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BACKGROUND: Low back pain has become a globally challenging health problem, and about 90% of cases are nonspecific. Due to the risks associated with opioid use and the limited effectiveness of drug treatment, acupuncture and other non-drug methods have become the first-line treatment for this disease. However, the best acupuncture method has not yet been determined. In this study, the effects of different acupuncture methods on chronic nonspecific low back pain (CNLBP) were compared by network meta-analysis, aiming at identifying the best option and providing a basis for precise treatment of CNLBP. METHODS: Clinical randomized controlled trials (RCTs) on acupuncture in the treatment of NSLBP were searched in eight databases including PubMed, Embase, Cochrane Library, Web of Science, Sinomed, CNKI, Wanfang Data and VIP from the inception of databases to January 21, 2024. The Cochrane risk-of-bias tool 2.0 (RoB 2.0) and Stata 15.0 (Stata Corp, College Station, Texas, USA) were used to evaluate the literature quality and meta-analysis, and the evidence quality was assessed based on GRADE guidelines. This systematic review was registered at the International Prospective Register of Systematic Reviews. RESULTS: A total of 27 articles were included, involving 2579 patients. The results of the network meta-analysis showed that the top three treatment schemes were warm needle acupuncture, intensive silver needle therapy and meridian-sinew theory-based treatment. In terms of relieving pain, the top three treatments were electrical warm needling, intensive silver needle therapy and warm needle acupuncture. In improving mobility, the top three were meridian-sinew theory-based treatment, routine acupuncture and electroacupuncture. CONCLUSION: For CNLBP patients, warm needle acupuncture, electrical warm needling and meridian-sinew theory-based treatment are mainly recommended. If patients have significant pain, electroacupuncture is strongly suggested. On the contrary, for patients with decreased joint mobility, meridian-sinew theory-based treatment is advocated.
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Terapia por Acupuntura , Dor Crônica , Dor Lombar , Metanálise em Rede , Ensaios Clínicos Controlados Aleatórios como Assunto , Dor Lombar/terapia , Humanos , Terapia por Acupuntura/métodos , Dor Crônica/terapia , Resultado do Tratamento , Masculino , Feminino , Adulto , Pessoa de Meia-IdadeRESUMO
Modern life is essentially homochiral, containing D-sugars in nucleic acid backbones and L-amino acids in proteins. Since coded proteins are theorized to have developed from a prebiotic RNA World, the homochirality of L-amino acids observed in all known life presumably resulted from chiral transfer from a homochiral D-RNA World. This transfer would have been mediated by aminoacyl-RNAs defining the genetic code. Previous work on aminoacyl transfer using tRNA mimics has suggested that aminoacylation using D-RNA may be inherently biased toward reactivity with L-amino acids, implying a deterministic path from a D-RNA World to L-proteins. Using a model system of self-aminoacylating D-ribozymes and epimerizable activated amino acid analogs, we test the chiral selectivity of 15 ribozymes derived from an exhaustive search of sequence space. All of the ribozymes exhibit detectable selectivity, and a substantial fraction react preferentially to produce the D-enantiomer of the product. Furthermore, chiral preference is conserved within sequence families. These results are consistent with the transfer of chiral information from RNA to proteins but do not support an intrinsic bias of D-RNA for L-amino acids. Different aminoacylation structures result in different directions of chiral selectivity, such that L-proteins need not emerge from a D-RNA World.
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Aminoácidos , Aminoacilação , RNA Catalítico , RNA Catalítico/metabolismo , RNA Catalítico/química , RNA Catalítico/genética , Aminoácidos/química , Aminoácidos/metabolismo , Estereoisomerismo , Conformação de Ácido Nucleico , RNA/metabolismo , RNA/genética , RNA/química , Código GenéticoRESUMO
Introduction: The relationship between schizophrenia and violence is heterogeneous and complex. The aim of this study was to explore the characteristics and the potential risk factors for violence crime in patients with schizophrenia. Methodology: We conducted a retrospective case-control study at the Judicial Psychiatric Identification Unit of Xiangya Second Hospital of Central South University from January 1, 2013 to December 31, 2016. The case group included violent offenders diagnosed with schizophrenia, while the control group comprised non-violent individuals with the same diagnosis. Results: There were 308 individuals in the violent group [subdivided into the homicide group (n = 155) and the intentional injury group (n = 153)] and 139 individuals in the non-violent group. A risk model showed that a history of violence (odds ratio (OR) = 2.88, 95% CI [1.79-4.64]), persecutory delusions (OR = 2.57, 95% CI [1.63-4.06]), regular treatment in the previous four weeks (OR = 0.29, 95% CI [0.16-0.51]) and insight (OR = 0.30, 95% CI [0.14-0.62]) were independently associated with violence. Conclusion: This study provided useful clinical information to identify risk factors for violence and develop better strategic programs to manage violence in patients with schizophrenia.
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Esquizofrenia , Violência , Humanos , Estudos Retrospectivos , Masculino , Esquizofrenia/epidemiologia , Fatores de Risco , Violência/psicologia , Violência/estatística & dados numéricos , Adulto , Feminino , Estudos de Casos e Controles , Pessoa de Meia-Idade , Psicologia do Esquizofrênico , China/epidemiologia , Crime/estatística & dados numéricos , Crime/psicologiaRESUMO
INTRODUCTION: This study combined the bioinformatics and in vitro experiment-related technologies to analyze the impact of steroid 5 alpha-reductase 3 (SRD5A3) on the prognosis and immune microenvironment of Liver Hepatocellular Carcinoma (LIHC). METHOD: Gene expression and clinical data were obtained from public databases. The prognosis was evaluated using survival, multifactor Cox, enrichment, and mutation analyses. This was then verified through in vitro experiments. RESULTS: The expression level of SRD5A3 in LIHC tissues was significantly higher than that in the adjacent tissues. Kaplan-Meier survival analysis showed that high SRD5A3 expression was associated with poor overall survival (OS) and short progression-free survival in patients with LIHC. Multivariate Cox regression analysis revealed that positive SRD5A3 expression was an independent risk factor for OS in patients with LIHC. Expression of SRD5A3 was negatively correlated with immune cell infiltration of CD4+ T, CD8+ T, and B cells. GO and KEGG enrichment analyses showed that SRD5A3 was significantly enriched in signaling- and tumor metastasis-related pathways. Nomogram and calibration curve showed that the predicted performance of the model was consistent with the actual results. In vitro results confirmed that SRD5A3 knockdown inhibited the migration, invasion, and proliferation of LIHC cells. CONCLUSIONS: SRD5A3 is actively expressed in LIHC, and positive expression of SRD5A3 is an independent risk factor for different prognoses in patients with LIHC. SRD5A3 can promote the proliferation, migration, and invasion of liver cancer cells and is related to short immune infiltration in patients with LIHC.
High SRD5A3 expression is significantly associated with poor prognosis in patients with LIHC and may help assess tumor progression.Highly expressed SRD5A3 promotes migration, invasion, and proliferation of liver cancer cells.SRD5A3 expression is related to the degree of tumor immune cell infiltration.
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3-Oxo-5-alfa-Esteroide 4-Desidrogenase , Carcinoma Hepatocelular , Neoplasias Hepáticas , Microambiente Tumoral , Humanos , Carcinoma Hepatocelular/imunologia , Carcinoma Hepatocelular/genética , Carcinoma Hepatocelular/patologia , Neoplasias Hepáticas/imunologia , Neoplasias Hepáticas/genética , Neoplasias Hepáticas/patologia , Microambiente Tumoral/imunologia , 3-Oxo-5-alfa-Esteroide 4-Desidrogenase/genética , 3-Oxo-5-alfa-Esteroide 4-Desidrogenase/metabolismo , Prognóstico , Masculino , Feminino , Pessoa de Meia-Idade , Regulação Neoplásica da Expressão Gênica , Proteínas de Membrana/genética , Proteínas de Membrana/metabolismo , Estimativa de Kaplan-Meier , Biomarcadores Tumorais/metabolismo , Biomarcadores Tumorais/genética , Proliferação de Células/genética , Linhagem Celular TumoralRESUMO
The inversion of substrate size specificity is an evolutionary roadblock for proteins. The Duf4243 dioxygenases GedK and BTG13 are known to catalyze the aromatic cleavage of bulky tricyclic hydroquinone. In this study, we discover a Duf4243 dioxygenase PaD that favors small monocyclic hydroquinones from the penicillic-acid biosynthetic pathway. Sequence alignments between PaD and GedK and BTG13 suggest PaD has three additional motifs, namely motifs 1-3, distributed at different positions in the protein sequence. X-ray crystal structures of PaD with the substrate at high resolution show motifs 1-3 determine three loops (loops 1-3). Most intriguing, loops 1-3 stack together at the top of the pocket, creating a lid-like tertiary structure with a narrow channel and a clearly constricted opening. This drastically changes the substrate specificity by determining the entry and binding of much smaller substrates. Further genome mining suggests Duf4243 dioxygenases with motifs 1-3 belong to an evolutionary branch that is extensively involved in the biosynthesis of natural products and has the ability to degrade diverse monocyclic hydroquinone pollutants. This study showcases how natural enzymes alter the substrate specificity fundamentally by incorporating new small motifs, with a fixed overall scaffold-architecture. It will also offer a theoretical foundation for the engineering of substrate specificity in enzymes and act as a guide for the identification of aromatic dioxygenases with distinct substrate specificities.
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Motivos de Aminoácidos , Dioxigenases , Especificidade por Substrato , Dioxigenases/metabolismo , Dioxigenases/genética , Dioxigenases/química , Cristalografia por Raios X , Hidroquinonas/metabolismo , Proteínas de Bactérias/metabolismo , Proteínas de Bactérias/genética , Proteínas de Bactérias/química , Sequência de Aminoácidos , Modelos Moleculares , Alinhamento de SequênciaRESUMO
Postharvest decay, primarily caused by pathogenic fungi in ripening fruits and fresh vegetables, poses a challenge to agricultural sustainability and results in significant economic losses. The regulation of the fruit ripening by DNA methylation has been well demonstrated, while defense response of fruit underlying epigenetic regulation against postharvest decay remains uncertain. In the present study, treatment of tomato fruits with the DNA methyltransferase inhibitor 5-Azacytidine (5-Aza) notably decreased their susceptibility to gray mold. Following 5-Aza treatment, we observed a substantial increase in activities of chitinase (CHI) and glucanase (GLU) in tomato fruits, as well as an increase in the expression of the dicer-like SlDCL2 gene family. Suppression of SlDCL2c through double-stranded RNA-induced RNA interference (RNAi) resulted in a decrease in the expression of chitinases CHI3, CHI9, Class V chitinase, and endochitinase 4 by 71%, 29%, 55%, 64%, as well as glucanases Cel1, Cel2, and GluB by 19%, 93%, and 87%, respectively. This was accompanied by decreased activities of resistance-related enzymes, including CHI and GLU. The expression levels of genes phenylalanine ammonia-lyase PAL2, peroxidase POD 12, POD P7, CCR1, CYP84A2, and COMT in phenylpropanoid biosynthesis pathway also decreased by 33%, 53%, 18%, 50%, 30%, and 24% in SlDCL2c-RNAi fruit, resulting in decreased activities of PAL and POD. Consequently, the lesion diameter of gray mold in SlDCL2c-RNAi fruit increased by 55% compared to the control group. Overall, the present study indicated that DNA methyltransferase inhibitor 5-Aza reduces susceptibility of tomato fruit to gray mold through regulation of DCL2c-mediated inducible defense response.
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The preparation of histology slides is a critical step in histopathology, and poor-quality histology slides with weak adhesion of tissue sections to the substrate often affect diagnostic accuracy and sometimes lead to diagnostic failure due to tissue section detachment. This issue has been of concern and some methods have been proposed to enhance tissue-substrate adhesion. Unfortunately, quantitative analysis of the adhesion between tissue sections and glass slides is still challenging. In this work, the adhesion of mouse brain tissue sections on gold-coated glass slides was analyzed using a laboratory-fabricated hyperspectral surface plasmon resonance microscopy (HSPRM) system that enabled single-pixel spectral SPR sensing and provided two-dimensional (2D) distribution of resonance wavelengths (RWs). The existence of the nanoscale water gap between the tissue section and the substrate was verified by fitting the RW measured in each pixel using the five-layer Fresnel reflection model. In addition, a 2D image of the tissue-substrate adhesion distance (AD) was obtained from the measured 2D distribution of RWs. The results showed that tissue-substrate AD was 20-35 nm in deionized water and 4-24 nm in saline solution. The HSPRM system used in this work has a wide wavelength range of 400-1000 nm and can perform highly sensitive and label-free detection over a large dynamic detection range with high spectral and spatial resolutions, showing significant potential applications in stain-free tissue imaging, quantitative analysis of tissue-substrate adhesion, accurate identification of tumor cells, and rapid histopathological diagnosis.
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Video-to-Video synthesis (Vid2Vid) gains remarkable performance in generating a photo-realistic video from a sequence of semantic maps, such as segmentation, sketch and pose. However, this pipeline is heavily limited to high computational cost and long inference latency, mainly attributed to two essential factors: 1) network architecture parameters, 2) sequential data stream. Recently, the parameters of image-based generative models have been significantly reduced via more efficient network architectures. Existing methods mainly focus on slimming network architectures but ignore the size of the sequential data stream. Moreover, due to the lack of temporal coherence, image-based compression is not sufficient for the compression of the video task. In this paper, we present a spatial-temporal hybrid distillation compression framework, Fast-Vid2Vid++, which focuses on knowledge distillation of the teacher network and the data stream of generative models on both space and time. Fast-Vid2Vid++ makes the first attempt at time dimension to transfer hierarchical features and time coherence knowledge to reduce computational resources and accelerate inference. Specifically, we compress the data stream spatially and reduce the temporal redundancy. We distill the knowledge of the hierarchical features and the final response from the teacher network to the student network in high-resolution and full-time domains. We transfer the long-term dependencies of the features and video frames to the student model. After the proposed spatial-temporal hybrid knowledge distillation (Spatial-Temporal-HKD), our model can synthesize high-resolution key-frames using the low-resolution data stream. Finally, Fast-Vid2Vid++ interpolates intermediate frames by motion compensation with slight latency and generates full-length sequences with motion-aware inference (MAI). On standard benchmarks, Fast-Vid2Vid++ achieves a real-time performance of 30-59 FPS and saves 28-35× computational cost on a single V100 GPU. Code and models are publicly available.
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Image- and video-based 3D human recovery (i.e., pose and shape estimation) have achieved substantial progress. However, due to the prohibitive cost of motion capture, existing datasets are often limited in scale and diversity. In this work, we obtain massive human sequences by playing the video game with automatically annotated 3D ground truths. Specifically, we contribute GTA-Human, a large-scale 3D human dataset generated with the GTA-V game engine, featuring a highly diverse set of subjects, actions, and scenarios. More importantly, we study the use of game-playing data and obtain five major insights. First, game-playing data is surprisingly effective. A simple frame-based baseline trained on GTA-Human outperforms more sophisticated methods by a large margin. For videobased methods, GTA-Human is even on par with the in-domain training set. Second, we discover that synthetic data provides critical complements to the real data that is typically collected indoor. We highlight that our investigation into domain gap provides explanations for our data mixture strategies that are simple yet useful, which offers new insights to the research community. Third, the scale of the dataset matters. The performance boost is closely related to the additional data available. A systematic study on multiple key factors (such as camera angle and body pose) reveals that the model performance is sensitive to data density. Fourth, the effectiveness of GTA-Human is also attributed to the rich collection of strong supervision labels (SMPL parameters), which are otherwise expensive to acquire in real datasets. Fifth, the benefits of synthetic data extend to larger models such as deeper convolutional neural networks (CNNs) and Transformers, for which a significant impact is also observed. We hope our work could pave the way for scaling up 3D human recovery to the real world. Homepage: https://caizhongang.github.io/projects/GTA-Human/.
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Panoptic Scene Graph (PSG) is a challenging task in Scene Graph Generation (SGG) that aims to create a more comprehensive scene graph representation using panoptic segmentation instead of boxes. Compared to SGG, PSG has several challenging problems: pixel-level segment outputs and full relationship exploration (It also considers thing and stuff relation). Thus, current PSG methods have limited performance, which hinders downstream tasks or applications. This work aims to design a novel and strong baseline for PSG. To achieve that, we first conduct an in-depth analysis to identify the bottleneck of the current PSG models, finding that inter-object pair-wise recall is a crucial factor that was ignored by previous PSG methods. Based on this and the recent query-based frameworks, we present a novel framework: Pair then Relation (Pair-Net), which uses a Pair Proposal Network (PPN) to learn and filter sparse pair-wise relationships between subjects and objects. Moreover, we also observed the sparse nature of object pairs for both. Motivated by this, we design a lightweight Matrix Learner within the PPN, which directly learns pair-wised relationships for pair proposal generation. Through extensive ablation and analysis, our approach significantly improves upon leveraging the segmenter solid baseline. Notably, our method achieves over 10% absolute gains compared to our baseline, PSGFormer. The code of this paper is publicly available at https://github.com/king159/Pair-Net.
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Implantable neural devices that record neurons in various states, including static states, light activities such as walking, and vigorous activities such as running, offer opportunities for understanding brain functions and dysfunctions. However, recording neurons under vigorous activities remains a long-standing challenge because it leads to intense brain deformation. Thus, three key requirements are needed simultaneously for neural devices, that is, low modulus, low specific interfacial impedance, and high electrical conductivity, to realize stable device/brain interfaces and high-quality transmission of neural signals. However, they always contradict each other in current material strategies. Here, a soft fiber neural device capable of stably tracking individual neurons in the deep brain of medium-sized animals under vigorous activity is reported. Inspired by the axon architecture, this fiber neural device is constructed with a conductive gel fiber possessing a network-in-liquid structure using conjugated polymers and liquid matrices and then insulated with soft fluorine rubber. This strategy reconciles the contradictions and simultaneously confers the fiber neural device with low modulus (300 kPa), low specific impedance (579 kΩ µm2), and high electrical conductivity (32 700 S m-1) - ≈1-3 times higher than hydrogels. Stable single-unit spike tracking in running cats, which promises new opportunities for neuroscience is demonstrated.
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Axônios , Condutividade Elétrica , Animais , Axônios/fisiologia , Neurônios/citologia , Neurônios/fisiologia , Polímeros/químicaRESUMO
Deep models, e.g., CNNs and Vision Transformers, have achieved impressive achievements in many vision tasks in the closed world. However, novel classes emerge from time to time in our ever-changing world, requiring a learning system to acquire new knowledge continually. Class-Incremental Learning (CIL) enables the learner to incorporate the knowledge of new classes incrementally and build a universal classifier among all seen classes. Correspondingly, when directly training the model with new class instances, a fatal problem occurs - the model tends to catastrophically forget the characteristics of former ones, and its performance drastically degrades. There have been numerous efforts to tackle catastrophic forgetting in the machine learning community. In this paper, we survey comprehensively recent advances in class-incremental learning and summarize these methods from several aspects. We also provide a rigorous and unified evaluation of 17 methods in benchmark image classification tasks to find out the characteristics of different algorithms empirically. Furthermore, we notice that the current comparison protocol ignores the influence of memory budget in model storage, which may result in unfair comparison and biased results. Hence, we advocate fair comparison by aligning the memory budget in evaluation, as well as several memory-agnostic performance measures. The source code is available at https://github.com/zhoudw-zdw/CIL_Survey/.
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A great challenge in the commercialization process of layered Ni-rich cathode material LiNixCoyMn1-x-yO2 (NCM, x ≥ 80%) for lithium-ion batteries is the surface instability, which is exacerbated by the increase in nickel content. The high surface alkalinity and unavoidable cathode/electrolyte interface side reactions result in significant decrease for the capacity of NCM material. Surface coating and doping are common and effective ways to improve the electrochemical performance of Ni-rich cathode material. In this study, an in situ reaction is induced on the surface of secondary particles of NCM material to construct a stable lithium sulfate coating, while achieving sulfur doping in the near surface region. The synergistic modification of lithium sulfate coating and lattice sulfur doping significantly reduced the content of harmful residual lithium compounds (RLCs) on the surface of NCM material, suppressed the side reactions between the cathode material surface and electrolyte and the degradation of surface structure of the NCM material, effectively improved the rate capability and cycling stability of the NCM material.
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Visual segmentation seeks to partition images, video frames, or point clouds into multiple segments or groups. This technique has numerous real-world applications, such as autonomous driving, image editing, robot sensing, and medical analysis. Over the past decade, deep learning-based methods have made remarkable strides in this area. Recently, transformers, a type of neural network based on self-attention originally designed for natural language processing, have considerably surpassed previous convolutional or recurrent approaches in various vision processing tasks. Specifically, vision transformers offer robust, unified, and even simpler solutions for various segmentation tasks. This survey provides a thorough overview of transformer-based visual segmentation, summarizing recent advancements. We first review the background, encompassing problem definitions, datasets, and prior convolutional methods. Next, we summarize a meta-architecture that unifies all recent transformer-based approaches. Based on this meta-architecture, we examine various method designs, including modifications to the meta-architecture and associated applications. We also present several specific subfields, including 3D point cloud segmentation, foundation model tuning, domain-aware segmentation, efficient segmentation, and medical segmentation. Additionally, we compile and re-evaluate the reviewed methods on several well-established datasets. Finally, we identify open challenges in this field and propose directions for future research. The project page can be found at https://github.com/lxtGH/Awesome-Segmentation-With-Transformer.
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The size of vision models has grown exponentially over the last few years, especially after the emergence of Vision Transformer. This has motivated the development of parameter-efficient tuning methods, such as learning adapter layers or visual prompt tokens, which allow a tiny portion of model parameters to be trained whereas the vast majority obtained from pre-training are frozen. However, designing a proper tuning method is non-trivial: one might need to try out a lengthy list of design choices, not to mention that each downstream dataset often requires custom designs. In this paper, we view the existing parameter-efficient tuning methods as "prompt modules" and propose Neural prOmpt seArcH (NOAH), a novel approach that learns, for large vision models, the optimal design of prompt modules through a neural architecture search algorithm, specifically for each downstream dataset. By conducting extensive experiments on over 20 vision datasets, we demonstrate that NOAH (i) is superior to individual prompt modules, (ii) has good few-shot learning ability, and (iii) is domain-generalizable. The code and models are available at https://github.com/ZhangYuanhan-AI/NOAH.
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BACKGROUND: The introduction of non-native species is a primary driver of biodiversity loss in freshwater ecosystems. The redclaw crayfish (Cherax quadricarinatus) is a freshwater species that exhibits tolerance to hypoxic stresses, fluctuating temperatures, high ammonia concentration. These hardy physiological characteristics make C. quadricarinatus a popular aquaculture species and a potential invasive species that can negatively impact tropical and subtropical ecosystems. Investigating the genomic basis of environmental tolerances and immune adaptation in C. quadricarinatus will facilitate the development of management strategies of this potential invasive species. RESULTS: We constructed a chromosome-level genome of C. quadricarinatus by integrating Nanopore and PacBio techniques. Comparative genomic analysis suggested that transposable elements and tandem repeats drove genome size evolution in decapod crustaceans. The expansion of nine immune-related gene families contributed to the disease resistance of C. quadricarinatus. Three hypoxia-related genes (KDM3A, KDM5A, HMOX2) were identified as being subjected to positive selection in C. quadricarinatus. Additionally, in vivo analysis revealed that upregulating KDM5A was crucial for hypoxic response in C. quadricarinatus. Knockdown of KDM5A impaired hypoxia tolerance in this species. CONCLUSIONS: Our results provide the genomic basis for hypoxic tolerance and immune adaptation in C. quadricarinatus, facilitating the management of this potential invasive species. Additionally, in vivo analysis in C. quadricarinatus suggests that the role of KDM5A in the hypoxic response of animals is complex.
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Adaptação Fisiológica , Astacoidea , Genoma , Animais , Astacoidea/genética , Astacoidea/imunologia , Adaptação Fisiológica/genética , Hipóxia/genética , GenômicaRESUMO
Myelofibrosis is a rare and often fatal hematological neoplasm, and the treatment of myelofibrosis-associated anemia remains suboptimal, with no improved therapies. Luspatercept was shown to display some efficacy in a phase 2 clinical trial for Myelofibrosis with anemia, yet relevant research are limited. Threrfore, data from patients diagnosed with refractory anemic primary or post-essential thrombocythemia/polycythemia vera myelofibrosis, who were treated with luspatercept for at least 9 weeks, were retrospectively collected. Eighteen patients with myelofibrosis treated with luspatercept were enrolled. Median age was 68 years (range, 44-80 years), and 27.8% were males. Ten (55.6%) were transfusion-dependent. Ten (55.6%) were Dynamic International Prognostic Scoring System intermediate-1, and eight (44.4%) were intermediate-2. The median follow-up was 7 (4-16) months. Erythroid response occurred in eight patients (44.4%) at week 12, four patients (30.8%) at week 24, and nine (50%) at the end of follow-up. Patients who were transfusion-dependent and not transfusion-dependent had similar HI-E responses, at different time points (P > 0.05). Patients had a significantly higher hemoglobin level at 12 weeks, 24 weeks, and at the end of follow-up, than at baseline (P = 0.001, P = 0.021, and P = 0.005, respectively). Treatment-related adverse events occurred in five (16.7%) patients, with no serious adverse events. Two (11.1%) patients relapsed at weeks 15 and 31. One patient progressed to acute myeloid leukemia. No patients had died by the end of follow-up. Luspatercept induced a good response in patients with anemic myelofibrosis, with a low relapse rate and good tolerance.
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Mielofibrose Primária , Proteínas Recombinantes de Fusão , Humanos , Masculino , Mielofibrose Primária/tratamento farmacológico , Mielofibrose Primária/complicações , Feminino , Pessoa de Meia-Idade , Idoso , Adulto , Idoso de 80 Anos ou mais , Estudos Retrospectivos , Proteínas Recombinantes de Fusão/uso terapêutico , Proteínas Recombinantes de Fusão/efeitos adversos , China , Anemia Refratária/tratamento farmacológico , Receptores de Activinas Tipo II/uso terapêutico , Fragmentos Fc das Imunoglobulinas/uso terapêutico , Resultado do Tratamento , Seguimentos , Anemia/tratamento farmacológico , Anemia/etiologiaRESUMO
Implantable sensors, especially ion sensors, facilitate the progress of scientific research and personalized healthcare. However, the permanent retention of implants induces health risks after sensors fulfill their mission of chronic sensing. Biodegradation is highly anticipated; while; biodegradable chemical sensors are rare due to concerns about the leakage of harmful active molecules after degradation, such as ionophores. Here, a novel biodegradable fiber calcium ion sensor is introduced, wherein ionophores are covalently bonded with bioinert nanoparticles to replace the classical ion-selective membrane. The fiber sensor demonstrates comparable sensing performance to classical ion sensors and good flexibility. It can monitor the fluctuations of Ca2+ in a 4-day lifespan in vivo and biodegrade in 4 weeks. Benefiting from the stable bonding between ionophores and nanoparticles, the biodegradable sensor exhibits a good biocompatibility after degradation. Moreover, this approach of bonding active molecules on bioinert nanoparticles can serve as an effective methodology for minimizing health concerns about biodegradable chemical sensors.
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Cálcio , Nanopartículas , Nanopartículas/química , Cálcio/química , Animais , Camundongos , Materiais Biocompatíveis/química , Implantes Absorvíveis , Íons/química , Técnicas Biossensoriais/métodosRESUMO
Transpiration (T) is pivotal in the global water cycle, responding to soil moisture, atmospheric stress, climate changes, and human impacts. Therefore, establishing a reliable global transpiration dataset is essential. Collocation analysis methods have been proven effective for assessing the errors in these products, which can subsequently be used for multisource fusion. However, previous results did not consider error cross-correlation, rendering the results less reliable. In this study, we employ collocation analysis, taking error cross-correlation into account, to effectively analyze the errors in multiple transpiration products and merge them to obtain a more reliable dataset. The results demonstrate its superior reliability. The outcome is a long-term daily global transpiration dataset at 0.1°from 2000 to 2020. Using the transpiration after partitioning at FLUXNET sites as a reference, we compare the performance of the merged product with inputs. The merged dataset performs well across various vegetation types and is validated against in-situ observations. Incorporating non-zero ECC considerations represents a significant theoretical and proven enhancement over previous methodologies that neglected such conditions, highlighting its reliability in enhancing our understanding of transpiration dynamics in a changing world.
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Transpiração Vegetal , Mudança Climática , SoloRESUMO
Ovarian cancer is a common malignant tumor in women, with a high mortality rate ranking first among gynecological tumors. Currently, there is insufficient understanding of the causes, pathogenesis, recurrence and metastasis of ovarian cancer, and early diagnosis and treatment still face great challenges. The sensitivity and specificity of existing ovarian cancer screening methods are still unsatisfactory. Centromere protein O (CENP-O) is a recently discovered structural centromere protein that is involved in cell death and is essential for spindle assembly, chromosome separation, and checkpoint signaling during mitosis. The abnormal high expression of CENP-O was detected in various tumors such as bladder cancer and gastric cancer, and it participates in the regulation of tumor cell proliferation. In this study, we detect the expression abundance of CENP-O mRNA in different ovarian cancer cells ( ES-2, A2780, Caov-3, OVCAR-3 and SK-OV-3). The biological function changes of cell proliferation and apoptosis were detected and the role of CENP-O in ovarian cancer cell proliferation and apoptosis was explored by knocking down the expression of CENP-O gene. The results showed that CENP-O gene was significantly expressed in 5 types of ovarian cancer cell lines. After knocking down the CENP-O gene, the proliferation and cloning ability of ovarian cancer cells decreased, and the apoptosis increased. This study indicates that CENP-O has the potential to be a molecular therapeutic target, and downregulating the expression of CENP-O gene can break the unlimited proliferation ability of cancer cells and promote their apoptosis, providing a foundation and new ideas for subsequent molecular mechanism research and targeted therapy.