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

Molybdenum-Ruthenium-Carbon Solid-Solution Alloy Nanoparticles: Can They Be Pseudo-Technetium Carbide?

Technetium (Tc), atomic number 43, is an element that humans cannot freely use even in the 21st century because Tc is radioactive and has no stable isotope. In this report, we present molybdenum–ruthenium–carbon solid-solution alloy (Mo_xRu_{1-x}C_y) nanoparticles (NPs) that are expected to have an electronic structure similar to that of technetium carbide (TcC_y). Mo_xRu_{1-x}C_y NPs were synthesized by annealing under a helium/hydrogen atmosphere following thermal decomposition of metal precursors. The obtained NPs had a solid-solution structure in the whole composition range. Mo_xRu_{1-x}C_y with a cubic structure (down to 30 atom % Mo in the metal ratio) showed a superconducting state, and the transition temperature (Tc) increased with increasing Mo composition. The continuous change in Tc across that of TcC_y indicates the continuous control of the electronic structure by solid-solution alloying, leading to pseudo-TcC_y. Density functional theory calculations indicated that the synthesized Mo_{0.53}Ru_{0.47}C_{0.41} has a similar electronic structure to TcC_{0.41}

NY-60挿入眼の薄暮でのコントラスト感度

雑誌掲載版目的:着色非球面眼内レンズNY-60挿入眼の薄暮条件でのコントラスト感度を検討した。方法:屈折異常、白内障以外に眼疾患を有さず白内障手術を予定し ていた60例60眼を対象として、無作為に3群に分けて20眼にNY-60を、20眼にPY-60ADを、20眼にSN60ATを挿入した。術後6ヵ月で 矯正視力、瞳孔径、波面収差、および薄暮条件でのコントラスト感度を測定した。結果:コントラスト感度で、全周波数でNY-60群がSN60AT群と比べ て有意に高値(p<0.05)であり、NY-60群とPY-60AD群ではNY-60群が、PY-60AD群とSN60AT群ではPY-60AD群 がいくつかの周波数で有意に高値であった(p<0.05)。結論:NY-60はPY-60AD、SN60ATと比べて薄暮でのコントラスト感度向上に貢献すると思われる