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Abstract Small bowel vascular malformation disease (SBVM) commonly causes obscure gastrointestinal bleeding (OGIB). However, the pathogenetic mechanism and the role of lncRNAs in SBVM remain largely unknown. Here, we found that hypoxia and low-glucose environments co-augment angiogenesis and existed in SBVM. Mechanistically, hypoxia and low-glucose environments supported angiogenesis via activation of hypoxia and glucose deprivation-induced lncRNA (HGDILnc1) transcription by increasing binding of the NeuroD1 transcription factor to the HGDILnc1 promoter. Raised HGDILnc1 acted as a suppressor of α-Enolase 1 (ENO1) small ubiquitin-like modifier modification (SUMOylation)-triggered ubiquitination, and an activator of transcription of Aldolase C (ALDOC) via upregulation of Histone H2B lysine 16 acetylation (H2BK16ac) level in the promoter of ALDOC, and consequently promoting glycolysis and angiogenesis. Moreover, HGDILnc1 was clinically positively correlated with Neurogenic differentiation 1 (NeuroD1), ENO1, and ALDOC in SBVM tissues, and could function as a biomarker for SBVM diagnosis and therapy. These findings suggest that hypoxia and low-glucose environments were present in SBVM tissues, and co-augmented angiogenesis. Hypoxia and low-glucose environments co-induced HGDILnc1, which is higher expressed in SBVM tissue compared with normal tissue, could promoted glycolysis and angiogenesis.

Abstract Background Diabetic cardiomyopathy is a serious complication of obesity with type 2 diabetes and is a major cause of mortality. Metabolic surgery, such as duodenal‐jejunal bypass (DJB), can effectively improve diabetic cardiomyopathy; however, the underlying mechanisms remain elusive. Oxidative stress is one of the pivotal mechanisms of diabetic cardiomyopathy. Our objective was to investigate the effect and potential mechanisms of DJB on oxidative stress in the heart of diabetic cardiomyopathy rats. Methods High‐fat diet combined with intraperitoneal injection of streptozotocin was used to establish diabetic cardiomyopathy rats. DJB was performed on diabetic cardiomyopathy rats, and high glucose and palmitate were used to simulate diabetic cardiomyopathy in H9C2 cells in vitro. Sera from different groups of rats were used for experiments in vivo and in vitro. Results DJB effectively improved oxidative stress and activated the adenosine monophosphate (AMP)‐activated protein kinase (AMPK) pathway to increase endothelial nitric oxide synthase (eNOS) phosphorylation level and the expression of antioxidative system‐related proteins and genes in the heart of diabetic cardiomyopathy rats. AMPK agonists and serum from DJB rats activated the AMPK pathway to increase eNOS phosphorylation level and the expression of antioxidative system‐related proteins and genes and decreased the content of reactive oxygen species in H9C2 cells, but this improvement was almost eliminated by the addition of AMPK inhibitors. Conclusions DJB activates eNOS and enhances the antioxidant system by activating the AMPK pathway—and not solely by improving blood glucose—to improve oxidative stress in the heart of diabetic cardiomyopathy rats.

Despite recent progress in multiple myeloma (MM) treatments, most patients will relapse and require additional treatment. Intravenous daratumumab, a human IgGκ monoclonal antibody targeting CD38, has shown good efficacy in the treatment of MM. A subcutaneous version of daratumumab was formulated to reduce the burden of intravenous infusions. We aimed to investigate the efficacy and safety of subcutaneous daratumumab in Chinese patients with relapsed/refractory MM based on the demonstrated noninferiority of subcutaneous daratumumab to intravenous daratumumab, with a shorter administration time and reduced infusion-related reaction rate in global studies. This phase 1, multicenter study (MMY1010; ClinicalTrials.gov Identifier: NCT04121260) evaluated subcutaneous daratumumab in Chinese patients with relapsed/refractory MM after 1 prior line (n = 1) or ≥2 prior lines (n = 20) of therapy, including a proteasome inhibitor and an immunomodulatory drug. Primary endpoints were pharmacokinetics and safety. Mean (standard deviation) maximum trough concentration of daratumumab was 826 (335) μg/mL, which was consistent with prior studies of subcutaneous daratumumab and intravenous daratumumab. Safety was consistent with safety profiles observed in other daratumumab studies, with no new safety concerns identified. Incidences of infusion-related reactions and injection-site reactions were low and consistent with other subcutaneous daratumumab studies. At a median follow-up of 7.5 months, the overall response rate was 57.1%, with a very good partial response or better rate of 38.1% and complete response or better rate of 19.0%. Our results demonstrate a favorable benefit/risk profile of subcutaneous daratumumab in Chinese patients with relapsed/refractory MM, potentially impacting clinical administration of daratumumab in this population.

Many applications, such as agriculture and water monitoring, require frequent observations, as well as high spatial resolution. In practice, it is difficult to achieve both high resolution and temporal coverage for satellite sensors. Accordingly, several fusion algorithms for spatial and temporal images have been proposed. Among them, the unmixing-based methods are widely used. However, large-scale changes between categories cannot be detected accurately, as they cannot estimate the variance within individual categories, when the variances in the inhomogeneous areas are large. To solve these problems, a fusion method based on the frequency domain is proposed. It determines the reflectance of the high-resolution (HR) image using the frequency relationship between the HR image and the coarse-resolution one. It is faster and more accurate than the conventional spatial-domain-based fusion methods, as its operations are realized in the frequency domain. Both the large-scale textures and the small-scale textures can be preserved, even in the case of discern sudden or large-scale changes. Finally, experiments over simulated images and real satellite ones, using Landsat thematic mapper image and Sentinel-2 image, are carried out to demonstrate the performance of the proposed approach.

Digital Supply Chain Surveillance (DSCS) is the proactive monitoring and analysis of digital data that allows firms to extract information related to a supply network, without the explicit consent of firms involved in the supply chain. AI has made DSCS to become easier and larger-scale, posing significant opportunities for automated detection of actors and dependencies involved in a supply chain, which in turn, can help firms to detect risky, unethical and environmentally unsustainable practices. Here, we define DSCS, review priority areas using a survey conducted in the UK. Visibility, sustainability, resilience are significant areas that DSCS can support, through a number of machine-learning approaches and predictive algorithms. Despite anecdotal narrative on the importance of explainability of algorithmic results, practitioners often prefer accuracy over explainability; however, there are significant differences between industrial sectors and application areas. Using a case study, we highlight a number of concerns on the unchecked use of AI in DSCS, such as bias or misinterpretation resulting in erroneous conclusions, which may lead to suboptimal decisions or relationship damage. Building on this, we develop and discuss a number of illustrative cases to highlight risks that practitioners should be aware of, proposing key areas of further research. [ABSTRACT FROM AUTHOR]

Summary: Artificial intelligence (AI) has been found to assist in optical differentiation of hyperplastic and adenomatous colorectal polyps. We investigated whether AI can improve the accuracy of endoscopists’ optical diagnosis of polyps with advanced features. We introduced our AI system distinguishing polyps with advanced features with more than 0.870 of accuracy in the internal and external validation datasets. All 19 endoscopists with different levels showed significantly lower diagnostic accuracy (0.410–0.580) than the AI. Prospective randomized controlled study involving 120 endoscopists into optical diagnosis of polyps with advanced features with or without AI demonstration identified that AI improved endoscopists’ proportion of polyps with advanced features correctly sent for histological examination (0.960 versus 0.840, p

Abstract Background Preventive colostomy is required for colorectal surgery, and the incidence of complications associated with ileostomy and colostomy remains controversial. This study aimed to compare the incidence of postoperative complications between ileostomy and colostomy procedures. Methods Data analysis was conducted on 30 studies, and meta-analysis and trial sequential analysis (TSA) were performed on five studies. The basic indicators, such as stoma prolapse, leak, wound infection, ileus, and a series of other indicators, were compared. Results No statistically significant differences were observed with complications other than stoma prolapse. Meta-analysis and TSA showed that the incidence of ileostomy prolapse was lower than that of colostomy prolapse, and the difference was statistically significant. Apart from the four complications listed above, the general data analysis showed differences in incidence between the two groups. The incidence of skin irritation, parastomal hernia, dehydration, pneumonia, and urinary tract infections was higher with ileostomy than with colostomy. In contrast, the incidence of parastomal fistula, stenosis, hemorrhage, and enterocutaneous fistula was higher with colostomy than with ileostomy. Conclusions There were differences in the incidence of ileostomy and colostomy complications in the selected studies, with a low incidence of ileostomy prolapse. PROSPERO registration number CRD42022303133.

A risk-adapted treatment strategy is of crucial importance in patients with myelodysplastic syndromes (MDS). Previous risk prognostic scoring systems did not integrate molecular abnormalities. The new IPSS-Molecular (IPSS-M) model, combing genomic profiling with hematologic and cytogenetic parameters, was recently developed to evaluate the associations with leukemia-free survival (LFS), leukemic transformation, and overall survival (OS). However, it has not yet been widely validated in clinics. This study aims to further validate the prognostic power of IPSS-M based on real-world data and to compare the prognostic value of different scoring systems in patients with MDS. IPSS-M Web calculator was used to calculate a tailored IPSS-M score of the enrolled patient (N = 255), and the risk category was defined correspondingly. We next compared the IPSS-M prognostic power to that of IPSS, IPSS-R, and WPSS. We found that IPSS-M risk classification was statistically significant for 3-year OS and LFS. Compared with other tools, IPSS-M was superior in sensitivity and accuracy for 3-year OS and LFS. The mapping C-index between IPSS-R and IPSS-M categories resulted in improved discrimination across the OS, but not LFS and leukemic transformation. The result of different treatment options indicated that allogeneic hematopoietic stem cell transplantation (allo-HSCT) can result in a better OS than those without allo-HSCT. In conclusion, IPSS-M was a valuable tool for risk stratification compared with other risk prognostic scoring systems. However, more studies should be conducted to explore the appropriate treatment options for different groups stratified by IPSS-M.

[This corrects the article DOI: 10.1371/journal.pone.0125429.].

Abstract Background More effective targeted therapy and new combination regimens are needed for Acute myeloid leukemia (AML), owing to the unsatisfactory long-term prognosis of the disease. Here, we investigated the synergistic effect and the mechanism of a histone deacetylase inhibitor, Chidamide in combination with Cladribine, a purine nucleoside antimetabolite analog in the disease. Methods Cell counting kit-8 assays and Chou-Talalay’s combination index were used to examine the synergistic effect of Chidamide and Cladribine on AML cell lines (U937, THP-1, and MV4-11) and primary AML cells. PI and Annexin-V/PI assays were used to detect the cell cycle effect and apoptosis effect, respectively. Global transcriptome analysis, RT-qPCR, c-MYC Knockdown, western blotting, co-immunoprecipitation, and chromatin immunoprecipitation assays were employed to explore the molecule mechanisms. Results The combination of Chidamide with Cladribine showed a significant increase in cell proliferation arrest, the G0/G1 phase arrest, and apoptosis compared to the single drug control in AML cell lines along with upregulated p21Waf1/Cip1 expression and downregulated CDK2/Cyclin E2 complex, and elevated cleaved caspase-9, caspase-3, and PARP. The combination significantly suppresses the c-MYC expression in AML cells, and c-MYC knockdown significantly increased the sensitivity of U937 cells to the combination compared to single drug control. Moreover, we observed HDAC2 interacts with c-Myc in AML cells, and we further identified that c-Myc binds to the promoter region of RCC1 that also could be suppressed by the combination through c-Myc-dependent. Consistently, a positive correlation of RCC1 with c-MYC was observed in the AML patient cohort. Also, RCC1 and HDAC2 high expression are associated with poor survival in AML patients. Finally, we also observed the combination significantly suppresses cell growth and induces the apoptosis of primary cells in AML patients with AML1-ETO fusion, c-KIT mutation, MLL-AF6 fusion, FLT3-ITD mutation, and in a CMML-BP patient with complex karyotype. Conclusions Our results demonstrated the synergistic effect of Chidamide with Cladribine on cell growth arrest, cell cycle arrest, and apoptosis in AML and primary cells with genetic defects by targeting HDAC2/c-Myc/RCC1 signaling in AML. Our data provide experimental evidence for the undergoing clinical trial (Clinical Trial ID: NCT05330364) of Chidamide plus Cladribine as a new potential regimen in AML.

This study aimed to assess the outcomes of modified onlay island flap (MOIF) urethroplasty for hypospadias reoperation. A retrospective analysis was conducted using clinical data on children reoperated with MOIF or tubularized incised plate (TIP) urethroplasty for hypospadias in the Children's Hospital of Nanjing Medical University (Nanjing, China) from December 2016 to September 2020. A total of 90 children with hypospadias who failed primary repair and were reoperated on with MOIF (MOIF group, n = 46) or TIP urethroplasty (TIP group, n = 44) were recruited. All children were postoperatively followed up for at least 6 months. Six patients with postoperative urinary fistula, 2 patients with glans dehiscence, and 1 patient with urethral diverticulum were reported in the MOIF group, while 7 patients with postoperative urinary fistula, 2 patients with urethral stricture, and 1 patient with glans dehiscence were reported in the TIP group. Postoperative infection, flap necrosis and other complications were not recorded. A total of 37 (80.4%) patients in the MOIF group and 34 (77.3%) patients in the TIP group were successfully reoperated. Although the success rates of MOIF and TIP urethroplasty are comparable, pediatricians should master different types of repair techniques for individualized hypospadias reoperation. MOIF urethroplasty can be recommended after failure of the primary repair.

A double potential well flat ion trap was designed as an ion accumulating and focusing device for multi-reflection time-of-flight mass analyzer. Two potential wells of the trap were severally used for accumulation of injected ions, controlled cooling and ejection of trapped ions, which had features to accumulate ions continuously and cool ion for almost one period. While a flat ion trap with only one potential well is a traditional and excellent ion accumulating and focusing device, which has an intermittent ion accumulation and an ion cooling around half period. Compared with the single potential well flat ion trap, the double potential well flat ion trap had better performance on ion accumulation and focusing in principle. Four steps to run the trap were simulated and researched by an ion optics simulator of SIMION 8.1. The first step was ion accumulation, ions were successively injected into the trap, and then stored and accumulated in the first potential well. The second step was ion transfer, the trapped ions were transferred from the first potential well to the second potential well. The third step was ion cooling, the trapped ions were confined in the second potential well, and were simultaneously cooled by collisions with 0.5 Pa He. The fourth step was ion ejection, the trapped ions were ejected from the trap and focused into the next device. Ion optics simulations indicated that the trap had a period from 1 ms to above 10 ms, a total ion transmission efficiency of 83%, and an ion flux of at most 1.6×106 ions. In addition, ion packets ejected from the trap had thermalized with radial diameters of 1.0 and 1.0 mm, angular standard deviations of 24 mrad and 16 mrad, and an energy standard deviation of 15 eV. This trap can be coupled with and provide high-flux and full-focusing ion packets for multi-reflection time-of-flight mass analyzers, due to an excellent suitability between the operation of the analyzer and the period, the ion transmission efficiency, the ion flux, and ejected ion packets of the trap.

The use of Artificial Intelligence (AI) for predicting supply chain risk has gained popularity. However, proposed approaches are based on the premise that organisations act alone, rather than a collective when predicting risk, despite the interconnected nature of supply chains. This yields a problem: organisations that have inadequate datasets cannot predict risk. While data-sharing has been proposed to evaluate risk, in practice this does not happen due to privacy concerns. We propose a federated learning approach for collective risk prediction without the risk of data exposure. We ask: Can organisations who have inadequate datasets tap into collective knowledge? This raises a second question: Under what circumstances would collective risk prediction be beneficial? We present an empirical case study where buyers predict order delays from their shared suppliers before and after Covid-19. Results show that federated learning can indeed help supply chain members predict risk effectively, especially for buyers with limited datasets. Training data-imbalance, disruptions, and algorithm choice are significant factors in the efficacy of this approach. Interestingly, data-sharing or collective risk prediction is not always the best choice for buyers with disproportionately larger order-books. We thus call for further research on on local and collective learning paradigms in supply chains. [ABSTRACT FROM AUTHOR]