Explore the vast range of titles available.

One of the most striking hallmarks shared by various neurodegenerative diseases, including Parkinson’s disease, Alzheimer’s disease (AD), and amyotrophic lateral sclerosis, is microglia-mediated neuroinflammation. Increasing evidence indicates that microglial activation in the central nervous system is heterogeneous, which can be categorized into two opposite types: M1 phenotype and M2 phenotype. Depending on the phenotypes activated, microglia can produce either cytotoxic or neuroprotective effects. In this review, we focus on the potential role of M1 and M2 microglia and the dynamic changes of M1/M2 phenotypes that are critically associated with the neurodegenerative diseases. Generally, M1 microglia predominate at the injury site at the end stage of disease, when the immunoresolution and repair process of M2 microglia are dampened. This phenotype transformation is very complicated in AD due to the phagocytosis of regionally distributed β-amyloid (Aβ) plaque and tangles that are released into the extracellular space. The endogenous stimuli including aggregated α-synuclein, mutated superoxide dismutase, Aβ, and tau oligomers exist in the milieu that may persistently activate M1 pro-inflammatory responses and finally lead to irreversible neuron loss. The changes of microglial phenotypes depend on the disease stages and severity; mastering the stage-specific switching of M1/M2 phenotypes within appropriate time windows may provide better therapeutic benefit.

This study explored the transformative potential of generative artificial intelligence (GAI) for achieving the UN Sustainable Development Goal on Quality Education (SDG4), emphasizing its interconnectedness with the other SDGs. A proprietary algorithm and cocitation network analysis were used to identify and analyze the network of SDG features in GAI research publications (n = 1501). By examining GAI’s implications for ten SDG4 targets, the findings advocate for a collaborative, ethical approach to integrating GAI, emphasizing policy and practice developments that ensure that technological advancements align with the overarching goals of SDG4. The results highlight the multifaceted impact of GAI on the SDGs. First, this paper outlines a framework that leverages GAI to enhance educational equity, quality, and lifelong learning opportunities. By highlighting the synergy between GAI and the SDGs, such as reducing inequalities (SDG10) and promoting gender equality (SDG5), this study underscores the need for an integrated approach to utilizing GAI. Moreover, it advocates for personalized learning, equitable technology access, adherence to ethical AI principles, and fostering global citizenship, proposing a strategic alignment of GAI applications with the broader SDG agenda. Next, the results highlight that GAI introduces significant challenges, including ethical concerns, data privacy, and the risk of exacerbating the digital divide. Overall, our findings underscore the critical role of policy reforms and innovative practices in navigating the challenges and harnessing the opportunities presented by GAI in education, thereby contributing to a comprehensive discourse on technology’s role in advancing global education and sustainable development.

We report exact black hole solutions in asymptotically flat or (A)dS four-dimensional spacetime with a conformally coupled self-interacting scalar field in f(R) gravity. We first consider the asymptotically flat model f(R)=R-2αR and derive an exact black hole solution. Then, we consider the asymptotically (A)dS model f(R)=R-2Λ-2αR-4Λ and derive an exact black hole solution. In both cases the modified gravity parameter α, which has the dimension of the inverse mass, cannot be set to zero and the self-interacting potential is determined from the Klein–Gordon equation, preserving the conformal invariance. The thermodynamics of the solutions is also studied.

Major depressive disorder is a devastating psychiatric disease that afflicts up to 17% of the world’s population. Postmortem brain analyses and imaging studies of patients with depression have implicated basal lateral amygdala (BLA) dysfunction in the pathophysiology of depression. However, the circuit and molecular mechanisms through which BLA neurons modulate depressive behavior are largely uncharacterized. Here, in mice, we identified that BLA cholecystokinin (CCK) glutamatergic neurons mediated negative reinforcement via D2 medium spiny neurons (MSNs) in the nucleus accumbens (NAc) and that chronic social defeat selectively potentiated excitatory transmission of the CCKBLA–D2NAc circuit in susceptible mice via reduction of presynaptic cannabinoid type-1 receptor (CB1R). Knockdown of CB1R in the CCKBLA–D2NAc circuit elevated synaptic activity and promoted stress susceptibility. Notably, selective inhibition of the CCKBLA–D2NAc circuit or administration of synthetic cannabinoids in the NAc was sufficient to produce antidepressant-like effects. Overall, our studies reveal the circuit and molecular mechanisms of depression.Activating cannabinoid receptors in a newly identified neural circuit ameliorates depressive-like behaviors in mice.

C-reactive protein (CRP) is a plasma protein that is evolutionarily conserved, found in both vertebrates and many invertebrates. It is a member of the pentraxin superfamily, characterized by its pentameric structure and calcium-dependent binding to ligands like phosphocholine (PC). In humans and various other species, the plasma concentration of this protein is markedly elevated during inflammatory conditions, establishing it as a prototypical acute phase protein that plays a role in innate immune responses. This feature can also be used clinically to evaluate the severity of inflammation in the organism. Human CRP (huCRP) can exhibit contrasting biological functions due to conformational transitions, while CRP in various species retains conserved protective functions in vivo . The focus of this review will be on the structural traits of CRP, the regulation of its expression, activate complement, and its function in related diseases in vivo .

Fusarium graminearum is a destructive wheat pathogen. No fully resistant cultivars are available. Knowledge concerning the molecular weapons of F. graminearum to achieve infection remains limited. Here, we report that deletion of the putative secondary metabolite biosynthesis gene cluster fg3_54 compromises the pathogen’s ability to infect wheat through cell-to-cell penetration. Ectopic expression of fgm4 , a pathway-specific bANK-like regulatory gene, activates the transcription of the fg3_54 cluster in vitro. We identify a linear, C- terminally reduced and d -amino acid residue-rich octapeptide, fusaoctaxin A, as the product of the two nonribosomal peptide synthetases encoded by fg3_54 . Chemically-synthesized fusaoctaxin A restores cell-to-cell invasiveness in fg3_54 -deleted F. graminearum , and enables colonization of wheat coleoptiles by two Fusarium strains that lack the fg3_54 homolog and are nonpathogenic to wheat. In conclusion, our results identify fusaoctaxin A as a virulence factor required for cell-to-cell invasion of wheat by F. graminearum . Fusarium graminearum is a fungal pathogen of wheat and other cereals. Here the authors identify a gene cluster in F. graminearum encoding the production of a non-ribosomal peptide that is required for infection of wheat through cell-to-cell penetration.

Background Anaphase-promoting complex subunit 1 (ANAPC1) is a regulator of cellular mitosis and an important factor in tumorigenesis. To date, a comprehensive assessment of the potential role, biological behaviours, and clinical significance of ANAPC1 in colorectal cancer (CRC) is still lacking. Materials and methods This study integrated 2329 mRNA expression data, single-cell RNA sequencing (scRNA-seq), and internal immunohistochemistry of 416 tissue samples to comprehensively evaluate the abnormal expression pattern of ANAPC1 in CRC. It also incorporated evidence from immune infiltration analysis, functional enrichment analysis, and weighted gene co-expression network analysis to explore the biological behaviour of ANAPC1 in CRC. In addition, in vitro cell biology experiments such as real-time polymerase chain reaction (RT-PCR), western blot (WB), cholecystokinin 8 (CCK-8), wound healing, cell cycle, and apoptosis assays were conducted to verify the potential effect of ANAPC1 on CRC cells. Results ANAPC1 mRNA was significantly overexpressed in CRC tissue (SMD = 2.07, 95% CI 1.59-2.55, P < .05) and malignant epithelial cells (P < .05). Validation at the protein level similarly confirmed the overexpression of ANAPC1 in CRC tissue (P < .05). ANAPC1 in CRC may play a role in abnormal ribosome biogenesis, DNA replication, ATP-dependent activity acting on DNA, nuclear division, chromosome segregation, and other pathways. In vitro experiments demonstrated that HCT-116 cells with ANAPC1 knockdown had reduced proliferation and migration abilities, increased cell apoptosis rate, and altered cell cycle distribution. In addition, CRC patients with low ANAPC1 expression were more likely to benefit from treatment with immune checkpoint inhibitors. ANAPC1 was significantly downregulated in malignant epithelial cells of CRC treated with PD-1 inhibitors (P < .05). Conclusion ANAPC1 may have a positive impact on the development of CRC by being involved in pathways related to DNA replication, chromosome segregation, and ribosomes.

Diabetes, a group of metabolic disorders, constitutes an important global health problem. Diabetes and its complications place a heavy financial strain on both patients and the global healthcare establishment. The lack of effective treatments contributes to this pessimistic situation and negative outlook. Exosomes released from mesenchymal stromal cells (MSCs) have emerged as the most likely new breakthrough and advancement in treating of diabetes and diabetes‐associated complication due to its capacity of intercellular communication, modulating the local microenvironment, and regulating cellular processes. In the present review, we briefly outlined the properties of MSCs-derived exosomes, provided a thorough summary of their biological functions and potential uses in diabetes and its related complications.

As one of the most ubiquitous diagnostic imaging tests in medical practice, chest radiography requires timely reporting of potential findings and diagnosis of diseases in the images. Automated, fast, and reliable detection of diseases based on chest radiography is a critical step in radiology workflow. In this work, we developed and evaluated various deep convolutional neural networks (CNN) for differentiating between normal and abnormal frontal chest radiographs, in order to help alert radiologists and clinicians of potential abnormal findings as a means of work list triaging and reporting prioritization. A CNN-based model achieved an AUC of 0.9824 ± 0.0043 (with an accuracy of 94.64 ± 0.45%, a sensitivity of 96.50 ± 0.36% and a specificity of 92.86 ± 0.48%) for normal versus abnormal chest radiograph classification. The CNN model obtained an AUC of 0.9804 ± 0.0032 (with an accuracy of 94.71 ± 0.32%, a sensitivity of 92.20 ± 0.34% and a specificity of 96.34 ± 0.31%) for normal versus lung opacity classification. Classification performance on the external dataset showed that the CNN model is likely to be highly generalizable, with an AUC of 0.9444 ± 0.0029. The CNN model pre-trained on cohorts of adult patients and fine-tuned on pediatric patients achieved an AUC of 0.9851 ± 0.0046 for normal versus pneumonia classification. Pretraining with natural images demonstrates benefit for a moderate-sized training image set of about 8500 images. The remarkable performance in diagnostic accuracy observed in this study shows that deep CNNs can accurately and effectively differentiate normal and abnormal chest radiographs, thereby providing potential benefits to radiology workflow and patient care.

Background: Screening or diagnosis for the elderly with dementia in rural regions might be delayed and underestimated due to limited utilization of healthcare resources. This study aimed to evaluate the disparities of prevalence and risk factors of mild cognitive impairment (MCI) and dementia between urban and rural residence.Methods: In this nationwide door-to-door survey, 10,432 participants aged 65 years and more were selected through computerized random sampling from all administrative districts in Taiwan and were assessed using an in-person interview. We calculated the prevalence of MCI and dementia, with their risk factors examined using multivariable logistic regression.Results: The prevalence of dementia in rural, suburban, and urban areas among the elderly was 8.69% (95% CI, 8.68–8.69), 6.63% (95% CI, 6.62–6.63), and 4.46% (95% CI, 4.46–4.47), respectively. A similar rural-suburban-urban gradient relationship on the dementia prevalence was observed in any age and sex group. The rural:urban ratio was higher in women than in men for both MCI and dementia. Urbanization remained to be an independent factor for both MCI and dementia after adjustment for age, gender, education, lifestyle, and health status. The beneficial effects of exercise on dementia were more evident in rural areas than in urban ones.Conclusion: Significantly higher prevalence of MCI and dementia were found in rural areas than in urban ones, especially for women. The odds of risk factors for MCI and dementia varied by urbanization status. Focus on the rural-urban inequality and the modification of associated factors specifically for different urbanization levels are needed.