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Advanced systemic mastocytosis (AdvSM) is a rare hematologic neoplasm driven by the KIT D816V mutation and associated with poor survival. This phase 1 study ( NCT02561988 ) evaluated avapritinib (BLU-285), a selective KIT D816V inhibitor, in patients with AdvSM. The primary endpoints were the maximum tolerated dose, recommended phase 2 dose and safety of avapritinib. Secondary endpoints included overall response rate and changes in measures of mast cell burden. Avapritinib was evaluated at doses of 30–400 mg once daily in 86 patients, 69 with centrally confirmed AdvSM. Maximum tolerated dose was not reached, and 200 mg and 300 mg daily were studied in dose-expansion cohorts. The most frequent adverse events observed were periorbital edema (69%), anemia (55%), diarrhea (45%), thrombocytopenia (44%) and nausea (44%). Intracranial bleeding occurred in 13% overall, but in only 1% of patients without severe thrombocytopenia (platelets <50 × 10 9 /l). In 53 response-evaluable patients, the overall response rate was 75%. The complete remission rate was 36%. Avapritinib elicited ≥50% reductions in marrow mast cells and serum tryptase in 92% and 99% of patients, respectively. Avapritinib induced deep and durable responses, including molecular remission of KIT D816V in patients with AdvSM, and was well tolerated at the recommended phase 2 dose of 200 mg daily. In a phase 1 trial of patients with advanced systemic mastocytosis, avapritinib, a selective KIT inhibitor, was generally well tolerated, elicited durable clinical responses and led to reductions in mast cell disease burden.

Advanced systemic mastocytosis (AdvSM) is a rare, KIT D816V-driven hematologic neoplasm characterized by mast cell infiltration and shortened survival. We report the results of a prespecified interim analysis of an ongoing pivotal single-arm phase 2 trial (no. NCT03580655 ) of avapritinib, a potent, selective KIT D816V inhibitor administered primarily at a once-daily starting dose of 200 mg in patients with AdvSM ( n  = 62). The primary endpoint was overall response rate (ORR). Secondary endpoints included mean baseline change in AdvSM–Symptom Assessment Form Total Symptom Score and quality of life, time to response, duration of response, progression-free survival, overall survival, changes in measures of disease burden and safety. The primary endpoint was successfully met ( P  = 1.6 × 10 -9 ), with an ORR of 75% (95% confidence interval 57–89) in 32 response-evaluable patients with AdvSM who had sufficient follow-up for response assessment, including 19% with complete remission with full or partial hematologic recovery. Reductions of ≥50% from baseline in serum tryptase (93%), bone marrow mast cells (88%) and KIT D816V variant allele fraction (60%) were observed. The most frequent grade ≥3 adverse events were neutropenia (24%), thrombocytopenia (16%) and anemia (16%). Avapritinib demonstrated a high rate of clinical, morphological and molecular responses and was generally well tolerated in patients with AdvSM. In a prespecified interim analysis of a pivotal phase 2 trial, avapritinib, a selective KIT inhibitor, elicited robust clinical and molecular responses, was generally well tolerated and led to improved patient-reported outcomes in patients with advanced systemic mastocytosis.

Background The exclusive breastfeeding condition in China is not optimism now. Maternal breastfeeding self-efficacy stands as a pivotal factor influencing exclusive breastfeeding. Interestingly, studies have suggested that father support breastfeeding self-efficacy is a pivotal mediator in infant breastfeeding. Thus, the current research aimed to investigate the association between father support breastfeeding self-efficacy and exclusive breastfeeding at six weeks postpartum, and the influencing factors of father support breastfeeding self-efficacy. Methods This research was structured as a multi-centre cross-sectional study, involving 328 fathers, whose partners were six weeks postpartum, and recruited from two public hospitals in Southeast China. Self-designed demographic questionnaires, namely, Father Support Breastfeeding Self-Efficacy Scale-Short Form, Breastfeeding Knowledge Questionnaire, Positive Affect Scale and the 14-item Fatigue Scale, were applied. Descriptive statistics, Chi-square test, logistic regression univariate analysis and multiple linear regression were used to analyse data. Results Results indicate a significant difference between the infant feeding methods at six weeks postpartum and fathers with different levels of support breastfeeding self-efficacy ( p  < 0.05). Particularly, father support breastfeeding self-efficacy positively affected exclusive breastfeeding at six weeks postpartum after adjusting all the demographic characteristics of fathers (OR: 2.407; 95% CI: 1.017–4.121). Moreover, results show that the significant influencing factors of father support breastfeeding self-efficacy include breastfeeding knowledge, fatigue, positive affect, successfully experienced helping mothers to breastfeed, spousal relationships and companionship time. Conclusions High-level father support breastfeeding self-efficacy effectively increased exclusive breastfeeding rate at six weeks postpartum. To enhance the exclusive breastfeeding rate, nurses or midwives can endeavour to design educational programmes or take supportive interventions customised for fathers, such as enhancing their breastfeeding knowledge education, reducing fatigue and mobilising positive emotions, thereby bolstering paternal self-efficacy in breastfeeding.

Background As high-throughput genomic technologies become accurate and affordable, an increasing number of data sets have been accumulated in the public domain and genomic information integration and meta-analysis have become routine in biomedical research. In this paper, we focus on microarray meta-analysis, where multiple microarray studies with relevant biological hypotheses are combined in order to improve candidate marker detection. Many methods have been developed and applied in the literature, but their performance and properties have only been minimally investigated. There is currently no clear conclusion or guideline as to the proper choice of a meta-analysis method given an application; the decision essentially requires both statistical and biological considerations. Results We performed 12 microarray meta-analysis methods for combining multiple simulated expression profiles, and such methods can be categorized for different hypothesis setting purposes: (1) HS A : DE genes with non-zero effect sizes in all studies, (2) HS B : DE genes with non-zero effect sizes in one or more studies and (3) HS r : DE gene with non-zero effect in \"majority\" of studies. We then performed a comprehensive comparative analysis through six large-scale real applications using four quantitative statistical evaluation criteria: detection capability, biological association, stability and robustness. We elucidated hypothesis settings behind the methods and further apply multi-dimensional scaling (MDS) and an entropy measure to characterize the meta-analysis methods and data structure, respectively. Conclusions The aggregated results from the simulation study categorized the 12 methods into three hypothesis settings ( HS A , HS B , and HS r ). Evaluation in real data and results from MDS and entropy analyses provided an insightful and practical guideline to the choice of the most suitable method in a given application. All source files for simulation and real data are available on the author’s publication website.

Entropy images, representing the complexity of original fundus photographs, may strengthen the contrast between diabetic retinopathy (DR) lesions and unaffected areas. The aim of this study is to compare the detection performance for severe DR between original fundus photographs and entropy images by deep learning. A sample of 21,123 interpretable fundus photographs obtained from a publicly available data set was expanded to 33,000 images by rotating and flipping. All photographs were transformed into entropy images using block size 9 and downsized to a standard resolution of 100 × 100 pixels. The stages of DR are classified into 5 grades based on the International Clinical Diabetic Retinopathy Disease Severity Scale: Grade 0 (no DR), Grade 1 (mild nonproliferative DR), Grade 2 (moderate nonproliferative DR), Grade 3 (severe nonproliferative DR), and Grade 4 (proliferative DR). Of these 33,000 photographs, 30,000 images were randomly selected as the training set, and the remaining 3,000 images were used as the testing set. Both the original fundus photographs and the entropy images were used as the inputs of convolutional neural network (CNN), and the results of detecting referable DR (Grades 2–4) as the outputs from the two data sets were compared. The detection accuracy, sensitivity, and specificity of using the original fundus photographs data set were 81.80%, 68.36%, 89.87%, respectively, for the entropy images data set, and the figures significantly increased to 86.10%, 73.24%, and 93.81%, respectively (all p values <0.001). The entropy image quantifies the amount of information in the fundus photograph and efficiently accelerates the generating of feature maps in the CNN. The research results draw the conclusion that transformed entropy imaging of fundus photographs can increase the machinery detection accuracy, sensitivity, and specificity of referable DR for the deep learning-based system.

Within Internet of Things (IoT) sensors, the challenge is how to dig out the potentially valuable information from the collected data to support decision making. This paper proposes a method based on machine learning to predict long cycle maintenance time of wind turbines for efficient management in the power company. Long cycle maintenance time prediction makes the power company operate wind turbines as cost-effectively as possible to maximize the profit. Sensor data including operation data, maintenance time data, and event codes are collected from 31 wind turbines in two wind farms. Data aggregation is performed to filter out some errors and get significant information from the data. Then, the hybrid network is built to train the predictive model based on the convolutional neural network (CNN) and support vector machine (SVM). The experimental results show that the prediction of the proposed method reaches high accuracy, which helps drive up the efficiency of wind turbine maintenance.

Background Indolent systemic mastocytosis (ISM) is a rare, clonal mast cell neoplasm characterized by severe, unpredictable symptoms. The Indolent Systemic Mastocytosis Symptom Assessment Form (ISM-SAF) items compose a Total Symptom Score (TSS), Gastrointestinal Symptom Score (GSS), and Skin Symptom Score (SSS) to assess symptom severity. This study evaluated the psychometric performance of ISM-SAF among ISM patients. Methods In PIONEER, a Phase 2 trial evaluating safety and efficacy of selective kinase inhibitor avapritinib in patients with ISM, the 12-item ISM-SAF was administered daily. Psychometric evaluation of score reliability, validity, and clinical interpretation was conducted using the trial data. Results Thirty-eight patients contributed to analyses (78.9% female; mean age = 49). Baseline internal consistency reliability (α) for bi-weekly TSS, GSS, and SSS was 0.86, 0.83, and 0.82, respectively. Test–retest reliability among patients exhibiting no change in Patient Global Impression of Symptom Severity (PGIS) between Baseline and Day 15 exceeded 0.74 universally. Construct validity and known-groups analysis showed moderate to strong ISM-SAF score correlation (r = 0.382–0.881) to supportive patient-reported questionnaires (e.g., PGIS and Mastocytosis Quality of Life Questionnaire) symptom and skin scores, and ability to distinguish among clinically unique groups. Correlations of ISM-SAF and other assessment change scores reflect evidence of score sensitivity. Clinically important difference and response estimates were 7–10 and 19, respectively. Discussion ISM-SAF produced reliable, construct-valid, sensitive scores when administered in PIONEER to patients in the target population. Results of this study support the use of the ISM-SAF as a reliable and valid measure to evaluate disease symptomology in ISM patients. Trial registration ClinicalTrials.gov, NCT03731260. Registered 10 October 2018, https://clinicaltrials.gov/ct2/show/study/NCT03731260 .

Serum lactate dehydrogenase (LDH) is a prognostic factor for patients with stage IV melanoma. To gain insights into the biology underlying this prognostic factor, we analyzed total serum LDH, serum LDH isoenzymes, and serum lactate in up to 49 patients with metastatic melanoma. Our data demonstrate that high serum LDH is associated with a significant increase in LDH isoenzymes 3 and 4, and a decrease in LDH isoenzymes 1 and 2. Since LDH isoenzymes play a role in both glycolysis and oxidative phosphorylation (OXPHOS), we subsequently determined using tissue microarray (TMA) analysis that the levels of proteins associated with mitochondrial function, lactate metabolism, and regulators of glycolysis were all elevated in advanced melanomas compared with nevic melanocytes. To investigate whether in advanced melanoma, the glycolysis and OXPHOS pathways might be linked, we determined expression of the monocarboxylate transporters (MCT) 1 and 4. Analysis of a nevus-to-melanoma progression TMA revealed that MCT4, and to a lesser extend MCT1, were elevated with progression to advanced melanoma. Further analysis of human melanoma specimens using the Seahorse XF24 extracellular flux analyzer indicated that metastatic melanoma tumors derived a large fraction of energy from OXPHOS. Taken together, these findings suggest that in stage IV melanomas with normal serum LDH, glycolysis and OXPHOS may provide metabolic symbiosis within the same tumor, whereas in stage IV melanomas with high serum LDH glycolysis is the principle source of energy.

The secondary motor cortex (MOs) plays crucial roles in cognitive and executive processes and has reciprocal connections with numerous cortices in rodents. However, descriptions of the neuronal morphologies and projection patterns of the MOs at the level of a single neuron are lacking, severely hindering the comprehensive understanding of the wiring diagram of the MOs. Herein, we used a Cre-dependent adeno-associated virus (AAV) to fluorescently label ~80 pyramidal neurons nearby or in the MOs and acquired an uninterrupted whole-brain 3D dataset at a voxel resolution of 0.2 × 0.2 × 1 μm with a whole-brain fluorescence imaging system (fMOST). Based on our 3D dataset, we reconstructed the complete morphologies of 36 individual intratelencephalic (IT) neurons nearby or in the MOs and analyzed the projection patterns and projection strengths of these neurons at a single-neuron level based on several parameters, including the projection areas, the total number of branches, the fiber length, and the total number of terminal tips. We obtained a neuron with an axonal length of 318.43 mm, which is by far the longest reported axonal length. Our results show that all individual neurons in the MOs, regardless of whether they are located in layer 2/3 or layer 5, display diverse projection patterns and projection strengths, implying that these neurons might be involved in different brain circuits at different intensities. The results lay a solid foundation for exploring the relationship between neuronal morphologies and behavioral functions of the MOs at the level of a single neuron.

The red imported fire ant (RIFA, Solenopsis invicta) is an exotic aggressive pest that is notorious for its ability to seriously harm humans and animals, cause economic loss to agriculture, and damage ecosystems. This is the first study to validate the capability of filter paper adsorption as a feasible odor bearer of RIFAs and evaluate its use in detection dog training. Two live RIFA-experienced detection dogs achieved a mean 92% positive indication rate (PIR) on RIFA-scented papers with a relatively low false response rate (0.8%). The similar accuracies in recognizing live RIFAs (96%) and scented papers (92%) suggest that a filter paper is an effective odor reservoir. After training with live RIFA and scented filter papers, both RIFA-experienced and inexperienced detection dogs successfully indicated filter papers that were scented with at least 10 RIFAs for 4 h with a high PIR (>93%) and low false response rate (2%). Detection dogs correctly recognized the filter papers scented by 10 RIFAs for 24 h with a 97.6% PIR. Even for scented samples stored at −20 °C and 4 °C for 13 weeks, the positive indication rates (PIRs) were as high as 90%. These results suggest that filter paper is an effective RIFA odor bearer, and the scent can be maintained at least 13 weeks for dog identification. After RIFA-scented paper training, detection dogs showed high (>95%) PIRs for both RIFA-scented paper and live RIFAs and also successfully performed field studies. Using filter paper as a RIFA odor bearer is an effective and economical method for detection dog training and RIFA identification.