Resistance to immune checkpoint blockade (ICB) therapy represents a formidable clinical challenge limiting the efficacy of immunotherapy. In particular, prostate cancer poses a challenge for ICB therapy due to its immunosuppressive features. A ketogenic diet (KD) has been reported to enhance response to ICB therapy in some other cancer models. However, adverse effects associated with continuous KD were also observed, demanding better mechanistic understanding and optimized regimens for using KD as an immunotherapy sensitizer. In this study, we established a series of ICB-resistant prostate cancer cell lines and developed a highly effective strategy of combining anti-PD1 and anti-CTLA4 antibodies with histone deacetylase inhibitor (HDACi) vorinostat, a cyclic KD (CKD), or dietary supplementation of the ketone body β-hydroxybutyrate (BHB), which is an endogenous HDACi. CKD and BHB supplementation each delayed prostate cancer tumor growth as monotherapy, and both BHB and adaptive immunity were required for the antitumor activity of CKD. Single-cell transcriptomic and proteomic profiling revealed that HDACi and ketogenesis enhanced ICB efficacy through both cancer cell–intrinsic mechanisms, including upregulation of MHC class I molecules, and -extrinsic mechanisms, such as CD8+ T-cell chemoattraction, M1/M2 macrophage rebalancing, monocyte differentiation toward antigen-presenting cells, and diminished neutrophil infiltration. Overall, these findings illuminate a potential clinical path of using HDACi and optimized KD regimens to enhance ICB therapy for prostate cancer. Significance: Optimized cyclic ketogenic diet and 1,3-butanediol supplementation regimens enhance the efficacy of immune checkpoint blockade in prostate cancer through epigenetic and immune modulations, providing dietary interventions to sensitize tumors to immunotherapy.

It has been nearly 2 years since the first case of COVID-19 was reported. Governments worldwide have introduced numerous non-pharmaceutical interventions (NPIs) to combat this disease. Many of these NPIs were designed in response to initial outbreaks but are unsustainable in the long term. Governments are exploring how to adjust their current NPIs to resume normal activities while effectively protecting their population. As one of the most controversial NPIs, the implementation of travel restrictions varies across regions. Some governments have abandoned their previous travel restrictions because of the induced costs to society and on the economy. Other areas, including Hong Kong (Special Administrative Region of China) and Singapore, continue employing these NPIs as a long-term disease prevention tactic. However, the multidimensional impacts of travel restrictions require careful consideration of how to apply restrictions more appropriately. We have proposed an adapted framework to examine Hong Kong and Singapore’s travel restrictions. We aimed to study these two regions’ experiences in balancing disease control efforts with easing the burden on lives and livelihoods. Based on the experiences of Hong Kong and Singapore, we have outlined six policy recommendations to serve as the cornerstone for future research and policy practices.

Integrative analysis of multiple single-cell RNA-sequencing datasets allows for more comprehensive characterizations of cell types, but systematic technical differences between datasets, known as ‘batch effects’, need to be removed before integration to avoid misleading interpretation of the data. Although many batch-effect-removal methods have been developed, there is still a large room for improvement: most existing methods only give dimension-reduced data instead of expression data of individual genes, are based on computationally demanding models and are black-box models and thus difficult to interpret or tune. Here, we present a new batch-effect-removal method called SCIBER (Single-Cell Integrator and Batch Effect Remover) and study its performance on real datasets. SCIBER matches cell clusters across batches according to the overlap of their differentially expressed genes. As a simple algorithm that has better scalability to data with a large number of cells and is easy to tune, SCIBER shows comparable and sometimes better accuracy in removing batch effects on real datasets compared to the state-of-the-art methods, which are much more complicated. Moreover, SCIBER outputs expression data in the original space, that is, the expression of individual genes, which can be used directly for downstream analyses. Additionally, SCIBER is a reference-based method, which assigns one of the batches as the reference batch and keeps it untouched during the process, making it especially suitable for integrating user-generated datasets with standard reference data such as the Human Cell Atlas. SCIBER is publicly available as an R package on CRAN: https://cran.r-project.org/web/packages/SCIBER/. A vignette is included in the CRAN R package. Supplementary data are available at Bioinformatics online.

Advanced prostate cancer (PCa) is overwhelmingly resistant to immune checkpoint blockade (ICB) therapy, representing a formidable clinical challenge. In this study, we developed a syngeneic murine PCa model with acquired ICB resistance. Using this model, synergistic efficacy was achieved by combining anti-PD1 and anti-CTLA4 antibodies with histone deacetylase inhibitor (HDACi) vorinostat, a cyclic ketogenic diet (CKD), or supplementation of ketone body β-hydroxybutyrate (BHB, endogenous HDACi) via 1,3-butanediol-admixed food. CKD and BHB supplementation delayed PCa tumors as monotherapy, and both BHB and adaptive immunity are required for the anti-tumor activity of CKD. Single-cell transcriptomic and proteomic profiling revealed that the HDACi and ketogenesis-enhanced ICB therapy involves cancer-cell-intrinsic (upregulated MHC class I molecules) and extrinsic mechanisms (CD8 ⁺ T cell chemoattraction, M1/M2 macrophage rebalancing, monocyte differentiation toward antigen presenting cells, and diminished neutrophils). Overall, these findings underscore the potential of using HDACi and optimized KD to enhance ICB therapy for PCa.

While foundation transformer-based models developed for gene expression data analysis can be costly to train and operate, a recent approach known as GenePT offers a low-cost and highly efficient alternative. GenePT utilizes OpenAI’s text-embedding function to encode background information, which is in textual form, about genes. However, the closed-source, online nature of OpenAI’s text-embedding service raises concerns regarding data privacy, among other issues. In this paper, we explore the possibility of replacing OpenAI’s models with open-source transformer-based text-embedding models. We identified ten models from Hugging Face that are small in size, easy to install, and light in computation. Across all four gene classification tasks we considered, some of these models have outperformed OpenAI’s, demonstrating their potential as viable, or even superior, alternatives. Additionally, we find that fine-tuning these models often does not lead to significant improvements in performance.

Compute-in-memory accelerators built upon nonvolatile memory devices excel in energy efficiency and latency when performing deep neural network (DNN) inference, thanks to their in-situ data processing capability. However, the stochastic nature and intrinsic variations of nonvolatile memory devices often result in performance degradation during DNN inference. Introducing these nonideal device behaviors in DNN training enhances robustness, but drawbacks include limited accuracy improvement, reduced prediction confidence, and convergence issues. This arises from a mismatch between the deterministic training and nondeterministic device variations, as such training, though considering variations, relies solely on the model’s final output. In this work, inspired by control theory, we propose negative feedback training (NeFT)—a novel concept supported by theoretical analysis—to more effectively capture the multiscale noisy information throughout the network. We instantiate this concept with two specific instances, oriented variational forward (OVF) and intermediate representation snapshot (IRS). Based on device variation models extracted from measured data, extensive experiments show that our NeFT outperforms existing state-of-the-art methods with up to a 45.08% improvement in inference accuracy while reducing epistemic uncertainty, boosting output confidence, and improving convergence probability. These results underline the generality and practicality of our NeFT framework for increasing the robustness of DNNs against device variations. The source code for these two instances is available at https://github.com/YifanQin-ND/NeFT_CIM.