CNS-Native Myeloid Cells Drive Immune Suppression in the Brain Metastatic Niche through Cxcl10.

Brain metastasis (br-met) develops in an immunologically unique br-met niche. Central nervous system-native myeloid cells (CNS-myeloids) and bone-marrow-derived myeloid cells (BMDMs) cooperatively regulate brain immunity. The phenotypic heterogeneity and specific roles of these myeloid subsets in shaping the br-met niche to regulate br-met outgrowth have not been fully revealed. Applying multimodal single-cell analyses, we elucidated a heterogeneous but spatially defined CNS-myeloid response during br-met outgrowth. We found Ccr2+ BMDMs minimally influenced br-met while CNS-myeloid promoted br-met outgrowth. Additionally, br-met-associated CNS-myeloid exhibited downregulation of Cx3cr1. Cx3cr1 knockout in CNS-myeloid increased br-met incidence, leading to an enriched interferon response signature and Cxcl10 upregulation. Significantly, neutralization of Cxcl10 reduced br-met, while rCxcl10 increased br-met and recruited VISTAHi PD-L1+ CNS-myeloid to br-met lesions. Inhibiting VISTA- and PD-L1-signaling relieved immune suppression and reduced br-met burden. Our results demonstrate that loss of Cx3cr1 in CNS-myeloid triggers a Cxcl10-mediated vicious cycle, cultivating a br-met-promoting, immune-suppressive niche.

Mapping cellular interactions from spatially resolved transcriptomics data.

Cell-cell communication (CCC) is essential to how life forms and functions. However, accurate, high-throughput mapping of how expression of all genes in one cell affects expression of all genes in another cell is made possible only recently through the introduction of spatially resolved transcriptomics (SRT) technologies, especially those that achieve single-cell resolution. Nevertheless, substantial challenges remain to analyze such highly complex data properly. Here, we introduce a multiple-instance learning framework, Spacia, to detect CCCs from data generated by SRTs, by uniquely exploiting their spatial modality. We highlight Spacia's power to overcome fundamental limitations of popular analytical tools for inference of CCCs, including losing single-cell resolution, limited to ligand-receptor relationships and prior interaction databases, high false positive rates and, most importantly, the lack of consideration of the multiple-sender-to-one-receiver paradigm. We evaluated the fitness of Spacia for three commercialized single-cell resolution SRT technologies: MERSCOPE/Vizgen, CosMx/NanoString and Xenium/10x. Overall, Spacia represents a notable step in advancing quantitative theories of cellular communications.

Intelligent Clinic Nurse Scheduling Considering Nurses Paired with Doctors and Preference of Nurses.

The nurse scheduling problem (NSP) has been a crucial and challenging research issue for hospitals, especially considering the serious deterioration in nursing shortages in recent years owing to long working hours, considerable work pressure, and irregular lifestyle, which are important in the service industry. This study investigates the NSP that aims to maximize nurse satisfaction with the generated schedule subject to government laws, internal regulations of hospitals, doctor-nurse pairing rules, shift and day off preferences of nurses, etc. The computational experiment results show that our proposed hybrid metaheuristic outperforms other metaheuristics and manual scheduling in terms of both computation time and solution quality. The presented solution procedure is implemented in a real-world clinic, which is used as a case study. The developed scheduling technique reduced the time spent on scheduling by 93% and increased the satisfaction of the schedule by 21%, which further enhanced the operating efficiency and service quality.

AM-MSFF: A Pest Recognition Network Based on Attention Mechanism and Multi-Scale Feature Fusion.

Traditional methods for pest recognition have certain limitations in addressing the challenges posed by diverse pest species, varying sizes, diverse morphologies, and complex field backgrounds, resulting in a lower recognition accuracy. To overcome these limitations, this paper proposes a novel pest recognition method based on attention mechanism and multi-scale feature fusion (AM-MSFF). By combining the advantages of attention mechanism and multi-scale feature fusion, this method significantly improves the accuracy of pest recognition. Firstly, we introduce the relation-aware global attention (RGA) module to adaptively adjust the feature weights of each position, thereby focusing more on the regions relevant to pests and reducing the background interference. Then, we propose the multi-scale feature fusion (MSFF) module to fuse feature maps from different scales, which better captures the subtle differences and the overall shape features in pest images. Moreover, we introduce generalized-mean pooling (GeMP) to more accurately extract feature information from pest images and better distinguish different pest categories. In terms of the loss function, this study proposes an improved focal loss (FL), known as balanced focal loss (BFL), as a replacement for cross-entropy loss. This improvement aims to address the common issue of class imbalance in pest datasets, thereby enhancing the recognition accuracy of pest identification models. To evaluate the performance of the AM-MSFF model, we conduct experiments on two publicly available pest datasets (IP102 and D0). Extensive experiments demonstrate that our proposed AM-MSFF outperforms most state-of-the-art methods. On the IP102 dataset, the accuracy reaches 72.64%, while on the D0 dataset, it reaches 99.05%.

Compassionate Use of Remdesivir for Patients with Severe Covid-19.

BACKGROUND: Remdesivir, a nucleotide analogue prodrug that inhibits viral RNA polymerases, has shown in vitro activity against SARS-CoV-2. METHODS: We provided remdesivir on a compassionate-use basis to patients hospitalized with Covid-19, the illness caused by infection with SARS-CoV-2. Patients were those with confirmed SARS-CoV-2 infection who had an oxygen saturation of 94% or less while they were breathing ambient air or who were receiving oxygen support. Patients received a 10-day course of remdesivir, consisting of 200 mg administered intravenously on day 1, followed by 100 mg daily for the remaining 9 days of treatment. This report is based on data from patients who received remdesivir during the period from January 25, 2020, through March 7, 2020, and have clinical data for at least 1 subsequent day. RESULTS: Of the 61 patients who received at least one dose of remdesivir, data from 8 could not be analyzed (including 7 patients with no post-treatment data and 1 with a dosing error). Of the 53 patients whose data were analyzed, 22 were in the United States, 22 in Europe or Canada, and 9 in Japan. At baseline, 30 patients (57%) were receiving mechanical ventilation and 4 (8%) were receiving extracorporeal membrane oxygenation. During a median follow-up of 18 days, 36 patients (68%) had an improvement in oxygen-support class, including 17 of 30 patients (57%) receiving mechanical ventilation who were extubated. A total of 25 patients (47%) were discharged, and 7 patients (13%) died; mortality was 18% (6 of 34) among patients receiving invasive ventilation and 5% (1 of 19) among those not receiving invasive ventilation. CONCLUSIONS: In this cohort of patients hospitalized for severe Covid-19 who were treated with compassionate-use remdesivir, clinical improvement was observed in 36 of 53 patients (68%). Measurement of efficacy will require ongoing randomized, placebo-controlled trials of remdesivir therapy. (Funded by Gilead Sciences.).

TGSA: protein-protein association-based twin graph neural networks for drug response prediction with similarity augmentation.

MOTIVATION: Drug response prediction (DRP) plays an important role in precision medicine (e.g. for cancer analysis and treatment). Recent advances in deep learning algorithms make it possible to predict drug responses accurately based on genetic profiles. However, existing methods ignore the potential relationships among genes. In addition, similarity among cell lines/drugs was rarely considered explicitly. RESULTS: We propose a novel DRP framework, called TGSA, to make better use of prior domain knowledge. TGSA consists of Twin Graph neural networks for Drug Response Prediction (TGDRP) and a Similarity Augmentation (SA) module to fuse fine-grained and coarse-grained information. Specifically, TGDRP abstracts cell lines as graphs based on STRING protein-protein association networks and uses Graph Neural Networks (GNNs) for representation learning. SA views DRP as an edge regression problem on a heterogeneous graph and utilizes GNNs to smooth the representations of similar cell lines/drugs. Besides, we introduce an auxiliary pre-training strategy to remedy the identified limitations of scarce data and poor out-of-distribution generalization. Extensive experiments on the GDSC2 dataset demonstrate that our TGSA consistently outperforms all the state-of-the-art baselines under various experimental settings. We further evaluate the effectiveness and contributions of each component of TGSA via ablation experiments. The promising performance of TGSA shows enormous potential for clinical applications in precision medicine. AVAILABILITY AND IMPLEMENTATION: The source code is available at https://github.com/violet-sto/TGSA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

AM3F-FlowNet: Attention-Based Multi-Scale Multi-Branch Flow Network.

Micro-expressions are the small, brief facial expression changes that humans momentarily show during emotional experiences, and their data annotation is complicated, which leads to the scarcity of micro-expression data. To extract salient and distinguishing features from a limited dataset, we propose an attention-based multi-scale, multi-modal, multi-branch flow network to thoroughly learn the motion information of micro-expressions by exploiting the attention mechanism and the complementary properties between different optical flow information. First, we extract optical flow information (horizontal optical flow, vertical optical flow, and optical strain) based on the onset and apex frames of micro-expression videos, and each branch learns one kind of optical flow information separately. Second, we propose a multi-scale fusion module to extract more prosperous and more stable feature expressions using spatial attention to focus on locally important information at each scale. Then, we design a multi-optical flow feature reweighting module to adaptively select features for each optical flow separately by channel attention. Finally, to better integrate the information of the three branches and to alleviate the problem of uneven distribution of micro-expression samples, we introduce a logarithmically adjusted prior knowledge weighting loss. This loss function weights the prediction scores of samples from different categories to mitigate the negative impact of category imbalance during the classification process. The effectiveness of the proposed model is demonstrated through extensive experiments and feature visualization on three benchmark datasets (CASMEII, SAMM, and SMIC), and its performance is comparable to that of state-of-the-art methods.

Globally optimal OCT surface segmentation using a constrained IPM optimization.

Segmentation of multiple surfaces in optical coherence tomography (OCT) images is a challenging problem, further complicated by the frequent presence of weak boundaries, varying layer thicknesses, and mutual influence between adjacent surfaces. The traditional graph-based optimal surface segmentation method has proven its effectiveness with its ability to capture various surface priors in a uniform graph model. However, its efficacy heavily relies on handcrafted features that are used to define the surface cost for the "goodness" of a surface. Recently, deep learning (DL) is emerging as a powerful tool for medical image segmentation thanks to its superior feature learning capability. Unfortunately, due to the scarcity of training data in medical imaging, it is nontrivial for DL networks to implicitly learn the global structure of the target surfaces, including surface interactions. This study proposes to parameterize the surface cost functions in the graph model and leverage DL to learn those parameters. The multiple optimal surfaces are then simultaneously detected by minimizing the total surface cost while explicitly enforcing the mutual surface interaction constraints. The optimization problem is solved by the primal-dual interior-point method (IPM), which can be implemented by a layer of neural networks, enabling efficient end-to-end training of the whole network. Experiments on spectral-domain optical coherence tomography (SD-OCT) retinal layer segmentation demonstrated promising segmentation results with sub-pixel accuracy.

Label-free visualization and characterization of extracellular vesicles in breast cancer.

Despite extensive interest, extracellular vesicle (EV) research remains technically challenging. One of the unexplored gaps in EV research has been the inability to characterize the spatially and functionally heterogeneous populations of EVs based on their metabolic profile. In this paper, we utilize the intrinsic optical metabolic and structural contrast of EVs and demonstrate in vivo/in situ characterization of EVs in a variety of unprocessed (pre)clinical samples. With a pixel-level segmentation mask provided by the deep neural network, individual EVs can be analyzed in terms of their optical signature in the context of their spatial distribution. Quantitative analysis of living tumor-bearing animals and fresh excised human breast tissue revealed abundance of NAD(P)H-rich EVs within the tumor, near the tumor boundary, and around vessel structures. Furthermore, the percentage of NAD(P)H-rich EVs is highly correlated with human breast cancer diagnosis, which emphasizes the important role of metabolic imaging for EV characterization as well as its potential for clinical applications. In addition to the characterization of EV properties, we also demonstrate label-free monitoring of EV dynamics (uptake, release, and movement) in live cells and animals. The in situ metabolic profiling capacity of the proposed method together with the finding of increasing NAD(P)H-rich EV subpopulations in breast cancer have the potential for empowering applications in basic science and enhancing our understanding of the active metabolic roles that EVs play in cancer progression.

CEP164 is essential for efferent duct multiciliogenesis and male fertility.

Cilia are evolutionarily conserved microtubule-based structures that perform diverse biological functions. Cilia are assembled on basal bodies and anchored to the plasma membrane via distal appendages. In the male reproductive tract, multicilia in efferent ducts (EDs) move in a whip-like motion to prevent sperm agglutination. Previously, we demonstrated that the distal appendage protein CEP164 recruits Chibby1 (Cby1) to basal bodies to facilitate basal body docking and ciliogenesis. Mice lacking CEP164 in multiciliated cells (MCCs) (FoxJ1-Cre;CEP164fl/fl) show a significant loss of multicilia in the trachea, oviduct, and ependyma. In addition, we observed male sterility; however, the precise role of CEP164 in male fertility remained unknown. Here, we report that the seminiferous tubules and rete testis of FoxJ1-Cre;CEP164fl/fl mice exhibit substantial dilation, indicative of dysfunctional multicilia in the EDs. We found that multicilia were hardly detectable in the EDs of FoxJ1-Cre;CEP164fl/fl mice although FoxJ1-positive immature cells were present. Sperm aggregation and agglutination were commonly noticeable in the lumen of the seminiferous tubules and EDs of FoxJ1-Cre;CEP164fl/fl mice. In FoxJ1-Cre;CEP164fl/fl mice, the apical localization of Cby1 and the transition zone marker NPHP1 was severely diminished, suggesting basal body docking defects. TEM analysis of EDs further confirmed basal body accumulation in the cytoplasm of MCCs. Collectively, we conclude that male infertility in FoxJ1-Cre;CEP164fl/fl mice is caused by sperm agglutination and obstruction of EDs due to loss of multicilia. Our study, therefore, unravels an essential role of the distal appendage protein CEP164 in male fertility.

Three-dimensional visualization and a deep-learning model reveal complex fungal parasite networks in behaviorally manipulated ants.

Some microbes possess the ability to adaptively manipulate host behavior. To better understand how such microbial parasites control animal behavior, we examine the cell-level interactions between the species-specific fungal parasite Ophiocordyceps unilateralis sensu lato and its carpenter ant host (Camponotus castaneus) at a crucial moment in the parasite's lifecycle: when the manipulated host fixes itself permanently to a substrate by its mandibles. The fungus is known to secrete tissue-specific metabolites and cause changes in host gene expression as well as atrophy in the mandible muscles of its ant host, but it is unknown how the fungus coordinates these effects to manipulate its host's behavior. In this study, we combine techniques in serial block-face scanning-electron microscopy and deep-learning-based image segmentation algorithms to visualize the distribution, abundance, and interactions of this fungus inside the body of its manipulated host. Fungal cells were found throughout the host body but not in the brain, implying that behavioral control of the animal body by this microbe occurs peripherally. Additionally, fungal cells invaded host muscle fibers and joined together to form networks that encircled the muscles. These networks may represent a collective foraging behavior of this parasite, which may in turn facilitate host manipulation.

Decoding Calcium Signaling Dynamics during Drosophila Wing Disc Development.

The robust specification of organ development depends on coordinated cell-cell communication. This process requires signal integration among multiple pathways, relying on second messengers such as calcium ions. Calcium signaling encodes a significant portion of the cellular state by regulating transcription factors, enzymes, and cytoskeletal proteins. However, the relationships between the inputs specifying cell and organ development, calcium signaling dynamics, and final organ morphology are poorly understood. Here, we have designed a quantitative image-analysis pipeline for decoding organ-level calcium signaling. With this pipeline, we extracted spatiotemporal features of calcium signaling dynamics during the development of the Drosophila larval wing disc, a genetic model for organogenesis. We identified specific classes of wing phenotypes that resulted from calcium signaling pathway perturbations, including defects in gross morphology, vein differentiation, and overall size. We found four qualitative classes of calcium signaling activity. These classes can be ordered based on agonist stimulation strength Gαq-mediated signaling. In vivo calcium signaling dynamics depend on both receptor tyrosine kinase/phospholipase C γ and G protein-coupled receptor/phospholipase C ß activities. We found that spatially patterned calcium dynamics correlate with known differential growth rates between anterior and posterior compartments. Integrated calcium signaling activity decreases with increasing tissue size, and it responds to morphogenetic perturbations that impact organ growth. Together, these findings define how calcium signaling dynamics integrate upstream inputs to mediate multiple response outputs in developing epithelial organs.

Single Cells Exhibit Differing Behavioral Phases during Early Stages of Pseudomonas aeruginosa Swarming.

Pseudomonas aeruginosa is among the many bacteria that swarm, where groups of cells coordinate to move over surfaces. It has been challenging to determine the behavior of single cells within these high-cell-density swarms. To track individual cells within P. aeruginosa swarms, we imaged a fluorescently labeled subset of the larger population. Single cells at the advancing swarm edge varied in their motility dynamics as a function of time. From these data, we delineated four phases of early swarming prior to the formation of the tendril fractals characteristic of P. aeruginosa swarming by collectively considering both micro- and macroscale data. We determined that the period of greatest single-cell motility does not coincide with the period of greatest collective swarm expansion. We also noted that flagellar, rhamnolipid, and type IV pilus motility mutants exhibit substantially less single-cell motility than the wild type.IMPORTANCE Numerous bacteria exhibit coordinated swarming motion over surfaces. It is often challenging to assess the behavior of single cells within swarming communities due to the limitations of identifying, tracking, and analyzing the traits of swarming cells over time. Here, we show that the behavior of Pseudomonas aeruginosa swarming cells can vary substantially in the earliest phases of swarming. This is important to establish that dynamic behaviors should not be assumed to be constant over long periods when predicting and simulating the actions of swarming bacteria.

Single molecule sequencing-guided scaffolding and correction of draft assemblies.

BACKGROUND: Although single molecule sequencing is still improving, the lengths of the generated sequences are inevitably an advantage in genome assembly. Prior work that utilizes long reads to conduct genome assembly has mostly focused on correcting sequencing errors and improving contiguity of de novo assemblies. RESULTS: We propose a disassembling-reassembling approach for both correcting structural errors in the draft assembly and scaffolding a target assembly based on error-corrected single molecule sequences. To achieve this goal, we formulate a maximum alternating path cover problem. We prove that this problem is NP-hard, and solve it by a 2-approximation algorithm. CONCLUSIONS: Our experimental results show that our approach can improve the structural correctness of target assemblies in the cost of some contiguity, even with smaller amounts of long reads. In addition, our reassembling process can also serve as a competitive scaffolder relative to well-established assembly benchmarks.

Invasive Pneumococcal Disease Among Immunocompromised Persons: Implications for Vaccination Programs.

BACKGROUND: In 2012/2013, a single dose of 13-valent pneumococcal conjugate vaccine (PCV13) was recommended for immunocompromised adults in the United States and Canada. To assess the potential benefits of this recommendation, we assessed the serotype-specific burden of invasive pneumococcal disease (IPD) among immunocompromised individuals. METHODS: From 1995 to 2012, population-based surveillance for IPD was conducted in Metropolitan Toronto and Peel Region, Canada. Disease incidence and case fatality were measured in immunocompromised populations over time, and the contribution of different serotypes determined. RESULTS: Overall, 2115/7604 (28%) episodes of IPD occurred in immunocompromised persons. IPD incidence was 12-fold higher (95% confidence interval [CI], 8.7-15) in immunocompromised compared to immunocompetent persons; the case fatality rate was elevated in both younger (odds ratio [OR] 1.8) and older (OR 1.3) adults. Use of immunosuppressive medications was associated with a 2.1-2.7 fold increase in the risk of IPD. Five years after PPV23 program implementation, IPD incidence had declined significantly in immunocompromised adults (IRR 0.57, 95% CI, .40-.82). Ten years after pediatric PCV7 authorization, IPD due to PCV7 serotypes had decreased by 90% (95% CI, 77%-96%) in immunocompromised persons of all ages. In 2011/2012, 37% of isolates causing IPD in immunocompromised persons were PCV13 serotypes and 27% were PPV23/not PCV13 serotypes. CONCLUSIONS: Immunocompromised individuals comprised 28% of IPD. Both PPV23 and herd immunity from pediatric PCV7 were associated with reductions in IPD in immunocompromised populations. PCV13 vaccination of immunocompromised adults may substantially reduce the residual burden until herd immunity from pediatric PCV13 is fully established.

Kinetic Transition Networks for the Thomson Problem and Smale's Seventh Problem.

The Thomson problem, arrangement of identical charges on the surface of a sphere, has found many applications in physics, chemistry and biology. Here, we show that the energy landscape of the Thomson problem for N particles with N=132, 135, 138, 141, 144, 147, and 150 is single funneled, characteristic of a structure-seeking organization where the global minimum is easily accessible. Algorithmically, constructing starting points close to the global minimum of such a potential with spherical constraints is one of Smale's 18 unsolved problems in mathematics for the 21st century because it is important in the solution of univariate and bivariate random polynomial equations. By analyzing the kinetic transition networks, we show that a randomly chosen minimum is, in fact, always "close" to the global minimum in terms of the number of transition states that separate them, a characteristic of small world networks.

A seeding-searching-ensemble method for gland segmentation in H&E-stained images.

BACKGROUND: Glands are vital structures found throughout the human body and their structure and function are affected by many diseases. The ability to segment and detect glands among other types of tissues is important for the study of normal and disease processes and helps their analysis and visualization by pathologists in microscopic detail. METHODS: In this paper, we develop a new approach for segmenting and detecting intestinal glands in H&E-stained histology images, which utilizes a set of advanced image processing techniques: graph search, ensemble, feature extraction, and classification. Our method is computationally fast, preserves gland boundaries robustly and detects glands accurately. RESULTS: We tested the performance of our gland detection and segmentation method by analyzing a dataset of over 1700 glands in digitized high resolution clinical histology images obtained from normal and diseased human intestines. The experimental results show that our method outperforms considerably the state-of-the-art methods for gland segmentation and detection. CONCLUSIONS: Our method can produce high-quality segmentation and detection of non-overlapped glands that obey the natural property of glands in histology tissue images. With accurately detected and segmented glands, quantitative measurement and analysis can be developed for further studies of glands and computer-aided diagnosis.

An updated Alzheimer's disease progression model: incorporating non-linearity, beta regression, and a third-level random effect in NONMEM.

Our objective was to expand our understanding of the predictors of Alzheimer's disease (AD) progression to help design a clinical trial on a novel AD medication. We utilized the Coalition Against Major Diseases AD dataset consisting of control-arm data (both placebo and stable background AD medication) from 15 randomized double-blind clinical trials in mild-to-moderate AD patients (4,495 patients; July 2013). Our ADAS-cog longitudinal model incorporates a beta-regression with between-study, -subject, and -residual variability in NONMEM; it suggests that faster AD progression is associated with younger age and higher number of apolipoprotein E type 4 alleles (APOE*4), after accounting for baseline disease severity. APOE*4, in particular, seems to be implicated in the AD pathogenesis. In addition, patients who are already on stable background AD medications appear to have a faster progression relative to those who are not receiving AD medication. The current knowledge does not support a causality relationship between use of background AD medications and higher rate of disease progression, and the correlation is potentially due to confounding covariates. Although causality has not necessarily been demonstrated, this model can inform inclusion criteria and stratification, sample size, and trial duration.

MR2CPPIS: Accurate prediction of protein-protein interaction sites based on multi-scale Res2Net with coordinate attention mechanism.

Proteins play a vital role in various biological processes and achieve their functions through protein-protein interactions (PPIs). Thus, accurate identification of PPI sites is essential. Traditional biological methods for identifying PPIs are costly, labor-intensive, and time-consuming. The development of computational prediction methods for PPI sites offers promising alternatives. Most known deep learning (DL) methods employ layer-wise multi-scale CNNs to extract features from protein sequences. But, these methods usually neglect the spatial positions and hierarchical information embedded within protein sequences, which are actually crucial for PPI site prediction. In this paper, we propose MR2CPPIS, a novel sequence-based DL model that utilizes the multi-scale Res2Net with coordinate attention mechanism to exploit multi-scale features and enhance PPI site prediction capability. We leverage the multi-scale Res2Net to expand the receptive field for each network layer, thus capturing multi-scale information of protein sequences at a granular level. To further explore the local contextual features of each target residue, we employ a coordinate attention block to characterize the precise spatial position information, enabling the network to effectively extract long-range dependencies. We evaluate our MR2CPPIS on three public benchmark datasets (Dset 72, Dset 186, and PDBset 164), achieving state-of-the-art performance. The source codes are available at https://github.com/YyinGong/MR2CPPIS.

A Protein-Context Enhanced Master Slave Framework for Zero-Shot Drug Target Interaction Prediction.

Drug Target Interaction (DTI) prediction plays a crucial role in in-silico drug discovery, especially for deep learning (DL) models. Along this line, existing methods usually first extract features from drugs and target proteins, and use drug-target pairs to train DL models. However, these DL-based methods essentially rely on similar structures and patterns defined by the homologous proteins from a large amount of data. When few drug-target interactions are known for a newly discovered protein and its homologous proteins, prediction performance can suffer notable reduction. In this paper, we propose a novel Protein-Context enhanced Master/Slave Framework (PCMS), for zero-shot DTI prediction. This framework facilitates the efficient discovery of ligands for newly discovered target proteins, addressing the challenge of predicting interactions without prior data. Specifically, the PCMS framework consists of two main components: a Master Learner and a Slave Learner. The Master Learner first learns the target protein context information, and then adaptively generates the corresponding parameters for the Slave Learner. The Slave Learner then perform zero-shot DTI prediction in different protein contexts. Extensive experiments verify the effectiveness of our PCMS compared to state-of-the-art methods in various metrics on two public datasets. The Code and the processed Data will be open once the paper is accepted.