Biomechanical behavior of deep anterior lamellar keratoplasty: a finite element analysis.

Deep Anterior Lamellar Keratoplasty (DALK) is a surgical procedure used to restore sight and manage corneal diseases by replacing cloudy corneal tissue with allogeneic normal corneal tissue or artificial corneal material. However, the limited availability and mechanical defects of artificial corneal materials pose challenges in DALK. To predicting postoperative mechanical behavior of Deep Anterior Lamellar Keratoplasty (DALK), a three-dimensional finite element model of the postoperative DALK cornea with suture holes was developed. The postoperative corneal displacement and von Mises (VM) stress changes were also simulated under varying depths of cut (DOC: 0.16-0.26 µm), intraocular pressure (IOP: 12, 15, 18 mmHg), and central corneal thickness (CCT: 420-620 µm). The model indicated that higher IOP and CCT were associated with improved postoperative corneal stability. The postoperative corneal displacement increased from the edge to the center, while the maximum VM stress value occurs at the corneal suture hole. Corneal displacement and VM stress decrease with increasing CCT and decreasing IOP. DOC has a slight effect on corneal displacement and VM stress, with an overall positive relationship. The model has potential application in the preoperative assessment of risk in keratoplasty.

DeepAIR: A deep learning framework for effective integration of sequence and 3D structure to enable adaptive immune receptor analysis.

Structural docking between the adaptive immune receptors (AIRs), including T cell receptors (TCRs) and B cell receptors (BCRs), and their cognate antigens are one of the most fundamental processes in adaptive immunity. However, current methods for predicting AIR-antigen binding largely rely on sequence-derived features of AIRs, omitting the structure features that are essential for binding affinity. In this study, we present a deep learning framework, termed DeepAIR, for the accurate prediction of AIR-antigen binding by integrating both sequence and structure features of AIRs. DeepAIR achieves a Pearson's correlation of 0.813 in predicting the binding affinity of TCR, and a median area under the receiver-operating characteristic curve (AUC) of 0.904 and 0.942 in predicting the binding reactivity of TCR and BCR, respectively. Meanwhile, using TCR and BCR repertoire, DeepAIR correctly identifies every patient with nasopharyngeal carcinoma and inflammatory bowel disease in test data. Thus, DeepAIR improves the AIR-antigen binding prediction that facilitates the study of adaptive immunity.

An Accurate Recognition of Infrared Retro-Reflective Markers in Surgical Navigation.

Marker-based optical tracking systems (OTS) are widely used in clinical image-guided therapy. However, the emergence of ghost markers, which is caused by the mistaken recognition of markers and the incorrect correspondences between marker projections, may lead to tracking failures for these systems. Therefore, this paper proposes a strategy to prevent the emergence of ghost markers by identifying markers based on the features of their projections, finding the correspondences between marker projections based on the geometric information provided by markers, and fast-tracking markers in a 2D image between frames based on the sizes of their projections. Apart from validating its high robustness, the experimental results show that the proposed strategy can accurately recognize markers, correctly identify their correspondences, and meet the requirements of real-time tracking.

[A Maternal Health Care System Based on Mobile Health Care].

Wearable devices are used in the new design of the maternal health care system to detect electrocardiogram and oxygen saturation signal while smart terminals are used to achieve assessments and input maternal clinical information. All the results combined with biochemical analysis from hospital are uploaded to cloud server by mobile Internet. Machine learning algorithms are used for data mining of all information of subjects. This system can achieve the assessment and care of maternal physical health as well as mental health. Moreover, the system can send the results and health guidance to smart terminals.

[Motor Cortex Functional Mapping Using Electrocorticography].

The main shortcomings of using electrocortical stimulation (ECS) in identifying the motor functional area around the focus in neurosurgery are certainly time-consuming, possibly cerebral cortex injuring and perhaps triggering epilepsy. To solve these problems, we in our research presented an intraoperative motor cortex functional mapping based on electrocorticography (ECoG). At first, using power spectrum estimation, we analyzed the characteristic of ECoG which was related to move task, and selected Mu rhythm as the move-related feature. Then we extracted the feature from original ECoG by multi-resolution wavelet analysis. By calculating the sum value of feature in every channel and observing the distribution of these sum values, we obtained the correlation between the cortex area under the electrode and motor cortex functional area. The results showed that the distribution of the relationship between the cortex under the electrode and motor cortex functional area was almost consistent with those identified by ECS which was called as the gold-standard. It indicated that this method was basically feasible, and it just needed five minutes totally. In conclusion, ECoG-based and passive identification of motor cortical function may serve as a useful adjunct to ECS in the intraoperative mapping.

Improved therapeutic effect of folate-decorated PLGA-PEG nanoparticles for endometrial carcinoma.

Folate (FOL) mediated poly-lactide-co-glycolide-polyethylene glycol nanoparticles (FOL-PEG-PLGA NPs) bearing paclitaxel (PTX) were prepared for the effective delivery of drug to endometrial carcinoma. The average size, zeta potential and encapsulation efficiency of FOL-targeted NPs were found to be around 220 nm, -30.43 mV and 95.6%. Cellular uptake was observed. The accumulation of FOL-targeted NPs depends on dual effects of passive and active targeting. The FOL-targeted PTX NPs showed a greater cytotoxicity against HEC-1A cancer cells in vitro and in vivo, which might be induced by apoptosis. H&E staining did not showed apparent tissue damage to liver and kidney of the mice after injecting NPs intravenously. These results suggest that the novel FOL-PEG-PLGA NPs could be a potential delivery system with excellent therapeutic efficacy for targeting the drugs to cancer cells.