A deep learning model adjusting for infant gender, age, height, and weight to determine whether the individual infant suit ultrasound examination of developmental dysplasia of the hip (DDH)

ObjectiveTo examine the correlation between specific indicators and the quality of hip joint ultrasound images in infants and determine whether the individual infant suit ultrasound examination for developmental dysplasia of the hip (DDH).MethodWe retrospectively selected infants aged 0–6 months, undergone ultrasound imaging of the left hip joint between September 2021 and March 2022 at Shenzhen Children’s Hospital. Using the entropy weighting method, weights were assigned to anatomical structures. Moreover, prospective data was collected from infants aged 5–11 months. The left hip joint was imaged, scored and weighted as before. The correlation between the weighted image quality scores and individual indicators were studied, with the last weighted image quality score used as the dependent variable and the individual indicators used as independent variables. A Long-short term memory (LSTM) model was used to fit the data and evaluate its effectiveness. Finally, The randomly selected images were manually measured and compared to measurements made using artificial intelligence (AI).ResultsAccording to the entropy weight method, the weights of each anatomical structure as follows: bony rim point 0.29, lower iliac limb point 0.41, and glenoid labrum 0.30. The final weighted score for ultrasound image quality is calculated by multiplying each score by its respective weight. Infant gender, age, height, and weight were found to be significantly correlated with the final weighted score of image quality (P < 0.05). The LSTM fitting model had a coefficient of determination (R2) of 0.95. The intra-class correlation coefficient (ICC) for the α and β angles between manual measurement and AI measurement was 0.98 and 0.93, respectively.ConclusionThe quality of ultrasound images for infants can be influenced by the individual indicators (gender, age, height, and weight). The LSTM model showed good fitting efficiency and can help clinicians select whether the individual infant suit ultrasound examination of DDH

A novel dilated contextual attention module for breast cancer mitosis cell detection

Background and object: Mitotic count (MC) is a critical histological parameter for accurately assessing the degree of invasiveness in breast cancer, holding significant clinical value for cancer treatment and prognosis. However, accurately identifying mitotic cells poses a challenge due to their morphological and size diversity.Objective: We propose a novel end-to-end deep-learning method for identifying mitotic cells in breast cancer pathological images, with the aim of enhancing the performance of recognizing mitotic cells.Methods: We introduced the Dilated Cascading Network (DilCasNet) composed of detection and classification stages. To enhance the model’s ability to capture distant feature dependencies in mitotic cells, we devised a novel Dilated Contextual Attention Module (DiCoA) that utilizes sparse global attention during the detection. For reclassifying mitotic cell areas localized in the detection stage, we integrate the EfficientNet-B7 and VGG16 pre-trained models (InPreMo) in the classification step.Results: Based on the canine mammary carcinoma (CMC) mitosis dataset, DilCasNet demonstrates superior overall performance compared to the benchmark model. The specific metrics of the model’s performance are as follows: F1 score of 82.9%, Precision of 82.6%, and Recall of 83.2%. With the incorporation of the DiCoA attention module, the model exhibited an improvement of over 3.5% in the F1 during the detection stage.Conclusion: The DilCasNet achieved a favorable detection performance of mitotic cells in breast cancer and provides a solution for detecting mitotic cells in pathological images of other cancers

Embryonic diapause due to high glucose is related to changes in glycolysis and oxidative phosphorylation, as well as abnormalities in the TCA cycle and amino acid metabolism

IntroductionThe adverse effects of high glucose on embryos can be traced to the preimplantation stage. This study aimed to observe the effect of high glucose on early-stage embryos. Methods and resultsSeven-week-old ICR female mice were superovulated and mated, and the zygotes were collected. The zygotes were randomly cultured in 5 different glucose concentrations (control, 20mM, 40mM, 60mM and 80mM glucose). The cleavage rate, blastocyst rate and total cell number of blastocyst were used to assess the embryo quality. 40 mM glucose was selected to model high glucose levels in this study. 40mM glucose arrested early embryonic development, and the blastocyst rate and total cell number of the blastocyst decreased significantly as glucose concentration was increased. The reduction in the total cell number of blastocysts in the high glucose group was attributed to decreased proliferation and increased cell apoptosis, which is associated with the diminished expression of GLUTs (GLUT1, GLUT2, GLUT3). Furthermore, the metabolic characterization of blastocyst culture was observed in the high-glucose environment. DiscussionThe balance of glycolysis and oxidative phosphorylation at the blastocyst stage was disrupted. And embryo development arrest due to high glucose is associated with changes in glycolysis and oxidative phosphorylation, as well as abnormalities in the TCA cycle and amino acid metabolism

Case report: Clinical features of pediatric acute myeloid leukemia presenting with cardiac tamponade: a case series study and literature review

ObjectiveThis study aims to elucidate the clinical features observed in cases of pediatric acute myeloid leukemia (AML) initially presenting with cardiac tamponade and to share treatment experiences.Materials and methodsFive pediatric patients were initially diagnosed with AML accompanied by cardiac myeloid sarcoma (MS). The diagnosis was established by examining our hospital records and reviewing pertinent literature from 1990 to July 2023, accessible through MEDLINE/PubMed. We comprehensively assessed the clinical characteristics and treatment modalities employed for these patients.ResultFive pediatric patients presented with acute symptoms, including shortness of breath, malaise, cough, and fever, leading to their hospitalization. Physical examination revealed irritability, hypoxia, tachypnea, tachycardia, and hypotension. Initial detection utilized chest X-ray or echocardiogram, leading to subsequent diagnoses based on pericardial effusion and/or bone marrow examination. Two patients received chemotherapy at the time of initial diagnosis, one with cytarabine and etoposide, and the other with cytarabine and cladribine. Post-treatment, their bone marrow achieved remission, and over a 2.5-year follow-up, their cardiac function remained favorable. Unfortunately, the remaining three patients succumbed within two weeks after diagnosis, either due to receiving alternative drugs or without undergoing chemotherapy.ConclusionThis is the first and largest case series of pediatric AML patients with cardiac MS, manifesting initially with cardiac tamponade. It highlights the rarity and high mortality associated with this condition. The critical factors for reducing mortality include identifying clinical manifestations, conducting thorough physical examinations, performing echocardiography promptly, initiating early and timely pericardial drainage, and avoiding cardiotoxic chemotherapy medications

Ultra-short lifetime isomer studies from photonuclear reactions using laser-driven ultra-intense {\gamma}-ray

Isomers, ubiquitous populations of relatively long-lived nuclear excited states, play a crucial role in nuclear physics. However, isomers with half-life times of several seconds or less barely had experimental cross section data due to the lack of a suitable measuring method. We report a method of online {\gamma} spectroscopy for ultra-short-lived isomers from photonuclear reactions using laser-driven ultra-intense {\gamma}-rays. The fastest time resolution can reach sub-ps level with {\gamma}-ray intensities >10^{19}/s ({\geqslant} 8 MeV). The ^{115}In({\gamma}, n)^{114m2}In reaction (T_{1/2} = 43.1 ms) was first measured in the high-energy region which shed light on the nuclear structure studies of In element. Simulations showed it would be an efficient way to study ^{229m}Th (T_{1/2} = 7 {\mu}s), which is believed to be the next generation of nuclear clock. This work offered a unique way of gaining insight into ultra-short lifetimes and promised an effective way to fill the gap in relevant experimental data

Inter-patient ECG heartbeat classification for arrhythmia classification: a new approach of multi-layer perceptron with weight capsule and sequence-to-sequence combination

Objective: The objective of this research is to construct a method to alleviate the problem of sample imbalance in classification, especially for arrhythmia classification. This approach can improve the performance of the model without using data enhancement.Methods: In this study, we have developed a new Multi-layer Perceptron (MLP) block and have used a Weight Capsule (WCapsule) network with MLP combined with sequence-to-sequence (Seq2Seq) network to classify arrhythmias. Our work is based on the MIT-BIH arrhythmia database, the original electrocardiogram (ECG) data is classified according to the criteria recommended by the American Association for Medical Instrumentation (AAMI). Also, our method’s performance is further evaluated.Results: The proposed model is evaluated using the inter-patient paradigm. Our proposed method shows an accuracy (ACC) of 99.88% under sample imbalance. For Class N, sensitivity (SEN) is 99.79%, positive predictive value (PPV) is 99.90%, and specificity (SPEC) is 99.19%. For Class S, SEN is 97.66%, PPV is 96.14%, and SPEC is 99.85%. For Class V, SEN is 99.97%, PPV is 99.07%, and SPEC is 99.94%. For Class F, SEN is 97.94%, PPV is 98.70%, and SPEC is 99.99%. When using only half of the training sample, our method shows that the SEN of Class N and V is 0.97% and 5.27% higher than the traditional machine learning algorithm.Conclusion: The proposed method combines MLP, weight capsule network with Seq2seq network, effectively addresses the problem of sample imbalance in arrhythmia classification, and produces good performance. Our method also shows promising potential in less samples