Indoor environmental quality in chemistry and chemical engineering laboratories at Izmir Institute of Technology

AbstractIndoor air pollution in university research laboratories may be important to building occupants, especially for those who work in the laboratories. In this study, indoor air quality (IAQ) and indoor environmental comfort were investigated in research laboratories of two departments at a university. PM2.5, PM10, TVOC (total volatile organic compounds), and CO concentrations, and three comfort variables which are temperature, relative humidity, and CO2 were measured. PM2.5 concentration was determined gravimetrically by collecting particles on glass fiber filters, whereas the remaining pollutants and comfort variables were measured using a monitoring device. IAQ measurements showed that levels of all pollutants were under the limits in both of the departments except for TVOC in one laboratory which had a mean concentration of 182ppb. The comfort variables were in the comfort ranges for laboratories in both of the departments except for temperature in one laboratory with a mean value of 30 °C. In conclusion, measures are needed for extensive uses of organic solvents because ventilation may not be sufficient to keep VOC concentrations within the limits, and to provide thermal comfort

Turkish Neonatal Society guideline to the approach, follow-up, and treatment of neonatal jaundice.

Jaundice is one of the most common problems in the newborn. It is generally accepted as a physiologic condition; most cases are benign and transient. However, in a small portion of jaundiced newborn infants, serum bilirubin concentrations increase to a level at which irreversible brain damage can occur. The timely diagnosis and management of severe hyperbilirubinemia is essential to prevent acute bilirubin encephalopathy and kernicterus. Kernicterus still occurs although it is almost always preventable. The focus of this guideline is to reduce the incidence of severe hyperbilirubinemia and bilirubin encephalopathy. Therefore, a system-based approach using the recommendations of this guideline should be implemented in all birthing facilities and continued in ambulatory care of the newborn infants

Association between insulin resistance and serum and salivary irisin levels in patients with psoriasis vulgaris

Background/Objectives: Psoriasis is an inflammatory skin disease, which is associated with metabolic syndrome and insulin resistance. Irisin is an adipokine and myokine that regulates the metabolic status during times of increased insulin sensitivity. In this study, we aimed to investigate changes in the serum level of irisin in psoriasis patients in comparison with participants who did not have any disease (control group). We hope the results of our study would also aid in establishing a protocol aimed at understanding the etiopathogenesis and treatment of psoriasis. Materials and methods: The study included 30 patients with psoriasis vulgaris, who presented to the dermatology outpatient clinic and were not receiving systemic treatment. The control group included voluntary participants who did not have any disease (n = 30). In addition to venous and salivary irisin levels, glucose, triglyceride, cholesterol, high-density lipoprotein, and low-density lipoprotein levels, and Homeostasis Model Assessment of Insulin Resistance scores were measured in both control and patient groups. Results: Serum irisin and salivary irisin levels were significantly lower in the patient group compared with the control group (p < 0.05). In the patient group, serum irisin levels had a positive correlation with salivary irisin levels (r = 418; p = 0.022) and a negative correlation with Psoriasis Area and Severity Index (r = −437, p = 0.016) and Dermatological Life Quality Index (r = −424; p = 0.02) scores. Conclusion: This is the first study evaluating irisin levels in patients with psoriasis vulgaris in the literature. The results of our study show that serum and salivary irisin levels were significantly lower in the patient group when compared with the control group. Irisin levels in patients with severe psoriasis were low, suggesting that irisin may have a role in the pathogenesis of psoriasis and may be a marker showing the severity of psoriasis, which could warn us against the development of insulin resistance and diabetes mellitus

Value Of Twelfth Hour Bilirubin Level In Predicting Significant Hyperbilirubinemia In Preterm Infants

Background As hyperbilirubinemia is a significant cause of brain injury, it is important to predict the cases who are at risk. Data for preterm infants are scarce. The aim of this study is to predict significant hyperbilirubinemia in preterm infants by measuring capillary bilirubin at 12th hour of life. Methods One hundred and fifty neonates born ≤ 35 weeks were included in the study. They were categorized into two groups according to their birth weights (group 1: 1,000 - 1,499 g; group 2: 1,500 - 2,000 g). Their bilirubin levels were measured at 12th hour and daily thereafter for 5 days. Risk nomograms were generated based on their bilirubin measurements and postnatal ages. On the age-specific percentile-based nomogram, the zone above the 90th percentile was determined as high risk and those below the fifth percentile as low risk. Infants who had bilirubin levels over the limits defined according to their postnatal ages and birth weights were accepted to have significant hyperbilirubinemia and received phototherapy and predictive value of the 12th hour bilirubin was asssessed. Results Fifty-four of 57 infants (94.7%) in group 1 and 75/93 infants (80.7%) in group 2 received phototherapy. Capillary bilirubin levels of 3.55 mg/dL and 4.55 mg/dL for group 1 and group 2 measured at the 12th hour of life had the highest sensitivity, negative and positive predictive value to predict the neonates who will develop significant hyperbilirubinemia. Conclusion Bilirubin levels of preterm infants should be monitored closely. More attention should be paid to the ones who had 12th hour bilirubin level above the cutoff values.PubMe

Can cerebrospinal fluid uric acid levels differentiate intraventricular hemorrhage from traumatic tap?

Aslan, Ayse Tana/0000-0002-5360-8517WOS: 000241774800010PubMed: 16809910Objective: To measure blood and cerebrospinal fluid (CSF) uric acid (UA) levels of neonates with intraventricular hemorrhage (IVH), and to examine whether or not UA can be used to differentiate traumatic tap from IVH. Material and Methods: The control group (n = 19, group I) consisted of neonates presenting with signs requiring analysis of CSF but whose CSF indices proved to be normal. Traumatic taps (n = 15, group II) were mimicked by adding 2 drops of homologous blood to normal CSF samples. The IVH group (n = 21, group III) consisted of neonates who had been diagnosed by cranial ultrasonography or computed tomography scans. Data are presented as median ( range). Results: There was no significant difference between groups with respect to serum UA levels. While no significant difference was observed between CSF UA levels of the control [0.6 (0.1-1.8) mg/dl] and traumatic tap group [0.5 (0.3-1.1) mg/dl], the IVH group [1.6 (0.7-6.9) mg/dl] was found to have significantly higher CSF UA levels than groups I and II. Furthermore, although there were significant correlations between serum and CSF UA levels in the control and traumatic tap groups, no correlation was observed in the IVH group. Conclusion: CSF UA levels are increased in neonates with IVH and they may be used to differentiate a real hemorrhage from a traumatic tap. Copyright (c) 2006 S. Karger AG, Basel

3D spatial organization and network-guided comparison of mutation profiles in Glioblastoma reveals similarities across patients.

Glioblastoma multiforme (GBM) is the most aggressive type of brain tumor. Molecular heterogeneity is a hallmark of GBM tumors that is a barrier in developing treatment strategies. In this study, we used the nonsynonymous mutations of GBM tumors deposited in The Cancer Genome Atlas (TCGA) and applied a systems level approach based on biophysical characteristics of mutations and their organization in patient-specific subnetworks to reduce inter-patient heterogeneity and to gain potential clinically relevant insights. Approximately 10% of the mutations are located in "patches" which are defined as the set of residues spatially in close proximity that are mutated across multiple patients. Grouping mutations as 3D patches reduces the heterogeneity across patients. There are multiple patches that are relatively small in oncogenes, whereas there are a small number of very large patches in tumor suppressors. Additionally, different patches in the same protein are often located at different domains that can mediate different functions. We stratified the patients into five groups based on their potentially affected pathways that are revealed from the patient-specific subnetworks. These subnetworks were constructed by integrating mutation profiles of the patients with the interactome data. Network-guided clustering showed significant association between the groups and patient survival (P-value = 0.0408). Also, each group carries a set of signature 3D mutation patches that affect predominant pathways. We integrated drug sensitivity data of GBM cell lines with the mutation patches and the patient groups to analyze the possible therapeutic outcome of these patches. We found that Pazopanib might be effective in Group 3 by targeting CSF1R. Additionally, inhibiting ATM that is a mediator of PTEN phosphorylation may be ineffective in Group 2. We believe that from mutations to networks and eventually to clinical and therapeutic data, this study provides a novel perspective in the network-guided precision medicine

How to Attack and Defend NextG Radio Access Network Slicing With Reinforcement Learning

In this paper, reinforcement learning (RL) for network slicing is considered in next generation (NextG) radio access networks, where the base station (gNodeB) allocates resource blocks (RBs) to the requests of user equipments and aims to maximize the total reward of accepted requests over time. Based on adversarial machine learning, a novel over-the-air attack is introduced to manipulate the RL algorithm and disrupt NextG network slicing. The adversary observes the spectrum and builds its own RL based surrogate model that selects which RBs to jam subject to an energy budget with the objective of maximizing the number of failed requests due to jammed RBs. By jamming the RBs, the adversary reduces the RL algorithm&#x0027;s reward. As this reward is used as the input to update the RL algorithm, the performance does not recover even after the adversary stops jamming. This attack is evaluated in terms of both the recovery time and the (maximum and total) reward loss, and it is shown to be much more effective than benchmark (random and myopic) jamming attacks. Different reactive and proactive defense schemes such as suspending the RL algorithm&#x0027;s update once an attack is detected, introducing randomness to the decision process in RL to mislead the learning process of the adversary, or manipulating the feedback (NACK) mechanism such that the adversary may not obtain reliable information are introduced to show that it is viable to defend NextG network slicing against this attack, in terms of improving the RL algorithm&#x0027;s reward

How to Attack and Defend 5G Radio Access Network Slicing with Reinforcement Learning

Reinforcement learning (RL) for network slicing is considered in the 5G radio access network, where the base station, gNodeB, allocates resource blocks (RBs) to the requests of user equipments and maximizes the total reward of accepted requests over time. Based on adversarial machine learning, a novel over-the-air attack is introduced to manipulate the RL algorithm and disrupt 5G network slicing. Subject to an energy budget, the adversary observes the spectrum and builds its own RL-based surrogate model that selects which RBs to jam with the objective of maximizing the number of failed network slicing requests due to jammed RBs. By jamming the RBs, the adversary reduces the RL algorithm's reward. As this reward is used as the input to update the RL algorithm, the performance does not recover even after the adversary stops jamming. This attack is evaluated in terms of the recovery time and the (maximum and total) reward loss, and it is shown to be much more effective than benchmark (random and myopic) jamming attacks. Different reactive and proactive defense mechanisms (protecting the RL algorithm's updates or misleading the adversary's learning process) are introduced to show that it is viable to defend 5G network slicing against this attack