A rare cause of deep peroneal nerve palsy due to compression of synovial cyst – Case report

AbstractINTRODUCTIONSynovial cyst is a rare cause of compression neuropathy and its differential diagnosis can be misleading.PRESENTATION OF CASEThis article presents clinical, radiological, and histological findings of deep peroneal nerve palsy due to compression of a synovial cyst in a 30-year-old patient admitted with sudden drop foot.DISCUSSIONFocal nerve entrapment in lower extremity due to synovial cystis a rare entity. Differential diagnosis is important. Surgical excision is the main treatment method with high success rate.CONCLUSIONSynovial cyst compression which can be treated easily with surgical excision should be considered in rapidly progressed drop foot

AMELIORATING EFFECT OF HAWTHORN (CRATAEGUS OXYACANTHA) AND PHYSICAL EXERCISE ON ACUTE PENICILLIN INDUCED SEIZURES IN GERBILS

The aim of the present study was to assess the effect of Hawthorn (Crataegus oxyacantha) and physical activity. We studied its effect on penicilline induced epilepticus (Pie) in gerbils. Pie was induced by administration of penicilline G (500 IU, ip). The gerbils were divided randomly in four groups (6 animals per each group) and studied as described below: 1) Control group 2) Exercise group (30 min/each day for 8 weeks) (Eg) 3) Extract group, 50mg/kg/day/animal in 1 ml saline, 3 h prior to exercise (Exe) 4) Exercise+Extract + (Exe+Ex). The severity of Pie was observed and recorded. The means of latencies (Mean±SE) were 236±45, 369±36, 386±58 and 433±37 ms in groups of control, Exe, Ex, and Exe+Ex respectively. The mean spike latency significantly (P=0,033 F=3,560) decreased in Exe, Ex and Exe+Ex when compared control. Although spike frequency significantly (

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Prediction of Water Level in Lakes by RNN-Based Deep Learning Algorithms to Preserve Sustainability in Changing Climate and Relationship to Microcystin

In recent years, intensive water use combined with global climate change has increased fluctuations in freshwater lake levels, hydrological characteristics, water quality, and water ecosystem balance. To provide a sustainable management plan in the long term, deep learning models (DL) can provide fast and reliable predictions of lake water levels (LWLs) in challenging future scenarios. In this study, artificial neural networks (ANNs) and four recurrent neural network (RNN) algorithms were investigated to predict LWLs that were applied in time series such as one day, five days, ten days, twenty days, one month, two months, and four months ahead. The results show that the performance of the Long Short-Term Memory (LSTM) model with a prediction of 60 days is in the very good range and outperforms the benchmark, the Naïve Method, by 78% and the ANN at the significance level (p < 0.05) with an RMSE = 0.1762 compared to other DL algorithms. The RNN-based DL algorithms show better prediction performance, specifically, for long time horizons, 57.98% for 45 days, 78.55% for 60 days, and 58% for 120 days, and it is better to use a prediction period of at least 20 days with an 18.45% performance increase to take advantage of the gated RNN algorithms for predicting future water levels. Additionally, microcystin concentration was tightly correlated with temperature and was most elevated between 15 and 20 m water depths during the summer months. Evidence on LWL forecasting and microcystin concentrations in the context of climate change could help develop a sustainable water management plan and long-term policy for drinking water lakes

Integrated Scheduling and Tool Management in Flexible Manufacturing Systems

A multistage algorithm is proposed that will solve the scheduling problem in a flexible manufacturing system by..

Right-to-Work Laws and State-Level Economic Outcomes: Evidence from the Case Studies of Idaho and Oklahoma Using Synthetic Control Method

The role of right-to-work laws on state economies, labor organizations and employees are controversial and important policy questions. Empirical evidence is far from being conclusive predominantly due to identification issues. Using a recently developed econometric technique and exploiting the two most recent cases, -Idaho and Oklahoma- we examine the effectiveness of right-to-work laws on state-level outcomes. Our results indicate that the passage of right-to-work laws in Oklahoma affected union membership and coverage rates and, possibly to some extent, foreign direct investment. As for manufacturing employment, per capita income and average wage rates, we do not observe any impact. Our findings for Idaho, on the other hand, suggest that the laws increased the manufacturing employment, while it had no effect on per capita income and are inconclusive for foreign direct investment..Manufacturing Employment, Right-to-Work Laws, Synthetic Control Method, Unionization