Likelihood-based feature representation learning combined with neighborhood information for predicting circRNA-miRNA associations.

Connections between circular RNAs (circRNAs) and microRNAs (miRNAs) assume a pivotal position in the onset, evolution, diagnosis and treatment of diseases and tumors. Selecting the most potential circRNA-related miRNAs and taking advantage of them as the biological markers or drug targets could be conducive to dealing with complex human diseases through preventive strategies, diagnostic procedures and therapeutic approaches. Compared to traditional biological experiments, leveraging computational models to integrate diverse biological data in order to infer potential associations proves to be a more efficient and cost-effective approach. This paper developed a model of Convolutional Autoencoder for CircRNA-MiRNA Associations (CA-CMA) prediction. Initially, this model merged the natural language characteristics of the circRNA and miRNA sequence with the features of circRNA-miRNA interactions. Subsequently, it utilized all circRNA-miRNA pairs to construct a molecular association network, which was then fine-tuned by labeled samples to optimize the network parameters. Finally, the prediction outcome is obtained by utilizing the deep neural networks classifier. This model innovatively combines the likelihood objective that preserves the neighborhood through optimization, to learn the continuous feature representation of words and preserve the spatial information of two-dimensional signals. During the process of 5-fold cross-validation, CA-CMA exhibited exceptional performance compared to numerous prior computational approaches, as evidenced by its mean area under the receiver operating characteristic curve of 0.9138 and a minimal SD of 0.0024. Furthermore, recent literature has confirmed the accuracy of 25 out of the top 30 circRNA-miRNA pairs identified with the highest CA-CMA scores during case studies. The results of these experiments highlight the robustness and versatility of our model.

Biolinguistic graph fusion model for circRNA-miRNA association prediction.

Emerging clinical evidence suggests that sophisticated associations with circular ribonucleic acids (RNAs) (circRNAs) and microRNAs (miRNAs) are a critical regulatory factor of various pathological processes and play a critical role in most intricate human diseases. Nonetheless, the above correlations via wet experiments are error-prone and labor-intensive, and the underlying novel circRNA-miRNA association (CMA) has been validated by numerous existing computational methods that rely only on single correlation data. Considering the inadequacy of existing machine learning models, we propose a new model named BGF-CMAP, which combines the gradient boosting decision tree with natural language processing and graph embedding methods to infer associations between circRNAs and miRNAs. Specifically, BGF-CMAP extracts sequence attribute features and interaction behavior features by Word2vec and two homogeneous graph embedding algorithms, large-scale information network embedding and graph factorization, respectively. Multitudinous comprehensive experimental analysis revealed that BGF-CMAP successfully predicted the complex relationship between circRNAs and miRNAs with an accuracy of 82.90% and an area under receiver operating characteristic of 0.9075. Furthermore, 23 of the top 30 miRNA-associated circRNAs of the studies on data were confirmed in relevant experiences, showing that the BGF-CMAP model is superior to others. BGF-CMAP can serve as a helpful model to provide a scientific theoretical basis for the study of CMA prediction.

Multiomics analysis reveals the molecular mechanisms underlying virulence in Rhizoctonia and jasmonic acid-mediated resistance in Tartary buckwheat (Fagopyrum tataricum).

Rhizoctonia solani is a devastating soil-borne pathogen that seriously threatens the cultivation of economically important crops. Multiple strains with a very broad host range have been identified, but only 1 (AG1-IA, which causes rice sheath blight disease) has been examined in detail. Here, we analyzed AG4-HGI 3 originally isolated from Tartary buckwheat (Fagopyrum tataricum), but with a host range comparable to AG1-IA. Genome comparison reveals abundant pathogenicity genes in this strain. We used multiomic approaches to improve the efficiency of screening for disease resistance genes. Transcriptomes of the plant-fungi interaction identified differentially expressed genes associated with virulence in Rhizoctonia and resistance in Tartary buckwheat. Integration with jasmonate-mediated transcriptome and metabolome changes revealed a negative regulator of jasmonate signaling, cytochrome P450 (FtCYP94C1), as increasing disease resistance probably via accumulation of resistance-related flavonoids. The integration of resistance data for 320 Tartary buckwheat accessions identified a gene homolog to aspartic proteinase (FtASP), with peak expression following R. solani inoculation. FtASP exhibits no proteinase activity but functions as an antibacterial peptide that slows fungal growth. This work reveals a potential mechanism behind pathogen virulence and host resistance, which should accelerate the molecular breeding of resistant varieties in economically essential crops.

Winter warming post floral initiation delays flowering via bud dormancy activation and affects yield in a winter annual crop.

Winter annual life history is conferred by the requirement for vernalization to promote the floral transition and control the timing of flowering. Here we show using winter oilseed rape that flowering time is controlled by inflorescence bud dormancy in addition to vernalization. Winter warming treatments given to plants in the laboratory and field increase flower bud abscisic acid levels and delay flowering in spring. We show that the promotive effect of chilling reproductive tissues on flowering time is associated with the activity of two FLC genes specifically silenced in response to winter temperatures in developing inflorescences, coupled with activation of a BRANCHED1-dependent bud dormancy transcriptional module. We show that adequate winter chilling is required for normal inflorescence development and high yields in addition to the control of flowering time. Because warming during winter flower development is associated with yield losses at the landscape scale, our work suggests that bud dormancy activation may be important for effects of climate change on winter arable crop yields.

MHESMMR: a multilevel model for predicting the regulation of miRNAs expression by small molecules.

According to the expression of miRNA in pathological processes, miRNAs can be divided into oncogenes or tumor suppressors. Prediction of the regulation relations between miRNAs and small molecules (SMs) becomes a vital goal for miRNA-target therapy. But traditional biological approaches are laborious and expensive. Thus, there is an urgent need to develop a computational model. In this study, we proposed a computational model to predict whether the regulatory relationship between miRNAs and SMs is up-regulated or down-regulated. Specifically, we first use the Large-scale Information Network Embedding (LINE) algorithm to construct the node features from the self-similarity networks, then use the General Attributed Multiplex Heterogeneous Network Embedding (GATNE) algorithm to extract the topological information from the attribute network, and finally utilize the Light Gradient Boosting Machine (LightGBM) algorithm to predict the regulatory relationship between miRNAs and SMs. In the fivefold cross-validation experiment, the average accuracies of the proposed model on the SM2miR dataset reached 79.59% and 80.37% for up-regulation pairs and down-regulation pairs, respectively. In addition, we compared our model with another published model. Moreover, in the case study for 5-FU, 7 of 10 candidate miRNAs are confirmed by related literature. Therefore, we believe that our model can promote the research of miRNA-targeted therapy.

Discovery of novel RNA viruses through analysis of fungi-associated next-generation sequencing data.

BACKGROUND: Like all other species, fungi are susceptible to infection by viruses. The diversity of fungal viruses has been rapidly expanding in recent years due to the availability of advanced sequencing technologies. However, compared to other virome studies, the research on fungi-associated viruses remains limited. RESULTS: In this study, we downloaded and analyzed over 200 public datasets from approximately 40 different Bioprojects to explore potential fungal-associated viral dark matter. A total of 12 novel viral sequences were identified, all of which are RNA viruses, with lengths ranging from 1,769 to 9,516 nucleotides. The amino acid sequence identity of all these viruses with any known virus is below 70%. Through phylogenetic analysis, these RNA viruses were classified into different orders or families, such as Mitoviridae, Benyviridae, Botourmiaviridae, Deltaflexiviridae, Mymonaviridae, Bunyavirales, and Partitiviridae. It is possible that these sequences represent new taxa at the level of family, genus, or species. Furthermore, a co-evolution analysis indicated that the evolutionary history of these viruses within their groups is largely driven by cross-species transmission events. CONCLUSIONS: These findings are of significant importance for understanding the diversity, evolution, and relationships between genome structure and function of fungal viruses. However, further investigation is needed to study their interactions.

Detection of Helicobacter pylori Infection in Human Gastric Fluid Through Surface-Enhanced Raman Spectroscopy Coupled With Machine Learning Algorithms.

Diagnostic methods for Helicobacter pylori infection include, but are not limited to, urea breath test, serum antibody test, fecal antigen test, and rapid urease test. However, these methods suffer drawbacks such as low accuracy, high false-positive rate, complex operations, invasiveness, etc. Therefore, there is a need to develop simple, rapid, and noninvasive detection methods for H. pylori diagnosis. In this study, we propose a novel technique for accurately detecting H. pylori infection through machine learning analysis of surface-enhanced Raman scattering (SERS) spectra of gastric fluid samples that were noninvasively collected from human stomachs via the string test. One hundred participants were recruited to collect gastric fluid samples noninvasively. Therefore, 12,000 SERS spectra (n = 120 spectra/participant) were generated for building machine learning models evaluated by standard metrics in model performance assessment. According to the results, the Light Gradient Boosting Machine algorithm exhibited the best prediction capacity and time efficiency (accuracy = 99.54% and time = 2.61 seconds). Moreover, the Light Gradient Boosting Machine model was blindly tested on 2,000 SERS spectra collected from 100 participants with unknown H. pylori infection status, achieving a prediction accuracy of 82.15% compared with qPCR results. This novel technique is simple and rapid in diagnosing H. pylori infection, potentially complementing current H. pylori diagnostic methods.

Multi-omics identification of a key glycosyl hydrolase gene FtGH1 involved in rutin hydrolysis in Tartary buckwheat (Fagopyrum tataricum).

Rutin, a flavonoid rich in buckwheat, is important for human health and plant resistance to external stresses. The hydrolysis of rutin to quercetin underlies the bitter taste of Tartary buckwheat. In order to identify rutin hydrolysis genes, a 200 genotypes mini-core Tartary buckwheat germplasm resource was re-sequenced with 30-fold coverage depth. By combining the content of the intermediate metabolites of rutin metabolism with genome resequencing data, metabolite genome-wide association analyses (GWAS) eventually identified a glycosyl hydrolase gene FtGH1, which could hydrolyse rutin to quercetin. This function was validated both in Tartary buckwheat overexpression hairy roots and in vitro enzyme activity assays. Mutation of the two key active sites, which were determined by molecular docking and experimentally verified via overexpression in hairy roots and transient expression in tobacco leaves, exhibited abnormal subcellular localization, suggesting functional changes. Sequence analysis revealed that mutation of the FtGH1 promoter in accessions of two haplotypes might be necessary for enzymatic activity. Co-expression analysis and GWAS revealed that FtbHLH165 not only repressed FtGH1 expression, but also increased seed length. This work reveals a potential mechanism behind rutin metabolism, which should provide both theoretical support in the study of flavonoid metabolism and in the molecular breeding of Tartary buckwheat.

A biomedical knowledge graph-based method for drug-drug interactions prediction through combining local and global features with deep neural networks.

Drug-drug interactions (DDIs) prediction is a challenging task in drug development and clinical application. Due to the extremely large complete set of all possible DDIs, computer-aided DDIs prediction methods are getting lots of attention in the pharmaceutical industry and academia. However, most existing computational methods only use single perspective information and few of them conduct the task based on the biomedical knowledge graph (BKG), which can provide more detailed and comprehensive drug lateral side information flow. To this end, a deep learning framework, namely DeepLGF, is proposed to fully exploit BKG fusing local-global information to improve the performance of DDIs prediction. More specifically, DeepLGF first obtains chemical local information on drug sequence semantics through a natural language processing algorithm. Then a model of BFGNN based on graph neural network is proposed to extract biological local information on drug through learning embedding vector from different biological functional spaces. The global feature information is extracted from the BKG by our knowledge graph embedding method. In DeepLGF, for fusing local-global features well, we designed four aggregating methods to explore the most suitable ones. Finally, the advanced fusing feature vectors are fed into deep neural network to train and predict. To evaluate the prediction performance of DeepLGF, we tested our method in three prediction tasks and compared it with state-of-the-art models. In addition, case studies of three cancer-related and COVID-19-related drugs further demonstrated DeepLGF's superior ability for potential DDIs prediction. The webserver of the DeepLGF predictor is freely available at http://120.77.11.78/DeepLGF/.

A novel circRNA-miRNA association prediction model based on structural deep neural network embedding.

A large amount of clinical evidence began to mount, showing that circular ribonucleic acids (RNAs; circRNAs) perform a very important function in complex diseases by participating in transcription and translation regulation of microRNA (miRNA) target genes. However, with strict high-throughput techniques based on traditional biological experiments and the conditions and environment, the association between circRNA and miRNA can be discovered to be labor-intensive, expensive, time-consuming, and inefficient. In this paper, we proposed a novel computational model based on Word2vec, Structural Deep Network Embedding (SDNE), Convolutional Neural Network and Deep Neural Network, which predicts the potential circRNA-miRNA associations, called Word2vec, SDNE, Convolutional Neural Network and Deep Neural Network (WSCD). Specifically, the WSCD model extracts attribute feature and behaviour feature by word embedding and graph embedding algorithm, respectively, and ultimately feed them into a feature fusion model constructed by combining Convolutional Neural Network and Deep Neural Network to deduce potential circRNA-miRNA interactions. The proposed method is proved on dataset and obtained a prediction accuracy and an area under the receiver operating characteristic curve of 81.61% and 0.8898, respectively, which is shown to have much higher accuracy than the state-of-the-art models and classifier models in prediction. In addition, 23 miRNA-related circular RNAs (circRNAs) from the top 30 were confirmed in relevant experiences. In these works, all results represent that WSCD would be a helpful supplementary reliable method for predicting potential miRNA-circRNA associations compared to wet laboratory experiments.

miR-126a-5p inhibits H1N1-induced inflammation and matrix protease secretion in lung fibroblasts by targeting ADAMTS-4.

Upregulation of ADAMTS-4 has been reported to have an important role in lung injury, and ADAMTS-4 expression is regulated by miR-126a-5p in abdominal aortic aneurysms. The aim of this study was to investigate whether miR-126a-5p/ADAMTS-4 plays a role in influenza-virus-induced lung injury. Lung fibroblasts were infected with H1N1 influenza virus to detect changes in miR-126a-5p and ADAMTS-4 expression, and cell viability was measured by CCK-8 assay. Inflammatory factors and matrix protease levels were examined using ELISA kits, and cell apoptosis was assessed by measuring the levels of apoptosis-related proteins. A dual luciferase assay was used to verify the regulatory relationship between miR-126a-5p and ADAMTS-4. H1N1 influenza virus reduced fibroblast viability, inhibited miR-126a-5p expression, and promoted ADAMTS-4 expression. Overexpression of miR-126a-5p attenuated the cellular inflammatory response, apoptosis, matrix protease secretion, and virus replication. Luciferase reporter assays revealed that miR-126a-5p inhibited ADAMTS-4 expression by targeting ADAMTS-4 mRNA. Further experiments showed that overexpression of ADAMTS-4 significantly reversed the inhibitory effects of miR-126a-5p on fibroblast inflammation, apoptosis, matrix protease secretion, and virus replication. Upregulation of miR-126a-5p inhibits H1N1-induced apoptosis, inflammatory factors, and matrix protease secretion, as well as virus replication in lung fibroblasts.

Design, synthesis, and evaluation of pirfenidone-NSAIDs conjugates for the treatment of idiopathic pulmonary fibrosis.

Idiopathic pulmonary fibrosis (IPF) is a fatal, chronic and progressive lung disease that threaten public health like many cancers. In this study, targeting the significant driving factor, inflammatory response, of the IPF, several conjugates of pirfenidone (PFD) with non-steroidal anti-inflammatory drugs (NSAIDs), along with their derivatives, were designed and synthesized to enhance the anti-IPF potency of PFD. Among these compounds, the (S)-ibuprofen-PFD conjugate 5b exhibited the most potent anti-proliferation activity against NIH3T3 cells, demonstrating up to a 343-fold improvement compared to PFD (IC50 = 0.04 mM vs IC50 = 13.72 mM). Notably, 5b exhibited superior activity in inhibiting the migration of macrophages induced by TGF-ß compared to PFD. Additionally, 5b demonstrated significant suppression of TGF-ß-induced migration of NIH3T3 cells and induction of apoptosis in NIH3T3 cells. Mechanistic studies revealed that 5b reduced the expression of collagen I and α-SMA by inhibiting the TGF-ß/SMAD3 pathway. In a bleomycin-induced pulmonary fibrosis model, treatment with 5b (40 mg/kg/day, orally) exhibited a more pronounced effect on reducing the degree of histopathological changes in lung tissue and alleviating collagen deposition compared to PFD (100 mg/kg/day, orally). Moreover, 5b could block the expression of collagen I, α-SMA, fibronectin, and pro-inflammatory factors (IL-6, IFN-γ, and TNF-α) compared to PFD, while demonstrating low toxicity in vivo. These preliminary results indicated that the hybridization of PFD with NSAIDs represented an effective modification approach to improve the anti-IPF potency of PFD. Consequently, 5b emerged as a promising candidate for the further development of new anti-IPF agents.

Exogenous Indole-3-Acetic Acid Suppresses Rice Infection of Magnaporthe oryzae by Affecting Plant Resistance and Fungal Growth.

Auxin is an important phytohormone that regulates diverse biologic processes, including plant growth and immunity. Indole-3-acetic acid (IAA), known as one of the main forms of auxin, is able to activate plant immunity. However, it is unknown whether IAA enhances plant resistance and/or suppresses the growth of the fungal pathogen Magnaporthe oryzae. Here, we found that IAA could induce expression levels of pathogenesis-related genes to enhance disease resistance and could control the development of blast disease through inhibiting M. oryzae infection. Exogenous IAA suppressed mycelial growth and delayed spore germination by inhibiting fungal endogenous IAA biosynthesis and impairing redox homeostasis, respectively. When applied to a field test, two IAA analogues, 1-naphthaleneacetic acid and 2,4-dichlorophenoxy acetic acid, can effectively control rice blast disease. Our study advances the understanding of IAA in controlling rice blast disease through suppressing pathogen growth and enhancing plant resistance.

The body mass index and the risk of ectopic pregnancy: a 5-year retrospective case-control study.

PURPOSE: Acknowledging the associated risk factors may have a positive impact on reducing the incidence of ectopic pregnancy (EP). In recent years, body mass index (BMI) has been mentioned in research. However, few studies are available and controversial on the relationship between EP and BMI. METHODS: We retrospectively studied the EP women as a case group and the deliveries as a control group in the central hospital of Wuhan during 2017 ~ 2021. χ2 test of variables associated with ectopic pregnancy was performed to find differences. Univariate and multivariate binary logistic regression analysis was conducted to analyze the association of the variables of age, parity, history of induced abortion, history of ectopic pregnancy, history of spontaneous abortion, history of appendectomy surgery and BMI (< 18.5 kg/m2, 18.5 ~ 24.9 kg/m2, 25 kg/m2 ~ 29.9 kg/m2, ≥ 30 kg /m2) with EP. RESULTS: They were 659 EP and 1460 deliveries. The variables of age, parity, history of induced abortion, history of ectopic pregnancy and BMI were different significantly(P < 0.05). Multivariate analysis showed that the variables of age > 35 years old [(OR (Odds Ratio), 5.415; 95%CI (Confidence Interval), 4.006 ~ 7.320, P < 0.001], history of ectopic pregnancy (OR, 3.944; 95%CI, 2.405 ~ 6.467; P < 0.001), history of induced abortion(OR, 3.365; 95%CI, 2.724 ~ 4.158, P < 0.001) and low BMI (< 18.5 kg/m2) (OR, 1.929; 95%CI, 1.416 ~ 2.628, P < 0.001])increased the risk of EP. CONCLUSION: The history of ectopic pregnancy, history of induced abortion and age > 35 years old were the risk factors with EP. In addition to these traditional factors, we found low BMI (< 18.5 kg/m2) with women may increase the risk to EP.

Quality of life, household income, and dietary habits are associated with the risk of sarcopenia among the Chinese elderly.

BACKGROUND: Health-related quality of life (HRQoL), which can be influenced by various aspects, especially socioeconomic status and lifestyle, has been identified as an important predictor of the prognosis of older adults. Dietary habit, a major part of lifestyle, can affect the nutritional status, which is closely correlated with the development of geriatric syndromes in the elderly. AIMS: The aim of the study was to examine the association of HRQoL, socioeconomic status, and lifestyle with the risk and severity of sarcopenia, a geriatric syndrome characterized by progressive loss of skeletal muscle mass, strength and function. METHODS: A cross-sectional retrospective study with 2877 participants aged ≥65 years was performed. HRQoL was assessed using EuroQoL Five Dimensions questionnaire. Socioeconomic status was assessed by the educational attainment, occupation, and household income. Lifestyle was assessed using 12 items closely related to Chinese living habits. The information of daily dietary habits including tea, alcohol, type of diet, and volume of drinking water were collected. The associations of HRQoL, socioeconomic status, and lifestyle with the risk of sarcopenia were examined by multivariate regression logistical analysis. The potential causal role of age, body mass index, and waist circumference in the effect of HRQoL on sarcopenia risk was analyzed by causal mediation analysis. RESULTS: High HRQoL [adjusted odds ratio (OR) =0.85, 95% confidence interval (CI) =0.69-0.95, P=0.034] and household income levels (adjusted OR =0.74, 95% CI =0.57-0.95, P=0.019) were inversely associated with the risk of sarcopenia. Meanwhile, more consumption of spicy food (adjusted OR =1.34, 95% CI =1.09-1.81, P =0.037) and occasionally drinking (adjusted OR =1.46, 95% CI =1.07-2.00, P =0.016, as compared to those never drinking) were associated with higher risk of sarcopenia, while skipping breakfast occasionally (adjusted OR =0.37, 95% CI =0.21-0.64, P <0.001, as compared to those eating breakfast every day) and less consumption of salt (adjusted OR =0.71, 95% CI =0.52-0.96, P =0.026, as compared to those consuming high amount of salt) were associated with lower risk of sarcopenia. Further causal mediation analysis aimed to explore how much age, body mass index, and waist circumference might explain the effect of HRQoL on the risk of sarcopenia showed that the estimated proportion that mediated the effect of HRQoL on the risk of sarcopenia by age was 28.0%. CONCLUSIONS: In summary, our findings demonstrate that low levels of HRQoL and household income, more intake of salt and spicy food, and occasional intake of alcohol are correlated with higher risk of sarcopenia, while skipping breakfast occasionally is associated with lower risk of sarcopenia in a Chinese population of older adults.

The association of alcohol consumption with the risk of sarcopenia: a dose-response meta-analysis.

Background: Sarcopenia is defined as a progressive loss of skeletal muscle mass plus a decline in muscle strength and/or reduced physical performance with advancing age. The results of current studies on the relationship between drinking and sarcopenia remain controversial.Objectives: The aim of this meta-analysis was to evaluate the association of alcohol consumption with the risk of sarcopenia.Methods: Systematic searches were conducted without language restrictions from the beginning of each database to September 20, 2023 on PubMed, Embase, Cochrane Library, Web of Science, Wanfang Data, Chinese BioMedical Literature, and China national knowledge infrastructure databases. Meta-analysis was conducted to pool the study-specific odds ratios (ORs) with 95% confidence interval (CI).Results: Sixty-two studies with 454,643 participants were enrolled. The meta-analysis of proportions revealed that alcohol consumption was not associated with the presence of sarcopenia, with a pooled OR of 0.964 (95% CI = 0.912-1.019). Further subgroup analysis indicated that alcohol consumption was correlated with lower risk of sarcopenia in men (OR = 0.763; 95% CI = 0.622-0.938; P = .010). The nonlinear dose-response analysis suggested a J-shaped association between alcohol consumption and the risk of sarcopenia, with a nadir at the amounts of alcohol consumption of 6.6 grams/day (OR = 0.765; 95% CI = 0.608-0.957; P < .05).Conclusions: The results of this meta-analysis indicate that alcohol consumption is not a risk factor for the development of sarcopenia. Any suggestion of a putative protective effect of alcohol should be treated with caution, particularly in light of the overall lack of relationship reported in the present comprehensive meta-analysis.

[Effects of α1-antitrypsin on motor function in mice with immature brain white matter injury]. / α1-æŠ—èƒ°è›‹ç™½é ¶å¯¹æœªæˆç†Ÿè„‘ç™½è´¨æŸä¼¤å°é¼ è¿åŠ¨åŠŸèƒ½çš„å½±å“.

OBJECTIVES: To investigate the effects of α1-antitrypsin (AAT) on motor function in adult mice with immature brain white matter injury. METHODS: Five-day-old C57BL/6J mice were randomly assigned to the sham surgery group (n=27), hypoxia-ischemia (HI) + saline group (n=27), and HI+AAT group (n=27). The HI white matter injury mouse model was established using HI methods. The HI+AAT group received intraperitoneal injections of AAT (50 mg/kg) 24 hours before HI, immediately after HI, and 72 hours after HI; the HI+saline group received intraperitoneal injections of the same volume of saline at the corresponding time points. Brain T2-weighted magnetic resonance imaging scans were performed at 7 and 55 days after modeling. At 2 months of age, adult mice were evaluated for static, dynamic, and coordination parameters using the Catwalk gait analysis system. RESULTS: Compared to the sham surgery group, mice with HI injury showed high signal intensity on brain T2-weighted magnetic resonance imaging at 7 days after modeling, indicating significant white matter injury. The white matter injury persisted at 55 days after modeling. In comparison to the sham surgery group, the HI+saline group exhibited decreased paw print area, maximum contact area, average pressure, maximum pressure, paw print width, average velocity, body velocity, stride length, swing speed, percentage of gait pattern AA, and percentage of inter-limb coordination (left hind paw → left front paw) (P<0.05). The HI+saline group showed increased inter-paw distance, percentage of gait pattern AB, and percentage of phase lag (left front paw → left hind paw) compared to the sham surgery group (P<0.05). In comparison to the HI+saline group, the HI+AAT group showed increased average velocity, body velocity, stride length, and swing speed (right front paw) (P<0.05). CONCLUSIONS: The mice with immature brain white matter injury may exhibit significant motor dysfunction in adulthood, while the use of AAT can improve some aspects of their motor function.

A predictive paradigm for COVID-19 prognosis based on the longitudinal measure of biomarkers.

Novel coronavirus disease 2019 (COVID-19) is an emerging, rapidly evolving crisis, and the ability to predict prognosis for individual COVID-19 patient is important for guiding treatment. Laboratory examinations were repeatedly measured during hospitalization for COVID-19 patients, which provide the possibility for the individualized early prediction of prognosis. However, previous studies mainly focused on risk prediction based on laboratory measurements at one time point, ignoring disease progression and changes of biomarkers over time. By using historical regression trees (HTREEs), a novel machine learning method, and joint modeling technique, we modeled the longitudinal trajectories of laboratory biomarkers and made dynamically predictions on individual prognosis for 1997 COVID-19 patients. In the discovery phase, based on 358 COVID-19 patients admitted between 10 January and 18 February 2020 from Tongji Hospital, HTREE model identified a set of important variables including 14 prognostic biomarkers. With the trajectories of those biomarkers through 5-day, 10-day and 15-day, the joint model had a good performance in discriminating the survived and deceased COVID-19 patients (mean AUCs of 88.81, 84.81 and 85.62% for the discovery set). The predictive model was successfully validated in two independent datasets (mean AUCs of 87.61, 87.55 and 87.03% for validation the first dataset including 112 patients, 94.97, 95.78 and 94.63% for the second validation dataset including 1527 patients, respectively). In conclusion, our study identified important biomarkers associated with the prognosis of COVID-19 patients, characterized the time-to-event process and obtained dynamic predictions at the individual level.

Inside Out Computational Redesign of Cavities for Improving Thermostability and Catalytic Activity of Rhizomucor Miehei Lipase.

Cavities are created by hydrophobic interactions between residue side chain atoms during the folding of enzymes. Redesigning cavities can improve the thermostability and catalytic activity of the enzyme; however, the synergistic effect of cavities remains unclear. In this study, Rhizomucor miehei lipase (RML) was used as a model to explore volume fluctuation and spatial distribution changes of the internal cavities, which could reveal the roles of internal cavities in the thermostability and catalytic activity. We present an inside out cavity engineering (CE) strategy based on computational techniques to explore how changes in the volumes and spatial distribution of cavities affect the thermostability and catalytic activity of the enzyme. We obtained 12 single-point mutants, among which the melting temperatures (Tm) of 8 mutants showed an increase of more than 2°C. Sixteen multipoint mutations were further designed by spatial distribution rearrangement of internal cavities. The Tm of the most stable triple variant, with mutations including T21V (a change of T to V at position 21), S27A, and T198L (T21V/S27A/T198L), was elevated by 11.0°C, together with a 28.7-fold increase in the half-life at 65°C and a specific activity increase of 9.9-fold (up to 5,828 U mg-1), one of the highest lipase activities reported. The possible mechanism of decreased volumes and spatial rearrangement of the internal cavities improved the stability of the enzyme, optimizing the outer substrate tunnel to improve the catalytic efficiency. Overall, the inside out computational redesign of cavities method could help to deeply understand the effect of cavities on enzymatic stability and activity, which would be beneficial for protein engineering efforts to optimize natural enzymes. IMPORTANCE In the present study, R. miehei lipase, which is widely used in various industries, provides an opportunity to explore the effects of internal cavities on the thermostability and catalytic activity of enzymes. Here, we execute high hydrostatic pressure molecular dynamics (HP-MD) simulations to screen the critical internal cavity and reshape the internal cavities through site-directed mutation. We show that as the global internal cavity volume decreases, cavity rearrangement can improve the stability of the protein while optimizing the substrate channel to improve the catalytic efficiency. Our results provide significant insights into understanding the mechanism of action of the internal cavity. Our strategy is expected to be applied to other enzymes to promote increases in thermostability and catalytic activity.

Development of a multi-recombinase polymerase amplification assay for rapid identification of COVID-19, influenza A and B.

The coronavirus disease 2019 (COVID-19) pandemic caused extensive loss of life worldwide. Further, the COVID-19 and influenza mix-infection had caused great distress to the diagnosis of the disease. To control illness progression and limit viral spread within the population, a real-time reverse-transcription PCR (RT-PCR) assay for early diagnosis of COVID-19 was developed, but detection was time-consuming (4-6 h). To improve the diagnosis of COVID-19 and influenza, we herein developed a recombinase polymerase amplification (RPA) method for simple and rapid amplification of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the causative agent of COVID-19 and Influenza A (H1N1, H3N2) and B (influenza B). Genes encoding the matrix protein (M) for H1N1, and the hemagglutinin (HA) for H3N2, and the polymerase A (PA) for Influenza B, and the nucleocapsid protein (N), the RNA-dependent-RNA polymerase (RdRP) in the open reading frame 1ab (ORF1ab) region, and the envelope protein (E) for SARS-CoV-2 were selected, and specific primers were designed. We validated our method using SARS-CoV-2, H1N1, H3N2 and influenza B plasmid standards and RNA samples extracted from COVID-19 and Influenza A/B (RT-PCR-verified) positive patients. The method could detect SARS-CoV-2 plasmid standard DNA quantitatively between 102 and 105 copies/ml with a log linearity of 0.99 in 22 min. And this method also be very effective in simultaneous detection of H1N1, H3N2 and influenza B. Clinical validation of 100 cases revealed a sensitivity of 100% for differentiating COVID-19 patients from healthy controls when the specificity was set at 90%. These results demonstrate that this nucleic acid testing method is advantageous compared with traditional PCR and other isothermal nucleic acid amplification methods in terms of time and portability. This method could potentially be used for detection of SARS-CoV-2, H1N1, H3N2 and influenza B, and adapted for point-of-care (POC) detection of a broad range of infectious pathogens in resource-limited settings.