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Diagnostic pathology has long relied on the morphological interpretation of hematoxylin and eosin (H&E)-stained tissues to guide diagnosis and assess prognostic features. While pathologists intuitively recognize spatial patterns and architectural organization, these assessments remain largely qualitative and difficult to quantify systematically. Immunohistochemistry and immunofluorescence have introduced molecular specificity but are limited in multiplexing capacity, whereas bulk genomic and transcriptomic assays provide high molecular depth but lose spatial context by averaging signals across heterogeneous cell populations. Recent advances in spatial proteomics-including mass spectrometry-based imaging and cyclic immunofluorescence-now enable multiplexed, single-cell protein analysis within intact tissue architecture. These technologies have revealed complex immune and stromal microenvironments, spatially organized biomarkers predictive of therapeutic response, and molecular gradients underlying disease progression. By integrating histological and molecular information, spatial proteomics bridges traditional microscopy with high-dimensional omics, allowing quantitative, spatially resolved insights into tissue organization and disease mechanisms. This review summarizes recent developments in multiplexed spatial proteomics from both scientific and pathological perspectives, highlighting how these technologies extend beyond morphology to quantify histologic patterns, refine biomarker discovery, and facilitate clinical translation. The review also examines translational challenges and barriers to clinical implementation, including costs, standardization requirements, and workflow integration.

Mosquitoes are vectors of deadly, life-threatening diseases worldwide. There is limited information on the interactions between microbes and local mosquito fauna in the Nigerian ecotype and Osun State. This study employed molecular techniques to characterize microorganisms isolated from the internal tissues of adult mosquitoes reared from larvae and pupae collected from various breeding sites in the Osogbo metropolis, Nigeria. Bacteria and yeasts were isolated from immobilized, surface-sterilized, and homogenized mosquitoes. Molecular identification of microbes was based on the Polymerase Chain Reaction (PCR) technique and sequencing of the 16S rDNA gene and internal transcribed spacer (ITS) region for bacteria and yeasts, respectively. The genus Wolbachia was screened with PCR using Wolbachia-specific primers. The adult mosquitoes harboured bacteria, namely Enterobacter bugandensis, Staphylococcus haemolyticus, Staphylococcus capitis, Enterobacter hormaechei, Pantoea dispersa, Sphingobium yanoikuyae, and Aerococcus urinaeequi. Yeasts identified were Meyerozyma caribbica, Rhodotorula mucilaginosa, and Candida orthopsilosis. The genus Wolbachia was not detected in the mosquitoes. The bacteria and yeast isolates from this study can play important roles in the biology of mosquitoes. Notably, S. yanoikuyae possesses bio-degradative potential, as reported in previous studies. Hence, this underscores the need for further investigation of the role of Sphingobium species in mosquito resistance to insecticides.

KRAS mutations are commonly found in 90% of pancreatic ductal adenocarcinoma (PDAC) cases, making it one of the deadliest cancers. Key oncogenic signaling networks, such as KRAS, TP53-MDM2, EGFR, and PI3K/AKT/mTOR, are frequently altered in this invasive disease. These networks function within a dense desmoplastic tumor environment that inhibits drug delivery and fosters therapeutic resistance. Although KRAS mutations are a primary oncogenic driver and occur in approximately 90% of patients with PDAC, variant-specific biology (e.g., G12D, G12R, G12V, and G12C) affects downstream signaling dependency and treatment response. Although specific KRASG12C inhibitors have been developed, their use in PDAC remains limited because of compensatory pathway activation and mutation prevalence. Similarly, whereas EGFR amplification and adaptive signaling bypass pathways decrease the durability of EGFR-targeted therapies, TP53 inactivation and MDM2 axis dysregulation contribute to genomic instability and treatment resistance. Although resistance to chemotherapy and targeted therapies, survival signaling, and metabolic reprogramming are all significantly affected by the PI3K/AKT/mTOR system, the therapeutic results with pathway inhibitors have been mixed. Significantly, these signaling pathways function within a coordinated, interdependent network, wherein single-agent approaches are compromised by crosstalk and feedback activation. This review synthesizes these main signaling axes, emphasizing molecular pathology, including mutation-specific biology, diagnostic techniques such as liquid biopsy and NGS, the role of natural compounds, the tumor microenvironment (TME) in pancreatic cancer (PC), and the limitations noted in therapeutic trials. Novel therapeutic approaches include KRAS-directed degradation techniques, pathway co-inhibition, rational combination methods, and therapy paradigms driven by the TME. To discover new molecules with long-lasting therapeutic effects, a system-level understanding of pathway interactions within the PDAC microenvironment is necessary.

In this study, a novel strategy integrating aptamer-gated cell-free synthetic biology with electrochemical analysis has been developed for thrombin detection (using thrombin as a model target). A DNA template embedded with a target-specific aptamer is designed as a "molecular switch". Target binding induces a conformational change in the aptamer, creating steric hindrance for T7 RNA polymerase and thereby inhibiting transcription of the RNA reporter strand. The transcribed RNA can hybridize with methylene blue (MB)-labeled DNA probes immobilized on the electrode surface, causing electroactive species to move away from the electrode surface and resulting in a relatively low electrochemical signal. Conversely, a reduced amount of transcribed RNA leads to an increased electrochemical signal. Under optimized conditions, the biosensor exhibits a linear range of 0.06 nM - 6 μM and a limit of detection (LOD) of 0.032 nM (S/N = 3). The sensor demonstrates excellent specificity against interfering proteins. Spike-recovery tests in 20-fold diluted human serum yield recoveries of 95.35% - 105.20% with RSD values of 2.32% - 4.90%, confirming its anti-interference ability in complex biological matrices. This methodological innovation realizes sensitive and specific detection of thrombin and provides a generalizable strategy for cell-free electrochemical analysis of protein targets. By replacing the aptamer sequence, the platform can be extended to diverse proteins, opening new avenues for the application of cell-free synthetic biology in biosensing and clinical diagnostics.

While single-omics analyses of Parkinson's Disease (PD) have demonstrated their ability in revealing the underlying molecular mechanisms, they often fail to provide a comprehensive view of the complete disease mechanisms. In this study, we leveraged multi-omics data from 64 heterogeneous, well-phenotyped PD patients, generated plasma metabolomics data and Olink proteomics data together with the gut and saliva metagenomics data, and investigated the altered molecular mechanisms and their interactions in association with the severity of motor function disorders in PD patients. Based on our multi-omics approach, we identified a panel of 58 biomarkers comprising one clinical variable, 10 proteins, and 17 metabolites from plasma, 26 gut species, and 4 saliva species for PD severity. These biomarkers exhibited superior predictive performance for assessing PD severity compared to those derived from single-omics datasets. The predictive power of our machine learning models based on these biomarkers was validated using additional multi-omics data from the same group of PD patients after a 3-month follow-up. The contribution of each omics dataset was evaluated by both supervised and unsupervised machine learning approaches, highlighting the importance of plasma metabolomics in disease stratification. Our study unveiled disease-related molecular alterations across multiple omics datasets, offering potential diagnostic and therapeutic insights for PD. Moreover, it underpinned the significance of employing multi-omics analyses when studying complex diseases like PD.

Restoring the tumor-suppressor function of p53 by inhibiting its negative regulator, MDM2, represents a significant therapeutic avenue for cancers that maintain wild-type p53. This research aimed to identify new MDM2 inhibitors through a phylogenetically guided strategy that involved the construction of a focused virtual library of metabolites derived from the Penicillium genus. A comprehensive computational framework was developed, employing machine learning-based quantitative structure-activity relationship (ML-QSAR) modeling, ensemble molecular docking, network pharmacology, molecular dynamics (MD) simulations, and ADMET profiling. The gradient boosting ML-QSAR model achieved a test set R2 of 0.80 and was externally validated against 39 known MDM2 inhibitors (R2 = 0.82, RMSE = 0.80 pIC50 units), confirming its predictive reliability. Ensemble docking studies against 13 conformations of MDM2 highlighted three leading candidates (CNP0147553.1, CNP0154476.3, and CNP0154476.4) demonstrating binding affinities comparable to the known control inhibitor Nutlin-3a, with docking scores validated against experimental binding data. Further investigations through 500 ns MD simulations provided insights into the stability of the CNP0147553.1-MDM2 complex, which maintained a mean ligand RMSD of 0.039 nm and a complex RMSD of 0.176 nm, alongside a favorable binding free energy of -25.82 kcal/mol. Key residue analysis revealed that CNP0147553.1 achieved pronounced stabilization of critical binding pocket residues, including an 81.6% reduction in flexibility of HIS96. Network pharmacology analysis revealed a polypharmacology potential, indicating that the hub genes related to the identified compounds predominantly converged on the PI3K-AKT-mTOR and RAS-RAF-MAPK signaling pathways. ADMET profiling suggested promising pharmacokinetic and safety profiles for the lead candidates, establishing the basis for future experimental validation.

Sarcopenia, an age-related degenerative disease, corresponds to "Qi Xu" syndrome in traditional Chinese medicine. Juyuanjian (JYJ), a classical Qi-tonifying formula, has shown potential against muscle atrophy and functional decline, but its molecular mechanisms are not well understood. To investigate whether JYJ protects against sarcopenia and to elucidate its underlying mechanisms. Caenorhabditis elegans (C. elegans) RW1596, C2C12 myotube cells and senescence-accelerated mouse prone 8 (SAMP8) transgenic mice were used to explore the alleviative effect of JYJ on sarcopenia and the molecular mechanism in vivo and in vitro. This study systematically evaluated the therapeutic effects of JYJ using multiple biological models. Ultra-high performance liquid chromatography-high-resolution mass spectrometry (UPLC-MS) was employed to characterize its chemical constituents, followed by network pharmacology to predict potential targets and pathways. These mechanisms were further validated through molecular biology experiments. Additionally, molecular docking and molecular dynamics (MD) simulations were conducted to elucidate the interactions and binding stability between key bioactive components and target proteins. JYJ significantly alleviated muscle fiber damage in C. elegans RW1596 and mitigated the decline in skeletal muscle mass and strength in SAMP8 mice. Furthermore, JYJ inhibited chronic low-grade inflammation by reducing tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6) levels, decreasing macrophage infiltration, and suppressing nuclear factor-kappa B (NF-κB) activation. Network pharmacology analysis indicated that mitochondrial biogenesis and proteasome-mediated ubiquitin-dependent processes were the main biological processes, with protein kinase B (Akt), forkhead box O1 (FoxO1), sirtuin 1 (SIRT1), and peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PGC-1α) as key targets. Consistent with this, JYJ enhanced the phosphorylation of Akt and FoxO1 both in vitro and in vivo, while downregulating the expression of muscle atrophy-related E3 ubiquitin ligases muscle ring finger 1 (MuRF1) and muscle atrophy F-box (MAFbx); it also upregulated the expression of SIRT1 and PGC-1α, promoting mitochondrial biogenesis and adenosine triphosphate (ATP) production. Molecular docking and 100-nanosecond MD simulations showed stable interactions between the bioactive components of JYJ and Akt/SIRT1, supported by favorable binding free energy and stable conformational dynamics. JYJ exerts anti-sarcopenic effects by dual regulation of protein degradation (Akt/FoxO1) and mitochondrial biogenesis (SIRT1/PGC-1α), providing a phytotherapeutic for sarcopenia.

Obesity is an established risk factor for invasive breast cancer; however, the specific molecular heterogeneity distinguishing invasive ductal carcinoma (IDC) from ductal carcinoma in situ (DCIS) within the obese tumor microenvironment is not well defined. In the current study, spatially resolved transcriptomics was utilized to profile the epithelial, stromal, and immune compartments of DCIS and IDC lesions stratified by host body mass index, categorized as non-obese (≤29.9 kg/m2) or obese (≥30 kg/m2). These analyses reveal that the transcriptional signatures defining the invasive state differ significantly across BMI categories. In non-obese patients, IDC lesions exhibited canonical profiles driven by proliferation and epithelial-to-mesenchymal transition, compared with DCIS. Conversely, the obese setting was characterized by a distinct "stress-adaptive" phenotype, enriched for metabolic adjustment, oxidative stress response, and inflammatory signaling. The epithelial component was accompanied by a fibro-inflammatory stromal signature and an immunosuppressive niche characterized by B cell depletion and M2 macrophage enrichment. Furthermore, SULF2, an extracellular endosulfatase involved in extracellular matrix organization and signaling, was consistently upregulated within the obese epithelium, providing a plausible link between metabolic stress and structural remodeling. Collectively, these data indicate obesity-associated differences consistent with an alternative invasive transcriptional program that is less dominated by classical proliferative drivers in this cohort. Consequently, standard prognostic markers may be context-dependent, highlighting the need to integrate metabolic health into precision risk stratification.

This study evaluated the polyphenol content of leaf extracts from Artemisia monosperma (AM) and investigated their antioxidant properties, cytotoxic effects, and potential to induce DNA damage in human cancer cell lines. High-performance liquid chromatography (HPLC) quantified polyphenols in methanolic (AMM), ethanolic (AME), and aqueous (AMA) extracts, identifying 13 compounds in AME and 12 in AMA. AMM exhibited the strongest antioxidant activity (IC50 = 24 µg/ml). Both AME and AMM demonstrated potent anticancer activity against HCT-116 (IC₅₀ = 0.38 µg/mL for AMM) and HUH-7 (IC₅₀ = 21.95 µg/mL for AMM) cells, while exhibiting minimal cytotoxicity toward normal skin fibroblast cells (BJ-1; IC₅₀ = 13.05 µg/mL for AMM), with AMM demonstrating particular selectivity for HCT-116 cells. AMM induced DNA fragmentation and modulated apoptosis-related gene expression (Bax, Bcl-2, p53) in HUH-7 cells and caused cell cycle arrest at G0/G1 phase in HCT-116 cells. Molecular docking further supported AMM's apoptosis activity. These results position A. monosperma as a rich source of bioactive polyphenols and antioxidants, with AMM showing promise as a therapeutic agent, especially for colorectal cancer.

Allexiviruses (family Alphaflexiviridae) are widespread pathogens of vegetatively propagated allium crops, but their occurrence has not previously been documented in Ukraine. We surveyed cultivated allium plants collected in eight Ukrainian regions (2022-2025) and screened their samples for garlic virus B (GarV-B), garlic virus C (GarV-C) and shallot virus X (ShVX) using enzyme-linked immunosorbent assay (ELISA). GarV-B, GarV-C and ShVX were detected in 39/108 (36.1%), 23/108 (21.3%) and 21/108 (19.4%) plants, respectively, with infections which were strongly host-associated: garlic (n = 63) had high frequencies of indicated viruses (GarV-B-61.9%; GarV-C-36.5%; ShVX-28.6%), whereas onion samples (n = 33) were largely negative (ShVX-3.0%; GarV-B and GarV-C-not detected). Co-occurrence analysis within garlic revealed a nested allexivirus module in which GarV-C and ShVX occurred only in GarV-B-positive plants. RT-PCR and Sanger sequencing generated 11 partial genomes representing GarV-B, GarV-C, ShVX, GarV-A and GarV-D. Maximum-likelihood phylogenies placed Ukrainian allexivirus isolates within established global diversity and indicated both European- and Asian-affiliated lineages. These findings provide the first evidence of allexiviruses in Ukrainian allium crops, and support their inclusion in plant health surveillance and planting-material certification.

The metabolic enzyme lactate dehydrogenase C4 (LDHC4) is aberrantly expressed in cancers and linked to poor prognosis. However, its role in lung adenocarcinoma (LUAD) and the molecular mechanisms beyond glycolysis remain unclear. This study investigates whether LDHC4 promotes LUAD by modulating protein lactylation, a lactate-derived post-translational modification, focusing on the tumor suppressor retinoblastoma protein (RB1). LDHC4 expression and its correlation with clinicopathological features and survival were analyzed using public databases (UALCAN, Kaplan-Meier Plotter, LOGpc) and validated in a cohort of 90 paired LUAD tissues via immunohistochemistry. The functional impact of LDHC4 on proliferation, migration, and invasion was assessed in A549 and PC-9 cells using gain- and loss-of-function models. The global lactylation profile was analyzed using DIA-based lactylation proteomics on the Astral platform. The interaction between RB1 and E2F1 (E2F transcription factor 1) was examined through molecular dynamics simulations, co-immunoprecipitation (Co-IP), and immunofluorescence. The functional consequences of site-specific RB1 lactylation at lysine 900 (RB1-K900lac) were determined using RB1-K900R mutant constructs and cell cycle analysis. LDHC4 was significantly overexpressed in LUAD tissues, correlating with poor patient survival, and was an independent prognostic risk factor. In vitro, LDHC4 promoted LUAD cell proliferation, migration, and invasion, and its tumor-promoting role was corroborated in an LUAD xenograft model, in which derived tumors exhibited increased volume and weight compared with mock-transfected controls. Mechanistically, LDHC4 overexpression elevated global protein lactylation levels and specifically increased lactylation of RB1. Bioinformatics and molecular dynamics simulations identified K900 as a key conserved residue for RB1-E2F1 binding; its lactylation destabilized the complex by increasing structural fluctuation and weakening intermolecular interactions. Cellular experiments confirmed that the lactylation-resistant RB1-K900R mutant bound E2F1 more strongly than wild-type RB1. Functionally, cells expressing RB1-K900R exhibited suppressed malignant phenotypes and G1/S cell cycle arrest, accompanied by downregulation of CDKs/cyclins and upregulation of P21. This study uncovers a novel LDHC4-driven oncogenic axis in LUAD. LDHC4 facilitates RB1 lactylation at the K900 residue, which disrupts the RB1-E2F1 tumor-suppressive complex, leading to cell cycle dysregulation and tumor progression. These findings may position the "LDHC4-RB1 lactylation" axis as a promising therapeutic target for LUAD.

Second generation tyrosine kinase inhibitors (TKIs) have improved response rates in patients with chronic phase chronic myeloid leukaemia (CP-CML). Phase 2 trials demonstrated increased deep molecular response rates when combining second generation TKIs with pegylated interferon alfa (Peg-IFN). This trial aimed to evaluate the efficacy and the safety of combining nilotinib with Peg-IFN alfa-2a in patients with newly diagnosed CP-CML. In PETALs, this open-label, randomised, multicentre phase 3 trial, we enrolled patients with newly diagnosed CP-CML from 27 French academic institutions via a centrally-generated electronic system in a 1:1 ratio to two groups: 300 mg oral nilotinib alone twice a day (the nilotinib only group) or 300 mg nilotinib twice a day combined with subcutaneous Peg-IFN (30 μg per week for the first month of treatment and 45 μg per week thereafter) for a maximum of 2 years. The randomly allocated patients were stratified by their Sokal index and European Treatment and Outcome Study long-term survival index. Eligible patients had major BCR::ABL1 transcripts, an Eastern Cooperative Oncology Group performance score of two or lower, who had never received TKIs, and were aged between 18 and 65 years. The primary endpoint was the cumulative rate of molecular response 4·5 (MR4·5; defined as BCR::ABL1 international scale [IS] of 0·0032% and lower), analysed in the intention-to-treat population (n=200). This trial is registered at ClinicalTrials.gov, NCT02201459, and is completed. 205 patients were enrolled between Aug 6, 2014, and Sept 29, 2016, after which five patients were declared ineligible and excluded, resulting in 200 patients being randomly allocated (99 to the nilotinib group and 101 to the combination group). The median age at diagnosis was 45 years (IQR 36-55); 130 patients (65%) were male and 70 (35%) were female. Median follow-up in this cohort was 67 months (IQR 32·6-70·6). The primary objective was met, with higher rates of MR4·5 in the combination group (24% [95% CI 16·0-34·1] vs 15% [8·6-24·2], p=0·048) at month 12. There were equivalent grade 3-4 haematological side effects in the both groups (14 vs 14) with a predominance for grade 3-4 thrombocytopenia without haemorrhages (six in the combination group vs five in the nilotinib group). Psychiatric grade 3-4 events occurred in six (6%) patients in the combination group (including three unsuccessful suicide attempts) compared with five (5%) in the nilotinib group (including one unsuccessful suicide attempt). Six vascular events also occurred in six patients in the combination group and seven vascular events in five patients in the nilotinib group (all grades 3-4). In this setting, Peg-IFN combined with nilotinib induced higher initial rates of MR4·5 compared to TKI monotherapy, despite additional side effects. The onset of psychiatric events might promote immediate cease of Peg-IFN and psychiatrist advice Whether this early molecular response translates into sustained treatment-free survival should be studied in a randomised trial sufficiently powered for this outcome. Novartis Pharma.

Pancreatic ductal adenocarcinoma (PDAC) is frequently preceded by new-onset diabetes mellitus (NODM), yet differentiating PDAC-associated DM from type 2 diabetes (T2D) remains clinically challenging. We investigated whether plasma proteomic profiling combined with machine learning could discriminate these conditions. Plasma samples from individuals with PDAC (with and without DM), long-standing T2D, and controls were analyzed by MALDI-TOF mass spectrometry. Spectral features were processed through a nested cross-validation framework to prevent data leakage, and model interpretability was explored using SHAP values. In parallel, low-molecular-weight proteins were characterized by GeLC-MS followed by LC-MS/MS and differential abundance analysis. Machine learning models distinguished PDAC-associated DM from T2D with a balanced accuracy of 85%. Proteomic analyses identified distinct signatures in PDAC- associated DM, including downregulation of erythrocyte-related proteins and PPBP, and upregulation of acute-phase reactants such as FGA, CP, and SERPINA3. Treatment-naïve cases displayed increased circulating epithelial and keratin-associated proteins, which were attenuated after therapy, suggesting dynamic tumor-related remodeling. These findings demonstrate that integrating MALDI-TOF profiling with machine learning can capture plasma signatures associated with PDAC-associated DM. Although exploratory, this approach supports further validation in prospective cohorts aimed at improving PDAC risk stratification among individuals with NODM. SIGNIFICANCE: Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy with a dismal 5-year survival rate, primarily due to late-stage diagnosis. The frequent occurrence of new-onset diabetes mellitus (NODM) as a paraneoplastic syndrome offers a critical window for early detection. However, the clinical challenge of distinguishing PDAC-associated diabetes (PDAC-DM) from type 2 diabetes mellitus (T2D) has hindered the implementation of effective screening strategies. This study addresses this significant clinical problem by leveraging a multi-faceted proteomics approach. We demonstrate that the integration of MALDI-TOF mass spectrometry peptide profiling with machine learning algorithms can accurately discriminate PDAC-DM from T2D with 85% accuracy. Furthermore, we used LC-MS/MS to identify specific low molecular weight proteins that are differentially regulated between these conditions, providing a molecular basis for the observed discrimination. Our work is significant as it presents a novel, high-throughput pipeline for biomarker discovery that combines the scalability of MALDI-TOF with the analytical power of LC-MS/MS and machine learning. The identified plasma signatures hold strong translational potential to improve risk stratification in patients with new-onset diabetes, ultimately enabling earlier diagnosis of PDAC and improving patient survival prospects. This research directly contributes to the field of clinical proteomics by providing a robust methodological framework and candidate biomarkers for the early detection of one of oncology's most challenging diseases.

Myelofibrosis (MF) is a chronic myeloproliferative neoplasm characterized by progressive cytopenias, splenomegaly, and constitutional symptoms. The hallmark of MF pathophysiology is constitutive activation of JAK/STAT signaling, which, in the majority of cases, is associated with an acquired mutation in one of three driver mutations, JAK2, CALR, or MPL. Our growing understanding of the molecular biology of MPNs has resulted in regulatory approval of four JAK inhibitors (JAKi), which have demonstrated efficacy in improving symptom burden and reducing spleen size. Despite clear benefits of JAKi therapy, including evidence of improved survival, these therapeutic interventions have not established an ability to modify disease in terms of resolution of bone marrow fibrosis or molecular remissions. Therefore, recent emphasis has been on the development of novel therapies with informed targets outside of the JAK/STAT signaling pathway. Moreover, combination approaches utilizing JAK and non-JAK targeting agents underscore the potential for disease modification along with deeper and more durable clinical responses. Emerging combination strategies and their clinical development will be reviewed here, including investigations that pair JAKi therapy with BCL-2 family inhibitors, BET inhibitors, restored p53 cell death signals, telomerase inhibitors, PIM1 kinase inhibitors, and mutant CALR targeted therapies. While several combination clinical trials suggest improved spleen and symptom responses and the possibility of disease modification, toxicity profiles and optimal sequencing remain areas of active investigation.

Medicinal plants are widely used for applications in agriculture, food, medicine, and cosmetics due to their abundant bioactive secondary metabolites (SMs) such as terpenoids, phenylpropanoids, and alkaloids. The biosynthesis and accumulation of SMs are highly associated with multiple environmental factors. Among these abiotic stresses, drought plays a pivotal role in regulating the quality of medicinal plants. Understanding the regulatory mechanisms of medicinal plants in response to drought is beneficial for (i) cultivating high-quality traditional Chinese medicinal plants via targeted water management strategies; (ii) screening candidate marker genes to breed high-quality novel cultivars with enhanced bioactive compound accumulation under drought conditions, thereby addressing the adverse impacts of drought induced by global climate change; (iii) mining dual-functional genes that confer drought tolerance while maintaining high bioactive compound content, thus ensuring both the yield and quality of medicinal plants. To summarize the latest advances in the transcriptional regulation of SM biosynthesis with a focus on terpenoids, phenylpropanoids, and alkaloids in medicinal plants under drought conditions. A comprehensive literature search was conducted in three electronic databases including PubMed, Scopus, and Web of Science using the search terms "regulatory mechanism", "secondary metabolites", "medicinal plants", "drought stress", "transcription factor", "bioactive compound", "synthetic biology", "smart irrigation", "terpenoid biosynthesis", "phenylpropanoid biosynthesis", "phenolic biosynthesis" and "alkaloid biosynthesis". All the retrieved data were then critically reviewed and summarized. Drought affects secondary metabolite biosynthesis via a complex molecular regulatory network, including shifts in microbial community composition, epigenetic remodeling, changes in global gene expression profiles, altered catalytic activity of core biosynthetic enzymes, as well as modifications of transcription factors. This review offers novel insights into unraveling the underlying transcriptional regulatory networks, and practical implications for researchers in the fields of medicinal plant biology, natural product chemistry, and crop stress physiology.

Artificial intelligence (AI) is reshaping drug repurposing by integrating systems biology with molecular design. Here, we present a unified framework combining AI-enhanced Kinase Enrichment Analysis (KEA), geometric deep learning, and federated learning to enable scalable and privacy-preserving therapeutic discovery. KEA prioritizes disease-relevant kinases from multi-omics data, while geometric deep learning captures structure-activity relationships (SARs) at atomic resolution. Federated learning facilitates secure, multi-institutional model training across heterogeneous datasets. This integrative pipeline enhances identification of repurposable kinase inhibitors and supports emerging modalities, such as proteolysis-targeting chimeras (PROTACs). A case study in Alzheimer's disease (AD) highlights improved target prioritization and predictive performance. By bridging kinase signaling networks with AI-driven modeling, this framework provides a robust strategy for accelerating precision drug discovery and repurposing.

Respiratory syncytial virus (RSV) remains a major cause of severe acute respiratory infections across the life course, particularly in infants, older adults, and immunocompromised individuals. For decades, clinical management relied almost exclusively on supportive care, while ribavirin, the only licensed antiviral, offered limited therapeutic benefit. The recent introduction of prefusion F (pre-F)-based vaccines and long-acting monoclonal antibodies has reshaped RSV prevention and represents the most significant advance since the discovery of the virus. Nevertheless, effective pharmacological treatment of established infection continues to be an unmet need, and the burden of RSV-associated hospitalizations and mortality persists worldwide. This review critically synthesizes current and emerging RSV therapeutic strategies from a pharmacological and translational perspective, integrating approved interventions with emerging antiviral pipelines. Licensed vaccines and monoclonal antibodies have demonstrated high efficacy in preventing lower respiratory tract disease; however, their impact is constrained by limited access and uptake, as well as the absence of complementary direct-acting antivirals (DAAs). Investigational agents targeting the fusion protein and the N/L replication complex have shown potent antiviral activity, but clinical trials have highlighted challenges related to the timing of administration, host immunity, and resistance selection. Advances in structural biology, air-liquid interface models, high-throughput screening, and artificial intelligence are accelerating the identification of new molecular targets and host-directed strategies. Overall, RSV control will require an integrated therapeutic framework in which vaccines and monoclonal antibodies prevent severe disease, while early-administered DAAs and resistance-aware combination strategies treat established infection and reduce breakthrough disease in high-risk populations.

Major Depressive Disorder (MDD) is characterized by heterogeneous pathogenesis that extends beyond traditional monoamine deficits. A paradigm shift is recognizing neuroinflammation as a central, critical driver of both illness onset and resistance to treatment. The CXCL12/CXCR4 system is traditionally associated with immune cell trafficking, but increasing evidence reveals its powerful regulatory role in neuropsychiatric disorders. We performed a comprehensive synthesis demonstrating that CXCL12/CXCR4 axis acts as a direct molecular modulator of neurotransmission, neuroplasticity, and glial cell signaling. Specifically, this axis can modulate a multiple molecular pathways linked with the glutaminergic, GABAergic, and serotonergic systems, and mediating neuroplasticity and glial cell function. Functionally, CXCL12/CXCR4 axis has twofold character - it can strengthen neurotoxic processes through overactivation of NMDAR and excessive Ca2+ influx. On the other hand, it can also play protective role by preventing excitotoxicity, supporting neurogenesis, enhancing GABA synthesis, and dendritic spines stabilization. This review focuses on identifying potential mechanisms across in vitro, animal, and human studies to establish the CXCL12/CXCR4 axis as a powerful biomarker and, critically, an unexploited therapeutic target.

Dermatophytoses are common superficial fungal infections, most frequently caused by Trichophyton species. Among them, Trichophyton tonsurans is increasingly recognized as an important cause of tinea capitis and other dermatophytic infections. We conducted a retrospective review spanning three- years and six months (January 2022 - June 2025) in the Parasitology-Mycology Laboratory of La Rabta University Hospital (Tunis, Tunisia). The study included patients with dermatophytosis confirmed to be caused by Trichophyton tonsurans. Clinical samples (skin, scalp, nails) were examined by direct microscopy, following potassium hydroxide clarification and cultured on Sabouraud agar with chloramphenicol and with or without actidione at 27 °C. Identification was based on colony morphology, microscopic examinations and confirmed by ITS2 sequencing. Eight cases of T. tonsurans infection were diagnosed during the study period. Patients ranged in age from 8 to 59 years. Seven presented with scaly alopecic patches on the scalp and one with generalized erythematous-squamous lesions associated with onychomycosis. Four patients (three children and one adult) reported similar infections among close contacts, suggesting possible intra-familial or community transmission. Direct microscopy was positive in six cases, revealing endothrix parasitism or septate hyphae. Cultures yielded powdery white to beige colonies with a yellowbrown reverse within 8-14 days. Molecular analysis confirmed the isolates as T. tonsurans. This case series documents the occurrence of T. tonsurans infections identified over a recent three-year period in Tunisia. Accurate laboratory diagnosis and molecular confirmation remain essential for clinical management and for monitoring evolving dermatophyte epidemiology in the region.