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.

[Objective] To explore the temperature parameters affecting the polybrene test and determine the optimal temperature conditions for detecting unexpected antibodies of the Rh system. [Methods] The reaction of IgG human anti-D antibody with different dilutions (undiluted, 1∶2, 1∶4, 1∶8, 1∶16, 1∶32，1∶64) with D antigen-positive red blood cells was detected by manual polybrene test (MPT). Different temperatures (25℃ and 37℃) were set, and the reaction time with low ionic medium was 4 minutes. The agglutination integral value of anti-D and red cell depolymerization time were compared to observe the effect of enhanced agglutination reaction, thereby establishing the test temperature reaction conditions for enhancing the MPT. The same reaction condition was applied to 36 blood samples containing unexpected antibodies of the Rh system, and the effect of enhanced MPT was observed in comparison with the polybrene method and the antiglobulin test (column agglutination). [Results] With all other conditions held constant, when low ionic medium was added, the incubation temperature of 25℃ and 37℃ resulted in different total agglutination integral values for anti-D (20.9±2.025 vs 25.5±2.635), and the comparison showed a significant difference (P<0.05). When the antibody dilution was 1∶16, the incubation temperature of 25℃ and 37℃ resulted in different agglutination integral values (3.9±0.738 vs 5.8±0.632), and the comparison showed a significant difference (P<0.05). Erythrocyte depolymerization time (62.8±8.149 vs 90.1±10.713) was significantly different (P<0.05). At a dilution of 1∶32, the incubation temperatures of 25℃ and 37℃ resulted in different agglutination integral values (2.5±0.527 vs 4.3±0.675), as well as different red blood cell dissociation times (35.4±7.792 vs 57.4±10.885)(P<0.05), and the comparison showed a significant difference (P<0.05), with no differences observed in the other groups. In the detection of 36 Rh system unexpected antibody samples, when the antibody titer was ≤2, the enhanced polybrene method had a higher positive rate, and when the antibody titer was ≥4, the detection rates of the three methods were consistent. [Conclusion] The reference temperature condition for the modified MPT is incubation at 37℃ for 4 min after the addition of low ionic medium. The application of this temperature condition to unexpected antibody samples of Rh system could achieve a significant enhancement effect, thereby increasing transfusion safety for the treatment of emergency patients, and is worth popularizing.

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

Mesenchymal stem cells (MSCs) have been widely used in the treatment of various autoimmune and inflammation-related diseases due to their potent immunomodulatory properties. Several studies have demonstrated that MSC-mediated immunomodulation is complex and bidirectional, with the in vivo microenvironment influencing the direction of this modulation. Indoleamine-2,3-dioxygenase (IDO), an immunosuppressive factor, has been identified as a key "switch" in the immunomodulatory role of MSCs. In this review, we explore how IDO functions as a critical regulator of MSC immunoregulatory plasticity. We delve into the mechanisms by which changes in IDO expression affect the function of various immune cells, summarize relevant research and clinical advances regarding the role of IDO expression in MSC-based therapies for various diseases, and discuss potential therapeutic strategies that target IDO to enhance the stability of MSC therapeutic effects. This provides a theoretical foundation for optimizing MSCs as safer and more effective clinical therapeutic agents.

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.

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/.

Objective:To investigate the clinical characteristics and cardiac magnetic resonance (CMR) features of cardiac sarcoidosis (CS).Methods:This retrospective study included 8 consecutive patients with pathologically confirmed CS by endomyocardial biopsy from Fuwai Hospital, Chinese Academy of Medical Sciences, between January 2012 and September 2024. All patients underwent comprehensive CMR examinations including cine imaging and late gadolinium enhancement (LGE) imaging. Clinical data, including electrocardiographic findings, were collected. CMR phenotyping was performed based on imaging characteristics, and cardiac structure and function parameters were evaluated. LGE analysis was conducted using the American Heart Association 17-segment model to assess the distribution patterns and involvement sites.Results:The most common clinical symptoms were chest tightness (4 patients), palpitations (4 patients), and shortness of breath (6 patients). Electrocardiographic abnormalities included atrioventricular block in 4 patients, right bundle branch block in 2 patients, left bundle branch block in 2 patients, frequent premature ventricular contractions in 4 patients, and non-sustained ventricular tachycardia in 5 patients. CMR phenotyping revealed hypertrophic cardiomyopathy pattern in 3 patients, with 2 patients maintaining normal biventricular function and 1 patient showing significantly reduced biventricular systolic function. Dilated cardiomyopathy pattern was identified in 4 patients, all demonstrating significantly impaired biventricular systolic function. One patient exhibited another phenotype with preserved biventricular systolic function. LGE analysis demonstrated that the anterior wall and anterior septum (segments 1, 2, 7, 8) were the most frequently involved regions, followed by the lateral and inferior walls (segments 5, 6, 11, 12). Subepicardial involvement was the predominant pattern of myocardial enhancement.Conclusions:Cardiac sarcoidosis exhibits diverse clinical manifestations and heterogeneous imaging characteristics. CMR not only provides a comprehensive assessment of cardiac structure and function but also reveals distinctive myocardial tissue characteristics, particularly the extent and distribution patterns of LGE involvement. These findings have significant reference value for early identification of CS and differential diagnosis from other cardiomyopathies.

Objective:To explore the feasibility and effectiveness of full free-breathing cardiac magnetic resonance (CMR) in clinical practice.Methods:The study prospectively included patients who underwent full free-breathing CMR and traditional breath-holding cine imaging between June 1 and June 30, 2024. An analysis and comparison were conducted on the image acquisition time, image quality, and left ventricular function parameters under two scanning methods, including left ventricular ejection fraction (LVEF), left ventricular cardiac output (LVCO),left ventricular end diastolic volume (LVEDV), left ventricular end diastolic volume index (LVEDVI), left ventricular end systolic volume (LVESV), left ventricular end systolic volume index (LVESVI), left ventricular stroke volume (LVSV), and left ventricular mass (LVM). In addition, the study conducted both quantitative and qualitative analyses of other sequences in full free-breathing CMR, including T 1 mapping, T 2 mapping, flow imaging, and late gadolinium enhancement (LGE). Group comparisons were performed using the Wilcoxon signed-rank test or paired t-test. Consistency assessments included Bland-Altman analysis, intraclass correlation coefficient ( ICC), and linear regression analysis. Results:Totally, 150 patients were recruited into the study. The average acquisition time of full free-breathing CMR was (22.1±3.1) min, with an average short axis cine sequence examination time of (2.7±0.4) min; The average acquisition time of short axis images in a breath-holding state was (4.9±1.4) min, which was significantly longer than the cine scan in the free-breathing state ( P0.001). The cine and LGE images quality scores obtained from full free-breathing CMR were 4 (4, 4) points and 5 (4, 5) points, respectively, while the cine image quality score obtained in a breath-holding state was 5 (4, 5) points. Compared with traditional breath-hold CMR, free-breathing CMR measurements showed slightly higher LVESV, and LVESVI, while LVEDV, LVEDVI, LVSV, LVCO, LVEF, and LVM were slightly lower, except for LVSV and LVCO, which showed no statistically significant difference, the differences in other cardiac function parameters were statistically significant ( P0.05). However, the two methods demonstrated good consistency( ICC0.947) and correlation (0.808 r0.993, P0.001). The Bland-Altman analysis showed that the bias for all cardiac function parameters was within 8.0%. The Native T 1 and T 2 values for free-breathing CMR were (1 277.5±57.0) ms and 40.1 (38.5, 41.4) ms, respectively, and the results of flow imaging and echocardiography were basically consistent. Conclusions:Free-breathing CMR is feasible and effective in clinical practice, showing a high level of consistency with left ventricular functional parameters obtained from traditional breath-hold scanning. It significantly shortens examination time and holds great clinical value for the promotion and widespread use of CMR.

Objective To explore the efficacy of low-frequency repetitive transcranial magnetic stimulation(rTMS)combined with virtual reality interactive robot training in improving upper limb function of patients with stroke.Methods From February to December 2023,92 patients in the hos-pital were randomly divided into control group(n=30),virtual reality group(n=31),and com-bined group(n=31).The control group received conventional rehabilitation therapy;the virtual re-ality group received conventional rehabilitation therapy and virtual reality interactive robot training for upper limb;the combined group received low-frequency rTMS on the therapeutic basis of the virtual reality group.Before treatment and 4 weeks after treatment,the Upper Extremity Fugl-Meyer Assess-ment(UFMA)score,the Functional Test for the Hemiplegic Upper Extremity-Hong Kong Version(FTHUE-HK)score,motor evoked potential(MEP)amplitude,cortical latency(CL)value,and the ratio of root mean square of myoelectricity(RMS)of wrist dorsiflexor muscles between the affect-ed and unaffected sides were compared among the three groups.Results Four weeks after treatment,the UFMA and FTHUE-HK scores of the three groups significantly improved compared with those before treatment,the UFMA and FTHUE-HK scores of the combined group were significantly higher than those of the control group and the virtual reality group,and the UFMA score of the virtual reality group was significantly higher than that of the control group(P＜0.05);the RMS ratios and MEP amplitudes of the three groups significantly increased compared with those before treatment,the RMS ratios and MEP amplitudes of the combined group were significantly higher than those of the control group and the virtual reality group,and the virtual reality group had higher values than the control group,with significant between-group differences(P＜0.05);the CL of the three groups significantly shortened compared with that before treatment,the CL of the combined group was significantly shorter than that of the control group and the virtual reality group,and the CL of the virtual reality group was significantly shorter than that of the control group(P＜0.05).Conclusion The rTMS assisted virtu-al reality interactive robot training can effectively improve upper limb function in stroke patients.