Miracle-dz/UniFAR-FTUs huggingface dataset:
Albumin-binding recombinant human IL-18BP ameliorates macrophage activation syndrome and atopic dermatitis via direct IL-18 inactivation

Given the clinical success of cytokine blockade in managing diverse inflammatory human conditions, this approach could be exploited for numerous refractory or uncontrolled inflammatory conditions by identifying novel targets for functional blockade. IL-18, a pro-inflammatory cytokine, is relatively underestimated as a therapeutic target, despite accumulated evidence indicating the unique roles of IL-18 in acute and chronic inflammatory conditions, such as macrophage activation syndrome. Herein, we designed a new form of IL-18 blockade, i.e., APB-R3, a long-acting recombinant human IL-18BP linked to human albumin-binding Fab fragment, SL335, for extending half-life. We then explored the pharmacokinetics and pharmacodynamics of APB-R3. In addition to an extended serum half-life, APB-R3 alleviates liver inflammation and splenomegaly in a model of the macrophage activation syndrome induced in IL-18BP knockout mice. Moreover, APB-R3 substantially controlled skin inflammation in a model of atopic dermatitis. Thus, we report APB-R3 as a new potent IL-18 blocking agent that could be applied to treat IL-18-mediated inflammatory diseases.
Yeast Ixr1 mediates the DNA replication stress response through it HMGB DNA binding domains and interaction with checkpoint Mrc1

Background High mobility group box (HMGB) family protein Ixr1 has been shown to be involved in DNA damage repair, however, its role and mechanism remain largely unclear. Methods Genes of S. cerevisiae were deleted or tagged with myc, GFP, or mcherry using the lithium acetate method. Sensitivity of strains to hydroxyurea (HU), methyl methanesulfonate (MMS), camptothe-cin (CPT), 4-nitroquinoline N-oxide (4-NQ), or Zeocin was tested. Distribution of GFP or mcherry fusion proteins was visualized with laser scanning confocal microscopy. RNA-seq was used to determine differential gene expression between mutant and control strains. Results Ixr1 deletion (ixr1Δ) mutant strain was sensitive to HU. Additionally, phosphorylation of effector of DNA damage checkpoint kinase Rad53 was lower in ixr1Δ than WT. Deletion of DNA damage checkpoint mediators ixr1Δ Rad9Δ was more sensitive to HU than ixr1Δ or Rad9Δ, and ixr1Δ mrc1Δ had similar sensitivity to HU as mrc1Δ but stronger than ixr1Δ. Deletion of ribonucleotide reductase inhibitors sml1Δ or crt10Δ didn’t reduce the sensitivity of ixr1Δ induced by HU. Repli-cation fork nuclease exo1Δ ixr1Δ or helicase sgs1Δ ixr1Δ double deletions were more sensitive to HU than single deletion. In addition, laser scanning confocal microscopy imaging indicated that in response to HU, Ixr1 may be in the same pathway as Mrc1, possibly downstream. Gene Ontol-ogy enrichment analysis of differentially expressed genes (DEGs) between ixr1Δ and wildtype, untreated and treated with HU, confirmed that Ixr1 plays an important role in regulating the transcription of genes related to DNA replication or DNA damage repair. We also found that, re-gardless of HU exposure, Ixr1 localized to the nucleus and may bind DNA through its two HMG-boxes. Conclusion Ixr1 participates in the DNA replication stress response through a DNA damage checkpoint pathway mediated by Mrc1, and regulates expression of genes related to DNA damage repair.
Nf1 Deficiency Increases Mammary Collagen Deposition and Restricts Adipocyte Differentiation Before Tumor Formation

BACKGROUND Neurofibromin, coded by the NF1 tumor suppressor gene, is the main negative regulator of the RAS pathway and is frequently mutated in various cancers. Women with Neurofibromatosis Type I (NF1) – a tumor predisposition syndrome caused by a germline NF1 mutation – have an increased risk of developing aggressive breast cancer with poorer prognosis. The mechanism by which NF1 mutations lead to breast cancer tumorigenesis is not well understood. Therefore, the objective of this work was to identify stromal alterations before tumor formation that result in the increased risk and poorer outcome seen among NF1 patients with breast cancer. METHODS To accurately model the germline monoallelic NF1 mutations in NF1 patients, we utilized an Nf1-deficient rat model with accelerated mammary development before presenting with highly penetrant breast cancer. RESULTS We identified increased collagen content in Nf1-deficient rat mammary glands before tumor formation that correlated with age of tumor onset. Additionally, gene expression analysis revealed that Nf1-deficient mature adipocytes in the rat mammary gland have increased collagen expression and shifted to a fibroblast and preadipocyte expression profile. This alteration in lineage commitment was also observed with in vitro differentiation, however, flow cytometry analysis did not show a change in mammary adipose-derived mesenchymal stem cell abundance. CONCLUSION Collectively, these studies uncovered the previously undescribed role of Nf1 in mammary collagen deposition and regulating adipocyte differentiation. In addition to unraveling the mechanism of tumor formation, further investigation of adipocytes and collagen modifications in preneoplastic mammary glands will create a foundation for developing early detection strategies of breast cancer among NF1 patients. GRAPHICAL ABSTRACT
TMS-evoked potential in the dorsolateral prefrontal cortex to assess the severity of depression disease: a TMS-EEG study

Objective: The combined use of transcranial magnetic stimulation and electroencephalography (TMS-EEG), as a powerful technique that can non-invasively probe the state of the brain, can be used as a method to study neurophysiological markers in the field of psychiatric disorders and discover potential diagnostic predictors. This study used TMS-evoked potentials (TEPs) to study the cortical activity of patients with major depressive disorder depression (MDD) and the correlation with clinical symptoms to provide an electrophysiological basis for the clinical diagnosis. Methods: A total of 41 patients and 42 healthy controls were recruited to study. Using TMS-EEG techniques to measure the left dorsolateral prefrontal cortex (DLPFC) ‘s TEP index and evaluate the clinical symptoms of MDD patients using the Hamilton Depression Scale-24 (HAMD-24). Results: MDD subjects performing TMS-EEG on the DLPFC showed lower cortical excitability P60 index levels than healthy controls. Further analysis revealed that the degree of P60 excitability within the DLPFC of MDD patients was significantly negatively correlated with the severity of depression. Conclusion: The low levels of P60 exhibited in DLPFC reflect low excitability in MDD; the P60 component can be used as a biomarker for MDD in clinical assessment tools.
Speakers are more cooperative and less individual when interacting in larger group sizes

Introduction Cooperation, acoustically signaled through vocal convergence, is facilitated when group members are more similar. Excessive vocal convergence may, however, weaken individual recognizability. This study aimed to explore whether constraints to convergence can arise in circumstances where interlocutors need to enhance their vocal individuality. Therefore, we tested the effects of group size (3 and 5 interactants) on vocal convergence and individualization in a social communication scenario in which individual recognition by voice is at stake. Methods In an interactive game, players had to recognize each other through their voices while solving a cooperative task online. The vocal similarity was quantified through similarities in speaker i-vectors obtained through probabilistic linear discriminant analysis (PLDA). Speaker recognition performance was measured through the system Equal Error Rate (EER). Results Vocal similarity between-speakers increased with a larger group size which indicates a higher cooperative vocal behavior. At the same time, there was an increase in EER for the same speakers between the smaller and the larger group size, meaning a decrease in overall recognition performance. Discussion The decrease in vocal individualization in the larger group size suggests that ingroup cooperation and social cohesion conveyed through acoustic convergence have priority over individualization in larger groups of unacquainted speakers.
Cytokine-induced killer cell treatment is superior to chemotherapy alone in esophageal cancer

Background: The therapeutic efficacy of cytokine-induced killer (CIK) cells versus dendritic cells (DC) co-cultured with CIK cells (DC-CIK) in treating esophageal cancer (EC) remains unclear due to the absence of a direct comparison of these two regimens. This study evaluated the comparative efficacy and safety of CIK cells versus DC-CIK using network meta-analysis in treating EC. Material and methods: We identified eligible studies from previous meta-analyses, then conducted an updated search to retrieve additional trials between February 2020 and July 2021. The primary outcomes included overall survival (OS), objective response rate (ORR), and disease control rate (DCR), and the secondary outcomes included quality of life improved rate (QLIR) and adverse events (AEs). A network meta-analysis of 12 studies was conducted using ADDIS software. Results: Twelve studies were identified, including six comparing CIK or DC-CIK plus chemotherapy (CT) with CT alone. Immunotherapy plus CT significantly improved overall survival (OS) (odds ratio [OR] 4.10, 95% confidence interval [CI] 1.23–13.69), objective response rate (ORR) (OR 2.72, 95% CI 1.79–4.11), disease control rate (DCR) (OR 3.45, 95% CI 2.32–5.14), and quality of life improvement rate (QLIR) (OR 3.54, 95% CI 2.31–5.41). DC-CIK+CT decreased the risk of leukopenia compared with CT alone. However, no statistical difference was detected between CIK-CT and DC-CIK+CT. Conclusion: Based on the available evidence, we concluded that CIK cell treatment is superior to CT alone, but CIK-CT and DC-CIK+CT may be comparable in treating EC. However, comparing CIK-CT and DC-CIK+CT is only based on indirect evidence, so it is undoubtedly necessary to conduct studies to compare CIK-CT with DC-CIK+CT in EC patients directly.
How does climate risk matter for corporate green innovation? Empirical evidence from heavy-polluting listed companies in China

Chinese heavy-polluting companies have been facing enormous challenges in responding to climate risk and energy transformation. This paper uses panel regression model and investigates the impact of climate risk on corporate green innovation in Chinese heavy-polluting listed companies from 2011 to 2020. The empirical results show that climate risk adversely affects green innovation in heavy-polluting companies, and this effect persists throughout a series of robustness and endogeneity tests. Climate risk may affect corporate green innovation through decreasing R&D investment, lowing resource allocation efficiency and increasing company risk. Climate risk has a greater negative impact on mid-western, state-owned and large-size heavy-polluting companies, but can be mitigated by the development of green finance, digital finance and marketization. These findings may help heavy-polluting companies fully utilize existing resources, policies, and channels for green innovation and mitigate climate risks.
High stress related to COVID-19 among health workers in the Plateau Central healthcare region (BURKINA FASO): a cross-sectional study

Background The COVID-19 pandemic challenged the mental wellbeing of health workers. The objective of this study was to assess health workers' perceived stress during the response to COVID-19 in the Central Plateau region (Burkina Faso). Methods We conducted a cross-sectional study of health workers in the Central Plateau health region from September 20 to October 20, 2021. Agents' perceived stress was assessed by the Perceived Stress Scale (PSS-10). Factors associated with high stress (PSS-10 score ≥ 27) were identified by logistic regression. Results A total of 272 officers participated in the survey. The mean PSS-10 score was 29.3 points (standard deviation: 6.2). Three out of ten agents (68%) had a high level of stress. The main sources of stress were the risk of being exposed to contamination (70%) and being the source of contamination (78%). Working at the referral health center [adjusted odds ratio (aOR): 2.29; 95% confidence interval (95% CI): 1.19–4.41], the hospital as the main source of COVID-19 information (aOR: 1.17; 95% CI: 1.01-3.04), fear of COVID-19 patients being managed at one's center (aOR: 1.8; 95% CI: 1.06–3.07) were factors associated with high health worker stress levels during the first wave of COVID-19. Conclusion The COVID-19 pandemic caused high stress among health care workers in Burkina Faso. Psychological support for health center workers in responding to future epidemics would improve their mental health.
Study on the Beam-shaping of high-power LD Bar Arrays

A new beam-shaping method for high-power LD bar is presented.In this method,firstly beams are separated into several layers along fast axis direction,and then they are rearranged along slow axis direction,finally a high brightness rectangular beam is received at the output.The principles and shaping mechanism of the shaping element are discussed in detail.The spot size of the output beam is calculated by using the geometrical optics method.The output beam quality is evaluated.Numerical results show that the beam quality has been improved significantly.
A novel visual brain-computer interfaces paradigm based on evoked related potentials evoked by weak and small number of stimuli

Introduction Traditional visual Brain-Computer Interfaces (v-BCIs) usually use large-size stimuli to attract more attention from users and then elicit more distinct and robust EEG responses, which would cause visual fatigue and limit the length of use of the system. On the contrary, small-size stimuli always need multiple and repeated stimulus to code more instructions and increase separability among each code. These common v-BCIs paradigms can cause problems such as redundant coding, long calibration time, and visual fatigue. Methods To address these problems, this study presented a novel v-BCI paradigm using weak and small number of stimuli, and realized a nine-instruction v-BCI system that controlled by only three tiny stimuli. Each of these stimuli were located between instructions, occupied area with eccentricities subtended 0.4°, and flashed in the row-column paradigm. The weak stimuli around each instruction would evoke specific evoked related potentials (ERPs), and a template-matching method based on discriminative spatial pattern (DSP) was employed to recognize these ERPs containing the intention of users. Nine subjects participated in the offline and online experiments using this novel paradigm. Results The average accuracy of the offline experiment was 93.46% and the online average information transfer rate (ITR) was 120.95 bits/min. Notably, the highest online ITR achieved 177.5 bits/min. Discussion These results demonstrate the feasibility of using a weak and small number of stimuli to implement a friendly v-BCI. Furthermore, the proposed novel paradigm achieved higher ITR than traditional ones using ERPs as the controlled signal, which showed its superior performance and may have great potential of being widely used in various fields.
Live cell imaging of DNA and RNA with fluorescent signal amplification and background reduction techniques

Illuminating DNA and RNA dynamics in live cell can elucidate their life cycle and related biochemical activities. Various protocols have been developed for labeling the regions of interest in DNA and RNA molecules with different types of fluorescent probes. For example, CRISPR-based techniques have been extensively used for imaging genomic loci. However, some DNA and RNA molecules can still be difficult to tag and observe dynamically, such as genomic loci in non-repetitive regions. In this review, we will discuss the toolbox of techniques and methodologies that have been developed for imaging DNA and RNA. We will also introduce optimized systems that provide enhanced signal intensity or low background fluorescence for those difficult-to-tag molecules. These strategies can provide new insights for researchers when designing and using techniques to visualize DNA or RNA molecules.
Loss of FAM111B protease mutated in hereditary fibrosing poikiloderma negatively regulates telomere length

Hereditary fibrosing poikiloderma (HFP) is a rare human dominant negative disorder caused by mutations in the FAM111B gene that encodes a nuclear trypsin-like serine protease. HFP patients present with symptoms including skin abnormalities, tendon contractures, myopathy and lung fibrosis. We characterized the cellular roles of human FAM111B using U2OS and MCF7 cell lines and report here that the protease interacts with components of the nuclear pore complex. Loss of FAM111B expression resulted in abnormal nuclear shape and reduced telomeric DNA content suggesting that FAM111B protease is required for normal telomere length; we show that this function is independent of telomerase or recombination driven telomere extension. Even though FAM111B-deficient cells were proficient in DNA repair, they showed hallmarks of genomic instability such as increased levels of micronuclei and ultra-fine DNA bridges. When mutated as in HFP, FAM111B was more frequently localized to the nuclear envelope, suggesting that accumulation of the mutated protease at the nuclear periphery may drive the disease pathology.
Time-related survival prediction in molecular subtypes of breast cancer using time-to-event deep-learning-based models

Background Breast cancer (BC) survival prediction can be a helpful tool for identifying important factors selecting the effective treatment reducing mortality rates. This study aims to predict the time-related survival probability of BC patients in different molecular subtypes over 30 years of follow-up. Materials and methods This study retrospectively analyzed 3580 patients diagnosed with invasive breast cancer (BC) from 1991 to 2021 in the Cancer Research Center of Shahid Beheshti University of Medical Science. The dataset contained 18 predictor variables and two dependent variables, which referred to the survival status of patients and the time patients survived from diagnosis. Feature importance was performed using the random forest algorithm to identify significant prognostic factors. Time-to-event deep-learning-based models, including Nnet-survival, DeepHit, DeepSurve, NMLTR and Cox-time, were developed using a grid search approach with all variables initially and then with only the most important variables selected from feature importance. The performance metrics used to determine the best-performing model were C-index and IBS. Additionally, the dataset was clustered based on molecular receptor status (i.e., luminal A, luminal B, HER2-enriched, and triple-negative), and the best-performing prediction model was used to estimate survival probability for each molecular subtype. Results The random forest method identified tumor state, age at diagnosis, and lymph node status as the best subset of variables for predicting breast cancer (BC) survival probabilities. All models yielded very close performance, with Nnet-survival (C-index=0.77, IBS=0.13) slightly higher using all 18 variables or the three most important variables. The results showed that the Luminal A had the highest predicted BC survival probabilities, while triple-negative and HER2-enriched had the lowest predicted survival probabilities over time. Additionally, the luminal B subtype followed a similar trend as luminal A for the first five years, after which the predicted survival probability decreased steadily in 10- and 15-year intervals. Conclusion This study provides valuable insight into the survival probability of patients based on their molecular receptor status, particularly for HER2-positive patients. This information can be used by healthcare providers to make informed decisions regarding the appropriateness of medical interventions for high-risk patients. Future clinical trials should further explore the response of different molecular subtypes to treatment in order to optimize the efficacy of breast cancer treatments.
Holo-omic applications to unveil microbiome shaping sustainable horticultural production

Plant-associated microorganisms play a key role in the future sustainability of terrestrial ecosystems. In nature, plants and their associated microbiome can form an assemblage of species, considered interacting metaorganisms or holobionts. Horticultural plants, including vegetables, fruit and berries, and ornamentals live in intimate association with complex and diverse microbial communities that have evolved a myriad of cooperative and competitive interaction mechanisms that shape the holobiont fitness. Nonetheless, our understanding of these interactions in shaping more complex microbial horticulture communities, along with their relevance for host growth, fitness, stress resilience, and health in a more natural context remains sparse. In this perspective, the holo-omic landscape that requires careful consideration of study design and integrates multi-omic data from both host and microbiota domains is a promising approach to unveil the interplay between the two to advance horticultural production. The analysis of horticulture (meta)-omics and phenotyping, along with mechanistic experiments in model systems, is revolutionizing research by enhancing our ability to understand the structure and core function of the plant-associated microbiome. These advancements are complemented by improvements in the throughput and accuracy of DNA sequencing, enabling us to delve deeper into the genomes of microbial communities. Yet, many challenges remain. We focus this review on the potential for holo-omics to generate a more holistic perspective of molecular networks, and we further discuss the implementation and current limitations of the holo-omic approach within microbial horticulture. We argue that holo-omics will pave the way to improve, from a horticultural perspective, food security and safety, sustainability, breeding practices, development of microbiota- and host-tailored horticultural crops’ health treatments and increasing production efficiency.
c-Abl tyrosine kinase down-regulation as target for memory improvement in Alzheimer’s disease

Background Growing evidence suggests that the non-receptor tyrosine kinase, c-Abl, plays a significant role in the pathogenesis of Alzheimer’s disease (AD). Here, we analyzed the effect of c-Abl on the cognitive performance decline of APPSwe/PSEN1ΔE9 (APP/PS1) mouse model for AD. Methods We used the conditional genetic ablation of c-Abl in the brain (c-Abl-KO) and pharmacological treatment with neurotinib, a novel allosteric c-Abl inhibitor with high brain penetrance, imbued in rodent’s chow. Results We found that APP/PS1/c-Abl-KO mice and APP/PS1 neurotinib-fed mice had improved performance in hippocampus-dependent tasks. In the object location and Barnes-maze tests, they recognized the displaced object and learned the location of the escape hole faster than APP/PS1 mice. Also, APP/PS1 neurotinib-fed mice required fewer trials to reach the learning criterion in the memory flexibility test. Accordingly, c-Abl absence and inhibition caused fewer amyloid plaques, reduced astrogliosis, and preserved neurons in the hippocampus. Discussion Our results further validate c-Abl as a target for AD, and the neurotinib, a novel c-Abl inhibitor, as a suitable preclinical candidate for AD therapies.