StreamLearner: Distributed Incremental Machine Learning on Event Streams: Grand Challenge

Today, massive amounts of streaming data from smart devices need to be analyzed automatically to realize the Internet of Things. The Complex Event Processing (CEP) paradigm promises low-latency pattern detection on event streams. However, CEP systems need to be extended with Machine Learning (ML) capabilities such as online training and inference in order to be able to detect fuzzy patterns (e.g., outliers) and to improve pattern recognition accuracy during runtime using incremental model training. In this paper, we propose a distributed CEP system denoted as StreamLearner for ML-enabled complex event detection. The proposed programming model and data-parallel system architecture enable a wide range of real-world applications and allow for dynamically scaling up and out system resources for low-latency, high-throughput event processing. We show that the DEBS Grand Challenge 2017 case study (i.e., anomaly detection in smart factories) integrates seamlessly into the StreamLearner API. Our experiments verify scalability and high event throughput of StreamLearner.Comment: Christian Mayer, Ruben Mayer, and Majd Abdo. 2017. StreamLearner: Distributed Incremental Machine Learning on Event Streams: Grand Challenge. In Proceedings of the 11th ACM International Conference on Distributed and Event-based Systems (DEBS '17), 298-30

Convalescent plasma in patients admitted to hospital with COVID-19 (RECOVERY): a randomised controlled, open-label, platform trial

SummaryBackground Azithromycin has been proposed as a treatment for COVID-19 on the basis of its immunomodulatoryactions. We aimed to evaluate the safety and efficacy of azithromycin in patients admitted to hospital with COVID-19.Methods In this randomised, controlled, open-label, adaptive platform trial (Randomised Evaluation of COVID-19Therapy [RECOVERY]), several possible treatments were compared with usual care in patients admitted to hospitalwith COVID-19 in the UK. The trial is underway at 176 hospitals in the UK. Eligible and consenting patients wererandomly allocated to either usual standard of care alone or usual standard of care plus azithromycin 500 mg once perday by mouth or intravenously for 10 days or until discharge (or allocation to one of the other RECOVERY treatmentgroups). Patients were assigned via web-based simple (unstratified) randomisation with allocation concealment andwere twice as likely to be randomly assigned to usual care than to any of the active treatment groups. Participants andlocal study staff were not masked to the allocated treatment, but all others involved in the trial were masked to theoutcome data during the trial. The primary outcome was 28-day all-cause mortality, assessed in the intention-to-treatpopulation. The trial is registered with ISRCTN, 50189673, and ClinicalTrials.gov, NCT04381936.Findings Between April 7 and Nov 27, 2020, of 16 442 patients enrolled in the RECOVERY trial, 9433 (57%) wereeligible and 7763 were included in the assessment of azithromycin. The mean age of these study participants was65·3 years (SD 15·7) and approximately a third were women (2944 [38%] of 7763). 2582 patients were randomlyallocated to receive azithromycin and 5181 patients were randomly allocated to usual care alone. Overall,561 (22%) patients allocated to azithromycin and 1162 (22%) patients allocated to usual care died within 28 days(rate ratio 0·97, 95% CI 0·87–1·07; p=0·50). No significant difference was seen in duration of hospital stay (median10 days [IQR 5 to >28] vs 11 days [5 to >28]) or the proportion of patients discharged from hospital alive within 28 days(rate ratio 1·04, 95% CI 0·98–1·10; p=0·19). Among those not on invasive mechanical ventilation at baseline, nosignificant difference was seen in the proportion meeting the composite endpoint of invasive mechanical ventilationor death (risk ratio 0·95, 95% CI 0·87–1·03; p=0·24).Interpretation In patients admitted to hospital with COVID-19, azithromycin did not improve survival or otherprespecified clinical outcomes. Azithromycin use in patients admitted to hospital with COVID-19 should be restrictedto patients in whom there is a clear antimicrobial indication

Molecular and biologic biomarkers of Ewing sarcoma: A systematic review

With an annual incidence of less than 1%, Ewing sarcoma mainly occurs in children and young adults. It is not a frequent tumor but is the second most common bone malignancy in children. It has a 5-year survival rate of 65–75%; however, it has a poor prognosis when it relapses in patients. A genomic profile of this tumor can potentially help identify poor prognosis patients earlier and guide their treatment. A systematic review of the articles concerning genetic biomarkers in Ewing sarcoma was conducted using the Google Scholar, Cochrane, and PubMed database. There were 71 articles discovered. Numerous diagnostic, prognostic, and predictive biomarkers were found. However, more research is necessary to confirm the role of some of the mentioned biomarkers.

Cytokine Patterns in COVID-19 Patients: Which Cytokines Predict Mortality and Which Protect Against?

(1) Background/Aim: People infected with SARS-CoV-2 may develop COVID-19 in a wide range of clinical severity. Pulmonary fibrosis is characterized by several grades of chronic inflammation and collagen deposition in the interalveolar space. SARS-CoV-2 infection has been demonstrated to cause lung fibrosis without a currently elucidated mechanism. Some studies emphasize the role of proinflammatory cytokines. This research studies the correlation of the released cytokines with mortality or lung injury in COVID-19 patients. (2) Methods: Electronic medical record data from 40 patients diagnosed with COVID-19 in the COVID-19 Department, Galilee Medical Center, Nahariya, Israel, were collected. Epidemiological, clinical, laboratory, and imaging variables were analyzed. The cytokine levels were measured upon admission and discharge. A correlation between cytokine levels and severity and mortality or lung involvement was undertaken. (3) Results: IFN-gamma and IL-10 are the most powerful risk factors for mortality in the COVID-19 patient groups in a multivariate analysis. However, in a univariate analysis, TGF-β, CXCL-10, IFN gamma, and IL-7 affected mortality in COVID-19 patients. MMP-7 was significantly correlated with a cytokine storm and a high 4-C (severity) score in COVID-19 patients. MMP-7, TGF-β, IL-10, IL-7, TNF-α, and IL-6 were correlated with high lung involvement in COVID-19 patients. Serum concentrations of IGF-1 were significantly increased upon discharge, but MMP-7 was decreased. (4) Conclusions: Proinflammatory cytokines predict clinical severity, lung fibrosis, and mortality in COVID-19 patients. High concentrations of TGF-β, CXCL-10, IL-10, IL-6, and TNF-α are correlated to severity and lung injury. However, certain cytokines have protective effects and higher levels of these cytokines increase survival levels and lower lung damage. High levels of INF-γ, IL-7, MMP-7, and IGF-1 have protection probabilities against lung injury and severity

Epidemiology of bone tumors in Lebanon: a retrospective study from 2000 to 2022 at a tertiary center

Aim: Bone tumors are rare and have an uneven geographic distribution. Methods: 730 patients diagnosed with bone tumors were included in this retrospective analysis. Results: With a 64% rate of malignancy, the most common tumors were metastasis (40%) mostly in the axial skeleton, Osteosarcoma (9%) mostly in the femur, Osteochondroma (8%) mostly in the femur, giant cell tumors (7%) mostly in the knee, and Ewing's sarcoma (6%) mostly in the axial skeleton. Conclusion: Even though a some of the tumors have a predilection for certain localizations in the human body, they may differ in the middle-eastern population. One must also pay attention to the higher rates of malignancies as compared with other cohorts

SARS-CoV-2 vaccination modelling for safe surgery to save lives: data from an international prospective cohort study

Background: Preoperative SARS-CoV-2 vaccination could support safer elective surgery. Vaccine numbers are limited so this study aimed to inform their prioritization by modelling. Methods: The primary outcome was the number needed to vaccinate (NNV) to prevent one COVID-19-related death in 1 year. NNVs were based on postoperative SARS-CoV-2 rates and mortality in an international cohort study (surgical patients), and community SARS-CoV-2 incidence and case fatality data (general population). NNV estimates were stratified by age (18-49, 50-69, 70 or more years) and type of surgery. Best- and worst-case scenarios were used to describe uncertainty. Results: NNVs were more favourable in surgical patients than the general population. The most favourable NNVs were in patients aged 70 years or more needing cancer surgery (351; best case 196, worst case 816) or non-cancer surgery (733; best case 407, worst case 1664). Both exceeded the NNV in the general population (1840; best case 1196, worst case 3066). NNVs for surgical patients remained favourable at a range of SARS-CoV-2 incidence rates in sensitivity analysis modelling. Globally, prioritizing preoperative vaccination of patients needing elective surgery ahead of the general population could prevent an additional 58 687 (best case 115 007, worst case 20 177) COVID-19-related deaths in 1 year. Conclusion: As global roll out of SARS-CoV-2 vaccination proceeds, patients needing elective surgery should be prioritized ahead of the general population