Quality of recovery and safety of deep intravenous sedation compared to general anesthesia in facial plastic surgery: A prospective cohort study.

BACKGROUND: Facial plastic surgical procedures are performed under either general anesthesia (GA) or sedation. GA is often associated with post-operative nausea and longer recovery, while deep sedation is thought to greatly facilitate perioperative patient comfort and expedite recovery. The objective of this study was to compare these two anesthetic techniques in a relatively healthy patient population undergoing facial plastic surgery and to discuss optimizing patient safety with a deep sedation technique. METHODS: A non-randomized, prospective cohort study was conducted to evaluate patients undergoing facial plastic surgery with a focus on rhinoplasty under either deep intravenous sedation (DIVS) in an ambulatory surgery center or under GA in a community hospital. Patients were between ages 18-65 and agreed to participate in the study and complete a quality of recovery (QoR-40) survey. Two-tailed Student's t-test was done for numerical data and Chi-squared test for categorical data. RESULTS: Twenty-three patients and 16 patients had surgery under DIVS and GA, respectively. Compared to the GA group, the DIVS group had less post-operative nausea and vomiting (21.7% vs 50%, P = 0.04; 4.3% vs 37.5%; P = 0.004, respectively), shorter emergence time (4 vs 13 min, P < 0.001), and higher QoR-40 scores for almost all the categories except for physical independence. There were no post-operative medical or surgical complications. CONCLUSION: DIVS appeared to be safe in the office-based setting and provided a higher quality recovery after a predominantly rhinoplasty-based practice compared to the GA group. Vigilant monitoring of the patient is crucial for careful titration of sedation to avoid respiratory depression and possible compromise of the surgical result from having to rescue the airway.

Automatic text classification of drug-induced liver injury using document-term matrix and XGBoost.

Introduction: Regulatory agencies generate a vast amount of textual data in the review process. For example, drug labeling serves as a valuable resource for regulatory agencies, such as U.S. Food and Drug Administration (FDA) and Europe Medical Agency (EMA), to communicate drug safety and effectiveness information to healthcare professionals and patients. Drug labeling also serves as a resource for pharmacovigilance and drug safety research. Automated text classification would significantly improve the analysis of drug labeling documents and conserve reviewer resources. Methods: We utilized artificial intelligence in this study to classify drug-induced liver injury (DILI)-related content from drug labeling documents based on FDA's DILIrank dataset. We employed text mining and XGBoost models and utilized the Preferred Terms of Medical queries for adverse event standards to simplify the elimination of common words and phrases while retaining medical standard terms for FDA and EMA drug label datasets. Then, we constructed a document term matrix using weights computed by Term Frequency-Inverse Document Frequency (TF-IDF) for each included word/term/token. Results: The automatic text classification model exhibited robust performance in predicting DILI, achieving cross-validation AUC scores exceeding 0.90 for both drug labels from FDA and EMA and literature abstracts from the Critical Assessment of Massive Data Analysis (CAMDA). Discussion: Moreover, the text mining and XGBoost functions demonstrated in this study can be applied to other text processing and classification tasks.

Denoising and decoding spontaneous vagus nerve recordings with machine learning.

Neural interfaces that electrically stimulate the peripheral nervous system have been shown to successfully improve symptom management for several conditions, such as epilepsy and depression. A crucial part for closing the loop and improving the efficacy of implantable neuromodulation devices is the efficient extraction of meaningful information from nerve recordings, which can have a low Signal-to-Noise ratio (SNR) and non-stationary noise. In recent years, machine learning (ML) models have shown outstanding performance in regression and classification problems, but it is often unclear how to translate and assess these for novel tasks in biomedical engineering. This paper aims to adapt existing ML algorithms to carry out unsupervised denoising of neural recordings instead. This is achieved by applying bandpass filtering and two novel ML algorithms to in-vivo spontaneous, low-SNR vagus nerve recordings. The performance of each approach is compared using the task of extracting respiratory afferent activity and validated using cross-correlation, MSE, and accuracy in terms of extracting the true respiratory rate. A variational autoencoder (VAE) model in particular produces results that show better correlation with respiratory activity compared to bandpass filtering, highlighting that these models have the potential to preserve relevant features in complex neural recordings.

Anaerobic treatment of a chemical synthesis-based pharmaceutical wastewater in a hybrid upflow anaerobic sludge blanket reactor.

In this study, performance of a lab-scale hybrid up-flow anaerobic sludge blanket (UASB) reactor, treating a chemical synthesis-based pharmaceutical wastewater, was evaluated under different operating conditions. This study consisted of two experimental stages: first, acclimation to the pharmaceutical wastewater and second, determination of maximum loading capacity of the hybrid UASB reactor. Initially, the carbon source in the reactor feed came entirely from glucose, applied at an organic loading rate (OLR) 1 kg COD/m(3) d. The OLR was gradually step increased to 3 kg COD/m(3) d at which point the feed to the hybrid UASB reactor was progressively modified by introducing the pharmaceutical wastewater in blends with glucose, so that the wastewater contributed approximately 10%, 30%, 70%, and ultimately, 100% of the carbon (COD) to be treated. At the acclimation OLR of 3 kg COD/m(3) d the hydraulic retention time (HRT) was 2 days. During this period of feed modification, the COD removal efficiencies of the anaerobic reactor were 99%, 96%, 91% and 85%, and specific methanogenic activities (SMA) were measured as 240, 230, 205 and 231 ml CH(4)/g TVS d, respectively. Following the acclimation period, the hybrid UASB reactor was fed with 100% (w/v) pharmaceutical wastewater up to an OLR of 9 kg COD/m(3) d in order to determine the maximum loading capacity achievable before reactor failure. At this OLR, the COD removal efficiency was 28%, and the SMA was measured as 170 ml CH(4)/g TVS d. The hybrid UASB reactor was found to be far more effective at an OLR of 8 kg COD/m(3) d with a COD removal efficiency of 72%. At this point, SMA value was 200 ml CH(4)/g TVS d. It was concluded that the hybrid UASB reactor could be a suitable alternative for the treatment of chemical synthesis-based pharmaceutical wastewater.

Effect of full and partial-bed configuration on carbon removal performance of biological aerated filters.

A comparative study to explore the characteristics of partially and fully packed biological aerated filters (BAFs) in the removal of carbon pollutant, reveals that the partial-bed reactor can perform comparably well with the full-bed reactor. The organic removal rate was 5.34 kg COD m(-3) d(-1) at Organic Loading Rates (OLR) 5.80+/-0.31 kg COD m(-3) d(-1) for the full-bed, and 5.22 kg COD m(-3) d(-1) at OLR 5.79+/-0.29 kg COD m(-3) d(-1) for the partial-bed. In the partial-bed system, where the masses of biomass were only 41-51% of those of the full-bed, the maximum carbon removal limit was still between 5 to 6 kg COD m(-3) d(-1). At organic loadings above 5.0 kg COD m(-3) d(-1), the carbon removal capacity in both systems was limited by the mass and activity of microorganisms. The SRT in the full and partial-bed reactors was primarily controlled by the biomass loss in the effluent and during backwash operation. The SRT was reduced from 20.08 days at OLR 4.18+/-0.20 kg COD m(-3) d(-1) to 7.62 days at OLR 5.80+/-0.31 kg COD m(-3) d(-1) in the full-bed, and from 7.17 days to 4.21 days in the partial-bed. After all, SRT values in the partial-bed were always lower than those in the full-bed.

Patient counseling materials: The effect of patient health literacy on the comprehension of printed prescription drug information.

BACKGROUND: Counseling patients with written materials relies equally on patients' health literacy to understand their disease and its treatment, and the written materials' effectiveness communicating clearly in accessible and actionable ways. Only about 12% of the US population is adequately health literate. OBJECTIVES: To explore the impact of reducing the health literacy demands of written patient health information. METHODS: 805 patients were screened for health literacy, and recruited for balanced cohorts of adequate and low literacy, and high and normal blood pressure. Half of each patient cohort received either standard or "health literacy-friendly" drug summaries (i.e. Patient Package Inserts, or PPIs or "leaflets") along with a standardized health literacy assessment scale. RESULTS: The literacy-friendly drug summary improved comprehension of drug-related information overall from 50% to 71% correct responses. Adequate literacy patients improved from 58% correct to 90%, while lower literacy patients improved from 42% to 52% correct in response to the health literacy-friendly PPIs. CONCLUSIONS: Health literacy demands require special attention in developing and using written drug summary materials. Additionally, pharmacists should be provided additional information and counseling support materials to facilitate communications with low health literacy level patients.

Fate of estrogens during the biological treatment of synthetic wastewater in a nitrite-accumulating sequencing batch reactor.

The fate of estrone (El), 17beta-estradiol (E2), and 17alpha-ethynylestradiol (EE2) was investigated in two nitrite-accumulating sequencing batch reactors operating under strictly aerobic (SBR1) conditions at different sludge ages (SRT, 1.7 to 17.1 d) and anoxic/anaerobic/aerobic (SBR2) conditions with different phases and durations of redox conditions, using a modified GC-MS analytical method for estrogen detection to ng/L concentrations. In SBR1, > or =98% of E2 was removed (specific E2 removal rate ranged from 0.375 (at SRT 17.1 d) to 2.625 (at SRT 1.7 d) microg E2 x g MLVSS(-1) x d(-1)) regardless of SRT or DO (<1.0 to >5.0 mg/L). Removal of E1 and, to a greater extent, EE2 was adversely affected when this reactor was operated at SRT shorterthan 5.7 d. However, whereas E1 was removed efficiently as long as SRT was long enough for AOB to bring about nitritation, EE2 removal efficiency was significantly lower when SBR1 was operated at SRT longer than 7.5 d. This reduced removal of EE2 may have been caused by the inhibition and toxicity of nitrite, both to the ammonium monooxygenase (AMO) and to the microbial population generally. In SBR2, less removal of E2 was found at the lower MLVSS concentrations, and E1 was not removed by sludge with poor settling qualities. The removal of EE2 observed in SBR2 was considered to be mainly a result of sorption. However, the binding of estrogens to the sludge in this reactor was apparently not as strong as the binding observed in the sludge of the strictly aerobic SBR1, since desorption was observed during the aeration phase in SBR2.