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.

Rapid detection of murine-derived ingredients in meat products using real-time enzymatic recombinase amplification.

BACKGROUND: Accurate identification of meat species is critical to prevent economic fraud and safeguard public health. The use of inappropriate meat sources, such as murine, poses significant health risks because of potential contamination with pathogens and allergens, leading to foodborne illnesses. The present study aimed to develop a novel real-time enzymatic recombinase amplification (ERA) method for the rapid and specific detection of murine DNA in meat products. RESULTS: A novel ERA primer and probe set was designed, targeting a murine-specific single-copy nuclear gene identified through bioinformatics analysis. The assay demonstrates high specificity, showing no amplification in commonly consumed meats, other animals or major crops. Additionally, it exhibits remarkable sensitivity, detecting as few as five copies of murine genomic DNA. For practical application, the ERA method could effectively identify mouse DNA in laboratory-prepared samples at concentrations as low as 0.5% and also quantify samples with mouse DNA content as low as 5%. It also accurately detects the presence of murine-derived ingredients in commercially available meat products. The detection process is straightforward, utilizing a simple isothermal device for incubation, blue light excitation and a smartphone camera for result interpretation. This rapid analysis can be completed within 20 min. CONCLUSION: The newly developed real-time ERA method provides a valuable tool for standardizing meat trade practices, promoting food safety and enhancing consumer confidence in the authenticity of meat products. © 2024 Society of Chemical Industry.

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.

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.

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.

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.

Smooth 3D Dubins Curves Based Mobile Data Gathering in Sparse Underwater Sensor Networks.

Sensory data collection is one of the most important concerns in underwater sensor networks (USNs). Because full connectivity cannot be guaranteed, mobile data gathering with autonomous underwater vehicles (AUVs) is widely used in sparse three-dimensional (3D) USNs to solve energy-imbalance problems between different sensor nodes. AUVs with relatively abundant energy and storage can collect sensory data from one sensor node to transmit to another node, so as to avoid energy-intensive multi-hop transmission. As a result, movement control strategy and data collecting path planning for AUVs are very crucial for the performance of data acquisition. This paper proposes a smooth 3D Dubins curves based mobile data gathering mechanism to overcome the kinematic nonholonomic constraints of AUVs. The objective of our proposed method is to collect sensory data along smooth 3D Dubins paths, which are interpolated by continuous Bezier curves in the Z-axis from 2D Dubins curves. Extensive simulation results verify that the proposed method has a more efficient performance in terms of path smoothness and energy consumption; thus it is very suitable for mobile data collection in 3D underwater sensor networks.

Task Assignment and Path Planning for Multiple Autonomous Underwater Vehicles Using 3D Dubins Curves †.

This paper investigates the task assignment and path planning problem for multiple AUVs in three dimensional (3D) underwater wireless sensor networks where nonholonomic motion constraints of underwater AUVs in 3D space are considered. The multi-target task assignment and path planning problem is modeled by the Multiple Traveling Sales Person (MTSP) problem and the Genetic Algorithm (GA) is used to solve the MTSP problem with Euclidean distance as the cost function and the Tour Hop Balance (THB) or Tour Length Balance (TLB) constraints as the stop criterion. The resulting tour sequences are mapped to 2D Dubins curves in the X - Y plane, and then interpolated linearly to obtain the Z coordinates. We demonstrate that the linear interpolation fails to achieve G 1 continuity in the 3D Dubins path for multiple targets. Therefore, the interpolated 3D Dubins curves are checked against the AUV dynamics constraint and the ones satisfying the constraint are accepted to finalize the 3D Dubins curve selection. Simulation results demonstrate that the integration of the 3D Dubins curve with the MTSP model is successful and effective for solving the 3D target assignment and path planning problem.

Association of the systemic immune-inflammation index with anemia: a population-based study.

Background: Inflammation has been reported to be related to anemia. As a novel inflammatory marker, Systemic immune-inflammation index (SII) has not been studied with Anemia. The aim of this study was to investigate the possible relationship between SII and anemia. Methods: This retrospective cross-sectional survey was conducted using data from the 2005-2018 National Health and Nutrition Examination Survey (NHANES) population. In total, 19851 American adults aged ≥18 years were included. SII was calculated as the platelet count×neutrophil count/lymphocyte count. Anemia was defined as hemoglobin (Hgb) levels of < 13 g/dL in males and < 12 g/dL in females. Logistic regression analyses, subgroup analyses and sensitivity analyses were performed to investigate the relationship between SII and anemia. Results: Our study included a total of 19851 patients, of which 1501 (7.6%) had anemia. After adjusting for all covariates, the multivariate logistic regression analysis showed that a higher SII (In-transform) level was associated with increased likelihood of anemia (OR=1.51, 95% CI: 1.36-1.68, P<0.001). The association between SII and anemia exhibited a nonlinear manner. The positive correlation between SII and anemia was related to the severity of anemia. Subgroup analysis showed that there was no significant dependence on age, family income, body mass index, hypertension, kidney disease and cancer except gender on this positive association. Furthermore, sensitivity analyses confirmed the robustness of our results. Conclusion: Our study demonstrated that SII was positively associated with anemia especially among female participants. And this positive correlation was related to the severity of anemia. Further large-scale prospective studies are still needed to analyze the role of SII in anemia.

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.

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.

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.

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.

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.

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.