Identification of Proteome-Based Immune Subtypes of Early Hepatocellular Carcinoma and Analysis of Potential Metabolic Drivers.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality worldwide, ranking fourth in frequency. The relationship between metabolic reprogramming and immune infiltration has been identified as having a crucial impact on HCC progression. However, a deeper understanding of the interplay between the immune system and metabolism in the HCC microenvironment is required. In this study, we used a proteomic dataset to identify three immune subtypes (IM1-IM3) in HCC, each of which has distinctive clinical, immune, and metabolic characteristics. Among these subtypes, IM3 was found to have the poorest prognosis, with the highest levels of immune infiltration and T-cell exhaustion. Furthermore, IM3 showed elevated glycolysis and reduced bile acid metabolism, which was strongly correlated with CD8 T cell exhaustion and regulatory T cell accumulation. Our study presents the proteomic immune stratification of HCC, revealing the possible link between immune cells and reprogramming of HCC glycolysis and bile acid metabolism, which may be a viable therapeutic strategy to improve HCC immunotherapy.

CDS-DB, an omnibus for patient-derived gene expression signatures induced by cancer treatment.

Patient-derived gene expression signatures induced by cancer treatment, obtained from paired pre- and post-treatment clinical transcriptomes, can help reveal drug mechanisms of action (MOAs) in cancer patients and understand the molecular response mechanism of tumor sensitivity or resistance. Their integration and reuse may bring new insights. Paired pre- and post-treatment clinical transcriptomic data are rapidly accumulating. However, a lack of systematic collection makes data access, integration, and reuse challenging. We therefore present the Cancer Drug-induced gene expression Signature DataBase (CDS-DB). CDS-DB has collected 78 patient-derived, paired pre- and post-treatment transcriptomic source datasets with uniformly reprocessed expression profiles and manually curated metadata such as drug administration dosage, sampling time and location, and intrinsic drug response status. From these source datasets, 2012 patient-level gene perturbation signatures were obtained, covering 85 therapeutic regimens, 39 cancer subtypes and 3628 patient samples. Besides data browsing, download and search, CDS-DB also supports single signature analysis (including differential gene expression, functional enrichment, tumor microenvironment and correlation analyses), signature comparative analysis and signature connectivity analysis. This provides insights into drug MOA and its heterogeneity in patients, drug resistance mechanisms, drug repositioning and drug (combination) discovery, etc. CDS-DB is available at http://cdsdb.ncpsb.org.cn/.

Structure-based characterization and compound identification of the wild-type THF class-II riboswitch.

Riboswitches are conserved regulatory RNA elements participating in various metabolic pathways. Recently, a novel RNA motif known as the folE RNA motif was discovered upstream of folE genes. It specifically senses tetrahydrofolate (THF) and is therefore termed THF-II riboswitch. To unravel the ligand recognition mechanism of this newly discovered riboswitch and decipher the underlying principles governing its tertiary folding, we determined both the free-form and bound-form THF-II riboswitch in the wild-type sequences. Combining structural information and isothermal titration calorimetry (ITC) binding assays on structure-based mutants, we successfully elucidated the significant long-range interactions governing the function of THF-II riboswitch and identified additional compounds, including alternative natural metabolites and potential lead compounds for drug discovery, that interact with THF-II riboswitch. Our structural research on the ligand recognition mechanism of the THF-II riboswitch not only paves the way for identification of compounds targeting riboswitches, but also facilitates the exploration of THF analogs in diverse biological contexts or for therapeutic applications.

Combining the dynamic model and deep neural networks to identify the intensity of interventions during COVID-19 pandemic.

During the COVID-19 pandemic, control measures, especially massive contact tracing following prompt quarantine and isolation, play an important role in mitigating the disease spread, and quantifying the dynamic contact rate and quarantine rate and estimate their impacts remain challenging. To precisely quantify the intensity of interventions, we develop the mechanism of physics-informed neural network (PINN) to propose the extended transmission-dynamics-informed neural network (TDINN) algorithm by combining scattered observational data with deep learning and epidemic models. The TDINN algorithm can not only avoid assuming the specific rate functions in advance but also make neural networks follow the rules of epidemic systems in the process of learning. We show that the proposed algorithm can fit the multi-source epidemic data in Xi'an, Guangzhou and Yangzhou cities well, and moreover reconstruct the epidemic development trend in Hainan and Xinjiang with incomplete reported data. We inferred the temporal evolution patterns of contact/quarantine rates, selected the best combination from the family of functions to accurately simulate the contact/quarantine time series learned by TDINN algorithm, and consequently reconstructed the epidemic process. The selected rate functions based on the time series inferred by deep learning have epidemiologically reasonable meanings. In addition, the proposed TDINN algorithm has also been verified by COVID-19 epidemic data with multiple waves in Liaoning province and shows good performance. We find the significant fluctuations in estimated contact/quarantine rates, and a feedback loop between the strengthening/relaxation of intervention strategies and the recurrence of the outbreaks. Moreover, the findings show that there is diversity in the shape of the temporal evolution curves of the inferred contact/quarantine rates in the considered regions, which indicates variation in the intensity of control strategies adopted in various regions.

Metal-ligand cross-link strategy engineered iron-doped dopamine-based superstructure as peroxidase-like nanozymes for detection of glucose.

Nanozymes are nanomaterials with mimetic enzyme properties and the related research has attracted much attention. It is of great value to develop methods to construct nanozymes and to study their application in bioanalysis. Herein, the metal-ligand cross-linking strategy was developed to fabricate superstructure nanozymes. This strategy takes advantage of being easy to operate, adjustable, cheap, and universal. The fabricated superstructure nanozymes possess efficient peroxidase-like catalytic activity. The enzyme reaction kinetic tests demonstrated that for TMB and H2O2, the Km is 0.229 and 1.308 mM, respectively. Furthermore, these superstructure nanozymes are applied to highly efficient and sensitive detection of glucose. The linear range for detecting glucose is 20-2000 µM, and the limit of detection is 17.5 µM. Furthermore, mechanistic research illustrated that this integrated system oxidizes glucose to produce hydrogen peroxide and further catalyzes the production of ·OH and O2·-, which results in a chromogenic reaction of oxidized TMB for the detection of glucose. This work could not only contribute to the development of efficient nanozymes but also inspire research in the highly sensitive detection of other biomarkers.

CTR-DB, an omnibus for patient-derived gene expression signatures correlated with cancer drug response.

To date, only some cancer patients can benefit from chemotherapy and targeted therapy. Drug resistance continues to be a major and challenging problem facing current cancer research. Rapidly accumulated patient-derived clinical transcriptomic data with cancer drug response bring opportunities for exploring molecular determinants of drug response, but meanwhile pose challenges for data management, integration, and reuse. Here we present the Cancer Treatment Response gene signature DataBase (CTR-DB, http://ctrdb.ncpsb.org.cn/), a unique database for basic and clinical researchers to access, integrate, and reuse clinical transcriptomes with cancer drug response. CTR-DB has collected and uniformly reprocessed 83 patient-derived pre-treatment transcriptomic source datasets with manually curated cancer drug response information, involving 28 histological cancer types, 123 drugs, and 5139 patient samples. These data are browsable, searchable, and downloadable. Moreover, CTR-DB supports single-dataset exploration (including differential gene expression, receiver operating characteristic curve, functional enrichment, sensitizing drug search, and tumor microenvironment analyses), and multiple-dataset combination and comparison, as well as biomarker validation function, which provide insights into the drug resistance mechanism, predictive biomarker discovery and validation, drug combination, and resistance mechanism heterogeneity.

UbiBrowser 2.0: a comprehensive resource for proteome-wide known and predicted ubiquitin ligase/deubiquitinase-substrate interactions in eukaryotic species.

As an important post-translational modification, ubiquitination mediates ∼80% of protein degradation in eukaryotes. The degree of protein ubiquitination is tightly determined by the delicate balance between specific ubiquitin ligase (E3)-mediated ubiquitination and deubiquitinase-mediated deubiquitination. In 2017, we developed UbiBrowser 1.0, which is an integrated database for predicted human proteome-wide E3-substrate interactions. Here, to meet the urgent requirement of proteome-wide E3/deubiquitinase-substrate interactions (ESIs/DSIs) in multiple organisms, we updated UbiBrowser to version 2.0 (http://ubibrowser.ncpsb.org.cn). Using an improved protocol, we collected 4068/967 known ESIs/DSIs by manual curation, and we predicted about 2.2 million highly confident ESIs/DSIs in 39 organisms, with >210-fold increase in total data volume. In addition, we made several new features in the updated version: (i) it allows exploring proteins' upstream E3 ligases and deubiquitinases simultaneously; (ii) it has significantly increased species coverage; (iii) it presents a uniform confidence scoring system to rank predicted ESIs/DSIs. To facilitate the usage of UbiBrowser 2.0, we also redesigned the web interface for exploring these known and predicted ESIs/DSIs, and added functions of 'Browse', 'Download' and 'Application Programming Interface'. We believe that UbiBrowser 2.0, as a discovery tool, will contribute to the study of protein ubiquitination and the development of drug targets for complex diseases.

Dual-Functional Capping Agent-Mediated Transformation of Silver Nanotriangles to Silver Nanoclusters for Dual-Mode Biosensing.

The localized surface plasmon resonance (LSPR) property, depending on the structure (morphology and assembly) of nanoparticles, is very sensitive to the environmental fluctuation. Retaining the colorimetric effect derived from the LSPR property while introducing new optical properties (such as fluorescence) that provide supplementary information is an effective means to improve the controllability in structures and reproducibility in optical properties. DNA as a green and low-cost etching agent has been demonstrated to effectively control the morphology and optical properties (the blue shift of the LSPR peak) of the plasmonic nanoparticles. Herein, taking silver nanotriangles (AgNTs) as a proof of concept, we report a novel strategy to induce precisely tunable LSPR and fluorescence-composited dual-mode signals by using mono-DNA first as an etching agent for etching the morphology of AgNTs and later as a template for synthesizing fluorescent silver nanoclusters (AgNCs). In addition, common templates for synthesizing AgNCs, such as l-glutathione and bovine serum albumin, were demonstrated to have the capability to serve as etching agents. More importantly, these biomolecules as dual-functional capping agents (etching agents and templates) follow the size-dependent rule: as the size of the thiolated biomolecule increases, the blue shift of the LSPR peak increases; at the same time, the fluorescence intensity increases. The enzyme that can change the molecular weight (size) of the biomolecular substrates (DNA, peptides, and proteins) through an enzymatic cleavage reaction was explored to regulate the LSPR and fluorescent properties of the resulting nanoparticles (by etching of AgNTs and synthesis of AgNCs), achieving excellent performance in detection of cancer-related proteases. This study can be expanded to other biopolymers to impact both fundamental nanoscience and applications and provide powerful new tools for bioanalytical biosensors and nanomedicine.

A cross-sectional study: a breathomics based pulmonary tuberculosis detection method.

BACKGROUND: Diagnostics for pulmonary tuberculosis (PTB) are usually inaccurate, expensive, or complicated. The breathomics-based method may be an attractive option for fast and noninvasive PTB detection. METHOD: Exhaled breath samples were collected from 518 PTB patients and 887 controls and tested on the real-time high-pressure photon ionization time-of-flight mass spectrometer. Machine learning algorithms were employed for breathomics analysis and PTB detection mode, whose performance was evaluated in 430 blinded clinical patients. RESULTS: The breathomics-based PTB detection model achieved an accuracy of 92.6%, a sensitivity of 91.7%, a specificity of 93.0%, and an AUC of 0.975 in the blinded test set (n = 430). Age, sex, and anti-tuberculosis treatment does not significantly impact PTB detection performance. In distinguishing PTB from other pulmonary diseases (n = 182), the VOC modes also achieve good performance with an accuracy of 91.2%, a sensitivity of 91.7%, a specificity of 88.0%, and an AUC of 0.961. CONCLUSIONS: The simple and noninvasive breathomics-based PTB detection method was demonstrated with high sensitivity and specificity, potentially valuable for clinical PTB screening and diagnosis.

[Review of classical prescriptions in treatment of ulcerative colitis].

Ulcerative colitis(UC) is a continuous inflammatory bowel disease with the main clinical manifestations of abdominal pain, diarrhea, and mucous bloody stools, mainly attacking the colorectal mucosa and submucosa. It is characterized by high recurrence rate, difficult cure, and clustering and regional occurrence. Chinese medicinal prescriptions for the treatment of UC have good therapeutic effect, multi-target regulation, slight toxicity, and no obvious side effects. In particular, the classical prescriptions highlight the characteristics and advantages of traditional Chinese medicine theory and have attracted much attention in recent years. To enable researchers to timely and comprehensively understand the classical prescriptions in the treatment of UC, we reviewed the studies about the pharmacodynamic material basis, quality control, action mechanism, and clinical application of relevant classical prescriptions. We first introduced the latest research progress in the active components such as alkaloids, polysaccharides, saponins, and flavonoids in relevant classical prescriptions. Then, we reviewed the latest research achievements on the quality control of classical prescriptions for the treatment of UC by gas chromatography, liquid chromatography, mass spectrometry, liquid chromatography-mass spectrometry and the like. Further, we summarized the research advances in the mechanisms of relevant prescriptions in the treatment of UC based on network pharmacology, molecular docking, integrated pharmacology platform, and animal experiments. Finally, we generalized the clinical application of the classical prescriptions for clearing heat and removing dampness, mildly regulating cold and heat, soothing liver and regulating spleen, strengthening spleen and invigorating Qi, and tonifying spleen and stomach. By systematic summary of the research progress in relevant classical prescriptions, we hope to promote the application and development of such prescriptions in UC treatment.

Integral Multielement Signals by DNA-Programmed UCNP-AuNP Nanosatellite Assemblies for Ultrasensitive ICP-MS Detection of Exosomal Proteins and Cancer Identification.

Exosomes are expected to be used as cancer biomarkers because they carry a variety of cancer-related proteins inherited from parental cells. However, it is still challenging to develop a sensitive, robust, and high-throughput technique for simultaneous detection of exosomal proteins. Herein, three aptamers specific to cancer-associated proteins (CD63, EpCAM, and HER2) are selected to connect gold nanoparticles (AuNPs) as core with three different elements (Y, Eu, and Tb) doped up-conversion nanoparticles (UCNPs) as satellites, thereby forming three nanosatellite assemblies. The presence of exosomes causes specific aptamers to recognize surface proteins and release the corresponding UCNPs, which can be simultaneously detected by inductively coupled plasma-mass spectrometry (ICP-MS). It is worth noting that rare earth elements are scarcely present in living systems, which minimize the background for ICP-MS detection and exclude potential interferences from the coexisting species. Using this method, we are able to simultaneously detect three exosomal proteins within 40 min, and the limit of detection for exosome is 4.7 × 103 particles/mL. The exosomes from seven different cell lines (L-02, HepG2, GES-1, MGC803, AGS, HeLa, and MCF-7) can be distinguished with 100% accuracy by linear discriminant analysis. In addition, this analytical strategy is successfully used to detect exosomes in clinical samples to distinguish stomach cancer patients from healthy individuals. These results suggest that this sensitive and high-throughput analytical strategy based on ICP-MS has the potential to play an important role in the detection of multiple exosomal proteins and the identification of early cancer.

Capping Ligand Size-Dependent LSPR Property Based on DNA Nanostructure-Mediated Morphological Evolution of Gold Nanorods for Ultrasensitive Visualization of Target DNA.

Systematically tuning the structures and properties of noble-metal nanoparticles through biomolecule-mediated overgrowth is of significant importance for their applications in biosensing and imaging. Herein thiolated biomolecules with different concentrations and sizes (molecular weight and spatial structure) were used as a class of capping ligands to control the longitudinal surface plasmon resonance (LSPR) property of gold nanorods (GNRs). The LSPR peaks were red-shifted by increasing the capping agent concentration. The size effect could be divided to two aspects: (1) When the ligands are small molecules, the LSPR peak is blue-shifted as the size of the capping ligand increases. (2) When the ligands are macromolecular proteins, the LSPR property is similar to that of the overgrown nanoparticle (Au@gap@GNR) without thiolated biomolecules as capping agents. Interestingly, thiol-free and nonhomooligomeric DNA strands as capping agents present a similar influence in shaping the overgrowth of GNRs by varying their concentrations and sizes. In addition, the size effect of a DNA nanostructure was used to construct a ΔλLSPR-based catalytic nucleic acid biosensor using a DNA dendritic nanostructure as a capping agent combined with LSPR signals generated from the Au@gap@GNRs with morphological evolution. More importantly, the ΔλLSPR-based biosensor possesses three advantages in nucleic acid biosensing: (1) It is completely label- and wash-free, (2) it has an ultrahigh sensitivity and signal-to-noise ratio, and (3) it can be visualized without any instrumental aid, indicating a significant potential for ultrasensitive biosensing.

Precisely Tuning LSPR Property via "Peptide-Encoded" Morphological Evolution of Gold Nanorods for Quantitative Visualization of Enzyme Activity.

Longitudinal surface plasmon resonance (LSPR)-based optical signals possess unique advantages in biomolecular sensing and detection which can be attributed to their ultrahigh sensitivity and signal-to-noise ratio. However, the lack of effective strategies for morphological control of gold nanorods (GNRs) complicates the precise tuning of their LSPR property. Herein, a "peptide-encoded" strategy was first developed to precisely control the morphologies of GNRs via overgrowth of GNR seeds in the presence of thiol-containing peptides. Significantly, the "peptide-encoded" GNRs exhibit a tunable LSPR peak ranging from 685 to 877 nm by altering the amount of peptide. A few obvious colorimetric changes were accompanied from pink to purple and even to blue. Other parameters, e.g., pH, temperature, and Ag+ concentration, could also be utilized to regulate the morphologies of the "peptide-encoded" GNRs. The ultrasensitive detection of tumor-related protease activities based on LSPR peak shifts was further successfully performed without the need for labeling or instrumental aid, achieving a limit of detection of 60 fM. It is much lower than traditional absorption-based analysis (1 nM) and enzyme-linked immunosorbent assay (ELISA) method (1 pM), indicating the great potential of this peptide-encoded strategy in the application of ultrasensitive biomarker assay and clinical diagnosis.

A simple enzyme-assisted cascade amplification strategy for ultrasensitive and label-free detection of DNA.

A simple fluorescence biosensor is developed based on the enzyme-assisted cascade amplification strategy. The amplification system consists of a hairpin-structure DNA (H-DNA) and exonuclease III. The target DNA can hybridize with the H-DNA and initiate exonuclease III-assisted target recycling amplification to generate abundant G-rich DNA (G-DNA). One region of G-DNA is designed to possess the same sequence as target DNA. Thus, the G-DNA can also hybridize with H-DNA and initiate the digestion of H-DNA. The cascade strategy in this amplification system causes the concentration of G-DNA to grow exponentially. The fluorescence intensity of N-methylmesoporphyrin IX (NMM) is highly enhanced due to the formation of G-quadruplex configuration. Under optimal conditions, the cascade system could achieve an admirable sensitivity with a detection limit of 52 fM for HIV DNA, and guarantees a satisfactory specificity. Moreover, the cascade system could be implemented for other target DNA detections by substituting the recognition region of the H-DNA. In this way, a detection limit of 65 fM for HBV DNA could be achieved by the cascade system. The target DNA analysis in a real serum sample further indicates that this biosensor has potential for future application in clinical diagnosis. Graphical abstract A simple and label-free cascade amplification strategy is developed by exploiting hairpin DNA and EXO III for sensitive DNA detection.

Smart DNA Machine for Carcinoembryonic Antigen Detection by Exonuclease III-Assisted Target Recycling and DNA Walker Cascade Amplification.

A synthetic DNA machine performs quasi-mechanical movements in response to external intervention, suggesting the promise of constructing sensitive and specific biosensors. Herein, a smart DNA walker biosensor for label-free detection of carcinoembryonic antigen (CEA) is developed for the first time by a novel cascade amplification strategy of exonuclease (Exo) III-assisted target recycling amplification (ERA) and DNA walker. ERA as the first stage of amplification generates the walker DNA, while the autonomous traveling of the walker DNA on the substrate-modified silica microspheres as the second stage of amplification produces an ultrasensitive fluorescent signal with the help of N-methylmesoporphyrin IX (NMM). The DNA machine as a biosensor could be applied for transducing and quantifying signals from isothermal molecular amplifications, avoiding the complicated reporter elements and thermal cycling. The present biosensor achieves a detection limit of 1.2 pg·mL-1 within a linear range of 10 pg·mL-1 to 100 ng·mL-1 for CEA, along with a favorable specificity. The practical applicability of the biosensor is demonstrated by the detection of CEA in human serum with satisfactory results; thus, it shows great potential in clinical diagnosis.

A sensitive aptasensor based on molybdenum carbide nanotubes and label-free aptamer for detection of bisphenol A.

To specifically and sensitively identify bisphenol A (BPA) with a simple and rapid method is very important for food safety. Using an anti-BPA aptamer and Mo2C nanotubes, we developed a label-free and low-background signal biosensor for BPA detection. The anti-BPA aptamer drastically increased the fluorescence signal of N-methylmesoporphyrin IX under an assistance of Help-DNA. Additionally, BPA can interact with the anti-BPA aptamer and switch its conformation to prevent the formation of a G-quadruplex, resulting in fluorescence quenching. Simultaneously, Mo2C nanotubes can reduce the background signals due to the adsorption of Help-DNA on their surface. This method shows a linear range of 2-20 nM with a detection limit of 2 nM for detecting BPA. This label-free BPA aptasensor with low background signal is inexpensive, easy to use, and can be applied to determine BPA in real water samples. Graphical Abstract A low-background and label-free biosensor was designed based on Mo2C nanotubes and aptamer for BPA detection.

The Excellent Mechanical Performance of Polymer-Coated Ceramsite Particles for Efficient Fracturing: An Explanation from a Surface, Tribological Perspective.

Hydraulic fracturing using micro-particles is an effective technology in the petroleum industry since the particles facilitate crack propagation of the shale layer, creating pathways for oil and gas. A new kind of polymer-coated ceramsite particles (PCP) was generated. The friction and wear properties of the particles under different loads and speeds were also studied. The tribological relationship between the newly fabricated polymer-coated ceramsite particles and the fracturing fluid was studied through tribological experiments under the condition of fracturing fluid lubrication. The results show that, in contrast, the wear of the new-generation particles is relatively stable, indicating that it has good adjustable friction properties. In addition, under the lubrication condition of fracturing fluid, the new-generation particles have better hydrophobicity, high-pressure resistance, and low reflux rate, which have an important value as a practical engineering application for improving shale gas production efficiency and production.

Mechanism interpretation of Guhan Yangshengjing for protection against Alzheimer's disease by network pharmacology and molecular docking.

ETHNOPHARMACOLOGICAL RELEVANCE: Guhan Yangshengjing (GHYSJ) is an effective prescription for delaying progression of Alzheimer's disease (AD) based on the ancient Chinese medical classics excavated from Mawangdui Han Tomb. Comprising a combination of eleven traditional Chinese herbs, the precise protective mechanism through which GHYSJ acts on AD progression remains unclear and has significant implications for the development of new drugs to treat AD. AIM OF THE STUDY: To investigate the mechanism of GHYSJ in the treatment of AD through network pharmacology and validate the results through in vitro experiments. MATERIALS AND METHODS: Chemical composition-target-pathway network and protein-protein interaction network were constructed by network pharmacology to predict the potential targets of GHYSJ for the treatment of AD. The interaction relationship between active ingredients and targets was verified by molecular docking and molecular force. Furthermore, the chemical constituents of GHYSJ were analyzed by LC-MS and HPLC, the effects of GHYSJ on animal tissues were analyzed by H&E staining. An Aß-induced SH-SY5Y cellular model was established to validate the core pathways and targets predicted by network pharmacology and molecular docking. RESULTS: The results of the network pharmacology analysis revealed a total of 155 bioactive compounds capable of crossing the blood-brain barrier and interacting with 677 targets, among which 293 targets specifically associated with AD, which mainly participated in and regulated the amyloid aggregation pathway and PI3K/Akt signaling pathway, thereby treating AD. In addition, molecular docking analysis revealed a robust binding affinity between the principal bioactive constituents of GHYSJ and crucial targets implicated in AD. Our findings were further substantiated by in vitro experiments, which demonstrated that Liquiritigenin and Ginsenosides Rh4, crucial constituents of GHYSJ, as well as GHYSJ pharmaceutic serum, exhibited a significant down-regulation of BACE1 expression in Aß-induced damaged SH-SY5Y cells. This study provides valuable data and theoretical underpinning for the potential therapeutic application of GHYSJ in the treatment of AD and secondary development of GHYSJ prescription. CONCLUSION: Through network pharmacology, molecular docking, LC-MS, and cellular experiments, GHYSJ was initially confirmed to delay the progression of AD by regulating the expression of BACE1 in Amyloid aggregation pathway. Our observations provided valuable data and theoretical underpinning for the potential therapeutic application of GHYSJ in the treatment of AD.

Transmission dynamics informed neural network with application to COVID-19 infections.

Since the end of 2019 the COVID-19 repeatedly surges with most countries/territories experiencing multiple waves, and mechanism-based epidemic models played important roles in understanding the transmission mechanism of multiple epidemic waves. However, capturing temporal changes of the transmissibility of COVID-19 during the multiple waves keeps ill-posed problem for traditional mechanism-based epidemic compartment models, because that the transmission rate is usually assumed to be specific piecewise functions and more parameters are added to the model once multiple epidemic waves involved, which poses a huge challenge to parameter estimation. Meanwhile, data-driven deep neural networks fail to discover the driving factors of repeated outbreaks and lack interpretability. In this study, aiming at developing a data-driven method to project time-dependent parameters but also merging the advantage of mechanism-based models, we propose a transmission dynamics informed neural network (TDINN) by encoding the SEIRD compartment model into deep neural networks. We show that the proposed TDINN algorithm performs very well when fitting the COVID-19 epidemic data with multiple waves, where the epidemics in the United States, Italy, South Africa, and Kenya, and several outbreaks the Omicron variant in China are taken as examples. In addition, the numerical simulation shows that the trained TDINN can also perform as a predictive model to capture the future development of COVID-19 epidemic. We find that the transmission rate inferred by the TDINN frequently fluctuates, and a feedback loop between the epidemic shifting and the changes of transmissibility drives the occurrence of multiple waves. We observe a long response delay to the implementation of control interventions in the four countries, while the decline of the transmission rate in the outbreaks in China usually happens once the implementation of control interventions. The further simulation show that 17 days' delay of the response to the implementation of control interventions lead to a roughly four-fold increase in daily reported cases in one epidemic wave in Italy, which suggest that a rapid response to policies that strengthen control interventions can be effective in flattening the epidemic curve or avoiding subsequent epidemic waves. We observe that the transmission rate in the outbreaks in China is already decreasing before enhancing control interventions, providing the evidence that the increasing of the epidemics can drive self-conscious behavioural changes to protect against infections.

NAD+ biosynthesis metabolism predicts prognosis and indicates immune microenvironment for breast cancer.

The growing evidence implies that tumor cells need to increase NAD+ levels by upregulating NAD+ biosynthesis to satisfy their growth demand. NAD+ biosynthesis metabolism is implicated in tumor progression. Breast cancer (BC) is the most common malignant malignancy in the world. Nevertheless, the prognostic significance of NAD+ biosynthesis and its relationship with the tumor immune microenvironment in breast cancer still need further investigation. In this study, we obtained the mRNA expression data and clinical information of BC samples from public databases and calculated the level of NAD+ biosynthesis activity by single-sample gene set enrichment analysis (ssGSEA). We then explored the relationship between the NAD+ biosynthesis score, infiltrating immune cells, prognosis significance, immunogenicity and immune checkpoint molecules. The results demonstrated that patients with high NAD+ biosynthetic score displayed poor prognosis, high immune infiltration, high immunogenicity, elevated PD-L1 expression, and might more benefit from immunotherapy. Taken together, our studies not only deepened the understanding of NAD+ biosynthesis metabolism of breast cancer but also provided new insights into personalized treatment strategies and immunological therapies to improve the outcomes of breast cancer patients.