Search Results (139)

Three common agricultural beneficial microbes, Trichoderma harzianum, Bacillus cereus, and Pseudomonas fluorescens, are widely used in the growth cycle of crops, and increase the yield of agricultural products through disease prevention and sterilization. As a biodegradable biological macromolecular material, polylactic acid (PLA) is also widely used in agricultural production as a biodegradable film. The addition of agricultural microbes will affect the degradation rate of polylactic acid and thus its agricultural use. Under specific conditions (Tri15), the degradation rate of PLA film exceeds 30%. Scanning electron microscopy (SEM) images show that the degradation of the PLA happened after 360 days of exposure to these three specific microbe environments, which makes the surface of PLA films crack. Gel permeation chromatography (GPC) analysis reveals that in the presence of these microbes, the molecular weight of PLA is reduced. The analysis of 16S rDNA sequences demonstrates that the introduction of these microbes alters the soil microbial community, resulting in an enhanced abundance of Betaproteomicrobes, promoting the degradation of PLA. These results indicate that the three microbes species significantly promote the degradation of PLA, and the effects of microbes vary for the different concentrations. This study establishes practical guidelines for the deployment of PLA in real-world farmland environments. Full article

The FDA plans to gradually replace animal testing with organoid and organ-on-a-chip technologies for drug safety assessment, driving surging demand for gut-on-a-chip in food and drug safety evaluation and highlighting the need for efficient, precise chip designs. Oxygen gradients are central to these devices because they shape epithelial metabolism, microbial co-culture, and overall gut homeostasis. We coupled machine learning with finite element analysis to build a parametric COMSOL Multiphysics model linking channel geometry, transport coefficients, and cellular oxygen uptake to the resulting oxygen field. For numerical prediction, three models—Random Forest (RF), XGBoost, and MLP—were employed, with XGBoost achieving the highest accuracy (RMSE = 1.68%). SHAP analysis revealed that medium flow rate (39.7%), external flux (26.9%), and cellular oxygen consumption rate (24.8%) contributed most importantly to the prediction. For oxygen distribution mapping, an innovative Boundary-Guided Generative Network (BG-Net) model was employed, yielding an average concentration error of 0.012 mol/m³ (~4.8%), PSNR of 33.71 dB, and SSIM of 0.9220, demonstrating excellent image quality. Ablation experiment verified the necessity of each architectural component of BG-Net. This pipeline offers quantitative, data-driven guidance for tuning oxygen gradients in gut-on-a-chip. Future work will explore extensions including real experimental data integration, real-time prediction, and multi-task scenarios. Full article

This study evaluated the combined effect of the modified atmosphere packaging (MAP1: 60% CO₂, 10% O₂/30% N₂ & MAP2: 40% CO₂, 30% O₂/30% N₂), and active packaging of oregano essential oil (1% v/w) used as a natural preservative, on the quality and shelf-life extension of fresh sea bream fillets. The samples were stored at four different temperatures (0, 4, 8, and 12 °C), and a microbiological analysis, pH measurements, and sensory evaluations were performed. In parallel, spectral data were obtained using three different spectroscopic sensors (two MultiSpectral Imaging devices and an FT-IR one), and nine different machine-learning regression models were applied to predict the microbiological counts. Oregano essential oil positively affected preservation, reducing microbial growth by 0.5 to 2 log CFU/g, and extending the fillets’ shelf life by up to 48 h based on sensory evaluation. Regarding the sensors’ data, the examined nine models exhibited encouraging results for the rapid microbiological assessment, with the FT-IR data showing the best performance for evaluating the microbiological population. Among the tested algorithms, the least Angle Regression (lars) achieved the best performance for both the flesh and skin datasets, with RMSE values of 0.6075 and 0.5953, MAE of 0.3008 and 0.4567, R² of 0.8858 and 0.7532, and accuracy of 87% and 91%, respectively. The Benchtop-MSI showed the best predictive performance for flesh (RMSE = 0.5926, MAE = 0.4876, R² = 0.7338, and Accuracy = 92%), while the artificial neural network (nnet) performed best for skin (RMSE = 0.6761, MAE = 0.5247, R² = 0.6560, and Accuracy = 84%). Regarding the Portable-MSI, the artificial neural network model gave the highest accuracy for flesh (RMSE = 0.5908, MAE = 0.4663, R² = 0.5903, and Accuracy = 87%), whereas principal component regression was the most effective for skin (RMSE = 0.6600, MAE = 0.5413, R² = 0.5534, and Accuracy = 83%). Full article

The rhenium(I) tricarbonyl complex fac-[Re(CO)₃(5,6-epoxy-5,6-dihydro-1,10-phenanthroline)Br] (ReL) has previously demonstrated promising luminescent properties, enabling its direct application as a probe for walled cells such as Candida albicans and Salmonella enterica. In this new study, we present a significantly expanded and comprehensive characterization of ReL, incorporating a wide range of experimental and computational techniques not previously reported. These include variable-temperature ¹H and ¹³C NMR spectroscopy, CH-COSY, single-crystal X-ray diffraction, Hirshfeld surface analysis, DFT calculations, Fukui functions, non-covalent interaction (NCI) indices, and electrochemical profiling. Structural analysis confirmed a pseudo-octahedral geometry with the bromide ligand positioned cis to the epoxy group. NMR data revealed the coexistence of cis and trans isomers in solution, with the trans form being slightly more stable. DFT calculations and aromaticity descriptors indicated minimal electronic differences between isomers, supporting their unified treatment in subsequent analyses. Electrochemical studies revealed two oxidation and two reduction events, consistent with ECE and EEC mechanisms, including a Re(I) → Re(0) transition at −1.50 V vs. SCE. Theoretical redox potentials showed strong agreement with experimental data. Biological assays revealed a dose-dependent cytotoxic effect on HeLa cells, contrasting with previously reported low toxicity in microbial systems. These findings, combined with ReL’s luminescent and antimicrobial properties, underscore its multifunctional nature and highlight its potential as a bioactive and imaging agent for advanced therapeutic and microbiological applications. Full article

Background: Standardized Er:YAG laser settings for microbial reduction remain undefined, and existing studies rarely compare multiple species under identical conditions. This work aimed to characterize susceptibility across selected microorganisms using a controlled agar-based surface growth model. Methods: Six reference strains (E. coli, S. aureus MSSA, S. aureus MRSA, E. faecalis, P. aeruginosa, and C. albicans) were cultured on agar and exposed to Er:YAG irradiation. Two experimental phases were conducted: (1) inhibition zone mapping using energies between 30 and 400 mJ at 1 Hz, with tapered and flat laser tips; and (2) quantification of viable surface coverage after irradiating mature 96 h cultures with 80, 130, 180, and 230 mJ at 10 Hz in contact mode. ImageJ analysis was used to measure inhibition diameters and remaining coverage. Data were evaluated using two-way ANOVA. Results: All microorganisms showed measurable inhibition at every tested energy level, with diameter increasing proportionally to energy. E. coli and E. faecalis produced the largest inhibition zones in the mapping phase, while P. aeruginosa and C. albicans required higher energies to reach comparable levels. Mature surface cultures showed progressive reductions in viable coverage; the strongest effects occurred at 230 mJ. The tapered tip generated broader inhibition zones at lower energies compared with the flat tip. Conclusions: Er:YAG laser irradiation produces consistent, energy-dependent antimicrobial effects on single-species agar-based surface growth, with clear differences in species susceptibility and tip performance. The identified parameter ranges provide a quantitative foundation for future in vitro studies aiming to refine Er:YAG-based microbial reduction strategies. Full article

Determining the postmortem interval remains one of the most persistent and fragmented challenges in forensic science. Conventional approaches—thermal, biochemical, molecular, or entomological—capture only isolated fragments of a single physical reality: the irreversible drift of a once-living system toward equilibrium. This Perspective proposes a unifying paradigm in which death is understood as a progressive rise in entropy, encompassing the loss of biological order across thermal, chemical, structural, and ecological domains. Each measurable postmortem variable—temperature decay, metabolite diffusion, macromolecular breakdown, tissue disorganization, and microbial succession—represents a distinct expression of the same universal law. Within this framework, entropy becomes a dimensionless index of disorder that can be normalized and compared across scales, transforming scattered empirical data into a coherent continuum. A Bayesian formulation further integrates these entropic signals according to their temporal reliability, yielding a probabilistic, multidomain equation for PMI estimation. By merging thermodynamics, information theory, and biology, the concept of death as rising entropy offers a comprehensive physical description of the postmortem process and a theoretical foundation for future computational, imaging, and metabolomic models in forensic time analysis. Full article

Microplastic pollution poses growing risks to aquatic zooplankton, yet its impact on Daphnia magna life history remains incompletely understood. This study explored the influences of micro/nanoplastics (MPs/NPs) on D. magna by exposing organisms to size- and concentration-varied microplastics, tracking microplastic distribution via fluorescence imaging. Results demonstrated significant microplastic-induced impairments in growth and reproduction. Gut microbiota analysis revealed microplastic-altered microbial communities, with functional prediction identifying disrupted glucose metabolism as a key driver of life-history changes. Intestinal structure observations further showed microplastic-accelerated aging. Collectively, our findings highlight that microplastic accumulation in D. magna disrupts gut microbiota and tissue integrity, ultimately impairing life-history traits. These alterations in growth and gut characteristics of D. magna may further propagate through the aquatic food web, potentially damaging the intestinal structure and function of plankton communities. Given the pivotal role of zooplankton in nutrient cycling and energy transfer, our findings underscore that microplastic-induced disruptions in key species like D. magna could threaten the stability and sustainability of aquatic ecosystems. Full article

Bacteria underpin human health, environmental balance, and industrial processes. Rapid and accurate identification is essential for diagnosis and responsible antibiotic use. Culture, biochemical tests, and microscopy are slow, expensive, and depend on expert judgment, which introduces subjectivity and errors. This research aims to recommend a new generation deep learning architecture for bacterial species classification. We curated a bacterial image dataset, and this dataset contains 18,221 microscopic images from 24 species under standard laboratory conditions. All images passed clarity and focus checks. We developed a compact CNN, the Multiple Activation Network (MACNeXt). The recommended MACNeXt preserves local feature extraction and improves representation with two activation functions (GELU and ReLU) and a multi-branch design. The aim is high accuracy with low computational cost for routine clinical use. MACNeXt achieved 90.97% accuracy, 89.63% precision, 88.64% recall, and 88.99% F1-score on the test set. The calculated results and findings showcase balanced and stable performance across species with an efficient, lightweight design since the introduced MACNeXt has about 4.4 million learnable parameters. The results of the MACNeXt openly demonstrate that this CNN is a compact, lightweight, and highly accurate CNN model. Full article

The recirculating aquaculture system (RAS) provides a sustainable approach to sustaining aquaculture output while reducing environmental pollution and excessive water consumption. Nonetheless, high concentrations of Total Ammonia Nitrogen (TAN) continue to be a significant obstacle in RAS operations. To address this issue, the Moving Bed Biofilm Reactor (MBBR), with bubble aeration, is important for promoting ammonia degradation. Bubble size impacts the effectiveness of bubble aeration, influencing both oxygen transfer and microbial activity. This research involved a 35-day experiment to evaluate the effects of bubble size, produced by nanobubble and coarse bubble aerators, on biofilm development and TAN decrease. The maximum biofilm thickness of 172.88 µm was recorded during nanobubble aeration, which also produced a higher quantity of microbial colonies (293 × 10⁷ CFU) in comparison to coarse bubble aeration (89 × 10⁷ CFU), as validated by Total Plate Count analysis. SEM–EDX imaging additionally demonstrated a more compact and consistent biofilm structure in the presence of nanobubbles. These results align with an increased TAN degradation efficiency, achieving 83.33% with nanobubble aeration, while coarse bubble aeration reached only 50%. The findings indicate that nanobubble aeration enhances biofilm functionality by improving bacterial dispersion and oxygen availability within the biofilm matrix, thereby promoting a more uniform distribution of microorganisms and nutrients. This mechanism represents a promising approach for improving water quality and overall treatment efficiency in recirculating aquaculture systems (RAS). Full article

Skin pathogenic microbes continue to seriously endanger humans, particularly resistant strains. Nanomaterials/composites are promising answers for this. Black mulberry (MB) polysaccharides were employed for biosynthesizing/capping selenium nanoparticles (SeNPs); their conjugations alongside chitosan (Cht) nanoforms were constructed and assessed for skin pathogens’ (Staphylococcus aureus bacteria and Candida albicans yeast) suppression and destruction. The biosynthesis of SeNPs with MB was verified using FTIR analysis and UV-vis spectroscopy. The nanocomposites were constructed from Cht–MB-SeNPs at concentrations of 2:1 (F1), 1:1 (F2), and 1:2 (F3). The SeNPs had a mean diameter of 46.19 nm, whereas the F-2 nanocomposites had the lowest particle diameter (212.42 nm) compared to F-1 (239.88 nm) and F-3 (266.16 nm) nanocomposites. The F-2 nanocomposites significantly exhibited the strongest antimicrobial efficacy against skin pathogens, with 26.3 and 27.1 mm inhibition zones and 22.5 and 20.0 μg/mL inhibitory concentrations against bacteria and C. albicans yeast, respectively. The scanning imaging of microbes exposed to nanocomposite emphasized the severe destruction/lyses of microbial cells within 10 h. Loading of cotton fabrics with nanomaterials, particularly with Cht/MB-SeNP nanocomposites, generated potent durable antimicrobial textiles that could prohibit microbial growth, with inhibition zones of 6.2 mm against C. albicans and 3.7 mm against S. aureus; the textiles could preserve their antimicrobial actions after two washing cycles. The biogenic construction of Cht/MB-SeNP nanocomposites can provide innovative solutions to manage and control skin pathogens. Full article

The many uses of biochar extend to microbial enhancement in fermentation processes because it acts as a catalyst and a support medium in agricultural industries, particularly for biofertilizer production. This study explores how three key biochar parameters, concentration (0.05–0.25% w/v), temperature (30–50 °C), and particle size (250 μm–1.40 mm) affect hyper-ammonia-producing bacteria (HAB) growth during fermentation using commercially sourced pine wood-derived biochar. Fermentation experiments utilized enriched cow rumen fluid under controlled conditions, monitoring bacterial growth via optical density (OD₆₀₀) over 48 h. Microbial proliferation was strongly influenced by all tested parameters (concentration, temperature, particle size). Highest growth occurred at 0.15% biochar concentration, 45 °C, and 250 μm particle size within the tested parameter ranges. Lower concentrations and smaller particles promoted microbial adhesion and colonization. Higher biochar levels hindered growth due to surface saturation and reduced pore accessibility. SEM imaging supported these findings by revealing structural changes on the biochar surface at different concentrations. Regression analysis demonstrated strong correlation between biochar parameters and microbial activity (R² = 0.9931), though multicollinearity limited individual variable significance. These findings support biochar optimization for enhanced microbial processing in biotechnological applications. Full article

This study reports the development and optimization of a Melissa officinalis oil-based nanoemulgel for transdermal delivery using a Design-of-Experiments (DoE) approach. A Central Composite Design (CCD) was applied to optimize Tween 80 concentration and homogenization time, resulting in a nanoemulsion with a droplet size of 127.31 nm, PDI of 17.7%, and zeta potential of −25.0 mV, indicating good colloidal stability. FTIR analysis confirmed the presence of functional groups such as O–H, C=O, and C–O–C, supporting the oil’s phytochemical richness and therapeutic potential. DSC analysis revealed enhanced thermal stability and successful encapsulation, while SEM imaging showed a uniform and spherical microstructure. The drug release followed Higuchi kinetics (R² = 0.900), indicating diffusion-driven release, with the Korsmeyer–Peppas model (n = 0.88) suggesting anomalous transport. Antibacterial studies showed inhibition of Staphylococcus aureus (MIC = 250 µg/mL) and Escherichia coli (MIC = 500 µg/mL). In vivo anti-inflammatory testing demonstrated significant edema reduction (p < 0.05) using a carrageenan-induced rat paw model. These results support the potential of Melissa nanoemulgel as a stable and effective topical therapeutic for inflammatory and microbial skin disorders. Full article

The laser speckle technique provides a non-invasive remote sensing method for monitoring biological dynamics. In this study, we focus on assessing microbial growth through systematic comparison of correlation-based speckle image analysis methods. We compare conventional techniques, NCC, ZNCC, the Lewis method, and Phase correlation, with two newly proposed variants: frequency-domain correlation of normalized images and ZNCC with limited shifts around the peak. We analyze these methods in terms of precision and computational efficiency. Our results demonstrate that the proposed techniques offer optimal trade-offs for tracking subtle microbial activity, particularly in early-stage growth. This paper aims not only to identify the most effective tools for laser speckle analysis, but also to justify the use of laser speckle imaging for microbial activity assessment. Full article

Background: Dental caries arise from a multifactorial interplay between microbial dysbiosis, host immune responses, and enamel degradation visible on radiographs. Deep learning excels in image-based caries detection; however, integrative analyses that combine radiographic, microbiome, and transcriptomic data remain rare because public cohorts are seldom aligned. Objective: To determine whether three independent deep-learning pipelines—radiographic segmentation, microbiome regression, and transcriptome regression—can be reproducible implemented on non-aligned datasets, and to demonstrate the feasibility of estimating microbiome heritability in a matched twin cohort. Methods: (i) A U-Net with ResNet-18 encoder was trained on 100 annotated panoramic radiographs to generate a continuous caries-severity score from a predicted lesion area. (ii) Feed-forward neural networks (FNNs) were trained on supragingival 16S rRNA profiles (81 samples, 750 taxa) and gingival transcriptomes (247 samples, 54,675 probes) using randomly permuted severity scores as synthetic targets to stress-test preprocessing, training, and SHAP-based interpretability. (iii) In 49 monozygotic and 50 dizygotic twin pairs (n = 198), Bray–Curtis dissimilarity quantified microbial heritability, and an FNN was trained to predict recorded TotalCaries counts. Results: The U-Net achieved IoU = 0.564 (95% CI 0.535–0.594), precision = 0.624 (95% CI 0.583–0.667), recall = 0.877 (95% CI 0.827–0.918), and correlated with manual severity scores (r = 0.62, p < 0.01). The synthetic-target FNNs converged consistently but—as intended—showed no predictive power (R² ≈ −0.15 microbiome; −0.18 transcriptome). Twin analysis revealed greater microbiome similarity in monozygotic versus dizygotic pairs (0.475 ± 0.107 vs. 0.557 ± 0.117; p = 0.0005) and a modest correlation between salivary features and caries burden (r = 0.25). Conclusions: Modular deep-learning pipelines remain computationally robust and interpretable on non-aligned datasets; radiographic severity provides a transferable quantitative anchor. Twin-cohort findings confirm heritable patterns in the oral microbiome and outline a pathway toward future clinical translation once patient-matched multi-omics are available. This framework establishes a scalable, reproducible foundation for integrative caries research. Full article

Fermentation has been a crucial process in the preparation of foods and beverages for consumption, especially for the purpose of adding value to nutrients and bioactive compounds; however, conventional approaches have certain drawbacks such as not being able to fulfill the requirements of the ever-increasing global population as well as the sustainability goals. This review aims to evaluate how the application of advanced fermentation techniques can transform the food production system to be more effective, nutritious, and environmentally friendly. The techniques discussed include metabolic engineering, synthetic biology, AI-driven fermentation, quorum sensing regulation, and high-pressure processing, with an emphasis on their ability to enhance microbial activity with a view to enhancing product output. Authentic, wide-coverage scientific research search engines were used such as Google Scholar, Research Gate, Science Direct, PubMed, and Frontiers. The literature search was carried out for reports, articles, as well as papers in peer-reviewed journals from 2010 to 2024. A statistical analysis with a graphical representation of publication trends on the main topics was conducted using PubMed data from 2010 to 2024. In this present review, 112 references were used to investigate novel fermentation technologies that fortify the end food products with nutritional and functional value. Images that illustrate the processes involved in novel fermentation technologies were designed using Adobe Photoshop. The findings indicate that, although there are issues regarding costs, the scalability of the process, and the acceptability of the products by the consumers, the technologies provide a way of developing healthy foods and products produced using sustainable systems. This paper thus calls for more research and development as well as for the establishment of a legal frameworks to allow for the integration of these technologies into the food production system and make the food industry future-proof. Full article