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.

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.

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.

Self-supervised semantic segmentation of retinal pigment epithelium cells in flatmount fluorescent microscopy images.

MOTIVATION: Morphological analyses with flatmount fluorescent images are essential to retinal pigment epithelial (RPE) aging studies and thus require accurate RPE cell segmentation. Although rapid technology advances in deep learning semantic segmentation have achieved great success in many biomedical research, the performance of these supervised learning methods for RPE cell segmentation is still limited by inadequate training data with high-quality annotations. RESULTS: To address this problem, we develop a Self-Supervised Semantic Segmentation (S4) method that utilizes a self-supervised learning strategy to train a semantic segmentation network with an encoder-decoder architecture. We employ a reconstruction and a pairwise representation loss to make the encoder extract structural information, while we create a morphology loss to produce the segmentation map. In addition, we develop a novel image augmentation algorithm (AugCut) to produce multiple views for self-supervised learning and enhance the network training performance. To validate the efficacy of our method, we applied our developed S4 method for RPE cell segmentation to a large set of flatmount fluorescent microscopy images, we compare our developed method for RPE cell segmentation with other state-of-the-art deep learning approaches. Compared with other state-of-the-art deep learning approaches, our method demonstrates better performance in both qualitative and quantitative evaluations, suggesting its promising potential to support large-scale cell morphological analyses in RPE aging investigations. AVAILABILITY AND IMPLEMENTATION: The codes and the documentation are available at: https://github.com/jkonglab/S4_RPE.

Regeneration of the lung alveolus by an evolutionarily conserved epithelial progenitor.

Functional tissue regeneration is required for the restoration of normal organ homeostasis after severe injury. Some organs, such as the intestine, harbour active stem cells throughout homeostasis and regeneration; more quiescent organs, such as the lung, often contain facultative progenitor cells that are recruited after injury to participate in regeneration. Here we show that a Wnt-responsive alveolar epithelial progenitor (AEP) lineage within the alveolar type 2 cell population acts as a major facultative progenitor cell in the distal lung. AEPs are a stable lineage during alveolar homeostasis but expand rapidly to regenerate a large proportion of the alveolar epithelium after acute lung injury. AEPs exhibit a distinct transcriptome, epigenome and functional phenotype and respond specifically to Wnt and Fgf signalling. In contrast to other proposed lung progenitor cells, human AEPs can be directly isolated by expression of the conserved cell surface marker TM4SF1, and act as functional human alveolar epithelial progenitor cells in 3D organoids. Our results identify the AEP lineage as an evolutionarily conserved alveolar progenitor that represents a new target for human lung regeneration strategies.

Anisotropic materials with abnormal Poisson's ratios and acoustic velocities.

Isotropic materials are required to adhere to various mechanical principles due to their limited thermal stability. For instance, it is essential for Poisson's ratio to be within the range of -1 to 0.5, and the longitudinal wave velocity must exceed the transverse wave velocity. Nevertheless, perfect crystals, as anisotropic materials, have the ability to defy conventional rules. Through the integration of high-throughput processes and first-principles calculations, a comprehensive exploration of known materials was conducted, resulting in the establishment of a database featuring an extreme anisotropic mechanism. This included the identification of abnormal Poisson's ratios (with the directional Poisson's ratio ranging from -3.00 to 3.67), the discovery of extreme negative linear compressibility, the determination of the upper and lower limits of the sound velocity, and other associated properties. Several materials with abnormal Poisson's ratios (<-1 or >0.5) were listed, and their peculiar mechanical behavior, wherein the volume decreased counterintuitively with uniaxial tension, was discussed. Finally, this study focused on the velocities of longitudinal and transverse waves, with specific emphasis on materials exhibiting transverse wave velocities that exceeded the longitudinal wave velocities.

Malware Detection for Internet of Things Using One-Class Classification.

The increasing usage of interconnected devices within the Internet of Things (IoT) and Industrial IoT (IIoT) has significantly enhanced efficiency and utility in both personal and industrial settings but also heightened cybersecurity vulnerabilities, particularly through IoT malware. This paper explores the use of one-class classification, a method of unsupervised learning, which is especially suitable for unlabeled data, dynamic environments, and malware detection, which is a form of anomaly detection. We introduce the TF-IDF method for transforming nominal features into numerical formats that avoid information loss and manage dimensionality effectively, which is crucial for enhancing pattern recognition when combined with n-grams. Furthermore, we compare the performance of multi-class vs. one-class classification models, including Isolation Forest and deep autoencoder, that are trained with both benign and malicious NetFlow samples vs. trained exclusively on benign NetFlow samples. We achieve 100% recall with precision rates above 80% and 90% across various test datasets using one-class classification. These models show the adaptability of unsupervised learning, especially one-class classification, to the evolving malware threats in the IoT domain, offering insights into enhancing IoT security frameworks and suggesting directions for future research in this critical area.

Effect of seasonal variations in sea level on residual saltwater removal upstream of subsurface dam in coastal layered heterogeneity aquifers.

Subsurface dams have been recognized as one of the most effective measures for preventing saltwater intrusion. However, it may result in large amounts of residual saltwater being trapped upstream of the dam and take years to decades to remove, which may limit the utilization of fresh groundwater in coastal areas. In this study, field-scale numerical simulations were used to investigate the mechanisms of residual saltwater removal from a typical stratified aquifer, where an intermediate low-permeability layer (LPL) exists between two high-permeability layers, under the effect of seasonal sea level fluctuations. The study quantifies and compares the time of residual saltwater removal (Tre) for constant sea level (CSL) and seasonally varying sea level (FSL) scenarios. The modelling results indicate that, in most cases, seasonal fluctuations in sea level facilitate the dilution of residual saltwater and thus accelerate residual saltwater removal compared to a static sea level scenario. However, accounting for seasonal sea level variations may increase the required critical dam height (the minimum dam height required to achieve complete residual saltwater removal). Sensitivity analyses show that Tre decreases with increasing height of subsurface dam (Hd) under CSL or weaker sea level fluctuation scenarios; however, when the magnitude of sea level fluctuation is large, Tre changes non-monotonically with Hd. Tre decreases with increasing distance between subsurface dam and ocean for both CSL and FSL scenarios. We also found that stratification model had a significant effect on Tre. The increase in LPL thickness for both CSL and FSL scenarios leads to a decrease in Tre and critical dam height. Tre generally shows a non-monotonically decreasing trend as LPL elevation increases. These quantitative analyses provide valuable insights into the design of subsurface dams in complex situations.

Genome-wide identification of GRF transcription factors and their expression profile in stem meristem of broomcorn millet (Panicum miliaceum L.).

To understand the genome-wide information of the GRF family genes in broomcorn millet and their expression profile in the vegetative meristems, bioinformatic methods and transcriptome sequencing were used to analyze the characteristics, physical and chemical properties, phylogenetic relationship, chromosome distribution, gene structure, cis-acting elements and expression profile in stem meristem for the GRF family members. The results showed that the GRF gene family of millet contains 21 members, and the PmGRF gene is unevenly distributed on 12 chromosomes. The lengths of PmGRF proteins vary from 224 to 618 amino acids, and the isoelectric points are between 4.93-9.69. Each member of the family has 1-4 introns and 2-5 exons. The protein PmGRF13 is localized in both the nucleus and chloroplast, and the rest PmGRF proteins are located in the nucleus. Phylogenetic analysis showed that the 21 GRF genes were divided into 4 subfamilies (A,B,C and D) in broomcorn millet. The analysis of cis-acting elements showed that there were many cis-acting elements involved in light response, hormone response, drought induction, low temperature response and other environmental stress responses in the 2000 bp sequence upstream of the GRF genes. Transcriptome sequencing and qRT-PCR analyses showed that the expression levels of PmGRF3 and PmGRF12 in the dwarf variety Zhang778 were significantly higher than those of the tall variety Longmi12 in the internode and node meristems at the jointing stage, while the expression patterns of PmGRF4, PmGRF16 and PmGRF21 were reverse. In addition, the expression levels of PmGRF2 and PmGRF5 in the internode of Zhang778 were significantly higher than Longmi12. The other GRF genes were not or insignificantly expressed. These results indicated that seven genes, PmGRF2, PmGRF3, PmGRF4, PmGRF5, PmGRF12, PmGRF16 and PmGRF21, were related to the formation of plant height in broomcorn millet.

Polyploid giant cancer cell characterization: New frontiers in predicting response to chemotherapy in breast cancer.

Although polyploid cells were first described nearly two centuries ago, their ability to proliferate has only recently been demonstrated. It also becomes increasingly evident that a subset of tumor cells, polyploid giant cancer cells (PGCCs), play a critical role in the pathophysiology of breast cancer (BC), among other cancer types. In BC, PGCCs can arise in response to therapy-induced stress. Their progeny possess cancer stem cell (CSC) properties and can repopulate the tumor. By modulating the tumor microenvironment (TME), PGCCs promote BC progression, chemoresistance, metastasis, and relapse and ultimately impact the survival of BC patients. Given their pro- tumorigenic roles, PGCCs have been proposed to possess the ability to predict treatment response and patient prognosis in BC. Traditionally, DNA cytometry has been used to detect PGCCs.. The field will further derive benefit from the development of approaches to accurately detect PGCCs and their progeny using robust PGCC biomarkers. In this review, we present the current state of knowledge about the clinical relevance of PGCCs in BC. We also propose to use an artificial intelligence-assisted image analysis pipeline to identify PGCC and map their interactions with other TME components, thereby facilitating the clinical implementation of PGCCs as biomarkers to predict treatment response and survival outcomes in BC patients. Finally, we summarize efforts to therapeutically target PGCCs to prevent chemoresistance and improve clinical outcomes in patients with BC.

Effects of melatonin on dopaminergic neuron development via IP3-mediated mitochondrial Ca2+ regulation in autism spectrum disorder.

Melatonin entrainment of suprachiasmatic nucleus-regulating circadian rhythms is mediated by MT1 and MT2 receptors. Melatonin also has neuroprotective and mitochondrial activating effects, suggesting it may affect neurodevelopment. We studied melatonin's pharmacological effects on autism spectrum disorder (ASD) neuropathology. Deciduous tooth-derived stem cells from children with ASD were used to model neurodevelopmental defects and differentiated into dopaminergic neurons (ASD-DNs) with or without melatonin. Without melatonin, ASD-DNs had reduced neurite outgrowth, mitochondrial dysfunction, lower mitochondrial Ca2+ levels, and Ca2+ accumulation in the endoplasmic reticulum (ER) compared to control DNs from typically developing children-derived stem cells. Melatonin enhanced IP3-dependent Ca2+ release from ER to mitochondria, improving mitochondrial function and neurite outgrowth in ASD-DNs. Luzindole, an MT1/MT2 antagonist, blocked these effects. Thus, melatonin supplementation may improve dopaminergic system development in ASD by modulating mitochondrial Ca2+ homeostasis via MT1/MT2 receptors.

A spatial attention guided deep learning system for prediction of pathological complete response using breast cancer histopathology images.

MOTIVATION: Predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in triple-negative breast cancer (TNBC) patients accurately is direly needed for clinical decision making. pCR is also regarded as a strong predictor of overall survival. In this work, we propose a deep learning system to predict pCR to NAC based on serial pathology images stained with hematoxylin and eosin and two immunohistochemical biomarkers (Ki67 and PHH3). To support human prior domain knowledge-based guidance and enhance interpretability of the deep learning system, we introduce a human knowledge-derived spatial attention mechanism to inform deep learning models of informative tissue areas of interest. For each patient, three serial breast tumor tissue sections from biopsy blocks were sectioned, stained in three different stains and integrated. The resulting comprehensive attention information from the image triplets is used to guide our prediction system for prognostic tissue regions. RESULTS: The experimental dataset consists of 26 419 pathology image patches of 1000×1000 pixels from 73 TNBC patients treated with NAC. Image patches from randomly selected 43 patients are used as a training dataset and images patches from the rest 30 are used as a testing dataset. By the maximum voting from patch-level results, our proposed model achieves a 93% patient-level accuracy, outperforming baselines and other state-of-the-art systems, suggesting its high potential for clinical decision making. AVAILABILITY AND IMPLEMENTATION: The codes, the documentation and example data are available on an open source at: https://github.com/jkonglab/PCR_Prediction_Serial_WSIs_biomarkers. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Expanding the Pressure Frontier in Grüneisen Parameter Measurement: Study of Sodium Chloride.

The Grüneisen parameter (γ) is crucial for determining many thermal properties, including the anharmonic effect, thermostatistics, and equation of state of materials. However, the isentropic adiabatic compression conditions required to measure the Grüneisen parameter under high pressure are difficult to achieve. Thus, direct experimental Grüneisen parameter data in a wide range of pressures is sparse. In this work, we developed a new device that can apply pressure (up to tens of GPa) with an extremely short time of about 0.5 ms, confidently achieving isentropic adiabatic compression. Then, we applied our new technique to sodium chloride and measured its Grüneisen parameter, which conforms to previous theoretical predictions. According to our obtained sodium chloride Grüneisen parameters, the calculated Hugoniot curve of the NaCl B1 phase appears up to 20 GPa and 960 K, which compares very well with the shock compression experimental data by Fritz et al. and other calculation works. Our results suggest that this new method can reliably measure the Grüneisen parameter of even more materials, which is significant for researching the equation of state in substances.

Banff Digital Pathology Working Group: Image Bank, Artificial Intelligence Algorithm, and Challenge Trial Developments.

The Banff Digital Pathology Working Group (DPWG) was established with the goal to establish a digital pathology repository; develop, validate, and share models for image analysis; and foster collaborations using regular videoconferencing. During the calls, a variety of artificial intelligence (AI)-based support systems for transplantation pathology were presented. Potential collaborations in a competition/trial on AI applied to kidney transplant specimens, including the DIAGGRAFT challenge (staining of biopsies at multiple institutions, pathologists' visual assessment, and development and validation of new and pre-existing Banff scoring algorithms), were also discussed. To determine the next steps, a survey was conducted, primarily focusing on the feasibility of establishing a digital pathology repository and identifying potential hosts. Sixteen of the 35 respondents (46%) had access to a server hosting a digital pathology repository, with 2 respondents that could serve as a potential host at no cost to the DPWG. The 16 digital pathology repositories collected specimens from various organs, with the largest constituent being kidney (n = 12,870 specimens). A DPWG pilot digital pathology repository was established, and there are plans for a competition/trial with the DIAGGRAFT project. Utilizing existing resources and previously established models, the Banff DPWG is establishing new resources for the Banff community.

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.

Foveal blur-boosted segmentation of nuclei in histopathology images with shape prior knowledge and probability map constraints.

MOTIVATION: In most tissue-based biomedical research, the lack of sufficient pathology training images with well-annotated ground truth inevitably limits the performance of deep learning systems. In this study, we propose a convolutional neural network with foveal blur enriching datasets with multiple local nuclei regions of interest derived from original pathology images. We further propose a human-knowledge boosted deep learning system by inclusion to the convolutional neural network new loss function terms capturing shape prior knowledge and imposing smoothness constraints on the predicted probability maps. RESULTS: Our proposed system outperforms all state-of-the-art deep learning and non-deep learning methods by Jaccard coefficient, Dice coefficient, Accuracy and Panoptic Quality in three independent datasets. The high segmentation accuracy and execution speed suggest its promising potential for automating histopathology nuclei segmentation in biomedical research and clinical settings. AVAILABILITY AND IMPLEMENTATION: The codes, the documentation and example data are available on an open source at: https://github.com/HongyiDuanmu26/FovealBoosted. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Monitoring of post-transplant MLL-PTD as minimal residual disease can predict relapse after allogeneic HSCT in patients with acute myeloid leukemia and myelodysplastic syndrome.

BACKGROUND: The mixed-lineage leukemia (MLL) gene is located on chromosome 11q23. The MLL gene can be rearranged to generate partial tandem duplications (MLL-PTD), which occurs in about 5-10% of acute myeloid leukemia (AML) with a normal karyotype and in 5-6% of myelodysplastic syndrome (MDS) patients. Allogeneic hematopoietic stem cell transplantation (allo-HSCT) is currently one of the curative therapies available for AML and MDS with excess blasts (MDS-EB). However, how the prognosis of patients with high levels of MLL-PTD after allo-HSCT, and whether MLL-PTD could be used as a reliable indicator for minimal residual disease (MRD) monitoring in transplant patients remains unknown. Our study purposed to analyze the dynamic changes of MLL-PTD peri-transplantation and the best threshold for predicting relapse after transplantation. METHODS: We retrospectively collected the clinical data of 48 patients with MLL-PTD AML or MDS-EB who underwent allo-HSCT in Peking University People's Hospital. The MLL-PTD was examined by real-time quantitative polymerase chain reaction (RQ-PCR) at the diagnosis, before transplantation and the fixed time points after transplantation. Detectable MLL-PTD/ABL > 0.08% was defined as MLL-PTD positive in this study. RESULTS: The 48 patients included 33 AML patients and 15 MDS-EB patients. The median follow-up time was 26(0.7-56) months after HSCT. In AML patients, 7 patients (21.2%) died of treatment-related mortality (TRM), 6 patients (18.2%) underwent hematological relapse and died ultimately. Of the 15 patients with MDS-EB, 2 patients (13.3%) died of infection. The 3-year cumulative incidence of relapse (CIR), overall survival (OS), disease-free survival (DFS) and TRM were 13.7 ± 5.2, 67.8 ± 6.9, 68.1 ± 6.8 and 20.3% ± 6.1%, respectively. ROC curve showed that post-transplant MLL-PTD ≥ 1.0% was the optimal cut-off value for predicting hematological relapse after allo-HSCT. There was statistical difference between post-transplant MLL-PTD ≥ 1.0% and MLL-PTD < 1.0% groups (3-year CIR: 75% ± 15.3% vs. 0%, P < 0.001; 3-year OS: 25.0 ± 15.3% vs. 80.7% ± 6.6%, P < 0.001; 3-year DFS: 25.0 ± 15.3% vs. 80.7 ± 6.6%, P < 0.001; 3-year TRM: 0 vs. 19.3 ± 6.6%, P = 0.277). However, whether MLL-PTD ≥ 1% or MLL-PTD < 1% before transplantation has no significant difference on the prognosis. CONCLUSIONS: Our study indicated that MLL-PTD had a certain stability and could effectively reflect the change of tumor burden. The expression level of MLL-PTD after transplantation can serve as an effective indicator for predicting relapse.

Effect of "timing it right" on comprehensive unmet needs and psychological pain in patients with head and neck cancer undergoing radiotherapy: a randomized controlled trial.

PURPOSE: We assessed the effectiveness of "timing it right" (TIR) applications in patients undergoing radiotherapy for head and neck cancer through a carefully designed TIR intervention program. The assessment in this study emphasized the impact of the TIR intervention on the unmet needs and psychological pain of patients with head and neck cancer at different stages of radiotherapy. METHODS: In total, 140 radiotherapy patients were randomly recruited into two study groups: (1) the TIR intervention group received routine nursing follow-up and comprehensive nursing intervention based on TIR, and (2) the control group received routine nursing care. Assessments were conducted at baseline, before discharge, and 3 months and 6 months after discharge. The comprehensive needs and psychological pain of patients with radiotherapy were measured using the Comprehensive Needs Assessment Tool in Cancer for Patients (CNAT) and the Distress Thermometer (DT), respectively. A linear mixed model was applied to analyze the effects. RESULTS: A total of 137 patients completed the study. Compared to the control group, the TIR group showed significant improvements in information needs, health and psychological problems, healthcare staff, physical symptoms, health facilities and services, religious/spiritual support, and psychological pain (F=8.503, p=0.004; F=1.896, p=0.003; F=12.422, p<0.001; F=9.634, p=0.001; F=7.310, p=0.006; F=1.684, p=0.009; F=1.692, p=0.041). In addition to practical support, the remaining indicators differed significantly by time point, group, and group-time interaction. CONCLUSIONS: The patient support application based on TIR can effectively address unmet needs and improve psychological pain, supporting TIR as an effective psychological management and intervention strategy for radiotherapy patients in the early stage of long-term rehabilitation. TRIAL REGISTRATION: Chictr.org.cn Chi CTR2100047960.

A Case Series Exploration of Multi-Regional Expression Heterogeneity in Triple-Negative Breast Cancer Patients.

Extensive intratumoral heterogeneity (ITH) is believed to contribute to therapeutic failure and tumor recurrence, as treatment-resistant cell clones can survive and expand. However, little is known about ITH in triple-negative breast cancer (TNBC) because of the limited number of single-cell sequencing studies on TNBC. In this study, we explored ITH in TNBC by evaluating gene expression-derived and imaging-derived multi-region differences within the same tumor. We obtained tissue specimens from 10 TNBC patients and conducted RNA sequencing analysis of 2-4 regions per tumor. We developed a novel analysis framework to dissect and characterize different types of variability: between-patients (inter-tumoral heterogeneity), between-patients across regions (inter-tumoral and region heterogeneity), and within-patient, between-regions (regional intratumoral heterogeneity). We performed a Bayesian changepoint analysis to assess and classify regional variability as low (convergent) versus high (divergent) within each patient feature (TNBC and PAM50 subtypes, immune, stroma, tumor counts and tumor infiltrating lymphocytes). Gene expression signatures were categorized into three types of variability: between-patients (108 genes), between-patients across regions (183 genes), and within-patients, between-regions (778 genes). Based on the between-patient gene signature, we identified two distinct patient clusters that differed in menopausal status. Significant intratumoral divergence was observed for PAM50 classification, tumor cell counts, and tumor-infiltrating T cell abundance. Other features examined showed a representation of both divergent and convergent results. Lymph node stage was significantly associated with divergent tumors. Our results show extensive intertumoral heterogeneity and regional ITH in gene expression and image-derived features in TNBC. Our findings also raise concerns regarding gene expression based TNBC subtyping. Future studies are warranted to elucidate the role of regional heterogeneity in TNBC as a driver of treatment resistance.

Profiles of NK cell subsets are associated with successful tyrosine kinase inhibitor discontinuation in chronic myeloid leukemia and changes following interferon treatment.

Recent studies have shown that approximately 50% of patients with chronic myeloid leukemia (CML) receiving tyrosine kinase inhibitor (TKI) therapy with a sustained deep molecular response (DMR) (BCR-ABL1IS ≤ 0.01%) can achieve treatment-free remission (TFR, stopping TKI without relapse) and that prior interferon (IFN)-α therapy and higher NK cell counts at and after TKI discontinuation are associated with TFR. We recently reported that post-TKI discontinuation of IFN-α therapy could prevent molecular relapse (MR, BCR-ABL1IS > 0.1%). Here, we evaluated whether NK cells are associated with MR and investigated the effects of post-TKI discontinuation IFN-α therapy on lymphocyte subsets. A total of 34 patients measuring blood lymphocyte subclasses were included. In the 22 patients who did not receive IFN-α therapy, at 1 month after TKI discontinuation, the nonrelapsed patients showed a significantly higher proportion and count of NK cells than the relapsed patients. In particular, the proportion and count of CD56dim NK cells were significantly higher in the nonrelapsed patients than in the relapsed patients. In the 12 patients who received IFN-α therapy, the level of CD56bright NK cells increased significantly after 3 and 6 months of IFN-α therapy. In summary, NK cells, in particular CD56dim NK cells, were associated with MR after TKI discontinuation in patients with CML. Additionally, IFN-α therapy gradually increased the level of CD56bright NK cells in patients with CML.