Yttrium-90 selective internal radiation therapy (⁹⁰Y-SIRT) is a treatment technique that delivers radioactive microspheres precisely to the arterial vascular bed of neoplasms, utilizing beta radiation to administer a high local dose of radiation to the neoplasm tissues. This technology has demonstrated significant efficacy in patients with unresectable pirmary liver cancers and liver metastases. This article systematically reviews the development history and clinical application status of ⁹⁰Y-SIRT in the treatment of liver cancer, and looks forward to future development directions.

Natural product-based drug combinations (NPDCs) present distinctive advantages in treating complex diseases. While high-throughput screening (HTS) and conventional computational methods have partially accelerated synergistic drug combination discovery, their applications remain constrained by experimental data fragmentation, high costs, and extensive combinatorial space. Recent developments in artificial intelligence (AI), encompassing traditional machine learning and deep learning algorithms, have been extensively applied in NPDC identification. Through the integration of multi-source heterogeneous data and autonomous feature extraction, prediction accuracy has markedly improved, offering a robust technical approach for novel NPDC discovery. This review comprehensively examines recent advances in AI-driven NPDC prediction, presents relevant data resources and algorithmic frameworks, and evaluates current limitations and future prospects. AI methodologies are anticipated to substantially expedite NPDC discovery and inform experimental validation.
Artificial Intelligence ; Biological Products/chemistry* ; Humans ; Drug Combinations ; Drug Discovery/methods* ; Machine Learning ; Algorithms

The αPD-L1 antibody-based immune checkpoint blockade therapy is still limited by the poor clinical response rate as it is mainly utilized to block surface PD-L1 on tumor cells while ignoring abundant PD-L1 exosomes secreted in the environment, causing tumor immune evasion. Here, we proposed an exosome biogenesis inhibition strategy to suppress tumor exosomes secretion from the source, reducing the inhibitory effect on T cells and enhancing chemo-immunotherapy efficacy. We developed sulfafurazole homodimers (SAS) with disulfide linkages, effectively releasing the drug in response to glutathione (GSH) and inhibiting 4T1 tumor-derived exosomes secretion. Subsequently, gemcitabine (Gem) was encapsulated to induce immunogenic cell death (ICD). Consequently, Gem@SAS inhibited the secretion of tumor exosomes by more than 70%, increased proliferation and granzyme B secretion ability of T cells by more than 2 times, and showed superior efficacy in breast cancer treatment as well as lung metastasis of breast cancer.

Advancements in artificial intelligence (AI) and emerging technologies are rapidly expanding the exploration of chemical space, facilitating innovative drug discovery. However, the transformation of novel compounds into safe and effective drugs remains a lengthy, high-risk, and costly process. Comprehensive early-stage evaluation is essential for reducing costs and improving the success rate of drug development. Despite this need, no comprehensive tool currently supports systematic evaluation and efficient screening. Here, we present druglikeFilter, a deep learning-based framework designed to assess drug-likeness across four critical dimensions: 1) physicochemical rule evaluated by systematic determination, 2) toxicity alert investigated from multiple perspectives, 3) binding affinity measured by dual-path analysis, and 4) compound synthesizability assessed by retro-route prediction. By enabling automated, multidimensional filtering of compound libraries, druglikeFilter not only streamlines the drug development process but also plays a crucial role in advancing research efforts towards viable drug candidates, which can be freely accessed at https://idrblab.org/drugfilter/.

Increasing evidence showed that histone deacetylase 6 (HDAC6) dysfunction is directly associated with the onset and progression of various diseases, especially cancers, making the development of HDAC6-targeted anti-tumor agents a research hotspot. In this study, artificial intelligence (AI) technology and molecular simulation strategies were fully integrated to construct an efficient and precise drug screening pipeline, which combined Voting strategy based on compound-protein interaction (CPI) prediction models, cascade molecular docking, and molecular dynamic (MD) simulations. The biological potential of the screened compounds was further evaluated through enzymatic and cellular activity assays. Among the identified compounds, Cmpd.18 exhibited more potent HDAC6 enzyme inhibitory activity (IC₅₀ = 5.41 nM) than that of tubastatin A (TubA) (IC₅₀ = 15.11 nM), along with a favorable subtype selectivity profile (selectivity index ≈ 117.23 for HDAC1), which was further verified by the Western blot analysis. Additionally, Cmpd.18 induced G2/M phase arrest and promoted apoptosis in HCT-116 cells, exerting desirable antiproliferative activity (IC₅₀ = 2.59 μM). Furthermore, based on long-term MD simulation trajectory, the key residues facilitating Cmpd.18's binding were identified by decomposition free energy analysis, thereby elucidating its binding mechanism. Moreover, the representative conformation analysis also indicated that Cmpd.18 could stably bind to the active pocket in an effective conformation, thus demonstrating the potential for in-depth research of the 2-(2-phenoxyethyl)pyridazin-3(2H)-one scaffold.

Hemoglobin, the principal protein in red blood cells, is crucial for oxygen transport in the bloodstream. The quantification of hemoglobin concentration is indispensable in medical diagnostics and health management, which encompass the diagnosis of anemia and the screening of various blood disorders. Immunological methods, based on antigen-antibody interactions, are distinguished by their high sensitivity and accuracy. Consequently, it is necessary to develop hemoglobin-specific antibodies characterized by high specificity and affinity to enhance detection accuracy. In this study, we immunized a Bactrian camel (Camelus bactrianus) with human hemoglobin and subsequently constructed a nanobody library. Utilizing a solid-phase screening method, we selected nanobodies and evaluated the binding activity of the screened nanobodies to hemoglobin. Initially, human hemoglobin was used to immunize a Bactrian camel. Following four immunization sessions, blood was withdrawn from the jugular vein, and a nanobody library with a capacity of 2.85×10⁸ colony forming units (CFU) was generated. Subsequently, ten hemoglobin-specific nanobody sequences were identified through three rounds of adsorption-elution-enrichment assays, and these nanobodies were subjected to eukaryotic expression. Finally, enzyme-linked immunosorbent assay and biolayer interferometry were employed to evaluate the stability, binding activity, and specificity of these nanobodies. The results demonstrated that the nanobodies maintained robust binding activity within the temperature range of 20-40 ℃ and exhibited the highest binding activity at pH 7.0. Furthermore, the nanobodies were capable of tolerating a 10% methanol solution. Notably, among the nanobodies tested, VHH-12 displayed the highest binding activity to hemoglobin, with a half maximal effective concentration (EC₅₀) of 10.63 nmol/L and a equilibrium dissociation constant (K_D) of 2.94×10^-7 mol/L. VHH-12 exhibited no cross-reactivity with a panel of eight proteins, such as ovalbumin and bovine serum albumin, while demonstrating partial cross-reactivity with hemoglobin derived from porcine, goat, rabbit, and bovine sources. In this study, a hemoglobin-specific high-affinity nanobody was successfully isolated, demonstrating potential applications in disease diagnosis and health monitoring.
Animals ; Camelus/immunology* ; Single-Domain Antibodies/immunology* ; Hemoglobins/immunology* ; Humans ; Peptide Library

Humans ; Amyloidosis ; Amyloid ; Cardiomyopathies

Objective To explore the effect of task-oriented training of intelligent mirror gloves combined with low-frequency repeti-tive transcranial magnetic stimulation(rTMS)on hand function recovery in stroke patients. Methods From October 1st,2022 to June 30th,2023,136 stroke patients in Northern Jiangsu People's Hospital were ran-domly divided into control group,mirror group,rTMS group and combination group,with 34 patients in each group.All the groups received routine rehabilitation treatment.In addition,the mirror group received task-orient-ed training of intelligent mirror gloves,rTMS group received low-frequency rTMS,and the combination group received task-oriented training combined with low-frequency rTMS,for four weeks.The Fugl-Meyer Assess-ment-Upper Extremities(FMA-UE)score,Wolf Motor Function Test(WMFT)score,and surface electromyo-graphic root mean square(RMS)of forearm extensor and flexor muscle groups on the affected/healthy side be-fore and after treatment were compared.And the differences of transcranial magnetic stimulation-motor-evoked potentials(MEP)between rTMS group and combination group before and after treatment were also compared. Results Four cases in the control group,seven in the mirror group,five in rTMS group and six in the combination group dropped off.The intra-group effect(F>996.656,P<0.001),inter-group effect(F>20.333,P<0.001)and inter-action effect(F>72.796,P<0.001)were significant in the scores of FMA-UE and WMFT,and the RMS ratio of forearm extensor and flexor muscle groups among four groups,in which the combination group was the best.After treatment,the amplitude of MEP increased in rTMS group and combination group(|t|>3.842,P<0.05),and was higher in the combination group than in rTMS group(t=-3.060,P<0.01). Conclusion The task-oriented training of intelligent mirror gloves combined with low-frequency rTMS could effectively promote the recovery of hand function in stroke patients.

Objectives:To analyze the consistency of evaluating left ventricular hemodynamics (HDF) based on single plane and multi plane cine sequences of magnetic resonance mitral valve orifice.Methods:A prospective study was conducted on 48 healthy adults, and two methods were used to measure the mitral valve diameter and calculate HDF parameters. The first method was to measure the diameter of the mitral valve opening in the left ventricular three chamber cine sequence; The second method is to measure the mitral valve diameter using cine sequences of two chamber, three chamber, and four chamber hearts, and then take the average value. Paired t-tests were used to compare the differences in HDF measured by two methods, and Pearson correlation coefficient ( r), intra group correlation coefficient ( ICC), and Bland-Altman analysis were used to test the consistency and reproducibility of the two methods. Results:The root mean square (RMS) of longitudinal HDF calculated using single plane and multi plane mitral valve diameters were [(17.28±4.41)% vs (17.21±4.61)%] ( P=0.379) for the entire cardiac cycle, [(21.45±5.54)% vs (21.49±5.68)%] ( P=0.646) for systolic phase, and [(12.78±4.10)% vs (12.54±4.24)%] ( P=0.106) for diastolic phase, respectively. The difference in the calculation results of HDF parameters related to ventricular function was not statistically significant (all P>0.05), and there was good consistency ( r=0.924-0.996, ICC=0.924-0.995). The two HDF parameters related to atrial function were sensitive to the measurement method of mitral valve orifice diameter [RMS of longitudinal HDF during active atrial emptying: (3.26±1.51)% vs (3.32±1.55)%, P=0.006; longitudinal HDF pulse during active atrial emptying: (-2.60±1.28)% vs (-2.76±1.30)%, P<0.001]. Conclusions:The ventricular function related HDF parameters obtained from the analysis of mitral valve orifice diameter using single plane and multi plane methods have good consistency, and can be evaluated using relatively simple single plane methods for left ventricular HDF.

Non-small cell lung cancer (NSCLC) is one of the most common types of cancer in the world and is an important cause for cancer death. Although the application of immunotherapy in recent years has greatly improved the prognosis of NSCLC, there are still huge challenges in the treatment of NSCLC. The immune microenvironment plays an important role in the process of NSCLC development, infiltration and metastasis, and they can interact and influence each other, forming a vicious circle. Notably, single-cell RNA sequencing enables high-resolution analysis of individual cells and is of great value in revealing cell types, cell evolution trajectories, molecular mechanisms of cell differentiation, and intercellular regulation within the immune microenvironment. Single-cell RNA sequencing is expected to uncover more promising immunotherapies. This article reviews the important researches and latest achievements of single-cell RNA sequencing in the immune microenvironment of NSCLC, and aims to explore the significance of applying single-cell RNA sequencing to analyze the immune microenvironment of NSCLC.