Multitask machine learning-based tumor-associated collagen signatures predict peritoneal recurrence and disease-free survival in gastric cancer.

BACKGROUND: Accurate prediction of peritoneal recurrence for gastric cancer (GC) is crucial in clinic. The collagen alterations in tumor microenvironment affect the migration and treatment response of cancer cells. Herein, we proposed multitask machine learning-based tumor-associated collagen signatures (TACS), which are composed of quantitative collagen features derived from multiphoton imaging, to simultaneously predict peritoneal recurrence (TACSPR) and disease-free survival (TACSDFS). METHODS: Among 713 consecutive patients, with 275 in training cohort, 222 patients in internal validation cohort, and 216 patients in external validation cohort, we developed and validated a multitask machine learning model for simultaneously predicting peritoneal recurrence (TACSPR) and disease-free survival (TACSDFS). The accuracy of the model for prediction of peritoneal recurrence and prognosis as well as its association with adjuvant chemotherapy were evaluated. RESULTS: The TACSPR and TACSDFS were independently associated with peritoneal recurrence and disease-free survival in three cohorts, respectively (all P < 0.001). The TACSPR demonstrated a favorable performance for peritoneal recurrence in all three cohorts. In addition, the TACSDFS also showed a satisfactory accuracy for disease-free survival among included patients. For stage II and III diseases, adjuvant chemotherapy improved the survival of patients with low TACSPR and low TACSDFS, or high TACSPR and low TACSDFS, or low TACSPR and high TACSDFS, but had no impact on patients with high TACSPR and high TACSDFS. CONCLUSIONS: The multitask machine learning model allows accurate prediction of peritoneal recurrence and survival for GC and could distinguish patients who might benefit from adjuvant chemotherapy.

Leptin A deficiency affecting the mitochondrial dynamics of aged oocytes in medaka (Oryzias latipes).

Mitochondrial dysfunction and metabolic disorder have been associated to age-related subfertility, however, the precise molecular mechanism controlling the development of fertile oocytes in aging females remains elusive. Leptin plays an important role in the maintenance of energy homeostasis, as both excessive or insufficient levels can affect the body weight and fertility of mice. Here, we report that leptin A deficiency affects growth and shortens reproductive lifespan by reducing fertility in medaka (Oryzias latipes). Targeted disruption of lepa (lepa-/-) females reduced their egg laying and fertility compared to normal 3-month-old females (lepa+/+ sexual maturity), with symptoms worsening progressively at the age of 6 months and beyond. Transcriptomic analysis showed that differentially expressed genes involved in metabolic and mitochondrial pathways were significantly altered in lepa-/- ovaries compared with the normal ovaries at over 6 months old. The expression levels of the autophagy-promoting genes ulk1a, atg7 and atg12 were significantly differentiated between normal and lepa-/- ovaries, which were further confirmed by quantitative polymerase chain reaction analysis, indicating abnormal autophagy activation and mitochondrial dysfunction in oocyte development lacking lepa. Transmission electron microscopy observations further confirmed these mitochondrial disorders in lepa-deficient oocytes. In summary, these research findings provide novel insights into how leptin influences female fertility through mitochondrial-mediated oocyte development.

Ovarian cancer identification technology based on deep learning and second harmonic generation imaging.

Ovarian cancer is among the most common gynecological cancers and the eighth leading cause of cancer-related deaths among women worldwide. Surgery is among the most important options for cancer treatment. During surgery, a biopsy is generally required to screen for lesions; however, traditional case examinations are time consuming and laborious and require extensive experience and knowledge from pathologists. Therefore, this study proposes a simple, fast, and label-free ovarian cancer diagnosis method that combines second harmonic generation (SHG) imaging and deep learning. Unstained fresh human ovarian tissues were subjected to SHG imaging and accurately characterized using the Pyramid Vision Transformer V2 (PVTv2) model. The results showed that the SHG imaged collagen fibers could quantify ovarian cancer. In addition, the PVTv2 model could accurately differentiate the 3240 SHG images obtained from our imaging collection into benign, normal, and malignant images, with a final accuracy of 98.4%. These results demonstrate the great potential of SHG imaging techniques combined with deep learning models for diagnosing the diseased ovarian tissues.

Neuroprotective effects of anti-TRAIL-ICG nanoagent and its multimodal imaging evaluation in cerebral ischemia-reperfusion injury.

Cerebral ischemia-reperfusion injury (CIRI) is a major challenge to neuronal survival in acute ischemic stroke (AIS). However, effective neuroprotective agents remain to be developed for the treatment of CIRI. In this work, we have developed an Anti-TRAIL protein-modified and indocyanine green (ICG)-responsive nanoagent (Anti-TRAIL-ICG) to target ischemic areas and then reduce CIRI and rescue the ischemic penumbra. In vitro and in vivo experiments have demonstrated that the carrier-free nanoagent can enhance drug transport across the blood-brain barrier (BBB) in stroke mice, exhibiting high targeting ability and good biocompatibility. Anti-TRAIL-ICG nanoagent played a better neuroprotective role by reducing apoptosis and ferroptosis, and significantly improved ischemia-reperfusion injury. Moreover, the multimodal imaging platform enables the dynamic in vivo examination of multiple morphofunctional information, so that the dynamic molecular events of nanoagent can be detected continuously and in real time for early treatment in transient middle cerebral artery occlusion (tMCAO) models. Furthermore, it has been found that Anti-TRAIL-ICG has great potential in the functional reconstruction of neurovascular networks through optical coherence tomography angiography (OCTA). Taken together, our work effectively alleviates CIRI after stoke by blocking multiple cell death pathways, which offers an innovative strategy for harnessing the apoptosis and ferroptosis against CIRI.

Long-term Intravital Investigation of an Orthotopic Glioma Mouse Model via Optical Coherence Tomography Angiography.

BACKGROUND/AIM: Probing brain tumor microvasculature holds significant importance in both basic cancer research and medical practice for tracking tumor development and assessing treatment outcomes. However, few imaging methods commonly used in clinics can noninvasively monitor the brain microvascular network at high precision and without exogenous contrast agents in vivo. The present study aimed to investigate the characteristics of microvasculature during brain tumor development in an orthotopic glioma mouse model. MATERIALS AND METHODS: An orthotopic glioma mouse model was established by surgical orthotopic implantation of U87-MG-luc cells into the mouse brain. Then, optical coherence tomography angiography (OCTA) was utilized to characterize the microvasculature progression within 14 days. RESULTS: The orthotopic glioma mouse model evaluated by bioluminescence imaging and MRI was successfully generated. As the tumor grew, the microvessels within the tumor area slowly decreased, progressing from the center to the periphery for 14 days. CONCLUSION: This study highlights the potential of OCTA as a useful tool to noninvasively visualize the brain microvascular network at high precision and without any exogenous contrast agents in vivo.

Intravital photoacoustic brain stimulation with high-precision.

Significance: Neural regulation at high precision vitally contributes to propelling fundamental understanding in the field of neuroscience and providing innovative clinical treatment options. Recently, photoacoustic brain stimulation has emerged as a cutting-edge method for precise neuromodulation and shows great potential for clinical application. Aim: The goal of this perspective is to outline the advancements in photoacoustic brain stimulation in recent years. And, we also provide an outlook delineating several prospective paths through which this burgeoning approach may be substantively refined for augmented capability and wider implementations. Approach: First, the mechanisms of photoacoustic generation as well as the potential mechanisms of photoacoustic brain stimulation are provided and discussed. Then, the state-of-the-art achievements corresponding to this technology are reviewed. Finally, future directions for photoacoustic technology in neuromodulation are provided. Results: Intensive research endeavors have prompted substantial advancements in photoacoustic brain stimulation, illuminating the unique advantages of this modality for noninvasive and high-precision neuromodulation via a nongenetic way. It is envisaged that further technology optimization and randomized prospective clinical trials will enable a wide acceptance of photoacoustic brain stimulation in clinical practice. Conclusions: The innovative practice of photoacoustic technology serves as a multifaceted neuromodulation approach, possessing noninvasive, high-accuracy, and nongenetic characteristics. It has a great potential that could considerably enhance not only the fundamental underpinnings of neuroscience research but also its practical implementations in a clinical setting.

Inhibition of asparagine endopeptidase (AEP) effectively treats sporadic Alzheimer's disease in mice.

Alzheimer's disease (AD) is a progressive neurodegenerative disease with cognitive dysfunction as its major clinical symptom. However, there is no disease-modifying small molecular medicine to effectively slow down progression of the disease. Here, we show an optimized asparagine endopeptidase (AEP, also known as δ-secretase) inhibitor, #11 A, that displays an orderly in vivo pharmacokinetics/pharmacodynamics (PK/PD) relationship and robustly attenuates AD pathologies in a sporadic AD mouse model. #11 A is brain permeable with great oral bioavailability. It blocks AEP cleavage of APP and Tau dose-dependently, and significantly decreases Aß40 and Aß42 and p-Tau levels in APP/PS1 and Tau P301S mice after oral administration. Notably, #11 A strongly inhibits AEP and prevents mouse APP and Tau fragmentation by AEP, leading to reduction of mouse Aß42 (mAß42), mAß40 and mouse p-Tau181 levels in Thy1-ApoE4/C/EBPß transgenic mice in a dose-dependent manner. Repeated oral administration of #11 A substantially decreases mAß aggregation as validated by Aß PET assay, Tau pathology, neurodegeneration and brain volume reduction, resulting in alleviation of cognitive impairment. Therefore, our results support that #11 A is a disease-modifying preclinical candidate for pharmacologically treating AD.

Cooperation between IRTKS and deubiquitinase OTUD4 enhances the SETDB1-mediated H3K9 trimethylation that promotes tumor metastasis via suppressing E-cadherin expression.

Elevated expression and genetic aberration of IRTKS, also named as BAIAP2L1, have been observed in many tumors, especially in tumor progression. however, the molecular and cellular mechanisms involved in the IRTKS-enhanced tumor progression are obscure. Here we show that higher IRTKS level specifically increases histone H3 lysine 9 trimethylation (H3K9me3) by promoting accumulation of the histone methyltransferase SETDB1. Furthermore, we reveal that IRTKS recruits the deubiquitinase OTUD4 to remove Lys48-linked polyubiquitination at K182/K1050 sites of SETDB1, thus blocking SETDB1 degradation via the ubiquitin-proteasome pathway. Interestingly, the enhanced IRTKS-OTUD4-SETDB1-H3K9me3 axis leads to a general decrease in chromatin accessibility, which inhibits transcription of CDH1 encoding E-cadherin, a key molecule essential for maintaining epithelial cell phenotype, and therefore results in epithelial-mesenchymal transition (EMT) and malignant cell metastasis. Clinically, the elevated IRTKS levels in tumor specimens correlate with SETDB1 levels, but negatively associate with survival time. Our data reveal a novel mechanism for the IRTKS-enhanced tumor progression, where IRTKS cooperates with OTUD4 to enhance SETDB1-mediated H3K9 trimethylation that promotes tumor metastasis via suppressing E-cadherin expression. This study also provides a potential approach to reduce the activity and stability of the known therapeutic target SETDB1 possibly through regulating IRTKS or deubiquitinase OTUD4.

Targetoid-like lesions and chilblain-like erythema manifested on hands and feet: A case of Rowell syndrome from China.

BACKGROUND: Rowell syndrome (RS) is an uncommon condition characterized by erythema multiforme (EM)-like lesions and lupus erythematosus. It is more common in females, and EM may be the first manifestation of the disease with positive autoantibodies, such as antinuclear antibody (ANA), SSA, SSB and rheumatoid factor. The pathogenesis of RS is unknown and is likely caused by drug induction, ultraviolet exposure and infection. METHOD: We describe a case of RS from China which presented as characteristic targetoid-like lesions and chilblain-like erythema on hands and feet. This is a case of RS in a female patient from the inpatient department of dermatology. RESULTS: A 41-year-old female with systemic lupus erythematosus exhibited chilblain-like erythema and characteristic EM lesions on her extremities. She tested positive for serum ANA (1:320) and anti-double-stranded DNA, as well as other autoantibodies. Systemic glucocorticoids and hydroxychloroquine worked effectively for her. CONCLUSION: The present case met diagnostic criteria of RS. Notably, there was a co-occurrence of facial butterfly erythema, chilblain-like erythema and EM lesions distributed on the limbs in this case.

Zein-Based Nanoparticles Improve the Therapeutic Efficacy of a TrkB Agonist toward Alzheimer's Disease.

The brain-derived neurotrophic factor (BDNF)/TrkB pathway plays a crucial role in neural plasticity and neuronal survival but is often deficient in neurodegenerative diseases like Alzheimer's disease (AD). CF3CN acts as a specific TrkB agonist that displays therapeutic effects in the AD mouse model, but its brain/plasma ratio (B/P ratio) distribution is not satisfactory. To increase its brain exposure, we synthesized several derivatives and employed nanoparticle (NP) formulation to optimize the most potent #2 derivative's in vivo PK profiles. We generated stable #2-loaded zein/lactoferrin composite NPs (#2/zein/LF) using the antisolvent co-precipitation method. In vivo PK studies revealed that nanoencapsulation improved #2's oral bioavailability by approximately 2-fold and significantly enhanced its plasma Cmax and t1/2, but the brain profiles were comparable. Pharmacodynamics showed that #2/zein/LF activates TrkB signaling that phosphorylates asparagine endopeptidase (AEP) T322 and decreases its enzymatic activity, resulting in reduced AEP-cleaved amyloid precursor protein and Tau fragments in the brains of AD mice, correlating with its PK profiles. After 3 months of treatment in 3xTg mice, #2/zein/LF decreased AD pathologies and alleviated cognitive dysfunction. Hence, zein/LF composite nanoencapsulation is a promising drug delivery method for improving the PK profiles of a potential preclinical candidate for treating neurodegenerative diseases.

AutoFibroNet: A deep learning and multi-photon microscopy-derived automated network for liver fibrosis quantification in MAFLD.

BACKGROUND: Liver fibrosis is the strongest histological risk factor for liver-related complications and mortality in metabolic dysfunction-associated fatty liver disease (MAFLD). Second harmonic generation/two-photon excitation fluorescence (SHG/TPEF) is a powerful tool for label-free two-dimensional and three-dimensional tissue visualisation that shows promise in liver fibrosis assessment. AIM: To investigate combining multi-photon microscopy (MPM) and deep learning techniques to develop and validate a new automated quantitative histological classification tool, named AutoFibroNet (Automated Liver Fibrosis Grading Network), for accurately staging liver fibrosis in MAFLD. METHODS: AutoFibroNet was developed in a training cohort that consisted of 203 Chinese adults with biopsy-confirmed MAFLD. Three deep learning models (VGG16, ResNet34, and MobileNet V3) were used to train pre-processed images and test data sets. Multi-layer perceptrons were used to fuse data (deep learning features, clinical features, and manual features) to build a joint model. This model was then validated in two further independent cohorts. RESULTS: AutoFibroNet showed good discrimination in the training set. For F0, F1, F2 and F3-4 fibrosis stages, the area under the receiver operating characteristic curves (AUROC) of AutoFibroNet were 1.00, 0.99, 0.98 and 0.98. The AUROCs of F0, F1, F2 and F3-4 fibrosis stages for AutoFibroNet in the two validation cohorts were 0.99, 0.83, 0.80 and 0.90 and 1.00, 0.83, 0.80 and 0.94, respectively, showing a good discriminatory ability in different cohorts. CONCLUSION: AutoFibroNet is an automated quantitative tool that accurately identifies histological stages of liver fibrosis in Chinese individuals with MAFLD.

Development and validation of a collagen signature to predict the prognosis of patients with stage II/III colorectal cancer.

The tumor, nodes and metastasis (TNM) classification system provides useful but incomplete prognostic information and lacks the assessment of the tumor microenvironment (TME). Collagen, the main component of the TME extracellular matrix, plays a nonnegligible role in tumor invasion and metastasis. In this cohort study, we aimed to develop and validate a TME collagen signature (CSTME) for prognostic prediction of stage II/III colorectal cancer (CRC) and to compare the prognostic values of "TNM stage + CSTME" with that of TNM stage alone. Results indicated that the CSTME was an independent prognostic risk factor for stage II/III CRC (hazard ratio: 2.939, 95% CI: 2.180-3.962, p < 0.0001), and the integration of the TNM stage and CSTME had a better prognostic value than that of the TNM stage alone (AUC(TNM+CSTME) = 0.772, AUC TNM = 0.687, p < 0.0001). This study provided an application of "seed and soil" strategy for prognosis prediction and individualized therapy.

Mapping variation of extracellular matrix in human keloid scar by label-free multiphoton imaging and machine learning.

Significance: Rapid diagnosis and analysis of human keloid scar tissues in an automated manner are essential for understanding pathogenesis and formulating treatment solutions. Aim: Our aim is to resolve the features of the extracellular matrix in human keloid scar tissues automatically for accurate diagnosis with the aid of machine learning. Approach: Multiphoton microscopy was utilized to acquire images of collagen and elastin fibers. Morphological features, histogram, and gray-level co-occurrence matrix-based texture features were obtained to produce a total of 28 features. The minimum redundancy maximum relevancy feature selection approach was implemented to rank these features and establish feature subsets, each of which was employed to build a machine learning model through the tree-based pipeline optimization tool (TPOT). Results: The feature importance ranking was obtained, and 28 feature subsets were acquired by incremental feature selection. The subset with the top 23 features was identified as the most accurate. Then stochastic gradient descent classifier optimized by the TPOT was generated with an accuracy of 96.15% in classifying normal, scar, and adjacent tissues. The area under curve of the classification results (scar versus normal and adjacent, normal versus scar and adjacent, and adjacent versus normal and scar) was 1.0, 1.0, and 0.99, respectively. Conclusions: The proposed approach has great potential for future dermatological clinical diagnosis and analysis and holds promise for the development of computer-aided systems to assist dermatologists in diagnosis and treatment.

Mapping and dynamic monitoring of military training-induced vegetation cover loss using Sentinel-2 images and method comparison.

Sustainable management of the US Army installations is critical for military training and readiness of forces. However, monitoring military training-induced vegetation cover disturbances using remote sensing data is challenging due to the lack of methodology for optimizing the selection of spectral variables or predictors and spatial modeling methods. This study aimed to propose and demonstrate a methodological solution for this purpose. The study was conducted in the Fort Riley installation in which three training areas were selected to map and monitor the training-induced vegetation cover loss. Sentinel-2 images and field observations of percentage vegetation cover (PVC) were combined at a spatial resolution of 10 m by 10 m to map PVC and its dynamics by comparison of two predictor selection methods and five spatial modeling algorithms based on a total of 304 spectral variables from the images before and after the training. Results showed that overall, the correlation-based predictor selection method reduced the relative root mean square error (RRMSE) of PVC predictions by 4.44% than the random forest (RF)-based predictor selection. Machine learning methods including RF, neural network, and support vector machine overall reduced the RRMSE of PVC predictions by 42.83% compared with multiple linear regression and k-nearest neighbors. Combining correlation-based predictor selection and RF modeling, coupled with leave one out cross validation, provided the greatest potential of increasing the accuracy of monitoring the vegetation cover loss. The findings provided useful implications to develop a near real-time system of monitoring military training-induced vegetation cover loss.

Robust functional principal component analysis via a functional pairwise spatial sign operator.

Functional principal component analysis (FPCA) has been widely used to capture major modes of variation and reduce dimensions in functional data analysis. However, standard FPCA based on the sample covariance estimator does not work well if the data exhibits heavy-tailedness or outliers. To address this challenge, a new robust FPCA approach based on a functional pairwise spatial sign (PASS) operator, termed PASS FPCA, is introduced. We propose robust estimation procedures for eigenfunctions and eigenvalues. Theoretical properties of the PASS operator are established, showing that it adopts the same eigenfunctions as the standard covariance operator and also allows recovering ratios between eigenvalues. We also extend the proposed procedure to handle functional data measured with noise. Compared to existing robust FPCA approaches, the proposed PASS FPCA requires weaker distributional assumptions to conserve the eigenspace of the covariance function. Specifically, existing work are often built upon a class of functional elliptical distributions, which requires inherently symmetry. In contrast, we introduce a class of distributions called the weakly functional coordinate symmetry (weakly FCS), which allows for severe asymmetry and is much more flexible than the functional elliptical distribution family. The robustness of the PASS FPCA is demonstrated via extensive simulation studies, especially its advantages in scenarios with nonelliptical distributions. The proposed method was motivated by and applied to analysis of accelerometry data from the Objective Physical Activity and Cardiovascular Health Study, a large-scale epidemiological study to investigate the relationship between objectively measured physical activity and cardiovascular health among older women.

Sex-specific meiosis responses to Gsdf in medaka (Oryzias latipes).

The meiotic entry of undifferentiated germ cells is sexually specific and strictly regulated by the testicular or ovarian environment. Germline stem cells with a set of abnormal sex chromosomes and associated autosomes undergo defective meiotic processes and are eventually eliminated by yet to be defined post-transcriptional modifications. Herein, we report the role of gsdf, a member of BMP/TGFß family uniquely found in teleost, in the regulation of meiotic entry in medaka (Oryzias latipes) via analyses of gametogenesis in gsdf-deficient XX and XY gonads in comparison with their wild-type siblings. Several differentially expressed genes, including the FKB506-binding protein 7 (fkbp7), were significantly upregulated in pubertal gsdf-deficient gonads. The increase in alternative pre-mRNA isoforms of meiotic synaptonemal complex gene sycp3 was visualized using Integrative Genomics Viewer and confirmed by real-time qPCR. Nevertheless, immunofluorescence analysis showed that Sycp3 protein products reduced significantly in gsdf-deficient XY oocytes. Transmission electron microscope observations showed that normal synchronous cysts were replaced by asynchronous cysts in gsdf-deficient testis. Breeding experiments showed that the sex ratio deviation of gsdf-/- XY gametes in a non-Mendelian manner might be due to the non-segregation of XY chromosomes. Taken together, our results suggest that gsdf plays a role in the proper execution of cytoplasmic and nuclear events through receptor Smad phosphorylation and Sycp3 dephosphorylation to coordinate medaka gametogenesis, including sex-specific mitotic divisions and meiotic recombination.

Calibration of an Accelerometer Activity Index among Older Women and Its Association with Cardiometabolic Risk Factors.

Purpose: Traditional summary metrics provided by accelerometer device manufacturers, known as counts, are proprietary and manufacturer specific, making them difficult to compare studies using different devices. Alternative summary metrics based on raw accelerometry data have been introduced in recent years. However, they were often not calibrated on ground truth measures of activity-related energy expenditure for direct translation into continuous activity intensity levels. Our purpose is to calibrate, derive, and validate thresholds among women 60 years and older based on a recently proposed transparent raw data based accelerometer activity index (AAI), and to demonstrate its application in association with cardiometabolic risk factors. Methods: We first built calibration equations for estimating metabolic equivalents (METs) continuously using AAI and personal characteristics using internal calibration data (n=199). We then derived AAI cutpoints to classify epochs into sedentary behavior and intensity categories. The AAI cutpoints were applied to 4,655 data units in the main study. We then utilized linear models to investigate associations of AAI sedentary behavior and physical activity intensity with cardiometabolic risk factors. Results: We found that AAI demonstrated great predictive accuracy for METs (R2=0.74). AAI-based physical activity measures were associated in the expected directions with body mass index (BMI), blood glucose, and high density lipoprotein (HDL) cholesterol. Conclusion: The calibration framework for AAI and the cutpoints derived for women older than 60 years can be applied to ongoing epidemiologic studies to more accurately define sedentary behavior and physical activity intensity exposures which could improve accuracy of estimated associations with health outcomes.

Identification of human ovarian cancer relying on collagen fiber coverage features by quantitative second harmonic generation imaging.

Ovarian cancer has the highest mortality rate among all gynecological cancers, containing complicated heterogeneous histotypes, each with different treatment plans and prognoses. The lack of screening test makes new perspectives for the biomarker of ovarian cancer of great significance. As the main component of extracellular matrix, collagen fibers undergo dynamic remodeling caused by neoplastic activity. Second harmonic generation (SHG) enables label-free, non-destructive imaging of collagen fibers with submicron resolution and deep sectioning. In this study, we developed a new metric named local coverage to quantify morphologically localized distribution of collagen fibers and combined it with overall density to characterize 3D SHG images of collagen fibers from normal, benign and malignant human ovarian biopsies. An overall diagnosis accuracy of 96.3% in distinguishing these tissue types made local and overall density signatures a sensitive biomarker of tumor progression. Quantitative, multi-parametric SHG imaging might serve as a potential screening test tool for ovarian cancer.

Research progress on the application of optical coherence tomography in the field of oncology.

Optical coherence tomography (OCT) is a non-invasive imaging technique which has become the "gold standard" for diagnosis in the field of ophthalmology. However, in contrast to the eye, nontransparent tissues exhibit a high degree of optical scattering and absorption, resulting in a limited OCT imaging depth. And the progress made in the past decade in OCT technology have made it possible to image nontransparent tissues with high spatial resolution at large (up to 2mm) imaging depth. On the one hand, OCT can be used in a rapid, noninvasive way to detect diseased tissues, organs, blood vessels or glands. On the other hand, it can also identify the optical characteristics of suspicious parts in the early stage of the disease, which is of great significance for the early diagnosis of tumor diseases. Furthermore, OCT imaging has been explored for imaging tumor cells and their dynamics, and for the monitoring of tumor responses to treatments. This review summarizes the recent advances in the OCT area, which application in oncological diagnosis and treatment in different types: (1) superficial tumors:OCT could detect microscopic information on the skin's surface at high resolution and has been demonstrated to help diagnose common skin cancers; (2) gastrointestinal tumors: OCT can be integrated into small probes and catheters to image the structure of the stomach wall, enabling the diagnosis and differentiation of gastrointestinal tumors and inflammation; (3) deep tumors: with the rapid development of OCT imaging technology, it has shown great potential in the diagnosis of deep tumors such in brain tumors, breast cancer, bladder cancer, and lung cancer.

DLK1-directed chimeric antigen receptor T-cell therapy for hepatocellular carcinoma.

BACKGROUND: Delta-like homologue 1 (DLK1), a transmembrane protein, is highly expressed in hepatocellular carcinoma (HCC). We explored whether DLK1-directed chimeric antigen receptor (CAR) T cells can specifically eliminate DLK1-positive HCC cells and serve as a therapeutic strategy for HCC immunotherapy. METHODS: We first characterized a homemade anti-human DLK1 monoclonal antibody, sequenced the single-chain Fragment variable (scFv) and integrated it into the second-generation CAR lentiviral vector, and then developed the DLK1-directed CAR-T cells. The cytotoxic activities of DLK1-directed CAR-T cells against different HCC cells were evaluated in vitro and in vivo. RESULTS: The genetically modified human T cells with the DLK1-directed CARs produced cytotoxic activity against DLK1-positive HCC cells. Additionally, the DLK1-directed CARs enhanced T cell proliferation and activation in a DLK1-dependent manner. Interestingly, the DLK1-targeted CAR-T cells significantly inhibited both subcutaneous and peritoneal xenograft tumours derived from human liver cancer cell lines HepG2 or Huh-7. CONCLUSION: DLK1-directed CAR-T cells specifically suppresses DLK1-positive HCC cells in vitro and in vivo. This study provides a novel transmembrane antigen DLK1 as a potential therapeutic target appropriate for CAR-T cell therapy, which may be further developed as a clinical therapeutic strategy for HCC immunotherapy.