A One-Size-Fits-All Approach to Pressure Ulcers: Whole-Buttock Fasciocutaneous Advancement Flap.

BACKGROUND: Buttock pressure injuries can be difficult to treat. There are many choices of flaps to reconstruct these wounds, but few are large, technically simple, and easily recycled. AIM AND OBJECTIVE: We are presenting our experience on surgical reconstruction of buttock pressure injuries using large whole-buttock fasciocutaneous flaps that are easily designed for ulcers regardless of location and size and are easily recycled for treatment of recurrences. MATERIAL AND METHODS: We conducted a retrospective review of all patients who received reconstruction with fasciocutaneous rotational flaps for buttock region pressure injuries from January 2013 to December 2018. The key steps of this one-size-fits-all flap include elevation of a large, oversized flap to achieve tension-free closure, avoiding fascial incisions over bony prominences, placing the V-Y type closure wound in the posteromedial thigh, and the use of closed incisional negative wound therapy postoperatively. RESULTS: Fifty patients underwent 54 flaps reconstruction for coverage of stage 4 gluteal pressure injuries between January 2013 and December 2018. Seventy-four percent healed without the need for further operation. The average size of the defect was 90 cm2 (maximum = 300 cm2). The average follow-up period was 31 months. Four of the 54 flaps were "recycled" flaps, 3 were performed for the coverage of recurrent ulcers and 1 flap was performed for treatment of a postoperative wound dehiscence. CONCLUSIONS: We recommend this simple, one-size-fits-all approach, whole-buttock fasciocutaneous flap when surgically treating gluteal pressure injuries for selected patients.

A substructure-based screening approach to uncover N-nitrosamines in drug substances.

Drug substances are at risk of contamination with N-nitrosamines (NAs), well-known carcinogenic agents, during synthesis processes and/or long-term storage. Therefore, in this study, we developed an efficient data-based screening approach to systemically assess marketed products and investigated its scalability for benefiting both regulatory agencies and pharmaceutical industries. A substructure-based screening method employing DataWarrior, an open-source software, was established to evaluate the risks of NA impurities in drug substances. Eight NA substructures containing susceptible amino sources for N-nitrosation have been identified as screening targets: dimethylamine (DMA), diethylamine, isopropylethylamine, diisopropylamine, N-methyl-2-pyrrolidone, dibutylamine, methylphenylamine, and tetrazoles. Our method detected 192 drug substances with a theoretical possibility of NA impurity, 141 of which had not been reported previously. In addition, the DMA moiety was significantly dominant among the eight NA substructures. The results were validated using data from the literature, and a high detection sensitivity of 0.944 was demonstrated. Furthermore, our approach has the advantage of scalability, owing to which 31 additional drugs with suspected NA-contaminated substructures were identified using the substructures of 1-methyl-4-piperazine in rifampin and 1-cyclopentyl-4-piperazine in rifapentine. In conclusion, the reported substructure-based approach provides an effective and scalable method for the screening and investigation of NA impurities in various pharmaceuticals and might be used as an ancillary technique in the field of pharmaceutical quality control for risk assessments of potential NA impurities.

A proactive remote pharmaceutical care for rural elderly population: The Houston-Apollo polypharmacy project.

AIMS: To evaluate polypharmacy-related problems in the elderly people who live in rural through a proactive pharmaceutical care project under a novel remote medical service infrastructure (the Houston-Apollo polypharmacy project). METHODS: It is a prospectively cross-sectional study. The elderly aged 65 years old lived in communities executed the congregate meal service and joined the Houston-Apollo project were included. During March and July on 2020, the pharmaceutical care team of Houston-Apollo polypharmacy project interviewed old people and collected their medications by remote video. Polypharmacy situation and drug-related problems, including potentially inappropriate medications (PIMs), anticholinergic burden (ACB) and risk of sarcopaenia, were evaluated by clinical pharmacists. In addition, we analysed the categories of the prescription types between polypharmacy and non-polypharmacy users, polypharmacy users with and without PIMs or ACB. A patient-specific integrated pharmacist's note for medication education and a dear doctor letter (as needed) were generated and delivered within 2-weeks postinterviewed. Age- and sex-adjusted logistic regression model was used to evaluate the association between polypharmacy and these potential medication problems. RESULTS: There were 87 older people (mean age = 75.9) and 536 long-term medications were collected. Among them, 52% were defined as polypharmacy users. Polypharmacy was significantly associated with higher risk of PIMs and ACB. The adjusted odd ratio was 5.31 (95% CI: 2.02-13.9) and 10.1 (95% CI: 3.4-29.7), respectively. Among polypharmacy users, there were nearly double the prescriptions for the nervous system and musculoskeletal system among patients with PIMs compared with those without PIMs. Besides, polypharmacy users with ACB showed higher rate of prescriptions for the nervous system and the alimentary tract and metabolism system compared with those without ACB. CONCLUSION: Polypharmacy was significantly associated with negative impact of medication safety among the elderly people in rural area. A persistent remote pharmaceutical care intervention was crucial for improving this problem.

Backpropagation With N -D Vector-Valued Neurons Using Arbitrary Bilinear Products.

Vector-valued neural learning has emerged as a promising direction in deep learning recently. Traditionally, training data for neural networks (NNs) are formulated as a vector of scalars; however, its performance may not be optimal since associations among adjacent scalars are not modeled. In this article, we propose a new vector neural architecture called the Arbitrary BIlinear Product NN (ABIPNN), which processes information as vectors in each neuron, and the feedforward projections are defined using arbitrary bilinear products. Such bilinear products can include circular convolution, 7-D vector product, skew circular convolution, reversed-time circular convolution, or other new products that are not seen in the previous work. As a proof-of-concept, we apply our proposed network to multispectral image denoising and singing voice separation. Experimental results show that ABIPNN obtains substantial improvements when compared to conventional NNs, suggesting that associations are learned during training.

Improving Cross-Day EEG-Based Emotion Classification Using Robust Principal Component Analysis.

Constructing a robust emotion-aware analytical framework using non-invasively recorded electroencephalogram (EEG) signals has gained intensive attentions nowadays. However, as deploying a laboratory-oriented proof-of-concept study toward real-world applications, researchers are now facing an ecological challenge that the EEG patterns recorded in real life substantially change across days (i.e., day-to-day variability), arguably making the pre-defined predictive model vulnerable to the given EEG signals of a separate day. The present work addressed how to mitigate the inter-day EEG variability of emotional responses with an attempt to facilitate cross-day emotion classification, which was less concerned in the literature. This study proposed a robust principal component analysis (RPCA)-based signal filtering strategy and validated its neurophysiological validity and machine-learning practicability on a binary emotion classification task (happiness vs. sadness) using a five-day EEG dataset of 12 subjects when participated in a music-listening task. The empirical results showed that the RPCA-decomposed sparse signals (RPCA-S) enabled filtering off the background EEG activity that contributed more to the inter-day variability, and predominately captured the EEG oscillations of emotional responses that behaved relatively consistent along days. Through applying a realistic add-day-in classification validation scheme, the RPCA-S progressively exploited more informative features (from 12.67 ± 5.99 to 20.83 ± 7.18) and improved the cross-day binary emotion-classification accuracy (from 58.31 ± 12.33% to 64.03 ± 8.40%) as trained the EEG signals from one to four recording days and tested against one unseen subsequent day. The original EEG features (prior to RPCA processing) neither achieved the cross-day classification (the accuracy was around chance level) nor replicated the encouraging improvement due to the inter-day EEG variability. This result demonstrated the effectiveness of the proposed method and may shed some light on developing a realistic emotion-classification analytical framework alleviating day-to-day variability.

Developing a benchmark for emotional analysis of music.

Music emotion recognition (MER) field rapidly expanded in the last decade. Many new methods and new audio features are developed to improve the performance of MER algorithms. However, it is very difficult to compare the performance of the new methods because of the data representation diversity and scarcity of publicly available data. In this paper, we address these problems by creating a data set and a benchmark for MER. The data set that we release, a MediaEval Database for Emotional Analysis in Music (DEAM), is the largest available data set of dynamic annotations (valence and arousal annotations for 1,802 songs and song excerpts licensed under Creative Commons with 2Hz time resolution). Using DEAM, we organized the 'Emotion in Music' task at MediaEval Multimedia Evaluation Campaign from 2013 to 2015. The benchmark attracted, in total, 21 active teams to participate in the challenge. We analyze the results of the benchmark: the winning algorithms and feature-sets. We also describe the design of the benchmark, the evaluation procedures and the data cleaning and transformations that we suggest. The results from the benchmark suggest that the recurrent neural network based approaches combined with large feature-sets work best for dynamic MER.

Fusion of electroencephalographic dynamics and musical contents for estimating emotional responses in music listening.

Electroencephalography (EEG)-based emotion classification during music listening has gained increasing attention nowadays due to its promise of potential applications such as musical affective brain-computer interface (ABCI), neuromarketing, music therapy, and implicit multimedia tagging and triggering. However, music is an ecologically valid and complex stimulus that conveys certain emotions to listeners through compositions of musical elements. Using solely EEG signals to distinguish emotions remained challenging. This study aimed to assess the applicability of a multimodal approach by leveraging the EEG dynamics and acoustic characteristics of musical contents for the classification of emotional valence and arousal. To this end, this study adopted machine-learning methods to systematically elucidate the roles of the EEG and music modalities in the emotion modeling. The empirical results suggested that when whole-head EEG signals were available, the inclusion of musical contents did not improve the classification performance. The obtained performance of 74~76% using solely EEG modality was statistically comparable to that using the multimodality approach. However, if EEG dynamics were only available from a small set of electrodes (likely the case in real-life applications), the music modality would play a complementary role and augment the EEG results from around 61-67% in valence classification and from around 58-67% in arousal classification. The musical timber appeared to replace less-discriminative EEG features and led to improvements in both valence and arousal classification, whereas musical loudness was contributed specifically to the arousal classification. The present study not only provided principles for constructing an EEG-based multimodal approach, but also revealed the fundamental insights into the interplay of the brain activity and musical contents in emotion modeling.

Demethoxycurcumin, a major active curcuminoid from Curcuma longa, suppresses balloon injury induced vascular smooth muscle cell migration and neointima formation: an in vitro and in vivo study.

SCOPE: Curcumin has been shown to affect platelet-derived growth factor (PDGF)- and tumor necrosis factor (TNF)-α-elicited vascular smooth muscle cell (VSMC) migration and inhibit neointima formation following vascular injury. However, whether two other curcuminoids isolated from Curcuma longa, demethoxycurcumin (DMC) and bisdemethoxycurcumin (BDMC), also demonstrate antimigratory activity in VSMCs similar to that of curcumin remain uncharacterized. METHODS AND RESULTS: Based on 3-(4,5-dimethylthiazolyl-2)-2,5-diphenyltetrazolium bromide and proliferating cell nuclear antigen immunostaining analyses as well as changes in intima/media ratios, we show that DMC exhibits more potent effects than the other curcuminoids. We aimed to evaluate the effects and characterize the molecular mechanisms of DMC on VSMC migration and neointima formation in a carotid injury model. DMC decreased the expression of matrix metalloproteinase 2/9 and inhibited VSMC migration as demonstrated by in vitro scratch wound and transwell assays. Furthermore, DMC may inhibit the migration of VSMCs by reducing the expression of matrix metalloproteinase 2/9 via downregulation of the focal adhesion kinase/phosphatidylinositol 3-kinase (PI3K)/AKT (protein kinase B) and phosphoglycerate kinase 1/extracellular signal regulated kinase 1/2 signaling pathways. Using a rat carotid arterial injury model, we show that DMC treatment was more potent than treatment with the other curcuminoids with respect to reducing intima/media ratios and the number of proliferating cells. CONCLUSION: DMC should be considered for therapeutic use in preventing VSMC migration and attenuating restenosis following balloon-mediated vascular injury.

Effects of altitude in high-rise building on the autonomic nervous modulation in healthy subjects.

This study intended to study the effects of altitude in the high-rise building on the automatic nervous modulation in healthy subjects. Heart rate variability (HRV) analysis was performed to assess the automatic nervous modulation of the subjects at three different altitudes in the air-conditioned high-rise building, i.e., the first basement (4 m beneath sea level), the 31st floor (133 m above sea level), and the 46 th floor (200 m above sea level). We found that the heart rate was significantly decreased, whereas the standard deviation of RR intervals (SD(RR)), total power and high frequency power were significantly increased when the subject was elevated to a higher altitude. The normalized low frequency power and low-/high-frequency power ratio on the 31st and 46 th floors were significantly different between genders; however, no such difference was found on the first basement. The age correlated significantly and positively with the percentage change in the SD(RR) and coefficient of variation of RR intervals when the subjects were elevated from the first basement to the 46 th floor. In conclusion, higher altitude in an air-conditioned high-rise building can lead to an increase in HRV/vagal modulation. The stay at a higher altitude in a high-rise building may lead to increased overall HRV and vagal modulation of a subject, especially for the elder people and the people who had a small HRV at ground level.