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The corpus cavernosum is the most important structure for penile erection, and its dysfunction causes many physiological and psychological problems. However, its cellular heterogeneity and signalling networks at the molecular level are poorly understood because of limited access to samples. Here, we profile 64,993 human cavernosal single-cell transcriptomes from three males with normal erection and five organic erectile dysfunction patients. Cell communication analysis reveals that cavernosal fibroblasts are central to the paracrine signalling network and regulate microenvironmental homeostasis. Combining with immunohistochemical staining, we reveal the cellular heterogeneity and describe a detailed spatial distribution map for each fibroblast, smooth muscle and endothelial subcluster in the corpus cavernosum. Furthermore, comparative analysis and related functional experiments identify candidate regulatory signalling pathways in the pathological process. Our study provides an insight into the human corpus cavernosum microenvironment and a reference for potential erectile dysfunction therapies.

Taking a hydrogen-fueled truck frame as the research object, based on the topology optimization theory of variable density method, the goal of maximizing the static multi-working stiffness and improving the dynamic low-order natural frequency of the frame is realized. The attachments of the frame were retained, and the topological optimization space was established according to the original frame outline size. A multi-objective topology optimization was carried out by using the isotropic material penalty function to obtain the best frame structure layout, and then the new frame structure was designed. The results show that the middle bearing capacity of the new frame is better than that of the original frame which has been verified by modal testing, and the maximum stress decreases by 35.6%. The first-order natural frequency increases by 1.9 Hz, which can avoid the maximum excitation energy region of the road. Meanwhile, the frame weight is reduced by 7.8%, which realizes the specialized and lightweight design of a hydrogen-fueled truck frame.

Combining medical IoT and artificial intelligence technology is an effective approach to achieve the intelligence of medical equipment. This integration can address issues such as low image quality caused by fluctuations in power quality and potential equipment damage, and this study proposes a predictive model, ISSA-TCN-BiLSTM, based on a bi-directional long short-term memory network (BiLSTM). Firstly, power quality data and other data from MRI and CT equipment within a 6-month period are collected using current fingerprint technology. The key factors affecting the active power of medical equipment are explored using the Pearson coefficient method. Subsequently, a Temporal Convolutional Network (TCN) is employed to conduct multi-layer convolution operations on the input temporal feature sequences, enabling the learning of global temporal feature information while minimizing the interference of redundant data. Additionally, bidirectional long short-term memory (BiLSTM) is integrated to model the intermediate active power features, facilitating accurate prediction of medical equipment power quality. Additionally, an improved Sparrow Search Algorithm (ISSA) is utilized for hyperparameter optimization of the TCN-BiLSTM model, enabling optimization of the active power of different medical equipment. Experimental results demonstrate that the ISSA-TCN-BiLSTM model outperforms other comparative models in terms of RMSE, MSE, and R2, with values of 0.1143, 0.1157, 0.0873, 0.0817, 0.95, and 0.96, respectively, for MRI and CT equipment. This model exhibits both prediction speed and accuracy in power prediction for medical equipment, providing valuable guidance for equipment maintenance and diagnostic efficiency enhancement. [ABSTRACT FROM AUTHOR]

Background: Patients with symptomatic chronic internal carotid artery occlusion (ICAO) face a high risk of recurrent stroke despite receiving aggressive medical therapy. This study aimed to evaluate the effectiveness and safety of hybrid surgery in treating symptomatic chronic ICAO. Methods: This retrospective case series was conducted at a single center. From January 2019 to December 2022, patients with symptomatic chronic ICAO who underwent hybrid surgery were included. We collected baseline data, lesion characteristics, revascularization rates, perioperative complications, and follow-up outcomes. Results: The study enrolled 27 patients, comprising 22 males and 5 females, with symptomatic chronic ICAO. The hybrid surgery achieved a technical success rate of 100% for revascularization (n = 27), with a perioperative complication rate of 14.8% (n = 4). Following a median follow-up of 6.0 months (IQR, 4–10), 21 patients underwent a DSA or CT angiography reexamination, confirming a vascular patency rate of 90.5% (n = 19). One patient required surgery for severe in-stent restenosis, and another experienced asymptomatic occlusion. Clinical follow-ups were conducted for all 26 patients; no new strokes were reported in the qualifying artery territory, with 13 patients scoring 0, 12 scoring 1, and 1 scoring 2 on the mRS. Conclusion: Although hybrid surgery represent a promising option for treating chronic ICAO, they are also associated with a relatively high incidence of treatment-related complications. The application of composite surgery should be based on standardized technical guidelines and the careful selection of patients who are genuinely at high risk for recurrent strokes. [ABSTRACT FROM AUTHOR]