Epidemiology, clinical presentation and management of COVID-19 associated mucormycosis: A single centre experience from Pune, Western India.

BACKGROUND: The second COVID-19 wave in India has been associated with an unprecedented increase in cases of COVID-19 associated mucormycosis (CAM), mainly Rhino-orbito-cerebral mucormycosis (ROCM). METHODS: This retrospective cohort study was conducted at Noble hospital and Research Centre (NHRC), Pune, India, between 1 April, 2020, and 1 August, 2021, to identify CAM patients and assess their management outcomes. The primary endpoint was incidence of all-cause mortality due to CAM. RESULTS: 59 patients were diagnosed with CAM. Median duration from the first positive COVID-19 RT PCR test to diagnosis of CAM was 17 (IQR: 12,22) days. 90% patients were diabetic with 89% having uncontrolled sugar level (HbA1c >7%). All patients were prescribed steroids during treatment for COVID-19. 56% patients were prescribed steroids for non-hypoxemic, mild COVID-19 (irrational steroid therapy), while in 9%, steroids were prescribed in inappropriately high dose. Patients were treated with a combination of surgical debridement (94%), intravenous liposomal Amphotericin B (91%) and concomitant oral Posaconazole (95.4%). 74.6% patients were discharged after clinical and radiologic recovery while 25.4% died. On relative risk analysis, COVID-19 CT severity index ≥18 (p = .017), presence of orbital symptoms (p = .002), presence of diabetic ketoacidosis (p = .011) and cerebral involvement (p = .0004) were associated with increased risk of death. CONCLUSIONS: CAM is a rapidly progressive, angio-invasive, opportunistic fungal infection, which is fatal if left untreated. Combination of surgical debridement and antifungal therapy leads to clinical and radiologic improvement in majority of cases.

Efficient Channel Allocation using Matching Theory for QoS Provisioning in Cognitive Radio Networks.

The focus of research efforts in cognitive radio networks (CRNs) has primarily remained confined to maximizing the utilization of the discovered resources. However, it is also important to enhance the user satisfaction in CRNs by finding a suitable match between the secondary users and the idle channels available from the primary network while taking into consideration not only the quality of service (QoS) requirements of the secondary users but the quality of the channels as well. In this work, the Gale Shapley matching theory was applied to find the best match, so that the most suitable channels from the available pool were allocated that satisfy the QoS requirements of the secondary users. Before applying matching theory, two objective functions were defined from the secondary user's perspective as well as from the channel's perspective. The objective function of secondary users is the weighted sum of the data rate of the secondary users and the probability of reappearance of the primary user on the channel. Whereas, the objective function of the channel is the maximum utilization of the channel. The weight factors included in the objective functions allow for diverse service classes of secondary users (SUs) or varying channel quality characteristics. The objective functions were used in developing the preference lists for the secondary users and the idle channels. The preference lists were then used by the Gale Shapely matching algorithm to determine the most suitably matched SU-channel pairs. The performance of the proposed scheme was evaluated using Monte-Carlo simulations. The results show significant improvement in the overall satisfaction of the secondary users with the proposed scheme in comparison to other contemporary techniques. Further, the impact of changing the weight factors in the objective functions on the secondary user's satisfaction and channel utilization was also analyzed.

Analysis of Efficient Spectrum Handoff in a Multi-Class Hybrid Spectrum Access Cognitive Radio Network Using Markov Modelling.

Cognitive radio networks (CRNs) rely on sensing of the licensed spectrum of a primary network to dynamically ascertain underutilized portion of the spectrum, thus affording additional communication opportunities. In a CRN, a single homogeneous spectrum access, such as interweave only deprives the secondary users (SUs) of channel access during handoff, particularly at high primary network traffic. Therefore, providing quality-of-service (QoS) to multi-class SUs with diverse delay requirements during handoff becomes a challenging task. In this paper, we have evolved a Markov-based analytical model to ascertain the gain in non-switching spectrum handoff scheme for multi-class SUs employing hybrid interweave-underlay spectrum access strategy. To satisfy the QoS requirements of the delay-sensitive traffic, we have analyzed the impact of hybrid spectrum access scheme for prioritized multi-class SUs traffic. The results show substantial improvement in spectrum utilization, average system throughput and extended data delivery time compared to conventional CRN using interweave only spectrum access. This demonstrates the suitability of the proposed scheme towards meeting QoS requirements of the delay-sensitive SU traffic while improving the overall performance for delay-tolerant SU traffic as well.

Spectrum Handoff based on Imperfect Channel State Prediction Probabilities with Collision Reduction in Cognitive Radio Ad Hoc Networks.

The spectrum handoff is highly critical as well as challenging in a cognitive radio ad hoc network (CRAHN) due to lack of coordination among secondary users (SUs), which leads to collisions among the SUs and consequently affects the performance of the network in terms of spectrum utilization and throughput. The target channel selection mechanism as part of handoff process can play an enormously significant role in minimizing the collisions among the SUs and improving the performance of a cognitive radio network (CRN). In this paper, an enhanced target channel selection scheme based on imperfect channel state prediction is proposed for the spectrum handoff among the SUs in a CRAHN. The proposed scheme includes an improved frame structure that increases coordination among the SUs in the ad hoc environment and helps in organizing the SUs according to the shortest job first principle during channel access. Unlike the existing prediction-based spectrum handoff techniques, the proposed scheme takes into account the accuracy of channel state prediction; the SUs affected due to false prediction are compensated by allowing them to contend for channel access within the same transmission cycle and thus enabling them to achieve higher throughput. The proposed scheme has been compared with the contemporary spectrum handoff schemes and the results have demonstrated substantial improvement in throughput and extended data delivery time by virtue of the reduced number of collisions.