Research Progress on Individualized Treatment of Splenic Aneurysm

Splenic artery aneurysm has a hidden onset and atypical symptoms.There are various diagnostic methods for splenic aneurysms,among which ultrasound is the first choice for aneurysm screening.The diagnostic rate of CTA and DSA reaches 100%.In clinical practice,it is necessary to integrate medical history,symptoms and signs,and multiple imaging results to improve the diagnostic rate.In terms of treatment plans,both intervention and surgical procedures have their strengths,and the application of intervention will be more extensive.We need to adhere to the principle of individualization and choose a reasonable treatment plan for patients.At present,there are many reports on pregnant women with large splenic aneurysms both domestically and internationally.Some scholars believe that large splenic aneurysms may be related to hormonal imbalances during pregnancy,and the specific pathogenic mechanism will become a hot topic in future research.

Nondestructive testing algorithm of building concrete material defects based on machine learning

In order to pay more attention to the quality of construction concrete and accurately judge whether concrete material meets the standard,a nondestructive testing algorithm of building concrete material defects based on machine learning is proposed.Through the ray tracing algorithm of Snell’s theorem,the shortest path between two random punctuation marks of building concrete is calculated.The original coordinate system and grid size were set,the trend and length of the line in the grid were calculated,and the coordinates between the grid corner points and the transmitting probe were calculated so as to obtain the position of the intermediate refractive points of the two probes.Finally,the vector dot product of the local defects is obtained by the optimal hyperplane calculation of the binary classification in the support vector machine.Experimental results show that the proposed method has the advantages of high precision.

Mutation in a non-force-bearing region of protein L influences force-dependent unfolding behavior

Single-molecule magnetic tweezers(MTs) have revealed multiple transition barriers along the unfolding pathway of several two-state proteins, such as GB1 and Csp. In this study, we utilized MTs to measure the force-dependent folding and unfolding rates of both protein L(PLWT) and its Y47W mutant(PLY47W) where the mutation point is not at the force-bearing β-strands. The measurements were conducted within a force range of 3–120 pN. Notably, the unfolding rates of both PLWT and PWY47W exhibit distinct force sensitivities below 50 pN and above 60 pN, implying a two-barrier free energy landscape. Both PLWT and PLY47W share the same force-dependent folding rate and the same transition barriers,but the unfolding rate of PLY47W is faster than that of PLWT. Our finding demonstrates that the residue outside of the force-bearing region will also affect the force-induced unfolding dynamics.

A fast antibiotic detection method for simplified pretreatment through spectra-based machine learning

Antibiotics are widely used in medicine and animal husbandry.However,due to the resistance of antibiotics to degradation,large amounts of antibiotics enter the environment,posing a potential risk to the ecosystem and public health.Therefore,the detection of antibiotics in the environment is necessary.Nevertheless,conventional detection methods usually involve complex pretreatment techniques and expensive instrumentation,which impose considerable time and economic costs.In this paper,we proposed a method for the fast detection of mixed antibiotics based on simplified pretreatment using spectral machine learning.With the help of a modified spectrometer,a large number of characteristic images were generated to map antibiotic information.The relationship between characteristic images and antibiotic concentrations was established by machine learning model.The coefficient of determination and root mean squared error were used to evaluate the prediction performance of the machine learning model.The results show that a well-trained machine learning model can accurately predict multiple antibiotic concentrations simultaneously with almost no pretreatment.The results from this study have some referential value for promoting the development of environmental detection technologies and digital environmental management strategies.