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Minimal sternotomy surgery in comparison to standard sternotomy in the coronary bypass Surgery
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Background Median sternotomy is the gold standard incision for most cardiac operations. However, with the advent of minimal invasive surgery, a new approach emerged in cardiac surgery named mini-sternotomy and has been successfully used to perform a variety of operations.

 The aim of this paper is to present our experience of using mini-sternotomy to harvest the left internal mammary artery (LIMA) for off-pump revascularization of the left anterior descending artery (LAD)

 Methodology Over a 2-year period (October 2012-October 2014), 100 patients underwent coronary artery bypass grafting (CABG) via conventional median sternotomy (CMS) (n=80) and mini-sternotomy (MS) (n=20). The 2 groups were compared regarding length and difficulty of surgery, postoperative pain and respiratory function, stay in the intensive care unit (ICU), wound infection, shoulder stability and other variables.

 Results One patient (5%) with LMS was converted into CMS due to inadequate exposure. The blood loss was less in LMS patients. Lung atelectasis and pleural effusions were less in group 2. A higher PaO2, lower PaCO2 and a shorter assisted-ventilation time were observed in LMS group. Early postoperative pain score & analgesic requirements were less in LMS patients and their hospital stay was shorter (4-5 days) than CMS. Moreover, LMS patients could return to their jobs and drove cars earlier than group 2 patients. There were 9 deaths (11.3%) in CMS group vs. one death (5%) in LMS group; however, this difference was not statistically significant (p˂0.05)

 Conclusions This study shows that off-pump coronary surgery through mini-sternotomy incision is feasible and safe.

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Publication Date
Thu Jul 02 2026
Journal Name
Innovative Construction And Petrochemical Technologies
Improving Arabic Text Classification Accuracy Using Lightweight NLP Techniques
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Arabic text classification is a challenging task because of the complex morphology of the language, the existence of different writing forms and a multitude of dialects, which can result in sparser common text representations. While transformer models such as AraBERT have obtained superior results on many Arabic NLP tasks, their high computational requirements make them difficult to deploy in environments with limited hardware resources. In some cases this can also make the model less practical for researchers working with basic computer systems. This study focuses on a more practical issue: how much accuracy a simple classifier may lose when the amount of required computation is reduced. We use a combined TF-IDF representation based on bo

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