Categories
Nevin Manimala Statistics

Relationship between cocaine and cocaethylene blood concentration with the severity of clinical manifestations

Am J Emerg Med. 2021 Aug 24;50:404-408. doi: 10.1016/j.ajem.2021.08.057. Online ahead of print.

ABSTRACT

BACKGROUND: Poisonings resulting from the abuse of drugs currently represent a serious problem for public health. Among the main agents involved, cocaine stands out. It became one of the most abused drugs around the world, and one of the main reasons for visits to the emergency department due to the use of illicit substances. The use of cocaine is primarily in combination with alcoholic beverages. There are few studies that correlate cocaine blood concentration and the severity of clinical manifestations in patients evaluated at Emergency Department. The aim of the present study was to verify the possible relationship between the blood concentration of cocaine and cocaethylene (product of the interaction of cocaine with ethanol) with the severity of the clinical manifestations presented by patients with cocaine intoxication.

METHODS: Blood levels were measured by high-performance liquid chromatography (HPLC) and the severity of clinical manifestations was assessed using the Stimulant Intoxication Score (SIS). To establish this relationship, Pearson’s chi-square statistical test (x2) was used for categorical variables and Student’s t for continuous variables, with statistical significance of 5% (p < 0.05).

RESULTS: Of the 81 patients included in the study, 77.8% were men with a mean age of 32.5 years ± 8.5 and mean of SIS 3.4 ± 2.5. Considering the toxicological analysis results, 24.7% of the blood samples were positive. The mean of cocaine and cocaethylene concentrations were 0.34 μg/mL ± 0.45 and 0.38 μg/mL ± 0.34, respectively. The blood concentration of cocaine and cocaethylene has not been shown to be useful information for the treatment and prognosis of patients, but blood levels of these substances at the time of treatment, regardless of their concentration, may be an indicator of severity, showing that any concentrations of these substances should be considered as potentially toxic.

CONCLUSION: The application of the SIS score proved to be an important alternative capable of predicting the severity of the patients due to cocaine intoxication in a fast and simplified way.

PMID:34481259 | DOI:10.1016/j.ajem.2021.08.057

Categories
Nevin Manimala Statistics

Mental health and resilience: Arts on Prescription for children and young people in a school setting

Public Health. 2021 Aug 31;198:196-199. doi: 10.1016/j.puhe.2021.07.021. Online ahead of print.

ABSTRACT

OBJECTIVES: Arts on Prescription (AoP) programmes were among the first forms of social prescribing in the UK. Most of the studies of AoP programmes focus on adults and currently there is no published research on the impact of AoP on children and young people. This study investigates the impact of 10 weekly AoP workshops delivered in a school setting on the mental well-being and resilience of adolescents aged 13-16 years at risk of emotional or behavioural problems.

STUDY DESIGN: The study design used is a longitudinal cohort study of an AoP programme implemented in 10 schools in the East of England.

METHODS: Changes in mental well-being and resilience of school children were assessed using the Warwick-Edinburgh Mental Well-being Scale (WEMWBS), and the True Resilience Scale applied pre- and post-intervention, with follow-up at 3 months. In total, 91 young people participated in the programme and 65 completed pre- and post-intervention measures.

RESULTS: Data from the WEMWBS and True Resilience Scale indicated that the AoP Programme had a positive impact on both well-being and resilience of participants with a statistically significant increase recorded immediately post-intervention. However, these improvements were not sustained upon observation at 3-month follow-up.

CONCLUSION: This article presents the first indication of the effectiveness of a programme of AoP workshops on the mental well-being and resilience of children and young people. It suggests the potential of AoP as a means of support the mental health and well-being of secondary school aged children.

PMID:34481274 | DOI:10.1016/j.puhe.2021.07.021

Categories
Nevin Manimala Statistics

Interpretable prediction of 3-year all-cause mortality in patients with heart failure caused by coronary heart disease based on machine learning and SHAP

Comput Biol Med. 2021 Aug 28;137:104813. doi: 10.1016/j.compbiomed.2021.104813. Online ahead of print.

ABSTRACT

BACKGROUND: This study sought to evaluate the performance of machine learning (ML) models and establish an explainable ML model with good prediction of 3-year all-cause mortality in patients with heart failure (HF) caused by coronary heart disease (CHD).

METHODS: We established six ML models using follow-up data to predict 3-year all-cause mortality. Through comprehensive evaluation, the best performing model was used to predict and stratify patients. The log-rank test was used to assess the difference between Kaplan-Meier curves. The association between ML risk and 3-year all-cause mortality was also assessed using multivariable Cox regression. Finally, an explainable approach based on ML and the SHapley Additive exPlanations (SHAP) method was deployed to calculate 3-year all-cause mortality risk and to generate individual explanations of the model’s decisions.

RESULTS: The best performing extreme gradient boosting (XGBoost) model was selected to predict and stratify patients. Subjects with a higher ML score had a high hazard of suffering events (hazard ratio [HR]: 10.351; P < 0.001), and this relationship persisted with a multivariable analysis (adjusted HR: 5.343; P < 0.001). Age, N-terminal pro-B-type natriuretic peptide, occupation, New York Heart Association classification, and nitrate drug use were important factors for both genders.

CONCLUSIONS: The ML-based risk stratification tool was able to accurately assess and stratify the risk of 3-year all-cause mortality in patients with HF caused by CHD. ML combined with SHAP could provide an explicit explanation of individualized risk prediction and give physicians an intuitive understanding of the influence of key features in the model.

PMID:34481185 | DOI:10.1016/j.compbiomed.2021.104813

Categories
Nevin Manimala Statistics

Communication attitude of Kannada-speaking adults who do and do not stutter

J Fluency Disord. 2021 Aug 28;70:105866. doi: 10.1016/j.jfludis.2021.105866. Online ahead of print.

ABSTRACT

The Communication Attitude Test for Adults who stutter (BigCAT) is an established measure of cognitive traits in adults who stutter (AWS). The primary purpose of the present study was to adapt and validate the BigCAT to the Kannada language. The secondary purpose was to compare AWS’ and adults who do not stutter (AWNS) BigCAT-K scores and compare AWS’ score in sub-populations in terms of severity and age. The study included a purposive sample of 100 AWS and 317 AWNS. There was high test-retest reliability and solid construct validity, as made evident by the results of the discriminant analysis and cross-validation. Further, as in other investigations with the BigCAT (Vanryckeghem & Brutten, 2019), this self-report test revealed a statistically significant group mean difference between AWS and AWNS, suggesting the presence of a negative attitude towards communication in Kannada-speaking AWS. Further, individuals with severe stuttering had a significantly higher level of speech-associated negative attitude compared to those with mild stuttering. Age does not seem to influence the AWS’ speech-associated belief system. Both of these findings augment the existing scant literature on exploring the association between stuttering severity and age on the cognitive dimension of stuttering. The outcomes establish the BigCAT-K as an effective tool in the assessment and subsequent management of stuttering.

PMID:34481196 | DOI:10.1016/j.jfludis.2021.105866

Categories
Nevin Manimala Statistics

Semantic influence on visual working memory of object identity and location

Cognition. 2021 Sep 1;217:104891. doi: 10.1016/j.cognition.2021.104891. Online ahead of print.

ABSTRACT

Does semantic information-in particular, regularities in category membership across objects-influence visual working memory (VWM) processing? We predict that the answer is “yes”. Four experiments evaluating this prediction are reported. Experimental stimuli were images of real-world objects arranged in either one or two spatial clusters. On coherent trials, all objects belonging to a cluster also belonged to the same category. On incoherent trials, at least one cluster contained objects from different categories. Experiments using a change-detection paradigm (Experiments 1-3) and an experiment in which participants recalled the locations of objects in a scene (Experiment 4) yielded the same result: participants showed better memory performance on coherent trials than on incoherent trials. Taken as a whole, these experiments provide the best (perhaps only) data to date demonstrating that statistical regularities in semantic category membership improve VWM performance. Because a conventional perspective in cognitive science regards VWM as being sensitive solely to bottom-up visual properties of objects (e.g., shape, color, orientation), our results indicate that cognitive science may need to modify its conceptualization of VWM so that it is closer to “conceptual short-term memory”, a short-term memory store representing current stimuli and their associated concepts (Potter, 1993, 2012).

PMID:34481197 | DOI:10.1016/j.cognition.2021.104891

Categories
Nevin Manimala Statistics

Mutational profiling of myeloid neoplasms associated genes may aid the diagnosis of acute myeloid leukemia with myelodysplasia-related changes

Leuk Res. 2021 Aug 31;110:106701. doi: 10.1016/j.leukres.2021.106701. Online ahead of print.

ABSTRACT

AML with myelodysplasia-related changes (AML-MRC) is a subtype of AML known to have adverse prognosis. The karyotype abnormalities in AML-MRC have been well established; however, relatively little has been known about the role of gene mutation profiles by next generation sequencing. 177 AML patients (72 AML-MRC and 105 non-MRC AML) were analyzed by NGS panel covering 53 AML related genes. AML-MRC showed statistically significantly higher frequency of TP53 mutation, but lower frequencies of mutations in NPM1, FLT3-ITDLow, FLT3-ITDHigh, FLT3-TKD, NRAS, and PTPN11 than non-MRC AML. Supervised tree-based classification models including Decision tree, Random forest, and XGboost, and logistic regression were used to evaluate if the mutation profiles could be used to aid the diagnosis of AML-MRC. All methods showed good accuracy in differentiating AML-MRC from non-MRC AML with AUC (area under curve) of ROC ranging from 0.69 to 0.78. Additionally, logistic regression indicated 3 independent factors (age and mutations of TP53 and FLT3) could aid the diagnosis AML-MRC. Using weighted factors, a AML-MRC risk scoring equation was established for potential application in clinical setting: +1x(Age ≥ 65) + 3 x (TP53 mutation) – 2 x (FLT3 mutation). Using a cutoff score of 0, the accuracy of the risk score was 0.76 with sensitivity of 0.77 and specificity of 0.75 for predicting the diagnosis of AML-MRC. Further studies with larger sample sizes are warranted to further evaluate the potential of using gene mutation profiles to aid the diagnosis of AML-MRC.

PMID:34481124 | DOI:10.1016/j.leukres.2021.106701

Categories
Nevin Manimala Statistics

ABLE: Attention based learning for enzyme classification

Comput Biol Chem. 2021 Aug 19;94:107558. doi: 10.1016/j.compbiolchem.2021.107558. Online ahead of print.

ABSTRACT

Classifying proteins into their respective enzyme class is an interesting question for researchers for a variety of reasons. The open source Protein Data Bank (PDB) contains more than 1,60,000 structures, with more being added everyday. This paper proposes an attention-based bidirectional-LSTM model (ABLE) trained on over sampled data generated by SMOTE to analyse and classify a protein into one of the six enzyme classes or a negative class using only the primary structure of the protein described as a string by the FASTA sequence as an input. We achieve the highest F1-score of 0.834 using our proposed model on a dataset of proteins from the PDB. We baseline our model against eighteen other machine learning and deep learning networks, including CNN, LSTM, Bi-LSTM, GRU, and the state-of-the-art DeepEC model. We conduct experiments with two different oversampling techniques, SMOTE and ADASYN. To corroborate the obtained results, we perform extensive experimentation and statistical testing.

PMID:34481129 | DOI:10.1016/j.compbiolchem.2021.107558

Categories
Nevin Manimala Statistics

Antibiotic resistance and biofilm formation of Acinetobacter baumannii isolated from high risk effluent water in tertiary hospitals in South Africa

J Glob Antimicrob Resist. 2021 Sep 1:S2213-7165(21)00196-X. doi: 10.1016/j.jgar.2021.08.004. Online ahead of print.

ABSTRACT

INTRODUCTION: The discharge of drug-resistant, biofilm-forming pathogens from hospital effluent water into municipal wastewater treatment plants poses a public health concern. The present study examined the relationship between antibiotic resistance levels and biofilm formation of Acinetobacter baumannii strains isolated from hospital effluents.

METHODS: Antibiotic susceptibility of 71 A. baumannii isolates was evaluated using the Kirby Bauer disc diffusion method. The minimum inhibitory concentration was performed by the agar dilution method, while the minimum biofilm eradication concentration was performed by the broth dilution method. Genotyping was performed with plasmid DNA. Biofilm formation was evaluated by the microtitre plate method and quantified using crystal violet. P-values < 0.05 were regarded as statistically significant in all the tests conducted.

RESULTS: The extended spectrum resistant (XDR) strains made up 58% of the isolates while MDR and pan drug resistance (PDR) were observed in 50% of the isolates from the final effluent. The MBEC of ciprofloxacin increased by 255-fold while that of ceftazidime was as high as 63-1310-fold compared to their respective MICs. Isolates were classified into four plasmid pattern groups and no significance difference exists between biofilm formation and plasmid type (P = 0.0921). The degree of biofilm formation was independent of the level of antibiotic resistance, although MDRs, XDRs and PDRs produced significant biofilm biomass (P = 0.2580).

CONCLUSION: The results suggest that hospital effluent is a potential risk for multidrug-resistant biofilm-forming A. baumannii strains. Appropriate treatment and disposal for effluents are essential to prevent presence of drug resistance pathogens in waste water.

PMID:34481121 | DOI:10.1016/j.jgar.2021.08.004

Categories
Nevin Manimala Statistics

USPIO-SWI Shows Fingolimod Enhanced Alteplase Action on Angiographic Reperfusion in eMCAO Rats

J Magn Reson Imaging. 2021 Sep 4. doi: 10.1002/jmri.27914. Online ahead of print.

ABSTRACT

BACKGROUND: Noninvasive evaluation of the status of cerebral arteriole perfusion remains a practical challenge in murine stroke models, because conventional magnetic resonance imaging (MRI) is no longer capable of capturing these very small vessels.

PURPOSE: To investigate the feasibility of ultrasmall superparamagnetic iron oxide particles (USPIO)-based susceptibility weighted imaging (SWI)-MRI (USPIO-SWI) and T2* map-MRI (USPIO-T2* map) for monitoring angiographic perfusion in stroke rats.

STUDY TYPE: A preclinical randomized controlled trial.

ANIMAL MODEL: Normal rats (N = 9), embolic middle cerebral artery occlusion (eMCAO) rats (N = 66).

FIELD STRENGTH/SEQUENCE: 7 T; T2* map (multigradient echo), SWI (3D gradient echo).

ASSESSMENT: Experiment 1: To develop a method for angiographic reperfusion evaluation with USPIO-SWI. Normal rats were used to optimize the USPIO dosage (5.6, 16.8, and 56 mg/kg ferumoxytol) as well as scan time points for cerebral arterioles. Contrast-to-noise ratio (CNR) was measured. Stroke rats were further used and the number of visual cortical vessels were counted. Experiment 2: To examine whether fingolimod (lymphocytes inhibitor) enhances the action of tissue plasminogen activator (tPA) in eMCAO rats on cerebral angiographic reperfusion.

STATISTICAL TESTS: Mann-Whitney test and two way-ANOVA were used. P < 0.05 was considered statistically significant.

RESULTS: CNR values of cerebral cortical penetrating arteries in normal rats were significantly increased to 4.4 ± 0.5 (5.6 mg/kg), 6.1 ± 0.5 (16.8 mg/kg), and 3.4 ± 0.9 (56 mg/kg) after USPIO injection. The number of visual cortical vessels on USPIO-SWI images in ischemic regions was significantly less than in control regions (5 ± 2 vs. 56 ± 20) of eMCAO rats. Compared with eMCAO rats who received tPA only, eMCAO rats who received the combination of fingolimod and tPA exhibited significantly higher proportion of complete angiographic reperfusion (69% vs. 17%).

DATA CONCLUSION: This study supports the feasibility of angiographic perfusion evaluation with USPIO-SWI in stroke rats.

LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY STAGE: 1.

PMID:34480787 | DOI:10.1002/jmri.27914

Categories
Nevin Manimala Statistics

Coupling isotopic analysis and Ecopath model to detect the ecosystem features based on functional groups of the southwestern Yellow Sea, China

Environ Sci Pollut Res Int. 2021 Sep 4. doi: 10.1007/s11356-021-16032-5. Online ahead of print.

ABSTRACT

The paper evaluates the species richness, material transfer, energy flow, and system function of the southwestern Yellow Sea Ecosystem (SYSE) indicating intensive human intervention affecting this large marine ecosystem. Twenty functional groups were chosen to represent the basic components of the SYSE for Ecopath modeling based on offshore surveys, annual bird observations, and the China Fisheries Statistical Yearbooks. Forty-nine species based on 15 functional groups of Ecopath model were assessed by stable isotopic analysis (SIA) to verify ecosystem features, energy flow, and trophic structure of the SYSE derived from Ecopath model. Results showed there was a clear correlation of the estimated trophic structure calculated from SIA and the Ecopath model with R2=0.7184. The SYSE Ecopath model was still at an immature and unstable stage according to outputs of the modeling parameters. This paper provides a verification method of detecting the ecosystem features and maturity, stability, and resilience of marine ecosystems by comparing outputs from Ecopath models with SIA.

PMID:34480702 | DOI:10.1007/s11356-021-16032-5