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Periodicals

Tweet Influence on Market Trends: Analyzing the Impact of Social Media Sentiment on Biotech Stocks

This study investigates the relationship between tweet sentiment across diverse categories: news, company opinions, CEO opinions, competitor opinions, and stock market behavior in the biotechnology sector, with a focus on understanding the impact of social media discourse on investor sentiment and decision-making processes. We analyzed historical stock market data for ten of the largest and most influential pharmaceutical companies alongside Twitter data related to COVID-19, vaccines, the companies, and their respective CEOs. Using VADER sentiment analysis, we examined the sentiment scores of tweets and assessed their relationships with stock market performance. We employed ARIMA (AutoRegressive Integrated Moving Average) and VAR (Vector AutoRegression) models to forecast stock market performance, incorporating sentiment covariates to improve predictions. Our findings revealed a complex interplay between tweet sentiment, news, biotech companies, their CEOs, and stock market performance, emphasizing the importance of considering diverse factors when modeling and predicting stock prices. This study provides valuable insights into the influence of social media on the financial sector and lays a foundation for future research aimed at refining stock price prediction models.

Biotechnology• 2024• arXiv
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Periodicals

Wavelength and Polarization Multiplexed Nonlocal Metasurface for Quantitative Phase Microscopy

Imaging transparent samples remains an ongoing challenge in the study of unstained biological cells and material samples. Widely used methods trade off system complexity, cost and bulk, computational efficiency and information content. Here we demonstrate the use of a nonlocal metasurface located in the object plane for obtaining single-shot, low-noise differential phase contrast images visualising phase gradients along orthogonal directions in a sample obtained at wavelengths of 613 nm and 656 nm. Furthermore, we show that these images are sufficient to calculate the quantitative phase introduced into the transmitted optical field by the sample. We find that the recovered phase of an optical field generated by a spatial light modulator is in good agreement with specified values. We also present information-rich differential phase contrast images of unstained HeLa cells with the recovered phase excursion values consistent with the literature. Our results demonstrate the potential for metasurfaces as a platform for extracting information from an optical field for use in next-generation compact imaging systems with applications in medical diagnostics, biotechnology, and materials science.

Biotechnology• 2026• arXiv
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Periodicals

Structure and Electrical Properties of DNA Nanotubes Embedded in Lipid Bilayer Membranes

Engineering the synthetic nanopores through lipid bilayer membrane to access the interior of a cell is a long persisting challenge in biotechnology. Here, we demonstrate the stability and dynamics of a tile-based 6-helix DNA nanotube (DNT) embedded in POPC lipid bilayer using the analysis of 0.2 microsecond long equilibrium MD simulation trajectories. We observe that the head groups of the lipid molecules close to the lumen cooperatively tilt towards the hydrophilic sugar-phosphate backbone of DNA and form a toroidal structure around the patch of DNT protruding in the membrane. Further, we explore the effect of ionic concentrations to the in-solution structure and stability of the lipid-DNT complex. Transmembrane ionic current measurements for the constant electric field MD simulation provide the I-V characteristics of the water filled DNT lumen in lipid membrane. With increasing salt concentrations, the measured values of transmembrane ionic conductance of the porous DNT lumen vary from 4.3 nS to 20.6 nS. Simulations of the DNTs with ssDNA and dsDNA overhangs at the mouth of the pore show gating effect with remarkable difference in the transmembrane ionic conductivities for open and close state nanopores.

Biotechnology• 2017• arXiv
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Periodicals

Removing Biases from Molecular Representations via Information Maximization

High-throughput drug screening -- using cell imaging or gene expression measurements as readouts of drug effect -- is a critical tool in biotechnology to assess and understand the relationship between the chemical structure and biological activity of a drug. Since large-scale screens have to be divided into multiple experiments, a key difficulty is dealing with batch effects, which can introduce systematic errors and non-biological associations in the data. We propose InfoCORE, an Information maximization approach for COnfounder REmoval, to effectively deal with batch effects and obtain refined molecular representations. InfoCORE establishes a variational lower bound on the conditional mutual information of the latent representations given a batch identifier. It adaptively reweighs samples to equalize their implied batch distribution. Extensive experiments on drug screening data reveal InfoCORE's superior performance in a multitude of tasks including molecular property prediction and molecule-phenotype retrieval. Additionally, we show results for how InfoCORE offers a versatile framework and resolves general distribution shifts and issues of data fairness by minimizing correlation with spurious features or removing sensitive attributes. The code is available at https://github.com/uhlerlab/InfoCORE.

Biotechnology• 2023• arXiv
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Periodicals

A small PAM optimises target recognition in the CRISPR-Cas immune system

CRISPR-Cas is an adaptive immune mechanism that has been harnessed for a variety of genetic engineering applications: the Cas9 protein recognises a 2-5nt DNA motif, known as the PAM, and a programmable crRNA binds a target DNA sequence that is then cleaved. While off-target activity is undesirable, it occurs because cross-reactivity was beneficial in the immune system on which the machinery is based. Here, a stochastic model of the target recognition reaction was derived to study the specificity of the innate immune mechanism in bacteria. CRISPR systems with Cas9 proteins that recognised PAMs of varying lengths were tested on self and phage DNA. The model showed that the energy associated with PAM binding impacted mismatch tolerance, cleavage probability, and cleavage time. Small PAMs allowed the CRISPR to balance catching mutant phages, avoiding self-targeting, and quickly dissociating from critically non-matching sequences. Additionally, the results revealed a lower tolerance to mismatches in the PAM and a PAM-proximal region known as the seed, as seen in experiments. This work illustrates the role that the Cas9 protein has in dictating the specificity of DNA cleavage that can aid in preventing off-target activity in biotechnology applications.

Biotechnology• 2021• arXiv
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Periodicals

BeeRNA: tertiary structure-based RNA inverse folding using Artificial Bee Colony

The Ribonucleic Acid (RNA) inverse folding problem, designing nucleotide sequences that fold into specific tertiary structures, is a fundamental computational biology problem with important applications in synthetic biology and bioengineering. The design of complex three-dimensional RNA architectures remains computationally demanding and mostly unresolved, as most existing approaches focus on secondary structures. In order to address tertiary RNA inverse folding, we present BeeRNA, a bio-inspired method that employs the Artificial Bee Colony (ABC) optimization algorithm. Our approach combines base-pair distance filtering with RMSD-based structural assessment using RhoFold for structure prediction, resulting in a two-stage fitness evaluation strategy. To guarantee biologically plausible sequences with balanced GC content, the algorithm takes thermodynamic constraints and adaptive mutation rates into consideration. In this work, we focus primarily on short and medium-length RNAs ($<$ 100 nucleotides), a biologically significant regime that includes microRNAs (miRNAs), aptamers, and ribozymes, where BeeRNA achieves high structural fidelity with practical CPU runtimes. The lightweight, training-free implementation will be publicly released for reproducibility, offering a promising bio-inspired approach for RNA design in therapeutics and biotechnology.

Biotechnology• 2025• arXiv
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Periodicals

Bioconvection and Bloom in Porous environment and Its Assisted Technologies: A Review

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.

Biotechnology• 2025• arXiv
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Periodicals

Bayesian Variable Selection with Structure Learning: Applications in Integrative Genomics

Significant advances in biotechnology have allowed for simultaneous measurement of molecular data points across multiple genomic and transcriptomic levels from a single tumor/cancer sample. This has motivated systematic approaches to integrate multi-dimensional structured datasets since cancer development and progression is driven by numerous co-ordinated molecular alterations and the interactions between them. We propose a novel two-step Bayesian approach that combines a variable selection framework with integrative structure learning between multiple sources of data. The structure learning in the first step is accomplished through novel joint graphical models for heterogeneous (mixed scale) data allowing for flexible incorporation of prior knowledge. This structure learning subsequently informs the variable selection in the second step to identify groups of molecular features within and across platforms associated with outcomes of cancer progression. The variable selection strategy adjusts for collinearity and multiplicity, and also has theoretical justifications. We evaluate our methods through simulations and apply them to a motivating genomic (DNA copy number and methylation) and transcriptomic (mRNA expression) data for assessing important markers associated with Glioblastoma progression.

Biotechnology• 2015• arXiv
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Periodicals

Solving Maximum Clique Problem for Protein Structure Similarity

A basic assumption of molecular biology is that proteins sharing close three-dimensional (3D) structures are likely to share a common function and in most cases derive from a same ancestor. Computing the similarity between two protein structures is therefore a crucial task and has been extensively investigated. Evaluating the similarity of two proteins can be done by finding an optimal one-to-one matching between their components, which is equivalent to identifying a maximum weighted clique in a specific "alignment graph". In this paper we present a new integer programming formulation for solving such clique problems. The model has been implemented using the ILOG CPLEX Callable Library. In addition, we designed a dedicated branch and bound algorithm for solving the maximum cardinality clique problem. Both approaches have been integrated in VAST (Vector Alignment Search Tool) - a software for aligning protein 3D structures largely used in NCBI (National Center for Biotechnology Information). The original VAST clique solver uses the well known Bron and Kerbosh algorithm (BK). Our computational results on real life protein alignment instances show that our branch and bound algorithm is up to 116 times faster than BK for the largest proteins.

Biotechnology• 2009• arXiv
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Periodicals

The light quantum mechanism of PCR efficiency oscillation with gold nanoparticle concentration

The widespread application of nanomaterials in polymerase chain reaction (PCR) technology has opened new avenues for improving detection methods in the biomedical field. Recent experiments (Chem. Eur. J. 2023, e202203513) have revealed oscillatory behavior between PCR efficiency and the concentration of gold nanoparticles in the pM range, potentially linked to the long-range Coulomb interactions among charged colloidal particles and the quantum size effect of nanoparticle electronic states. Through Monte Carlo simulation, we discovered that the radial distribution function of gold nanoparticles in solution gradually exhibits peak characteristics with increasing charge, triggering coherent photon behavior in Rayleigh scattering within the solution, thereby influencing the efficiency of reusing released photons in the PCR chain reaction. The study demonstrates that the oscillation period aligns with the wavelength of downstream reaction photons, while their energy matches the width of energy levels near the Fermi level of gold nanoparticles. The latter can absorb and store electron states internally, promoting upstream PCR reactions through subsequent re-release, and compensating for energy deficiencies through the Boltzmann distribution of electrons. This work is poised to advance the application of PCR-specific precise detection methods in the field of quantum biotechnology.

Biotechnology• 2024• arXiv
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Periodicals

Real-time Monitoring of Cellular Cultures with Electrolyte-gated Carbon Nanotube Transistors

Cell-based biosensors constitute a fundamental tool in biotechnology, and their relevance has greatly increased in recent years as a result of a surging demand for reduced animal testing and for high-throughput and cost-effective in vitro screening platforms dedicated to environmental and biomedical diagnostics, drug development and toxicology. In this context, electrochemical/electronic cell-based biosensors represent a promising class of devices that enable long-term and real-time monitoring of cell physiology in a non-invasive and label-free fashion, with a remarkable potential for process automation and parallelization. Common limitations of this class of devices at large include the need for substrate surface modification strategies to ensure cell adhesion and immobilization, limited compatibility with complementary optical cell-probing techniques, and need for frequency-dependent measurements, which rely on elaborated equivalent electrical circuit models for data analysis and interpretation. We hereby demonstrate the monitoring of cell adhesion and detachment through the time-dependent variations in the quasi-static characteristic current curves of a highly stable electrolyte-gated transistor, based on an optically transparent network of printable polymer-wrapped semiconducting carbon-nanotubes.

Biotechnology• 2019• arXiv
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Periodicals

PersGNN: Applying Topological Data Analysis and Geometric Deep Learning to Structure-Based Protein Function Prediction

Understanding protein structure-function relationships is a key challenge in computational biology, with applications across the biotechnology and pharmaceutical industries. While it is known that protein structure directly impacts protein function, many functional prediction tasks use only protein sequence. In this work, we isolate protein structure to make functional annotations for proteins in the Protein Data Bank in order to study the expressiveness of different structure-based prediction schemes. We present PersGNN - an end-to-end trainable deep learning model that combines graph representation learning with topological data analysis to capture a complex set of both local and global structural features. While variations of these techniques have been successfully applied to proteins before, we demonstrate that our hybridized approach, PersGNN, outperforms either method on its own as well as a baseline neural network that learns from the same information. PersGNN achieves a 9.3% boost in area under the precision recall curve (AUPR) compared to the best individual model, as well as high F1 scores across different gene ontology categories, indicating the transferability of this approach.

Biotechnology• 2020• arXiv
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Periodicals

Goal assembly as a formalism of evolvable design

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.

Biotechnology• 2025• arXiv
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Periodicals

CMADiff: Cross-Modal Aligned Diffusion for Controllable Protein Generation

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.

Biotechnology• 2025• arXiv
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Periodicals

Human Factors in Biocybersecurity Wargames

Within the field of biocybersecurity, it is important to understand what vulnerabilities may be uncovered in the processing of biologics as well as how they can be safeguarded as they intersect with cyber and cyberphysical systems, as noted by the Peccoud Lab, to ensure not only product and brand integrity, but protect those served. Recent findings have revealed that biological systems can be used to compromise computer systems and vice versa. While regular and sophisticated attacks are still years away, time is of the essence to better understand ways to deepen critique and grasp intersectional vulnerabilities within bioprocessing as processes involved become increasingly digitally accessible. Wargames have been shown to be successful with-in improving group dynamics in response to anticipated cyber threats, and they can be used towards addressing possible threats within biocybersecurity. Within this paper, we discuss the growing prominence of biocybersecurity, the importance of biocybersecurity to bioprocessing , with respect to domestic and international contexts, and reasons for emphasizing the biological component in the face of explosive growth in biotechnology and thus separating the terms biocybersecurity and cyberbiosecurity. Additionally, a discussion and manual is provided for a simulation towards organizational learning to sense and shore up vulnerabilities that may emerge within an organization's bioprocessing pipeline

Biotechnology• 2020• arXiv
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Periodicals

Automatic Raman Measurements in a High-Throughput Bioprocess Development Lab

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.

Biotechnology• 2025• arXiv
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Periodicals

The Budapest Amyloid Predictor and its Applications

The amyloid state of proteins is widely studied with relevancy in neurology, biochemistry, and biotechnology. In contrast with amorphous aggregation, the amyloid state has a well-defined structure, consisting of parallel and anti-parallel $β$-sheets in a periodically repeated formation. The understanding of the amyloid state is growing with the development of novel molecular imaging tools, like cryogenic electron microscopy. Sequence-based amyloid predictors were developed by using mostly artificial neural networks (ANNs) as the underlying computational techniques. From a good neural network-based predictor, it is a very difficult task to identify those attributes of the input amino acid sequence, which implied the decision of the network. Here we present a Support Vector Machine (SVM)-based predictor for hexapeptides with correctness higher than 84\%, i.e., it is at least as good as the published ANN-based tools. Unlike the artificial neural networks, the decision of the SVMs are much easier to analyze, and from a good predictor, we can infer rich biochemical knowledge. Availability and Implementation: The Budapest Amyloid Predictor webserver is freely available at https://pitgroup.org/bap.

Biotechnology• 2020• arXiv
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Periodicals

Changing topic bias in biomedical science maps by linking documents through alternative data sources: policy documents, patents, authors, Facebook, and Twitter

Traditional science maps visualize topics by clustering documents within a network, but they are inherently biased toward clustering certain topics over others. If these topics could be chosen, then the science maps could be tailored for different needs. In this paper, we explore the extent to which the topic bias of a science map can be changed by choosing different data sources to build the document network. We analyze this by evaluating the clustering effectiveness of several topic categories over two sources that are traditionally used for the creation of science maps (citations and text similarity) and six non-traditional data sources, which we found favor different kinds of topics: Health issues for Facebook users, biotechnology topics for patent families, government and social issues for policy documents, food topics for Twitter conversations, nursing topics for Twitter users, and geographical entities for document authors (the favoring in this latter source was particularly strong). Our results show that diverse data sources can be used to control topic bias, which opens up the possibility of creating science maps tailored for different needs.

Biotechnology• 2024• arXiv
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Periodicals

Multimodal Regression for Enzyme Turnover Rates Prediction

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.

Biotechnology• 2025• arXiv
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Periodicals

Emergent swimming strategies of a smart three-bead swimmer

Low-Reynolds-number microswimmers have recently attracted much interest for their ubiquity in biology and their applications in biotechnology and medicine. However, a key obstacle for the design and deployment of artificial microswimmers lies in their autonomy: to successfully perform tasks in any real-world scenario, these swimmers need to be able to interact with and adapt to their environment without external control. Here, we train a simple two-dimensional model microswimmer (consisting of three-bead) to learn autonomous swimming strategies via Reinforcement Learning, focusing on neuroevolution techniques to derive controller architectures with minimal complexity. We identify five different, characteristic swimming gaits: three of these gaits lead to directed locomotion with varying grades of efficiency and two gaits result in a rotational, inefficient movement. Remarkably, all of these gaits can be achieved by very simple neural networks (with less than ten nodes and weights), showing that low-Reynolds-number swimming can be achieved efficiently and robustly while requiring only minimal computational power. These results are of particular interest to the experimental design of artificial microswimmers and may have implications for modeling biological microorganisms such as Chlamydomonas reinhardtii.

Biotechnology• 2026• arXiv
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Periodicals

Improving Company Valuations with Automated Knowledge Discovery, Extraction and Fusion

Performing company valuations within the domain of biotechnology, pharmacy and medical technology is a challenging task, especially when considering the unique set of risks biotech start-ups face when entering new markets. Companies specialized in global valuation services, therefore, combine valuation models and past experience with heterogeneous metrics and indicators that provide insights into a company's performance. This paper illustrates how automated knowledge discovery, extraction and data fusion can be used to (i) obtain additional indicators that provide insights into the success of a company's product development efforts, and (ii) support labor-intensive data curation processes. We apply deep web knowledge acquisition methods to identify and harvest data on clinical trials that is hidden behind proprietary search interfaces and integrate the extracted data into the industry partner's company valuation ontology. In addition, focused Web crawls and shallow semantic parsing yield information on the company's key personnel and respective contact data, notifying domain experts of relevant changes that get then incorporated into the industry partner's company data.

Biotechnology• 2020• arXiv
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Periodicals

Photonic-integrated quantum sensor array for microscale magnetic localisation

Nitrogen-vacancy centres (NVs) are promising solid-state nanoscale quantum sensors for applications ranging from material science to biotechnology. Using multiple sensors simultaneously offers advantages for probing spatiotemporal correlations of fluctuating fields or the dynamics of point defects. In this work, by integrating NVs with foundry silicon-nitride photonic integrated circuits, we realise the scalable operation of eight localised NV sensors in an array, with simultaneous, distinct readout of the individual sensors. Using the eight NV sensors and machine-learning methods for multi-point magnetic field reconstruction, we demonstrate microscale magnetic localisation of a 30 $μ$m-sized needle tip. Experimentally, the needle tip can be localised with an error below its dimension and tracked dynamically with high fidelity. We further simulate the feasibility of our platform for monitoring the position and orientation of magnetic microrobots designed for biological and clinical purposes. Without the complexity of bulk optics, our photonic-integrated multi-sensor platform presents a step towards real-life biomedical applications under out-of-the-lab conditions.

Biotechnology• 2025• arXiv
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Periodicals

Generalized Inverse Optimal Control and its Application in Biology

Living organisms exhibit remarkable adaptations across all scales, from molecules to ecosystems. We believe that many of these adaptations correspond to optimal solutions driven by evolution, training, and underlying physical and chemical laws and constraints. While some argue against such optimality principles due to their potential ambiguity, we propose generalized inverse optimal control to infer them directly from data. This novel approach incorporates multi-criteria optimality, nestedness of objective functions on different scales, the presence of active constraints, the possibility of switches of optimality principles during the observed time horizon, maximization of robustness, and minimization of time as important special cases, as well as uncertainties involved with the mathematical modeling of biological systems. This data-driven approach ensures that optimality principles are not merely theoretical constructs but are firmly rooted in experimental observations. Furthermore, the inferred principles can be used in forward optimal control to predict and manipulate biological systems, with possible applications in bio-medicine, biotechnology, and agriculture. As discussed and illustrated, the well-posed problem formulation and the inference are challenging and require a substantial interdisciplinary effort in the development of theory and robust numerical methods.

Biotechnology• 2024• arXiv
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Cell-in-cell structures are involved in the competition between cells in breast cancer

Breast cancer is the most common cancer in women worldwide, and discovering the biomarkers of this disease became so vital nowadays and Cell in Cell structure could be one of them, and it may be used as an available proxy for tumor malignancy. (CICs) are unusual in that keep morphologically healthy cells within another cell. They are found in various human cancers and result from active cell-cell interaction, and it has different kinds. In this study, we analyzed the microarray data from GEO (GSE103865) to genetically evaluate CICs' incidence in samples obtained from breast cancer patients to understand the relationship between the rate of CIC and the prognosis of breast cancer. The preprocessing was performed using R software. The DAVID website was used to analyze gene ontology (GO) and Gene and Genome (KEGG) pathways. The protein-protein interactions (PPIs) of the obtained DEGs were assessed using the STRING website, and hub modules in Cytoscape and cytoHubba were screened. According to the results from analyzing the 20 hub genes, we understood that overexpression of our Top genes is effective in focal adhesion, ECM-receptor interaction, platelet activation and PI3K-Akt signaling pathway, which shows that changes in these pathways could be the reason the overexpression of CICs in breast cancer. These data and research by many others have uncovered various genes involved in CIC formation and have started to give us an idea of why they are formed and how they could contribute to breast cancer

Biotechnology• 2021• arXiv
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