Wenhua Su’s research while affiliated with Fudan University and other places

The precise determination of surgical margins is essential for the management of multifocal cutaneous cancers, including extramammary Paget’s disease. This study introduces a novel strategy for precise margin identification in such tumors, employing multichannel autofluorescence lifetime decay (MALD), fluorescence lifetime imaging microscopy (FLIM), and machine learning, including confidence learning algorithms. Using FLIM, 51 unstained frozen sections were analyzed, of which 13 (25%) sections, containing 5003 FLIM patches, were used for training the residual network model (ResNet–FLIM). The remaining 38 (75%) sections, including 16 918 patches, were retained for external validation. Application of confidence learning with deep learning reduced the reliance on extensive pathologist annotation. Refined labels obtained by ResNet–FLIM were then incorporated into a support vector machine (SVM) model, which utilized fiber-optic-based MALD data. Both models exhibited substantial agreement with the pathological assessments. Of the 35 MALD-measured tissue segments, six (17%) segments were selected as the training dataset, including 900 decay profiles. The remaining 29 segments (83%), including 2406 decay profiles, were reserved for external validation. The ResNet–FLIM model achieved 100% sensitivity and specificity. The SVM–MALD model demonstrated 94% sensitivity and 83% specificity. Notably, fiber-optic-MALD allows assessing 12 sites per patient and delivering predictions within 10 min. Variations in the necessary safe margin length were observed among patients, highlighting the necessity for patient-specific approaches to determine surgical margins. This innovative approach holds potential for wide clinical application, providing a rapid and accurate margin evaluation method that significantly reduces a pathologist’s workload and improves patient outcomes through personalized medicine.

Non‐invasive screening for bladder cancer is crucial for treatment and postoperative follow‐up. This study combines digital microfluidics (DMF) technology with fluorescence lifetime imaging microscopy (FLIM) for urine analysis and introduces a novel non‐invasive bladder cancer screening technique. Initially, the DMF was utilized to perform preliminary screening and enrichment of urine exfoliated cells from 54 participants, followed by cell staining and FLIM analysis to assess the viscosity of the intracellular microenvironment. Subsequently, a deep learning residual convolutional neural network was employed to automatically classify FLIM images, achieving a three‐class prediction of high‐risk (malignant), low‐risk (benign), and minimal risk (normal) categories. The results demonstrated a high consistency with pathological diagnosis, with an accuracy of 91% and a precision of 93%. Notably, the method is sensitive for both high‐grade and low‐grade bladder cancer cases. This highly accurate non‐invasive screening method presents a promising approach for bladder cancer screening with significant clinical application potential.

Mesenchymal stem cells (MSCs) play a crucial role in tissue engineering, as their differentiation status directly affects the quality of the final cultured tissue, which is critical to the success of transplantation therapy. Furthermore, the precise control of MSC differentiation is essential for stem cell therapy in clinical settings, as low-purity stem cells can lead to tumorigenic problems. Therefore, to address the heterogeneity of MSCs during their differentiation into adipogenic or osteogenic lineages, numerous label-free microscopic images were acquired using fluorescence lifetime imaging microscopy (FLIM) and stimulated Raman scattering (SRS), and an automated evaluation model for the differentiation status of MSCs was built based on the K-means machine learning algorithm. The model is capable of highly sensitive analysis of individual cell differentiation status, so it has great potential for stem cell differentiation research.

Cervical cancer has high morbidity and mortality rates, affecting hundreds of thousands of women worldwide and requiring more accurate screening for early intervention and follow-up treatment. Cytology is the current dominant clinical screening approach, and though it has been used for decades, it has unsatisfactory sensitivity and specificity. In this work, fluorescence lifetime imaging microscopy (FLIM) was used for the imaging of exfoliated cervical cells in which an endogenous coenzyme involved in metabolism, namely, reduced nicotinamide adenine dinucleotide (phosphate) [NAD(P)H], was detected to evaluate the metabolic status of cells. FLIM images from 71 participants were analyzed by the unsupervised machine learning method to build a prediction model for cervical cancer risk. The FLIM method combined with unsupervised machine learning (FLIM-ML) had a sensitivity and specificity of 90.9% and 100%, respectively, significantly higher than those of the cytology approach. One cancer recurrence case was predicted as high-risk several months earlier using this method as compared to using current clinical methods, implying that FLIM-ML may be very helpful for follow-up cancer care. This study illustrates the clinical applicability of FLIM-ML as a detection method for cervical cancer screening and a convenient tool for follow-up cancer care.

While it is known that air borne ultrafine particulate matter (PM) may pass through the pulmonary circulation of blood at the alveolar level between lung and heart and cross the air-blood barrier, the mechanism and effects are not completely clear. In this study the imaging method fluorescence lifetime imaging microscopy is adopted for visualization with high spatial resolution and quantification of ultrafine PM particles in mouse lung and heart tissues. The results showed that the median numbers of particles in lung of mice exposed to ultrafine particulate matter of diameter less than 2.5 µm was about 2.0 times more than that in the filtered air (FA)-treated mice, and about 1.3 times more in heart of ultrafine PM-treated mice than in FA-treated mice. Interestingly, ultrafine PM particles were more abundant in heart than lung, likely due to how ultrafine PM particles are cleared by phagocytosis and transport via circulation from lungs. Moreover, heart tissues showed inflammation and amyloid deposition. The component analysis of concentrated airborne ultrafine PM particles suggested traffic exhausts and industrial emissions as predominant sources. Our results suggest association of ultrafine PM exposure to chronic lung and heart tissue injuries. The current study supports the contention that industrial air pollution is one of the causative factors for rising levels of chronic pulmonary and cardiac diseases.

The ability to sort yeast cells is important for diverse applications in the industry and research. We determined the aging level of yeast by the fluorescence lifetime imaging microscopy (FLIM) by an endogenous fluorophore NAD(P)H, which was label‐free and non‐invasive. Young and active yeast cells were sorted by the laser‐induced forward transfer (LIFT) system at the single cell level. The high viability of sorted cells was achieved. Further details can be found in the article by Yawei Kong, Yinping Zhao, Yao Yu, Wenhua Su, Zhijia Liu, Yiyan Fei, Jiong Ma, and Lan Mi ( e202100344 ) image

While it is known that air borne particulate matter (PM 2.5 ) may pass through the pulmonary circulation of blood at the alveolar level between lung and heart and cross the air-blood barrier, the mechanism and effects are not completely clear. In this study the imaging method fluorescence lifetime imaging microscopy (FLIM) is adopted for visualization with high spatial resolution and quantification of PM particles in mouse lung and heart tissues. The results showed that the median numbers of particles in lung of mice exposed to particulate matter of diameter less than 2.5 µm (PM 2.5 ) was about 2.0 times more than that in the filtered air (FA)-treated mice, and about 1.3 times more in heart of PM 2.5 -treated mice than in FA-treated mice. Interestingly, PM 2.5 particles were more abundant in heart than lung, likely due to how PM particles are cleared by phagocytosis and transport via circulation from lungs. It is proposed that the powerful flow of blood through the heart may contribute to invasion of PM 2.5 particles into heart muscles. The histopathological evaluations revealed that exposure of PM 2.5 to lungs dilated air spaces and showed signs of inflammation. Moreover, heart tissues showed inflammation and amyloid deposition. The component analysis of concentrated airborne PM 2.5 particles suggested traffic exhausts and industrial emissions as predominant sources. Our results strongly suggest association of PM 2.5 exposure to chronic lung and heart tissue injuries. The current study supports the contention that industrial air pollution is one of the causative factors for rising levels of chronic pulmonary and cardiac diseases.

Intracellular pH plays a critical role in biological functions, and abnormal pH values are related to various diseases. Here, we report on an intracellular pH sensor AgInS2 (AIS)/ZnS quantum dots (QDs) that show long fluorescence lifetimes of hundreds of nanoseconds and low toxicity. Fluorescence lifetime imaging microscopy (FLIM) combined with AIS/ZnS QDs is used for the imaging of live cells in different pH buffers and different cell lines. The FLIM images of AIS/ZnS QDs in live cells demonstrate different intracellular pH values in different regions, such as in lysosomes or cytoplasm. This method can also distinguish cancer cells from normal cells, and the fluorescence lifetime difference of the AIS/ZnS QDs between the two types of cells is 100 ± 7 ns. Most importantly, the exfoliated cervical cells from 20 patients are investigated using FLIM combined with AIS/ZnS QDs. The lifetime difference value between the normal and cervical cancer (CC) groups is 115 ± 9 ns, and the difference between the normal and the precancerous lesion group is 64 ± 9 ns. For the first time, the noninvasive method has been used for cervical cancer screening, and it has shown great improvement in sensitivity compared with a clinical conventional cytology examination.