Mesenchymal stromal/stem cells (MSCs) possess immunoregulatory properties and their regulatory functions represent a potential therapy for acute lung injury (ALI). However, uncertainties remain with respect to defining MSCs-derived immunomodulatory pathways.Therefore, this study aimed to investigate the mechanism underlying the enhanced effect of human recombinant bone morphogenic protein-2 (rhBMP-2) primed ES-MSCs (MSC BMP2 ) in promoting Tregs in ALI mice. MSC were preconditioned with 100 ng/ml rhBMP-2 for 24 h, and then administrated to mice by intravenous injection after intratracheal injection of 1 mg/kg LPS. Treating MSCs with rhBMP-2 significantly increased cellular proliferation and migration, and cytokines array reveled that cytokines release by MSC BMP2 were associated with migration and growth. MSC BMP2 ameliorated LPS induced lung injury and reduced myeloperoxidase activity and permeability in mice exposed to LPS. Levels of inducible nitric oxide synthase were decreased while levels of total glutathione and superoxide dismutase activity were further increased via inhibition of phosphorylated STAT1 in ALI mice treated with MSC BMP2 . MSC BMP2 treatment increased the protein level of IDO1, indicating an increase in Treg cells, and Foxp3 + CD25 + Treg of CD4 + cells were further increased in ALI mice treated with MSC BMP2 . In co-culture assays with MSCs and RAW264.7 cells, the protein level of IDO1 was further induced in MSC BMP2 . Additionally, cytokine release of IL-10 was enhanced while both IL-6 and TNF-α were further inhibited. In conclusion, these findings suggest that MSC BMP2 has therapeutic potential to reduce massive inflammation of respiratory diseases by promoting Treg cells.

Background and Objectives: O-cyclic phytosphingosine-1-phosphate (cP1P) is a synthetic chemical and has a structure like sphingosine-1-phosphate (S1P). S1P is known to promote cell migration, invasion, proliferation, and anti-apoptosis through hippocampal signals. However, S1P mediated cellular-, molecular mechanism is still remained in the lung.Acute lung injury (ALI) and its severe form acute respiratory distress syndrome (ARDS) are characterized by excessive immune response, increased vascular permeability, alveolar-peritoneal barrier collapse, and edema. In this study, we determined whether cP1P primed human dermal derived mesenchymal stem cells (hdMSCs) ameliorate lung injury and its therapeutic pathway in ALI mice. Methods: and Results: cP1P treatment significantly stimulated MSC migration and invasion ability. In cytokine array, secretion of vascular-related factors was increased in cP1P primed hdMSCs (hdMSCcP1P ), and cP1P treatment induced inhibition of Lats while increased phosphorylation of Yap. We next determined whether hdMSCcP1P reduce inflammatory response in LPS exposed mice. hdMSCcP1P further decreased infiltration of macrophage and neutrophil, and release of TNF-α, IL-1β, and IL-6 were reduced rather than naïve hdMSC treatment. In addition, phosphorylation of STAT1 and expression of iNOS were significantly decreased in the lungs of MSCcP1P treated mice. Conclusions Taken together, these data suggest that cP1P treatment enhances hdMSC migration in regulation of Hippo signaling and MSCcP1P provide a therapeutic potential for ALI/ARDS treatment.

PURPOSE: We studied the feasibility and results of a tissue-engineered ileal conduit using a poly (epsilon-caprolactone) (PCL) nano-sheet seeded with muscle-derived stem cells to replace a conventional ileal conduit in rats. MATERIALS AND METHODS: Muscle-derived stem cells were isolated from the gastrocnemius muscle of female Sprague-Dawley rats (200-250 g, n=6) by use of a preplate technique and were cultured on a PCL nano-sheet. The PCL nano-sheet was implanted into the omentum of rats and was then made into a conical shaped conduit. Rats were sacrificed 4 and 8 weeks after implantation, and morphologic changes were assessed by H&E and immunofluorescence staining, including DAPI staining and staining for myogenin and myosin heavy chain (MyHC). RESULTS: All rats survived until the end of the experiment. A minimal inflammatory reaction was observed around the PCL nano-sheet in the 4 week specimens but was found to be reduced in the 8 week specimen. Muscle bundles were identified at week 4 as well as week 8 after implantation on H&E staining. Around the PCL sheet, immunostaining for both myogenin and MyHC were positive, indicating skeletal muscle differentiation and ingrowth into the PCL sheet. CONCLUSIONS: A PCL nano-sheet seeded with muscle-derived stem cells showed successful skeletal muscle differentiation at 4 and 8 weeks after implantation. This preliminary result supports the feasibility of a tissue-engineered ileal conduit using a PCL nano-sheet (seeded with muscle-derived stem cells) in place of conventional ileal conduits.
Animals ; Female ; Fluorescent Antibody Technique ; Humans ; Hypogonadism ; Indoles ; Mitochondrial Diseases ; Muscle, Skeletal ; Muscles ; Myogenin ; Myosin Heavy Chains ; Omentum ; Ophthalmoplegia ; Rats ; Rats, Sprague-Dawley ; Seeds ; Stem Cells ; Tissue Engineering ; Urinary Diversion

Objective: To evaluate the diagnostic performance of a deep learning algorithm for the automated detection of developmental dysplasia of the hip (DDH) on anteroposterior (AP) radiographs. Materials and Methods: Of 2601 hip AP radiographs, 5076 cropped unilateral hip joint images were used to construct a dataset that was further divided into training (80%), validation (10%), or test sets (10%). Three radiologists were asked to label the hip images as normal or DDH. To investigate the diagnostic performance of the deep learning algorithm, we calculated the receiver operating characteristics (ROC), precision-recall curve (PRC) plots, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) and compared them with the performance of radiologists with different levels of experience. Results: The area under the ROC plot generated by the deep learning algorithm and radiologists was 0.988 and 0.988–0.919, respectively. The area under the PRC plot generated by the deep learning algorithm and radiologists was 0.973 and 0.618– 0.958, respectively. The sensitivity, specificity, PPV, and NPV of the proposed deep learning algorithm were 98.0, 98.1, 84.5, and 99.8%, respectively. There was no significant difference in the diagnosis of DDH by the algorithm and the radiologist with experience in pediatric radiology (p = 0.180). However, the proposed model showed higher sensitivity, specificity, and PPV, compared to the radiologist without experience in pediatric radiology (p < 0.001). Conclusion The proposed deep learning algorithm provided an accurate diagnosis of DDH on hip radiographs, which was comparable to the diagnosis by an experienced radiologist.

Objective: To evaluate the diagnostic performance of a deep learning algorithm for the automated detection of developmental dysplasia of the hip (DDH) on anteroposterior (AP) radiographs. Materials and Methods: Of 2601 hip AP radiographs, 5076 cropped unilateral hip joint images were used to construct a dataset that was further divided into training (80%), validation (10%), or test sets (10%). Three radiologists were asked to label the hip images as normal or DDH. To investigate the diagnostic performance of the deep learning algorithm, we calculated the receiver operating characteristics (ROC), precision-recall curve (PRC) plots, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) and compared them with the performance of radiologists with different levels of experience. Results: The area under the ROC plot generated by the deep learning algorithm and radiologists was 0.988 and 0.988–0.919, respectively. The area under the PRC plot generated by the deep learning algorithm and radiologists was 0.973 and 0.618– 0.958, respectively. The sensitivity, specificity, PPV, and NPV of the proposed deep learning algorithm were 98.0, 98.1, 84.5, and 99.8%, respectively. There was no significant difference in the diagnosis of DDH by the algorithm and the radiologist with experience in pediatric radiology (p = 0.180). However, the proposed model showed higher sensitivity, specificity, and PPV, compared to the radiologist without experience in pediatric radiology (p < 0.001). Conclusion The proposed deep learning algorithm provided an accurate diagnosis of DDH on hip radiographs, which was comparable to the diagnosis by an experienced radiologist.

Abstract: Edible offal is easily contaminated by Escherichia coli (E. coli) and fluoroquinolone (FQ)-resistant E. coli is considered a serious public health problem, thus, this study investigated the genetic characteristics of FQ-resistant E. coli from edible offal. A total of 22 FQ-resistant E. coli isolates were tested. A double mutation in each gyrA and parC led the highest MIC. Four (18.2%) isolates carried plasmid-mediated quinolone resistance genes. The fimH, eaeA, escV, astA, and iucC genes were confirmed. Seventeen isolates (77.3%) were positive for plasmid replicons. The isolates showed high genetic heterogeneity based on pulsed-field gel electrophoresis patterns.