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Nevin Manimala Statistics

An Uncertainty-Guided Deep Learning Method Facilitates Rapid Screening of CYP3A4 Inhibitors

J Chem Inf Model. 2023 Dec 6. doi: 10.1021/acs.jcim.3c01241. Online ahead of print.

ABSTRACT

Cytochrome P450 3A4 (CYP3A4), a prominent member of the P450 enzyme superfamily, plays a crucial role in metabolizing various xenobiotics, including over 50% of clinically significant drugs. Evaluating CYP3A4 inhibition before drug approval is essential to avoiding potentially harmful pharmacokinetic drug-drug interactions (DDIs) and adverse drug reactions (ADRs). Despite the development of several CYP inhibitor prediction models, the primary approach for screening CYP inhibitors still relies on experimental methods. This might stem from the limitations of existing models, which only provide deterministic classification outcomes instead of precise inhibition intensity (e.g., IC50) and often suffer from inadequate prediction reliability. To address this challenge, we propose an uncertainty-guided regression model to accurately predict the IC50 values of anti-CYP3A4 activities. First, a comprehensive data set of CYP3A4 inhibitors was compiled, consisting of 27,045 compounds with classification labels, including 4395 compounds with explicit IC50 values. Second, by integrating the predictions of the classification model trained on a larger data set and introducing an evidential uncertainty method to rank prediction confidence, we obtained a high-precision and reliable regression model. Finally, we use the evidential uncertainty values as a trustworthy indicator to perform a virtual screening of an in-house compound set. The in vitro experiment results revealed that this new indicator significantly improved the hit ratio and reduced false positives among the top-ranked compounds. Specifically, among the top 20 compounds ranked with uncertainty, 15 compounds were identified as novel CYP3A4 inhibitors, and three of them exhibited activities less than 1 μM. In summary, our findings highlight the effectiveness of incorporating uncertainty in compound screening, providing a promising strategy for drug discovery and development.

PMID:38055780 | DOI:10.1021/acs.jcim.3c01241

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Nevin Manimala Statistics

Family planning uptake and its associated factors among women of reproductive age in Uganda: An insight from the Uganda Demographic and Health Survey 2016

PLOS Glob Public Health. 2023 Dec 6;3(12):e0001102. doi: 10.1371/journal.pgph.0001102. eCollection 2023.

ABSTRACT

Despite the government efforts to reduce the high fertility levels and increase the uptake of family planning services in Uganda, family planning use was still low at 30% in 2020 which was the lowest in the East African region. This study was undertaken to determine the prevalence and factors associated with the uptake of family planning methods among women of reproductive age in Uganda. This community-based cross-sectional study utilized secondary data from the Uganda Demographic and Health Survey (UDHS) of 2016. The survey data was downloaded from the Measure Demographic Health Survey website after data use permission was granted. Data was collected from a representative sample of women of the reproductive age group (15-49 years) from all 15 regions in Uganda. A total of 19,088 eligible women were interviewed but interviews were completed with 18,506 women. Data analysis was performed using SPSS statistical software version 32.0 where univariable, bivariable, and multivariable analyses were conducted. The prevalence of family planning use was found to be 29.3% and that of modern contraceptive use was found to be 26.6%. Multivariable analysis showed higher odds of current family planning use among older women (40-44 years) (aOR = 2.09, 95% CI: 1.40-3.12); women who had attained the secondary level of education (aOR = 1.91, 95% CI: 1.32-2.76); those living in households with the highest wealth index (aOR = 1.87, 95% CI: 1.29-2.72); and awareness of the availability of family planning methods (aOR = 1.41, 95% CI: 1.17-1.72). In conclusion, the study suggests improving women’s education attainment, socio-economic position, and awareness may help increase use in the population.

PMID:38055707 | DOI:10.1371/journal.pgph.0001102

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Nevin Manimala Statistics

Re-examination of the risk of dementia after dengue virus infection: A population-based cohort study

PLoS Negl Trop Dis. 2023 Dec 6;17(12):e0011788. doi: 10.1371/journal.pntd.0011788. eCollection 2023 Dec.

ABSTRACT

Dengue infection can affect the central nervous system and cause various neurological complications. Previous studies also suggest dengue was associated with a significantly increased long-term risk of dementia. A population-based cohort study was conducted using national health databases in Taiwan and included 37,928 laboratory-confirmed dengue patients aged ≥ 45 years between 2002 and 2015, along with 151,712 matched nondengue individuals. Subdistribution hazard regression models showed a slightly increased risk of Alzheimer’s disease, and unspecified dementia, non-vascular dementia, and overall dementia in dengue patients than the nondengue group, adjusted for age, sex, area of residence, urbanization level, income, comorbidities, and all-cause clinical visits within one year before the index date. After considering multiple comparisons using Bonferroni correction, only overall dementia and non-vascular dementia remained statistically significant (adjusted SHR 1.13, 95% CI 1.05-1.21, p = 0.0009; E-value 1.51, 95% CI 1.28-NA). Sensitivity analyses in which dementia cases occurring in the first three or five years after the index dates were excluded revealed no association between dengue and dementia. In conclusion, this study found dengue patients had a slightly increased risk of non-vascular dementia and total dementia than those without dengue. However, the small corresponding E-values and sensitivity analyses suggest the association between dengue and dementia may not be causal.

PMID:38055695 | DOI:10.1371/journal.pntd.0011788

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Nevin Manimala Statistics

Deep learning hybrid model for analyzing and predicting the impact of imported malaria cases from Africa on the rise of Plasmodium falciparum in China before and during the COVID-19 pandemic

PLoS One. 2023 Dec 6;18(12):e0287702. doi: 10.1371/journal.pone.0287702. eCollection 2023.

ABSTRACT

BACKGROUND: Plasmodium falciparum cases are rising in China due to the imported malaria cases from African countries. The main goal of this study is to examine the impact of imported malaria cases in African countries on the rise of P. falciparum cases in China before and during the COVID-19 pandemic.

METHODS: A generalized regression model was used to investigate the association of time trends between imported malaria cases from 45 African countries and P. falciparum cases in 31 provinces of China from 2012 to 2018 before the COVID-19 pandemic and during the COVID-19 pandemic from October 2020 to May 2021. Based on the analysis, we proposed a statistical and deep learning hybrid approach to model the resurgence of malaria in China using monthly data of P. falciparum from 2004 to 2016. This study builds a hybrid model known as the ARIMA-GRU approach for modeling the P. falciparum cases in all provinces of China and the number of malaria deaths in China before and during the COVID-19 pandemic.

RESULTS: The analysis showed an emerging link between the rise of imported malaria cases from Africa and P. falciparum cases in many provinces of China. Many imported malaria cases from Africa were P. falciparum cases. The proposed deep learning model achieved a high prediction accuracy score on the testing dataset of 96%.

CONCLUSION: The study provided an analysis of the reduction of P. falciparum cases and deaths caused by imported P. falciparum cases during the COVID-19 pandemic due to the control measures regarding the limitation of international travel in China. The Chinese government has to prepare the imported malaria control measures after the normalization of international travel, to prevent the resurgence of malaria disease in China.

PMID:38055693 | DOI:10.1371/journal.pone.0287702

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Nevin Manimala Statistics

Sex-dependent effects of Canagliflozin on kidney protection in mice with combined hypertension-type 1 diabetes

PLoS One. 2023 Dec 6;18(12):e0295284. doi: 10.1371/journal.pone.0295284. eCollection 2023.

ABSTRACT

Canagliflozin (CANA) is a sodium-glucose cotransporter 2 (SGLT2) inhibitor with blood glucose lowering effects. CANA also promotes kidney protection in patients with cardiovascular diseases and type 2 diabetes (T2D), as well as in normoglycemic patients with hypertension or heart failure. Clinical studies, although conduct in both sexes, do not report sex-dependent differences in T2DM treated with CANA. However, the impact of CANA in type 1 diabetes, as well in sex-dependent outcomes in such cohort needs further understanding. To analyze the effects of CANA in mice with combined hypertension and type 1 diabetes, diabetes was induced by STZ injection (5 days, 50mg/kg/day) in both male and female 8 weeks old genetic hypertensive mice (Lin), whereas the control (Lin) received 0.1M sodium citrate injections. 8 weeks after STZ. Mice were fed either regular or CANA-infused diet for 4 weeks. 8 weeks after STZ, hyperglycemia was present in both male and female mice. CANA reversed BG increase mice fed regular diet. Male LinSTZ mice had elevated water intake, urine output, urinary albumin to creatinine ratio (ACR), kidney lesion score, and creatinine clearance compared to the Lin control group. Kidney injury was improved in male LinSTZ + CANA group in male mice. Water intake and urine output were not statistically significantly different in female LinSTZ compared to female LinSTZ+ CANA. Moreover, CANA did not improve kidney injury in female mice, showing no effect in creatinine clearance, lesion score and fibrosis when compared to LinSTZ fed regular diet. Here we show that Canagliflozin might exert different kidney protection effects in male compared to female mice with hypertension and type 1 diabetes. Sex-dimorphisms were previously found in the pathophysiology of diabetes induced by STZ. Therefore, we highlight the importance of in-depth investigation on sex-dependent effects of CANA, taking in consideration the unique characteristics of disease progression for each sex.

PMID:38055691 | DOI:10.1371/journal.pone.0295284

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Proprotein convertase subtilisn/kexin type 9 inhibitors and small interfering RNA therapy for cardiovascular risk reduction: A systematic review and meta-analysis

PLoS One. 2023 Dec 6;18(12):e0295359. doi: 10.1371/journal.pone.0295359. eCollection 2023.

ABSTRACT

BACKGROUND: Atherosclerotic cardiovascular disease (ASCVD) is the leading cause of mortality worldwide. Atherosclerosis occurs due to accumulation of low-density lipoprotein cholesterol (LDL-c) in the arterial system. Thus, lipid lowering therapy is essential for both primary and secondary prevention. Proprotein convertase subtilisn/kexin type 9 (PCSK9) inhibitors (Evolocumab, Alirocumab) and small interfering RNA (siRNA) therapy (Inclisiran) have been demonstrated to lower LDL-c and ASCVD events in conjunction with maximally tolerated statin therapy. However, the degree of LDL-c reduction and the impact on reducing major adverse cardiac events, including their impact on mortality, remains unclear.

OBJECTIVE: The purpose of this study is to examine the effects of PCSK9 inhibitors and small interfering RNA (siRNA) therapy on LDL-c reduction and major adverse cardiac events (MACE) and mortality by conducting a meta-analysis of randomized controlled trials.

METHODS: Using Pubmed, Embase, Cochrane Library and clinicaltrials.gov until April 2023, we extracted randomized controlled trials (RCTs) of PCSK9 inhibitors (Evolocumab, Alirocumab) and siRNA therapy (Inclisiran) for lipid lowering and risk of MACE. Using random-effects models, we pooled the relative risks and 95% CIs and weighted least-squares mean difference in LDL-c levels. We estimated odds ratios with 95% CIs among MACE subtypes and all-cause mortality. Fixed-effect model was used, and heterogeneity was assessed using the I2 statistic.

RESULTS: In all, 54 studies with 87,669 participants (142,262 person-years) met criteria for inclusion. LDL-c percent change was reported in 47 studies (n = 62,634) evaluating two PCSK9 inhibitors and siRNA therapy. Of those, 21 studies (n = 41,361) included treatment with Evolocumab (140mg), 22 (n = 11,751) included Alirocumab (75mg), and 4 studies (n = 9,522) included Inclisiran (284mg and 300mg). Compared with placebo, after a median of 24 weeks (IQR 12-52), Evolocumab reduced LDL-c by -61.09% (95% CI: -64.81, -57.38, p<0.01) and Alirocumab reduced LDL-c by -46.35% (95% CI: -51.75, -41.13, p<0.01). Inclisiran 284mg reduced LDL-c by -54.83% (95% CI: -59.04, -50.62, p = 0.05) and Inclisiran 300mg reduced LDL-c by -43.11% (95% CI: -52.42, -33.80, p = 0.01). After a median of 8 months (IQR 6-15), Evolocumab reduced the risk of myocardial infarction (MI), OR 0.72 (95% CI: 0.64, 0.81, p<0.01), coronary revascularization, 0.77 (95% CI: 0.70, 0.84, p<0.01), stroke, 0.79 (95% CI: 0.66, 0.94, p = 0.01) and overall MACE 0.85 (95% CI: 0.80, 0.89, p<0.01). Alirocumab reduced MI, 0.57 (0.38, 0.86, p = 0.01), cardiovascular mortality 0.35 (95% CI: 0.16, 0.77, p = 0.01), all-cause mortality 0.60 (95% CI: 0.43, 0.84, p<0.01), and overall MACE 0.35 (0.16, 0.77, p = 0.01).

CONCLUSION: PCSK9 inhibitors (Evolocumab, Alirocumab) and siRNA therapy (Inclisiran) significantly reduced LDL-c by >40% in high-risk individuals. Additionally, both Alirocumab and Evolocumab reduced the risk of MACE, and Alirocumab reduced cardiovascular and all-cause mortality.

PMID:38055686 | DOI:10.1371/journal.pone.0295359

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Nevin Manimala Statistics

Heart girth best predicts live weights of market-age pigs in Tanzania

PLoS One. 2023 Dec 6;18(12):e0295433. doi: 10.1371/journal.pone.0295433. eCollection 2023.

ABSTRACT

The aim of this study was to use linear body measurements to develop and validate a regression-based model for prediction of live weights (LW) of pigs reared under smallholder settings in rural areas in the southern highlands of Tanzania. LW of 400 pigs (range 7 to 91 kg) was measured, along with their heart girths (HG) and body lengths (BL). BL was measured from the midpoint between the ears to the tail base. HG was measured as chest circumference just behind the front legs. LW was determined using a portable hanging scale. An analysis of covariance was performed to test for differences in LW between male and female pigs, including age, HG and BL as covariates. LW was regressed on HG and BL using simple and multiple linear regressions. Models were developed for all pig ages, and separately for market/breeding-age pigs and those below market/breeding age. Model validation was done using a split-samples approach, followed by PRESS-related statistics. Model efficiency and accuracy were assessed using the coefficient of determination, R2, and standard deviation of the random error, respectively. Model stability was determined by assessing ‘shrinkage’ of R2 value. Results showed that HG was the best predictor of LW in market/breeding-age pigs (model equation: LW = 1.22HG-52.384; R2 = 0.94, error = 3.7). BL, age and sex of pigs did not influence LW estimates. It is expected that LW estimation tools will be developed to enable more accurate estimation of LW in the pig value chain in the area.

PMID:38055667 | DOI:10.1371/journal.pone.0295433

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Nevin Manimala Statistics

Trend and determinants of home delivery in Gambia, evidence from 2013 and 2020 Gambia Demographic and Health Survey: A multivariate decomposition analysis

PLoS One. 2023 Dec 6;18(12):e0295219. doi: 10.1371/journal.pone.0295219. eCollection 2023.

ABSTRACT

BACKGROUND: Home delivery is defined as is an even of pregnant women getting giving birth in a woman her home or other homes without an unskilled health professional assistance. It is continuing as public health problem since its responsible for death of women and newborn. In Gambia there is a high maternal mortality rate, which may be related to home delivery. Therefore, this study aimed to assess the trend of home delivery and identify predictors using Gambia Demographic and Health Survey (GDHS) 2013 and 2019-2020 data sets.

METHODS: A Cross-Section survey was conducted based on GDHS 2013 and 2019-2020 among reproductive age group women. A total of 8607 women participated in this study. A bivariate decomposition model was fitted, and variables that had a p-value > 0.25 were dropped. Finally, variables that got a p-value of < 0.05 with 95% confidence interval (CI) in the multivariate decomposition analysis were considered as statistical significance variables in the overall decomposition.

RESULTS: There has been a dramatic decrement in maternal home delivery in Gambia. It was 36.18% (95% CI:34.78, 37.58) in 2013 GDHS and 14.39% (95% CI:13.31,15.47) in 2019-2020 GDHS. This reduction is real because there was a change in the characteristics effect of the population and the coefficient effect some variables in the home delivery. Changes in characteristics effect of husband education, women education, rural residents, more than three antenatal cares follow up, and no problem reaching health facilities played a significant role in the reduction of home delivery. Being urban resident and women who had occupation were variables that had a positive effect on coefficient effect change.

CONCLUSION: In this study, the home delivery rate had steeply declined in the Gambia during the study period of the two surveys. Just above nine-tenths decrement in home delivery rate resulted because there was a change in the characteristics effect of the study participants. Enhancing more citizens to attend high school and above, narrowing the gap between rural and urban in terms of accessing health facilities, and improving the availability of infrastructure should be done.

PMID:38055662 | DOI:10.1371/journal.pone.0295219

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Nevin Manimala Statistics

Smartphone sensor data estimate alcohol craving in a cohort of patients with alcohol-associated liver disease and alcohol use disorder

Hepatol Commun. 2023 Dec 7;7(12):e0329. doi: 10.1097/HC9.0000000000000329. eCollection 2023 Dec 1.

ABSTRACT

BACKGROUND: Sensors within smartphones, such as accelerometer and location, can describe longitudinal markers of behavior as represented through devices in a method called digital phenotyping. This study aimed to assess the feasibility of digital phenotyping for patients with alcohol-associated liver disease and alcohol use disorder, determine correlations between smartphone data and alcohol craving, and establish power assessment for future studies to prognosticate clinical outcomes.

METHODS: A total of 24 individuals with alcohol-associated liver disease and alcohol use disorder were instructed to download the AWARE application to collect continuous sensor data and complete daily ecological momentary assessments on alcohol craving and mood for up to 30 days. Data from sensor streams were processed into features like accelerometer magnitude, number of calls, and location entropy, which were used for statistical analysis. We used repeated measures correlation for longitudinal data to evaluate associations between sensors and ecological momentary assessments and standard Pearson correlation to evaluate within-individual relationships between sensors and craving.

RESULTS: Alcohol craving significantly correlated with mood obtained from ecological momentary assessments. Across all sensors, features associated with craving were also significantly correlated with all moods (eg, loneliness and stress) except boredom. Individual-level analysis revealed significant relationships between craving and features of location entropy and average accelerometer magnitude.

CONCLUSIONS: Smartphone sensors may serve as markers for alcohol craving and mood in alcohol-associated liver disease and alcohol use disorder. Findings suggest that location-based and accelerometer-based features may be associated with alcohol craving. However, data missingness and low participant retention remain challenges. Future studies are needed for further digital phenotyping of relapse risk and progression of liver disease.

PMID:38055637 | DOI:10.1097/HC9.0000000000000329

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Prediction of Suicide Attempts Among Persons with Depression: A Population-Based Case Cohort Study

Am J Epidemiol. 2023 Dec 5:kwad237. doi: 10.1093/aje/kwad237. Online ahead of print.

ABSTRACT

Studies have highlighted the potential importance of modeling interactions for suicide attempt prediction. This case-cohort study identified risk factors for suicide attempts among persons with depression in Denmark using statistical approaches that do (random forests) or do not model interactions (least absolute shrinkage and selection operator regression [LASSO]). Cases made a non-fatal suicide attempt (n = 6,032) between 1995 and 2015. The comparison subcohort was a 5% random sample of all persons in Denmark on January 1, 1995 (n = 11,963). We used random forests and LASSO for sex-stratified prediction of suicide attempts from demographic variables, psychiatric and somatic diagnoses, and treatments. Poisonings, psychiatric disorders, and medications were important predictors for both sexes. Area under the receiver operating characteristic curve (AUC) values were higher in LASSO models (0.85 [95% CI = 0.84, 0.86] in men; 0.89 [95% CI = 0.88, 0.90] in women) than random forests (0.76 [95% CI = 0.74, 0.78] in men; 0.79 [95% CI = 0.78, 0.81] in women). Automatic detection of interactions via random forests did not result in better model performance than LASSO models that did not model interactions. Due to the complex nature of psychiatric comorbidity and suicide, modeling interactions may not always be the optimal statistical approach to enhancing suicide attempt prediction in high-risk samples.

PMID:38055633 | DOI:10.1093/aje/kwad237