Corrigendum: Sesquiterpene lactones attenuate paclitaxel resistance via inhibiting MALAT1/STAT3/ FUT4 axis and P-Glycoprotein transporters in lung cancer cells.

[This corrects the article DOI: 10.3389/fphar.2022.795613.].

Aspiration Thrombectomy Using Inari FlowTriever System for Inferior Vena Cava Tumor Thrombus: A Case Report.

Pharmacomechanical therapy and catheter-directed thrombolysis are potent treatments for venous thromboembolism. However, limited data exist regarding the management of thrombi in the inferior vena cava (IVC). IVC thrombus resulting from tumors is a particularly uncommon condition. Managing IVC tumor thrombi poses even greater challenges, as conventional therapies such as systemic anticoagulation and thrombolysis are often ineffective. In this report, we present the case of a 73-year-old male with an inferior vena cava tumor thrombus successfully managed through aspiration thrombectomy utilizing the Inari FlowTriever system.

DEEP-EP: Identification of epigenetic protein by ensemble residual convolutional neural network for drug discovery.

Epigenetic proteins (EP) play a role in the progression of a wide range of diseases, including autoimmune disorders, neurological disorders, and cancer. Recognizing their different functions has prompted researchers to investigate them as potential therapeutic targets and pharmacological targets. This paper proposes a novel deep learning-based model that accurately predicts EP. This study introduces a novel deep learning-based model that accurately predicts EP. Our approach entails generating two distinct datasets for training and evaluating the model. We then use three distinct strategies to transform protein sequences to numerical representations: Dipeptide Deviation from Expected Mean (DDE), Dipeptide Composition (DPC), and Group Amino Acid (GAAC). Following that, we train and compare the performance of four advanced deep learning models algorithms: Ensemble Residual Convolutional Neural Network (ERCNN), Generative Adversarial Network (GAN), Convolutional Neural Network (CNN), and Gated Recurrent Unit (GRU). The DDE encoding combined with the ERCNN model demonstrates the best performance on both datasets. This study demonstrates deep learning's potential for precisely predicting EP, which can considerably accelerate research and streamline drug discovery efforts. This analytical method has the potential to find new therapeutic targets and advance our understanding of EP activities in disease.

Blood cell image segmentation and classification: a systematic review.

Background: Blood diseases such as leukemia, anemia, lymphoma, and thalassemia are hematological disorders that relate to abnormalities in the morphology and concentration of blood elements, specifically white blood cells (WBC) and red blood cells (RBC). Accurate and efficient diagnosis of these conditions significantly depends on the expertise of hematologists and pathologists. To assist the pathologist in the diagnostic process, there has been growing interest in utilizing computer-aided diagnostic (CAD) techniques, particularly those using medical image processing and machine learning algorithms. Previous surveys in this domain have been narrowly focused, often only addressing specific areas like segmentation or classification but lacking a holistic view like segmentation, classification, feature extraction, dataset utilization, evaluation matrices, etc. Methodology: This survey aims to provide a comprehensive and systematic review of existing literature and research work in the field of blood image analysis using deep learning techniques. It particularly focuses on medical image processing techniques and deep learning algorithms that excel in the morphological characterization of WBCs and RBCs. The review is structured to cover four main areas: segmentation techniques, classification methodologies, descriptive feature selection, evaluation parameters, and dataset selection for the analysis of WBCs and RBCs. Results: Our analysis reveals several interesting trends and preferences among researchers. Regarding dataset selection, approximately 50% of research related to WBC segmentation and 60% for RBC segmentation opted for manually obtaining images rather than using a predefined dataset. When it comes to classification, 45% of the previous work on WBCs chose the ALL-IDB dataset, while a significant 73% of researchers focused on RBC classification decided to manually obtain images from medical institutions instead of utilizing predefined datasets. In terms of feature selection for classification, morphological features were the most popular, being chosen in 55% and 80% of studies related to WBC and RBC classification, respectively. Conclusion: The diagnostic accuracy for blood-related diseases like leukemia, anemia, lymphoma, and thalassemia can be significantly enhanced through the effective use of CAD techniques, which have evolved considerably in recent years. This survey provides a broad and in-depth review of the techniques being employed, from image segmentation to classification, feature selection, utilization of evaluation matrices, and dataset selection. The inconsistency in dataset selection suggests a need for standardized, high-quality datasets to strengthen the diagnostic capabilities of these techniques further. Additionally, the popularity of morphological features indicates that future research could further explore and innovate in this direction.

Deep-VEGF: deep stacked ensemble model for prediction of vascular endothelial growth factor by concatenating gated recurrent unit with two-dimensional convolutional neural network.

Vascular endothelial growth factor (VEGF) is involved in the development and progression of various diseases, including cancer, diabetic retinopathy, macular degeneration and arthritis. Understanding the role of VEGF in various disorders has led to the development of effective treatments, including anti-VEGF drugs, which have significantly improved therapeutic methods. Accurate VEGF identification is critical, yet experimental identification is expensive and time-consuming. This study presents Deep-VEGF, a novel computational model for VEGF prediction based on deep-stacked ensemble learning. We formulated two datasets using primary sequences. A novel feature descriptor named K-Space Tri Slicing-Bigram position-specific scoring metrix (KSTS-BPSSM) is constructed to extract numerical features from primary sequences. The model training is performed by deep learning techniques, including gated recurrent unit (GRU), generative adversarial network (GAN) and convolutional neural network (CNN). The GRU and CNN are ensembled using stacking learning approach. KSTS-BPSSM-based ensemble model secured the most accurate predictive outcomes, surpassing other competitive predictors across both training and testing datasets. This demonstrates the potential of leveraging deep learning for accurate VEGF prediction as a powerful tool to accelerate research, streamline drug discovery and uncover novel therapeutic targets. This insightful approach holds promise for expanding our knowledge of VEGF's role in health and disease.Communicated by Ramaswamy H. Sarma.

An ensemble computational model for prediction of clathrin protein by coupling machine learning with discrete cosine transform.

Clathrin protein (CP) plays a pivotal role in numerous cellular processes, including endocytosis, signal transduction, and neuronal function. Dysregulation of CP has been associated with a spectrum of diseases. Given its involvement in various cellular functions, CP has garnered significant attention for its potential applications in drug design and medicine, ranging from targeted drug delivery to addressing viral infections, neurological disorders, and cancer. The accurate identification of CP is crucial for unraveling its function and devising novel therapeutic strategies. Computational methods offer a rapid, cost-effective, and less labor-intensive alternative to traditional identification methods, making them especially appealing for high-throughput screening. This paper introduces CL-Pred, a novel computational method for CP identification. CL-Pred leverages three feature descriptors: Dipeptide Deviation from Expected Mean (DDE), Bigram Position Specific Scoring Matrix (BiPSSM), and Position Specific Scoring Matrix-Tetra Slice-Discrete Cosine Transform (PSSM-TS-DCT). The model is trained using three classifiers: Support Vector Machine (SVM), Extremely Randomized Tree (ERT), and Light eXtreme Gradient Boosting (LiXGB). Notably, the LiXGB-based model achieves outstanding performance, demonstrating accuracies of 94.63% and 93.65% on the training and testing datasets, respectively. The proposed CL-Pred method is poised to significantly advance our comprehension of clathrin-mediated endocytosis, cellular physiology, and disease pathogenesis. Furthermore, it holds promise for identifying potential drug targets across a spectrum of diseases.Communicated by Ramaswamy H. Sarma.

In Silico Characterisation of the Aedes aegypti Gustatory Receptors.

Aedes aegypti, also known as the dengue mosquito or the yellow fewer mosquito, is the vector of dengue, chikungunya, Zika, Mayaro and yellow fever viruses. The A. aegypti genome contains an array of gustatory receptor (GR) proteins that are related to the recognition of taste. In this study, we performed in silico molecular characterization of all 72 A. aegypti GRs reported in the latest version of A. aegypti genome AaegL5. Phylogenetic analysis classified the receptors into three major clads. Multiple GRs were found to encode multiple transcripts. Physicochemical attributes such as the aliphatic index, hydropathicity index and isoelectric point indicated that A. aegypti gustatory receptors are highly stable and are tailored to perform under a variety of cellular environments. Analysis for subcellular localization indicated that all the GRs are located either in the extracellular matrix or the plasma membrane. Results also indicated that the GRs are distributed mainly on chromosomes 2 and 3, which house 22 and 49 GRs, respectively, whereas chromosome 1 houses only one GR. NCBI-CDD analysis showed the presence of a highly conserved 7tm_7 chemosensory receptor protein superfamily that includes gustatory and odorant receptors from insect species Anopheles gambiae and Drosophila melanogaster. Further, three significantly enriched ungapped motifs in the protein sequence of all 72 A. aegypti gustatory receptors were found. High-quality 3D models for the tertiary structures were predicted with significantly higher confidence, along with ligand-binding residues. Prediction of S-nitrosylation sites indicated the presence of target cysteines in all the GRs with close proximity to the ligand-bindings sites within the 3D structure of the receptors. In addition, two highly conserved motifs inside the GR proteins were discovered that house a tyrosine (Y) and a cysteine (C) residue which may serve as targets for NO-mediated tyrosine nitration and S-nitrosylation, respectively. This study will help devise strategies for functional genomic studies of these important receptor molecules in A. aegypti and other mosquito species through in vitro and in vivo studies.

Pediatric Malignant Pelvic Paraganglioma-A Diagnostic and Surgical Challenge.

Pheochromocytoma are chromaffin cell-derived tumors that have an exceptionally high genetic predisposition. The presentation of extra-adrenal and pelvic paraganglioma (PGL) in children is uncommon. Due to the relative rarity, PGL tumors' presentation and disease behavior may vary. Genetic testing, imaging, and biochemical investigation are employed to diagnose PGL. Surgical resection with preoperative angioembolization has been practiced in alleviating the burden of torrential intraoperative bleeding.

A bi-layer model for identification of piwiRNA using deep neural learning.

piwiRNA is a kind of non-coding RNA (ncRNA) that cannot be translated into proteins. It helps in understanding the study of gametes generation and regulation of gene expression over both transcriptional and post-transcriptional levels. piwiRNA has the function of instructing deadenylation, animal fertility, silencing transposons, fighting viruses, and regulating endogenous genes. Due to the great significance of piwiRNA, prediction of piwiRNA is essential for crucial cellular functions. Several predictors were established for prediction of piwiRNA. However, improving the prediction of piwiRNA is highly desirable. In the current study, we developed a more promising predictor named, BLP-piwiRNA. The features are explored by reverse complement k-mer, gapped-k-mer composition, and k-mer composition. The feature set of all descriptors is fused and the best features are selected by cascade and relief feature selection strategies. The best feature sets are provided to random forest (RF), deep neural network (DNN), and support vector machine (SVM). The models validation are examined by 10-fold test. DNN with optimal features of Cascade feature selection approach secured the highest prediction results. The results illustrate that BLP-piwiRNA effectively outperforms the existing studies. The proposed approach would be beneficial for both research community and drug development industry. BLP-piwiRNA would serve as novel biomarkers and therapeutic targets for tumor diagnostics and treatment.Communicated by Ramaswamy H. Sarma.

Based on network pharmacology, gastrodin attenuates hypertension-induced vascular smooth muscle cell proliferation and PI3K/AKT pathway activation.

The effects and underlying mechanisms of gastrodin treatment on hypertensive vascular dysfunction and proliferation of vascular smooth muscle cells (VSMCs) were determined in vitro and in vivo. Using a pharmacological target network interaction analysis, 151 common targets and a PPI network were identified containing the top 10 hub genes. Kyoto encyclopedia of genes and genomes (KEGG) analysis identified the PI3K/AKT pathway as a significantly enriched pathway. Both spontaneous hypertensive rats (SHRs) and Wistar Kyoto rats were used to assess the therapeutic effects of gastrodin on hypertension. Gastrodin treatment of the SHRs resulted in a marked attenuation of elevated blood pressure, pulse wave velocity, and pathological changes in the abdominal aorta. Moreover, gastrodin treatment significantly inhibited cell growth and downregulated the expression of PCNA as well as the p-PI3K/PI3K and p-AKT/AKT levels in angiotensin II-stimulated VSMCs. Taken together, gastrodin treatment attenuates blood pressure elevation, vascular dysfunction, and proliferation of VSMCs and inhibits the activation of the PI3K/AKT pathway.

Deep-AGP: Prediction of angiogenic protein by integrating two-dimensional convolutional neural network with discrete cosine transform.

Angiogenic proteins (AGPs) play a primary role in the formation of new blood vessels from pre-existing ones. AGPs have diverse applications in cancer, including serving as biomarkers, guiding anti-angiogenic therapies, and aiding in tumor imaging. Understanding the role of AGPs in cardiovascular and neurodegenerative diseases is vital for developing new diagnostic tools and therapeutic approaches. Considering the significance of AGPs, in this research, we first time established a computational model using deep learning for identifying AGPs. First, we constructed a sequence-based dataset. Second, we explored features by designing a novel feature encoder, called position-specific scoring matrix-decomposition-discrete cosine transform (PSSM-DC-DCT) and existing descriptors including Dipeptide Deviation from Expected Mean (DDE) and bigram-position-specific scoring matrix (Bi-PSSM). Third, each feature set is fed into two-dimensional convolutional neural network (2D-CNN) and machine learning classifiers. Finally, the performance of each learning model is validated by 10-fold cross-validation (CV). The experimental results demonstrate that 2D-CNN with proposed novel feature descriptor achieved the highest success rate on both training and testing datasets. In addition to being an accurate predictor for identification of angiogenic proteins, our proposed method (Deep-AGP) might be fruitful in understanding cancer, cardiovascular, and neurodegenerative diseases, development of their novel therapeutic methods and drug designing.

Quercetin attenuates angiotensin II-induced proliferation of vascular smooth muscle cells and p53 pathway activation in vitro and in vivo.

Quercetin is an essential flavonoid mostly found in herbal plants, fruits, and vegetables, which exhibits anti-hypertension properties. However, its pharmacological impact on angiotensin II (Ang II) induced the increase of blood pressure along with in-depth mechanism needs further exploration. The present study pointed out the anti-hypertensive role of quercetin and its comprehensive fundamental mechanisms. Our data showed that quercetin treatment substantially reduced the increase in blood pressure, pulse wave velocity, and aortic thickness of abdominal aorta in Ang II-infused C57BL/6 mice. RNA sequencing revealed that quercetin treatment reversed 464 differentially expressed transcripts in the abdominal aorta of Ang II-infused mice. Moreover, overlapping KEGG-enriched signaling pathways identified multiple common pathways between the comparison of Ang II versus control and Ang II + quercetin versus Ang II. Likewise, these pathways included cell cycle as well as p53 pathways. Transcriptome was further validated by immunohistochemistry, indicating that quercetin treatment significantly decreased the Ang II-induced expression of proliferating cell nuclear antigen (PCNA), cyclin-dependent kinase-4 (CDK4), and cyclin D1, while increased protein expression of p53, and p21 in abdominal aortic tissues of mice. In vitro, quercetin treatment meaningfully decreased the cell viability, arrested cell cycle at G0/G1 phase, and up-regulated the p53 and p21 proteins expression, as well as down-regulated the protein expression of cell cycle-related markers, for example, CDK4, cyclin D1 in Ang II stimulated vascular smooth muscle cells (VSMCs). This study addresses pharmacologic and mechanistic perspectives of quercetin against Ang-II-induced vascular injury and the increase of blood pressure.

Dihydropyrimidone Derivatives as Thymidine Phosphorylase Inhibitors: Inhibition Kinetics, Cytotoxicity, and Molecular Docking.

Overexpression of the thymidine phosphorylase (TP) enzyme induces angiogenesis, which eventually leads to metastasis and tumor growth. The crucial role of TP in cancer development makes it an important target for anticancer drug discovery. Currently, there is only one US-FDA-approved drug, i.e., Lonsurf, a combination of trifluridine and tipiracil, for the treatment of metastatic colorectal cancer. Unfortunately, numerous adverse effects are associated with its use, such as myelosuppression, anemia, and neutropenia. Since the last few decades, the discovery of new, safe, and effective TP inhibitory agents has been rigorously pursued. In the present study, we evaluated a series of previously synthesized dihydropyrimidone derivatives 1-40 for their TP inhibitory potential. Compounds 1, 12, and 33 showed a good activity with IC50 = 314.0 ± 0.90, 303.5 ± 0.40, and 322.6 ± 1.60 µM, respectively. The results of mechanistic studies revealed that compounds 1, 12, and 33 were the non-competitive inhibitors. These compounds were also evaluated for cytotoxicity against 3T3 (mouse fibroblast) cells and were found to be non-cytotoxic. Finally, the molecular docking suggested the plausible mechanism of non-competitive inhibition of TP. The current study thus identifies some dihydropyrimidone derivatives as potential inhibitors of TP, which can be further optimized as leads for cancer treatment.

Magnetically recoverable poly (methyl methacrylate-acrylic acid)/iron oxide magnetic composites nanomaterials with hydrophilic wettability for efficient oil-water separation.

Due to the environmental and production problems of emulsion, it is important to efficiently separate oil-water emulsion to meet the refinery requirement and clean up oil spills. Synthesis of a universal demulsifier is not an easy task because the physical properties of crude oil vary, which makes its characterization and demulsification procedure difficult. To overcome this problem, hydrophilic and magnetically recoverable poly (methyl methacrylate-acrylic acid)/iron oxide magnetic composite nanoparticles ((P(MMA-AA)/Fe3O4 NPs) were developed as an efficient and economical demulsifier via soap-free emulsion polymerization. To characterize the magnetic composite NPs for their appropriate surface morphology and magnetic domain, TEM, FTIR, VSM, and TGA analyses were carried out. The newly synthesized NPs displayed good hydrophilic properties as they migrated quickly to the aqueous emulsion phase, which was also reassured by their water contact angle of 75°. They exhibit strong magnetic characteristics (20 amu/g) in the oil-water emulsion, makings the hydrophilic wettability capable and attractive to the external magnet. Experimental results revealed that the prepared magnetic composite NPs separated 99% of the water from stable emulsion in 30 min and could be recycled 8 times through magnetic separation. The recycled magnetic composite NPs maintain their hydrophilic wettability and efficiency in separating oil-water emulsion, making them economical and commercially viable. The migration of magnetic composite NPs to the aqueous phase in the stable emulsion with a strong magnetic domain explains the coalescence of emulsified water droplets and their quick separation from the stable emulsions through the external magnet.

Target-DBPPred: An intelligent model for prediction of DNA-binding proteins using discrete wavelet transform based compression and light eXtreme gradient boosting.

DNA-protein interaction is a critical biological process that performs influential activities, including DNA transcription and recombination. DBPs (DNA-binding proteins) are closely associated with different kinds of human diseases (asthma, cancer, and AIDS), while some of the DBPs are used in the production of antibiotics, steroids, and anti-inflammatories. Several methods have been reported for the prediction of DBPs. However, a more intelligent method is still highly desirable for the accurate prediction of DBPs. This study presents an intelligent computational method, Target-DBPPred, to improve DBPs prediction. Important features from primary protein sequences are investigated via a novel feature descriptor, called EDF-PSSM-DWT (Evolutionary difference formula position-specific scoring matrix-discrete wavelet transform) and several other multi-evolutionary methods, including F-PSSM (Filtered position-specific scoring matrix), EDF-PSSM (Evolutionary difference formula position-specific scoring matrix), PSSM-DPC (Position-specific scoring matrix-dipeptide composition), and Lead-BiPSSM (Lead-bigram-position specific scoring matrix) to encapsulate diverse multivariate features. The best feature set from the features of each descriptor is selected using sequential forward selection (SFS). Further, four models are trained using Adaboost, XGB (eXtreme gradient boosting), ERT (extremely randomized trees), and LiXGB (Light eXtreme gradient boosting) classifiers. LiXGB, with the best feature set of EDF-PSSM-DWT, has attained 6.69% and 15.07% higher performance in terms of accuracies using training and testing datasets, respectively. The obtained results verify the improved performance of our proposed predictor over the existing predictors.

Sesquiterpene Lactones Attenuate Paclitaxel Resistance Via Inhibiting MALAT1/STAT3/ FUT4 Axis and P-Glycoprotein Transporters in Lung Cancer Cells.

Paclitaxel resistance is a challenging factor in chemotherapy resulting in poor prognosis and cancer recurrence. Signal transducer and activator of transcription factor 3 (STAT3), a key transcription factor, performs a critical role in cancer development, cell survival and chemoresistance, while its inactivation overwhelms drug resistance in numerous cancer types including lung cancer. Additionally, the fucosyltransferase 4 (FUT4) is a crucial enzyme in post-translational modification of cell-surface proteins involved in various pathological conditions such as tumor multidrug resistance (MDR). The P-glycoprotein (P-GP) is the well-known ABC transporter member that imparts drug resistance in different cancer types, most notably paclitaxel resistance in lung cancer cells. LncRNA-MALAT1 exerts a functional role in the cancer development as well as the drug resistance and is linked with STAT3 activation and activity of FUT4. Moreover, STAT3-mediated induction of P-GP is well-documented. Natural compounds of Sesquiterpene Lactone (SL) family are well-known for their anticancer properties with particular emphasis over STAT3 inhibitory capabilities. In this study, we explored the positive correlation of MALAT1 with STAT3 and FUT4 activity in paclitaxel resistant A549 (A549/T) lung cancer cells. Additionally, we investigated the anticancer activity of two well-known members of SLs, alantolactone (ALT) and Brevilin A (Brv-A), in A549/T lung cancer cells. ALT and Brv-A induced apoptosis in A549/T cells. Furthermore, these two natural SLs suppressed MALAT1 expression, STAT3 activation, and FUT4 and P-GP expression which are the hallmarks for paclitaxel resistance in A549 lung cancer cells. The inhibition of MALAT1 enhanced the competence of these SLs members significantly, which accounted for the growth inhibition as well as anti-migratory and anti-invasive effects of ALT and Brv-A. These findings suggest SLs to be the promising agents for overcoming paclitaxel resistance in A549 lung cancer cells.

iAtbP-Hyb-EnC: Prediction of antitubercular peptides via heterogeneous feature representation and genetic algorithm based ensemble learning model.

Tuberculosis (TB) is a worldwide illness caused by the bacteria Mycobacterium tuberculosis. Owing to the high prevalence of multidrug-resistant tuberculosis, numerous traditional strategies for developing novel alternative therapies have been presented. The effectiveness and dependability of these procedures are not always consistent. Peptide-based therapy has recently been regarded as a preferable alternative due to its excellent selectivity in targeting specific cells without affecting the normal cells. However, due to the rapid growth of the peptide samples, predicting TB accurately has become a challenging task. To effectively identify antitubercular peptides, an intelligent and reliable prediction model is indispensable. An ensemble learning approach was used in this study to improve expected results by compensating for the shortcomings of individual classification algorithms. Initially, three distinct representation approaches were used to formulate the training samples: k-space amino acid composition, composite physiochemical properties, and one-hot encoding. The feature vectors of the applied feature extraction methods are then combined to generate a heterogeneous vector. Finally, utilizing individual and heterogeneous vectors, five distinct nature classification models were used to evaluate prediction rates. In addition, a genetic algorithm-based ensemble model was used to improve the suggested model's prediction and training capabilities. Using Training and independent datasets, the proposed ensemble model achieved an accuracy of 94.47% and 92.68%, respectively. It was observed that our proposed "iAtbP-Hyb-EnC" model outperformed and reported ~10% highest training accuracy than existing predictors. The "iAtbP-Hyb-EnC" model is suggested to be a reliable tool for scientists and might play a valuable role in academic research and drug discovery. The source code and all datasets are publicly available at https://github.com/Farman335/iAtbP-Hyb-EnC.

DBP-GAPred: An intelligent method for prediction of DNA-binding proteins types by enhanced evolutionary profile features with ensemble learning.

DNA-binding proteins (DBPs) perform an influential role in diverse biological activities like DNA replication, slicing, repair, and transcription. Some DBPs are indispensable for understanding many types of human cancers (i.e. lung, breast, and liver cancer) and chronic diseases (i.e. AIDS/HIV, asthma), while other kinds are involved in antibiotics, steroids, and anti-inflammatory drugs designing. These crucial processes are closely related to DBPs types. DBPs are categorized into single-stranded DNA-binding proteins (ssDBPs) and double-stranded DNA-binding proteins (dsDBPs). Few computational predictors have been reported for discriminating ssDBPs and dsDBPs. However, due to the limitations of the existing methods, an intelligent computational system is still highly desirable. In this work, features from protein sequences are discovered by extending the notion of dipeptide composition (DPC), evolutionary difference formula (EDF), and K-separated bigram (KSB) into the position-specific scoring matrix (PSSM). The highly intrinsic information was encoded by a compression approach named discrete cosine transform (DCT) and the model was trained with support vector machine (SVM). The prediction performance was further boosted by the genetic algorithm (GA) ensemble strategy. The novel predictor (DBP-GAPred) acquired 1.89%, 0.28%, and 6.63% higher accuracies on jackknife, 10-fold, and independent dataset tests, respectively than the best predictor. These outcomes confirm the superiority of our method over the existing predictors.

Effect of protein and oil volume concentrations on emulsifying properties of acorn protein isolate.

This study aims to extract acorn protein isolate (API) from locally abundant waste acorn fruit and investigate its emulsification behavior by mixing different protein (0.1-2% w/v) and oil volume concentrations (5-45% v/v). Significant decrease in emulsifying activity index (EAI) and an increase in emulsifying stability index (ESI) were observed with an increase in API concentrations (P < 0.05). Droplet sizes of emulsions and viscosity were observed to decrease significantly (P < 0.05) with increase in API concentration while the increase was observed in interfacial protein concentration (Г). In contrast, increase in oil volume concentration results in increase of droplet sizes, packing fractions and viscosity, while decrease in Г values was observed. The results reveal that main fractions of API (66.2-14.4 kDa) were migrated to oil-water interface for emulsion stabilization. These results demonstrate the potential application of API in food formulation and development.

Comparison of Miniplate and K-wire in the Treatment of Metacarpal and Phalangeal Fractures.

Introduction Metacarpal and phalangeal fractures are common upper limb fractures due to direct blows, axial loading, and torsional loading injuries. The universal goal in treating all fractures for the patient to achieve normal motion, but the ideal technique for stabilization is still debated. For internal fixation, Kirschner wires (K-wires) or miniplates can be used, and each carries certain advantages. No previous study has compared K-wire use to miniplate use in treating metacarpal and phalangeal fractures. Therefore, we conducted this randomized control trial to evaluate the outcomes of K-wire and miniplate use in treating metacarpal and phalangeal fractures. Materials and methods This randomized controlled trial was conducted in the Department of Orthopaedic Surgery, Bahawal Victoria Hospital, from February 2017 to February 2018. Seventy-five patients were included in this study and randomly assigned into two groups. One group was treated with K-wire fixation, and the other group was treated with miniplate fixation. We assessed total active motion (TAM), range of motion (ROM), duration of injury, and complication rate. Data were analyzed using IBM SPSS Statistics for Windows, Version 23.0 (Armonk, NY: IBM Corp). P values ≤ 0.05 were considered significant. Results Mean surgical time, pain scale, and time of union of K-wire treated patients was 38.63±3.64 minutes, 4.17±1.11, and 12.95±3.38 weeks, respectively. The success of the union was noted in 38 K-wire patients (95%). Total active ROM was greater in miniplate fixation patients compared with K-wire treated patients, but this difference was statistically significant. Similarly, TAM was also greater in the miniplate fixation patients compared to the K-wire treated patients, but this difference was also not statistically significant. Conclusion Both K-wire fixation and miniplate fixation are equally effective in terms of TAM, ROM, and complications when used to treat metacarpal and phalangeal fractures.