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Abstract Background It was estimated that about 1.3 billion people were diagnosed to be hypertensive in 2015. All countries consistently show this high prevalence. Ischemic heart disease stands as the most common cause of systolic blood pressure-related deaths per year. Left ventricular hypertrophy determined by echocardiography can predict cardiovascular morbidity and mortality. The question of whether the LV geometric pattern has an additional prognostic value is still not clearly answered. Currently, coronary computed tomography is widely used in clinical practice with a great capability of simultaneous evaluation of the LV mass and the coronary arterial tree. Our study aims to examine the relationship between LV mass and geometry and coronary artery disease using an ECG-gated 320-detector- row CT scanner. Results Two hundred ninety-eight hypertensive Egyptian individuals were enrolled in our study, the mean age was 57.5 ± 10.5, and males comprised 76.5% of the study population. The mean LV mass and LV mass index were 193 ± 60 gm and 95.2 ± 27.5 g/m2 respectively. One-fifth of the patient had CAD luminal stenosis ≥ 50%. Normal LV geometric pattern was observed in about 37% of the study population. About one-third of the patients showed concentric remodeling. Patients with increased LV mass index represented one-third of the study population with a greater percentage of the concentric hypertrophy pattern than the eccentric hypertrophy pattern. Patients with high CAD-RADS showed statistically significant higher LV mass, LV mass index, and septal wall thickness. Patients with high CAD-RADS showed a greater percentage of concentric and eccentric hypertrophy. The LV geometric pattern was the only independent predictor of the high CAD-RADS. The LV geometric patterns associated with high RADS ordered from the highest to the lowest, were concentric LVH, Eccentric LV, and concentric remodeling. Conclusions LV geometric pattern is the only independent predictor of high CAD-RADS after adjustment for LV mass index and septal wall thickness. Among abnormal LV geometric patterns, concentric hypertrophy stands as the most important predictor of high CAD-RADS.

Background and Aims Although progesterone role on TBI patients and its antitumor activity have been proved, its perioperative use in surgical brain tumors remains unclear. Primary outcome is to investigate any neuroprotective effects of perioperative intramuscular progesterone on the brain’s cellular inflammatory and neuro-excitotoxic response to tumor resection evidenced by neuronal cells immunohistochemistry and CT scanning. Secondary outcome is to explore the postoperative clinical course, any related complications, and post craniotomy short-term (3 months) outcome.Methods Two hundred fifty-two (252) adult patients, ASA class І-ІІ undergoing elective craniotomy were randomly allocated into two groups: progesterone (PR) and control (C) groups to receive either 1.0 mg/kg progesterone (diluted to a total volume of 2 ml) or 2 ml of isotonic saline daily intramuscular for five days before and after craniotomy.Results Histopathological biopsies revealed significant increase the expression of active nuclear PR receptors on oligodendrocytes and astrocytes (P-value = 0.0001), cytoplasmic and nuclear neuro-quiescence endorsed by keeping blood vessel integrity and preventing neutrophilic infiltration and cytoplasmic oedema (P-value = 0.005) in PR group. Follow up CT2 (on the morning of surgery) and CT3 (on the 3rd postoperative day) recorded a significant reduction of brain oedema (P = 0.0001&0.001). Also, significant earlier weaning with shorter ICU stay (P = 0.0001) and better postoperative 3 months outcome (P = 0.001) were demonstrated in progesterone group without any detectable complications.Conclusion Perioperative progesterone offered anti-neuroinflammatory and neuroprotective effect and reduced cytoplasmic brain oedema evidenced by histopathological biopsies and CT scanning. Also, it improved postoperative course and 3 months neurological outcome in surgical brain tumors.

Background and Aims Despite of the effectiveness of on pump cardiac surgeries in valvular diseases, cardioplegic arrest and myocardial reperfusion injury are still an obstacle. Many cardioprotective additives are tried but the obtained laboratory results are mixed. Our objective was to compare the effects of supplementing the cardioplegia solution with propofol or insulin evaluated not only by laboratory biomarkers but also with papillary muscle biopsy in patients undergoing on pump valvular surgeries.Methods Sixty adult patients were randomly assigned into three equal groups to receive: cold blood cadioplegia (control (C) group), supplemented with either 9 mg/L propofol 10% (P group), or 10 IU/L regular insulin (I group)Results Propofol induced significant higher myocardial protection presented by better histopathological grading of the obtained muscle biopsies when compared to either group C (P = 0.0460) or group I (P = 0.014), lower postoperative dysrhythmia (P = 0.004), lower troponin I release (40.57 ± 8.5 vs 47.7 ± 6.22-fold increase), and more eukalemic state with lower need for K supplementation than insulin.Conclusion Propofol was superior to insulin in providing higher grade of myocardial protection with lower troponin I release, more steady K level and lower postoperative complications.

Annually, many people worldwide lose their lives due to breast cancer, making it one of the most prevalent cancers in the world. Since the disease is becoming more common, early detection of breast cancer is essential to avoiding serious complications and possibly death as well. This research provides a novel Breast Cancer Discovery (BCD) strategy to aid patients by providing prompt and sensitive detection of breast cancer. The two primary steps that form the BCD are the Breast Cancer Discovery Step (BCDS) and the Pre-processing Step (P2S). In the P2S, the needed data are filtered from any non-informative data using three primary operations: data normalization, feature selection, and outlier rejection. Only then does the diagnostic model in the BCDS for precise diagnosis begin to be trained. The primary contribution of this research is the novel outlier rejection technique known as the Combined Outlier Rejection Technique (CORT). CORT is divided into two primary phases: (i) the Quick Rejection Phase (QRP), which is a quick phase utilizing a statistical method, and (ii) the Accurate Rejection Phase (ARP), which is a precise phase using an optimization method. Outliers are rapidly eliminated during the QRP using the standard deviation, and the remaining outliers are thoroughly eliminated during ARP via Binary Harris Hawk Optimization (BHHO). The P2S in the BCD strategy indicates that data normalization is a pre-processing approach used to find numeric values in the datasets that fall into a predetermined range. Information Gain (IG) is then used to choose the optimal subset of features, and CORT is used to reject incorrect training data. Furthermore, based on the filtered data from the P2S, an Ensemble Classification Method (ECM) is utilized in the BCDS to identify breast cancer patients. This method consists of three classifiers: Naïve Bayes (NB), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). The Wisconsin Breast Cancer Database (WBCD) dataset, which contains digital images of fine-needle aspiration samples collected from patients' breast masses, is used herein to compare the BCD strategy against several contemporary strategies. According to the outcomes of the experiment, the suggested method is very competitive. It achieves 0.987 accuracy, 0.013 error, 0.98 recall, 0.984 precision, and a run time of 3 s, outperforming all other methods from the literature. [ABSTRACT FROM AUTHOR]

Background: The oils obtained from the seeds of Nigella sativa, also named black cumin, are rich in bioactive compounds that strengthen immunity and support human health. This study aimed to compare Nigella sativa oils pressed from Egyptian (Eg-NSSO), Ethiopian (Et-NSSO), and Syrian (Sy-NSSO) seeds. Methods: The analyzed oils were obtained from a local company. The content of phenolic compounds, tocochromanols, phytosterols, volatile compounds, triglycerides, and fatty acids composition was determined using chromatographic methods. The oxidative stability was determined by Rancimat technique as well as the determination of DPPH and ABTS scavenging activity. As an assessment of bioactivity, the antimicrobial and anti-aflatoxigenic properties of oils were evaluated. Results: Ethiopian oil had highest content of phenolic compounds, flavonoids, phytosterols, and tocochromanols and was characterized by the longest induction period (IP = 7.89 h). The share of thymoquinone was the highest in Ethiopian oil (34.84%), followed by Egyptian (27.36%), then Syrian (22.59%). Ethiopian oil recorded a high antibacterial activity, while Egyptian oil showed a unique antifungal activity against toxigenic fungi. Aflatoxins' secretion into liquid medium containing NSSO was reduced, especially with Egyptian oil. [ABSTRACT FROM AUTHOR]

Simple Summary: A novel mammogram image-based BCa explainable AI (BCaXAI) model is proposed, with a deep learning-based framework, for precise, noninvasive, objective, and timely manner diagnosis of BCa. The proposed BCaXAI leverages the Inception-ResNet v2 architecture, where the integration of explainable AI components, such as Grad-CAM, provides radiologists with valuable visual insights into the model's decision-making process, fostering trust and confidence in the AI-based system. Breast cancer (BCa) poses a severe threat to women's health worldwide as it is the most frequently diagnosed type of cancer and the primary cause of death for female patients. The biopsy procedure remains the gold standard for accurate and effective diagnosis of BCa. However, its adverse effects, such as invasiveness, bleeding, infection, and reporting time, keep this procedure as a last resort for diagnosis. A mammogram is considered the routine noninvasive imaging-based procedure for diagnosing BCa, mitigating the need for biopsies; however, it might be prone to subjectivity depending on the radiologist's experience. Therefore, we propose a novel, mammogram image-based BCa explainable AI (BCaXAI) model with a deep learning-based framework for precise, noninvasive, objective, and timely manner diagnosis of BCa. The proposed BCaXAI leverages the Inception-ResNet V2 architecture, where the integration of explainable AI components, such as Grad-CAM, provides radiologists with valuable visual insights into the model's decision-making process, fostering trust and confidence in the AI-based system. Based on using the DDSM and CBIS-DDSM mammogram datasets, BCaXAI achieved exceptional performance, surpassing traditional models such as ResNet50 and VGG16. The model demonstrated superior accuracy (98.53%), recall (98.53%), precision (98.40%), F1-score (98.43%), and AUROC (0.9933), highlighting its effectiveness in distinguishing between benign and malignant cases. These promising results could alleviate the diagnostic subjectivity that might arise as a result of the experience-variability between different radiologists, as well as minimize the need for repetitive biopsy procedures. [ABSTRACT FROM AUTHOR]

Background: Sepsis is the 10th leading cause of death and one of the leading causes for admission to the intensive care unit. Combining serum lactate and albumin in the form of lactate albumin ratio could yield more accurate prediction about prognosis of critically ill patients especially in septic patients. The aim of this work was to to evaluate the ability of Lactate/albumin ratio to predict outcome regarding morbidity, mortality and disease severity. Methods: This study is done as a case control study that enrolled fifty one critically ill infants and children (cases group), aged from 1 year to 14 years, with the criteria of severe sepsis or septic shock during the duration from October 2023 to March 2024 at Al-Hussein university hospital. 21 healthy infants and children of matched age and sex were included as a control group. All studied patients were subjected to history and examination, laboratory investigations including serum albumin, serum lactate in day 0 and 1 using enzymatic colorimetric, CBC, ESR, CRP, Liver function test and renal function test. Results: Lactate/albumin ratio at day 0 could significantly predict sepsis with area under the curve of 1, P value <0.001, and at cut-off value >0.16 ng/mL with 100% sensitivity, 100% specificity, 100% positive predictive value (PPV) and 100% negative predictive value (NPV). Lactate/albumin ratio at day 1 could significantly predict sepsis with area under the curve of 0.998, P value <0.001, and at cut-off value >0.12 ng/mL with 100% sensitivity, 85.71% specificity, 94.4% PPV and 100% NPV. Conclusions: The lactate/albumin ratio could serve as useful biomarkers integrated with a panel for risk-stratification, prognosis and management of paediatric patients with sepsis/septic shock. [ABSTRACT FROM AUTHOR]