Saccharomyces cerevisiae is a cornerstone organism in industrial biotechnology, valued for its genetic tractability and robust fermentative capacity. Accurately predicting biomass flux across diverse environmental and genetic perturbations remains a significant challenge for rational strain design. We present a computational framework combining the Yeast9 genome-scale metabolic model with machine learning and optimization to predict, interpret, and enhance biomass flux. Flux balance analysis generated 2,000 flux profiles by varying glucose, oxygen, and ammonium uptake rates. Random Forest and XGBoost regressors achieved R2 of 0.99989 and 0.9990, respectively. A variational autoencoder revealed four distinct metabolic clusters, and SHAP analysis identified glycolysis, the TCA cycle, and lipid biosynthesis as key biomass determinants. In silico overexpression achieved a biomass flux of 0.979 gDW/hr, while Bayesian optimization of nutrient constraints produced a 12-fold increase (0.0858 to 1.041 gDW/hr). A generative adversarial network proposed stoichiometrically feasible novel flux configurations. This framework demonstrates how genome-scale simulation, interpretable ML, and generative modeling can advance yeast metabolic engineering.
Carbon dots (CDs) are a class of nanobiomaterials with significant potential in bone regeneration. Their excellent biocompatibility, tunable fluorescence, high stability, low toxicity, and abundant functional groups make CDs promising candidates for efficient drug delivery and bone tissue regeneration. CDs contribute to targeted drug release, enhance osteogenic differentiation, and interact with cellular components to facilitate bone formation. Recent research highlights the roles of CDs in scaffold-based approaches, offering controlled drug delivery and real-time bioimaging capabilities. This review provides a comprehensive overview of CDs in bone regeneration, with a focus on their synthesis, functionalization, and biomedical applications. It begins by exploring CD synthesis methods, physicochemical properties, and mechanisms of action. Next, it discusses CD-based drug delivery systems and their applications in bone regeneration. Finally, the review highlights the challenges and future perspectives in optimizing CDs for enhanced therapeutic outcomes.
Flávio Rodrigues, Mariana Fernandes, Filipe Samuel Silva
et al.
In the pursuit of addressing the persistent challenge of bacterial adhesion and biofilm formation in dental care, this study investigates the efficacy of electric current as an alternative strategy, specifically focusing on its application in dental contexts. Polyether ether ketone (PEEK), known for its excellent biocompatibility and resistance to bacterial plaque, was enhanced with conductive properties by incorporating silver (Ag), a well-known antibacterial material. Through systematic in vitro experiments, the effectiveness of alternating current (AC) and direct current (DC) in reducing bacterial proliferation was evaluated. The tests were conducted using two bacterial strains: the Gram-positive <i>Staphylococcus aureus</i> and the Gram-negative <i>Pseudomonas aeruginosa</i>. Various configurations, current parameters, and two different electrode configurations were assessed to determine their impact on bacterial reduction. A notable finding from this study is that alternating current (AC) demonstrates superior efficacy compared to direct current (DC). The more significant decrease in CFUs/mL for <i>P. aeruginosa</i> with AC was recorded at the current levels of 5 mA and 500 nA. In opposition, <i>S. aureus</i> exhibited the greatest reduction at 5 mA and 1 mA. This study highlights the potential of using electric current within specific intensity ranges as an alternative strategy to effectively mitigate bacterial challenges in dental care.
BackgroundScrew fixation is pivotal for prosthetic stability. For 3D-printed customized acetabular revision prostheses designed for complex, large-scale bone defects, precise adherence to preoperative screw trajectory planning is critical. However, there remains a lack of standardized three-dimensional (3D) evaluation protocols to quantify intraoperative screw angular alignment fidelity relative to preoperative digital plans, hindering universal validation criteria.MethodsA total of 11 patients were stratified into two groups based on postoperative Harris Hip Scores and severe complication rates: the better outcome group and the regular outcome group. A 3D pelvic coordinate system was established using anatomical landmarks. Two biomechanically critical screws per prosthesis were selected to quantify 3D angular deviations between postoperative and preoperative plans. The efficacy of this method was compared with simulated anteroposterior radiograph-based 2D measurements. Inter- and intra-observer consistency (kappa statistics) were evaluated to assess reproducibility.ResultsHigh inter-observer (κ = 0.88) and intra-observer (κ = 0.87, 0.75) agreement confirmed method reliability. While individual critical screw deviations showed no significant intergroup differences, the cumulative angular deviation of critical screws was significantly lower in the better outcome group (p = 0.0147). In contrast, 2D radiographic analysis failed to distinguish intergroup differences in cumulative deviations (p = 0.1489), demonstrating reduced clinical relevance.ConclusionThis 3D assessment protocol robustly correlates with clinical outcomes, providing a validated tool for evaluating preoperative-to-postoperative fidelity in customized acetabular revision prostheses. To our knowledge, this is the first CT-based 3D coordinate system study quantifying critical screw alignment accuracy in patient-specific prostheses, with clinical validation.
AI-assisted protein design has emerged as a critical tool for advancing biotechnology, as deep generative models have demonstrated their reliability in this domain. However, most existing models primarily utilize protein sequence or structural data for training, neglecting the physicochemical properties of proteins.Moreover, they are deficient to control the generation of proteins in intuitive conditions. To address these limitations,we propose CMADiff here, a novel framework that enables controllable protein generation by aligning the physicochemical properties of protein sequences with text-based descriptions through a latent diffusion process. Specifically, CMADiff employs a Conditional Variational Autoencoder (CVAE) to integrate physicochemical features as conditional input, forming a robust latent space that captures biological traits. In this latent space, we apply a conditional diffusion process, which is guided by BioAligner, a contrastive learning-based module that aligns text descriptions with protein features, enabling text-driven control over protein sequence generation. Validated by a series of evaluations including AlphaFold3, the experimental results indicate that CMADiff outperforms protein sequence generation benchmarks and holds strong potential for future applications. The implementation and code are available at https://github.com/HPC-NEAU/PhysChemDiff.
Christoph Lange, Simon Seidel, Madeline Altmann
et al.
This study presents a collection of physical devices and software services that fully automate Raman spectra measurements for liquid samples within a robotic facility. This method is applicable to various fields, with demonstrated efficacy in biotechnology, where Raman spectroscopy monitors substrates, metabolites, and product-related concentrations. Our system specifically measures 50 $\micro L$ samples using a liquid handling robot capable of taking 8 samples simultaneously. We record multiple Raman spectra for 10s each. Furthermore, our system takes around 20s for sample handling, cleaning, and preparation of the next measurement. All spectra and metadata are stored in a database, and we use a machine learning model to estimate concentrations from the spectra. This automated approach enables gathering spectra for various applications under uniform conditions in high-throughput fermentation processes, calibration procedures, and offline evaluations. This allows data to be combined to train sophisticated machine learning models with improved generalization. Consequently, we can develop accurate models more quickly for new applications by reusing data from prior applications, thereby reducing the need for extensive calibration data.
In the age of artificial intelligence and biotechnology, a unified understanding of technology and biology is critically needed but still lacking. A cornerstone of such unification is evolvable design. I present a formalism, called goal assembly, that unifies three key characteristics of evolvable, biology-like design: (i) hierarchical integration of small competencies into larger competencies; (ii) highly non-uniform (power-law or log-normal distributed) representation of phenotypes by genotypes; and (iii) evolution of hierarchical modularity. It does so by making the hierarchy of physical goal states corresponding to substructures explicit, focusing on their composition across scales. In particular, higher-level goal variables approximate achievable joint goal states of interacting lower-level goal variables. Mechanisms of evolvability include goal state gradient backpropagation across scales, hierarchical decision making among alternative goal states, and structural recombination of modules. In learning theory terms, goal assembly proposes architectural inductive biases of emergent engineering systems operating at a complexity regime where goals arise as necessary abstractions. Beyond asking what goals are and how they are achieved, goal assembly suggests we can almost independently talk about their compositionality.
Bioconvection, a phenomenon arising from the collective motion of motile microorganisms, plays a crucial role in shaping microbial distributions, fluid dynamics, and bloom formation in aquatic environments. While extensive research has explored bioconvection in open waters, a significant proportion of microbial life exists within porous habitats, including soils, sediments, and subsurface environments, where the interplay between microbial motility and structural heterogeneity remains less understood. This review synthesizes recent advancements in the study of bioconvective dynamics within porous media, emphasizing the impacts of various external parameters on microbial transport, and spatial organization in structured environments. Additionally, we explore the various scenarios where bloom formation interacts with porous ecosystems. We further discuss the implications of microbial interactions with porous substrates for bioremediation, health management, and industrial applications. A dedicated section explores genetic engineering approaches aimed at modulating microbial motility and growth characteristics to enhance bioconvective behaviour and bloom regulation. By integrating experimental, numerical, and theoretical insights, this review highlights key knowledge gaps and outlines future research directions toward a deeper understanding of microbial transport and bioconvection in porous systems, with potential applications in environmental sustainability and biotechnology.
The enzyme turnover rate is a fundamental parameter in enzyme kinetics, reflecting the catalytic efficiency of enzymes. However, enzyme turnover rates remain scarce across most organisms due to the high cost and complexity of experimental measurements. To address this gap, we propose a multimodal framework for predicting the enzyme turnover rate by integrating enzyme sequences, substrate structures, and environmental factors. Our model combines a pre-trained language model and a convolutional neural network to extract features from protein sequences, while a graph neural network captures informative representations from substrate molecules. An attention mechanism is incorporated to enhance interactions between enzyme and substrate representations. Furthermore, we leverage symbolic regression via Kolmogorov-Arnold Networks to explicitly learn mathematical formulas that govern the enzyme turnover rate, enabling interpretable and accurate predictions. Extensive experiments demonstrate that our framework outperforms both traditional and state-of-the-art deep learning approaches. This work provides a robust tool for studying enzyme kinetics and holds promise for applications in enzyme engineering, biotechnology, and industrial biocatalysis.
Fernanda Fernandes Coan, Sabrina de Sousa Campelo, Caio Brandão e Vasconcelos
et al.
Trata-se de uma revisão integrativa que busca enumerar e compreender pelas revisões de literatura as circunstâncias que permeiam o pós-operatório da fratura de fêmur. A revisão do processo baseou-se nas recomendações da lista de conferência do Rayyan Enterprise, Faster Sistematic Review. Foram examinados artigos publicados entre 2010 e 2023 que fizessem estudo qualitativo relacionado à fratura de quadril em idosos. As buscas ocorreram em agosto nas bases de dados Pubmed, MEDLINE, Scopus e Capes. A seleção compreendeu três etapas: busca, pré-seleção e inclusão de artigos. Foram utilizados os seguintes descritores em ciências da saúde (DeCS): (hip fracture) AND (elderly) AND (qualitative). Foram identificados 41 artigos nas bases de dados pesquisadas através das estratégias de busca. Após leitura dos títulos e resumos, 25 artigos foram considerados potencialmente elegíveis para inclusão no estudo e foram recuperados para leitura na íntegra. Após a leitura completa, os 9 artigos foram selecionados mediante aplicação dos critérios de inclusão e exclusão estabelecido. O pós-operatório da fratura de fêmur em idosos é um momento frágil que gera a experiência de uma nova rotina e a incerteza de uma recuperação de suas atividades fisiológicas do passado. A concepção dos profissionais de saúde e cuidadores são decisivos para sua melhor recuperação.
The production of food packaging membranes with antibacterial activity is of great significance because it can inactivate bacteria in food and protect the human body from food-borne diseases. Herein, a novel polyacrylonitrile (PAN)/cellulose acetate (CA) composite nanofibers membrane with citral as an antibacterial agent was fabricated by utilizing electrospinning technology. Subsequently, the PAN/regenerated cellulose (RC)/citral composite nanofibers membrane was obtained through an alkaline hydrolysis process and citral grafting modification strategy. At the same time, the preservation efficacy of this membrane in refrigerated chicken breast was investigated. Results indicate that the PAN/RC/citral composite nanofibers membrane, modified by grafting citral, exhibits uniform fiber diameter, favorable morphology, and excellent mechanical properties. Moreover, citral crosslinks with RC components in fiber membranes significantly reduce the total bacterial count and total volatile basic nitrogen value in chicken breast during the packaging and storage process, thereby extending the shelf life of refrigerated chicken breast. This research provides a new approach to the production of antibacterial food packaging films and demonstrates their broad potential application value in the field of food packaging.
Abstract Catalytic hairpin assembly (CHA)-DNA walker allows nanostructures to spontaneously hybridize to the nucleic acids. The localized surface plasmon resonance provides the ability of color-shift for Au nanoparticles (AuNPs) to design a colorimetric biosensor by implementing CHA-DNA walker reaction on AuNPs. A target gene in Klebsiella pneumoniae as the reaction cascade trigger, was selected. H1 and H2 oligonucleotides as the components of the system were designed and verified by NUPACK. The AuNPs were conjugated to H1. The conjugation of the probes to the AuNPs was evaluated using FT-IR. The signal amplification process was conducted at 25℃. TEM imaging, zeta potential, spectroscopy, and gel-electrophoresis were used to examine the conduction of the reaction cascade and specificity. The sensitivity of the method was analyzed using serial dilution of the target. The formation of over-52 bp intermediate secondary structures (which only exist when the reaction happens) was confirmed by gel-electrophoresis. The color distinction between positive (0.08 to 0.058) and negative samples (0.098 to 0.05) was evidenced instantly and in a period of 90 min of the reaction as a drop change of 520 nm intensity absorbance. TEM imaging confirmed the further distance of AuNPs in the positive sample in comparison to that of the negative sample which reveals effective detection of the pathogen. The LOD of the technique was measured as 2.5 nM of the target sequence. The diagnostic approach is a label-free, enzyme-independent approach and can be executed in a single step. It has been designed by employing the CHA-DNA walker system along with the colorimetric properties of AuNPs for the first time, thereby paving the way for more rapid and accurate diagnostic kits.
Aaliyawani Ezzerin Sinin, Sinin Hamdan, Khairul A. Mohamad Said
et al.
This work was conducted using the Picoscope signal extraction procedure, which revealed significant insights regarding the belian wood and its application in Gendang Melayu Sarawak (GMS) production. The amplitude of belian wood GMS signal remains constant, allowing it to sustain its timbre for a longer duration compared to durian wood GMS using the same procedure. Considering that the dimensions of the big belian (BB) and big durian (BD) GMS are almost the same, both GMS yield almost the same note, i.e. G1# (51.9 Hz). Considering that the dimensions of both the small belian (SB) and small durian (SD) GMS are almost the same, both GMS yield almost similar note, i.e. F3 (174 Hz) and E3 (164 Hz). Although both BB and BD showed consistent harmonics, BD only displays 2 harmonics. The SB and SD both display consistent harmonics. Both BB and BD showed pleasing tonal qualities. These occurred due to the closeness of the principal overtones to the consonant interval.