Target For Design Research Articles

Targeting chronic lymphocytic leukemia with B-cell activating factor receptor CAR T cells.

The challenge of disease relapsed/refractory (R/R) remains a therapeutic hurdle in chimeric antigen receptor (CAR) T-cell therapy, especially for hematological diseases, with chronic lymphocytic leukemia (CLL) being particularly resistant to CD19 CAR T cells. Currently, there is no approved CAR T-cell therapy for CLL patients. In this study, we aimed to address this unmet medical need by choosing the B-cell activating factor receptor (BAFF-R) as a promising target for CAR design against CLL. BAFF-R is essential for B-cell survival and is consistently expressed on CLL tumors. Our research discovered that BAFF-R CAR T-cell therapy exerted the cytotoxic effects on both CLL cell lines and primary B cells derived from CLL patients. In addition, the CAR T cells exhibited cytotoxicity against CD19-knockout CLL cells that are resistant to CD19 CAR T therapy. Furthermore, we were able to generate BAFF-R CAR T cells from small blood samples collected from CLL patients and then demonstrated the cytotoxic effects of these patient-derived CAR T cells against autologous tumor cells. Given these promising results, BAFF-R CAR T-cell therapy has the potential to meet the long-standing need for an effective treatment on CLL patients.

Bioinformatics and Chemoinformatics Analysis Explored the Role of Linum usitatissimum in Diabetic Heart Conditions: Experimental Analysis in H9c2 Rat Embryonic Cardiomyocytes Cell Lines.

Cytokine storms and inflammation lead to heart failure (HF). Bioactive compounds, as complementary medicine, can be the primary source of compounds with anti-inflammatory properties. Linum usitatissimum (LiU) has antioxidant capacity and anti-inflammatory activity. Here, candidate hugeness was selected based on the in silico studies, bio-cheminformatics, and bioinformatic analysis for excremental validation. We selected the vital genes with differential expression from the GSE26887 dataset. Based on the bioinformatics analysis, several parameters are determined to choose switchable genes involved in diabetic HF (DHF). We designed the protein-protein interactions network to consider the nodes' degree, modularity, and betweenness centrality. Hence, we selected the interleukin (IL)-6 protein as a target for drug design and discovery to reduce diabetes complications in the heart. Here, H9c2 cell lines of rat embryonic cardiomyocytes induce HF using hyperglycemic and hyperlipidemic conditions. Real-time polymerase chain reaction evaluated the relative expression of SMAD7/NRF-2/STAT3. Furthermore, we assessed the concentration of IL-6 using the enzyme-linked immunosorbent assay technique. Based on the bioinformatic analysis, we found that IL-6 with the highest network parameters score might be presented as a druggable protein in the DHF. Bioactive compounds and phytochemicals have potential strategies to manage DHF. LiUs decreased the expression level of the SMAD7 (P <0.0001) and STAT3 (P < 0.0001), and increased the expression level of the NRF2 (P < 0.0001). In addition, LiUs significantly reduced the concentration of IL-6 (P < 0.0001). Our data proposed that LiUs regulated inflammation and triggered the antioxidant defense in HF. Moreover, LiUs could have potential approaches to managing and preventing DHF.

Properties analysis on interlayer within lithium target and its beam uniformity for accelerator-based Boron Neutron Capture Therapy

In order to enhance the performance and lifetime of the lithium target used in accelerator-based neutron sources for Boron Neutron Capture Therapy (BNCT) treatment, an exploration of target design was conducted based on the 2.8 MeV, 20 mA proton beam. A comparison between scanning magnets and octupole magnets was performed for beam uniform, with octupole magnets selected to effectively avoid localized high thermal densities over short durations. Exploration was conducted on the performance of tantalum and vanadium as interlayers within the lithium target, considering aspects such as cooling, hydrogen diffusion, and neutron performances. This study revealed that, as the majority of energy deposition occurs within the interlayer, the presence of an appropriately thick tantalum or vanadium interlayer has minimal impact on cooling effectiveness, ensuring temperatures remain below 144 °C. The addition of an interlayer effectively reduces the maximum hydrogen concentration in copper, thus preventing copper blistering. Within the investigated thickness range, the interlayer does not affect neutron spectrum in the forward direction of the target, mitigating concerns regarding its impact on beam shaping.

The long road to ignition: An eyewitness account

This paper reviews the many twists and turns in the long journey that culminated in ignition in late 2022 using the laser heated indirect-drive approach to imploding DT filled targets at the National Ignition Facility (NIF), located at the Lawrence Livermore National Laboratory (LLNL). We describe the early origins of the Laser Program at LLNL and key developments such as the paradigm shifting birth of high energy density physics (HEDP) studies with lasers, changes in choice of laser wavelength, and the development of key diagnostics and computer codes. Fulfilling the requirements of the multi-faceted Nova Technical Contract was a necessary condition for the approval of the NIF, but more importantly, the end of the Cold War and the cessation of nuclear testing were key catalysts in that approval, along with the ready-and-waiting field of HEDP. The inherent flexibility of the field of laser driven inertial confinement fusion played a fundamental role in achieving success at the NIF. We describe how the ultimately successful ignition target design evolved from the original “point design” target, through the lessons of experiment. All key aspects of that original design changed: The capsule's materials and size were changed; the hohlraum's materials, size, laser entrance hole size, and gas fills were also all changed, as were the laser pulse shapes that go along with all those changes. The philosophy to globally optimize performance for stability (by raising the adiabat and thus lowering the implosion convergence) was also key, as was progress in target fabrication, and in increasing NIF's energy output. The persistence of the research staff and the steadfast backing of our supporters were also necessary elements in this success. We gratefully acknowledge seven decades of researcher endeavors and four decades of the dedicated efforts of many hundreds of personnel across the globe who have participated in NIF construction, operation, target fabrication, diagnostic, and theoretical advances that have culminated in ignition.

Learning Joint 2-D and 3-D Graph Diffusion Models for Complete Molecule Generation.

Designing new molecules is essential for drug discovery and material science. Recently, deep generative models that aim to model molecule distribution have made promising progress in narrowing down the chemical research space and generating high-fidelity molecules. However, current generative models only focus on modeling 2-D bonding graphs or 3-D geometries, which are two complementary descriptors for molecules. The lack of ability to jointly model them limits the improvement of generation quality and further downstream applications. In this article, we propose a joint 2-D and 3-D graph diffusion model (JODO) that generates geometric graphs representing complete molecules with atom types, formal charges, bond information, and 3-D coordinates. To capture the correlation between 2-D molecular graphs and 3-D geometries in the diffusion process, we develop a diffusion graph transformer (DGT) to parameterize the data prediction model that recovers the original data from noisy data. The DGT uses a relational attention mechanism that enhances the interaction between node and edge representations. This mechanism operates concurrently with the propagation and update of scalar attributes and geometric vectors. Our model can also be extended for inverse molecular design targeting single or multiple quantum properties. In our comprehensive evaluation pipeline for unconditional joint generation, the experimental results show that JODO remarkably outperforms the baselines on the QM9 and GEOM-Drugs datasets. Furthermore, our model excels in few-step fast sampling, as well as in inverse molecule design and molecular graph generation. Our code is provided in https://github.com/GRAPH-0/JODO.

Intervention studies with group design targeting expressive phonology for children with developmental speech and language disorder: A systematic review and meta-analysis.

Phonological difficulties are prevalent in children with speech and/or language disorders and may hamper their later language outcomes and academic achievements. These children often form a significant proportion of speech and language therapists' caseloads. There is a shortage of information on evidence-based interventions for improving phonological skills in children and adolescents with speech and language disorder. The aim of this systematic literature review and meta-analysis was to systematically examine the effects of different intervention approaches on speech production accuracy and phonological representation skills in children with speech and language disorders. A preregistered systematic review (International Prospective Register of Systematic Reviews ID: CRD42017076075) adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines was completed. Seven electronic databases (PubMed, Web of Science, ERIC, PsychINFO, Cochrane Library, SCOPUS and Linguistics & Language Behavior Abstracts) were searched for studies related to oral language interventions with children with developmental speech and/or language disorder (mean age ranging from 3-18 years) published between January 2006 and August 2022. The included articles reported intervention studies with a group design in which speech production accuracy was the outcome measure. Studies were appraised using the Cochrane risk of bias tool, and individual effect sizes were calculated using standardised means differences when enough data was available. A meta-analysis was conducted obtaining the average standardised mean difference d. Heterogeneity, influence of possible moderator variables and publication bias were explored. The 23 studies that met the inclusion criteria presented low-medium risk of bias. Nine effect sizes were obtained from seven of these studies that presented a pre-post-test with a control group design. Medium-high average effect sizes were found in phonological accuracy. Heterogeneity was found between individual effect sizes. Significant moderator variables and publication bias were not detected. The results of this meta-analysis indicate positive effects on speech production accuracy. Based on this review, further improvements in the quality of reporting for intervention research are required in developing the evidence base for practice. What is already known on the subject An increasing number of interventions is available for children and adolescents with developmental speech and/or language disorders. Previous reviews suggest relatively low levels of evidence of interventions having phonology as an outcome measure. What this paper adds to the existing knowledge This review and meta-analysis summarise the intervention evidence from a substantial body of group design studies, indicating positive results from a range of interventions with phonological outcomes. It highlights the need to systematically implement and replicate different intervention procedures to understand factors that will maximise positive outcomes and to grow the evidence base for best practice. What are the potential or actual clinical implications of this study? Tentative evidence is emerging for the effectiveness of various approaches in enhancing speech production accuracy skills of children and adolescents with developmental speech and/or language disorder.

Identification of immunity- and ferroptosis-related signature genes as potential design targets for mRNA vaccines in AML patients.

Immune-associated ferroptosis plays an important role in the progression of acute myeloid leukemia (AML); however, the targets that play key roles in this process are currently unknown. This limits the development of mRNA vaccines based on immune-associated ferroptosis for clinical therapeutic applications. In this study, based on the rich data resources of the TCGA-LAML cohort, we analyzed the tumor mutational burden (TMB), gene mutation status, and associations between immune and ferroptosis genes to reveal the disease characteristics of AML patients. To gain a deeper understanding of differentially expressed genes, we applied the Limma package for differential expression analysis and integrated data sources such as ImmPort Shared Data and FerrDb V2. Moreover, we established gene modules related to TMB according to weighted gene coexpression network analysis (WGCNA) and explored the functions of these modules in AML and their relationships with TMB. We focused on the top 30 most frequent genes through a detailed survey of missense mutations and single nucleotide polymorphisms (SNPs) and selected potentially critical gene targets for subsequent analysis. Based on the expression of these genes, we successfully subgrouped AML patients and found that the subgroups associated with TMB (C1 and C2) exhibited significant differences in survival. The differences in the tumor microenvironment and immune cells between C1 and C2 patients were investigated with the ESTIMATE and MCP-counter algorithms. A predictive model of TMB-related genes (TMBRGs) was constructed, and the validity of the model was demonstrated by categorizing patients into high-risk and low-risk groups. The differences in survival between the high-risk patients and high-TMB patients were further investigated, and potential vaccine targets were identified via immune cell-level analysis. The identification of immunity- and ferroptosis-associated signature genes is an independent predictor of survival in AML patients and provides new information on immunotherapy for AML.

Discovery of Inhibitors Targeting Protein-Protein Interaction between Bacterial RNA Polymerase and NusG as Novel Antimicrobials.

Bacterial RNA polymerase (RNAP), the core enzyme responsible for bacterial transcription, requires the NusG factor for efficient transcription elongation and termination. As the primary binding site for NusG, the RNAP clamp-helix (CH) domain represents a potential protein-protein interaction (PPI) target for novel antimicrobial agent design and discovery. In this study, we designed a pharmacophore model based on the essential amino acids of the CH for binding to NusG, such as R270, R278, and R281 (Escherichia coli numbering), and identified a hit compound with mild antimicrobial activity. Subsequent rational design and synthesis of this hit compound led to improved antimicrobial activity against Streptococcus pneumoniae, with the minimum inhibitory concentration (MIC) reduced from 128 to 1 μg/mL. Additional characterization of the antimicrobial activity, inhibitory activity against RNAP-NusG interaction, and cell-based transcription and fluorescent assays of the optimized compounds demonstrated their potential for further lead optimization.

NOX1-mediated oxidative stress induces chondrocyte ferroptosis by inhibiting the Nrf2/HO-1 pathway

Osteoarthritis (OA) is a common joint disease associated with the aging of the population, and it reduces the quality of life of patients. It is characterized by the destruction of articular cartilage and the secretion of inflammatory cytokines. Owing to the unclear pathogenesis of OA, current treatment methods have significant limitations. Oxidative stress has been revealed to play an important role in the development of OA. Our experiments indicated that the levels of GSH decreased and the level of MDA increased in chondrocytes, which induced ferroptosis in chondrocytes in OA. We also revealed that ferroptosis was the main mechanism of cartilage destruction caused by the addition of the ferroptosis activator erastin and the ferroptosis inhibitor ferrostatin-1. NOX1 is the main modulator of oxidative stress by increasing the generation of reactive oxidative species (ROS). We suppressed the expression of NOX1 in chondrocytes through cell transfection. The expression of collagen II and MMP13, and the secretion of IL-1β and TNF-α were reversed. An increase in the mitochondrial membrane potential and a decrease in the level of intracellular ROS indicate an improvement in oxidative damage. Additionally, we determined the effect of the Nrf2/HO-1 pathway on NOX1-mediated chondrocyte injury. We found that NOX1 inhibited the expression of Nrf2/HO-1, but the activation of Nrf2 improved the oxidative damage to chondrocytes in vivo and vitro. This study revealed that NOX1-mediated oxidative stress induces chondrocyte ferroptosis by inhibiting the Nrf2/HO-1 pathway. Our findings contribute to revealing the pathogenesis of OA, providing targets for drug design and optimizing the clinical treatment of OA.

Design Optimization and Reliability Evaluation in 1.2 kV SiC Trench MOSFET with Deep P Structure

In this paper, 1.2 kV SiC trench MOSFET with deep P structure has been proposed to effectively shield the trench bottom oxide. The various design splits, such as N concentration between deep P and deep P to trench distance, were experimentally evaluated and TCAD simulations were performed to extract maximum oxide electric field at trench bottom. Based on trade off results, critical design parameters were optimized to obtain low Rdson and stable breakdown voltage with acceptable oxide electric field. To evaluate trench gate oxide reliability in wafer level, gate oxide integrity (GOI/Vramp), charge to breakdown (QBD), and time dependent dielectric breakdown (TDDB) tests were conducted. Also, high temperature gate bias (HTGB) and high temperature reverse bias (HTRB) stress tests were carried out for assembled samples to compare device reliability depending on different designs. For the target design, the promising reliability results were confirmed in both wafer level and assembled samples.

RLpMIEC: High-Affinity Peptide Generation Targeting Major Histocompatibility Complex-I Guided and Interpreted by Interaction Spectrum-Navigated Reinforcement Learning.

Major histocompatibility complex (MHC) plays a vital role in presenting epitopes (short peptides from pathogenic proteins) to T-cell receptors (TCRs) to trigger the subsequent immune responses. Vaccine design targeting MHC generally aims to find epitopes with a high binding affinity for MHC presentation. Nevertheless, to find novel epitopes usually requires high-throughput screening of bulk peptide database, which is time-consuming, labor-intensive, more unaffordable, and very expensive. Excitingly, the past several years have witnessed the great success of artificial intelligence (AI) in various fields, such as natural language processing (NLP, e.g., GPT-4), protein structure prediction and engineering (e.g., AlphaFold2), and so on. Therefore, herein, we propose a deep reinforcement-learning (RL)-based generative algorithm, RLpMIEC, to quantitatively design peptide targeting MHC-I systems. Specifically, RLpMIEC combines the energetic spectrum (namely, the molecular interaction energy component, MIEC) based on the peptide-MHC interaction and the sequence information to generate peptides with strong binding affinity and precise MIEC spectra to accelerate the discovery of candidate peptide vaccines. RLpMIEC performs well in all the generative capability evaluations and can generate peptides with strong binding affinities and precise MIECs and, moreover, with high interpretability, demonstrating its powerful capability in participation for accelerating peptide-based vaccine development.

A Futuristic Development in 3D Printing Technique Using Nanomaterials with a Step Toward 4D Printing.

3D bioprinting has shown great promise in tissue engineering and regenerative medicine for creating patient-specific tissue scaffolds and medicinal devices. The quickness, accurate imaging, and design targeting of this emerging technology have excited biomedical engineers and translational medicine researchers. Recently, scaffolds made from 3D bioprinted tissue have become more clinically effective due to nanomaterials and nanotechnology. Because of quantum confinement effects and high surface area/volume ratios, nanomaterials and nanotechnological techniques have unique physical, chemical, and biological features. The use of nanomaterials and 3D bioprinting has led to scaffolds with improved physicochemical and biological properties. Nanotechnology and nanomaterials affect 3D bioprinted tissue engineered scaffolds for regenerative medicine and tissue engineering. Biomaterials and cells that respond to stimuli change the structural shape in 4D bioprinting. With such dynamic designs, tissue architecture can change morphologically. New 4D bioprinting techniques will aid in bioactuation, biorobotics, and biosensing. The potential of 4D bioprinting in biomedical technologies is also discussed in this article.

Novel physics-informed optimization framework for complex multi-physics problems: Implementation for a sweeping gas membrane distillation module

The application of advanced machine learning algorithms such as deep learning is limited for the surrogate-based process optimization of complex multi-physics problems because high-quality experimental and numerical training samples required for deep-learning model training are expensive and scarce. To address this issue, in this study, a novel physics-informed process optimization (PIPO) framework was introduced. In the first step, surrogate models based on conventional neural networks (NN) were trained using a few available high-quality training samples. In the second step, the trained NN models were coupled to the process optimization solver in which the loss of physical laws was added to the optimizer’s objective function to find optimal design points that satisfy the laws of physics. As a result, the generalization performance of the framework was greatly improved for design targets outside the training range of NN models. PIPO is substantially different from the physics-informed neural networks where the loss of physics is added to the loss function used during NN model training. The PIPO framework was used to optimize a sweeping gas membrane distillation (SGMD) module. Eight input design variables, including process and geometrical parameters, were optimized for different challenging targets to achieve the best SGMD performance in terms of ammonia recovery ratio and concentration. It was shown that for noticeably few training samples of 68 experiments, the proposed framework was able to achieve the optimization targets within a reasonable computational cost. The optimum designs were verified and analyzed in detail by high-resolution computational fluid dynamics models.

The application of NiCrPt alloy targets for magnetron sputter deposition: Characterization of targets and deposited thin films

NiSi thin films are extensively used in advanced semiconductor devices due to their desirable electrical properties. Recently, the incorporation of platinum (Pt) into NiSi thin films, resulting in NiPtSi, has been pursued to mitigate the agglomeration of NiSi and delay its transformation into NiSi2. Typically, in semiconductor manufacturing, NiPt films are deposited onto silicon wafers through magnetron sputtering using NiPt sputtering targets. To further enhance the properties of these thin films, chromium (Cr) was introduced into the NiPt target. Initially, NiCrPt targets were fabricated through thermal mechanical treatment. Subsequently, NiCrPt thin films were deposited via magnetron sputtering using these NiCrPt targets. The study results indicate that, in comparison to NiPt targets, the addition of chromium offers several advantages: (1) it refines the grain size of NiPt in both bulk and thin film states; (2) it effectively reduces microcracks in the thin films; and (3) it increases the sputtering rate of NiCrPt targets, owing to an increased pass-through flux of 100 % and the refined grain size of the NiCrPt target. These findings provide valuable insights into the design and development of NiPt alloy sputtering targets and the production of crack-free thin films via magnetron sputtering, marking a significant advancement in the field of semiconductor manufacturing.

A genome-scale metabolic model of a globally disseminated hyperinvasive M1 strain of Streptococcus pyogenes.

Streptococcus pyogenes is responsible for a range of diseases in humans contributing significantly to morbidity and mortality. Among more than 200 serotypes of S. pyogenes, serotype M1 strains hold the greatest clinical relevance due to their high prevalence in severe human infections. To enhance our understanding of pathogenesis and discovery of potential therapeutic approaches, we have developed the first genome-scale metabolic model (GEM) for a serotype M1 S. pyogenes strain, which we name iYH543. The curation of iYH543 involved cross-referencing a draft GEM of S. pyogenes serotype M1 from the AGORA2 database with gene essentiality and autotrophy data obtained from transposon mutagenesis-based and growth screens. We achieved a 92.6% (503/543 genes) accuracy in predicting gene essentiality and a 95% (19/20 amino acids) accuracy in predicting amino acid auxotrophy. Additionally, Biolog Phenotype microarrays were employed to examine the growth phenotypes of S. pyogenes, which further contributed to the refinement of iYH543. Notably, iYH543 demonstrated 88% accuracy (168/190 carbon sources) in predicting growth on various sole carbon sources. Discrepancies observed between iYH543 and the actual behavior of living S. pyogenes highlighted areas of uncertainty in the current understanding of S. pyogenes metabolism. iYH543 offers novel insights and hypotheses that can guide future research efforts and ultimately inform novel therapeutic strategies.IMPORTANCEGenome-scale models (GEMs) play a crucial role in investigating bacterial metabolism, predicting the effects of inhibiting specific metabolic genes and pathways, and aiding in the identification of potential drug targets. Here, we have developed the first GEM for the S. pyogenes highly virulent serotype, M1, which we name iYH543. The iYH543 achieved high accuracy in predicting gene essentiality. We also show that the knowledge obtained by substituting actual measurement values for iYH543 helps us gain insights that connect metabolism and virulence. iYH543 will serve as a useful tool for rational drug design targeting S. pyogenes metabolism and computational screening to investigate the interplay between inhibiting virulence factor synthesis and growth.

Temporal structures that determine consistency and quality of care: a case study in hyperacute stroke services

BackgroundTemporal structuring is determined by practices and social norms and affects the quality and timing of care. In this case study of hyperacute stroke wards which provide initial stroke investigation,...

Osteoporosis treatment: current drugs and future developments.

Osteoporosis is a common systemic metabolic disease characterized by a decrease in bone density and bone mass, destruction of bone tissue microstructure, and increased bone fragility leading to fracture susceptibility. Pharmacological treatment of osteoporosis is the focus of current research, and anti-osteoporosis drugs usually play a role in inhibiting bone resorption, promoting bone formation, and having a dual role. However, most of the drugs have the disadvantages of single target and high toxic and side effects. There are many types of traditional Chinese medicines (TCM), from a wide range of sources and mostly plants. Herbal plants have unique advantages in regulating the relationship between osteoporosis and the immune system, acupuncture therapy has significant therapeutic effects in combination with medicine for osteoporosis. The target cells and specific molecular mechanisms of TCM in preventing and treating osteoporosis have not been fully elucidated. At present, there is a lack of comprehensive understanding of the pathological mechanism of the disease. Therefore, a better understanding of the pathological signaling pathways and key molecules involved in the pathogenesis of osteoporosis is crucial for the design of therapeutic targets and drug development. In this paper, we review the development and current status of anti-osteoporosis drugs currently in clinical application and under development to provide relevant basis and reference for drug prevention and treatment of osteoporosis, with the aim of promoting pharmacological research and new drug development.

How immersive technologies impact behavioral responses in destination marketing: the role of physiological arousal, presence, and age

PurposeThis paper aims to investigate the effectiveness of immersive virtual reality (VR) media and the influence of user’s age in the context of destination marketing by exploring their impact on cognition (presence), affection (arousal), and behavioral (intention to visit and intention to recommend the destination) outcomes.Design/methodology/approachA laboratory experiment was conducted to compare the impact of using immersive VR (vs. 2D desktop) to experience a 360-degree virtual tour of Valencia on consumers’ behavior. The sample included 187 participants. Both self-reported and physiological measures were collected during the experimentation.FindingsResults showed that participants in the immersive condition experienced a stronger sense of presence and higher physiological arousal than those exposed to nonimmersive content. Presence significantly mediated both the intention to visit and the intention to recommend the promoted venue. Physiological arousal mediated the relationship between media typology and the intention to recommend the destination. Upon introducing age as a moderating variable, the effect of physiological arousal on behavioral outcomes proves to be significant.Practical implicationsThe study presents destination marketing organizations with a compelling use case for immersive technologies. It also offers design principles, potential applications and targeting strategies for VR marketing in hospitality management.Originality/valueTo the best of the authors’ knowledge, this study is the first to investigate the combined effect of physiological arousal and presence on behavioral intentions in VR destination marketing, while also examining the impact of age as an individual characteristic.

Exploring the Development of Photoresists in Lacquer under the Background of Global Carbon Neutrality: A Mini Review

With the establishment of a global carbon-neutral target, green and sustainable design concepts, materials, and processes have become the key to scientific and technological innovation, which highlights the development and utilization of environmentally-friendly materials and renewable energy sources. At present, almost all the raw materials in commercial photoresist are from oil resources. In order to implement the policy of “green chemistry”, there is an urgent need to take the place of petroleum with renewable, biodegradable, and green biomass resources in photoresist production. As a kind of green and natural high polymer material from China, lacquer has excellent physical and chemical properties and has great application potential in the field of electronics. In this paper, the potential connections between photoresist and lacquer, especially in the green development of photoresist, are sorted out. The introduction of lacquer and its derivatives may help to improve the environmentally-friendly production of photoresist and its performance. Therefore, in the context of global carbon neutrality, the study of photoresists in the “lacquer” is of great significance in promoting the application of eco-friendly materials in the field of microelectronics.

The French Medicinal Chemistry Society-SCT, an Historical Member of EFMC.

The French Society of Medicinal Chemistry or " Société de Chimie Thérapeutique " (SCT) was founded in 1966. Since its inception, its mission has been to promote knowledge in the main fields of pharmaceutical research and development, in particular the research and validation of biological targets of therapeutic interest, the screening, design and optimization of drug candidates, chemical biology, medicinal chemistry, pharmacokinetics, metabolism and toxicity. Since 1964, the Society has organized an annual international congress (RICT), and later thematic days for young researchers and workshops on specific topics. The SCT is also a member of the European Federation for Medicinal Chemistry (EFMC) and organized the International Symposium on Medicinal Chemistry (ISMC) in Nice in 2022. Several new trends can be identified in the activities of the SCT, such as the organization of regular webinars, but also the recent creation of the Young MedChem Forum, as well as the distribution of a newsletter reporting scientific achievements in the French community and abroad, and an improved presence on social networks. These trends are in line with the current changes in the field in terms of scientific progress, means of communication in the community and with the public and inclusiveness.