The SGLT2 inhibitor empagliflozin inhibits skeletal muscle fibrosis in naturally aging male mice through the AMPKα/MMP9/TGF-ß1/Smad pathway.

ABSTACT: With advancing age, the incidence of sarcopenia increases, eventually leading to a cascade of adverse events. However, there is currently a lack of effective pharmacological treatment for sarcopenia. Sodium-glucose co-transporter 2 inhibitor (SGLT2i) empagliflozin demonstrates anti-fibrotic capabilities in various organs. This study aims to determine whether empagliflozin can improve skeletal muscle fibrosis induced by sarcopenia in naturally aging mice. A natural aging model was established by feeding male mice from 13 months of age to 19 months of age. A fibrosis model was created by stimulating skeletal muscle fibroblasts with TGF-ß1. The Forelimb grip strength test assessed skeletal muscle function, and expression levels of COL1A1, COL3A1, and α-SMA were analyzed by western blot, qPCR, and immunohistochemistry. Additionally, levels of AMPKα/MMP9/TGFß1/Smad signaling pathways were examined. In naturally aging mice, skeletal muscle function declines, expression of muscle fibrosis markers increases, AMPKα expression is downregulated, and MMP9/TGFß1/Smad signaling pathways are upregulated. However, treatment with empagliflozin reverses this phenomenon. At the cellular level, empagliflozin exhibits similar anti-fibrotic effects, and these effects are attenuated by Compound C and siAMPKα. Empagliflozin exhibits anti-fibrotic effects, possibly associated with the AMPK/MMP9/TGFß1/Smad signaling pathways.

Predictive power of deep-learning segmentation based prognostication model in non-small cell lung cancer.

Purpose: The study aims to create a model to predict survival outcomes for non-small cell lung cancer (NSCLC) after treatment with stereotactic body radiotherapy (SBRT) using deep-learning segmentation based prognostication (DESEP). Methods: The DESEP model was trained using imaging from 108 patients with NSCLC with various clinical stages and treatment histories. The model generated predictions based on unsupervised features learned by a deep-segmentation network from computed tomography imaging to categorize patients into high and low risk groups for overall survival (DESEP-predicted-OS), disease specific survival (DESEP-predicted-DSS), and local progression free survival (DESEP-predicted-LPFS). Serial assessments were also performed using auto-segmentation based volumetric RECISTv1.1 and computer-based unidimensional RECISTv1.1 patients was performed. Results: There was a concordance between the DESEP-predicted-LPFS risk category and manually calculated RECISTv1.1 (φ=0.544, p=0.001). Neither the auto-segmentation based volumetric RECISTv1.1 nor the computer-based unidimensional RECISTv1.1 correlated with manual RECISTv1.1 (p=0.081 and p=0.144, respectively). While manual RECISTv1.1 correlated with LPFS (HR=6.97,3.51-13.85, c=0.70, p<0.001), it could not provide insight regarding DSS (p=0.942) or OS (p=0.662). In contrast, the DESEP-predicted methods were predictive of LPFS (HR=3.58, 1.66-7.18, c=0.60, p<0.001), OS (HR=6.31, 3.65-10.93, c=0.71, p<0.001) and DSS (HR=9.25, 4.50-19.02, c=0.69, p<0.001). The promising results of the DESEP model were reproduced for the independent, external datasets of Stanford University, classifying survival and 'dead' group in their Kaplan-Meyer curves (p = 0.019). Conclusion: Deep-learning segmentation based prognostication can predict LPFS as well as OS, and DSS after SBRT for NSCLC. It can be used in conjunction with current standard of care, manual RECISTv1.1 to provide additional insights regarding DSS and OS in NSCLC patients receiving SBRT. Summary: While current standard of care, manual RECISTv1.1 correlated with local progression free survival (LPFS) (HR=6.97,3.51-13.85, c=0.70, p<0.001), it could not provide insight regarding disease specific survival (DSS) (p=0.942) or overall survival (OS) (p=0.662). In contrast, the deep-learning segmentation based prognostication (DESEP)-predicted methods were predictive of LPFS (HR=3.58, 1.66-7.18, c=0.60, p<0.001), OS (HR=6.31, 3.65-10.93, c=0.71, p<0.001) and DSS (HR=9.25, 4.50-19.02, c=0.69, p<0.001). DESEP can be used in conjunction with current standard of care, manual RECISTv1.1 to provide additional insights regarding DSS and OS in NSCLC patients.

Rational design of a cobalt sulfide nanoparticle-embedded flexible carbon nanofiber membrane electrocatalyst for advanced lithium-sulfur batteries.

Both the sluggish redox kinetics and severe polysulfide shuttling behavior hinders the commercialization of lithium-sulfur (Li-S) battery. To solve these obstacles, we design a cobalt sulfide nanoparticle-embedded flexible carbon nanofiber membrane (denoted as CoS2@NCF) as sulfiphilic functional interlayer materials. The hierarchically porous structure of carbon nanofiber is conducive to immobilizing sulfur species and facilitating lithium-ion penetration. Moreover, electrocatalytic CoS2nanoparticles can significantly enhance the catalytic effect, achieving favorable adsorption-diffusion-conversion interface of polysulfide. Combined with these synergistic features, the assembled Li-S cell with CoS2@NCF interlayer exhibited a great discharge capacity of 950.9 mAh g-1with prolonged cycle lifespan at 1 C (maintained 648.1 mAh g-1over 500 cycles). This multifunctional interlayer material used in this contribution provides an advanced route for developing high-energy-density Li-S battery.

Deep segmentation networks predict survival of non-small cell lung cancer.

Non-small-cell lung cancer (NSCLC) represents approximately 80-85% of lung cancer diagnoses and is the leading cause of cancer-related death worldwide. Recent studies indicate that image-based radiomics features from positron emission tomography/computed tomography (PET/CT) images have predictive power for NSCLC outcomes. To this end, easily calculated functional features such as the maximum and the mean of standard uptake value (SUV) and total lesion glycolysis (TLG) are most commonly used for NSCLC prognostication, but their prognostic value remains controversial. Meanwhile, convolutional neural networks (CNN) are rapidly emerging as a new method for cancer image analysis, with significantly enhanced predictive power compared to hand-crafted radiomics features. Here we show that CNNs trained to perform the tumor segmentation task, with no other information than physician contours, identify a rich set of survival-related image features with remarkable prognostic value. In a retrospective study on pre-treatment PET-CT images of 96 NSCLC patients before stereotactic-body radiotherapy (SBRT), we found that the CNN segmentation algorithm (U-Net) trained for tumor segmentation in PET and CT images, contained features having strong correlation with 2- and 5-year overall and disease-specific survivals. The U-Net algorithm has not seen any other clinical information (e.g. survival, age, smoking history, etc.) than the images and the corresponding tumor contours provided by physicians. In addition, we observed the same trend by validating the U-Net features against an extramural data set provided by Stanford Cancer Institute. Furthermore, through visualization of the U-Net, we also found convincing evidence that the regions of metastasis and recurrence appear to match with the regions where the U-Net features identified patterns that predicted higher likelihoods of death. We anticipate our findings will be a starting point for more sophisticated non-intrusive patient specific cancer prognosis determination. For example, the deep learned PET/CT features can not only predict survival but also visualize high-risk regions within or adjacent to the primary tumor and hence potentially impact therapeutic outcomes by optimal selection of therapeutic strategy or first-line therapy adjustment.

Ternary Sulfur/Polyacrylonitrile/SiO2 Composite Cathodes for High-Performance Sulfur/Lithium Ion Full Batteries.

In the present study, a novel sulfur/lithium-ion full battery was assembled while using ternary sulfur/polyacrylonitrile/SiO2 (S/PAN/SiO2) composite as the cathode and prelithiated graphite as the anode. For anode, Stabilized Lithium Metal Powder (SLMP) was successfully transformed into lithiated graphite anode. For cathode, scanning electron microscopy (SEM) and transmission electron microscopy (TEM) revealed that SiO2 was uniformly distributed on S/PAN composites, where SiO2 served as an effective additive due to its ultra high absorb ability and enhanced ability in trapping soluble polysulfide. The tested half-cell based on S/PAN/SiO2 composite revealed high discharge capacity of 1106 mAh g-1 after 100 cycles at 0.2 C. The full cell based on prelithiated graphite//S/PAN/SiO2 composite system delivered a specific capacity of 810 mAh g-1 over 100 cycles.

Biomass Derived Nitrogen-Doped Highly Porous Carbon Material with a Hierarchical Porous Structure for High-Performance Lithium/Sulfur Batteries.

A novel nitrogen doped mesoporous carbon (NMPC) with a hierarchical porous structure is prepared by simple carbonizing the green algae, which is applied as a host material to encapsulate sulfur for lithium/sulfur (Li/S) battery. The NMPC exhibits high pore volume as well as large specific surface area, and thus sulfur content in the S/NMPC composite reaches up to 63 wt %. When tested in a Li/S battery, the S/NMPC composite yields a high initial capacity of 1327 mAh·g-1 as well as 757 mAh·g-1 after 100 cycles at a current rate of 0.1 C, a reversible capacity of 642 was achieved even at 1 C. This good electrochemical performance of the S/NMPC composite could be attributed to a unique combination of mesopority and surface chemistry, allowing for the retention of the intermediate polysuflides within the carbon framework.