Category: Nevin Manimala

BMC Womens Health. 2023 Jan 2;23(1):2. doi: 10.1186/s12905-022-02136-8.

ABSTRACT

BACKGROUND: The knowledge of ovulatory cycle (KOC) is the base for natural family planning methods, yet few studies have paid attention to women’s KOC. This study aimed to assess the prevalence of correct KOC and its determinants among women of childbearing age in Haiti.

METHODS: Data from the nationally representative cross-sectional Haiti Demographic and Health Survey 2016/17 were used. STATA/SE version 14 was employed to analyse the data by computing descriptive statistics, Chi‑square, and binary logistic regression model to assess the socio-economic and demographic predictors of correct KOC. P-value less than 0.05 was taken as a significant association.

RESULTS: Out of 14,371 women of childbearing age who constituted our sample study, 24.1% (95% CI 23.4-24.8) had correct KOC. In addition, the findings showed that place of residence, respondent’s education level, wealth index, currently working, husband/partner’s education level, contraceptive use, exposure to mass media FP messages, and fieldworker visit were significantly associated with correct KOC.

CONCLUSION: Policies should include increasing the literacy at community level as well as of individual women and their partners. Moreover, increasing awareness about family planning should be prioritized, especially for women from poor households and rural areas.

PMID:36593445 | DOI:10.1186/s12905-022-02136-8

BMC Psychiatry. 2023 Jan 3;23(1):1. doi: 10.1186/s12888-022-04495-w.

ABSTRACT

BACKGROUND: Incidents of suicide can be categorized into three main types: solitary suicides, suicides following homicide, and suicide pacts. Although these three suicide incidents vary by definition, no studies to-date have simultaneously examined and compared them for potential differences. The objective of the current study was to empirically and descriptively compare solitary suicides, suicides following homicide, and suicide pacts in the United States.

METHODS: Restricted-access data from the National Violent Death Report System for 2003-2019 for 262,679 solitary suicides, 4,352 suicides following homicide, and 450 suicide pacts were used. Pairwise comparisons of the three suicide incident types were made for demographic factors, method of suicide, preceding circumstances, mental health status, and toxicology findings.

RESULTS: Solitary suicides, suicides following homicide, and suicide pacts have distinct profiles, with statistically significant (p < 0.05) differences across all pairwise comparisons of sex, race, ethnicity, marital status, education, method of suicide, financial problems, interpersonal relationship problems, physical health problems, mental health problems, mood disorders, suicide attempt history, and opiate use at the time of death.

CONCLUSION: Despite sharing a few commonalities, solitary suicides, suicides following homicide, and suicide pacts represent distinct phenomena. Each of these suicide incident types likely have their own unique prevention pathways.

PMID:36593442 | DOI:10.1186/s12888-022-04495-w

Lifetime Data Anal. 2023 Jan 2. doi: 10.1007/s10985-022-09580-6. Online ahead of print.

ABSTRACT

Quantile residual lifetime (QRL) is of significant interest in many clinical studies as an easily interpretable quantity compared to other summary measures of survival distributions. In cancer or other chronic diseases, treatments are often compared based on the distributions or quantiles of the residual lifetime. Thus a common problem of interest is to test the equality of the QRL between two populations. In this paper, we propose two classes of tests to compare two QRLs; one class is based on the difference between two estimated QRLs, and the other is based on the estimating function of the QRL, where the estimated QRL from one sample is plugged into the QRL-estimating-function of the other sample. We outline the asymptotic properties of these test statistics. Simulation studies demonstrate that the proposed tests produced Type I errors closer to the nominal level and are superior to some existing tests based on both Type I error and power. Our proposed test statistics are also computationally less intensive and more straightforward compared to tests based on the confidence intervals. We applied the proposed methods to a randomized multicenter phase III trial for breast cancer patients.

PMID:36593432 | DOI:10.1007/s10985-022-09580-6

Nat Biotechnol. 2023 Jan 2. doi: 10.1038/s41587-022-01520-x. Online ahead of print.

ABSTRACT

The application of multiple omics technologies in biomedical cohorts has the potential to reveal patient-level disease characteristics and individualized response to treatment. However, the scale and heterogeneous nature of multi-modal data makes integration and inference a non-trivial task. We developed a deep-learning-based framework, multi-omics variational autoencoders (MOVE), to integrate such data and applied it to a cohort of 789 people with newly diagnosed type 2 diabetes with deep multi-omics phenotyping from the DIRECT consortium. Using in silico perturbations, we identified drug-omics associations across the multi-modal datasets for the 20 most prevalent drugs given to people with type 2 diabetes with substantially higher sensitivity than univariate statistical tests. From these, we among others, identified novel associations between metformin and the gut microbiota as well as opposite molecular responses for the two statins, simvastatin and atorvastatin. We used the associations to quantify drug-drug similarities, assess the degree of polypharmacy and conclude that drug effects are distributed across the multi-omics modalities.

PMID:36593394 | DOI:10.1038/s41587-022-01520-x

Br J Cancer. 2023 Jan 2. doi: 10.1038/s41416-022-02124-7. Online ahead of print.

ABSTRACT

BACKGROUND: Dysregulation of alternative splicing (AS) triggers many tumours, understanding the roles of splicing events during tumorigenesis would open new avenues for therapies and prognosis in multiple myeloma (MM).

METHODS: Molecular, genetic, bioinformatic and statistic approaches are used to determine the mechanism of the candidate splicing factor (SF) in myeloma cell lines, myeloma xenograft models and MM patient samples.

RESULTS: GSEA reveals a significant difference in the expression pattern of the alternative splicing pathway genes, notably enriched in MM patients. Upregulation of the splicing factor SRSF1 is observed in the progression of plasma cell dyscrasias and predicts MM patients’ poor prognosis. The c-indices of the Cox model indicated that SRSF1 improved the prognostic stratification of MM patients. Moreover, SRSF1 knockdown exerts a broad anti-myeloma activity in vitro and in vivo. The upregulation of SRSF1 is caused by the transcription factor YY1, which also functions as an oncogene in myeloma cells. Through RNA-Seq, we systematically verify that SRSF1 promotes the tumorigenesis of myeloma cells by switching AS events.

CONCLUSION: Our results emphasise the importance of AS for promoting tumorigenesis of MM. The candidate SF might be considered as a valuable therapeutic target and a potential prognostic biomarker for MM.

PMID:36593359 | DOI:10.1038/s41416-022-02124-7

Sci Rep. 2023 Jan 2;13(1):59. doi: 10.1038/s41598-022-27318-0.

ABSTRACT

Decision support systems can seriously help medical doctors in the diagnosis of different diseases, especially in complicated cases. This article is devoted to recognizing and diagnosing heart disease based on automatic computer processing of the electrocardiograms (ECG) of patients. In the general case, the change of the ECG parameters can be presented as a random sequence of the signals under processing. Developing new computational methods for such signal processing is an important research problem in creating efficient medical decision support systems. Authors consider the possibility of increasing the diagnostic accuracy of cardiovascular diseases by implementing of the new proposed computational method of information processing. This method is based on the generalized nonlinear canonical decomposition of a random sequence of the change of cardiogram parameters. The use of a nonlinear canonical model makes it possible to significantly simplify the maximum likelihood criterion for classifying diseases. This simplification is provided by the transition from a multi-dimensional distribution density of cardiogram parameters to a product of one-dimensional distribution densities of independent random coefficients of a nonlinear canonical decomposition. The absence of any restrictions on the class of random sequences under study makes it possible to achieve maximum accuracy in diagnosing cardiovascular diseases. Functional diagrams for implementing the proposed method reflecting the features of its application are presented. The quantitative parameters of the core of the computational diagnostic procedure can be determined in advance based on the preliminary statistical data of the ECGs for different heart diseases. That is why the developed method is quite simple in terms of computation (computing complexity, accuracy, computing time, etc.) and can be implemented in medical computer decision systems for monitoring cardiovascular diseases and for their diagnosis in real time. The results of the numerical experiment confirm the high accuracy of the developed method for classifying cardiovascular diseases.

PMID:36593356 | DOI:10.1038/s41598-022-27318-0

Eur J Epidemiol. 2023 Jan 3. doi: 10.1007/s10654-022-00921-1. Online ahead of print.

ABSTRACT

Laboratory and animal research support a protective role for vitamin D in breast carcinogenesis, but epidemiologic studies have been inconclusive. To examine comprehensively the relationship of circulating 25-hydroxyvitamin D [25(OH)D] to subsequent breast cancer incidence, we harmonized and pooled participant-level data from 10 U.S. and 7 European prospective cohorts. Included were 10,484 invasive breast cancer cases and 12,953 matched controls. Median age (interdecile range) was 57 (42-68) years at blood collection and 63 (49-75) years at breast cancer diagnosis. Prediagnostic circulating 25(OH)D was either newly measured using a widely accepted immunoassay and laboratory or, if previously measured by the cohort, calibrated to this assay to permit using a common metric. Study-specific relative risks (RRs) for season-standardized 25(OH)D concentrations were estimated by conditional logistic regression and combined by random-effects models. Circulating 25(OH)D increased from a median of 22.6 nmol/L in consortium-wide decile 1 to 93.2 nmol/L in decile 10. Breast cancer risk in each decile was not statistically significantly different from risk in decile 5 in models adjusted for breast cancer risk factors, and no trend was apparent (P-trend = 0.64). Compared to women with sufficient 25(OH)D based on Institute of Medicine guidelines (50- < 62.5 nmol/L), RRs were not statistically significantly different at either low concentrations (< 20 nmol/L, 3% of controls) or high concentrations (100- < 125 nmol/L, 3% of controls; ≥ 125 nmol/L, 0.7% of controls). RR per 25 nmol/L increase in 25(OH)D was 0.99 [95% confidence intervaI (CI) 0.95-1.03]. Associations remained null across subgroups, including those defined by body mass index, physical activity, latitude, and season of blood collection. Although none of the associations by tumor characteristics reached statistical significance, suggestive inverse associations were seen for distant and triple negative tumors. Circulating 25(OH)D, comparably measured in 17 international cohorts and season-standardized, was not related to subsequent incidence of invasive breast cancer over a broad range in vitamin D status.

PMID:36593337 | DOI:10.1007/s10654-022-00921-1

Sci Rep. 2023 Jan 2;13(1):71. doi: 10.1038/s41598-022-26858-9.

ABSTRACT

In insect-pollinated plants, the foraging behavior of pollinators affects their pattern of movement. If distinct bee species vary in their foraging behaviors, different models may best describe their movement. In this study, we quantified and compared the fine scale movement of three bee species foraging on patches of Medicago sativa. Bee movement was described using distances and directions traveled between consecutive racemes. Bumble bees and honey bees traveled shorter distances after visiting many flowers on a raceme, while the distance traveled by leafcutting bees was independent of flower number. Transition matrices and vectors were calculated for bumble bees and honey bees to reflect their directionality of movement within foraging bouts; leafcutting bees were as likely to move in any direction. Bee species varied in their foraging behaviors, and for each bee species, we tested four movement models that differed in how distances and directions were selected, and identified the model that best explained the movement data. The fine-scale, within-patch movement of bees could not always be explained by a random movement model, and a general model of movement could not be applied to all bee species.

PMID:36593317 | DOI:10.1038/s41598-022-26858-9

Int Arch Occup Environ Health. 2023 Jan 3. doi: 10.1007/s00420-022-01950-9. Online ahead of print.

ABSTRACT

OBJECTIVE: Despite the continuous development of occupational safety, the prevalence of work-related head injuries is excessive. To promote prevention, we conducted a study evaluating the risks and pathways that precede head injuries in different economic activity sectors.

METHODS: In Finland, more than 90% of employees are covered by inclusive statutory workers’ compensation. We obtained data on occupational head injuries in 2010-2017 from an insurance company database. The European Statistics on Accidents at Work (ESAW) variables represented the characteristics of the accidents and the injury. We analysed the risk factors, contributing events and injury mechanisms in 20 industry sectors, based on the Statistical Classification of Economic Activities in the European Community (NACE).

RESULTS: In the 32,898 cases, the most commonly affected area was the eyes (49.6%). The highest incidence of head injuries was in construction (15.7 per 1000 insurance years). Construction, manufacturing, and human health and social work activities stood out due to their distinctive ESAW category counts. ‘Working with hand-held tools’ [risk ratio (RR) 2.23, 95% confidence interval (CI) 2.14-2.32] in construction and ‘operating machines’ (RR 3.32, 95% CI 3.01-3.66) and ‘working with hand-held tools’ (1.99, 1.91-2.07) in manufacturing predicted head injury. The risk related to parameters of violence and threats in health and social work activities was nearly ninefold the risk of other sectors.

CONCLUSION: The risks and pathways preceding head injuries varied considerably. The highest head injury rates were in construction and manufacturing. Violence emerged as a major risk factor in human health and social work activities.

PMID:36593301 | DOI:10.1007/s00420-022-01950-9

Sci Rep. 2023 Jan 2;13(1):13. doi: 10.1038/s41598-022-27264-x.

ABSTRACT

Risk prediction models are frequently used to identify individuals at risk of developing hypertension. This study evaluates different machine learning algorithms and compares their predictive performance with the conventional Cox proportional hazards (PH) model to predict hypertension incidence using survival data. This study analyzed 18,322 participants on 24 candidate features from the large Alberta’s Tomorrow Project (ATP) to develop different prediction models. To select the top features, we applied five feature selection methods, including two filter-based: a univariate Cox p-value and C-index; two embedded-based: random survival forest and least absolute shrinkage and selection operator (Lasso); and one constraint-based: the statistically equivalent signature (SES). Five machine learning algorithms were developed to predict hypertension incidence: penalized regression Ridge, Lasso, Elastic Net (EN), random survival forest (RSF), and gradient boosting (GB), along with the conventional Cox PH model. The predictive performance of the models was assessed using C-index. The performance of machine learning algorithms was observed, similar to the conventional Cox PH model. Average C-indexes were 0.78, 0.78, 0.78, 0.76, 0.76, and 0.77 for Ridge, Lasso, EN, RSF, GB and Cox PH, respectively. Important features associated with each model were also presented. Our study findings demonstrate little predictive performance difference between machine learning algorithms and the conventional Cox PH regression model in predicting hypertension incidence. In a moderate dataset with a reasonable number of features, conventional regression-based models perform similar to machine learning algorithms with good predictive accuracy.

PMID:36593280 | DOI:10.1038/s41598-022-27264-x