Lidocaine Spray for Acute Postsurgical Pain Control After Posterior Pharyngeal Flap Surgery.

OBJECTIVES: This study evaluated the use of lidocaine spray for acute postsurgical pain control after posterior pharyngeal flap surgery. METHODS: Fifty patients aged 4 to 14 years who were scheduled to undergo elective posterior pharyngeal flap surgery were randomized to receive 2.4% lidocaine spray (Group L) or an identical volume of placebo spray (Group C) on the surgical field at the end of the surgery. The primary outcome was the maximum postoperative pain score in the postanesthesia care unit. RESULTS: The maximum pain score in Group L was significantly lower than that in Group C (p = 0.001). The incidence of moderate-to-severe pain in the postanesthesia care unit was significantly lower in Group L than that in Group C (p < 0.001). In the postanesthesia care unit, more patients in Group C were prescribed rescue analgesics (p < 0.001). The time to the first rescue analgesic was also significantly shorter in Group L (p < 0.001). The incidence and maximum score of emergence agitation were lower in Group L than in Group C. Compared with Group C, Group L showed earlier postoperative fluid intake (p = 0.001). Moreover, the score for parental satisfaction with pain control was higher in Group L than in Group C (p < 0.001). CONCLUSIONS: Our findings indicated that the use of 2.4% lidocaine aerosol spray on the surgical site at the end of the surgery could produce good analgesia for acute postoperative pain, reduce the incidence and severity of EA, and shorten the time to restore fluid intake. LEVEL OF EVIDENCE: 2 Laryngoscope, 134:2438-2443, 2024.

Determination of the ED90 of Dexmedetomidine Infusion to Prevent Emergence Agitation in Children Undergoing Dental Rehabilitation With Sevoflurane Anesthesia: A Biased-Coin Up-and-Down Sequential Allocation Trial.

BACKGROUND: Emergence agitation (EA) is an adverse complication during early recovery from sevoflurane anesthesia. Continuous intravenous infusion of dexmedetomidine (DEX) is commonly used for EA prevention. However, a wide dose range is used for preventing EA, and the optimal dose remains unknown. This study was aimed at determining the optimal dose (the 90% effective dose [ED90]) of DEX for continuous intraoperative infusion for EA prevention in children. METHODS: We enrolled children aged 3 to 7 years who underwent dental treatment under sevoflurane anesthesia. DEX was continuously infused from the time of the establishment of the intravenous access until 5 minutes before the end of surgery. The initial DEX dose was 0.5 µg/kg/h, and subsequent dose adjustments were determined based on the response of the previous patient by using an up-down sequential allocation with a biased-coin design. The primary outcome was the ED90 for continuous DEX infusion based on the success or failure of the EA-preventing dose. RESULTS: Forty-five patients were enrolled in the study. The DEX dose ranged from 0.50 to 0.90 µg/kg/h. The estimated ED90 (95% confidence interval [CI]) for preventing EA was 0.74 µg/kg/h (0.67-1.05 µg/kg/h). The duration of surgery (mean ± standard deviation [SD]) was 113 ± 30 minutes. The times (mean ± SD) for extubation, time to emergence, and recovery time were 5 ± 2 minutes, 27 ± 9 minutes, and 39 ± 7 minutes, respectively. CONCLUSIONS: The ED90 for continuous intraoperative DEX infusion for EA prevention in pediatric patients receiving dental treatment under sevoflurane anesthesia was 0.74 µg/kg/h (95% CI, 0.67-1.05 µg/kg/h).

Impact of choice of nostril on nasotracheal intubation when using video rigid stylet: a randomized clinical trial.

BACKGROUND: Patients undergoing oral and maxillofacial surgeries under general anesthesia usually require nasotracheal intubation. When presented with patients with equally patent nostrils, selection of the nostril to use for intubation is an important decision for facilitating intubation. The objective of this trial is to determine whether choice of nostril impacts nasotracheal intubation when using a video rigid stylet in patients undergoing oral and maxillofacial surgery. METHODS: Fifty patients scheduled for elective oral and maxillofacial surgery requiring nasotracheal intubation were randomly allocated into two groups to undergo nasotracheal intubation through the left nostril (Group L, n = 25) or the right nostril (Group R, n = 25). Intubation was performed by experienced anesthesiologists using a video rigid stylet. The primary endpoint was time to successful intubation, which was defined as the duration from when the tip of the stylet-tube assembly entered the selected nostril to when the tube entered the trachea. Secondary outcomes included: length of time for device insertion; length of time for tube insertion; total success rate; first-attempt success rate; number of intubation attempts; requirement of airway assisted maneuvers; incidence and severity of epistaxis. Intubation-related adverse events were monitored for up to postoperative 24 h. RESULTS: Median time (interquartile range) to tracheal intubation was 25.3 seconds (20.7 to 27.6) in Group L and 26.8 seconds (22.5 to 30.0) in Group R (median difference (MD) = 1.9; 95% confidence interval (CI) -1.8 to 5.7, P = 0.248). Nasotracheal intubation was successful in all patients in both groups and the first-attempt success rates in both groups were similar (Group L: 96% (24/25); Group R: 96% (24/25); relative risk (RR) 1.0; 95% CI 0.9 to 1.1; P > 0.999). No significant difference of requirement of assisted maneuvers was noted between the two groups (Group L: 36% (9/25); Group R: 28% (7/25); RR 0.8; 95% CI 0.3-1.8; P = 0.544). Furthermore, all patients showed a high quality of visualization of the glottis (Cormack and Lehane Grade I). For safety outcomes, the incidence and severity of epistaxis during intubation was comparable between the two groups. There were no significant differences between the selection of nostrils and intubation-related adverse events up to 24 h after surgery. CONCLUSIONS: When considering which nostril to use for intubation with video rigid stylet, either nostril can be used similarly. TRIAL REGISTRATION: Clinicaltrials.gov . Identifier: NCT05218590.

Machine learning to predict metabolic drug interactions related to cytochrome P450 isozymes.

Drug-drug interaction (DDI) often causes serious adverse reactions and thus results in inestimable economic and social loss. Currently, comprehensive DDI evaluation has become a major challenge in pharmaceutical research due to the time-consuming and costly process of the experimental assessment and it is of high necessity to develop effective in silico methods to predict and evaluate DDIs accurately and efficiently. In this study, based on a large number of substrates and inhibitors related to five important CYP450 isozymes (CYP1A2, CYP2C9, CYP2C19, CYP2D6 and CYP3A4), a series of high-performance predictive models for metabolic DDIs were constructed by two machine learning methods (random forest and XGBoost) and 4 different types of descriptors (MOE_2D, CATS, ECFP4 and MACCS). To reduce the uncertainty of individual models, the consensus method was applied to yield more reliable predictions. A series of evaluations illustrated that the consensus models were more reliable and robust for the DDI predictions of new drug combination. For the internal validation, the whole prediction accuracy and AUC value of the DDI models were around 0.8 and 0.9, respectively. When it was applied to the external datasets, the model accuracy was 0.793 and 0.795 for multi-level validation and external validation, respectively. Furthermore, we also compared our model with some recently published tools and then applied the final model to predict FDA-approved drugs and proposed 54,013 possible drug pairs with potential DDIs. In summary, we developed a powerful DDI predictive model from the perspective of the CYP450 enzyme family and it will help a lot in the future drug development and clinical pharmacy research.

A novel multi-layer prediction approach for sweetness evaluation based on systematic machine learning modeling.

Nowadays, computational approaches have drawn more and more attention when exploring the relationship between sweetness and chemical structure instead of traditional experimental tests. In this work, we proposed a novel multi-layer sweetness evaluation system based on machine learning methods. It can be used to evaluate sweet properties of compounds with different chemical spaces and categories, including natural, artificial, carbohydrate, non-carbohydrate, nutritive and non-nutritive ones, suitable for different application scenarios. Furthermore, it provided quantitative predictions of sweetness. In addition, sweetness-related chemical basis and structure transforming rules were obtained by using molecular cloud and matched molecular pair analysis (MMPA) methods. This work systematically improved the data quality, explored the best machine learning algorithm and molecular characterizing strategy, and finally obtained robust models to establish a multi-layer prediction system (available at: https://github.com/ifyoungnet/ChemSweet). We hope that this study could facilitate food scientists with efficient screening and precise development of high-quality sweeteners.

Penehyclidine for prevention of postoperative nausea and vomiting following bimaxillary orthognathic surgery: a randomized, double-blind, controlled trial.

PURPOSE: To investigate the efficacy and safety of low-dose bolus plus continuous infusion of penehyclidine in preventing postoperative nausea and vomiting (PONV) following bimaxillary surgery. METHODS: Three hundred fifty-four patients were randomly allocated into three groups. In the Control group, placebo (normal saline) was injected before anesthesia and infused over 48 h after surgery; in the Bolus group, 0.5 mg penehyclidine was injected before anesthesia, whereas placebo was infused after surgery; in the Infusion group, 0.25 mg penehyclidine were injected before anesthesia, another 0.25 mg penehyclidine was infused after surgery. The primary endpoint was the incidence of PONV within 72 h. RESULTS: A total of 353 patients were included in intention-to-treat analysis. The PONV incidence was 61.0% (72/118) in the Control group, 40.2% (47/117) in the Bolus group, and 28.0% (33/118) in the Infusion group. The incidence was significantly lower in the Bolus group than in the Control group (RR 0.66; 95% CI 0.51-0.86; adjusted P = 0.003) and in the Infusion group than in the Control group (RR 0.46; 95% CI 0.33-0.63; adjusted P < 0.001); the difference between the Infusion and Bolus groups was not statistically significant (RR 0.70; 95% CI 0.48-1.00; adjusted P = 0.144). Emergence agitation occurred more frequently in the Bolus group than in the Control group (36.8% [43/117] vs. 21.2% [25/118], adjusted P = 0.027), but did not differ significantly between the Infusion and Control groups. CONCLUSIONS: A low-dose bolus plus continuous infusion of penehyclidine was effective in preventing PONV without increasing emergence agitation. TRIAL REGISTRATION: Clinicaltrials.gov. Identifier: NCT04454866.

Systematic comparison of ligand-based and structure-based virtual screening methods on poly (ADP-ribose) polymerase-1 inhibitors.

The poly (ADP-ribose) polymerase-1 (PARP1) has been regarded as a vital target in recent years and PARP1 inhibitors can be used for ovarian and breast cancer therapies. However, it has been realized that most of PARP1 inhibitors have disadvantages of low solubility and permeability. Therefore, by discovering more molecules with novel frameworks, it would have greater opportunities to apply it into broader clinical fields and have a more profound significance. In the present study, multiple virtual screening (VS) methods had been employed to evaluate the screening efficiency of ligand-based, structure-based and data fusion methods on PARP1 target. The VS methods include 2D similarity screening, structure-activity relationship (SAR) models, docking and complex-based pharmacophore screening. Moreover, the sum rank, sum score and reciprocal rank were also adopted for data fusion methods. The evaluation results show that the similarity searching based on Torsion fingerprint, six SAR models, Glide docking and pharmacophore screening using Phase have excellent screening performance. The best data fusion method is the reciprocal rank, but the sum score also performs well in framework enrichment. In general, the ligand-based VS methods show better performance on PARP1 inhibitor screening. These findings confirmed that adding ligand-based methods to the early screening stage will greatly improve the screening efficiency, and be able to enrich more highly active PARP1 inhibitors with diverse structures.

Computational Bioactivity Fingerprint Similarities To Navigate the Discovery of Novel Scaffolds.

As one of the central tasks of modern medicinal chemistry, scaffold hopping is expected to lead to the discovery of structural novel biological active compounds and broaden the chemical space of known active compounds. Here, we report the computational bioactivity fingerprint (CBFP) for easier scaffold hopping, where the predicted activities in multiple quantitative structure-activity relationship models are integrated to characterize the biological space of a molecule. In retrospective benchmarks, the CBFP representation shows outstanding scaffold hopping potential relative to other chemical descriptors. In the prospective validation for the discovery of novel inhibitors of poly [ADP-ribose] polymerase 1, 35 predicted compounds with diverse structures are tested, 25 of which show detectable growth-inhibitory activity; beyond this, the most potent (compound 6) has an IC50 of 0.263 nM. These results support the use of CBFP representation as the bioactivity proxy of molecules to explore uncharted chemical space and discover novel compounds.

Improving structure-based virtual screening performance via learning from scoring function components.

Scoring functions (SFs) based on complex machine learning (ML) algorithms have gradually emerged as a promising alternative to overcome the weaknesses of classical SFs. However, extensive efforts have been devoted to the development of SFs based on new protein-ligand interaction representations and advanced alternative ML algorithms instead of the energy components obtained by the decomposition of existing SFs. Here, we propose a new method named energy auxiliary terms learning (EATL), in which the scoring components are extracted and used as the input for the development of three levels of ML SFs including EATL SFs, docking-EATL SFs and comprehensive SFs with ascending VS performance. The EATL approach not only outperforms classical SFs for the absolute performance (ROC) and initial enrichment (BEDROC) but also yields comparable performance compared with other advanced ML-based methods on the diverse subset of Directory of Useful Decoys: Enhanced (DUD-E). The test on the relatively unbiased actives as decoys (AD) dataset also proved the effectiveness of EATL. Furthermore, the idea of learning from SF components to yield improved screening power can also be extended to other docking programs and SFs available.

Improving Docking-Based Virtual Screening Ability by Integrating Multiple Energy Auxiliary Terms from Molecular Docking Scoring.

Virtual Screening (VS) based on molecular docking is an efficient method used for retrieving novel hit compounds in drug discovery. However, the accuracy of the current docking scoring function (SF) is usually insufficient. In this study, in order to improve the screening power of SF, a novel approach named EAT-Score was proposed by directly utilizing the energy auxiliary terms (EAT) provided by molecular docking scoring through eXtreme Gradient Boosting (XGBoost). Here, EAT specifically refers to the output of the Molecular Operating Environment (MOE) scoring, including the energy scores of five different classical SFs and the Protein-Ligand Interaction Fingerprint (PLIF) terms. The performance of EAT-Score to discriminate actives from decoys was strictly validated on the DUD-E diverse subset by using different performance metrics. The results showed that EAT-Score performed much better than classical SFs in VS, with its AUC values exhibiting an improvement of around 0.3. Meanwhile, EAT-Score could achieve comparable even better prediction performance compared with other state-of-the-art VS methods, such as some machine learning (ML)-based SFs and classical SFs implemented in docking programs, in terms of AUC, LogAUC, or BEDROC. Furthermore, the EAT-Score model can capture important binding pattern information from protein-ligand complexes by Shapley additive explanations (SHAP) analysis, which may be very helpful in interpreting the ligand binding mechanism for a certain target and thereby guiding drug design.