[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.

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

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.

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.

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/.

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.