Effects of the different intensities of task-oriented training on the upper extremity motor function and self-care ability of stroke survivors: study protocol for a multiarm parallel-group randomised controlled trial in a tertiary hospital in China.

INTRODUCTION: Although task-oriented training (TOT) may provide added therapeutic value to stroke survivors, current TOT guidance offers insufficient advice on standardisation, particularly choices of intensity, for clinical practice. Therefore, this study aims to explore the effects of different intensities of TOT on motor function and self-care ability of stroke survivors. METHODS AND ANALYSIS: This randomised controlled clinical trial will recruit 72 participants from a tertiary hospital, diagnosed with decreased self-care ability and upper limb motor dysfunction post-stroke. All participants will be randomly assigned to one of three experimental groups or the control group to undergo intervention for 8 weeks. The participants in the control group (n=18) will receive traditional occupational therapy, while those in the experimental groups will receive different intensities of TOT (50%, 75% and 100% intensity for groups A, B and C, respectively). This protocol designed a more innovative and optimised TOT programme to explore the optimal intensity of TOT on stroke survivors. The primary outcome is Canadian Occupational Performance Measure, and the secondary outcomes include Modified Barthel Index, Wolf Motor Function Test and Motor Activity Log. ETHICS AND DISSEMINATION: The Second Affiliated Hospital of Hainan Medical University Ethics Committee approved the medical and ethical protocol for this study (LW2022015). All participants will sign an informed consent form. The results will be presented in scientific conferences and published in peer-reviewed journals. TRIAL REGISTRATION NUMBER: ChiCTR2200056390.

Fast and Adaptive Construction of Gyral Morphological Networks Based on Morphometric Features of Cortical Surface.

The organization of cortical folding patterns are related to brain function, cognition and behaviors. Due to the enormous complexity and high inter-subject variability in cortical morphology, it has been a challenging task to effectively and efficiently quantify the gyrification patterns of cerebral cortex. To tackle these issues, the gyral net approach used a graph-based representation of cortical architecture by segmenting the gyral crests from the cortical meshes based on its morphological metrics. However, current morphology-based approaches are very time-consuming and not applicable for large-scale dataset. In this study, we develop a fast and adaptive method to automatically construct the gyral morphological graph within 10 seconds. Our method is robust to low contrast conditions and more computationally efficient, approximately 5 times faster than classical approaches. We evaluated the proposed method on 1081 young adults acquired from the HCP dataset and uncovered significant differences among functional brain networks from the perspective of morphological networks.

MosaicNet: A deep-learning-based multi-tile biomedical image stitching method.

Multi-tile image stitching aims to merge multiple natural or biomedical images into a single mosaic. This is an essential step in whole-slide imaging and large-scale pathological imaging systems. To tackle this task, a multi-step framework is usually used by first estimating the optimal transformation for each image and then fusing them into a whole image. However, the traditional approaches are usually time-consuming and require manual adjustments. Advances in deep learning techniques provide an end-to-end solution to register and fuse information of multiple tile images. In this paper, we present a deep learning model for multi-tile biomedical image stitching, namely MosaicNet, consisting of an aligning network and a fusion network. We trained the MosaicNet network on a large simulation dataset based on the VOC2012 dataset and evaluated the model on multiple types of datasets, including simulated natural images, mouse brain T2-weighted Magnetic Resonance Imaging (T2w-MRI) data, and mouse brain polarization sensitive-optical coherence tomography (PS-OCT) data. Our method outperformed traditional approaches on both natural images and brain imaging data. The proposed method is robust to different settings of hyper-parameters and shows high computational efficiency, up to approximately 32 times faster than the conventional methods.

Artificial Intelligence-Assisted Ultrasound Diagnosis on Infant Developmental Dysplasia of the Hip Under Constrained Computational Resources.

OBJECTIVES: Ultrasound (US) is important for diagnosing infant developmental dysplasia of the hip (DDH). However, the accuracy of the diagnosis depends heavily on expertise. We aimed to develop a novel automatic system (DDHnet) for accurate, fast, and robust diagnosis of DDH. METHODS: An automatic system, DDHnet, was proposed to diagnose DDH by analyzing static ultrasound images. A five-fold cross-validation experiment was conducted using a dataset containing 881 patients to verify the performance of DDHnet. In addition, a blind test was conducted on 209 patients (158 normal and 51 abnormal cases). The feasibility and performance of DDHnet were investigated by embedding it into ultrasound machines at low computational cost. RESULTS: DDHnet obtained reliable measurements and accurate diagnosis predictions. It reported an intra-class correlation coefficient (ICC) on α angle of 0.96 (95% CI: 0.93-0.97), ß angle of 0.97 (95% CI: 0.95-0.98), FHC of 0.98 (95% CI: 0.96-0.99) and PFD of 0.94 (95% CI: 0.90-0.96) in abnormal cases. DDHnet achieved a sensitivity of 90.56%, specificity of 100%, accuracy of 98.64%, positive predictive value (PPV) of 100%, and negative predictive value (NPV) of 98.44% for the diagnosis of DDH. For the measurement task on the US device, DDHnet took only 1.1 seconds to operate and complete, whereas the experienced senior expert required an average 41.4 seconds. CONCLUSIONS: The proposed DDHnet demonstrate state-of-the-art performance for all four indicators of DDH diagnosis. Fast and highly accurate DDH diagnosis is achievable through DDHnet, and is accessible under constrained computational resources.

High-Intensity Focused Ultrasound for Patients With Cervical Intraepithelial Neoplasia 2/3: A Prospective One-Arm Study.

The aim of this study was to ascertain the safety of high-intensity focused ultrasound (HIFU) for high-grade cervical intraepithelial neoplasia grade 2/3 (CIN 2/3) in patients with fertility requirements. This was a prospective one-arm study. Consecutive CIN 2/3 patients diagnosed with histopathology were screened, enrolled and treated from September 2019 to September 2020 in the Affiliated Hospital of North Sichuan Medical College. All patients were treated with a combination of HIFU and antiviral treatment with REBACIN. The scheduled follow-up visits were 1 week, 1 mo, 3 mo, 6 mo and 12 mo after surgery. The primary outcomes included cure and human papillomavirus clearance rates. We screened 287 consecutive CIN 2/3 patients in our hospital, 29 of whom were enrolled and treated in this study. The cure rate reached 82.8% at 7 mo after treatment and 96.6% within 1 y. The HPV-negative rate reached 72.4% (21/29) around 6 mo after treatment, with mild side effects during and after the procedure. Our study suggests that in CIN 2/3 study participants with fertility requirements, HIFU + REBACIN therapy is a safe and effective therapeutic option with a high cure rate, HPV clearance and few side effects.

Effects of exogenous Fe addition on soil respiration rate and dissolved organic carbon structure in temperate forest swamps of northeastern China.

Fe as an important redox-active transition metal plays a key role in the carbon cycle of ecosystems. To date, the mechanisms by which Fe affects organic carbon (soil respiration rate [Rs] and dissolved organic carbon [DOC] structure) remain unclear, because most studies only focused on the effect of Fe on soil organic carbon content. To understand these effects, a 30-day laboratory incubation experiment was conducted using forest swamp soils from northeastern China amended with different concentrations of exogenous Fe (no exogenous Fe added [L0], add exogenous Fe at 1 time the soil background value [L1], add exogenous Fe at 2 times the soil background value [L2]). Our results showed that exogenous Fe addition reduced the soil respiration rate by 54.8% during the incubation time. The DOC concentration decreased by 40.5% with exogenous Fe addition during the incubation time. The dissolved organic matter (DOM) characteristic parameters showed apparent variations (p < 0.05), including significant increases in the fluorescence and biological index and significant decreases in the humification index, which indicate that exogenous Fe addition reduced humification, which may lead to the increased fixation of dissolved organic carbon. In addition significant increases in tryptophan-like DOM was observed when exogenous Fe addition resulted in a soil Fe concentration of twice the background value (p < 0.05). These findings indicate that exogenous Fe addition promotes the production of endogenous soil DOC by microorganisms. Overall, Our study uses three-dimensional fluorescence spectroscopy techniques combined with the parallel factor analysis (PARAFAC) to characterize the dissolved organic matter components in soil samples under exogenous Fe addition conditions, with a view to exploring the differences in the effects of Fe on the DOC concentration and structure of wetland soils, providing a theoretical basis for the mechanisms of soil carbon fixation and soil organic matter transformation in wetland soils.

CircRNA VPRBP inhibits tumorigenicity of cervical cancer via miR-93-5p/FRMD6 axis.

BACKGROUND: Cervical cancer is a malignant tumor that threatens the life and health of women. Circular RNA (circRNA) is a research hotspot in human diseases including cervical cancer. However, the research of circRNA viral protein R-binding protein (circ_VPRBP) in cervical cancer is blank. METHODS: Real-time quantitative polymerase chain reaction (RT-qPCR) was used to detect the expression of target genes in cervical cancer tissues and cells. The expression of related proteins was detected by western blot. The localization of circ_VPRBP was detected by nuclear cytoplasmic separation, and the stability of circ_VPRBP was verified by actinomycin D. After transfection with oligonucleotides and/or plasmids, cell proliferation, migration, invasion and apoptosis were detected by 3-(4, 5-dimethylthiazol-2-yl) -2, 5-diphenyl-2-H-tetrazolium bromide (MTT), colony formation, 5-ethynyl-2'-deoxyuridine (EdU), transwell, or flow cytometry assays. Mechanistically, the interaction between microRNA-93-5p (miR-93-5p) and circ_VPRBP/FERM domain containing 6 (FRMD6) was verified by dual luciferase reporter assay. Animal experiment was conducted to investigate the role of circ_VPRBP in vivo. RESULTS: Circ_VPRBP was down-regulated in cervical cancer tissues and cells, and overexpression of circ_VPRBP inhibited proliferation and promoted apoptosis of Caski and C33A cells. MiR-93-5p was a target of circ_VPRBP, and miR-93-5p mimic reversed the effect of circ_VPRBP on cell behavior. FRMD6 was a downstream target of miR-93-5p, and down-regulated FRMD6 reversed the cell viability, migration and invasion of cervical cancer cells inhibited by anti-miR-93-5p. Circ_VPRBP inhibited tumor growth by regulating miR-93-5p and FRMD6 in vivo. CONCLUSION: Circ_VPRBP inhibited cell proliferation, migration and invasion and promoted cell apoptosis of cervical cancer cells by regulating miR-93-5p/FRMD6 axis.

NAS-optimized topology-preserving transfer learning for differentiating cortical folding patterns.

Increasing evidence suggests that cortical folding patterns of human cerebral cortex manifest overt structural and functional differences. However, for interpretability, few studies leverage advanced techniques (e.g., deep learning) to investigate the difference among cortical folds, resulting in more differences yet to be extensively explored. To this end, we proposed an effective topology-preserving transfer learning framework to differentiate cortical fMRI time series extracted from cortical folds. Our framework consists of three main parts: (1) Neural architecture search (NAS), which is used to devise a well-performing network structure based on an initialized hand-designed super-graph in an image dataset; (2) Topology-preserving transfer, which takes the model searched by NAS as the source network, keeping the topological connectivity in the network unchanged, while transforming all 2D operations including convolution and pooling into 1D, therefore resulting in a topology-preserving network, named TPNAS-Net; (3) Classification and correlation analysis, which involves using the TPNAS-Net to classify 1D cortical fMRI time series for each individual brain, and performing a group difference analysis between autism spectrum disorder (ASD) and healthy control (HC) and correlation analysis with clinical information (i.e., age). Extensive experiments on two ASD datasets obtain consistent results, demonstrating that the TPNAS-Net not only discriminates cortical folding patterns at high classification accuracy, but also captures subtle differences between ASD and HC (p-value = 0.042). In addition, we discover that there is a positive correlation between the classification accuracy and age in ASD (r = 0.39, p-value = 0.04). These findings together suggest that structural and functional differences in cortical folding patterns between ASD and HC may provide a potentially useful biomarker for the diagnosis of ASD.

Joint Landmark and Structure Learning for Automatic Evaluation of Developmental Dysplasia of the Hip.

The ultrasound (US) screening of the infant hip is vital for the early diagnosis of developmental dysplasia of the hip (DDH). The US diagnosis of DDH refers to measuring alpha and beta angles that quantify hip joint development. These two angles are calculated from key anatomical landmarks and structures of the hip. However, this measurement process is not trivial for sonographers and usually requires a thorough understanding of complex anatomical structures. In this study, we propose a multi-task framework to learn the relationships among landmarks and structures jointly and automatically evaluate DDH. Our multi-task networks are equipped with three novel modules. Firstly, we adopt Mask R-CNN as the basic framework to detect and segment key anatomical structures and add one landmark detection branch to form a new multi-task framework. Secondly, we propose a novel shape similarity loss to refine the incomplete anatomical structure prediction robustly and accurately. Thirdly, we further incorporate the landmark-structure consistent prior to ensure the consistency of the bony rim estimated from the segmented structure and the detected landmark. In our experiments, 1231 US images of the infant hip from 632 patients are collected, of which 247 images from 126 patients are tested. The average errors in alpha and beta angles are 2.221 ° and 2.899 °. About 93% and 85% estimates of alpha and beta angles have errors less than 5 degrees, respectively. Experimental results demonstrate that the proposed method can accurately and robustly realize the automatic evaluation of DDH, showing great potential for clinical application.

WITHDRAWN: Analysis of Clinical Stage and Nerve Damage of Endometrial Carcinoma Diagnosed by Hysteroscopy of Artificial Intelligence Computer Information.

This article has been withdrawn at the request of the Editor-in-Chief. The Publisher apologizes for any inconvenience this may cause. The full Elsevier Policy on Article Withdrawal can be found at https://www.elsevier.com/about/our-business/policies/article-withdrawal.

CR-Unet: A Composite Network for Ovary and Follicle Segmentation in Ultrasound Images.

Transvaginal ultrasound (TVUS) is widely used in infertility treatment. The size and shape of the ovary and follicles must be measured manually for assessing their physiological status by sonographers. However, this process is extremely time-consuming and operator-dependent. In this study, we propose a novel composite network, namely CR-Unet, to simultaneously segment the ovary and follicles in TVUS. The CR-Unet incorporates the spatial recurrent neural network (RNN) into a plain U-Net. It can effectively learn multi-scale and long-range spatial contexts to combat the challenges of this task, such as the poor image quality, low contrast, boundary ambiguity, and complex anatomy shapes. We further adopt deep supervision strategy to make model training more effective and efficient. In addition, self-supervision is employed to iteratively refine the segmentation results. Experiments on 3204 TVUS images from 219 patients demonstrate the proposed method achieved the best segmentation performance compared to other state-of-the-art methods for both the ovary and follicles, with a Dice Similarity Coefficient (DSC) of 0.912 and 0.858, respectively.

A Generic Quality Control Framework for Fetal Ultrasound Cardiac Four-Chamber Planes.

Quality control/assessment of ultrasound (US) images is an essential step in clinical diagnosis. This process is usually done manually, suffering from some drawbacks, such as dependence on operator's experience and extensive labors, as well as high inter- and intra-observer variation. Automatic quality assessment of US images is therefore highly desirable. Fetal US cardiac four-chamber plane (CFP) is one of the most commonly used cardiac views, which was used in the diagnosis of heart anomalies in the early 1980s. In this paper, we propose a generic deep learning framework for automatic quality control of fetal US CFPs. The proposed framework consists of three networks: (1) a basic CNN (B-CNN), roughly classifying four-chamber views from the raw data; (2) a deeper CNN (D-CNN), determining the gain and zoom of the target images in a multi-task learning manner; and (3) the aggregated residual visual block net (ARVBNet), detecting the key anatomical structures on a plane. Based on the output of the three networks, overall quantitative score of each CFP is obtained, so as to achieve fully automatic quality control. Experiments on a fetal US dataset demonstrated our proposed method achieved a highest mean average precision (mAP) of 93.52% at a fast speed of 101 frames per second (FPS). In order to demonstrate the adaptability and generalization capacity, the proposed detection network (i.e., ARVBNet) has also been validated on the PASCAL VOC dataset, obtaining a highest mAP of 81.2% when input size is approximately 300 × 300.

Discriminating schizophrenia using recurrent neural network applied on time courses of multi-site FMRI data.

BACKGROUND: Current fMRI-based classification approaches mostly use functional connectivity or spatial maps as input, instead of exploring the dynamic time courses directly, which does not leverage the full temporal information. METHODS: Motivated by the ability of recurrent neural networks (RNN) in capturing dynamic information of time sequences, we propose a multi-scale RNN model, which enables classification between 558 schizophrenia and 542 healthy controls by using time courses of fMRI independent components (ICs) directly. To increase interpretability, we also propose a leave-one-IC-out looping strategy for estimating the top contributing ICs. FINDINGS: Accuracies of 83·2% and 80·2% were obtained respectively for the multi-site pooling and leave-one-site-out transfer classification. Subsequently, dorsal striatum and cerebellum components contribute the top two group-discriminative time courses, which is true even when adopting different brain atlases to extract time series. INTERPRETATION: This is the first attempt to apply a multi-scale RNN model directly on fMRI time courses for classification of mental disorders, and shows the potential for multi-scale RNN-based neuroimaging classifications. FUND: Natural Science Foundation of China, the Strategic Priority Research Program of the Chinese Academy of Sciences, National Institutes of Health Grants, National Science Foundation.

Linked 4-Way Multimodal Brain Differences in Schizophrenia in a Large Chinese Han Population.

Multimodal fusion has been regarded as a promising tool to discover covarying patterns of multiple imaging types impaired in brain diseases, such as schizophrenia (SZ). In this article, we aim to investigate the covarying abnormalities underlying SZ in a large Chinese Han population (307 SZs, 298 healthy controls [HCs]). Four types of magnetic resonance imaging (MRI) features, including regional homogeneity (ReHo) from resting-state functional MRI, gray matter volume (GM) from structural MRI, fractional anisotropy (FA) from diffusion MRI, and functional network connectivity (FNC) resulted from group independent component analysis, were jointly analyzed by a data-driven multivariate fusion method. Results suggest that a widely distributed network disruption appears in SZ patients, with synchronous changes in both functional and structural regions, especially the basal ganglia network, salience network (SAN), and the frontoparietal network. Such a multimodal coalteration was also replicated in another independent Chinese sample (40 SZs, 66 HCs). Our results on auditory verbal hallucination (AVH) also provide evidence for the hypothesis that prefrontal hypoactivation and temporal hyperactivation in SZ may lead to failure of executive control and inhibition, which is relevant to AVH. In addition, impaired working memory performance was found associated with GM reduction and FA decrease in SZ in prefrontal and superior temporal area, in both discovery and replication datasets. In summary, by leveraging multiple imaging and clinical information into one framework to observe brain in multiple views, we can integrate multiple inferences about SZ from large-scale population and offer unique perspectives regarding the missing links between the brain function and structure that may not be achieved by separate unimodal analyses.

A Schizophrenia-Related Genetic-Brain-Cognition Pathway Revealed in a Large Chinese Population.

BACKGROUND: In the past decades, substantial effort has been made to explore the genetic influence on brain structural/functional abnormalities in schizophrenia, as well as cognitive impairments. In this work, we aimed to extend previous studies to explore the internal mediation pathway among genetic factor, brain features and cognitive scores in a large Chinese dataset. METHODS: Gray matter (GM) volume, fractional amplitude of low-frequency fluctuations (fALFF), and 4522 schizophrenia-susceptible single nucleotide polymorphisms (SNP) from 905 Chinese subjects were jointly analyzed, to investigate the multimodal association. Based on the identified imaging-genetic pattern, correlations with cognition and mediation analysis were then conducted to reveal the potential mediation pathways. FINDINGS: One linked imaging-genetic pattern was identified to be group discriminative, which was also associated with working memory performance. Particularly, GM reduction in thalamus, putamen and bilateral temporal gyrus in schizophrenia was associated with fALFF decrease in medial prefrontal cortex, both were also associated with genetic factors enriched in neuron development, synapse organization and axon pathways, highlighting genes including CSMD1, CNTNAP2, DCC, GABBR2 etc. This linked pattern was also replicated in an independent cohort (166 subjects), which although showed certain age and clinical differences with the discovery cohort. A further mediation analysis suggested that GM alterations significantly mediated the association from SNP to fALFF, while fALFF mediated the association from SNP and GM to working memory performance. INTERPRETATION: This study has not only verified the impaired imaging-genetic association in schizophrenia, but also initially revealed a potential genetic-brain-cognition mediation pathway, indicating that polygenic risk factors could exert impact on phenotypic measures from brain structure to function, thus could further affect cognition in schizophrenia.

Suctioning Minimally Invasive Percutaneous Nephrolithotomy with a Patented System Is Effective to Treat Renal Staghorn Calculi: A Prospective Multicenter Study.

OBJECTIVES: To investigate the safety, efficacy, and practicability of minimally invasive percutaneous nephrolithotomy (MPCNL) with the aid of a patented irrigation clearance system in treating renal staghorn calculi. METHODS: From August 2009 to July 2014, 4 hospitals had executed a prospective multicenter study with a total of 912 cases. The patients were randomly divided into 3 groups: suctioning MPCNL, standard percutaneous nephrolithotomy (PCNL), and traditional MPCNL groups. Multiple operative and perioperative parameters were compared. RESULTS: Blood loss and intrapelvic pressure in the suctioning MPCNL group were significantly less than those in the standard PCNL group. The average operation time, intrapelvic pressure, and amount of bleeding in the suctioning MPCNL group were better than those in the traditional MPCNL group. The suctioning MPCNL used one tract more frequently and 2 or 3 tracts less frequently than the standard MPCNL and traditional MPCNL groups. The stone-free rate by one surgery in the suctioning MPCNL group was significantly higher than that in standard PCNL and traditional MPNCL groups. CONCLUSIONS: Suctioning MPCNL using our patented system shows several advantages in treating renal staghorn calculi, including minimal invasion, shorter operation time, lower intrapelvic pressure, less bleeding and the need for a smaller number of -percutaneous tracts, and higher stone clearance rate by one -surgery.

MicroRNA132 associated multimodal neuroimaging patterns in unmedicated major depressive disorder.

There is compelling evidence that epigenetic factors contribute to the manifestation of depression, in which microRNA132 (miR-132) is suggested to play a pivotal role in the pathogenesis and neuronal mechanisms underlying the symptoms of depression. Additionally, several depression-associated genes [MECP2, ARHGAP32 (p250GAP), CREB, and period genes] were experimentally validated as miR-132 targets. However, most studies regarding miR-132 in major depressive disorder are based on post-mortem, animal models or genetic comparisons. This work will be the first attempt to investigate how miR-132 dysregulation may impact covariation of multimodal brain imaging data in 81 unmedicated major depressive patients and 123 demographically-matched healthy controls, as well as in a medication-naïve subset of major depressive patients. MiR-132 values in blood (patients > controls) was used as a prior reference to guide fusion of three MRI features: fractional amplitude of low frequency fluctuations, grey matter volume, and fractional anisotropy. The multimodal components correlated with miR-132 also show significant group difference in loadings. Results indicate that (i) higher miR-132 levels in major depressive disorder are associated with both lower fractional amplitude of low frequency fluctuations and lower grey matter volume in fronto-limbic network; and (ii) the identified brain regions linked with increased miR-132 levels were also associated with poorer cognitive performance in attention and executive function. Using a data-driven, supervised-learning method, we determined that miR-132 dysregulation in major depressive disorder is associated with multi-facets of brain function and structure in fronto-limbic network (the key network for emotional regulation and memory), which deepens our understanding of how miR-132 dysregulation in major depressive disorders contribute to the loss of specific brain areas and is linked to relevant cognitive impairments.

In vitro anti-tumor effect of mGM-CSF-GnRH3 and mGM-CSF-GRP6 recombinant fusion protein and theirbioinformatics prediction / 中国肿瘤生物治疗杂志

@#Objective: To prepare the fusion protein mGM-CSF-GnRH3 (mGGn) of mouse granulocyte-macrophage colony stimulating factor (mGM-CSF) combining with gonadotropin releasing hormone (GnRH) and the fusion protein mGM-CSF-GRP6 (mG6) of mGM-CSF combining with gastrin-releasing peptide (GRP), and to investigate the inhibitory effect of the above two fusion proteins on B16F10 melanoma in vitro as well as to preliminarily predict their isoelectric point, relative molecular weight, hydrophobicity, stability, subcellular localization, signal peptide, spatial structure and potential epitopes. Methods:After the successful preparation of mGGn and mG6, the effects of different concentrations of fusion proteins on tumor cell morphology, migration, proliferation and cell cycle were detected by microscopic observation, scratch test, CCK-8 method and flow cytometry, respectively. The protein online analysis systems EXPASY, GOR4, SWISS MODEL were used to predict the basic properties and secondary/tertiary structure of recombinant fusion proteins. The B cell epitopes were predicted by IEDB and ABCpred software, the CTL epitopes were comprehensively predicted by SYFPEITHI, BlMAS and NetCTL software, and the Th epitopes were predicted by NetMHCIIpan 3.1 Server and IEDB software. Results:Both mGGn and mG6 inhibited the migration and proliferation of tumor cells. mGGn could block B16F10 cell cycle at G1 phase while mG6 could block B16F10 cell cycle at S phase, all of which prevented cells entering into G2 phase to inhibit tumor cell growth. The mGGn and mG6 fusion proteins got diverse structures and had multiple potential B epitopes, CTL epitopes and Th epitopes. Conclusion: mGGn and mG6 have inhibitory effect on B16F10 melanoma in vitro, and bioinformatics predictions have laid a foundation for further study of the biological functions and immunological activities of these fusion proteins.

A Randomized Study of Minimally Invasive Percutaneous Nephrolithotomy (MPCNL) with the aid of a patented suctioning sheath in the treatment of renal calculus complicated by pyonephrosis by one surgery.

BACKGROUND: Calculus pyonephrosis is difficult to manage. The aim of this study is to explore the value of a patented suctioning sheath assisted minimally invasive percutaneous nephrolithotomy (MPCNL) in the treatment of calculus pyonephrosis. METHODS: One hundred and eighty two patients with calculus pyonephrosis were randomizely divided into observation group (n = 91) and control group (n = 91). The control group was treated with MPCNL traditionally using peel-away sheath while the observation group was treated with MPCNL using the patented suctioning sheath. RESULTS: All the patients in the observation group underwent one stage surgical treatment, 14 patients in the control group underwent first-stage surgery with the rest of the group underwent one stage surgery. The complication rate was 12.1% in the observation group, significantly lower than the rate in the control group which was 51.6%; One surgery stone clearance in the observation group was 96.7% while it was 73.6% in the control group; operative time in the observation group was (54.5 ± 14.5) min, compared to (70.2 ± 11.7) min in the control group; the bleeding amount in the observation group was (126.4 ± 47.2) ml, compared to (321.6 ± 82.5) ml in the control group; the hospitalization duration for the observation group was (6.4 ± 2.3) days, compared to (10.6 ± 3.7) days in the control group. Comparison of the above indicators, the observation group was better than the control group with significant difference (p < 0.001 each). CONCLUSIONS: Minimally invasive percutaneous nephrolithotomy with the aid of the patented suctioning sheath in the treatment of calculus pyonephrosis in one surgery is economic, practical, and warrants clinical promotion. TRIAL REGISTRATION: This study was registered with Chinese Clinical Trial Registry on May 18, 2016 (retrospective registration) with a trial registration number of ChiCTR-IOR-16008490 .

A Novel Flexible Ureteroscopy with Intelligent Control of Renal Pelvic Pressure: An Initial Experience of 93 Cases.

BACKGROUND: Flexible ureteroscopic lithotripsy (URS) is rapidly becoming a first-line therapy for patients with renal and ureteral calculi. Most current medical infusion devices can only monitor infusion flow and pressure, but not renal pelvic pressure (RPP). PATIENTS AND METHODS: We designed a patented intelligent system to facilitate flexible URS that included an irrigation and suctioning platform and a ureteral access sheath (UAS) with a pressure-sensitive tip, enabling regulation of the infusion flow precisely and control of the vacuum suctioning by computerized real-time recording and monitoring of RPP. A stable RPP was ensured by pressure feedback technology. Ninety-three patients with renal or ureteral calculi participated in the study and received flexible URS. Gravel particles were sucked out automatically during the flexible URS. Patients were evaluated on postoperative days 1 and 30 by X-ray of kidneys, ureters, and bladder to assess stone-free status. RESULTS: In 81 of the 93 patients, only one surgery was needed to remove the stone. There were nine cases who failed the first surgery due to difficulty in placing the UAS, but flexible URS was performed in these patients after indwelling a Double-J stent to the ureter with the calculus for 2 weeks. Three cases were converted to percutaneous nephrolithotomy due to significant ureteral stenosis. For the 90 patients who underwent flexible URS, the actual RPP was controlled under 20 mmHg with clear operative visualization. The stone-free rates at postoperative days 1 and 30 were 90.0% (81/90) and 95.6% (86/90), respectively. Clavien I complications were noted in 13 cases, while Clavien II complications were noted in two cases. No major complications (Clavien III-V) were noted. CONCLUSIONS: Our patented system is technically feasible, safe, and efficient for treating upper urinary calculi. The advantages include breaking stones effectively and low complication rates because of its automatic control of RPP.