The diversity of peripheral blood T cells and myeloid-derived suppressor cells in patients with idiopathic membranous nephropathy.

OBJECTIVE: Membranous nephropathy is a leading cause of adult-onset nephrotic syndrome. Peripheral T cells and myeloid-derived suppressor cells (MDSCs) are closely associated with autoimmune diseases, while their exact roles and interaction in these processes are unclear. Here, we studied the roles of T cells, MDSCs, and their subsets in patients with idiopathic membranous nephropathy (IMN). MATERIALS AND METHODS: 35 IMN patients and 30 healthy controls were included in this retrospective study. Flow cytometry was performed to determine the phenotype of human T cells and MDSCs in peripheral blood mononuclear cells (PBMCs). Anti-PLA2R was measured by ELISA. Values ≥ 20 RU/mL were defined as positive and < 14 RU/mL as negative. RESULTS: A higher ratio of CD4/CD8 T cells with a lower proportion of Tregs, a remarkably lower proportion of G-MDSCs (but not M-MDSCs), lower frequency of PD-L2+G-MDSCs, and higher frequency of PD-L1+M-MDSCs were found in IMN patients compared to healthy controls. The ratio of CD4/CD8 T cells was higher, and the frequencies of PD-1+CD4+ T cells, CTLA-4+CD4+ T cells, PD-1+Tregs, and CTLA-4+Tregs were lower in PBMCs of PLA2R-positive IMN patients compared to PLA2R-negative IMN patients. CONCLUSION: Tregs and G-MDSCs were reduced in the circulation of the IMN patients, which may promote understanding of the crucial functions that are mediated by these cells in the pathogenesis of IMN.

Altered Intestinal Microbial Flora and Metabolism in Patients with Idiopathic Membranous Nephropathy.

INTRODUCTION: Dysbiosis of the intestinal microbiome and related metabolites have been observed in chronic kidney disease, yet their roles in idiopathic membranous nephropathy (IMN) are poorly understood. METHODS: In this study, we describe the variation of intestinal bacteria and fecal metabolites in patients with IMN in Chinese population. Stool samples were collected from 41 IMN patients at the beginning of diagnosis confirmation and 41 gender- and age-matched healthy control (HC). Microbial communities are investigated by sequencing of 16S rRNA genes and functional profiles predicted using Tax4Fun, and the correlation between intestinal bacteria and IMN clinical characteristics is also analyzed. Untargeted metabolomic analysis is performed to explore the relationship between colon's microbiota and fecal metabolites. RESULTS: IMN gastrointestinal microbiota demonstrates lower richness and diversity compared to HC, and exhibits a marked taxonomic and inferred functional dysbiosis when compared to HC. Some genera are closely related to the clinical parameters, such as Citrobacter and Akkermansia. Twenty characteristic microbial biomarkers are selected to establish a disease prediction model with a diagnostic accuracy of 93.53%. Fecal metabolomics shows that tryptophan metabolism is reduced in IMN patients but uremic toxin accumulation in feces is not noticeable. Fecal microbiota transplantation demonstrates that gut dysbiosis impairs gut permeability in microbiota-depleted mice and induces NOD-like receptor activation in the kidneys. CONCLUSIONS: Clarifying the changes in intestinal microbiota in IMN patients will help further know the pathogenesis of this disease, and microbiota-targeted biomarkers will provide a potentially powerful tool for diagnosing and treating IMN.

Multilayer Perceptron Network Optimization for Chaotic Time Series Modeling.

Chaotic time series are widely present in practice, but due to their characteristics-such as internal randomness, nonlinearity, and long-term unpredictability-it is difficult to achieve high-precision intermediate or long-term predictions. Multi-layer perceptron (MLP) networks are an effective tool for chaotic time series modeling. Focusing on chaotic time series modeling, this paper presents a generalized degree of freedom approximation method of MLP. We then obtain its Akachi information criterion, which is designed as the loss function for training, hence developing an overall framework for chaotic time series analysis, including phase space reconstruction, model training, and model selection. To verify the effectiveness of the proposed method, it is applied to two artificial chaotic time series and two real-world chaotic time series. The numerical results show that the proposed optimized method is effective to obtain the best model from a group of candidates. Moreover, the optimized models perform very well in multi-step prediction tasks.

Overlapping Community Detection Based on Membership Degree Propagation.

A community in a complex network refers to a group of nodes that are densely connected internally but with only sparse connections to the outside. Overlapping community structures are ubiquitous in real-world networks, where each node belongs to at least one community. Therefore, overlapping community detection is an important topic in complex network research. This paper proposes an overlapping community detection algorithm based on membership degree propagation that is driven by both global and local information of the node community. In the method, we introduce a concept of membership degree, which not only stores the label information, but also the degrees of the node belonging to the labels. Then the conventional label propagation process could be extended to membership degree propagation, with the results mapped directly to the overlapping community division. Therefore, it obtains the partition result and overlapping node identification simultaneously and greatly reduces the computational time. The proposed algorithm was applied to a synthetic Lancichinetti-Fortunato-Radicchi (LFR) dataset and nine real-world datasets and compared with other up-to-date algorithms. The experimental results show that our proposed algorithm is effective and outperforms the comparison methods on most datasets. Our proposed method significantly improved the accuracy and speed of the overlapping node prediction. It can also substantially alleviate the computational complexity of community structure detection in general.

Efficacy of bisphosphonates in patients with synovitis, acne, pustulosis, hyperostosis, and osteitis syndrome: a prospective open study.

OBJECTIVES: To evaluate the clinical efficacy of bisphosphonates treatment for spinal bone marrow oedema (BME) in patients with synovitis, acne, pustulosis, hyperostosis, and osteitis (SAPHO) syndrome. METHODS: SAPHO syndrome patients presenting to Peking Union Medical College Hospital from 2015 to 2016 were recruited. Patients were administered pamidronate disodium 1 mg/kg/d intravenously, for 3 days, at baseline and 3 months later. The symptoms were evaluated using the Visual Analog Score (VAS) for pain, and other clinical measures including, spinal BME scores, ß-crosslaps, osteocalcin, and inflammatory factors, were collected. RESULTS: A total of 30 patients (20 women and 10 men) with a median age of 47.2 (interquartile range 8.8) years were recruited. In a short time, the patients showed a significant decrease in VAS (before vs. after; first treatment: 5.70±1.62 vs. 2.30±1.29 cm, second treatment: 4.03±1.88 vs. 2.17±1.23 cm) and ß-crosslaps (first treatment: 0.4441±0.1923 vs. 0.0859±0.0374 pg/ml, second treatment: 0.2891±0.1983 vs. 0.0962±0.0324 pg/ml) (all p<0.05). At 12-month follow-up, compared with the baseline, we noticed a significant drop in the VAS (5.70±1.62 vs. 2.43±1.25 cm), erythrocyte sedimentation rate (28.87±25.26 vs. 18.00±18.65 mm/h), high-sensitivity C-reactive protein level (11.76±10.19 vs. 5.84±5.88 mg/L), osteocalcin (2.30±1.27 vs. 1.65±0.80 ng/ml), and BME (30.50±24.09 vs. 22.13±27.79) (all p<0.05). No one had serious adverse events. CONCLUSIONS: Bisphosphonates can significantly and rapidly relieve symptoms in patients with SAPHO syndrome and have a long-term effect on inflammation and spinal BME. We suggest that bisphosphonates could be used as the first-line therapeutic drug for SAPHO syndrome, especially in patients with spinal BME.

RNA-TVcurve: a Web server for RNA secondary structure comparison based on a multi-scale similarity of its triple vector curve representation.

BACKGROUND: RNAs have been found to carry diverse functionalities in nature. Inferring the similarity between two given RNAs is a fundamental step to understand and interpret their functional relationship. The majority of functional RNAs show conserved secondary structures, rather than sequence conservation. Those algorithms relying on sequence-based features usually have limitations in their prediction performance. Hence, integrating RNA structure features is very critical for RNA analysis. Existing algorithms mainly fall into two categories: alignment-based and alignment-free. The alignment-free algorithms of RNA comparison usually have lower time complexity than alignment-based algorithms. RESULTS: An alignment-free RNA comparison algorithm was proposed, in which novel numerical representations RNA-TVcurve (triple vector curve representation) of RNA sequence and corresponding secondary structure features are provided. Then a multi-scale similarity score of two given RNAs was designed based on wavelet decomposition of their numerical representation. In support of RNA mutation and phylogenetic analysis, a web server (RNA-TVcurve) was designed based on this alignment-free RNA comparison algorithm. It provides three functional modules: 1) visualization of numerical representation of RNA secondary structure; 2) detection of single-point mutation based on secondary structure; and 3) comparison of pairwise and multiple RNA secondary structures. The inputs of the web server require RNA primary sequences, while corresponding secondary structures are optional. For the primary sequences alone, the web server can compute the secondary structures using free energy minimization algorithm in terms of RNAfold tool from Vienna RNA package. CONCLUSION: RNA-TVcurve is the first integrated web server, based on an alignment-free method, to deliver a suite of RNA analysis functions, including visualization, mutation analysis and multiple RNAs structure comparison. The comparison results with two popular RNA comparison tools, RNApdist and RNAdistance, showcased that RNA-TVcurve can efficiently capture subtle relationships among RNAs for mutation detection and non-coding RNA classification. All the relevant results were shown in an intuitive graphical manner, and can be freely downloaded from this server. RNA-TVcurve, along with test examples and detailed documents, are available at: http://ml.jlu.edu.cn/tvcurve/ .

Clinical features of 30 patients with cryoglobulinemia.

OBJECTIVE: To summarize the clinical features of cryoglobulinemia. METHOD: We retrospectively analyzed the clinical data of 30 patients admitted to Peking Union Medical College Hospital from January 2003 to March 2013 due to cryoglobulinemia. RESULTS: The average age was(53.8±11.9)years in these 30 patients(12 men and 18 women),among whom 22 patient(73.3%)developed infectious diseases including hepatitis B(n=11)and hepatitis C(n=11);in addition,3 hepatitis B patients and 1 hepatitis C patient also had malignancies. Four patients(13.3%)were accompanied with malignant lymphocytic proliferation diseases,and three(10.0%)with connective tissue diseases. The cause of disease was unclear in 5 patients(16.7%). The clinical manifestations varied due to the primary diseases;notably,20 patients(66.7%)had an onset of purpura rash,22(73.3%)and 19(63.3%)were accompanied with hypertension and chronic renal insufficiency,respectively. The severity of renal involvement was relevant with the increase of C reactive protein,erythrocytes,sedimentation rate,and IgM and the decrease of complements. Treatment should be directed at the primary diseases. Glucocorticoid and immunosuppressants were good choices for relieving renal involvement. Elderly, type 1 cryoglobulinemia,and poor renal function were associated with the poor prognosis. CONCLUSIONS: Cryoglobulinemia is mainly seen in middle and elderly patients. It can often affect multiple systems,in particular the kidney. Inflammatory markers,IgM,and complements is related with the disease severity. Age,primary disease,and renal function are related with prognosis.

Acquired Fanconi syndrome in mixed cryoglobulinemia patients: a single-center case series.

PURPOSE: Cryoglobulinemia is a pathological condition characterized by the presence of cryoglobulins in the blood, with cryoglobulinemic glomerulonephritis being the most frequent form of renal involvement. Fanconi syndrome presents as a generalized dysfunction of the proximal tubule, characterized by the presence of polyuria, phosphaturia, glycosuria, proteinuria, proximal renal tubular acidosis, and osteomalacia. We aimed to present five cases co-occurring with Fanconi syndrome and cryoglobulinemia. METHODS: We retrospectively summarized the cases of five patients with Fanconi syndrome and cryoglobulinemia at Peking Union Medical College Hospital from January 2012 to June 2022. The clinical features, diagnosis, treatment, and prognosis were systematically analyzed. RESULTS: All five patients exhibited typical features of Fanconi syndrome, and cryoglobulinemia was concurrently detected in all cases. These patients also exhibit positive anti-nuclear antibody spectrum and hyperglobulinemia, and IgM constitutes the predominant monoclonal component in cryoglobulins. In addition to supplemental treatment, timely immunosuppressive therapy may potentially benefit the long-term renal prognosis of patients with this condition. CONCLUSION: Our findings highlight the rare co-occurrence of Fanconi syndrome and cryoglobulinemia in clinical practice. Despite the lack of causal evidence, the coexistence of Fanconi syndrome and tubulointerstitial injury is also noteworthy in patients with cryoglobulinemia, underscoring the importance of thorough evaluation and tailored management in patients presenting with overlapping renal manifestations. Key Points • Patients with mixed cryoglobulinemia can clinically present with tubulointerstitial injury, specifically manifesting as Fanconi syndrome. • In addition to typical symptoms of Fanconi syndrome, these patients also exhibit positive anti-nuclear antibody spectrum and hyperglobulinemia, while IgM constitutes the monoclonal component in cryoglobulins. • Timely immunosuppressive therapy may improve long-term renal prognosis in these patients.

An Ultra-Conductive and Patternable 40 nm-Thick Polymer Film for Reliable Emotion Recognition.

Understanding psychology is an important task in modern society which helps predict human behavior and provide feedback accordingly. Monitoring of weak psychological and emotional changes requires bioelectronic devices to be stretchable and compliant for unobtrusive and high-fidelity signal acquisition. Thin conductive polymer film is regarded as an ideal interface; however, it is very challenging to simultaneously balance mechanical robustness and opto-electrical property. Here, a 40 nm-thick film based on photolithographic double-network conductive polymer mediated by graphene layer is reported, which concurrently enables stretchability, conductivity, and conformability. Photolithographic polymer and graphene endow the film photopatternability, enhance stress dissipation capability, as well as improve opto-electrical conductivity (4458 S cm-1@>90% transparency) through molecular rearrangement by π-π interaction, electrostatic interaction, and hydrogen bonding. The film is further applied onto corrugated facial skin, the subtle electromyogram is monitored, and machine learning algorithm is performed to understand complex emotions, indicating the outstanding ability for stretchable and compliant bioelectronics.

Paraffin-Enabled Superlattice Customization for a Photostimulated Gradient-Responsive Artificial Reflex Arc.

The development of photostimulated-motion artificial reflex arcs - a neural circuit inspired by light-driven motion reflexes - holds significant promises for advancements in robotic perception, navigation, and motion control. However, the fabrication of such systems, especially those that accommodate multiple actions and exhibit gradient responses, remains challenging. Here, a gradient-responsive photostimulated-motion artificial reflex arc is developed by integrating a programmable and tunable photoreceptor based on folded MoS2 at different twist angles. The twisted folded bilayer MoS2 used as photoreceptors can be customized via the transfer technique using patternable paraffin, where the twist angle and fold-line could be controlled. The photoluminescence (PL) intensity is 3.7 times higher at a twist angle of 29° compared to that at 0°, showing a monotonically decreasing indirect bandgap. Through tunable interlayer carrier transport, photoreceptors fabricated using folded bilayer MoS2 at different twist angles demonstrate gradient response time, enabling the photostimulated-motion artificial reflex arc for multiaction responses. They are transformed to digital command flow and studied via machine learning to control the gestures of a robotic hand, showing a prototype of photostimulated gradient-responsive artificial reflex arcs for the first time. This work provides a unique idea for developing intelligent soft robots and next-generation human-computer interfaces.

Improving transmembrane protein consensus topology prediction using inter-helical interaction.

Alpha helix transmembrane proteins (αTMPs) represent roughly 30% of all open reading frames (ORFs) in a typical genome and are involved in many critical biological processes. Due to the special physicochemical properties, it is hard to crystallize and obtain high resolution structures experimentally, thus, sequence-based topology prediction is highly desirable for the study of transmembrane proteins (TMPs), both in structure prediction and function prediction. Various model-based topology prediction methods have been developed, but the accuracy of those individual predictors remain poor due to the limitation of the methods or the features they used. Thus, the consensus topology prediction method becomes practical for high accuracy applications by combining the advances of the individual predictors. Here, based on the observation that inter-helical interactions are commonly found within the transmembrane helixes (TMHs) and strongly indicate the existence of them, we present a novel consensus topology prediction method for αTMPs, CNTOP, which incorporates four top leading individual topology predictors, and further improves the prediction accuracy by using the predicted inter-helical interactions. The method achieved 87% prediction accuracy based on a benchmark dataset and 78% accuracy based on a non-redundant dataset which is composed of polytopic αTMPs. Our method derives the highest topology accuracy than any other individual predictors and consensus predictors, at the same time, the TMHs are more accurately predicted in their length and locations, where both the false positives (FPs) and the false negatives (FNs) decreased dramatically. The CNTOP is available at: http://ccst.jlu.edu.cn/JCSB/cntop/CNTOP.html.

Recruitment of myeloid­derived suppressor cells and regulatory T­cells is associated with the occurrence of acute myocardial infarction.

The roles of myeloid-derived suppressor cells (MDSCs) and regulatory T-cells (Tregs) in acute myocardial infarction (AMI) remain elusive. The present study aimed to analyze the proportions of the granulocytic and monocytic populations of MDSCs (G-MDSCs and M-MDSCs, respectively), and Tregs in the peripheral blood mononuclear cells (PBMCs) of patients with AMI. The present study recruited 34 patients with AMI and 37 healthy controls without clinical signs of myocardial ischemia. PBMCs were isolated from the peripheral blood samples of patients with AMI within 24 h following admission to the hospital and from those of the healthy controls during a physical examination. Two subsets of MDSCs, G-MDSCs (CD15+CD33+CD11b+CD14-HLA-DRlow) and M-MDSCs (CD14+CD15-CD11b+HLA-DRlow), and Tregs (CD3+CD4+CD25highCD127low T-cells) in the PBMCs derived from the patients with AMI and healthy controls were analyzed using flow cytometry. The effects of MDSCs derived from patients with AMI on naïve CD4+ T-cells were examined in the co-culture system. The results revealed that the proportions of G-MDSCs and M-MDSCs were higher in the peripheral blood of patients with AMI than in that of the healthy controls. The patients with AMI had significantly higher numbers of programmed death-ligand (PD-L)1- and PD-L2-positive G-MDSCs and M-MDSCs compared with the healthy controls (P<0.05). The MDSCs could acquire a granulocytic phenotype following AMI, and the G-MDSCs and M-MDSCs would be more likely to express PD-L2 and PD-L1, respectively. The ratios of Tregs to CD4+ T-cells and PD-1+ Tregs in the peripheral blood of patients with AMI were significantly higher than those in the healthy controls (P<0.05). The results of flow cytometry demonstrated an increase in the numbers of inducible Tregs in the co-culture system with the G-MDSCs derived from patients with AMI compared with the G-MDSCs derived from the healthy controls (P<0.01). On the whole, the findings presented herein demonstrate the accumulation of MDSCs, and the upregulation of PD-L1 and PD-L2 expression on the surface of MDSCs in patients with AMI. MDSCs can induce the expansion of Tregs by binding PD-1 on the surface of Tregs, thus playing a crucial role in AMI.

Decreased programmed cell death ligand 2-positive monocytic myeloid-derived suppressor cells and programmed cell death protein 1-positive T-regulatory cells in patients with type 2 diabetes: implications for immunopathogenesis.

Objectives: The activation of immune cells plays a significant role in the progression of type 2 diabetes. This study aimed to investigate the potential role of myeloid-derived suppressor cells (MDSCs) and T-regulatory cells (Tregs) in type 2 diabetes. Methods: A total of 61 patients diagnosed with type 2 diabetes were recruited. Clinical characteristics were reviewed and peripheral blood samples were collected. We calculated the percentage of different cells. Frequencies of MDSC subsets refered to the percentage of G-MDSCs (CD15+CD33+CD11b+CD14-HLA-DR-/low) in CD45 positive cells and the percentage of M-MDSCs (CD14+CD15-CD11b+CD33+HLA-DR-/low) in lymphocytes plus monocytes. Results: Frequencies of programmed cell death ligand 1-positive granulocytic MDSCs (PD-L1+ G-MDSCs), programmed cell death ligand 2-positive monocytic MDSCs (PD-L2+ M-MDSCs), PD-L2+ G-MDSC, and programmed cell death protein 1-positive Tregs (PD-1+Tregs) were decreased in patients with type 2 diabetes. The frequency of PD-1+ Tregs was positively related to PD-L2+ M-MDSCs (r= 0.357, P = 0.009) and negatively related to HbA1c (r = -0.265, P = 0.042), fasting insulin level (r = -0.260, P = 0.047), and waist circumference (r = -0.373, P = 0.005). Conclusions: Decreased PD-L2+ M-MDSCs and PD-1+ Tregs may promote effector T cell activation, leading to chronic low-grade inflammation in type 2 diabetes. These findings highlight the contribution of MDSCs and Tregs to the immunopathogenesis of type 2 diabetes and suggest their potential as targets for new therapeutic approaches.

Herbal small RNAs in patients with COVID-19 linked to reduced DEG expression.

In China, more than 80% of patients with coronavirus disease 2019 (COVID-19) received traditional Chinese medicine (TCM) as a treatment and their clinical efficacy have been reported. However, the underlying molecular mechanism remains unclear. Previous studies have identified herbal small RNAs (sRNAs) as novel functional components. In this study, a cohort of 22 patients with COVID-19 treated with Toujie Quwen (TQ) granules was analyzed. We observed thousands of herbal small RNAs that entered the blood cells of patients after the consumption of TQ granules. In response to this treatment, the reduced differentially expressed genes (DEGs) were highly correlated with the predicted target genes of the most prevalent herbal sRNAs detected in the blood. Moreover, the predicted target genes of the top 30 sRNAs from each of the 245 TCMs in the Bencao sRNA Atlas overlapped with 337 upregulated DEGs in patients with mild COVID-19, and 33 TCMs, with more than 50% overlapping genes were identified as effective TCMs. These predicted target genes of top 30 sRNAs from Juhong, Gualoupi and Foshou were confirmed experimentally. Our results not only elucidated a novel molecular mechanism of TCM potential clinical efficacy for COVID-19 patients, but also provided 33 effective COVID-19 TCMs for prescription optimization.

7-hydroxycoumarin-ß-D-glucuronide protects against cisplatin-induced acute kidney injury via inhibiting p38 MAPK-mediated apoptosis in mice.

AIMS: Cisplatin is a widely-used drug in the clinical treatment of tumors, but kidney nephrotoxicity is one of the reasons that limits its widespread use. We previously found that 7-hydroxycoumarin-ß-D-glucuronide (7-HCG) was one of metabolites of skimmin and highly enriched in the kidneys and maintained a high blood concentration in skimmin-treated rats. Therefore, we investigated whether 7-HCG has a protective effect on cisplatin-induced acute kidney injury. MATERIALS AND METHODS: Male C57BL/6 mice were continuously administered 7-HCG for five days, and on the third day, an intraperitoneal injection of cisplatin was given to induce acute kidney injury. After 72 h, the mice were sacrificed for analysis. Serum and renal tissue were collected for renal function evaluation. RNA sequencing was used to explore mechanism, and further validated by western blot and immunohistochemistry. In addition, pharmacokinetic study of oral 7-HCG administration was performed to examine how much 7-hydroxycoumarin (7-HC) was metabolized and 7-HC possible effect on renal protection. KEY FINDINGS: 7-HCG significantly reduced serum BUN and SCR levels, and alleviated pathological damage in renal tissue, and reduced the renal index. RNA sequencing revealed that 7-HCG could reverse p38 MAPK regulation and apoptosis. By western blotting, it was found that 7-HCG could reduce renal injury by reducing p-p38, p-ERK, p-JNK, cleaved-caspase3 and Bax. The immunohistochemical results of cleaved-caspase3 were consistent with western blotting. 7-HCG also significantly reduced the production of ROS in kidney tissue. Pharmacokinetic experiments have shown that 7-HCG in the blood increased rapidly and was eliminated slowly, with an average t1/2ß of 18.3 h. And the concentration of 7-HCG in the target organ kidney was about 4 times higher than that in blood. SIGNIFICANCE: Our findings indicate that 7-HCG could exert its protective effect against cisplatin-induced acute kidney injury by inhibiting apoptosis via p38 MAPK regulation and elucidates its pharmacokinetics.

A comprehensive analysis of the Bencao (herbal) small RNA Atlas reveals novel RNA therapeutics for treating human diseases.

Cross-kingdom herbal miRNA was first reported in 2012. Using a modified herbal extraction protocol, we obtained 73,677,287 sequences by RNA-seq from 245 traditional Chinese Medicine (TCM), of which 20,758,257 were unique sequences. We constructed a Bencao (herbal) small RNA (sRNA) Atlas ( http://bencao.bmicc.cn ), annotated the sequences by sequence-based clustering, and created a nomenclature system for Bencao sRNAs. The profiles of 21,757 miRNAs in the Atlas were highly consistent with those of plant miRNAs in miRBase. Using software tools, our results demonstrated that all human genes might be regulated by sRNAs from the Bencao sRNA Atlas, part of the predicted human target genes were experimentally validated, suggesting that Bencao sRNAs might be one of the main bioactive components of herbal medicines. We established roadmaps for oligonucleotide drugs development and optimization of TCM prescriptions. Moreover, the decoctosome, a lipo-nano particle consisting of 0.5%-2.5% of the decoction, demonstrated potent medical effects. We propose a Bencao (herbal) Index, including small-molecule compounds (SM), protein peptides (P), nucleic acid (N), non-nucleic and non-proteinogenic large-molecule compounds (LM) and elements from Mendeleev's periodic table (E), to quantitatively measure the medical effects of botanic medicine. The Bencao sRNA Atlas is a resource for developing gene-targeting oligonucleotide drugs and optimizing botanical medicine, and may provide potential remedies for the theory and practice of one medicine.

Refined Contact Map Prediction of Peptides Based on GCN and ResNet.

Predicting peptide inter-residue contact maps plays an important role in computational biology, which determines the topology of the peptide structure. However, due to the limited number of known homologous structures, there is still much room for inter-residue contact map prediction. Current models are not sufficient for capturing the high accuracy relationship between the residues, especially for those with a long-range distance. In this article, we developed a novel deep neural network framework to refine the rough contact map produced by the existing methods. The rough contact map is used to construct the residue graph that is processed by the graph convolutional neural network (GCN). GCN can better capture the global information and is therefore used to grasp the long-range contact relationship. The residual convolutional neural network is also applied in the framework for learning local information. We conducted the experiments on four different test datasets, and the inter-residue long-range contact map prediction accuracy demonstrates the effectiveness of our proposed method.

Protein Subcellular Localization Prediction Model Based on Graph Convolutional Network.

Protein subcellular localization prediction is an important research area in bioinformatics, which plays an essential role in understanding protein function and mechanism. Many machine learning and deep learning algorithms have been employed for this task, but most of them do not use structural information of proteins. With the advances in protein structure research in recent years, protein contact map prediction has been dramatically enhanced. In this paper, we present GraphLoc, a deep learning model that predicts the localization of proteins at the subcellular level. The cores of the model are a graph convolutional neural network module and a multi-head attention module. The protein topology graph is constructed based on a contact map predicted from protein sequences, which is used as the input of the GCN module to take full advantage of the structural information of proteins. Multi-head attention module learns the weighted contribution of different amino acids to subcellular localization in different feature representation subspaces. Experiments on the benchmark dataset show that the performance of our model is better than others. The code can be accessed at https://github.com/GoodGuy398/GraphLoc . The proposed GraphLoc model consists of three parts. The first part is a graph convolutional network (GCN) module, which utilizes the predicted contact maps to construct protein graph, taking benefit of protein information accordingly. The second part is the multi-head attention module, which learns the weighted contribution of different amino acids in different feature representation subspace, and weighted average the feature map across all amino acid nodes. The last part is a fully connected layer that maps the flatten graph representation vector to another vector with a category number dimension, followed by a softmax layer to predict the protein subcellular localization.

Amino acid environment affinity model based on graph attention network.

Proteins are engines involved in almost all functions of life. They have specific spatial structures formed by twisting and folding of one or more polypeptide chains composed of amino acids. Protein sites are protein structure microenvironments that can be identified by three-dimensional locations and local neighborhoods in which the structure or function exists. Understanding the amino acid environment affinity is essential for additional protein structural or functional studies, such as mutation analysis and functional site detection. In this study, an amino acid environment affinity model based on the graph attention network was developed. Initially, we constructed a protein graph according to the distance between amino acid pairs. Then, we extracted a set of structural features for each node. Finally, the protein graph and the associated node feature set were set to input the graph attention network model and to obtain the amino acid affinities. Numerical results show that our proposed method significantly outperforms a recent 3DCNN-based method by almost 30%.

Quercetin Attenuates Diabetic Peripheral Neuropathy by Correcting Mitochondrial Abnormality via Activation of AMPK/PGC-1α Pathway in vivo and in vitro.

The AMPK/PGC-1α pathway-mediated mitochondrial dysfunction has been supposed to play a crucial role in pathogenesis of diabetic peripheral neuropathy (DPN). The present study investigated the neuroprotective potential of quercetin, a natural AMPK activator. Streptozotocin (STZ)-induced diabetic rats that developed DPN phenotype were orally administrated with quercetin (30 and 60 mg/kg per day) for 6 weeks. The morphologic changes in the sciatic nerves (SN), the pathological structure of neurons in dorsal root ganglion (DRG), and the expressions of myelin proteins were assessed. The ATP content and the mitochondrial ultrastructure were measured. Furthermore, key proteins in the AMPK/PGC-1α pathway were determined. As a result, quercetin administration at both doses improved the paw withdrawal threshold, nerve conduction velocity, and the pathologic changes in SN and DRG of DPN rats. The expressions of myelin basic protein and myelin protein zero were also increased by quercetin. The oxidative stress, decreased ATP generation, and morphological changes of mitochondria were corrected by quercetin. In vitro study found that quercetin treatment significantly decreased the high-glucose-induced generation of reactive oxygen species, as well as attenuated the mitochondrial morphologic injuries and oxidative DNA damages of RSC96 cells. Quercetin treatment promoted the expressions of phosphorylated AMPK, PGC-1α, SIRT1, NRF1, and TFAM under hyperglycemic state in vivo and in vitro. This study revealed that the neuroprotective effect of quercetin was mainly related to mitochondrial protection by activation of the AMPK/PGC-1α pathway for the first time and proved quercetin as a potential therapeutic agent in the management of diabetic neuropathy.