Effect of virtual reality on upper limb function after stroke: a study of diffusion tensor imaging / 中国康复理论与实践

ObjectiveTo explore the effect of virtual reality on upper limb function in stroke patients through diffusion tensor imaging (DTI). MethodsFrom September, 2021 to March, 2023, 80 stroke patients in the Fuzhou Second General Hospital were randomly divided into control group (n = 40) and experimental group (n = 40). Both groups received routine rehabilitation, while the experimental group received virtual reality training additionally, for four weeks. They were assessed with Fugl-Meyer Assessment-Upper Extremities (FMA-UE) and Action Research Arm Test (ARAT) before treatment, after treatment and after four-week follow-up; and they were scaned with DTI to measure the fractional anisotropy (FA) and relative anisotropy (RA) of cerebral peduncle and posterior limb of inner capsule of the affected side before and after treatment. ResultsTwo cases dropped in each group. The FMA-UE and ARAT scores increased in both groups after treatment and follow-up (F > 2.790, P < 0.001), and increased more in the experimental group than in the control group (t > 2.297, P < 0.05). FA and RA in the posterior limb of inner capsule increased in both groups after treatment (t > 21.013, P < 0.001), and increased more in the experimental group (t > 2.006, P < 0.05). The d-value of FA of the posterior limb of internal capsule before and after treatment (ΔFA) was positively correlated with the d-value of FMA-UE score (r > 0.362, P < 0.05) in both groups, the ΔFA of the posterior limb of internal capsule was positively correlated with the d-value of ARAT score (r = 0.459, P < 0.01). ConclusionVirtual reality training can promote the recovery of upper limb function in stroke patients, which may associate with the conductivity of posterior limb of inner capsule.

Willingness and influencing factors of multitarget stool DNA testing among individuals receiving colonoscopy screening / 预防医学

Abstract@#Objective To investigate the willingness to receiving multitarget stool DNA (MT-sDNA) testing and factors affecting the payment among individuals receiving colonoscopy screening, so as to provide the evidence for the formulation and health economic evaluation of colorectal cancer screening strategies. @*Methods@# Individuals at ages of 40 to 75 years that received colonoscopy screening in The Affiliated Hospital of Ningbo University Medical School from August 2021 to March 2022 were sampled. Participants' demographics, living behaviors, family history, willingness to receive MT-sDNA testing and willingness to pay for MT-sDNA testing were collected using questionnaire surveys, and factors affecting the willingness to receive and pay for MT-sDNA testing were analyzed using a multivariable logistic regression model.@*Results @#A total of 546 respondents were enrolled, with a mean age of (56.25±8.66) years and including 282 men (51.65%). There were 504 respondents that were willing to receiving MT-sDNA testing (92.31%) and 480 that were willing to pay for the MT-sDNA testing (88.24%). Multivariable logistic regression analysis showed that a family history of colorectal cancer in first-degree relatives (OR=0.246, 95%CI: 0.068-0.888), history of hemorrhoids (OR=0.300, 95%CI: 0.109-0.826) resulted in low willingness to receive MT-sDNA testing, and recognizing the reliability of MT-sDNA testing (OR=5.749, 95%CI: 1.480-22.323), considering no difficulty in sampling for MT-sDNA testing (OR=32.042, 95%CI: 6.666-154.021) and considering a difficulty in sampling for MT-sDNA testing (OR=20.278, 95%CI: 4.405-93.354) resulted in high willingness to receive MT-sDNA testing, while recognizing the reliability of MT-sDNA testing (OR=5.003, 95%CI: 1.761-14.216), concern about the reliability of MT-sDNA testing (OR=4.166, 95%CI: 1.285-13.501), considering no difficulty in sampling for MT-sDNA testing (OR=6.558, 95%CI: 2.105-20.428) and considering a difficulty in sampling for MT-sDNA testing (OR=5.820, 95%CI: 1.810-18.720) resulted in high willingness to pay for the MT-sDNA testing among individuals receiving colonoscopy screening.@*Conclusion @#A family history of colorectal cancer in first-degree relatives, history of hemorrhoids and awareness of MT-sDNA testing are factors affecting the willingness to receive and pay for the MT-sDNA testing among individuals receiving colonoscopy screening.

Prediction of lipid nanoparticles for mRNA vaccines by the machine learning algorithm

Lipid nanoparticle (LNP) is commonly used to deliver mRNA vaccines. Currently, LNP optimization primarily relies on screening ionizable lipids by traditional experiments which consumes intensive cost and time. Current study attempts to apply computational methods to accelerate the LNP development for mRNA vaccines. Firstly, 325 data samples of mRNA vaccine LNP formulations with IgG titer were collected. The machine learning algorithm, lightGBM, was used to build a prediction model with good performance (R 2 > 0.87). More importantly, the critical substructures of ionizable lipids in LNPs were identified by the algorithm, which well agreed with published results. The animal experimental results showed that LNP using DLin-MC3-DMA (MC3) as ionizable lipid with an N/P ratio at 6:1 induced higher efficiency in mice than LNP with SM-102, which was consistent with the model prediction. Molecular dynamic modeling further investigated the molecular mechanism of LNPs used in the experiment. The result showed that the lipid molecules aggregated to form LNPs, and mRNA molecules twined around the LNPs. In summary, the machine learning predictive model for LNP-based mRNA vaccines was first developed, validated by experiments, and further integrated with molecular modeling. The prediction model can be used for virtual screening of LNP formulations in the future.