A fitting method of calculating local helix parameters of proteins based on dual quaternions registration fitting (DQRFit) is proposed in this paper. First, the C and N atom coordinates of each residue in the protein structure data are extracted. Then the unregistered data and reference data are constructed using the sliding windows. The square sum of the distance of the data points before and after registration is regarded as an optimization goal. We calculate the optimal rotation matrix and the translation vector using the dual quaternion registration algorithm, and get the helix parameters of the secondary structure which contain the number of residues per turn(τ), helix radius(ρ)and helix pitch(p). Furthermore, we can achieve the fitting of three-helix parameters of τ, ρ, p simultaneously with the dual quaternion registration, and can adjust the sliding windows to adapt to different error levels. Compared with the traditional helix fitting method, DQRFit has some advantages such as low computational complexity, strong anti-interference, and high fitting accuracy. It is proven that the precision of proposed DQRFit for α helix detection is comparable to that of the dictionary of secondary structure of proteins (DSSP), and is better than that of other traditional methods. This is of great significance for the protein structure classification and functional prediction, drug design, protein structure visualization and other fields in the future.
R software is a free, powerful statistical and graphing software, including metafor, meta as well as metaplus packages. They can be used to conduct meta-analysis. This article introduces detailed operations of the metaplus package for meta-analysis using cases.
Patients with acute human immunodeficiency virus (HIV) infection are the critical source of infection due to high viral load and strong transmission ability. The vast majority of patients in the acute infection stage have no or only mild clinical symptoms, and their screening and diagnosis often rely on laboratory tests. However, there are still some difficulties in early screening and detection for HIV infection due to the detection window period. In recent years, laboratory testing for acute HIV infection has made great progress. This article reviews the progress in laboratory testing of acute HIV infection, in order to provide a reference for follow-up related research.
Fatigue driving is one of the leading causes of traffic accidents, posing a significant threat to drivers and road safety. Most existing methods focus on studying whole-brain multi-channel electroencephalogram (EEG) signals, which involve a large number of channels, complex data processing, and cumbersome wearable devices. To address this issue, this paper proposes a fatigue detection method based on frontal EEG signals and constructs a fatigue driving detection model using an asymptotic hierarchical fusion network. The model employed a hierarchical fusion strategy, integrating an attention mechanism module into the multi-level convolutional module. By utilizing both cross-attention and self-attention mechanisms, it effectively fused the hierarchical semantic features of power spectral density (PSD) and differential entropy (DE), enhancing the learning of feature dependencies and interactions. Experimental validation was conducted on the public SEED-VIG dataset. The proposed model achieved an accuracy of 89.80% using only four frontal EEG channels. Comparative experiments with existing methods demonstrate that the proposed model achieves high accuracy and superior practicality, providing valuable technical support for fatigue driving monitoring and prevention.
The diaphragm is the main respiratory muscle in the body. The onset detection of the surface diaphragmatic electromyography (sEMGdi) can be used in the respiratory rehabilitation training of the hemiparetic stroke patients, but the existence of electrocardiography (ECG) increases the difficulty of onset detection. Therefore, a method based on sample entropy (SampEn) and individualized threshold, referred to as SampEn method, was proposed to detect onset of muscle activity in this paper, which involved the extraction of SampEn features, the optimization of the SampEn parameters w and r0, the selection of individualized threshold and the establishment of the judgment conditions. In this paper, three methods were used to compare onset detection accuracy with the SampEn method, which contained root mean square (RMS) with wavelet transform (WT), Teager-Kaiser energy operator (TKE) with wavelet transform and TKE without wavelet transform, respectively. sEMGdi signals of 12 healthy subjects in 2 different breathing ways were collected for signal synthesis and methods detection. The cumulative sum of the absolute value of error τ was used as an judgement value to evaluate the accuracy of the four methods. The results show that SampEn method can achieve higher and more stable detection precision than the other three methods, which is an onset detection method that can adapt to individual differences and achieve high detection accuracy without ECG denoising, providing a basis for sEMGdi based respiratory rehabilitation training and real time interaction.
ObjectiveTo explore the application value of the combined detection of CA19-9, CA72-4, carcinoembryonic antigen (CEA), serum pepsinogen Ⅰ(PGⅠ), serum pepsinogen Ⅱ(PGⅡ), ratio of PGⅠ and PGⅡ (PGR), and gastrin-17 (G17) in the diagnosis of gastric cancer.MethodsOne hundred cases of gastric cancer admitted to the Joint Logistic Support Force 940 Hospital of the People’s Liberation Army from January 2016 to August 2018 were respectively collected as the observation group, 110 cases of benign gastric lesions as the control group during the same period, the levels of serum CA19-9, CA72-4, CEA, PGⅠ, PGⅡ, PGR, and G17 were tested among patients in the two groups, the diagnostic value of single and combined detection (included CA19-9, CA72-4, CEA, PGⅠ, PGⅡ, PGR, and G17) were explored.ResultsThe levels of CA19-9, CA72-4, CEA, and G17 in the observation group were higher than those of the control group (P<0.05), the levels of PGⅠ and PGR were lower than those of the control group (P<0.05). The positive detection rates of CA19-9, CA72-4, CEA, G17, PGⅠ, PGR, and combined detection in the observation group were all higher than those of the control group (P<0.05). The sensitivity and accuracy of the combined detection in the diagnosis of gastric cancer were higher than that of single serum index (P<0.05). The levels of serum CA19-9, CA72-4, CEA, and G17 in the patients of Ⅲ+Ⅳ period, low and moderate degree of differentiation, the tumor diameter was larger than five centimeters, signet-ring cell carcinoma, and distance metastasis of gastric cancer patients were on the high side compared with Ⅰ+Ⅱ period, high differentiation, the tumor diameter was less than or equal to five centimeters, glandular cancer, and no distance metastasis of gastric cancer patients, as well as the levels of serum PGⅠ and PGR on the low side (P<0.05).ConclusionThe combined detection of CA19-9, CA72-4, CEA, PGⅠ, PGⅡ, PGR, and G17 can effectively improve the diagnose rate of gastric cancer, and they are closely related to the pathological characteristics of gastriccancer patients.
ObjectiveTo suggest the importance of taking notice of oral chemotherapy drugs in cancer patients, and the importance of drug-use evaluation in patients with insufficient kidney functions, by reporting one death case caused by multiple organ failure because of myelosuppression after oral tegafur, gimeracil and oteracil potassium (S-1) capsules for 10 days in a patient with insufficient kidney functions.
MethodsThrough the analysis of one patient who died of multiple organ failure due to degree-Ⅳ myelosuppression and the related literature review, we discussed the necessity of individualized administration of clinical chemotherapy.
ResultsThe patient had grade-Ⅱ renal insufficiency before chemotherapy and did not undergo dihydropyrimidine dehydrogenase (DPYD) gene test. Myelosuppression occurred 10 days after oral chemotherapy drugs. The white blood cells, neutrophils and platelets decreased progressively, and then developed into degree-Ⅳ suppression. Finally the patient died of multiple organ failure.
Conclusions Genetic variation and renal insufficiency may cause differences in drug metabolism. The reduced urinary excretion of guimet pyrimidine (CDHP), the inhibitors of dihydropyrimidine dehydrogenase which is the 5-fluorouracil (5-FU) metabolic enzyme, may lead to elevated plasma concentration of 5-FU, thereby increasing myelosuppression and other adverse reactions. If DPYD gene detection results show low enzyme activity, it can cause lethal toxicity through deceleration of 5-FU metabolism and high concentration of blood. DPYD gene dzetection should be performed if allowed, and individualized treatment plan should be formulated after comprehensive evaluation. The overall situation of the patients should be considered before treatment, and then individualized drugs should be administered.
To solve the problems of noise interference and edge signal weakness for the existing medical image, we used two-dimensional wavelet transform to process medical images. Combined the directivity of the image edges and the correlation of the wavelet coefficients, we proposed a medical image processing algorithm based on wavelet characteristics and edge blur detection. This algorithm improved noise reduction capabilities and the edge effect due to wavelet transformation and edge blur detection. The experimental results showed that directional correlation improved edge based on wavelet transform fuzzy algorithm could effectively reduce the noise signal in the medical image and save the image edge signal. It has the advantage of the high-definition and de-noising ability.
Medical magnetic nanoparticles are nano-medical materials with superparamagnetism, which can be collected in the tumor tissue through blood circulation, and magnetic particle imaging technology can be used to visualize the concentration of magnetic nanoparticles in the living body to achieve the purpose of tumor imaging. Based on the nonlinear magnetization characteristics of magnetic particles and the frequency characteristics of their magnetization, a differential detection method for the third harmonic of magnetic particle detection signals is proposed. It was modeled and analyzed, to study the nonlinear magnetization response characteristics of magnetic particles under alternating field, and the spectral characteristics of magnetic particle signals. At the same time, the relationship between each harmonic and the amount of medical magnetic nanoparticle samples was studied. On this basis, a signal detection experimental system was built to analyze the spectral characteristics and power spectral density of the detected signal, and to study the relationship between the signal and the excitation frequency. The signal detection experiment was carried out by the above method. The experimental results showed that under the alternating excitation field, the medical magnetic nanoparticles would generate a spike signal higher than the background sensing signal, and the magnetic particle signal existed in the odd harmonics of the detected signal spectrum. And the spectral energy was concentrated at the third harmonic, that is, the third harmonic magnetic particle signal detection that meets the medical detection requirement could be realized. In addition, the relationship between each harmonic and the particle sample volume had a positive growth relationship, and the detected medical magnetic nanoparticle sample volume could be determined according to the relationship. At the same time, the selection of the excitation frequency was limited by the sensitivity of the system, and the detection peak of the third harmonic of the detection signal was reached at the excitation frequency of 1 kHz. It provides theoretical and technical support for the detection of medical magnetic nanoparticle imaging signals in magnetic particle imaging research.
Traditional speech detection methods regard the noise as a jamming signal to filter, but under the strong noise background, these methods lost part of the original speech signal while eliminating noise. Stochastic resonance can use noise energy to amplify the weak signal and suppress the noise. According to stochastic resonance theory, a new method based on adaptive stochastic resonance to extract weak speech signals is proposed. This method, combined with twice sampling, realizes the detection of weak speech signals from strong noise. The parameters of the system a, b are adjusted adaptively by evaluating the signal-to-noise ratio of the output signal, and then the weak speech signal is optimally detected. Experimental simulation analysis showed that under the background of strong noise, the output signal-to-noise ratio increased from the initial value-7 dB to about 0.86 dB, with the gain of signal-to-noise ratio is 7.86 dB. This method obviously raises the signal-to-noise ratio of the output speech signals, which gives a new idea to detect the weak speech signals in strong noise environment.
