A Microfluidic Chip-Based Automated System for Whole-Course Monitoring the Drug Responses of Organoids.

Tumor patients-derived organoids, as a promising preclinical prediction model, have been utilized to evaluate ex vivo drug responses for formulating optimal therapeutic strategies. Detecting adenosine triphosphate (ATP) has been widely used in existing organoid-based drug response tests. However, all commercial ATP detection kits containing the cell lysis procedure can only be applied for single time point ATP detection, resulting in the neglect of dynamic ATP variations in living cells. Meanwhile, due to the limited number of viable organoids from a single patient, it is impractical to exhaustively test all potential time points in search of optimal ones. In this work, a multifunctional microfluidic chip was developed to perform all procedures of organoid-based drug response tests, including establishment, culturing, drug treatment, and ATP monitoring of organoids. An ATP sensor was developed to facilitate the first successful attempt on whole-course monitoring the growth status of fragile organoids. To realize a clinically applicable automatic system for the drug testing of lung cancer, a microfluidic chip based automated system was developed to perform entire organoid-based drug response test, bridging the gap between laboratorial manipulation and clinical practices, as it outperformed previous methods by improving data repeatability, eliminating human error/sample loss, and more importantly, providing a more accurate and comprehensive evaluation of drug effects.

Clonal expansion of shared T cell receptors reveals the existence of immune commonality among different lesions of synchronous multiple primary lung cancer.

The increase in the detection rate of synchronous multiple primary lung cancer (MPLC) has posed remarkable clinical challenges due to the limited understanding of its pathogenesis and molecular features. Here, comprehensive comparisons of genomic and immunologic features between MPLC and solitary lung cancer nodule (SN), as well as different lesions of the same patient, were performed. Compared with SN, MPLC displayed a lower rate of EGFR mutation but higher rates of BRAF, MAP2K1, and MTOR mutation, which function exactly in the upstream and downstream of the same signaling pathway. Considerable heterogeneity in T cell receptor (TCR) repertoire exists among not only different patients but also among different lesions of the same patient. Invasive lesions of MPLC exhibited significantly higher TCR diversity and lower TCR expansion than those of SN. Intriguingly, different lesions of the same patient always shared a certain proportion of TCR clonotypes. Significant clonal expansion could be observed in shared TCR clonotypes, particularly in those existing in all lesions of the same patient. In conclusion, this study provided evidences of the distinctive mutational landscape, activation of oncogenic signaling pathways, and TCR repertoire in MPLC as compared with SN. The significant clonal expansion of shared TCR clonotypes demonstrated the existence of immune commonality among different lesions of the same patient and shed new light on the individually tailored precision therapy for MPLC.

The EEG-Based Fusion Entropy-Featured Identification of Isometric Contraction Forces under the Same Action.

This study explores the important role of assessing force levels in accurately controlling upper limb movements in human-computer interfaces. It uses a new method that combines entropy to improve the recognition of force levels. This research aims to differentiate between different levels of isometric contraction forces using electroencephalogram (EEG) signal analysis. It integrates eight different entropy measures: power spectrum entropy (PSE), singular spectrum entropy (SSE), logarithmic energy entropy (LEE), approximation entropy (AE), sample entropy (SE), fuzzy entropy (FE), alignment entropy (PE), and envelope entropy (EE). The findings emphasize two important advances: first, including a wide range of entropy features significantly improves classification efficiency; second, the fusion entropy method shows exceptional accuracy in classifying isometric contraction forces. It achieves an accuracy rate of 91.73% in distinguishing between 15% and 60% maximum voluntary contraction (MVC) forces, along with 69.59% accuracy in identifying variations across 15%, 30%, 45%, and 60% MVC. These results illuminate the efficacy of employing fusion entropy in EEG signal analysis for isometric contraction detection, heralding new opportunities for advancing motor control and facilitating fine motor movements through sophisticated human-computer interface technologies.

A dual-functional microfluidic chip for guiding personalized lung cancer medicine: combining EGFR mutation detection and organoid-based drug response test.

Many efforts have been paid to advance the effectiveness of personalized medicine for lung cancer patients. Sequencing-based molecular diagnosis of EGFR mutations has been widely used to guide the selection of anti-lung-cancer drugs. Organoid-based assays have also been developed to ex vivo test individual responses to anti-lung-cancer drugs. After addressing several technical difficulties, a new combined strategy, in which anti-cancer medicines are first selected based on molecular diagnosis and then ex vivo tested on organoids, has been realized in a single dual-functional microfluidic chip. A DNA-based nanoruler has been developed to detect the existence of EGFR mutations and shrink the detection period from weeks to hours, compared with sequencing. The employment of the DNA-based nanoruler creates a possibility to purposively test anti-cancer drugs, either EGFR-TKIs or chemotherapy drugs, not both, on limited amounts of organoids. Moreover, a DNA-based nanosensor has been developed to recognize intracellular ATP variation without harming cell viability, realizing in situ monitoring of the whole course growth status of organoids for on-chip drug response test. The dual-functional microfluidic chip was validated by both cell lines and clinical samples from lung cancer patients. Furthermore, based on the dual-functional microfluidic chip, a fully automated system has been developed to span the divide between experimental procedures and therapeutic approaches. This study constitutes a novel way of combining EGFR mutation detection and organoid-based drug response test on an individual patient for guiding personalized lung cancer medicine.

[Application and Research Progress of Lung Cancer Organoid in Precision Medicine 
for Lung Cancer].

The continuous advancement of molecular detection technology has greatly propelled the development of precision medicine for lung cancer. However, tumor heterogeneity is closely associated with tumor metastasis, recurrence, and drug resistance. Additionally, different lung cancer patients with the same genetic mutation may exhibit varying treatment responses to different therapeutic strategies. Therefore, the development of modern precision medicine urgently requires the precise formulation of personalized treatment strategies through personalized tumor models. Lung cancer organoid (LCO) can highly simulate the biological characteristics of tumor in vivo, facilitating the application of innovative drugs such as antibody-drug conjugate in precision medicine for lung cancer. With the development of co-culture model of LCO with tumor microenvironment and tissue engineering technology such as microfluidic chip, LCO can better preserve the biological characteristics and functions of tumor tissue, further improving high-throughput and automated drug sensitivity experiment. In this review, we combine the latest research progress to summarize the application progress and challenges of LCO in precision medicine for lung cancer.
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[Relationship between Bacteria in the Lower Respiratory Tract/Lung Cancer 
and the Development of Lung Cancer as well as Its Clinical Application].

Due to the advancement of 16S rRNA sequencing technology, the lower respiratory tract microbiota, which was considered non-existent, has been revealed. The correlation between these microorganisms and diseases such as tumor has been a hot topic in recent years. As the bacteria in the surrounding can infiltrate the tumors, researchers have also begun to pay attention to the biological behavior of tumor bacteria and their interaction with tumors. In this review, we present the characteristic of the lower respiratory tract bacteria and summarize recent research findings on the relationship between these microbiota and lung cancer. On top of that, we also summarize the basic feature of bacteria in tumors and focus on the characteristic of the bacteria in lung cancer. The relationship between bacteria in lung cancer and tumor development is also been discussed. Finally, we review the potential clinical applications of bacterial communities in the lower respiratory tract and lung cancer, and summarize key points of sample collection, sequencing, and contamination control, hoping to provide new ideas for the screening and treatment of tumors.
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