Objectives: The current COVID-19 pandemic needs unconventional therapies to tackle the resulted high morbidity and mortality. Convalescent plasma is one of the therapeutic approaches that might be of benefit. Methods: Forty nine early-stage severe and critically-ill COVID-19 patients residing in RCU of three hospitals in Baghdad, Iraq were included, 21 received convalescent plasma while 28 did not receive, namely control group. Recovery or death, length of stay in hospital, and improvement in the clinical course of the disease were monitored clinically along with laboratory monitoring through SARS-CoV-2 RNA detection via PCR, and SARS-CoV-2 IgG and IgM serological monitoring. Results: Patients received convalescent plasma showed reduced duration of infection in about 4 days, and showed less death rate, 1/21 versus 8/28 in control group. In, addition, all of the patients received convalescent plasma showed high levels of SARS-CoV-2 IgG and IgM 3 days after plasma transfusion. Plasma from donors with high levels of SARS-CoV-2 IgG and donors with positive SRAS-CoV-2 IgM showed better therapeutic results than other donors. Conclusions: Convalescent plasma therapy is an effective mode of therapy if donors with high level of SARS-Cov2 antibodies are selected and if recipients were at their early stage of critical illness, being no more than 3 days in RCU.

This paper formulates the attitude filtering problem as a nonlinear stochastic filter problem evolved directly on the Special Orthogonal Group SO (3). One of the traditional potential functions for nonlinear deterministic complimentary filters is studied and examined against angular velocity measurements corrupted with noise. This work demonstrates that the careful selection of the attitude potential function allows to attenuate the noise associated with the angular velocity measurements and results into superior convergence properties of estimator and correction factor. The problem is formulated as a stochastic problem through mapping SO (3) to Rodriguez vector parameterization. Two nonlinear stochastic complimentary filters are developed on SO (3). The first stochastic filter is driven in the sense of Ito and the second one considers Stratonovich. The two proposed filters guarantee that errors in the Rodriguez vector and estimates are semi-globally uniformly ultimately bounded in mean square, and they converge to a small neighborhood of the origin. Simulation results are presented to illustrate the effectiveness of the proposed filters considering high level of uncertainties in angular velocity as well as body-frame vector measurements.

Objectives: COVID-19 patients suffer from the lack of curative therapy. Hence, there is an urgent need to try repurposed old drugs on COVID-19. Methods: Randomized controlled study on 70 COVID-19 patients (48 mild-moderate, 11 severe, and 11 critical patients) treated with 200ug/kg PO of Ivermectin per day for 2-3 days along with 100mg PO doxycycline twice per day for 5-10 days plus standard therapy; the second arm is 70 COVID-19 patients (48 mild-moderate and 22 severe and zero critical patients) on standard therapy. The time to recovery, the progression of the disease, and the mortality rate were the outcome-assessing parameters. Results: among all patients and among severe patients, 3/70 (4.28%) and 1/11 (9%), respectively progressed to a more advanced stage of the disease in the Ivermectin-Doxycycline group versus 7/70 (10%) and 7/22 (31.81%), respectively in the control group (P>0.05). The mortality rate was 0/48 (0%), 0/11 (0%), and 2/11 (18.2%) in mild-moderate, severe, and critical COVID-19 patients, respectively in Ivermectin-Doxycycline group versus 0/48 (0%), and 6/22 (27.27%) in mild-moderate and severe COVID-19 patients, respectively in standard therapy group (p=0.052). Moreover, the mean time to recovery was 6.34, 20.27, and 24.13 days in mild-moderate, severe, and critical COVID-19 patients, respectively in Ivermectin-Doxycycline group versus 13.66 and 24.25 days in mild-moderate and severe COVID-19 patients, respectively in standard therapy group (P<0.01). Conclusions: Ivermectin with doxycycline reduced the time to recovery and the percentage of patients who progress to more advanced stage of disease; in addition, Ivermectin with doxycycline reduced mortality rate in severe patients from 22.72% to 0%; however, 18.2% of critically ill patients died with Ivermectin and doxycycline therapy. Taken together, the earlier administered Ivermectin with doxycycline, the higher rate of successful therapy.

This paper introduces two novel nonlinear stochastic attitude estimators developed on the Special Orthogonal Group SO (3) with the tracking error of the normalized Euclidean distance meeting predefined transient and steady-state characteristics. The tracking error is confined to initially start within a predetermined large set such that the transient performance is guaranteed to obey dynamically reducing boundaries and decrease smoothly and asymptotically to the origin in probability from almost any initial condition. The proposed estimators produce accurate attitude estimates with remarkable convergence properties using measurements obtained from lowcost inertial measurement units. The estimators proposed in continuous form are complemented by their discrete versions for the implementation purposes. The simulation results illustrate the effectiveness and robustness of the proposed estimators against uncertain measurements and large initialization error, whether in continuous or discrete form.

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Ivermectin is an antiparasitic drug being investigated for repurposing to SARS-CoV-2. In-vitro, ivermectin showed limited antiviral activity and a COVID-19 animal model demonstrated pathological benefits but no effect on viral RNA. This meta-analysis investigated ivermectin in 18 randomized clinical trials (2282 patients) identified through systematic searches of PUBMED, EMBASE, MedRxiv and trial registries. Ivermectin was associated with reduced inflammatory markers (C-Reactive Protein, d-dimer and ferritin) and faster viral clearance by PCR. Viral clearance was treatment dose- and duration-dependent. In six randomized trials of moderate or severe infection, there was a 75% reduction in mortality (Relative Risk=0.25 [95%CI 0.12-0.52]; p=0.0002); 14/650 (2.1%) deaths on ivermectin; 57/597 (9.5%) deaths in controls) with favorable clinical recovery and reduced hospitalization. Many studies included were not peer reviewed and meta-analyses are prone to confounding issues. Ivermectin should be validated in larger, appropriately controlled randomized trials before the results are sufficient for review by regulatory authorities.

Two novel robust nonlinear stochastic full pose (i.e, attitude and position) estimators on the Special Euclidean Group SE (3) are proposed using the available uncertain measurements. The resulting estimators utilize the basic structure of the deterministic pose estimators adopting it to the stochastic sense. The proposed estimators for six degrees of freedom (DOF) pose estimations consider the group velocity vectors to be contaminated with constant bias and Gaussian random noise, unlike nonlinear deterministic pose estimators which disregard the noise component in the estimator derivations. The proposed estimators ensure that the closed loop error signals are semi-globally uniformly ultimately bounded in mean square. The efficiency and robustness of the proposed estimators are demonstrated by the numerical results which test the estimators against high levels of noise and bias associated with the group velocity and body-frame measurements and large initialization error.