Notes

[Management involving surgical complications right after extented warfarin treatment inside a common operative hospital].
The prediction of hypertensive disorders in pregnancy (HDP) mainly involves various aspects such as maternal characteristics and biomarkers.

We aimed to study the effect of the HDP prediction model with or without placental growth factor (PlGF).

This study used maternal factors and PlGF, and standardized the data uniformly. At 12-20 weeks, the comprehensive comparison of model quality with or without PlGF was conducted by logistic regression.

The area under curve and the model accuracy of the model with PlGF were higher than those of the model without PlGF. The accuracy of the model with PlGF was above 90%.

Adding PlGF to the model for predicting HDP improved the accuracy and effectiveness of the model. This study confirmed the predictive performance of PlGF.
Adding PlGF to the model for predicting HDP improved the accuracy and effectiveness of the model. This study confirmed the predictive performance of PlGF.
The SARS-CoV-2 pneumonia infection is associated with high rates of hospitalization and mortality and this has placed healthcare systems under strain. Our study provides a novel method for the progress prediction, clinical treatment and prognosis of NCP, and has important clinical value for timely treatment of severe NCP patients.

To summarize the clinical features and severe illness risk factors of the patients with novel coronavirus pneumonia (NCP), in order to provide support for the progression prediction, clinical treatment and prognosis of NCP patients.

A total of 196 NCP patients treated in our hospital from January 25, 2020 to June 21, 2020 were divided into the severe group and the mild group. The clinical features of the two groups were analyzed and compared. The risk factors were explored by using multivariate logistic regression, and the receiver operating characteristic (ROC) curve was obtained. The correlations of the risk factors with the prognosis of NCP were investigated combined with tecreased, while the levels of creatine kinase (CK), lactate dehydrogenase (LDH), c-reactive protein (CRP), erythrocyte sedimentation rate (ESR) and D-dimer (D-D) increased. 44 cases (22.45%) were found to be severely ill. The multivariate logistic regression analysis demonstrated that age, underlying disease, length of hospital stay, body mass index (BMI), LDH, chest CT visual score, absolute lymphocyte count (ALC) and CRP were risk factors for severe.
Digital hearing aids are based on technology that amplifies sound and removes noise according to the frequency of hearing loss in hearing loss patients. However, within the noise removed is a warning sound that alert the listener; the listener may be exposed to danger because the warning sound is not recognized.

In this paper, a deep learning model was used to improve these limits and propose a method to distinguish the warning sound in speech signals mixed with noise. In addition, the improved speech and warning sound were derived by removing noise present in the classification sound signals.

To classify the sound dataset, an adaptive convolution filter that changes according to two signals is proposed. The proposed convolution filter is applied to the PCNNs model to analyze the characteristics of the time and frequency domains of the dataset and classify the presence or absence of warning sound. In addition, the CEDN model was used to improve the intelligibility of the warning and the speech in the simitation in clearly recognizing speech as the noise ratio increased.
There is a great demand for the extraction of organ models from three-dimensional (3D) medical images in clinical medicine diagnosis and treatment.

We aimed to aid doctors in seeing the real shape of human organs more clearly and vividly.

The method uses the minimum eigenvectors of Laplacian matrix to automatically calculate a group of basic matting components that can properly define the volume image. These matting components can then be used to build foreground images with the help of a few user marks.

We propose a direct 3D model segmentation method for volume images. check details This is a process of extracting foreground objects from volume images and estimating the opacity of the voxels covered by the objects.

The results of segmentation experiments on different parts of human body prove the applicability of this method.
The results of segmentation experiments on different parts of human body prove the applicability of this method.
Mastication improves cognitive function by activating cerebral cortical activity, and it is important to demonstrate the cognitive effects of masticatory training using a variety of different interventions.

This study aimed to evaluate the effects of masticatory exercise on cognitive function in healthy older adults living in the community.

For six weeks, twelve participants performed a masticatory exercise using a NOSICK exerciser device, and thirteen subjects performed daily life without masticatory exercises. Trail Making Test, Digit Span Test, and Stroop test were used to measure the cognitive function.

The participants in the experimental group showed significant improvements in TMT-A/B (p= 0.001 and 0.004), DST-forward (p= 0.001), and ST-word (p= 0.001). The effect sizes after the intervention were calculated as (1.2 and 0.8) for TMT-A/B, (0.8 and 0.2) for Digit Span Test forward/backward, and (0.6 and 0.2) for Stroop test color/word.

We suggest that the masticatory exercises improve cognitive function in healthy older adults. Therefore, masticatory exercises can be used as a therapeutic exercise during cognitive rehabilitation.
We suggest that the masticatory exercises improve cognitive function in healthy older adults. Therefore, masticatory exercises can be used as a therapeutic exercise during cognitive rehabilitation.
DNA sequence alignment is one of the most fundamental and important operation to identify which gene family may contain this sequence, pattern matching for DNA sequence has been a fundamental issue in biomedical engineering, biotechnology and health informatics.

To solve this problem, this study proposes an optimal multi pattern matching with wildcards for DNA sequence.

This proposed method packs the patterns and a sliding window of texts, and the window slides along the given packed text, matching against stored packed patterns.

Three data sets are used to test the performance of the proposed algorithm, and the algorithm was seen to be more efficient than the competitors because its operation is close to machine language.

Theoretical analysis and experimental results both demonstrate that the proposed method outperforms the state-of-the-art methods and is especially effective for the DNA sequence.
Theoretical analysis and experimental results both demonstrate that the proposed method outperforms the state-of-the-art methods and is especially effective for the DNA sequence.
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