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High-risk and relapsed/refractory (R/R) acute lymphoblastic leukemia poses significant therapeutic challenges due to emergent drug resistance and dose-limiting toxicities. Natural products, with their diverse chemical structures and pharmacological activities, provide a promising avenue for multi-target therapies. This review analyzes key natural product classes, such as phenolic compounds, terpenoids, flavonoids, and alkaloids. It aims to elucidate their mechanisms by modulating critical oncogenic pathways to overcome drug resistance, paving the way for rational therapeutic design strategies. A comprehensive literature review was conducted by systematically searching Web of Science, PubMed, and Google Scholar. Search queries combined "acute lymphoblastic leukemia" with various natural product classes, focusing on publications from 2000 to 2025. The analysis synthesized data on molecular mechanisms, pharmacokinetics, safety, and synergistic strategies for combination therapy. The database search yielded 303,464 hits, with 13,839 hits from Web of Science, 14,625 hits from PubMed, and 275,000 hits from Google Scholar. After removal of duplicates, commentary articles, clearly ineligible literature, and records not within the predefined topic range were removed, 280 records were screened. 73 publications were assessed for eligibility, and 53 studies were included in the final synthesis. The studies considered in this review largely focused on chemically characterized natural products and derivatives comprising phenolic compounds, terpenoids, flavonoids, alkaloids, and artemisinin-related compounds. Most evidence was collected from preclinical ALL models and involved modulation of apoptosis, oxidative stress, cell-cycle arrest, autophagy, ferroptosis, and ALL-related signaling pathways. In conclusion, these natural products showed multi-target and pathway-intersecting activities that could improve conventional anti-leukemic treatment and overcome chemoresistance, although clinical translation remains limited by insufficient in vivo validation, insufficient PK/PD characterization, poor bioavailability, and incomplete safety evaluation. Natural products offer a valuable resource for developing novel anti-ALL therapeutics. Their multi-target capability fosters synergistic combinations to combat resistance, highlighting the need for advanced technologies and precision medicine approaches in ALL treatment.

Parkinson's disease (PD) is a progressive neurodegenerative disease characterized by the loss of dopaminergic neurons and resulting in physical and mental issues. Dopamine-based treatments have long been used for Parkinson's disease; however, they do not provide a complete cure, highlighting the need for alternative therapeutic strategies. Phosphodiesterase-10 A (PDE10A), a dual-substrate enzyme regulates dopamine signalling by hydrolyzing cAMP and cGMP. In PD, dopamine depletion disrupts cyclic nucleotide signalling, impairing motor function and overactived PDE10A further reduces cAMP/cGMP levels, worsening the motor deficits. Therefore, PDE10A inhibition may restore cyclic nucleotide signalling, rebalance basal ganglia pathways, and improve motor function, making it a promising therapeutic target for PD. Triazolopyridines are reported as potent PDE10A inhibitors and in the current research work this scaffold is selected for the in silico studies to build 3D QSAR model. Subsequently, after conducting pharmacophore modelling, the AAHHHR_1 model with two hydrogen bond acceptors (A1, A2), three hydrophobic interactions (H5, H6, H9), and one aromatic ring (R13) was selected to screen the NPASS database. After analysing molecular docking, MMGBSA and ADME, the top five hits were selected. These compounds exhibited docking scores ranging from - 11.27 to -13.210 kcal/mol, outperforming the standard reference molecule (-9.204 kcal/mol). MM/GBSA binding free energy calculations further revealed comparable scores ranging from - 59.60 to -68.75 kcal/mol for hits, compared to -71.67 kcal/mol for the standard. MD studies confirmed the stability of hits with PDE10A, forming favourable hydrogen bond interactions throughout a trajectory of 200 ns. Lastly, MM/PBSA showed that out of the five hits, complex NPASS_6815 showed better binding free energy of -30.26 kcal/mol when compared to the reference molecules (-23.38 kcal/mol). Thus, it was concluded that this molecule can act as potent PDE10A inhibitors and could serve as lead compounds for the development of new therapeutic agents for the Parkinson's disease.

The Encyclopedia of Domains (TED) provides domain annotations for proteins in the AlphaFold Protein Structure Database (AFDB) using a consensus of three state-of-the-art structure-based methods. We used these annotations to construct profile Hidden Markov models (HMMs), collectively forming the TED Library of HMMs (TEDLH). TEDLH enables sensitive sequence and profile searches, supporting systematic exploration of protein domain families and their evolutionary relationships. TEDLH links 934,186 domain HMMs to experimentally determined CATH-PDB structures through direct (primary) and transitive (secondary and tertiary) relationships. Fewer than half of TEDLH HMMs are directly linked to a CATH-PDB domain; the remaining models are connected through transitive relationships. These transitive links extend coverage into more divergent regions of sequence space and better represent CATH superfamily diversity.HMM-HMM comparisons within CATH superfamily 3.30.70.100 illustrate how transitive relationships expand sequence coverage. In this superfamily, 5,640 TEDLH HMMs are connected to 173 CATH-PDB representatives. Primary, secondary, and tertiary relationships progressively capture more divergent sequences (pairwise sequence identity <20%) that retain structural similarity (TM-score &#x2265;0.6) and a conserved two-layer &#x3b1;/&#x3b2; sandwich core fold.All-against-all HMM-HMM comparisons across TEDLH also reveal sequence similarities across the CATH hierarchy (cross-hits). At low query coverage (<50%), cross-hits are more frequent between CATH classes, architectures and topologies, whereas at higher coverage thresholds (&#x2265;70%) they predominantly occur between superfamilies. These cross-hits are not driven by superfamily size or sequence diversity and can provide guidance for CATH curation. As an example, analysis of cross-hits between superfamilies 2.170.130.30 and 3.10.20.30 reveals evolutionary relationships between these groups. TEDLH is compatible with HH-suite3 and is available from FigShare https://doi.org/10.6084/m9.figshare.28531754 for local use. Supplementary data are available at Bioinformatics online.

Currently, clustering algorithms are mainly used to classify fiber-reinforced composite cylinder damage. However, the number of clustering categories is heavily influenced by the evaluation criteria, and the real damage type categorization cannot be determined. Therefore, we propose a semi-supervised algorithm that obtains higher damage classification information with a small number of labels. Specifically, we first performed a phased fiber-reinforced composite cylinder pressurization experiment and collected damage signals through acoustic emission (AE) hits. We analyzed the damage types of the collected burst-type acoustic emission hits (each hit corresponds to a single waveform captured when the hit's amplitude exceeds the preset threshold) and marked a small number of these hits. Then, we constructed a mean-teacher semi-supervised network structure based on transfer learning, achieving a classification accuracy of 85.92%. Compared to traditional supervised learning and clustering algorithms, the accuracy improved by nearly 30%.

Tuberculosis remains one of the leading major global health challenges, driven by the emergence of multidrug-resistant and extensively drug-resistant bacterial strains. Resistant strains complicate treatment, which often requires prolonged use of toxic second- and third-line drugs. Protein phosphorylation plays critical roles in Mycobacterium tuberculosis, with serine/threonine kinases PknA, PknB, and PknG being essential for survival, virulence, and persistence. In this study, we screened an in-house kinase inhibitor library to identify compounds targeting these kinases. Four structurally diverse hits from the screening inhibiting all three kinases in vitro were selected for further analysis. Hits were evaluated for their ability to inhibit M. tuberculosis growth and characterized structurally using X-ray crystallography, molecular docking, and isothermal titration calorimetry. Our findings provide a structural framework for the development of multitargeting kinase inhibitors, offering a potential strategy to combat drug-resistant M. tuberculosis.IMPORTANCEDrug-resistant tuberculosis is a growing global health threat that is increasingly difficult to treat with existing antibiotics, necessitating the discovery of new therapeutic strategies. This study focuses on protein kinases, key regulatory enzymes that help Mycobacterium tuberculosis survive, cause disease, and persist in the host. By identifying small molecules that can simultaneously block multiple essential kinases, this work introduces a promising multitarget approach to combat tuberculosis. Using structural and biophysical methods, we reveal how these compounds interact with their targets, providing a clear blueprint for improving their effectiveness. These insights advance the rational design of next-generation antitubercular drugs and open new avenues for tackling multidrug- and extensively drug-resistant tuberculosis.

Orientia tsutsugamushi strain Ikeda is a scrub typhus reference strain originally described in Japan, and Ikeda 56-kDa type-specific antigen sequence types have also been reported in South Korea. However, complete genome resources for South Korean Ikeda-genotype isolates remain limited. Here, we generated complete genomes for two archived clinical O. tsutsugamushi isolates from northern South Korea, CH219 and K4-135, using a PacBio HiFi and Illumina hybrid assembly approach and compared them with the Japanese reference strain Ikeda and the South Korean reference strain Boryong. Both genomes were assembled as single circular chromosomes and contained a substantial fraction of duplicated identical coding sequences, consistent with the highly repetitive nature of O. tsutsugamushi genomes. Strains CH219 and K4-135 had identical 56-kDa TSA sequences and MLST profiles to those of the Ikeda reference strain and clustered within the Ikeda-associated clade in recombination-filtered core-genome phylogeny and ANI analyses. Within this comparison, the two South Korean isolates showed more closely related to each other than to the Japanese Ikeda reference genome. Whole-genome dot plots further indicated structural variation among the Ikeda-associated genomes. Insertion sequence (IS) profiling showed differences in IS-related CDS copy number patterns between the Boryong reference genome and the Ikeda-associated genomes, with strain Boryong showing a higher observed number of ISOt6-related CDS hits and fewer high-identity ISOt3-related hits under the applied thresholds. Together, these genomes provide new resources for Ikeda-like O. tsutsugamushi strains detected in South Korea and support the need for expanded complete and well-supported whole-genome data to better understand genome diversity in scrub typhus agents.

Age-based stereotype threat (ABST) occurs when older adults worry about being judged by a negative ageist stereotype. Although ABST can impair cognitive performance, its underlying mechanisms remain unclear, and two prominent frameworks offer different explanations. The integrated process model proposes that ABST heightens arousal, negative affect, and self-monitoring, which together consume working-memory resources and reduce processing efficiency. In a working-memory task, ABST should therefore lead to reduced hits, slower responding, increased false alarms, and slower evidence accumulation (i.e., lower drift rates in a drift diffusion model). In contrast, the regulatory focus account proposes that ABST induces a prevention focus that prioritizes vigilance and avoids commission errors. ABST should therefore lead to reduced hits, slower responding, reduced false alarms, increased omissions, and increased response caution (i.e., higher boundary separation in a drift diffusion model). To test these predictions, older adults completed a baseline 2-back task and then repeated the task under either control instructions or instructions designed to elicit ABST. Compared with the control condition, participants in the ABST condition showed reduced practice-related gains in hit rates and a selective increase in omissions. No ABST effects were observed for response times, false alarms, drift rates, or boundary separation. Although this pattern aligns with some aspects of the regulatory focus account, ABST did not consistently result in more cautious responding, and the ABST-related increase in omissions could also reflect reduced working-memory efficiency. Overall, the findings suggest that ABST can undermine correct responding without producing clear changes in underlying decision parameters. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Proton transfer reaction time-of-flight mass spectrometry (PTR-TOFMS) is a powerful tool for real-time analysis of volatile organic compounds (VOCs), including tetrachloroethylene (PCE). However, data analysis of large datasets of PTR-TOFMS data can be challenging to optimally extract chemical information. To address this challenge, we present a MATLAB-based workflow that processes PTR-TOFMS data directly from raw HDF5 files and analyzes all measurement cycles (i.e., spectra) in an untargeted manner. The workflow integrates mass spectrum preprocessing, alignment, and feature selection to maximize signal-to-noise ratio (S/N) and improve analyte discovery. Preprocessing included averaging, smoothing, and baseline correction, followed by correlation optimized warping (COW) alignment adapted to one-dimensional mass spectral data to correct m/z peak shifting across all spectra and samples. We implemented a tile-based F-ratio analysis to the one-dimensional (1D) PTR-TOFMS spectra to discover analytes correlated with PCE concentration. Samples with the five highest and five lowest PCE concentrations were separated into two classes, and 1D tile-based Fisher-ratio (F-ratio) analysis was followed by a 95% confidence interval t-test to generate a statistically filtered hit list. Principal component analysis (PCA) was used to visualize class separation, and performance was quantitatively assessed using the degree of class separation (DCS). PCE was ranked as the top hit (F-ratio&#x202f;=&#x202f;129), and 20 of 487 discovered analyte hits passed the statistical significance threshold. Receiver operating characteristic analysis yielded an area under the curve (AUC) of 0.98, indicating effective discrimination of PCE-correlated analytes. The DCS between high- and low-PCE classes increased from 1.9 to 5.1, representing nearly a three-fold improvement when PCA was applied to only the top 20&#x202f;F-ratio hits. Overall, this study demonstrates a workflow incorporating preprocessing and alignment across all mass spectra and samples and applies a tile-based automated F-ratio calculation framework to one-dimensional PTR-TOFMS data, enabling robust untargeted detection and quantification of analytes correlated with PCE.

Single-cell (SC) metabolomics holds great potential in the development of novel diagnostic tools and mechanistic insights into cell biology. Using high-resolution mass spectrometry (HRMS), the masses of a single cell's constituents can be determined with an accuracy high enough to derive their respective elemental compositions. Using a molecule's mass and its MS fragmentation pattern, in many cases a molecular structure can be assigned or looked up in databases. Due to the small measurement cell volume of an Orbitrap HRMS instrument, samples need to be scanned multiple times, which necessitates across-scan clustering per sample, and across-sample alignment of m/z values. However, existing HRMS data processing software is not designed to process SC HRMS data, as it typically requires liquid chromatography retention times or reference spectra for m/z clustering and alignment. Herein, a novel, robust SC HRMS m/z clustering and alignment algorithm is presented and compared with two commercially available and industry standard algorithms used by Sciex MarkerView and Thermo FreeStyle. Furthermore, output is compared with clustering results from DBSCAN and MaldiQuant binning. Our algorithm, Global Clustering unTargeted Analysis (GCTA), enforces a strict maximum on the cluster size, thereby reducing the chance of peak aggregation. Furthermore, by design, GCTA enables noise filtering based on intensity and number of peaks. Across-scan clustering and across-sample alignment were contrasted for accuracy in finding peaks identified by commercial software output and peaks with known m/z values corresponding to standards and HMDB and LipidMaps database hits. Comparisons are made based on data recorded for quality control samples containing standard mixes as well as SC HRMS data recorded for two different cell lines. This work shows that the presented algorithm is comparable in accuracy with respect to MarkerView and FreeStyle, reliably identifies compounds, is less prone to peak splitting than MaldiQuant binning while providing similar levels of error in clustering peaks, and successfully filters noise. Furthermore, it is shown to be competitive with DBSCAN, MaldiQuant binning and MarkerView when compared to theoretical m/z values based on database hits. GCTA encompasses both m/z clustering and reference-free alignment, which makes it pivotal to further development of untargeted SC HRMS metabolomics.

Toll-like receptor 2 (TLR2) is a key pattern recognition receptor in the innate immune system, and its aberrant activation is implicated in numerous inflammatory and immune-related disorders, making it an attractive therapeutic target. However, the development of potent and drug-like TLR2 antagonists remains challenging. In this study, we report the discovery and biological evaluation of novel small-molecule TLR2 antagonists targeting its extracellular domain through a structure-based virtual screening campaign. A hierarchical docking protocol (HTVS, SP and XP) was performed against the TLR2 crystal structure (PDB ID: 2Z7X) using the TopScience compound database containing approximately 1.2 million compounds. After Lipinski filtering (&#x223c;680,000 compounds) and stepwise docking, 18 hit compounds were identified. Top hits were prioritized by MM/GBSA binding free energy calculations and comprehensive ADMET predictions. Experimental validation using a HEK-Blue&#x2122; hTLR2 reporter cell assay confirmed that five hit compounds (T2, T5, T9, T12, T15) exhibit concentration-dependent antagonistic activity, with T9 demonstrating the most potent IC&#x2085;&#x2080; of 13.32&#xa0;&#x3bc;M, comparable to the reference MMG-11 (IC&#x2085;&#x2080;&#xa0;=&#xa0;5.36&#xa0;&#x3bc;M). Notably, all novel hits displayed superior predicted metabolic stability compared to MMG-11, overcoming its major developmental bottleneck. Extensive 200&#xa0;ns molecular dynamics simulations, complemented by PCA and free energy landscape analyses, revealed that T9 and T2 induce a more rigid and thermodynamically stable binding mode than MMG-11. MM/PBSA calculations identified van der Waals interactions as the dominant driving force, with per-residue decomposition highlighting PHE-322, PHE-349, and ILE-319 as critical binding hotspots. Density functional theory and interaction region indicator analyses further confirmed that the novel scaffolds possess extensive neutral, lipophilic surfaces and are intrinsically pre-organized for receptor binding. This integrated computational experimental approach has successfully identified promising new chemotypes with validated biological activity and improved drug-like properties, providing a robust foundation for developing next-generation TLR2 antagonists.

Cardiovascular diseases are the major cause of death worldwide. Atherosclerosis is a chronic inflammatory condition characterized by the accumulation of atherosclerotic plaques in the artery walls of medium and large arteries. Receptor-interacting protein kinase 3 (RIPK3) plays a major role in the development and instability of atherosclerotic plaque. Targeting RIPK3 is a promising therapeutic approach, as current inhibitors have reported side effects and lack specificity. In order to identify new and targeted RIPK3 inhibitors, this study focused on computational approach using methods such as virtual screening with essential descriptors such as drug-likeness and pharmacokinetic properties and docking studies. Three top compounds viz. ZINC96307758, ZINC96136342 and ZINC40012267 were identified that exhibit better binding scores compared to the reference inhibitor GSK843. Further analysis using density functional theory showed that the top compound was more reactive and stable compared to GSK843. The two complexes RIPK3-ZINC40012267 and RIPK3-ZINC96307758 showed the best stability, based on molecular dynamics simulations, as shown by its favorable free energy landscape and low RMSD and RMSF values. Strong binding free energy for the top hits was validated by MMPBSA calculations. Our results point to ZINC40012267 and ZINC96307758 as a particularly strong and stable RIPK3 inhibitor candidate, wet bench experimental validations are required to establish this finding.

Small cell lung cancer (SCLC) responds exceptionally well to cytotoxic chemotherapy. However, relapse with the emergence of chemoresistant disease is rapid and accompanied by poor treatment outcomes. To understand the genetic basis of chemoresistance in SCLC, we apply in vivo CRISPR deletion screening to patient-derived xenograft (PDX) models. Top screen hits include genes encoding components of the transcriptional co-activator SAGA (Spt-Ada-Gcn5 acetyltransferase) complex. We demonstrate that deletion of the SAGA deubiquitylase USP22 confers cisplatin-etoposide resistance in two chemosensitive PDX models, and that restoring expression in a PDX model harboring homozygous truncating mutation of USP22 re-sensitizes tumors to chemotherapy. USP22 loss increases gene body histone H2AK119 monoubiquitylation at key regulators of neuronal differentiation and suppresses neural and neuroendocrine gene expression including targets of ASCL1. Chemoresistance following USP22 loss reflects attenuated DNA damage-driven phosphorylation events and apoptosis, in conjunction with increased expression of glycolysis and hypoxia-related genes. Glycolysis program upregulation may reflect a targetable vulnerability, as inhibition of GLUT1 re-sensitizes USP22-null tumors to chemotherapy.

Carbapenem-resistant Acinetobacter baumannii (CRAB) represents a critical global health threat for which existing antibiotics are increasingly inadequate. This study aimed to establish a comprehensive genomic framework for the rational prioritization of virulent Acinetobacter bacteriophages as therapeutic candidates. We performed large-scale comparative genomic analysis of 340 virulent Acinetobacter bacteriophages, integrating phylogenetic reconstruction, pangenome analysis, CRISPR spacer-based host interaction mapping, Anti-CRISPR protein identification, and systematic antimicrobial resistance (AMR) gene screening. Genome sizes spanned a nearly 20-fold range, with a significant negative correlation between genome size and GC content (R&#xb2; = 0.139, &#x3c1; = -0.630). Phylogenetic analysis revealed extensive divergence across multiple lineages with no dominant clade. Pangenome analysis identified 20,982 unique protein families, of which 76.2% were cloud genes, confirming a highly open genome architecture. CRISPR spacer matching yielded 1,480 high-confidence matches across 100 phage genomes, providing molecular evidence of broad historical infectivity. Anti-CRISPR profiling identified Acinetobacter phage XC1 as an exceptional therapeutic candidate harboring 55 predicted Anti-CRISPR proteins with canonical regulatory locus architecture. AMR screening identified 21 distinct AMR gene homologs (Loose RGI hits, 22.5 to 47.1% amino acid identity) distributed heterogeneously across the dataset, confirming abundant therapeutically clean candidates while flagging a subset warranting further scrutiny before therapeutic exclusion. These findings provide a multi-criteria genomic framework for rational phage candidate prioritization against multidrug-resistant Acinetobacter infections, with direct implications for evidence-based phage therapy development.

Neonatal pneumonia and bronchopulmonary dysplasia (BPD) are major causes of morbidity and mortality in preterm infants, driven by excessive inflammation involving the NOD-like receptor family pyrin domain-containing 3 (NLRP3) inflammasome. This study employed in silico drug discovery, including virtual screening, molecular docking, ADMET profiling, molecular dynamics (MD) simulations, and MM/PBSA calculations, followed by preliminary in vitro validation to identify novel NLRP3 inhibitors from Traditional Chinese Medicine (TCM) compounds for these conditions. The NLRP3 NACHT domain (PDB ID: 7ALV) served as the target. A library of FDA-approved drugs and TCM-derived compounds underwent molecular docking with AutoDock Vina. Top hits were evaluated for ADMET properties using SwissADME, pkCSM, and admetSAR. Selected complexes (Hinokiflavone, Theaflavin, Sciadopitysin, Liquiritin apioside, Tigogenin) were subjected to 200 ns all-atom MD simulations in GROMACS and MM/PBSA binding free energy analysis. In vitro cytoprotective effects were assessed via MTT assay in LPS-stimulated BEAS-2B and MLE-12 lung epithelial cells, with glyburide as positive control. Hinokiflavone and Theaflavin exhibited the strongest docking scores (-10.8 and -10.4&#xa0;kcal/mol), superior MD stability (lowest RMSD: 0.21 &#xb1; 0.02 nm and 0.23 &#xb1; 0.03 nm; high hydrogen bond occupancy: 78% and 82%), and most favorable MM/PBSA binding energies (-55.8 and -55.2&#xa0;kcal/mol), driven by van der Waals and electrostatic interactions. They showed acceptable ADMET profiles with high intestinal absorption and low BBB penetration. In vitro, Hinokiflavone restored cell viability to 89.6% &#xb1; 3.2% at 50&#xa0;&#xb5;M (comparable to glyburide at 91.2% &#xb1; 2.8%), while Theaflavin reached 82.4% &#xb1; 3.9%, demonstrating dose-dependent protection against LPS-induced cytotoxicity. Hinokiflavone and Theaflavin emerge as promising NLRP3 inhibitors with stable binding to the NACHT domain and cytoprotective effects in lung epithelial cells. These TCM-derived compounds warrant further preclinical investigation as potential targeted therapies to mitigate inflammation in neonatal pneumonia and BPD.

High-throughput screening (HTS) remains the cornerstone of early phase small molecule discovery yet consistently underperforms against immunotherapy targets, yielding validated hit rates below 0.1%. Here we introduce HTS-Oracle v2, which features rigorous cross-validation that ensures honest performance estimates. HTS-Oracle v2 was trained and validated across four clinically significant immune checkpoint targets (CD28, ICOS, LAG-3, and TIGIT) achieving ROC-AUC values of 0.968, 0.969, 0.875, and 0.928, respectively, under rigorous cross-validation. For prospective experimental validation, HTS-Oracle v2 was applied to an 8960-compound Enamine Protein Mimetic Library, selecting only 25 compounds per target for experimental testing using temperature-related intensity change (TRIC) technology, a 99.7% reduction in screening burden. HTS-Oracle v2 identified 4, 5, 4, and 6 validated binders from 25 prospectively selected compounds per target, corresponding to validated hit rates of 16%, 20%, 16%, and 24%, respectively. Notably, 67-80% of all experimentally confirmed hits across the full 8960-compound library were captured within just 25 model-selected compounds per target. These results establish HTS-Oracle v2 as an efficient platform for AI-guided prospective hit discovery across immunotherapy targets.

Neritidae is one of the most diverse families in Neritomorpha, with approximately 300 extant species inhabiting various aquatic environments worldwide. Despite their ecological importance and popularity among aquarium hobbyists, genomic resources for this family remain limited, and their taxonomy is incompletely resolved. Here, we present de novo assembled transcriptomes from seven neritid species representing three genera collected from China, including Clithon pulchellum, C. retropictum, Neripteron violaceum, N. pileolus, Nerita insculpta, N. albicilla, and N. ocellata. Assembly was performed using Trinity, resulting in average contig lengths ranging from 1,098 to 1,336&#x2009;bp and transcript numbers ranging from 94,216 to 160,086. All species exhibited N50 values exceeding 2,200&#x2009;bp. Benchmarking Universal Single-Copy Ortholog (BUSCO) analysis showed complete BUSCO percentages ranging from 48.7% to 71.8%. Functional annotation of transcripts for each species yielded over 18,000 BLAST hits against the UniProtKB/Swiss-Prot database, with more than 17,000 GO terms, 15,000 KEGG pathways, and 7,750 Pfam accessions. Additionally, the major mitochondrial genes (comprising all 13 protein-coding genes and 2 rRNAs) were successfully assembled, among which the COI and 16S genes were utilized for species identification verification. This study provides valuable transcriptomic resources for Neritidae research, which can be applied to investigations of biodiversity, phylogenetic relationships, comparative genomics, physiological ecology, and conservation strategies for this ecologically important gastropod family.

Differential scanning fluorimetry (DSF) is a common and straightforward method to evaluate the thermal stability of proteins and has been heavily used for ligand binding characterisation as well as for screening. One class of compounds that is less typically evaluated by DSF are covalent binders. Here, we assessed the contribution of the covalent bond to protein thermal stabilisation. We evaluated selective covalent binders, as well as non-selective reactive electrophiles, against five model protein targets. To assess DSF in the context of fragment-based electrophile screening, we compared DSF measurements to covalent labelling over a subset of electrophilic fragments. We show that, in the context of selective binders, the formation of the covalent bond increases thermal stabilisation. However, it is not the covalent bond itself that stabilises the protein, because non-selective irreversible binding was typically neutral or, more often, destabilised the protein. In the context of fragment screening, the magnitude of the thermal shift tended to increase with irreversible labelling, whereas the more reactive fragments tended to destabilise the protein. Taken together, we suggest DSF as a complementary approach to triage covalent fragment hits, in which fragments that show both labelling and protein stabilisation are predicted to display molecular recognition driven binding and serve as more productive starting points for covalent ligand development.

Metabolic dysfunction-associated fatty liver disease(MAFLD) is a prevalent chronic liver disease worldwide. Due to its complex pathogenesis, there is currently no specific drug capable of intervening throughout the entire pathological process, nor a unified and definitive treatment protocol in clinical practice. In traditional Chinese medicine, MAFLD falls under the category of diseases such as "hypochondriac pain" and "liver disease", with the core pathogenesis being "stagnation of the liver meridian and obstruction of Qi movement". The pharmacological characteristics of Bupleuri Radix(BR), which "soothes the liver, relieves stagnation, and promotes Qi movement", are highly consistent with this pathogenesis. Furthermore, data mining studies have shown that BR is among the most frequently used herbs in TCM clinical protocols for treating MAFLD, and its herb pairs and classic formulas have demonstrated favorable therapeutic effects in clinical application. Modern pharmacological studies have also confirmed that BR is rich in active ingredients, including saponins(e.g., saikosaponin A/D/B2), volatile oils(e.g., D-limonene, hexanoic acid), flavonoids(e.g., quercetin, rutin, kaempferol), and polysaccharides. These active ingredients can target multiple pathological aspects of the "multiple parallel hits" in MAFLD, such as improving insulin resistance(IR), alleviating endoplasmic reticulum stress(ERS), repairing mitochondrial function, regulating oxidative stress(OS) response, modulating intestinal microbiota imbalance, and inhibiting inflammasome activation, thereby slowing the progress of MAFLD. Its mechanism of action is closely related to the regulation of PI3K/Akt, PPAR, Nrf2, AMPK, MAPK, PINK1/Parkin, NLRP3 inflammasome and the "gut-liver axis", reflecting the integrative regulatory advantages of TCM's multi-component, multi-target and multi-mechanism approach. Future in-depth studies should focus on precise component profiling of BR, validation of key targets, and the synergistic mechanisms within "formula-component" interactions to better leverage the value of BR in the prevention and treatment of MAFLD.