Because of the diverse etiologies of community-acquired pneumonia (CAP) and the limitations of current diagnostic modalities, serum procalcitonin levels have been proposed as a novel tool to guide antibiotic therapy. Outcome data from procalcitonin-guided therapy trials have shown similar mortality, but the essential question is whether the sensitivity and specificity of procalcitonin levels enable the practitioner to distinguish bacterial pneumonia, which requires antibiotic therapy, from viral pneumonia, which does not. In this meta-analysis of 12 studies in 2408 patients with CAP that included etiologic diagnoses and sufficient data to enable analysis, the sensitivity and specificity of serum procalcitonin were 0.55 (95% confidence interval [CI], .37-.71; I2 = 95.5%) and 0.76 (95% CI, .62-.86; I2 = 94.1%), respectively. Thus, a procalcitonin level is unlikely to provide reliable evidence either to mandate administration of antibiotics or to enable withholding such treatment in patients with CAP.
In recent years, the use of wireless sensor networks for vibration monitoring is emphasized, because of its capability to continuously monitor at hard-to-reach locations of complex machines. Low power consumption is one of the main requirements for the sensor nodes in continuous and long-term vibration monitoring. However, the power consumption of state-of-the-art wireless sensor nodes is significantly increased by wireless radio in continuously transmitting the raw vibration data to the base station. One of the ways to reduce the power consumption is to reduce the duty-cycle of wireless transmission. Accurately processing the vibration data on the sensor node and transmitting only the critical information, such as natural frequency, defective frequency and amplitude of the vibration, will not only reduce the amount of data transmitted but also the duty cycle of the wireless communication. It eventually leads to reduction of total power consumed by the sensor nodes. In this paper the capability of a sensor node to accurately process the real-time vibration data is analyzed and the corresponding power consumption is measured. In particular, impact-based analysis of real-time vibration data is performed by breaking complex signal-processing tasks into manageable segments on the sensor nodes to minimize algorithmic complexity while still meeting real-time deadlines of the algorithm. As a result, it is found that the accuracy of the on-node signal processing is comparable with conventional off-node monitoring methods, whilst reducing total power consumption.
Introduction: Spaceflight alters normal physiology of cells and tissues seen on Earth. Immune cells and signaling molecules appear to be particularly affected, resulting in changes in leukocyte populations, killing ability and effector function, and signaling molecule response. Akin to spaceflight, diabetes mellitus produces significant immune system dysfunction. Applying observations and interventions from spaceflight to conditions such as diabetes mellitus may help to identify new approaches that combat the high clinical and financial burden of terrestrial disease.Discussion: A literature review was conducted using PubMed, MEDLINE, and Google Scholar. Papers of immune cells conducted in space and studies on diabetes mellitus-related immune dysfunction were included. Broad themes of immunosuppression were seen in both spaceflight and diabetes mellitus. Effects on lymphocytes, neutrophils, eosinophils, monocytes, fibroblasts, growth factors, and inflammatory factors are presented.Conclusions: Immune responses to spaceflight and DM are inconsistent. The innate immune system responds similarly to spaceflight and DM. In contrast, the adaptive immune system responds differently to spaceflight than to DM. This difference may be the result of a glucocorticoid dominant response linked to innate suppression and a Th2 lymphocyte shift.Relevance: Diabetes mellitus causes major morbidity and mortality on Earth. Further research is needed to elucidate mechanisms behind these differences and develop countermeasures for immunosuppression in space with application towards diabetic therapy on earth. Furthermore, commercial spaceflight makes it all the more necessary to elucidate these mechanisms as civilian participants with diabetes mellitus or other immune-altering conditions may be space bound.
Ongoing advancements in Body Sensor Networks (BSN) have enabled continuous health monitoring of chronically ill patients, with the use of implantable and body worn sensor nodes. Inevitable day-to-day activities such as walking, running, and sleeping cause severe disruptions in the wireless link among these sensor nodes, resulting in temporary shadowing of wireless signals. These disruptions in the wireless link not only reduce the reliability of the network but also increase the power consumption. Both signal disruption and power consumption must be reduced in order to achieve long term monitoring of physiological signals in chronic patients. In this paper we propose a MAC protocol called DiNAMAC (Disruption tolerant reiNforcement leArning-based MAC), which is not only aware of the wireless link quality but also is aware of network resource availability and application requirements. DiNAMAC uses reinforcement learning to adapt the scheduling based on channel conditions and to prioritize data transmission and availability according to the application requirements. In addition, we design DiNAMAC based on a model-free learning technique to make it more practical in real-world applications. Our simulation results show that DiNAMAC performs better than conventional MAC protocols in terms of latency and throughput even with when the wireless link quality is challenged by large temporal variations.
Abstract Background Although studies have investigated the risk of second primary malignancies (SPMs) associated with lymphoma of various sites, limited studies have investigated this risk in patients with lymphoma originating within the ocular adnexa. We conducted a retrospective study to assess incidence of secondary malignancies in patients with a prior diagnosis of ocular adnexal lymphoma (OAL) and to determine latency periods and age-groups at increased risk for SPM occurrence. Methods Retrospective analysis was performed on data obtained from Surveillance, Epidemiology, and End Results (SEER) 9 database. Patients with an initial primary malignancy diagnosis of OAL between 1973 and 2015 were included in the study. Standardized incidence ratios (SIR) and excess absolute risks (EAR) compared to a SEER reference population with similar sex, race, age, and calendar year were computed for SPMs. Excess absolute risk is per 10,000 individuals; alpha of 0.05 was used. Results Of 1834 patients with primary ocular adnexal lymphoma, 279 developed a secondary malignancy during average follow-up of 110.03 months (+/− 88.46), denoting higher incidence than expected (SIR 1.20; 95% CI, 1.07 to 1.35; EAR 30.56). Amongst the primary lymphoma cohort, 98.7% (1810/1834) of patients had non-Hodgkin’s lymphoma and amongst those that developed secondary malignancies, 99.6% (278/279) had non-Hodgkin’s lymphoma. Patients exhibited increased incidence of lymphohematopoietic and non-lymphohematopoietic second malignancies and no secondary malignancies of the eye or orbit. Patients had increased incidence of secondary malignancies in the first year (SIR 2.07; 95% CI, 1.49 to 2.79; EAR 150.37) and 1–5 years following lymphoma diagnosis (SIR 1.24; 95% CI, 1.01 to 1.51; EAR 34.89). Patients with various OAL subtypes demonstrated differing patterns of site-specific and overall SPM risk. Conclusions Patients with prior diagnosis of ocular adnexal lymphoma possess increased risk of hematologic and non-hematologic secondary malignancies. Risk of secondary malignancy could vary by lymphoma subtype. Patients with ocular adnexal lymphoma may benefit from regular surveillance to promote early detection of second primary malignancies.
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