ObjectiveTo analyze the effects of the progestin-primed ovarian stimulation （PPOS） protocol on embryo euploidy rate and clinical pregnancy outcomes in infertile patients. MethodsWomen who underwent Preimplantation genetic testing for aneuploidy （PGT-A） cycles were selected as study participants （a total of 656 cycles and 3 081 blastocysts）. Participants were divided into two age groups （≤35 years and >35 years） and further stratified by ovarian stimulation protocol： the Progestin-Primed Ovarian Stimulation （PPOS） group （n=48 and 60， respectively）， the Luteal Phase Long Protocol （LP） group （n=160 and 57， respectively）， and the Gonadotropin-Releasing Hormone Antagonist Protocol （AP） group （n=220 and 111， respectively）. Baseline characteristics， ovarian stimulation outcomes， and embryo development parameters were compared among the three protocols within each age group. Additionally， clinical pregnancy outcomes of the first frozen embryo transfer （FET） cycle were compared. The primary outcome measure was the embryo euploidy rate. Results① In the ≤35 years age group， baseline follicle-stimulating hormone （bFSH） levels were significantly higher in the PPOS group compared to the LP and AP groups， and the bFSH/bLH ratio was significantly higher in the PPOS group than in the AP group （P < 0.05）. In the >35 years age group， bFSH levels in both the PPOS and AP groups were significantly higher than in the LP group （P < 0.05）.② In the ≤35 years group， there were no significant differences in the rates of metaphase II （MII） oocytes， 2-pronuclei （2 PN） zygotes， cleavage， 2 PN cleavage， blastocyst formation， high-quality embryos， or embryo euploidy between the PPOS group and either the LP or AP groups. However， the PPOS group showed significantly lower values for the following parameters compared to the other two groups： total oocytes retrieved， number of MII oocytes， number of 2 PN zygotes， number of cleaved embryos， number of 2 PN cleaved embryos， number of blastocysts formed， number of high-quality embryos， number of high-grade blastocysts， number of transferable embryos， number of cryopreserved embryos， number of biopsied blastocysts， number of euploid embryos， and the rate of obtaining at least one euploid embryo. In the >35 years group， no significant differences were observed among the PPOS， LP， and AP groups in the rates of MII oocytes， 2 PN cleavage， blastocyst formation， or in the number of euploid embryos， euploidy rate， and the rate of obtaining at least one euploid embryo. However， the PPOS group had significantly lower numbers of total oocytes retrieved， MII oocytes， 2 PN zygotes， cleaved embryos， 2 PN cleaved embryos， blastocysts formed， transferable embryos， and cryopreserved embryos compared to the LP group （P<0.05）. The high-quality embryo rate was significantly lower in the PPOS group than in the AP group （P<0.05）. The numbers of high-quality embryos， high-grade blastocysts， and biopsied blastocysts were significantly lower in the PPOS group than in both the LP and AP groups （P<0.05）. Notably， the 2 PN fertilization rate was significantly higher in the PPOS group than in the AP group， and the cleavage rate was significantly higher in the PPOS group than in both the LP and AP groups （P<0.05）.③ No significant differences were found among the three groups in the biochemical pregnancy rate， clinical pregnancy rate， live birth rate， early miscarriage rate， and late miscarriage rate following the first FET cycle. ConclusionCompared to the other two protocols， the PPOS protocol does not significantly affect embryo euploidy or clinical pregnancy rates. However， in freeze-all cycles with PGT-A， the PPOS protocol does not reduce the rate of chromosomally normal embryos but may decrease the total number of available embryos. It may not be the optimal choice for younger women， but it can be considered as a viable option for women of advanced maternal age.

ObjectiveTo focused on patients with unexplained repeated implantation failure （URIF） and compare the clinical efficacy of different administration routes of granulocyte colony-stimulating factor （G-CSF） in patients undergoing frozen-thawed embryo transfer cycles. MethodsA retrospective case-control study was conducted. A total of 187 patients with URIF who received medical treatment were enrolled in this study. According to whether G-CSF was used on the day of endometrial transformation and its administration route in frozen-thawed embryo transfer cycles， the patients were divided into three groups： control group （without G-CSF， n=71）， subcutaneous injection group （subcutaneous G-CSF， n=72）， and intrauterine infusion group （intrauterine G-CSF infusion， n=44）. The pregnancy outcomes among the three groups were compared， and Logistic regression analysis was performed. ResultsThere were significant differences in biochemical pregnancy rates among the three groups （P<0.05）， indicating statistical significance in the overall comparison. In terms of numerical trend， the biochemical pregnancy rate was in the order： subcutaneous injection group > intrauterine infusion group > control group. Further pairwise comparison showed that only the difference between the subcutaneous injection group and the control group was statistically significant （P<0.017）， while no significant differences were found between the other groups （P>0.017）. The overall comparison of early miscarriage rates among the three groups also showed a significant difference （P<0.05）， indicating a statistically significant difference in the distribution among the groups. The numerical trend was： intrauterine perfusion group > subcutaneous injection group > control group. Pairwise comparison results showed that only the difference between the intrauterine perfusion group and the control group was statistically significant （P<0.017）， while there was no statistically significant difference between any other two groups （P>0.017）.Multivariate Logistic regression analysis showed that endometrial thickness on the day of transformation， number of transferred embryos， and subcutaneous G-CSF injection were independent factors associated with an increased biochemical pregnancy rate， where as age was an independent factor for a decreased biochemical pregnancy rate. For clinical pregnancy outcome， endometrial thickness on the day of transformation， number of transferred embryos， and subcutaneous G-CSF injection were also independent factors for an increased clinical pregnancy rate， while BMI was an independent factor for a decreased clinical pregnancy rate. ConclusionSubcutaneous injection of G-CSF only improved the biochemical pregnancy rate in URIF patients. Multivariate Logistic regression analysis showed that subcutaneous G-CSF injection was a favorable factor for clinical pregnancy rate in URIF patients； however， no significant improvement in clinical pregnancy rate was observed among the three groups. Both the comparison of pregnancy outcomes and the results of multivariate logistic regression analysis indicates that intrauterine perfusion of G-CSF has no significant therapeutic effect in the treatment of patients with URIF.

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.

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/.

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.

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.

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.