Complexity Of Disease Research Articles

Exosomal MicroRNAs in Alzheimer's Disease: Unveiling Their Role and Pioneering Tools for Diagnosis and Treatment.

Alzheimer's disease (AD) is a common neurodegenerative disorder that presents a significant health concern, often leading to substantial cognitive decline among older adults. A prominent feature of AD is progressive dementia, which eventually disrupts daily functioning and the ability to live independently. A major challenge in addressing AD is its prolonged pre-symptomatic phase, which makes early detection difficult. Moreover, the disease's complexity and the inefficiency of current diagnostic methods impede the development of targeted therapies. Therefore, there is an urgent need to enhance diagnostic methodologies for detection and treating AD even before clinical symptoms appear. Exosomes are nanoscale biovesicles secreted by cells, including nerve cells, into biofluids. These exosomes play essential roles in the central nervous system (CNS) by facilitating neuronal communication and thus influencing major physiological and pathological processes. Exosomal cargo, particularly microRNAs (miRNAs), are critical mediators in this cellular communication, and their dysregulation affects various pathological pathways related to neurodegenerative diseases, including AD. This review discusses the significant roles of exosomal miRNAs in the pathological mechanisms related to AD, focusing on the promising use of exosomal miRNAs as diagnostic biomarkers and targeted therapeutic interventions for this devastating disease.

Recent Therapeutic Advancements for Gaucher Disease

AbstractGaucher Disease (GD) is a well‐known lysosomal storage disease resulting from mutations in the GBA1 gene. GD exhibits a range of clinical manifestations, each with unique symptoms and severity levels. This review explores the genetic foundations of GD, highlighting the significance of Glucocerebrosidase (GCase) deficiency, resulting in the skeletal complications associated with GD, such as osteonecrosis, fractures, and bone pain, all of which significantly negatively influence the quality of life for patients. Over 700 mutations in the GBA1 gene are found to cause variations in gene expression in GD, indicating the disease's complexity and the need for continued research. Early diagnosis and prognosis evaluation depend heavily on diagnostic approaches integrating laboratory assessments, genetic testing, and clinical symptoms. Treatment strategies like enzyme replacement therapy (ERT) and substrate reduction therapy (SRT) have advanced, but issues like high costs and invasiveness still exist. This review focuses on novel therapeutic approaches that show promise in treating GD, including gene and cell‐based therapies, pharmacological chaperone therapy (PCT), and drug delivery via nanoparticles. Finally, discussions on current clinical trials, limitations of the advanced therapies, and future scope are summarized.

Impact of computed tomography/magnetic resonance imaging registration on rehabilitation after percutaneous endoscopic decompression for lumbar stenosis: Retrospective study.

Percutaneous endoscopic lumbar decompression (PELD) shows promise for lumbar spinal stenosis (LSS) treatment, but its use is limited by the disease's complexity and procedural challenges. In this study, the effects of preoperative planning and intraoperative guidance with computed tomography (CT)/magnetic resonance imaging (MRI) registration techniques on PELD for LSS and postoperative rehabilitation outcomes were evaluated. This retrospective study was conducted with data from patients who underwent PELD for LSS between January 2021 and December 2023. Patients were assigned to preoperative CT/MRI registration and control groups. Data collected included the operative time, length of hospital stay, visual analog scale (VAS) scores for low back and leg pain, and the Japanese Orthopaedic Association (JOA) lumbar spine score. Differences between groups were assessed using Student's t test. Data from 135 patients (71 in the CT/MRI registration group, 64 in the control group) were analyzed. The operative time was significantly shorter in the CT/MRI registration group (P = 0.007). At 2 months postoperatively, both groups showed significant reductions in VAS leg and low back pain scores (all P < 0.001) and improvements in the JOA score (both P < 0.001). No complication or death occurred. Preoperatively, pain and JOA scores were similar between groups (P = 0.830, P = 0.470, and P = 0.287, respectively). At 2 months postoperatively, patients in the CT/MRI registration group reported lower leg and low back pain levels (P < 0.001 and P = 0.001, respectively) and had higher JOA scores (P = 0.004) than did patients in the control group. Preoperative CT/MRI registration for PELD for LSS reduced the operative time and VAS pain scores at 2 months and improved JOA scores, demonstrating enhanced effectiveness and safety.

Diagnosis of acute lymphoblastic leukaemia: an overview of the current genomic classification, diagnostic approaches, and future directions.

B-acute lymphoblastic leukaemia (B-ALL) is a haematological disease resulting from haematopoietic system dysfunction, leading to the unchecked growth of immature B lymphoblasts. The disease's complexity is underscored by the spectrum of genetic aberrations that underlie B-ALL entities, necessitating advanced genetic analyses for precise classification and risk determination. Prior to the adoption of next-generation sequencing into standard diagnostic practices, up to 30% of B-ALL cases were not assigned to specific entities due to the limitations of traditional diagnostic methods. The advent of comprehensive genomic analysis, especially whole-genome transcriptome sequencing, has significantly enhanced our understanding of B-ALL's molecular heterogeneity, paving the way for the exploration of novel, tailored treatment strategies. Furthermore, recent technological innovations, such as optical genome mapping, methylation profiling, and single-cell sequencing, have propelled forward the fields of cancer research and B-ALL management. These innovations introduce novel diagnostic approaches and prognostic markers, facilitating a deeper, more nuanced understanding of individual patient disease profiles. This review focuses on the latest diagnostic standards and assays for B-ALL, the importance of new technologies and biomarkers in enhancing diagnostic accuracy, and the expected role of innovative advancements in the future diagnosis and treatment of B-ALL.

Two novel deep intronic variants cause Duchenne muscular dystrophy by splice-altering mechanism

Duchenne muscular dystrophy (DMD) is a genetic disorder characterized by progressive muscle degeneration and weakness, due to mutations in the DMD gene, which encodes the dystrophin protein. While mutations within the coding regions of DMD have been extensively studied, recent focus has shifted to deep intronic variants for their potential impact on disease severity. Here, we characterize two deep intronic variants, c.8669-19_8669-24del and c.6439-1016_6439-3376del, in unrelated DMD patients. These variants were identified using targeted long-read sequencing on patients' DNA. RNA sequencing/reverse transcription polymerase chain reaction on RNA extracted from muscle biopsies revealed the presence of a pseudoexon or retention of part of the intron in the transcript, resulting in the introduction of premature termination codons. This study enhances our understanding of pseudoexon activation mechanisms in DMD and underscores the diverse genetic abnormalities contributing to the disease's complexity.

Enhancing Ovarian Cancer Detection Using Self-Organizing Maps and Improved Recurrent Neural Networks with Extended Harmony Search Optimization

Background: Ovarian cancer (OC) is a highly fatal malignancy of the female reproductive system, characterized by its high mortality rate and the challenges associated with clinical research due to the disease's complexity and late-stage diagnosis. Advances in technology, such as the Internet of Medical Things (IoMT), offer new opportunities for improving OC detection and diagnosis. Objective: This study aimed to develop and evaluate a novel method for OC detection using IoMT data, leveraging Self-Organizing Maps (SOM) and Improved Recurrent Neural Networks (IRNN) enhanced with the Extended Harmony Search Optimization (EHSO) algorithm to improve feature selection and classification accuracy. Methods: The study utilized OC data from the IoMT and applied SOM for feature selection, which helps in managing and classifying large datasets. SOM was employed to improve data representation and address challenges in labeling and classifying data. The IRNN model, optimized using the EHSO algorithm, was developed to enhance classification performance. The model was tested using a dataset from Kaggle comprising 179 benign and 172 malignant OC images with 50 attributes. Results: The IRNN model with EHSO demonstrated superior performance compared to other methods. In the training dataset, it achieved an accuracy of 95.72% and a Root Mean Square Error (RMSE) of 4.8%. For the testing dataset, the model maintained a high accuracy of 90.4% and an RMSE of 6.8%. The IRNN with EHSO outperformed alternative methods in terms of specificity and sensitivity, while the Genetic Algorithm (GA) showed the lowest performance across all metrics. Conclusion: The proposed method using SOM and IRNN with EHSO significantly improves the detection of ovarian cancer by optimizing feature selection and classification accuracy. This approach offers a promising advancement in utilizing IoMT data for early and accurate OC diagnosis, potentially enhancing patient outcomes through more effective detection and treatment strategies.

A scoping review on control strategies for Echinococcus granulosus sensu lato.

Cystic echinococcosis (CE) is a widespread neglected zoonotic disease caused by Echinococcus granulosus sensu lato (EG) with a global burden of control in the billions of dollars. E. granulosus' life cycle involves definitive, intermediate, and humans as dead-end hosts. Echinococcosis control programs use strategies that focus on any of these hosts. We aimed to provide a comprehensive and up-to-date overview of the EG control interventions worldwide. We conducted a scoping review by mapping all studies on interventions for EG control following the Arksey and O'Malley Framework. We screened identified articles, and charted and coded selected papers. We classified the data based on target host, type of study, and control mechanism. We described the efficacy or safety outcomes, and the associated barriers/facilitators for the intervention. Critical appraisal was conducted. From 7,853 screened studies, we analyzed 45: seven centered on human interventions, 21 on animals, and 17 on both. Studies on humans focused on educational strategies and human CE monitoring. The studies on animals were field trials and most were based on Praziquantel (PZQ) for dogs. Studies focused on both animals and humans had, in general, more participants, lasted longer, and covered larger geographical areas. Overall, the quality of studies was moderate to low. Available evidence suggests that long-term interventions aimed at both animals and humans can achieve significant reduction in EG transmission, particularly when PZQ treatment for dogs is included. Higher quality evidence, standardization of methodologies, and better reporting on post-intervention outcomes are necessary for drawing stronger conclusions. Further evidence is needed to assess the sustainability and scalability of control measures. Nonetheless, an integrative One Health approach is essential for overcoming the multiple challenges associated with sustaining long-term control efforts for Echinococcosis.

A gene set enrichment analysis for the cancer hallmarks

The "hallmarks of cancer" concept provides a valuable framework for understanding fundamental organizing principles common to various cancers. However, without a consensus gene set for cancer hallmarks, data comparison and integration result in diverse biological interpretations across studies. Therefore, we aimed to form a consensus cancer hallmark gene set by merging data from available mapping resources and establishing a framework for mining these gene sets. By consolidating data from seven projects, 6,763 genes associated with ten cancer hallmarks were identified. A cancer hallmarks enrichment analysis was performed for prognostic genes associated with overall survival across twelve types of solid tumors. "Tissue invasion and metastasis" was most prominent in cancers of the stomach (P = 2.2×10−11), pancreas (P = 4.2×10−9), bladder (P = 3.3×10−8), and ovaries (P = 0.0007), aligning with their heightened potential to spread. "Sustained angiogenesis" was most prominent in squamous cell carcinomas of the lung (P = 2.5×10−7), while "genome instability" showed strong enrichment in lung adenocarcinomas (P = 1.5×10−8) and cancers of the liver (P = 5.5×10−10), pancreas (P = 2.1×10−5), and kidney (P = 0.018). Pancreatic cancers displayed the highest enrichment of hallmarks, emphasizing the disease's complexity, while in melanomas, and cancers of the liver, prostate, and kidney, a single hallmark was enriched among the prognostic markers of survival. Additionally, an online tool (www.cancerhallmarks.com) that allows the identification of cancer-associated hallmarks from new gene sets was established. In summary, our aim of establishing a consensus list of cancer hallmark genes was achieved. Furthermore, the analysis of survival-associated genes revealed a unique pattern of hallmark enrichment with potential pharmacological implications in different tumor types.

Exploring Angiogenesis and Artificial Intelligence in Alzheimer’s Disease: Implications for Early Detection and Therapeutic Strategies

Background: Alzheimer’s disease (AD) presents a significant global health challenge, affecting millions and imposing substantial socioeconomic burdens, with healthcare costs exceeding US$100 billion annually. The disease's complexity is underscored by its slow progression and the pivotal role of amyloid plaques in its pathology. Despite advancements in understanding AD, critical questions regarding the mechanisms of amyloid plaque development and their relationship with neuroinflammation and angiogenesis remain. Methods: This study employed advanced imaging techniques and artificial intelligence (AI) to investigate the interplay between amyloid beta (Aβ) accumulation, neuroinflammation, and angiogenesis in AD. Magnetic resonance imaging (MRI) facilitated detailed assessments of brain structure alterations, while machine learning models enhanced diagnostic accuracy by analyzing imaging data and identifying patterns linked to disease progression. Results: The findings revealed a significant association between Aβ deposition and increased angiogenesis, contributing to neurovascular dysfunction and exacerbating neuroinflammation. AI-driven analyses demonstrated improved diagnostic capabilities, detecting early changes in brain structure associated with mild cognitive impairment (MCI) and AD. Specifically, convolutional neural networks (CNNs) such as AlzheimerNet achieved remarkable accuracy in distinguishing AD from other neurodegenerative conditions. Conclusion: This research underscores the multifaceted nature of AD, highlighting the critical roles of amyloid plaques, neuroinflammation, and angiogenesis. The integration of AI and advanced imaging modalities offers promising avenues for early diagnosis and intervention, potentially transforming patient management strategies. Continued exploration of these pathways may yield effective therapeutic targets to mitigate the progression of AD and enhance patient outcomes.

Functional Genomics in Psoriasis.

Psoriasis is an autoimmune cutaneous condition that significantly impacts quality of life and represents a burden on society due to its prevalence. Genome-wide association studies (GWASs) have pinpointed several psoriasis-related risk loci, underlining the disease's complexity. Functional genomics is paramount to unveiling the role of such loci in psoriasis and disentangling its complex nature. In this review, we aim to elucidate the main findings in this field and integrate our discussion with gold-standard techniques in molecular biology-i.e., Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-and high-throughput technologies. These tools are vital to understanding how disease risk loci affect gene expression in psoriasis, which is crucial in identifying new targets for personalized treatments in advanced precision medicine.

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: the biology of a neglected disease.

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a chronic, debilitating disease characterised by a wide range of symptoms that severely impact all aspects of life. Despite its significant prevalence, ME/CFS remains one of the most understudied and misunderstood conditions in modern medicine. ME/CFS lacks standardised diagnostic criteria owing to variations in both inclusion and exclusion criteria across different diagnostic guidelines, and furthermore, there are currently no effective treatments available. Moving beyond the traditional fragmented perspectives that have limited our understanding and management of the disease, our analysis of current information on ME/CFS represents a significant paradigm shift by synthesising the disease's multifactorial origins into a cohesive model. We discuss how ME/CFS emerges from an intricate web of genetic vulnerabilities and environmental triggers, notably viral infections, leading to a complex series of pathological responses including immune dysregulation, chronic inflammation, gut dysbiosis, and metabolic disturbances. This comprehensive model not only advances our understanding of ME/CFS's pathophysiology but also opens new avenues for research and potential therapeutic strategies. By integrating these disparate elements, our work emphasises the necessity of a holistic approach to diagnosing, researching, and treating ME/CFS, urging the scientific community to reconsider the disease's complexity and the multifaceted approach required for its study and management.

CancerHallmarks.com: A tool for the enrichment analysis of the cancer hallmarks.

e15079 Background: The "Hallmarks of Cancer" concept is a useful framework for understanding the fundamental principles that are shared among different types of cancers. However, the lack of agreement on a specific set of genes for these hallmarks makes it difficult to compare and integrate data, leading to varying interpretations of the biological processes involved in cancer across different studies. In this study, we created a consensus set of cancer hallmark genes by combining data from multiple mapping resources and the scientific literature. Methods: We identified 6,763 candidate cancer hallmark genes associated with ten cancer hallmarks by consolidating information from seven studies. There was a considerable variation in the number of genes within the available resources, ranging between 91 and 7,779. We observed a sizable variation in the number of genes connected to each hallmark. The number of genes across cancer hallmarks ranges between 574 („enabling replicative immortality”) and 3,574 ("sustaining proliferative signaling"). The least number of genes are associated with the more recent hallmarks, published in 2011, and the hallmarks „enabling replicative immortality” and „sustaining angiogenesis”. We identified six genes linked to each of the ten cancer hallmarks: TP53, AKT1, MAP2K1, CTNNB1, HRAS, and PIK3CB. Results: As a practical utility of the hallmark concept, we analyzed the enrichment of cancer hallmarks by evaluating genes associated with overall survival. Only genes with a false discovery rate below 0.01, a hazard rate over 1, and a cutoff value over 100 were analyzed to ensure sufficient robustness of the gene sets. The hallmark "genome instability" was most prominent in liver (p=1.0E-05), lung (p=1.0E-05), and pancreatic cancers (p=0.00164). Renal cancer had higher “invasion and metastasis” (p=0.002). Both pancreatic cancer and lung adenocarcinomas were enriched for “resisting cell death” (p=0.00096 and p=0.037, respectively). Overall, while eight cancer types had no significant hallmark enrichment among druggable genes, pancreatic cancers had the most enriched hallmarks (5 out of 10), emphasizing the disease's complexity. These results highlight the usefulness of the hallmark concept as a valuable tool for organizing information, especially in establishing a direct link between genes and biological functions. Conclusions: We have created a unified list of genes linked to cancer hallmarks. We integrated the data into an easily accessible online tool (accessible at www.cancerhallmarks.com) that enables users to identify the most pertinent cancer-associated “candidate hallmark genes” in their own datasets.

Novel drug discovery: Advancing Alzheimer's therapy through machine learning and network pharmacology

Alzheimer's disease (AD), marked by tau tangles and amyloid-beta plaques, leads to cognitive decline. Despite extensive research, its complex etiology remains elusive, necessitating new treatments. This study utilized machine learning (ML) to analyze compounds with neuroprotective potential. This approach exposed the disease's complexity and identified important proteins, namely MTOR and BCL2, as central to the pathogenic network of AD. MTOR regulates neuronal autophagy and survival, whereas BCL2 regulates apoptosis, both of which are disrupted in AD. The identified compounds, including Armepavine, Oprea1_264702,1-cyclopropyl-7-fluoro-8-methoxy-4-oxoquinoline-3-carboxylic acid,(2S)-4′-Hydroxy-5,7,3′-trimethoxyflavan,Oprea1_130514,Sativanone,5-hydroxy-7,8-dimethoxyflavanone,7,4′-Dihydroxy-8,3′-dimethoxyflavanone,N,1-dicyclopropyl-6,Difluoro-Methoxy-Gatifloxacin,6,8-difluoro-1-(2-fluoroethyl),1-ethyl-6-fluoro-7-(4-methylpiperidin-1-yl),Avicenol C, demonstrated potential modulatory effects on these proteins. The potential for synergistic effects of these drugs in treating AD has been revealed via network pharmacology. By targeting numerous proteins at once, these chemicals may provide a more comprehensive therapeutic approach, addressing many aspects of AD's complex pathophysiology. A Molecular docking, dynamic simulation, and Principle Component Analysis have confirmed these drugs' efficacy by establishing substantial binding affinities and interactions with important proteins such as MTOR and BCL2. This evidence implies that various compounds may interact within the AD pathological framework, providing a sophisticated and multifaceted therapy strategy. In conclusion, our study establishes a solid foundation for the use of these drugs in AD therapy. Thus current study highlights the possibility of multi-targeted, synergistic therapeutic approaches in addressing the complex pathophysiology of AD by integrating machine learning, network pharmacology, and molecular docking simulations. This holistic technique not only advances drug development but also opens up new avenues for developing more effective treatments for this difficult and widespread disease.

Recent Advances in Understanding and Treating Alzheimer’s Disease

Alzheimer’s disease (AD) is a significant global health challenge characterized by progressive cognitive decline and neurodegeneration. Refinements in the amyloid and tau hypotheses have deepened insights into the molecular mechanisms underlying AD, while research on neuroinflammation and genetic factors, including APOE ε4, has expanded our understanding of the disease's complexity. Diagnostic advancements include cerebrospinal fluid and blood-based biomarkers, advanced imaging techniques like PET and MRI, and innovative neuropsychological assessments, enabling earlier and more accurate detection. Therapeutically, notable progress has been made with disease-modifying treatments targeting amyloid and tau proteins, including the recent approval of aducanumab. Symptomatic treatments continue to evolve, providing better management of cognitive and behavioral symptoms. Additionally, lifestyle interventions, such as diet, exercise, and cognitive training, show promise in slowing disease progression. Emerging research in gene therapy and stem cell applications offers potential for future breakthroughs in AD treatment. Despite these advances, challenges persist, including the heterogeneity of AD, difficulties in clinical trial design and patient recruitment, and ethical considerations related to early diagnosis and treatment access. This review underscores the necessity of ongoing research and interdisciplinary collaboration to develop more effective management strategies and ultimately achieve a cure for Alzheimer’s disease. Continued efforts in these areas are crucial for transforming the landscape of AD diagnosis and treatment, offering hope for patients and their families.

A Novel Human Interleukin-23A Overexpressing Mouse Model of Systemic Lupus Erythematosus.

Interleukin-23 (IL-23) is a crucial cytokine implicated in chronic inflammation and autoimmunity, associated with various diseases such as psoriasis, psoriatic arthritis, and systemic lupus erythematosus (SLE). This study aimed to create and characterize a transgenic mouse model overexpressing human IL-23A (TghIL-23A), providing a valuable tool for investigating the pathogenic role of human IL-23A and evaluating the efficacy of anti-human IL-23A therapeutics. TghIL-23A mice were generated via microinjection of CBA × C57BL/6 zygotes with a fragment of the human IL23A gene, flanked by its 5'-regulatory sequences and the 3' untranslated region of human β-globin. The TghIL-23A pathology was assessed through hematologic and biochemic analyses, cytokine and antinuclear antibody detection, and histopathologic examination of skin and renal tissues. The response to the anti-human IL-23A therapeutic agent guselkumab was evaluated in groups of eight mixed-sex mice receiving subcutaneous treatment twice weekly for 10 weeks using clinical, biomarker, and histopathologic readouts. TghIL-23A mice exhibited interactions between human IL-23A and mouse IL-23/IL-12p40 and developed a chronic multiorgan autoimmune disease marked by proteinuria, anti-double-stranded DNA antibodies, severe inflammatory lesions in the skin, and milder phenotypes in the kidneys and lungs. The TghIL-23A pathologic features exhibited significant similarities to those observed in human patients with SLE, and they were reversed following guselkumab treatment. We have generated and characterized a novel genetic mouse model of SLE, providing proof-of-concept for the etiopathogenic role of human IL-23A. This new model has a normal life span and integrates several characteristics of the human disease's complexity and chronicity, making it an attractive preclinical tool for studying IL-23-dependent pathogenic mechanisms and assessing the efficacy of anti-human IL-23A or modeled disease-related therapeutics.

Suitability of Fourier transform infrared microscopy for the diagnosis of cystic echinococcosis in human tissue sections.

Cystic echinococcosis (CE) is a global health concern caused by cestodes, posing diagnostic challenges due to nonspecific symptoms and inconclusive radiographic results. Diagnosis relies on histopathological evaluation of affected tissue, demanding comprehensive tools. In this retrospective case study, Fourier transform infrared microscopy was explored for detecting and identifying CE through biochemical changes in human tissue sections. Tissue samples from 11 confirmed CE patients were analyzed. Archived FFPE blocks were cut and stained, and then CE-positive unstained sections were examined using Fourier transform infrared microscopy post-deparaffinization. Results revealed the method's ability to distinguish echinococcus elements from human tissue, irrespective of organ type. This research showcases the potential of mid-infrared microscopy as a valuable diagnostic tool for CE, offering promise in enhancing diagnostic precision in the face of the disease's complexities.

Abstract 3546: Putting genes in context by linking gene signatures to established cancer hallmarks

Abstract The concept of the "Hallmarks of Cancer" provides a valuable framework for understanding fundamental organizing principles common to various types of cancers. However, the absence of a consensus gene set for cancer hallmarks poses data comparison and integration challenges, resulting in diverse biological interpretations across studies. Here, we first established a consensus cancer hallmark-gene set by merging data from available mapping resources. We searched the scientific literature for relevant publications from which a list of hallmark genes could be reconstructed. By consolidating data from seven publications, we identified 6,763 candidate cancer hallmark genes associated with ten cancer hallmarks. Of these, 1574 genes were part of at least two resources, forming a "core" hallmark gene set. Then, we compared the enrichment of cancer hallmarks by analyzing the prognostic genes associated with overall survival across twelve solid tumor types. Notably, the hallmark "tissue invasion and metastasis" was most prominent in the stomach (p=1.2E-11), pancreatic (p=1.5E-09), bladder (p=1.8E-08), and ovarian cancers (p=0.0002), aligning with their heightened potential to spread. "Sustained angiogenesis" predominated in lung squamous carcinomas (p=4.5E-08), while "genome instability" showed strong enrichment in lung adenocarcinomas (p=1.3E-09), liver (p=1.4E-10), pancreatic (p=1E-5) and kidney cancers (p=0.0025). Compelling evidence links lifestyle choices like alcohol consumption and smoking to genomic instability across tumors, suggesting a potential common underlying mechanism of tumorigenesis. Pancreatic cancers displayed the highest enrichment of hallmarks (8 out of 10), emphasizing the disease's complexity, while in melanomas, liver, and kidney cancer, a single hallmark (,,genomic instability”) was enriched among the prognostic markers of survival. These findings underscore the utility of the hallmark concept as an effective organizational tool, particularly when establishing a clear connection between genes and biological functions. Finally, we integrated the data into a publicly accessible and user-friendly online tool (available at www.cancerhallmarks.com) that allows users to identify the most relevant cancer-associated "candidate hallmark genes" in their datasets. In summary, we have established a consensus list of genes associated with cancer hallmarks. The analysis of survival-associated genes revealed a unique pattern of hallmark enrichment for each tumor type. Citation Format: Otília Menyhart, William Kothalawala, Balázs Győrffy. Putting genes in context by linking gene signatures to established cancer hallmarks [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 3546.

The association of Neuromedin U levels and non-alcoholic fatty liver disease: A comparative analysis

This comprehensive study delves into the potential link between Neuromedin U (NmU) serum levels and the development of non-alcoholic fatty liver disease (NAFLD), a condition of increasing global prevalence and significant public health concern. The research provides a nuanced understanding of the disease's etiology by examining a cohort of 112 participants, including individuals with and without NAFLD. The study meticulously considers a spectrum of variables such as demographic factors, body composition metrics, and blood parameters. Advanced diagnostic tools like Fibroscan® are employed to ascertain NAFLD presence, ensuring accurate and reliable results.The investigation reveals a noteworthy correlation between NAFLD and several risk factors, notably obesity, increased waist and neck circumferences, hypertriglyceridemia, and insulin resistance. These findings underscore the multifactorial nature of NAFLD and its intricate connection with metabolic syndromes. Intriguingly, the study observes lower NmU levels in individuals diagnosed with NAFLD. However, the role of NmU as an independent risk factor for NAFLD remains inconclusive, warranting further investigation. Although triglyceride level was observed to be an independent risk factor for NAFLD, this relationship was not associated with NmU.This research contributes significantly to the existing knowledge on NAFLD, highlighting the disease's complexity and the interplay of various risk factors. It also opens up new avenues for future research, particularly in exploring the role of NmU within the metabolic pathways associated with NAFLD. The insights gained from this study could guide the development of novel diagnostic and therapeutic strategies for NAFLD, addressing a crucial need in contemporary healthcare.In conclusion, the findings of this study not only enhance the understanding of NAFLD's pathophysiology but also emphasize the importance of comprehensive risk factor analysis in the management and prevention of this growing health concern.

Utilizing 3D Models to Unravel the Dynamics of Myeloma Plasma Cells' Escape from the Bone Marrow Microenvironment.

Recent therapeutic advancements have markedly increased the survival rates of individuals with multiple myeloma (MM), doubling survival compared to pre-2000 estimates. This progress, driven by highly effective novel agents, suggests a growing population of MM survivors exceeding the 10-year mark post-diagnosis. However, contemporary clinical observations indicate potential trends toward more aggressive relapse phenotypes, characterized by extramedullary disease and dominant proliferative clones, despite these highly effective treatments. To build upon these advances, it is crucial to develop models of MM evolution, particularly focusing on understanding the biological mechanisms behind its development outside the bone marrow. This comprehensive understanding is essential to devising innovative treatment strategies. This review emphasizes the role of 3D models, specifically addressing the bone marrow microenvironment and development of extramedullary sites. It explores the current state-of-the-art in MM modelling, highlighting challenges in replicating the disease's complexity. Recognizing the unique demand for accurate models, the discussion underscores the potential impact of these advanced 3D models on understanding and combating this heterogeneous and still incurable disease.

Proposed Etiotypes for Chronic Obstructive Pulmonary Disease: Controversial Issues.

The 2023 Global Initiative for Chronic Obstructive Lung Disease (GOLD) revised the definition of chronic obstructive pulmonary disease (COPD) to broadly include a variety of etiologies. A new taxonomy, composed of etiotypes, aims to highlight the heterogeneity in causes and pathogenesis of COPD, allowing more personalized management strategies and emphasizing the need for targeted research to understand and manage COPD better. However, controversy arises with including some diseases under the umbrella term of COPD, as their clinical presentations and treatments differ from classical COPD, which is smoking-related. COPD due to infection (COPD-I) and COPD due to environmental exposure (COPD-P) are classifications within the new taxonomy. Some disease entities in these categories show distinct clinical features and may not benefit from conventional COPD treatments, raising questions about their classification as COPD subtypes. There is also controversy regarding whether bronchiectasis with airflow limitations should be classified as an etiotype of COPD. This article discusses controversial issues associated with the proposed etiotypes for COPD in terms of COPD-I, COPD-P, and bronchiectasis. While the updated COPD definition by GOLD 2023 is a major step towards recognizing the disease's complexity, it also raises questions about the classification of related respiratory conditions. This highlights the need for further research to improve our understanding and approach to COPD management.