Analysis on the Effects of Whole-process Exercise Combined with Dietary Nutrition Intervention in the Treatment of Gestational Diabetes

Objective: To investigate the effect of whole-process exercise combined with dietary nutrition intervention in the treatment of gestational diabetes mellitus. Methods: A total of 84 patients with gestational diabetes admitted to our hospital within 1 year from 2022.05 to 2023.05 were selected as research subjects, and they were divided into control group (42 cases, using conventional intervention) and observation group (42 cases, using whole-process exercise combined with dietary nutrition intervention) according to the random number table method. The treatment effects of the two groups were analyzed. Results: Both groups achieved certain results after receiving the intervention, but the blood glucose level, weight gain level, maternal and infant outcomes of the observation group after the whole process of exercise combined with dietary nutrition intervention were better than those in the control group, and the differences were statistically significant (P<0.05). Conclusion: The use of whole-process exercise combined with dietary nutrition intervention in patients with gestational diabetes mellitus can effectively reduce their blood glucose level, control their weight gain, and reduce the risk of adverse maternal and infant outcomes

Breast cancer metastasis to thyroid: a retrospective analysis

Background: Breast cancers metastasizing to thyroid gland are relatively uncommon in clinical practice.Objective: Retrospective analysis of data from breast cancer patients with thyroid metastasis (TM).Methods: The US suspected, fine-needle aspiration cytology (FNAC) confirmed TM in breast cancer patients, treated between 2005 and 2015 at our hospital, was retrospectively analyzed. The data were re-evaluated by the pathologist and radiologist who were blinded to the patients’ data.Results: FNAC and immunohistochemistry confirmed the ultrasonography (US) suspected TM in eight breast cancer patients. Clinically both unilateral and bilateral TM was seen, which were symptomless and metachronously (6-121 months) metastasized. Six of eight cases exhibited recurrence/distant metastasis and were treated with chemotherapy/ thyroidectomy of which two cases passed away. The remaining two patients had no recurrences/distant metastases and were treated with partial/total thyroidectomy. Post-chemotherapy US showed more homogenous thyroid parenchyma with gathering of calcification that reduced in size, revealing the sensitiveness of TM to chemotherapy.Conclusion: US was useful in screening TM in breast cancer patients. Both partial and total thyroidectomy was effective in disease free survival of isolated TM cases, with controlled primary condition. TM responded well to chemotherapy in most of the recurrent breast cancer cases with or without distant metastasis.Keywords: Thyroid, ultrasonography, breast cancer, metastasis

Neurotoxicity of nanoparticles : insight from studies in zebrafish

Nanoparticles are widely used in industry and personal care, and they inevitably end up in people's bodies and the environment. The widespread use of nanoparticles has raised new concerns about their neurotoxicity, as nanoparticles can enter the nervous system by blood-brain barrier. In neurotoxicity testing, the zebrafish provides powerful tools to overcome the limitations of other models. This paper will provide a comprehensive review of the power of zebrafish in neurotoxicity tests and the neurotoxic effects of nanoparticles, including inorganic, organic, and metal-based nanoparticles, on zebrafish from different perspectives. Such information can be used to predict not only the effects of nanoparticles on other species exposed to the aquatic environment but also the neurotoxicity of nanoparticles in humans

Structural water as an essential comonomer in supramolecular polymerization

Water is an essential comonomer in a supramolecular polymer that is used as a recyclable, water-activated glue.</jats:p

Linear brain measurement: a new screening method for cognitive impairment in elderly patients with cerebral small vessel disease

BackgroundThe old adults have high incidence of cognitive impairment, especially in patients with cerebral small vessel disease (CSVD). Cognitive impairment is not easy to be detected in such populations. We aimed to develop clinical prediction models for different degrees of cognitive impairments in elderly CSVD patients based on conventional imaging and clinical data to determine the better indicators for assessing cognitive function in the CSVD elderly.Methods210 CSVD patients were screened out by the evaluation of Magnetic Resonance Imaging (MRI). Then, participants were divided into the following three groups according to the cognitive assessment results: control, mild cognitive impairment (MCI), and dementia groups. Clinical data were collected from all patients, including demographic data, biochemical indicators, carotid ultrasound, transcranial Doppler (TCD) indicators, and linear measurement parameters based on MRI.ResultsOur results showed that the brain atrophy and vascular lesions developed progressive worsening with increased degree of cognitive impairment. Crouse score and Interuncal distance/Bitemporal distance (IUD/BTD) were independent risk factors for MCI in CSVD patients, and independent risk factors for dementia in CSVD were Crouse Score, the pulsatility index of the middle cerebral artery (MCAPI), IUD/BTD, and Sylvian fissure ratio (SFR). Overall, the parameters with high performance were the IUD/BTD (OR 2.28; 95% CI 1.26–4.10) and SFR (OR 3.28; 95% CI 1.54–6.91), and the AUC (area under the curve) in distinguishing between CSVD older adults with MCI and with dementia was 0.675 and 0.724, respectively. Linear brain measurement parameters had larger observed effect than other indexes to identify cognitive impairments in CSVD patients.ConclusionThis study shows that IUD/BTD and SFR are good predictors of cognitive impairments in CSVD elderly. Linear brain measurement showed a good predictive power for identifying MCI and dementia in elderly subjects with CSVD. Linear brain measurement could be a more suitable and novel method for screening cognitive impairment in aged CSVD patients in primary healthcare facilities, and worth further promotion among the rural population

Bridging photochemistry and photomechanics with NMR crystallography: the molecular basis for the macroscopic expansion of an anthracene ester nanorod

Crystals composed of photoreactive molecules represent a new class of photomechanical materials with the potential to generate large forces on fast timescales. An example is the photodimerization of 9-tert-butyl-anthracene ester (9TBAE) in molecular crystal nanorods that leads to an average elongation of 8%. Previous work showed that this expansion results from the formation of a metastable crystalline product. In this article, it is shown how a novel combination of ensemble oriented-crystal solid-state NMR, X-ray diffraction, and first principles computational modeling can be used to establish the absolute unit cell orientations relative to the shape change, revealing the atomic-resolution mechanism for the photomechanical response and enabling the construction of a model that predicts an elongation of 7.4%, in good agreement with the experimental value. According to this model, the nanorod expansion does not result from an overall change in the volume of the unit cell, but rather from an anisotropic rearrangement of the molecular contents. The ability to understand quantitatively how molecular-level photochemistry generates mechanical displacements allows us to predict that the expansion could be tuned from +9% to −9.5% by controlling the initial orientation of the unit cell with respect to the nanorod axis. This application of NMR-assisted crystallography provides a new tool capable of tying the atomic-level structural rearrangement of the reacting molecular species to the mechanical response of a nanostructured sample

Genome-Wide Identification and Immune Response Analysis of Serine Protease Inhibitor Genes in the Silkworm, Bombyx mori

In most insect species, a variety of serine protease inhibitors (SPIs) have been found in multiple tissues, including integument, gonad, salivary gland, and hemolymph, and are required for preventing unwanted proteolysis. These SPIs belong to different families and have distinct inhibitory mechanisms. Herein, we predicted and characterized potential SPI genes based on the genome sequences of silkworm, Bombyx mori. As a result, a total of eighty SPI genes were identified in B. mori. These SPI genes contain 10 kinds of SPI domains, including serpin, Kunitz_BPTI, Kazal, TIL, amfpi, Bowman-Birk, Antistasin, WAP, Pacifastin, and alpha-macroglobulin. Sixty-three SPIs contain single SPI domain while the others have at least two inhibitor units. Some SPIs also contain non-inhibitor domains for protein-protein interactions, including EGF, ADAM_spacer, spondin_N, reeler, TSP_1 and other modules. Microarray analysis showed that fourteen SPI genes from lineage-specific TIL family and Group F of serpin family had enriched expression in the silk gland. The roles of SPIs in resisting pathogens were investigated in silkworms when they were infected by four pathogens. Microarray and qRT-PCR experiments revealed obvious up-regulation of 8, 4, 3 and 3 SPI genes after infection with Escherichia coli, Bacillus bombysepticus, Beauveria bassiana or B. mori nuclear polyhedrosis virus (BmNPV), respectively. On the contrary, 4, 11, 7 and 9 SPI genes were down-regulated after infection with E. coli, B. bombysepticus, B. bassiana or BmNPV, respectively. These results suggested that these SPI genes may be involved in resistance to pathogenic microorganisms. These findings may provide valuable information for further clarifying the roles of SPIs in the development, immune defence, and efficient synthesis of silk gland protein

An Adaptive Target Tracking Algorithm Based on EKF for AUV with Unknown Non-Gaussian Process Noise

An adaptive target tracking method based on extended Kalman filter (TT-EKF) is proposed to simultaneously estimate the state of an Autonomous Underwater Vehicle (AUV) and an mobile recovery system (MRS) with unknown non-Gaussian process noise in homing process. In the application scenario of this article, the process noise includes the measurement noise of AUV heading and forward speed and the estimation error of MRS heading and forward speed. The accuracy of process noise covariance matrix (PNCM) can affect the state estimation performance of the TT-EKF. The variational Bayesian based algorithm is applied to estimate the process noise statistics. We use a Gaussian mixture distribution to model the non-Gaussian noisy forward speed of AUV and MRS. We use a von-Mises distribution to model the noisy heading of AUV and MRS. The variational Bayesian algorithm is applied to estimate the parameters of these distributions, and then the PNCM can be calculated. The prediction error of TT-EKF is online compensated by using a multilayer neural network, and the neural network is online trained during the target tracking process. Matlab simulation and experimental data analysis results verify the effectiveness of the proposed method

Athlete target detection method in dynamic scenario based on nonlinear filtering and YOLOv5

In this paper firefly optimization algorithm is improved, including the method of relative firefly fluorescence brightness, the method of attraction and the method of firefly position update. The dynamic step size factor and dynamic difference factor are introduced, and the improved firefly algorithm is used to optimize the particle filter, so that the particle swarm can be concentrated to the high likelihood region as much as possible, so as to ensure the overall quality of the particle swarm. In this paper, the most commonly used non-maximum suppression algorithm in the post-processing stage of target detection model is discussed. The original YOLOv5 model and the fusion model of nonlinear filtering and YOLOv5 were respectively used to simulate the two data sets after data enhancement. Some mainstream object detection models and the improved model in this paper are analyzed experimentally in two datasets. In dataset 1, the small increase in mAP value is the addition of CBAM's attention module, which increases by 2.8%. For data set 2, when the original Focal loss was replaced by VFLoss, the mAP increased to 89.8%, an increase of 0.95%. For the case where all the improvements were added, the mAP value increased by 7%

An Adaptive Target Tracking Algorithm Based on EKF for AUV with Unknown Non-Gaussian Process Noise

An adaptive target tracking method based on extended Kalman filter (TT-EKF) is proposed to simultaneously estimate the state of an Autonomous Underwater Vehicle (AUV) and an mobile recovery system (MRS) with unknown non-Gaussian process noise in homing process. In the application scenario of this article, the process noise includes the measurement noise of AUV heading and forward speed and the estimation error of MRS heading and forward speed. The accuracy of process noise covariance matrix (PNCM) can affect the state estimation performance of the TT-EKF. The variational Bayesian based algorithm is applied to estimate the process noise statistics. We use a Gaussian mixture distribution to model the non-Gaussian noisy forward speed of AUV and MRS. We use a von-Mises distribution to model the noisy heading of AUV and MRS. The variational Bayesian algorithm is applied to estimate the parameters of these distributions, and then the PNCM can be calculated. The prediction error of TT-EKF is online compensated by using a multilayer neural network, and the neural network is online trained during the target tracking process. Matlab simulation and experimental data analysis results verify the effectiveness of the proposed method