Tag: nevin manimala

Bone Marrow Transplant. 2024 Nov 25. doi: 10.1038/s41409-024-02480-3. Online ahead of print.

ABSTRACT

Chronic myelomonocytic leukemia (CMML) is a clonal hematopoietic stem cell malignancy and the only curable therapy is allogeneic hematopoietic stem cell transplantation (allo-HSCT). However, allo-HSCT is not appropriate for all CMML patients, and relapse is the leading cause of treatment failure. This project conducted a nationwide multicenter real-world study to develop a novel prediction scoring system for early relapse. A total of 238 CMML patients from twenty-seven medical centers treated with allo-HSCT, and 307 adult patients with CMML who underwent allo-HSCT in a publicly available research dataset from the Center for International Blood and Marrow Transplantation Registry (CIBMTR) database were included. Independent prognostic factors for the early relapse of CMML posttransplantation were identified according to competing risk regression methods. Four prognostic factors were identified: bone marrow blasts >10% (hazard ratio [HR], 4.262; P = 0.014), age >60 years (HR, 6.221; P = 0.007), hemoglobin level <100 g/L (HR, 3.695; P = 0.004), and non TET2 gene mutation (HR, 3.425; P = 0.017). A risk-grading scoring system was developed based on the regression coefficients and patients were stratified into low-risk (0-1 point), intermediate-risk (1.5-2 points) and high-risk ( > 2 points) groups. The validated internal c-statistic was 0.767 (95% confidence interval [CI], 0.674-0.860), and the external c-statistic was 0.769 (95% CI, 0.703-0.836). In the derivation cohort, the cumulative incidence rates of early relapse in the low-risk, intermediate-risk, and high-risk groups were 1.35% (95% CI: 1-4%), 10.40% (95% CI: 4-16%), and 29.54% (95% CI: 16-39%) (P < 0.001), respectively. This scoring system can be utilized to early identification of patients at a high risk of relapse and contributing to the implementation of urgent medical support.

PMID:39587323 | DOI:10.1038/s41409-024-02480-3

Ecol Appl. 2024 Nov 25:e3066. doi: 10.1002/eap.3066. Online ahead of print.

ABSTRACT

Earthworms are a key faunal group in agricultural soils, but little is known on how farming systems affect their communities across wide climatic gradients and how farming system choice might mediate earthworms’ exposure to climate conditions. Here, we studied arable soil earthworm communities on wheat fields across a European climatic gradient, covering nine pedo-climatic zones, from Mediterranean to Boreal (S to N) and from Lusitanian to Pannonian (W to E). In each zone, 20-25 wheat fields under conventional or organic farming were sampled. Community metrics (total abundance, fresh mass, and species richness and composition) were combined with data on climate conditions, soil properties, and field management and analyzed with mixed models. There were no statistically discernible differences between organic and conventional farming for any of the community metrics. The effects of refined arable management factors were also not detected, except for an elevated proportion of subsurface-feeding earthworms when crop residues were incorporated. Soil properties were not significantly associated with earthworm community variations, which in the case of soil texture was likely due to low variation in the data. Pedo-climatic zone was an overridingly important factor in explaining the variation in community metrics. The Boreal zone had the highest mean total abundance (179 individuals m^-2) and fresh mass (86 g m^-2) of earthworms while the southernmost Mediterranean zones had the lowest metrics (<1 individual m^-2 and <1 g m^-2). Within each field, species richness was low across the zones, with the highest values being recorded at the Nemoral and North Atlantic zones (mean of 2-3 species per field) and declining from there toward north and south. No litter-dwelling species were found in the southernmost, Mediterranean zones. These regional trends were discernibly related to climate, with the community metrics declining with the increasing mean annual temperature. The current continent-wide warming of Europe and related increase of severe and rapid onsetting droughts will likely deteriorate the living conditions of earthworms, particularly in southern Europe. The lack of interaction between the pedo-climatic zone and the farming system in our data for any of the earthworm community metrics may indicate limited opportunities for alleviating the negative effects of a warming climate in cereal field soils of Europe.

PMID:39587320 | DOI:10.1002/eap.3066

Sci Rep. 2024 Nov 26;14(1):29258. doi: 10.1038/s41598-024-80610-z.

ABSTRACT

This study aimed to evaluate the predictive value of metabolic parameters in preoperative non-enhancing peritumoral regions (NEPTRs) for glioblastoma recurrence, using multivoxel hydrogen proton magnetic resonance spectroscopy (1H-MRS). Clinical and imaging data from patients with recurrent glioblastoma were analyzed. Through co-registration of preoperative and post-recurrence MRI, we identified future tumor recurrence regions (FTRRs) and future non-tumor recurrence regions (FNTRRs) within the NEPTRs. Metabolic parameters were recorded separately for each region. Cox regression analysis was applied to assess the association between metabolic parameters and glioblastoma recurrence. Compared to FNTRRs, FTRRs exhibited a higher Cho/Cr ratio, higher Cho/NAA ratio, and lower NAA/Cr ratio. Both Cho/NAA and Cho/Cr ratios were recognized as risk factors in univariate and multivariate analyses (P < 0.05). The Cox regression model indicated that Cho/NAA > 1.99 and Cho/Cr > 1.73 are independent risk factors for early glioblastoma recurrence. Based on these cut-off values, patients were stratified into low-risk and high-risk groups, with a statistically significant difference in recurrence rates between the two groups (P < 0.01). The Cho/NAA and Cho/Cr ratios in NEPTRs are independent predictors of future glioblastoma recurrence. Specifically, Cho/NAA > 1.99 and/or Cho/Cr > 1.73 in NEPTRs may indicate a higher risk of early postoperative recurrence at these regions.

PMID:39587278 | DOI:10.1038/s41598-024-80610-z

Sci Rep. 2024 Nov 26;14(1):29261. doi: 10.1038/s41598-024-80849-6.

ABSTRACT

The dynamic and multimodal nature of photovoltaic (PV) systems makes it challenging to examine all solar photovoltaic characteristics. Consequently, this study recommends a recently developed optimization method called the marine predator algorithm (MPA) for developing reliable PV models. In the traditional MPA, the two main search processes are Lévy flight (LF) and Brownian walk (BW), and the switch across them is unpredictable. This is while the transition between these two mechanisms is naturally continuous and dynamic. To rectify the limitation mentioned above, this research paper presents an innovative, dynamic shift function that effectively modulates the interplay that exists between the BW and LF procedures. By enhancing the changeover pattern between the primary phases of MPA, the suggested dynamic walk substantially boosts the performance of MPA. The dynamic Lévy-Brownian MPA (DLBMPA) is also made to be resilient in dealing with the parameterization limitations of PV Modeling approaches by using a constraint handling technique. The performance of DLBMPA is tested using ten popular optimization methods. Employing the DLBMPA achieved an average RMSE of 9.7 × 10^{– 4} in the parameter estimation across a number of multiple PV models, including the SDM, DDM, and TDM, where out of the ten optimization algorithms experimented, this was statistically significant (p < 0.05) better. In terms of averaged computation time, DLBMPA was 13 ms and still showed high accuracy in dealing with different irradiance and temperature levels. These improvements allow for MBPA to be credited as having a high efficiency when estimating the PV parameters since its speed of convergence and accuracy level surpass the previous techniques used.

PMID:39587262 | DOI:10.1038/s41598-024-80849-6

Nat Cancer. 2024 Nov 25. doi: 10.1038/s43018-024-00863-5. Online ahead of print.

ABSTRACT

Refractoriness to initial chemotherapy and relapse after remission are the main obstacles to curing T cell acute lymphoblastic leukemia (T-ALL). While tumor heterogeneity has been implicated in treatment failure, the cellular and genetic factors contributing to resistance and relapse remain unknown. Here we linked tumor subpopulations with clinical outcome, created an atlas of healthy pediatric hematopoiesis and applied single-cell multiomic analysis to a diverse cohort of 40 T-ALL cases. We identified a bone marrow progenitor (BMP)-like leukemia subpopulation associated with treatment failure and poor overall survival. The single-cell-derived molecular signature of BMP-like blasts predicted poor outcome across multiple subtypes of T-ALL and revealed that NOTCH1 mutations additively drive T-ALL blasts away from the BMP-like state. Through in silico and in vitro drug screenings, we identified a therapeutic vulnerability of BMP-like blasts to apoptosis-inducing agents including venetoclax. Collectively, our study establishes multiomic signatures for rapid risk stratification and targeted treatment of high-risk T-ALL.

PMID:39587259 | DOI:10.1038/s43018-024-00863-5

Sci Rep. 2024 Nov 25;14(1):29229. doi: 10.1038/s41598-024-80650-5.

ABSTRACT

A novel, optimized, polysaccharide and biochar-based, compostable hydrogel horticultural growing substrate for use in hydroponics and vertical farming was created based upon empirical methods and statistical design of experiments. A 15-run D-optimal mixture design of experiments was completed that increased the 14-day plant growing ability of a five-component hydrogel nearly ten-fold from 4.3695 g to 41.2623 g per 100 plants. The data were analyzed using a standard least squares method with an effect screening emphasis, and a model was created that maximized the signal to noise ratio. There was a good correlation between the measured and predicted values of the model, with an r-squared value of 0.90. The predictions of efficacy and compostability were confirmed with subsequent experiments that showed the hydrogel was composted in less than 84 days and that the plant growth predicted by the model differed from the experimental growth by 0.65%. The resulting optimized formulation had a high fertilizer content for a growth medium. We therefore suggest that an empirical approach to formulation research can produce superior outcomes with a statistically designed study.

PMID:39587252 | DOI:10.1038/s41598-024-80650-5

Nat Ecol Evol. 2024 Nov 25. doi: 10.1038/s41559-024-02594-3. Online ahead of print.

ABSTRACT

A pressing question resulting from global warming is how climate change will affect infectious diseases. Answering this question requires research into the effects of weather on the population dynamics of transmission and infection; elucidating these effects, however, has proved difficult due to the challenges of assessing causality from the predominantly observational data available in epidemiological research. Here we show how concepts from causal inference-the sub-field of statistics aiming at inferring causality from data-can guide that research. Through a series of case studies, we illustrate how such concepts can help assess study design and strategically choose a study’s location, evaluate and reduce the risk of bias, and interpret the multifaceted effects of meteorological variables on transmission. More broadly, we argue that interdisciplinary approaches based on explicit causal frameworks are crucial for reliably estimating the effect of weather and accurately predicting the consequences of climate change.

PMID:39587221 | DOI:10.1038/s41559-024-02594-3

Sci Rep. 2024 Nov 25;14(1):29205. doi: 10.1038/s41598-024-80554-4.

ABSTRACT

Insecticide based paint formulations have been available since years, however the concept of using such paint products at household level did not get attention due to various reasons. The advancement in insecticidal paint technology has steered toward the development and evaluation of such formulations for use against arthropod vectors. The improved insecticidal paint formulations may contain two or more active agents, hence could display different type of activity against the target vectors. In the present study, optimum concentrations of deltamethrin (1%), chlorpyriphos (0.5%) and pyriproxyfen (0.075%) were used as active ingredients (AIs) to develop flame retardant slow-release insecticides paint (FRSRIP) formulation. The developed formulation was tested for physico-chemical properties, toxicity and efficacy against two important mosquito vectors. The formulation was glossy, smooth, uniform and scratch proof. Furthermore, the formulation was flame retardant and conformed to class-A according to the guidelines. Acute oral, dermal and inhalation toxicity suggested that the formulation is safe for use in human dwellings. The formulation was evaluated against Ae. aegypti and An. stephensi mosquitoes in laboratory upto four years. It was found that KDT₅₀ after 24 months (T24) was 14.8 ± 0.8 min and 17.1 ± 1.0 min, while after 48 month (T48) was 21.3 ± 2.0 min and 22.4 ± 1.4 min in both Ae. aegypti and An. stephensi respectively. KDT₅₀ was found varying during the different time intervals (T6 to T48) in both Ae. aegypti (p = 0.01) and An. stephensi (p = 0.0003). Furthermore the corrected mortality (CM) also found statistically declined during the period of evaluation (T6 to T48) in both the test species (F ≥ 42. 4; p ≤ 0.0001). Ae. aegypti mosquitoes that survived FRSRIP exposure exhibited overall decline in total eggs laid, eggs hatched, pupae formed and adult emerged at different time intervals upto T21. Behavioural experiments showed that both the tested species elicited negative response to the test formulation. The concentrations of all the three active agents were estimated by HPLC after different time intervals, however only deltamethrin (0.24%) was found after T48. The developed formulation was stable, safe and effective against mosquito vectors for a considerably longer time. In the pretext of continuous toll of vector borne diseases and trans-boundary expansion of mosquito vectors into new geographical areas, the idea of using insecticidal paint could be a game changer.

PMID:39587220 | DOI:10.1038/s41598-024-80554-4

Commun Med (Lond). 2024 Nov 25;4(1):247. doi: 10.1038/s43856-024-00680-y.

ABSTRACT

BACKGROUND: The advantage of intensified myeloablative conditioning (MAC) over standard MAC has not been determined in haematopoietic stem cell transplantation (HSCT) for adult acute lymphoblastic leukemia (ALL) patients.

METHODS: To evaluate heterogeneous effects of intensified MAC among individuals, we analyzed the registry database of adult ALL patients between 2000 and 2021. After propensity score matching, we applied a machine-learning Bayesian causal forest algorithm to develop a prediction model of individualized treatment effect (ITE) of intensified MAC on reduction in overall mortality at 1 year after HSCT.

RESULTS: Among 2440 propensity score-matched patients, our model shows heterogeneity in the association between intensified MAC and 1-year overall mortality. Individuals in the high-benefit group (n = 1220), defined as those with ITEs greater than the median, are more likely to be younger, male, and to have higher refined Disease Risk Index (rDRI), T-cell phenotype, and grafts from related donors than those in the low-benefit group (n = 1220). The high-benefit approach (applying intensified MAC to individuals in the high-benefit group) shows the largest reduction in overall mortality at 1 year (risk difference [95% confidence interval], +5.94 percentage points [0.88 to 10.51], p = 0.011). In contrast, the high-risk approach (targeting patients with high or very high rDRI) does not achieve statistical significance (risk difference [95% confidence interval], +3.85 percentage points [-1.11 to 7.90], p = 0.063).

CONCLUSIONS: These findings suggest that the high-benefit approach, targeting patients expected to benefit from intensified MAC, has the capacity to maximize HSCT effectiveness using intensified MAC.

PMID:39587218 | DOI:10.1038/s43856-024-00680-y

Sci Rep. 2024 Nov 26;14(1):29272. doi: 10.1038/s41598-024-80488-x.

ABSTRACT

To meet the challenges of increasing food production demand globally, extracting insights regarding the persistent agriculture-related problems on a nationwide scale is the need of the hour. Policymakers now have limited possibilities for acquiring a comprehensive knowledge of the difficulties that farmers face on a national level. In this direction, the presented work proposes a new artificial intelligence-based pipeline to gain insights at country level regarding the farmers’ demand for assistance in India. The presented study uses the data from the Kisan Call Centres, a nationwide network of farmer’s helplines, including 28.6 million call-log records, made available by the Ministry of Agriculture & Farmers’ Welfare, Government of India. Additionally, the extracted insights are presented in the form of “Topic-wise Problems’ Trend Clusters” (TPTC), which can be used by policymakers in both the government and private sectors to aid decision-making. The article also introduces a pipeline for designing forecasting models to estimate the monthly frequency of farmer inquiries (in terms of the number of query calls). The seven statistical forecasting models were examined in the study with the TBATP1 (Trigonometric seasonal components with Box-Cox transformation incorporating ARIMA errors and Trend including the Seasonal components) model attaining the lowest error rates in terms of Root Mean Square Error (0.034) and Mean Absolute Error (0.107). The study also explores numerous applications of the derived insights in the real world as well as the future scope of the presented work.

PMID:39587214 | DOI:10.1038/s41598-024-80488-x