Inferring gene regulatory networks from single-cell gene expression data via deep multi-view contrastive learning.

The inference of gene regulatory networks (GRNs) is of great importance for understanding the complex regulatory mechanisms within cells. The emergence of single-cell RNA-sequencing (scRNA-seq) technologies enables the measure of gene expression levels for individual cells, which promotes the reconstruction of GRNs at single-cell resolution. However, existing network inference methods are mainly designed for data collected from a single data source, which ignores the information provided by multiple related data sources. In this paper, we propose a multi-view contrastive learning (DeepMCL) model to infer GRNs from scRNA-seq data collected from multiple data sources or time points. We first represent each gene pair as a set of histogram images, and then introduce a deep Siamese convolutional neural network with contrastive loss to learn the low-dimensional embedding for each gene pair. Moreover, an attention mechanism is introduced to integrate the embeddings extracted from different data sources and different neighbor gene pairs. Experimental results on synthetic and real-world datasets validate the effectiveness of our contrastive learning and attention mechanisms, demonstrating the effectiveness of our model in integrating multiple data sources for GRN inference.

Clustering single-cell multi-omics data via graph regularized multi-view ensemble learning.

MOTIVATION: Single-cell clustering plays a crucial role in distinguishing between cell types, facilitating the analysis of cell heterogeneity mechanisms. While many existing clustering methods rely solely on gene expression data obtained from single-cell RNA sequencing techniques to identify cell clusters, the information contained in mono-omic data is often limited, leading to suboptimal clustering performance. The emergence of single-cell multi-omics sequencing technologies enables the integration of multiple omics data for identifying cell clusters, but how to integrate different omics data effectively remains challenging. In addition, designing a clustering method that performs well across various types of multi-omics data poses a persistent challenge due to the data's inherent characteristics. RESULTS: In this paper, we propose a graph-regularized multi-view ensemble clustering (GRMEC-SC) model for single-cell clustering. Our proposed approach can adaptively integrate multiple omics data and leverage insights from multiple base clustering results. We extensively evaluate our method on five multi-omics datasets through a series of rigorous experiments. The results of these experiments demonstrate that our GRMEC-SC model achieves competitive performance across diverse multi-omics datasets with varying characteristics. AVAILABILITY AND IMPLEMENTATION: Implementation of GRMEC-SC, along with examples, can be found on the GitHub repository: https://github.com/polarisChen/GRMEC-SC.

MARS: a motif-based autoregressive model for retrosynthesis prediction.

MOTIVATION: Retrosynthesis is a critical task in drug discovery, aimed at finding a viable pathway for synthesizing a given target molecule. Many existing approaches frame this task as a graph-generating problem. Specifically, these methods first identify the reaction center, and break a targeted molecule accordingly to generate the synthons. Reactants are generated by either adding atoms sequentially to synthon graphs or by directly adding appropriate leaving groups. However, both of these strategies have limitations. Adding atoms results in a long prediction sequence that increases the complexity of generation, while adding leaving groups only considers those in the training set, which leads to poor generalization. RESULTS: In this paper, we propose a novel end-to-end graph generation model for retrosynthesis prediction, which sequentially identifies the reaction center, generates the synthons, and adds motifs to the synthons to generate reactants. Given that chemically meaningful motifs fall between the size of atoms and leaving groups, our model achieves lower prediction complexity than adding atoms and demonstrates superior performance than adding leaving groups. We evaluate our proposed model on a benchmark dataset and show that it significantly outperforms previous state-of-the-art models. Furthermore, we conduct ablation studies to investigate the contribution of each component of our proposed model to the overall performance on benchmark datasets. Experiment results demonstrate the effectiveness of our model in predicting retrosynthesis pathways and suggest its potential as a valuable tool in drug discovery. AVAILABILITY AND IMPLEMENTATION: All code and data are available at https://github.com/szu-ljh2020/MARS.

Colloidal Synthesis of Cu3N Nanorods and Construction of Cu3N-Cu2O Heteronanostructures via Epitaxial Growth.

The synthesis of transition metal nitrides nanocrystals (TMNs NCs) has posed a significant challenge due to the limited reactivity of nitrogen sources at lower temperatures and the scarcity of available synthesis methods. In this study, we present a novel colloidal synthesis strategy for the fabrication of Cu3N nanorods (NRs). It is found that the trace oxygen (O2) plays an important role in the synthesis process. And a new mechanism for the formation of Cu3N is proposed. Subsequently, by employing secondary lateral epitaxial growth, the Cu3N-Cu2O heteronanostructures (HNs) can be prepared. The Cu3N NRs and Cu3N-Cu2O HNs were evaluated as precursor electrocatalysts for the CO2 reduction reaction (CO2RR). The Cu3N-Cu2O HNs demonstrate remarkable selectivity and stability with ethylene (C2H4) Faradaic efficiency (FE) up to 55.3%, surpassing that of Cu3N NRs. This study provides innovative insights into the reaction mechanism of colloidal synthesis of TMNs NCs and presents alternative options for designing cost-effective electrocatalysts to achieve carbon neutrality.

60Co-γ Irradiation-Induced Shift and Polarity Reversal in the Triboelectric Series of Poly(ether sulfone)s.

The sensitivity of triboelectric nanogenerators (TENGs) to the surface charge density highlights the significance of triboelectric materials and their modifications. Efforts have been directed toward developing effective strategies for increasing the surface charge density, expanding the potential applications of TENGs. This study proposes the use of irradiation technology for grafting to modify the electron-donating capability of poly(ether sulfone) (PES), thereby affording a dual benefit of enhancing the surface charge density and inducing a shift in the position of PES from negative to positive within the triboelectric series. The TENG based on grafted PES has resulted in a significant 3-fold increase in surface charge density compared to that of pristine PES, reaching 263 µC m-2. The surface charge density can be further increased to 502 µC m-2 through charge pumping. Notably, irradiation technology presents advantages over chemical grafting methods, particularly in terms of sustainability and environmental friendliness. This innovative approach shows great potential in advancing the domain of TENGs.

DEMOC: a deep embedded multi-omics learning approach for clustering single-cell CITE-seq data.

Advances in single-cell RNA sequencing (scRNA-seq) technologies has provided an unprecedent opportunity for cell-type identification. As clustering is an effective strategy towards cell-type identification, various computational approaches have been proposed for clustering scRNA-seq data. Recently, with the emergence of cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq), the cell surface expression of specific proteins and the RNA expression on the same cell can be captured, which provides more comprehensive information for cell analysis. However, existing single cell clustering algorithms are mainly designed for single-omic data, and have difficulties in handling multi-omics data with diverse characteristics efficiently. In this study, we propose a novel deep embedded multi-omics clustering with collaborative training (DEMOC) model to perform joint clustering on CITE-seq data. Our model can take into account the characteristics of transcriptomic and proteomic data, and make use of the consistent and complementary information provided by different data sources effectively. Experiment results on two real CITE-seq datasets demonstrate that our DEMOC model not only outperforms state-of-the-art single-omic clustering methods, but also achieves better and more stable performance than existing multi-omics clustering methods. We also apply our model on three scRNA-seq datasets to assess the performance of our model in rare cell-type identification, novel cell-subtype detection and cellular heterogeneity analysis. Experiment results illustrate the effectiveness of our model in discovering the underlying patterns of data.

Matrix factorization for biomedical link prediction and scRNA-seq data imputation: an empirical survey.

Advances in high-throughput experimental technologies promote the accumulation of vast number of biomedical data. Biomedical link prediction and single-cell RNA-sequencing (scRNA-seq) data imputation are two essential tasks in biomedical data analyses, which can facilitate various downstream studies and gain insights into the mechanisms of complex diseases. Both tasks can be transformed into matrix completion problems. For a variety of matrix completion tasks, matrix factorization has shown promising performance. However, the sparseness and high dimensionality of biomedical networks and scRNA-seq data have raised new challenges. To resolve these issues, various matrix factorization methods have emerged recently. In this paper, we present a comprehensive review on such matrix factorization methods and their usage in biomedical link prediction and scRNA-seq data imputation. Moreover, we select representative matrix factorization methods and conduct a systematic empirical comparison on 15 real data sets to evaluate their performance under different scenarios. By summarizing the experimental results, we provide general guidelines for selecting matrix factorization methods for different biomedical matrix completion tasks and point out some future directions to further improve the performance for biomedical link prediction and scRNA-seq data imputation.

scDEA: differential expression analysis in single-cell RNA-sequencing data via ensemble learning.

The identification of differentially expressed genes between different cell groups is a crucial step in analyzing single-cell RNA-sequencing (scRNA-seq) data. Even though various differential expression analysis methods for scRNA-seq data have been proposed based on different model assumptions and strategies recently, the differentially expressed genes identified by them are quite different from each other, and the performances of them depend on the underlying data structures. In this paper, we propose a new ensemble learning-based differential expression analysis method, scDEA, to produce a more stable and accurate result. scDEA integrates the P-values obtained from 12 individual differential expression analysis methods for each gene using a P-value combination method. Comprehensive experiments show that scDEA outperforms the state-of-the-art individual methods with different experimental settings and evaluation metrics. We expect that scDEA will serve a wide range of users, including biologists, bioinformaticians and data scientists, who need to detect differentially expressed genes in scRNA-seq data.

CD276 enhances sunitinib resistance in clear cell renal cell carcinoma by promoting DNA damage repair and activation of FAK-MAPK signaling pathway.

OBJECTIVE: This study aimed to explore the effect of CD276 expression on the sunitinib sensitivity of clear cell renal cell carcinoma (ccRCC) cell and animal models and the potential mechanisms involved. METHODS: CD276 expression levels of ccRCC and normal samples were analyzed via online databases and real-time quantitative PCR (RT-qPCR). CD276 was knocked down in ccRCC cell models (sunitinib-resistant 786-O/R cells and sunitinib-sensitive 786-O cells) using shRNA transfection, and the cells were exposed to a sunitinib (2 µM) environment. Cells proliferation was then analyzed using MTT assay and colony formation experiment. Alkaline comet assay, immunofluorescent staining, and western blot experiments were conducted to assess the DNA damage repair ability of the cells. Western blot was also used to observe the activation of FAK-MAPK pathway within the cells. Finally, a nude mouse xenograft model was established and the nude mice were orally administered sunitinib (40 mg/kg/d) to evaluate the in vivo effects of CD276 knockdown on the therapeutic efficacy of sunitinib against ccRCC. RESULTS: CD276 was significantly upregulated in both ccRCC clinical tissue samples and cell models. In vitro experiments showed that knocking down CD276 reduced the survival rate, IC50 value, and colony-forming ability of ccRCC cells. Knocking down CD276 increased the comet tail moment (TM) values and γH2AX foci number, and reduced BRCA1 and RAD51 protein levels. Knocking down CD276 also decreased the levels of p-FAK, p-MEK, and p-ERK proteins. CONCLUSION: Knocking down CD276 effectively improved the sensitivity of ccRCC cell and animal models to sunitinib treatment.

Mechanisms and clinical landscape of N6-methyladenosine (m6A) RNA modification in gastrointestinal tract cancers.

Epigenetics encompasses reversible and heritable chemical modifications of non-nuclear DNA sequences, including DNA and RNA methylation, histone modifications, non-coding RNA modifications, and chromatin rearrangements. In addition to well-studied DNA and histone methylation, RNA methylation has emerged as a hot topic in biological sciences over the past decade. N6-methyladenosine (m6A) is the most common and abundant modification in eukaryotic mRNA, affecting all RNA stages, including transcription, translation, and degradation. Advances in high-throughput sequencing technologies made it feasible to identify the chemical basis and biological functions of m6A RNA. Dysregulation of m6A levels and associated modifying proteins can both inhibit and promote cancer, highlighting the importance of the tumor microenvironment in diverse biological processes. Gastrointestinal tract cancers, including gastric, colorectal, and pancreatic cancers, are among the most common and deadly malignancies in humans. Growing evidence suggests a close association between m6A levels and the progression of gastrointestinal tumors. Global m6A modification levels are substantially modified in gastrointestinal tumor tissues and cell lines compared to healthy tissues and cells, possibly influencing various biological behaviors such as tumor cell proliferation, invasion, metastasis, and drug resistance. Exploring the diagnostic and therapeutic potential of m6A-related proteins is critical from a clinical standpoint. Developing more specific and effective m6A modulators offers new options for treating these tumors and deeper insights into gastrointestinal tract cancers.

The future of blood services amid a tight balance between the supply and demand of blood products: Perspectives from the ISBT Young Professional Council.

BACKGROUND AND OBJECTIVES: Blood services manage the increasingly tight balance between the supply and demand of blood products, and their role in health research is expanding. This review explores the themes that may define the future of blood banking. MATERIALS AND METHODS: We reviewed the PubMed database for articles on emerging/new blood-derived products and the utilization of blood donors in health research. RESULTS: In high-income countries (HICs), blood services may consider offering these products: whole blood, cold-stored platelets, synthetic blood components, convalescent plasma, lyophilized plasma and cryopreserved/lyophilized platelets. Many low- and middle-income countries (LMICs) aim to establish a pool of volunteer, non-remunerated blood donors and wean themselves off family replacement donors; and many HICs are relaxing the deferral criteria targeting racial and sexual minorities. Blood services in HICs could achieve plasma self-sufficiency by building plasma-dedicated centres, in collaboration with the private sector. Lastly, blood services should expand their involvement in health research by establishing donor cohorts, conducting serosurveys, studying non-infectious diseases and participating in clinical trials. CONCLUSION: This article provides a vision of the future for blood services. The introduction of some of these changes will be slower in LMICs, where addressing key operational challenges will likely be prioritized.

Blocking reverse electron transfer-mediated mitochondrial DNA oxidation rescues cells from PANoptosis.

PANoptosis is a new type of cell death featured with pyroptosis, apoptosis and necroptosis, and is implicated in organ injury and mortality in various inflammatory diseases, such as sepsis and hemophagocytic lymphohistiocytosis (HLH). Reverse electron transport (RET)-mediated mitochondrial reactive oxygen species (mtROS) has been shown to contribute to pyroptosis and necroptosis. In this study we investigated the roles of mtROS and RET in PANoptosis induced by TGF-ß-activated kinase 1 (TAK1) inhibitor 5Z-7-oxozeaenol (Oxo) plus lipopolysaccharide (LPS) as well as the effects of anti-RET reagents on PANoptosis. We showed that pretreatment with anti-RET reagents 1-methoxy PMS (MPMS) or dimethyl fumarate (DMF) dose-dependently inhibited PANoptosis in macrophages BMDMs and J774A.1 cells induced by Oxo/LPS treatment assayed by propidium iodide (PI) staining. The three arms of the PANoptosis signaling pathway, namely pyroptosis, apoptosis and necroptosis signaling, as well as the formation of PANoptosomes were all inhibited by MPMS or DMF. We demonstrated that Oxo/LPS treatment induced RET and mtROS in BMDMs, which were reversed by MPMS or DMF pretreatment. Interestingly, the PANoptosome was co-located with mitochondria, in which the mitochondrial DNA was oxidized. MPMS and DMF fully blocked the mtROS production and the formation of PANoptosome induced by Oxo plus LPS treatment. An HLH mouse model was established by poly(I:C)/LPS challenge. Pretreatment with DMF (50 mg·kg-1·d-1, i.g. for 3 days) or MPMS (10 mg·kg-1·d-1, i.p. for 2 days) (DMF i.g. MPMS i.p.) effectively alleviated HLH lesions accompanied by decreased hallmarks of PANoptosis in the liver and kidney. Collectively, RET and mtDNA play crucial roles in PANoptosis induction and anti-RET reagents represent a novel class of PANoptosis inhibitors by blocking oxidation of mtDNA, highlighting their potential application in treating PANoptosis-related inflammatory diseases. PANoptotic stimulation induces reverse electron transport (RET) and reactive oxygen species (ROS) in mitochondia, while 1-methoxy PMS and dimethyl fumarate can inhibit PANoptosis by suppressing RETmediated oxidation of mitochondrial DNA.

Novel STAT3 oligonucleotide compounds suppress tumor growth and overcome the acquired resistance to sorafenib in hepatocellular carcinoma.

Signal transducer and activator of transcription 3 (STAT3) plays an important role in the occurrence and progression of tumors, leading to resistance and poor prognosis. Activation of STAT3 signaling is frequently detected in hepatocellular carcinoma (HCC), but potent and less toxic STAT3 inhibitors have not been discovered. Here, based on antisense technology, we designed a series of stabilized modified antisense oligonucleotides targeting STAT3 mRNA (STAT3 ASOs). Treatment with STAT3 ASOs decreased the STAT3 mRNA and protein levels in HCC cells. STAT3 ASOs significantly inhibited the proliferation, survival, migration, and invasion of cancer cells by specifically perturbing STAT3 signaling. Treatment with STAT3 ASOs decreased the tumor burden in an HCC xenograft model. Moreover, aberrant STAT3 signaling activation is one of multiple signaling pathways involved in sorafenib resistance in HCC. STAT3 ASOs effectively sensitized resistant HCC cell lines to sorafenib in vitro and improved the inhibitory potency of sorafenib in a resistant HCC xenograft model. The developed STAT3 ASOs enrich the tools capable of targeting STAT3 and modulating STAT3 activity, serve as a promising strategy for treating HCC and other STAT3-addicted tumors, and alleviate the acquired resistance to sorafenib in HCC patients. A series of novel STAT3 antisense oligonucleotide were designed and showed potent anti-cancer efficacy in hepatocellular carcinoma in vitro and in vivo by targeting STAT3 signaling. Moreover, the selected STAT3 ASOs enhance sorafenib sensitivity in resistant cell model and xenograft model.

Acetylcytidine modification of Amotl1 by N-acetyltransferase 10 contributes to cardiac fibrotic expansion in mice after myocardial infarction.

Cardiac fibrosis is a pathological scarring process that impairs cardiac function. N-acetyltransferase 10 (Nat10) is recently identified as the key enzyme for the N4-acetylcytidine (ac4C) modification of mRNAs. In this study, we investigated the role of Nat10 in cardiac fibrosis following myocardial infarction (MI) and the related mechanisms. MI was induced in mice by ligation of the left anterior descending coronary artery; cardiac function was assessed with echocardiography. We showed that both the mRNA and protein expression levels of Nat10 were significantly increased in the infarct zone and border zone 4 weeks post-MI, and the expression of Nat10 in cardiac fibroblasts was significantly higher compared with that in cardiomyocytes after MI. Fibroblast-specific overexpression of Nat10 promoted collagen deposition and induced cardiac systolic dysfunction post-MI in mice. Conversely, fibroblast-specific knockout of Nat10 markedly relieved cardiac function impairment and extracellular matrix remodeling following MI. We then conducted ac4C-RNA binding protein immunoprecipitation-sequencing (RIP-seq) in cardiac fibroblasts transfected with Nat10 siRNA, and revealed that angiomotin-like 1 (Amotl1), an upstream regulator of the Hippo signaling pathway, was the target gene of Nat10. We demonstrated that Nat10-mediated ac4C modification of Amotl1 increased its mRNA stability and translation in neonatal cardiac fibroblasts, thereby increasing the interaction of Amotl1 with yes-associated protein 1 (Yap) and facilitating Yap translocation into the nucleus. Intriguingly, silencing of Amotl1 or Yap, as well as treatment with verteporfin, a selective and potent Yap inhibitor, attenuated the Nat10 overexpression-induced proliferation of cardiac fibroblasts and prevented their differentiation into myofibroblasts in vitro. In conclusion, this study highlights Nat10 as a crucial regulator of myocardial fibrosis following MI injury through ac4C modification of upstream activators within the Hippo/Yap signaling pathway.

Association of sleep quality on the night of operative day with postoperative delirium in elderly patients: A prospective cohort study.

BACKGROUND: Sleep disturbances in the peri-operative period have been associated with adverse outcomes, including postoperative delirium (POD). However, research on sleep quality during the immediate postoperative period is limited. OBJECTIVES: This study aimed to investigate the association between sleep quality on the night of the operative day assessed using the Sleep Quality Numeric Rating Scale (SQ-NRS), and the incidence of POD in a large cohort of surgical patients. DESIGN: A prospective cohort study. SETTING: A tertiary hospital in China. PATIENTS: This study enrolled patients aged 65 years or older undergoing elective surgery under general anaesthesia. The participants were categorised into the sleep disturbance and no sleep disturbance groups according to their operative night SQ-NRS. MAIN OUTCOME MEASURES: The primary outcome was delirium incidence, whereas the secondary outcomes included acute kidney injury, stroke, pulmonary infection, cardiovascular complications and all-cause mortality within 1 year postoperatively. RESULTS: In total, 3072 patients were included in the analysis of this study. Among them, 791 (25.72%) experienced sleep disturbances on the night of operative day. Patients in the sleep disturbance group had a significantly higher risk of developing POD (adjusted OR 1.43, 95% CI 1.11 to 1.82, P  = 0.005). Subgroup analysis revealed that age 65-75 years; male sex; ASA III and IV; haemoglobin more than 12 g l -1 ; intra-operative hypotension; surgical duration more than 120 min; and education 9 years or less were significantly associated with POD. No interaction was observed between the subgroups. No significant differences were observed in the secondary outcomes, such as acute kidney injury, stroke, pulmonary infection, cardiovascular complications and all-cause mortality within 1 year postoperatively. CONCLUSIONS: The poor subjective sleep quality on the night of operative day was independently associated with increased POD risk, especially in certain subpopulations. Optimising peri-operative sleep may reduce POD. Further research should investigate potential mechanisms and causal relationships. TRIAL REGISTRY: chictr.org.cn: ChiCTR1900028545.

Treatment of Spinal Infections.

The treatment of spinal infections is not well defined, and a cursory review of the literature can lead to conflicting treatment strategies. To add to the complexity, infections can include primary infection of the spine, infection secondary to another primary source, and postoperative infections including epidural abscesses, discitis, osteomyelitis, paraspinal soft-tissue infections, or any combination. Furthermore, differing opinions often exist within the medical and surgical communities regarding the outcomes and effectiveness of varying treatment strategies. Given the paucity of defined treatment protocols and long-term follow-up, it is important to develop multidisciplinary treatment teams and treatment strategies. This, along with defined protocols for the treatment of varying infections, can provide the data needed for improved treatment of spinal infections.

A Novel Three-Point Localization Method for Bladder Volume Estimation.

The measurement of bladder volume is crucial for the diagnosis and treatment of urinary system diseases. Ultrasound imaging, with its non-invasive, radiation-free, and repeatable scanning capabilities, has become the preferred method for measuring residual urine volume. Nevertheless, it still faces some challenges, including complex imaging methods leading to longer measurement times and lower spatial resolution. Here, we propose a novel three-point localization method that does not require ultrasound imaging to calculate bladder volume. A corresponding triple-element ultrasound probe has been designed based on this method, enabling the ultrasound probe to transmit and receive ultrasound waves in three directions. Furthermore, we utilize the Hilbert Transform algorithm to extract the envelope of the ultrasound signal to enhance the efficiency of bladder volume measurements. The experiment indicates that bladder volume estimation can be completed within 5 s, with a relative error rate of less than 15%. These results demonstrate that this novel three-point localization method offers an effective approach for bladder volume measurement in patients with urological conditions.

Production of Bioactive Porcine Lactoferrin through a Novel Glucose-Inducible Expression System in Pichia pastoris: Unveiling Antimicrobial and Anticancer Functionalities.

Lactoferrin (LF) stands as one of the extensively investigated iron-binding glycoproteins within milk, exhibiting diverse biological functionalities. The global demand for LF has experienced consistent growth. Biotechnological strategies aimed at enhancing LF productivity through microbial expression systems offer substantial cost-effective advantages and exhibit fewer constraints compared to traditional animal bioreactor technologies. This study devised a novel recombinant plasmid, wherein the AOX1 promoter was replaced with a glucose-inducible G1 promoter (PG1) to govern the expression of recombinant porcine LF (rpLF) in Pichia pastoris GS115. High-copy-number PG1-rpLF yeast clones were meticulously selected, and subsequent induction with 0.05 g/L glucose demonstrated robust secretion of rpLF. Scaling up production transpired in a 5 L fermenter, yielding an estimated rpLF productivity of approximately 2.8 g/L by the conclusion of glycerol-fed fermentation. A three-step purification process involving tangential-flow ultrafiltration yielded approximately 6.55 g of rpLF crude (approximately 85% purity). Notably, exceptional purity of rpLF was achieved through sequential heparin and size-exclusion column purification. Comparatively, the present glucose-inducible system outperformed our previous methanol-induced system, which yielded a level of 87 mg/L of extracellular rpLF secretion. Furthermore, yeast-produced rpLF demonstrated affinity for ferric ions (Fe3+) and exhibited growth inhibition against various pathogenic microbes (E. coli, S. aureus, and C. albicans) and human cancer cells (A549, MDA-MB-231, and Hep3B), similar to commercial bovine LF (bLF). Intriguingly, the hydrolysate of rpLF (rpLFH) manifested heightened antimicrobial and anticancer effects compared to its intact form. In conclusion, this study presents an efficient glucose-inducible yeast expression system for large-scale production and purification of active rpLF protein with the potential for veterinary or medical applications.

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In the clinical diagnosis and treatment of children with short stature, mental health issues merit special attention. It is widely acknowledged that the psychological well-being of children with short stature is lower than that of their peers with normal height. Therefore, during the diagnosis, treatment, and care of short stature, it is crucial to actively monitor the mental health of these children, promptly identify potential psychological and behavioral issues, and intervene accordingly. Such measures play a positive role in enhancing the quality of life of these children and improving their physical and mental health. This article analyses and discusses the current state of psychological assessment and psycho-behavioral interventions for children with short stature, aiming to provide insights for improving their mental health.

WDNE: an integrative graphical model for inferring differential networks from multi-platform gene expression data with missing values.

The mechanisms controlling biological process, such as the development of disease or cell differentiation, can be investigated by examining changes in the networks of gene dependencies between states in the process. High-throughput experimental methods, like microarray and RNA sequencing, have been widely used to gather gene expression data, which paves the way to infer gene dependencies based on computational methods. However, most differential network analysis methods are designed to deal with fully observed data, but missing values, such as the dropout events in single-cell RNA-sequencing data, are frequent. New methods are needed to take account of these missing values. Moreover, since the changes of gene dependencies may be driven by certain perturbed genes, considering the changes in gene expression levels may promote the identification of gene network rewiring. In this study, a novel weighted differential network estimation (WDNE) model is proposed to handle multi-platform gene expression data with missing values and take account of changes in gene expression levels. Simulation studies demonstrate that WDNE outperforms state-of-the-art differential network estimation methods. When applied WDNE to infer differential gene networks associated with drug resistance in ovarian tumors, cell differentiation and breast tumor heterogeneity, the hub genes in the estimated differential gene networks can provide important insights into the underlying mechanisms. Furthermore, a Matlab toolbox, differential network analysis toolbox, was developed to implement the WDNE model and visualize the estimated differential networks.