The COVID-19 has been spreading all over the world, and human health is at stake. Conventional medicine played an important role in the treatment and syndrome relief, while there is limited research evidence to support any prevention or antiviral treatment for COVID-19. Yupingfengsan is a classic formula with the effect of replenishing qi, consolidating exterior, and arresting sweating. Yupingfengsan has been widely used to treat various diseases in China and Southeast Asia for hundreds of years. Accumulating evidence suggests that YPFS has both immune-regulatory and anti-virus effects clinically, but leaving the mechanism elusive. In this paper, we reviewed the recent progress of Yupingfengsan in immune-regulatory and anti-virus activities, including the effect on mucosal immunity, biological antagonism, macrophages, adaptive immunity, and natural killer cells. From this systematic review, we speculate that YPFS can play an effective role in the prevention of SARS-CoV-2, but further experimental studies are expected to investigate the specific mechanisms. This review suggests an alternative direction for the prevention of COVID-19.

It can be argued that in silico studies do not receive enough attention despite being a key part of addressing the limitations of our laboratory facilities, the high cost of chemicals, and the equipment required for wet laboratory activities. Natural product studies are demanding higher costs of chemicals, reagents, and varied laboratory facilities. This becomes a serious limitation in getting data from natural product studies. In silico studies use chemical structures as inputs as well as software and online web servers to generate data to support, predict, and validate wet laboratory activities. Interaction studies use computational tools to calculate binding energies and other associated properties. Predictions are based on the structure-activity relationships derived from previously conducted preclinical and clinical studies. As a main component of in silico studies, the physicochemical and pharmacokinetic properties of small molecules can be determined using online web servers such as SwissADME and absorption, distribution, metabolism, excretion, and toxicity web servers. An interaction study uses molecular docking software such as AutoDock, AutoDock Vina, GOLD, and online servers such as SwissDock. Furthermore, the stabilities of complexes considered in interaction studies can be confirmed using molecular dynamics simulation software such as VMD. Prediction of activity spectra for substances (PASS) is widely used to predict biological activities for molecules based on multilevel neighborhoods of atom descriptors. In silico studies have played an important role in medicinal chemistry, pharmacology, and related research for screening, interaction studies, prediction, and other related purposes. Results of in silico predictions will not be far from wet lab activities as in most cases these studies consider previously attempted clinical and preclinical biological activities. Some examples are presented here to encourage the use of in silico studies.

Bruton’s tyrosine kinase (BTK) is a kinase of the TEC family expressed in B cells and other hematopoietic cells, but it is not expressed in T cells. B-cell malignancies such as multiple myeloma and chronic lymphocytic leukemia have been shown to have a high expression of BTK, thereby displaying oncogenic activities in these diseases, triggering the discovery of BTK inhibitors. The study investigated computationally the phytochemical present in M cordata as a novel BTK inhibitor with high efficacy in treating B-cell malignancies. Chelidimerine, Bocconarborine A, and Bocconarborine B show a high binding affinity of −13.7, −13.3, and −12.9, respectively. This study was validated using molecular dynamic stimulation to indicate the stability and interaction of the ligand with the targeted protein. Bocconarborine B has the best binding energy of −30.94 kcal/mol compared to ibrutinib, with a binding energy of −22.46 kcal/mol. The identified hit compounds from this study were subjected to half maximum inhibitory concentration prediction (IC₅₀) using machine learning modeling; the result shows that Bocconarborine B has the best IC₅₀ of 48.98 nM. This study is subject to validation via in vivo and in vitro studies.

Deep learning methods for many medical image segmentation task encounter challenges like smaller datasets and class imbalance. This study proposes a variant SegNet (vSegNet) designed to deliver significantly accurate and reliable segmentation results on such datasets. The novelty lies in designing encoder and decoder blocks with an appropriate number of convolution layers and using the Dice score and Hausdorff distance (HD) as compound loss function in learning. This study used public datasets consisting of chest X-rays, axial CT slices, foot ulcer images, and subset of SPIDER dataset to benchmark the segmentation task of the proposed neural network model with other popular networks like U-Net, SegNet, DeepLabv3+, VGG16, MobileNetV2, and fully convolutional network (FCN). For the segmentation of lungs in chest X-rays, vertebral body in CT, augmented data for the previous case, foot ulcer dataset, and segmentation of vertebrae, intervertebral disks, and spinal canal in SPIDER dataset (MRI dataset) respectively, the proposed vSegNet performed with a Dice score of 0.96 ± 0.01, 0.90 ± 0.20, 0.95 ± 0.02, 0.86 ± 0.07, and 0.95 ± 0.01 and the HD of 14.33 ± 7.74, 8.45 ± 7.08, 7.99 ± 6.05, 29.32 ± 25.64, and 8.45 ± 2.81 with respect to the ground truth on the test dataset. These results highlight the effectiveness of the proposed model in delivering both higher segmentation accuracy and improved boundary delineation. The proposed network, vSegNet, has been demonstrated as an effective model for semantic segmentation on class-imbalanced smaller datasets, surpassing all other networks considered in this study in terms of mIoU, BF score, Dice score, HD, accuracy, precision, recall, and F1 score on a variety of anatomical regions and medical imaging modalities.

In this study, we combined discrete mathematics with computational biology to identify Brucella species using clustering algorithms. By analyzing the Matrix-Assisted Laser Desorption/Ionization Time of Flight Mass Spectrometry (MALDI-TOF MS) spectra of 44 Brucella isolates, which included 21 Brucella melitensis, 12 Brucella suis, and 11 Brucella abortus, we utilized a feature selection strategy to pinpoint 22 key characteristics critical for species differentiation. We then developed a spectral clustering-based model for Brucella traceability. This model offers a rapid and cost-effective alternative to traditional, labor-intensive identification methods, significantly improving the efficiency and accuracy of Brucella strain identification. Additionally, it aids in monitoring disease transmission trends, identifying outbreak sources, and formulating effective control strategies to mitigate risks. Our findings demonstrate the practical application of discrete mathematics in computational biology, contributing significantly to both scientific research and educational methodologies. This approach illustrates how mathematical concepts can be effectively applied to solve real-world biological problems, providing valuable insights for future interdisciplinary studies and innovative solutions.

Angiogenesis is an important process in tumor progression. Vascular endothelial growth factor (VEGF) is the key factor regulating angiogenesis, and hence, anti-VEGF therapy is considered a useful therapeutic approach in tumor conditions. However, the drug resistance and lack of efficacy of existing drugs limit the potential of such a therapeutic approach in certain cases, and the tumor growth will continue through alternative mechanisms. Glioblastoma (GBM) is one such type of tumor that shows resistance to anti-VEGF therapy. Previously, we identified the hub genes differentially expressed in anti-VEGF resistance in GBM. Medhya Rasayana, an Ayurvedic formulation, is used for the management of neurological disorders. In the present study, we used computational docking methods to identify the phytochemicals present in the medicinal plants of Medhya Rasayana, which can target the proteins expressed by the hub genes associated with anti-VEGF resistance. Network pharmacological analysis was also performed to identify the highly effective phytochemicals for a possible adjuvant therapy. Results showed that multiple phytochemicals of Glycirrhiza glabra Linn, Evolvulus alsinoides, and Celastrus paniculatus target the anti-VGEF resistant proteins in GBM. This indicates the multi-targeting property of phytocompounds of Medhya Rasayana plants, which may be considered for adjuvant therapy along with anti-VEGF therapy.