Learning locality-sensitive bucketing functions.

MOTIVATION: Many tasks in sequence analysis ask to identify biologically related sequences in a large set. The edit distance, being a sensible model for both evolution and sequencing error, is widely used in these tasks as a measure. The resulting computational problem-to recognize all pairs of sequences within a small edit distance-turns out to be exceedingly difficult, since the edit distance is known to be notoriously expensive to compute and that all-versus-all comparison is simply not acceptable with millions or billions of sequences. Among many attempts, we recently proposed the locality-sensitive bucketing (LSB) functions to meet this challenge. Formally, a (d1,d2)-LSB function sends sequences into multiple buckets with the guarantee that pairs of sequences of edit distance at most d1 can be found within a same bucket while those of edit distance at least d2 do not share any. LSB functions generalize the locality-sensitive hashing (LSH) functions and admit favorable properties, with a notable highlight being that optimal LSB functions for certain (d1,d2) exist. LSB functions hold the potential of solving above problems optimally, but the existence of LSB functions for more general (d1,d2) remains unclear, let alone constructing them for practical use. RESULTS: In this work, we aim to utilize machine learning techniques to train LSB functions. With the development of a novel loss function and insights in the neural network structures that can potentially extend beyond this specific task, we obtained LSB functions that exhibit nearly perfect accuracy for certain (d1,d2), matching our theoretical results, and high accuracy for many others. Comparing to the state-of-the-art LSH method Order Min Hash, the trained LSB functions achieve a 2- to 5-fold improvement on the sensitivity of recognizing similar sequences. An experiment on analyzing erroneous cell barcode data is also included to demonstrate the application of the trained LSB functions. AVAILABILITY AND IMPLEMENTATION: The code for the training process and the structure of trained models are freely available at https://github.com/Shao-Group/lsb-learn.

Extracellular vesicle surface display enhances the therapeutic efficacy and safety profile of cancer immunotherapy.

Immunotherapy has emerged as a mainstay in cancer therapy, yet its efficacy is constrained by the risk of immune-related adverse events. In this study, we present a nanoparticle-based delivery system that enhances the therapeutic efficacy of immunomodulatory ligands while concurrently limiting systemic toxicity. We demonstrate that extracellular vesicles (EVs), lipid bilayer enclosed particles released by cells, can be efficiently engineered via inverse electron demand Diels-Alder (iEDDA)-mediated conjugation to display multiple immunomodulatory ligands on their surface. Display of immunomodulatory ligands on the EV surface conferred substantial enhancements in signaling efficacy, particularly for tumor necrosis factor receptor superfamily (TNFRSF) agonists, where the EV surface display served as an alternative FcγR-independent approach to induce ligand multimerization and efficient receptor crosslinking. EVs displaying a complementary combination of immunotherapeutic ligands were able to shift the tumor immune milieu toward an anti-tumorigenic phenotype and significantly suppress tumor burden and increase survival in multiple models of metastatic cancer to a greater extent than an equivalent dose of free ligands. In summary, we present an EV-based delivery platform for cancer immunotherapeutic ligands that facilitates superior anti-tumor responses at significantly lower doses with fewer side effects than is possible with conventional delivery approaches.

A novel diabetic foot ulcer diagnostic model: identification and analysis of genes related to glutamine metabolism and immune infiltration.

BACKGROUND: Diabetic foot ulcer (DFU) is one of the most common and severe complications of diabetes, with vascular changes, neuropathy, and infections being the primary pathological mechanisms. Glutamine (Gln) metabolism has been found to play a crucial role in diabetes complications. This study aims to identify and validate potential Gln metabolism biomarkers associated with DFU through bioinformatics and machine learning analysis. METHODS: We downloaded two microarray datasets related to DFU patients from the Gene Expression Omnibus (GEO) database, namely GSE134431, GSE68183, and GSE80178. From the GSE134431 dataset, we obtained differentially expressed Gln-metabolism related genes (deGlnMRGs) between DFU and normal controls. We analyzed the correlation between deGlnMRGs and immune cell infiltration status. We also explored the relationship between GlnMRGs molecular clusters and immune cell infiltration status. Notably, WGCNA to identify differentially expressed genes (DEGs) within specific clusters. Additionally, we conducted GSVA to annotate enriched genes. Subsequently, we constructed and screened the best machine learning model. Finally, we validated the predictions' accuracy using a nomogram, calibration curves, decision curve analysis (DCA), and the GSE134431, GSE68183, and GSE80178 dataset. RESULTS: In both the DFU and normal control groups, we confirmed the presence of deGlnMRGs and an activated immune response. From the GSE134431 dataset, we obtained 20 deGlnMRGs, including CTPS1, NAGS, SLC7A11, GGT1, GCLM, RIMKLA, ARG2, ASL, ASNS, ASNSD1, PPAT, GLS2, GLUD1, MECP2, ASS1, PRODH, CTPS2, ALDH5A1, DGLUCY, and SLC25A12. Furthermore, two clusters were identified in DFU. Immune infiltration analysis indicated the presence of immune heterogeneity in these two clusters. Additionally, we established a Support Vector Machine (SVM) model based on 5 genes (R3HCC1, ZNF562, MFN1, DRAM1, and PTGDS), which exhibited excellent performance on the external validation datasetGSE134431, GSE68183, and GSE80178 (AUC = 0.929). CONCLUSION: This study has identified five Gln metabolism genes associated with DFU, revealing potential novel biomarkers and therapeutic targets for DFU. Additionally, the infiltration of immune-inflammatory cells plays a crucial role in the progression of DFU.

The Impact of Electron Donating and Withdrawing Groups on Electrochemical Hydrogenolysis and Hydrogenation of Carbonyl Compounds.

The hydrogenolysis or hydrodeoxygenation of a carbonyl group, where the C═O group is converted to a CH2 group, is of significant interest in a variety of fields. A challenge in electrochemically achieving hydrogenolysis of a carbonyl group with high selectivity is that electrochemical hydrogenation of a carbonyl group, which converts the C═O group to an alcohol group (CH-OH), is demonstrated not to be the initial step of hydrogenolysis. Instead, hydrogenation and hydrogenolysis occur in parallel, and they are competing reactions. This means that although both hydrogenolysis and hydrogenation require adding H atoms to the carbonyl group, they involve different intermediates formed on the electrode surface. Thus, revealing the difference in intermediates, transition states, and kinetic barriers for hydrogenolysis and hydrogenation pathways is the key to understanding and controlling hydrogenolysis/hydrogenation selectivity of carbonyl compounds. In this study, we aimed to identify features of reactant molecules that can affect their hydrogenolysis/hydrogenation selectivity on a Zn electrode that was previously shown to promote hydrogenolysis over hydrogenation. In particular, we examined the electrochemical reduction of para-substituted benzaldehyde compounds with substituent groups having different electron donating/withdrawing abilities. Our results show a strikingly systematic impact of the substituent group where a stronger electron-donating group promotes hydrogenolysis and a stronger electron-withdrawing group promotes hydrogenation. These experimental results are presented with computational results explaining the substituent effects on the thermodynamics and kinetics of electrochemical hydrogenolysis and hydrogenation pathways, which also provide critically needed information and insights into the transition states involved with these pathways.

Single-cell RNA sequencing reveals the transcriptional heterogeneity of Tbx18-positive cardiac cells during heart development.

The T-box family transcription factor 18 (Tbx18) has been found to play a critical role in regulating the development of the mammalian heart during the primary stages of embryonic development while the cellular heterogeneity and landscape of Tbx18-positive (Tbx18+) cardiac cells remain incompletely characterized. Here, we analyzed prior published single-cell RNA sequencing (scRNA-seq) mouse heart data to explore the heterogeneity of Tbx18+ cardiac cell subpopulations and provide a comprehensive transcriptional landscape of Tbx18+ cardiac cells during their development. Bioinformatic analysis methods were utilized to identify the heterogeneity between cell groups. Based on the gene expression characteristics, Tbx18+ cardiac cells can be classified into a minimum of two distinct cell populations, namely fibroblast-like cells and cardiomyocytes. In terms of temporal heterogeneity, these cells exhibit three developmental stages, namely the MEM stage, ML_P0 stage, and P stage Tbx18+ cardiac cells. Furthermore, Tbx18+ cardiac cells encompass several cell types, including cardiac progenitor-like cells, cardiomyocytes, and epicardial/stromal cells, as determined by specific transcriptional regulatory networks. The scRNA-seq results revealed the involvement of extracellular matrix (ECM) signals and epicardial epithelial-to-mesenchymal transition (EMT) in the development of Tbx18+ cardiac cells. The utilization of a lineage-tracing model served to validate the crucial function of Tbx18 in the differentiation of cardiac cells. Consequently, these findings offer a comprehensive depiction of the cellular heterogeneity within Tbx18+ cardiac cells.

Magnetic Microbead-Based Herringbone Chip for Sensitive Detection of Human Immunodeficiency Virus.

The microfluidic chip-based nucleic acid detection method significantly improves the sensitivity since it precisely controls the microfluidic flow in microchannels. Nonetheless, significant challenges still exist in improving the detection efficiency to meet the demand for rapid detection of trace substances. This work provides a novel magnetic herringbone (M-HB) structure in a microfluidic chip, and its advantage in rapid and sensitive detection is verified by taking complementary DNA (cDNA) sequences of human immunodeficiency virus (HIV) detection as an example. The M-HB structure is designed based on controlling the magnetic field distribution in the micrometer scale and is formed by accumulation of magnetic microbeads (MMBs). Hence, M-HB is similar to a nanopore microstructure, which has a higher contact area and probe density. All of the above is conducive to improving sensitivity in microfluidic chips. The M-HB chip is stable and easy to form, which can linearly detect cDNA sequences of HIV quantitatively ranging from 1 to 20 nM with a detection limit of 0.073 nM. Compared to the traditional herringbone structure, this structure is easier to form and release by controlling the magnetic field, which is flexible and helps in further study. Results show that this chip can sensitively detect the cDNA sequences of HIV in blood samples, demonstrating that it is a powerful platform to rapidly and sensitively detect multiple nucleic acid-related viruses of infectious diseases.

Integrated analysis reveals ceRNA network of cardiac remodeling by SGLT2 inhibitor in middle-aged hypertensive rats.

Sodium-glucose cotransporter 2 inhibitors (SGLT2i) represent an innovative class of antidiabetic agents that have demonstrated promise in mitigating cardiac remodeling. However, the transcriptional regulatory mechanisms underpinning their impact on blood pressure and the reversal of hypertension-induced cardiac remodeling remain largely unexplored. Given this context, our study concentrated on comparing the cardiac expression profiles of lncRNAs and mRNAs between Wistar-Kyoto (WKY) rats and spontaneously hypertensive rats (SHR). To validate our results, we performed blood pressure measurements, tissue staining, and qRT-PCR. The treatment led to a significant reduction in systolic blood pressure and improved cardiac remodeling by reducing myocardial fibrosis and regulating the inflammatory response. Our examination disclosed that ventricular tissue mRNA, regulated by hypertension, was primarily concentrated in the complement and coagulation cascades and cytokine-cytokine receptor interactions. Compared with SHR, the SGLT2i treatment group was associated with myocardial contraction. Investigation into the lncRNA-mRNA regulatory network and competing endogenous RNA (ceRNA) network suggested that the potential roles of these differentially expressed (DE) lncRNAs and mRNAs were tied to processes such as collagen fibril organization, inflammatory response, and extracellular matrix (ECM) modifications. We found that the expression of Col3a1, C1qa, and lncRNA NONRATT007139.2 were altered in the SHR group and that SGLT2i treatment reversed these changes. Our results suggest that dapagliflozin effectively reverses hypertension-induced myocardial remodeling through a lncRNA-mRNA transcriptional regulatory network, with immune cell-mediated ECM deposition as a potential regulatory target. This underlines the potentiality of SGLT2i and genes related to immunity as promising targets for the treatment of hypertension.

Predicting long-term progression of Alzheimer's disease using a multimodal deep learning model incorporating interaction effects.

BACKGROUND: Identifying individuals with mild cognitive impairment (MCI) at risk of progressing to Alzheimer's disease (AD) provides a unique opportunity for early interventions. Therefore, accurate and long-term prediction of the conversion from MCI to AD is desired but, to date, remains challenging. Here, we developed an interpretable deep learning model featuring a novel design that incorporates interaction effects and multimodality to improve the prediction accuracy and horizon for MCI-to-AD progression. METHODS: This multi-center, multi-cohort retrospective study collected structural magnetic resonance imaging (sMRI), clinical assessments, and genetic polymorphism data of 252 patients with MCI at baseline from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Our deep learning model was cross-validated on the ADNI-1 and ADNI-2/GO cohorts and further generalized in the ongoing ADNI-3 cohort. We evaluated the model performance using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and F1 score. RESULTS: On the cross-validation set, our model achieved superior results for predicting MCI conversion within 4 years (AUC, 0.962; accuracy, 92.92%; sensitivity, 88.89%; specificity, 95.33%) compared to all existing studies. In the independent test, our model exhibited consistent performance with an AUC of 0.939 and an accuracy of 92.86%. Integrating interaction effects and multimodal data into the model significantly increased prediction accuracy by 4.76% (P = 0.01) and 4.29% (P = 0.03), respectively. Furthermore, our model demonstrated robustness to inter-center and inter-scanner variability, while generating interpretable predictions by quantifying the contribution of multimodal biomarkers. CONCLUSIONS: The proposed deep learning model presents a novel perspective by combining interaction effects and multimodality, leading to more accurate and longer-term predictions of AD progression, which promises to improve pre-dementia patient care.

Roles of vaporization and thermal decomposition in the dynamic evolution of laser-induced bubble on the surface of a submerged metal plate.

This paper aims to explain when the vaporization or thermal decomposition prevails during laser-induced bubble growth and how they influence bubble morphology. Bubbles were generated by irradiating a 304 stainless steel plate submerged in degassed water using millisecond lasers with a pulse width of 0.4 ms and powers of 1.6 kW and 3.2 kW, respectively. The dynamic evolution of bubbles was recorded by a high-speed camera. Moreover, the numerical models were developed to obtain a vaporization model and a decomposition model by incorporating the source terms due to the vaporization and decomposition mass fluxes into the governing equations, respectively. The simulated dynamic bubble evolution is consistent with the experimental results. When the laser power is 1.6 kW, a thin-layer bubble is formed, which gradually shrinks and eventually disappears after the laser stops irradiating. When the laser power is 3.2 kW, a spherical bubble is formed, and its volume decreases significantly after the laser stops irradiating. Subsequently, it remains relatively stable during the observation period. The fundamental reason for the difference between the bubble morphologies obtained from the vaporization model and the decomposition model lies in the presence of a condensation zone in the gas phase. When water vaporization or thermal decomposition dominates, the temperatures obtained from the models align with the decomposition ratios at varying temperatures reported in the literature. Our findings are significant for understanding the dynamic behavior of bubbles, with implications for various laser processing underwater.

Temporal structured illumination and vision-transformer enables large field-of-view binary snapshot ptychography.

Ptychography, a widely used computational imaging method, generates images by processing coherent interference patterns scattered from an object of interest. In order to capture scenes with large field-of-view (FoV) and high spatial resolution simultaneously in a single shot, we propose a temporal-compressive structured-light Ptychography system. A novel three-step reconstruction algorithm composed of multi-frame spectra reconstruction, phase retrieval, and multi-frame image stitching is developed, where we employ the emerging Transformer-based network in the first step. Experimental results demonstrate that our system can expand the FoV by 20× without losing spatial resolution. Our results offer huge potential for enabling lensless imaging of molecules with large FoV as well as high spatial-temporal resolutions. We also notice that due to the loss of low-intensity information caused by the compressed sensing process, our method so far is only applicable to binary targets.

Snapshot compressive structured illumination microscopy.

We propose a snapshot compressive structured illumination microscopy (SoSIM) system to increase the number of reconstructed resolution-enhanced (RE) images per second and reduce the data bandwidth by capturing compressed measurements. In this system, multiple low-resolution images are encoded by a high-speed digital micro-mirror device with random binary masks. These images are then captured by a low-speed camera as a snapshot compressed measurement. Following this, we adopt an efficient deep neural network to reconstruct nine images with different structured illumination patterns from a single measurement. The reconstructed images are then combined into a single-frame RE image using the method of spectral synthesis in the frequency domain. When the camera operates at 100 frames per second (fps), we can eventually recover dynamic RE videos at the same speed with 100 fps.

Video snapshot compressive imaging using adaptive progressive coding for high-quality reconstruction under different illumination circumstances.

We consider capturing high-speed color video under different illumination conditions using a video snapshot compressive imaging system (video SCI). An adaptive progressive coding method is proposed, and we conduct an integrated design of the imaging system in terms of optics, mechanics, and control. Compared to previous video SCI systems, this adaptive progressive coding method mitigates the image stability issues in various illumination conditions, ensuring high-quality imaging while greatly improving the light throughput of the system. Based on the analysis of both simulation and real experimental results, we found that this imaging system can achieve color video shooting under an illumination range of 2 lux to 60 lux.

Image-free single-pixel keypoint detection for privacy preserving human pose estimation.

Computer vision technology has been applied in various fields such as identification, surveillance, and robot vision. However, computer vision algorithms used for human-related tasks operate on human images, which raises data security and privacy concerns. In this Letter, we propose an image-free human keypoint detection technique using a few coded illuminations and a single-pixel detector. Our proposed method can complete the keypoint detection task at an ultralow sampling rate on a measured one-dimensional sequence without image reconstruction, thus protecting privacy from the data collection stage and preventing the acquisition of detailed visual information from the source. The network is designed to optimize both the illumination patterns and the human keypoint predictor with an encoder-decoder framework. For model training and validation, we used 2000 images from Leeds Sport Dataset and COCO Dataset. By incorporating EfficientNet backbone, the inference time is reduced from 4 s to 0.10 s. In the simulation, the proposed network achieves 91.7% average precision. Our experimental results show an average precision of 88.4% at a remarkably low sampling rate of 0.015. In summary, our proposed method has the advantages of privacy protection and resource efficiency, which can be applied to many monitoring and healthcare tasks, such as clinical monitoring, construction site monitoring, and home service robots.

Broadband lens and graphene/silicon photodiode for wide spectral imaging.

We designed a broadband lens along with a graphene/silicon photodiode for wide spectral imaging ranging from ultraviolet to near-infrared wavelengths. By using five spherical glass lenses, the broadband lens, with the modulation transfer function of 0.38 at 100 lp/mm, corrects aberrations ranging from 340 to 1700 nm. Our design also includes a broadband graphene/silicon Schottky photodiode with the highest responsivity of 0.63 A/W ranging from ultraviolet to near-infrared. By using the proposed broadband lens and the broadband graphene/silicon photodiode, several single-pixel imaging designs in ultraviolet, visible, and near-infrared wavelengths are demonstrated. Experimental results show the advantages of integrating the lens with the photodiode and the potential to realize broadband imaging with a single set of lens and a detector.

Cholinium Pyridinolate Ionic Pair-Catalyzed Fixation of CO2 into Cyclic Carbonates.

A halide-free ionic pair organocatalyst was proposed for the cycloaddition of CO2 into epoxide reactions. Cholinium pyridinolate ionic pairs with three different substitution positions were designed. Under conditions of temperature of 120 °C, pressure of 1 MPa CO2, and catalyst loading of 5 mol %, the optimal catalyst cholinium 4-pyridinolate ([Ch]+[4-OP]-) was employed. After a reaction time of 12 h, styrene oxide was successfully converted into the corresponding cyclic carbonate, and its selectivity was improved to 90%. A series of terminal epoxides were converted into cyclic carbonates within 12 h, with yields ranging from 80 to 99%. The proposed mechanism was verified by 1H NMR and 13C NMR titrations. Cholinium cations act as a hydrogen bond donor to activate epoxides, and pyridinolate anions combine with carbon dioxide to form intermediate carbonate anions that attack epoxides as nucleophiles and lead to ring opening. In summary, a halide-free ionic pair organocatalyst was designed and the catalytic mechanism in the cycloaddition of CO2 into epoxides reactions was proposed.

Integrated analysis of transcriptome and metabolome reveals the regulatory mechanism of largemouth bass (Micropterus salmoides) in response to Nocardia seriolae infection.

Nocardia seriolae is a severe bacterial pathogen that has seriously affected the development of aquaculture industry. Largemouth bass (Micropterus salmoides) is a commercially significant freshwater fish that suffers a variety of environmental threats, including bacterial pathogens. However, the immune responses and metabolic alterations of largemouth bass to N. seriolae infection remain largely unclear. We discovered that N. seriolae caused pathological alterations in largemouth bass and shifted the transcript of immune-related and apoptotic genes in head kidney after infection. To answer the aforementioned question, a combined transcriptome and metabolome analysis was employed to explore the alterations in genes, metabolites, and metabolic pathways in largemouth bass following bacterial infection. A total of 3579 genes and 1929 metabolites are significant differentially changed in the head kidney post infection. In response to N. seriolae infection, host modifies the PI3K-Akt signaling pathway, TCA cycle, glycolysis, and amino acid metabolism. The integrated analysis of transcriptome and metabolome suggested that with the arginine metabolism pathway as the core, multiple biomarkers (arg gene, arginine) are involved in the antibacterial and immune functions of largemouth bass. Thus, we hypothesized that arginine plays a crucial role in the immune responses of largemouth bass against N. seriolae infection, and increasing arginine levels suitably is beneficial for the host against bacterial infection. Our results shed light on the regulatory mechanism of largemouth bass resistance to N. seriolae infection and contributed to the development of more effective N. seriolae resistance strategies.

Preparation, biological activity and antibacterial properties of tantalum surface-doped Ca2+/Zn2+nanorods.

In this research, we utilize porous tantalum, known for its outstanding elastic modulus and biological properties, as a base material in biomedical applications. The human skeletal system is rich in elements like Ca and Zn. The role of Zn is crucial for achieving a spectrum of sterilizing effects, while Ca is known to effectively enhance cell differentiation and boost cellular activity. The focus of this study is the modification of porous tantalum using a hydrothermal method to synthesize Ca2+/Zn2+-doped Ta2O5nanorods. These nanorods are subjected to extensive characterization techniques to confirm their structure and composition. Additionally, their biological performance is evaluated through a range of tests, including antibacterial assessments, MTT assays, and bacteria/cell scanning electron microscopy (SEM) analyses. The objective is to determine the most effective method of surface modification for porous tantalum, thereby laying a foundational theoretical framework for its surface enhancement.

Molecular transmission network analysis of newly diagnosed HIV-1 infections in Nanjing from 2019 to 2021.

OBJECTIVE: The objective of this study was to conduct a comprehensive analysis of the molecular transmission networks and transmitted drug resistance (TDR) patterns among individuals newly diagnosed with HIV-1 in Nanjing. METHODS: Plasma samples were collected from newly diagnosed HIV patients in Nanjing between 2019 and 2021. The HIV pol gene was amplified, and the resulting sequences were utilized for determining TDR, identifying viral subtypes, and constructing molecular transmission network. Logistic regression analyses were employed to investigate the epidemiological characteristics associated with molecular transmission clusters. RESULTS: A total of 1161 HIV pol sequences were successfully extracted from newly diagnosed individuals, each accompanied by reliable epidemiologic information. The analysis revealed the presence of multiple HIV-1 subtypes, with CRF 07_BC (40.57%) and CRF01_AE (38.42%) being the most prevalent. Additionally, six other subtypes and unique recombinant forms (URFs) were identified. The prevalence of TDR among the newly diagnosed cases was 7.84% during the study period. Employing a genetic distance threshold of 1.50%, the construction of the molecular transmission network resulted in the identification of 137 clusters, encompassing 613 nodes, which accounted for approximately 52.80% of the cases. Multivariate analysis indicated that individuals within these clusters were more likely to be aged ≥ 60, unemployed, baseline CD4 cell count ≥ 200 cells/mm3, and infected with the CRF119_0107 (P < 0.05). Furthermore, the analysis of larger clusters revealed that individuals aged ≥ 60, peasants, those without TDR, and individuals infected with the CRF119_0107 were more likely to be part of these clusters. CONCLUSIONS: This study revealed the high risk of local HIV transmission and high TDR prevalence in Nanjing, especially the rapid spread of CRF119_0107. It is crucial to implement targeted interventions for the molecular transmission clusters identified in this study to effectively control the HIV epidemic.

Real-world effectiveness of nirmatrelvir-ritonavir versus azvudine in hospitalized patients with COVID-19 during the omicron wave in Beijing: a multicenter retrospective cohort study.

BACKGROUND AND AIM: Two oral antivirals (Nirmatrelvir- ritonavir and Azvudine) are widely used in China practice during the Omicron wave of the pandemic. However, little evidence regarding the real-world effectiveness of these two oral antivirals in in-hospital patients. We aimed to evaluate the clinical effectiveness of nirmatrelvir-ritonavir versus azvudine among adult hospitalized patients with COVID-19. METHODS: This retrospective cohort study used data from three Chinese PLA General Hospital medical centres. Hospitalized patients with COVID-19 treated with azvudine or nirmatrelvir-ritonavir from Dec 10, 2022, to February 20, 2023, and did not require invasive ventilation support on admission were eligible for inclusion. RESULTS: After exclusions and propensity-score matching, the final analysis included 486 azvudine recipients and 486 nirmatrelvir-ritonavir recipients. By 28 days of initiation of the antivirus treatment, the crude incidence rate of all-cause death was similar in both types of antivirus treatment (nirmatrelvir-ritonavir group 2.8 events 1000 person-days [95% CI, 2.1-3.6] vs azvudine group 3.4 events/1000 person-days [95% CI, 2.6-4.3], P = 0.38). Landmark analysis showed that all-cause death was lower in the nirmatrelvir-ritonavir (3.5%) group than the azvudine (6.8%, P = 0.029) within the initial 10-day admission period, while no significant difference was observed for results between 10 and 28 days follow-up. There was no significant difference between the nirmatrelvir-ritonavir group and the azvudine group in cumulative incidence of the composite disease progression event (8.6% with nirmatrelvir-ritonavir vs. 10.1% with azvudine, HR, 1.22; 95% CI 0.80-1.86, P = 0.43). CONCLUSION: Among patients hospitalized with COVID-19 during the omicron wave in Beijing, similar in-hospital clinical outcomes on 28 days were observed between patients receiving nirmatrelvir-ritonavir and azvudine. However, it is worth noticing that nirmatrelvir-ritonavir appears to hold an advantage over azvudine in reducing early mortality. Further randomized controlled trials are needed to verify the efficacy of those two antivirus medications especially in early treatment.

miR-1915-3p regulates megakaryocytic and erythroid differentiation by targeting SOCS4.

BACKGROUND: Proper control of the lineage bias of megakaryocytic and erythroid progenitor cells (MEPs) is of significant importance, the disorder of which will lead to abnormalities in the number and function of platelets and erythrocytes. Unfortunately, the signaling pathways regulating MEP differentiation largely remain to be elucidated. This study aimed to analyze the role and the underlying molecular mechanism of miR-1915-3p in megakaryocytic and erythroid differentiation. METHODS: We utilized miRNA mimics and miRNA sponge to alter the expression of miR-1915-3p in megakaryocytic and/or erythroid potential cells; siRNA and overexpression plasmid to change the expression of SOCS4, a potential target of miR-1915-3p. The expression of relevant surface markers was detected by flow cytometry. We scanned for miR-1915-3p target genes by mRNA expression profiling and bioinformatic analysis, and confirmed the targeting by dual-luciferase reporter assay, western blot and gain- and lost-of-function studies. One-way ANOVA and t-test were used to analyze the statistical significance. RESULTS: In this study, overexpression or knockdown of miR-1915-3p inhibited or promoted erythroid differentiation, respectively. Accordingly, we scanned for miR-1915-3p target genes and confirmed that SOCS4 is one of the direct targets of miR-1915-3p. An attentive examination of the endogenous expression of SOCS4 during megakaryocytic and erythroid differentiation suggested the involvement of SOCS4 in erythroid/megakaryocytic lineage determination. SOCS4 knockdown lessened erythroid surface markers expression, as well as improved megakaryocytic differentiation, similar to the effects of miR-1915-3p overexpression. While SOCS4 overexpression resulted in reversed effects. SOCS4 overexpression in miR-1915-3p upregulated cells rescued the effect of miR-1915-3p. CONCLUSIONS: miR-1915-3p acts as a negative regulator of erythropoiesis, and positively in thrombopoiesis. SOCS4 is one of the key mediators of miR-1915-3p during the differentiation of MEPs.