ASD2023: towards the integrating landscapes of allosteric knowledgebase.

Allosteric regulation, induced by perturbations at an allosteric site topographically distinct from the orthosteric site, is one of the most direct and efficient ways to fine-tune macromolecular function. The Allosteric Database (ASD; accessible online at http://mdl.shsmu.edu.cn/ASD) has been systematically developed since 2009 to provide comprehensive information on allosteric regulation. In recent years, allostery has seen sustained growth and wide-ranging applications in life sciences, from basic research to new therapeutics development, while also elucidating emerging obstacles across allosteric research stages. To overcome these challenges and maintain high-quality data center services, novel features were curated in the ASD2023 update: (i) 66 589 potential allosteric sites, covering > 80% of the human proteome and constituting the human allosteric pocketome; (ii) 748 allosteric protein-protein interaction (PPI) modulators with clear mechanisms, aiding protein machine studies and PPI-targeted drug discovery; (iii) 'Allosteric Hit-to-Lead,' a pioneering dataset providing panoramic views from 87 well-defined allosteric hits to 6565 leads and (iv) 456 dualsteric modulators for exploring the simultaneous regulation of allosteric and orthosteric sites. Meanwhile, ASD2023 maintains a significant growth of foundational allosteric data. Based on these efforts, the allosteric knowledgebase is progressively evolving towards an integrated landscape, facilitating advancements in allosteric target identification, mechanistic exploration and drug discovery.

Janus Cr2BN Monolayer with Ferroelectricity and Room-Temperature Ferromagnetism.

Janus monolayers, a special kind of two-dimensional materials, offer an exciting platform for the development of novel electronic/spintronic devices because of their out-of-plane asymmetry. Herein, we propose a sandwich liked Janus tetragonal Cr2BN monolayer with ferroelectricity and ferromagnetism through first-principles calculations. The predicted magnetic moment is up to ~3.0 µB/Cr originating from the distorted square crystal field induced by out-of-plane asymmetry. The Cr2BN monolayer possesses an intrinsic ferromagnetism with a high Curie temperature of 383 K and a sizeable magnetic anisotropy energy of 171 µeV/Cr. Its robust ferromagnetism, dominating by the multi-anion mediated super-exchange interactions, can even resist -5 % ~5 % biaxial strain. Its large cohesive energy and high dynamical/thermal stability provide a strong feasibility for experimental synthesis. These intriguing properties render the Cr2BN monolayer a promising material for nanoscale spintronic devices.

Toxicological effects and transcriptome mechanisms of rice (Oryza sativa L.) under stress of quinclorac and polystyrene nanoplastics.

The absorption and accumulation of nanoplastics (NPs) by plants is currently attracting considerable attention. NPs also tend to adsorb surrounding organic pollutants, such as pesticides, which can damage plants. However, molecular mechanisms underlying the phytotoxicity of NPs are not sufficiently researched. Therefore, we analyzed the toxicological effects of 50 mg/L polystyrene NPs (PS 50 nm) and 5 mg/L the herbicide quinolinic (QNC) on rice (Oryza sativa L.) using 7-day hydroponic experiments, explaining the corresponding mechanisms by transcriptome analysis. The main conclusion is that all treatments inhibit rice growth and activate the antioxidant level. Compared with CK, the inhibition rates of PS, QNC, and PS+QNC on rice shoot length were 3.95%, 6.68%, and 11.43%, respectively. The gene ontology (GO) term photosynthesis was significantly enriched by QNC, and the combination PS+QNC significantly enriched the GO terms of amino sugar and nucleotide sugar metabolisms. The chemicals QNC and PS+QNC significantly affected the Kyoto Encyclopedia of Genes and Genomes (KEGG) of the MAPK signaling pathway, plant hormone signal transduction, and plant-pathogen interaction. Our findings provide a new understanding of the phytotoxic mechanisms and environmental impacts of the interactions between NPs and pesticides. It also provides insights into the impact of NPs and pesticides on plants in the agricultural system.

Posterior cruciate ligament reconstruction with independent internal brace reinforcement: surgical technique and clinical outcomes with a minimum two year follow-up.

PURPOSE: We developed an augmentation technique for PCL reconstruction with independent internal brace reinforcement and evaluated the functional outcome after PCL reconstruction employing autologous hamstrings augmented with an internal brace system for patients with isolated or combined grade 3 posterior instability who were treated with this technique. METHODS: From January 2016 to January 2018, patients with isolated or combined grade 3 PCL tears who underwent single-bundle PCL reconstruction using autologous hamstrings augmented with independent internal braces were studied. The function of the operated knee was evaluated according to the International Knee Documentation Committee (IKDC) score, Lysholm score, and Tegner activity score. The patients were asked the level of returned to their previous sport. Posterior knee laxity was examined with a KT-1000 arthrometer, and data on range of motion (ROM), re-operation, and other complications were collected. RESULTS: A total of 33 consecutive patients who received single-bundle PCL reconstruction using autologous hamstrings augmented with independent internal braces with a minimum two years follow-up were included in this study. Two patients had undergone this procedure during the study period and were not included in this study (one had combined bone fractures, and one patient had previous meniscus surgery). Thirty-one patients were available for final analysis. The mean follow-up was 45.35 ± 10.88 months (range 29-66 months). The average IKDC subjective knee evaluation scores from 51.65 ± 12.35 to 84.52 ± 6.42, the Lysholm score from 53.90 ± 11.86 to 85.68 ± 4.99, and the Tegner score from 2.81 ± 0.79 to 6.71 ± 1.83 (P < 0.05 for all). The mean total posterior side-to-side difference in knee laxity, assessed using a KT-1000 arthrometer, decreased from 12.13 ± 2.66 mm pre-operatively to 1.87 ± 0.56 mm post-operatively at 70° (P < 0.05). Most patients (29/31) had normal or near normal knee ROM post-operatively; two patients revealed a 6-15° loss of knee flexion compared with the contralateral knee. Twenty-nine patients (93.55%) returned to a normal daily exercise level. Twenty-three patients (74.19%) returned to competitive sports with high-level sports (Tegner score of 6 or above; eleven patients (35.48%) reported to be on the same level as well as the Tegner level); six patients (19.35%) returned to recreational sports (Tegner score of 4 or 5). Two patients had Tegner scores of 2 and 3, indicating poor function level. No patient needed PCL revision surgery during the follow-up period. CONCLUSION: Single-bundle PCL reconstruction with internal brace augmentation for PCL injury exhibited satisfactory posterior stability and clinical outcomes in patients with isolated or combined grade 3 PCL injuries at a minimum two year follow-up.

A Unified Fourth-Order Tensor-Based Smart Community System.

Empowered by the ubiquitous sensing capabilities of Internet of Things (IoT) technologies, smart communities could benefit our daily life in many aspects. Various smart community studies and practices have been conducted, especially in China thanks to the government's support. However, most intelligent systems are designed and built individually by different manufacturers in diverging platforms with different functionalities. Therefore, multiple individual systems must be deployed in a smart community to have a set of functions, which could lead to hardware waste, high energy consumption and high deployment cost. More importantly, current smart community systems mainly focus on the technologies involved, while the effects of human activity are neglected. In this paper, a fourth-order tensor model representing object, time, location and human activity is proposed for human-centered smart communities, based on which a unified smart community system is designed. Thanks to the powerful data management abilities of a high-order tensor, multiple functions can be integrated into our system. In addition, since the tensor model embeds human activity information, complex functions could be implemented by exploring the effects of human activity. Two exemplary applications are presented to demonstrate the flexibility of the proposed unified fourth-order tensor-based smart community system.

Erlotinib as Neoadjuvant Therapy in Stage IIIA (N2) EGFR Mutation-Positive Non-Small Cell Lung Cancer: A Prospective, Single-Arm, Phase II Study.

LESSONS LEARNED: The findings of this prospective, single-arm, phase II study showed that neoadjuvant erlotinib was well tolerated and might improve the radical resection rate in patients with stage IIIA-N2 epidermal growth factor receptor mutation-positive non-small cell lung cancer (NSCLC).Erlotinib shows promise as a neoadjuvant therapy option in this patient population.Next-generation sequencing may be useful for predicting outcomes with preoperative tyrosine kinase inhibitors (TKIs) in patients with NSCLC.Large-scale randomized controlled trials investigating the role of TKIs in perioperative therapy, combining neoadjuvant and adjuvant treatments to enhance personalized therapy for patients in this precision medicine era, are warranted. BACKGROUND: Information on epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) as neoadjuvant therapy in non-small cell lung cancer (NSCLC) is scarce. We evaluated whether neoadjuvant erlotinib improves operability and survival in patients with stage IIIA-N2 EGFR mutation-positive NSCLC. METHODS: We conducted a prospective, single-arm, phase II study. Patients received erlotinib 150 mg per day for 56 days in the neoadjuvant period. The primary endpoint was the radical resection rate. RESULTS: Nineteen patients were included in the final analysis. After erlotinib treatment, 14 patients underwent surgery. The radical resection rate was 68.4% (13/19) with a 21.1% (4/19) rate of pathological downstaging. The objective response rate was 42.1%; 89.5% (17/19) of patients achieved disease control, with a 10.3-month median disease-free survival among patients who underwent surgery. Among all 19 patients who received neoadjuvant therapy, median progression-free survival (PFS) and overall survival were 11.2 and 51.6 months, respectively. Adverse events (AEs) occurred in 36.8% (7/19) of patients, with the most common AE being rash (26.3%); 15.8% experienced grade 3/4 AEs. Quality of life (QoL) improvements were observed after treatment with erlotinib for almost all QoL assessments. Effects of TP53 mutation on prognosis were evaluated in eight patients with adequate tissue samples. Next-generation sequencing revealed that most patients had a TP53 gene mutation (7/8) in addition to an EGFR mutation. No TP53 mutation, or very low abundance, was associated with longer PFS (36 and 38 months, respectively), whereas high abundance was associated with short PFS (8 months). CONCLUSION: Neoadjuvant erlotinib was well tolerated and may improve the radical resection rate in this patient population. Next-generation sequencing may predict outcomes with preoperative TKIs.

Research on Fault Extraction Method of Variational Mode Decomposition Based on Immunized Fruit Fly Optimization Algorithm.

In recent years, a new method of fault diagnosis, named variational mode decomposition (VMD), has been widely used in industrial production, but the decomposition accuracy of VMD is determined by two parameters, which are respectively the decomposition layer number k and the penalty factor α, if the parameters are not properly selected, there will be over-decomposition or under-decomposition. In order to find an approach to determine the parameters adaptively, a method to optimize VMD by using the immune fruit fly optimization algorithm (IFOA) is proposed in this paper. In this method, permutation entropy is used as the fitness function, firstly, the immune fruit fly optimization algorithm is used to search the combined parameters of k and α in VMD, searching for the best combination parameters of k and α by iteration, and then uses the combined parameters to perform VMD, finally, the center frequency is determined through frequency spectrum analysis. The method mentioned is applied to the fault extraction of a simulated signal and a measured signal of a wind turbine gearbox, and the fault frequency is successfully extracted. Using ensemble empirical mode decomposition (EEMD) and singular spectrum decomposition (SSD) to compare with the proposed method, which validated feasibility of the proposed method.

A New Fuzzy Logic Classifier Based on Multiscale Permutation Entropy and Its Application in Bearing Fault Diagnosis.

The self-organizing fuzzy (SOF) logic classifier is an efficient and non-parametric classifier. Its classification process is divided into an offline training stage, an online training stage, and a testing stage. Representative samples of different categories are obtained through the first two stages, and these representative samples are called prototypes. However, in the testing stage, the classification of testing samples is completely dependent on the prototype with the maximum similarity, without considering the influence of other prototypes on the classification decision of testing samples. Aiming at the testing stage, this paper proposed a new SOF classifier based on the harmonic mean difference (HMDSOF). In the testing stage of HMDSOF, firstly, each prototype was sorted in descending order according to the similarity between each prototype in the same category and the testing sample. Secondly, multiple local mean vectors of the prototypes after sorting were calculated. Finally, the testing sample was classified into the category with the smallest harmonic mean difference. Based on the above new method, in this paper, the multiscale permutation entropy (MPE) was used to extract fault features, linear discriminant analysis (LDA) was used to reduce the dimension of fault features, and the proposed HMDSOF was further used to classify the features. At the end of this paper, the proposed fault diagnosis method was applied to the diagnosis examples of two groups of different rolling bearings. The results verify the superiority and generalization of the proposed fault diagnosis method.

Research on Fault Diagnosis of Gearbox with Improved Variational Mode Decomposition.

Variational Mode Decomposition (VMD) can decompose signals into multiple intrinsic mode functions (IMFs). In recent years, VMD has been widely used in fault diagnosis. However, it requires a preset number of decomposition layers K and is sensitive to background noise. Therefore, in order to determine K adaptively, Permutation Entroy Optimization (PEO) is proposed in this paper. This algorithm can adaptively determine the optimal number of decomposition layers K according to the characteristics of the signal to be decomposed. At the same time, in order to solve the sensitivity of VMD to noise, this paper proposes a Modified VMD (MVMD) based on the idea of Noise Aided Data Analysis (NADA). The algorithm first adds the positive and negative white noise to the original signal, and then uses the VMD to decompose it. After repeated cycles, the noise in the original signal will be offset to each other. Then each layer of IMF is integrated with each layer, and the signal is reconstructed according to the results of the integrated mean. MVMD is used for the final decomposition of the reconstructed signal. The algorithm is used to deal with the simulation signals and measured signals of gearbox with multiple fault characteristics. Compared with the decomposition results of EEMD and VMD, it shows that the algorithm can not only improve the signal to noise ratio (SNR) of the signal effectively, but can also extract the multiple fault features of the gear box in the strong noise environment. The effectiveness of this method is verified.

Application of Improved Singular Spectrum Decomposition Method for Composite Fault Diagnosis of Gear Boxes.

Aiming at the problem that the composite fault signal of the gearbox is weak and the fault characteristics are difficult to extract under strong noise environment, an improved singular spectrum decomposition (ISSD) method is proposed to extract the composite fault characteristics of the gearbox. Singular spectrum decomposition (SSD) has been proved to have higher decomposition accuracy and can better suppress modal mixing and pseudo component. However, noise has a great influence on it, and it is difficult to extract weak impact components. In order to improve the limitations of SSD, we chose the minimum entropy deconvolution adjustment (MEDA) as the pre-filter of the SSD to preprocess the signal. The main function of the minimum entropy deconvolution adjustment is to reduce noise and enhance the impact component, which can make up for the limitations of SSD. However, the ability of MEDA to reduce noise and enhance the impact signal is greatly affected by its parameter, the filter length. Therefore, to improve the shortcomings of MEDA, a parameter adaptive method based on Cuckoo Search (CS) is proposed. First, construct the objective function as the adaptive function of CS to optimize the MEDA algorithm. Then, the pre-processed signal is decomposed into singular spectral components (SSC) by SSD, and the meaningful components are selected by Correlation coefficient. For the existing modal mixing phenomenon, the SSC component is reconstructed to eliminate the misjudgment of the result. Then, the frequency spectrum analysis is performed to obtain the frequency information for fault diagnosis. Finally, the effectiveness and superiority of ISSD are validated by simulation signals and applying to compound faults of a Gear box test rig.

Modified Singular Spectrum Decomposition and Its Application to Composite Fault Diagnosis of Gearboxes.

Under the strong noise environment, the composite fault signal of gearbox is weak, which makes it difficult to extract fault features. For this problem, based on noise-assisted method, we propose a novel method called Modified Singular Spectrum Decomposition (MSSD). Singular Spectrum Decomposition (SSD) has many advantages such as high decomposition precision and strong ability to restrain mode mixing, etc. However, the ability of SSD to extract a weak signal is not ideal, the decomposition results usually contain a lot of redundant noise and mode mixing caused by intermittency, which is also a troubling problem. In order to improve the decomposition efficiency and make up for the defects of SSD, the new method MSSD adds an adaptive and particular noise in every SSD decomposition stage for each trial, and in addition, whenever the input signal is decomposed to obtain an intrinsic module function (IMF), a unique residual is obtained. After multiple decomposition, the average value of the residual is used as input to the next stage, until the residual cannot continue to decompose, which means that the residual component has, at most, one extreme value. Finally, analyzing simulated signals to explain the advantages of MSSD compared to ensemble empirical mode decomposition (EEMD) and complete ensemble local mean decomposition with adaptive noise (CEEMDAN). In order to further prove the effectiveness of MSSD, this new method, MSSD, is applied to the fault diagnosis of an engineering gearbox test stand in an actual engineer project case. The final results show that MSSD can extract more fault feature information, and mode mixing has been improved and suffers less interference compared to SSD.

Effect of Apixaban on All-Cause Death in Patients with Atrial Fibrillation: a Meta-Analysis Based on Imputed Placebo Effect.

PURPOSE: Vitamin K antagonists (VKAs) are the standard of care for stroke prevention in patients with atrial fibrillation (AF); therefore, there is not equipoise when comparing newer oral anticoagulants with placebo in this setting. METHODS: To explore the effect of apixaban on mortality in patients with AF, we performed a meta-analysis of apixaban versus placebo using a putative placebo analysis based on randomized controlled clinical trials that compared warfarin, aspirin, and no antithrombotic control. We used data from two prospective randomized controlled trials for our comparison of apixaban versus warfarin (Apixaban for Reduction in Stroke and Other Thromboembolic Events in Atrial Fibrillation) and apixaban versus aspirin (Apixaban Versus Acetylsalicylic Acid to Prevent Stroke in Atrial Fibrillation Patients Who Have Failed or Are Unsuitable for Vitamin K Antagonist Treatment). Using meta-analysis approaches, we indirectly compared apixaban with an imputed placebo with respect to the risk of death in patients with AF. We used results from meta-analyses of randomized trials as our reference for the comparison between warfarin and placebo/no treatment, and aspirin and placebo/no treatment. RESULTS: In these meta-analyses, a lower rate of death was seen both with warfarin (odds ratio [OR] 0.74, 95% confidence interval [CI] 0.57-0.97) and aspirin (OR 0.86, 95% CI 0.69-1.07) versus placebo/no treatment. Using data from ARISTOTLE and AVERROES, apixaban reduced the risk of death by 34% (95% CI 12-50%; p = 0.004) and 33% (95% CI 6-52%; p = 0.02), respectively, when compared with an imputed placebo. The pooled reduction in all-cause death with apixaban compared with an imputed placebo was 34% (95% CI 18-47%; p = 0.0002). CONCLUSIONS: In patients with AF, indirect comparisons suggest that apixaban reduces all-cause death by approximately one third compared with an imputed placebo.

A Novel Method for Multi-Fault Feature Extraction of a Gearbox under Strong Background Noise.

Strong background noise and complicated interfering signatures when implementing vibration-based monitoring make it difficult to extract the weak diagnostic features due to incipient faults in a multistage gearbox. This can be more challenging when multiple faults coexist. This paper proposes an effective approach to extract multi-fault features of a wind turbine gearbox based on an integration of minimum entropy deconvolution (MED) and multipoint optimal minimum entropy deconvolution adjusted (MOMEDA). By using simulated periodic transient signals with different noise to signal ratios (SNR), it evaluates the outstanding performance of MED in noise suppression and reveals the deficient in extract multiple impulses. On the other hand, MOMEDA can performs better in extracting multiple pulses but not robust to noise influences. To compromise the merits of them, therefore the diagnostic approach is formalized by extracting the multiple weak features with MOMEDA based on the MED denoised signals. Experimental verification based on vibrations from a wind turbine gearbox test bed shows that the approach allows successful identification of multiple faults occurring simultaneously on the shaft and bearing in the high speed transmission stage of the gearbox.

Meta-analysis framework for exact inferences with application to the analysis of rare events.

The usefulness of meta-analysis has been recognized in the evaluation of drug safety, as a single trial usually yields few adverse events and offers limited information. For rare events, conventional meta-analysis methods may yield an invalid inference, as they often rely on large sample theories and require empirical corrections for zero events. These problems motivate research in developing exact methods, including Tian et al.'s method of combining confidence intervals (2009, Biostatistics, 10, 275-281) and Liu et al.'s method of combining p-value functions (2014, JASA, 109, 1450-1465). This article shows that these two exact methods can be unified under the framework of combining confidence distributions (CDs). Furthermore, we show that the CD method generalizes Tian et al.'s method in several aspects. Given that the CD framework also subsumes the Mantel-Haenszel and Peto methods, we conclude that the CD method offers a general framework for meta-analysis of rare events. We illustrate the CD framework using two real data sets collected for the safety analysis of diabetes drugs.

Clinical events after transitioning from apixaban versus warfarin to warfarin at the end of the Apixaban for Reduction in Stroke and Other Thromboembolic Events in Atrial Fibrillation (ARISTOTLE) trial.

BACKGROUND: We sought to assess the occurrence of events after blinded study drug discontinuation and transition to open-label vitamin K antagonist (VKA) in ARISTOTLE. METHODS: At the end of ARISTOTLE, blinded study drug was stopped, and open-label VKA was recommended. For patients completing the trial on blinded study drug, a 2-day bridging period with apixaban or apixaban placebo was recommended (while beginning open-label VKA). Outcomes were assessed during the 30 days after stopping blinded study drug. RESULTS: Of the 6,809 patients in the apixaban group and 6,588 in the warfarin group who completed the trial on study drug, there were 21 strokes or systemic emboli (4.02%/year) and 26 major bleeding (4.97%/year) events in the apixaban group (transitioning to VKA) and 5 strokes or systemic emboli (0.99%/year) and 10 major bleeding (1.97%/year) events in the warfarin group (continuing on VKA), with most of the imbalance between groups being after the first week. Similar results were seen in the first 30 days of the trial where warfarin-naive patients starting warfarin had a higher rate of stroke or systemic emboli (5.41%/year) than warfarin-experienced patients (1.42%/year), a pattern not seen when starting apixaban. No similar increase in events with apixaban versus warfarin was seen during temporary or permanent study drug discontinuation during the trial. CONCLUSIONS: The excess in thrombotic and bleeding events in the apixaban group after study drug discontinuation appears to be related to an increased risk associated with the initiation of a VKA rather than a direct effect of apixaban. Whether ≥2 days of apixaban bridging improves outcomes during VKA transition is unknown and deserves further evaluation.

Rational design of small-sized peptidomimetic inhibitors disrupting protein-protein interaction.

Protein-protein interactions are fundamental to nearly all biological processes. Due to their structural flexibility, peptides have emerged as promising candidates for developing inhibitors targeting large and planar PPI interfaces. However, their limited drug-like properties pose challenges. Hence, rational modifications based on peptide structures are anticipated to expedite the innovation of peptide-based therapeutics. This review comprehensively examines the design strategies for developing small-sized peptidomimetic inhibitors targeting PPI interfaces, which predominantly encompass two primary categories: peptidomimetics with abbreviated sequences and low molecular weights and peptidomimetics mimicking secondary structural conformations. We have also meticulously detailed several instances of designing and optimizing small-sized peptidomimetics targeting PPIs, including MLL1-WDR5, PD-1/PD-L1, and Bak/Bcl-xL, among others, to elucidate the potential application prospects of these design strategies. Hopefully, this review will provide valuable insights and inspiration for the future development of PPI small-sized peptidomimetic inhibitors in pharmaceutical research endeavors.

SNRPB promotes the progression of hepatocellular carcinoma via regulating cell cycle, oxidative stress, and ferroptosis.

Small Nuclear Ribonucleoprotein Polypeptides B and B1 (SNRPB) have been linked to multiple human cancers. However, the mechanism of SNRPB in hepatocellular carcinoma (HCC) and whether SNRPB has a synergistic effect with sorafenib in the treatment of HCC remain unclear. In this study, bioinformatic analysis found that SNRPB was an independent prognostic factor for HCC that exerted a critical effect on the progression of HCC. SNRPB was linked with immune checkpoints, cell cycle, oxidative stress and ferroptosis in HCC. Single cell sequencing analysis found that HCC cell subset with high expression of SNRPB, accounted for a higher proportion in HCC cells with higher stages, had higher expression levels of the genes which promote cell cycle, inhibit oxidative stress and ferroptosis, and had higher cell cycle score, lower oxidative stress score and ferroptosis score. Single-sample gene set enrichment analysis (ssGSEA) analysis found that 17 oxidative stress pathways and 68 oxidative stress-ferroptosis related genes were significantly correlated with SNRPB risk scores. SNRPB knockdown induced cell cycle G2/M arrest and restrained cell proliferation, while downregulated the expression of CDK1, CDK4, and CyclinB1. The combined treatment (SNRPB knockdown+sorafenib) significantly inhibited tumor growth. In addition, the expression of SLC7A11, which is closely-related to ferroptosis, decreased significantly in vitro and in vivo. Therefore, SNRPB may promote HCC progression by regulating immune checkpoints, cell cycle, oxidative stress and ferroptosis, while its downregulation inhibits cell proliferation, which enhances the therapeutic effect of sorafenib, providing a novel basis for the development of HCC therapies.

Computer-aided molecular design and optimization of potent inhibitors disrupting APC‒Asef interaction.

Colorectal cancer (CRC) is the second leading cause of cancer mortality worldwide. At initial diagnosis, approximately 20% of patients are diagnosed with metastatic CRC (mCRC). Although the APC‒Asef interaction is a well-established target for mCRC therapy, the discovery and development of effective and safe drugs for mCRC patients remains an urgent and challenging endeavor. In this study, we identified a novel structural scaffold based on MAI inhibitors, the first-in-class APC‒Asef inhibitors we reported previously. ONIOM model-driven optimizations of the N-terminal cap and experimental evaluations of inhibitory activity were performed, and 24-fold greater potency was obtained with the best inhibitor compared to the parental compound. In addition, the cocrystal structure validated that the two-layer π‒π stacking interactions were essential for inhibitor stabilization in the bound state. Furthermore, in vitro and in vivo studies have demonstrated that novel inhibitors suppressed lung metastasis in CRC by disrupting the APC‒Asef interaction. These results provide an intrinsic structural basis to further explore drug-like molecules for APC‒Asef-mediated CRC therapy.

Hydrogel Loaded with Peptide-Containing Nanocomplexes: Symphonic Cooperation of Photothermal Antimicrobial Nanoparticles and Prohealing Peptides for the Treatment of Infected Wounds.

Current treatment for chronic infectious wounds is limited due to severe drug resistance in certain bacteria. Therefore, the development of new composite hydrogels with nonantibiotic antibacterial and pro-wound repair is important. Here, we present a photothermal antibacterial composite hydrogel fabricated with a coating of Fe2+ cross-linked carboxymethyl chitosan (FeCMCS) following the incorporation of melanin nanoparticles (MNPs) and the CyRL-QN15 peptide. Various physical and photothermal properties of the hydrogel were characterized. Cell proliferation, migration, cycle, and free-radical scavenging activity were assessed, and the antimicrobial properties of the hydrogel were probed by photothermal therapy. The effects of the hydrogel were validated in a model of methicillin-resistant Staphylococcus aureus (MRSA) infection with full-thickness injury. This effect was further confirmed by changes in cytokines associated with inflammation, re-epithelialization, and angiogenesis on the seventh day after wound formation. The MNPs demonstrated robust photothermal conversion capabilities. The composite hydrogel (MNPs/CyRL-QN15/FeCMCS) promoted keratinocyte and fibroblast proliferation and migration while exhibiting high antibacterial efficacy, effectively killing more than 95% of Gram-positive and Gram-negative bacteria. In vivo study using an MRSA-infected full-thickness injury model demonstrated good therapeutic efficacy of the hydrogel in promoting regeneration and remodeling of chronically infected wounds by alleviating inflammatory response and accelerating re-epithelialization and collagen deposition. The MNPs/CyRL-QN15/FeCMCS hydrogel showed excellent antibacterial and prohealing effects on infected wounds, indicating potential as a promising candidate for wound healing promotion.

Newly identified peptide Nigrocin-OA27 inhibits UVB induced melanin production via the MITF/TYR pathway.

Melasma is a common skin disease induced by an increase in the content of melanin in the skin, which also causes serious physical and mental harm to patients. In this research, a novel peptide (Nigrocin-OA27) from Odorrana andersonii is shown to exert a whitening effect on C57 mice pigmentation model. The peptide also demonstrated non-toxic and antioxidant capacity, and can significantly reduce melanin content in B16 cells. Topical application effectively delivered Nigrocin-OA27 to skin's epidermal and dermal layers and exhibited significant preventive and whitening effects on the UVB-induced ear pigmentation model in C57 mice. The whitening mechanism of Nigrocin-OA27 may be related to reduced levels of the microphthalmia-associated transcription factor and the key enzyme for melanogenesis-tyrosinase (TYR). Nigrocin-OA27 also inhibited the catalytic activity by adhering to the active core of TYR, thereby reducing melanin formation and deposition. In conclusion, Nigrocin-OA27 may be a potentially effective external agent to treat melasma by inhibiting aberrant skin melanin synthesis.