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Formaldehyde is widely utilized across various industries such as food, textiles, and leather, yet its presence raises significant health concerns due to its irritation and carcinogenicity. Numerous methods have been developed to probe it in solution and gas states. Among them, absorption spectroscopy-based methods commonly employed. However, these methods require complex preprocessing for colored solutions to avoid interference. In response to this challenge, a novel interference-resistant detection kit leveraging surface-enhanced Raman spectroscopy (SERS) coupled with reaction-induced self-amplification for rapid and direct detection of formaldehyde was developed. The kit needed only ten minutes and achieved a limit of detection lower than 10−4 μg/mL with a handheld Raman spectrometer. Thanks to the kit’s reaction with formaldehyde, colloidal nanoparticles were linked to form “hot-spots” to further enhance the SERS signals. The signal demonstrated outstanding resistance to interference, including different dyes, ensuring a practical tool for direct detection of formaldehyde in complex solutions.

The Raman spectroscopy analysis technique has found extensive applications across various disciplines due to its exceptional convenience and efficiency, facilitating the analysis and identification of diverse substances. In recent years, owing to the escalating demand for high-efficiency analytical methods, deep learning models have progressively been introduced into the realm of Raman spectroscopy. However, the application of these models to portable Raman spectrometers has posed a series of challenges due to the computational intensity inherent to deep learning approaches. This paper proposes a lightweight classification model, named RepDwNet, for identifying 28 different types of biological blood. The model integrates advanced techniques such as multi-scale convolutional kernels, depth-wise separable convolutions, and residual connections. These innovations enable the model to capture features at different scales while preserving the coherence of feature data to the maximum extent. The experimental results demonstrate that the average recognition accuracy of the model on the reflective Raman blood dataset and the transmissive Raman blood dataset are 97.31% and 97.10%, respectively. Furthermore, by applying structural reparameterization to compress the well-trained model, it maintains high classification accuracy while significantly reducing the parameter size, thereby enhancing the speed of classification inference. This makes the model more suitable for deployment in portable and mobile devices. Additionally, the proposed model can be extended to various Raman spectroscopy classification scenarios.

Abstract Tyrosine phosphorylation by protein tyrosine kinases (PTKs) is a type of post-translational modification. Tec kinases, which are a subfamily of non-receptor PTKs, were originally discovered in the hematopoietic system and include five members: Tec, Btk, Itk/Emt/Tsk, Etk/Bmx, and Txk/Rlk. With the progression of modern research, certain members of the Tec family of kinases have been found to be expressed outside the hematopoietic system and are involved in the development and progression of a variety of diseases. The role of Tec family kinases in cardiovascular disease is receiving increasing attention. Tec kinases are involved in the occurrence and progression of ischemic heart disease, atherosclerosis, cardiac dysfunction associated with sepsis, atrial fibrillation, myocardial hypertrophy, coronary atherosclerotic heart disease, and myocardial infarction and post-myocardial. However, no reviews have comprehensively clarified the role of Tec kinases in the cardiovascular system. Therefore, this review summarizes research on the role of Tec kinases in cardiovascular disease, providing new insights into the prevention and treatment of cardiovascular disease.

Returning extraterrestrial samples to Earth has become essential for future deep space exploration. Achieving a comprehensive evaluation of the physical and chemical properties of samples with minimal damage is key to analyzing extraterrestrial samples in the future, as well as to the future sampling and returning of heterogeneous solid samples. This article aims to reconstruct the three-dimensional internal structure of high-contrast objects, select sections of interest through internal structure and detail features, and then analyze the physical and chemical properties of the samples based on laser spectroscopy technology. This paper proposes a strategy based on Raman mapping and X-ray phase-contrast imaging technology to reconstruct the three-dimensional internal structure of a heterogeneous solid sample and detect the substance composition of the region of interest. This study takes meteorite samples as an example and uses X-ray phase-contrast imaging technology to distinguish and reconstruct the spatial distribution of different components in the meteorite, providing a three-dimensional visualization reference with a high spatial resolution for the spatial positioning of the region of interest. Raman spectroscopy, in combination with LIBS, was used to further identify the meteorite as pallasite and to achieve the spectral image fusion of high spatial and high spectral resolutions. The experimental results show that the unknown meteorite’s three-dimensional structure and its components’ spatial distribution can be evaluated based on Raman mapping combined with X-ray phase-contrast imaging technology. This article provides a highly valuable analytical strategy by which to analyze samples returned from deep space exploration.

PurposeThis study aimed to clarify the relationship between liver fibrosis scores (Fibrosis-4, BARD score, and BAAT score) and chronic kidney disease (CKD).MethodsWe collected a range of data from 11,503 subjects (5,326 men and 6,177 women) from the rural regions of Northeastern China. Three liver fibrosis scores (LFSs) including fibrosis-4 (FIB-4), BARD score, and BAAT score were adopted. A logistic regression analysis was used to calculate odds ratios and the 95% confidence interval. A subgroup analysis showed the association between LFSs and CKD under different stratifications. Restricted cubic spline could further explore whether there is a linear relationship between LFSs and CKD. Finally, we used C-statistics, Net Reclassification Index (NRI), and Integrated Discrimination Improvement (IDI) to assess the effect of each LFS on CKD.ResultsThrough the baseline characteristics, we observed that LFSs were higher in the CKD population than in non-CKD. The proportion of participants with CKD also increased with LFSs. In a multivariate logistic regression analysis, the ORs of CKD were 6.71 (4.45–10.13) in FIB-4, 1.88 (1.29–2.75) in the BAAT score, and 1.72 (1.28–2.31) in the BARD score by comparing the high level with the low level in each LFSs. Moreover, after adding LFSs to the original risk prediction model, which consisted of age, sex, drinking, smoking, diabetes, low-density lipoprotein cholesterol, total cholesterol, triglycerides, and mean waist circumference, we found the new models have higher C-statistics. Furthermore, NRI and IDI both indicate LFSs had a positive effect on the model.ConclusionsOur study showed that LFSs are associated with CKD among middle-aged populations in rural areas of northeastern China.