Catalytical performance of heteroatom doped and undoped carbon-based materials

Developing cost-effective, eco-friendly, efficient, stable, and unique catalytic systems remains a crucial issue in catalysis. Due to their superior physicochemical and electrochemical properties, exceptional structural characteristics, environmental friendliness, economic productivity, minimal energy demand, and abundant supply, a significant amount of research has been devoted to the development of various doped carbon materials as efficient catalysts. In addition, carbon-based materials (CBMs) with specified doping have lately become significant members of the carbon group, showing promise for a broad range of uses (e.g., catalysis, environmental remediation, critical chemical production, and energy conversion and storage). This study will, therefore, pay attention to the function of heteroatom-based doped and undoped CBMs for catalytical applications and discuss the underlying chemistries of catalysis. According to the findings, doping CBMs may greatly improve their catalytic activity, and heteroatom-doped CBMs may be a promising option for further metal doping to attach them to an appropriate place. This paper also covers the potential applications of both doped and undoped CBMs in the future

Recent Advances in Centrifugal Spinning and Their Applications in Tissue Engineering

Over the last decade, researchers have investigated the potential of nano and microfiber scaffolds to promote wound healing, tissue regeneration, and skin protection. The centrifugal spinning technique is favored over others due to its relatively straightforward mechanism for producing large quantities of fiber. Many polymeric materials have yet to be investigated in search of those with multifunctional properties that would make them attractive in tissue applications. This literature presents the fundamental process of fiber generation, and the effects of fabrication parameters (machine, solution) on the morphologies such as fiber diameter, distribution, alignment, porous features, and mechanical properties. Additionally, a brief discussion is presented on the underlying physics of beaded morphology and continuous fiber formation. Consequently, the study provides an overview of the current advancements in centrifugally spun polymeric fiber-based materials and their morphological features, performance, and characteristics for tissue engineering applications

Machine Learning and Bioinformatics Models to Identify Pathways that Mediate Influences of Welding Fumes on Cancer Progression

Abstract: Welding generates and releases fumes that are hazardous to human health. Welding fumes (WFs) are a complex mix of metallic oxides, fluorides and silicates that can cause or exacerbate health problems in exposed individuals. In particular, WF inhalation over an extended period carries an increased risk of cancer, but how WFs may influence cancer behaviour or growth is unclear. To address this issue we employed a quantitative analytical framework to identify the gene expression effects of WFs that may affect the subsequent behaviour of the cancers. We examined datasets of transcript analyses made using microarray studies of WF-exposed tissues and of cancers, including datasets from colorectal cancer (CC), prostate cancer (PC), lung cancer (LC) and gastric cancer (GC). We constructed gene-disease association networks, identified signaling and ontological pathways, clustered protein-protein interaction network using multilayer network topology, and analyzed survival function of the significant genes using Cox proportional hazards (Cox PH) model and product-limit (PL) estimator. We observed that WF exposure causes altered expression of many genes (36, 13, 25 and 17 respectively) whose expression are also altered in CC, PC, LC and GC. Gene-disease association networks, signaling and ontological pathways, protein-protein interaction network, and survival functions of the significant genes suggest ways that WFs may influence the progression of CC, PC, LC and GC. This quantitative analytical framework has identified potentially novel mechanisms by which tissue WF exposure may lead to gene expression changes in tissue gene expression that affect cancer behaviour and, thus, cancer progression, growth or establishment

Machine Learning and Bioinformatics Models to Identify Pathways that Mediate Influences of Welding Fumes on Cancer Progression

Abstract: Welding generates and releases fumes that are hazardous to human health. Welding fumes (WFs) are a complex mix of metallic oxides, fluorides and silicates that can cause or exacerbate health problems in exposed individuals. In particular, WF inhalation over an extended period carries an increased risk of cancer, but how WFs may influence cancer behaviour or growth is unclear. To address this issue we employed a quantitative analytical framework to identify the gene expression effects of WFs that may affect the subsequent behaviour of the cancers. We examined datasets of transcript analyses made using microarray studies of WF-exposed tissues and of cancers, including datasets from colorectal cancer (CC), prostate cancer (PC), lung cancer (LC) and gastric cancer (GC). We constructed gene-disease association networks, identified signaling and ontological pathways, clustered protein-protein interaction network using multilayer network topology, and analyzed survival function of the significant genes using Cox proportional hazards (Cox PH) model and product-limit (PL) estimator. We observed that WF exposure causes altered expression of many genes (36, 13, 25 and 17 respectively) whose expression are also altered in CC, PC, LC and GC. Gene-disease association networks, signaling and ontological pathways, protein-protein interaction network, and survival functions of the significant genes suggest ways that WFs may influence the progression of CC, PC, LC and GC. This quantitative analytical framework has identified potentially novel mechanisms by which tissue WF exposure may lead to gene expression changes in tissue gene expression that affect cancer behaviour and, thus, cancer progression, growth or establishment

Convalescent plasma in patients admitted to hospital with COVID-19 (RECOVERY): a randomised controlled, open-label, platform trial

SummaryBackground Azithromycin has been proposed as a treatment for COVID-19 on the basis of its immunomodulatoryactions. We aimed to evaluate the safety and efficacy of azithromycin in patients admitted to hospital with COVID-19.Methods In this randomised, controlled, open-label, adaptive platform trial (Randomised Evaluation of COVID-19Therapy [RECOVERY]), several possible treatments were compared with usual care in patients admitted to hospitalwith COVID-19 in the UK. The trial is underway at 176 hospitals in the UK. Eligible and consenting patients wererandomly allocated to either usual standard of care alone or usual standard of care plus azithromycin 500 mg once perday by mouth or intravenously for 10 days or until discharge (or allocation to one of the other RECOVERY treatmentgroups). Patients were assigned via web-based simple (unstratified) randomisation with allocation concealment andwere twice as likely to be randomly assigned to usual care than to any of the active treatment groups. Participants andlocal study staff were not masked to the allocated treatment, but all others involved in the trial were masked to theoutcome data during the trial. The primary outcome was 28-day all-cause mortality, assessed in the intention-to-treatpopulation. The trial is registered with ISRCTN, 50189673, and ClinicalTrials.gov, NCT04381936.Findings Between April 7 and Nov 27, 2020, of 16 442 patients enrolled in the RECOVERY trial, 9433 (57%) wereeligible and 7763 were included in the assessment of azithromycin. The mean age of these study participants was65·3 years (SD 15·7) and approximately a third were women (2944 [38%] of 7763). 2582 patients were randomlyallocated to receive azithromycin and 5181 patients were randomly allocated to usual care alone. Overall,561 (22%) patients allocated to azithromycin and 1162 (22%) patients allocated to usual care died within 28 days(rate ratio 0·97, 95% CI 0·87–1·07; p=0·50). No significant difference was seen in duration of hospital stay (median10 days [IQR 5 to >28] vs 11 days [5 to >28]) or the proportion of patients discharged from hospital alive within 28 days(rate ratio 1·04, 95% CI 0·98–1·10; p=0·19). Among those not on invasive mechanical ventilation at baseline, nosignificant difference was seen in the proportion meeting the composite endpoint of invasive mechanical ventilationor death (risk ratio 0·95, 95% CI 0·87–1·03; p=0·24).Interpretation In patients admitted to hospital with COVID-19, azithromycin did not improve survival or otherprespecified clinical outcomes. Azithromycin use in patients admitted to hospital with COVID-19 should be restrictedto patients in whom there is a clear antimicrobial indication

Mechanistic insight into antibiotics and endocrine disruptors removal by wood-derived biochar, functionalized biochar and biochar composite

University of Technology Sydney. Faculty of Engineering and Information Technology.Today water pollution has become one of the most critical issues in many regions of the world. The presence of antibiotics and endocrine disruptors (EDCs) in the aquatic environment causes critical problems to aquatic life and eventually on human. The efficacy for removing antibiotics (such as sulfamethazine (SMT), sulfamethoxazole (SMX), sulfathiazole (STZ) and chloramphenicol (CP)) and EDCs (such as estrone (E1), 17β-estradiol (E2), estriol (E3), 17α-ethynylestradiol (EE2), bisphenol A (BPA) and 4-tert-butylphenol (4tBP)) in traditional wastewater treatment processes is not satisfactory. However, adsorptive materials are cost effective and highly suitable for removal of different pollutants. Adsorptive materials such as biochar, functionalized biochar (fBC) and biochar composite with zero-valent-iron were prepared by the utilization of Australian abundantly grown woody biomasses (e.g. bamboo and eucalyptus wood). The materials were applied, for the first time, to remove antibiotics and EDCs, in both single and competitive modes, from water and wastewaters (e.g. synthetic wastewater, Lake Water and sewage sludge wastewater). Special focus was given to both single and competitive sorption performance based on experimental and theoretical findings. Sorption mechanism was determined based on pH profile, speciation, ionic strength, materials characteristics, temperature, thermodynamics, solution chemistry, and water matrixes. Different isotherm models such as the Langmuir isotherm model, Freundlich isotherm model, and Polani-Mane model were applied to analyse the sorption data. For competitive sorption, summarized isotherm models were proposed. Several kinetic models such as pseudo first order kinetic model, pseudo second order kinetic model, intra-particle diffusion model and Boyd model were also employed. Effects of different synthetic wastewater composition, ions and competitive solute were also studied. The findings suggested that antibiotics and EDCs sorption occurred mainly through pseudo-second order and external mass transfer diffusion processes, by forming different types of H-bonds along with π-π electron-donor–acceptor (EDA) interactions at different pH. More importantly specific direction of π-π EDA interaction was proposed based on 1ᴴ NMR spectroscopic study. fBC was regenerated and reused several times after treatment of different water. Therefore, fBC can be a potent sorbent to adsorb a wide varieties of organic contaminants from water and wastewater

Identifying molecular insight of synergistic complexities for SARS-CoV-2 infection with pre-existing type 2 diabetes

The ongoing COVID-19 outbreak, caused by SARS-CoV-2, has posed a massive threat to global public health, especially to people with underlying health conditions. Type 2 diabetes (T2D) is lethal comorbidity of COVID-19. However, its pathogenetic link remains unclear. This research aims to determine the genetic factors and processes contributing to the synergistic severity of SARS-CoV-2 infection among T2D patients through bioinformatics approaches. We analyzed two sets of transcriptomic data of SARS-CoV-2 infection obtained from lung epithelium cells and PBMCs, and two sets of T2D data from pancreatic islet cells and PBMCs to identify the associated differentially expressed genes (DEGs) followed by their functional enrichment analyses in terms of protein-protein interaction (PPI) to detect hub-proteins and associated comorbidities, transcription factors (TFs), microRNAs (miRNAs) as well as the potential drug candidates. In PPI analysis, four potential hub-proteins (i.e., BIRC3, C3, MME, and IL1B) were identified among 25 DEGs shared between the disease pair. Enrichment analyses using the mutually overlapped DEGs revealed the most prevalent GO and cell signalling pathways, including TNF signalling, cytokine-cytokine receptor interaction, and IL-17 signalling, which are related to cytokine activities. Furthermore, as significant TFs, we identified IRF1, KLF11, FOSL1, and CREB3L1 while miRNAs including miR-1-3p, 34a-5p, 16-5p, 155-5p, 20a-5p, and let-7b-5p were found to be noteworthy. The findings illustrated the significant association between COVID-19 and T2D at the molecular level. These genetic determinants can further be explored for their specific roles in disease progression and therapeutic intervention, while significant pathways can also be studied as molecular checkpoints. Finally, the identified drug candidates may be evaluated for their potency to minimize the severity of COVID-19 patients with pre-existing T2D

Super-Adsorptive Biodegradable Hydrogel from Simply Treated Sugarcane Bagasse

There is a great demand for biodegradable hydrogel, and cellulose enriched wastes materials are widely used to serve this purpose for various advance applications (e.g., biomedical and environmental). Sugarcane bagasse is cellulose-enriched agro-waste, abundantly grown in Bangladesh. This study aimed to treat sugarcane bagasse-based agro-waste using a sustainable and ecofriendly approach to produce hydrogel with super-swelling capacity for adsorption of copper, chromium, iron ions, methylene blue and drimaren red dyes. To increase the swelling property of hydrogels, copolymerization of hydrophilic monomers is an effective technique. Therefore, this study aimed to prepare hydrogel via free radical graft-copolymerization reaction among acrylamide, methyl methacrylate and treated bagasse in the presence of N,N-methylene-bis-acrylamide as a crosslinker and potassium persulphate as an initiator. To obtain maximum yield, reaction conditions were optimized. It was found that hydrogel obtained from chemically treated sugarcane bagasse showed maximum water absorption capacity of 228.0 g/g, whereas untreated bagassebased hydrogel could absorb similar to 50 g/g of water. Maximum adsorption capacity of 247.0 mg/g was found for copper ion. In addition, organic pollutant removal from industrial effluent also showed good performance, removing >90% of methylene blue and 62% of drimaren red dye, with shorter kinetics. The biodegradability study showed that after 90 days of exposure, the hydrogels degraded to about 43% of their own mass. Therefore, the produced hydrogel could be an alternative adsorbent to remove pollutants and also for other potential applications

Industrial metal pollution in water and probabilistic assessment of human health risk

Concentration of eight heavy metals in surface and groundwater around Dhaka Export Processing Zone (DEPZ) industrial area were investigated, and the health risk posed to local children and adult residents via ingestion and dermal contact was evaluated using deterministic and probabilistic approaches. Metal concentrations (except Cu, Mn, Ni, and Zn) in Bangshi River water were above the drinking water quality guidelines, while in groundwater were less than the recommended limits. Concentration of metals in surface water decreased as a function of distance. Estimations of non-carcinogenic health risk for surface water revealed that mean hazard index (HI) values of As, Cr, Cu, and Pb for combined pathways (i.e., ingestion and dermal contact) were >1.0 for both age groups. The estimated risk mainly came from the ingestion pathway. However, the HI values for all the examined metals in groundwater were 1\ua0×\ua010 for adult and children, respectively. Deterministic and probabilistic estimations of cancer risk through exposure to groundwater were well below the safety limit. Overall, the population exposed to Bangshi River water remained at carcinogenic and non-carcinogenic health threat and the risk was higher for adults. Sensitivity analysis identified exposure duration (ED) and ingestion rate (IR) of water as the most relevant variables affecting the probabilistic risk estimation model outcome

Machine Learning and Bioinformatics Models to Identify Pathways that Mediate Influences of Welding Fumes on Cancer Progression.

Welding generates and releases fumes that are hazardous to human health. Welding fumes (WFs) are a complex mix of metallic oxides, fluorides and silicates that can cause or exacerbate health problems in exposed individuals. In particular, WF inhalation over an extended period carries an increased risk of cancer, but how WFs may influence cancer behaviour or growth is unclear. To address this issue we employed a quantitative analytical framework to identify the gene expression effects of WFs that may affect the subsequent behaviour of the cancers. We examined datasets of transcript analyses made using microarray studies of WF-exposed tissues and of cancers, including datasets from colorectal cancer (CC), prostate cancer (PC), lung cancer (LC) and gastric cancer (GC). We constructed gene-disease association networks, identified signaling and ontological pathways, clustered protein-protein interaction network using multilayer network topology, and analyzed survival function of the significant genes using Cox proportional hazards (Cox PH) model and product-limit (PL) estimator. We observed that WF exposure causes altered expression of many genes (36, 13, 25 and 17 respectively) whose expression are also altered in CC, PC, LC and GC. Gene-disease association networks, signaling and ontological pathways, protein-protein interaction network, and survival functions of the significant genes suggest ways that WFs may influence the progression of CC, PC, LC and GC. This quantitative analytical framework has identified potentially novel mechanisms by which tissue WF exposure may lead to gene expression changes in tissue gene expression that affect cancer behaviour and, thus, cancer progression, growth or establishment