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X-irradiation of blood products is an alternative for gamma-ray to prevent post-transfusion GvHD. However, commercial X-irradiators are not widely available while little is known about their safety and efficacy for platelet products. This study introduces an efficient, accessible and cost-effective "X irradiation system" for platelet concentrates (PCs). By constructing a suitable radiation box (phantom) for a clinically available linear accelerator, an "X irradiation system" was designed specifically for PCs. PCs were divided into three equal bags either exposed to X- and gamma-irradiation or kept unirradiated (control). Irradiation-induced inhibition of T cells proliferation was examined by MTT and cell cycle assays on mononuclear cells (MNCs) obtained from PCs. The inhibitory effect of irradiation on allorecognition ability of MNCs was assessed by mixed lymphocyte reaction where MTT evaluated lymphocyte proliferation responses and flowcytometry examined CD8 + T lymphocytes activity. Platelet activation was also examined with P-selectin expression and PAC-1 binding by flowcytometry. X- and gamma-irradiation reduced T cell proliferation while disturbing the cell-cycle with reduced entry of T-cells into the S phase and their G2 arrest. Both types of irradiations also effectively reduced "lymphocyte allorecognition responses" while inactivating CD8 + T lymphocytes in platelet products but with no significant effect on platelet activity. This is the first study that showed "X irradiation system" effectively suppresses T cell proliferation and CD8 + T lymphocyte activity in platelet products, with no effect to platelet quality and activation markers. This may suggest the LINAC-based "X irradiation system" with a dose of 30Gy as efficient and safe as gamma-irradiation for platelet products., Competing Interests: Declarations Competing interests The authors declare no competing interests. Ethics approval This study was approved by the Ethics Committee of the High Institute for Research and Education in Transfusion Medicine, Tehran, Iran (Ethic approval numbers: IR.TMI.REC.1401.017& IR.TMI.REC.1402.001)., (© 2024. The Author(s).)

β-Ionone is the end-ring counterpart of β-carotenoids, which are widely found in fruits and vegetables. Recent studies have illustrated the antimetastatic, anti-proliferative, and apoptosis-inducing activities of β-ionone both in vitro and in vivo. We aimed to explore the anti-cancer potency of β-Ionone-derived ester, (E)-4-(2,6,6-trimethylcyclohex-1-enyl) but-3-en-2-ylpyrazine-2-carboxylate (4-TM.P). The cytotoxic effects of the compound on K562 cells were evaluated by MTT assay. The mechanisms of apoptosis induction were investigated by acridine orange/ethidium bromide (AO/EtBr) double staining, cell cycle analysis, and Annexin V/PI staining. Furthermore, the 4-TM.P-DNA interactions have been thoroughly elucidated by various methods, such as ultraviolet-visible spectroscopy, fluorescence assays, viscosity measurements, molecular docking, and dynamic simulation. The MTT cytotoxicity assay revealed that the growth of K562 cells was inhibited by treatment with β-ionone-derived ester, with an IC50 of 25 ± 5.0 µM at 72 h. Morphological studies revealed the occurrence of apoptosis in treated cells, and G0/G1 cell cycle arrest was observed after treatment of the cells with the IC50 value of the compound. Analyses of multi-spectroscopy and viscosity assays revealed that 4-TM.P binds to DNA in the minor groove mode, which was supported by molecular docking studies. The dynamic stability of the complex was also confirmed using molecular dynamic simulation analyses., Competing Interests: Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

This study addresses the challenges associated with emergency department (ED) overcrowding and emphasizes the need for efficient risk stratification tools to identify high-risk patients for early intervention. While several scoring systems, often based on logistic regression (LR) models, have been proposed to indicate patient illness severity, this study aims to compare the predictive performance of ensemble learning (EL) models with LR for in-hospital mortality in the ED. A cross-sectional single-center study was conducted at the ED of Imam Reza Hospital in northeast Iran from March 2016 to March 2017. The study included adult patients with one to three levels of emergency severity index. EL models using Bagging, AdaBoost, random forests (RF), Stacking and extreme gradient boosting (XGB) algorithms, along with an LR model, were constructed. The training and validation visits from the ED were randomly divided into 80% and 20%, respectively. After training the proposed models using tenfold cross-validation, their predictive performance was evaluated. Model performance was compared using the Brier score (BS), The area under the receiver operating characteristics curve (AUROC), The area and precision-recall curve (AUCPR), Hosmer-Lemeshow (H-L) goodness-of-fit test, precision, sensitivity, accuracy, F1-score, and Matthews correlation coefficient (MCC). The study included 2025 unique patients admitted to the hospital's ED, with a total percentage of hospital deaths at approximately 19%. In the training group and the validation group, 274 of 1476 (18.6%) and 152 of 728 (20.8%) patients died during hospitalization, respectively. According to the evaluation of the presented framework, EL models, particularly Bagging, predicted in-hospital mortality with the highest AUROC (0.839, CI (0.802-0.875)) and AUCPR = 0.64 comparable in terms of discrimination power with LR (AUROC (0.826, CI (0.787-0.864)) and AUCPR = 0.61). XGB achieved the highest precision (0.83), sensitivity (0.831), accuracy (0.842), F1-score (0.833), and the highest MCC (0.48). Additionally, the most accurate models in the unbalanced dataset belonged to RF with the lowest BS (0.128). Although all studied models overestimate mortality risk and have insufficient calibration (P > 0.05), stacking demonstrated relatively good agreement between predicted and actual mortality. EL models are not superior to LR in predicting in-hospital mortality in the ED. Both EL and LR models can be considered as screening tools to identify patients at risk of mortality., (© 2024. The Author(s).)

Liposome nanoparticles have emerged as promising drug delivery systems due to their unique properties. Assessing particle size and polydispersity index (PDI) is critical for evaluating the quality of these liposomal nanoparticles. However, optimizing these parameters in a laboratory setting is both costly and time-consuming. This study aimed to apply a machine learning technique to assess the impact of specific factors, including sonication time, extrusion temperature, and compositions, on the size and PDI of liposomal nanoparticles. Liposomal solutions were prepared and subjected to sonication with varying values for these parameters. Two compositions: (A) HSPC:DPPG:Chol:DSPE-mPEG2000 at 55:5:35:5 molar ratio and (B) HSPC:Chol:DSPE-mPEG2000 at 55:40:5 molar ratio, were made using remote loading method. Ensemble learning (EL), a machine learning technique, was employed using the Least-squares boosting (LSBoost) algorithm to accurately model the data. The dataset was randomly split into training and testing sets, with 70% allocated for training. The LSBoost algorithm achieved mean absolute errors of 1.652 and 0.0105 for modeling the size and PDI, respectively. Under conditions where the temperature was set at approximately 60 °C, our EL model predicted a minimum particle size of 116.53 nm for composition (A) with a sonication time of approximately 30 min. Similarly, for composition (B), the model predicted a minimum particle size of 129.97 nm with sonication times of approximately 30 or 55 min. In most instances, a PDI of less than 0.2 was achieved. These results highlight the significant impact of optimizing independent factors on the characteristics of liposomal nanoparticles and demonstrate the potential of EL as a decision support system for identifying the best liposomal formulation. We recommend further studies to explore the effects of other independent factors, such as lipid composition and surfactants, on liposomal nanoparticle characteristics., (© 2023. Springer Nature Limited.)

Upregulation of neuroplasticity might help maximize stroke recovery. One intervention that appears worthy of investigation is aerobic exercise. This study aimed to determine whether a single bout of moderate intensity aerobic exercise can enhance neuroplasticity in people with stroke. Participants were randomly assigned (1:1) to a 20-min moderate intensity exercise intervention or remained sedentary (control). Transcranial magnetic stimulation measured corticospinal excitability of the contralesional hemisphere by recording motor evoked potentials (MEPs). Intermittent Theta Burst Stimulation (iTBS) was used to repetitively activate synapses in the contralesional primary motor cortex, initiating the early stages of neuroplasticity and increasing excitability. It was surmised that if exercise increased neuroplasticity, there would be a greater facilitation of MEPs following iTBS. Thirty-three people with stroke participated in this study (aged 63.87 ± 10.30 years, 20 male, 6.13 ± 4.33 years since stroke). There was an interaction between Time*Group on MEP amplitudes (P = 0.009). Participants allocated to aerobic exercise had a stronger increase in MEP amplitude following iTBS. A non-significant trend indicated time since stroke might moderate this interaction (P = 0.055). Exploratory analysis suggested participants who were 2-7.5 years post stroke had a strong MEP facilitation following iTBS (P < 0.001). There was no effect of age, sex, resting motor threshold, self-reported physical activity levels, lesion volume or weighted lesion load (all P > 0.208). Moderate intensity cycling may enhance neuroplasticity in people with stroke. This therapy adjuvant could provide opportunities to maximize stroke recovery., (© 2023. Springer Nature Limited.)

Foot-and-mouth disease (FMD) is a highly contagious animal disease caused by a ribonucleic acid (RNA) virus, with significant economic costs and uneven distribution across Asia, Africa, and South America. While spatial analysis and modeling of FMD are still in their early stages, this research aimed to identify socio-environmental determinants of FMD incidence in Iran at the provincial level by studying 135 outbreaks reported between March 21, 2017, and March 21, 2018. We obtained 46 potential socio-environmental determinants and selected four variables, including percentage of population, precipitation in January, percentage of sheep, and percentage of goats, to be used in spatial regression models to estimate variation in spatial heterogeneity. In our analysis, we employed global models, namely ordinary least squares (OLS), spatial error model (SEM), and spatial lag model (SLM), as well as local models, including geographically weighted regression (GWR) and multiscale geographically weighted regression (MGWR). The MGWR model yielded the highest adjusted [Formula: see text] of 90%, outperforming the other local and global models. Using local models to map the effects of environmental determinants (such as the percentage of sheep and precipitation) on the spatial variability of FMD incidence provides decision-makers with helpful information for targeted interventions. Our findings advocate for multiscale and multidisciplinary policies to reduce FMD incidence., (© 2023. Springer Nature Limited.)

Amyotrophic Lateral Sclerosis (ALS) is recognised to be a complex neurodegenerative disease involving both genetic and non-genetic risk factors. The underlying causes and risk factors for the majority of cases remain unknown; however, ever-larger genetic data studies and methodologies promise an enhanced understanding. Recent analyses using published summary statistics from the largest ALS genome-wide association study (GWAS) (20,806 ALS cases and 59,804 healthy controls) identified that schizophrenia (SCZ), cognitive performance (CP) and educational attainment (EA) related traits were genetically correlated with ALS. To provide additional evidence for these correlations, we built single and multi-trait genetic predictors using GWAS summary statistics for ALS and these traits, (SCZ, CP, EA) in an independent Australian cohort (846 ALS cases and 665 healthy controls). We compared methods for generating the risk predictors and found that the combination of traits improved the prediction (Nagelkerke-R 2 ) of the case-control logistic regression. The combination of ALS, SCZ, CP, and EA, using the SBayesR predictor method gave the highest prediction (Nagelkerke-R 2 ) of 0.027 (P value = 4.6 × 10 -8 ), with the odds-ratio for estimated disease risk between the highest and lowest deciles of individuals being 3.15 (95% CI 1.96-5.05). These results support the genetic correlation between ALS, SCZ, CP and EA providing a better understanding of the complexity of ALS., (© 2021. The Author(s), under exclusive licence to European Society of Human Genetics.)

Schizophrenia has a heritability of 60-80% 1 , much of which is attributable to common risk alleles. Here, in a two-stage genome-wide association study of up to 76,755 individuals with schizophrenia and 243,649 control individuals, we report common variant associations at 287 distinct genomic loci. Associations were concentrated in genes that are expressed in excitatory and inhibitory neurons of the central nervous system, but not in other tissues or cell types. Using fine-mapping and functional genomic data, we identify 120 genes (106 protein-coding) that are likely to underpin associations at some of these loci, including 16 genes with credible causal non-synonymous or untranslated region variation. We also implicate fundamental processes related to neuronal function, including synaptic organization, differentiation and transmission. Fine-mapped candidates were enriched for genes associated with rare disruptive coding variants in people with schizophrenia, including the glutamate receptor subunit GRIN2A and transcription factor SP4, and were also enriched for genes implicated by such variants in neurodevelopmental disorders. We identify biological processes relevant to schizophrenia pathophysiology; show convergence of common and rare variant associations in schizophrenia and neurodevelopmental disorders; and provide a resource of prioritized genes and variants to advance mechanistic studies., (© 2022. The Author(s), under exclusive licence to Springer Nature Limited.)

This work reports a three-dimensional polymer interdigitated pillar electrostatic actuator that can produce force densities 5-10× higher than those of biological muscles. The theory of operation, scaling, and stability is investigated using analytical and FEM models. The actuator consists of two high-density arrays of interdigitated pillars that work against a restoring force generated by an integrated flexure spring. The actuator architecture enables linear actuation with higher displacements and pull-in free actuation to prevent the in-use stiction associated with other electrostatic actuators. The pillars and springs are 3D printed together in the same structure. The pillars are coated with a gold-palladium alloy layer to form conductive electrodes. The space between the pillars is filled with liquid dielectrics for higher breakdown voltages and larger electrostatic forces due to the increase in the dielectric constant. We demonstrated a prototype actuator that produced a maximum work density of 54.6 µJ/cc and an electrical-to-mechanical energy coupling factor of 32% when actuated at 4000 V. The device was operated for more than 100,000 cycles with no degradation in displacements. The flexible polymer body was robust, allowing the actuator to operate even after high mechanical force impact, which was demonstrated by operation after drop tests. As it is scaled further, the reported actuator will enable soft and flexible muscle-like actuators that can be stacked in series and parallel to scale the resulting forces. This work paves the way for high-energy density actuators for microrobotic applications., Competing Interests: Conflict of interestThe authors declare no competing interests., (© The Author(s) 2022.)

Adipocytes increase energy expenditure in response to prolonged sympathetic activation via persistent expression of uncoupling protein 1 (UCP1) 1,2 . Here we report that the regulation of glycogen metabolism by catecholamines is critical for UCP1 expression. Chronic β-adrenergic activation leads to increased glycogen accumulation in adipocytes expressing UCP1. Adipocyte-specific deletion of a scaffolding protein, protein targeting to glycogen (PTG), reduces glycogen levels in beige adipocytes, attenuating UCP1 expression and responsiveness to cold or β-adrenergic receptor-stimulated weight loss in obese mice. Unexpectedly, we observed that glycogen synthesis and degradation are increased in response to catecholamines, and that glycogen turnover is required to produce reactive oxygen species leading to the activation of p38 MAPK, which drives UCP1 expression. Thus, glycogen has a key regulatory role in adipocytes, linking glucose metabolism to thermogenesis., (© 2021. The Author(s), under exclusive licence to Springer Nature Limited.)

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterised by the loss of upper and lower motor neurons. ALS exhibits high phenotypic variability including age and site of onset, and disease duration. To uncover epigenetic and transcriptomic factors that may modify an ALS phenotype, we used a cohort of Australian monozygotic twins (n = 3 pairs) and triplets (n = 1 set) that are discordant for ALS and represent sporadic ALS and the two most common types of familial ALS, linked to C9orf72 and SOD1. Illumina Infinium HumanMethylation450K BeadChip, EpiTYPER and RNA-Seq analyses in these ALS-discordant twins/triplets and control twins (n = 2 pairs), implicated genes with consistent longitudinal differential DNA methylation and/or gene expression. Two identified genes, RAD9B and C8orf46, showed significant differential methylation in an extended cohort of >1000 ALS cases and controls. Combined longitudinal methylation-transcription analysis within a single twin set implicated CCNF, DPP6, RAMP3, and CCS, which have been previously associated with ALS. Longitudinal transcriptome data showed an 8-fold enrichment of immune function genes and under-representation of transcription and protein modification genes in ALS. Examination of these changes in a large Australian sporadic ALS cohort suggest a broader role in ALS. Furthermore, we observe that increased methylation age is a signature of ALS in older patients.

Photothermal hyperthermia is proven to be an effective diagnostic tool for cancer therapy. The efficacy of this method directly relies on understanding the localization of the photothermal effect in the targeted region. Realizing the safe and effective concentration of nano-particles and the irradiation intensity and time requires spatiotemporal temperature monitoring during and after laser irradiation. Due to uniformities of the nanoparticle distribution and the complexities of the microenvironment, a direct temperature measurement in micro-scale is crucial for achieving precise thermal dose control. In this study, a 50 nm thin film nickel resistive temperature sensor was fabricated on a 300 nm SiN membrane to directly measure the local temperature variations of a hydrogel-GNR mixture under laser exposure with 2 mK temperature resolution. The chip-scale approach developed here is an effective tool to investigate localization of photothermal heating for hyperthermia applications for in-vitro and ex-vivo models. Considering the connection between thermal properties, porosity and the matrix stiffness in hydrogels, we present our results using the interplay between matrix stiffness of the hydrogel and its thermal properties: the stiffer the hydrogel, the higher the thermal conductivity resulting in lower photothermal heating. We measured 8.1, 7.4, and 5.6 °C temperature changes (from the room temperature, 20 °C) in hydrogel models with stiffness levels corresponding to adipose (4 kPa), muscle (13 kPa) and osteoid (30 kPa) tissues respectively by exposing them to 2 W/cm 2 laser (808 nm) intensity for 150 seconds.

Haematopoiesis is an essential process that evolved in multicellular animals. At the heart of this process are haematopoietic stem cells (HSCs), which are multipotent and self-renewing, and generate the entire repertoire of blood and immune cells throughout an animal's life 1 . Although there have been comprehensive studies on self-renewal, differentiation, physiological regulation and niche occupation in vertebrate HSCs, relatively little is known about the evolutionary origin and niches of these cells. Here we describe the haematopoietic system of Botryllus schlosseri, a colonial tunicate that has a vasculature and circulating blood cells, and interesting stem-cell biology and immunity characteristics 2-8 . Self-recognition between genetically compatible B. schlosseri colonies leads to the formation of natural parabionts with shared circulation, whereas incompatible colonies reject each other 3,4,7 . Using flow cytometry, whole-transcriptome sequencing of defined cell populations and diverse functional assays, we identify HSCs, progenitors, immune effector cells and an HSC niche, and demonstrate that self-recognition inhibits allospecific cytotoxic reactions. Our results show that HSC and myeloid lineage immune cells emerged in a common ancestor of tunicates and vertebrates, and also suggest that haematopoietic bone marrow and the B. schlosseri endostyle niche evolved from a common origin.

Micro-fabricated single-layer graphenes (SLGs) on a silicon dioxide (SiO 2 )/Si substrate, a silicon nitride (SiN) membrane, and a suspended architecture are presented for their use as temperature sensors. These graphene temperature sensors act as resistance temperature detectors, showing a quadratic dependence of resistance on the temperature in a range between 283 K and 303 K. The observed resistance change of the graphene temperature sensors are explained by the temperature dependent electron mobility relationship (~T -4 ) and electron-phonon scattering. By analyzing the transient response of the SLG temperature sensors on different substrates, it is found that the graphene sensor on the SiN membrane shows the highest sensitivity due to low thermal mass, while the sensor on SiO 2 /Si reveals the lowest one. Also, the graphene on the SiN membrane reveals not only the fastest response, but also better mechanical stability compared to the suspended graphene sensor. Therefore, the presented results show that the temperature sensors based on SLG with an extremely low thermal mass can be used in various applications requiring high sensitivity and fast operation.

We develop a novel approach to identify regions of the genome underlying population genetic differentiation in any genetic data where the underlying population structure is unknown, or where the interest is assessing divergence along a gradient. By combining the statistical framework for genome-wide association studies (GWASs) with eigenvector decomposition (EigenGWAS), which is commonly used in population genetics to characterize the structure of genetic data, loci under selection can be identified without a requirement for discrete populations. We show through theory and simulation that our approach can identify regions under selection along gradients of ancestry, and in real data we confirm this by demonstrating LCT to be under selection between HapMap CEU-TSI cohorts, and we then validate this selection signal across European countries in the POPRES samples. HERC2 was also found to be differentiated between both the CEU-TSI cohort and within the POPRES sample, reflecting the likely anthropological differences in skin and hair colour between northern and southern European populations. Controlling for population stratification is of great importance in any quantitative genetic study and our approach also provides a simple, fast and accurate way of predicting principal components in independent samples. With ever increasing sample sizes across many fields, this approach is likely to be greatly utilized to gain individual-level eigenvectors avoiding the computational challenges associated with conducting singular value decomposition in large data sets. We have developed freely available software, Genetic Analysis Repository (GEAR), to facilitate the application of the methods.

Anaemia is a chief determinant of global ill health, contributing to cognitive impairment, growth retardation and impaired physical capacity. To understand further the genetic factors influencing red blood cells, we carried out a genome-wide association study of haemoglobin concentration and related parameters in up to 135,367 individuals. Here we identify 75 independent genetic loci associated with one or more red blood cell phenotypes at P < 10(-8), which together explain 4-9% of the phenotypic variance per trait. Using expression quantitative trait loci and bioinformatic strategies, we identify 121 candidate genes enriched in functions relevant to red blood cell biology. The candidate genes are expressed preferentially in red blood cell precursors, and 43 have haematopoietic phenotypes in Mus musculus or Drosophila melanogaster. Through open-chromatin and coding-variant analyses we identify potential causal genetic variants at 41 loci. Our findings provide extensive new insights into genetic mechanisms and biological pathways controlling red blood cell formation and function.

Most information in linkage analysis for quantitative traits comes from pairs of relatives that are phenotypically most discordant or concordant. Confounding this, within-family outliers from non-genetic causes may create false positives and negatives. We investigated the influence of within-family outliers empirically, using one of the largest genome-wide linkage scans for height. The subjects were drawn from Australian twin cohorts consisting of 8447 individuals in 2861 families, providing a total of 5815 possible pairs of siblings in sibships. A variance component linkage analysis was performed, either including or excluding the within-family outliers. Using the entire dataset, the largest LOD scores were on chromosome 15q (LOD 2.3) and 11q (1.5). Excluding within-family outliers increased the LOD score for most regions, but the LOD score on chromosome 15 decreased from 2.3 to 1.2, suggesting that the outliers may create false negatives and false positives, although rare alleles of large effect may also be an explanation. Several regions suggestive of linkage to height were found after removing the outliers, including 1q23.1 (2.0), 3q22.1 (1.9) and 5q32 (2.3). We conclude that the investigation of the effect of within-family outliers, which is usually neglected, should be a standard quality control measure in linkage analysis for complex traits and may reduce the noise for the search of common variants of modest effect size as well as help identify rare variants of large effect and clinical significance. We suggest that the effect of within-family outliers deserves further investigation via theoretical and simulation studies.