Volume 69, Issue 6

The aging population has led to a widening gap between the supply and demand for defecation care. To address this issue, the development of defecation care devices is the most direct and effective solution. Pre-defecation care devices offer a more personalized and comfortable alternative to the conventional post-defecation care devices currently available on the market. Furthermore, they facilitate greater patient involvement in the care process. Real-time monitoring and accurate identification of defecation intention are key technologies in the development of pre-defecation nursing devices. Automatic and accurate online monitoring of defecation intention can provide accurate early warning information for differentiated defecation assistance and cleansing care, effectively reducing nursing workload and improving patients’ quality of life. However, there are relatively few studies on real-time monitoring and accurate identification of defecation intention. This review summarizes the existing defecation intention sensing technologies and their monitoring principles and research status, and explores the potential development direction of defecation intention sensing systems by comparing the characteristics and application conditions of various sensing technologies, which provides a direction for perception strategies for future defecation intention monitoring and early warning research.

Aside from human bones, epoxy-based synthetic bones are regarded as the gold standard for biomechanical testing os osteosyntheses. There is a significant discrepancy in biomechanical testing between the determination of fracture stability due to implant treatment in experimental methods and their ability to predict the outcome of stability and fracture healing in a patient. One possible explanation for this disparity is the absence of population-specific variables such as age, gender, and ethnicity in artificial bone, which may influence the geometry and mechanical properties of bone. The goal of this review was to determine whether commercially available artificial bones adequately represent human anatomical variability for mechanical testing of femoral osteosyntheses. To summarize, the availability of suitable bone surrogates currently limits the validity of mechanical evaluations of implant-bone constructs. The currently available synthetic bones neither accurately reflect the local mechanical properties of human bone, nor adequately represent the necessary variability between various populations, limiting their generalized clinical relevance.

Objectives This study aimed to evaluate the intensity of the subacute local biological effects after implantation and osseoconductive potential of novel hydroxyapatite-based bone substitute coated with poly (lactide-co-glycolide), named ALBO-OS, in comparison to Bio-Oss ® . Methods Fifteen male Wistar rats, randomly assigned into groups: 10, 20, and 30 days (n꞊5), were subcutaneously implanted with ALBO-OS and Bio-Oss ® . Furthermore, artificially made bone defects on both rat’s tibias were implanted with experimental materials. Unimplanted defects represented negative control. After the animals’ euthanizing, tissue samples were prepared and analyzed histologically and histomorphometrically. Results Normal healing of the epithelial tissue was observed, with no signs of infection or necrosis. Minimal vascular congestion was noted immediately around the graft, with no signs of tissue oedema, with a minimal capsule thickness. The applied material did not cause an inflammatory response (IR) of significant intensity, and 20 days after implantation, the IR was mainly assessed as minimal. The tibial specimen showed that ALBO-OS has good osseoconductive potential, similar to Bio-Oss ® , as well as low levels of acute and subacute inflammation. Conclusions The tested material exhibits satisfying biocompatibility, similar to Bio-Oss ® .

Objectives Due to the dynamic character of the stomatognathic system, fatigue life experiments simulating the cyclic loading experienced by implant-supported restorations are critical consideration. The aim of this study was to examine the effect of different crown and abutment materials on fatigue failure of single implant-supported crowns. Methods Models were created for 10 different designs of implant-supported single crowns including two zirconia-reinforced lithium silicates (crystallized and precrystallized), monolithic lithium disilicate, polymer-infiltrated ceramic networks, and polyetheretherketone supported by zirconia and titanium abutments. A cyclic load of 179 N with a frequency of 1 Hz was applied on palatal cusp of a maxillary first premolar at a 30° angle in a buccolingual direction. Results In the models with titanium abutments, the polymer-infiltrated ceramic network model had a lower number of cycles to fatigue failure values in the implant (5.07), abutment (2.30), and screw (1.07) compared to others. In the models with zirconia abutments, the crystallized zirconia-reinforced lithium silicate model had a higher number of cycles to fatigue failure values in the abutment (8.52) compared to others. Depending on the fatigue criteria, polyetheretherketone implant crown could fail in less than five year while the other implant crowns exhibits an infinite life on all models. Conclusions The type of abutment material had an effect on the number of cycles to fatigue failure values for implants, abutments, and screws, but had no effect on crown materials. The zirconia abutment proved longer fatigue lifetime, and should thus be considered for implant-supported single crowns.

Objectives The objective of this study is to develop a system for automatic sign language recognition to improve the quality of life for the mute-deaf community in Egypt. The system aims to bridge the communication gap by identifying and converting right-hand gestures into audible sounds or displayed text. Methods To achieve the objectives, a convolutional neural network (CNN) model is employed. The model is trained to recognize right-hand gestures captured by an affordable web camera. A dataset was created with the help of six volunteers for training, testing, and validation purposes. Results The proposed system achieved an impressive average accuracy of 99.65 % in recognizing right-hand gestures, with high precision value of 95.11 %. The system effectively addressed the issue of gesture similarity between certain alphabets by successfully distinguishing between their respective gestures. Conclusions The proposed system offers a promising solution for automatic sign language recognition, benefiting the mute-deaf community in Egypt. By accurately identifying and converting right-hand gestures, the system facilitates communication and interaction with the wider world. This technology has the potential to greatly enhance the quality of life for individuals who are unable to speak or hear, promoting inclusivity and accessibility.

Objectives To overcome the limitations of traditional diagnosis of orbicularis oris muscle function in mouth-breathing patients, this study aims to propose a surface electromyographic (sEMG) based method for reliable and accurate quantitative assessment of lip closure ability. Methods A total of 21 volunteers (16 patients and 5 healthy subjects, aged 8–16) were included in the study. Three nonlinear onset detection algorithms – Teager–Kaiser Energy (TKE) operator, Sample Entropy (SampEn), and Fuzzy Entropy (FuzzyEn) – were compared for their ability to identify lip closure in sEMG signals. Lip Closure EMG Activity Index (LCEAI) was proposed based on the action segments detected by the best performing algorithm for the quantitative assessment of lip closure. Results The results indicated that FuzzyEn had the highest lip closure identification rate at 93.78 %, the lowest average onset delay of 47.50 ms, the lowest average endpoint delay of 73.10 ms, and the minimal time error of 111.61 ms, exhibiting superior performance. The calculation results of the LCEAI closely corresponded with the actual degree of lip closure in patients. Conclusions The lip closure ability assessment method proposed in this study can provide a quantitative basis for the diagnosis of mouth breathing.

Objectives This research investigates designing a continuum soft robot and proposing a kinematic matching control to enable the robot to perform a specified medical task, which in this paper is the transesophageal echocardiography (TEE). Methods A multi-chamber soft robot was designed and fabricated based on the molding of separate layers. The method of transformation matrices was used to develop the kinematic models, and a control method using Jacobian matrices was proposed to manipulate the robot. Results A prototype was made based on a multi-chamber multi-layer design. The system contains three segments that can be actuated independently to mimic the active bending part of the respective probe. Kinematic models were developed. Negative pressure (vacuum) was used as actuation input. An open-loop controller inspired by a redundancy resolution technique was proposed to make the soft robot tip follow the desired path, i.e. the path of the rigid ultrasound probe. Conclusions It is concluded that the soft solution can perform the required task as the reachable points of the TEE tip cover the proposed robot workspace and the proposed control can be used for maneuvering in arbitrary trajectories.

In this paper, the multi-task dense-feature-fusion survival prediction (DFFSP) model is proposed to predict the three-year survival for glioblastoma (GBM) patients based on radiogenomics data. The contrast-enhanced T1-weighted (T1w) image, T2-weighted (T2w) image and copy number variation (CNV) is used as the input of the three branches of the DFFSP model. This model uses two image extraction modules consisting of residual blocks and one dense feature fusion module to make multi-scale fusion of T1w and T2w image features as backbone. Also, a gene feature extraction module is used to adaptively weight CNV fragments. Besides, a transfer learning module is introduced to solve the small sample problem and an image reconstruction module is adopted to make the model anatomy-aware under a multi-task framework. 256 sample pairs (T1w and corresponding T2w MRI slices) and 187 CNVs of 74 patients were used. The experimental results show that the proposed model can predict the three-year survival of GBM patients with the accuracy of 89.1 %, which is improved by 3.2 and 4.7 % compared with the model without genes and the model using last fusion strategy, respectively. This model could also classify the patients into high-risk and low-risk groups, which will effectively assist doctors in diagnosing GBM patients.