Biological Components Research Articles

Dynamic transformation and mechanisms of volatile sulfur compound releasing during anaerobic digestion of sludge.

Dynamic transformation and mechanisms of volatile sulfur compound releasing during anaerobic digestion of sludge.

G-quadruplex in cancer energy metabolism: A potential therapeutic target.

G-quadruplex in cancer energy metabolism: A potential therapeutic target.

Paving the way for bacteria-based drug delivery: biohybrid microrobots emerging from microrobotics and synthetic biology.

Advances in microrobotics and synthetic biology are paving the way for innovative solutions to long-standing challenges in drug delivery. Both fields have independently worked on engineering bacteria as a therapeutic system, focusing on enhancing propulsion, cargo delivery, detection, and biocompatibility. Bacteria, with their inherent adaptability and functional versatility, serve as an ideal foundation for these efforts, enabling them to navigate complex biological environments such as the human body. This review explores the convergence of microrobotics and synthetic biology, which has catalysed the development of biohybrid bacterial microrobots that integrate the strengths of both disciplines. By incorporating external control modalities - such as light, ultrasound, and magnetic fields - these hybrid systems address the limitations of purely microrobotic or biological approaches, offering new opportunities to enhance precision and efficacy in targeted therapies. However, realising the full potential of biohybrid bacterial microrobots requires overcoming critical challenges, such as ensuring compatibility between biological and synthetic components, scaling manufacturing processes, and defining regulatory pathways tailored to living therapeutics. Addressing these hurdles through joint, interdisciplinary research efforts, can unlock the transformative possibilities of these systems in modern medicine.

Amazonian natural products used as functional food and medicine.

Amazonian natural products used as functional food and medicine.

Hybrid Plasmonic Bioresins and dECM-Based Materials for Volumetric Bioprinting of Vascular-Inspired Architectures.

Synergizing nanomaterial technology with advanced 3D printing techniques creates new opportunities for developing smart, stimuli-responsive materials suitable for tissue engineering scaffolds. By incorporation of stimuli-responsive nanoparticles into extracellular matrix mimetics, these composites gain functional elements capable of replicating dynamic biological processes in vitro. Herein, we propose combining hybrid multifunctional inorganic-organic materials with the emerging volumetric bioprinting (VBP) technique. We present two hybrid materials, a light stimuli-responsive polymer-based resin and a biocompatible porcine-derived decellularized extracellular matrix (dECM)-based bioresin, thus expanding the library of materials suitable for VBP. Plasmonic nanoparticles are combined with a thermoresponsive polymeric matrix, formulating the stimuli-responsive plasmonic resin, while a dECM-based bioresin with embedded smooth muscle cells (SMCs) is employed to include the biological component in the system. As proof of concept to demonstrate the versatility of the hybrid materials, we investigated the generation of highly complex structures, including multiwalled channels, using sequential VBP. Overall, this study broadens the range of materials compatible with VBP, thereby enabling the use of smart multicomponent materials in the fabrication of dynamic, stimuli-responsive 3D in vitro models.

The predictive power of profiling the DNA methylome in human health and disease.

Early and accurate diagnosis significantly improves the chances of disease survival. DNA methylation (5mC), the major DNA modification in the human genome, is now recognized as a biomarker of immense clinical potential. This is due to its ability to delineate precisely cell-type, quantitate both internal and external exposures, as well as tracking chronological and biological components of the aging process. Here, we survey the current state of DNA methylation as a biomarker and predictor of traits and disease. This includes Epigenome-wide association study (EWAS) findings that inform Methylation Risk Scores (MRS), EpiScore long-term estimators of plasma protein levels, and machine learning (ML) derived DNA methylation clocks. These all highlight the significant benefits of accessible peripheral blood DNA methylation as a surrogate measure. However, detailed DNA methylation biopsy analysis in real-time is also empowering pathological diagnosis. Furthermore, moving forward, in this multi-omic and biobank scale era, novel insights will be enabled by the amplified power of increasing sample sizes and data integration.

Tumor-associated long non-coding RNAs show variable expression across diffuse gliomas and effect on cell growth upon silencing in glioblastoma

Long noncoding RNAs (lncRNAs) have been recently recognized as critical components of cancer biology linked to oncogenic processes. Certain lncRNAs are known to act as oncogenes, and the disease-specific expression of many lncRNAs makes them informative biomarkers. We identified 22 uncharacterized lncRNAs from RNA-seq data of 169 glioblastoma (GBM) tumor samples sequenced by The Cancer Genome Atlas (TCGA) consortium and studied their expression in TCGA diffuse glioma cohort including also IDH-mutant astrocytomas and oligodendrogliomas as well as in normal brain samples from the Genotype-Tissue Expression cohort. All of the 22 lncRNAs were clearly upregulated in diffuse gliomas samples compared to the normal brain. Interestingly, 20 (91%) of these lncRNAs had significant expression differences between tumor grades and/or entities, and 14 (64%) were associated with overall patient survival. All 22 lncRNAs were expressed in at least one of the studied GBM cell lines and 10 (45%) were expressed in all four. When six of the lncRNAs were silenced in the SNB19 GBM cell line, the knock-down was associated with reduced growth and colony formation for three lncRNAs: TCONS_l2_00001282, lnc-GBMT-6, and lnc-NBN-1. In conclusion, the studied lncRNAs are associated with survival in patients with diffuse glioma and have functional relevance in GBM.

GIS-based integration of marine data for assessment and management of a highly anthropized coastal area

Monitoring coastal marine environments by evaluating and comparing their chemical, physical, biological, and anthropogenic components is essential for ecological assessment and socio-economic development. In this study, we conducted an integrated multivariate analysis to assess the descriptors of the Marine Strategy Framework Directive at a regional scale in the Tyrrhenian Sea (Italy), with a specific focus on the densely populated coastal zone of the Campania region. Physical, chemical, and biological data were collected and analyzed in 22 sampling sites during three oceanographic surveys in the Gulf of Gaeta (GoG), Naples (GoN), and Salerno (GoS) in autumn 2020. Our results indicated that these three gulfs were distinct overall, with GoN being more divergent and heterogeneous than GoG and GoS. The marine area studied in the GoN had more favorable hydrographic and trophic conditions and food web characteristics, except for the mesozooplankton biomass, and was closer to socio-economic factors compared to the GoS and GoG. Our analysis helped us find the key ecological features that define different sub-regions and connect them to social and economic factors, including human activities. We highlighted the relevance of primary and secondary variables in terms of the comprehensive ecological assessment of a marine area and its impact on specific socio-economic activities. These findings support the need to describe and integrate multiple descriptors at the spatial scale.

Transfer learning from inorganic materials to ivory detection

This paper describes the automatic identification of ivory using Raman spectroscopy data and deep neural network (DNN) models pre-trained on open-source data from inorganic minerals. The proposed approach uses transfer learning (TL) from foundation models trained on a larger inorganic (minerals) spectroscopy dataset (MLROD). The results demonstrate, for the first time, the ability to transfer machine learning (ML) models from a Raman spectroscopy dataset of geological substances to classify biological ivory samples. Current identification methods, such as DNA analysis and radiocarbon dating, are costly and destructive. Recently, it was demonstrated that the use of Raman spectroscopy, a laser-based, non-destructive technique, in combination with well-known statistical techniques, has the potential to differentiate between mammoth and elephant ivory. However, this previous study had a small sample size due to difficulties in obtaining large amounts of labeled ivory data. To date, there has been no reported work on ivory classification using DNNs, and only limited studies using Raman spectra. The work proposed in this paper suggests that ML can provide high levels of accuracy in the classification of Raman spectroscopy data from ivory samples of different elephant species (up to 99.7%). This has the potential to create a quick and inexpensive method of identifying legal and illegal types of ivory to aid in enforcement of ivory trade bans. This study also demonstrated that DNN models initially pre-trained on inorganic minerals (from the MLROD dataset) that were not finetuned on ivory data had a high accuracy rate of 92%, alleviating the need for large amounts of training data from ivory specimens. Finally, the approach proposed in this paper, provides insight into the decision making and interpretation of the results using prototype-based models. This novel work demonstrates that: (1) ML methods can provide highly accurate classification of ivory from different species of elephant using data obtained using Raman spectroscopy and providing insight into the decision making (2) TL enables re-purposing the models trained on larger mineral datasets of inorganic materials (such as MLROD) to discriminating between the classes of ivory, containing inorganic and organic biological components, for the first time transgressing between non-biological and biological samples (3) the proposed method allows both training from labelled samples of ivory and the identification of unknown ivory samples through prototype-based methods.

Medications used in dementia: their management and the role of the community nurse.

Dementia care embraces biological, psychological, spiritual and social components, commonly referred to as a biopsychosocial model. From a biological standpoint, dementia is caused by different diseases, each resulting in damage to nerve cells and transmitter pathways. Most people with dementia reside in a community setting, such as care homes or in their own homes, whether living alone or with family carers and supporters. This article considers the place of medications of the biopsychosocial model and discusses common medications used for: (i) for cognitive symptoms; (ii) non-cognitive symptoms; (iii) other medical conditions that occur alongside dementia. Nurses are in a prime position to support safe and effective care in medications management. The article also examines the implications and effects of polypharmacy on people with dementia, as well as some of the challenges involved in administration, such as covert administration of medicines, polypharmacy and a person's non-adherence.

Fundamental Principles of Cognitive Biology 2.0

Abstract Cognitive biology, as a scientific program-in-waiting, is the direct (if unacknowledged) offspring of the 20th century revolution in molecular biology, which revealed for the first time the deep, nonmetaphorical parallels between the activities of biological components and processes and the knowledge-generating capabilities characteristic of cognition. The article examines cognitive biology’s parentage—Brian C. Goodwin and Ladislav Kováč—and the context which gave birth to it, twice. Special reference is made to Kováč, without whose work, which is honored in this special issue, cognitive biology as such could have perished. Putting to one side Kováč’s own continuing work in the area, cognitive biology developed in the 21st century both in ways he and Goodwin (who died in 2009) would recognize and in ways they would not. One of the paths taken within their lineage is my own, which has travelled under different labels (the biogenic approach to cognition, basal cognition) and developed, also independently, from unorthodox beginnings. It is important to emphasize that cognitive biology is not simply the “biologizing” of the study of cognition. In a very real sense, cognitive biology is not about cognition—as a biological function of whole organisms—at all. It is a recognition that biological processes, what normally passes for mere physiology and development, have properties traditionally associated with cognitive capacities in animals, properties that are inadequately captured by a generic (usually poorly specified) notion of “information processing.” Cognitive biology is related to the search for the biological basis of cognition, and does much to illuminate that search, but was never motivated by that search. It was motivated entirely by the search for a more general biological theory. Inspired by Kováč’s seminal “Fundamental Principles of Cognitive Biology,” a considerably expanded set of principles is gathered here for the first time from multiple sources. Together they show how cognitive biology reunites the sciences of life and cognition on a foundation that is gratifyingly substantial, and which may point the way to a future science.

Going around the bend to understand the role of leg coalescence in metachronal swimming.

Many of the most abundant aquatic invertebrates display metachronal swimming by sequentially beating closely spaced flexible appendages. Common biophysical mechanisms like appendage spatial asymmetry and phase drive the success and performance of this locomotor mode, which is generally explained by the need to maximize thrust production. However, the potential role of these mechanisms in drag reduction, another important contributor to the overall swimming performance, has yet to be evaluated. We present several morphological, functional, and physical mechanisms promoting drag reduction during metachronal swimming by exploring appendage differential bending and leg grouping (coalescence). We performed μ-CT and in-vivo velocimetry measurements of shrimp (Palaemonetes vulgaris) to design a five-legged robotic metachronal analog. This test platform enabled simultaneous flow and force measurements to quantify the thrust and drag forces produced by flexible and stiff pleopods (legs) beating independently or coalescing. We tested the hypothesis that coalescence and bending effectively reduce drag during the recovery stroke (RS). The curved cross-section of the pleopods enables passive asymmetrical bending during the RS to reduce their drag coefficient by up to 75.8% relative to stiff pleopods. Bending promotes physical interactions, facilitating the coalescence of three pleopods at any time during the RS to reduce drag such that the mean net thrust produced during coalescence is increased by 30.2%. These improvements are explained by the production of a weaker wake compared with stiff and non-coalescing pleopods. Our results describe fundamental biological and physical components of metachronal propulsion that may aid the development of novel bio-inspired underwater vehicles.

Micropatterned Styrene-Butadiene-Styrene Thin Films Doped with Barium Titanate Nanoparticles: Effects on Myoblast Differentiation.

Biohybrid actuators exploit the contraction of biological components (muscle cells) to produce a force. In particular, bottom-up approaches use tissue engineering techniques, by coupling cells with a proper scaffold to obtain constructs undergoing contraction and guaranteeing actuation in biohybrid devices. However, the fabrication of actuators able to recapitulate the organization and maturity of native muscle is not trivial. In this field, quasi-two-dimensional (2D) substrates are raising interest due to their high surface/thickness ratio and the possibility of functionalizing their surface. In this work, we fabricated micropatterned thin films made of poly(styrene-butadiene-styrene) (SBS) doped with barium titanate nanoparticles (BTNPs) for fostering myogenic differentiation. We investigated material concentrations and fabrication process parameters to obtain thin microgrooved films with an average thickness below 1 μm, thus featured by a relatively low flexural rigidity and with an anisotropic topography to guide cell alignment and myotube formation. The embodiment of BTNPs did not significantly affect the film's mechanical properties. Interestingly, the presence of BTNPs enhanced the expression of myogenic differentiation markers (i.e., MYH1, MYH4, MYH8, and ACTA1). The results show the promising potential of SBS thin films doped with BTNPs, opening avenues in the fields of biohybrid actuation and skeletal muscle tissue engineering.

Intelligent biomanufacturing of water-soluble vitamins.

Intelligent biomanufacturing of water-soluble vitamins.

Bioheterojunctions Prevent Tooth Caries via Cascade Antibacterial Strategy.

Tooth caries is a prevalent chronic oral disease with microorganisms as the initiating factor and carbohydrates as the key environmental factor. Clinical antimicrobial therapies rely mainly on broad-spectrum antibiotics, which usually increase the risk of bacterial resistance. Recently, phototherapy has shown powerful antibacterial effects, although it cannot effectively eliminate cariogenic microenvironments and the antibacterial effect is not sustained after the light is removed. Here, we developed novel bioheterojunctions (bio-HJs) comprising MXene/Ag3PO4 (MX/AgP) and glucose oxidase (GOx), denoted MX/AgP-GOx, aiming at both the chemical and biological components of dental plaque biofilm. The bio-HJs decomposed the glucose rich in the cariogenic environment through GOx while providing abundant H2O2 for subsequent Fenton reaction. Under near-infrared (NIR) light, the bio-HJs produced hyperthermia and generated large amounts of reactive oxygen species based on the above H2O2, exerting powerful phototherapy properties (log reduction: 1.45 log 10 CFU/mL). It is worth noting that MX/AgP-GOx still exerted antibacterial effects in the dark via Ag+ bactericidal effects, Ag0 NPs catalytic activity, and GOx-mediated glucose depletion (log reduction: 0.39 log 10 CFU/mL), ensuring a sustained anticaries effect after the removal of NIR light. In addition, the rat caries model revealed that MX/AgP-GOx significantly reduced enamel mineral loss and had good biocompatibility. This study constructed efficacy-cascade bio-HJs targeting the sugar-rich cariogenic microenvironment, which promotes subsequent photodynamic therapy and combines photothermal and metal ion synergistic antibacterial means to continuously and effectively eliminate biofilm and prevent the occurrence and development of tooth caries.

Effects of Airborne Particulate Matter in Biomass Treatment Plants on the Expression of DNA Repair and IL-8 Genes

Biogas plants for sewage and organic waste treatment are rapidly expanding. While these facilities provide valuable benefits, such as renewable energy production and the promotion of circular economy practices, they also emit airborne particles of biological origin, which may pose potential health risks. This study aims to evaluate, by in vitro assay, the cytotoxic and genotoxic potential of PM10 sub-fractions (0.49–10 µm and <0.49 µm) generated in eight different plants, also assessing the endotoxin component using the Limulus Amebocyte Lysate (LAL) assay. Human embryonic lung fibroblasts (HELF) were exposed to organic extracts of particulate matter (PM). Cytotoxic effects (XTT assay) were analyzed, along with the modulation of gene expression involved in DNA repair (ERCC1, XRCC1, XPA, and XPF) and IL-8 production as a marker of inflammatory response. PM10 and endotoxin concentrations varied significantly among the plants (ANOVA, p < 0.01), with PM10 levels ranging from 14 to 18,000 µg/m3 and endotoxin content from 1 to 138 EU/m3. Exposure significantly increased ERCC1 and IL-8 expression by 25% and 53%, respectively (paired t-test, p < 0.01). IL-8 expression correlated with endotoxin exposure (Spearman’s rho = 0.35; p < 0.01). A deeper understanding of the biological component of airborne PM10 can enhance risk assessments for occupational and nearby resident communities’ safety.

Implementation of Marine Environmental Impact Assessment Mechanism for Marine Spatial Planning in Taiwan

To undertake a detailed and comprehensive research on the marine environmental impact assessment mechanism of marine spatial planning in Taiwan with a specific focus on the environmental impact assessment (EIA) technique. The present research adopts methodological model based on the principles of environmental impact assessment (EIA) for analysis of the Taiwan marine ecosystem. The cumulative environmental impact of human activities within the area of scope of the study is computed as the sum of the impacts of identified human activities on selected components of the ecosystem. The collection of data related to the human activities and ecosystem components variables was limited to the last five years, between 2018 and 2023, and the pre-existing data that were openly available were sourced from different sources including the Taiwan marine spatial planning (MSP) related governmental agencies, consultancies and academic research. The impactful human activities on the Taiwan marine environment include ocean bottom trawls, oil spill shipping and sand loss extraction activities as well as turbidity ocean bottom trawl, boating effects, mining activities and marine infrastructural projects and the most affected ecosystem components include the artificial and natural coastal reefs, the shoreline shallows, the deep ocean spaces and the biological components which include the plants and the animals. The most impactful human activity on the Taiwan marine environment is attributed to dumping activities within the coast zone areas which has had the most significant impact on the coastal zone ecosystem.

Challenges and Pathways in Regulating Next-Gen Biological Therapies.

Current medicine could benefit from gene and cell therapies for genetic defects, cancer, and degenerative disorders. These therapies modify genetic material or biological components. CRISPR-Cas9 gene editing, stem cell, and CAR-T treatments are examples. Complex products need rigorous regulations to ensure quality, efficacy, and patient safety. This paper discusses international gene and cell-based treatment regulatory regimes, highlighting key issues and recent developments. It also includes gene and cell-based therapy classes and mechanisms. The publications on gene and cell therapy challenges and their regulatory approvals in the US, Europe, Japan, Australia, Brazil, Canada, and China were collected over the last 20 years from PubMed, Scopus, and Google Scholar and analyzed to determine the differences. Gene treatments correct genetic defects or disease processes by adding, removing, or changing cell genetic information. In contrast, cell-based therapies restore damaged tissues with modified or unmodified cells. Highly customized and patient-specific drugs make regulatory monitoring challenging. US FDA CBER controls gene and cell-based therapies. Before clinical trials, these biologic drugs must file BLAs for market approval and INDs. FDA's Breakthrough Therapy and Regenerative Medicine Advanced Therapy (RMAT) designations accelerate biological development. The EMA oversees EU Advanced Therapy Medicinal Products. ATMP quality, safety, and efficacy are CAT's top priorities. The Conditional Marketing Authorization process expedites access to life-threatening disease medicines while the MAA regulates them. Japan's PMDA's Conditional Time-Limited Approval for regenerative medicines provides early commercialization and rigorous post-market supervision. Similarly, each country has adopted some ways to expedite the approval of biologicals. Geneediting drugs require specialized methods, long-term follow-up, and better safety to avoid offtarget effects. GMPs ensure production uniformity, sterility, and safety, complicating manufacturing and quality control. The review concludes that there is a need for worldwide regulatory harmonization and regulatory framework developments, including R.W.E., adaptive pathways, and personalization of biologics.

Abstract 6260: Characterization of the tumor microenvironment’s histologic landscape through histology-based deep learning spatial transcriptomic cell-type deconvolution of colon tumors

Abstract The tumor microenvironment (TME) is an essential component of cancer biology. TME-derived measures such as tumor-infiltrating lymphocytes proportions (TILs) and tumor-stroma ratio (TSR) are used to evaluate TME composition. We leveraged spatial transcriptomics (ST) data from colon tumors to deconvolve cell-type populations and developed a deep learning (DL) algorithm to infer local ST-derived TIL and TSR estimates reflecting TME heterogeneity from held-out H&E whole slide images (WSIs). A total of 44 colon tumors surgical sections were profiled using matched H&E WSI and Visium ST, with 10 patients profiled for single-cell RNASeq. A pathologist annotated tumor microarchitecture within each slide. A total of 284,770 512x512 pixel2 image patches were extracted and paired with corresponding Visium ST spots. Using SCANVI, cell type labels from the Human colon cancer atlas (c295) were lifted over to our in-house scRNASeq data. Cells were mapped spatially to estimate spot-level cell-type proportions using Cell2Location (C2L). Cell-type mapping was validated through human-in-the-loop (HITL) testing, where 512-pixel image crops around 400 randomly selected spots were annotated at each pixel. Spot-level TIL/TSR measures were estimated using cell-type proportions from the C2L-transformed ST data and proportion of pixels within each patch assigned to lymphocytes and tumor. Next, Virchow, a state-of-the-art deep learning pathology foundation model, was used to extract spot-level histological features- a Random Forest model was trained on spot-level features to infer cell-type proportions. Spot-level performance for cell-type mapping (evaluated through HITL) and cell-type proportion inference from histology was measured using the area under the curve (AUC) on a held-out test set of 9 patients. HITL tests demonstrated high agreement between H/ST-TIL (AUC=0.71; 95% CI: [0.60-0.82]) and H/ST-TSR (AUC=0.86; 95% CI: [0.81-0.92]). DL spot-level cell-type deconvolution within tumor-only slide regions achieved a macro-averaged AUC of 0.812 (Tumor: 0.85, Mesenchymal: 0.91, Lymphocytes: 0.79, Monocytes: 0.76, Mast: 0.79, Plasma: 0.78). Previous work inferred spatial expression patterns across broader regions, reporting cell-type AUCs of Tumor: 0.93, Mesenchymal: 0.82, Lymphocytes: 0.76, Monocytes: 0.74, and Mast: 0.91. Our results show that DL can infer ST-labeled cell types from H&E WSI, capturing TME features beyond tissue architecture. HITL testing demonstrated strong concordance between derived spot-level ST-TSR/TIL metrics with their histology-based counterparts (H-TSR/TIL), with utility for pathologist validation of spatially mapped cell-type data. This study highlights future potential of DL to capture spatial distribution of TME measures as prognostic biomarkers through ST inference from histology. Citation Format: Minh-Khang Le, Gokul Srinivasan, Fred Kolling, Keluo Yao, Alos Diallo, Zarif Azher, Louis J. Vaickus, Xiaoying Liu, Joshua Levy. Characterization of the tumor microenvironment’s histologic landscape through histology-based deep learning spatial transcriptomic cell-type deconvolution of colon tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 6260.

Utilizing eDNA from bacteria, fungi, plants, and arthropods associated with mock geologic evidence for sample-to-sample comparisons and study site separation: A feasibility study.

Utilizing eDNA from bacteria, fungi, plants, and arthropods associated with mock geologic evidence for sample-to-sample comparisons and study site separation: A feasibility study.