Predicting herb-disease associations using network-based measures in human protein interactome.

BACKGROUND: Natural herbs are frequently used to treat diseases or to relieve symptoms in many countries. Moreover, as their safety has been proven for a long time, they are considered as main sources of new drug development. However, in many cases, the herbs are still prescribed relying on ancient records and/or traditional practices without scientific evidences. More importantly, the medicinal efficacy of the herbs has to be evaluated in the perspective of MCMT (multi-compound multi-target) effects, but most efforts focus on identifying and analyzing a single compound experimentally. To overcome these hurdles, computational approaches which are based on the scientific evidences and are able to handle the MCMT effects are needed to predict the herb-disease associations. RESULTS: In this study, we proposed a network-based in silico method to predict the herb-disease associations. To this end, we devised a new network-based measure, WACP (weighted average closest path length), which not only quantifies proximity between herb-related genes and disease-related genes but also considers compound compositions of each herb. As a result, we confirmed that our method successfully predicts the herb-disease associations in the human protein interactome (AUROC = 0.777). In addition, we observed that our method is superior than the other simple network-based proximity measures (e.g. average shortest and closest path length). Additionally, we analyzed the associations between Brassica oleracea var. italica and its known associated diseases more specifically as case studies. Finally, based on the prediction results of the WACP, we suggested novel herb-disease pairs which are expected to have potential relations and their literature evidences. CONCLUSIONS: This method could be a promising solution to modernize the use of the natural herbs by providing the scientific evidences about the molecular associations between the herb-related genes targeted by multiple compounds and the disease-related genes in the human protein interactome.

A case of disorder of arousal with prolonged postarousal hypersynchronous delta activity.

Disorder of arousal (DOA) is a form of non-rapid eye movement sleep parasomnia caused by partial or incomplete arousal from deep sleep. Most previous studies of patients with DOA analyzed prearousal hypersynchronous delta activity (HSDA), but few studies have described postarousal HSDA. Herein, we report a 23-year-old man with a history of abrupt arousal during sleep and confused behavior and speech since he was 14 years old. During video electroencephalography monitoring, he had 9 arousal events of getting up, sitting on the bed, looking around, or simple arousal, including eyes open, looking at the ceiling, or head flexion. During all arousal events, the postarousal electroencephalography pattern was prolonged HSDA for approximately 40 seconds. The patient was treated unsuccessfully for more than 2 years with an antiseizure medication (lacosamide); eventually, he responded to clonazepam that was administered for the possibility of DOA. Prolonged rhythmic HSDA without spatiotemporal evolution can appear as a postarousal electroencephalography pattern of DOA. When diagnosing DOA, it is important to recognize that postarousal HSDA can appear as a characteristic electroencephalography pattern of DOA. CITATION: Kang M, Shin D, Lee HC, Provini F, Jung KY. A case of disorder of arousal with prolonged postarousal hypersynchronous delta activity. J Clin Sleep Med. 2023;19(7):1365-1368.

IJA: an efficient algorithm for query processing in sensor networks.

One of main features in sensor networks is the function that processes real time state information after gathering needed data from many domains. The component technologies consisting of each node called a sensor node that are including physical sensors, processors, actuators and power have advanced significantly over the last decade. Thanks to the advanced technology, over time sensor networks have been adopted in an all-round industry sensing physical phenomenon. However, sensor nodes in sensor networks are considerably constrained because with their energy and memory resources they have a very limited ability to process any information compared to conventional computer systems. Thus query processing over the nodes should be constrained because of their limitations. Due to the problems, the join operations in sensor networks are typically processed in a distributed manner over a set of nodes and have been studied. By way of example while simple queries, such as select and aggregate queries, in sensor networks have been addressed in the literature, the processing of join queries in sensor networks remains to be investigated. Therefore, in this paper, we propose and describe an Incremental Join Algorithm (IJA) in Sensor Networks to reduce the overhead caused by moving a join pair to the final join node or to minimize the communication cost that is the main consumer of the battery when processing the distributed queries in sensor networks environments. At the same time, the simulation result shows that the proposed IJA algorithm significantly reduces the number of bytes to be moved to join nodes compared to the popular synopsis join algorithm.

New bone formation in unilateral rhinosinusitis.

BACKGROUND: Computed tomography (CT) frequently shows abnormal bone thickening in patients with chronic rhinosinusitis. The sinus bone may be not in a static state, and remodeling occurs in response to chronic inflammation. METHODS: Ostiomeatal unit CT scans were reviewed in 29 patients with unilateral rhinosinusitis (URS) undergoing endoscopic sinus surgery. We defined new bone formation (NBF) as a remarkable bone thickening or hyperostosis of the intrinsic sinus walls in comparison with the normal side. Bony CT scores of sinus walls were expressed by the Hounsfield unit (HU) and soft tissue CT scores were measured by the Lund-Mackay system. RESULTS: Almost all of the NBF was located at the maxillary and anterior ethmoid sinuses (83.7%), and it was significantly increased in patients with higher Lund-Mackay scores (p = 0.043). The HU values were significantly different between NBF and non-NBF contralateral sides (p < 0.05). CONCLUSION: The HU may be helpful to diagnose and quantify the bone remodeling in URS.