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Age-related osteoporosis is characterized by the deterioration in bone volume and strength, partly due to the dysfunction of bone marrow mesenchymal stromal/stem cells (MSCs) during aging. Alpha-ketoglutarate (αKG) is an essential intermediate in the tricarboxylic acid (TCA) cycle. Studies have revealed that αKG extends the lifespan of worms and maintains the pluripotency of embryonic stem cells (ESCs). Here, we show that the administration of αKG increases the bone mass of aged mice, attenuates age-related bone loss, and accelerates bone regeneration of aged rodents. αKG ameliorates the senescence-associated (SA) phenotypes of bone marrow MSCs derived from aged mice, as well as promoting their proliferation, colony formation, migration, and osteogenic potential. Mechanistically, αKG decreases the accumulations of H3K9me3 and H3K27me3, and subsequently upregulates BMP signaling and Nanog expression. Collectively, our findings illuminate the role of αKG in rejuvenating MSCs and ameliorating age-related osteoporosis, with a promising therapeutic potential in age-related diseases. α-ketoglutarate is an intermediate of the Krebs Cycle that was recently reported to extend lifespan in C.Elegans. Here, the authors show that administration of α-ketoglutarate to mice reduces age-related bone loss, by ameliorating senescence of bone-marrow derived mesenchymal stem cells.

Long non-coding RNAs （lncRNAs）, which represent a new frontier in molecular biology, play important roles in regulating gene expression at epigenetic, transcriptional and post-transcriptional levels. More and more lncRNAs have been found to play important roles in normal cell physiological activities, and participate in the development of varieties of tumors and other dis- eases. Previously, we have only been able to determine the function of lncRNAs through multiple mechanisms, including ge- netic imprinting, chromatin remodeling, splicing regulation, mRNA decay, and translational regulation. Application of techno- logical advances to research into the function of lncRNAs is extremely important. The major tools for exploring lncRNAs in- clude microarrays, RNA sequencing （RNA-seq）, Northern blotting, real-time quantitative reverse transcription-polymerase chain reaction （qRT-PCR）, fluorescence in situ hybridization （FISH）, RNA interference （RNAi）, RNA-binding protein im- munoprecipitation （RIP）, chromatin isolation by RNA purification （CHIRP）, crosslinking-immunopurification （CLIP）, and bi- oinformatic prediction. In this review, we highlight the functions of lncRNAs, and advanced methods to research lncRNA-protein interactions.

Transcript stability is associated with many biological processes, and the factors affecting mRNA stability have been extensively studied. However, little is known about the features related to human long noncoding RNA (lncRNA) stability. By inhibiting transcription and collecting samples in 10 time points, genome-wide RNA-seq studies was performed in human lung adenocarcinoma cells (A549) and RNA half-life datasets were constructed. The following observations were obtained. First, the half-life distributions of both lncRNAs and messanger RNAs (mRNAs) with one exon (lnc-human1 and m-human1) were significantly different from those of both lncRNAs and mRNAs with more than one exon (lnc-human2 and m-human2). Furthermore, some factors such as full-length transcript secondary structures played a contrary role in lnc-human1 and m-human2. Second, through the half-life comparisons of nucleus- and cytoplasm-specific and common lncRNAs and mRNAs, lncRNAs (mRNAs) in the nucleus were found to be less stable than those in the cytoplasm, which was derived from transcripts themselves rather than cellular location. Third, kmers-based protein−RNA or RNA−RNA interactions promoted lncRNA stability from lnc-human1 and decreased mRNA stability from m-human2 with high probability. Finally, through applying deep learning−based regression, a non-linear relationship was found to exist between the half-lives of lncRNAs (mRNAs) and related factors. The present study established lncRNA and mRNA half-life regulation networks in the A549 cell line and shed new light on the degradation behaviors of both lncRNAs and mRNAs.

Objectives Osteoporosis, prevalent among the elderly population, is primarily diagnosed through bone mineral density (BMD) testing, which has limitations in early detection. This study aims to develop and validate a machine learning approach for osteoporosis identification by integrating demographic data, laboratory and questionnaire data, offering a more practical and effective screening alternative. Methods In this study, data from the National Health and Nutrition Examination Survey were analyzed to explore factors linked to osteoporosis. After cleaning, 8766 participants with 223 variables were studied. Minimum Redundancy Maximum Relevance and SelectKBest were employed to select the import features. Four Machine learning algorithms (RF, NN, LightGBM and XGBoost.) were applied to examine osteoporosis, with performance comparisons made. Data balancing was done using SMOTE, and metrics like F1 score, and AUC were evaluated for each algorithm. Results The LightGBM model outperformed others with an F1 score of 0.914, an MCC of 0.831, and an AUC of 0.970 on the training set. On the test set, it achieved an F1 score of 0.912, an MCC of 0.826, and an AUC of 0.972. Top predictors for osteoporosis were height, age, and sex. Conclusions This study demonstrates the potential of machine learning models in assessing an individual’s risk of developing osteoporosis, a condition that significantly impacts quality of life and imposes substantial healthcare costs. The superior performance of the LightGBM model suggests a promising tool for early detection and personalized prevention strategies. Importantly, identifying height, age, and sex as top predictors offers critical insights into the demographic and physiological factors that clinicians should consider when evaluating patients’ risk profiles.

Background A coracoid process fracture combined with an acromioclavicular (AC) joint dislocation is an uncommon injury that typically causes significant pain and limits shoulder movement. Open reduction and internal fixation have been the traditional treatment approach. However, arthroscopic techniques are emerging as a promising alternative for managing these injuries. Case representation A 35-year-old woman presented with right shoulder pain following an accidental fall. Imaging studies revealed a coracoid process fracture along with an AC joint dislocation. The fracture was classified as an Eyres Type IIIA, which warranted surgical intervention. Our team performed arthroscopic coracoid fracture reduction and internal fixation surgery, as well as AC joint dislocation repair using Kirschner wires. Six months after surgery, the patient demonstrated a satisfactory functional outcome with complete bone healing. Conclusion This case report highlights the potential of arthroscopic reduction and fixation as a novel treatment option for fractures of the coracoid base.

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. 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. 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. 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). 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.

Fracture-related infection (FRI) is one of the most common and intractable complications in orthopedic trauma surgery. This complication can impose severe psychological burdens and socio-economic impacts on patients. Although the definition of FRI has been proposed recently by an expert group, the diagnostic criteria for FRI are not yet standardized. A total of 4761 FRI patients and 4761 fracture patients (Non-FRI) were included in the study. The feature set of patients included imaging characteristics, demographic information, clinical symptoms, microbiological findings, and serum inflammatory markers, which were reduced by the Principal Component Analysis. To optimize the Support Vector Machine (SVM) model, the Traction Switching Delay Particle Swarm Optimization (TSDPSO) algorithm, a recognition method was proposed. Moreover, five machine learning models, including TSDPSO-SVM, were employed to distinguish FRI from Non-FRI. The Area under the Curve of TSDPSO-SVM was 0.91, at least 5% higher than that of other models. Compared with the Random Forest, Backpropagation Neural Network (BP), SVM and eXtreme Gradient Boosting (XGBoost), TSDPSO-SVM demonstrated remarkable accuracy in the test set ( χ 2 = 29.17 , 50.46 , 56.66 , 35.88 , P < 0.01 ). The recall of TSDPSO-SVM was 98.32%, indicating a significant improvement ( χ 2 = 91.78 , 107.42 , 135.69 , P < 0.01 ). Compared with BP and SVM, TSDPSO-SVM exhibited significantly superior specificity, false positive rate and precision ( χ 2 > 3.84 , P < 0.05 ) . The five models yielded consistent results in the training and testing of FRI patients across different age groups. TSDPSO-SVM is validated to have the maximum overall prediction ability and can effectively distinguish between FRI and Non-FRI. For the early diagnosis of FRI, TSDPSO-SVM may provide a reference basis for clinicians, especially those with insufficient experience. These results also lay a foundation for the intelligent diagnosis of FRI. Furthermore, these findings exhibit the application potential of this model in the diagnosis and classification of other diseases.

Pregnancy-induced hypertension (PIH), a prominent determinant of maternal mortality and morbidity worldwide, is hindered by the absence of efficacious biomarkers for early diagnosis, contributing to suboptimal outcomes. Here, we explored potential causal relationships between blood metabolites and the risk of PIH using Mendelian randomization (MR). We employed a two-sample univariable MR approach to empirically estimate the causal relationships between 249 circulating metabolites and PIH. Inverse variance weighted, MR-egger, weight median, simple mode, and weighted mode methods were used for causal estimates. The exposure-to-outcome directionality was confirmed with the MR Steiger test. The Bayesian model averaging MR (MR-BMA) method was applied to detect the predominant causal metabolic traits with alignment for pleiotropy effects. In the primary analysis, analyzing 249 metabolites, we identified 25 causally linked to PIH, including 11 lipid-related traits and 6 associated with fatty acid (un)saturation. Importantly, MR-BMA analyses corroborated the total concentration of branched-chain amino acids(total-BCAA) to be the highest rank causal metabolite, followed by leucine (Leu), phospholipids to total lipids ratio in medium LDL (M-LDL-PL-pct), and Val (all P < 0.05). The directionality of causality predicted by univariable MR and MR-BMA for these metabolites remained consistent. This study highlights the causal connection between metabolites and PIH risk. It highlighted BCAAs as the strongest causal candidates warranting further investigation. Since PIH typically occurs in the second and third trimesters, extending these findings could inform earlier strategies to reduce its risk. Directed acyclic graph of the MR framework investigating the causal relationship between metabolites and PIH. MR: Mendelian randomization; GIVs: genetic instrument variables; SNPs: single-nucleotide polymorphism; IVW: inverse variance weighted; WM: weighted median; PIH: pregnancy-induced hypertension; SM: significant metabolite; MR-BMA: Bayesian model averaging MR.

Maternally Expressed Gene 3 (MEG3) encodes a lncRNA which is suggested to function as a tumor suppressor. Previous studies suggested that MEG3 functioned through activation of p53, however, the functional properties of MEG3 remain obscure and their relevance to human diseases is under continuous investigation. Here, we try to illuminate the relationship of MEG3 and p53, and the consequence in hepatoma cells. We find that transfection of expression construct of MEG3 enhances stability and transcriptional activity of p53. Deletion analysis of MEG3 confirms that full length and intact structure of MEG3 are critical for it to activate p53-mediated transactivation. Interestingly, our results demonstrate for the first time that MEG3 can interact with p53 DNA binding domain and various p53 target genes are deregulated after overexpression of MEG3 in hepatoma cells. Furthermore, results of qRT-PCR have shown that MEG3 RNA is lost or reduced in the majority of HCC samples compared with adjacent non-tumorous samples. Ectopic expression of MEG3 in hepatoma cells significantly inhibits proliferation and induces apoptosis. In conclusion, our data demonstrates that MEG3 functions as a tumor suppressor in hepatoma cells through interacting with p53 protein to activate p53-mediated transcriptional activity and influence the expression of partial p53 target genes.

ChatGPT is a natural language processing model developed by OpenAI, which can be iteratively updated and optimized to accommodate the changing and complex requirements of human verbal communication. The study aimed to evaluate ChatGPT's accuracy in answering orthopedics-related multiple-choice questions (MCQs) and assess its short-term effects as a learning aid through a randomized controlled trial. In addition, long-term effects on student performance in other subjects were measured using final examination results. We first evaluated ChatGPT's accuracy in answering MCQs pertaining to orthopedics across various question formats. Then, 129 undergraduate medical students participated in a randomized controlled study in which the ChatGPT group used ChatGPT as a learning tool, while the control group was prohibited from using artificial intelligence software to support learning. Following a 2-week intervention, the 2 groups' understanding of orthopedics was assessed by an orthopedics test, and variations in the 2 groups' performance in other disciplines were noted through a follow-up at the end of the semester. ChatGPT-4.0 answered 1051 orthopedics-related MCQs with a 70.60% (742/1051) accuracy rate, including 71.8% (237/330) accuracy for A1 MCQs, 73.7% (330/448) accuracy for A2 MCQs, 70.2% (92/131) accuracy for A3/4 MCQs, and 58.5% (83/142) accuracy for case analysis MCQs. As of April 7, 2023, a total of 129 individuals participated in the experiment. However, 19 individuals withdrew from the experiment at various phases; thus, as of July 1, 2023, a total of 110 individuals accomplished the trial and completed all follow-up work. After we intervened in the learning style of the students in the short term, the ChatGPT group answered more questions correctly than the control group (ChatGPT group: mean 141.20, SD 26.68; control group: mean 130.80, SD 25.56; P=.04) in the orthopedics test, particularly on A1 (ChatGPT group: mean 46.57, SD 8.52; control group: mean 42.18, SD 9.43; P=.01), A2 (ChatGPT group: mean 60.59, SD 10.58; control group: mean 56.66, SD 9.91; P=.047), and A3/4 MCQs (ChatGPT group: mean 19.57, SD 5.48; control group: mean 16.46, SD 4.58; P=.002). At the end of the semester, we found that the ChatGPT group performed better on final examinations in surgery (ChatGPT group: mean 76.54, SD 9.79; control group: mean 72.54, SD 8.11; P=.02) and obstetrics and gynecology (ChatGPT group: mean 75.98, SD 8.94; control group: mean 72.54, SD 8.66; P=.04) than the control group. ChatGPT answers orthopedics-related MCQs accurately, and students using it excel in both short-term and long-term assessments. Our findings strongly support ChatGPT's integration into medical education, enhancing contemporary instructional methods. Chinese Clinical Trial Registry Chictr2300071774; https://www.chictr.org.cn/hvshowproject.html ?id=225740&v=1.0.