RETRACTED: Jain et al. Lysosomes in Stem Cell Quiescence: A Potential Therapeutic Target in Acute Myeloid Leukemia. Cancers 2022, 14, 1618.

The Cancers Editorial Office retracts the article entitled 'Lysosomes in Stem Cell Quiescence: A Potential Therapeutic Target in Acute Myeloid Leukemia' [...].

Soil and Mineral Nutrients in Plant Health: A Prospective Study of Iron and Phosphorus in the Growth and Development of Plants.

Plants being sessile are exposed to different environmental challenges and consequent stresses associated with them. With the prerequisite of minerals for growth and development, they coordinate their mobilization from the soil through their roots. Phosphorus (P) and iron (Fe) are macro- and micronutrient; P serves as an important component of biological macromolecules, besides driving major cellular processes, including photosynthesis and respiration, and Fe performs the function as a cofactor for enzymes of vital metabolic pathways. These minerals help in maintaining plant vigor via alterations in the pH, nutrient content, release of exudates at the root surface, changing dynamics of root microbial population, and modulation of the activity of redox enzymes. Despite this, their low solubility and relative immobilization in soil make them inaccessible for utilization by plants. Moreover, plants have evolved distinct mechanisms to cope with these stresses and coregulate the levels of minerals (Fe, P, etc.) toward the maintenance of homeostasis. The present study aims at examining the uptake mechanisms of Fe and P, and their translocation, storage, and role in executing different cellular processes in plants. It also summarizes the toxicological aspects of these minerals in terms of their effects on germination, nutrient uptake, plant-water relationship, and overall yield. Considered as an important and indispensable component of sustainable agriculture, a separate section covers the current knowledge on the cross-talk between Fe and P and integrates complete and balanced information of their effect on plant hormone levels.

Unleashed Treasures of Solanaceae: Mechanistic Insights into Phytochemicals with Therapeutic Potential for Combatting Human Diseases.

Plants that possess a diverse range of bioactive compounds are essential for maintaining human health and survival. The diversity of bioactive compounds with distinct therapeutic potential contributes to their role in health systems, in addition to their function as a source of nutrients. Studies on the genetic makeup and composition of bioactive compounds have revealed them to be rich in steroidal alkaloids, saponins, terpenes, flavonoids, and phenolics. The Solanaceae family, having a rich abundance of bioactive compounds with varying degrees of pharmacological activities, holds significant promise in the management of different diseases. Investigation into Solanum species has revealed them to exhibit a wide range of pharmacological properties, including antioxidant, hepatoprotective, cardioprotective, nephroprotective, anti-inflammatory, and anti-ulcerogenic effects. Phytochemical analysis of isolated compounds such as diosgenin, solamargine, solanine, apigenin, and lupeol has shown them to be cytotoxic in different cancer cell lines, including liver cancer (HepG2, Hep3B, SMMC-772), lung cancer (A549, H441, H520), human breast cancer (HBL-100), and prostate cancer (PC3). Since analysis of their phytochemical constituents has shown them to have a notable effect on several signaling pathways, a great deal of attention has been paid to identifying the biological targets and cellular mechanisms involved therein. Considering the promising aspects of bioactive constituents of different Solanum members, the main emphasis was on finding and reporting notable cultivars, their phytochemical contents, and their pharmacological properties. This review offers mechanistic insights into the bioactive ingredients intended to treat different ailments with the least harmful effects for potential applications in the advancement of medical research.

Study of Amiloride Binding to Human Serum Albumin: Insights from Thermodynamic, Spectroscopic, and Molecular Docking Investigations.

This study was undertaken to investigate the interaction between the sodium channel blocker amiloride (AML) and human serum albumin (HSA). A combination of multi-spectroscopic techniques and computational methods were employed to identify the AML binding site on HSA and the forces responsible for the formation of the HSA-AML complex. Our findings revealed that AML specifically binds to Sudlow's site II, located in subdomain IIIA of HSA, and that the complex formed is stabilized using van der Waals hydrogen-bonding and hydrophobic interactions. FRET analysis showed that the distance between AML and Trp214 was optimal for efficient quenching. UV-Vis spectroscopy and circular dichroism indicated minor changes in the structure of HSA after AML binding, and molecular dynamics simulations (MDS) conducted over 100 ns provided additional evidence of stable HSA-AML-complex formation. This study enhances understanding of the interaction between AML and HSA and the mechanism responsible.

Pollutants in aquatic system: a frontier perspective of emerging threat and strategies to solve the crisis for safe drinking water.

Water is an indispensable natural resource and is the most vital substance for the existence of life on earth. However, due to anthropogenic activities, it is being polluted at an alarming rate which has led to serious concern about water shortage across the world. Moreover, toxic contaminants released into water bodies from various industrial and domestic activities negatively affect aquatic and terrestrial organisms and cause serious diseases such as cancer, renal problems, gastroenteritis, diarrhea, and nausea in humans. Therefore, water treatments that can eliminate toxins are very crucial. Unfortunately, pollution treatment remains a difficulty when four broad considerations are taken into account: effectiveness, reusability, environmental friendliness, and affordability. In this situation, protecting water from contamination or creating affordable remedial techniques has become a serious issue. Although traditional wastewater treatment technologies have existed since antiquity, they are both expensive and inefficient. Nowadays, advanced sustainable technical approaches are being created to replace traditional wastewater treatment processes. The present study reviews the sources, toxicity, and possible remediation techniques of the water contaminants.

Elevated CDKN1A (P21) mediates ß-thalassemia erythroid apoptosis, but its loss does not improve ß-thalassemic erythropoiesis.

ß-thalassemias are common hemoglobinopathies due to mutations in the ß-globin gene that lead to hemolytic anemias. Premature death of ß-thalassemic erythroid precursors results in ineffective erythroid maturation, increased production of erythropoietin (EPO), expansion of erythroid progenitor compartment, extramedullary erythropoiesis, and splenomegaly. However, the molecular mechanism of erythroid apoptosis in ß-thalassemia is not well understood. Using a mouse model of ß-thalassemia (Hbbth3/+), we show that dysregulated expression of the FOXO3 transcription factor is implicated in ß-thalassemia erythroid apoptosis. In Foxo3-/-/Hbbth3/+ mice, erythroid apoptosis is significantly reduced, whereas erythroid cell maturation, and red blood cell and hemoglobin production are substantially improved even with elevated reactive oxygen species in double-mutant erythroblasts. However, persistence of elevated reticulocytes and splenomegaly suggests that ineffective erythropoiesis is not resolved in Foxo3-/-/Hbbth3/+. We found the cell cycle inhibitor Cdkn1a (cyclin-dependent kinase inhibitor p21), a FOXO3 target gene, is markedly upregulated in both mouse and patient-derived ß-thalassemic erythroid precursors. Double-mutant p21/Hbbth3/+ mice exhibited embryonic lethality with only a fraction of mice surviving to weaning. Notably, studies in adult mice displayed greatly reduced apoptosis and circulating Epo in erythroid compartments of surviving p21-/-/Hbbth3/+ mice relative to Hbbth3/+ mice, whereas ineffective erythroid cell maturation, extramedullary erythropoiesis, and splenomegaly were not modified. These combined results suggest that mechanisms that control ß-thalassemic erythroid cell survival and differentiation are uncoupled from ineffective erythropoiesis and involve a molecular network including FOXO3 and P21. Overall, these studies provide a new framework for investigating ineffective erythropoiesis in ß-thalassemia.

An Efficient and Effective Framework for Intestinal Parasite Egg Detection Using YOLOv5.

Intestinal parasitic infections pose a grave threat to human health, particularly in tropical and subtropical regions. The traditional manual microscopy system of intestinal parasite detection remains the gold standard procedure for diagnosing parasite cysts or eggs. This approach is costly, time-consuming (30 min per sample), highly tedious, and requires a specialist. However, computer vision, based on deep learning, has made great strides in recent years. Despite the significant advances in deep convolutional neural network-based architectures, little research has been conducted to explore these techniques' potential in parasitology, specifically for intestinal parasites. This research presents a novel proposal for state-of-the-art transfer learning architecture for the detection and classification of intestinal parasite eggs from images. The ultimate goal is to ensure prompt treatment for patients while also alleviating the burden on experts. Our approach comprised two main stages: image pre-processing and augmentation in the first stage, and YOLOv5 algorithms for detection and classification in the second stage, followed by performance comparison based on different parameters. Remarkably, our algorithms achieved a mean average precision of approximately 97% and a detection time of only 8.5 ms per sample for a dataset of 5393 intestinal parasite images. This innovative approach holds tremendous potential to form a solid theoretical basis for real-time detection and classification in routine clinical examinations, addressing the increasing demand and accelerating the diagnostic process. Our research contributes to the development of cutting-edge technologies for the efficient and accurate detection of intestinal parasite eggs, advancing the field of medical imaging and diagnosis.

Apoptotic proteins with non-apoptotic activity: expression and function in cancer.

Apoptosis is a process of programmed cell death in which a cell commits suicide while maintaining the integrity and architecture of the tissue as a whole. Apoptosis involves activation of one of two major pathways: the extrinsic pathway, where extracellular pro-apoptotic signals, transduced through plasma membrane death receptors, activate a caspase cascade leading to apoptosis. The second, the intrinsic apoptotic pathway, where damaged DNA, oxidative stress, or chemicals, induce the release of pro-apoptotic proteins from the mitochondria, leading to the activation of caspase-dependent and independent apoptosis. However, it has recently become apparent that proteins involved in apoptosis also exhibit non-cell death-related physiological functions that are related to the cell cycle, differentiation, metabolism, inflammation or immunity. Such non-conventional activities were predominantly reported in non-cancer cells although, recently, such a dual function for pro-apoptotic proteins has also been reported in cancers where they are overexpressed. Interestingly, some apoptotic proteins translocate to the nucleus in order to perform a non-apoptotic function. In this review, we summarize the unconventional roles of the apoptotic proteins from a functional perspective, while focusing on two mitochondrial proteins: VDAC1 and SMAC/Diablo. Despite having pro-apoptotic functions, these proteins are overexpressed in cancers and this apparent paradox and the associated pathophysiological implications will be discussed. We will also present possible mechanisms underlying the switch from apoptotic to non-apoptotic activities although a deeper investigation into the process awaits further study.

Plant Growth Promoting Rhizobacteria in Plant Health: A Perspective Study of the Underground Interaction.

Plants are affected by various environmental stresses such as high or low temperatures, drought, and high salt levels, which can disrupt their normal cellular functioning and impact their growth and productivity. These stressors offer a major constraint to the morphological, physiological, and biochemical parameters; thereby attributing serious complications in the growth of crops such as rice, wheat, and corn. Considering the strategic and intricate association of soil microbiota, known as plant growth-promoting rhizobacteria (PGPR), with the plant roots, PGPR helps plants to adapt and survive under changing environmental conditions and become more resilient to stress. They aid in nutrient acquisition and regulation of water content in the soil and also play a role in regulating osmotic balance and ion homeostasis. Boosting key physiological processes, they contribute significantly to the alleviation of stress and promoting the growth and development of plants. This review examines the use of PGPR in increasing plant tolerance to different stresses, focusing on their impact on water uptake, nutrient acquisition, ion homeostasis, and osmotic balance, as well as their effects on crop yield and food security.

Advances Towards Automatic Detection and Classification of Parasites Microscopic Images Using Deep Convolutional Neural Network: Methods, Models and Research Directions.

In the developing world, parasites are responsible for causing several serious health problems, with relatively high infections in human beings. The traditional manual light microscopy process of parasite recognition remains the golden standard approach for the diagnosis of parasitic species, but this approach is time-consuming, highly tedious, and also difficult to maintain consistency but essential in parasitological classification for carrying out several experimental observations. Therefore, it is meaningful to apply deep learning to address these challenges. Convolution Neural Network and digital slide scanning show promising results that can revolutionize the clinical parasitology laboratory by automating the process of classification and detection of parasites. Image analysis using deep learning methods have the potential to achieve high efficiency and accuracy. For this review, we have conducted a thorough investigation in the field of image detection and classification of various parasites based on deep learning. Online databases and digital libraries such as ACM, IEEE, ScienceDirect, Springer, and Wiley Online Library were searched to identify sufficient related paper collections. After screening of 200 research papers, 70 of them met our filtering criteria, which became a part of this study. This paper presents a comprehensive review of existing parasite classification and detection methods and models in chronological order, from traditional machine learning based techniques to deep learning based techniques. In this review, we also demonstrate the summary of machine learning and deep learning methods along with dataset details, evaluation metrics, methods limitations, and future scope over the one decade. The majority of the technical publications from 2012 to the present have been examined and summarized. In addition, we have discussed the future directions and challenges of parasites classification and detection to help researchers in understanding the existing research gaps. Further, this review provides support to researchers who require an effective and comprehensive understanding of deep learning development techniques, research, and future trends in the field of parasites detection and classification.

Gut Microbiota and Cardiovascular System: An Intricate Balance of Health and the Diseased State.

Gut microbiota encompasses the resident microflora of the gut. Having an intricate relationship with the host, it plays an important role in regulating physiology and in the maintenance of balance between health and disease. Though dietary habits and the environment play a critical role in shaping the gut, an imbalance (referred to as dysbiosis) serves as a driving factor in the occurrence of different diseases, including cardiovascular disease (CVD). With risk factors of hypertension, diabetes, dyslipidemia, etc., CVD accounts for a large number of deaths among men (32%) and women (35%) worldwide. As gut microbiota is reported to have a direct influence on the risk factors associated with CVDs, this opens up new avenues in exploring the possible role of gut microbiota in regulating the gross physiological aspects along the gut-heart axis. The present study elaborates on different aspects of the gut microbiota and possible interaction with the host towards maintaining a balance between health and the occurrence of CVDs. As the gut microbiota makes regulatory checks for these risk factors, it has a possible role in shaping the gut and, as such, in decreasing the chances of the occurrence of CVDs. With special emphasis on the risk factors for CVDs, this paper includes information on the prominent bacterial species (Firmicutes, Bacteriodetes and others) towards an advance in our understanding of the etiology of CVDs and an exploration of the best possible therapeutic modules for implementation in the treatment of different CVDs along the gut-heart axis.

Lysosomes and Their Role in Regulating the Metabolism of Hematopoietic Stem Cells.

Hematopoietic stem cells (HSCs) have the capacity to renew blood cells at all stages of life and are largely quiescent at a steady state. It is essential to understand the processes that govern quiescence in HSCs to enhance bone marrow transplantation. It is hypothesized that in their quiescent state, HSCs primarily use glycolysis for energy production rather than mitochondrial oxidative phosphorylation (OXPHOS). In addition, the HSC switch from quiescence to activation occurs along a continuous developmental path that is driven by metabolism. Specifying the metabolic regulation pathway of HSC quiescence will provide insights into HSC homeostasis for therapeutic application. Therefore, understanding the metabolic demands of HSCs at a steady state is key to developing innovative hematological therapeutics. Lysosomes are the major degradative organelle in eukaryotic cells. Catabolic, anabolic, and lysosomal function abnormalities are connected to an expanding list of diseases. In recent years, lysosomes have emerged as control centers of cellular metabolism, particularly in HSC quiescence, and essential regulators of cell signaling have been found on the lysosomal membrane. In addition to autophagic processes, lysosomal activities have been shown to be crucial in sustaining quiescence by restricting HSCs access to a nutritional reserve essential for their activation into the cell cycle. Lysosomal activity may preserve HSC quiescence by altering glycolysis-mitochondrial biogenesis. The understanding of HSC metabolism has significantly expanded over the decade, revealing previously unknown requirements of HSCs in both their dividing (active) and quiescent states. Therefore, understanding the role of lysosomes in HSCs will allow for the development of innovative treatment methods based on HSCs to fight clonal hematopoiesis and HSC aging.

Extragenital lichen sclerosus: A comprehensive review.

Lichen sclerosus (LS) is a chronic inflammatory mucocutaneous disease of unknown aetiology. About 85% of total cases of LS are genital cases, while extragenital form is seen in only 15-20% of cases. Extragenital LS (EGLS) can occur simultaneously with genital form; however, in 6% of the cases, only extragenital form has been described. Genetic, autoimmune, infectious, environmental and hormonal factors are implicated in its aetiology. Extragenital LS presents as asymptomatic white opalescent papules, which cluster in plaques and slowly progress over time resulting in parchment-like skin usually involving upper trunk, neck and shoulders. Lesions are frequently accompanied by purpura/haemorrhagic spots. The relationship with morphoea has been a topic of debate. Association with several autoimmune diseases has been observed. Diagnosis is usually based on clinical and dermoscopic examination and further supported by histopathological findings. LS needs to be differentiated from several other dermatological conditions such as discoid lupus erythematosus, vitiligo, mycosis fungoides (hypopigmented variant), lichen planus, graft-versus-host disease and morphoea depending upon the stage of the disease. Generally, extragenital LS is believed to lack carcinogenic potential. However, case reports with possible malignant transformation have been described. In this article, the authors have described a concise review of the extragenital form of LS.

Myostatin and its Regulation: A Comprehensive Review of Myostatin Inhibiting Strategies.

Myostatin (MSTN) is a well-reported negative regulator of muscle growth and a member of the transforming growth factor (TGF) family. MSTN has important functions in skeletal muscle (SM), and its crucial involvement in several disorders has made it an important therapeutic target. Several strategies based on the use of natural compounds to inhibitory peptides are being used to inhibit the activity of MSTN. This review delivers an overview of the current state of knowledge about SM and myogenesis with particular emphasis on the structural characteristics and regulatory functions of MSTN during myogenesis and its involvements in various muscle related disorders. In addition, we review the diverse approaches used to inhibit the activity of MSTN, especially in silico approaches to the screening of natural compounds and the design of novel short peptides derived from proteins that typically interact with MSTN.

Performance analysis and prediction of type 2 diabetes mellitus based on lifestyle data using machine learning approaches.

Objective: Diabetes is a chronic fatal disease that has affected millions of people all over the globe. Type 2 Diabetes Mellitus (T2DM) accounts for 90% of the affected population among all types of diabetes. Millions of T2DM patients remain undiagnosed due to lack of awareness and under resourced healthcare system. So, there is a dire need for a diagnostic and prognostic tool that shall help the healthcare providers, clinicians and practitioners with early prediction and hence can recommend the lifestyle changes required to stop the progression of diabetes. The main objective of this research is to develop a framework based on machine learning techniques using only lifestyle indicators for prediction of T2DM disease. Moreover, prediction model can be used without visiting clinical labs and hospital readmissions. Method: A proposed framework is presented and implemented based on machine learning paradigms using lifestyle indicators for better prediction of T2DM disease. The current research has involved different experts like Diabetologists, Endocrinologists, Dieticians, Nutritionists, etc. for selecting the contributing 1552 instances and 11 attributes lifestyle biological features to promote health and manage complications towards T2DM disease. The dataset has been collected through survey and google forms from different geographical regions. Results: Seven machine learning classifiers were employed namely K-Nearest Neighbour (KNN), Linear Regression (LR), Support Vector Machine (SVM), Naive Bayes (NB), Decision Tree (DT), Random Forest (RF) and Gradient Boosting (GB). Gradient Boosting classifier outperformed best with an accuracy rate of 97.24% for training and 96.90% for testing separately followed by RF, DT, NB, SVM, LR, and KNN as 95.36%, 92.52%, 90.72%, 90.20%, 90.20% and 77.06% respectively. However, in terms of precision, RF achieved high performance (0.980%) and KNN performed the lowest (0.793%). As far as recall is being concerned, GB achieved the highest rate of 0.975% and KNN showed the worst rate of 0.774%. Also, GB is top performed in terms of f1-score. According to the ROCs, GB and NB had a better area under the curve compared to the others. Conclusion: The research developed a realistic health management system for T2DM disease based on machine learning techniques using only lifestyle data for prediction of T2DM. To extend the current study, these models shall be used for different, large and real-time datasets which share the commonality of data with T2DM disease to establish the efficacy of the proposed system.

Lysosomes in Stem Cell Quiescence: A Potential Therapeutic Target in Acute Myeloid Leukemia.

Lysosomes are cellular organelles that regulate essential biological processes such as cellular homeostasis, development, and aging. They are primarily connected to the degradation/recycling of cellular macromolecules and participate in cellular trafficking, nutritional signaling, energy metabolism, and immune regulation. Therefore, lysosomes connect cellular metabolism and signaling pathways. Lysosome's involvement in the critical biological processes has rekindled clinical interest towards this organelle for treating various diseases, including cancer. Recent research advancements have demonstrated that lysosomes also regulate the maintenance and hemostasis of hematopoietic stem cells (HSCs), which play a critical role in the progression of acute myeloid leukemia (AML) and other types of cancer. Lysosomes regulate both HSCs' metabolic networks and identity transition. AML is a lethal type of blood cancer with a poor prognosis that is particularly associated with aging. Although the genetic landscape of AML has been extensively described, only a few targeted therapies have been produced, warranting the need for further research. This review summarizes the functions and importance of targeting lysosomes in AML, while highlighting the significance of lysosomes in HSCs maintenance.

Mini-Review on SARS-CoV-2 Infection and Neurological Manifestations: A Perspective.

The coronavirus, also known as SARS-CoV-2 (Severe Acute Respiratory Syndrome Corona Virus-19), with its rapid rate of transmission, has progressed with a great impact on respiratory function and mortality worldwide. The nasal cavity is the promising gateway of SARS-CoV-2 to reach the brain via systemic circulatory distribution. Recent reports have revealed that the loss of involuntary process of breathing control into the brainstem that results in death is a signal of neurological involvement. Early neurological symptoms, like loss of smell, convulsions, and ataxia, are the clues of the involvement of the central nervous system that makes the entry of SARS-CoV-2 further fatal and life-threatening, requiring artificial respiration and emergency admission in hospitals. Studies performed on patients infected with SARS-CoV-2 has revealed three-stage involvement of the Central Nervous System (CNS) in the progression of SARS-CoV-2 infection: Direct involvement of CNS with headache, ataxia, dizziness, altered or impaired consciousness, acute stroke or seizures as major symptoms, peripheral involvement with impaired taste, smell, vision, and altered nociception, and skeletal muscle impairment that includes skeletal muscle disorders leading to acute paralysis in a particular area of the body. In the previous era, most studied and researched viruses were beta coronavirus and mouse hepatitis virus, which were studied for acute and chronic encephalitis and Multiple Sclerosis (MS). Although the early symptoms of SARS-CoV are respiratory pathogenesis, the differential diagnosis should always be considered for neurological perspective to stop the mortalities.

Automated Bacterial Classifications Using Machine Learning Based Computational Techniques: Architectures, Challenges and Open Research Issues.

Bacteria are important in a variety of practical domains, including industry, agriculture, medicine etc. A very few species of bacteria are favourable to humans. Whereas, majority of them are extremely dangerous and causes variety of life threatening illness to different living organisms. Traditionally, this class of microbes is detected and classified using different approaches like gram staining, biochemical testing, motility testing etc. However with the availability of large amount of data and technical advances in the field of medical and computer science, the machine learning methods have been widely used and have shown tremendous performance in automatic detection of bacteria. The inclusion of latest technology employing different Artificial Intelligence techniques are greatly assisting microbiologist in solving extremely complex problems in this domain. This paper presents a review of the literature on various machine learning approaches that have been used to classify bacteria, for the period 1998-2020. The resources include research papers and book chapters from different publishers of national and international repute such as Elsevier, Springer, IEEE, PLOS, etc. The study carried out a detailed and critical analysis of penetrating different Machine learning methodologies in the field of bacterial classification along with their limitations and future scope. In addition, different opportunities and challenges in implementing these techniques in the concerned field are also presented to provide a deep insight to the researchers working in this field.