Vanadium(IV) chalcogenide materials are of increasing interest for use in catalysis and energy conversion-related applications. Since no ternary compounds are yet known in the V–Se–Te system, we studied ternary VwSeyTe2–y (w = 1.10, 1.13; y = 0.42, 0.72) phases crystallizing in space group P3m1 (no. 164). Two single-crystal specimens with differing compositions of a solid solution were obtained using the ceramic method. All products were characterized by either single-crystal or powder X-ray diffraction. The lattice parameters increase with rising tellurium content in accordance to the larger ionic radius of the tellurium anion compared to selenium. The chemical compositions were confirmed by energy dispersive X-ray spectroscopy. Further, magnetic measurements revealed mostly antiferromagnetic properties. Simultaneous differential scanning calorimetry/thermogravimetric analyses in nitrogen atmosphere showed endothermic decomposition accompanied by the formation of VN. The results of this work can serve as a basis for the synthesis of new phases in the V–Se–Te and related vanadium chalcogenide systems.

In the past decade, targeted therapies for solid tumors, including non-small cell lung cancer (NSCLC), have advanced significantly, offering tailored treatment options for patients. However, individuals without targetable mutations pose a clinical challenge, as they may not respond to standard treatments like immune-checkpoint inhibitors (ICIs) and novel targeted therapies. While the mechanism of action of ICIs seems promising, the lack of a robust response limits their widespread use. Although the expression levels of programmed death ligand 1 (PD-L1) on tumor cells are used to predict ICI response, identifying new biomarkers, particularly those associated with the tumor microenvironment (TME), is crucial to address this unmet need. Recently, inflammatory cytokines such as interleukin-1 beta (IL-1β) have emerged as a key area of focus and hold significant potential implications for future clinical practice. Combinatorial approaches of IL-1β inhibitors and ICIs may provide a potential therapeutic modality for NSCLC patients without targetable mutations. In this review, we discuss the role of IL-1β in NSCLC, its involvement in inflammatory pathways, and explore its potential role in the treatment of NSCLC.

Placenta-derived mesenchymal stem cells (PD-MSCs) have been highlighted as therapeutic sources in several degenerative diseases. Recently, microRNAs (miRNAs) were mediated one of the therapeutic mechanisms of PD-MSCs in regenerative medicine. To enhance the therapeutic effects of PD-MSCs, we established functionally enhanced PD-MSCs with phosphatase of regenerating liver-1 overexpression (PRL-1(+)). However, the profile and functions of miRNAs induced by PRL-1(+) PD-MSCs in a rat model with hepatic failure prepared by bile duct ligation (BDL) remained unclear. Hence, the objectives of the present study were to analyze the expression of miRNAs and investigate their therapeutic mechanisms for hepatic regeneration via PRL-1(+) in a rat model with BDL. We selected candidate miRNAs based on microarray analysis. Under hypoxic conditions, compared with invaded naïve PD-MSCs, invaded PRL-1(+) PD-MSCs showed improved integrin-dependent migration ability through RHO family-targeted miRNA expression (e.g., hsa-miR-30a-5p, 340-5p, and 146a-3p). Moreover, rno-miR-30a-5p and 340-5p regulated engraftment into injured rat liver by transplanted PRL-1(+) PD-MSCs through the integrin family. Additionally, an increase in PDGFRA by suppressing rno-miR-27a-3p improved vascular structure in rat liver tissues after PRL-1(+) PD-MSCs transplantation. Furthermore, decreased rno-miR-122-5p was significantly correlated with increased proliferation of hepatocytes in liver tissues by PRL-1(+) PD-MSCs by activating IL-6 signaling pathway through the repression of rno-miR-21-5p. Taken together, these findings improve the understanding of therapeutic mechanisms based on miRNA-mediated stem cell therapy in liver diseases.

This comprehensive analysis thoroughly examines the topic of adversarial attacks in artificial intelligence (AI), providing a detailed overview of the various methods used to compromise machine learning models. It explores different attack techniques, ranging from the simple Fast Gradient Sign Method (FGSM) to the intricate Carlini and Wagner Attack (C&W), emphasising the wide range of adversarial approaches and their intended goals. The discussion also distinguishes between targeted and non-targeted attacks, highlighting the adaptability and versatility of these malicious efforts. Additionally, the study delves into the realm of black-box attacks, revealing the capability of adversarial strategies to compromise models even with limited knowledge. Real-life examples illustrate the tangible consequences and potential dangers of adversarial attacks in various fields such as self-driving cars, multimedia, and voice assistants. These cases highlight the difficulties in ensuring the legitimacy and dependability of AI-powered technologies and programs. The article stresses the importance of ongoing research and innovation to address the growing difficulties posed by advanced methods like deepfakes and disguised voice commands in preserving the security of AI systems. This study provides valuable insights on how different adversarial strategies and defence mechanisms interact within AI. The results emphasise the urgent need for stronger and more secure AI models to combat the increasing number of adversarial threats in today's AI landscape. These findings can guide future research and innovations in developing more resilient AI technologies that can better withstand various adversarial vulnerabilities and challenges.

The generation of isogenic induced pluripotent stem cell (iPSC) lines using CRISPR-Cas9 technology is a technically challenging, time-consuming process with variable efficiency. Here we use fluorescence-activated cell sorting (FACS) to sort biallelic CRISPR-Cas9 edited single-cell iPS clones into high-throughput 96-well microtiter plates. We used high-content screening (HCS) technology and generated an in-house developed algorithm to select the correctly edited isogenic clones for continued expansion and validation. In our model we have gene-corrected the iPSCs of a Parkinson’s disease (PD) patient carrying the autosomal dominantly inherited heterozygous c.88G>C mutation in the SNCA gene, which leads to the pathogenic p.A30P form of the alpha-synuclein protein. Undertaking a PCR restriction-digest mediated clonal selection strategy prior to sequencing, we were able to post-sort validate each isogenic clone using a quadruple screening strategy. Subsequent transfection with mRNA encoding excision-only transposase allows for the generation of footprint-free isogenic iPSC lines. These monoclonal isogenic iPSC lines retain a normal molecular genotype, express pluripotency markers and have the ability to differentiate into the three germ layers. This combinatory approach of FACS, HCS and post-sorted restriction digestion facilitates the generation of isogenic cell lines for disease modelling to be scaled-up on an automated platform.

Using multi-color flow cytometry analysis, we studied the immunophenotypical differences between leukemic blasts/LSCs from patients with AML/MDS and hematopoietic stem and progenitor cells (HSPCs) from patients in complete remission (CR) following their successful treatment. The panel of markers included CD34, CD38, CD45RA, CD123 as representatives for a hierarchical hematopoietic stem and progenitor cell (HSPC) classification as well as programmed death ligand 1 (PD-L1). Rather than restricting the evaluation on a 2- or 3-dimensional analysis, we applied a t-distributed stochastic neighbor embedding (t-SNE) approach to get a deeper insight and segregation between leukemic and normal HPSCs. For that purpose, we created a t-SNE map, which resulted in the visualization of 27 cell clusters based on their similarity concerning the composition and intensity of antigen expression. Two of these clusters were “leukemia-related” containing a great proportion of CD34⁺/CD38^- hematopoietic stem cells (HSCs) or CD34⁺ cells with a strong coexpression of CD45RA/CD123, respectively. CD34⁺ cells within the latter cluster were also highly positive for PD-L1 reflecting their immunosuppressive capacity. Beyond this proof of principle study, the inclusion of additional markers will be helpful to refine the differentiation between normal HSPCs and leukemic cells, particularly in the context of minimal disease detection and antigen-targeted therapeutic interventions. Furthermore, we suggest a protocol for the assignment of new cell ensembles in quantitative terms, via a numerical value, the Pearson coefficient, based on a similarity comparison of the t-SNE pattern with a reference.

Human endogenous retroviruses (HERV) are remnants of ancient retroviral infections that, over millions of years, became integrated into the human genome. While normally inactive, environmental stimuli such as infections have contributed to the transcriptional reactivation of HERV promoting pathological conditions including the development of autoimmunity, neurodegenerative disease and cancer. What infections then trigger HERV activation? Mycobacterium avium subspecies paratuberculosis (MAP) is a pluripotent driver of human disease. Aside from granulomatous diseases Crohn’s, sarcoidosis and Blau syndrome, MAP is associated with autoimmune disease: type one diabetes (T1D), multiple sclerosis (MS), rheumatoid arthritis and autoimmune thyroiditis. MAP is also associated with Parkinson’s disease. Autoimmune diabetes, multiple sclerosis and rheumatoid arthritis are the diseases with the strongest MAP/HERV association. There are several other diseases associated with HERV activation including diseases whose epidemiology and/or pathology would prompt speculation for a causal role of MAP. These include non-solar uveal melanoma, colon cancer, glioblastoma and amyotrophic lateral sclerosis (ALS). This article further points to MAP infection as a contributor to autoimmunity, neurodegenerative disease and cancer via unsilencing of HERV. We examine the link between the ever-increasing number of MAP- associated diseases, the MAP/HERV intersection with these diverse medical conditions and propose treatment opportunities based upon this association.

Coronavirus Disease-19 (COVID-19) is caused by the infection of severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2). The virus enters host cells through receptor-mediated endocytosis of angiotensin-converting enzyme-2 (ACE2), leading to systemic inflammation, also known as a “cytokine storm,” and neuroinflammation. COVID-19’s upstream regulator, interferon-gamma (IFNG), is downregulated upon the infection of SARS-CoV-2, which leads to the downregulation of ACE2. The neuroinflammation signaling pathway (NISP) can lead to neurodegenerative diseases, such as Parkinson's disease (PD), which is characterized by the formation of Lewy Bodies made of α-Synuclein protein encoded by synuclein alpha (SNCA). This study presents the pathways involved in the downregulation of ACE2 following SARS-CoV-2 infection and its effect on PD progression. Through QIAGEN’s Ingenuity Pathway Analysis, the study identifies the NISP as a top-five canonical pathway/signaling pathway and SNCA as a top-five upstream regulator. Core Analysis was also conducted on the associated molecules between COVID-19 and SNCA to construct a network connectivity map. The Molecule Activity Predictor tool was used to simulate the infection of SARS-CoV-2 by downregulating IFNG, which leads to the predicted activation of SNCA, and subsequently PD, through a dataset of intermediary molecules. Downstream Effect analysis was further used to quantify the downregulation of ACE2 on SNCA activation.

This study focuses on examining various approaches for Tensor Decompositions (TDs) and their potential applications in satellite imaging (SI) and deep learning (DL). The research highlights how these decompositions can contribute to the advancement of SI and DL, providing valuable insights for future utilization of TDs in research. It explores how these techniques contribute to the advancement of SI and DL methods, providing insights into their potential applications and suggesting future research directions. The study aims to enhance the use of TDs techniques to further advance research efforts in these fields. More importantly, the current investigation offers a thorough analysis of the potential advantages and reasons for employing TDs techniques in different domains, including satellite imaging and deep learning. The aim is to enhance research outcomes by utilizing TDs. The review also identifies unresolved issues and proposes future directions for further investigation in this field. Fundamentally, these proposed open problems will open new grounds to the research community to articulate, innovate, and provide more real-life applications to improve the current state of the art by delving into a wider vision for a higher-level performance of both satellite imaging (SI) and deep learning (DL). Looking at the bigger scenario, this also suggests that TDs could be potentially employed to revolutionize existing machine learning technologies as well as the current space AI industry.