracy of convolutive neural networks was 100 %, and the recognition sensitivity was less than 10 CFU/mL. We constructed a probiotic isolation and enrichment method by velocity gradient centrifugation and density gradient differential centrifugation, and the average recoveries of lactobacilli and yeasts were greater than 98 % in the practical application, and the accuracy of the verified classification was 100 %. In conclusion, this study has established a preliminary screening strategy of “screening before cultivating” for lactic acid bacteria and yeasts, which breaks through the principle limitation of the current traditional paradigm of “cultivating before screening”. It can greatly improve the preliminary screening rate of probiotics in fermented foods, and provide a good technical support for the mining of probiotic resources from environmental or complex matrix samples.

Cocrystal screening in minutes by solution-mediated phase transformation (SMPT): Preparation and characterization of ketoconazole cocrystals with nine aliphatic dicarboxylic acids

The rapid and efficient cocrystal screening, based on solution-mediated phase transformation (SMPT), was applied to the screening of cocrystals between ketoconazole (KTZ) and nine aliphatic dicarboxylic acids. Cocrystals formed successfully, in minutes, with a change of suspension characteristics, either a cake formation or the formation of large particles. Bulk cocrystals were characterized by powder X-ray diffraction, thermal analysis, and Raman spectroscopy. Single crystals were grown, and molecular structures were determined. Three previously reported cocrystals were reproduced, and six new cocrystals were discovered, including one that was reported as a failure in literature by solution or grinding method. Two hydrogen-bonded motifs are observed in these nine cocrystals: Most cocrystals form hydrogen bonded discrete tetramer with two KTZ and two acids molecules; while two cocrystals form infinite chain. This study demonstrated the high efficacy of cocrystal generation using the slurry screening method. It should be fully utilized in future cocrystal screening.

Use of NIR in COVID-19 Screening: Proof of Principles for Future Application.

The COVID-19 pandemic that affected the world between 2019 and 2022 showed the need for new tools to be tested and developed to be applied in global emergencies. Although standard diagnostic tools exist, such as the reverse-transcription polymerase chain reaction (RT-PCR), these tools have shown severe limitations when mass application is required. Consequently, a pressing need remains to develop a rapid and efficient screening test to deliver reliable results. In this context, near-infrared spectroscopy (NIRS) is a fast and noninvasive vibrational technique capable of identifying the chemical composition of biofluids. This study aimed to develop a rapid NIRS testing methodology to identify individuals with COVID-19 through the spectral analysis of swabs collected from the oral cavity. Swab samples from 67 hospitalized individuals were analyzed using NIR equipment. The spectra were preprocessed, outliers were removed, and classification models were constructed using partial least-squares for discriminant analysis (PLS-DA). Two models were developed: one with all the original variables and another with a limited number of variables selected using ordered predictors selection (OPS-DA). The OPS-DA model effectively reduced the number of redundant variables, thereby improving the diagnostic metrics. The model achieved a sensitivity of 92%, a specificity of 100%, an accuracy of 95%, and an AUROC of 94% for positive samples. These preliminary results suggest that NIRS could be a potential tool for future clinical application. A fast methodology for COVID-19 detection would facilitate medical diagnoses and laboratory routines, helping to ensure appropriate treatment.

Molecular-properties based formulation guidance tree for amorphous and supersaturable mesoporous silica preparations of poorly soluble compounds

A huge majority of new chemical entities (NCEs) advancing through the drug discovery pipeline often have poor aqueous solubility. This requires formulation scientists to search for solubility enhancement strategies, within the constraints of time and material. To address these challenges, a strategic platform formulation is often required for a rapid compound screening to enable early exploratory PK and toxicology studies. Through this work, we present an option of a material-sparing, high yielding and solubility-enabling amorphous API and HPMCAS-L co-loaded mesoporous silica-based formulation. The usability of this platform formation strategy was assessed for a physico-chemically diverse set of eleven compounds. The formulation approach was successful in stabilizing the model compounds mesoporous silica. Additionally, through the presence of HPMCAS-L, the precipitation risk in supersaturable aqueous environment was significantly reduced. Finally, this manuscript provides fundamental, computational and experimental molecular-properties based formulation guidance tree to a priori gauge the (1) possibility of generating solid-state stable amorphous formulations and (2) sustaining in vitro supersaturation in extreme non-sink dissolution conditions. This unique and conceptual formulation guidance tree is believed to be extremely beneficial to drug discovery formulators to triage NCEs and streamline solubility-enabling formulation efforts.

Multi-objective optimisation and verification of creep-resistant Ni-base superalloy for electron-beam powder-bed-fusion

This paper reports the use of integrated computational alloy design, coupled with a rapid printability screening method, to downselect from a total of 70000 datasets in design space to five candidates in the first step, and then from five to one in the second step. The new Ni-base superalloy with compositions of Ni-5.03Al-2.69Co-5.63Cr-0.04Hf-1.91Mo-2.36Re-3.32Ta-0.57Ti-8.46W-0.05C-0.019B exhibits an optimal balance of density (8.82 g/cm2), printability (freezing range of 107 °C), thermal stability (γ′-volume fraction of 50.7% at 980 °C and low Md‾ value) and creep (rupture time of 612 h at 980 °C/120 MPa). The micro-hardness varies mildly from 417.2 ± 18.5 to 434.7 ± 14.6 HV, suggesting good phase stability. This is substantiated by microstructure observations, which revealed the absence of a topologically close-packed phase. Machine-learning tools of the artificial neural network (ANN), random forest, and support vector regression, respectively, were used to predict creep rupture time. The ANN algorithm achieves the highest accuracy in predicting creep life. By recognising the “black box” nature of the ANN, interpretability analysis was conducted using the local interpretable model-agnostic method. The analysis supports that the ANN model truly learned meaningful functional relationships, and thus is judged as reliable. Feature correlation evaluation outcome emphasises the importance of incorporating microstructure-related input features.

Evaluation of Cardiotoxicity of Cancer Chemotherapeutics Using Daphnia magna as a Preclinical Model.

One of the major concerns following cancer treatment is cardiotoxicity. Therefore, it is important to predict potential cardiotoxicity of cancer chemotherapeutics at the preclinical phase. Current models of cardiotoxicity testing involve either cell culture models or rodent models. We developed a simple invertebrate animal model for rapid screening of cardiotoxicity of cancer chemotherapeutics. Daphnia magna (water flea, a crustacean) has a transparent body and a large myogenic heart that can be easily monitored under a microscope. Using this model, we have previously described comparative cardiotoxicity of several kinase inhibitors that were approved for the treatment of multiple cancers. In this article, we describe the step-wise protocols for evaluating the heart rate and survival of D. magna with relevant information on troubleshooting. © 2024 Wiley Periodicals LLC. Basic Protocol 1: Culturing and maintenance of D. magna Basic Protocol 2: Experimental design for evaluating heart rate of Daphnia Basic Protocol 3: Long-term effect on Daphnia survival upon drug exposure.

Zebrafish as a model organism to study sporadic Alzheimer's disease: Behavioural, biochemical and histological validation

Alzheimer's disease (AD) is a global burden to the healthcare system with no viable treatment options till date. Rodents and primates have been extensively used as models for understanding AD pathogenesis and identifying therapeutic targets. However, the focus is now shifting towards developing alternate models. Zebrafish is emerging as a preferred model for neurodegenerative conditions because of its simple nervous system, highly conserved genome and short duration required to model disease condition. The present study is aimed to develop streptozotocin (STZ)-induced model of sporadic AD (sAD) in zebrafish. STZ was administered to adult zebrafish (4–6 mo) at different doses (1 to 50 mg/kg body weight, intracerebroventricularly). Kaplan-Meier survival analysis revealed time and dose dependent mortality in the zebrafish administered with STZ. Based on survival analysis, 1 to 10 mg/kg body weight of STZ was selected for behavioural, molecular and histological studies. STZ administered fish had anxiety and stress-like behaviour in novel tank and light/dark preference tests. STZ-induced cognitive and memory deficits assessed using novel object recognition and spatial alternation tests. Further, expression of markers of amyloidogenic pathway (appa and bace1) were increased in terms of mRNA and protein levels in a time and dose dependent manner following STZ administration. However, expression of non-amyloidogenic pathway mediator (adam10) was reduced at both mRNA and protein level. Histological assessment using hematoxylin and eosin, and Nissl stain revealed loss of neurons in STZ administered fish. The ratio of phosphor-tauser396/total-tau was increased in STZ administered fish. Based on these findings, 5 mg/kg body weight of STZ was found to be most appropriate dose to exhibit sAD phenotype. Mass spectrometric analysis confirmed the presence of amyloid beta oligomers in brains of STZ administered fish. Transmission electron microscopy also showed the presence of higher order insoluble amyloid fibrils with twists. Immunohistochemical analysis revealed amyloid beta deposits in brain of STZ administered fish. Golgi-cox staining indicated decreased number of dendrites, whereas microglia had increased density, span ratio, soma area and lacunarity. The results of the present study demonstrate presence of AD hallmarks and phenotype in zebrafish 7 days post STZ administration (5 mg/kg). The study validates the potential of STZ-induced sAD in zebrafish as a reliable model for studying pathophysiology and rapid screening of therapeutic molecules against sAD.

ProTformer: Transformer-based model for superior prediction of protein content in lablab bean (Lablab purpureus L.) using Near-Infrared Reflectance spectroscopy

Lablab bean (Lablab purpureus L.), known for its higher protein content provides a promising alternative to reduce reliance on animal-based proteins and support sustainable agriculture. Nowadays, traditional methods for nutritional profiling have been outperformed by Convolutional Neural Networks (CNNs) integrated with Near-Infrared Reflectance Spectroscopy (NIRS). However, an advanced technique called Transformers, despite their potential, remains underexplored for prediction of key quality traits of crops. Thus, in the present study, we aimed to predict lablab bean protein content using a Transformer-based model (termed ProTformer) coupled with NIRS. The Dumas combustion method was used to determine protein content from 112 lablab bean genotypes. The performance of this model was compared with 1D CNN and Modified Partial Least Squares (MPLS)-based models. These models were evaluated using the Coefficient of Determination (RSQ), Residual Prediction Deviation (RPD), bias, and Corrected Standard Error of Prediction (SEP(C)). Our findings show that the Transformer-based model achieved the highest predictive accuracy (RSQ = 0.977, RPD = 13.92), surpassing the 1D CNN (RSQ = 0.956, RPD = 11.33) and MPLS (RSQ = 0.943, RPD = 10.53)- based models by 22.86% and 32.21% in RPD, respectively. The multi-head self-attention mechanism present in the Transformer-based model enables it to concurrently focus on different spectral regions, resulting in superior predictive performance. This study shows the potential of using Transformer-based models coupled with NIRS for nutritional profiling which could be effectively adapted for other crops for rapid screening of large germplasm available at global repositories.