Heterogeneous Complex Research Articles

Standardizing Chondrocyte Isolation and Articular Cartilage Decellularization: A Versatile Bioink for Tissue Engineering Applications.

The extracellular matrix (ECM) is a noncellular component of tissues that provides structural and biochemical support to cells. The purpose of decellularization is to provide a tissue-specific niche to preserve the architecture, composition, and signaling molecules of the ECM. The current protocol discusses the standardization of chondrocyte isolation and the preparation of acellular ECM as a bioink additive from human native articular cartilage. Isolated chondrocytes with bioink additives provide a tissue-specific microenvironment. Herein, we discuss a standardized protocol with multiple applications in the area of organ-on-a-chip model development, spheroid formation, microfluidics platform, bioprinting, and tissue engineering. Cartilage tissue engineering is complex owing to the heterogeneous complex proteins, which are a challenge to synthesize; hence, this protocol in many ways offers cues to exploit the acellular ECM for multiple ongoing research studies.

Glycosort: A Computational Solution to Post-process Quantitative Large-Scale Intact Glycopeptide Analyses.

Protein glycosylation is a post-translational modification involving the addition of carbohydrates to proteins and plays a crucial role in protein folding and various biological processes such as cell recognition, differentiation, and immune response. The vast array of natural sugars available allows the generation of plenty of unique glycan structures in proteins, adding complexity to the regulation and biological functions of glycans. The diversity is further increased by enzymatic site preferences and stereochemical conjugation, leading to an immense amount of different glycan structures. Understanding glycosylation heterogeneity is vital for unraveling the impact of glycans on different biological functions. Evaluating site occupancies and structural heterogeneity aids in comprehending glycan-related alterations in biological processes. Several software tools are available for large-scale glycoproteomics studies; however, integrating identification and quantitative data to assess heterogeneity complexity often requires extensive manual data processing. To address this challenge, we present a python script that automates the integration of Byonic and MaxQuant outputs for glycoproteomic data analysis. The script enables the calculation of site occupancy percentages by glycans and facilitates the comparison of glycan structures and site occupancies between two groups. This automated tool offers researchers a means to organize and interpret their high-throughput quantitative glycoproteomic data effectively.

A new Takagi-Sugeno fuzzy system approach for fuzzy state feedback controller design and its application to malware propagation on heterogeneous complex network

Abstract— Nowadays, digital transformation has brought many great changes and is becoming an essential part of real life, however, it also goes along with a considerable likelihood of being targeted in cyberattack. Indeed, the more businesses embrace digital transformation or use online services, the more opportunities hackers have to expand their cyberattacks. Hence, there is an essential need on analyzing and predicting of cyberattack on network systems. For this aim, we propose to study a controlled fractional network-based SCIRS (Susceptible - Carrier - Infectious - Recovered - Susceptible) malware propagation model and its stabilization problem based on fractional interconnected Takagi-Sugeno fuzzy system. A fuzzy state feedback controller is proposed to asymptotically stabilize the unstable malware-free equilibrium of the proposed malware propagation model and then, we establish sufficient conditions in terms of linear matrix inequalities. The effectiveness of proposed approach is illustrated by a case study of SCIRS malware propagation model on heterogeneous complex network.

Microbial Melanin: Recent Developments and Challenges in the Production, Extraction, Purification and Application of Microbial Melanin

A class of biopolymers known by the common name melanin has several potential uses in the medicinal sciences, bioelectronics, cosmeceutical and bioremediation. These pigments wide distribution indicates that they help a variety of life forms- fight biotic and abiotic stressors. Different types of melanin, such as eumelanin, pheomelanin, allomelanin, pyomelanin, and neuromelanin, are produced by the oxidative polymerization of phenolic compounds in fungi and bacteria, primarily via the 1,8-dihydroxynaphthalene [DHN] or 3,4-dihydroxyphenylalanine [DOPA] pathways. The tyrosinase, laccase, and polyketide synthase are the groups of enzymes that are primarily responsible for the production of melanin in many microorganisms. Research has indicated that utilising recombinant technologies in conjunction with culture parameter manipulation can enhance melanin yield for industrial production. Its low solubility has hindered the development of extraction techniques despite tremendous efforts, and its heterogeneous structural complexity has hindered structural elucidation, which limits the full potential of its biotechnological applications. The process of extraction may differ slightly depending on the kind of tissue and where the melanin is located. Melanin pigments from various taxa of life (like Sepia, bacteria, fungi) have been the subject of countless investigations to expand our understanding of them and enable their effective use in a wide range of applications. Due to these studies, there is an immediate need for a thorough review of melanin pigments that have been isolated from microorganisms. A review that covers biosynthesis, bioproduction, characterization, and possible applications will aid researchers from a variety of backgrounds in appreciating the significance of microbial melanins and in organizing their own melanin-related research projects. In order to achieve this, the current study contrasts traditional and cutting-edge concepts for environmentally sustainable melanin extraction processes.

Clinical challenges and strategies on delivery of Oncolytic Virus

Oncolytic Virus therapy, including HSV-1 and adenovirus, is a new type of tumour immunotherapy that uses specific viruses to directly kill tumour cells or to induce the body's intrinsic and acquired immunity to generate anti-tumour immune responses. In addition, Oncolytic Virus is capable of recruiting immune cells to improve the tumour microenvironment, so the development of The clinical significance of oncolytic virus therapy lies in its potential to enhance the efficacy of other immunotherapeutic approaches. However, the delivery of Oncolytic Virus in current clinical trials has huge defects, which greatly limits the benefits of Oncolytic Virus and even produces certain side effects. Therefore, this review discusses the definition and mechanism of Oncolytic Virus and focuses on the defects in Oncolytic Virus delivery, such as systemic delivery and intratumoural delivery. Further, this review also summarizes some improvement strategies against the challenges of intratumoural heterogeneity and vascular complexity.

Crustal heterogeneity effects on coseismic deformation: numerical simulation of the 2008 MW 7.9 Wenchuan earthquake

Coseismic deformation of large earthquakes causes significant property damages and fatalities, which requires quantitative research of multiple disciplines such as geodesy, geological investigation, seismic tomography, and seismic dislocation theory. The finite element method accounts for material heterogeneity and geometric complexity, making it suitable for studying the coseismic deformation of large earthquakes. This paper develops a parallel elastic finite element program that utilizes split nodes and high-performance parallel computing technology on the FELAC software platform to study the coseismic deformation of large earthquakes. We verify the accuracy of the parallel elastic finite element program by comparing its results with the analytical solutions from seismic dislocation theory for four ideal earthquake cases. Finally, we established parallel elastic finite element models to study the coseismic deformation of the 2008 Wenchuan earthquake. The simulation results are consistent with the GPS and InSAR data. Coseismic surface deformation results are significantly influenced by medium regional heterogeneity with different layered structures besides the Longmenshan fault. The finite element program lay the foundation for the inversion of the coseismic fault rupture process based on the heterogeneous medium model and complex geometric model.

A new nuclear magnetic resonance-based permeability model based on two pore structure characterization methods for complex pore structure rocks: Permeability assessment in Nanpu Sag, China

The nuclear magnetic resonance (NMR) estimate of permeability is a fundamental method that has numerous applications in reservoir engineering and petrophysics. To improve the accuracy of the NMR-based permeability model, many variables are introduced into NMR-based permeability prediction models due to geometric complexity and pore structure heterogeneity. In this paper, two pore structure characterization methods are investigated based on the Kozeny-Carman model and equivalent component model. Furthermore, an NMR-based permeability model accounting for the effect of pore structure is developed based on the analysis of the relationship between two pore structure parameters, and it is applied to practically predict permeability. Results indicate that the new model-calculated permeability has good agreement with experimental data; moreover, the adaptability of the new NMR-based permeability prediction model is highly improved through reducing undetermined variables, and key parameters can be measured directly using NMR. The new model provides a valuable scientific resource and assists in the evaluation of hydrocarbon-bearing reservoirs with complex pore structure, such as tight sandstone, shale, and carbonate rock.

How does urbanization affect the fauna of the largest urban forest in Mexico?

Animals in urban forests provide ecological and cultural services, but the quality of these services often depends on floral diversity and animal diversity. In turn, animal diversity in urban forests is influenced by various aspects of habitat quality, in spatial scale- and taxon-dependent ways. Understanding the effect of individual habitat factors on different animal groups can provide valuable information for urban forest management. We estimated the relationships of 25 environmental, vegetation, land cover and anthropogenic perturbation variables to the fauna of Chapultepec Forest within Mexico City, Mexico. We included flower-visiting flies (Diptera: Bombyliidae, Syrphidae), bees (Hymenoptera: Apoidea), butterflies (Lepidoptera: Papilionoidea), birds, and reptiles (Squamata and Testudines). Insect populations were more strongly affected by herb and flower variables, while variables related to trees and shrubs were more important to vertebrate taxa. In general, human density, buildings cover and pavement cover were the most relevant anthropogenic perturbation factors. Birds and butterflies were more tolerant of urbanization than the other groups. Effects for each taxon are discussed. Our results suggest that urban park management plans should focus on enhancing habitat heterogeneity and vegetation structure complexity. Specifically, maintaining herb and tree diversity would likely promote animal diversity and ecosystem services in the Chapultepec Forest.

Determination of regions prone to sand production and the linkage to fluid flow rates by integrating rock strength parameters and microphotographs in the southern onshore basin, India

One of the main challenges for healthy hydrocarbon yields from the production wells is the sand production, and its control assumes significant importance for successful production operations. The prime objective remains to control the sand production without compromising on the fluid flow rates. Sand production is conventionally associated with diverse heterogeneous formations, especially poorly consolidated formations. Scrutinizing fluid flow rates at different orientations of perforations is a valuable analysis in controlling the sand production. This study was conducted to examine the case of sand production in a few development wells from Malleshwaram field of the Krishna Godavari (KG) basin in south India and provide alternatives to arrest this sand production. The influence of oriented perforations on fluid flow rates to minimize sanding tendencies was examined. Sand grain diameter was estimated using the distribution plots of the grain sizes obtained from the photomicrographs, and the average value of the grain size was estimated around 100 μm. The fluid flow model was simulated for actual scenario in the hydrocarbon bearing Nandigama formation using finite element modeling (FEM). Sanding tendencies in these formations were studied at varying depletion rates. Critical draw down pressure (CDDP), which is a function of both critical bottom hole pressure and reservoir pore pressure, for well 1 and well 2 is estimated as 4650 Psi and 3400 Psi, respectively, at 0% depletion. CDDP was used to estimate the onset of sanding, and the flow rate for horizontal perforation was calculated as 0.024548 m3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathrm{m}}^{3}$$\\end{document}/sec. Strength parameters, namely the unconfined compressive strength (UCS) and cohesion, were also investigated in all the wells to decipher the formation strength and a possible linkage to sanding. These results present a completely fresh analysis of the sanding potential and the related parameters responsible for it in these formations. Fluid flow rates for the producing reservoir are studied at different set of oriented and sized perforations. It was observed that a hiked flow rate in horizontal perforations maintains the production in a safe and no-sanding zone. This study provides vital information and a workflow for similar heterogeneous complex formations for designing the well completions, augmenting economic production and reinforcing future reservoir management.

GCN-Based Heterogeneous Complex Feature Learning to Enhance Predictability for LncRNA-Disease Associations.

Using computational models to predict potential lncRNA-disease associations (LDAs) has emerged as an effective supplement to bioexperiments for exploring the pathogenesis of diseases. However, current computational models still face limitations in their ability to learn the complex features of bionetworks. In this study, HGCNLDA, a model which combines graph convolutional network (GCN)-based aggregation, heterogeneous information fusion, and a bilinear-decoder to infer LDAs was proposed. Recognizing the need to extract essential features during data processing, our HGCNLDA explored four key steps for uncovering interaction patterns within the bionetwork: (1) a novel type of tripartite heterogeneous network, known as the lncRNA-disease-miRNA network (LDMN), was constructed using computed similarities and known associations. (2) Homogeneous and heterogeneous features of nodes were extracted from domains within the LDMN by a GCN-based encoder. (3) Feature fusions, including bipolymerization operations and attention mechanism, were employed to capture a more accurate and comprehensive representation of nodes. (4) Bilinear-decoder was used to rebuild the edge type (or rating type) for a specific node pair, resulting in the predicted association score. Through a 5-fold cross-validation on two data sets, namely, data set1 and data set2, our HGCNLDA consistently demonstrated superior performance compared to five related models. It almost achieved the highest AUROC and AUPR values on both data sets, especially on data set2 where the results obtained were more challenging and objective. Case studies involving three real cancer scenarios further validated the practicality of HGCNLDA in identifying potential LDAs in real-world contexts. The source code and data for this study are available at https://github.com/zywait/HGCNLDA.

A comprehensive investigation on performance heterogeneity of commercial-size fuel cell stacks during dynamics operation

The performance heterogeneity of commercial-size proton exchange membrane (PEM) fuel cells comprises both inter-cell consistency and intra-cell uniformity, which can lead to the generation of degrading stressors, such as harmful variations in local cell voltage and current density. However, there is a notable absence of a thorough investigation of the potential correlation between inter-cell consistency and intra-cell uniformity. To address this knowledge gap, a comprehensive experimental study was conducted via the current distribution model to assess the impact of several operating variables, including loading rate, cathode stoichiometry ratio, and cathode pressure, on the performance heterogeneity of commercial-size fuel cell stacks. The results indicate that cells situated on both end sides of the stack are more susceptible to non-equipotential phenomena owing to liquid water accumulation and the inhomogeneity of oxygen distribution, which in turn lead to the redistribution of the in-plane current as well as the deviation of the bipolar plate potential. Moreover, a significant correlation exists between inter-cell consistency and intra-cell uniformity of cells placed at both ends of the stack with a correlation coefficient > 0.85. The enhancement of fuel cell stack output performance and reduction of performance heterogeneity may be achieved by optimizing pressure and stoichiometric ratio. However, it should be noted that there exists a discrepancy in the operating conditions necessary to achieve optimal performance and minimal performance heterogeneity. Finally, the recommendations given for the implementation of cell voltage monitors (CVMs) were summarized. This study provides a comprehensive reference for analyzing complex heterogeneity issues within fuel cells, revealing significant potential in their application.

Oncogenes and cancer associated thrombosis: what can we learn from single cell genomics about risks and mechanisms?

Single cell analysis of cancer cell transcriptome may shed a completely new light on cancer-associated thrombosis (CAT). CAT causes morbid, and sometimes lethal complications in certain human cancers known to be associated with high risk of venous thromboembolism (VTE), pulmonary embolism (PE) or arterial thromboembolism (ATE), all of which worsen patients' prognosis. How active cancers drive these processes has long evaded scrutiny. While "unspecific" microenvironmental effects and consequences of patient care (e.g., chemotherapy) have been implicated in pathogenesis of CAT, it has also been suggested that oncogenic pathways driven by either genetic (mutations), or epigenetic (methylation) events may influence the coagulant phenotype of cancer cells and stroma, and thereby modulate the VTE/PE risk. Consequently, the spectrum of driver events and their downstream effector mechanisms may, to some extent, explain the heterogeneity of CAT manifestations between cancer types, molecular subtypes, and individual cases, with thrombosis-promoting, or -protective mutations. Understanding this molecular causation is important if rationally designed countermeasures were to be deployed to mitigate the clinical impact of CAT in individual cancer patients. In this regard, multi-omic analysis of human cancers, especially at a single cell level, has brought a new meaning to concepts of cellular heterogeneity, plasticity, and multicellular complexity of the tumour microenvironment, with profound and still relatively unexplored implications for the pathogenesis of CAT. Indeed, cancers may contain molecularly distinct cellular subpopulations, or dynamic epigenetic states associated with different profiles of coagulant activity. In this article we discuss some of the relevant lessons from the single cell "omics" and how they could unlock new potential mechanisms through which cancer driving oncogenic lesions may modulate CAT, with possible consequences for patient stratification, care, and outcomes.

Dynamic Queries through Augmented Reality for Intelligent Video Systems

Artificial vision system applications have generated significant interest as they allow information to be obtained through one or several of the cameras that can be found in daily life in many places, such as parks, avenues, squares, houses, etc. When the aim is to obtain information from large areas, it can become complicated if it is necessary to track an object of interest, such as people or vehicles, due to the vision space that a single camera can cover; this opens the way to distributed zone monitoring systems made up of a set of cameras that aim to cover a larger area. Distributed zone monitoring systems add great versatility, becoming more complex in terms of the complexity of information analysis, communication, interoperability, and heterogeneity in the interpretation of information. In the literature, the development of distributed schemes has focused on representing data communication and sharing challenges. Currently, there are no specific criteria for information exchange and analysis in a distributed system; hence, different models and architectures have been proposed. In this work, the authors present a framework to provide homogeneity in a distributed monitoring system. The information is obtained from different cameras, where a global reference system is defined for generated trajectories, which are mapped independently of the model used to obtain the dynamics of the movement of people within the vision area of a distributed system, thus allowing for its use in works where there is a large amount of information from heterogeneous sources. Furthermore, we propose a novel similarity metric that allows for information queries from heterogeneous sources. Finally, to evaluate the proposed performance, the authors developed several distributed query applications in an augmented reality system based on realistic environments and historical data retrieval using a client–server model.

Machine learning and deep learning for mineralogy interpretation and CO2 saturation estimation in geological carbon Storage: A case study in the Illinois Basin

Carbon capture and storage (CCS) is a promising approach to simultaneously maintaining energy security and reducing carbon dioxide (CO2) emissions under the current energy portfolio that is dominated by fossil fuel energy. Pre-injection formation characterization and post-injection CO2 monitoring are two critical tasks to guarantee storage efficiency in CCS. The CCS projects in the Illinois Basin, the first large-scale CO2 injection into saline aquifers in the United States, employed conventional and the latest pulsed neutron logging (PNL) tools for mineralogy interpretation and CO2 saturation estimation, which provide valuable references for future CCS projects. Because of the inherent fuzziness of petrophysical measurements and complex subsurface heterogeneity, interpreting well-logging data is time-consuming, and its accuracy can be user-biased. In recent years, data-driven methods have been widely used to capture the non-linear patterns between input features and interpretation results. This work applied and evaluated four commonly used machine learning (ML) models, including ridge regression (RR), random forest (RF), gradient boosting regression (GBR), support vector regression (SVR), and one deep learning (DL) model, the artificial neural network (ANN). We optimized the hyperparameters of the four ML models and the DL model using the simulated annealing algorithm and the grid search strategy, respectively. The input features of the mineralogy interpretation models were eleven conventional well-logging parameters, and the label data (i.e., ground truth) were the porosity and volumetric fractions of six minerals, including quartz, feldspar, dolomite, calcite, clay, and iron minerals. The results demonstrated that the GBR and RF models were superior in predicting volumetric fractions of minerals and porosity; label data with low coefficient of variation (CV) values tended to yield better performance. For CO2 saturation estimation, the RF was the best-performing model, followed by SVR, ANN, GBR, and RR. Furthermore, we conducted feature importance ranking using the permutation importance algorithm and found that the formation sigma and well pressure were the most important features in this study. The study of CCS projects in the Illinois Basin bridges the gap between the limited knowledge and understanding of geological carbon storage and the increasing demand for reliable, cost-effective, and sustainable energy solutions.

Spatial evolution of pore and fracture structures in coal under unloading confining pressure: A stratified nuclear magnetic resonance approach

The inherent spatial complexity and strong heterogeneity characterise the pore and fracture structures (PFS) in coal. Understanding the spatial evolution of PFS in coal under unloading confining pressure is crucial for ensuring the safety of coal mining operations. In this study, the stratified nuclear magnetic resonance (NMR) technique and a triaxial mechanical loading system were combined to realise real-time observation of the spatial evolution of PFS in coal during the unloading confining pressure process. A conceptual model depicting the spatial evolution of PFS in coal under unloading confining pressure was formulated. Based on our experimental findings, the mesoscopic mechanical behaviour of coal subjected to unloading confining pressure was simulated using PFC 2D. Our research reveals that throughout the unloading confining pressure process, the PFS within coal samples simultaneously exhibits characteristics of both compacted and fractured states, with mutual transformation occurring in the adsorption and seepage pores. A reduction in confining pressure results in a notable escalation of observed damage within the coal samples and intensified development of PFS. Furthermore, our numerical simulation results closely align with NMR test results, providing additional validation to our findings.

Structure and dynamics of endogenous cardiac troponin complex in human heart tissue captured by native nanoproteomics

Protein complexes are highly dynamic entities that display substantial diversity in their assembly, post-translational modifications, and non-covalent interactions, allowing them to play critical roles in various biological processes. The heterogeneity, dynamic nature, and low abundance of protein complexes in their native states present challenges to study using conventional structural biology techniques. Here we develop a native nanoproteomics strategy for the enrichment and subsequent native top-down mass spectrometry (nTDMS) analysis of endogenous cardiac troponin (cTn) complex directly from human heart tissue. The cTn complex is enriched and purified using peptide-functionalized superparamagnetic nanoparticles under non-denaturing conditions to enable the isotopic resolution of cTn complex, revealing their complex structure and assembly. Moreover, nTDMS elucidates the stoichiometry and composition of the cTn complex, localizes Ca2+ binding domains, defines cTn-Ca2+ binding dynamics, and provides high-resolution mapping of the proteoform landscape. This native nanoproteomics strategy opens a paradigm for structural characterization of endogenous native protein complexes.

International standards for monoclonal antibodies for assessing the biological activity of medicines: A status update

Scientific relevance. The clinical effects and the expiration of patents for original (reference) biotechnological medicines based on monoclonal antibodies (mAbs) stimulated the development of biosimilar mAbs. The quality profile of a biosimilar mAb should correspond to the quality of the reference medicinal product. When demonstrating biosimilarity and determining the activity of medicines as part of batch quality control, analysts should study the biological properties of mAbs using suitable reference standards. The lack of international standards (ISs) makes mAb manufacturers use in-house reference standards. There is a risk of obtaining non-uniform quality and efficacy data because of the use of in-house reference standards, the heterogeneity and structural complexity of mAbs, and the relationship between the biological activity and efficacy of mAbs.Aim. This study aimed to analyse the relevance of and need for ISs for the biological activity of biotherapeutic mAbs and to define the role of reference medicinal products and ISs in assessing biosimilarity and testing medicines throughout their lifecycle.Discussion. This review covers the issues arising from the lack of ISs for assessing the biological activity of mAbs and the role and significance of reference products and ISs for biosimilars. The authors describe the specifics of studying the biological properties of mAbs and summarise the data on the need to develop and use ISs for the standardisation of biological tests. This review presents the results of studies on the first ISs established by the World Health Organisation to assess the biological activity of mAbs; these results suggest the need to standardise mAbs using ISs to ensure the quality, safety, and efficacy of mAb therapy.Conclusions. The use of ISs for mAbs plays a key role in harmonising biological activity assessments. Publicly available ISs serve as primary standards for the calibration of secondary reference materials. Moreover, ISs are required for the harmonisation of activity evaluation (in IU) between laboratories and for the consistency of the activity of various medicinal products from different manufacturers that share the same INN. The use of ISs by mAb manufacturers will contribute to ensuring the quality of mAbs and clinical monitoring of the effectiveness of their use.

Multi-MSIProcessor: Data Visualizing and Analysis Software for Spatial Metabolomics Research.

Mass spectrometry imaging (MSI) has emerged as a revolutionary analytical strategy in biomedical research for molecular visualization. By linking the characterization of functional metabolites with tissue architecture, it is now possible to reveal unknown biological functions of tissues. However, due to the complexity and high dimensionality of MSI data, mining bioinformatics-related peaks from batch MSI data sets and achieving complete spatially resolved metabolomics analysis remain a great challenge. Here, we propose novel MSI data processing software, Multi-MSIProcessor (MMP), which integrates the data read-in, MSI visualization, processed data preservation, and biomarker discovery functions. The MMP focuses on the AFADESI-MSI data platform but also supports mzXML and imzmL data input formats for compatibility with data generated by other MSI platforms such as MALDI/SIMS-MSI. MMP enables deep mining of batch MSI data and has flexible adaptability with the source code opened that welcomes new functions and personalized analysis strategies. Using multiple clinical biosamples with complex heterogeneity, we demonstrated that MMP can rapidly establish complete MSI analysis workflows, assess batch sample data quality, screen and annotate differential MS peaks, and obtain abnormal metabolic pathways. MMP provides a novel platform for spatial metabolomics analysis of multiple samples that could meet the diverse analysis requirements of scholars.

Functional and Molecular Heterogeneity in Glioma Stem Cells Derived from Multiregional Sampling.

Glioblastoma (GBM) is an aggressive and highly heterogeneous primary brain tumor. Glioma stem cells represent a subpopulation of tumor cells with stem cell traits that are presumed to be the cause of tumor relapse. There exists complex tumor heterogeneity in drug sensitivity patterns between glioma stem cell (GSC) cultures derived from different patients. Here, we describe that heterogeneity also exists between GSC cultures derived from multiple biopsies within a single tumor. From biopsies harvested within spatially distinct regions representing the entire tumor mass, we established seven GSC cultures and compared their stem cell properties, mutations, gene expression profiles, and drug sensitivity patterns against 115 different anticancer drugs. The results were compared to 14 GSC cultures derived from other patients. Between the multiregional-derived GSC cultures, we observed only minor differences in their phenotype, proliferative capacity, and global gene expression. Further, they displayed intratumoral heterogeneity in mutational profiles and sensitivity patterns to anticancer drugs. This heterogeneity, however, did not exceed the extensive heterogeneity found between GSC cultures derived from other GBM patients. Our results suggest that the use of GSC cultures from one single focal biopsy may underestimate the overall complexity of the GSC population and display the importance of including GSC cultures reflecting the entire tumor mass in drug screening strategies.

A sojourn on mitochondria targeted drug delivery systems for cancer: Strategies, clinical and future prospects

Mitochondria, often referred to as the powerhouses of the cell, have emerged as promising targets for cancer therapy due to their pivotal roles in cell survival, apoptosis, and energy metabolism. This sojourn emphasizes the significance of mitochondria-targeted drug delivery systems in cancer therapeutics. The unique characteristics of cancer cell mitochondria, such as altered membrane potential and distinct lipid composition, offer an avenue for selective drug targeting. Several strategies have been explored to exploit these features, including the use of lipophilic cations, mitochondria-penetrating peptides, and nanocarriers tailored for mitochondrial delivery. Mitochondria-targeted drug delivery systems have demonstrated enhanced therapeutic efficacy and reduced systemic toxicity in preclinical models. Some of these systems have made a successful transition to clinical trials, illustrating their potential in real-world oncology settings. However, there remain challenges like intracellular barriers, potential off-target effects, and the complexity of tumor heterogeneity that must be addressed to fully harness the potential of mitochondria-targeted drug delivery systems. As research progresses, it is anticipated that innovative approaches and technologies will be developed to improve the specificity and efficacy of mitochondrial targeting, paving the way for more effective and safer cancer treatments in the future. This review serves as a comprehensive guide to the current state of mitochondria-targeted drug delivery systems for cancer, highlighting key strategies, clinical progress, and prospective avenues for future research.