Validation Of Biomarkers Research Articles

Neurofilaments light chains as a diagnostic and predictive biomarker for Tunisian Multiple Sclerosis patients

BackgroundMultiple Sclerosis (MS) course was shown to be more severe among North Africans compared to Caucasians. Validation of prognostic biomarkers of disease activity and severity is a priority in our practice. ObjectiveWe aimed to investigate the association between baseline cerebrospinal fluid (CSF) and serum NfL (sNFL) levels and disease activity and disability accrual in a cohort of Tunisian patients with MS. MethodsA cross-sectional study was conducted, in the department of Neurology of Razi Hospital, including patients diagnosed with MS. Patient's data were retrieved from our local MS database. Blood and CSF sampling were performed at the first visit. sNFL levels were measured using the Enzyme-Linked Immuno-Sorbent Assay (ELISA) sandwich technique. ResultsThree hundred MS patients were enrolled (sex-ratio= 3.05; mean age at MS onset=28.83 years+9.55, mean MS course = 10.21 years+8.96). MS phenotype was predominately relapsing (73%). CSF NfL levels were significantly correlated to the serum ones. NfL concentrations were significantly associated with MS activity (p = 0.012), disease progression (p = 0.001), and higher Multiple Sclerosis Severity Scores (MSSS) (p = 0.0017, r = 0.28). ConclusionsThese results support the value of NfL as a sensitive and clinically meaningful CSF and blood biomarker to evaluate MS activity and outcomes among Tunisian MS patients.

Biomarker relationships with small bowel histopathology among malnourished children with environmental enteric dysfunction in a multicountry cohort study

BackgroundValidated biomarkers could catalyze environmental enteric dysfunction (EED) research. ObjectivesLeveraging an EED histology scoring system, this multicountry analysis examined biomarker associations with duodenal histology features among children with EED. We also examined differences in 2-h compared with 1-h urine collections in the lactulose rhamnose (LR) dual sugar test. MethodsThree cohorts of undernourished children unresponsive to nutrition intervention underwent esophagogastroduodenoscopy and duodenal biopsies. Histopathology scores were compared to fecal calprotectin (CAL), myeloperoxidase (MPO), neopterin (NEO), and urinary LR ratio and lactulose percentage recovery. Log-transformed biomarkers were used in linear regressions adjusted for age, center, and sample collection–biopsy time interval in multivariable models. ResultsData on >1 biomarker were available for 120 Bangladeshi (CAL, MPO, NEO, and LR), 63 Pakistani (MPO, NEO, and LR), and 63 Zambian children (CAL). Median age at endoscopy was similar (19 mo) across centers. Median sample collection prior to endoscopy was consistent with each center’s study design: 2 wk in Bangladesh (urine and stool) and Zambia (stool), and 6 (urine) and 11 (stool) mo in Pakistan. In multivariable models, intraepithelial lymphocytes were associated with CAL (exponentiated [exp.] coefficient: 1.19; 95% confidence interval [CI]: 1, 1.41), intramucosal Brunner’s glands with MPO (exp. coefficient: 1.33; 95% CI: 1.05, 1.69) and NEO (exp. coefficient: 1.37; 95% CI: 1.1, 1.7), and chronic inflammation with NEO (exp. coefficient: 1.61; 95% CI: 1.17, 2.17). Intraepithelial lymphocytes were associated with lactulose % recovery (exp. coefficient: 1.22; 95% CI: 1.05, 1.41). LR recovery was substantially lower in 1-h collections than in 2-h collections. ConclusionsFour commonly used markers of enteric dysfunction were associated with specific histologic features. One-hour urine collection may be insufficient to reflect small bowel permeability in LR testing. While acknowledging the challenges with obtaining relevant tissue, these findings form the basis for further EED biomarker validation research.

Olfactory event-related potentials (OERPs) and trigeminal event-related potentials (TERPs) in subjects after Covid-19 infection: single-center prospective study.

Olfactory dysfunction (OD) is a common symptom associated with Covid-19. During the Covid-19 pandemic, the importance of psychophysical olfactory tests and electrophysiological olfactory assessment increased. The purpose of the study was to analyze the psychophysical olfactory tests and the post-covid curves of olfactory event-related potentials (OERPs) and trigeminal event-related potentials (TERPs). The prospective study included 98 subjects (62 females / 36 males). The average age was 42 years (range 21-84 years). Group I (n = 77) contained participants who had been infected with Covid-19. They were enrolled in the study at least 1 year after Covid-19. Group II (n = 21) was the healthy normosmic control group. In Group I, the OERPs of 18% participants were absent. Patients in Group I were statistically more likely to have an absence of OERPs (p = 0.036) than subjects in Group II. We did not detect a statistical difference in amplitudes and latencies of the OERPs between Group I and Group II. In Group I, N1 latency of the TERPs was significantly longer (p = 0.002) than in Group II. The amplitude of the N1-P2 interval of the TERPs was significantly lower (p = 0.025) in Group I than in Group II. According to the psychophysical Sniffin stick identification test, hyposmia was detected in 39% in Group I versus 0% in the control Group II. OD is a common post-covid symptom. The presence of OERPs is a significant prognostic factor for olfactory function after Covid 19. We detected a lower percentage of absence of OERPs after Covid-19 compared to the previously published studies of post-viral OD and post-infectious OD. For TERPs, we detected a longer N1 latency and a lower amplitude for the N1-P2 interval after Covid-19. OERPs and TERPs can be considered valid biomarkers to evaluate the progress of post-covid OD.

Metabolomics and Lipidomics for Studying Metabolic Syndrome: Insights into Cardiovascular Diseases, Type 1 & 2 Diabetes, and Metabolic Dysfunction-Associated Steatotic Liver Disease.

Metabolomics and lipidomics have emerged as tools in understanding the connections of metabolic syndrome (MetS) with cardiovascular diseases (CVD), type 1 and type 2 diabetes (T1D, T2D), and metabolic dysfunction-associated steatotic liver disease (MASLD). This review highlights the applications of these omics approaches in large-scale cohort studies, emphasizing their role in biomarker discovery and disease prediction. Integrating metabolomics and lipidomics has significantly advanced our understanding of MetS pathology by identifying unique metabolic signatures associated with disease progression. However, challenges such as standardizing analytical workflows, data interpretation, and biomarker validation remain critical for translating research findings into clinical practice. Future research should focus on optimizing these methodologies to enhance their clinical utility and address the global burden of MetS-related diseases.

Hair mercury isotopes, a noninvasive biomarker for dietary methylmercury exposure and biological uptake.

Background. Fish and rice are the main dietary sources of methylmercury (MeHg); however, rice does not contain the same beneficial nutrients as fish, and these differences can impact the observed health effects of MeHg. Hence, it is important to validate a biomarker, which can distinguish among dietary MeHg sources. Methods. Mercury (Hg) stable isotopes were analyzed in hair samples from peripartum mothers in China (n = 265). Associations between mass dependent fractionation (MDF) (δ202Hg) and mass independent fractionation (MIF) (Δ199Hg) (dependent variables) and dietary MeHg intake (independent variable) were investigated using multivariable regression models. Results. In adjusted models, hair Δ199Hg was positively correlated with serum omega-3 fatty acids (a biomarker for fish consumption) and negatively correlated with maternal rice MeHg intake, indicating MIF recorded in hair can be used to distinguish MeHg intake predominantly from fish versus rice. Conversely, in adjusted models, hair δ202Hg was not correlated with measures of dietary measures of MeHg intake. Instead, hair δ202Hg was strongly, negatively correlated with hair Hg, which explained 27-29% of the variability in hair δ202Hg. Conclusions. Our results indicated that hair Δ199Hg can be used to distinguish MeHg intake from fish versus rice. Results also suggested that lighter isotopes were preferentially accumulated in hair, potentially reflecting Hg binding to thiols (i.e., cysteine); however, more research is needed to elucidate this hypothesis. Broader impacts include 1) validation of a non-invasive biomarker to distinguish MeHg intake from rice versus fish, and 2) the potential to use Hg isotopes to investigate Hg binding in tissues.

Systematic discovery of gene-environment interactions underlying the human plasma proteome in UK Biobank

Understanding how gene-environment interactions (GEIs) influence the circulating proteome could aid in biomarker discovery and validation. The presence of GEIs can be inferred from single nucleotide polymorphisms that associate with phenotypic variability - termed variance quantitative trait loci (vQTLs). Here, vQTL association studies are performed on plasma levels of 1463 proteins in 52,363 UK Biobank participants. A set of 677 independent vQTLs are identified across 568 proteins. They include 67 variants that lack conventional additive main effects on protein levels. Over 1100 GEIs are identified between 101 proteins and 153 environmental exposures. GEI analyses uncover possible mechanisms that explain why 13/67 vQTL-only sites lack corresponding main effects. Additional analyses also highlight how age, sex, epistatic interactions and statistical artefacts may underscore associations between genetic variation and variance heterogeneity. This study establishes the most comprehensive database yet of vQTLs and GEIs for the human proteome.

Methods for causality assessment of idiosyncratic drug-induced liver injury.

The diagnosis of idiosyncratic drug-induced liver injury (DILI) is a challenging task due to the lack of specific features or definitive diagnostic tools. A minimum of clinical and pharmacological information is required, together with laboratory and imaging tests to exclude other causes of liver injury. Several standardized methods have been developed to support clinical judgement and establish causality assessment, the most widely used being the Roussel Uclaf Causality Assessment Method-RUCAM-and structured Expert Opinion. More recently, an evidence-based, revised RUCAM, Electronic Causality Assessment Method-RECAM-has been developed and, although still a work in progress, may replace RUCAM scoring in the future. International collaborative networks and ongoing research efforts are key to advancing biomarker qualification and validation and developing new invitro patient-based methods that will help improve DILI diagnosis and move towards a personalized medicine approach.

Pancreatic cancer - epidemiology, risk factors, nutritional and infl ammatory prognostic and predictive factors.

Pancreatic cancer remains one of the most challenging malignancies to treat, with consistently low survival rates despite advances in medical research. The identification and validation of effective prognostic biomarkers are crucial for improving diagnostic accuracy and treatment outcomes. The aim of the work is to analyze the latest data of the pancreatic cancer incidence and mortality, comparing them with global epidemiological data. The narrative review also aims to summarize current knowledge about various prognostic biomarkers in the pancreatic cancer treatment, including indicators of performance status, nutritional and inflammatory markers. The most recently available national epidemiological data on pancreatic cancer are analyzed. The literature review is focused on markers that evaluate the general condition of patients, such as performance status, body mass index, prognostic nutritional index and markers of the inflammatory response, such as Glasgow prognostic score, C-reactive protein, neutrophil to lymphocyte ratio, systemic inflammatory response index and systemic immune inflammation index. These biomarkers are analyzed for their role in predicting prognosis and response to systemic therapy for pancreatic cancer. Both the Slovak Republic and the Czech Republic are globally ranked in the leading places in terms of pancreatic cancer incidence and mortality, both in estimates and real data. Indicators of nutritional and performance status play a critical role in patient assessment and influence treatment decisions, with potential impact on treatment outcomes. Inflammatory markers have shown significant prognostic value, correlating with the patient's immune response to the tumor and inflammatory processes that may promote disease progression. However, despite their promising predictive capabilities, these biomarkers are not routinely used in clinical practice due to the need for further validation. Integration of new biomarkers into clinical practice could lead to more personalized therapeutic decisions and improved treatment outcomes. Further research is needed for a more comprehensive assessment of the validity of these biomarkers and their use in common clinical conditions.

Validation of candidate protein biomarkers previously identified by genetic instruments for prostate cancer risk: A prospective cohort analysis of directly measured protein levels in the ARIC study.

Multiple novel protein biomarkers have been shown to be associated with prostate cancer risk using genetic instruments. This study aimed to externally validate the associations of 30 genetically predicted candidate proteins with prostate cancer risk using aptamer-based levels in US Black and White men in the Atherosclerosis Risk in Communities (ARIC) study. Plasma protein levels were previously measured by SomaScan® using the blood collected in 1990-1992. Among 4864 eligible participants, we ascertained 667 first primary prostate cancer cases through 2015. Hazard ratios (HRs) of prostate cancer and 95% confidence intervals (CIs) were estimated using Cox proportional hazards regression for tertiles of each protein. We adjusted for age, race, and other risk factors. Of the 30 proteins and considering a nominal p trend < 0.05, two were positively associated with prostate cancer risk-RF1ML (tertile 3 vs. 1: HR = 1.23; 95% CI 1.02-1.48; p trend = 0.037) and TPST1 (1.28, 95% CI 1.06-1.55; p trend = 0.0087); two were inversely associated-ATF6A (HR = 0.80, 95% CI 0.65-0.98; p trend = 0.028) and SPINT2 (HR = 0.74, 95% CI 0.61-0.90; p trend = 0.0025). One protein, KDEL2, which was nonlinearly associated (test-for-linearity: p < 0.01) showed a statistically significant lower risk in the second tertile (HR = 0.79, 95% CI 0.65-0.95). Of these five, four proteins-ATF6A, KDEL2, RF1ML, and TPST1-were consistent in the direction of association with the discovery studies. This study validated some pre-diagnostic protein biomarkers of the risk of prostate cancer.

Biomarker Detection and Validation for Corneal Involvement in Patients With Acute Infectious Conjunctivitis

Infectious conjunctivitis can lead to corneal involvement and result in ocular morbidity. The identification of biomarkers associated with corneal involvement has the potential to improve patient care. To identify biomarkers in patients with acute infectious conjunctivitis. This cross-sectional study took place from December 2016 to March 2024. Analyses were performed in 3 phases. First, logistic regression and machine learning algorithms were used to predict the probability of demonstrating corneal involvement in patients with presumed infectious conjunctivitis. Second, quantitative reverse transcription polymerase chain reaction (RT-qPCR) was used to confirm the most important biomarker gene identified by the algorithm. Third, the biomarker gene was validated in prospectively collected conjunctival samples of adult patients from 3 outpatient centers in Thailand and 1 in India. Patients with signs and symptoms of infectious conjunctivitis and onset within less than 14 days were eligible. Exclusion criteria were the inability to consent, presumed toxicity, or allergic conjunctivitis. Acute infectious conjunctivitis. The identification and validation of ocular surface gene expression associated with corneal findings on slitlamp examination. Thirteen genes exhibited a 1.5-log2 fold change in expression in patients with corneal involvement compared to patients without corneal involvement. Using the 13 genes to train and cross validate, logistic regression produced the highest mean area under the receiver operating characteristic curve (AUROC; 0.85; 95% CI, 0.84-0.86) for corneal involvement. The removal of apolipoprotein E (APOE) from the gene ensemble led to a decline in predictive performance of the logistic regression classifier (from mean AUROC 0.85 [95% CI, 0.84-0.86] to 0.74 [95% CI, 0.73-0.75]; adjusted P = .001 [Tukey test]). Orthogonal testing of APOE expression level with RT-qPCR showed that APOE expression was higher in patients with corneal involvement compared to patients without (median [IQR], 0.23 [0.04-0.47] vs 0.04 [0.02-0.06]; P = .004 [Mann-Whitney U test]). Using a Youden index of 0.23 Δ threshold cycle, APOE had a sensitivity of 56% (95% CI, 33-77) and a specificity of 88% (95% CI, 79-93) in 106 samples with conjunctivitis at Aravind, India (P < .001 [Fisher exact test]). When applied to a different patient population in Thailand, the same criteria could discriminate between disease states (58 samples; sensitivity, 47%; 95% CI, 30-64 and specificity, 93%; 95% CI, 77-99; P = .001 [Fisher exact test]). The results from this study suggest that the host conjunctival immune response can be meaningfully interrogated to identify biomarkers for ocular surface diseases.

New Biomarkers for Renal Transporter-Mediated Drug-Drug Interactions: Metabolomic Effects of Cimetidine, Probenecid, Verapamil, and Rifampin in Humans.

The inhibition of renal transport proteins organic cation transporter 2 (OCT2), multidrug and toxin extrusion proteins (MATE1, MATE2-K), and organic anion transporters (OAT1, OAT3) causes clinically relevant drug-drug interactions (DDI). Endogenous biomarkers could be used to improve risk prediction of such renal DDIs. While a number of biomarkers for renal DDIs have been described so far, multiple criteria for valid biomarkers have frequently not been investigated, for example, specificity, metabolism, or food effects. Therefore, there is a need for novel biomarkers of renal DDIs. Here, we investigated the global metabolomic effects following the administration of two classical inhibitors of renal transport proteins [cimetidine (OCT2/MATEs), probenecid (OATs)] in human plasma and urine of healthy volunteers. Additionally, we investigated metabolomic effects of two inhibitors of other transporters [verapamil (P-glycoprotein), rifampin (organic anion transporting polypeptides)] as controls. This analysis shows that both cimetidine and probenecid affect compounds involved in caffeine metabolism, carnitines, and sulfates. Hierarchical cluster analysis of the effects of all four inhibitors on endogenous compounds identified multiple promising new sensitive and specific biomarker candidates for OCT2/MATE- or OAT-mediated DDIs. For OCT2/MATEs, 5-amino valeric acid betaine (median log2-fold change of estimated renal elimination: -3.62) presented itself as a promising candidate. For OATs, estimated renal elimination of 7-methyluric acid and cinnamoylglycine (median log2-fold changes -3.10 and -1.92, respectively) was both sensitive and specific. This study provides comprehensive information on metabolomic effects of transport protein inhibition in humans and identifies putative new sensitive and specific biomarkers for renal transporter-mediated DDIs.

The informatics of ADNI.

The Alzheimer's Disease Neuroimaging Initiative (ADNI) has revolutionized the landscape of Alzheimer's research through its Informatics Core, which has facilitated unprecedented data standardization and sharing. Over 20 years, ADNIestablished a robust informatics framework, enabling the validation of biomarkers and supporting global research efforts. The Informatics Core, centered at the Laboratory of Neuro Imaging (LONI), provides a comprehensive data hub that ensures data quality, accessibility, and security, fostering over 5600 publications and significant scientific advancements. By embracing open data sharing principles, ADNIset a gold standard in data transparency, allowing over 26,000 investigators from 169 countries to access and download a wealth of multimodal data. This collaborative approachnot only accelerated biomarker discovery and drug development and advanced our understanding of Alzheimer's disease but also has served as a model for other research initiatives, demonstrating the transformative potential of carefully designed informatics models and shared data in driving global scientific progress. HIGHLIGHTS: Accelerating biomarker discovery and drug development for Alzheimer's disease. Alzheimer's Disease Neuroimaging Initiative's (ADNI's) open data sharing drives scientific progress. Data exploration and coupled analytics to data archives.

External validation of serum biomarkers predicting short-term and mid/long-term relapse in patients with Crohn’s disease stopping infliximab

ObjectiveIn patients with Crohn’s disease (CD) on combination therapy (infliximab and immunosuppressant) and stopping infliximab (cohort from the study of infliximab diSconTinuation in CrOhn’s disease patients in stable Remission on...

Closing the gap: prognostic and predictive biomarker validation for personalized care in a Latin American hormone-dependent breast cancer cohort.

Several guidelines recommend the use of different classifiers to determine the risk of recurrence (ROR) and treatment decisions in patients with HR+HER2- breast cancer. However, data are still lacking for their usefulness in Latin American (LA) patients. Our aim was to evaluate the comparative prognostic and predictive performance of different ROR classifiers in a real-world LA cohort. The Molecular Profile of Breast Cancer Study (MPBCS) is an LA case-cohort study with 5-year follow-up. Stages I and II, clinically node-negative HR+HER2- patients (n = 340) who received adjuvant hormone therapy and/or chemotherapy, were analyzed. Time-dependent receiver-operator characteristic-area under the curve, univariate and multivariate Cox proportional hazards regression (CPHR) models were used to compare the prognostic performance of several risk biomarkers. Multivariate CPHR with interaction models tested the predictive ability of selected risk classifiers. Within this cohort, transcriptomic-based classifiers such as the recurrence score (RS), EndoPredict (EP risk and EPClin), and PAM50-risk of recurrence scores (ROR-S and ROR-PC) presented better prognostic performances for node-negative patients (univariate C-index 0.61-0.68, adjusted C-index 0.77-0.80, adjusted hazard ratios [HR] between high and low risk: 4.06-9.97) than the traditional classifiers Ki67 and Nottingham Prognostic Index (univariate C-index 0.53-0.59, adjusted C-index 0.72-0.75, and adjusted HR 1.85-2.54). RS (and to some extent, EndoPredict) also showed predictive capacity for chemotherapy benefit in node-negative patients (interaction P = .0200 and .0510, respectively). In summary, we could prove the clinical validity of most transcriptomic-based risk classifiers and their superiority over clinical and immunohistochemical-based methods in the heterogenous, real-world node-negative HR+HER2- MPBCS cohort.

A Comprehensive Review of Bioinformatics Tools for Genomic Biomarker Discovery Driving Precision Oncology.

The rapid advancement of high-throughput technologies, particularly next-generation sequencing (NGS), has revolutionized cancer research by enabling the investigation of genetic variations such as SNPs, copy number variations, gene expression, and protein levels. These technologies have elevated the significance of precision oncology, creating a demand for biomarker identification and validation. This review explores the complex interplay of oncology, cancer biology, and bioinformatics tools, highlighting the challenges in statistical learning, experimental validation, data processing, and quality control that underpin this transformative field. This review outlines the methodologies and applications of bioinformatics tools in cancer genomics research, encompassing tools for data structuring, pathway analysis, network analysis, tools for analyzing biomarker signatures, somatic variant interpretation, genomic data analysis, and visualization tools. Open-source tools and repositories like The Cancer Genome Atlas (TCGA), Genomic Data Commons (GDC), cBioPortal, UCSC Genome Browser, Array Express, and Gene Expression Omnibus (GEO) have emerged to streamline cancer omics data analysis. Bioinformatics has significantly impacted cancer research, uncovering novel biomarkers, driver mutations, oncogenic pathways, and therapeutic targets. Integrating multi-omics data, network analysis, and advanced ML will be pivotal in future biomarker discovery and patient prognosis prediction.

Development of a Fit-For-Purpose Multi-Marker Panel for Early Diagnosis of Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) suffers from a lack of an effective diagnostic method, which hampers improvement in patient survival. Carbohydrate antigen 19-9 (CA19-9) is the only FDA-approved blood biomarker for PDAC, yet its clinical utility is limited due to suboptimal performance. Liquid chromatography-mass spectrometry (LC-MS) has emerged as a burgeoning technology in clinical proteomics for the discovery, verification, and validation of novel biomarkers. A plethora of protein biomarker candidates for PDAC have been identified using LC-MS, yet few has successfully transitioned into clinical practice. This translational standstill is owed partly to insufficient considerations of practical needs and perspectives of clinical implementation during biomarker development pipelines, such as demonstrating the analytical robustness of proposed biomarkers which is critical for transitioning from research-grade to clinical-grade assays. Moreover, the throughput and cost-effectiveness of proposed assays ought to be considered concomitantly from the early phases of the biomarker pipelines for enhancing widespread adoption in clinical settings. Here, we developed a fit-for-purpose multi-marker panel for PDAC diagnosis by consolidating analytically robust biomarkers as well as employing a relatively simple LC-MS protocol. In the discovery phase, we comprehensively surveyed putative PDAC biomarkers from both in-house data and prior studies. In the verification phase, we developed a multiple-reaction monitoring (MRM)-MS-based proteomic assay using surrogate peptides that passed stringent analytical validation tests. We adopted a high-throughput protocol including a short gradient (<10min) and simple sample preparation (no depletion or enrichment steps). Additionally, we developed our assay using serum samples, which are usually the preferred biospecimen in clinical settings. We developed predictive models based on our final panel of 12 protein biomarkers combined with CA19-9, which showed improved diagnostic performance compared to using CA19-9 alone in discriminating PDAC from non-PDAC controls including healthy individuals and patients with benign pancreatic diseases. A large-scale clinical validation is underway to demonstrate the clinical validity of our novel panel.

Developing an RNA Signature for Radiation Injury Using a Human Liver-on-a-Chip Model.

Radiation exposure in a therapeutic setting or during a mass casualty event requires improved medical triaging, where the time to delivery and quantity of medical countermeasures are critical to survival. Radiation-induced liver injury (RILI) and fibrosis can lead to death, but clinical symptoms manifest late in disease pathogenesis and there is no simple diagnostic test to determine RILI. Because animal models do not completely recapitulate clinical symptoms, we used a human liver-on-a-chip model to identify biomarkers of RILI. The goals of this study were: 1. to establish a microfluidic liver-on-a-chip device as a physiologically relevant model for studying radiation-induced tissue damage; and 2. to determine acute changes in RNA expression and biological pathway regulation that identify potential biomarkers and mechanisms of RILI. To model functional human liver tissue, we used the Emulate organ-on-a-chip system to establish a co-culture of human liver sinusoidal endothelial cells (LSECs) and hepatocytes. The chips were subject to 0 Gy (sham), 1 Gy, 4 Gy, or 10 Gy irradiation and cells were collected at 6 h, 24 h, or 7 days postirradiation for RNA isolation. To identify significant expression changes in messenger RNA (mRNA) and long non-coding RNA (lncRNA), we performed RNA sequencing (RNASeq) to conduct whole transcriptome analysis. We found distinct differences in expression patterns by time, dose, and cell type, with higher doses of radiation resulting in the most pronounced expression changes, as anticipated. Ingenuity Pathway Analysis indicated significant inhibition of the cell viability pathway 24 h after 10 Gy exposure in LSECs but activation of this pathway in hepatocytes, highlighting differences between cell types despite receiving the same radiation dose. Overall, hepatocytes showed fewer gene expression changes in response to radiation, with only 3 statistically significant differentially expressed genes at 7 days: APOBEC3H, PTCHD4, and GDNF. We further highlight lncRNA of interest including DINO and PURPL in hepatocytes and TMPO-AS1 and PRC-AS1 in LSECs, identifying potential biomarkers of RILI. We demonstrated the potential utility of a human liver-on-a-chip model with primary cells to model organ-specific radiation injury, establishing a model for radiation medical countermeasure development and further biomarker validation. Furthermore, we identified biomarkers that differentiate radiation dose and defined cell-specific targets for potential radiation mitigation therapies.

Review of the Longitudinal Management of Autoimmune Encephalitis, Potential Biomarkers, and Novel Therapeutics.

Increasing awareness and earlier diagnosis of autoimmune encephalitis (AE) have led to a greater number of patients being cared for longitudinally by neurologists. Although many neurologists are now familiar with the general approach to diagnosis and acute immunosuppression, this review aims to provide neurologists with guidance related to management beyond the acute phase of disease, including long-term immunosuppression, monitoring, potential biomarkers of disease activity, outcome measures, and symptom management. Observational studies in AE have demonstrated that early diagnosis and treatment is associated with improved neurologic outcomes, particularly in AE with antibodies targeting neuronal cell surface/synaptic proteins. The literature regarding long-term management is evolving. In addition to traditional immunosuppressive approaches, there is emerging use of novel immunosuppressive therapies (ISTs) in case series, and several randomized controlled trials are planned. Novel biomarkers of disease activity and methods to measure outcomes and response to treatment are being explored. Furthermore, it is increasingly recognized that many individuals have chronic symptoms affecting quality of life including seizures, cognitive impairment, fatigue, sleep disorders, and mood disorders, and there are emerging data supporting the use of patient centered outcome measures and multidisciplinary symptom-based care. This review aims to summarize recent literature and offer a practical approach to long-term management of adult patients with AE through a multidisciplinary approach. We summarize current knowledge on ISTs, potential biomarkers of disease activity, outcome measures, and long-term sequelae. Further research is needed to answer questions regarding optimal IST, biomarker validity, and sequelae of disease.

Primate cerebrospinal fluid CHI3L1 reflects brain TREM2 agonism.

Triggering receptor expressed on myeloid cells 2 (TREM2) agonists are being clinically evaluated as disease-modifying therapeutics for Alzheimer's disease. Clinically translatable pharmacodynamic (PD) biomarkers are needed to confirm drug activity and select the appropriate therapeutic dose in clinical trials. We conducted multi-omic analyses on paired non-human primate brain and cerebrospinal fluid (CSF), and stimulation of human induced pluripotent stem cell-derived microglia cultures after TREM2 agonist treatment, followed by validation of candidate fluid PD biomarkers using immunoassays. We immunostained microglia to characterize proliferation and clustering. We report CSF soluble TREM2 (sTREM2) and CSF chitinase-3-like protein 1 (CHI3L1/YKL-40) as PD biomarkers for the TREM2 agonist hPara.09. The respective reduction of sTREM2 and elevation of CHI3L1 in brain and CSF after TREM2 agonist treatment correlated with transient microglia proliferation and clustering. CSF CHI3L1 and sTREM2 reflect microglial TREM2 agonism and can be used as clinical PD biomarkers to monitor TREM2 activity in the brain. CSF soluble triggering receptor expressed on myeloid cells 2 (sTREM2) reflects brain target engagement for a novel TREM2 agonist, hPara.09. CSF chitinase-3-like protein 1 reflects microglial TREM2 agonism. Both can be used as clinical fluid biomarkers to monitor TREM2 activity in brain.

Peripheral immune biomarkers for immune checkpoint inhibition of solid tumours.

With the rapid adoption of immunotherapy for the treatment of cancer comes the pressing need for readily accessible biomarkers to guide immunotherapeutic strategies and offer insights into outcomes with specific treatments. Regular sampling of solid tumour tissues outside of melanoma for immune monitoring is not often feasible; conversely, routine, frequent interrogation of circulating immune biomarkers is entirely possible. As immunotherapies and immune checkpoint inhibitors, in particular, are more widely used in first-line, neoadjuvant, and metastatic settings, the discovery and validation of peripheral immune biomarkers are urgently needed across solid tumour types for improved prediction and prognostication of clinical outcomes in response to immunotherapy, as well as elucidation of mechanistic underpinnings of the intervention. Careful experimental design, encompassing both retrospective and prospective studies, is required in such biomarker identification studies, and concerted efforts are essential for their advancement into clinical settings. In this review, we summarize shared immune features between the tumour microenvironment and systemic circulation, evaluate exploratory peripheral immune biomarker studies, and discuss associations between candidate biomarkers with clinical outcomes. We also consider integration of multiple peripheral immune parameters for better prediction and prognostication and discuss considerations in study design to further evaluate the clinical utility of candidate peripheral immune biomarkers for immunotherapy of solid tumours. Peripheral immune biomarkers are critical for improved prediction and prognostication of clinical outcomes for patients with solid tumours treated with immune checkpoint inhibition. Candidate peripheral biomarkers, such as cytokines, soluble factors, and immune cells, have potential as biomarkers to guide immunotherapy of solid tumours. Multiple peripheral immune parameters may be integrated to improve prediction and prognostication. The potential of peripheral immune biomarkers to guide immunotherapy of solid tumours requires critical work in biomarker discovery, validation, and standardization.