Predicting survival in patients with myelodysplastic/myeloproliferative neoplasms with SF3B1 mutation and thrombocytosis.

We investigated data from 180 consecutive patients with myelodysplastic/myeloproliferative neoplasms with SF3B1 mutation and thrombocytosis (MDS/MPN-SF3B1-T) who were diagnosed according to the 2022 World Health Organization (WHO) classification of myeloid neoplasms to identify covariates associated with survival. At a median follow-up of 48 months (95% confidence interval [CI] 35-61 months), the median survival was 69 months (95% CI 59-79 months). Patients with bone marrow ring sideroblasts (RS) < 15% had shorter median overall survival (OS) than did those with bone marrow RS ≥ 15% (41 months [95% CI 32-50 months] versus 76 months [95% CI 59-93 months]; P < 0.001). According to the univariable analyses of OS, age ≥ 65 years (P < 0.001), hemoglobin concentration (Hb) < 80 g/L (P = 0.090), platelet count (PLT) ≥ 800 × 10E + 9/L (P = 0.087), bone marrow RS < 15% (P < 0.001), the Revised International Prognostic Scoring System (IPSS-R) cytogenetic category intermediate/poor/very poor (P = 0.005), SETBP1 mutation (P = 0.061) and SRSF2 mutation (P < 0.001) were associated with poor survival. Based on variables selected from univariable analyses, two separate survival prediction models, a clinical survival model, and a clinical-molecular survival model, were developed using multivariable analyses with the minimum value of the Akaike information criterion (AIC) to specifically predict outcomes in patients with MDS/MPN-SF3B1-T according to the 2022 WHO classification.

Validation and comparison of 5th edition World Health Organization classification (WHO 2022) and International Consensus Classification proposals for MDS-SFB31.

The criteria of myelodysplastic syndromes (MDS) with mutated SFB31 (MDS-SFB31) proposed by the 5th edition of the WHO classification (WHO 2022) and the International Consensus Classification (ICC) need validation. We analysed 125 consecutive MDS cases with SFB31 mutation or ring sideroblasts (RS) ≥15% without excess blasts. We found that SFB31-negative MDS with RS had significantly different clinical features and worse prognosis. According to WHO 2022, the detection of ≥15% RS may substitute for SF3B1 mutation and our analyses support this proposal for similar prognosis of two groups after excluding high-risk genetic features referred by WHO 2022. Patients with variant allele frequency (VAF) <10% SFB31 tend to have briefer survival, supporting the VAF 10% threshold of ICC. Patients with multilineage dysplasia (MLD) had significantly shorter OS than those with single lineage dysplasia. MLD is still a powerful morphological marker of worse outcome in WHO 2022 and ICC-defined MDS-SF3B1.

Case of cryptic TNIP1::PDGFRB rearrangement presenting with myelodysplastic syndrome achieved hematologic and cytogenetic remission with low-dose imatinib plus decitabine therapy.

For a long time, FIP1L1::PDGFRA fusion seems to be the only cryptic rearrangement of myeloid/lymphoid neoplasm with tyrosine kinase gene fusions. Recently, with the wide application of RNA sequencing, more cryptic rearrangements of other TK genes have been identified, especially the PDGFRB. Here we report a case of myelodysplastic syndrome with severe thrombocytopenia. Conventional karyotype analysis revealed a t (5;19) (q33; p13.2) but no PDGFRB rearrangement was detected by the PDGFRB break-apart probe. The TNIP1::PDGFRB fusion was eventually found by RNA sequencing, leading us to treat with low-dose imatinib plus decitabine, and the patient achieved hematologic improvement and cytogenetic remission.

Lower vitamin D levels and VDR variants are risk factors for breast cancer: an updated meta-analysis.

Inadequate vitamin D levels and vitamin D variants have been shown to be associated with breast cancer (BC), however the results are inconsistent. To reach a definitive conclusion the present meta-analysis was conducted. When compared to healthy controls, BC patients had reduced vitamin D levels (standard difference in means = -0.564, p = 0.003). The meta-analysis revealed that the FokI mutation was linked with an increased BC susceptibility (CC vs. TT: OR = 1.107, p = 0.001, CC vs. TC + TT: OR = 1.114, p = 0.020). There was no role of other VDR variants (BsmI, TaqI, and ApaI). FokI mutation and diminished vitamin D increase the likelihood of developing BC.

Impact of the International Consensus Classification of myelodysplastic syndromes.

The impact of the 2022 International Consensus Classification (ICC) of myelodysplastic syndromes (MDS) needs study. We analysed data from 989 MDS subjects classified using the 2016 World Health Organization (WHO) criteria to determine the impact of the new proposal. Our analyses suggested the ICC criteria of MDS-SF3B1 identifies a more homogenous disease entity than the WHO 2016 criteria of myelodysplastic syndromes with ring sideroblasts (MDS-RS). MDS, not otherwise specified with single lineage dysplasia (MDS, NOS-SLD) patients had a better prognosis than MDS, NOS with multilineage dysplasia (MDS, NOS-MLD) patients. MDS with mutated TP53 and MDS/acute myeloid leukaemia with mutated TP53 patients had the briefest survivals. These data support the ICC of MDS, which allows more accurate diagnoses and risk stratification.

A Parallel Classification Model for Marine Mammal Sounds Based on Multi-Dimensional Feature Extraction and Data Augmentation.

Due to the poor visibility of the deep-sea environment, acoustic signals are often collected and analyzed to explore the behavior of marine species. With the progress of underwater signal-acquisition technology, the amount of acoustic data obtained from the ocean has exceeded the limit that human can process manually, so designing efficient marine-mammal classification algorithms has become a research hotspot. In this paper, we design a classification model based on a multi-channel parallel structure, which can process multi-dimensional acoustic features extracted from audio samples, and fuse the prediction results of different channels through a trainable full connection layer. It uses transfer learning to obtain faster convergence speed, and introduces data augmentation to improve the classification accuracy. The k-fold cross-validation method was used to segment the data set to comprehensively evaluate the prediction accuracy and robustness of the model. The evaluation results showed that the model can achieve a mean accuracy of 95.21% while maintaining a standard deviation of 0.65%. There was excellent consistency in performance over multiple tests.

IPSS-M has greater survival predictive accuracy compared with IPSS-R in persons ≥ 60 years with myelodysplastic syndromes.

There are considerable new data on mutation topography in persons with myelodysplastic syndromes (MDS). These data have been used to update conventional risk models such as the Revised International Prognostic Scoring System (IPSS-R). Whether the molecular IPSS (IPSS-M) which includes these data improves survival prediction accuracy is untested. To answer this question, we compared survival prediction accuracies of the IPSS-R and IPSS-M in 852 consecutive subjects with de novo MDS. Concordance statistics (C-statistics) of the IPSS-R and IPSS-M in the entire cohort were similar, 0.67 (95% Confidence Interval [CI] 0.64, 0.71) and 0.68 (0.64, 0.71). Average numbers of mutations and of IPSS-M related mutations were greater in persons ≥ 60 years (2.0 [Interquartile Range [IQR], 1, 3] vs. 1.6 [0, 2], P = 0.003; 1.6 [0, 2] vs. 1.3 [0, 2], P = 0.006). Subjects ≥ 60 years had a higher incidence of mutations in RUNX1, TP53, TET2, SRSF2, DNMT3A, STAG2, EZH2 and DDX41. In contrast, mutations in U2AF1 were more common in persons < 60 years. Next we tested survival prediction accuracy based on age < or ≥ 60 years. C-statistics of the IPSS-R and IPSS-M in subjects ≥ 60 years were 0.66 (0.61, 0.71) and 0.69 (0.64, 0.73) whereas in subjects < 60 years they were 0.67 (0.61, 0.72) and 0.65 (0.59, 0.71). These data indicate an advantage for the IPSS-M over the IPSS-R in subjects ≥ 60 years but not in those < 60 years probably because of a great frequency of mutations correlated with survival in those ≥ 60 years.

Influencing factors of perinatal bereavement care confidence in nurses and midwives: A cross-sectional study.

BACKGROUND: Most nurses and midwives are not prepared to provide bereavement care. The conflict between the need for high-quality care of bereaved parents and the lack of confidence in providing perinatal bereavement care among nursing staff is becoming increasingly prominent in China. OBJECTIVE: To describe the current situation and identify influencing factors of perinatal bereavement care confidence (PBCC) among nurses and midwives in China. METHODS: This descriptive and cross-sectional survey was conducted in 2021. A convenience sample was created by recruiting 571 nurses and midwives in 11 hospitals in Zhejiang Province. Collected the data of PBCC, secondary traumatic stress (STS) and emotional exhaustion (EE) in this study. RESULTS: The average score of the PBCC was 67.83 ± 10.78. Average levels of STS were (23.32 ± 7.39) and EE (17.87 ± 8.62). PBCC was found to be most often associated with self-awareness, organisational support and training in perinatal bereavement care. CONCLUSIONS: Managers should take measures to improve PBCC and optimise perinatal bereavement care practice from the perspective of enhancing self-awareness of nursing staff, strengthening organisational support and providing training in perinatal bereavement care. The mental health of nursing professionals in the context of perinatal bereavement care needs to be emphasised. Nursing managers should make clear policies and establish a communication platform for nursing staff. Professional training should be conducted in the future.

ASXL2 mutated myelodysplastic syndrome in a novel germline G6b variant.

The 2016 revised World Health Organization classification identified myeloid neoplasms with germline predisposition as a new diagnostic category. Germline loss-of-function mutations in G6b (G6b-B, C6orf25 or MPIG6B) are associated with congenital macro-thrombocytopenia with focal myelofibrosis, a rare autosomal recessive disease. It is unclear whether germline G6b variants increase the risk of developing a myeloid neoplasm. Here we describe an adult with Myelodysplastic syndromes and a homozygous germline G6b mutation who achieved hematopoietic reconstitution by hematopoietic stem cell transplantation. As far as we know, this is the first report of adult Myelodysplastic syndromes with germline G6b homozygous variant in the literatures.

Heart Disease Prediction Based on the Embedded Feature Selection Method and Deep Neural Network.

In recent decades, heart disease threatens people's health seriously because of its prevalence and high risk of death. Therefore, predicting heart disease through some simple physical indicators obtained from the regular physical examination at an early stage has become a valuable subject. Clinically, it is essential to be sensitive to these indicators related to heart disease to make predictions and provide a reliable basis for further diagnosis. However, the large amount of data makes manual analysis and prediction taxing and arduous. Our research aims to predict heart disease both accurately and quickly through various indicators of the body. In this paper, a novel heart disease prediction model is given. We propose a heart disease prediction algorithm that combines the embedded feature selection method and deep neural networks. This embedded feature selection method is based on the LinearSVC algorithm, using the L1 norm as a penalty item to choose a subset of features significantly associated with heart disease. These features are fed into the deep neural network we built. The weight of the network is initialized with the He initializer to prevent gradient varnishing or explosion so that the predictor can have a better performance. Our model is tested on the heart disease dataset obtained from Kaggle. Some indicators including accuracy, recall, precision, and F1-score are calculated to evaluate the predictor, and the results show that our model achieves 98.56%, 99.35%, 97.84%, and 0.983, respectively, and the average AUC score of the model reaches 0.983, confirming that the method we proposed is efficient and reliable for predicting heart disease.

An ECG Heartbeat Classification Method Based on Deep Convolutional Neural Network.

The electrocardiogram (ECG) is one of the most powerful tools used in hospitals to analyze the cardiovascular status and check health, a standard for detecting and diagnosing abnormal heart rhythms. In recent years, cardiovascular health has attracted much attention. However, traditional doctors' consultations have disadvantages such as delayed diagnosis and high misdiagnosis rate, while cardiovascular diseases have the characteristics of early diagnosis, early treatment, and early recovery. Therefore, it is essential to reduce the misdiagnosis rate of heart disease. Our work is based on five different types of ECG arrhythmia classified according to the AAMI EC57 standard, namely, nonectopic, supraventricular ectopic, ventricular ectopic, fusion, and unknown beat. This paper proposed a high-accuracy ECG arrhythmia classification method based on convolutional neural network (CNN), which could accurately classify ECG signals. We evaluated the classification effect of this classification method on the supraventricular ectopic beat (SVEB) and ventricular ectopic beat (VEB) based on the MIT-BIH arrhythmia database. According to the results, the proposed method achieved 99.8% accuracy, 98.4% sensitivity, 99.9% specificity, and 98.5% positive prediction rate for detecting VEB. Detection of SVEB achieved 99.7% accuracy, 92.1% sensitivity, 99.9% specificity, and 96.8% positive prediction rate.

Improved Self-Organizing Map-Based Unsupervised Learning Algorithm for Sitting Posture Recognition System.

As the intensity of work increases, many of us sit for long hours while working in the office. It is not easy to sit properly at work all the time and sitting for a long time with wrong postures may cause a series of health problems as time goes by. In addition, monitoring the sitting posture of patients with spinal disease would be beneficial for their recovery. Accordingly, this paper designs and implements a sitting posture recognition system from a flexible array pressure sensor, which is used to acquire pressure distribution map of sitting hips in a real-time manner. Moreover, an improved self-organizing map-based classification algorithm for six kinds of sitting posture recognition is proposed to identify whether the current sitting posture is appropriate. The extensive experimental results verify that the performance of ISOM-based sitting posture recognition algorithm (ISOM-SPR) in short outperforms that of four kinds of traditional algorithms including decision tree-based (DT), K-means-based (KM), back propagation neural network-based (BP), self-organizing map-based (SOM) sitting posture recognition algorithms. Finally, it is proven that the proposed system based on ISOM-SPR algorithm has good robustness and high accuracy.

Is race important in genomic classification of hematological neoplasms?

In recent years, genome-based classifications for hematological neoplasms have been proposed successively and proved to be more accurate than histologic classifications. However, some previous studies have reported the racial differences of genetic landscape in persons with hematological neoplasms including myelodysplastic syndromes (MDS), which may cause a genomic classification based on a particular ethnic group does not operate in other races. To determine whether race plays an important role in the genomic-based classification, we validated a newly proposed genomic classification of MDS (J Clin Oncol.2021; JCO2001659), which was based on a large European database, in Chinese patients from our center. Our results showed significant differences between Chinese and European patients including proportion of each group to overall cohort when applying this novel genomic classification. Our data indicate that a genomic classification of hematological neoplasms probably should be revised according to specific genetic features in different races.

VEXAS syndrome in myelodysplastic syndrome with autoimmune disorder.

VEXAS (vacuoles, E1 enzyme, X-linked, autoinflammatory, somatic) syndrome is a newly-described adult-onset inflammatory syndrome characterized by vacuoles in myeloid and erythroid precursor cells and somatic mutations affecting methionine-41 (p.Met41) in UBA1. The VEXAS syndrome often overlaps with myelodysplastic syndromes (MDS) with autoimmune disorders (AD). By screening the UBA1 gene sequences derived from MDS patients with AD from our center, we identified one patient with a p.Met41Leu missense mutation in UBA1, who should have been diagnosed as MDS comorbid with VEXAS syndrome. This patient respond poorly to immune suppressive drugs. Patients with MDS and AD who have characteristic vacuoles in myeloid and erythroid precursor cells should be screened for UBA1 mutation, these patients are likely to have VEXAS syndrome and unlikely to improve with immunosuppressive drugs and should be considered for other alternative therapies.

Severe ineffective erythropoiesis discriminates prognosis in myelodysplastic syndromes: analysis based on 776 patients from a single centre.

The underlying mechanisms and clinical significance of ineffective erythropoiesis in myelodysplastic syndromes (MDS) remain to be fully defined. We conducted the ex vivo erythroid differentiation of megakaryocytic-erythroid progenitors (MEPs) from MDS patients and discovered that patient-derived erythroblasts exhibit precocity and premature aging phenotypes, partially by inducing the pro-aging genes, like ERCC1. Absolute reticulocyte count (ARC) was chosen as a biomarker to evaluate the severity of ineffective erythropoiesis in 776 MDS patients. We found that patients with severe ineffective erythropoiesis displaying lower ARC (<20 × 109/L), were more likely to harbor complex karyotypes and high-risk somatic mutations (p < 0.05). Lower ARCs are associated with shorter overall survival (OS) in univariate analysis (p < 0.001) and remain significant in multivariable analysis. Regardless of patients of lower-risk who received immunosuppressive therapy or higher-risk who received decitabine treatment, patients with lower ARC had shorter OS (p < 0.001). Whereas no difference in OS was found between patients receiving allo-hematopoietic stem cell transplantations (Allo-HSCT) (p = 0.525). Our study revealed that ineffective erythropoiesis in MDS may be partially caused by premature aging and apoptosis during erythroid differentiation. MDS patients with severe ineffective erythropoiesis have significant shorter OS treated with immunosuppressive or hypo-methylating agents, but may benefit from Allo-HSCT.

GBDT-Based Fall Detection with Comprehensive Data from Posture Sensor and Human Skeleton Extraction.

Since fall is happening with increasing frequency, it has been a major public health problem in an aging society. There are considerable demands to distinguish fall down events of seniors with the characteristics of accurate detection and real-time alarm. However, some daily activities are erroneously signaled as falls and there are too many false alarms in actual application. In order to resolve this problem, this paper designs and implements a comprehensive fall detection framework on the basis of inertial posture sensors and surveillance cameras. In the proposed system framework, data sources representing behavior characteristics to indicate potential fall are derived from wearable triaxial accelerometers and monitoring videos of surveillance cameras. Moreover, the NB-IoT based communication mode is adopted to transmit wearable sensory data to the Internet for subsequent analysis. Furthermore, a Gradient Boosting Decision Tree (GBDT) classifier-based fall detection algorithm (GBDT-FD in short) with comprehensive data fusion of posture sensor and human video skeleton is proposed to improve detection accuracy. Experimental results verify the good performance of the proposed GBDT-FD algorithm compared to six kinds of existing fall detection algorithms, including SVM-based fall detection, NN-based fall detection, etc. Finally, we implement the proposed integrated systems including wearable posture sensors and monitoring software on the Cloud Server.

Robust and Cooperative Localization for Underwater Sensor Networks in the Existence of Malicious Anchors.

Precise and robust localization in three-dimensional underwater sensor networks is still an important research problem. This problem is particularly challenging if there are some malicious anchors among ordinary anchor nodes that will broadcast their locations falsely and deliberately. In this paper, we study how to self-localize large teams of underwater sensor nodes under the condition that some malicious anchor nodes mixed with ordinary anchors. Due to malicious characteristic of some deliberate anchor nodes, an iterative and cooperative 3D-localization algorithm for underwater sensor networks in the existence of malicious anchors is proposed in this paper. The proposed robust localization algorithm takes advantage of distributed reputation voting method within 1-Hop neighboring reference nodes to detect and eliminate malicious anchor nodes. Moreover, one kind of Minimum Mean Squared Error estimation based iterative localization method is applied to determine accurate location information. Additionally, we analyze and prove that our localization algorithm would have a bounded error when the number of malicious anchors is smaller than a certain threshold. Extensive simulation results are provided to demonstrate performance improvements comparing to traditional Minimum Mean Squared Error and Attack Resistant Minimum Mean Squared Error based localization methods in terms of localization accuracy and coverage ratio.

A Low-Cost Electromagnetic Docking Guidance System for Micro Autonomous Underwater Vehicles.

As important observational platforms for the Smart Ocean concept, autonomous underwater vehicles (AUVs) that perform long-term observation in fleets are beneficial because they provide large-scale sampling data with a sufficient spatiotemporal resolution. Therefore, a large number of low-cost micro AUVs with docking capability for power recharge and data transmission are essential. This study designed a low-cost electromagnetic docking guidance (EMDG) system for micro AUVs. The EMDG system is composed of a transmitter coil located on the dock and a three-axial search coil magnetometer acting as a receiver. The search coil magnetometer was optimized for small sizes while maintaining sufficient sensitivity. The signal conditioning and processing subsystem was designed to calculate the deflection angle (ß) for docking guidance. Underwater docking tests showed that the system can detect the electromagnetic signal and successfully guide AUV docking. The AUV can still perform docking in extreme positions, which cannot be realized through normal optical or acoustic guidance. This study is the first to focus on the EM guidance system for low-cost micro AUVs. The search coil sensor in the AUV is inexpensive and compact so that the system can be equipped on a wide range of AUVs.