Sequential Sampling Research Articles

Spatial distribution and sequential sampling plan for Oligonychus punicae (Acari: Trombidiformes: Tetranychidae) on grapevine.

Sequential sampling plans are employed for the rapid characterization of infestations to facilitate decision-making. This study aimed to (i) investigate the spatial distribution of Oligonychus punicae (Hirst) in grapevine crops, (ii) determine the most representative branch, leaf, and leaf region for monitoring, and (iii) develop a sequential sampling plan for decision-making to control O. punicae in the table grape varieties Arra 15, BRS Vitória, Cotton Candy, Sugar Crisp, and Timpson at different phenological stages. O. punicae distribution was analyzed across all varieties and developmental stages. Notably, O. punicae distribution varied among the tested varieties, with no consistent pattern observed in branches and leaves. However, a distinct distribution pattern was evident within leaves, with larger populations concentrated in the central regions. The lower and upper economic thresholds implement control measures varied according to the phenological stage, with the lower thresholds (economic threshold) set at 36% and the upper thresholds (economic injury level) at 40%. Consequently, a minimum of 3 and maximum of 20 plants were sampled. Overall, this study provides a robust approach to optimizing resource allocation and minimizing the environmental impact of O. punicae management.

Advancing diagnosis and early risk assessment of preeclampsia through noninvasive cell-free DNA methylation profiling

BackgroundAberrant embryo implantation and suboptimal placentation can lead to (severe) complications such as preeclampsia and fetal growth restriction later in pregnancy. Current identification of high-risk pregnancies relies on a combination of risk factors, biomarkers, and ultrasound examinations, a relatively inaccurate approach.Previously, aberrant DNA methylation due to placental hypoxia has been identified as a potential marker of placental insufficiency and, hence, potential (future) pregnancy complications. The goal of the Early Prediction of prEgnancy Complications Testing, or the ExPECT study, is to validate a genome-wide, cell-free DNA (cfDNA) methylation strategy to diagnose preeclampsia accurately. More importantly, the predictive potential of this strategy is also explored to reliably identify high-risk pregnancies early in gestation. Furthermore, a longitudinal study was conducted, including sequential blood samples from pregnant individuals experiencing both uneventful and complicated gestations, to assess the methylation dynamics of cfDNA throughout these pregnancies.A significant strength of this study is its enzymatic digest, which enriches CpG-rich regions across the genome without the need for proprietary reagents or prior selection of regions of interest. This makes it useful for the cost-effective discovery of novel markers.ResultsInvestigation of methylation patterns throughout pregnancy showed different methylation trends between unaffected and affected pregnancies. We detected differentially methylated regions (DMRs) in pregnancies complicated with preeclampsia as early as 12 weeks of gestation, with distinct differences in the methylation profile between early and late pregnancy. Two classification models were developed to diagnose and predict preeclampsia, demonstrating promising results on a small set of validation samples.ConclusionsThis study offers valuable insights into methylation changes at specific genomic regions throughout pregnancy, revealing critical differences between normal and complicated pregnancies. The power of noninvasive cfDNA methylation profiling was successfully proven, suggesting the potential to integrate this noninvasive approach into routine prenatal care.

Linking multiple serological assays to infer dengue virus infections from paired samples using mixture models.

Dengue virus (DENV) is an increasingly important human pathogen, with already half of the globe's population living in environments with transmission potential. Since only a minority of cases are captured by direct detection methods (RT-PCR or antigen tests), serological assays play an important role in the diagnostic process. However, individual assays can suffer from low sensitivity and specificity and interpreting results from multiple assays remains challenging, particularly because interpretations from multiple assays may differ, creating uncertainty over how to generate finalized interpretations. We develop a Bayesian mixture model that can jointly model data from multiple paired serological assays, to infer infection events from paired serological data. We first test the performance of our model using simulated data. We then apply our model to 677 pairs of acute and convalescent serum collected as a part of illness and household investigations across two longitudinal cohort studies in Kamphaeng Phet, Thailand, including data from 232 RT-PCR confirmed infections (gold standard). We compare the classification of the new model to prior standard interpretations that independently utilize information from either the hemagglutination inhibition assay (HAI) or the enzyme-linked immunosorbent assay (EIA). We find that additional serological assays improve accuracy of infection detection for both simulated and real world data. Models incorporating paired IgG and IgM data as well as those incorporating IgG, IgM, and HAI data consistently have higher accuracy when using PCR confirmed infections as a gold standard (87-90% F1 scores, a combined metric of sensitivity and specificity) than currently implemented cut-point approaches (82-84% F1 scores). Our results provide a probabilistic framework through which multiple serological assays across different platforms can be leveraged across sequential serum samples to provide insight into whether individuals have recently experienced a DENV infection. These methods are applicable to other pathogen systems where multiple serological assays can be leveraged to quantify infection history.

Value construction through sequential sampling explains serial dependencies in decision making.

Deciding between a pair of familiar items is thought to rely on a comparison of their subjective values. When the values are similar, decisions take longer, and the choice may be inconsistent with stated value. These regularities are thought to be explained by the same mechanism of noisy evidence accumulation that leads to perceptual errors under conditions of low signal to noise. However, unlike perceptual decisions, subjective values may vary with internal states (e.g. desires, priorities) that change over time. This raises the possibility that the apparent stochasticity of choice reflects changes in value rather than mere noise. We hypothesized that these changes would manifest in serial dependencies across decision sequences. We analyzed data from a task in which participants chose between snack items. We developed an algorithm, Reval, that revealed significant fluctuations of the subjective values of items within an experimental session. The dynamic values predicted choices and response times more accurately than stated values. The dynamic values also furnished a superior account of the BOLD signal in ventromedial prefrontal cortex. A novel bounded-evidence accumulation model with temporally correlated evidence samples supports the idea that revaluation reflects the dynamic construction of subjective value during deliberation, which in turn influences subsequent decisions.

Value construction through sequential sampling explains serial dependencies in decision making

Deciding between a pair of familiar items is thought to rely on a comparison of their subjective values. When the values are similar, decisions take longer, and the choice may be inconsistent with stated value. These regularities are thought to be explained by the same mechanism of noisy evidence accumulation that leads to perceptual errors under conditions of low signal to noise. However, unlike perceptual decisions, subjective values may vary with internal states (e.g. desires, priorities) that change over time. This raises the possibility that the apparent stochasticity of choice reflects changes in value rather than mere noise. We hypothesized that these changes would manifest in serial dependencies across decision sequences. We analyzed data from a task in which participants chose between snack items. We developed an algorithm, Reval, that revealed significant fluctuations of the subjective values of items within an experimental session. The dynamic values predicted choices and response times more accurately than stated values. The dynamic values also furnished a superior account of the BOLD signal in ventromedial prefrontal cortex. A novel bounded-evidence accumulation model with temporally correlated evidence samples supports the idea that revaluation reflects the dynamic construction of subjective value during deliberation, which in turn influences subsequent decisions.

EFEKTIVITAS EKSTRAK DAUN SIRIH MERAH TERHADAP JUMLAH KOLONI BAKTERI PADA REMAJA PEREMPUAN YANG MENGALAMI KEPUTIHAN

Leukorrhea, or vaginal discharge, is a common issue faced by adolescent girls during puberty. The Indonesian Adolescent Reproductive Health Survey reports that 31.8% of young women experience this condition, indicating their increased vulnerability to vaginal infections. Bacterial growth is a primary cause of leukorrhea, particularly in adolescent girls whose thin vaginal mucosa provides a favorable environment for bacterial proliferation, resulting in excessive fluid production and discomfort. Red betel leaf extract, containing natural antiseptics and antibiotics, offers an effective treatment for leukorrhea. The research sought to examine how extract from Piper crocatum (red betel leaf) impacts the growth of bacteria responsible for leukorrhea in young female adolescents. The research employed the Total Plate Count method to measure aerobic bacterial colony numbers in the participants' leukorrhea samples The research employed a quasi-experimental methodology, utilizing a single group pre-post test design and sequential sampling. Results demonstrated a notable impact of red betel leaf extract on the quantity of bacterial colonies in teenage girls. The statistical evaluation produced a p-value below 0.05 (specifically, 0.02), validating the extract's effectiveness. The research concludes that red betel leaf extract, a readily available natural remedy, can serve as an effective therapy to reduce bacterial colonies responsible for leukorrhea in adolescent girls. This herbal treatment's accessibility makes it a practical option for community use.

Sequential adaptive design for emulating costly computer codes

Gaussian processes (GPs) are generally regarded as the gold standard surrogate model for emulating computationally expensive computer-based simulators. However, the problem of training GPs as accurately as possible with a minimum number of model evaluations remains challenging. We address this problem by suggesting a novel adaptive sampling criterion called VIGF (variance of improvement for global fit). The improvement function at any point is a measure of the deviation of the GP emulator from the nearest observed model output. At each iteration of the proposed algorithm, a new run is performed where VIGF is the largest. Then, the new sample is added to the design and the emulator is updated accordingly. A batch version of VIGF is also proposed which can save the user time when parallel computing is available. Additionally, VIGF is extended to the multi-fidelity case where the expensive high-fidelity model is predicted with the assistance of a lower fidelity simulator. This is performed via hierarchical kriging. The applicability of our method is assessed on a bunch of test functions and its performance is compared with several sequential sampling strategies. The results suggest that our method has a superior performance in predicting the benchmark functions in most cases. An implementation of VIGF is available in the dgpsi R package, which can be found on CRAN.

Perbandingan Hasil Pemeriksaan Asam Urat Metode Point of Care Testing (POCT) dengan Metode Fotometrik

The techniques used in laboratory examinations are crucial for diagnosing illnesses, including figuring out how much uric acid is in the body. The photometric technique and the Point Of Care Testing (POCT) approach are two of them. The aim of this research is to examine the findings of uric acid level examinations in persons between the ages of 20 and 40 in families and communities in Ciparay District, Bandung Regency, using POCT and photometric techniques. This is a cross-sectional research that employs a sequential random sample approach on 30 populations for sampling. A normality test utilizing the Paired Sample T-Test and the One-Sample Kolmogorov-Smirnov Test was used to analyze the data. The analysis's findings revealed that the uric acid level determined by the photometric technique was 4,920±1,480, whereas the average and standard deviation for the POCT method was 5,266±1,439. This indicates that the two approaches' average uric acid levels differ from one another. With a p-value of 0.045, the paired T-test demonstrated a significant difference between the photometric and POCT methods.

Supplemental Material for Probing the Origins of Subjective Confidence in Source Memory Decisions in Young and Older Adults: A Sequential Sampling Account

Supplemental Material for Probing the Origins of Subjective Confidence in Source Memory Decisions in Young and Older Adults: A Sequential Sampling Account

Drug use patterns and health problems among people who use drugs in Guinea-Bissau (2022): A cross-sectional survey using respondent-driven sampling

BackgroundLittle data exists on the use of cocaine, methamphetamine, tramadol and heroin or related health conditions in Guinea Bissau. We aimed to estimate drug use practices and the prevalence of selected blood-borne infections, depression and population size estimates of people who use injectable drugs in Guinea-Bissau. MethodsWe used respondent-driven sampling to recruit adults who use injectable drugs in this cross sectional survey in three cities (Bissau, Bafatá and Gabú) between July and September 2022. Participants completed an interviewer administered survey enquiring about sociodemographic characteristics, drug use practices and mental health. Rapid diagnostic testing was done for HIV, hepatitis B surface antigen (HBsAg) and hepatitis C antibody (anti-HCV). Data was weighted in RDS-Analyst using self-reported network size and Gile's Sequential Sampling Estimator. Population size estimates were generated using the two point capture-recapture method. ResultsOverall, 750 participants were recruited. People who use drugs were estimated to be mostly unemployed males aged between 25 and 49 years. Methamphetamine and crack cocaine were most commonly used. Prevalence of ever injecting ranged from 6 % to 44 %. Between 44 % and 52 % of people experience symptoms of depression. Prevalence ranges from 1.9 % to 5.2 % for HIV, and 5.7–8.3 % for HBsAg and 0.42–0.66 % for anti-HCV. The population estimates of people who use injectable drugs were 1637 in Bissau, 1314 in Bafatá and 424 in Gabú. ConclusionMethamphetamine and crack cocaine are the most commonly used injectable drugs in Guinea-Bissau. Symptoms of depression are common among people who use drugs in the country. Access to evidence-based drug use treatment and harm reduction interventions that integrate mental health care services are needed to improve the health and wellbeing of people who use drugs in Guinea-Bissau.

Unsupervised data-driven response regime exploration and identification for dynamical systems.

Data-Driven Response Regime Exploration and Identification (DR2EI) is a novel and fully data-driven method for identifying and classifying response regimes of a dynamical system without requiring human intervention. This approach is a valuable tool for exploring and discovering response regimes in complex dynamical systems, especially when the governing equations and the number of distinct response regimes are unknown, and the system is expensive to sample. Additionally, the method is useful for order reduction, as it can be used to identify the most dominant response regimes of a given dynamical system. DR2EI utilizes unsupervised learning algorithms to transform the system's response into an embedding space that facilitates regime classification. An active sequential sampling approach based on Gaussian Process Regression is used to efficiently sample the parameter space, quantify uncertainty, and provide optimal trade-offs between exploration and exploitation. The performance of the DR2EI method was evaluated by analyzing three established dynamical systems: the mathematical pendulum, the Lorenz system, and the Duffing oscillator, and its robustness to noise was validated across a range of noise magnitudes. The method was shown to effectively identify a variety of response regimes with both similar and distinct topological features and frequency content, demonstrating its versatility in capturing a wide range of behaviors. While it may not be possible to guarantee that all possible regimes will be identified, the method provides an automated and efficient means for exploring the parameter space of a dynamical system and identifying its underlying "sufficiently dominant" response regimes without prior knowledge of the system's equations or behavior.

Clinical Utility of a Novel Triplex Digital PCR Assay for Clone Monitoring in Sequential and Relapsed Pediatric B-Cell Acute Lymphoblastic Leukemia Patients.

Digital polymerase chain reaction (PCR) studies for clonal disease monitoring in B-acute lymphoblastic leukemia patients are currently limited due to the heterogeneous nature of mutations, which limit cost-effective assay designs. In this study, 70 samples (14 relapse and 56 sequential therapy samples) were tested for 13 recurrent mutations identified on deep sequencing in our published cohort (KRAS, NRAS, NT5C2, PMS2, UHRF1, KMT2D, and TP53 genes) via a novel triplex digital PCR assay. A total of seven major clones of NRAS [five] and NT5C2 [two] were noted in six out of 14 (43%) relapse patients, accounting for 44% of early relapses. In addition, 10 minor clones (PMS2 [two], NRAS [four], NT5C2 [three], and TP53 [one]) were noted in five out of 14 (36%) patients. In the 56 sequential therapy samples, six major clones were noted (NRAS [five], KRAS [one]) in four out of 14 (28.5%) patients, with two increasing in size in maintenance samples, leading to subsequent relapse in both cases. In addition, therapy-acquired minor clones in NT5C2 [four] and PMS2 [one] were seen to emerge in maintenance samples in four out of 14 (28.5%) patients, with concordant detection of such major and minor clones in unpaired relapse samples, indicating the need for their active surveillance during therapy. Overall, digital PCR validated NRAS and NT5C2 major clones in one-third (10 out of 27; 37%) of cases, driving nearly half of early relapses.

A novel looped functional for stability analysis of asynchronous sampled‐data systems

AbstractThis paper is concerned with the stability analysis problems of asynchronous sampled‐data systems via an input‐delay approach. Between two sequential sampling times, a sampled‐data system is controlled by previously sampled states and thus can be modeled as a time‐delay system with a saw‐tooth delay. Inevitably, information at the sampling instants have played essential roles in controlling the systems and reducing the conservatism of the stability criteria. Therefore, to further utilize the information at sampling instants, this paper proposes novel looped functionals consisting of integral state variables and their interval‐normalized terms. Three numerical examples including a practical helicopter system demonstrate the effectiveness of the proposed approaches in terms of allowable sampling intervals.

A Control Chart for the Joint Monitoring of Mean and Variance With Klein's Supplementary Rule

ABSTRACTSupplementary run rules have been employed since the 1950s to enhance the performance of control charts, such as the control chart, which aims to control the process mean, or control chart, which aims to control the process variance. One simple, effective, and popular suggestion is Klein's 2‐of‐2, which signals an out‐of‐control process only when two sequential sample averages are observed above the upper control limit or below the lower control limit. We propose to implement Klein's 2‐of‐2 for a more complex control chart, the joint control chart, which aims to simultaneously monitor the mean and the variance of a process. Compared with the traditional control chart, using this rule for the control chart reduces the average run length (ARL) and standard deviation of the run length (SDRL) for detecting small process shifts while maintaining high applicability owing to the ease of use in employing the 2‐of‐2 rule in practical environments. Klein's run rule was implemented in the control chart through a 15‐state Markov chain, and its performance was compared with the basic control chart as well as the exponentially weighted moving average (EWMA) chart, known to have excellent capabilities in detecting small process average shifts at the cost of a more complex control scheme. The newly proposed Klein control chart demonstrated superior performance compared to the traditional control chart and competitive performance even against the more complex joint EWMA control chart.

LVCD: Reference-based Lineart Video Colorization with Diffusion Models

We propose the first video diffusion framework for reference-based lineart video colorization. Unlike previous works that rely solely on image generative models to colorize lineart frame by frame, our approach leverages a large-scale pretrained video diffusion model to generate colorized animation videos. This approach leads to more temporally consistent results and is better equipped to handle large motions. Firstly, we introduce Sketch-guided ControlNet which provides additional control to finetune an image-to-video diffusion model for controllable video synthesis, enabling the generation of animation videos conditioned on lineart. We then propose Reference Attention to facilitate the transfer of colors from the reference frame to other frames containing fast and expansive motions. Finally, we present a novel scheme for sequential sampling, incorporating the Overlapped Blending Module and Prev-Reference Attention , to extend the video diffusion model beyond its original fixed-length limitation for long video colorization. Both qualitative and quantitative results demonstrate that our method significantly outperforms state-of-the-art techniques in terms of frame and video quality, as well as temporal consistency. Moreover, our method is capable of generating high-quality, long temporal-consistent animation videos with large motions, which is not achievable in previous works. Our code and model are available at https://luckyhzt.github.io/lvcd.

18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography in Large-Vessel Vasculitis During Active and Inactive Disease Stages Is Associated with the Metabolic Profile, but Not the Macrophage-Related Cytokines: A Proof-of-Concept Study.

Giant cell arteritis (GCA) is an autoimmune/autoinflammatory disease affecting large vessels in patients over 50 years old. The disease presents as an acute inflammatory response with two phenotypes, cranial GCA and large-vessel vasculitis (LV)-GCA, involving the thoracic aorta and its branches. 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET-CT) is among the imaging techniques contributing to diagnosing patients with systemic disease. However, its association with soluble inflammatory markers is still elusive. This proof-of-concept study aims to identify novel soluble serum biomarkers in PET/CT-positive patients with LV-GCA and associate them with active (0 months) and inactive disease (6 months following treatment), in sequential samples. The most-diseased-segment target-to-background ratio (TBRMDS) was calculated for 13 LV-GCA patients, while 14 cranial GCA and 14 Polymyalgia Rheumatica patients with negative initial PET/CT scans served as disease controls. Serum macrophage-related cytokines were evaluated by cytometric bead array (CBA). Finally, previously published NMR/metabolomics data acquired from the same blood sampling were analyzed along with PET/CT findings. TBRMDS was significantly increased in active versus inactive disease (3.32 vs. 2.65, p = 0.006). The analysis identified nine serum metabolites as more sensitive to change from the active to inactive state. Among them, choline levels were exclusively altered in the LV-GCA group but not in the disease controls. Cytokine levels were not associated with PET/CT activity. Combining CRP, ESR, and TBRMDS with choline levels, a composite index was generated to distinguish active and inactive LV-GCA (20.4 vs. 11.62, p = 0.001). These preliminary results could pave the way for more extensive studies integrating serum metabolomic parameters with PET/CT imaging data to extract sensitive composite disease indexes useful for everyday clinical practice.

Radiocarbon dating and isotopic paleoecology of Notiomastodon platensis (Mammalia: Proboscidea) from the late Pleistocene of Minas Gerais, Brazil

Proboscidea is a group of large mammals abundant in the fossil record and in Brazil it is represented by Notiomastodon platensis (Ameghino, 1888). To improve the understanding on its taxonomy, chronology, annual diet, and habitats paleoenvironmental aspects, we conducted a morphological description, absolute dating, and analysis of stable isotopes of carbon and oxygen (δ13C and δ18O) in Notiomastodon' remains from the northern (Norte de Minas) and western (Triângulo Mineiro) mesoregions of Minas Gerais State, Brazil. The material includes isolated teeth, mandibular portions, and postcranial bones at different levels of fragmentation, associated with at least eight adults and one juvenile. The only individual from Triângulo, found in Campina Verde municipality, was dated at 27,715–27,903 Cal yr BP. It presented a mixed diet based on C4 plants (piC4 = 90 %; δ13C = 0.5 ‰), which implies the occupation of an open environment, mostly pasture. The region was relatively humid (δ18O = 24.7 ‰), which is supported by the existence of humidity corridors in the Amazon region. A similar phytophysiognomy was inferred for the Norte de Minas region between at least 21,966–22,279 Cal yr BP and 18,944–19,157 Cal yr BP. However, one specimen showed a different δ13C value (piC3 = 0.87 %; δ13C = −10.2 ‰). This indicates it lived in a transitional environment between low-density forest and arboreal savannah. This likely occurred during a period that favored the expansion of trees and shrubs. The region underwent climate change, from relatively humid conditions (δ18O = 25.6 ± 0.2 ‰) to expressively dry (δ18O = 34.6 ‰) between the dated periods, a change corroborated by some palynological data. The multiannual paleoecological analysis, based on sequential sampling of three dentin layers of an incisor, indicated the relative stability of vegetation and climate despite fluctuations in hydrology. The perceived disassociation between vegetation dynamics and local hydrology corroborates the idea that factors other than precipitation may play a significant role in the environmental dynamics of the Cerrado biome.

Sequential search-based Latin hypercube sampling scheme for digital twin uncertainty quantification with application in EHA

For uncertainty quantification of complex models with high-dimensional, nonlinear, multi-component coupling like digital twins, traditional statistical sampling methods, such as random sampling and Latin hypercube sampling, require a large number of samples, which entails huge computational costs. Therefore, how to construct a small-size sample space has been a hot issue of interest for researchers. To this end, this paper proposes a sequential search-based Latin hypercube sampling scheme to generate efficient and accurate samples for uncertainty quantification. First, the sampling range of the samples is formed by carving the polymorphic uncertainty based on theoretical analysis. Then, the optimal Latin hypercube design is selected using the Latin hypercube sampling method combined with the “space filling” criterion. Finally, the sample selection function is established, and the next most informative sample is optimally selected to obtain the sequential test sample. Compared with the classical sampling method, the generated samples can retain more information on the basis of sparsity. A series of numerical experiments are conducted to demonstrate the superiority of the proposed sequential search-based Latin hypercube sampling scheme, which is a way to provide reliable uncertainty quantification results with small sample sizes.

Cell-free DNA from ascites identifies clinically relevant variants and tumour evolution in patients with advanced ovarian cancer.

The emergence of targeted therapies has transformed ovarian cancer treatment. However, biomarker profiling for precision medicine is limited by access to quality, tumour-enriched tissue samples. The use of cell-free DNA (cfDNA) in ascites presents a potential solution to this challenge. In this study, next-generation sequencing was performed on ascites-derived cfDNA samples (26 samples from 15 human participants with ovarian cancer), with matched DNA from ascites-derived tumour cells (n = 5) and archived formalin-fixed paraffin-embedded (FFPE) tissue (n = 5). Similar tumour purity and variant detection were achieved with cfDNA compared to FFPE and ascites cell DNA. Analysis of large-scale genomic alterations, loss of heterozygosity and tumour mutation burden identified six cases of high genomic instability (including four with pathogenic BRCA1 and BRCA2 mutations). Copy number profiles and subclone prevalence changed between sequential ascites samples, particularly in a case where deletions and chromothripsis in Chr17p13.1 and Chr8q resulted in changes in clinically relevant TP53 and MYC variants over time. Ascites cfDNA identified clinically actionable information, concordant to tissue biopsies, enabling opportunistic molecular profiling. This advocates for analysis of ascites cfDNA in lieu of accessing tumour tissue via biopsy.

Post-Neutralisation Activated Clotting Time and Postoperative Transfusions in Cardiac Surgery Outcome.

To assess the impact of post-protamine neutralisation activated clotting time (ACT) values on postoperative outcomes including chest drain output, transfusion requirements, and CICU stay, in patients undergoing cardiac surgery. Observational comparative study. Place and Duration of the Study: Department of Anaesthesiology, The Aga Khan University Hospital, Karachi, Pakistan, from February to August 2023. Ethical approval was obtained to collect data from elective cardiac surgery patients' charts. A sequential sampling approach analysed the baseline and post-protamine neutralisation ACT values, categorising patients into two groups. Group A maintained ACT within 10% of baseline, while Group B deviated. The outcomes measured included transfusion needs, chest drain output, additional protamine, cardiac intensive care unit (CICU) stay, and postoperative reopening. Statistical analysis included mean, median, frequency, t-test / Mann-Whitney U test, and Chi-square test. The study comprised 101 patients (39 in Group A, 62 in Group B), with similar baseline health. No significant differences were found in tranexamic acid use, CICU stay, chest drain output, or transfusion rates between the groups (p >0.05). Maintaining ACT within 10% of baseline post-protamine neutralisation results in similar intraoperative and postoperative outcomes, suggesting potential benefits in avoiding the aggressive protamine therapy and ensuring haemostasis in cardiac surgery. Coronary Artery bypass grafting, Cardiopulmonary bypass, Activated clotting time (ACT), Heparin, Postoperative bleeding, Blood transfusions.