Superlative Porous Organic Polymers for Photochemical and Electrochemical CO2 Reduction Applications: From Synthesis to Functionality.

To mimic the carbon cycle at a kinetically rapid pace, the sustainable conversion of omnipresent CO2 to value-added chemical feedstock and hydrocarbon fuels implies a remarkable prototype for utilizing released CO2. Porous organic polymers (POPs) have been recognized as remarkable catalytic systems for achieving large-scale applicability in energy-driven processes. POPs offer mesoporous characteristics, higher surface area, and superior optoelectronic properties that lead to their relatively advanced activity and selectivity for CO2 conversion. In comparison to the metal organic frameworks, POPs exhibit an enhanced tendency toward membrane formation, which governs their excellent stability with regard to remarkable ultrathinness and tailored pore channels. The structural ascendancy of POPs can be effectively utilized to develop cost-effective catalytic supports for energy conversion processes to leapfrog over conventional noble metal catalysts that have nonlinear techno-economic equilibrium. Herein, we precisely surveyed the functionality of POPs from scratch, classified it, and provided a critical commentary of its current methodological advancements and photo/electrochemical achievements in the CO2 reduction reaction.

Improved Early Outcomes With Off-Pump Coronary Artery Bypass Grafting in Patients With Left Ventricular Dysfunction: A Systematic Review and Meta-Analysis.

The ongoing debate surrounding coronary artery bypass grafting (CABG) with or without cardiopulmonary bypass persists, particularly in individuals with left ventricular dysfunction. The objective of this study was to evaluate the safety and efficacy of these 2 strategies through a comprehensive meta-analysis of existing studies. A systematic search of PubMed, EMBASE, Web of Science, and the Cochrane Central Registry was conducted from inception to July 2023. The primary focus was on studies comparing on-pump versus off-pump CABG as the primary treatment for multivessel coronary artery disease in patients with left ventricular dysfunction (ejection fraction ≤40%), with mortality as the primary outcome. The meta-analysis included 26 studies with a total of 35,863 patients. The results revealed a significant reduction in mortality risk [risk ratio (RR), 0.75; 95% confidence interval (CI), 0.60-0.93; P = 0.009] and other perioperative morbidities associated with off-pump CABG. These included stroke (RR, 0.67; 95% CI, 0.54-0.82; P = 0.0002), myocardial infarction (RR, 0.74; 95% CI, 0.56-0.97; P = 0.03), pulmonary complications (RR, 0.71; 95% CI, 0.55-0.92; P = 0.010), postoperative transfusion (RR, 0.70; 95% CI, 0.55-0.88; P = 0.002), neurological dysfunction (RR, 0.80; 95% CI, 0.64-1.00; P = 0.05), infection (RR, 0.74; 95% CI, 0.56-0.97; P = 0.03), renal failure (RR, 0.79; 95% CI, 0.67-0.95; P = 0.010), and reoperation for bleeding (RR, 0.66; 95% CI, 0.52-0.84; P = 0.0006). However, no significant difference was observed between the 2 groups regarding postoperative atrial fibrillation (RR, 0.97; 95% CI, 0.84-1.12; P = 0.69). In conclusion, off-pump CABG demonstrates a lower perioperative mortality risk and improved overall early outcomes compared with on-pump techniques in individuals with reduced left ventricular function.

Cortical signals analysis to recognize intralimb mobility using modified RNN and various EEG quantities.

Electroencephalogram (EEG) signals are critical in interpreting sensorimotor activities for predicting body movements. However, their efficacy in identifying intralimb movements, such as the dorsiflexion and plantar flexion of the foot, remains suboptimal. This study aims to explore whether various EEG signal quantities can effectively recognize intralimb movements to facilitate the development of Brain-Computer Interface (BCI) devices for foot rehabilitation. This research involved twenty-two healthy, right-handed participants. EEG data were collected using 21 electrodes positioned over the motor cortex, while two electromyography (EMG) electrodes recorded the onset of ankle joint movements. The study focused on analyzing slow cortical potential (SCP) and sensorimotor rhythms (SMR) in alpha and beta bands from the EEG. Five key features-fourth-order Autoregressive feature, variance, waveform length, standard deviation, and permutation entropy-were extracted. A modified Recurrent Neural Network (RNN) including Long Short-term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms was developed for movement recognition. These were compared against conventional machine learning algorithms, including nonlinear Support Vector Machine (SVM) and k Nearest Neighbourhood (kNN) classifiers. The performance of the proposed models was assessed using two data schemes: within-subject and across-subjects. The findings demonstrated that the GRU and LSTM models significantly outperformed traditional machine learning algorithms in recognizing different EEG signal quantities for intralimb movement. The study indicates that deep learning models, particularly GRU and LSTM, hold superior potential over standard machine learning techniques in identifying intralimb movements using EEG signals. Where the accuracies of LSTM for within and across subjects were 98.87 ± 1.80 % and 87.38 ± 0.86 % respectively. Whereas the accuracy of GRU within and across subjects were 99.18 ± 1.28 % and 86.44 ± 0.69 % respectively. This advancement could significantly benefit the development of BCI devices aimed at foot rehabilitation, suggesting a new avenue for enhancing physical therapy outcomes.

Brain MRI sequence and view plane identification using deep learning.

Brain magnetic resonance imaging (MRI) scans are available in a wide variety of sequences, view planes, and magnet strengths. A necessary preprocessing step for any automated diagnosis is to identify the MRI sequence, view plane, and magnet strength of the acquired image. Automatic identification of the MRI sequence can be useful in labeling massive online datasets used by data scientists in the design and development of computer aided diagnosis (CAD) tools. This paper presents a deep learning (DL) approach for brain MRI sequence and view plane identification using scans of different data types as input. A 12-class classification system is presented for commonly used MRI scans, including T1, T2-weighted, proton density (PD), fluid attenuated inversion recovery (FLAIR) sequences in axial, coronal and sagittal view planes. Multiple online publicly available datasets have been used to train the system, with multiple infrastructures. MobileNet-v2 offers an adequate performance accuracy of 99.76% with unprocessed MRI scans and a comparable accuracy with skull-stripped scans and has been deployed in a tool for public use. The tool has been tested on unseen data from online and hospital sources with a satisfactory performance accuracy of 99.84 and 86.49%, respectively.

Evaluation of adjective and adverb types for effective Twitter sentiment classification.

Twitter, the largest microblogging platform, has reported more than 330 million active users in recent years. Many users express their sentiments about politics, sports, products, personalities, etc. Sentiment analysis has emerged as a specialized branch of machine learning in which tweets are binary-classified to provide sentimental insights. A major step in sentiment classification is feature selection, which primarily revolves around parts of speech (POS). Few techniques merely focused on single features such as adjectives, adverbs, and verbs, while other techniques examined types of these features, such as comparative adjectives, superlative adjectives, or general adverbs. Furthermore, POS as linguistic entities have also been studied and extensively classified by researchers, such as CLAWS-C7. For sentiment analysis, none of the studies conceptualized all possible POS features under similar conditions to draw firm conclusion. This research is centered on the following objectives: 1) examining the impact of various types of adjectives and adverbs that have not been previously explored for sentiment classification; 2) analyzing potential combinations of adjectives and adverbs types 3) conducting a comparison with a benchmark dataset for better classification accuracy. To assess the concept, a renowned human annotated dataset of tweets is investigated. Results showed that classification accuracy for adjectives is improved up to 83% based on the general superlative adjective whereas for adverbs, comparative general adverb also depicted significant accuracy improvement. Their combination with general adjectives and general adverbs also played a substantial role. The unexplored potential of adjectives and adverb types proved better in accuracy against state-of-the-art probabilistic model. In comparison to lexicon-based model, proposed research model overruled the dependency of lexicon-based dictionary where each term first needs to be matched for semantic orientation. The evident outcomes also help in time reduction aspect where huge volume of data need to be processed swiftly. This noteworthy contribution brought up significant knowledge and direction for domain experts. In the future, the proposed technique will be explored for other types of textual data across different domains.

Key determinants to supply chain resilience to face pandemic disruption: An interpretive triple helix framework.

Today, supply chain (SC) networks are facing more disruptions compared to the past. While disruptions are rare, they can have catastrophic long-term economic or societal repercussions, and the recovery processes can be lengthy. These can tremendously affect the SC and make it vulnerable, as observed during the COVID-19 pandemic. The identification of these concerns has prompted the demand for improved disruption management by developing resilient, agile, and adaptive SC. The aim of this study is to introduce an assessment framework for prioritizing and evaluating the determinants to supply chain resilience (SCR). To analyze the empirical data, fuzzy criteria importance through intercriteria correlation (fuzzy CRITIC) and fuzzy technique for order of preference by similarity to ideal solution (fuzzy TOPSIS) have been incorporated. Fuzzy CRITIC method was used to identify the critical determinants and fuzzy TOPSIS method was applied for determining relative ranking of some real-world companies. Finally, by developing propositions an interpretive triple helix framework was proposed to achieve SCR. This research stands out for its originality in both methodology and implications. By introducing the novel combination of Fuzzy CRITIC and Fuzzy TOPSIS in the assessment of determinants to SCR and applying these determinants with the help of interpretive triple helix framework to establish a resilient SC, this study offers a unique and valuable contribution to the field of SCR. The key findings suggest that 'Responsiveness' followed by 'Managerial coordination and information integration' are the most significant determinant to achieve SCR. The outcome of this work can assist the managers to achieve SCR with improved agility and adaptivity.

Anhydroparthenin as a dual-target inhibitor against Sterol C-24 methyltransferase and Sterol 14-α demethylase of Leishmania donovani: A comprehensive in vitro and in silico study.

Parthenium hysterophorus plant has a diverse chemical profile and immense bioactive potential. It exhibits excellent pharmacological properties such as anti-cancer, anti-inflammatory, anti-malarial, microbicidal, and anti-trypanosomal. The present study aims to evaluate the anti-leishmanial potential and toxicological safety of anhydroparthenin isolated from P. hysterophorus. Anydroparthenin was extracted from the leaves of P. hysterophorus and characterized through detailed analysis of 1H, 13C NMR, and HRMS. Dye-based in vitro and ex vivo assays confirmed that anhydroparthenin significantly inhibited both promastigote and amastigote forms of the Leishmania donovani parasites. Both the cytotoxicity experiment and hemolytic assay revealed its non-toxic nature and safety index in the range of 10 to 15. Further, various mechanistic assays suggested that anhydroparthenin led to the generation of oxidative stress, intracellular ATP depletion, alterations in morphology and mitochondrial membrane potential, formation of intracellular lipid bodies, and acidic vesicles, ultimately leading to parasite death. As a dual targeting approach, computational studies and sterol quantification assays confirmed that anhydroparthenin inhibits the Sterol C-24 methyl transferase and Sterol 14-α demethylase proteins involved in the ergosterol biosynthesis in Leishmania parasites. These results suggest that anhydroparthenin could be a promising anti-leishmanial molecule and can be developed as a novel therapeutic stratagem against leishmaniasis.

New regulatory role of Znf1 in transcriptional control of pentose phosphate pathway and ATP synthesis for enhanced isobutanol and acid tolerance.

To develop a cost-effective microbial cell factory for the production of biofuels and biochemicals, an understanding of tolerant mechanisms is vital for the construction of robust host strains. Here, we characterized a new function of a key metabolic transcription factor named Znf1 and its involvement in stress response in Saccharomyces cerevisiae to enhance tolerance to advanced biofuel, isobutanol. RNA-sequencing analysis of the wild-type versus the znf1Δ deletion strains in glucose revealed a new role for transcription factor Znf1 in the pentose phosphate pathway (PPP) and energy generation. The gene expression analysis confirmed that isobutanol induces an adaptive cell response, resulting in activation of ATP1-3 and COX6 expression. These genes were Znf1 targets that belong to the electron transport chain, important to produce ATPs. Znf1 also activated PPP genes, required for the generation of key amino acids, cellular metabolites, and maintenance of NADP/NADPH redox balance. In glucose, Znf1 also mediated the upregulation of valine biosynthetic genes of the Ehrlich pathway, namely ILV3, ILV5, and ARO10, associated with the generation of key intermediates for isobutanol production. Using S. cerevisiae knockout collection strains, cells with deleted transcriptional regulatory gene ZNF1 or its targets displayed hypersensitivity to isobutanol and acid inhibitors; in contrast, overexpression of ZNF1 enhanced cell survival. Thus, the transcription factor Znf1 functions in the maintenance of energy homeostasis and redox balance at various checkpoints of yeast metabolic pathways. It ensures the rapid unwiring of gene transcription in response to toxic products/by-products generated during biofuel production. Importantly, we provide a new approach to enhance strain tolerance during the conversion of glucose to biofuels.

Molecular Assessments of Antimicrobial Protein Enterocins and Quorum Sensing Genes and Their Role in Virulence of the Genus Enterococcus.

Enterococcus has emerged as an opportunistic pathogen because of its antibiotic resistance and virulence profile, which makes it a causative agent of several diseases like endocarditis, surgical site, and urinary tract infections. Currently, species of this genus are the 2nd most frequently isolated microorganisms from hospital-acquired infections. Significant association with hospitals and unhygienic conditions of the environments has made them resistant to a wide range of antibiotics. On the brighter side, enterococci have the ability to produce antimicrobial proteins (i.e., enterocins) that exhibit wide antagonistic activity, thus making them useful microbes in the food and pharmaceutical industries. Enterocins are also involved in niche control in gut microbiota which is regulated by the quorum sensing (QS) system. A bacterial communication system that is controlled by the fsr operon in enterococci consists of FsrABDC, ef1097, and GelE/SprE genes. Hence, the present study was conducted for molecular assessment of enterocins and quorum sensing genes, inter-environmental correlation, and species prevalence of enterococci isolated from different environmental niches of Karachi, Pakistan. Obtained results revealed the highest prevalence of E. faecium and E. faecalis in all environments. Bacterial antagonism and enterocin genes were observed significantly high in poultry environments. The inter-environmental correlation indicated a strong positive correlation of freshwater with sewage and soil environments. Similarly, the fsr regulatory system was mostly identified in poultry-related environments, and a significant correlation between QS system and biofilm formation was established. In conclusion, this study confirmed the high prevalence of E. faecium in all tested sources, high enterocin production in non-clinical environments, and more fsr regulatory genes in poultry-related environments.

Insight into mechanism of excellent visible-light photocatalytic activity of CuO/MgO/ZnO nanocomposite for advanced solution of environmental remediation.

Environmental remediation has sought several innovative ways for the treatment of wastewater and captivated researchers around the globe towards it. Through this study, we aim to proceed with the efforts to foster sustainable and feasible ways for the treatment of wastewater. In this work, we report the sol-gel synthesis of CuO/MgO/ZnO nanocomposite and carry out their systematic characterization with the help of state-of-the-art analytical techniques, such as FTIR, SEM, TEM, PL, XRD, Raman, and AFM. The SEM along with TEM and AFM provided useful insights into the surface morphology of the synthesized nanocomposite on both 2D and 3D surfaces and concluded the well-dispersed behavior of the nanocomposite. The characteristic functional groups responsible for carrying out the reaction of Cu-O, Mg-O, and Zn-O were identified by FTIR spectroscopy. On the other hand, crystal size, dislocation density, and microstrain of the nanocomposite were calculated by XRD. For optical studies, photoluminescence spectroscopy was performed. Once the characterization of the nanocomposite was done, they were eventually treated against the toxic organic dye, methylene blue. The calculated rate constant values of k for CuO was 2.48 × 10-3 min-1, for CuO/MgO (2.04 × 10-3 min-1), for CuO/ZnO (1.82 × 10-3 min-1) and CuO/MgO/ZnO was found to be 2.00 × 10-3 min-1. It has become increasingly evident that nanotechnology can be used in various facets of modern life, and its implementation in wastewater treatment has recently received much attention.

Temporal trends with the Evolut family of self-expanding transcatheter heart valves: A single-center experience.

BACKGROUND: The Evolut self-expanding valve (SEV) systems (Medtronic), were designed to accommodate varying valve sizes and reduce paravalvular leak (PVL) while maintaining a low delivery profile. These systems have evolved between product generations, alongside valve deployment techniques changing over time. AIMS: This study aimed to examine whether these changes impacted clinical outcomes. METHODS: EPROMPT is a prospective, investigator-initiated, postmarketing registry of consecutive patients undergoing transfemoral transcatheter aortic valve replacement (TAVR) using the Evolut PRO/PRO+ SEV system. A total of 300 patients were divided into three consecutive cohorts of 100 patients according to implantation date (January to October 2018, November 2018 to July 2020, and August 2020 to November 2021). Procedural and clinical outcomes over these time periods were compared. RESULTS: Valve Academic Research Consortium (VARC)-2 device implantation success improved over time (70.0% vs. 78.0% vs. 88.8%, p = 0.01), with a similar trend for VARC-3 device success (94.7% vs. 81.7% vs. 96.8%, p < 0.001). PVL (all degrees) frequency was likewise reduced over time (31.0% vs. 17.0% vs. 19.2%, p = 0.04). Furthermore, a trend was noticed toward shorter procedure times and shorter length of stay. However, postprocedural pacemaker implantation rates did not significantly differ (15.2% vs. 21.1% vs. 14.0%, p = 0.43). CONCLUSION: During a 3-year period, we demonstrated better TAVR outcomes with newer SEV iterations, alongside changes in implantation techniques, which might result in better procedural and clinical outcomes. However, we did not see a significant change in peri-procedural pacemaker rates for SEV.

MBenes for Energy Conversion: Advances, Bottlenecks, and Prospects.

The advent of two-dimensional layered materials has bolstered the development of catalytic endeavors for energy conversion and storage. MXenes (transition metal carbides/nitrides) have already consolidated their candidature in the past decade due to their enhanced compositional and structural tunabilities through surface modifications. Perseverant research in engineering MXene based materials has led to the inception of MBenes (transition metal borides) as promising catalytic systems for energy-driven operations. Physicochemical superiorities of MBenes such as escalated conductivity and hydrophilicity, unique surface and geometrical domains, and higher stability and modulus of elasticity provide the reaction-friendly milieu to exploit these materials. Nevertheless, the research on MBenes is embryonic and requires the thorough realization of their scientific significance. Herein, we aim to discuss the advancements, challenges, and outlooks of MBenes with respect to their energy conversion HER, CO2RR, and NRR applications.

In Vitro and In Vivo Anti-Inflammatory Activity of a Combination of Extracts from Cinnamomum zeylanicum, Alpinia galanga, and Withania somnifera used in Unani Medicine.

Objectives: Continuous and excessive secretion of pro-inflammatory and anti-inflammatory chemicals and cytokines may further deteriorate inflammation. Anti-inflammatory drugs play an imperative role in inhibiting the evolution of inflammatory diseases. As per the Unani doctrine, a holistic treatment approach is used to treat illnesses. Therefore, drugs having different actions are used to achieve the synergic effect. Three drugs (Cinnamomum zeylanicum, Alpinia galanga, and Withania somnifera), which are frequently used in Unani medicine for joint disorders were selected to evaluate the anti-inflammatory activity of the extract derived from them. Methods: We used RAW 264.7 macrophage cells to see the expression of inflammatory markers IL-1ß, IL-6, and TNF-α. Cytotoxic activity was assessed with MTT assay and Nitric Oxide (NO) was evaluated using Griess reagent. Further, anti-inflammatory activity was evaluated in Wistar Albino rats using carrageenan-induced paw oedema and immunohistochemistry assays for Cyclooxygenase-2 (COX-2). All the data were analyzed using ANOVA and Dunnett t test for multiple comparisons. Results: This extract did not show any cytotoxic effect and the gene expression was significantly reduced for IL-1ß, IL-6, and TNF-α in a dose-dependent manner. Further, NO production was also significantly reduced in the test groups. Immunohistochemistry revealed that the test groups had less inflammation as compared to the control group. Conclusion: It may be inferred that the ethanolic extract of the three herbs has strong anti-inflammatory activity in the tested inflammatory models and the extract is safe as it did not show any cytotoxic effects in both in vitro and in vivo conditions.

Investigation of ultra wide bandgap Flouro-perovskite materials RBeF3 (R[bond, double bond]K and Li) for smart window applications: A DFT study.

The human body is affected by ultraviolet radiation because it can penetrate and harm bodily cells. Although skin cancer and early aging are consequences of prolonged exposure to ultraviolet (UV) rays, sun rays signify immediate excessive exposure. In this context, some structural, optical, electrical, and mechanical properties of the beryllium-based cubic fluoro-perovskite RBeF3 (R[bond, double bond]K and Li) compounds are examined through the use of density functional theory (DFT) within generalized gradient approximation (GGA) using the Perdew-Burke-Ernzerhof (PBE) approximations (GGA-PBE). The compounds KBeF3 and LiBeF3 have space group 221-pm3m, and their lattice constants and volumes are (3.765, 3.566) Å and (53.380, 45.379) Å3, respectively, based on their structural properties. Computed results indicate that the compounds' bandgaps are 7.35 eV and 7.12 eV, respectively, with an indirect nature for KBeF3 and LiBeF3. The properties of the band structure indicate that both compounds are insulators. The bonding properties of these compounds, RBeF3, are a combination of covalent and ionic. Optical properties of the compounds are examined which reflect the light-matter interaction like reflectivity, conductivity, and absorption. These materials were likely very hard but brittle, based on a higher bulk modulus B from elastic features, the B/G ratio, Pugh's ratio, and Vickers hardness. The compound RBeF3, as determined by the findings, is used as a UV protection and reflection layer for car and room windows.

Discovery and technical validation of high-performance methylated DNA markers for the detection of cervical lesions at risk of malignant progression in low- and middle-income countries.

BACKGROUND: Cervical cancer remains a leading cause of death, particularly in developing countries. WHO screening guidelines recommend human papilloma virus (HPV) detection as a means to identify women at risk of developing cervical cancer. While HPV testing identifies those at risk, it does not specifically distinguish individuals with neoplasia. We investigated whether a quantitative molecular test that measures methylated DNA markers could identify high-risk lesions in the cervix with accuracy. RESULTS: Marker discovery was performed in TCGA-CESC Infinium Methylation 450 K Array database and verified in three other public datasets. The panel was technically validated using Quantitative Multiplex-Methylation-Specific PCR in tissue sections (N = 252) and cervical smears (N = 244) from the USA, South Africa, and Vietnam. The gene panel consisted of FMN2, EDNRB, ZNF671, TBXT, and MOS. Cervical tissue samples from all three countries showed highly significant differential methylation in squamous cell carcinoma (SCC) with a sensitivity of 100% [95% CI 74.12-100.00], and specificity of 91% [95% CI 62.26-99.53] to 96% [95% CI 79.01-99.78], and receiver operating characteristic area under the curve (ROC AUC) = 1.000 [95% CI 1.00-1.00] compared to benign cervical tissue, and cervical intraepithelial neoplasia 2/3 with sensitivity of 55% [95% CI 37.77-70.84] to 89% [95% CI 67.20-98.03], specificity of 93% [95% CI 84.07-97.38] to 96% [95% CI 79.01-99.78], and a ROC AUC ranging from 0.793 [95% CI 0.68-0.89] to 0.99 [95% CI 0.97-1.00] compared to CIN1. In cervical smears, the marker panel detected SCC with a sensitivity of 87% [95% CI 77.45-92.69], specificity 95% [95% CI 88.64-98.18], and ROC AUC = 0.925 [95% CI 0.878-0.974] compared to normal, and high-grade squamous intraepithelial lesion (HSIL) at a sensitivity of 70% (95% CI 58.11-80.44), specificity of 94% (95% CI 88.30-97.40), and ROC AUC = 0.884 (95% CI 0.822-0.945) compared to low-grade intraepithelial lesion (LSIL)/normal in an analysis of pooled data from the three countries. Similar to HPV-positive, HPV-negative cervical carcinomas were frequently hypermethylated for these markers. CONCLUSIONS: This 5-marker panel detected SCC and HSIL in cervical smears with a high level of sensitivity and specificity. Molecular tests with the ability to rapidly detect high-risk HSIL will lead to timely treatment for those in need and prevent unnecessary procedures in women with low-risk lesions throughout the world. Validation of these markers in prospectively collected cervical smear cells followed by the development of a hypermethylated marker-based cervical cancer detection test is warranted.

Recent Advances in Carbon Nanotube Utilization in Perovskite Solar Cells: A Review.

Due to their exceptional optoelectronic properties, halide perovskites have emerged as prominent materials for the light-absorbing layer in various optoelectronic devices. However, to increase device performance for wider adoption, it is essential to find innovative solutions. One promising solution is incorporating carbon nanotubes (CNTs), which have shown remarkable versatility and efficacy. In these devices, CNTs serve multiple functions, including providing conducting substrates and electrodes and improving charge extraction and transport. The next iteration of photovoltaic devices, metal halide perovskite solar cells (PSCs), holds immense promise. Despite significant progress, achieving optimal efficiency, stability, and affordability simultaneously remains a challenge, and overcoming these obstacles requires the development of novel materials known as CNTs, which, owing to their remarkable electrical, optical, and mechanical properties, have garnered considerable attention as potential materials for highly efficient PSCs. Incorporating CNTs into perovskite solar cells offers versatility, enabling improvements in device performance and longevity while catering to diverse applications. This article provides an in-depth exploration of recent advancements in carbon nanotube technology and its integration into perovskite solar cells, serving as transparent conductive electrodes, charge transporters, interlayers, hole-transporting materials, and back electrodes. Additionally, we highlighted key challenges and offered insights for future enhancements in perovskite solar cells leveraging CNTs.

Network pharmacology combined with molecular docking and experimental verification to elucidate the effect of flavan-3-ols and aromatic resin on anxiety.

This study investigated the potential anxiolytic properties of flavan-3-ols and aromatic resins through a combined computational and experimental approach. Network pharmacology techniques were utilized to identify potential anxiolytic targets and compounds by analyzing protein-protein interactions and KEGG pathway data. Molecular docking and simulation studies were conducted to evaluate the binding interactions and stability of the identified targets. Behavioral tests, including the elevated plus maze test, open field test, light-dark test, actophotometer, and holeboard test, were used to assess anxiolytic activity. The compound-target network analysis revealed complex interactions involving 306 nodes and 526 edges, with significant interactions observed and an average node degree of 1.94. KEGG pathway analysis highlighted pathways such as neuroactive ligand-receptor interactions, dopaminergic synapses, and serotonergic synapses as being involved in anxiety modulation. Docking studies on EGCG (Epigallocatechin gallate) showed binding energies of -9.5 kcal/mol for MAOA, -9.2 kcal/mol for SLC6A4, and -7.4 kcal/mol for COMT. Molecular dynamic simulations indicated minimal fluctuations, suggesting the formation of stable complexes between small molecules and proteins. Behavioral tests demonstrated a significant reduction in anxiety-like behavior, as evidenced by an increased number of entries into and time spent in the open arm of the elevated plus maze test, light-dark test, open field center activity, hole board head dips, and actophotometer beam interruptions (p < 0.05 or p < 0.01). This research provides a comprehensive understanding of the multi-component, multi-target, and multi-pathway intervention mechanisms of flavan-3-ols and aromatic resins in anxiety treatment. Integrated network and behavioral analyses collectively support the anxiolytic potential of these compounds and offer valuable insights for future research in this area.