Unsupervised OCT image despeckling with ground-truth- and repeated-scanning-free features.

Optical coherence tomography (OCT) can resolve biological three-dimensional tissue structures, but it is inevitably plagued by speckle noise that degrades image quality and obscures biological structure. Recently unsupervised deep learning methods are becoming more popular in OCT despeckling but they still have to use unpaired noisy-clean images or paired noisy-noisy images. To address the above problem, we propose what we believe to be a novel unsupervised deep learning method for OCT despeckling, termed Double-free Net, which eliminates the need for ground truth data and repeated scanning by sub-sampling noisy images and synthesizing noisier images. In comparison to existing unsupervised methods, Double-free Net obtains superior denoising performance when trained on datasets comprising retinal and human tissue images without clean images. The efficacy of Double-free Net in denoising holds significant promise for diagnostic applications in retinal pathologies and enhances the accuracy of retinal layer segmentation. Results demonstrate that Double-free Net outperforms state-of-the-art methods and exhibits strong convenience and adaptability across different OCT images.

Perovskite Oxides Toward Oxygen Evolution Reaction: Intellectual Design Strategies, Properties and Perspectives.

Developing electrochemical energy storage and conversion devices (e.g., water splitting, regenerative fuel cells and rechargeable metal-air batteries) driven by intermittent renewable energy sources holds a great potential to facilitate global energy transition and alleviate the associated environmental issues. However, the involved kinetically sluggish oxygen evolution reaction (OER) severely limits the entire reaction efficiency, thus designing high-performance materials toward efficient OER is of prime significance to remove this obstacle. Among various materials, cost-effective perovskite oxides have drawn particular attention due to their desirable catalytic activity, excellent stability and large reserves. To date, substantial efforts have been dedicated with varying degrees of success to promoting OER on perovskite oxides, which have generated multiple reviews from various perspectives, e.g., electronic structure modulation and heteroatom doping and various applications. Nonetheless, the reviews that comprehensively and systematically focus on the latest intellectual design strategies of perovskite oxides toward efficient OER are quite limited. To bridge the gap, this review thus emphatically concentrates on this very topic with broader coverages, more comparative discussions and deeper insights into the synthetic modulation, doping, surface engineering, structure mutation and hybrids. More specifically, this review elucidates, in details, the underlying causality between the being-tuned physiochemical properties [e.g., electronic structure, metal-oxygen (M-O) bonding configuration, adsorption capacity of oxygenated species and electrical conductivity] of the intellectually designed perovskite oxides and the resulting OER performances, coupled with perspectives and potential challenges on future research. It is our sincere hope for this review to provide the scientific community with more insights for developing advanced perovskite oxides with high OER catalytic efficiency and further stimulate more exciting applications.

Hybrid surgery for the treatment of lumbar spine surgery complicated with iliac artery pseudoaneurysm, arteriovenous fistula, and lower limb artery embolism: A rare case report.

INTRODUCTION AND IMPORTANCE: Vascular injuries during lumbar surgery are rare, but complications such as false aneurysm of the iliac artery, arteriovenous fistula, and lower limb artery embolism are even rarer. These complications can easily be misdiagnosed and result in the inability to choose an appropriate surgical approach, leading to serious consequences. CASE PRESENTATION: A 36-year-old male patient experienced swelling in both lower limbs, along with numbness, coldness, and dysfunction in his right lower limb, after undergoing a "posterior lumbar discectomy" surgery. On the 20th day post-surgery, a clear diagnosis was established through CTV: 1) Right common iliac artery injury with pseudoaneurysm formation; 2) Right iliac arteriovenous fistula; 3) Right popliteal artery embolism. The patient underwent hybrid surgery to address multiple complications simultaneously and made a good recovery after the procedure. CLINICAL DISCUSSION: Rarely, lumbar spine surgery can concurrently lead to conditions such as pseudoaneurysm, arteriovenous fistula, and lower limb artery embolism. Due to atypical symptoms and signs, it is often misdiagnosed. Hybrid surgery involves incising the femoral artery, using a thrombectomy catheter to remove clots from the iliac artery above and the popliteal artery below, and then re-implanting a covered stent to treat pseudoaneurysm and arteriovenous fistula. CONCLUSION: With a solid clinical knowledge, one can make a timely diagnosis and choose an appropriate surgical method to intervene, thereby improving the prognosis. Hybrid surgery combines the minimally invasive and safe effects of endovascular techniques with the precise effects of open surgery, and it also allows for the simultaneous treatment of multiple comorbidities.

Toward ground-truth optical coherence tomography via three-dimensional unsupervised deep learning processing and data.

Optical coherence tomography (OCT) can perform non-invasive high-resolution three-dimensional (3D) imaging and has been widely used in biomedical fields, while it is inevitably affected by coherence speckle noise which degrades OCT imaging performance and restricts its applications. Here we present a novel speckle-free OCT imaging strategy, named toward-ground-truth OCT (tGT-OCT), that utilizes unsupervised 3D deep-learning processing and leverages OCT 3D imaging features to achieve speckle-free OCT imaging. Specifically, our proposed tGT-OCT utilizes an unsupervised 3D-convolution deep-learning network trained using random 3D volumetric data to distinguish and separate speckle from real structures in 3D imaging volumetric space; moreover, tGT-OCT effectively further reduces speckle noise and reveals structures that would otherwise be obscured by speckle noise while preserving spatial resolution. Results derived from different samples demonstrated the high-quality speckle-free 3D imaging performance of tGT-OCT and its advancement beyond the previous state-of-the-art. The code is available online: https://github.com/Voluntino/tGT-OCT.

Self-supervised Self2Self denoising strategy for OCT speckle reduction with a single noisy image.

Optical coherence tomography (OCT) inevitably suffers from the influence of speckles originating from multiple scattered photons owing to its low-coherence interferometry property. Although various deep learning schemes have been proposed for OCT despeckling, they typically suffer from the requirement for ground-truth images, which are difficult to collect in clinical practice. To alleviate the influences of speckles without requiring ground-truth images, this paper presents a self-supervised deep learning scheme, namely, Self2Self strategy (S2Snet), for OCT despeckling using a single noisy image. Specifically, in this study, the main deep learning architecture is the Self2Self network, with its partial convolution being updated with a gated convolution layer. Specifically, both the input images and their Bernoulli sampling instances are adopted as network input first, and then, a devised loss function is integrated into the network to remove the background noise. Finally, the denoised output is estimated using the average of multiple predicted outputs. Experiments with various OCT datasets are conducted to verify the effectiveness of the proposed S2Snet scheme. Results compared with those of the existing methods demonstrate that S2Snet not only outperforms those existing self-supervised deep learning methods but also achieves better performances than those non-deep learning ones in different cases. Specifically, S2Snet achieves an improvement of 3.41% and 2.37% for PSNR and SSIM, respectively, as compared to the original Self2Self network, while such improvements become 19.9% and 22.7% as compared with the well-known non-deep learning NWSR method.

Coaxial Bright and Dark Field Optical Coherence Tomography.

To improve the signal collection efficiency of Optical Coherence Tomography (OCT) for biomedical applications. A novel coaxial optical design was implemented, utilizing a wavefront-division beam splitter in the sample arm with a 45-degree rod mirror. This design allowed for the simultaneous collection of bright and dark field signals. The bright field signal was detected within its circular aperture in a manner similar to standard OCT, while the dark field signal passed through an annular-shaped aperture and was collected by the same spectrometer via a fiber array. This new configuration improved the signal collection efficiency by ∼3 dB for typical biological tissues. Dark-field OCT images were found to provide higher resolution, contrast and distinct information compared to standard bright-field OCT. By compounding bright and dark field images, speckle noise was suppressed by ∼ √2 . These advantages were validated using Teflon phantoms, chicken breast ex vivo, and human skin in vivo. This new OCT configuration significantly enhances signal collection efficiency and image quality, offering great potential for improving OCT technology with better depth, contrast, resolution, speckles, and signal-to-noise ratio. We believe that the bright and dark field signals will enable more comprehensive tissue characterization with the angled scattered light. This advancement will greatly promote the OCT technology in various clinical and biomedical research applications.

Axial super-resolution optical coherence tomography via complex-valued network.

Optical coherence tomography (OCT) is a fast and non-invasive optical interferometric imaging technique that can provide high-resolution cross-sectional images of biological tissues. OCT's key strength is its depth resolving capability which remains invariant along the imaging depth and is determined by the axial resolution. The axial resolution is inversely proportional to the bandwidth of the OCT light source. Thus, the use of broadband light sources can effectively improve the axial resolution and however leads to an increased cost. In recent years, real-valued deep learning technique has been introduced to obtain super-resolution optical imaging. In this study, we proposed a complex-valued super-resolution network (CVSR-Net) to achieve an axial super-resolution for OCT by fully utilizing the amplitude and phase of OCT signal. The method was evaluated on three OCT datasets. The results show that the CVSR-Net outperforms its real-valued counterpart with a better depth resolving capability. Furthermore, comparisons were made between our network, six prevailing real-valued networks and their complex-valued counterparts. The results demonstrate that the complex-valued network exhibited a better super-resolution performance than its real-valued counterpart and our proposed CVSR-Net achieved the best performance. In addition, the CVSR-Net was tested on out-of-distribution domain datasets and its super-resolution performance was well maintained as compared to that on source domain datasets, indicating a good generalization capability.

A Retrospective Study of Anlotinib Combined with Anti-PD-1 Inhibitors in the 2nd or Later-Line Treatment of Advanced Solid Tumors.

Objective: To investigate the clinical efficacy and safety of anlotinib combined with anti-PD-1 inhibitors in the 2nd or later-line treatment of advanced solid tumors. Patients and Methods: A total of 63 patients with advanced solid tumors who had failed or could not endure the adverse reactions after receiving first-line or more systematic treatment in the Second Affiliated Hospital of Harbin Medical University from March 2019 to April 2023 were treated with anlotinib Hydrochloride capsule combined with anti-PD-1 inhibitors. The efficacy and adverse reactions were evaluated according to RECIST1.1 and NCICTC4.0 standards. Results: The percentage of overall response rate of 63 patients during the combination administration indicated that complete response was 1.6% (n=1), partial response was 23.8% (n=15), stable disease was 39.7% (n=25) and progressive disease was 34.9% (n=22), yielding objective response rate (ORR) of 25.4% and disease control rate (DCR) of 65.1%. Furthermore, the median PFS of 63 patients with advanced solid tumors was 7 months and the median OS was not reached, and the median follow-up time is 4.5 months. In subgroup analysis, there was no significant difference in PFS between first-line, second-line, third-line and above (p=0.631); there was no significant difference in PFS between PD-1 positive patients and PD-1 negative patients (p=0.094); there was no significant difference in PFS between patients who had previously used anti-PD-1 inhibitors and patients who had not used before (p=0.204). The most common adverse reactions were hypertension, hand-foot syndrome, and fatigue, with an incidence of 28.4% (18/63), 25.6% (14/63), and 25.6% (14/63), respectively. Most of the adverse reactions were grade 1-2, and there were no grade 4 adverse reactions. Conclusion: Anlotinib combined with anti-PD-1 inhibitors demonstrated promising efficacy and tolerable safety for patients with advanced solid tumors in the 2nd or later-line treatment.

Impact of Porphyromonas gingivalis-odontogenic infection on the pathogenesis of non-alcoholic fatty liver disease.

Aim: Non-alcoholic fatty liver disease is characterized by diffuse hepatic steatosis and has quickly risen to become the most prevalent chronic liver disease. Its incidence is increasing yearly, but the pathogenesis is still not fully understood. Porphyromonas gingivalis (P. gingivalis) is a major pathogen widely prevalent in periodontitis patients. Its infection has been reported to be a risk factor for developing insulin resistance, non-alcoholic fatty liver disease (NAFLD), non-alcoholic steatohepatitis (NASH), and metabolic syndrome. The aim of this review is to evaluate the association between P. gingivalis infection and NAFLD, identify the possible etiopathogenetic mechanisms, and raise public awareness of oral health to prevent and improve NAFLD.Methods: After searching in PubMed and Web of Science databases using 'Porphyromonas gingivalis', 'non-alcoholic fatty liver disease', and 'hepatic steatosis' as keywords, studies related were compiled and examined.Results: P. gingivalis infection is a direct risk factor for NAFLD based on clinical and basic research. Moreover, it induces systematic changes and systemic abnormalities by disrupting metabolic, inflammatory, and immunologic homeostasis.Conclusion: P. gingivalis-odontogenic infection promotes the occurrence and development of NAFLD. Further concerns are needed to emphasize oral health and maintain good oral hygiene for the prevention and treatment of NAFLD.

Porphyromonas gingivalis exacerbates the development of non-alcoholic fatty liver disease.Porphyromonas gingivalis aggravates a homeostasis imbalance in hepatic lipid metabolism and an insulin resistance phenotype.

Loss-balanced parallel decoding network for retinal fluid segmentation in OCT.

As a leading cause of blindness worldwide, macular edema (ME) is mainly determined by sub-retinal fluid (SRF), intraretinal fluid (IRF), and pigment epithelial detachment (PED) accumulation, and therefore, the characterization of SRF, IRF, and PED, which is also known as ME segmentation, has become a crucial issue in ophthalmology. Due to the subjective and time-consuming nature of ME segmentation in retinal optical coherence tomography (OCT) images, automatic computer-aided systems are highly desired in clinical practice. This paper proposes a novel loss-balanced parallel decoding network, namely PadNet, for ME segmentation. Specifically, PadNet mainly consists of an encoder and three parallel decoder modules, which serve as segmentation, contour, and diffusion branches, and they are employed to extract the ME's characteristics, the contour area features, and to expand the ME area from the center to edge, respectively. A new loss-balanced joint-loss function with three components corresponding to each of the three parallel decoding branches is also devised for training. Experiments are conducted with three public datasets to verify the effectiveness of PadNet, and the performances of PadNet are compared with those of five state-of-the-art methods. Results show that PadNet improves ME segmentation accuracy by 8.1%, 11.1%, 0.6%, 1.4% and 8.3%, as compared with UNet, sASPP, MsTGANet, YNet, RetiFluidNet, respectively, which convincingly demonstrates that the proposed PadNet is robust and effective in ME segmentation in different cases.

Synergistic efficacy of PI3Kδ inhibitor with anti-PD-1 mAbs in immune-humanized PDX model of endocrine resistance hormone receptor-positive advanced breast cancer.

Purpose: Endocrine resistance hormone receptor-positive (HR+) advanced breast cancer (ABC) is generally insensitive to immunecheckpoint inhibitors (ICIs). This study sought to determine whether PI3Kδ inhibitor could enhance the sensitivity of endocrine resistance HR + advanced BC to ICIs by reducing immune evasion. Methods: Patient-derived HR + ABC xenografts were implanted into immune-humanized NSG mice and subsequently treated with YY20394 (PI3Kδ inhibitor) and camrelizumab. The mice were monitored for tumor progression, biochemical blood indicators, and peripheral blood T-cell subsets. The xenografted tumors were collected at the end of the treatment cycle and subjected to HE staining, immunohistochemistry and protein phosphorylation analysis. Besides, the xenografted tumors were also used to isolate primary breast cancer cells (BCCs) and regulatory T-cells (Tregs), which were subsequently used to evaluate drug sensitivity in vitro. Results: The humanized PDX model showed a favorable initial treatment response to camrelizumab combined with YY20394 and manageable toxicity. YY20394 plus camrelizumab showed a strong inhibitory effect on HR + BC in vivo mediated by suppression of Treg activity and an increased proportion of CD8+ T cells. Mice bearing tumors treated with YY20394 and camrelizumab had less invasion, mitotic figures, and ki67 expression, while having higher IL-12 expression compared with other groups. Mechanistically, YY20394 only effectively inhibited the PI3K pathway and proliferation activity in Tregs but not in BCCs. Conclusion: Our study suggests PI3Kδ inhibitor could the enhance the efficacy of ICIs in HR + BC PDX models by combating immune suppression and provides a feasible approach that may overcome the resistance of ICIs in HR + BC patients.

Self-supervised Blind2Unblind deep learning scheme for OCT speckle reductions.

As a low-coherence interferometry-based imaging modality, optical coherence tomography (OCT) inevitably suffers from the influence of speckles originating from multiply scattered photons. Speckles hide tissue microstructures and degrade the accuracy of disease diagnoses, which thus hinder OCT clinical applications. Various methods have been proposed to address such an issue, yet they suffer either from the heavy computational load, or the lack of high-quality clean images prior, or both. In this paper, a novel self-supervised deep learning scheme, namely, Blind2Unblind network with refinement strategy (B2Unet), is proposed for OCT speckle reduction with a single noisy image only. Specifically, the overall B2Unet network architecture is presented first, and then, a global-aware mask mapper together with a loss function are devised to improve image perception and optimize sampled mask mapper blind spots, respectively. To make the blind spots visible to B2Unet, a new re-visible loss is also designed, and its convergence is discussed with the speckle properties being considered. Extensive experiments with different OCT image datasets are finally conducted to compare B2Unet with those state-of-the-art existing methods. Both qualitative and quantitative results convincingly demonstrate that B2Unet outperforms the state-of-the-art model-based and fully supervised deep-learning methods, and it is robust and capable of effectively suppressing speckles while preserving the important tissue micro-structures in OCT images in different cases.

MAS-Net OCT: a deep-learning-based speckle-free multiple aperture synthetic optical coherence tomography.

High-resolution spectral domain optical coherence tomography (SD-OCT) is a vital clinical technique that suffers from the inherent compromise between transverse resolution and depth of focus (DOF). Meanwhile, speckle noise worsens OCT imaging resolving power and restricts potential resolution-enhancement techniques. Multiple aperture synthetic (MAS) OCT transmits light signals and records sample echoes along a synthetic aperture to extend DOF, acquired by time-encoding or optical path length encoding. In this work, a deep-learning-based multiple aperture synthetic OCT termed MAS-Net OCT, which integrated a speckle-free model based on self-supervised learning, was proposed. MAS-Net was trained on datasets generated by the MAS OCT system. Here we performed experiments on homemade microparticle samples and various biological tissues. Results demonstrated that the proposed MAS-Net OCT could effectively improve the transverse resolution in a large imaging depth as well as reduced most speckle noise.

Harnessing interventions during the immediate perioperative period to improve the long-term survival of patients following radical gastrectomy.

Although the incidence and mortality of gastric cancer (GC) have been decreasing steadily worldwide, especially in East Asia, the disease burden of this malignancy is still very heavy. Except for tremendous progress in the management of GC by multidisciplinary treatment, surgical excision of the primary tumor is still the cornerstone intervention in the curative-intent treatment of GC. During the relatively short perioperative period, patients undergoing radical gastrectomy will suffer from at least part of the following perioperative events: Surgery, anesthesia, pain, intraoperative blood loss, allogeneic blood transfusion, postoperative complications, and their related anxiety, depression and stress response, which have been shown to affect long-term outcomes. Therefore, in recent years, studies have been carried out to find and test interventions during the perioperative period to improve the long-term survival of patients following radical gastrectomy, which will be the aim of this review.

Massive increases in C31 alkane on Zygophyllum xanthoxylum leaves contribute to its excellent abiotic stress tolerance.

BACKGROUND AND AIMS: Desert plants possess excellent water-conservation capacities to survive in extreme environments. Cuticular wax plays a pivotal role in reducing water loss through plant aerial surfaces. However, the role of cuticular wax in water retention by desert plants is poorly understood. METHODS: We investigated leaf epidermal morphology and wax composition of five desert shrubs from north-west China and characterized the wax morphology and composition for the typical xerophyte Zygophyllum xanthoxylum under salt, drought and heat treatments. Moreover, we examined leaf water loss and chlorophyll leaching of Z. xanthoxylum and analysed their relationships with wax composition under the above treatments. KEY RESULTS: The leaf epidermis of Z. xanthoxylum was densely covered by cuticular wax, whereas the other four desert shrubs had trichomes or cuticular folds in addition to cuticular wax. The total amount of cuticular wax on leaves of Z. xanthoxylum and Ammopiptanthus mongolicus was significantly higher than that of the other three shrubs. Strikingly, C31 alkane, the most abundant component, composed >71 % of total alkanes in Z. xanthoxylum, which was higher than for the other four shrubs studied here. Salt, drought and heat treatments resulted in significant increases in the amount of cuticular wax. Of these treatments, the combined drought plus 45 °C treatment led to the largest increase (107 %) in the total amount of cuticular wax, attributable primarily to an increase of 122 % in C31 alkane. Moreover, the proportion of C31 alkane within total alkanes remained >75 % in all the above treatments. Notably, the water loss and chlorophyll leaching were reduced, which was negatively correlated with C31 alkane content. CONCLUSION: Zygophyllum xanthoxylum could serve as a model desert plant for study of the function of cuticular wax in water retention because of its relatively uncomplicated leaf surface and because it accumulates C31 alkane massively to reduce cuticular permeability and resist abiotic stressors.

Multiscale joint segmentation method for retinal optical coherence tomography images using a bidirectional wave algorithm and improved graph theory.

Morphology and functional metrics of retinal layers are important biomarkers for many human ophthalmic diseases. Automatic and accurate segmentation of retinal layers is crucial for disease diagnosis and research. To improve the performance of retinal layer segmentation, a multiscale joint segmentation framework for retinal optical coherence tomography (OCT) images based on bidirectional wave algorithm and improved graph theory is proposed. In this framework, the bidirectional wave algorithm was used to segment edge information in multiscale images, and the improved graph theory was used to modify edge information globally, to realize automatic and accurate segmentation of eight retinal layer boundaries. This framework was tested on two public datasets and two OCT imaging systems. The test results show that, compared with other state-of-the-art methods, this framework does not need data pre-training and parameter pre-adjustment on different datasets, and can achieve sub-pixel retinal layer segmentation on a low-configuration computer.

Strain Sensor-Inserted Microchannel for Gas Viscosity Measurement.

Quantifying the viscosity of a gas is of great importance in determining its properties and can even be used to identify what the gas is. While many techniques exist for measuring the viscosities of gases, it is still challenging to probe gases with a simple, robust setup that will be useful for practical applications. We introduce a facile approach to estimating gas viscosity using a strain gauge inserted in a straight microchannel with a height smaller than that of the gauge. Using a constrained geometry for the strain gauge, in which part of the gauge deforms the channel to generate initial gauge strain that can be transduced into pressure, the pressure change induced via fluid flow was measured. The change was found to linearly correlate with fluid viscosity, allowing estimation of the viscosities of gases with a simple device.

Physiological and transcriptomic analyses provide insight into thermotolerance in desert plant Zygophyllum xanthoxylum.

BACKGROUND: Heat stress has adverse effects on the growth and reproduction of plants. Zygophyllum xanthoxylum, a typical xerophyte, is a dominant species in the desert where summer temperatures are around 40 °C. However, the mechanism underlying the thermotolerance of Z. xanthoxylum remained unclear. RESULTS: Here, we characterized the acclimation of Z. xanthoxylum to heat using a combination of physiological measurements and transcriptional profiles under treatments at 40 °C and 45 °C, respectively. Strikingly, moderate high temperature (40 °C) led to an increase in photosynthetic capacity and superior plant performance, whereas severe high temperature (45 °C) was accompanied by reduced photosynthetic capacity and inhibited growth. Transcriptome profiling indicated that the differentially expressed genes (DEGs) were related to transcription factor activity, protein folding and photosynthesis under heat conditions. Furthermore, numerous genes encoding heat transcription shock factors (HSFs) and heat shock proteins (HSPs) were significantly up-regulated under heat treatments, which were correlated with thermotolerance of Z. xanthoxylum. Interestingly, the up-regulation of PSI and PSII genes and the down-regulation of chlorophyll catabolism genes likely contribute to improving plant performance of Z. xanthoxylum under moderate high temperature. CONCLUSIONS: We identified key genes associated with of thermotolerance and growth in Z. xanthoxylum, which provide significant insights into the regulatory mechanisms of thermotolerance and growth regulation in Z. xanthoxylum under high temperature conditions.

The establishment of COPD organoids to study host-pathogen interaction reveals enhanced viral fitness of SARS-CoV-2 in bronchi.

Chronic obstructive pulmonary disease (COPD) is characterised by airflow limitation and infective exacerbations, however, in-vitro model systems for the study of host-pathogen interaction at the individual level are lacking. Here, we describe the establishment of nasopharyngeal and bronchial organoids from healthy individuals and COPD that recapitulate disease at the individual level. In contrast to healthy organoids, goblet cell hyperplasia and reduced ciliary beat frequency were observed in COPD organoids, hallmark features of the disease. Single-cell transcriptomics uncovered evidence for altered cellular differentiation trajectories in COPD organoids. SARS-CoV-2 infection of COPD organoids revealed more productive replication in bronchi, the key site of infection in severe COVID-19. Viral and bacterial exposure of organoids induced greater pro-inflammatory responses in COPD organoids. In summary, we present an organoid model that recapitulates the in vivo physiological lung microenvironment at the individual level and is amenable to the study of host-pathogen interaction and emerging infectious disease.

TriD-LAMP: A pump-free microfluidic chip for duplex droplet digital loop-mediated isothermal amplification analysis.

Digital nucleic acid amplification techniques are powerful and attractive approaches for providing sensitive and absolute quantification in biology. Among these, digital loop-mediated isothermal amplification (dLAMP) shows the potential for field detection, since its robustness and independence from thermal cycling. The key of dLAMP is to generate a large number of individual droplets or microwells. However, an auxiliary precision pump is always required for sample digitalization. In addition, current systems for droplet dLAMP usually need to transfer the droplets after digitalization or amplification. Herein, we developed and evaluated a pump-free microfluidic chip for duplex droplet dLAMP (TriD-LAMP) detection. This chip was designed based on step emulsification and contains a droplet generation zone and a droplet storage zone. Droplets are formed through the step due to the difference in Laplace pressure. There are 64 parallel nozzles that could generate tens of thousands of uniform droplets manually (variation <5%). The storage zone for droplets collection was previously filled with oil containing fluorosurfactant that keeps the droplets from fusing and evaporation during the amplification. Therefore, this custom chip is able to perform all stages of the dLAMP process without transferring droplets. Combined with the optimized fluorescent probe method, the chip achieves accurate quantification of the E. coli DNA down to 19.8 copies/µL. As a proof of concept, the simultaneous quantification of two targets was successfully realized on this custom chip. Conclusively, this integrated, pump-free TriD-LAMP chip can provide a promising tool for multiple targets detection in clinical diagnostics and point-of-care applications.