The aims of this study were (1) to evaluate the incidence of poststent findings detected by OCT from multicenter experience, (2) to correlate these poststent findings with 1-year device-oriented clinical end points (DoCEs), including cardiac death, target vessel-related myocardial infarction, target Background-Optical coherence tomography (OCT) was recently introduced to optimize percutaneous coronary intervention. However, the exact incidence and significance of poststent OCT findings are unknown. Methods and Results-A total of 900 lesions treated with 1001 stents in 786 patients who had postprocedure OCT imaging were analyzed to evaluate the incidence of poststent OCT findings and to identify the OCT predictors for device-oriented clinical end points, including cardiac death, target vessel-related myocardial infarction, target lesion revascularization, and stent thrombosis. Patients were followed up to 1 year. Stent edge dissection was detected in 28.7% of lesions, and incomplete stent apposition was detected in 39.1% of lesions. The incidences of smooth protrusion, disrupted fibrous tissue protrusion, and irregular protrusion were 92.9%, 61.0%, and 53.8%, respectively. Small minimal stent area, defined as a lesion with minimal stent area <5.0 mm 2 in a drug-eluting stent or <5.6 mm 2 in a bare metal stent, was observed in 40.4% of lesions. One-year device-oriented clinical end points occurred in 33 patients (4.5%). Following adjustment, irregular protrusion and small minimal stent area were independent OCT predictors of 1-year device-oriented clinical end points (P=0.003 and P=0.012, respectively). Conclusions-Abnormal

Background Electroencephalogram patterns observed during sedation with dexmedetomidine appear similar to those observed during general anesthesia with propofol. This is evident with the occurrence of slow (0.1–1 Hz), delta (1–4 Hz), propofol-induced alpha (8–12 Hz), and dexmedetomidine-induced spindle (12–16 Hz) oscillations. However, these drugs have different molecular mechanisms and behavioral properties, and are likely accompanied by distinguishing neural circuit dynamics. Methods We measured 64-channel electroencephalogram under dexmedetomidine (n = 9) and propofol (n = 8) in healthy volunteers, 18–36 years of age. We administered dexmedetomidine with a 1mcg/kg loading bolus over 10 minutes, followed by a 0.7mcg/kg/hr infusion. For propofol, we used a computer controlled infusion to target the effect-site concentration gradually from and 0 µg/mL to 5 µg/mL. Volunteers listened to auditory stimuli and responded by button-press to determine unconsciousness. We analyzed the electroencephalogram using multitaper spectral and coherence analysis. Results Dexmedetomidine was characterized by spindles with maximum power and coherence at ~13 Hz, (mean±std; power, −10.8dB±3.6; coherence, 0.8±0.08), while propofol was characterized with frontal alpha oscillations with peak frequency at ~11 Hz (power, 1.1dB±4.5; coherence, 0.9±0.05). Notably, slow oscillation power during a general anesthetic state under propofol (power, 13.2dB±2.4) was much larger than during sedative states under both propofol (power, −2.5dB±3.5) and dexmedetomidine (power, −0.4dB±3.1). Conclusion Our results indicate that dexmedetomidine and propofol place patients into different brain states, and suggests that propofol enables a deeper state of unconsciousness by inducing large amplitude slow oscillations that produce prolonged states of neuronal silence.

ObjectiveTo determine the associations between individual metabolic syndrome (MetS) components and peripheral neuropathy in a large population‐based cohort from Pinggu, China.MethodsA cross‐sectional, randomly selected, population‐based survey of participants from Pinggu, China was performed. Metabolic phenotyping and neuropathy outcomes were performed by trained personnel. Glycemic status was defined according to the American Diabetes Association criteria, and the MetS using modified consensus criteria (body mass index instead of waist circumference). The primary peripheral neuropathy outcome was the Michigan Neuropathy Screening Instrument (MNSI) examination. Secondary outcomes were the MNSI questionnaire and monofilament testing. Multivariable models were used to assess for associations between individual MetS components and peripheral neuropathy. Tree‐based methods were used to construct a classifier for peripheral neuropathy using demographics and MetS components.ResultsThe mean (SD) age of the 4002 participants was 51.6 (11.8) and 51.0% were male; 37.2% of the population had normoglycemia, 44.0% prediabetes, and 18.9% diabetes. The prevalence of peripheral neuropathy increased with worsening glycemic status (3.25% in normoglycemia, 6.29% in prediabetes, and 15.12% in diabetes, P < 0.0001). Diabetes (odds ratio [OR] 2.60, 95% CI 1.77–3.80) and weight (OR 1.09, 95% CI 1.02–1.18) were significantly associated with peripheral neuropathy. Age, diabetes, and weight were the primary splitters in the classification tree for peripheral neuropathy.InterpretationSimilar to previous studies, diabetes and obesity are the main metabolic drivers of peripheral neuropathy. The consistency of these results reinforces the urgent need for effective interventions that target these metabolic factors to prevent and/or treat peripheral neuropathy.

Background Circadian disturbances are commonly seen in people with Alzheimer's disease and have been reported in individuals without symptoms of dementia but with Alzheimer's pathology. We aimed to assess the temporal relationship between circadian disturbances and Alzheimer's progression. MethodsWe did a prospective cohort study of 1401 healthy older adults (aged >59 years) enrolled in the Rush Memory and Aging Project (Rush University Medical Center, Chicago, IL, USA) who had been followed up for up to 15 years. Participants underwent annual assessments of cognition (with a battery of 21 cognitive performance tests) and motor activities (with actigraphy). Four measures were extracted from actigraphy to quantify daily and circadian rhythmicity, which were amplitude of 24-h activity rhythm, acrophase (representing peak activity time), interdaily stability of 24-h activity rhythm, and intradaily variability for hourly fragmentation of activity rhythm. We used Cox proportional hazards models and logistic regressions to assess whether circadian disturbances predict an increased risk of incident Alzheimer's dementia and conversion of mild cognitive impairment to Alzheimer's dementia. We used linear mixed-effects models to investigate how circadian rhythms changed longitudinally and how the change integrated to Alzheimer's progression. FindingsParticipants had a median age of 81•8 (IQR 76•3-85•7) years. Risk of developing Alzheimer's dementia was increased with lower amplitude (1 SD decrease, hazard ratio [HR] 1•39, 95% CI 1•19-1•62) and higher intradaily variability (1 SD increase, 1•22, 1•04-1•43). In participants with mild cognitive impairment, increased risk of Alzheimer's dementia was predicted by lower amplitude (1 SD decrease, HR 1•46, 95% CI 1•24-1•72), higher intradaily variability (1 SD increase, 1•36, 1•15-1•60), and lower interdaily stability (1 SD decrease, 1•21, 1•02-1•44). A faster transition to Alzheimer's dementia in participants with mild cognitive impairment was predicted by lower amplitude (1 SD decrease, odds ratio [OR] 2•08, 95% CI 1•53-2•93), increased intradaily variability (1 SD increase, 1•97, 1•43-2•79), and decreased interdaily stability (1 SD decrease, 1•35, 1•01-1•84). Circadian amplitude, acrophase, and interdaily stability progressively decreased over time, and intradaily variability progressively increased over time. Alzheimer's progression accelerated these aging effects by doubling or more than doubling the annual changes in these measures after the diagnosis of mild cognitive impairment, and further doubled them after the diagnosis of Alzheimer's dementia. The longitudinal change of global cognition positively correlated with the longitudinal changes in amplitude and interdaily stability and negatively correlated with the longitudinal change in intradaily variability. Interpretation Our results indicate a link between circadian dysregulation and Alzheimer's progression, implying either a bidirectional relation or shared common underlying pathophysiological mechanisms.

Artificial intelligence has been advancing in fields including anesthesiology. This scoping review of the intersection of artificial intelligence and anesthesia research identified and summarized six themes of applications of artificial intelligence in anesthesiology: (1) depth of anesthesia monitoring, (2) control of anesthesia, (3) event and risk prediction, (4) ultrasound guidance, (5) pain management, and (6) operating room logistics. Based on papers identified in the review, several topics within artificial intelligence were described and summarized: (1) machine learning (including supervised, unsupervised, and reinforcement learning), (2) techniques in artificial intelligence (e.g., classical machine learning, neural networks and deep learning, Bayesian methods), and (3) major applied fields in artificial intelligence. The implications of artificial intelligence for the practicing anesthesiologist are discussed as are its limitations and the role of clinicians in further developing artificial intelligence for use in clinical care. Artificial intelligence has the potential to impact the practice of anesthesiology in aspects ranging from perioperative support to critical care delivery to outpatient pain management.

These results suggest that α2a adrenergic agonists may be developed as a new class of sleep enhancing medications with neurocognitive sparing benefits.

Background A clear understanding of the neural basis of consciousness is fundamental to research in clinical and basic neuroscience disciplines and anesthesia. Recently, decreased efficiency of information integration was suggested as a core network feature of propofol-induced unconsciousness. However, it is unclear whether this finding can be generalized to dexmedetomidine, which has a different molecular target. Methods Dexmedetomidine was administered as a 1-μg/kg bolus over 10 min, followed by a 0.7-μg · kg−1 · h−1 infusion to healthy human volunteers (age range, 18 to 36 yr; n = 15). Resting-state functional magnetic resonance imaging data were acquired during baseline, dexmedetomidine-induced altered arousal, and recovery states. Zero-lag correlations between resting-state functional magnetic resonance imaging signals extracted from 131 brain parcellations were used to construct weighted brain networks. Network efficiency, degree distribution, and node strength were computed using graph analysis. Parcellated brain regions were also mapped to known resting-state networks to study functional connectivity changes. Results Dexmedetomidine significantly reduced the local and global efficiencies of graph theory–derived networks. Dexmedetomidine also reduced the average brain connectivity strength without impairing the degree distribution. Functional connectivity within and between all resting-state networks was modulated by dexmedetomidine. Conclusions Dexmedetomidine is associated with a significant drop in the capacity for efficient information transmission at both the local and global levels. These changes result from reductions in the strength of connectivity and also manifest as reduced within and between resting-state network connectivity. These findings strengthen the hypothesis that conscious processing relies on an efficient system of information transfer in the brain.

Mobile healthcare increasingly relies on analytical tools that can extract meaningful information from ambulatory physiological recordings. We tested whether a nonlinear tool of fractal physiology could predict long-term health consequences in a large, elderly cohort. Fractal physiology is an emerging field that aims to study how fractal temporal structures in physiological fluctuations generated by complex physiological networks can provide important information about system adaptability. We assessed fractal temporal correlations in the spontaneous fluctuations of ambulatory motor activity of 1275 older participants at baseline, with a follow-up period of up to 13 years. We found that people with reduced temporal correlations (more random activity fluctuations) at baseline had increased risk of frailty, disability, and all-cause death during follow-up. Specifically, for 1-SD decrease in the temporal activity correlations of this studied cohort, the risk of frailty increased by 31%, the risk of disability increased by 15 to 25%, and the risk of death increased by 26%. These incidences occurred on average 4.7 years (frailty), 3 to 4.2 years (disability), and 5.8 years (death) after baseline. These observations were independent of age, sex, education, chronic health conditions, depressive symptoms, cognition, motor function, and total daily activity. The temporal structures in daily motor activity fluctuations may contain unique prognostic information regarding wellness and health in the elderly population.