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

Release from optimal compressive force suppresses osteoclast differentiation

Bone remodeling is an important factor in orthodontic tooth movement. During orthodontic treatment, osteoclasts are subjected to various mechanical stimuli, and this promotes or inhibits osteoclast differentiation and fusion. It has been previously reported that the release from tensile force induces osteoclast differentiation. However, little is known about how release from compressive force affects osteoclasts. The present study investigated the effects of release from compressive force on osteoclasts. The number of tartrate-resistant acid phosphatase (TRAP) -positive multinucleated osteoclasts derived from RAW264.7 cells was counted, and gene expression associated with osteoclast differentiation and fusion in response to release from compressive force was evaluated by reverse transcription-quantitative polymerase chain reaction. Osteoclast number was increased by optimal compressive force application. On release from this force, osteoclast differentiation and fusion were suppressed. mRNA expression of NFATc1 was inhibited for 6 h subsequent to release from compressive force. mRNA expression of the other osteoclast-specific genes, TRAP, RANK, matrix metalloproteinase-9, cathepsin-K, chloride channel 7, ATPase H+ transporting vacuolar proton pump member I, dendritic cell-specific transmembrane protein and osteoclast stimulatory transmembrane protein (OC-STAMP) was significantly inhibited at 3 h following release from compressive force compared with control cells. These findings suggest that release from optimal compressive force suppresses osteoclast differentiation and fusion, which may be important for developing orthodontic treatments

Modeling Heterogeneous Brain Dynamics of Depression and Melancholia Using Energy Landscape Analysis

Our current understanding of melancholic depression is shaped by its position in the depression spectrum. The lack of consensus on how it should be treated-whether as a subtype of depression, or as a distinct disorder altogethe-interferes with the recovery of suffering patients. In this study, we analyzed brain state energy landscape models of melancholic depression, in contrast to healthy and non-melancholic energy landscapes. Our analyses showed significant group differences on basin energy, basin frequency, and transition dynamics in several functional brain networks such as basal ganglia, dorsal default mode, and left executive control networks. Furthermore, we found evidences suggesting the connection between energy landscape characteristics (basin characteristics) and depressive symptom scores (BDI-II and SHAPS). These results indicate that melancholic depression is distinguishable from its non-melancholic counterpart, not only in terms of depression severity, but also in brain dynamics