Intratumoral and peritumoral habitat imaging based on multiparametric MRI to predict cervical stromal invasion in early-stage endometrial carcinoma.

RATIONALE AND OBJECTIVES: To evaluate the validity of multiparametric MRI-based intratumoral and peritumoral habitat imaging for predicting cervical stromal invasion (CSI) in patients with early-stage endometrial carcinoma (EC) and to compare the performance of structural and functional habitats. MATERIALS AND METHODS: The preoperative MRI and clinical data of 680 patients with early-stage EC from three centers were retrospectively analyzed. Based on cohort-level, gaussian mixture model (GMM) algorithm was used for habitat clustering of MRI images. Structural habitats were clustered using T2-weighted imaging (T2WI) and contrast-enhanced T1-weighted imaging (CE-T1WI), and functional habitats were clustered using apparent diffusion coefficient (ADC) mapping and CE-T1WI. Habitat parameters were extracted from four volumes of interest (VOIs): intratumoral regions (ROI), peritumoral loops of 3 mm dilation (L3), intratumoral regions + peritumoral loops of 3 mm dilation (R3), and peritumoral loops of 3 mm dilation + peritumoral loops of 3 mm erosion (DE3). Clinical-habitat models were constructed by combining clinical independent predictors and optimal habitat models. The model performance was evaluated by the area under the curve (AUC). RESULTS: Deep myometrial invasion (DMI) was an independent predictor. L3 models showed the best performance for both structural and functional habitats, and the L3 functional habitat model had the highest average AUC (0.807) in external test groups, and the average AUC increased to 0.815 when combing with the clinical independent predictor. CONCLUSION: Multiparametric MRI-based intratumoral and peritumoral habitat imaging provides a noninvasive approach to predict CSI in EC patients. The combination of the clinical predictor with the L3 functional habitat model improved predictive performance.

High-Order Topological Pumping on a Superconducting Quantum Processor.

High-order topological phases of matter refer to the systems of n-dimensional bulk with the topology of m-th order, exhibiting (n-m)-dimensional boundary modes and can be characterized by topological pumping. Here, we experimentally demonstrate two types of second-order topological pumps, forming four 0-dimensional corner localized states on a 4×4 square lattice array of 16 superconducting qubits. The initial ground state of the system at half-filling, as a product of four identical entangled 4-qubit states, is prepared using an adiabatic scheme. During the pumping procedure, we adiabatically modulate the superlattice Bose-Hubbard Hamiltonian by precisely controlling both the hopping strengths and on-site potentials. At the half pumping period, the system evolves to a corner-localized state in a quadrupole configuration. The robustness of the second-order topological pump is also investigated by introducing different on-site disorder. Our Letter studies the topological properties of high-order topological phases from the dynamical transport picture using superconducting qubits, which would inspire further research on high-order topological phases.

Rethinking Noise Sampling in Class-Imbalanced Diffusion Models.

In the practical application of image generation, dealing with long-tailed data distributions is a common challenge for diffusion-based generative models. To tackle this issue, we investigate the head-class accumulation effect in diffusion models' latent space, particularly focusing on its correlation to the noise sampling strategy. Our experimental analysis indicates that employing a consistent sampling distribution for the noise prior across all classes leads to a significant bias towards head classes in the noise sampling distribution, which results in poor quality and diversity of the generated images. Motivated by this observation, we propose a novel sampling strategy named Bias-aware Prior Adjusting (BPA) to debias diffusion models in the class-imbalanced scenario. With BPA, each class is automatically assigned an adaptive noise sampling distribution prior during training, effectively mitigating the influence of class imbalance on the generation process. Extensive experiments on several benchmarks demonstrate that images generated using our proposed BPA showcase elevated diversity and superior quality.

The impact of processing voltage of wire electric discharge machining on the performance of Mo doped V-VO0.2 based Archimedean micro-supercapacitors.

Vanadium oxide-based electrode materials have attracted increasing attention owing to their extraordinary capacitance and prolonged lifespan, excellent conductivity and outstanding electrochemical reversibility. However, the development of vanadium oxide-based integrated electrodes with outstanding capacitive performance is an enduring challenge. This research reports a facile method for structuring 3D Archimedean micro-supercapacitors (AMSCs) composed of Mo doped V-VO0.2 (Mo@V-VO0.2) based integrated electrodes with designable geometric shape, using computer-aided wire electric discharge machining (WEDM). The performance of Mo@V-VO0.2 based AMSCs manufactured by different processing voltages of 60 V, 80 V and 100 V were evaluated. It was found that 80 V is the optimal processing voltage for manufacturing Mo@V-VO0.2 based AMSCs with the best electrochemical performance. This device demonstrates superior capacitive behavior even at an ultra-high scan rate of 50, 000 mV s-1, and achieves a good capacitance retention rate of 94.4% after 2000 cycles. Additionally, the characteristics of electric field distribution were also simulated for optimizing the geometric structure of the microdevices. This WEDM fabrication technique, which is easy, secure, patternable, efficient, economical, eco-friendly, and does not require binders or conductive additives, enables the development of high-capacity 3D pseudocapacitive micro-supercapacitors and demonstrates the great potential for metal oxide synthesis and microdevice manufacturing.

Multiparametric MRI-based radiomics combined with 3D deep transfer learning to predict cervical stromal invasion in patients with endometrial carcinoma.

OBJECTIVE: To develop and compare various preoperative cervical stromal invasion (CSI) prediction models, including radiomics, three-dimensional (3D) deep transfer learning (DTL), and integrated models, using single-sequence and multiparametric MRI. METHODS: Data from 466 early-stage endometrial carcinoma (EC) patients from three centers were collected. Radiomics models were constructed based on T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC) mapping, contrast-enhanced T1-weighted imaging (CE-T1WI), and four combined sequences as well as 3D DTL models. Two integrated models were created using ensemble and stacking algorithms based on optimal radiomics and DTL models. Model performance and clinical benefits were assessed using area under the curve (AUC), decision curve analysis (DCA), net reclassification index (NRI), integrated discrimination index (IDI), and the Delong test for model comparisons. RESULTS: Multiparametric MRI models were superior to single-sequence models for radiomics or DTL models. Ensemble and stacking integrated models displayed excellent performance. The stacking model had the highest average AUC (0.908) and accuracy (0.883) in external validation groups 1 and 2 (AUC = 0.965 and 0.851, respectively) and emerged as the best predictive model for CSI. All models significantly outperformed the radiologist (P < 0.05). In terms of net benefits, all models demonstrated favorable outcomes in DCA, NRI, and IDI, with the stacking model yielding the highest net benefit. CONCLUSION: Multiparametric MRI-based radiomics combined with 3D DTL can be used to noninvasively predict CSI in EC patients with greater diagnostic accuracy than the radiologist. Stacking integrated models showed significant potential utility in predicting CSI. Which helps to provide new treatment strategy for clinicians to treat early-stage EC patients.

Bioinspired triple-layered membranes for periodontal guided bone regeneration applications.

Barrier membranes have been used for the treatment of alveolar bone loss caused by periodontal diseases or trauma. However, an optimal barrier membrane must satisfy multiple requirements simultaneously, which are challenging to combine into a single material. We herein report the design of a bioinspired membrane consisting of three functional layers. The primary layer is composed of clay nanosheets and chitin, which form a nacre-inspired laminated structure. A calcium phosphate mineral layer is deposited on the inner surface of the nacre-inspired layer, while a poly(lactic acid) layer is coated on the outer surface. The composite membrane integrates good mechanical strength and deformability because of the nacre-inspired structure, facilitating operations during the implant surgery. The mineral layer induces the osteogenic differentiation of bone marrow mesenchymal stem cells and increases the stiffness of the membrane, which is an important factor for the regeneration process. The poly(lactic acid) layer can prevent unwanted mineralization on the outer surface of the membrane in oral environments. Cell experiments reveal that the membrane exhibits good biocompatibility and anti-infiltration capability toward connective tissue/epithelium cells. Furthermore, in vitro analyses show that the membrane does not degrade too fast, allowing enough time for bone regeneration. In vivo experiments prove that the membrane can effectively induce better bone regeneration and higher trabecular bone density in alveolar bone defects. This study demonstrates the potential of this bioinspired triple-layered membrane with hierarchical structures as a promising barrier material for periodontal guided tissue regeneration.

Rational design of self-amplifying virus-like vesicles with Ebola virus glycoprotein as vaccines.

As emerging and re-emerging pathogens, filoviruses, especially Ebola virus (EBOV), pose a great threat to public health and require sustained attention and ongoing surveillance. More vaccines and antiviral drugs are imperative to be developed and stockpiled to respond to unpredictable outbreaks. Virus-like vesicles, generated by alphavirus replicons expressing homogeneous or heterogeneous glycoproteins (GPs), have demonstrated the capacity of self-propagation and shown great potential in vaccine development. Here, we describe a novel class of EBOV-like vesicles (eVLVs) incorporating both EBOV GP and VP40. The eVLVs exhibited similar antigenicity as EBOV. In murine models, eVLVs were highly attenuated and elicited robust GP-specific antibodies with neutralizing activities. Importantly, a single dose of eVLVs conferred complete protection in a surrogate EBOV lethal mouse model. Furthermore, our VLVs strategy was also successfully applied to Marburg virus (MARV), the representative member of the genus Marburgvirus. Taken together, our findings indicate the feasibility of an alphavirus-derived VLVs strategy in combating infection of filoviruses represented by EBOV and MARV, which provides further evidence of the potential of this platform for universal live-attenuated vaccine development.

Tensorized Soft Label Learning Based on Orthogonal NMF.

Recently, a strong interest has been in multiview high-dimensional data collected through cross-domain or various feature extraction mechanisms. Nonnegative matrix factorization (NMF) is an effective method for clustering these high-dimensional data with clear physical significance. However, existing multiview clustering based on NMF only measures the difference between the elements of the coefficient matrix without considering the spatial structure relationship between the elements. And they often require postprocessing to achieve clustering, making the algorithms unstable. To address this issue, we propose minimizing the Schatten p -norm of the tensor, which consists of a coefficient matrix of different views. This approach considers each element's spatial structure in the coefficient matrices, crucial for effectively capturing complementary information presented in different views. Furthermore, we apply orthogonal constraints to the cluster index matrix to make it sparse and provide a strong interpretation of the clustering. This allows us to obtain the cluster label directly without any postprocessing. To distinguish the importance of different views, we utilize adaptive weights to assign varying weights to each view. We introduce an unsupervised optimization scheme to solve and analyze the computational complexity of the model. Through comprehensive evaluations of six benchmark datasets and comparisons with several multiview clustering algorithms, we empirically demonstrate the superiority of our proposed method.

Application of CT and MRI images based on artificial intelligence to predict lymph node metastases in patients with oral squamous cell carcinoma: a subgroup meta-analysis.

Background: The performance of artificial intelligence (AI) in the prediction of lymph node (LN) metastasis in patients with oral squamous cell carcinoma (OSCC) has not been quantitatively evaluated. The purpose of this study was to conduct a systematic review and meta-analysis of published data on the diagnostic performance of CT and MRI based on AI algorithms for predicting LN metastases in patients with OSCC. Methods: We searched the Embase, PubMed (Medline), Web of Science, and Cochrane databases for studies on the use of AI in predicting LN metastasis in OSCC. Binary diagnostic accuracy data were extracted to obtain the outcomes of interest, namely, the area under the curve (AUC), sensitivity, and specificity, and compared the diagnostic performance of AI with that of radiologists. Subgroup analyses were performed with regard to different types of AI algorithms and imaging modalities. Results: Fourteen eligible studies were included in the meta-analysis. The AUC, sensitivity, and specificity of the AI models for the diagnosis of LN metastases were 0.92 (95% CI 0.89-0.94), 0.79 (95% CI 0.72-0.85), and 0.90 (95% CI 0.86-0.93), respectively. Promising diagnostic performance was observed in the subgroup analyses based on algorithm types [machine learning (ML) or deep learning (DL)] and imaging modalities (CT vs. MRI). The pooled diagnostic performance of AI was significantly better than that of experienced radiologists. Discussion: In conclusion, AI based on CT and MRI imaging has good diagnostic accuracy in predicting LN metastasis in patients with OSCC and thus has the potential for clinical application. Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO/#recordDetails, PROSPERO (No. CRD42024506159).

Structural basis of neuropeptide Y signaling through Y1 and Y2 receptors.

Neuropeptide Y (NPY), a 36-amino-acid peptide, functions as a neurotransmitter in both the central and peripheral nervous systems by activating the NPY receptor subfamily. Notably, NPY analogs display varying selectivity and exert diverse physiological effects through their interactions with this receptor family. [Pro34]-NPY and [Leu31, Pro34]-NPY, mainly acting on Y1R, reportedly increases blood pressure and postsynaptically potentiates the effect of other vasoactive substances above all, while N-terminal cleaved NPY variants in human body primary mediates angiogenesis and neurotransmitter release inhibition through Y2R. However, the recognition mechanisms of Y1R and Y2R with specific agonists remain elusive, thereby hindering subtype receptor-selective drug development. In this study, we report three cryo-electron microscopy (cryo-EM) structures of Gi2-coupled Y1R and Y2R in complexes with NPY, as well as Y1R bound to a selective agonist [Leu31, Pro34]-NPY. Combined with cell-based assays, our study not only reveals the conserved peptide-binding mode of NPY receptors but also identifies an additional sub-pocket that confers ligand selectivity. Moreover, our analysis of Y1R evolutionary dynamics suggests that this sub-pocket has undergone functional adaptive evolution across different species. Collectively, our findings shed light on the molecular underpinnings of neuropeptide recognition and receptor activation, and they present a promising avenue for the design of selective drugs targeting the NPY receptor family.

WNT16 as a promising biomarker for systemic lupus erythematosus and its role in regulating cell proliferation and apoptosis.

OBJECTIVES: To investigate the expression and function of WNT16, a member of the WNT family protein, in the context of systemic lupus erythematosus (SLE). METHODS: WNT16 expression was assessed in peripheral blood mononuclear cells (PBMCs) from 35 SLE patients and 25 healthy individuals using quantitative polymerase chain reaction. Additionally, serum WNT16 protein levels were quantified via enzyme-linked immunosorbent assay in 162 SLE patients, 96 healthy controls (HC), and disease controls comprised 154 individuals with rheumatoid arthritis (RA) and Sjögren's syndrome (SS). We investigated the associations between WNT16 protein levels and clinical manifestations, laboratory indices, and disease activity in SLE patients. Receiver operating characteristic (ROC) curve analysis was employed to evaluate the diagnostic potential of serum WNT16 for SLE. Furthermore, we performed a knockdown assay on Jeko-1 cells and assessed cell proliferation and apoptosis using Cell Counting Kit-8 and flow cytometry. RESULTS: WNT16 mRNA in SLE patients' PBMCs were significantly lower than those in HC. Furthermore, serum WNT16 in SLE patients were markedly reduced compared to HC, RA, and SS cohorts. ROC curve analysis indicated that plasma WNT16 levels could serve as a potential biomarker for SLE identification (AUC=0.809, SLE vs. HC; AUC=0.760, SLE vs. RA; AUC=0.710, SLE vs. SS). Notably, a weak positive correlation was observed between WNT16 protein and both alkaline phosphatase and lymphocyte percentages. Conversely, a weak negative correlation existed between WNT16 and low-density lipoprotein, neutrophil percentage, and the incidence of pleurisy and disease activity. Additionally, our study confirmed that WNT16 knockdown impairs cell proliferation and enhances apoptosis. CONCLUSIONS: Serum WNT16 levels effectively differentiate SLE patients from healthy controls and individuals with other autoimmune disorders. WNT16 serves as a potential biomarker with high sensitivity. The diminished expression of WNT16 in SLE may have a significant role in its pathogenesis through the regulation of cell proliferation and apoptosis.

Early patient-reported outcomes after robotic-assisted versus video-assisted thoracoscopic lobectomy.

BACKGROUND: Robotic-assisted thoracoscopic surgery (RATS) can achieve traditional clinical outcomes comparable to those of video-assisted thoracoscopic surgery (VATS). However, patient-reported outcomes (PROs) during the early period after RATS and VATS remain unclear. This study aimed to utilize longitudinal electronic PRO (ePRO) assessments to evaluate symptom burden and functional status between these approaches from patients' perspective. METHODS: This study comprised patients who underwent lobectomy via RATS or VATS for non-small cell lung cancer. We collected multiple-time-point PROs data from the prospective longitudinal study via an ePRO system. Symptom severity and function status were assessed using the perioperative symptom assessment for patients undergoing lung surgery and were analyzed between groups using linear mixed-effects models. RESULTS: Of the 164 patients, 42 underwent RATS and 122 underwent VATS. After propensity score matching (PSM), 42 RATS and 84 VATS exhibited similar baseline characteristics. During the 7-day postoperative period, participants underwent RATS reported milder pain (p = 0.014), coughing (p < 0.001), drowsiness (p = 0.001), and distress (p = 0.045) compared with those underwent VATS. Moreover, participants in RATS group showed less functional interference with walking (p < 0.001) and general activity (p < 0.001). RATS exhibited a shorter postoperative hospitalization (p = 0.021) but higher hospital cost (p < 0.001). Meanwhile, short-term clinical outcomes of operative time, dissected lymph node stations, chest tube drainage, and postoperative complication rates were comparable. CONCLUSION: PROs are important metrics for assessing patients' recovery after lobectomy. Compared with VATS, RATS may induce less symptom burden and better functional status for patients in the early postoperative period.

Cross-Linking CdSO4-Type Nets with Hexafluorosilicate Anions to Form an Ultramicroporous Material for Efficient C2H2/CO2 and C2H2/C2H4 Separation.

A 44.610.8 topology hybrid ultramicroporous material (HUM), {[Cu1.5F(SiF6)(L)2.5]·G}n, (L = 4,4'-bisimidazolylbiphenyl, G = guest molecules), 1, formed by cross-linking interpenetrated 3D four-connected CdSO4-type nets with hexafluorosilicate anions is synthesized and evaluated in the context of gas sorption and separation herein. 1 is the first HUM functionalized with two different types of fluorinated sites (SiF6 2- and F- anions) lining along the pore surface. The optimal pore size (≈5 Å) combining mixed and high-density electronegative fluorinated sites enable 1 to preferentially adsorb C2H2 over CO2 and C2H4 by hydrogen bonding interactions with a high C2H2 isosteric heat of adsorption (Qst) of ≈42.3 kJ mol-1 at zero loading. The pronounced discriminatory sorption behaviors lead to excellent separation performance for C2H2/CO2 and C2H2/C2H4 that surpasses many well-known sorbents. Dynamic breakthrough experiments are conducted to confirm the practical separation capability of 1, which reveal an impressive separation factor of 6.1 for equimolar C2H2/CO2 mixture. Furthermore, molecular simulation and density functional theory (DFT) calculations validate the strong binding of C2H2 stems from the chelating fix of C2H2 between SiF6 2- anion and coordinated F- anion.

Cushioning mechanism of the metatarsals during landing for the skateboarding ollie maneuver.

Skateboarding is an Olympic event with frequent jumping and landing, where the cushioning effect by the foot structure (from the arch, metatarsals, etc.) and damping performance by sports equipment (shoes, insoles, etc.) can greatly affect an athlete's sports performance and lower the risk of limb injury. Skateboarding is characterized by the formation of a "man-shoe-skateboard system," which makes its foot cushioning mechanism different from those of other sports maneuvers, such as basketball vertical jump and gymnastics broad jump. Therefore, it is necessary to clarify the cushioning mechanism of the foot structure upon landing on a skateboard. To achieve this, a multibody finite element model of the right foot, shoe, and skateboard was created using Mimics, Geomagic, and ANSYS. Kinetic data from the ollie maneuver were used to determine the plantar pressure and Achilles tendon force at three characteristics (T1, T2, and T3). The stress and strain on the foot and metatarsals (MT1-5) were then simulated. The simulation results had an error of 6.98% compared to actual measurements. During landing, the force exerted on the internal soft tissues tends to increase. The stress and strain variations were highest on MT2, MT3, and MT4. Moreover, the torsion angle of MT1 was greater than those of the other metatarsals. Additionally, the displacements of MT2, MT3, and MT4 were higher than those of the other parts. This research shows that skateboarders need to absorb the ground reaction force through the movements of the MTs for ollie landing. The soft tissues, bones, and ligaments in the front foot may have high risks of injury. The developed model serves as a valuable tool for analyzing the foot mechanisms in skateboarding; furthermore, it is crucial to enhance cushioning for the front foot during the design of skateboard shoes to reduce potential injuries.

Multi-Relational Deep Hashing for Cross-Modal Search.

Deep cross-modal hashing retrieval has recently made significant progress. However, existing methods generally learn hash functions with pairwise or triplet supervisions, which involves learning the relevant information by splicing partial similarity between data pairs; notably, this approach only captures the data similarity locally and incompletely, resulting in sub-optimal retrieval performance. In this paper, we propose a novel Multi-Relational Deep Hashing (MRDH) approach, which can fully bridge the modality gap by comprehensively modeling the similarity relationship between data in different modalities. In more detail, to investigate the inter-modal relationships, we constrain the consistency of cross-modal pairwise similarities to maintain the semantic similarity across modalities. Moreover, to further capture complete similarity information, we design a new similarity metric, which we term cross-modal global similarity, by encouraging hash codes of similar data pairs from different modalities to approach a common center and hash codes for dissimilar pairs to converge to different centers. Adopting this approach enables our model to generate more discriminative hash codes. Extensive experiments on three benchmark datasets demonstrate the superiority of our method on cross-modal hashing retrieval.

Glutamate rescues heat stress-induced apoptosis of Sertoli cells by enhancing the activity of antioxidant enzymes and activating the Trx1-Akt pathway in vitro.

Heat stress reduces the number of Sertoli cells, which is closely related to an imbalanced redox status. Glutamate functions to maintain the equilibrium of redox homeostasis. However, the role of glutamate in heat treated Sertoli cells remains unclear. Herein, Sertoli cells from 3-week-old piglets were treated at 44 °C for 30 min (heat stress). Glutamate levels increased significantly following heat stress treatment, followed by a gradual decrease during recovery, while glutathione (GSH) showed a gradual increase. The addition of exogenous glutamate (700 µM) to Sertoli cells before heat stress significantly reduced the heat stress-induced apoptosis rate, mediated by enhanced levels of antioxidant substances (superoxide dismutase (SOD), total antioxidant capacity (TAC), and GSH) and reduced levels of oxidative substances (reactive oxygen species (ROS) and malondialdehyde (MDA)). Glutamate addition to Sertoli cells before heat stress upregulated the levels of glutamate-cysteine ligase, modifier subunit (Gclm), glutathione synthetase (Gss), thioredoxin (Trx1) and B-cell leukemia/lymphoma 2 (Bcl-2), and the ratio of phosphorylated Akt (protein kinase B)/total Akt. However, it decreased the levels of Bcl2-associated X protein (Bax) and cleaved-caspase 3. Addition of the inhibitor of glutaminase (Gls1), Bptes (Bis-2-(5-phenylacetamido-1,3,4-thiadiazol-2-yl)ethyl sulfide, 30 µM)to Sertoli cells before heat stress reversed these effects. These results inferred that glutamate rescued heat stress-induced apoptosis in Sertoli cells by enhancing activity of antioxidant enzymes and activating the Trx1-Akt pathway. Thus, glutamate supplementation might represent a novel strategy to alleviate the negative effect of heat stress.

Asynchronous Parallel Large-Scale Gaussian Process Regression.

Gaussian process regression (GPR) is an important nonparametric learning method in machine learning research with many real-world applications. It is well known that training large-scale GPR is a challenging task due to the required heavy computational cost and large volume memory. To address this challenging problem, in this article, we propose an asynchronous doubly stochastic gradient algorithm to handle the large-scale training of GPR. We formulate the GPR to a convex optimization problem, i.e., kernel ridge regression. After that, in order to efficiently solve this convex kernel problem, we first use the random feature mapping method to approximate the kernel model and then utilize two unbiased stochastic approximations, i.e., stochastic variance reduced gradient and stochastic coordinate descent, to update the solution asynchronously and in parallel. In this way, our algorithm scales well in both sample size and dimensionality, and speeds up the training computation. More importantly, we prove that our algorithm has a global linear convergence rate. Our experimental results on eight large-scale benchmark datasets with both regression and classification tasks show that the proposed algorithm outperforms the existing state-of-the-art GPR methods.

The role of TGF-ß signaling in muscle atrophy, sarcopenia and cancer cachexia.

Skeletal muscle, comprising a significant proportion (40 to 50 percent) of total body weight in humans, plays a critical role in maintaining normal physiological conditions. Muscle atrophy occurs when the rate of protein degradation exceeds protein synthesis. Sarcopenia refers to age-related muscle atrophy, while cachexia represents a more complex form of muscle wasting associated with various diseases such as cancer, heart failure, and AIDS. Recent research has highlighted the involvement of signaling pathways, including IGF1-Akt-mTOR, MuRF1-MAFbx, and FOXO, in regulating the delicate balance between muscle protein synthesis and breakdown. Myostatin, a member of the TGF-ß superfamily, negatively regulates muscle growth and promotes muscle atrophy by activating Smad2 and Smad3. It also interacts with other signaling pathways in cachexia and sarcopenia. Inhibition of myostatin has emerged as a promising therapeutic approach for sarcopenia and cachexia. Additionally, other TGF-ß family members, such as TGF-ß1, activin A, and GDF11, have been implicated in the regulation of skeletal muscle mass. Furthermore, myostatin cooperates with these family members to impair muscle differentiation and contribute to muscle loss. This review provides an overview of the significance of myostatin and other TGF-ß signaling pathway members in muscular dystrophy, sarcopenia, and cachexia. It also discusses potential novel therapeutic strategies targeting myostatin and TGF-ß signaling for the treatment of muscle atrophy.

A novel FK506-loading mesoporous silica nanoparticle homing to lymph nodes for transplant rejection treatment.

Tacrolimus (FK506) is an effective therapeutic for transplant rejection in clinical practice, primarily inhibiting rejection by suppressing the activation and proliferation of allogeneic T cells in the lymph nodes (LNs). However, conventional administration methods face challenges in directly delivering free FK506 to the LNs. In this study, we introduce a novel LN-targeted delivery system based on mesoporous silica nanoparticles (MSNs-FK506-MECA79). These particles were designed to selectively target high endothelial venules in LNs; this was achieved through surface modification with MECA79 antibodies. Their mean size and zeta potential were 201.18 ± 5.98 nm and - 16.12 ± 0.36 mV, respectively. Our findings showed that MSNs-FK506-MECA79 could accumulate in LNs and increase the local concentration of FK506 from 28.02 ± 7.71 ng/g to 123.81 ± 76.76 ng/g compared with the free FK506 treatment group. Subsequently, the therapeutic efficacy of MSNs-FK506-MECA79 was evaluated in a skin transplantation model. The treatment with MSNs-FK506-MECA79 could lead to a decrease in the infiltration of T cells in the grafts, a reduction in the grade of rejection, and a significant prolongation of survival. Consequently, this study presents a promising strategy for the active LN-targeted delivery of FK506 and improving the immunotherapeutic effects on transplant rejection.

[Multi-index evaluation of different parts of Amomum villosum].

To investigate the quality differences between the seeds and husks of Amomum villosum and explore the rationality of using the seeds without husks, this study determined the content of protocatechuic acid, vanillic acid, epicatechin, quercitrin, volatile oil, water extract, and ethanol extract. The 2,2-diphenyl-1-picrylhydrazyl(DPPH), 2,2-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid)(ABTS), and hydroxyl radical scavenging activities were determined to evaluate the antioxidant activities of seeds and husks. The quality differences between the seeds and husks were assessed through orthogonal partial least squares-discriminant analysis(OPLS-DA) and analytic hierarchy process(AHP) combined with the entropy weight method(EWM). Significant differences(P<0.05) were observed in all 10 indicators between the seeds and husks. The levels of epicatechin, quercetin, and volatile oil were higher in the seeds, whereas those of protocatechuic acid, vanillic acid, water extract, and ethanol extract were higher in the husks. The seeds showed stronger scavenging ability against DPPH and ABTS radicals, while the husks showed a stronger scavenging effect on hydroxyl radicals. OPLS-DA significantly discriminated between the seeds and husks. Furthermore, volatile oil, water extract, DPPH radical scavenging rate, quercitrin, ABTS radical scavenging rate, hydroxyl radical scavenging rate, and vanillic acid were selected as the main differential indicators by variable importance in projection(VIP). Comprehensive scores calculated by AHP combined with EWM indicated that the seeds were superior to husks in terms of overall quality. However, there are still some dominant components and a certain antioxidant effect in the husks. Therefore, it is suggested to using Amomi Fructus with a certain amount of husks or utilizing the husks for other purposes.