Search

Perovskite materials have garnered significant attention within a very short period of time by achieving competitive efficiency. In addition, this material demonstrated intriguing optoelectronic properties and versatile applications. Although they have confirmed amazing efficiency in solar cells at the laboratory scale, mass commercial manufacturing of perovskite solar cells (PSCs) is still a problem due to their poor longevity. Researchers have identified several intrinsic and extrinsic factors contributing to the instability of perovskite compounds and PSCs, and various approaches are being used to increase material quality and stability in order to extend the lifespan of PSCs. Despite these challenges, the potential of perovskite materials in revolutionizing solar energy remains a central point of scientific investigation and development. In this review, a comprehensive analysis is provided to discern the intrinsic and extrinsic factors contributing to the degradation of PSCs which certainly helps us to understand the underlying degradation mechanisms. In addition, we discussed some novel approaches that have already been adopted to augment the stability of the devices., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (This journal is © The Royal Society of Chemistry.)

Objectives: The prevalence of tobacco use is high in Bangladesh, with a cigarette being the most consumed tobacco product. Taxing tobacco is highly effective for tobacco control, raising costs and encouraging smokers to quit or reduce cigarette consumption. Garment workers are the major group of low-wage earners in Bangladesh. This study aims to determine the perceived effects of raising cigarette prices on smoking behaviors and their influences on quitting the decision of male garment workers in Bangladesh., Study Design: Cross-sectional study., Methods: The Study adopted a cross-sectional method among 739 male garment workers from Gazipur and Savar, Dhaka, for six months, using a semi-structured questionnaire. Factors such as the prevalence of lifetime tobacco use and rising cigarette prices on smoking behaviors were analyzed., Results: The study found that most participants began smoking before age 16, and increased tobacco prices influenced quitting decisions for both smoking and smokeless tobacco users, with reductions of 79% and 68%, respectively. Higher prices notably impacted smoking cessation among lower-income individuals, decreasing smoking frequency by 12-fold. Significantly changing their smoking behavior, on average, daily cigarette or biri consumption decreased from 7 to 5, with some participants switching to less expensive tobacco products., Conclusion: Findings from the study depict that imposing a high tax on tobacco products such as cigarette or biri has a desirable impact on the smoking behavior of low earning community. The price of all tobacco products should be increased to auspicate a considerable positive change in the community., (Crown Copyright © 2024. Published by Elsevier Ltd. All rights reserved.)

T-cell engagers (TCEs) represent a promising therapeutic strategy for various cancers and autoimmune disorders. These bispecific antibodies act as bridges, connecting T-cell receptors (TCRs) to target cells (either malignant or autoreactive) via interactions with specific tumour-associated antigens (TAAs) or autoantigens to form trimeric synapses, or trimers, that co-localise T-cells with target cells and stimulate their cytotoxic function. Bispecific TCEs are expected to exhibit a bell-shaped dose-response curve, with a defined optimal TCE exposure for maximizing trimer formation. The shape of the dose-response is determined by a non-trivial interplay of binding affinities, exposure and antigens expression levels. Furthermore, excessively low binding to the TCR may reduce efficacy, but mitigate risk of over-stimulating cytokine secretion or induce effector cell exhaustion. These inevitable trade-off highlights the importance of quantitatively understanding the relationship between TCE concentration, target expression, binding affinities, and trimer formation. We utilized a mechanistic target engagement model to show that, if the TCE design parameters are close to the recommended ranges found in the literature, relative affinities for TCR, TAA and target expression levels have qualitatively different, but predictable, effects on the resulting dose-response curve: higher expression levels shift the curve upwards, higher antigen affinity shifts the curve to the left, and higher TCR affinity shifts the curve upwards and to the left., Competing Interests: All authors are employees and shareholders of AstraZeneca Plc., (Copyright © 2024 Lai, Pichardo-Almarza, Verma, Shahinuzzaman, Zhu and Kimko.)

This study delves into enhancing the efficiency and stability of perovskite solar cells (PSCs) by optimizing the surface morphologies and optoelectronic properties of the electron transport layer (ETL) using tungsten (W) doping in zinc oxide (ZnO). Through a unique green synthesis process and spin-coating technique, W-doped ZnO films were prepared, exhibiting improved electrical conductivity and reduced interface defects between the ETL and perovskite layers, thus facilitating efficient electron transfer at the interface. High-quality PSCs with superior ETL demonstrated a substantial 30% increase in power conversion efficiency (PCE) compared to those employing pristine ZnO ETL. These solar cells retained over 70% of their initial PCE after 4000 h of moisture exposure, surpassing reference PSCs by 50% PCE over this period. Advanced numerical multiphysics solvers, employing finite-difference time-domain (FDTD) and finite element method (FEM) techniques, were utilized to elucidate the underlying optoelectrical characteristics of the PSCs, with simulated results corroborating experimental findings. The study concludes with a thorough discussion on charge transport and recombination mechanisms, providing insights into the enhanced performance and stability achieved through W-doped ZnO ETL optimization.

Objective: Cancer is a major cause of death globally, and places a substantial burden on both patients and their caregivers. Frequent stress among caregivers often affects their mental well-being. This study was aimed at assessing anxiety and depression levels among informal caregivers of patients with cancer treated at selected tertiary hospitals in Nepal. An additional aim was to identify socio-demographic factors associated with these mental health outcomes., Methods: In this cross-sectional study, 383 informal caregivers were surveyed with the Hospital Anxiety and Depression Scale (HADS). Statistical analyses, including descriptive analysis and binary logistic regression, were conducted to explore associations with socio-demographic variables., Results: The mean age of participants was 36.1 ± 13.1 years, and 56.1% were 12-36 years old. Most participants were married (81.5%), were unemployed (66.6%), and had primary to secondary education (66.6%). The prevalence of moderate to severe anxiety (52%) and depression (45%) among caregivers was notable. Caregivers of patients treated at non-governmental hospitals were six times more likely (OR 6.3, 95% CI: [3.62-10.95], P  = 0.001) to have anxiety and five times more likely (OR 5.3, 95% CI: [8.28-19.32], P  = 0.001) to have depression., Conclusion: People who take care of patients with cancer in Nepal often feel substantial stress. Determining the causes of these feelings can aid in the creation of programs to support caregivers' mental health. Caring for mental well-being among caregivers is critical to achieving better cancer care and quality of life., (© 2024 The Authors.)

This study provides an efficient and straightforward approach to eliminate carbon dioxide (CO 2 ) by absorption using a calcium oxide (CaO) sorbent derived from chicken eggshells. The sorbent concentration, stirring speed, and contact time were varied. The optimal condition for CO 2 removal was a 10% calcium hydroxide (Ca(OH) 2 ) suspension at 600 rpm with 20 min interaction. This optimum condition conferred the ever-highest absorption (98.71%) of CO 2 through Ca(OH) 2 suspensions from eggshell-derived CaO. X-ray diffraction was used to identify crystallographic phases and optimum conditions revealed calcium carbonate (CaCO 3 ) formation with the highest intensity, Fourier transform infrared spectroscopy revealed peaks for the carbonate (CO 3 2- ) group, field emission scanning electron microscopy was used to investigate the morphological and structural properties of the sorbent before and after CO 2 absorption, and thermogravimetric analysis was performed to understand the reaction mechanism. According to the kinetic analysis, the sorbent can be fully decomposed with a minimum activation energy ( E a ) of 89.09 kJ/mol., Competing Interests: The authors declare no competing financial interest., (© 2023 The Authors. Published by American Chemical Society.)

In this study, lead sulfide (PbS) nanoparticles were synthesized by the chemical precipitation method using Aloe Vera extract with PbCl 2 and Thiourea (H 2 N-CS-NH 2 ). The synthesized nanoparticles have been investigated using x-ray diffraction (XRD), UV-Vis, energy-dispersive x-ray spectroscopy (EDX), scanning electron microscopy (SEM), and transmission electron microscopy (TEM). XRD and TEM results confirm that the films are in the cubic phase. The crystallite size, lattice constant, micro-strain, dislocation density, optical bandgap, etc. have been determined using XRD and UV-Vis for investigating the quality of prepared nanoparticles. The possible application of these synthesized nanoparticles in the solar cells was investigated by fabricating the thin films on an FTO-coated and bare glass substrate. The properties of nanoparticles were found to be nearly retained in the film state as well. The experimentally found properties of thin films have been implemented for perovskite solar cell simulation and current-voltage and capacitance-voltage characteristics have been investigated. The simulation results showed that PbS nanoparticles could be a potential hole transport layer for high-efficiency perovskite solar cell applications.

Individual cells in a solution display variable uptake of nanomaterials, peptides, and nutrients. Such variability reflects their heterogeneity in endocytic capacity. In a recent work, we have shown that the endocytic capacity of a cell depends on its size and surface density of endocytic components (transporters). We also demonstrated that in MDA-MB-231 breast cancer cells, the cell-surface transporter density (n) may decay with cell radius (r) following the power rule n ∼ r α , where α ≈ -1. In this work, we investigate how n and r may independently contribute to the endocytic heterogeneity of a cell population. Our analysis indicates that the smaller cells display more heterogeneity because of the higher stochastic variations in n. By contrast, the larger cells display a more uniform uptake, reflecting less-stochastic variations in n. We provide analyses of these dependencies by establishing a stochastic model. Our analysis reveals that the exponent α in the above relationship is not a constant; rather, it is a random variable whose distribution depends on cell size r. Using Bayesian analysis, we characterize the cell-size-dependent distributions of α that accurately capture the particle uptake heterogeneity of MDA-MB-231 cells., (Copyright © 2020 Biophysical Society. Published by Elsevier Inc. All rights reserved.)

The size of a cell is central to many functions, including cellular communication and exchange of materials with the environment. This modeling and experimental study focused on understanding how the size of a cell determines its ability to uptake nanometer-scale extracellular materials from the environment. Several mechanisms in the cell plasma membrane mediate cellular uptake of nutrients, biomolecules, and particles. These mechanisms involve recognition and internalization of the extracellular molecules via endocytic components, such as clathrin-coated pits, vacuoles, and micropinocytic vesicles. Because the demand for an external resource could be different for cells of different sizes, the collective actions of these various endocytic routes should also vary based on the cell size. Here, using a reaction-diffusion model, we analyze single-cell data to interrogate the one/one mapping between the size of the MDA-MB 231 breast cancer cells and their ability to uptake nanoparticles. Our analysis indicates that under both reaction- and diffusion-controlled regimes, cellular uptake follows a linear relationship with the cell radius. Furthermore, this linear dependency is insensitive to particle size variation within 20-200 nm range. This result is counterintuitive because the general perception is that cellular uptake is proportional to the cell volume (mass) or surface area and hence follow a cubic or square relationship with the cell radius. A further analysis using our model reveals a potential mechanism underlying this linear relationship., (Copyright © 2018 Biophysical Society. Published by Elsevier Inc. All rights reserved.)

Receptor tyrosine kinases (RTKs) typically contain multiple autophosphorylation sites in their cytoplasmic domains. Once activated, these autophosphorylation sites can recruit downstream signaling proteins containing Src homology 2 (SH2) and phosphotyrosine-binding (PTB) domains, which recognize phosphotyrosine-containing short linear motifs (SLiMs). These domains and SLiMs have polyspecific or promiscuous binding activities. Thus, multiple signaling proteins may compete for binding to a common SLiM and vice versa. To investigate the effects of competition on RTK signaling, we used a rule-based modeling approach to develop and analyze models for ligand-induced recruitment of SH2/PTB domain-containing proteins to autophosphorylation sites in the insulin-like growth factor 1 (IGF1) receptor (IGF1R). Models were parameterized using published datasets reporting protein copy numbers and site-specific binding affinities. Simulations were facilitated by a novel application of model restructuration, to reduce redundancy in rule-derived equations. We compare predictions obtained via numerical simulation of the model to those obtained through simple prediction methods, such as through an analytical approximation, or ranking by copy number and/or KD value, and find that the simple methods are unable to recapitulate the predictions of numerical simulations. We created 45 cell line-specific models that demonstrate how early events in IGF1R signaling depend on the protein abundance profile of a cell. Simulations, facilitated by model restructuration, identified pairs of IGF1R binding partners that are recruited in anti-correlated and correlated fashions, despite no inclusion of cooperativity in our models. This work shows that the outcome of competition depends on the physicochemical parameters that characterize pairwise interactions, as well as network properties, including network connectivity and the relative abundances of competitors., Competing Interests: The authors have declared that no competing interests exist.

Aggregation of cell surface receptor proteins by multivalent antigens is an essential early step for immune cell signalling. A number of experimental and modelling studies in the past have investigated multivalent ligand-mediated aggregation of IgE receptors (Fc ɛ RI) in the plasma membrane of mast cells. However, understanding of the mechanisms of Fc ɛ RI aggregation remains incomplete. Experimental reports indicate that Fc ɛ RI forms relatively small and finite-sized clusters when stimulated by a multivalent ligand. By contrast, modelling studies have shown that receptor cross-linking by a trivalent ligand may lead to the formation of large receptor superaggregates that may potentially give rise to hyperactive cellular responses. In this work, we have developed a Brownian dynamics-based spatio-temporal model to analyse Fc ɛ RI aggregation by a trivalent antigen. Unlike the existing models, which implemented non-spatial simulation approaches, our model explicitly accounts for the coarse-grained site-specific features of the multivalent species (molecules and complexes). The model incorporates membrane diffusion, steric collisions and sub-nanometre-scale site-specific interaction of the time-evolving species of arbitrary structures. Using the model, we investigated temporal evolution of the species and their diffusivities. Consistent with a recent experimental report, our model predicted sharp decay in species mobility in the plasma membrane in response receptor cross-linking by a multivalent antigen. We show that, due to such decay in the species mobility, post-stimulation receptor aggregation may become self-limiting. Our analysis reveals a potential regulatory mechanism suppressing hyperactivation of immune cells in response to multivalent antigens., Competing Interests: We declare we have no competing interests.

This article introduces a multiscale framework for spatiotemporal modeling of protein-protein interaction. Cellular protein molecules represent multivalent species that contain modular features, such as binding domains and phosphorylation motifs. The binding and transformations of these features occur at a small time and spatial scale. On the other hand, space and time involved in protein diffusion, colocalization, and formation of complexes could be relatively large. Here, we present an agent-based framework integrated with a multiscale Brownian Dynamics (BD) simulation algorithm. The framework employs spatial graphs to describe multivalent molecules and complexes with their site-specific details. By implementing a time-adaptive feature, the BD algorithm enables efficient computation while capturing the site-specific interactions of the diffusing species at the sub-nanometer scale. We demonstrate these capabilities by modeling two multivalent molecules, one representing a ligand and the other a receptor, in a two-dimensional plane (cell membrane). Using the model, we show that the algorithm can accelerate computation by orders of magnitudes in both concentrated and dilute regimes. We also show that the algorithm enables robust model predictions against a wide range of selection of time step sizes.