Survival analysis and predictors for hemoglobin level and time-to-default from HIV treatment among first-line female HIV-positive patients within the reproductive age group – Nature.com 03:17 

Scientific Reports volume 15, Article number: 15348 (2025) Cite this article

Abstract

Ethiopia has the highest proportion of the HIV population receiving ART of any African country. The objective of this study was to identify survival analysis and predictors for hemoglobin level and time-to-default from HIV treatment among first-line female HIV-positive patients within the reproductive age group. Secondary data source conducted at the University of Gondar Comprehensive Specialized Hospital from September 2015 to March 2022. In this study, the generalized linear mixed model and the Cox PHs model were jointly used to get a wide range of information about female HIV data. The mean (standard deviation) of white blood cells and red blood cells was 6.11 (1.8) and 4.02 (1.26), respectively. Out of 201 study participants, 27.9% defaulted from treatment, and the remaining were considered censored. In this study, the association parameter (gamma_1) for hemoglobin level and default from HIV treatment is negative and statistically significant (β=−0.3659,andp−value=<0.0001) at the 5% level of significance. These findings suggested that the patient’s association parameter had an inverse link with hemoglobin and treatment default. The result of the study also shows low red blood cell and white blood cells lead to low hemoglobin levels and a high hazard of defaulting. Likewise, patients under the categories of World Health Organization (WHO) clinical stage III and IV, ambulatory, bedridden, primary education, Opportunistic Infections (OIs), and substance abuse were at high hazard of being defaulters. Conversely, secondary and tertiary education and patients expressing diseases status to family members were low hazards for defaulters. In addition, WHO clinical stage III and IV patients, bedridden patients, primary educators, OIs, and substance abusers had low hemoglobin level concentrations, and tertiary education and disclosing the diseases to family members were high hemoglobin level concentrations. Healthcare workers in similar settings should pay more attention to clients related to hemoglobin levels and time to default from medication based on these important factors.

Introduction

Among the few nations in Africa, Ethiopia has the largest proportion of the population taking antiretroviral therapy (ART)1. In the Amhara region, which includes the catchment area of the University of Gondar Comprehensive Specialized Hospital, the distribution of HIV and ART users is currently relatively high2,3,4,5. Many of the country’s enormous population who are infected with the virus are receiving treatment6. Certain individuals undergoing treatment exhibit notable improvements in their health state; however, this is not the case for others due to medication non-adherence, which results in patients becoming treatment defaulters7.

For people living with HIV (PLWHIV), ART has made to live long and healthy lives. However, the virus is one of the most significant worldwide health concerns of the twenty-first century and a serious threat to human society due to patient’s defaulters from treatment. The risk of defaulters for AIDS patients rises when the quantity of hemoglobin level declines. On the other hand, adult HIV-positive patients can have low concentrations of hemoglobin levels that strongly lead to high HIV progression and patient default from ART treatment8.

The majority of earlier studies were conducted with hemoglobin level and survival time to default separately. Some of the studies separately done with default from ART indicated that the predictors affected by time to default were female patients, patients who disclosed the disease, patients who got social support, patients living with a partner, patients with ownership of cell phones, urban HIV patients, patients with working functional status, patients with normal BMI, patients with high baseline CD4 cell count9, low BMI, WHO stage-IV, OIs, weight, rural residence10, lack of transport, unemployment11, age12, and functional status13.

Another study also indicated older age, higher viral load, 1c ART treatment, BMI, smoker, alcohol addiction14, increasing weight, CD4 cells < 350/mm3 15, tuberculosis, advanced WHO clinical stage16, OIs, adherence to ART, and rural residence17 were significantly associated with hemoglobin level.

Then, there is a limitation of a study now done in this study among first-line female HIV-positive patients within the reproductive age group. Therefore, the objective of this study was to identify survival analysis, and predictors for hemoglobin level and time-to-default from HIV treatment among first-line female HIV-positive patients within reproductive age group.

Methods and participants

Study area, and population

This study was conducted at the University of Gondar Comprehensive Specialized Hospital. The study population was all female HIV-positive patients under reproductive age groups.

Study design

A retrospective cohort study was carried out to retrieve relevant information from patient’s charts. On the other hand, this study is a repeated measure study, because of the nature of the response variable in which we look back to a certain point to analyze a particular group of patients who have already experienced an outcome of interest.

Study period and source of data

The study focused on female patients who began ART treatment between September 2015 and March 2022. The source of data was secondary data.

Inclusion criteria

This study included all female HIV-positive patients within reproductive age groups who had a minimum of two visits for repeated measurement of hemoglobin levels, patients whose age groups between 15 and 49 years, patients who had a baseline viral load count, and patients who had started ART the treatment within treatment follow-up study period.

Sample size determination

Based on the above inclusion criteria, 201 female HIV-positive participants were selected for the current study.

Data collection procedures and quality

The data-gathering methodologies in this cohort study were based on the patient’s chart and an electronic database. Before collecting the real data, the checklist’s appropriateness was assessed, and unclear questions were adjusted. The final data extraction format is amended to ensure completeness and consistency, and the whole forms are reviewed by ART data management.

Variables under study

Outcome variable

The response variables in the current study were hemoglobin level measured in grams per deciliter (g/dl) and time to default from ART treatment. Hemoglobin level was measured at every 6-months interval. The number of times for the outcome variable of hemoglobin levels was measured not the same for each patient. Therefore, we approximate every six months starting from the baseline months, 6 months, 12 months, 18 months, 24 months, 30 months, 36 months, 42 months, 48 months, 54 months, 60 months, 66 months and 72 months. Hence, there were about 13 measures for hemoglobin level for those of the patients who had complete data.

The survival time to default from ART treatment is categorized as;

survivalstatus={0,ifcensored1,ifdefault

Explanatory variable

The explanatory variables were CD4 cell count, viral load count, white blood cell count, red blood cell count, platelet cell count, hematocrit, monocyte count, lymphocyte count, weight, BMI, age, functional status, residence, religion, marital status, educational status, disclosure status, treatment adherence, TB screen, opportunistic infections, other comorbidity conditions, WHO clinical stage, and substance abuse.

Methods of data analysis

Longitudinal outcome data analysis

The current study consists of one cohort outcome (hemoglobin level) and one survival outcome (time to default from treatment) variable. The nature of the cohort variable is continuous, and an appropriate model for continuous outcome was the generalized linear mixed effect model (GLMM). The GLMM is the most often used random-effects model for non-Gaussian repeated data. The model for such a continuous outcome consists of fixed and random components. Where the random effect contains the interdependence variable, which is associated the two outcome variables. Hence, the random effect contains subject-specific effects and the fixed effect contains the set of covariates that are fixed across the subjects10,18, and19.

The general form of the model in matrix notation is:

βlnE(yij|bi)=xij′β+zij′bi

Where E(yij|bi) is a set of expected values of the conditional distribution of the N×1 column vector of an outcome variable given the random effects. xij′β is the “fixed-effects” part of the model, x is an N×p matrix of the p predictor variables, β is a p×1 column vector of the fixed-effects regression coefficients (the βs). zzij′bbii is the random effects part of the model, z is the N by q design matrix for the q random effects (the random complements to the fixed X), and bi is a q×1 vector of the random effects (the random complement to the fixed β).

Survival outcome data analysis

The survival function S(t) represents the probability that the survival time of a randomly selected subject is greater than or equal to a specified time. Thus, it gives the probability that an individual survives beyond a specified time. Let T be a random variable associated with the survival times, let t be the specified value of the random variable, and let f(t) be the underlying probability density function of the survival time T4. In a single event analysis, the survival function is defined as the probability that the survival time is greater than or equal to t which is given by:

S(t)=P(T≥t)=1−F(t)=∫∞tf(t)dtfort≥0

Where; F(t) is cumulative distribution function, f(t) is the probability density function of event time. Whereas the hazard rate is the instantaneous risk of experiencing the event at a given time, given that it has survived. The hazard rate of an event is defined as;

h(t)=limΔt→0P(t≤T<ΔtT≥t)Δt,t≥0

h(t)=f(t)s(t)=−dlogs(t)dt

Non-parametric survival model

Non-parametric approaches are used to analyze the data preliminarily, giving insight into the shape of each group’s survival function and determining if the groupings are proportionate, that is, whether the estimated survival functions of two groups are roughly parallel. The survival function can be estimated non-parametrically using the sequence in which events occur rather than the actual observed event and censoring timings11,20. This estimator is known as the Kaplan-Meier estimator. Assume that n observations (t1,tn…) and related censoring indicators (δδδ1,…,δn) exist. Let r be the number of different event times (r ≤ n), and lett(1)<,…,t(r) be the ordered event time and the equivalent number of occurrences, d(1),…,d(r). Furthermore, let R (t(j)) represent the risk level at event time t(j), that is, the group of subjects who were still at risk for the event at that time since they had not yet experienced the event and had not yet been censored before the timet(j). As a result, the survival function’s Kaplan-Meier estimate at time t is provided by:

S(t)^=∏i=1k[R(t(j))−d(j)R(t(j))],fort(j)<t<t(j+1),k=1,2,……r,

Data analysis for the longitudinal and survival outcomes jointly

The most widely used random effects joint models combine a GLMM for the cohort process and a Cox proportional hazards model for the survival process. The two sub-models are then linked through their shared random effects, which account for the association between both outcomes. The formulation of a link (shared random effects) between the cohort and survival processes depends on the focus on the joint model. The joint model in the current study was formulated as follows. First, a generalized linear mixed effect model conducted to obtain the estimate of the cohort process.

βlnE(yij|bi)=xij′β+zij′bi

Next, a proportional hazards model was conducted for the survival data21.

γhi(t)=h0(t)exp(γTTwi+δbi)

Where; δ represents the influence of the longitudinal random effects on the survival process. A separate association for the influence of the random effects on the survival process can also be assumed by allowing different coefficients for the random intercept and slope (e.g., δδ0b0i + δδ1b1i). Finally, assume that given the random effects, bi, the Cohort process and the survival outcome was jointly conducted19,22,. Therefore, it is able to get more precise and accurate estimates of the strength of the relationship between the Cohort process and the survival time.

Variable selection

Purposeful variable selection was appropriate in this study for covariates to be used by deciding on each step of the modeling process at a 25% level of significance.

Model selection criteria

To select the better model using the Akakie Information Criteria (AIC), and Bayesian Information Criteria (BIC) is the most commonly used method of model selection criteria. Therefore, the model with the smallest value is the appropriate model for the given data set.

Results

Adult female HIV patients socio-demographic features

Out of 201 female participants in these studies, 65.2% were in the age group between 25 and 34 years, of which 22.1% defaulted from treatment. Given residence, 52.2% were rural, of which 23.8% were default from treatment. Furthermore, 22.5% of non-educator, 23.2% of primary, 28.6% of secondary, and 44.8% of tertiary educator patients defaulted from treatment. More than half (70.6%) were classified as non-substance user patients, of whom 55.9% defaulted from treatment. Because of the marital status of female patients, almost more than half of the female patients 101(50.2%) were married, and 31.7% defaulted from ART treatment (Table 1).

Adult female HIV patients clinical features

In this study, 81.1% of study participants were incorporated as non-TB patients, of whom 25.8% defaulted from treatment. Less than one-third of female participants (28.9%) had Other Comorbidity Conditions (OCC) of which 19.0% had defaulted. Similarly, less than one-fourth of female patients (25.9%) were affected by opportunistic infections (OIs) other than TB of which 21.2% had defaulted from treatment. Regarding treatment adherence, 26.4% of the study participants had fair treatment adherence status, of which around half of the patients (47.2%) had the outcome of defaulting (Table 2).Table 1 Baseline socio-demographic and behavioral characteristics of patients.

Full size table

Survival rate of adult female patients

Out of 201 study participants, 27.9% defaulted from treatment, and the remaining were considered as censored. The estimated values of the 50 th percentile (median) and 75 th percentile were 60.0 and 30.0, and the standard errors of 3.364 and 3.604, respectively. Similarly, the mean estimated values and standard error were 51.5 and 2.1 respectively (Table 3).Table 2 Baseline clinical characteristics of patients.

Full size tableTable 3 Percentile, mean, and survival status of adult female HIV patients.

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Kaplan Meier curve for survival function

The overall survivor function was decreasing monotonically as the patient’s visit time increased. On the other hand, the visiting time increases, and the survival probability of adult HIV-positive patients decreases (Fig. 1).

Kaplan Meier curve for some selected covariates

In addition, the Kaplan-Meier survival curves difference in survival time between disclosure, residence, substance abuse, WHO clinical stage, adherence, TB screen and functional status. However, there was no difference Kaplan-Meier survival curves for marital status (Fig. 2).

figure 1
Fig. 1
figure 2
Fig. 2

Cox PH model assumption check

The goodness of fit test for a global p-value is 0.084, which is greater than 5%. This suggested that the assumption of Cox PHs model was satisfied (Table 4).Table 4 Cox phs model assumption check for time to default.

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Covariance structure and random effect models selection

Based on the smaller value of Akaike Information Criteria (AIC) and Bayesian Information Criterion (BIC), auto regressive first-order (AR1) covariance structures were selected (Table 5). Similarly, random intercept and slope models were selected (Table 6).

Variable selection

At a 25% level of significance, by using the purposeful variable selection method, the covariates hematocrit, BMI, WBC, RBC, platelet cell count, lymphocyte count, monocyte count, functional status, TB screen, OIs, WHO clinical stage, educational status, substance abuse, and disclosure status were statistically significant for hemoglobin level. As well as, the covariate CD4 cell count, hematocrit, weight, BMI, WBC, RBC, platelet cell count, lymphocyte, monocyte, adherence, WHO clinical stage, functional status, TB screen, OIs, residence, religion, educational status, marital status, substance abuse, and disclosure were considered as statistically significant for time to default from treatment.Table 5 Covariance structure for cohort hemoglobin level.

Full size tableTable 6 Random effect model selection for cohort hemoglobin level. Key: AIC indicates Akaike information criteria and BIC indicates bayesian information criterion.

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Correlation for hemoglobin and time to default

The association parameter under the survival sub-model was negative, statistically different from zero, and significant (β=−0.3659,p−value=<0.0001) (Table 7). Then, there is an inverse (negative) relationship between cohort hemoglobin and time to default.

Predictors of hemoglobin and time to default

WBC and RBC are predictors for hemoglobin level and time to default. RBC increased by one unit, and the expected hemoglobin level was decreased by 0.04 g/dl (β=−0.0434,p−value=0.0205). Similarly, with one unit increase in RBC of female patients, the hazard of defaulter increased by (HR = 1.167, p-value = 0.01285). WBC increased by one unit, and the expected hemoglobin level was decreased by 0.1 g/dl (β=−0.1105,p−value=0.0142). On the other hand, with one unit increase in WBC of female patients, the hazard of defaulter increased by (HR = 1.0380, p-value = 0. 00216) (Table 7).

Table 7 also revealed that the primary and tertiary education levels of female patients had 0.2 g/dl (β= −0.2100, p-value = 0.0457) and 0.9 g/dl (β= 0.9917, p-value = 0.0354) decremented and incremented in their expected hemoglobin level than non-educator patients, respectively. Patients with OIs (β=−0.2974, p-value = 0.0381) had 0.3 g/dl had decremented in their expected hemoglobin level than those without OIs. On the other hand, patients with OIs had a higher hazard of defaulting (HR = 1.1926, p-value=0.0258) than patients without OIs.

Female patients who disclosed the disease to a family member(β=0.5093, p-value = 0.0243) had an incremented by 0.4 g/dl in their expected hemoglobin than patients who not express the status of the disease to a family member. Also, patients who disclosed the disease to a family member had less hazard of defaulting (HR = 0.6128, p-value = 0.01409) than patients who didn’t’ express the status of the disease to a family member. Substance abuse patients (β= −0.7115, p-value = 0.0208) had a 0.7 g/dl decremented in expected hemoglobin compared than patients who were not abuse. Besides, abuser patients had more hazard of defaulting (HR = 2.5234, p-value=0.00510) than not abusers.

WHO clinical stage III (β=−0.5934, p-value = 0.0450) and IV (β= −0.1684, p-value = 0.0475) patients had 0.6 g/dl and 0.2 g/dl decremented in their expected hemoglobin compared than WHO clinical stage I patients. Inversely, WHO clinical stages III and IV had more hazard of defaulting (HR = 1.7102, p-value = 0.00281) and (HR = 4.9458, p-value = < 0.0001) respectively, than WHO clinical stage I patients (Table 7). Bedridden patients (β=−1.0210, p-value = 0.0475) had 1.02 g/dl lower expected hemoglobin than working patients. Inversely, ambulatory and bedridden patients had more hazard of defaulting (HR = 1.1.4319, p-value = 0.0264) and (HR = 1.1773, p-value = 0105), respectively than working patients (Table 7).Table 7 Parameter estimates for time to default and hemoglobin level jointly. Key: p- values indicated probability values, Std. Error indicated a standard errors for β, Gamma_1 indicated the association parameter under the survival sub-model, HR is hazard ratio, β is estimated values for each model and * indicated statistically significant at a 5% level of significance.

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Discussions

As far as we are aware, this is the first study on the joint determinant factors of female adult HIV-positive patient’s cohort hemoglobin levels and time to default from ART treatment at the University of Gondar Comprehensive Specialized Hospital. None of the literature is labeled as survival rate, correlation, and predictors for hemoglobin level and time-to-default from HIV treatment among first-line female HIV-positive patients within the reproductive age group. Therefore, to assess the survival rate and find out the effects of predictors on hemoglobin levels and time to default, the joint model is better to fit the data18,19.

The results in the current study indicate that about 27.9% of female HIV patients defaulted from treatment. The result of this study is in line with a study done at Kenya21. However, this result is contradicted by one of the previous study23. The potential reason for this variation might be due to the different lifestyles of populations, differences in the study area, study time, sample size, and differences in methodology. Hence, it needs further investigation.

The association parameter under the survival sub-model was negative and significant. This negative value indicates that there is an inverse relationship (correlation) between hemoglobin level and time to default. In this result, patients had a higher risk of defaulting from treatment. This result is similar with previous studies18,19. The potential reason for this similarity might be similar methodological analysis.

A low patient’s RBC relates with low hemoglobin and leads to be default from the treatment. These finding is consistent with one of a previous study13. Hence, RBC is positively associated with hemoglobin levels. However, the result in this study is contradicted by one of the other previous study16. The potential reason for this variation might be the study area and study time, sample size, and difference in methodology applied. Further investigation is needed for the one to be accepted.

Similarly, high patients’ WBC leads to high hemoglobin levels, leading to good adherence to the treatment. The result of this finding is consistent with previous study24. Female patients had a high hazard of defaulting due to a decrease of WBC. These result contradict previous study21. The potential reasons for these contradictions might vary due to study area, study population, study time, sample size, and difference in methodology applied. This study was based on first-line female HIV-positive patients within the reproductive age group. Hence, it needs further investigation.

The result of this study shows that higher educational levels of female patient’s incremental variations of hemoglobin levels and low default from treatment due to a better understanding of HIV progression and ART treatment follow-up lead to good survival of life. This study demonstrates the advantages of education for females with HIV are improving access to health services, decreasing social and economic vulnerability that exposes women to risky activities, and an increasing chance of joining community groups that promote AIDS prevention. This study is consistent with previous study25. However, this study finding contradicts with previous study done in Ethiopia26. This variation might be different in study area, study population, study time, sample size, and difference in methodology applied.

These results suggested patients with OIs had lower hemoglobin levels than patients without OIs. OIs in HIV patients are infections that become more common or severe due to HIV-mediated immunosuppression. This idea is consistent with previous literature22. On the other hand, patients with OIs had a higher hazard of defaulting than patients without OIs. However, OIs patients properly follow-up of ART leads to improving, and reducing the occurrence of OIs. This study was supported by a former study27.

This study also suggested disclosure status was another factor significantly associated with both the variables of interest. On the other hand, disclosure patients had high hemoglobin and low survival time of defaulting. Female HIV-positive patients might be associated with a health-related quality of life. Furthermore, female patients with improving general health status had an increasing hemoglobin. Moreover, family members can share the emotional load, which helps patients feel less depressed, worn out, anxious and patients better understand of ART follow-up. Then, patients with a good quality of life have a better understood about ART is results for less hazard of default and high hemoglobin. This finding is supported by previous studies28,29,30,31.

Substance abuser patients who are addicted to alcohol and non-injection drugs, including crack cocaine, increase a woman’s risk of sexually transmitted HIV infection through increased engagement in high-risk sexual behaviors, such as unprotected sex and sex exchange for drugs. Due to this reason, patients with lower hemoglobin levels leads to poor health outcomes. The result of this study is consistent with previous studies done on American women’s32. Addicted patients had a high chance of defaulting due to misunderstandings of ART. Patients with alcohol or other substance use disorders often have complex presentations of addictive behaviors and medical comorbidities, making these patients challenging to treat by health professionals and leading to poor ART follow-up. The idea of this study is supported by former literature23.

Ambulatory and bedridden was another factor significantly associated with hemoglobin levels and the hazard of defaulting from the treatment. Ambulatory and bedridden female patients have lower level of hemoglobin as compared to working female patients. Correspondingly, the result of this study indicates that ambulatory and bedridden HIV-positive female patients had a higher risk of defaulting from ART as compared to working female patients. This indicated, based on the functional status of patients, higher default and low hemoglobin were significantly associated with bedridden and ambulatory patients. This finding is consistent with previous studies33,34,35.

In this study, WHO stages III and IV had a low concentration of hemoglobin levels and resulted to poor health status. Besides, stages III and IV patients had defaulted from treatment due to inappropriate health status. Patients under stage III (the moderately symptomatic stage) have disease progression, and additional clinical manifestations may appear. Those encompassed by the WHO clinical stage III are weight loss, prolonged unexplained diarrhea, pulmonary tuberculosis, and severe systemic bacterial infections including pneumonia, pyelonephritis, empyema, meningitis, bone and joint infections, and bacteremia. WHO clinical stage IV (the severely symptomatic stage) allows presumptive diagnosis of AIDS and related illnesses. The result of this study is supported by former literatures11,18,36,37,38,39,40,41.

Conclusion and recommendations

These results concluded that the association parameter of the patient had an inverse relationship between hemoglobin and default from treatment. The result of the study also shows low red blood cell and white blood cell counts lead to low hemoglobin levels and a high hazard of being defaulting. Likewise, patients under the categories of WHO clinical stage III and IV, ambulatory, bedridden, primary education, OIs, and substance abuser were a high hazard of a defaulter. Conversely, secondary and tertiary educators and patients expressed disease status to family member were low hazard of defaulters. In addition, WHO clinical stage III and IV patients, bedridden patients, primary educators, OIs, and substance abusers had low hemoglobin level concentrations, and tertiary education and disclosing the diseases to family members were high hemoglobin level concentrations. Healthcare workers in similar settings should pay more attention to clients related to hemoglobin levels and time to default from medication based on these important factors.

Data availability

The data used in the current investigation is available from the corresponding author and can be attached upon request.

Code availability

All codes organized in this study are available from the corresponding author upon reasonable request.

Abbreviations

AIC:

Akaike information criteriaAIDS:

Acquired immune deficiency syndromeART:

Antiretroviral therapyBIC:

Bayesian information criterionBMI:

Body Mass IndexCD4:

Cluster Differentiation4HIV:

Human Immune Deficiency VirusOIs:

Opportunistic InfectionsPH:

Proportional HazardTB:

TuberculosisWHO:

World Health Organization

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Acknowledgements

The authors extend gratitude to University of Gondar Compressive Specialized Hospital (UGCSH), especially, the healthcare ART center professionals. Finally, the authors would like to thank Mekdela Amba and Bahir Dar University for initiating this crucial study.

Funding

No funding was received for this research.

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Authors and Affiliations

  1. Department of Statistics, Mekdela Amba University, Tulu Awulia, EthiopiaNurye Seid Muhie
  2. Department of Bio-Statistics, Bahir Dar University, Bahir Dar, EthiopiaAwoke Seyoum Tegegne

Contributions

NSM was involved in this study from data management, data analysis, drafting, and revising the final manuscript, contributed to the conception, design, and interpretation of data, as well as to revision and approval of the final manuscript. AST was involved in revising the final manuscript, contributed to the conception, as well revised and approved the final manuscript.

Corresponding author

Correspondence to Nurye Seid Muhie.

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Competing interests

The authors declare no competing interests.

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Not applicable.

Ethics approval and consent to participate

A statement to confirm that all methods were performed by the ethical standards as laid down in the Declaration of Helsinki. Hence, informed consent was waived by the Bahir Dar University research technical and ethical review board with Clinical Trial Number Stat-S/166/2022 because of retrospective nature of the study. The study was approved by the Bahir Dar University Research Technical and Ethical Review Board.

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Muhie, N.S., Tegegne, A.S. Survival analysis and predictors for hemoglobin level and time-to-default from HIV treatment among first-line female HIV-positive patients within the reproductive age group. Sci Rep 15, 15348 (2025). https://doi.org/10.1038/s41598-025-00033-2

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