Introduction and Objective: Drugs targeting the glucagon-like peptide-1 receptor (GLP-1R), particularly dual or triple agonist chimeras that co-activate the glucose-dependent insulinotropic polypeptide receptor (GIPR) and/or the glucagon receptor (GcgR), are currently the most effective treatments for obesity and Type 2 Diabetes. However, common adverse side effects of such compounds, including nausea, often results in early treatment dropout. Peptide YY (PYY) can further reduce food intake and weight loss via Y2 receptor (Y2R) agonism, and co-treatment with GIP can mitigate PYY-induced nausea. Thus, unimolecular tetra-receptor agonists (TRA) chimeras that co-induce Y2R with GLP1-R/GIPR/GcgR agonism may have superior therapeutic efficacy with better tolerability to treat obesity and diabetes. Methods: We investigated the acute impact of three novel differentially balanced TRAs on glucose metabolism and food intake. Following a single subcutaneous injection of each TRA (1-30 nmol/kg), we measured glucose tolerance and food intake in chow and high fat diet (HFD)-fed young (6-months) and old (24-29-months) female and male C57BL/6 mice. Results: Each TRA dose dependently improved glucose tolerance in young and old female and male mice versus vehicle controls, with comparable efficacies as tirzepatide (established GLP-1R/GIPR dual agonist, 30 nmol/kg) as a positive control. TRAs decreased 24-hour food intake in young HFD-fed female and male mice by up to 50% versus vehicle controls to levels similar to tirzepatide. Of note, in both sexes of young chow-fed mice, the TRAs decreased food intake to a slightly greater extent than observed after injection of tirzepatide. Conclusion: These data indicate that TRAs have beneficial acute metabolic effects that are comparable to those induced by tirzepatide and rely on co-activation of four (vs. two) different receptors. Future studies will reveal whether TRAs have superior long-term efficacy with reduced adverse effects than existing drugs for treating obesity and diabetes. Disclosure T.C. Dinsmore: None. K. Wellenstein: None. M. Beinborn: None. K. Kumar: None. J. Lee: None.

The expansion of global population and industrialization has resulted in an increasing demand for energy in various sectors including petrochemicals, energy storage, pharmaceuticals, and electronics and electricals leads to several challenges such as environmental degradation, conventional resource depletion, and energy insecurity. As a result, for balancing daily energy needs efficient and sustainable energy storage solutions, such as supercapacitors are required that provide rapid energy storage and release, along with long cycle life and minimal environmental impact. While existing literature primarily discusses conventional materials for energy storage which lacks comprehensive analysis of fabrication strategies and morphological structures of biomass-based electrodes. Therefore, the present review comprehensively highlights the substantial potential of carbonized biomass precursors as a sustainable alternative. Several fabrication strategies for carbonized biomass concerning various morphological dimensions such as zero dimensional (0-D), one dimensional (1-D), two dimensional (2-D), and three dimensional (3-D) are comprehensively explored for enhanced electrode performance, along with recent advancements in biomass conversion and activation techniques. In addition, the influence of nanostructure-based dopants on the performance of biomass-derived carbon electrodes, especially focusing on the charge transfer efficiency, cycling stability, and energy storage capacity is thoroughly discussed. Furthermore, the review addresses current challenges and future directions for synthesizing nanostructure-doped carbonized biomass materials for large-scale supercapacitor applications. Thus, this review offers a valuable source for researchers and industries seeking to innovate in sustainable energy storage solutions by bridging the existing knowledge gaps.

Understanding the impact of composition and interfaces between metals and oxides is a goal of interest for many chemical reactions. Herein, we propose a framework to map correlations between the electrochemical behavior of the oxide and the stability and reactivity of metal|oxide interfaces, exemplified by Cu|oxide for the electrochemical CO2 reduction reaction (CO2RR). Copper materials interfaced with metal oxides have emerged as promising CO2RR catalysts for selectivity toward multicarbon products, including alcohols; stability under operation has been reported for some of them. However, design rules are currently lacking. Herein, we propose the synthesis of well-defined Cu-MOx core-shell nanoparticles to investigate and compare the behavior of Cu-ZrOx, Cu-MgOx, and Cu-TiOx. By tracking the speciation and morphological evolution of these model catalyst materials, we find that the cathodic stability of the formed interfaces is determined by the operating potential and phase stability of the pure oxides and by their chemical interaction with copper. We learn that the interplay between these factors shapes the restructuring pathways for Cu-MOx catalysts and eventually drives their selectivity in the CO2RR. The developed understanding can be applied beyond this reaction, and the developed nanomaterials can be used beyond catalysis.

E-waste management (EWM) refers to the operation-management of discarded or unproductive electronic devices and components, a challenge exacerbated due to overindulgent urbanization. This article presents a multidimensional cost-function-based analysis of the EWM framework structured on three modules - environmental, economic, and social uncertainties - which contemplate the 3 pillars of sustainability in an e-waste recycling plant, including the production-delivery-utilization process. The framework incorporates material recovery from a single e-waste facility provisioning for chemical and mechanical recycling. Each module is ranked using independent Machine Learning (ML) protocols: a) Analytical Hierarchical Process (AHP) and b) combined AHP and Principal Component Analysis (PCA). From a long list of possible contributors, the model identifies and ranks two key sustainability contributors to the EWM supply chain: overall energy consumption and volume of carbon dioxide generated. Another key finding is a precise time window for policy resurrection, which for the data considered, happens to be 400-600 days from the start of operation. Another interesting outcome is the quality of prediction using a combination of AHP and PCA, which consistently produced better results than any of these ML methods individually implemented. Model outcomes have been verified using a case study to outline a future E-waste sustained roadmap.

Glucose-dependent insulinotropic peptide (GIP) is a 42-amino acid peptide hormone that regulates postprandial glucose levels. GIP binds to its cognate receptor, GIPR, and mediates metabolic physiology by improved insulin sensitivity, β-cell proliferation, increased energy consumption, and stimulated glucagon secretion. Dipeptidyl peptidase-4 (DPP4) catalyzes the rapid inactivation of GIP within 6 min in vivo. Here, we report a molecular platform for the design of GIP analogues that are refractory to DPP4 action and exhibit differential activation of the receptor, thus offering potentially hundreds of GIP-based compounds to fine-tune pharmacology. The lead compound from our studies, which harbored a combination of N-terminal alkylation and side-chain lipidation, was equipotent and retained full efficacy at GIPR as the native peptide, while being completely refractory toward DPP4, and was resistant to trypsin. The GIP analogue identified from these studies was further evaluated in vivo and is one of the longest-acting GIPR agonists to date.

Purpose: E-waste management (EWM) refers to the operation management of discarded electronic devices, a challenge exacerbated due to overindulgent urbanization. The main purpose of this paper is to amalgamate production engineering, statistical methods, mathematical modelling, supported with Machine Learning to develop a dynamic e-waste supply chain model. Method Used: This article presents a multidimensional, cost function-based analysis of the EWM framework structured on three modules including environmental, economic, and social uncertainties in material recovery from an e-waste (MREW) plant, including the production–delivery–utilization process. Each module is ranked using Machine Learning (ML) protocols—Analytical Hierarchical Process (AHP) and combined AHP-Principal Component Analysis (PCA). Findings: This model identifies and probabilistically ranks two key sustainability contributors to the EWM supply chain: energy consumption and carbon dioxide emission. Additionally, the precise time window of 400–600 days from the start of the operation is identified for policy resurrection. Novelty: Ours is a data-intensive model that is founded on sustainable product designing in line with SDG requirements. The combined AHP-PCA consistently outperformed traditional statistical tools, and is the second novelty. Model ratification using real e-waste plant data is the third novelty. Implications: The Machine Learning framework embeds a powerful probabilistic prediction algorithm based on data-based decision making in future e-waste sustained roadmaps.

Glucagon-like peptide-1, glucose-dependent insulinotropic polypeptide, and glucagon are three naturally occurring peptide hormones that mediate glucoregulation. Several agonists representing appropriately modified native ligands have been developed to maximize metabolic benefits with reduced side-effects and many have entered the clinic as type 2 diabetes and obesity therapeutics. In this work, we describe strategies for improving the stability of the peptide ligands by making them refractory to dipeptidyl peptidase-4 catalyzed hydrolysis and inactivation. We describe a series of alkylations with variations in size, shape, charge, polarity, and stereochemistry that are able to engender full activity at the receptor(s) while simultaneously resisting enzyme-mediated degradation. Utilizing this strategy, we offer a novel method of modulating receptor activity and fine-tuning pharmacology without a change in peptide sequence.

Automatic creation of realistic images is a tedious process even though the state-of-the-art AI/ML algorithms are employed. There is a lot of demand for such automatic image generators that could create high quality images. Many have this problem of visualizing things from the explanations they hear about. Thus, the text to image generation problem is necessary because it has significant applications in CAD, art generation and many more. This is a challenging task since the image should be realistic and consistent with the text. One of the most common uses of modern conditional generative models is the generation of visuals from natural language. Viewing an image makes us easily understand what that image is, rather than hearing someone describing the image. To bridge the semantic gap between text and image, Generative Adversarial Network (GAN) systems are used to achieve high accuracy. The proposed system helps in generation of superior quality images that are meaningfully consistent with the text.

As a Life Member of TIES and as a student of Dr. C. R. Rao the author pays a tribute to Dr. Rao on his being chosen for the International Statistics Prize for 2023. The importance of Rao’s contributions to errors in variables modeling, information geometry, improving the estimators through Rao-Blackwellization, fractional factorial orthogonal designs for randomized trials etc. are stressed.

Globally, cervical cancer is the fourth most common cancer among women. After being cloned from a recurring cervical lesion in 1987, Human papillomavirus (HPV) type-45 was identified as a high-risk HPV type. It is the third most common cancer-causing HPV subtype, after HPV-16 and HPV-18. Immunogenic epitopes and structural features provide the most useful information for vaccine development. Computational algorithms provide quick, simple, trustworthy, and cost-efficient methods for predicting immunogenic epitopes. In this study, both B and T cell epitopes have been identified as potential immunogens that can elicit a response from the host system. Three potential B-cell epitopes, i.e., SIAGQYRGQCNTCCDQ, LQEIVLHLEPQNELDP, and DSTVYLPPPSVARVVS, were identified in this study. A potential epitope for E6 (ATLERTEVY) was predicted to 8 MHC-I alleles (HLA-A*30:02, HLA-B*15:01, HLA-A*01:01, HLA-A*26:01, HLA-A*32:01, HLA-B*35:01, HLA-B*58:01, HLA-A*11:01) and for L1 epitope (NVFPIFLQM) was predicted for 4 MHC-I alleles (HLA-A*30:02, HLA-A*32:01, HLA-B*53:01, HLA-B*51:01). To conclude, the epitopes identified here might potentially be useful for developing a cervical cancer vaccine against HPV-45 strains, but in vitro and in vivo trials are needed to validate their safety and efficacy.

Copper sulfide (CuS) based material has been broadly used in recent years because of its semiconducting and non-toxic nature. CuS nanocubes (NCs) were synthesized at low temperature by the solvothermal method using copper diethyldithiocarbamate (Cu[DTC]2) as a single-source precursor and the presence of hexadecylamine (HDA) as shape directing agent. As-prepared CuS NCs properties like thermal, phase, vibrational group, morphology and elemental were explored by different analytic tools. Further, the electrochemical behaviours of CuS NCs modified working electrode were examined in 1 M KOH electrolyte solution. It exhibits remarkable specific capacitance (Csp) of 1472.3 Fg- 1 at 1 Ag-1 with excellent capacitive retention up to 93.6 % after 5000 charge-discharge cycles at the current density of 10 Ag-1. Besides, the two electrode asymmetric supercapacitor (ASC) device was constructed and it exhibits the Csp of 54 Fg-1 at 3 Ag-1. Moreover, even after 5000 cycles at a 7 Ag-1 of current density, the ASC device was able to retain internal capacitance retention of 91.2 % due to its high energy density of 21.68 Wh kg -1 and power density of 1272.92 W kg -1. After 5000 cycle's electrochemical evaluation of CuS NCs modified electrode were analyzed. These outcomes of electrochemical performance suggested that fabricated CuS||AC device has a bright future in energy storage application.

The cobalt sulfide (Co9S8) nanoparticles (NPs) have been synthesized by the solvothermal techniques by utilizing Cobalt diethyldithiocarbamate (Co[DTC]2) as single-source precursor and hexadecylamine (HDA) as shape directing agent. As-prepared Co9S8 NPs were characterized with structural, morphological, thermal, Spectroscopic and surface analysis using PXRD, TEM, SEM-EDS, TG/DTA, FTIR, Raman and XPS studies re-spectively. The electrochemical performances were investigated by galvanostatic charge-discharge (GCD), cyclic voltammetry (CV), and electrochemical impedance spectroscopy (EIS) analysis with Co9S8 NPs modified working electrode. The Co9S8 NPs modified electrode delivered excellent specific capacitances of 502 Fg(-1) at current densities of 1 Ag-1. The capacitance retention of Co9S8 was found to be 87 % over the examination even after 7000 cycles. Furthermore, a hybrid supercapacitor (HSC) device assembled with cathode and anode materials delivers a high energy density of 15.47 Wh kg(-1) with power density of 1274.9 W kg(-1). (C) 2022 Published by Elsevier B.V.

Resilient supply chains are often inherently dependent on the nature of their complex interconnected networks that are simultaneously multi-dimensional and multi-layered. This article presents a Supply Chain Network (SCN) model that can be used to regulate downstream relationships towards a sustainable SME using a 4-component cost function structure — Environmental (E), Demand (D), Economic (E), and Social (S). As a major generalization to the existing practice of using phenomenological interrelationships between the EDES cost kernels, we propose a complementary time varying model of a cost function, based on Lagrangian mechanics (incorporating SCN constraints through Lagrange multipliers), to analyze the time evolution of the SCN variables to interpret the competition between economic inertia and market potential. Multicriteria decision making, based on an Analytic Hierarchy Process (AHP), ranks performance quality, identifying key business decision makers. The model is first solved numerically and then validated against real data pertaining to two Small and Medium Enterprises (SMEs) from diverse domains, establishing the domain-independent nature of the model. The results quantify how increases in a production line without appropriate consideration of market volatility can lead to bankruptcy, and how high transportation cost together with increased production may lead to a break-even state. The model also predicts the time it takes a policy change to reinvigorate sales, thereby forecasting best practice operational procedure that ensures holistic sustainability on all four sustainability fronts.

Silk fibroin protein is a biomaterial with excellent biocompatibility and low immunogenicity. These properties have catapulted the material as a leader for extensive use in stents, catheters, and wound dressings. Modulation of hydrophobicity of silk fibroin protein to further expand the scope and utility however has been elusive. We report that installing perfluorocarbon chains on the surface of silk fibroin transforms this water-soluble protein into a remarkably hydrophobic polymer that can be solvent-cast. A clear relationship emerged between fluorine content of the modified silk and film hydrophobicity. Water contact angles of the most decorated silk fibroin protein exceeded that of Teflon®. We further show that water uptake in prefabricated silk bars is dramatically reduced, extending their lifetimes, and maintaining mechanical integrity. These results highlight the power of chemistry under moderate conditions to install unnatural groups onto the silk fibroin surface and will enable further exploration into applications of this versatile biomaterial.

Alzheimer is a kind of dementia that affects reasoning and social aptitudes which results in constant decline of individual’s capacity to work. The development of amyloid protein and tau protein in cerebrum stimulates cell passing. The prevention and early prediction is a big challenge. The proposed model addresses this need with the analysis of parameters such as Mini-Mental State Examination score and so on. In addition to these parameters, one more parameter dice coefficient value that shows difference between normal and affected scan images. The SVM and CNN are applied on various neuro parameters and evaluated for prediction accuracy.

Weaving a tunable hydrophobic web! Silk fibroin, a protein extracted from the silkworm cocoon can be tailored to possess different properties by means of chemical functionalization. In this study, we sought to induce hydrophobicity using fluorination. Fluorocarbon-appending iodonium salts accomplish the fluorination in one step. A trend of increasing hydrophobicity with the number of fluorine atoms was observed, with the most hydrophobic material exhibiting a water contact angle greater than that of Teflon! This work shows the first development of directly modified silk fibroin to create hydrophobic films. More information can be found in the Research Article by L. M. Davis, D. L. Kaplan, K. Kumar et al.

In the history of nanotechnology, Dendrimers are rolling in as a highly tempting class of drug delivery system for cancer therapy. Dendrimers are the best and smart choice as Nanocarriers to deliver one or more therapeutic agents safely and selectively to cancer cells. Dendrimers that have remarkable properties including membrane interaction, monodispersity, well-defined size, shape and molecular weight, etc. Functional groups that are present in the Dendrimers exterior also permit other chemical moieties that can actively target certain diseases which are now widely used as tumor-targeting strategies. There are three ways by which drugs interact with dendrimers, (a) physical encapsulation, (b) electrostatic interactions, and (c) covalent conjugations. This review represents the advantages of Dendrimers over conventional chemotherapy, toxicity, and its management. The anti-cancer drugs are delivered by using Dendrimers and recent advances in drug delivery by different types of Dendrimers.

We report the comparison of a series of 2D molecular crystals formed from the intermediates of the dehalogenation reaction of iodoethane versus various fluorinated iodoalkanes on Cu(111). High-resolution scanning tunneling microscopy enables us to distinguish the alkyl groups from the iodine atoms, and we find that the ethyl groups and iodine atoms formed from the dissociation of ethyl iodide are well mixed. However, fluorination of the alkyl tail changes this behavior and leads to local segregation of the two species on the surface. We postulate that the low-polarizability and relatively large dipole moment of the fluorinated species drive the ordered assemblies of the fluorinated alkyl species on the surface and discuss this in the context of how solvophobicity can drive the clustering of fluorinated groups and, hence, phase separation.