Evidence-based interventions to improve sleep quality after thoracic surgery:A retrospective analysis of clinical studies.

OBJECTIVE: To investigate and rank the evidence for the efficacy of interventions in improving sleep quality after cardiac surgery using comprehensive comparisons. BACKGROUND: Clinical evidence suggests that over 80 % of adult cardiac surgery patients experience sleep disturbances during the first week postoperatively. While certain interventions have been shown to improve post-thoracic surgery sleep quality, a systematic description of the effects of these varied interventions is lacking. METHODS: This systematic search was conducted across PubMed, Web of Science, Cochrane, Embase, and CINAHL databases to collate all published randomized clinical trials as evidence. Two researchers independently extracted pertinent information from eligible trials and assessed the quality of included studies. Based on statistical heterogeneity, traditional meta-analysis using fixed or random-effects models was employed to assess the efficacy of interventions, and a Frequentist network meta-analysis using a consistency model was conducted to rank the effectiveness of intervention protocols. RESULTS: Our review incorporated 37 articles (n = 3569), encompassing 46 interventions, including 9 reports on pharmacological interventions (24.3 %), 28 on non-pharmacological interventions (75.7 %), and 5 on anesthetic management interventions (13.5 %). The analysis indicated the efficacy of Benson's relaxation technique, Progressive muscle relaxation, Education, Aromatherapy, Acupressure, Massage, and Eye masks in enhancing postoperative sleep quality. Specifically, Benson's relaxation technique (cumulative ranking curve area: 0.80; probability: 98.3 %) and Acupressure (cumulative ranking curve area: 0.96; probability: 58.3 %) were associated with the highest probability of successfully improving postoperative sleep quality, while Progressive muscle relaxation (cumulative ranking curve area: 0.70; probability: 35.2 %) and Eye masks (cumulative ranking curve area: 0.81; probability: 78.8 %) were considered secondary options. Eye masks and Massage significantly reduced postoperative sleep latency, with Eye masks (cumulative ranking curve area: 0.82; probability: 51.0 %) being most likely to enhance sleep quality postoperatively, followed by Massage (cumulative ranking curve area: 0.60; probability: 27.2 %). Education, Music, Massage, Eye masks, and Handholding were effective in alleviating pain intensity, with Education being most likely to successfully reduce postoperative pain (cumulative ranking curve area: 0.92; probability: 54.3 %), followed by Music (cumulative ranking curve area: 0.91; probability: 54 %). CONCLUSIONS: Our findings can be utilized to optimize strategies for managing post-thoracic surgery sleep disturbances and to develop evidence-based approaches for this purpose. Benson's relaxation technique, Progressive muscle relaxation, Education, Aromatherapy, Acupressure, Massage, and Eye masks significantly improve sleep quality in postoperative patients. KEY: disorders of initiating and maintaining sleep, sleep wake disorders, thoracic surgical procedures, cardiac surgical procedures, sleep quality, pain, network meta-analysis.

Development and validation of a deep learning system to screen vision-threatening conditions in high myopia using optical coherence tomography images.

BACKGROUND/AIMS: To apply deep learning technology to develop an artificial intelligence (AI) system that can identify vision-threatening conditions in high myopia patients based on optical coherence tomography (OCT) macular images. METHODS: In this cross-sectional, prospective study, a total of 5505 qualified OCT macular images obtained from 1048 high myopia patients admitted to Zhongshan Ophthalmic Centre (ZOC) from 2012 to 2017 were selected for the development of the AI system. The independent test dataset included 412 images obtained from 91 high myopia patients recruited at ZOC from January 2019 to May 2019. We adopted the InceptionResnetV2 architecture to train four independent convolutional neural network (CNN) models to identify the following four vision-threatening conditions in high myopia: retinoschisis, macular hole, retinal detachment and pathological myopic choroidal neovascularisation. Focal Loss was used to address class imbalance, and optimal operating thresholds were determined according to the Youden Index. RESULTS: In the independent test dataset, the areas under the receiver operating characteristic curves were high for all conditions (0.961 to 0.999). Our AI system achieved sensitivities equal to or even better than those of retina specialists as well as high specificities (greater than 90%). Moreover, our AI system provided a transparent and interpretable diagnosis with heatmaps. CONCLUSIONS: We used OCT macular images for the development of CNN models to identify vision-threatening conditions in high myopia patients. Our models achieved reliable sensitivities and high specificities, comparable to those of retina specialists and may be applied for large-scale high myopia screening and patient follow-up.

Conformational antibody binding to a native, cell-free expressed GPCR in block copolymer membranes.

G-protein coupled receptors (GPCRs) play a key role in physiological processes and are attractive drug targets. Their biophysical characterization is, however, highly challenging because of their innate instability outside a stabilizing membrane and the difficulty of finding a suitable expression system. We here show the cell-free expression of a GPCR, CXCR4, and its direct embedding in diblock copolymer membranes. The polymer-stabilized CXCR4 is readily immobilized onto biosensor chips for label-free binding analysis. Kinetic characterization using a conformationally sensitive antibody shows the receptor to exist in the correctly folded conformation, showing binding behaviour that is commensurate with heterologously expressed CXCR4.