Nevin Manimala

BMC Bioinformatics. 2024 Mar 9;25(1):105. doi: 10.1186/s12859-024-05723-8.

ABSTRACT

MOTIVATION: The prediction of cancer drug response is a challenging subject in modern personalized cancer therapy due to the uncertainty of drug efficacy and the heterogeneity of patients. It has been shown that the characteristics of the drug itself and the genomic characteristics of the patient can greatly influence the results of cancer drug response. Therefore, accurate, efficient, and comprehensive methods for drug feature extraction and genomics integration are crucial to improve the prediction accuracy.

RESULTS: Accurate prediction of cancer drug response is vital for guiding the design of anticancer drugs. In this study, we propose an end-to-end deep learning model named DeepAEG which is based on a complete-graph update mode to predict IC50. Specifically, we integrate an edge update mechanism on the basis of a hybrid graph convolutional network to comprehensively learn the potential high-dimensional representation of topological structures in drugs, including atomic characteristics and chemical bond information. Additionally, we present a novel approach for enhancing simplified molecular input line entry specification data by employing sequence recombination to eliminate the defect of single sequence representation of drug molecules. Our extensive experiments show that DeepAEG outperforms other existing methods across multiple evaluation parameters in multiple test sets. Furthermore, we identify several potential anticancer agents, including bortezomib, which has proven to be an effective clinical treatment option. Our results highlight the potential value of DeepAEG in guiding the design of specific cancer treatment regimens.

PMID:38461284 | DOI:10.1186/s12859-024-05723-8

BMC Oral Health. 2024 Mar 9;24(1):319. doi: 10.1186/s12903-024-04064-5.

ABSTRACT

BACKGROUND: In the regenerative endodontic procedures, scaffolds could influence the prognosis of affected teeth. Currently, there is controversy regarding the postoperative evaluation of various scaffolds for pulp regeneration. The objective of this study was to access whether other scaffolds, used alone or in combination with blood clot (BC), are more effective than BC in regenerative endodontic procedures.

METHODS: We systematically search the PubMed, the Cochrane Central Register of Controlled Trials (CENTRAL), Embase, and Google Scholar databases. Randomized controlled trials examining the use of BC and other scaffold materials in the regenerative endodontic procedures were included. A random effects model was used for the meta-analysis. The GRADE method was used to determine the quality of the evidence.

RESULTS: We screened 168 RCTs related to young permanent tooth pulp necrosis through electronic and manual retrieval. A total of 28 RCTs were related to regenerative endodontic procedures. Ultimately, 12 articles met the inclusion criteria and were included in the relevant meta-analysis. Only 2 studies were assessed to have a low risk of bias. High quality evidence indicated that there was no statistically significant difference in the success rate between the two groups (RR=0.99, 95% CI=0.96 to 1.03; 434 participants, 12 studies); low-quality evidence indicated that there was no statistically significant difference in the increase in root length or root canal wall thickness between the two groups. Medium quality evidence indicated that there was no statistically significant difference in pulp vitality testing between the two groups.

CONCLUSIONS: For clinical regenerative endodontic procedures, the most commonly used scaffolds include BC, PRP, and PRF. All the different scaffolds had fairly high clinical success rates, and the difference was not significant. For regenerative endodontic procedures involving young permanent teeth with pulp necrosis, clinical practitioners could choose a reasonable scaffold considering the conditions of the equipment and patients.

PMID:38461281 | DOI:10.1186/s12903-024-04064-5

Alzheimers Res Ther. 2024 Mar 9;16(1):54. doi: 10.1186/s13195-024-01421-y.

ABSTRACT

BACKGROUND: Due to the heterogeneity among patients with Mild Cognitive Impairment (MCI), it is critical to predict their risk of converting to Alzheimer’s disease (AD) early using routinely collected real-world data such as the electronic health record data or administrative claim data.

METHODS: The study used MarketScan Multi-State Medicaid data to construct a cohort of MCI patients. Logistic regression with tree-guided lasso regularization (TGL) was proposed to select important features and predict the risk of converting to AD. A subsampling-based technique was used to extract robust groups of predictive features. Predictive models including logistic regression, generalized random forest, and artificial neural network were trained using the extracted features.

RESULTS: The proposed TGL workflow selected feature groups that were robust, highly interpretable, and consistent with existing literature. The predictive models using TGL selected features demonstrated higher prediction accuracy than the models using all features or features selected using other methods.

CONCLUSIONS: The identified feature groups provide insights into the progression from MCI to AD and can potentially improve risk prediction in clinical practice and trial recruitment.

PMID:38461266 | DOI:10.1186/s13195-024-01421-y

BMC Oral Health. 2024 Mar 9;24(1):313. doi: 10.1186/s12903-024-04012-3.

ABSTRACT

OBJECTIVES: To predict and identify the key demographic and clinical exposure factors associated with dental anxiety among young adults, and to compare if the traditional statistical modelling approach provides similar results to the machine learning (ML) approach in predicting factors for dental anxiety.

METHODS: A cross-sectional study of Western Illinois University students. Three survey instruments (sociodemographic questionnaire, modified dental anxiety scale (MDAS), and dental concerns assessment tool (DCA)) were distributed via email to the students using survey monkey. The dependent variable was the mean MDAS scores, while the independent variables were the sociodemographic and dental concern assessment variables. Multivariable analysis was done by comparing the classical statistical model and the machine learning model. The classical statistical modelling technique was conducted using the multiple linear regression analysis and the final model was selected based on Akaike information Criteria (AIC) using the backward stepwise technique while the machine learining modelling was performed by comparing two ML models: LASSO regression and extreme gradient boosting machine (XGBOOST) under 5-fold cross-validation using the resampling technique. All statistical analyses were performed using R version 4.1.3.

RESULTS: The mean MDAS was 13.73 ± 5.51. After careful consideration of all possible fitted models and their interaction terms the classical statistical approach yielded a parsimonious model with 13 predictor variables with Akaike Information Criteria (AIC) of 2376.4. For the ML approach, the Lasso regression model was the best-performing model with a mean RMSE of 0.617, R² of 0.615, and MAE of 0.483. Comparing the variable selection of ML versus the classical statistical model, both model types identified 12 similar variables (out of 13) as the most important predictors of dental anxiety in this study population.

CONCLUSION: There is a high burden of dental anxiety within this study population. This study contributes to reducing the knowledge gap about the impact of clinical exposure variables on dental anxiety and the role of machine learningin the prediction of dental anxiety. The predictor variables identified can be used to inform public health interventions that are geared towards eliminating the individual clinical exposure triggers of dental anxiety are recommended.

PMID:38461263 | DOI:10.1186/s12903-024-04012-3

BMC Public Health. 2024 Mar 9;24(1):750. doi: 10.1186/s12889-024-18254-x.

ABSTRACT

BACKGROUND: Women in their reproductive age have tremendous health implications that affect their health and well-being. Anaemia is an indicator of inadequate dietary intake and poor health. Maternal malnutrition significantly impacts maternal and child health outcomes, increasing the mother’s risk of dying during delivery. High-risk fertility behaviour is a barrier to reducing mother and child mortality. This study aims to examine the level of high-risk fertility behaviour and anaemia among ever-married urban Indian women and also examine the linkages between the both.

METHODS: Based on the National Family Health Survey’s fifth round of data, the study analyzed 44,225 samples of ever-married urban women. Univariate and bivariate analysis and binary logistic regression have been used for the analysis.

RESULTS: Findings suggested that more than half (55%) of the urban women were anaemic, and about one-fourth (24%) of women had any high-risk fertility behaviour. Furthermore, the results suggest that 20% of women were more vulnerable to anaemia due to high-risk fertility behaviour. For the specific category, 19% and 28% of women were more likely to be anaemic due to single and multiple high-risk fertility. However, after controlling for sociodemographic factors, the findings showed a statistically significant link between high-risk fertility behaviour and anaemia. As a result, 16% of the women were more likely to be anaemic due to high-risk fertility behaviour, and 16% and 24% were more likely to be anaemic due to single and multiple high-risk fertility behaviour, respectively.

CONCLUSIONS: The findings exposed that maternal high-risk fertility behaviour is a significant factor in raising the chance of anaemia in ever-married urban women of reproductive age in forms of the short birth interval, advanced maternal age, and advanced maternal age & higher order. Policy and choice-based family planning techniques should be employed to minimize the high-risk fertility behaviour among Indian urban women. This might aid in the reduction of the malnutrition status of their children.

PMID:38461259 | DOI:10.1186/s12889-024-18254-x

BMC Med Res Methodol. 2024 Mar 9;24(1):62. doi: 10.1186/s12874-024-02184-8.

ABSTRACT

INTRODUCTION: Interrupted time series (ITS) design is a commonly used method for evaluating large-scale interventions in clinical practice or public health. However, improperly using this method can lead to biased results.

OBJECTIVE: To investigate design and statistical analysis characteristics of drug utilization studies using ITS design, and give recommendations for improvements.

METHODS: A literature search was conducted based on PubMed from January 2021 to December 2021. We included original articles that used ITS design to investigate drug utilization without restriction on study population or outcome types. A structured, pilot-tested questionnaire was developed to extract information regarding study characteristics and details about design and statistical analysis.

RESULTS: We included 153 eligible studies. Among those, 28.1% (43/153) clearly explained the rationale for using the ITS design and 13.7% (21/153) clarified the rationale of using the specified ITS model structure. One hundred and forty-nine studies used aggregated data to do ITS analysis, and 20.8% (31/149) clarified the rationale for the number of time points. The consideration of autocorrelation, non-stationary and seasonality was often lacking among those studies, and only 14 studies mentioned all of three methodological issues. Missing data was mentioned in 31 studies. Only 39.22% (60/153) reported the regression models, while 15 studies gave the incorrect interpretation of level change due to time parameterization. Time-varying participant characteristics were considered in 24 studies. In 97 studies containing hierarchical data, 23 studies clarified the heterogeneity among clusters and used statistical methods to address this issue.

CONCLUSION: The quality of design and statistical analyses in ITS studies for drug utilization remains unsatisfactory. Three emerging methodological issues warranted particular attention, including incorrect interpretation of level change due to time parameterization, time-varying participant characteristics and hierarchical data analysis. We offered specific recommendations about the design, analysis and reporting of the ITS study.

PMID:38461257 | DOI:10.1186/s12874-024-02184-8

Lipids Health Dis. 2024 Mar 9;23(1):73. doi: 10.1186/s12944-024-02059-3.

ABSTRACT

BACKGROUND: Abdominal aortic calcification (AAC) is a crucial indicator of cardiovascular health. This study aims investigates the associations between dietary fatty acid intake and AAC.

METHODS: In this study, a cross-sectional assessment was performed on a group of 2,897 individuals aged 40 and above, utilizing data from the NHANES. The focus was on examining dietary consumption of various fatty acids, including Saturated (SFA), Monounsaturated (MUFA), Polyunsaturated (PUFA), as well as Omega-3 and Omega-6. The evaluation of AAC was done by applying the Kauppila AAC score to results obtained from dual-energy X-ray absorptiometry scans. For statistical analysis, weighted multivariate linear and logistic regression were employed, with adjustments for variables like gender, age, ethnicity, and overall health condition.

RESULTS: Participants with higher intake of SFA and PUFA showed a positive association with AAC score, while higher levels of dietary Omega-3 and Omega-6 fatty acids was connected with a negative correlation. Subgroup analyses indicated consistent associations across different sexes and age groups. The study found that an increase in SFA and PUFA intake correlated with an increase in AAC score, whereas Omega-3 and Omega-6 intake correlated with a decrease.

CONCLUSION: This study underscores the importance of dietary fatty acid composition in the prevalence of AAC and its potential implications for dietary guidelines and cardiovascular disease prevention strategies.

PMID:38461250 | DOI:10.1186/s12944-024-02059-3

BMC Oral Health. 2024 Mar 9;24(1):317. doi: 10.1186/s12903-024-04080-5.

ABSTRACT

BACKGROUND: Surgical guides have been proposed in an attempt to reach more predictable outcomes for esthetic crown lengthening. The objective of the present study was to evaluate the effectiveness of esthetic crown lengthening using 3D-printed surgical guides in the management of excessive gingival display due to altered passive eruption type 1B.

MATERIALS AND METHODS: Sixteen patients diagnosed with altered passive eruption type 1B, were divided into two groups. In the control group, the procedure was carried out conventionally, and in the study group, a dual surgical guide was used. The parameters of wound healing (swelling, color, probing depth, bleeding index, and plaque index), pain scores, gingival margin stability, and operating time were assessed at 1 week, 2 weeks, 3 months, and 6 months postoperatively.

RESULTS: There was no statistically significant difference in terms of wound healing, pain scores, and gingival margin stability between both groups at different time intervals (P = 1), however, there was a statistical difference between both groups in terms of operating time with the study group being significantly lower (P < 0.001).

CONCLUSION: Digitally assisted esthetic crown lengthening helps shorten the operating time and reduces the possibility of human errors during the measurements. This will be useful in helping practitioners achieve better results.

PRACTICAL IMPLICATIONS: The conventional method remains to be the gold standard. However, shorter operating time and lower margins for errors will help reduce costs as the chair side time is reduced as well as the possibility for a second surgery is lower. This will improve patient satisfaction as well.

PMID:38461241 | DOI:10.1186/s12903-024-04080-5

BMC Womens Health. 2024 Mar 9;24(1):168. doi: 10.1186/s12905-024-03005-2.

ABSTRACT

BACKGROUND: South African women have been exposed to epidemic proportions of intimate partner violence (IPV) amongst heterosexual relationships but not much is known about same-sex partnerships. Sexual minorities are excluded from research but are subject to intimate partner violence as much as heteronormative persons. The purpose of this study is to determine the association between age-disparity and IPV outcomes among females with same-sex partners in South Africa.

METHODS: A cross-sectional study of the nationally representative South African National HIV Prevalence, Incidence, Behaviour and Communication Survey (SABSSM 2017) is used. A weighted sample of 63,567 female respondents identified as having a same-sex partner are analysed. IPV is measured as ever been physically and/ or sexually abused. Any experience of IPV is included in the dependent variable of this study. Descriptive and inferential statistics are used to estimate the relationship between demographic, socioeconomic, age-disparity and IPV.

RESULTS: Almost 16% of females in same-sex relationships experienced IPV and about 22% from younger partners. In female same-sex partnerships, partner age-disparity (OR: 1.30, CI: 1.18 – 1.51), type of place of residence (OR: 2.27, CI: 1.79 – 3.79), highest level of education (OR: 1.07, CI: 0.97 – 1.17), marital status (OR: 1.60, CI: 1.37 – 1.88), and race (OR: 1.47, CI: 1.41 – 1.54) are associated with an increased likelihood of violence.

CONCLUSION: IPV programs that are specifically targeted for non-heteronormative orientations are needed. These programs should promote health equity and safety for non-confirmative sexual identities in the country.

PMID:38461233 | DOI:10.1186/s12905-024-03005-2

Popul Health Metr. 2024 Mar 9;22(1):4. doi: 10.1186/s12963-024-00324-2.

ABSTRACT

BACKGROUND: Studying long-term trends in educational inequalities in health is important for monitoring and policy evaluation. Data issues regarding the allocation of people to educational groups hamper the study and international comparison of educational inequalities in mortality. For the UK, this has been acknowledged, but no satisfactory solution has been proposed.

OBJECTIVE: To enable the examination of long-term mortality trends by educational level for England and Wales (E&W) in a time-consistent and internationally comparable manner, we propose and implement an approach to deal with the data issues regarding mortality data by educational level.

METHODS: We employed 10-year follow-ups of individuals aged 20+ from the Office for National Statistics Longitudinal Study (ONS-LS), which include education information from each decennial census (1971-2011) linked to individual death records, for a 1% representative sample of the E&W population. We assigned the individual cohort data to single ages and calendar years, and subsequently obtained aggregate all-cause mortality data by education, sex, age (30+), and year (1972-2017). Our data adjustment approach optimised the available education information at the individual level, and adjusts-at the aggregate level-for trend discontinuities related to the identified data issues, and for differences with country-level mortality data for the total population.

RESULTS: The approach resulted in (1) a time-consistent and internationally comparable categorisation of educational attainment into the low, middle, and high educated; (2) the adjustment of identified data-quality related discontinuities in the trends over time in the share of personyears and deaths by educational level, and in the crude and the age-standardised death rate by and across educational levels; (3) complete mortality data by education for ONS-LS members aged 30+ in 1972-2017 which aligns with country-level mortality data for the total population; and (4) the estimation of inequality measures using established methods. For those aged 30+ , both absolute and relative educational inequalities in mortality first increased and subsequently decreased.

CONCLUSION: We obtained additional insights into long-term trends in educational inequalities in mortality in E&W, and illustrated the potential effects of different data issues. We recommend the use of (part of) the proposed approach in other contexts.

PMID:38461232 | DOI:10.1186/s12963-024-00324-2