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Objectives There is a lack of consensus regarding what constitutes cognitively normal performance on the Montreal Cognitive Assessment (MoCA) based on demographic characteristics. Further, research regarding normative data on the MoCA for middle‐aged individuals is relatively limited. The current study sought to provide age‐ and education‐corrected normative data for the MoCA in a large epidemiological cohort of cognitively healthy middle‐aged and older adults with characteristics similar to the original validation sample of the MoCA. Methods Participants were from Generation 3 and Omni 2 cohorts of the Framingham Heart Study (n = 2637; 91.43% non‐Hispanic White) who were determined to be cognitively unimpaired at the time of MoCA assessment (Mean age = 53.56 years, age range = 32–83 years, 63.71% ≥ college‐educated). Normative data were generated by age in 10‐year intervals and education (≤ high school, some college, or ≥ college degree). Analysis of variance was used to examine the relationship between MoCA performance, age, and education. Results The average MoCA score across all participants was close to the revised MCI cutoff of 23 (M = 24.69, SD = 3.03). The average MoCA score for individuals over the age of 60 was below the recently suggested MCI cutoff score of 23 points. Similarly, individuals above the age of 70 scored below the revised cutoff score of 23 points, irrespective of level of education. Further, performance of participants below the age of 40 who were college educated was similar to the frequently used original MCI cutoff score of 26 (M = 26.28, SD = 2.41). Conclusions Results are consistent with previous literature suggesting that the original MoCA cutoff score of 26 may result in a high rate of false positives. Findings indicate that the recently suggested MCI cutoff score of 23 on the MoCA may also be artificially high. Using inappropriate normative data for the MoCA can impact diagnostic accuracy as well as misclassification in research settings. These findings highlight the need for the use of demographically appropriate, population‐based normative data for the MoCA in clinical and research settings.

Dementia and its most common form Alzheimer’s disease (AD) are urgent yet complicated problems to decipher and as such predictive modeling for AD outcomes attracted researchers’ attention for years. Numerous works in literature have identified categories of predictors that are linked to AD such as inflammatory cerebrospinal fluid (CSF) biomarkers and brain MRI measurements. A major contribution of the thesis is introducing covariance structures as a tool to signify AD. In clinical settings, it is often imperative to account for demographic covariates as potential confounding factors. We explore two ways to account for these covariates: one by removing their effects via partial covariance and the other by calculating the covariance matrices for given values of covariates via function covariance matrix estimators. For both covariance estimating methods we use hypothesis testing methods to determine if the covariance estimates are significantly different between AD outcome groups. These methods are the parametric Tracy-Widom, the semi-parametric Forkman’s test, and the nonparametric Permutation method. We evaluate the utility of the covariance estimation as well as the hypothesis testing methods via extensive simulation studies. Additionally, we apply these methods to real world data studies such as the FHS and the ADNI.Moreover, we explore the scenario where the cases are rare compared to the controls in a binary outcome (aka rare event), as often the case in bio-medical application such as AD data. The imbalance between the outcomes has been shown to introduce bias in the estimation of the model parameters, which in turn affects the predictive probabilities. The problem becomes more severe as the imbalance becomes starker, therefore methods that adjust for the imbalance could be beneficial in such situations. As part of the thesis, we explore the adequacy of logistic regression model which is known to suffer from the problem of bias in rare event cases. Additionally, we evaluate derivative methods that aim to compensate for rare event cases such as prior correction, weighting, Firth’s logistic regression, FLIC, and FLAC using the ADNI data set. Our investigation into the performances of the various methods show that the weighting method provides a significant improvement in the predictive utility of the regression model.

Although there are many methods available in the literature to compare the covariance structures of two populations, few are suitable for clinical application due to the inability to account for covariate(s) that affect the dependence structure of the variables being investigated. A common method is to adjust the effect of the covariates via a linear model and work with the resulting residuals. However, removing the effects of the covariates could potentially eliminate valuable information from the analysis. We propose a functional nonparametric covariance matrix estimator to account for any given value in the covariate(s), which allows a comparison of the functional covariance structures of the multivariate data. This comparison is facilitated via a test statistic involving the first eigenvalue of the combined form of covariance matrices of the two groups. Three different approaches, namely, the parametric Tracy-Widom, the semi-parametric Forkman's test, and the nonparametric Permutation method, are used to compute the approximate p-values of the test statistic. We have conducted extensive simulation studies to determine the type I error and power of the proposed hypothesis testing methods and developed practical recommendations for implementing this novel approach. Finally, we apply our methods to the Alzheimer's Disease Neuroimaging Initiative (ADNI) study to compare cerebrospinal fluid (CSF) biomarkers between dementia and non-dementia cohorts, which offers a fascinating insight into the differences between covariance structures of biomarkers amyloid , total tau (tau), and phosphorylated tau (ptau) for given values of age, sex, and years of education.

Numerous studies have shown that individuals with dementia have exhibited activation of inflammatory pathways in their brains. Typically, these studies use traditional and well-established regression methods for data analysis. In this paper, a new approach is introduced that utilizes the analysis of the covariance structure using methods related to the principal component analysis (PCA) theory. Eleven biomarkers related to neuroinflammation were used to determine the association with the onset of dementia. Various demographic covariates were adjusted to account for possible confounding effects of the covariance structure. Three hypothesis testing methods were considered to discern differences between partial covariance matrices for comparing power and Type I errors through simulation studies. Application of hypothesis testing methods using data from Framingham Heart Study (FHS) found significant differences in covariance matrices between the non-dementia and dementia groups. Competing Interest Statement The authors have declared no competing interest.

ObjectiveTo investigate the incidence of gastric cancer (GC) attributed to gastric intestinal metaplasia (IM), and validate the Operative Link on Gastric Intestinal Metaplasia (OLGIM) for targeted endoscopic surveillance in regions with low-intermediate incidence of GC.MethodsA prospective, longitudinal and multicentre study was carried out in Singapore. The study participants comprised 2980 patients undergoing screening gastroscopy with standardised gastric mucosal sampling, from January 2004 and December 2010, with scheduled surveillance endoscopies at year 3 and 5. Participants were also matched against the National Registry of Diseases Office for missed diagnoses of early gastric neoplasia (EGN).ResultsThere were 21 participants diagnosed with EGN. IM was a significant risk factor for EGN (adjusted-HR 5.36; 95% CI 1.51 to 19.0; p<0.01). The age-adjusted EGN incidence rates for patients with and without IM were 133.9 and 12.5 per 100 000 person-years. Participants with OLGIM stages III–IV were at greatest risk (adjusted-HR 20.7; 95% CI 5.04 to 85.6; p<0.01). More than half of the EGNs (n=4/7) attributed to baseline OLGIM III–IV developed within 2 years (range: 12.7–44.8 months). Serum trefoil factor 3 distinguishes (Area Under the Receiver Operating Characteristics 0.749) patients with OLGIM III–IV if they are negative for H. pylori. Participants with OLGIM II were also at significant risk of EGN (adjusted-HR 7.34; 95% CI 1.60 to 33.7; p=0.02). A significant smoking history further increases the risk of EGN among patients with OLGIM stages II–IV.ConclusionsWe suggest a risk-stratified approach and recommend that high-risk patients (OLGIM III–IV) have endoscopic surveillance in 2 years, intermediate-risk patients (OLGIM II) in 5 years.

Artificial Intelligence (AI) techniques have made great advances in assisting antibody design. However, antibody design still heavily relies on isolating antigen-specific antibodies from serum, which is a resource-intensive and time-consuming process. To address this issue, we propose a Pre-trained Antibody generative large Language Model (PALM-H3) for the de novo generation of artificial antibodies heavy chain complementarity-determining region 3 (CDRH3) with desired antigen-binding specificity, reducing the reliance on natural antibodies. We also build a high-precision model antigen-antibody binder (A2binder) that pairs antigen epitope sequences with antibody sequences to predict binding specificity and affinity. PALM-H3-generated antibodies exhibit binding ability to SARS-CoV-2 antigens, including the emerging XBB variant, as confirmed through in-silico analysis and in-vitro assays. The in-vitro assays validate that PALM-H3-generated antibodies achieve high binding affinity and potent neutralization capability against spike proteins of SARS-CoV-2 wild-type, Alpha, Delta, and the emerging XBB variant. Meanwhile, A2binder demonstrates exceptional predictive performance on binding specificity for various epitopes and variants. Furthermore, by incorporating the attention mechanism inherent in the Roformer architecture into the PALM-H3 model, we improve its interpretability, providing crucial insights into the fundamental principles of antibody design. Antibody design still heavily relies on isolating antigen-specific antibodies from serum. Here the authors report a Pre-trained Antibody generative large Language Model (PALM-H3) for the de novo generation of artificial antibodies heavy chain complementarity-determining region 3 with desired antigen-binding specificity.

ObjectiveAn unmet need exists for a non-invasive biomarker assay to aid gastric cancer diagnosis. We aimed to develop a serum microRNA (miRNA) panel for identifying patients with all stages of gastric cancer from a high-risk population.DesignWe conducted a three-phase, multicentre study comprising 5248 subjects from Singapore and Korea. Biomarker discovery and verification phases were done through comprehensive serum miRNA profiling and multivariant analysis of 578 miRNA candidates in retrospective cohorts of 682 subjects. A clinical assay was developed and validated in a prospective cohort of 4566 symptomatic subjects who underwent endoscopy. Assay performance was confirmed with histological diagnosis and compared with Helicobacter pylori (HP) serology, serum pepsinogens (PGs), ‘ABC’ method, carcinoembryonic antigen (CEA) and cancer antigen 19–9 (CA19-9). Cost-effectiveness was analysed using a Markov decision model.ResultsWe developed a clinical assay for detection of gastric cancer based on a 12-miRNA biomarker panel. The 12-miRNA panel had area under the curve (AUC)=0.93 (95% CI 0.90 to 0.95) and AUC=0.92 (95% CI 0.88 to 0.96) in the discovery and verification cohorts, respectively. In the prospective study, overall sensitivity was 87.0% (95% CI 79.4% to 92.5%) at specificity of 68.4% (95% CI 67.0% to 69.8%). AUC was 0.848 (95% CI 0.81 to 0.88), higher than HP serology (0.635), PG 1/2 ratio (0.641), PG index (0.576), ABC method (0.647), CEA (0.576) and CA19-9 (0.595). The number needed to screen is 489 annually. It is cost-effective for mass screening relative to current practice (incremental cost-effectiveness ratio=US$44 531/quality-of-life year).ConclusionWe developed and validated a serum 12-miRNA biomarker assay, which may be a cost-effective risk assessment for gastric cancer.Trial registration numberThis study is registered with ClinicalTrials.gov (Registration number: NCT04329299).

Multiple myeloma (MM) is an incurable cancer of plasma cells with a 5‐year survival rate of 59%. Dysregulation of fatty acid (FA) metabolism is associated with MM development and progression; however, the underlying mechanisms remain unclear. Herein, we explore the roles of long‐chain fatty acid coenzyme A ligase (ACSL) family members in MM. ACSLs convert free long‐chain fatty acids into fatty acyl‐CoA esters and play key roles in catabolic and anabolic fatty acid metabolism. Analysis of the Multiple Myeloma Research Foundation (MMRF) CoMMpassSM study showed that high ACSL1 and ACSL4 expression in myeloma cells are both associated with worse clinical outcomes for MM patients. Cancer Dependency Map (DepMap) data showed that all five ACSLs have negative Chronos scores, and ACSL3 and ACSL4 were among the top 25% Hallmark Fatty Acid Metabolism genes that support myeloma cell line fitness. Inhibition of ACSLs in myeloma cell lines in vitro, using the pharmacological inhibitor Triacsin C (TriC), increased apoptosis, decreased proliferation, and decreased cell viability, in a dose‐ and time‐dependent manner. RNA‐sequencing analysis of MM.1S cells treated with TriC showed a significant enrichment in apoptosis, ferroptosis, and endoplasmic reticulum (ER) stress, and proteomic analysis of these cells revealed enriched pathways for mitochondrial dysfunction and oxidative phosphorylation. TriC also rewired mitochondrial metabolism by decreasing mitochondrial membrane potential, increasing mitochondrial superoxide levels, decreasing mitochondrial ATP production rates, and impairing cellular respiration. Overall, our data support the hypothesis that suppression of ACSLs in myeloma cells is a novel metabolic target in MM that inhibits their viability, implicating this family as a promising therapeutic target in treating myeloma. Triacsin C inhibition of the acyl‐CoA synthetase long chain (ACSL) family decreases multiple myeloma cell survival, proliferation, mitochondrial respiration, and membrane potential. Made with Biorender.com.

Targeted therapy is effective in many tumor types including lung cancer, the leading cause of cancer mortality. Paradigm defining examples are targeted therapies directed against non-small cell lung cancer (NSCLC) subtypes with oncogenic alterations in EGFR, ALK and KRAS. The success of targeted therapy is limited by drug-tolerant persister cells (DTPs) which withstand and adapt to treatment and comprise the residual disease state that is typical during treatment with clinical targeted therapies. Here, we integrate studies in patient-derived and immunocompetent lung cancer models and clinical specimens obtained from patients on targeted therapy to uncover a focal adhesion kinase (FAK)-YAP signaling axis that promotes residual disease during oncogenic EGFR-, ALK-, and KRAS-targeted therapies. FAK-YAP signaling inhibition combined with the primary targeted therapy suppressed residual drug-tolerant cells and enhanced tumor responses. This study unveils a FAK-YAP signaling module that promotes residual disease in lung cancer and mechanism-based therapeutic strategies to improve tumor response. Remaining drug-tolerant persistent (DTP) cancer cells limit the efficacy of targeted therapy in EGFR, ALK and KRAS mutant non-small cell lung cancer (NSCLC). Here, the authors show that focal adhesion kinase (FAK)-YAP signalling supports DTP cells promoting residual disease and targeting this pathway improved tumour response in NSCLC preclinical models.

The emergence and rapid global spread of SARS-CoV-2 demonstrates the importance of infectious disease surveillance, particularly during the early stages. Viral genomes can provide key insights into transmission chains and pathogenicity. Nasopharyngeal swabs were obtained from thirty-two of the first SARS-CoV-2 positive cases (March 18–30) in Kingston Ontario, Canada. Viral genomes were sequenced using Ion Torrent (n = 24) and MinION (n = 27) sequencing platforms. SARS-CoV-2 genomes carried forty-six polymorphic sites including two missense and three synonymous variants in the spike protein gene. The D614G point mutation was the predominate viral strain in our cohort (92.6%). A heterozygous variant (C9994A) was detected by both sequencing platforms but filtered by the ARTIC network bioinformatic pipeline suggesting that heterozygous variants may be underreported in the SARS-CoV-2 literature. Phylogenetic analysis with 87,738 genomes in the GISAID database identified global origins and transmission events including multiple, international introductions as well as community spread. Reported travel history validated viral introduction and transmission inferred by phylogenetic analysis. Molecular epidemiology and evolutionary phylogenetics may complement contact tracing and help reconstruct transmission chains of emerging diseases. Earlier detection and screening in this way could improve the effectiveness of regional public health interventions to limit future pandemics.