Utility of Platelet Indices as a Predictive Marker in Sepsis: An Observational Study From North East India.

Background Unraveling sepsis remains the holy grail of clinical medicine and the commonest cause of in-hospital mortality worldwide. Various newer biomarkers have emerged in recent years that aid in the diagnosis and prognostication of sepsis. However, the widespread use of these is limited by availability, cost, and long turnaround times. Considering the crucial role of hematological parameters in infectious conditions, the present study aimed to evaluate the association of various platelet indices with the severity and outcomes in patients diagnosed with sepsis. Methods This was a single-center, prospective, observational study comprising 100 consecutive patients who fulfilled the selection criteria in the emergency department of a tertiary care hospital from June 2021 to May 2022. All patients underwent history taking, physical examination, and necessary laboratory investigations, including complete blood counts, biochemistry panel, and radiographic and microbiological tests. A detailed assessment of various platelet indices (platelet count, mean platelet volume, and platelet distribution width) was performed, and its association with outcomes was derived. The Sequential Organ Failure Assessment (SOFA) score was recorded for all patients. Results The majority of the study population was male (52%) with a mean age of 48.05±19.27 years. Respiratory infection (38%) was the most common origin of sepsis followed by genitourinary infections in 27%. The mean platelet count on admission was 1.83±1.21 lakhs/mm3. The incidence of thrombocytopenia (<1.5 lakhs/ mm3) in our study sample was 35%. The overall in-hospital mortality of the study group was 30%. Thrombocytopenia was significantly associated with a higher SOFA score (7.4±3 vs. 3.7±1.9, P<0.05), longer hospital stays (10.8±4.6 vs. 7.8±3.9; p<0.05), and mortality (17 vs. 13; p<0.05). The change in platelet count, platelet distribution width, and mean platelet volume from Day 1 to Day 3 also correlated with outcomes. There was a decrease in platelet count among the non-survivors compared to an increase in platelet count among survivors from Day 1 to Day 3 (p<0.05). Similarly, the change in platelet distribution width showed a decreasing trend among the survivors compared to an increasing trend among the non-survivors (p<0.05). The mean platelet volume of non-survivors increased from Day 1 to Day 3 compared to a downward trend among the survivors (p<0.05). Conclusion Septic patients with thrombocytopenia on admission had a higher SOFA score and were associated with worse outcomes. Additionally, platelet indices, such as platelet distribution width and mean platelet volume, serve as important prognostic markers among sepsis patients. Change in these parameters from Day 1 to Day 3 also correlated with outcomes. These indices are simple and affordable, allowing for their serial assessment to aid in the prognosis of sepsis.

I-Health: SDN-Based Fog Architecture for IIoT Applications in Healthcare.

The Industrial Internet of Things (IIoT) has been introduced in an era of increasingly broad potentials in the medical industry. In recent years, IIoT-based healthcare applications have grown in popularity, with the majority of them relying on Wireless Body Area Network (WBAN) for flexibility. There have been a few recent works that have investigated SDN-based fog architecture for constructing smart healthcare systems. However, the best fog node from the fog layer must be identified and limit the transmission of unnecessary data. To address this issue, the Intelligent Software-defined Fog Architecture (i-Health) is developed in this work. Based on the prior data pattern of each patient, the controller will decide whether to send the data to the fog layer. Furthermore, we introduced the Fog Ranking Service (FRS) and Fog Probing Service (FPS) to select the best fog node. The performance comparison reveals that the proposed i-Health outperforms existing benchmark approaches.

Second-Order Probability Matching Priors for the Person Parameter in Unidimensional IRT Models.

In applications of item response theory (IRT), it is often of interest to compute confidence intervals (CIs) for person parameters with prescribed frequentist coverage. The ubiquitous use of short tests in social science research and practices calls for a refinement of standard interval estimation procedures based on asymptotic normality, such as the Wald and Bayesian CIs, which only maintain desirable coverage when the test is sufficiently long. In the current paper, we propose a simple construction of second-order probability matching priors for the person parameter in unidimensional IRT models, which in turn yields CIs with accurate coverage even when the test is composed of a few items. The probability matching property is established based on an expansion of the posterior distribution function and a shrinkage argument. CIs based on the proposed prior can be efficiently computed for a variety of unidimensional IRT models. A real data example with a mixed-format test and a simulation study are presented to compare the proposed method against several existing asymptotic CIs.

Design and analysis of a dynamic mobility management scheme for wireless mesh network.

Seamless mobility management of the mesh clients (MCs) in wireless mesh network (WMN) has drawn a lot of attention from the research community. A number of mobility management schemes such as mesh network with mobility management (MEMO), mesh mobility management (M(3)), and wireless mesh mobility management (WMM) have been proposed. The common problem with these schemes is that they impose uniform criteria on all the MCs for sending route update message irrespective of their distinct characteristics. This paper proposes a session-to-mobility ratio (SMR) based dynamic mobility management scheme for handling both internet and intranet traffic. To reduce the total communication cost, this scheme considers each MC's session and mobility characteristics by dynamically determining optimal threshold SMR value for each MC. A numerical analysis of the proposed scheme has been carried out. Comparison with other schemes shows that the proposed scheme outperforms MEMO, M(3), and WMM with respect to total cost.