Integrated assessment of PD-L1 expression and molecular classification facilitates therapy selection and prognosis prediction in gastric cancer.

PURPOSE: Targeting the PD-1/PD-L1 pathway has emerged as a novel therapy for cancer. To identify rational candidates for anti-PD-1/PD-L1 immunotherapy in gastric cancer (GC), the abundance of PD-L1 expression was evaluated on a kind of biomarker-based molecular classification for shaping prognosis and treatment planning. METHODS: One hundred and sixty-five GCs were classified into five subgroups using immunohistochemistry (IHC) and in situ hybridization (ISH) methods, based on a panel of seven markers (MLH1, PMS2, MSH2, MSH6, E-cadherin, P53, and Epstein-Barr virus mRNA). The expression of PD-L1 in GC tissues was analyzed immunohistochemically. RESULTS: The five categories (Epstein-Barr virus positivity, microsatellite instability, aberrant E-cadherin, aberrant P53 expression, and normal P53 expression) correspond to the reported molecular subgroups for similar proportions and clinicopathologic characteristics. Survival analysis indicated that subgroups with aberrant E-cadherin expression independently predicted a worse prognosis in GC patients (HR=2.51, P=0.010). The clinical and prognostic profiles produced by this stratification in nonintestinal-type GC were distinguishable from those in intestinal-type. Although PD-L1 was not a significant prognostic factor, that more frequent presence of PD-L1-positive in microsatellite instability tumors than other subtypes (P<0.010) hinted at a prolonged clinical course. Moreover, the lowest level of PD-L1 but the highest of Her2 was observed in the group of aberrant P53, namely it was suggested that there was a negative correlation between PD-L1 and Her2 overexpression. CONCLUSION: Different molecular subtypes in GC may have a tendency to react differently to anti-PD-L1/PD-1 immunotherapy or anti-Her2 therapy. A combination of PD-L1 expression and this cost-effective classification strategy would be helpful for predicting prognosis and promoting personalized therapy in clinical practice.

Human body contour data based activity recognition.

This research work is aimed to develop autonomous bio-monitoring mobile robots, which are capable of tracking and measuring patients' motions, recognizing the patients' behavior based on observation data, and providing calling for medical personnel in emergency situations in home environment. The robots to be developed will bring about cost-effective, safe and easier at-home rehabilitation to most motor-function impaired patients (MIPs). In our previous research, a full framework was established towards this research goal. In this research, we aimed at improving the human activity recognition by using contour data of the tracked human subject extracted from the depth images as the signal source, instead of the lower limb joint angle data used in the previous research, which are more likely to be affected by the motion of the robot and human subjects. Several geometric parameters, such as, the ratio of height to weight of the tracked human subject, and distance (pixels) between centroid points of upper and lower parts of human body, were calculated from the contour data, and used as the features for the activity recognition. A Hidden Markov Model (HMM) is employed to classify different human activities from the features. Experimental results showed that the human activity recognition could be achieved with a high correct rate.

Psycho-physiological assessment of a prosthetic hand sensory feedback system based on an auditory display: a preliminary study.

BACKGROUND: Prosthetic hand users have to rely extensively on visual feedback, which seems to lead to a high conscious burden for the users, in order to manipulate their prosthetic devices. Indirect methods (electro-cutaneous, vibrotactile, auditory cues) have been used to convey information from the artificial limb to the amputee, but the usability and advantages of these feedback methods were explored mainly by looking at the performance results, not taking into account measurements of the user's mental effort, attention, and emotions. The main objective of this study was to explore the feasibility of using psycho-physiological measurements to assess cognitive effort when manipulating a robot hand with and without the usage of a sensory substitution system based on auditory feedback, and how these psycho-physiological recordings relate to temporal and grasping performance in a static setting. METHODS: 10 male subjects (26+/-years old), participated in this study and were asked to come for 2 consecutive days. On the first day the experiment objective, tasks, and experiment setting was explained. Then, they completed a 30 minutes guided training. On the second day each subject was tested in 3 different modalities: Auditory Feedback only control (AF), Visual Feedback only control (VF), and Audiovisual Feedback control (AVF). For each modality they were asked to perform 10 trials. At the end of each test, the subject had to answer the NASA TLX questionnaire. Also, during the test the subject's EEG, ECG, electro-dermal activity (EDA), and respiration rate were measured. RESULTS: The results show that a higher mental effort is needed when the subjects rely only on their vision, and that this effort seems to be reduced when auditory feedback is added to the human-machine interaction (multimodal feedback). Furthermore, better temporal performance and better grasping performance was obtained in the audiovisual modality. CONCLUSIONS: The performance improvements when using auditory cues, along with vision (multimodal feedback), can be attributed to a reduced attentional demand during the task, which can be attributed to a visual "pop-out" or enhance effect. Also, the NASA TLX, the EEG's Alpha and Beta band, and the Heart Rate could be used to further evaluate sensory feedback systems in prosthetic applications.