Harnessing plant extracts for eco-friendly synthesis of iron nanoparticle (Fe-NPs): Characterization and their potential applications for ameliorating environmental pollutants.

Iron-nanoparticles (Fe-NPs) are increasingly been utilized in environmental applications due to their efficacy and strong catalytic activities. The novelty of nanoparticle science had attracted many researchers and especially for their green synthesis, which can effectively reuse biological resources during the polymerization reactions. Thus, the synthesis of Fe-NPs utilizing plant extracts could be considered as the eco-friendly, simple, rapid, energy-efficient, sustainable, and cost-effective. The green synthesis route can be recognized as a practical, valuable, and economically effective alternative for large-scale production. During the production process, some biomolecules present in the extracts undergo metal salts reduction, which can serve as both a capping and reducing mechanism, enhancing the reactivity and stability of green-synthesized Fe-NPs. The diversity of species provided a wide range of potential sources for green synthesis of Fe-NPs. With improved understanding of the specific biomolecules involved in the bioreduction and stabilization processes, it will become easier to identify and utilize new, potential plant materials for Fe-NPs synthesis. Newly synthesized Fe-NPs require different characterization techniques such as transmission electron microscope, ultraviolet-visible spectrophotometry, and X-ray absorption fine structure, etc, for the determination of size, composition, and structure. This review described and assessed the recent advancements in understanding green-synthesized Fe-NPs derived from plant-based material. Detailed information on various plant materials suitable of yielding valuable biomolecules with potential diverse applications in environmental safety. Additionally, this review examined the characterization techniques employed to analyze Fe-NPs, their stability, accumulation, mobility, and fate in the environment. Holistically, the review assessed the applications of Fe-NPs in remediating wastewaters, organic residues, and inorganic contaminants. The toxicity of Fe-NPs was also addressed; emphasizing the need to refine the synthesis of green Fe-NPs to ensure safety and environmental friendliness. Moving forward, the future challenges and opportunities associated with the green synthesis of Fe-NPs would motivate novel research about nanoparticles in new directions.

Integration of transcriptomics, metabolomics, and hormone analysis revealed the formation of lesion spots inhibited by GA and CTK was related to cell death and disease resistance in bread wheat (Triticum aestivum L.).

BACKGROUND: Wheat is one of the important grain crops in the world. The formation of lesion spots related to cell death is involved in disease resistance, whereas the regulatory pathway of lesion spot production and resistance mechanism to pathogens in wheat is largely unknown. RESULTS: In this study, a pair of NILs (NIL-Lm5W and NIL-Lm5M) was constructed from the BC1F4 population by the wheat lesion mimic mutant MC21 and its wild genotype Chuannong 16. The formation of lesion spots in NIL-Lm5M significantly increased its resistance to stripe rust, and NIL-Lm5M showed superiour agronomic traits than NIL-Lm5W under stripe rust infection.Whereafter, the NILs were subjected to transcriptomic (stage N: no spots; stage S, only a few spots; and stage M, numerous spots), metabolomic (stage N and S), and hormone analysis (stage S), with samples taken from normal plants in the field. Transcriptomic analysis showed that the differentially expressed genes were enriched in plant-pathogen interaction, and defense-related genes were significantly upregulated following the formation of lesion spots. Metabolomic analysis showed that the differentially accumulated metabolites were enriched in energy metabolism, including amino acid metabolism, carbohydrate metabolism, and lipid metabolism. Correlation network diagrams of transcriptomic and metabolomic showed that they were both enriched in energy metabolism. Additionally, the contents of gibberellin A7, cis-Zeatin, and abscisic acid were decreased in leaves upon lesion spot formation, whereas the lesion spots in NIL-Lm5M leaves were restrained by spaying GA and cytokinin (CTK, trans-zeatin) in the field. CONCLUSION: The formation of lesion spots can result in cell death and enhance strip rust resistance by protein degradation pathway and defense-related genes overexpression in wheat. Besides, the formation of lesion spots was significantly affected by GA and CTK. Altogether, these results may contribute to the understanding of lesion spot formation in wheat and laid a foundation for regulating the resistance mechanism to stripe rust.

Evaluation of machine learning models for predicting TiO2 photocatalytic degradation of air contaminants.

The escalation of global urbanization and industrial expansion has resulted in an increase in the emission of harmful substances into the atmosphere. Evaluating the effectiveness of titanium dioxide (TiO2) in photocatalytic degradation through traditional methods is resource-intensive and complex due to the detailed photocatalyst structures and the wide range of contaminants. Therefore in this study, recent advancements in machine learning (ML) are used to offer data-driven approach using thirteen machine learning techniques namely XG Boost (XGB), decision tree (DT), lasso Regression (LR2), support vector regression (SVR), adaBoost (AB), voting Regressor (VR), CatBoost (CB), K-Nearest Neighbors (KNN), gradient boost (GB), random Forest (RF), artificial neural network (ANN), ridge regression (RR), linear regression (LR1) to address the problem of estimation of TiO2 photocatalytic degradation rate of air contaminants. The models are developed using literature data and different methodical tools are used to evaluate the developed ML models. XGB, DT and LR2 models have high R2 values of 0.93, 0.926 and 0.926 in training and 0.936, 0.924 and 0.924 in test phase. While ANN, RR and LR models have lowest R2 values of 0.70, 0.56 and 0.40 in training and 0.62, 0.63 and 0.31 in test phase respectively. XGB, DT and LR2 have low MAE and RMSE values of 0.450 min-1/cm2, 0.494 min-1/cm2 and 0.49 min-1/cm2 for RMSE and 0.263 min-1/cm2, 0.285 min-1/cm2 and 0.29 min-1/cm2 for MAE in test stage. XGB, DT, and LR2 have 93% percent errors within 20% error range in training phase. XGB has 92% and DT, and LR2 have 94% errors with 20% range in test phase. XGB, DT, LR2 models remained the highest performing models and XGB is the most robust and effective in predictions. Feature importances reveal the role of input parameters in prediction made by developed ML models. Dosage, humidity, UV light intensity remain important experimental factors. This study will impact positively in providing efficient models to estimate photocatalytic degradation rate of air contaminants using TiO2.

Post-Transplant Maintenance Therapy in Acute Myeloid Leukemia.

Allogeneic hematopoietic cell transplantation (allo-HCT) is potentially curative for patients with acute myeloid leukemia (AML). However, the post-transplant relapse rate ranges from 40 to 70%, particularly with reduced intensity conditioning, and remains a major cause of treatment failure for these patients due to the limited efficacy of salvage therapy options. Strategies to mitigate this risk are urgently needed. In the past few years, the basic framework of post-transplant maintenance has been shaped by several clinical trials investigating targeted therapy, chemotherapy, and immunomodulatory therapies. Although the practice of post-transplant maintenance in AML has become more common, there remain challenges regarding the feasibility and efficacy of this strategy. Here, we review major developments in post-transplant maintenance in AML, along with ongoing and future planned studies in this area, outlining the limitations of available data and our future goals.

Comparative analysis of various machine learning algorithms to predict strength properties of sustainable green concrete containing waste foundry sand.

The use of waste foundry sand (WFS) in concrete production has gained attention as an eco-friendly approach to waste reduction and enhancing cementitious materials. However, testing the impact of WFS in concrete through experiments is costly and time-consuming. Therefore, this study employs machine learning (ML) models, including support vector regression (SVR), decision tree (DT), and AdaBoost regressor (AR) ensemble model to predict concrete properties accurately. Moreover, SVR was employed in conjunction with three robust optimization algorithms: the firefly algorithm (FFA), particle swarm optimization (PSO), and grey wolf optimization (GWO), to construct hybrid models. Using 397 experimental data points for compressive strength (CS), 146 for elastic modulus (E), and 242 for split tensile strength (STS), the models were evaluated with statistical metrics and interpreted using the SHapley Additive exPlanation (SHAP) technique. The SVR-GWO hybrid model demonstrated exceptional accuracy in predicting waste foundry sand concrete (WFSC) strength characteristics. The SVR-GWO hybrid model exhibited correlation coefficient values (R) of 0.999 for CS and E, and 0.998 for STS. Age was found to be a significant factor influencing WFSC properties. The ensemble model (AR) also exhibited comparable prediction accuracy to the SVR-GWO model. In addition, SHAP analysis revealed an optimal content of input variables in the concrete mix. Overall, the hybrid and ensemble models showed exceptional prediction accuracy compared to individual models. The application of these sophisticated soft computing prediction techniques holds the potential to stimulate the widespread adoption of WFS in sustainable concrete production, thereby fostering waste reduction and bolstering the adoption of environmentally conscious construction practices.

Topological indices and patterns in iron telluride networks.

This paper explores the complex interplay between topological indices and structural patterns in networks of iron telluride (FeTe). We want to analyses and characterize the distinct topological features of (FeTe) by utilizing an extensive set of topological indices. We investigate the relationship that these indicators have with the network's physical characteristics by employing sophisticated statistical techniques and curve fitting models. Our results show important trends that contribute to our knowledge of the architecture of the (FeTe) network and shed light on its physiochemical properties. This study advances the area of material science by providing a solid foundation for using topological indices to predict and analyses the behavior of intricate network systems. More preciously, we study the topological indices of iron telluride networks, an artificial substance widely used with unique properties due to its crystal structure. We construct a series of topological indices for iron telluride networks with exact mathematical analysis and determine their distributions and correlations using statistical methods. Our results reveal significant patterns and trends in the network structure when the number of constituent atoms increases. These results shed new light on the fundamental factors that influence material behavior, thus offering a deeper understanding of the iron telluride network and may contribute to future research and engineering of these materials.

Divacancy and resonance level enables high thermoelectric performance in n-type SnSe polycrystals.

N-type polycrystalline SnSe is considered as a highly promising candidates for thermoelectric applications due to facile processing, machinability, and scalability. However, existing efforts do not enable a peak ZT value exceeding 2.0 in n-type polycrystalline SnSe. Here, we realized a significant ZT enhancement by leveraging the synergistic effects of divacancy defect and introducing resonance level into the conduction band. The resonance level and increased density of states resulting from tungsten boost the Seebeck coefficient. The combination of the enhanced electrical conductivity (achieved by increasing carrier concentration through WCl6 doping and Se vacancies) and large Seebeck coefficient lead to a high power factor. Microstructural analyses reveal that the co-existence of divacancy defects (Se vacancies and Sn vacancies) and endotaxial W- and Cl-rich nanoprecipitates scatter phonons effectively, resulting in ultralow lattice conductivity. Ultimately, a record-high peak ZT of 2.2 at 773 K is achieved in n-type SnSe0.92 + 0.03WCl6.

Exploring the Impact of Personalized Nutritional Approaches on Metabolism and Immunity: A Systematic Review of Various Nutrients and Dietary Patterns.

The systematic review investigates the impact of different nutrients and dietary patterns on metabolism and immunity to answer the research question: "Can personalized nutritional approaches boost immunity?" The importance of diet in supporting the immune system has come to light in today's environment, where a strong immune system is crucial for protection against infectious illnesses, as highlighted by the COVID-19 pandemic. This systematic review adhered to the guidelines outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020. Four databases were screened for relevant data published in 2022-2023: PubMed, PubMed Central (PMC), MEDLINE, and Cochrane Library. Inclusion and exclusion criteria were utilized, and 13 papers were finalized after screening and employing the quality appraisal tool Cochrane Bias assessment for randomized controlled trials (RCT). Personalized nutrition can strengthen immunity and enhance overall health by adjusting dietary recommendations and following a person's genetic makeup, lifestyle, and health state. An adequate supply of vitamins, minerals, proteins, and fatty acids as well as an optimum caloric intake are essential for immune health, and individual requirements can vary significantly due to genetic factors, lifestyle choices, and underlying health conditions. Personalized nutrition considers these factors, enabling tailored dietary recommendations to address specific nutrient needs and optimize nutrient intake, leading to better health outcomes. The review concludes that personalized nutrition is more effective than a one-size-fits-all approach in boosting immunity, and its potential impact on health and immune function is highly important.

Effectiveness of Probiotic Use in Alleviating Symptoms of Irritable Bowel Syndrome: A Systematic Review.

Irritable bowel syndrome (IBS) is a common functional gastrointestinal (GI) condition, and changes in the gut microbiota's composition contribute to the development of symptoms. Although the precise mechanisms of probiotic use in the human body are not fully understood, probiotic supplements are believed to reduce symptoms, such as abdominal pain, by regulating neurotransmitters and receptors associated with pain modulation in IBS patients compared to placebo by altering the gut flora. This systematic review aimed to assess the most current randomized controlled trials (RCTs) on how probiotic supplementation affects the symptoms in people with IBS. The effects of probiotic supplements on IBS symptoms were studied in RCTs published between January 2018 and June 2023. After a search through PubMed and Google Scholar using the keywords probiotics, gut microbiota, irritable bowel syndrome, and IBS; eight articles matched the inclusion criteria and were reviewed. Four trials used a multistrain probiotic, whereas the remaining four trials examined the effects of a monostrain supplement. All eight trials came to the same conclusion: Probiotic treatment may significantly reduce symptoms.

The therapeutic potential of isosakuranetin against perfluorooctane sulfonate instigated cardiac toxicity via modulating Nrf-2/Keap-1 pathway, inflammatory, apoptotic, and histological profile.

Perfluorooctane sulfonate (PFOS) is a pervasive organic toxicant that damages body organs, including heart. Isosakuranetin (ISN) is a plant-based flavonoid that exhibits a broad range of pharmacological potentials. The current investigation was conducted to evaluate the potential role of ISN to counteract PFOS-induced cardiac damage in rats. Twenty-four albino rats (Rattus norvegicus) were distributed into four groups, including control, PFOS (10 mg/kg) intoxicated, PFOS + ISN (10 mg/kg + 20 mg/kg) treated, and ISN (20 mg/kg) alone supplemented group. It was revealed that PFOS intoxication reduced the expressions of Nrf-2 and its antioxidant genes while escalating the expression of Keap-1. Furthermore, PFOS exposure reduced the activities of glutathione reductase (GSR), superoxide dismutase (SOD), catalase (CAT), glutathione peroxidase (GPx), glutathione S-transferase (GST), Heme oxygenase-1 (HO-1) and glutathione (GSH) contents while upregulating the levels of reactive oxygen species (ROS) and malondialdehyde (MDA). Besides, PFOS administration upregulated the levels of creatine kinase-MB (CK-MB), troponin I, creatine phosphokinase (CPK), and lactate dehydrogenase (LDH). Moreover, the levels of tumor necrosis factor-alpha (TNF-α), nuclear factor kappa-B (NF-κB), interleukin-6 (IL-6), and interleukin-1ß (IL-1ß) were increased after PFOS intoxication. Additionally, PFOS exposure downregulated the expression of Bcl-2 while upregulating the expressions of Bax and Caspase-3. Furthermore, PFOS administration disrupted the normal architecture of cardiac tissues. Nonetheless, ISN treatment remarkably protected the cardiac tissues via regulating aforementioned dysregulations owing to its antioxidative, anti-inflammatory, and antiapoptotic properties.

Child-related factors associated with depressive symptoms among mothers of school-going children in urban Bangladesh: A cross-sectional study.

OBJECTIVE: This study aimed to identify the child-related factors associated with depressive symptoms among mothers of school-going children in Dhaka city of Bangladesh. METHODS: The study followed a cross-sectional design and was conducted between June and December 2019 among mothers of school-going children from Dhaka City, Bangladesh. A multistage sampling technique was adopted, and a total of 324 mothers of school-going children studying in the same school for at least six months were selected. Depressive symptoms of mothers were measured using a 20-item Self-Rating Depression Scale weighted to 100 percent, with 25-49 categorized as no depression and ≥ 50 as having depression. A binary logistic regression model was executed to identify the child-related factors associated with depressive symptoms among mothers. All statistical analyses were performed using the statistical software, Stata (Version 14.0). RESULTS: More than half of the participants (54.3%) were aged 40 years or above and had up to HSC level education (52.5%). The majority of the participants were homemakers (67.0%), mothers of a girl child (53.1%), and had a family income of 50,000 BDT or more (52.8%). Adjusted analyses revealed that the mother's depressive symptoms were associated with their child's frequent complaints of headaches or stomach aches (aOR = 13.19, 95% CI 3.03-57.37), having an injury (aOR = 4.05, 95% CI 1.44-11.41), and unfriendly relationship with mothers (aOR = 21.46, 95% CI 5.04-91.28). CONCLUSION: The present study highlighted several child-related factors that are associated with depressive symptoms among mothers that need to be considered while designing any intervention to address depressive symptoms among mothers of school-going children. It is also important to counsel mothers and fathers of the children about the importance of having a sound mother-child relationship while addressing depressive symptoms among mothers.

Numerical and artificial intelligence based investigation on the development of design guidelines for pultruded GFRP RHS profiles subjected to web crippling.

This article presents a numerical and artificial intelligence (AI) based investigation on the web crippling performance of pultruded glass fiber reinforced polymers' (GFRP) rectangular hollow section (RHS) profiles subjected to interior-one-flange (IOF) loading conditions. To achieve the desired research objectives, a finite element based computational model was developed using one of the popular simulating software ABAQUS CAE. This model was then validated by utilizing the results reported in experimental investigation-based article of Chen and Wang. Once the finite element model was validated, an extensive parametric study was conducted to investigate the aforementioned phenomenon on the basis of which a comprehensive, universal, and coherent database was assembled. This database was then used to formulate the design guidelines for the web crippling design of pultruded GFRP RHS profiles by employing AI based gene expression programming (GEP). Based on the findings of numerical investigation, the web crippling capacity of abovementioned structural profiles subjected to IOF loading conditions was found to be directly related to that of section thickness and bearing length whereas inversely related to that of section width, section height, section's corner radii, and profile length. On the basis of the findings of AI based investigation, the modified design rules proposed by this research were found to be accurately predicting the web crippling capacity of aforesaid structural profiles. This research is a significant contribution to the literature on the development of design guidelines for pultruded GFRP RHS profiles subjected to web crippling, however, there is still a lot to be done in this regard before getting to the ultimate conclusions.

Impact of dichromatic lighted incubation on hatching result and post-hatch performance of broiler chickens.

This study was planned to evaluate the impact of dichromatic lights during incubation on the hatching and post-hatch performance of broiler chickens. A total of 500 eggs of broiler breeder (Ross 308; Age 44 weeks) were evenly divided according to a completely randomized design into 4 treatments having 5 replicates and 25 eggs each. Treatments consisted of dichromatic lights Blue + Red (BR), Green + Red (GR) and Green + Blue (GB) provided at an intensity of 250 lx for 12 h a day along with a Dark (D) environment. After hatching 200 chicks (50 from each respective light group) were divided into 4 treatments with 5 replicates each having 10 chicks. Results indicated a higher embryo index (13.12%) in the GR group on the 12th day of incubation; while an ideal hatch window was observed in GR and GB (98.18% and 96.00% hatched chicks) lighting groups. In hatching traits, higher hatchability (86.15) and hatch of fertile (93.85) percentages were observed in GR lighting followed by GB, BR and Dark treatment groups; while dead-in shell embryos were lowest in the GR group. In growth performance, higher feed intake (513.20 g) and body weight (479.20 g) were observed in the GB group followed by GR, BR and dark group; and feed conversion ratio (FCR) was better in the GR group (1.06). In welfare parameters, improved physical asymmetry (0.90 mm) and tonic immobility (54.40 s) were measured in the GR group followed by GB, BR and the dark group. It was concluded that under experimental conditions when broiler breeder eggs are provided with GR lighting during incubation, it can help to improve hatchability, growth performance and welfare traits in chicks.

Phytoremediation of Arsenic (As) in rice plants, mediated by Bacillus subtilis strain IU31 through antioxidant responses and phytohormones synthesis.

Bacteria-assisted phytoremediation uses bacteria to promote plant health and improve its ability to remediate toxic heavy metals like Arsenic (As). Here, we isolated rhizobacteria and identified them as Bacillus subtilis strain IU31 using 16S rDNA sequencing. IU31 showed phosphate solubilization potential on Pikovskaya agar medium and produced siderophores, which were detected on Chromium Azurol-S (CAS) agar medium. Indole-3-acetic acid (IAA) and gibberellins (GAs), namely GA1, GA3, GA4, GA7, GA9, GA12, GA15, and GA24, were quantified by GC/MS-SIM analysis. The expression levels of genes involved in GA and IAA biosynthesis, such as cyp112, cyp114, trpA, and trpB, were assessed using semi-quantitative RT-PCR. Plant bioassays showed that As at a 15 mg/kg concentration reduced plant growth, chlorophyll content, and biomass. However, IU31 inoculation significantly improved plant growth dynamics, enhancing As accumulation by up to 50% compared with uninoculated plants. IU31 inoculation induced the bioconcentration factor (BCF) and bioaccumulation factor (BAF) of As in plants compared to uninoculated plants, but the translocation factor (TF) of As was unaffected by IU31 inoculation. IU31 inoculation effectively restored glutathione-S-transferase (GST) and catalase (CAT) enzyme activities, as well as glutathione (GSH) and hydrogen peroxide concentrations to nearly normal levels, which were significantly elevated in plants exposed to As stress. These results show that IU31 improves plant health and growth by producing IAA and GAs, which might contribute to the uptake and detoxification of As.

Computational analysis of antiviral drugs using topological descriptors.

Many health challenges are attributed to viral infections, which represent significant concerns in public health. Among these infections, diseases such as herpes simplex virus (HSV), cytomegalovirus (CMV), and varicella-zoster virus (VZV) infections have garnered attention due to their prevalence and impact on human health. There are specific antiviral medications available for the treatment of these viral infections. Drugs like Cidofovir, Valacyclovir, and Acyclovir are commonly prescribed. These antiviral drugs are known for their efficacy against herpesviruses and related viral infections, leveraging their ability to inhibit viral DNA polymerase. A molecular descriptor is a numerical value that correlates with specific physicochemical properties of a molecular graph. This article explores the calculation of distance-based topological descriptors, including the Trinajstic, Mostar, Szeged, and PI descriptors for the aforementioned antiviral drugs. These descriptors provide insights into these drugs' structural and physicochemical characteristics, aiding in understanding their mechanism of action and the development of new therapeutic agents.

Topological Insights into Nanostar Dendrimers by Computing the Augmented Zagreb Index.

BACKGROUND: The field of nanobiotechnology uses precise nanofabrication techniques to advance our understanding and control of biological systems. Due to their remarkable properties, dendrimers, which are hyperbranched macromolecular structures with distinct and well-defined architectures, have emerged as pivotal entities within this field. They are gaining increasing attention for their potential to catalyze a paradigm shift in medical therapeutics, biotechnological applications, and advanced material sciences. OBJECTIVE: This paper focuses on a novel analytical expression and determines the precise value of the augmented Zagreb index, a topological descriptor, for eight classes of nanostar dendrimers. METHODS: The Zagreb index is a topological invariant to predict molecular behaviour and reactivity. In this paper, we have explored its application in characterizing the branching of nanostar dendrimers through computational modelling and mathematical rigor. RESULTS: Our research has measured the augmented Zagreb index for nanostar dendrimers, which fall into eight distinct classes. The results better explain the relationship between the dendrimers' topology and chemical properties. This correlation has implications for their structural stability and reactivity, potentially leading to new applications. CONCLUSION: Developing the augmented Zagreb index for nanostar dendrimers is a significant breakthrough in nanobiotechnology. Based on the correlation between the calculated topological index and the corresponding molecular attributes, our analytical approach has opened up new possibilities for designing and synthesizing dendrimers tailored to specific functions in medical and material science applications. This precise topological quantification could significantly enhance the utility and functionalization of dendrimers in cutting-edge nanotechnological applications.

Biosynthesized metallic nanoparticles: A new era in cancer therapy.

Cancer remains a global health crisis, claiming countless lives throughout the years. Traditional cancer treatments like chemotherapy and radiation often bring about severe side effects, underscoring the pressing need for innovative, more efficient, and less toxic therapies. Nanotechnology has emerged as a promising technology capable of producing environmentally friendly anticancer nanoparticles. Among various nanoparticle types, metal-based nanoparticles stand out due to their exceptional performance and ease of use in methods of imaging. The widespread accessibility of biological precursors for synthesis based on plants of metal nanoparticles has made large-scale, eco-friendly production feasible. This evaluation provides a summary of the green strategy for synthesizing metal-based nanoparticles and explores their applications. Moreover, this review delves into the potential of phyto-based metal nanoparticles in combating cancer, shedding light on their probable mechanisms of action. These insights are invaluable for enhancing both biomedical and environmental applications. The study also touches on the numerous potential applications of nanotechnology in the field of medicine. Consequently, this research offers a concise and well-structured summary of nanotechnology, which should prove beneficial to researchers, engineers, and scientists embarking on future research endeavors.

Comparison of Low-Dose Interleukin 2 Therapy in Conjunction With Standard Therapy in Patients With Systemic Lupus Erythematosus vs Rheumatoid Arthritis: A Systematic Review.

This systematic review aims to compare the efficacy and safety of a novel immunotherapy with low-dose interleukin 2 (IL2) across two of the most prevalent autoimmune diseases i.e. systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA). Contemporary therapeutic practices have not been able to achieve complete remission from these autoimmune disorders. In contrast, low-dose IL2 has shown promise in achieving this therapeutic goal via inducing self-tolerance in patients with autoimmune diseases; however, due to variable irregularities among autoimmune processes of variable diseases, the benefit of low-dose IL2 could not be determined among different autoimmune diseases. Therefore, we conducted a study to compare low-dose IL2 therapy effects on SLE and RA. We systematically screened four databases: PubMed, Medical Literature Analysis and Retrieval System Online (MEDLINE), PubMed Central (PMC), and Google Scholar. Inclusion and exclusion criteria were implemented. Quality appraisal of studies chosen for the review was done using the Cochrane Risk-of-Bias (RoB) assessment tool for randomized controlled trials, and the Newcastle-Ottawa Scale (NOS) and JBI critical appraisal tool for non-randomized clinical trials. Information was gathered from seven articles: three randomized controlled trials and four non-randomized clinical trials. Our review concluded that low-dose IL2 therapy in conjunction with respective standard therapies for SLE and RA has a higher efficacy and safety profile as compared to standard therapy alone and the therapeutic effects were comparable in both SLE and RA patients treated with low-dose IL2; however, this novel intervention does not seem to have a significant corrective effect on the biomarkers of RA as it does for SLE biomarkers.

Breaking It Down: A Systematic Review Unravelling the Impact of Attention Deficit Hyperactivity Disorder and Methylphenidate on Childhood Fractures.

Limb fractures are a common cause of pediatric hospital admissions and surgeries, with a significant prevalence in the United Kingdom across all injury categories. Among psychiatric conditions in children, attention deficit hyperactivity disorder (ADHD) stands out as frequently associated with fractures, particularly those involving extremities. ADHD, with diagnoses prevalent among a significant proportion of school-age children and adolescents, has witnessed a growing global incidence. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 checklist for our systematic literature search, using various databases and specific search terms related to ADHD and fractures. We considered articles from 2018 to 2023, focusing on English language papers with free full-text access. Our selection process used the PRISMA flowchart. We began with 1,890 articles and, after deduplication, title screening, abstract assessment, and quality evaluation included nine research papers in our review. Our primary focus was on examining fracture-related outcomes in individuals with ADHD compared to those without, considering medication status. These studies encompassed various designs, with a focus on the ADHD-fracture relationship and methylphenidate's (MPH) impact. Our study confirms that ADHD increases fracture risk and suggests that MPH may help mitigate this risk. Early ADHD detection is vital for nonpharmacological interventions. Orthopedic surgeons should proactively identify ADHD, while healthcare professionals should offer injury prevention guidance, particularly for at-risk groups.