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Head injury is reported to be associated with increased risks of dementia and Alzheimer's disease (AD) in many but not all the epidemiological studies. We conducted a systematic review and meta-analysis to estimate the relative effect of head injury on dementia and AD risks. Relevant cohort and case-control studies published between Jan 1, 1990, and Mar 31, 2015 were searched in PubMed, Web of Science, Scopus, and ScienceDirect. We used the random-effect model in this meta-analysis to take into account heterogeneity among studies. Data from 32 studies, representing 2,013,197 individuals, 13,866 dementia events and 8,166 AD events, were included in the analysis. Overall, the pooled relative risk (RR) estimates showed that head injury significantly increased the risks of any dementia (RR = 1.63, 95% CI 1.34-1.99) and AD (RR = 1.51, 95% CI 1.26-1.80), with no evidence of publication bias. However, when considering the status of unconsciousness, head injury with loss of consciousness did not show significant association with dementia (RR = 0.92, 95% CI 0.67-1.27) and AD (RR = 1.49, 95% CI 0.91-2.43). Additionally, this positive association did not reach statistical significance in female participants. The findings from this meta-analysis indicate that head injury is associated with increased risks of dementia and AD.

Treatment planning is an essential step of the radiotherapy workflow. It has become more sophisticated over the past couple of decades with the help of computer science, enabling planners to design highly complex radiotherapy plans to minimize the normal tissue damage while persevering sufficient tumor control. As a result, treatment planning has become more labor intensive, requiring hours or even days of planner effort to optimize an individual patient case in a trial-and-error fashion. More recently, artificial intelligence has been utilized to automate and improve various aspects of medical science. For radiotherapy treatment planning, many algorithms have been developed to better support planners. These algorithms focus on automating the planning process and/or optimizing dosimetric trade-offs, and they have already made great impact on improving treatment planning efficiency and plan quality consistency. In this review, the smart planning tools in current clinical use are summarized in 3 main categories: automated rule implementation and reasoning, modeling of prior knowledge in clinical practice, and multicriteria optimization. Novel artificial intelligence–based treatment planning applications, such as deep learning–based algorithms and emerging research directions, are also reviewed. Finally, the challenges of artificial intelligence–based treatment planning are discussed for future works.

Transcriptome-wide association studies (TWAS) are commonly used to prioritize causal genes underlying associations found in genome-wide association studies (GWAS) and have been extended to identify causal genes through multivariate TWAS methods. However, recent studies have shown that widespread infinitesimal effects due to polygenicity can impair the performance of these methods. In this report, we introduce a multivariate TWAS method named tissue-gene pairs, direct causal variants, and infinitesimal effects selector (TGVIS) to identify tissue-specific causal genes and direct causal variants while accounting for infinitesimal effects. In simulations, TGVIS maintains an accurate prioritization of causal gene-tissue pairs and variants and demonstrates comparable or superior power to existing approaches, regardless of the presence of infinitesimal effects. In the real data analysis of GWAS summary data of 45 cardiometabolic traits and expression/splicing quantitative trait loci from 31 tissues, TGVIS is able to improve causal gene prioritization and identifies novel genes that were missed by conventional TWAS. Polygenic background effects can confound transcriptome-wide association studies aimed at identifying causal genes. Here, the authors show that TGVIS improves gene and variant prioritisation by accounting for infinitesimal effects in multivariate TWAS across tissues.

Platinum is the most efficient catalyst for hydrogen evolution reaction in acidic conditions, but its widespread use has been impeded by scarcity and high cost. Herein, Pt atomic clusters (Pt ACs) containing Pt-O-Pt units were prepared using Co/N co-doped carbon (CoNC) as support. Pt ACs are anchored to single Co atoms on CoNC by forming strong interactions. Pt-ACs/CoNC exhibits only 24 mV overpotential at 10 mA cm −2 and a high mass activity of 28.6 A mg −1 at 50 mV, which is more than 6 times higher than commercial Pt/C with any Pt loadings. Spectroscopic measurements and computational modeling reveal the enhanced hydrogen generation activity attributes to the charge redistribution between Pt and O atoms in Pt-O-Pt units, making Pt atoms the main active sites and O linkers the assistants, thus optimizing the proton adsorption and hydrogen desorption. This work opens an avenue to fabricate noble-metal-based ACs stabilized by single-atom catalysts with desired properties for electrocatalysis. Modulating single-metal sites at the atomic level can boost the intrinsic catalytic activity. Here, the authors describe the design of Pt atomic clusters containing Pt-O-Pt units supported on Co single atoms and N co-doped carbon for enhanced hydrogen evolution catalysis.

Winding inductance always plays a key role in the electromagnetic performances of stator surface-mounted permanent magnet (SSPM) machines, including their flux-weakening capability, prospective fault current, power factor, current ripple, etc. Generally speaking, winding inductance mainly comprises three components: an air-gap component, a slot-leakage component, and an end-leakage component. In this paper, firstly, the winding pole pairs of SSPM machines are investigated based on the magneto-motive force-permeance model, through which the winding configurations can also be determined. Then, according to the winding configurations, three analytical expressions for each inductance component are derived to evaluate the winding inductance per phase. In addition, finite element analysis (FEA) is employed to verify the effectiveness of the derived analytical expressions. Meanwhile, three prototyped SSPM machines are manufactured, and their winding inductances are measured to further verify the analytical expressions. The measured results agree with both the analytical and FEA results very well.

Drug delivery systems (DDSs) based on nanomaterials have shown a promise for cancer chemotherapy; however, it remains a great challenge to localize on-demand release of anticancer drugs in tumor tissues to improve therapeutic effects and minimize the side effects. In this regard, photoresponsive DDSs that employ light as an external stimulus can offer a precise spatiotemporal control of drug release at desired sites of interest. Most photoresponsive DDSs are only responsive to ultraviolet-visible light that shows phototoxicity and/or shallow tissue penetration depth, and thereby their applications are greatly restricted. To address these issues, near-infrared (NIR) photoresponsive DDSs have been developed. In this review, the development of NIR photoresponsive DDSs in last several years for cancer photo-chemotherapy are summarized. They can achieve on-demand release of drugs into tumors of living animals through photothermal, photodynamic, and photoconversion mechanisms, affording obviously amplified therapeutic effects in synergy with phototherapy. Finally, the existing challenges and further perspectives on the development of NIR photoresponsive DDSs and their clinical translation are discussed.

Background Soybean cyst nematode (SCN) is the most devastating pathogen of soybean. Our previous study showed that the plant growth-promoting rhizobacterium Bacillus simplex strain Sneb545 promotes soybean resistance to SCN. Here, we conducted a combined metabolomic and transcriptomic analysis to gain information regarding the biological mechanism of defence enhancement against SCN in Sneb545-treated soybean. To this end, we compared the transcriptome and metabolome of Sneb545-treated and non-treated soybeans under SCN infection. Results Transcriptomic analysis showed that 6792 gene transcripts were common in Sneb545-treated and non-treated soybeans. However, Sneb545-treated soybeans showed a higher concentration of various nematicidal metabolites, including 4-vinylphenol, methionine, piperine, and palmitic acid, than non-treated soybeans under SCN infection. Conclusions Overall, our results validated and expanded the existing models regarding the co-regulation of gene expression and metabolites in plants, indicating the advantage of integrated system-oriented analysis.

Large language models (LLMs), such as ChatGPT, have demonstrated impressive capabilities in various natural language processing tasks, particularly in text generation. However, their effectiveness in summarizing radiology report impressions remains uncertain. This study aims to evaluate the capability of nine LLMs, that is, Tongyi Qianwen, ERNIE Bot, ChatGPT, Bard, Claude, Baichuan, ChatGLM, HuatuoGPT, and ChatGLM-Med, in summarizing Chinese radiology report impressions for lung cancer. We collected 100 Chinese computed tomography (CT), positron emission tomography (PET)-CT, and ultrasound (US) reports each from Peking University Cancer Hospital and Institute. All these reports were from patients with suspected or confirmed lung cancer. Using these reports, we created zero-shot, one-shot, and three-shot prompts with or without complete example reports as inputs to generate impressions. We used both automatic quantitative evaluation metrics and five human evaluation metrics (completeness, correctness, conciseness, verisimilitude, and replaceability) to assess the generated impressions. Two thoracic surgeons (SZ and BL) and one radiologist (QL) compared the generated impressions with reference impressions, scoring them according to the five human evaluation metrics. In the automatic quantitative evaluation, ERNIE Bot, Tongyi Qianwen, and Claude demonstrated the best overall performance in generating impressions for CT, PET-CT, and US reports, respectively. In the human semantic evaluation, ERNIE Bot outperformed the other LLMs in terms of conciseness, verisimilitude, and replaceability on CT impression generation, while its completeness and correctness scores were comparable to those of other LLMs. Tongyi Qianwen excelled in PET-CT impression generation, with the highest scores for correctness, conciseness, verisimilitude, and replaceability. Claude achieved the best conciseness, verisimilitude, and replaceability scores on US impression generation, and its completeness and correctness scores are close to the best results obtained by other LLMs. The generated impressions were generally complete and correct but lacked conciseness and verisimilitude. Although one-shot and few-shot prompts improved conciseness and verisimilitude, clinicians noted a significant gap between the generated impressions and those written by radiologists. Current LLMs can produce radiology impressions with high completeness and correctness but fall short in conciseness and verisimilitude, indicating they cannot yet fully replace impressions written by radiologists.

Purpose breast cancer is common and highly malignant, currently, STMN1 was found to be associated with several human malignancies. The purpose of this study is to investigate STMN1 expression in breast cancer and explore its role in disease progression and its interaction with neoadjuvant therapy efficacy. Methods we analyzed the tissue STMN1 mRNA expression in BC tissue samples from 105 patients received with neoadjuvant therapy using qPCR between 2019 and 2022. Results Statistical analysis showed that a high expression of STMN1 before neoadjuvant chemotherapy (NACT) was a trend positively related to non-pCR in the ITT (Intention to Treat) population, while in patients with paclitaxel or docetaxel regimens, before-NACT STMN1 expression was obviously higher in non-pCR (failure to achieve pathologically complete response) patients. Additionally, compared to pCR, high expression of STMN1 after NACT was obviously related to non-pCR. Interestingly, Kaplan-Meier analysis demonstrated that patients with mid-high STMN1 expression before and post-NACT had a poorer PFS to compared to those with low expression. Conclusions STMN1 is the potential biomarker of NACT and prognosis for breast cancer.

Edge intelligence can enable fast intelligent services by integrating edge computing with machine learning, thereby facilitating intelligent information processing for Internet of Things (IoT) devices on the edge. However, intelligent data processing at the edge may expose IoT devices to the risk of private information leakage. To mitigate this issue, we propose a federated meta-learning-aided data processing framework to cope with complex tasks in edge IoT networks. Unfortunately, communications between edge IoT devices and edge servers in federated frameworks incur significant overhead. To address this challenge, we propose a parameter optimization algorithm that alleviates communication costs between edge IoT devices and edge servers, thereby reducing classification errors induced by parameter optimization. Moreover, the convergence of the federated meta-learning method is derived, which theoretically confirms the feasibility of the proposed approach. Simulation results demonstrate that the error minimization-based quantization compression optimization algorithm can substantially enhance communication efficiency while incurring only negligible precision losses.