Simultaneous quantification of mirabegron and vibegron in human plasma by HPLC-MS/MS and its application in the clinical determination in patients with tumors associated with overactive bladder.

Mirabegron and vibegron, both newly identified beta-3 adrenergic agonists, have significantly improved the quality of life for patients suffering from overactive bladder. In order to comprehensively assess the plasma exposure levels of these agents, the development of a rapid and highly sensitive bioanalytical method becomes imperative. The primary objective of this study was to establish a robust high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) method for the concurrent quantification of mirabegron and vibegron in human plasma. The analytes were extracted from a 100 µL plasma sample through protein precipitation, employing 300 µL of methanol. Subsequently, samples underwent separation and quantification using a Waters XBridge C18 column (2.1 × 100 mm, 3.5 µm), with a mobile phase consisting of 0.1% formic acid in water and 0.1% formic acid in acetonitrile. The mass analysis was conducted using positive electrospray ionization (ESI+) operated in a multiple reaction monitoring (MRM) mode. The proposed method was meticulously validated in accordance with the guidelines set forth by the U.S. Food and Drug Administration (FDA) for bioanalytical method validation. The regression equations demonstrated exceptional linearity for both mirabegron (r² ≥ 0.994) and vibegron (r² ≥ 0.996) across the concentration range of 0.5 - 200 ng/mL. Furthermore, the assay exhibited accuracy (inter-day relative error ≤ 6.90%) and precision (inter-day coefficient of variation ≤ 8.88%). The average recoveries of the analytes were found to range from 81.94% to 102.02%, with mean matrix effects falling within the range of 89.77% to 110.58%. As a result, this method was deemed highly suitable for the precise determination of the concentrations of both mirabegron and vibegron in the context of therapeutic drug monitoring and bioequivalence studies.

Single-cell transcriptome analysis indicates fatty acid metabolism-mediated metastasis and immunosuppression in male breast cancer.

Male breast cancer (MBC) is a rare but aggressive malignancy with cellular and immunological characteristics that remain unclear. Here, we perform transcriptomic analysis for 111,038 single cells from tumor tissues of six MBC and thirteen female breast cancer (FBC) patients. We find that that MBC has significantly lower infiltration of T cells relative to FBC. Metastasis-related programs are more active in cancer cells from MBC. The activated fatty acid metabolism involved with FASN is related to cancer cell metastasis and low immune infiltration of MBC. T cells in MBC show activation of p38 MAPK and lipid oxidation pathways, indicating a dysfunctional state. In contrast, T cells in FBC exhibit higher expression of cytotoxic markers and immune activation pathways mediated by immune-modulatory cytokines. Moreover, we identify the inhibitory interactions between cancer cells and T cells in MBC. Our study provides important information for understanding the tumor immunology and metabolism of MBC.

The FACT-targeted drug CBL0137 enhances the effects of rituximab to inhibit B-cell non-Hodgkin's lymphoma tumor growth by promoting apoptosis and autophagy.

BACKGROUND: Aggressive B-cell non-Hodgkin's lymphoma (B-NHL) patients often develop drug resistance and tumor recurrence after conventional immunochemotherapy, for which new treatments are needed. METHODS: We investigated the antitumor effects of CBL0137. In vitro, cell proliferation was assessed by CCK-8 and colony formation assay. Flow cytometry was performed to analyze cell cycle progression, apoptosis, mitochondrial depolarization, and reactive oxygen species (ROS) production. Autophagy was detected by transmission electron microscopy and mGFP-RFP-LC3 assay, while western blotting was employed to detect proteins involved in apoptosis and autophagy. RNA-sequencing was conducted to analyze the transcription perturbation after CBL0137 treatment in B-NHL cell lines. Finally, the efficacy and safety of CBL0137, rituximab, and their combination were tested in vivo. RESULTS: CBL0137, a small molecule anticancer agent that has significant antitumor effects in B-NHL. CBL0137 sequesters the FACT (facilitates chromatin transcription) complex from chromatin to produce cytotoxic effects in B-NHL cells. In addition, we discovered novel anticancer mechanisms of CBL0137. CBL0137 inhibited human B-NHL cell proliferation by inducing cell cycle arrest in S phase via the c-MYC/p53/p21 pathway. Furthermore, CBL0137 triggers ROS generation and induces apoptosis and autophagy in B-NHL cells through the ROS-mediated PI3K/Akt/mTOR and MAPK signaling pathways. Notably, a combination of CBL0137 and rituximab significantly suppressed B-NHL tumor growth in subcutaneous models, consistent with results at the cellular level in vitro. CONCLUSIONS: CBL0137 has potential as a novel approach for aggressive B-NHL, and its combination with rituximab can provide new therapeutic options for patients with aggressive B-NHL. Video Abstract.

Phagocytosis of Glioma Cells Enhances the Immunosuppressive Phenotype of Bone Marrow-Derived Macrophages.

Tumor-associated macrophages (TAM) play a crucial role in immunosuppression. However, how TAMs are transformed into immunosuppressive phenotypes and influence the tumor microenvironment (TME) is not fully understood. Here, we utilized single-cell RNA sequencing and whole-exome sequencing data of glioblastoma (GBM) tissues and identified a subset of TAMs dually expressing macrophage and tumor signatures, which were termed double-positive TAMs. Double-positive TAMs tended to be bone marrow-derived macrophages (BMDM) and were characterized by immunosuppressive phenotypes. Phagocytosis of glioma cells by BMDMs in vitro generated double-positive TAMs with similar immunosuppressive phenotypes to double-positive TAMs in the GBM TME of patients. The double-positive TAMs were transformed into M2-like macrophages and drove immunosuppression by expressing immune-checkpoint proteins CD276, PD-L1, and PD-L2 and suppressing the proliferation of activated T cells. Together, glioma cell phagocytosis by BMDMs in the TME leads to the formation of double-positive TAMs with enhanced immunosuppressive phenotypes, shedding light on the processes driving TAM-mediated immunosuppression in GBM. SIGNIFICANCE: Bone marrow-derived macrophages phagocytose glioblastoma cells to form double-positive cells, dually expressing macrophage and tumor signatures that are transformed into M2-like macrophages and drive immunosuppression.

Single-cell analysis reveals the chemotherapy-induced cellular reprogramming and novel therapeutic targets in relapsed/refractory acute myeloid leukemia.

Chemoresistance and relapse are the leading cause of AML-related deaths. Utilizing single-cell RNA sequencing (scRNA-seq), we dissected the cellular states of bone marrow samples from primary refractory or short-term relapsed AML patients and defined the transcriptional intratumoral heterogeneity. We found that compared to proliferating stem/progenitor-like cells (PSPs), a subpopulation of quiescent stem-like cells (QSCs) were involved in the chemoresistance and poor outcomes of AML. By performing longitudinal scRNA-seq analyses, we demonstrated that PSPs were reprogrammed to obtain a QSC-like expression pattern during chemotherapy in refractory AML patients, characterized by the upregulation of CD52 and LGALS1 expression. Flow cytometric analysis further confirmed that the preexisting CD99+CD49d+CD52+Galectin-1+ (QSCs) cells at diagnosis were associated with chemoresistance, and these cells were further enriched in the residual AML cells of refractory patients. Interaction of CD52-SIGLEC10 between QSCs and monocytes may contribute to immune evading and poor outcomes. Furthermore, we identified that LGALS1 was a promising target for chemoresistant AML, and LGALS1 inhibitor could help eliminate QSCs and enhance the chemotherapy in patient-derived primary AML cells, cell lines, and AML xenograft models. Our results will facilitate a better understanding of the AML chemoresistance mechanism and the development of novel therapeutic strategies for relapsed/refractory AML patients.

Single-cell RNA sequencing unveils the communications between malignant T and myeloid cells contributing to tumor growth and immunosuppression in cutaneous T-cell lymphoma.

Cutaneous T cell lymphoma (CTCL) is characterized by the accumulation of malignant T cells in the skin. However, advanced CTCL pathophysiology remains elusive and therapeutic options are limited due to the high intratumoral heterogeneity and complicated tumor microenvironment (TME). By comparing the single-cell RNA-seq (scRNA-seq) data from advanced CTCL patients and healthy controls (HCs), we showed that CTCL had a higher enrichment of T/NK and myeloid cells. Subpopulations of T cells (CXCR3+, GNLY+, CREM+, and MKI67+ T cells), with high proliferation, stemness, and copy number variation (CNV) levels, contribute to the malignancy of CTCL. Besides, CCL13+ monocytes/macrophages and LAMP3+ cDC cells were enriched and mediated the immunosuppression via inhibitory interactions with malignant T cells, such as CD47-SIRPA, MIF-CD74, and CCR1-CCL18. Notably, elevated expressions of S100A9 and its receptor TLR4, as well as the activation of downstream toll-like receptor and NF-κB pathway were observed in both malignant cells and myeloid cells in CTCL. Cell co-culture experiments further confirmed that the interaction between malignant CTCL cells and macrophages contributed to tumor growth via S100A9 upregulation and NF-kb activation. Our results showed that blocking the S100A9-TLR4 interaction using tasquinimod could inactivate the NF-κB pathway and inhibit the growth of CTCL tumor cells, and trigger cell apoptosis. Collectively, our study revealed a landscape of immunosuppressive TME mediated by interactions between malignant T cells and myeloid cells, and provided novel targets and potential treatment strategies for advanced CTCL patients.

Natural Coevolution of Tumor and Immunoenvironment in Glioblastoma.

Isocitrate dehydrogenase (IDH) wild-type glioblastoma (GBM) has a dismal prognosis. A better understanding of tumor evolution holds the key to developing more effective treatment. Here we study GBM's natural evolutionary trajectory by using rare multifocal samples. We sequenced 61,062 single cells from eight multifocal IDH wild-type primary GBMs and defined a natural evolution signature (NES) of the tumor. We show that the NES significantly associates with the activation of transcription factors that regulate brain development, including MYBL2 and FOSL2. Hypoxia is involved in inducing NES transition potentially via activation of the HIF1A-FOSL2 axis. High-NES tumor cells could recruit and polarize bone marrow-derived macrophages through activation of the FOSL2-ANXA1-FPR1/3 axis. These polarized macrophages can efficiently suppress T-cell activity and accelerate NES transition in tumor cells. Moreover, the polarized macrophages could upregulate CCL2 to induce tumor cell migration. SIGNIFICANCE: GBM progression could be induced by hypoxia via the HIF1A-FOSL2 axis. Tumor-derived ANXA1 is associated with recruitment and polarization of bone marrow-derived macrophages to suppress the immunoenvironment. The polarized macrophages promote tumor cell NES transition and migration. This article is highlighted in the In This Issue feature, p. 2711.

Unraveling the drug distribution in brain enabled by MALDI MS imaging with laser-assisted chemical transfer.

Accurate localization of central nervous system (CNS) drug distribution in the brain is quite challenging to matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI), owing to the ionization competition/suppression of highly abundant endogenous biomolecules and MALDI matrix. Herein, we developed a highly efficient sample preparation technique, laser-assisted chemical transfer (LACT), to enhance the detection sensitivity of CNS drugs in brain tissues. A focused diode laser source transilluminated the tissue slide coated with α-cyano-4-hydroxycinnamic acid, an optimal matrix to highly absorb the laser radiation at 405 nm, and a very thin-layer chemical film mainly containing drug molecule was transferred to the acceptor glass slide. Subsequently, MALDI MSI was performed on the chemical film without additional sample treatment. One major advantage of LACT is to minimize ionization competition/suppression from the tissue itself by removing abundant endogenous lipid and protein components. The superior performance of LACT led to the successful visualization of regional distribution patterns of 16 CNS drugs in the mouse brain. Furthermore, the dynamic spatial changes of risperidone and its metabolite were visualized over a 24-h period. Also, the brain-to-plasma (B/P) ratio could be obtained according to MALDI MSI results, providing an alternative means to assess brain penetration in drug discovery.

Detection and Localization of Solid Tumors Utilizing the Cancer-Type-Specific Mutational Signatures.

Accurate detection and location of tumor lesions are essential for improving the diagnosis and personalized cancer therapy. However, the diagnosis of lesions with fuzzy histology is mainly dependent on experiences and with low accuracy and efficiency. Here, we developed a logistic regression model based on mutational signatures (MS) for each cancer type to trace the tumor origin. We observed MS could distinguish cancer from inflammation and healthy individuals. By collecting extensive datasets of samples from ten tumor types in the training cohort (5,001 samples) and independent testing cohort (2,580 samples), cancer-type-specific MS patterns (CTS-MS) were identified and had a robust performance in distinguishing different types of primary and metastatic solid tumors (AUC:0.76 ∼ 0.93). Moreover, we validated our model in an Asian population and found that the AUC of our model in predicting the tumor origin of the Asian population was higher than 0.7. The metastatic tumor lesions inherited the MS pattern of the primary tumor, suggesting the capability of MS in identifying the tissue-of-origin for metastatic cancers. Furthermore, we distinguished breast cancer and prostate cancer with 90% accuracy by combining somatic mutations and CTS-MS from cfDNA, indicating that the CTS-MS could improve the accuracy of cancer-type prediction by cfDNA. In summary, our study demonstrated that MS was a novel reliable biomarker for diagnosing solid tumors and provided new insights into predicting tissue-of-origin.

Association of blood glucose level and prognosis of inpatients with coexistent diabetes and COVID-19.

Type 2 diabetes (T2D) increases the risk of coronavirus disease (COVID-19). This study investigates the association between glucose control of COVID-19 patients with T2D in first 7 days after hospital admission and prognosis. A total of 252 infected inpatients with T2D in China were included. Well-controlled blood glucose was defined as stable fasting blood glucose (FBG) levels in the range of 3.9-7.8 mmol/L during first 7 days using indicators of average (FBGA), maximum (FBGM) or first-time (FBG1) FBG levels. The primary endpoint was admission to intensive care unit or death. Hazard ratio (HR) of poorly controlled glucose level group compared with well-controlled group were 4.96 (P = 0.021) for FBGM and 5.55 (P = 0.014) for FBGA. Well-controlled blood glucose levels in first 7 days could improve the prognosis of COVID-19 inpatients with diabetes.

Characterizing the spatial distribution of dipeptides in rodent tissue using MALDI MS imaging with on-tissue derivatization.

A highly efficient on-tissue derivatization method for the matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) of dipeptides in tissues has been developed via a combination of a home-built laser-assisted chemical transfer (LACT) technique and hexafluoroisopropanol (HFIP).

Laboratory Testing Implications of Risk-Stratification and Management of COVID-19 Patients.

Objective: To distinguish COVID-19 patients and non-COVID-19 viral pneumonia patients and classify COVID-19 patients into low-risk and high-risk at admission by laboratory indicators. Materials and methods: In this retrospective cohort, a total of 3,563 COVID-19 patients and 118 non-COVID-19 pneumonia patients were included. There are two cohorts of COVID-19 patients, including 548 patients in the training dataset, and 3,015 patients in the testing dataset. Laboratory indicators were measured during hospitalization for all patients. Based on laboratory indicators, we used the support vector machine and joint random sampling to risk stratification for COVID-19 patients at admission. Based on laboratory indicators detected within the 1st week after admission, we used logistic regression and joint random sampling to develop the survival mode. The laboratory indicators of COVID-10 and non-COVID-19 were also compared. Results: We first identified the significant laboratory indicators related to the severity of COVID-19 in the training dataset. Neutrophils percentage, lymphocytes percentage, creatinine, and blood urea nitrogen with AUC >0.7 were included in the model. These indicators were further used to build a support vector machine model to classify patients into low-risk and high-risk at admission in the testing dataset. Results showed that this model could stratify the patients in the testing dataset effectively (AUC = 0.89). Our model still has good performance at different times (Mean AUC: 0.71, 0.72, 0.72, respectively for 3, 5, and 7 days after admission). Moreover, laboratory indicators detected within the 1st week after admission were able to estimate the probability of death (AUC = 0.95). We identified six indicators with permutation p < 0.05, including eosinophil percentage (p = 0.007), white blood cell count (p = 0.045), albumin (p = 0.041), aspartate transaminase (p = 0.043), lactate dehydrogenase (p = 0.002), and hemoglobin (p = 0.031). We could diagnose COVID-19 and differentiate it from other kinds of viral pneumonia based on these laboratory indicators. Conclusions: Our risk-stratification model based on laboratory indicators could help to diagnose, monitor, and predict severity at an early stage of COVID-19. In addition, laboratory findings could be used to distinguish COVID-19 and non-COVID-19.

Comparative Characterization and Risk Stratification of Asymptomatic and Presymptomatic Patients With COVID-19.

The identification of asymptomatic, non-severe presymptomatic, and severe presymptomatic coronavirus disease 2019 (COVID-19) in patients may help optimize risk-stratified clinical management and improve prognosis. This single-center case series from Wuhan Huoshenshan Hospital, China, included 2,980 patients with COVID-19 who were hospitalized between February 4, 2020 and April 10, 2020. Patients were diagnosed as asymptomatic (n = 39), presymptomatic (n = 34), and symptomatic (n = 2,907) upon admission. This study provided an overview of asymptomatic, presymptomatic, and symptomatic COVID-19 patients, including detection, demographics, clinical characteristics, and outcomes. Upon admission, there was no significant difference in clinical symptoms and CT image between asymptomatic and presymptomatic patients for diagnosis reference. The mean area under the receiver operating characteristic curve (AUC) of the differential diagnosis model to discriminate presymptomatic patients from asymptomatic patients was 0.89 (95% CI, 0.81-0.98). Importantly, the severe and non-severe presymptomatic patients can be further stratified (AUC = 0.82). In conclusion, the two-step risk-stratification model based on 10 laboratory indicators can distinguish among asymptomatic, severe presymptomatic, and non-severe presymptomatic COVID-19 patients on admission. Moreover, single-cell data analyses revealed that the CD8+T cell exhaustion correlated to the progression of COVID-19.

Sex-based clinical and immunological differences in COVID-19.

BACKGROUND: Males and females differ in their immunological responses to foreign pathogens. However, most of the current COVID-19 clinical practices and trials do not take the sex factor into consideration. METHODS: We performed a sex-based comparative analysis for the clinical outcomes, peripheral immune cells, and severe acute respiratory syndrome coronavirus (SARS-CoV-2) specific antibody levels of 1558 males and 1499 females COVID-19 patients from a single center. The lymphocyte subgroups were measured by Flow cytometry. The total antibody, Spike protein (S)-, receptor binding domain (RBD)-, and nucleoprotein (N)- specific IgM and IgG levels were measured by chemiluminescence. RESULTS: We found that male patients had approximately two-fold rates of ICU admission (4.7% vs. 2.7% in males and females, respectively, P = 0.005) and mortality (3% vs. 1.4%, in males and females, respectively, P = 0.004) than female patients. Survival analysis revealed that the male sex is an independent risk factor for death from COVID-19 (adjusted hazard ratio [HR] = 2.22, 95% confidence interval [CI]: 1.3-3.6, P = 0.003). The level of inflammatory cytokines in peripheral blood was higher in males during hospitalization. The renal (102/1588 [6.5%] vs. 63/1499 [4.2%], in males and females, respectively, P = 0.002) and hepatic abnormality (650/1588 [40.9%] vs. 475/1499 [31.7%], P = 0.003) were more common in male patients than in female patients. By analyzing dynamic changes of lymphocyte subsets after symptom onset, we found that the percentage of CD19+ B cells and CD4+ T cells was generally higher in female patients during the disease course of COVID-19. Notably, the protective RBD-specific IgG against SARS-CoV-2 sharply increased and reached a peak in the fourth week after symptom onset in female patients, while gradually increased and reached a peak in the seventh week after symptom onset in male patients. CONCLUSIONS: Males had an unfavorable prognosis, higher inflammation, a lower percentage of lymphocytes, and indolent antibody responses during SARS-CoV-2 infection and recovery. Early medical intervention and close monitoring are important, especially for male COVID-19 patients.

High Expression of ACE2 and TMPRSS2 at the Resection Margin Makes Lung Cancer Survivors Susceptible to SARS-CoV-2 With Unfavorable Prognosis.

BACKGROUND: Coronavirus disease 2019 (COVID-19) has rapidly spread worldwide. Systematic analysis of lung cancer survivors at molecular and clinical levels is warranted to understand the disease course and clinical characteristics. METHODS: A single-center, retrospective cohort study was conducted in 65 patients with COVID-19 from Wuhan Huoshenshan Hospital, of which 13 patients were diagnosed with lung cancer. The study was conducted from February 4 to April 11, 2020. RESULTS: During the course of treatment, lung cancer survivors infected with severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) had shorter median time from symptom onset to hospitalization (P = 0.016) and longer clinical symptom remission time (P = 0.020) than non-cancer individuals. No differences were observed among indicators such as time from symptom onset to hospitalization and symptom remission time between medium-term and short-term survivors. The expression of ACE2 (P = 0.013) and TMPRSS2 (P <0.001) was elevated in lung cancer survivors as compared with that in non-cancer individuals. CONCLUSIONS: ACE2 and TMPRSS2 levels were higher at resection margins of lung cancer survivors than those in normal tissues of non-cancerous individuals and may serve as factors responsible for the high susceptibility to COVID-19 among lung cancer survivors. Lung cancer patients diagnosed with COVID-19, including medium-term survivors, have worse outcomes than the general population.

Implications of cardiac markers in risk-stratification and management for COVID-19 patients.

BACKGROUND: COVID-19 has resulted in high mortality worldwide. Information regarding cardiac markers for precise risk-stratification is limited. We aim to discover sensitive and reliable early-warning biomarkers for optimizing management and improving the prognosis of COVID-19 patients. METHODS: A total of 2954 consecutive COVID-19 patients who were receiving treatment from the Wuhan Huoshenshan Hospital in China from February 4 to April 10 were included in this retrospective cohort. Serum levels of cardiac markers were collected after admission. Coronary artery disease diagnosis and survival status were recorded. Single-cell RNA-sequencing and bulk RNA-sequencing from different cohorts of non-COVID-19 were performed to analyze SARS-CoV-2 receptor expression. RESULTS: Among 2954 COVID-19 patients in the analysis, the median age was 60 years (50-68 years), 1461 (49.5%) were female, and 1515 (51.3%) were severe/critical. Compared to mild/moderate (1439, 48.7%) patients, severe/critical patients showed significantly higher levels of cardiac markers within the first week after admission. In severe/critical COVID-19 patients, those with abnormal serum levels of BNP (42 [24.6%] vs 7 [1.1%]), hs-TNI (38 [48.1%] vs 6 [1.0%]), α- HBDH (55 [10.4%] vs 2 [0.2%]), CK-MB (45 [36.3%] vs 12 [0.9%]), and LDH (56 [12.5%] vs 1 [0.1%]) had a significantly higher mortality rate compared to patients with normal levels. The same trend was observed in the ICU admission rate. Severe/critical COVID-19 patients with pre-existing coronary artery disease (165/1,155 [10.9%]) had more cases of BNP (52 [46.5%] vs 119 [16.5%]), hs-TNI (24 [26.7%] vs 9.6 [%], α- HBDH (86 [55.5%] vs 443 [34.4%]), CK-MB (27 [17.4%] vs 97 [7.5%]), and LDH (65 [41.9%] vs 382 [29.7%]), when compared with those without coronary artery disease. There was enhanced SARS-CoV-2 receptor expression in coronary artery disease compared with healthy controls. From regression analysis, patients with five elevated cardiac markers were at a higher risk of death (hazards ratio 3.4 [95% CI 2.4-4.8]). CONCLUSIONS: COVID-19 patients with pre-existing coronary artery disease represented a higher abnormal percentage of cardiac markers, accompanied by high mortality and ICU admission rate. BNP together with hs-TNI, α- HBDH, CK-MB and LDH act as a prognostic biomarker in COVID-19 patients with or without pre-existing coronary artery disease.

Implications of liver injury in risk-stratification and management of patients with COVID-19.

BACKGROUND: Infection with SARS-CoV-2 has been associated with liver dysfunction, aggravation of liver burden, and liver injury. This study aimed to assess the effects of liver injuries on the clinical outcomes of patients with COVID-19. METHODS: A total of 1520 patients with severe or critical COVID-19 from Huoshenshan Hospital, Wuhan, were enrolled. Chronic liver disease (CLD) was confirmed by consensus diagnostic criteria. Laboratory test results were compared between different groups. scRNA-seq data and bulk gene expression profiles were used to identify cell types associated with liver injury. RESULTS: A total of 10.98% of patients with severe or critical COVID-19 developed liver injury after admission that was associated with significantly higher rates of mortality (21.74%, p < 0.001) and intensive care unit admission (26.71%, p < 0.001). Pre-existing CLDs were not associated with a higher risk. However, fatty liver disease and cirrhosis were associated with higher risks, supported by evidences from single cell and bulk transcriptome analysis that showed more TMPRSS2+ cells in these tissues. By generating a model, we were able to predict the risk and severity of liver injury during hospitalization. CONCLUSION: We demonstrate that liver injury occurring during therapy as well as pre-existing CLDs like fatty liver disease and cirrhosis in patients with COVID-19 is significantly associated with the severity of disease and mortality, but the presence of other CLD is not associated. We provide a risk-score model that can predict whether patients with COVID-19 will develop liver injury or proceed to higher-risk stages during subsequent hospitalizations.

Dynamic changes in anti-SARS-CoV-2 antibodies during SARS-CoV-2 infection and recovery from COVID-19.

Deciphering the dynamic changes in antibodies against SARS-CoV-2 is essential for understanding the immune response in COVID-19 patients. Here we analyze the laboratory findings of 1,850 patients to describe the dynamic changes of the total antibody, spike protein (S)-, receptor-binding domain (RBD)-, and nucleoprotein (N)-specific immunoglobulin M (IgM) and G (IgG) levels during SARS-CoV-2 infection and recovery. The generation of S-, RBD-, and N-specific IgG occurs one week later in patients with severe/critical COVID-19 compared to patients with mild/moderate disease, while S- and RBD-specific IgG levels are 1.5-fold higher in severe/critical patients during hospitalization. The RBD-specific IgG levels are 4-fold higher in older patients than in younger patients during hospitalization. In addition, the S- and RBD-specific IgG levels are 2-fold higher in the recovered patients who are SARS-CoV-2 RNA negative than those who are RNA positive. Lower S-, RBD-, and N-specific IgG levels are associated with a lower lymphocyte percentage, higher neutrophil percentage, and a longer duration of viral shedding. Patients with low antibody levels on discharge might thereby have a high chance of being tested positive for SARS-CoV-2 RNA after recovery. Our study provides important information for COVID-19 diagnosis, treatment, and vaccine development.

Bioinformatics Approaches for Anti-cancer Drug Discovery.

Drug discovery is important in cancer therapy and precision medicines. Traditional approaches of drug discovery are mainly based on in vivo animal experiments and in vitro drug screening, but these methods are usually expensive and laborious. In the last decade, omics data explosion provides an opportunity for computational prediction of anti-cancer drugs, improving the efficiency of drug discovery. High-throughput transcriptome data were widely used in biomarkers' identification and drug prediction by integrating with drug-response data. Moreover, biological network theory and methodology were also successfully applied to the anti-cancer drug discovery, such as studies based on protein-protein interaction network, drug-target network and disease-gene network. In this review, we summarized and discussed the bioinformatics approaches for predicting anti-cancer drugs and drug combinations based on the multi-omic data, including transcriptomics, toxicogenomics, functional genomics and biological network. We believe that the general overview of available databases and current computational methods will be helpful for the development of novel cancer therapy strategies.