Evaluating radiofrequency electromagnetic field exposure in confined spaces: a systematic review of recent studies and future directions.

This study reviews recent research on Radiofrequency Electromagnetic Field (RF-EMF) exposure in confined environments, focusing on methodologies and parameters. Studies typically evaluate RF-EMF exposure using an electric field and specific absorption rate but fail to consider temperature rise in the tissues in confined environments. The study highlights the investigation of RF-EMF exposure in subterranean environments such as subways, tunnels and mines. Future research should evaluate the exposure of communication devices in such environments, considering the surrounding environment. Such studies will aid in understanding the risks and developing effective mitigation strategies to protect workers and the general public.

Is it real or not? Toward artificial intelligence-based realistic synthetic cytology image generation to augment teaching and quality assurance in pathology.

INTRODUCTION: Urine cytology offers a rapid and relatively inexpensive method to diagnose urothelial neoplasia. In our setting of a public sector laboratory in South Africa, urothelial neoplasia is rare, compromising pathology training in this specific aspect of cytology. Artificial intelligence-based synthetic image generation-specifically the use of generative adversarial networks (GANs)-offers a solution to this problem. MATERIALS AND METHODS: A limited, but morphologically diverse, dataset of 1000 malignant urothelial cytology images was used to train a StyleGAN3 model to create completely novel, synthetic examples of malignant urine cytology using computer resources within reach of most pathology departments worldwide. RESULTS: We have presented the results of our trained GAN model, which was able to generate realistic, morphologically diverse examples of malignant urine cytology images when trained using a modest dataset. Although the trained model is capable of generating realistic images, we have also presented examples for which unrealistic and artifactual images were generated-illustrating the need for manual curation when using this technology in a training context. CONCLUSIONS: We have presented a proof-of-concept illustration of creating synthetic malignant urine cytology images using machine learning technology to augment cytology training when real-world examples are sparse. We have shown that despite significant morphologic diversity in terms of staining variations, slide background, variations in the diagnostic malignant cellular elements, the presence of other nondiagnostic cellular elements, and artifacts, visually acceptable and varied results are achievable using limited data and computing resources.

The Dynamics of Pathology Dataset Creation Using Urine Cytology as an Example.

INTRODUCTION: Dataset creation is one of the first tasks required for training AI algorithms but is underestimated in pathology. High-quality data are essential for training algorithms and data should be labelled accurately and include sufficient morphological diversity. The dynamics and challenges of labelling a urine cytology dataset using The Paris System (TPS) criteria are presented. METHODS: 2,454 images were labelled by pathologist consensus via video conferencing over a 14-day period. During the labelling sessions, the dynamics of the labelling process were recorded. Quality assurance images were randomly selected from images labelled in previous sessions within this study and randomly distributed throughout new labelling sessions. To assess the effect of time on the labelling process, the labelled set of images was split into 2 groups according to the median relative label time and the time taken to label images and intersession agreement were assessed. RESULTS: Labelling sessions ranged from 24 m 11 s to 41 m 06 s in length, with a median of 33 m 47 s. The majority of the 2,454 images were labelled as benign urothelial cells, with atypical and malignant urothelial cells more sparsely represented. The time taken to label individual images ranged from 1 s to 42 s with a median of 2.9 s. Labelling times differed significantly among categories, with the median label time for the atypical urothelial category being 7.2 s, followed by the malignant urothelial category at 3.8 s and the benign urothelial category at 2.9 s. The overall intersession agreement for quality assurance images was substantial. The level of agreement differed among classes of urothelial cells - benign and malignant urothelial cell classes showed almost perfect agreement and the atypical urothelial cell class showed moderate agreement. Image labelling times seemed to speed up, and there was no evidence of worsening of intersession agreement with session time. DISCUSSION/CONCLUSION: Important aspects of pathology dataset creation are presented, illustrating the significant resources required for labelling a large dataset. We present evidence that the time taken to categorise urine cytology images varies by diagnosis/class. The known challenges relating to the reproducibility of the AUC (atypical) category in TPS when compared to the NHGUC (benign) or HGUC (malignant) categories is also confirmed.

Using text mining techniques to extract prostate cancer predictive information (Gleason score) from semi-structured narrative laboratory reports in the Gauteng province, South Africa.

BACKGROUND: Prostate cancer (PCa) is the leading male neoplasm in South Africa with an age-standardised incidence rate of 68.0 per 100,000 population in 2018. The Gleason score (GS) is the strongest predictive factor for PCa treatment and is embedded within semi-structured prostate biopsy narrative reports. The manual extraction of the GS is labour-intensive. The objective of our study was to explore the use of text mining techniques to automate the extraction of the GS from irregularly reported text-intensive patient reports. METHODS: We used the associated Systematized Nomenclature of Medicine clinical terms morphology and topography codes to identify prostate biopsies with a PCa diagnosis for men aged > 30 years between 2006 and 2016 in the Gauteng Province, South Africa. We developed a text mining algorithm to extract the GS from 1000 biopsy reports with a PCa diagnosis from the National Health Laboratory Service database and validated the algorithm using 1000 biopsies from the private sector. The logical steps for the algorithm were data acquisition, pre-processing, feature extraction, feature value representation, feature selection, information extraction, classification, and discovered knowledge. We evaluated the algorithm using precision, recall and F-score. The GS was manually coded by two experts for both datasets. The top five GS were reported, with the remaining scores categorised as "Other" for both datasets. The percentage of biopsies with a high-risk GS (≥ 8) was also reported. RESULTS: The first output reported an F-score of 0.99 that improved to 1.00 after the algorithm was amended (the GS reported in clinical history was ignored). For the validation dataset, an F-score of 0.99 was reported. The most commonly reported GS were 5 + 4 = 9 (17.6%), 3 + 3 = 6 (17.5%), 4 + 3 = 7 (16.4%), 3 + 4 = 7 (14.7%) and 4 + 4 = 8 (14.2%). For the validation dataset, the most commonly reported GS were: (i) 3 + 3 = 6 (37.7%), (ii) 3 + 4 = 7 (19.4%), (iii) 4 + 3 = 7 (14.9%), (iv) 4 + 4 = 8 (10.0%) and (v) 4 + 5 = 9 (7.4%). A high-risk GS was reported for 31.8% compared to 17.4% for the validation dataset. CONCLUSIONS: We demonstrated reliable extraction of information about GS from narrative text-based patient reports using an in-house developed text mining algorithm. A secondary outcome was that late presentation could be assessed.

Design, synthesis, and molecular docking of novel 3,5-disubstituted-1,3,4-oxadiazole derivatives as iNOS inhibitors.

To obtain new anti-inflammatory agents, recent studies have aimed to replace the carboxylate functionality of nonsteroidal anti-inflammatory drugs with less acidic heterocyclic bioisosteres like 1,3,4-oxadiazole to protect the gastric mucosa from free carboxylate moieties. In view of these observations, we designed and synthesized a series of 3,5-disubstituted-1,3,4-oxadiazole derivatives as inhibitors of prostaglandin E2 (PGE2 ) and NO production with an improved activity profile. As initial screening, and to examine the anti-inflammatory activities of the compounds, the inhibitions of the productions of lipopolysaccharide-induced NO and PGE2 in RAW 264.7 macrophages were evaluated. The biological assays showed that, compared with indomethacin, compounds 5a, 5g, and 5h significantly inhibited NO production with 12.61 ± 1.16, 12.61 ± 1.16, and 18.95 ± 3.57 µM, respectively. Consequently, the three compounds were evaluated for their in vivo anti-inflammatory activities. Compounds 5a, 5g, and 5h showed a potent anti-inflammatory activity profile almost equivalent to indomethacin at the same dose in the carrageenan-induced paw edema test. Moreover, the treatment with 40 mg/kg of 5h produced significant anti-inflammatory activity data. Furthermore, docking studies were performed to reveal possible interactions with the inducible nitric oxide synthase enzyme. Docking results were able to rationalize the biological activity data of the studied inhibitors. In summary, our data suggest that compound 5h is identified as a promising candidate for further anti-inflammatory drug development with an extended safety profile.

A New Artificial Chordae Implantation Method in Mitral Valve Surgery.

A novel method is described for artificial chordae replacement with expanded polytetrafluoroethylene suture in mitral valve repair procedures. The technique does not involve knots over or beneath the free edge of the mitral valve leaflets. Artificial chords suspend the exact free margin of leaflets as if it were a continuation of the free margin, such that the smooth zone of the coapting area can be preserved. This technique is simple, reproducible, and applicable to both anterior and posterior leaflets. Moreover, the length of the artificial chords can be adjusted rapidly and accurately at the first attempt.

Mesenchymal stem cell therapy for the streptozotocin-induced neurodegeneration in rats.

BACKGROUND AND AIM: Bone marrow-derived mesenchymal stem cells (BM-MSCs) are one of the sources of adult stem cells being explored for potential use in repairing neurodegenerative disorders. In this study, it was aimed to investigate the useful effects of BM-MSCs therapy on the streptozotocin-induced neurodegeneration in rats. MATERIALS AND METHODS: Adult female Wistar rats were bilaterally injected intra-cerebroventricularly with streptozotocin (3 mg/kg) for neurodegeneration. Water maze tests were used to monitor spatial learning and memory. One or two intravenous injections of BM-MSCs were administrated to rat via the tail veins. At the end of the study, all rats were sacrificed for histological evaluation and immunohistochemistry. RESULTS: Streptozotocin group demonstrated a significant increase in escape latency in comparison with both control groups (Sham and Saline), whereas rats treated with BM-MSCs exhibited a decrease in escape latency in comparison with streptozotocin group. The percentage of time spent in the target quadrant and the mean number of platform crossings did not change in all the groups. BM-MSCs administration improved spatial learning but not memory. However, improvement in neuronal cells in hippocampal CA1 region was only observed in the rats treated with BM-MSCs twice as opposed to the rats treated with BM-MSCs once or with saline. CONCLUSIONS: In this study, mesenchymal stem cells therapy failed to improve the streptozotocin-induced neurodegeneration like Alzheimer's disease in rats.

Endocan: A novel inflammatory indicator in cardiovascular disease?

Endothelial dysfunction is considered as an early change in atherogenesis. Raised levels of systemic inflammatory markers are associated with cardiovascular disease (CVD). Endocan (previously known as endothelial cell specific molecule-1, ESM-1), is a potential immunoinflammatory marker that may be linked to CVD. Endocan is released by vascular endothelial cells in several organs. Endocan may play an important role in regulating cell adhesion and raised plasma levels may reflect endothelial dysfunction. Endocan levels are elevated in conditions such as chronic kidney disease, renal transplant rejection, tumor progression and hypertension. Endocan is a potential inflammatory and CVD marker. Further studies are needed to assess the relevance of endocan in clinical practice.