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Abstract Background Several computational and mathematical models of protein synthesis have been explored to accomplish the quantitative analysis of protein synthesis components and polysome structure. The effect of gene sequence (coding and non-coding region) in protein synthesis, mutation in gene sequence, and functional model of ribosome needs to be explored to investigate the relationship among protein synthesis components further. Ribosomal computing is implemented by imitating the functional property of protein synthesis. Result In the proposed work, a general framework of ribosomal computing is demonstrated by developing a computational model to present the relationship between biological details of protein synthesis and computing principles. Here, mathematical abstractions are chosen carefully without probing into intricate chemical details of the micro-operations of protein synthesis for ease of understanding. This model demonstrates the cause and effect of ribosome stalling during protein synthesis and the relationship between functional protein and gene sequence. Moreover, it also reveals the computing nature of ribosome molecules and other protein synthesis components. The effect of gene mutation on protein synthesis is also explored in this model. Conclusion The computational model for ribosomal computing is implemented in this work. The proposed model demonstrates the relationship among gene sequences and protein synthesis components. This model also helps to implement a simulation environment (a simulator) for generating protein chains from gene sequences and can spot the problem during protein synthesis. Thus, this simulator can identify a disease that can happen due to a protein synthesis problem and suggest precautions for it.

Background/objectives: As a primary source of mortality and disability, bacterial infections continue to develop a severe threat to humanity. Nuclear medicine imaging (NMI) is known for its promising potential to diagnose deep-seated bacterial infections. This work aims to develop a new technetium-99m ( 99m Tc) labeled tigecycline radiopharmaceutical as an infection imaging agent., Methods: Reduced 99m Tc was used to make a coordinate complex with tigecycline at pH 7.7-7.9 at room temperature. Instantaneous thin-layer chromatography impregnated with silica gel (ITLC-SG) and ray detector equipped high-performance liquid chromatography (ray-HPLC) was performed to access the radiolabeling yield and radiochemical purity (RCP)., Results: More than 91% labeling efficiency was achieved after 25 min of mild shaking of the reaction mixture. The radiolabeled complex was found intact up to 4 h in saline. Staphylococcus aureus ( S. aureus ) and Escherichia coli ( E. coli ) infection-induced rats were used to record the biodistribution of the radiopharmaceutical and its target specificity; 2 h' post-injection biodistribution revealed a 2.39 ± 0.29 target/non-target (T/NT) ratio in the E. coli infection-induced animal model, while a 2.9 ± 0.31 T/NT value was recorded in the S. aureus bacterial infection-induced animal model. [ 99m Tc]Tc-tigecycline scintigraphy was performed in healthy rabbits using a single photon emission computed tomography (SPECT) camera. Scintigrams showed normal kidney perfusion and excretion into the bladder., Conclusion: In conclusion, the newly developed [ 99m Tc]Tc-tigecycline radiopharmaceutical could be considered to diagnose broad-spectrum bacterial infections.

Nuclear receptors (NRs) function as crucial transcription factors in orchestrating essential functions within the realms of development, host defense, and homeostasis of body. NRs have garnered increased attention due to their potential as therapeutic targets, with drugs directed at NRs demonstrating significant efficacy in impeding chronic disease progression. Consequently, these pharmacological agents hold promise for the treatment and management of various diseases. Accumulating evidence emphasizes the regulatory role of exosome-derived microRNAs (miRNAs) in chronic inflammation, disease progression, and therapy resistance, primarily by modulating transcription factors, particularly NRs. By exploiting inflammatory pathways such as protein kinase B (Akt)/mammalian target of rapamycin (mTOR), nuclear factor kappa-B (NF-κB), signal transducer and activator of transcription 3 (STAT3), and Wnt/β-catenin signaling, exosomes and NRs play a pivotal role in the panorama of development, physiology, and pathology. The internalization of exosomes modulates NRs and initiates diverse autocrine or paracrine signaling cascades, influencing various processes in recipient cells such as survival, proliferation, differentiation, metabolism, and cellular defense mechanisms. This comprehensive review meticulously examines the involvement of exosome-mediated NR regulation in the pathogenesis of chronic ailments, including atherosclerosis, cancer, diabetes, liver diseases, and respiratory conditions. Additionally, it elucidates the molecular intricacies of exosome-mediated communication between host and recipient cells via NRs, leading to immunomodulation. Furthermore, it outlines the implications of exosome-modulated NR pathways in the prophylaxis of chronic inflammation, delineates current limitations, and provides insights into future perspectives. This review also presents existing evidence on the role of exosomes and their components in the emergence of therapeutic resistance., (© 2024. The Author(s).)

Cardiomegaly is among the disorders categorized by a structural enlargement of the heart by any of the situations including pregnancy, resulting in damage to heart muscles and causing trouble in normal heart functioning. Cardiomegaly can be defined in terms of dilatation with an enlarged heart and decreased left or biventricular contraction. The genetic origin of cardiomegaly is becoming more evident due to extensive genomic research opening up new avenues to ensure the use of precision medicine. Cardiomegaly is usually assessed by using an array of radiological modalities, including computed tomography (CT) scans, chest X-rays, and MRIs. These imaging techniques have provided an important opportunity for the physiology and anatomy of the heart. This review aims to highlight the complexity of cardiomegaly, highlighting the contribution of both ecological and genetic variables to its progression. Moreover, we further highlight the worth of precise clinical diagnosis, which comprises blood biomarkers and electrocardiograms (EKG ECG), demonstrating the significance of distinguishing between numerous basic causes. Finally, the analysis highlights the extensive variation of treatment lines, such as lifestyle modifications, prescription drugs, surgery, and implantable devices, although highlighting the critical need for individualized and personalized care., Competing Interests: Declaration of competing interest The authors declared that they have no competing interests among them., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Cancer has become a burgeoning global healthcare concern marked by its exponential growth and significant economic ramifications. Though advancements in the treatment modalities have increased the overall survival and quality of life, there are no definite treatments for the advanced stages of this malady. Hence, understanding the diseases etiologies and the underlying molecular complexities, will usher in the development of innovative therapeutics. Recently, YAP/TAZ transcriptional regulation has been of immense interest due to their role in development, tissue homeostasis and oncogenic transformations. YAP/TAZ axis functions as coactivators within the Hippo signaling cascade, exerting pivotal influence on processes such as proliferation, regeneration, development, and tissue renewal. In cancer, YAP is overexpressed in multiple tumor types and is associated with cancer stem cell attributes, chemoresistance, and metastasis. Activation of YAP/TAZ mirrors the cellular "social" behavior, encompassing factors such as cell adhesion and the mechanical signals transmitted to the cell from tissue structure and the surrounding extracellular matrix. Therefore, it presents a significant vulnerability in the clogs of tumors that could provide a wide window of therapeutic effectiveness. Natural compounds have been utilized extensively as successful interventions in the management of diverse chronic illnesses, including cancer. Owing to their capacity to influence multiple genes and pathways, natural compounds exhibit significant potential either as adjuvant therapy or in combination with conventional treatment options. In this review, we delineate the signaling nexus of YAP/TAZ axis, and present natural compounds as an alternate strategy to target cancer., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Ultrasound imaging is commonly used to aid in fetal development. It has the advantage of being real-time, low-cost, non-invasive, and easy to use. However, fetal organ detection is a challenging task for obstetricians, it depends on several factors, such as the position of the fetus, the habitus of the mother, and the imaging technique. In addition, image interpretation must be performed by a trained healthcare professional who can take into account all relevant clinical factors. Artificial intelligence is playing an increasingly important role in medical imaging and can help solve many of the challenges associated with fetal organ classification. In this paper, we propose a deep-learning model for automating fetal organ classification from ultrasound images. We trained and tested the model on a dataset of fetal ultrasound images, including two datasets from different regions, and recorded them with different machines to ensure the effective detection of fetal organs. We performed a training process on a labeled dataset with annotations for fetal organs such as the brain, abdomen, femur, and thorax, as well as the maternal cervical part. The model was trained to detect these organs from fetal ultrasound images using a deep convolutional neural network architecture. Following the training process, the model, DenseNet169, was assessed on a separate test dataset. The results were promising, with an accuracy of 99.84%, which is an impressive result. The F1 score was 99.84% and the AUC was 98.95%. Our study showed that the proposed model outperformed traditional methods that relied on the manual interpretation of ultrasound images by experienced clinicians. In addition, it also outperformed other deep learning-based methods that used different network architectures and training strategies. This study may contribute to the development of more accessible and effective maternal health services around the world and improve the health status of mothers and their newborns worldwide., (© 2023. Springer Nature Limited.)

This paper presents an approach that combines conventional image processing with deep learning by fusing the features from the individual techniques. We hypothesize that the two techniques, with different error profiles, are synergistic. The conventional image processing arm uses three handcrafted biologically inspired image processing modules and one clinical information module. The image processing modules detect lesion features comparable to clinical dermoscopy information-atypical pigment network, color distribution, and blood vessels. The clinical module includes information submitted to the pathologist-patient age, gender, lesion location, size, and patient history. The deep learning arm utilizes knowledge transfer via a ResNet-50 network that is repurposed to predict the probability of melanoma classification. The classification scores of each individual module from both processing arms are then ensembled utilizing logistic regression to predict an overall melanoma probability. Using cross-validated results of melanoma classification measured by area under the receiver operator characteristic curve (AUC), classification accuracy of 0.94 was obtained for the fusion technique. In comparison, the ResNet-50 deep learning based classifier alone yields an AUC of 0.87 and conventional image processing based classifier yields an AUC of 0.90. Further study of fusion of conventional image processing techniques and deep learning is warranted.