In our study, we addressed the challenge of the brain tumor segmentation task using a range of MRI modalities. While leading models show proficiency on standardized datasets, their versatility across different clinical environments remains uncertain. We introduced ‘Region-Focused Selection Plus (RFS+)’, enhancing the segmentation performance for clinically defined labels like gross tumor volume in our local dataset. RFS+ integrates segmentation approaches and normalization techniques, leveraging the strengths of each approach and minimizing their drawbacks by selecting the top three models. RFS+ demonstrated efficient brain tumor segmentation, using 67% less memory and requiring 92% less training time than the state-of-the-art model. The strategy achieved better performance compared to the leading model, with a 79.22% dice score. These findings highlight the potential of RFS+ in amplifying the adaptability of deep learning models for brain tumor segmentation in clinical applications. However, further research is needed to validate the broader clinical efficacy of RFS+.Automated brain tumor segmentation has significant importance, especially for disease diagnosis and treatment planning. The study utilizes a range of MRI modalities, namely T1-weighted (T1), T1-contrast-enhanced (T1ce), T2-weighted (T2), and fluid-attenuated inversion recovery (FLAIR), with each providing unique and vital information for accurate tumor localization. While state-of-the-art models perform well on standardized datasets like the BraTS dataset, their suitability in diverse clinical settings  remains a subject of debate. This research aims to address this gap by introducing a novel ‘Region-Focused Selection Plus (RFS+)’ strategy designed to efficiently improve the generalization and quantification capabilities of deep learning (DL) models for automatic brain tumor segmentation. RFS+ advocates a targeted approach, focusing on one region at a time. It presents a holistic strategy that maximizes the benefits of various segmentation methods by customizing input masks, activation functions, loss functions, and normalization techniques. Upon identifying the top three models for each specific region in the training dataset, RFS+ employs a weighted ensemble learning technique to mitigate the limitations inherent in each segmentation approach. In this study, we explore three distinct approaches, namely, multi-class, multi-label, and binary class for brain tumor segmentation, coupled with various normalization techniques applied to individual sub-regions. The combination of different approaches with diverse normalization techniques is also investigated. A comparative analysis is conducted among three U-net model variants, including the state-of-the-art models that emerged victorious in the BraTS 2020 and 2021 challenges. These models are evaluated using the dice similarity coefficient (DSC) score on the 2021 BraTS validation dataset. The 2D U-net model yielded DSC scores of 77.45%, 82.14%, and 90.82% for enhancing tumor (ET), tumor core (TC), and the whole tumor (WT), respectively. Furthermore, on our local dataset, the 2D U-net model augmented with the RFS+ strategy demonstrates superior performance compared to the state-of-the-art model, achieving the highest DSC score of 79.22% for gross tumor volume (GTV). The model utilizing RFS+ requires 10% less training dataset, 67% less memory and completes training in 92% less time compared to the state-of-the-art model. These results confirm the effectiveness of the RFS+ strategy for enhancing the generalizability of DL models in brain tumor segmentation. The global age-standardized rate (ASR) of incidence of brain cancer is 3.9 per 100,000 in males and 3.0 per 100,000 in females, while the ASR of mortality is 3.2 per 100,000 in males and 2.4 per 100,000 in females [Patients suffering from GBM need an accurate diagnosis and prognosis for treatment decisions. Although positron emission tomography (PET) and computed tomography (CT) are occasionally used for non-invasive diagnosis of GBM, MRI (magnetic resonance imaging) has gained widespread practice in brain imaging due to its non-ionizing, high-resolution scans, and ability to generate images with high soft tissue contrast. MRI stands out as the pivotal medical imaging method, utilizing strong magnetic fields and radiofrequency waves to create detailed images of the internal structures of the brain . DiffereIn most cases, an expert neuroradiologist is required to carry out the precise manual segmentation of tumors. This, in contrast to manual and semi-automatic segmentation, is time-consuming, labor-intensive, and sensitive to observer bias. Conversely, automatic segmentation appears as an alternative with the capability to eliminate intra- and inter-observer segmentation variability . In addiAlthough machine learning (ML) was used with hand-designed features for the brain tumor segmentation task at the beginning of the brain tumor segmentation challenge (BraTS) , deep leEven though early applications of computer vision (CV) techniques have demonstrated success under certain conditions for similar tasks, the task of medical image segmentation remains challenging due to the difficulties of feature representation . DespiteMulti-class segmentation is an effective method to delineate multiple classes of tumors. However, in the context of the segmentation task, binary class segmentation may present exclusive benefits compared to multi-class segmentation such as simpler optimization methodology . For binThere are two representations of tumor classes: labels (non-overlapping masks) and sub-regions (overlapping masks). While the labels are classified as enhancing tumor, necrotic, and edema, the sub-regions being segmented are (i) enhancing tumor (ET), (ii) tumor core , and (iii) whole tumor  . DespiteLastly, it is required to note that the generalizability of DL models can be seen as one of the prominent issues, especially in medical imaging research. For example, a state-of-the-art model was trained on the BraTS dataset but was validated on a local dataset . The outThis study aims to improve the versatility of DL models for brain tumor segmentation in different clinical scenarios while also prioritizing time and memory efficiency to address real-world practical limitations. The aforementioned issues guided our research to propose a novel strategy called RFS+, which represents a pioneering approach distinct from the region-focused selection (RFS) . RFS covThis paper, for the first time in the literature, provides a thorough investigation of different normalization techniques of MRI scans on segmentation tasks for DL models;A novel strategy called RFS+ is introduced as a versatile solution for any DL model, optimizing brain tumor segmentation through a fusion of various segmentation approaches, and normalization techniques with ensemble learning, thereby enhancing accuracy and generalizability across different datasets;For each region, RFS+ method provides the best DSC score by investigating the effect of normalization techniques on U-net models. It helps in the selection of the best method for each region when the aim is to use only one model. For example, transferring the trained models with one approach (such as multi-class) on ET, TC, and WT to segment GTVs cannot always give the best results. In contrast, RFS+ gives the best model for specific contours when transferring information from one contour style (TC) to another (GTV) ;RFS+ offers ensemble learning by using models with the top three DSC scores on the training dataset from the proposed models. The segmentation outcomes, when utilizing a 2D U-net model through ensemble learning, outperform those of the state-of-the-art model, indicating a substantial enhancement in segmentation accuracy provided by RFS+. Additionally, the introduction of RFS+ led to a DSC enhancement up to 1% when compared to its predecessor;A state-of-the-art model, with its original Docker image that triumphed in the BraTS 2021 challenge, was evaluated using a local dataset, addressing a notable gap as most models, trained on the BraTS training dataset, have predominantly been tested on the BraTS validation and test datasets, leaving the exploration on local datasets largely untouched. This study seeks to illuminate the generalizability of DL models by showcasing the segmentation results of a state-of-the-art DL model on local datasets, serving as a pivotal guide for future research and applications in this domain.RFS+ proposes three types of approaches for image segmentation, i.e., binary class, multi-class, and multi-label. It utilizes combinations of models with different normalization techniques  and pickThe deployment of DL in medical imaging is increasingly prevalent. In the BraTS challenge, most of the applications are based on DL where the best-performing model is based on CNNs  . EncoderMagadza et al. categorized CNN architectures into four subgroups which are single pathway, dual pathway, cascaded architectures, and U-net . An examOther feature extraction-based methods or transformer-based DL models are not widely utilized in clinical applications ,35 or doIn this research, we utilized 2D, 2.5D , 3D U-neThis section explains the details of the proposed architectures, pre-processing steps, and hyperparameters used in the models. Additionally, further information is given about two datasets that are utilized in this paper: the BraTS 2021 dataset  and our local dataset (STORM_GLIO).The methodology depicted in The distinction from RFS lies in the utilization of ensemble learning, incorporating various normalization techniques that can be customized for specific regions. This strategy consists of two main components. Firstly, various normalization techniques are applied during the pre-processing of MRI scans. Secondly, three distinct segmentation approaches are employed. When using the combinations of segmentation approaches and normalization techniques , the segmentation target should be selected. We showed the transferability of a trained model on the TC contour to the GTV contour . The topTo mitigate the intensity differences between different MRI scanners, we utilized two types of normalization techniques. These are: Z-score and piecewise linear histogram matching (Nyul) which were selected from a larger set as described in Reinhold et al. , based oThe segmentation strategy we propose employs three distinct approaches: multi-class segmentation, multi-label segmentation, and binary class segmentation. In the multi-class approach, non-overlapping masks were utilized, whereas for both the binary class and multi-label approaches, overlapping masks were employed, as illustrated in We used three types of U-net architectures: 2D, 2.5D, and 3D U-net. The 2D U-net architecture is shown in The 2.5D U-net architecture  employs The proposed 2D U-net model has three versions. The only difference in the architecture lies in the last layer, where sigmoid is applied for binary class and multi-label segmentation, while softmax is utilized for multi-class segmentation. The activation function selection is related to the input mask.The input image dimensions for the 2D model are 240 × 240 × 4, whereas for the 2.5D model, they are 240 × 240 × 12. These input images have a resolution of 240 × 240 pixels and include the concatenation of four registered modalities  for the third dimension. In the encoder path, there are four blocks, each utilizing convolutional layers and incorporating batch normalization for feature map normalization, followed by a ReLU activation layer. After the application of two convolutional layers, a max-pooling layer is employed. The initial feature maps start with 64 channels and are doubled after each max-pooling operation. Furthermore, the bottleneck kernel consists of 1024 channels. In the decoder path, a convolutional transpose layer is applied following two convolutional layers. Skip connections connect the encoder and decoder paths. Finally, the output layer produces a segmentation with a size of 240 × 240 pixels.In the multi-class approach, as illustrated in The loss function employed in all the previously mentioned U-net architectures is chosen as follows: the binary cross-entropy for multi-label/binary class segmentations and multi-class cross-entropy for multi-class segmentation. The selection of these two loss functions is based on the nature of the input masks, distinguishing between mutually exclusive and non-mutually exclusive classes see .The choice of activation function is closely linked to the type of input mask used. For multi-class segmentation with non-overlapping input masks  and p is the predicted probability that the pixel belongs to the object. For an entire image, the total loss is the average of the losses for all pixels. This is for only the binary class segmentation approach. After the loss function calculation, the binary class provides computationally friendly simple analysis like the provided input mask having only one region for each training. For the multi-label segmentation approach, additionally, the calculated losses for each region are averaged to obtain a total loss. The multi-label segmentation approach provides complex and flexible analysis compared to others. However, this increases complexity, computational resources, and label correlation challenges.In binary class segmentation, each input mask is processed individually using the sigmoid function. On the other hand, for multi-label due to utilizing overlapping input masks c.f. B, the siEach segmentation approach has its inherent advantages and drawbacks. The conventional methods typically employ the same segmentation approach to delineate all tumor regions. Our solution diverges from this, by advocating for a region-focused ensemble learning model. This model assimilates the most efficacious segmentation approaches, each complemented by a distinct normalization technique, thereby leveraging the strengths of each to achieve superior performance on the segmentation of the targeted tumor region.In this paper, two different datasets are utilized for the purposes of training and testing the proposed approaches. The first one is the BraTS dataset which defines the retrospective collection of brain tumor MRI scans from multiple institutions ,41,42. I3 and a matrix size of 240 × 240 × 155.In the BraTS competition, there are 3 different sub-tumors for GBM shown in The second dataset utilized in this study is the local dataset (STORM_GLIO), collected between April 2014 and April 2018 in Wales. This dataset comprises 108 glioblastoma patients, with only 53 of them having all four modalities similar to the BraTS dataset. Notably, the MRI scan format for STORM_GLIO is DICOM, differing from the format of the BraTS dataset. The resolution and matrix size of each patient is different. Additionally, these specifications can vary inter-modalities. For further details, please refer to The 3D MRI of the BraTS 2021 dataset has the dimension of 240 × 240 × 155. Before the collection of the slices (240 × 240) or patches (192 × 192 × 128), the training dataset samples are split into 70% training, 15% validation, and 15% testing shown in Since STORM_GLIO is a clinical dataset, it included scans of different dimensions and a patient co-ordinate system that needs to be registered before use. Original MRI scans of the BraTS dataset are registered based on T1ce and SRI24 Atlas   are computed as follows:All metrics are calculated by using a python library: Segmentationmetrics v1.0.1. For the targeted region, We evaluated the proposed method through three principal test scenarios. Initially, to identify the top-performing architecture among the models, the BraTS 2021 dataset was employed. A comparative analysis of each model was conducted to facilitate the model selection phase for the RFS+ strategy. Subsequently, the BraTS validation dataset was utilized to contrast the proposed U-net with the extended nnU-net (the winning model of BraTS 2021). Ultimately, in the third experimental scenario, we executed the RFS+ strategy with the highest-performing architectures against both the extended nnU-net and the models devoid of the RFS+ strategy.This section presents experiments, evaluations, and comparisons of the proposed DL models with different approaches using several intensity normalization techniques. The effectiveness of the proposed models was tested with the BraTS 2021 and STORM_GLIO datasets and compared to each other in order to pursue a model selection step. The results of the aforementioned analysis are presented in On STORM_GLIO, According to Investigating the results presented in On the BraTS 2021 validation dataset, the evaluation in this section will compare the performance of the extended nnU-net and the proposed U-net variants and nnU-net models. Intensity normalization is the Z-score for the proposed U-net variants and nnU-net model. As can be seen in To improve the performance of the ensemble learning, several combinations of segmentation approaches and normalization techniques were employed. To demonstrate the importance of each segmentation approach, an ablation study was conducted. The U-net model employing the multi-class segmentation approach served as the baseline (base U-net) due to the nature of the training dataset masks (non-overlapping).This section presents the usage of the proposed strategy—RFS+—for the purposes of GTV segmentation for the local dataset. We tested the best-performing U-net and nnU-net models with and without RFS+ along with the extended nnU-net model. Additionally, the predecessor method (the RFS)  for 2D UUpon scrutinizing the outcomes detailed in The comparative illustration between the 2D U-net model with RFS+ and the extended nnU-net model is depicted in The RFS+ strategy marks a significant innovation in brain tumor segmentation, notably augmenting the adaptability of DL models within clinical environments. Compared to the current state-of-the-art model, the RFS+ on 2D U-net models exhibits remarkable efficiency, necessitating 10% less training dataset, 67% less memory, and reducing training time by 92% see , therebyThe RFS+ appears to address dataset variability and generalization more explicitly than DeepMedic (v0.8.4), which is crucial for real-world clinical applicability. The multi-scale approach of DeepMedic (v0.8.4), which captures both local and global contextual details, presents a potential area of limitation for the RFS+. The refined, cascading structure of cascade U-net is tailored for precise tumor boundary delineation, which could potentially yield more accurate segmentations compared to those by the RFS+. Cascade U-net’s layered methodology may exhibit superior performance in managing intricate tumor structures. 3D-DSN employs a deep supervision mechanism, ensuring comprehensive feature extraction at multiple levels, potentially resulting in more detailed segmentations than those achieved by the RFS+ approach. The capacity of 3D-DSN to extract learning from its intermediate layers might endow it with an edge in identifying subtle features, a facet that could pose a challenge for RFS+, depending on its learning methodology. The nnU-net’s proven ability to generalize across various medical segmentation tasks stands in contrast to RFS+, which has yet to demonstrate similar breadth in generalizability across diverse datasets and segmentation challenges. Known for its automated configuration, nnU-net can adjust to different tasks and datasets: this is a level of adaptability and automation that RFS+ has not yet developed. The nnU-net has undergone rigorous validation and benchmarking on multiple datasets, including the well-regarded medical segmentation decathlon, whereas RFS+ is yet to be as comprehensively validated, particularly on diverse and complex datasets. Demonstrating robustness under a variety of imaging conditions and with different medical image types, nnU-net’s versatility might present a benchmark for RFS+, whose robustness in such a wide array of conditions necessitates further investigation. With a design conducive to scaling effectively across different tasks and dataset sizes, nnU-net’s flexibility might highlight potential scaling or adaptability challenges for RFS+ in handling exceptionally large or complex datasets. Although the RFS+ strategy demonstrates promising results when compared with the leading model, the primary benchmark used for comparison is the nnU-net. Future work should involve more comprehensive analyses involving a wider array of state-of-the-art models. The performance on standard datasets like BraTS is well established, but its effectiveness on a wider range of datasets might need more exploration. The varying clinical environments present a challenge for the generalizability of DL models, including those using RFS+. Our study focused on the GTV/TC label which include both ET and NCR labels from our local dataset. Finally, the effect of intensity normalization on outcomes differs, based on the specific local dataset and this is an area that necessitates additional research.This study highlights the benefits of adopting the RFS+ strategy for the purposes of brain tumor segmentation in MR images. Although the extended nnU-net resulted in the best DSC score on the BraTS validation dataset, the performance of the proposed 2D U-net model with RFS+ strategy on GTV delineation had the best DSC score in the local dataset. The main reason for this is that the local dataset has no fixed resolution and matrix size for each patient, unlike the BraTS dataset. It is necessary to determine which normalization technique will be most effective for the targeted sub-tumor which is TC/GTV in this research. When using U-net with a multi-class approach on the BraTS 2021 validation dataset, the best DSC scores among the proposed models are 77.45%, 82.87%, and 90.82% for ET, TC, and WT segmentation, respectively. The model incorporating RFS+ yields the best DSC score on our local dataset, achieving 79.22% for GTV segmentation. Moreover, it is worth noting that the model requires 10% less training dataset, 67% less memory, and takes 92% less time for training, as detailed in In conclusion, this study effectively improves segmentation accuracy on the local dataset with our proposed strategy RFS+, setting the stage for future state-of-the-art models to similar benefits from the RFS+ strategy for enhanced generalizability in brain tumor segmentation. The RFS+ model shows encouraging progress, especially in its computational efficiency and ability to adapt to varied clinical contexts. However, to comprehensively assess its place in the domain of brain tumor segmentation, it is essential to conduct further validations and engage in more extensive comparisons with a diverse set of algorithms, thereby mitigating the risk of potential biases. We are planning to extend the application of RFS+ to not only time and memory efficiency but also performance improvement in brain tumor segmentation compared to a wider array of state-of-the-art models, under different clinical settings. The development of data augmentation methods for clinical applications is crucial to align training data with real-world data.


MicroRNAs (miRNAs) play a crucial role in regulating adaptive and maladaptive responses in cardiovascular diseases, making them attractive targets for potential biomarkers. However, their potential as novel biomarkers for diagnosing cardiovascular diseases requires systematic evaluation.In this study, we aimed to identify a key set of miRNA biomarkers using integrated bioinformatics and machine learning analysis. We combined and analyzed three gene expression datasets from the Gene Expression Omnibus (GEO) database, which contains peripheral blood mononuclear cell (PBMC) samples from individuals with myocardial infarction (MI), stable coronary artery disease (CAD), and healthy individuals. Additionally, we selected a set of miRNAs based on their area under the receiver operating characteristic curve (AUC-ROC) for separating the CAD and MI samples. We designed a two-layer architecture for sample classification, in which the first layer isolates healthy samples from unhealthy samples, and the second layer classifies stable CAD and MI samples. We trained different machine learning models using both biomarker sets and evaluated their performance on a test set.We identified hsa-miR-21-3p, hsa-miR-186-5p, and hsa-miR-32-3p as the differentially expressed miRNAs, and a set including hsa-miR-186-5p, hsa-miR-21-3p, hsa-miR-197-5p, hsa-miR-29a-5p, and hsa-miR-296-5p as the optimum set of miRNAs selected by their AUC-ROC. Both biomarker sets could distinguish healthy from not-healthy samples with complete accuracy. The best performance for the classification of CAD and MI was achieved with an SVM model trained using the biomarker set selected by AUC-ROC, with an AUC-ROC of 0.96 and an accuracy of 0.94 on the test data.Our study demonstrated that miRNA signatures derived from PBMCs could serve as valuable novel biomarkers for cardiovascular diseases. Cardiovascular diseases (CVDs) are the leading cause of human mortality, accounting for 32% of all global deaths. It is estimated that approximately 85% of CVD mortality is due to myocardial infarction (MI) . MI is aOver the last 50 years, numerous attempts have been made to use biomarkers to facilitate diagnosis, assess the risk, follow-up therapy, and determine therapeutic efficacy in CVD candidates. Based on released guidelines, cardiac troponins (cTns) are used as a highly sensitive and accurate approach for detecting MI. Despite these inherent advantages, the high sensitivity of cTn-based assays has also led to more false-positive results , necessiNumerous studies have investigated the potential of miRNAs as biomarkers for MI, revealing promising findings. For instance, miR-1 has been proposed as a potential biomarker for MI . This miIt has been postulated that the function and diagnostic properties of miRNAs are beyond the myocardium in patients with CVD. Specifically, the expression of miRNAs can vary in different biofluids and cell components such as serum and peripheral blood mononuclear cells (PBMCs) . PBMCs aIn recent years, the advent and application of machine learning (ML) has been an exciting prospect for advancing scientific research. Although the concept of ML and its initial algorithms were conceived many years ago, recent improvements in computing power and access to vast amounts of data have demonstrated that ML techniques outperform classical statistical methods in various fields. Furthermore, the progress made in omics technologies has enabled the analysis of massive and intricate biological datasets, consisting of hundreds to thousands of samples, which makes it possible for ML to extract valuable biological insights and information from such data . ConsequIn this study, we aimed to identify potential miRNA biomarkers in patients with MI by combining and analyzing three different microarray datasets from PBMCs. The integration of omics data with bioinformatics and ML techniques could be a promising tool in the discovery of new and more accurate biomarkers for monitoring MI. Additionally, this approach can deepen our understanding of the underlying mechanisms of MI and aid in the development of valid diagnostic biomarkers and patient stratification.https://www.ncbi.nlm.nih.gov/geo/). To obtain robust classification performance between MI, healthy control, and CAD samples, sufficiently large sample sizes for each group are required. For this purpose, the GSE59867 dataset was selected, as it contains sizable numbers of both MI and CAD samples. To provide an equally large set of healthy controls, the GSE56609 and GSE54475 datasets containing healthy samples were also included. Combining these three datasets enabled comparative analysis between MI, CAD, and healthy control groups with adequate statistical power. All samples were produced using Affymetrix Human Gene 1.0 ST Array platform (GPL6244). This platform contains 189 miRNA probes based on the annotation data from the GEO database. Only healthy, CAD, and early-stage MI samples were selected from these datasets for further analysis. Early-stage MI samples were analyzed to enable detection of miRNA biomarkers specific to the initial ischemia and infarction event, before extensive myocardial necrosis and remodeling occurs. Using samples from the early phase enhances identification of miRNA signals related to plaque rupture and MI onset versus stable CAD. Additionally, early-stage samples allow investigation of mechanisms initiating myocardial injury. The basic information for the three datasets evaluated in this study is provided in Table  Microarray datasets were obtained from the Gene Expression Omnibus (GEO) database  below 0.05, calculated using the Benjamini-Hochberg procedure to account for multiple testing issues were identified as differentially expressed genes (DEGs). The same procedures were applied to the CAD versus healthy control comparison, as well as to the MI versus CAD group, to identify DEGs specific to each comparison.The differentially expressed miRNAs were defined as those miRNAs within the total DEGs  exceeding 0.8 for separating MI and CAD. This AUC-based approach identifies miRNAs with the best classification performance, unconstrained by statistical cutoffs. Using both the differentially expressed and AUC-based selection provides complementary methods to uncover miRNA biomarkers from both a biological and diagnostic perspective.In this approach, a two-layer architecture is deployed to the data to maximize the prediction values. The first layer predicted whether a sample was healthy or not, and the second layer separated MI from CAD in the samples that were predicted as not healthy in the first layer. To this end, a distinct ML model was trained for each layer. Because there were a limited number of miRNAs in the DEGs, both layers were trained with all of them. For further comparison with the models’ performance, the ROC curve of each miRNA for classifying healthy and not-healthy, as well as CAD and MI, was generated using a logistic regression model.A support vector machine (SVM) model using RBF kernels was trained and hypertuned using all miRNAs in the DEGs. To account for the substantial class imbalance between the healthy and not-healthy groups, with 51 samples in the minority healthy class compared to 157 combined CAD and MI samples, adjustments were made to the sample weights used during model training. Without compensating for the imbalance, the machine learning model would be biased towards the majority class and potentially ignore the minority class. To counteract this, the sample weights were empirically tuned, with the weight for healthy samples set to 1 and the weight for not-healthy samples set to 0.5. These values were determined through iterative testing to produce a model with strong performance on both classes despite the imbalance. The ROC curve and confusion matrix for the model are reported.Different models were investigated to achieve the highest classification performance. To do so, SVM , logistic regression (LR), random forests (RF), k-nearest neighbor (kNN), gradient boosting (GB), XGBoost (XGB) and decision tree (DT) models were trained. All models were trained with their preset parameters using 10-fold cross-validation. The criteria for selecting the best model were the highest accuracy and AUC-ROC for the test set. The best model was hypertuned using the scikit-opt package  for the As in the previous approach, a two-layer strategy was employed. The first layer classified samples into healthy and not-healthy, and the second layer separated the MI and CAD samples. However, to keep the number of miRNAs as low as possible, miRNAs were selected from the second layer and their performance was evaluated in the first layer. The AUC-ROC of all miRNAs for classifying MI and CAD samples was calculated, and miRNAs with AUC-ROC An SVM model with an RBF kernel is trained using the selected set of miRNAs. Additionally, the model was hypertuned to find the hyperparameters for the highest AUC-ROC and accuracy. The same sample weights as in the previous approach  were used. The ROC curve and confusion matrix for the model were reported.The selected miRNA set was used to train different algorithms to determine the best model. Similar to the previous approach, the SVM , LR, RF, kNN, GB, XGB, and DT models were trained. All models were trained with their preset parameters using 10-fold cross-validation. The models with the highest AUC-ROC and accuracy on the test set were selected and hypertuned using the scikit-opt package . The ROCThe PCA plots of the samples are shown in Fig.  According to the cutoff criterion of entclass1pt{minimaThe Venn diagram in Fig. Among the DEGs for MI/healthy and CAD/healthy comparison, hsa-miR-186-5p, hsa-miR-32-3p, and hsa-miR-21-3p were identified as differentially expressed miRNAs. The only differentially expressed miRNA in MI/CAD comparison was hsa-miR-186-5p ., 32.3, 3PBMCs can respond to several insulting conditions, such as MI, in the shortest possible time with notable changes in their miRNA profile . ConsideIn this study, we combined three GEO datasets for healthy, CAD, and MI samples. Having these sample sets alongside bioinformatics analysis and ML methods enabled us to identify potential biomarker sets and effective therapeutic targets. The results of the DEG analysis Table   prove thML models trained with miRNA sets selected by both DEG and AUC-ROC approaches, showed better classification performance than each miRNA. To avoid unwanted complexity and poor predictive values, a two-layer architecture was designed. The first layer was used to discriminate between healthy and not-healthy samples, and the second layer was was used to separate CAD from MI candidates. As expected, in both approaches, a hypertuned SVM model could flawlessly separate healthy and not-healthy samples using distinct miRNA sets. ML models are also capable of effectively separating CAD from MI patients. Although both miRNA sets had nearly the same AUC-ROC using the best model, their accuracy, sensitivity, and specificity were different. The model trained with AUC-selected miRNAs showed better performance in all predictive values, which is logical because of the higher number of miRNAs in the set.Numerous studies have reported that different biological processes can affect the miRNA expression in PBMCs. However, the exact role of miRNAs in the function of immune cells and the correlation between specific pathological conditions and miRNA profiles remain controversial. Several studies have proven the activation of particular miRNA types in PBMCs under cardiovascular events . For insormation . In lineAs mentioned before, hsa-miR-21–3p was also up-regulated in both MI and CAD patients compared to healthy controls. Moreover, the expression value of hsa-miR-21–3p was significantly higher in the MI group than in the CAD group , osteopontin, and bone-specific phosphoprotein matrix GLA protein . LikewisMolecular analyses have indicated the regulatory role of miRNAs selected using the AUC-ROC approach in PBMCs after a cardiovascular event. The biological importance of two common miRNAs in the DEGs and AUC-ROC approaches, hsa-miR-21–3p and hsa-miR-186–5p, have already been discussed. Based on numerous reports, hsa-miR-29a-5p can be activated in different diseases . Data anThe data indicated that hsa-miR-197–5p was significantly up-regulated in both the CAD/healthy and MI/healthy groups. Previous studies have demonstrated that hsa-miR-197–5p may play a crucial role in controlling the anti-inflammatory response of IL-35 by influencing the secretion of cytokines that can either promote or suppress inflammation, the ratio of M1/M2 macrophages, and the proliferation of Tesponses . AlongsiThe findings of this study demonstrate the potential of hsa-miR-296–5p as a biomarker with high discriminatory power to distinguish between samples from individuals with MI and CAD. Hsa-miR-296–5p has been identified as a key regulator in the development and advancement of atherosclerosis by controlling the expression of target genes associated with various biological processes, including angiogenesis, cholesterol metabolism, inflammation, cellular proliferation, hypertension, and apoptosis . In a prcument}α . These fIn summary, we derived a set of miRNA biomarkers by comparing MI samples with both healthy and CAD samples. We found that the SVM model performed best in both the first layer, which separated healthy and unhealthy samples, and the second layer, which classified the MI/CAD samples. The set of miRNAs selected based on their AUC-ROC values performed better in the second layer. Overall, our two-layer structure achieved an accuracy of 0.96. This demonstrates the potential of combining bioinformatics and machine learning techniques to identify novel biomarkers and gain a deeper understanding of myocardial infarction.


Sepsis is an overwhelming reaction to infection that comes with high morbidity and mortality. It requires urgent interventions in order to improve outcomes. Intravenous immunoglobulins (IVIG) are considered as potential therapy in sepsis patients. Results of trials on IVIG as adjunctive therapy for sepsis have been conflicting due to the variability in population characteristics, country geography and drug dosage form in different studies.A systematic article search was performed for eligible studies published up to January, 31, 2023, through the PubMed, Embase, Cochrane Library and Chinese National Knowledge Infrastructure database. The included articles were screened by using rigorous inclusion and exclusion criteria. Subgroup analyses were conducted according to different IVIG types, ages and economic regions. All analyses were conducted using Review Manager 5.4. Quality of studies and risk of bias were evaluated.p = 0.005), the hospital stay , and the APACHE II scores . Additionally, the results showed that IgM-enriched IVIG was effective in treating sepsis , while standard IVIG failed to be effective . And the effect of IVIG in reducing neonatal mortality was inconclusive , but it played a large role in reducing sepsis mortality in adults . Besides, from the subgroup of different economic regions, it indicated that IVIG was effective for sepsis in high-income  and middle-income countries , while no benefit was demonstrated in low-income countries .In total, 31 randomized controlled trials were included with a sample size of 6,276 participants. IVIG could reduce the mortality (RR 0.86, 95% CI: 0.77–0.95, There is sufficient evidence to support that IVIG reduces sepsis mortality. IgM-enriched IVIG is effective in both adult and neonatal sepsis, while standard IVIG is only effective in adult sepsis. IVIG for sepsis has shown efficacy in high- and middle-income countries, but is still debatable in low-income countries. More RCTs are needed in the future to confirm the true clinical potential of IVIG for sepsis in low-income countries.The online version contains supplementary material available at 10.1186/s12967-023-04592-8. Sepsis is a syndrome of life-threatening organ dysfunction caused by a dysregulated host response to infection . It affeIntravenous immunoglobulin (IVIG), containing the full range of antibody spectrum, is derived from the plasma of thousands of healthy donors . It contTo date, the factors that influence the efficacy of IVIG for sepsis have not been thoroughly explored. It was reported that different types of IVIG are key to the efficacy of sepsis. The pentameric structure of IgM improves the activation of the complement system compared to IgG in standard IVIG . Thus, IThis study conducted a meta-analysis of the efficacy of IVIG in the treatment of sepsis based on patients ages, IVIG types and economic regions, providing a basis for the application of IVIG in clinical treatment.This study was designed and conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline . The preThe search included articles in English language published in the PubMed, Embase, Cochrane library, Chinese National Knowledge Infrastructure database through January, 31, 2023. The search was conducted using the following keywords: Intravenous Immunoglobulin or IVIG or Intravenous Antibodies and Sepsis or Septicemia or Pyemia or Septic Shock and randomized controlled trial or controlled clinical trial or placebo or groups. The detailed retrieval strategy can be found in the Additional file Inclusion criteria for the systematic review were (1) randomized controlled study of sepsis; (2) all subjects were diagnosed with sepsis; (3) experimental group was not given intervention other than IVIG administration under the guarantee of basic medical care. Studies were excluded if (1) study design was not an RCT; (2) study reported insufficient details to derive the study outcomes; (3) study had other interventions; (4) the full text of the study was not available in the databases; (5) study was on an animal model.We assessed the primary outcomes of this study is an efficacy measure, all-cause mortality at the end of the follow-up period. The secondary outcomes including the length of hospital stay among survivors and APACHE II, were assessed for efficacy.Two investigator  performed the literature search and screening, and 2 investigators  independently performed data extraction. Discrepancies were resolved through discussion between investigators. The extracted data items include (1) study design, country, year of publication; (2) participant characteristics, including age, size, source, income level; (3) details of the intervention, treatment duration. An additional file shows this in more detail  of sepsis patients showed IVIG did play a great role in reducing the mortality and middle-income countries , while no benefit was demonstrated in low-income countries .Of the 31 RCTs included, 22 were conducted in high-income countries, 6 in middle-income countries and 3 in low-income countries Figs. , 8.The rThis is a high-quality comprehensive meta-analysis to evaluate the efficacy of IVIG therapy for sepsis. In order to ensure the credibility of the meta-analysis, all nonrandomized controlled trials such as cohort studies were excluded. Similarly, preprints that have not been peer-reviewed were not included.In this meta-analysis, IVIG treatment significantly reduced all-cause mortality in patients with sepsis compared with placebo plus standard treatment or standard treatment alone. However, in 2016 surviving sepsis campaign suggested against immunoglobulin use in sepsis [When the subjects were subgrouped by 2 different ages, IVIG therapy was associated with a significantly decreased all-cause mortality in adults with sepsis, whereas its efficacy in neonatal sepsis patients was not applicable. From the perspective of disease incidence, neonates are a special group, and they are more likely to develop into sepsis . StudiesWhen neonatal group was subgrouped by different types of IVIG administration, it was found that standard IVIG did not reduce all-cause mortality in neonatal sepsis in the present study. It was consistent with the results of the meta-analysis by Alejandria . HoweverNotably, accurate estimates of neonatal sepsis burden vary by countries, with different estimates of disease burden reported in high-income countries compared to those reported in low- and middle-income countries . It was Several limitations may affect the results of our meta-analysis. The collected data were limited because this meta-analysis only included randomised controlled trials with high-quality evidence. Since studies in different subgroups concentrated either in high-income countries or in low- and middle-income countries, the included neonatal RCTs lacked regional representation after subgroup analysis. So the data were only marginally representative. Moreover, we can not exclude the effect of publication bias and the potential effect of some confounders.In summary, there is sufficient evidence to support that IVIG reduces sepsis mortality. IgM-enriched IVIG is effective in both adult and neonatal sepsis, while standard IVIG is only effective in adult sepsis. IVIG for sepsis has shown efficacy in high- and middle-income countries, but is still debatable in low-income countries. More RCTs are needed in the future to confirm the true clinical potential of IVIG for sepsis in low-income countries.Additional file 1. The detailed search strategy.Additional file 2: Table S1. Characteristics of RCTs included in this systematic review (adults). Table S2. Characteristics of RCTs included in this systematic review (neonates).


Background: In 2019, Germany introduced a law to reimburse high-incidence populations for pre-exposure prophylaxis (PrEP), prescribed as tenofovir-disoproxil/emtricitabine (TDF/FTC), via statutory health insurance (SHI). We studied changes in TDF/FTC-prescriptions after the implementation of this law and during the COVID-19 pandemic.Methods: We performed an interrupted time series analysis with monthly prescriptions per defined time period as the outcome. We considered the introduction of SHI-covered PrEP (09/2019) as an interruption, and four COVID-19 waves and two national lockdowns (2020–2021) as explanatory variables. We extrapolated prescriptions had the lockdowns not occurred, and compared this to the actual prescriptions. We performed sub-analyses based on stratification by five federal states with the highest proportion of PrEP users. We assessed the models’ goodness-of-fit based on the adjusted R-squared using RStudio.Results: The best fitting model included SHI-covered PrEP and the first COVID-19 lockdown (04/2020). The decrease in prescriptions during the first lockdown was significant nationally, and in the five federal states for single-month prescriptions. The first lockdown resulted in reductions of 57.7% : 23.0%–92.4%) for single-month prescriptions, while 17.4% (95% PI: 0.28%–34.5%) nationally, and 13.9% (95% PI: -3.67%–31.5%) for 3-month prescriptions.Conclusion: Introduction of SHI-covered PrEP resulted in a doubling of TDF/FTC-prescriptions nationwide in the first month alone. A drop in prescriptions was most apparent after the first lockdown, and particularly affected PrEP initiations, possibly due to reduced healthcare access and behavioural changes. Ongoing monitoring of TDF/FTC-prescriptions is needed to safeguard access to preventative care such as PrEP and particularly PrEP initiation during public health crises like COVID-19. Human immunodeficiency virus (HIV) pre-exposure prophylaxis (PrEP) based on daily oral antiretroviral medication is a safe and effective preventive measure that, when taken as prescribed, strongly reduces the risk of HIV acquisition among persons at risk . PrEP isFormally, a clinical assessment is needed prior to initiating PrEP. During treatment with PrEP, it is recommended that individuals pay regular healthcare visits for PrEP counselling, and that clinicians regularly follow up on renal function, and test for HIV and other sexually transmitted infections (STIs) .In Germany, PrEP became officially available in 2016 when the European Medicines Agency approved the use of tenofovir disoproxil and emtricitabine (TDF/FTC) for PrEP . However® (TDF/FTC) patent expired. Still, costs of approximately 240 Euros per month in the case of continuous use  . In GermThe global COVID-19 pandemic emerged in early 2020. Subsequent national and regional public health measures such as lockdown policies, and COVID-19-related reductions of outpatient hours affected the availability of health services, and personal concerns about possible SARS-CoV-2 exposure affected social and healthcare-seeking behaviours . This inThe objective of this study is to examine the changes in TDF/FTC prescription trends in Germany  after the implementation of SHI-covered PrEP in September 2019, and during the COVID-19 pandemic until December 2021. Understanding trends in PrEP prescriptions over time can help guide public health efforts to facilitate access to PrEP and ensure that PrEP coverage is successfully expanded. Moreover, it can help prevent potential negative effects of future pandemic responses on successful preventive health measures such as PrEP.In Germany, medical prescriptions are electronically recorded, since SHI reimburses pharmacies via specialized pharmacy billing centres. From September 2019 onwards, data on TDF/FTC prescribed on SHI included prescriptions for PrEP. To include the period prior to SHI-covered PrEP, monthly prescription data on TDF/FTC in Germany were obtained from January 2018 to December 2021. The data were provided by Insight Health™, which claims >99% coverage of the SHI prescription market (≈73 million people). The data on the sale of TDF/FTC prescriptions from pharmacies originate from a database that is not person-specific and cannot be linked to other health insurance data or treatment indication. No accessible national data source for the SHI system currently exists, which would allow the validation of prescriptions according to treatment indication. The analysed anonymous prescription data included, for each month between January 2018 to December 2021: substance name, pack size, number of prescriptions, and location of prescription reimbursement. The data used here have a specific legal status in Germany and can be purchased under the regulations of the German Social Code Book V (§300 SGB V) with a delay of approximately 3 months.The only drug currently approved for PrEP in Germany is TDF/FTC, which individuals take as a fixed dose combination tablet. TDF/FTC is also used for HIV therapy and postexposure prophylaxis (PEP), in which cases it is combined with a third compound. Since our dataset of pharmacy billing data is anonymous and aggregated, there is no information on how often TDF/FTC was combined with a third compound. Therefore, TDF/FTC in this data source may have been prescribed for PrEP, HIV therapy, or PEP. However, we made attributions based on the ratio of prescription shares, previously stable trends in TDF/FTC as part of HIV therapy, and the knowledge that the standard treatment duration for PEP is 28–30 days. Moreover, HIV therapy is usually prescribed as a 3-month package, while PrEP is usually initiated with a single-month package, followed by 3-month TDF/FTC packages. We therefore analysed single-month and 3-month data separately.In terms of the ratio of TDF/FTC prescription shares, the overall number of PEP treatments relative to PrEP as well as HIV therapy is low. In the recent years before SHI-covered PrEP, the number of TDF/FTC prescriptions slightly decreased. Recent analyses of unpublished, routine health insurance data used for surveillance purposes also suggest that the number of TDF/FTC prescriptions as part of HIV therapy remained relatively constant after the introduction of SHI-covered PrEP in September 2019. Hence, we assume that any potential abrupt changes in TDF/FTC prescriptions after September 2019 were mainly due to changes in PrEP use.Since HIV treatment involves vital, lifelong therapy that must be taken daily, we assumed that HIV therapy was continued throughout the COVID-19 pandemic. Therefore, with respect to potential changes during the COVID-19 waves and lockdowns, we assumed that in the case of 3-month data, these changes primarily represented alterations in continued PrEP use, while changes in the single-month data primarily represent alterations in PrEP initiation, and to a lesser extent, in PEP.We conducted an interrupted time series approach involving regression methods in which we modelled the implementation of SHI-covered PrEP as an interruption. The four COVID-19 waves and first two corresponding national lockdowns in Germany between January 2020 and December 2021 were included as potential explanatory variables in the regression models.We defined the time periods as follows : First wt = Intercept + SHI_coveraget + slope_before_SHI × t + slope_after_SHI × t + COVID_waveti) TDF_FTCt = Intercept + SHI_coveraget + slope_before_SHI × t + slope_after_SHI × t + COVID_lockdowntii) TDF_FTCGenerally, the regression model was defined as follows accounting for either i) waves or ii) lockdowns:t: TDF and FTC prescriptions in month t; SHI_coveraget: shift in prescriptions if PrEP is covered by SHI in month t; slope_before_SHI: monthly change of TDF/FTC prescriptions before PrEP was covered by SHI; slope_after_SHI: monthly change of TDF/FTC prescriptions after the law change that SHI covers PrEP; COVID_wavet: shift in prescriptions if month t is within the first COVID-19 pandemic wave; COVID_lockdownt: shift in prescriptions if month t is within the first COVID-19 COVID-19 lockdown.Where TDF_FTCThe estimated shift in prescriptions directly after SHI coverage of PrEP is based on the difference between, respectively, the upper and lower ≥95% confidence interval bounds of the estimated mean number of prescriptions before and after the SHI coverage of PrEP in September 2019. The monthly changes before and after SHI coverage of PrEP were included as slopes in the regression models. The outcome was the number of monthly TDF/FTC prescriptions in Germany per defined period of time.The hypothetical monthly prescription data had the four COVID-19 waves or two lockdowns not taken place were determined as linear extrapolations and subsequently compared to the actual observed number of prescriptions during the same time period. To assess potential changes during the four waves and two lockdowns, linear regression models were considered in which the interruptions were represented as binary explanatory variables, defined as 1 during the corresponding timespan of the pandemic wave or lockdown, and 0 if outside this timespan.We performed sub-analyses based on single-month  and 3-month data (84 tablets or 90 tablets), and stratified by the 16 German federal states . We also compared separate trend analyses for the different regions and treatment durations (single-month versus 3-month prescriptions). For this purpose, we considered the five federal states with the largest metropoles in Germany where the proportion of PrEP users was highest in 2020: Berlin/Brandenburg, Bavaria, Hamburg, Hesse, North Rhine-Westphalia . Berlin We assessed the overall goodness-of-fit of the considered models based on the adjusted R-squared. Likewise, the adjusted R-squared was used to compare and choose among potential model variations. We conducted all analyses using R and RStudio.Ethical approval and informed consent were not required, because we used routinely collected, anonymized secondary data from pharmacies, which cannot be traced back to individual patients. The German Social Code Book V (§300 SGB V) ensures safe use of this data.From January 2018 to December 2021, there were a total of 697,555 TDF/FTC prescriptions in Germany , and 3-month prescriptions: 2,496 for the overall TDF/FTC prescriptions , and 1,843 for the 3-month prescriptions . However, for the single-month prescriptions, this was 645 .p = 0.0595)  . We did  0.0595) .Following the analysis of the TDF/FTC prescription data for the five federal states with the largest metropoles, we assessed the number of single-month and 3-month prescriptions for each of these federal states using the same linear regression models. Generally, these models exhibited acceptable goodness-of-fit with the actual single-month and 3-month data from these five federal states. The linear regression model with the best fit included the variables SHI coverage of PrEP and the first wave, closely followed by the model including SHI-covered PrEP and the first lockdown .Finally, we plotted the linear regression models including SHI coverage of PrEP and the first COVID-19 lockdown against actual single-month and 3-month TDF/FTC prescriptions for the five federal states . The treWhen comparing the five federal states to one another, the trends in single-month prescriptions appeared similar , and corHowever, in each of the five federal states, the single-month and 3-month trends clearly differed. Although in both cases we observed a slightly declining trend from January 2018 to August 2019 (before the SHI coverage of PrEP), the subsequent increase was markedly more pronounced for the single-month data compared to the 3-month data . MoreoveThis study addresses changes in TDF/FTC prescriptions in Germany from January 2018 to December 2021. During this 4-year period, an important national law establishing the right for individuals at substantial risk of acquiring HIV to receive insurance-covered PrEP was implemented in September 2019. In the months that followed, the COVID-19 pandemic struck, resulting in several successive COVID-19 waves and lockdowns in Germany.Our data show a remarkable jump in TDF/FTC prescriptions in the same month that SHI-covered PrEP was introduced in September 2019. Taken as a proxy for PrEP initiation, this clearly shows that being given the chance to start fully insurance-reimbursed PrEP motivated a large group of eligible persons to initiate this preventive treatment via their health insurance within 1 month of implementation of the law.During the COVID-19 pandemic, the largest downward change of TDF/FTC prescriptions was observed during the first wave and lockdown in April 2020, similar to observations in France . This isThis study shows that the most notable change during the first lockdown was on the single-month packages (rather than on the 3-month packages), both at the national level and in the five federal states with the largest German metropoles with the highest proportion of PrEP users. This implies that the first lockdown affected persons initiating PrEP more severely than persons who were continuing PrEP or HIV therapy. This is indeed likely, assuming that implementation of insurance-reimbursed PrEP caused a sharp increase in persons who initiated PrEP, and given that during the first wave, nationwide pandemic control measures and restrictions were in place which affected the federal states in much the same way.From September 2019 until December 2021, overall trends in single-month prescription data decreased while 3-month prescriptions increased, most likely as a result of fewer new PrEP initiations and more PrEP and HIV therapy continuations. Possibly, the overall downward trend in PrEP initiations is also associated with the commonly given advice to reduce social contacts as part of restrictive measures in place during the four lockdowns that occurred between September 2019 and December 2021. Notably, North Rhine-Westphalia was the only federal state where we did not observe a significant association in the overall trend with the first lockdown, and in the federal states Hesse and North Rhine-Westphalia, the first lockdown significantly affected single-month TDF/FTC prescriptions, but not 3-month prescriptions, indicating that although PrEP initiations decreased during that time, the continuation of PrEP and HIV therapy was in fact maintained in these federal states. This drop in PrEP demand in particular for PrEP initiations is similar to what was reported by HIV specialty care centres in North Rhine-Westphalia, Hesse, and other federal states in surveys about PrEP availability, demand and uptake during the COVID-19 pandemic . HoweveTo effectively prevent HIV, PrEP requires compliant use for which continued access is important. Previous research found that PrEP use and adherence decreased after the implementation of pandemic-related control measures. Other research found that HIV testing rates decreased during the COVID-19 pandemic  which miRobust surveillance and monitoring systems both within and outside Germany, which include data collection on the extent of informal online access to PrEP and health outcomes of existing monitoring are important, and indeed are part of recent recommendations by the European Centre for Disease Control (ECDC) as well as part of a current project that aims to establish a continuous PrEP surveillance in Germany .The monthly aggregated TDF/FTC prescription data for PrEP, PEP, and HIV treatment in Germany used in this study do not represent individual prescription data, and would equal the monthly number of prescriptions only in case of daily use. Based on the ratio of prescription shares, treatment duration, and stable trends in TDF/FTC prescriptions as part of HIV therapy, the monthly changes after September 2019 in this dataset most likely primarily reflect changes in PrEP use. However, no accessible national data source for the SHI system currently exists in Germany, and TDF/FTC prescriptions as part of HIV therapy or PEP cannot be directly distinguished. As such, we were unable to validate prescriptions according to treatment indication. Therefore, these data are an indirect measure and might under- or overestimate actual PrEP use. Within the dataset it is not possible to formally distinguish between new prescriptions and refills, nor can these data be linked to any demographic or behavioural data.Furthermore, in October 2020 a reference price level regulation for TAF/FTC came into effect in Germany , which cIn summary, PrEP use in Germany doubled after the establishment of the legal right to SHI-covered PrEP in September 2019. Since then, continuous PrEP use in Germany has steadily increased, although to what extent PrEP use would have increased in the absence of the COVID-19 pandemic cannot be known. Based on these data, it appears that only the first COVID-19 wave and corresponding lockdown negatively affected PrEP prescriptions, possibly as a result of reduced access to care and a reduction in risk behaviour. PrEP initiations were most negatively affected during the first lockdown, and to a lesser extent, PrEP continuation and HIV therapy.The public health relevance of this finding is that in times of infectious disease outbreaks, maintenance of access to preventative care such as PrEP, and particularly PrEP initiation, merits attention. To this end, expanding PrEP coverage, such as through telemedicine, targeted communication, and low-threshold, outreach-based programs is vital. In conclusion, ongoing monitoring of trends in prescription, coupled with the assessment of contextual factors, is needed to investigate possible long-term effects, and to understand how PrEP use changes over time. This can guide future efforts to expand PrEP coverage, and help avoid lapses in access to PrEP in responses to future pandemics.


The technical advisory group of the World Health Organization  has suggested person-centered and community-based mental health services in response to the long-term and far-reaching mental health impacts of the COVID-19 pandemic. Task shifting is a pragmatic approach to tackle the mental health treatment gap in low- and middle-income countries. Pakistan is dismally resourced to address the mental health challenges. Pakistan’s government has established a lady health worker’s program (LHW-P) which can be effectively utilized to provide some basic mental health services at community doorsteps. However, lady health workers’ current curriculum does not include mental health as a subject. WHO’s Mental Health Gap Intervention Guide (mhGAP-IG) Version 2.0 for mental, neurological, and substance use disorders in non-specialist health settings can be adapted and utilized to be included as part of the LHW-P curriculum in Pakistan. Thus, the historical lack of access to mental health support workers, counsellors, and specialists can be addressed. Additionally, this will also help to reduce the stigma associated with seeking mental health care outside the boundaries of home, mostly at a huge cost. Mental health impacts of the pandemic have been declared to be long-term and far-reaching. WHO’s technical advisory group suggested critical actions to be taken by national authorities in response to COVID-19. Of the many recommendations, one is to provide person-centered, community-based mental health services using innovative approaches . A systeDespite the high rate of psychiatric disorders in the country, help-seeking is very low due to prevailing stigma, lack of awareness, unemployment, poverty, and a low literacy rate . CurrentWHO has advised low- and middle-income countries (LMICs) to tackle the mental health treatment gap through ‘task-shifting’, i.e., training non-specialists such as nurses, teachers, and community health workers (CHWs) to provide mental health services under the guidance of specialists . This apSantiago, Chile, has used stepped care approach successfully. This approach was a 3-month, multi-component intervention led by a group of non-medical health workers. They provided psychoeducation, structured and systematic follow-up, and medicines to 240 adult female primary care patients with severe depression. They found nearly 70% of patients recovered using a stepped care program at an additional cost of just 216 Chilean pesos (US$0.32) . In a ruGovernment of Pakistan established a CHWs’ program known as the Lady Health Worker-Programme (LHW-P) comprising LHWs and their Supervisors (LHSs) to strengthen health systems at the household and community levels and to connect local communities with hospital-based services. LHWs in Pakistan have minimum 8 years of education and undergo 15 months of classroom and field-based training. Each LHW has a designated catchment area of 100–150 households, catering to 1000 community members , 15. A lOne of the key strengths of LHW-P is that LHWs are recruited from within local communities, and thus can deliver services in a culturally appropriate and acceptable manner. They have established acceptability in the community and are respected as a healthcare workforce. This workforce is responsible for providing basic preventive maternal, newborn, and child health (MNCH) and family planning services at community doorsteps and cover almost 60% of the rural population . In receFor successful integration of mental health into primary healthcare in low and middle-income countries like Pakistan, grass-root level workers like LHWs need to acquire relevant knowledge and skills to recognize, refer and support people experiencing mental health disorders. They need to be trained in screening symptoms of mental ill health, communication skills, stress, and emotion management strategies. In this regard, WHO’s Mental Health Gap Intervention Guide (mhGAP-IG) version 2.0 for mental, neurological, and substance use disorders in non-specialist health settings  can be uCurrent curriculum of LHW-P includes health education, promotion of healthy behaviors, antenatal and postnatal care, and dispensing of ORS and Zinc . Thus, tWHO recommends that mhGAP-IG should be adapted by countries to suit their local context, resources, and priorities . This mhEarly results (yet to be published) of adapting mhGAP guide to the context of non-specialist LHWs in one district of Sind, Pakistan, showed that these front-line workers easily understood the causes, signs, and symptoms of common mental illnesses and found this guide useful in improving their communication and counselling skills for improving mental health . CHWs caThus, to bridge the mental health delivery gap in Pakistan, a task-shifting approach is recommended. mhGAP guidelines offer a promising solution to build the mental health capacity of LHW-P. Once the content has been contextually and linguistically adapted considering the current mental health literacy of the LHWs, it has the potential to be a supplementary chapter in the LHW-P curriculum. A pilot feasibility study can later test the effectiveness of introducing mental health into LHW-P mandate and its impact on reducing mental distress.Task-sharing models require increased numbers of human resources, adequate training, compensation for health workers who take on new mental health tasks, and ongoing structured supportive supervision at the community and primary health care level. It is now for the Government of Pakistan, academic institutions, non-governmental organizations, and development agencies to invest in implementing this approach to reduce the huge gap that is responsible for a substantial burden of disease in the country.


As part of the EU Joint Action on Antimicrobial Resistance (AMR) and Healthcare-Associated Infections, an initiative has been launched to build the European AMR Surveillance network in veterinary medicine (EARS-Vet). So far, activities included mapping national systems for AMR surveillance in animal bacterial pathogens, and defining the EARS-Vet objectives, scope, and standards. Drawing on these milestones, this study aimed to pilot test EARS-Vet surveillance, namely to (i) assess available data, (ii) perform cross-country analyses, and (iii) identify potential challenges and develop recommendations to improve future data collection and analysis.Eleven partners from nine EU/EEA countries participated and shared available data for the period 2016–2020, representing a total of 140,110 bacterial isolates and 1,302,389 entries (isolate-antibiotic agent combinations).Collected data were highly diverse and fragmented. Using a standardized approach and interpretation with epidemiological cut-offs, we were able to jointly analyze AMR trends of 53 combinations of animal host-bacteria–antibiotic categories of interest to EARS-Vet. This work demonstrated substantial variations of resistance levels, both among and within countries .Key issues at this stage include the lack of harmonization of antimicrobial susceptibility testing methods used in European surveillance systems and veterinary diagnostic laboratories, the absence of interpretation criteria for many bacteria–antibiotic combinations of interest, and the lack of data from a lot of EU/EEA countries where little or even surveillance currently exists. Still, this pilot study provides a proof-of-concept of what EARS-Vet can achieve. Results form an important basis to shape future systematic data collection and analysis. Salmonella and Campylobacter from human infections  has been widely recognized as a major public health problem, responsible for an estimated 23,100 human deaths every year in Western and Central Europe . To addrfections .Salmonella and Campylobacter), indicator bacteria (Escherichia coli) and extended-spectrum-cephalosporin-resistant and carbapenemase-producing E. coli from healthy food-producing animals  at slaughter and meat thereof, according to Directive 2003/99/EC  coordinates a mandatory active monitoring of AMR in zoonotic , an initiative was launched in 2017 to build the European Antimicrobial Resistance Surveillance network in veterinary medicine (EARS-Vet) . An initFollowing this review, a group of experts from 14 EU/EEA countries, the majority of which were actively involved in the 15 identified programs, joined forces to establish a methodological basis for EARS-Vet, including the EARS-Vet objectives, standards  methods and interpretation criteria), and scope  , 2022a. Drawing on the EARS-Vet milestones achieved so far, the objective of this study was to pilot test surveillance by EARS-Vet. Specifically, we aimed to (i) assess available data from partner countries, (ii) perform cross-country analyses for selected bacteria–antibiotic combinations, (iii) identify potential challenges, and (iv) formulate recommendations for future improvement of EARS-Vet data collection and analysis.Upon signature of a data sharing agreement, 11 partners from nine European countries participated in the EARS-Vet pilot study and shared their available data for the years 2016 to 2020. The majority of partners retrieved data from national monitoring systems of AMR in bacterial isolates from diseased animals, the structures and operations of which have been described previously . Additio®) data , and Laboklin which used MERLIN Diagnostika GmbH, partners who used broth microdilution all used commercial microtiter plates (either VetMIC before production ceased in 2018 or Thermo Fisher Scientific Inc.). Eight partners referred to methodological standards of the Clinical and Laboratory Standards Institute . NORM-V®) and standards used , AST quantitative result (minimum inhibitory concentration (MIC) or inhibition zone diameter), as well as production of AmpC- or extended-spectrum beta-lactamase – ESBL, virulence profile (for swine Escherichia coli), and presence of mecA or mecC gene (for Staphylococcus spp.). For disk diffusion data, disk concentrations used for AST were also collected. Data were pseudo-anonymized so that no link could be made to the animal or owner of origin.A data collection template was circulated to all partners in the form of a Microsoft Excel sheet . Taking into consideration the study objectives, six variables were deemed essential: year, isolate ID, animal host, bacterial species, antibiotic agent, and MIC/disk diameter value. Entries for which those variables were incorrect, ambiguous, or missing  were removed from the dataset . Other vSemantic errors in the dataset were also looked for. First, all true duplicated entries  were deleted. Then we checked that (i) each isolate ID corresponded to a unique animal species, bacterial species, country and year, and (ii) each antibiotic agent tested on a given isolate had a unique MIC/disk diameter value. When one or two of these properties were violated, the data were either corrected or excluded from the dataset. For each partner, feedback was provided in the form of an individual report on data quality, to facilitate data verification and improve future data extractions.For each combination of bacterial species–antibiotic, distribution of MIC and disk diameter values were graphically displayed and visually checked for concordance with existing EUCAST epidemiological cut-off values (ECOFFs) or tentative ECOFFs (TECOFFs), when available by May 2022. Of note, several partners tested only for a narrow range of antibiotic concentrations and for a proportion of the isolates provided semi-quantitative MIC data , instead of an exact MIC; these data were arranged by ascending order and visualized on the graph to better capture the overall MIC distribution.To evaluate proportions of resistance across the years, MIC/disk diameter values were used to categorize isolates as wild-type (WT) or non-wild-type (non-WT) in reference to an antibiotic, using either (T)ECOFFs from EUCAST (for MIC data)  or epideCombinations of bacterial species–antibiotic agents with no available (T)ECOFF or CA-SFM Vet cut-off were not categorized as WT/non-WT, and were excluded from the analyses. Similarly, MIC data for which the tested concentration ranges were insufficient to allocate WT vs. non-WT categories based on EUCAST (T)ECOFFs were excluded from the analyses. Spanish CLSI disk diffusion data were also excluded from the analyses, since CLSI currently has no epidemiological cut-off values publicly available.E. coli data, where sufficient information was available to split between milk versus other types of specimens. All analyses were performed using R statistical software version 3.6.1  initially collected, a total of 1,302,389 entries (68.4%) were retained in further analyses, representing 140,110 isolates and 317 combinations of animal species–bacterial species–antibiotic. Main reasons for exclusion were animal species–bacterial species–antibiotic agent combinations out of the EARS-Vet scope as defined previously  or non-rThe number of isolates for which data were provided per partner was highly variable, with RESAPATH and SVA being the largest contributors and providing 77.1 and 11.8% of all available isolates, respectively . BacteriE. coli  followed by Staphylococcus pseudintermedius (14.0%)  . E. colimecA/mecC presence confirmation by three partners (151 isolates) , ESBL/AmpC phenotype confirmation by four partners (140 isolates), and solates) .Streptococcus dysgalactiae–ceftiofur and Streptococcus uberis–ceftiofur).Out of the 148 bacterial species–antibiotic agent combinations retained in this study, 81 (54.7%) had a EUCAST (T)ECOFF available for MIC data interpretation. For six of them, the antibiotic concentration ranges used by some laboratories were insufficient to apply the EUCAST (T)ECOFF, leading to discard more than 30% of the collected data for these six combinations; in this case, the data from the partner were removed entirely for this particular bacterial species–antibiotic agent combination. For French disk diffusion data, 146 (98.6%) combinations had a CA-SFM Vet epidemiological cut-off value available , animal species , critically and non-critically important antibiotic classes, and countries. AMR trends for the 51 other combinations of bacterial species–antibiotic categories of interest with sufficient data and available interpretation criteria are provided in supplementary material  and swine  compared to dogs  and cats . Similarly, the proportion of fluoroquinolone-resistant S. pseudintermedius from dogs was variable between countries, with Sweden having proportions below 5%, Denmark and France between 5 and 10%, and Germany between 10 and 20% , and from one animal/bacterial species to another. This not only depended on the amount of data owned by the partners, but also on their ability to easily curate and extract them, since the lack of efficient data management tools was reported as a major weakness across existing systems . Except vs beef should be considered to be replaced by calf vs adult cattle, since these tend to have distinct bacterial infections. In addition, dairy vs beef production are not always easy to distinguish, since calves born in dairy herds are commonly transferred and later reared for beef production. Similarly, specimen data were difficult to retrieve (except for milk in cattle), and some partners had difficulties to allocate recorded specimen into the EARS-Vet predefined list. Alternatively, EARS-Vet could consider working with a list of predefined infection types , to which data could be allocated more easily, hence facilitating data interpretation for the next data collection rounds. While almost the entire scope initially envisaged for EARS-Vet , its applicability to fastidious organisms  is somewhat questionable due to differences between CLSI vs EUCAST methodology; this can be considered as a minor limitation to this study. Conversely, recognizing that EUCAST and CLSI standards differ for disk diffusion , and since CLSI epidemiological cut-off values (ECVs) are not publicly available, disk diffusion data based on CLSI standards provided by the Spanish partner were excluded from trends analysis. The French data were interpreted with national (CA-SFM Vet) epidemiological cut-off values, since CA-SFM Vet disk diffusion standards differ from EUCAST standards.As described by Still, working with EUCAST (T)ECOFFs was not straightforward because several partners worked with narrow concentration ranges that did not necessarily include the EUCAST (T)ECOFF value. For those working with MICs, providing exact MICs (instead of semi-quantitative ones) should strongly be recommended for future EARS-Vet data collection. Should these not be routinely available, a representative subset of isolates could be re-tested using MIC panels with a wider range of concentrations. Similar to the EFSA monitoring, EARS-Vet could consider obtaining MIC plates customized to the needs of the network. Tentative concentration ranges to be used for broth microdilution in animal pathogens have recently been proposed  and coulIn the meantime, alternative strategies are needed. An option that was initially explored as part of this study was to use CLSI breakpoints  to interDespite these limitations, we managed to perform cross-country data analysis for selected bacterium–antibiotic category combinations with sufficient data entries and available interpretation criteria. This work showed substantial variations in reported levels and trends of resistance, both within and between countries, and between animal species . These dTo conclude, this pilot study provided a proof-of-concept of what EARS-Vet can achieve, and formed a basis to improve future data collection and analysis. The next steps will consist in adjusting data collection tools, addressing key methodological issues , improving harmonization and comparability across countries, and more generally strengthening capacities for AMR surveillance in animal pathogens across Europe. In the mid- to long-term, EARS-Vet aims to become a sustainable initiative, with regular release of surveillance reports and the development of an online dashboard for dissemination of the results to a large audience, hence complementing the existing pool of data to support evidence-based management of AMR in animals in Europe.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.Ethical review and approval was not required for the animal study because ethical approval was not necessary since data originated from routine diagnostic laboratories activities. Written informed consent for participation was not obtained from the owners because ethical approval was not necessary since data were anonymized and aggregated, preventing any breach in confidentiality.LC, GC, and NJ conceived the study and supervised data collection and analysis. JL performed data cleaning and analysis. J-PA, CB, PD, TG, MH, EJ, HK, BK, AL, J-YM, CS, EM, CM, ON, MN, SN, GO, KP, TP, CJ, AU, and EZ provided data from their country or region and significantly contributed to data analysis and interpretation. KK, RS, and CZ assisted with data analysis and interpretation. LC drafted the original manuscript. All authors substantially edited the text and approved the final version of the manuscript.For Sweden, data collection and reporting was in part funded by the SvarmPat program. For Switzerland, additional financial support was received from the Food Safety and Veterinary Office (FSVO) (grant no. 071-40011-65). Other partners participated with their own funding.BK and EM work for a commercial veterinary laboratory with EARS-Vet activity not influencing the laboratory work.The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


A 75-year-old male was diagnosed with carcinoma in-situ of the bladder. He failed standard therapy and was started on pembrolizumab to prevent the need for cystectomy. His malignancy recurred, and he was treated with intravesical valrubicin and gemcitabine/docetaxel. Three years after starting pembrolizumab, he developed severe neutropenia and thrombocytopenia. He was treated for suspected auto-immune cytopenias but was later found to have acute promyelocytic leukemia on peripheral blood smear and cytometry. He was hospitalized, treated with all-trans retinoic acid and arsenic trioxide, and is currently in molecular remission.This case describes therapy-related acute promyelocytic leukemia (t-APL) diagnosed while on pembrolizumab. Pembrolizumab is an immune checkpoint inhibitor that exhibits anti-tumor effects. Development of hematologic malignancies after immune checkpoint inhibitor therapy is rare. The definitive etiology of our patient's t-APL is uncertain; however, it is more likely that he developed de novo acute promyelocytic leukemia (APL), which was suppressed by pembrolizumab and later revealed when pembrolizumab was discontinued. Acute promyelocytic leukemia (APL) is a rare type of acute myeloid leukemia (AML) that arises from a chromosomal translocation and subsequent fusion of promyelocytic leukemia (PML) and retinoic acid receptor alpha (RAR) genes from chromosome 15 and 17 .Therapy-related APL (t-APL) accounts for up to 12-14% of APL cases . T-APL tHere we present a case of pembrolizumab-related acute promyelocytic leukemia.A 75-year-old male was diagnosed with non-muscle invasive carcinoma in-situ of the bladder in August 2018. Over the next year, he was treated with and failed two rounds of bacillus Calmette-Guérin (BCG) therapy. In October 2019, he started pembrolizumab treatment to prevent the need for a cystectomy. Over the next few years of treatment, he subsequently developed multiple autoimmune side effects from pembrolizumab, including adrenal insufficiency, hypophysitis, hypothyroidism, hepatitis, and arthritis, which were all successfully treated with either temporarily discontinuing pembrolizumab or hormonal replacement. Due to the recurrence of his cancer, throughout 2021, he received intravesical valrubicin and intravesical gemcitabine/docetaxel while on pembrolizumab. His bladder cancer was successfully treated with this regimen, and he continued on pembrolizumab to prevent recurrence.On routine labs in the fall of 2022, he was found to have severe neutropenia (ANC 0.34 x 10^6/uL) and moderate thrombocytopenia (PLT 58 10^3/uL), which were thought to be autoimmune cytopenias, a known side effect of pembrolizumab. Peripheral blood smear did not demonstrate any dysplasia or immaturity. Pembrolizumab was stopped, and he was given steroids, a dose of pegfilgrastim, and two doses of intravenous immune globulin (IVIG). He developed an appropriate leukocytosis to this therapy; however, his platelet count did not improve. When the leukocytosis did not spontaneously resolve after two weeks, he underwent further testing, and peripheral blood smear showed the presence of promyelocytes  translocation on fluorescence in situ hybridization (FISH) analysis. Upon diagnosis, he was quickly admitted to the hospital for treatment of APL and was found to be in moderate DIC on presentation with severe thrombocytopenia  monoclonal antibody against the checkpoint programmed cell death-1 (PD-1) . PD-1 isPembrolizumab tends to cause fatigue, diarrhea, nausea, rash, and arthralgias . BecauseIt is difficult to specifically identify the etiology of our patient's t-APL. It presented approximately 18 months after receiving intravesical valrubicin, 10 months after intravesical gemcitabine/docetaxel, and three years after initiation of pembrolizumab. Topoisomerase II inhibitors are known causes of t-APL . HoweverHematologic malignancies following treatment with immune checkpoint inhibitors have rarely been reported , althougIn summary, this case describes therapy-related APL diagnosed while on pembrolizumab. Development of hematologic malignancies after immune checkpoint inhibitor therapy is a rare occurrence. The definitive etiology of our patient's t-APL is uncertain; however, it is more likely that he developed de novo APL, which was suppressed by pembrolizumab and later revealed when pembrolizumab was discontinued. Further research is needed to evaluate the efficacy of immune checkpoint inhibitors in APL and AML treatment.


In the 19th century, Gregor Mendel identified heritable units, today known as genes, and laid the foundation for an emerging form of treatment known as gene therapy (GT). Subsequently, from the description of double-stranded DNA to completion of the Human Genome Project, GT has become a powerful treatment option for several gene-based diseases. GT involves the intracellular introduction of nucleic acid (NA)-materials for altering host protein expressions in order to cure the diseased state. However, despite nearly 3,000 clinical trials being undertaken (completed or ongoing), GT continues to exist largely only in the experimental phase. A primary challenge holding it back from realising its true potential is the delivery of target gene/NA into cells or tissues . A delivIn this setting, non-viral vectors are emerging with increasing relevance as safer alternatives for bypassing the pathogenicity of viral systems. Lipid-based nanoparticles and cationic polymers represent conventional chemicals that aid with NA-delivery. Such nano/microsystems are the only non-viral vectors in clinical trials but are still impeded by their tendency to aggregate in serum . On a prTherefore, challenges of transfection efficacy, cargo protection and systemic aggregation are key areas requiring further research-led improvements in this field. However, less than 1% research articles involving delivery systems focus on non-viral options. This slack is picking up, as a plethora of novel strategies, such as unique materials, formulations and innovative applications, are emerging to aid in the advancement towards bench-to-bedside translation . In thisLohchania et al. Transfection efficacies differ between delivery of plasmid versus mRNA formats. Possible reasons could be differential loading/protection capacities, or varying interactions of the mRNA/protein with the delivery system as compared to DNA. While possessing multiple benefits including safety and ease of optimisations, efficiency continues to be a critical aspect withholding non-viral vectors from reaching the same level of applications as viral vectors, and this study highlights good research carried out in addressing such key issues.GT modalities are broadly classified into 3 main groups: immunomodulatory, corrective, and cytoreductive or oncolytic approaches. Immunomodulation boosts the host immune system to promote detection and destruction of diseased cells. In such an approach, it is necessary to account for immunomodulation imparted by the delivery system itself. Corrective GT accounts for the bulk of experimental applications with non-viral vectors. It involves replacement or deactivation of target gene expression using materials delivering either targeted or proof-of-concept NAs. For instance, in this Research Topic, diosgenin is reported for the first time to aid in liposome-mediated intracellular NA delivery by facilitating cell membrane permeabilization in vitro as well as in 3D-spheroids Chattopadhyay et al. Such findings pave the way towards increasing therapeutic options for difficult-to-treat diseases with the aid of GT. The following articles in the Research Topic further demonstrate benefits in diseases that are particularly susceptible to GT using a combination of corrective gene-editing paired with other biotechnological methods. Osteogenesis imperfecta (OI) occurs due to abnormal collagen from a mutant COL1A1 gene leading to brittle bones. The authors, in a significant approach, utilise OI patient-derived induced pluripotent stem cells (iPSCs) and transfect them with normal COL1A1 gene through a specialised ‘STAR’ polymer; achieving a remarkable 85% mutation repair efficiency in the iPSCs Fus-Kujawa et al. In the next article, the impact on neurological disorders is evaluated through a well-crafted in vivo somatic gene-editing study Neuman et al. Notably, a non-viral novel nanocapsule and a viral delivery system comprising adeno-associated-virus 9 formulations are investigated, with both achieving significant gene editing. Slightly higher efficiency is seen with the viral system. While this difference did not achieve statistical significance, it is consistent with the recurrent theme of general lower efficiency seen with non-viral vectors as compared to viruses.The cytoreductive method, also known as oncolytic or suicide GT, involves delivery of genetic material whose intracellular expression induces cell death. Such utility is illustrated in lung cancer in the next article of the Research Topic. The authors develop a novel suicidal GT system that delivers specific miRNAs and caspase genes leading to apoptosis of non-small-cell-lung cancer (NSCLC) cells In summary, the different approaches included in this Research Topic have the unified goal of creating safer, less immunogenic, or non-pathogenic alternatives to virus-based systems as GT carriers. Currently, the common challenge for future research in non-viral vectors appear to be the optimisation of transfection efficiency. While such optimisations are required, it is also prudent to keep in mind that if the efficiency achieved by non-viral systems is sufficient to effect clinical relevance, then increasing efficiency may be unnecessary. Furthermore, a better understanding of the immunomodulatory impact of non-viral vectors, for all GT strategies, is necessary. It is critical to address the major factors in gene delivery, i.e., maintaining cargo integrity; successful cellular entry; endosomal escape and nuclear uptake where required; without inciting unwanted immune responses. Overall, we present the reader with a valuable reference of research findings based on the above factors and provide an overview of the great scope of various non-viral gene-delivery systems in the preclinical setting.