Background/Introduction: Immune-related adverse events (irAEs) remain unpredictable complications of immune checkpoint inhibitor (ICI) therapy. Current biomarkers, such as inflammatory blood parameter lack specificity and clinical robustness. Because DNA methylation reflects stable immune programming and predisposition to autoimmune diseases, blood methylation profiling may offer a tumour-agnostic approach for irAE prediction.

Material & methods: We analysed baseline leukocyte DNA methylation in a prospectively enrolled real-world cohort of advanced pan-cancer patients treated with ICI in a palliative setting. Genome-wide methylation was profiled using Infinium MethylationEPIC v1.0 BeadChips. Differentially methylated positions (DMPs) were identified by rank-based comparison and tested for Gene Ontology enrichment. Blood-cell deconvolution using EpiDISH was applied to exclude compositional bias.

Results: Among 195 patients (median age 65 years; 34.9% female), 32.8% experienced at least one irAE, most frequently involving thyroid, skin, or lung. Despite globally concordant DNA methylation between groups (ρ = 0.9996), DMP analysis revealed subtle but distinct site-specific differences. A methylation-based classifier discriminated patients who developed irAEs with good accuracy (area under the curve [AUC] 0.831, 95% confidence interval [CI] 0.735–0.927), which remained robust in an internal validation subset (AUC 0.874, 95% CI 0.757–0.992). EpiDISH confirmed no significant variation in immune cell fractions between groups. Functional enrichment of top-ranked DMPs showed significant overrepresentation of neuronal development, cell-cell signalling, and epithelial morphogenesis pathways, networks linked to immune-synapse formation, cytokine signalling, and tissue repair responses.

Discussion: Baseline blood DNA methylation captures systemic immune-developmental states predisposing to irAEs. The enrichment of cell-cell signalling and epithelial morphogenesis pathways suggests an epigenetically encoded predisposition for dysregulated immune-tissue communication. Blood-based methylation profiling thus represents a promising, tumour-agnostic biomarker approach for individualised irAE risk prediction in cancer immunotherapy.

Background: Skin cancer (SC) is among the most prevalent malignancies, yet causal circulating proteins and tractable therapeutic targets remain poorly defined.

Methods: We performed a proteome-wide Mendelian randomization (MR) analysis of 4972 proteins across large GWAS datasets (discovery: 23 694 cases and 372 016 controls; replication cohorts: > 42 000 cases and > 810 000 controls). Sensitivity analyses, bidirectional MR, Steiger filtering and SMR testing supported robustness. Based on an integrated prioritization framework, DPEP1 was selected for downstream analyses. We then explored DPEP1-associated transcriptional programs using bulk and single-cell transcriptomic datasets and performed virtual screening, docking and molecular dynamics (MD) simulations to prioritize candidate DPEP1-binding ligands for future validation.

Results: MR identified six genetically supported proteins associated with SC risk, and DPEP1 was prioritized for downstream characterization based on an explicit target-prioritization framework. In the murine Dpep1-knockout dataset, Dpep1 deficiency was associated with altered stromal and immune programs, including reduced fibroblast abundance, lower YAP-TEAD/TGF-β pathway activity and increased CD8⁺ T-cell cytotoxicity scores. In human melanoma single-cell analyses, DPEP1 expression was enriched in fibroblasts and inferred ligand–receptor analyses suggested potential communication between DPEP1⁺ ·fibroblasts and immune subsets, including CXCL12–CXCR4 and MIF–CD74/CXCR4 interactions. Structure-based screening identified Balsaminone B as a top-ranked predicted high-affinity ligand, supported by stable docking and MD trajectories, with additional candidates including Ergotamine, Evobioside, MOL012579 and Venetoclax.

Conclusion: Together, these findings prioritize DPEP1 as a genetically supported candidate associated with skin cancer risk and provide supportive transcriptomic and computational evidence for future mechanistic and therapeutic investigation.

Background: TCF/LEF transcription factors (TFs) are indispensable for canonical Wnt signal transduction, acting through the recruitment of β-catenin and its co-activators to Wnt response elements at target gene promoters. Dysregulation of TCF/LEF family TFs is implicated in various cancers, yet their comprehensive molecular landscape and prognostic implications across cancers remain incompletely characterised.

Methods: We analysed data from The Cancer Genome Atlas and GTEx databases to assess the expression patterns and correlations of four TCF/LEF family TFs (TCF1/TCF7, LEF1, TCF7L1 and TCF7L2) across 33 cancer types, validating findings in tumour cell lines using the CCLE database. We conducted a systematic characterisation of the molecular landscape, encompassing copy number alterations, somatic mutations and DNA methylation patterns. In parallel, we built molecular regulatory networks leveraging motif‒TF relationships and mRNA‒miRNA interactions. Using gene set variation analysis, we calculated TCF/LEF scores and stratified pan-cancer samples into high-score (H-score) and low-score (L-score) subgroups.

Results: Functional enrichment analysis confirmed the previously reported significant positive correlation between TCF/LEF gene expression and Wnt/β-catenin signalling, as well as pathways involved in cell differentiation and development. Immune infiltration analysis showed strong associations between TCF/LEF scores and specific immune subsets: positive correlations with CAFs, NKT, MAIT, CD4 T cells (naïve and regulatory subtypes), dendritic cells (various subsets), B cells (naïve) and Tfh cells; and negative correlations with Th1, Th17, pDC, pro-B cells, M2 macrophages, neutrophils and NK cells. We also examined seven metabolic features and uncovered significant associations between TCF/LEF scores and metabolic reprogramming. Drug sensitivity analysis using the CellMiner database and connectivity map identified potential compounds targeting the TCF/LEF score. Prognostic analysis demonstrated the efficacy of the TCF/LEF score in predicting patient survival (overall survival or progression-free interval). We also constructed a THYM-specific nomogram for prognostic prediction.

Conclusions: In summary, we developed a TCF/LEF family TF-based pan-cancer transcriptomic signature with promising potential for predicting prognosis and therapeutic response.

Background: Exosomes and other small extracellular vesicles carry molecular cargo that can reflect disease status, therapeutic response, and relapse dynamics. Currently, most exosome-analysis workflows are centralized, require milliliter-sized blood samples, involve extended isolation procedures, and depend on separate downstream assays. Single-drop microfluidic nanochips, which can process finger-prick blood volumes of approximately 1050 µL within a closed cartridge, offer a promising route toward integrated sample-to-answer liquid biopsy platforms.

Main Body: These systems may integrate plasma handling, exosome enrichment, cargo access, multiplex biomarker detection, and algorithmic classification. This review highlights key on-chip enrichment techniques, including immunoaffinity, size selection, electrokinetic, acoustofluidic, and hybrid methods. It also discusses nano-biointerface designs that improve vesicle capture efficiency while reducing fouling and shear-induced vesicle damage. In addition, multiplex sensing architectures for optical, electrochemical, and plasmonic transduction are explored, with particular attention to challenges associated with whole-blood measurements, including anticoagulation, sample variability, platelet activation, viscosity, and hemolysis.

Conclusions: Single-drop exosome nanochips have the potential to transform liquid biopsy by enabling rapid, low-volume, integrated analysis of extracellular vesicles. However, successful clinical translation will require clear validation requirements, standardized reporting expectations, and defined use-case roadmaps for screening, disease monitoring, and therapeutic-response assessment. Establishing realistic performance standards and robust engineering parameters will be essential for making single-lab exosome nanochip platforms reproducible and clinically applicable.

Purpose: The prevalence of obstructive sleep apnoea (OSA) is high in children, for whom accurate diagnosis and timely intervention are crucial. Current single-modal clinical evaluations have limitations. This study aimed to build a multimodal intelligent system to assist diagnosis and surgical decision-making in children presenting with clinical symptoms.

Methods: A single-centre retrospective study at Shanghai Children's Hospital enrolled 220 paediatric OSA patients between March and June 2025. Comprehensive multimodal data, including medical histories, nasopharyngoscopy images, and snoring recordings, were collected. Among them, 192 children were included in the surgical indication-positive group and 28 in the surgical indication-negative group according to the guidelines. To predict surgical need, a multimodal fusion model was trained on a split of 120 training and 100 test samples. Class imbalance was addressed using Focal Loss, inverse weighting, and minority augmentation with regularization.

Results: The multimodal fusion model demonstrated excellent performance, achieving a sensitivity of over 97% in identifying the necessity of adenoidal or tonsillar surgery. Compared to single-modal and bi-modal models, the multi-modal fusion model showed substantial advantages in reducing both misdiagnosis and missed diagnosis, exhibiting strong screening capabilities.

Conclusions: The proposed system, integrating multimodal data, provides a sensitive and accurate tool to assist diagnosis and surgical decision-making in this symptomatic population, offering a promising approach for improving clinical outcomes.

Trial registration: ChiCTR, ChiCTR2500098075.

Background: Pulmonary fibrosis is a progressive and often fatal disease featuring the disruption of lung architecture by excessive extracellular matrix deposition.

Main Body: Mesenchymal stem cells (MSCs) have strong immunomodulatory, anti-inflammatory and regenerative capacities that enable them to restore lung function through the targeting of key fibrotic mechanisms. Their migration into sites of injury and interaction with immune and structural cells through cytokines and growth factors underlines their therapeutic value. Recently, exosomes derived from MSCs have emerged as a promising cell-free treatment option capable of crossing biological barriers and delivering anti-fibrotic molecules like miRNAs, siRNAs, and proteins directly to fibrotic tissues. These vesicles enhance therapeutic precision and safety compared with cell therapy. Despite challenges in standardization, large-scale production, and heterogeneity among MSCs sources, ongoing advances in biotechnology and clinical trial design are rapidly bridging the gap between preclinical promise and clinical application.

Conclusion: This present review discusses the pathogenesis and modelling of pulmonary fibrosis and underlines MSCs and exosome-based regenerative strategies as an effective and transforming approach in the future therapy of pulmonary fibrosis.

Background: Microsatellite instability-high/deficient mismatch repair (MSI-H/dMMR) colorectal cancer (CRC) is a biologically distinct subtype with important clinical implications. While tissue-based assays remain the standard, accessible preoperative surrogate markers are of interest. Systemic inflammatory indices, including the systemic immune-inflammation index (SII) and neutrophil-to-lymphocyte ratio (NLR), have been explored, but their relationship with MSI-H/dMMR status remains unclear.

Objective: To evaluate the association between systemic inflammatory indices and MSI-H/dMMR status using published evidence, local clinical data and pathological corroboration.

Methods: This study included three components: (1) evidence synthesis of published cohorts comparing inflammatory indices between MSI-H/dMMR and MSS/pMMR CRC; (2) a retrospective cohort of 28 patients (20 MSS and eight MSI-H) assessing peripheral blood profiles and exploratory associations; and (3) immunohistochemical quantification of CD8 and myeloperoxidase (MPO) using a Python/OpenCV workflow.

Results: Six studies (n = 1598) were included in the NLR synthesis. The pooled random-effects standardised mean difference was 0.93 (95% confidence interval [CI] 0.016–1.852), suggesting higher NLR in MSI-H/dMMR cases, although heterogeneity was high (I² = 97.8%). Excluding one influential study reduced the effect to 0.34 (95% CI −0.00 to 0.68). One study of SII suggested a modest increase in MSI-H/dMMR cases. In the local cohort, lymphocyte count was higher in MSI-H cases (p = .009), whereas SII and NLR were lower but not significant. Receiver operating characteristic analysis showed modest discrimination (SII area under the curve [AUC] = 0.675; NLR AUC = 0.656). Pathological quantification showed increased CD8 and reduced MPO/CD8 ratio in MSI-H tumours.

Conclusions: MSI-H/dMMR CRC exhibits a distinct but context-dependent immune-inflammatory phenotype. Systemic indices show heterogeneous patterns, while tissue findings indicate a lymphocyte-enriched microenvironment. These markers may provide supportive immune context, but further validation is required.

Trial registration: PROSPERO CRD420261323742 (https://www.crd.york.ac.uk/PROSPERO/view/CRD420261323742).

Background: Placental insufficiency is a key pathological process underlying major obstetric complications, including pre-eclampsia, fetal growth restriction (FGR), placental abruption, recurrent pregnancy loss, preterm birth and stillbirth. Emerging evidence identifies thromboinflammation—the interplay between coagulation, inflammation, endothelial dysfunction, platelet activation and complement pathways—as a central contributor to these adverse outcomes.

Objective: To critically evaluate the current evidence on thromboinflammatory biomarkers associated with placental insufficiency and adverse feto-maternal outcomes, emphasising their biological relevance, predictive value and clinical applicability.

Methods: A narrative review of literature from PubMed, Scopus, Web of Science, Embase and Google Scholar was conducted. Priority was given to systematic reviews, meta-analyses, prospective studies and international guidelines. Evidence on coagulation, inflammatory, angiogenic, endothelial, complement and emerging molecular biomarkers was synthesised.

Results: Among available biomarkers, the soluble fms-like tyrosine kinase-1/placental growth factor (sFlt-1/PlGF) ratio demonstrates the strongest clinical utility for predicting and managing placental dysfunction and pre-eclampsia. Conventional markers, including D-dimer, fibrinogen, platelet indices, neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio, provide additional prognostic information, but show variable predictive performance. Emerging biomarkers such as neutrophil extracellular traps, extracellular vesicles, complement activation products, cell-free DNA and circulating microRNAs offer mechanistic insights but remain investigational due to limited standardisation and validation. Multimarker approaches appear superior to individual biomarkers for risk prediction.

Conclusion: Thromboinflammatory biomarkers provide valuable insights into placental dysfunction and adverse pregnancy outcomes. While angiogenic biomarkers currently have the greatest clinical utility, integrated multimarker models may enhance future precision obstetric care. Further large-scale validation studies are required for clinical translation.

Objective: Few trials have explored associations between diets, diet-induced metabolomic changes and metabolic outcomes in type 2 diabetes mellitus (T2DM), especially weight-stratified management. This study aimed to identify diet-specific metabolomic signatures by defining ketogenic diet (KD) and low-carbohydrate diet (LCD) applicability in T2DM patients with varying body mass index (BMI).

Methods: A total of 611 patients with T2DM were prescreened, and 505 eligible participants were included after exclusions. All participants received metformin and were allocated to either the LCD or KD group according to the study protocol and clinical dietary management process. BMI-stratified medical phenotype analysis was performed, with clinical/serum profiles measured at baseline and 3 months. High-throughput untargeted chromatography-mass spectrometry analysed serum metabolome. Associations between metabolites and diet intake were examined and diet-induced metabolomic changes versus diabetes prognosis were analysed via correlation.

Results: Of 611 prescreened participants, 505 eligible participants were included in the final analysis after completing the 12-week intervention: Obese patients in the KD group had the most significant glycated haemoglobin (HbA1c) (8.18 ± 2.2 vs. 5.87 ± 0.98%) and weight (88.51 ± 15.8 vs. 80.5 ± 15.3 kg) reductions. Overweight patients benefited from both diets for weight and glucose control. Metabolomic analysis identified 2410 markers post-intervention: KD group (279 vs. 259, up and downregulated); LCD group (300 vs. 288). Correlation regression: for the LCD group, weight negatively with histidinyl hydroxyproline; HbA1c positively with 4-hydroxyphenylacetylglutamic acid. As for the KD group, HbA1c positively correlated with glutamyl isoleucine and negatively correlated with diethanolamine.

Conclusion: Both LCD and KD are effective for overweight and obese patients with T2DM. In the obese subgroup, LCD showed a numerically greater HbA1c reduction than KD, while both diets were suitable for overweight patients. For patients with normal BMI, LCD demonstrated no weight loss benefit. Metabolomic signatures sensitively reflect the effects of different diets and are closely associated with clinical metabolic improvements in T2DM, which is crucial for optimising dietary strategies.

Trial Registration: https://www.chictr.org.cn/ChiCTR2500102119. Registration Date: 8 May, 2025

Background: Tyrosine sulfation, a widespread post-translational modification in mammals mainly associated with secretory and transmembrane proteins, has recently been found to occur on histones. However, the molecular landscape and prognostic signature of pan-cancer based on histone tyrosine sulfation (HYsulf) remain poorly understood.

Methods: We investigated the expression diversity, molecular regulation landscape, DNA methylation and mutation patterns of 51 HYsulf-related genes across 33 cancer types from the Cancer Genome Atlas (TCGA). Additionally, we classified pan-cancer samples into high- and low-score subgroups (Hscore and Lscore) by establishing the HYsulf score.

Result: Enrichment analysis revealed high enrichment of immune-related pathways, cancer-associated hallmarks and pathways, hypoxia, metabolism and cell fate-related pathways in the Hscore subgroup. Infiltration analysis indicated correlations between immune infiltration and HYsulf score, with negative associations observed primarily with activated and resting NK cells, while positive correlations were prominent with Tregs, macrophages M1 and M0. HYsulf score showed significant correlations with immuno-inhibitors, immuno-stimulators, MHC, chemokines and their receptors, as well as emerging biomarkers of immune therapeutic response such as TMB, MSI and neoantigens. Furthermore, evaluations of seven metabolic features suggested a strong link between HYsulf and metabolic reprogramming in pan-cancer. Additionally, drug response evaluation using the CellMiner database uncovered candidate compounds associated with HYsulf-related genes. Importantly, the HYsulf score demonstrated prognostic stratification capacity in predicting overall survival or progression-free time in 10 tumour types. Finally, the nomogram based on the HYsulf score was established for prognostic prediction of PCPG and UVM.

Conclusion: We developed a HYsulf-associated transcriptomic signature based on HYsulf that holds promise for predicting prognosis and drug response.

Background: For clinical trials, the vector-based comparison (VBC) technique presented provides an alternative approach to solve a traditional problem caused by multiple endpoints. VBC uses vector algebra to form an endpoint vector and decompose it into different parts, thus reducing heterogeneity, increasing precision, and establishing a structure for the evaluation of clinical trial outcomes.

Methods: In this study, VBC was applied to 5 clinical trials with 695 patients. The primary clinical endpoint (E1) was defined as the reference endpoint, and the remaining endpoints were expressed relative to the primary endpoint. A paired t-test of the confidence interval widths, per decomposed endpoint, was conducted to test the effect of the VBC. Furthermore, machine learning and principal component analysis were applied to analyze whether the decomposed endpoints maintained the properties of the initial measures.

Results: The vector angle between E1 and the decomposed endpoints remained smaller than 45° in a typical study, which, before decomposing, was found to be correlated. Use of VBC brought down confidence interval widths by approximately 50%, implying less variability. Principal component analysis confirmed preservation of endpoint-specific properties, while Type I error simulations showed that VBC did not elevate false-positive rates under null conditions. Endpoint-specific type I error rates have been close to the nominal value of 0.05.

Conclusions: VBC can decompose clinical endpoints into nonredundant components while preserving endpoint-specific information and reducing variability without inflating Type I error. VBC serves as a reproducible and practical preprocessing method for clinical trials with many endpoints.

Plant-derived biomimetic nanoparticles (PD-BNPs) have emerged as a very promising and sustainable alternative to traditional nanocarriers for targeted drug delivery. Synthetic nanoparticles provide great tunability but suffer from issues such as cytotoxicity, fast immunological elimination, and sustainability problems, while those of animal origin are restricted by limitations such as difficulties associated with scale-up and safety. On the other hand, plant-based biomimetic nanoparticles (PD-BNPs), especially plant-derived exosome-like nanoparticles, show great biocompatibility, low immunogenicity, and inherent therapeutic properties due to their phytochemicals, lipids, proteins, and RNA. They can provide antioxidant, anti-inflammatory, and immunomodulating activity. With developments in ultracentrifugation techniques, size exclusion chromatography, nanoemulsion technology and green synthesis methods, it has become possible to enhance the purification, stability, and scaling up of PD-BNPs. Natural tissue targeting capabilities and responsive properties make these nanoparticles ideal candidates for drug delivery. Despite some of the limitations, such as variations in sources and non-standardized production, these nanoparticles have immense applications in precision drug delivery and nanomedicine.

Background: Gastric cancers (GCs) arising in the proximal and distal stomach are increasingly recognized as biologically and clinically heterogeneous subtypes of GC. However, individualized prognostic tools specifically designed for these two tumour locations remain insufficient.

Methods: The clinical records, pathological findings, laboratory indicators, and follow-up data from 813 patients with GC were retrospectively analysed. Survival-related variables were examined separately in the proximal GC (PGC) and distal GC (DGC) cohorts using Cox proportional hazards modelling. Factors that remained relevant in the final models were integrated into two tumour location-based nomograms for overall survival (OS) estimation. The models were examined using receiver operating characteristic (ROC) curves, Harrell's concordance index (C-index), calibration plots and decision curve to assess discrimination, agreement and clinical usefulness.

Results: In patients with PGC, the final model retained tumour-stroma percentage (TSP) (hazard ratio [HR] = 2.485, 95% confidence interval [CI] 1.258–4.908, p = .009), vascular invasion (HR = 1.911, 95% CI 1.018–3.587, p = .044) and pathological TNM stage (pTNM) (HR = 1.943, 95% CI 1.093–3.453, p = .024) regard as survival-related predictors. For DGC, nerve invasion (HR = 2.174, 95% CI 1.227–3.851, p = .005) and pTNM stage (HR = 2.070, 95% CI 1.373–4.177, p = .004) were retained in the final model. The location-specific nomograms demonstrated favourable discriminatory ability for predicting 5-year OS, with AUC values of 0.924 for PGC and 0.861 for DGC.

Conclusions: TSP was associated with survival outcomes in GC and showed particular prognostic relevance in PGC. Developing separate prognostic nomograms for PGC and DGC may provide a practical approach for improving individualized clinical management.

Background: Cardiovascular diseases remain the leading cause of mortality worldwide, accounting for approximately one-third of global deaths annually. Despite advances in pharmacological and interventional therapies, the adult mammalian heart exhibits minimal intrinsic regenerative capacity. Mesenchymal stem cell-derived small extracellular vesicles (MSC-sEVs), commonly referred to as exosomes, have emerged as a promising cell-free therapeutic approach for cardiac repair by delivering bioactive molecules that reduce apoptosis, fibrosis and inflammation while promoting angiogenesis.

Objective: This review critically assesses current production, purification and engineering methods for MSC-sEVs, highlighting translational challenges and proposing actionable solutions for clinical development.

Key findings: Preclinical efficacy shows wide variations due to inconsistent isolation methods, dosing protocols and animal models. A standardized potency assay correlating with cardiac functional outcomes is lacking. While completed phase I trials confirm safety, no controlled phase II/III efficacy trials for cardiac indications have been reported. Engineered sEVs demonstrate improved targeting in rodents, but human translation remains unproven. Major barriers include the absence of validated potency assays, GMP-compatible standardization, and large-animal efficacy data.

Conclusion: MSC-sEVs represent a promising investigational platform requiring substantial methodological advancement before clinical translation. Significant hurdles, particularly the absence of validated potency assays, manufacturing standardization, and controlled efficacy trials, must be overcome before regulatory authorization can realistically be contemplated. The timeline for regulatory approval remains uncertain and depends on successful completion of these prerequisites.

Metabolic reprogramming and glycosylation remodelling are hallmarks of cancer, and selected monosaccharides are increasingly recognized as key regulators of both processes. Beyond serving as fuels, glucose, galactose, mannose and fructose shape glycosylation, lipid synthesis, antitumour immunity and tumour–host communication. Here, we discuss how these monosaccharides are rerouted across tumour, immune and stromal compartments to generate context-dependent tumour-promoting or antitumour effects. We further evaluate pharmacological opportunities, including metabolic intervention, glycosylation-directed therapy and rational combination strategies, while highlighting major translational barriers such as tumour heterogeneity, biomarker selection and systemic toxicity. This framework positions monosaccharide metabolism as a potential entry point for precision oncology.

Background: Spinal tuberculosis (TB) is a potentially severe form of musculoskeletal TB. Early diagnosis of the disease is challenging due to variable and non-specific initial symptoms. We aimed to evaluate Mycobacterium tuberculosis antigen-specific host blood protein biomarkers that are detectable in QuantiFERON supernatants as candidate tools to distinguish spinal TB from mechanical back pain.

Methods: We enrolled patients who presented with symptoms requiring investigation for spinal TB or mechanical back pain at Tygerberg Hospital, in the Western Cape Province of South Africa. Participants were diagnosed with spinal TB or mechanical back pain based on the results of microbiological tests, imaging, and/or clinical assessment. Following QuantiFERON Plus testing, the concentrations of 33 protein biomarkers were evaluated in the harvested supernatants using the Luminex platform. The diagnostic potential of the biomarkers was assessed using receiver operating characteristic curve analysis.

Results: Out of 36 participants enrolled on the study, 20 were diagnosed with spinal TB and 16 with mechanical back pain. Unstimulated and TB-antigen-specific concentrations of interleukin (IL)-9, I-309, and interferon-gamma significantly differentiated patients diagnosed with spinal TB from those with mechanical back pain, with areas under the curve (AUCs) > 0.70, with the most accurate individual biomarker being I-309 (AUC = 0.84, 95% confidence interval [CI], 0.70–0.98). Four- and five-marker combinations between different biomarkers showed potential for diagnosing spinal TB with the most optimal being FGF-Basic_(TB2-Nil), IL-15_(TB1-Nil), TGF-α_(TB2-Nil), IL-9_(Nil), and I-309_(Nil), with an AUC of 1.00 (95% CI, 1.00–1.00), sensitivity of 95.0% (95% CI, 75.1–99.9) and specificity of 100.0% (95% CI,82.9–100.0) after leave-one-out cross validation.

Conclusion: Host protein biomarkers that are detectable in QuantiFERON supernatants may be valuable for early diagnosis of spinal TB. Our findings are preliminary and need validation in larger prospective studies.

Background: The APOE gene encodes ApoE, a lipid-transport protein implicated in neuroinflammatory processes. Whether APOE expression independently predicts outcome in lower-grade glioma (LGG), after accounting for IDH mutation and 1p/19q codeletion, remains unclear.

Methods: We analysed The Cancer Genome Atlas (TCGA) pan-cancer and LGG transcriptomic and clinical data. In LGG, APOE was modelled continuously in Cox regression adjusted for age, histological grade and IDH/1p/19q molecular class; molecular-class-stratified and interaction analyses were performed. Results were evaluated in the Chinese Glioma Genome Atlas (CGGA)_693 and CGGA_325 cohorts. Immune deconvolution, myeloid/lipid programme scores and author-annotated GSE197543 single-cell data were used to define the immune and cellular context of APOE.

Results: Higher APOE expression was associated with favourable survival in univariable TCGA-LGG analyses, but not after adjustment for molecular class (overall survival hazard ratio per 1-SD increase, 1.021; 95% confidence interval, 0.830–1.256; p = 0.846). The APOE–survival association differed across molecular classes (global interaction p = 1.92 × 10⁻⁴), including an adverse association in IDH-wild-type tumours. External results were heterogeneous: the unadjusted association in CGGA_693 attenuated after adjustment and was not consistently reproduced in CGGA_325. APOE correlated positively with tumour-associated macrophage/macrophage transcriptional programmes in TCGA and both CGGA cohorts. Single-cell data localised APOE expression primarily to microglial and macrophage populations.

Conclusion: The apparent favourable association between APOE and LGG survival is molecular-context dependent and not consistently independent across cohorts. APOE may primarily mark a myeloid immune–metabolic context rather than an independently protective prognostic determinant.

The transition from acute kidney injury (AKI) to chronic kidney disease (CKD) represents a critical determinant of long-term prognosis, yet the underlying immunometabolic mechanisms remain incompletely defined. Indoleamine 2,3-dioxygenase (IDO), the rate-limiting enzyme of the kynurenine pathway (KP) of tryptophan (Trp) metabolism, has emerged as a pivotal metabolic-immune checkpoint linking cellular metabolism to the immune microenvironment. This review systematically delineates the dual, time-dependent roles of IDO across the AKI-to-CKD continuum. During early AKI, IDO induction confers protection through expansion of regulatory T cells, M2 macrophage polarisation and IDO2-mediated suppression of ferroptosis via glutathione peroxidase 4 stabilisation. However, sustained IDO activation drives a pathogenic phenotype: persistent Trp depletion activates general control non-derepressible 2 kinase and the DNA damage response, toxic metabolites, including quinolinic acid (QA), promote tubular dysfunction and fibrosis, nicotinamide adenine dinucleotide (NAD⁺) supply contracts, and sustained aryl hydrocarbon receptor (AhR) signalling reshapes the immune microenvironment towards fibrosis. Concurrently, IDO impairs peritubular capillary stability, and hypoxia-inducible factor (HIF)–AhR crosstalk via their shared aryl hydrocarbon receptor nuclear translocator (ARNT/HIF-1β) dimerisation partner amplifies pro-fibrotic transcription. We synthesise the mechanisms driving this functional reversal, evaluate the kynurenine-to-tryptophan ratio as a candidate biomarker for AKI-to-CKD risk stratification benchmarked against conventional renal biomarkers, and propose a precision-timed intervention toolkit, including selective IDO1 inhibitors, AhR modulators, NAD⁺ precursors, QA scavengers, and senolytic combinations—informed by clinical trial experience from oncology. Gene knockout studies that establish causal roles of IDO and downstream targets are systematically reviewed. The framework reframes IDO as a stage-specific metabolic-immune switch whose targeted modulation offers a tractable route to prevent renal fibrosis after AKI.