OBJECTIVE To construct a predictive model for evaluating the efficacy of a triple antiemetic regimen （neurokinin- 1 receptor antagonist+5-hydroxytryptamine 3 receptor antagonist+dexamethasone） for preventing nausea and vomiting induced by highly emetogenic chemotherapy （HEC） based on interpretable deep learning algorithms. METHODS Clinical data of cancer patients who received HEC and were treated with the standard triple antiemetic regimen in the oncology department of Tianjin Medical University General Hospital from January 2018 to December 2022 were collected retrospectively. Demographic， clinical and metabolism-related variables were integrated. After data pre-processing， two deep learning algorithms （deep random forest and dense neural network） and four machine learning algorithms （support vector machine， categorical boosting， random forest and decision tree） were used to build predictive models. Subsequently， model performance evaluation and model interpretability analysis were conducted. RESULTS Among the six candidate models， the deep random forest model demonstrated the best predictive performance on the test set， with an area under the receiver operating characteristic curve of 0.850， an accuracy of 0.911， a precision of 0.805， a recall of 0.783， an F1 score of 0.793， and a Brier score of 0.075. Interpretability analysis revealed that creatinine clearance rate （Ccr） was the key predictive factor， and low Ccr levels， female gender， younger age， highly emetogenic drugs （particularly cisplatin-containing chemotherapy regimens）， and anticipatory nausea and vomiting were positively correlated with the risk of HEC-related nausea and vomiting. CONCLUSIONS The deep random forest model exhibits the best performance in predicting the efficacy of triple antiemetic regimen for preventing HEC-related nausea and vomiting. The key predictors in this model primarily include Ccr，anticipatory nausea and vomiting， gender， age， and highly emetogenic drugs.

OBJECTIVE To study the improvement effect and mechanism of Tripterygium wilfordii multiglucoside （TWM） on nephrotic syndrome in rats. METHODS The nephrotic syndrome model was established by intravenous injection of adriamycin via the tail vein. The modeling rats were randomly divided into the model group （distilled water）， prednisone group （10 mg/kg）， and TWM high- and low-dose groups （10 and 5 mg/kg， respectively）. Additionally， blank group （distilled water） without model induction was established. Each group consisted of 9 rats. Rats in each group were administered the corresponding drugs or distilled water by gavage， once a day， for 6 consecutive weeks. The histopathological morphology of kidney tissues in rats was observed； the levels of 24-hour urinary protein （24 h-UTP） and serum biochemical indicators [albumin （ALB）， blood urea nitrogen （BUN）， serum creatinine （SCr）， cholesterol （CHOL）， and triglyceride （TG）] in rats were determined； the levels of oxidative stress indicators [superoxide dismutase （SOD）， malondialdehyde （MDA）] in kidney tissue of rats were determined； expressions of hypoxia-inducible factor-1α （HIF-1α）/microRNA-155-5p （miR-155-5p）/nuclear factor erythriod 2- related factor 2 （Nrf2） signaling pathway-related mRNA and protein in the renal tissues of rats were detected. RESULTS Compared with the blank group， the rats in the model group exhibited disordered renal tissue structure， with a small amount of glomerular necrosis and edema of the renal tubular epithelial cells. 24 h-UTP， serum levels of SCr， BUN， CHOL and TG， MDA content， mRNA and protein expressions of HIF-1α and Keap1 as well as the expression of miR-155-5p in renal tissues were increased significantly （ P ＜0.05）. Serum level of ALB， SOD level in renal tissue as well as mRNA and protein expressions of Nrf2 were decreased significantly （ P ＜0.05）. Compared with the model group， TWM high-dose and low-dose groups exhibited significant improvements in renal injury， with notable reversals in the levels of the above quantitative indicators （ P ＜0.05）. CONCLUSIONS TWM can alleviate oxidative stress-induced damage and thereby improve nephrotic syndrome in rats by regulating the HIF-1α/miR-155-5p/Nrf2 signaling pathway.

ObjectiveDiscriminating atypical hepatocellular carcinoma (HCC) from other malignancies in liver nodules classified as Liver Imaging Reporting and Data System category M (LR-M) remains a significant diagnostic challenge on conventional ultrasound examination. The LR-M category, originally intended to capture non-HCC malignancies, paradoxically contains up to 63% of atypical HCCs that deviate from classic enhancement patterns, leading to potential misdiagnosis and suboptimal treatment planning. While deep learning has shown promise in HCC diagnosis, most existing models rely exclusively on single-modality ultrasound, overlooking the diagnostic benefits of integrating complementary information from multiple imaging sources. To address this gap, we propose a novel attention-weighted tri-modal ultrasound network (TUS-Net) that integrates contrast-enhanced ultrasound (CEUS), B-mode ultrasound (BUS), and time-intensity curves (TICs) to improve diagnostic accuracy for these clinically challenging lesions. MethodsOur framework incorporates a three-dimensional convolutional neural network (C3D) backbone to extract spatiotemporal features from CEUS videos, capturing dynamic vascular patterns critical for lesion characterization. To effectively fuse complementary modalities, we introduce a dual-channel feature fusion module (DCFFM) that adaptively combines features from CEUS and BUS through channel-wise attention mechanisms, allowing the model to dynamically weigh the contribution of each modality based on diagnostic relevance. Additionally, we propose a temporal intensity feature fusion module (TIFFM) that leverages quantitative hemodynamic information from TICs to guide the model’s attention toward diagnostically critical temporal phases, such as arterial wash-in and portal venous washout. The model is further enhanced by automated lesion localization using YOLOX and class activation mapping for interpretability, ensuring that predictions align with clinically meaningful imaging features. ResultsEvaluated on a tri-modal ultrasound dataset comprising 161 patients with pathologically confirmed LR-M nodules (131 atypical HCC and 30 non-HCC malignancies), our model achieved an accuracy of 86.83%, a sensitivity of 92.50%, a specificity of 75.50%, and an AUC of 89.32% in screening atypical HCC. Compared to single-modality baselines, TUS-Net demonstrated superior specificity, a clinically critical metric given the higher risk associated with misclassifying non-HCC malignancies. Ablation studies confirmed the contribution of each module, with the full model outperforming both standard C3D and 3D ResNet backbones integrated with attention mechanisms. A reader study involving junior and senior radiologists further validated the clinical utility of AI assistance, showing consistent improvements in specificity and inter-reader consistency, particularly for less experienced clinicians. ConclusionThese results surpass existing benchmark models and demonstrate the potential of our approach to enhance diagnostic precision in clinically specific cases. By intelligently fusing multi-modal ultrasound data with attention-guided mechanisms, TUS-Net offers a reliable and interpretable tool that holds promise for improving the non-invasive diagnosis of atypical HCC in challenging LR-M liver nodules.

ObjectiveDiscriminating atypical hepatocellular carcinoma (HCC) from other malignancies in liver nodules classified as Liver Imaging Reporting and Data System category M (LR-M) remains a significant diagnostic challenge on conventional ultrasound examination. The LR-M category, originally intended to capture non-HCC malignancies, paradoxically contains up to 63% of atypical HCCs that deviate from classic enhancement patterns, leading to potential misdiagnosis and suboptimal treatment planning. While deep learning has shown promise in HCC diagnosis, most existing models rely exclusively on single-modality ultrasound, overlooking the diagnostic benefits of integrating complementary information from multiple imaging sources. To address this gap, we propose a novel attention-weighted tri-modal ultrasound network (TUS-Net) that integrates contrast-enhanced ultrasound (CEUS), B-mode ultrasound (BUS), and time-intensity curves (TICs) to improve diagnostic accuracy for these clinically challenging lesions. MethodsOur framework incorporates a three-dimensional convolutional neural network (C3D) backbone to extract spatiotemporal features from CEUS videos, capturing dynamic vascular patterns critical for lesion characterization. To effectively fuse complementary modalities, we introduce a dual-channel feature fusion module (DCFFM) that adaptively combines features from CEUS and BUS through channel-wise attention mechanisms, allowing the model to dynamically weigh the contribution of each modality based on diagnostic relevance. Additionally, we propose a temporal intensity feature fusion module (TIFFM) that leverages quantitative hemodynamic information from TICs to guide the model’s attention toward diagnostically critical temporal phases, such as arterial wash-in and portal venous washout. The model is further enhanced by automated lesion localization using YOLOX and class activation mapping for interpretability, ensuring that predictions align with clinically meaningful imaging features. ResultsEvaluated on a tri-modal ultrasound dataset comprising 161 patients with pathologically confirmed LR-M nodules (131 atypical HCC and 30 non-HCC malignancies), our model achieved an accuracy of 86.83%, a sensitivity of 92.50%, a specificity of 75.50%, and an AUC of 89.32% in screening atypical HCC. Compared to single-modality baselines, TUS-Net demonstrated superior specificity, a clinically critical metric given the higher risk associated with misclassifying non-HCC malignancies. Ablation studies confirmed the contribution of each module, with the full model outperforming both standard C3D and 3D ResNet backbones integrated with attention mechanisms. A reader study involving junior and senior radiologists further validated the clinical utility of AI assistance, showing consistent improvements in specificity and inter-reader consistency, particularly for less experienced clinicians. ConclusionThese results surpass existing benchmark models and demonstrate the potential of our approach to enhance diagnostic precision in clinically specific cases. By intelligently fusing multi-modal ultrasound data with attention-guided mechanisms, TUS-Net offers a reliable and interpretable tool that holds promise for improving the non-invasive diagnosis of atypical HCC in challenging LR-M liver nodules.

Plant-derived extracellular vesicles (PDEVs) are nanoscale extracellular vesicles secreted by plant cells, characterized by a lipid bilayer structure. These vesicles carry a variety of bioactive molecules, including proteins, nucleic acids, and lipids, and play essential roles in intercellular communication and physiological regulation in plants. Compared to animal-derived extracellular vesicles, PDEVs offer several advantages, such as a broad range of sources, high biocompatibility, low immunogenicity, and low production costs. Furthermore, PDEVs have demonstrated remarkable potential as natural nanocarriers for drug delivery, due to their ability to efficiently traverse biological barriers, such as the blood-brain barrier, making them promising candidates for drug delivery systems. This review systematically elaborates on the complex composition of PDEVs, which consists of lipids, proteins, and nucleic acids, the typical structural characteristics of their lipid bilayers ranging from 30 to 150 nm, and their versatile loading capabilities as drug carriers, efficiently encapsulating various types of therapeutic agents such as hydrophilic small molecules, hydrophobic drugs, nucleic acids, and proteins. We systematically summarize the recent advancements in strategies for enhancing the loading efficiency of PDEVs, which include methods such as co-incubation, ultrasound-assisted loading, electroporation, freeze-thaw cycles, and microfluidic technology. These techniques are evaluated based on their underlying principles, suitable drug types, and their respective advantages. In addition to loading strategies, we focus on the engineered approaches to achieve targeted delivery using PDEVs, such as genetic engineering modifications, chemical ligand conjugation, membrane fusion technology, and polyethylene glycol (PEG) modification. We discuss the mechanisms of these strategies in enhancing targeting efficiency, prolonging in vivo circulation time, and improving therapeutic efficacy. Further, this review highlights the application of PDEVs in various disease models, including tumor, skin inflammation, metabolic disorders, and neurodegenerative diseases, showcasing their therapeutic potential as multifunctional delivery platforms. The ability of PDEVs to encapsulate diverse therapeutic agents and target specific tissues or cells opens up new avenues for the treatment of complex diseases, offering advantages over conventional drug delivery systems. However, despite the promising applications of PDEVs, several challenges remain in their development and clinical translation. These challenges include variability in source materials, standardization of preparation processes, quality control, scalability of production, and the need for clinical validation. To overcome these obstacles, the integration of advanced technologies such as artificial intelligence-assisted design and multi-omics analysis is proposed as a way to facilitate the precise development of PDEVs. These emerging technologies hold the potential to further enhance the precision and effectiveness of plant-based drug delivery systems, ultimately advancing the field of precision medicine. In conclusion, the use of PDEVs as a platform for drug delivery represents a promising area of research with the potential to revolutionize therapeutic strategies. Their ability to encapsulate and deliver a wide variety of bioactive molecules, along with their inherent advantages in biocompatibility and versatility, makes them a valuable tool in the development of more efficient and targeted therapeutic interventions. Continued research and innovation in this field will pave the way for the clinical implementation of PDEVs in the treatment of various diseases, offering new hope for more effective and sustainable therapeutic options.

This study investigated the effects of Rehmanniae Radix Praeparata on striatal neuronal apoptosis in rats with attention deficit hyperactivity disorder(ADHD) based on the B-cell lymphoma-2(Bcl-2)/Bcl-2-associated X protein(Bax)/caspase-3 signaling pathway. Twenty-four 3-week-old male spontaneously hypertensive rats(SHR) were randomly divided into a model group, a methylphenidate group(2 mg·kg~(-1)·d~(-1)), and a Rehmanniae Radix Praeparata group(2.4 mg·kg~(-1)·d~(-1)). Age-matched male Wistar Kyoto(WKY) rats were used as the normal control group, with 8 rats in each group. The rats were administered by gavage for 28 days. Body weight and food intake were recorded for each group. The open field test and elevated plus maze test were used to assess hyperactivity and impulsive behaviors. Nissl staining was used to detect changes in striatal neurons and Nissl bodies. Terminal deoxynucleotidyl transferase dUTP nick end labeling(TUNEL) fluorescence staining was used to detect striatal cell apoptosis. Western blot was employed to detect the expression levels of Bcl-2, Bax, and caspase-3 proteins in the striatum. The results showed that compared with the model group, Rehmanniae Radix Praeparata significantly reduced the total movement distance, average movement speed, and central area residence time in the open field test, and significantly reduced the ratio of open arm entries, open arm stay time, and head dipping in the elevated plus maze test. Furthermore, it increased the number of Nissl bodies in striatal neurons, significantly downregulated the apoptosis index, significantly increased Bcl-2 protein expression and the Bcl-2/Bax ratio, and reduced Bax and caspase-3 protein expression. In conclusion, Rehmanniae Radix Praeparata can reduce hyperactivity and impulsive behaviors in ADHD rats. Its mechanism may be related to the regulation of the Bcl-2/Bax/caspase-3 signaling pathway in the striatum, enhancing the anti-apoptotic capacity of striatal neurons.
Animals ; Male ; Apoptosis/drug effects* ; Rats ; Drugs, Chinese Herbal/administration & dosage* ; Caspase 3/genetics* ; Proto-Oncogene Proteins c-bcl-2/genetics* ; bcl-2-Associated X Protein/genetics* ; Rehmannia/chemistry* ; Attention Deficit Disorder with Hyperactivity/physiopathology* ; Signal Transduction/drug effects* ; Neurons/cytology* ; Rats, Inbred SHR ; Rats, Inbred WKY ; Humans ; Corpus Striatum/cytology* ; Plant Extracts

The phase changes and quantity-quality transfer of 17 batches of Ostreae Concha(Ostrea rivularis) during the raw material-calcined decoction pieces-standard decoction process were analyzed. The content of calcium carbonate(CaCO_3), the main component, was determined by chemical titration, and the extract yield and transfer rate were calculated. The CaCO_3 content in the raw material, calcined decoction pieces, and standard decoction was 94.39%-98.80%, 95.03%-99.22%, and 84.58%-90.47%, respectively. The process of raw material to calcined decoction pieces showed the yield range of 96.85% to 98.55% and the CaCO_3 transfer rate range of 96.92% to 99.27%. The process of calcined decoction pieces to standard decoction showed the extract yield range of 2.86% to 5.48% and the CaCO_3 transfer rate range of 2.59% to 5.13%. The results of X-ray fluorescence(XRF) assay showed that the raw material, calcined decoction pieces, and standard decoction mainly contained Ca, Na, Mg, Si, Br, Cl, Al, Fe, Cr, Mn, and K. The chemometric results showed an increase in the relative content of Cr, Fe, and Si from raw material to calcined decoction pieces and an increase in the relative content of Mg, Al, Br, K, Cl, and Na from calcined decoction pieces to standard decoction. X-ray diffraction(XRD) was employed to establish XRD characteristic patterns of the raw material, calcined decoction pieces, and standard decoction. The XRD results showed that the main phase of all three was calcite, and no transformation of crystalline form or generation of new phase was observed. Fourier transform infrared spectroscopy(FTIR) was employed to establish the FTIR characteristic spectra of the raw material, calcined decoction pieces, and standard decoction. The FTIR results showed that the raw material had internal vibrations of O-H, C-H, C=O, C-O, and CO■ groups. Due to the loss of organic matter components after calcination, no information about the vibrations of C-H, C=O, and C-O groups was observed in the spectra of calcined decoction pieces and standard decoction. In summary, this study elucidated the quantity-quality transfer and phase changes in the raw material-calcined decoction pieces-standard decoction process by determining the CaCO_3 content, calculating the extract yield and transfer rate, and comparing the element changes, FTIR characteristic spectra, and XRD characteristic pattern. The results were reasonable and reliable, laying a foundation for the subsequent process research and quality control of the formula granules of calcined Ostreae Concha(O. rivularis Gould), and providing ideas and methods for the quality control of the whole process of raw material-decoction pieces-standard decoction-formula granules of Ostreae Concha and other testacean traditional Chinese medicine.
Drugs, Chinese Herbal/isolation & purification* ; Calcium Carbonate/analysis* ; Quality Control

Repeated transcranial magnetic stimulation (rTMS) is one of the commonly used brain stimulation techniques. In order to investigate the effects of rTMS on the excitability of different types of neurons, this study is conducted to investigate the effects of rTMS on the cognitive function of mice and the excitability of hippocampal glutaminergic neurons and gamma-aminobutyric neurons from the perspective of electrophysiology. In this study, mice were randomly divided into glutaminergic control group, glutaminergic magnetic stimulation group, gamma-aminobutyric acid energy control group, and gamma-aminobutyric acid magnetic stimulation group. The four groups of mice were injected with adeno-associated virus to label two types of neurons and were implanted optical fiber. The stimulation groups received 14 days of stimulation and the control groups received 14 days of pseudo-stimulation. The fluorescence intensity of calcium ions in mice was recorded by optical fiber system. Behavioral experiments were conducted to explore the changes of cognitive function in mice. The patch-clamp system was used to detect the changes of neuronal action potential characteristics. The results showed that rTMS significantly improved the cognitive function of mice, increased the amplitude of calcium fluorescence of glutamergic neurons and gamma-aminobutyric neurons in the hippocampus, and enhanced the action potential related indexes of glutamergic neurons and gamma-aminobutyric neurons. The results suggest that rTMS can improve the cognitive ability of mice by enhancing the excitability of hippocampal glutaminergic neurons and gamma-aminobutyric neurons.
Animals ; Mice ; Hippocampus/cytology* ; Transcranial Magnetic Stimulation ; Neurons/physiology* ; Male ; Cognition/physiology* ; gamma-Aminobutyric Acid/metabolism* ; Action Potentials/physiology*

OBJECTIVE: To systematically assess the differences in gait parameters and clinical efficacy between HURWA robot-assisted total knee arthroplasty(TKA) and MAKO robotic-assisted TKA during the 1-year postoperative follow-up period. METHODS: From November 2023 to March 2024, 40 patients with unilateral knee osteoarthritis were enrolled and randomly divided into two groups:HURWA robotic-assisted TKA group(HURWA group) and MAKO robotic-assisted TKA group (MAKO group) using a random number table. In the HURWA group, there were 20 patients, comprising 5 males and 15 females, with an age range of 59 to 79 years old with a mean age of (69.45±4.36) years old, and body mass index(BMI) ranging from 22.96 to 33.87 kg·m^-2 with a mean BMI of (27.28±3.12) kg·m^-2. In the MAKO group, there were also 20 patients, consisting of 4 males and 16 females, with an age range of 58 to 80 years old with an average of (67.50±6.88) years old, BMI ranging from 25.39 to 29.30 kg·m-2 with an average of(26.86 ±1.23) kg·m-2. To comprehensively evaluate the improvement in knee joint function, the Western Ontario and McMaster Universities osteoarthritis index (WOMAC) and American Knee Society score (KSS) were used for clinical efficacy evaluation. In gait analysis, an innovative computer vision-based human pose estimation framework, MediaPipe, was used to quantitatively measure the spatiotemporal parameters (such as walking speed, step frequency, stride length, step width, etc.) and kinematic parameters (such as gait cycle, stance time, stance phase, swing time, swing phase, knee joint active flexion angle, etc.) of both groups preoperatively and 1 year postoperatively. A dynamic evaluation of the maximum hip flexion and knee flexion angles during functional activities (such as squatting) was also conducted to fully reflect the recovery of patients' motor function. RESULTS: There were no significant differences in the WOMAC and KSS scores between the HURWA robotic-assisted TKA group and the MAKO robotic-assisted TKA group preoperatively and 1 year postoperatively (P>0.05). In terms of gait function, there were no significant differences in the spatiotemporal parameters (including walking speed, step frequency, stride length, step width, etc.) and kinematic parameters(such as gait cycle, stance time, stance phase, swing time, swing phase, knee joint active flexion angle, etc.) between the two groups preoperatively and 1 year postoperatively(P>0.05). CONCLUSION Both HURWA robot-assisted TKA and MAKO robot-assisted TKA demoonstrated equivalent outcones in terms of functional recovery and gait improvement 1 year postoperatively.
Humans ; Male ; Female ; Arthroplasty, Replacement, Knee/methods* ; Aged ; Middle Aged ; Gait ; Robotic Surgical Procedures/methods* ; Osteoarthritis, Knee/physiopathology* ; Aged, 80 and over ; Knee Joint/physiopathology* ; Motion Capture