Kelleyrafferty5514

cond procedures were required. The mean follow-up period was 8.4months (range 3-12 months). At the last follow-up evaluation all the patients reached an acceptable cosmetic result and, when limbs were affected, complete range of motion restoration.

The present case series provide promising evidence regarding the reliability and versatility of the chimeric conjoint flap technique for large and delicate defect reconstructions throughout the body.

The present case series provide promising evidence regarding the reliability and versatility of the chimeric conjoint flap technique for large and delicate defect reconstructions throughout the body.Actinobacteria is an ancient phylum of Gram-positive bacteria with a characteristic high GC content to their DNA. The ActinoBase Wiki is focused on the filamentous actinobacteria, such as Streptomyces species, and the techniques and growth conditions used to study them. These organisms are studied because of their complex developmental life cycles and diverse specialised metabolism which produces many of the antibiotics currently used in the clinic. ActinoBase is a community effort that provides valuable and freely accessible resources, including protocols and practical information about filamentous actinobacteria. It is aimed at enabling knowledge exchange between members of the international research community working with these fascinating bacteria. ActinoBase is an anchor platform that underpins worldwide efforts to understand the ecology, biology and metabolic potential of these organisms. There are two key differences that set ActinoBase apart from other Wiki-based platforms [1] ActinoBase is specifically aimed at researchers working on filamentous actinobacteria and is tailored to help users overcome challenges working with these bacteria and [2] it provides a freely accessible resource with global networking opportunities for researchers with a broad range of experience in this field.

Accurate and rapid measurement of the MRI volume of meningiomas is essential in clinical practice to determine the growth rate of the tumor. Imperfect automation and disappointing performance for small meningiomas of previous automated volumetric tools limit their use in routine clinical practice.

To develop and validate a computational model for fully automated meningioma segmentation and volume measurement on contrast-enhanced MRI scans using deep learning.

Retrospective.

A total of 659 intracranial meningioma patients (median age, 59.0 years; interquartile range 53.0-66.0 years) including 554 women and 105 men.

The 1.0 T, 1.5T, and 3.0 T; three-dimensional, T

-weighted gradient-echo imaging with contrast enhancement.

The tumors were manually segmented by two neurosurgeons, H.K. and C.-K.P., with 10 and 26 years of clinical experience, respectively, for use as the ground truth. Deep learning models based on U-Net and nnU-Net were trained using 459 subjects and tested for 100 patients from a single institution (internal validation set [IVS]) and 100 patients from other 24 institutions (external validation set [EVS]), respectively. The performance of each model was evaluated with the Sørensen-Dice similarity coefficient (DSC) compared with the ground truth.

According to the normality of the data distribution verified by the Shapiro-Wilk test, variables with three or more categories were compared by the Kruskal-Wallis test with Dunn's post hoc analysis.

A two-dimensional (2D) nnU-Net showed the highest median DSCs of 0.922 and 0.893 for the IVS and EVS, respectively. The nnU-Nets achieved superior performance in meningioma segmentation than the U-Nets. The DSCs of the 2D nnU-Net for small meningiomas less than 1cm

were 0.769 and 0.780 with the IVS and EVS, respectively.

A fully automated and accurate volumetric measurement tool for meningioma with clinically applicable performance for small meningioma using nnU-Net was developed.

3 TECHNICAL EFFICACY Stage 2.

3 TECHNICAL EFFICACY Stage 2.Halide perovskites, possessing unique electronic and photovoltaic properties, have been intensively investigated over the past decade. The excellent polarization, piezoelectricity, dielectricity and photoelectricity of halide perovskites provide new opportunities for the applications of mechanical energy harvesting. Although various studies have been conducted to develop halide perovskite-based triboelectric and piezoelectric nanogenerators, strategies for their electrical performance optimization are rarely mentioned. In this review, we systematically introduce the recent research progress of halide perovskite-based mechanical energy harvesters and summarize the different optimization strategies for improving both the piezoelectric and triboelectric output of the devices, bringing some inspiration to guide future material and structure design for halide perovskite-based energy devices. A summary of the current challenges and future perspectives is also presented, offering some possible directions for development in this emerging field.Two fluorescent chemosensors based on MOF-5 and curcumin (A-curcumin@MOF-5 and B-curcumin@MOF-5) were designed and synthesized. The two curcumin@MOF-5 materials retained the framework structure of MOF-5 and the fluorescence properties of curcumin. Importantly, both curcumin@MOF-5 materials in ethanol displayed highly selective fluorescence enhancement responses toward Al3+ with the special pore structure of MOF-5 as the main reason for the high selectivity. The fluorescence intensities of the two curcumin@MOF-5 materials showed a good linear relationship with Al3+ concentration with low detection limits of 3.10 μM for A-curcumin@MOF-5 and 2.84 μM for B-curcumin@MOF-5, respectively. The complexation of curcumin with Al3+ inhibited the photoinduced electron-transfer (PET) process and further enhanced the fluorescence of the curcumin molecules. Both the curcumin@MOF-5 materials can be used as fluorescent sensors to identify and detect Al3+ in water samples.As a kind of thermo-responsive hydrogel, amphiphilic block copolymers are widely investigated. However, the molecular mechanism of their structural change during the gelation process is still limited. Here, a well-controlled triblock copolymer poly(N,N-dimethylacrylamide)-b-poly(diacetone acrylamide)-b-poly(N,N-dimethylacrylamide) (PDMAA-b-PDAAM-b-PDMAA) was synthesized. Its optical microrheology results suggest a gelation temperature range from 42 to 50 °C, showing a transition from viscosity to elasticity. The morphological transition from spheres to worms occurs. Temperature-dependent IR spectra through two-dimensional correlation spectroscopy (2D-COS) and the Gaussian fitting technique were analyzed to obtain the transition information of the molecular structure within the triblock copolymer. The N-way principal component analysis (NPCA) on the temperature-dependent NIR spectra was performed to understand the molecular interaction between water and the copolymer. The intramolecular hydrogen bonds within the hydrophobic PDAAM block tend to dissociate with temperature, resulting in improved hydration and a relative volume increase of the PDAAM block. The dissociation of intermolecular hydrogen bonds within the PDAAM block was the driving force for the morphological transition. Moreover, the hydrophilic PDMAA block dehydrates with temperature, and three stages can be found. The dehydration rate of the second stage with temperature from 42 to 50 °C was obviously higher than those in the lower (first stage) and higher (third stage) temperature ranges.HIV/AIDS pandemic remains the world's most severe public health challenge, especially for HIV/AIDS immunological nonresponders (HIV/AIDS-INRs), who tend to have higher mortality. Due to the advantages in promoting patients' immune reconstitution, Traditional Chinese medicine (TCM) has become one of the mainstays of complementary treatments for HIV/AIDS-INRs. Given that effective TCM treatments largely depend on precise syndrome differentiation, there is an increasing interest in exploring biological evidence for the classification of TCM syndromes in HIV/AIDS-INRs. In our study, to identify the typical HIV/AIDS-INRs syndrome, an epidemiological survey was first conducted in the Liangshan prefecture (China), a high HIV/AIDS prevalence region. The key TCM syndrome, Yang deficiency of spleen and kidney (YDSK), was evaluated by using a tandem mass tag combined with liquid chromatography-tandem mass spectrometry (TMT-LC-MS/MS). A total of 62 differentially expressed proteins (DEPs) of YDSK syndrome compared with healthy people were screened out. Comparative bioinformatics analyses showed that DEPs in YDSK syndrome were mainly associated with response to wounding and acute inflammatory response in the biological process. The pathway annotation is mainly enriched in complement and coagulation cascades. Finally, the YDSK syndrome-specific DEPs such as HP and S100A9 were verified by ELISA, and confirmed as potential biomarkers for YDSK syndrome. Our study may lay the biological and scientific basis for the specificity of TCM syndromes in HIV/AIDs-INRs, and may provide more opportunities for the deep understanding of TCM syndromes and the developing more effective and stable TCM treatment for HIV/AIDS-INRs.

Disease diagnosis-oriented dialog system models the interactive consultation procedure as the Markov decision process, and reinforcement learning algorithms are used to solve the problem. Existing approaches usually employ a flat policy structure that treat all symptoms and diseases equally for action making. This strategy works well in a simple scenario when the action space is small; however, its efficiency will be challenged in the real environment. Inspired by the offline consultation process, we propose to integrate a hierarchical policy structure of two levels into the dialog system for policy learning. PQR309 nmr The high-level policy consists of a master model that is responsible for triggering a low-level model, the low-level policy consists of several symptom checkers and a disease classifier. The proposed policy structure is capable to deal with diagnosis problem including large number of diseases and symptoms.

Experimental results on three real-world datasets and a synthetic dataset demonstrate that our hierarchical framework achieves higher accuracy and symptom recall in disease diagnosis compared with existing systems. We construct a benchmark including datasets and implementation of existing algorithms to encourage follow-up researches.

The code and data are available from https//github.com/FudanDISC/DISCOpen-MedBox-DialoDiagnosis.

Supplementary data are available at Bioinformatics online.

Supplementary data are available at Bioinformatics online.Pancreatic ductal adenocarcinoma (PDAC) remains one of the most challenging cancers to treat. For patients with advanced and metastatic disease, chemotherapy has yielded only modest incremental benefits, which are not durable. Immunotherapy has revolutionized the treatment of other solid tumors by leading to cures where none existed only a decade ago, yet it has made few inroads with PDAC. A host of trials with promising preclinical data have failed, except for in a small minority of patients with selected biomarkers. There is, however, a glimmer of hope, which we seek to cultivate. In this review, we discuss recent advances in the understanding of the uniquely immunosuppressive tumor microenvironment (TME) in PDAC, learnings from completed trials of checkpoint inhibitors, TME modifiers, cellular and vaccine therapies, oncolytic viruses, and other novel approaches. We go on to discuss our expectations for improved preclinical models of immunotherapy in PDAC, new approaches to modifying the TME including the myeloid compartment, and emerging biomarkers to better select patients who may benefit from immunotherapy.