Objective To determine prevalence and factors predictive of periodontitis by using a standardized assessment model in adults with type 2 diabetes. Research design and methods We performed an observational cross-sectional study to determine the burden of periodontitis in adults with type 2 diabetes attending urban, ambulatory referral centers in the USA and UK. Full-mouth probing was performed and periodontitis was diagnosed based on either a low (≥5 mm at ≥1 site) or high pocket probing-depth threshold (≥6 mm at ≥1 site). Results were stratified into a five-stage schema and integrated with other clinical variables into the novel Diabetes Cross-Disciplinary Index to function as a balanced health scorecard. Corresponding demographic and routinely collected health data were obtained and comparisons were made between patients with and without periodontitis. Multivariable logistic regression was performed to identify factors predictive of the presence or absence of periodontitis. Results Between our two cohorts, 253 patients were screened. Caucasians comprised >90% and Hispanic Americans >75% of the UK and US cohorts, respectively. Males and females were equally distributed; mean age was 53.6±11 years; and 17 (6.7%) were edentulous. Of the 236 dentate patients, 128 (54.2%) had periodontitis by low threshold and 57 (24.2%) by high threshold. Just 17 (7.2%) were periodontally healthy. No significant differences in age, HbA1c, blood pressure, body mass index, low-density lipoprotein cholesterol, or smoking status (all p>0.05) were identified between those with or without periodontitis (regardless of threshold) and none was found to be a significant predictor of disease. Conclusions Periodontitis is frequent in adults with type 2 diabetes and all should be screened. Periodontal health status can be visualized with other comorbidities and complications using a novel balanced scorecard that could facilitate patient–clinician communication, shared decision-making, and prioritization of individual healthcare needs.

Aptamers are highly selective nucleic acid ligands that overcome many challenges of antibodies, including structural instability and high production costs. The implementation of aptamers into routine research and commercial applications has advanced steadily, though it has been tempered by the limitations of existing discovery methods, which remain relatively slow and low‐throughput. SELEX, the gold‐standard method for aptamer discovery, has played a pivotal role in the field. As the demand for faster and more tailored solutions grows, there is increasing recognition of the value of newer approaches that can expedite the discovery and optimization of aptamers for specific applications. This review discusses recent advances in the methods used for aptamer engineering, including both wet lab methods performed in vitro, and dry lab methods performed in silico. Applications of engineered aptamers described in recent literature and discuss new developments that could further lead to innovative new applications of aptamers for use both within and outside of the laboratory are discussed. Within the laboratory, these applications include (but are not limited to) target‐binding assays and integration into analytical nanomaterials, and outside of the laboratory, diagnostics and biosensing, which will be discussed at length.

While many natural instances of adhesion between cells and biological macromolecules have been elucidated, understanding how to mimic these adhesion events remains to be a challenge. Discovering new biointerface materials that can provide an appropriate environment, and in some cases, also providing function similar to the body’s own extracellular matrix, would be highly beneficial to multiple existing applications in biomedical and biological engineering, and provide the necessary insight for the advancement of new technology. Such examples of current applications that would benefit include biosensors, high-throughput screening and tissue engineering. From a mechanical perspective, these biointerfaces would function as bioactuators that apply focal adhesion points onto cells, allowing them to move and migrate along a surface, making biointerfaces a very relevant application in the field of actuators. While it is evident that great strides in progress have been made in the area of synthetic biointerfaces, we must also acknowledge their current limitations as described in the literature, leading to an inability to completely function and dynamically respond like natural biointerfaces. In this review, we discuss the methods, materials and, possible applications of biointerface materials used in the current literature, and the trends for future research in this area.

The generation of combinatorial oligonucleotide libraries is desirable for applications such as DNA aptamers, data storage, DNA origami, or synthetic genomes, but conventional libraries present challenges in detection and analysis. Synthesis of unique oligonucleotide sequences on magnetic nanoparticles would enhance the ability to manipulate, recover, and detect them. Here, we present a protocol for generating a scalable combinatorial oligonucleotide library on magnetic nanoparticles using split-and-pool synthesis. We then describe the process for preparing the library for conventional and next-generation sequencing (NGS) DNA sequencing. For complete details on the use and execution of this protocol, please refer to Nguyen et al.¹.

Non-invasive quantitative assessment of oral inflammatory burden could complement conventional periodontal evaluation; however, current clinical methods primarily characterize disease after structural and tissue changes have occurred. In our previously reported DePerio framework, we demonstrated the feasibility of enriching oral polymorphonuclear neutrophils (oPMNs) from saliva through their differential adhesion to a hydrophilic cornstarch (CS)-coated surface, followed by brightfield imaging and AI-assisted quantification. Building on this framework, here we present BioSA, with an emphasis on the mechanistic and quantitative characterization of this biologically inspired adhesion-based oPMN isolation process, analogous to leukocyte adhesion behavior in blood capillaries. We systematically investigate the contributions of surface physicochemical properties, oPMN surface characteristics, and physical forces governing preferential oPMN retention and epithelial-cell removal. The optimized adhesion-based isolation reduced epithelial-cell contamination by 91% while preserving >98% of oPMNs, outperforming the evaluated filtration- and poly-L-lysine (PLL)-based approaches. Following isolation, brightfield images were analyzed using an AI-assisted deep-learning model for automated oPMN quantification. Across 30 independent test days, BioSA measurements were strongly associated with those obtained using the reference HEMO method (R² ≈ 0.99; MAE ≈ 2.06 × 10⁵ cells/10 mL), while method agreement and systematic differences were further evaluated using Bland–Altman analysis. The deep-learning detector achieved 98% sensitivity, 97% precision, and an F1 score of 0.97 on a dataset containing 4617 manually annotated oPMNs. Using data-driven oPMN concentration ranges informed by prior literature, we further explored five oral inflammatory load (OIL) strata. These strata are hypothesis-generating and should not be interpreted as validated diagnostic stages or as replacements for the 2017 periodontitis staging and grading framework. Operating with standard brightfield microscopy and a cloud-based AI interface, BioSA reduces analysis time from 15 to 20 min to less than 2 min per sample. Collectively, this study extends the DePerio framework by providing mechanistic insight into biologically inspired adhesion-based oPMN isolation and demonstrates its potential for rapid, quantitative, and exploratory assessment of oral inflammatory burden.