Test Samples Research Articles

Multi-objective decision optimization design for building energy-saving retrofitting design based on improved grasshopper optimization algorithm

With the national emphasis on building energy efficiency planning, energy efficiency optimization in existing buildings requires renovation measures based on multi-objective factors. In order to get the optimal solution in the multi-objective decision-making of renovation, the study proposes a class of improved grasshopper optimization algorithms. The process employs a systematic methodology to identify an optimal energy renovation method, taking into account the specific characteristics of the building environment. It then classifies and formulates the energy reduction substitution items for building renovation, and finally, it synchronizes the cost of the renovation project as a measure for decision-making. The elite inverse strategy approach enhances the grasshopper optimization algorithm to facilitate the multi-objective decision-making process associated with building renovation measures. The results showed that the improved grasshopper optimization algorithm could achieve a decision accuracy of 98.8% for the test samples, which was 5.5% higher than the accuracy of the particle swarm optimization algorithm. Repeated run tests of the research algorithm for multi-objective decision making yielded a mean decision fitness value of 2.34×104 and a data extreme value of 0.38×104. Compared to other algorithms improved grasshopper optimization algorithm converged in a lower range of fitness values, which indicated that the algorithm worked well for multi-objective optimization and the model repeatability was good. The research algorithm was used to decide the energy efficient renovation planning of the building and the power consumption of the renovated power supply system was reduced by 23.7%-49.6%. This indicates that the renovated building has better energy efficiency and can provide a reliable technical direction for decision-making optimization of building energy efficiency renovation.

Ultra-high-rate Manufacturing of Thermoplastic Window Plug using Hybrid Overmolding

Highly loaded complex parts are machined from a solid block of metal by removing material through cutting, boring, drilling, and grinding to obtain the three-dimensional shape. Most cases, this involves removing as much as 80 percent of the material from a billet, which is time-consuming and inefficient considering the cost of the materials. Alternatively, these complex parts can be manufactured using hybrid overmolding process which involves injecting plastics over a reinforced substrate with continuous fiber-reinforcement eliminating subtractive machining or joining of multiple parts. The substrate is typically heated using an infrared (IR) oven and preformed over a solid mold to a three-dimensional shape using thermoforming. Injection overmolding material can also be reinforced with discontinuous fibers based on the design requirements. Both thermoforming and injection molding are matured automotive ultra-high-rate manufacturing processes. Adopting these technologies to aerospace requires advanced aerospace-grade thermoplastic material systems and developing the hybrid overmolding process that combines both thermoforming and injection molding processes. Material compatibility is one of the key enablers for the fusion across the overmolded interfaces. To demonstrate the overmolding technology for an aerospace application, Victrex AE250 low-melt polyaryletherketone (LM-PAEK) system reinforced with AS4 fibers and a 30% carbon fiber-reinforced polyetheretherketone (PEEK) were used to overmold aircraft window plugs to be used for cargo conversion of passenger aircraft. The fully automated overmolding process was demonstrated using KraussMaffei multi-robotic thermoforming and injection molding system. Thermal and mechanical test evaluations were employed to assess the material compatibility and overall part quality. Photomicrographs and flexure tests conducted on test samples extracted from the window plugs indicated satisfactory fusion across the overmolded interface and optimized holding of PEEK materials.

Performance and mechanism of the structure formation and physical-mechanical properties of concrete by modification with nanodiamonds

Nanomodifying additives in the building materials production is a relevant and widely studied topic in current building materials science. Nanodiamonds are used in various fields of science and technology. However, they were not used as a modifying additive in the production of building composite materials. The purpose of this study is to investigate the mechanism of interaction of chemically pure cavitation synthesis nanodiamonds (КНА-НС) with cement concrete on the structure formation and mechanical properties of fresh and hardened concrete. These KHA-HC nanodiamonds are carbon sp3 hybridized nanoparticles with a diamond-like structure and a particle size in a cross section of not more than 3nm, shaped close to spherical and a clear mono modal distribution throughout the entire fractional composition, with a maximum of 1nm to 3nm. During the research, the initial components were selected, the compatibility of these components was checked, including the formation of test samples, the properties of fresh concrete, the density of hardened concrete, compressive and flexural strength at various ages of hardening (2, 7, 14 and 28 days) were studied using standard methods, and X-ray diffraction and scanning electron microscopy of concrete were also carried out. It has been established that the addition of KHA-HC accelerates the process of concrete strength gain. The greatest effect was recorded for concrete aged 2 days. With an optimal KHA-HC content of 0.4%, the increases in compressive and flexural strength were 28.6% and 26.1%, respectively. The addition of KHA-HC in all considered ranges from 0.1% to 1.0% has a positive effect on the strength properties of concrete. The most effective dosage is 0.4%. The increases in compressive and flexural strength of concrete at an age of 28 days were 15.1% and 15.7%, respectively. Nanomodification of concrete with the KHA-HC additive does not affect the phase composition of concrete. The effect of “self-reinforcement of cement matrix” put forward in this study is confirmed by images of the microstructure of the cement matrix with a large accumulation of calcium hydrosilicate zones.

Research on an aeroengine bearing fault diagnosis method based on WMSST-CNN-BKA-LSSVM

Abstract To effectively extract the fault feature from small sample datasets and improve the accuracy of the model, an aeroengine bearing fault diagnosis method is proposed in this research based on wavelet multi-synchrosqueezed transform-convolutional neural network-black-winged kite algorithm-least squares support vector machine (WMSST-CNN-BKA-LSSVM). WMSST was used to perform modal decomposition on the collected fault data to obtain the time-frequency images. The time-frequency images were input into CNN for fault data feature extraction. The results of the CNN fully connected layer were used as input to the LSSVM, and the BKA was used to optimize the key parameters of the LSSVM to classify aeroengine bearing faults. The method was verified by aeroengine bearing test data from the Harbin Institute of Technology. The research showed that the accuracy is 99.86% when the faulty test samples are 70%, and the accuracy is 93.90% when the faulty samples are 30%. The accuracy showed the applicability of this method in small sample fault diagnosis. By comparing the method with a one-dimensional convolutional neural network (1D-CNN) and the convolutional neural network - long short-term memory (CNN-LSTM), it is found that the accuracy has been dramatically improved.

“Comparative evaluation of cytotoxicity of three herbal endodontic irrigants at three intervals of time” – An in vitro study

Aim: The aim of the study was to evaluate and compare the cytotoxicity of 25% of neem leaf extract (Azadirachta indica), 20% of guava leaf extracts (Psidium guajava), and 20% of cinnamon extract (Cinnamomum zeylanicum) irrigants at three intervals of time. Methodology: Four groups were formed (n = 15), Group 1 (control group) – normal saline solution, Group 2 – 25% of neem extract, Group 3 – 20% of guava extract, and Group 4 – 20% of cinnamon extract. Each group was further divided into three subgroups based on intervals (n = 5). Subgroup A – at 10 min, Subgroup B – at 20 min, and Subgroup C – at 30 min. One hundred microliters of each irrigant was added to 2 mL of the diluted red blood cells suspension obtained from a human volunteer. Hemoglobin (Hb) estimation was done with an automated hematology analyzer after incubating the test samples at 10, 20, and 30 min intervals. Results: The reduction in the mean Hb values was not statistically significant in the normal saline, guava, and cinnamon groups. However, in the neem extract group, the mean Hb values reduced significantly at P < 0.001. Among the subgroups, Subgroup A (10 min) showed the least cytotoxicity. Conclusion: In the present study, 20% guava extract had the lowest cytotoxicity and cytotoxicity increased with time. Hence, 20% guava extract can be used as an alternative to conventional irrigants as it has been shown to have the least cytotoxicity.

Multi-species cryoEM calibration and workflow verification standard.

Cryogenic electron microscopy (cryoEM) is a rapidly growing structural biology modality that has been successful in revealing molecular details of biological systems. However, unlike established biophysical and analytical techniques with calibration standards, cryoEM has lacked comprehensive biological test samples. Here, a cryoEM calibration sample consisting of a mixture of compatible macromolecules is introduced that can not only be used for resolution optimization, but also provides multiple reference points for evaluating instrument performance, data quality and image-processing workflows in a single experiment. This combined test specimen provides researchers with a reference point for validating their cryoEM pipeline, benchmarking their methodologies and testing new algorithms.

FEATURES OF THE FORMATION OF THE PHASE COMPOSITION AND PROPERTIES OF HIGH ALLOY STEEL 03Х17Н3Г9МБДЮЧ

Purpose. Evaluation of the influence of alloying elements of corrosion-resistant steel 03Х17Н3Г9МБДЮч on the phase composition and indicators of high-temperature corrosion. Research methods. In order to check the effect of the chemical composition on the structure of steel 03Х17Н3Г9МБДЮч, laboratory steels were made. The production of these steels was carried out in an induction furnace with a main lining with a capacity of 50 kg. The resulting castings were forged into blanks 40 ´ 80 ´100 mm in size, with subsequent hot rolling into samples 25, 20, and 15 mm thick. Determination of the yield strength of steels was carried out after exposure at a temperature of 850 °С for 10,000 hours. The corrosion resistance of the test samples was determined by the gravimetric method after the samples were completely immersed in molten magnesium. Results. The choice of the main alloying elements was justified and their influence on durability in the aggressive environment of the recovery process of spongy titanium production was determined. A thermodynamic calculation of the changed Gibbs energy in the operating temperature range was performed. The required phase ratio of 03Х17Н3Г9МБДЮч steel was considered and selected based on Scheffler de Long and Potak-Sahalevich diagrams. Scientific novelty. The effect of changing the chemical composition of steel 03Х17Н3Г9МБДЮч within technical conditions on the creep limit and durability in the aggressive environment of the recovery process of the production of spongy titanium was determined. Practical value. It is shown that the alloying complex of corrosion-resistant steel is capable of significantly improving the physical, mechanical and technological properties, and expanding the functional application (purpose).

A prediction model for differential resilience to the effects of combat-related stressors in US army soldiers.

To develop a composite score for differential resilience to effects of combat-related stressors (CRS) on persistent DSM-IV post-traumatic stress disorder (PTSD) among US Army combat arms soldiers using survey data collected before deployment. A sample of n=2542 US Army combat arms soldiers completed a survey shortly before deployment to Afghanistan and then again two to three and 8-9months after redeployment. Retrospective self-reports were obtained about CRS. Precision treatment methods were used to determine whether differential resilience to persistent PTSD in the follow-up surveys could be developed from pre-deployment survey data in a 60% training sample and validated in a 40% test sample. 40.8% of respondents experienced high CRS and 5.4% developed persistent PTSD. Significant test sample heterogeneity was found in resilience (t=2.1, p=0.032), with average treatment effect (ATE) of high CRS in the 20% least resilient soldiers of 17.1% (SE=5.5%) compared to ATE=3.8% (SE=1.2%) in the remaining 80%. The most important predictors involved recent and lifetime pre-deployment distress disorders. A reliable pre-deployment resilience score can be constructed to predict variation in the effects of high CRS on persistent PTSD among combat arms soldiers. Such a score could be used to target preventive interventions to reduce PTSD or other resilience-related outcomes.

Novel Pla Composites Modified with Steel Fibres and (3-Thiopropyl) Polysilsesquioxane Derivatives

Abstract In recent years, a significant increase in the development of new composite materials with desirable mechanical, thermal or surface properties has been observed. One of the popular polymers on the market is polylactide. This article explores how to modify the polymer using steel fibres and organosilicon compounds (SSQ-SH, SSQ-SH-OCT, and SSQ-SH-OFP) to enhance its properties. Test samples were obtained by injection molding with varying concentrations of 0.5%, 1%, 1.5%, 2.5%, and 5% of steel fibres. Mechanical tests, including tensile strength, elongation at break, and impact strength, were conducted, along with an analysis of the contact angle. The modified samples showed higher impact strength values, with the PLA /steel fibres /SSQ-SH sample seeing an increase of 12%. The addition of modifiers with fluoroalkyl groups led to a contact angle increase of 8.5% compared to neat PLA. Thermal tests (TGA) were also carried out to determine the influence of fibres and organosilicon compounds on decomposition.

Multiscale Analysis of Surface Topography for Engineering Applications in the Casting Industry

This paper presents the results of studies aimed at assessing the impact of the molding process on the variability of surface irregularities of casting models. This research was conducted using a selected multiscale method, i.e., wavelet transformation, in both discrete and continuous perspective. The test samples were made both based on traditional methods of manufacturing casting models, i.e., machining of aluminum and wood, as well as using three additive technologies. The impact of the forming process on the variability of the topography of the produced models was evaluated. This research comprehensively relates to the assessment of the applicability of additive technologies, which are increasingly used in various industrial areas, as well as the impact of the process on surface topography in relation to scale. The statistical assessment based on the ANOVA analysis demonstrated that it is possible to distinguish between the surfaces before and after a specific number of forming cycles. Studies have shown that the impact of the forming process is relatively small, mainly affecting the long-term irregularity components, and there are no functional dependencies in terms of the impact of the forming process on the variation in surface topography in relation to the manufacturing method or its parameters.

Research of Dynamic Processes in a Layer During Collision With an Impactor

Abstract The article concerns the modeling of the transverse impact of an impactor (test sample) on the surface of an infinite elastic layer. The Laplace transform with respect to time and the Hankel transform with respect to the radius for the axisymmetric case were applied. The propagation of elastic waves in the layer and local deformations in the contact zone are taken into account. Impact force, impact time and the coefficient of restitution were examined. The results are compared with the elastic half-space. The calculations carried out showed that for layer thicknesses of more than five impactor diameters, the layer can be considered as a half-space.

Analysis of Big Data Network Security Defense Mechanism Application of Artificial Intelligence

In the context of the close integration of computer network technology and daily life, big data network security faces many challenges. With its powerful information processing ability, this AI can effectively identify and handle potential threats in the network, thereby improving network security. This paper discusses the application of artificial intelligence (AI) in enhancing big data network security defense, especially in the threat detection link. This paper proposes a detection algorithm that combines support vector machine (SVM) and K nearest neighbor (KNN), namely SVM-KNN algorithm, for network attack detection and defense. When the hazard data is 60 under the test sample, the detection accuracy of KNN, SVM, and SVM-KNN is 86.88%, 89.51%, and 96.75% respectively; when the hazard data is 60 under the experimental sample, the detection accuracy of KNN, SVM, and SVM-KNN is 82.70%, 83.88%, and 89.26% respectively.response.

Ergonomic design of mastectomy bra based on emotion measurements

The mastectomy bra (M-bra) serves as an essential rehabilitative product for individuals who have undergone breast cancer surgery. This study aimed to develop an ergonomic design method for M-bras that addresses both the physiological and psychological needs of post-mastectomy breast cancer patients. Over 270 M-bras with diverse design options were collected from online markets, and more than 100 post-mastectomy patients participated in an online survey to rank their preferences regarding eight M-bra components and 19 design options. An orthogonal experimental design was applied to reduce the vast array of design combinations to 20 M-bra test samples. The Pleasure-Arousal-Dominance (PAD) emotion model and the Self-Assessment Manikin (SAM) scale were then used to collect PAD scores from 40 patients evaluating these test samples. Extreme difference analysis was conducted to evaluate the significance of the M-bra components and identify combinations that generated the highest and lowest PAD levels. Based on the PAD scores, 11 samples were categorized as "delighted" or "relaxed" M-bras due to their positive emotional polarity. Linear regression analysis showed strong correlations between the design options and PAD scores, with R2 values of 0.962 for P, 0.819 for A, and 0.949 for D. A backpropagation neural network was developed to predict PAD values for various M-bra design options, achieving prediction errors of 8.06% for P, 6.15% for A, and 13.29% for D, demonstrating satisfactory performance given the subjective nature of the evaluations.

Association of serum bilirubin levels and glycemic measurements in Type 2 diabetic patients

Introduction: Bilirubin, a waste product of the heme catabolic pathway, has proven to be a natural antioxidant associated with a lower prevalence of oxidative stress-mediated diseases. This study evaluates the association of serum bilirubin levels with Glycated Hemoglobin (HbA1c), Fasting Blood Sugar (FBS) and Post Prandial Blood Sugar (PPBS) in diabetic patients. Methods: A retrospective analytical study was conducted at the Department of Haematology of the Sri Jayewardenepura General Hospital. Data was obtained through a laboratory information system of randomly selected 201 adult patients diagnosed with T2DM and grouped as poorly and well controlled using HbA1c values over and below 7%, respectively. Normal ranges for serum bilirubin, FBS, and PPBS were (0.3-1.2) mg/dL, (75-110) mg/dL and (80-140) mg/dL, respectively. Data were analyzed using (SPSS) version 20, descriptive statistical methods, Pearson’s Correlation test and independent sample t-test. P<0.05 was considered significant. Results: A negative correlation between total bilirubin and HbA1c (r = -0.234, p=0.001) was observed. There was no statistically significant correlation between total bilirubin level and FBS (p=0.131), PPBS (p=0.408), and age (p=0147). However, the mean values of total bilirubin (p=0.001), FBS (p<0.001) and PPBS (p<0.001) were significant between diabetes well controlled and poorly controlled groups. A statistically positive correlation was observed between HbA1c and FBS (p<0.001) and PPBS (p<0.001), but not with age (p=0.887). Conclusion: There was a significant relationship between total bilirubin with FBS, and PPBS in both groups. A significant negative correlation was found between total bilirubin and HbA1c, suggesting a potential protective or modulatory role of bilirubin in glycemic control. No significant correlation was observed between total bilirubin levels and FBS, PPBS, or age in the total population.Keywords: T2DM, HbA1c, bilirubin, FBS, PPBS

The value of multiple diffusion metrics based on whole-lesion histogram analysis in evaluating the subtypes and proliferation status of non-small cell lung cancer

ObjectiveLimited studies have explored the utility of whole-lesion histogram analysis in discerning the subtypes and proliferation status of non-small cell lung cancer (NSCLC), despite its potential to provide comprehensive tissue assessment through the computation of additional quantitative metrics. This study sought to assess the significance of intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) histogram parameters in discriminating between squamous cell carcinoma (SCC) and adenocarcinoma (AC), and to examine the correlation of each parameter with the proliferative marker Ki-67.Materials and methodsPatients with space-occupying lesions detected by chest CT examination and with further routine MRI, DKI and IVIM functional sequence scans were enrolled. Based on the pathological results, seventy patients with NSCLC were selected and divided into AC and SCC groups. Histogram parameters of IVIM (D, D*, f) and DKI (Dapp, Kapp) were calculated, and the Mann–Whitney U test or independent samples t test was used to analyze the differences in each histogram parameter of the SCC and AC groups. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic performance of the histogram parameters. The correlation coefficient between histogram parameters and Ki-67 was calculated using Spearman’s or Pearson’s methods.ResultsThe D 10th percentile, D 90th percentile, D mean, D median, Dapp10th percentile, Dapp90th percentile, Dappmean, Dappmedian, Dappskewness, DappSD of the AC groups were significantly higher than those of the SCC groups, while the Kappentropy and KappSD of the SCC groups were significantly higher than those of the AC groups. All the above differences were statistically significant (all P < 0.05). ROC curve analysis revealed that Dappmean showed the best performance for differentiating AC from SCC lesions, with an area under the ROC curve of 0.832 (95% confidence interval [CI]: 0.707-0.919). But there was no statistically significant difference in diagnostic efficacy compared to other histogram parameters (all P>0.05). Dapp90thpercentile, Dappmean, Kappskewnes showed a slight negative correlation with Ki-67 expression (r value -0.340, -0.287, -0.344, respectively; P< 0.05), while the other histogram parameters showed no significant correlation with Ki-67 (all P > 0.05).ConclusionsOur study demonstrates the utility of IVIM and DKI histogram analyses in differentiating NSCLC subtypes, particularly AC and SCC. Correlations with the Ki-67 index suggest that Dappmean, Dapp90th percentile, and Kappskewness may serve as markers of tumor aggressiveness, supporting their use in NSCLC diagnosis and treatment planning.

Financial Performance of Banking in Indonesia: A Comparison Before and During the Covid-19 Pandemic

This research aims to examine the financial performance of the banking sector in Indonesia both before and during the pandemic. In the course of this investigation, financial performance was evaluated using several factors namely Loan to deposit ratio (LDR), non-performing loan (NPL), return on assets (ROA), as well as operating expenses and operating income (BOPO). Furthermore, the data used comprised information on the banking financial conditions before (2018-2019) and during the pandemic-affected periods (2020-2021). Purposive sampling was used for sample selection and the analytical methodology adopted comprised the Wilcoxon signed rank test and paired samples t-test analysis. It is also crucial to establish that the present research utilized statistical computations conducted via the Statistical Package for the Social Sciences (SPSS) application, with a significance level of 5%. The obtained results showed that no significant difference was identified between LDR and BOPO both before and during the pandemic. However, a substantial difference was identified between NPL and ROA over the two examined periods.

A seed-epidermis-feature-recognition-based lightweight peanut seed selection method for embedded systems

High-quality seeds improve the germination rate. Therefore, seed selection before sowing peanuts is crucial. Current peanut seed selection methods include color sorters and sieving machines, which are efficient but lack accuracy due to their reliance on a single indicator. Manual selection is inefficient and subject to subjective influences. Although machine vision and deep learning have performed well in crops such as corn and pepper, most existing research is based on PC or MATLAB platforms, which are not portable and are prone to interference, making them unsuitable for field applications. This study developed a lightweight model for recognizing peanut seed epidermal features. The model was based on deep learning and model quantization techniques. The transfer learning method was used to use four pre-trained models, EfficientNet_b0, EfficientNetv2-b0, MobileNet_v2_35_224, and NasNet_Mobile, as feature extraction layers, the input layer was added before the feature extraction layer, and the dropout and dense layers were added after the feature extraction layer to construct a classifier. The peanut seed selection network(PSSNet) models were constructed and named PSSNet-E, PSSNet-E2, PSSNet-M, and PSSNet-N, respectively, and trained on the Huayu 22 peanut seed dataset constructed in this study. The constructed models were compressed using model quantization technology, and four quantized models were obtained, namely PSSNet-Ef, PSSNet-E2f, PSSNet-Mf, and PSSNet-Nf. Finally, PSSNet-Mf, which had the best model evaluation, was selected as the peanut selection model for this study and deployed on a prototype for testing. Compared with the unquantized model, the size of the quantized model was reduced by two-thirds and the running speed was increased by 37%. A total of 400 Huayu 22 peanut seeds were selected as test samples, and 5 selection tests were conducted.The results showed that the average accuracy was 95.3%, which met the requirements of peanut selection at the production site.

Two-Dimensional X-Ray Diffraction (2D-XRD) and Micro-Computed Tomography (Micro-CT) Characterization of Additively Manufactured 316L Stainless Steel

In-depth quality assessment of 3D-printed parts is vital in determining their overall characteristics. This study focuses on the use of 2D X-Ray diffraction (2D-XRD) and X-Ray micro-computed tomography (micro-CT) techniques to evaluate the crystallography and internal defects of 316L SS parts fabricated by the powder-based direct energy deposition (DED) technique. The test samples were printed in a controlled argon environment with variable laser power and print speeds, using a customized deposition pattern to achieve a high-density print (>99%). Multiple features, including hardness, elastic modulus, porosity, crystallographic orientation, and grain morphology and size were evaluated as a function of print parameters. Micro-CT was used for in-depth internal defect analysis, revealing lack-of-fusion and gas-induced (keyhole) pores and no observable micro-cracks or inclusions in most of the printed body. Some porosity was found mostly concentrated in the initial layers of print and decreased along the build direction. 2D-XRD was used for phase analysis and grain size determination. The phase analysis revealed single phase γ-austenitic FCC phase without any detectable presence of the δ-ferrite phase. A close correlation was found between Electron Backscatter Diffraction (EBSD) and 2D-XRD results on the average size distribution and the crystallographic orientation of grains in the sample. This work demonstrates the fast and reliable as-printed crystallography analysis using 2D-XRD compared to the EBSD technique, with potential for in-line integration.

Development of an Improved Thiosulfate-Utilizing Denitrifying Bacteria-Based Ecotoxicity Test with High Detection Sensitivity and Reproducibility

Microorganism-based ecotoxicity assessment has been widely used as a reliable tool showing direct biochemical impacts of contaminants on ecosystems and the environment. The present study aimed at developing a thiosulfate-utilizing denitrifying bacteria (TUDB)-based ecotoxicity test with high detection sensitivity and favorable reproducibility. To achieve this goal, existing TUDB toxicity tests were improved by employing a pure culture of Thiobacillus thioparus ATCC 8158 and optimizing test conditions, particularly in terms of inoculated microbial biomass, incubating temperature, and operational pH. From control tests, it was found that 4 h is a sufficient processing time for TUDB test kits. As a result of optimization, 20 mg VSS/L of initial bacterial biomass, 25 °C of incubating temperature, and 6 of operational pH were determined as the most favorable test conditions, providing enhanced detection sensitivity and reproducibility. Under these optimal test conditions, I conducted toxicity tests for diverse toxic metals and obtained 0.65 ± 0.03, 1.09 ± 0.04, 1.21 ± 0.07, 0.13 ± 0.01, 0.56 ± 0.04, 1.42 ± 0.03, 0.98 ± 0.02, and 2.12 ± 0.05 mg/L of 4 h EC50 values for Ag+, As3+, Cd2+, Cr6+, Cu2+, Hg2+, Ni2+, and Pb2+, respectively. These EC50 values are substantially lower than those from earlier TUDB tests, demonstrating the high detection sensitivity of the current TUDB tests. Moreover, the present TUDB tests attained very low coefficient of variation (CV) values (1.6–6.3%) for the EC50, showing favorable reproducibility of the test methodology. In addition, the current TUDB toxicity tests offer numerous advantages for ecotoxicity assessment, including versatility for diverse test samples, no requirement for advanced equipment, and no distortion of end-point measurement. These refinements render the TUDB tests a favorable ecotoxicity assessment with enhanced sensitivity and reproducibility.

Comparative small molecule screening of primary human acute leukemias, engineered human leukemia and leukemia cell lines.

Targeted therapeutics for high-risk cancers remain an unmet medical need. Here we report the results of a large-scale screen of over 11,000 molecules for their ability to inhibit the survival and growth in vitro of human leukemic cells from multiple sources including patient samples, de novo generated human leukemia models, and established human leukemic cell lines. The responses of cells from de novo models were most similar to those of patient samples, both of which showed striking differences from the cell-line responses. Analysis of differences in subtype-specific therapeutic vulnerabilities made possible by the scale of this screen enabled the identification of new specific modulators of apoptosis, while also highlighting the complex polypharmacology of anti-leukemic small molecules such as shikonin. These findings introduce a new platform for uncovering new therapeutic options for high-risk human leukemia, in addition to reinforcing the importance of the test sample choice for effective drug discovery.