Zeolites in wastewater treatment: A comprehensive review on scientometric analysis, adsorption mechanisms, and future prospects.

Zeolites possess a microporous crystalline structure, a large surface area, and a uniform pore size. Natural or synthetic zeolites are commonly utilized for adsorbing organic and inorganic compounds from wastewater because of their unique physicochemical properties and cost-effectiveness. The present review work comprehensively revealed the application of zeolites in removing a diverse range of wastewater contaminates, such as dyes, heavy metal ions, and phenolic compounds, within the framework of contemporary research. The present review work offers a summary of the existing literature about the chemical composition of zeolites and their synthesis by different methods. Subsequently, the article provides a wide range of factors to examine the adsorption mechanisms of both inorganic and organic pollutants using natural zeolites and modified zeolites. This review explores the different mechanisms through which zeolites effectively eliminate pollutants from aquatic matrices. Additionally, this review explores that the Langmuir and pseudo-second-order models are the predominant models used in investigating isothermal and kinetic adsorption and also evaluates the research gap on zeolite through scientometric analysis. The prospective efficacy of zeolite materials in future wastewater treatment may be assessed by a comparative analysis of their capacity to adsorb toxic inorganic and organic contaminates from wastewater, with other adsorbents.

Long-Term Follow-Up of Abatacept, Post-Transplantation Cyclophosphamide, and Sirolimus-Based Haploidentical Transplantation in Younger Patients with Nonmalignant Diseases.

Haploidentical (haplo) hematopoietic cell transplantation (HCT) for nonmalignant disease (NMD) carries inherent challenges of both alloreactivity and graft failure. Building on promising results from pilot studies in which abatacept was combined with post-transplantation cyclophosphamide (PTCy) and sirolimus (AbaCyS) in younger NMD patients undergoing haplo-HCT, we present the long-term outcomes of this protocol. On the back of uniform disease-specific conditioning regimens containing antithymocyte globulin 4.5 mg/kg from day -9 to day -7, GVHD prophylaxis with AbaCyS consisted of abatacept administered on days 0, +5, +20, +35, and monthly until 180 days with PTCy and sirolimus. The patients were followed up with longitudinal assessment of immune reconstitution, growth, and reproductive development and quality of life (QoL) analyses. Among 40 patients (aplastic anemia, n = 24; hemoglobinopathies, n = 14; and primary immunodeficiencies, n = 2) with a median age of 10 years (range, 2 to 25 years), 95% achieved sustained engraftment. Post-transplantation hemophagocytic syndrome was detected in 3 patients, leading to graft failure in 2 cases. The incidence of acute graft-versus-host disease (GVHD) was 2.6%, and that of chronic GVHD (cGVHD) was 14.3%. Cytomegalovirus, adenovirus, and Epstein-Barr virus infections were observed in 45%, 5%, and 0% respectively. Rates of nonrelapse mortality, overall survival, event-free survival, and GVHD-free, event-free survival were 5%, 95%, 90%, and 82%, respectively, at a median follow-up of 4.6 years. Absence of cGVHD correlated with younger patient age and early sustained recovery of regulatory T cells and mature natural killer cells, which in turn was associated with improved QoL and lack of late infections. The AbaCyS protocol was associated with excellent long-term survival, with attenuation of both early and late alloreactivity in >80% of younger patients undergoing haplo-HCT for NMD. This study sheds light on predispositions to cGVHD and its impact on QoL, warranting further optimization of this approach.

Unlocking the potential of oncology biomarkers: advancements in clinical theranostics.

INTRODUCTION: Cancer biomarkers have revolutionized the field of oncology by providing valuable insights into tumor changes and aiding in screening, diagnosis, prognosis, treatment prediction, and risk assessment. The emergence of "omic" technologies has enabled biomarkers to become reliable and accurate predictors of outcomes during cancer treatment. CONTENT: In this review, we highlight the clinical utility of biomarkers in cancer identification and motivate researchers to establish a personalized/precision approach in oncology. By extending a multidisciplinary technology-based approach, biomarkers offer an alternative to traditional techniques, fulfilling the goal of cancer therapeutics to find a needle in a haystack. SUMMARY AND OUTLOOK: We target different forms of cancer to establish a dynamic role of biomarkers in understanding the spectrum of malignancies and their biochemical and molecular characterization, emphasizing their prospective contribution to cancer screening. Biomarkers offer a promising avenue for the early detection of human cancers and the exploration of novel technologies to predict disease severity, facilitating maximum survival and minimum mortality rates. This review provides a comprehensive overview of the potential of biomarkers in oncology and highlights their prospects in advancing cancer diagnosis and treatment.

Potential role of p53 deregulation in modulating immune responses in human malignancies: A paradigm to develop immunotherapy.

The crucial role played by the oncogenic expression of TP53, stemming from mutation or amyloid formation, in various human malignancies has been extensively studied over the past two decades. Interestingly, the potential role of TP53 as a crucial player in modulating immune responses has provided new insight into the field of cancer biology. The loss of p53's transcriptional functions and/or the acquisition of tumorigenic properties can efficiently modulate the recruitment and functions of myeloid and lymphoid cells, ultimately leading to the evasion of immune responses in human tumors. Consequently, the oncogenic nature of the tumor suppressor p53 can dynamically alter the function of immune cells, providing support for tumor progression and metastasis. This review comprehensively explores the dual role of p53 as both the guardian of the genome and an oncogenic driver, especially in the context of regulation of autophagy, apoptosis, the tumor microenvironment, immune cells, innate immunity, and adaptive immune responses. Additionally, the focus of this review centers on how p53 status in the immune response can be harnessed for the development of tailored therapeutic strategies and their potential application in immunotherapy against human malignancies.

LncRNA-mediated orchestrations of alternative splicing in the landscape of breast cancer.

Alternative splicing (AS) is a fundamental post-transcriptional process in eukaryotes, enabling a single gene to generate diverse mRNA transcripts, thereby enhancing protein variability. This process involves the excision of introns and the joining of exons in pre-mRNA(s) to form mature mRNA. The resulting mature mRNAs exhibit various combinations of exons, contributing to functional diversity. Dysregulation of AS can substantially modulate protein functions, impacting the onset and progression of numerous diseases, including cancer. Non-coding RNAs (ncRNAs) are distinct from protein-coding RNAs and consist of short and long types. Long non-coding RNAs (lncRNAs) play an important role in regulating several cellular processes, particularly alternative splicing, according to new research. This review provides insight into the latest discoveries concerning how lncRNAs influence alternative splicing within the realm of breast cancer. Additionally, it explores potential therapeutic strategies focused on targeting lncRNAs.

Role of p53 transcription factor in determining the efficacy of telomerase inhibitors in cancer treatment.

AIM: Telomerase expression is unique to cancer cells, making it a promising target for therapy. However, a major drawback of telomerase inhibition is that it affects cancer cell proliferation only when telomeres shorten, creating a lag phase post-continuous drug treatment. Acute cytotoxicity of telomerase inhibitors is dependent on their ability to induce DNA damage. p53 senses DNA damage and is the primary effector required for sensitizing cells towards apoptosis. MAIN METHODS: Isogenic p53+/+ and p53-/- ovarian cancer cell lines were generated using the CRISPR/Cas9 system and the anti-cancer effect of telomerase inhibitors MST-312 and BIBR1532 were determined. Flow cytometry, real-time PCR, and western blot were performed to study cell cycle, apoptosis, and gene expression. KEY FINDINGS: We report that MST-312 exhibits p53-dependent cytotoxicity, while BIBR1532 exhibits p53-independent cytotoxicity. Colony-forming ability also confirms the p53-dependent effect of MST-312. Re-expression of p53 in p53-/- cells could rescue MST-312 sensitivity. In p53+/+ cells, MST-312 causes S phase arrest and activation of p53-dependent target genes like anti-apoptosis markers (Fas and Puma) and cell cycle markers (p21 and cyclinB). In p53-/- cells, MST-312 causes S/G2/M arrest. BIBR1532 induces S/G2/M phase cell cycle arrest irrespective of p53 status. This correlates with the expression of the DNA damage marker (γ-H2AX). Long-term continuous treatment with MST-312 or BIBR1532 results in p53-independent telomere shortening. SIGNIFICANCE: In summary, we demonstrate that acute anti-cancer effects of MST-312 are dependent on p53 expression. Hence, it is important to consider the p53 expression status in cancer cells when selecting and administering telomerase inhibitors.

Machine learning approaches for the treatment of textile wastewater using sugarcane bagasse (Saccharum officinarum) biochar.

Most dyes present in wastewater from the textile industry exhibit toxicity and are resistant to biodegradation. Hence, the imperative arises for the environmentally significant elimination of textile dye by utilising agricultural waste. The achievement of this objective can be facilitated through the utilisation of the adsorption mechanism, which entails the passive absorption of pollutants using biochar. In this study, we compare the efficacy of the response surface methodology (RSM), the artificial neural network (ANN), the k-nearest neighbour (kNN), and adaptive neuro-fuzzy inference system (ANFIS) in removing crystal violet (CV) from wastewater. The characterisation of biochar is carried out by scanning electron microscope (SEM) and Fourier transform infrared (FTIR). The impacts of the solution pH, adsorbent dosage, initial dye concentration, and temperature were investigated using a variety of models (RSM, ANN, kNN, and ANFIS). The statistical analysis of errors was conducted, resulting in a maximum removal effectiveness of 97.46% under optimised settings. These conditions included an adsorbent dose of 0.4 mg, a pH of 5, a CV concentration of 40.1 mg/L, and a temperature of 20 °C. The ANN, RSM, kNN, and ANFIS models all achieved R2 0.9685, 0.9618, 0.9421, and 0.8823, respectively. Even though all models showed accuracy in predicting the removal of CV dye, it was observed that the ANN model exhibited greater accuracy compared to the other models.

AI-enhanced adsorption modeling: Challenges, applications, and bibliographic analysis.

Inorganic and organic contaminants, such as fertilisers, heavy metals, and dyes, are the primary causes of water pollution. The field of artificial intelligence (AI) has received significant interest due to its capacity to address challenges across various fields. The use of AI techniques in water treatment and desalination has recently shown useful for optimising processes and dealing with the challenges of water pollution and scarcity. The utilization of AI in the water treatment industry is anticipated to result in a reduction in operational expenditures through the lowering of procedure costs and the optimisation of chemical utilization. The predictive capabilities of artificial intelligence models have accurately assessed the efficacy of different adsorbents in removing contaminants from wastewater. This article provides an overview of the various AI techniques and how they can be used in the adsorption of contaminants during the water treatment process. The reviewed publications were analysed for their diversity in journal type, publication year, research methodology, and initial study context. Citation network analysis, an objective method, and tools like VOSviewer are used to find these groups. The primary issues that need to be addressed include the availability and selection of data, low reproducibility, and little proof of uses in real water treatment. The provision of challenges is essential to ensure the prospective success of AI associated with technologies. The brief overview holds importance to everyone involved in the field of water, encompassing scientists, engineers, students, and stakeholders.