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Large language models (LLMs) have flourished and gradually become an important research and application direction in the medical field. However, due to the high degree of specialization, complexity, and specificity of medicine, which results in extremely high accuracy requirements, controversy remains about whether LLMs can be used in the medical field. More studies have evaluated the performance of various types of LLMs in medicine, but the conclusions are inconsistent. This study uses a network meta-analysis (NMA) to assess the accuracy of LLMs when answering clinical research questions to provide high-level evidence-based evidence for its future development and application in the medical field. In this systematic review and NMA, we searched PubMed, Embase, Web of Science, and Scopus from inception until October 14, 2024. Studies on the accuracy of LLMs when answering clinical research questions were included and screened by reading published reports. The systematic review and NMA were conducted to compare the accuracy of different LLMs when answering clinical research questions, including objective questions, open-ended questions, top 1 diagnosis, top 3 diagnosis, top 5 diagnosis, and triage and classification. The NMA was performed using Bayesian frequency theory methods. Indirect intercomparisons between programs were performed using a grading scale. A larger surface under the cumulative ranking curve (SUCRA) value indicates a higher ranking of the corresponding LLM accuracy. The systematic review and NMA examined 168 articles encompassing 35,896 questions and 3063 clinical cases. Of the 168 studies, 40 (23.8%) were considered to have a low risk of bias, 128 (76.2%) had a moderate risk, and none were rated as having a high risk. ChatGPT-4o (SUCRA=0.9207) demonstrated strong performance in terms of accuracy for objective questions, followed by Aeyeconsult (SUCRA=0.9187) and ChatGPT-4 (SUCRA=0.8087). ChatGPT-4 (SUCRA=0.8708) excelled at answering open-ended questions. In terms of accuracy for top 1 diagnosis and top 3 diagnosis of clinical cases, human experts (SUCRA=0.9001 and SUCRA=0.7126, respectively) ranked the highest, while Claude 3 Opus (SUCRA=0.9672) performed well at the top 5 diagnosis. Gemini (SUCRA=0.9649) had the highest rated SUCRA value for accuracy in the area of triage and classification. Our study indicates that ChatGPT-4o has an advantage when answering objective questions. For open-ended questions, ChatGPT-4 may be more credible. Humans are more accurate at the top 1 diagnosis and top 3 diagnosis. Claude 3 Opus performs better at the top 5 diagnosis, while for triage and classification, Gemini is more advantageous. This analysis offers valuable insights for clinicians and medical practitioners, empowering them to effectively leverage LLMs for improved decision-making in learning, diagnosis, and management of various clinical scenarios. PROSPERO CRD42024558245; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024558245.

While cryoablation demonstrates therapeutic potential for lung adenocarcinoma, the precise molecular mechanisms underlying its antitumor effects require further elucidation. We conducted the first transcriptome-based investigation of cryoablation’s anti-tumor mechanisms using the Lewis lung carcinoma (LLC) model. Subcutaneous LLC tumors were established in C57BL/6 mice, with subsequent randomization into the model, cryoablation, and cisplatin control groups (DDP). Systematic evaluations included the tumor volume and weight, histopathology (H&E staining), Ki-67 proliferative index (IHC). RNA sequencing identified differentially expressed genes (DEGs), and bioinformatics analysis constructed lncRNA/miRNA-mRNA regulatory networks, with key targets validated by RT-qPCR and Western blotting. Our findings revealed that cryoablation significantly inhibited tumor growth (volume/weight reduction) and downregulated the Ki-67 proliferative index. Transcriptomic profiling identified 1136 mRNA, 215 lncRNA, and 39 miRNA DEGs, unveiling critical PDK1-VEGFA axis regulation and mmu-miR-210-5p-ANKFY1 targeting. Western blot analysis confirmed that cryoablation blocked the HIF-1 signaling pathway by inhibiting HIF-1α and VEGF protein expression. This study elucidates that cryoablation exerts anti-tumor effects via HIF-1 pathway blockade and non-coding RNA network remodeling, providing a theoretical foundation for optimizing cryotherapeutic strategies.

Intracellular tau accumulation is a hallmark pathology of Alzheimer’s disease (AD) and other tauopathies. Tau protein, in the hyperphosphorylated form, is the component of paired helical filaments (PHFs) and neurofibrillary tangles (NFTs) in AD. Blocking tau aggregation and/or phosphorylation is currently a promising strategy for AD treatment. Here, we elucidate that quercetagitrin, a natural compound derived from African marigold (Tagetes erecta), could inhibit tau aggregation and reduce tau phosphorylation at multiple disease-related sites in vitro. Moreover, the in vivo effect of quercetagitrin was assessed in P301S-tau transgenic via oral administration. The compound treatment restored the cognitive deficits and neuron loss in the mice. The formation of NFTs and tau phosphorylations in the hippocampus and cortex of the mice was also prevented by the compound. Moreover, quercetagitrin feeding displayed neuroinflammation protection through the inhibition of NF-κB activation in the mice. Together, our data reveal that quercetagitrin possesses the potential to further develop as a therapeutic medicine for AD and other tauopathies.

Existing research treated all sentences in the text on an equal basis during the training process and did not consider that key sentences tend to have a stronger influence. We propose a Convolutional Neural Network text sentiment classification model based on the key sentences enhancement. The proposed model can identify key sentences in the text and generate text representation based on these key sentences to reduce noise and improve the accuracy of the model sentiment classification. The experiment results show that the proposed model improves the accuracy of the text sentiment classification compared with other classic text sentiment classification models.

Objective: Parkinson’s disease (PD) is a degenerative disease of the nervous system that frequently occurs in the aged. Transcranial magnetoacoustic stimulation (TMAS) is a neuronal adjustment method that combines sound fields and magnetic fields. It has the characteristics of high spatial resolution and noninvasive deep brain focusing. Methods: This paper constructed a simulation model of TMAS based on volunteer’s skull computer tomography, phased controlled transducer and permanent magnet. It simulates a transcranial focused sound pressure field with the Westervelt equation and builds a basal ganglia and thalamus neural network model in the PD state based on the Hodgkin-Huxley model. Results: A biased sinusoidal pulsed ultrasonic TMAS induced current with 0.3 T static magnetic field induction and 0.2 W⋅cm –2 sound intensity can effectively modulate PD states with RI ≥ 0.633. The magnitude of magnetic induction strength was changed to 0.2 and 0.4 T. The induced current was the same when the sound intensity was 0.4 and 0.1 W⋅cm –2 . And the sound pressure level is in the range of −1 dB (the induced current difference is less than or equal to 0.019 μA⋅cm –2 ). TMAS with a duty cycle of approximately 50% can effectively modulates the error firings in the PD neural network with a relay reliability not less than 0.633. Conclusion: TMAS can modulates the state of PD.

This study examines the combined effects of life satisfaction and employability on student learning outcomes, addressing a critical gap in existing research. Specifically, it explores how these factors interact to enhance academic performance, motivation, and career preparedness. By integrating Self-Determination Theory (SDT) and Social Cognitive Theory (SCT), this research develops a novel theoretical framework for understanding these dynamics within an educational context. Employing a purposive sampling method, this study focuses on junior and senior college students from management schools in eight coastal cities in China, recognizing that these students are at a pivotal stage in shaping their future careers. A total of 875 valid responses were collected, ensuring the robustness and reliability of the analysis. The findings indicate that both life satisfaction and employability play a significant role in enhancing learning outcomes by fostering intrinsic motivation and self-efficacy. Furthermore, this study underscores the mediating role of social capital and the moderating influence of collaborative learning, offering valuable insights into their impact on student engagement and academic success. These findings have important practical implications for educational strategies aimed at improving student engagement and performance in higher education settings. By adopting a comprehensive approach, this study provides critical insights for educators and policymakers striving to foster an academic environment that not only supports educational achievement but also promotes student well-being and long-term career development.

Background The prevalence of cardiovascular metabolic comorbidities (CMM) among adults is relatively high, imposing a heavy burden on individuals, families, and society. Dietary patterns play a significant role in the occurrence and development of CMM. This study aimed to identify the combined types of CMM in adult populations and explore the association between dietary patterns and CMM. Methods Participants in this study were from the sixth wave of the China Health and Nutrition Survey (CHNS) in 2009. Dietary intake was assessed using a three-day unconsecutive 24-hour dietary recall method among 4,963 participants. Latent profile analysis was used to determine dietary pattern types. Two-step cluster analysis was performed to identify the combined types of CMM based on the participants’ conditions of hyperuricemia, dyslipidemia, diabetes, renal dysfunction, hypertension, and stroke. Logistic regression analysis with robust standard errors was used to determine the impact of dietary patterns on CMM. Results Participants were clustered into three dietary patterns (Pattern a, b and c) and five CMM types (Class I to V). Class I combined six diseases, with a low proportion of diabetes. Class II also combined six diseases but with a high proportion of diabetes. Class III combined four diseases, with a high proportion of hypertension. Class IV combined three diseases, with the highest proportions of hyperuricemia, diabetes, and renal dysfunction. Class V combined two diseases, with high proportions of dyslipidemia and renal dysfunction. Patients with Class III CMM had a significantly higher average age than the other four classes ( P  < 0.05). Compared to those with isolated dyslipidemia, individuals with a low-grain, high-fruit, milk, and egg (LCHFM) dietary pattern had a higher risk of developing dyslipidemia combined with renal dysfunction (Class V CMM) with an odds ratio of 2.001 (95% CI : 1.011–3.960, P  < 0.05). Conclusion Individuals with isolated dyslipidemia should avoid a low-grain, high-fruit, milk, and egg (LCHFM) dietary pattern to reduce their dyslipidemia combined with renal dysfunction.

Lightweight multi-rotor unmanned aerial vehicles (UAVs) have shown great potential in flexible Earth observation, but they impose strict restrictions on payload, volume, and power consumption. Traditional pulse-Doppler synthetic aperture radar (SAR) systems offer high imaging performance but suffer from high peak power and large volume, making them unsuitable for lightweight UAV platforms. To meet the low-power demand, most existing lightweight UAV SAR systems adopt frequency-modulated continuous-wave (FMCW) schemes, which are compact and low cost yet limited by a low range resolution, poor anti-interference ability, and single imaging modes. Therefore, it is urgent to develop an SAR system that combines the high performance of pulse radar with the lightweight advantage of FMCW radar. To this end, this paper proposes a compact, low-power Ka-band pulse-Doppler SAR system for multi-rotor UAVs. With 1.2 GHz bandwidth and highly integrated RF and antenna design, the system achieves miniaturization and low power consumption while maintaining high-resolution imaging capability. Furthermore, two-step waveform error correction and a signal predistortion method are presented to compensate amplitude and phase errors and improve the purity of the transmitted signal. Experimental results show that the proposed system can obtain clear SAR images with a resolution better than 0.3 m, providing a practical high-performance pulse-SAR solution for lightweight UAV platforms.

Maize trait recognition plays a crucial role in agricultural production and breeding research; however, the adaptability of existing object detection methods in complex environments remains limited. In this study, a maize trait detection method based on the neighborhood attention mechanism is proposed to enhance the accuracy of kernel and ear morphology detection. Experimental results demonstrate that the proposed method outperforms existing approaches across multiple evaluation metrics, achieving an overall mAP@50 of 0.92 and mAP@50-95 of 0.65, with precision and recall reaching 0.95 and 0.92, respectively. Compared to traditional attention mechanisms and loss functions, the proposed method significantly improves both detection accuracy and stability, providing reliable technical support for precision agriculture.

China has witnessed a considerable expansion of private higher education institutions (HEIs) over the last two decades, and research has shown that teacher leadership (TL) is an essential aspect of providing quality higher education. This study proposed a model to explain TL and the factors that contribute to it in private HEIs. A sample of 4,152 participants responded to an 11-item questionnaire using a 5-point scale designed to measure three variables: TL, teacher self-efficacy (TSE), and teacher competence (TC). The results showed that the three variables were valid in explaining TL and the factors that contribute to it. Hypothesis tests revealed that both hypotheses were supported. Finally, the results revealed that TSE and TC are both significantly associated with TL. The practical and theoretical implications of these findings and the scope for future research are discussed.