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Background: Ambiguities and anomalies in the Activity of Daily Living (ADL) patterns indicate deviations from Wellness. The monitoring of lifestyles could facilitate remote physicians or caregivers to give insight into symptoms of the disease and provide health improvement advice to residents; Objective: This research work aims to apply lifestyle monitoring in an ambient assisted living (AAL) system by diagnosing conduct and distinguishing variation from the norm with the slightest conceivable fake alert. In pursuing this aim, the main objective is to fill the knowledge gap of two contextual observations (i.e., day and time) in the frequent behavior modeling for an individual in AAL. Each sensing category has its advantages and restrictions. Only a single type of sensing unit may not manage composite states in practice and lose the activity of daily living. To boost the efficiency of the system, we offer an exceptional sensor data fusion technique through different sensing modalities; Methods: As behaviors may also change according to other contextual observations, including seasonal, weather (or temperature), and social interaction, we propose the design of a novel activity learning model by adding behavioral observations, which we name as the Wellness indices analysis model; Results: The ground-truth data are collected from four elderly houses, including daily activities, with a sample size of three hundred days plus sensor activation. The investigation results validate the success of our method. The new feature set from sensor data fusion enhances the system accuracy to (98.17% ± 0.95) from (80.81% ± 0.68). The performance evaluation parameters of the proposed model for ADL recognition are recorded for the 14 selected activities. These parameters are Sensitivity (0.9852), Specificity (0.9988), Accuracy (0.9974), F1 score (0.9851), False Negative Rate (0.0130).

Objective In this paper, we propose to evaluate the use of pre-trained convolutional neural networks (CNNs) as a features extractor followed by the Principal Component Analysis (PCA) to find the best discriminant features to perform classification using support vector machine (SVM) algorithm for neonatal sleep and wake states using Fluke ® facial video frames. Using pre-trained CNNs as a feature extractor would hugely reduce the effort of collecting new neonatal data for training a neural network which could be computationally expensive. The features are extracted after fully connected layers (FCL’s), where we compare several pre-trained CNNs, e.g., VGG16, VGG19, InceptionV3, GoogLeNet, ResNet, and AlexNet. Results From around 2-h Fluke ® video recording of seven neonates, we achieved a modest classification performance with an accuracy, sensitivity, and specificity of 65.3%, 69.8%, 61.0%, respectively with AlexNet using Fluke ® (RGB) video frames. This indicates that using a pre-trained model as a feature extractor could not fully suffice for highly reliable sleep and wake classification in neonates. Therefore, in future work a dedicated neural network trained on neonatal data or a transfer learning approach is required.

Key Clinical Message Because of the complex embryonic origin of the abdominal venous structures, IVC and azygous systems can show numerous and even previously unreported anatomical variations and anomalies. Also, evaluating major vascular structures should not be dismissed in non‐contrast‐enhanced CT as it can provide valuable information about these structures. Double IVC is a rare occurrence of IVC anatomical variations and congenital anomalies. Herein, we discuss a case of a very rare type of double IVC that has not been reported in the literature before. A non‐contrast‐enhanced CT study was performed for a 34‐year‐old patient who visited our ER to evaluate for urolithiasis, during which two IVCs were noted. Each renal vein joined the ipsilateral IVC at a perpendicular angle. Unusually, the right IVC was formed from the confluence of both left and right common iliac veins (CIV), and the left IVC—Instead of crossing the midline at the renal veins level and reuniting the right IVC—cranially contributed to the azygos vein formation and caudally joined the left CIV. Also, there were some small communicating veins between the two IVCs and the left gonadal vein was slightly dilated before suggesting a reflux from the left renal vein (LRV). A complimentary doppler ultrasound exam confirmed the diagnosis and revealed a left‐side varicocele. Although rare cases of hemiazygos continuation and interiliac connections of left‐side IVC in the cases of double‐IVC have been reported previously, a complete confluence of CIVs is rare. The main differential diagnosis is retro‐aortic left renal vein (RLRV) type IV which seems to have an oblique course. Radiologists and surgeons should expect previously unreported variations in the vena cava system. Furthermore, reviewing the main abdominal vasculature should not be dismissed in non‐contrast CT exams. The operator assisted 3D reconstruction of the main retroperitoneal vasculature: the Aorta is shown in red; the right renal vein, right Inferior Vena Cava (IVC), and common iliac veins are depicted in dark blue; the left renal vein, left IVC, azygos vein, and accessory hemiazygos vein are illustrated in cyan; and the gonadal veins are represented in green. The left IVC exhibits multiple connections to the right IVC and the left Common Iliac Vein (CIV) caudally. It follows a vertical path and intersects with the left renal vein at a right angle. The superior segment of the left IVC, (arrow) does not merge with the right IVC after crossing the midline above the confluence of the left renal vein. Instead, it contributes to the formation of the azygos vein at the T12 vertebral level.

IntroductionRecent research has revealed that exposure to toxins and substances like cannabis, opium, heroin, and tramadol can inhibit the growth of lymphocytes. One of the key factors identified is the impact of probiotics on decreasing and managing inflammation in individuals affected by poisoning. This study examined the impact of probiotic treatment on interleukin-6 (IL-6) and IL-10 levels in opioid-poisoned patients.MethodsThis randomized clinical trial included 70 patients with opioid poisoning who were admitted to the poisoned intensive care unit for 6 months. Patients were randomly assigned to two intervention groups that received oral probiotics or a control group that received placebo. Data collected were analyzed using IBM SPSS version 22 software.ResultsThe intervention group had methadone (28.6%) and tramadol as the most common causes of poisoning, whereas the control group had tramadol (45.7%) and methadone (28.6%). There was a noticeable decrease in Acute Physiology and Chronic Health Evaluation scores in the intervention group before and after the intervention, whereas no significant difference was observed in the control group. Significantly, the levels of IL-6 and IL-10 were lower in the probiotic group than in the control group.ConclusionProbiotic use improved the condition of patients and decreased IL-6 and IL-10. This shows the positive effect of probiotics as an adjunctive treatment in opioid-poisoned patients, improving the condition of the patients’ immune system.

Although cardiac calcifications are described in the literature, calcification of atria is less frequently reported. There have been few case studies about atrial wall calcification in the literature, most of which were in middle-age females and were attributable to chronic heart diseases including rheumatic heart disease and valve replacement. In majority of the reported cases, interatrial septum has been spared. Only one case of bilateral atrial wall calcification has been reported prior to the current report, which has been in a patient with renal failure, calciphylaxis and long-term haemodialysis and calcium supplement intake. Atrial wall calcification is a rare finding but is important to report since it can complicate cardiac surgeries. It's also probable that this kind of dystrophic calcification could not be detected during routine echocardiography and CT scan should be performed in suspected cases.

Akbarzadeh and Saeed explore one of the most challenging issues facing the Muslim world: the Islamisation of political power. They present a comparative analysis of Muslim societies in West, South, Central and South East Asia and highlight the immediacy of the challenge for the political leadership in those societies. Islam and Political Legitimacy contends that the growing reliance on Islamic symbolism across the Muslim world, even in states that have had a strained relationship with Islam, has contributed to the evolution of Islam as a social and cultural factor to an entrenched political force. The geographic breadth of this book offers readers a nuanced appraisal of political Islam that transcends parochial eccentricities. Contributors to this volume examine the evolving relationship between Islam and political power in Bangladesh, Indonesia, Iran, Malaysia, Pakistan, Saudi Arabia and Uzbekistan.Researchers and students of political Islam and radicalism in the Muslim world will find Islam and Political Legitimacy of special interest. This is a welcome addition to the rich literature on the politics of the contemporary Muslim world.

Objective: In this paper, we propose to evaluate the use of a pre-trained convolutional neural networks (CNNs) as a features extractor followed by the Principal Component Analysis (PCA) to find the best discriminant features to perform classification using support vector machine (SVM) algorithm for neonatal sleep and wake states using Fluke® facial video frames. Using pre-trained CNNs as feature extractor would hugely reduce the effort of collecting new neonatal data for training a neural network which could be computationally very expensive. The features are extracted after fully connected layers (FCL’s), where we compare several pre-trained CNNs, e.g., VGG16, VGG19, InceptionV3, GoogLeNet, ResNet, and AlexNet. Results: From around 2-h Fluke® video recording of seven neonate, we achieved a modest classification performance with an accuracy, sensitivity, and specificity of 65.3%, 69.8%, 61.0%, respectively with AlexNet using Fluke® (RGB) video frames. This indicates that using a pre-trained model as a feature extractor could not fully suffice for highly reliable sleep and wake classification in neonates. Therefore, in future a dedicated neural network trained on neonatal data or a transfer learning approach is required.

Despite advancements in vital pulp therapy (VPT), a subset of cases fails to achieve desired outcomes. This study based on a previous large-scale cohort study involving 1257 VPT-treated teeth, aiming to describe the demographic data and clinical characteristics of all failed cases and their management protocols. Clinical records/images of 105 failed cases treated by a single endodontist (2011–2022) were examined, including 10 extracted teeth. Asymptomatic cases with PDL widening received no intervention, while others underwent management protocols, including (selective) RCT and (tampon) re-VPT. These retreatments were assessed for success (defined as radiographic evidence of healing) and survival (characterized by the retention/function of the treated tooth) using Kaplan–Meier analysis. While 51.4% of all initial failures were diagnosed due to symptoms, 48.6% were symptom-free. Notably, failed cases with symptomatic irreversible pulpitis, and apical periodontitis/widened PDL before initial treatment significantly outnumbered asymptomatic cases and normal PDL, respectively (P = 0.001). Moreover, most of the initial failures were observed in teeth with composite resin rather than amalgam restorations (P = 0.002). The success and survival rates for the management protocols were 91.78% and 95.79%, respectively, over an average follow-up period of 36.94 (± 23.30) months. RCT and re-VPT procedures provide successful outcomes for managing unsuccessful VPTs.

This cohort study evaluated the long-term success/survival of vital pulp therapies (VPTs) after carious pulp exposure in adult teeth. Additionally, factors influencing long-term success were identified. Teeth treated during 2011–2022 in a private clinic were studied with clinical/radiographic follow-ups. Data included patient demographics, tooth specifics, and treatment details. Outcomes were classified as success/failure based on clinical/radiographic findings, with tooth functionality determining the survival rate. Encompassing 1149 patients and 1257 VPT-treated teeth, the average monitoring period was 42.2 months. Overall VPTs’ survival and success rates were 99.1% and 91.6%, respectively. Success rates for 768 direct pulp cappings, 217 miniature pulpotomies, and 272 full pulpotomies were 91.9%, 92.6%, and 90.1%, respectively ( P  > 0.05). Influencing factors included symptomatic irreversible pulpitis (SIP; HR 1.974, 95% CI 1.242–3.137; P  = 0.004), radiographic signs of apical periodontitis (AP; HR 2.983, 95% CI 1.961–4.540; P  < 0.001), restoration type (HR 2.263, 95%CI 1.423–3.600; P  = 0.001), and restoration surfaces (HR 1.401, 95%CI 1.034–1.899; P  = 0.030). This study concludes that VPT techniques consistently exhibit high long-term success/survival rates in treating carious pulp exposures. Critical predictors include initial clinical signs of SIP/AP, caries extent, and use of composite restorations.

Promoting the use of agricultural wastes/byproducts in concrete production can significantly reduce environmental effects and contribute to sustainable development. Several experimental investigations on such concrete’s compressive strength ( f c ) and behavior have been done. The results of 229 concrete samples made by oil palm shell ( OPS ) as a lightweight aggregate ( LWA ) were used to develop models for predicting the f c of the high-strength lightweight aggregate concrete ( H S - L W A C ). To this end, gene expression programming ( GEP ), adaptive neuro-fuzzy inference system ( ANFIS ), artificial neural network ( ANN ), and multiple linear regression ( MLR ) are employed as machine learning ( ML ) and regression methods. The water-to-binder ( W / B ) ratio, ordinary Portland cement ( OPC ), fly ash ( FA ), silica fume ( SF ), fine aggregate ( Sand ), natural coarse aggregate ( Gravel ), OPS , superplasticizer ( SP ) contents, and specimen age are among the nine input parameters used in the developed models. The results show that all ML -based models efficiently predict the H S - L W A C ’s f c , which comprised OPS agricultural wastes. According to the results, the ANN model outperformed the GEP and ANFIS models. Moreover, an uncertainty analysis through the Monte Carlo simulation (MCS) method was applied to the prediction results. The growing demand for sustainable development and the crucial role of eco-friendly concrete in the construction industry can pave the way for further application of the developed models due to their superior robustness and high accuracy in future codes of practice.