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The intestine is the core digestive and absorptive organ and the largest immune barrier. Intestinal dysfunction impairs physiological function, but current treatments remain suboptimal because of its complex etiologies. This study investigated whether Codonopsis pilosula water extract (CPWE) repairs intestinal mucosal barrier damage in mice and elucidated the underlying mechanisms. Intestinal dysfunction was induced in male ICR mice by continuous gavage with rhubarb water extract to establish a diarrhea model. The efficacy of CPWE was evaluated by measuring body weight, fecal water content, and the small intestinal propulsion rate. Histopathological changes in intestinal tissues were examined using H&E and AB-PAS staining. Intestinal barrier damage was assessed by immunofluorescence and Western blotting. Furthermore, transcriptomic and microbiome sequencing were employed to explore the specific mechanisms by which CPWE repairs intestinal mucosal barrier damage. CPWE effectively alleviated rhubarb-induced diarrhea and restored the intestinal mucosal barrier in mice. It increased onula occludens-1 (ZO-1) and occludin (OCLN) expression, promoted mucin 2 (MUC2) secretion, reduced the proportions of Th1 and Th17 cells in the colonic lamina propria, and modulated gut microbial composition. These changes were associated with regulation of tryptophan metabolism and aryl hydrocarbon receptor (AhR) signaling, consistent with a role in maintaining the integrity of the mechanical, chemical, immune, and biological barriers. These findings suggest that CPWE ameliorates rhubarb-induced intestinal barrier dysfunction through multi-barrier repair associated with tryptophan metabolism and AhR signaling. This study provides a theoretical basis for multi-component strategies in diarrhea-related intestinal injury and supports the application of traditional Chinese medicine in precision treatment of intestinal diseases.

This study aimed to develop and validate a machine learning (ML)-based predictive model to identify risk factors associated with intensive care unit (ICU) admission among patients with comorbid Chronic Obstructive Pulmonary Disease (COPD) and depression. Data of patients diagnosed with both COPD and depression were extracted from the MIMIC-IV version 3.1 database. A total of 1121 patients with first-time hospitalization and comorbid COPD and depression were included. Feature selection was performed using the Boruta algorithm. Five ML algorithms were employed to construct prediction models. Model performance was evaluated using the receiver operating characteristic curve, area under the curve, calibration curves, and decision curve analysis. Shapley additive explanations (SHAP) plots were used to interpret the contribution of each feature to the model's predictions. Sixteen variables identified by the Boruta algorithm were used to build the predictive models, including chloride, prothrombin time, phosphorus, bicarbonate, international normalized ratio, mean corpuscular hemoglobin (MCH) concentration (MCHC), mean corpuscular hemoglobin, hemoglobin, hematocrit (HCT), total calcium, paraplegia, cerebrovascular disease, smoker status, invasivevent, sepsis, and acute kidney injury (AKI). The Random Forest model performed the best: specificity (0.948), positive predictive value (0.924), precision (0.924), F1 score (0.887), balanced accuracy (0.901), sensitivity (0.853), negative predictive value (0.898), and recall (0.853). SHAP analysis indicated that AKI (0.212), sepsis (0.092), and invasivevent (0.073) were the most influential predictors, followed by anemia-related features such as hemoglobin (0.023), HCT (0.023), and MCHC (0.022). ML analysis revealed that AKI, sepsis, invasivevent, and anemia-related indicators are key risk factors for ICU admission among COPD patients with depression. However, it is important to note that the findings lack external validation, which warrants further investigation in diverse patient populations.

Vitamin D deficiency persists worldwide, exacerbating Ca loss and Mg inadequacy. Current supplements suffer from vitamin D3 (VD3) photolability, mineral segregation, and poor bioavailability. In this study, oil-in-water freeze-dried chocolate was developed for the simultaneous delivery of insoluble Ca, soluble Mg, and lipid-soluble VD3. Cryo-EDS, ICP-MS and HPLC revealed homogeneous distributions of Mg in the aqueous phase, VD3 in the oil droplets and Ca throughout the matrix. The medium formula (16.4 g/kg Ca, 8.2 g/kg Mg, and 926 μg/kg VD3) delivered excellent emulsion stability, rheological performance, and acceptable sensory and purchase intention scores. The stability of lipid-encapsulated VD3 markedly increased in the presence of heat, oxidation and UV. In 0.1 M HCl, >88.9% of each nutrient disintegrated within 10 min. In the simulated gastrointestinal fluid, Ca showed a burst release, whereas Mg and VD3 provided sustained release. This shelf-stable, palatable one-bite system resolves multinutrient incompatibility through colloidal structuring, demonstrating strong potential for functional foods and precision nutrition applications.

Polygenic risk scores (PRSs) quantify genetic susceptibilities, yet ancestry imbalance in genome-wide association studies (GWASs) limits the accuracy of monoracial PRSs in non-European populations. Here, we perform a multiancestry GWAS meta-analysis for lung cancer (76,953 cases and 1,886,372 controls), identifying 87 conditionally independent genome-wide significant loci, including two unreported cytobands. We use a PRS construction method, PRS-CSx, to develop a multiancestry PRS (PRSMA) which outperforms 32 published PRSs. To enhance predictive power, we construct a multitrait PRS (PRSMT) using CatBoost, integrating 32 cross-trait PRSs across three ancestries. Combining PRSMA and PRSMT, we generate PRSMAMT and validate it in independent cohorts (OncoArray, TRICL and All of Us). PRSMAMT demonstrates superior discriminability in European, Asian, and African populations, improves risk stratification, and identifies approximately 10% additional lung cancer cases in the UK Biobank. Individuals with elevated PLCOm2012 scores and high genetic risk exhibit a 12.64-fold higher cumulative risk than those with low scores and low genetic risk, supporting precision prevention strategies.

Acupuncture, as a significant component of Traditional Chinese Medicine (TCM), has attracted increased attention for its mechanism of controlling the physiological functions of the human body and promoting the recovery of diseases by stimulating specific acupoints. Despite its long history and wide clinical application, the mechanism of action of acupuncture is still not fully understood. More research needs to be done on how acupuncture affects molecular communication and cellular function. Exosomes are nanoscale vesicles that rely on cellular multivesicular bodies (MVBs) fused with cell membranes to be released into the extracellular matrix. They are crucial for information transfer between cells and are currently a hot research topic in the world's cutting-edge life sciences. Previous research has demonstrated that the therapeutic effect of acupuncture may be related to stimulating certain cells to secrete exosomes, that exosomes released may contain "acupuncture information", and that manipulating the back-injection of exosomes to produces "acupuncture-like" effects. These findings suggest that exosomes could serve as a bridge between conventional acupuncture therapy and modern precision medicine. They also offer fresh prospects and difficulties for acupuncture translational medicine research. To better define the relationship between exosomes and acupuncture, we reviewed and systematized the literature on past studies related to exosomes and acupuncture. This paper provides a significant theoretical and experimental foundation for applying exosomes in precision medicine by summarizing and analyzing the relationship between acupuncture stimulation and exosome function. This is expected to promote the combination of traditional acupuncture therapy and modern biotechnology and bring innovation and progress to future medical practice.

Human skin, the body's largest organ, hosts a diverse ecosystem of bacteria, fungi, viruses, and mites collectively known as the skin microbiome. This microbiome supports cutaneous homeostasis through barrier defense, immune education, and metabolic functions. To narratively review the historical evolution of skin microbiome research, synthesize current knowledge on its composition, biogeography, and functional roles in health and disease, and highlight emerging microbiome-based therapeutic strategies in dermatology. This review integrates seminal historical works with contemporary evidence from culture-independent sequencing and multi-omic investigations of the skin microbiome, identified through a selective search of recent dermatology and microbiome literature. Modern molecular and multi-omic approaches have revealed microbial diversity across sebaceous, moist, and dry skin niches and clarified key functions of the skin microbiome, including colonization resistance, immune modulation, metabolite production, and participation in the gut-skin axis. Dysbiosis of these communities is linked to inflammatory conditions such as atopic dermatitis, acne vulgaris, psoriasis, and chronic wounds. A growing body of work supports microbiome-targeted interventions, including probiotics, prebiotics, postbiotics, and microbiome engineering, as promising personalized strategies. As a narrative review, this work may be subject to selection bias and does not provide a quantitative synthesis of all available studies on the skin microbiome. By integrating historical context with mechanistic insights from modern microbiome research, this review underscores the skin microbiome as a central ecological determinant of cutaneous health and disease and provides a framework for translating microbiome science into clinical applications and precision dermatology.

Within the central nervous system (CNS), motor neurons constitute the principal and highly specialized functional units responsible for the precise regulation of somatic motor activity. Their developmental processes and plasticity mechanisms directly underpin the establishment and maintenance of neural circuits. This review offers a focused overview of the developmental processes and functional characteristics of motor neurons, while clarifying the definition of motor neuron plasticity. It further elucidates the intricate interplay between plastic alterations and the onset of injury, whereby aberrant plasticity acts as both a critical determinant in motor neuron injury and an accelerator of motor function deterioration. Building on these insights, the review constructs a multidimensional classification framework of motor neuron injury and further elaborates on the core molecular mechanisms of motor neuron regeneration, including signaling pathway regulation, epigenetic modifications, and maintenance of microenvironmental homeostasis. Conclusively, it summarizes the existing therapeutic strategies for motor neuron injury-related disorders, such as targeted gene therapy, cell replacement therapy, and neuromodulation technology, while dissecting the intervention mechanisms and limitations of each strategy. Although extensive studies have separately investigated various aspects of motor neurons, this review establishes a comprehensive framework that integrates findings from developmental mechanisms to therapeutic strategies and provides a comprehensive theoretical reference for future research on precision therapeutics, and facilitates bridging preclinical research and clinical translation.

Inflammatory diseases arise from complex interactions between immune signaling and cellular stress. Although endoplasmic reticulum (ER) stress is a key modulator of immunity, the mechanisms by which it promotes inflammatory pathology remain incompletely understood. Notably, ER stress-induced NF-κB activation alone is insufficient to account for robust IL-6 production, thus suggesting the involvement of additional regulators. Using bone marrow-derived macrophages and sepsis model mice, we identified the inducible transcription factor IκBζ as a critical mediator of this response, with ER stress synergizing with TLR signaling to markedly upregulate IκBζ. Mechanistically, ER stress triggered calcium-dependent signaling that led to IκB kinase-mediated degradation of the RNase Regnase-1, likely stabilizing Nfkbiz mRNA and promoting the accumulation of IκBζ, which was found to cooperate with the ER stress factor XBP1s to drive transcription of selected secondary-response genes, particularly Il6 and Nos2. Importantly, this synergy was required for excessive IL-6 production in septic mice, highlighting a gene-specific amplification pathway. Together, these findings identify a dual mechanism in which transcriptional synergy between IκBζ and XBP1s is coupled to posttranscriptional mRNA stabilization via Regnase-1 degradation, thereby linking proteotoxic stress to hyperinflammatory responses. Our results establish ER stress-mediated IκBζ accumulation as a key driver of inflammatory pathogenesis and a potential therapeutic target in ER stress-associated inflammatory disorders.

Artificial intelligence (AI) is transforming drug discovery and development, fields historically constrained by long timelines, high costs, and substantial attrition. Recent advances, particularly in generative modeling, enable an accelerated and increasingly systematic exploration of vast chemical and biological spaces, improving molecular interaction modeling and streamlining the identification and optimization of therapeutic candidates. However, the true utility of this expanded search space remains strictly bounded by the quality of upstream data and the logistical constraints of downstream experimental validation. Emerging platforms, including scaffold-aware and 3D molecular design tools (e.g., AlphaFold, MoleR, and PocketCrafter), single-cell foundation models, and large language models (LLMs), are expanding AI's applicability across the research and development pipeline, spanning target identification, drug discovery, lead optimization, phenotypic screening, and precision biology.AI is also increasingly integrated into preclinical and clinical research workflows, informing adaptive trial design, enabling AI-driven drug repurposing, and supporting the development of safer and more personalized therapies. While the U.S. FDA has approved numerous AI-enabled medical devices and software tools, no fully AI-discovered and AI-designed drug has yet received marketing approval. Nonetheless, several AI-originated candidates have progressed into clinical development, underscoring AI's growing translational impact. Collectively, these advances position AI as a collaborative "lab partner," capable of uncovering non-intuitive molecular designs, accelerating target and lead optimization, and enabling exploration of previously inaccessible chemical and biological space to inform downstream development and clinical decision-making. Despite gains in efficiency, scalability, and cost reduction, the broader impact of AI depends on access to high-quality multimodal data, robust regulatory and ethical frameworks, and careful recognition of methodological limitations. This review critically examines the evolution of AI approaches, highlighting key challenges and opportunities that shape the future of data-driven therapeutic innovation.

Research misconduct poses a serious threat to academic integrity, particularly in medical sciences. This study aimed to estimate the prevalence of various forms of research misconduct include plagiarism, data fabrication or falsification, authorship misconduct, salami slicing, and purchasing research work among postgraduate students in Iranian medical universities using the Unmatched Count Technique (UCT). A cross-sectional survey was conducted among postgraduate students from multiple Iranian medical universities using a double-list version of the unmatched count technique (UCT). The questionnaire was administered in two sequential waves, with approximately half of participants completing List A and the remaining participants completing List B, ensuring that each respondent received only one list version. For each research misconduct behavior, prevalence was estimated by calculating the mean difference in endorsement counts between lists containing the sensitive item and corresponding control lists with only non-sensitive items. In the double-list design, prevalence estimates were computed separately for List A and List B, with sensitive items counterbalanced across list positions to control for order effects. The final prevalence was calculated as the average of the two list-specific estimates, improving precision and reducing list-order bias. The most commonly reported misconduct was using others' ideas or phrases without proper citation (43%), followed by dishonest result reporting (38%), data fabrication or deletion (34%), and authorship misrepresentation (34%). Salami slicing was reported by 26%, and 20% admitted to purchasing parts or all of a research project. The UCT survey tool demonstrated acceptable reliability, with intraclass correlation coefficients (ICCs) ranging from 0.64 to 0.84. The findings indicate a troubling level of research misconduct among postgraduatestudents in Iran's medical sciences universities. This highlights the need for effective ethics training, stronger academic integrity policies, and enforceable institutional mechanisms to promote responsible research conduct and protect the future credibility of medical professionals.

Prenatal exposure to air pollutants has been linked to developmental delays in early childhood. This study investigated the association between prenatal exposure to ambient manganese (Mn) and delays in achieving specific neurodevelopmental milestones. Data were obtained from a nationwide population-based cohort study that recruited children born in 2005 and their mothers. Developmental outcomes were assessed by conducting home interviews at 6 and 18 months of age. Gestational exposure to ambient Mn was estimated using a land-use regression model enhanced with machine learning. Associations between Mn exposure and delayed milestone attainment were evaluated using multivariable logistic regression, adjusting for child, maternal, household factors, and co-exposure to particulate matter and nitrogen dioxide. A total of 17,683 term singleton births without congenital anomalies were included. Mn exposure during mid-gestation showed more consistent associations with later attainment of selected developmental milestones. After adjustment for relevant covariates, each 1 ng/m³ increase in second-trimester Mn exposure was associated with higher odds of delayed gross motor, fine motor, language, and social milestones ("walking with support": adjusted odds ratio [aOR] = 1.028; 95% confidence interval [CI]: 1.004-1.053), fine motor skills ("drawing arbitrarily": aOR = 1.081; 95% CI: 1.053-1.109), language milestones ("waving goodbye": aOR = 1.048; 95% CI: 1.022-1.075; "calling a parent meaningfully": aOR = 1.050; 95% CI: 1.021-1.080), and social interaction ("drinking with both hands": aOR = 1.045; 95% CI: 1.021-1.071). These findings suggest modest associations between prenatal ambient Mn exposure, particularly during mid-gestation, and early neurodevelopmental timing.

Colorectal cancer is one of the most prevalent malignant tumors worldwide. Early screening relies on accurate polyp detection during colonoscopy. Polyps in colonoscopic images exhibit diverse morphologies, indistinct boundaries, and low contrast. Specular reflections, fold occlusions, and imaging artifacts further complicate detection, which fail to meet the requirements of real-time clinical assistance. To address these challenges, we propose BCP-YOLO (You Only Look Once), a high-precision, relatively lightweight polyp detection framework built upon an improved YOLOv8 architecture, designed to achieve a well-balanced trade-off between detection accuracy and computational efficiency. First, to mitigate complex background interference and improve small polyp detection, a BiFormer module is integrated into the backbone network to enhance focus on salient polyp regions while suppressing noise. To alleviate boundary ambiguity, the CARAFE content-aware upsampling operator is incorporated into the feature fusion stage, to refine lesion boundaries and spatial details. PConv module is employed to optimize network efficiency, reducing computational cost while maintaining detection performance. Experimental results on the Kvasir-SEG and CVC-ClinicDB datasets demonstrate that BCP-YOLO achieves a mean average precision (mAP0.5) of 88.5% on Kvasir-SEG, representing a 3.4% improvement over the YOLOv8 baseline. Precision and recall increase by 5.5% and 1.3%, respectively. The model contains 11.7 M parameters and achieves an inference speed of 104.1 frames per second (FPS). Five-fold cross-validation on both datasets validates its strong generalization capability and robustness. The method provides a high-accuracy and deployable solution for computer-aided diagnosis in real-time colonoscopy, offering significant potential to improve the reliability and efficiency of early colorectal cancer screening.

To evaluate whether early canakinumab initiation may provide treatment advantages in Still's disease (SD) patients, particularly in terms of therapy discontinuation due to long-term disease remission, glucocorticoid sparing effect, and increase in the frequency of monocyclic disease course rather than a polycyclic or chronic articular pattern. SD patients treated with canakinumab were grouped according to time between disease onset and canakinumab initiation (&#x2264;3 months vs. >3 months). Patients were enrolled from the international AutoInflammatory Disease Alliance (AIDA) Network registry for SD. Overall, 190 patients were enrolled, 35 (19%) treated with canakinumab within three months from SD onset and 155 (82%) starting canakinumab later. Glucocorticoids use decreased more rapidly in patients receiving canakinumab within 3 months from SD onset than among patients treated later, with reductions of 50% vs 6% at month 3 (p=0.0001), and 75% vs 32% at month 6 (p=0.004). In logistic regression analysis, canakinumab initiation within 3 months from disease onset was significantly associated with treatment discontinuation due to long-term remission (OR 4.83, 95% CI 1.08-23.19; p=0.04). A monocyclic course occurred in 49% of patients starting canakinumab &#x2264;3 months versus 8% starting later (p<0.0001). Starting canakinumab within 3 months from disease onset was significantly associated with a monocyclic disease course compared with the chronic-articular (RRR 4.43, 95% CI 1.12-17.60; p=0.034) and polycyclic courses (RRR 8.97, 95% CI 1.29-62.3; p=0.03). Early canakinumab initiation is associated with treatment discontinuation due to long-term remission and appears linked to a greater frequency of a monocyclic disease course.

Organophosphate esters (OPEs) are widely used as flame retardants and plasticizers and are considered emerging environmental pollutants. However, whether they affect human and rat gonadal 3&#x3b2;-hydroxysteroid dehydrogenase (3&#x3b2;-HSD) isoforms, crucial enzymes in steroidogenesis, remains unclear. A total of 22 trialkyl OPEs were comprehensively screened for their inhibitory effects on human and rat gonadal 3&#x3b2;-HSDs, and structure-activity relationships (SAR), mode of action, and in silico docking were explored. C4-C6 alkyl OPEs inhibited human KGN cell 3&#x3b2;-HSD2 activity with the inhibitory strength order of trihexyl (IC50: 20.92&#x202f;&#xb5;M) >&#x202f;triamyl (52.58&#x202f;&#xb5;M) >&#x202f;tributyl (90.16&#x202f;&#xb5;M), with a V-shaped turn observed for C7-C10 trialkyl OPEs, and the same pattern was seen for the inhibition on rat testicular 3&#x3b2;-HSD1 enzyme. Most trialkyl OPEs functioned as mixed inhibitors and reduced progesterone secretion in human KGN cells. Rat 3&#x3b2;-HSD1 was found to be more susceptible than human 3&#x3b2;-HSD2. Docking studies showed that OPEs bound to the NAD+ and steroid binding sites. SAR and 3D-quantitative SAR indicated that hydrophobicity and carbon chain length were critical for the inhibition activity and the V-shaped pattern while docking analysis emphasized the importance of the optimal size of trialkyl OPEs for binding affinity to human 3&#x3b2;-HSD2 active sites. In conclusion, trialkyl OPEs can inhibit human and rat gonadal 3&#x3b2;-HSDs, depending on factors such as carbon chain length and lipophilicity, with a distinct V-shaped shift at C6. These findings suggest that OPEs may have endocrine-disrupting properties and pose risks to reproductive health.

To evaluate the fat fraction (FF) and morphologic characteristics of the mandibular condyles in patients with temporomandibular disorders (TMDs) using quantitative Dixon (Q-Dixon). The bilateral condyles of 109 TMDs patients and 25 healthy volunteers were prospectively enrolled. Histogram metrics and shape-based radiomics features were calculated to describe the FF and morphologic characteristics of the condyles, respectively. The FF and morphologic characteristics between health control (HC) and TMDs groups, and among TMDs subgroups were compared. The diagnostic ability of FF and morphologic characteristics were revealed. Between HC and TMDs groups, 10 percentile, 90 percentile, mean, median, root mean squared, and skewness showed significant differences (all P&#xa0;<&#xa0;0.05). The surface volume ratio differs between the two groups. Except for major axis length, all of the FF and morphologic characteristics showed significant differences among TMDs subgroups (all P&#xa0;<&#xa0;0.05). The FF and morphologic characteristics of condyles showed good diagnostic performance in distinguishing HC from TMDs (area under the receiver operating curve [ROC-AUC]: 0.705&#xa0;&#x223c;&#xa0;0.835 and precision&#x2011;recall curve [PR-AUC]: 0.920&#xa0;&#x223c;&#xa0;0.958) group and differentiating subtypes of TMDs (ROC-AUC: 0.761&#xa0;&#x223c;&#xa0;0.837; PR-AUC: 0. 823&#xa0;&#x223c;&#xa0;0.916). The histogram metrics of FF and shape-based radiomics features derived from the Q-Dixon were capable of detecting and quantifying the pathologic changes of the condyles in TMDs patients, providing novel and promising MRI biomarkers for clinical applications.

Residual disease after cytoreductive surgery is a dominant, surgeon-modifiable determinant of outcome in advanced ovarian cancer, yet white-light inspection cannot reliably identify microscopic implants, plaque-like deposits, or therapy-altered fibrotic foci across complex peritoneal surfaces. Intraoperative molecular imaging aims to close this visibility gap by translating tumor-associated biology into real-time signal that can prompt additional resection, direct sampling, and support intraoperative decision-making. Across probe classes, folate receptor-&#x3b1;-targeted agents represent the most clinically mature approach, while activatable tracers and multimodal platforms are expanding capabilities beyond superficial visualization. However, improved detection alone does not establish patient benefit. We therefore frame molecular guidance as a workflow intervention and organize evidence along a clinically oriented hierarchy-lesion detection, decision impact, and patient outcomes-highlighting the need for standardized acquisition/quantification and pathology-linked validation. We propose pragmatic, trial-compatible definitions for pathology-confirmed completeness and prespecified molecular field clearance, and discuss how AI-assisted interpretation, multimodality, and radionuclide-enabled strategies could strengthen reproducibility and extend management toward microscopic residual disease.

Breast cancer remains a major challenge to public health. Biomarkers may be useful to improve prediction of breast cancer survival. Several epigenetic markers of cell division and ageing based on DNA methylation have been proposed. In this study, we measured these epigenetic markers in breast tumours and assessed their prognostic value. We used genome-wide DNA methylation data measured in 1992 breast cancer tumours from the Melbourne Collaborative Cohort Study and publicly available datasets. We calculated four markers of cell division (epiTOC2, stemTOC, MiAge and CellDRIFT), two markers of chronological age (Horvath age and BTEC), and four markers of biological age (PhenoAge, GrimAge, MRscore and DunedinPACE). Cox regression models were used to assess the associations of age-adjusted epigenetic markers with 5-year overall survival, with adjustment for clinical variables. Effect modification by estrogen receptor (ER) status and molecular subtype was also investigated. After adjustment for age and stratification by study, higher levels of cell division markers were associated with poorer survival (e.g., epiTOC2: per one-standard-deviation increase, hazard ratio [HR]&#x2009;=&#x2009;1.14, 95% CI: 1.03-1.27), whereas higher chronological age markers were linked to better prognosis (e.g., Horvath age: HR&#x2009;=&#x2009;0.70, 95% CI: 0.61-0.82). Epigenetic markers of biological age showed variable associations. These associations were partly explained by the main clinicopathological variables at diagnosis and varied across subtypes. Our study revealed associations of several epigenetic markers with breast cancer survival. The associations were quite weak, suggesting these markers may have limited prognostic value. The varying associations observed among subtypes may reflect underlying biological differences that should be further investigated.

Cell-surface glycans play essential roles in cell communication, immune recognition, and disease progression. Their medical applications are supported by two important technological pillars: glycan recognition and glycan editing. This Review focuses on three key directions: recognition technologies, namely, in situ imaging and glycomic profiling, for detecting and profiling glycans; functional editing technologies, including genetic, enzymatic, chemical, and metabolic approaches, for precisely remodelling glycans; and medical applications driven by the synergy between recognition and editing, with a focus on four areas: biomarkers, lectin-based therapies, precision glycan editing, and cancer immunotherapy. Throughout the Review, five representative glycan classes-high-mannose N-glycans, mucin-type O-GalNAc, heparan sulfate, ganglioside GM3, and glycoRNAs-are used as recurring examples wherever possible. This Review offers an integrated perspective to navigate the process by which glycan recognition and editing technologies collectively drive the translation of cell-surface glycan research into clinical practice.