Category: Nevin Manimala

Ann Plast Surg. 2022 Dec 1;89(6):600-609. doi: 10.1097/SAP.0000000000003247.

ABSTRACT

PURPOSE: The aim of this study was to understand how opioid prescribing practices of plastic surgery residents changed after instituting opioid prescribing education (OPE) interventions.

METHODS: Plastic surgery residents at a single academic institution completed a survey (fall 2017) assessing opioid prescribing following 8 common procedures. The Division then completed 3 multidisciplinary OPE interventions over 2.5 years, which provided passive learning to raise awareness without top-down prescribing guidelines. Residents were resurveyed at 2 time points after the interventions (fall 2018 and spring 2020). The primary outcome measure was self-reported morphine milligram equivalents prescribed.

RESULTS: Survey response rates were 84% to 100%. Preintervention opioid prescriptions were characterized by high variability and absolute doses for all procedures. We observed statistically significant decreases in prescribed doses for most procedures at 9 months post intervention and further decreases at 2.5 years. In the most recently surveyed cohort, only 3 of 16 residents (18.8%) had OPE before residency, whereas 12 of 16 residents (75.0%) participated in OPE during residency. Eighty-eight percent of respondents “always” (8/16, 50%) or “usually” (6/16, 38%) considered the opioid epidemic when prescribing opioids, suggesting an improved prescribing culture. Barriers to better prescribing included duplicate prescriptions/accessing state-run prescription drug monitoring programs (75.0%), remote prescribing (75%), providing refills (56%), and prescribing opioids for patients on chronic opioid therapy (56%).

CONCLUSION: This prospective cohort study demonstrates that a simple multidisciplinary, didactic OPE intervention that aimed to increase residents’ awareness has the potential to reduce self-reported opioid prescribing and sustain prescribing practices over many years. We identify persistent barriers facing our resident prescribers today, enabling more opioid educational interventions in the future.

PMID:36416684 | DOI:10.1097/SAP.0000000000003247

Pharmacol Res Perspect. 2022 Dec;10(6):e01024. doi: 10.1002/prp2.1024.

ABSTRACT

The immunosuppressive agents sirolimus and everolimus are sensitive CYP3A4 substrates with narrow therapeutic index. Ritonavir is a strong CYP3A inhibitor. A phase 1 study was conducted to evaluate the pharmacokinetics, safety, and tolerability of the co-administration of sirolimus or everolimus with the ritonavir-containing 3D regimen of the direct-acting antiviral agents ombitasvir, ritonavir-boosted paritaprevir, and dasabuvir in healthy subjects. This study had two independent arms, each with a two-period, single-sequence, crossover study design. A single dose of sirolimus 2 mg (N = 12) or everolimus 0.75 mg (N = 12) was administered in Period 1. In Period 2, multiple doses of the 3D regimen (ombitasvir/paritaprevir/ritonavir 25/150/100 mg once daily and dasabuvir 250 mg twice daily) were administered for 34 or 28 days, with a single dose of sirolimus 0.5 mg or everolimus 0.75 mg co-administered on Day 15. Following co-administration with the 3D regimen, the sirolimus dose-normalized maximum observed blood concentration (C_max ) and area under the blood concentration-time curve from time zero to infinity (AUC_inf ) increased to 6.4-fold and 38-fold, respectively. Following co-administration with the 3D regimen, the everolimus C_max and AUC_inf increased to 4.7-fold and 27-fold, respectively. Sirolimus and everolimus half-lives increased from 96 to 249 h, and 42 to 118 h, respectively. There were no major safety or tolerability issues in this study. The ritonavir-containing 3D regimen resulted in a significant increase in sirolimus or everolimus exposure, consistent with the known strong inhibitory effect of ritonavir on CYP3A requiring dose and/or frequency modification when co-administered with each other.

PMID:36416673 | DOI:10.1002/prp2.1024

Dentomaxillofac Radiol. 2022 Nov 23:20220225. doi: 10.1259/dmfr.20220225. Online ahead of print.

ABSTRACT

OBJECTIVE: To define which are and how the radiomics features of jawbone pathologies are extracted for diagnosis, predicting prognosis and therapeutic response.

METHODS: A comprehensive literature search was conducted using eight databases and gray literature. Two independent observers rated these articles according to exclusion and inclusion criteria. 23 papers were included to assess the radiomics features related to jawbone pathologies. Included studies were evaluated by using JBI Critical Appraisal Checklist for Analytical Cross-Sectional Studies.

RESULTS: Agnostic features were mined from periapical, dental panoramic radiographs, cone beam CT, CT and MRI images of six different jawbone alterations. The most frequent features mined were texture-, shape- and intensity-based features. Only 13 studies described the machine learning step, and the best results were obtained with Support Vector Machine and random forest classifier. For osteoporosis diagnosis and classification, filtering, shape-based and Tamura texture features showed the best performance. For temporomandibular joint pathology, gray-level co-occurrence matrix (GLCM), gray level run length matrix (GLRLM), Gray Level Size Zone Matrix (GLSZM), first-order statistics analysis and shape-based analysis showed the best results. Considering odontogenic and non-odontogenic cysts and tumors, contourlet and SPHARM features, first-order statistical features, GLRLM, GLCM had better indexes. For odontogenic cysts and granulomas, first-order statistical analysis showed better classification results.

CONCLUSIONS: GLCM was the most frequent feature, followed by first-order statistics, and GLRLM features. No study reported predicting response, prognosis or therapeutic response, but instead diseases diagnosis or classification. Although the lack of standardization in the radiomics workflow of the included studies, texture analysis showed potential to contribute to radiologists’ reports, decreasing the subjectivity and leading to personalized healthcare.

PMID:36416666 | DOI:10.1259/dmfr.20220225

Tidsskr Nor Laegeforen. 2022 Nov 21;142(17). doi: 10.4045/tidsskr.22.0256. Print 2022 Nov 22.

ABSTRACT

BACKGROUND: Studies have shown a high incidence of emergency caesarean sections among immigrant women, especially those born in Sub-Saharan Africa, but the risk of planned and emergency caesarean section varies with the mother’s level of education. The proportion of women with little or no education is higher among those born in Sub-Saharan Africa and other low- and middle-income countries than those born in Norway. We therefore wanted to investigate the relationship between maternal birthplace, level of education and risk of caesarean section.

MATERIAL AND METHOD: The study was based on all births recorded in the Medical Birth Registry of Norway between 2008 and 2017 linked to data from Statistics Norway. Maternal birthplace, divided into four categories, was the exposure variable. The outcome was planned or emergency caesarean section. We used multinomial logistic regression and stratified the analyses by level of education. Norwegian-born women constituted the reference group.

RESULTS: Of 572 349 births, immigrant women accounted for 26.6 %. Caesarean sections and emergency caesareans made up 15.1 % and 9.6 % of all births respectively. Norwegian-born women had the highest proportion of planned caesarean sections (5.7 %), while women born in Sub-Saharan Africa had the highest proportion of emergency caesareans (16.3 %). Among women with a higher education, the proportion of emergency caesareans was 8.3 % among Norwegian-born women and 18.1 % among women born in Sub-Saharan Africa (adjusted relative risk 2.41, 95 % confidence interval 2.18 to 2.66).

INTERPRETATION: The impact of education level on risk of caesarean section differed between immigrant women and Norwegian-born women.

PMID:36416648 | DOI:10.4045/tidsskr.22.0256

J Am Nutr Assoc. 2022 Nov 23:1-10. doi: 10.1080/27697061.2022.2144540. Online ahead of print.

ABSTRACT

OBJECTIVE: The aim of this study is to evaluate the effect of daily consumption of high-polyphenol (HP) olive oil on neurogenesis by investigating neuronal cell proliferation and maturation in the hippocampus of old rats, and to evaluate the relationship between neurogenesis, spatial memory, and anxiety-like behavior.

METHODS: A total of 34 female, 20-22-month-old Sprague Dawley rats were divided into three groups: control group, low-polyphenol (LP) group, and high-polyphenol (HP) group. The animals were fed distilled water, LP olive oil and HP-extra virgin olive oil, respectively for 6 weeks using an oral gavage. At 43 days, animals were tested using the Morris Water Maze to evaluate spatial memory, and the Open-field test to evaluate anxiety-like behavior. Neural cell proliferation in the dentate gyrus (DG) was determined by BrdU labeling and Nestin protein expression. Neuronal maturation was determined by NeuN labeling. Synaptic density in the hippocampus and prefrontal cortex was examined by measuring Synaptophysin (SYN) levels. Hippocampal Calbindin levels were measured to assess cellular calcium metabolism.

RESULTS: Daily consumption of HP olive oil significantly improved cell proliferation and neuronal maturation in the DG of old rats. HP-olive oil significantly increased SYN levels in the prefrontal cortex, and nestin and calbindin levels in the hippocampus (p < 0.05). LP olive oil diet has shown no effect on any parameter (p > 0.05). We also did not find any statistically significant difference between the groups in terms of spatial memory and anxiety-like behavior (p > 0.05).

CONCLUSION: Our study is first to show that daily consumption of HP-olive oil enhances hippocampal neurogenesis in old rats, which has been confirmed by proliferation and maturation biomarkers. In addition, increased SYN and calbindin levels showed that the generated cells were also functionally developed in the HP group. We suggest that daily consumption of HP olive oil may have beneficial effects on brain aging by triggering neurogenesis.

PMID:36416641 | DOI:10.1080/27697061.2022.2144540

Ear Nose Throat J. 2022 Nov 23:1455613221140281. doi: 10.1177/01455613221140281. Online ahead of print.

ABSTRACT

OBJECTIVES: Eustachian tube dysfunction (ETD) is frequent in children with adenoid hypertrophy (AH). Although the most common treatment of AH is surgical removal of adenoid tissue, numerous studies have reported the efficacy of intranasal steroids. The effects of the intranasal steroid and azelastine combination on AH and ETD have not been reported before. In this study, we tried to determine the effects of 3-month intranasal Azelastine-Fluticasone dipropionate combination (Aze-Flu) treatment in children with ETD and AH.

MATERIALS AND METHODS: 100 children who had open mouth sleep, snoring, and sleep apnea and were diagnosed with AH and ETD participated in this study. The mean age was 7.73 ± 2.37 (4-14 years). The rates of adenoid tissue hypertrophy and choanal occlusion were evaluated using a rigid pediatric nasal endoscope and reassessed after 3 months of Aze-Flu nasal spray treatment. The function of the Eustachian tube (ET) was evaluated before and after medical treatment using the Eustachian tube score, the Eustachian dysfunction test-7 (ETS-7) and tubomanometry (TMM).

RESULTS: The results were evaluated in 100 patients with AH and ETD. The adenoid tissue to choana rate was 82% before treatment and decreased to 37% after treatment. The ETS-7 test score was 6.36 before treatment and increased to 9.72 at the end of 3 months. Both the regression of the adenoid tissue and the improvement in the Eustachian function scores were statistically significant (p < 0.05).

CONCLUSIONS: AH significantly increases the frequency of ETD. In this study, it was observed that Aze-Flu treatment was significantly effective in both regression of the adenoid tissue and Eustachian tube dysfunction. We believe that it can be applied as an initial therapy in children with AH and associated ETD.

PMID:36416201 | DOI:10.1177/01455613221140281

Biometrics. 2022 Nov 22. doi: 10.1111/biom.13800. Online ahead of print.

ABSTRACT

In biomedical science, analyzing treatment effect heterogeneity plays an essential role in assisting personalized medicine. The main goals of analyzing treatment effect heterogeneity include estimating treatment effects in clinically relevant subgroups and predicting whether a patient subpopulation might benefit from a particular treatment. Conventional approaches often evaluate the subgroup treatment effects via parametric modeling and can thus be susceptible to model mis-specifications. In this paper, we take a model-free semiparametric perspective and aim to efficiently evaluate the heterogeneous treatment effects of multiple subgroups simultaneously under the one-step targeted maximum-likelihood estimation (TMLE) framework. When the number of subgroups is large, we further expand this path of research by looking at a variation of the one-step TMLE that is robust to the presence of small estimated propensity scores in finite samples. From our simulations, our method demonstrates substantial finite sample improvements compared to conventional methods. In a case study, our method unveils the potential treatment effect heterogeneity of rs12916-T allele (a proxy for statin usage) in decreasing Alzheimer’s disease risk. This article is protected by copyright. All rights reserved.

PMID:36416173 | DOI:10.1111/biom.13800

J Am Heart Assoc. 2022 Nov 23:e027339. doi: 10.1161/JAHA.122.027339. Online ahead of print.

ABSTRACT

Background Eosinophil count (EOS) has been proposed to provide prognostic information in multiple cardiovascular disorders. However, few researchers have investigated the predictive value of EOS for patients with type B aortic dissection who had thoracic endovascular repair. Methods and Results The authors reviewed the records of 912 patients with type B aortic dissection who were treated with thoracic endovascular repair in Changhai Hospital, Shanghai. By using receiver operating characteristic curve analysis, patients were divided into 2 groups based on the admission EOS cutoff value (<7.4×10⁶/L [n=505] and ≥7.4×10⁶/L [n=407]). To reduce selection bias, propensity score matching was applied. Multivariable regression analysis and Kaplan-Meier curves were performed to assess the association between EOS and long-term outcomes. Furthermore, we investigated nonlinear correlations between EOS and outcomes using general additive models with restricted cubic splines. In the matched population, lower EOS was associated with significantly higher 30-day mortality (4.1% vs 0%, P=0.007). There was no statistically difference in 30-day adverse events between the 2 groups (all P>0.05). Kaplan-Meier analysis revealed that patients with an EOS <7.4×10⁶/L had a higher incidence of 1-year all-cause death (7.95% vs. 2.34%, P=0.008) and aortic-related death (5.98% vs 1.81%, P=0.023) than those with higher EOS. Multivariable Cox analysis showed that continuous EOS was independently associated with 1-year mortality (hazard ratio, 3.23 [95% CI, 1.20-8.33], P=0.019). In addition, we discovered a nonlinear association between EOS and 1-year outcomes. Conclusions Lower admission EOS values predict higher short- and long-term mortality after thoracic endovascular repair.

PMID:36416154 | DOI:10.1161/JAHA.122.027339

Bioinformatics. 2022 Nov 23:btac755. doi: 10.1093/bioinformatics/btac755. Online ahead of print.

ABSTRACT

MOTIVATION: Dynamic mechanistic modelling in systems biology has been hampered by the complexity and variability associated with the underlying interactions, and by uncertain and sparse experimental measurements. Ensemble modelling, a concept initially developed in statistical mechanics, has been introduced in biological applications with the aim of mitigating those issues. Ensemble modelling uses a collection of different models compatible with the observed data to describe the phenomena of interest. However, since systems biology models often suffer from lack of identifiability and observability, ensembles of models are particularly unreliable when predicting non-observable states.

RESULTS: We present a strategy to assess and improve the reliability of a class of model ensembles. In particular, we consider kinetic models described using ordinary differential equations (ODEs) with a fixed structure. Our approach builds an ensemble with a selection of the parameter vectors found when performing parameter estimation with a global optimization metaheuristic. This technique enforces diversity during the sampling of parameter space and it can quantify the uncertainty in the predictions of state trajectories. We couple this strategy with structural identifiability and observability analysis, and when these tests detect possible prediction issues we obtain model reparameterizations that surmount them. The end result is an ensemble of models with the ability to predict the internal dynamics of a biological process. We demonstrate our approach with models of glucose regulation, cell division, circadian oscillations, and the JAK-STAT signalling pathway.

AVAILABILITY: The code that implements the methodology and reproduces the results is available at https://doi.org/10.5281/zenodo.6782638.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:36416122 | DOI:10.1093/bioinformatics/btac755

Brief Bioinform. 2022 Nov 22:bbac483. doi: 10.1093/bib/bbac483. Online ahead of print.

ABSTRACT

DNA-binding proteins (DBPs) play crucial roles in numerous cellular processes including nucleotide recognition, transcriptional control and the regulation of gene expression. Majority of the existing computational techniques for identifying DBPs are mainly applicable to human and mouse datasets. Even though some models have been tested on Arabidopsis, they produce poor accuracy when applied to other plant species. Therefore, it is imperative to develop an effective computational model for predicting plant DBPs. In this study, we developed a comprehensive computational model for plant specific DBPs identification. Five shallow learning and six deep learning models were initially used for prediction, where shallow learning methods outperformed deep learning algorithms. In particular, support vector machine achieved highest repeated 5-fold cross-validation accuracy of 94.0% area under receiver operating characteristic curve (AUC-ROC) and 93.5% area under precision recall curve (AUC-PR). With an independent dataset, the developed approach secured 93.8% AUC-ROC and 94.6% AUC-PR. While compared with the state-of-art existing tools by using an independent dataset, the proposed model achieved much higher accuracy. Overall results suggest that the developed computational model is more efficient and reliable as compared to the existing models for the prediction of DBPs in plants. For the convenience of the majority of experimental scientists, the developed prediction server PlDBPred is publicly accessible at https://iasri-sg.icar.gov.in/pldbpred/.The source code is also provided at https://iasri-sg.icar.gov.in/pldbpred/source_code.php for prediction using a large-size dataset.

PMID:36416116 | DOI:10.1093/bib/bbac483