Dual mutations in the whitefly nicotinic acetylcholine receptor ß1 subunit confer target-site resistance to multiple neonicotinoid insecticides.

Neonicotinoid insecticides, which target insect nicotinic acetylcholine receptors (nAChRs), have been widely and intensively used to control the whitefly, Bemisia tabaci, a highly damaging, globally distributed, crop pest. This has inevitably led to the emergence of populations with resistance to neonicotinoids. However, to date, there have been no reports of target-site resistance involving mutation of B. tabaci nAChR genes. Here we characterize the nAChR subunit gene family of B. tabaci and identify dual mutations (A58T&R79E) in one of these genes (BTß1) that confer resistance to multiple neonicotinoids. Transgenic D. melanogaster, where the native nAChR Dß1 was replaced with BTß1A58T&R79E, were significantly more resistant to neonicotinoids than flies where Dß1 were replaced with the wildtype BTß1 sequence, demonstrating the causal role of the mutations in resistance. The two mutations identified in this study replace two amino acids that are highly conserved in >200 insect species. Three-dimensional modelling suggests a molecular mechanism for this resistance, whereby A58T forms a hydrogen bond with the R79E side chain, which positions its negatively-charged carboxylate group to electrostatically repulse a neonicotinoid at the orthosteric site. Together these findings describe the first case of target-site resistance to neonicotinoids in B. tabaci and provide insight into the molecular determinants of neonicotinoid binding and selectivity.

Wnt5a-mediated autophagy contributes to the epithelial-mesenchymal transition of human bronchial epithelial cells during asthma.

BACKGROUND: The epithelial-mesenchymal transition (EMT) of human bronchial epithelial cells (HBECs) is essential for airway remodeling during asthma. Wnt5a has been implicated in various lung diseases, while its role in the EMT of HBECs during asthma is yet to be determined. This study sought to define whether Wnt5a initiated EMT, leading to airway remodeling through the induction of autophagy in HBECs. METHODS: Microarray analysis was used to investigate the expression change of WNT5A in asthma patients. In parallel, EMT models were induced using 16HBE cells by exposing them to house dust mites (HDM) or interleukin-4 (IL-4), and then the expression of Wnt5a was observed. Using in vitro gain- and loss-of-function approaches via Wnt5a mimic peptide FOXY5 and Wnt5a inhibitor BOX5, the alterations in the expression of the epithelial marker E-cadherin and the mesenchymal marker protein were observed. Mechanistically, the Ca2+/CaMKII signaling pathway and autophagy were evaluated. An autophagy inhibitor 3-MA was used to examine Wnt5a in the regulation of autophagy during EMT. Furthermore, we used a CaMKII inhibitor KN-93 to determine whether Wnt5a induced autophagy overactivation and EMT via the Ca2+/CaMKII signaling pathway. RESULTS: Asthma patients exhibited a significant increase in the gene expression of WNT5A compared to the healthy control. Upon HDM and IL-4 treatments, we observed that Wnt5a gene and protein expression levels were significantly increased in 16HBE cells. Interestingly, Wnt5a mimic peptide FOXY5 significantly inhibited E-cadherin and upregulated α-SMA, Collagen I, and autophagy marker proteins (Beclin1 and LC3-II). Rhodamine-phalloidin staining showed that FOXY5 resulted in a rearrangement of the cytoskeleton and an increase in the quantity of stress fibers in 16HBE cells. Importantly, blocking Wnt5a with BOX5 significantly inhibited autophagy and EMT induced by IL-4 in 16HBE cells. Mechanistically, autophagy inhibitor 3-MA and CaMKII inhibitor KN-93 reduced the EMT of 16HBE cells caused by FOXY5, as well as the increase in stress fibers, cell adhesion, and autophagy. CONCLUSION: This study illustrates a new link in the Wnt5a-Ca2+/CaMKII-autophagy axis to triggering airway remodeling. Our findings may provide novel strategies for the treatment of EMT-related diseases.

IMN4NPD: An Integrated Molecular Networking Workflow for Natural Product Dereplication.

Molecular networking has emerged as a standard approach for natural product (NP) discovery. However, the current pipeline based on molecular networks tends to prioritize larger clusters comprising multiple nodes. To address this issue, we present the integrated molecular networking workflow for NP dereplication (IMN4NPD). This approach not only expedites the rapid dereplication of extensive clusters within the molecular network but also places specific emphasis on self-looped or pairs of nodes, which are often overlooked by the current methods. By amalgamating the outputs from various computational tools, we efficiently dereplicate compounds falling into specific categories and provide annotations for both large cluster nodes and self-looped or pair of nodes within the molecular network. Furthermore, we have incorporated several fundamentally distinct similarity algorithms, namely, Spec2Vec and MS2DeepScore, for constructing the t-SNE network. Through comparison with modified cosine similarity, we have observed that integrating additional diverse spectral similarity measures, the resulting t-SNE network enhanced the ability to dereplicate NPs. Demonstrating the use case of an ethanol extract of Plumula nelumbinis, we illustrate that an integration of multiple computational solutions with IMN4NPD aids the dereplication, especially self-looped nodes, and in the discovery of novel compounds in NPs.

Controlling nonlinear collapse of ellipticity and orientation of a co-variant vector optical field.

A vector optical field with inhomogeneous spatial polarization distribution offers what we believe to be a new paradigm to form controllable filaments. However, it is challenging to steer multiple performances (e.g. number, orientation, and interval) of filaments in transparent nonlinear media at one time. Herein, we theoretically self-design and generate a kind of believed to be novel ellipticity and orientation co-variant vector optical field to interact with Kerr medium to solve this issue. The collapsing behaviors of such a new hybrid vector optical field reveal that, by judiciously adjusting the inherent topological charge and initial phase of incident optical field, we are able to give access to stable collapsing filamentation with tunable numbers, orientations and interval. Additionally, the collapsing patterns presented are immune nearly to the extra random noise. The relevant mechanism behind the collapse of the vector optical field is elucidated as well. The findings in this work may have huge potential in optical signal processing, laser machining, and other related applications.

Integrating trans-omics, cellular experiments and clinical validation to identify ILF2 as a diagnostic serum biomarker and therapeutic target in gastric cancer.

BACKGROUND: Gastric cancer (GC) lacks serum biomarkers with clinical diagnostic value. Multi-omics analysis is an important approach to discovering cancer biomarkers. This study aimed to identify and validate serum biomarkers for GC diagnosis by cross-analysis of proteomics and transcriptomics datasets. METHODS: A cross-omics analysis was performed to identify overlapping differentially expressed genes (DEGs) between our previous aptamer-based GC serum proteomics dataset and the GC tissue RNA-Seq dataset in The Cancer Genome Atlas (TCGA) database, followed by lasso regression and random forest analysis to select key overlapping DEGs as candidate biomarkers for GC. The mRNA levels and diagnostic performance of these candidate biomarkers were analyzed in the original and independent GC datasets to select valuable candidate biomarkers. The valuable candidate biomarkers were subjected to bioinformatics analysis to select those closely associated with the biological behaviors of GC as potential biomarkers. The clinical diagnostic value of the potential biomarkers was validated using serum samples, and their expression levels and functions in GC cells were validated using in vitro cell experiments. RESULTS: Four candidate biomarkers (ILF2, PGM2L1, CHD7, and JCHAIN) were selected. Their mRNA levels differed significantly between tumor and normal tissues and showed different diagnostic performances for GC, with areas under the receiver operating characteristic curve (AUROCs) of 0.629-0.950 in the TCGA dataset and 0.736-0.840 in the Gene Expression Omnibus (GEO) dataset. In the bioinformatics analysis, only ILF2 (interleukin enhancer-binding factor 2) gene levels were associated with immune cell infiltration, some checkpoint gene expression, chemotherapy sensitivity, and immunotherapy response. Serum levels of ILF2 were higher in GC patients than in controls, with an AUROC of 0.944 for the diagnosis of GC, and it was also detected in the supernatants of GC cells. Knockdown of ILF2 by siRNA significantly reduced the proliferation and colony formation of GC cells. Overexpression of ILF2 significantly promotes the proliferation and colony formation of gastric cancer cells. CONCLUSIONS: Trans-omics analysis of proteomics and transcriptomics is an efficient approach for discovering serum biomarkers, and ILF2 is a potential diagnostic biomarker and therapeutic target of gastric cancer.

Comprehensive Review of Drug-Drug Interaction Prediction Based on Machine Learning: Current Status, Challenges, and Opportunities.

Detecting drug-drug interactions (DDIs) is an essential step in drug development and drug administration. Given the shortcomings of current experimental methods, the machine learning (ML) approach has become a reliable alternative, attracting extensive attention from the academic and industrial fields. With the rapid development of computational science and the growing popularity of cross-disciplinary research, a large number of DDI prediction studies based on ML methods have been published in recent years. To give an insight into the current situation and future direction of DDI prediction research, we systemically review these studies from three aspects: (1) the classic DDI databases, mainly including databases of drugs, side effects, and DDI information; (2) commonly used drug attributes, which focus on chemical, biological, and phenotypic attributes for representing drugs; (3) popular ML approaches, such as shallow learning-based, deep learning-based, recommender system-based, and knowledge graph-based methods for DDI detection. For each section, related studies are described, summarized, and compared, respectively. In the end, we conclude the research status of DDI prediction based on ML methods and point out the existing issues, future challenges, potential opportunities, and subsequent research direction.

A Very Deep Graph Convolutional Network for 13C NMR Chemical Shift Calculations with Density Functional Theory Level Performance for Structure Assignment.

Nuclear magnetic resonance (NMR) chemical shift calculations are powerful tools for structure elucidation and have been extensively employed in both natural product and synthetic chemistry. However, density functional theory (DFT) NMR chemical shift calculations are usually time-consuming, while fast data-driven methods often lack reliability, making it challenging to apply them to computationally intensive tasks with a high requirement on quality. Herein, we have constructed a 54-layer-deep graph convolutional network for 13C NMR chemical shift calculations, which achieved high accuracy with low time-cost and performed competitively with DFT NMR chemical shift calculations on structure assignment benchmarks. Our model utilizes a semiempirical method, GFN2-xTB, and is compatible with a broad variety of organic systems, including those composed of hundreds of atoms or elements ranging from H to Rn. We used this model to resolve the controversial J/K ring junction problem of maitotoxin, which is the largest whole molecule assigned by NMR calculations to date. This model has been developed into user-friendly software, providing a useful tool for routine rapid structure validation and assignation as well as a new approach to elucidate the large structures that were previously unsuitable for NMR calculations.

DDInter: an online drug-drug interaction database towards improving clinical decision-making and patient safety.

Drug-drug interaction (DDI) can trigger many adverse effects in patients and has emerged as a threat to medicine and public health. Despite the continuous information accumulation of clinically significant DDIs, there are few open-access knowledge systems dedicated to the curation of DDI associations. To facilitate the clinicians to screen for dangerous drug combinations and improve health systems, we present DDInter, a curated DDI database with comprehensive data, practical medication guidance, intuitive function interface, and powerful visualization to the scientific community. Currently, DDInter contains about 0.24M DDI associations connecting 1833 approved drugs (1972 entities). Each drug is annotated with basic chemical and pharmacological information and its interaction network. For DDI associations, abundant and professional annotations are provided, including severity, mechanism description, strategies for managing potential side effects, alternative medications, etc. The drug entities and interaction entities are efficiently cross-linked. In addition to basic query and browsing, the prescription checking function is developed to facilitate clinicians to decide whether drugs combinations can be used safely. It can also be used for informatics-based DDI investigation and evaluation of other prediction frameworks. We hope that DDInter will prove useful in improving clinical decision-making and patient safety. DDInter is freely available, without registration, at http://ddinter.scbdd.com/.

Co-culturing Propionibacterium freudenreichii and Bifidobacterium animalis subsp. lactis improves short-chain fatty acids and vitamin B12 contents in soy whey.

The lack of vitamin B12 in unprocessed plant-based foods can lead to health problems in strict vegetarians and vegans. The main aim of this study was to investigate the potential synergy of co-culturing Bifidobacterium animalis subsp. lactis and Propionibacterium freudenreichii in improving production of vitamin B12 and short-chain fatty acids in soy whey. Different strategies including mono-, sequential and simultaneous cultures were adopted. Growth, short-chain fatty acids and vitamin B12 were assessed throughout the fermentation while free amino acids, volatiles, and isoflavones were determined on the final day. P. freudenreichii monoculture grew well in soy whey, whereas B. lactis monoculture entered the death phase by day 4. Principal component analysis demonstrates that metabolic changes in both sequential cultures did not show drastic differences to those of P. freudenreichii monoculture. However, simultaneous culturing significantly improved vitamin B12, acetic acid and propionic acid contents (1.3 times, 5 times, 2.5 times, compared to the next highest treatment [sequential cultures]) in fermented soy whey relative to other culturing modes. Hence, co-culturing of P. freudenreichii and B. lactis would provide an alternative method to improve vitamin B12, acetic acid and propionic acid contents in fermented foods.

Properties of low-fat Cheddar cheese prepared from bovine-camel milk blends: Chemical composition, microstructure, rheology, and volatile compounds.

Making cheese from camel milk (CM) presents various challenges due to its different physicochemical properties compared with bovine milk (BM). In this study, we investigated the chemical composition, proteolysis, meltability, oiling off, texture profile, color, microstructure, and rheological properties of low-fat Cheddar cheese (LFCC) prepared from BM-CM blends. LFCC was produced from BM or BM supplemented with 15% CM (CM15) and 30% CM (CM30), and analyzed after 14, 60, 120, and 180 d of ripening at 8°C. Except for salt content, no significant differences were observed among LFCC from BM, CM15, and CM30. The addition of CM increased the meltability and oiling off in the resulting cheese throughout storage. With respect to color properties, after melting, LFCC CM30 showed lower L* values than LFCC made from BM and CM15, and a* and b* values were higher than those of BM and CM15 samples. LFCC from CM30 also exhibited lower hardness compared with the other cheeses. Moreover, LFCC made from BM showed a rough granular surface, but cheese samples made from BM-CM blends exhibited a smooth surface. The rheological parameters, including storage modulus, loss modulus, and loss tangent, varied among cheese treatments. The determined acetoin and short-chain volatile acids (C2-C6) in LFCC were affected by the use of CM, because CM15 showed significantly higher amounts than BM and CM30, respectively. The detailed interactions between BM and CM in the cheese matrix should be further investigated.

Invited review: Camel milk and gut health-Understanding digestibility and the effect on gut microbiota.

Camel milk (CM), known for its immune-regulatory, anti-inflammatory, antiapoptotic, and antidiabetic properties, is a natural healthy food. It is easily digestible due to the high levels of ß-casein and diverse secreted antibodies, exhibiting superior antibacterial and antiviral activities compared with bovine milk. ß-casein is less allergic and more digestible because it is more susceptible to digestive hydrolysis in the gut; therefore, higher levels of ß-casein make CM advantageous for human health. Furthermore, antibodies help the digestive system by destroying the antigens, which are then overwhelmed and digested by macrophages. The connection between the gut microbiota and human health has gained substantial research attention, as it offers potential benefits and supports disease treatment. The gut microbiota has a vital role in regulating the host's health because it helps in several biological functions, such as protection against pathogens, immune function regulation, energy harvesting from digested foods, and reinforcement of digestive tract biochemical barriers. These functions could be affected by the changes in the gut microbiota profile, and gut microbiota differences are associated with several diseases, such as inflammatory bowel disease, colon cancer, irritable bowel disorder, mental illness, allergy, and obesity. This review focuses on the digestibility of CM components, particularly protein and fat, and their influence on gut microbiota modulation. Notably, the hypoallergenic properties and small fat globules of CM contribute to its enhanced digestibility. Considering the rapid digestion of its proteins under conditions simulating infant gastrointestinal digestion, CM exhibits promise as a potential alternative for infant formula preparation due to the high ß-/αs-casein ratio and protective proteins, in addition to the absence of ß-lactoglobulin.

Comprehensive profiling of the chemical constituents in Dayuanyin decoction using UPLC-QTOF-MS combined with molecular networking.

CONTEXT: Dayuanyin decoction is a traditional Chinese medicine formulation that is commonly used in modern clinical practice to treat viral infections such as viral pneumonia, viral fever, influenza, and hepatitis. Although the usage rate of Dayuanyin decoction is gradually increasing in clinical practice, its pharmacological constituents are still unclear. OBJECTIVE: This study comprehensively characterized the chemical constituents in Dayuanyin decoction using ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) and molecular networking. MATERIALS AND METHODS: The overall strategy involved retrieving structural information, such as fragment ions and precursor ion masses, from self-built databases to identify the target constituents of the Dayuanyin decoction extract. The identification of non-targeted constituents was achieved by analyzing different categories, fragment pathways, mass spectrometry data, and the relationships between clusters of structures in molecular networking. Unannotated constituents were inferred from secondary mass spectrometry similarity and molecular weight differences and annotated constituents in the same constituent cluster. A few predicted constituents were selected and validated by comparing them to reference standards under identical mass spectrometry conditions. RESULTS: This study preliminarily identified 216 constituents, including flavonoids, amino acids, alkaloids, triterpenes, steroidal saponins, phenylpropanoids, and other constituents. CONCLUSIONS: This integrated strategy using UPLC-QTOF-MS and molecular networking lays the foundation for clinical research on pharmacologically active substances in Dayuanyin decoction and could be popularized for the comprehensive profiling of chemical constituents of other traditional Chinese medicines.

Surgical treatment of multiple magnet ingestion by children: A single-center experience from China.

BACKGROUND: The incidence of magnet ingestion in children has escalated concurrent with the rise in popularity of magnetic playthings, bearing the capacity to induce substantial morbidity. AIM: The objective of this study was to encapsulate our accumulated expertise in handling pediatric cases featuring multiple magnetic foreign bodies within the gastrointestinal tract sometimes necessitating surgical intervention, as well as to formulate a clinical management algorithm. METHODS: This was a retrospective review of patients with multiple magnetic foreign bodies in the digestive tract, admitted to Shenzhen Children's Hospital, between January 2018 and December 2022. RESULTS: A total of 100 cases were included in this study, including 66 males and 34 females. The main clinical manifestation ns were abdominal pain and vomiting. All patients had abdominal x-ray, all of which indicated foreign bodies in the digestive tract. 33 patients had to undergo a surgical intervention. Among these cases, the gastrointestinal complications occurred in 31 patients, including gastric rupture (n = 9), intestinal obstruction (n = 11) and intestinal perforation (n = 30). Postoperative intestinal obstruction occurred in 6 children. There was no statistical significant difference in age and gender between the Surgical group and Non-surgical group, but the Surgical group had a higher number of magnets ([7.5(2-44) vs 4(2-20)], p = 0.009), a longer interval between time of misingestion to clinical visit ([48(7.2-480) vs 5(2-336)]hours, p < 0.001), and a longer length of hospital stay ([10(6-19) vs 2(1-8)]days, p < 0.001). CONCLUSIONS: Multiple magnet ingestion in children can lead to serious complications and carry severe risks. Timely diagnosis and effective treatment are crucial for managing such patients.

Smart Target-Initiated Catalytic DNA Junction Circuit Amplification Strategy for the Ultrasensitive Electrochemiluminescence Detection of MicroRNA.

One of the highly attractive research directions in the electrochemiluminescence (ECL) field is how to regulate and improve ECL efficiency. Quantum dots (QDs) are highly promising ECL materials due to their adjustable luminescence size and strong luminous efficiency. MoS2 NSs@QDs, an ECL emitter, is synthesized via hydrothermal methods, and its ECL mechanism is investigated using cyclic voltammetry and ECL-potential curves. Then, a stable and vertical attachment of a triplex DNA (tsDNA) probe to the MoS2 nanosheets (NSs) is applied to the electrode. Next, an innovative ECL sensor is courageously empoldered for precise and ultrasensitive detection of target miRNA-199a through the agency of ECL-resonance energy transfer (RET) strategy and a dextrous target-initiated catalytic three-arm DNA junction assembly (CTDJA) based on a toehold strand displacement reaction (TSDR) signal amplification approach. Impressively, the ingenious system not only precisely regulates the distance between energy donor-acceptor pairs leave energy less loss and more ECL-RET efficiency, but also simplifies the operational procedure and verifies the feasibility of this self-assembly process without human intervention. This study can expand MoS2 NSs@QDs utilization in ECL biosensing applications, and the proposed nucleic acid amplification strategy can become a miracle cure for ultrasensitive detecting diverse biomarkers, which helps researchers to better study the tumor mechanism, thereby unambiguously increasing cancer cure rates and reducing the risk of recurrence.

A long-acting recombinant FSH supports high-quality mouse follicle development and oocyte maturation in vitro by coordinating somatic and germ cell transcriptomes.

Strategies to maximize individual fertility chances are constant requirements of ART. In vitro folliculogenesis may represent a valid option to create a large source of immature ovarian follicles in ART. Efforts are being made to set up mammalian follicle culture protocols with suitable FSH stimuli. In this study, a new type of recombinant FSH (KN015) with a prolonged half-life is proposed as an alternative to canonical FSH. KN015 supports the in vitro development of mouse follicles from primary to preovulatory stage with higher efficiency than canonical FSH and enhanced post-fertilization development rates of the ovulated oocytes. The use of KN015 also allows us to compare the dynamic transcriptome changes in oocytes and granulosa cells at different stages, in vivo and in vitro. In particular, KN015 facilitates mRNA accumulation in growing mouse oocytes and prevents spontaneous luteinization of granulosa cells in vitro. Novel analyses of transcriptome changes in this study reveal that the in vivo oocytes were more efficient than in vitro oocytes in terms of maternal mRNA clearing during meiotic maturation. KN015 promotes the degradation of maternal mRNA during in vitro oocyte maturation, improves cytoplasmic maturation and, therefore, enhances embryonic developmental potential. These findings establish new transcriptome data for oocyte and granulosa cells at the key stages of follicle development, and should help to widen the use of KN015 as a valid and commercially available hormonal support enabling optimized in vitro development of follicles and oocytes.

QSAR-assisted-MMPA to expand chemical transformation space for lead optimization.

Matched molecular pairs analysis (MMPA) has become a powerful tool for automatically and systematically identifying medicinal chemistry transformations from compound/property datasets. However, accurate determination of matched molecular pair (MMP) transformations largely depend on the size and quality of existing experimental data. Lack of high-quality experimental data heavily hampers the extraction of more effective medicinal chemistry knowledge. Here, we developed a new strategy called quantitative structure-activity relationship (QSAR)-assisted-MMPA to expand the number of chemical transformations and took the logD7.4 property endpoint as an example to demonstrate the reliability of the new method. A reliable logD7.4 consensus prediction model was firstly established, and its applicability domain was strictly assessed. By applying the reliable logD7.4 prediction model to screen two chemical databases, we obtained more high-quality logD7.4 data by defining a strict applicability domain threshold. Then, MMPA was performed on the predicted data and experimental data to derive more chemical rules. To validate the reliability of the chemical rules, we compared the magnitude and directionality of the property changes of the predicted rules with those of the measured rules. Then, we compared the novel chemical rules generated by our proposed approach with the published chemical rules, and found that the magnitude and directionality of the property changes were consistent, indicating that the proposed QSAR-assisted-MMPA approach has the potential to enrich the collection of rule types or even identify completely novel rules. Finally, we found that the number of the MMP rules derived from the experimental data could be amplified by the predicted data, which is helpful for us to analyze the medicinal chemical rules in local chemical environment. In summary, the proposed QSAR-assisted-MMPA approach could be regarded as a very promising strategy to expand the chemical transformation space for lead optimization, especially when no enough experimental data can support MMPA.

PySmash: Python package and individual executable program for representative substructure generation and application.

BACKGROUND: Substructure screening is widely applied to evaluate the molecular potency and ADMET properties of compounds in drug discovery pipelines, and it can also be used to interpret QSAR models for the design of new compounds with desirable physicochemical and biological properties. With the continuous accumulation of more experimental data, data-driven computational systems which can derive representative substructures from large chemical libraries attract more attention. Therefore, the development of an integrated and convenient tool to generate and implement representative substructures is urgently needed. RESULTS: In this study, PySmash, a user-friendly and powerful tool to generate different types of representative substructures, was developed. The current version of PySmash provides both a Python package and an individual executable program, which achieves ease of operation and pipeline integration. Three types of substructure generation algorithms, including circular, path-based and functional group-based algorithms, are provided. Users can conveniently customize their own requirements for substructure size, accuracy and coverage, statistical significance and parallel computation during execution. Besides, PySmash provides the function for external data screening. CONCLUSION: PySmash, a user-friendly and integrated tool for the automatic generation and implementation of representative substructures, is presented. Three screening examples, including toxicophore derivation, privileged motif detection and the integration of substructures with machine learning (ML) models, are provided to illustrate the utility of PySmash in safety profile evaluation, therapeutic activity exploration and molecular optimization, respectively. Its executable program and Python package are available at https://github.com/kotori-y/pySmash.

Resonance coupling between chiral quasi-BICs and achiral molecular excitons in dielectric metasurface J-aggregate heterostructures.

The realization of flexible tuning and enhanced chiral responses is vital for many applications in nanophotonics. This study proposes to manipulate the collective optical responses with heterostructures consisting of chiral dielectric metasurfaces and achiral J-aggregates. Owing to the resonance coupling between the chiral quasi-bound states in the continuum (QBICs) and the achiral exciton mode, large mode splitting and anticrossing are observed in both the transmission and circular dichroism (CD) spectra, which indicates the formation of hybrid chiral eigenmodes and the realization of the strong coupling regime. Considering that the radiative and dissipative damping of the hybrid eigenmodes depends on the coherent energy exchange, the chiral resonances can be flexibly tuned by adjusting the geometry and optical constants for the heterostructure, and the CD of the three hybrid eigenmodes approach the maximum (∼1) simultaneously when the critical coupling conditions are satisfied, which can be promising for enhanced chiral light-matter interactions.

The potential of postbiotics as a novel approach in food packaging and biopreservation: a systematic review of the latest developments.

Metabolic by-products are part of the so-called postbiotics of probiotics and other beneficial microorganisms, particularly lactic acid bacteria, which have gained popularity as a feasible alternative to improving food quality and safety. Postbiotics in dry and liquid forms can be easily integrated into food formulations and packaging materials, exhibiting antimicrobial and antioxidant effects owing to the presence of multiple antimicrobials, such as organic acids, bacteriocins, exopolysaccharides and bioactive peptides. Postbiotics can thus control the growth of pathogens and spoilage microorganisms, thereby extending the shelf life of food products. Because of their ability to be easily manufactured without requiring extensive processing, postbiotics are regarded as a safer and more sustainable alternative to synthetic preservatives, which can have negative environmental consequences. Additionally, food manufacturers can readily adopt postbiotics in food formulations without significant modifications. This systematic review provides an in-depth analysis of studies on the use of postbiotics in the biopreservation and packaging of a wide range of food products. The review evaluates and discusses the types of microorganisms, postbiotics preparation and modification techniques, methods of usage in dairy products, meat, poultry, seafood, fruits, vegetables, bread, and egg, and their effects on food quality and safety.

Georgenia halotolerans sp. nov., a halotolerant actinobacterium isolated from Taklamakan desert soil.

A Gram-stain-positive, aerobic, non-motile, non-spore-forming and rod-shaped actinobacterium, designated strain 10Sc9-8T, was isolated from Taklamakan desert soil sampled in the Xinjiang Uygur Autonomous Region, China. Strain 10Sc9-8T grew at 8‒37 °C (optimum, 28‒30 °C), pH 6.0‒10.0 (optimum, pH 7.0-8.0) and in the presence of 0‒15 % (w/v) NaCl (optimum, 0-3 %). Phylogenetic analysis based on 16S rRNA gene sequence suggested that strain 10Sc9-8T was affiliated with members of the genus Georgenia and showed the highest 16S rRNA gene sequence similarity to Georgenia yuyongxinii Z443T (97.4 %). Phylogenomic analysis based on the whole genome sequences indicated that strain 10Sc9-8T should be assigned into the genus Georgenia. The average nucleotide identity and digital DNA-DNA hybridization values calculated from the whole genome sequences indicated that strain 10Sc9-8T was clearly separated from other closely related species of the genus Georgenia with values below the thresholds for species delineation. Chemotaxonomic analyses showed that the cell-wall peptidoglycan was in a variant of A4α type with an interpeptide bridge comprising l-Lys-l-Ala-Gly-l-Asp. The predominant menaquinone was MK-8(H4). The polar lipids comprised diphosphatidylglycerol, phosphatidylglycerol, phosphatidylinositol, phosphatidylinositol mannoside, several unidentified phospholipids, glycolipids and one unidentified lipid. The major fatty acids were anteiso-C15 : 0, anteiso-C15 : 1 A and C16 : 0. The genomic DNA G+C content was 72.7 mol%. On the basis of phenotypic, phylogenetic and phylogenomic data, strain 10Sc9-8T represents a novel species of the genus Georgenia, for which the name Georgenia halotolerans sp. nov. is proposed. The type strain is 10Sc9-8T (=JCM 33946T=CPCC 206219T).