NACNet: A histology context-aware transformer graph convolution network for predicting treatment response to neoadjuvant chemotherapy in Triple Negative Breast Cancer.

Neoadjuvant chemotherapy (NAC) response prediction for triple negative breast cancer (TNBC) patients is a challenging task clinically as it requires understanding complex histology interactions within the tumor microenvironment (TME). Digital whole slide images (WSIs) capture detailed tissue information, but their giga-pixel size necessitates computational methods based on multiple instance learning, which typically analyze small, isolated image tiles without the spatial context of the TME. To address this limitation and incorporate TME spatial histology interactions in predicting NAC response for TNBC patients, we developed a histology context-aware transformer graph convolution network (NACNet).

Treatment of minimal residual disease in myeloid malignancies after allo-HSCT with venetoclax-based regimens in patients ineligible for or failed in the immunotherapy.

Background: Relapse was the major cause of treatment failure in patients with myeloid malignancies after allogeneic hematopoietic stem cell transplantation (allo-HSCT). Patients who still suffer from the disease while cannot be detected by morphological analysis can be identified by the minimal residual disease (MRD) monitoring. The most used first-line regimens for MRD are immunotherapies.

Distinct Immune Homeostasis Remodeling Patterns after HLA-Matched and Haploidentical Transplantation.

Allogeneic hematopoietic stem cell transplantation (allo-HSCT) is a widely used treatment for a variety of hematopoietic disorders, and also provides a valuable platform for investigating the development of donor-derived immune cells in recipients post-HSCT. The immune system remodels from the donor to the recipient during allo-HSCT. However, little is known about the cell profile alterations as donor homeostasis rebalances to recipient homeostasis following HSCT.

Clinical features and prognostic model for viral encephalitis after allogeneic haematopoietic stem cell transplantation.

The objective of this study was to identify independent prognostic factors of viral encephalitis (VE) after allogeneic haematopoietic stem cell transplantation (allo-HSCT) and establish a prognostic model to identify post-transplant VE patients with a greater likelihood of mortality. Among 5380 patients in our centre from 2014 to 2022, 211 patients who developed VE after allo-HSCT were reviewed in this retrospective study. Prognostic factors were selected, and a prognostic model was constructed using Cox regression analysis.

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.

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.

Entropy and distance-guided super self-ensembling for optic disc and cup segmentation.

Segmenting the optic disc (OD) and optic cup (OC) is crucial to accurately detect changes in glaucoma progression in the elderly. Recently, various convolutional neural networks have emerged to deal with OD and OC segmentation. Due to the domain shift problem, achieving high-accuracy segmentation of OD and OC from different domain datasets remains highly challenging.

Enhancing Clinical Predictive Modeling through Model Complexity-Driven Class Proportion Tuning for Class Imbalanced Data: An Empirical Study on Opioid Overdose Prediction.

Class imbalance issues are prevalent in the medical field and significantly impact the performance of clinical predictive models. Traditional techniques to address this challenge aim to rebalance class proportions. They generally assume that the rebalanced proportions are derived from the original data, without considering the intricacies of the model utilized.

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.

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.

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).

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.

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.

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).

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

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.

Retinal vessel segmentation based on multi-scale feature and style transfer.

Retinal vessel segmentation is very important for diagnosing and treating certain eye diseases. Recently, many deep learning-based retinal vessel segmentation methods have been proposed; however, there are still many shortcomings (e.g.

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.

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.

Effects of organic carbon and subsurface dams on saltwater intrusion and nitrate pollution in sandy coastal aquifers.

This study explores the impact of a novel approach on the levels of SWI (saltwater intrusion) and NO (nitrate) contamination. Some numerical simulations were conducted utilizing a coupled model that incorporates variably saturation and density, as well as convection diffusion reaction within a sandy coastal aquifer. We verified the reliability of the model for SWI based on comparison lab experiments and for chemical reactions based on a comparison of previous in situ observations.

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 30-40% of TNBC patients.

TRPV4-mediated Ca 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 Ca permeable nonselective cation channel, cause TRPV4-related disorders. TRPV4 is widely expressed in the brain; however, the pathogenesis underlying TRPV4-mediated Ca 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.