Medical image colorization for better visualization and segmentation

Medical images contain precious anatomical information for clinical procedures. Improved understanding of medical modality may contribute significantly in arena of medical image analysis. This paper investigates enhancement of monochromatic medical modality into colorized images. Improving the contrast of anatomical structures facilitates precise segmentation. The proposed framework starts with pre-processing to remove noise and improve edge information. Then colour information is embedded to each pixel of a subject image. A resulting image has a potential to portray better anatomical information than a conventional monochromatic image. To evaluate the performance of colorized medical modality, the structural similarity index and the peak signal to noise ratio are computed. Supremacy of proposed colorization is validated by segmentation experiments and compared with greyscale monochromatic images

Water treatment technologies in removing heavy metal ions from wastewater: A review

The scarcity of clean water resources leads to many scientific and technological advancements in wastewater treatment processes. The recalcitrance of heavy metals in wastewater has been proven to be a challenging concern. Therefore, there is a need for more water treatment technologies to completely remove heavy metals down to a non-hazardous level. In this article, current development in recent waste-water treatment technologies to remove heavy metals has been reviewed and summarized. This review evaluates the state-of-the-art removal processes, including chemical precipitation, photocatalysis, flotation, ion exchange, remediation, electrochemical treatment, adsorption, membrane technologies, and coagulation/flocculation. These processes are leading technologies with industrial efficiency and practical feasibility. In general, the key factors in selecting the most appropriate process for wastewater treatment are the cost required and characteristics of wastewater plus the applicability of the process

Alginate-Based Sustainable Green Composites of Polymer and Reusable Birm for Mitigation of Malachite Green Dye: Characterization and Application for Water Decontamination

Environmental sustainability appraisal of adsorption for exclusion of the malachite green toxic dye was the center of attention in this work. The influenced goals were to analyze the consequences of novel composites fabricated by sodium alginate with guar gum and birm (SA@GG@B composites) by ion gelation. This work not only explains the feasibility of the sorbent and its application for the removal of dye stuff but also proclaimed various effects of different parameters affecting the removal efficiency. Adsorption processes were carried out in the batch process. The composite was characterized by SEM, which revealed that the irregular surface of composites has pores present for high adsorption, FTIR (for functional groups detection) reveals the presence of –OH group which provides attachment sites for dye, and BET (surface analysis) with a surface area of 5.01 m2/g shows that it has a wide surface area for greater adsorption process. Adsorption was performed on synthetic composites by varying different parameters like contact time, the concentration of sorbent and sorbate, and pH. Maximum adsorption was achieved (92.7%) at 100 ppm initial concentration, 120 min interaction time, and pH 9. Adsorption isotherms (Freundlich, Langmuir, Dubnin, and Elvoich isotherm) were applied in this work and evaluated the adsorption phenomenon and nature of adsorption. Freundlich adsorption capacity KF (9.45) reveals effective adsorption of dye by the proposed adsorbent. The kinetics models show that it was better with the pseudo-second-order reaction. Effective removal of malachite green by synthesized composites reveals their importance for the industrial water purification from hazardous dyes

Approaches to multiple myeloma management in gulf countries: A narrative review insights from the Kingdom of Saudi Arabia and gulf multiple myeloma experts

Multiple myeloma (MM) is neoplasm of the plasma cells derived from the postgerminal B-cell lineage and it ranges from premalignant conditions like monoclonal gammopathy of unknown significance and smoldering MM (SMM) to malignant diseases such as overt MM. With advances in science and technology, the understanding of the disease has increased paving the way for advanced therapeutic options and better patient outcomes. Thus, this article is a narrative review summarizing the recent advances in the epidemiology, clinical presentation, risk stratification, and MM patient populations treatment and to provide insights by the authors who are experts in the field of MM management who are considered as Gulf Myeloma Working Group and who were the members of 'Approaches to MM Management' Advisory Board meeting held on October 29, 2021. The expert panel provided several recommendations and drawn consensus statements pertaining to MM management in the Gulf countries

eWound-PRIOR: An Ensemble Framework for Cases Prioritization After Orthopedic Surgeries

© 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG. Patient follow-up appointments are an imperative part of the healthcare model to ensure safe patient recovery and proper course of treatment. The use of mobile devices can help patient monitoring and predictive approaches can provide computational support to identify deteriorating cases. Aiming to aggregate the data produced by those devices with the power of predictive approaches, this paper proposes the eWound-PRIOR framework to provide a remote assessment of postoperative orthopedic wounds. Our approach uses Artificial Intelligence (AI) techniques to process patients’ data related to postoperative wound healing and makes predictions as to whether the patient requires an in-person assessment or not. The experiment results showed that the predictions are promising and adherent to the application context, even if the on-line questionnaire had impaired the training model and the performance