Given the widespread use of esters and polyesters in products like cosmetics, fishing nets, lubricants and adhesives, whose specific application(s) may cause their dispersion in open environments, there is a critical need for stringent eco-design criteria based on biodegradability and ecotoxicity evidence. Our approach integrates experimental and computational methods based on short oligomers, offering a screening tool for the rapid identification of sustainable monomers and oligomers, with a special focus on bio-based alternates. We provide insights into the relationships between the chemical structure and properties of bio-based oligomers in terms of biodegradability in marine environments and toxicity in benchmark organisms. The experimental results reveal that the considered aromatic monomers (terephthalic acid and 2,5-furandicarboxylic acid) accumulate under the tested conditions (OECD 306), although some slight biodegradation is observable when the inoculum derives from sites affected by industrial and urban pollution, which suggests that ecosystems adapt to non-natural chemical pollutants. While clean seas are more susceptible to toxic chemical buildup, biotic catalytic activities offer promise for plastic pollution mitigation. Without prejudice to the fact that biodegradability inherently signifies a desirable trait in plastic products, nor that it automatically grants them a sustainable “license”, this study is intended to facilitate the rational design of new polymers and materials on the basis of specific uses and applications. Full article

In South China, the large quantity of rainfall in the pre-summer rainy season can easily lead to natural disasters, which emphasizes the importance of improving the accuracy of precipitation forecasting during this period for the social and economic development of the region. In this paper, the back-propagation neural network (BPNN) is used to establish the model for precipitation forecasting. Three schemes are applied to improve the model performance: (1) predictors are selected based on individual meteorological stations within the region rather than the region as a whole; (2) the triangular irregular network (TIN) is proposed to preprocess the observed precipitation data for input of the BPNN model, while simulated/forecast precipitation is the expected output; and (3) a genetic algorithm is used for the hyperparameter optimization of the BPNN. The first scheme reduces the mean absolute percentage error (MAPE) and the root mean square error (RMSE) of the simulation by roughly 5% and more than 15 mm; the second reduces the MAPE and RMSE by more than 15% and 15 mm, respectively, while the third improves the simulation inapparently. Obviously, the second scheme raises the upper limit of the model simulation capability greatly by preprocessing the precipitation data. During the training and validation periods, the MAPE of the improved model can be controlled at approximately 35%. For precipitation hindcasting in the test period, the anomaly rate is less than 50% in only one season, and the highest is 64.5%. According to the anomaly correlation coefficient and Ps score of the hindcast precipitation, the improved model performance is slightly better than the FGOALS-f2 model. Although global climate change makes the predictors more variable, the trend of simulation is almost identical to that of the observed values over the whole period, suggesting that the model is able to capture the general characteristics of climate change. Full article

The clinical integration of prostate membrane specific antigen (PSMA) positron emission tomography and computed tomography (PET/CT) scans represents potential for advanced data analysis techniques in prostate cancer (PC) prognostication. Among these tools is the use of radiomics, a computer-based method of extracting and quantitatively analyzing subvisual features in medical imaging. Within this context, the present review seeks to summarize the current literature on the use of PSMA PET/CT-derived radiomics in PC risk stratification. A stepwise literature search of publications from 2017 to 2023 was performed. Of 23 articles on PSMA PET/CT-derived prostate radiomics, PC diagnosis, prediction of biopsy Gleason score (GS), prediction of adverse pathology, and treatment outcomes were the primary endpoints of 4 (17.4%), 5 (21.7%), 7 (30.4%), and 7 (30.4%) studies, respectively. In predicting PC diagnosis, PSMA PET/CT-derived models performed well, with receiver operator characteristic curve area under the curve (ROC-AUC) values of 0.85–0.925. Similarly, in the prediction of biopsy and surgical pathology results, ROC-AUC values had ranges of 0.719–0.84 and 0.84–0.95, respectively. Finally, prediction of recurrence, progression, or survival following treatment was explored in nine studies, with ROC-AUC ranging 0.698–0.90. Of the 23 studies included in this review, 2 (8.7%) included external validation. While explorations of PSMA PET/CT-derived radiomic models are immature in follow-up and experience, these results represent great potential for future investigation and exploration. Prior to consideration for clinical use, however, rigorous validation in feature reproducibility and biologic validation of radiomic signatures must be prioritized. Full article

A crimping and pultruding forming process for truss rods using Carbon Fiber (CF)/Polyether–Ether–Ketone (PEEK) prepreg tape as the raw material is proposed to address the problem of continuous manufacturing of space trusses on orbit. The proposed process provides material rods for continuous truss manufacturing. Through numerical simulation and experimental verification, the effects of relevant parameters on the forming process are determined, an efficient method of rod curl pultrusion, in-rail, equal material forming is proposed, and the structural configuration of the rod curl pultrusion forming mold is determined. The equivalent macroscopic mechanical properties of unidirectional CF/PEEK prepreg strips are considered, and the rod-forming process is investigated. Rod samples with different process parameters are prepared, and several tests are conducted on them. The results show that the forming load pull is negatively correlated with the temperature at the same forming speed, and forming speed is positively correlated with the forming load pull at a certain temperature. Temperature and speed affect the surface quality of the rod, the density of the material filling, and the mechanical properties of the rod. The optimal forming process parameters are determined through numerical simulation and experimental verification. The developed molding technology has the advantages of high efficiency, low energy consumption, and high integration. It reduces manufacturing costs and improves manufacturing efficiency, so it can serve as a new and effective solution for the manufacturing of high-performance truss rods in the aerospace field. Full article

Participant movement is a major source of artifacts in functional near-infrared spectroscopy (fNIRS) experiments. Mitigating the impact of motion artifacts (MAs) is crucial to estimate brain activity robustly. Here, we suggest and evaluate a novel application of the nonlinear Hammerstein–Wiener model to estimate and mitigate MAs in fNIRS signals from direct-movement recordings through IMU sensors mounted on the participant’s head (head-IMU) and the fNIRS probe (probe-IMU). To this end, we analyzed the hemodynamic responses of single-channel oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) signals from 17 participants who performed a hand tapping task with different levels of concurrent head movement. Additionally, the tapping task was performed without head movements to estimate the ground-truth brain activation. We compared the performance of our novel approach with the probe-IMU and head-IMU to eight established methods (PCA, tPCA, spline, spline Savitzky–Golay, wavelet, CBSI, RLOESS, and WCBSI) on four quality metrics: SNR, △AUC, RMSE, and R. Our proposed nonlinear Hammerstein–Wiener method achieved the best SNR increase (p < 0.001) among all methods. Visual inspection revealed that our approach mitigated MA contaminations that other techniques could not remove effectively. MA correction quality was comparable with head- and probe-IMUs. Full article

Knowing the tolerance interval capabilities (TICs) of a manufacturing process is of prime interest, especially if specifications link the manufacturer to a customer. These TICs can be determined using the machine performance concept of ISO 22514. However, few works have applied this to Additive Manufacturing printers, while testing most of the printing area as recommended takes a very long time (nearly 1 month is common). This paper, by proposing a novel part design called COMPAQT (Component for Machine Performances Assessment in Quick Time), aims at giving the same level of printing area coverage, while keeping the manufacturing time below 24 h. The method was successfully tested on a material extrusion printer. It allowed the determination of potential and real machine tolerance interval capabilities. Independently of the feature size, those aligned with the X axis achieved lower TICs than those aligned with the Y axis, while the Z axis exhibited the best performance. The measurements specific to one part exhibited a systematic error centered around 0 mm ± 0.050 mm, while those involving two parts reached up to 0.314 mm of deviation. COMPAQT can be used in two applications: evaluating printer tolerance interval capabilities and tracking its long-term performance by incorporating it into batches of other parts. Full article

This work presents the detection of hypoxanthine (HXA), a purine derivative that is similar to nucleic acids who overconsumption can cause health issues, by using hydrothermally synthesized cerium phosphate (CePO₄) followed by a sonochemical approach for CePO₄ decorated with a functionalized carbon nanofiber (CePO₄@f-CNF) nanocomposite. The formation of the nanocomposite was confirmed with X-ray powder diffraction (XRD), scanning electron microscopy (SEM), and energy-dispersive spectroscopy (EDS). A CePO₄@f-CNF nanocomposite is used to modify a glassy carbon electrode (GCE) to analyze the electrochemical detection of HXA. Cyclic voltammetry (CV), Electrochemical impedance spectroscopy (EIS), and Differential pulse voltammetry (DPV) were used to examine the electrochemical properties of the composite. As a result, the modified electrode exhibits a larger active surface area (A = 1.39 cm²), a low limit of detection (LOD) at 0.23 µM, a wide linear range (2.05–629 µM), and significant sensitivity. Therefore, the CePO₄@f-CNF nanocomposite was used to study the real-time detection in chicken and fish samples, and it depicted significant results. Full article

Microarray experiments, a mainstay in gene expression analysis for nearly two decades, pose challenges due to their complexity. To address this, we introduce DExplore, a user-friendly web application enabling researchers to detect differentially expressed genes using data from NCBI’s GEO. Developed with R, Shiny, and Bioconductor, DExplore integrates WebGestalt for functional enrichment analysis. It also provides visualization plots for enhanced result interpretation. With a Docker image for local execution, DExplore accommodates unpublished data. To illustrate its utility, we showcase two case studies on cancer cells treated with chemotherapeutic drugs. DExplore streamlines microarray data analysis, empowering molecular biologists to focus on genes of biological significance. Full article

Accurate quantification of uncertainty in solar photovoltaic (PV) generation forecasts is imperative for the efficient and reliable operation of the power grid. In this paper, a data-driven non-parametric probabilistic method based on the Naïve Bayes (NB) classification algorithm and Dempster–Shafer theory (DST) of evidence is proposed for day-ahead probabilistic PV power forecasting. This NB-DST method extends traditional deterministic solar PV forecasting methods by quantifying the uncertainty of their forecasts by estimating the cumulative distribution functions (CDFs) of their forecast errors and forecast variables. The statistical performance of this method is compared with the analog ensemble method and the persistence ensemble method under three different weather conditions using real-world data. The study results reveal that the proposed NB-DST method coupled with an artificial neural network model outperforms the other methods in that its estimated CDFs have lower spread, higher reliability, and sharper probabilistic forecasts with better accuracy. Full article

In recent years, there has been a surge in demand for and research focus on cell therapy, driven by the tissue-regenerative and disease-treating potentials of stem cells. Among the candidates, dental pulp stem cells (DPSCs) or human exfoliated deciduous teeth (SHED) have garnered significant attention due to their easy accessibility (non-invasive), multi-lineage differentiation capability (especially neurogenesis), and low immunogenicity. Utilizing these stem cells for clinical purposes requires careful culture techniques such as excluding animal-derived supplements. Human platelet lysate (hPL) has emerged as a safer alternative to fetal bovine serum (FBS) for cell culture. In our study, we assessed the impact of hPL as a growth factor supplement for culture medium, also conducting a characterization of SHED cultured in hPL-supplemented medium (hPL-SHED). The results showed that hPL has effects in enhancing cell proliferation and migration and increasing cell survivability in oxidative stress conditions induced by H₂O₂. The morphology of hPL-SHED exhibited reduced size and elongation, with a differentiation capacity comparable to or even exceeding that of SHED cultured in a medium supplemented with fetal bovine serum (FBS-SHED). Moreover, no evidence of chromosome abnormalities or tumor formation was detected. In conclusion, hPL-SHED emerges as a promising candidate for cell therapy, exhibiting considerable potential for clinical investigation. Full article

Background: Rare diseases, predominantly caused by genetic factors and often presenting neurological manifestations, are significantly underrepresented in research. This review addresses the urgent need for advanced research in rare neurological diseases (RNDs), which suffer from a data scarcity and diagnostic challenges. Bridging the gap in RND research is the integration of machine learning (ML) and omics technologies, offering potential insights into the genetic and molecular complexities of these conditions. Methods: We employed a structured search strategy, using a combination of machine learning and omics-related keywords, alongside the names and synonyms of 1840 RNDs as identified by Orphanet. Our inclusion criteria were limited to English language articles that utilized specific ML algorithms in the analysis of omics data related to RNDs. We excluded reviews and animal studies, focusing solely on studies with the clear application of ML in omics data to ensure the relevance and specificity of our research corpus. Results: The structured search revealed the growing use of machine learning algorithms for the discovery of biomarkers and diagnosis of rare neurological diseases (RNDs), with a primary focus on genomics and radiomics because genetic factors and imaging techniques play a crucial role in determining the severity of these diseases. With AI, we can improve diagnosis and mutation detection and develop personalized treatment plans. There are, however, several challenges, including small sample sizes, data heterogeneity, model interpretability, and the need for external validation studies. Conclusions: The sparse knowledge of valid biomarkers, disease pathogenesis, and treatments for rare diseases presents a significant challenge for RND research. The integration of omics and machine learning technologies, coupled with collaboration among stakeholders, is essential to develop personalized treatment plans and improve patient outcomes in this critical medical domain. Full article

Due to the long-term deformation settlement of foundations, issues such as damage and functional failure of buildings and structures have long been a concern in the engineering field. The creep of soil is one of the primary causes leading to long-term deformation of foundations. In this paper, the consolidation deformation, creep characteristics, and creep model of reconstituted saturated silty clay were studied using the isotropic consolidation creep test and triaxial compression creep test. The results show that for the isotropic consolidation creep test, although the applied load adopted different stages of loading, as long as the final applied confining pressure was the same, the number of stages applied by the confining pressure had little effect on the final isotropic consolidation deformation of the sample and the triaxial undrained shear strength after creep. However, for the triaxial shear creep test, it was found that under the same final deviatoric stress, the final deviatoric strain of the sample was closely related to the number of loading stages of deviatoric stress. The test showed that the more loading stages with the same deviatoric stress, the smaller the final deviatoric strain, and the triaxial undrained shear strength of the sample after creep increased. In addition, it was reasonable to set the pore pressure dissipation of the sample at 95% ((u₀ − u)/u₀ = 95%) as the time (t₁₀₀) at which the primary consolidation of the soil sample was completed. The isotropic consolidation creep curves and the triaxial compression creep curves showed certain non-linearity. Then, the logarithmic model and the hyperbolic model were used to fit the creep curves of the samples. It was found that the hyperbolic model had a better fitting effect than the logarithmic model, but for the triaxial compression creep test, the creep parameters of the sample changed greatly. Therefore, studying the creep characteristics of soil under different pre-loading steps is of significant engineering importance for evaluating the long-term deformation of underground structures. Full article

This research presents a new, eco-friendly, and swift method combining solid-phase extraction and hydrophobic deep eutectic solvents (DES) with high-performance liquid chromatography (SPE-DES-HPLC) for extracting and quantifying catechin and epicatechin in Shanxi aged vinegar (SAV). The parameters, such as the elution solvent type, the XAD-2 macroporous resin dosage, the DES ratio, the DES volume, the adsorption time, and the desorption time, were optimized via a one-way experiment. A central composite design using the Box–Behnken methodology was employed to investigate the effects of various factors, including 17 experimental runs and the construction of three-dimensional response surface plots to identify the optimal conditions. The results show that the optimal conditions were an HDES (tetraethylammonium chloride and octanoic acid) ratio of 1:3, an XAD-2 macroporous resin dosage of 188 mg, and an adsorption time of 11 min. Under these optimal conditions, the coefficients of determination of the method were greater than or equal to 0.9917, the precision was less than 5%, and the recoveries ranged from 98.8% to 118.8%. The environmentally friendly nature of the analytical process and sample preparation was assessed via the Analytical Eco-Scale and AGREE, demonstrating that this method is a practical and eco-friendly alternative to conventional determination techniques. In summary, this innovative approach offers a solid foundation for the assessment of flavanol compounds present in SAV samples. Full article

As a transitional flow pattern, slug–annular flow occurs over a wide range of operating conditions in micro-channels while its distribution in parallel micro-channels has not been well characterized. Herein, we conducted an experiment to study the phase distribution of slug–annular flow in parallel micro-channels. The test section consists of a header with a diameter of 0.48 mm and six branch channels with a diameter of 0.40 mm. Nitrogen and 0.03 wt% sodium dodecyl sulfate (SDS) solution were used as the test fluids. It was found that the phase distribution of the slug–annular flow was unstable and the duration of the varying process showed regularity with different inlet conditions. Increasing the liquid superficial velocity facilitated the liquid phase to flow into channels at the fore part of the header, while the channels at the rear part of the header were more supplied with liquid as the gas superficial velocity, volume fraction of gas, and volume flow rate increased. Furthermore, the results indicated that the channels located at the rear part of the header experienced a pronounced enhancement in the supply of both the liquid and gas phases, with the spacing between the branches increasing. A predictive correlation was formulated to ascertain the distribution of the liquid phase within slug–annular flow across parallel micro-channels. Full article

This study theoretically investigates the temperature and velocity spatial distributions in the flow of a copper–water nanofluid induced by a rotating rigid disk in a porous medium. Unlike previous work on similar systems, we assume that the disk surface is well polished (coated); therefore, there are velocity and temperature slips between the nanofluid and the disk surface. The importance of considering slip conditions in modeling nanofluids comes from practical applications where rotating parts of machines may be coated. Additionally, this study examines the influence of heat generation on the temperature distribution within the flow. By transforming the original Navier–Stokes partial differential equations (PDEs) into a system of ordinary differential equations (ODEs), numerical solutions are obtained. The boundary conditions for velocity and temperature slips are formulated using the effective viscosity and thermal conductivity of the copper–water nanofluid. The dependence of the velocity and temperature fields in the nanofluid flow on key parameters is investigated. The major findings of the study are that the nanoparticle volume fraction significantly impacts the temperature distribution, particularly in the presence of a heat source. Furthermore, polishing the disk surface enhances velocity slips, reducing stresses at the disk surface, while a pronounced velocity slip leads to distinct changes in the radial, azimuthal, and axial velocity components. The study highlights the influence of slip conditions on fluid velocity as compared to previously considered non-slip conditions. This suggests that accounting for slip conditions for coated rotating disks would yield more accurate predictions in assessing heat transfer, which would be potentially important for the practical design of various devices using nanofluids. Full article

Storing energy in hydrogen has been recognized by scientists as one of the most effective ways of storing energy for many reasons. The first of these reasons is the availability of technology for producing hydrogen from water using electrolytic methods. Another aspect is the availability of relatively cheap energy from renewable energy sources. Moreover, you can count on the availability of large amounts of this energy. The aim of this article is to support the decision-making processes related to the production of yellow hydrogen using a strategic model which exploits the metalog family of probability distributions. This model allows us to calculate, with accuracy regarding the probability distribution, the amount of energy produced by photovoltaic systems with a specific peak power. Using the model in question, it is possible to calculate the expected amount of electricity produced daily from the photovoltaic system and the corresponding amount of yellow hydrogen produced. Such a strategic model may be appropriate for renewable energy developers who build photovoltaic systems intended specifically for the production of yellow and green hydrogen. Based on our model, they can estimate the size of the photovoltaic system needed to produce the assumed hydrogen volume. The strategic model can also be adopted by producers of green and yellow hydrogen. Due to precise calculations, up to the probability distribution, the model allows us to calculate the probability of providing the required energy from a specific part of the energy mix. Full article

In recent years, an increasing number of viruses have triggered outbreaks that pose a severe threat to both human and animal life, as well as caused substantial economic losses. It is crucial to understand the genomic structure and epidemiology of these viruses to guide effective clinical prevention and treatment strategies. Nanopore sequencing, a third-generation sequencing technology, has been widely used in genomic research since 2014. This technology offers several advantages over traditional methods and next-generation sequencing (NGS), such as the ability to generate ultra-long reads, high efficiency, real-time monitoring and analysis, portability, and the ability to directly sequence RNA or DNA molecules. As a result, it exhibits excellent applicability and flexibility in virus research, including viral detection and surveillance, genome assembly, the discovery of new variants and novel viruses, and the identification of chemical modifications. In this paper, we provide a comprehensive review of the development, principles, advantages, and applications of nanopore sequencing technology in animal and human virus research, aiming to offer fresh perspectives for future studies in this field. Full article

Digital economy has emerged as one of the primary driving forces for economic globalization. However, assessing digital economy development in a robust and scientific manner remains a great challenge. This paper proposes an evaluation system with measurement errors correction to accurately research the regional differences in and the spatial-temporal evolution characteristics of digital economy development in the Yangtze River Economic Belt (YEB), combining the entropy method, the Dagum–Gini coefficient, an σ convergence model and grey correlation analysis. The results present that the digital economy development index in the YEB rose from 2012 to 2021, with the greatest weight being social livelihood benefits. Meanwhile, there were noticeable regional differences in digital economy development in the YEB; in particular, the middle reaches showed obvious convergence. The grey correlation degree between the influence factors and the digital economy development ranged from 0.5286 to 0.9144, demonstrating a robust positive correlation. The theoretical framework of this paper integrates economic development models with advanced statistical analysis techniques, providing a robust analytical perspective for examining the complexities of digital economy evolution. The insights offer a blueprint for policymakers seeking to foster a robust and equitable digital economy, underscoring the potential of data-driven policy formulations in navigating the intricate landscape of economic globalization. Full article

Building information modeling (BIM) and finite element method (FEM) models have a wide range of applications in underground engineering design, construction, and operation and maintenance. This study employs a BIM-FEM framework to numerically simulate the impact of excavation on existing subway stations, using the Yanjiang New City Station TOD project as a case study. This framework simplifies the smooth integration of BIM and FEM models, automating functions such as assigning material properties, conducting construction simulations, and generating high-quality meshes. Simulation results reveal significant horizontal and vertical displacements in diaphragm walls, support structures, and subway station structures, with the greatest impacts occurring closest to the excavation site. The BIM-FEM framework is validated as an effective tool for designing foundation pit support structures, enhancing numerical modeling accuracy and efficiency in underground engineering. The findings contribute to a better understanding of the dynamic interactions between excavation and underground structures, informing the development of construction strategies and protective measures to ensure structural safety. Full article

This paper presents a novel class of interior solutions for anisotropic stars under the imposition of a self-similar symmetry. This means proposing exact solutions to the Einstein field equations to describe charged matter distribution with radiation flow. The Einstein–Maxwell system by employing specific choices of mass function is formulated to describe the gravitational collapse of charged, anisotropic, spherically symmetric distributions using the Schwarzschild metric. Two ordinary differential equations governing the dynamics are derived by matching a straightforward solution of the symmetry equations to the charged exterior (Reissner–Nordström–Vaidya). Models with satisfactory physical behavior are constructed by extensively exploring self-similar solutions for a set of parameters and initial conditions. Finally, the paper presents the evolution of physical variables and the collapsing radius, demonstrating the inevitable collapse of the matter distribution. Full article

In the evolving landscape of portable electronics, there is a critical demand for components that meld stretchability with optical transparency, especially in supercapacitors. Traditional materials fall short in harmonizing conductivity, stretchability, transparency, and capacity. Although poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) stands out as an exemplary candidate, further performance enhancements are necessary to meet the demands of practical applications. This study presents an innovative and effective method for enhancing electrochemical properties by homogeneously incorporating Ru(III) into PEDOT:PSS. These Ru(III) PEDOT:PSS complexes are readily synthesized by dipping PEDOT:PSS films in RuCl₃ solution for no longer than one minute, leveraging the high specific capacitance of Ru(III) while minimizing interference with transmittance. The supercapacitor made with this Ru(III) PEDOT:PSS complex demonstrated an areal capacitance of 1.62 mF cm⁻² at a transmittance of 73.5%, which was 155% higher than that of the supercapacitor made with PEDOT:PSS under comparable transparency. Notably, the supercapacitor retained 87.8% of its initial capacitance even under 20% tensile strain across 20,000 cycles. This work presents a blueprint for developing stretchable and transparent supercapacitors, marking a significant stride toward next-generation wearable electronics. Full article

Background/Objectives: This study explores the impact of QMAC-DST, a rapid, fully automated phenotypic drug susceptibility test (pDST), on the treatment of tuberculosis (TB) patients. Methods: This pre–post comparative study, respectively, included pulmonary TB patients who began TB treatment between 1 December 2020 and 31 October 2021 (pre-period; pDST using the Löwenstein–Jensen (LJ) DST (M-kit DST)) and between 1 November 2021 and 30 September 2022 (post-period; pDST using the QMAC-DST) in five university-affiliated tertiary care hospitals in South Korea. We compared the turnaround times (TATs) of pDSTs and the time to appropriate treatment for patients whose anti-TB drugs were changed based on these tests between the groups. All patients were permitted to use molecular DSTs (mDSTs). Results: A total of 182 patients (135 in the M-kit DST group and 47 in the QMAC-DST group) were included. The median TAT was 36 days for M-kit DST (interquartile range (IQR), 30–39) and 12 days for QMAC-DST (IQR, 9–15), with the latter being significantly shorter (p < 0.001). Of the total patients, 10 (5.5%) changed their anti-TB drugs based on the mDST or pDST results after initiating TB treatment (8 in the M-kit DST group and 2 in the QMAC-DST group). In the M-kit DST group, three (37.5%) patients changed anti-TB drugs based on the pDST results. In the QMAC-DST group, all changes were due to mDST results; therefore, calculating the time to appropriate treatment for patients whose anti-TB drugs were changed based on pDST results was not feasible. In the QMAC-DST group, 46.8% of patients underwent the first-line line probe assay compared to 100.0% in the M-kit DST group (p < 0.001), indicating that rapid QMAC-DST results provide quicker assurance of the ongoing treatment by confirming susceptibility to the current anti-TB drugs. Conclusions: QMAC-DST delivers pDST results more rapidly than LJ-DST, ensuring faster confirmation for the current treatment regimen. Full article