In recent years, there have been frequent cases of seizures among adolescents after using e-cigarettes, which has aroused deep concern about the potential health risks of excessive nicotine intake. Compared to adults, adolescents exhibit heightened sensitivity to nicotine’s reinforcing effects and greater tolerance to its adverse effects, which may lead to increased nicotine intake. Evidence indicates that nicotine possesses seizure potential. While clinical research mainly focuses on nicotine addiction, the impact of seizure-inducing doses of nicotine on adolescent brain development remains understudied. This study investigates the impact of intraperitoneal injections of nicotine at seizure-inducing doses on the brain structure and behavior of adolescent female rats. The results indicate that nicotine exposure leads to both short-term and long-term increases in gray matter volume in the hippocampal dCA1/DG regions of female rats. Additionally, nicotine exposure triggers short-term activation of microglia in the hippocampal dCA1/DG regions and a long-term decline in recognition memory function.

Prostate cancer (PCa) is one of the most common cancers in men, and imaging techniques have been increasingly used in its diagnosis. This study develops a prostate cancer model based on magnetic resonance T2-weighted imaging (T2WI) combined with diffusion-weighted imaging (DWI) texture analysis. Significant feature parameters were selected to construct a predictive model for prostate cancer, and the diagnostic value of texture features for disease discrimination was evaluated using the receiver operating characteristic (ROC) curve and the area under the curve (AUC). The results showed that the prostate cancer prediction model incorporating ${\sigma }_{\text{T2WI}}^{\text{2}}$ and ${\mu }_{\text{DWI}}^{}$ achieved an AUC value of 0.994, demonstrating high predictive performance. This study confirms that the textural features based on T2WI and DWI have good application value in distinguishing prostate cancer.

Hyperpolarized ¹²⁹Xe magnetic resonance imaging (MRI) is an emerging medical imaging technique that plays an important role in the diagnosis and treatment of numerous lung diseases. However, the noise generated during the acquisition process affects the data quality and limits the reliability of the technique in clinical diagnosis and treatment. In this paper, we propose a multimodal feature-enhanced conditional diffusion model based on deep learning that aims to remove noise and improve image quality. The model inputs acquired ¹H MRI as constraints, and a multimodal feature enhancement module is specially designed, which aims to enhance the effectiveness of the model in exploiting multimodal information and the sensitivity to changes in local details of the image. The experimental results show that the method has the best denoising performance and detail preservation compared to other methods, and demonstrate in a ventilation defect segmentation task that the method can enhance the reliability of ¹²⁹Xe MRI in clinical practice.

Pancreatic health is closely linked to diabetes, making accurate fat quantification crucial for early diagnosis. This study proposes a deep learning-based method for automatic pancreatic segmentation and fat quantification. The nnU-Net model achieves high-precision segmentation on m-Dixon Imaging, with a Dice similarity coefficient (DSC) of 0.92. A novel sub-region partitioning and quantification method enables precise delineation of the pancreatic head, body, and tail. Analysis of 256 subjects (healthy, prediabetic, diabetic) reveals a significant association between pancreatic tail fat and type 2 diabetes (p < 0.05). Using random forest classifiers, diabetes risk was effectively predicted based on tail fat content and a composite fat index, yielding an area under the curve (AUC) of 0.68 and 0.73, respectively. This method offers a promising tool for the early diagnosis of diabetes.

Parallel magnetic resonance imaging (PMRI) is an imaging technique that uses multiple receiver coils for undersampling. It utilizes spatial information to supplement the insufficient gradient phase encoding and reconstructs aliasing-free images with specific algorithms to accelerate the imaging process. To address the issue of overfitting or poor generalization when using high acceleration factors with a limited number of auto calibration signals (ACS) in PMRI algorithms based on specific scans, a reconstruction method based on virtual coils and GRAPPA-enhanced networks is proposed. This method expands the sample by using virtual conjugate coils and enhances the ACS using the GRAPPA algorithm for training a nonlinear deep learning network. Experimental results show that the proposed PMRI method can effectively reduce aliasing artifacts caused by insufficient reference data, significantly improving image reconstruction quality with fewer ACS and higher acceleration factors.

Diffusion-weighted magnetic resonance spectroscopy (DW-MRS) measures metabolite apparent diffusion coefficients (ADC) to characterize cellular microstructures. However, physiological motion artifacts and low reproducibility limit its clinical application. This study investigates the effects of different gating methods and cycling modes on DW-MRS results. A total of 21 healthy subjects were included: 6 underwent DW-MRS scans under electrocardiogram (ECG) gating, respiratory gating, and no gating; the remaining 15 were scanned using internal and external b-value cycling to evaluate the impact of cycling modes on ADC reproducibility. Results showed that ECG gating reduced motion artifacts and mitigated ADC overestimation, while internal cycling improved reproducibility. The combination of ECG gating and internal cycling enhances ADC reliability, supporting the broader clinical application of DW-MRS.

The chemical structure of one seized yellow powder sample was elucidated by Fourier transform infrared spectroscopy (FTIR), gas chromatography-mass spectrometry (GC-MS), ultra-performance liquid chromatography-high resolution mass spectrometry (UPLC-HRMS), and nuclear magnetic resonance (NMR). The yellow powder was identified as 2-[2-(4-ethoxyphenyl)ethyl]-N,N-diethyl-5-nitro-1H-benzimidazole-1-ethanamine and was abbreviated as phenylethyl etonitazene. It is a nitazene-type new psychoactive substance (NPS), which has not been controlled in China. The characteristic absorption peaks in IR spectrum and product ions in mass spectra were discussed. The ¹H and ¹³C NMR signals of phenylethyl etonitazene were fully assigned, aided by its two-dimensional NMR data. The information provided herein will assist forensic science laboratories in identifying this compound or other structurally similar substances in their casework.

Fluopimomide is a broad-spectrum fungicide whose structure was characterized using high-resolution mass spectrometry (HR-MS), ultraviolet spectroscopy (UV), infrared spectroscopy (IR), and nuclear magnetic resonance spectroscopy (NMR). The characteristic absorption bands in the UV spectrum, as well as the characteristic absorption peaks in the IR spectrum and their corresponding vibrational modes of functional groups were identified. Furthermore, the structure was confirmed through comprehensive analysis of ¹H NMR spectrum, ¹³C NMR spectrum, distortionless enhancement by polarization transfer (DEPT), homonuclear correlation spectrum (¹H-¹H COSY), heteronuclear single quantum coherence spectroscopy (¹H-¹³C HSQC), and heteronuclear multiple bond correlation spectroscopy (¹H-¹³C HMBC). The connectivity of relevant groups was assigned, thereby unequivocally verifying the compound's structure. The spectroscopic data and structural confirmation are crucial for quality control during pesticide production and provide a valuable reference for the synthesis of fluopimomide analogs.

In this paper, one-dimensional (¹H NMR, ¹³C NMR, DEPT, and ¹⁹F NMR) and two-dimensional NMR (¹H-¹H COSY, ¹H-¹³C HMBC, and ¹H-¹³C HSQC) techniques were applied to perform structural identification and NMR spectral assignments of the fluorine-containing compound 4-isopropoxy-1-(trifluoroacetyl)naphthalene. This paper could serve as a reference for further research on this class of compounds.

Hydrogen-deuterium exchange nuclear magnetic resonance (HDX-NMR) and hydrogen-deuterium exchange mass spectrometry (HDX-MS) are key techniques for studying protein structure and dynamics, and have been widely applied to analyzing protein conformational changes in recent years. HDX-NMR provides dynamic information at single amino acid resolution by detecting nuclear magnetic resonance signals after exchange, and is suitable for studying slow exchange regions and conformational changes over long time scales. HDX-MS integrates the advantages of hydrogen-deuterium exchange and high-resolution mass spectrometry, enabling the determination of protein solution structures under near-physiological conditions, and is applicable to complex systems such as macromolecular complexes and membrane proteins. Compared with traditional techniques like X-ray crystallography and cryo-electron microscopy, HDX-MS is characterized by high sensitivity and low sample demand, while HDX-NMR is superior in site resolution and kinetic analysis. Ongoing technological and methodological optimizations are further broadening the application prospects of both techniques in areas such as protein conformational changes and drug screening. This article reviews their principles, procedures, and analytical methods, compares their similarities and differences, discusses their complementarity and integrated applications, and outlines future directions, aiming to provide references for related research.