The clinical outcome of pembrolizumab for patients with recurrent or metastatic squamous cell carcinoma of the head and neck: a single center, real world study in China

BackgroundThe KEYNOTE-048 and KEYNOTE-040 study have demonstrated the efficacy of pembrolizumab in recurrent or metastatic squamous cell carcinoma of the head and neck (R/M HNSCC), we conducted this real-world study to investigate the efficacy of pembrolizumab in patients with R/M HNSCC.MethodsThis is a single-center retrospective study conducted in the Shanghai Ninth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine (Shanghai, China). Between December 2020 and December 2022, a total of 77 patients with R/M HNSCC were included into analysis. The primary endpoint of the study was overall survival (OS), and the secondary endpoints were progression-free survival (PFS), overall response rate (ORR)and toxicity.Efficacy was assessed according to RECIST version 1.1.SPSS 27.0 and GraphPad Prism 8.0 software were utilized to perform the statistical analysis.ResultsBy the cut-off date (February 28, 2023), the median OS,PFS and ORR were 15.97 months,8.53 months and 48.9% in patients treated with the pembrolizumab regimen in the first line therapy. Among these patients, 17 patients received pembrolizumab with cetuximab,and 18 received pembrolizumab with chemotherapy.We observed no significant differences between two groups neither in median OS (13.9 vs 19.4 months, P=0.3582) nor PFS (unreached vs 8.233 months, P= 0.2807). In the ≥2nd line therapy (n=30), the median OS, PFS and ORR were 5.7 months, 2.58 months and 20% respectively. Combined positive score (CPS) was eligible from 54 patients. For first line therapy, the median OS and PFS were 14.6 and 8.53 months in patients with CPS ≥1, and median OS and PFS were 14.6 and 12.33 months in patients with CPS ≥20. The immune-related adverse events (irAEs) were occurred in the 31 patients (31/77, 40.26%), and the most common potential irAEs were hypothyroidism (25.97%), and pneumonitis (7.79%).ConclusionOur real-world results indicated that pembrolizumab regimen is a promising treatment in patients with R/M HNSC

Dynamic calibration of drifting sensor arrays for real-time monitoring

Traditional sensor calibration is restricted to mathematically relating the steady-state sensor responses to the target analyte concentrations to realize environment monitoring. However, commonly-used chemical sensors usually require a relatively long time, on the order of minutes, to reach steady-state operation, and exhibit nonlinear drifting behaviors. To achieve real-time monitoring of rapidly-changing environments while accommodating drifting behaviors, this work develops statistical methods for both forward descriptive calibration and inverse dynamic calibration of sensor arrays.;Forward calibration is performed based on experimental data. In this work, multivariate Gaussian processes (GPs) were adapted to obtain the forward calibration model, which quantifies the sensor response as a function of the analyte concentration, the drifting variables, and the sensors\u27 exposure time. The multivariate GP method synergistically models all calibration data collected under a range of drifting conditions, and seeks to produce the calibration model of highest quality with the given experimental data. The forward calibration model is a descriptive model, relating sensors\u27 time-dependent responses to a static environment specified by several variables, hence it is not able to assist in real-time monitoring of rapidly-changing environments.;To achieve real-time monitoring of analyte concentrations while fully utilizing the efficiency of forward calibration rendered by multivariate GPs, an inverse calibration method was developed. This inverse model takes the form of a transfer function regression, infers the time-varying analyte concentrations from the dynamic sensor responses, and thus can be coupled with sensors for real-time monitoring. The inverse transfer function model is estimated from the pseudo-calibration data generated by the forward multivariate GP model, which captures the sensors\u27 dynamic and drifting behaviors as reflected in the real experimental data.;Simulated sensor arrays have been developed from real sensor data, and were used to demonstrate the calibration methods developed in this work

Simulation-Based Optimum Design of Sensor Arrays Using Multi-Objective Tabu Search

This thesis is concerned with the optimum design of sensor arrays (i.e., electronic noses or tongues) using simulation experiments. The proposed design method adopts a set of new criteria, which more adequately represent the desirable properties of a sensor array in practice. A number of best non-dominated array designs are selected through a multiple objective (criteria) Tabu search algorithm. The evaluation of a candidate sensor array is based on its multivariate calibration model, which is efficiently estimated from well-designed simulation experiments. The method can be used to optimize the design of both linear and nonlinear sensor arrays

T: Randomized Decimation HyperPipes

This paper represents an experimental investigation into the commonly asserted notion that the data mining algorithm “HyperPipes ” works best on sparse data sets, i.e. datasets whose individual instances contain very few values in proportion to the number of attributes. To test this hypothesis, we have developed a tool, Randomized Decimation HyperPipes (RDH), which allows the user to adjust the level of sparseness of the training sets for datasets that would normally be considered full, i.e. entries existent for almost all attributes for every instance of the data. We then conduct 10-way, cross validated, experimental evaluations to measure the performance of HyperPipes on twenty-five different datasets. Our results show that the experiment provides information that can confirm the hypothesis pertaining to certain types of datasets. In datasets with certain dominant classes, RDH provides the best results when the training set is made very sparse. Analysis of our experimental results also consistently shows that, when using approximately three quarters of the data, selected by semi-intelligent randomization during training, our method worked the same or better than traditional HyperPipes in over sixty percent of our trials

Accelerating Analyte Quantification through Dynamic Sensor Calibration

Abstract not Available.</jats:p