ACS Omega最新文献

Nucleic acid testing with high sensitivity and specificity is of great importance for accurate disease diagnostics. Here, we developed an in situ one-tube nucleic acid testing assay. In this assay, the target nucleic acid is captured using magnetic silica beads, avoiding an elution step, followed directly by the polymerase chain reaction (PCR) and clustered regularly interspaced short palindromic repeats (CRISPR)-Cas12a detection. This assay achieved visual readout and a sensitivity of 120 copies/mL for detecting SARS-CoV-2. More importantly, the assay demonstrated over 95% sensitivity and 100% specificity compared to the gold standard real-time quantitative PCR (RT-qPCR) test by using 75 SARS-CoV-2 clinical samples. By integrating nested PCR and Cas12a, this all-in-one nucleic acid testing approach enables ultrasensitive, highly specific, and cost-effective diagnosis at community clinics and township hospitals.

CARM1 is an arginine methyltransferase that has crucial roles in a number of cellular pathways and is being explored as a therapeutic target in diseases such as cancer and neurodegenerative disorders. Its deregulation at the protein level was found to have potential prognostic value, and as such, its protein levels are regularly assessed through the common practice of western blotting (WB). Our group uncovered that CARM1 has biochemical properties that complicate its analysis by standard WB sample preparation techniques. Here, we show that CARM1 has the ability to form SDS-resistant aggregates that effectively hinder gel migration in SDS-PAGE. CARM1 levels and the temperature at the denaturation step can both influence CARM1 aggregation, which prompts the use of additional measures to ensure representative detection at the protein level. We have demonstrated the formation of CARM1 aggregates in both cell and tissue extracts, making these findings an important consideration for any CARM1-related study. We also show how aggregate formation in models of CARM1 overexpression can hinder proteomic studies. Having identified factors that can induce CARM1 aggregation, we suggest alternative sample preparation techniques that allow for clear resolution of the protein in stringent denaturing conditions while avoiding aggregation.

The so-called Distributed Activation Energy Model (DAEM) has been used extensively, mainly to analyze pyrolysis reactions of solid reactants. The model expresses many parallel first-order reactions using the distributions of activation energy f(E) and frequency factor k ₀(E). Miura and Maki presented a method to estimate both f(E) and k ₀(E) in the DAEM in 1998. This model has been used successfully by many researchers. In this paper more general basic equations are derived for describing an infinite number of parallel first-order reactions by extending the basic equations for the finite number of parallel first-order reactions. Revisiting the Miura-Maki method based on the general basic equations, a graphical analysis method that may be called "Pseudo Master Curve Analysis" is presented. The method not only supplements the Miura-Maki method but gives the underlying concept of the Miura-Maki method clearly. It is also shown that the graphical method can be applicable to analyze single reactions and the experimental data obtained using isothermal reaction techniques. Next, a method that improves the estimation accuracy of k ₀(E) is presented. Practical examples analyzing several experimental data are also given to show the usefulness and validity of the Miura-Maki method and the graphical method. Through the examination, it is proposed that the DAEM should be renamed, for example, as the Distributed Rate Constant Model (DRCM).

G protein-coupled receptors (GPCRs) play a central role in cellular signaling and are linked to many diseases. Accordingly, computational methods to explore potential allosteric sites for this class of proteins to facilitate the identification of potential modulators are needed. Importantly, the availability of rich structural data providing the locations of the orthosteric ligands and allosteric modulators targeting different GPCRs allows for the validation of approaches to identify new allosteric binding sites. Here, we validate the combination of two computational techniques, the residue interaction network (RIN) model and the site identification by ligand competitive saturation (SILCS) method, to predict putative allosteric binding sites of class A GPCRs. RIN analysis identifies hub residues that mediate allosteric signaling within a receptor and have a high capacity to alter receptor dynamics upon ligand binding. The known orthosteric (and allosteric) binding sites of 18 distinct class A GPCRs were successfully predicted by RIN through a dataset of 105 crystal structures (91 ligand-bound, 14 unbound) with up to 77.8% (76.9%) sensitivity, 92.5% (95.3%) specificity, 51.9% (50%) precision, and 86.2% (92.4%) accuracy based on the experimental and theoretical binding site data. Moreover, graph spectral analysis of the residue networks revealed that the proposed sites were located at the interfaces of highly interconnected residue clusters with a high ability to coordinate the functional dynamics. Then, we employed the SILCS-Hotspots method to assess the druggability of the novel sites predicted for 7 distinct class A GPCRs that are critical for a variety of diseases. While the known orthosteric and allosteric binding sites are successfully explored by our approach, numerous putative allosteric sites with the potential to bind drug-like molecules are proposed. The computational approach presented here promises to be a highly effective tool to predict putative allosteric sites of GPCRs to facilitate the design of effective modulators.

Preventing microbial infections and accelerating wound closure are essential in the process of wound healing. In this study, various concentrations of carvacrol (CA) were loaded into polyacrylonitrile/poly(ethylene oxide) (PAN/PEO) nanofiber membranes to develop potential wound dressing materials via an electrospinning technique. The morphology and structure of the PAN/PEO/CA nanofiber membrane were analyzed by scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FTIR), respectively. Subsequently, antimicrobial performance testing showed that the PAN/PEO/CA nanofiber membrane exhibited antimicrobial activity in a concentration-dependent manner. Moreover, SEM and transmission electron microscopy revealed that the number of Staphylococcus aureus decreased significantly and the microstructure of the biofilm was seriously damaged. Next, compared with the control and PAN/PEO groups, the PAN/PEO/5% CA group in a full-thickness skin infection model not only exhibited reduced wound exudate on day 2 after infection but also displayed a greater ability to achieve complete skin regeneration, with faster wound healing. Finally, the Kyoto Encyclopedia of Genes and Genomes pathway analysis revealed that the downregulated differentially expressed genes between PAN/PEO- and PAN/PEO/5% CA-treated S. aureus were enriched in the two-component system and S. aureus infection. In conclusion, the antimicrobial materials of PAN/PEO/CA inhibited microbial growth and promoted wound healing with potential applications in the clinical management of wounds.

Lithium-ion batteries (LIBs) serve as the backbone of modern technologies with ongoing efforts to enhance their performance and sustainability driving the exploration of new electrode materials. This study introduces a new type of alloy-conversion-based gallium ferrite (GFO: GaFeO₃) as a potential anode material for Li-ion battery applications. The GFO was synthesized by a one-step mechanochemistry-assisted solid-state method. The powder X-ray diffraction analysis confirms the presence of an orthorhombic phase with the Pc2₁n space group. The photoelectron spectroscopy studies reveal the presence of Ga³⁺ and Fe³⁺ oxidation states of gallium and iron atoms in the GFO structure. The GFO was evaluated as an anode material for Li-ion battery applications, displaying a high discharge capacity of ∼887 mA h g^–1 and retaining a stable capacity of ∼200 mA h g^–1 over 450 cycles, with a Coulombic efficiency of 99.6 % at a current density of 100 mA g^–1. Cyclic voltammetry studies confirm an alloy-conversion-based reaction mechanism in the GFO anode. Furthermore, density functional theory studies reveal the reaction mechanism during cycling and Li-ion diffusion pathways in the GFO anode. These results strongly suggest that the GFO could be an alternative anode material in LIBs.

The current study focuses on the idea of “Energy from Waste” that intends to address energy crises and manage waste. Fruit waste is one of the most common forms of organic waste due to its inedible portion and perishable nature. In Pakistani regions, an extensive amount of mango pulp (MP)/juice waste is produced due to excessive consumption during summers, which poses huge environmental challenges. The study aims at effective valorization of perishable waste and elimination of deteriorating waste that causes a polluting environment. Experimental work has been conducted to evaluate the sucrolytic potential of Bacillus cereus FA3 for the bioconversion of sucrose from mango waste into reducing sugars for ethanologenesis. The Plackett–Burman model was designed to analyze enzymatic hydrolytic parameters for sugar conversion. The model was significant for reducing sugars with F and p values of 43.99 and 0.0013 correspondingly. 11.43 ± 0.068 g/L maximum reducing sugars were analyzed in MP after hydrolysis with 12.58 IU of crude enzyme dosage of B. cereus FA3 at 30 °C within 5 days with a 22% enzyme conversion rate. Additionally, the ethanologenic potentials of experimental Metschnikowia cibodasensis Y34 and standard Saccharomyces cerevisiae K7 yeasts were investigated from mango hydrolyzate when subjected to central composite design as a statistical optimization tool. These findings exhibited significantly higher response outcomes and good development for waste management.

CARM1 is an arginine methyltransferase that has crucial roles in a number of cellular pathways and is being explored as a therapeutic target in diseases such as cancer and neurodegenerative disorders. Its deregulation at the protein level was found to have potential prognostic value, and as such, its protein levels are regularly assessed through the common practice of western blotting (WB). Our group uncovered that CARM1 has biochemical properties that complicate its analysis by standard WB sample preparation techniques. Here, we show that CARM1 has the ability to form SDS-resistant aggregates that effectively hinder gel migration in SDS-PAGE. CARM1 levels and the temperature at the denaturation step can both influence CARM1 aggregation, which prompts the use of additional measures to ensure representative detection at the protein level. We have demonstrated the formation of CARM1 aggregates in both cell and tissue extracts, making these findings an important consideration for any CARM1-related study. We also show how aggregate formation in models of CARM1 overexpression can hinder proteomic studies. Having identified factors that can induce CARM1 aggregation, we suggest alternative sample preparation techniques that allow for clear resolution of the protein in stringent denaturing conditions while avoiding aggregation.

G protein-coupled receptors (GPCRs) play a central role in cellular signaling and are linked to many diseases. Accordingly, computational methods to explore potential allosteric sites for this class of proteins to facilitate the identification of potential modulators are needed. Importantly, the availability of rich structural data providing the locations of the orthosteric ligands and allosteric modulators targeting different GPCRs allows for the validation of approaches to identify new allosteric binding sites. Here, we validate the combination of two computational techniques, the residue interaction network (RIN) model and the site identification by ligand competitive saturation (SILCS) method, to predict putative allosteric binding sites of class A GPCRs. RIN analysis identifies hub residues that mediate allosteric signaling within a receptor and have a high capacity to alter receptor dynamics upon ligand binding. The known orthosteric (and allosteric) binding sites of 18 distinct class A GPCRs were successfully predicted by RIN through a dataset of 105 crystal structures (91 ligand-bound, 14 unbound) with up to 77.8% (76.9%) sensitivity, 92.5% (95.3%) specificity, 51.9% (50%) precision, and 86.2% (92.4%) accuracy based on the experimental and theoretical binding site data. Moreover, graph spectral analysis of the residue networks revealed that the proposed sites were located at the interfaces of highly interconnected residue clusters with a high ability to coordinate the functional dynamics. Then, we employed the SILCS-Hotspots method to assess the druggability of the novel sites predicted for 7 distinct class A GPCRs that are critical for a variety of diseases. While the known orthosteric and allosteric binding sites are successfully explored by our approach, numerous putative allosteric sites with the potential to bind drug-like molecules are proposed. The computational approach presented here promises to be a highly effective tool to predict putative allosteric sites of GPCRs to facilitate the design of effective modulators.

The so-called Distributed Activation Energy Model (DAEM) has been used extensively, mainly to analyze pyrolysis reactions of solid reactants. The model expresses many parallel first-order reactions using the distributions of activation energy f(E) and frequency factor k₀(E). Miura and Maki presented a method to estimate both f(E) and k₀(E) in the DAEM in 1998. This model has been used successfully by many researchers. In this paper more general basic equations are derived for describing an infinite number of parallel first-order reactions by extending the basic equations for the finite number of parallel first-order reactions. Revisiting the Miura–Maki method based on the general basic equations, a graphical analysis method that may be called “Pseudo Master Curve Analysis” is presented. The method not only supplements the Miura–Maki method but gives the underlying concept of the Miura–Maki method clearly. It is also shown that the graphical method can be applicable to analyze single reactions and the experimental data obtained using isothermal reaction techniques. Next, a method that improves the estimation accuracy of k₀(E) is presented. Practical examples analyzing several experimental data are also given to show the usefulness and validity of the Miura–Maki method and the graphical method. Through the examination, it is proposed that the DAEM should be renamed, for example, as the Distributed Rate Constant Model (DRCM).