Accurate Method For Assessment Research Articles

Assessing soil remediation effect of Cr and Pb based on bioavailability using DGT, BCR and standardized determination method

In the field of soil remediation, the importance of bioavailability of pollutants has not received adequate attention, leading to the excessive application of remediation measures. Therefore, to ensure the safe use of farmland soil, a scientific method is needed to assess labile contaminants and their translocation in plants. To evaluate soil remediation effect based on bioavailability, the concentrations of these heavy metals in soil were analyzed using by the method for total metal content, the Community Bureau of Reference (BCR) extraction, and the diffusive gradients in thin films (DGT) technique. The results reveal that the correlation coefficients between metal concentrations measured by DGT and those accumulated in rice grains are the highest (Cr-R2 = 0.8966, Pb-R2 = 0.9045). However, the capability of method for total metal content to evaluate the remediation effect of heavy metals is very limited. In contrast, although Cr and Pb measured by BCR show a high correlation with HMs in rice plants, the method still falls short in precisely assessing bioavailability. Significantly, DGT proves to be more effective, successfully distinguishing the remediation effects of different treatments. Generally, DGT offers a more accurate and simpler assessment method, underscoring its practical significance for monitoring soil remediation and environmental management.

Construction and validation of academic burnout scale

This study constructs and validates a scale for measuring academic burnout among university students. Drawing from empirical evidence and systematic scale construction procedures, the questionnaire items were developed through literature review and focus group discussions. Data from 348 and 774 respondents respectively underwent exploratory factor analysis and confirmatory factor analysis. The resulting scale identifies six dimensions: parental/Guardian pressure, exhaustion, negative peer relations, subjective overload, academic inefficacy, and negative teacher-student relation. By providing a specialized tool for identifying academic burnout, this study aims to facilitate research efforts in higher education institutions. This scale addresses the lack of accurate assessment methods for academic burnout, offering insights into its components.

The assessment method of lip closure ability based on sEMG nonlinear onset detection algorithms.

To overcome the limitations of traditional diagnosis of orbicularis oris muscle function in mouth-breathing patients, this study aims to propose a surface electromyographic (sEMG) based method for reliable and accurate quantitative assessment of lip closure ability. A total of 21 volunteers (16 patients and 5 healthy subjects, aged 8-16) were included in the study. Three nonlinear onset detection algorithms- Teager-Kaiser Energy (TKE) operator, Sample Entropy (SampEn), and Fuzzy Entropy (FuzzyEn)- were compared for their ability to identify lip closure in sEMG signals. Lip Closure EMG Activity Index (LCEAI) was proposed based on the action segments detected by the best performing algorithm for the quantitative assessment of lip closure. The results indicated that FuzzyEn had the highest lip closure identification rate at 93.78 %, the lowest average onset delay of 47.50 ms, the lowest average endpoint delay of 73.10 ms, and the minimal time error of 111.61 ms, exhibiting superior performance. The calculation results of the LCEAI closely corresponded with the actual degree of lip closure in patients. The lip closure ability assessment method proposed in this study can provide a quantitative basis for the diagnosis of mouth breathing.

Decision Support System for the Assessment and Enhancement of Agrobiodiversity Performance

The industrialisation of agriculture and changes in production systems have contributed to a biodiversity decline worldwide. Developing accurate and adequate assessment methods can encourage farmers to support more sustainable agricultural management. This study presents a decision support system to promote agrobiodiversity that incorporates not only a quantitative assessment of relevant indicators of agrobiodiversity performance but also provides enhancement practice recommendations and associated benefits, presenting an action plan in order of priority. Additionally, the decision support system allows a visual comparison between biodiversity composite indicators and indicators representing pest control and crop yield. Since grape cultivation is considered one of the most intensive agricultural systems, thus significantly impacting biodiversity, the elaborated decision support system was tested on a viticultural agroecosystem in the demarcated Douro region in Portugal. The results demonstrated the decision support system functioning according to the selected methodology and allowed the identification of future lines for investigation. During the analysed period, the following were verified: an increase of 2% in the biodiversity indicator, 130% in harvest yield, and 2077% in the enemy-to-pest ratio. It is expected that the elaborated DSS will offer a significant contribution by bridging communication gaps on alternative management options to improve biodiversity performance in agricultural systems.

Drug-drug interactions and synergy: from pharmacological models to clinical application.

This review explores the concept of synergy in pharmacology, emphasizing its importance in optimizing treatment outcomes through the combination of drugs with different mechanisms of action. Synergy, defined as an effect greater than the expected additive effect elicited by individual agents according to specific predictive models, offers a promising approach to enhance therapeutic efficacy while minimizing adverse events. The historical evolution of synergy research, from ancient civilizations to modern pharmacology, highlights the ongoing quest to understand and harness synergistic interactions. Key concepts such as concentration-response curves, additive effects, and predictive models are discussed in detail, emphasizing the need for accurate assessment methods throughout translational drug development. While various mathematical models exist for synergy analysis, selecting the appropriate model and software tools remains a challenge, necessitating careful consideration of experimental design and data interpretation. Furthermore, this review addresses practical considerations in synergy assessment, including preclinical and clinical approaches, mechanism of action, and statistical analysis. Optimizing synergy requires attention to concentration/dose ratios, target site localization, and timing of drug administration, ensuring that the benefits of combination therapy detected at bench-side are translatable into clinical practice. Overall, the review advocates for a systematic approach to synergy assessment, incorporating robust statistical analysis, effective and simplified predictive models, and collaborative efforts across pivotal sectors such as academic institutions, pharmaceutical companies, and regulatory agencies. By overcoming critical challenges and maximizing therapeutic potential, effective synergy assessment in drug development holds promise for advancing patient care. Significance Statement Combining drugs with different mechanisms of action for synergistic interactions optimizes treatment efficacy and safety. Accurate interpretation of synergy requires the identification of the expected additive effect. Despite innovative models to predict the additive effect, consensus in drug interaction research is lacking, hindering the bench-to-bedside development of combination therapies. Collaboration among science, industry, and regulation is crucial for advancing combination therapy development, ensuring rigorous application of predictive models in clinical settings.

Validity of dietary intake methods in cancer cachexia.

Accurate assessment of dietary intake, especially energy and protein intake, is crucial for optimizing nutritional care and outcomes in patients with cancer. Validation of dietary assessment methods is necessary to ensure accuracy, but the validity of these methods in patients with cancer, and especially in those with cancer cachexia, is uncertain. Validating nutritional intake is complex because of the variety of dietary methods, lack of a gold standard method, and diverse validation measures. Here, we review the literature on validations of dietary intake methods in patients with cancer, including those with cachexia, and highlight the gap between current validation efforts and the need for accurate dietary assessment methods in this population. We analyzed eight studies involving 1479 patients with cancer to evaluate the accuracy and reliability of 24-hour recalls, food records, and food frequency questionnaires in estimating energy and protein intake. We discuss validation methods, including comparison with biomarkers, indirect calorimetry, and relative validation of dietary intake methods. Few have validated dietary intake methods against objective markers in patients with cancer. While food records and 24-hour recalls show potential accuracy for energy and protein intake, this may be compromised in hypermetabolic patients. Additionally, under- and overreporting of intake may be less frequent, and the reliability of urinary nitrogen as a protein intake marker in patients with cachexia needs further investigation. Accurate dietary assessment is important for enhancing nutritional care outcomes in cachexia trials, requiring validation at multiple time points throughout the cancer trajectory.

Introducing and Validating the Cranial-Dorsal-Hip Angle (∠CDH): A Method for Accurate Fetal Position Assessment in the First Trimester and Future AI Applications

Abstract Purpose To introduce the cranial-dorsal-hip angle (∠CDH) as a novel quantitative tool for assessing fetal position in the first trimester and to validate its feasibility for future AI applications. Materials and Methods 2520 first-trimester fetal NT exams with 2582 CRL images (January-August 2022) were analyzed at a tertiary hospital as the pilot group. Additionally, 1418 cases with 1450 fetal CRL images (September-December 2022) were examined for validation. Three expert sonographers defined a standard for fetal positions. ∠CDH measurements, conducted by two ultrasound technicians, were validated for consistency using Bland-Altman plots and the intra-class correlation coefficient (ICC). This method allowed for categorizing fetal positions as hyperflexion, neutral, and hyperextension based on ∠CDH. Comparative accuracy was assessed against Ioannou, Wanyonyi, and Roux methods using the weighted Kappa coefficient (k value). Results The pilot group comprised 2186 fetal CRL images, and the validation group included 1193 images. Measurement consistency was high (ICCs of 0.993; P<0.001). The established 95% reference range for ∠CDH in the neutral fetal position was 118.3° to 137.8°. The ∠CDH method demonstrated superior accuracy over the Ioannou, Wanyonyi, and Roux methods in both groups, with accuracy rates of 94.5% (k values: 0.874, 95%CI: 0.852–0.896) in the pilot group, and 92.6% (k values: 0.838, 95%CI: 0.806–0.871) in the validation group. Conclusion The ∠CDH method has been validated as a highly reproducible and accurate technique for first-trimester fetal position assessment. This sets the stage for its potential future integration into intelligent assessment models.

Individual Variability in Brain Connectivity Patterns and Driving-Fatigue Dynamics.

Mental fatigue during driving poses significant risks to road safety, necessitating accurate assessment methods to mitigate potential hazards. This study explores the impact of individual variability in brain networks on driving fatigue assessment, hypothesizing that subject-specific connectivity patterns play a pivotal role in understanding fatigue dynamics. By conducting a linear regression analysis of subject-specific brain networks in different frequency bands, this research aims to elucidate the relationships between frequency-specific connectivity patterns and driving fatigue. As such, an EEG sustained driving simulation experiment was carried out, estimating individuals' brain networks using the Phase Lag Index (PLI) to capture shared connectivity patterns. The results unveiled notable variability in connectivity patterns across frequency bands, with the alpha band exhibiting heightened sensitivity to driving fatigue. Individualized connectivity analysis underscored the complexity of fatigue assessment and the potential for personalized approaches. These findings emphasize the importance of subject-specific brain networks in comprehending fatigue dynamics, while providing sensor space minimization, advocating for the development of efficient mobile sensor applications for real-time fatigue detection in driving scenarios.

Machine Learning and Deep Learning Methods for Fast and Accurate Assessment of Transthoracic Echocardiogram Image Quality.

High-quality echocardiogram images are the cornerstone of accurate and reliable measurements of the heart. Therefore, this study aimed to develop, validate and compare machine learning and deep learning algorithms for accurate and automated assessment of transthoracic echocardiogram image quality. In total, 4090 single-frame two-dimensional transthoracic echocardiogram images were used from apical 4-chamber, apical 2-chamber and parasternal long-axis views sampled from 3530 adult patients. The data were extracted from CAMUS and Unity Imaging open-source datasets. For every raw image, additional grayscale block histograms were developed. For block histogram datasets, six classic machine learning algorithms were tested. Moreover, convolutional neural networks based on the pre-trained EfficientNetB4 architecture were developed for raw image datasets. Classic machine learning algorithms predicted image quality with 0.74 to 0.92 accuracy (AUC 0.81 to 0.96), whereas convolutional neural networks achieved between 0.74 and 0.89 prediction accuracy (AUC 0.79 to 0.95). Both approaches are accurate methods of echocardiogram image quality assessment. Moreover, this study is a proof of concept of a novel method of training classic machine learning algorithms on block histograms calculated from raw images. Automated echocardiogram image quality assessment methods may provide additional relevant information to the echocardiographer in daily clinical practice and improve reliability in clinical decision making.

Multi-scale image-based damage recognition and assessment for reinforced concrete structures in post-earthquake emergency response

As cities continue to develop, the significance of resilience and intelligence is increasing. In post-earthquake emergency response, there is a continuous demand for efficient and accurate methods of structural state assessment. This study employs deep learning (DL) techniques to propose a multi-scale image-based damage recognition and assessment method for reinforced concrete (RC) structures. First, an RC structural damage recognition task framework and a structural mechanical damage image dataset are established. Second, a DL model is selected to conduct the experiments and enhance its performance through transfer learning. Then, a multi-scale correlated structural state assessment procedure is introduced where local, component, structural, and regional scales are linked. Finally, an engineering case is presented to describe the application steps of the method in real-world scenarios, demonstrating its feasibility. Nevertheless, the proposed method lacks comprehensive validation across various geotechnical conditions and detailed structural configurations, which may limit its generalizability. This study has the potential to enhance the efficiency and scope of post-disaster emergency response and contribute to the development of sustainable cities.

Diagnosis of the accuracy of land cover classification using bootstrap resampling

ABSTRACT Since the 1970s, there has been a long history of assessing the accuracy of remotely sensed land cover types. Various accuracy assessment methods have been proposed, including the method of bootstrap resampling. However, the potential of the bootstrap resampling method to diagnose the accuracy of remote sensing monitoring of land cover has not been fully explored. Here, we used the land cover map classified in the Gannan Tibetan Autonomous Region as an example and investigated the impact of two parameters in the bootstrap resampling method on the assessment of land cover classification accuracy: the number of validation points and the number of bootstrap resampling times. Varying these two parameters can help diagnose the accuracy of remote sensing classifications of land cover. The main conclusions are as follows. First, the number of validation points required to achieve a stabilized accuracy estimate varies among land cover types. It indicates not only the level of homogeneity of the land cover type but also the level of difficulty associated with classifying it. Second, it is not necessary to have a very large number of bootstrap resampling times to estimate the classification accuracy – 50 was sufficient. In summary, we recommend that various numbers of validation points be employed in bootstrap to objectively and comprehensively assess the overall, producer’s, and user’s accuracies of remote sensing classifications of land cover.

Assessing the Influence of Vehicular Traffic-Associated Atmospheric Pollutants on Pulmonary Function Using Spirometry and Impulse Oscillometry in Healthy Participants: Insights from Bogotá, 2020–2021

Air pollution, particularly from particulate matter (PM2.5) and black carbon (eBC), has been implicated in airway pathologies. This study aims to assess the relationship between exposure to these pollutants and respiratory function in various populations, including healthy individuals, while seeking an accurate assessment method. A cross-sectional study was conducted in Bogotá, evaluating respiratory function in the users of bicycles, minivans, and buses through spirometry and impulse oscillometry. Measurements were taken along two main avenues, assessing the PM2.5 and eBC concentrations. The results reveal higher pollutant levels on AVE KR 9, correlating with changes in oscillometry values post-travel. Cyclists exhibited differing pre- and post-travel values compared to bus and minivan users, suggesting aerobic exercise mitigates pollutant impacts. However, no statistically significant spirometry or impulse oscillometry variations were observed among routes or modes. Public transport and minivan users showed greater PM2.5 and eBC exposure, yet no significant changes associated with environmental contaminants were found in respiratory function values. These findings underscore the importance of further research on pollutant effects and respiratory health in urban environments, particularly concerning different transport modes.

A critical analysis of circular product attributes and limitations of product circularity assessment methods

The Circular Economy (CE) has been proposed as a strategy to promote the efficient use of resources, maximizing the benefits derived from materials and products through value recovery strategies, and minimizing waste generation. However, ambiguity remains in defining what makes a product circular and its characteristics when adapting the CE concept for application at the product level. More clarity about the constitutive characteristics of Circular Products (CPs) is also vital to facilitate their design. To address this challenge, and with the intention to increase the adoption of CE concept within the industry, the descriptions and attributes of a Circular Product (CP) were examined through a scoped literature review and analysis. Findings were then synthesized to establish a preliminary framework of CP attributes. These attributes were used to review the existing product circularity assessment methods. The results highlight limitations in the coverage of CP attributes in the existing methods and research opportunities for their improvement. The findings from this study will contribute to the development of a comprehensive and accurate product circularity assessment method.

Evaluating Residual Strength of RCC Tanks Affected by ASR Using NDT Methods

This case study focuses on the groundwater level tanks (GLR) located in Netra Village, 35 km from Jodhpur, on Nagour Road. The investigation revealed that people and livestock in the area were grappling with water scarcity issues. Non-Destructive Testing (NDT) methods were employed to assess the residual strength of three tanks in the region. The results showed that Tank-1 had a residual strength of 35.41%, Tank-2 had 63.08%, and Tank-3 had 53.8%. Alkali-silica reaction (ASR) poses a significant threat to the structural integrity of concrete tanks, necessitating accurate and non-destructive methods for residual strength assessment. This research paper delves into the effectiveness of NDT methods, specifically rebound hammer and Ultrasonic Pulse Velocity (UPV), in evaluating the residual strength of ASR-affected tanks. Through an extensive review of literature, case studies, and experimental data, this study aims to shed light on the practical application of rebound hammer and UPV for ASR assessment in tank structures. The paper discusses the principles, advantages, and limitations of each NDT method, emphasizing their ability to detect ASR-induced damage and predict the remaining structural capacity of tanks. Moreover, the research addresses the challenges associated with implementing rebound hammer and UPV techniques in ASR-affected environments, offering recommendations for enhancing their reliability and accuracy. By harnessing the combined capabilities of rebound hammer and UPV, engineers and asset managers can make well-informed decisions regarding the maintenance, repair, and retrofitting of ASR-affected tanks, ensuring their long-term safety and functionality in critical infrastructure applications.

Spatial–Temporal Analysis-Based Video Quality Assessment: A Two-Stream Convolutional Network Approach

In system processing, video inevitably suffers from distortion, which leads to quality degradation and affects the user experience. Therefore, it is of great importance to design an accurate and effective objective video quality assessment (VQA) method. In this paper, by considering the multi-dimensional characteristics for video and visual perceptual mechanism, a two-stream convolutional network for VQA is proposed based on spatial–temporal analysis, named TSCNN-VQA. Specifically, for feature extraction, TSCNN-VQA first extracts spatial and temporal features by two different convolutional neural network branches, respectively. After that, the spatial–temporal joint feature fusion is constructed to obtain the joint spatial–temporal features. Meanwhile, the TSCNN-VQA also integrates an attention module to guarantee that the process conforms to the mechanism that the visual system perceives video information. Finally, the overall quality is obtained by non-linear regression. The experimental results in both the LIVE and CSIQ VQA datasets show that the performance indicators obtained by TSCNN-VQA are higher than those of existing VQA methods, which demonstrates that TSCNN-VQA can accurately evaluate video quality and has better consistency with the human visual system.

Developing a smart green supplier risk assessment system integrating natural language processing and life cycle assessment based on AHP framework: An empirical study

Global carbon reduction drives the transformations of corporations, emphasizing carbon and environmental impact within supply chains. A critical challenge in risk management is converting unstructured data into quantifiable information, and ruining the conversion often leads to ineffective risk assessments. While artificial intelligence is increasingly used in supply chain risk management, previous studies seldom integrate text analysis for risk extraction and lack accurate risk assessment methods. This study pioneers an intelligent green supplier risk assessment system integrating natural language processing (NLP) and life cycle assessment (LCA) based on an analytic hierarchy process (AHP) framework. The proposed approach utilizes NLP keyword extraction to identify and assess risk and uses LCA to uncover environmental risks, minimizing negative impacts. The text classification model attains 81.7 % accuracy, ensuring a trustworthy risk assessment system. The proposed method could significantly improve risk analysis efficiency, foster sustainable procurement, and elevate enterprise competitiveness in sustainable business practices.

The distribution of global tidal marshes from Earth observation data

AbstractAimTidal marsh ecosystems are heavily impacted by human activities, highlighting a pressing need to address gaps in our knowledge of their distribution. To better understand the global distribution and changes in tidal marsh extent, and identify opportunities for their conservation and restoration, it is critical to develop a spatial knowledge base of their global occurrence. Here, we develop a globally consistent tidal marsh distribution map for the year 2020 at 10‐m resolution.LocationGlobal.Time period2020.Major taxa studiedTidal marshes.MethodsTo map the location of the world's tidal marshes at 10‐m resolution, we applied a random forest classification model to Earth observation data from the year 2020. We trained the classification model with a reference dataset developed to support distribution mapping of coastal ecosystems, and predicted the spatial distribution of tidal marshes between 60° N and 60° S. We validated the tidal marsh map using standard accuracy assessment methods, with our final map having an overall accuracy score of 0.85.ResultsWe estimate the global extent of tidal marshes in 2020 to be 52,880 km2 (95% CI: 32,030 to 59,780 km2) distributed across 120 countries and territories. Tidal marsh distribution is centred in temperate and Arctic regions, with nearly half of the global extent of tidal marshes occurring in the temperate Northern Atlantic (45%) region. At the national scale, over a third of the global extent (18,510 km2; CI: 11,200–20,900) occurs within the USA.Main conclusionsOur analysis provides the most detailed spatial data on global tidal marsh distribution to date and shows that tidal marshes occur in more countries and across a greater proportion of the world's coastline than previous mapping studies. Our map fills a major knowledge gap regarding the distribution of the world's coastal ecosystems and provides the baseline needed for measuring changes in tidal marsh extent and estimating their value in terms of ecosystem services.

BDHE-Net: A Novel Building Damage Heterogeneity Enhancement Network for Accurate and Efficient Post-Earthquake Assessment Using Aerial and Remote Sensing Data

Accurate and efficient post-earthquake building damage assessment methods enable key building damage information to be obtained more quickly after an earthquake, providing strong support for rescue and reconstruction efforts. Although many methods have been proposed, most have limited effect on accurately extracting severely damaged and collapsed buildings, and they cannot meet the needs of emergency response and rescue operations. Therefore, in this paper, we develop a novel building damage heterogeneity enhancement network for pixel-level building damage classification of post-earthquake unmanned aerial vehicle (UAV) and remote sensing data. The proposed BDHE-Net includes the following three modules: a data augmentation module (DAM), a building damage attention module (BDAM), and a multilevel feature adaptive fusion module (MFAF), which are used to alleviate the weight deviation of intact and slightly damaged categories during model training, pay attention to the heterogeneous characteristics of damaged buildings, and enhance the extraction of house integrity contour information at different resolutions of the image. In addition, a combined loss function is used to focus more attention on the small number of severely damaged and collapsed classes. The proposed model was tested on remote sensing and UAV images acquired from the Afghanistan and Baoxing earthquakes, and the combined loss function and the role of the three modules were studied. The results show that compared with the state-of-the-art methods, the proposed BDHE-Net achieves the best results, with an F1 score improvement of 6.19–8.22%. By integrating the DBA, BDAM, and MFAF modules and combining the loss functions, the model’s classification accuracy for severely damaged and collapsed categories can be improved.

Acaricide resistance status of livestock ticks from East and West Africa and in vivo efficacy of acaricides to control them

Through a collaborative effort across six Sub-Saharan African countries, using recognized international assessment techniques, 23 stocks of three tick species (Rhipicephalus microplus, Rhipicephalus appendiculatus and Amblyomma variegatum) of economic importance for rural small holder farming communities from East and West Africa were collected from cattle, and evaluated in in vitro larval packet tests (LPT). The results demonstrated medium to high resistance to chlorfenvinphos and amitraz across species. Rhipicephalus microplus demonstrated high level alpha-cypermethrin and cypermethrin resistance. Stocks of A. variegatum (West Africa) and R. appendiculatus (Uganda) demonstrated medium level ivermectin resistance.The four least susceptible stocks (East and West African R. microplus, A. variegatum and R. appendiculatus) were taken into in vivo controlled cattle studies where fipronil was found effective against West and East African R. microplus isolates although persistent efficacy failed to reach 90%. Cymiazole and cypermethrin, and ivermectin based acaricides were partially effective against R. microplus without persistent efficacy. Flumethrin spray-on killed A. variegatum within 72 h for up to 10 days posttreatment, however product application was directly to tick attachment sites, which may be impractical under field conditions. A flumethrin pour-on formulation on goats provided persistent efficacy against A. variegatum for up to one-month. Therapeutic control was achieved against R. appendiculatus through weekly spraying cattle with flumethrin, amitraz or combined cymiazole and cypermethrin. A fipronil pour-on product offered four-week residual control against R. appendiculatus (with slow onset of action).Few studies have assessed and directly compared acaricidal activity in vitro and in vivo. There was some discordance between efficacy indicated by LPT and in vivo results. This observation calls for more research into accurate and affordable assessment methods for acaricide resistance.No single active or product was effective against all three tick species, emphasising the need for the development of alternative integrated tick management solutions.

Comparison of treatment adherence among TB patients with and without COVID-19 in South India

AimThe present study aimed to compare the adherence to anti-TB treatment among post-COVID-19 pulmonary TB cases and TB patients without a history of COVID-19 in South India. MethodsA prospective, cross sectional study was conducted in all drug-sensitive pulmonary tuberculosis patients in National TB Elimination Program. The research investigates the impact of the COVID-19 pandemic on TB management, considering both subjective and objective measures of adherence. Data were collected using a validated instrument for subjective assessment and urine metabolite testing for objective evaluation. ResultsThe results reveal significant differences (p < 0.05) between subjective and objective adherence measures, emphasizing the need for accurate and comprehensive assessment methods. However, there is no statistically significant difference (p > 0.05) in adherence to anti-TB treatment among post-COVID-19 pulmonary TB cases and TB patients without a history of COVID-19 in South India. The reason for non-adherence in both groups were ATT side effects, loss of daily wages, forgetting to take medication and lazy to take medications. ConclusionThe study concluded that there is no statistically significant difference (p > 0.05) in adherence to anti-TB treatment among post-COVID-19 pulmonary TB cases and TB patients without a history of COVID-19.