There is an urgent need for new ways to treat Alzheimer’s disease (AD), the most common cause of dementia in the elderly. Current therapies are modestly effective at treating the symptoms, and do not significantly alter the course of the disease. Over the years, a range of epidemiological and experimental studies have demonstrated interactions between diabetes mellitus and AD. As both diseases are leading causes of morbidity and mortality in the elderly and are frequent co-morbid conditions, it has raised the possibility that treating diabetes might be effective in slowing AD. This is currently being attempted with drugs such as the insulin sensitizer rosiglitazone. These two diseases share many clinical and biochemical features, such as elevated oxidative stress, vascular dysfunction, amyloidogenesis and impaired glucose metabolism suggesting common pathogenic mechanisms. The main thrust of this review will be to explore the evidence from a pathological point of view to determine whether diabetes can cause or exacerbate AD. This was supported by a number of animal models of AD that have been shown to have enhanced pathology when diabetic conditions were induced. The one drawback in linking diabetes and insulin to AD has been the postmortem studies of diabetic brains demonstrating that AD pathology was not increased; in fact decreased pathology has often been reported. In addition, diabetes induces its own distinct features of neuropathology different from AD. There are common pathological features to be considered including vascular abnormalities, a major feature arising from diabetes; there is increasing evidence that vascular abnormalities can contribute to AD. The most important common mechanism between insulin-resistant (type II) diabetes and AD could be impaired insulin signaling; a form of toxic amyloid can damage neuronal insulin receptors and affect insulin signaling and cell survival. It has even been suggested that AD could be considered as “type 3 diabetes” since insulin can be produced in brain. Another common feature of diabetes and AD are increased advanced glycation endproduct-modified proteins are found in diabetes and in the AD brain; the receptor for advanced glycation endproducts plays a prominent role in both diseases. In addition, a major role for insulin degrading enzyme in the degradation of Aβ peptide has been identified. Although clinical trials of certain types of diabetic medications for treatment of AD have been conducted, further understanding the common pathological processes of diabetes and AD are needed to determine whether these diseases share common therapeutic targets. Depending on populations being studied, the incidence of AD is 10% in those over 65 years of old, rising to 50% in those over 85 years of age. By the middle of this century, as the population progressively ages, AD is becoming an epidemic that will affect 13.2 to 16 million in the United States . CurrentAlthough the majority of research in AD has focused on the amyloid beta (Aβ) peptide and how it is generated and metabolized, the role of insulin, glucose regulation and diabetes has been a constant area of research. Glucose metabolism has a pivotal role in maintaining all aspects of the health of the brain; glucose dysregulation has a multitude of pathological effects with many of these being observed in patients with diabetes. It has been known for many years that reduced glucose metabolism, as demonstrated by positron emission tomography using Fluorodeoxyglucose as ligand, is an early indication of the development of AD -6. The sWhy are there not enhanced AD pathological changes in the brains of elderly with diabetes? Does this mean the two diseases have no interrelationship or are the pathological mechanisms of diabetes and AD only sharing some of the features? We will examine many of the common features of diabetes and AD to determine how they might interact and also address the issue of using diabetic medication and/or insulin as AD therapy.Insulin is the major hormone that regulates glucose metabolism in the periphery and the brain, so it can be expected that diseases with glucose dysregulation, namely Type I and Type II diabetes, will also have consequences on brain function. There has been a large body of basic scientific and epidemiological studies showing how diabetes can accelerate AD. Based on these findings one might expect that treating diabetes will slow down or reverse AD neuropathological changes. There have been many excellent reviews over the last 5 years that make this case in a compelling manner; however the aim of this review is to consider this question from a human pathological point of view. 1), a simplified scheme of the interaction between insulin deficiency/resistance (not necessarily diabetes) is proposed. As we will examine, some of the features of how failure of insulin signaling leads to AD pathology has been better elucidated in animal models rather than in humans.In Fig. (1.1Positron emission tomography (PET) using radiolabeled fluorodeoxyglucose (FDG) has become one of the oldest and most accurate biomarker tools for identification of mild cognitive impairment (MCI) and early stage AD in living subjects -12. Resu1.2Diabetics develop a diverse range of pathological conditions, both in brain and periphery, due to the consequences of glucose dysregulation; as many of the pathologies share features with AD or putative AD processes, research has hypothesized that diabetes (Type I or Type II) is a risk factor for AD. Insulin, a polypeptide hormone consisting of a 21 amino acid α chain and a 30 amino acid β chain, plays the central role in regulating energy metabolism in the body. Diabetes as the “sugar” disease has been identified for centuries, but it has been shown that insulin has numerous neurotrophic, metabolic and endocrine functions in the brain, being centrally involved in memory and neuronal survival (reviewed in ). This h1.3Type-2 diabetes (T2D) also referred to as insulin-resistant diabetes or non-insulin dependent diabetes, tends to be an age-related disease frequently associated with obesity, smoking, and failure to exercise, and characterized by endogenous insulin resistance. Subjects have hyperinsulinemia, hyperglycemia, glucose intolerance, atherosclerosis, hypertension and adiposity , which aClinical studies have examined the shared risk for AD and T2D. It has been observed that 81% of AD patients had either T2D or impaired fasting glucose levels . These s2AD is the most common cause of dementia in the elderly whose incidence is increasing as the population is becoming older. The disease manifests as short-term memory loss that progresses in severity to global dementia. Usually the cognitive decline in AD is associated with the progressive development of extracellular senile plaques, which consist of fibrillar aggregates of amyloid-β (Aβ) deposits, and intracellular neurofibrillary tangles (NFT), composed of hyperphosphorylated tau protein located in the cortex and hippocampus of the affected brain; the disruption of synaptic function; and cerebral atrophy. The central issue for understanding the interaction of diabetes and AD has been to determine if and how diabetes/insulin deficiency can accelerate these above listed pathological features. This has brought up a number of potential mechanisms, including the interaction of insulin degrading enzyme (IDE) in AD and diabetes -44; the 2.1whether there is an insulin deficit or insulin resistance in the brain. Additionally, insulin and IGF-I have proved to have direct effects on AD pathogenic processes. They affect the metabolism and clearance of Aβ, and reduce the formation of neurofibrillary tangles by regulating the levels of phosphorylated tau protein. This, in theory could give them potent neuroprotective characteristics [Insulin and insulin-like growth factor (IGF) have been proven to have vital roles in the brain. They control the neuronal functions by directing the metabolic, neurotrophic, neuromodulatory and neuroendocrine actions that are necessary elements for learning and establishing memory . Studieseristics , 61.2.2The majority of the body’s insulin is produced by the pancreas; however insulin can cross the BBB from the circulation to the brain by a receptor-dependent mechanism , 62, 63.2.3It is incontrovertible that diabetes, particularly T2D, and AD are coexistent diseases of the elderly. The association between diabetes mellitus, insulin resistance and the degree of hippocampal and amygdalar atrophy was investigated in Rotterdam study by volumetric magnetic resonance imaging . In thisIn general, diabetes leads to its own type of neuropathology, cerebral neuropathology of type-2 diabetes, which consists of cerebrovascular and white matter pathology -73. In tThis raises the question whether diabetes can accelerate AD pathology, or there are common coexisting factors for both diseases. The reduced AD neuropathology in the brains of diabetics may indicate that treatments for diabetes are reversing the AD pathology. This is germane in deciding whether diabetic medications should be tested for treating non-diabetic AD patients. In a further neuropathology study, investigators examined relation of diabetes to the most common neuropathology causes of dementia, cerebral infarction and AD . They fo2.4Vascular abnormalities including atherosclerosis are increased in diabetics, and also are a feature of those possessing the apoE4 allele. Possession of a single or two copies of the apoE4 allele is the most significant risk factor for developing sporadic AD , 77. Sub.In a study of an Italian population aimed at the diagnosis and treatment of dementia, vascular factors such as hypertension and hypercholesterolemia were not found to be significantly associated with disease progression, but, surprisingly, patients with diabetes had a 65% reduced risk of fast cognitive decline compared to AD patients without diabetes. Their findings suggest a slower disease progression in AD patients with diabetes, when based on clinical progression of AD severity . As most2.5We are still left with the conflicting situation that epidemiological studies show increased frequency of dementia in diabetics, while the series of neuropathology studies did not show enhanced AD pathology in this same group -88. The 2.6The effect of antihypertensive drug use on dementia in a cohort of the population-based Rotterdam Study was investigated . They de33.1ras and transcription factor nuclear factor-kappa B (NF-κB). A common pathological feature between diabetes and AD has been the presence of advanced glycation endproduct (AGE)-modified proteins in both diseases , 95. Thein vivo is still unclear, but increased RAGE expression along with increased amounts of AGE-modified proteins is believed to be a major cause of the vascular complications that are a common feature of diabetes and AD [The activation of NF-κB is considered to be the most common feature in RAGE signaling, as it up regulates cellular expression of RAGE , 101. Ths and AD -107. In s and AD , 108-110s and AD , 111-114s and AD , and it s and AD , supports and AD . In addis and AD .3.22). The effects of excess type Aβ is generated as a result of proteolytic cleavage of amyloid precursor protein (APP) and has been considered the main toxic mediating factor in AD. It has been shown that Aβ binds to a number of neuronal or glial receptors (reviewed [2). As studies have concluded that diabetic neuropathology and AD are separate entities, the mechanism of insulin’s involvement in AD needs to be resolved. As mentioned, up to 80% of AD patients have type II insulin resistance diabetes (T2D). In T2D, there is an excess production of insulin due to the insulin receptors becoming unresponsive to insulin binding. The increased production of insulin can compensate for the lack of signaling to some extent, but this excess insulin is degraded by the pool of IDE, which also functions to degrade monomeric Aβ. This results in increased levels of both, and the resulting lack of insulin signaling. Blocking insulin signaling results in reduced glucose metabolism, increased glycogen synthase kinase-3β (GSK-3β) activation which can promote hypophosphorylation of tau and increased Aβ production. It has even been suggested that insulin can promote Aβ aggregation. The possible interactions of insulin and Aβ on insulin signaling are shown in Fig. , ireviewed , with threviewed ,121. Morreviewed . As it ireviewed . Feature3.3When considering the potential interactions between diabetes, particularly hyperinsulinemia, and AD, the most convincing evidence has come from a range of studies on insulin degrading enzyme (IDE). IDE also referred to as insulysin or insulin protease, is a zinc binding metalloprotease involved in regulating insulin levels and is one of the proteases involved in Aβ degradation (primarily the monomeric and soluble forms of Aβ) -127. Mut3.4Insulin resistance syndrome is a condition in which there is an impaired response of body cells  to insulin resulting in impaired endothelial function and enhanced atherosclerosis -138; whi3.5The human brain uses up to 25% of total body glucose, as it is the main nutrient for maintaining brain function . However3.6One of the problems of human clinical studies of diabetes and AD has been the fact that measurements of glucose metabolism and insulin levels at the time of death in an AD patient are not reflective of the pathological mechanisms that lead to this stage. Most of the metabolic processes will have been altered prior to death. For this reason, relevant animal models have been invaluable for dissecting insulin related mechanisms in the CNS and whether they predispose to AD.A widely used reagent for inducing experimental diabetes is streptozotocin, broad spectrum antibiotics with a nitrosamine-related compound that destroys beta cells in the pancreatic islets. Injection of streptozotocin into the ventricles of the brains of rats resulted in 10-30% reduction in glucose metabolism. The mechanism of this cytotoxic diabetogenic drug is mainly mediated by the development of ROS, in addition to the formation of nitric oxide (NO), which induces destruction by necrosis . It is p3.6.1in vitro. This finding contrasts with the decrease in IDE shown in human AD brains [Increased generation of Aβ appears to stimulate expression of IDE in AD transgenic mice model. As plaque formation progresses, IDE levels increase. This was particularly noticeable with the increased numbers of IDE-immunoreactive astrocytes surrounding plaques . These iD brains . This fiD brains . Of moreD brains .3.6.2ob/ob (leptin deficient) mice resulted in enhanced diabetes symptoms (blood glucose levels and plasma insulin) compared to ob/ob mice alone, but with reduced insulin levels in the brain. These mice also had significantly reduced cognitive function and insulin signaling, even though there was no increased Aβ compared to APP non-diabetic mice. Similarly, crossing APP23 with the mouse model of T2D (Nagoya-Shibata-Yasuda (NSY)) resulted in more severe memory deficits and insulin resistance compared to NSY mice. In this model, brain Aβ levels correlated positively with circulating glucose levels. This study concluded that the enhancement of Aβ-associated memory impairment and pathology in these mouse models was due to increased cerebrovascular inflammation mediated by increased RAGE expression on endothelial cells. It still remains to be determined how vascular inflammation can affect insulin signaling, but this and other studies suggest that it can reduce BBB transport of insulin into brain [Studies of insulin deficiency, as a model of type 1 diabetes, in transgenic mice provided some parallel features with AD. Nine weeks of insulin deficiency resulted in reduced IDE, reduced learning capacity, and reduced insulin receptor signaling. Reduced insulin signaling resulted in increased tau phosphorylation and Aβ levels. These effects were partially reversed with insulin treatment. These pathological features were not observed in a parallel T2D model . Two newto brain . Two recto brain , 156. Boto brain . Both stto brain . At pres4The literature on diabetes, dementia and therapy has drawn many divergent conclusions possibly due to the differing designs of the clinical studies. To address the issue whether insulin therapy or anti-diabetic medication could affect cognitive function, a group of AD patients with T2D were either treated with anti-diabetic medication, or with insulin and anti-diabetic medication. After 12 months, the group receiving insulin and anti-diabetic medication had deteriorated at half the rate of those receiving the non-insulin therapy . Another4.1The treatment strategies that are now used in AD are mainly targeting symptom improvement. AD has a major inflammatory component due to the neurodegeneration and Aβ peptide deposition. As a result, many studies have shown that non-steroidal anti inflammatory drugs (NSAIDs) may affect AD process, and may reduce the risk and delay the onset of AD. Some NSAIDs are also ligands for peroxisome proliferator activated receptors  , 161.PPARs are ligand-inducible transcription factors that regulate the expression of a number of inflammatory and insulin-regulated genes . The besRecent findings on the safety of rosiglitazone might preclude this drug for further testing in AD, but alternative drugs of this family with similar modes of action are available.4.2Another new approach in treating AD is in the administration of intranasal insulin. The fact that there are direct pathways between the nasal cavity and the central nervous system has guaranteed an easy access for insulin through the BBB, without the risk of systemic hypoglycemia that results from the peripheral administration of insulin . In thisInitially, insulin was thought to not cross the BBB as the brain was perceived to be an insulin–insensitive organ, however this was shown not to be true. It is still probable that most insulin in the brain is transported from the periphery but further studies on CNS synthesized insulin are needed . Insulin


AbstractTroglophilus.Fifteen populations were studied for sequence variation in a fragment (543 base pairs) of the mitochondrial DNA (mtDNA) 16S rDNA gene (16S) to reconstruct their phylogenetic relationships and biogeographic history. Genetic data retrieved three main clades and at least three divergent lineages that could not be attributed to any of the taxa known for the area. Molecular time estimates suggest that the diversification of the group took place between the Messinian and the Plio-Pleistocene.This study focuses on the evolutionary relationships among Turkish species of the cave cricket genus  PageBreakWhite 1969; Troglophilus from Turkish caves. This genus belongs to family Rhaphidophoridae, which has a worldwide distribution and typically includes wingless crickets with a clear preference for dump environments, including natural and artificial caves. In the Northern hemisphere these crickets are essentially confined to natural and artificial caves. Overall 10 subfamilies have been recognized to date .Caves are traditionally considered as natural laboratories to understand evolutionary processes related to allopatric divergence because, similarly to remote oceanic islands, by their very nature greatly reduce or hamper gene flow among populations (Poulson and Dolichopoda and Troglophilus) with a fairly overlapping Eastern-Mediterranean distribution. Dolichopoda (49 described species) is by far more species-rich than Troglophilus (17 described species). Until now, seven species of Dolichopoda , Dolichopoda noctivaga Di Russo & Rampini, 2007, Dolichopoda sutini Rampini & Taylan, 2012) and five species of Troglophilus  have been reported from Anatolian caves. As far as Troglophilus is concerned, the first species to be described from the area was Troglophilus escalerai  by Bolivar in Troglophilus adamovici  and Troglophilus gajaci  in 1974. About thirty years later Troglophilus bicakcii , while the description of Troglophilus tatyanae  was presented in In the peri-Mediterranean area the family is represented by two genera only  up to the phylogenetic relationships among the vast majority of taxa ascribed to the genus  and four new taxa/populations from Muğla, Alanya, Seydişehir and Balıkesir provinces .Ten caves have been checked for each region in Turkey  to collect cave crickets and fifteen sampled populations belonged to the genus a region . All thences see  were incoC for 5 minutes, followed by 39 cycles of denaturation at 95 oC for 30 s, annealing of primers at 53 oC for 30 s, elongation at 72 oC for 1 min and one final extension step at 72 oC for 5 min. PCR products were purified using the QIAquick PCR Purification Kit (Qiagen); in some circumstances PCR products were excised from gel and purified with the QIAquick Gel Extraction Kit (Qiagen). Sequencing was carried out on an ABI 3730XL sequencer in both directions and with the same primer pair used for PCRs. Sequences data were edited and compiled using Codoncode Aligner .Genomic DNA was extracted from the hind femoral muscle using I-genomic CTB DNA Extraction Mini Kit . A 532-535 base pair (bp) fragment of the mitochondrial 16S rDNA gene was amplified through the Polymerase Chain Reaction (PCR) from each individual samples. The primers used were ER232 (5’-CGCCTGTTTAACAAAAACAT-3’) and ER233 (5’-CCGGTCTGAACTCAG ATGACTG-3’) . PCR ampPageBreakJMODELTEST ; the species belongs to the only other Rhaphidophoridae genus present in the Mediterranean area and was used as the outgroup for all phylogenetic searches. We calculated Maximum Likelihood (ML) genetic distances among the main lineages retrieved from the phylogenetic searches using the settings yielded by JMODELTEST.Sequences were aligned in ClustalX  with defODELTEST ; the choODELTEST . In addiDolichopoda to date age of divergence among haplotypes. We used a Yule prior on rates of evolution because this more accurately resembles phylogenetic processes at the species level. We adopted the same GTR+ G model as in the ML and Bayesian searches. We ran five independent analyses of 50,000,000 generations each; the corresponding outputs were analyzed using Tracer 1.4, TreeAnnotator 1.4.6 and FigTree 1.0  through the western Taurus Mountain, Southern Central Anatolian regions with a Mediterranean climate and Southern Aegean region . Clade 2 contains Troglophilus bicakci and Troglophilus sp. 2 populations, which are distributed in the Southern Central Anatolian region through Kembos Polye and Konya, Seydişehir, Derebucak and Beyşehir Provinces. This clade overlaps with Clade 1 in the Seydişehir Province (Ferzene cave). Clade 3 comprises Troglophilus sp. 3 population only and it is geographically restricted to the Antalya area . The cave is located near the Dim River in the Southern Mediterranean Region.Clade 1 includes PageBreakAverage GTR + G distance between Clade 1 and 2 is 0.063 ± 0.025, between Clade 2 and 3 is 0.058 ± 0.021 and between Clade 1 and 3 is 0.050 ± 0.005. Time estimates retrieved from the Bayesian MCMC analyses for the three main clades are illustrated in -2 . The 95% High Posterior Density spans zero; this implies that branches with fast and slow rates are next to each other in the phylogenetic tree. There is thus no evidence of autocorrelation of rates in the tree. Ages of Clades 1, 2, and 3 ranges between 5.8 and 2.3 million years; the lack of a clear calibration point resulted in a chronogram with relatively ample confidence intervals  could not be attributed to any of the previously described species and could hence represent new taxa. The mean GTR + G genetic distance between the described Troglophilus species included in our study  diverge from all the described species for a GTR + G distance range comprised between 0.023-0.132 ± 0.026. Hence, these four new lineages are genetically as divergent as the formally described species are, and in some cases even more.. In addition, they also show morphological differences in the shape of the ovipositor, which is one of the most important discriminating characters traditionally used for taxonomic purposes in Troglophilus  both from the Konya province, while Clade 3 includes Troglophilus sp. 3 population distributed in the Dim Cave in Antalya. The phylogenetic placement of Troglophilus gajaci, Troglophilus escalerai, Troglophilus tatyanae, and Troglophilus sp. 4 could not be resolved by the data and remains controversial. Troglophilus gajaci basal to a group of non-described forms, including a population corresponding to Troglophilus sp. 3 in our study. The placement of Troglophilus tatyanae is not resolved in either study, while Troglophilus adamovici and Troglophilus bicakcii. Those authors did not analyze Troglophilus escalerai. It is evident that these discrepancies could be reconciled only by maximizing the overlap of both species and markers. Another point that shouldn’t be overlooked is that a phylogenetic hypothesis for the whole genus Troglophilus is still missing. A study based on a multi-gene approach and aimed at producing such a hypothesis is in progress, which will likely shed light on the questions left open by this and previous studies.Overall, we could distinguish three main clades; all received strong support in our phylogenetic analyses  and 2. CDolichopoda). As a matter of fact,Dolichopoda. The difference in the genetic structure between Troglophilus and Dolichopoda could be due to a higher tendency for the latter to maintain gene flow among caves. On the other hand, it shouldn’t be overlooked that our sampling across Turkey is rather sparse and isolation by distance could fail to emerge from the data just because we missed too many intervening locations in our sampling. Finally, our study is based on a single marker with moderate evolving rates. On a more local scale, with a denser sampling and a multi-gene approach, isolation by distance was found in Troglophilus cavicola in Northern Italy . This pattern suggests a secondary contact of these lineages after allopatric divergence, a phenomenon reported multiple times in cave dwelling-organisms , dating to the Plio-Pleistocene, which was characterized by alternating dry/cold and warm/humid phases. The climatic fluctuations during the Plio-Pleistocene likely led to ecological fragmentation with subsequent genetic isolation and speciation in the area. This hypothesis is also supported by the results from the Dolichopoda species, whose radiation also appears to have followed the climatic changes of the Plio-Pleistocene (The estimated divergence time for istocene , 2009.Troglophilus adamovici and Troglophilus sp. 2 in the Ferzene cave do not interbreed, their secondary contact must have taken place after the diversification within Clades 1 and 2, certainly more recently than the Messinian. Even though we are not in the position to date when the secondary contact actually happened, we suspected that this was favored by one of the many warm and humid climatic phases of the Quaternary, which allegedly promoted epigean dispersal among lineages that had been previously confined to caves.Since the syntopic Troglophilus are in agreement with those reported in Troglophilus.Our time estimates for the splitting events within the Anatolian representatives of Finally, it should not be overlooked that this study is limited to the Turkish area and is based on a single mitochondrial marker. To place these results in a broader perspective and to understand in details the evolutionary trajectories followed by the genus, we need to expand our sampling by covering its whole distribution range and by combining multiple mitochondrial and nuclear loci. To these aims our ongoing research activity is currently devoted.


This pilot study compared usual care alone with usual care plus meditation in relation to anxiety, depression, pain, and quality of life in community-dwelling older adults with postherpetic neuralgia (PHN). PHN may occur after shingles, and has been described as one of the most intractable neuropathic pain disorders. Older adults develop PHN more often than younger adults. Mindfulness meditation, found to be beneficial in the management of some chronic pain conditions, has not been specifically examined in older adults with PHN.Using a two-group pretest-posttest design with repeated measures, data were collected at entry to the study (Time 1), after a 2-week control period (Time 2), and 6 weeks later (Time 3). After Time 2 testing, participants were randomly assigned to usual care alone or usual care plus meditation. Daily pain diaries were kept by all participants for 8 weeks.The 27 study participants were between 55 and 90 years of age, with a mean of 72 years. Fifteen participants were female, and 12 were male. Fourteen participants were White, 11 were Hispanic, and 2 were American Indian. At entry to the study, 18 participants reported moderate or greater pain seven days per week due to PHN. Data analysis included repeated measures ANOVA for the three time periods. Although no statistically significant differences were found for time or interaction effects, trends indicating a favorable response to mindfulness meditation over time were identified in the majority of the outcome variables.Participants were able to commit to the protocol for the duration of the study. The small sample size is a limitation; improvement in either group could be due to the natural course of PHN or even investigator attention. Future studies will include a sample size that is powered to detect significant differences at α=.05.


This risk was similar to that of thrombin generation(MaxR OR 2.6). Grouping defined by oral contraceptive (OC) use in the control population showed the biggest differences in fXa generation; a >60% increase in the MaxR upon OC use. FXa generation can distinguish between a subset of individuals characterized by overlapping thrombin generation profiles. Analysis of fXa generation is a phenotypic characteristic which may prove to be a more sensitive discriminator than thrombin generation among all individuals.Factor (f)Xa is a critical enzyme in blood coagulation that is responsible for the initiation and propagation of thrombin generation. Previously we have shown that analysis of computationally generated thrombin profiles is a tool to investigate hemostasis in various populations. In this study, we evaluate the potential of computationally derived time courses of fXa generation as another approach for investigating thrombotic risk. Utilizing the case (n = 473) and control (n = 426) population from the Leiden Thrombophilia Study and each individual's plasma protein factor composition for fII, fV, fVII, fVIII, fIX, fX, antithrombin and tissue factor pathway inhibitor, tissue factor-initiated total active fXa generation was assessed using a mathematical model. FXa generation was evaluated by the area under the curve (AUC), the maximum rate (MaxR) and level (MaxL) and the time to reach these, TMaxR and TMaxL, respectively. FXa generation was analyzed in the entire populations and in defined subgroups . The maximum rates and levels of fXa generation occur over a 10- to 12- fold range in both cases and controls. This variation is larger than that observed with thrombin (3–6 fold) in the same population. The greatest risk association was obtained using either MaxR or MaxL of fXa generation; with an ∼2.2 fold increased risk for individuals exceeding the 90 The inventory of blood and vessel wall components associated with the blood coagulation system for hemorrhage control is extensive, and this inventory increases substantially with the inclusion of intermediate species that emerge in the individual processes leading to thrombin generation. Monitoring thrombin generation has been the focus of providing a global description of the blood coagulation process, due to the multifactorial nature of thrombin in procoagulant, anticoagulant, fibrinolytic and cellular events 2+). Factor Xa is the serine protease enzyme in the prothrombinase complex that catalytically activates prothrombin to thrombin. Factor Xa is a unique regulatory enzyme in that it is formed through both the extrinsic tenase (tissue factor (Tf)-fVIIa-membrane-Ca2+) and intrinsic tenase (fVIIIa-fIXa-membrane-Ca2+) complexes. During the initial stages of the hemostatic event triggered by the exposure of blood to Tf, low levels of both fXa and fIXa are generated Factor Xa can be thought of as the transducer of the thrombin generation signal. The main function of fXa is to participate in the prothrombinase complex (fXa-fVa-membrane-CaProthrombin circulates in plasma at a concentration of 1.4 µM (100 µg/mL) while the plasma concentration of circulating fX is approximately 10-fold less . Studies from a number of experimental systems have indicated that as a coagulation reaction proceeds, consumption of fX is quite limited (<10%) in contrast to prothrombin Factor X levels in healthy populations vary on average over a two- to three-fold range. High levels of fX alone have been shown to predict the risk of venous thrombosis, but were not a risk factor for venous thrombosis when the levels of other vitamin K-dependent proteins were taken into account Mathematical modeling allows for these measurements and comparisons. An empirically validated numerical model of the extrinsic coagulation system Our population was selected from the LETS case-control study Several individual characteristics were used to define subpopulations. We separated the data by sex, age and body mass index. When we investigated the effect of oral contraceptives we excluded individuals who were pregnant, post-menopausal, within 30 days postpartum or had a recent miscarriage at the time of thrombosis for patients (index date). Since the median time between occurrence of DVT and venipuncture was 18 months, we only evaluated women who either used oral contraceptives at the index date and the venipuncture date or did not at both dates. Seven individuals were excluded from analyses involving alcohol since information was missing, and three individuals were excluded from analyses involving BMI who were not 18 at the time of the blood draw. Factor Xa generation curves of each subpopulation were generated by averaging fXa concentrations at each time point. Subpopulation fXa profiles are shown in the figures as the mean time point values with the 95% confidence interval.Citrated plasma previously collected from all individuals et al. et al. The mathematical model is based upon prior publications by Hockin A systematic analyses of the contribution of the plasma factors to either the fXa or thrombin generation output was conducted on the subgroups, as previously described th percentiles for MaxL, MaxR and AUC were determined for the entire population. Odds ratios (signifying the odds of being a case for those above the 90th percentile vs those with lower levels) were calculated for each parameter.Data are presented as mean (SD). The Kolmogorov-Smirnov test was used to assess conformity with a normal distribution. The t-test was utilized for comparisons between two groups provided a normal distribution. Non-parametric methods were utilized if the assumption of non-normality was not met. Significance was defined as p<0.05. 90The fX activation profiles for all cases and controls are represented separately in th percentile cut off, the OR for MaxR-fXa generation is 2.1 (95% CI: 1.3–3.2) and for MaxL-fXa generation is 2.2 (95% CI: 1.4–3.4) , MaxR-Xa generation also varied by 12-fold (of 2.1–25.1 pM/s) and MaxL-Xa and AUC varied over 11-fold . In comparison to thrombin generation in the case population, fXa discrimination was consistently broader than thrombin generation; which showed only a 5.8 fold variation in MaxR-IIa (11.3–65.3 nM/s) and 5.3 fold in MaxL-IIa (170–903 nM) 1.4–3.4) . Thrombie.g. BMI, sex, age, oral contraceptive use) and generating an average profile for the entire group analogous to our previously published work for thrombin Factor Xa generation in individuals was analyzed by grouping fXa generation profiles of individuals with shared clinical characteristics  showed an odds ratio of 2.4 (95% CI: 1.1–4.9) for MaxR and 2.5 (95% CI: 1.1–5.6) for MaxL. Thrombin generation, as previously reported, also showed a greater risk prediction (MaxR OR: 2.9 (95% CI: 1.6–5.0)) in individuals with a lower BMI ≤26 kg/m2When cases and controls are compared , individWhen control women (n = 272) are compared to men (n = 201) , women gWhen control premenopausal women aged 15 to 49 are split into users and non-users of oral contraceptives , we obseth percentile compared to cases  were above the 90To determine if fXa generation provides any additional discrimination between individuals relative to thrombin generation, we reevaluated our previously determined thrombin generation profiles When we evaluate fXa generation in the individuals with overlapping pairs of thrombin generation curves, fXa generation appears to discriminate between the individuals in two of the groupings. There also appears to be a relationship between the timing of thrombin generation and the level of fXa discrimination. The greatest discrimination appears when the overlapping thrombin generation profiles are faster than the mean of the population (bottom row). These results show that fXa generation can provide additional discrimination to thrombin generation profiles. In addition, these studies were performed with a Tf-initiator concentration of 5 pM to reflect empirically validated data We have previously demonstrated that the thrombin generation profile is not dependent upon one factor alone, but the synergy between all the factors We have reported differences in thrombin generation profiles between acute (ACS) and stable coronary artery disease (CAD) populations In this study we showed that there is a large variation in predicted fXa generation in an apparently healthy population and in individuals with a known DVT. The maximum rates and levels of fXa generation occur over a 10- to 12- fold range, respectively in both populations. This variation is larger than that observed with thrombin (3–6 fold) Factor Xa generation discriminated well between defined clinical risk subgroups. Most notably, healthy women on oral contraceptives showed >60% increase in some fXa parameters compared to women not on oral contraceptives. In addition, individuals with similar thrombin generation profiles displayed differing fXa generation profiles, and there appeared to be a linkage between the extent of the differences in the fXa generation profiles and the timing of thrombin generation in these pairs of individuals. Analysis of simulated fXa generation appears to be a more sensitive discriminator than simulated thrombin generation among individuals. In addition, these studies were performed with a Tf-initiator concentration of 5 pM to reflect empirically validated data Thrombin generation and fXa generation appear to have a similar ability to identify individuals at risk for DVT, and can tell us about the most thrombotic segment of the population. However, it is not a tool for predicting which individuals in the less thrombotic group (90% of the population) will transition to a thrombotic state. A measure of procoagulant capacity that shows a wider range of variation (fXa vs. thrombin generation) across an entire population, in principle, may have a stronger potential to prospectively identify individuals who will transition to a procoagulant state. At the peak of prothrombinase activity, the concentration of active fXa participating in prothrombinase represents less than 0.004% of the zymogen fX Utilizing empirically validated mathematical modeling, investigations and comparisons into protein interactions, intermediates or complex formations can be accomplished to develop a broader view of the dynamics associated with the coagulant response. In this study, these simulations account for all the plasma pro- and anti-coagulant proteins of the Tf-pathway that are both empirically validated and show correlation to clinical events and other clinical assays. Currently, these simulations do not include the contribution of the anticoagulant protein C pathway, the contribution of platelets, the contact pathway or the vasculature. In general, our theoretical approach allows us to evaluate the impact of each individual factor to the overall fXa or thrombin generation profile which may not be obvious using standard multivariate methods. We have previously shown that it is the integration of an individual's blood composition data at any one time into an assessment of that individual's thrombin generation potential that can be used to discriminate between various hemostatic states Since thrombin is the key enzyme in Tf-initiated coagulation, but requires fXa formation and function for its generation, targeting fXa generation for analysis should increase our knowledge regarding the results that are seen in thrombin generation profiles. This is the first study that we are aware of that compares fXa generation to thrombin generation in the same population. To date, available diagnostic methods for fXa generation include chromogenic, immunological and clot based assays. Recently, a large focus has been on developing better methods for detecting fXa generation with increased sensitivity th percentile and was 15.7 for MaxL. These data are consistent with the established link between oral contraceptive use and thrombotic risk in vitro fertilization, had increased thrombin generation profiles The most dramatic separation of fXa generation profiles in was between users and non-users of oral contraceptives in control premenopausal women. The increased rate and ultimate levels of fXa generated suggests an extremely primed procoagulant response. The OR was similar to thrombin generation in this group e.g. factor deficiencies or factor over expression). These outliers appear to be obvious candidates in efforts to identify at risk individuals in the healthy population. However, most healthy individuals have relatively similar thrombin generation profiles, whether thrombin formation is evaluated by empirical or computational methods. The significance of small differences in these thrombin profiles is unclear, in part because an operational definition establishing what magnitude of difference is required to signal a consequence to an individual's hemostatic balance, and thereby his/her immediate or long term health, is lacking. An alternative approach for discriminating among these “thrombin similar” individuals may be to use additional analyte(s). This study potentially identifies the utility of fXa generation as a segregating tool for investigating groups of such individuals.There is considerable interest in developing methods of analysis that relate an individual's thrombin generation profile derived directly from Tf-initiation of their plasma sample to their current or future risk of thrombosis One limitation of the computational approach is the expense of factor analysis. We have previously reported that differences in thrombin generation between some populations may depend only on a subset of factors, all varying within the normal range e.g. Tf-stimulus, antithrombotics, oral contraceptives). These approaches can ultimately aid in the development of individualized therapies. These studies illustrate the importance of fXa specifically and prothrombinase as a therapeutic target.Utilizing plasma composition based modeling we can begin to develop individual blood response profiles that can be used to evaluate how an individual responds to varying triggers (


Much work has been performed on understanding the effects of additives on protein thermodynamics and degradation kinetics, in particular addressing the Hofmeister series and other broad empirical phenomena. Little attention, however, has been paid to the effect of additive-additive interactions on proteins. Our group and others have recently shown that such interactions can actually govern protein events, such as aggregation. Here we use dendrimers, which have the advantage that both size and surface chemical groups can be changed and therein studied independently. Dendrimers are a relatively new and broad class of materials which have been demonstrated useful in biological and therapeutic applications, such as drug delivery, perturbing amyloid formation, etc. Guanidinium modified dendrimers pose an interesting case given that guanidinium can form multiple attractive hydrogen bonds with either a protein surface or other components in solution, such as hydrogen bond accepting counterions. Here we present a study which shows that the behavior of such macromolecule species (modified PAMAM dendrimers) is governed by intra-solvent interactions. Attractive guanidinium-anion interactions seem to cause clustering in solution, which inhibits cooperative binding to the protein surface but at the same time, significantly suppresses nonnative aggregation. Understanding how solution components interact with proteins and modulate biological processes is essential in applications such as researching methods for stabilizing protein based therapeutics and treating neurodegenerative diseases resulting from amyloid formation When considering the behavior of solution additives or macromolecular species with charged surfaces, the influence of the counterion is often overlooked. The influence individual ions have on protein stability has been empirically ranked for more than a century in the well-known Hofmeister Series Here, we present a study which shows that attractive guanidinium-anion interactions strongly influence the solution behavior of guanidinium modified PAMAM dendrimers. As with other similar compounds, the guanidinium chloride (GdmCl) form disrupted attractive protein-protein interactions at low concentrations but reduced thermostability, which led to enhanced aggregation. However, the aggregation suppression by the sulfate and dihydrogen phosphate salt forms was more significant and observed at all concentrations. They slowed the rate of aggregation of model proteins  to about 2% of the original aggregation rate at concentrations as low as 0.2 mol/L, which is around 10 times slower than when in the presence of arginine HCl or other aggregation suppressing excipients . Preferential interaction and computational studies of the modified generation 0 dendrimer salts in α-Chymotrypsinogen A aqueous solutions establish that attractive ion-ion interactions alter how the dendrimers interact with each other and with proteins through the formation of clusters. Such behavior is also observed for the unmodified ammonium surface but to a much lesser degree The most notable consequence of perturbing a protein's environment is the enhancement or inhibition of protein aggregation These results are comparable to other large compounds with surfaces modified to Gdm, which exhibit a strong interaction with proteins that results in destabilization at moderate to high concentrations 2PO4) has an ability to slow the rate of aCgn aggregation far beyond that of ArgHCl. To elaborate, with no cosolute present, half of the original amount of protein is lost within only 30 minutes. When in the presence of a high concentration of ArgHCl (0.67 M), the half-life is extended to about 2.5 hours, which is similar to other commonly used excipients. However, when in the presence of the H2PO4 form of the surface modified dendrimer at a concentration of 0.2 M, the half-life is extended to about 25 hours  was used as another model protein for the aggregation study, which demonstrated that the shelf-life of this protein at pH 6.5 and 37°C is extended by a factor greater than 15 when in the presence of the generation 1 dendrimer with a guanidinium sulfate surface  of the compounds were used and Shelf-Life Extension Factor values for commonly used excipients at isotonic concentrations are shown as well. Shelf-life values of aCgn were determined at 52.5°C and at 37°C for Con A. It is clear from these results, that when utilized at a practical maximum concentration, the surface modified dendrimers, in the form of either a dihydrogen phosphate or sulfate salt, significantly improve the shelf life of these two proteins, either at high or moderate temperatures. For aCgn, the shelf life is extended by a factor between 16 and 27 when formulated with these dendrimers, which is 5 to 8 times longer than when in the presence of other aggregation suppressing additives, such as arginine HCl, sucrose, or sodium sulfate.Improved aggregation suppression is also observed when the counterion is exchanged to other hydrogen bond accepting anions. When exchanged to acetate, aggregation suppression is only improved slightly, which was anticipated given that acetate cannot form as many hydrogen bonds with Gdm as compared to other ions face see , which iThe results for Con A are more significant and show a much clearer relationship with the size of the dendrimers. At 37°C and pH 6.5, Con A aggregates quite rapidly. Commonly used additives can extend the shelf life, at most, by factor of only 1.5. The sulfate form of the generation 0 modified dendrimer can quadruple this to a factor of 5.9 and the sulfate form of the generation 1 modified dendrimer extends the shelf life even further, by a factor of 16.7. The phosphate form only shows a minimal improvement in the shelf life, likely due to Con A being very sensitive to ionic strength at pH 6.5 because arginine HCl, sodium chloride, and sodium phosphate all speed up Con A aggregation. The sulfate form of the dendrimers has fewer ions per mole than the phosphate form and therefore, the detrimental effect imparted on proteins that are sensitive to ionic strength is lessened when this form of the dendrimer is used.It should be noted that these results do not extend completely to the original, unmodified dendrimer structure, which has an ammonium surface. The chloride form of the unmodified dendrimer is more destabilizing and the phosphate form is less effective at suppressing aggregation see . This inm) from DSC scans, which is a qualitative indicator of how the conformational stability of the protein is perturbed 2PO4 salt forms of the surfaced modified generation 0 dendrimers increase Tm at a rate of 15.2 and 37.4°C*M−1, respectively, for concentrations less than 0.2 mol/L, while the chloride salt form decreases Tm at a rate of 13.9°C*M−1. One can speculate that this indicates that the sulfate and H2PO4 salt forms shift the protein folding equilibrium toward the native structure while the chloride salt form promotes unfolding. However, given that the unfolding of aCgn is irreversible, it could also indicate that the sulfate and H2PO4 salt forms reduce the rate at which aCgn aggregates during the DSC scan. The apparent thermodynamic stabilization by these forms is quite significant when compared to other conformational stabilizers (e.g. sucrose) The thermostability of aCgn at 1 mg/mL in the presence of the modified dendrimers was assessed by determining the denaturation midpoint temperature  is nearly an order of magnitude more than that for the chloride form (∼20), leading to numerous bridged interactions (73 to 124), which is almost nonexistent for the chloride form. These guanidinium-anion and bridged interactions have a direct impact on the number of dendrimer-protein interactions, reducing the number by nearly half when compared to the chloride form. The extent of clustering in these solutions can also be quantified in terms of the loss of the solvent-accessible area (SAA) of dendrimer molecules, as shown in 2PO4 (∼60%), followed by sulfate (∼40%) and chloride (20%). In the case of chloride, the loss of SAA is mainly due to the presence of counterions near the dendrimer. For sulfate and H2PO4, the dominant component to the loss of SAA is due to the overlap of dendrimer molecules. The number of H2PO4 ions is twice the number of sulfate ions per dendrimer molecule, which contributes to the higher loss of SAA as compared to sulfate.These results also show that counterions can act as a bridge between dendrimer molecules due to attractive guanidinium-anion interactions, leading to the formation of large clusters in solution see . To veri23, values at various concentrations were determined, both experimentally via vapor pressure osmometry (VPO) measurements, and computationally via MD simulations. The experimental results for the interaction between modified generation 0 PAMAM dendrimers and aCgn are expressed in To gain insight into how the modified dendrimer salts inhibit protein-protein interactions, preferential interaction coefficient, Γ23 values for salts are a weighted average of the Γ23 values for individual ions. Γ23 for the dendrimer cation was found to have a positive value of 1, which shows that the local concentration of dendrimer molecules around the protein is higher than the bulk concentration. However, due to the negative preferential interaction value for the chloride ion (−7), the overall preferential interaction coefficient was found to be negative. The observed preferential binding of the dendrimer cation stems from the fact that the modified dendrimers can interact favorably with a variety of amino acids on the protein surface due to the presence of the Gdm group, which can form hydrogen bonds with negatively charged amino acids and the protein backbone and can also interact with aromatic amino acids via cation-π interactions. Furthermore, the dendrimer molecule can bind cooperatively with the protein surface due to multiple Gdm surface groups simultaneously interacting with the protein surface  and H2PO4 (−2.3) salt forms match well with their corresponding experimental values. The values of Γ23 for the counterions are −10 for the H2PO4 ion and −7 for the sulfate ion, which is present in half the quantity as the phosphate and chloride ions. On the basis of the observed attractive interaction between the dendrimer and these counterions, it can be argued that sulfate and H2PO4 inhibit the dendrimer molecule from binding to the protein surface. In essence, the dendrimer molecules are pulled away from the surface to interact with bulk solution components. This is verified by the individual Γ23 values for the dendrimer molecule . These results are similar to the results of our recent work on the interaction of arginine with proteins, where the carboxylate group and various counterions limited the interaction between a protein and the Gdm group in arginine Γ2PO4 salt forms. This result further supports that for the dendrimer with a GdmCl surface, multiple arms simultaneously interact with the protein surface but for the sulfate and H2PO4 salts, only one arm can interact with the protein while the other arms face away from the surface and interact with the bulk solution. Furthermore, for the sulfate and H2PO4 salts, there are additional peaks further away from the surface for the closest dendrimer arm, which is the result of the anions acting as a bridge between the protein and the dendrimer. This interaction with the Gdm group is clearly impeding direct binding of the dendrimer to the protein surface.RDF's between the four dendrimer arms and the protein surface see  highlighThe original intent of this work was to determine if attractive protein-protein interactions could be inhibited by large additives which tend to crowd the local domain around proteins rather than being excluded to the bulk solution From these results, it is obvious that volume exclusion effects , rather than multiple simultaneous interactions (i.e. multiple strong guanidinium-protein peaks), a behavior more consistent with the predicted outcome of making large compounds with protein-binding groups. The ion-ion interactions described here have only recently been taken into consideration in describing the solution behavior of different guanidinium salt forms. The MD simulations performed verify that attractive ion-ion interactions bridge together multiple dendrimer molecules into clusters. This clustering leads to three important impications: (i) these clusters enhance the effective size of the additives in solution, with the size of the additive determining its ability to crowd out protein-protein interactions, (ii) these clusters are expected to reduce the mobility of the proteins in solutions due to a network of large hydrogen-bonded clusters around them , the large molecules are capable of significantly reducing the rate of aggregation at all concentrations. This stems from the behavior resulting from attractive guanidinium-anion interactions, which are lacking for the guanidinium chloride modified dendrimers. As indicated during molecular simulation snapshots, attractive guanidinium-sulfate/H2PO4 interactions cause dendrimer molecules to form clusters in solution and in return, inhibit multiple dendrimer arms from simultaneously binding to the protein, as indicated by radial distribution function plots. This reduced level of preferential binding producing a scenario in which the additive clusters solvate the surface of the protein, which reduces protein-protein interactions, without promoting unfolding. The elucidation of this particular type of additive gives insight into the behavior of PAMAM dendrimers in general, but more importantly, it demonstrates the role additive-additive interactions play in proteins stability.In conclusion, using α-Chymotrypsinogen and Concanavalin A as a model proteins, we investigated the aggregation suppressing performance of PAMAM dendrimers with surfaces modified to a variety of guanidinium salts and give a molecular level mechanistic insight into the behavior of this new class of additive. The most significant observation was that attractive additive-additive interactions dominated the behavior of the dendrimer molecules. The results presented indicate that all of the dendrimers form an attractive interaction with aCgn, leading to suppressed protein-protein interactions, which is more significant than other additives due to the size of the dendrimer molecules. The dendrimers with guanidinium chloride surfaces suppressed aggregation at low concentrations but DSC scans indicate that the additive promotes aCgn unfolding, leading to enhanced aggregation at high concentrations. Under conditions when the conformation of aCgn is not destabilized , and jack bean Concanavalin A (C2010) were obtained from Sigma-Aldrich . All other reagents were obtained from Sigma-Aldrich in the highest available grade. The concentration of aCgn and Con A were determined spectrophotometrically using extinction coefficient of 1.97 mL*mgtert-butoxycarbonyl)-2-methyl-2-thiopseudourea in dimethylformamide (DMF). The DMF was evaporated and the residue dissolved in diethyl ether. The product was purified by repeated precipitation with n-hexane. The BOC protecting groups were removed with 4 M HCl dissolved in dioxane and the resulting salts were washed with acetone. For alternate salt forms, the counterions were exchanged using Amberlite IRA 400 anion exchange resin loaded using the appropriate sodium salt. The purity and structure were analyzed with NMR and mass spectrometry.The PAMAM dendrimer surface amine groups were guanylated with an excess of 1,3-Bis size exclusion column and a UV-Vis detector.The thermodynamic stability of 1 mg/mL solutions of aCgn in the presence of the modified dendrimers was determined by differential scanning calorimetry  using a scan rate of 90°C/hour.Molecular dynamics (MD) simulations of aqueous solutions of the guanidinium modified generation 0 dendrimer salts with and without aCgn (PDB Id: 2CGA) were performed using NAMD 2.7 23) values were calculated using a statistical mechanical method applied to and all-atom model with no adjustable parameters Theoretical preferential interaction coefficient Click here for additional data file.Figure S123) of α-Chymotripsinogen A in aqueous dendrimer (GdmCl surface) salt solution.Convergence of preferential interaction coefficient Click here for additional data file.Figure S2The influence of generation 0 PAMAM dendrimers, with surfaces modified to guanidinium, on aCgn monomer loss due to aggregation at 52.5°C. The figure depicts aCgn monomer concentration, M, versus time relative to the initial monomer concentration, M0, of 10 mg/mL, with all solutions prepared in a 20 mM sodium citrate pH 5 buffer and all profiles fitted to a 2nd order rate law. (A) Monomer loss profiles for solutions containing the guanidinium chloride salt form at varying concentrations. (B) Monomer loss profile for a solution containing the guanidinium dihydrogen phosphate salt form at a concentration of 0.2 M. The profiles for a solution containing no additive and a solution containing arginine hydrochloride at a concentration of 0.67 M are included for comparison.(TIFF)Click here for additional data file.Figure S3Preferential Interaction Coefficient, Γμ3, values versus additive concentration. Values are for the interaction between generation 0 PAMAM dendrimers, with surfaces modified to guanidinium, and aCgn, as determined from VPO measurements. Error bars left off for clarity and curves drawn through the plots to aid the eye (see (TIF)Click here for additional data file.Figure S4Snapshots of PAMAM dendrimer and counter-ions within 0.6 nm of the protein surface. Guanidinium chloride surface (left) and Guanidinium sulfate surface (right).(TIF)Click here for additional data file.Table S1Setup of simulation systems.(DOCX)Click here for additional data file.Table S2V), preferential interactions with aCgn as determined by VPO, and aCgn denaturation midpoint temperature increment as determined by DSC for surface modified PAMAM dendrimers.Summary of additive molecular weight, partial molar volume ((DOCX)Click here for additional data file.


The unique properties of microporous zeolites, including ion-exchange properties, adsorption, molecular sieving, catalysis, conductivity have been exploited in improving the performance of gas sensors. Zeolites have been employed as physical and chemical filters to improve the sensitivity and selectivity of gas sensors. In addition, direct interaction of gas molecules with the extraframework cations in the nanoconfined space of zeolites has been explored as a basis for developing new impedance-type gas/vapor sensors. In this review, we summarize how these properties of zeolites have been used to develop new sensing paradigms. There is a considerable breadth of transduction processes that have been used for zeolite incorporated sensors, including frequency measurements, optical and the entire gamut of electrochemical measurements. It is clear from the published literature that zeolites provide a route to enhance sensor performance, and it is expected that commercial manifestation of some of the approaches discussed here will take place. The future of zeolite-based sensors will continue to exploit its unique properties and use of other microporous frameworks, including metal organic frameworks. Zeolite composites with electronic materials, including metals will lead to new paradigms in sensing. Use of nano-sized zeolite crystals and zeolite membranes will enhance sensor properties and make possible new routes of miniaturized sensors. ZeoliThere are several physical and structural features of zeolites that have been exploited for sensing. The versatility of these materials is evident from the fact that the zeolite can be used to improve the performance of an existing sensor, as well as zeolites have been used as the sensing medium. We briefly discuss these properties, followed by examples detailing their use in sensor development.The internal microporosity of zeolites that gives rise to the high surface area also provides sites for adsorption of molecules. Upon adsorption, mass changes as well as optical properties are altered, which have been used for the sensor transduction. Selectivity towards analytes has also been observed by adsorption of species within a zeolite. Microporous spaces within the zeolite also serve as a host for guest species. Optical/electrical properties change of the guest species upon interaction with gases has been used for sensing.n-butane can enter through the pore openings of calcium-exchanged zeolite A , quartz crystal microbalance (QCM) and microcantilevers have employed the zeolite layer as a functional element. When the zeolite layer adsorbs the gas molecules from the surrounding, the mass changes (Δm) per unit surface/volume results in a proportional frequency shift (Δf), which can be measured:Zeolite films were grown on piezoelectric sensor devices  via seeding of nanocrystals followed by secondary growth. Both zeolite A and zeolite beta of thicknesses <1,000 nm have been grown on QCM ,11. Thes+-ZSM-5 on QCM, a detection limit of 0.26 ppm of acetone with a response time of 90 s was reported  preferred-orientation silicalite-1 layer. Improved sensitivity was proposed to arise from preferential adsorption of ethylene into the silicalite-1, as well as the fact that the oriented silicalite-1 preconcentrates the ethylene into its vertical channels.An excellent example of the improved selectivity by employing zeolite layer is provided by silicalite-1 on SnOof water . Also, b3.2.2 and CO2 with a bias voltage of 1.8 V [2 and CO2 decreased, but more so for CO2. It was suggested that the preferable diffusion of O2 through zeolite led to this effect. The preferential selectivity of oxygen over CO2 could not be directly correlated to molecular sieving , but diffusional aspects of the zeolite film were considered important.An amperometric sensor made with a zeolite A coating on a Pt-yttria stabilized zirconia used as sensing and reference electrodes (zeolite coating on only one electrode) and yttria stabilized zirconia as electrolyte exhibited response to both Oof 1.8 V . Upon co4.There are many examples where the catalytic properties of zeolites have been exploited to promote sensor performance, with a variety of gas-zeolite interactions towards this goal, as explained below.4.1.n-alkanes. Using catalytic Cr-zeolite Y and Cr-zeolite beta film coated on semiconducting chromium titanium oxide, the cracking patterns and shape and size effects were found to improve selectivity to linear alkanes [7-C10) as analytes. Chromatographic (GC-MS) studies showed that over Cr-zeolite Y, heptanes was unreactive, nonane completely reacts making methylpropanol and water, whereas octane and decane produce small linear and branched chain alkanes (<C6). In keeping with these results, the nonane produced a response with chromium titanium oxide that was distinct from the other alkanes. With Mo-zeolite Y, the products were primarily water, aldehydes and ketones, with acetaldehyde as the primary product, indicating that cracking and partial oxidation of the n-alkanes is dominant. The sensing properties observed for the n-alkanes were arising from the reactivity of the reaction products on the chromium titanium dioxide, rather than the analytes themselves. We will see other examples below, where the analyte has been modified chemically and the resulting product used for the sensor response, establishing a new paradigm for sensing.Acid sites in zeolite promote cracking reaction of  alkanes ,30. Two 0.8Fe0.2O3-δ led to selective detection of propane with discrimination against H2, NO and CO [Pt-doped ZSM-5 as a cover layer on semiconducting SrTiO and CO . Besides2. It was reported that the Pt-zeolite-TiO2 composite exhibited selectivity to propane and discriminated against CO. It was proposed that the resistance change of TiO2 was arising from interaction with water, the oxidation product of propane catalyzed by the embedded Pt zeolite within the TiO2 [Noble metals are often added to semiconducting metal oxides to promote selectivity and sensitivity for gas detection. At high temperatures of operation, there is the possibility of interaction of the metal with the sensing metal oxide, thereby altering its electrical properties. In order to avoid this interaction, the Pt was included in the supercages of zeolites Y, which was then mixed with the semiconducting oxide, TiOthe TiO2 .3 as the sensing oxide layer, it was reported that the use of H-ZSM5 as an overlayer led to a 19 fold increase in sensor response to NO2 as compared to an unmodified sensor. Tests were also done with acetone, with the best results obtained for Cr-zeolite layer. It was proposed that the Cr-zeolite  catalytically converted the acetone to a reaction product that was more sensitive on the WO3 sensor. The H-ZSM5 did not exhibit an increase in sensing response to acetone [With WO acetone .4.2.x, in a complex background of other gases. NOx is primarily present as NO/NO2 and often the interest is in determining total NOx (NO + NO2). There are many examples of potentiometric devices that can detect NO and NO2. However, since NO gets oxidized and NO2 gets reduced, their response are opposite, and mixtures of NO and NO2 will lead to cancellation of signal, making calibration impossible. Using a Pt-zeolite Y layer separated and ahead of a potentiometric (yttria stabilized zirconia (YSZ) as electrolyte) sensor  than the zeolite layer (T1), and the signal is a measure of total NOx (NO + NO2) [In all these examples discussed, the zeolite layer is in contact with the active oxide sensing layer. It is likely that the sensing semiconducting surface is altered because of the interaction with the zeolite, especially at the high temperatures often necessary for sensing. Another drawback for such design lies in that the physiochemical filters have to work at the same temperature as the active sensing layers which can limit the performance of either the filters or the sensing layers, since both necessarily do not have the same optimum operating temperature. The concept of separating the zeolite layer from the sensor has led to a new paradigm in sensing. This is best demonstrated for detection of NO) sensor , the NOxO + NO2) –38.2. The results from such a device is shown in 2 provide similar signals. The use of the zeolite catalytic layer was also shown to reduce interference to CO, C3H8 and ammonia, gases expected to be interferents in combustion-based processes, such as lean burn engines. The active sensing unit YSZ is a mixed-potential type sensor working in accordance with an asymmetrical open circuit potential as the analyte reacts at the triple-phase-boundary with oxygen at each electrode. One of the electrodes is Pt covered with Pt-Na zeolite Y (reference) and the other one was covered with tungsten oxide (sensing) which was reported to exhibit a good sensitivity to NO. The reference Pt-Na zeolite Y equilibrates the NO and NOx sensors has been in human breath analysis for diagnosis of asthma [x sensor discussed above  for equilibration. This imbalance in equilibration can also be accomplished by applying a voltage across the working/reference electrodes, and the ensuing chemical equilibration at the sensor leads to a measurable current. Using this strategy, an amperometric sensor has been reported [In the zeolite-based potentiometric design –38, the reported .x (NO + NO2) can be measured, and interference to gases like CO and hydrocarbons are eliminated. x. Stability tests for such sensors were reported over a 30-day period, and indicted that the background signal approached steady state after 15 days.With the use of a zeolite layer covering the sensor, total NOx sensor just discussed with a reference-free O2 sensor has led to a bifunctional total NOx/O2 sensor, which measures NOx and O2 simultaneously [2 as compared to the amperometric device, as shown in The combination of the potentiometric and amperometric NOaneously . To the 4.3.Catalytic reactions inside zeolite cages that lead to light emission have been developed as sensors. Examples include detection of acetaldehyde via reaction with O atoms in zeolite supercages, with discrimination against other aldehydes, such as formaldehyde, cinnamaldehyde, glutaraldehyde and benzaldehyde . The seln-hexane was found to be 0.55 μg/mL and minimal interference was noted from alcohols, other alkanes and aromatics. Using similar principles of light emission from an intrazeolitic catalytic reaction, a sensor for hexane has been reported . The bas4.4.Heat liberated during a catalytic reaction on a zeolite has been coupled with a micromachined thin-film calorimeter for selective sensing of analytes . With Pd5.There are reports of electrochemical and optical sensors that exploit the intrazeolitic space as a site for guest species which interact in specific ways with the analyte of interest.5.1.4. The response times for adsorption and desorption were ∼26.5 and 38.6 s with humidity change from 0 to 63.2% [By loading LiCl into stilbite with high and optimal dispersion, a conductive material was obtained that behaved as a humidity sensor with a dynamic range of 10to 63.2% .60 was deposited on zeolite Y films on a tantalum toothcomb type electrode, followed by thermal treatment to get the C60 into the zeolite cages [60 introduction into the zeolite led to observable conductivity under ambient conditions. In the presence of ethylene, these composites exhibited changes in conductivity. For recovery of the device to stable baseline values, evacuation was necessary, making it impractical for sensor use. Also, initial introduction of ethylene led to an abrupt current increase, explained as arising from temperature increase from heat of adsorption of ethylene into the zeolite.In another study, Cte cages . The zeo5.2.All of the other reported examples of zeolite host-guest sensors are optical in nature, in which changes of spectroscopic properties of dye molecules held in zeolite cages upon interaction with analytes were used as the transduction mechanism. The two analytes that have been examined are humidity and oxygen.Protonation/deprotonation of methylene blue in H-mordenite changes as a function of humidity and manifests itself as a change in the diffuse reflectance spectra of the composite, which has been exploited as a sensor for humidity in the 9–92% range ,49. Two Change in the diffuse reflectance spectra of the solvatochromic Nile red dye in zeolite NaY led to a sensor with detection limits of 200 ppm humidity and response times of ∼4 min. The discrimination against hexane was proposed as arising from its poor adsorption on the hydrophilic zeolite Y .For oxygen sensing, 2-hydroxymethyl anthraquinone in zeolite NaY provides a convenient visual probe because of color change with oxygen . This seThere are several reports exploiting the quenching of fluorescence of tris-bipyridine ruthenium held in zeolite cages as a measure of oxygen content. Dissolved oxygen as well as oxygen inside macrophage cells has been measured in this fashion ,53. Figu6.There are a number of recent reports on gas sensing exploiting the changes in conductivity of the extraframework cations in the zeolite. The manifestation of this change is measured by impedance spectroscopy, potentiometry, resistance and microwave methods.6.1.x reduction in the latest generation of lean burn engines, an impedance sensor using H-ZSM-5 has been reported [The interaction of basic molecules with intrazeolitic cations via Bronsted and Lewis acid-base reactions has been examined. Driven by the need for detecting ammonia used for NOreported .4+ ion. In the case of pyridine (0.60 nm) on zeolite beta and ZSM-5, the lower sensitivity in ZSM-5 was correlated with the lower mobility of pyridinium ions (factor of 5) in MFI (pore diameter 0.51–0.56 nm) as compared to zeolite beta (0.56–0.77 nm).The role of zeolite acidity and the zeolite architecture upon detection of basic molecules  using H-forms of ZSM-5, mordenite and zeolite beta has been reported . For mol2 atmosphere has been reported [2) was exploited in this study. Typically, metal oxide or polymer based sensors designed for humidity detection will not function in reducing environment or at high temperature. 2 at 400 °C in the presence of ppm levels of water (measurement frequency is 10 Hz). The decrease in impedance arises since the presence of water lowers the activation energy for proton motion within the zeolite.Using acid-base chemistry, a humidity sensor using H-ZSM-5 capable of operation at temperatures of 600 °C in Hreported . The exc+). Interaction of dimethylmethylphosphonate DMMP,  with mon2O3 on top of an ion-conducting Pt-doped ZSM-5 exhibited several semicircles, with a hydrocarbon dependent semicircle in the medium frequency range [2, but with interference to NH3. The proposed mechanism considered the interface between the Cr2O3/zeolite to be important rather than a filter effect from the zeolite or the semiconducting properties of Cr2O3. This interfacial effect between Cr2O3 and Pt-ZSM-5 (on the electrodes) that leads to the selective sensing of propane and propene is a novel observation. Later studies showed that the effect was not due to Cr2O3 on the zeolite layer, but Cr2O3 on the interdigitated Au electrodes produced during the photolithography process. A lower cost fabrication technology for the Cr2O3/zeolite sensor has also been proposed. A recent paper examined the mechanism at the Cr2O3/zeolite interface more carefully, and concluded that the hydrocarbon alters the charge carrier density in the Cr2O3 layer, which leads to the observed impedance changes [2O3 – zeolite – Cr2O3 – electrode, or electrode – zeolite – Cr2O3 – zeolite-electrode. For samples prepared with Cr2O3 bridging the two electrodes, the unique sensitivity to hydrocarbon was not observed. Modeling studies indicated that the reaction of hydrocarbons with the Cr2O3 alters the intrinsic carrier density, which alters the depletion zone within the zeolite and results in the observed impedance changes at low frequencies. The generality of this phenomenon was demonstrated with other semiconducting oxides.The other group of molecules that has been extensively studied are hydrocarbons –60. Impecy range   and Na+ inside the zeolite. ZSM-5 was examined as the zeolite, and optimum sensitivity to propane was obtained with high Si/Al ratio of the framework. Interaction of propane with intrazeolitic Na+ altered its activity and resulted in the observed voltage. The zeolite thin films exhibited faster response times as compared to pellets.Zeolites have also been examined as electrolytes for gas sensing, in particular hydrocarbons –64. Usin6.3.Zeolite films of differing Si/Al ratios were coated on interdigitated electrodes (10–50 μm spacing) and these devices were responsive to humidity, zeolites of higher Al content exhibiting the highest sensitivity . In orde6.4.The sensitivity for ethanol on H-ZSM-5, zeolite Y and mordenite exhibited a decrease with increasing Si/Al ratio of the framework . The mec6.5.x emissions via reaction with NH3 in heavy duty diesel trucks. Of considerable interest is the NH3 loading of the catalysts. It has been demonstrated that a microwave cavity perturbation method in which electromagnetic waves at specific frequency are reflected by the catalyst and subsequently measured provide a method to estimate the stored ammonia [Zeolite-based selective reduction catalysts (SCR) are being used for reducing NO ammonia . The basAs the ammonia loading of the zeolite changes, the electrical properties of the catalyst change and is reflected in a change in the propagation of electromagnetic waves. Cross sensitivity to water was minimized by choice of the resonant frequency peak.7.Quality and practicality of a sensor for a specific application is determined by its selectivity and sensitivity for the particular analyte. Long-term stability as well as reproducible fabrication of sensors is also critical, and often not satisfactorily reported in the literature. Other relevant aspects include operational conditions such as temperature, power requirements, footprint and ease of use. Based on the examples discussed in this review, most sensors were developed to serve the environmental industry and for optimizing combustion processes and only a few examples are found for the biomedical and food and agriculture industries. The impact of zeolites as physical and chemical filters has primarily been on enhancing selectivity, especially in the case of electrochemical sensors, which suffer often from poor selectivity. Considering the novel adsorption, diffusion and catalytic properties of zeolites, the zeolite as a selective filter is going to continue to find new applications. Newer sensing paradigms with zeolites have included chemical transformations which result in products that provide higher sensitivity than the analyte itself. This can be distinguished from sensor amplification due to preconcentration of analyte from the enhanced adsorptive property of the zeolite. With development of new microporous frameworks with novel compositions, signal enhancement will be an important feature for future applications. High throughput through very thin zeolitic films (nanometer dimensions) will improve response and recovery times. Oriented films also hold promise of rapid response. Interfacial effects in zeolite-based composites for improving sensor performance are another area that will lead to novel sensing effects. Nano sized zeolites have the potential to be incorporated into polymer packaging and can act as visual sensors, primarily for the food industry. Appropriate dye molecules need to be discovered. Intrazeolite cation mobility continues to hold promise for new applications, it is also one of the few examples that have led to commercial devices, e.g., for ammonia slip sensors. More commercial successes using zeolite incorporated sensors will accelerate development of this field. The high thermal and environmental stability of zeolites is in its favor for industrial applications. Zeolites also lends itself well for miniaturization and novel deposition and zeolite self-assembly techniques being developed will impact sensor development. Stabilization of metals, magnetic oxides, quantum dots in the nanoconfined spaces of zeolites provides opportunities for new sensors paradigms with optical or magnetic transduction methods. In summary, there appears to be considerable promise for zeolites to influence gas sensing through a variety of novel pathways, many of which have been articulated in this review article.


Current consensus definitions of Acute Kidney Injury (AKI) utilise thresholds of change in serum or plasma creatinine and urine output. Biomarkers of renal injury have been validated against these definitions. These biomarkers have also been shown to be independently associated with mortality and need for dialysis. For AKI definitions to include these structural biomarkers, there is a need for an independent outcome against which to judge both markers of functional change and structural markers of injury. We illustrate how sensitivity to need for dialysis and death can be used to link functional and structural (biomarker) based definitions of AKI. We demonstrated the methodology in a representative cohort of critically ill patients, in which an increase of plasma creatinine of >26.4 µmol/L in 48 hours or >50% in 7 days  had a sensitivity of 62% for death or dialysis within 30 days. In a development sub-cohort the urinary neutrophil-gelatinase-associated-lipocalin threshold with a 62% sensitivity for death or dialysis was 140 ng/ml . Using these thresholds in a validation sub-cohort, the risk of death or dialysis relative to those with no AKI by either definition was, for combined Structural-AKI and Functional-AKI 3.11 , for those with Structural-AKI but not Functional-AKI 1.51 (1.26 to 1.62), and for those with Functional-AKI but not Structural-AKI 1.34 (1.16 to 1.42). Linking functional and structural biomarkers via sensitivity for death and dialysis is a viable method by which to define thresholds for novel biomarkers of AKI. AKI is common and associated with increased in-hospital mortality, length of stay and subsequent development of chronic kidney disease A major limitation in the evaluation of injury biomarkers is that performance is usually only assessed by the ability to detect or predict increases in plasma creatinine, the same parameter used to diagnose AKI Although we do not yet know how much structural damage is required to reduce GFR acutely, we need an independent outcome against which to judge both markers of functional change and structural injury. The outcomes on which consensus is most likely are the requirement for dialysis and short-term mortality. While only dialysis links easily to AKI, there is increasing support for both direct and indirect linkage to death If structural biomarkers are to have clinical utility, then a suitable methodology needs to be developed to determine a clinically relevant threshold. We hypothesised that sensitivity for a significant outcome  is the ideal parameter for defining the threshold required for diagnosis of AKI by either a single structural or functional biomarker or for any combination of structural or functional biomarkers. We suggest that sensitivity for dialysis and death could provide the benchmark against which further enhancements should be judged. The use of sensitivity provides an independent parameter, to link these potentially disparate biomarkers. We chose, by way of example, to assess this methodology for a single candidate biomarker, urinary NGAL, by retrospective analysis of the well-documented dataset from the EARLYARF  trial. However, the method can equally be applied using any other candidate biomarker for structural injury in AKI.Patients from the EARLYARF trial in two intensive care units All biomarkers were measured at entry to ICU, and then at 12 and 24 hours. The maximum concentration for each patient was used in the analysis. Details of analysis have been reported previously The common outcome was need for dialysis or death in 30 days. In the development cohort the sensitivity of Functional-AKI was determined. The threshold concentration of the structural biomarker, urinary NGAL, which had the same sensitivity for this outcome was then determined. Patients with urinary NGAL above this threshold were deemed to have Structural-AKI. This threshold was then used to determine the proportions of patients with the outcome in both the development and validation cohorts. Urinary NGAL was chosen for this demonstration because it is the most studied of candidate biomarkers and has been evaluated to be independently associated with mortality and need for dialysis 2 test. All confidence intervals are 95%. To compare the development and validation cohorts a χ2 Goodness-of-fit test was applied. This required a logistic regression model predicting the outcome of [Cohort + AKI + Cohort*AKI], where Cohort equals 0 for the development cohort and 1 for the validation cohort, and the AKI cell equals 0 for no-AKI, 1 for Structural-AKI only, 2 for Functional-AKI only, and 3 for both Structural-AKI and Functional-AKI.Results are presented as means ± standard deviation, medians (interquartile range), or n (%). Comparisons were made for normally distributed variables by Students t-test, for non-normally distributed by Mann Whitney U test, and for categorical variables by χhttp://www.actr.org.au). Screening on entry to ICU was by presumptive consent, followed by written consent from the patient or family.The EARLYARF trial was approved by the multiregional ethics committee of New Zealand (MEC/050020029) and registered under the Australian and New Zealand Clinical Trials Registry  patients had Functional-AKI and 28 of these needed dialysis or died within 30 days. Seventeen patients without Functional-AKI also needed dialysis or died. The sensitivity was thus 62%.The threshold for urinary NGAL with 62% sensitivity for need for dialysis or death in 30 days was 140 ng/ml. We therefore defined Structural-AKI as NGAL >140 ng/ml. This threshold resulted in 26% (n = 38) of the no Functional-AKI (n = 144) being diagnosed as Structural-AKI . CompareThe validation cohort was similar to the development cohort. There was no difference in the proportions of Structural-AKI (urinary NGAL>140 ng/ml) and Functional-AKI to the development cohort  concentration reduces risk, illustrates additional potential benefits from this strategy. We consider it important that when applying this methodology to determine thresholds the biomarker reflects injury specific to the kidney and not some other disease, such as sepsis that may also be independently related to mortality. This may mean different thresholds in different cohorts, such as for urinary Cystatin C We proposed sensitivity of Functional-AKI for determining thresholds because early stage AKI management choices are low-risk (see KDIGO guidelines, figure four It is a point of debate whether mortality alone or mortality and dialysis need together should be the outcome measure used. We proposed the combined outcome of need for dialysis and mortality because of clinical relevance and the association of both Functional-AKI and kidney injury biomarkers with both dialysis and mortality. A rapidly increasing creatinine is clearly an indication for dialysis. Multiple biomarkers originating in the kidney have been shown to be associated with dialysis need As suggested by Siew et al there is a need to examine large datasets to assess the agreement or disagreement with creatinine data In conclusion, sensitivity to need for dialysis and death can be used to link and give equal weight to functional or structural biomarker-based definitions of AKI. This hypothesis awaits validation in large multicentre datasets.