Maternal N-Acetyl-Cysteine Prevents Neonatal Hypoxia-Induced Brain Injury in a Rat Model

Perinatal hypoxia is a major cause of infant brain damage, lifelong neurological disability, and infant mortality. N-Acetyl-Cysteine (NAC) is a powerful antioxidant that acts directly as a scavenger of free radicals. We hypothesized that maternal-antenatal and offspring-postnatal NAC can protect offspring brains from hypoxic brain damage.Sixty six newborn rats were randomized into four study groups. Group 1: Control (CON) received no hypoxic intervention. Group 2: Hypoxia (HYP)-received hypoxia protocol. Group 3: Hypoxia-NAC (HYP-NAC). received hypoxia protocol and treated with NAC following each hypoxia episode. Group 4: NAC Hypoxia (NAC-HYP) treated with NAC during pregnancy, pups subject to hypoxia protocol. Each group was evaluated for: neurological function (Righting reflex), serum proinflammatory IL-6 protein levels (ELISA), brain protein levels: NF-κB p65, neuronal nitric oxide synthase (nNOS), TNF-α, and IL-6 (Western blot) and neuronal apoptosis (histology evaluation with TUNEL stain). Hypoxia significantly increased pups brain protein levels compared to controls. NAC administration to dams or offspring demonstrated lower brain NF-κB p65, nNOS, TNF-α and IL-6 protein levels compared to hypoxia alone. Hypoxia significantly increased brain apoptosis as evidenced by higher grade of brain TUNEL reaction. NAC administration to dams or offspring significantly reduce this effect. Hypoxia induced acute sensorimotor dysfunction. NAC treatment to dams significantly attenuated hypoxia-induced acute sensorimotor dysfunction. Prophylactic NAC treatment of dams during pregnancy confers long-term protection to offspring with hypoxia associated brain injury, measured by several pathways of injury and correlated markers with pathology and behavior. This implies we may consider prophylactic NAC treatment for patients at risk for hypoxia during labor.     

A procedure to synthesize silica aerogels in a wide range of densities by a single-step base catalyzed recipe

Journal of Porous Materials - Silicon alkoxides based synthesis of silica aerogels, could be divided into two main approaches; a single-step recipe for a fixed density, benefiting from exceptional...     

Loss of Fer Jeopardizes Metabolic Plasticity and Mitochondrial Homeostasis in Lung and Breast Carcinoma Cells

Metabolic plasticity is a hallmark of the ability of metastatic cancer cells to survive under stressful conditions. The intracellular Fer kinase is a selective constituent of the reprogramed mitochondria and metabolic system of cancer cells. In the current work, we deciphered the modulatory roles of Fer in the reprogrammed metabolic systems of metastatic, lung (H358), non-small cell lung cancer (NSCLC), and breast (MDA-MB-231), triple-negative breast cancer (TNBC), carcinoma cells. We show that H358 cells devoid of Fer (H358ΔFer), strictly depend on glucose for their proliferation and growth, and fail to compensate for glucose withdrawal by oxidizing and metabolizing glutamine. Furthermore, glucose deficiency caused increased reactive oxygen species (ROS) production and induction of a DNA damage response (DDR), accompanied by the onset of apoptosis and attenuated cell-cycle progression. Analysis of mitochondrial function revealed impaired respiratory and electron transport chain (ETC) complex 1 (comp. I) activity in the Fer-deficient H358ΔFer cells. This was manifested by decreased levels of NAD⁺ and ATP and relatively low abundance of tricarboxylic acid (TCA) cycle metabolites. Impaired electron transport chain comp. I activity and dependence on glucose were also confirmed in Fer-deficient, MDA-MB-231ΔFer cells. Although both H358ΔFer and MDA-MB-231ΔFer cells showed a decreased aspartate level, this seemed to be compensated by the predominance of pyrimidines synthesis over the urea cycle progression. Notably, absence of Fer significantly impeded the growth of H358ΔFer and MDA-MB-231ΔFer xenografts in mice provided with a carb-deficient, ketogenic diet. Thus, Fer plays a key role in the sustention of metabolic plasticity of malignant cells. In compliance with this notion, targeting Fer attenuates the progression of H358 and MDA-MB-231 tumors, an effect that is potentiated by a glucose-restrictive diet.     

De-anonymizing Ethereum blockchain smart contracts through code attribution

Blockchain users are identified by addresses (public keys), which cannot be easily linked back to them without out-of-network information. This provides pseudo-anonymity, which is amplified when the user generates a new address for each transaction. Since all transaction history is visible to all users in public blockchains, finding affiliation between related addresses undermines pseudo-anonymity. Such affiliation information can be used to discriminate against addresses linked with undesired activities or can lead to de-anonymization if out-of-network information becomes available. In this work, we propose an approach to undermine pseudo-anonymity of blockchain transactions by linking together addresses that were used to deploy smart contracts, which were produced by the same authors. In our approach, we leverage stylometry techniques, widely used in the social science field for attribution of literary texts to their corresponding authors. The assumption underlying authorship attribution is the existence of a distinctive writing style, unique to an author and easily distinguishable from others. Drawing an analogy between literary text and smart contracts' source code, we explore the extent to which unique features of source code and byte code of Ethereum smart contracts can represent the coding style of smart contract developers. We show that even a small number of representative features leads to a sufficiently high accuracy in attributing smart contracts' code to its deployer's address. We further validate our approach on real-world scammers' data and Ponzi scheme-related contracts. Additionally, we provide an algorithm to extract distinctly contributing features per an entire dataset or per specific authors. We use this algorithm to extract and explore such features in our dataset and in the Ponzi scheme-related dataset.