原发性中枢神经系统淋巴瘤(primary central nervous system lymphoma,PCNSL)是一种罕见、高度侵袭性的结外非霍奇金淋巴瘤,其局限于脑、软脑膜、眼、脊髓,无全身侵犯,占所有原发性脑肿瘤3%~4%[1]。PCNSL的临床和影像学表现与其他常见...原发性中枢神经系统淋巴瘤(primary central nervous system lymphoma,PCNSL)是一种罕见、高度侵袭性的结外非霍奇金淋巴瘤,其局限于脑、软脑膜、眼、脊髓,无全身侵犯,占所有原发性脑肿瘤3%~4%[1]。PCNSL的临床和影像学表现与其他常见中枢神经系统肿瘤相似。展开更多
Background: Cryptococcus neoformans is an opportunistic fungal pathogen that primarily affects immunocompromised individuals. While traditional histologic methods such as hematoxylin and eosin (H&E) staining can s...Background: Cryptococcus neoformans is an opportunistic fungal pathogen that primarily affects immunocompromised individuals. While traditional histologic methods such as hematoxylin and eosin (H&E) staining can sometimes identify fungal organisms, definitive diagnosis typically requires microbiological culture or molecular testing. Stimulated Raman Histology (SRH) is an emerging imaging technology that enables rapid, label-free tissue analysis, potentially improving intraoperative diagnostic workflows. Aim: This case report explores the utility of SRH for the real-time identification of pulmonary cryptococcosis, highlighting its potential to enhance tissue triage and expedite diagnosis. Case Presentation: We report a 44-year-old man with a history of smoking and alcohol use who presented with a right lower lung mass. An ION robotic-assisted bronchoscopy was performed, and SRH was used intraoperatively for real-time tissue evaluation. Within approximately 90 seconds, SRH provided morphologic findings indicative of Cryptococcus neoformans, prompting additional microbiological testing, which confirmed the diagnosis. The patient required a six-week hospitalization with antifungal therapy. Conclusion: This case demonstrates the potential of SRH as a rapid, intraoperative diagnostic tool for detecting fungal infections in pulmonary specimens. By enabling real-time morphological assessment, SRH can optimize biopsy specimen triage, reduce the need for repeat procedures, and improve patient management. Integrating SRH into diagnostic workflows may be particularly beneficial in resource-limited settings, where timely cryptococcosis diagnosis is critical.展开更多
To promote behavioral change among adolescents in Zambia, the National HIV/AIDS/STI/TB Council, in collaboration with UNICEF, developed the Zambia U-Report platform. This platform provides young people with improved a...To promote behavioral change among adolescents in Zambia, the National HIV/AIDS/STI/TB Council, in collaboration with UNICEF, developed the Zambia U-Report platform. This platform provides young people with improved access to information on various Sexual Reproductive Health topics through Short Messaging Service (SMS) messages. Over the years, the platform has accumulated millions of incoming and outgoing messages, which need to be categorized into key thematic areas for better tracking of sexual reproductive health knowledge gaps among young people. The current manual categorization process of these text messages is inefficient and time-consuming and this study aims to automate the process for improved analysis using text-mining techniques. Firstly, the study investigates the current text message categorization process and identifies a list of categories adopted by counselors over time which are then used to build and train a categorization model. Secondly, the study presents a proof of concept tool that automates the categorization of U-report messages into key thematic areas using the developed categorization model. Finally, it compares the performance and effectiveness of the developed proof of concept tool against the manual system. The study used a dataset comprising 206,625 text messages. The current process would take roughly 2.82 years to categorise this dataset whereas the trained SVM model would require only 6.4 minutes while achieving an accuracy of 70.4% demonstrating that the automated method is significantly faster, more scalable, and consistent when compared to the current manual categorization. These advantages make the SVM model a more efficient and effective tool for categorizing large unstructured text datasets. These results and the proof-of-concept tool developed demonstrate the potential for enhancing the efficiency and accuracy of message categorization on the Zambia U-report platform and other similar text messages-based platforms.展开更多
文摘原发性中枢神经系统淋巴瘤(primary central nervous system lymphoma,PCNSL)是一种罕见、高度侵袭性的结外非霍奇金淋巴瘤,其局限于脑、软脑膜、眼、脊髓,无全身侵犯,占所有原发性脑肿瘤3%~4%[1]。PCNSL的临床和影像学表现与其他常见中枢神经系统肿瘤相似。
文摘Background: Cryptococcus neoformans is an opportunistic fungal pathogen that primarily affects immunocompromised individuals. While traditional histologic methods such as hematoxylin and eosin (H&E) staining can sometimes identify fungal organisms, definitive diagnosis typically requires microbiological culture or molecular testing. Stimulated Raman Histology (SRH) is an emerging imaging technology that enables rapid, label-free tissue analysis, potentially improving intraoperative diagnostic workflows. Aim: This case report explores the utility of SRH for the real-time identification of pulmonary cryptococcosis, highlighting its potential to enhance tissue triage and expedite diagnosis. Case Presentation: We report a 44-year-old man with a history of smoking and alcohol use who presented with a right lower lung mass. An ION robotic-assisted bronchoscopy was performed, and SRH was used intraoperatively for real-time tissue evaluation. Within approximately 90 seconds, SRH provided morphologic findings indicative of Cryptococcus neoformans, prompting additional microbiological testing, which confirmed the diagnosis. The patient required a six-week hospitalization with antifungal therapy. Conclusion: This case demonstrates the potential of SRH as a rapid, intraoperative diagnostic tool for detecting fungal infections in pulmonary specimens. By enabling real-time morphological assessment, SRH can optimize biopsy specimen triage, reduce the need for repeat procedures, and improve patient management. Integrating SRH into diagnostic workflows may be particularly beneficial in resource-limited settings, where timely cryptococcosis diagnosis is critical.
文摘To promote behavioral change among adolescents in Zambia, the National HIV/AIDS/STI/TB Council, in collaboration with UNICEF, developed the Zambia U-Report platform. This platform provides young people with improved access to information on various Sexual Reproductive Health topics through Short Messaging Service (SMS) messages. Over the years, the platform has accumulated millions of incoming and outgoing messages, which need to be categorized into key thematic areas for better tracking of sexual reproductive health knowledge gaps among young people. The current manual categorization process of these text messages is inefficient and time-consuming and this study aims to automate the process for improved analysis using text-mining techniques. Firstly, the study investigates the current text message categorization process and identifies a list of categories adopted by counselors over time which are then used to build and train a categorization model. Secondly, the study presents a proof of concept tool that automates the categorization of U-report messages into key thematic areas using the developed categorization model. Finally, it compares the performance and effectiveness of the developed proof of concept tool against the manual system. The study used a dataset comprising 206,625 text messages. The current process would take roughly 2.82 years to categorise this dataset whereas the trained SVM model would require only 6.4 minutes while achieving an accuracy of 70.4% demonstrating that the automated method is significantly faster, more scalable, and consistent when compared to the current manual categorization. These advantages make the SVM model a more efficient and effective tool for categorizing large unstructured text datasets. These results and the proof-of-concept tool developed demonstrate the potential for enhancing the efficiency and accuracy of message categorization on the Zambia U-report platform and other similar text messages-based platforms.
