Miracle-dz/UniFAR-FTUs huggingface dataset:
Single-cell mapping of leukocyte immunoglobulin-like receptors in kidney transplant rejection

Leukocyte immunoglobulin-like receptors (LILRs) are a family of inhibitory or stimulatory receptors expressed by immune cell types belonging to both myeloid and lymphoid lineage. Several members of the LILR family recognize major histocompatibility complex class I and thus play important roles in a range of clinical situations including pregnancy. Moreover, paired immunoglobulin-like receptors (PIRs), the murine orthologs of LILRs, are implicated in experimental transplant allorecognition by monocytes and contribute to the induction of donor-specific monocyte-memory. After non-self recognition, activating PIRs are transiently overexpressed at the surface of monocytes and participate in donor-specific monocyte recruitment, leading to graft rejection in vivo. In the present study, we mapped LILR expression and also their respective reported ligands at single cell level in the renal allograft and circulating cells in the context of kidney transplant rejection. Recipient-derived monocytes were shown to infiltrate the donor tissue and to differentiate into macrophages. We thus also investigate LILR expression during in vitro monocyte-to-macrophage differentiation in order to characterize the myeloid population that directly contribute to allorecognition. Altogether our results emphasize non-classical monocytes and CD68+ M1 macrophages as key players in LILRs-ligand interaction in kidney transplantation.
Identifying immune cells-related phenotype to predict immunotherapy and clinical outcome in gastric cancer

Background The tumor microenvironment is mainly composed of tumor-infiltrating immune cells (TIICs), fibroblast, extracellular matrix, and secreted factors. TIICs are often associated with sensitivity to immunotherapy and the prognosis of multiple cancers, yet the predictive role of individual cells on tumor prognosis is limited. Methods Based on single-sample gene set enrichment analysis, we combined three Gene Expression Omnibus (GEO) cohorts to build a TIIC model for risk stratification and prognosis prediction. The performance of the TIIC model was validated using our clinical cohort and the TCGA cohort. To assess the predictive power of the TIIC model for immunotherapy, we plotted the receiver operating characteristic curve with the IMvigor210 and GSE135222 cohorts. Results Chemokines, tumor-infiltrating immune cells, and immunomodulators differed between the two TIIC groups. The TIIC model was vital for predicting the outcome of immunotherapy. In our clinical samples, we verified that the expression levels of PD-1 and PD-L1 were higher in the low TIIC score group than in the high TIIC score group, both in the tumor and stroma. Conclusions Collectively, the TIIC model could provide a novel idea for immune cell targeting strategies in gastric cancer and predict the survival outcome of patients.
Viral infections in hospitalized children in Germany during the COVID-19 pandemic: Association with non-pharmaceutical interventions

Background Non-pharmaceutical interventions (NPI) during the COVID-19 pandemic aimed at prevention of SARS-CoV-2 transmission also influenced transmission of viruses other than SARS-CoV-2. The aim of this study was to describe and compare the burden of common viral respiratory and gastrointestinal infections in children admitted to Berlin University Children's Hospital (BCH) before and during the COVID-19 pandemic at different levels of public NPI measures. Methods In this retrospective study, we analyzed the frequency of detection of common human respiratory and gastrointestinal viruses from January 2016 through January 2022 in all patients admitted to BCH. We compared virus detection before and during the COVID-19 pandemic at different levels of public NPI measures. Results The frequency of detection of seasonal enveloped and non-enveloped viruses [Boca-, Corona-, Influenza-, Metapneumo-, Parainfluenza-, Rota-, and Respiratory Syncytial Viruses (RSV)] was diminished during the COVID-19 pandemic, whereas detection rates of non-seasonal viruses (Rhino-/Entero-, and Adenoviruses) were stable during the pandemic. After withdrawal of major NPI measures, we observed an out of season surge of the detection rates of Boca-, Corona-, Parainfluenzaviruses, and RSV. In contrast, no increased detection frequency was observed for Influenza-, Metapneumo-, and Rotaviruses as of January 2022. Conclusion Corona-, Boca-, Parainfluenzaviruses, and RSV returned as frequently detected pathogens after withdrawal of major NPI measures. The out of season rise might be attributed to an “immune-debt” due to missing contact to viral antigens resulting in waning of population immunity during the COVID-19 pandemic.
Reconstitution of the immune system and clinical correlates after stem cell transplantation for systemic sclerosis

Systemic sclerosis (SSc) is a chronic autoimmune disease that includes fibrosis, diffuse vasculopathy, inflammation, and autoimmunity. Autologous hematopoietic stem cell transplantation (auto-HSCT) is considered for patients with severe and progressive SSc. In recent decades, knowledge about patient management and clinical outcomes after auto-HSCT has significantly improved. Mechanistic studies have contributed to increasing the comprehension of how profound and long-lasting are the modifications to the immune system induced by transplantation. This review revisits the immune monitoring studies after auto-HSCT for SSc patients and how they relate to clinical outcomes. This understanding is essential to further improve clinical applications of auto-HSCT and enhance patient outcomes.
Prevalence and distribution of extended-spectrum β-lactamase and AmpC-producing Escherichia coli in two New Zealand dairy farm environments

Antimicrobial resistance (AMR) is a global threat to human and animal health, with the misuse and overuse of antimicrobials being suggested as the main driver of resistance. In a global context, New Zealand (NZ) is a relatively low user of antimicrobials in animal production. However, the role antimicrobial usage on pasture-based dairy farms, such as those in NZ, plays in driving the spread of AMR within the dairy farm environment remains equivocal. Culture-based methods were used to determine the prevalence and distribution of extended-spectrum β-lactamase (ESBL)- and AmpC-producing Escherichia coli from farm environmental samples collected over a 15-month period from two NZ dairy farms with contrasting management practices. Whole genome sequencing was utilised to understand the genomic epidemiology and antimicrobial resistance gene repertoire of a subset of third-generation cephalosporin resistant E. coli isolated in this study. There was a low sample level prevalence of ESBL-producing E. coli (faeces 1.7%; farm dairy effluent, 6.7% from Dairy 4 and none from Dairy 1) but AmpC-producing E. coli were more frequently isolated across both farms (faeces 3.3% and 8.3%; farm dairy effluent 38.4%, 6.7% from Dairy 1 and Dairy 4, respectively). ESBL- and AmpC-producing E. coli were isolated from faeces and farm dairy effluent in spring and summer, during months with varying levels of antimicrobial use, but no ESBL- or AmpC-producing E. coli were isolated from bulk tank milk or soil from recently grazed paddocks. Hybrid assemblies using short- and long-read sequence data from a subset of ESBL- and AmpC-producing E. coli enabled the assembly and annotation of nine plasmids from six E. coli, including one plasmid co-harbouring 12 antimicrobial resistance genes. ESBL-producing E. coli were infrequently identified from faeces and farm dairy effluent on the two NZ dairy farms, suggesting they are present at a low prevalence on these farms. Plasmids harbouring several antimicrobial resistance genes were identified, and bacteria carrying such plasmids are a concern for both animal and public health. AMR is a burden for human, animal and environmental health and requires a holistic “One Health” approach to address.
Using Recurrent Neural Networks To Forecasting of Forex

This paper reports empirical evidence that a neural networks model is applicable to the statistically reliable prediction of foreign exchange rates. Time series data and technical indicators such as moving average, are fed to neural nets to capture the underlying "rules" of the movement in currency exchange rates. The trained recurrent neural networks forecast the exchange rates between American Dollar and four other major currencies, Japanese Yen, Swiss Frank, British Pound and EURO. Various statistical estimates of forecast quality have been carried out. Obtained results show, that neural networks are able to give forecast with coefficient of multiple determination not worse then 0.65. Linear and nonlinear statistical data preprocessing, such as Kolmogorov-Smirnov test and Hurst exponents for each currency were calculated and analyzed.
HIV-1 induction of tolerogenic dendritic cells is mediated by cellular interaction with suppressive T cells

HIV-1 infection gives rise to a multi-layered immune impairment in most infected individuals. The chronic presence of HIV-1 during the priming and activation of T cells by dendritic cells (DCs) promotes the expansion of suppressive T cells in a contact-dependent manner. The mechanism behind the T cell side of this HIV-induced impairment is well studied, whereas little is known about the reverse effects exerted on the DCs. Herein we assessed the phenotype and transcriptome profile of mature DCs that have been in contact with suppressive T cells. The HIV exposed DCs from cocultures between DCs and T cells resulted in a more tolerogenic phenotype with increased expression of e.g., PDL1, Gal-9, HVEM, and B7H3, mediated by interaction with T cells. Transcriptomic analysis of the DCs separated from the DC-T cell coculture revealed a type I IFN response profile as well as an activation of pathways involved in T cell exhaustion. Taken together, our data indicate that the prolonged and strong type I IFN signaling in DCs, induced by the presence of HIV during DC-T cell cross talk, could play an important role in the induction of tolerogenic DCs and suppressed immune responses seen in HIV-1 infected individuals.
Spontaneous Activity Predicts Survival of Developing Cortical Neurons

Spontaneous activity plays a crucial role in brain development by coordinating the integration of immature neurons into emerging cortical networks. High levels and complex patterns of spontaneous activity are generally associated with low rates of apoptosis in the cortex. However, whether spontaneous activity patterns directly encode for survival of individual cortical neurons during development remains an open question. Here, we longitudinally investigated spontaneous activity and apoptosis in developing cortical cultures, combining extracellular electrophysiology with calcium imaging. These experiments demonstrated that the early occurrence of calcium transients was strongly linked to neuronal survival. Silent neurons exhibited a higher probability of cell death, whereas high frequency spiking and burst behavior were almost exclusively detected in surviving neurons. In local neuronal clusters, activity of neighboring neurons exerted a pro-survival effect, whereas on the functional level, networks with a high modular topology were associated with lower cell death rates. Using machine learning algorithms, cell fate of individual neurons was predictable through the integration of spontaneous activity features. Our results indicate that high frequency spiking activity constrains apoptosis in single neurons through sustained calcium rises and thereby consolidates networks in which a high modular topology is reached during early development.
Dispersion of subsurface lagrangian drifters in the northeastern Gulf of Mexico

The dispersion of subsurface Lagrangian floats by eddies was observed directly in DeSoto Canyon, located in the northeastern Gulf of Mexico. Key elements of dispersion include the capture and release of floats by variations in eddy structure and intensity. Two separate eddy events were revealed through 60-day trajectories from five subsurface drifters deployed at 400 m depth in DeSoto Canyon. A changing background flow in DeSoto Canyon allowed for the contraction and expansion of the eddy’s “trap zone,” resulting in the capture and release of several drifters deployed in the area. To investigate the variability of dispersion due to this capture-and-release effect, virtual particle tracks from a 5-year numerical model simulation of the Gulf of Mexico were used. Large interannual variability was observed in eddy activity over the 5-year simulation. When coupled with a variable background flow, this greatly affected Lagrangian particle transport within the entire eastern Gulf of Mexico. During years of increased eddy activity, more virtual particles were “captured” from the along-slope flow and “released” offshore, increasing dispersion and residence time within the eastern Gulf of Mexico. The opposite was observed during minimal eddy activity, where more virtual particles remained within the along-slope flow and thus were funneled toward two main exit points out of the eastern Gulf of Mexico. Regions such as DeSoto Canyon with strong topographic constraints, a highly variable background flow, and considerable eddy activity are likely to spread tracers such as nutrients and contaminants over a substantial area due to this capture-and-release effect.
CCL5 mediates breast cancer metastasis and prognosis through CCR5/Treg cells

Background and aims CCL5 is considered to contribute to the biological function of a variety of cancer types, but its specific mechanism is still unclear. This study aimed to reveal the mechanism of CCL5 in the invasion, metastasis, and prognosis of breast cancer. Methods The expression of CCL5 in tumor tissue and serum was measured with a Luminex protein detection kit, and the correlation between CCL5 and clinical parameters was evaluated. Kaplan–Meier analysis was used to analyze the effect of CCL5 on the prognosis of breast cancer patients. Protein interaction network analysis and gene coexpression were used to determine the receptor that has the strongest interaction with CCL5. Enrichment analysis was used to study the possible pathway by which CCL5 affects breast cancer progression. We used immunofluorescence staining and flow cytometry to estimate the fraction of immunity-related components in the tumor microenvironment. Results The expression level of CCL5 in breast cancer patients was positively correlated with the degree of axillary lymph node metastasis; CCL5 in tumor tissue was correlated with estrogen receptor status (P = 0.034), progesterone receptor (P = 0.009), nuclear grade (P = 0.013), clinical stage (P < 0.001) and molecular subtype (P = 0.024) in breast cancer patients. Breast cancer patients with high CCL5 expression had worse disease-free survival (P = 0.031) and breast cancer-specific survival (P = 0.043); however, CCL5 had no effect on overall survival (P = 0.077). CCL5 affected tumor progression through CCR5, and the T-cell-related immune pathway may be the main pathway; the CD4+/CD8+, CCR5+/CD4+ and Treg/CCR5+ cell ratios were significantly increased in the lymph node metastasis group. Conclusion CCL5 affects the Treg/CD4+CCR5+ cell ratio in breast cancer patients through CCR5, thus affecting breast cancer metastasis and prognosis.
Identification of the key ferroptosis-related genes involved in sepsis progression and experimental validation in vivo

Background: Ferroptosis has a vital role in sepsis, but the mechanism is not known. Understanding the mechanism of ferroptosis during sepsis will aid in developing improved therapeutic strategies. Methods: We used the Gene Expression Omnibus database and FerrDb database to obtain ferroptosis-related differentially expressed genes (DEGs) between sepsis patients and healthy volunteers (HVs). Analyses of PPI networks, functional enrichment, as well as use of the MCODE algorithm were used to identify key ferroptosis-related DEGs. Expression of key ferroptosis-related DEGs was verified using: GSE57065 and GSE65682 datasets; rats in which ferroptosis was induced with erastin; sepsis-induced acute lung injury (siALI) rats. The effects of acupoint catgut embedding (ACE) on ferroptosis and expression of key ferroptosis-related DEGs in the lungs of siALI rats were also observed. A Cox proportional hazard model was used to verify the effect of key ferroptosis-related DEGs on the survival of sepsis patients. Cytoscape was used to construct ceRNA networks and gene–transcription factor networks. Results: Between sepsis patients and HVs, we identified 33 ferroptosis-related DEGs. According to analyses of PPI networks and the MCODE algorithm, we obtained four modules, of which the most significant module contained nine ferroptosis-related DEGs. Functional-enrichment analyses showed that four of the nine DEGs were enriched in the MAPK signaling pathway: MAPK14, VEGFA, TGFBR1, and DUSP1. We verified expression of these four genes in GSE57065 and GSE65682 datasets and ferroptosis rats. In addition, expression of these four genes and that of the oxidative-stress indicators GSSG and MDA was upregulated, and glutathione peroxidase-4 (GPX4) expression was downregulated, in siALI rats, but ACE reversed these changes. The Cox proportional hazard model showed that survival of sepsis patients in the high-risk group was shorter than that in the low-risk group. We found that the XIST−hsa-let-7b-5p−TGFBR1/DUSP1 ceRNA network and transcription factor E2F1 may be important regulators of these four DEGs. Conclusion: Our results suggest that MAPK14, VEGFA, TGFBR1, and DUSP1 may be key regulatory targets of ferroptosis in sepsis, and that ACE pretreatment may be antioxidant treatment for sepsis and alleviate ferroptosis. These findings provide a basis for further ferroptosis-related study in sepsis and provide new targets for its treatment.
Cerebellar cognitive affective syndrome after acute cerebellar stroke

Introduction The cerebellum modulates both motor and cognitive behaviors, and a cerebellar cognitive affective syndrome (CCAS) was described after a cerebellar stroke in 1998. Yet, a CCAS is seldom sought for, due to a lack of practical screening scales. Therefore, we aimed at assessing both the prevalence of CCAS after cerebellar acute vascular lesion and the yield of the CCAS-Scale (CCAS-S) in an acute stroke setting. Materials and methods All patients admitted between January 2020 and January 2022 with acute onset of a cerebellar ischemic or haemorrhagic first stroke at the CUB-Hôpital Erasme and who could be evaluated by the CCAS-S within a week of symptom onset were included. Results Cerebellar acute vascular lesion occurred in 25/1,580 patients. All patients could complete the CCAS-S. A definite CCAS was evidenced in 21/25 patients. Patients failed 5.2 ± 2.12 items out of 8 and had a mean raw score of 68.2 ± 21.3 (normal values 82–120). Most failed items of the CCAS-S were related to verbal fluency, attention, and working memory. Conclusion A definite CCAS is present in almost all patients with acute cerebellar vascular lesions. CCAS is efficiently assessed by CCAS-S at bedside tests in acute stroke settings. The magnitude of CCAS likely reflects a cerebello-cortical diaschisis.
A possible trigger for a wave-function collapse

The measurement problem is usually analysed with the quantum decoherence or wave-function collapse theory. Both theories seem to be equivalent with respect to experiments. The trigger/reason for a wave-function collapse is not clearly defined (in the corresponding theory) and the present work is an attempt to close this gap. The present thought experiment describes a measurement to determine the localization of an electron inside a box. The present work assumes that the determination of information (about the localization of the electron) induces a wave-function collapse. It seems possible to identify a clearly defined trigger for a wave-function collapse in the described model system.
The vertical light-gradient and its potential impact on animal distribution and behavior

The visual environment provides vital cues allowing animals to assess habitat quality, weather conditions or measure time of day. Together with other sensory cues and physiological conditions, the visual environment sets behavioral states that make the animal more prone to engage in some behaviors, and less in others. This master-control of behavior serves a fundamental and essential role in determining the distribution and behavior of all animals. Although it is obvious that visual information contains vital input for setting behavioral states, the precise nature of these visual cues remains unknown. Here we use a recently described method to quantify the distribution of light reaching animals’ eyes in different environments. The method records the vertical gradient (as a function of elevation angle) of intensity, spatial structure and spectral balance. Comparison of measurements from different types of environments, weather conditions, times of day, and seasons reveal that these aspects can be readily discriminated from one another. The vertical gradients of radiance, spatial structure (contrast) and color are thus reliable indicators that are likely to have a strong impact on animal behavior and spatial distribution.
Design and experimental validation of OPERA_MET-A panel for deep methylation analysis by next generation sequencing

DNA methylation is the most recognized epigenetic mark that leads to a massive distortion in cancer cells. It has been observed that a large number of DNA aberrant methylation events occur simultaneously in a group of genes, thus providing a growth advantage to the cell in promoting cell differentiation and neoplastic transformation. Due to this reason, methylation profiles have been suggested as promising cancer biomarkers. Here, we designed and performed a first step of validation of a novel targeted next generation sequencing (NGS) panel for methylation analysis, which can simultaneously evaluate the methylation levels at CpG sites of multiple cancer-related genes. The OPERA_MET-A methylation panel was designed using the Ion AmpliSeq™ technology to amplify 155 regions with 125-175 bp mean length and covers a total of 1107 CpGs of 18 cancer-related genes. The performance of the panel was assessed by running commercially available fully methylated and unmethylated control human genomic DNA (gDNA) samples and a variable mixture of them. The libraries were run on Ion Torrent platform and the sequencing output was analyzed using the “methylation_analysis” plugin. DNA methylation calls on both Watson (W) and Crick (C) strands and methylated:unmethylated ratio for each CpG site were obtained. Cell lines, fresh frozen and formalin-fixed paraffin-embedded (FFPE) lung cancer tissues were tested. The OPERA_MET-A panel allows to run a minimum of 6 samples/530 chip to reach an observed mean target depth ≥2,500X (W and C strands) and an average number of mapped reads >750,000/sample. The conversion efficiency, determined by spiking-in unmethylated Lambda DNA into each sample before the bisulfite conversion process, was >97% for all samples. The observed percentage of global methylation for all CpGs was >95% and <5% for fully methylated and unmethylated gDNA samples, respectively, and the observed results for the variable mixtures were in agreement with what was expected. Methylation-specific NGS analysis represents a feasible method for a fast and multiplexed screening of cancer patients by a high-throughput approach. Moreover, it offers the opportunity to construct a more robust algorithm for disease prediction in cancer patients having a low quantity of biological material available.