The management of 21-hydroxylase deficiency (21OHD), the most common form of congenital adrenal hyperplasia, remains challenging as both over- and undertreatment with hormone replacement therapy are associated with short and long-term complications. Monitoring of treatment efficacy typically combines clinical assessment with biochemical evaluation by measuring specific steroids. Currently, androstenedione and 17-hydroxyprogesterone are the most commonly measured biomarkers, and their concentrations are interpreted using available reference intervals. However, inter-center variation in the concentrations of these steroids has been observed, likely due to the heterogeneity in monitoring practices and analytical methods. Additional sources of variation include the selection of biological matrix, timing of sample collection relative to diurnal rhythm and medication administration, and interpretative challenges of biomarker levels. Age-dependent fluctuations in steroid concentrations, particularly within the pediatric population, underscore the necessity for age-specific reference intervals. This review evaluates current monitoring strategies and reported reference intervals, and explores emerging biomarkers, including 11-oxygenated androgens and indicators of glucocorticoid receptor sensitivity, along with non-invasive sampling approaches. Together, these developments may enhance the precision and ease of disease monitoring in patients with 21OHD. Overall, this review emphasizes the need for standardized monitoring practices and method- and age-specific reference ranges, aiming for assay harmonization and optimal disease control.

Aging and aging-related diseases are increasingly viewed as systemic disorders arising from disrupted inter-organ communication, yet the mechanisms linking local metabolic stress to organism-wide dysfunction remain unclear. The liver occupies a central position in this network, but how hepatic mitochondrial stress is translated into circulating signals that remodel distant tissues is incompletely understood. Here, we synthesize evidence identifying hepatic mitochondria as a systemic signaling hub that integrates metabolic and inflammatory stress and disseminates blood-borne cues during aging. We focus on three major classes of mitochondrial outputs: UPRmt-driven mitokines, including fibroblast growth factor 21 (FGF21) and growth differentiation factor 15 (GDF15); metabolites generated through mitochondrial metabolic reprogramming; and mitochondrial danger signals such as mitochondrial reactive oxygen species (mtROS) and oxidized mitochondrial DNA (mtDNA). These signals act through endocrine, metabolic, and immune pathways to reshape mitochondrial function, inflammation, and energy homeostasis across multiple organs. We further discuss how aging shifts hepatic mitochondrial signaling from adaptive to maladaptive states and emphasize that liver-centered regulation operates within bidirectional networks involving the gut, skeletal muscle, and immune system. Finally, we outline translational challenges and potential strategies for modulating hepatic mitochondrial outputs to restore systemic homeostasis in aging and aging-related diseases.

Parkinson’s disease (PD) is characterized by the progressive loss of dopaminergic neurons, with growing evidence underscoring the critical role of immunosenescence—the age-related dysregulation of the immune system—in its pathogenesis. This review delineates the intricate interplay between systemic immunosenescence, chronic neuroinflammation, and neurodegeneration in PD. We explore how age-related remodeling of the peripheral immune system, termed “inflammaging,” promotes a pro-inflammatory milieu that compromises blood-brain barrier integrity and drives microglial activation within the central nervous system. A central focus is the senescence-associated secretory phenotype, a cocktail of pro-inflammatory factors released by senescent glial cells, which perpetuates a self-sustaining cycle of neuroinflammation, facilitates the propagation of pathological α-synuclein, and ultimately accelerates neuronal loss. The review further examines the disruption of vital neuroimmune communication pathways, including aberrant neuron-glia and gut-brain axis signaling, which are corrupted in the aging brain. We evaluate the translational promise of emerging therapeutic strategies designed to target this immunosenescence-neuroinflammation axis. These include senolytic agents to clear senescent cells, adoptive regulatory T-cell therapy, cytokine-targeted immunomodulation, and immune rejuvenation approaches. Finally, we discuss significant translational challenges and outline future research directions, emphasizing the need for advanced model systems, biomarker development, and AI-driven personalized medicine to successfully develop disease-modifying immunotherapies that disrupt the vicious cycle of immunosenescence and neurodegeneration in PD.

Chronic wounds and implanted medical devices remain highly vulnerable to biofilm-associated infections, which resist conventional antibiotics and immune clearance. Synthetic antimicrobial peptides (AMPs) have emerged as promising alternatives, offering tunable sequences, short lengths for cost-effective synthesis, and functional modifications that enhance stability and antibiofilm potency. Hydrogels provide an optimal delivery matrix by enabling localized AMP release, maintaining a moist wound environment, and supporting stimuli-responsive or sustained therapeutic action. This review highlights recent advances in peptide engineering strategies—including rational sequence design, chemical modifications, and self-assembling nanostructures—alongside hydrogel integration approaches ranging from physical entrapment to covalent tethering and infection-triggered release systems. Mechanistic insights into antibiofilm activity are discussed, supported by in vitro, ex vivo, and in vivo evaluation models. Beyond antimicrobial efficacy, multifunctional AMP–hydrogel systems can deliver complementary benefits such as hemostasis, anti-inflammation, or enzymatic biofilm dispersal, further accelerating tissue repair. Despite significant progress, translational challenges remain, including peptide stability, manufacturing costs, regulatory hurdles, and host safety. Future directions point toward AI-driven peptide design, programmable hydrogels, and point-of-care integration to realize safe, effective, and multifunctional AMP–hydrogel therapies for chronic wound management and biofilm eradication.

Optogenetic vision-restorative approaches have entered the clinical stage of development. This article provides an overview of the current status of translation. It discusses specific aspects relating to the identification of asuitable study group and the selection of endpoints. It also focuses on patients' expectations on this novel therapy. A narrative review article is presented. Clinicaltrials.gov lists eight different treatment studies pursuing the approach of optogenetic restoration of vision. The results from three of these have been published in peer-reviewed journals. Now that proof of concept has been successfully demonstrated in patients, future approaches must focus on restoring more complex visual perception in order to offer maximum added value. When identifying suitable patient groups, the status of retinal remodelling must be considered, as must the fact that patients with no remaining native visual impressions already benefit from alower functional gain. Study endpoints should be chosen so that they specifically probe the function of the optogenetic actuator and meet the requirements of the regulatory authorities for treatment approval. Optogenetics has reached the clinical development phase. Following proof of concept, the focus is now on further developing the approach so that it will also enable complex vision.

The emergence of stem-cell-derived enamel organoids and dentin-producing dental pulp stem cell constructs presents new possibilities for restoring carious lesions using autologous enamel–dentin inlays. This overview outlines the biological and technological advances supporting this approach and proposes a workflow oriented toward clinical application. The benefits of tissue-based inlays, including inherent biomechanical compatibility, aesthetic accuracy, and potential for biological integration, are contrasted with those of purely artificial materials. Significant regenerative developments include the formation of human enamel organoids and odontoblast-lineage cells in vitro, 3D bioprinting of tooth-shaped constructs with demineralised dentin matrix and poly(ε‑caprolactone) scaffolds, and fibre-guiding periodontal ligament scaffolds that restore Sharpey’s fibres in vivo. The mechanical performance of adhesive resin cements, with bond strengths of approximately 4–7 MPa to enamel and dentin, and their durability in reattaching natural tooth fragments, supports the feasibility of bonding biological inlays. Practical considerations include controlling the slow degradation and hydrophobicity of poly(ε-caprolactone) through the use of ceramic or natural polymer additives, employing multi-material 3D printing to co-print mineralized enamel and cell-laden dentin layers, and achieving the desired shade, microstructure, and mechanical properties, exemplified by a compressive strength of approximately 677 MPa for 3D-printed zirconia crowns. Despite regulatory and translational challenges, the integration of digital dentistry, bioprinting, and stem cell science points toward future “grow and glue” restorations that may replace traditional drill-and-fill methods.

Pancreatic ductal adenocarcinoma (PDAC) remains a highly lethal malignancy due to late presentation, limited resectability, therapeutic resistance, and a dense desmoplastic immunosuppressive tumor microenvironment that impairs drug penetration and antitumor immunity. Focused ultrasound (FUS) is an emerging non-invasive, image-guided therapeutic platform capable of delivering spatially confined acoustic energy to induce tumor ablation, disrupt stromal barriers, and enhance delivery of drugs, nanoparticles, and nucleic acids. Depending on acoustic parameters, FUS can produce thermal effects resulting in coagulative necrosis or non-thermal mechanical effects, including cavitation, sonoporation, and histotripsy which remodel extracellular matrix architecture, increase vascular and cellular permeability, and facilitate tumor debulking. In addition, FUS-induced cell injury can promote immunogenic cell death and release tumor-associated antigens and danger signals, providing a rationale for combination strategies with chemotherapy, radiation, and immunotherapy. This review synthesizes the mechanistic foundations, preclinical modeling advances, and emerging clinical applications of FUS in PDAC, with emphasis on treatment integration, patient selection, real-time monitoring, and acoustic parameter optimization, while acknowledging current safety considerations and limited clinical toxicity data. Key limitations, translational challenges, and priority knowledge gaps are also discussed to define the role of FUS in multimodal PDAC care.

Architectural modulation of polymeric hydrogen sulfide donors: Structure-responsive materials for controlled therapeutics.

Cardiovascular diseases （CVDs） are among the leading cause of global morbidity and mortality. Due to their high prevalence and often asymptomatic progression， there is a pressing need for diagnostic tools that enable the early， accurate， and accessible detection of them. Acute coronary syndrome （ACS）， as a common and severe CVDs with high morbidity and mortality rates， has attracted considerable scientific interest. Various methods have been developed to detect ACS rapidly and accurately. Traditional diagnostic methods relying on antibody-based assays are effective. However， they face significant limitations， including high production costs， poor stability under varying environmental conditions， batch-to-batch variability， and cross-reactivity leading to false positives. These challenges have motivated the search for robust， cost-effective alternatives capable of detecting biomarkers with high sensitivity and specificity. Molecularly imprinted polymers （MIPs） have emerged as a promising alternative solution， offering antibody-like molecular recognition capabilities， superior stability， lower production costs， and resistance to harsh environmental conditions. This review systematically examines the latest advancements in MIP-based sensors for ACS biomarker detection in the last fifteen years， including imprinting strategies for key ACS biomarkers， sensor development and integration， and current challenges along with future perspectives. The first section focuses on the molecular imprinting techniques for essential ACS biomarkers， such as cardiac troponin （cTnI/cTnT）， myoglobin （Myo）， and creatine kinase （CK）. It compares whole-protein imprinting with epitope imprinting， highlighting the advantages of the latter in reducing template costs and enhancing binding specificity. Epitope imprinting using short peptide sequences has demonstrated femtomolar detection limits while overcoming challenges associated with large protein templates， such as structural denaturation and difficult template removal. The review also explores innovative approaches like dummy template imprinting， where structurally similar but cheaper molecules are used to create MIPs for high-cost biomarkers， achieving comparable specificity and sensitivity. The second section discusses the integration of MIPs with advanced biosensing platforms. Electrochemical sensors， using MIP-modified electrodes， have achieved remarkable sensitivity and rapid response times， making them suitable for point-of-care testing （POCT）. Optical sensors， particularly those based on surface-enhanced Raman spectroscopy and surface plasmon resonance， enable label-free， real-time detection with ultra-low detection limits. The review also addresses the integration of MIPs with microfluidic technology， where miniaturized devices facilitate automated， high-throughput biomarker analysis. Examples include paper-based microfluidic sensors that combine capillary action with MIP-SERs tags for multiplexed detection， achieving low detection limits without complex instrumentation. Despite these advancements， the review identifies key challenges hindering widespread clinical adoption of the MIP's based ACS sensor. Although the sensitivity and specificity of MIPs are impressive， they still lag behind those of monoclonal antibodies in some applications， particularly for low-abundance biomarkers. Reproducibility issues arise from variations in polymerization conditions and template removal efficiency. Commercialization barriers include the lack of standardized production protocols and regulatory frameworks for MIP-based diagnostics. The review proposes several strategic directions to address these limitations. Computational modeling and machine learning could optimize monomer selection and polymerization conditions to enhance MIP's performance. The development of hybrid systems combining MIPs with nanomaterials may further improve sensitivity and signal transduction. Multidisciplinary collaborations among chemists， engineers， and clinicians will be essential to translate laboratory innovations into commercially viable diagnostic tools. Additionally， the integration of MIPs with artificial intelligence machine learning algorithms could support the development of personalized diagnosis and treatment strategies. These future perspectives are likely to have a significant impact on the early diagnosis and treatment of cardiovascular diseases. In conclusion， MIP-based sensors represent a promising direction in ACS diagnostics， offering a unique combination of affordability， stability， and precision. By addressing current technical and translational challenges， MIP technology has the potential to revolutionize early disease detection， particularly in resource-limited areas. This review not only summarizes a decade of research progress but also provides a plan for future developments that could make personalized， decentralized cardiovascular diagnostics a widespread reality.

As an emerging class of smart nanomaterials, pH-responsive nanozymes are capable of realizing dynamic regulation of catalytic activity according to microenvironmental acidity and alkalinity. The material system encompasses noble metals, metal oxides, metal sulfides, carbon-based nanozymes, and metal-organic frameworks. Through engineering strategies such as surface ligand modification, heterogeneous atom doping and core-shell structure design, these nanozymes can achieve precise response to complex biological microenvironments, and show unique catalytic properties in the lesion site with specific pH. In recent years, pH-responsive nanozymes have been applied in various biomedical fields, such as tumor therapy, antimicrobial, wound healing, and anti-inflammation, to enhance therapeutic efficacy through controlled activation and targeted drug delivery. However, they still face many challenges in clinical translation, such as in vivo stability, toxicity assessment and precise regulation of activity. This paper reviews the research progress of pH-responsive nanozyme therapeutic systems and discusses the potential and challenges of integrating them with other nanotechnologies and therapeutic modalities, aiming to provide a reference and outlook for promoting their wider application in clinical diseases.

Current single-cell profiling technologies enable the capture of multiple cellular modalities, providing valuable insights into complex biological systems. While a substantial amount of single-cell multimodal data has been generated and accumulated, most of these datasets are unpaired, characterized by distinct feature spaces and a lack of cell-wise correspondence. The absence of explicit linkages between modalities poses a fundamental challenge for data integration and interpretation. To address this, we introduce SuperMap, a statistical learning method designed for the integrative analyses of unpaired multimodal data. SuperMap directly learns cross-modal mappings from unpaired data to effectively bridge and link different modalities, facilitating a variety of downstream analysis tasks. Comprehensive benchmarking and real-world applications demonstrate the superior performance of SuperMap in enhancing cell-type identification, improving diagonal integration, enabling regulatory analysis, and revealing epigenomic priming events to specify cell differentiation directions for trajectory inference.

Data-independent acquisition (DIA) mass spectrometry is a technique used in proteomics to identify and quantify proteins in complex biological samples. While this comprehensive approach yields more complete and reproducible protein profiles than data-independent acquisition (DDA), the resulting data are substantially larger and more complex, presenting significant challenges for data analysis and interpretation. These challenges can be effectively addressed using dedicated workflow managers that support parallel execution of complex analysis pipelines on high-performance computing infrastructure. Nextflow, in particular, is well-suited for streamlining data analysis, as it automates key aspects of workflow management, allowing researchers to efficiently analyze large-scale data sets with minimal manual intervention. Here, we describe glaDIAtor-nf, a Nextflow version of our software package glaDIAtor for untargeted analysis of DIA mass spectrometry proteomics data. We first demonstrate its technical accuracy through rigorous testing on gold standard data sets. Building on this, we then reveal known proteome patterns from public breast cancer data that remained hidden in the processed data of the original study. This illustrates the potential of reanalyzing the accumulating public data, but also highlights the need for convenient tools to facilitate such reanalysis in large-scale.

Remanent magnetisation is a critical challenge in magnetic interpretation, often leading to mispositioned anomalies and unreliable inversion results when neglected. This study applies and validates a practical, sequential two‑step workflow that integrates existing techniques to improve magnetic modelling in scenarios where remanent components significantly influence the anomaly geometry. In the first stage, the total magnetisation direction is estimated using the Equivalent Layer technique, which reconstructs the effective magnetisation vector from the observed anomaly without decomposing it into induced and remanent components. The magnetisation direction estimated via the equivalent layer technique was used to perform the reduction to the pole (RTP), and the inversion was subsequently carried out assuming a vertical inducing field (D = 0°, I = 90°). The methodology was tested on synthetic models with varying remanent contributions to evaluate its performance in controlled conditions. Results demonstrate that incorrect directional assumptions lead to distorted source geometries and underestimated susceptibilities, whereas using the Equivalent Layer estimated direction significantly improves inversion accuracy. The approach was also applied to a real airborne magnetic dataset from Espírito Santo State, Brazil, where it successfully recovered a westward‑dipping magnetic body with coherent susceptibility structure. Residual analysis confirmed strong agreement between predicted and observed fields, reinforcing the method’s robustness. While the current implementation assumes a constant magnetisation direction within the target volume, making it less suitable for geologically complex bodies, it offers a stable and interpretable solution for cases where remanence is spatially coherent. This study provides a practical, reproducible workflow for the integrated application of EL‑based direction estimation, RTP, and VOXI susceptibility inversion to remanence‑affected datasets. This workflow is compatible with standard modelling platforms and provides a practical reference for remanence‑affected magnetic interpretation.

ABSTRACT Introduction Endoglin has been extensively studied as an anti-angiogenic target, with preclinical work highlighting its critical role in tumor vasculature. TRC105 (carotuximab), a monoclonal antibody against endoglin, progressed through multiple clinical trials and was well tolerated, yet outcomes were disappointing, revealing a gap between preclinical findings and patient benefit. Recent insights into endoglin biology in the tumor microenvironment suggest that more precise, informed strategies are needed to fully realize its therapeutic potential. Areas covered This review summarizes key advances in endoglin biology, including its endothelial and non-endothelial roles and context-dependent effects across tumor and stromal compartments. We discuss preclinical therapeutic strategies and clinical trial experience with TRC105 and examine the translational challenges and future considerations needed to achieve potential clinical benefit. Expert opinion Preclinical studies have greatly advanced our understanding of endoglin biology, but the key challenge lies in identifying the biologic context where endoglin drives tumor progression. Future progress requires a deeper mechanistic resolution of endoglin’s roles across tumor type and disease stage, along with the development of biomarkers incorporating spatial expression patterns. Successful translation will depend on matching endoglin-targeted therapy to patients and tumor ecosystems most likely to benefit, shifting from broad anti-angiogenic application to precision stromal targeting.

Breast cancer remains a leading cause of cancer-related mortality despite advances in targeted therapies and immune checkpoint inhibitors (ICIs). While ICIs have reshaped therapeutic paradigms in multiple solid tumors, their clinical efficacy in breast cancer is predominantly restricted to triple-negative breast cancer (TNBC), underscoring the necessity for innovative immunotherapeutic modalities with broader applicability. Bispecific antibodies (BsAbs) are engineered molecules capable of simultaneously engaging tumor-associated antigens (TAAs) such as EGFR, EpCAM, Trop-2, CEACAM5, MUC1, and PSMA, in addition to B7-H4, HER2, and PD-L1, alongside immune effector cells, primarily CD3+ T lymphocytes. This dual targeting promotes the formation of a cytolytic immunological synapse, leading to T-cell activation, proliferation, and targeted tumor cell lysis via granzyme/perforin pathways. Advances in BsAb design-including optimization of affinity constants, valency, Fc engineering to modulate effector functions, and molecular formats (e.g., BiTEs, DARTs, tandem scFvs)-have improved tumor penetration, pharmacokinetics, and reduced off-target toxicity. Preclinical and early-phase clinical data demonstrate robust antitumor activity of BsAbs, both as monotherapy and in synergistic combinations with chemotherapy, targeted agents, or ICIs, potentially overcoming tumor microenvironment-mediated immunosuppression and immune escape mechanisms. Major challenges include heterogeneity and temporal variability of TAA expression, cytokine release syndrome (CRS) mitigation strategies, pharmacodynamic optimization, and delivery methods to maximize tumor bioavailability while limiting systemic toxicity. This review details the molecular mechanisms, preclinical evidence, clinical trial outcomes, and translational challenges for BsAbs in breast cancer, highlighting their transformative potential in expanding immunotherapeutic efficacy beyond current limitations.

External ventricular drain (EVD) placement is the most commonly performed bedside emergent neurosurgical procedure. However, catheter misplacement occurs in approximately 25% of cases, potentially resulting in neurological injury or the need for repositioning, highlighting the need for safer and more accurate placement techniques. This review aims to (1) summarize technological innovations developed to enhance the accuracy and safety of bedside EVD placement, (2) evaluate their reported benefits and limitations, and (3) identify key barriers to their clinical implementation in critical care settings. We conducted a systematic review of PubMed, Embase, and Scopus combining terms related to EVD placement and assistive technologies. All the studies evaluated EVD placement using simulations, phantoms, cadavers, and patient procedures conducted at the bedside and in the operating room. We identified 3898 records, and 76 studies met the inclusion criteria. Of these, 30 studies compared stereotactic systems, ultrasound guidance, frameless neuronavigation, and augmented or mixed reality technologies to the freehand technique. These studies consistently reported improved accuracy with advanced systems although complication rates and procedural times were variably reported. An additional 35 and 12 studies evaluated nonimmersive and immersive technologies, respectively, in noncomparative settings. Most of these demonstrated high first-pass success and mean tip deviations under 3 mm. Variability in the definitions of accuracy and procedural metrics limited data synthesis, but the overall findings suggest a promising trajectory for technology-assisted EVD placement. Assistive technologies for EVD placement show potential to improve accuracy and safety over the freehand technique. These early successes signal potential to improve bedside neurosurgical care. Realizing this promise will require future studies to standardize accuracy metrics, report time, and safety outcomes consistently and validate performance in real-world critical care settings.

Diabetic kidney disease (DKD) represents the leading cause of end-stage renal disease (ESRD) worldwide, characterized by a complex pathophysiology and heterogeneous progression. Accurate prediction of the onset, progression, and adverse outcomes of DKD is critical for early intervention and personalized management. This review systematically summarizes the current research on prediction models in DKD, encompassing both diagnostic and prognostic models. It discusses key methodological considerations in model development and validation, with a specific focus on the application of machine learning (ML) techniques in model construction. Furthermore, this article also evaluates the performance of prediction models based on routine clinical parameters and multimodal models integrating multi-omics, imaging, retinal parameters, and renal pathological features. The primary challenges in clinical translation are analyzed, and future directions for optimizing DKD prediction are proposed. In summary, advancing the optimization and clinical translation of DKD prediction models holds significant potential to improve patient care. Future research should focus on addressing the existing challenges, aiming to advance risk-stratified and personalized management and inform future precision medicine approaches in nephrology.

LEF1 and IL13RA2 in testicular sex cord-stromal tumors: LEF1 as a potential diagnostic marker for Sertoli cell tumors.

Multi-omic biomarker detection in ovarian cancer.

Non-coding RNAs as emerging biomarkers in melanoma.