Magnetic Microrobots Fabricated by Photopolymerization and Assembly.

Magnetic soft microrobots have great potential to access narrow spaces and conduct multiple tasks in the biomedical field. Until now, drug delivery, microsurgery, disease diagnosis, and dredging the blocked blood vessel have been realized by magnetic soft microrobots in vivo or in vitro. However, as the tasks become more and more complex, more functional units have been embedded in the body of the developed magnetic microrobots. These magnetic soft microrobots with complex designed geometries, mechanisms, and magnetic orientation are now greatly challenging the fabrication of the magnetic microrobots. In this paper, we propose a new method combining photopolymerization and assembly for the fabrication of magnetic soft microrobots. Utilizing the micro-hand assembly system, magnetic modules with different shapes and materials are firstly arrayed with precise position and orientation control. Then, the developed photopolymerization system is employed to fix and link these modules with soft materials. Based on the proposed fabrication method, 3 kinds of soft magnetic microrobots were fabricated, and the fundamental locomotion was presented. We believe that the presented fabrication strategy could help accelerate the clinical application of magnetic microrobots.

Field-Controlled Microrobots Fabricated by Photopolymerization.

Field-controlled microrobots have attracted extensive research in the biological and medical fields due to the prominent characteristics including high flexibility, small size, strong controllability, remote manipulation, and minimal damage to living organisms. However, the fabrication of these field-controlled microrobots with complex and high-precision 2- or 3-dimensional structures remains challenging. The photopolymerization technology is often chosen to fabricate field-controlled microrobots due to its fast-printing velocity, high accuracy, and high surface quality. This review categorizes the photopolymerization technologies utilized in the fabrication of field-controlled microrobots into stereolithography, digital light processing, and 2-photon polymerization. Furthermore, the photopolymerized microrobots actuated by different field forces and their functions are introduced. Finally, we conclude the future development and potential applications of photopolymerization for the fabrication of field-controlled microrobots.

Clinical characteristics and prognostic analysis of SMARCA4-deficient non-small cell lung cancer.

PURPOSE: To improve the understanding of special types of tumors, we summarized and analyzed the clinicopathological features and prognostic factors of SMARCA4-deficient non-small cell lung cancer (SMARCA4-dNSCLC). METHODS: We selected 105 patients with SMARCA4-dNSCLC and 221 patients with SMARCA4-intact non-small cell lung cancer (SMARCA4-iNSCLC) by performing immunohistochemical analysis of 1520 NSCLC samples, and we assessed the patients' clinicopathological features and survival state. RESULTS: (1) SMARCA4-dNSCLC was significantly associated with older age, male sex, smoking history, larger invasive tumor size, higher tumor proliferation index (Ki-67), more adrenal metastases, more lymph node metastases, and few EGFR mutations (p < 0.05). The tumors were mostly negative for thyroid transcription factor-1 (TTF-1), CD34, and p40 and positive for cytokeratin 7 (CK7) in immunohistochemistry (IHC). Nineteen SMARCA4-dNSCLC patients mostly had TP53, SMARCA4, and LRP1B mutations, and 48% of them had SMARCA4 frameshift mutations. SMARCA4-dNSCLC patients have a worse prognosis than SMARCA4-iNSCLC patients (HR: 0.27; 95% CI: 0.17-0.45). The overall survival (OS) of patients with stage III SMARCA4-dNSCLC was worse than that of patients with SMARCA4-iNSCLC, and the OS of stage IV SMARCA4-dNSCLC patients was also worse than that of SMARCA4-iNSCLC patients (p < 0.01). (2) Multivariate regression analysis showed that sex (HR: 4.12; 95% CI: 1.03-16.39) and smoking history (HR: 2.29; 95% CI: 1.04-5.02) had significant effects on the survival time of SMARCA4-dNSCLC patients. In SMARCA4-dNSCLC patients without distant metastases (stage I-III), patients with stage N2 or N3 lymph node metastases (HR: 6.35; 95% CI: 1.07-37.47) had a poor prognosis. Among patients with SMARCA4-dNSCLC who were treated and had distant metastases (stage IV), male patients and patients treated with immunotherapy combined with chemotherapy showed a longer median overall survival (mOS). CONCLUSION: SMARCA4-dNSCLC has unique clinicopathological features and a shorter survival prognosis than SMARCA4-iNSCLC. The efficacy of immunotherapy combined with chemotherapy needs to be observed for longer periods.

A Novel Method Based on Nonlinear Binary Grasshopper Whale Optimization Algorithm for Feature Selection.

Feature Selection (FS) is considered as an important preprocessing step in data mining and is used to remove redundant or unrelated features from high-dimensional data. Most optimization algorithms for FS problems are not balanced in search. A hybrid algorithm called nonlinear binary grasshopper whale optimization algorithm (NL-BGWOA) is proposed to solve the problem in this paper. In the proposed method, a new position updating strategy combining the position changes of whales and grasshoppers population is expressed, which optimizes the diversity of searching in the target domain. Ten distinct high-dimensional UCI datasets, the multi-modal Parkinson's speech datasets, and the COVID-19 symptom dataset are used to validate the proposed method. It has been demonstrated that the proposed NL-BGWOA performs well across most of high-dimensional datasets, which shows a high accuracy rate of up to 0.9895. Furthermore, the experimental results on the medical datasets also demonstrate the advantages of the proposed method in actual FS problem, including accuracy, size of feature subsets, and fitness with best values of 0.913, 5.7, and 0.0873, respectively. The results reveal that the proposed NL-BGWOA has comprehensive superiority in solving the FS problem of high-dimensional data.

ISW-LM: An intensive symptom weight learning mechanism for early COVID-19 diagnosis.

The novel coronavirus disease 2019 (COVID-19) pandemic has severely impacted the world. The early diagnosis of COVID-19 and self-isolation can help curb the spread of the virus. Besides, a simple and accurate diagnostic method can help in making rapid decisions for the treatment and isolation of patients. The analysis of patient characteristics, case trajectory, comorbidities, symptoms, diagnosis, and outcomes will be performed in the model. In this paper, a symptom-based machine learning (ML) model with a new learning mechanism called Intensive Symptom Weight Learning Mechanism (ISW-LM) is proposed. The proposed model designs three new symptoms' weight functions to identify the most relevant symptoms used to diagnose and classify COVID-19. To verify the efficiency of the proposed model, multiple laboratory and clinical datasets containing epidemiological symptoms and blood tests are used. Experiments indicate that the importance of COVID-19 infection symptoms varies between countries and regions. In most datasets, the most frequent and significant predictive symptoms for diagnosing COVID-19 are fever, sore throat, and cough. The experiment also compares the state-of-the-art methods with the proposed method, which shows that the proposed model has a high accuracy rate of up to 97.1711%. The positive results indicate that the proposed learning mechanism can help clinicians quickly diagnose and screen patients for COVID-19 at an early stage.

DNA Melting Analysis with Optofluidic Lasers Based on Fabry-Pérot Microcavity.

We conduct DNA high-resolution melting (HRM) analysis using optofluidic lasers based on a Fabry-Pérot microcavity. Compared to the fluorescence-based HRM, the laser-based HRM has advantages of higher emission intensity for better signal-to-noise ratio and sharper transition for better temperature resolution. In addition, the melting temperature can be lowered by optimizing the laser conditions such as external pump and cavity Q-factor. In this work, we first theoretically analyze the laser-based HRM. Then experiments are performed on three long DNA sequences as model systems, one being 99 bases and the other two being 130 bases long but with different GC contents. We show that the laser-based HRM is able to distinguish the target and the single-base mismatched DNA as long as 130 bases and with nearly 50% GC content. The dependence of laser threshold on the temperature for each DNA sample is first experimentally investigated and by optimizing the external pump, the melting temperature is reduced by more than 10 °C, compared to the fluorescence-based HRM for long DNA sequences up to 130 bases. Finally, we demonstrate an alternative method of using the laser-based HRM for rapid DNA screening that does not exist for the fluorescence-based HRM, in which laser excitation is scanned at a fixed temperature to distinguish the target and the base-mismatched DNA sequences. It is shown that the 130-bases-long DNA with nearly 50% GC content can have as much as 20% difference in the laser threshold and 40% difference in the laser output slope between the target and the single-base mismatched sequences, despite only 0.5 °C difference in their melting temperature, indicating that the laser-excitation-scanning method can also be suitable for long DNA sequences with higher GC content.