Modified Technique of Pancreaticogastrostomy for Soft Pancreas with Two Continuous Hemstitch Sutures: A Single-Center Prospective Study

Postoperative pancreatic fistula (POPF) remains a persistent problem after pancreaticoduodenectomy (PD), especially in the presence of a soft, nonfibrotic pancreas. To reduce the risk of POPF, pancreaticogastrostomy (PG) is an optional reconstruction technique for surgeons after PD. This study presents a new technique of PG for a soft, nonfibrotic pancreas with double-binding continuous hemstitch sutures and evaluates its safety and reliability. From January 2011 to June 2012, 92 cases of patients with periampullary malignancy with a soft pancreas underwent this technique. A modified technique of PG was performed with two continuous hemstitch sutures placed in the mucosal and seromuscular layers of the posterior gastric wall, respectively. Then the morbidity and mortality was calculated. This technique was applied in 92 patients after PD all with soft pancreas. The median time for the anastomosis was 12 min (range, 8–24). Operative mortality was zero, and morbidity was 16.3 % (n = 15), including hemorrhage (n = 2), biliary fistula (n = 2), pulmonary infection (n = 1), delayed gastric emptying (DGE; n = 5, 5.4 %), abdominal abscess (n = 3, one caused by PF), and POPF (n = 2, 2.2 %). Two patients developed a pancreatic fistula (one type A and one type B) classified according to the International Study Group on Pancreatic Fistula. The described technique is a simple and safe reconstruction procedure after PD, especially for patients with a soft and fragile pancreas. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11605-013-2183-8) contains supplementary material, which is available to authorized users

An E3 ubiquitin-proteasome gene signature for predicting prognosis in patients with pancreatic cancer

Pancreatic cancer is the seventh leading cause of cancer death worldwide, which is demonstrated with remarkable resistance to radiotherapy and chemotherapy. The identification of prognosis signature and novel prognostic markers will facilitate patient stratification and an individualized precision therapy strategy. In this study, TCGA-PAAD was used to screen prognostic E3 ubiquitin ligases and establish prognostic signatures, and GEO database was used to verify the accuracy of prognostic signatures. Functional analysis, in vitro experiments and clinical cohort studies were used to analyze the function and prognostic efficacy of the target gene. An E3 ligase-based signature of 9 genes and the nomogram were developed, and the signature was proved to accurately predict the prognosis of patients with pancreatic cancer. WDR37 might be the most prognostic E3 ubiquitin ligase in pancreatic cancer, and the clinical cohort analyses suggested a tumor‐suppressive role. The results of functional analysis and in vitro experiments indicated that WDR37 may promote the degradation of TCP1 complex to inhibit tumor and improve immune cell infiltration. The E3 ligase-based signature accurately predicted the prognosis of patients with pancreatic cancer, so it can be used as a decision-making tool to guide the treatment of patients with pancreatic cancer. At the same time, WDR37, the main gene in E3PMP signature, can be used as the most prognostic E3 ubiquitin ligase in the treatment of pancreatic cancer

A Metagenomic Approach to Characterization of the Vaginal Microbiome Signature in Pregnancy

While current major national research efforts (i.e., the NIH Human Microbiome Project) will enable comprehensive metagenomic characterization of the adult human microbiota, how and when these diverse microbial communities take up residence in the host and during reproductive life are unexplored at a population level. Because microbial abundance and diversity might differ in pregnancy, we sought to generate comparative metagenomic signatures across gestational age strata. DNA was isolated from the vagina (introitus, posterior fornix, midvagina) and the V5V3 region of bacterial 16S rRNA genes were sequenced (454FLX Titanium platform). Sixty-eight samples from 24 healthy gravidae (18 to 40 confirmed weeks) were compared with 301 non-pregnant controls (60 subjects). Generated sequence data were quality filtered, taxonomically binned, normalized, and organized by phylogeny and into operational taxonomic units (OTU); principal coordinates analysis (PCoA) of the resultant beta diversity measures were used for visualization and analysis in association with sample clinical metadata. Altogether, 1.4 gigabytes of data containing >2.5 million reads (averaging 6,837 sequences/sample of 493 nt in length) were generated for computational analyses. Although gravidae were not excluded by virtue of a posterior fornix pH >4.5 at the time of screening, unique vaginal microbiome signature encompassing several specific OTUs and higher-level clades was nevertheless observed and confirmed using a combination of phylogenetic, non-phylogenetic, supervised, and unsupervised approaches. Both overall diversity and richness were reduced in pregnancy, with dominance of Lactobacillus species (L. iners crispatus, jensenii and johnsonii, and the orders Lactobacillales (and Lactobacillaceae family), Clostridiales, Bacteroidales, and Actinomycetales. This intergroup comparison using rigorous standardized sampling protocols and analytical methodologies provides robust initial evidence that the vaginal microbial 16S rRNA gene catalogue uniquely differs in pregnancy, with variance of taxa across vaginal subsite and gestational age

Achieving Higher Resolution Lake Area from Remote Sensing Images Through an Unsupervised Deep Learning Super-Resolution Method

Lakes have been identified as an important indicator of climate change and a finer lake area can better reflect the changes. In this paper, we propose an effective unsupervised deep gradient network (UDGN) to generate a higher resolution lake area from remote sensing images. By exploiting the power of deep learning, UDGN models the internal recurrence of information inside the single image and its corresponding gradient map to generate images with higher spatial resolution. The gradient map is derived from the input image to provide important geographical information. Since the training samples are only extracted from the input image, UDGN can adapt to different settings per image. Based on the superior adaptability of the UDGN model, two strategies are proposed for super-resolution (SR) mapping of lakes from multispectral remote sensing images. Finally, Landsat 8 and MODIS (moderate-resolution imaging spectroradiometer) images from two study areas on the Tibetan Plateau in China were used to evaluate the performance of UDGN. Compared with four unsupervised SR methods, UDGN obtained the best SR results as well as lake extraction results in terms of both quantitative and visual aspects. The experiments prove that our approach provides a promising way to break through the limitations of median-low resolution remote sensing images in lake change monitoring, and ultimately support finer lake applications

Meta-analysis on resected pancreatic cancer: a comparison between adjuvant treatments and gemcitabine alone

Abstract Background Pancreatic cancer is a highly malignant tumor with a poor prognosis. Chemotherapy such as gemcitabine is still an important treatment. Gemcitabine (Gem) may prolong survival time and delay the development of recurrent disease after complete resection of pancreatic cancer. Currently, some control studies have been performed between certain drugs and gemcitabine monotherapy after pancreatic cancer surgery, but the outcomes were uncertain. Here, we implemented meta-analysis to compare the efficacy between adjuvant treatments and gemcitabine monotherapy in patients with resected pancreatic cancer. Methods PubMed, Embase and the Central Registry of Controlled Trials of the Cochrane Library searches were undertaken to identify randomized controlled trials (RCTs). Date of search ranged from January 1997 to December 2017. The meta-analysis included six RCTs. The major endpoints involved overall survival (OS), disease-free survival/progress free survival/relapse-free survival (DFS/PFS/RFS) and grade 3–4 toxicity. Results Pooled meta-analytic estimates were derived using random-effects model. Subgroup analysis used fixed-effects model. The outcome showed that there was no difference in OS (hazard ratio (HR), 0.87; 95% CI, 0.70–1.07; P = 0.19) and DFS (HR, 0.85; 95% CI, 0.71–1.02; P = 0.08) between the adjuvant treatments group (fluorouracil+folinic acid, S-1, gemcitabine+capecitabine, gemcitabine+erlotinib and gemcitabine+uracil/tegafur) and Gem monotherapy group. However, the subgroup analysis showed that only S-1 chemotherapy, which is an oral fluoropyrimidine agent containing tegafur, gimeracil and oteracil, was significant in OS (HR, 0.59; 95% CI, 0.46–0.74; P < 0.0001) and DFS (HR, 0.63; 95% CI, 0.52–0.75; P < 0.00001) compared with Gem alone. Toxicity analysis showed there was an increased incidence of grade 3/4 diarrhea (risk ratio (RR), 5.11; 95%CI, 3.24–8.05; P < 0.00001) and decreased incidence of grade 3/4 leucopenia (RR, 0.55; 95%CI, 0.31–0.98; P = 0.04), thrombocytopenia (RR, 0.61; 95%CI, 0.39–0.97; P = 0.04) in adjuvant treatments group. Neutropenia (RR, 0.69; 95%CI, 0.36–1.29; P = 0.24) and fatigue (RR, 1.29; 95%CI, 0.95–1.77; P = 0.11) for patients between the two groups were not significantly different. Conclusions In our meta-analysis, a significant survival benefit is only observed in the S-1 regimen, but the results are yet to be determined. Optimal cytotoxicity or targeted drug regimens need further validation in clinical trials in the future

Remote Sensing Single-Image Resolution Improvement Using A Deep Gradient-Aware Network with Image-Specific Enhancement

International audienceSuper-resolution (SR) is able to improve the spatial resolution of remote sensing images, which is critical for many practical applications such as fine urban monitoring. In this paper, a new single-image SR method, deep gradient-aware network with image-specific enhancement (DGANet-ISE) was proposed to improve the spatial resolution of remote sensing images. First, DGANet was proposed to model the complex relationship between low-and high-resolution images. A new gradient-aware loss was designed in the training phase to preserve more gradient details in super-resolved remote sensing images. Then, the ISE approach was proposed in the testing phase to further improve the SR performance. By using the specific features of each test image, ISE can further boost the generalization capability and adaptability of our method on inexperienced datasets. Finally, three datasets were used to verify the effectiveness of our method. The results indicate that DGANet-ISE outperforms the other 14 methods in the remote sensing image SR, and the cross-database test results demonstrate that our method exhibits satisfactory generalization performance in adapting to new data

Improved Light and In Vitro Digestive Stability of Lutein-Loaded Nanoparticles Based on Soy Protein Hydrolysates via Pepsin

In order to improve the water solubility and stability of lutein, soy protein isolates (SPI) and their hydrolysates via pepsin (PSPI) and alcalase (ASPI) were used as nanocarriers for lutein to fabricate the lutein-loaded nanoparticles (LNPS) of SPI, PSPI, and ASPI. The encapsulation properties, light, and in vitro digestive stability of lutein in nanoparticles, and protein–lutein interactions were investigated. Compared with SPI-LNPS and ASPI-LNPS, PSPI-LNPS was characterized by uniform morphology (approximately 115 nm) with a lower polydispersity index (approximately 0.11) and higher lutein loading capacity (17.96 μg/mg protein). In addition, PSPI-LNPS presented the higher lutein retention rate after light exposure (85.05%) and simulated digestion (77.73%) than the unencapsulated lutein and SPI-LNPS. Fluorescence spectroscopy revealed that PSPI had stronger hydrophobic interaction with lutein than SPI, which positively correlated with their beneficial effects on the light and digestive stability of lutein. This study demonstrated that PSPI possessed significant potential for lutein delivery

Using Machine Learning to Unravel the Value of Radiographic Features for the Classification of Bone Tumors

Objectives. To build and validate random forest (RF) models for the classification of bone tumors based on the conventional radiographic features of the lesion and patients’ clinical characteristics, and identify the most essential features for the classification of bone tumors. Materials and Methods. In this retrospective study, 796 patients (benign bone tumors: 412 cases, malignant bone tumors: 215 cases, intermediate bone tumors: 169 cases) with pathologically confirmed bone tumors from Nanfang Hospital of Southern Medical University, Foshan Hospital of TCM, and University of Hong Kong-Shenzhen Hospital were enrolled. RF models were built to classify tumors as benign, malignant, or intermediate based on conventional radiographic features and potentially relevant clinical characteristics extracted by three musculoskeletal radiologists with ten years of experience. SHapley Additive exPlanations (SHAP) was used to identify the most essential features for the classification of bone tumors. The diagnostic performance of the RF models was quantified using receiver operating characteristic (ROC) curves. Results. The features extracted by the three radiologists had a satisfactory agreement and the minimum intraclass correlation coefficient (ICC) was 0.761 (CI: 0.686-0.824, P<.001). The binary and tertiary models were built to classify tumors as benign, malignant, or intermediate based on the imaging and clinical features from 627 and 796 patients. The AUC of the binary (19 variables) and tertiary (22 variables) models were 0.97 and 0.94, respectively. The accuracy of binary and tertiary models were 94.71% and 82.77%, respectively. In descending order, the most important features influencing classification in the binary model were margin, cortex involvement, and the pattern of bone destruction, and the most important features in the tertiary model were margin, high-density components, and cortex involvement. Conclusions. This study developed interpretable models to classify bone tumors with great performance. These should allow radiographers to identify imaging features that are important for the classification of bone tumors in the clinical setting