Automatic Nonnutritive Suck Waveform Discrimination and Feature Extraction in Preterm Infants

Background and Objective: The emergence of the nonnutritive suck (NNS) pattern in preterm infants reflects the integrity of the brain and is used by clinicians in the neonatal intensive care unit (NICU) to assess feeding readiness and oromotor development. A critical need exists for an integrated software platform that provides NNS signal preprocessing, adaptive waveform discrimination, feature detection, and batch processing of big data sets across multiple NICU sites. Thus, the goal was to develop and describe a crossplatform graphical user interface (GUI) and terminal application known as NeoNNS for single and batch file time series and frequency-domain analyses of NNS compression pressure waveforms using analysis parameters derived from previous research on NNS dynamics. Methods. NeoNNS was implemented with Python and the Tkinter GUI package. The NNS signal-processing pipeline included a low-pass filter, asymmetric regression baseline correction, NNS peak detection, and NNS burst classification. Data visualizations and parametric analyses included time- and frequency-domain view, NNS spatiotemporal index view, and feature cluster analysis to model oral feeding readiness. Results. 568 suck assessment files sampled from 30 extremely preterm infants were processed in the batch mode (\u3c50 minutes) to generate time- and frequency-domain analyses of infant NNS pressure waveform data. NNS cycle discrimination and NNS burst classification yield quantification of NNS waveform features as a function of postmenstrual age. Hierarchical cluster analysis (based on the Tsfresh python package and NeoNNS) revealed the capability to label NNS records for feeding readiness. Conclusions. NeoNNS provides a versatile software platform to rapidly quantify the dynamics of NNS development in time and frequency domains at cribside over repeated sessions for an individual baby or among large numbers of preterm infants at multiple hospital sites to support big data analytics. The hierarchical cluster feature analysis facilitates modeling of feeding readiness based on quantitative features of the NNS compression pressure waveform

Automatic Nonnutritive Suck Waveform Discrimination and Feature Extraction in Preterm Infants

Background and Objective: +e emergence of the nonnutritive suck (NNS) pattern in preterm infants reflects the integrity of the brain and is used by clinicians in the neonatal intensive care unit (NICU) to assess feeding readiness and oromotor development. A critical need exists for an integrated software platform that provides NNS signal preprocessing, adaptive waveform discrimination, feature detection, and batch processing of big data sets across multiple NICU sites. +us, the goal was to develop and describe a crossplatform graphical user interface (GUI) and terminal application known as NeoNNS for single and batch file time series and frequency-domain analyses of NNS compression pressure waveforms using analysis parameters derived from previous research on NNS dynamics. Methods. NeoNNS was implemented with Python and the Tkinter GUI package. +e NNS signal-processing pipeline included a low-pass filter, asymmetric regression baseline correction, NNS peak detection, and NNS burst classification. Data visualizations and parametric analyses included time- and frequency-domain view, NNS spatiotemporal index view, and feature cluster analysis to model oral feeding readiness. Results. 568 suck assessment files sampled from 30 extremely preterm infants were processed in the batch mode (\u3c50 minutes) to generate time- and frequency-domain analyses of infant NNS pressure waveform data. NNS cycle discrimination and NNS burst classification yield quantification of NNS waveform features as a function of postmenstrual age. Hierarchical cluster analysis (based on the Tsfresh python package and NeoNNS) revealed the capability to label NNS records for feeding readiness. Conclusions. NeoNNS provides a versatile software platform to rapidly quantify the dynamics of NNS development in time and frequency domains at cribside over repeated sessions for an individual baby or among large numbers of preterm infants at multiple hospital sites to support big data analytics. +e hierarchical cluster feature analysis facilitates modeling of feeding readiness based on quantitative features of the NNS compression pressure waveform

Editorial: Advances of Targeted Therapy in Gynecologic Malignancies

Gynecologic malignancies, a heterogeneous group of female reproductive system tumors (including ovarian, endometrial, cervical, vaginal and vulvar cancer), are the second most commonly diagnosed female cancers worldwide (1). Among these cancers, cervical cancer is the most common malignancy of the female genital tract, followed by endometrial cancer and ovarian cancer. Most patients with early stage gynecological cancers are cured with surgery alone or a combination of surgery, radiation and chemotherapy. However, patients with advanced, recurrent, or metastatic disease lack effective therapeutic options and often have poor prognosis despite appropriate managements. Therefore, there is an urgent need to develop new-targeted therapies based on the molecular features of gynecological cancers and their microenvironment

Accuracy of 2D and point shear wave elastography-based measurements for diagnosis of esophageal varices: a systematic review and meta-analysis

PURPOSEThe aim of this meta-analysis is to summarize the diagnostic accuracies of point shear wave elas- tography (pSWE) and two-dimensional (2D) SWE for esophageal varices (EV) and varices needing treatment (VNT).METHODSWe conducted a systematic review and meta-analysis of diagnostic accuracy studies. We searched for studies reporting the EV and VNT diagnostic accuracy of pSWE and 2D SWE using PubMed Cen- tral, SCOPUS, MEDLINE, Embase, and Cochrane databases. STATA software“Midas”package was used for meta-analysis.RESULTSA total of 24 studies with 3867 patients were included in the review. Pooled score sensitivities of pSWE were 91% (95% CI, 80%-96%) for EV, and 94% (95% CI, 86%-97%) for VNT. Pooled score sensi- tivities of 2D SWE were 78% (95% CI, 69%-85%) for EV, and 79% (95% CI, 72%-85%) for VNT. Pooled score specificities of pSWE were 70% (95% CI, 60%-78%) for EV, and 59% (95% CI, 40%-75%) for VNT. Pooled score specificities of 2D SWE for EV were 79% (95% CI, 72%-85%) 72% (95% CI, 66%-77%) for VNT. We found significant heterogeneity for all the elastography-based measurements with the chi- square test results and an I2 statistic >75%.CONCLUSIONBoth pSWE and 2D SWE can diagnose EV and VNT with moderate diagnostic accuracy. Further large- scale setting-specific longitudinal studies are required to establish the best modality

Tourist choice processing: evaluating decision rules and methods of their measurement

A detailed understanding of decision rules is essential in order to better explain consumption behavior, yet the variety of decision rules used have been somewhat neglected in tourism research. This study adopts an innovative method, greedoid analysis, to estimate a noncompensatory type of decision rule known as lexicographic by aspect (LBA). It is quite different from the weighted additive (WADD) model commonly assumed in tourism studies. By utilizing an experimental research design, this study enables the evaluation of the two types of decision rules regarding their predictive and explanatory power. Additionally, we introduce a novel evaluation indicator (“cost”), which allows further investigation of the heterogeneity in the use of decision rules. The results suggest that although the out-of-sample accuracy is lower, the LBA model has a better explanatory performance on respondents’ preference order. Moreover, the different perspective provided by the LBA model is useful for obtaining managerial implications

Integrated analysis identifies microRNA-195 as a suppressor of Hippo-YAP pathway in colorectal cancer

Figure S2. KEGG cell signaling pathway was shown for HIPPO pathway. The most significantly enriched by the predicted targets of miR-195 (P = 6.47E-05). Red frame shows the predicted miR-195 targets. (TIF 83 kb

Longitudinal associations of concurrent falls and fear of falling with functional limitations differ by living alone or not

BackgroundFalls and fear of falling (FOF) are independent risk factors for functional limitations in older adults. However, the combined effect of falls and FOF on functional limitations and the moderating role of living alone or not is unclear. We aimed to examine (1) the independent and combined effect of falls and FOF on functional limitations in older adults and (2) whether living alone moderates these associations.MethodsWe used data from the National Health and Aging Trends Study (NHATS) and included 5,950 U.S. community-dwelling older adults aged 65 and older from Round 1 (Year 2011) and Round 2 (Year 2012). Falls and FOF were ascertained by asking participants whether they had any falls in the last year and whether they had worried about falling in the previous month at R1. Assessed functional limitations included any difficulties with mobility, self-care, or household activities at R2. Poisson regression models were used to examine the longitudinal associations of falls and FOF with functional limitations and the moderation effects of baseline living alone.ResultsOf the 5,950 participants, 16.3% had falls only; 14.3% had FOF only; 14.3% had both, and 55.1% had neither at baseline. In the adjusted model, those who experienced concurrent falls and FOF in R1 had a higher risk of functional limitations at R2 than those with neither (Mobility: Incidence risk ratio [IRR] = 1.34, 95% CI: 1.24–1.45; Self-care: IRR = 1.18, 95% CI: 1.11–1.26; Household: IRR = 1.20, 95% CI: 1.11–1.30). Moreover, living alone significantly moderated the longitudinal associations of concurrent falls and FOF with mobility activity limitations.ConclusionThe findings suggest that strategies to improve falls and FOF together could potentially help prevent functional limitations. Older adults who live with others and have falls or FOF should receive interventions to promote their mobility activities

A New Convolutional Neural Network Architecture for Automatic Segmentation of Overlapping Human Chromosomes

In clinical diagnosis, karyotyping is carried out to detect genetic disorders due to chromosomal aberrations. Accurate segmentation is crucial in this process that is mostly operated by experts. However, it is time-consuming and labor-intense to segment chromosomes and their overlapping regions. In this research, we look into the automatic segmentation of overlapping pairs of chromosomes. Different from standard semantic segmentation applications that mostly detect object regions or boundaries, this study attempts to predict not only non-overlapping regions but also the order of superposition and opaque regions of the underlying chromosomes. We propose a novel convolutional neural network called Compact Seg-UNet with enhanced deep feature learning capability and training efficacy. To address the issue of unrealistic images in use characterized by overlapping regions of higher color intensities, we propose a novel method to generate more realistic images with opaque overlapping regions. On the segmentation performance of overlapping chromosomes for this new dataset, our Compact Seg-UNet model achieves an average IOU score of 93.44% ± 0.26 which is significantly higher than the result of a simplified U-Net reported by literature by around 6.08%. The corresponding F1 score also increases from 0.9262 ± 0.1188 to 0.9596 ± 0.0814

Evaluation of the antitumor effects of c-Myc-Max heterodimerization inhibitor 100258-F4 in ovarian cancer cells

Abstract Epithelial ovarian carcinoma is the most lethal gynecological cancer due to its silent onset and recurrence with resistance to chemotherapy. Overexpression of oncogene c-Myc is one of the most frequently encountered events present in ovarian carcinoma. Disrupting the function of c-Myc and its downstream target genes is a promising strategy for cancer therapy. Our objective was to evaluate the potential effects of small-molecule c-Myc inhibitor, 10058-F4, on ovarian carcinoma cells and the underlying mechanisms by which 10058-F4 exerts its actions. Using MTT assay, colony formation, flow cytometry and Annexin V FITC assays, we found that 10058-F4 significantly inhibited cell proliferation of both SKOV3 and Hey ovarian cancer cells in a dose dependent manner through induction of apoptosis and cell cycle G1 arrest. Treatment with 10058-F4 reduced cellular ATP production and ROS levels in SKOV3 and Hey cells. Consistently, primary cultures of ovarian cancer treated with 10058-F4 showed induction of caspase-3 activity and inhibition of cell proliferation in 15 of 18 cases. The response to 10058-F4 was independent the level of c-Myc protein over-expression in primary cultures of ovarian carcinoma. These novel findings suggest that the growth of ovarian cancer cells is dependent upon c-MYC activity and that targeting c-Myc-Max heterodimerization could be a potential therapeutic strategy for ovarian cancer