Early Disease Research Articles

Zero shot plant disease classification with semantic attributes

In the rapidly evolving field of plant disease detection, the number and complexity of crop diseases are increasing, made worse by factors like climate change. Addressing these challenges requires robust and efficient methodologies capable of early and accurate disease identification. This paper explores the integration of advanced deep learning techniques, including pre-trained models, zero-shot learning, and semantic attributes to enhance the effectiveness of plant disease detection systems. High level features extracted from the images by these pretrained models capture crucial patterns, while domain-specific semantic attributes, such as leaf texture and color variations, enhance the understanding. Incorporating zero-shot learning enables adaptation to new and unseen diseases using semantic descriptions. Experimental validation across diverse plant species and disease types underscores the approach’s reliability in real-world agricultural scenarios. Our approach has demonstrated superior performance with plant village dataset, showing a significant improvement in accuracy and generalization. These results underscore the potential of our method to revolutionize plant disease detection and management in agricultural practices.

A comparative study of Autogluonand H2O for early predictionof coronary artery disease using automated machine learningandXAI

A comparative study of Autogluonand H2O for early predictionof coronary artery disease using automated machine learningandXAI

Evaluation of Novel Biomarker of Early Atherosclerosis in Patients With Chronic Kidney Disease in Wasit Governorate

The aim of the study was to evaluate some Biomarkers in patients with athreoscelerosis in Wasit Province. Atherosclerosis is a chronic arterial disease and a major cause of vascular death. Fatty streaks in arterial walls gradually develop into atheroma and characteristic plaques. The acute rupture of these atheromatous plaques causes local thrombosis, leading to partial or total occlusion of the affected artery. The clinical consequences of these plaques depend on their site and the degree and speed of vessel occlusion . Its major clinical manifestations include ischemic heart disease (IHD), ischemic stroke, and peripheral arterial disease. The study took place in the a hemodialysis unit Al-Zahraa Teaching Hospital in Wasit Governorate during the period from November 2023 to April 2024. This study aimed to examine risk factors (age, gender, high blood pressure, smoking, left ventricular hypertrophy, diabetes mellitus, dyslipidemia, physical inactivity) for atherosclerosis in the general population and patients with chronic kidney disease. Also studying the effect of some novel biomarkers (Homocysteine, Fibrinogen, myeloperoxidase, lipoprotein-associated phospholipase A2, pentraxin 3). The study include (90) samples: (60) patients suffering from early atherosclerosis and chronic kidney disease (dialysis stage) and 30 samples of healthy people (control group), their ages ranged from (40 to 70 years). The samples were divided into two age groups, where the first group had an average age of 40-55 years and the second group was 56-70 years old , the categories were divided equally into males and females Samples were collected based on the incidence of kidney disease first, and then the main risk factors of early atherosclerosis were relied upon, which are (smoking, age, sex, dyslipidemia, hypertension, diabetes, left ventricle valve, physical inactivity). The results of our study regarding diagnostic markers of atherosclerosis showed a significant increase in biomarkers (MPO, PTX3, HCy, FG and LpPLA2)of infected people at p ≥ 0.5. In patients with atherosclerosis the level of new biomarkers showed a significant increase in serum of infected people compared to healthy people at p<0.5. This is an important indication of the importance of these biomarkers in detecting the disease.

Spectral Intelligence: AI-Driven Hyperspectral Imaging for Agricultural and Ecosystem Applications

Ensuring global food security amid mounting challenges, such as population growth, disease infestations, resource limitations, and climate change, is a pressing concern. Anticipated increases in food demand add further complexity to this critical issue. Plant pathogens, responsible for substantial crop losses (up to 41%) in major crops like wheat, rice, maize, soybean, and potato, exacerbate the situation. Timely disease detection is crucial, yet current practices often identify diseases at advanced stages, leading to severe infestations. To address this, remote sensing and Hyperspectral imaging (HSI) have emerged as robust and nondestructive techniques, exhibiting promising results in early disease identification. Integrating machine learning algorithms with image data sets enables precise spatial–temporal disease identification, facilitating timely detection, predictive modeling, and effective disease management without compromising fitness or climate adaptability. By harnessing these cutting-edge technologies and data-driven decision-making, growers can optimize input costs while achieving enhanced yields, making significant strides toward global food security in the face of climate change risks. This review will discuss some of the foundational concepts of remote sensing, several platforms used for remote sensing data collection, successful application of the approach, and its future perspective.

Advanced Pressure-Sending Wristband for Disease Detection Based on Traditional Chinese Medicine Pulse Diagnosis

This paper explores the integration of Traditional Chinese Medicine (TCM) pulse diagnosis with modern sensor technology to develop a sophisticated wristband system for monitoring vital organ health. Leveraging the principles of TCM, the proposed system focuses on the “Three regions and nine indicators” method to accurately detect pulse variations at different wrist positions and depths. By utilizing a pressure sensor array that covers the Cun, Guan, and Chi pulse positions, the wristband aims to enhance diagnostic accuracy beyond conventional wearable technologies. The system incorporates advanced micro-electro-mechanical techniques to minimize size while maintaining functionality, enabling continuous health monitoring through a coplanar crossed sensing array. Additionally, the use of machine learning algorithms facilitates the analysis of pulse data to identify potential health issues related to the heart, lungs, liver, kidneys, and spleen. An accompanying mobile app provides real-time feedback and health insights, promoting proactive health management. This innovative approach merges TCM’s holistic diagnosis methods with cutting-edge technology to offer a powerful tool for early disease detection and health monitoring, potentially transforming the landscape of personal healthcare devices.

Brain Tumor Detection using Improved Binomial Thresholding Segmentation and Sparse Bayesian Extreme Learning Machine Classification

People are dying these days from numerous deadliest diseases. One such illness is brain tumour, in which the unusual cells within the tumour quickly begin to damage the brain's healthy cells. Owing to this rapid growth, a person may pass away before the disease receives a correct diagnosis. Early disease detection is essential for any disease to help save the patient by providing them with better care. In a similar vein, a patient's life depends on early brain tumour detection. Brain tumour detection is an extremely challenging procedure that we would like to simplify in order to save time. The proposed model facilitates the quicker and more accurate identification of abnormal brain cells, leading to the early detection of brain tumours. In this work, an improved binomial thresholding-based segmentation (IBTBS) is introduced for segmentation purpose. From this segmented image, information theoretic based, wavelet transform (WT) based, and wavelet scattering transform (WST) based features are extracted. An optimization-based feature selection approach (OBFSA) is incorporated between feature selection and tumour classification in order to reduce the dimension of this retrieved feature. Finally, classification is performed using the Sparse Bayesian extreme learning machine (SBELM) classifier. The execution process of this proposed methodology takes an MRI image from the free accessible source. By computing and detecting four different parameters, the experimental analysis of the proposed approach displays the accuracy, specificity, and sensitivity values. This model can assist us in quickly diagnosing brain tumours, potentially saving the lives of patients.

Implementation of Random Forest with Synthetic Minority Oversampling Technique and Particle Swarm Optimization for Predicting Survival of Heart Failure Patients

Abstract. Heart failure is caused by a disruption in the heart’s muscle wall, which results in the heart’s inability to pump blood in sufficient quantities to meet the body’s demand for blood. The increasing prevalence and mortality rates of heart failure can be reduced through early disease detection using data mining processes. Data mining is believed to aid in discovering and interpreting specific patterns in decision-making based on processed information. Data mining has also been applied in various fields, one of which is the healthcare sector. One of the data mining techniques used to predict a decision is the classification technique. Purpose: This research aims to apply SMOTE and PSO to the Random Forest classification algorithm in predicting the survival of heart failure patients and to determine its accuracy results. Methods/Study design/approach: To predict the survival of heart failure patients, we utilize the Random Forest classification algorithm and incorporate data imbalance handling with SMOTE and feature selection techniques with PSO on the Heart Failure Clinical Records Dataset. The data mining process consists of three distinct phases. Result/Findings: The application of SMOTE and PSO on the Heart Failure Clinical Records Dataset in the Random Forest classification process resulted in an accuracy rate of 93.9%. In contrast, the Random Forest classification process without SMOTE and PSO resulted in an accuracy rate of only 88.33%. This indicates that the proposed method combination can optimize the performance of the classification algorithm, achieving a higher accuracy compared to previous research. Novelty/Originality/Value: Data imbalance and irrelevant features in the Heart Failure Clinical Records Dataset significantly impact the classification process. Therefore, this research utilizes SMOTE as a data balancing method and PSO as a feature selection technique in the Heart Failure Clinical Records Dataset before the classification process of the Random Forest algorithm.

Machine learning models for personalised healthcare on marketable generative-AI with ethical implications

Personalised healthcare, underpinned by a deep understanding of individual patient variability, demands innovative solutions. Machine Learning (ML) models offer a promising avenue to achieve this by facilitating the development of enhanced Digital Twins (DTs). This research proposes a novel framework for creating DTs tailored to individual patients, considering not only physical attributes but also the intricate interplay of social, and biological factors. By capturing this comprehensive patient profile, ML-powered DTs have the potential to revolutionise healthcare by enabling predictive, preventive, and personalised care strategies, ultimately leading to improved patient outcomes and the development of a marketable, trust-worthy product. Current healthcare solutions lack personalisation, often failing to consider individual differences in disease presentation and response to treatment. Traditional DTs in healthcare often adopt a disease-centric or organ-specific approach, thereby restricting their capacity to deliver comprehensive, personalised care. To address this limitation, we propose a holistic Artificial Intelligence (AI) framework centered on ML models. Initially focusing on diabetes, our research aims to enhance diagnosis, treatment, and predictive capabilities through personalised insights, thereby optimising patient outcomes and care management. The benefits identified with our ML model are early disease prevention and risk stratification, optimised treatment planning and therapy selection, enhanced patient-physician communication and shared decision-making, reduced healthcare costs and improved resource allocation. Our models are designed to optimise patient care while prioritising safety and societal benefit. To ensure this, we have conducted a thorough assessment of potential ethical implications. Key challenges identified include data privacy and security, algorithmic bias, diagnostic accuracy, data interoperability and standardisation, integration with existing healthcare systems, ethical management of sensitive patient data, refinement of ML methodologies, addressing legal and ethical AI challenges, and suggesting robust ethical guidelines. A comprehensive evaluation of accuracy, reliability, and associated risks will be conducted prior to full-scale integration into the healthcare ecosystem to establish a robust ethical framework for our research models.

Biomaterials for reliable wearable health monitoring: Applications in skin and eye integration

Recent advancements in biomaterials have significantly impacted wearable health monitoring, creating opportunities for personalized and non-invasive health assessments. These developments address the growing demand for customized healthcare solutions. Durability is a critical factor for biomaterials in wearable applications, as they must withstand diverse wearing conditions effectively. Therefore, there is a heightened focus on developing biomaterials that maintain robust and stable functionalities, essential for advancing wearable sensing technologies. This review examines the biomaterials used in wearable sensors, specifically those interfaced with human skin and eyes, highlighting essential strategies for achieving long-lasting and stable performance. We specifically discuss three main categories of biomaterials—hydrogels, fibers, and hybrid materials—each offering distinct properties ideal for use in durable wearable health monitoring systems. Moreover, we delve into the latest advancements in biomaterial-based sensors, which hold the potential to facilitate early disease detection, preventative interventions, and tailored healthcare approaches. We also address ongoing challenges and suggest future directions for research on material-based wearable sensors to encourage continuous innovation in this dynamic field.

Chemical sensors: From fundamentals to the future—A review

<p>Chemical sensors bridge the gap between the chemical and electrical/optical domains, offering a powerful tool for analyzing our environment. These ingenious devices, with detection limits reaching parts-per-billion (ppb) for some analytes, rely on interactions between a specific material and the target molecule. This interaction, which can involve changes in electrical current, light emission, or mass, is translated into a measurable signal. This review delves into the core working principles of various sensor types, highlighting their diverse applications. From environmental monitoring (tracking air and water pollutants at concentrations as low as 10 ppb) to medical diagnostics (detecting biomarkers for early disease identification), chemical sensors play a crucial role in shaping a safer and healthier future. Recent advancements, such as miniaturization and integration with nanomaterials, promise even greater sensitivity, portability, and affordability, paving the way for a new era of sensor-driven innovation. This review article explores these advancements and their potential impact on various fields, inspiring further development and exploration of this transformative technology.</p>

Neutrophil gelatinase-associated lipocalin as a biomarker for detection of early renal impairment in Iraqi patients with multiple myeloma.

To evaluate the serum neutrophil gelatinase-associated lipocalin levels in multiple myeloma patients as an indicator of disease progression. The case-control study was conducted at the Clinical Biochemistry Department of the National Centre of Haematology, Mustansiriyah University, Baghdad, Iraq, from October 2020 to July 2021, amd comprised diagnosed multiple myeloma patients and healthy controls of either gender aged 40-60 years. Neutrophil gelatinaseassociated lipocalin and renal functions were measured for all patients in addition to obtaining a complete history and physical examination. Data was analysed using MedCalc. Of the 60 sujects, 30(50) were cases with mean age 64±2.1 years and 30(50%) were controls with mean age 60 ±3.2 years. There were 18(68%) males among the cases and 17(55%) among the controls. In the cases group, mean levels of creatinine, urea and neutrophil gelatinase-associated lipocalin were 1.62±0.85mg/dl, 55.56±28.05mg/dl and 389.39±116.12pg/mL, respectively. The corresponding values for the controls were 0.9518±0.1623mg/dl, 30.17±8.47mg/dl and 120.82±68'52pg/ml. The neutrophil gelatinase-associated lipocalin cutoff level >190pg/ml in multiple myeloma patients was strongly associated with renal impairment (p<0.001). Neutrophil gelatinase-associated lipocalin was found to be an accurate indicator of early kidney disease in patients with de novo multiple myeloma.

Impact of Glycine Powder Air-Abrasive Debridement on Peri-Implant Mucositis: A Randomized Control Trial of Clinical, Microbial, and Immunological Changes.

Peri-implant mucositis, a dysbiosis-driven inflammatory disease, is a precursor to peri-implantitis, underscoring the need for early disease management. Therefore, we investigated the efficacy of glycine powder in resolving clinical inflammation and restoring host-microbial homeostasis. Thirty subjects were randomized to receive either glycine powder air-abrasive debridement or ultrasonic instrumentation. Clinical parameters (probe depth [PD], modified Sulcular Bleeding Index [mSBI], modified Plaque Index [mPlI]), biofilm and peri-implant crevicular fluid were collected at baseline and at 1-day, 1-, 3-, 6-weeks and 3- and 6-months post-therapy. Microbial recolonization was examined using 16S rDNA sequencing and immune response was semi-quantified using a bead-based 17-plex microarray. At 6-months, both groups demonstrated non-significant reductions in mSBI when compared to baseline (p > 0.05, Wald test, mixed model for repeated measures). However, mSBI and PD decreased in the test group from week-1 to 3-months, while control group decreased at 1- and 3-weeks only. mSBI was lower in the test group when compared to controls from Week-1 to 3-months, while PD differed between groups at 6 weeks and 3-months. Glycine group demonstrated significant microbial shifts after 24-h, increases in species richness and health-compatible species, and loss of pathobionts (p < 0.001, Dunn test). Pro-inflammatory cytokines decreased from 1- to 6-weeks or 3-months (p < 0.05, Wald test). Comparable results were obtained in the ultrasonic group at 3-weeks and sustained over 6-weeks post-therapy. Glycine therapy leads to early and sustained change in host-microbial interactions when compared to ultrasonics, however, the changes wrought by both therapies were sustained for a maximum of 3 months. ClinicalTrials.gov identifier: NCT05810558.

Generating a Pro-Inflammatory Organ Culture Model to Simulate an Early Intervertebral Disc Disease

Generating a Pro-Inflammatory Organ Culture Model to Simulate an Early Intervertebral Disc Disease

Generating a Pro-Inflammatory Organ Culture Model to Simulate an Early Intervertebral Disc Disease

Generating a Pro-Inflammatory Organ Culture Model to Simulate an Early Intervertebral Disc Disease

Macular Pigment Optical Density as a Measurable Modifiable Clinical Biomarker.

Carotenoids are present throughout retina and body its dense deposition leads to an identifiable yellow spot in the macula. Macular pigment optical density (MPOD) measured in the macula is vital to macular well-being and high-resolution visual acuity. MPOD has also been associated with various health and disease states. We sought to review the literature on this topic and summarize MPODs role as a measurable modifiable clinical biomarker, particularly as a measure of the eye's antioxidant capacity in the context of oxidative damage and retinal ischemia. A literature review collated the articles relevant to MPOD, carotenoid intake or supplementation, and their influence on various health and disease states. Literature reveals that MPOD can serve as a reliable biomarker for assessing the retinal defense mechanisms against oxidative stress and the deleterious effects of excessive light exposure. Elevated MPOD levels offer robust protection against the onset and progression of age-related macular degeneration (AMD), a prevalent cause of vision impairment among the elderly population. MPOD's implications in diverse ocular conditions, including diabetic retinopathy and glaucoma, have been explored, underscoring the real need for clinical measurement of MPOD. The integration of MPOD measurement into routine eye examinations presents an unparalleled opportunity for early disease detection, precise treatment planning, and longitudinal disease monitoring. Longitudinal investigations underscore the significance of MPOD in the context of age-related ocular diseases. These studies show promise and elucidate the dynamic nuances of MPOD's status and importance as a measurable, modifiable clinical biomarker.

Multiplex Determination of Glycan Profiles on Urinary Prostate-Specific Antigen by Quartz-Crystal Microbalance Combined with Surface-Enhanced Raman Scattering.

Prostate cancer remains a major health concern, with prostate-specific antigen (PSA) being a key biomarker for its detection and monitoring. However, PSA levels often fall into a "gray zone", where PSA levels are not clearly indicative of cancer, thus complicating early diagnosis and treatment decisions. Glycosylation profiles, which often differ between healthy and diseased cells, have emerged as potential biomarkers to enhance the specificity and sensitivity of cancer diagnosis in these ambiguous cases. We propose the integration of two complementary techniques, namely quartz-crystal microbalance with dissipation (QCM-D) and surface-enhanced Raman scattering (SERS) to study PSA glycan profiles. QCM-D offers real-time operation, PSA mass quantification, and label-free detection with high sensitivity, as well as enhanced specificity and reduced cross-reactivity when using nucleic acid aptamers as capture ligands. Complementary SERS sensing enables the determination of the glycosylation pattern on PSA, at low concentrations and without the drawbacks of photobleaching, thereby facilitating multiplexed glycosylation pattern analysis. This integrated setup could retrieve a data set comprising analyte concentrations and associated glycan profiles in relevant biological samples, which may eventually improve early disease detection and monitoring. Prostate-specific antigen (PSA), a glycoprotein secreted by prostate epithelial cells, serves as our proof-of-concept analyte. Our platform allows multiplex targeting of PSA multiplex glycosylation profiles of PSA at "gray zone" concentrations for prostate cancer diagnosis. We additionally show the use of SERS for glycan analysis in PSA secreted from prostate cancer cell lines after androgen-based treatment. Differences in PSA glycan profiles from resistant cell lines after androgen-based treatment may eventually improve cancer treatment.

Determining Long-term Prostate Cancer Outcomes for Active Surveillance Patients Without Early Disease Progression: Implications for Slowing or Stopping Surveillance

Background and objectiveActive surveillance (AS) of prostate cancer (PCa) is the standard of care for low-grade disease, but there is limited guidance on tailoring protocols for stable patients. We investigated long-term outcomes for patients without initial progression and risk factors for upgrade. MethodsMen on AS with Gleason grade group (GG) 1 PCa on three serial biopsies, ≥5 yr without progression, and ≥10 yr of follow-up were included. Outcomes were upgrade (GG ≥2), major upgrade (GG ≥3), progression to treatment, metastasis, PCa-specific survival, and overall survival. Cox proportional hazards regression models were used to estimate the associations between patient characteristics and risk of upgrade. Key findings and limitationsA total of 774 men met the inclusion criteria. At 10, 12, and 15 yr, upgrade-free survival rates were 56%, 45%, and 21%; major upgrade–free survival rates were 88%, 83%, and 61%; treatment-free survival rates were 86%, 83%, and 73%; metastasis-free survival rates were 99%, 99%, and 98%; and overall survival rates were 98%, 96%, and 95%, respectively. PCa-specific survival was 100% at 15 yr. On a multivariable analysis, year of diagnosis, age, body mass index (BMI), and biopsy core positivity were associated with upgrade (all p < 0.01), whereas age and prostate-specific antigen (PSA) density were associated with major upgrade. Conclusions and clinical implicationsPatients without progression for 5 yr on AS had modest rates of upgrade and low rates of metastasis, and mortality at 15 yr of follow-up. Year of diagnosis, older age, increased BMI, and increased biopsy core positivity were associated with upgrade, whereas older age and greater PSA density were associated with an increased risk of major upgrade. A subset of these patients may benefit from deintensification of AS protocols. Patient summaryThere are little reported data or clinical guidelines for patients with prostate cancer (PCa) who are stable for many years on active surveillance (AS). We show, in a large cohort, that PCa patients without progression for 5 yr on AS have modest rates of upgrade and very low rates of metastasis, and mortality rates at 15 yr of follow-up, and that older age, increased body mass index, and increased PCa volume are associated with an increased likelihood of future upgrade. This study supports continued AS in this patient population and deintensification in select patients.

Use of biomarkers in the diagnosis of Alzheimer's disease in adults with intellectual disability.

People with intellectual disability are a vulnerable cohort who face challenges accessing health care. Compared with the general population, people with intellectual disability have an elevated risk of developing dementia, which often presents at a younger age and with atypical symptoms. The lifelong cognitive and functional difficulties faced by people with intellectual disability further complicate the diagnostic process. Specialised intellectual disability memory services and evaluation using reliable biomarkers of neurodegeneration are needed to improve diagnostic and prognostic certainty in this group. Inadequate specialist services and paucity of research on biomarkers in this population hinders progress and impedes the delivery of adequate health care. Although cerebrospinal fluid-based biomarkers and radiological biomarkers are used routinely in the evaluation of Alzheimer's disease in the general population, biological variation within the clinically heterogenous group of people with intellectual disability could affect the clinical utility of existing biomarkers. As disease-modifying therapies become available for the treatment of early Alzheimer's disease, and hopefully other neurodegenerative conditions in the future, biomarkers will serve as gatekeepers to establish the eligibility for such therapies. Inadequate representation of adults with intellectual disability in biomarker research will result in their exclusion from treatment with disease-modifying therapies, thus perpetuating the inequity in health care that is already faced by this group. The aim of this Series paper is to summarise current evidence on the application of biomarkers for Alzheimer's disease in a population with intellectual disability (that is not attributable to Down syndrome) and suspected cognitive decline.

Polygenic scores for complex traits are associated with changes in concentration of circulating lipid species.

Understanding perturbations in circulating lipid levels that often occur years or decades before clinical symptoms may enhance our understanding of disease mechanisms and provide novel intervention opportunities. Here, we assessed if polygenic scores (PGSs) for complex traits could detect lipid dysfunctions related to the traits and provide new biological insights. We constructed genome-wide PGSs (approximately 1 million genetic variants) for 50 complex traits in 7,169 Finnish individuals with routine clinical lipid profiles and lipidomics measurements (179 lipid species). We identified 678 associations (P < 9.0 × 10-5) involving 26 traits and 142 lipids. Most of these associations were also validated with the actual phenotype measurements where available (89.5% of 181 associations where the trait was available), suggesting that these associations represent early signs of physiological changes of the traits. We detected many known relationships (e.g., PGS for body mass index (BMI) and lysophospholipids, PGS for type 2 diabetes and triacyglycerols) and those that suggested potential target for prevention strategies (e.g., PGS for venous thromboembolism and arachidonic acid). We also found association of PGS for favorable adiposity with increased sphingomyelins levels, suggesting a probable role of sphingomyelins in increased risk for certain disease, e.g., venous thromboembolism as reported previously, in favorable adiposity despite its favorable metabolic effect. Altogether, our study provides a comprehensive characterization of lipidomic alterations in genetic predisposition for a wide range of complex traits. The study also demonstrates potential of PGSs for complex traits to capture early, presymptomatic lipid alterations, highlighting its utility in understanding disease mechanisms and early disease detection.

Associated Factors for Liver Fibrosis and Histological Activity Index in Treatment-Naïve HBeAg-Positive Chronic Hepatitis B Infection: Insights from a Retrospective Analysis.

The study aimed to identify predictors of advanced fibrosis score and histological activity index (HAI) in HBeAg-positive patients to facilitate early disease detection and reduce the need for invasive biopsies. The single-center retrospective study included treatment-naïve HBeAg-positive chronic hepatitis B (CHB) patients. Patients with HAI ≥6 and/or fibrosis ≥2 were considered to have significant liver damage. Independent determinants were identified by univariate and multivariate logistic regression analysis. Cut-off values for variables were determined by receiver operating characteristics (ROCs) curve analysis. The study enrolled a cohort of 66 patients, with 51.5% male and a median age of 26 (22.7-34.2) years. In assessing necroinflammation, no significant differences were observed in age and gender between patients with HAI <6 and HAI ≥6. However, patients with HAI ≥6 exhibited higher aspartate aminotransferase (AST) levels compared to those with HAI <6. Furthermore, lower albumin levels and platelet (PLT) counts, along with higher fibrosis-4 (FIB-4) scores, were associated with HAI ≥6. In the evaluation of fibrosis, while gender distribution did not differ significantly, patients with fibrosis grade ≥2 were older and had higher HAI scores, HAI ≥6 ratios, and FIB-4 scores compared to those with fibrosis grade <2. Multivariate analysis identified albumin as a significant predictor for both HAI ≥6 and fibrosis grade ≥2. The area under ROC (AUROC) values of albumin for predicting HAI ≥6 and fibrosis ≥2 were 0.696 and 0.698, respectively, indicating moderate predictive ability. Albumin was found to be an independent predictor of liver damage in HBeAg-positive CHB patients. The fact that the optimal threshold values detected for both HAI ≥6 and fibrosis ≥2 in this patient group were close to normal values suggests that clinicians should be more cautious in monitoring albumin levels and other pre-defined parameters to avoid delays in diagnosis.