Proanthocyanidin B2 inhibits proliferation and induces apoptosis of osteosarcoma cells by suppressing the PI3K/AKT pathway.

Osteosarcoma (OS) is the most common primary malignant bone tumour in children and adolescents. The long-term survival rate of OS patients is stubbornly low mainly due to the chemotherapy resistance. We therefore aimed to investigate the antitumoral effects and underlying mechanisms of proanthocyanidin B2 (PB2) on OS cells in the current study. The effect of PB2 on the proliferation and apoptosis of OS cell lines was assessed by CCK-8, colony formation, and flow cytometry assays. The target gene and protein expression levels were measured by qRT-PCR and Western blotting. A xenograft mouse model was established to assess the effects of PB2 on OS proliferation and apoptosis in vivo. Results from in vitro experiments showed that PB2 inhibited the proliferation and induced apoptosis of OS cells, and also increased the expression levels of apoptosis-related proteins. Moreover, PB2 induced OS cell apoptosis through suppressing the PI3K/AKT signalling pathway. The in vivo experiments further confirmed that PB2 could inhibit OS tumour growth and induce its apoptosis. Taken together, these results suggested that PB2 inhibited the proliferation and induced apoptosis of OS cells through the suppression of the PI3K/AKT signalling pathway.

Photoelectrochemical NADH regeneration is highly sensitive to the nature of electrode surface.

(Photo)electrochemistry enables the synthesis of high-value fine chemicals and highly selective activation of molecules that are difficult to prepare using conventional chemical methods. In this work, light-driven NADH (reduced nicotinamide adenine dinucleotide) regeneration is achieved using a molecular Rh(III) mediator on Si photoelectrodes. This process is observed to be highly sensitive to the surface nature of Si photoelectrodes, exhibiting an overpotential reduction up to 600 mV on Si nanowires (SiNWs) as compared to planar Si. The use of a molecular mediator and SiNWs enables 100% selectivity toward NADH synthesis within a broad potential window. The origin of the striking difference is identified as the multifaceted nature of SiNWs.

Tomatidine Alleviates Osteoporosis by Downregulation of p53.

BACKGROUND As a common metabolic disorder, osteoporosis is characterized by decreasing bone mass density and increased possibility of fragility fracture. The incidence of senile osteoporosis increases year by year. There is no gold standard of treatment for osteoporosis. Tomatidine is the aglycone derivative of tomatine, having the ability to treat various diseases, including osteoporosis. However, the mechanism by which tomatidine improves osteoporosis has not been fully elucidated. Tomatidine is a potential and promising drug for osteoporosis. MATERIAL AND METHODS In this study, the KEGG pathways that tomatidine-targeted genes enriched in were obtained using bioinformatics methods. The KEGG pathways involved in osteoporosis that were also associated with tomatidine-targeted genes were selected. After analysis of these pathways, essential genes that may be involved in this biological process were identified and validated experimentally. RESULTS We found 110 osteoporosis related KEGG pathways and 76 tomatidine-targeted genes-related KEGG pathways were obtained. 39 shared KEGG pathways were identified. The top 5 pathways were: pathway of chronic myeloid leukemia, pathway of B cell receptor signaling, pathway in cancer, bladder cancer pathway, and progesterone-mediated oocyte maturation pathway. MAPK1, MAP2K1, MAPK3, RAF1 were involved in all the 5 pathways. The p53 signaling pathway and the MAPK signaling pathway were involved in the 5 KEGG pathways. In vitro experiments showed that downregulating p53 expression could be potentially protective for osteoporosis. CONCLUSIONS Tomatidine can improve osteoporosis, and one of the mechanisms of its action is achieved by modulating p53. Tomatidine may be a promising drug for osteoporosis.

A Real-Time Gait Phase Detection Method Based on BiLSTM-Attention Model.

Real-time gait phase detection is essential to achieve accurate and stable walking assistance in intelligent rehabilitation training for patients with motor disorders. This study proposed an efficient real-time detection method to detect three gait phases (loading response, stance, and swing) based on a bidirectional long short-term memory network with an attention layer (BiLSTM-Attention). We validated our method on a public dataset where eight healthy subjects' data during treadmill walking were employed. A single inertial measurement unit (IMU) was attached to the shank to measure the sagittal plane acceleration of the lower leg and the angular velocity around the central lateral axis. These data were transposed and segmented into data sequences based on labels using a sliding window method. The data from 8 participants were divided into the training, validation, and test sets (5:1:2). Results showed the average recognition accuracy of the proposed model on new subjects was 97.40% with an average time delay of 15.7±10.1ms, showing the method's potential to be applied for practice use.

Differentiating brain states via multi-clip random fragment strategy-based interactive bidirectional recurrent neural network.

EEG is widely adopted to study the brain and brain computer interface (BCI) for its non-invasiveness and low costs. Specifically EEG can be applied to differentiate brain states, which is important for better understanding the working mechanisms of the brain. Recurrent neural network (RNN)-based learning strategy has been widely utilized to differentiate brain states, because its optimization architectures improve the classification performance for differentiating brain states at the group level. However, present classification performance is still far from satisfactory. We have identified two major focal points for improvements: one is about organizing the input EEG signals, and the other is related to the design of the RNN architecture. To optimize the above-mentioned issues and achieve better brain state classification performance, we propose a novel multi-clip random fragment strategy-based interactive bidirectional recurrent neural network (McRFS-IBiRNN) model in this work. This model has two advantages over previous methods. First, the McRFS component is designed to re-organize the input EEG signals to make them more suitable for the RNN architecture. Second, the IBiRNN component is an innovative design to model the RNN layers with interaction connections to enhance the fusion of bidirectional features. By adopting the proposed model, promising brain states classification performances are obtained. For example, 96.97% and 99.34% of individual and group level four-category classification accuracies are successfully obtained on the EEG motor/imagery dataset, respectively. A 99.01% accuracy can be observed for four-category classification tasks with new subjects not seen before, which demonstrates the generalization of our proposed method. Compared with existing methods, our model outperforms them with superior results. Overall, the proposed McRFS-IBiRNN model demonstrates great superiority in differentiating brain states on EEG signals.

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.

Cost-utility analysis of extensile lateral approach versus sinus tarsi approach in Sanders type II/III calcaneus fractures.

BACKGROUND: Extensile lateral approach had been recognized as the gold standard technique for displaced intra-articular calcaneus fractures (DIACFs) while sinus tarsi approach had been increasingly valued by surgeons and comparative clinical outcome was shown in both techniques. Appropriate decisions could be made by the clinicians with the help of cost-utility analysis (CUA) about optimal healthcare for type II/III calcaneus fracture. METHOD: A single-center, retrospective study was conducted in which basic characteristics, clinical outcomes, and health care costs of 109 patients had been obtained and analyzed. Changes in health-related quality of life (HRQoL) scores, validated by EuroQol five-dimensional-three levels (EQ-5D-3L), were used to enumerate quality-adjusted life years (QALYs). Cost-effectiveness was determined by the incremental cost per QALY. RESULTS: One hundred nine patients were enrolled in our study including 62 in the ELA group and 47 in the STA group. There were no significant differences between these two groups in mean total cost, laboratory, and radiographic evaluation expense, surgery, anesthesia, and antibiotic expense. The expense of internal fixation materials ($3289.0 ± 543.9) versus ($2630.6 ± 763.7) and analgesia ($145.8 ± 85.6) versus ($102.9 ± 62.7) in ELA group were significantly higher than in the STA group (P < .001, P = .008, respectively). Visual Analogue Scale (VAS) scores showed significant difference at postoperative 3 and 5 days (P < .001). American Orthopaedic Foot and Ankle Society (AOFAS) ankle-hindfoot scores and the Bohlers' and Gissane angle showed no significant differences between the two groups before and after the operation. The cost-effectiveness ratios of ELA and STA were $8766.8 ± 2835.2/QALY and $7914.9 ± 1822.0/QALY respectively, and incremental cost-effectiveness ratio (ICERs) of ELA over STA was $32110.00/QALY, but both showed no significant difference. CONCLUSION: Both ELA and STA techniques are effective operative procedures for the patients with calcaneus fracture. Moreover, STA seems to be more reasonable for its merits including less postoperative pain, and less expense of analgesia as well as internal fixation materials. LEVEL OF EVIDENCE: 5.

MicroRNA-130a regulates chondrocyte proliferation and alleviates osteoarthritis through PTEN/PI3K/Akt signaling pathway.

The function of microRNA­130a in development and progression of osteoarthritis was determined. In osteoarthritis patients, the serum levels of microRNA­130a were decreased, compared with normal group. Overexpression of microRNA­130a increased cell proliferation and decreased apoptosis of chondrocytes, and downregulation of microRNA­130a also decreased cell proliferation and induced apoptosis in chondrocytes. Downregulation of microRNA­130a promoted Bax and caspase­3/9 protein expression, increased inflammation divisors and suppressed the PTEN/PI3K/Akt signaling pathway. PTEN inhibitor, VO­Ohpic trihydrate increased the destructive effect of microRNA­130a on cell proliferation of chondrocytes. PI3K inhibitor, wortmannin also increased the destructive effect of microRNA­130a on osteoarthritis. In conclusion, microRNA­130a is an important regulator of osteoarthritis in chondrocytes through PTEN/PI3K/Akt signaling pathway.