In recent years, immune checkpoint inhibitors (ICIs) has emerged as a fundamental component of the standard treatment regimen for patients with head and neck squamous cell carcinoma (HNSCC). However, accurately predicting the treatment effectiveness of ICIs for patients at the same TNM stage remains a challenge. In this study, we first combined multi-omics data (mRNA, lncRNA, miRNA, DNA methylation, and somatic mutations) and 10 clustering algorithms, successfully identifying two distinct cancer subtypes (CSs) (CS1 and CS2). Subsequently, immune-regulated genes (IRGs) and machine learning algorithms were utilized to construct a consensus machine learning-driven prediction immunotherapy signature (CMPIS). Further, the prognostic model was validated and compared across multiple datasets, including clinical characteristics, external datasets, and previously published models. Ultimately, the response of different CMPIS patients to immunotherapy, targeted therapy, radiotherapy and chemotherapy was also explored. First, Two distinct molecular subtypes were successfully identified by integrating immunomics data with machine learning techniques, and it was discovered that the CS1 subtype tended to be classified as “cold tumors” or “immunosuppressive tumors”, whereas the CS2 subtype was more likely to represent “hot tumors” or "immune-activated tumors". Second, 303 different algorithms were employed to construct prognostic models and the average C-index value for each model was calculated across various cohorts. Ultimately, the StepCox [forward] + Ridge algorithm, which had the highest average C-index value of 0.666, was selected and this algorithm was used to construct the CMPIS predictive model comprising 16 key genes. Third, this predictive model was compared with patients’ clinical features, such as age, gender, TNM stage, and grade stage. The findings indicated that this prognostic model exhibited the best performance in terms of C-index and AUC values. Additionally, it was compared with previously published models and it was found that the C-index of CMPIS ranked in the top 5 among 94 models across the TCGA, GSE27020, GSE41613, GSE42743, GSE65858, and META datasets. Lastly, the study revealed that patients with lower CMPIS were more sensitive to immunotherapy and chemotherapy, while those with higher CMPIS were more responsive to radiation therapy and EGFR-targeted treatments. In summary, our study identified two CSs (CS1 and CS2) of HNSCC using multi-omics data and predicted patient prognosis and treatment response by constructing the CMPIS model with IRGs and 303 machine learning algorithms, which underscores the importance of immunotherapy biomarkers in providing more targeted, precise, and personalized immunotherapy plans for HNSCC patients, significantly contributing to the optimization of clinical treatment outcomes.

Diffusion models, a class of deep learning models based on probabilistic generative processes, progressively transform data into noise and then reconstruct the original data through an inverse process. Recently, diffusion models have gained attention in microscopic image analysis for their ability to process complex data, extract valuable information, and enhance image quality. This review provides an overview of diffusion models in microscopic images and micro-alike images, focusing on three commonly used models: DDPM, DDIM, and SDEs. We explore their applications in image generation, segmentation, denoising, classification, reconstruction and super-resolution. It shows their notable advantages, particularly in image generation and segmentation. Through simulating the imaging process of biological samples under the microscope, diffusion model can generate high-quality synthetic microscopic images. The generated images serve as a powerful tool for data augmentation when training deep learning models. Diffusion model also excels in microscopic image segmentation. It enables to accurately segment different cellular regions and tissue structures by simulating the interactions between pixels in an image. The review includes 31 papers, with 13 on image generation, nine on segmentation, and the remainder on other applications. We also discuss the strengths, limitations, and future directions for diffusion models in biomedical image processing.

This study introduces an innovative master-slave cardiac ablation catheter robot system that employs magnetorheological fluids. The system incorporates magnetorheological fluid to enable collision detection through haptic feedback, thereby enhancing the operator’s situational awareness. A modular clamping and propulsion mechanism has been engineered for the ablation catheter, facilitating omnidirectional operation and force feedback within the cardiac cavity. To evaluate the proposed system, an in vitro experiment was performed. Results from the experiment indicate that the system demonstrates high motion transmission accuracy. Furthermore, the system effectively alerts operators to potential collisions, enabling swift catheter position adjustments, minimizing the risk of vascular perforation, and ultimately enhancing the overall safety and efficiency of the procedure.

In this study, we propose a rehabilitation training system that incorporates active and passive rehabilitation modes to enhance the convenience, efficacy, and safety of rehabilitation training for patients with upper limb hemiplegia. This system facilitates elbow flexion and extension as well as wrist and palm flexion and extension. The experimental results demonstrate that the exoskeleton robot on the affected limb exhibits a rapid response and maintains a highly synchronized movement with the unaffected upper limb equipped with an angle sensor, preserving stability and coordination throughout the movement process without significant delay affecting the overall motion. When the movement of the unaffected upper limb exceeds the predetermined angle threshold, the affected limb promptly initiates a protective mechanism to maintain its current posture. Upon equalization of the angles between the two limbs, the affected limb resumes synchronized movement, thereby ensuring the safety of the rehabilitation training. This research provides some insights into the functional improvements of safe and reliable upper limb exoskeleton rehabilitation training systems.

Alzheimer disease (AD) is the most commonly occurring neurodegenerative disease. However, current diagnostics for AD primarily rely on invasive tests, which limit the application of diagnostic procedures in early screening. Speech, as a noninvasive biomarker, is closely associated with AD but has not been fully leveraged as a diagnostic tool. This study develops a novel early AD diagnosis method that uses primitive speech and explores its potential application in community screening. Moreover, the study proposes an innovative multimodal method for speech feature fusion that combines acoustic and semantic information to differentiate patients with AD from normal controls. This method uses the ImageBind audio encoder to extract acoustic features and the Embeddings from Language Model to extract semantic features, thereby effectively integrating the features by mid-level fusion. The training set comprises 166 speech recordings, which comprise 87 samples from individuals with AD and 79 samples from healthy control subjects. The ratio of training set to test set is 7:3. Evaluation of the Alzheimer dementia recognition through spontaneous speech only dataset showed that the proposed model achieved a classification accuracy of 0.903 and a recall rate of 1, and it considerably outperformed existing baseline models, thereby confirming the effectiveness of the proposed approach to AD diagnosis. This study applies the multimodal fusion of speech features to an early AD diagnostic procedure and achieves excellent performance. The findings of this study not only provide a new approach to noninvasive AD screening but also open new pathways to the early diagnosis of other neurodegenerative diseases.

Acupuncture, as a traditional medical therapy, has gained widespread application globally. However, traditional acupoint localization has relied heavily on the personal experience of clinicians. With the advancement of AI, especially deep learning, researchers have begun exploring automated acupoint localization in recent years. This study systematically reviews the literature on acupoint localization in different anatomical regions using traditional algorithms and deep learning to evaluate the effectiveness, limitations, and future directions of these technologies. Drawing insights from the surveyed studies, we anticipate that the challenges outlined in this research will be addressed in the near future. As these challenges are overcome, automated acupoint localization techniques are expected to yield more promising outcomes. We hope our efforts will inspire further exploration of advanced AI methods for acupoint localization.