Nevin Manimala

Clin Neurol Neurosurg. 2022 Feb 1;214:107150. doi: 10.1016/j.clineuro.2022.107150. Online ahead of print.

ABSTRACT

BACKGROUND: Spinocerebellar ataxia (SCA) presents different rates of functional decline depending on the type of ataxia.

OBJECTIVE: To compare the progression of disability, imbalance and severity of ataxia in patients with the three most common types of SCA in southern Brazil.

METHODS: 126 patients (31-SCA2, 58-SCA3 and 37-SCA10) were stratified into four groups based on disease duration. Progression rates were calculated in each group for ataxia severity (SARA), functioning (FIM-ADL and Lawton-IADL), and balance (Berg Balance Scale).

RESULTS: Differences across groups in terms of disease severity revealed a linear pattern of decline in SCA3, with a faster rate over time (p = 0.039) compared to SCA2 and SCA10. The pattern was nonlinear for SCA2 and SCA10, with a twofold faster rate in patients with up to seven years of disease compared to all other periods in SCA10 (p < 0.001) and to the longer follow up period in SCA2 (p = 0.049). Differences across groups regarding worsening of balance scores was significantly faster in SCA3 compared to SCA10 (p = 0.028) and SCA2 (p = 0.028). The rate of loss of independence of ADLs tended to diminish over time in the three types of ataxia and was faster in SCA3. Similarly, the rate for loss of independence (IADLs) was faster in SCA3 compared to SCA2 (p = 0.057) and significantly faster compared to SCA10 (p = 0.028).

CONCLUSION: The present findings suggest that the progression of the disease (severity/functioning/balance) varies according to the SCA subtype and the period in disease course. Progression is more linear and aggressive in patients with SCA3.

PMID:35123369 | DOI:10.1016/j.clineuro.2022.107150

Drug Alcohol Depend. 2022 Jan 29;232:109336. doi: 10.1016/j.drugalcdep.2022.109336. Online ahead of print.

ABSTRACT

BACKGROUND: Research on the impact of Medicaid expansion on buprenorphine utilization has largely focused on the Medicaid program. Less is known about its associations with total buprenorphine utilization and non-Medicaid payers.

METHODS: Monthly prescription data (June 2013-May 2018) for proprietary and generic sublingual as well as buccal buprenorphine products were purchased from IQVIA®. Population-adjusted state-level utilization measures were constructed for Medicaid, commercial insurance, Medicare, cash, and total utilization. A difference-in-differences (DID) approach with population weights estimated the association between Medicaid expansion and buprenorphine utilization, while controlling for treatment capacity.

RESULTS: Monthly total buprenorphine prescriptions increased by 68% overall and increased 283% for Medicaid, 30% for commercial insurance, and 143% for Medicare. Cash prescriptions decreased by 10%. The DID estimate for Medicaid expansion was not statistically significant for total utilization (-19.780, 95% CI = -45.118, 5.558, p = .123). For Medicaid buprenorphine utilization, there was a significant increase of 27.120 prescriptions per 100,000 total state residents (95% CI = 9.458, 44.782, p = .003) in expansion states versus non-expansion states post-Medicaid expansion. Medicaid expansion had a negative effect on commercial insurance (DID estimate = -37.745, 95% CI = -62.946, -12.544, p = .004), cash utilization (DID estimate = -6.675, 95% CI = -12.627, -0.723, p = .029), and Medicare utilization (DID estimate = -1.855, 95% CI = -3.697, -0.013, p = .048).

DISCUSSION: The associations between Medicaid expansion and buprenorphine utilization varied across different types of payers, such that the overall impact of Medicaid expansion on buprenorphine utilization was not significant.

PMID:35123365 | DOI:10.1016/j.drugalcdep.2022.109336

Drug Alcohol Depend. 2022 Jan 29;232:109332. doi: 10.1016/j.drugalcdep.2022.109332. Online ahead of print.

ABSTRACT

BACKGROUND: This study examined associations of local cannabis retail outlet availability and neighborhood disadvantage with cannabis use and related risk factors among young adults.

METHODS: Data were from annual cross-sectional surveys administered from 2015 to 2019 to individuals ages 18-25 residing in Washington State (N = 10,009). As outcomes, this study assessed self-reported cannabis use at different margins/frequencies (any past year, at least monthly, at least weekly, at least daily) and perceived ease of access to cannabis and acceptability of cannabis use in the community. Cannabis retail outlet availability was defined as the presence of at least one retail outlet within a 1-kilometer road network buffer of one’s residence. Sensitivity analyses explored four other spatial metrics to define outlet availability (any outlet within 0.5-km, 2-km, and the census tract; and census tract density per 1000 residents). Census tract level disadvantage was a composite of five US census variables.

RESULTS: Adjusting for individual- and area-level covariates, living within 1-kilometer of at least one cannabis retail outlet was statistically significantly associated with any past year and at least monthly cannabis use as well as high perceived access to cannabis. Results using a 2-km buffer and census tract-level metrics for retail outlet availability showed similar findings. Neighborhood disadvantage was statistically significantly associated with at least weekly and at least daily cannabis use and with greater perceived acceptability of cannabis use.

CONCLUSIONS: Results may have implications for regulatory and prevention strategies to reduce the population burden of cannabis use and related harms.

PMID:35123361 | DOI:10.1016/j.drugalcdep.2022.109332

J Colloid Interface Sci. 2022 Jan 25;615:50-58. doi: 10.1016/j.jcis.2022.01.141. Online ahead of print.

ABSTRACT

Nanoparticle tracking analysis (NTA) is a single particle tracking technique that in principle provides a more direct measure of particle size distribution compared to dynamic light scattering (DLS). Here, we demonstrate how statistical mixture distribution analysis can be used in combination with NTA to quantitatively characterize the amount and extent of particle binding in a mixture of nanomaterials. The combined approach is used to study the binding of gold nanoparticles to two types of phospholipid vesicles, those containing and lacking the model ion channel peptide gramicidin A. This model system serves as both a proof of concept for the method and a demonstration of the utility of the approach in studying nano-bio interactions. Two diffusional models (Stokes-Einstein and Kirkwood-Riseman) were compared in the determination of particle size, extent of binding, and nanoparticle:vesicle binding ratios for each vesicle type. The combination of NTA and statistical mixture distributions is shown to be a useful method for quantitative assessment of the extent of binding between particles and determination of binding ratios.

PMID:35123359 | DOI:10.1016/j.jcis.2022.01.141

Leg Med (Tokyo). 2022 Jan 31;56:102031. doi: 10.1016/j.legalmed.2022.102031. Online ahead of print.

ABSTRACT

After death human body is subject to the processes of autolysis and putrefaction. Notably, the changes in cadaver during decomposition complicate its forensic analysis and particularly the estimation of the post-mortem interval (PMI). The process and rate of decomposition is impacted by various intrinsic and extrinsic factors that vary across countries and regions. Studying the decomposition pattern in different regions in the world helps us to understand the process and improve the precision of the PMI estimation of decomposed bodies. With the aim to develop a taphonomic model of decomposition in the province of Barcelona (Catalonia, Spain), this study analyses the influence of several intrinsic and extrinsic factors in the pattern and rate of decomposition in this geographical area. Our statistical model concluded that the most significant factors affecting the decomposition pattern and rate are temperature and PMI. Nevertheless, there are other intrinsic factors such as cause, manner of death and underlying pathological conditions which also have an important role. Moreover, considering the various variables studied in this research, two predictive machine learning algorithms were developed as a probabilistic approach to estimate the PMI. Reliable classification results are obtained for three interval groups (1-2 days, 3-10 days, and > 10 days) and two interval groups (>1 week, < 1 week). Machine learning algorithm is a promising tool to gain objectivity in forensic PMI assessments. The results of this study could potentially assist further research in forensic taphonomy.

PMID:35123354 | DOI:10.1016/j.legalmed.2022.102031

Poult Sci. 2022 Jan 8;101(4):101700. doi: 10.1016/j.psj.2022.101700. Online ahead of print.

ABSTRACT

Large White male turkey genetic lines (GL) comparison in performance and processing yields under the same conditions are rare in the literature. Two rearing experiments (EXP) were conducted to accomplish 2 objectives. The first objective was to test the effects of poult source and genetic lines on performance and processing yields. The second objective was to extract season and growth patterns when comparing both EXP common treatments. In EXP 1, male poults from 5 different sources were randomly assigned to 48 concrete: litter-covered floor pens. In EXP 2, male poults from 7 different genetic lines were randomly assigned to 48 concrete: litter-covered floor pens. For both EXP, the experimental design was a completely randomized block design with a one-factor arrangement. Both EXP were placed in the same house with the same management and nutrition in two separate seasons of the same year. Bird performance and carcass processing yield were analyzed in SAS 9.4 or JMP 15.1 in a mixed model. In EXP 1 no significant difference in BW or processing yield was observed. However, a similar GL from a commercial hatchery had an improved feed conversion ratio (FCR) over the same GL sourced directly from the genetic company hatchery. In EXP 2, statistical differences were observed in performance and breast meat yield depending on the GL. A season effect was observed when comparing the two EXP. Birds raised in the fall season had a 2 kg BW increase, on average, over their spring counterparts. This difference in BW can also be observed in a statistically higher breast meat yield by the birds raised in the fall over the ones raised in the spring. In conclusion, a comparison between GL resulted in effects due to genetic line, poult source, and rearing season on bird performance and carcass yield.

PMID:35123351 | DOI:10.1016/j.psj.2022.101700

Comput Methods Programs Biomed. 2022 Jan 26;216:106663. doi: 10.1016/j.cmpb.2022.106663. Online ahead of print.

ABSTRACT

BACKGROUND AND OBJECTIVE: Alert of patient deterioration is essential for prompt medical intervention in the Medical Intensive Care Unit (MICU). Logistic Regression (LR) has been used for the development of most conventional severity-of-illness scoring systems to anticipate the risk of mortality in the MICU. Machine Learning (ML) models such as probabilistic graphical models and Extreme Gradient Boosting (XGB) have demonstrated improved prediction accuracy in patient outcomes compared to LR. The aim was to compare three ML models to the SAPS, SAPS II, SAPS III, SOFA, serial SOFA, LODS, and OASIS for prediction of MICU mortality.

METHODS: A Bayesian Network (BN), Naïve Bayes network (NB), and a XGB model were developed. 9893 adult MICU-stays from the MIMIC-III database were studied. The primary outcome was MICU mortality prediction and the secondary outcome was 1-year mortality prediction. Data analyzed consisted on routine physiological measurements collected during 5 hours in the MICU, demographic and diagnoses/procedure features. The performance was evaluated by accuracy statistics, discrimination and calibration measures. Limitations of the study were discussed.

RESULTS: The AUROC for MICU mortality prediction was 0.919 for XGB, 0.905 for BN, and 0.864 for NB, while the conventional systems displayed much lower values with the serial SOFA having the best value (0.814). The Diagnostic Odds Ratio was ≤7.099 for all the conventional systems, reaching values of 30.115 for XGB and 22.648 for BN. The XGB achieved a sensitivity of 0.831 and specificity of 0.86 assuring an acceptable precision (0.528), whose values were much lower for the conventional systems. The Brier score was better for the ML models, except for the NB (0.119), with 0.072 for XGB and 0.081 for BN.

CONCLUSIONS: The XGB and BN substantially outperformed the conventional systems for discrimination, calibration and the accuracy statistics assessed. The NB showed inferior performance to the XGB and BN but improved the discrimination and all accuracy statistics of the conventional systems except for an inferior calibration and 1-year mortality discrimination. The XGB showed the best performance among all models. These ML models have the potential to improve the monitoring of MICU patients, which must be evaluated in future studies.

PMID:35123348 | DOI:10.1016/j.cmpb.2022.106663

Dev Cogn Neurosci. 2022 Jan 15;54:101072. doi: 10.1016/j.dcn.2022.101072. Online ahead of print.

ABSTRACT

Developmental researchers are often interested in event-related potentials (ERPs). Data-analytic approaches based on the observed ERP suffer from major problems such as arbitrary definition of analysis time windows and regions of interest and the observed ERP being a mixture of latent underlying components. Temporal principal component analysis (PCA) can reduce these problems. However, its application in developmental research comes with the unique challenge that the component structure differs between age groups (so-called measurement non-invariance). Separate PCAs for the groups can cope with this challenge. We demonstrate how to make results from separate PCAs accessible for inferential statistics by re-scaling to original units. This tutorial enables readers with a focus on developmental research to conduct a PCA-based ERP analysis of amplitude differences. We explain the benefits of a PCA-based approach, introduce the PCA model and demonstrate its application to a developmental research question using real-data from a child and an adult group (code and data openly available). Finally, we discuss how to cope with typical challenges during the analysis and name potential limitations such as suboptimal decomposition results, data-driven analysis decisions and latency shifts.

PMID:35123341 | DOI:10.1016/j.dcn.2022.101072

J Psychiatr Res. 2022 Jan 15;148:127-130. doi: 10.1016/j.jpsychires.2022.01.017. Online ahead of print.

ABSTRACT

OBJECTIVE: COVID-19 and associated measures to control the spread of the COVID-19 has significantly impacted clinical research. This study aimed to determine the impact COVID-19 has had on psychiatric clinical trials and to assess whether certain trial areas or trial types were differentially affected.

METHODS: We used information from ClinicalTrials.gov, the largest online database of clinical trial information, to examine changes in psychiatric clinical trials from January 2010-December 2020.

RESULTS: Clinical trial initiation decreased in 2020, with a year-on-year percent change in trial initiation of -5.4% versus an expected percent change based on forecasting observed trends from 2010 to 2019 of 8.6%. When broken down by disease area, the distribution of trials observed in 2020 was significantly different from the predicted distribution (p < 0.00001). The greatest decrease in trial initiation was seen in Schizophrenia-specific trials, with an observed percent change of -29.2% versus an expected percent change of 3.2%. Conversely, anxiety trials saw a significant increase in trial initiation during 2020, with an observed percent change of 24.6% versus an expected percent change of 16.0%. When assessing interventional versus observational studies, data showed a significant increase in initiation of observational psychiatric clinical trials (p < 0.05), and a significant decrease in initiation of interventional psychiatric clinical trials (p < 0.01). When data was analyzed on a month-by-month time scale, 7/12 months in 2020 showed significant decreases when compared to initiation during matching months over prior years, and a single month, June, showed a significant increase.

CONCLUSION: COVID-19 has had significant impacts on the initiation of psychiatric clinical trials over 2020, and this decrease in trial initiation may have long-term impacts on the development and assessment of psychiatric treatments and therapeutics.

PMID:35123324 | DOI:10.1016/j.jpsychires.2022.01.017

Mult Scler Relat Disord. 2022 Jan 31;59:103558. doi: 10.1016/j.msard.2022.103558. Online ahead of print.

ABSTRACT

BACKGROUND: Aim of our study was to estimate the prevalence of comorbid conditions and adverse health behaviors in relapsing-remitting multiple sclerosis (RRMS) patients and evaluate association between comorbidity and MS outcomes (relapse rate, fatigue and quality of life) in Lithuanian setting.

METHODS: A prospective cohort study was carried out in the MS center of Lithuanian University of Health Sciences Hospital Kaunas clinics from November 2016 to March 2021. People with MS filled a self-report comorbidity and adverse health behavior questionnaire, visual analogue fatigue scale (VAFS), a Short Form 36 (SF-36) v2 health related quality of life questionnaire (QoL). Information about disability and relapses was acquired from medical documentation and Lithuanian MS registry at baseline and after 24-month observational period. Chi square, t-test, ANOVA, Mann-Whitney U were used for basic statistical evaluation. Multivariable logistic regression models were used to prognose MS outcomes in association to comorbidity and adverse health behaviors, adjusting for age and baseline disability.

RESULTS: Of 230 RRMS patients, 167 (72.6%) were women, average age was 42 years. 207 persons were followed through the observational period and included into relapse analysis. 112 (48.7%) of participants reported having at least one comorbidity, the most prevalent were arterial hypertension (19.1%) depression (16.5%) and anxiety (14.8%). People with comorbidities had higher fatigue (6.6 vs. 5.3, p < 0.001) and lower quality of life (overall SF-36 46.3 vs 59.1, p < 0.001). People consuming alcohol had fewer relapses per 24 months (0.56 vs. 0.82, p = 0.01), lower fatigue (5.7 vs. 6.4, p = 0.03), better quality of life (overall SF-36: 56.8 vs. 45.6, p < 0.001), compared to abstinents. In regression models, comorbidities were associated with severe (>7 VAFS) fatigue (Exp(B)=1.98, 95% CI [1.02, 3.86], p = 0.043), diminished (<50 SF-36) QoL (Exp(B)=3.50, 95% CI [1.72, 7.09], p = 0.001). Depression was independently associated with lower QoL (Exp(B)= 2.86, 95% CI [1.04, 7.88], p = 0.042) and severe fatigue (Exp(B)=4.65, 95% CI [2.39, 9.01], p < 0.001); anxiety with diminished QoL (Exp(B)= 4.99, 95%CI [1.67, 14.92], p = 0.002). Light alcohol consumption (compared to abstinents) was associated with decreased risk for: relapse during 24 months (Exp(B)=0.44, 95% CI[0.24, 0.77], p = 0.005), severe fatigue (Exp(B)=0.48, 95% CI [0.24, 0.98], p = 0.042) and lower QoL (Exp(B)= 0.32, 95% CI [0.16, 0.65], p = 0.002).

CONCLUSION: Comorbidity is a relevant issue in multiple sclerosis as half of people with MS report concomitant conditions. Hypertension, depression, and anxiety are especially prevalent in MS. In our study, comorbidity is associated with quality of life and fatigue, but not relapse rate. Depression and anxiety are independently associated with lower quality of life and higher fatigue. Light alcohol consumption is associated with reduced relapse risk, less fatigue and better quality of life. Overweight and tobacco smoking do not seem to have negative impact on MS outcomes in our sample.

PMID:35123292 | DOI:10.1016/j.msard.2022.103558