Abnormal synaptic architecture in iPSC-derived neurons from a multi-generational family with genetic Creutzfeldt-Jakob disease.

Genetic prion diseases are caused by mutations in PRNP, which encodes the prion protein (PrPC). Why these mutations are pathogenic, and how they alter the properties of PrPC are poorly understood. We have consented and accessed 22 individuals of a multi-generational Israeli family harboring the highly penetrant E200K PRNP mutation and generated a library of induced pluripotent stem cells (iPSCs) representing nine carriers and four non-carriers. iPSC-derived neurons from E200K carriers display abnormal synaptic architecture characterized by misalignment of postsynaptic NMDA receptors with the cytoplasmic scaffolding protein PSD95. Differentiated neurons from mutation carriers do not produce PrPSc, the aggregated and infectious conformer of PrP, suggesting that loss of a physiological function of PrPC may contribute to the disease phenotype. Our study shows that iPSC-derived neurons can provide important mechanistic insights into the pathogenesis of genetic prion diseases and can offer a powerful platform for testing candidate therapeutics.

Prion protein pathology in Ubiquilin 2 models of ALS.

Mutations in UBQLN2 cause ALS and frontotemporal dementia (FTD). The pathological signature in UBQLN2 cases is deposition of highly unusual types of inclusions in the brain and spinal cord that stain positive for UBQLN2. However, what role these inclusions play in pathogenesis remains unclear. Here we show cellular prion protein (PrPC) is found in UBQLN2 inclusions in both mouse and human neuronal induced pluripotent (IPSC) models of UBQLN2 mutations, evidenced by the presence of aggregated forms of PrPC with UBQLN2 inclusions. Turnover studies indicated that the P497H UBQLN2 mutation slows PrPC protein degradation and leads to mislocalization of PrPC in the cytoplasm. Immunoprecipitation studies indicated UBQLN2 and PrPC bind together in a complex. The abnormalities in PrPC caused by UBQLN2 mutations may be relevant in disease pathogenesis.

Transarterial doxorubicin-eluting beads embolization for the treatment of desmoid fibromatosis in the pubic region.

Desmoid fibromatosis (DFs) is rare, low-grade neoplasm. Although it poses no risk of metastasis, DFs exhibits a range of clinical manifestations characterized by local infiltrative growth tendencies and a propensity for recurrence. Despite its nonmalignant nature, DFs can be highly debilitating and occasionally life-threatening, causing severe pain and functional limitations. Traditionally, surgery served as the conventional primary treatment approach; nevertheless, a recent shift in paradigm towards a more conservative management has emerged, accompanied by efforts to standardize the strategy among clinicians. Systemic doxorubicin has been demonstrated to be effective in treating DFs; however, it carries potential risks of adverse effects on the cardiovascular, digestive, and hematologic systems. The novel intravascular intervention employing drug-eluting beads loaded with doxorubicin represents an effective treatment for DFs, optimizing drug delivery to the target lesion and reducing systemic toxicity. In this article, we present a rare case of DFs in the right pubic region treated with transarterial doxorubicin-eluting beads embolization.

Antimicrobial Resistance Patterns of Pathogens Isolated from Patients with Wound Infection at a Teaching Hospital in Vietnam.

Purpose: At a teaching Hospital in Vietnam, the persistently high incidence of diagnosed wound infection poses ongoing challenges to treatment. This study seeks to explore the causative agents of wound infection and their antimicrobial and multidrug resistance patterns. Methods: A cross-sectional study was conducted at the Department of Microbiology, Military Hospital 103, Vietnam. Data on microorganisms that caused wound infection and their antimicrobial resistance patterns was recorded from hospitalized patients from 2014 to 2021. Using the chi-square test, we analyzed the initial isolation from wound infection specimens collected from individual patients. Results: Over a third (34.9%) of wound infection samples yielded bacterial cultures. Staphylococcus aureus was the most prevalent bacteria, followed by Pseudomonas aeruginosa. Worryingly high resistance rates were observed for several antibiotics, particularly among Gram-negative bacteria. Ampicillin displayed the highest resistance (91.9%), while colistin and ertapenem remained the most effective. In Gram-positive bacteria, glycopeptides like teicoplanin and vancomycin (0% and 3.3% resistance, respectively) were most effective, but their use was limited. Clindamycin and tetracycline showed decreasing effectiveness. Resistance rates differed between surgical and non-surgical wards, highlighting the complex dynamics of antimicrobial resistance within hospitals. Multidrug resistance (MDR) was substantial, with Gram-negative bacteria exhibiting a 63.6% MDR rate. Acinetobacter baumannii showed the highest MDR rate (88.0%). Conclusion: This study investigated wound infection characteristics, antibiotic resistance patterns of common bacteria, and variations by hospital ward. S. aureus was the most prevalent bacteria, and concerning resistance rates were observed, particularly among Gram-negative bacteria. These findings highlight the prevalence of multidrug resistance in wound infections, emphasizing the importance of infection control measures and judicious antibiotic use.

A Potential Role for MAGI-1 in the Bi-Directional Relationship Between Major Depressive Disorder and Cardiovascular Disease.

PURPOSE OF REVIEW: Major Depressive Disorder (MDD) is characterized by persistent symptoms such as fatigue, loss of interest in activities, feelings of sadness and worthlessness. MDD often coexist with cardiovascular disease (CVD), yet the precise link between these conditions remains unclear. This review explores factors underlying the development of MDD and CVD, including genetic, epigenetic, platelet activation, inflammation, hypothalamic-pituitary-adrenal (HPA) axis activation, endothelial cell (EC) dysfunction, and blood-brain barrier (BBB) disruption. RECENT FINDINGS: Single nucleotide polymorphisms (SNPs) in the membrane-associated guanylate kinase WW and PDZ domain-containing protein 1 (MAGI-1) are associated with neuroticism and psychiatric disorders including MDD. SNPs in MAGI-1 are also linked to chronic inflammatory disorders such as spontaneous glomerulosclerosis, celiac disease, ulcerative colitis, and Crohn's disease. Increased MAGI-1 expression has been observed in colonic epithelial samples from Crohn's disease and ulcerative colitis patients. MAGI-1 also plays a role in regulating EC activation and atherogenesis in mice and is essential for Influenza A virus (IAV) infection, endoplasmic reticulum stress-induced EC apoptosis, and thrombin-induced EC permeability. Despite being understudied in human disease; evidence suggests that MAGI-1 may play a role in linking CVD and MDD. Therefore, further investigation of MAG-1 could be warranted to elucidate its potential involvement in these conditions.

Sigma Receptor Ligands Are Potent Antiprion Compounds that Act Independently of Sigma Receptor Binding.

Prion diseases are invariably fatal neurodegenerative diseases of humans and other animals for which there are no effective treatment options. Previous work from our laboratory identified phenethylpiperidines as a novel class of anti-prion compounds. While working to identify the molecular target(s) of these molecules, we unexpectedly discovered ten novel antiprion compounds based on their known ability to bind to the sigma receptors, σ1R and σ2R, which are currently being tested as therapeutic or diagnostic targets for cancer and neuropsychiatric disorders. Surprisingly, however, knockout of the respective genes encoding σ1R and σ2R (Sigmar1 and Tmem97) in prion-infected N2a cells did not alter the antiprion activity of these compounds, demonstrating that these receptors are not the direct targets responsible for the antiprion effects of their ligands. Further investigation of the most potent molecules established that they are efficacious against multiple prion strains and protect against downstream prion-mediated synaptotoxicity. While the precise details of the mechanism of action of these molecules remain to be determined, the present work forms the basis for further investigation of these compounds in preclinical studies. Given the therapeutic utility of several of the tested compounds, including rimcazole and haloperidol for neuropsychiatric conditions, (+)-pentazocine for neuropathic pain, and the ongoing clinical trials of SA 4503 and ANAVEX2-73 for ischemic stroke and Alzheimer's disease, respectively, this work has immediate implications for the treatment of human prion disease.

Adaptive response of Pseudomonas aeruginosa under serial ciprofloxacin exposure.

Understanding the evolution of antibiotic resistance is important for combating drug-resistant bacteria. In this work, we investigated the adaptive response of Pseudomonas aeruginosa to ciprofloxacin. Ciprofloxacin-susceptible P. aeruginosa ATCC 9027, CIP-E1 (P. aeruginosa ATCC 9027 exposed to ciprofloxacin for 14 days) and CIP-E2 (CIP-E1 cultured in antibiotic-free broth for 10 days) were compared. Phenotypic responses including cell morphology, antibiotic susceptibility, and production of pyoverdine, pyocyanin and rhamnolipid were assessed. Proteomic responses were evaluated using comparative iTRAQ labelling LC-MS/MS to identify differentially expressed proteins (DEPs). Expression of associated genes coding for notable DEPs and their related regulatory genes were checked using quantitative reverse transcriptase PCR. CIP-E1 displayed a heterogeneous morphology, featuring both filamentous cells and cells with reduced length and width. By contrast, although filaments were not present, CIP-E2 still exhibited size reduction. Considering the MIC values, ciprofloxacin-exposed strains developed resistance to fluoroquinolone antibiotics but maintained susceptibility to other antibiotic classes, except for carbapenems. Pyoverdine and pyocyanin production showed insignificant decreases, whereas there was a significant decrease in rhamnolipid production. A total of 1039 proteins were identified, of which approximately 25 % were DEPs. In general, there were more downregulated proteins than upregulated proteins. Noted changes included decreased OprD and PilP, and increased MexEF-OprN, MvaT and Vfr, as well as proteins of ribosome machinery and metabolism clusters. Gene expression analysis confirmed the proteomic data and indicated the downregulation of rpoB and rpoS. In summary, the response to CIP involved approximately a quarter of the proteome, primarily associated with ribosome machinery and metabolic processes. Potential targets for bacterial interference encompassed outer membrane proteins and global regulators, such as MvaT.

MoViT: Memorizing Vision Transformers for Medical Image Analysis.

The synergy of long-range dependencies from transformers and local representations of image content from convolutional neural networks (CNNs) has led to advanced architectures and increased performance for various medical image analysis tasks due to their complementary benefits. However, compared with CNNs, transformers require considerably more training data, due to a larger number of parameters and an absence of inductive bias. The need for increasingly large datasets continues to be problematic, particularly in the context of medical imaging, where both annotation efforts and data protection result in limited data availability. In this work, inspired by the human decision-making process of correlating new "evidence" with previously memorized "experience", we propose a Memorizing Vision Transformer (MoViT) to alleviate the need for large-scale datasets to successfully train and deploy transformer-based architectures. MoViT leverages an external memory structure to cache history attention snapshots during the training stage. To prevent overfitting, we incorporate an innovative memory update scheme, attention temporal moving average, to update the stored external memories with the historical moving average. For inference speedup, we design a prototypical attention learning method to distill the external memory into smaller representative subsets. We evaluate our method on a public histology image dataset and an in-house MRI dataset, demonstrating that MoViT applied to varied medical image analysis tasks, can outperform vanilla transformer models across varied data regimes, especially in cases where only a small amount of annotated data is available. More importantly, MoViT can reach a competitive performance of ViT with only 3.0% of the training data. In conclusion, MoViT provides a simple plug-in for transformer architectures which may contribute to reducing the training data needed to achieve acceptable models for a broad range of medical image analysis tasks.

Improved synthesis, molecular modeling and anti-inflammatory activity of new fluorinated dihydrofurano-naphthoquinone compounds.

A series of new fluorinated dihydrofurano-napthoquinone compounds were sucessfully synthesized in good yields using microwave-assisted multi-component reactions of 2-hydroxy-1,4-naphthoquinone, fluorinated aromatic aldehydes, and pyridinium bromide. The products were fully characterized using spectroscopic techniques and evaluated for their anti-inflammatory activity using lipopolysaccharide (LPS)-stimulated RAW264.7 macrophage cells. Among 12 new compounds, compounds 8b, 8d, and 8e showed high potent NO inhibitory activity in lipopolysaccharide (LPS)-stimulated RAW264.7 macrophage cells with IC50 values ranging from 1.54 to 3.92 µM. The levels of pro-inflammatory cytokines IL-1ß and IL-6 in LPS-stimulated RAW264.7 macrophages were remarkably decreased after the application of 8b, 8d, 8e and 8k. Molecular docking simulations revealed structure-activity relationships of 8b, 8d, and 8e toward NO synthase, cyclooxygenase (COX-2 over COX-1), and prostaglandin E synthase-1 (mPGES-1). Further physicochemical and pharmacokinetic computations also demonstrated the drug-like characteristics of synthesized compounds. These findings demonstrated the importance of fluorinated dihydrofurano-napthoquinone moieties in the development of potential anti-inflammatory agents.

A bibliometric literature review of stock price forecasting: From statistical model to deep learning approach.

We introduce a comprehensive analysis of several approaches used in stock price forecasting, including statistical, machine learning, and deep learning models. The advantages and limitations of these models are discussed to provide an insight into stock price forecasting. Traditional statistical methods, such as the autoregressive integrated moving average and its variants, are recognized for their efficiency, but they also have some limitations in addressing non-linear problems and providing long-term forecasts. Machine learning approaches, including algorithms such as artificial neural networks and random forests, are praised for their ability to grasp non-linear information without depending on stochastic data or economic theory. Moreover, deep learning approaches, such as convolutional neural networks and recurrent neural networks, can deal with complex patterns in stock prices. Additionally, this study further investigates hybrid models, combining various approaches to explore their strengths and counterbalance individual weaknesses, thereby enhancing predictive accuracy. By presenting a detailed review of various studies and methods, this study illuminates the direction of stock price forecasting and highlights potential approaches for further studies refining the stock price forecasting models.