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Chemokine receptor 4 (CXCR4) is a clinically significant G protein-coupled receptor implicated in HIV-1 entry, cancer progression, immune regulation, and metastatic dissemination, making it an attractive therapeutic target. This study employed an integrated computational and experimental framework to identify novel small-molecule CXCR4 inhibitors. A curated dataset of 608 compounds from peer-reviewed literature and patents was used to train machine-learning classification models. Decision Tree, Logistic Regression, and AdaBoost models showed balanced performance across key metrics, and external validation on 2146 in-house compounds identified 44 consensus CXCR4 inhibitors. Molecular docking analyses suggested favorable binding modes and key interactions comparable to those predicted for the reference inhibitor IT1t. One hundred-nanosecond molecular dynamics simulations indicated stable CXCR4-ligand complexes, with equilibration occurring within approximately 20 ns and backbone RMSD values maintained between 4 and 8 Å. MM/GBSA free-energy calculations demonstrated favorable energetics, with IS00622 exhibiting the strongest affinity (-70 kcal/mol), followed by IT1t, IS00998, and IS00179. In vitro assays identified IS00127 as a promising lead, showing strong antiproliferative activity against MDA-MB-231 cells and minimal toxicity toward HEK293 cells. ELISA assays confirmed dose-dependent CXCR4 downregulation with negligible effects on CXCR7, indicating high functional selectivity. Overall, this integrative strategy accelerates the discovery of potent, selective CXCR4 inhibitors for translational research.

The overexpression of the transcriptional enhanced associate domain (TEAD), which regulates gene transcription linked to cell growth, drives the proliferation in cases of hepatocellular carcinoma (HCC). In order to discover novel TEAD inhibitors that are more effective and have better efficacy and pharmacokinetic properties for treating HCC, this study employed a cyclization strategy to generate a novel indole-based scaffold of TEAD inhibitors. A comprehensive and systematic structure-activity relationship (SAR) analysis identified the most promising compound: LC-TD-05, a non-covalent, partial TEAD inhibitor with selective activity against TEAD1, TEAD2 and TEAD4, but reduced potency against TEAD3. LC-TD-05 exhibits good potency against TEAD1/2/4 (TEAD1 IC50 = 116.6 ± 21.7 nM, TEAD2 IC50 = 168.7 ± 17.1 nM, TEAD4 IC50 = 68.3 ± 18.2 nM), demonstrates favorable oral bioavailability (F = 53.7%), and exhibits significant anti-tumor activity in HCC LM3 models in vitro (LM3 cell IC50 = 248 ± 27.9 nM) and in vivo (TGI = 75%). Overall, this study provides a novel scaffold for TEAD inhibitors, enabling more effective interventions against HCC.

Determination of estrogen receptor (ER) and progesterone receptor (PR) status is critical for breast cancer subtyping and guiding endocrine therapy. Although immunohistochemistry (IHC) remains the diagnostic gold standard, it is costly, labor-intensive, and prone to interobserver variability. These limitations are particularly restrictive in low-resource settings where access to standardized receptor testing is limited. This study presents a proof-of-concept evaluation of attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy combined with artificial intelligence (AI) for label-free classification of ER and PR status in formalin-fixed paraffin-embedded (FFPE) breast cancer tissues. A total of 72 samples (33 ER-positive, 39 ER-negative) were analyzed for ER classification, and 74 samples for PR classification (20 PR-positive, 54 PR-negative), generating 2328 and 1804 spectra, respectively. Spectra were acquired from pathologist-annotated tumor regions exhibiting definitive nuclear staining (positive) or absence thereof (negative) using a grid-based mapping strategy. Preprocessing included baseline correction (rubber-band algorithm) and z-score normalization. Seven AI models - logistic regression, support vector machine (SVM), decision tree, XGBoost, feedforward neural network (FNN), recurrent neural network (RNN), and convolutional neural network (CNN) - were trained and optimized using a genetic algorithm. Model performance was assessed via repeated cross-validation using AUC-ROC, accuracy, sensitivity, specificity, PPV, NPV, and F1 score. CNN achieved the highest classification performance for both ER (AUC = 95.93% ± 6.64%, accuracy = 90.06% ± 4.85%) and PR (AUC = 97.46% ± 0.64%, accuracy = 91.51% ± 3.28%). FNN, RNN, and XGBoost also demonstrated strong performance, whereas SVM yielded the lowest accuracy and F1 scores. Statistically significant spectral differences between receptor-positive and -negative tumor regions were observed across biochemical bands corresponding to proteins, lipids, nucleic acids, and phosphorylated biomolecules. AI-enhanced ATR-FTIR spectroscopy demonstrates high diagnostic potential for hormone receptor subtyping in FFPE tissues. As a label-free, scalable platform, it offers a promising alternative to IHC, particularly in resource-constrained environments. These findings establish the technical feasibility of this approach and warrant further validation in multicenter clinical cohorts.

Point-of-care testing (POCT) platforms frequently suffer from a fundamental bottleneck: while advances in molecular amplification improve signal intensity, the reliability of signal readout in complex clinical matrices remains poorly controlled. Here, we present an integrated biosensing framework that treats readout reliability as an explicit engineering objective rather than a post hoc correction problem. The platform integrates three complementary components: (i) a heptameric nanobody probe employed as a multivalent recognition element for target capture, (ii) a DNA-assisted clustering interface that spatially organizes gold nanoparticle reporters for robust signal amplification, and (iii) a few-shot learning module based on Prototypical Networks that enables robust classification with minimal training data while providing interpretable decision-making through metric-based reasoning. Alpha-fetoprotein was selected as the model analyte because it remains a clinically important biomarker for hepatocellular carcinoma screening and follow-up, while also representing a realistic POCT challenge in which clinically meaningful detection must be achieved with low instrumentation burden and reliable readout under matrix variability. In this setting, the system achieves a visual limit of detection of 2 ng/mL and demonstrates quantitative consistency across representative clinical serum samples. Importantly, the AI module functions as an integral system component, identifying diagnostically relevant regions and mitigating readout uncertainty arising from matrix effects and imaging variability. By jointly engineering the sensing interface and the interpretive layer, this work establishes a generalizable strategy for constructing trustworthy POCT systems in which chemical signal generation and digital interpretation are co-designed.

The therapeutic gain ratio (TGR) of radiotherapy for hepatocellular carcinoma (HCC) remains limited by two major barriers: insufficient precision in adaptive radiotherapy (ART) on the physical dimension and the lack of effective radiosensitization on the biological dimension. Although advances have been made separately in accurate dose delivery and tumor-sensitizing strategies, no approach has yet integrated both dimensions to achieve a coordinated improvement in TGR, representing a critical gap in current practice. In this study, we propose an integrated physical-biological strategy that combines nanomaterials with artificial intelligence (AI). We first constructed charge-engineered gold-platinum nanoparticles that respond to the acidic tumor microenvironment and enable prolonged, high-contrast computed tomography imaging of HCC. These enhanced images were then used to develop the first Transformer-convolutional neural network hybrid model (3D STS-Net) tailored for this scenario, enabling high-accuracy three-dimensional segmentation of small HCC for image-guided adaptive radiotherapy. In parallel, we systematically evaluated the nanoparticles' radiosensitizing effects in vitro and in vivo. The nanoparticles provided stable imaging enhancement for up to 120 h and markedly improved tumor-liver contrast. The 3D STS-Net achieved high segmentation accuracy, supporting more precise contouring for HCC ART. Moreover, the nanoparticles significantly increased radiation-induced reactive oxygen species and enhanced tumor control in animal models. Together, these findings demonstrate that the proposed strategy simultaneously strengthens radiotherapy performance in both physical and biological dimensions, leading to a coordinated improvement in TGR. This integrated "nanomaterial-AI" framework offers a systematic and generalizable approach for enhancing radiotherapy effectiveness in HCC.

Functional constipation (FC) is a common gastrointestinal condition often accompanied by anxiety and depression status (FCAD). Gastrointestinal symptoms in FCAD patients are not fully resolved with medication, and the responses to treatment can differ significantly from those of patients without anxiety and depression (FCNAD). Given the distinct effects of FC and mental status on brain function and structure, we hypothesize that these brain differences could serve as imaging features to differentiate FCAD and FCNAD. Patients with FC (N = 187) underwent structural magnetic resonance imaging, diffusion tensor imaging scans, and completed self-reported assessments of depression and anxiety. The current study first identified FCNAD and FCAD patients with high- and low-confidence labels based on self-reported ratings. Brain structural imaging features were subsequently extracted to train and refine the classification model using a stagewise training approach. The classification model achieved an average accuracy of 89.23% during the cross-validation, and the predicted probability of FCAD was significantly correlated with mental ratings and gastrointestinal symptoms. The top 30 imaging features contributing to classification were primarily located in brain regions involved in emotional processing (temporal pole, amygdala, orbitofrontal cortex), somatosensory (insula), and motor control (corticospinal tract, inferior cerebellar peduncle). These findings highlight potential brain imaging features distinguishing FCAD and FCNAD, which may provide the incremental value over standard behavioral assessments for future personalized diagnosis and treatment strategies.

Accurate capture and molecular analysis of circulating tumor cells (CTCs) from whole blood are crucial for early cancer diagnosis, prognosis and personalized therapy. Conventional enrichment methods tend to induce cellular damage, compromising cell viability and limiting downstream functional analysis. To address this, we present an Acousto-SERS Integrated Trapping Chip (ASSIT Chip) which combines acoustic sorting with surface enhanced Raman scattering (SERS) fingerprinting for label-free, non-invasive CTC identification and recovery. In this platform, interdigital transducers generate a finely tuned acoustic field that gently sorts CTCs from whole blood via contactless manipulation. The sorted cells are then transported to an integrated SERS region for label-free spectral fingerprinting. From acoustic sorting to optical identification, the fully non-invasive workflow minimizes cellular damage. Additionally, the dual-mode approach integrates physical size selection with chemical biomolecular identification, thus significantly improving the enrichment purity. Through precise control of acoustic and hydrodynamic fields, the ASSIT Chip achieves high sorting efficiency (86.7%) and recovery rate (96.9%). By employing a microsphere-templated plasmonic substrate, label-free SERS detection accurately distinguishes CTCs from blood cells with an accuracy of 96.6%. More importantly, the retrieved CTCs retain high viability and undiminished proliferative capacity, enabling subsequent cell culture and downstream biological studies. The validation with whole blood samples confirms that the ASSIT Chip provides a robust platform for cancer diagnostics and functional CTC analysis, holding considerable promise for clinical translation and personalized medicine.

Cervical cancer (CC) is recognized as a significant health concern impacting females worldwide. The pathogenicity of CC is associated with human papillomavirus (HPV). Timely diagnosis, therefore, becomes imperative for reducing mortality and improving patient outcomes. Various conventional detection strategies (like Pap Smear, Colposcopy, Cervical Biopsy, Visual inspection with Acetic Acid, Southern blot hybridization assay), along with modern techniques (such as genotyping, PCR, HCII Hybrid Capture, ELISA, and sequencing), are being adopted for the time being. These traditional detection methods come with their own sets of limitations, such as false positives, the need for specialized equipment, lower sensitivity, and specificity. To overcome these challenges, new-age biosensing techniques are being developed. The advent of biosensors has brought new possibilities for innovations in point-of-care devices for the timely, sensitive, and specific detection of HPV. This article provides a comprehensive overview of the pathogenicity and diagnostic strategies for cervical cancer, with special emphasis on recently developed biosensing techniques. The role of AI and machine learning, along with challenges, is also outlined. This article intends to highlight the contribution of biosensors in reducing global CC burden by providing an affordable, reliable, and rapid diagnostic solution. However, interdisciplinary collaborations and future research are required to increase their robustness for early CC detection. The continued development and clinical translation of biosensors hold immense prospects in redefining the future of CC diagnostics.

Ubiquitin-specific protease 7 (USP7) is a key member of the deubiquitinating enzyme family. It is abnormally overexpressed in various malignancies, including breast cancer, chronic lymphocytic leukemia, and prostate cancer. By regulating pathways such as the p53-MDM2 signaling axis, USP7 promotes tumorigenesis and progression, making it a highly promising therapeutic target for anticancer treatment. Although multiple USP7 inhibitors have been reported, existing screening and evaluation assays exhibit limitations: the ubiquitin-phospholipase A2 (Ub-PLA2) assay frequently produces false-positive results, while the ubiquitin-rhodamine (Ub-Rho) assay is susceptible to interference from compound autofluorescence. To address this challenge, we developed a fluorescence polarization (FP) assay. This employs a rationally designed strategy that exhibits excellent characteristics, making it a simple-to-operate and cost-effective method, suitable for the evaluation of compound bioactivity against USP7. To further validate the practicality and reliability of this FP assay, we conducted a structure-based drug design campaign involving two rounds of systematic structural optimization, yielding 51 novel derivatives featuring pyrazolo[4,3-d]pyrimidine and piperidol scaffolds. Following FP evaluation and Ub-Rho enzyme activity validation, we performed a comprehensive structure-activity relationship (SAR) analysis. Ultimately, in vitro cellular assays identified three compounds (LC-U7-44, LC-U7-48, and LC-U7-50) that exhibit potent USP7 inhibitory activity alongside favorable cellular anti-proliferative effects. Overall, the established FP assay in this study closes a methodological gap in the evaluation of USP7 inhibitors, and the detailed SAR analysis provides a foundation for the further development of potent USP7 inhibitors.

Cancer represents a significant challenge to people's health and safety. Tumor biomarker detection plays a vital role in the precise diagnosis of cancer and finds widespread applications in cancer screening and pathological diagnosis. Existing methods for tumor biomarker detection have drawbacks such as susceptibility to false positives, complexity of operation, and high costs. As a molecular-level fingerprint spectrum, Raman spectroscopy holds promise as a rapid and accurate method for tumor biomarker protein detection. This paper presents a high-confidence Raman spectra collection method for tumor biomarker proteins based on experimental and theoretical cross-validation. On the one hand, through ultrafiltration purification of protein samples, high-confidence spectra for four tumor biomarker proteins of breast cancer were experimentally acquired. On the other hand, using first-principles Density Functional Theory (DFT), the Raman spectra of the proteins were calculated theoretically. Experimental and theoretical spectra were mutually validated, confirming differences in spectral peak characteristics and their assignments for the four biomarker proteins. We also demonstrate improvement in AI-based protein classification through theoretical-experimental cross-validation, with 7.62% accuracy gain. The method proposed in this paper is well-suited for integration with high-throughput spectral analysis algorithms based on artificial intelligence. It holds the potential for developing deep learning models constrained by biological knowledge in the field of cancer screening and tissue biopsy pathological diagnosis in the future.