Small molecule metabolites: discovery of biomarkers and therapeutic targets.

Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject's phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.

Potential urinary biomarkers in young adults with short-term exposure to particulate matter and bioaerosols identified using an unbiased metabolomic approach.

Numerous epidemiological studies have shown a close relationship between outdoor air pollution and increased risks for cancer, infection, and cardiopulmonary diseases. However, very few studies have investigated the potential health effects of coexposure to airborne particulate matter (PM) and bioaerosols through the transmission of infectious agents, particularly under the current circumstances of the coronavirus disease 2019 pandemic. In this study, we aimed to identify urinary metabolite biomarkers that might serve as clinically predictive or diagnostic standards for relevant diseases in a real-time manner. We performed an unbiased gas/liquid chromatography-mass spectroscopy (GC/LC-MS) approach to detect urinary metabolites in 92 samples from young healthy individuals collected at three different time points after exposure to clean air, polluted ambient, or purified air, as well as two additional time points after air repollution or repurification. Subsequently, we compared the metabolomic profiles between the two time points using an integrated analysis, along with Kyoto Encyclopedia of Genes and Genomes-enriched pathway and time-series analysis. We identified 33 and 155 differential metabolites (DMs) associated with PM and bioaerosol exposure using GC/LC-MS and follow-up analyses, respectively. Our findings suggest that 16-dehydroprogesterone and 4-hydroxyphenylethanol in urine samples may serve as potential biomarkers to predict or diagnose PM- or bioaerosol-related diseases, respectively. The results indicated apparent differences between PM- and bioaerosol-associated DMs at five different time points and revealed dynamic alterations in the urinary metabolic profiles of young healthy humans with cyclic exposure to clean and polluted air environments. Our findings will help in investigating the detrimental health effects of short-term coexposure to airborne PM and bioaerosols in a real-time manner and improve clinically predictive or diagnostic strategies for preventing air pollution-related diseases.

Deciphering the potential anti-COVID-19 active ingredients in Andrographis paniculata (Burm. F.) Nees by combination of network pharmacology, molecular docking, and molecular dynamics.

Currently, coronavirus disease 2019 (COVID-19) caused by Severe Acute Respiratory Syndrome Coronavirus 2 has posed an enormous threat to public health worldwide. An andrographolide sulfonates preparation, named Xiyanping injection in Chinese, which was prepared from the aqueous extract of Andrographis paniculata (Burm. F.) Nees, showed favorable therapeutic effectiveness on COVID-19, suggesting A. paniculata could contain powerful therapeutic ingredients against COVID-19. In this study, to search for the potential drug candidates for COVID-19 in the herb, 68 potential target proteins and 24 active ingredients from A. paniculata were screened out using TCMSP, STP, Genecards and TTD databases firstly. A. paniculata-Compound-Target network constructed by cytoscape software showed that the protein targets PTGS2, EGFR, MAPK14, etc. had a high network relevance value. GO and KEGG enrichment analysis indicated that the 24 compounds in A. paniculata might exert their therapeutic effects by the biological processes, cellular response to biotic stimulus, response to lipopolysaccharide, response to molecule of bacterial origin, etc. And AGE-RAGE signaling pathway in diabetic complications (hsa04933), Kaposi sarcoma-associated herpesvirus infection (hsa05167), Human cytomegalovirus infection (hsa05163), etc. were predicted as the most significant effect pathways. Andrographidine C (MOL008223) and andrographolide (MOL008232) were found with strong binding affinity to the target active sites of the potential targets by molecular docking. Ultimately, the application of molecular dynamics simulations demonstrated that andrographidine C could bind well to the ACE2 and PIK3CG proteins. This research identified novel molecules against COVID-19 for developing natural medicines from A. paniculate and also provides a possible explanation for the molecular mechanisms of Xiyanping Injection against COVID-19.

The application framework of big data technology in the COVID-19 epidemic emergency management in local government-a case study of Hainan Province, China.

BACKGROUND: As COVID-19 continues to spread globally, traditional emergency management measures are facing many practical limitations. The application of big data analysis technology provides an opportunity for local governments to conduct the COVID-19 epidemic emergency management more scientifically. The present study, based on emergency management lifecycle theory, includes a comprehensive analysis of the application framework of China's SARS epidemic emergency management lacked the support of big data technology in 2003. In contrast, this study first proposes a more agile and efficient application framework, supported by big data technology, for the COVID-19 epidemic emergency management and then analyses the differences between the two frameworks. METHODS: This study takes Hainan Province, China as its case study by using a file content analysis and semistructured interviews to systematically comprehend the strategy and mechanism of Hainan's application of big data technology in its COVID-19 epidemic emergency management. RESULTS: Hainan Province adopted big data technology during the four stages, i.e., migration, preparedness, response, and recovery, of its COVID-19 epidemic emergency management. Hainan Province developed advanced big data management mechanisms and technologies for practical epidemic emergency management, thereby verifying the feasibility and value of the big data technology application framework we propose. CONCLUSIONS: This study provides empirical evidence for certain aspects of the theory, mechanism, and technology for local governments in different countries and regions to apply, in a precise, agile, and evidence-based manner, big data technology in their formulations of comprehensive COVID-19 epidemic emergency management strategies.

A potential impact of SARS-CoV-2 on pituitary glands and pituitary neuroendocrine tumors.

INTRODUCTION: Angiotensin-converting enzyme 2 (ACE2) is the receptor of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The effects of SARS-CoV-2 on normal pituitary glands function or pituitary neuroendocrine tumors (PitNETs) have not yet been elucidated. Thus, the present study aimed to investigate the potential risks of SARS-CoV-2 infection on the impairment of pituitary glands and the development of PitNETs. METHODS: PitNETs tissues were obtained from 114 patients, and normal pituitary gland tissues were obtained from the autopsy. The mRNA levels of ACE2 and angiotensin II receptor type 1 (AGTR1) were examined by quantitative real-time PCR. Immunohistochemical staining was performed for ACE2 in 69 PitNETs and 3 normal pituitary glands. The primary tumor cells and pituitary cell lines (MMQ, GH3 and AtT-20/D16v-F2) were treated with diminazene aceturate (DIZE), an ACE2 agonist, with various dose regimens. The pituitary hormones between 43 patients with SARS-CoV-2 infection were compared with 45 healthy controls. RESULTS: Pituitary glands and the majority of PitNET tissues showed low/negative ACE2 expression at both the mRNA and protein levels, while AGTR1 showed high expression in normal pituitary and corticotroph adenomas. ACE2 agonist increased the secretion of ACTH in AtT-20/D16v-F2 cells through downregulating AGTR1. The level of serum adrenocorticotropic hormone (ACTH) was significantly increased in COVID-19 patients compared to normal controls (p < 0.001), but was dramatically decreased in critical cases compared to non-critical patients (p = 0.003). CONCLUSIONS: This study revealed a potential impact of SARS-CoV-2 infection on corticotroph cells and adenomas.

Observations on the particle pollution of the cities in China in the Coronavirus 2019 closure: Characteristics and lessons for environmental management.

Particulate matter in the air seriously affects human health and has been a hot topic of discussion. Because of the coronavirus disease 2019 (COVID-19) lockdown in cities in China, sources of particulate matter, including gasoline-burning vehicles, dust-producing building sites, and coal-fired factories, almost all ceased at the end of January 2020. It was not until early April that outdoor activities recovered. Ten cities were selected as observation sites during the period from 19 December 2019 to 30 April 2020, covering the periods of preclosure, closure, and gradual resumption. A total of 11 720 groups of data were obtained, and 4 indicators were used to assess the characteristics of the particle pollution in the period. The quality of the atmospheric environment was visibly influenced by human activities in those 5 mo. The concentrations of particulate matter with particle sizes below 10 µm (PM10) decreased slightly in February and March and then began to increase slowly after April with the gradual recovery of production. The concentrations of particulate matter with particle sizes below 2.5 µm (PM2.5) decreased greatly in most regions, especially in northern cities, during closure and maintained a relatively stable level in the following 3 mo. The trends of PM10 and PM2.5 indicated that the reduced human activities during the COVID-19 lockdown decreased the concentrations of particulate matter in the air, and the difference between the PM10 and PM2.5 trends might be due to the different sources of the 2 particles and their different aerodynamics. However, during closure, the particulate matter pollution in the cities remained at a high level, which indicated that some ignored factors other than outdoor production activities, automobile exhaust, and construction site dust might have contributed greatly to the PM10 and PM2.5 concentrations, and the tracing of the particulate matter should be given further attention in environmental management. Integr Environ Assess Manag 2021;17:1014-1024. © 2021 SETAC.

Observations on the Particle Pollution of the Cities in China in the COVID-19 Closure: Characteristics and Lesson for Environmental Management

Abstract Particulate matter in the air seriously affects human health and has been a hot topic being discussed. Because of the COVID-19 lockdown of the cities in China, sources of the particulate matter including gasoline burning vehicles, dust producing building sites, coal-fired factories, almost all stopped working since the end of January 2020. It was not until early April that outdoor activities recovered. Ten cities were selected as the observation sites in the period from 12/19/2019 to 04/30/2020, covering the periods of the pre-closure, the closure and the gradual resumption. 11720 groups of data were obtained and four indicators were applied to assess the characteristics of the particle pollution in the period. The quality of the atmospheric environment was visually influenced by human activities in the four months. The PM10 concentrations decreased slightly in February and March, and then began to increase gradually after April with the gradual recovery of production. The concentrations of PM2.5 reduced greatly in most regions especially in northern cities, during the closure and maintained a relatively stable level in the following three months. The trends of PM10 and PM2.5 indicated that the reduced human activities during the COVID-19 lockdown made the concentrations of the particulate matter in the air decreased, and the difference between the PM10 and PM2.5 trends might be due to the different sources of the two particles and their different aerodynamics. However, during the closure, the pollution of the particulate matter in the cities still remained at a high level, which indicated that some ignored factors except the outdoor production activities, automobile exhaust and sites? dust might have been contributing much to the PM10 and PM2.5 concentrations, and the traceability of the particulate matter should be paid further attention in environmental management. This article is protected by copyright. All rights reserved.

Digital Contact Tracing Based on a Graph Database Algorithm for Emergency Management During the COVID-19 Epidemic: Case Study.

BACKGROUND: The COVID-19 epidemic is still spreading globally. Contact tracing is a vital strategy in epidemic emergency management; however, traditional contact tracing faces many limitations in practice. The application of digital technology provides an opportunity for local governments to trace the contacts of individuals with COVID-19 more comprehensively, efficiently, and precisely. OBJECTIVE: Our research aimed to provide new solutions to overcome the limitations of traditional contact tracing by introducing the organizational process, technical process, and main achievements of digital contact tracing in Hainan Province. METHODS: A graph database algorithm, which can efficiently process complex relational networks, was applied in Hainan Province; this algorithm relies on a governmental big data platform to analyze multisource COVID-19 epidemic data and build networks of relationships among high-risk infected individuals, the general population, vehicles, and public places to identify and trace contacts. We summarized the organizational and technical process of digital contact tracing in Hainan Province based on interviews and data analyses. RESULTS: An integrated emergency management command system and a multi-agency coordination mechanism were formed during the emergency management of the COVID-19 epidemic in Hainan Province. The collection, storage, analysis, and application of multisource epidemic data were realized based on the government's big data platform using a centralized model. The graph database algorithm is compatible with this platform and can analyze multisource and heterogeneous big data related to the epidemic. These practices were used to quickly and accurately identify and trace 10,871 contacts among hundreds of thousands of epidemic data records; 378 closest contacts and a number of public places with high risk of infection were identified. A confirmed patient was found after quarantine measures were implemented by all contacts. CONCLUSIONS: During the emergency management of the COVID-19 epidemic, Hainan Province used a graph database algorithm to trace contacts in a centralized model, which can identify infected individuals and high-risk public places more quickly and accurately. This practice can provide support to government agencies to implement precise, agile, and evidence-based emergency management measures and improve the responsiveness of the public health emergency response system. Strengthening data security, improving tracing accuracy, enabling intelligent data collection, and improving data-sharing mechanisms and technologies are directions for optimizing digital contact tracing.

High resolution computed tomography for the diagnosis of 2019 novel coronavirus (2019-nCoV) pneumonia: a study from multiple medical centers in western China.

BACKGROUND: To evaluate the role of high-resolution computed tomography (HRCT) in the diagnosis of 2019 novel coronavirus (2019-nCoV) pneumonia and to provide experience in the early detection and diagnosis of 2019-nCoV pneumonia. METHODS: Seventy-two patients confirmed to be infected with 2019-nCoV from multiple medical centers in western China were retrospectively analyzed, including epidemiologic characteristics, clinical manifestations, laboratory findings and HRCT chest features. RESULTS: All patients had lung parenchymal abnormalities on HRCT scans, which were mostly multifocal in both lungs and asymmetric in all patients, and were mostly in the peripheral or subpleural lung regions in 52 patients (72.22%), in the central lung regions in 16 patients (22.22%), and in both lungs with "white lung" manifestations in 4 patients (5.56%). Subpleural multifocal consolidation was a predominant abnormality in 38 patients (52.78%). Ground-glass opacity was seen in 34 patients (47.22%). Interlobular septal thickening was found in 18 patients, 8 of whom had only generally mild thickening with no zonal predominance. Reticulation was seen in 8 patients (11.11%), and was mild and randomly distributed. In addition, both lungs of 28 patients had 2 or 3 CT imaging features. Out of these 72 patients, 36 were diagnosed as early stage, 32 patients as progressive stage, and 4 patient as severe stage pneumonia. Moreover, the diagnostic accuracy of HRCT features combined with epidemiological history was not significantly different from the detection of viral nucleic acid (all P >0.05). CONCLUSIONS: The HRCT features of 2019-nCoV pneumonia are characteristic to a certain degree, which when combined with epidemiological history yield high clinical value in the early detection and diagnosis of 2019-nCoV pneumonia.

Epidemiological and clinical characteristics of COVID-19 patients in Hengyang, Hunan Province, China.

BACKGROUND: In December 2019, an ongoing outbreak of coronavirus disease 2019 (COVID-19) was first identified in Wuhan, China. The characteristics of COVID-19 patients treated in local hospitals in Wuhan are not fully representative of patients outside Wuhan. Therefore, it is highly essential to analyze the epidemiological and clinical characteristics of COVID-19 in areas outside Wuhan or Hubei Province. To date, a limited number of studies have concentrated on the epidemiological and clinical characteristics of COVID-19 patients with different genders, clinical classification, and with or without basic diseases. AIM: To study the epidemiological and clinical characteristics of COVID-19 patients in Hengyang (China) and provide a reliable reference for the prevention and control of COVID-19. METHODS: From January 16 to March 2, 2020, a total of 48 confirmed cases of COVID-19 were reported in Hengyang, and those cases were included in this study. The diagnostic criteria, clinical classification, and discharge standard related to COVID-19 were in line with the Diagnosis and Treatment Protocol for Novel Coronavirus Pneumonia (Trial Version 7) released by National Health Commission and National Administration of Traditional Chinese Medicine. The presence of SARS-CoV-2 in pharyngeal swab specimens was detected by quantitative reverse transcription polymerase chain reaction. All the data were imported into the excel worksheet and statistically analyzed by using SPSS 25.0 software. RESULTS: A total of 48 cases of COVID-19 were collected, of which 1 was mild, 38 were moderate, and 9 were severe. It was unveiled that there were 31 (64.6%) male patients and 17 (35.4%) female patients, with a female-to-male ratio of 1.82:1. The range of age of patients with COVID-19 was dominantly 30-49 years old [25 (52.1%) of 48], followed by those aged over 60 years old [11 (22.9%)]. Besides, 29.2% (14 of 48) of patients had basic diseases, and 57.2% (8 of 14) of patients with basic diseases were aged over 60 years old. The occupations of 48 COVID-19 patients were mainly farmers working in agricultural production [15 (31.5%) of 48], rural migrant workers from Hengyang to Wuhan [15 (31.5%)], and service workers operating in the service sector [8 (16.7%)]. The mean latent period was 6.86 ± 3.57 d, and the median was 7 [interquartile range (IQR): 4-9] d. The mean time from onset of symptoms to the first physician visit was 3.38 ± 2.98 (95%CI: 2.58-9.18) d, with a median of 2 (IQR: 1-5) d, and the mean time from hospital admission to confirmed diagnosis was 2.29 ± 2.11 (95%CI: 1.18-6.42) d, with a median of 2 (IQR: 1-3) d. The main symptoms were fever [43 (89.6%) of 48], cough and expectoration [41 (85.4%)], fatigue [22 (45.8%)], and chills [22 (45.8%)]. Other symptoms included poor appetite [13 (27.1%)], sore throat [9 (18.8%)], dyspnea [9 (18.8%)], diarrhea [7 (14.6%)], dizziness [5 (10.4%)], headache [5 (10.4%)], muscle pain [5 (10.4%)], nausea and vomiting [4 (8.3%)], hemoptysis [4 (8.3%)], and runny nose [1 (2.1%)]. The numbers of peripheral blood leukocytes, lymphocytes, and eosinophils were significantly reduced in the majority of the patients. The levels of C-reactive protein, fibrinogen, blood glucose, lactate dehydrogenase, D-dimer, alanine aminotransferase (ALT), aspartate aminotransferase (AST), gamma-glutamyl transferase (γ-GT), myoglobin (MB), and creatine kinase (CK) were increased in 64.6%, 44.7%, 43.2%, 37.0%, 29.5%, 22.9%,20.8%, 21.6%, 13.6%, and 12.8% of patients, respectively. The incidence of ALT elevation in male patients was remarkably higher than that in females (P < 0.01), while the incidences of AST, CK, and blood glucose elevations in severe patients were remarkably higher than those in moderate patients (P < 0.05, respectively). Except for the mild patients, chest computed tomography showed characteristic pulmonary lesions. All the patients received antiviral drugs, 38 (79.2%) accepted traditional Chinese medicine, and 2 (4.2%) received treatment of human umbilical-cord mesenchymal stem cells. On March 2, 2020, 48 patients with COVID-19 were all cured and discharged. CONCLUSION: Based on our results, patients with COVID-19 often have multiple organ dysfunction or damage. The incidences of ALT elevation in males, and AST, CK, and blood glucose elevations in severe patients are remarkably higher.