Rejected

Artificial intelligence provides the opportunity to reveal important information buried in large amounts of complex data. Electronic health records (eHRs) are a source of such big data that provide a multitude of health related clinical information about patients. However, text data from eHRs, e.g., discharge summary notes, are challenging in their analysis because these notes are free-form texts and the writing formats and styles vary considerably between different records. For this reason, in this paper we study deep learning neural networks in combination with natural language processing to analyze text data from clinical discharge summaries. We provide a detail analysis of patient phenotyping, i.e., the automatic prediction of ten patient disorders, by investigating the influence of network architectures, sample sizes and information content of tokens. Importantly, for patients suffering from Chronic Pain, the disorder that is the most difficult one to classify, we find the largest performance gain for a combined word- and sentence-level input convolutional neural network (ws-CNN). As a general result, we find that the combination of data quality and data quantity of the text data is playing a crucial role for using more complex network architectures that improve significantly beyond a word-level input CNN model. From our investigations of learning curves and token selection mechanisms, we conclude that for such a transition one requires larger sample sizes because the amount of information per sample is quite small and only carried by few tokens and token categories. Interestingly, we found that the token frequency in the eHRs follow a Zipf law and we utilized this behavior to investigate the information content of tokens by defining a token selection mechanism. The latter addresses also issues of explainable AI.

Unfortunately, the author names in the author group section were incorrectly captured in the published online paper.

Multimodal pain management for surgery patients may include the use of a combination of scheduled oral pain medications with as-needed (PRN) oral opioids. Multiple concurrent time demands on nursing staff frequently cause delays in the delivery of oral PRN pain medication compromising pain management.

Currently, the role of transient receptor potential vanilloid type 4 (TRPV4), a nonselective cation channel in the pathology of spinal cord injury (SCI) is not recognized. Herein, we report the expression and contribution of TRPV4 in the pathology of scarring, endothelial and secondary damage after SCI. TRPV4 expression increased during the inflammatory phase in female rats after SCI and was expressed primarily by cells at endothelial-microglial junctions. Two-photon microscopy of intracellular free Ca levels revealed a biphasic increase at similar time points after SCI. Expression of TRPV4 at the injury epicenter, but not intracellular free Ca, progressively increases with the severity of the injury. Activation of TRPV4 with specific agonist altered the organization of endothelial cells, affected tight junctions in the hCMEC/D3 BBB cell line , and increases the scarring in rat spinal cord as well as induced endothelial damage. By contrast, suppression of TRPV4 with a specific antagonist or in female knockout mouse attenuated inflammatory cytokines and chemokines, prevented the degradation of tight junction proteins, and preserve blood-spinal cord barrier integrity thereby attenuate the scarring after SCI. Likewise, secondary damage was reduced, and behavioral outcomes were improved in knockout mice after SCI. These results suggest that increased TRPV4 expression disrupts endothelial cell organization during the early inflammatory phase of SCI, resulting in tissue damage, vascular destabilization, blood-spinal cord barrier breakdown, and scarring. Thus, TRPV4 inhibition/knockdown represents a promising therapeutic strategy to stabilize/protect endothelial cells, attenuate nociception and secondary damage, and reduce scarring after SCI.

The study compare two tests for evaluating the driving abilities of patients undergoing opioid therapy for chronic pain: the Vienna Test System (VTS), a software developed for this purpose, and a new free APP for smartphones (SafeDrive) measuring visual and auditory reaction times.

The epidemic of opioid crisis started getting recognised as a public health emergency in view of increasing opioid-related deaths occurring due to undetected respiratory depression. Prescribing opioids at discharge has become an independent risk factor for chronic opioid use, following which, prescription practices have undergone a radical change. A call to action has been voiced recently to end the opioid epidemic although with the pain practitioners still struggling to make opioids readily available. American Society of Anesthesiologist (ASA) has called for reducing patient exposure to opioids in the surgical setting. Opioid sparing strategies have emerged embracing loco-regional techniques and non-opioid based multimodal pain management whereas opioid free anesthesia is the combination of various opioid sparing strategies culminating in complete elimination of opioid usage. The movement away from opioid usage perioperatively is a massive but necessary shift in anesthesia which has rationalised perioperative opioid usage. Ideal way moving forward would be to adapt selective low opioid effective dosing which is both procedure and patient specific while reserving it as rescue analgesia, postoperatively. Many unknowns persist in the domain of immunologic effects of opioids, as complex interplay of factors gets associated during real time surgery towards outcome. At present it would be too premature to conclude upon opioid-induced immunosuppression from the existing evidence. Till evidence is established, there are no recommendations to change current clinical practice. At the same time, consideration for multimodal opioid sparing strategies should be initiated in each patient undergoing surgery.

Keratoconjunctivitis sicca ("dry eye") is a common (14%-30% of adults over age 48) though difficult to treat condition that causes both discomfort and disability with associated dryness, pain, and visual disturbances. Etiology is not clearly understood but is likely varied, with a subset of patients suffering from chronic neuropathic pain referred to as "burning eye syndrome." This review of existing literature summarizes the clinical presentation, natural history, pathophysiology, and treatment modalities of burning eye syndrome. Chronicity of burning eye syndrome is likely secondary to increased nociception from the cornea, decrease in inhibitory signals, and nerve growth factor expression alterations. Treatment centers around symptomatic alleviation and reduction of inflammation. Conservative treatments focus on well-being and perception and include exercise, acupuncture, and cognitive behavioral therapy. Topical treatment consists of the anti-adhesion T-cell antagonist lifitegrast, corticosteroids, and cyclosporine; all have moderate efficacy and good safety. Autologous serum eye drops are a second-line topical that may promote corneal and neural healing on top of symptomatic relief. When these treatments fail, patients may trial neuromodulation with transcranial magnetic stimulation. Despite general treatment safety, more research is needed to develop novel approaches to this condition, possibly focusing more directly on the neurological component.