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Image forgery detection is important for sensitive domains such as courts of law. The main challenge is to develop a robust model that is sensitive to tampering traces. Existing techniques perform well on a limited dataset but do not generalize well across the datasets. Moreover, these techniques cannot reliably detect tampering that distorts the texture pattern of the image. The noise patterns remain consistent throughout a digital image if its contents are not altered. Based on this hypothesis, a robust descriptor FFT-DRLBP (Fast Fourier Transformation - Discriminative Robust Local Binary Patterns) is introduced, which first estimates noise patterns using FFT and encodes the discrepancies in noise patterns using DRLBP. Features extracted are passed to Support Vector Machine (SVM) for deciding whether the image is authentic or tampered. Intensive experiments are performed on benchmark datasets to validate the performance of the method. It achieved an accuracy of 99.21% on the combination of two challenging datasets. The comparison shows that it outperforms state-of-the-art methods and is vigorous to image forgery attacks even in the presence of various post-processing operations. The performance of the method is also validated using cross-dataset experiments, which ensures its robustness and generalization.

Angela Bonaccorso,1,2,* Alessandro Ortis,3,* Teresa Musumeci,1,2 Claudia Carbone,1,2 Mazhar Hussain,3 Valentina Di Salvatore,1 Sebastiano Battiato,3 Francesco Pappalardo,1,2 Rosario Pignatello1,2 1Department of Drug and Health Sciences, University of Catania, Catania, Italy; 2NANOMED–Research Centre for Nanomedicine and Pharmaceutical Nanotechnology, University of Catania, Catania, 95125, Italy; 3Department of Mathematics and Computer Science, University of Catania, Catania, Italy*These authors contributed equally to this workCorrespondence: Teresa Musumeci, Email teresa.musumeci@unict.itBackground: In the last few decades, nose-to-brain delivery has been investigated as an alternative route to deliver molecules to the Central Nervous System (CNS), bypassing the Blood–Brain Barrier. The use of nanotechnological carriers to promote drug transfer via this route has been widely explored. The exact mechanisms of transport remain unclear because different pathways (systemic or axonal) may be involved. Despite the large number of studies in this field, various aspects still need to be addressed. For example, what physicochemical properties should a suitable carrier possess in order to achieve this goal? To determine the correlation between carrier features (eg, particle size and surface charge) and drug targeting efficiency percentage (DTE%) and direct transport percentage (DTP%), correlation studies were performed using machine learning.Methods: Detailed analysis of the literature from 2010 to 2021 was performed on Pubmed in order to build “NANOSE” database. Regression analyses have been applied to exploit machine-learning technology.Results: A total of 64 research articles were considered for building the NANOSE database (102 formulations). Particle-based formulations were characterized by an average size between 150– 200 nm and presented a negative zeta potential (ZP) from − 10 to − 25 mV. The most general-purpose model for the regression of DTP/DTE values is represented by Decision Tree regression, followed by K-Nearest Neighbors Regressor (KNeighbor regression).Conclusion: A literature review revealed that nose-to-brain delivery has been widely investigated in neurodegenerative diseases. Correlation studies between the physicochemical properties of nanosystems (mean size and ZP) and DTE/DTP parameters suggest that ZP may be more significant than particle size for DTP/DTE predictability.Keywords: nanomedicine, intranasal administration, pharmacokinetic, DTE, DTP, machine learning

Ultrasound imaging is frequently employed to aid with fetal development. It benefits from being real-time, inexpensive, non-intrusive, and simple. Artificial intelligence is becoming increasingly significant in medical imaging and can assist in resolving many problems related to the classification of fetal organs. Processing fetal ultrasound (US) images increasingly uses deep learning (DL) techniques. This paper aims to assess the development of existing DL classification systems for use in a real maternal-fetal healthcare setting. This experimental process has employed two publicly available datasets, such as FPSU23 Dataset and Fetal Imaging. Two novel deep learning architectures have been designed in the proposed architecture based on 3-residual and 4-residual blocks with different convolutional filter sizes. The hyperparameters of the proposed architectures were initialized through Bayesian Optimization. Following the training process, deep features were extracted from the average pooling layers of both models. In a subsequent step, the features from both models were optimized using an improved version of the Generalized Normal Distribution Optimizer (GNDO). Finally, neural networks are used to classify the fused optimized features of both models, which were first combined using a new fusion technique. The best classification scores, 98.5 and 88.6% accuracy, were obtained after multiple steps of analysis. Additionally, a comparison with existing state-of-the-art methods revealed a notable improvement in the suggested architecture’s accuracy.

In this study, copper was removed from an aqueous solution through electromembrane extraction (EME), a new approach that utilises a two-chamber electrochemical cell. It consists of two electrodes (stainless steel cathode and graphite anode) and a solid liquid membrane (SLM). SLM is composed of supporting polypropylene membrane impregnated with1-octanol as an organic solvent and bis(2-ethylhexyl) phosphate (DEHP) as a carrier. The effects of process parameters such as applied voltage, pH and copper concentration on the removal of copper were investigated. Response surface methodology (RSM) was applied to optimise these parameters and their interactions. Results indicated that pH has the major effect on the removal of copper, followed by applied voltage. The squared interaction term in the RSM model has the highest contribution (70.5%), followed by the linear term, thereby confirming the significant interaction among the variables. The optimum conditions include an applied voltage of 60V, pH of 5.18 and initial copper concentration of 5 ppm, which yield to a removal efficiency of 80% after 6 hours of operation. The findings demonstrate the use of electromembrane extraction as an efficient method for the removal of heavy metals and provide valuable insights for future application to other environmental and water treatment processes.

Abstract Background Despite the efficacy and safety of DIGIFab, it is relatively expensive and has limited availability. In addition, alternative interventions, such as therapeutic plasma exchange, may need to be considered in massive digoxin overdoses. Although few case reports describe its efficacy. Case presentation We report a case of a 17-year-old white male patient brought by family members to our emergency department in Riyadh, Saudi Arabia. After intentionally ingesting 48 mg of digoxin tablets to commit suicide, the patient’s initial digoxin serum level was 8.04 ng/mL. The patient was resuscitated in the emergency department. After admission to the intensive care unit, the patient underwent therapeutic plasma exchange, because of insufficient DIGIFab doses. Afterward, the serum digoxin levels drastically decreased, and his symptoms reverted. The patient was successfully managed and discharged 7 days after admission. Conclusion Despite insufficient evidence and a limited number of case reports describing the use of extracorporeal treatment in digoxin overdose, we noted the significant impact of therapeutic plasma exchange on our patient. However, therapeutic plasma exchange’s use in routine treatment requires stronger evidence to confirm its benefits.

Background Computed tomography perfusion (CTP) predictions of infarct core play an important role in the determination of treatment eligibility in large‐vessel occlusion acute ischemic stroke. Prior studies have demonstrated that blood glucose can affect cerebral blood flow. Here, we examine the influence of acute and chronic hyperglycemia on CTP estimations of infarct core. Methods From our prospectively collected multicenter observational cohort, we identified patients with large‐vessel occlusion acute ischemic stroke who underwent CTP with RAPID (IschemaView, Stanford, CA) postprocessing, followed by endovascular therapy with substantial reperfusion (Thrombolysis in Cerebral Infarction 2b–3) within 90 minutes, and final infarct volume determination by magnetic resonance imaging 48 to 72 hours posttreatment. Core volume overestimations and underestimations were defined as a difference of at least 20 mL between CTP‐RAPID predicted infarct core and Diffusion Weighted Imaging (DWI) final infarct volume. Primary outcome was the association of presentation glucose and hemoglobin A1c (HgbA1c) with underestimation of core volume and was measured using multivariable logistic regression adjusted for comorbidities and presentation characteristics. Secondary outcomes included frequency of overestimation of infarct core. Results Among 256 patients meeting inclusion criteria, median age was 67 (interquartile range [IQR], 57–77) years, 51.6% were women, and 132 (51.6%) and 93 (36.3%) had elevated presentation glucose and elevated HgbA1c, respectively. Median CTP‐predicted core was 6 mL (IQR, 0–30 mL), median DWI final infarct volume was 14 mL (IQR, 6‐43 mL), and median difference was 12 mL (IQR, 5–35 mL). Twenty‐eight (10.9%) patients had infarct core overestimation and 68 (26.6%) had underestimation. Compared with those with no underestimation, patients with underestimation had elevated blood glucose (median, 119 [IQR, 103–155] versus 138 [IQR, 117–195] mg/dL; P = 0.002) and HgbA1c (median, 5.80% [IQR, 5.40–6.40] versus 6.40% [IQR, 5.50–7.90]; P = 0.009). In multivariable analysis, underestimation was independently associated with elevated glucose (adjusted odds ratio [OR], 2.10; P = 0.038) and HgbA1c (adjusted OR, 2.37; P = 0.012). Overestimation was associated with lower presentation blood glucose (median, 109 [IQR, 99–132] in overestimation versus 127 [IQR, 107–172] mg/dL in no overestimation; P = 0.003) and HgbA1c (5.6%[IQR 5.1–6.2] in overestimation versus 5.90%[IQR, 5.50–6.70] in no overestimation; P = 0.012). Conclusions Acute and chronic hyperglycemia were strongly associated with CTP underestimation in patients with large‐vessel occlusion acute ischemic stroke undergoing endovascular therapy. Glycemic state should be considered when interpreting CTP findings in patients with large‐vessel occlusion acute ischemic stroke.

The superhcater zone of the boiler is the convective heat transfer region of the boiler, that is the heat in this region mostly transferred by the convection between the flew gases outside and steam inside tubes. The factors that effected the heat transfer rate and heat transfer coefficient in the Mosiab power station boiler (980000kgihr capacity under pressure of 175bar) at its superlieater is studied here. The super heater is divided into two parts, primary and secondary super heater. Each part is divided into many zones depending on the pipe size and material. The effect of boiler load mass flow rate of steam on the heat transfer rate, heat transfer coefficient, steam temperature is studied. It is concluded that as the load on the boiler increased the heat transfer rate is also increased and the heat transfer coefficient. The heat transfer coefficient in secondary superheater is less that that in the primary. The reduction in mass flow rate of steam increases the wall temperature of tubes because of low capacity of water to receive heat.

Dalal Alkhudir,1 Adeeba Al-Herz,1 Khulood Saleh,2 Adel Alawadhi,1,3 Waleed Al-Kandari,2 Eman Hasan,1 Khaled Mokaddem,1 Aqeel Ghanem,4 Youssef Attia Bartella,1 Mohammed Hussain,1 Naser AlHadhood,2 Yaser Ali,4 Ebrahim Nahar,4 Ahmad Alenizi,5 Ali Aldei,1 Fatemah Abutiban,5 Sawsan Hayat,4 Hussain Behbehani,2 Fatemah Baron,5 Hebah Alhajeri,4 Amjad Alkadi,6 Ahmed Alsaber7 1Department of Rheumatology, Amiri Hospital, Kuwait city, Kuwait; 2Department of Rheumatology, Farwania Hospital, Farwaniya, Kuwait; 3Department of Rheumatology, Faculty of Medicine, Kuwait University, Jabriya, Kuwait; 4Department of Rheumatology, Mubarak Al-Kabeer Hospital, Jabriya, Kuwait; 5Department of Rheumatology, Jahra Hospital, Jahra, Kuwait; 6Department of Rheumatology, Sabah Hospital, Sabah, Kuwait; 7College of Business and Economics, The American University of Kuwait, Salmiya, KuwaitCorrespondence: Dalal Alkhudir, Amiri Hospital, Ministry of Health, Kuwait, Email drdalalkh@gmail.comBackground: An association between serum uric acid (UA) and disease activity in rheumatoid arthritis (RA) patients has not been well studied. We describe RA patients with high and normal UA and study its association with RA activity.Methods: Adult RA patients from the Kuwait Registry for Rheumatic Diseases (KRRD) were studied from February 2012 through March 2022. Patients with documented UA levels were included. UA of > 357 μmol/L (6mg/dL) was considered high. Statistical comparison and correlation were made using multivariate logistic regression.Results: Overall, 1054 patients with documented UA. A total of 158 patients (15%) had high UA level with a mean of 409± 44.4μmol/L. The mean age for the high UA group and low UA group were 59.3 ± 10.7 years and 54.5 ± 12.4 years, respectively (p< 0.001). 49.4% were female in high UA group, and 62.2% were female in low UA group, respectively (p< 0.05). Logistic analysis showed an inverse relation between DAS28 and UA, as lower DAS28 score was associated with higher UA level (p=0.032) OR 1.39. There was a direct relation with HAQ, creatinine and UA. A higher HAQ is associated with a higher UA level (p=0.019) OR 0.78. High creatinine level is also associated with high UA level (p< 0.001) OR 0.24. The use of antirheumatic drugs was similar among patients with high and normal UA.Conclusion: RA patients with a higher UA had a lower disease activity despite using similar antirheumatic drugs. The reasons behind this association need to be further studied.Keywords: rheumatoid arthritis, uric acid, disease activity, DAS28, registry, KRRD, Kuwait, Middle East

Munawwar Hussain,1 Sarvari Yellapragada,2 Samer Al Hadidi1 1Myeloma Center, Winthrop P. Rockefeller Cancer Institute, University of Arkansas for Medical Sciences, Little Rock, AR, USA; 2Michael E. DeBakey VA Medical Center and Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TX, USACorrespondence: Samer Al Hadidi, Myeloma Center, Winthrop P. Rockefeller Cancer Institute, University of Arkansas for Medical Sciences, Little Rock, AR, USA, Tel +1 501-526-6990, Fax +1 501-320-7861, Email salhadidi@uams.eduAbstract: Multiple myeloma (MM) is a hematologic malignancy characterized by the abnormal clonal proliferation of plasma cells that may result in focal bone lesions, renal failure, anemia, and/or hypercalcemia. Recently, the diagnosis and treatment of MM have evolved due to a better understanding of disease pathophysiology, improved risk stratification, and new treatments. The incorporation of new drugs, including proteasome inhibitors, immunomodulatory drugs, anti-CD38 antibodies and high-dose chemotherapy followed by hematopoietic stem cell transplantation, has resulted in a significant improvement in patient outcomes and QoL. In this review, we summarize differential diagnoses and therapeutic advances in MM.Keywords: multiple myeloma, myeloma, smoldering myeloma, Waldenstrom macroglobulinemia, light-chain amyloidosis