Background: The combination of systematic evolution of ligands by exponential enrichment (SELEX) and deep sequencing is termed high-throughput (HT)-SELEX, which enables searching aptamer candidates from a massive amount of oligonucleotide sequences. A clustering method is an important procedure to identify sequence groups including aptamer candidates for evaluation with experimental analysis. In general, aptamer includes a specific target binding region, which is necessary for binding to the target molecules. The length of the target binding region varies depending on the target molecules and/or binding styles. Currently available clustering methods for HT-SELEX only estimate clusters based on the similarity of full-length sequences or limited length of motifs as target binding regions. Hence, a clustering method considering the target binding region with different lengths is required. Moreover, to handle such huge data and to save sequencing cost, a clustering method with fast calculation from a single round of HT-SELEX data, not multiple rounds, is also preferred.Results: We developed fast string-based clustering (FSBC) for HT-SELEX data. FSBC was designed to estimate clusters by searching various lengths of over-represented strings as target binding regions. FSBC was also designed for fast calculation with search space reduction from a single round, typically the final round, of HT-SELEX data considering imbalanced nucleobases of the aptamer selection process. The calculation time and clustering accuracy of FSBC were compared with those of four conventional clustering methods, FASTAptamer, AptaCluster, APTANI, and AptaTRACE, using HT-SELEX data (>15 million oligonucleotide sequences). FSBC, AptaCluster, and AptaTRACE could complete the clustering for all sequence data, and FSBC and AptaTRACE performed higher clustering accuracy. FSBC showed the highest clustering accuracy and had the second fastest calculation speed among all methods compared.Conclusion: FSBC is applicable to a large HT-SELEX dataset, which can facilitate the accurate identification of groups including aptamer candidates.Availability of data and materials: FSBC is available athttp://www.aoki.ecei.tohoku.ac.jp/fsbc/.

Although nurses’ fatigue affects their well-being and patient safety, no effective objective measurements exist. We explored the profiles of salivary biomarkers associated with nurses’ chronic fatigue across several shifts. This longitudinal study involved 45 shiftwork nurses and collected their saliva samples before two night and two day shifts for a month. Chronic fatigue was measured using the Cumulative Fatigue Symptom Index before the first night shift. Biomarker profiles were analyzed using hierarchical cluster analysis, and chronic fatigue levels were compared between the profiles. Cortisol profiles were classified into high- and low-level groups across two day shifts; the low-level group presented significantly higher irritability and unwillingness to work. Secretory immunoglobulin A (s-IgA) profiles across the four shifts were classified into high- and low-level groups; the high-level group had significantly higher depressive feelings, decreased vitality, irritability, and unwillingness to work. Cortisol (two day shifts) and s-IgA (four shifts) profiles were combined, and (i) cortisol low-level and s-IgA high-level and (ii) cortisol high-level and s-IgA low-level groups were identified. The former group had significantly higher chronic fatigue sign and irritability than the latter group. The profiles of salivary cortisol and s-IgA across several shifts were associated with nurses’ chronic fatigue.

Tools developed to assess individuals’ comprehensive health status would be beneficial for personalized prevention and treatment. This study aimed to develop a set of risk prediction models to estimate the risk for multiple diseases such as heart, blood vessel, brain, metabolic, liver, and kidney diseases using health checkup data only. This is a retrospective study that used health checkup data combined with diagnostic information from electronic health records of Kurashiki Central Hospital in Okayama, Japan. All exposure factors were measured at the first health checkup visit, including demographic characteristics, laboratory test results, lifestyle questionnaires, medication use, and medical history. Primary outcomes were the diagnoses of 15 diseases during the follow-up period. Cox proportional hazard regression was applied to develop risk prediction models for heart, blood vessel, brain, metabolic, liver, and kidney diseases. Area under the curve with 4-year risk assessments were performed to evaluate the models. From January 2012 to September 2022, a total of 92,174 individuals aged 15–96 years underwent general health checkups. The area under the curve of the models in validation datasets was as follows: atrial fibrillation, 0.81; acute myocardial infarction, 0.81; heart failure, 0.76; cardiomyopathy, 0.72; angina pectoris, 0.70; atherosclerosis, 0.82; hypertension, 0.80; cerebral infarction, 0.77; intracerebral hemorrhage, 0.68; subarachnoid hemorrhage, 0.50; type-2 diabetes mellitus, 0.82; hyperlipidemia, 0.70; alcoholic liver disease, 0.91; liver fibrosis, 0.92; and chronic kidney disease, 0.80. A set of prediction models to estimate multi-disease risk simultaneously from health checkup results may help to assess comprehensive individual health status and facilitate personalized prevention and early diagnosis.

Growth/differentiation factor–15 (GDF15) is a secreted cytokine strongly associated with dementia risk. However, the extent to which GDF15 represents a biomarker and driver of dementia risk remains unclear. Across multiple cohorts, we demonstrated that plasma GDF15 is associated with greater dementia risk over 15- to 25-year follow-up periods when measured in midlife, with stronger associations observed for vascular, compared to Alzheimer’s disease (AD), dementia. Two-sample Mendelian randomization supported plasma GDF15’s mechanistic role in AD and related dementias, while cohort studies linked it to cerebral small vessel disease, neurodegeneration, phosphorylated tau, and a cerebrospinal fluid proteomic signature indicative of neuroimmune activation. Exposure of cultured myeloid cells to recombinant GDF15 altered biological pathways that we subsequently demonstrated are predictive of dementia risk, including interferon/antiviral responses. These findings support circulating GDF15’s role as an early biomarker—particularly for vascular dementia and neuroinflammation—and identify the mechanisms by which it may drive dementia risk.