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Abstract Background Incremental peritoneal dialysis (IPD) refers to the use of less than standard full-dose peritoneal dialysis (SPD) in end-stage renal disease patients. While the use of IPD is being reported in the literature, its safety and efficacy vs. SPD is unclear. We hereby performed a systematic review of studies comparing mortality, peritonitis, technique survival, anuria-free survival and residual renal function (RRF) between IPD and SPD. Methods All comparative studies published on PubMed, Embase, CENTRAL, Scopus, and Web of Science databases from inception to 5th September 2023 and reporting on given outcomes were eligible. Results Ten studies were included. Definitions of IPD were heterogenous and hence mostly a qualitative synthesis was undertaken. Majority of studies found no difference in patient survival between IPD and SPD. Meta-analysis of crude mortality data also presented no significant difference. Peritonitis and technique survival were also not significantly different between IPD and SPD in the majority of studies. Data on RRF was conflicting. Some studies showed that IPD was associated with the preservation of RRF while others found no such difference. Conclusion IPD may be a safe alternative to SPD in incident dialysis patients. There seems to be no difference in patient survival, peritonitis, and technique survival between the two modalities. However, the impact of IPD on RRF is still questionable. Evidence is heterogeneous and conflicting to derive firm conclusions.

Abstract CT and MR tools are commonly used to diagnose lumbar fractures (LF). However, numerous limitations have been found in practice. The aims of this study were to innovate and develop a spinal disease-specific neural network and to evaluate whether synthetic MRI of the LF affected clinical diagnosis and treatment strategies. A total of 675 LF patients who met the inclusion and exclusion criteria were included in the study. For each participant, two mid-sagittal CT and T2-weighted MR images were selected; 1350 pairs of LF images were also included. A new Self-pix based on Pix2pix and Self-Attention was constructed. A total of 1350 pairs of CT and MR images, which were randomly divided into a training group (1147 pairs) and a test group (203 pairs), were fed into Pix2pix and Self-pix. The quantitative evaluation included PSNR and SSIM (PSNR1 and SSIM1: real MR images and Pix2pix-generated MR images; PSNR2 and SSIM2: real MR images and Self-pix-generated MR images). The qualitative evaluation, including accurate diagnosis of acute fractures and accurate selection of treatment strategies based on Self-pix-generated MRI, was performed by three spine surgeons. In the LF group, PSNR1 and PSNR2 were 10.884 and 11.021 (p

Abstract Background posterior pedicle screw fixation is common method, one of the most severe complications is iatrogenic vascular damage, no report investigated association of different introversion angles (INTAs) and length of pedicle screw. The aims were to investigate the optimal introversion angle and length of pedicle screw for improving the safety of the operation, and to analyze the differences of vascular damage types at L1-S1. Methods Lumbar CT imaging data from110 patients were analyzed by DICOM software, and all parameters were measured by new Cartesian coordinate system, INTAs (L1-L5:5°,10°,15°,S1: 0°, 5°,10°,15°), DO−AVC (the distance between the origin (O) with anterior vertebral cortex (AVC)), DAVC−PGVs (the distance between AVC and the prevertebral great vessels (PGVs)), DO−PGVs (the distance between the O and PGVs). At different INTAs, DAVC−PGVs were divided into four grades: Grade III: DAVC−PGVs ≤ 3 mm, Grade II: 3 mm 5 mm, and N: the not touching PGVs. Results The optimal INTA was 5° at L1-L3, the left was 5° and the right was 15° at L4, and screw length was less than 50 mm at L1-L4. At L5, the left optimal INTA was 5° and the right was 10°, and screw length was less than 45 mm. The optimal INTA was 15° at S1, and screw length was less than 50 mm. However, screw length was less than 40 mm when the INTA was 0° or 5° at S1. Conclusions At L5-S1, the risk of vascular injury is the highest. INTA and length of the pedicle screw in lumbar operation are closely related. 3 mm interval of screw length may be more preferable to reduce vascular damage.

Abstract The microstructure of the spinal cord in syringomyelia has not been well studied. The aim of this study was to evaluate the microstructure of the cervical cord in patients with syringomyelia using diffusion tensor imaging (DTI) and to investigate the association between DTI parameters and the size of the syrinx cavity. Thirty patients with syringomyelia and 11 age-matched controls were included in this study. DTI and T1/T2-weighted MRI were used to estimate spinal microstructure. The patients were divided into a clinical symptom group (group A) and a non-clinical symptom group (group B) according to ASIA assessments. The fractional anisotropy (FA) and apparent diffusion coefficient (ADC) values (mm2/s) were measured and compared between patients and controls. Correlation between FA/ADC and the size of the syrinx cavity was examined with a bivariate analysis. FA values were lower (P 0.05). FA values and ADC values at all cervical levels were not significantly different either in controls or outside of the syrinx (all P > 0.05). FA values of group A was significantly lower than those of group B (P

Purpose The influence of prior failed kidney transplants on outcomes of peritoneal dialysis (PD) is unclear. Thus, we conducted a systematic review and meta-analysis to compare the outcomes of patients initiating PD after a failed kidney transplant with those initiating PD without a prior history of kidney transplantation. Methods We searched PubMed, Embase, CENTRAL, and Google Scholar databases from inception until 25 November 2020. Our meta-analysis considered the absolute number of events of mortality, technical failures, and patients with peritonitis, and we also pooled multi-variable adjusted hazard ratios (HR). Results We included 12 retrospective studies. For absolute number of events, our analysis indicated no statistically significant difference in technique failure [RR, 1.14; 95% CI, 0.80–1.61; I2=52%; p = 0.48], number of patients with peritonitis [RR, 1.13; 95% CI, 0.97–1.32; I2=5%; p = 0.11] and mortality [RR, 1.00; 95% CI, 0.67–1.50; I2=63%; p = 0.99] between the study groups. The pooled analysis of adjusted HRs indicated no statistically significant difference in the risk of technique failure [HR, 1.25; 95% CI, 0.88–1.78; I2=79%; p = 0.22], peritonitis [HR, 1.04; 95% CI, 0.72–1.50; I2=76%; p = 0.85] and mortality [HR, 1.24; 95% CI, 0.77–2.00; I2=66%; p = 0.38] between the study groups. Conclusion Patients with kidney transplant failure initiating PD do not have an increased risk of mortality, technique failure, or peritonitis as compared to transplant-naïve patients initiating PD. Further studies are needed to evaluate the impact of prior and ongoing immunosuppression on PD outcomes.

Summary: Decreasing projection views to a lower X-ray radiation dose usually leads to severe streak artifacts. To improve image quality from sparse-view data, a multi-domain integrative Swin transformer network (MIST-net) was developed and is reported in this article. First, MIST-net incorporated lavish domain features from data, residual data, image, and residual image using flexible network architectures, where a residual data and residual image sub-network was considered as a data consistency module to eliminate interpolation and reconstruction errors. Second, a trainable edge enhancement filter was incorporated to detect and protect image edges. Third, a high-quality reconstruction Swin transformer (i.e., Recformer) was designed to capture image global features. The experimental results on numerical and real cardiac clinical datasets with 48 views demonstrated that our proposed MIST-net provided better image quality with more small features and sharp edges than other competitors. The bigger picture: Decreasing projection views to a lower X-ray radiation dose usually leads to severe streak artifacts. To improve reconstructed image quality from sparse-view data, we develop a multi-domain integrative Swin transformer (MIST) network in this study. The proposed MIST-net incorporates lavish domain features from data, residual data, image, and residual image using flexible network architectures, which help deeply mine the data and image features. To detect image features and protect image edges, the trainable edge enhancement filter is further incorporated to the network for improving encode-decode ability. A high-quality reconstruction transformer was designed to improve the ability of global feature extraction. Our results from both simulation and real cardiac data demonstrated the great potential of MIST.

Background. Ultrafiltration failure remains one of the most severe complications of long-term peritoneal dialysis (PD), which results in death. This study aimed to characterize the circulating exosomal microRNA (miRNA) profiles associated with ultrafiltration failure and explore its underlying mechanisms. Methods. Exosomes were isolated from the peritoneal dialysis effluent (PDE) of patients with ultrafiltration failure or success using the ultracentrifugation method, and then transmission electron microscopy (TEM), nanoparticle tracking analysis (NTA), and western blot were used for exosome characterization. After that, the isolated exosomes were sent for small RNA sequencing, and eight differentially expressed miRNAs (DE-miRNAs) were chosen for RT-qPCR validation. Results. TEM, NTA, and western blot revealed that exosomes were successfully isolated. After sequencing, 70 DE-miRNAs involved in ultrafiltration were identified, including 41 upregulated ones and 29 downregulated ones. Functional analyses revealed that these DE-miRNAs were significantly enriched in pathways of cancer, ubiquitin-mediated proteolysis, axon orientation, and the Rap1 and Ras signaling pathways. In addition, the consistency rate of RT-qPCR and sequencing results was 75%, which indicated the relatively high reliability of the sequencing data. Conclusions. Our findings implied that these DE-miRNAs may be potential biomarkers of ultrafiltration failure, which would help us to discover novel therapeutic targets/pathways for ultrafiltration failure in patients with end-stage renal disease.

Abstract Background To investigate the incidence of scoliosis and trunk shift in patients with LDH (lumbar disc herniation) and analyze the differences in spinopelvic alignment among patients with or without trunk shift and non-symptom controls. Materials and methods All included subjects had standard upright antero-posterior and lateral radiographs of the whole spine taken. Evidence of disc herniation was confirmed by computed tomography or magnetic resonance imaging. The parameters measured included trunk shift and Cobb angle, TK (thoracic kyphosis), TLK (thoraco-lumbar junction kyphosis), LL (lumbar lordosis), PI (pelvic incidence), SS (sacral slope), PT (pelvic tilt) and SVA (sagittal vertical axis). Results Sixty-eight patients with LDH and 61 controls were included. There were significantly more male patients with trunk shift than the patients without trunk shift. Forty-two patients had curve magnitudes ranging from 5 to 38°. The trunk shift ranged from 0.5 to 7.3 cm. A total of 54.76% of patients had a disc herniation on the concave side of the main curve. Fifty percent of patients showed a trunk shift towards the opposite side of disc herniation. There were significant differences in spinopelvic parameters among groups. Significant correlations were also observed between several spinopelvic parameters in the three groups. However, the degrees of correlations among the spinopelvic parameters differed among the three groups. Conclusion Spinal sagittal morphology in LDH patients with trunk shift exhibits a more anterior shift of the C7 plumb line, less LL, and a more horizontal sacrum. Correlation analysis indicated a disharmonious spinopelvic interaction and a change in the compensatory model in patients with LDH.

Malware classification plays an important role in tracing the attack sources of computer security. However, existing static analysis methods are fast in classification, but they are inefficient in some malware using packing and obfuscation techniques; the dynamic analysis methods have better universality for packing and obfuscation, but they will cause excessive classification cost. To overcome these shortcomings, in this paper, we propose a classification system Malscore based on the probability scoring and machine learning, which sets the probability threshold to concatenate static analysis (called Phase 1) and dynamic analysis (called Phase 2). The convolutional neural networks with spatial pyramid pooling were used to analyze the grayscale images (static features) in Phase 1, and the variable n-grams and machine learning were used to analyze the native API call sequences (dynamic features) in Phase 2. Malscore combined static analysis with dynamic analysis not only accelerated the static analysis process by taking advantage of the CNN in image recognition but also appeared to be more resilient to obfuscation by the dynamic analysis. Different from other static and dynamic analysis techniques, when malware is detected, due to the fact that malware will most likely be labeled only by static analysis, we could reduce the overheads by dynamically analyzing a few malware that has less obvious features or greater confusion in static analysis. We performed experiments on 174607 malware samples from 63 malware families. The result showed that Malscore achieved 98.82% accuracy for malware classification. Furthermore, Malscore was compared with the method of using static and dynamic analysis. The preprocessing and test time represented a reduction of 59.58% and 61.70%, respectively.

One of the advantages of spectral computed tomography (CT) is it can achieve accurate material components using the material decomposition methods. The image-based material decomposition is a common method to obtain specific material components, and it can be divided into two steps: image reconstruction and post material decomposition. To obtain accurate material maps, the image reconstruction method mainly focuses on improving image quality by incorporating regularization priors. Very recently, the regularization priors are introduced into the post material decomposition procedure in the iterative image-based methods. Since the regularization priors can be incorporated into image reconstruction and post image-domain material decomposition, the performance of regularization by combining these two cases is still an open problem. To realize this goal, the material accuracy from those steps are first analyzed and compared. Then, to further improve the accuracy of decomposition materials, a two-step regularization based method is developed by incorporating priors into image reconstruction and post material decomposition. Both numerical simulation and preclinical mouse experiments are performed to demonstrate the advantages of the two-step regularization based method in improving material accuracy.

Abstract To give full play to the high efficiency and parallelism of multi-processor systems, the fireworks algorithm (FWA) is improved, and a multi-processor task scheduling algorithm based on improved FWA, named IMFWA, is proposed. IMFWA maps continuous space to discrete space by designing the fireworks location coding method, improves the Gaussian mutation process, and sets adaptive dimensions to accelerate the convergence speed of the algorithm. At the same time, in order to reduce the time complexity of the algorithm and shorten the time finding the optimal task scheduling sequence, the fitness-based tournament selection strategy is used instead of the rule based on Euclidean distance. Finally, IMFWA is compared with the basic fireworks algorithm and the genetic algorithms on the Matlab platform for performance analysis. The results show that the IMFWA has advantages in the convergence speed, and the negative impact of the number of tasks is also lower than the fireworks algorithm and genetic algorithm.

[This corrects the article DOI: 10.1371/journal.pone.0211373.].

With the exponential increase in malware, homology analysis has become a hot research topic in the malware detection field. This paper proposes MHAS, a malware homology analysis system based on ensemble learning and multifeatures. MHAS generates grayscale images from malware binary files and then uses the opcode tool IDA Pro to extract opcode sequences and system call graphs. Thus, RGB images and M-images are generated on the image matrix. Then, MHAS uses convolutional neural networks (CNNs) as base learners to perform bagging ensemble learning to learn features from the grayscale images, RGB images and M-images. Next, MHAS integrates the nine base learners using voting, learning and selective ensemble (in that order) and maps the integration results to the result matrix. Finally, the result matrix is again integrated using the learning method to obtain the final malware classification result. To verify the accuracy of MHAS, we performed a malware family classification experiment, that included samples of 10 malware families. The results showed that MHAS can reach an accuracy rate of 99.17%, meaning that it can effectively analyze and identify malware families.

Transfer learning can enhance classification performance of a target domain with insufficient training data by utilizing knowledge relating to the target domain from source domain. Nowadays, it is common to see two or more source domains available for knowledge transfer, which can improve performance of learning tasks in the target domain. However, the classification performance of the target domain decreases due to mismatching of probability distribution. Recent studies have shown that deep learning can build deep structures by extracting more effective features to resist the mismatching. In this paper, we propose a new multi-source deep transfer neural network algorithm, MultiDTNN, based on convolutional neural network and multi-source transfer learning. In MultiDTNN, joint probability distribution adaptation (JPDA) is used for reducing the mismatching between source and target domains to enhance features transferability of the source domain in deep neural networks. Then, the convolutional neural network is trained by utilizing the datasets of each source and target domain to obtain a set of classifiers. Finally, the designed selection strategy selects classifier with the smallest classification error on the target domain from the set to assemble the MultiDTNN framework. The effectiveness of the proposed MultiDTNN is verified by comparing it with other state-of-the-art deep transfer learning on three datasets.