Objective To assess the ability of urinary acute kidney injury biomarkers and renal near-infrared spectroscopy (NIRS) to predict outcomes in infants following congenital heart surgery. Methods Urinary levels of neutrophil gelatinase-associated lipocalin (NGAL), interleukin-18 (IL-18), kidney injury molecule-1 (KIM-1), and cystatin C were measured pre- and post-operatively in 49 infants <6 months of age. Renal NIRS was monitored for the first 24 h following surgery. A composite poor outcome was defined as death, the need for renal replacement therapy, prolonged time to first extubation, or prolonged ICU length of stay. Results Forty-two patients (86%) developed acute kidney injury by meeting at least AKIN/KDIGO stage 1 criteria, and 17 (35%) patients experienced poor outcomes, including three deaths. With the exception of KIM-1, all biomarkers demonstrated significant increases within 24 h post-operatively among patients with poor outcomes. Low levels of NGAL and IL-18 demonstrated high negative predictive values (91%) within 2 h post-operatively. Poor outcome infants had greater cumulative time with NIRS saturations <50% (60 vs. 1.5 min, p=0.02) in the first 24 h. Conclusions Within the first 24 h following cardiopulmonary bypass, infants at increased risk for poor outcomes demonstrated elevated urinary NGAL, IL-18, and cystatin C, and increased time with low NIRS saturations. These findings suggest that urinary biomarkers and renal NIRS may differentiate patients with good vs. poor outcomes in the early post-operative period which could assist clinicians when counseling families and inform the development of future clinical trials.

Cell patterning technologies that are fast, easy to use and affordable will be required for the future development of high throughput cell assays, platforms for studying cell-cell interactions and tissue engineered systems. This detailed protocol describes a method for generating co-cultures of cells using biocompatible solutions of dextran (DEX) and polyethylene glycol (PEG) that phase-separate when combined above threshold concentrations. Cells can be patterned in a variety of configurations using this method. Cell exclusion patterning can be performed by printing droplets of DEX on a substrate and covering them with a solution of PEG containing cells. The interfacial tension formed between the two polymer solutions causes cells to fall around the outside of the DEX droplet and form a circular clearing that can be used for migration assays. Cell islands can be patterned by dispensing a cell-rich DEX phase into a PEG solution or by covering the DEX droplet with a solution of PEG. Co-cultures can be formed directly by combining cell exclusion with DEX island patterning. These methods are compatible with a variety of liquid handling approaches, including manual micropipetting, and can be used with virtually any adherent cell type. Video LinkThe video component of this article can be found at

Conventional immunostaining methods consume large quantities of expensive antibodies and are limited in terms of the number of antigens that can be detected from a single sample. In order to achieve multiplexed immunostaining, we micropatterning antibodies using aqueous two-phase systems formed from polyethylene glycol (PEG) and dextran. Multiple antigens can be detected on a single fixed sample by incorporating antibodies within dextran solutions, which are then patterned by micropipetting at specific sites on the sample in a solution of PEG. The antibodies are retained within the dextran phase due to biomolecular partitioning, allowing multiple protein markers to be visualized simultaneously by way of chromogenic, chemiluminescent or immunofluorescent detection. This aqueous two-phase system-mediated antibody micropatterning approach allows antibody dilutions to be easily optimized, reduces the consumption of expensive primary antibodies and can prevent antibody cross-reactions, since the antibodies are retained at separate sites within the dextran microdroplets.

No abstract

According to data protection studies, "Distributed Denial-of-Service (DDoS)" threats have cost governments and businesses throughout the globe a large number of financial resources. Despite this, the existing practices fall short of the standards set by "Cloud Computing (CC)" monitoring technology. They ignore the "Intrusion Detection Systems (IDS)" techniques, which take advantage of the CC's multiple tenants and elasticity qualities, and also the hardware limitations. Attackers are finding increasing ways to effectively exploit them because of their rising complexity. DDoS assaults of this scale have never been observed online before 2018. As online services get more popular, so does the amount of DDoS assaults and malevolent hackers leading to terrible. Numerous IDS for DDoS are already in place to address this problem. One of the most challenging aspects of virtualization is establishing a "Trust Model (TM)" between the many "Virtual Machines (VMs)". The lack of a standard formulation for generating a TM would be the primary reason. As a consequence, the integrity of every VM might not have been recognized by an independent trust, which might lead to a decrease in trust value. In this research for TM creation, "Enhanced Graph Based Clustering (EGC)" is proposed, while "Enhanced Fuzzy (EF)" is used for detecting attacks, and the "Enhanced Cuckoo Search (ECS)" method is used to find the ideal "Load Balancing (LB)" distribution. By creating a new TM, the proposed (EGC-EF-ECS) system strengthens trust value. To expand the CC model's stability, it optimizes attacker recognition percentage and makes better use of resources by restricting each VM's processing, bandwidth, and storage requirements. The proposed EGC-EF-ECS outperformed the previously used BPA-SAB, and DCRI-RI approaches in terms of the "Intrusion-Detection-Rate (IDR)", "Load-Balancing-Efficiency (LBE)", and "Data-Accessing-Time (DAT)" evaluation metrics.