Life Science Applications Research Articles

Characterization, production optimization and ecotoxicity of a lipopeptide biosurfactant by Pseudomonas citronellolis using oily wastewater

Considering both fundamental principles and application perspectives, it is imperative to characterize biosurfactants (BSFs) for replacing their synthetic counterparts. In this study, a BSF produced by the Pseudomonas citronellolis 620 C bacterium, using oily wastewater, was identified as a lipopeptide based on ultraviolet spectroscopy, thin layer chromatography, high-pressure liquid chromatography and nuclear magnetic resonance spectroscopy. The latter detected the lactone ring at the 5.1–4.9 ppm region, suggesting it as a cyclic lipopeptide (CLP). Gas chromatography/mass spectrometry detected high percentage of C16:0, C18:0 fatty acids, and aliphatic amino acids. A novel CLP is here proposed. To reduce its bioprocess costs, different nitrogen (N)-sources were tested using oily wastewater as the main nutrient source. Ammonium sulfate was the best N-source for the bacterium, reaching BSF production of 556 mg/L, at 10/2 C/N ratio. The BSF ecotoxicity for Aliivibrio fischeri, expressed as effective concentration (EC), was 0.45 and 3.04 g/L as EC20 and EC50, respectively. For Daphnia magna, the EC50 at 24 h and 48 h were 0.980 and 0.936 g/L, respectively. The lipopetide had no estrogenic activity up to 8 g/L. These data make the novel lipopeptide suitable for agriculture, environmental restoration, as dispersant in waste(water) biodisposal and life sciences applications.

Single‐Photon Time‐Stretch Infrared Spectroscopy

AbstractSensitive mid‐infrared (MIR) spectroscopy is highly demanded in various fields ranging from industrial inspection, biomedical diagnosis to astronomical observation. However, the detection sensitivity of conventional MIR spectrometers is severely limited by excessive noises for existing infrared sensors, which hinders widespread use in photon‐scarce scenarios. Here, a broadband MIR single‐photon time‐stretch spectrometer is devised and implemented based on high‐fidelity spectral upconversion and time‐correlated coincidence counting. Specifically, a nanophotonic supercontinuum illumination covering 2.4–4.2 µm is nonlinearly converted to the near‐infrared band, where low‐loss single‐mode fiber and high‐performance silicon detector can be leveraged to facilitate dispersive operation and sensitive detection, respectively. The arrival time for the dispersed upconversion photons is precisely registered with a low‐timing‐jitter photon counter, which enables us to obtain a high spectral resolution about 0.5 cm−1 under a low‐light‐level illumination down to 0.14 photons/nm/pulse. In comparison to previous MIR upconversion spectrometers, the presented time‐stretch architecture favors single‐pixel simplicity and high‐throughput acquisition for the single‐photon spectral measurement. The achieved MIR spectroscopic features of broadband spectral coverage, sub‐wavenumber resolution, single‐photon sensitivity, and room‐temperature operation would stimulate immediate applications in material and life sciences.

Bioinformatics – Supporting modern life science research, applications, and challenges

Bioinformatics is an interdisciplinary field that develops methods, software tools for understanding biological data and aims to investigate questions about biological composition, structure, function, and evolution of molecules, cells, tissues, and organisms using mathematics, informatics, statistics, and computer science. As we are moving towards the era of cutting-edge technologies there will be a lot of data to store, process and analyze. It offers analysis software for data studies and comparisons and provides tools for modeling, visualizing, exploring and interpreting data. It includes analysis, structural and functional characterization of biomolecules leading to the development of Genomics, Proteomics, Transcriptomics, and Metabolomics, etc. Drug discovery and development tools, supported by recent advancements in machine learning and cloud computing should shorten the time to find and produce an efficient drug compound with fewer side effects and more results emerge as a branch called Chemo-informatics. Personalized medicine where bioinformatics can help a lot to make drug molecules based on the genetic makeup of individuals for better outcomes is a prime area of research and need of the society at present. The major futures challenge of the scientific community is to create an in-vitro model of whole-cell or organism and further simulating a whole cell or an organism by applying in-silico approaches. To achieve that, reliable tools that utilize those technologies need to be developed and tested. Bioinformatics reduces the search space/size of the problem by thousand times. The main goal is to convert a multitude of complex data into useful information and knowledge. As a consequence of understanding such data, one can basically engineer longer life for society.

Spatiotemporal omics of life energy: Towards medicine of frequencies and terahertz drugs

AbstractThe quantum mechanisms how life efficiently utilizes energy and transmits information remain unclear yet. Frequency medicine, an emerging cross‐discipline of both quantum mechanics and biomedicine, is a promising turning point for biomaterials and medicine developing from the matter level to the energy level. Recognizing the pivotal role of molecular vibrational frequencies in resonant energy coupling and transmission underscores the potential of frequency medicine to precisely regulate biomaterial vibrations, influencing interactions and reactions in living organisms. At present, scientists have unveiled sophisticated phototherapeutics; nevertheless, their advancement necessitates the precise mapping of the life energy. In contrast to genomics, proteomics, and metabolomics studied on the matter level, omics of frequencies related to medicine should be on the energy level. Herein, starting from the history of frequency medicine, and followed by the introduction of vibrational strong coupling applications in life sciences, we emphasize the significance, necessity, and urgency of studying spatiotemporal omics of medicine frequencies. By decoding the energy atlas of life, we can acquire profound insights into the quantum mechanisms that govern life processes from an energy standpoint. We anticipate that the integration of biomaterials with spatiotemporal frequency omics related to medical research will contribute significantly to advancing the goals of precision medicine, potentially revolutionizing pharmaceuticals, such as terahertz drugs.

Fabrication of Silk Fibroin Film with Strong Anticoagulant Property from the Synergy of Deep Eutectic Solvents and UV-Light Irradiation

As a biological macromolecule, silk fibroin (SF) has great application potential in anticoagulant. However, its complex molecular structure leads to its poor solubility, which limits the application of SF. In this study, a novel SF film with strong anticoagulant properties was fabricated through the synergistic effect of LiBr deep eutectic solvents (DESs) and UV-light irradiation. Under the optimum conditions, the plasma recalcification time of SF film was 182 s, the hemolysis rate was 3.93%, and the dynamic coagulation time remained at a high level up to 60 min. The synergistic effect of UV-light and DES can promote the enhancement of hydrophilicity and the decrease of surface roughness of SF films, and thus the anticoagulant properties of the films were enhanced. The structure change, wettability, and roughness of SF films were characterized by XRD, FTIR, contact angle, and AFM. In the meantime, the wavelength of the UV-light and the irradiation time have an effect on the anticoagulant properties of the SF films. Furthermore, the improvement of the anticoagulant properties of SF films exhibits good universality for different LiBr DESs. The study provides a new direction for the preparation of strong anticoagulation SF films, as well as its application in life sciences.

Copper oxide nanoparticles impairs oocyte meiosis maturation by inducing mitochondrial dysfunction and oxidative stress

Copper oxides nanoparticles (CuO NPs) are widely used for a variety of industrial and life science applications. In addition to cause neurotoxicity, hepatotoxicity, immunotoxicity, CuO NPs have also been reported to adversely affect the reproductive system in animals; However, little is known about the effects and potential mechanism of CuO NPs exposure on oocyte quality, especially oocyte maturation. In the present study, we reported that CuO NPs exposure impairs the oocyte maturation by disrupting meiotic spindle assembly and chromosome alignment, as well as kinetochore-microtubule attachment. In addition, CuO NPs exposure also affects the acetylation level of α-tubulin in mice oocyte, which hence impairs microtubule dynamics and organization. Besides, CuO NPs exposure would result in the mis-localization of Juno and Ovastacin, which might be one of the critical factors leading to the failure of oocyte maturation. Finally, CuO NPs exposure impairs the mitochondrial distribution and induced high levels of ROS, which led to the accumulation of DNA damage and occurrence of apoptosis. In summary, our results indicated that CuO NPs exposure had potential toxic effects on female fertility and led to the poor oocyte quality in female mice.

More generalized k(ε)-Fibonacci sequence, series, and its applications

In this study, we present a generalized higher-order delta operator with the co-efficient of falling factorial and its inverse, both of which allow us to get more generalized k(ε)-Fibonacci sequences along with their sums, a few theorems, and some intriguing conclusions regarding the sum of more generalized terms of k(ε)-Fibonacci sequences with falling factorial. In addition, the n-fold Fibonacci ratio and a few applications in life sciences and crystal growth were presented in the work. In addition, suitable examples are presented using MATLAB to show our results.

Cascaded four-wave mixing in liquid-core optical fibers

Ultrafast nonlinear interactions in optical fibers are commonly employed for generating light with tailored properties, with four-wave mixing (FWM) being a widely used mechanism. Existing systems mainly rely on fibers with solid glass cores, facing limitations due to a lack of tunability and susceptibility to noise. Here, fibers with fluidic cores emerge as a promising alternative for efficient FWM, offering novel functionalities and expanded parameter ranges. In this study, we investigate single and cascaded FWM in liquid-core fibers regarding spectral tunability and interplay with the Raman effect. The study relies on binary liquids used as core materials in combination with ultrashort ps-pulses and seeding. Strong side bands were observed whose spectral position could be adjusted by the liquid composition and the seed wavelength. Seeding additionally leads to higher-order side bands, which we assign to cascaded FWM. Furthermore, we explore the interaction between FWM and stimulated Raman scattering by adjusting the FWM peaks to overlap or deviate from the Raman bands through variations of the core liquid and the seed wavelength. The presented results shed light on the unique characteristics of the liquid-core fiber platform in the context of parametric nonlinear interactions, particularly regarding tunability and interaction with Raman scattering. These findings offer new possibilities for the development of light sources capable of Raman-free photon pair generation for quantum technology or for creating tunable narrowband spectra for imaging applications in life sciences.

One-step electrodeposited hybrid nanofilms in amperometric biosensor development.

This review summarizes recent developments in amperometric biosensors, based on one-step electrodeposited organic-inorganic hybrid layers, used for analysis of low molecular weight compounds. The factors affecting self-assembly of one-step electrodeposited films, methods for verifying their composition, advantages, limitations and approaches affecting the electroanalytical performance of amperometric biosensors based on organic-inorganic hybrid layers were systemized. Moreover, issues related to the formation of one-step organic-inorganic hybrid functional layers with different structures in biosensors produced under the same electrodeposition parameters are discussed. The systemized dependencies can support the preliminary choice of functional sensing layers with architectures tuned for specific biotechnology and life science applications. Finally, the capabilities of one-step electrodeposition of organic-inorganic hybrid functional films beyond amperometric biosensors were highlighted.

SEM-Cathodoluminescence Imaging and Spectroscopy - Applications in Archeology and Life Science

SEM-Cathodoluminescence Imaging and Spectroscopy - Applications in Archeology and Life Science

Geometric method to determine planar anchoring strength for chromonics.

Chromonic nematics are lyotropic liquid crystals that have already been known for half a century, but have only recently raised interest for their potential applications in life sciences. Determining elastic constants and anchoring strengths for rigid substrates has thus become a priority in the characterization of these materials. Here we present a method to determine chromonics' planar anchoring strength. We call it geometric as it is based on recognition and fitting of the stable equilibrium shapes of droplets surrounded by the isotropic phase in a thin cell with plates enforcing parallel alignments of the nematic director. We apply our method to shapes observed in experiments; they resemble elongated rods with round ends, which are called bâtonnets. Our theory also predicts other droplets' equilibrium shapes, which are either slender and round, called discoids, or slender and pointed, called tactoids. In particular, sufficiently small droplets are expected to display shape bistability, with two equilibrium shapes, one tactoid and one discoid, exchanging roles as stable and metastable shapes upon varying their common area.

Evaluation methods for development and selection of novel probiotics

Probiotics is currently one of the science–driven products which have undergone considerable evolution with acclaimed health benefit. Besides the discovery of antibiotics some years ago, probiotics has found considerable applications in life sciences, aquaculture, poultry, piggery, animal health, and human healthcare. There are many novel putative probiotic organisms that could be found in different substrates or carbon sources among bacteria, bacteriophages, fungi, yeasts, microalgae etc. A search approach to developing candidate probionts could be made among these variable sources. Invariably, the methods for isolation and evaluation of the probiotic organisms are many depending on the purpose of use. To wit, different methods are used in aquaculture industry or animal health and human healthcare. For instance, the Food and Agriculture organization FAO of the United Nation/ World Health organization UNO/WHO has already developed international guidelines for the evaluation of probiotics meant for the later. Consequently, this chapter reviews the different methods and approaches for development and evaluation of novel potential probiotics for aquaculture production, taking into considerations the differences in environments and the complex needs and nature of aquatic species. Qualifying a strain of bacterium as a probiotics and selection of such strain for probiotic purposes has not been easy. Principally, scientific driven approaches have been used to primarily decipher the specific trait a desirable probiotic strain should possess, and also developed methods used for selecting and evaluating candidate probiotics. This review will addressed the different methodologies which have been used to analyze microbial cells, which promises to serves for probiotic strains for use in aquaculture industry

Explaining Deep Graph Networks via Input Perturbation.

Deep graph networks (DGNs) are a family of machine learning models for structured data which are finding heavy application in life sciences (drug repurposing, molecular property predictions) and on social network data (recommendation systems). The privacy and safety-critical nature of such domains motivates the need for developing effective explainability methods for this family of models. So far, progress in this field has been challenged by the combinatorial nature and complexity of graph structures. In this respect, we present a novel local explanation framework specifically tailored to graph data and DGNs. Our approach leverages reinforcement learning to generate meaningful local perturbations of the input graph, whose prediction we seek an interpretation for. These perturbed data points are obtained by optimizing a multiobjective score taking into account similarities both at a structural level as well as at the level of the deep model outputs. By this means, we are able to populate a set of informative neighboring samples for the query graph, which is then used to fit an interpretable model for the predictive behavior of the deep network locally to the query graph prediction. We show the effectiveness of the proposed explainer by a qualitative analysis on two chemistry datasets, TOX21 and Estimated SOLubility (ESOL) and by quantitative results on a benchmark dataset for explanations, CYCLIQ.

High Volume 3D Printer and Developed of High-Volume Extruders for Gelatin and Liquids

Over the last decade, the development of 3D printing technology has led to both increased interest and availability of these machines. The steady growth and demand have made 3D printing an affordable and consumer-friendly craft. Due to the shrinking barrier-of-entry, the technology is increasingly integrated into wider areas of science and research, as well as the manufacturing industry in general. The demand for individualized medical solutions is continuously expanding in multiple fields, such as medical training and patient-specific surgical guides—exposing a need for further development of these technologies for mainstream applications. The research areas of material science, life science, biotechnology, and medical applications have greatly benefited from the increasing availability of this technology—especially in regard to 3D printing high viscosity substrates which are biological, biocompatible, or bioresorbable. In this area of research and development, mainly micro-extrusion syringe-based systems are used. However, these extruders are limited to the volume of the utilized syringe. The presented work primary objective was to develop a multi-material 3D printer capable of processing substrates with a wide variety of viscosities, with a particular focus on biological-based substrates. The entire project was developed with an open-source mindset—with the intent of furthering future research possibilities within this space. A coreXY printer (0.5x0.5x0.5 m) was developed, including an automatic tool changing mechanism. A peristaltic pump and ink jet extruder were developed and manufactured to improve their processability for additive manufacturing and extrusion of hydrogels and as well as liquids.

Composition-Dependent Protein-Material Interaction of Poly(Methyl Methacrylate-co-styrene) Nanoparticle Series.

Polymer nanoparticles continue to be of high interest in life science applications. Still, adsorption processes occurring in protein-containing media and their implications for biological responses are not generally predictable. Here, the effect of nanoparticle composition on the adsorption of bovine serum albumin (BSA), fibronectin (FN) and immunoglobulin G (IgG) as structurally and functionally different model proteins was explored by systematically altering the composition of poly(methyl methacrylate-co-styrene) nanoparticles with sizes in a range of about 550 nm. As determined by protein depletion from the suspension medium via a colorimetric assay, BSA and IgG adsorbed at similar quantities, while FN reached larger masses of adsorbed protein (up to 0.4 ± 0.06 µg·cm-2 BSA, 0.42 ± 0.09 µg·cm-2 IgG, 0.72 ± 0.04 µg·cm-2 FN). A higher content of styrene as the more hydrophobic polymer component enhanced protein binding, which suggests a contribution of hydrophobic interactions despite the particles exhibiting strongly negatively charged surfaces with zeta potentials of -44 to -52 mV. The quantities of adsorbed proteins were estimated to correspond to a confluent surface coverage. Overall, this study illustrated how protein binding can be controlled by systematically varying the nanoparticle bulk composition and may serve as a basis for establishing interfaces with a targeted level of protein retention and/or presentation.

Advances in sparse dynamic scanning in spectromicroscopy through compressive sensing.

Scanning microscopies and spectroscopies like X-ray Fluorescence (XRF), Scanning Transmission X-ray Microscopy (STXM), and Ptychography are of very high scientific importance as they can be employed in several research fields. Methodology and technology advances aim at analysing larger samples at better resolutions, improved sensitivities and higher acquisition speeds. The frontiers of those advances are in detectors, radiation sources, motors, but also in acquisition and analysis software together with general methodology improvements. We have recently introduced and fully implemented an intelligent scanning methodology based on compressive sensing, on a soft X-ray microscopy beamline. This demonstrated sparse low energy XRF scanning of dynamically chosen regions of interest in combination with STXM, yielding spectroimaging data in the megapixel-range and in shorter timeframes than were previously not feasible. This research has been further developed and has been applied to scientific applications in biology. The developments are mostly in the dynamic triggering decisional mechanism in order to incorporate modern Machine Learning (ML) but also in the suitable integration of the method in the control system, making it available for other beamlines and imaging techniques. On the applications front, the method was previously successfully used on different samples, from lung and ovarian human tissues to plant root sections. This manuscript introduces the latest methodology advances and demonstrates their applications in life and environmental sciences. Lastly, it highlights the auxiliary development of a mobile application, designed to assist the user in the selection of specific regions of interest in an easy way.

Application of thermal analysis methods in biology and medicine

Thermal analysis methods are widely used in the characterization of substances and materials in chemistry and engineering. But they also find their application in life sciences: biology and medicine. This paper exhaustively and critically reviews the application of the most commonly used thermal analysis methods for the characterization of organs and tissues of plants, animals, and humans. The methods are suitable for differentiating between several types of water in a cell, optimizing treatment, storage, or cultivation conditions, following plant or animal development, medical diagnostics and modelling, and more. Expertise developed in the characterization of synthetic materials and molecules can be transferred to that of biological tissues and biomolecules and opens a perspective for interdisciplinary research. Still, researchers should take into consideration the inherent complexity of biological samples, as well as inevitable changes when isolating the tissue from the living organism.

ASD2023: towards the integrating landscapes of allosteric knowledgebase.

Allosteric regulation, induced by perturbations at an allosteric site topographically distinct from the orthosteric site, is one of the most direct and efficient ways to fine-tune macromolecular function. The Allosteric Database (ASD; accessible online at http://mdl.shsmu.edu.cn/ASD) has been systematically developed since 2009 to provide comprehensive information on allosteric regulation. In recent years, allostery has seen sustained growth and wide-ranging applications in life sciences, from basic research to new therapeutics development, while also elucidating emerging obstacles across allosteric research stages. To overcome these challenges and maintain high-quality data center services, novel features were curated in the ASD2023 update: (i) 66589 potential allosteric sites, covering>80% of the human proteome and constituting the human allosteric pocketome; (ii) 748 allosteric protein-protein interaction (PPI) modulators with clear mechanisms, aiding protein machine studies and PPI-targeted drug discovery; (iii) 'Allosteric Hit-to-Lead,' a pioneering dataset providing panoramic views from 87 well-defined allosteric hits to 6565 leadsand (iv) 456 dualsteric modulators for exploring the simultaneous regulation of allosteric and orthosteric sites. Meanwhile, ASD2023 maintains a significant growth of foundational allosteric data. Based on these efforts, the allosteric knowledgebase is progressively evolving towards an integrated landscape, facilitating advancements in allosteric target identification, mechanistic explorationand drug discovery.

Mechano-Driven Tribo-Electrophoresis Enabled Human-Droplet Interaction in Three-dimensional Space.

Electronically controlled droplet manipulation has widespread applications in biochemistry, life sciences and industry. However, current technologies such as electrowetting, electrostatics, and surface charge printing rely on complex electrode arrays and external power supplies, leading to inefficient manipulation. In light of these limitations, w e propose a novel method that leverages tribo-electrophoresis (TEP) to pipette in an oil medium, thereby enabling human-droplet interactions to be constructed with greater efficiency. O ur approach involves in the rational design of a triboelectric nanogenerator-electrostatic tweezer that generates an electric field to charge the droplet and improves the maneuverability of the charged droplet, including aligned/non-aligned pipetting, stable transport in clamped state, which can be accomplished solely by hand motion. The TEP method not only provides droplets with the freedom to move in three dimensions, but also offers a feasibility case for chemical reactions in the liquid phase and non-invasive sample extraction. This breakthrough establishes a cornerstone for human-droplet interactions capitalized on triboelectric nanogenerators, opening new avenues for research in droplet manipulation. This article is protected by copyright. All rights reserved.

Unknown cell class distinction via neural network based scattering snapshot recognition.

Neural network-based image classification is widely used in life science applications. However, it is essential to extrapolate a correct classification method for unknown images, where no prior knowledge can be utilised. Under a closed set assumption, unknown images will be inevitably misclassified, but this can be genuinely overcome choosing an open-set classification approach, which first generates an in-distribution of identified images to successively discriminate out-of-distribution images. The testing of such image classification for single cell applications in life science scenarios has yet to be done but could broaden our expertise in quantifying the influence of prediction uncertainty in deep learning. In this framework, we implemented the open-set concept on scattering snapshots of living cells to distinguish between unknown and known cell classes, targeting four different known monoblast cell classes and a single tumoral unknown monoblast cell line. We also investigated the influence on experimental sample errors and optimised neural network hyperparameters to obtain a high unknown cell class detection accuracy. We discovered that our open-set approach exhibits robustness against sample noise, a crucial aspect for its application in life science. Moreover, the presented open-set based neural network reveals measurement uncertainty out of the cell prediction, which can be applied to a wide range of single cell classifications.