Summary Composite expended graphite (EG)/Ba(OH)2·8H2O form‐stable phase change material (PCM) is prepared with porous adsorption method in this study to solve the problem of the leakage risk in the application process based on barium hydroxide octahydrate. In addition, the thermal properties and stability have been measured and verified. Thermal conductivity of each group composite material was enhanced by about two to four times, while the addition has little negative effect on the other properties. With microscopic features being characterized by scanning electron microscope (SEM), the experimental result demonstrates that the composite material with 7 wt% of EG is optimal to be a saturated state between two phases. After the thermal cyclings of 100, 300, and 500 times, thermal properties did not change dramatically, which can be used as an ideal material for solar thermal storage system within finite thermal cyclings. In order to simulate the operating condition of solar energy or waste heat storage, the composite material was encapsulated in the heat storage unit with pipe bundle, and heat storage/release experiences were performed to test the performance of the material and hot water supply. The results indicate that the composite material is qualified to storage and release sufficient heat. The experimental data can provide necessary technical reference for engineering design and effect prediction in practical application.

The effects of ionizing radiation on the reproductive system have always been a matter of great interest. Both artificial and naturally occurring ionizing radiation can directly or indirectly affect the reproductive system via the introduction of DNA single-strand and double-strand breaks, the excitation of water molecules, and the generation of free radicals. In order to quantitatively investigate the effects of ionizing radiation on reproductive function, 60Co γ irradiation was applied on a model organism, Caenorhabditis elegans (C. elegans). The egg-laying and embryo-hatching activities were observed for the parent (F0) and the first 2 progeny (F1 and F2) generations. The incidence rate of ovipositor malformation was also recorded. Acridine orange was used to detect the number of apoptotic germ cells. With the above metrics, the effects of 60Co γ irradiation on the reproductive function of C. elegans were systematically evaluated. The results showed that the postirradiation egg-laying and embryo-hatching activities of the F0 generation were increasingly suppressed by increasing doses of 60Co γ irradiation. Those of the F1 generation showed a trend toward recovery although also suppressed by the radiation to the F0 generation compared with the control. Those activities were restored to normal or near-normal levels for the F2 generation. The incidence rate of ovipositor malformation was greatly increased by 60Co γ irradiation according to radiation doses. Gamma irradiation by 60Co also substantially induced germ cell apoptosis, and the apoptosis rate increased with increasing radiation doses. Therefore, 60Co γ irradiation affects the reproductive function of C. elegans. The suppression on its reproductive function increases with increasing radiation doses. The reproductive functions of progeny generations are also affected and weakened.

Tyrosine kinase inhibitors (TKIs) have greatly improved the prognosis of unresectable and metastatic gastrointestinal stromal tumors (GISTs) in the last two decades. Imatinib and sunitinib are recommended as first-line and second-line therapies, respectively. However, there is a lack of precision therapy for refractory GISTs regarding therapy after imatinib and sunitinib. We comprehensively searched electronic databases, including PubMed, EMBASE, Web of Science, Cochrane Library, and ClinicalTrials, from inception to October 2022. Randomized controlled trials featuring comparisons with third-line or over third-line therapies against GISTs were eligible. The primary outcome was progression-free survival (PFS). All network calculations were performed using random effect models, and the ranking of regimens were numerically based on the surface under the cumulative ranking (SUCRA) statistics. A total of seven studies were eligible for inclusion in this network meta-analysis. After analysis, ripretinib was ranked at the top in progression-free survival (PFS), overall survival (OS), and disease control rate (DCR) (SUCRA statistics: 83.1%, 82.5%, and 86.5%, respectively), whereas nilotinib and pimitespib presented better tolerability (SUCRA statistics: 64.9% and 63.8%, respectively). We found that regorafenib seemed more reliable for clinical administration, and ripretinib showed good effectiveness for the over third-line therapy. Precise targeted therapy is a critical direction for the future treatment of GIST, and more high-quality studies of new agents are expected.

ObjectiveMultiple mechanical learning models were used to predict the therapeutic dose of 131I radionuclide in patients with hyperthyroidism, and to compare the calculation results of each prediction model to obtain the optimal model for dose prediction. Meanwhile, the classification model was used to classify the prognosis of the existing clinical hyperthyroidism case data in order to evaluate the administration results and provide reference for the dose given by clinicians.MethodsAccording to the data of hyperthyroidism patients treated with 131I in nuclear medicine department of many hospitals, a prediction model was established based on MATLAB. Firstly, the prediction results of BP neural network, radial basis function (RBF) neural network and support vector machine (SVM) were compared with small sample data, and then the optimal model was selected to predict the drug dose. BP-AdaBoost, SVM and random forest were used to classify the patients after recovery and evaluate whether the dose was accurate.ResultsThe average errors of BP neural network, RBF neural network and SVM models trained with small samples were 6.58%, 17.25% and 14.09% respectively. After comparison, BP neural network was selected to establish the prediction model. The data of 30 cases were randomly selected to verify BP neural network, and average error of the prediction results was 11.99%. Using SVM, BP-AdaBoost and random forest models, 100 groups of case data were selected as the training set and 10 groups as the test set. The classification accuracy were 80%, 90% and 100% respectively. The random forest model with the highest accuracy was selected as the large sample prediction. When 318 groups of cases were trained and 35 groups of cases were used for the test, the classification accuracy was 97.14%.ConclusionThis study compared the prediction effects of various prediction models on 131I therapeutic dose in patients with hyperthyroidism and the accuracy of prognosis classification. BP neural network and random forest achieved the best results respectively. The two models provide reference for clinicians when giving the dose, which has clinical practical significance.