Quality and Dependability of ChatGPT and DingXiangYuan Forums for Remote Orthopedic Consultations: Comparative Analysis.

BACKGROUND: The widespread use of artificial intelligence, such as ChatGPT (OpenAI), is transforming sectors, including health care, while separate advancements of the internet have enabled platforms such as China's DingXiangYuan to offer remote medical services. OBJECTIVE: This study evaluates ChatGPT-4's responses against those of professional health care providers in telemedicine, assessing artificial intelligence's capability to support the surge in remote medical consultations and its impact on health care delivery. METHODS: We sourced remote orthopedic consultations from "Doctor DingXiang," with responses from its certified physicians as the control and ChatGPT's responses as the experimental group. In all, 3 blindfolded, experienced orthopedic surgeons assessed responses against 7 criteria: "logical reasoning," "internal information," "external information," "guiding function," "therapeutic effect," "medical knowledge popularization education," and "overall satisfaction." We used Fleiss κ to measure agreement among multiple raters. RESULTS: Initially, consultation records for a cumulative count of 8 maladies (equivalent to 800 cases) were gathered. We ultimately included 73 consultation records by May 2023, following primary and rescreening, in which no communication records containing private information, images, or voice messages were transmitted. After statistical scoring, we discovered that ChatGPT's "internal information" score (mean 4.61, SD 0.52 points vs mean 4.66, SD 0.49 points; P=.43) and "therapeutic effect" score (mean 4.43, SD 0.75 points vs mean 4.55, SD 0.62 points; P=.32) were lower than those of the control group, but the differences were not statistically significant. ChatGPT showed better performance with a higher "logical reasoning" score (mean 4.81, SD 0.36 points vs mean 4.75, SD 0.39 points; P=.38), "external information" score (mean 4.06, SD 0.72 points vs mean 3.92, SD 0.77 points; P=.25), and "guiding function" score (mean 4.73, SD 0.51 points vs mean 4.72, SD 0.54 points; P=.96), although the differences were not statistically significant. Meanwhile, the "medical knowledge popularization education" score of ChatGPT was better than that of the control group (mean 4.49, SD 0.67 points vs mean 3.87, SD 1.01 points; P<.001), and the difference was statistically significant. In terms of "overall satisfaction," the difference was not statistically significant between the groups (mean 8.35, SD 1.38 points vs mean 8.37, SD 1.24 points; P=.92). According to how Fleiss κ values were interpreted, 6 of the control group's score points were classified as displaying "fair agreement" (P<.001), and 1 was classified as showing "substantial agreement" (P<.001). In the experimental group, 3 points were classified as indicating "fair agreement," while 4 suggested "moderate agreement" (P<.001). CONCLUSIONS: ChatGPT-4 matches the expertise found in DingXiangYuan forums' paid consultations, excelling particularly in scientific education. It presents a promising alternative for remote health advice. For health care professionals, it could act as an aid in patient education, while patients may use it as a convenient tool for health inquiries.

The role of mitochondria-related lncRNAs in characterizing the immune landscape and supervising the prognosis of osteosarcoma.

Mitochondrial damage is related to the functional properties of immune cells as well as to tumorigenesis and progression. Nevertheless, there is an absence concerning the systematic evaluation of mitochondria-associated lncRNAs (MALs) in the immune profile and tumor microenvironment of osteosarcoma patients. Based on transcriptomic and clinicopathological data from the TARGET database, MAL-related patterns were ascertained by consistent clustering, and gene set variation analysis of the different patterns was completed. Next, a MAL-derived scoring system was created using Cox and LASSO regression analyses and validated by Kaplan-Meier and ROC curves. The GSEA, ESTIMATE, and CIBERSORT algorithms were utilized to characterize the immune status and underlying biological functions in the different MAL score groups. MAL-derived risk scores were well stabilized and outperformed traditional clinicopathological features to reliably predict 5-year survival in osteosarcoma cohorts. Moreover, patients with increased MAL scores were observed to suffer from poorer prognosis, higher tumor purity, and an immunosuppressive microenvironment. Based on estimated half-maximal inhibitory concentrations, the low-MAL score group benefited more from gemcitabine and docetaxel, and less from thapsigargin and sunitinib compared to the high-MAL score group. Pan-cancer analysis demonstrated that six hub MALs were strongly correlated with clinical outcomes, immune subtypes, and tumor stemness indices in various common cancers. Finally, we verified the expression patterns of hub MALs in osteosarcoma with qRT-PCR. In summary, we identified the crosstalk between prognostic MALs and tumor-infiltrating immune cells in osteosarcoma, providing a potential strategy to ameliorate clinical stratification management.

Comprehensive multi-omics analysis reveals m7G-related signature for evaluating prognosis and immunotherapy efficacy in osteosarcoma.

Background: Osteosarcoma is one of the most prevalent bone malignancies with a poor prognosis. The N7-methylguanosine (m7G) modification facilitates the modification of RNA structure and function tightly associated with cancer. Nonetheless, there is a lack of joint exploration of the relationship between m7G methylation and immune status in osteosarcoma. Methods: With the support of TARGET and GEO databases, we performed consensus clustering to characterize molecular subtypes based on m7G regulators in all osteosarcoma patients. The least absolute shrinkage and selection operator (LASSO) method, Cox regression, and receiver operating characteristic (ROC) curves were employed to construct and validate m7G-related prognostic features and derived risk scores. In addition, GSVA, ssGSEA, CIBERSORT, ESTIMATE, and gene set enrichment analysis were conducted to characterize biological pathways and immune landscapes. We explored the relationship between risk scores and drug sensitivity, immune checkpoints, and human leukocyte antigens by correlation analysis. Finally, the roles of EIF4E3 in cell function were verified through external experiments. Results: Two molecular isoforms based on regulator genes were identified, which presented significant discrepancies in terms of survival and activated pathways. Moreover, the six m7G regulators most associated with prognosis in osteosarcoma patients were identified as independent predictors for the construction of prognostic signature. The model was well stabilized and outperformed traditional clinicopathological features to reliably predict 3-year (AUC = 0.787) and 5-year (AUC = 0.790) survival in osteosarcoma cohorts. Patients with increased risk scores had a poorer prognosis, higher tumor purity, lower checkpoint gene expression, and were in an immunosuppressive microenvironment. Furthermore, enhanced expression of EIF4E3 indicated a favorable prognosis and affected the biological behavior of osteosarcoma cells. Conclusions: We identified six prognostic relevant m7G modulators that may provide valuable indicators for the estimation of overall survival and the corresponding immune landscape in patients with osteosarcoma.

VEGFA-Enriched Exosomes from Tendon-Derived Stem Cells Facilitate Tenocyte Differentiation, Migration, and Transition to a Fibroblastic Phenotype.

Tendon-derived stem cells (TDSCs) play a vital role in repair of rotator cuff tear injuries by secreting paracrine proteins that regulate resident cell functions. Secreted exosomes may play a role in tendon injury repair by mediating intercellular communication; however, the detailed mechanisms by which TDSC-derived exosomes affect tenocyte development remain unknown. Here, we examined the effects of exosomes isolated from conditioned medium of TDSCs on tenocyte differentiation, migration, and transition to a fibroblastic phenotype in vitro. Successful isolation of exosomes from TDSCs was confirmed by high expression levels of CD81, CD63, CD9, and TSG101. Treatment with TDSC-derived exosomes promoted the growth and migration of cultured rat tenocytes, and increased the levels of the fibrosis markers collagen I, collagen III, scleraxis, tenascin C, and α-smooth muscle actin. Furthermore, vascular endothelial growth factor A (VEGFA) expression was higher in TDSC-derived exosomes than in TDSCs, and genetic knockdown of VEGFA suppressed the stimulatory effect of TDSC-derived exosomes on tenocyte development. Overall, these results demonstrate that VEGFA-enriched exosomes isolated from TDSCs promote differentiation and migration of cultured tenocytes and their transition to a fibroblastic phenotype. These data provide a new potential clinical treatment strategy for tendon injury.

Long noncoding RNA H19 accelerates tenogenic differentiation by modulating miR-140-5p/VEGFA signaling.

Rotator cuff tear (RCT) is a common tendon injury, but the mechanisms of tendon healing remain incompletely understood. Elucidating the molecular mechanisms of tenogenic differentiation is essential to develop novel therapeutic strategies in clinical treatment of RCT. The long noncoding RNA H19 plays a regulatory role in tenogenic differentiation and tendon healing, but its detailed mechanism of action remains unknown. To elucidate the role of H19 in tenogenic differentiation and tendon healing, tendon-derived stem cells were harvested from the Achilles tendons of Sprague Dawley rats and a rat model of cuff tear was established for the exploration of the function of H19 in promoting tenogenic differentiation. The results showed that H19 overexpression promoted, while H19 silencing suppressed, tenogenic differentiation of tendon-derived stem cells (TDSCs). Furthermore, bioinformatic analyses and a luciferase reporter gene assay showed that H19 directly targeted and inhibited miR-140-5p to promote tenogenic differentiation. Further, inhibiting miR-140-5p directly increased VEGFA expression, revealing a novel regulatory axis between H19, miR-140-5p, and VEGFA in modulating tenogenic differentiation. In rats with RTC, implantation of H19-overexpressing TDSCs at the lesion promoted tendon healing and functional recovery. In general, the data suggest that H19 promotes tenogenic differentiation and tendon-bone healing by targeting miR-140-5p and increasing VEGFA levels. Modulation of the H19/miR-140-5p/VEGFA axis in TDSCs is a new potential strategy for clinical treatment of tendon injury.