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Shoulder periarthritis, a prevalent musculoskeletal disorder, causes significant pain and functional impairment, severely affecting patients' quality of life. With the increasing incidence of shoulder periarthritis linked to modern lifestyle changes, effective prevention and treatment strategies remain elusive. This study explores two areas: first, identifying risk factors for shoulder periarthritis through Mendelian randomization (MR) analysis, and second, designing a motion intervention system incorporating MediaPipe and virtual reality (VR) technology. The MR analysis revealed positive causal relationships between shoulder periarthritis and body mass index (BMI), cigarettes per day, insomnia, and sedentary behavior, while higher educational attainment was inversely associated with the condition. Based on these findings, we developed a "VR Harvest" exercise system aimed at alleviating shoulder stiffness due to prolonged sitting and reducing shoulder pain. By adjusting task difficulty to increase physical activity, the system also facilitates BMI reduction, potentially lowering the risk of shoulder periarthritis. The experimental results indicate that the "VR Harvest" therapy effectively alleviates pain and, compared to traditional exercise therapy, offers greater enjoyment, thereby enhancing participant engagement. Moreover, participants across different BMI categories experienced reductions in BMI after completing the intervention. This study offers a novel approach for shoulder periarthritis prevention and treatment, leveraging VR technology to improve both symptom relief and underlying risk factors., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2024 Yucheng Tian et al.)

The deregulation of long noncoding RNAs (lncRNAs) holds great potential in the treatment of multiple cancers, including pancreatic cancer (PC). However, the specific molecular mechanisms by which LINC01133 contributes to pancreatic cancer remain unknown. Subsequent to bioinformatics analysis, we predicted and analyzed differentially expressed lncRNAs, microRNAs, and genes in pancreatic cancer. We determined the expression patterns of LINC01133, miR-1299, and insulin-like growth factor 2 mRNA binding protein 3 (IGF2BP3) in pancreatic cancer cells, and validated their interactions through luciferase reporter and RNA immunoprecipitation assays. We implemented loss-of-function and gain-of-function experiments for LINC01133, miR-1299, and IGF2BP3 to assay their potential effects on pancreatic cancer cell functions. We observed high expression of LINC01133 and IGF2BP3, but low expression of miR-1299, in pancreatic cancer cells. Furthermore, we found that LINC01133 enhances IGF2BP3 through binding with miR-1299. Silencing LINC01133 or IGF2BP3 and/or overexpressing miR-1299 limited pancreatic cancer cell proliferation, invasion, epithelial-mesenchymal transition, and suppressed tumorigenic abilities in mice lacking T cells (nude mice). Overall, our findings identified that silencing LINC01133 downregulates IGF2BP3 by upregulating miR-1299 expression, ultimately leading to the prevention of pancreatic cancer., (© 2023 Wiley Periodicals LLC.)

N6-methyladenosine (m6A) methyltransferase METTL3 has been implicated in carcinogenesis, which may be associated the overexpression of MALAT1. However, the downstream mechanics actions remain largely unknown. This study intends to probe the downstream mechanism of the N6-methyladenosine (m 6 A) methyltransferase METTL3 and MALAT1 in adriamycin resistance in breast cancer. Through Bioinformatics databases lncMAP, TCGA and GTEx, we predicted the downstream transcription factors E2F1 and AGR2 of MALAT1 in breast cancer. The Cancer Genome Atlas and Genotype-Tissue Expression (GTEx) databases were used to screen the downstream target genes of MALAT1. MeRIP-qPCR was used to detect the m 6 A level of MALAT1 in cells. RIP was used to detect the binding between MALAT1 and E2F1, and chromatin immunoprecipitation (ChIP) for the binding of E2F1 to AGR2 promoter. Cell Counting Kit-8 and colony formation assays were used to detect cell viability. Transwell was used to detect cell invasion. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) and western blot were used to detect the expression of related genes and proteins. A nude mouse xenograft tumor model was established to observe the effect of METTL3 on adriamycin resistance of breast cancer. The total survival of mice after exogenous gene silencing was analyzed by the Kaplan-Meier method. METTL3 was highly expressed in adriamycin-resistant breast cancer cells. METTL3 promotes adriamycin resistance in breast cancer cells. METTL3 mediates the expression of MALAT1 in adriamycin-resistant breast cancer through m 6 A. MALAT1 increases adriamycin resistance in breast cancer cells by recruiting E2F1 to activate AGR2 transcription. METTL3 can regulate the expression of MALAT1 through m 6 A, mediate the E2F1/AGR2 axis, and promote the adriamycin resistance of breast cancer. METTL3 may modify MALAT1 protein through m 6 A, recruit E2F1 and activate downstream AGR2 expression, thus promoting adriamycin resistance in breast cancer., (© 2021 Wiley Periodicals LLC.)

Purpose: To accelerate and improve multishot diffusion-weighted MRI reconstruction using deep learning., Methods: An unrolled pipeline containing recurrences of model-based gradient updates and neural networks was introduced for accelerating multishot DWI reconstruction with shot-to-shot phase correction. The network was trained to predict results of jointly reconstructed multidirection data using single-direction data as input. In vivo brain and breast experiments were performed for evaluation., Results: The proposed method achieves a reconstruction time of 0.1 second per image, over 100-fold faster than a shot locally low-rank reconstruction. The resultant image quality is comparable to the target from the joint reconstruction with a peak signal-to-noise ratio of 35.3 dB, a normalized root-mean-square error of 0.0177, and a structural similarity index of 0.944. The proposed method also improves upon the locally low-rank reconstruction (2.9 dB higher peak signal-to-noise ratio, 29% lower normalized root-mean-square error, and 0.037 higher structural similarity index). With training data from the brain, this method also generalizes well to breast diffusion-weighted imaging, and fine-tuning further reduces aliasing artifacts., Conclusion: A proposed data-driven approach enables almost real-time reconstruction with improved image quality, which improves the feasibility of multishot DWI in a wide range of clinical and neuroscientific studies., (© 2020 International Society for Magnetic Resonance in Medicine.)

Purpose: To develop a motion-robust wave-encoding technique for highly accelerated free-breathing abdominal MRI., Methods: A comprehensive 3D wave-encoding-based method was developed to enable fast free-breathing abdominal imaging: (a) auto-calibration for wave-encoding was designed to avoid extra scan for coil sensitivity measurement; (b) intrinsic butterfly navigators were used to track respiratory motion; (c) variable-density sampling was included to enable compressed sensing; (d) golden-angle radial-Cartesian hybrid view-ordering was incorporated to improve motion robustness; and (e) localized rigid motion correction was combined with parallel imaging compressed sensing reconstruction to reconstruct the highly accelerated wave-encoded datasets. The proposed method was tested on six subjects and image quality was compared with standard accelerated Cartesian acquisition both with and without respiratory triggering. Inverse gradient entropy and normalized gradient squared metrics were calculated, testing whether image quality was improved using paired t-tests., Results: For respiratory-triggered scans, wave-encoding significantly reduced residual aliasing and blurring compared with standard Cartesian acquisition (metrics suggesting P < 0.05). For non-respiratory-triggered scans, the proposed method yielded significantly better motion correction compared with standard motion-corrected Cartesian acquisition (metrics suggesting P < 0.01)., Conclusion: The proposed methods can reduce motion artifacts and improve overall image quality of highly accelerated free-breathing abdominal MRI. Magn Reson Med 78:1757-1766, 2017. © 2016 International Society for Magnetic Resonance in Medicine., (© 2016 International Society for Magnetic Resonance in Medicine.)