Background: Despite the impressive outcomes with immune checkpoint inhibitor (ICI) in non-small cell lung cancer (NSCLC), only a minority of the patients show long-term benefits from ICI. In this study, we used retrospective cohorts of ICI treated patients with NSCLC to discover and validate spatially resolved protein markers associated with resistance to programmed cell death protein-1 (PD-1) axis inhibition. Methods: Pretreatment samples from 56 patients with NSCLC treated with ICI were collected and analyzed in a tissue microarray (TMA) format in including four different tumor regions per patient using the GeoMx platform for spatially informed transcriptomics. 34 patients had assessable tissue with tumor compartment in all 4 TMA spots, 22 with leukocyte compartment and 12 with CD68 compartment. The patients’ tissue that was not assessable in fourfold redundancy in each compartment was designated as the validation cohort; cytokeratin (CK) (N=22), leukocytes CD45 (N=31), macrophages, CD68 (N=43). The human whole transcriptome, represented by~18,000 individual genes assessed by oligonucleotide-tagged in situ hybridization, was sequenced on the NovaSeq platform to quantify the RNAs present in each region of interest. Results: 54,000 gene variables were generated per case, from them 25,740 were analyzed after removing targets with expression lower than a prespecified frequency. Cox proportional-hazards model analysis was performed for overall and progression-free survival (OS, PFS, respectively). After identifying genes significantly associated with limited survival benefit (HR>1)/progression per spot per patient, we used the intersection of them across the four TMA spots per patient. This resulted in a list of 12 genes in the tumor-cell compartment (RPL13A, GNL3, FAM83A, CYBA, ACSL4, SLC25A6, EPAS1, RPL5, APOL1, HSPD1, RPS4Y1, ADI1). RPL13A, GNL3 in tumor-cell compartment were also significantly associated with OS and PFS, respectively, in the validation cohort (CK: HR, 2.48; p=0.02 and HR, 5.33; p=0.04). In CD45 compartment, secreted frizzled-related protein 2, was associated with OS in the discovery cohort but not in the validation cohort. Similarly, in the CD68 compartment ARHGAP and PNN interacting serine and arginine rich protein were significantly associated with PFS and OS, respectively, in the majority but not all four spots per patient. Conclusion: This work highlights RPL13A and GNL3 as potential indicative biomarkers of resistance to PD-1 axis blockade that might help to improve precision immunotherapy strategies for lung cancer.

We define an iterative method for dimensionality reduction using categorical gradient boosted trees and Shapley values and created four machine learning models which potentially could be used as diagnostic tests for acute myeloid leukaemia (AML). For the final Catboost model we use a dataset of 2177 individuals using as features 16 probe sets and the age in order to classify if someone has AML or is healthy. The dataset is multicentric and consists of data from 27 organizations, 25 cities, 15 countries and 4 continents. The performance of our last model is specificity: 0.9909, sensitivity: 0.9985, F1-score: 0.9976 and its ROC-AUC: 0.9962 using ten fold cross validation. On an inference dataset the perormance is: specificity: 0.9909, sensitivity: 0.9969, F1-score: 0.9969 and its ROC-AUC: 0.9939. To the best of our knowledge the performance of our model is the best one in the literature, as regards the diagnosis of AML using similar or not data. Moreover, there has not been any bibliographic reference which associates AML or any other type of cancer with the 16 probe sets we used as features in our final model.

Objectives: Periodontitis (PD) patients frequently suffer from comorbidities, necessitating increased attention to disease management and monitoring. The aim of this study is to describe the prevalence and patterns of comorbidities among patients with PD in a private periodontal referral practice. Methods: This study involved 3171 adults with PD. Data on demographics, lifestyle, number of teeth, pockets of size ≥ 6 mm, bleeding on probing, periodontal inflammatory surface area, and comorbidities were extracted from electronic patient records. Descriptive and statistical analyses, including t-tests, chi-square tests, cluster analysis, binomial logistic regression analysis, and hypergraph network analysis, were performed. Results: Among this PD population, 47% had a comorbidity, and 20% had multimorbidity (≥2 diseases). Based on the disease patterns, two distinct clusters emerged: Cluster 1 was dominated by respiratory tract conditions (asthma, lung disease, and allergic rhinitis), allergies, and hypothyroidism, while Cluster 2 primarily included cardiometabolic diseases (angina pectoris, hypertension, diabetes mellitus (DM), and hyperthyroidism). The hypergraph network analysis for those with multimorbidity identified two main groups: (i) pulmonary conditions (lung disease, asthma, allergic rhinitis, and allergies) and (ii) cardiometabolic disorders (hypertension, myocardial infarction, cerebrovascular disease, and DM). Hypertension, allergies, and allergic rhinitis showed high centrality, serving as central nodes frequently co-occurring with other diseases. Conclusions: Nearly half of the PD patients in a private periodontal referral practice were found to have comorbidities, primarily clustering into cardiometabolic and respiratory tract diseases. These findings, based on real-world data, should encourage dental professionals to integrate systemic conditions into their care strategies. They could also guide policymakers and practitioners in developing evidence-based approaches to mitigate the reciprocal negative effects of PD and comorbidities.

Purpose: To investigate the effect of vitamin C supplementation with or without flavonoids on periodontal conditions, and microbial and systemic variables before and after non-surgical periodontal treatment (NSPT). Materials and Methods: A case series derived from a randomized controlled trial was conducted to explore the effects of daily vitamin C supplementation, with or without flavonoids, on periodontal conditions. The study population was recruited from patients with periodontitis who had been referred to the Department of Periodontology at the Academic Centre for Dentistry Amsterdam (ACTA). The study consisted of a 2-month observation of untreated periodontitis followed by a 3-month period after NSPT. Descriptive statistics, correlation and clustering analyses, and dimensionality reduction methods were used to evaluate the interventions’ impact. Results: Due to COVID-19, the study was prematurely terminated and reported findings from 13 patients. Results indicate a correlation between higher plasma vitamin C levels and reduced gingival inflammation, suggesting benefits for untreated periodontal conditions. Clustering analysis showed no differences based on supplementation type, indicating it did not affect outcomes, and microbiological data had limited effects. Principal Component Analysis visualized clusters and illustrated no distinct groups corresponding to supplementation types. Violin plots highlighted variability, with one cluster comprising individuals with more severe periodontal conditions. Conclusions: Higher plasma vitamin C levels were associated with lower gingival inflammation. However, daily vitamin C supplementation, with or without flavonoids, did not show additional benefits on periodontal conditions before or after treatment. Clustering suggests that periodontal severity, rather than supplementation, influenced patient profiles. The study’s small sample size limits the generalizability of the findings.

Applying the iterative methodology for dimensionality reduction/feature selection using categorical gradient boosted trees, as it has been defined in and has been successfully applied on similar datasets in and , on a dataset consisted of 12708 gene expressions coming from 5052 individuals from 105 studies, we classify whether a person has acute myeloid leukaemia (AML) or is healthy. A CatBoost model on a dataset with reduced dimensions of 72 genes reached a ROC-AUC score of 0.9973 using ten fold cross validation (10CV) and ROC-AUC: 0.9988 on an inference dataset. We further investigate the options of using less genes that potentially could be used in clinical practise and genes than have not been associated to AML yet, or to blood cancer in general. On the same folds of the 10CV and on the same inference dataset the performance of the tuned CatBoost models show that it could be the case that not all genes with an association to AML have been found yet and 19 genes could be enough to predict AML: CatBoost63 (ROC-AUC: 0.9941, Test: 0.9942), CatBoost19: (ROC-AUC: 0.9946, Test: 0.9941) and CatBoost15: (ROC-AUC: 0.9922, Test: 0.9900). In addition, our results verify that a gene diagnostic test for AML could be possible in the future as well as further research is needed on these 15 genes that it could lead to new and better drugs.