Data-driven categorization of postoperative delirium symptoms using unsupervised machine learning

BackgroundPhenotyping analysis that includes time course is useful for understanding the mechanisms and clinical management of postoperative delirium. However, postoperative delirium has not been fully phenotyped. Hypothesis-free categorization of heterogeneous symptoms may be useful for understanding the mechanisms underlying delirium, although evidence is currently lacking. Therefore, we aimed to explore the phenotypes of postoperative delirium following invasive cancer surgery using a data-driven approach with minimal prior knowledge.MethodsWe recruited patients who underwent elective invasive cancer resection. After surgery, participants completed 5 consecutive days of delirium assessments using the Delirium Rating Scale-Revised-98 (DRS-R-98) severity scale. We categorized 65 (13 questionnaire items/day × 5 days) dimensional DRS-R-98 scores using unsupervised machine learning (K-means clustering) to derive a small set of grouped features representing distinct symptoms across all participants. We then reapplied K-means clustering to this set of grouped features to delineate multiple clusters of delirium symptoms.ResultsParticipants were 286 patients, of whom 91 developed delirium defined according to Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, criteria. Following the first K-means clustering, we derived four grouped symptom features: (1) mixed motor, (2) cognitive and higher-order thinking domain with perceptual disturbance and thought content abnormalities, (3) acute and temporal response, and (4) sleep–wake cycle disturbance. Subsequent K-means clustering permitted classification of participants into seven subgroups: (i) cognitive and higher-order thinking domain dominant delirium, (ii) prolonged delirium, (iii) acute and brief delirium, (iv) subsyndromal delirium-enriched, (v) subsyndromal delirium-enriched with insomnia, (vi) insomnia, and (vii) fit.ConclusionWe found that patients who have undergone invasive cancer resection can be delineated using unsupervised machine learning into three delirium clusters, two subsyndromal delirium clusters, and an insomnia cluster. Validation of clusters and research into the pathophysiology underlying each cluster will help to elucidate the mechanisms of postoperative delirium after invasive cancer surgery

Continuous-Flow Chemical Synthesis for Sub-2 nm Ultra-Multielement Alloy Nanoparticles Consisting of Group IV to XV Elements

Multielement alloy nanoparticles have attracted much attention due to their attractive catalytic properties derived from the multiple interactions of adjacent multielement atoms. However, mixing multiple elements in ultrasmall nanoparticles from a wide range of elements on the periodic table is still challenging because the elements have different properties and miscibility. Herein, we developed a benchtop 4-way flow reactor for chemical synthesis of ultra-multielement alloy (UMEA) nanoparticles composed of d-block and p-block elements. BiCoCuFeGaInIrNiPdPtRhRuSbSnTi 15-element alloy nanoparticles composed of group IV to XV elements were synthesized by sequential injection of metal precursors using the reactor. This methodology realized the formation of UMEA nanoparticles at low temperature (66 °C), resulting in a 1.9 nm ultrasmall average particle size. The UMEA nanoparticles have high durability and activity for electrochemical alcohol oxidation reactions and high tolerance to CO poisoning. These results suggest that the multiple interactions of UMEA efficiently promote the multistep alcohol oxidation reaction

Continuous-Flow Chemical Synthesis for Sub‑2 nm Ultra-Multielement Alloy Nanoparticles Consisting of Group IV to XV Elements

Multielement alloy nanoparticles have attracted much attention due to their attractive catalytic properties derived from the multiple interactions of adjacent multielement atoms. However, mixing multiple elements in ultrasmall nanoparticles from a wide range of elements on the periodic table is still challenging because the elements have different properties and miscibility. Herein, we developed a benchtop 4-way flow reactor for chemical synthesis of ultra-multielement alloy (UMEA) nanoparticles composed of d-block and p-block elements. BiCoCuFeGaInIrNiPdPtRhRuSbSnTi 15-element alloy nanoparticles composed of group IV to XV elements were synthesized by sequential injection of metal precursors using the reactor. This methodology realized the formation of UMEA nanoparticles at low temperature (66 °C), resulting in a 1.9 nm ultrasmall average particle size. The UMEA nanoparticles have high durability and activity for electrochemical alcohol oxidation reactions and high tolerance to CO poisoning. These results suggest that the multiple interactions of UMEA efficiently promote the multistep alcohol oxidation reaction