Highlights in Science, Engineering and Technology
Highlights in Science, Engineering and Technology is an open access, peer-reviewed publication series published by Darcy & Roy Press. It aims to publish conference proceedings dealing with all areas of science, engineering and technology.
Study on the Correlation Characteristics Between Fetal Y Chromosome Concentration and Maternal Indicators Based on a Multiple Linear Regression Model
This study investigates the association between fetal Y chromosome concentration and maternal indicators in non-invasive prenatal testing (NIPT). It details the modeling process for quantitative relationships between fetal Y chromosome concentration and maternal factors including gestational age, BMI, and age. The research first standardized raw data and removed outliers based on the 3 sigma criterion to ensure foundational data quality. Following data visualization and Shapiro-Wilk normality tests confirming non-normal distribution, Spearman correlation coefficients were applied. Results indicated extremely weak correlations between gestational age, BMI, and age with Y chromosome concentration. A multiple linear regression model was then constructed using OLS estimation for regression coefficients, with significance assessed via t-tests and F-tests. The test results clearly indicated that gestational age and BMI exerted highly significant effects on Y chromosome concentration (p<0.05), while the influence of age was insignificant. Given the initial model's low coefficient of determination <!-- [if gte msEquation 12]><m:oMath><m:sSup><m:sSupPr><m:ctrlPr></m:ctrlPr></m:sSupPr><m:e><m:r>R</m:r></m:e><m:sup><m:r>2</m:r></m:sup></m:sSup></m:oMath><![endif]--> of only 0.0522, this study successfully optimized the model by clustering participants based on age and parity. This approach improved the <!-- [if gte msEquation 12]><m:oMath><m:sSup><m:sSupPr><m:ctrlPr></m:ctrlPr></m:sSupPr><m:e><m:r>R</m:r></m:e><m:sup><m:r>2</m:r></m:sup></m:sSup></m:oMath><![endif]--> value to between 0.1 and 0.3, significantly enhancing the model's explanatory power and validating the rationale for refining the basic model through detailed subgrouping.
Multiple linear regression; Y chromosome concentration; Spearman correlation coefficient.
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Esearch on Muscle Protein Mobilization in Lactating Sows Based on the Tracer Kinetics of 3-[Methyl-³H₃] histidine
This study investigated the effects of dietary protein levels on muscle protein breakdown (MPB) in lactating sows using 3-[methyl-²H₃]histidine (3MH) as an isotopic tracer. Objectives included the evaluation of candidate exponential models and a comparison between nonlinear regression and the traditional curve peeling method for analyzing 3MH dynamics. Eight sows were randomly assigned to a standard protein (SP; 18.0% CP) or a low-protein diet (LP; 14.0% CP + crystalline amino acids). Plasma 3MH kinetics were modeled using single-, two-, and three-exponential models. The three-exponential model (three-pool model) coupled with a nonlinear regression approach provided the most accurate characterization of 3MH dynamics. Model parameters derived from nonlinear regression and curve peeling were comparable, with the former being more objective and precise. ANOVA results indicated that the LP diet significantly increased MPB rates (P<0.05P<0.05), suggesting escalated mobilization of muscle reserves to sustain lactation. These findings highlight the trade-off between reduced dietary protein supply and increased maternal muscle catabolism, underscoring the necessity for precision diet formulation in lactating sows.
Exponential Model, Non-linear Regression, Curve Peeling Method, Muscle Protein Breakdown.
Determination of Optimal Timing for Non-Invasive Prenatal Testing and Robustness Analysis of Errors Based on Multi-Objective Optimization and K-Means Clustering
This study addresses the challenge of determining the optimal timing for non-invasive prenatal testing (NIPT) based on BMI grouping. It aims to identify the optimal NIPT testing window for different pregnant women populations to minimize potential risks and maximize the pass rate for Y chromosome concentration. First, to eliminate subjective bias and uncover potential associations between BMI and test performance, virtual data were generated via Monte Carlo simulation to compensate for sparse real samples. Combined with the K-means elbow point method, pregnant women carrying male fetuses were grouped into four BMI intervals: [20.0, 25.7), [25.7, 32.2), [32.2, 39.4), and [39.4, 47.0). Second, a segmented sigmoid function was constructed to quantitatively define maternal potential risk, aligning with clinical risk trends. This function exhibits a three-stage progression: “initial slow growth,” “rapid growth,” and “extremely slow growth.” Subsequently, a multi-objective optimization model was established to minimize potential risk while maximizing Y chromosome concentration compliance rates. Solving this model using the NSGA-II algorithm yielded optimal NIPT timing points of 11.21, 12.69, 14.25, and 15.83 weeks for each BMI interval. Finally, error analysis revealed that detection errors significantly amplify false-negative risks in high-BMI and early-pregnancy-week cases, markedly narrowing the feasible testing window.
K-means elbow point method, Multi-objective Optimization model, NSGA-II algorithm.
Research on Optimal NIPT Timing Decision and Intelligent Fetal Abnormality Detection Based on Machine Learning and Optimization Algorithms
With the popularization of non-invasive prenatal testing (NIPT) technology, determining a personalized optimal testing time for pregnant women with different characteristics. To address this issue, this paper proposes a comprehensive intelligent decision-making framework that integrates prediction, optimization, and classification. First, the framework constructs a quadratic multivariate nonlinear regression model to quantify the complex relationships between key factors such as maternal BMI and gestational age and the concentration of the Y chromosome in male fetuses. On this basis, it designs an optimal timing decision model aimed at minimizing potential risks for pregnant women, which is solved using the particle swarm optimization (PSO) algorithm, thus generating NIPT gestational age recommendations for groups of pregnant women in different BMI ranges. Finally, addressing the issue of detecting chromosomal aneuploidy in female fetuses, this study establishes and trains a support vector machine (SVM) binary classification model. Experimental results show that this framework can effectively formulate personalized testing plans, with the SVM classifier demonstrating superior discriminative ability on independent test sets, achieving an area under the receiver operating characteristic curve (AUC) of 0.924, thus enabling efficient and precise identification of abnormal conditions. The findings of this study provide a scientific, data-driven theoretical basis and methodological support for NIPT clinical decision-making, showcasing the application value of integrating “prediction” with “decision-making.”
NIPT, Non-linear Regression, Particle Swarm Optimization, Random Forest, Support Vector Machine.
Optimizing the Timing of Male Fetal NIPT Testing: A Model Based on Linear Regression, Survival Analysis, and Monte Carlo Simulation
The accuracy of non-invasive prenatal testing (NIPT) is significantly influenced by gestational age and maternal body mass index (BMI), and individual variability makes a single standardized testing time suboptimal for both sensitivity and specificity. In this study, a univariate linear regression model was developed based on fetal Y-chromosome concentration data to examine its association with gestational age and BMI, revealing significant positive correlations for both factors (p < 0.01). Subsequently, K-means clustering combined with survival analysis was employed to stratify pregnant individuals into three BMI groups. Optimal testing times were estimated at 13.71, 15.14, and 19.57 weeks, respectively, using Kaplan-Meier curves, with the Log-rank test confirming significant intergroup differences. Accounting for assay variability, Monte Carlo simulation suggested a delay of 0.5–1 week in testing. Furthermore, incorporating maternal age and parity into a multivariate integrated model revealed that individuals with low BMI and prior delivery history could be tested earlier, whereas those with high BMI and no prior delivery required later testing. After optimization, recommended gestational ages were adjusted later by 0.12–0.15 weeks. Notably, in parous individuals with BMI ranging from 27.7 to 33.8, the testing time increased from 13.86 to 15.26 weeks, achieving a success rate of 84.48%.
Non-invasive prenatal testing, Y-chromosome concentration, K-means clustering, Monte Carlo simulation.
BMI-Stratification-Driven Optimization of NIPT Timing and Fetal Abnormality Determination
Noninvasive prenatal testing (NIPT) analyzes cell-free fetal DNA fragments in maternal blood and faces two key challenges: precisely selecting the testing time point and efficiently determining fetal chromosomal abnormalities. Integrating generalized linear modeling and clustering, this study focuses on BMI-driven optimization of NIPT timing and the determination of fetal anomalies. First, we employ a generalized linear regression model with Pearson correlation analysis to quantitatively characterize the associations between fetal Y-chromosome concentration and indicators such as gestational age and BMI. The results indicate that the quadratic BMI term exhibits a pronounced nonlinear inhibitory effect;evaluation based on GLM metrics shows that the model stably captures the positive effect of gestational age and the overall suppressive effect of BMI. Next, this article combines K-means clustering with decision-tree regression to stratify maternal BMI among pregnancies with male fetuses, yielding a grouping scheme with within-cluster homogeneity of 92%, and construct a composite risk function R by integrating window-period risk and accuracy risk to determine the optimal time point. Finally, this article adopts the pipeline “fit a Gaussian mixture model and use the midpoint c of the two component means as a data-driven threshold→ multi-model cross-validation for model selection→ gestational-age grid computation→ use τ to obtain the earliest attainment time T*→ Pareto frontier trade-off, ”and, with K-means–based BMI stratification and τ sensitivity analysis, shows that the recommended time point exhibits notable robustness.
Fetal abnormality determination, Generalized linear regression, K-means clustering, Gaussian mixture model.
Precise Estimation of Fetal Y Chromosome Concentration Based on XGBoost Regression and Entropy-Weighted TOPSIS Optimization
Addressing challenges in fetal Y chromosome concentration estimation for non-invasive prenatal testing (NIPT)—including inconsistent measurement units, significant noise, weak monotonic correlations among variables, and strong potential nonlinearity—this study focuses on constructing and optimizing a Y chromosome concentration prediction model. First, bubble plots and correlation analysis revealed significant weak nonlinear relationships between fetal Y chromosome concentration and gestational age at testing, as well as maternal BMI. Building upon this, an XGBoost regression model was developed as the primary predictor and compared with Support Vector Machine Regression. Results demonstrated significantly improved model fit for XGBoost, achieving value of 0.958 and a low standardized root mean square error of 0.707, substantially outperforming the SVR model. To further enhance model robustness and interpretability, this study introduced the entropy weighting method to determine objective weights for key features. Combined with the Two-Operator Partial Indexed System of Selection method, this approach performed weighted optimization of model parameters. The optimized model successfully simplified complex nonlinear relationships into a clearer structure while significantly improving fitting accuracy for extreme and marginal samples. Ultimately, paired t-tests on the same sample error confirmed that the improvement of the optimized model over the baseline model was highly statistically significant.
XGBoost Regression, Entropy-Based Method, TOPSIS Method.
A Study on Optimal NIPT Timing and Fetal Anomaly Detection Based on Multi-Model Approach
The accuracy of Non-Invasive Prenatal Testing (NIPT) critically depends on selecting the optimal testing time point, which is influenced by maternal factors such as gestational week and Body Mass Index (BMI). This paper develops a comprehensive modeling framework to address two key challenges: optimizing NIPT timing for male fetuses and accurately detecting chromosomal abnormalities in female fetuses. A quadratic polynomial regression model is established to quantify the relationship between Y-chromosome concentration, gestational week, and BMI. To determine the optimal NIPT time point, K-Means clustering is integrated with a logistic regression-based risk prediction model, which minimizes a composite risk function. For the classification of fetal anomalies in females, a fusion model combining Random Forest and Logistic Regression is proposed. The results demonstrate that Y-chromosome concentration exhibits a weak but statistically significant positive correlation with gestational week and a negative correlation with BMI. The risk optimization model identifies distinct optimal NIPT timings for different maternal BMI and age groups. The fusion classification model achieves an accuracy of 0.9505, a precision of 0.8235, a recall of 0.7000, and an F1-score of 0.7570, with an optimal fusion weight of 0.67 and a decision threshold of 0.4459. This study provides a robust theoretical framework and actionable recommendations for personalized NIPT scheduling and diagnosis in clinical practice.
NIPT, Multi-Model Approach, Optimal Timing, Fetal Anomaly Detection.
Photoelectrochemical Sensors for the Detection of Disease Biomarkers: Principles and Applications
The rapid and accurate detection of disease biomarkers is crucial for the early diagnosis, prognosis assessment, and therapeutic monitoring of major diseases, including cancer, cardiovascular diseases, inflammatory responses, and infectious diseases. Conventional analytical techniques such as enzyme-linked immunosorbent assay (ELISA), mass spectrometry, and nucleic acid amplification, while reliable, are often constrained by complicated procedures, high cost, and limited applicability in point-of-care settings. In response to the aforementioned challenges, photoelectrochemical (PEC) sensors have garnered significant attention in recent years due to their advantages of low background noise, high sensitivity, and simple instrumentation, emerging as a promising direction in biosensing. This review systematically elucidates the fundamental operating principles of PEC biosensors and summarizes the key functional materials involved in their construction (such as semiconductor optoelectronic materials, photosensitizers, and electron transport aids) and their interfacial regulation strategies. Furthermore, recent progress in the application of PEC sensors for the detection of disease-related biomarkers is critically examined, with representative examples targeting cancer-associated proteins, cardiovascular disease markers, inflammatory mediators, pathogen-derived nucleic acids, and antigens. The performance of different detection strategies is also compared in terms of sensitivity, selectivity, and practical feasibility. Finally, the challenges hindering the broader application of PEC biosensors are discussed. Future perspectives are outlined, highlighting material innovation, interfacial optimization, and device miniaturization as key directions to advance PEC biosensors toward clinical translation and portable diagnostic applications.
Photoelectrochemical Biosensors, Disease Biomarkers, Functional Materials, Clinical Diagnostics.
Optimization Study on Non-Invasive Prenatal Testing for Y Chromosome Target Concentration Using Box-Cox Transformation and K-Means Clustering
Non-invasive prenatal testing (NIPT) faces challenges in clinical practice, including difficulties in achieving target Y chromosome concentrations in high-BMI pregnant women and the lack of individualized testing timepoints. This study addresses these two critical challenges by constructing a precise nonlinear regression model and developing a timing optimization strategy. To quantify the nonlinear effects of gestational age and BMI on Y chromosome concentration, data preprocessing was performed, including mean imputation for missing BMI values and linear interpolation for gestational age to ensure data stability and continuity. Pearson correlation analysis revealed a strong positive correlation between Y chromosome concentration and gestational age (r=0.72) and a strong negative correlation with BMI (r=−0.58), both identified as critical influencing factors. To capture nonlinear relationships, this study employed Box-Cox transformation combined with stepwise regression. The constructed nonlinear regression model achieved an R² value of 0.875, with both gestational age and BMI exhibiting extremely significant effects (p=0.000), enabling precise prediction of Y chromosome concentration. Building upon this predictive framework, to determine optimal testing timepoints for pregnant women with varying BMIs, K-means clustering (optimal k=5) was employed to categorize female fetus pregnancies into reasonable groups. Clustering results were validated via ANOVA (F=124.7, p<0.001), demonstrating significant differences and clinical interpretability. Subsequently, the optimal testing window with minimal risk was determined using a piecewise risk function, constrained to ensure a pass rate ≥90%. Results indicate that the optimal timing is delayed with increasing BMI, enabling personalized timing recommendations for high-BMI pregnant women.
Non-Invasive Prenatal Testing (NIPT), Box-Cox Transformation, K-means Clustering.
Modeling and Optimization for Optimal Timing Decision of Non-Invasive Prenatal Testing Based on Multi-Model Fusion
The accuracy of non-invasive prenatal testing (NIPT) is significantly affected by individual differences among pregnant women (especially body mass index, BMI) and the gestational age at which the test is performed, making it challenging to determine an individualized optimal testing time in clinical practice. To address this issue, this study aims to develop a multi-model integrated decision framework to provide precise optimal NIPT timing recommendations for pregnant women with different characteristics. Firstly, this paper employs linear mixed-effects models and gradient boosting decision trees (GBDT) to reveal the intrinsic associations between key factors—such as gestational age at testing and BMI—and fetal Y chromosome concentration from both linear and nonlinear perspectives. Based on this, for various BMI groups, a novel multi-objective optimization model integrating genetic algorithms (GA) and particle swarm optimization (PSO) is constructed, and—combined with LASSO-Cox regression for multifactorial survival analysis—achieves a transition from “prediction” to “decision-making”. Key results show that the optimized optimal testing time plan enables detection accuracy to exceed 96% for all groups of pregnant women, with a significant reduction in high-risk probability, which dropped to 0% in some groups. For female fetal samples without a Y chromosome, this study constructs a “random forest-LSTM” dual-model collaborative determination mechanism. After addressing the issue of sample imbalance using the SMOTEENN method, the model achieved a recall rate of 1.0000 and a precision rate of 0.9939 on the test set, with a missed diagnosis rate of 0%. The multi-model decision system proposed in this study provides critical theoretical and methodological support for the clinical precision application of NIPT technology and the construction of a quality control system.
Non-invasive Prenatal Testing, Optimal Timing, Particle Swarm Optimization, Random Forest, Multi-Model Integration.
Research on Decision-Making for NIPT Testing Timing Based on Mixed-Effects Models and Risk Optimization
Noninvasive prenatal testing (NIPT), which analyzes fetal cell-free DNA in maternal blood to detect chromosomal aneuploidies early in pregnancy, faces challenges in balancing accuracy and timeliness due to the influence of individual variations such as gestational age and body mass index (BMI). To address this issue, this study proposes a dynamic modeling framework integrating clustering and machine learning to optimize the timing of NIPT and improve individualized adaptability. Using fetal Y-chromosome concentration as a core variable, we constructed individual growth curves and mixed-effects models to characterize interactions between gestational age and BMI, combined with Gaussian mixture modeling (GMM) for population stratification. By incorporating survival analysis and risk optimization functions, optimal testing windows were identified for different maternal subgroups, with model robustness verified via Monte Carlo simulations. Results demonstrate that the optimal testing window concentrates between 12–14 weeks of gestation, with the model exhibiting insensitivity to error perturbations. Further integration of multidimensional features—including maternal age, X chromosome concentration, and reproductive history—enabled feature importance analysis through random forest methods, establishing an interpretable high-dimensional clustering prediction framework. This study provides quantitative evidence to support intelligent decision-making in the scheduling and strategy of NIPT.
Non-invasive prenatal testing, Mixed-effects model, Gaussian mixture model, Risk optimization.
A Study on Factor Analysis and Timing Optimization for NIPT in High-BMI Pregnant Women Based on Linear Mixed-Effects Models and Dynamic Programming
Non-invasive prenatal testing (NIPT) is a pivotal prenatal screening tool, yet its accuracy for male fetuses depends on achieving a sufficient fetal Y chromosome concentration (fYCC), which is influenced by individual factors like maternal BMI and gestational age (GA). This study aims to resolve the clinical conflict between early detection and reliable results by optimizing the NIPT timing for high-BMI pregnant women. First, a linear mixed-effects model (LMM) was employed to identify key factors affecting fYCC, revealing significant positive correlations with GA, X chromosome concentration, and number of blood draws, and a negative correlation with BMI. Building on these findings, a personalized BMI grouping strategy was developed using a hierarchical risk mapping function optimized via a dynamic programming (DP) algorithm. This approach defined five optimal BMI intervals and determined the recommended testing time as the 90th percentile GA for fYCC attainment within each group. Error analysis demonstrated the scheme's high robustness with a detection error (σ) of ≤1%. The proposed model fusion framework provides a data-driven solution for personalized NIPT scheduling, effectively minimizing risk for high-BMI individuals.
Non-Invasive Prenatal Testing, Linear Mixed-Effects Model, Dynamic Programming.
Genome-Wide Identification and Functional Analysis of The FRA8 Gene Family in Eucalyptus Grandis
FRA8 is a key glycosyltransferase in the GT47 family that mediates the addition of glucuronic acid side chains to the structural polysaccharide xylan, playing a central role in the formation of plant secondary cell walls and the regulation of wood quality. However, the systematic characteristics of the FRA8 family in woody plants remain unclear, particularly in the fast-growing economic timber species Eucalyptus grandis. This study utilized bioinformatic methods to perform genome-wide identification, evolutionary analysis, and expression characterization of the FRA8 family in E. grandis. Results identified 31 EgrFRA8 genes. Most members contain the conserved Exostosin domain (PF03016) of the GT47 family, while one unique member carries an RXYLT1-like domain. Phylogenetic analysis categorized them into five groups, where members such as EgrFRA8_1-3 are closely related to functionally validated Arabidopsis glucuronyltransferases AtFRA8 and AtFRA8H. Cis-acting elements in promoters were enriched with light, hormone, and stress-responsive motifs, suggesting regulation by abiotic factors. RNA-seq data confirmed high expression of EgrFRA8_2, EgrFRA8_3, and EgrFRA8_26 in immature and mature xylem, suggesting involvement in wood formation. Twenty-nine EgrFRA8 members were mapped to 11 chromosomes, with segmental duplication identified as the primary driver of family expansion. This study provides a systematic analysis of the EgrFRA8 family, offering a basis for elucidating xylan synthesis mechanisms in woody plants and identifying high-priority candidate genes for the genetic improvement of wood quality in E. grandis.
FRA8 gene family; Grand eucalyptus; Whole-genome identification; Xylan- biosynthesis; Wood formation.
Construction of Injectable Tetra-Polyethylene Glycol Hydrogel Adhesive for Wound Healing
Hydrogel wound dressings are medical materials that accelerate wound healing by maintaining a moist environment, promoting tissue repair, and absorbing exudate. Its core efficacy is based on the material's high-water content (typically 60%-90%) and biocompatibility, which can provide ideal repair conditions for wounds while reducing the risk of complications. In this work, we constructed a class of tetra-polyethylene glycol hydrogel systems based on rapid chemical reactions, with a fast gelation rate, significant mechanical strength, and sufficient adhesion. Cytotoxicity tests demonstrated that the hydrogels had good biocompatibility, the hydrogel adhesives did not cause secondary damage to the surrounding tissues, and could promote tissue repair and regeneration, providing ideas for the development of a new generation of bioadhesives.
Hydrogel, strong tissue adhesion, microstructure, mechanical properties, tissue binder.
Preparation, Characterization and Application of Injectable Tissue Bioadhesives
Hydrogel are medical wound dressings for accelerating wound healing, because it can maintain a moist environment, promote tissue regeneration, and absorb wound exudates, which provide a favorable microenvironment for tissue repair while reducing the risk of complications. In this work, aminated gelatin and four-arm polyethylene glycol activated ester were employed to construct an injectable and mechanically robust bioadhesive. After optimization, the bioadhesive exhibits good handling properties, rapid gelation, and excellent biocompatibility. It can firmly adhere to skin tissue surfaces and effectively promote wound healing, demonstrating that this material is a convenient, effective, and biosafe tissue adhesive. Notably, the bioadhesive’s overall performance is significantly superior to that of commercially available fibrin glue. This study provides a new strategy for development of next-generation tissue bioadhesives.
Whole-Blood Transcriptomic Profiling Unveils An HERV-Interferon Axis as A Novel Immunological Signature in Latent Autoimmune Diabetes in Adults (LADA)
Latent Autoimmune Diabetes in Adults (LADA) is characterized by progressive autoimmune beta-cell destruction, yet its upstream molecular triggers remain elusive. In this study, we utilized whole-blood RNA sequencing to investigate the global transcriptomic and endogenous virome landscapes of LADA patients. Principal Component Analysis revealed a robust systemic separation driven by both host immune gene remodeling and a distinct human endogenous retrovirus (HERV) expression signature. Differential expression profiling uncovered widespread host immune dysregulation coupled with a highly targeted and bidirectional modulation of specific HERV loci, including the profound upregulation of distinct viral elements alongside the significant downregulation of others. Crucially, functional enrichment demonstrated a profound "viral mimicry" state, characterized by overwhelming antiviral innate immune activation and Type I interferon (IFN) signaling, despite the absence of exogenous viral infection. Gene Set Variation Analysis (GSVA) further confirmed the Type I IFN storm as the singular, significantly upregulated systemic module in peripheral circulation. Finally, a highly stringent Spearman co-expression network and node degree analysis directly linked specific upregulated HERV hubs (e.g., loci 1371) to core IFN pathway integrators and molecular switches (e.g., IFI27, USP18, ISG15, IFITM3). Together, our findings provide compelling bioinformatic evidence that specific HERV activation serves as a crucial upstream trigger orchestrating the pathogenic interferon cascade in LADA.
LADA, ERV, Type 1 Interferon, Autoimmune, Transcriptome.
3D-Printed Functional Materials for Cell Culture and Tissue Engineering: Fabrication Strategies and Applications
3D printing technology offers a precise and highly customizable approach for constructing biomimetic three-dimensional tissue engineering scaffolds, largely overcoming the limitations of traditional manufacturing methods in balancing complex microstructures with individual needs. This article systematically reviews the latest advancements in 3D-printable functional materials for cell culture and tissue engineering, focusing on three levels: fabrication concepts, functionalization strategies, and practical applications. Firstly, it examines the key "inks" for 3D printing, comparing the printing characteristics and biocompatibility of natural and synthetic polymers, reversible-dynamic hydrogels, and various inorganic materials, and discusses how to balance printability with scaffold mechanical strength through blending in different ratios or layered structures. Subsequently, the article summarizes strategies to actively regulate cell adhesion, proliferation, differentiation, and even the immune microenvironment by fine-tuning physical structures (pore size gradient, channel orientation, surface roughness, etc.) and chemical modifications (covalent immobilization of peptides, growth factors, or calcium/phosphorus ions, etc.). Based on this, it further illustrates the core application pathways of this technology in constructing highly biomimetic in vitro models—such as tumor microenvironment-on-a-chip, liver or intestinal organoids—and in accelerating the regeneration and repair of bone defects, skin, muscle, and other soft tissues. Although challenges remain regarding the balance between printing resolution and speed, scaffold functional maturation cycles, large-scale quality control, and clinical translation pathways, deep integration with smart responsive materials and immunomodulatory design concepts holds the potential for 3D printing to push "tailor-made, truly functional" artificial tissues into the clinic, bringing substantial breakthroughs to regenerative medicine.
3D printing, Cell culture, Tissue engineering, porous materials, Bone repair.
Isoform-Specific Divergent Roles of Class I HDACs in Cardiac Hypertrophic Remodeling
Class I histone deacetylases are emerging targets to manage cardiac hypertrophy. To delineate their individual roles, we used siRNA to knock down each HDAC isoform in human AC16 cardiomyocytes, in the presence or absence of angiotensin II (AngII). We found that HDAC1 and HDAC3 silencing could effectively reduce MYH7 expression and partially reverse cellular enlargement, whereas HDAC2 knockdown surprisingly exacerbated hypertrophy. Cross-isoform compensatory effects were also observed. RNA sequencing revealed distinct gene regulation profiles driven by different HDAC inhibitions. These results suggest that Class I HDAC isoforms mediate distinct, sometimes opposing, effects on cardiac hypertrophy.
cardiac hypertrophy, Class I HDAC, knockdown, siRNA.
Pathway-Derived Features for Machine Learning-Based Prediction of Multidrug Resistance in Pseudomonas Aeruginosa
Antimicrobial resistance (AMR) in Pseudomonas aeruginosa poses a critical threat to healthcare, particularly due to surgical site infections. Current phenotypic testing methods are time-consuming, while existing antimicrobial resistance gene-based machine learning approaches suffer from limited data availability, poor multi-antibiotic prediction performance, and lack of interpretability. This study presents a novel framework using KEGG pathway-derived features to predict multidrug resistance in P. aeruginosa. We annotated whole genome sequences to identify AMR-related metabolic and regulatory pathways, then evaluated these pathway-based features across multiple algorithms, including traditional machine learning methods like logistic regression, random forest, and KNN; deep learning models like MLP, CNN, LSTM, and GRU; and the tabular foundation model TabPFN. All algorithms achieved an accuracy above 0.85, demonstrating the robustness of using pathway-derived features for multi-drug AMR prediction. TabPFN showed superior performance with an accuracy of 0.926, successfully detecting resistance phenotypes across multiple antibiotics simultaneously while providing interpretable insights. In conclusion, the pathway-based features capture complex biological mechanisms underlying resistance beyond simple gene presence/absence, offering a meaningful framework for rapid resistance profiling. We hope this method enables accurate antibiotic selection in clinical settings, potentially reducing inappropriate antibiotic use and combating the spread of the AMR phenomenon.
Antimicrobial resistance (AMR); Pseudomonas aeruginosa; KEGG pathway-based features; Machine learning, TabPFN
Deep Learning Framework for RNA Inverse Folding with Geometric Structure Potentials
RNA’s functional diversity arises from its rich three-dimensional structural landscape, yet designing RNA sequences that adopt a target 3D conformation (inverse folding) remains a fundamental challenge. I present a geometric deep learning framework that enables end-to-end RNA inverse folding by integrating Geometric Vector Perceptron (GVP) layers into a Transformer architecture, thereby explicitly encoding the spatial and directional features of RNA structures. A curated dataset of experimentally solved RNA 3D structures was constructed from the BGSU RNA database through rigorous filtering and deduplication. Model performance was evaluated using sequence recovery rate and TM-score to assess sequence accuracy and structural fidelity, respectively. Across standard benchmarks and RNA-Puzzles, the proposed method achieves state-of-the-art performance, improving the recovery rate by 85% and the TM-Score by 40% compared to existing approaches. Masked family-level validation using Rfam annotations demonstrates strong generalization to unseen RNA families, with a 132% improvement in generalization performance. Furthermore, inverse-folded sequences refolded using AlphaFold3 closely recapitulate native structures, underscoring the importance of explicit geometric representations in RNA design. This work establishes a “3D-aware” paradigm for RNA inverse folding, enabling the rapid generation of structurally valid RNA sequences in hours rather than months. By combining geometric learning with sequence modeling, the framework offers a scalable foundation for accelerating mRNA vaccine development, RNA-based therapeutics, and synthetic biology, and positions geometric deep learning as a key technology for next-generation biomolecular design.
Geometric Vector Perceptron; Transformer; Deep Learning
Active Targeting Design and Preclinical Progress of Liposome-Based Nanocarriers for Cancer Therapy
Cancer remains a critical threat to human health, yet its clinical treatments are severely hindered by multiple limitations, such as solid tumor physical barriers, poor drug solubility, short in vivo circulation half-life, acquired drug resistance, and non-targeted side effects. These challenges have underscored the urgency of developing advanced drug delivery systems to improve therapeutic efficacy and safety. This paper focuses on actively targeted nanoliposomes as a promising strategy to overcome the drawbacks of traditional cancer therapies and passive targeting liposomes. Unlike passive targeting relying on the EPR effect, actively targeted liposomes are modified with ligands (antibodies, aptamers, peptides, etc.) that specifically bind to overexpressed receptors on tumor cells, thereby enhancing tumor cell recognition, endocytosis, and drug accumulation at lesion sites while reducing normal tissue toxicity. The paper further outlines two major cellular entry pathways of these liposomes (membrane fusion and endocytosis) and highlights that carrier physical properties determine the dominant entry route. Additionally, it summarizes key bottlenecks in clinical translation, including long-term safety validation, blood-brain barrier penetration, antigen/receptor heterogeneity, and large-scale manufacturing, as well as corresponding optimization frameworks for scenario-based delivery, immunoliposome construction and industrialization. This paper provides a comprehensive reference for advancing the clinical application of actively targeted liposomes in cancer therapy.
Cancer therapy; liposome; nanoparticles.
Mechanistic Roles of Gut Microbiota Regulation in Cancer Immunotherapy
Cancer immunotherapy has become an important approach in the treatment of many malignant tumors, especially with the development of immune checkpoint inhibitors. However, clinical responses vary substantially, and a considerable number of patients gain limited benefits or develop resistance during treatment. Recent evidence indicates that the gut microbiota is a crucial factor underlying such heterogeneous therapeutic outcomes. This paper focuses on the role of gut microbiota in tumor immunotherapy by summarizing the latest findings regarding its involvement in immune regulation. The gut microbiota modulates immune system development, reshapes the tumor microenvironment and affects responses to immune checkpoint blockade. Specific microbial communities correlate with enhanced anti-tumor immunity and improved therapeutic efficacy, whereas microbial dysbiosis induces immunosuppression and attenuates treatment effects. Microbial metabolites also act as key mediators linking the gut microbiota and host immunity. Potential strategies to improve immunotherapeutic outcomes via microbiota modulation are also outlined, including fecal microbiota transplantation, probiotic supplementation and dietary intervention. Elucidating gut microbiota-immune interactions holds great promise for optimizing effective and personalized cancer immunotherapy strategies.
Challenges of EGFR Exon 20 Insertion Targeted Therapy in Non-Small Cell Lung Cancer
Epidermal growth factor receptor (EGFR) exon 20 insertion mutations are a distinct molecular subtype of non-small cell lung cancer that inherently resists conventional therapies and has poor clinical outcomes. However, with recent advancements in molecular profiling and drug development, several emerging targeted therapies possess the potential to overcome these obstacles partially. This paper summarizes the biological and structural features of EGFR ex20ins mutations and evaluates the currently available targeted therapies, most notably Amivantamab, Mobocertinib and Poziotinib, with respect to clinical efficacy and safety. While Amivantamab has shown encouraging clinical trial data compared with the other two, several challenges remain, including drug heterogeneity, treatment-related toxicity, acquired resistance and diagnostic limitations. The results of this paper imply that future directions should prioritize the development of more selective inhibitors, combination therapies and improved mutation-detection strategies. This paper also indicates that continued translational and clinical research will be essential to optimize therapeutic outcomes and establish more durable, personalized treatment for patients with EGFR ex20ins mutant NSCLC.
Non-small-cell lung cancer; EGFR exon 20 insertion; Amivantamab; Mobocertinib; Poziotinib.
Therapeutic Strategy and Resistance Mechanism of PD-L1 Monoclonal Antibody in Precision Population of Gastric Cancer
PD-L1 is a core inhibitory immune checkpoint that finely regulates immune homeostasis. PD-L1 is closely linked to the pathogenesis of autoimmune diseases and malignant tumors, especially tumor immune escape, making it a pivotal target for cancer immunotherapy. This paper focuses on the biological functions of this immune checkpoint pathway, how to regulate the progression and metastasis of gastric cancer, and real-world use of PD-L1 monoclonal antibodies as well as their resistance mechanisms and safety profiles. Atezolizumab combined with chemotherapy or trastuzumab-based targeted therapy significantly improves the pathological complete response rate in gastric cancer patients. PD-L1 CPS ≥5 is an indicator used to judge the therapeutic effect. Durvalumab exhibits robust therapeutic benefits in neoadjuvant/adjuvant therapy, second-line treatment, and maintenance therapy for advanced gastric cancer, particularly in patients with high PD-L1 expression. Therapeutic resistance to PD-L1 inhibitors involves multiple factors including T-cell exhaustion, myeloid cell infiltration, exosomal PD-L1 secretion, and post-translational modifications of PD-L1. Immune-mediated toxicities arising from PD-L1 inhibitor therapy are manageable and not correlated with PD-L1 expression levels. This paper comprehensively summarizes the advances in PD-L1-targeted immunotherapy for gastric cancer, providing valuable insights for optimizing clinical treatment strategies and exploring novel solutions to overcome drug resistance.
Gastric cancer; PD-L1; tumor immunotherapy; ICI; CPS.
Current and Future Perspectives of CAR T-Cell Therapy in Pancreatic Cancer
One of the deadliest cancers in the world is pancreatic cancer with contributing factors resulting from late diagnosis, rapid disease progression, and limited effective therapies. Fortunately, in recent years, immunotherapies like chimeric antigen receptors (CAR T-cells) have become very effective and successful in the treatment of hematologic cancers. With these favorable results, researchers are focusing on using CAR T-cells for solid cancers. This review examines some of the recent pre-clinical and clinical studies conducted over the past few years on promising targeted antigens for CAR T-Cells in pancreatic cancer, including but not limited to mesothelin, Claudin18.2, CD318, and PSCA. While these antigen CAR T-Cells showed positive results, multiple limitations reduce the effectiveness of the treatment. These major barriers include suppressive tumor microenvironment (TME), antigen loss and heterogeneity, or toxicity. Nonetheless, researchers continue to investigate new target antigens and strategies that could overcome these barriers and enhance the therapy’s safety and outcome like novel CAR designs.
Drug Response Prediction using Deep Learning Models and Single-Cell Perturbation Data across Pan Cancer Cell Lines
Cancer remains a leading cause of death. Effective treatments remain limited, because of complications during therapies that contribute to poor survival outcomes. Developing new drugs can be expensive and time-consuming. One promising approach is drug repurposing by identifying potent treatments from existing drugs. This study aims at developing an efficient and effective framework for drug efficacy prediction. To achieve this goal, this study first processed publicly available drug perturbation cancer cell line datasets to enhance their usefulness for drug repurposing to cancer, in which this paper integrated perturbed pan cancer cell line gene expression data with their drug responses. This study integrated 10 selected cancer cell line data covering 8 cancers from public databases including CMAP, CTRP, CCLE, and GDSC. Secondly, this study applied advanced machine learning methods, particularly deep learning to learn useful features from these data, and transfer learning to make the predictive models generalized and interpretable. With deep learning and these datasets, this study developed a highly accurate deep neural network framework (DeepDrugCancer) that combines the drug molecular structures and gene expression data of cancer cell lines to predict the drug responsiveness. DeepDrugCancer achieved excellent performance with over 90% AUC test score across multiple cell lines. By integration with scRNA-seq data of breast cancer, predicted drugs such as aminocaproic acid, pancuronium bromide, and pseudopelletierine showed high cell-type specificity. Finally, this study conducted in-vitro (wet lab) experiments to validate the viability of DeepDrugCancer. The model predicted Mifanertinib to be sensitive on breast cancer cell lines and the in-vitro experiments confirmed the trend of such effects. The experiments have demonstrated that this framework efficiently utilizes drug-induced molecular signatures to predict therapeutic responses across certain cancers. By linking transcriptomic perturbations to drug responses, this study advances drug repurposing to expanded therapeutic options for cancer treatments as well as to precision oncology.
Drugs repurpose, machine learning, transcriptomics, cancer.
Rapamycin and Rapalogs in Cancer Therapy
mTOR signaling is frequently upregulated in cancer and supports tumor growth by promoting protein synthesis, metabolism and survival programs. Rapamycin and rapalogs, as they inhibit the mTOR pathway, remain important anticancer agents. This paper summarizes how rapamycin-centered cancer treatment strategies can inhibit mTORC1 outputs and explains why pathway inhibition does not always translate into significant tumor control. Mechanistically, rapamycin reduces mTORC1-dependent translation signals, but the clinical effect varies because tumors differ in pathway dependence and can engage compensatory circuits. Key determinants of heterogeneous response include feedback activation within the mTOR network, incomplete suppression of mTOR-complex functions, and autophagy. Evidence from tumor studies shows that rapalogs can achieve tumor suppression in most cases, yet these effects are often limited. Preclinical findings, on the other hand, support that cellular context can determine whether mTORC1 inhibition slows proliferation or is bypassed by alternative growth pathways. In conclusion, the paper argues that rapamycin is most useful when used with biomarker guidance and combinations that anticipate feedback and adaptive responses. These results motivate future work focused on improving patient selection and clarifying tumor-specific resistance mechanisms.
Evolutionary Mechanisms of Cancer Drug Resistance and Their Implications for Adaptive Therapy
Cancer treatment outcomes are fundamentally limited by the evolutionary processes that enable drug resistance to emerge and expand within heterogeneous tumour ecosystems. This essay explains how genetic changes, cellular and molecular adaptations, tumour microenvironment impacts, and reversible non-genetic processes that enable subclones to withstand therapeutic pressure all contribute to the development of resistance. Together, these several mechanisms show that the therapy itself functions as a selection agent, encouraging the dominance of resistant populations while eradicating sensitive rivals. Evolution-informed therapy approaches have been developed to control tumour populations rather than destroy them in response to these obstacles. This change is best shown by adaptive treatment, which modifies medication dosage to protect sensitive cells and decrease resistant ones, extending disease control and lowering toxicity. Drug cycling, collateral sensitivity exploitation and evolutionary steering are among the tactics that show how changing selection pressures can restrict or delay resistance. To incorporate evolutionary ideas into oncology, the paper underlines the need for improved surveillance, biomarker discovery and interdisciplinary collaboration. It also presents clinical and computational data that support these methods. All things considered, acknowledging cancer as a dynamic and changing system reframes treatment planning and facilitates a shift from maximum cytotoxicity to long-term ecological management, providing a potential route towards more long-lasting therapeutic results.
Cancer; cancer resistance; adaptive therapy.
Research on the Mechanism of Covalent Interactions in Targeted Drug Design
Covalent interactions are one of the important intermolecular forces and play a significant role in the design of targeted drugs. This article provides a detailed account of the role covalent interactions play in drug-target binding and the thermodynamic and kinetic characteristics of covalent bond formation. By reviewing the relevant literature and combining with theoretical analysis, the differences in pharmacodynamics and pharmacokinetics between covalent inhibitors and traditional non-covalent inhibitors were compared. The results showed that covalent interactions could significantly enhance the binding affinity and specificity of the drug to the target protein, thereby prolonging the drug's action time and reducing the number of dosing times. And because the covalent bond is irreversible, it can avoid conformational changes of the target protein and competitive inhibition, and has great advantages in tumor treatment, etc. However, covalent drugs can also bring about problems such as off-target toxicity and immunogenicity. This article also summarizes several currently successful covalent targeted drugs, such as afatinib, osimertinib and other EGFR covalent inhibitors, and summarizes their design ideas and improvement schemes. Some design principles and development directions of covalent drugs were proposed in terms of the selection of reactive groups, the design of linkers, the properties of target amino acid residues, etc. The findings suggest that the rational use of covalent interactions can significantly increase the success rate of drug design, which is of great significance for the development of highly effective and low-toxicity targeted therapeutic drugs.
Covalent interactions; Targeted drug design; Molecular mechanisms; Drug-target binding; Covalent inhibito.