In vitro fertilization (IVF) therapy is an important treatment for human infertility. However, the methods for clinical IVF have only changed slightly over decades: culture medium is held in oil-covered drops in Petri dishes and manipulation occurs by manual pipetting. Here we report a novel microwell-structured microfluidic device that integrates single oocyte trapping, fertilization and subsequent embryo culture. A microwell array was used to capture and hold individual oocytes during the flow-through process of oocyte and sperm loading, medium substitution and debris cleaning. Different microwell depths were compared by computational modeling and flow washing experiments for their effectiveness in oocyte trapping and debris removal. Fertilization was achieved in the microfluidic devices with similar fertilization rates to standard oil-covered drops in Petri dishes. Embryos could be cultured to blastocyst stages in our devices with developmental status individually monitored and tracked. The results suggest that the microfluidic device may bring several advantages to IVF practices by simplifying oocyte handling and manipulation, allowing rapid and convenient medium changing, and enabling automated tracking of any single embryo development.

The high prevalence of polycystic ovary syndrome (PCOS) among reproductive-aged women has attracted more and more attention. As a common disorder that is likely to threaten women’s health physically and mentally, the detection of PCOS is a growing public health concern worldwide. In this paper, we proposed an automated deep learning algorithm for the auxiliary detection of PCOS, which explores the potential of scleral changes in PCOS detection. The algorithm was applied to the dataset that contains the full-eye images of 721 Chinese women, among which 388 are PCOS patients. Inputs of the proposed algorithm are scleral images segmented from full-eye images using an improved U-Net, and then a Resnet model was applied to extract deep features from scleral images. Finally, a multi-instance model was developed to achieve classification. Various performance indices such as AUC, classification accuracy, precision, recall, precision, and F1-score were adopted to assess the performance of our algorithm. Results show that our method achieves an average AUC of 0.979 and a classification accuracy of 0.929, which indicates the great potential of deep learning in the detection of PCOS.

An air-insulated microfluidic chip was designed for the automatic centrifugal distribution of samples to 24-test cells, enabling the parallel identification of multiple clinical pneumonia-related pathogens in 1.45-μL reactions without cross-contamination in 45 min. A portable nucleic acid analyzer that integrates mechanical, confocal optical, electronic, and software functions was also developed to collect fluorescence data in a Ø3 mm imaging field near the optical diffraction limit for highly sensitive fluorescence detection of nucleic acid amplification in real time. This microfluidic chip-based portable nucleic acid analyzer could detect low abundance nucleic acids present at as few as 10 copies. In a blinded experiment, specific identification of Mycoplasma pneumoniae, Staphylococcus aureus, and methicillin-resistant S. aureus was achieved with 229 clinical patient sputum samples. The total coincidence rate of our system and traditional RT-PCR with an ABI 7500 was 99.56%. Four samples accounting for the 0.44% inconformity were retested by gene sequencing, revealing that our system reported the correct results. This novel microfluidic chip-based detection system is cost-effective, rapid, sensitive, specific, and has a relatively high throughput for parallel identification, which is especially suitable for resource-limited facilities/areas and point-of-care testing.

Rh-catalyzed regio-switchable cross-coupling reactions of gem -difluorinated cyclopropanes with allylboronates have been developed. Fluorinated 1,4-dienes, 1,5-dienes and 1,3-dienes are produced in good yields with excellent regioselectivity.

Multimodal imaging-guided near-infrared (NIR) photothermal therapy (PTT) is an interesting and promising cancer theranostic method. However, most of the multimodal imaging systems provide structural and functional information used for imaging guidance separately by directly combining independent imaging systems with different detectors, and many problems arise when trying to fuse different modal images that are serially taken by inviting extra markers or image fusion algorithms. Further, most imaging and therapeutic agents passively target tumors through the enhanced permeability and retention (EPR) effect, which leads to low utilization efficiency. To address these problems and systematically improve the performance of the imaging-guided PTT methodology, we report a novel simultaneous dual-modal imaging system combined with cancer cell membrane-coated nanoparticles as a platform for PTT-based cancer theranostics. A novel detector with the ability to detect both high-energy X-ray and low-energy visible light at the same time, as well as a dual-modal imaging system based on the detector, was developed for simultaneous dual-modal imaging. Cancer cell membrane-coated upconversion nanoparticles (CC-UCNPs) and gold nanoparticles (CC-AuNPs) with the capacity for immune evasion and active tumor targeting were engineered for highly specific imaging and high-efficiency PTT therapy. In vitro and in vivo evaluation of macrophage escape and active homologous tumor targeting were performed. Cancer cell membrane-coated nanoparticles (CC-NPs) displayed excellent immune evasion ability, longer blood circulation time, and higher tumor targeting specificity compared to normal PEGylated nanoparticles, which led to highly specific upconversion luminescence (UCL) imaging and PTT-based anti-tumor efficacy. The anti-cancer efficacy of the dual-modal imaging-guided PTT was also evaluated both in vitro and in vivo. Dual-modal imaging yielded precise anatomical and functional information for the PTT process, and complete tumor ablation was achieved with CC-AuNPs. Our biomimetic UCNP/AuNP and novel simultaneous dual-modal imaging combination could be a promising platform and methodology for cancer theranostics.