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Laboratory automation technologies have revolutionized biomedical research. However, the availability of automation solutions at the single-cell level remains scarce, primarily owing to the inherent challenges of handling cells with such small dimensions in a precise, biocompatible manner. Here, we present a single-cell-level laboratory automation solution that configures various experiments onto standardized, microscale test-tube matrices via our precise ultrasonic liquid sample ejection technology, known as PULSE. PULSE enables the transformation of titer plates into microdroplet arrays by printing nanodrops and single cells acoustically in a programmable, scalable, and biocompatible manner. Unlike pipetting robots, PULSE enables researchers to conduct biological experiments using single cells as anchoring points (e.g., 1 cell vs. 1000 cells per "tube"), achieving higher resolution and potentially more relevant data for modeling and downstream analyses. We demonstrate the ability of PULSE to perform biofabrication, precision gating, and deterministic array barcoding via preallocated droplet-addressable primers. Single cells can be gently printed at a speed range of 5–20 cell⋅s−1 with an accuracy of 90.5–97.7%, which can then adhere to the substrate and grow for up to 72 h while preserving cell integrity. In the deterministic barcoding experiment, 95.6% barcoding accuracy and 2.7% barcode hopping were observed by comparing the phenotypic data with known genotypic data from two types of single cells. Our PULSE platform allows for precise and dynamic analyses by automating experiments at the single-cell level, offering researchers a powerful tool in biomedical research. [ABSTRACT FROM AUTHOR]

Recent technological advancements in single-cell genomics have enabled joint profiling of gene expression and alternative modalities at unprecedented scale. Consequently, the complexity of multi-omics data sets is increasing massively. Existing models for multi-modal data are typically limited in functionality or scalability, making data integration and downstream analysis cumbersome. We present multiDGD, a scalable deep generative model providing a probabilistic framework to learn shared representations of transcriptome and chromatin accessibility. It shows outstanding performance on data reconstruction without feature selection. We demonstrate on several data sets from human and mouse that multiDGD learns well-clustered joint representations. We further find that probabilistic modeling of sample covariates enables post-hoc data integration without the need for fine-tuning. Additionally, we show that multiDGD can detect statistical associations between genes and regulatory regions conditioned on the learned representations. multiDGD is available as an scverse-compatible package on GitHub. Understanding single-cell multi-omics data requires powerful solutions. Here, authors present a data-efficient machine learning approach for paired data. It enables integration from unseen covariates and can link distal regulatory elements to promoters, presenting a computational version of HiC. [ABSTRACT FROM AUTHOR]

Isocitrate dehydrogenase (IDH) and O6-methylguanine-DNA methyltransferase (MGMT) genes are critical molecular markers in determining treatment options and predicting the prognosis of adult-type diffuse gliomas. Objectives: this study aimed to investigate whether multimodal MRI enables the differentiation of genotypes in adult-type diffuse gliomas. Methods: a total of 116 adult-type diffuse glioma patients (61 males, 51.5 (37, 62) years old) who underwent multimodal MRI before surgery were retrospectively analysed. Multimodal MRI included conventional MRI, proton magnetic resonance spectroscopy (1H-MRS), and diffusion tensor imaging (DTI). Conventional visual features, N-acetyl-aspartate (NAA)/Creatine (Cr), Choline (Cho)/Cr, Cho/NAA, fractional anisotropy (FA), mean diffusivity (MD), and diffusion histogram parameters were extracted on the whole tumour. Multimodal MRI parameters of IDH-mutant and IDH-wildtype gliomas were compared using the Mann–Whitney U test, Student's t-test, or Pearson chi-square tests. Logistic regression was used to select the MRI parameters to predict IDH-mutant gliomas. Furthermore, multimodal MRI parameters were selected to establish models for predicting MGMT methylation in the IDH-wildtype gliomas. The performance of models was evaluated by the receiver operating characteristics curve. Results: a total of 56 patients with IDH-mutant gliomas and 60 patients with IDH-wildtype glioblastomas (GBM) (37 with methylated MGMT and 17 with unmethylated MGMT) were diagnosed by 2021 WHO classification criteria. The enhancement degree (OR = 4.298, p < 0.001), necrosis/cyst (OR = 5.381, p = 0.011), NAA/Cr (OR = 0.497, p = 0.037), FA-Skewness (OR = 0.497, p = 0.033), MD-Skewness (OR = 1.849, p = 0.035), FAmean (OR = 1.924, p = 0.049) were independent factors for the multimodal combined prediction model in predicting IDH-mutant gliomas. The combined modal based on conventional MRI, 1H-MRS, DTI parameters, and histogram performed best in predicting IDH-wildtype status (AUC = 0.890). However, only NAA/Cr (OR = 0.17, p = 0.043) and FA (OR = 0.38, p = 0.015) were associated with MGMT methylated in IDH-wildtype GBM. The combination of NAA/Cr and FA-Median is more accurate for predicting MGMT methylation levels than using these elements alone (AUC, 0.847 vs. 0.695/0.684). Conclusions: multimodal MRI based on conventional MRI, 1H-MRS, and DTI can provide compound imaging markers for stratified individual diagnosis of IDH mutant and MGMT promoter methylation in adult-type diffuse gliomas. [ABSTRACT FROM AUTHOR]

Patient-derived xenografts (PDXs) are tumour fragments engrafted into mice for preclinical studies. PDXs offer clear advantages over simpler in vitro cancer models - such as cancer cell lines (CCLs) and organoids - in terms of structural complexity, heterogeneity, and stromal interactions. Here, we characterise 231 colorectal cancer PDXs at the genomic, transcriptomic, and epigenetic levels, along with their response to cetuximab, an EGFR inhibitor used clinically for metastatic colorectal cancer. After evaluating the PDXs' quality, stability, and molecular concordance with publicly available patient cohorts, we present results from training, interpreting, and validating the integrative ensemble classifier CeSta. This model takes in input the PDXs' multi-omic characterisation and predicts their sensitivity to cetuximab treatment, achieving an area under the receiver operating characteristics curve > 0.88. Our study demonstrates that large PDX collections can be leveraged to train accurate, interpretable drug sensitivity models that: (1) better capture patient-derived therapeutic biomarkers compared to models trained on CCL data, (2) can be robustly validated across independent PDX cohorts, and (3) could contribute to the development of future therapeutic biomarkers. Patient-derived xenografts (PDX) could contribute to understanding how colorectal cancer (CRC) responds to targeted therapies like cetuximab. Here, the authors characterise the response to cetuximab in 231 CRC PDXs using multiomics and develop an integrative ensemble classifier - CeSta - to predict sensitivity to cetuximab. [ABSTRACT FROM AUTHOR]

The genus Allium is a prominent member of the Amaryllidaceae family and encompasses approximately 1,063 species worldwide. These diverse species vary in appearance and flavor but they share biochemical similarities. Underutilized Alliums present a promising alternative to onion and garlic in various Indian regions, given their adaptability, versatile utility, and nutraceutical properties. Their adaptation in the plains of India is a great opportunity to add medicinal and nutraceutical value to Indian cuisine. Therefore, the present study aimed to evaluate the underutilized raw Allium leaves for their sensory perception by hedonic test (n = 60), consumer perceptions and willingness to purchase (n = 180), and the development and validation of value chain model (n = 30) to popularize these species in plains. For the various sensory attributes evaluated, Allium tuberosum Kazakhstan CGN-1587 emerged out to be the most suitable for its advancement into the consumer survey. The consumer perception study recorded 74.44% willingness to purchase of total respondents and a majority preference for consuming it in cooked form (38.30%). A value chain model of Allium tuberosum Kazakhstan CGN-1587 with supply chain agencies was subsequently developed. Validation of this value chain model was done with a panel of different stakeholders. Panelists perceived the value chain as highly valid, particularly in terms of economic feasibility (4.30 ± 0.70) and sustainability (4.20 ± 0.81). Thus, the present study provides valuable insights that can support and promote the use of underutilized Alliums in non-traditional areas of India. [ABSTRACT FROM AUTHOR]

Hemp, also known as Cannabis sativa L., contains over 80 cannabinoids, with cannabidiol (CBD) being the primary neuroactive component. CBD possesses various pharmacological properties and is considered a non-psychoactive compound, making it a promising component for various applications, such as pharmaceuticals, cosmetics, and nutraceuticals. The aim of this study was to identify the optimal conditions for extracting CBD from hemp using supercritical fluid extraction (SFE). Response surface methodology (RSM) was employed to optimize the SFE conditions. The Box–Behnken design and the central composite design were utilized to refine the extraction parameters, including extraction time, temperature, and pressure. The statistical significance and reliability of the optimized conditions were confirmed by the significant influence of these independent variables on CBD yield. The extracted CBD was purified to a high level of purity and converted from cannabidiolic acid (CBDA) through heat treatment and then analyzed using high-performance liquid chromatography (HPLC). The following extraction conditions were considered optimal and led to a CBD yield of approximately 70.46 g/kg: pressure of 48.3 MPa, temperature of 60 °C, and extraction time of 109.2 min. Validation experiments confirmed the accuracy of the model, with experimental values closely matching the predicted values (69.93 ± 0.88 g/kg). This study demonstrates that SFE is an efficient method for obtaining high-purity CBD from hemp, highlighting its potential for industrial applications. The findings suggest that optimizing SFE conditions through RSM can significantly enhance the efficiency and yield of CBD extraction, providing a robust framework for industrial-scale production. [ABSTRACT FROM AUTHOR]

Despite the great advances in basic research results, glioblastoma multiforme (GBM) still remains an incurable tumour. To date, a GBM diagnosis is a death sentence within 15–18 months, due to the high recurrence rate and resistance to conventional radio- and chemotherapy approaches. The effort the scientific community is lavishing on the never-ending battle against GBM is reflected by the huge number of clinical trials launched, about 2003 on 10 September 2024. However, we are still far from both an in-depth comprehension of the biological and molecular processes leading to GBM onset and progression and, importantly, a cure. GBM is provided with high intratumoral heterogeneity, immunosuppressive capacity, and infiltrative ability due to neoangiogenesis. These features impact both tumour aggressiveness and therapeutic vulnerability, which is further limited by the presence in the tumour core of niches of glioblastoma stem cells (GSCs) that are responsible for the relapse of this brain neoplasm. Epigenetic alterations may both drive and develop along GBM progression and also rely on changes in the expression of the genes encoding histone-modifying enzymes, including histone deacetylases (HDACs). Among them, HDAC6—a cytoplasmic HDAC—has recently gained attention because of its role in modulating several biological aspects of GBM, including DNA repair ability, massive growth, radio- and chemoresistance, and de-differentiation through primary cilia disruption. In this review article, the available information related to HDAC6 function in GBM will be presented, with the aim of proposing its inhibition as a valuable therapeutic route for this deadly brain tumour. [ABSTRACT FROM AUTHOR]

Post-traumatic stress disorder (PTSD) is a debilitating neuropsychiatric condition closely linked to neuroinflammation, with a higher prevalence in women. Cannabidiol (CBD), a non-psychoactive cannabinoid, has shown promise as a potential treatment for PTSD. In this study, we used a PTSD model in which female rats were subjected to a severe foot shock followed by contextual situational reminders (SRs). Testing was conducted one month after exposure. The rats received daily CBD injections for three weeks during the SRs, from days 7 to 28. Two days after the final SR, the rats underwent five extinction trials, followed by the forced swim test (FST). After a five-day rest period, the rats were sacrificed, and brain tissues from the medial prefrontal cortex (mPFC) and ventral subiculum (vSUB) were analyzed for inflammatory markers. Chronic CBD treatment reversed impairments in fear extinction caused by shock and SR. It also reduced learned helplessness in the FST and decreased the upregulation of mPFC-il1β induced by shock and SRs. Additionally, exposure to shock and SRs downregulated mPFC-il6 while upregulating vSUB-il6. CBD treatment further downregulated il6 expression in the vSUB compared to the vehicle groups. Our findings show that CBD effectively inhibited the development of PTSD-like behaviors and suppressed neuroinflammation in the mPFC. [ABSTRACT FROM AUTHOR]

Emulsification is a feared and common complication of the use of silicone oil (SO) as tamponade fluid after vitrectomy as it potentially associated with significant risks to ocular health, including elevated intraocular pressure (IOP), glaucoma, corneal and retinal changes. The aim of this study was to investigate the role and interplay of physical factors on the formation of SO emulsion. Experiments were performed in a model of the vitreous chamber with a realistic shape, filled with SO and an aqueous solution containing different concentrations of albumin, an endogenous protein known to modify the interfacial properties between SO and aqueous solutions. The model was subjected to harmonic and saccadic rotations and kept at body temperature. Results indicated that no emulsions were detected in the absence of albumin in the aqueous solution, while the presence of the protein facilitated emulsion formation, acting as a surfactant. Mechanical energy from eye movements was also found to be a key mechanism to produce emulsification, with higher mechanical energy provided to the system leading to smaller droplet sizes. The emulsions formed were stable over extended times. This study highlights the complex interplay of factors influencing SO emulsification in the vitreous chamber. A better understanding of the mechanisms underlying SO emulsification is crucial for developing strategies to mitigate SO emulsion and the related complications. [ABSTRACT FROM AUTHOR]

A key advantage of single-cell multimodal joint profiling is the modality interplay, which is essential for deciphering the cell fate. However, while current analytical methods can leverage the additive benefits, they fall short to explore the synergistic insights of joint profiling, thereby diminishing the advantage of joint profiling. Here, we introduce CellMATE, a M ulti-head A dversarial T raining-based E arly-integration approach specifically developed for multimodal joint profiling. CellMATE can capture both additive and synergistic benefits inherent in joint profiling through auto-learning of multimodal distributions and simultaneously represents all features into a unified latent space. Through extensive evaluation across diverse joint profiling scenarios, CellMATE demonstrated its superiority in ensuring utility of cross-modal properties, uncovering cellular heterogeneity and plasticity, and delineating differentiation trajectories. CellMATE uniquely unlocks the full potential of joint profiling to elucidate the dynamic nature of cells during critical processes as differentiation, development, and diseases. [ABSTRACT FROM AUTHOR]

Spatially resolved transcriptomics (SRT) technologies facilitate gene expression profiling with spatial resolution in a naïve state. Nevertheless, current SRT technologies exhibit limitations, manifesting as either low transcript detection sensitivity or restricted gene throughput. These constraints result in diminished precision and coverage in gene measurement. In response, we introduce SpaGDA, a sophisticated deep learning–based graph domain adaptation framework for both scenarios of gene expression imputation and cell type identification in spatially resolved transcriptomics data by impartially transferring knowledge from reference scRNA-seq data. Systematic benchmarking analyses across several SRT datasets generated from different technologies have demonstrated SpaGDA's superior effectiveness compared to state-of-the-art methods in both scenarios. Further applied to three SRT datasets of different biological contexts, SpaGDA not only better recovers the well-established knowledge sourced from public atlases and existing scientific literature but also yields a more informative spatial expression pattern of genes. Together, these results demonstrate that SpaGDA can be used to overcome the challenges of current SRT data and provide more accurate insights into biological processes or disease development. The SpaGDA is available in https://github.com/shenrb/SpaGDA. [ABSTRACT FROM AUTHOR]

The integration of data from multiple modalities generated by single-cell omics technologies is crucial for accurately identifying cell states. One challenge in comprehending multi-omics data resides in mosaic integration, in which different data modalities are profiled in different subsets of cells, as it requires simultaneous batch effect removal and modality alignment. Here, we develop Multi-omics Mosaic Auto-scaling Attention Variational Inference (mmAAVI), a scalable deep generative model for single-cell mosaic integration. Leveraging auto-scaling self-attention mechanisms, mmAAVI can map arbitrary combinations of omics to the common embedding space. If existing well-annotated cell states, the model can perform semisupervised learning to utilize existing these annotations. We validated the performance of mmAAVI and five other commonly used methods on four benchmark datasets, which vary in cell numbers, omics types, and missing patterns. mmAAVI consistently demonstrated its superiority. We also validated mmAAVI's ability for cell state knowledge transfer, achieving balanced accuracies of 0.82 and 0.97 with less 1% labeled cells between batches with completely different omics. The full package is available at https://github.com/luyiyun/mmAAVI. [ABSTRACT FROM AUTHOR]

A number of consequences of climate change, notably salinity, put global food security at risk by impacting the development and production of lentils. Salinity-induced stress alters lentil genetics, resulting in severe developmental issues and eventual phenotypic damage. Lentils have evolved sophisticated signaling networks to combat salinity stress. Lentil genomics and transcriptomics have discovered key genes and pathways that play an important role in mitigating salinity stress. The development of saline-smart cultivars can be further revolutionized by implementing proteomics, metabolomics, miRNAomics, epigenomics, phenomics, ionomics, machine learning, and speed breeding approaches. All these cutting-edge approaches represent a viable path toward creating saline-tolerant lentil cultivars that can withstand climate change and meet the growing demand for high-quality food worldwide. The review emphasizes the gaps that must be filled for future food security in a changing climate while also highlighting the significant discoveries and insights made possible by omics and other state-of-the-art biotechnological techniques. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this research is to elucidate the correlation between open innovation, digital strategies and networking in enhancing agricultural enterprises within the new perspective of Agrifood 5.0. As such, it contributes to making businesses more competitive, especially in the Italian agricultural sector, where small and medium-sized enterprises are highly fragmented. Numerous studies have asserted that the competitiveness of actors operating within a specific territory is closely linked to local identity and image enhancement. Agricultural organizations are undergoing a profound transformation, with technological assets emerging as catalysts for new synergies. Advanced technologies such as robotics, the Internet of Things (IoT) and automation (AI) are emerging as differentiating elements capable of further advancing the agricultural sector, transitioning it from Agrifood 4.0 to Agrifood 5.0. The empirical analysis of the research shows a positive correlation between a collaborative attitude and a propensity for innovation. Indeed, the data demonstrated that digital strategies and open innovation positively influence competitiveness in agricultural SMEs. Design/methodology/approach: The methodology employed in this study is mixed, incorporating both qualitative and quantitative approaches. The quantitative aspect involves analysis of the dataset from the Italian Statistical Institute (ISTAT) through logistic regression, while the qualitative component entails analysis of semi-structured interviews conducted with a sample of 174 agricultural cooperatives in southern Italian regions (Campania). This approach allows for a comprehensive understanding of the research topic, capturing both numerical trends and nuanced insights from interviews. Findings: After analyzing the data from the 7th General Census of Agriculture conducted by ISTAT, a clear understanding of the sector has emerged, revealing several potential research avenues. It is evident that innovation in the agricultural sector is often driven by the largest and best-capitalized production entities, primarily located in Italy. Conversely, smaller agricultural entities can benefit from networking as new technological assets act as catalysts for new synergies, innovation and competitiveness. Practical implications: Enhancing the relational contribution within the network and humanizing a fragmented sector are crucial elements for promoting open innovation. Network structuring facilitates the transmission of managerial knowledge, contributing to an overall increase in the intellectual and relational capital of the agricultural sector. These factors, combined with open innovation, enhance the competitiveness of individual firms and elevate the brand of the entire sector, creating a conducive environment for transitioning toward Agrifood 5.0. This transition is characterized by increased interconnection, continuous innovation and overall prosperity. Specific studies on this topic are lacking in Italy, particularly in the southern regions. Therefore, this contribution focuses on investigating the Campania region. Originality/value: The novelty of this study lies in its investigation of the relationship between agricultural enterprises and innovation in the context of enterprises networking strategies (i.e. associationism and/or cooperation), promoting competitiveness. The limitations of this study are related to the dimension of the sample selected and its relationship with other productive sectors. [ABSTRACT FROM AUTHOR]

Background: Explanations for why social media users propagate misinformation include failure of classical reasoning (over-reliance on intuitive heuristics), motivated reasoning (conforming to group opinion), and personality traits (e.g., narcissism). However, there is a lack of consensus on which explanation is most predictive of misinformation spread. Previous work is also limited by not distinguishing between passive (i.e., "liking") and active (i.e., "retweeting") propagation behaviors. Methods: To examine this issue, 858 Twitter users were recruited to engage in a Twitter simulation task in which they were shown real tweets on public health topics (e.g., COVID-19 vaccines) and given the option to "like", "reply", "retweet", "quote", or select "no engagement". Survey assessments were then given tomeasure variables corresponding to explanations for: classical reasoning [cognitive reflective thinking (CRT)], motivated reasoning (religiosity, political conservatism, and trust in medical science), and personality traits (openness to new experiences, conscientiousness, empathy, narcissism). Results: Cognitive reflective thinking, conscientiousness, openness, and emotional concern empathy were all negatively associated with liking misinformation, but not significantly associated with retweeting it. Trust in medical scientists was negatively associated with retweeting misinformation, while grandiose narcissism and religiosity were positively associated. An exploratory analysis on engagement with misinformation corrections shows that conscientiousness, openness, and CRT were negatively associated with liking corrections while political liberalism, trust in medical scientists, religiosity, and grandiose narcissism were positively associated. Grandiose narcissism was the only factor positively associated with retweeting corrections. Discussion: Findings support an inhibitory role for classical reasoning in the passive spread of misinformation (e.g., "liking"), and a major role for narcissistic tendencies and motivated reasoning in active propagating behaviors ("retweeting"). Results further suggest differences in passive and active propagation, as multiple factors influence liking behavior while retweeting is primarily influenced by two factors. Implications for ecologically valid study designs are also discussed to account for greater nuance in social media behaviors in experimental research. [ABSTRACT FROM AUTHOR]

Background: Explanations for why social media users propagate misinformation include failure of classical reasoning (over-reliance on intuitive heuristics), motivated reasoning (conforming to group opinion), and personality traits (e.g., narcissism). However, there is a lack of consensus on which explanation is most predictive of misinformation spread. Previous work is also limited by not distinguishing between passive (i.e., “liking”) and active (i.e., “retweeting”) propagation behaviors. Methods: To examine this issue, 858 Twitter users were recruited to engage in a Twitter simulation task in which they were shown real tweets on public health topics (e.g., COVID-19 vaccines) and given the option to “like”, “reply”, “retweet”, “quote”, or select “no engagement”. Survey assessments were then given to measure variables corresponding to explanations for: classical reasoning [cognitive reflective thinking (CRT)], motivated reasoning (religiosity, political conservatism, and trust in medical science), and personality traits (openness to new experiences, conscientiousness, empathy, narcissism). Results: Cognitive reflective thinking, conscientiousness, openness, and emotional concern empathy were all negatively associated with liking misinformation, but not significantly associated with retweeting it. Trust in medical scientists was negatively associated with retweeting misinformation, while grandiose narcissism and religiosity were positively associated. An exploratory analysis on engagement with misinformation corrections shows that conscientiousness, openness, and CRT were negatively associated with liking corrections while political liberalism, trust in medical scientists, religiosity, and grandiose narcissism were positively associated. Grandiose narcissism was the only factor positively associated with retweeting corrections. Discussion: Findings support an inhibitory role for classical reasoning in the passive spread of misinformation (e.g., “liking”), and a major role for narcissistic tendencies and motivated reasoning in active propagating behaviors (“retweeting”). Results further suggest dierences in passive and active propagation, as multiple factors influence liking behavior while retweeting is primarily influenced by two factors. Implications for ecologically valid study designs are also discussed to account for greater nuance in social media behaviors in experimental research. [ABSTRACT FROM AUTHOR]

The genus Allium is a prominent member of the Amaryllidaceae family and encompasses approximately 1,063 species worldwide. These diverse species vary in appearance and flavor but they share biochemical similarities. Underutilized Alliums present a promising alternative to onion and garlic in various Indian regions, given their adaptability, versatile utility, and nutraceutical properties. Their adaptation in the plains of India is a great opportunity to add medicinal and nutraceutical value to Indian cuisine. Therefore, the present study aimed to evaluate the underutilized raw Allium leaves for their sensory perception by hedonic test (n = 60), consumer perceptions and willingness to purchase (n = 180), and the development and validation of value chain model (n = 30) to popularize these species in plains. For the various sensory attributes evaluated, Allium tuberosum Kazakhstan CGN-1587 emerged out to be the most suitable for its advancement into the consumer survey. The consumer perception study recorded 74.44% willingness to purchase of total respondents and a majority preference for consuming it in cooked form (38.30%). A value chain model of Allium tuberosum Kazakhstan CGN-1587 with supply chain agencies was subsequently developed. Validation of this value chain model was done with a panel of different stakeholders. Panelists perceived the value chain as highly valid, particularly in terms of economic feasibility (4.30 ± 0.70) and sustainability (4.20 ± 0.81). Thus, the present study provides valuable insights that can support and promote the use of underutilized Alliums in non-traditional areas of India. [ABSTRACT FROM AUTHOR]

The genus Allium is a prominent member of the Amaryllidaceae family and encompasses approximately 1,063 species worldwide. These diverse species vary in appearance and flavor but they share biochemical similarities. Underutilized Alliums present a promising alternative to onion and garlic in various Indian regions, given their adaptability, versatile utility, and nutraceutical properties. Their adaptation in the plains of India is a great opportunity to add medicinal and nutraceutical value to Indian cuisine. Therefore, the present study aimed to evaluate the underutilized raw Allium leaves for their sensory perception by hedonic test (n = 60), consumer perceptions and willingness to purchase (n = 180), and the development and validation of value chain model (n = 30) to popularize these species in plains. For the various sensory attributes evaluated, Allium tuberosum Kazakhstan CGN-1587 emerged out to be the most suitable for its advancement into the consumer survey. The consumer perception study recorded 74.44% willingness to purchase of total respondents and a majority preference for consuming it in cooked form (38.30%). A value chain model of Allium tuberosum Kazakhstan CGN-1587 with supply chain agencies was subsequently developed. Validation of this value chain model was done with a panel of different stakeholders. Panelists perceived the value chain as highly valid, particularly in terms of economic feasibility (4.30 ± 0.70) and sustainability (4.20 ± 0.81). Thus, the present study provides valuable insights that can support and promote the use of underutilized Alliums in non-traditional areas of India. [ABSTRACT FROM AUTHOR]

Background: Explanations for why social media users propagate misinformation include failure of classical reasoning (over-reliance on intuitive heuristics), motivated reasoning (conforming to group opinion), and personality traits (e.g., narcissism). However, there is a lack of consensus on which explanation is most predictive of misinformation spread. Previous work is also limited by not distinguishing between passive (i.e., "liking") and active (i.e., "retweeting") propagation behaviors. Methods: To examine this issue, 858 Twitter users were recruited to engage in a Twitter simulation task in which they were shown real tweets on public health topics (e.g., COVID-19 vaccines) and given the option to "like", "reply", "retweet", "quote", or select "no engagement". Survey assessments were then given tomeasure variables corresponding to explanations for: classical reasoning [cognitive reflective thinking (CRT)], motivated reasoning (religiosity, political conservatism, and trust in medical science), and personality traits (openness to new experiences, conscientiousness, empathy, narcissism). Results: Cognitive reflective thinking, conscientiousness, openness, and emotional concern empathy were all negatively associated with liking misinformation, but not significantly associated with retweeting it. Trust in medical scientists was negatively associated with retweeting misinformation, while grandiose narcissism and religiosity were positively associated. An exploratory analysis on engagement withmisinformation corrections shows that conscientiousness, openness, and CRT were negatively associated with liking corrections while political liberalism, trust in medical scientists, religiosity, and grandiose narcissism were positively associated. Grandiose narcissism was the only factor positively associated with retweeting corrections. Discussion: Findings support an inhibitory role for classical reasoning in the passive spread of misinformation (e.g., "liking"), and a major role for narcissistic tendencies and motivated reasoning in active propagating behaviors ("retweeting"). Results further suggest differences in passive and active propagation, as multiple factors influence liking behavior while retweeting is primarily influenced by two factors. Implications for ecologically valid study designs are also discussed to account for greater nuance in social media behaviors in experimental research. [ABSTRACT FROM AUTHOR]

Cannabinoids represent a highly researched group of plant-derived ingredients. The substantial investment of funds from state and commercial sources has facilitated a significant increase in knowledge about these ingredients. Cannabinoids can be classified into three principal categories: plant-derived phytocannabinoids, synthetic cannabinoids and endogenous cannabinoids, along with the enzymes responsible for their synthesis and degradation. All of these compounds interact biologically with type 1 (CB1) and/or type 2 (CB2) cannabinoid receptors. A substantial body of evidence from in vitro and in vivo studies has demonstrated that cannabinoids and inhibitors of endocannabinoid degradation possess anti-inflammatory, antioxidant, antitumour and antifibrotic properties with beneficial effects. This review, which spans the period from 1940 to 2024, offers an overview of the potential therapeutic applications of natural and synthetic cannabinoids. The development of these substances is essential for the global market of do-it-yourself drugs to fully exploit the promising therapeutic properties of cannabinoids. [ABSTRACT FROM AUTHOR]

Background: The foot racing disciplines include sprints, middle distances, and long distances, which vary in terms of intensities, duration of training, and metabolic demands. The aim of our study was to evaluate the differences in morpho-functional parameters describing cardiac remodeling in a large cohort of Olympic athletes practicing the different track subspecialties. Methods: We evaluated 140 track and field (52.1% males, mean age 26.3 ± 4.3 years) Olympic athletes divided into four groups according to the distance performed: Group A (46, 32.9%): 100 and 200 mt; Group B (34, 24.3%): 400 mt; Group C (25, 17.9%): 800, 1500, and 3000 mt; Group D (35, 24.9%): 5000, 10,000 mt, and marathon distance. The athletes underwent a pre-participation screening, which included transthoracic echocardiography and exercise stress testing. Results: In Group A and in Group B, most athletes presented normal cardiac geometry (41/46, 89.1% in Group A and 31/34, 91.2% in Group B, p < 0.0001). Instead, in Groups C and D, more than half presented eccentric cardiac remodeling (13\25, 52% in Group C and 23\35, 65.7% in Group D). No significant differences were found between subspecialties in LVEF (p = 0.587), diastolic function (p = 0.431), and training hours/week (p = 0.078). Conclusions: In conclusion, the presence and extent of cardiac remodeling vary according to the distance of the discipline practiced, with the largest dimensional increase in both left and right ventricles and atria in mid- and long-distance runners and the lowest in sprinters. [ABSTRACT FROM AUTHOR]

This study investigates the impact of vertical greenery systems (VGSs) applied to several typical wall configurations on indoor thermal conditions in a building module situated in the Mediterranean climate of Catania, Italy. By means of dynamic simulations in TRNSYS vers.18, the research compares the thermal behavior of walls made of either hollow clay blocks (Poroton) or lava stone blocks against a lightweight wall setup already in place at the University of Catania. The primary focus is on evaluating the VGSs' capability of reducing peak inner surface temperatures and moderating heat flux fluctuations entering the building. The findings indicate that adding an outer vertical greenery layer to heavyweight walls can decrease the peak inner surface temperature by up to 1.0 °C compared to the same bare wall. However, the greenery's positive impact is less pronounced than in the case of the lightweight wall. This research underscores the potential of green facades in enhancing the indoor thermal environment in buildings in regions with climates like the Mediterranean one, providing valuable insights for sustainable building design and urban planning. [ABSTRACT FROM AUTHOR]

Construction using earth materials demonstrates ecological sustainability using locally sourced natural materials and environmentally friendly demolition methods. In this study, the environmental impact of adding cement to soil materials for rammed earth farmhouse construction in rural China was investigated and comparatively simulated using the One Click LCA database, focusing on the conflict between sustainability objectives and the practical aspects of cement addition. By analyzing how the addition of cement aligns with local construction practices and addressing the debate surrounding the inclusion of cement in rammed-earth construction, our objective is to provide insights into achieving a balance between the environmental impact and the pragmatic considerations of using cement in earthen building practices. Three local structure scenarios are evaluated via simulations: cement-stabilized rammed earth wall, fired brick wall, and a localized reinforced concrete frame structure. The quantitative environmental impacts are assessed, and the qualitative differences in adaptation, economic sustainability, and other factors are examined in the context of present-day development in rural China. The results show that the use of cement-stabilized rammed earth wall-supported structures is associated with higher embodied carbon emissions compared to structures supported by reinforced concrete frames and enclosed by brick walls; however, these emissions are lower than those for brick wall-supported structures while effectively meeting the structural requirements. In addition, the use of cement-stabilized earth for perimeter walls simplifies material management and disposal throughout the building's life cycle, and the cost-effectiveness of cement has been found to be substantially greater than that of reinforced concrete frames and brick structures, improving economic viability and social acceptability, especially among low-income communities in rural areas [ABSTRACT FROM AUTHOR]

Multiple sclerosis (MS) is an immune-mediated demyelinating disease of the nervous system that leads to neurological dysfunctions and severe disabilities. It is worth noting that conventional pharmacotherapy is poorly selective and causes toxicity problems and several systemic side effects. Thus, there is a need to develop new approaches to this medical challenge. The use of nanocarriers for drug delivery represents a good strategy to overcome several issues such as high therapeutic drug doses with side effects, such as diarrhea, nausea, and abdominal pain, and drug degradation processes; in addition, nanocarriers can provide controlled and targeted drug release. This review describes the application of liposomes for the delivery of pharmaceutical actives to target MS. Firstly, MS is explained. Then, liposomes are described along with their preparation, characterization, and stability. The literature about the use of liposomes for the treatment of MS is then analyzed. [ABSTRACT FROM AUTHOR]

During the last two decades, an increased number of molecules with multiple mechanisms of action have been approved for the treatment of inflammatory bowel disease (IBD), with a substantial increase in the costs related to therapy, which has become a concern for payers, regulators, and healthcare professionals. Biosimilars are biologic medical products that are highly structurally similar to their reference products; have no clinically meaningful differences in terms of immunogenicity, safety, or effectiveness; and are available at a lower price. Materials and Methods: This was an observational prospective study conducted in two IBD centres in Bucharest and included 53 patients, 27 male (M) and 26 female (F), diagnosed with IBD according to standard clinical, endoscopic, radiological, and histological criteria, who were non-medically switched at the indication of the National Insurance House to a biosimilar of Adalimumab. Aims: The aim was to determine the rates of clinical remission, adverse effects, and treatment persistence at one year. Results: No significant differences were found in terms of the faecal calprotectin (FC) and C-reactive protein (CRP) levels 6 and 12 months after changing from the originator biologic treatment to a biosimilar. Only one patient required a change in their biological treatment following the clinical and biological loss of response. The main adverse effect reported by the patients was pain at the injection site. Of the 53 patients, only 2 reported pain at the injection site, and 1 patient reported experiencing abdominal pain and rectal bleeding immediately after the switch, but no recurrence was observed clinically or endoscopically. Conclusions: This observational study is the first to be carried out in Romania that shows that, after a non-medical switch, biosimilars of Adalimumab are as efficient and safe as the originator Adalimumab in the clinical treatment of patients with IBD. [ABSTRACT FROM AUTHOR]

Colorectal cancer (CRC) is a global problem. Oncology currently practices conventional methods of treating this carcinoma, including surgery, chemotherapy, and radiotherapy. Unfortunately, their efficacy is low; hence, the exploration of new therapies is critical. Recently, many efforts have focused on developing safe and effective anticancer compounds. Some of them include cannabinoids. In the present study, we obtained cannabinoids, such as cannabidiol (CBD), abnormal cannabigerol (abn-CBG), cannabichromene (CBC), and cannabicitran (CBT), by chemical synthesis and performed the biological evaluation of their activity on colon cancer cells. In this study, we analyzed the effects of selected cannabinoids on the lifespan and metabolic activity of normal colonic epithelial cells and cancer colon cells. This study demonstrated that cannabinoids can induce apoptosis in cancer cells by modulating mitochondrial dehydrogenase activity and cellular membrane integrity. The tested cannabinoids also influenced cell cycle progression. We also investigated the antioxidant activity of cannabinoids and established a relationship between the type of cannabinoid and nitric oxide (NO) production in normal and cancerous colon cells. To conclude, it seems that, due to their interesting properties, the cannabinoids studied may constitute an interesting target for further research aimed at their use in alternative or combined therapies for human colon cancer. [ABSTRACT FROM AUTHOR]

Integrating single-cell datasets produced by multiple omics technologies is essential for defining cellular heterogeneity. Mosaic integration, in which different datasets share only some of the measured modalities, poses major challenges, particularly regarding modality alignment and batch effect removal. Here, we present a deep probabilistic framework for the mosaic integration and knowledge transfer (MIDAS) of single-cell multimodal data. MIDAS simultaneously achieves dimensionality reduction, imputation and batch correction of mosaic data by using self-supervised modality alignment and information-theoretic latent disentanglement. We demonstrate its superiority to 19 other methods and reliability by evaluating its performance in trimodal and mosaic integration tasks. We also constructed a single-cell trimodal atlas of human peripheral blood mononuclear cells and tailored transfer learning and reciprocal reference mapping schemes to enable flexible and accurate knowledge transfer from the atlas to new data. Applications in mosaic integration, pseudotime analysis and cross-tissue knowledge transfer on bone marrow mosaic datasets demonstrate the versatility and superiority of MIDAS. MIDAS is available at https://github.com/labomics/midas. Single-cell, multiomic datasets are integrated using dimensionality reduction, imputation and batch correction. [ABSTRACT FROM AUTHOR]

The precise mechanism underlying the therapeutic effects of dihydroartemisinin (DHA) in alleviating colitis remains incompletely understood. A strong correlation existed between the elevation of glial fibrillary acidic protein (GFAP)+/S100 calcium binding protein B (S100β)+ enteric glial cells (EGCs) in inflamed colonic tissues and the disruption of the intestinal epithelial barrier (IEB) and gut vascular barrier (GVB) observed in chronic colitis. DHA demonstrated efficacy in restoring the functionality of the dual gut barrier while concurrently attenuating intestinal inflammation. Mechanistically, DHA inhibited the transformation of GFAP+ EGCs into GFAP+/S100β+ EGCs while promoting the differentiation of GFAP+/S100β+ EGCs back into GFAP+ EGCs. Furthermore, DHA induced apoptosis in GFAP+/S100β+ EGCs by inducing cell cycle arrest at the G0/G1 phase. The initial mechanism is further validated that DHA regulates EGC heterogeneity by improving dysbiosis in colitis. These findings underscore the multifaceted therapeutic potential of DHA in ameliorating colitis by improving dysbiosis, modulating EGC heterogeneity, and preserving gut barrier integrity, thus offering promising avenues for novel therapeutic strategies for inflammatory bowel diseases. [ABSTRACT FROM AUTHOR]

Numerous prevalence studies on Fabry disease (FD, OMIM #301500) have been conducted in dialysis populations across the world with variable and controversial results. The FABRYDIAL study aimed to estimate the prevalence of FD in patients aged 18 to 74 years on chronic dialysis in France. This cross-sectional study was conducted in patients undergoing dialysis. One hundred and twenty-four dialysis centers participated. Patients with proven causes of nephropathy unrelated to FD were excluded. Alpha-galactosidase A activity was assayed in men, and both α-galactosidase A and lyso-Gb3 were assayed in women from dried blood spots. GLA gene sequencing was performed in case of abnormal values. If a variant was identified, a diagnosis validation committee was consulted for adjudication. Among the 6032 targeted patients, 3088 were included (73.6% of the eligible patients). Biochemical results were available for 2815 (1721 men and 1094 women). A genetic variant of GLA was identified in five patients: a benign c.937G>T/p.(Asp313Tyr) variant in two individuals, a likely benign c.427G>A/(p.Ala143Thr) variant, a likely benign c.416A>G/(p.Asn139Ser) variant, and a pathogenic c.1185dupG/p.Phe396Glyfs variant. Among the screened patients, the prevalence was 0.058% [0.010;0.328] in males, 0% [0.000;0.350] in females, and 0.035% [0.006;0.201] when both genders were pooled. Among all patients aged 18–74 years undergoing dialysis without a previously known cause of nephropathy unlinked to FD, the prevalence was 0.028% [0.006;0.121]. The prevalence of FD in a cohort of French dialysis patients was low. However, considering the prognostic impact of earlier diagnosis, signs of FD should be sought in patients with nephropathies of uncertain etiology. [ABSTRACT FROM AUTHOR]

The pursuit of sustainable development has become a global priority, with the United Nations Sustainable Development Goals (UN SDGs) serving as a comprehensive framework for addressing various social, economic, and environmental challenges. Employing the stimulus–organization–response (SOR) model, this study examines the impact of customer's perceptions of the firm's compliance with the SDGs on customers' perceptions and loyalty through the mediation roles of image, trust, and reputation in the customer's evaluation of the firm in the banking sector in Turkey. To achieve these objectives, a quantitative research approach was used, and 455 valid responses were collected from retail banking customers through an online survey. Data from the study were statistically analyzed using partial least-squares–structural equation modeling (PLS-SEM). The findings reveal significant positive relationships between SDG implementation and customers' perceptions of banking institutions' image, trust, and reputation. Furthermore, favorable image, trust, and favorable reputation are found to positively influence customer loyalty in the banking sector. The mediation analyses confirm that each of the mediating variables partially mediates the relationships between SDG implementation and customer loyalty, underscoring the significance of these intermediate variables in shaping customers' loyalty behaviors. This research contributes to both theoretical and practical domains by providing insights into the role of SDG implementation in enhancing customer perceptions and loyalty in the banking sector. Managerial implications are discussed, and recommendations are provided for banking institutions seeking to leverage the SDGs to strengthen customer relationships and achieve sustainable growth. [ABSTRACT FROM AUTHOR]

Despite the numerous advantages of using natural materials, such as fast-growing, living, and earthen materials, their widespread application in the construction industry remains limited. This research presents a perception survey, which investigates stakeholders' perceptions regarding the market, regulatory barriers, and educational barriers, exploring experiences, motivations, and attitudes toward the adoption of natural materials in construction projects. The results capture variations in current practices and identify patterns for future directions, analyzed in a comparative manner to assess two geographical regions: Europe and North America. The results show that contractor availability, a lack of professional knowledge (mostly in Europe), and cost-to-value perceptions (mostly in the USA) are key barriers to adopting natural materials. The lack of awareness among construction professionals regarding technical aspects highlights the need for targeted training, while the lack of regulatory distinction between living and earth-based materials underscores the need for harmonized policies. By elucidating stakeholders' perspectives and identifying key challenges, this research aims to inform policymaking, industry practices, and research initiatives aimed at promoting the use of a wider lexicon of construction materials. Ultimately, this study hopes to facilitate the development of strategies to overcome scalability challenges and accelerate the transition toward their implementation in mainstream projects. [ABSTRACT FROM AUTHOR]

We addressed the damage caused by internal and external factors on the skin, as well as the aging phenomenon caused by delayed repair after damage. We prepared supramolecular hyaluronic acid–ectoin (HA–ECT) by combining theoretical calculations and experimental research, using intermolecular forces between hyaluronic acid and ectoin. This supramolecule has good stability, safety, and skin permeability and can penetrate the stratum corneum of the skin, reaching the epidermis and dermis of the skin. Compared with ectoin, the permeability of the supramolecule HA–ECT was 3.39-fold higher. Supramolecular HA–ECT can promote the proliferation of keratinocytes and fibroblasts, significantly increase the content of type collagen-I, reduce the expression of inflammatory factors in keratinocytes, and enhance skin hydration and repair effects. HA–ECT can reduce intracellular reactive oxygen species and inhibit the expression of matrix metalloproteinase-1 (reduced by 1.27-fold) to improve skin photoaging. Therefore, supramolecular HA–ECT has potential application in the field of cosmetics for skin antioxidants, anti-aging, and repair. [ABSTRACT FROM AUTHOR]

Melatonin (N‐acetyl‐5‐methoxytryptamine) is reported to improve mood disorders in perimenopausal women and gut microbiome composition is altered during menopausal period. The possible role of microbiome in the treatment effect of melatonin on menopausal depression remains unknown. Here, it is shown that melatonin treatment reverses the gut microbiota dysbiosis and depressive‐like behaviors in ovariectomy (OVX) operated mice. This effect of melatonin is prevented by antibiotic cocktails (ABX) treatment. Transferring microbiota harvested from adolescent female mice to OVX‐operated mice is sufficient to ameliorate depressive‐like behaviors. Conversely, microbiota transplantation from OVX‐operated mice or melatonin‐treated OVX‐operated mice to naïve recipient mice exhibits similar phenotypes to donors. The colonization of Alistipes Inops, which is abundant in OVX‐operated mice, confers the recipient with depressive‐like behaviors. Further investigation indicates that the expansion of Alistipes Inops induced by OVX leads to the degradation of intestinal tryptophan, which destroys systemic tryptophan availability. Melatonin supplementation restores systemic tryptophan metabolic disorders by suppressing the growth of Alistipes Inops, which ameliorates depressive‐like behaviors. These results highlight the previously unrecognized role of Alistipes Inops in the modulation of OVX‐induced behavioral disorders and suggest that the application of melatonin to inhibit Alistipes Inops may serve as a potential strategy for preventing menopausal depressive symptoms. [ABSTRACT FROM AUTHOR]

Background: Despite the numerous benefits of Internet use, improper Internet utilization may produce many psychological illnesses, such as Internet Addiction (IA). Young people are considered a vulnerable population for IA. Purpose: The current study is conducted to determine the prevalence of IA among youth in Jordan. Methods: This study employed a cross-sectional descriptive approach. The final sample consisted of 405 Jordanian young people. The researchers recruited the study participants from two universities and four schools located at different regions in Jordan. The study utilized a self-report questionnaire composed of four sections; Basic Information Questionnaire, Internet Use Patterns Questionnaire, IA Test, and Generalized Anxiety Disorder Assessment. Ethical principles of nursing research were adhered to. The study was conducted during the second semester of 2023/2024 academic year. Descriptive and inferential statistics were used for data analysis. Results: The study results revealed that about one third of the participants (n= 121, 29.9%) met the criteria for IA. There is a variability among the study participants regarding their online activities, where social media was the main online activity (66.4 %). The study results also reported that there is a positive association between anxiety and IA (r = 0.66; p = 0.001). Conclusion: From a Jordanian viewpoint, this study adds to the understanding of IA among youth in the international literature. To mitigate the negative effects of IA, stakeholders must work together interdisciplinarily and with concerted effort. Implications for Nursing: Nurses can play an important role in raising awareness about IA among youth and making referral for the affected individuals. [ABSTRACT FROM AUTHOR]

Polyploidization drives regulatory and phenotypic innovation. How the merger of different genomes contributes to polyploid development is a fundamental issue in evolutionary developmental biology and breeding research. Clarifying this issue is challenging because of genome complexity and the difficulty in tracking stochastic subgenome divergence during development. Recent single-cell sequencing techniques enabled probing subgenome-divergent regulation in the context of cellular differentiation. However, analyzing single-cell data suffers from high error rates due to high dimensionality, noise, and sparsity, and the errors stack up in polyploid analysis due to the increased dimensionality of comparisons between subgenomes of each cell, hindering deeper mechanistic understandings. In this study, we develop a quantitative computational framework, called "pseudo-genome divergence quantification" (pgDQ), for quantifying and tracking subgenome divergence directly at the cellular level. Further comparing with cellular differentiation trajectories derived from single-cell RNA sequencing data allows for an examination of the relationship between subgenome divergence and the progression of development. pgDQ produces robust results and is insensitive to data dropout and noise, avoiding high error rates due to multiple comparisons of genes, cells, and subgenomes. A statistical diagnostic approach is proposed to identify genes that are central to subgenome divergence during development, which facilitates the integration of different data modalities, enabling the identification of factors and pathways that mediate subgenome-divergent activity during development. Case studies have demonstrated that applying pgDQ to single-cell and bulk tissue transcriptomic data promotes a systematic and deeper understanding of how dynamic subgenome divergence contributes to developmental trajectories in polyploid evolution. [ABSTRACT FROM AUTHOR]

Multidrug-resistant Candida auris has recently caused major outbreaks in healthcare facilities. Rapid and accurate antifungal susceptibility testing (AST) of C. auris is crucial for proper management of invasive infections. The Commercial Sensititre Yeast One and Vitek 2 methods underestimate or overestimate the resistance of C. auris to fluconazole and amphotericin B (AMB). This study evaluated the AST results of C. auris against fluconazole and AMB by gradient-MIC-strip (Etest) and broth microdilution-based MICRONAUT-AM-EUCAST (MCN-AM) assays. Clinical C. auris isolates (n = 121) identified by phenotypic and molecular methods were tested. Essential agreement (EA, ±1 two-fold dilution) between the two methods and categorical agreement (CA) based on the Centers for Disease Control and Prevention's (CDC's) tentative resistance breakpoints were determined. Fluconazole resistance-associated mutations were detected by PCR-sequencing of ERG11. All isolates identified as C. auris belonged to South Asian clade I and contained the ERG11 Y132F or K143R mutation. The Etest–MCN-AM EA was poor (33%) for fluconazole and moderate (76%) for AMB. The CA for fluconazole was higher (94.2%, 7 discrepancies) than for AMB (91.7%, 10 discrepancies). Discrepancies were reduced when an MCN-AM upper-limit value of 4 µg/mL for fluconazole-susceptible C. auris and an Etest upper-limit value of 8 µg/mL for the wild type for AMB were used. Our data show that resistance to fluconazole was underestimated by MCN-AM, while resistance to AMB was overestimated by Etest when using the CDC's tentative resistance breakpoints of ≥32 µg/mL for fluconazole and ≥2 µg/mL for AMB. Method-specific resistance breakpoints should be devised for accurate AST of clinical C. auris isolates for proper patient management. [ABSTRACT FROM AUTHOR]

Motivation The growing number of single-cell RNA-seq (scRNA-seq) studies highlights the potential benefits of integrating multiple datasets, such as augmenting sample sizes and enhancing analytical robustness. Inherent diversity and batch discrepancies within samples or across studies continue to pose significant challenges for computational analyses. Questions persist in practice, lacking definitive answers: Should we use a specific integration method or opt for simply merging the datasets during joint analysis? Among all the existing data integration methods, which one is more suitable in specific scenarios? Result To fill the gap, we introduce SCIntRuler, a novel statistical metric for guiding the integration of multiple scRNA-seq datasets. SCIntRuler helps researchers make informed decisions regarding the necessity of data integration and the selection of an appropriate integration method. Our simulations and real data applications demonstrate that SCIntRuler streamlines decision-making processes and facilitates the analysis of diverse scRNA-seq datasets under varying contexts, thereby alleviating the complexities associated with the integration of heterogeneous scRNA-seq datasets. Availability and implementation The implementation of our method is available on CRAN as an open-source R package with a user-friendly manual available: https://cloud.r-project.org/web/packages/SCIntRuler/index.html [ABSTRACT FROM AUTHOR]

Single-cell RNA sequencing (scRNA-seq) technologies have become essential tools for characterizing cellular landscapes within complex tissues. Large-scale single-cell transcriptomics holds great potential for identifying rare cell types critical to the pathogenesis of diseases and biological processes. Existing methods for identifying rare cell types often rely on one-time clustering using partial or global gene expression. However, these rare cell types may be overlooked during the clustering phase, posing challenges for their accurate identification. In this paper, we propose a Cluster decomposition-based Anomaly Detection method (scCAD), which iteratively decomposes clusters based on the most differential signals in each cluster to effectively separate rare cell types and achieve accurate identification. We benchmark scCAD on 25 real-world scRNA-seq datasets, demonstrating its superior performance compared to 10 state-of-the-art methods. In-depth case studies across diverse datasets, including mouse airway, brain, intestine, human pancreas, immunology data, and clear cell renal cell carcinoma, showcase scCAD's efficiency in identifying rare cell types in complex biological scenarios. Furthermore, scCAD can correct the annotation of rare cell types and identify immune cell subtypes associated with disease, thereby offering valuable insights into disease progression. Identifying rare cells is essential for advancing our understanding of complex biological systems and disease mechanisms. Here, authors propose scCAD, a method that combines cluster decomposition and anomaly detection to effectively identify rare cell types across diverse biological scenarios. [ABSTRACT FROM AUTHOR]

Triple-negative breast cancer (TNBC) accounts for about 10-20% of all breast cancer cases and is associated with an unfavorable prognosis. Until recently, treatment options for TNBC were limited to chemotherapy. A new successful systemic treatment is immunotherapy with immune checkpoint inhibitors, but new tumor-specific biomarkers are needed to improve patient outcomes. Cannabinoids show antitumor activity in most preclinical studies in TNBC models and do not appear to have adverse effects on chemotherapy. Clinical data are needed to evaluate efficacy and safety in humans. Importantly, the endocannabinoid system is linked to the immune system and immunosuppression. Therefore, cannabinoid receptors could be a potential biomarker for immune checkpoint inhibitor therapy or a novel mechanism to reverse resistance to immunotherapy. In this article, we provide an overview of the currently available information on how cannabinoids may influence standard therapy in TNBC. [ABSTRACT FROM AUTHOR]

Recently, products of plant origin have been utilized to extend the shelf life of meat products. This study examined the impact of hemp seed oil as a replacement for animal fat and sodium nitrite on the nutritional, physicochemical, technological, and sensory traits of fermented salamis. Five treatments were prepared: S0 (100 mg/kg NaNO2), S1 (2% hemp oil and 50 mg/kg NaNO2), S2 (4% hemp oil and 50 mg/kg NaNO2), S3 (2% hemp oil), and S4 (4% hemp oil). The addition of hemp seed oil did not affect proximate composition but improved fatty acid composition and lipid quality nutritional indices. Microbial growth was consistent across all treatments. Active acidity (pH) and water activity (aw) were influenced by hemp seed oil and/or sodium nitrite. Salamis containing only hemp seed oil exhibited lower redness and chroma values during storage. Hemp seed oil led to higher lipid peroxidation, mitigated by sodium nitrite. The addition of hemp seed oil and varying levels of sodium nitrite significantly impacted salami texture. Sensory evaluation showed consumer acceptance of hemp seed oil-enhanced salamis. In conclusion, hemp seed oil can be used as a functional ingredient to improve the nutritional value and healthiness of fermented meat products when combined with reduced sodium nitrite content. [ABSTRACT FROM AUTHOR]

Amidst the escalating threat of global warming, which manifests in more frequent forest fires, the prompt and accurate detection of forest fires has ascended to paramount importance. The current surveillance algorithms employed for forest fire monitoring—including, but not limited to, fixed threshold algorithms, multi-channel threshold algorithms, and contextual algorithms—rely primarily upon the degree of deviation between the pixel temperature and the background temperature to discern pyric events. Notwithstanding, these algorithms typically fail to account for the spatial heterogeneity of the background temperature, precipitating the consequential oversight of low-temperature fire point pixels, thus impeding the expedited detection of fires in their initial stages. For the amelioration of this deficiency, the present study introduces a spatial feature-based (STF) method for forest fire detection, leveraging Himawari-8/9 imagery as the main data source, complemented by the Shuttle Radar Topography Mission (SRTM) DEM data inputs. Our proposed modality reconstructs the surface temperature information via selecting the optimally designated machine learning model, subsequently identifying the fire point through utilizing the difference between the reconstructed surface temperatures and empirical observations, in tandem with the spatial contextual algorithm. The results confirm that the random forest model demonstrates superior efficacy in the reconstruction of the surface temperature. Benchmarking the STF method against both the fire point datasets disseminated by the China Forest and Grassland Fire Prevention and Suppression Network (CFGFPN) and the Wild Land Fire (WLF) fire point product validation datasets from Himawari-8/9 yielded a zero rate of omission errors and a comprehensive evaluative index, predominantly surpassing 0.74. These findings show that the STF method proposed herein significantly augments the identification of lower-temperature fire point pixels, thereby amplifying the sensitivity of forest surveillance. [ABSTRACT FROM AUTHOR]

Purpose: Customer lifetime value (CLV) is one of the key indicators to measure the success or health of an organization. How can an organization assess the organization's customers' lifetime value (LTV) and offer relevant strategies to retain prospective and profitable customers? This study offers an integrated view of different methods for calculating CLVs for both loyalty members and non-membership customers. Design/methodology/approach: This study outlines eleven methods for calculating CLV considering (1) the deterministic aspect of NPV (Net present value) models in both finite and infinite timespans, (2) the geometric pattern and (3) the probabilistic aspect of parameter estimates through simulation modeling along with (4) the migration models for including "the probability that customers will return in the future" as a key input for CLV calculation. Findings: The CLV models are validated in the context of complementary and alternative medicine (CAM)in the healthcare industry. The results show that understanding CLV can help the organization develop strategies to retain valuable customers while maintaining profit margins. Originality/value: The integrated CLV models provide an overview of the mathematical estimation of LTVs depending on the nature of the customers and the business circumstances and can be applied to other business settings. [ABSTRACT FROM AUTHOR]

Fentanyl is an opioid with powerful analgesic effects and a high speed of action. Due to its pharmacological properties, this molecule has therapeutic application as an anesthetic in surgery or as palliative therapy for cancer patients. Unfortunately, in recent years, the easy availability of this substance, the low cost and the illegal online market have favored the large-scale diffusion of fentanyl. Fentanyl is available in different forms, including nasal spray, oral patches, soluble capsules, aerosol or the new version of fentanyl mixed with other drugs, making its use very widespread. Subjects of various ages are involved in fentanyl consumption, including minors that have not yet reached adolescence. In this work, we performed a literature review using the search engines PubMed NCBI and SCOPUS regarding episodes of acute fentanyl intoxication occurring in those of a pediatric age using the Mesh Terms "fentanyl" AND "overdose" AND "children". The inclusion criteria were English papers published in the last 10 years regarding the cases of children under the age of 10. We evaluated the most frequent methods of intake and the circumstances of such episodes. In cases of death, we analyzed the autopsy, the toxicological findings and the investigations carried out. The review results show that in this age group (under < 10 y.o. s), it is possible to identify the risk factors for fentanyl intake, such as the presence of this molecule within the family unit due to drug addiction or medical therapy. The results also demonstrate a significant risk of underestimation of this phenomenon, since the molecule is often not investigated through adequate toxicological analysis. These results, therefore, suggest always carrying out toxicological investigations in the case of suspected fentanyl intoxication, both on patients or cadavers. The investigations must always include a urinary screening for opiates, and the request for a second level analysis with molecule dosage in cases of positivity or in cases of strong suspicion for assumption. In cases of intoxication in a family context of drug addiction, it is necessary to investigate the chronicity of the intake through hair analysis and evaluate the possible co-administration of other drugs. In conclusion, we suggest a protocol, applicable both on patients or cadavers, which can be useful for physicians and forensic pathologists in order to promptly identify these cases and allow for the reporting of them to the judicial authorities with the adoption of strict prevention and control measures. [ABSTRACT FROM AUTHOR]

The blood-brain barrier (BBB) plays a critical role in maintaining ion and fluid homeostasis, essential for brain metabolism and neuronal function. Regulation of nutrient, water, and ion transport across the BBB is tightly controlled by specialized ion transporters and channels located within its unique cellular components. These dynamic transport processes not only influence the BBB’s structure but also impact vital signaling mechanisms, essential for its optimal function. Disruption in ion, pH, and fluid balance at the BBB is associated with brain pathology and has been implicated in various neurological conditions, including stroke, epilepsy, trauma, and neurodegenerative diseases such as Alzheimer’s disease (AD). However, knowledge gaps exist regarding the impact of ion transport dysregulation on BBB function in neurodegenerative dementias. Several factors contribute to this gap: the complex nature of these conditions, historical research focus on neuronal mechanisms and technical challenges in studying the ion transport mechanisms in in vivo models and the lack of efficient in vitro BBB dementia models. This review provides an overview of current research on the roles of ion transporters and channels at the BBB and poses specific research questions: 1) How are the expression and activity of key ion transporters altered in AD and vascular dementia (VaD); 2) Do these changes contribute to BBB dysfunction and disease progression; and 3) Can restoring ion transport function mitigate BBB dysfunction and improve clinical outcomes. Addressing these gaps will provide a greater insight into the vascular pathology of neurodegenerative disorders. [ABSTRACT FROM AUTHOR]