Advances in genomics and genome editing for improving strawberry (Fragaria ×ananassa).

The cultivated strawberry, Fragaria ×ananassa, is a recently domesticated fruit species of economic interest worldwide. As such, there is significant interest in continuous varietal improvement. Genomics-assisted improvement, including the use of DNA markers and genomic selection have facilitated significant improvements of numerous key traits during strawberry breeding. CRISPR/Cas-mediated genome editing allows targeted mutations and precision nucleotide substitutions in the target genome, revolutionizing functional genomics and crop improvement. Genome editing is beginning to gain traction in the more challenging polyploid crops, including allo-octoploid strawberry. The release of high-quality reference genomes and comprehensive subgenome-specific genotyping and gene expression profiling data in octoploid strawberry will lead to a surge in trait discovery and modification by using CRISPR/Cas. Genome editing has already been successfully applied for modification of several strawberry genes, including anthocyanin content, fruit firmness and tolerance to post-harvest disease. However, reports on many other important breeding characteristics associated with fruit quality and production are still lacking, indicating a need for streamlined genome editing approaches and tools in Fragaria ×ananassa. In this review, we present an overview of the latest advancements in knowledge and breeding efforts involving CRISPR/Cas genome editing for the enhancement of strawberry varieties. Furthermore, we explore potential applications of this technology for improving other Rosaceous plant species.

LordNet: An efficient neural network for learning to solve parametric partial differential equations without simulated data.

Neural operators, as a powerful approximation to the non-linear operators between infinite-dimensional function spaces, have proved to be promising in accelerating the solution of partial differential equations (PDE). However, it requires a large amount of simulated data, which can be costly to collect. This can be avoided by learning physics from the physics-constrained loss, which we refer to it as mean squared residual (MSR) loss constructed by the discretized PDE. We investigate the physical information in the MSR loss, which we called long-range entanglements, and identify the challenge that the neural network requires the capacity to model the long-range entanglements in the spatial domain of the PDE, whose patterns vary in different PDEs. To tackle the challenge, we propose LordNet, a tunable and efficient neural network for modeling various entanglements. Inspired by the traditional solvers, LordNet models the long-range entanglements with a series of matrix multiplications, which can be seen as the low-rank approximation to the general fully-connected layers and extracts the dominant pattern with reduced computational cost. The experiments on solving Poisson's equation and (2D and 3D) Navier-Stokes equation demonstrate that the long-range entanglements from the MSR loss can be well modeled by the LordNet, yielding better accuracy and generalization ability than other neural networks. The results show that the Lordnet can be 40× faster than traditional PDE solvers. In addition, LordNet outperforms other modern neural network architectures in accuracy and efficiency with the smallest parameter size.

[The Role of NK Cells in Allogeneic Hematopoietic Stem Cell Micro-Transplantation for Acute Myeloid leukemia].

OBJECTIVE: To explore the role of NK cells in allogeneic hematopoietic stem cell micro-transplantation(MST) in the treatment of patients with acute myeloid leukemia(AML). METHODS: Data from 93 AML patients treated with MST at our center from 2013-2018 were retrospectively analyzed. The induction regimen was anthracycline and cytarabine combined with peripheral blood stem cells transplantation mobilization by granulocyte colony stimulating factor (GPBSC), followed by 2-4 courses of intensive treatment with medium to high doses of cytarabine combined with GPBSC after achieving complete remission (CR). The therapeutic effects of one and two courses of MST induction therapy on 42 patients who did not reach CR before transplantation were evaluated. Cox proportional hazards regression analysis was used to analyze the impact of donor NK cell dose and KIR genotype, including KIR ligand mismatch, 2DS1, haplotype, and HLA-Cw ligands on survival prognosis of patients. RESULTS: Forty-two patients received MST induction therapy, and the CR rate was 57.1% after 1 course and 73.7% after 2 courses. Multivariate analysis showed that, medium and high doses of NK cells was significantly associated with improved disease-free survival (DFS) of patients (HR=0.27, P =0.005; HR=0.21, P =0.001), and high doses of NK cells was significantly associated with improved overall survival (OS) of patients (HR=0.15, P =0.000). Donor 2DS1 positive significantly increases OS of patients (HR=0.25, P =0.011). For high-risk patients under 60 years old, patients of the donor-recipient KIR ligand mismatch group had longer DFS compared to the nonmismatch group (P =0.036); donor 2DS1 positive significantly prolonged OS of patients (P =0.009). CONCLUSION: NK cell dose, KIR ligand mismatch and 2DS1 influence the therapeutic effect of MST, improve the survival of AML patients.

Mesenchymal stem cell-derived exosomes as new drug carrier for the treatment of spinal cord injury: A review.

Spinal cord injury (SCI) is a devastating traumatic disease seriously impairing the quality of life in patients. Expectations to allow the hopeless central nervous system to repair itself after injury are unfeasible. Developing new approaches to regenerate the central nervous system is still the priority. Exosomes derived from mesenchymal stem cells (MSC-Exo) have been proven to robustly quench the inflammatory response or oxidative stress and curb neuronal apoptosis and autophagy following SCI, which are the key processes to rescue damaged spinal cord neurons and restore their functions. Nonetheless, MSC-Exo in SCI received scant attention. In this review, we reviewed our previous work and other studies to summarize the roles of MSC-Exo in SCI and its underlying mechanisms. Furthermore, we also focus on the application of exosomes as drug carriers in SCI. In particular, it combs the advantages of exosomes as drug carriers for SCI, imaging advantages, drug types, loading methods, etc., which provides the latest progress for exosomes in the treatment of SCI, especially drug carriers.

Tryptophan catabolism via the kynurenine pathway regulates infection and inflammation: from mechanisms to biomarkers and therapies.

BACKGROUND: L-Tryptophan (L-Trp), an essential amino acid, is the only amino acid whose level is regulated specifically by immune signals. Most proportions of Trp are catabolized via the kynurenine (Kyn) pathway (KP) which has evolved to align the food availability and environmental stimulation with the host pathophysiology and behavior. Especially, the KP plays an indispensable role in balancing the immune activation and tolerance in response to pathogens. SCOPE OF REVIEW: In this review, we elucidate the underlying immunological regulatory network of Trp and its KP-dependent catabolites in the pathophysiological conditions by participating in multiple signaling pathways. Furthermore, the KP-based regulatory roles, biomarkers, and therapeutic strategies in pathologically immune disorders are summarized covering from acute to chronic infection and inflammation. MAJOR CONCLUSIONS: The immunosuppressive effects dominate the functions of KP induced-Trp depletion and KP-produced metabolites during infection and inflammation. However, the extending minor branches from the KP are not confined to the immune tolerance, instead they go forward to various functions according to the specific condition. Nevertheless, persistent efforts should be made before the clinical use of KP-based strategies to monitor and cure infectious and inflammatory diseases.

Targeted inhibition of the PI3K/AKT/mTOR pathway by (+)-anthrabenzoxocinone induces cell cycle arrest, apoptosis, and autophagy in non-small cell lung cancer.

Non-small cell lung cancer (NSCLC), characterized by low survival rates and a high recurrence rate, is a major cause of cancer-related mortality. Aberrant activation of the PI3K/AKT/mTOR signaling pathway is a common driver of NSCLC. Within this study, the inhibitory activity of (+)-anthrabenzoxocinone ((+)-ABX), an oxygenated anthrabenzoxocinone compound derived from Streptomyces, against NSCLC is demonstrated for the first time both in vitro and in vivo. Mechanistically, it is confirmed that the PI3K/AKT/mTOR signaling pathway is targeted and suppressed by (+)-ABX, resulting in the induction of S and G2/M phase arrest, apoptosis, and autophagy in NSCLC cells. Additionally, the augmentation of intracellular ROS levels by (+)-ABX is revealed, further contributing to the inhibition of the signaling pathway and exerting inhibitory effects on tumor growth. The findings presented in this study suggest that (+)-ABX possesses the potential to serve as a lead compound for the treatment of NSCLC.

Examining preharvest genetic and morphological factors contributing to lettuce (Lactuca sativa L.) shelf-life.

Lettuce is a highly perishable horticultural crop with a relatively short shelf-life that limits its commercial value and contributes to food waste. Postharvest senescence varies with influences of both environmental and genetic factors. From a larger pool of romaine lettuce genotypes, we identified three genotypes with variable shelf lives and evaluated their leaf morphology characteristics and transcriptomic profiles at preharvest to predict postharvest quality. Breeding line 60184 had the shortest shelf-life (SSL), cultivar 'Manatee' had an intermediate shelf-life (ISL), and 'Okeechobee' had the longest shelf-life (LSL). We observed significantly larger leaf lamina thickness and higher stomatal index in the SSL genotypes relative to the LSL cultivar. To identify molecular indicators of shelf-life, we used a transcriptional approach between two of the contrasting genotypes, breeding line 60184 and cultivar 'Okeechobee' at preharvest. We identified 552 upregulated and 315 downregulated differentially expressed genes between the genotypes, from which 27% of them had an Arabidopsis thaliana ortholog previously characterized as senescence associated genes (SAGs). Notably, we identified several SAGs including several related to jasmonate ZIM-domain jasmonic acid signaling, chlorophyll a-b binding, and cell wall modification including pectate lyases and expansins. This study presented an innovative approach for identifying preharvest molecular factors linked to postharvest traits for prolonged shelf.

Influenza and COVID-19 co-infection and vaccine effectiveness against severe cases: a mathematical modeling study.

Background: Influenza A virus have a distinctive ability to exacerbate SARS-CoV-2 infection proven by in vitro studies. Furthermore, clinical evidence suggests that co-infection with COVID-19 and influenza not only increases mortality but also prolongs the hospitalization of patients. COVID-19 is in a small-scale recurrent epidemic, increasing the likelihood of co-epidemic with seasonal influenza. The impact of co-infection with influenza virus and SARS-CoV-2 on the population remains unstudied. Method: Here, we developed an age-specific compartmental model to simulate the co-circulation of COVID-19 and influenza and estimate the number of co-infected patients under different scenarios of prevalent virus type and vaccine coverage. To decrease the risk of the population developing severity, we investigated the minimum coverage required for the COVID-19 vaccine in conjunction with the influenza vaccine, particularly during co-epidemic seasons. Result: Compared to the single epidemic, the transmission of the SARS-CoV-2 exhibits a lower trend and a delayed peak when co-epidemic with influenza. Number of co-infection cases is higher when SARS-CoV-2 co-epidemic with Influenza A virus than that with Influenza B virus. The number of co-infected cases increases as SARS-CoV-2 becomes more transmissible. As the proportion of individuals vaccinated with the COVID-19 vaccine and influenza vaccines increases, the peak number of co-infected severe illnesses and the number of severe illness cases decreases and the peak time is delayed, especially for those >60 years old. Conclusion: To minimize the number of severe illnesses arising from co-infection of influenza and COVID-19, in conjunction vaccinations in the population are important, especially priority for the elderly.

Randomness Regularization with Simple Consistency Training for Neural Networks.

Randomness is widely introduced in neural network training to simplify model optimization or avoid the over-fitting problem. Among them, dropout and its variations in different aspects (e.g., data, model structure) are prevalent in regularizing the training of deep neural networks. Though effective and performing well, the randomness introduced by these dropout-based methods causes nonnegligible inconsistency between training and inference. In this paper, we introduce a simple consistency training strategy to regularize such randomness, namely R-Drop, which forces two output distributions sampled by each type of randomness to be consistent. Specifically, R-Drop minimizes the bidirectional KL-divergence between two output distributions produced by dropout-based randomness for each training sample. Theoretical analysis reveals that R-Drop can reduce the above inconsistency by reducing the inconsistency among the sampled sub structures and bridging the gap between the loss calculated by the full model and sub structures. Experiments on 7 widely-used deep learning tasks ( 23 datasets in total) demonstrate that R-Drop is universally effective for different types of neural networks (i.e., feed-forward, recurrent, and graph neural networks) and different learning paradigms (supervised, parameter-efficient, and semi-supervised). In particular, it achieves state-of-the-art performances with the vanilla Transformer model on WMT14 English → German translation ( 30.91 BLEU) and WMT14 English → French translation ( 43.95 BLEU), even surpassing models trained with extra large-scale data and expert-designed advanced variants of Transformer models. Our code is available at GitHub https://github.com/dropreg/R-Drop.

Tissue-specific proteome profile analysis reveals regulatory and stress responsive networks in passion fruit during storage.

Passiflora edulis, commonly known as passion fruit, is a crop with a fragrant aroma and refreshingly tropical flavor that is a valuable source of antioxidants. It offers a unique opportunity for growers because of its adaptability to tropical and subtropical climates. Passion fruit can be sold in the fresh market or used in value-added products, but its postharvest shelf life has not been well-researched, nor have superior cultivars been well-developed. Understanding the proteins expressed at the tissue level during the postharvest stage can help improve fruit quality and extend shelf life. In this study, we carried out comparative proteomics analysis on four passion fruit tissues, the epicarp, mesocarp, endocarp, and pulp, using multiplexed isobaric tandem mass tag (TMT) labeling quantitation. A total of 3352 proteins were identified, including 295 differentially expressed proteins (DEPs). Of these DEPs, 213 showed a fold increase greater than 1.45 (50 proteins) or a fold decrease less than 0.45 (163 proteins) with different patterns among tissue types. Among the DEPs, there were proteins expressed with functions in oxygen scavenging, lipid peroxidation, response to heat stress, and pathogen resistance. Thirty-six proteins were designated as hypothetical proteins were characterized for potential functions in immunity, cell structure, homeostasis, stress response, protein metabolism and miraculin biosynthesis. This research provides insight into tissue-specific pathways that can be further studied within fruit physiology and postharvest shelf life to aid in implementing effective plant breeding programs. Knowing the tissue-specific function of fruit is essential for improving fruit quality, developing new varieties, identifying health benefits, and optimizing processing techniques.

Shrinkage Reduction in Nanopore-Rich Cement Paste Based on Facile Organic Modification of Montmorillonite.

The organic modification of montmorillonite was successfully achieved using cetyltrimethyl ammonium bromide under facile conditions. The modified montmorillonite was subsequently used for the fabrication of montmorillonite-induced nanopore-rich cement paste (MNCP), and the shrinkage behavior and fundamental performance of MNCP were also investigated. The results indicate that alkali cations on a montmorillonite layer surface were exchanged by using CTAB under 80 °C, successfully achieving the organic modification of montmorillonite. As a pore-forming agent, the modified montmorillonite caused a reduction in shrinkage: the 28-day autogenous shrinkage at a design density of 400 kg/m3 and 800 kg/m3 was reduced to 2.05 mm/m and 0.24 mm/m, and the highest reduction percentages during the 28-day drying shrinkage were 68.1% and 62.2%, respectively. The enlarged interlamellar pores and hydrophobic effects caused by the organic modification of montmorillonite aided this process. Organic-modified montmorillonite had a minor influence on dry density and thermal conductivity and could contribute to an enhancement of strength in MNCP.

Dynamics of algal blooms in typical low-latitude plateau lakes: Spatiotemporal patterns and driving factors.

The alpine lakes distributed on the plateau are crucial for the hydrological, and biogeochemical cycle, and also serve as a guarantee for regional economic development and human survival. However, under the influence of human interference and climate fluctuations, lakes are facing problems of eutrophication and subsequent algal blooms (ABs) with acceleration, and the development and driving factors of this phenomenon need to be considered as a whole. In this study, ten lakes located on the Yunnan-Guizhou Plateau were selected as the study area to analyze the spatiotemporal distribution of ABs and possible controlling forces. The FAI (Floating Algae Index) derived from multiple MODIS products and water quality data under high-frequency monitoring were selected as the data sources for characterizing ABs. Three nutrient parameters and five meteorological variables were used to explore the driving factors affecting ABs. Various methods of trend detection and correlation analysis have been applied. The main results are as follows: (1) Dianchi Lake (in lake area) and Xingyun Lake (in area proportion) are the two lakes with the most serious ABs in the historical period; (2) ABs are mainly distributed on the shoreline and northern edge of lakes, and tend to stay away from the lake center during high-temperature periods of the day; (3) Six lakes show a decreasing trend in ABs, especially after 2018, while other lakes (including Fuxian, Chenghai, Yangzong, and Erhai) are increasing, not only in peak value but also in duration; (4) Lakes with severe ABs are all P-restricted lakes, the minimum temperature is the most sensitive meteorological factor, while the impact of precipitation against ABs has a time lag; (5) Establishing a warning system of temperature and nutrient concentration is critical in ABs adaptive strategy. This study is expected to provide scientific references for regional water management and the restoration of the eutrophic aquatic ecosystem.

Assessing Face Image Quality: A Large-Scale Database and a Transformer Method.

The amount of face images has been witnessing an explosive increase in the last decade, where various distortions inevitably exist on transmitted or stored face images. The distortions lead to visible and undesirable degradation on face images, affecting their quality of experience (QoE). To address this issue, this paper proposes a novel Transformer-based method for quality assessment on face images (named as TransFQA). Specifically, we first establish a large-scale face image quality assessment (FIQA) database, which includes 42,125 face images with diversifying content at different distortion types. Through an extensive crowdsource study, we obtain 712,808 subjective scores, which to the best of our knowledge contribute to the largest database for assessing the quality of face images. Furthermore, by investigating the established database, we comprehensively analyze the impacts of distortion types and facial components (FCs) on the overall image quality. Accordingly, we propose the TransFQA method, in which the FC-guided Transformer network (FT-Net) is developed to integrate the global context, face region and FC detailed features via a new progressive attention mechanism. Then, a distortion-specific prediction network (DP-Net) is designed to weight different distortions and accurately predict final quality scores. Finally, the experiments comprehensively verify that our TransFQA method significantly outperforms other state-of-the-art methods for quality assessment on face images.

NaturalSpeech: End-to-End Text-to-Speech Synthesis With Human-Level Quality.

Text-to-speech (TTS) has made rapid progress in both academia and industry in recent years. Some questions naturally arise that whether a TTS system can achieve human-level quality, how to define/judge that quality, and how to achieve it. In this paper, we answer these questions by first defining the human-level quality based on the statistical significance of subjective measure and introducing appropriate guidelines to judge it, and then developing a TTS system called NaturalSpeech that achieves human-level quality on benchmark datasets. Specifically, we leverage a variational auto-encoder (VAE) for end-to-end text-to-waveform generation, with several key modules to enhance the capacity of the prior from text and reduce the complexity of the posterior from speech, including phoneme pre-training, differentiable duration modeling, bidirectional prior/posterior modeling, and a memory mechanism in VAE. Experimental evaluations on the popular LJSpeech dataset show that our proposed NaturalSpeech achieves -0.01 CMOS (comparative mean opinion score) to human recordings at the sentence level, with Wilcoxon signed rank test at p-level p >> 0.05, which demonstrates no statistically significant difference from human recordings for the first time.

Enhancing geometric representations for molecules with equivariant vector-scalar interactive message passing.

Geometric deep learning has been revolutionizing the molecular modeling field. Despite the state-of-the-art neural network models are approaching ab initio accuracy for molecular property prediction, their applications, such as drug discovery and molecular dynamics (MD) simulation, have been hindered by insufficient utilization of geometric information and high computational costs. Here we propose an equivariant geometry-enhanced graph neural network called ViSNet, which elegantly extracts geometric features and efficiently models molecular structures with low computational costs. Our proposed ViSNet outperforms state-of-the-art approaches on multiple MD benchmarks, including MD17, revised MD17 and MD22, and achieves excellent chemical property prediction on QM9 and Molecule3D datasets. Furthermore, through a series of simulations and case studies, ViSNet can efficiently explore the conformational space and provide reasonable interpretability to map geometric representations to molecular structures.

Functional Composite Dual-Phase In Situ Self-Reconstruction Design for High-Energy-Density Li-Rich Cathodes.

The unique anionic redox mechanism provides, high-capacity, irreversible oxygen release and voltage/capacity degradation to Li-rich cathode materials (LRO, Li1.2 Mn0.54 Co0.13 Ni0.13 O2 ). In this study, an integrated stabilized carbon-rock salt/spinel composite heterostructured layers (C@spinel/MO) is constructed by in situ self-reconstruction, and the generation mechanism of the in situ reconstructed surface is elucidated. The formation of atomic-level connections between the surface-protected phase and bulk-layered phase contributes to electrochemical performance. The best-performing sample shows a high increase (63%) of capacity retention compared to that of the pristine sample after 100 cycles at 1C, with an 86.7% reduction in surface oxygen release shown by differential electrochemical mass spectrometry. Soft X-ray results show that Co3+ and Mn4+ are mainly reduce in the carbothermal reduction reaction and participate in the formation of the spinel/MO rock-salt phase. The results of oxygen release characterized by Differential electrochemical mass spectrometry (DEMS) strongly prove the effectiveness of surface reconstruction.

Antimicrobial ingredients of Zanthoxylum motuoense and potential in fresh pork meat preservation.

Zanthoxylum motuoense (Tibetan prickly ash, MTHJ), different from the Chinese prickly ash species, is distributed only in the Tibet. Now the chemical characterization and antibacterial activity of MTHJ extracts were analyzed for the first time. As a result, Schinifoline (12), γ-Fagarine (8), (2E,7E,9E)-6 S-Hydroxy-N-(2-methylpropyl)-11-oxo-2, 7, 9-Dodecatrienamide (6), and Neoechinulin A (17) were found to be the major different factors by untarget LC-MS metabolomics together with quantitative analysis on target. These four compounds were also the major antibacterial constituents. Then, the antimicrobial activity of MTHJ fractions was evaluated with colony forming units (CFU), fluorescence microscopy imaging, SEM and investigating the potential food preservation. Nutritional composition, colour and sensory evaluation of extract-treated samples were evaluated along storage time. The results suggested the MTHJ may be used for meat products preservation, and the scores were significantly higher for its unique flavor, which offered a promising choice for food safety, preservation and reducing foodborne illness.

Flexible Organic Photovoltaic-Powered Hydrogel Bioelectronic Dressing With Biomimetic Electrical Stimulation for Healing Infected Diabetic Wounds.

Electrical stimulation (ES) is proposed as a therapeutic solution for managing chronic wounds. However, its widespread clinical adoption is limited by the requirement of additional extracorporeal devices to power ES-based wound dressings. In this study, a novel sandwich-structured photovoltaic microcurrent hydrogel dressing (PMH dressing) is designed for treating diabetic wounds. This innovative dressing comprises flexible organic photovoltaic (OPV) cells, a flexible micro-electro-mechanical systems (MEMS) electrode, and a multifunctional hydrogel serving as an electrode-tissue interface. The PMH dressing is engineered to administer ES, mimicking the physiological injury current occurring naturally in wounds when exposed to light; thus, facilitating wound healing. In vitro experiments are performed to validate the PMH dressing's exceptional biocompatibility and robust antibacterial properties. In vivo experiments and proteomic analysis reveal that the proposed PMH dressing significantly accelerates the healing of infected diabetic wounds by enhancing extracellular matrix regeneration, eliminating bacteria, regulating inflammatory responses, and modulating vascular functions. Therefore, the PMH dressing is a potent, versatile, and effective solution for diabetic wound care, paving the way for advancements in wireless ES wound dressings.

[Efficacy and Prognostic Factors of Nonmyeloablative Allogeneic Hematopoietic Stem Cell Transplantation for Patients with Acute Myeloid Leukemia].

OBJECTIVE: To retrospectively analyze the efficacy and complications of our institution's modified nonmyeloablative allogeneic hematopoietic stem cell transplantation (NST) in treating intermediate-risk acute myeloid leukemia (AML) - first complete remission (CR1) and prognostic factors. METHODS: Clinical data of 50 intermediate-risk AML-CR1 patients who underwent matched related NST at the Fifth Medical Center of Chinese People's Liberation Army General Hospital from August 2004 to April 2021 were collected, the hematopoietic recovery, donor engraftment and complications were observed, and overall survival (OS) rate, leukemia-free survival (LFS) rate, treatment-related mortality (TRM), and cumulative relapse rate were calculated. Statistical analysis of factors affecting prognosis was also preformed. RESULTS: The median times for neutrophil and platelet recovery after transplantation were 10 (6-16) and 13 (6-33) days, respectively. One month after transplantation, 22 patients (44%) achieved full donor chimerism (FDC), and 22 patients (44%) achieved mixed chimerism (MC), among whom 18 cases gradually transited to FDC during 1-11 months, 4 cases maintained MC status. The overall incidence of acute graft-versus-host disease (aGVHD) was 36%, with a rate of 18% for grade II-IV aGVHD and a median onset time of 45 (20-70) days after transplantation. The overall incidence of chronic GVHD (cGVHD) was 34%, with 20% and 14% of patients having limited or extensive cGVHD, respectively. The incidence rates of infections, interstitial pneumonia, and hemorrhagic cystitis were 30%, 10%, and 16%, respectively. The 5-year OS rate, LFS rate, TRM, and cumulative relapse rate were 68%, 64%, 16%, and 20%, respectively. The increase of the number of CD34+ cells infused had shortened the recovery time for neutrophils and platelets (r =0.563, r =0.350). The number of CD34+ cells infused significantly influenced the occurrence of extensive cGVHD (OR =1.36, 95%CI : 1.06-1.84, P =0.024). CONCLUSION: Modified NST is effective in treating intermediate-risk AML-CR1 patients, however, further expansion of sample size is needed to study prognostic factors.

Temporary impact on medical system and effectiveness of mitigation strategies after COVID-19 policy adjustment in China: a modeling study.

Background: As China amends its "zero COVID" strategy, a sudden increase in the number of infections may overwhelm medical resources and its impact has not been quantified. Specific mitigation strategies are needed to minimize disruption to the healthcare system and to prepare for the next possible epidemic in advance. Method: We develop a stochastic compartmental model to project the burden on the medical system (that is, the number of fever clinic visits and admission beds) of China after adjustment to COVID-19 policy, which considers the epidemiological characteristics of the Omicron variant, age composition of the population, and vaccine effectiveness against infection and severe COVD-19. We also estimate the effect of four-dose vaccinations (heterologous and homologous), antipyretic drug supply, non-pharmacological interventions (NPIs), and triage treatment on mitigating the domestic infection peak. Result: As to the impact on the medical system, this epidemic is projected to result in 398.02 million fever clinic visits and 16.58 million hospitalizations, and the disruption period on the healthcare system is 18 and 30 days, respectively. Antipyretic drug supply and booster vaccination could reduce the burden on emergency visits and hospitalization, respectively, while neither of them could not reduce to the current capacity. The synergy of several different strategies suggests that increasing the heterologous booster vaccination rate for older adult to over 90% is a key measure to alleviate the bed burden for respiratory diseases on the basis of expanded healthcare resource allocation. Conclusion: The Omicron epidemic followed the adjustment to COVID-19 policy overloading many local health systems across the country at the end of 2022. The combined effect of vaccination, antipyretic drug supply, triage treatment, and PHSMs could prevent overwhelming medical resources.