Search Results (133)

Amyotrophic lateral sclerosis (ALS) is still a heterogeneous neurodegenerative disorder that can be identified clinically and biologically, without a strong set of biomarkers that can adequately measure its fast rate of progression and molecular heterogeneity. In this review, we intend to consolidate the most relevant and timely advances in ALS biomarker discovery, in order to begin to bring molecular, imaging, genetic, and digital areas together for potential integration into a precision medicine approach to ALS. Our goal is to begin to display how several biomarkers in development (e.g., neurofilament light chain (NfL), phosphorylated neurofilament heavy chain (pNfH), TDP-43 aggregates, mitochondrial stress markers, inflammatory markers, etc.) are changing our understanding of ALS and ALS dynamics. We will attempt to provide a framework for thinking about biomarkers in a systematic way where our candidates are not signals alone but part of a tethered pathophysiological cascade. We are particularly interested in the fast progressor phenotype, a devastating and under-characterized subset of ALS due to a rapid axonal degeneration, early respiratory failure, and very short life span. We will try to highlight the salient molecular features of this ALS subtype, including SOD1 A5V toxicity, C9orf72 repeats, FUS variants, mitochondrial collapse, and impaired autophagy mechanisms, and relate these features to measurable blood and CSF (biomarkers) and imaging platforms. We will elaborate on several interesting tools, for example, single-cell transcriptomics, CSF exosomal cargo analysis, MRI techniques, and wearable sensor outputs that are developing into high-resolution windows of disease progression and onset. Instead of providing a static catalog, we plan on providing a conceptual roadmap to integrate biomarker panels that will allow for earlier diagnosis, real-time disease monitoring, and adaptive therapeutic trial design. We hope this synthesis will make a meaningful contribution to the shift from observational neurology to proactive biologically informed clinical care in ALS. Although there are still considerable obstacles to overcome, the intersection of a precise molecular or genetic association approach, digital phenotyping, and systems-level understandings may ultimately redefine how we monitor, care for, and treat this challenging neurodegenerative disease. Full article

Background: Glioblastoma multiforme (GBM) is the most common and aggressive primary brain tumour, associated with poor survival outcomes and significant clinical challenges. Conventional diagnostic methods, including MRI, CT, and histopathological analysis of tissue biopsies, are limited by their inability to reliably distinguish treatment effects from true tumour progression, often resulting in misdiagnosis and delayed intervention. Repeated tissue biopsies are also invasive and unsuitable for longitudinal monitoring. Liquid biopsy, a minimally invasive approach analysing tumour-derived material in biofluids such as blood and cerebrospinal fluid (CSF), offers a promising alternative. This review aims to evaluate current evidence on circulating biomarkers including circulating tumour cells (CTCs), circulating tumour DNA (ctDNA), microRNAs (miRNAs), extracellular vesicles (EVs), and proteins in GBM diagnosis and monitoring, and to assess the potential role of artificial intelligence (AI) in enhancing their clinical application. Methods: A narrative synthesis of the literature was undertaken, focusing on studies that have investigated blood- and CSF-derived biomarkers in GBM patients. Key aspects evaluated included biomarker biology, detection techniques, diagnostic and prognostic value, current technical challenges, and progress towards clinical translation. Studies exploring AI and machine learning (ML) approaches for biomarker integration and analysis were also reviewed. Results: Liquid biopsy enables repeated and minimally invasive sampling of tumour-derived material, reflecting the genetic, epigenetic, proteomic, and metabolomic landscape of GBM. Although promising, its translation into routine clinical practice is hindered by the low abundance of circulating biomarkers and lack of standardised collection and analysis protocols. Evidence suggests that combining multiple biomarkers improves sensitivity and specificity compared with single-marker approaches. Emerging AI and ML tools show significant potential for improving biomarker discovery, integrating multi-omic datasets, and enhancing diagnostic and prognostic accuracy. Conclusions: Liquid biopsy represents a transformative tool for GBM management, with the capacity to overcome limitations of conventional diagnostics and provide real-time insights into tumour biology. By integrating multiple circulating biomarkers and leveraging AI-driven approaches, liquid biopsy could enhance diagnostic precision, enable dynamic disease monitoring, and improve clinical decision-making. However, large-scale validation and standardisation are required before routine clinical adoption can be achieved. Full article

Background: This study presents an innovative approach to cardiovascular disease (CVD) risk prediction based on a comprehensive analysis of clinical, immunological and biochemical markers using mathematical modelling and machine learning methods. Baseline data include indices of humoral and cellular immunity (CD59, CD16, IL-10, CD14, CD19, CD8, CD4, etc.), cytokines and markers of cardiovascular disease, inflammatory markers (TNF, GM-CSF, CRP), growth and angiogenesis factors (VEGF, PGF), proteins involved in apoptosis and cytotoxicity (perforin, CD95), as well as indices of liver function, kidney function, oxidative stress and heart failure (albumin, cystatin C, N-terminal pro B-type natriuretic peptide (NT-proBNP), superoxide dismutase (SOD), C-reactive protein (CRP), cholinesterase (ChE), cholesterol, and glomerular filtration rate (GFR)). Clinical and behavioural risk factors were also considered: arterial hypertension (AH), previous myocardial infarction (PICS), aortocoronary bypass surgery (CABG) and/or stenting, coronary heart disease (CHD), atrial fibrillation (AF), atrioventricular block (AB block), and diabetes mellitus (DM), as well as lifestyle (smoking, alcohol consumption, physical activity level), education, and body mass index (BMI). Methods: The study included 52 patients aged 65 years and older. Based on the clinical, biochemical and immunological data obtained, a model for predicting the risk of premature cardiovascular aging was developed using mathematical modelling and machine learning methods. The aim of the study was to develop a predictive model allowing for the early detection of predisposition to the development of CVDs and their complications. Numerical methods of mathematical modelling, including Runge–Kutta, Adams–Bashforth and backward-directed Euler methods, were used to solve the prediction problem, which made it possible to describe the dynamics of changes in biomarkers and patients’ condition over time with high accuracy. Results: HLA-DR (50%), CD14 (41%) and CD16 (38%) showed the highest association with aging processes. BMI was correlated with placental growth factor (37%). The glomerular filtration rate was positively associated with physical activity (47%), whereas SOD activity was negatively correlated with it (48%), reflecting a decline in antioxidant defence. Conclusions: The obtained results allow for improving the accuracy of cardiovascular risk prediction, and form personalised recommendations for the prevention and correction of its development. Full article

Chemotherapeutic agents and radiotherapy are highly effective in treating malignancies. However, they carry a significant risk of harming the gonads and may lead to endocrine dysfunction and reproductive issues. This review outlines the molecular mechanisms of gonadotoxic therapies, focusing on radiation, alkylating agents, and platinum compounds. It discusses the loss of PMFs due to gonadotoxic exposure, including DNA double-strand breaks, oxidative stress, and dysregulated signaling pathways like PI3K/PTEN/Akt/mTOR and TAp63-mediated apoptosis. Furthermore, it explores strategies to mitigate gonadal damage, including GnRH agonists, AMH, imatinib, melatonin, sphingolipid metabolites, G-CSF, mTOR inhibitors, AS101, and LH. These therapies, paired with existing fertility preservation methods, could safeguard reproductive and hormonal functions and improve the quality of life for young cancer patients. Despite the progress made in recent years in understanding gonadotoxic mechanisms, gaps remain due to questionable reliance on mouse models and the lack of models replicating human ovarian dynamics. Long-term studies are vital for wider analyses and exploration of protective strategies based on various animal models and clinical trials. It is essential to verify that these substances do not hinder the anti-cancer effectiveness of treatments or cause lasting DNA changes in granulosa cells, raising the risk of miscarriages and infertility. Full article

Although intracranial hypertension (ICH) has traditionally been framed as simply a numerical escalation of intracranial pressure (ICP) and usually dealt with in its clinical form and not in terms of its complex underlying pathophysiology, an emerging body of evidence indicates that ICH is not simply an elevated ICP process but a complex process of molecular dysregulation, glymphatic dysfunction, and neurovascular insufficiency. Our aim in this paper is to provide a complete synthesis of all the new thinking that is occurring in this space, primarily on the intersection of glymphatic dysfunction and cerebral vein physiology. The aspiration is to review how glymphatic dysfunction, largely secondary to aquaporin-4 (AQP4) dysfunction, can lead to delayed cerebrospinal fluid (CSF) clearance and thus the accumulation of extravascular fluid resulting in elevated ICP. A range of other factors such as oxidative stress, endothelin-1, and neuroinflammation seem to significantly impair cerebral autoregulation, making ICH challenging to manage. Combining recent studies, we intend to provide a revised conceptualization of ICH that recognizes the nuance and complexity of ICH that is understated by previous models. We wish to also address novel diagnostics aimed at better capturing the dynamic nature of ICH. Recent advances in non-invasive imaging (i.e., 4D flow MRI and dynamic contrast-enhanced MRI; DCE-MRI) allow for better visualization of dynamic changes to the glymphatic and cerebral blood flow (CBF) system. Finally, wearable ICP monitors and AI-assisted diagnostics will create opportunities for these continuous and real-time assessments, especially in limited resource settings. Our goal is to provide examples of opportunities that exist that might augment early recognition and improve personalized care while ensuring we realize practical challenges and limitations. We also consider what may be therapeutically possible now and in the future. Therapeutic opportunities discussed include CRISPR-based gene editing aimed at restoring AQP4 function, nano-robotics aimed at drug targeting, and bioelectronic devices purposed for ICP modulation. Certainly, these proposals are innovative in nature but will require ethically responsible confirmation of long-term safety and availability, particularly to low- and middle-income countries (LMICs), where the burdens of secondary ICH remain preeminent. Throughout the review, we will be restrained to a balanced pursuit of innovative ideas and ethical considerations to attain global health equity. It is not our intent to provide unequivocal answers, but instead to encourage informed discussions at the intersections of research, clinical practice, and the public health field. We hope this review may stimulate further discussion about ICH and highlight research opportunities to conduct translational research in modern neuroscience with real, approachable, and patient-centered care. Full article

The effective suppression of inflammation using disease-modifying therapies is essential in the treatment of multiple sclerosis (MS). Anti-CD20 monoclonal antibodies are commonly used long-term as maintenance therapies, largely due to the lack of reliable biomarkers to guide dosing and evaluate treatment response. However, prolonged use increases the risk of infections and other immune-mediated side effects. The unique ability of brain-derived blood extracellular vesicles (EVs) to cross the blood–brain barrier and reflect the central nervous system (CNS) immune status has sparked interest in their potential as biomarkers. This study aimed to assess whether blood-derived L1CAM⁺ EVs could serve as biomarkers of treatment response to rituximab (RTX) in patients with relapsing-remitting MS (RRMS). Serum samples (n = 25) from the baseline (month 0) and after 6 months were analyzed from the RTX arm of the ongoing randomized clinical trial OVERLORD-MS (comparing anti-CD20 therapies in RRMS patients) and were compared with serum samples from healthy controls (n = 15). Baseline cerebrospinal fluid (CSF) samples from the same study cohort were also included. EVs from both serum and CSF samples were characterized, considering morphology, size, and concentration, using transmission electron microscopy (TEM) and nanoparticle tracking analysis (NTA). The immunophenotyping of EV surface receptors was performed using flow cytometry with the MACSPlex exosome kit, while label-free quantitative proteomics of EV protein cargo was conducted using a proximity extension assay (PEA). TEM confirmed the presence of EVs with the expected round morphology with a diameter of 50–150 nm. NTA showed significantly higher concentrations of L1CAM⁺ EVs (p < 0.0001) in serum total EVs and EBNA1⁺ EVs (p < 0.01) in serum L1CAM⁺ EVs at baseline (untreated) compared to in healthy controls. After six months of RTX therapy, there was a significant reduction in L1CAM⁺ EV concentration (p < 0.0001) and the downregulation of TNFRSF13B (p = 0.0004; FC = −0.49) in serum total EVs. Additionally, non-significant changes were observed in CD79B and CCL2 levels in serum L1CAM⁺ EVs at baseline compared to in controls and after six months of RTX therapy. In conclusion, L1CAM⁺ EVs in serum showed distinct immunological profiles before and after rituximab treatment, underscoring their potential as dynamic biomarkers for individualized anti-CD20 therapy in MS. Full article

Due to their clinical heterogeneity, nonspecific symptoms, and the limitations of existing biomarkers and imaging modalities, metabolic brain diseases (MBDs), such as mitochondrial encephalopathies, lysosomal storage disorders, and glucose metabolism syndromes, pose significant diagnostic challenges. This review examines the growing potential of cell-free DNA (cfDNA) derived from cerebrospinal fluid (CSF) epigenetic profiling as a dynamic, cell-type-specific, minimally invasive biomarker approach for MBD diagnosis and monitoring. We review important technological platforms and their use in identifying CNS-specific DNA methylation patterns indicative of neuronal injury, neuroinflammation, and metabolic reprogramming, including cfMeDIP-seq, enzymatic methyl sequencing (EM-seq), and targeted bisulfite sequencing. By synthesizing current findings across disorders such as MELAS, Niemann–Pick disease, Gaucher disease, GLUT1 deficiency syndrome, and diabetes-associated cognitive decline, we highlight the superior diagnostic and prognostic resolution offered by CSF cfDNA methylation signatures relative to conventional CSF markers or neuroimaging. We also address technical limitations, interpretive challenges, and translational barriers to clinical implementation. Ultimately, this review explores CSF cfDNA epigenetic analysis as a liquid biopsy modality. The central objective is to assess whether epigenetic profiling of CSF-derived cfDNA can serve as a reliable and clinically actionable biomarker for improving the diagnosis and longitudinal monitoring of metabolic brain diseases. Full article

Background: Immune checkpoint inhibitors (ICIs) have transformed cancer treatment, yet severe immune-related adverse events (irAEs) often necessitate immunotherapy discontinuation and cause life-threatening complications. Circulating plasma proteins, dynamically accessible and functionally linked to immunity, may predict and offer novel targets for irAEs. Methods: Leveraging multi-omics integration, we conducted bidirectional two-sample Mendelian randomization (MR) using protein quantitative trait loci (pQTLs) from 4998 plasma proteins and genome-wide association data of irAE phenotypes. A causal inference framework combining colocalization analysis, multivariable MR (MVMR) adjusting for body mass index (BMI) confounding, and mediation MR elucidated BMI-independent pathways. Systems biology approaches including tissue-specific expression profiling, pathway enrichment, and protein interaction network analysis revealed spatial and functional drivers of irAE pathogenesis. Results: Proteome-wide MR mapping identified eight plasma proteins (CCL20, CSF1, CXCL9, CD40, TGFβ1, CLSTN2, TNFSF12, TGFα) causally associated with all-grade irAEs, and five (CCL20, CCL25, CXCL10, ADA, TGFα) with high-grade irAEs. Colocalization prioritized CD40/TNFSF12 (all-grade) and ADA/CCL25 (high-grade) as therapeutic targets (PPH4 > 0.7). CXCL9/TNFSF12 (all-grade) and CCL25 (high-grade) exerted BMI-independent effects, suggesting intrinsic immune dysregulation mechanisms. Tissue-specific gene expression patterns, CSF1, TGFβ1 in lung, TNFSF12 in the ileum may explain organ-specific irAE vulnerabilities. High-grade irAEs correlated with compartmentalized immune dysregulation and IL-17/immunodeficiency pathway activation. Conclusions: This study establishes the causal atlas of plasma proteins in irAE pathogenesis, bridging biomarker discovery with actionable therapeutic targets. These advances align with next-generation immunotherapy goals: maximizing efficacy while taming the immune storm. Full article

Background/Objectives: Tick-borne encephalitis (TBE) is a notifiable disease in Poland, with the highest incidence in the northeastern region. Although vaccination is highly effective, breakthrough infections occasionally occur. This study aimed to describe the clinical features of vaccinated and unvaccinated TBE cases, assess long-term hospitalization trends, and estimate vaccine effectiveness (VE) in a highly endemic region. Methods: We retrospectively analyzed 1518 laboratory-confirmed TBE cases hospitalized at the University Clinical Hospital in Białystok, Poland, from 1988 to 2020. Clinical and cerebrospinal fluid (CSF) parameters were compared between vaccinated and unvaccinated individuals. Vaccine effectiveness was estimated using the screening method, based on aggregated regional vaccine uptake data from 1999 to 2020. Results: Among all cases, 13 (0.9%) occurred in individuals who had received at least one dose of vaccine, including 4 who had completed the full primary vaccination schedule. Hospitalized vaccinated patients showed similar demographic and clinical characteristics compared to unvaccinated patients, though CSF findings suggested an earlier and more dynamic immune response. Seasonal analysis revealed a sustained increase in TBE hospitalizations and a possible extension of the transmission season into late summer and autumn. Estimated VE was 94.4% (95% CI 85.2–97.9%), though this should be interpreted with caution due to the small number of vaccinated cases and assumptions regarding population-level coverage. Conclusions: This study provides detailed clinical data on breakthrough TBE cases and long-term epidemiological insights from an endemic region in Poland. While vaccine effectiveness appears high, low uptake remains a public health concern. These findings underscore the need for improved vaccination coverage and ongoing surveillance to monitor evolving transmission patterns. Full article

Aquaporins (AQPs) are a family of transmembrane water channel proteins facilitating the transport of water and, in some cases, small solutes such as glycerol, lactate, and urea. In the central nervous system (CNS), several aquaporins play crucial roles in maintaining water homeostasis, modulating cerebrospinal fluid (CSF) circulation, regulating energy metabolism, and facilitating neuroprotection under pathological conditions. Among them, AQP2, AQP4, AQP9, and AQP11 have been implicated in traumatic and non-traumatic brain injuries. The most abundant aquaporin (AQP) in the brain, AQP4, is essential for fluid regulation, facilitating water transport across the blood–brain barrier and glymphatic clearance. AQP2 is primarily known for its function in the kidneys, but it is also expressed in brain regions related to vasopressin signaling and CSF dynamics. AQP9 acts as a channel for glycerol and lactate, thus playing a role in metabolic adaptation during brain injury. AQP11, an intracellular aquaporin, is involved in oxidative stress responses and cellular homeostasis, with emerging evidence suggesting its role in neuroprotection. Aquaporins play a dual role in brain injury; while they help maintain homeostasis, their dysregulation can exacerbate cerebral edema, metabolic dysfunction, and inflammation. In traumatic brain injury (TBI), aquaporins regulate the formation and resolution of cerebral edema. In non-traumatic brain injuries, including ischemic stroke, aneurysmal subarachnoid hemorrhage (aSAH), and intracerebral hemorrhage (ICH), aquaporins influence fluid balance, energy metabolism, and oxidative stress responses. Understanding the specific roles of AQP2, AQP4, AQP9, and AQP11 in these brain injuries may lead to new therapeutic strategies to mitigate secondary damage and improve neurological outcomes. This review explores the function of the above aquaporins in both traumatic and non-traumatic brain injuries, highlighting their potential and limitations as therapeutic targets for neuroprotection and recovery. Full article

In China’s coastal regions, severe seawater eutrophication has led to frequent occurrences of red tides, causing significant damage to marine fisheries and aquatic resources. Therefore, red tide detection and prediction are of great research importance. Although current deep learning-based red tide detection methods perform well in detecting single-day red tides, they struggle with continuous multi-day detection due to insufficient mining of temporal features and difficulties in accurately capturing dynamic variations, limiting further improvements in detection accuracy. To address these issues, this study proposes a time-series fusion network model (CSF-RTDNet) for red tide detection using time-continuous GOCI data from the East China Sea. By integrating multi-temporal GOCI data, the model comprehensively captures spatiotemporal characteristics of red tides, enhancing dynamic process modeling. The CSF-RTDNet method improves feature discrimination by introducing NDVI to enhance red tide characteristics and increase separability between red tides and seawater. Additionally, an ECA channel attention mechanism is employed to fully exploit spectral features across different bands for deeper feature extraction. A novel feature extraction module, ASPC-DSC, combines atrous spatial pyramid convolution with depthwise separable convolution to effectively fuse multi-scale contextual features while improving computational efficiency. Furthermore, ConvLSTM is introduced to integrate temporal and spatial features, effectively addressing the insufficient mining of sequential characteristics in multi-day red tide detection. Experimental results demonstrate that CSF-RTDNet achieves robust detection of red tides with complex boundaries and continuous temporal patterns, attaining an accuracy of 95.89%, precision of 93.03%, recall of 96.34%, and a Kappa coefficient of 0.95. This method significantly enhances red tide detection accuracy and provides valuable technical support for marine environmental monitoring. Full article

Background: Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that often begins decades before clinical symptoms manifest. Early detection remains critical for effective intervention, particularly in younger adults, where biomarker deviations may signal pre-symptomatic risk. This research presents a computational modeling framework to predict cognitive impairment progression and stratify individuals into risk zones based on age-specific biomarker thresholds. Methods: The model integrates sigmoid-based data generation to simulate non-linear biomarker trajectories reflective of real-world disease progression. Core biomarkers—including cerebrospinal fluid (CSF) amyloid-beta 42 (A${\beta }_{42}$), amyloid positron emission tomography (amyloid PET), cerebrospinal fluid Tau protein (CSF Tau), and magnetic resonance imaging with fluorodeoxyglucose positron emission tomography (MRI FDG-PET)—were analyzed simultaneously to compute the cognitive impairment (CI) score of instances, dynamically adjusted for age. Higher CSF A${\beta }_{42}$ levels consistently demonstrated a protective effect, while elevated amyloid PET and Tau levels increased cognitive risk. Age-specific CI thresholds prevented the overestimation of risk in younger individuals and the underestimation in older cohorts. To demonstrate its applicability, we applied the full four-stage framework—comprising data aggregation and cleaning, sigmoid-based synthetic biomarker simulation with descriptive analysis, parameter accumulation modeling, and correlation-driven CI classification—on a curated dataset of 307 instances (ages 10–110) from Kaggle, the Alzheimer’s Disease Neuroimaging Initiative (ANDI), and the Open Access Series of Imaging Studies (OASIS) to evaluate age-specific stratification of preclinical AD risk. Results: The study highlights the model’s potential to identify individuals in risk zones from a pool of 150 instances, enabling targeted early interventions. Furthermore, the framework supports retrospective disease trajectory analysis, offering clinicians insights into optimal intervention windows even after symptom onset. Conclusions: Future work aims to validate the model using longitudinal, inclusive, real-world datasets and expand its predictive capacity through machine learning techniques and integrating genetic and lifestyle factors. Ultimately, this research contributes to advancing precision medicine approaches in Alzheimer’s disease by providing a scalable computational tool for early risk assessment and intervention planning. Full article

Neural stem cells (NSCs) are crucial components of the nervous system, primarily located in the subventricular zone (SVZ) and subgranular zone (SGZ). The SVZ neural stem cell niche (NSCN) is a specialized microenvironment where growth factors and extracellular matrix (ECM) components collaborate to regulate NSC self-renewal and differentiation. Despite its importance, our understanding of the SVZ remains incomplete due to the inherent challenges of animal research, particularly given the tissue’s dynamic nature. To address these limitations, we developed a proof-of-concept, dynamic, and tissue-specific 3D organotypic SVZ model to reduce reliance on animal models. This static 3D organotypic model integrates a region-specific decellularized ECM derived from the SVZ, mimicking the native NSCN and supporting mouse-derived ependymal cells (ECs), radial glial cells (RGCs), astrocytes, and NSCs. To further improve physiological relevance, we incorporated a dynamic microfluidic culture system (SVZonChip), replicating cerebrospinal fluid (CSF) flow as observed in vivo. The resulting SVZonChip platform, combining region-specific ECM proteins with dynamic culture conditions, provides a sustainable and reproducible tool to minimize animal model use. It holds significant promise for studying SVZ-related diseases, such as congenital hydrocephalus, stroke, and post-stroke neurogenesis, while advancing translational research and enabling personalized medicine protocols. Full article

Hydrocephalus is a chronic neurological condition caused by abnormal cerebrospinal fluid (CSF) accumulation, significantly impacting patients’ quality of life. Its causes remain poorly understood, making neurosurgery the primary treatment. Research suggests that hydrocephalus may result from impaired macromolecular clearance, leading to increased osmotic load in the ventricles. Macromolecules are cleared via processes such as transcytosis, involving caveolae- and clathrin-dependent pathways, soluble N-ethylmaleimide-sensitive factor activating protein receptor (SNARE) proteins, and vesicular trafficking. Abnormalities in transcytosis components, such as mutations in alpha-SNAP (α-soluble NSF attachment protein) and SNARE complexes, disrupt membrane organization and vesicle fusion, potentially contributing to hydrocephalus. Other factors, including alpha-synuclein and Rab proteins, may also play roles in vesicle dynamics. Insights from animal models, such as hyh (hydrocephalus with hop gait) mice, highlight the pathological consequences of these disruptions. Understanding transcytosis abnormalities in hydrocephalus could lead to novel therapeutic strategies aimed at enhancing macromolecular clearance, reducing ventricular fluid buildup, and improving patient outcomes. Full article

Tumor-associated macrophages’ (TAMs) origin, polarization, and dynamic interaction in the tumor microenvironment (TME) influence cancer development. They are essential for homeostasis, monitoring, and immune protection. Cells from bone marrow or embryonic progenitors dynamically polarize into pro- or anti-tumor M2 or M1 phenotypes based on cytokines and metabolic signals. Recent advances in TAM heterogeneity, polarization, characterization, immunological responses, and therapy are described here. The manuscript details TAM functions and their role in resistance to PD-1/PD-L1 blockade. Similarly, TAM-targeted approaches, such as CSF-1R inhibition or PI3Kγ-driven reprogramming, are discussed to address anti-tumor immunity suppression. Furthermore, innovative biomarkers and combination therapy may enhance TAM-centric cancer therapies. It also stresses the relevance of this distinct immune cell in human health and disease, which could impact future research and therapies. Full article