The distribution of lactate between the corpuscles and the plasma in the blood

Evidence has been accumulating in the laboratories of Henderson and Van Slyke to prove that blood is a physico-chemical system. The distribution of acid and basic ions in general and that of bicarbonate and chloride in particular have been studied in blood under varying conditions. Reduced and oxygenated blood under different conditions were subjected to different pressures of carbon dioxide and the behaviour of the various ions had been studied carefully. The Lactate ion, however, has not received the same attention by the workers in these laboratories. No doubt it is supposed that other acid ions behave like chloride in this respect. So far as the blood in resting subjects is concerned, the importance of chloride in the buffering mechanism is predominant, and the similar behaviour of other acid ions, lactate and phosphate, relatively small, for the amounts present in the blood are only 15 and 10 mg. per 100 cc. respectively as against 300 mg. of chloride. In the case of blood in fatigued subjects, though the phosphate is nearly doubled in concentration, its rôle may not be important; but the lactate which can on occasion increase to 200 mg. per 100 cc. may present a different case.The amount of lactate present in the blood has been estimated at 20 to 25 mg. per 100 co. in resting subjects.Recently H. Owles (1) and Noshi (2) have shown separately that the concentration of lactic acid in blood during rest is 10 to 12 mg. per 100 cc. In the literature (3) it is stated that the ratio of concentration of lactate in corpuscles to concentration of lactate in plasma is 0.60: Noshi gives the value 0.85 before and 0.45 after muscular exercise. He has not accounted for the different ratios obtained, but merely states that the plasma has a higher concentration than the corpuscles. The ratio of the concentration of lactic acid in corpuscles to that in plasma may differ in arterial and venous blood. Hill, Long and Lupton (4) in 1924 noticed that in human blood when the lactic acid has been liberated in the body as a result of muscular exercise the ratio is smaller; the concentration in the corpuscles is only about 43 per cent of that in the plasma. The ratio was not derived by direct estimation of lactic acid in the corpuscles but was calculated from the amounts of lactic acid present in the blood and in the plasma, assuming that the corpuscles make up 40 per cent of the whole blood. The different ratios for lactate arrived at by various workers may have some significance, especially in the light of the well -known phenomenon of the chloride shift. The unequal distribution of the lactate between plasma and corpuscles was not studied further by Hill, Long and Lupton, and the present work aims at a systematic study of the behaviour of lactate towards the corpuscles. The previous workers did not estimate the amount of lactic acid in the corpuscles directly. In the work presented here, however, this has been done.Recently Kerr (5) has noticed that the sodium and potassium ions can diffuse through the corpuscles, when they are suspended in Ringer solution; but the diffusion is hindered by the presence of serum. This peculiar property of serum needed to be studied in relation to the lactate ion. Therefore an attempt has been made to study the diffusion of lactate into the corpuscles, when they are suspended in serum and saline containing different amounts of sodium lactate.In addition, the present work takes into account the behaviour of the lactate in relation to muscle cells.Reference is often made to "lactate ratio" C/P and the ratio of concentration Of lactate in plasma to that in corpuscles, which mean, the ratio of the amount of lactate in 100 gms. of corpuscles to the amount of lactate in 100 gms. of plasma

CARDIOPROTECTIVE POTENTIAL OF PLANTS AND PLANT-DERIVED PRINCIPLES – A REVIEW

Cardiovascular diseases (CVDs) are a class of diseases involving heart or blood vessels. Coronary artery diseases include angina, myocardial infarction (MI), stroke, heart failure, hypertensive heart disease, cardiomyopathy, and arrhythmias. CVDs are the leading cause of death globally. Risk factors include high blood pressure, smoking, obesity, poor diet, blood cholesterol, and lack of exercise. It is approximated that 90% of CVDs is preventable. High blood pressure results in 13% of CVD deaths, whereas tobacco outcomes in 9%, diabetes and lack of exercise in around 6%, and obesity in 5%. Due to certain medications such as anticancer drugs like doxorubicin, adverse effects result in MI. Since ancient times, medicinal plants have been widely used in the treatment of diseases. This information may serve as a primer in identifying novel prophylactic as well as therapeutic studies of plant-derived principles. The parts of the plants such as seeds, leaves, flowers, roots, and bark contain these phytoconstituents which are obtained through different extraction processes. Phytoconstituents are broadly classified into alkaloids, saponins, polyphenols, essential oils, carotenoids, glycosides, omega fatty acids, and flavonoids. Each class is responsible for its own pharmacological effects. The underlying mechanism in which they exert the action is different. This review presents an overview of the MI and therapeutic strategies of plant-derived principles that are available to mitigate the effect of MI

An adaptive placement framework for efficient near-data stream processing over data source-edge-cloud systems

Large amounts of data are being generated across many different domains including datacenters, surveillance cameras, mobile devices and other Internet-of-things (IoT) systems. This data is generated as streams, i.e., an unbounded sequence of data items which needs to be processed in near real-time. Large-scale datacenters report generating up to 10s of PBs of monitoring data per day from hundreds of thousands of server nodes. High bandwidth video data at rates of 1.4 TB/hr or more, will be generated from autonomous vehicles and other IoT systems. Processing this data is challenging due to limited network and compute resources in the cloud. Utilizing compute resources closer to the data source helps reduce network transfer costs and alleviates the compute load on cloud nodes. However, near-data compute resources are limited. E.g. data source nodes may have a small fraction of compute resources (e.g., 1-2 CPU cores) for near-data processing compared to cloud servers. Analytics applications which process streaming data are also becoming complex and increasingly resource-intensive. Thus, all the analytics processing cannot be done on nodes near the data source, while using remote resources incurs network transfer costs. To alleviate the resulting compute-communication bottlenecks, applications are partitioned to reduce network transfer costs, by making placement decisions to split the execution of its operations between cloud and near-data resource nodes. Resource conditions are frequently changing in such systems, thus requiring the placement decisions to also adapt. As an example, monitoring queries on server nodes (i.e., data sources in monitoring pipelines) are co-located with foreground applications such as web services which have different compute resource needs based on incoming traffic requests. Thus, resources available to the monitoring queries change over time. Furthermore, compute resource needs of applications on edge and cloud nodes are also affected by changing input rates. E.g., surveillance camera applications consist of dynamic camera networks, with cameras being added or removed from the network to handle tasks such as object detection and target tracking. In this dissertation, we argue for the need to make highly efficient placement decisions to meet the application resource demands under fast changing resource conditions. Existing systems place application components between near-data resources and cloud nodes, with the goal to better utilize the available compute resources and improve application performance. However, they do not scale well to efficiently handle the increasing number of application operations/resource nodes in modern stream processing systems. Furthermore, placement techniques which optimize application power consumption on battery-driven data source nodes depend on accurate power prediction tools. Current tools typically rely on analytical power models based on hardware resource parameters, which are challenging to develop for modern hardware with complex and heterogeneous compute resources (e.g., multi-core CPUs, GPUs, TPUs). Moreover, modern application operations have complex power characteristics which depend on the input data stream. We address the above challenges by building a framework to make scalable placement decisions for near-data stream processing applications. We develop algorithms that identify effective placement decisions for application partitioning between near-data resources and the cloud. We build proof-of-concept systems to implement the proposed algorithms and validate their effectiveness in scaling to large workloads. The first algorithm focuses on optimizing the application’s end-to-end processing time, which is designed for making partitioning decisions between edges and cloud nodes. We map application operations to resource nodes on which they need to execute, using dynamic programming techniques to scale to large application graphs. The second system investigates the use of a fine-grained placement strategy on data source nodes, which can effectively utilize the limited compute resources on these nodes. An algorithm to quickly adapt the placement plan (in the order of seconds) is designed to handle fast changing resource conditions on data source nodes. Fast adaptation results from combining a query cost model-based and model agnostic heuristic to make placement decisions. Novel system-level abstractions are introduced in the stream processing pipeline to implement and evaluate the proposed placement algorithm. The third system is a first step towards extending our framework to support energy-aware placement decisions. Power cost of application operators is predicted without using complex hardware or software based analytical power models, which enables our system to be easily applied to devices with heterogeneous hardware resources. The power predictions can then be used to make energy-aware placement decisions.Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-08-01The student, Atul Sandur, accepted the attached license on 2022-07-15 at 12:02.The student, Atul Sandur, submitted this Dissertation for approval on 2022-07-15 at 12:42.This Dissertation was approved for publication on 2022-07-15 at 14:24.DSpace SAF Submission Ingestion Package generated from Vireo submission #18332 on 2022-11-15 at 21:40:2

eScope: A Fine-Grained Power Prediction Mechanism for Mobile Applications

Managing the limited energy on mobile platforms executing long-running, resource intensive streaming applications requires adapting an application's operators in response to their power consumption. For example, the frame refresh rate may be reduced if the rendering operation is consuming too much power. Currently, predicting an application's power consumption requires (1) building a device-specific power model for each hardware component, and (2) analyzing the application's code. This approach can be complicated and error-prone given the complexity of an application's logic and the hardware platforms with heterogeneous components that it may execute on. We propose eScope, an alternative method to directly estimate power consumption by each operator in an application. Specifically, eScope correlates an application's execution traces with its device-level energy draw. We implement eScope as a tool for Android platforms and evaluate it using workloads on several synthetic applications as well as two video stream analytics applications. Our evaluation suggests that eScope predicts an application's power use with 97% or better accuracy while incurring a compute time overhead of less than 3%

Curcumin and derivatives function through protein phosphatase 2A and presenilin orthologues in Dictyostelium discoideum.

Natural compounds often have complex molecular structures and unknown molecular targets. These characteristics make them difficult to analyse using a classical pharmacological approach. Curcumin, the main curcuminoid of turmeric, is a complex molecule possessing wide-ranging biological activities, cellular mechanisms and roles in potential therapeutic treatment including Alzheimer's disease and cancer. Here, we investigate the physiological effects and molecular targets of curcumin in Dictyostelium discoideum We show curcumin causes acute effects on cell behaviour, reduces cell growth, and slows multicellular development. We then employ a range of structurally related compounds to show the distinct role of different structural groups cell behaviour, growth, and development, highlighting active moieties in cell function, and showing that these cellular effects are unrelated to the well-known antioxidant activity of curcumin. Molecular mechanisms underlying the effect of curcumin and one synthetic analogue (EF24) were then investigated to identify a curcumin-resistant mutant lacking the protein phosphatase 2A regulatory subunit (PsrA) and an EF24-resistant mutant lacking the presenilin 1 orthologue (PsenB). Using in-silico docking analysis, we then show that curcumin may function through direct binding to a key regulatory region of PsrA. These findings reveal novel cellular and molecular mechanisms for the function of curcumin and related compounds

Complexity analysis for DROPLET: Distributed Operator Placement for IoT Applications Spanning Edge and Cloud Resources

Internet of Things (IoT) applications generate massive amounts of real-time data. Owners of such data strive to make predictions/inference from large streams of complex input such as video feeds, often by deploying applications that involve machine learning and image processing operations. A typical deployment of IoT applications includes edge devices to acquire the input data and provide processing/storage capacity closer to the location where the data is captured. An important challenge for IoT applications is deciding which operations to be executed on an edge device and which operations should be carried out on the cloud, in order to minimize the completion time of all operations. We call this the distributed operator placement problem. In this report, we show that the distributed operator placement problem is NP-complete. We then provide computational complexity analysis of a dynamic programming algorithm called Droplet, which is a heuristic to scalably partition operations in IoT applications across shared edge and cloud resources, while minimizing the completion time of all operations. Our analysis shows that Droplet scales log-linearly in the total number of operations.Ope

Obstructive Fibrinous Tracheal Pseudomembrane After Tracheal Intubation: A Case Report

Obstructive fibrinous tracheal pseudomembrane is a rare, but potentially fatal complication associated with endotracheal intubation. It has been known that the formation of tracheal pseudomembrane is related with intracuff pressure during endotracheal intubation or infectious cause. But in the patient described in this case, pseudomembrane formation in the trachea was associated with subglottic epithelial trauma or caustic injuries to the trachea caused by aspirated gastric contents during intubation rather than tracheal ischemia due to high cuff pressure. We report a patient with obstructive fibrinous tracheal pseudomembrane after endotracheal intubation who presented with dyspnea and stridor and was treated successfully with mechanical removal using rigid bronchoscopy

Curcumin―The Paradigm of a Multi-Target Natural Compound with Applications in Cancer Prevention and Treatment

As cancer is a multifactor disease, it may require treatment with compounds able to target multiple intracellular components. We summarize here how curcumin is able to modulate many components of intracellular signaling pathways implicated in inflammation, cell proliferation and invasion and to induce genetic modulations eventually leading to tumor cell death. Clinical applications of this natural compound were initially limited by its low solubility and bioavailability in both plasma and tissues but combination with adjuvant and delivery vehicles was reported to largely improve bio-availability of curcumin. Moreover, curcumin was reported to act in synergism with several natural compounds or synthetic agents commonly used in chemotherapy. Based on this, curcumin could thus be considered as a good candidate for cancer prevention and treatment when used alone or in combination with other conventional treatments

Curcumin: novel treatment in neonatal hypoxic-ischaemic brain injury

Hypoxic-ischaemic encephalopathy (HIE) is a major cause of mortality and morbidity in neonates, with an estimated global incidence of 3/1000 live births. HIE brain damage is associated with an inflammatory response and oxidative stress, resulting in the activation of cell death pathways. At present, therapeutic hypothermia is the only clinically approved treatment available for HIE. This approach, however, is only partially effective. There is therefore an unmet clinical need for the development of novel therapeutic interventions for the treatment of HIE. Curcumin is an antioxidant reactive oxygen species scavenger, with reported anti-tumour and anti-inflammatory activity. Curcumin has been shown to attenuate mitochondrial dysfunction, stabilise the cell membrane, stimulate proliferation, and reduce injury severity in adult models of spinal cord injury, cancer, and cardiovascular disease. The role of curcumin in neonatal HIE has not been widely studied due to its low bioavailability and limited aqueous solubility. The aim of this study was to investigate the effect of curcumin treatment in neonatal HIE, including time of administration and dose-dependent effects. Our results indicate that curcumin administration prior to HIE in neonatal mice elevated cell and tissue loss, as well as glial activation compared to HI alone. However, immediate post-treatment with curcumin was significantly neuroprotective, reducing grey and white matter tissue loss, TUNEL+ cell death, microglia activation, reactive astrogliosis and iNOS oxidative stress when compared to vehicle-treated littermates. This effect was dose-dependent, with 200µg/g body weight as the optimal dose-regimen, and was maintained when curcumin treatment was delayed by 60min or 120min post-HI. Cell proliferation measurements showed no changes between curcumin and HI alone, suggesting that the protective effects of curcumin on the neonatal brain following HI are most likely due to curcumin’s anti-inflammatory and antioxidant properties, as seen in the reduced glial and iNOS activity. In conclusion, this study suggests curcumin as a potent neuroprotective agent with potential for the treatment of HIE. The delayed application of curcumin further increases its clinical relevance