Comparative analysis of the expression patterns of TM9SF family members in mice.

Transmembrane 9 superfamily proteins (TM9SFs) define a highly conserved protein family, each member of which is characterized by a variable extracellular domain and presumably nine transmembrane domains. Although previous studies have delineated the potential cytological roles of TM9SFs like autophagy and secretory pathway, their functions during development are largely unknown. To establish the basis for dissecting the functions of TM9SFs in vivo, we employed the open-source database, structure prediction, immunofluorescence and Western blot to describe the gene and protein expression patterns of TM9SFs in human and mouse. While TM9SFs are ubiquitously and homogeneously expressed in all tissues in human with RNA sequencing and proteomics analysis, we found that all mice Tm9sf proteins are preferentially expressed in lung except Tm9sf1 which is enriched in brain although they all distributed in various tissues we examined. In addition, we further explored their expression patterns in the mice central nervous system (CNS) and its extension tissue retina. Interestingly, we could show that Tm9sf1is developmentally up-regulated in brain. In addition, we also detected all Tm9sf proteins are located in neurons and microglia instead of astrocytes. Importantly, Tm9sf3 is localized in the nuclei which is distinct from the other members that are dominantly targeted to the plasma membrane/cytoplasm as expected. Finally, we also found that Tm9sf family members are broadly expressed in the layers of INL, OPL, and GCL of retina and likely targeted to the plasma membrane of retinal cells. Thus, our data provided a comprehensive overview of TM9SFs expression patterns, illustrating their ubiquitous roles in different organs, implying the possible roles of Tm9sf2/3/4 in lung functions and Tm9sf1 in neurodevelopment, and highlighting a unique cell biological functions of TM9SF3 in neuronal and microglia.

Differentiating ChatGPT-Generated and Human-Written Medical Texts: Quantitative Study.

BACKGROUND: Large language models, such as ChatGPT, are capable of generating grammatically perfect and human-like text content, and a large number of ChatGPT-generated texts have appeared on the internet. However, medical texts, such as clinical notes and diagnoses, require rigorous validation, and erroneous medical content generated by ChatGPT could potentially lead to disinformation that poses significant harm to health care and the general public. OBJECTIVE: This study is among the first on responsible artificial intelligence-generated content in medicine. We focus on analyzing the differences between medical texts written by human experts and those generated by ChatGPT and designing machine learning workflows to effectively detect and differentiate medical texts generated by ChatGPT. METHODS: We first constructed a suite of data sets containing medical texts written by human experts and generated by ChatGPT. We analyzed the linguistic features of these 2 types of content and uncovered differences in vocabulary, parts-of-speech, dependency, sentiment, perplexity, and other aspects. Finally, we designed and implemented machine learning methods to detect medical text generated by ChatGPT. The data and code used in this paper are published on GitHub. RESULTS: Medical texts written by humans were more concrete, more diverse, and typically contained more useful information, while medical texts generated by ChatGPT paid more attention to fluency and logic and usually expressed general terminologies rather than effective information specific to the context of the problem. A bidirectional encoder representations from transformers-based model effectively detected medical texts generated by ChatGPT, and the F1 score exceeded 95%. CONCLUSIONS: Although text generated by ChatGPT is grammatically perfect and human-like, the linguistic characteristics of generated medical texts were different from those written by human experts. Medical text generated by ChatGPT could be effectively detected by the proposed machine learning algorithms. This study provides a pathway toward trustworthy and accountable use of large language models in medicine.

Efficacy of Retrobulbar Hyaluronidase Injection for Vision Loss Resulting from Hyaluronic Acid Filler Embolization.

BACKGROUND: Vision loss is a rare but serious complication of facial hyaluronic acid (HA) filler injection, for which there is no proven rescue therapy. Retrobulbar hyaluronidase injection is advocated by many plastic surgeons as an emergency treatment, but has not been carefully assessed for its efficacy. OBJECTIVES: To evaluate the efficacy of retrobulbar hyaluronidase injection as a rescue treatment for vision loss caused by HA filler embolization. METHODS: Patients with vision loss caused by HA filler embolization were treated with retrobulbar hyaluronidase injection. Their visual acuity and fundoscopic images before and after treatment were analyzed for efficacy assessment. RESULTS: One patient with branch retinal artery occlusion (BRAO), one patient with posterior ischemic optic neuropathy (PION), one patient with ophthalmic artery occlusion, and one patient with both BRAO and PION were treated with one or two retrobulbar injections of 1500 or 3000 units hyaluronidase. No patients demonstrated substantial retinal artery recanalization or vision acuity improvement after treatment. CONCLUSIONS: One or two retrobulbar injections of 1500 to 3000 IU hyaluronidase are unable to recanalize retinal artery occlusion or improve the visual outcome of patients who presented with vision loss caused by HA filler embolization at least four hours after onset. LEVEL OF EVIDENCE: 4.