Fast Proliferation Research Articles

A Novel Cloud-Enabled Cyber Threat Hunting Platform for Evaluating the Cyber Risks Associated with Smart Health Ecosystems

The fast proliferation of Internet of Things (IoT) devices has dramatically altered healthcare, increasing the efficiency and efficacy of smart health ecosystems. However, this expansion has created substantial security risks, as cybercriminals increasingly target IoT devices in order to exploit their weaknesses and relay critical health information. The rising threat landscape poses serious concerns across various domains within healthcare, where the protection of patient information and the integrity of medical devices are paramount. Smart health systems, while offering numerous benefits, are particularly vulnerable to cyber-attacks due to the integration of IoT devices and the vast amounts of data they generate. Healthcare providers, although unable to control the actions of cyber adversaries, can take proactive steps to secure their systems by adopting robust cybersecurity measures, such as strong user authentication, regular system updates, and the implementation of advanced security technologies. This research introduces a groundbreaking approach to addressing the cybersecurity challenges in smart health ecosystems through the deployment of a novel cloud-enabled cyber threat-hunting platform. This platform leverages deception technology, which involves creating decoys, traps, and false information to divert cybercriminals away from legitimate health data and systems. By using this innovative approach, the platform assesses the cyber risks associated with smart health systems, offering actionable recommendations to healthcare stakeholders on how to minimize cyber risks and enhance the security posture of IoT-enabled healthcare solutions. Overall, this pioneering research represents a significant advancement in safeguarding the increasingly interconnected world of smart health ecosystems, providing a promising strategy for defending against the escalating cyber threats faced by the healthcare industry.

Reduction of chromosomal instability and inflammation is a common aspect of adaptation to aneuploidy.

Aneuploidy, while detrimental to untransformed cells, is notably prevalent in cancer. Aneuploidy is found as an early event during tumorigenesis which indicates that cancer cells have the ability to surmount the initial stress responses associated with aneuploidy, enabling rapid proliferation despite aberrant karyotypes. To generate more insight into key cellular processes and requirements underlying adaptation to aneuploidy, we generated a panel of aneuploid clones in p53-deficient RPE-1 cells and studied their behavior over time. As expected, de novo-generated aneuploid clones initially display reduced fitness, enhanced levels of chromosomal instability (CIN), and an upregulated inflammatory response. Intriguingly, after prolonged culturing, aneuploid clones exhibit increased proliferation rates while maintaining aberrant karyotypes, indicative of an adaptive response to the aneuploid state. Interestingly, all adapted clones display reduced CIN and reduced inflammatory signaling, suggesting that these are common aspects of adaptation to aneuploidy. Collectively, our data suggests that CIN and concomitant inflammation are key processes that require correction to allow for fast proliferation in vitro. Finally, we provide evidence that amplification of oncogenic KRAS can promote adaptation.

On the Anticancer Drug Structures and Their Locating Numbers

Cancer is the fast proliferation of unwanted cells in the body. Carcinogens are chemicals that can cause cancer. A carcinogen is a kind of chemical compound found in cigarette smoke. It has the ability to spread to many places in the body. Some of the signs and symptoms of this sickness include a lump, irregular bleeding, a chronic cough, weight gain or loss, and so on. Tobacco chewing is a major factor in this deadly disease. Obesity, a poor diet, laziness, and increased alcohol usage are all factors. Anticancer drugs are being utilized to treat this condition. In this work, we studied some anticancer medicines in terms of locating set, where locating set is a set to settle the entire atom set of a graph into a unique way to access each atom independently. By locating numbers, a particular resolving set is chosen and this resolving set or locating set creates the entire structure into a unique form this helps to study the chemical structure in more depth and detail. So, we can say that locating number helps to understand the chemical structure in more detail and depth. Locating number is a part of resolvability parameters and it also plays a role in developing a unique way to access each node/vertex of a network. When each of its vertices is easily accessible with a unique code, working on a network becomes easy. So this method makes structures of anticancer disease easily understandable and researchers can easily extend working on these structures in medical diagnostics.

Photo-Clickable Triazine-Trione Thermosets as Promising 3D Scaffolds for Tissue Engineering Applications.

There is an overwhelming demand for new scaffolding materials for tissue engineering (TE) purposes. Polymeric scaffolds have been explored as TE materials; however, their high glass transition state (Tg) limits their applicability. In this study, a novel materials platform for fabricating TE scaffolds is proposed based on solvent-free two-component heterocyclic triazine-trione (TATO) formulations, which cure at room temperature via thiol-ene/yne photochemistry. Three ester-containing thermosets, TATO-1, TATO-2, and TATO-3, are used for the fabrication of TE scaffolds including rigid discs, elastic films, microporous sponges, and 3D printed objects. After 14 days' incubation the materials covered a wide range of properties, from the soft TATO-2 having a compression modulus of 19.3MPa and a Tg of 30.4°C to the hard TATO-3 having a compression modulus of 411MPa and a Tg of 62.5°C. All materials exhibit micro- and nano-surface morphologies suited for bone tissue engineering, and in vitro studies found them all to be cytocompatible, supporting fast cell proliferation while minimizing cell apoptosis and necrosis. Moreover, bone marrow-derived mesenchymal stem cells on the surface of the materials are successfully differentiated into osteoblasts, adipocytes, and neuronal cells, underlining the broad potential for the biofabrication of TATO materials for TE clinical applications.

A low fouling and high biocompatibility electrochemical sensor based on the electrospun gelatin‐PLGA‐CNTs nanofibers for dopamine detection in blood

AbstractThe ability to fabricate resisting nonspecific protein adsorption electrochemical sensor capable of high biocompatibility in vivo will undoubtedly underpin key future developments in life sciences. Herein, a gelatin‐poly(lactide‐co‐glycolide)‐carbon nanotubes nanofibers‐membrane in three‐dimensional porous structure without any chemical crosslinking is constructed on the carbon fiber microelectrode (e‐Gelatin‐PLGA‐CNTs/CFME) using a one‐step electrospinning technology. The nanofibers‐membrane still presents good three‐dimensional porous structure and excellent hydrophily after implantation in BSA solution. In addition, the dopamine hydrochloride (DA) sensitivity at e‐Gelatin‐PLGA‐CNTs/CFME after implantation in human blood samples exhibits almost the same as preimplantation (91% ± 9%, n = 3). Importantly, the nanofibers‐membrane possesses fast cell proliferation and a low hemolysis rate (2.27% ± 0.76%), satisfying the required biocompatibility as a constructed material for the detection in vivo. The constructed micro‐electrochemical sensor realizes the detection of DA in human blood samples. Consequently, this strategy offers a new and facile platform for the development of implanted electrochemical sensor.

Estimation of Serum TLR-9,TNF-α, and IL-6 Levels in the Iraqi Patients Diagnosed as Acute Myelogenous Leukemia

إن ابيضاض الدم النقوي الحاد (AML) هو مجموعة متنوعة من الأمراض التي تتضمن تغايرات حيوية وسريرية. تتميز بالتكاثر السريع للخلايا الشاذة في نخاع العظام، والتي تتداخل مع تكوين خلايا الدم الطبيعية. يتم إطلاق السيتوكينات بواسطة العديد من أنواع الخلايا استجابةً للمنبهات المختلفة ، وهي تؤثر على الاستجابة المناعية. تظهر العديد من الدراسات أن السيتوكينات التي تطلقها خلايا سرطان الدم بطريقة autocrine أو paracrine تؤثر على تكاثر خلايا AML. صُممت دراستنا لتقييم مستوى البروتين لـ TLR-9 و TNF-α و IL-6 في مرضى AML. تم إجراء الدراسة عن طريق تقنية المقايسة الإمتصاصية المناعية المرتبطة بالإنزيم (ELISA). اظهرت النتائج وجود فروق معنوية عالية في مستوى المصل لـكل من TLR-9 و TNF-α و IL-6 . يظهر الارتباط بين المعلمات المناعية لهذه الدراسة باستخدام معامل ارتباط بيرسون وللمرضى أن هناك علاقة ضعيفة بين TLR-9 وكلا من TNF- α, IL-6 مع (P- value) 0.47) و 0.23( على التوالي ، مما قد يشير إلى البيئة المعقدة الناتجة عن الالتهاب. ومع ذلك ، هناك علاقة قوية بين TNF- α و IL-6 بقيمة P=0.001)) مما يشير إلى أن إفراز هذه السيتوكينات كان مرتفعًا عند المرضى.تم تقييم جميع المعلمات المناعية (TLR-9 و TNF- α و IL-6) للمرضى باستخدام منحنى خصائص تشغيل المستلم (ROC) لإثبات أن أيًا منهم يمكن استخدامه كواسم حيوي لتحديد الالتهاب وامكانية تتبع اعراض المرض لمرضى ابيضاض الدم النقوي الحاد. كشفت نتائج منحنى ROC أن جميع المتغيرات لديها حساسية وخصوصية جيدة للكشف عن الالتهاب ونشاط المرض لدى مرضى ابيضاض الدم النقوي الحاد. وفقًا لنتائج الدراسة الحالية ، فأن مرض ابيضاض الدم النقوي الحاد مرتبطًا بأرتفاع مستويات البروتين لكل من TLR-9 و TNF-α و IL-6. قد يؤدي زيادة إنتاج هذه السيتوكينات في المرضى إلى تحفيز وزيادة الخطر على تطور المرض وربما قلة الاستجابة للعلاج مما يؤثر سلبا على المرضى .

Small Cell Lung Cancer Neuronal Features and Their Implications for Tumor Progression, Metastasis, and Therapy.

Small cell lung cancer (SCLC) is an epithelial neuroendocrine form of lung cancer for which survival rates remain dismal and new therapeutic approaches are greatly needed. Key biological features of SCLC tumors include fast growth and widespread metastasis, as well as rapid resistance to treatment. Similar to pulmonary neuroendocrine cells, SCLC cells have traits of both hormone-producing cells and neurons. In this study, we specifically discuss the neuronal features of SCLC. We consider how neuronal G protein-coupled receptors and other neuronal molecules on the surface of SCLC cells can contribute to the growth of SCLC tumors and serve as therapeutic targets in SCLC. We also review recent evidence for the role of neuronal programs expressed by SCLC cells in the fast proliferation, migration, and metastasis of these cells. We further highlight how these neuronal programs may be particularly relevant for the development of brain metastases and how they can assist SCLC cells to functionally interact with neurons and astrocytes. A greater understanding of the molecular and cellular neuronal features of SCLC is likely to uncover new vulnerabilities in SCLC cells, which may help develop novel therapeutic approaches. More generally, the epithelial-to-neuronal transition observed during tumor progression in SCLC and other cancer types can contribute significantly to tumor development and response to therapy.

Feature-enhanced deep learning method for electric vehicle charging demand probabilistic forecasting of charging station

The fast proliferation of electric vehicles (EVs) profoundly affects the stable operation of the power system. Since EV charging demand is affected by users volatile behaviors and many other environmental and social factors, their uncertainty is quite pronounced. Traditional deterministic forecasting methods which provide little information on the uncertainty may not be informative to the decision-making of power system with massive EVs. This paper proposes a novel feature-enhanced deep learning method (FEDM) for EV charging demand probabilistic forecasting of individual station, in which a two-stage feature selection model is constructed to introduce the Pearson correlation coefficient between charging demand and external features as a prior knowledge to reweight the initial weights. The performance is improved compared to which is without feature-enhanced. In the comparison with eight benchmarks, FEDM shows superiority in both deterministic and probabilistic evaluation metrics. With high computational efficiency, FEDM has low dependence on the historical data stock, which can still achieve good performance in the case of low data volume. It provides a fast and accurate forecasting scheme for the demand forecasting of individual stations under the limitation of low data stock.

Single and Mixed Sensory Anomaly Detection in Connected and Automated Vehicle Sensor Networks

Connected and automated vehicles (CAVs), integrated with sensors, cameras, and communication networks, are transforming the transportation industry and providing new opportunities for consumers to enjoy personalized and seamless experiences. The fast proliferation of connected vehicles on the road and the growing trend of autonomous driving create vast amounts of data that need to be analyzed in real time. Anomaly detection in CAVs refers to identifying any unusual or unforeseen behavior in the data generated by vehicles’ various sensors and components. Anomaly detection aims to identify any unusual behavior that might indicate a problem or a malfunction in the vehicle. To identify and detect anomalies efficiently, a method must deal with noisy data, missing data, dynamic frequency data, and low- and high-magnitude data, and it must be accurate enough to detect anomalies in a dynamic sensor streaming environment. Therefore, this paper proposes a fast and efficient hard-voting-based technique named FT-HV, comprising three fine-tuned machine learning algorithms to detect and classify anomaly behavior in CAVs for single and mixed sensory datasets. In experiments, we evaluate our approach on the benchmark Sensor Anomaly dataset that contains data from various vehicle sensors at low and high magnitudes. Further, it contains single and mixed anomaly types that are challenging to detect and identify. The results reveal that the proposed approach outperforms existing solutions for detecting single anomaly types at low magnitudes and detecting mixed anomaly types in all settings. Furthermore, this research is envisioned to help detect and identify anomalies early and efficiently promote safer and more resilient CAVs.

Proliferation in malaria parasites: How resource limitation can prevent evolution of greater virulence.

For parasites, robust proliferation within hosts is crucial for establishing the infection and creating opportunities for onward transmission. While faster proliferation enhances transmission rates, it is often assumed to curtail transmission duration by killing the host (virulence), a trade-off constraining parasite evolution. Yet in many diseases, including malaria, the preponderance of infections with mild or absent symptoms suggests that host mortality is not a sufficient constraint, raising the question of what restrains evolution toward faster proliferation. In malaria infections, the maximum rate of proliferation is determined by the burst size, the number of daughter parasites produced per infected red blood cell. Larger burst sizes should expand the pool of infected red blood cells that can be used to produce the specialized transmission forms needed to infect mosquitoes. We use a within-host model parameterized for rodent malaria parasites (Plasmodium chabaudi) to project the transmission consequences of burst size, focusing on initial acute infection where resource limitation and risk of host mortality are greatest. We find that resource limitation restricts evolution toward higher burst sizes below the level predicted by host mortality alone. Our results suggest resource limitation could represent a more general constraint than virulence-transmission trade-offs, preventing evolution towards faster proliferation.

Abstract 1829: Brusatol overcomes tyrosine kinase inhibitors resistance in myeloid leukemia cells by inhibiting translation of fast turnover protein cyclin D1 and Mcl-1

Abstract Myeloid leukemia is characterized with fast proliferation and apoptosis evasion, which are controlled by mutant or activated tyrosine kinase signalings. Tyrosine kinase inhibitors (TKI) targeting BCR-ABL in chronic myeloid leukemia (CML) and FLT3-ITD in acute myeloid leukemia (AML) significantly improved the therapy, but not cure, due to the general resistance. Overactivated mTOR protein translation pathway is believed to be one of the mechanisms causing resistance and that targeting protein translation should be one effective approach to overcome resistance. Brusatol is a quassinoid extracted from the fruit of Brucea javanica and an inhibitor of protein translation. We tested its antiproliferation and apoptosis induction in a variety of AML and CML cell lines as well as in TKI resistant cell lines. Brusatol effectively inhibits the growth of seven leukemia cell lines with GI50 values less than 20 nM. Brusatol induces apoptosis in AML cell lines and induces G0/G1 phase arrest in CML cell lines. Brusatol decreases the levels of fast turnover protein cyclin D1 and Mcl-1. In FLT3-ITD inhibitor sorafenib resistant MOLM-13/Sor cell line brusatol exhibits similar growth inhibition and apoptosis induction with the parental MOLM-13 cells, associated with inhibition of Mcl-1 and FLT3-ITD proteins. In BCR-ABL inhibitor imatinib resistant K562/STI cell line brusatol is effective as in parental K562 cells, associated with inhibition of cyclin D1 and BCR-ABL proteins. Brusatol at 2 mg/kg effectively inhibit tumor growth of K562 xenografts without causing toxicity to normal organs. These data indicate that brusatol inhibits oncogenic protein translation and has potential to be used to treat TKI resistant leukemia. Citation Format: Huiming Hua, Ying Yang, Guangfuxin Lin, Jingyi Zhang, Samuel Waxman, Yongkui Jing. Brusatol overcomes tyrosine kinase inhibitors resistance in myeloid leukemia cells by inhibiting translation of fast turnover protein cyclin D1 and Mcl-1 [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1829.

Brain Tumor Detection based on Multiple Deep Learning Models for MRI Images

INTRODUCTION: Medical imaging techniques are used to analyze the inner workings of the human body. In today's scientific world, medical image analysis is the most demanding and rising discipline, with brain tumor being the most deadly and destructive kind of malignancy. A brain tumor is an abnormal growth of cells within the skull that disrupts normal brain function by damaging neighboring cells. Brain tumors are regarded as one of the most dangerous, visible, and potentially fatal illnesses in the world. Because of the fast proliferation of tumor cells, brain tumors kill thousands of people each year all over the world. To save the lives of thousands of individuals worldwide, prompt analysis and automated identification of brain tumors are essential.
 OBJECTIVES: To design a enhanced deep learning model for brain tumor detection and classification from MRI analysis.
 METHODS: The proposed models Densenet-121, Resnet-101 Mobilenet-V2 is used to perform the task of Brain tumor detection for multi- class classification.
 RESULTS: The proposed models achieved an accuracy of up to 99% in our evaluations, and when compared to competing models, they yield superior results.
 CONCLUSION: The MRI image collection has been used to train deep learning models. The experimental findings show that the Densnet-121 model delivers the highest accuracy (99%) compared to other models. The system will have significant applications in the medical field. The presence or absence of a tumour can be ascertained using the proposed method.

DMRTA2 supports glioma stem-cell mediated neovascularization in glioblastoma

Glioblastoma (GBM) is the most common and lethal brain tumor in adults. Due to its fast proliferation, diffusive growth and therapy resistance survival times are less than two years for patients with IDH-wildtype GBM. GBM is noted for the considerable cellular heterogeneity, high stemness indices and abundance of the glioma stem-like cells known to support tumor progression, therapeutic resistance and recurrence. Doublesex- and mab-3–related transcription factor a2 (DMRTA2) is involved in maintaining neural progenitor cells (NPC) in the cell cycle and its overexpression suppresses NPC differentiation. Despite the reports showing that primary GBM originates from transformed neural stem/progenitors cells, the role of DMRTA2 in gliomagenesis has not been elucidated so far. Here we show the upregulation of DMRTA2 expression in malignant gliomas. Immunohistochemical staining showed the protein concentrated in small cells with high proliferative potential and cells localized around blood vessels, where it colocalizes with pericyte-specific markers. Knock-down of DMRTA2 in human glioma cells impairs proliferation but not viability of the cells, and affects the formation of the tumor spheres, as evidenced by strong decrease in the number and size of spheres in in vitro cultures. Moreover, the knockdown of DMRTA2 in glioma spheres affects the stabilization of the glioma stem-like cell-dependent tube formation in an in vitro angiogenesis assay. We conclude that DMRTA2 is a new player in gliomagenesis and tumor neovascularization and due to its high expression in malignant gliomas could be a biomarker and potential target for new therapeutic strategies in glioblastoma.

Key Proteins of Replication Stress Response and Cell Cycle Control as Cancer Therapy Targets.

Replication stress (RS) is a characteristic state of cancer cells as they tend to exchange precision of replication for fast proliferation and increased genomic instability. To overcome the consequences of improper replication control, malignant cells frequently inactivate parts of their DNA damage response (DDR) pathways (the ATM-CHK2-p53 pathway), while relying on other pathways which help to maintain replication fork stability (ATR-CHK1). This creates a dependency on the remaining DDR pathways, vulnerability to further destabilization of replication and synthetic lethality of DDR inhibitors with common oncogenic alterations such as mutations of TP53, RB1, ATM, amplifications of MYC, CCNE1 and others. The response to RS is normally limited by coordination of cell cycle, transcription and replication. Inhibition of WEE1 and PKMYT1 kinases, which prevent unscheduled mitosis entry, leads to fragility of under-replicated sites. Recent evidence also shows that inhibition of Cyclin-dependent kinases (CDKs), such as CDK4/6, CDK2, CDK8/19 and CDK12/13 can contribute to RS through disruption of DNA repair and replication control. Here, we review the main causes of RS in cancers as well as main therapeutic targets-ATR, CHK1, PARP and their inhibitors.

Current Advances on Nanomaterials Interfering with Lactate Metabolism for Tumor Therapy.

Increasing numbers of studies have shown that tumor cells prefer fermentative glycolysis over oxidative phosphorylation to provide a vast amount of energy for fast proliferation even under oxygen-sufficient conditions. This metabolic alteration not only favors tumor cell progression and metastasis but also increases lactate accumulation in solid tumors. In addition to serving as a byproduct of glycolytic tumor cells, lactate also plays a central role in the construction of acidic and immunosuppressive tumor microenvironment, resulting in therapeutic tolerance. Recently, targeted drug delivery and inherent therapeutic properties of nanomaterials have attracted great attention, and research on modulating lactate metabolism based on nanomaterials to enhance antitumor therapy has exploded. In this review, the advanced tumor therapy strategies based on nanomaterials that interfere with lactate metabolism are discussed, including inhibiting lactate anabolism, promoting lactate catabolism, and disrupting the "lactate shuttle". Furthermore, recent advances in combining lactate metabolism modulation with other therapies, including chemotherapy, immunotherapy, photothermal therapy, and reactive oxygen species-related therapies, etc., which have achieved cooperatively enhanced therapeutic outcomes, are summarized. Finally, foreseeable challenges and prospective developments are also reviewed for the future development of this field.

Hyper-parameter tuned deep learning approach for effective human monkeypox disease detection

Human monkeypox is a very unusual virus that can devastate society. Early identification and diagnosis are essential to treat and manage an illness effectively. Human monkeypox disease detection using deep learning models has attracted increasing attention recently. The virus that causes monkeypox may be passed to people, making it a zoonotic illness. The latest monkeypox epidemic has hit more than 40 nations. Computer-assisted approaches using Deep Learning techniques for automatically identifying skin lesions have shown to be a viable alternative in light of the fast proliferation and ever-growing problems of supplying PCR (Polymerase Chain Reaction) Testing in places with limited availability. In this research, we introduce a deep learning model for detecting human monkeypoxes that is accurate and resilient by tuning its hyper-parameters. We employed a mixture of convolutional neural networks and transfer learning strategies to extract characteristics from medical photos and properly identify them. We also used hyperparameter optimization strategies to fine-tune the Model and get the best possible results. This paper proposes a Yolov5 model-based method for differentiating between chickenpox and Monkeypox lesions on skin pictures. The Roboflow skin lesion picture dataset was subjected to three different hyperparameter tuning strategies: the SDG optimizer, the Bayesian optimizer, and Learning without Forgetting. The proposed Model had the highest classification accuracy (98.18%) when applied to photos of monkeypox skin lesions. Our findings show that the suggested Model surpasses the current best-in-class models and may be used in clinical settings for actual Human Monkeypox disease detection and diagnosis.

A Review of Recent Advances in Brain Tumor Diagnosis Based on AI-Based Classification.

Uncontrolled and fast cell proliferation is the cause of brain tumors. Early cancer detection is vitally important to save many lives. Brain tumors can be divided into several categories depending on the kind, place of origin, pace of development, and stage of progression; as a result, tumor classification is crucial for targeted therapy. Brain tumor segmentation aims to delineate accurately the areas of brain tumors. A specialist with a thorough understanding of brain illnesses is needed to manually identify the proper type of brain tumor. Additionally, processing many images takes time and is tiresome. Therefore, automatic segmentation and classification techniques are required to speed up and enhance the diagnosis of brain tumors. Tumors can be quickly and safely detected by brain scans using imaging modalities, including computed tomography (CT), magnetic resonance imaging (MRI), and others. Machine learning (ML) and artificial intelligence (AI) have shown promise in developing algorithms that aid in automatic classification and segmentation utilizing various imaging modalities. The right segmentation method must be used to precisely classify patients with brain tumors to enhance diagnosis and treatment. This review describes multiple types of brain tumors, publicly accessible datasets, enhancement methods, segmentation, feature extraction, classification, machine learning techniques, deep learning, and learning through a transfer to study brain tumors. In this study, we attempted to synthesize brain cancer imaging modalities with automatically computer-assisted methodologies for brain cancer characterization in ML and DL frameworks. Finding the current problems with the engineering methodologies currently in use and predicting a future paradigm are other goals of this article.

A Survey of Malware Analysis Using Community Detection Algorithms

In recent years, we have witnessed an overwhelming and fast proliferation of different types of malware targeting organizations and individuals, which considerably increased the time required to detect malware. The malware developers make this issue worse by spreading many variants of the same malware [ 13 ]. To deal with this issue, graph theory techniques, and particularly community detection algorithms, can be leveraged to achieve bulk detection of malware families and variants to identify malicious communities instead of focusing on the detection of an individual instance of malware, which could significantly reduce the detection time. In this article, we review the state-of-the-art malware analysis solutions that employ community detection algorithms and provide a taxonomy that classifies the solutions with respect to five facets: analysis task, community detection approach, target platform, analysis type, and source of features. We present the solutions with respect to the analysis task, which covers malware detection, malware classification, cyber-threat infrastructure detection, and feature selection. The findings of this survey indicate that there is still room for contributions to further improve the state of the art and address research gaps. Finally, we discuss the advantages and the limitations of the solutions, identify open issues, and provide future research directions.

2-Methoxyestradiol as an Antiproliferative Agent for Long-Term Estrogen-Deprived Breast Cancer Cells.

To identify effective treatment modalities for breast cancer with acquired resistance, we first compared the responsiveness of estrogen receptor-positive breast cancer MCF-7 cells and long-term estrogen-deprived (LTED) cells (a cell model of endocrine therapy-resistant breast cancer) derived from MCF-7 cells to G-1 and 2-methoxyestradiol (2-MeO-E2), which are microtubule-destabilizing agents and agonists of the G protein-coupled estrogen receptor 1 (GPER1). The expression of GPER1 in LTED cells was low (~0.44-fold), and LTED cells displayed approximately 1.5-fold faster proliferation than MCF-7 cells. Although G-1 induced comparable antiproliferative effects on both MCF-7 and LTED cells (IC50 values of >10 µM), 2-MeO-E2 exerted antiproliferative effects selective for LTED cells with an IC50 value of 0.93 μM (vs. 6.79 μM for MCF-7 cells) and induced G2/M cell cycle arrest. Moreover, we detected higher amounts of β-tubulin proteins in LTED cells than in MCF-7 cells. Among the β-tubulin (TUBB) isotype genes, the highest expression of TUBB2B (~3.2-fold) was detected in LTED cells compared to that in MCF-7 cells. Additionally, siTUBB2B restores 2-MeO-E2-mediated inhibition of LTED cell proliferation. Other microtubule-targeting agents, i.e., paclitaxel, nocodazole, and colchicine, were not selective for LTED cells. Therefore, 2-MeO-E2 can be an antiproliferative agent to suppress LTED cell proliferation.

Preservation Strategies for Biodiversity Data

Preservation Strategies for Biodiversity Data