Establishment and validation of an artificial intelligence web application for predicting postoperative in-hospital mortality in patients with hip fracture: a National cohort study of 52,707 cases.

BACKGROUND: In-hospital mortality following hip fractures is a significant concern, and accurate prediction of this outcome is crucial for appropriate clinical management. Nonetheless, there is a lack of effective prediction tools in clinical practice. By utilizing artificial intelligence and machine learning techniques, this study aims to develop a predictive model that can assist clinicians in identifying geriatric hip fracture patients at a higher risk of in-hospital mortality. METHODS: A total of 52,707 geriatric hip fracture patients treated with surgery from 90 hospitals were included in this study. The primary outcome was postoperative in-hospital mortality. The patients were randomly divided into two groups, with a ratio of 7:3. The majority of patients, assigned to the training cohort, were used to develop the AI models. The remaining patients, assigned to the validation cohort, were used to validate the models. Various machine learning algorithms, including logistic regression (LR), decision tree (DT), naïve Bayesian (NB), neural network (NN), eXGBoosting machine (eXGBM), and random forest (RF), were employed for model development. A comprehensive scoring system, incorporating 10 evaluation metrics, was developed to assess the prediction performance, with higher scores indicating superior predictive capability. Based on the best machine learning-based model, an AI application was developed on the Internet. In addition, a comparative testing of prediction performance between doctors and the AI application. FINDINGS: The eXGBM model exhibited the best prediction performance, with an AUC of 0.908 (95% CI: 0.881-0.932), as well as the highest accuracy (0.820), precision (0.817), specificity (0.814), and F1 score (0.822), and the lowest Brier score (0.120) and log loss (0.374). Additionally, the model showed favorable calibration, with a slope of 0.999 and an intercept of 0.028. According to the scoring system incorporating 10 evaluation metrics, the eXGBM model achieved the highest score (56), followed by the RF model (48) and NN model (41). The LR, DT, and NB models had total scores of 27, 30, and 13, respectively. The AI application has been deployed online at https://in-hospitaldeathinhipfracture-l9vhqo3l55fy8dkdvuskvu.streamlit.app/ , based on the eXGBM model. The comparative testing revealed that the AI application's predictive capabilities significantly outperformed those of the doctors in terms of AUC values (0.908 vs. 0.682, P <0.001). CONCLUSIONS: The eXGBM model demonstrates promising predictive performance in assessing the risk of postoperative in-hospital mortality among geriatric hip fracture patients. The developed AI model serves as a valuable tool to enhance clinical decision-making.

Ptehosides A-I: Nine undescribed iridoids with in vitro cytotoxicity from the whole plant of Pterocephalus hookeri (C.B. Clarke) Höeck.

Nine previously undescribed iridoids, ptehosides A-I (1-9), together with 12 known ones (10-21), were isolated from Pterocephalus hookeri (C.B. Clarke) Höeck. Their structures were elucidated using various spectroscopic methods including HR-ESI-MS, NMR, UV, IR and CD, etc. The cytotoxic activities of all isolates were evaluated using MTT method in three human cancer cell lines (Caco2, Huh-7, and SW982). As result, compound 9 exhibited substantial inhibitory activity on Caco2, Huh-7, and SW982 cells with IC50 values of 1.17 ± 0.05, 1.15 ± 0.05 and 1.14 ± 0.04 µM, respectively. A preliminary mechanism study showed that 9 arrested the cell cycle of SW982 cells in the G0/G1 phase and induced apoptosis by upregulating Bax expression and downregulating Bcl-2 expression.

Upconversion fluorescence nanosensor based on enzymatic inhibited and copper-triggered o-phenylenediamine oxidation for the detection of dimethoate pesticides.

Pesticide residues in agricultural products pose a significant threat to human health. Herein, a sensitive fluorescence method employing upconversion nanoparticles was developed for detecting organophosphorus pesticides (OPs) based on the principle of enzyme inhibition and copper-triggered o-phenylenediamine (OPD) oxidation. Copper ions (Cu2+) oxidized the colorless OPD to a yellow 2,3-diaminophenazine (oxOPD). The yellow solution oxOPD quenched the fluorescence of upconversion nanoparticles due to the fluorescence resonance energy transfer. The high affinity of Cu2+ for thiocholine reduced the level of oxOPD, resulting in almost no fluorescence quenching. The addition of dimethoate led to the inhibition of acetylcholinesterase activity and thus prevented the formation of thiocholine. Subsequently, Cu2+ oxidized OPD to form oxOPD, which attenuated the fluorescence signal of the system. The detection system has a good linear range of 0.01 ng/mL to 50 ng/mL with a detection limit of 0.008 ng/mL, providing promising applications for rapid detection of dimethoate.

Correction: Innovative wound management: creating dynamic Alg-Mg/SF hydrogels for controlled Mg2+ release in wound healing.

[This corrects the article DOI: 10.1039/D4RA00793J.].

Rare Earth Nanoprobes for Targeted Delineation of Triple Negative Breast Cancer and Enhancement of Radioimmunotherapy.

Radiotherapy demonstrates a synergistic effect with immunotherapy by inducing a transformation of "immune cold" tumors into "immune hot" tumors in triple negative breast cancer (TNBC). Nevertheless, the effectiveness of immunotherapy is constrained by low expression of tumor-exposed antigens, inadequate inflammation, and insufficient tumor infiltrating lymphocyte (TILs). To address this predicament, novel lutecium-based rare earth nanoparticles (RENPs) are synthesized with the aim of amplifying radiation effect and tumor immune response. The nanoprobe is characterized by neodymium-based down-conversion fluorescence, demonstrating robust photostability, biocompatibility, and targetability. The conjugation of RENPs with a CXCR4 targeted drug enables precise delineation of breast tumors using a near-infrared imaging system and improves radiation efficacy via lutetium-based radio-sensitizer in vivo. Furthermore, the study shows a notable enhancement of immune response through the induction of immunogenic cell death and recruitment of TILs, resulting in the inhibition of tumor progression both in vitro and in vivo models following the administration of nanoparticles. Hence, the novel multifunctional nanoprobes incorporating various lanthanide elements offer the potential for imaging-guided tumor delineation, radio-sensitization, and immune activation post-radiation, thus presenting an efficient radio-immunotherapeutic approach for TNBC.

JIMR: Joint Semantic and Geometry Learning for Point Scene Instance Mesh Reconstruction.

Point scene instance mesh reconstruction is a challenging task since it requires both scene-level instance segmentation and instance-level mesh reconstruction from partial observations simultaneously. Previous works either adopt a detection backbone or a segmentation one, and then directly employ a mesh reconstruction network to produce complete meshes from incomplete instance point clouds. To further boost the mesh reconstruction quality with both local details and global smoothness, in this work, we propose JIMR, a joint framework with two cascaded stages for semantic and geometry understanding. In the first stage, we propose to perform both instance segmentation and object detection simultaneously. By making both tasks promote each other, this design facilitates subsequent mesh reconstruction by providing more precisely-segmented instance points and better alignment benefiting from predicted complete bounding boxes. In the second stage, we propose a complete-then-reconstruct procedure, where the completion module explicitly disentangles completion from reconstruction, and enables the usage of pre-trained weights of existing powerful completion and reconstruction networks. Moreover, we propose a comprehensive confidence score to filter proposals considering the quality of instance segmentation, bounding box detection, semantic classification, and mesh reconstruction at the same time. Experiments show that our proposed JIMR outperforms state-of-the-art methods regarding instance reconstruction qualitatively and quantitatively.

Collagen degradation assessment with an in vitro rotator cuff tendinopathy model using multiparametric ultrashort-TE magnetization transfer (UTE-MT) imaging.

PURPOSE: This study aims to assess ultrashort-TE magnetization transfer (UTE-MT) imaging of collagen degradation using an in vitro model of rotator cuff tendinopathy. METHODS: Thirty-six supraspinatus tendon specimens were divided into three groups and treated with 600 U collagenase (Group 1), 150 U collagenase (Group 2), and phosphate buffer saline (Group 3). UTE-MT imaging was performed to assess changes in macromolecular fraction (MMF), macromolecule transverse relaxation time (T2m), water longitudinal relaxation rate constant (R1m), the magnetization exchange rate from the macromolecular to water pool (Rm0 w) and from water to the macromolecular pool (Rm0 m), and magnetization transfer ratio (MTR) at baseline and following digestion and their differences between groups. Biochemical and histological studies were conducted to determine the extent of collagen degradation. Correlation analyses were performed with MMF, T2m, R1m, Rm0 w, Rm0 m, and MTR, respectively. Univariate and multivariate linear regression analyses were performed to evaluate combinations of UTE-MT parameters to predict collagen degradation. RESULTS: MMF, T2m, R1m, Rm0 m, and MTR decreased after digestion. MMF (r = -0.842, p < 0.001), MTR (r = -0.78, p < 0.001), and Rm0 m (r = -0.662, p < 0.001) were strongly negatively correlated with collagen degradation. The linear regression model of differences in MMF and Rm0 m before and after digestion explained 68.9% of collagen degradation variation in the tendon. The model of postdigestion in MMF and T2m and the model of MTR explained 54.2% and 52.3% of collagen degradation variation, respectively. CONCLUSION: This study highlighted the potential of UTE-MT parameters for evaluation of supraspinatus tendinopathy.

Salmonella Grumpensis Causing Diarrhea in Children - Shanghai Municipality, China, 2023.

What is already known about this topic?: Foodborne diseases are a growing public health concern with a notable disease burden in China. What is added by this report?: Two children with diarrhea visited a healthcare facility within 24 hours on August 1 and 2, 2023. Salmonella Grumpensis was detected in their stool samples by the public health laboratory. Whole genome sequencing (WGS) analysis revealed characteristics typical of outbreak strains. Although the origin of the outbreak was unknown, the possibility of a hidden shared infection was deemed feasible. What are the implications for public health practice?: It underscores the importance of thorough genomic surveillance to promptly detect emerging pathogens. Public health laboratories play a crucial role by utilizing advanced genomic technologies for accurate pathogen identification and timely warning systems.

Metabolomic screening of radioiodine refractory thyroid cancer patients and the underlying chemical mechanism of iodine resistance.

Radioiodine refractory (RAIR) patients do not benefit from iodine-131 therapy. Thus, timely identification of RAIR patients is critical for avoiding ineffective radioactive iodine therapy. In addition, determining the causes of iodine resistance will facilitate the development of novel treatment strategies. This study was comprised of 20 RAIR and 14 non-radioiodine refractory (non-RAIR) thyroid cancer patients. Liquid chromatography-mass spectrometry was used to identify differences in the serum metabolites of RAIR and non-RAIR patients. In addition, chemical assays were performed to determine the effects of the differential metabolites on iodine uptake. Metabolic pathway enrichment analysis of the differential metabolites revealed significant differences in the phenylalanine and tyrosine metabolic pathways. Notably, quinate and shikimic acid, metabolites of the tyrosine pathway, were significantly increased in the RAIR group. In contrast, the phenylalanine pathway metabolites, hippuric acid and 2-phenylacetamide, were markedly decreased in the RAIR group. Thyroid peroxidase plays an important role in catalyzing the iodination of tyrosine residues, while the ionic state of iodine promotes the iodination reaction. Quinate, shikimic acid, hippuric acid, and 2-phenylacetamide were found to be involved in the iodination of tyrosine, which is a key step in thyroid hormone synthesis. Specifically, quinate and shikimic acid were found to inhibit iodination, while hippuric acid and 2-phenylacetamide promoted iodination. Abnormalities in phenylalanine and tyrosine metabolic pathways are closely associated with iodine resistance. Tyrosine is required for thyroid hormone synthesis and could be a potential cause of iodine resistance.

A multi-channel electrochemical biosensor based on polyadenine tetrahedra for the detection of multiple drug resistance genes.

Antimicrobial resistance poses a serious threat to human health due to the high morbidity and mortality caused by drug-resistant microbial infections. Therefore, the development of rapid, sensitive and selective identification methods is key to improving the survival rate of patients. In this paper, a sandwich-type electrochemical DNA biosensor based on a polyadenine-DNA tetrahedron probe was constructed. The key experimental conditions were optimized, including the length of polyadenine, the concentration of the polyadenine DNA tetrahedron, the concentration of the signal probe and the hybridization time. At the same time, poly-avidin-HRP80 was used to enhance the electrochemical detection signal. Finally, excellent biosensor performance was achieved, and the detection limit for the synthetic DNA target was as low as 1 fM. In addition, we verified the practicability of the system by analyzing E. coli with the MCR-1 plasmid and realized multi-channel detection of the drug resistance genes MCR-1, blaNDM, blaKPC and blaOXA. With the ideal electrochemical interface, the polyA-based biosensor exhibits excellent stability, which provides powerful technical support for the rapid detection of antibiotic-resistant strains in the field.

Regulating Interfacial Microenvironment in Aqueous Electrolyte via a N2 Filtering Membrane for Efficient Electrochemical Ammonia Synthesis.

Electrochemical synthesis of ammonia (NH3) in aqueous electrolyte has long been suffered from poor nitrogen (N2) supply owing to its low solubility and sluggish diffusion kinetics. Therefore, creating a N2 rich microenvironment around catalyst surface may potentially improve the efficiency of nitrogen reduction reaction (NRR). Herein, a delicately designed N2 filtering membrane consisted of polydimethylsiloxane is covered on catalyst surface via superspreading. Because this membrane let the dissolved N2 molecules be accessible to the catalyst but block excess water, the designed N2 rich microenvironment over catalyst leads to an optimized Faradaic efficiency of 39.4% and an NH3 yield rate of 109.2 µg h-1 mg-1, which is superior to those of the most report metal-based catalysts for electrochemical NRR. This study offers alternative strategy for enhancing NRR performance.

GEnDDn: An lncRNA-Disease Association Identification Framework Based on Dual-Net Neural Architecture and Deep Neural Network.

Accumulating studies have demonstrated close relationships between long non-coding RNAs (lncRNAs) and diseases. Identification of new lncRNA-disease associations (LDAs) enables us to better understand disease mechanisms and further provides promising insights into cancer targeted therapy and anti-cancer drug design. Here, we present an LDA prediction framework called GEnDDn based on deep learning. GEnDDn mainly comprises two steps: First, features of both lncRNAs and diseases are extracted by combining similarity computation, non-negative matrix factorization, and graph attention auto-encoder, respectively. And each lncRNA-disease pair (LDP) is depicted as a vector based on concatenation operation on the extracted features. Subsequently, unknown LDPs are classified by aggregating dual-net neural architecture and deep neural network. Using six different evaluation metrics, we found that GEnDDn surpassed four competing LDA identification methods (SDLDA, LDNFSGB, IPCARF, LDASR) on the lncRNADisease and MNDR databases under fivefold cross-validation experiments on lncRNAs, diseases, LDPs, and independent lncRNAs and independent diseases, respectively. Ablation experiments further validated the powerful LDA prediction performance of GEnDDn. Furthermore, we utilized GEnDDn to find underlying lncRNAs for lung cancer and breast cancer. The results elucidated that there may be dense linkages between IFNG-AS1 and lung cancer as well as between HIF1A-AS1 and breast cancer. The results require further biomedical experimental verification. GEnDDn is publicly available at https://github.com/plhhnu/GEnDDn.

Nasal solitary chemosensory cells govern daily rhythm in mouse model of allergic rhinitis.

BACKGROUND: While the daily rhythm of allergic rhinitis (AR) has long been recognized, the molecular mechanism underlying this phenomenon remains enigmatic. OBJECTIVE: We aim to investigate the role of circadian clock in AR development and to clarify the mechanism by which the daily rhythm of AR is generated. METHODS: AR was induced in mice using the ovalbumin method. Toluidine blue staining, LC-MS/MS analysis, qPCR, and immunoblotting were performed with AR and control mice. RESULTS: Ovalbumin-induced AR is diurnally rhythmic and associated with clock gene disruption in nasal mucosa. In particular, Rev-erbα is generally down-regulated, and its rhythm retained but with a near 12-h phase shift. Furthermore, global knockout of the core clock gene Bmal1 or Rev-erbα increases the susceptibility of mice to AR and blunts AR rhythmicity. Importantly, nasal SCCs (solitary chemosensory cells) are rhythmically activated, and inhibition of the SCC pathway leads to attenuated AR and a loss of its rhythm. Moreover, rhythmic activation of SCCs is accounted for by diurnal expression of ChAT (an enzyme responsible for the synthesis of acetylcholine) and temporal generation of the neurotransmitter acetylcholine. Mechanistically, REV-ERBα trans-represses Chat through direct binding to a specific response element, generating a diurnal oscillation in this target gene. CONCLUSION: These findings identify SCCs, under the control of REV-ERBα, as a driver of AR rhythmicity, and suggest targeting SCCs as a new avenue for AR management.

A Fruit-Expressed MYB Transcription Factor Regulates Anthocyanin Biosynthesis in Atropa belladonna.

Anthocyanins are water-soluble flavonoid pigments that play a crucial role in plant growth and metabolism. They serve as attractants for animals by providing plants with red, blue, and purple pigments, facilitating pollination and seed dispersal. The fruits of solanaceous plants, tomato (Solanum lycopersicum) and eggplant (Solanum melongena), primarily accumulate anthocyanins in the fruit peels, while the ripe fruits of Atropa belladonna (Ab) have a dark purple flesh due to anthocyanin accumulation. In this study, an R2R3-MYB transcription factor (TF), AbMYB1, was identified through association analysis of gene expression and anthocyanin accumulation in different tissues of A. belladonna. Its role in regulating anthocyanin biosynthesis was investigated through gene overexpression and RNA interference (RNAi). Overexpression of AbMYB1 significantly enhanced the expression of anthocyanin biosynthesis genes, such as AbF3H, AbF3'5'H, AbDFR, AbANS, and Ab3GT, leading to increased anthocyanin production. Conversely, RNAi-mediated suppression of AbMYB1 resulted in decreased expression of most anthocyanin biosynthesis genes, as well as reduced anthocyanin contents in A. belladonna. Overall, AbMYB1 was identified as a fruit-expressed R2R3-MYB TF that positively regulated anthocyanin biosynthesis in A. belladonna. This study provides valuable insights into the regulation of anthocyanin biosynthesis in Solanaceae plants, laying the foundation for understanding anthocyanin accumulation especially in the whole fruits of solanaceous plants.

NADPH and NAC synergistically inhibits chronic ocular hypertension-induced neurodegeneration and neuroinflammation through regulating p38/MAPK pathway and peroxidation.

Glaucoma, the leading cause of irreversible blindness worldwide, is characterized by neurodegeneration and neuroinflammation with retinal NAD/NADP and GSH decline. Nicotinamide adenine dinucleotide (NAD)/NAD phosphate (NADP) and glutathione (GSH) are two redox reducers in neuronal and glial metabolism. However, therapeutic strategies targeting NAD/NADP or GSH do not exert ideal effects, and the underlying mechanisms are still poorly understood. We assessed morphological changes in retinal ganglion cells (RGCs), the affected neurons in glaucoma, and Müller cells, the major glial cells in the retina, as well as the levels of phosphorylated p38 (p-p38) and Caspase-3 in glaucoma patients. We constructed a modified chronic ocular hypertensive rat model and an oxygen-glucose deprivation (OGD) cell model. After applying NADPH and N-acetylcysteine (NAC), a precursor to cysteine, the rate-limiting substrate in GSH biosynthesis, to cells, apoptosis, axonal damage and peroxidation were reduced in the RGCs of the NAC group and p-p38 levels were decreased in the RGCs of the NADPH group, while in stimulated Müller cells cultured individually or cocultured with RGCs, gliosis and p38/MAPK, rather than JNK/MAPK, activation were inhibited. The results were more synergistic in the rat model, where either NADPH or NAC showed crossover effects on inhibiting peroxidation and p38/MAPK pathway activation. Moreover, the combination of NADPH and NAC ameliorated RGC electrophysiological function and prevented Müller cell gliosis to the greatest extent. These data illustrated conjoined mechanisms in glaucomatous RGC injury and Müller cell gliosis and suggested that NADPH and NAC collaborate as a neuroprotective and anti-inflammatory combination treatment for glaucoma and other underlying human neurodegenerative diseases.

Recurrence Characteristics on Optical Coherence Tomography and Treatments of Reis-Bücklers Corneal Dystrophy after Phototherapeutic Keratectomy or Penetrating Keratoplasty: Recurrence characteristics and treatments of RBCD.

PURPOSE: To evaluate the recurrence characteristics on optical coherence tomography and clinical outcomes after phototherapeutic keratectomy (PTK) or penetrating keratoplasty (PKP) in patients with Reis-Bücklers corneal dystrophy (RBCD). DESIGN: Retrospective interventional case series. METHODS: Seventeen patients with RBCD (31 eyes, including six surgery-naïve eyes and 25 surgical eyes) received 44 surgical interventions from 1996 through 2022. PTK or PKP was performed as the initial surgical procedure. Significant recurrence was determined when best spectacle-corrected visual acuity decreased at least two lines with increased opacity in the superficial cornea. Repeated PTK or PTK on the corneal graft (CG-PTK) was considered if patients could not endure poor vision due to significant recurrence. Recurrence depth and annual increase in thickness of the central cornea and subepithelial deposits were assessed by anterior segment optical coherence tomography. RESULTS: The mean follow-up time was 12.8±8.5 years (range, 2.0-25.5 years). The mean logMAR best spectacle-corrected visual acuity improved from 1.24±0.48 preoperatively to 0.27±0.09 postoperatively in the initial PTK group (13 eyes, P<0.001), from 1.84±0.69 to 0.40±0.13 in the PKP group (12 eyes, P<0.001), from 1.04±0.46 to 0.30±0.07 in the repeated PTK group (12 times in 7 eyes, P<0.001), and from 1.29±0.43 to 0.39±0.11 in the CG-PTK group (7 times in 5 eyes, P=0.001). The median significant recurrence time was 27 months (95% confidence interval 23.9-30.1), 96 months (84.1-107.9), 31 months (28.8-33.1), and 24 months (19.8-28.2), respectively (P<0.001). The depth of superficial deposits located between the epithelium and the anterior stroma was approximately 115µm (85-159µm). The annual thickening of subepithelial deposits was 14±2µm after initial PTK, 7±3µm after PKP, 14±3µm after repeated PTK, and 30±11µm after CG-PTK, compared to 4±2µm in surgery-naïve eyes (P=0.002, 0.515, 0.002, <0.001). The thickness of the central cornea increased by 15±2µm, 7±2µm, 15±3µm, and 31±10µm per year in the four surgery groups, respectively, compared to 5±2µm in surgery-naïve eyes (P=0.001, 0.469, 0.001, <0.001). CONCLUSIONS: Better visual acuity can be achieved after PTK than PKP for treatment of RBCD. The annual thickening of subepithelial deposits may approximate an increase in central corneal thickness. The superficial distribution of subepithelial deposits makes it feasible to perform repeated PTK, even on the corneal allograft, for recurrent RBCD.

Identification of Molecular Subtypes Associated with PCD in Esophageal Squamous Cell Carcinoma and Analysis of Immune Microenvironment.

BACKGROUND: Esophageal squamous cell carcinoma (ESCC) is a highly fatal malignancy with increasing incidence, and programmed cell death (PCD) plays an important role in homeostasis. AIMS: This study aimed to explore the ESCC of heterogeneity based on the PCD signatures for the diagnosis and treatment of patients. METHODS: The clinical information and RNA-seq data of patients with ESCC and the PCD-related genes set were used to identify PCD signatures.The "limma" package was used to identify the differentially expressed genes (DEGs). "Clusterprofiler" package was used for function enrichment analysis, and the "ConsensusClusterPlus" package was performed for consensus clustering. Finally, the "GSVA" package and the Cibersort algorithm were used for the immune infiltration analysis. RESULTS: We performed differential expression analysis between ESCC and normal samples and identified 1659 DEGs, of which 124 DEGs were PCD genes. Then, the patients were divided into cluster1 and cluster2 based on the expression of 124 PCD genes. There was a significant difference in immune infiltration between the two clusters. The patients in cluster 1 had a higher immune score and more CD56dim natural killer cells, monocytes, activated CD4 T cells, eosinophil, and activated B cells infiltration, while cluster2 had a higher stromal score, more immune regulation, and immune checkpoint genes expression. CONCLUSION: We identified two clusters based on PCD gene expression and characterized their tumor microenvironment and immune checkpoint difference. Our findings may provide some new insight into the treatment of ESCC.

SCPLPA: An miRNA-disease association prediction model based on spatial consistency projection and label propagation algorithm.

Identifying the association between miRNA and diseases is helpful for disease prevention, diagnosis and treatment. It is of great significance to use computational methods to predict potential human miRNA disease associations. Considering the shortcomings of existing computational methods, such as low prediction accuracy and weak generalization, we propose a new method called SCPLPA to predict miRNA-disease associations. First, a heterogeneous disease similarity network was constructed using the disease semantic similarity network and the disease Gaussian interaction spectrum kernel similarity network, while a heterogeneous miRNA similarity network was constructed using the miRNA functional similarity network and the miRNA Gaussian interaction spectrum kernel similarity network. Then, the estimated miRNA-disease association scores were evaluated by integrating the outcomes obtained by implementing label propagation algorithms in the heterogeneous disease similarity network and the heterogeneous miRNA similarity network. Finally, the spatial consistency projection algorithm of the network was used to extract miRNA disease association features to predict unverified associations between miRNA and diseases. SCPLPA was compared with four classical methods (MDHGI, NSEMDA, RFMDA and SNMFMDA), and the results of multiple evaluation metrics showed that SCPLPA exhibited the most outstanding predictive performance. Case studies have shown that SCPLPA can effectively identify miRNAs associated with colon neoplasms and kidney neoplasms. In summary, our proposed SCPLPA algorithm is easy to implement and can effectively predict miRNA disease associations, making it a reliable auxiliary tool for biomedical research.

Active metabolites combination therapies: towards the next paradigm for more efficient and more scientific Chinese medicine.

Traditional Chinese medicine (TCM) formulae have been studied extensively in various human diseases and have proven to be effective due to their multi-component, multi-target advantage. However, its active metabolites are not clear and the specific mechanisms are not well established, which limits its scientific application. Recently, combination therapies are attracting increasing attention from the scientific community in the past few years and are considered as the next paradigm in drug discovery. Here, we tried to define a new concept of "active metabolites combination therapies (AMCT)" rules to elucidate how the bioactive metabolites from TCMs to produce their synergistic effects in this review. The AMCT rules integrate multidisciplinary technologies like molecular biology, biochemistry, pharmacology, analytical chemistry and pharmacodynamics, etc. Meanwhile, emerging technologies such as multi-omics combined analysis, network analysis, artificial intelligence conduce to better elucidate the mechanisms of these combination therapies in disease treatment, which provides new insights for the development of novel active metabolites combination drugs. AMCT rules will hopefully further guide the development of novel combination drugs that will promote the modernization and international needs of TCM.

Millijoule Terahertz Radiation from Laser Wakefields in Nonuniform Plasmas.

We report the experimental measurement of millijoule terahertz (THz) radiation emitted in the backward direction from laser wakefields driven by a femtosecond laser pulse of few joules interacting with a gas target. By utilizing frequency-resolved energy measurement, it is found that the THz spectrum exhibits two peaks located at about 4.5 and 9.0 THz, respectively. In particular, the high frequency component emerges when the drive laser energy exceeds 1.26 J, at which electron acceleration in the forward direction is detected simultaneously. Theoretical analysis and particle-in-cell simulations indicate that the THz radiation is generated via mode conversion from the laser wakefields excited in plasma with an up-ramp profile, where radiations both at the local electron plasma frequency and its harmonics are produced. Such intense THz sources may find many applications in ultrafast science, e.g., manipulating the transient states of matter.