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Microtubules, an essential component of the eukaryotic cy-toskeleton, perform a variety of essential functions within cells. One mechanism that regulates these diverse functions is the post-translational modification of tubulin. Although these modifications have been known for decades, research into them has only really taken off in the last few years. In particular, the impact and importance of these modifications in the sensory epithelia of the inner ear have only been marginally explored. With the discovery of tubulin-modifying enzymes and the availability of knockout mice, it is now possible to investigate the biological functions and molecular mechanisms underlying these modifications. Here, we have initiated an immunohistochemical study of the effects of post-trans-lational modification knockout mice in the inner ear. Our results indicate that the absence or accumulation of polyglu-tamylated tubulin leads to different morphological changes in the mouse cochlea, whereas deacetylation results in a defective epithelium at an early age. A deeper understanding of the specific post-translational modification and the cochlea may provide new insights into the mechanisms of hearing and potential therapeutic approaches for the treatment of hearing disorders. [ABSTRACT FROM AUTHOR]

Short-term proteasome inhibition has been shown to prevent neuronal apoptosis. However, the key pro-survival proteins that must be degraded for triggering neuronal death are mostly unknown. Here, we show that Mcl-1, an anti-apoptotic Bcl-2 family member, is degraded by the proteasome during neuronal apoptosis. Using primary cultures of cerebellar granule neurons deprived of serum and KCl, we found that ubiquitination and proteasomal degradation of Mcl-1 depended on its prior phosphorylation by GSK3, providing the first insight into post-translational regulation of Mcl-1 in neurons. In a previous study, we have reported that the E3 ubiquitin-ligase Trim17 is both necessary and sufficient for neuronal apoptosis. Here, we identified Trim17 as a novel E3 ubiquitin-ligase for Mcl-1. Indeed, Trim17 co-immunoprecipitated with Mcl-1. Trim17 ubiquitinated Mcl-1 in vitro. Overexpression of Trim17 decreased the protein level of Mcl-1 in a phosphorylation- and proteasome-dependent manner. Finally, knock down of Trim17 expression reduced both ubiquitination and degradation of Mcl-1 in neurons. Moreover, impairment of Mcl-1 phosphorylation, by kinase inhibition or point mutations, not only decreased ubiquitination and degradation of Mcl-1, but also blocked the physical interaction between Trim17 and Mcl-1. As this stabilization of Mcl-1 increased its neuroprotective effect, our data strongly suggest that Trim17-mediated ubiquitination and degradation of Mcl-1 is necessary for initiating neuronal death. [ABSTRACT FROM AUTHOR]

Background: Chest X-rays (CXRs) are pivotal in clinical diagnostics, particularly in assessing cardiomegaly through the cardiothoracic ratio (CTR). This systematic review and meta-analysis evaluate the efficacy of artificial intelligence (AI) in automating CTR determination to enhance patient care and streamline diagnostic processes. They are concentrated on comparing the performance of AI models in determining the CTR against human assessments, identifying the most effective models for potential clinical implementation. This study was registered with PROSPERO (no. CRD42023437459). No funding was received. Methods: A comprehensive search of medical databases was conducted in June 2023. The search strategy adhered to the PICO framework. Inclusion criteria encompassed original articles from the last decade focusing on AI-assisted CTR assessment from standing-position CXRs. Exclusion criteria included systematic reviews, meta-analyses, conference abstracts, paediatric studies, non-original articles, and studies using imaging techniques other than X-rays. After initial screening, 117 articles were reviewed, with 14 studies meeting the final inclusion criteria. Data extraction was performed by three independent investigators, and quality assessment followed PRISMA 2020 guidelines, using tools such as the JBI Checklist, AMSTAR 2, and CASP Diagnostic Study Checklist. Risk of bias was assessed according to the Cochrane Handbook guidelines. Results: Fourteen studies, comprising a total of 70,472 CXR images, met the inclusion criteria. Various AI models were evaluated, with differences in dataset characteristics and AI technology used. Common preprocessing techniques included resizing and normalization. The pooled AUC for cardiomegaly detection was 0.959 (95% CI 0.944-0.975). The pooled standardized mean difference for CTR measurement was 0.0353 (95% CI 0.147-0.0760). Significant heterogeneity was found between studies (I 2 89.97%, p < 0.0001), with no publication bias detected. Conclusions: Standardizing methodologies is crucial to avoid interpretational errors and advance AI in medical imaging diagnostics. Uniform reporting standards are essential for the further development of AI in CTR measurement and broader medical imaging applications.

The physiological function of muscle glycogen is to meet the energy demands of muscle contraction. The breakdown of glycogen occurs through two distinct pathways, primarily cytosolic and partially lysosomal. To obtain the necessary energy for their function, skeletal muscles utilise also fatty acids in the β-oxidation. Ketogenesis is an alternative metabolic pathway for fatty acids, which provides an energy source during fasting and starvation. Diseases arising from impaired glycogenolysis lead to muscle weakness and dysfunction. Here, we focused on the lack of muscle glycogen phosphorylase (PYGM), a rate-limiting enzyme for glycogenolysis in skeletal muscles, which leads to McArdle disease. Metabolic myopathies represent a group of genetic disorders characterised by the limited ability of skeletal muscles to generate energy. Here, we discuss the metabolic aspects of glycogenosis with a focus on McArdle disease, offering insights into its pathophysiology. Glycogen accumulation may influence the muscle metabolic dynamics in different ways. We emphasize that a proper treatment approach for such diseases requires addressing three important and interrelated aspects, which include: symptom relief therapy, elimination of the cause of the disease (lack of a functional enzyme) and effective and early diagnosis., Competing Interests: Declaration of competing interest None, (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

As the number of smartphones increases, so does the number of medical apps. Medical mobile applications are widely used in many medical fields by both patients and doctors. However, there are still few approved mobile applications that can be used in the diagnostic-therapeutic process and radiological apps are affected as well. We conducted our research by classifying radiological applications from the Google Play® store into appropriate categories, according to our own qualification system developed by researchers for the purposes of this study. In addition, we also evaluated apps from the App Store®. The radiology application rating system we created has not been previously used in other articles. Out of 228 applications from the Google Play store, only 6 of them were classified as "A" category with the highest standard. Apps from the App Store (157) were not categorized due to the lack of download counts, which was necessary in our app-rating system. The vast majority of applications are for educational purposes and are not used in clinical practice. This is due to the need of obtaining special permits and certificates from relevant institutions in order to use them in medical practice. We recommend applications from the Google Play store that have been classified in the "A" category, evaluating them as the most valuable. App Store apps data is described and presented in the form of diagrams and tables., (© 2023. The Author(s).)

The application of negative polarity electrical pulse (↓) following positive polarity pulses (↑) may induce bipolar cancellation (BPC), a unique physiological response believed to be specific to nanosecond electroporation (nsEP). The literature lacks analysis of bipolar electroporation (BP EP) involving asymmetrical sequences composed of nanosecond and microsecond pulses. Moreover, the impact of interphase interval on BPC caused by such asymmetrical pulse needs consideration. In this study, the authors utilized the ovarian clear carcinoma cell line (OvBH-1) model to investigate the BPC with asymmetrical sequences. Cells were exposed to pulses delivered in 10-pulse bursts but as uni- or bipolar, symmetrical, or asymmetrical sequences with a duration of 600 ns or 10 µs and electric field strength equal to 7.0 or 1.8 kV/cm, respectively. It was shown that the asymmetry of pulses influences BPC. The obtained results have also been investigated in the context of calcium electrochemotherapy. The reduction of cell membrane poration, and cell survival have been observed following Ca 2+ electrochemotherapy. The effects of interphase delays (1 and 10 µs) on the BPC phenomenon were reported. Our findings show that the BPC phenomenon can be controlled using pulse asymmetry or delay between the positive and negative polarity of the pulse., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: ‘Julita Kulbacka reports financial support was provided by National Science Centre Poland. Vitalij Novickij reports financial support was provided by Research Council of Lithuania.’, (Copyright © 2023 Elsevier B.V. All rights reserved.)

Rhabdomyosarcoma (RMS) is the most common soft tissue sarcoma in children and represents a high-grade neoplasm of skeletal myoblast-like cells. About 40% of all registered soft tissue tumors are RMSs. This paper describes our current understanding of the RMS subtypes (alveolar (ARMS), embryonic (ERMS), pleomorphic (PRMS), and spindle cell/sclerosing (s/scRMS)), diagnostic methods, molecular bases, and characteristics. We also present the currently used treatment methods and the potential use of natural substances in the treatment of this type of cancer. Natural cytotoxic substances are compounds that have been the subject of numerous studies and discussions in recent years. Since anti-cancer therapies are often limited by a low therapeutic index and cancer resistance to pharmacotherapy, it is very important to search for new, effective compounds. Additionally, compounds of a natural origin are usually readily available and have a reduced cytotoxicity. Thus, the undiscovered potential of natural anti-cancer compounds makes this field of research a very important area. The introduction of model species into research examining the use of natural cytostatic therapies for RMS will allow for further assessment of the effects of these compounds on cancerous and healthy tissues.

Diagnostic imaging has become an integral part of the healthcare system. In recent years, scientists around the world have been working on artificial intelligence-based tools that help in achieving better and faster diagnoses. Their accuracy is crucial for successful treatment, especially for imaging diagnostics. This study used a deep convolutional neural network to detect four categories of objects on digital chest X-ray images. The data were obtained from the publicly available National Institutes of Health (NIH) Chest X-ray (CXR) Dataset. In total, 112,120 CXRs from 30,805 patients were manually checked for foreign objects: vascular port, shoulder endoprosthesis, necklace, and implantable cardioverter-defibrillator (ICD). Then, they were annotated with the use of a computer program, and the necessary image preprocessing was performed, such as resizing, normalization, and cropping. The object detection model was trained using the You Only Look Once v8 architecture and the Ultralytics framework. The results showed not only that the obtained average precision of foreign object detection on the CXR was 0.815 but also that the model can be useful in detecting foreign objects on the CXR images. Models of this type may be used as a tool for specialists, in particular, with the growing popularity of radiology comes an increasing workload. We are optimistic that it could accelerate and facilitate the work to provide a faster diagnosis.

Machine learning (ML), artificial neural networks (ANNs), and deep learning (DL) are all topics that fall under the heading of artificial intelligence (AI) and have gained popularity in recent years. ML involves the application of algorithms to automate decision-making processes using models that have not been manually programmed but have been trained on data. ANNs that are a part of ML aim to simulate the structure and function of the human brain. DL, on the other hand, uses multiple layers of interconnected neurons. This enables the processing and analysis of large and complex databases. In medicine, these techniques are being introduced to improve the speed and efficiency of disease diagnosis and treatment. Each of the AI techniques presented in the paper is supported with an example of a possible medical application. Given the rapid development of technology, the use of AI in medicine shows promising results in the context of patient care. It is particularly important to keep a close eye on this issue and conduct further research in order to fully explore the potential of ML, ANNs, and DL, and bring further applications into clinical use in the future.

Despite the growing popularity of mobile devices, they still have not found widespread use in medicine. This is due to the procedures in a given place, differences in the availability of mobile devices between individual institutions or lack of appropriate legal regulations and accreditation by relevant institutions. Numerous studies have been conducted and compared the usability of mobile solutions designed for diagnostic images evaluation on various mobile devices and applications with classic stationary descriptive stations. This study is an attempt to compare the usefulness of currently available mobile applications which are used in the medical industry, focusing on imaging diagnostics. As a consequence of the healthcare sector's diversity, it is also not possible to design a universal mobile application, which results in a multitude of software available on the market and makes it difficult to reliably compile and compare studies included in this systematic review. Despite these differences, it was possible to identify both positive and negative features of portable methods analyzing radiological images. The mobile application of the golden mean in hospital infrastructure should be widely available, with convenient and simple usage. Our future research will focus on development in the use of mobile devices and applications in the medical sector.