Accelerating antibody discovery and design with artificial intelligence: Recent advances and prospects.

Therapeutic antibodies are the largest class of biotherapeutics and have been successful in treating human diseases. However, the design and discovery of antibody drugs remains challenging and time-consuming. Recently, artificial intelligence technology has had an incredible impact on antibody design and discovery, resulting in significant advances in antibody discovery, optimization, and developability. This review summarizes major machine learning (ML) methods and their applications for computational predictors of antibody structure and antigen interface/interaction, as well as the evaluation of antibody developability. Additionally, this review addresses the current status of ML-based therapeutic antibodies under preclinical and clinical phases. While many challenges remain, ML may offer a new therapeutic option for the future direction of fully computational antibody design.

Flexible channel selection method based on optical combs for broadband signals receiving.

A flexible channel selection method based on optical combs is proposed for reconfigurable optical channels in this paper. Optical-frequency combs with a large frequency interval are used to modulate broadband radio frequency (RF) signals, and an on-chip reconfigurable optical filter [Proc. of SPIE, 11763, 1176370 (2021).10.1117/12.2587403] is used to perform periodic carrier separation of wideband and narrowband signals and channel selection. In addition, flexible channel selection is achieved by presetting the parameters of a fast-response programmable wavelength-selective optical switch and filter device. Channel selection only relies on the combs through the Vernier effect of the combs and the passbands for different periods and does not require the use an additional switch matrix. Finally, flexible switching between and selection of specific channels for 13 GHz and 19 GHz broadband RF signals are experimentally verified.

One-Step Crank-Nicolson Direct-Splitting Algorithm with Enhanced Absorption to Evaluate Low-Pressure Discharge for Satellite Sensors in Outer Space.

Low-pressure discharge causes air ionization resulting in performance degeneration or failure for the satellite sensors in outer space. Here, a one-step Crank-Nicolson Direct-Splitting (CNDS) algorithm is proposed to evaluate the electrical behavior of satellite sensors under the low-pressure discharge circumstance. To be more specific, the CNDS algorithm is proposed in the Lorentz medium, which can accurately analyze the ionized air and generated plasma. Higher order perfectly matched layer (PML) is modified in the Lorentz medium to efficiently terminate the unbounded lattice. It can be concluded that the proposed algorithm shows entire considerable performance in the low-pressure discharge evaluation. The proposed PML formulation behaviors enhanced absorbing performance compared with the existing algorithm. Through the experiments, it can be observed that the low-pressure discharge phenomenon causes performance variation, which shows a significant influence on the satellite sensors. Meanwhile, results show considerable agreement between the simulation and experiment results which indicates the effectiveness of the algorithm.

Narrow-Bandpass One-Step Leapfrog Hybrid Implicit-Explicit Algorithm with Convolutional Boundary Condition for Its Applications in Sensors.

A large number of sensors work in the narrow bandpass circumstance. Meanwhile, some of them hold fine details merely along one and two dimensions. In order to efficiently simulate these sensors and devices, the one-step leapfrog hybrid implicit-explicit (HIE) algorithm with the complex envelope (CE) method and absorbing boundary condition is proposed in the narrow bandpass circumstance. To be more precise, absorbing boundary condition is implemented by the higher order convolutional perfectly matched layer (CPML) formulation to further enhance the absorption during the entire simulation. Numerical examples and their experiments are carried out to further illustrate the effectiveness of the proposed algorithm. The results show considerable agreement with the experiment and theory resolution. The relationship between the time step and mesh size can break the Courant-Friedrichs-Levy condition which indicates the physical size/selection mesh size. Such a condition indicates that the proposed algorithm behaviors are considerably accurate due to the rational choice in discretized mesh. It also shows decrement in simulation duration and memory consumption compared with the other algorithms. In addition, absorption performance can be improved by employing the proposed higher order CPML algorithm during the whole simulation.

[A method of synthesizing cicada sound for treatment of tinnitus].

Masking therapy can make patients accustom to tinnitus. This therapy is safe and easy to implement, so that it has become a widely used treatment of curing tinnitus. According to surveys of tinnitus sounds, cicada sound is one of the most usual tinnituses. Meanwhile, we have not hitherto found published papers concerning how to synthesize cicada sound and to use it to ameliorate tinnitus. Inspired by the human acoustics theory, we proposed a method to synthesize medical masking sound and to realize the diversity by illustrating the process of synthesizing various cicada sounds. In addition, energy attenuation problem in spectrum shifting process has been successfully solved. Simulation results indicated that the proposed method achieved decent results and would have practical value for the future applications.

Colorectal gastrointestinal stromal tumors (GISTs): a retrospective study of reported cases.

BACKGROUND/AIMS: Although with malignant potential, there are limited information about colorectal GISTs due to their relatively low incidence among large intestine diseases and the lack of sufficient associated case reports. The aim of the study is to obtain a better understanding of colorectal GISTs and to investigate the optimal treatment for it. METHODOLOGY: Clinical data of 90 patients with colorectal GISTs published in the literature from 2003 to 2009 were analyzed with emphasis on pathology, treatment and prognosis. RESULTS: The patients with tumor necrosis had a high relapse rate (p = 0.003) and a bad prognosis (p < 0.001) after surgery with negative margins (R0). Tumor necrosis had a significantly positive correlation with risk grades (p < 0.001). On the other hand, the mixed cell type had a significantly positive correlation with tumor necrosis (p = 0.016). Of the 67 cases with R0 resection, the difference of disease-free survival between the low-risk and high-risk was statistically significant (p = 0.003). Although, this was not statistically significant between the low risk and intermediate risk (p = 0.160) as well as intermediate-risk and high-risk (p = 0.229). But the high-risk had a marked relapse rate (54.8%), in comparison with the intermediate-risk (7.1%). CONCLUSION: High-risk colorectal GISTs showed a poor outcome. A complete resection followed by routine use of tyrosine kinase inhibitors is recommended. Surgery with negative margins is the main treatment for non-high-risk group. The GISTs with tumor necrosis or mixed cell type implicate a malignant potential and a poor outcome. More attention should be paid and an extensive follow-up is warranted in this population.

Centromere protein F and survivin are associated with high risk and a poor prognosis in colorectal gastrointestinal stromal tumours.

AIMS: Colorectal gastrointestinal stromal tumours (GISTs) are considered to be tumours with a relatively poor prognosis. Few reports have been performed to investigate the mechanisms behind their malignant behaviour and to identify new therapeutic strategies for their treatment. The authors conducted this study to explore potential targets for the treatment of colorectal GISTs (CRGISTs). METHODS: In the current study, the authors focused on centromere protein F and survivin, two markers that are known to affect the malignant behaviour of other tumours. Expression of centromere protein F and survivin was detected through the immunohistochemical staining of paraffin-embedded tumour tissues and then scored. The relationship between the expression of the two markers and their clinical parameters was analysed. Associated Survival analysis was available based on follow-up information. RESULTS: The authors demonstrated for the first time that centromere protein F and survivin expression were significantly associated with high risk and a poor prognosis (p<0.05) in CRGISTs. The authors also found that centromere protein F expression was more prevalent in males (p=0.002). CONCLUSIONS: The results suggest that centromere protein F and survivin are malignant behaviour markers for CRGISTs. The expression of centromere protein F or survivin points to a poor clinical outcome. Interfering with centromere protein F and/or survivin expression might be a potentially therapeutic strategy for treating malignant CRGISTs.