Diagnostic value of exosomal noncoding RNA in lung cancer: a meta-analysis.

Background: Lung cancer is one of the most dangerous cancers in the world. Most lung cancer patients are diagnosed in the middle and later stages, which can lead to poor survival rates. The development of lung cancer is often accompanied by abnormal expression of exosomal non-coding RNAs, which means that they have the potential to serve as noninvasive novel molecular markers for lung cancer diagnosis. Methods: For this study, we conducted a comprehensive literature search in PubMed, Web of science, Science direct, Embase, Cochrane, and Medline databases, and by reviewing published literature, The diagnostic capacity of exosomal microRNAs (miRNAs), long-chain non-coding RNAs (lncRNAs), and circular RNAs (circRNAs) for lung cancer was evaluated. Functional enrichment analysis of miRNA target genes was performed. Results: The study included 41 papers, a total of 68 studies. More than 60 miRNAs, 9 lncRNAs and 14 circRNAs were involved. The combined sensitivity and specificity were 0.83(95%CI, 0.80~0.86) and 0.83(95% CI,0.79~0.87); 0.71(95% CI,0.68~0.74) and 0.79(95%CI, 0.75~0.82); 0.79(95%CI,0.67~0.87) and 0.81(95%CI,0.74~0.86), and constructed overall subject operating characteristic curves with the summarized area under the curve values of 0.90, 0.82, and 0.86. Conclusion: Our study shows that exosomes miRNAs, lncRNAs and circRNAs are effective in the diagnosis of lung cancer, providing evidence for studies related to novel lung cancer diagnostic markers. Systematic review registration: https://www.crd.york.ac.uk/prospero/, identifier CRD42023457087.

Bifunctional Smart Textiles with Simultaneous Motion Monitoring and Thermotherapy for Human Joint Injuries.

The motion detection and thermotherapy provides a convenient strategy for the diagnosis and rehabilitation assessment of joint injuries. However, it is still challenging to simultaneously achieve accurate joint motion monitoring and on-demand thermotherapy. Herein, core-sheath sensing yarns (CSSYs) is proposed and fabricated for excellent electrical and photothermal heating, which consists of carbon black (CB)-coated nylon (sheath layer), silver-plated nylon and elastic spandex yarns (core layer). The CSSYs demonstrates great joule heating performance, which reaches 75 °C at 2 V applied voltage. The good thermal management performance can be well maintained when weaving these yarns into bifunctional smart textile. Further, the optimized double-ply CSSYs (DPCSSYs) with helically twisted structure possess several appealing sensing performance, including preferable strain sensitivity (0.854), excellent linearity (0.962), and superior durability (over 5000 cycles). The as-woven bifunctional smart textile can provide instant and convenient thermotherapy to the injured joints, and simultaneously monitor the injury and recovery conditions of the joint. Therefore, the designed bifunctional smart textile can provide a promising route for developing next-generation healthcare smart textile.

Controlling the Fiber Stress Distribution with Variable-Frequency Step Roll for Tunable Spun Yarn Structures.

The dynamic regulation of fiber stress distribution in the yarn-forming triangle area is critical for controlling variable composite yarn structures, including siro and sirofil composite yarns. In this study, comparison analyses of the variable geometric structure and stress distribution during the yarn-forming process, which involves step rolls with asymmetrical fiber control, have been carried out using ring-spinning technology. The geometric analyses show that partly staple fibers are continuously controlled while other fibers intermittently lack stress restraint, resulting in cyclically changed helical angles and wrapping density in the yarn-forming triangle area. The yarn structure model displayed that periodically distributed staple fibers occur in siro composite yarn, while sirofil composite yarn shows gradual periodic changes with uniform thickness variations, caused by cyclical changes in the stress distribution between filaments, and the strand altered the yarn-forming zone shapes from symmetrical to offset. Then, a systematic comparison of variable composite yarns with different frequencies (high, medium and low frequency) revealed that low-frequency step roll with wider grooves resulted in an intermittent output of staple fibers with less stress restraint, resulting in more pronounced structural variation in the siro and sirofil composite yarns with a slight yarn quality deterioration.

All-Fabric Tactile Sensors Based on Sandwich Structure Design with Tunable Responsiveness.

Capacitance tactile sensors (TSs) based on electrode distance and contact area variations have been notably employed for various purposes due to their magnificent stress sensitivity. Nevertheless, developing TSs with tunable responsiveness in a broad pressure interval is crucial owing to the trade-off between sensitivity and linear identification range. Herein, a TS including Ag-coated Velcro and spacer fabric is constructed, where its sandwich framework provides a sizable expansion in compression deformation ability. In addition, a multilayered framework composed of the stacked TS from self-adhesive Velcro provides more contact area and significant deformation for stress distribution, further balancing the sensitivity, sensing range, and linearity for smart garment application. By utilizing the overlaid selection of multilayer structures, the all-textile TS demonstrates outstanding sensitivity with a one-layer structure (0.036 kPa-1) over a pressure range of 0.2-5 kPa and retains a sensitivity of 0.002 kPa-1 in a four-layer structure over a wide pressure range of 0.2-110 kPa, representing a significant improvement compared to previous results. The sensor possesses excellent performance in terms of response speed (104 ms), repeatability (10,000 cycles), and flexibility. In addition, its significant applications, involving human motion detection, pliable keyboards, and human-computer interface, are successfully shown. Based on the facile and scalable manufacturing approach, a suitable procedure is presented to construct next-generation wearable electronics.

Synthesis of a Novel Spherical-Shell-Structure Polymerized Ionic Liquid Microsphere PILM/Au/Al(OH)3 Catalyst for Benzyl Alcohol Oxidation.

In order to selectively oxidize benzyl alcohol, a novel noble metal catalyst based on polymer ionic liquids with a core-shell structure was created. First, polymer ionic liquid microspheres (PILMs) were prepared by free radical polymerization. Second, the in situ adsorption of Au nanoparticles on the surface of PILMs was accomplished, thanks to the strong electrostatic interaction between N atoms and metal ions on the diazole ring of PILMs. Additionally, the introduction of Al(OH)3 prevented the aggregation of Au nanoparticles and promoted the catalytic reaction. Finally, the PILM/Au/Al(OH)3 catalyst with a core-shell structure was formed. The effectiveness of the PILM/Au/Al(OH)3 catalyst was assessed by varying the catalyst's type, quantity, amount of Au, amount of H2O2, temperature, and reaction time. After five cycles of experiments, the catalyst was effective and reusable. In addition, the potential catalytic mechanism of the catalyst in the oxidation of benzyl alcohol was proposed.

Quaternion-based finite-time fault-tolerant trajectory tracking control for autonomous underwater vehicle without unwinding.

This brief disposes the finite-time anti-unwinding trajectory tracking control problem of the autonomous underwater vehicle (AUV) encountering model uncertainties, ocean disturbances and actuator failures. Primarily, the kinematic model of translational and rotational motions is depicted by unit quaternion in lieu of classical Euler angle such that the AUV's dynamics could be globally and uniquely formulated. Subsequently, two finite-time control strategies are presented here to leave the state variables of AUV can converge to an adjustable region. Additionally, the adaptive laws could be applicable to the existence of actuator faults by utilizing a passive fault-tolerant technology. By integrating the initial value of the scalar quaternion into the sliding mode surface, the proposed controllers are characterized with anti-unwinding property. Then, with the application of hyperbolic tangent function, finite-time stability will be achieved for tracking errors without singularity. Stability of the closed-loop system is verified via the Lyapunov theorem. Finally, numerical simulations are presented to demonstrate the effectiveness of the proposed controllers.

Aerosol Transmission of SARS-CoV-2 in Two Dormitories - Hubei and Shandong Provinces, China, 2020.

What is already known about this topic?: Aerosol transmission is one route for the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, uncertainty remains on the threshold of ventilation rate in its occurrence. What is added by this report?: Based on two cases in Shandong Province and Hubei Province, the effect of wearing masks and the minimum ventilation required to reduce coronavirus disease 2019 (COVID-19) aerosol transmission was determined. What are the implications for public health practice?: No masking and low ventilation rates lead to a relatively high contribution of aerosols to COVID-19 transmission. Thus, public awareness of wearing masks should increase and the ventilation rate should be sufficiently higher than the minimum required ventilation.

Enhancing the Spun Yarn Properties by Controlling Fiber Stress Distribution in the Spinning Triangle with Rotary Heterogeneous Contact Surfaces.

Control of tension distribution in the spinning triangle region that can facilitate fiber motion and transfer is highly desirable for high quality yarn production. Here, the key mechanisms and a mechanical model of gradient regulation of fiber tension and motion with rotary heterogeneous contact surfaces were theoretically analyzed. The linear velocity gradient, effected on a fiber strand using rotary heterogeneous contact surfaces, could balance and stabilize the structure and stress distribution of spinning triangle area, which could capture exposed fiber to reduce hairiness formation and enhance the internal and external fiber transfer to strengthen the fiber utilization rate. Then, varied yarns spun without and with the rotary grooved and rotary heterogeneous contact surfaces were tested to compare the property improvement for verifying above-mentioned theory. The hairiness, irregularity, and tensity of the yarns spun with rotary heterogeneous contact surfaces spun yarns were significantly improved compared to other spun yarns, which effectively corresponded well to the theoretical analysis. Based on this spinning method, this effective, low energy-consuming, easy spinning apparatus can be used with varied fiber materials for high-quality yarn production.

3D car-detection based on a Mobile Deep Sensor Fusion Model and real-scene applications.

Unmanned vehicles need to make a comprehensive perception of the surrounding environmental information during driving. Perception of automotive information is of significance. In the field of automotive perception, the sterevision of car-detection plays a vital role and sterevision can calculate the length, width, and height of a car, making the car more specific. However, under the existing technology, it is impossible to obtain accurate detection in a complex environment by relying on a single sensor. Therefore, it is particularly important to study the complex sensing technology based on multi-sensor fusion. Recently, with the development of deep learning in the field of vision, a mobile sensor-fusion method based on deep learning is proposed and applied in this paper--Mobile Deep Sensor Fusion Model (MDSFM). The content of this article is as follows. It does a data processing that projects 3D data to 2D data, which can form a dataset suitable for the model, thereby training data more efficiently. In the modules of LiDAR, it uses a revised squeezeNet structure to lighten the model and reduce parameters. In the modules of cameras, it uses the improved design of detecting module in R-CNN with a Mobile Spatial Attention Module (MSAM). In the fused part, it uses a dual-view deep fusing structure. And then it selects images from the KITTI's datasets for validation to test this model. Compared with other recognized methods, it shows that our model has a fairly good performance. Finally, it implements a ROS program on the experimental car and our model is in good condition. The result shows that it can improve performance of detecting easy cars significantly through MDSFM. It increases the quality of the detected data and improves the generalized ability of car-detection model. It improves contextual relevance and preserves background information. It remains stable in driverless environments. It is applied in the realistic scenario and proves that the model has a good practical value.

Identification of Genetic Mutations in Human Lung Cancer by Targeted Sequencing.

Lung cancer remains the most prevalent malignancy and the primary cause of cancer-related deaths worldwide. Unique mutations patterns can be found in lung cancer subtypes, in individual cancers, or within a single tumor, and drugs that target these genetic mutations and signal transduction pathways are often beneficial to patients. In this study, we used the Ion Torrent AmpliSeq Cancer Panel to sequence 737 loci from 45 cancer-related genes and oncogenes to identify genetic mutations in 48 formalin-fixed, paraffin-embedded (FFPE) human lung cancer samples from Chinese patients. We found frequent mutations in EGFR, KRAS, PIK3CA, and TP53 genes. Moreover, we observed that a portion of the lung cancer samples harbored two or more mutations in these key genes. This study demonstrates the feasibility of using the Ion Torrent sequencing to efficiently identify genetic mutations in individual tumors for targeted lung cancer therapy.

Rapid detection of genetic mutations in individual breast cancer patients by next-generation DNA sequencing.

Breast cancer is the most common malignancy in women and the leading cause of cancer deaths in women worldwide. Breast cancers are heterogenous and exist in many different subtypes (luminal A, luminal B, triple negative, and human epidermal growth factor receptor 2 (HER2) overexpressing), and each subtype displays distinct characteristics, responses to treatment, and patient outcomes. In addition to varying immunohistochemical properties, each subtype contains a distinct gene mutation profile which has yet to be fully defined. Patient treatment is currently guided by hormone receptor status and HER2 expression, but accumulating evidence suggests that genetic mutations also influence drug responses and patient survival. Thus, identifying the unique gene mutation pattern in each breast cancer subtype will further improve personalized treatment and outcomes for breast cancer patients. In this study, we used the Ion Personal Genome Machine (PGM) and Ion Torrent AmpliSeq Cancer Panel to sequence 737 mutational hotspot regions from 45 cancer-related genes to identify genetic mutations in 80 breast cancer samples of various subtypes from Chinese patients. Analysis revealed frequent missense and combination mutations in PIK3CA and TP53, infrequent mutations in PTEN, and uncommon combination mutations in luminal-type cancers in other genes including BRAF, GNAS, IDH1, and KRAS. This study demonstrates the feasibility of using Ion Torrent sequencing technology to reliably detect gene mutations in a clinical setting in order to guide personalized drug treatments or combination therapies to ultimately target individual, breast cancer-specific mutations.

Frequent KIT mutations in human gastrointestinal stromal tumors.

Identifying gene mutations in individual tumors is critical to improve the efficacy of cancer therapy by matching targeted drugs to specific mutations. Gastrointestinal stromal tumors (GIST) are stromal or mesenchymal subepithelial neoplasms affecting the gastrointestinal tract and frequently contain activating gene mutations in either KIT or platelet-derived growth factor A (PDGFRA). Although GIST is highly responsive to several selective tyrosine kinase inhibitors, combined use of inhibitors targeting other mutations is needed to further prolong survival in patients with GIST. In this study, we aim to screen and identify genetic mutations in GIST for targeted therapy using the new Ion Torrent next-generation sequencing platform. Utilizing the Ion Ampliseq Cancer Panel, we sequenced 737 loci from 45 cancer-related genes using DNA extracted from formalin-fixed and paraffin-embedded (FFPE) samples of 121 human gastrointestinal stromal tumors, set up stringent parameters for reliable variant calling by filtering out potential raw base calling errors, and identified frequent mutations in the KIT gene. This study demonstrates the utility of using Ion Torrent sequencing to efficiently identify human cancer mutations. This may provide a molecular basis for clinically developing new drugs targeting these gene mutations for GIST therapy.

Genetic mutation analysis of human gastric adenocarcinomas using ion torrent sequencing platform.

Gastric cancer is the one of the major causes of cancer-related death, especially in Asia. Gastric adenocarcinoma, the most common type of gastric cancer, is heterogeneous and its incidence and cause varies widely with geographical regions, gender, ethnicity, and diet. Since unique mutations have been observed in individual human cancer samples, identification and characterization of the molecular alterations underlying individual gastric adenocarcinomas is a critical step for developing more effective, personalized therapies. Until recently, identifying genetic mutations on an individual basis by DNA sequencing remained a daunting task. Recent advances in new next-generation DNA sequencing technologies, such as the semiconductor-based Ion Torrent sequencing platform, makes DNA sequencing cheaper, faster, and more reliable. In this study, we aim to identify genetic mutations in the genes which are targeted by drugs in clinical use or are under development in individual human gastric adenocarcinoma samples using Ion Torrent sequencing. We sequenced 737 loci from 45 cancer-related genes in 238 human gastric adenocarcinoma samples using the Ion Torrent Ampliseq Cancer Panel. The sequencing analysis revealed a high occurrence of mutations along the TP53 locus (9.7%) in our sample set. Thus, this study indicates the utility of a cost and time efficient tool such as Ion Torrent sequencing to screen cancer mutations for the development of personalized cancer therapy.

PIK3CA and TP53 gene mutations in human breast cancer tumors frequently detected by ion torrent DNA sequencing.

Breast cancer is the most common malignancy and the leading cause of cancer deaths in women worldwide. While specific genetic mutations have been linked to 5-10% of breast cancer cases, other environmental and epigenetic factors influence the development and progression of the cancer. Since unique mutations patterns have been observed in individual cancer samples, identification and characterization of the distinctive breast cancer molecular profile is needed to develop more effective target therapies. Until recently, identifying genetic cancer mutations via personalized DNA sequencing was impractical and expensive. The recent technological advancements in next-generation DNA sequencing, such as the semiconductor-based Ion Torrent sequencing platform, has made DNA sequencing cost and time effective with more reliable results. Using the Ion Torrent Ampliseq Cancer Panel, we sequenced 737 loci from 45 cancer-related genes to identify genetic mutations in 105 human breast cancer samples. The sequencing analysis revealed missense mutations in PIK3CA, and TP53 genes in the breast cancer samples of various histologic types. Thus, this study demonstrates the necessity of sequencing individual human cancers in order to develop personalized drugs or combination therapies to effectively target individual, breast cancer-specific mutations.

Frequent mutations in EGFR, KRAS and TP53 genes in human lung cancer tumors detected by ion torrent DNA sequencing.

Lung cancer is the most common malignancy and the leading cause of cancer deaths worldwide. While smoking is by far the leading cause of lung cancer, other environmental and genetic factors influence the development and progression of the cancer. Since unique mutations patterns have been observed in individual cancer samples, identification and characterization of the distinctive lung cancer molecular profile is essential for developing more effective, tailored therapies. Until recently, personalized DNA sequencing to identify genetic mutations in cancer was impractical and expensive. The recent technological advancements in next-generation DNA sequencing, such as the semiconductor-based Ion Torrent sequencing platform, has made DNA sequencing cost and time effective with more reliable results. Using the Ion Torrent Ampliseq Cancer Panel, we sequenced 737 loci from 45 cancer-related genes to identify genetic mutations in 76 human lung cancer samples. The sequencing analysis revealed missense mutations in KRAS, EGFR, and TP53 genes in the breast cancer samples of various histologic types. Thus, this study demonstrates the necessity of sequencing individual human cancers in order to develop personalized drugs or combination therapies to effectively target individual, breast cancer-specific mutations.