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

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.

Temporomandibular joint (TMJ) diseases are common clinical conditions. The number of patients with TMJ diseases is large, and the etiology, epidemiology, disease spectrum, and treatment of the disease remain controversial and unknown. To understand and master the current situation of the occurrence, development and prevention of TMJ diseases, as well as to identify the patterns in etiology, incidence, drug sensitivity, and prognosis is crucial for alleviating patients′suffering.This will facilitate in-depth medical research, effective disease prevention measures, and the formulation of corresponding health policies. Cohort construction and research has an irreplaceable role in precise disease prevention and significant improvement in diagnosis and treatment levels. Large-scale cohort studies are needed to explore the relationship between potential risk factors and outcomes of TMJ diseases, and to observe disease prognoses through long-term follw-ups. The consensus aims to establish a standard conceptual frame work for a cohort study on patients with TMJ disease while providing ideas for cohort data standards to this condition. TMJ disease cohort data consists of both common data standards applicable to all specific disease cohorts as well as disease-specific data standards. Common data were available for each specific disease cohort. By integrating different cohort research resources, standard problems or study variables can be unified. Long-term follow-up can be performed using consistent definitions and criteria across different projects for better core data collection. It is hoped that this consensus will be facilitate the development cohort studies of TMJ diseases.

Objective:To analyze the association between healthy lifestyle and mortality among Henan Province 35-74 years old individuals.Methods:Data from the programme of screening and intervention subjects with high-risk cardiovascular disease 99 133 adults were analyzed in a provincial cohort study of 16 counties. Four healthy lifestyle behaviors were assessed based on a questionnaire survey. Information on mortality endpoints was retrieved from the national death surveillance system. Cox proportional hazards regression models were used to estimate the associations between healthy lifestyles, mortality risk and population attributable fraction (PAF).Results:Out of the adult participants in Henan, 50.6% adhered to a healthy lifestyle, and only 0.1% adhered to 4 healthy lifestyle behaviours. During a mean of 4.5 years, 2 685 all-cause death and 1 283 cardiovascular deaths were documented. The decreased risk of mortality among individuals with non-smoking, moderate drinking, adequate exercise and healthy diet were 0.85 (95% CI: 0.77-0.94), 0.75 (95% CI: 0.63-0.89), 0.73 (95% CI: 0.67-0.79) and 0.86 (95% CI: 0.77-0.96), while the adjusted PAF for all-cause deaths were 5.2% (95% CI: 2.5%-7.9%), 24.0% (95% CI: 10.7%-36.4%), 19.4% (95% CI: 13.8%-24.8%) and 12.3% (95% CI: 3.4%-20.9%), respectively. A combined healthy lifestyle can bring more health benefits. Adherence to 4 healthy lifestyle behaviours could avoid 49.1% of all-cause death. Conclusion:Adherence to a healthy lifestyle can reduce the risk of death, and participants with a healthy lifestyle had a lower mortality risk.

Objective:To determine the motion detection uncertainty of the real-time CybeRay ? imaging system and patient intrafractional motion with thermoplastic mask-based immobilization. Methods:Real-time CybeRay ? imaging system was used for irradiation and treatment for head phantom and patients with brain tumors. All patients were immobilized with thermoplastic masks. Real-time imaging was delivered using kilovoltage projection images during radiotherapy. The detected patient motion data was collected from 5 head phantom measurements and 27 treatment fractions of 9 brain tumor patients admitted to Kaifeng Cancer Hospital. The accuracy and uncertainty of the motion monitoring system were determined. Results:The mean and standard deviation (SD) of the detected motion in the X, Y, and Z directions for phantom were (-0.02±0.41) mm, (-0.05±0.22) mm and (0.01±0.35) mm, respectively. The detected motion in the X, Y and Z directions for patents were (-0.13±0.48) mm, (-0.05±0.48) mm and (0.11±0.36) mm, respectively. After removing the motion detection uncertainty, the actual intrafractional motion of patients were (-0.11±0.25) mm, (0±0.43) mm and (0.10±0.08) mm in three directions, respectively. Conclusions:The uncertainty of real-time imaging-based motion monitoring system of CybeRay ? is less than 0.5 mm. It is feasible to apply thermoplastic masks for brain tumor patients in clinical practice, which can provide steady immobilization and limit the SD of patient intrafractional motion within 0.5 mm. Real-time imaging-based motion monitoring system of CybeRay ? is accurate for patient motion monitoring during frameless stereotactic radiosurgery/radiotherapy.