Molecular Knowledge Research Articles

Mass Spectrometry Reveals Molecular Effects of Citrulline Supplementation during Bone Fracture Healing in a Rat Model.

Bone fracture healing is a complex process in which specific molecular knowledge is still lacking. The citrulline-arginine-nitric oxide metabolism is one of the involved pathways, and its enrichment via citrulline supplementation can enhance fracture healing. This study investigated the molecular effects of citrulline supplementation during the different fracture healing phases in a rat model. Microcomputed tomography (μCT) was applied for the analysis of the fracture callus formation. Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) and liquid-chromatography tandem mass spectrometry (LC-MS/MS) were used for lipid and protein analyses, respectively. μCT analysis showed no significant differences in the fracture callus volume and volume fraction between the citrulline supplementation and control group. The observed lipid profiles for the citrulline supplementation and control group were distinct for the different fracture healing stages. The main contributing lipid classes were phosphatidylcholines (PCs) and lysophosphatidylcholines (LPCs). The changing effect of citrulline supplementation throughout fracture healing was indicated by changes in the differentially expressed proteins between the groups. Pathway analysis showed an enhancement of fracture healing in the citrulline supplementation group in comparison to the control group via improved angiogenesis and earlier formation of the soft and hard callus. This study showed the molecular effects on lipids, proteins, and pathways associated with citrulline supplementation during bone fracture healing, even though no effect was visible with μCT.

Human nonunion tissues display differential gene expression in comparison to physiological fracture callus

The healing of bone fractures can become aberrant and lead to nonunions which in turn have a negative impact on patient health. Understanding why a bone fails to normally heal will enable us to make a positive impact in a patient's life. While we have a wealth of molecular data on rodent models of fracture repair, it is not the same with humans. As such, there is still a lack of information regarding the molecular differences between normal physiological repair and nonunions. This study was designed to address this gap in our molecular knowledge of the human repair process by comparing differentially expressed genes (DEGs) between physiological fracture callus and two different nonunion types, hypertrophic (HNU) and oligotrophic (ONU). RNA sequencing data revealed over ∼18,000 genes in each sample. Using the physiological callus as the control and the nonunion samples as the experimental groups, bioinformatic analyses identified 67 and 81 statistically significant DEGs for HNU and ONU, respectively. Out of the 67 DEGs for the HNU, 34 and 33 were up and down-regulated, respectively. Similarly, out of the 81 DEGs for the ONU, 48 and 33 were up and down-regulated, respectively. Additionally, we also identified common genes between the two nonunion samples; 8 (10.8 %) upregulated and 12 (22.2 %) downregulated. We further identified many biological processes, with several statistically significant ones. Some of these were related to muscle and were common between the two nonunion samples. This study represents the first comprehensive attempt to understand the global molecular events occurring in human nonunion biology. With further research, we can perhaps decipher new molecular pathways involved in aberrant healing of human bone fractures that can be therapeutically targeted.

Pharmacognostical, Phytochemical, and Pharmacological Aspects of Aegle marmelos: An Update

Abstract: Due to its therapeutic characteristics, the Rutaceae family plant Aegle marmelos is one of the most important plants. The bilwa is used to treat a range of illnesses. The existence of this plant dates back to the age of the dinosaurs. The plant has a wide range of pharmacological properties, including the ability to heal wounds, act as an antipyretic, prevent diarrhoea, treat ulcers and constipation, exhibit radioprotective action, exert anti-arthritis activity, anti-thyroid activity, immunomodulatory activity, anti-fungal activity, anti-microbial activity, antioxidant activity, analgesic action, and cytoprotective action. The goal of this review is to summarise the molecular and scientific knowledge available regarding Aegle marmelos, including its phytochemical properties, pharmacological potential, and pharmacognostic traits. Data are acquired using Google, PubMed, Scholar, and other online sources.

Dual-Channel Learning Framework for Drug-Drug Interaction Prediction via Relation-Aware Heterogeneous Graph Transformer

Identifying novel drug-drug interactions (DDIs) is a crucial task in pharmacology, as the interference between pharmacological substances can pose serious medical risks. In recent years, several network-based techniques have emerged for predicting DDIs. However, they primarily focus on local structures within DDI-related networks, often overlooking the significance of indirect connections between pairwise drug nodes from a global perspective. Additionally, effectively handling heterogeneous information present in both biomedical knowledge graphs and drug molecular graphs remains a challenge for improved performance of DDI prediction. To address these limitations, we propose a Transformer-based relatIon-aware Graph rEpresentation leaRning framework (TIGER) for DDI prediction. TIGER leverages the Transformer architecture to effectively exploit the structure of heterogeneous graph, which allows it direct learning of long dependencies and high-order structures. Furthermore, TIGER incorporates a relation-aware self-attention mechanism, capturing a diverse range of semantic relations that exist between pairs of nodes in heterogeneous graph. In addition to these advancements, TIGER enhances predictive accuracy by modeling DDI prediction task using a dual-channel network, where drug molecular graph and biomedical knowledge graph are fed into two respective channels. By incorporating embeddings obtained at graph and node levels, TIGER can benefit from structural properties of drugs as well as rich contextual information provided by biomedical knowledge graph. Extensive experiments conducted on three real-world datasets demonstrate the effectiveness of TIGER in DDI prediction. Furthermore, case studies highlight its ability to provide a deeper understanding of underlying mechanisms of DDIs.

Abstract 3528: Constructing knowledge graph of N6-methyladnosion regulations in cancer from literature using ChatGPT-4

Abstract N6-methyladenosine (m6A) is the most abundant mRNA modification with a crucial role in cellular processes. Its involvement in cancers is well-established. Dysregulation of m6A is linked to cancer initiation, progression, and therapy resistance, impacting the tumor microenvironment (TME) and promoting metastasis. Understanding m6A's regulatory role in cancer is essential for identifying new therapeutic targets. We considered the construction of a knowledge graph (KG) representing molecular regulatory mechanisms (MRMs) of m6A in healthy conditions and different cancers from PubMed papers. Such a KG consists of interconnected triplets of (head node, regulatory relationship, tail node), each denoting regulation between genes or a gene and a phenotype/process. Advances in natural language processing (NLP) have provided tools such as SemRep and GNBR to automate the extraction of these triplets from the literature. However, these methods don’t extract contextual information, such as the type of cancers or cells in which these MRMs occur, leading to contradictory regulations in the constructed KG. In addition, existing approaches were designed to extract generic regulatory relations and therefore struggled in properly capturing unique concepts associated with m6A regulatory mechanisms. To tackle this challenge, we designed a novel ChatGPT-4 prompt to extract relational graphs from papers tailored for m6A regulatory mechanisms. An annotated dataset of 400 titles on m6A-related MRMs were created to evaluate the performance. We then applied the prompt to 1023 papers to create a m6A Molecular Regulatory Mechanism Knowledge Graph (m6A-MRM-KG). This graph illuminates m6A's roles in gene expression regulation, especially in cancer and immunity. Anticipated to enhance our understanding of cancer development, it provides insights into potential therapeutic strategies. The m6A-MRM-KG not only organizes information but also empowers researchers to uncover novel insights in the dynamic relationships of m6A modifications in diverse biological contexts. Citation Format: Sumin Jo, Xidong Wu, Yiming Zeng, Arun Das, Ting-He Zhang, David Alexander Spellman, Adam Ferris, Shou-Jiang Gao, Jianqiu (Michelle) Zhang, Yu-Chiao Chiu, Yufei Huang. Constructing knowledge graph of N6-methyladnosion regulations in cancer from literature using ChatGPT-4 [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 3528.

Abstract 1252: AI-derived predictions improve identification of real-world cancer driver mutations

Abstract AI-based variant effect predictors (VEPs), such as AlphaMissense, which is based on the protein structure prediction method AlphaFold, have gained significant attention for their potential to predict pathogenic effects of mutations. However, the utility of these methods for identifying pathogenic mutations in cancer remains unknown. We sought to quantify the prevalence of variants of unknown significance (VUSs) across the pan-cancer, multi-institution AACR GENIE cohort (N=160,965 samples) and test the utility of VEPs (SIFT, PolyPhen, MutationAssessor, REVEL, CADD, and AlphaMissense), using publicly available mutation annotations from each method, for 1) Annotating known pathogenic somatic cancer variants across the GENIE cohort, using known benign single nucleotide polymorphisms as negative controls 2) Identifying VUSs associated with overall survival (OS) in two cohorts of patients with non-small cell cancer (NSCLC), the MSK-IMPACT NCSLC (N=8,690) and non-MSK AACR GENIE Biopharma Collaborative NSCLC (N=977) cohorts, and 3) Identifying VUSs associated with known genomic patterns of mutual exclusivity in the MSK-IMPACT NSCLC cohort. In the GENIE cohort, 79% of mutations identified were VUSs according to an FDA-recognized molecular knowledge database, with a wide range of VUS frequencies across genes. Among VEPs trained without prior human-annotated knowledge, CADD and AlphaMissense had the highest AUROCs (0.940, 95%CI 0.937-0.943 and 0.917, 95%CI 0.913-0.922 respectively) for predicting known cancer drivers, demonstrating the power of protein structure modeling or functional genomic data for enhancing cancer drivers prediction. All VEPs identified known driver mutations in tumor suppressor genes more accurately than oncogenes, highlighting the challenge in identifying gain-of-function mutations. We tested whether VUSs reclassified as pathogenic by VEPs are associated with prognosis using inverse probability of treatment-weighted Cox’s propensity hazard models controlling for tumor mutational burden, treatment history, and other factors. VUSs reclassified as pathogenic by AlphaMissense and other VEPs in KEAP1 and SMARCA4 correlated with worse OS, comparable to that of known oncogenic mutations in those genes. Patients with tumors containing VUSs annotated as benign had similar OS to patients without analogous driver mutations. Finally, VUSs reclassified as oncogenic by AlphaMissense in genes within the RTK/RAS and NRF2 pathways followed expected patterns of mutual exclusivity, further suggesting biological validity. Despite not being trained to predict somatic effects in cancer, AI-derived mutation annotations can broaden the subset of annotated pathogenic variants in cancer and contribute to a more complete understanding of cancer genetics. Real-world outcomes and alteration patterns are additional benchmarks to consider when assessing the utility of VEPs in cancer. Citation Format: Thinh N. Tran, Chris Fong, Karl Pichotta, Anisha Luthra, Ronglai Shen, Yuan Chen, Michele Waters, Susie Kim, Gregory Riely, Debyani Chakravarty, Nikolaus Schultz, Justin Jee. AI-derived predictions improve identification of real-world cancer driver mutations [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 1252.

Phytopharmacological update on Cannabis sativa (Ganja): A Mini Review

Abstract: Cannabis sativa L., a significant herbaceous species in Central Asia, has long been valued for its use in traditional medicine and as a source of textile fiber. This fast-growing plant has recently drawn increased attention because of its many applications. It is an abundant source of wood and cellulosic fibers and a true treasure of phytochemicals. This plant greatly interests the construction and pharmaceutical sectors because its metabolites have potent biological ef-fects on human health. Its inner and outer stem tissues may be used to make concrete-like mate-rials and bioplastics. This study aims to summarize the molecular and scientific knowledge of Cannabis sativa, including its pharmacognostic properties, pharmacological potential, phyto-chemical properties, and prospective future research fields.

Pleural mesothelioma (PMe): The evolving molecular knowledge of a rare and aggressive cancer.

Mesothelioma is a type of late-onset cancer that develops in cells covering the outer surface of organs. Although it can affect the peritoneum, heart, or testicles, it mainly targets the lining of the lungs, making pleural mesothelioma (PMe) the most common and widely studied mesothelioma type. PMe is caused by exposure to fibres of asbestos, which when inhaled leads to inflammation and scarring of the pleura. Despite the ban on asbestos by most Western countries, the incidence of PMe is on the rise, also facilitated by a lack of specific symptomatology and diagnostic methods. Therapeutic options are also limited to mainly palliative care, making this disease untreatable. Here we present an overview of biological aspects underlying PMe by listing genetic and molecular mechanisms behind its onset, aggressive nature, and fast-paced progression. To this end, we report on the role of deubiquitinase BRCA1-associated protein-1 (BAP1), a tumour suppressor gene with a widely acknowledged role in the corrupted signalling and metabolism of PMe. This review aims to enhance our understanding of this devastating malignancy and propel efforts for its investigation.

Mutational spectrum in patients with dominant non-syndromic hearing loss in Austria.

Hearing loss (HL) is often monogenic. The clinical importance of genetic testing in HL may further increase when gene therapy products become available. Diagnoses are, however, complicated by a high genetic and allelic heterogeneity, particularly of autosomal dominant (AD) HL. This work aimed to characterize the mutational spectrumof AD HL in Austria. In an ongoing prospective study, 27 consecutive index patients clinically diagnosed with non-syndromic AD HL, including 18 previously unpublished cases, were analyzed using whole-exome sequencing (WES) and gene panels. Novel variants were characterized using literature and bioinformatic means. Two additional Austrian medical centers provided AD HL mutational data obtained with in-house pipelines. Other Austrian cases of AD HL were gathered from literature. The solve rate (variants graded as likely pathogenic (LP) or pathogenic (P)) within our cohort amounted to 59.26% (16/27). MYO6 variants were the most common cause. One third of LP/P variants were truncating variants in haploinsufficiency genes. Ten novel variants in HL genes were identified, including six graded as LP or P. In one cohort case and one external case, the analysis uncovered previously unrecognized syndromic presentations. More than half of AD HL cases analyzed at our center were solved with WES. Our data demonstrate the importance of genetic testing, especially for the diagnosis of syndromic presentations, enhance the molecular knowledge of genetic HL, and support other laboratories in the interpretation of variants.

Pathogenic Variants Associated with Epigenetic Control and the NOTCH Pathway Are Frequent in Classic Hodgkin Lymphoma.

Classic Hodgkin lymphoma (cHL) constitutes a B-cell neoplasm derived from germinal center lymphocytes. Despite high cure rates (80-90%) obtained with the current multiagent protocols, a significant proportion of cHL patients experience recurrences, characterized by a lower sensitivity to second-line treatments. The genomic background of chemorefractory cHL is still poorly understood, limiting personalized treatment strategies based on molecular features. In this study, using a targeted next-generation sequencing (NGS) panel specifically designed for cHL research, we compared chemosensitive and chemorefractory diagnostic tissue samples of cHL patients. Furthermore, we longitudinally examined paired diagnosis-relapsesamples of chemorefractory cHL in order to define patterns of dynamic evolution and clonal selection. Pathogenic variants in NOTCH1 and NOTCH2 genes frequently arise in cHL. Mutations in genes associated with epigenetic regulation (CREBBP and EP300) are particularly frequent in relapsed/refractory cHL. The appearance of novel clones characterized by mutations previously not identified at diagnosis is a common feature in cHL cases showing chemoresistance to frontline treatments. Our results expand current molecular and pathogenic knowledge of cHL and support the performance of molecular studies in cHL prior to the initiation of first-line therapies.

GLORI for absolute quantification of transcriptome-wide m6A at single-base resolution.

N6-methyladenosine (m6A) is the most abundant posttranscriptional chemical modification in mRNA, involved in regulating various physiological and pathological processes throughout mRNA metabolism. Recently, we developed GLORI, a sequencing method that enables the production of a globally absolute-quantitative m6A map at single-base resolution. Our technique utilizes the glyoxal- and nitrite-based chemical reaction, which selectively deaminates unmethylated adenosines while leaving m6A intact. The RNA library can then be prepared using a modified library construction protocol from enhanced UV crosslinking and immunoprecipitation (eCLIP) or commercial kits. Here we provide a detailed protocol for proper RNA sample handling and provide further guidelines for the use of a tailored bioinformatics pipeline (GLORI-tools) for subsequent data analysis. Compared with current methods, this new method is both exceptionally sensitive and robust, capable of identifying ~80,000 m6A sites with 50 Gb sequencing data in mammalian cells. It also provides a quantitative readout for m6A sites at single-base resolution. We hope the technique will provide a precise and unbiased tool for investigating m6A biology across various fields. Basic expertise in molecular biology and knowledge of bioinformatics are required for the protocol. The entire procedure can be completed within a week, with the library construction process taking ~4 d.

Diagnosing Cutaneous Melanocytic Tumors in the Molecular Era: Updates and Review of Literature.

Over the past decade, molecular and genomic discoveries have experienced unprecedented growth, fundamentally reshaping our comprehension of melanocytic tumors. This review comprises three main sections. The first part gives an overview of the current genomic landscape of cutaneous melanocytic tumors. The second part provides an update on the associated molecular tests and immunohistochemical stains that are helpful for diagnostic purposes. The third section briefly outlines the diverse molecular pathways now utilized for the classification of cutaneous melanomas. The primary goal of this review is to provide a succinct overview of the molecular pathways involved in melanocytic tumors and demonstrate their practical integration into the realm of diagnostic aids. As the molecular and genomic knowledge base continues to expand, this review hopes to serve as a valuable resource for healthcare professionals, offering insight into the evolving molecular landscape of cutaneous melanocytic tumors and its implications for patient care.

Regorafenib and glioblastoma: a literature review of preclinical studies, molecular mechanisms and clinical effectiveness.

Glioblastoma IDH wild type (GBM) is a very aggressive brain tumour, characterised by an infiltrative growth pattern and by a prominent neoangiogenesis. Its prognosis is unfortunately dismal, and the median overall survival of GBM patients is short (15 months). Clinical management is based on bulk tumour removal and standard chemoradiation with the alkylating drug temozolomide, but the tumour invariably recurs leading to patient's death. Clinical options for GBM patients remained unaltered for almost two decades until the encouraging results obtained by the phase II REGOMA trial allowed the introduction of the multikinase inhibitor regorafenib as a preferred regimen in relapsed GBM treatment by the National Comprehensive Cancer Network (NCCN) 2020 Guideline. Regorafenib, a sorafenib derivative, targets kinases associated with angiogenesis (VEGFR 1-3), as well as oncogenesis (c-KIT, RET, FGFR) and stromal kinases (FGFR, PDGFR-b). It was already approved for metastatic colorectal cancers and hepatocellular carcinomas. The aim of the present review is to focus on both the molecular and clinical knowledge collected in these first three years of regorafenib use in GBM.

Toward Unified AI Drug Discovery with Multimodal Knowledge.

Background: In real-world drug discovery, human experts typically grasp molecular knowledge of drugs and proteins from multimodal sources including molecular structures, structured knowledge from knowledge bases, and unstructured knowledge from biomedical literature. Existing multimodal approaches in AI drug discovery integrate either structured or unstructured knowledge independently, which compromises the holistic understanding of biomolecules. Besides, they fail to address the missing modality problem, where multimodal information is missing for novel drugs and proteins. Methods: In this work, we present KEDD, a unified, end-to-end deep learning framework that jointly incorporates both structured and unstructured knowledge for vast AI drug discovery tasks. The framework first incorporates independent representation learning models to extract the underlying characteristics from each modality. Then, it applies a feature fusion technique to calculate the prediction results. To mitigate the missing modality problem, we leverage sparse attention and a modality masking technique to reconstruct the missing features based on top relevant molecules. Results: Benefiting from structured and unstructured knowledge, our framework achieves a deeper understanding of biomolecules. KEDD outperforms state-of-the-art models by an average of 5.2% on drug-target interaction prediction, 2.6% on drug property prediction, 1.2% on drug-drug interaction prediction, and 4.1% on protein-protein interaction prediction. Through qualitative analysis, we reveal KEDD's promising potential in assisting real-world applications. Conclusions: By incorporating biomolecular expertise from multimodal knowledge, KEDD bears promise in accelerating drug discovery.

An effective framework for predicting drug–drug interactions based on molecular substructures and knowledge graph neural network

Drug–drug interactions (DDIs) play a central role in drug research, as the simultaneous administration of multiple drugs can have harmful or beneficial effects. Harmful interactions lead to adverse reactions, some of which can be life-threatening, while beneficial interactions can promote efficacy. Therefore, it is crucial for physicians, patients, and the research community to identify potential DDIs. Although many AI-based techniques have been proposed for predicting DDIs, most existing computational models primarily focus on integrating multiple data sources or combining popular embedding methods. Researchers often overlook the valuable information within the molecular structure of drugs or only consider the structural information of drugs, neglecting the relationship or topological information between drugs and other biological objects. In this study, we propose MSKG-DDI – a two-component framework that incorporates the Drug Chemical Structure Graph-based component and the Drug Knowledge Graph-based component to capture multimodal characteristics of drugs. Subsequently, a multimodal fusion neural layer is utilized to explore the complementarity between multimodal representations of drugs. Extensive experiments were conducted using two real-world datasets, and the results demonstrate that MSKG-DDI outperforms other state-of-the-art models in binary-class, multi-class, and multi-label prediction tasks under both transductive and inductive settings. Furthermore, the ablation analysis further confirms the practical usefulness of MSKG-DDI.

Importance of Bridging Molecular and Process Modeling to Design Optimal Adsorbents for Large-Scale CO2 Capture.

ConspectusCarbon capture, utilization, and storage have been identified as key technologies to decarbonize the energy and industrial sectors. Although postcombustion CO2 capture by absorption in aqueous amines is a mature technology, the required high regeneration energy, losses due to degradation and evaporation, and corrosion carry a high economic cost, precluding this technology to be used today at the scale required to mitigate climate change. Solid adsorbent-based systems with high CO2 capacities, high selectivity, and lower regeneration energy are becoming an attractive alternative for this purpose. Conscious of this opportunity, the search for optimal adsorbents for the capture of CO2 has become an urgent task. To accurately assess the performance of CO2 separation by adsorption at the needed scale, adsorbents should be synthesized and fully characterized under the required operating conditions, and the proper design and simulation of the process should be implemented along with techno-economic and environmental assessments. Several works have examined pure CO2 single-component adsorption or binary mixtures of CO2 with nitrogen for different families of adsorbents, primarily addressing their CO2 adsorption capacity and selectivity; however, very limited data is available under other conditions and/or with impurities, mainly due to the intensive experimental (modeling) efforts required for the large number of adsorbents to be studied, posing a challenge for their assessment under the needed conditions. In this regard, molecular simulations can be employed in synergy with experiments, reliably generating missing adsorption properties of mixtures while providing understanding at the molecular level of the mechanism of the adsorption process.This Account provides an outlook on strategies used for the rational design of materials for CO2 capture from different sources from the understanding of the adsorption mechanism at the molecular level. We illustrate with practical examples from our work and others' work how molecular simulations can be reliably used to link the molecular knowledge of novel adsorbents for which limited data exist for CO2 capture adsorption processes. Molecular simulation results of different adsorbents, including MOFs, zeolites, and carbon-based and silica-based materials, are discussed, focusing on understanding the role of physical and chemical adsorption obtained from simulations and quantifying the impact of impurities in the performance of the materials. Furthermore, simulation results can be used for screening adsorbents from basic key performance indicators, such as cycling the working capacity, selectivity, and energy requirement, or for feeding detailed dynamic models to assess their performance in swing adsorption processes on the industrial scale, additionally including monetized performance indicators such as operating expenses, equipment sizes, and compression cost. Moreover, we highlight the role of molecular simulations in guiding strategies for improving the performance of these materials by functionalization with amines or creating hybrid solid materials. We show how integrating models at different scales provides a robust and reliable assessment of the performance of the adsorbent materials under the required industrial conditions, rationally guiding the search for best performers. Trends in additional computational resources that can be used, including machine learning, and perspectives on practical requirements for leveraging CO2 capture adsorption technologies on the needed scale are also discussed.

Advancements in Glioma Care: Focus on Emerging Neurosurgical Techniques.

Despite significant advances in understanding the molecular pathways of glioma, translating this knowledge into effective long-term solutions remains a challenge. Indeed, gliomas pose a significant challenge to neurosurgical oncology because of their diverse histopathological features, genetic heterogeneity, and clinical manifestations. Relevant sections: This study focuses on glioma complexity by reviewing recent advances in their management, also considering new classification systems and emerging neurosurgical techniques. To bridge the gap between new neurosurgical approaches and standards of care, the importance of molecular diagnosis and the use of techniques such as laser interstitial thermal therapy (LITT) and focused ultrasound (FUS) are emphasized, exploring how the integration of molecular knowledge with emerging neurosurgical approaches can personalize and improve the treatment of gliomas. The choice between LITT and FUS should be tailored to each case, considering factors such as tumor characteristics and patient health. LITT is favored for larger, complex tumors, while FUS is standard for smaller, deep-seated ones. Both techniques are equally effective for small and superficial tumors. Our study provides clear guidance for treating pediatric low-grade gliomas and highlights the crucial roles of LITT and FUS in managing high-grade gliomas in adults. This research sets the stage for improved patient care and future developments in the field of neurosurgery.

The Search for System's Parameters: Statistical and Dynamical Description from Complex Network Analysis.

The integration of physical and biological science styles is the key for facing the deluge of molecular level information that is becoming a real threat for knowledge advancement. In this work, I will indicate a possible integration path based on the network formalization of molecular knowledge by two different (here named flux and dynamical) perspectives. Some theoretical and applicative cases are presented, focusing on the different physical models implicit in the two network analysis approaches.

Genetic diversity and phylogenetic relationships of tropical bed bugs (Hemiptera: Cimicidae) infestation from different regions of Iraq

Bed bug concerns have increased over the past three decades due to a sharp rise in bed bug infestations in human dwellings. Unfortunately, there is little information available regarding the population of bed bugs across Iraq, and no studies have been conducted on genetic diversity and phylogenetic relationships of bed bugs in Iraq. Hence, this study aims to determine the genetic diversity and phylogenetic relationships of bed bugs collected from major cities in Iraq utilizing partial gene sequences of the cytochrome oxidase I (CO1) and 16S ribosomal RNA 16S rRNA. A total of 1041 bed bug specimens representing various life stages were collected from 18 infested sites across eight of Iraq governorates. Based on the width-to-height ratio of the pronotum, most of the samples were provisionally identified as Cimex hemipterus. The 16S rRNA and CO1 genes that were very similar to C. hemipterus sequences (90–100 % similarity) on BLAST was applied to determine species identity. The 16S rRNA and CO1 gene sequence generated from samples collected in the study exhibited high similarity (90–100 %) to C. hemipterus sequences on BLAST. For constructing phylogenies, maximum likelihood and neighbor-joining algorithms were used. The sequences obtained clustered with C. hemipterus reference sequences retrieved from GenBank confirmed the species identification as C. hemipterus with bootstrap values of 100 % for the CO1 gene and 99 % for the 16SrRNA gene. Furthermore, low genetic diversity was determined among Iraq's bed bug populations. In summary, this study offers molecular demographic knowledge on bed bugs infestation in Iraq for more effective management in future.

Unboxing the bZIP transcription factor family exhibiting their role under cold and salt stresses in indica rice

Basic leucine zipper (bZIP) extensively studied transcription factor family and has been implicated in abiotic stress resistance in eukaryotes. Genes encoded by bZIP govern changes in molecular, physiological, and biological processes in response to abiotic stimuli. Developments in molecular knowledge and easy access to the drafted genome of indica rice open ways to explore bZIP and related features within the indica rice genome. We identified 82 TFs encoding bZIPs in the indica rice genome. The identified 82 bZIPs are unevenly distributed across all 12 chromosomes. The identified bZIP family expansion in the indica rice genome is mainly by segmental duplication relative to tandem duplication. The gene structure analysis exhibited that most genes are intron-less and distributed among all phylogenetic groups. We identified 20 different conserved motifs, supporting that bZIPs regulate the different biological processes in rice. The comparative phylogenetic analysis of indica rice bZIPs with Arabidopsis categorized the genes into 11 different groups, having several orthologs and paralogs. Apart from the comparative analysis, gene ontology (GO) analysis illustrated the involvement of bZIPs in different biological processes and the interaction between different processes are important for the metabolic activity of a living organism.Moreover, KEGG pathway analysis identified the 23 bZIPs involved in pathogen infection, carotenoid biosynthesis, and phenylalanine metabolism. Among 23 genes, 12 genes were selected for the expression analysis under salt and cold stress treatments in the Khusboo-95 indica rice variety. In this research, LOC_Os01g17260.2, LOC_Os02g52780.1, and LOC_Os09g10840.1 showed upregulated expression under salt stress conditions. This genome-wide analysis allows researchers to functionally characterize the genes, involved in different abiotic stresses that could be beneficial to improve plant performance under challenging conditions.