Introduction: Reliable biomarkers are in need to predict the prognosis of hepatocellular carcinoma (HCC). Whilst recent evidence has established the critical role of copper homeostasis in tumor growth and progression, no previous studies have dealt with the copper-related genes (CRGs) signature with prognostic potential in HCC.Methods: To develop and validate a CRGs prognostic signature for HCC, we retrospectively included 353 and 142 patients as the development and validation cohort, respectively. Copper-related Prognostic Signature (Copper-PSHC) was developed using differentially expressed CRGs with prognostic value. The hazard ratio (HR) and the area under the time-dependent receiver operating characteristic curve (AUC) during 3-year follow-up were utilized to evaluate the performance. Additionally, the Copper-PSHC was combined with age, sex, and cancer stage to construct a Copper-clinical-related Prognostic Signature (Copper-CPSHC), by multivariate Cox regression. We further explored the underlying mechanism of Copper-PSHC by analyzing the somatic mutation, functional enrichment, and tumor microenvironment. Potential drugs for the high-risk group were screened.Results: The Copper-PSHC was constructed with nine CRGs. Patients in the high-risk group demonstrated a significantly reduced overall survival (OS) . The Copper-PSHC achieved a 3-year AUC of 0.74  and 0.71  for OS in the development and validation cohort, respectively. Copper-CPSHC yield a 3-year AUC of 0.73  and 0.72  for OS in the development and validation cohort, respectively. Higher tumor mutation burden, downregulated metabolic processes, hypoxia status and infiltrated stroma cells were found for the high-risk group. Six small molecular drugs were screened for the treatment of the high-risk group.Conclusion: Copper-PSHC services as a promising tool to identify HCC with poor prognosis and to improve disease outcomes by providing potential clinical decision support in treatment. Liver cancer is the second leading cause of cancer-related death and the seventh most common cancer worldwide . HepatocATP7A, ATP7B, SLC31A1, and SLC31A2, were also found to be associated with poor prognosis in HCC patients  which regulate copper metabolisms including copper homeostasis, cuproptosis and copper binding  serve asTo construct a CRG-based prognostic signature , we retrospectively analyzed the RNA sequencing data from two public HCC cohorts. The overall study design was depicted in p < 0.05 chosen as the significance threshold, to determine the DEGs having prognostic value. Those prognostic DEGs measured in both cohorts were included to build the Copper-PSHC. Thereafter, we used the String Interaction Network to demonstrate the association between these genes , and the secondary endpoint was disease-free survival (DFS) which was not evaluated in the validation cohort owing to a lack of information on tumor recurrence. Proportional hazard assumption was not violated . The proSimilarly, we performed univariable analysis for Copper-CPSHC using Kaplan-Meier curve and compared the RMSTs of two risk groups. The performance of Copper-CPSHC was also evaluated by HR (in binary scenario) and AUC of time-dependent ROC curve at 3 years of follow-up (in continuous form). The c-index was calculated. Additionally, calibration curves were depicted to characterize the discrimination of Copper-CPSHC. Decision curve analysis (DCA) was applied to measure the net benefits, which was compared with tumor stage and the clinical model. Details of Copper-CPSHC validation can be found in In an attempt to explore the somatic mutations in high- and low-risk groups determined by Copper-PSHC in the development cohort, gene mutation data   were anaWe performed the functional enrichment analysis in both the development and validation cohorts. Biological function and pathways regarding Copper-PSHC were analyzed based on Gene Ontology (GO)  and the The correlation between Copper-PSHC and the tumor microenvironment (TME), which is comprised primarily of immune cells and stromal cells, was investigated in both the development and validation cohorts. We first applied ESTIMATE  algorithp < 0.005 as the significance level . To compare the difference in proportions, Chi-square test was implemented. Student’s  package  and c-inp < 0.05, ALB and LOX were identified as hub genes  and high-risk (risk score ≥ −0.021) groups .Copper-PSHC demonstrated outstanding prognostic value. In terms of the primary endpoint, patients from the high-risk group demonstrated a significantly reduced OS . After aTime-dependent ROC curves for OS and DFS exhibited an excellent discriminative power of Copper-PSHC at 3 years of follow-up . In devep < 0.001), margin residual (p = 0.021) and higher tumor grade , as shown in A higher risk score was observed in patients with undesirable biological behaviors or processes, including more advanced TMN stage  and low-risk (<0.677) groups according to the optimal cut-off determined by The significant prolonged OS was observed among the low-risk group in the development cohort  and validation cohort  , with thWe then constructed a nomogram to provide a handy quantitative instrument for clinical use . The calp < 0.01). The TMB was significantly higher for patients in the high-risk group , a well-known carcinogenic gene of P53 pathway, and DOCK2 , an intercellular regulator of the Rho family GTPase, RAC1  and −0.56 (p < 0.001), respectively. That echoed the observation of high stroma cell infiltration in the low-risk group (both p < 0.001). The enrichment of stroma cells in low-risk group is primarily driven by (pre-) adipocytes, pericytes, lymphatic endothelial cells, and skeletal muscle cells  were highly infiltrated among the low-risk group, while CD4+T cells memory activated, natural killer T cell (NKT), type 2T helper cell (Th2) were enriched among the high-risk group  expression and good prognosis . AnotherImportantly, we explored the association between Copper-PSHC and tumor microenvironment. Overall, more infiltration of stromal cells was found in the low-risk group, suggesting their low degree of tumor purity and well differentiation. Although no significant difference in overall immune cell infiltration was observed between high- and low-risk groups, great disparities exist for certain types of immune cells. Patients in the high-risk group were prone to possess more activated cells and T helper cells. Generally, the infiltration of immune cells including T cells, macrophages, and B cells would conduct to the desirable prognosis , especiaApart from the advantages and implications of this study as discussed above, several limitations also require due consideration. Firstly, restricted to the retrospective study, we may introduce the selection bias such as the exclusion of some patients who lacked data or were ineligible for sequencing, particularly those patients who are unable to undertake surgery treatment due to comorbidities or tumor metastasis. Secondly, the sample size was not considered large enough owing to the limited number of HCC patients in advanced stage. Further optimization of the Copper-PSHC index taking the stages of patient population into account is warranted for future studies. Thirdly, due to the unavailability of other clinical data including treatment history, comorbidities and laboratory values, and radiomic data, we were unable to incorporate more variables into our composite model. Notwithstanding, the index Copper-CPSHC developed in this study already owns satisfactory prognostic prediction value. Fourthly, with an aim of ensuring the robustness of results in tumor microenvironmental analysis, most immune cells were analyzed by two or more algorithms. However, certain cells, stromal cells for instance, were exclusively analyzed by xCell. Fifthly, the screening of potential drugs in this study is explorative and more efforts are warranted to further validate these finding in future studies. Lastly, the validation was performed using the public cohorts and we only provided a brief discussion of the potential mechanism of the genes in the model. More prospective studies and further exploration of the biological mechanism in the context of copper and HCC was in warranted.In conclusion, this study constructed a copper-related signature, Copper-PSHC, based on nine CRGs, which has been subsequently demonstrated to be a reliable biomarker for prognostic prediction. We then move forward to examine the hypothesis of metabolic process and tumor immunity being the mechanisms of this signature, illuminating the potential of certain small molecular drugs and immunotherapy for better management of HCC patients. It is envisaged that further investigation using different research tools will help to elucidate the underlying mechanism and verify its clinical utility in the real world.


Electrocatalytic materials are pivotal for clean chemical production and energy conversion in devices like electrolyzers and fuel cells. These materials usually consist of metallic nanoparticles which serve as active reaction sites, and support materials which provide high surface area, conductivity and stability. When designing novel electrocatalytic composites, the focus is often on the metallic sites, however, the significance of the support should not be overlooked. Carbon materials, valued for their conductivity and large surface area, are commonly used as support in benchmark electrocatalysts. However, using alternative support materials instead of carbon can be beneficial in certain cases. In this minireview, we summarize recent advancements and key directions in developing novel supports for electrocatalysis, encompassing both carbon and non-carbon materials. In this case, the biomolecule of DNA gives the sites for metal ion adsorption leading to the excellent activity for HER in the whole pH range.Combining the porous structure of graphene and doping with heteroatoms can significantly improve catalyst performance . Karaman3O4/rGO catalysts by recycling spent lithium-ion batteries. The activity of Co3O4/rGO for oxygen reduction reaction (ORR) is similar to that of commercial Pt/C but with improved stability. High conductivity of graphene facilitates electron transfer at the interface  and provCNTs are nanometer-sized cylindrical structures, which can be either single-walled (SW) or multi-walled (MW), depending on the number of concentric layers. Due to their chemical stability, they can be used in different pH environments. High electrical conductivity and large surface area of CNTs enable efficient electron transfer and more active sites  making tRegarding heteroatom doping, nitrogen-doped CNTs (N-CNTs) are the most widely studied. For example, excellent catalysis was obtained on N-CNTs supporting Pt-Ru for methanol oxidation reaction (MOR) . SimilarIn addition to graphene and CNTs, various other carbon-based materials have potential as catalyst substrates. Carbon nanofibers (CNFs) are similar to CNTs in structure but have a more disordered arrangement. Carbon aerogels (CA) are 3D structures that can be organic-based, graphitic materials-based, or biomass-based . The aerAnother strategy to create catalysts with excellent corrosion resistance involves the combination of fullerene C60 and platinum. The attachment of Pt clusters onto two-dimensional fullerene nanosheets , as wellDespite the mentioned advantages and feasibility of carbon supports, there are cases where alternative materials are necessary. In this section, we will explore recent advancements in the design of non-carbon supports for electrocatalysis.2, a cost-effective, low-toxicity semiconductor with excellent corrosion resistance. To address insufficient conductivity, Kwon et al. developed a TiO2/C composite by covering carbon with a sub-nanometer-thick TiO2 layer, which enhanced the durability of carbon support and the performance of supported Pt for ORR through MSI  are attractive in electrocatalysis due to their (electro)chemical stability and ability to trigger metal-support interaction (MSI) with active sites . Howeverough MSI . MSI weaposition . This led anodes . A similmposites . In summ2 conductivity is transition metal doping. For instance, incorporating W into TiO2 generates a highly conductive composite with a high surface area (2 (Pt/W-TiO2) exhibited significantly improved ORR activity compared to undoped Pt/TiO2 and surpassed the durability of Pt/C . Initial work by Bele et al. showed that TiON is a conductive material capable of anchoring small-sized Ir nanoparticles (Our group has developed a way to increase the conductivity of TiOarticles . The OERarticles . Anotherarticles . We havearticles . This coarticles  exhibitearticles . More rearticles : i) adjuarticles .2) is another interesting material in this context. Sasaki et al. demonstrated that Pt/SnO2 composite is more stable than Pt/C in fuel cell operations and that doping of SnO2 with hypervalent anions  can increase its conductivity  also found application in electrocatalysis research. Recent findings reveal that Pt/WO3 has remarkable HER activity, attributed to electron transfer from the support to Pt sites, leading to the adjustment of the free energy of H adsorption , enabling supported Pt to catalyze HOR. In contrast, HxWO3-x reverts to insulating WO3 in an oxygen atmosphere and suppresses ORR. Such selective catalysis of Pt-materials is beneficial for the fuel cell lifespan.Tungsten-oxide , the conductivity of TAP increases, allowing supported Pt to run HOR/HER, while at higher potentials limited TAP conductivity prevents Pt/TAP from running ORR (Our group recently explored tris(aza)pentacene (TAP) as a support for Pt nanoparticles . TAP belning ORR . Additioning ORR .This mini-review provides a concise overview of recent advancements in designing support materials for electrocatalysis. High surface area carbon-based materials have shown their versatility in electrocatalysis but also have room for further improvements. The most important challenge is in optimizing the activity of supported nanoparticles and enhancing the carbon corrosion resistance to prevent degradation of the catalytic composites. High-temperature treatments that lead to graphitization offer a path to more stable materials, but this comes with the trade-off of potentially reducing catalytic activity by removing defects and heteroatoms. Strategies for manufacturing advanced carbon-based materials will be the focus of future research. Despite all advantages, in some cases, however, it is necessary to replace carbon with some alternative supports. After addressing their low conductivity, TMOs offer several key advantages over carbon in tackling specific challenges in energy conversion devices. Their corrosion resistance enables the design of supported nanocatalysts for OER, which significantly improves the utilization of precious and scarce metals such as Ir and Ru. The ability to induce MSI and to improve the activity and durability of supported active sites can be used to create advanced electrocatalysts for a range of reactions. Materials such as ATO and TiON are certainly TMO derivates with interesting features as electrocatalytic supports for different reactions. Further work is needed to additionally understand the MSI provided by these supports for each particular case , which will allow more straightforward tailoring of the advanced electrocatalytic composites. Similar is valid for nitrides, carbides, MXenes, and organic matrices, which can be used for reactions in both fuel cells and electrolyzers.etc.) is in its early stages, offering ample opportunities for the synthesis of advanced electrocatalytic composites. In the case of non-carbon-based materials, the choice of support can be guided by the specific demand in front of the catalyst.Overall, we anticipate that carbon materials will remain a key focus in electrocatalysis, particularly in cases when they provide sufficient durability for stable catalyst operation. Non-carbon supports are foreseen to play a role when a specific issue is to be addressed (such as carbon corrosion) and when additional enhancement of the performance of active sites via MSI is desired. While TMOs, nitrides, and carbides are already well-known as supports for electrocatalysis, the research on MXenes and a few other types of 2D materials (phosphorenes, boridenes,


Three-dimensional LiDAR systems that capture point cloud data enable the simultaneous acquisition of spatial geometry and multi-wavelength intensity information, thereby paving the way for three-dimensional point cloud recognition and processing. However, due to the irregular distribution, low resolution of point clouds, and limited spatial recognition accuracy in complex environments, inherent errors occur in classifying and segmenting the acquired target information. Conversely, two-dimensional visible light images provide real-color information, enabling the distinction of object contours and fine details, thus yielding clear, high-resolution images when desired. The integration of two-dimensional information with point clouds offers complementary advantages. In this paper, we present the incorporation of two-dimensional information to form a multi-modal representation. From this, we extract local features to establish three-dimensional geometric relationships and two-dimensional color relationships. We introduce a novel network model, termed MInet (Multi-Information net), which effectively captures features relating to both two-dimensional color and three-dimensional pose information. This enhanced network model improves feature saliency, thereby facilitating superior segmentation and recognition tasks. We evaluate our MInet architecture using the ShapeNet and ThreeDMatch datasets for point cloud segmentation, and the Stanford dataset for object recognition. The robust results, coupled with quantitative and qualitative experiments, demonstrate the superior performance of our proposed method in point cloud segmentation and object recognition tasks. As autonomous driving and unmanned systems increase in application, the importance of environmental modeling, recognition, segmentation, and other processing tasks has escalated. Additionally, high spatial precision in complex environments has become vital, particularly for autonomous driving . ConsequAt present, established processing algorithms for LiDAR point clouds encompass PointNet , PointNeThe principal contributions of this research include:(1) The introduction of a novel 2D image feature, RGB normals, which estimates the RGB normals at a point based on the RGB values from neighboring points. This feature functions as a rendering effect in the surrounding environment, thereby further enhancing the collection of color information from neighboring points.(2) A novel end-to-end MInet for LiDAR point cloud recognition tasks is proposed in this study. The innovative aspect of this network is that it enhances the feature extraction of 2D data on the basis of 3D data, and integrates the features of both as multi-element information features for subsequent point cloud processing.(3) Comprehensive comparisons investigating the feasibility of multi-modal information in point cloud processing technology, thereby demonstrating the superior performance of the proposed architecture.In The pioneering PointNet model, brought forward by Charles R. Qi et al. at Stanford University , revolutFurthering the discourse in this field, Klokov and Lempitsky  presenteIn a similar vein, Li et al. proposed PointCNN , an elegAddressing the potential loss of graph structural information during transitions and the amalgamation of redundant information, Bo Wu offered a solution with his Dynamic Graph CNN (DGCNN) . This neDespite these strides in point cloud processing, challenges persist when dealing with diverse complex environments or situations demanding higher accuracy, often requiring complex parameter tuning. This process is not only time-intensive but also involves considerable technical effort to generate desired processing results from filtered point cloud data. To address this, our study proposes a multi-information-based deep point cloud processing algorithm. The enhanced neural network model uniquely separates the 3D geometric and 2D color information of the target object from the input data, forming distinct point cloud features after processing. Empirical results indicate that the proposed multi-information processing approach demonstrates robust performance across various environmental conditions and requires minimal manual intervention. Consequently, our user-friendly and efficient method stands as a promising algorithmic model in this field.This experiment capitalizes on multi-modal information for object feature extraction, and implements a novel local feature descriptor to enhance the accuracy and robustness of this extraction process. In this context, we propose a network model dubbed MInet, which accepts parallel inputs of multi-modal information. We utilize two network architectures to separately extract information from the three-dimensional and two-dimensional features of the target point cloud. In the following section, we will detail the feature extraction process for each dimension separately.Initially, the model ingests a dataset formatted as ng (FPS)  algorithC as follows:Building on the foundation of two-dimensional information, we propose the utilization of color normal vectors to encapsulate the two-dimensional color information. Color normals, serving as a specific type of normal vector, embody the influence of the surrounding colors on a point’s color information. This study also carried out the process of obtaining color normal vector for two-dimensional color features. We interpret the task of normal vector estimation as the projection of a normal vector tangent to the surface. Consequently, the problem of normal vector estimation is reformulated as a least-squares plane fitting estimation problem. Through an examination of the eigenvectors and eigenvalues of a covariance matrix , we simk is the nearest k points to j-th eigenvalue;j-th eigenvalue vector.Based on the neighborhood support estimation from the In previous studies, two-dimensional features are often used to discriminate data processed with three-dimensional features. However, relying on a single-dimensional feature has proven to be inadequate in addressing all potential scenarios. Consequently, in this study, we propose a method that concurrently processes the target using both two-dimensional and three-dimensional features. This methodology bolsters the robustness of our experiment in terms of target segmentation and recognition. Based on this, our experiment proposes the process framework shown in This model does not employ feature transformation matrices; hence, this study resorts to the cross-entropy loss function  for our OA) [mIoU) [recall [precision [f1 score [In order to provide a quantitative comparison and analysis of the classification results for LiDAR point clouds, we employ several evaluation metrics. These include overall accuracy (OA) , mean in) [mIoU) , recall  [recall , precisirecision , and thef1 score .The metrics are defined as follows:OA), average crossover and association (mIoU), recall, precision, and f1 scores.All the experiments were implemented in the ubuntu18.04 environment using a single GPU. A total of 200 epochs were used with 0.1 momentum and 0.001 initial learning rate as training parameters. Set batch_size to 8 for hardware performance. i7-12700, 2.10 GHz processor and RTX 3080 Ti graphics card were used in the experiment. Results were evaluated by overall accuracy  and instance mIoU, facilitating direct comparison with other state-of-the-art methods. We set the bar high, benchmarking our network against a wide range of robust models such as PointNet, PointNet++, DGCNN, RGCNN [In our study, we performed a rigorous evaluation of our network’s performance on the ShapeNet dataset using well-established metrics, namely, category-average Intersection over Union . Our methodology faithfully followed the same protocols of random scaling, rotation, and point expansion. The visualizations demonstrating the Stanford 3D test images can be viewed in In our approach to model training, we strategically extracted 4096 points at random from each block. Notably, the test points were spared from any sampling process. We deployed a comprehensive six-fold cross-validation, gauging the efficacy of our model based on the average mIoU and OA, registering scores of 57.2% and 87.1%, respectively. This underscores the effectiveness of our multi-information approach in handling complex point cloud recognition tasks.In mIoU of 0.893 on the ShapeNet dataset, while demonstrating recognition performance metrics of 0.855 OA, 0.922 mIoU on the 3DMatch dataset. Furthermore, the experimental outcomes showcase MInet’s superiority over other models in recognition tasks, in the semantic segmentation results of the Stanford 3D dataset, 0.572 of mIoU and 0.871 of OA were obtained.In this research, we introduced a novel model named MInet, which assimilates multi-modal information extraction techniques based on a multi-spectral LiDAR system. This model is designed for the execution of point cloud segmentation and recognition tasks within diverse environments. MInet can effectively capture the local geometric relationship between the points obtained from the non-uniformly sampled data, and enhance the extracted features by loading multiple information and utilizing the two-dimensional and three-dimensional characteristics of the target object. The evaluation results elucidate that our MInet attains a MInet possesses the ability to extract abundant two-dimensional information from images while maintaining three-dimensional coordinate data, considerably facilitating semantic segmentation. This model introduces an innovative approach of utilizing multi-modal information to process targets, employing both two-dimensional and three-dimensional features. This breaks away from the prevailing practice of using two-dimensional features solely as a discriminator for three-dimensional ones . In prac


The Internet of Things (IoT), projected to exceed 30 billion active device connections globally by 2025, presents an expansive attack surface. The frequent collection and dissemination of confidential data on these devices exposes them to significant security risks, including user information theft and denial-of-service attacks. This paper introduces a smart, network-based Intrusion Detection System (IDS) designed to protect IoT networks from distributed denial-of-service attacks. Our methodology involves generating synthetic images from flow-level traffic data of the Bot-IoT and the LATAM-DDoS-IoT datasets and conducting experiments within both supervised and self-supervised learning paradigms. Self-supervised learning is identified in the state of the art as a promising solution to replace the need for massive amounts of manually labeled data, as well as providing robust generalization. Our results showcase that self-supervised learning surpassed supervised learning in terms of classification performance for certain tests. Specifically, it exceeded the F1 score of supervised learning for attack detection by 4.83% and by 14.61% in accuracy for the multiclass task of protocol classification. Drawing from extensive ablation studies presented in our research, we recommend an optimal training framework for upcoming contrastive learning experiments that emphasize visual representations in the cybersecurity realm. This training approach has enabled us to highlight the broader applicability of self-supervised learning, which, in some instances, outperformed supervised learning transferability by over 5% in precision and nearly 1% in F1 score. These breaches primarily include denial-of-service attacks (DoS and DDoS) [The Internet of Things (IoT) encompasses a broad range of applications, spanning from smart homes to smart cities. It embodies the integration of physical objects, such as wireless healthcare devices, agricultural irrigation systems, and smart grid electric panels, with internet connectivity . The gloGiven the impulse that 5G networks  and SoftIndeed, the scarcity of recent and robust data collections has been identified as a significant gap in contemporary research ,10,11. GNonetheless, while the availability of the aforementioned datasets addresses the issue of suitable data quality for the IoT, the success of AI-based IDSs and IPSs is also contingent upon the chosen training strategy. Supervised learning necessitates copious amounts of labeled data to construct predictive models. In contrast, unsupervised learning does not require such ground truth information, but it presents challenges with generalization , specifiSelf-supervised learning (S-SL) is a promising solution to challenges such as the demand for vast amounts of manually labeled data and the imperative for robust generalization . In factS-SL stands as a promising direction for ML advancements . Today’sContrastive learning, a training strategy for S-SL, aims to draw similar (or positive) examples closer while distancing dissimilar (or negative) examples . This meTherefore, in this manuscript, we create a smart IDS for detecting DDoS attacks against IoT networks using S-SL with the contrastive learning strategy . SpecifiThe conversion process from flow-level traffic into synthetic images was motivated by the success of contrastive learning of visual representations discussed in . FurtherThe pioneering of experimentation in IoT networks by leveraging the self-supervised learning paradigm in tandem with synthetic image generation, enabling the application of computer vision (CV) techniques for denial-of-service attack detection;The pre-training of self-supervised learning models using MoCo v2 on the Bot-IoT and the LATAM-DDoS-IoT datasets, laying the groundwork for fine-tuning in future specialized research tasks;An optimized training framework for future studies focusing on the contrastive learning of visual representations for the detection of denial-of-service attacks within IoT networks.In summary, the primary contributions of this research are as follows:The remaining structure of this paper is divided as follows: the related work is presented in Here, we present related research on the creation of synthetic images for DDoS attack detection against IoT networks, as well as the learning strategies used to train the corresponding AI-based IDSs/IPSs.While the existing literature reports various detection techniques, including those that analyze network traffic at the flow-level through recurrent models such as Recurrent Neural Networks, Long Short-Term Memory, and Gated Recurrent Units ,37, our In , the authttps://www.unb.ca/cic/datasets/nsl.html, accessed on 12 July 2023), CIC-IDS2017, and UNSW-NB15 [Reference  proposedNSW-NB15 , coverinThe authors of  created In , a custohttps://registry.opendata.aws/cse-cic-ids2018, accessed on 13 July 2023) datasets for creating a network-based IDS using a custom model with a Multi-layer Perceptron (MLP) as the backbone. With respect to the data augmentation strategy, the authors generated adversarial examples based on [The authors of  used S-Sbased on . The accbased on  might beThis section presents the steps we followed to create our smart IDS. In The Bot-IoT and LATAM-DDoS-IoT datasets structure their input samples sequentially. We transformed this traffic into grayscale images to make the instances compatible with our 2D convolutional layers. This conversion involved applying min-max normalization and gamma correction, reshaping the input data, and then multiplying by 255 to represent various pixel intensities (as illustrated in Equation ). We utiGamma correction was employed with the aim of enhancing the magnitudes of features following the normalization process, thus preventing potential information loss ,55. In tAlthough converting sequential traffic into image form introduces an additional processing step in our classification tasks, the relevance of Convolutional Neural Networks in the state of the art has furthered the exploration of data transformation methodologies to leverage these architectures . FurtherFor this study, we tried two distinct data augmentation policies, both incorporating random cropping, Gaussian blur, and horizontal flipping, with the addition of random noise being the only difference between them. This noise was applied by multiplying each image pixel by a unique random value within the range of 0.8 to 1.2. This approach was inspired by , who fouThe number of samples used for pre-training was 108,452, with a batch size of 128. Given the relatively small batch size, we did not implement MoCo v3 , an incrAdditionally, we conducted an ablation study to assess the effect of initializing model weights and biases using ImageNet  and anotThe SGD optimizer was chosen for MoCo v2, while the Adam optimizer  was emplFor experiments involving cyclical learning rates, the SGD optimizer was consistently used. Both learning strategies typically employed the cosine annealing learning rate scheduler, except during evaluations of the one-cycle learning rate policy.Batch normalization yielded means and standard deviations of 0.4367 and 0.2715, respectively, for the LATAM-DDoS-IoT dataset and 0.3414 and 0.2202 for the Bot-IoT dataset.Details regarding the pre-training process are as follows:We used a batch size of 32, the Adam optimizer, and the cosine annealing learning rate scheduler.Overall, both the pre-training and fine-tuning phases spanned 100 epochs each.The details of the fine-tuning process are as follows:ResNet-34 served as the backbone for both the supervised learning paradigm and MoCo v2 in all experiments. The models were designed using PyTorch  and wereThe following metrics were chosen to measure the classification performance: accuracy, precision, recall, and F1 score. See Equations –5) for  for 5) ftraffic) .(2)AccuAttack detection: determining if an input image represents a DDoS attack;Protocol classification: classifying the input image based on its protocol ;OSI layer identification: recognizing the OSI layer the input image corresponds to, whether it is the transport layer, application layer, or standard traffic.We identified three specific downstream tasks:The multiclass protocol classification setup was also selected as the pre-training task for contrastive learning due to the complexity added by the number of classes  compared to the other tasks .The proposed OSI layer identification task provides the groundwork to tackle security in a layered approach since each layer presents specific protocols and vulnerabilities. In practical terms, 5G is a potential scenario that benefits from this end-to-end protection, where each OSI layer must properly work to meet the design objectives of increased connectivity, lower latency, and high reliability .For each downstream task, we employed only 300 labeled images per category. An exception was the OSI layer task, where the transport layer contained 600 samples split evenly between UDP and TCP images. Given the sparsely labeled data, these scenarios present a fitting examination of FSL capabilities for AI models.It should be noted that the 108,452 instances utilized in the pre-training phase are intended to establish a robust feature representation for the problem of network intrusion within the IoT domain. The limited sample size in the fine-tuning phase is designed to assess the rapid adaptability of our models, leveraging prior knowledge to ensure both robustness and performance .The results obtained from the different experiments are presented in the next section.Here, we show the classification results from the supervised learning and S-SL scenarios and a corresponding discussion.This subsection uses the novel LATAM-DDoS-IoT dataset for the pre-training and fine-tuning phases to determine the optimal training setting from the ablation studies. These studies evaluated the augmentation policy for contrastive learning, the initialization of the models, and the concept of super-convergence.See In our second experiment, we assessed the efficacy of employing pre-trained weights from ImageNet, as opposed to initializing the network randomly during the pre-training phase. As illustrated in The results in Lastly, we implemented the optimal training setting under new testing scenarios, namely, an entire classification performance evaluation using the Bot-IoT dataset and transferability experiments having the LATAM-DDoS-IoT as the target.See Generalization, understood as the ability to predict unseen related data correctly, is a potential benefit of S-SL models . TherefoThe modest gains in generalization attributed to S-SL may seem incongruent with the potential of this emerging training paradigm. One possible explanation for this discrepancy could be the selected pre-training task of multiclass protocol classification within the framework of contrastive learning. Identifying a sufficiently challenging pre-training task is often considered the most intricate aspect of effective S-SL, as it governs the quality of the learned feature representations . To poteAll the results presented in this section are the average of five runs, which indicates stable detection rates, and a benchmark where S-SL shows a competitive classification performance when compared to supervised learning.To the best of our knowledge, no existing study directly parallels our work. As discussed in In the next section, we provide a conclusion for this study and outline potential avenues for future research.In this study, we showcased the efficacy of S-SL, positioning it as a suitable alternative to supervised learning in the context of linear classification performance. Leveraging two cutting-edge IoT network datasets, we introduced an innovative downstream task for classifying attacks via the OSI layer. Through comprehensive ablation studies, we outlined an optimized training setting emphasizing an augmentation policy devoid of random noise, with ImageNet initialization and a one-cycle learning rate scheduler. S-SL’s superior generalization aptitude was evident, outclassing supervised learning by over 5% in precision and nearly 1% in F1 score in certain tests.Our results highlight the promise of S-SL in bolstering the security of IoT networks, thereby laying a foundational groundwork for future research endeavors and technological advancements. While we recognize an inherent limitation in our proposed solution, particularly concerning the computational overhead associated with converting each network flow into an image, our study nonetheless demonstrates the viability of leveraging S-SL to construct robust cybersecurity frameworks.Looking ahead, we aim to harness S-SL for sequential data, possibly tapping into the capabilities of Transformers . In addi


Ketamine has been recently approved to treat resistant depression; however preclinical studies showed sex differences in its efficacy. Sex steroids, such as estrogens and testosterone, both in the periphery and locally in the brain, are regarded as important modulators of these sex differences. Therefore, the present study evaluated how inhibiting the biosynthesis of estrogens with letrozole (an aromatase inhibitor) could affect the observed sex differences in ketamine's antidepressant-like-response.We performed several consecutive studies in adult Sprague–Dawley rats to evaluate potential sex differences in the antidepressant-like effects of ketamine , letrozole  and their combination (letrozole pre-treatment 3 h before ketamine). Acute and repeated antidepressant-like responses were ascertained in a series of behavioral tests .The main results proved clear sex differences in the antidepressant-like response induced by ketamine, which was observed following a repeated paradigm in adult male rats, but rendered inefficacious in female rats. Moreover, decreasing estrogens production with letrozole induced on itself an antidepressant-like response in female rats, while also increased ketamine's response in male rats . Interestingly, both the antidepressant-like effects induced by ketamine in male rats or letrozole in female rats persisted over time up to 65 days post-treatment, suggesting long-term sex-directed benefits for these drugs.The present results demonstrated a sex-specific role for aromatase inhibition with letrozole in the antidepressant-like response induced by ketamine in male rats. Moreover, letrozole itself presented as a potential antidepressant for females with persistent effects over time. Clearly, the production of estrogens is key in modulating, in a sex-specific manner, affective-like responses and thus deserve further studies.The online version contains supplementary material available at 10.1186/s13293-023-00560-5. There are clear sex differences in the antidepressant-like effects of ketamine.Ketamine showed efficacy in adult male rats while rendered ineffective in females.Aromatase inhibition with letrozole improved the effects observed in male rats.Aromatase inhibition with letrozole induced an antidepressant-like effect in females.The antidepressant-like effects of ketamine or letrozole persisted up to 2 months.The online version contains supplementary material available at 10.1186/s13293-023-00560-5. Ketamine is a novel fast-acting antidepressant recently approved for treatment-resistant depression. Since preclinical studies showed sex differences in its efficacy, probably driven by sex hormones (estrogens and testosterone), we evaluated the antidepressant-like effects of ketamine in male and female rats when the biosynthesis of estrogens was inhibited. To do so, we utilized letrozole, an inhibitor of the aromatase enzyme responsible for the conversion of testosterone into estrogens. The results showed, in line with the prior literature, sex-differences in the antidepressant-like response of ketamine; with efficacy in male rats and a lack of response for females. Aromatase inhibition with letrozole induced a faster response for ketamine in male rats, while did not change the lack of response for females. However, aromatase inhibition on itself was capable of inducing an antidepressant-like response in female rats. Interestingly, both ketamine's and letrozole's antidepressant-like effects in male and female rats respectively showed long-term beneficial effects, up to 65 days post-treatment.The online version contains supplementary material available at 10.1186/s13293-023-00560-5. Its great clinical outcome for depressed patients that are resistant to at least two other pharmacological options, confront with the scarce knowledge regarding the potential long-term impact of its repeated treatment , but otIn this context, recent studies not only supported a possible involvement of estrogens in the sex-differences reported in the pathogenesis of depression , but alA total of 145 adult Sprague–Dawley rats  were used in 3 consecutive studies  and female (n = 17) rats that were treated for 7 consecutive days  with saline 0.9% NaCl) or ketamine  and then stored at − 80 °C. The levels of accumulated testosterone through inhibiting the synthesis of estrogens with letrozole were used as an indicative of the hormonal status, and were quantified by standard ELISA kits , according to the manufacturer’s instructions. The sensitivity of the assay was 0.066 ng/ml.Blood samples were collected during Study II from 13 male and 11 female rats treated with either vehicle or letrozole for 8 consecutive days  and is This test was first described to assess differences in sensitivity to novelty in an anxiogenic-like environment , and couThis test is used as an indicator of hedonic-like responses , since t test, depending on the number of groups to compare, as recommended for preclinical data set analysis and when including sex as a biological variable . In line with the guidelines for displaying data and statistical methods in experimental pharmacology e.g., , 38), re, re38]),ble (see ). When ap = 0.031) as compared to male adult rats. Again, sex differences were observed when evaluating distance traveled (cm) in the novelty-suppressed feeding test, with females covering significant more distance  than male rats  than male adult rats  and increased climbing  behavior in the forced swim test  than males. Interestingly, and given the baseline sex difference in testosterone levels, when the effect of letrozole treatment was evaluated separately for each sex, it only rendered significant for females , but not for male rats , as an indicative of the correct inhibition of estrogens biosynthesis by this aromatase inhibitor. As expected, the results showed significant baseline sex differences in testosterone levels  and lower climbing  than males , and increasing climbing behavior  as measured 1 h post-treatment . Significant effects of Sex were observed for almost all data analyzed through two-way ANOVAs  and letrozole  pre-treated male rats  and increasing climbing  in the forced-swim test  and climbing  in the forced-swim test  by decreasing immobility  suggesting long-term sex-directed antidepressant-like effects for these drugs.The present results showed clear and interesting sex differences in the pharmacological actions exerted by ketamine. Particularly, while ketamine showed certain efficacy in male rats (following a repeated treatment of 5 mg/kg during 7 days), female rats were unresponsive to the expected beneficial antidepressant-like effects of ketamine. In line with our results prior data showed a beneficial response after a repeated paradigm with low doses of ketamine  in Wistar adult male rats . MoreoveSex hormones appear to be critical mediators of the antidepressant-like response of ketamine and needs further research, especially because it is overlooked in much of the available literature. For example, gonadal hormones have been implicated in the possible modulation of the antidepressant-like effect induced by ketamine at low doses, since the response was abolished in ovariectomized female rats and also recovered after receiving a hormone replacement treatment , 41. In In regards to the antidepressant-like effects of ketamine in letrozole pre-treated male and female rats, the results showed, as hypothesized, that letrozole improved the response of ketamine in male rats . Moreover, and replicating the data presented before, a repeated paradigm of ketamine was needed to induce an antidepressant-like response in vehicle-pretreated rats, as observed 1-day post-treatment. At this time point, combining letrozole and ketamine did not show a higher response than just ketamine, suggesting probably a maximum effect in the assay evaluated. However, letrozole did not improve the lack of response induced by ketamine in female rats, which still rendered inefficacious, but once again induced an overall antidepressant-like effect. The mechanism by which letrozole may potentiate the antidepressant-like effects of ketamine in adult male rats is unknown. One could speculate that letrozole could either be acting synergistically on the same molecular pathways targeted by ketamine , such aFinally, one of the most remarkable results presented in this study is the fact that, both the antidepressant-like effects induced by ketamine in adult male rats or letrozole in adult female rats persisted over a long time, since they were still present up to 65 days post-treatment, suggesting long-term sex-directed benefits for these drugs. This is relevant in the context that no prior studies have evaluated persistent effects for so long. For example, a single dose of ketamine induced an acute antidepressant-like effect that long-lasted up to 7 days , but notOverall, these findings have proven clear sex-differences in the antidepressant-like response of ketamine in rats, with male rats being more responsive to its beneficial effects proving long-term efficacy. Moreover, inhibiting the biosynthesis of estrogens with letrozole improved the efficacy of ketamine in male rats by advancing the response right after a single dose, in comparison to the repeated paradigm needed in normal conditions. Also, ketamine did not show an antidepressant-like potential in female rats, at least with the conditions tested, however, letrozole induced a remarkable antidepressant-like response in female rats, which persisted in time. Based on this study, ketamine should be utilized adhering to sex-specific considerations. Moreover, aromatase inhibition with letrozole presents itself as a great pharmacological option, since it improved ketamine's efficacy in male rats and induced on itself a promising beneficial response in female rats. The production of estrogens and/or the accumulation of testosterone are key players in modulating some sex-specific antidepressant-like responses in Sprague–Dawley rats. Future studies should center in evaluating the molecular mechanisms behind these effect at key points of behavioral changes.Additional file 1: Table S1. Antidepressant-like effects induced by ketamine or letrozole in adult male and female rats. Table S2. Antidepressant-like effects of ketamine in letrozole pre-treated male and female rats.


However, few computational methods have been developed to address this task. In addition, it remains a significant challenge to explain key functional groups determining metabolic stability.Metabolic stability plays a crucial role in the early stages of drug discovery and development. Accurately modeling and predicting molecular metabolic stability has great potential for the efficient screening of drug candidates as well as the optimization of lead compounds. Considering wet-lab experiment is time-consuming, laborious, and expensive, To address these issues, we develop a novel cross-modality graph contrastive learning model named CMMS-GCL for predicting the metabolic stability of drug candidates. In our framework, we design deep learning methods to extract features for molecules from two modality data, i.e. SMILES sequence and molecule graph. In particular, for the sequence data, we design a multihead attention BiGRU-based encoder to preserve the context of symbols to learn sequence representations of molecules. For the graph data, we propose a graph contrastive learning-based encoder to learn structure representations by effectively capturing the consistencies between local and global structures. We further exploit fully connected neural networks to combine the sequence and structure representations for model training. Extensive experimental results on two datasets demonstrate that our CMMS-GCL consistently outperforms seven state-of-the-art methods. Furthermore, a collection of case studies on sequence data and statistical analyses of the graph structure module strengthens the validation of the interpretability of crucial functional groups recognized by CMMS-GCL. Overall, CMMS-GCL can serve as an effective and interpretable tool for predicting metabolic stability, identifying critical functional groups, and thus facilitating the drug discovery process and lead compound optimization.https://github.com/dubingxue/CMMS-GCL.The code and data underlying this article are freely available at  Metabolic stability, which refers to the extent and rate of drug metabolism in the body, plays a crucial role in the early stages of drug discovery and development (in vitro by incubating hit/lead compounds with liver microsomes (LMs) to assess their metabolic properties (in vitro mouse liver microsomes (MLMs), rat liver microsomes (RLMs), and human liver microsomes (HLMs) have emerged as essential approaches for initial assessment of metabolic stability  in various biological research fields, such as drug–target interaction prediction -based encoder to learn the sequence representation of molecules by fully preserving the context of symbols. To capture the consistencies between local and global structures, we propose a graph contrastive learning framework to learn different structure representations. To the best of our knowledge, CMMS-GCL is the first method to simultaneously consider SMILES sequence and molecule structure for metabolic stability prediction. Comprehensive experiments show that our CMMS-GCL outperforms state-of-the-art methods. In addition, case studies further validate the interpretability of critical functional groups identified by CMMS-GCL. Overall, the main contributions can be summarized as follows:We propose a novel graph contrastive learning model named CMMS-GCL for metabolic stability prediction. Specifically, CMMS-GCL is an interpretable model, adept at identifying key functional groups crucial for metabolic stability. The model achieves this by analyzing specific sequence cases, with further validation provided by statistical analyses rooted in the graph structure.We design a new cross-modality representation learning framework where SMILES sequence and molecule structures are considered simultaneously to learn molecule features. To the best of our knowledge, this is the first attempt to integrate both modality data for effective metabolic stability prediction.Comprehensive experiments are conducted on two datasets and the results show that our CMMS-GCL outperforms 7 state-of-the-art methods. Case studies on several stable/unstable molecules further validate the interpretability of key functional groups identified by CMMS-GCL.n compounds i-th compound is stable; otherwise unstable. Our main task is to train a classifier We model the metabolic stability prediction problem as a classification task. Assuming that we are given a set of In this article, we propose a novel cross-modality graph contrastive learning framework called CMMS-GCL for metabolic stability prediction. As shown in m and Compounds are usually represented by the SMILES (Simplified Molecular-Input Line-Entry System). The local chemical context of an atom reveals its functionality in the SMILES sequence. To capture the context information, we first learn the preliminary features of atoms from SMILES sequence data using Smi2Vec  play a critical role in metabolic stability  and v and u are connected by a bond while v and u are disconnected by a bond. N is the number of atoms. The initial input features of the molecule i are defined by several atom-level properties such as the atom symbol, the number of adjacent atoms, the number of adjacent hydrogens, the implicit value of the atom, and the atom occurrence in an aromatic structure.In this section, we introduce the graph isomorphism network (GIN) to learn the representation of molecules from molecular structures. Given a molecule v in the molecular graph v in the kth layer is defined as follows:where v at the k-th iteration. v at the v. With K iterations, the representation K-hop neighborhood information. Eventually, we take the output of the K-th iteration as the final representation v. To obtain the molecule representation For each atom Inspired by the study that preserving consistencies between local and global structures in a graph is demonstrated to improve graph representation learning  graphs/subgraphs form negative pairs. The primary idea of graph contrastive learning is to maximize the agreement between the representations of positive pairs while minimizing the agreement between the representations of negative pairs  for molecules and calculated their Tanimoto similarity, a robust measure of structural similarity between molecules . FollowiWe performed two types of evaluation experiments on the above two datasets. In particular, we implemented 10-fold cross-validation on the HLM dataset for performance evaluation. And we repeat the experiment 10 times and take average values as the final result on the external dataset. We further conducted the external independent evaluation, by training the model on the HLM dataset and testing on the external dataset. Three well-known performance metrics are utilized for evaluation, including the average area under the receiver operating characteristic curve (AUC), Accuracy, F1 score, and Matthew’s correlation coefficient (MCC). For all these three metrics, a higher value means better performance.K of GIN layers as 2. In the stability predictor, the dimensions of the input layer, hidden layers, and output layer are set to 768, 256, and 1, respectively.In the atomic similarity-based sequence encoder, the dimensions k is set as 0.4. We evaluate the influence of the weight coefficient To train the model of CMMS-GCL, we set training epochs and learning rate as 200 and 0.0005, respectively. Adam  is selecFurthermore, CMMS-GCL is implemented in Python 3.8 and PyTorch 2.0, along with functions from PyG 2.3.1, Networkx 2.8.4, Scikit-learn 1.2.2, Numpy 1.23.5, Pandas 2.0.2, and RDKit 2023.03.1.To evaluate the performance of the proposed method, we compare CMMS-GCL with seven state-of-the-art methods, including three traditional machine learning-based methods and four deep learning-based methods, summarized as follows:FP-GBDT  for dimensionality reduction, as depicted in To validate our CMMS-GCL’s capacity for novel molecular structures, we calculated the Tanimoto similarity among the molecules within the HLM dataset using their ECFP fingerprints. We then performed a Furthermore, we conducted the leave-one-cluster-out cross-validation on the identified clusters, providing a robust validation of our approach on the molecules with novel structures. We compared our CMMS-GCL against various baseline methods under this leave-one-cluster-out setting in In conclusion, the assessment of structurally unique compounds manifests the superior predictive performance of CMMS-GCL, underscoring its competency in handling diverse chemical structures. This revelation accentuates the potential of CMMS-GCL as an invaluable asset for medicinal chemists in pinpointing compounds with novel structures and predicting their respective activities.Although deep learning is often known as a black-box model, it is crucial to comprehend the prediction mechanism employed by CMMS-GCL for distinguishing stable and unstable molecules, as well as to ascertain its potential influence on lead compound optimization. To thoroughly investigate the interpretability of our model, we embarked on an in-depth examination of its two primary modules, focusing distinctly on the sequence-based and the graph structure-based components.Since the atomic similarity-based sequence encoder module can capture the molecule’s important functional groups through its multihead attention module, we determined that two bonding atoms are regarded as a crucial functional group. The weight of a bond is the average of the weights of its constituent atoms  and are highlighted in acetylene functional groups, which are commonly associated with enhanced stability and are less prone to metabolic reactions . For these unstable molecules prone to metabolism, their prominent functional groups involve various metabolic pathways. As shown in ivatives . Ester fphenyl ring, ring, trifluoromethylbenzene, monofluorobenzene, chlorobenzene, ether, trifluoromethyl, primary amine, alkylbenzene, and carbonyl group have been screened out. They respectively account for approximately 17.9%, 8.2%, 4.5%, 4.4%, 4.2%, 1.2%, 1.1%, 0.9%, 0.9%, and 0.8% of instances where the main bond Shapley value exceeds 0.2. The functional groups that contribute strongly to stability are primarily the top five, accounting for 39.2% of instances, and trifluoromethylbenzene generally has a high Shapley value. Our results are essentially consistent with the literature (In terms of the graph structures, we employed the EdgeSHAPer method, which calculates the Shapley value for each edge as if they were individual contributors to the model’s predictive capability (sulfonate, amide, secondary amine, diazaheterocycle, primary amine, diazabenzene, amide or carboxylic acid, trifluorobenzene, and sulfonate, with respective proportions of 6.6%, 5.8%, 5.6%, 5.2%, 3.4%, 3.0%, 2.7%, 2.7%, 2.5%, and 2.4% of instances where the main bond Shapley value exceeds 0.2. Due to the extensive variety of metabolic pathway types (trifluorobenzene. The occurrence probability of trifluorobenzene in metabolic instability is significantly lower than its occurrence in the cases of stability. This discrepancy may potentially be attributed to statistical errors.In In summary, the alignment of our findings from the model and existing literature demonstrates the interpretability of CMMS-GCL, highlighting functional groups significantly associated with metabolic stability. This understanding enables the elucidation of the reasons behind a molecule’s specific stability or instability properties. Meanwhile, the insights derived from our analysis can contribute to the guidance of molecular design and the optimization of lead compounds.Assessing metabolic stability is essential for drug discovery and development. However, the high cost and risk associated with wet-lab experiments make it challenging to conduct this assessment. Therefore, computational methods can be a fast and effective complementary approach for predicting metabolic stability. However, it is noted that few computational methods have been developed for such tasks.In this article, we present CMMS-GCL, a novel cross-modality graph contrastive learning model named CMMS-GCL for predicting the metabolic stability of compound candidates. In the model, we design a dual-channel strategy to learn representations for molecules from two different modalities, i.e. SMILES sequence and graph structure. Especially, with the SMILES sequence data as inputs, a multihead BiGRU-based encoder is designed to learn the sequence representations of molecules by fully preserving the local chemical context of atoms. The introduction of multihead BiGRU enables the identification of crucial functional groups specific to metabolic stability. Meanwhile, we propose a GIN-based encoder to learn structure representations from the molecule structure, where graph contrastive learning is further introduced to enhance representation learning by capturing dependencies between local and global structures. Comprehensive experimental results on two datasets demonstrate that our proposed CMMS-GCL outperforms seven state-of-the-art methods in predicting metabolic stability. Additionally, we performed case studies on various stable and unstable molecules, demonstrating the interpretability of key functional groups recognized by CMMS-GCL in sequence analysis. Moreover, we conducted a statistical interpretability analysis utilizing the graph structure, further emphasizing the advantages and potential utility of our method. These statistical analyses not only corroborate our model but also provide valuable guidance for future candidate drug screening and lead compound optimization.in vitro data and in vivo predictions due to factors such as complex in vivo metabolism, physiological factors, and interindividual variability. To address these challenges, semisupervised and self-supervised learning techniques can be designed to integrate unlabeled data with the existing labeled data, thereby enhancing learning accuracy and overall prediction performance. Moreover, the incorporation of diverse data sources, validation of model performance using in vivo data, and exploration of methodologies to account for physiological factors and interindividual variability can also be investigated in future research.Although the CMMS-GCL method shows promising results in predicting metabolic stability, it still faces several limitations, including the challenges in effectively incorporating unlabeled data with experimental data and the discrepancies between


Following the introduction of all-oral treatment regimens for patients with drug-resistant tuberculosis (TB), second-line injectable drug applications have been reduced in the last few years. However, they are still important for anti-TB therapy. This study aims to analyze the occurrence of amikacin- and capreomycin-related adverse drug reactions (ADR) in patients with multidrug-resistant tuberculosis (MDR-TB) and evaluate the role of multiple patient-, disease-, and therapy-related factors on the frequency of the observed adverse events. In addition, the possible role of genetic risk factors was studied by full-length mitochondrial DNA sequencing. Toward this aim, we retrospectively evaluated 47 patients with MDR-TB who received amikacin and/or capreomycin. In total, 16 (34.0%) patients developed ototoxicity and 13 (27.7%) developed nephrotoxicity, including 3 (6.4%) patients who experienced both adverse events. Ototoxicity development was more common in patients who received amikacin. No other factors showed a significant impact. Nephrotoxicity was likely associated with previous renal health impairment. Full mitochondrial genome sequencing did not reveal any specific ADR-associated variants, and results showed no differences in adverse event occurrence for any specific variants, mutation count, or mitochondrial haplogroup. The absence of the previously reported ototoxicity-related mtDNA variants in our patients with ototoxicity and nephrotoxicity highlighted the complex nature of the ADR occurrence. Mycobacterium tuberculosis (Mtb) that are resistant to at least isoniazid and rifampicin, the most effective anti-TB drugs [Globally, tuberculosis (TB) remains a public health crisis and the second leading cause of death from an infectious agent (SARS-CoV-19 takes first place) [TB drugs . MDR-TB TB drugs . In the TB drugs . AlthougThe use of second-line injectable agents often comes with adverse drug reactions (ADR) that may become the cause of treatment discontinuation. All aminoglycosides have the potential to produce reversible and irreversible vestibular, cochlear, and renal toxicity and neuromuscular blockade . CapreomThe overall incidence of ototoxicity and nephrotoxicity in patients is difficult to determine, as different studies reported variable results . PatientAs an effective host-directed therapy is one of the perspectives to address TB drug resistance challenges, it is thus highly important to apprise factors that may impact the inter-individual variability in ADR occurrence. The aim of this study was to analyze the occurrence of amikacin- and capreomycin-related ADR in patients with MDR-TB in Latvia and to evaluate the role of multiple patient-, disease-, and therapy-related factors. In addition, the possible role of genetic risk factors was studied by full-length mitochondrial DNA (mtDNA) sequencing.In this retrospective study, 47 MDR-TB adult patients admitted to the Centre of Tuberculosis and Lung Diseases, Riga East Clinical University Hospital, were included. The inclusion criteria were: patients with informed consent; over 18 years old; an MDR-TB diagnosis; amikacin or capreomycin injections. No specific exclusion criteria were applied. The patient cohort was collected in the period from the year 2014 to 2017. DNA samples and patient data were obtained in collaboration with the national biobank Genome Database of the Latvian population according to the protocol described in Rovite et al., 2018 . InformaM. tuberculosis resistance data and following the WHO guidelines of the time [The treatment regimen for the patients with MDR-TB was chosen based on the the time ,17. The ® NGS Clean-up and Size Select magnetic beads .Human mitochondrial DNA (mtDNA) sequencing was performed using IonTorrent technology and the Personal Genome Machine (PGM) . First, the whole mitochondrial genome was amplified via two distinct PCR reactions to yield more than 8 kb long products—A and B fragments—as described previously . A and Busegalaxy.org to analyze the data [Homo sapiens mitochondrion complete genome). Aligned reads were analyzed with the FreeBayes tool for haploid genomes using a population model as shown in Nekrutenko and Ostrovsky, 2021 [The sequencing data were uploaded to the Galaxy web platform, and we used the public server at the data . BAM datSingle-nucleotide variants (SNVs) were defined as positions with a nucleotide different from the rCRS; indels were not analyzed. To exclude sequencing errors, each sample was also examined manually using the Integrative Genomics Viewer (IGV) tool . VCF files were uploaded to the HaploGrep 2.0 classification tool to determine a detailed mtDNA haplogroup . MutatioWe explored the following patient-, lifestyle- and disease-related factors: age, biological sex , type of antibiotic used during the therapy , number of doses received, smoking status (non-smoker/smoking experience), BMI , alcohol consumption , comorbidities (human immunodeficiency virus (HIV), diabetes mellitus (DM), hepatitis C virus (HCV); yes/no), simultaneous antiretroviral (ART) therapy (yes/no), TB incident case (new/recurrent), and we evaluated a possible impact of these variables on the ototoxicity and nephrotoxicity occurrence during the therapy.By using sequencing data, we assessed the occurrence of ototoxicity and nephrotoxicity in patients with MDR-TB depending on mtDNA genetic variants including mtDNA haplogroups and mutation frequencies in protein, 12S rRNA, and tRNA coding genes. p ≤ 0.05 and α = 0.05 were considered significant.Based on the data type, we used the following methods to find out if there was any association between these variables: Chi-square test of independence, Fisher’s exact test, logistic regression, ANOVA, and ANCOVA. XLSTAT and R (4.1.2.) software were used; values of Mtb resistance data). On average, during the treatment, patients with MDR-TB received 109 doses  of injectable anti-TB agents. The clinical characteristics of the patients are summarized in In our study, 47 patients with MDR-TB received injectable anti-TB drugs. The average age of the patients was 48 years (20–87). Amikacin, capreomycin, or both medications were used in two, twenty-nine, and sixteen patients, respectively ; some patients developed ototoxicity after receiving as little as 6 and 7 injections, whereas patients who received 206 and 346 injections did not experience ADR   . The tesp = 0.017; The univariate statistical analysis revealed a statistically significant difference in the nephrotoxicity occurrence in patients with MDR-TB who had or not had impaired renal function before the TB treatment  in 545 patients with TB . The repOur data indicated that nephrotoxicity was more frequently observed in older patients. This finding is in accordance with previous studies showing that patients who developed nephrotoxicity during aminoglycoside treatment had a higher average age . Renal fOur next question was about the possible effect of mtDNA genetic variables on the occurrence of ototoxicity and nephrotoxicity. Indeed, it was previously reported that ototoxicity manifestation can be influenced by patients’ age, nutrition and antioxidant deficiency, immunodeficiency, and also by mitochondrial functions and mtDNA variations . SeveralIn our study, a full mitochondrial genome analysis did not indicate any specific variants for the occurrence of adverse events, and none of the previously documented variants were present in our MDR-TB cohort. Thus, none of the ototoxicity events observed in our study could be linked to the previously reported ototoxicity-related mtDNA variants. Further, we looked deeper at the level of specific mtDNA variations to determine if they might be predisposition factors, as it is known that mitochondria are crucially necessary for cell metabolism, specific functions, and cell death regulation . OverallConsidering other mitochondrial genetic aspects, it is observed that the mtDNA haplogroup can influence the phenotypic manifestation of hearing loss-associated mutations . In our Personalized medicine addresses the challenges for tailoring the right therapeutic strategy for the right person at the right time. However, currently available technology limits our ability to draw significant conclusions from any single case; also, interaction between genetic and non-genetic risk factors can often be hard to measure, and studies require a sufficiently large number of patients. Thus, in our study, we used a population-based approach and combined individual genetic and clinical data to assess the risks of ototoxicity and nephrotoxicity in TB patients, aiming to add novel information on the path to the personalization of TB treatment. On the other hand, because of sporadic occurrence, the detected mtDNA SNVs were recorded on an individual level, thus contributing to the creation of large global data sets that capture individual health trajectories.Our study has several limitations. Our cohort size and the factor variability were the most significant limitations for the identification of all factors that may have an impact on ADR occurrence, as the individual variation is likely to obscure any factor–ADR relationship. The patient cohort was also collected in the period from 2014 to 2017, when injectable anti-TB medications were widely used in MDR-TB treatment. Nowadays, all oral regimens have been introduced according to the WHO guidelines on drug-resistant TB treatment; however, injectable agents such as amikacin can still be useful in severe cases of drug resistance . In addiIn conclusion, we analyzed the occurrence of ototoxicity and nephrotoxicity in patients with MDR-TB receiving amikacin and/or capreomycin and evaluated the possible role of different patient-, disease-, and lifestyle-related factors. Ototoxicity development was more common in patients who received amikacin injections. No other factors showed a significant impact. Nephrotoxicity was likely associated with a previous renal health condition. Full mitochondrial genome sequencing results did not reveal any specific ADR-associated variants and showed no differences in adverse event occurrence for specific variants or the mtDNA haplogroup. The absence of the previously reported pathogenic mtDNA variants in our patients with ototoxicity and nephrotoxicity highlighted the complex nature of the ADR mechanisms. Although the mitochondrial role in aminoglycoside- and capreomycin-induced adverse events is widely discussed, further research is needed to gain a more detailed understanding of the drug–mitochondrial interplay.