CRISPR-Responsive RCA-Based DNA Hydrogel Biosensing Platform with Customizable Signal Output for Rapid and Sensitive Nucleic Acid Detection.

Current nucleic acid-responsive DNA hydrogels face significant challenges, such as the requirement for high target concentrations, frequent redesigns, and increased costs, which limit their practical applications in biosensing. To address these issues, we developed a novel biosensing platform integrating a CRISPR/Cas12a system into an RCA-based DNA hydrogel. The hydrogel used in the platform could preencapsulate diverse signal molecules comprising GelRed, methylene blue, and gold nanoparticles, which were released upon Cas12a-mediated cleavage. This design enabled customizable signal output, including fluorescence, electrochemistry, and colorimetry, thereby ensuring the platform's adaptability to various detection scenarios. Our platform was highly specific for methicillin-resistant Staphylococcus aureus, with a mecA gene detection limit of 10 copies/µL, and provided fast and accurate results within 2 h for clinical samples. Hence, based on these advantages, the proposed biosensing platform exhibits promising application prospects in the field of nucleic acid detection.

Early Identification of Rotten Potatoes Using an Electronic Nose Based on Feature Discretization and Ensemble Convolutional Neural Network.

The early identification of rotten potatoes is one of the most important challenges in a storage facility because of the inconspicuous symptoms of rot, the high density of storage, and environmental factors (such as temperature, humidity, and ambient gases). An electronic nose system based on an ensemble convolutional neural network (ECNN, a powerful feature extraction method) was developed to detect potatoes with different degrees of rot. Three types of potatoes were detected: normal samples, slightly rotten samples, and totally rotten samples. A feature discretization method was proposed to optimize the impact of ambient gases on electronic nose signals by eliminating redundant information from the features. The ECNN based on original features presented good results for the prediction of rotten potatoes in both laboratory and storage environments, and the accuracy of the prediction results was 94.70% and 90.76%, respectively. Moreover, the application of the feature discretization method significantly improved the prediction results, and the accuracy of prediction results improved by 1.59% and 3.73%, respectively. Above all, the electronic nose system performed well in the identification of three types of potatoes by using the ECNN, and the proposed feature discretization method was helpful in reducing the interference of ambient gases.

Fingerprint Stimulated Raman Scattering Imaging Unveils Ergosteryl Ester as a Metabolic Signature of Azole-Resistant Candida albicans.

Candida albicans (C. albicans), a major fungal pathogen, causes life-threatening infections in immunocompromised individuals. Fluconazole (FLC) is recommended as first-line therapy for treatment of invasive fungal infections. However, the widespread use of FLC has resulted in increased antifungal resistance among different strains of Candida, especially C. albicans, which is a leading source of hospital-acquired infections. Here, by hyperspectral stimulated Raman scattering imaging of single fungal cells in the fingerprint window and pixel-wise spectral unmixing, we report aberrant ergosteryl ester accumulation in azole-resistant C. albicans compared to azole-susceptible species. This accumulation was a consequence of de novo lipogenesis. Lipid profiling by mass spectroscopy identified ergosterol oleate to be the major species stored in azole-resistant C. albicans. Blocking ergosterol esterification by oleate and suppressing sterol synthesis by FLC synergistically suppressed the viability of C. albicans in vitro and limited the growth of biofilm on mouse skin in vivo. Our findings highlight a metabolic marker and a new therapeutic strategy for targeting azole-resistant C. albicans by interrupting the esterified ergosterol biosynthetic pathway.

MiR-26a-5p exerts its influence by targeting EP300, a molecule known for its role in activating the PI3K/AKT/mTOR signaling pathway in CD8+tumor-infiltrating lymphocytes of colorectal cancer.

Surgical resection remains the primary approach for treating colorectal cancer, which is among the prevalent types of cancers affecting the digestive system. Tumor-infiltrating lymphocyte (TIL) therapy has emerged as a prominent area of study in the field of tumor immunotherapy in recent times, with the potential to serve as a supplementary treatment for colorectal cancer. For this investigation, we employed single-cell sequencing data to assess the manifestation extent of miR-26a-5p exists in healthy colon tissue, tissue affected by colorectal cancer, and tissue adjacent to the tumor. According to our findings, tumor-infiltrating T lymphocytes express comparatively less miR-26a-5p in comparison to normal T lymphocytes, the role of it in modulating the function of tumor-infiltrating T lymphocytes is suggested. Studies on miR-26a-5p's involvement in tumor-infiltrating T lymphocytes is limited, despite previous evidence indicating its ability to facilitate the development and advancement of cancerous cells. As a result of our experiments, we concluded that miR-26a-5p hindered the PI3K/AKT/mTOR(PAM) signaling pathway, reducing the ability of CD8+ tumor-infiltrating cells eradicate tumors. Using bioinformatics tools, we utilized prediction methods to identify EP300 as the specific gene targeted by miR-26a-5p. Subsequent research understood that downregulation of EP300 counteracted the suppressive impact exerted by miR-26a-5p on the stimulation of PAM signaling pathway, while it also diminishes the viability and cytotoxicity of CD8+ tumor-infiltrating lymphocytes. Therefore, miR-26a-5p emerges as a compelling option for the effective control of TIL therapy.

Left-primary & right-auxiliary operation mode in mediastinoscope-assisted radical esophagectomy.

BACKGROUND: Mediastinoscope-assisted transhiatal esophagectomy (MATHE) is the most minimally invasive esophagectomy procedure. It is a more challenging procedure and more difficult to be popularized than thoracoscopic surgery. We developed a new MATHE operation mode that provides a clearer visual field and makes the procedures simpler. METHODS: A total of 80 patients with esophageal cancer were divided into a control group (n = 29) and a study group (n = 51). The control group underwent classic MATHE, while the study group received modified MATHE. We compared the two groups on operation time; intraoperative blood loss; blood transfusion amount; incidence rate of lung infection, recurrent laryngeal nerves (RLNs) injury, chylothorax, and anastomotic leakage; and upper mediastinal lymph node dissection. RESULTS: The study group was significantly better than the control group in operation time (271.78 min vs. 322.90 min, p < 0.05), intraoperative blood loss (48.63 mL vs. 68.97 mL, p < 0.05), and left paratracheal lymph node (No. 4L) dissection rate (88.24% vs. 24.14%, p < 0.01). No significant differences were identified in the incidence rate of anastomotic leakage, lung complications, or RLNs injury between the two groups. CONCLUSION: The modified MATHE is easier to perform. Modified MATHE is significantly superior to classic MATHE in operation time, intraoperative blood loss, and upper mediastinal lymph node dissection rate.

An analysis of port congestion alleviation strategy based on system dynamics.

Port congestion has become a key factor restricting the international trade and economic development, especially during the COVID-19 epidemic. It is essential for the port to implement the effective alleviation strategies for handling the uncertain congestion. This paper aims to investigate the performance of the epidemic prevention alliance strategy (EPAS), shared berths strategy (SBS) around adjacent ports and their hybrid strategy in alleviating the port congestion. To simulate the effect of these three strategies, a system dynamics model of dual-port operation is developed considering the factors of the integrated service level of liner routes, empty container allocation, port congestion and regional economics, and so forth. The results indicate that the key issue of port congestion stems from the implementation of epidemic preventive measures. Among these three strategies, the hybrid strategy performs the best in alleviating the port congestion, improving integrated service levels, and curbing the fluctuation of container price. Moreover, the measures of investing more human resources and fixed assets are always taken in many current ports to alleviate the issue of port congestion. Therefore, the impacts of various investment in human resources and fixed assets on alleviating the port congestion are discussed. Finally, some suggestions are provided for the government to strengthen the cooperation between ports and promote the construction of port facility resources.

DeLTA: Automated cell segmentation, tracking, and lineage reconstruction using deep learning.

Microscopy image analysis is a major bottleneck in quantification of single-cell microscopy data, typically requiring human oversight and curation, which limit both accuracy and throughput. To address this, we developed a deep learning-based image analysis pipeline that performs segmentation, tracking, and lineage reconstruction. Our analysis focuses on time-lapse movies of Escherichia coli cells trapped in a "mother machine" microfluidic device, a scalable platform for long-term single-cell analysis that is widely used in the field. While deep learning has been applied to cell segmentation problems before, our approach is fundamentally innovative in that it also uses machine learning to perform cell tracking and lineage reconstruction. With this framework we are able to get high fidelity results (1% error rate), without human intervention. Further, the algorithm is fast, with complete analysis of a typical frame containing ~150 cells taking <700msec. The framework is not constrained to a particular experimental set up and has the potential to generalize to time-lapse images of other organisms or different experimental configurations. These advances open the door to a myriad of applications including real-time tracking of gene expression and high throughput analysis of strain libraries at single-cell resolution.

Will synchronous esophageal and lung resection increase the incidence of anastomotic leaks? A multicenter retrospective study.

BACKGROUND: Reports on combined resection for synchronous lung lesions and esophageal cancer (CRLE) cases are rare and mostly individual cases. Furthermore, the feasibility of CRLE has always been a controversial topic. In the current study, the authors retrospectively analyzed the feasibility of CRLE and established an individualized prediction model for esophageal anastomotic leaks after CRLE by performing a multicenter retrospective study. METHODS: Patients who underwent esophagectomy between January 2009 and June 2021 were extracted from a four-center prospectively maintained database, and those with CRLE at the same setting were matched in a 1:2 propensity score-matched (PSM) ratio to esophagectomy alone (EA) patients. A nomogram was then established based on the variables involved in multivariate logistic regression analysis. Internal validation of the nomogram was conducted utilizing Bootstrap resampling. Decision and clinical impact curve analysis were computed to assess the practical clinical utility of the nomogram. A prognosis analysis for CRLE and EA patients by Kaplan-Meier curves was conducted. RESULTS: Of the 7152 esophagectomies, 216 cases of CRLE were eligible, and 1:2 ratio propensity score-matched EA patients were matched. The incidence of anastomotic leaks following CRLE increased significantly ( P =0.035). The results of the multivariate analysis indicated the leaks varied according to the type of lung resection (anatomic>wedge resection, P =0.016) and site of resected lobe (upper>middle/low lobe; P =0.027), and a nomogram was established to predict the occurrence of leaks accurately (area under the curve=0.786). Although no statistically significant difference in overall survival (OS) was observed in the CRLE group ( P =0.070), a trend toward lower survival rates was noted. Further analysis revealed that combined upper lobe anatomic resection was significantly associated with reduced OS ( P =0.027). CONCLUSION: Our study confirms that CRLE is feasible but comes with a significantly increased risk of anastomotic leaks and a concerning trend of reduced survival, particularly when upper lobe anatomic resections are performed. These findings highlight the need for careful patient selection and surgical planning when considering CRLE.

Overtone photothermal microscopy for high-resolution and high-sensitivity vibrational imaging.

Photothermal microscopy is a highly sensitive pump-probe method for mapping nanostructures and molecules through the detection of local thermal gradients. While visible photothermal microscopy and mid-infrared photothermal microscopy techniques have been developed, they possess inherent limitations. These techniques either lack chemical specificity or encounter significant light attenuation caused by water absorption. Here, we present an overtone photothermal (OPT) microscopy technique that offers high chemical specificity, detection sensitivity, and spatial resolution by employing a visible probe for local heat detection in the C-H overtone region. We demonstrate its capability for high-fidelity chemical imaging of polymer nanostructures, depth-resolved intracellular chemical mapping of cancer cells, and imaging of multicellular C. elegans organisms and highly scattering brain tissues. By bridging the gap between visible and mid-infrared photothermal microscopy, OPT establishes a new modality for high-resolution and high-sensitivity chemical imaging. This advancement complements large-scale shortwave infrared imaging approaches, facilitating multiscale structural and chemical investigations of materials and biological metabolism.

High-content stimulated Raman histology of human breast cancer.

Histological examination is crucial for cancer diagnosis, however, the labor-intensive sample preparation involved in the histology impedes the speed of diagnosis. Recently developed two-color stimulated Raman histology could bypass the complex tissue processing to generates result close to hematoxylin and eosin staining, which is one of the golden standards in cancer histology. Yet, the underlying chemical features are not revealed in two-color stimulated Raman histology, compromising the effectiveness of prognostic stratification. Here, we present a high-content stimulated Raman histology (HC-SRH) platform that provides both morphological and chemical information for cancer diagnosis based on un-stained breast tissues. Methods: By utilizing both hyperspectral SRS imaging in the C-H vibration window and sparsity-penalized unmixing of overlapped spectral profiles, HC-SRH enabled high-content chemical mapping of saturated lipids, unsaturated lipids, cellular protein, extracellular matrix (ECM), and water. Spectral selective sampling was further implemented to boost the speed of HC-SRH. To show the potential for clinical use, HC-SRH using a compact fiber laser-based stimulated Raman microscope was demonstrated. Harnessing the wide and rapid tuning capability of the fiber laser, both C-H and fingerprint vibration windows were accessed. Results: HC-SRH successfully mapped unsaturated lipids, cellular protein, extracellular matrix, saturated lipid, and water in breast tissue. With these five chemical maps, HC-SRH provided distinct contrast for tissue components including duct, stroma, fat cell, necrosis, and vessel. With selective spectral sampling, the speed of HC-SRH was improved by one order of magnitude. The fiber-laser-based HC-SRH produced the same image quality in the C-H window as the state-of-the-art solid laser. In the fingerprint window, nucleic acid and solid-state ester contrast was demonstrated. Conclusions: HC-SRH provides both morphological and chemical information of tissue in a label-free manner. The chemical information detected is beyond the reach of traditional hematoxylin and eosin staining and heralds the potential of HC-SRH for biomarker discovery.

Kinetic Resolution of Acyclic Tertiary Propargylic Alcohols by NHC-Catalyzed Enantioselective Acylation.

We report herein an efficient NHC-catalyzed kinetic resolution of acyclic tertiary propargylic alcohols that provides them in high to excellent enantioselectivity. This is the first example of kinetic resolution realized by enantioselective acylation. The recovered enantioenriched alcohols can be facilely converted into other valuable compounds such as densely functionalized tertiary alcohols and carbmates in high yields and excellent stereopurity. Density functional theory calculations were performed to determine the reaction mechanism and to understand the origin of enantiodiscrimination.

Profiling single cancer cell metabolism via high-content SRS imaging with chemical sparsity.

Metabolic reprogramming in a subpopulation of cancer cells is a hallmark of tumor chemoresistance. However, single-cell metabolic profiling is difficult because of the lack of a method that can simultaneously detect multiple metabolites at the single-cell level. In this study, through hyperspectral stimulated Raman scattering (hSRS) imaging in the carbon-hydrogen (C-H) window and sparsity-driven hyperspectral image decomposition, we demonstrate a high-content hSRS (h2SRS) imaging approach that enables the simultaneous mapping of five major biomolecules, including proteins, carbohydrates, fatty acids, cholesterol, and nucleic acids at the single-cell level. h2SRS imaging of brain and pancreatic cancer cells under chemotherapy revealed acute and adapted chemotherapy-induced metabolic reprogramming and the unique metabolic features of chemoresistance. Our approach is expected to facilitate the discovery of therapeutic targets to combat chemoresistance. This study illustrates a high-content, label-free chemical imaging approach that measures metabolic profiles at the single-cell level and warrants further research on cellular metabolism.

Longitudinal Single-Cell Imaging of Engineered Strains with Stimulated Raman Scattering to Characterize Heterogeneity in Fatty Acid Production.

Understanding metabolic heterogeneity is critical for optimizing microbial production of valuable chemicals, but requires tools that can quantify metabolites at the single-cell level over time. Here, longitudinal hyperspectral stimulated Raman scattering (SRS) chemical imaging is developed to directly visualize free fatty acids in engineered Escherichia coli over many cell cycles. Compositional analysis is also developed to estimate the chain length and unsaturation of the fatty acids in living cells. This method reveals substantial heterogeneity in fatty acid production among and within colonies that emerges over the course of many generations. Interestingly, the strains display distinct types of production heterogeneity in an enzyme-dependent manner. By pairing time-lapse and SRS imaging, the relationship between growth and production at the single-cell level are examined. The results demonstrate that cell-to-cell production heterogeneity is pervasive and provides a means to link single-cell and population-level production.

High-throughput single-cell sorting by stimulated Raman-activated cell ejection.

Single-cell sorting is essential to explore cellular heterogeneity in biology and medicine. Recently developed Raman-activated cell sorting (RACS) circumvents the limitations of fluorescence-activated cell sorting, such as the cytotoxicity of labels. However, the sorting throughputs of all forms of RACS are limited by the intrinsically small cross-section of spontaneous Raman scattering. Here, we report a stimulated Raman-activated cell ejection (S-RACE) platform that enables high-throughput single-cell sorting based on high-resolution multi-channel stimulated Raman chemical imaging, in situ image decomposition, and laser-induced cell ejection. The performance of this platform was illustrated by sorting a mixture of 1 µm polymer beads, where 95% yield, 98% purity, and 14 events per second throughput were achieved. Notably, our platform allows live cell ejection, allowing for the growth of single colonies of bacteria and fungi after sorting. To further illustrate the chemical selectivity, lipid-rich Rhodotorula glutinis cells were successfully sorted from a mixture with Saccharomyces cerevisiae, confirmed by downstream quantitative PCR. Furthermore, by integrating a closed-loop feedback control circuit into the system, we realized real-time single-cell imaging and sorting, and applied this method to precisely eject regions of interest from a rat brain tissue section. The reported S-RACE platform opens exciting opportunities for a wide range of single-cell applications in biology and medicine.

Stimulated Raman photothermal microscopy toward ultrasensitive chemical imaging.

Stimulated Raman scattering (SRS) microscopy has shown enormous potential in revealing molecular structures, dynamics, and couplings in complex systems. However, the sensitivity of SRS is fundamentally limited to the millimolar level due to shot noise and the small modulation depth. To overcome this barrier, we revisit SRS from the perspective of energy deposition. The SRS process pumps molecules to their vibrationally excited states. The subsequent relaxation heats up the surroundings and induces refractive index changes. By probing the refractive index changes with a laser beam, we introduce stimulated Raman photothermal (SRP) microscopy, where a >500-fold boost of modulation depth is achieved. The versatile applications of SRP microscopy on viral particles, cells, and tissues are demonstrated. SRP microscopy opens a way to perform vibrational spectroscopic imaging with ultrahigh sensitivity.

Impact Ionization Coefficient Prediction of a Lateral Power Device Using Deep Neural Network.

Nowadays, the impact ionization coefficient in the avalanche breakdown theory is obtained using 1-D PN junctions or SBDs, and is considered to be a constant determined by the material itself only. In this paper, the impact ionization coefficient of silicon in a 2D lateral power device is found to be no longer a constant, but instead a function of the 2D coupling effects. The impact ionization coefficient of silicon that considers the 2D depletion effects in real-world devices is proposed and extracted in this paper. The extracted impact ionization coefficient indicates that the conventional empirical impact ionization in the Fulop equation is not suitable for the analysis of 2D lateral power devices. The veracity of the proposed impact ionization coefficient is validated by the simulations obtained from TCAD tools. Considering the complexity of direct modeling, a new prediction method using deep neural networks is proposed. The prediction method demonstrates 97.67% accuracy for breakdown location prediction and less than 6% average error for the impact ionization coefficient prediction compared with the TCAD simulation.

Stimulated Raman Photothermal Microscopy towards Ultrasensitive Chemical Imaging.

Stimulated Raman scattering (SRS) microscopy has shown enormous potential in revealing molecular structures, dynamics and coupling in a complex system. However, the bond-detection sensitivity of SRS microscopy is fundamentally limited to milli-molar level due to the shot noise and the small modulation depth in either pump or Stokes beam4. Here, to overcome this barrier, we revisit SRS from the perspective of energy deposition. The SRS process pumps molecules to their vibrational excited states. The thereafter relaxation heats up the surrounding and induces a change in refractive index. By probing the refractive index change with a continuous wave beam, we introduce stimulated Raman photothermal (SRP) microscopy, where a >500-fold boost of modulation depth is achieved on dimethyl sulfide with conserved average power. Versatile applications of SRP microscopy on viral particles, cells, and tissues are demonstrated. With much improved signal to noise ratio compared to SRS, SRP microscopy opens a new way to perform vibrational spectroscopic imaging with ultrahigh sensitivity and minimal water absorption.

Visualization of a Limonene Synthesis Metabolon Inside Living Bacteria by Hyperspectral SRS Microscopy.

Monitoring biosynthesis activity at single-cell level is key to metabolic engineering but is still difficult to achieve in a label-free manner. Using hyperspectral stimulated Raman scattering imaging in the 670-900 cm-1 region, localized limonene synthesis are visualized inside engineered Escherichia coli. The colocalization of limonene and GFP-fused limonene synthase is confirmed by co-registered stimulated Raman scattering and two-photon fluorescence images. The finding suggests a limonene synthesis metabolon with a polar distribution inside the cells. This finding expands the knowledge of de novo limonene biosynthesis in engineered bacteria and highlights the potential of SRS chemical imaging in metabolic engineering research.

Microsecond fingerprint stimulated Raman spectroscopic imaging by ultrafast tuning and spatial-spectral learning.

Label-free vibrational imaging by stimulated Raman scattering (SRS) provides unprecedented insight into real-time chemical distributions. Specifically, SRS in the fingerprint region (400-1800 cm-1) can resolve multiple chemicals in a complex bio-environment. However, due to the intrinsic weak Raman cross-sections and the lack of ultrafast spectral acquisition schemes with high spectral fidelity, SRS in the fingerprint region is not viable for studying living cells or large-scale tissue samples. Here, we report a fingerprint spectroscopic SRS platform that acquires a distortion-free SRS spectrum at 10 cm-1 spectral resolution within 20 µs using a polygon scanner. Meanwhile, we significantly improve the signal-to-noise ratio by employing a spatial-spectral residual learning network, reaching a level comparable to that with 100 times integration. Collectively, our system enables high-speed vibrational spectroscopic imaging of multiple biomolecules in samples ranging from a single live microbe to a tissue slice.

Ultrasensitive Vibrational Imaging of Retinoids by Visible Preresonance Stimulated Raman Scattering Microscopy.

High-sensitivity chemical imaging offers a window to decipher the molecular orchestra inside a living system. Based on vibrational fingerprint signatures, coherent Raman scattering microscopy provides a label-free approach to map biomolecules and drug molecules inside a cell. Yet, by near-infrared (NIR) pulse excitation, the sensitivity is limited to millimolar concentration for endogenous biomolecules. Here, the imaging sensitivity of stimulated Raman scattering (SRS) is significantly boosted for retinoid molecules to 34 micromolar via electronic preresonance in the visible wavelength regime. Retinoids play critical roles in development, immunity, stem cell differentiation, and lipid metabolism. By visible preresonance SRS (VP-SRS) imaging, retinoid distribution in single embryonic neurons and mouse brain tissues is mapped, retinoid storage in chemoresistant pancreatic and ovarian cancers is revealed, and retinoids stored in protein network and lipid droplets of Caenorahbditis elegans are identified. These results demonstrate VP-SRS microscopy as an ultrasensitive label-free chemical imaging tool and collectively open new opportunities of understanding the function of retinoids in biological systems.