Category: Nevin Manimala

Sci Rep. 2023 Dec 14;13(1):22238. doi: 10.1038/s41598-023-49491-6.

ABSTRACT

The risk of high-temperature-related diseases is increasing owing to global warming. This study aimed to assess the trend of disease burden caused by high temperatures in Mainland China from 1990 to 2019 and to predict the trend of disease burden over the next 10 years. The latest data were downloaded from the Global Burden of Disease Database (GBD) for analysis, and the disease burden related to high temperature was described by mortality and disability-adjusted life-years (DALYs) and stratified by etiology, sex, and age. Statistical analyses were performed using the R software. In 2019, there were 13,907 deaths attributed to high temperatures in Mainland China, and this was 29.55% higher than the 10,735 deaths in 1990. Overall, the age-standardized mortality and DALYs attributed to high temperatures showed a downward trend from 1990 to 2019. We observed an etiological shift in high-temperature-related diseases. The age-standardized DALYs contribution attributed to high temperatures in 1990 was mainly from communicable, maternal, neonatal, and nutritional diseases (CMNND) (21.81/100,000), followed by injury (18.30/100,000) and non-communicable diseases (10.40/100,000). In 2019, the largest contribution shifted to non-communicable diseases (10.07/100,000), followed by injuries (5.21/100,000), and CMNND (2.30/100,000). The disease burden attributed to high temperatures was higher in males than in females and increased with age. In 2030, the mortality rate and DALYs due to high temperatures are predicted to decrease further, and the largest contribution will come from chronic non-communicable diseases, the occurrence of which will remain at a high level over the next 10 years. The burden of disease due to high temperatures in Mainland China is still heavy, mainly due to population aging and an increase in non-communicable diseases.

PMID:38097708 | DOI:10.1038/s41598-023-49491-6

Sci Rep. 2023 Dec 14;13(1):22282. doi: 10.1038/s41598-023-49704-y.

ABSTRACT

The main goals of the Enhanced recovery after surgery (ERAS) protocol are focused on shortening the length of hospital stay (LOS), expediting convalescence, and reducing morbidity. A balanced perioperative fluid therapy is among the significant interventions incorporated by the ERAS protocol. The article contains extensive discussion surrounding the impact of this individual intervention on short-term outcomes. The aim of this study was to assess the impact of perioperative fluid therapy on short-term outcomes in patients after laparoscopic colorectal cancer surgery. The analysis included consecutive patients, who had undergone laparoscopic colorectal cancer operations between 2013 and 2020. Patients were divided into two groups: restricted (≤ 2500 ml) or excessive (> 2500 ml) perioperative fluid therapy. A standardized ERAS protocol was implemented in all patients. The study outcomes included recovery parameters and the morbidity rate, LOS and 30 days readmission rate. There were 361 and 80 patients in groups 1 and 2, respectively. There were no statistically significant differences between the groups in terms of demographic parameters and factors related to the surgical procedure. Logistic regression showed that restricted fluid therapy as a single intervention was associated with improvement in tolerance of diet on 1st postoperative day (OR 2.18, 95% CI 1.31-3.62, p = 0.003), accelerated mobilization on 1st postoperative day (OR 2.43, 95% CI 1.29-4.61, p = 0.006), lower risk of postoperative morbidity (OR 0.58, 95%CI 0.36-0.98, p = 0.046), shorter LOS (OR 0.49, 95% CI 0.29-0.81, p = 0.005) and reduced readmission rate (OR 0.48, 95% CI 0.23-0.98, p = 0.045). A balanced perioperative fluid therapy on the day of surgery may be associated with faster convalescence, lower morbidity rate, shorter LOS and lower 30 days readmission rate.

PMID:38097695 | DOI:10.1038/s41598-023-49704-y

Sci Rep. 2023 Dec 14;13(1):22261. doi: 10.1038/s41598-023-48426-5.

ABSTRACT

Traditional methods for assessing plant health often lack the necessary attributes for continuous and non-destructive monitoring. In this pilot study, we present a novel technique utilizing a customized fiber optic probe based on attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR) with a contact force control unit for non-invasive and continuous plant health monitoring. We also developed a normalized difference mid-infrared reflectance index through statistical analysis of spectral features, enabling differentiation of drought and age conditions in plants. Our research aims to characterize phytochemicals and plant endogenous status optically, addressing the need for improved analytical measurement methods for in situ plant health assessment. The probe configuration was optimized with a triple-loop tip and a 3 N contact force, allowing sensitive measurements while minimizing leaf damage. By combining polycrystalline and chalcogenide fiber probes, a comprehensive wavenumber range analysis (4000-900 cm^-1) was achieved. Results revealed significant variations in phytochemical composition among plant species, for example, red spinach with the highest polyphenolic content and green kale with the highest lignin content. Petioles displayed higher lignin and cellulose absorbance values compared to veins. The technique effectively monitored drought stress on potted green bok choy plants in situ, facilitating the quantification of changes in water content, antioxidant activity, lignin, and cellulose levels. This research represents the first demonstration of the potential of fiber optic ATR-FTIR probes for non-invasive and rapid plant health measurements, providing insights into plant health and advancements in quantitative monitoring for indoor farming practices, bioanalytical chemistry, and environmental sciences.

PMID:38097653 | DOI:10.1038/s41598-023-48426-5

Sci Rep. 2023 Dec 14;13(1):22268. doi: 10.1038/s41598-023-48886-9.

ABSTRACT

Amyloid proteins are often associated with the onset of diseases, including Alzheimer’s, Parkinson’s and many others. However, there is a wide class of functional amyloids that are involved in physiological functions, e.g., formation of microbial biofilms or storage of hormones. Recent studies showed that an amyloid fibril could affect the aggregation of another protein, even from a different species. This may result in amplification or attenuation of the aggregation process. Insight into amyloid cross-interactions may be crucial for better understanding of amyloid diseases and the potential influence of microbial amyloids on human proteins. However, due to the demanding nature of the needed experiments, knowledge of such interactions is still limited. Here, we present PACT (Prediction of Amyloid Cross-interaction by Threading) – the computational method for the prediction of amyloid cross-interactions. The method is based on modeling of a heterogeneous fibril formed by two amyloidogenic peptides. The resulting structure is assessed by the structural statistical potential that approximates its plausibility and energetic stability. PACT was developed and first evaluated mostly on data collected in the AmyloGraph database of interacting amyloids and achieved high values of Area Under ROC (AUC=0.88) and F1 (0.82). Then, we applied our method to study the interactions of CsgA – a bacterial biofilm protein that was not used in our in-reference datasets, which is expressed in several bacterial species that inhabit the human intestines – with two human proteins. The study included alpha-synuclein, a human protein that is involved in Parkinson’s disease, and human islet amyloid polypeptide (hIAPP), which is involved in type 2 diabetes. In both cases, PACT predicted the appearance of cross-interactions. Importantly, the method indicated specific regions of the proteins, which were shown to play a central role in both interactions. We experimentally confirmed the novel results of the indicated CsgA fragments interacting with hIAPP based on the kinetic characteristics obtained with the ThT assay. PACT opens the possibility of high-throughput studies of amyloid interactions. Importantly, it can work with fairly long protein fragments, and as a purely physicochemical approach, it relies very little on scarce training data. The tool is available as a web server at https://pact.e-science.pl/pact/ . The local version can be downloaded from https://github.com/KubaWojciechowski/PACT .

PMID:38097650 | DOI:10.1038/s41598-023-48886-9

Sci Rep. 2023 Dec 14;13(1):22240. doi: 10.1038/s41598-023-49544-w.

ABSTRACT

Accurate and in-time prediction of crop yield plays a crucial role in the planning, management, and decision-making processes within the agricultural sector. In this investigation, utilizing area under irrigation (%) as an exogenous variable, we have made an exertion to assess the suitability of different hybrid models such as ARIMAX (Autoregressive Integrated Moving Average with eXogenous Regressor)-TDNN (Time-Delay Neural Network), ARIMAX-NLSVR (Non-Linear Support Vector Regression), ARIMAX-WNN (Wavelet Neural Network), ARIMAX-CNN (Convolutional Neural Network), ARIMAX-RNN (Recurrent Neural Network) and ARIMAX-LSTM (Long Short Term Memory) as compared to their individual counterparts for yield forecasting of major Rabi crops in India. The accuracy of the ARIMA model has also been considered as a benchmark. Empirical outcomes reveal that the ARIMAX-LSTM hybrid modeling combination outperforms all other time series models in terms of root mean square error (RMSE) and mean absolute percentage error (MAPE) values. For these models, an average improvement of RMSE and MAPE values has been observed to be 10.41% and 12.28%, respectively over all other competing models and 15.83% and 18.42%, respectively over the benchmark ARIMA model. The incorporation of the area under irrigation (%) as an exogenous variable in the ARIMAX framework and the inbuilt capability of the LSTM model to process complex non-linear patterns have been observed to significantly enhance the accuracy of forecasting. The performance supremacy of other hybrid models over their individual counterparts has also been evident. The results also suggest avoiding any performance generalization of individual models for their hybrid structures.

PMID:38097613 | DOI:10.1038/s41598-023-49544-w

Transl Psychiatry. 2023 Dec 14;13(1):392. doi: 10.1038/s41398-023-02690-1.

ABSTRACT

Alcohol Use Disorder (AUD) adversely affects the lives of millions of people, but still lacks effective treatment options. Recent advancements in psychedelic research suggest psilocybin to be potentially efficacious for AUD. However, major knowledge gaps remain regarding (1) psilocybin’s general mode of action and (2) AUD-specific alterations of responsivity to psilocybin treatment in the brain that are crucial for treatment development. Here, we conducted a randomized, placebo-controlled crossover pharmaco-fMRI study on psilocybin effects using a translational approach with healthy rats and a rat model of alcohol relapse. Psilocybin effects were quantified with resting-state functional connectivity using data-driven whole-brain global brain connectivity, network-based statistics, graph theory, hypothesis-driven Default Mode Network (DMN)-specific connectivity, and entropy analyses. Results demonstrate that psilocybin induced an acute wide-spread decrease in different functional connectivity domains together with a distinct increase of connectivity between serotonergic core regions and cortical areas. We could further provide translational evidence for psilocybin-induced DMN hypoconnectivity reported in humans. Psilocybin showed an AUD-specific blunting of DMN hypoconnectivity, which strongly correlated to the alcohol relapse intensity and was mainly driven by medial prefrontal regions. In conclusion, our results provide translational validity for acute psilocybin-induced neural effects in the rodent brain. Furthermore, alcohol relapse severity was negatively correlated with neural responsivity to psilocybin treatment. Our data suggest that a clinical standard dose of psilocybin may not be sufficient to treat severe AUD cases; a finding that should be considered for future clinical trials.

PMID:38097569 | DOI:10.1038/s41398-023-02690-1

Nat Commun. 2023 Dec 14;14(1):8328. doi: 10.1038/s41467-023-43168-4.

ABSTRACT

The self-assembly of complex structures from a set of non-identical building blocks is a hallmark of soft matter and biological systems, including protein complexes, colloidal clusters, and DNA-based assemblies. Predicting the dependence of the equilibrium assembly yield on the concentrations and interaction energies of building blocks is highly challenging, owing to the difficulty of computing the entropic contributions to the free energy of the many structures that compete with the ground state configuration. While these calculations yield well known results for spherically symmetric building blocks, they do not hold when the building blocks have internal rotational degrees of freedom. Here we present an approach for solving this problem that works with arbitrary building blocks, including proteins with known structure and complex colloidal building blocks. Our algorithm combines classical statistical mechanics with recently developed computational tools for automatic differentiation. Automatic differentiation allows efficient evaluation of equilibrium averages over configurations that would otherwise be intractable. We demonstrate the validity of our framework by comparison to molecular dynamics simulations of simple examples, and apply it to calculate the yield curves for known protein complexes and for the assembly of colloidal shells.

PMID:38097568 | DOI:10.1038/s41467-023-43168-4

Nat Commun. 2023 Dec 14;14(1):7899. doi: 10.1038/s41467-023-43444-3.

ABSTRACT

Daily eating/fasting cycles synchronise circadian peripheral clocks, involved in the regulation of the cardiovascular system. However, the associations of daily meal and fasting timing with cardiovascular disease (CVD) incidence remain unclear. We used data from 103,389 adults in the NutriNet-Santé study. Meal timing and number of eating occasions were estimated from repeated 24 h dietary records. We built multivariable Cox proportional-hazards models to examine their association with the risk of CVD, coronary heart disease and cerebrovascular disease. In this study, having a later first meal (later than 9AM compared to earlier than 8AM) and last meal of the day (later than 9PM compared to earlier than 8PM) was associated with a higher risk of cardiovascular outcomes, especially among women. Our results suggest a potential benefit of adopting earlier eating timing patterns, and coupling a longer nighttime fasting period with an early last meal, rather than breakfast skipping, in CVD prevention.

PMID:38097547 | DOI:10.1038/s41467-023-43444-3

J Mech Behav Biomed Mater. 2023 Dec 7;150:106303. doi: 10.1016/j.jmbbm.2023.106303. Online ahead of print.

ABSTRACT

Underbody blasts (UBB) from mines and improvised explosive devices in military combat can cause debilitating spine injuries to vehicle mounted soldiers. Due to the exclusion of females in combat roles in prior US Department of Defense policy, UBB exposure and injury have predominantly affected male soldiers. Recent policy changes have opened many combat roles to women serving in the US Military (Carter, 2015) and have increased the need to understand the injury potential for female Warfighters. The goal of this study was to investigate the fracture response of adult female lumbar spines compared to adult male spines in UBB relevant loading to identify potential differences in either fracture mechanism or force. Results are presented for 15 simulated UBB spine compression tests using three small female (SF), five large female (LF), and seven mid-sized male (MM) post-mortem human subjects (PMHS). These PMHS groups align to 5th- and 75th-percentile female and 50th-percentile males, based on height and weight from the 2012 Anthropometric Survey of U.S. Army Personnel (Gordon et al., 2014). Both small females and large females (similar in size to the males) were included to assess the role of size and/or sex in the response. Tests were conducted at Virginia Tech on a cam-driven linear compression rig, which included a 6-axis load cell and ram accelerometer to evaluate the fracture. Fracture was visualized through high-speed x-ray video. All female and male spines exhibited similar fracture initiation at the end plates and progression through the vertebral body. The resulting severe compression and burst fractures were representative of reported theatre injuries (Freedman et al., 2014). Mean axial fracture forces were -4182 ± 940 N (SF), -6225 ± 1180 N (LF), -5459 ± 1472 N (All Females) and -7993 ± 2445 N (MM). The SF group was found to have statistically significant differences in mean fracture force compared to both LF and MM groups, while no significant difference was found between LF and MM groups, although the mean force at initial fracture was lower for the LF group. The All-Females group Fz mean was significantly different from the MM group. These data suggest that the significant difference in weight between the SF and LF groups, did have an influence on the Fz outcome, when controlling for sex. Conversely, controlling for size in the LF and MM comparison, sex did influence the mean Fz, but was not statistically significant. Groups with combined sex and size differences, however, did show significant differences in mean Fz. Further study is warranted to understand whether sex or size has a larger effect on fracture force. Mean ram displacement (spine compression) values at fracture initiation were -6.0 ± 5.3 mm (SF), -4.4 ± 0.8 mm (LF), -5.0 ± 3.0 mm (All Females), -6.2 ± 4.5 mm (MM). Spine compression did not seem to be largely influenced by either sex or size, and none of the groups was found to have significant differences in mean displacement values.

PMID:38096612 | DOI:10.1016/j.jmbbm.2023.106303

Bioinformatics. 2023 Dec 14:btad755. doi: 10.1093/bioinformatics/btad755. Online ahead of print.

ABSTRACT

In this article, we present IntelliGenes, a novel machine learning (ML) pipeline for the multi-genomics exploration to discover biomarkers significant in disease prediction with high accuracy. IntelliGenes is based on a novel approach, which consists of nexus of conventional statistical techniques and cutting-edge ML algorithms using multi-genomic, clinical, and demographic data. IntelliGenes introduces a new metric i.e., Intelligent Gene (I-Gene) score to measure the importance of individual biomarkers for prediction of complex traits. I-Gene scores can be utilized to generate I-Gene profiles of individuals to comprehend the intricacies of ML used in disease prediction. IntelliGenes is user-friendly, portable, and a cross-platform application, compatible with Microsoft Windows, macOS, and UNIX operating systems. IntelliGenes not only holds the potential for personalized early detection of common and rare diseases in individuals, but also opens avenues for broader research using novel ML methodologies, ultimately leading to personalized interventions and novel treatment targets.

AVAILABILITY: The source code of IntelliGenes is available on GitHub (https://github.com/drzeeshanahmed/intelligenes) and Code Ocean (https://codeocean.com/capsule/8638596/tree/v1).

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:38096588 | DOI:10.1093/bioinformatics/btad755