Simultaneous Quantification of Nirmatrelvir/Ritonavir in Human Serum by Lc-Ms/Ms (preprint)

In December 2021, the U.S. Food and Drug Administration (FDA) granted emergency authorization for Paxlovid® as an antiviral treatment for COVID-19. Paxlovid® is composed of two tablets, nirmatrelvir and ritonavir. Dose adjustment is necessary in cases of renal insufficiency. The aim of present study is to establish a LC-MS/MS method for simultaneous determination of nirmatrelvir/ritonavir in human serum for therapeutic drug monitoring. Internal standard saquinavir was added in 25 μl human serum samples, and then the samples were precipitated with methanol. The analytes were separated by gradient elution on a C18 column, using a mobile phase of 0.1% formic acid-water and methanol, at a flow rate of 0.4 ml/min. The injection volume was 2 μl, and the analysis time was 5 min. The determination of the analytes was performed by electrospray ionization in positive mode by full mass monitoring. The detected ions of nirmatrelvir, ritonavir and saquinavir were m/z 500.24792, 721.32004 and 671.39155, respectively. The linear concentration range for nirmatrelvir was 78.13~20000 ng/ml, for ritonavir was 15.63~4000 ng/ml (r2>0.9900). The accuracy ranged from 87.45%~104.63%, and the intra-day and inter-day precision RSD was < 15%. The recovery of Nirmatrelvir ranged from 98.72%~109.83%, and that of ritonavir was 95.41%~112.36%. The matrix effect of Nirmatrelvir was 88.31%~97.73%, and that of ritonavir was 85.17%~103.05%. This method was used to measure the trough concentrations of nirmatrelvir/ritonavir in 17 patients. The trough concentration of nirmatrelvir was 1331.7~8352.5 ng/ml, and that of ritonavir was 53.4~1325.5 ng/ml, with large individual differences. The method is simple, sensitive, specific, and reproducible, and can be used for monitoring the blood concentration and pharmacokinetic study of nirmatrelvir/ritonavir in COVID-19 patients.

DeepMPF: deep learning framework for predicting drug-target interactions based on multi-modal representation with meta-path semantic analysis.

BACKGROUND: Drug-target interaction (DTI) prediction has become a crucial prerequisite in drug design and drug discovery. However, the traditional biological experiment is time-consuming and expensive, as there are abundant complex interactions present in the large size of genomic and chemical spaces. For alleviating this phenomenon, plenty of computational methods are conducted to effectively complement biological experiments and narrow the search spaces into a preferred candidate domain. Whereas, most of the previous approaches cannot fully consider association behavior semantic information based on several schemas to represent complex the structure of heterogeneous biological networks. Additionally, the prediction of DTI based on single modalities cannot satisfy the demand for prediction accuracy. METHODS: We propose a multi-modal representation framework of 'DeepMPF' based on meta-path semantic analysis, which effectively utilizes heterogeneous information to predict DTI. Specifically, we first construct protein-drug-disease heterogeneous networks composed of three entities. Then the feature information is obtained under three views, containing sequence modality, heterogeneous structure modality and similarity modality. We proposed six representative schemas of meta-path to preserve the high-order nonlinear structure and catch hidden structural information of the heterogeneous network. Finally, DeepMPF generates highly representative comprehensive feature descriptors and calculates the probability of interaction through joint learning. RESULTS: To evaluate the predictive performance of DeepMPF, comparison experiments are conducted on four gold datasets. Our method can obtain competitive performance in all datasets. We also explore the influence of the different feature embedding dimensions, learning strategies and classification methods. Meaningfully, the drug repositioning experiments on COVID-19 and HIV demonstrate DeepMPF can be applied to solve problems in reality and help drug discovery. The further analysis of molecular docking experiments enhances the credibility of the drug candidates predicted by DeepMPF. CONCLUSIONS: All the results demonstrate the effectively predictive capability of DeepMPF for drug-target interactions. It can be utilized as a useful tool to prescreen the most potential drug candidates for the protein. The web server of the DeepMPF predictor is freely available at http://120.77.11.78/DeepMPF/ , which can help relevant researchers to further study.

Two-year impact of COVID-19 pandemic on hospitalized patients with skin diseases in China.

To investigate the clinical characteristics of skin disorders among hospitalized patients before and during the coronavirus disease 2019 (COVID-19) pandemic, a retrospective study was conducted based on hospitalized patients with skin diseases from Xiangya Hospital of Central South University, the largest hospital in the south-central region of China, between January 1, 2018, and December 31, 2021. A total of 3039 hospitalized patients were enrolled in the study, including 1681 patients in the prepandemic group and 1358 patients in the pandemic group. The total number of hospitalized patients in the pandemic group decreased by 19.2%, with an increased proportion of patients over 60 years of age (39.8% vs. 35.8%). Moreover, compared with the prepandemic group, there were decreases in the occurrence of most skin diseases in the pandemic group, but the proportions of keratinolytic carcinoma (6.6% vs. 5.2%), dermatitis (24.0% vs. 18.9%), and psoriasis (18.0% vs. 14.8%) were higher in the pandemic group. In addition, longer hospital stays (ß = 0.07, SE = 0.02, P = 1.35 × 10-3 ) and higher hospital costs (ß = 0.06, SE = 0.03, p = 0.031) were found in the pandemic group through general linear models, even after the corresponding adjustment. In summary, the COVID-19 pandemic has had a lasting impact on patients with skin diseases, with fewer hospitalized patients, increased proportions of older patients, longer hospital stays, and increased hospital costs. These findings will facilitate better preparation for the most effective response to future pandemics.

Efficacy and Safety of Shenfu Injection for Severe Pneumonia in the Elderly: A Systematic Review and Meta-Analysis Based on Western and Eastern Medicine.

Background: Although increasing clinical trials studying Shenfu injection (SFI) comprising panaxoside 0.8 mg/ml extracted from Panax ginseng C.A. Mey. and aconitine 0.1 mg/ml extracted from Aconitum carmichaeli Debeaux for elderly patients with severe pneumonia on biomarkers associated with COVID-19 progression are emerging, there is no evidence-based evaluation for the effect of SFI on elderly severe pneumonia. Objectives: To evaluate the effect of SFI on elderly patients with severe pneumonia providing hints for treating critical COVID-19, we conducted a systematic review and meta-analysis. Methods: Nine databases, namely, PubMed, EMBASE, Web of Science, Science Direct, Google Scholar, Wanfang, Chongqing VIP Database, CNKI, and SinoMed were used to search clinical trials reporting the effect of SFI as an adjuvant for elderly severe pneumonia on outcomes of interest. Primary outcomes were total effective rate, Acute Physiology and Chronic Health Evaluation (APACHE) II score, mortality, and safety. Secondary outcomes were predictors associated with COVID-19 progression. Duplicated or irrelevant articles with unavailable data were excluded. Cochrane Collaboration's tool was used to evaluate the risk of bias by two reviewers independently. All data were analyzed by Rev Man 5.4. Continuous variables were shown as weighted mean difference (WMD) or standard mean difference (SMD) with 95% confidence intervals (95% CI), whereas dichotomous data were calculated as the risk ratio (RR) with 95% CI. Results: We included 20 studies with 1, 909 participants, and the pooled data showed that compared with standard control, SFI could improve the total effective rate (RR = 1.25, 95% CI = 1.14-1.37, and n = 689), APACHE II score (WMD = -2.95, 95% CI = -3.35, -2.56, and n = 809), and predictors associated with COVID-19 progression (brain natriuretic peptide, creatine kinase, stroke volume, cardiac output, left ventricular ejection fraction, cardiac index, sE-selectin, von Willebrand factor, activated partial thromboplastin time, platelet counts, D-Dimer, procalcitonin, and WBC count). SFI may reduce mortality (RR = 0.52, 95% CI = 0.37-0.73, and n = 429) and safety concerns (RR = 0.29, 95% CI = 0.17-0.51, and n = 150) for elderly severe pneumonia. Conclusion: SFI as an adjuvant may improve the total effective rate, APACHE II score, gas exchange, and predictors associated with COVID-19 progression, reducing mortality and safety concerns for elderly patients with severe pneumonia.

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/.

CXCR6-based immunotherapy in autoimmune, cancer and inflammatory infliction

T cells, including both CD4+ and CD8+ T cells, play a pivotal role in mediating various inflammation and immune disorders. A long-standing challenge in T cell-based immunotherapy is to precisely inactivate or delete the pathogenic T cells in inflammation and autoimmune diseases, or to selectively expand the immunocompetent T cell in tumor or other immune compromised situations, without inducing global immunosuppression or zealous immune activation respectively. To achieve this, a specific marker is needed to differentiate the pathogenic or immunocompetent T cell among the rests. Indeed, recent progress of immunology strongly suggests that CXC chemokine receptor 6 (CXCR6, CD186) is such a kind of marker. Here, we review the emerging role of CXCR6 as a novel target for immunotherapy and discuss the underlying mechanism. We propose that CXCR6-based immunotherapy will play a significant role in autoimmune, nonalcoholic steatohepatitis (NASH), tumor, coronavirus disease 2019 (COVID-19) and even ageing-related inflammatory infliction. Graphical CXCR6+ T cells represent the immunocompetent or pathogenic T cells in anti-tumor immunity, autoimmune and inflammatory diseases, respectively. Targeting CXCR6+ cell population may be a promising immunotherapeutic strategy.Image 1

Dynamic SARS-CoV-2-specific B-cell and T-cell responses following immunization with an inactivated COVID-19 vaccine.

OBJECTIVE: The dynamic adaptive immune responses elicited by the inactivated virus vaccine CoronaVac remain elusive. METHODS: In a prospective cohort of 100 healthcare professionals naïve for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) who received two doses of CoronaVac, we analysed SARS-CoV-2-specific humoral and cellular responses at four different timepoints, including before vaccination (T1), 2 weeks after the first dose (T2), 2 weeks after the booster dose (T3), and 8-10 weeks after the booster dose (T4). SARS-CoV-2-specific antibodies, serum neutralizing activities, peripheral B cells, CD4+ and CD8+ T cells and their memory subsets were simultaneously measured in this cohort. RESULTS: SARS-CoV-2 spike-specific IgG responses reached a peak (geometric mean titre (GMT) 54827, 30969-97065) after two doses and rapidly declined (GMT 502, 212-1190) at T4, whereas suboptimal IgA responses were detected (GMT 5, 2-9). Spike-specific circulating B cells (0.60%, 0.46-0.73% of total B cells) and memory B cells (1.18%, 0.92-1.44% of total memory B cells) were effectively induced at T3 and sustained over time (0.33%, 0.23-0.43%; 0.87%, 0.05-1.67%, respectively). SARS-CoV-2-specific circulating CD4+ T cells (0.57%, 0.47-0.66%) and CD8+ T cells (1.29%, 1.04-1.54%) were detected at T3. At T4, 0.78% (0.43-1.20%) of memory CD4+ T cells and 0.68% (0.29-1.30%) of memory CD8+ T cells were identified as SARS-CoV-2-specific, while 0.62% (0.51-0.75%) of CD4+ T cells and 0.47% (0.38-0.58%) of CD8+ T cells were SARS-CoV-2-specific terminally differentiated effector memory cells. Furthermore, age and interval between doses affected the magnitude of CoronaVac-induced immune responses. SARS-CoV-2 memory CD4+ T cells were strongly associated with both receptor binding domain (RBD)-specific memory B cells (r 0.87, p <0.0001) and SARS-CoV-2-specific memory CD8+ T cells (r 0.48, p <0.0001). CONCLUSIONS: CoronaVac induced robust circulating and memory B cell and T cell responses. Our study offers new insight into the underlying immunobiology of inactivated virus vaccines in humans and may have implications for vaccine strategies in the future.

COVID-19 Lockdown Increased the Risk of Preterm Birth.

Purpose: To estimate whether the city-specific lockdown in Shanghai induced by the COVID-19 pandemic affected preterm birth rates among uninfected pregnant women in different trimesters. Methods: The population-based retrospective cohort study was conducted in the International Peace Maternity and Child Health Hospital (IPMCH) in Shanghai, China. Pregnant women without COVID-19 received perinatal healthcare during lockdown (from January 24, 2020 to March 24, 2020) and non-lockdown (from January 24, 2019 to March 24, 2019) period and giving birth to a live infant at IPMCH were enrolled. 1:1 propensity score matching and Inverse probability of treatment weighting were used to evaluate preterm birth (<37 weeks), very preterm birth (<34 weeks), preterm birth with premature rupture of membranes (PROM-PTB), spontaneous preterm birth with intact membranes (S-PTB), and medically induced preterm birth (MI-PTB) between two groups. Results: 8,270 pregnant women were in the lockdown group, and 9,815 were in the non-lockdown group. Pregnant women in second trimester during lockdown had a higher risk of PTB than those during the non-lockdown period [OR: 1.43 (CI 1.01-2.02), ARD: 1.7% (CI 0.04-3.4%), p = 0.045]. Furthermore, pregnant women in third trimester during lockdown had a higher risk of PROM-PTB than those during the non-lockdown period [OR: 1.64 (CI 1.09-2.47), ARD: 0.9% (CI 0.2-1.6%), p = 0.02]; no group differences were found related to rates of VPTB, S-PTB or MI-PTB. Conclusion: In this cohort study in China, we found that there was an increased risk in preterm birth for non-infected women in COVID-19 lockdown who were in their second trimester.

A Retrospective Study of Oral Emergency Services During COVID-19.

OBJECTIVES: This study was performed to examine changes in the number of patient visits and types of oral services in an oral emergency department from the beginning to the control stage of the coronavirus disease 2019 (COVID-19) outbreak in Beijing. METHODS: The numbers of daily oral emergency visits from January 20 to March 24, 2020, at a dental university hospital in Beijing and daily newly confirmed COVID-19 cases in Beijing during the same period were collected and analysed. All oral emergency patient information (including sex, age, and oral diagnosis) was also collected and analysed. Patients with incomplete medical data were excluded. RESULTS: In total, 12,416 patients were included in this study. The number of daily emergency visits was negatively correlated with the number of newly confirmed local COVID-19 cases in Beijing (P < .001). The number of daily emergency visits during the COVID-19 stable period in Beijing was greater than that during the outbreak period (P < .001). Compared to those in the COVID-19 outbreak period, the percentages of females, children and adolescents, patients with acute toothache, and patients with nonurgent cases were higher in the stable period, and the numbers of patients with toothache, trauma, infection, and nonemergency conditions increased in the COVID-19 stable period (P < .001). CONCLUSIONS: COVID-19 significantly influenced the number of patient visits and the percentages of patients with oral emergency situations in the oral emergency department. There were obvious differences in treatment seeking for oral emergencies between the COVID-19 periods in Beijing. There was an inverse relationship between daily oral emergency visits and daily confirmed COVID-19 cases in Beijing.

Disease-Modifying Therapies During the COVID-19 Outbreak: A Narrative Review of International and National Recommendations.

BACKGROUND: Managing multiple sclerosis (MS) during the novel coronavirus disease 2019 (COVID-19) pandemic is a challenge due to the lack of evidence from clinical studies. Disease-modifying therapies (DMTs) may affect the immune response and subsequently alter the risk of COVID-19 infections. METHODS: A literature search was conducted on the MEDLINE, Embase, and Cochrane databases. A focused Google search was also performed. Recommendations regarding the use of DMTs during the COVID-19 outbreak from national and international MS/neurology societies were identified and reviewed. RESULTS: The review included 16 recommendations from international and national MS organizations. All recommendations are based on expert opinions. The recommendations regarding DMT initiation and management during this outbreak are summarized. Moreover, the experts' views about the risk of COVID-19 infection with each DMT are discussed. CONCLUSIONS: There is significant agreement among most experts' recommendations from a variety of sources based on collective clinical experience. However, the recommendations will likely evolve because sufficient clinical data are limited. Several ongoing registries will help provide information for future recommendations.

Assessing risk factors for SARS-CoV-2 infection in patients presenting with symptoms in Shanghai, China: a multicentre, observational cohort study.

Background: The outbreak of COVID-19 has led to international concern. We aimed to establish an effective screening strategy in Shanghai, China, to aid early identification of patients with COVID-19. Methods: We did a multicentre, observational cohort study in fever clinics of 25 hospitals in 16 districts of Shanghai. All patients visiting the clinics within the study period were included. A strategy for COVID-19 screening was presented and then suspected cases were monitored and analysed until they were confirmed as cases or excluded. Logistic regression was used to determine the risk factors of COVID-19. Findings: We enrolled patients visiting fever clinics from Jan 17 to Feb 16, 2020. Among 53 617 patients visiting fever clinics, 1004 (1·9%) were considered as suspected cases, with 188 (0·4% of all patients, 18·7% of suspected cases) eventually diagnosed as confirmed cases. 154 patients with missing data were excluded from the analysis. Exposure history (odds ratio [OR] 4·16, 95% CI 2·74-6·33; p<0·0001), fatigue (OR 1·56, 1·01-2·41; p=0·043), white blood cell count less than 4 × 109 per L (OR 2·44, 1·28-4·64; p=0·0066), lymphocyte count less than 0·8 × 109 per L (OR 1·82, 1·00-3·31; p=0·049), ground glass opacity (OR 1·95, 1·32-2·89; p=0·0009), and having both lungs affected (OR 1·54, 1·04-2·28; p=0·032) were independent risk factors for confirmed COVID-19. Interpretation: The screening strategy was effective for confirming or excluding COVID-19 during the spread of this contagious disease. Relevant independent risk factors identified in this study might be helpful for early recognition of the disease. Funding: National Natural Science Foundation of China.

Insights into the Microbiological Safety of Wooden Cutting Boards Used for Meat Processing in Hong Kong's Wet Markets: A Focus on Food-Contact Surfaces, Cross-Contamination and the Efficacy of Traditional Hygiene Practices.

Hong Kong's wet markets play a crucial role in the country's supply of safe, fresh meat to satisfy the dietary needs of its population. Whilst food safety regulations have been introduced over the past few years to maintain the microbial safety of foods sold from these wet markets, it remains unclear whether the hygiene maintenance that is performed on the wooden cutting boards used for meat-processing is effective. In fact, hygiene maintenance may often be overlooked, and hygiene standards may be insufficient. If so, this may lead to the spread of harmful pathogens through cross-contamination, thereby causing severe risks to public health. The aim of this study was to determine the level of microbial transfer between wooden cutting boards and swine meat of various qualities, using 16S metagenomic sequencing, strain identification and biofilm screening of isolated strains. The results established that: (a) the traditional hygiene practices used for cleaning wooden cutting boards in Hong Kong's wet markets expose the surfaces to potentially harmful microorganisms; (b) the processing of microbially contaminated meat on cutting boards cleaned using traditional practices leads to cross-contamination; and (c) several potentially pathogenic microorganisms found on the cutting boards have good biofilm-forming abilities. These results reinforce the need to review the traditional methods used to clean wooden cutting boards after the processing of raw meat in Hong Kong' wet markets so as to prevent cross-contamination events. The establishment of proper hygiene protocols may reduce the spread of disease-causing microorganisms (including antibiotic-resistant microorganisms) in food-processing environments.