Tag: nevin manimala

Proc Natl Acad Sci U S A. 2023 Mar 14;120(11):e2122352120. doi: 10.1073/pnas.2122352120. Epub 2023 Mar 10.

ABSTRACT

A crucial challenge in medicine is choosing which drug (or combination) will be the most advantageous for a particular patient. Usually, drug response rates differ substantially, and the reasons for this response unpredictability remain ambiguous. Consequently, it is central to classify features that contribute to the observed drug response variability. Pancreatic cancer is one of the deadliest cancers with limited therapeutic achievements due to the massive presence of stroma that generates an environment that enables tumor growth, metastasis, and drug resistance. To understand the cancer-stroma cross talk within the tumor microenvironment and to develop personalized adjuvant therapies, there is a necessity for effective approaches that offer measurable data to monitor the effect of drugs at the single-cell level. Here, we develop a computational approach, based on cell imaging, that quantifies the cellular cross talk between pancreatic tumor cells (L3.6pl or AsPC1) and pancreatic stellate cells (PSCs), coordinating their kinetics in presence of the chemotherapeutic agent gemcitabine. We report significant heterogeneity in the organization of cellular interactions in response to the drug. For L3.6pl cells, gemcitabine sensibly decreases stroma-stroma interactions but increases stroma-cancer interactions, overall enhancing motility and crowding. In the AsPC1 case, gemcitabine promotes the interactions among tumor cells, but it does not affect stroma-cancer interplay, possibly suggesting a milder effect of the drug on cell dynamics.

PMID:36897966 | DOI:10.1073/pnas.2122352120

Sci Adv. 2023 Mar 10;9(10):eabo1638. doi: 10.1126/sciadv.abo1638. Epub 2023 Mar 10.

ABSTRACT

Increases in concurrent climate extremes in different parts of the world threaten the ecosystem and our society. However, spatial patterns of these extremes and their past and future changes remain unclear. Here, we develop a statistical framework to test for spatial dependence and show widespread dependence of temperature and precipitation extremes in observations and model simulations, with more frequent than expected concurrence of extremes around the world. Historical anthropogenic forcing has strengthened the concurrence of temperature extremes over 56% of 946 global paired regions, particularly in the tropics, but has not yet significantly affected concurrent precipitation extremes during 1901-2020. The future high-emissions pathway of SSP585 will substantially amplify the concurrence strength, intensity, and spatial extent for both temperature and precipitation extremes, especially over tropical and boreal regions, while the mitigation pathway of SSP126 can ameliorate the increase in concurrent climate extremes for these high-risk regions. Our findings will inform adaptation strategies to alleviate the impact of future climate extremes.

PMID:36897946 | DOI:10.1126/sciadv.abo1638

PLoS One. 2023 Mar 10;18(3):e0282730. doi: 10.1371/journal.pone.0282730. eCollection 2023.

ABSTRACT

BACKGROUND: Research into mood and emotion has often depended on slow and subjective self-report, highlighting a need for rapid, accurate, and objective assessment tools.

METHODS: To address this gap, we developed a method using digital image speckle correlation (DISC), which tracks subtle changes in facial expressions invisible to the naked eye, to assess emotions in real-time. We presented ten participants with visual stimuli triggering neutral, happy, and sad emotions and quantified their associated facial responses via detailed DISC analysis.

RESULTS: We identified key alterations in facial expression (facial maps) that reliably signal changes in mood state across all individuals based on these data. Furthermore, principal component analysis of these facial maps identified regions associated with happy and sad emotions. Compared with commercial deep learning solutions that use individual images to detect facial expressions and classify emotions, such as Amazon Rekognition, our DISC-based classifiers utilize frame-to-frame changes. Our data show that DISC-based classifiers deliver substantially better predictions, and they are inherently free of racial or gender bias.

LIMITATIONS: Our sample size was limited, and participants were aware their faces were recorded on video. Despite this, our results remained consistent across individuals.

CONCLUSIONS: We demonstrate that DISC-based facial analysis can be used to reliably identify an individual’s emotion and may provide a robust and economic modality for real-time, noninvasive clinical monitoring in the future.

PMID:36897921 | DOI:10.1371/journal.pone.0282730

PLoS One. 2023 Mar 10;18(3):e0281606. doi: 10.1371/journal.pone.0281606. eCollection 2023.

ABSTRACT

INTRODUCTION: Childhood illnesses, such as acute respiratory illness, fever, and diarrhoea, continue to be public health problems in low-income countries. Detecting spatial variations of common childhood illnesses and service utilisation is essential for identifying inequities and call for targeted actions. This study aimed to assess the geographical distribution and associated factors for common childhood illnesses and service utilisation across Ethiopia based on the 2016 Demographic and Health Survey.

METHODS: The sample was selected using a two-stage stratified sampling process. A total of 10,417 children under five years were included in this analysis. We linked data on their common illnesses during the last two weeks and healthcare utilisation were linked to Global Positioning System (GPS) information of their local area. The spatial data were created in ArcGIS10.1 for each study cluster. We applied a spatial autocorrelation model with Moran’s index to determine the spatial clustering of the prevalence of childhood illnesses and healthcare utilisation. Ordinary Least Square (OLS) analysis was done to assess the association between selected explanatory variables and sick child health services utilisation. Hot and cold spot clusters for high or low utilisation were identified using Getis-Ord Gi*. Kriging interpolation was done to predict sick child healthcare utilisation in areas where study samples were not drawn. All statistical analyses were performed using Excel, STATA, and ArcGIS.

RESULTS: Overall, 23% (95CI: 21, 25) of children under five years had some illness during the last two weeks before the survey. Of these, 38% (95%CI: 34, 41) sought care from an appropriate provider. Illnesses and service utilisation were not randomly distributed across the country with a Moran’s index 0.111, Z-score 6.22, P<0.001, and Moran’s index = 0.0804, Z-score 4.498, P< 0.001, respectively. Wealth and reported distance to health facilities were associated with service utilisation. Prevalence of common childhood illnesses was higher in the North, while service utilisation was more likely to be on a low level in the Eastern, South-western, and the Northern parts of the country.

CONCLUSION: Our study provided evidence of geographic clustering of common childhood illnesses and health service utilisation when the child was sick. Areas with low service utilisation for childhood illnesses need priority, including actions to counteract barriers such as poverty and long distances to services.

PMID:36897920 | DOI:10.1371/journal.pone.0281606

PLoS One. 2023 Mar 10;18(3):e0282249. doi: 10.1371/journal.pone.0282249. eCollection 2023.

ABSTRACT

INTRODUCTION: Contraceptive services utilization is an important intervention in averting the impact of unwanted and unplanned pregnancy among youth which is an obstacle to the higher learning institutions youth students in attaining their educational goals. Therefore, the current protocol aims to assess the motivators for family planning service utilization among youth student in higher learning institutions in Dodoma Tanzania.

METHODS: This study will be a cross-sectional study with quantitative approach. A multistage sampling technique will be employed in studying 421 youth students aged between 18 to 24 years using structured self-administered questionnaire adopted from the previous studies. The study outcome will be family planning service utilization and independent variables will be family planning service utilization environment, knowledge factors, and perception factors. Other factors such as socio-demographic characteristics will be assessed if they are confounding factors. A factor will be considered as a confounder if it associates with both the dependent and the independent variables. Multivariable Binary logistic regression will be employed in determining the motivators for family planning utilization. The results will be presented using percentages, frequencies, and Odds Ratios and the association will be considered statistically significant at p-value <0.05.

PMID:36897915 | DOI:10.1371/journal.pone.0282249

PLoS Comput Biol. 2023 Mar 10;19(3):e1010333. doi: 10.1371/journal.pcbi.1010333. Online ahead of print.

ABSTRACT

In many real-world applications, such as those based on electronic health records, prognostic prediction of patient survival is based on heterogeneous sets of clinical laboratory measurements. To address the trade-off between the predictive accuracy of a prognostic model and the costs related to its clinical implementation, we propose an optimized L0-pseudonorm approach to learn sparse solutions in multivariable regression. The model sparsity is maintained by restricting the number of nonzero coefficients in the model with a cardinality constraint, which makes the optimization problem NP-hard. In addition, we generalize the cardinality constraint for grouped feature selection, which makes it possible to identify key sets of predictors that may be measured together in a kit in clinical practice. We demonstrate the operation of our cardinality constraint-based feature subset selection method, named OSCAR, in the context of prognostic prediction of prostate cancer patients, where it enables one to determine the key explanatory predictors at different levels of model sparsity. We further explore how the model sparsity affects the model accuracy and implementation cost. Lastly, we demonstrate generalization of the presented methodology to high-dimensional transcriptomics data.

PMID:36897911 | DOI:10.1371/journal.pcbi.1010333

J Infect Dev Ctries. 2023 Feb 28;17(2):260-267. doi: 10.3855/jidc.15493.

ABSTRACT

INTRODUCTION: The present study evaluated the characteristics of the initial dengue outbreaks in the Jaffna peninsula, a region without dengue prior to mid-2009 in dengue-endemic Sri Lanka, a tropical island nation.

METHODOLOGY: This is a cross-sectional study conducted using a total of 765 dengue patients’ clinical data and samples collected from the Teaching Hospital, Jaffna during the initial dengue outbreaks. Clinical, non-specific, and specific virological laboratory characteristics including the platelet count, NS1 antigen, and anti-DENV IgM/IgG were evaluated as correlates of dengue virus (DENV) infection in the two initial outbreaks of 2009/2010 and 2011/2012 in Northern Sri Lanka.

RESULTS: Firstly, affected age and clinical characteristics were significantly different between the outbreaks (p < 0.005). Secondly, NS1 antigen detection in patients with fever days < 5 was statistically significant (p < 0.005). Thirdly, platelet count, detection of NS1 antigen, and anti-DENV IgM/IgG profiles were adequate to diagnose 90% of the patients; hepatomegaly and platelet count of < 25,000/mm3 were identified as predictors of severe disease. Fourthly, secondary DENV infections were detected in the early stages of the illness in many patients. Finally, infecting DENV serotypes were different between the two outbreaks.

CONCLUSIONS: Clinical and non-specific laboratory characteristics and the infecting DENV serotypes between the two initial outbreaks in Northern Sri Lanka were significantly different. NS1 antigen, anti-DENV IgM/IgG, and platelet counts were identified 90% of the dengue patients. Hepatomegaly and platelet count of < 25,000/mm3 were able to predict the disease severity in this study.

PMID:36897909 | DOI:10.3855/jidc.15493

ESC Heart Fail. 2023 Mar 10. doi: 10.1002/ehf2.14284. Online ahead of print.

ABSTRACT

BACKGROUND: Recent heart failure (HF) guidelines have re-classified HF patients with left ventricular ejection fraction (LVEF) between 41% and 49% as HF with mildly reduced ejection fraction (HFmrEF). HFmrEF treatment is often considered a grey zone as no randomized controlled trials (RCTs) were conducted exclusively on these patients.

AIMS: A network meta-analysis (NMA) was performed to compare treatment effect of mineralocorticoid receptor antagonists (MRA), angiotensin receptor neprilysin inhibitor (ARNi), angiotensin receptor blockers (ARB), angiotensin-converting-enzyme inhibitors (ACEi), sodium-glucose cotransporter-2 inhibitors (SGLT2i), and beta-blockers (BB) in HFmrEF cardiovascular (CV) outcomes.

METHODS AND RESULTS: RCTs sub-analyses evaluating the efficacy of pharmacological treatment in HFmrEF patients were searched. Hazard ratios (HRs) and their variance were extracted from each RCT for (i) composite of CV death or HF hospitalizations, (ii) CV death, and (iii) HF hospitalizations. A random-effects NMA was performed to compare and assess the treatment efficiency. Six RCTs with subgroup analysis according to participants’ ejection fraction, a patient-level pooled meta-analysis of two RCTs, and an individual patient-level analysis of eleven BB RCTs were included, totalling 7966 patients. To our primary endpoint, SGLT2i vs. placebo was the only comparison with significant results, with a 19% risk reduction in the composite of CV death or HF hospitalizations [HR 0.81, 95% confidence interval (CI) 0.67-0.98]. In HF hospitalizations, the impact of the pharmacological therapies was more notorious, and ARNi reduced in 40% the risk of HF hospitalizations (HR 0.60, 95% CI 0.39-0.92), SGLT2i in 26% (HR 0.74, 95% CI 0.59-0.93) and renin-angiotensin system inhibition (RASi) with ARB and ACEi in 28% (HR 0.72, 95% CI 0.53-0.98). Although BBs were globally less beneficial, they were the only class that supported a reduced risk of CV death (HR vs. placebo: 0.48, 95% CI 0.24-0.95). We did not observe a statistically significant difference in any comparison between active treatments. There was a sound reduction with ARNi on the primary endpoint (HR vs. BB: 0.81, 95% CI 0.47-1.41; HR vs. MRA 0.94, 95% CI 0.53-1.66) and on HF hospitalizations (HR vs. RASi 0.83, 95% CI 0.62-1.11; HR vs. SGLT2i 0.80, 95% CI 0.50-1.30).

CONCLUSIONS: In addition to SGLT2i, pharmacological treatment recommended for HF with reduced LVEF, namely, ARNi, MRA, and BB, can also be effective in HFmrEF. This NMA did not show significant superiority over any pharmacological class.

PMID:36896801 | DOI:10.1002/ehf2.14284

Psychol Med. 2023 Mar 10:1-9. doi: 10.1017/S0033291723000429. Online ahead of print.

ABSTRACT

BACKGROUND: Patients with remitted psychosis face a dilemma between the wish to discontinue antipsychotics and the risk of relapse. We test if an operationalized guided-dose-reduction algorithm can help reach a lower effective dose without increased risks of relapse.

METHODS: A 2-year open-label randomized prospective comparative cohort trial from Aug 2017 to Sep 2022. Patients with a history of schizophrenia-related psychotic disorders under stable medications and symptoms were eligible, randomized 2:1 into guided dose reduction group (GDR) v. maintenance treatment group (MT1), together with a group of naturalistic maintenance controls (MT2). We observed if the relapse rates would be different between 3 groups, to what extent the dose could be reduced, and if GDR patients could have improved functioning and quality of life.

RESULTS: A total of 96 patients, comprised 51, 24, and 21 patients in GDR, MT1, and MT2 groups, respectively. During follow-up, 14 patients (14.6%) relapsed, including 6, 4, and 4 from GDR, MT1, and MT2, statistically no difference between groups. In total, 74.5% of GDR patients could stay well under a lower dose, including 18 patients (35.3%) conducting 4 consecutive dose-tapering and staying well after reducing 58.5% of their baseline dose. The GDR group exhibited improved clinical outcomes and endorsed better quality of life.

CONCLUSIONS: GDR is a feasible approach as the majority of patients had a chance to taper antipsychotics to certain extents. Still, 25.5% of GDR patients could not successfully decrease any dose, including 11.8% experienced relapse, a risk comparable to their maintenance counterparts.

PMID:36896797 | DOI:10.1017/S0033291723000429

Mol Ecol. 2023 Mar 10. doi: 10.1111/mec.16912. Online ahead of print.

ABSTRACT

Deforestation threatens the integrity of the Amazon biome and the ecosystem services it provides, including greenhouse gas mitigation. Forest-to-pasture conversion has been shown to alter the flux of methane gas (CH₄ ) in Amazonian soils, driving a switch from acting as a sink to a source of atmospheric CH₄ . This study aimed to better understand this phenomenon by investigating soil microbial metagenomes, focusing on the taxonomic and functional structure of methane-cycling communities. Metagenomic data from forest and pasture soils were combined with in situ CH₄ fluxes and soil edaphic factors measurements and analysed using multivariate statistical approaches. We found a significantly higher abundance and diversity of methanogens in pasture soils. As inferred by co-occurrence networks, these microorganisms seem to be less interconnected within the soil microbiota in pasture soils. Metabolic traits were also different between land uses, with increased hydrogenotrophic and methylotrophic pathways of methanogenesis in pasture soils. Land-use change also induced shifts in taxonomic and functional traits of methanotrophs, with bacteria harboring genes encoding the soluble form of methane monooxygenase enzyme (sMMO) depleted in pasture soils. Redundancy analysis and multimodel inference revealed that the shift in methane-cycling communities was associated with high pH, organic matter, soil porosity, and micronutrients in pasture soils. These results comprehensively characterize the effect of forest-to-pasture conversion on the microbial communities driving the methane-cycling microorganisms in the Amazon rainforest, which will contribute to the efforts to preserve this important biome.

PMID:36896778 | DOI:10.1111/mec.16912