Safety of daratumumab in the real-world: a pharmacovigilance study based on FAERS database.

BACKGROUND: Daratumumab is widely used in multiple myeloma (MM) and light chain amyloidosis (AL amyloidosis). The purpose of this study was to identify adverse event (AE) signals for daratumumab through the FDA Adverse Event Reporting System (FAERS) database to assess its safety in a large sample of people. METHODS: Based on data from the FAERS database, three disproportionality analysis methods were used to mine AE signals for daratumumab, including reporting odd ratio (ROR), proportional reporting ratio (PRR), and bayesian configuration promotion neural network (BCPNN). RESULTS: A total of 9220 AE reports with daratumumab as the primary suspect drug were collected, containing 23,946 AEs. Within these reports, 252 preferred terms (PT) levels, 73 high level term (HLT) levels and 11 system organ class (SOC) levels of AE signals were detected, along with some new AEs. Most AEs occurred within the first month after drug administration. CONCLUSION: Our findings were consistent with the results of established studies that daratumumab has a good safety profile. The newly identified AEs are of concern and prospective clinical studies are needed to confirm whether they are causally related to daratumumab. This study provided an early warning for the safe use of daratumumab and also provided guidance for further safety studies.

Fault Detection in Power Distribution Networks Based on Comprehensive-YOLOv5.

Real-time fault detection in power distribution networks has become a popular issue in current power systems. However, the low power and computational capabilities of edge devices often fail to meet the requirements of real-time detection. To overcome these challenges, this paper proposes a lightweight algorithm, named Comprehensive-YOLOv5, for identifying defects in distribution networks. The proposed method focuses on achieving rapid localization and accurate identification of three common defects: insulator without loop, cable detachment from the insulator, and cable detachment from the spacer. Based on the You Only Look Once version 5 (YOLOv5) algorithm, this paper adopts GhostNet to reconstruct the original backbone of YOLOv5; introduces Bidirectional Feature Pyramid Network (BiFPN) structure to replace Path Aggregation Network (PANet) for feature fusion, which enhances the feature fusion ability; and replaces Generalized Intersection over Union GIOU with Focal Extended Intersection over Union (Focal-EIOU) to optimize the loss function, which improves the mean average precision and speed of the algorithm. The effectiveness of the improved Comprehensive-YOLOv5 algorithm is verified through a "morphological experiment", while an "algorithm comparison experiment" confirms its superiority over other algorithms. Compared with the original YOLOv5, the Comprehensive-YOLOv5 algorithm improves mean average precision (mAP) from 88.3% to 90.1% and increases Frames per second (FPS) from 20 to 52 frames. This improvement significantly reduces false positives and false negatives in defect detection. Consequently, the proposed algorithm enhances detection speed and improves inspection efficiency, providing a viable solution for real-time detection and deployment at the edge of power distribution networks.

Analysis of core mutation and TET2/ASXL1 mutations DNA methylation profile in myelodysplastic syndrome.

OBJECTIVES: The study aims to analyze genetic mutation and clinical characteristics and study their correlation with survival prognosis of patients with myelodysplastic syndromes (MDS). Moreover, the differential DNA methylation profiles between TET2 mutated (Mut)/ASXL1 wild-type (WT) and TET2-Mut/ASXL1-Mut MDS samples were investigated to explore the mechanism of MDS patients with TET2/ASXL1 mutations. METHODS: The clinical data of 195 patients diagnosed with MDS were selected and statistically analyzed. The DNA methylation sequencing data set was obtained from the GEO and bioinformatics analyzed. RESULTS: Of the 195 MDS patients, 42 (21.5%) carried TET2 mutations. 81% of TET2-Mut patients could detect comutated genes. The most commonly comutated gene in MDS patients with TET2-Mut was ASXL1, which had a tendency towards poorer prognosis (P = 0.08). GO analysis showed that highly methylated differentially methylated genes (DMGs) was mainly enriched in biological processes such as cell surface receptor signal pathway and cell secretion. Hypomethylated DMGs was mainly enriched in cell differentiation and cell development. KEGG analysis showed that hypermethylated DMGs was mainly enriched in Ras signal pathway and MAPK signal pathway. Hypomethylated DMGs was mainly enriched in extracellular matrix receptor interaction and focal adhesion. PPI network analysis identified 10 hub genes of hypermethylated and hypomethylated DMGs that may be associated with patients with TET2-Mut/ASXL1-Mut respectively. CONCLUSIONS: Our results illustrate the interrelationships between genetic mutations and clinical phenotypes and disease outcomes, with substantial potential for clinical application. Differentially methylated hub genes might represent potential biomarkers and provide novel insights and possible targets for MDS with double TET2/ASXL1 mutations.

A high performance dual-mode biosensor based on Nd-MOF nanosheets functionalized with ionic liquid and gold nanoparticles for sensing of ctDNA.

A dual-mode biosensor constructed based on photoelectrochemical (PEC) and electrochemical (EC) property was developed for assaying circulating tumor DNA (ctDNA), which is commonly used for triple-negative breast cancer diagnosis. Ionic liquid functionalized two-dimensional Nd-MOF nanosheets were successfully synthesized through a template-assisted reagent substituting reaction. Nd-MOF nanosheets integrated with gold nanoparticles (AuNPs) were able to improve photocurrent response and supply active sites for assembling sensing elements. To achieve selective detection of ctDNA, thiol-functionalized capture probes (CPs) were immobilized on the Nd-MOF@AuNPs modified glassy carbon electrode surface, thereby generating a "signal-off" photoelectrochemical biosensor for ctDNA under visible light irradiation. After the recognition of ctDNA, ferrocene-labeled signaling probes (Fc-SPs) were introduced into the biosensing interface. After hybridization between ctDNA and Fc-SPs, the oxidation peak current of Fc-SPs generated from square wave voltammetry can be employed as a "signal-on" electrochemical signal for ctDNA quantification. Under the optimized conditions, a linear relationship was obtained to the logarithm of ctDNA concentration in between 1.0 fmol L-1 to 10 nmol L-1 for the PEC model and 1.0 fmol L-1 to 1.0 nmol L-1 for the EC model. The dual-mode biosensor can provide accurate results for ctDNA assays, effectively eliminating the probable occurrence of false-positive or false-negative results in single-model assays. By switching DNA probe sequences, the proposed dual-mode biosensing platform can serve as a strategy for detecting other DNAs and possesses broad applications in bioassay and early disease diagnosis.

A nomogram based on clinical features and molecular abnormalities for predicting the prognosis of patients with acute myeloid leukemia.

Background: The high clinical and molecular heterogeneity of acute myeloid leukemia (AML) has led to an unsatisfactory clinical prognosis, thus we sought to incorporate both clinical features and molecular abnormalities to construct a new prognostic model. Methods: A database search of the Gene Expression Omnibus (GEO) revealed 238 cases of adult AML. The independent risk factors were assessed using both univariate and multivariate Cox regression, as well as least absolute shrinkage and selection operator (LASSO) regression. The predictive accuracy, discriminatory power and clinical applicability of the nomogram were determined by the consistency index (C-index), calibration curves and decision curve analysis (DCA). In addition, a single-centre cohort of 135 cases was used for external validation. Results: Multivariate Cox regression analysis showed that the independent influences on overall survival (OS) were age, type of disease, DNMT3A, IDH2 and TP53 mutations. The area under the curve (AUC) values for the training set were 0.755, 0.745 and 0.757 at 1, 2 and 3 years respectively; the AUC for the validation set were 0.648, 0.648 and 0.654 at 1, 2 and 3 years; and the AUC for the northwest China set were 0.692, 0.724 and 0.689 at 1, 2 and 3 years. The calibration and DCA indicated good consistency and clinical utility of the nomogram. Finally, younger (age <60 years) and elderly (age ≥60 years) patients were each divided into two risk groups with significantly different survival rates. Conclusions: A nomogram consisting of five risk factors was developed for forecasting the prognosis of AML with guaranteed reliability.

Development and validation of a novel prognosis prediction model for patients with myelodysplastic syndrome.

Background: Somatic mutations are widespread in patients with Myelodysplastic Syndrome (MDS) and are associated with prognosis. However, a practical prognostic model for MDS that incorporates somatic mutations urgently needs to be developed. Methods: A cohort of 201 MDS patients from the Gene Expression Omnibus (GEO) database was used to develop the model, and a single-center cohort of 115 MDS cohorts from Northwest China was used for external validation. Kaplan-Meier analysis was performed to compare the effects of karyotype classifications and gene mutations on the prognosis of MDS patients. Univariate and multivariate Cox regression analyses and Lasso regression were used to screen for key prognostic factors. The shinyapps website was used to create dynamic nomograms with multiple variables. The time-dependent receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA) were used to evaluate the model's discrimination, accuracy and clinical utility. Results: Six risk factors (age, bone morrow blast percentage, ETV6, TP53, EZH2, and ASXL1) were considered as predictor variables in the nomogram. The nomogram showed excellent discrimination, with respective the area under the ROC curve (AUC) values of 0.850, 0.839, 0.933 for the training cohort at 1 year, 3 years and 5 years; 0.715, 0.802 and 0.750 for the testing cohort at 1 year, 3 years and 5 years; and 0.668, 0.646 and 0.731 for the external validation cohort at 1 year, 3 years and 5 years. The calibration curves and decision curve showed that the nomogram had good consistency and clinical practical benefit. Finally, a stratified analysis showed that MDS patients with high risk had worse survival outcomes than patients with low risk. Conclusion: We developed a nomogram containing six risk factors, which provides reliable and objective predictions of prognosis for MDS patients.

Del(5q) and inv(3) in myelodysplastic syndrome: A rare case report.

BACKGROUND: Del(5q) is the most common molecular event in myelodysplastic syndrome (MDS), accounting for 10%-15% of cases. Inv(3) is an adverse cytogenetic abnormality observed in less than 1% of MDS patients. Few studies have reported the coexistence of del(5q) and inv(3) in MDS. Therefore, the pathological mechanism, treatment strategy and prognosis of this subtype need to be elucidated. CASE SUMMARY: A 66-year-old woman was admitted to the hospital due to chest tightness and shortness of breath. Combining clinical assessments with laboratory examinations, the patient was diagnosed with MDS containing both del(5q) and inv(3). Considering the deletion of chromosome 5q, we first treated the patient with lenalidomide. When drug resistance arose, we tried azacitidine, and the patient had a short remission. Finally, the patient refused treatment with haematopoietic stem cell transplantation and died of severe infection four months later. CONCLUSION: MDS patients with del(5) and inv(3) have a poor prognosis. Azacitidine may achieve short-term remission for such patients.

TBC1D2 Promotes Ovarian Cancer Metastasis via Inducing E-Cadherin Degradation.

Background: Ovarian cancer (OC) is the most lethal gynecological malignancy worldwide. Increasing evidence indicates that TBC domain family is implicated in various cellular events contributing to initiation and development of different cancers, including OC. However, the role of TBC1D2, a crucial member of TBC domain family, remains unclear in OC. Methods: IHC and qRT-PCR were employed to determine TBC1D2 expression in OC tissues and cells. In vitro and in vivo assays involving proliferation, migration, invasion were performed to explore the role of TBC1D2 in OC development. The underlying mechanism by which TBC1D2 promotes OC metastasis were elucidated using bioinformatics analysis, western blotting and co-immunoprecipitation. Results: Upregulation of TBC1D2 was found in OC and was associated with a poor prognosis. Meanwhile, TBC1D2 promoted OC cell proliferation, migration, and invasion in vitro and facilitated tumor growth and metastasis in vivo. Moreover, TBC1D2 contributed to OC cell invasion by E-cadherin degradation via disassembling Rac1-IQGAP1 complex. In addition, miR-373-3p was screened out and identified to inhibit OVCAR3 invasion via negative regulation of TBC1D2. Conclusion: Our findings indicated that TBC1D2 is overexpressed in OC and contributes to tumor metastasis via E-cadherin degradation. This study suggests that TBC1D2 may be an underlying therapeutic target for OC.