An AAV capsid reprogrammed to bind human transferrin receptor mediates brain-wide gene delivery.

Developing vehicles that efficiently deliver genes throughout the human central nervous system (CNS) will broaden the range of treatable genetic diseases. We engineered an adeno-associated virus (AAV) capsid, BI-hTFR1, that binds human transferrin receptor (TfR1), a protein expressed on the blood-brain barrier. BI-hTFR1 was actively transported across human brain endothelial cells and, relative to AAV9, provided 40 to 50 times greater reporter expression in the CNS of human TFRC knockin mice. The enhanced tropism was CNS-specific and absent in wild-type mice. When used to deliver GBA1, mutations of which cause Gaucher disease and are linked to Parkinson's disease, BI-hTFR1 substantially increased brain and cerebrospinal fluid glucocerebrosidase activity compared with AAV9. These findings establish BI-hTFR1 as a potential vector for human CNS gene therapy.

Targeting AAV vectors to the central nervous system by engineering capsid-receptor interactions that enable crossing of the blood-brain barrier.

Viruses have evolved the ability to bind and enter cells through interactions with a wide variety of cell macromolecules. We engineered peptide-modified adeno-associated virus (AAV) capsids that transduce the brain through the introduction of de novo interactions with 2 proteins expressed on the mouse blood-brain barrier (BBB), LY6A or LY6C1. The in vivo tropisms of these capsids are predictable as they are dependent on the cell- and strain-specific expression of their target protein. This approach generated hundreds of capsids with dramatically enhanced central nervous system (CNS) tropisms within a single round of screening in vitro and secondary validation in vivo thereby reducing the use of animals in comparison to conventional multi-round in vivo selections. The reproducible and quantitative data derived via this method enabled both saturation mutagenesis and machine learning (ML)-guided exploration of the capsid sequence space. Notably, during our validation process, we determined that nearly all published AAV capsids that were selected for their ability to cross the BBB in mice leverage either the LY6A or LY6C1 protein, which are not present in primates. This work demonstrates that AAV capsids can be directly targeted to specific proteins to generate potent gene delivery vectors with known mechanisms of action and predictable tropisms.

Explainable machine learning models based on multimodal time-series data for the early detection of Parkinson's disease.

BACKGROUND AND OBJECTIVES: Parkinson's Disease (PD) is a devastating chronic neurological condition. Machine learning (ML) techniques have been used in the early prediction of PD progression. Fusion of heterogeneous data modalities proved its capability to improve the performance of ML models. Time series data fusion supports the tracking of the disease over time. In addition, the trustworthiness of the resulting models is improved by adding model explainability features. The literature on PD has not sufficiently explored these three points. METHODS: In this work, we proposed an ML pipeline for predicting the progression of PD that is both accurate and explainable. We explore the fusion of different combinations of five time series modalities from the Parkinson's Progression Markers Initiative (PPMI) real-world dataset, including patient characteristics, biosamples, medication history, motor, and non-motor function data. Each patient has six visits. The problem has been formulated in two ways: ❶ a three-class based progression prediction with 953 patients in each time series modality, and ❷ a four-class based progression prediction with 1,060 patients in each time series modality. The statistical features of these six visits were calculated from each modality and diverse feature selection methods were applied to select the most informative feature sets. The extracted features were used to train a set of well-known ML models including Support vector machines (SVM), random forests (RF), extra tree classifier (ETC), light gradient boosting machines (LGBM), and stochastic gradient descent (SGD). We examined a number of data-balancing strategies in the pipeline with different combinations of modalities. ML models have been optimized using the Bayesian optimizer. A comprehensive evaluation of various ML methods has been conducted, and the best models have been extended to provide different explainability features. RESULTS: We compare the performance of ML models before and after optimization and using and without using feature selection. In the three-class experiment and with various modality fusions, the LGBM model produced the most accurate results with a 10-fold cross-validation (10-CV) accuracy of 90.73% using non-motor function modality. RF produced the best results in the four-class experiment with various modality fusions with a 10-CV accuracy of 94.57% using non-motor modality. With the fused dataset of non-motor and motor function modalities, the LGBM model outperformed the other ML models in both the 3-class and 4-class experiments (i.e., 10-CV accuracy of 94.89% and 93.73%, respectively). Using the Shapely Additive Explanations (SHAP) framework, we employed global and instance-based explanations to explain the behavior of each ML classifier. Moreover, we extended the explainability by implementing the LIME and SHAPASH local explainers. The consistency of these explainers has been explored. The resultant classifiers were accurate, explainable, and thus medically more relevant and applicable. CONCLUSIONS: The select modalities and feature sets were confirmed by the literature and medical experts. The various explainers suggest that the bradykinesia (NP3BRADY) feature was the most dominant and consistent. By providing thorough insights into the influence of multiple modalities on the disease risk, the suggested approach is expected to help improve the clinical knowledge of PD progression processes.

Arabic gum ameliorates systemic modulation in Alloxan monohydrate-induced diabetic rats.

Medicinal plants are considered an alternative therapy for diabetes mellitus as they regulate glucose levels. Moreover, a variety of plants offer a rich source of bioactive compounds that have potent pharmacological effects without any negative side effects. The present study aimed to clarify the effects of Arabic gum/Gum Acacia (GA) on the biochemical, histopathological, and immunohistochemical changes observed in diabetic rats. Further, the anti-inflammatory activity of GA in response to diabetes, through inflammatory mediators analysis. Male rats were divided into four groups: untreated control, diabetic, Arabic gum-treated, and Arabic gum-treated diabetic rats. Diabetes was induced using alloxan. Animals were sacrificed after 7 and 21 days of treatment with Arabic gum. Body weight, blood and pancreas tissue samples were collected for analysis. Alloxan injection significantly decreased body weight, increased glucose levels, decreased insulin levels, and caused depletion of islets of Langerhans and ß-cell damage in the pancreas. Arabic gum treatment of diabetic rats significantly increased body weight, decreased serum glucose levels, increased insulin levels, exerts anti-inflammatory effect, and improved the pancreas tissue structure. Arabic gum has beneficial pharmacological effects in diabetic rats; therefore, it might be employed as diabetic therapy to reduce the hyperglycemic damage and may be applicable for many autoimmune and inflammatory diseases treatment. Further, the new bioactive substances, such as medications made from plants, have larger safety margins, and can be used for a longer period of time.

An AAV capsid reprogrammed to bind human Transferrin Receptor mediates brain-wide gene delivery.

Developing vehicles that efficiently deliver genes throughout the human central nervous system (CNS) will broaden the range of treatable genetic diseases. We engineered an AAV capsid, BI-hTFR1, that binds human Transferrin Receptor (TfR1), a protein expressed on the blood-brain barrier (BBB). BI-hTFR1 was actively transported across a human brain endothelial cell layer and, relative to AAV9, provided 40-50 times greater reporter expression in the CNS of human TFRC knock-in mice. The enhanced tropism was CNS-specific and absent in wild type mice. When used to deliver GBA1, mutations of which cause Gaucher disease and are linked to Parkinson's disease, BI-hTFR1 substantially increased brain and cerebrospinal fluid glucocerebrosidase activity compared to AAV9. These findings establish BI-hTFR1 as a promising vector for human CNS gene therapy.

Identification of Cardiac Patients Based on the Medical Conditions Using Machine Learning Models.

Chronic diseases are the most severe health concern today, and heart disease is one of them. Coronary artery disease (CAD) affects blood flow to the heart, and it is the most common type of heart disease which causes a heart attack. High blood pressure, high cholesterol, and smoking significantly increase the risk of heart disease. To estimate the risk of heart disease is a complex process because it depends on various input parameters. The linear and analytical models failed due to their assumptions and limited dataset. The existing studies have used medical data for classification purposes, which help to identify the exact condition of the patient, but no one has developed any correlation equation which can be directly used to identify the patients. In this paper, mathematical models have been developed using the medical database of patients suffering from heart disease. Curve fitting and artificial neural network (ANN) have been applied to model the condition of patients to find out whether the patient is suffering from heart disease or not. The developed curve fitting model can identify the cardiac patient with accuracy, having a coefficient of determination (R 2-value) of 0.6337 and mean absolute error (MAE) of 0.293 at a root mean square error (RMSE) of 0.3688, and the ANN-based model can identify the cardiac patient with accuracy having a coefficient of determination (R 2-value) of 0.8491 and MAE of 0.20 at RMSE of 0.267, it has been found that ANN provides superior mathematical modeling than curve fitting method in identifying the heart disease patients. Medical professionals can utilize this model to identify heart patients without any angiography or computed tomography angiography test.

An Integrated Algorithmic MADM Approach for Heart Diseases' Diagnosis Based on Neutrosophic Hypersoft Set with Possibility Degree-Based Setting.

The possibility neutrosophic hypersoft set (pNHs-set) is a generalized version of the possibility neutrosophic soft set (pNs-set). It tackles the limitations of the pNs-set regarding the use of the multi-argument approximate function. This function maps sub-parametric tuples to a power set of the universe. It emphasizes the partitioning of each attribute into its respective attribute-valued set. These features make it a completely new mathematical tool for solving problems dealing with uncertainties. This makes the decision-making process more flexible and reliable. In this study, after characterizing some elementary notions and algebraic operations of the pNHs-set, Sanchez's method (a classical approach for medical diagnosis) is modified under the pNHs-set environment. A modified algorithm is proposed for the medical diagnosis of heart diseases by integrating the concept of the pNHs-set and the modified Sanchez's method. The authenticity of the proposed algorithm is evaluated through its implementation in a real-world scenario with real data from the Cleveland data set for heart diseases. The beneficial aspects of the proposed approach are evaluated through a structural comparison with some pertinent existing approaches.

Anti-inflammatory activity of ginger modulates macrophage activation against the inflammatory pathway of monosodium glutamate.

Monosodium glutamate (MSG) has been traditionally used as a flavor enhancer and is added to many foods. The chronic consumption of MSG has been suggested as causing toxicity, inflammation, obesity, type 2 diabetes, and pre-malignant changes. The use of medicinal plants and their products, such as ginger, against the effects of MSG has been suggested to have a protective effect. To evaluate the anti-inflammatory activity of ginger against the effects of MSG, we conducted a serial inflammatory analysis of MSG- and ginger-treated rats, focusing particularly on liver pathology. The consumption of ginger as an unconventional therapy against the effects of MSG resulted in significant anti-inflammatory activity. We found that it was possible to diagnose MSG-associated inflammatory pathogenesis using inflammatory mediators. Ginger consumption produced protective effects on health, minimized adverse effects, and may be applicable for food development and the treatment of many inflammatory diseases. PRACTICAL APPLICATIONS: The chronic administration of monosodium glutamate (MSG) as a flavor enhancer has been suggested to produce toxicity, inflammation, and pre-malignant changes in organs. Ginger has protective effects, with potent anti-inflammatory and anti-fibrotic activity against MSG administration. This study is the first to report that ginger modulated the inflammatory and fibrotic effects of MSG and improved immunological indices reflecting the involvement of inflammatory and fibrotic markers and polysaccharide content in the activation of macrophages. These findings support the further use of ginger as a supplement for food enhancement and as an anti-fibrotic, anti-inflammatory, and therapeutic agent in pharmaceutical therapies against autoimmune and inflammatory diseases, such as rheumatoid arthritis, lupus, and ulcerative colitis, as well as MSG-associated inflammatory diseases.

Systematic auditing is essential to debiasing machine learning in biology.

Biases in data used to train machine learning (ML) models can inflate their prediction performance and confound our understanding of how and what they learn. Although biases are common in biological data, systematic auditing of ML models to identify and eliminate these biases is not a common practice when applying ML in the life sciences. Here we devise a systematic, principled, and general approach to audit ML models in the life sciences. We use this auditing framework to examine biases in three ML applications of therapeutic interest and identify unrecognized biases that hinder the ML process and result in substantially reduced model performance on new datasets. Ultimately, we show that ML models tend to learn primarily from data biases when there is insufficient signal in the data to learn from. We provide detailed protocols, guidelines, and examples of code to enable tailoring of the auditing framework to other biomedical applications.

Characterization of protein-protein interactions between rice viruses and vector insects.

Planthoppers are the most notorious rice pests, because they transmit various rice viruses in a persistent-propagative manner. Protein-protein interactions (PPIs) between virus and vector are crucial for virus transmission by vector insects. However, the number of known PPIs for pairs of rice viruses and planthoppers is restricted by low throughput research methods. In this study, we applied DeNovo, a virus-host sequence-based PPI predictor, to predict potential PPIs at a genome-wide scale between three planthoppers and five rice viruses. PPIs were identified at two different confidence thresholds, referred to as low and high modes. The number of PPIs for the five planthopper-virus pairs ranged from 506 to 1985 in the low mode and from 1254 to 4286 in the high mode. After eliminating the "one-too-many" redundant interacting information, the PPIs with unique planthopper proteins were reduced to 343-724 in the low mode and 758-1671 in the high mode. Homologous analysis showed that 11 sets and 31 sets of homologous planthopper proteins were shared by all planthopper-virus interactions in the two modes, indicating that they are potential conserved vector factors essential for transmission of rice viruses. Ten PPIs between small brown planthopper and rice stripe virus (RSV) were verified using glutathione-S-transferase (GST)/His-pull down or co-immunoprecipitation assay. Five of the ten PPIs were proven positive, and three of the five SBPH proteins were confirmed to interact with RSV. The predicted PPIs provide new clues for further studies of the complicated relationship between rice viruses and their vector insects.

Effect of Transplanted Bone Marrow on Kidney Tissue of γ-Irradiated Pregnant Rats and Their Fetuses.

BACKGROUND AND OBJECTIVES: The damaging effects of ionizing radiation lead to cell death. The present study was performed to assess the possible ameliorating effects of bone marrow transplantation (BMT) on the histopathological and histochemical changes in the kidney tissue of γ-irradiated pregnant rats and their fetuses. MATERIALS AND METHODS: Pregnant rats were divided into 5 sets (6 females in each set): Group C (untreated pregnant rats), group R7 (pregnant rats exposed to 2Gy of γ-rays on the 7th day of pregnancy), group R7+BM (pregnant rats exposed to 2Gy of γ-rays on the 7th day of pregnancy then injected by freshly BMT (75×106±5 cells) intra peritoneally after 1 h of irradiation, group R14 (pregnant rats exposed to 2Gy of γ-rays on the 14th day of pregnancy), group R14+BM (pregnant rats exposed to 2Gy γ-rays on the 14th day of pregnancy and after 1 h received 1 dose of BMT). All pregnant rats were sacrificed on the 20th day of pregnancy and kidney samples of pregnant rats and their fetuses were removed for histopathological and histochemical studies. RESULTS: Gamma rays caused many histological and histochemical deviations in the kidney tissue of mothers and their fetuses on day 7 or 14 of gestation, but bone marrow transplantation highly improved the damage were occurred due to γ-rays. CONCLUSION: Bone marrow transplantation has the ability to decrease the injury of gamma rays.

Skeletal scintigraphy image enhancement based neutrosophic sets and salp swarm algorithm.

Recently, several schemes are proposed for enhancing the dark regions of the skeletal scintigraphy image. Nevertheless, most of them are flawed by some performance problems. This paper presents an adaptive scheme based on Salp Swarm algorithm (SSA) and a neutrosophic set (NS) under multi-criteria to enhance the dark regions of the skeletal scintigraphy image efficiently. Enhancing the dark regions is first converted into an optimization problem. The SSA algorithm is used to find the best improvement for each image separately, and then the neutrosophic algorithm is used to find similarity score to each image with adaptive weight coefficients obtained by the SSA algorithm. The proposed algorithm is applied to an Egyptian medical dataset collected from Menoufia University Hospital and it is a no-reference image. The experiments are done using 3 different resolutions 512*512, 256*256, and 128*128 and compared with Gamma Correction, the NS algorithm and the local enhance algorithm. The results demonstrate that the proposed algorithm achieves superior performance in almost criteria fitness function, entropy, eumber of edges, nNaturalness image quality Evaluator, sharpness, sharpness index, and contrast-distorted images using contrast enhancement. The results showed the idea of integration between the falsity membership of the neutrosophic set and the Salp swarm algorithm can be used to Skeletal Scintigraphy enhancement. This paper proved that it can depend on falsity membership of the neutrosophic set in the Image Enhancement field.

PEAK: Integrating Curated and Noisy Prior Knowledge in Gene Regulatory Network Inference.

With abundance of biological data, computational prediction of gene regulatory networks (GRNs) from gene expression data has become more feasible. Although incorporating other prior knowledge (PK), along with gene expression data, greatly improves prediction accuracy, the overall accuracy is still low. PK in GRN inference can be categorized into noisy and curated. In noisy PK, relations between genes do not necessarily correspond to regulatory relations and are thus considered inaccurate by inference algorithms such as transcription factor binding and protein-protein interactions. In contrast, curated PK is experimentally verified regulatory interactions in pathway databases. An issue in real data is that gene expression can poorly support the curated PK and thus most existing prediction algorithms cannot use these curated PK. Although several algorithms were proposed to incorporate noisy PK, none address curated PK with poor gene expression support. We present PEAK, a system to integrate both curated and noisy PK in GRN inference, especially with poor gene expression support. We introduce a novel method for GRN inference, CurInf, to effectively integrate curated PK, even when the gene expression data poorly support the PK. PEAK also uses the previously proposed method Modified Elastic Net to incorporate noisy PK, and we call it NoisInf. In our experiment, CurInf significantly incorporates curated PK, which was regarded as noise by previous methods. Using 100% curated PK, CurInf improves the area under precision-recall curve accuracy score over NoisInf by 27.3% in synthetic data, 86.5% in Escherichia coli data, and 31.1% in Saccharomyces cerevisiae data. Moreover, even when the noise in PK is 10 times more than true PK, PEAK performs better than inference without any PK. Better integration of curated PK helps biologists benefit from verified experimental data to predict more reliable GRN.

Possible Protective Role of Parsley Extract on the Diabetic Pregnant Rats and Their Fetuses.

BACKGROUND AND OBJECTIVE: Gestational diabetes mellitus (GDM) is one form of diabetes. It causes obstetrical complications and affects between 5-18% of all pregnancies and leads to congenital malformations and long-term postnatal disorders. Supportive therapy in treatment of diabetes during pregnancy takes place by anti-diabetic plants such as parsley. The current study has been undertaken to investigate the possible anti-diabetic and antioxidant role of aqueous parsley extract on streptozotocin (STZ) induced gestational diabetes mellitus in rats. MATERIALS AND METHODS: Fifty pregnant albino rats were categorized after mating into five groups: group C (control group), group D1 (pregnant rats injected with interperitoneally single dose of STZ (40 mg kg-1 b.wt.) in the 1st day of gestation, group D1+P: Pregnant rats were treated with parsley extract (1 m/150 g b.wt.) from the 1st to the 19th day of gestation post injection with STZ (40 mg kg-1 b.wt.), group D7: Pregnant rats were injected with STZ (40 mg kg-1 b.wt.) on day 7of gestation, group D7+P: Pregnant rats were treated with parsley extract (1 m/150 g b.wt.) from the 7th to the 19th day of gestation post injection with STZ (40 mg kg-1 b.wt.). The pregnant rats were dissected on the 19th day of pregnancy and the uterine horns were removed freshly and then photographed. Abnormalities or any morphological changes were recorded, weight of fetuses and placenta and placental index were determined. Blood samples were collected to estimate the glucose and biochemical parameters of the main kidney functions. Also, kidney samples of fetuses were taken for the histopathological study. RESULTS: Fetuses of the diabetic mothers showed some developmental changes such as very thin skin, very thin muscle layer under the skin, absence of eyelid and ear pinna, exencephaly and kyphosis. On the other hand, fetuses of the diabetic mothers which were treated with parsley leaves extract showed somewhat normal morphological development. According to the biochemical histopathological observations, the parsley leaf extract succeeded to minimize the drastic changes, which were observed in the diabetic rats and their fetuses. CONCLUSION: Administration of the parsley leaf extract has the ability to minimize the damage of hyperglycemia.

DeNovo: virus-host sequence-based protein-protein interaction prediction.

MOTIVATION: Can we predict protein-protein interactions (PPIs) of a novel virus with its host? Three major problems arise: the lack of known PPIs for that virus to learn from, the cost of learning about its proteins and the sequence dissimilarity among viral families that makes most methods inapplicable or inefficient. We develop DeNovo, a sequence-based negative sampling and machine learning framework that learns from PPIs of different viruses to predict for a novel one, exploiting the shared host proteins. We tested DeNovo on PPIs from different domains to assess generalization. RESULTS: By solving the challenge of generating less noisy negative interactions, DeNovo achieved accuracy up to 81 and 86% when predicting PPIs of viral proteins that have no and distant sequence similarity to the ones used for training, receptively. This result is comparable to the best achieved in single virus-host and intra-species PPI prediction cases. Thus, we can now predict PPIs for virtually any virus infecting human. DeNovo generalizes well; it achieved near optimal accuracy when tested on bacteria-human interactions. AVAILABILITY AND IMPLEMENTATION: Code, data and additional supplementary materials needed to reproduce this study are available at: https://bioinformatics.cs.vt.edu/~alzahraa/denovo CONTACT: alzahraa@vt.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.