RESUMEN

RGS (Regulator of G protein signaling) proteins have long captured the fascination of researchers due to their intricate involvement across a wide array of signaling pathways within cellular systems. Their diverse and nuanced functions have positioned them as continual subjects of scientific inquiry, especially given the implications of certain family members in various cancer types. Of particular note in this context is RGS20, whose clinical relevance and molecular significance in hepatocellular carcinoma we have recently investigated. These investigations have prompted questions into the prevalence of pathogenic mutations within the RGS20 gene and the intricate network of interacting proteins that could contribute to the complex landscape of cancer biology. In our study, we aim to unravel the mutations within the RGS20 gene and the multifaceted interplay between RGS20 and other proteins within the context of cancer. Expanding on this line of inquiry, our research is dedicated to uncovering the intricate mechanisms of RGS20 in various cancers. In particular, we have redirected our attention to examining the role of RGS20 within hematological malignancies, with a specific focus on multiple myeloma and follicular lymphoma. These hematological cancers hold significant promise for further investigation, as understanding the involvement of RGS20 in their pathogenesis could unveil novel therapeutic strategies and treatment avenues. Furthermore, our exploration has extended to encompass the latest discoveries concerning the potential involvement of RGS20 in diseases affecting the central nervous system, thereby broadening the scope of its implications beyond oncology to encompass neurobiology and related fields.

RESUMEN

KEY MESSAGE: This study provided a non-destructive detection method with Vis-NIR hyperspectral imaging combining with physio-biochemical parameters in Helianthus annuus in response to Orobanche cumana infection that took insights into the monitoring of sunflower weed. Sunflower broomrape (Orobanche cumana Wallr.) is an obligate weed that attaches to the host roots of sunflower (Helianthus annuus L.) leading to a significant reduction in yield worldwide. The emergence of O. cumana shoots after its underground life-cycle causes irreversible damage to the crop. In this study, a fast visual, non-invasive and precise method for monitoring changes in spectral characteristics using visible and near-infrared (Vis-NIR) hyperspectral imaging (HSI) was developed. By combining the bands sensitive to antioxidant enzymes (SOD, GR), non-antioxidant enzymes (GSH, GSH + GSSG), MDA, ROS (O2-, OH-), PAL, and PPO activities obtained from the host leaves, we sought to establish an accurate means of assessing these changes and conducted imaging acquisition using hyperspectral cameras from both infested and non-infested sunflower cultivars, followed by physio-biochemical parameters measurement as well as analyzed the expression of defense related genes. Extreme learning machine (ELM) and convolutional neural network (CNN) models using 3-band images were built to classify infected or non-infected plants in three sunflower cultivars, achieving accuracies of 95.83% and 95.83% for the discrimination of infestation as well as 97.92% and 95.83% of varieties, respectively, indicating the potential of multi-spectral imaging systems for early detection of O. cumana in weed management.

Asunto(s)

RESUMEN

Hyperaccumulators are the material basis and key to the phytoremediation of heavy metal contaminated soils. Conventional methods for screening hyperaccumulators are highly dependent on the time- and labor-consuming sampling and chemical analysis. In this study, a novel spectral approach assisted with multi-task deep learning was proposed to streamline accumulating ecotype screening, heavy metal stress discrimination, and heavy metals quantification in plants. The significant Cd/Zn co-hyperaccumulator Sedum alfredii and its non-accumulating ecotype were stressed by Cd, Zn, and Pb. Spectral images of leaves were rapidly acquired by hyperspectral imaging. The self-designed deep learning architecture was composed of a shallow network (ENet) for accumulating ecotype identification, and a multi-task network (HMNet) for heavy metal stress type and accumulation prediction simultaneously. To further assess the robustness of the networks, they were compared with conventional machine learning models (i.e., partial least squares (PLS) and support vector machine (SVM)) on a series of evaluation metrics of classification, multi-label classification, and regression. S. alfredii with heavy metals accumulation capability was identified by ENet with 100 % accuracy. HMNet reduced overfitting and outperformed machine learning models with the average exact match ratio (EMR) of heavy metal stress discrimination increased by 7.46 %, and residual prediction deviations (RPD) of heavy metal concentrations prediction increased by 53.59 %. The method succeeded in rapidly and accurately discriminating heavy metal stress with EMRs over 91 % and accuracies over 96 %, and in predicting heavy metals accumulation with an average RPD of 3.29 for Zn, 2.57 for Cd, and 2.53 for Pb, indicating the satisfactory practicability and potential for sensing heavy metals accumulation. This study provides a relatively novel spectral method to facilitate hyperaccumulator screening and heavy metals accumulation prediction in the phytoremediation process.

Asunto(s)

RESUMEN

OBJECTIVE: The objective of this study is to compare the differences in effective connectivity within the default mode network (DMN) subsystems between patients with Parkinson's disease with mild cognitive impairment (PD-MCI) and patients with Parkinson's disease with normal cognition (PD-CN). The mechanisms underlying DMN dysfunction in PD-MCI patients and its association with clinical cognitive function in PD-MCI are aimed to be investigated. METHODS: The spectral dynamic causal model (spDCM) was employed to analyze the effective connectivity of functional magnetic resonance imaging (fMRI) data in the resting state for the DMN subsystems, which include the medial prefrontal cortex (MPFC), posterior cingulate cortex (PCC), left and right angular gyrus (LAG, RAG) in 23 PD-MCI and 22 PD-CN patients, respectively. The effective connectivity values of DMN subsystems in the two groups were statistically analyzed using a two-sample t-test. The Spearman correlation analysis was used to test the correlation between the effective connectivity values of the subsystems with significant differences between the two groups and the clinical cognitive function (as measured by Montreal Cognitive Assessment Scale (MoCA) score). RESULTS: Statistical analysis revealed significant differences in the effective connections of MPFC-LAG and LAG-PCC between the two patient groups (MPFC-LAG: t = -2.993, p < 0.05; LAG-PCC: t = 2.174, p < 0.05). CONCLUSIONS: The study findings suggest that abnormal strength and direction of effective connections between DMN subsystems are found in PD-MCI patients.

Asunto(s)

RESUMEN

Rice stem is the sole conduit for cadmium translocation from underground to aboveground. The presence of cadmium can trigger responses of rice stem multi-phenotype, affecting metabolism, reducing yield, and altering composition, which is related to crop growth, food safety, and new energy utilization. Exploring the adversity response of plant phenotypes can provide a reliable assessment of growth status. However, the phytotoxicity and mechanism of cadmium stress on rice stem remain unclear. Here, we systematically revealed the response mechanisms of cadmium accumulation, adversity physiology, and morphological characteristic in rice stem under cadmium stress for the first time with concentration gradients of CK, 5, 25, 50, and 100 µM, and duration gradients of Day 5, Day 10, Day 15, and Day 20. The results indicated that cadmium stress led to a significant increase in cadmium accumulation, accompanied by the adversity response in stem phenotypes. Specifically, cadmium can cause fluctuations in soluble protein and disturbance of malondialdehyde (MDA), which reflects lipid peroxidation induced by cadmium accumulation. Lipid peroxidation inhibited rice growth by causing (1) a reduction in stem length, diameter, and weight, (2) suppression of air cavity, vascular bundle, parenchyma, and epidermal hair, and (3) disruption of cell structure. Furthermore, rapid detection of cadmium was realized based on the combination of laser-induced breakdown spectroscopy (LIBS) and machine learning, which took less than 3 min. The established qualitative model realized the precise discrimination of cadmium stress degrees with a prediction accuracy exceeding 92 %, and the quantitative model achieved the outstanding prediction effect of cadmium, with Rp of 0.9944. This work systematically revealed the phytotoxicity of cadmium on rice stem multi-phenotype from a novel perspective of lipid peroxidation and realized the rapid detection of cadmium in rice stem, which provided the technical tool and theoretical foundation for accurate prevention and efficient control of heavy metal risks in crops.

Asunto(s)

RESUMEN

The root is an important organ affecting cadmium accumulation in grains, but there is no comprehensive research involving rice root phenotype under cadmium stress yet. To assess the effect of cadmium on root phenotypes, this paper investigated the response mechanism of phenotypic information including cadmium accumulation, adversity physiology, morphological parameters, and microstructure characteristics, and explored rapid detection methods of cadmium accumulation and adversity physiology. We found that cadmium had the effect of "low-promotion and high-inhibition" on root phenotypes. In addition, the rapid detection of cadmium (Cd), soluble protein (SP), and malondialdehyde (MDA) were achieved based on spectroscopic technology and chemometrics, where the optimal prediction model was least squares support vector machine (LS-SVM) based on the full spectrum (Rp=0.9958) for Cd, competitive adaptive reweighted sampling-extreme learning machine (CARS-ELM) (Rp=0.9161) for SP and CARS-ELM (Rp=0.9021) for MDA, all with Rp higher than 0.9. Surprisingly, it took only about 3 min, which was more than 90% reduction in detection time compared with laboratory analysis, demonstrating the excellent ability of spectroscopy for root phenotype detection. These results reveal response mechanism to heavy metal and provide rapid detection method for phenotypic information, which can substantially contribute to crop heavy metal control and food safety supervision.

Asunto(s)

RESUMEN

Dendrobium nobile, an epiphytic plant, is a traditional medicinal herb with abundant endophytes. It is unclear whether the variation in the diversity and abundance of endophytes could stimulate the biosynthesis of medicinal compounds in the plant. In this study, we collected fresh stems of D. nobile from four habitats for investigating the fungal community structure, dendrobine content, and environment factors and their correlations. The results indicated no significant difference in endophytic fungal diversity among the habitats; however, different dominant or special endophytic genera were observed in the hosts from different habitats. The altitude was observed to be positively related to the dendrobine content, as the stems collected from the altitude of 692 m exhibited the highest level of dendrobine. Furthermore, the relative abundance of Toxicocladosporium was found to be positively correlated with the altitude and dendrobine content. The epiphytic matrix exhibited a significant negative correlation with the relative abundance of the endophytic fungus Gibberella but did not exhibit any significant correlation with the dendrobine content. The results indicated that the abundance of endophytes in D. nobile was affected by the altitude and epiphytic matrix and that high Toxicocladosporium abundance and high altitude were conducive to dendrobine production.

RESUMEN

BACKGROUND: Current available treatment modes against dermatophytoses are often tedious and sometimes unsatisfactory. As an emerging and promising approach, antimicrobial photodynamic therapy (aPDT) attracts much attention in the treatment of superficial or localised infections. OBJECTIVES: This work investigated the photodynamic efficacy and effects of haematoporphyrin monomethyl ether (HMME) on microconidia of Trichophyton rubrum in vitro. METHODS: The photodynamic killing efficacy of HMME on microconidia of two T rubrum strains was assessed by MTT assay. The effects of HMME-mediated aPDT on the growth of T rubrum and cellular structure of microconidia were also investigated. Confocal laser scanning microscopy (CLSM) and flow cytometry were employed to study the intracellular localisation of HMME and generation of reactive oxygen species (ROS). RESULTS: HMME showed no obvious toxicity in the dark, but after light irradiation it inactivated the T rubrum microconidia in a light energy dose-dependent manner, and inhibited the growth of T rubrum. CLSM demonstrated that HMME initially bound to the cell envelop and entered into the cell after light irradiation. HMME-mediated aPDT also damaged the cell cytoplasm and increased the accumulation of intracellular ROS, resulting in cell death. CONCLUSIONS: The results suggested that HMME-mediated aPDT had potential to be used in the treatment of superficial infections caused by T rubrum.

RESUMEN

High density diffuse optical tomography has become increasingly important to detect underlying neuronal activities. Conventional methods first estimate the time courses of the changes in the absorption coefficients for all the voxels, and then estimate the hemodynamic response function (HRF). Activation-level maps are extracted at last based on this HRF. However, the error propagation among the successive processes degrades and even misleads the final results. Besides, the computation burden is heavy. To address the above problems, a direct method is proposed in this paper to simultaneously estimate the HRF and the activation-level maps from the boundary fluxes. It is assumed that all the voxels in the same activated brain region share the same HRF but differ in the activation levels, and no prior information is imposed on the specific shape of the HRF. The dynamic simulation and phantom experiments demonstrate that the proposed method outperforms the conventional one in terms of the estimation accuracy and computation speed.

RESUMEN

In brain functional diffuse optical tomography, conventional indirect approaches first separately reconstruct the spatial changes in the absorption coefficients at every time point and then calculate the spatial excited levels in terms of hemodynamic models. Direct approaches combine the two steps necessary in the indirect approaches and obtain the spatial excited levels directly. Although reconstruction quality has been improved by the direct approaches to some extent, they still lack sharp edges and suffer from low spatial resolution because of the ill-posedness of the inverse problems. In this paper, a priori sparsity is introduced to obtain the sparse solutions and further improve reconstruction quality. Simulation experiments are conducted to illustrate the expected performance improvements of the proposed approaches.

Asunto(s)

RESUMEN

In functional near-infrared spectroscopy (fNIRS), the conventional indirect approaches first separately recover the spatial distribution of the changes in the optical properties at every time point, and then extract the activation levels by a time-course analysis process at every site. In the tomographic implementation of fNIRS, i.e., diffuse optical tomography (DOT), these approaches not only suffer from the ill-posedness of the optical inversions and error propagation between the two successive steps, but also fail to achieve satisfactory temporal resolution due to the requirement for a complete data set. To cope with the above adversities of the indirect approaches, we propose herein a direct approach to tomographically reconstructing the activation levels by incorporating a Kalman scheme. Dynamic simulative and phantom experiments were conducted for the performance validation of the proposed approach, demonstrating its potentials to improve the calculated images and to relax the speed limitation of the instruments.

Asunto(s)

RESUMEN

In the title centrosymmetric dimeric Cd(II) complex, [Cd(2)(C(7)H(5)O(4))(4)(C(12)H(8)N(2))(2)(H(2)O)(2)], the Cd(II) cation is coord-inated by a bidentate phenanthroline (phen) ligand, three dihy-droxy-benzoate (dhba) anions and one water mol-ecule in a distorted CdN(2)O(4) octa-hedral geometry. Among the dhba anions, two anions bridge two Cd(II) cations to form the dimeric complex with significant different Cd-O bond distances of 2.2215 (19) and 2.406 (2) Å. The centroid-centroid distance of 3.4615 (19) Šbetween two nearly parallel benzene rings of the dhba and phen ligands coordinating to the same Cd(II) cation indicates the existence of intra-molecular π-π stacking in the complex. Extensive O-H⋯O hydrogen bonding and inter-molecular weak C-H⋯O hydrogen bonding help to stabilize the crystal structure. One hy-droxy group of the monodentate dhba ligand is disordered over two sites with a site-occupancy ratio of 0.9:0.1.