ERStruct: a fast Python package for inferring the number of top principal components from whole genome sequencing data.

BACKGROUND: Large-scale multi-ethnic DNA sequencing data is increasingly available owing to decreasing cost of modern sequencing technologies. Inference of the population structure with such sequencing data is fundamentally important. However, the ultra-dimensionality and complicated linkage disequilibrium patterns across the whole genome make it challenging to infer population structure using traditional principal component analysis based methods and software. RESULTS: We present the ERStruct Python Package, which enables the inference of population structure using whole-genome sequencing data. By leveraging parallel computing and GPU acceleration, our package achieves significant improvements in the speed of matrix operations for large-scale data. Additionally, our package features adaptive data splitting capabilities to facilitate computation on GPUs with limited memory. CONCLUSION: Our Python package ERStruct is an efficient and user-friendly tool for estimating the number of top informative principal components that capture population structure from whole genome sequencing data.

DeepDetect: Deep Learning of Peptide Detectability Enhanced by Peptide Digestibility and Its Application to DIA Library Reduction.

In tandem mass spectrometry-based proteomics, proteins are digested into peptides by specific protease(s), but generally only a fraction of peptides can be detected. To characterize detectable proteotypic peptides, we have developed a series of methods to predict peptide digestibility and detectability. Here, we propose a bidirectional long short-term memory (BiLSTM)-based algorithm, named DeepDetect, for the prediction of peptide detectability enhanced by peptide digestibility. Compared with existing algorithms, DeepDetect is featured by its improved prediction accuracy for a wide range of commonly used proteases, covering trypsin, ArgC, chymotrypsin, GluC, LysC, AspN, LysN, and LysargiNase. On 11 test data sets from E. coli, yeast, mouse, and human samples, DeepDetect achieved higher prediction accuracies than PepFormer, a state-of-the-art deep-learning-based peptide detectability prediction algorithm. The results further demonstrated that peptide digestibility can substantially enhance the performance of peptide detectability predictors. As an application, DeepDetect was used to reduce the in silico predicted spectral libraries in data-independent acquisition mass spectrometry data analysis. Experiments using DIA-NN software showed that DeepDetect can significantly accelerate the library search without loss of peptide and protein identification sensitivity.

Notch signaling pathway: a new target for neuropathic pain therapy.

The Notch gene, a highly evolutionarily conserved gene, was discovered approximately 110 years ago and has been found to play a crucial role in the development of multicellular organisms. Notch receptors and their ligands are single-pass transmembrane proteins that typically require cellular interactions and proteolytic processing to facilitate signal transduction. Recently, mounting evidence has shown that aberrant activation of the Notch is correlated with neuropathic pain. The activation of the Notch signaling pathway can cause the activation of neuroglia and the release of pro-inflammatory factors, a key mechanism in the development of neuropathic pain. Moreover, the Notch signaling pathway may contribute to the persistence of neuropathic pain by enhancing synaptic transmission and calcium inward flow. This paper reviews the structure and activation of the Notch signaling pathway, as well as its potential mechanisms of action, to provide novel insights for future treatments of neuropathic pain.

Mesenchymal Stem Cells-derived Exosomes Ameliorate Lupus by Inducing M2 Macrophage Polarization and Regulatory T Cell Expansion in MRL/lpr Mice.

Previous studies have implicated that the transplantation of human umbilical cord mesenchymal stem cells (hUC-MSCs) effectively alleviates systemic lupus erythematosus (SLE) primarily due to immunomodulatory effects. However, little is known about the role of hUC-MSC-derived exosomes in SLE. This study is carried out to investigate the modifying effects of hUC-MSC-exosomes on the differentiation and function of immune cells in SLE. hUC-MSC-derived exosomes were extracted from the cultural supernatant of hUC-MSCs by ultrahigh speed centrifugation. Quantitative real-time polymerase chain reaction, western blot, enzyme-linked immunosorbent assay, and flow cytometry were performed to estimate the effect of hUC-MSC-derived exosomes on macrophage and regulatory T cell (Treg) polarization. In vivo, hUC-MSC-exosomes were injected intravenously into 28-week-old MRL/lpr mice. We had found that exosomes derived from hUC-MSC restrained the proliferation and inflammation of macrophages in vitro. Besides, MSC-exosomes inhibited CD68+M1 and HLA-DR+M1 but promoted CD206+M2 and CD163+M2 in vitro. Moreover, MRL/lpr mice administrated by intravenous injection of MSC-exosomes had less infiltration of CD14+CD11c+M1 cells but more CD14+CD163+M2 cells as well as Tregs in spleens compared with those in MRL/lpr mice treated by PBS. Additionally, MSC-exosomes could alleviate nephritis, liver and lung injuries of MRL/lpr mice. The survival of lupus mice could be improved after MSC-exosome treatment. This study has suggested that MSC-derived exosomes exert anti-inflammatory and immunomodulatory effects in SLE. MSC-exosomes ameliorate nephritis and other key organ injuries by inducing M2 macrophages and Tregs polarization. As natural nanocarriers, MSC-exosomes may serve as a promising cell-free therapeutic strategy for SLE.Abbreviations: SLE: Systemic lupus erythematosus; hUC-MSCs: Human umbilical cord mesenchymal stem cells; MSCs: Mesenchymal stem cells; qRT-PCR: Quantitative real-time polymerase chain reaction; ELISA: Enzyme-linked immunosorbent assay; Tregs: Regulatory cells; TNF-α: Tumor necrosis factor alfa; IL: Interleukin; COVID-19: Coronavirus disease 2019; pTHP-1: PMA-induced THP-1 macrophages; TEM: Transmission electron microscopy; LPS: Lipopolysaccharide; EVs: Extracellular vesicles; TRAF1: Tumor necrosis factor receptor-associated factor 1; IRAK1: Interferon-α-interleukin-1 receptor-associated kinase 1; NF-κB: Nuclear factor-κB; BLyS: B lymphocyte stimulator; APRIL: A proliferation-inducing ligand.

DeepDigest: Prediction of Protein Proteolytic Digestion with Deep Learning.

Proteolytic digestion of proteins by one or more proteases is a key step in shotgun proteomics, in which the proteolytic products, i.e., peptides, are taken as the surrogates of their parent proteins for further qualitative or quantitative analysis. The proteases generally cleave proteins at specific amino acid residue sites, but digestion is hardly complete (wide existence of missed cleavage sites). Therefore, it would be of great help to improve the prior experimental design and the posterior data analysis if the digestion behaviors of proteases can be accurately modeled and predicted. At present, systematic studies about the commonly used proteases in proteomics are insufficient, and there is a lack of easy-to-use tools to predict the cleavage sites of different proteases. Here, we propose a novel sequence-based deep learning algorithm-DeepDigest, which integrates convolutional neural networks and long short-term memory networks for protein digestion prediction. DeepDigest can predict the cleavage probability of each potential cleavage site on the protein sequences for eight popular proteases including trypsin, ArgC, chymotrypsin, GluC, LysC, AspN, LysN, and LysargiNase. We compared DeepDigest with three traditional machine learning algorithms, i.e., logistic regression, random forest, and support vector machine. On the eight training data sets, the 10-fold cross-validation accuracies (AUCs) of DeepDigest were 0.956-0.982, significantly higher than those of the three traditional algorithms. On the 11 independent test data sets, DeepDigest achieved AUCs between 0.849 and 0.978, outperforming the other traditional algorithms in most cases. Transfer learning then further improved the prediction accuracy. Besides, some interesting characteristics of different proteases were revealed and discussed. Ultimately, as an application, we used DeepDigest to predict the digestibilities of peptides and demonstrated that peptide digestibility is an informative new feature to discriminate between correct and incorrect peptide identifications.

Fabrication and Characterization of Prochloraz Nanoemulsion against Penicillium citrinum for the Postharvest Storage of Navel Oranges.

Nanoemulsions have become extremely popular water-insoluble pesticide delivery systems in recent years. In this study, prochloraz nanoemulsions were obtained by selecting the mixing ratio of surfactants (6:1, 3:1, 2:1, 1:1, 1:2, 1:3, and 1:6), surfactant concentration, and shearing time. The optimal formula was 10 wt % prochloraz, 6 wt % surfactant (2 wt % CO-100 + 4 wt % CO-360) dissolved in 6 wt % hydrocarbon solvent (S-100A), and deionized water replenished to 100 wt %. This formula meets the quality index standards of the Food and Agriculture Organization. Compared with oil-in-water emulsion (EW), the prochloraz nanoemulsion exhibited higher antifungal activity against Penicillium citrinum in vitro (lower LC50 of 1.17 mg L-1) and in vivo (fewer lesions). In addition, the L02 cells treated with the nanoemulsion had a higher survival rate and lower apoptosis rate at the same concentration. Results showed that the toxicity of the prochloraz nanoemulsion on L02 cells was lower than that of EW. The findings provide an important method for developing an efficient, safe, and environment-friendly nanoemulsion for postharvest fruit storage.

Gut Microbiota and Type 2 Diabetes Mellitus: Association, Mechanism, and Translational Applications.

Gut microbiota has attracted widespread attention due to its crucial role in disease pathophysiology, including type 2 diabetes mellitus (T2DM). Metabolites and bacterial components of gut microbiota affect the initiation and progression of T2DM by regulating inflammation, immunity, and metabolism. Short-chain fatty acids, secondary bile acid, imidazole propionate, branched-chain amino acids, and lipopolysaccharide are the main molecules related to T2DM. Many studies have investigated the role of gut microbiota in T2DM, particularly those butyrate-producing bacteria. Increasing evidence has demonstrated that fecal microbiota transplantation and probiotic capsules are useful strategies in preventing diabetes. In this review, we aim to elucidate the complex association between gut microbiota and T2DM inflammation, metabolism, and immune disorders, the underlying mechanisms, and translational applications of gut microbiota. This review will provide novel insight into developing individualized therapy for T2DM patients based on gut microbiota immunometabolism.

Genome-Wide Analysis of LncRNA in Bovine Mammary Epithelial Cell Injuries Induced by Escherichia Coli and Staphylococcus Aureus.

Escherichia coli and Staphylococcus aureus are two common pathogenic microorganisms that cause mastitis in dairy cows. They can cause clinical mastitis and subclinical mastitis. In recent studies, lncRNAs have been found to play an important role in the immune responses triggered by microbial inducers. However, the actions of lncRNAs in bovine mastitis remain unclear. The purpose of this study was to investigate the effects of bovine mammary epithelial cell injuries induced by treatment with E. coli and S. aureus, and to explore the lncRNA profile on cell injuries. The lncRNA transcriptome analysis showed a total of 2597 lncRNAs. There were 2234 lncRNAs differentially expressed in the E. coli group and 2334 in the S. aureus group. Moreover, we found that the E. coli and S. aureus groups of maternal genes targeted signaling pathways with similar functions according to KEGG and GO analyses. Two lncRNA-miRNA-mRNA interaction networks were constructed in order to predict the potential molecular mechanisms of regulation in the cell injuries. We believe that this is the first report demonstrating the dysregulation of lncRNAs in cells upon E. coli and S. aureus infections, suggesting that they have the potential to become important diagnostic markers and to provide novel insights into controlling and preventing mastitis.

Fabrication of abamectin-loaded mesoporous silica nanoparticles by emulsion-solvent evaporation to improve photolysis stability and extend insecticidal activity.

Mesoporous silica nanoparticles (MSNs) can be designed to effectively load, protect, and control the release of pesticides. In this study, emulsion-solvent evaporation was used to fabricate abamectin-loaded MSNs. Our method could deliver abamectin in the process of MSN self-assembly, resulting in simple operation, short preparation period, and outstanding drug carrying capacity. The characteristics of abamectin-loaded MSNs, including morphology, loading content, stability against photolysis, controlled release behavior, and toxicological effect, were systematically investigated. Abamectin-loaded MSNs were successfully produced, having spherical shape, rough surface, uniform particle sizes, typically hollow structure, high loading efficiency (44.8%), excellent photodegradation-reducing ability, and controlled-release properties. The biological activity survey for abamectin-loaded MSNs showed excellent toxicological properties against Plutella xylostella larvae, and maintained biological activity until the 15th day, with 70% mortality of the target insect. The results of this study are beneficial for the development of a delivery system for the rational and effective usage of pesticides.

AP3: An Advanced Proteotypic Peptide Predictor for Targeted Proteomics by Incorporating Peptide Digestibility.

The selection of proteotypic peptides, that is, detectable unique representatives of proteins of interest, is a key step in targeted proteomics. To date, much effort has been made to understand the mechanisms underlying peptide detection in liquid chromatography-tandem mass spectrometry (LC-MS/MS) based shotgun proteomics and to predict proteotypic peptides in the absence of experimental LC-MS/MS data. However, the prediction accuracy of existing tools is still unsatisfactory. We find that one crucial reason is their neglect of the significant influence of protein proteolytic digestion on peptide detectability in shotgun proteomics. Here, we present an Advanced Proteotypic Peptide Predictor (AP3), which explicitly takes peptide digestibility into account for the prediction of proteotypic peptides. Specifically, peptide digestibility is first predicted for each peptide and then incorporated as a feature into the peptide detectability prediction model. Our results demonstrated that peptide digestibility is the most important feature for the accurate prediction of proteotypic peptides in our model. Compared with the existing available algorithms, AP3 showed 10.3-34.7% higher prediction accuracy. On a targeted proteomics data set, AP3 accurately predicted the proteotypic peptides for proteins of interest, showing great potential for assisting the design of targeted proteomics experiments.

Dual-type fractal spiral zone plate for generating sequence of square optical vortices.

We propose a technique for generating a sequence of co-axial zero axial irradiance with a so-called dual-type fractal spiral zone plate (DTFSZP). Based on the Fresnel diffraction theory, we simulated the focusing performance of this optical device. The results reveal that DTFSZP has the remarkable ability of generating a sequence of optical vortices with larger depth of focus and high lateral resolution. The central diffracted image rotates in the vicinity of the focal plane. Moreover, the focusing performance follows a modulo-4 transmutation rule. Such optics promises a complementary and versatile high-resolution non-destructive tool for particle manipulation and provides potential application in three-dimensional optical alignment systems.

Removal of intracellular and extracellular antibiotic resistance genes and virulence factor genes using electricity-intensified constructed wetlands.

Constructed wetland (CW) is considered a promising technology for the removal of emerging contaminants. However, its removal performance for antibiotic resistance genes (ARGs) is not efficient and influence of virulence factor genes (VFGs) have not been elucidated. Here, removal of intracellular and extracellular ARGs as well as VFGs by electricity-intensified CWs was comprehensively evaluated. The two electrolysis-intensified CWs can improve the removal of intracellular ARGs and MGEs to 0.96- and 0.85-logs, respectively. But cell-free extracellular ARGs (CF-eARGs) were significantly enriched with 1.8-logs in the electrolysis-intensified CW. Interestingly, adding Fe-C microelectrolysis to the electrolysis-intensified CW is conducive to the reduction of CF-eARGs. However, the detected number and relative abundances of intracellular and extracellular VFGs were increased in all of the three CWs. The biofilms attached onto the substrates and rhizosphere are also hotspots of both intracellular and particle-associated extracellular ARGs and VFGs. Structural equation models and correlation analysis indicated that ARGs and VFGs were significantly cooccurred, suggesting that VFGs may affect the dynamics of ARGs. The phenotypes of VFGs, such as biofilm, may act as protective matrix for ARGs, hindering the removal of resistance genes. Our results provide novel insights into the ecological remediation technologies to enhance the removal of ARGs.

Diclofenac sodium nanomedicine results in pain-relief and differential expression of the RNA transcriptome in the spinal cord of SNI rats.

Neuropathic pain is chronic pain caused by a lesion or disease of the somatosensory nervous system. Neuropathic pain, with a high incidence and complex pathogenesis, is one of the most significant areas of clinical medicine and basic research. Currently, prescribed treatments are still unsatisfactory or have limited effectiveness. A medicinal preparation is required that relieves the neuropathic pain and prolongs action time, which has not yet been discovered. In this study, MIL-101 (Fe) was employed as a drug carrier to regulate the release of diclofenac sodium, thereby achieving the effect of analgesia and sustained release. The release curves demonstrated that diclofenac sodium could be continuously released from MIL-101 (Fe) for more than 48 h. There was no toxicity in vitro and in vivo, and the safety of MIL-101 (Fe) was confirmed by hematoxylin and eosin as well as ELISA tests in vivo. The results of behavioral testing, pharmacokinetics, and RNA sequencing analysis showed that MIL-101 (Fe) loaded with diclofenac sodium could enhance the mechanical withdrawal threshold and alleviate cold allodynia induced by Spared Nerve Injury, prolonging the work time by three days. The results indicated that MIL-101 (Fe) exhibited excellent biocompatibility, while the MIL-101 (Fe)-DS demonstrated analgesic and controlled-release properties. These findings provide a scientific foundation for the clinical management of neuropathic pain and the development of a novel formulation.

More potential uses of specific perforator flaps in the calf - A cadaveric study on the subdermal vascular structure of the lower leg.

BACKGROUND: The perforator flap has garnered significant interest since its inception due to its advantage of not needing a vascular network at the deep fascial level. Perforator flaps are commonly utilized in different flap transplant surgeries, and the thigh flap is presently the most widely used perforator flap. Is it possible for the calf to replace the thigh as a more suitable site for harvesting materials? Currently, there is a lack of relevant anatomical research. This study aims to address this question from an anatomical and imaging perspective. METHODS: This study used cadavers to observe the branches and courses of perforators on the calf and the distribution of skin branches using microdissection techniques, digital X-ray photography, and micro-computed tomography techniques. RESULTS: The perforators had three main branches: the vertical cutaneous branch, the oblique cutaneous branch, and the superficial fascial branch. The superficial fascial branch traveled in the superficial fascia and connected with the nearby perforators. The vertical and oblique cutaneous branches entered the subdermal layer and connected with each other to create the subdermal vascular network. CONCLUSIONS: We observed an intact calf cutaneous branch chain between the cutaneous nerve and the perforator of the infrapopliteal main artery at the superficial vein site. Utilizing this anatomical structure, the calfskin branch has the potential to serve as a substitute for thigh skin flap transplantation and may be applied to perforator flap transplantation in more locations.

Neural-SEIR: A flexible data-driven framework for precise prediction of epidemic disease.

Accurately modeling and predicting epidemic diseases is crucial to prevent disease transmission and reduce mortality. Due to various unpredictable factors, including population migration, vaccination, control efforts, and seasonal fluctuations, traditional epidemic models that rely on prior knowledge of virus transmission mechanisms may not be sufficient to forecast complex epidemics like coronavirus disease 2019(COVID-19). The application of traditional epidemiological models such as susceptible-exposed-infectious-recovered (SEIR) may face difficulties in accurately predicting such complex epidemics. Data-driven prediction approaches lack the ability to generalize and exhibit low accuracy on small datasets due to their reliance on large amounts of data without incorporating prior knowledge. To overcome this limitation, we introduce a flexible ensemble data-driven framework (Neural-SEIR) that "neuralizes" the SEIR model by approximating the core parameters through neural networks while preserving the propagation structure of SEIR. Neural-SEIR employs long short-term memory (LSTM) neural network to capture complex correlation features, exponential smoothing (ES) to model seasonal information, and prior knowledge from SEIR. By incorporating SEIR parameters into the neural network structure, Neural-SEIR leverages prior knowledge while updating parameters with real-world data. Our experimental results demonstrate that Neural-SEIR outperforms traditional machine learning and epidemiological models, achieving high prediction accuracy and efficiency in forecasting epidemic diseases.

Liquid biopsy biomarkers to guide immunotherapy in breast cancer.

Immune checkpoint inhibitors (ICIs) therapy has emerged as a promising treatment strategy for breast cancer (BC). However, current reliance on immunohistochemical (IHC) detection of PD-L1 expression alone has limited predictive capability, resulting in suboptimal efficacy of ICIs for some BC patients. Hence, developing novel predictive biomarkers is indispensable to enhance patient selection for immunotherapy. In this context, utilizing liquid biopsy (LB) can provide supplementary or alternative value to PD-L1 IHC testing for identifying patients most likely to benefit from immunotherapy and exhibit favorable responses. This review discusses the predictive and prognostic value of LB in breast cancer immunotherapy, as well as its limitations and future directions. We aim to promote the individualization and precision of immunotherapy in BC by elucidating the role of LB in clinical practice.

ALKBH5 Stabilized N6-Methyladenosine-Modified LOC4191 to Suppress E. coli-Induced Apoptosis.

E. coli is a ubiquitous pathogen that is responsible for over one million fatalities worldwide on an annual basis. In animals, E. coli can cause a variety of diseases, including mastitis in dairy cattle, which represents a potential public health hazard. However, the pathophysiology of E. coli remains unclear. We found that E. coli could induce global upregulation of m6A methylation and cause serious apoptosis in bovine mammary epithelial cells (MAC-T cells). Furthermore, numerous m6A-modified lncRNAs were identified through MeRIP-seq. Interestingly, we found that the expression of LOC4191 with hypomethylation increased in MAC-T cells upon E. coli-induced apoptosis. Knocking down LOC4191 promoted E. coli-induced apoptosis and ROS levels through the caspase 3-PARP pathway. Meanwhile, knocking down ALKBH5 resulted in the promotion of apoptosis through upregulated ROS and arrested the cell cycle in MAC-T cells. ALKBH5 silencing accelerated LOC4191 decay by upregulating its m6A modification level, and the process was recognized by hnRNP A1. Therefore, this indicates that ALKBH5 stabilizes m6A-modified LOC4191 to suppress E. coli-induced apoptosis. This report discusses an initial investigation into the mechanism of m6A-modified lncRNA in cells under E. coli-induced apoptosis and provides novel insights into infectious diseases.

Mechanism and effects of STING-IFN-I pathway on nociception: A narrative review.

Since the discovery of STING in 2008, numerous studies have investigated its functions in immunity, inflammation, and cancer. STING activates downstream molecules including IFN-I, NLRP3, and NF-κB. The STING-IFN-I pathway plays a vital role in nociception. After receiving the upstream signal, STING is activated and induces the expression of IFN-I, and after paracrine and autocrine signaling, IFN-I binds to IFN receptors. Subsequently, the activity of ion channels is inhibited by TYK2, which induces an acute antinociceptive effect. JAK activates PIK3 and MAPK-MNK-eIF4E pathways, which sensitize nociceptors in the peripheral nervous system. In the mid-late stage, the STING-IFN-I pathway activates STAT, increases pro-inflammatory and anti-inflammatory cytokines, inhibits ER-phagy, and promotes microglial M1-polarization in the central nervous system, leading to central sensitization. Thus, the STING-IFN-I pathway may exert complex effects on nociception at various stages, and these effects require further comprehensive elucidation. Therefore, in this review, we systematically summarized the mechanisms of the STING-IFN-I pathway and discussed its function in nociception.

Facile pathway to construct mesoporous silica nanoparticles loaded with pyraclostrobin: Physicochemical properties, antifungal activity, and biosafety.

BACKGROUND: A controlled-release formulation based on mesoporous silica nanoparticles (MSNs) provides an effective way for reducing pesticide use and protecting the ecological environment. In this study, MSNs loaded with pyraclostrobin (PYR@MSNs) were prepared using a one-pot method. RESULTS: The characteristics of PYR@MSNs were systematically investigated, including morphology, loading content, ultraviolet (UV) resistance, release behavior, control effects against pathogens, and safety to nontarget organisms. The results show that the prepared PYR@MSNs presented characteristics of regular spherical shapes, uniform particle size (200 nm), high drug loading (38.9%), and enhanced UV resistance. Compared with traditional formulation, PYR@MSNs exhibited improved control effects against Fusarium graminearum, an extended control period, and lower toxicity to zebrafish, earthworms and BEAS-2B cells. CONCLUSIONS: This research will facilitate the development of efficient and safe pesticide delivery systems. The PYR@MSNs has showed its potential as a new controlled-release formulation with increased efficacy and is expected to benefit the sustainable development of agriculture. © 2022 Society of Chemical Industry.

The Role of N6-Methyladenosine in Inflammatory Diseases.

N6-Methyladenosine (m6A) is the most abundant epigenetic RNA modification in eukaryotes, regulating RNA metabolism (export, stability, translation, and decay) in cells through changes in the activity of writers, erasers, and readers and ultimately affecting human life or disease processes. Inflammation is a response to infection and injury in various diseases and has therefore attracted significant attention. Currently, extensive evidence indicates that m6A plays an essential role in inflammation. In this review, we focus on the mechanisms of m6A in inflammatory autoimmune diseases, metabolic disorder, cardio-cerebrovascular diseases, cancer, and pathogen-induced inflammation, as well as its possible role as targets for clinical diagnosis and treatment.