Breathing gas carries important physiological information. Technology for detection of breathing gas has become a research focus because of the advantages of nondestructive sampling and convenient operation. Proton transfer reaction mass spectrometry (PTR-MS) plays an irreplaceable role because of the advantages of high sensitivity, fast response and good specificity. In this paper, the principle of PTR-MS is introduced first, followed by research progress of PTR-MS in the field of breathing gas detection. Factors influencing the test results are analyzed. Finally, future prospects of development for PTR-MS in the field of breathing gas detection are discussed.
In order to study the effect of light with different wavelengths on the motion behavior of carp robots, phototaxis experiment, anatomical experiment, light control experiment and speed measurement experiment were carried out in this study. Blue, green, yellow and red light with different wavelength were used to conduct phototaxis experiments on carp to observe their movement behavior. By dissecting the skull bones of the carp to determine the appropriate location to carry the light control device, we independently developed a light control carrying device which was suitable for any illumination intensity environment. The experiment of the light-controlled carp robots was carried out. The motion behavior of the carp robot was checked by using computer binocular stereo vision technology. The motion trajectory of the carp robot was tracked and obtained by applying kernel correlation filter (KCF) algorithm. The motion velocity of the carp robot at different wavelengths was calculated according to their motion trajectory. The results showed that carps’ sensitivity to different light changed from strong to weak in the order of blue, red, yellow and green, so that using light with different wavelengths to control the speed of the carp robot has certain laws to follow. A new method to avoid brain damage in carp robots control can be provided in this study.
In this study, loop-mediated isothermal amplification (LAMP) assay in conjunction with calcein for visualized detection of Mycobacterium tuberculosis (MTB) was established. Firstly, four LAMP primers were designed according to the region of 16S rDNA sequences of MTB. Secondly, clinical sputum samples were collected, decontaminated and their DNA was extracted. Thirdly, standard MTB strains were used to evaluate the specificity and sensitivity of LAMP. At the same time, electrophoresis was used for products detection and calcein was used for visualized verification. At last, Chi-squared test function in SPSS 17.0 software was used for consistency evaluation of LAMP assay as compared with the gold standard (culture method). Results showed that there was no nonspecific amplification appeared in the specificity assay and the detection limit was 10 copies/tube in the sensitivity assay. In addition, visualized method by calcein had a comparable sensitivity with that of electrophoresis method. After evaluation of clinical practicability, the sensitivity of LAMP was calculated as 94.74% and the specificity was 90%, respectively. And Chi-squared test showed that LAMP and culture method had no statistic difference, and the two methods were in good consistency (P>0.05). In conclusion, LAMP assay introduced in our study has the characteristics of high efficiency and visualized detection so that this technique has great application prospects in the resource-limited environment, such as work field and primary care hospitals.
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.
As one of the standard electrophysiological signals in the human body, the photoplethysmography contains detailed information about the blood microcirculation and has been commonly used in various medical scenarios, where the accurate detection of the pulse waveform and quantification of its morphological characteristics are essential steps. In this paper, a modular pulse wave preprocessing and analysis system is developed based on the principles of design patterns. The system designs each part of the preprocessing and analysis process as independent functional modules to be compatible and reusable. In addition, the detection process of the pulse waveform is improved, and a new waveform detection algorithm composed of screening-checking-deciding is proposed. It is verified that the algorithm has a practical design for each module, high accuracy of waveform recognition and high anti-interference capability. The modular pulse wave preprocessing and analysis software system developed in this paper can meet the individual preprocessing requirements for various pulse wave application studies under different platforms. The proposed novel algorithm with high accuracy also provides a new idea for the pulse wave analysis process.
Calculation of cardiac hemodynamic parameters is based on accurate detection of feature points in impedance cardiogram. According to these parameters, doctors can determine heart conditions, so it is very important to accurately detect the feature point of impedance differential signals. This article presents a process in which we used wavelet threshold method to de-noise signals, and then detected the feature points after six layers wavelet decomposition by using bior3.7. The experimental data were collected from healthy persons in our laboratory and twenty two clinical patients in Chongqing Daping Hospital by using KF_ICG instrument. The results indicated that this method could precisely detect feature points whether it was from healthy people or clinical patients. This helps to achieve the application of noninvasive detection cardiac hemodynamic parameters in clinical treatments by using impedance method.
Amanitin-containing mushroom poisoning is one of the most harmful and lethal types of mushroom poisoning events. Its basic medical and clinical medical knowledge has not been fully understood and mastered, so the basic and clinical diagnosis and treatment of amanitin-containing mushroom poisoning has always been a hot research field of acute mushroom poisoning. This article focuses on the new progress in the epidemiology, toxicological properties, poisoning mechanism, clinical diagnosis and treatment of amanitin-containing mushroom poisoning, in order to provide the basis for further study, diagnosis and treatment of amanitin-containing mushroom poisoning for basic researchers and clinical medical staff.
Objective To develop an innovative recognition algorithm that aids physicians in the identification of pulmonary nodules. MethodsPatients with pulmonary nodules who underwent thoracoscopic surgery at the Department of Thoracic Surgery, Affiliated Drum Tower Hospital of Nanjing University Medical School in December 2023, were enrolled in the study. Chest surface exploration data were collected at a rate of 60 frames per second and a resolution of 1 920×1 080. Frame images were saved at regular intervals for subsequent block processing. An algorithm database for lung nodule recognition was developed using the collected data. ResultsA total of 16 patients were enrolled, including 9 males and 7 females, with an average age of (54.9±14.9) years. In the optimized multi-topology convolutional network model, the test results demonstrated an accuracy rate of 94.39% for recognition tasks. Furthermore, the integration of micro-variation amplification technology into the convolutional network model enhanced the accuracy of lung nodule identification to 96.90%. A comprehensive evaluation of the performance of these two models yielded an overall recognition accuracy of 95.59%. Based on these findings, we conclude that the proposed network model is well-suited for the task of lung nodule recognition, with the convolutional network incorporating micro-variation amplification technology exhibiting superior accuracy. Conclusion Compared to traditional methods, our proposed technique significantly enhances the accuracy of lung nodule identification and localization, aiding surgeons in locating lung nodules during thoracoscopic surgery.
Bioartificial liver support system (BALSS) provides a new way to treat liver failure and leaves more time for patients who are waiting for liver transplantation. It has detoxification function as well as the human liver, at the same time it can provide nutrition and improve the internal environment inside human body. Bioreactors and hepatocytes with good biological activity are the cores of BALSS which determine the treatment effect. However, in the course of prolonged treatment, the function and activity of hepatocytes might be greatly changed which could influence the efficacy. Therefore, it is very important to detect the status of the hepatocytes in BALSS. This paper presents some common indicators of cell activity, detoxification and synthetic functions, and also introduces the commonly detection methods corresponding to each indicator. Finally, we summarize the application of detection methods of the hepatocyte status in BALSS and discuss its development trend.
ObjectiveTo evaluate the clinical value of a combined diagnostic model integrating circulating cell-free DNA (cfDNA) methylation markers and CT imaging features for differentiating benign and malignant lung nodules and for early lung cancer detection. This study pioneers a two-step multi-omics modeling approach to construct a robust diagnostic model. MethodsA retrospective cohort of 140 patients (70 malignant and 70 benign, confirmed by postoperative pathology) with lung nodules who underwent surgical treatment at West China Hospital, Sichuan University, from January 2014 to December 2024 was included. Methylation profiles of 54 cfDNA regions were detected via targeted methylation sequencing. CT imaging features (e.g., nodule size, type, and signs) were extracted. A two-step modeling strategy was applied: ① imaging features were modeled directly using binary logistic regression, while methylation features were selected via LASSO regression before modeling; ② a combined model was constructed using the scores from both models. Model performance was evaluated using receiver operating characteristic (ROC) curves, with internal validation via Bootstrap (1000 iterations). ResultsAll patients were split into a training set (n=84) and a test set (n=56). In the test set, the combined model achieved an area under the ROC curve (AUC) of 0.86 [95% confidence interval (CI): 0.74~0.95], with both sensitivity and specificity reaching 82%. This outperformed the individual imaging model (AUC=0.74) and methylation model (AUC=0.82). ConclusionThe multi-omics combined diagnostic model significantly improved the ability to distinguish benign from malignant lung nodules, particularly for early-stage lesions like ground-glass opacities. Its non-invasive and high-sensitivity features provide a promising translational tool for lung cancer screening, with promising clinical application prospects.