We propose a data-driven, model-free adaptive sliding mode control (MFASMC) approach to address the Haidou-1 ARV under-actuated motion control problem with uncertainties, including external disturbances and parameter perturbations. Firstly, we analyzed the two main difficulties in the motion control of Haidou-1 ARV. Secondly, in order to address these problems, a MFASMC control method was introduced. It is combined by a model-free adaptive control (MFAC) method and a sliding mode control (SMC) method. The main advantage of the MFAC method is that it relies only on the real-time measurement data of an ARV instead of any mathematical modeling information, and the SMC method guarantees the MFAC method’s fast convergence and low overshooting. The proposed MFASMC control method can maneuver Haidou-1 ARV cruising at the desired forward speed, heading, and depth, even when the dynamic parameters of the ARV vary widely and external disturbances exist. It also addresses the problem of under-actuated motion control for the Haidou-1 ARV. Finally, the simulation results, including comparisons with a PID method and the MFAC method, demonstrate the effectiveness of our proposed method. Full article

Recent reforms in undergraduate science education have highlighted the need for student-centered learning that challenges students to take ownership of the scientific process through conducting authentic research. As such, Undergraduate Research Experiences (UREs) have become more prevalent in higher education. However, extensive variations in the structures, durations, and contexts of UREs exist and long-term implications are not well documented. We used the Social Cognitive Career Theory to guide our exploration of student outcomes from completing a required three-semester capstone URE at a predominantly undergraduate institution located in the Midwest, United States. We sought to answer two central research questions: (1) What skills and competencies do alumni perceive to have gained from completing the URE capstone, and (2) What is the impact of the URE capstone on alumni success in the workforce and/or graduate school? We conducted in-depth, one-on-one interviews with 16 alumni who recently completed their undergraduate research capstone and who were currently in a science-based career or attending graduate school. Results indicate long-term benefits from URE capstones and are described through three primary themes: technical skill acquisition and future application, soft skill acquisition and future application, and scientific pursuits. Full article

The occurrence of ovarian dysfunction is often due to the imbalance between the formation of reactive oxygen species (ROS) and the ineffectiveness of the antioxidative defense mechanisms. Primary sources of ROS are respiratory electron transfer and the activity of NADPH oxidases (NOX) while superoxide dismutases (SOD) are the main key regulators that control the levels of ROS and reactive nitrogen species intra- and extracellularly. Because of their central role SODs are the subject of research on human ovarian dysfunction but sample acquisition is low. The high degree of cellular and molecular similarity between Drosophila melanogaster ovaries and human ovaries provides this model organism with the best conditions for analyzing the role of ROS during ovarian function. In this study we clarify the localization of the ROS-producing enzyme dNox within the ovaries of Drosophila melanogaster and by a tissue-specific knockdown we show that dNox-derived ROS are involved in the chorion hardening process. Furthermore, we analyze the dSod3 localization and show that reduced activity of dSod3 impacts egg-laying behavior but not the chorion hardening process. Full article

Background/Objectives: The effectiveness of a rhythm control strategy in patients with atrial fibrillation (AF) and reduced left ventricular ejection fraction (LVEF < 50%) in real-world practice remains uncertain. Our objective was to evaluate the real-world outcomes of a rhythm control strategy in patients with AF and reduced LVEF, focusing on changes in LV systolic function and brain natriuretic peptide (BNP) levels. Methods: The study retrospectively reviewed the medical records of 80 patients with concurrent AF and reduced LVEF who underwent rhythm control therapy between March 2015 and December 2021. Results: The patients had an average age of 63.6 years and an initial LVEF of 34.3%. Sinus rhythm was restored using anti-arrhythmic drugs (38, 47.5%) or electrical cardioversion (42, 52.5%). Over a follow-up period of 53.0 months, AF recurred in 65% of patients, with 57.7% undergoing catheter ablation. Significant improvements were noted in LVEF (from 34.3% to 55.1%, p < 0.001) and BNP levels (from 752 pg/mL to 72 pg/mL, p < 0.001) at the last follow-up. Nearly all patients (97.5%) continued with the rhythm control strategy during the follow-up period. Conclusions: In real-world settings, a rhythm control strategy proves to be feasible and effective for improving LVEF and reducing BNP levels in AF patients with reduced LVEF. Full article

This study presents a new approach to investigating the impact of repeated reflow on the failure of ball grid array (BGA) packages. The issue with the BGA package collapse is that the repeated reflow can lead to short circuits, particularly for BGAs with a very fine pitch between leads. A novel approach was developed to measure the collapse of BGA solder balls during the melting and solidification process, enabling in situ measurements. The study focused on two types of solders: Sn63Pb37 as a reference, and the commonly used SAC305, with measurements taken at various temperatures. The BGA samples were subjected to three different heating/cooling cycles in a thermomechanical analyzer (TMA) at temperatures of 250 °C, 280 °C, and 300 °C, with a subsequent cooling down to 100 °C. The results obtained from the TMA indicated differences in the collapse behavior of both BGA solder alloys at various temperatures. Short circuits between neighboring leads (later confirmed by an X-ray analysis) were also recognizable on the TMA. The novel approach was successfully developed and applied, yielding clear insights into the behavior of solder balls during repeated reflow. Full article

The COVID-19 pandemic, caused by SARS-CoV-2, poses a significant global health threat. The spike glycoprotein S1 of the SARS-CoV-2 virus is known to induce the production of pro-inflammatory mediators, contributing to hyperinflammation in COVID-19 patients. Triphala, an ancient Ayurvedic remedy composed of dried fruits from three plant species—Emblica officinalis (Family Euphorbiaceae), Terminalia bellerica (Family Combretaceae), and Terminalia chebula (Family Combretaceae)—shows promise in addressing inflammation. However, the limited water solubility of its ethanolic extract impedes its bioavailability. In this study, we aimed to develop nanoparticles loaded with Triphala extract, termed “nanotriphala”, as a drug delivery system. Additionally, we investigated the in vitro anti-inflammatory properties of nanotriphala and its major compounds, namely gallic acid, chebulagic acid, and chebulinic acid, in lung epithelial cells (A549) induced by CoV2-SP. The nanotriphala formulation was prepared using the solvent displacement method. The encapsulation efficiency of Triphala in nanotriphala was determined to be 87.96 ± 2.60% based on total phenolic content. In terms of in vitro release, nanotriphala exhibited a biphasic release profile with zero-order kinetics over 0–8 h. A549 cells were treated with nanotriphala or its active compounds and then induced with 100 ng/mL of spike S1 subunit (CoV2-SP). The results demonstrate that chebulagic acid and chebulinic acid are the active compounds in nanotriphala, which significantly reduced cytokine release (IL-6, IL-1β, and IL-18) and suppressed the expression of inflammatory genes (IL-6, IL-1β, IL-18, and NLRP3) (p < 0.05). Mechanistically, nanotriphala and its active compounds notably attenuated the expression of inflammasome machinery proteins (NLRP3, ASC, and Caspase-1) (p < 0.05). In conclusion, the nanoparticle formulation of Triphala enhances its stability and exhibits anti-inflammatory properties against CoV2-SP-induction. This was achieved by suppressing inflammatory mediators and the NLRP3 inflammasome machinery. Thus, nanotriphala holds promise as a supportive preventive anti-inflammatory therapy for COVID-19-related chronic inflammation. Full article

Typhoons bring great damages to transmission line systems located in coastal areas. Strong wind and extreme precipitation are the main sources of damaging effects. Transmission lines suffered from wind-driven rain exhibit more susceptibility to damage due to the coupled effect of wind and rainwater. This paper presents an integrated numerical simulation framework based on mesoscale WRF model, multiphase CFD model and FEM model to analyze the motions of a transmission line subjected to coupled wind and rain loads during typhoon events. A full-scale transmission line in Zhoushan Island is employed to demonstrate the effectiveness of the proposed framework by simulating typhoon evolution in terms of wind fields and rainfall, solving the coupled wind and rain fields around the conductor and predicting the dynamic responses of the transmission line during Super Typhoon Lekima in 2019. The results show that the horizontal displacements of the transmission line under the joint actions of wind and rain increase approximately 17%–18% compared to those of wind loads only. It is important to consider the coupled effects of wind-driven rain on conductors in the design of transmission lines under typhoon conditions. Full article

Methods based on light field information have shown promising results in depth estimation and underwater image restoration. However, improvements are still needed in terms of depth estimation accuracy and image restoration quality. Previous work on underwater image restoration employed an image formation model (IFM) that overlooked the effects of light attenuation and scattering coefficients in underwater environments, leading to unavoidable color deviation and distortion in the restored images. Additionally, the high blurriness and associated distortions in underwater images make depth information extraction and estimation very challenging. In this paper, we refine the light propagation model and propose a method to estimate the attenuation and backscattering coefficients of the underwater IFM. We simplify these coefficients into distance-related functions and design a relationship between distance and the darkest channel to estimate the water coefficients, effectively suppressing color deviation and distortion in the restoration results. Furthermore, to increase the accuracy of depth estimation, we propose using blur cues to construct a cost for refocusing in the depth direction, reducing the impact of high signal-to-noise ratio environments on depth information extraction, and effectively enhancing the accuracy and robustness of depth estimation. Finally, experimental comparisons show that our method achieves more accurate depth estimation and image restoration closer to real scenes compared to state-of-the-art methods. Full article

BlenderBot 2.0 represents a significant advancement in open-domain chatbots by incorporating real-time information and retaining user information across multiple sessions through an internet search module. Despite its innovations, there are still areas for improvement. This paper examines BlenderBot 2.0’s limitations and errors from three perspectives: model, data, and user interaction. From the data perspective, we highlight the challenges associated with the crowdsourcing process, including unclear guidelines for workers, insufficient measures for filtering hate speech, and the lack of a robust process for verifying the accuracy of internet-sourced information. From the user perspective, we identify nine types of limitations and conduct a thorough investigation into their causes. For each perspective, we propose practical methods for improvement and discuss potential directions for future research. Additionally, we extend our analysis to include perspectives in the era of large language models (LLMs), further broadening our understanding of the challenges and opportunities present in current AI technologies. This multifaceted analysis not only sheds light on BlenderBot 2.0’s current limitations but also charts a path forward for the development of more sophisticated and reliable open-domain chatbots within the broader context of LLM advancements. Full article

In this study, our primary aim was to explore the genomic landscape of Barka cattle, a breed recognized for high milk production in a semi-arid environment, by focusing on genes with known roles in milk production traits. We employed genome-wide analysis and three selective sweep detection methods (ZF_ST, θπ ratio, and ZHp) to identify candidate genes associated with milk production and composition traits. Notably, ACAA1, P4HTM, and SLC4A4 were consistently identified by all methods. Functional annotation highlighted their roles in crucial biological processes such as fatty acid metabolism, mammary gland development, and milk protein synthesis. These findings contribute to understanding the genetic basis of milk production in Barka cattle, presenting opportunities for enhancing dairy cattle production in tropical climates. Further validation through genome-wide association studies and transcriptomic analyses is essential to fully exploit these candidate genes for selective breeding and genetic improvement in tropical dairy cattle. Full article

Electro-conductive fabrics are key materials for designing and developing wearable smart textiles. The properties of textile materials depend on the production method, the technique which leads to high conductivity, and the structure. The aim of the research work was to determine the factors affecting the electrical conductivity of woven fabrics and elucidate the mechanism of electric current conduction through this complex, aperiodic textile material. The chemical composition of the material surface was identified using scanning electron microscopy energy dispersion X-ray spectroscopy. The van der Pauw method was employed for multidirectional resistance measurements. The coefficient was determined for the assessment of the electrical anisotropy of woven fabrics. X-ray micro-computed tomography was used for 3D woven structure geometry analysis. The anisotropy coefficient enabled the classification of electro-conductive fabrics in terms of isotropic or anisotropic materials. It was found that the increase in weft density results in an increase in sample anisotropy. The rise in thread width can lead to smaller electrical in-plane anisotropy. The threads are unevenly distributed in woven fabric, and their widths are not constant, which is reflected in the anisotropy coefficient values depending on the electrode arrangement. The smaller the fabric area covered by four electrodes, the fewer factors leading to structure aperiodicity. Full article

Appropriate soluble carbohydrate (SCHO)-to-NDF ratios in the diet are essential for rumen health. The effects of different SCHO-to-NDF ratios (1.0, 1.5, and 2.0) on rumen barrier function and inflammation in Dumont lambs (n = 18, 6 replicates per treatment) was investigated. The SCHO:NDF ratio was altered by replacing the forage (Leynus chinensis) with corn grain. With an increase in the proportion of SCHO, the final body weight (FBW), average daily gain (ADG), soluble carbohydrate intake (SCHOI), and LPS level increased; and the neutral detergent fiber intake (NDFI), ruminal papillae height, papillae area, and pH decreased (p < 0.05, p_lin < 0.05). The medium CHO:NDF group had increased claudin-1 mRNA (p < 0.05, p_lin = 0.005, p_quad = 0.003) and protein (p < 0.05, p_quad < 0.001) levels; the high CHO:NDF group had increased occludin mRNA and protein (p < 0.05, p_lin = 0.001) levels. The level of the anti-inflammatory cytokine IL-10 was significantly greater in the medium CHO:NDF group than in the high CHO:NDF group (p < 0.05, p_quad < 0.001). With an increase in the ratio of SCHO, the mRNA level and concentration of the proinflammatory cytokines IL-1β, IL-6, and TNF-α linearly increased (p < 0.05, p_lin < 0.05), and those in the high CHO:NDF group were significantly greater than those in the low CHO:NDF group. The levels of phosphorylated p65 (p_lin = 0.003), IκB-α (p_lin < 0.001), and JNK (p_lin = 0.001) increased linearly, and those in the high CHO:NDF group were significantly greater than those in the other two groups (p < 0.05). Therefore, when the SCHO-to-NDF ratio was increased to 1.5, the rumen epithelium was not affected, but when the ratio was increased to 2.0, NF-κB and MAPK were activated in the rumen epithelium, leading to impaired barrier function and inflammation. The suitable NFC:NDF ratio for the short-term fattening of Dumont lambs was found to be 1.50. Full article

Because of the absence of visual perception, visually impaired individuals encounter various difficulties in their daily lives. This paper proposes a visual aid system designed specifically for visually impaired individuals, aiming to assist and guide them in grasping target objects within a tabletop environment. The system employs a visual perception module that incorporates a semantic visual SLAM algorithm, achieved through the fusion of ORB-SLAM2 and YOLO V5s, enabling the construction of a semantic map of the environment. In the human–machine cooperation module, a depth camera is integrated into a wearable device worn on the hand, while a vibration array feedback device conveys directional information of the target to visually impaired individuals for tactile interaction. To enhance the system’s versatility, a Dobot Magician manipulator is also employed to aid visually impaired individuals in grasping tasks. The performance of the semantic visual SLAM algorithm in terms of localization and semantic mapping was thoroughly tested. Additionally, several experiments were conducted to simulate visually impaired individuals’ interactions in grasping target objects, effectively verifying the feasibility and effectiveness of the proposed system. Overall, this system demonstrates its capability to assist and guide visually impaired individuals in perceiving and acquiring target objects. Full article

Direct upgrading of methane into value-added products is one of the most significant technologies for the effective transformation of hydrocarbon feedstocks in the chemical industry. Both oxidative and non-oxidative methane conversion are broadly useful approaches, though the two reaction pathways are quite distinguished. Oxidative coupling of methane (OCM) has been widely studied, but suffers from the low selectivity to C₂ hydrocarbons because of the overoxidation leading to undesired byproducts. Therefore, non-oxidative coupling of methane is a worthy alternative approach to be developed for the efficient, direct utilization of methane. Recently, heterogeneous catalysts comprising singly dispersed metal sites, such as single-atom catalysts (SAC) and surface organometallic catalysts (SOMCat), have been proven to be effectively active for direct coupling of methane to product hydrogen and C₂ products. In this context, this review summarizes recent discoveries of these novel catalysts and provides a perspective on promising catalytic processes for methane transformation via non-oxidative coupling. Full article

The NeoCare home monitoring system aims to detect falls and other events using artificial intelligence. This study evaluated NeoCare’s accuracy and explored user perceptions through a 12-week in-home trial with 18 households of adults aged 65+ years old at risk of falls (mean age: 75.3 years old; 67% female). Participants logged events that were cross-referenced with NeoCare logs to calculate sensitivity and specificity for fall detection and response. Qualitative interviews gathered in-depth user feedback. During the trial, 28 falls/events were documented, with 12 eligible for analysis as others occurred outside the home or when devices were offline. NeoCare was activated 4939 times—4930 by everyday household sounds and 9 by actual falls. Fall detection sensitivity was 75.00% and specificity 6.80%. For responding to falls, sensitivity was 62.50% and specificity 17.28%. Users felt more secure with NeoCare but identified needs for further calibration to improve accuracy. Advantages included avoiding wearables, while key challenges were misinterpreting noises and occasional technical issues like going offline. Suggested improvements were visual indicators, trigger words, and outdoor capability. The study demonstrated NeoCare’s potential with modifications. Users found it beneficial, but highlighted areas for improvement. Real-world evaluations and user-centered design are crucial for healthcare technology development. Full article

The adequacy and efficacy of simple and hybrid machine learning and Computational Intelligence algorithms were evaluated for the classification of potential prostate cancer patients in two distinct categories, the high- and the low-risk group for PCa. The evaluation is based on randomly generated surrogate data for the biomarker PSA, considering that reported epidemiological data indicated that PSA values follow a lognormal distribution. In addition, four more biomarkers were considered, namely, PSAD (PSA density), PSAV (PSA velocity), PSA ratio, and Digital Rectal Exam evaluation (DRE), as well as patient age. Seven simple classification algorithms, namely, Decision Trees, Random Forests, Support Vector Machines, K-Nearest Neighbors, Logistic Regression, Naïve Bayes, and Artificial Neural Networks, were evaluated in terms of classification accuracy. In addition, three hybrid algorithms were developed and introduced in the present work, where Genetic Algorithms were utilized as a metaheuristic searching technique in order to optimize the training set, in terms of minimizing its size, to give optimal classification accuracy for the simple algorithms including K-Nearest Neighbors, a K-means clustering algorithm, and a genetic clustering algorithm. Results indicated that prostate cancer cases can be classified with high accuracy, even by the use of small training sets, with sizes that could be even smaller than 30% of the dataset. Numerous computer experiments indicated that the proposed training set minimization does not cause overfitting of the hybrid algorithms. Finally, an easy-to-use Graphical User Interface (GUI) was implemented, incorporating all the evaluated algorithms and the decision-making procedure. Full article

The mammalian target of rapamycin (mTOR) is a pivotal regulator, integrating diverse environmental signals to control fundamental cellular functions, such as protein synthesis, cell growth, survival, and apoptosis. Embedded in a complex network of signaling pathways, mTOR dysregulation is implicated in the onset and progression of a range of human diseases, including metabolic disorders such as diabetes and cardiovascular diseases, as well as various cancers. mTOR also has a notable role in aging. Given its extensive biological impact, mTOR signaling is a prime therapeutic target for addressing these complex conditions. The development of mTOR inhibitors has proven advantageous in numerous research domains. This review delves into the significance of mTOR signaling, highlighting the critical components of this intricate network that contribute to disease. Additionally, it addresses the latest findings on mTOR inhibitors and their clinical implications. The review also emphasizes the importance of developing more effective next-generation mTOR inhibitors with dual functions to efficiently target the mTOR pathways. A comprehensive understanding of mTOR signaling will enable the development of effective therapeutic strategies for managing diseases associated with mTOR dysregulation. Full article

Abstract: With the rapid growth in urban construction in Malaysia, road breakage has challenged traditional manual inspection methods. In order to quickly and accurately detect the extent of road breakage, it is crucial to apply automated road crack detection techniques. Researchers have long studied image-based road crack detection techniques, especially the deep learning methods that have emerged in recent years, leading to breakthrough developments in the field. However, many issues remain in road crack detection methods using deep learning techniques. The field lacks state-of-the-art systematic reviews that can scientifically and effectively analyze existing works, document research trends, summarize outstanding research results, and identify remaining shortcomings. To conduct a systematic review of the relevant literature, a bibliometric analysis and a critical analysis of the papers published in the field were performed. VOSviewer and CiteSpace text mining tools were used to analyze and visualize the bibliometric analysis of some parameters derived from the articles. The history and current status of research in the field by authors from all over the world are elucidated and future trends are analyzed. Full article

Soft to medium clay soil possesses major sources of damages to the pavement layers overlying them because of their potential failure under moisture changes and external heavy traffic load. In such situations, soil stabilization methods can be used to improve the soil properties and satisfy the desired engineering requirements. This study presents the use of sugarcane bagasse ash (SBA) and lime as chemical stabilizers for a clay soil subbase. Sugarcane bagasse ash and lime are used individually and as mixtures at varying percentages to stabilize a clay soil from Taxila, Pakistan. Various geotechnical laboratory tests such as Atterberg limits, compaction test, and California Bearing Ratio (CBR) are carried out on both pure and stabilized soils. These tests are performed at 2.5%, 5%, and 7.5% of either SBA or lime by weight of dry soil. In addition, mixtures of lime and SBA in ratios of 1:1, 2:1, 3:1, 1:2, and 1:3 are used in 5%, 7.5%, and 10% of dry soil weight, respectively. Results indicate that soil improved with 7.5% SBA showed a 28% increase in the liquid limit, while soil mixed with 2.5% lime in combination with 7.5% SBA showed an increase of 40% in the plastic limit. For the plasticity index, the soil mixed with 7.5% SBA showed an increase of 42%. Moreover, 2.5% lime in combination with 2.5% SBA showed the best improvement in soil consistency as this mixture reduced the soil plasticity from high to low according to the plasticity chart. Furthermore, 2.5% SBA in combination with 5% lime demonstrated the largest improvement on the CBR value, which is about a 69% increase above that of the pure soil. Finally, the cost analysis indicates a promising improvement method that reduces pavement cost, increases design life, and mitigates issues of energy consumption and pollution related to SBA as a solid waste material. Full article

The rise of the e-commerce industry has markedly changed the global economy, providing customers with unparalleled access to goods and services. This study empirically examines online shoppers’ perceptions and preferences, focusing on their experiences with last-mile delivery (LMD) services and its impact on their shopping behaviour. This research employs machine learning classification and regression models for a large-scale analysis of customers’ responses, collected using an online survey in the main cities in Saudi Arabia, which is experiencing rapid e-commerce growth amidst a broader digital transformation. The findings highlight a strong consumer preference for timely LMD services, typically within a day of purchase, while noting dissatisfaction with exceedingly early delivery windows. The research emphasises the need to address customer dissatisfaction with delivery services to retain clientele, as many may switch retailers without informing the retailers. Additionally, a considerable trend towards preferring digital over cash-on-delivery payment methods was observed among online shoppers. Overall, this study provides valuable insights into the significant influence of LMD services on customer satisfaction and behaviour in the e-commerce sector. The use of robust machine learning models has revealed critical factors that can guide retailers and LMD providers in enhancing service delivery and customer experience, contributing to the broader discourse on e-commerce logistics efficiency and customer satisfaction. Full article

Abstract: A decoupled model predictive control (MPC) power balancing strategy for a coupled inductor-based flying capacitor DC–DC converter (FCDC) is a proposed to solve the power imbalance caused by the parameter differences in the coupled inductor. The decoupled mathematical model of coupled inductor FCDC is firstly derived by analyzing the converter operation state under various modes. On this basis, the control relationship between inductor current and flying capacitor (FC) voltage is redefined and an MPC power balance strategy based on the inductor current with single-step optimization is proposed. The proposed MPC strategy not only achieves decoupled power balancing control but also solves multi-objective dynamic optimization control of the inductor current and FC voltage, greatly reducing the computation load. A detailed theoretical analysis of the proposed strategy is presented and the balancing performance is effectively verified through the experiments. Full article

Tyrosine kinase inhibitor (TKI) drugs have significantly improved chronic myeloid leukemia (CML) outcomes. Neopeptides from CML cells may induce specific immune responses, which are crucial for deep molecular (DMR) and treatment-free remission (TFR). In this study of Ethiopian patients with CML (n = 162), the HLA alleles and single-nucleotide polymorphisms of five cytokines revealed significant associations with clinical outcomes. Clinically unfavorable outcomes correlated with HLA alleles A*03:01/02, A*23:17:01, B*57:01/02/03, and HLA-DRB4*01:01 (p-value = 0.0347, p-value = 0.0285, p-value = 0.037, and p-value = 0.0127, respectively), while HLA-DRB4*01:03:01 was associated with favorable outcomes (p-value = 0.0058). After assigning values for the ‘low,’ ‘intermediate,’ and ‘high’ gene expression of the SNPs’ respective cytokine genes, Kaplan–Meier estimates for relapse-free survival, adjusted for age, treatment duration, and relapse risk among patients after the administration of TKIs, indicated that a gene expression ratio above the overall median of TNF-α, IL-6, and the combination of TGF-β1/IL-10, IFNγ, and IL-6/IL-10 TGF-β1 was correlated with a higher likelihood of treatment failure ((RR: 3.01; 95% CI: 1.1–8.3; p-value = 0.0261) and (RR: 2.4; 95% CI: 1.1–5.2; p-value = 0.022), respectively). Multi-SNPs, surpassing single-SNPs, and HLA allele polymorphisms showed promise in predicting outcomes of patients with CML during TKI treatment, prompting further exploration into their potential utility. Full article