Experimental Variables Research Articles

Investigation on the Hydrothermal Condition in Synthesis of Active Matrix from Metakaolin: Physicochemical Properties and Intrinsic Cracking Activities

The current trends in research and development of FCC catalyst is focused on the formulation of active matrices that serve as pre-crackers, with the objective of reducing the diffusional resistance of the longer chain hydrocarbon molecule in the feed. In this study, an aluminosilicate active matrix was synthesised from metakaolin using hydrothermal method. The experimental variables that were varied were hydrothermal temperature, in the range of 80 to 110 °C, and hydrothermal time, in the range of 12 to 72 hours, to investigate the best conditions for synthesising the active matrix. Subsequently, the active matrix was subjected to a series of analyses, including X-ray fluorescence, X-ray diffraction, N2 physisorption, NH3-temperature programmed desorption, Fourier transform infrared spectroscopy, scanning electron microscopy and thermogravimetry, with the objective of determining its composition, crystal characteristics, surface characteristics, acidity, functional groups, material structure, and thermal characteristics. Additionally, the active matrix was tested for its intrinsic cracking activity using the micro activity test (MAT). The results indicate that the best temperature for hydrothermal synthesis of the active matrix is 80 °C. The active matrix synthesised with a heating time of 24 hours demonstrated the highest light cycle oil yield, reaching 38.9 wt%. Meanwhile, the active matrix synthesised at 48 hours exhibited the most favourable characteristics, with a specific surface area of 144.23 m2/g and a pore volume of 0.9933 cm3/g, as well as the highest cracking conversion of 70.0 wt%. Copyright © 2024 by Authors, Published by BCREC Publishing Group. This is an open access article under the CC BY-SA License (https://creativecommons.org/licenses/by-sa/4.0).

Rationalised experiment design for parameter estimation with sensitivity clustering

Quantitative experiments are essential for investigating, uncovering, and confirming our understanding of complex systems, necessitating the use of effective and robust experimental designs. Despite generally outperforming other approaches, the broader adoption of model-based design of experiments (MBDoE) has been hindered by oversimplified assumptions and computational overhead. To address this, we present PARameter SEnsitivity Clustering (PARSEC), an MBDoE framework that identifies informative measurable combinations through parameter sensitivity (PS) clustering. We combined PARSEC with a new variant of Approximate Bayesian Computation-based parameter estimation for rapid, automated assessment and ranking of experiment designs. Using two kinetic model systems with distinct dynamical features, we show that PARSEC-based experiments improve the parameter estimation of a complex system. By its inherent formulation, PARSEC can account for experimental restrictions and parameter variability. Moreover, we demonstrate that there is a strong correlation between sample size and the optimal number of PS clusters in PARSEC, offering a novel method to determine the ideal sampling for experiments. This validates our argument for employing parameter sensitivity in experiment design and illustrates the potential to leverage both model architecture and system dynamics to effectively explore the experimental design space.

Seismic Performance of Precast Drift-Hardening Concrete Walls Connected by Grout–Sheath Duct

In order to find a suitable size of sheath duct and a reliable construction method for precast walls, a cast-in-place and five 1/2 scale precast drift-hardening concrete walls reinforced with weakly bonded ultra-high strength SBPDN rebars were fabricated and tested under reserved lateral load and constant compression. The experimental variables were the diameter of sheath ducts (45 mm, 100 mm, and 120 mm), embedded length (20d and 35d; d is the nominal diameter of SBPDN rebars), axial load ratio (0.075 and 0.15), and the construction method. The experimental observations, hysteresis behaviors, envelope curves, residual deformation, crack propagation, and energy dissipation were compared in the study. Moreover, a formula was applied to calculate the bond strength of the sheath duct. The experimental and calculated results revealed that increasing the axial load ratio and embedment length could not enhance the bond strength of the sheath ducts, and increasing the diameter decreased the bond strength significantly. Anchored the SBPDN rebars in smaller sheath ducts separately was a more stable connection method for precast concrete shear walls and provided sufficient drift-hardening capability, even at a large drift level (over 3%).

Modeling of the metal–insulator transition temperature in alio-valently doped VO2 through symbolic regression

The correlated semiconductor vanadium dioxide (VO2) exhibits an insulator–metal transition (IMT) near room temperature, which is of interest in various device applications. Precise IMT temperature control is crucial to determine the use cases across technologies such as thermochromic windows, actuators for robots or neuronal oscillators. Doping the cation or anion sites can modulate the IMT by several tens of degrees and control hysteresis. However, modeling the effects of control parameters (e.g., doping concentration, type of dopants) is challenging due to complex experimental procedures and limited data, hindering the use of traditional data-driven machine learning approaches. Symbolic regression (SR) can bridge this gap by identifying nonlinear expressions connecting key input parameters to target properties, even with small data sets. In this work, we develop SR models to capture the IMT trends in VO2 influenced by different dopant parameters. Using experimental data from the literature, our study reveals a dual nature of the IMT temperature with varying tungsten (W) doping concentrations. The symbolic model captures data trends and accounts for experimental variability, providing a complementary approach to first-principles calculations. Our feature-driven analysis across a broader class of dopants informs selectivity and provides qualitative insights into tuning phase transition properties valuable for neuromorphic computing and thermochromic windows.

Solving the B-SAT Problem Using Quantum Computing: Smaller Is Sometimes Better.

This paper aims to outline the effectiveness of modern universal gate quantum computers when utilizing different configurations to solve the B-SAT (Boolean satisfiability) problem. The quantum computing experiments were performed using Grover's search algorithm to find a valid solution. The experiments were performed under different variations to demonstrate their effects on the results. Changing the number of shots, qubit mapping, and using a different quantum processor were all among the experimental variables. The study also branched into a dedicated experiment highlighting a peculiar behavior that IBM quantum processors exhibit when running circuits with a certain number of shots.

From Simulations to Inference: Using Machine Learning to Tune Patient-Specific Finite-Element Models of the Middle Ear Towards Objective Diagnosis.

Computational models, particularly finite-element (FE) models, are essential for interpreting experimental data and predicting system behavior, especially when direct measurements are limited. A major challenge in tuning these models is the large number of parameters involved. Traditional methods, such as one-by-one sensitivity analyses, are time-consuming, subjective, and often return only a single set of parameter values, focusing on reproducing averaged data rather than capturing the full variability of experimental measurements. In this study, we applied simulation-based inference (SBI) using neural posterior estimation (NPE) to tune an FE model of the human middle ear. The training dataset consisted of 10,000 FE simulations of stapes velocity, ear-canal (EC) input impedance, and absorbance, paired with seven FE parameter values randomly sampled within plausible ranges. The neural network learned the association between parameters and simulation outcomes, returning the probability distribution of parameter values that can reproduce experimental data. Our approach successfully identified parameter sets that reproduced three experimental datasets simultaneously. By accounting for experimental noise and variability during training, the method provided a probability distribution of parameters, representing all valid combinations that could fit the data, rather than tuning to averaged values. The network demonstrated robustness to noise and exhibited an efficient learning curve due to the large training dataset. SBI offers an objective alternative to laborious sensitivity analyses, providing probability distributions for each parameter and uncovering interactions between them. This method can be applied to any biological FE model, and we demonstrated its effectiveness using a middle-ear model. Importantly, it holds promise for objective differential diagnosis of conductive hearing loss by providing insight into the mechanical properties of the middle ear.

A First-Passage Model of Intravitreal Drug Delivery and Residence Time-Influence of Ocular Geometry, Individual Variability, and Injection Location.

Standard of care for various retinal diseases involves recurrent intravitreal injections. This motivates mathematical modeling efforts to identify influential factors for ocular drug residence time, aiming to minimize administration frequency. We sought to describe the vitreal diffusion of therapeutics in nonclinical species frequently used during drug development assessments. In human eyes, we investigated the impact of variability in vitreous cavity size and eccentricity, and in injection location, on drug disposition. Using a first-passage time approach, we modeled the transport-controlled distribution of two standard therapeutic protein formats (Fab and IgG) and elimination through anterior and posterior pathways. Anatomical three-dimensional geometries of mouse, rat, rabbit, cynomolgus monkey, and human eyes were constructed using ocular images and biometry datasets. A scaling relationship was derived for comparison with experimental ocular half-lives. Model simulations revealed a dependence of residence time on ocular size and injection location. Delivery to the posterior vitreous resulted in increased vitreal half-life and retinal permeation. Interindividual variability in human eyes had a significant influence on residence time (half-life range of 5-7days), showing a strong correlation to axial length and vitreal volume. Anterior exit was the predominant route of drug elimination. Contribution of the posterior pathway displayed a 3% difference between protein formats but varied between species (10%-30%). The modeling results suggest that experimental variability in ocular half-life is partially attributed to anatomical differences and injection site location. Simulations further suggest a potential role of the posterior pathway permeability in determining species differences in ocular pharmacokinetics.

Single nucleotide polymorphism (SNP) characterisation of mouse inbred strains bred at MRC-National Institute for Medical Research.

Inbred mouse strains have long proved useful as tools for biomedical research. They remove the effects of genetic background as an experimental variable. Within all mouse colonies, genetic drift is a recognised phenomenon and monitoring and documenting changes is important for experimental design and consistency. This communication documents the initial characterisation through SNP analysis of the inbred mouse strains bred and used at the time at the Medical Research Council National Institute for Medical Research (MRC-NIMR), Mill Hill, now The Crick Institute. These inbred strains were part of the foundation colonies for the many genetically modified mouse strains made at Mill Hill. We found small genetic changes in four of the nine inbred strains. Although phenotypic differences have not yet been found between the NIMR and the correspondent parental strains, I cannot discard that these may arise or have already arisen. This work has also authenticated the 129/SvJEvNimr-Gpi1c strain that was widely used at MRC-NIMR for gene targeting. All these inbred strains have been renamed according to The International Committee on Standardized Genetic Nomenclature for Mice.

The effect of specimen size on the unconfined compressive strength of cement-stabilized granular mixtures made of natural and recycled aggregates

The cement-stabilization technique is employed on natural and recycled granular materials to improve their mechanical properties. The strength of these materials is assessed by the unconfined compressive strength on laboratory compacted specimens, typically after 7 days of curing. Standards and technical specifications specify different values of specimen height and diameter and different loading modes of testing. This makes the comparison between different materials and with the acceptance limits of technical specifications difficult. The research investigates the effect of specimen size and loading mode on the unconfined compressive strength of both natural and recycled cement-stabilized granular materials. The results revealed significant differences in strength due to variations in specimen size and loading mode. As expected, an increase in specimen slenderness resulted in a decrease in compressive strength. A linear regression model was developed to quantify the effect of the experimental variables on the compressive strength of the two cement-stabilized materials.

Variability assessment and screening of superior rice (Oryza sativa L.) genotypes for grain micronutrients and yield components

The present study was carried out to assess the genetic parameters and divergence in 38 rice genotypes for important yield and grain quality traits. Phenotypic coefficient of variation (PCV) exceeded genotypic coefficient of variation (GCV) for all the traits taken for the study, while high heritability was recorded for all the traits, except kernel length to breadth ratio and grain iron content. The trait number of grains per panicle exhibited high heritability (91.00%) and high genetic advance as a percentage of mean suggesting simple selection may be followed to improve the traits. Cluster analysis grouped the genotypes into seven clusters. The highest inter-cluster distance was observed between clusters VII and IV (537.17). Five genotypes excelled in grain yield, ten genotypes for grain zinc content and one genotype for grain iron content over the check variety Rajendra Bhagwati. Despite high variability in grain zinc and iron content, no genotype surpassed the check variety for all three crucial attributes i.e. grain yield, grain zinc and iron content. The experimental material's variability for grain nutrient status suggests their potential use in biofortification programs. Keywords: Rice, biofortification, micronutrients, variability, genetic diversity, heritability

Effect of Agronomic Biofortification of Zinc and Iron on Plant Height and Seed Yield of Chickpea (Cicer arietinum L.) Varieties

The present field experiment was conducted to study the effect of agronomic biofortification of zinc and iron on chickpea during Rabi season of 2021-22 and 2022-23 at the instructional farm, College of Agriculture, Jodhpur. The field experiment was laid out in split plot design comprised two varieties of chickpea (‘GNG-2144’ and ‘GNG-2171’) and three levels of iron fortification treatment including control (F0), 20 kg FeSO4 (SA) + 0.5 % FeSO4 (F1) and 25 kg FeSO4 (SA) + 0.5 % FeSO4 (F3) in the main plot and four-levels of zinc fortification viz. control (Z0), ZSB (SI) + 15 kg ZnSO4 (SA) + 0.5 % ZnSO4 (Z1), ZSB (SI) + 20 kg ZnSO4 (SA) + 0.5 % ZnSO4 (Z2) and ZSB (SI) + 25 kg ZnSO4 (SA) + 0.5 % ZnSO4 (Z3) in sub-plot. These three experimental variables make twenty-four treatment combinations were taken as experimental factors to study their effect on biofortification in chickpea. The results revealed that variety GNG-2144 recorded higher plant height and higher seed yield over variety GNG-2171. Among Iron levels treatment 25 kg FeSO4 (SA) + 0.5 % FeSO4 (F3) significantly recorded higher seed yield as compare to 20 kg FeSO4 (SA) + 0.5 % FeSO4 (F1). Moreover, Zinc fortification treatment ZSB (SI) + 25 kg ZnSO4 (SA) + 0.5 % ZnSO4 (Z3) was found significantly superior over the treatment ZSB (SI) + 15 kg ZnSO4 (SA) + 0.5 % ZnSO4 (Z1) and at par with ZSB (SI) + 20 kg ZnSO4 (SA) + 0.5 % ZnSO4 (Z2).

Stability assessment of housekeeping genes for qRT-PCR in Yersinia enterocolitica cultured at 22°C and 37°C.

Yersinia enterocolitica, a species within the genus Yersinia, thrives optimally at 22-25°C but can also grow at the mammalian core body temperature of 37°C. This dual temperature adaptability necessitates establishing both temperature conditions in research to examine the effects on various biological processes. In quantitative real-time PCR (qRT-PCR) assays, the selection of appropriate housekeeping genes is vital for data accuracy. Nevertheless, the lack of alternatives and information often leads to the default use of the 16S rRNA gene despite potential limitations. This investigation sourced 16 potential reference genes through a comprehensive review of the literature and transcriptome sequencing data analysis. We validated the expression stability of these genes via qRT-PCR across 12 Y. enterocolitica strains, representing the four prevalent serotypes O:3, O:5,27, O:8, and O:9, isolated from diarrheal patient stool samples. This approach aimed to minimize the impact of serotype heterogeneity. After acquiring Cq values, gene stability was evaluated using four established algorithms-ΔCq, geNorm, NormFinder, and BestKeeper-and subsequently synthesized into a consolidated ranking through the Robust Rank Aggregation (RRA) method. Our study suggests that the genes glnS, nuoB, glmS, gyrB, dnaK, and thrS maintain consistent expression across varying culture temperatures, supporting their candidacy as robust housekeeping genes. We advise against the exclusive use of 16S rRNA for this purpose. Should tradition prevail in its utilization, it must be employed with discernment, preferably alongside one or two of the housekeeping genes identified in this study as internal controls.IMPORTANCEIn our study, we focused on identifying stable reference genes for quantitative real-time PCR (qRT-PCR) experiments on Y. enterocolitica cultured at different temperatures (22°C and 37°C). After thoroughly evaluating 16 candidate genes, we identified six genes-glnS, nuoB, glmS, gyrB, dnaK, and thrS-as exhibiting stable expression across these temperature conditions, making them ideal reference genes for Y. enterocolitica studies. This discovery is crucial for ensuring the accuracy and reliability of qRT-PCR data, as the choice of appropriate reference genes is key to normalizing expression data and minimizing experimental variability. Importantly, our research extended beyond bioinformatics analysis by incorporating validation with clinical strains, bridging the gap between theoretical predictions and practical application. This approach not only underscores the robustness and reliability of our findings but also directly addresses the critical need for experimental validation in the field. By providing a set of validated, stably expressed reference genes, our work offers valuable guidance for designing experiments involving Y. enterocolitica, enhancing the reliability of research outcomes, and advancing our understanding of this significant pathogen.

Experimental and numerical investigation of low‐velocity impact behavior and failure mode of shear deficient RC beam strengthened with CFRP strips

AbstractIt is well‐known that shear failure is a collapse mechanism that is the riskiest, fastest, and catastrophic and occurs with no visible signs of damage or prior warning for RC beams. Therefore, to prevent brittle shear failure, the RC beams should include sufficient shear reinforcement, such as stirrups, and be designed to have sufficient shear capacity. However, the RC beams' shear capacity becomes inadequate for various reasons. One of these reasons may be the acting of the impulsive impact load, which is uncommon and disregarded in the design phase on the RC beams. An experimental program was conducted to examine the impact behavior and failure mode of shear‐deficient RC beams in the scope of the present study. Besides, it aims to investigate the effectiveness of the strengthening method using CFRP strips in improving the general behavior, failure mode, and performance of shear‐deficient RC beams exposed to impact load. The time histories of the accelerations, displacements, impact loads, and strains in the CFRP strips were measured. They were interpreted how they are affected by experimental variables examined in the experimental study. Furthermore, the finite element model of the specimens was generated in the LS‐DYNA software, and the experimental and numerical results were compared by performing finite element analysis in terms of failure modes and general behavior.

Organic matter mitigates biotic impact of copper in fruit orchard soil

Inorganic copper (Cu) fungicides and bactericides are widely used to control disease in fruit and vegetable crops and has led to widespread accumulation of the metal in soil beyond regulatory thresholds. We aimed to elucidate the impacts of Cu on soil health within cherry orchard soils in New Zealand, focusing on three biological indicators: earthworm behaviour, soil respiration, and plant growth. We sampled soils from four blocks of different ages within a single orchard, varying in amounts of accumulated soil Cu (7 - 263 mg kg-1) but also in Soil Organic Matter (SOM) content (3 - 10 %). Experimental work was designed to isolate the impacts of both Cu and SOM on three critical biological descriptors: earthworm behaviour, soil respiration and root growth. Soils were amended to standardise both variables in laboratory and glasshouse experiments. The results demonstrated a pronounced inhibition of soil respiration and root development, as well as adverse effects on earthworm behaviour, with increasing Cu concentrations. SOM played a mitigating role, reducing the bioavailability and toxicity of Cu to soil organisms. However, the buffering capacity of SOM is limited and long-term reliance on SOM to mitigate Cu toxicity is not sustainable. Currently Cu continues to accumulate in most orchard soils. This study highlights the importance of assessing Cu bioavailability and soil health in the context of orchard management.

Tapping strength variability in sensorimotor experiments on rhythmic tapping.

We report psychophysical experiments and time series analyses to investigate sensorimotor tapping strength fluctuations in human periodic tapping with and without a metronome. The power spectral density of tapping strength fluctuations typically decays in an inverse power law (1/fβ-noise) associated with long-range correlations, i.e., with a slow power-law decay of tapping strength autocorrelations and scale-free behavior. The power-law exponents β are scattered around β=1 ranging from 0.67 to 1.8. A log-linear representation of the power spectral densities reveals rhythmic peaks at frequencies f=0.25 (and f=0.5) and a tendency to slightly accentuate every fourth (and second) stroke when subjects try to synchronize their tapping with a metronome.

Test-retest and interrater reliability of experimental within-subject variability of pain reports as assessed by the focused analgesia selection test.

Within-subject variability (WSV) of pain intensity reports has been shown to predict the placebo response. The focused analgesia selection test (FAST), which allows to experimentally assess WSV of pain reports, has been used as a screening tool to identify participants who are likely to have a strong placebo response in drug-development clinical trials. Yet, the reliability of FAST has not been reported. To assess test-retest and interrater reliability of the FAST outcomes. To mimic pharma-sponsored clinical trials, we enlisted inexperienced assessors who underwent limited training. Healthy volunteers performed the FAST twice within a week and were randomly assigned to either the test-retest group or the interrater group. T-tests, partial Pearson correlations, intraclass correlations (ICC), and Bland-Altman plots were generated to assess FAST outcomes' reliability. Sixty-three participants completed the study and were assigned to the test-retest (N = 33) or interrater (N = 30) arms. No statistically significant differences in the FAST outcomes were detected between the 2 sessions, except for the FAST covariance (FAST CoV) in the interrater assessment (P = 0.009). Test-retest reliabilities of the FAST-main outcomes were r = 0.461, ICC = 0.385 for the FAST R 2 and r = 0.605, ICC = 0.539 for the FAST ICC and in the interrater cohort, they were FAST R 2: r = 0.321, ICC = 0.337 and FAST ICC: r = 0.355, ICC = 0.330. Using inexperienced assessors, the FAST outcomes test-retest ranged from moderate to strong, whereas the interrater reliability ranged from weak to poor. These results highlight the importance of adequately training study staff members before using this tool in multicentre clinical trials.

Chemometrics and electrochemistry joined hands to develop a novel and intelligent electronic device for simultaneous determination of malathion and diazinon in fruit juices: A progress in multidisciplinary studies

In this work, chemometrics and electrochemistry connected to each other to open a new way for assisting food industry specialists based on developing a novel electrochemical sensor for simultaneous determination of malathion (MT) and diazinon (DZ) in the presence of patulin (PT) and citrinin (CT) as uncalibrated interference in fruit juices. The sensor was fabricated based on modification of a glassy carbon electrode (GCE) by chitosan-ionic liquid (Ch-IL), electrodeposition of gold nanoparticles (Au NPs), drop-casting of multiwalled carbon nanotubes-IL (MWCNTs-IL), and electrochemical synthesis of dual templates molecularly imprinted polymers (DTMIPs) in which MT and DZ were used as templates. Effects of experimental variables on structure and response of the sensor were screened and optimized by Min Run screening and central composite design, respectively. After optimization, the third-order hydrodynamic differential pulse voltammetric (HDPV) data were generated based on changing modulation times and modulation amplitudes as instrumental parameters and modeled by N-PLS/RTL, U-PLS/RTL, U-PCA/RTL, APARAFAC, PARAFAC2 and MCR-ALS to select the best one to assist the sensor for ultra selective simultaneous determination of MT in the presence of PT and CT as uncalibrated interference in fruit samples. The results confirmed the MCR-ALS was the best assistance for DTMIPs/MWCNTs-IL/Au NPs/Ch-IL/GCE for simultaneous determination of MT and DZ in the presence of PT and CT as uncalibrated interference in both synthesis and real samples. Performance of the sensor assisted by MCR-ALS for ultra selective simultaneous determination of MT (0.1 pM to 12.5 pM, LOD=0.01 pM) and DZ (0.25 pM to 8.5 pM, LOD=0.15 pM) was really admirable which was comparable with HPLC with UV detection while it was faster, simpler and low-cost in comparison to HPLC-UV which motivated us to introduce it as a reliable method to assist food industry specialists for quality assurance purposes.

Experimental variables determine the outcome of RAS-RAS interactions

RAS clustering at the cell membrane is critical to activate signaling in cells, but whether this clustering is mediated exclusively by its c-terminal hypervariable region, receives contributions from the G-domain of RAS, and/or is influenced by secondary effectors has been intensely debated. Reports that G-domain mutations do not modulate RAS-RAS interactions, have led some to question the validity of previous experiments that indicate the G-domain plays a role in RAS clustering/interactions. Here we reconcile these findings by clarifying the impact of experimental variables, such as protein expression levels, cellular context, RAS zygosity, and secondary effector interactions on RAS clustering. Lack of control over these variables impact the results using G-domain mutations across various assay systems and can lead to unsound conclusions.

Impact of essential genes on the success of genome editing experiments generating 3313 new genetically engineered mouse lines

The International Mouse Phenotyping Consortium (IMPC) systematically produces and phenotypes mouse lines with presumptive null mutations to provide insight into gene function. The IMPC now uses the programmable RNA-guided nuclease Cas9 for its increased capacity and flexibility to efficiently generate null alleles in the C57BL/6N strain. In addition to being a valuable novel and accessible research resource, the production of 3313 knockout mouse lines using comparable protocols provides a rich dataset to analyze experimental and biological variables affecting in vivo gene engineering with Cas9. Mouse line production has two critical steps – generation of founders with the desired allele and germline transmission (GLT) of that allele from founders to offspring. A systematic evaluation of the variables impacting success rates identified gene essentiality as the primary factor influencing successful production of null alleles. Collectively, our findings provide best practice recommendations for using Cas9 to generate alleles in mouse essential genes, many of which are orthologs of genes linked to human disease.

Modified streptozotocin-induced diabetic model in rodents.

Streptozotocin (STZ)-induced type I diabetes mellitus (DM) models have been pivotal in diabetes research due to their ability to mimic the insulin-dependent hyperglycemia akin to human type I diabetes. However, these models often suffer from poor induction rates and low survival post-STZ induction, especially in long-term experiments, necessitating insulin supplementation, which introduces additional variables to experiments. To address this, we present a novel modification to the STZ-induced DM model in C57BL/6J mice to improve survival rates without insulin supplementation. Our method involves non-fasting, low-dose STZ injections dissolved in pH-neutral phosphate buffer saline instead of acidic sodium citrate buffer, administered over 5 days. We observed hyperglycemia induction in 94.28% of mice within a week post-injection, with stable high blood glucose levels, stable body weight, and minimal mortality up to 21 weeks. Notably, omitting 10% sucrose in water and fasting did not affect hyperglycemia induction. Our findings suggest that the modified protocol not only decreases the experimental effort of the researchers, but reduces animal stress and mortality, thus enhancing experimental outcomes and animal welfare. By optimizing the STZ-induced DM model in C57BL/6J mice, our study provides a valuable resource for researchers aiming to study diabetes and its complications while minimizing experimental variability and animal usage.