Chimeric antigen receptor-natural killer cell therapy: current advancements and strategies to overcome challenges.

Chimeric antigen receptor-natural killer (CAR-NK) cell therapy is a novel immunotherapy targeting cancer cells via the generation of chimeric antigen receptors on NK cells which recognize specific cancer antigens. CAR-NK cell therapy is gaining attention nowadays owing to the ability of CAR-NK cells to release potent cytotoxicity against cancer cells without side effects such as cytokine release syndrome (CRS), neurotoxicity and graft-versus-host disease (GvHD). CAR-NK cells do not require antigen priming, thus enabling them to be used as "off-the-shelf" therapy. Nonetheless, CAR-NK cell therapy still possesses several challenges in eliminating cancer cells which reside in hypoxic and immunosuppressive tumor microenvironment. Therefore, this review is envisioned to explore the current advancements and limitations of CAR-NK cell therapy as well as discuss strategies to overcome the challenges faced by CAR-NK cell therapy. This review also aims to dissect the current status of clinical trials on CAR-NK cells and future recommendations for improving the effectiveness and safety of CAR-NK cell therapy.

Salvage haploidentical transplantation for graft failure after first haploidentical allogeneic stem cell transplantation: an updated experience.

Graft failure is a fatal complication following allogeneic stem cell transplantation where a second transplantation is usually required for salvage. However, there are no recommended regimens for second transplantations for graft failure, especially in the haploidentical transplant setting. We recently reported encouraging outcomes using a novel method (haploidentical transplantation from a different donor after conditioning with fludarabine and cyclophosphamide). Herein, we report updated outcomes in 30 patients using this method. The median time of the second transplantation was 96.5 (33-215) days after the first transplantation. Except for one patient who died at +19d and before engraftment, neutrophil engraftments were achieved in all patients at 11 (8-24) days, while platelet engraftments were achieved in 22 (75.8%) patients at 17.5 (9-140) days. The 1-year OS and DFS were 60% and 53.3%, and CIR and TRM was 6.7% and 33.3%, respectively. Compared with the historical group, neutrophil engraftment (100% versus 58.5%, p < 0.001) and platelet engraftment (75.8% versus 32.3%, p < 0.001) were better in the novel regimen group, and OS was also improved (60.0% versus 26.4%, p = 0.011). In conclusion, salvage haploidentical transplantation from a different donor using the novel regimen represents a promising option to rescue patients with graft failure after the first haploidentical transplantation.

Novel Isoalantolactone-Based Derivatives as Potent NLRP3 Inflammasome Inhibitors: Design, Synthesis, and Biological Characterization.

The NLRP3 inflammasome has been recognized as a promising therapeutic target in drug discovery for inflammatory diseases. Our initial research identified a natural sesquiterpene isoalantolactone (IAL) as the active scaffold targeting NLRP3 inflammasome. To improve its activity and metabolic stability, a total of 64 IAL derivatives were designed and synthesized. Among them, compound 49 emerged as the optimal lead, displaying the most potent inhibitory efficacy on nigericin-induced IL-1ß release in THP-1 cells, with an IC50 value of 0.29 µM, approximately 27-fold more potent than that of IAL (IC50: 7.86 µM), and exhibiting higher metabolic stability. Importantly, 49 remarkably improved DSS-induced ulcerative colitis in vivo. Mechanistically, we demonstrated that 49 covalently bound to cysteine 279 in the NACHT domain of NLRP3, thereby inhibiting the assembly and activation of NLRP3 inflammasome. These results provided compelling evidence to further advance the development of more potent NLRP3 inhibitors based on this scaffold.

Severe Intraocular Inflammation Following Intravitreal Faricimab.

Importance: Monitoring for and reporting potential cases of intraocular inflammation (IOI) in clinical practice despite limited occurrences in clinical trials, including experiences with relatively new intravitreal agents, such as brolucizumab, pegcetacoplan, or faricimab, helps balance potential benefits and risks of these agents. Objective: To provide descriptions of 3 initially culture-negative cases of acute, severe, posterior-segment IOI events occurring within the same month following intravitreal faricimab injections at a single institution. Design, Setting, and Participants: In this case series, 3 patients manifesting acute, severe IOI following intravitreal injection of faricimab were identified between September 20, 2023, and October 20, 2023. Exposure: Faricimab, 6 mg (0.05 mL of 120 mg/mL solution), for neovascular age-related macular degeneration among patients previously treated with aflibercept; 1 patient also had prior exposure to bevacizumab. Main Outcomes and Measures: Visual acuity, vitreous taps for bacterial or fungal cultures, and retinal imaging. Results: All 3 patients received intravitreal faricimab injections between September 20 and October 20, 2023, from 2 different lot numbers (expiration dates, July 2025) at 3 locations of 1 institution among 3 of 19 retina physicians. Visual acuities with correction were 20/63 OS for patient 1, 20/40 OD for patient 2, and 20/20 OS for patient 3 prior to injection. All 3 patients developed acute, severe inflammation involving the anterior and posterior segment within 3 to 4 days after injection, with visual acuities of hand motion OS, counting fingers OD, and hand motion OS, respectively. Two patients were continuing faricimab treatment while 1 patient was initiating faricimab treatment. All received intravitreal ceftazidime, 2.2 mg/0.1 mL, and vancomycin, 1 mg/0.1 mL, immediately following vitreous taps. All vitreous tap culture results were negative. One patient underwent vitrectomy 1 day following presentation. Intraoperative vitreous culture grew 1 colony of Staphylococcus epidermidis, judged a likely contaminant by infectious disease specialists. All symptoms resolved within 1 month; visual acuities with correction were 20/100 OS for patient 1, 20/50 OD for patient 2, and 20/30 OS for patient 3. Conclusions and Relevance: In this case series, 3 patients with acute, severe IOI within 1 month at 3 different locations among 3 ophthalmologists of 1 institution following intravitreal faricimab could represent some unknown storage or handling problem. However, this cluster suggests such inflammatory events may be more common than anticipated from faricimab trial reports, emphasizing the continued need for vigilance to detect and report such cases following regulatory approval.

SwinUNet: a multiscale feature learning approach to cardiovascular magnetic resonance parametric mapping for myocardial tissue characterization.

Objective.Cardiovascular magnetic resonance (CMR) can measure T1 and T2 relaxation times for myocardial tissue characterization. However, the CMR procedure for T1/T2 parametric mapping is time-consuming, making it challenging to scan heart patients routinely in clinical practice. This study aims to accelerate CMR parametric mapping with deep learning.Approach. A deep-learning model, SwinUNet, was developed to accelerate T1/T2 mapping. SwinUNet used a convolutional UNet and a Swin transformer to form a hierarchical 3D computation structure, allowing for analyzing CMR images spatially and temporally with multiscale feature learning. A comparative study was conducted between SwinUNet and an existing deep-learning model, MyoMapNet, which only used temporal analysis for parametric mapping. The T1/T2 mapping performance was evaluated globally using mean absolute error (MAE) and structural similarity index measure (SSIM). The clinical T1/T2 indices for characterizing the left-ventricle myocardial walls were also calculated and evaluated using correlation and Bland-Altman analysis.Main results. We performed accelerated T1 mapping with ≤4 heartbeats and T2 mapping with 2 heartbeats in reference to the clinical standard, which required 11 heartbeats for T1 mapping and 3 heartbeats for T2 mapping. SwinUNet performed well in all the experiments (MAE < 50 ms, SSIM > 0.8, correlation > 0.75, and Bland-Altman agreement limits < 100 ms for T1 mapping; MAE < 1 ms, SSIM > 0.9, correlation > 0.95, and Bland-Altman agreement limits < 1.5 ms for T2 mapping). When the maximal acceleration was used (2 heartbeats), SwinUNet outperformed MyoMapNet and gave measurement accuracy similar to the clinical standard.Significance. SwinUNet offers an optimal solution to CMR parametric mapping for assessing myocardial diseases quantitatively in clinical cardiology.

BGF-Net: Boundary guided filter network for medical image segmentation.

How to fuse low-level and high-level features effectively is crucial to improving the accuracy of medical image segmentation. Most CNN-based segmentation models on this topic usually adopt attention mechanisms to achieve the fusion of different level features, but they have not effectively utilized the guided information of high-level features, which is often highly beneficial to improve the performance of the segmentation model, to guide the extraction of low-level features. To address this problem, we design multiple guided modules and develop a boundary-guided filter network (BGF-Net) to obtain more accurate medical image segmentation. To the best of our knowledge, this is the first time that boundary guided information is introduced into the medical image segmentation task. Specifically, we first propose a simple yet effective channel boundary guided module to make the segmentation model pay more attention to the relevant channel weights. We further design a novel spatial boundary guided module to complement the channel boundary guided module and aware of the most important spatial positions. Finally, we propose a boundary guided filter to preserve the structural information from the previous feature map and guide the model to learn more important feature information. Moreover, we conduct extensive experiments on skin lesion, polyp, and gland segmentation datasets including ISIC 2016, CVC-EndoSceneStil and GlaS to test the proposed BGF-Net. The experimental results demonstrate that BGF-Net performs better than other state-of-the-art methods.

Mesenchymal stromal cells plus basiliximab improve the response of steroid-refractory acute graft-versus-host disease as a second-line therapy: a multicentre, randomized, controlled trial.

BACKGROUND: For patients with steroid-refractory acute graft-versus-host disease (SR-aGVHD), effective second-line regimens are urgently needed. Mesenchymal stromal cells (MSCs) have been used as salvage regimens for SR-aGVHD in the past. However, clinical trials and an overall understanding of the molecular mechanisms of MSCs combined with basiliximab for SR-aGVHD are limited, especially in haploidentical haemopoietic stem cell transplantation (HID HSCT). METHODS: The primary endpoint of this multicentre, randomized, controlled trial was the 4-week complete response (CR) rate of SR-aGVHD. A total of 130 patients with SR-aGVHD were assigned in a 1:1 randomization schedule to the MSC group (receiving basiliximab plus MSCs) or control group (receiving basiliximab alone) (NCT04738981). RESULTS: Most enrolled patients (96.2%) received HID HSCT. The 4-week CR rate of SR-aGVHD in the MSC group was obviously better than that in the control group (83.1% vs. 55.4%, P = 0.001). However, for the overall response rates at week 4, the two groups were comparable. More patients in the control group used ≥ 6 doses of basiliximab (4.6% vs. 20%, P = 0.008). We collected blood samples from 19 consecutive patients and evaluated MSC-derived immunosuppressive cytokines, including HO1, GAL1, GAL9, TNFIA6, PGE2, PDL1, TGF-ß and HGF. Compared to the levels before MSC infusion, the HO1 (P = 0.0072) and TGF-ß (P = 0.0243) levels increased significantly 1 day after MSC infusion. At 7 days after MSC infusion, the levels of HO1, GAL1, TNFIA6 and TGF-ß tended to increase; however, the differences were not statistically significant. Although the 52-week cumulative incidence of cGVHD in the MSC group was comparable to that in the control group, fewer patients in the MSC group developed cGVHD involving ≥3 organs (14.3% vs. 43.6%, P = 0.006). MSCs were well tolerated, no infusion-related adverse events (AEs) occurred and other AEs were also comparable between the two groups. However, patients with malignant haematological diseases in the MSC group had a higher 52-week disease-free survival rate than those in the control group (84.8% vs. 65.9%, P = 0.031). CONCLUSIONS: For SR-aGVHD after allo-HSCT, especially HID HSCT, the combination of MSCs and basiliximab as the second-line therapy led to significantly better 4-week CR rates than basiliximab alone. The addition of MSCs not only did not increase toxicity but also provided a survival benefit.

Digital image analysis and machine learning-assisted prediction of neoadjuvant chemotherapy response in triple-negative breast cancer.

BACKGROUND: Pathological complete response (pCR) is associated with favorable prognosis in patients with triple-negative breast cancer (TNBC). However, only 30-40% of TNBC patients treated with neoadjuvant chemotherapy (NAC) show pCR, while the remaining 60-70% show residual disease (RD). The role of the tumor microenvironment in NAC response in patients with TNBC remains unclear. In this study, we developed a machine learning-based two-step pipeline to distinguish between various histological components in hematoxylin and eosin (H&E)-stained whole slide images (WSIs) of TNBC tissue biopsies and to identify histological features that can predict NAC response. METHODS: H&E-stained WSIs of treatment-naïve biopsies from 85 patients (51 with pCR and 34 with RD) of the model development cohort and 79 patients (41 with pCR and 38 with RD) of the validation cohort were separated through a stratified eightfold cross-validation strategy for the first step and leave-one-out cross-validation strategy for the second step. A tile-level histology label prediction pipeline and four machine-learning classifiers were used to analyze 468,043 tiles of WSIs. The best-trained classifier used 55 texture features from each tile to produce a probability profile during testing. The predicted histology classes were used to generate a histology classification map of the spatial distributions of different tissue regions. A patient-level NAC response prediction pipeline was trained with features derived from paired histology classification maps. The top graph-based features capturing the relevant spatial information across the different histological classes were provided to the radial basis function kernel support vector machine (rbfSVM) classifier for NAC treatment response prediction. RESULTS: The tile-level prediction pipeline achieved 86.72% accuracy for histology class classification, while the patient-level pipeline achieved 83.53% NAC response (pCR vs. RD) prediction accuracy of the model development cohort. The model was validated with an independent cohort with tile histology validation accuracy of 83.59% and NAC prediction accuracy of 81.01%. The histological class pairs with the strongest NAC response predictive ability were tumor and tumor tumor-infiltrating lymphocytes for pCR and microvessel density and polyploid giant cancer cells for RD. CONCLUSION: Our machine learning pipeline can robustly identify clinically relevant histological classes that predict NAC response in TNBC patients and may help guide patient selection for NAC treatment.

TRPV4-mediated Ca2+ deregulation causes mitochondrial dysfunction via the AKT/α-synuclein pathway in dopaminergic neurons.

Mutations in the gene encoding the transient receptor potential vanilloid member 4 (TRPV4), a Ca2+ permeable nonselective cation channel, cause TRPV4-related disorders. TRPV4 is widely expressed in the brain; however, the pathogenesis underlying TRPV4-mediated Ca2+ deregulation in neurodevelopment remains unresolved and an effective therapeutic strategy remains to be established. These issues were addressed by isolating mutant dental pulp stem cells from a tooth donated by a child diagnosed with metatropic dysplasia with neurodevelopmental comorbidities caused by a gain-of-function TRPV4 mutation, c.1855C > T (p.L619F). The mutation was repaired using CRISPR/Cas9 to generate corrected isogenic stem cells. These stem cells were differentiated into dopaminergic neurons and the pharmacological effects of folic acid were examined. In mutant neurons, constitutively elevated cytosolic Ca2+ augmented AKT-mediated α-synuclein (α-syn) induction, resulting in mitochondrial Ca2+ accumulation and dysfunction. The TRPV4 antagonist, AKT inhibitor, or α-syn knockdown, normalizes the mitochondrial Ca2+ levels in mutant neurons, suggesting the importance of mutant TRPV4/Ca2+/AKT-induced α-syn in mitochondrial Ca2+ accumulation. Folic acid was effective in normalizing mitochondrial Ca2+ levels via the transcriptional repression of α-syn and improving mitochondrial reactive oxygen species levels, adenosine triphosphate synthesis, and neurite outgrowth of mutant neurons. This study provides new insights into the neuropathological mechanisms underlying TRPV4-related disorders and related therapeutic strategies.

Thrombopoietin level predicts the response to avatrombopag treatment for persistent thrombocytopenia after haploidentical haematopoietic stem cell transplantation.

Persistent thrombocytopenia (PT) has an unsatisfactory response to therapy after haploidentical haematopoietic stem cell transplantation (haplo-HSCT). We retrospectively evaluated the safety and efficacy of avatrombopag treatment in 69 patients with PT following haplo-HSCT and assessed whether baseline thrombopoietin (TPO) levels could predict treatment response. Overall response (OR) and complete response (CR) were defined as increased platelet levels to over 20 × 109/L or 50 × 109/L independent of platelet transfusion during or within 7 days of the end of avatrombopag treatment, respectively. The incidences of OR and CR were 72.5% and 58.0%, with a median of 11 and 29 days to OR and CR, respectively. ROC analysis suggested that the optimally discriminant baseline TPO level threshold for both OR and CR to avatrombopag was ≤ 1714 pg/mL. In multivariate analysis, a lower baseline TPO level (P = 0.005) was a significant independent factor of response to avatrombopag. For patients resistant to other TPO receptor agonists (TPO-RAs), 9/16 (56.3%) exhibited a response after switching to avatrombopag. Avatrombopag was well tolerated, and responders achieved improved overall survival (79.0% vs. 91.1%, P = 0.001). In conclusion, avatrombopag is a potential safe and effective treatment for PT after haplo-HSCT, and lower baseline TPO levels predicted a better response.

The sulfate assimilation and reduction of marine microalgae and the regulation of illumination.

To examine the sulfate assimilation and reduction process and the regulation of illumination, diatom Phaeodactylum tricornutum and dinoflagellate Amphidinium carterae were selected for continuous simulation incubation under different photon flux densities (PFDs) (54, 108 and 162 µmol photons m-2 s-1), and concentration variations of related sulfur compounds sulfate, dimethylsulfoniopropionate (DMSP), dimethylsulfide (DMS) and acrylic acid (AA) in the culture system were observed. The optimal PFD for the growth of two microalgae was 108 µmol photons m-2 s-1. However, the maximum sulfate absorption occurred at 162 µmol photons m-2 s-1 for P. tricornutum and at 54 µmol photons m-2 s-1 for A. carterae. With the increase of PFD, the release of DMSP by P. tricornutum decreased while A. carterae increased. The largest release amount of DMS was 0.59 ± 0.05 fmol cells-1 for P. tricornutum and 2.61 ± 0.89 fmol cells-1 for A. carterae under their optimum growth light condition. The sulfate uptake of P. tricornutum was inhibited by the addition of amino acids, cysteine had a greater inhibitory effect than methionine, and the absorption process was controlled by light. The intermediate products of sulfur metabolism had an up-control effect on the sulfate uptake process of P. tricornutum. However, the addition of amino acids had no obvious effect on the sulfate absorption of A. carterae.

Digital image analysis and machine learning-assisted prediction of neoadjuvant chemotherapy response in triple-negative breast cancer.

Background: Pathological complete response (pCR) is associated with favorable prognosis in patients with triple-negative breast cancer (TNBC). However, only 30-40% of TNBC patients treated with neoadjuvant chemotherapy (NAC) show pCR, while the remaining 60-70% show residual disease (RD). The role of the tumor microenvironment (TME) in NAC response in patients with TNBC remains unclear. In this study, we developed a machine learning-based two-step pipeline to distinguish between various histological components in hematoxylin and eosin (H&E)-stained whole slide images (WSIs) of TNBC tissue biopsies and to identify histological features that can predict NAC response. Methods: H&E-stained WSIs of treatment-naïve biopsies from 85 patients (51 with pCR and 34 with RD) were separated through a stratified 8-fold cross validation strategy for the first step and leave one out cross validation strategy for the second step. A tile-level histology label prediction pipeline and four machine learning classifiers were used to analyze 468,043 tiles of WSIs. The best-trained classifier used 55 texture features from each tile to produce a probability profile during testing. The predicted histology classes were used to generate a histology classification map of the spatial distributions of different tissue regions. A patient-level NAC response prediction pipeline was trained with features derived from paired histology classification maps. The top graph-based features capturing the relevant spatial information across the different histological classes were provided to the radial basis function kernel support vector machine (rbfSVM) classifier for NAC treatment response prediction. Results: The tile-level prediction pipeline achieved 86.72% accuracy for histology class classification, while the patient-level pipeline achieved 83.53% NAC response (pCR vs. RD) prediction accuracy. The histological class pairs with the strongest NAC response predictive ability were tumor and tumor tumor-infiltrating lymphocytes for pCR and microvessel density and polyploid giant cancer cells for RD. Conclusion: Our machine learning pipeline can robustly identify clinically relevant histological classes that predict NAC response in TNBC patients and may help guide patient selection for NAC treatment.

Accuracy of Artificial Intelligence in Estimating Best-Corrected Visual Acuity From Fundus Photographs in Eyes With Diabetic Macular Edema.

Importance: Best-corrected visual acuity (BCVA) is a measure used to manage diabetic macular edema (DME), sometimes suggesting development of DME or consideration of initiating, repeating, withholding, or resuming treatment with anti-vascular endothelial growth factor. Using artificial intelligence (AI) to estimate BCVA from fundus images could help clinicians manage DME by reducing the personnel needed for refraction, the time presently required for assessing BCVA, or even the number of office visits if imaged remotely. Objective: To evaluate the potential application of AI techniques for estimating BCVA from fundus photographs with and without ancillary information. Design, Setting, and Participants: Deidentified color fundus images taken after dilation were used post hoc to train AI systems to perform regression from image to BCVA and to evaluate resultant estimation errors. Participants were patients enrolled in the VISTA randomized clinical trial through 148 weeks wherein the study eye was treated with aflibercept or laser. The data from study participants included macular images, clinical information, and BCVA scores by trained examiners following protocol refraction and VA measurement on Early Treatment Diabetic Retinopathy Study (ETDRS) charts. Main Outcomes: Primary outcome was regression evaluated by mean absolute error (MAE); the secondary outcome included percentage of predictions within 10 letters, computed over the entire cohort as well as over subsets categorized by baseline BCVA, determined from baseline through the 148-week visit. Results: Analysis included 7185 macular color fundus images of the study and fellow eyes from 459 participants. Overall, the mean (SD) age was 62.2 (9.8) years, and 250 (54.5%) were male. The baseline BCVA score for the study eyes ranged from 73 to 24 letters (approximate Snellen equivalent 20/40 to 20/320). Using ResNet50 architecture, the MAE for the testing set (n = 641 images) was 9.66 (95% CI, 9.05-10.28); 33% of the values (95% CI, 30%-37%) were within 0 to 5 letters and 28% (95% CI, 25%-32%) within 6 to 10 letters. For BCVA of 100 letters or less but more than 80 letters (20/10 to 20/25, n = 161) and 80 letters or less but more than 55 letters (20/32 to 20/80, n = 309), the MAE was 8.84 letters (95% CI, 7.88-9.81) and 7.91 letters (95% CI, 7.28-8.53), respectively. Conclusions and Relevance: This investigation suggests AI can estimate BCVA directly from fundus photographs in patients with DME, without refraction or subjective visual acuity measurements, often within 1 to 2 lines on an ETDRS chart, supporting this AI concept if additional improvements in estimates can be achieved.

Predicting neoadjuvant treatment response in triple-negative breast cancer using machine learning.

Background: Neoadjuvant chemotherapy (NAC) is the standard treatment for early-stage triple negative breast cancer (TNBC). The primary endpoint of NAC is a pathological complete response (pCR). NAC results in pCR in only 30%â€"40% of TNBC patients. Tumor-infiltrating lymphocytes (TILs), Ki67 and phosphohistone H3 (pH3) are a few known biomarkers to predict NAC response. Currently, systematic evaluation of the combined value of these biomarkers in predicting NAC response is lacking. In this study, the predictive value of markers derived from H&E and IHC stained biopsy tissue was comprehensively evaluated using a supervised machine learning (ML)-based approach. Identifying predictive biomarkers could help guide therapeutic decisions by enabling precise stratification of TNBC patients into responders and partial or non-responders. Methods: Serial sections from core needle biopsies (n=76) were stained with H&E, and immunohistochemically for the Ki67 and pH3 markers, followed by whole slide image (WSI) generation. The resulting WSI triplets were co-registered with H&E WSIs serving as the reference. Separate mask region-based CNN (MRCNN) models were trained with annotated H&E, Ki67 and pH3 images for detecting tumor cells, stromal and intratumoral TILs (sTILs and tTILs), Ki67 + , and pH3 + cells. Top image patches with a high density of cells of interest were identified as hotspots. Best classifiers for NAC response prediction were identified by training multiple ML models, and evaluating their performance by accuracy, area under curve, and confusion matrix analyses. Results: Highest prediction accuracy was achieved when hotspot regions were identified by tTIL counts and each hotspot was represented by measures of tTILs, sTILs, tumor cells, Ki67 + , and pH3 + features. Regardless of the hotspot selection metric, a complementary use of multiple histological features (tTILs, sTILs) and molecular biomarkers (Ki67 and pH3) resulted in top ranked performance at the patient level. Conclusions: Overall, our results emphasize that prediction models for NAC response should be based on biomarkers in combination rather than in isolation. Our study provides compelling evidence to support the use of ML-based models to predict NAC response in patients with TNBC.

Deep learning based registration of serial whole-slide histopathology images in different stains.

For routine pathology diagnosis and imaging-based biomedical research, Whole-slide image (WSI) analyses have been largely limited to a 2D tissue image space. For a more definitive tissue representation to support fine-resolution spatial and integrative analyses, it is critical to extend such tissue-based investigations to a 3D tissue space with spatially aligned serial tissue WSIs in different stains, such as Hematoxylin and Eosin (H&E) and Immunohistochemistry (IHC) biomarkers. However, such WSI registration is technically challenged by the overwhelming image scale, the complex histology structure change, and the significant difference in tissue appearances in different stains. The goal of this study is to register serial sections from multi-stain histopathology whole-slide image blocks. We propose a novel translation-based deep learning registration network CGNReg that spatially aligns serial WSIs stained in H&E and by IHC biomarkers without prior deformation information for the model training. First, synthetic IHC images are produced from H&E slides through a robust image synthesis algorithm. Next, the synthetic and the real IHC images are registered through a Fully Convolutional Network with multi-scaled deformable vector fields and a joint loss optimization. We perform the registration at the full image resolution, retaining the tissue details in the results. Evaluated with a dataset of 76 breast cancer patients with 1 H&E and 2 IHC serial WSIs for each patient, CGNReg presents promising performance as compared with multiple state-of-the-art systems in our evaluation. Our results suggest that CGNReg can produce promising registration results with serial WSIs in different stains, enabling integrative 3D tissue-based biomedical investigations.

Avapritinib is effective for treatment of minimal residual disease in acute myeloid leukemia with t (8;21) and kit mutation failing to immunotherapy after allogeneic hematopoietic stem cell transplantation.

In patients with t(8;21) acute myeloid leukemia (AML) with recurrent measurable residual disease (MRD) after allogeneic hematopoietic stem cell transplantation (allo-HSCT), pre-emptive interferon-α therapy and donor lymphocyte infusion are noneffective in 30%-50% of patients. Avapritinib is a novel tyrosine kinase inhibitor targeting KIT mutations. We retrospectively report about 20 patients with t(8;21) AML and KIT mutations treated with avapritinib after allo-HSCT with MRD and most failing to respond to immunotherapy. Reduction of RUNX1-RUNX1T1 after 1 month of treatment was ≥1 log in 12 patients (60%), which became negative in 4 patients (20%). In 13 patients who received avapritinib for ≥3 months, the reduction was ≥1 log in all patients, which became negative in 7 patients (53.8%). The median follow-up time was 5.5 (2.0-10.0) months from avapritinib initiation to the last follow-up. Three patients underwent hematologic relapse and survived. Among all 20 patients, RUNX1-RUNX1T1 transcripts turned negative in 9 patients (45%). The efficacy did not differ significantly between D816 and non-D816 KIT mutation groups. The main adverse effect was hematological toxicity, which could generally be tolerated. In summary, avapritinib was effective for MRD treatment in patients with t(8;21) AML with KIT mutations failing to respond to immunotherapy after allo-HSCT.

Unmanipulated haploidentical hematopoietic stem cell transplantation for pediatric de novo acute megakaryoblastic leukemia without Down syndrome in China: A single-center study.

Background: AMKL without DS is a rare but aggressive hematological malignant disease in children, and it is associated with inferior outcomes. Several researchers have regarded pediatric AMKL without DS as high-risk or at least intermediate-risk AML and proposed that upfront allogenic hematopoietic stem cell transplantation (HSCT) in first complete remission might improve long-term survival. Patients and method: We conducted a retrospective study with twenty-five pediatric (< 14 years old) AMKL patients without DS who underwent haploidentical HSCT in the Peking University Institute of Hematology, Peking University People's Hospital from July 2016 to July 2021. The diagnostic criteria of AMKL without DS were adapted from the FAB and WHO: ≥ 20% blasts in the bone marrow, and those blasts expressed at least one or more of the platelet glycoproteins: CD41, CD61, or CD42. AMKL with DS and therapy related AML was excluded. Children without a suitable closely HLA-matched related or unrelated donor (donors with more than nine out of 10 matching HLA-A, HLA-B, HLA-C, HLA-DR, and HLA-DQ loci), were eligible to receive haploidentical HSCT. Definition was adapted from international cooperation group. All statistical tests were conducted with SPSS v.24 and R v.3.6.3. Results: The 2-year OS was 54.5 ± 10.3%, and the EFS was 50.9 ± 10.2% in pediatric AMKL without DS undergoing haplo-HSCT. Statistically significantly better EFS was observed in patients with trisomy 19 than in patients without trisomy 19 (80 ± 12.6% and 33.3 ± 12.2%, respectively, P = 0.045), and OS was better in patients with trisomy 19 but with no statistical significance (P = 0.114). MRD negative pre-HSCT patients showed a better OS and EFS than those who were positive (P < 0.001 and P = 0.003, respectively). Eleven patients relapsed post HSCT. The median time to relapse post HSCT was 2.1 months (range: 1.0-14.4 months). The 2-year cumulative incidence of relapse (CIR) was 46.1 ± 11.6%. One patient developed bronchiolitis obliterans and respiratory failure and died at d + 98 post HSCT. Conclusion: AMKL without DS is a rare but aggressive hematological malignant disease in children, and it is associated with inferior outcomes. Trisomy 19 and MRD negative pre-HSCT might contribute to a better EFS and OS. Our TRM was low, haplo-HSCT might be an option for high-risk AMKL without DS.

Effective and efficient active learning for deep learning-based tissue image analysis.

MOTIVATION: Deep learning attained excellent results in digital pathology recently. A challenge with its use is that high quality, representative training datasets are required to build robust models. Data annotation in the domain is labor intensive and demands substantial time commitment from expert pathologists. Active learning (AL) is a strategy to minimize annotation. The goal is to select samples from the pool of unlabeled data for annotation that improves model accuracy. However, AL is a very compute demanding approach. The benefits for model learning may vary according to the strategy used, and it may be hard for a domain specialist to fine tune the solution without an integrated interface. RESULTS: We developed a framework that includes a friendly user interface along with run-time optimizations to reduce annotation and execution time in AL in digital pathology. Our solution implements several AL strategies along with our diversity-aware data acquisition (DADA) acquisition function, which enforces data diversity to improve the prediction performance of a model. In this work, we employed a model simplification strategy [Network Auto-Reduction (NAR)] that significantly improves AL execution time when coupled with DADA. NAR produces less compute demanding models, which replace the target models during the AL process to reduce processing demands. An evaluation with a tumor-infiltrating lymphocytes classification application shows that: (i) DADA attains superior performance compared to state-of-the-art AL strategies for different convolutional neural networks (CNNs), (ii) NAR improves the AL execution time by up to 4.3×, and (iii) target models trained with patches/data selected by the NAR reduced versions achieve similar or superior classification quality to using target CNNs for data selection. AVAILABILITY AND IMPLEMENTATION: Source code: https://github.com/alsmeirelles/DADA.

Real-time corneal thickness changes during phacoemulsification cataract surgery.

PURPOSE: To observe the changes in corneal thickness during phacoemulsification cataract surgery and to analyze the influencing factors. METHODS: One hundred two patients (102 eyes) with cataracts undergoing phacoemulsification cataract surgery at Shandong Eye Hospital between July and October 2021 were included. Intraoperative OCT was applied to capture real-time images preoperatively, before and after ultrasonic emulsification, at the end of irrigation aspiration and the end of surgery. Then, the corneal thickness at the above time points was measured using Photoshop software. RESULTS: The corneal thickness of 102 cataract patients was 511.79 ± 31.46 µm before operation and 512.71 ± 31.51 µm at the beginning of phacoemulsification, which increased by 0.91 ± 1.48 µm (0.2%). At the end of ultrasonic emulsification, the corneal thickness was 521.58 ± 32.75 µm and 8.87 ± 8.71 µm (1.7%) thicker than that before the procedure. After irrigation aspiration, the corneal thickness reached 528.09 ± 33.87 µm, which increased by 6.52 ± 6.38 µm (1.3%) compared with that of the previous step. At the end of the operation, the corneal thickness was 539.19 ± 33.88 µm, 11.09 ± 10.92 µm, and 27.37 ± 13.64 µm thicker than that of the previous step and the preoperative thickness, respectively, with an overall increase of 5.3%. The differences were statistically significant at all time points (all P < 0.001). Correlation analysis showed that postoperative corneal thickness changes were correlated with age, cataract lens nuclear grade, actual phacoemulsification time (APT), effective phacoemulsification time (EPT), average phacoemulsification energy (APE), total surgery time (TST), cell density (CD), maximum cell area (MAX), and cell area standard deviation (SD) (all P < 0.05), while the changes in thickness were not correlated with gender, cell area coefficient of variation (CV), percentage of hexagonal cells (6A), average cell area (AVE), or minimum cell area (MIN) (all P > 0.05). CONCLUSIONS: During phacoemulsification cataract surgery, corneal thickness gradually increases in real time with the increase of perfusion pressure and intraocular manipulation time. The real-time magnitude of intraoperative corneal thickness change is closely related to lens nucleus hardness, corneal endothelial cell density, ultrasound energy, and time for emulsification.