Outcomes after HSCT for mucolipidosis II (I-cell disease) caused by novel compound heterozygous GNPTAB mutations.

Background: Mucolipidosis type II (MLII), or I-cell disease, is a rare lysosomal storage disease (LSD) caused by variants in the GNPTAB gene. MLII patients exhibit clinical phenotypes in the prenatal or neonatal stage, such as marked dysmorphic features, cardiac involvement, respiratory symptoms, dysostosis multiplex, severe growth abnormalities, and mental and motor developmental abnormalities. The median age at diagnosis for MLII is 0.7 years, the median survival is 5.0 years, and the median age at death is 1.8 years. No cure for MLII exists. Methods: Sanger sequencing of the GNPTAB gene identified the compound heterozygous mutations c.673C > T in exon 7 and c.1090C > T in exon 9, which were novel double heterozygous mutations first reported in China. For the first time, we describe our experience in the use of HSCT for MLII. Our patient underwent HSCT with cells from a 9/10 human leukocyte antigen (HLA)-matched unrelated donor at 12 months of age. Myeloid neutrophil and platelet engraftment occurred on Days 10 and 11, respectively. Results: The patient's limb muscle tension was significantly reduced, and his gross and fine motor skills were improved four months after transplantation. DST(Developmental Screen Test) results showed that the patient's fine motor skills and mental development were improved compared with before HSCT. Conclusion: MLII is a very severe lysosomal storage disease, to date, only 3 cases have been reported on the use of HSCT to treat MLII. Our data show that HSCT is a potential way to prolong the life of patients and improve their quality of life. Due to the lack of comparable data and time, the exact benefit remains unclear in MLII patients. Longer-term follow-up and in-depth prospective studies are indispensable.

TED: Two-stage expert-guided interpretable diagnosis framework for microvascular invasion in hepatocellular carcinoma.

Microvascular invasion (MVI) has been clinically recognized as a prognostic factor for hepatocellular carcinoma (HCC) after surgical treatment. Detection of MVI before surgical operation greatly benefit patients' prognosis and survival. Most of the existing methods for automatic diagnosis of MVI directly use deep neural networks to make predictions, which do not take into account clinical knowledge and lack of interpretability. To simulate the radiologists' decision process, this paper proposes a Two-stage Expert-guided Diagnosis (TED) framework for MVI in HCC. Specifically, the first stage aims to predict key imaging attributes for MVI diagnosis, and the second stage leverages these predictions as a form of attention as well as soft supervision through a variant of triplet loss, to guide the fitting of the MVI diagnosis network. The attention and soft supervision are expected to jointly guide the network to learn more semantically correlated representations and thereafter increase the interpretability of the diagnosis network. Extensive experimental analysis on a private dataset of 466 cases has shown that the proposed method achieves 84.58% on AUC and 84.07% on recall, significantly exceeding the baseline methods.

LiSNet: An artificial intelligence -based tool for liver imaging staging of hepatocellular carcinoma aggressiveness.

BACKGROUND: Presurgical assessment of hepatocellular carcinoma (HCC) aggressiveness can benefit patients' treatment options and prognosis. PURPOSE: To develop an artificial intelligence (AI) tool, namely, LiSNet, in the task of scoring and interpreting HCC aggressiveness with computed tomography (CT) imaging. METHODS: A total of 358 patients with HCC undergoing curative liver resection were retrospectively included. Three subspecialists were recruited to pixel-wise annotate and grade tumor aggressiveness based on CT imaging. LiSNet was trained and validated in 193 and 61 patients with a deep neural network to emulate the diagnostic acumen of subspecialists for staging HCC. The test set comprised 104 independent patients. We subsequently compared LiSNet with an experience-based binary diagnosis scheme and human-AI partnership that combined binary diagnosis and LiSNet for assessing tumor aggressiveness. We also assessed the efficiency of LiSNet for predicting survival outcomes. RESULTS: At the pixel-wise level, the agreement rate of LiSNet with subspecialists was 0.658 (95% confidence interval [CI]: 0.490-0.779), 0.595 (95% CI: 0.406-0.734), and 0.369 (95% CI: 0.134-0.566), for scoring HCC aggressiveness grades I, II, and III, respectively. Additionally, LiSNet was comparable to subspecialists for predicting histopathological microvascular invasion (area under the curve: LiSNet: 0.668 [95% CI: 0.559-0.776] versus subspecialists: 0.699 [95% CI: 0.591-0.806], p > 0.05). In a human-AI partnered diagnosis, combining LiSNet and experience-based binary diagnosis can achieve the best predictive ability for microvascular invasion (area under the curve: 0.705 [95% CI: 0.589-0.820]). Furthermore, LiSNet was able to indicate overall survival after surgery. CONCLUSION: The designed LiSNet tool warrants evaluation as an alternative tool for radiologists to conduct automatic staging of HCC aggressiveness at the pixel-wise level with CT imaging. Its prognostic value might benefit patients' treatment options and survival prediction.

LINC00461 Regulates the Recurrence of Large B Cell Lymphoma through the miR-411-5p/BNIP3 Pathway.

Objective: To analyze the mechanism of LINC00461 regulating the recurrence of diffuse large B cell lymphoma (DLBCL) through microRNA (miR)-411-5p/BCL2 interacting protein 3 (BNIP3) pathway. Methods: DLBCL samples in TCGA and GSE12453 were used for differential analysis to find long noncoding RNA (lncRNA) related to DLBCL recurrence. The 4 DLBCL data with the highest and lowest expression levels of LINC00461 in the TCGA database were selected for GSEA enrichment analysis. The targeting relationships of miR-411-5p with LINC00461 and BNIP3 were verified by the dual luciferase report. Blood samples from DLBCL patients were used to analyze the correlation between miR-411-5p and LINC00461 or BNIP3. LINC00461, miR-411-5p, or BNIP3 was overexpressed or silenced by transfection, and a tumor-bearing nude mice model was constructed to detect their effects on proliferation and apoptosis. Results: The level of LINC00461 in DLBCL was significantly higher than that in normal cases, and the level in recurrence DLBCL was significantly higher than that in nonrecurrence. The enrichment analysis results showed that the function of LINC00461 was closely related to apoptosis. The results shown that miR-411-5p bound to LINC00461 and BNIP3 and was negatively correlated with LINC00461 and BNIP3 mRNA in blood of DLBCL patients. Suppressing the level of LINC00461 inhibited cell proliferation and induced apoptosis. The inhibition of LINC00461 or overexpression of miR-411-5p reduced the expression of BNIP3 protein, thereby inducing apoptosis at the in vivo and in vitro levels. Conclusion: LINC00461 may induce miR-411-5p to "sponge," thereby increasing the expression of BNIP3 protein, and exerting the function of inhibiting apoptosis and promoting DLBCL recurrence.

Excellent Outcome Following Sibling Peripheral Blood Hematopoietic Stem Cell Transplantation for Glanzmann Thrombasthenia: A Case Report.

Glanzmann thrombasthenia (GT) is a rare autosomal recessive platelet disorder due to a qualitative or quantitative anomaly of the platelet membrane glycoprotein GPIIb/IIIa. Its clinical manifestations include mild to severe bleeding. GT diagnosis mainly depends on platelet function analysis, flow cytometry, and gene detection. Treatment methods include conservative symptomatic treatment and allogeneic hematopoietic stem cell transplantation (allo-HSCT). Allo-HSCT is the only clinical radical method for GT. Herein, we report a 2-year-old boy with GT successfully cured by related identical peripheral blood stem cell transplantation (PBSCT). The platelet disorder was corrected to a normal level after PBSCT, with no significant complication related to the transplantation. Hematopoietic stem cell transplantation with full-matched donor in early stage could be a treatment option for GT.

Direct Comparison of Four Presurgical Stratifying Schemes for Prediction of Microvascular Invasion in Hepatocellular Carcinoma by Gadoxetic Acid-Enhanced MRI.

BACKGROUND: Microvascular invasion (MVI) is implicated in the poor prognosis of hepatocellular carcinoma (HCC). Presurgical stratifying schemes have been proposed for HCC-MVI but lack external validation. PURPOSE: To perform external validation and comparison of four presurgical stratifying schemes for the prediction of MVI using gadoxetic acid-based MRI in a cohort of HCC patients. STUDY TYPE: Retrospective. SUBJECTS: Included were 183 surgically resected HCCs from patients who underwent pretreatment MRI. FIELD STRENGTH/SEQUENCE: This includes 1.5-3.0 T with T2 , T1 , diffusion-weighted imaging (DWI), and dynamic gadoxetic acid contrast-enhancement imaging sequences. ASSESSMENT: A two-trait predictor of venous invasion (TTPVI), Lei model, Lee model, and Xu model were compared. We relied on preoperative characteristics and imaging findings via four independent radiologists who were blinded to histologic results, as required by the tested tools. STATISTICAL TEST: Tests of accuracy between predicted and observed HCC-MVI rates using receiver operating characteristic (ROC) curve and decision curve analysis. The intraclass correlation coefficient (ICC) and Cronbach's alpha statistics were used to evaluate reproducibility. RESULTS: HCC-MVI was identified in 52 patients (28.4%). The average ROC curves (AUCs) for HCC-MVI predictions were 0.709-0.880, 0.714-0.828, and 0.588-0.750 for the Xu model, Lei model, and Lee model, respectively. The rates of accuracy were 60.7-81.4%, 69.9-75.9%, and 65.6-73.8%, respectively. Decision curve analyses indicated a higher benefit for the Xu and Lei models compared to the Lee model. The ICC and Cronbach's alpha index were highest in the Lei model (0.896/0.943), followed by the Xu model (0.882/0.804), and the Lee model (0.769/0.715). The TTPVI resulted in a Cronbach's alpha index of 0.606 with a sensitivity of 34.6-61.5% and a specificity of 76.3-91.6%. DATA CONCLUSION: Stratifying schemes relying on gadoxetic acid-enhanced MRI provide an additional insight into the presence of preoperative MVI. The Xu model outperformed the other models in terms of accuracy when performed by an experienced radiologist. Conversely, the Lei model outperformed the other models in terms of reproducibility. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 2 J. Magn. Reson. Imaging 2020;52:433-447.

Machine Learning-Based Computational Models Derived From Large-Scale Radiographic-Radiomic Images Can Help Predict Adverse Histopathological Status of Gastric Cancer.

INTRODUCTION: Adverse histopathological status (AHS) decreases outcomes of gastric cancer (GC). With the lack of a single factor with great reliability to preoperatively predict AHS, we developed a computational approach by integrating large-scale imaging factors, especially radiomic features at contrast-enhanced computed tomography, to predict AHS and clinical outcomes of patients with GC. METHODS: Five hundred fifty-four patients with GC (370 training and 184 test) undergoing gastrectomy were retrospectively included. Six radiomic scores (R-scores) related to pT stage, pN stage, Lauren & Borrmann (L&B) classification, World Health Organization grade, lymphatic vascular infiltration, and an overall histopathologic score (H-score) were, respectively, built from 7,000+ radiomic features. R-scores and radiographic factors were then integrated into prediction models to assess AHS. The developed AHS-based Cox model was compared with the American Joint Committee on Cancer (AJCC) eighth stage model for predicting survival outcomes. RESULTS: Radiomics related to tumor gray-level intensity, size, and inhomogeneity were top-ranked features for AHS. R-scores constructed from those features reflected significant difference between AHS-absent and AHS-present groups (P < 0.001). Regression analysis identified 5 independent predictors for pT and pN stages, 2 predictors for Lauren & Borrmann classification, World Health Organization grade, and lymphatic vascular infiltration, and 3 predictors for H-score, respectively. Area under the curve of models using those predictors was training/test 0.93/0.94, 0.85/0.83, 0.63/0.59, 0.66/0.63, 0.71/0.69, and 0.84/0.77, respectively. The AHS-based Cox model produced higher area under the curve than the eighth AJCC staging model for predicting survival outcomes. Furthermore, adding AHS-based scores to the eighth AJCC staging model enabled better net benefits for disease outcome stratification. DISCUSSION: The developed computational approach demonstrates good performance for successfully decoding AHS of GC and preoperatively predicting disease clinical outcomes.

Performance of a machine learning-based decision model to help clinicians decide the extent of lymphadenectomy (D1 vs. D2) in gastric cancer before surgical resection.

BACKGROUND: Controversy still exists on the optimal surgical resection for potentially curable gastric cancer (GC). Use of radiologic evaluation and machine learning algorithms might predict extent of lymphadenectomy to limit unnecessary surgical treatment. We purposed to design a machine learning-based clinical decision-support model for predicting extent of lymphadenectomy (D1 vs. D2) in local advanced GC. METHODS: Clinicoradiologic features available from routine clinical assignments in 557 patients with GCs were retrospectively interpreted by an expert panel blinded to all histopathologic information. All patients underwent surgery using standard D2 resection. Decision models were developed with a logistic regression (LR), support vector machine (SVM) and auto-encoder (AE) algorithm in 371 training and tested in 186 test data, respectively. The primary end point was to measure diagnostic performance of decision model and a Japanese gastric cancer treatment guideline version 4th (JPN 4th) criteria for discriminate D1 (pT1 + pN0) versus D2 (≥ pT1 + ≥ pN1) lymphadenectomy. RESULTS: The decision model with AE analysis produced highest area under ROC curve (train: 0.965, 95% confidence interval (CI) 0.948-0.978; test: 0.946, 95% CI 0.925-0.978), followed by SVM (train: 0.925, 95% CI 0.902-0.944; test: 0.942, 95% CI 0.922-0.973) and LR (train: 0.886, 95% CI 0.858-0.910; test: 0.891, 95% CI 0.891-0.952). By this improvement, overtreatment was reduced from 21.7% (121/557) by treat-all pattern, to 15.1% (84/557) by JPN 4th criteria, and to 0.7-0.9% (4-5/557) by the new approach. CONCLUSIONS: The decision model with machine learning analysis demonstrates high accuracy for identifying patients who are candidates for D1 versus D2 resection. Its approximate 14-20% improvements in overtreatment compared to treat-all pattern and JPN 4th criteria potentially increase the number of patients with local advanced GCs who can safely avoid unnecessary lymphadenectomy.

High-Performance Ru@C4N Electrocatalyst for Hydrogen Evolution Reaction in Both Acidic and Alkaline Solutions.

We report a high-performance Ru@C4N electrocatalyst for the hydrogen evolution reaction (HER) in both acidic and alkaline solutions. This catalyst is synthesized by annealing a complex of a covalent organic framework compound coordinated with ruthenium synthesized by a "one-pot" solvothermal method. This Ru@C4N catalyst shows excellent electrocatalytic activity toward the hydrogen evolution reaction (HER) in both acidic and alkaline solutions with very low overpotentials at 10 mA/cm2 (6 mV in 0.5 M H2SO4 solution; 7 mV in 1.0 M KOH solution), which outperforms the commercial catalyst Pt/C. The Ru@C4N electrocatalyst also exhibits high HER turnover frequencies of 0.93 H2 per s in 0.5 M H2SO4 and 0.65 H2 per s in 1.0 M KOH solutions at 25 mV as well as superior performance stability.

Radiomic analysis of contrast-enhanced CT predicts microvascular invasion and outcome in hepatocellular carcinoma.

BACKGROUND & AIMS: Microvascular invasion (MVI) impairs surgical outcomes in patients with hepatocellular carcinoma (HCC). As there is no single highly reliable factor to preoperatively predict MVI, we developed a computational approach integrating large-scale clinical and imaging modalities, especially radiomic features from contrast-enhanced CT, to predict MVI and clinical outcomes in patients with HCC. METHODS: In total, 495 surgically resected patients were retrospectively included. MVI-related radiomic scores (R-scores) were built from 7,260 radiomic features in 6 target volumes. Six R-scores, 15 clinical factors, and 12 radiographic scores were integrated into a predictive model, the radiographic-radiomic (RR) model, with multivariate logistic regression. RESULTS: Radiomics related to tumor size and intratumoral heterogeneity were the top-ranked MVI predicting features. The related R-scores showed significant differences according to MVI status (p <0.001). Regression analysis identified 8 MVI risk factors, including 5 radiographic features and an R-score. The R-score (odds ratio [OR] 2.34) was less important than tumor capsule (OR 5.12), tumor margin (OR4.20), and peritumoral enhancement (OR 3.03). The RR model using these predictors achieved an area under the curve (AUC) of 0.909 in training/validation and 0.889 in the test set. Progression-free survival (PFS) and overall survival (OS) were significantly different between the RR-predicted MVI-absent and MVI-present groups (median PFS: 49.5 vs. 12.9 months; median OS: 76.3 vs. 47.3 months). RR-computed MVI probability, histologic MVI, tumor size, and Edmondson-Steiner grade were independently associated with disease-specific recurrence and mortality. CONCLUSIONS: The computational approach, integrating large-scale clinico-radiologic and radiomic features, demonstrates good performance for predicting MVI and clinical outcomes. However, radiomics with current CT imaging analysis protocols do not provide statistically significant added value to radiographic scores. LAY SUMMARY: The most effective treatment for hepatocellular carcinoma (HCC) is surgical removal of the tumor but often recurrence occurs, partly due to the presence of microvascular invasion (MVI). Lacking a single highly reliable factor able to preoperatively predict MVI, we developed a computational approach to predict MVI and the long-term clinical outcome of patients with HCC. In particular, the added value of radiomics, a newly emerging form of radiography, was comprehensively investigated. This computational method can enhance the communication with the patient about the likely success of the treatment and guide clinical management, with the aim of finding drugs that reduce the risk of recurrence.

An Intelligent Clinical Decision Support System for Preoperative Prediction of Lymph Node Metastasis in Gastric Cancer.

PURPOSE: The aim of this study was to develop and validate a computational clinical decision support system (DSS) on the basis of CT radiomics features for the prediction of lymph node (LN) metastasis in gastric cancer (GC) using machine learning-based analysis. METHODS: Clinicopathologic and CT imaging data were retrospectively collected from 490 patients who were diagnosed with GC between January 2002 and December 2016. Radiomics features were extracted from venous-phase CT images. Relevant features were selected, ranked, and modeled using a support vector machine classifier in 326 training and validation data sets. A model test was performed independently in a test set (n = 164). Finally, a head-to-head comparison of the diagnostic performance of the DSS and that of the conventional staging criterion was performed. RESULTS: Two hundred ninety-seven of the 490 patients examined had histopathologic evidence of LN metastasis, yielding a 60.6% metastatic rate. The area under the curve for predicting LN+ was 0.824 (95% confidence interval, 0.804-0.847) for the DSS in the training and validation data and 0.764 (95% confidence interval, 0.699-0.833) in the test data. The calibration plots showed good concordance between the predicted and observed probability of LN+ using the DSS approach. The DSS was better able to predict LN metastasis than the conventional staging criterion in the training and validation data (accuracy 76.4% versus 63.5%) and in the test data (accuracy 71.3% versus 63.2%) CONCLUSIONS: A DSS based on 13 "worrisome" radiomics features appears to be a promising tool for the preoperative prediction of LN status in patients with GC.

Copper-Nitrogen-Doped Graphene Hybrid as an Electrochemical Sensing Platform for Distinguishing DNA Bases.

An electrochemical sensor using ultralight and porous copper-nitrogen-doped graphene (CuNRGO) nanocomposite as the electrocatalyst has been constructed to simultaneously determine DNA bases such as guanine (G) and cytosine (C), adenine (A), and thymine (T). The nanocomposite is synthesized by thermally annealing an ice-templated structure of graphene oxide (GO) and Cu(phen)2. Because of the unique structure and the presence of Cu2+-N active sites, the CuNRGO exhibits outstanding electrocatalytic activity toward the oxidation of free DNA bases. After optimizing the experimental conditions, the CuNRGO-based electrochemical sensor shows good linear responses for the G, A, T, and C bases in the concentration ranges of 0.132-6.62 µM, 0.37-5.18 µM, 198.2-5551 µM, and 270.0-1575 µM, respectively. The results demonstrate that CuNRGO is a promising electrocatalyst for electrochemical sensing devices.

Expression and clinical significance of EGR-1 and PTEN in the pituitary tumors of elderly patients.

The aim of the study was to investigate the expression and clinical significance of early growth response protein 1 (EGR-1) and phosphatase and tensin homolog (PTEN) in the pituitary tumors of elderly patients. From January 2014 to December 2015, we collected 25 patient cases with non-invasive pituitary tumors, 10 cases with invasive pituitary tumors and 35 cases with healthy pituitary tissues (the healthy control group). Immunohistochemical staining was used to detect the expression of EGR-1 and PTEN, and analyze specific differences. The expression of EGR-1 and PTEN in patients with invasive and non-invasive pituitary tumors increased significantly, when compared with the healthy control group, and the difference was statistically significant (p<0.05). In patients with invasive tumors, EGR-1 levels were higher than levels in patients with non-invasive tumors. The difference was statistically significant (p<0.05). PTEN levels in patients with invasive tumors were significantly lower than levels in patients with non-invasive tumors. The difference was statistically significant (p<0.05). In conclusion, EGR-1 and PTEN levels in patients with pituitary tumors were significantly higher. In addition, EGR-1 levels were higher in patients with invasive pituitary tumors, while PTEN levels were lower. The combination of increases in both levels highlights an important role in the evaluation and prognosis of elderly patients with pituitary tumors.

Successful allogeneic hematopoietic stem cell transplantation in a boy with X-linked inhibitor of apoptosis deficiency presenting with hemophagocytic lymphohistiocytosis: A case report.

X-linked inhibitor of apoptosis (XIAP) deficiency, also known as X-linked lymphoproliferative syndrome type 2 (XLP2), is a rare inherited primary immunodeficiency resulting from the XIAP (also known as BIRC4) mutation. XIAP deficiency is mainly associated with familial hemophagocytic lymphohistiocytosis (HLH) phenotypes, and genetic testing is crucial in diagnosing this syndrome. Allogeneic hematopoietic stem cell transplantation (HSCT) is currently the only successful strategy for the treatment of this disease; however, a limited number of studies has been published concerning the outcomes of allogeneic HSCT in patients with XIAP deficiency. The present study reported a successful allogeneic HSCT performed to treat XIAP deficiency in a Chinese boy presenting with HLH. Polymerase chain reaction and DNA sequencing were performed to confirm the diagnosis of XIAP deficiency, and allogeneic HSCT was performed. Genetic tests revealed a two-nucleotide deletion (c.1021_1022delAA) in the patient, which was inherited from his mother, and resulted in frameshift mutation and premature stop codon (p.N341fsX348); this is considered to be a disease-causing mutation. The XIAP deficiency patient underwent allogeneic HSCT, receiving busulfan-containing reduced intensity myeloablative conditioning regimen, with a good intermediate follow-up result obtained. Therefore, genetic testing is essential to confirm the diagnosis of XIAP deficiency and detect the carrier of mutation. The present case study may promote the investigation of allogeneic HSCT in patients with XIAP deficiency.

6,6'-Dihydr-oxy-3,3'-dithio-dibenzoic acid.

In the title compound, C(14)H(10)O(6)S(2), the dihedral angle between the planes of the two phenyl-ene rings is 55.9 (1)°. Both hydr-oxy groups form intra-molecular hydrogen bonds; however, one of them also engages in inter-molecular hydrogen bonding. In the crystal, mol-ecules are connected into helical chains by O-H⋯O hydrogen bonds. The crystal studied was an inversion twin with a domain ratio of 0.51 (13):0.49 (13).