Identification of candidate single-nucleotide polymorphisms (SNPs) and genes associated with sugarcane leaf scald disease.

Leaf scald, caused by Xanthomonas albilineans, is a severe disease affecting sugarcane worldwide. One of the most practical ways to control it is by developing resistant sugarcane cultivars. It is essential to identify genes associated with the response to leaf scald. A panel of 170 sugarcane genotypes was evaluated for resistance to leaf scald in field conditions for 2 years, followed by a 1-year greenhouse experiment. The phenotypic evaluation data showed a wide continuous distribution, with heritability values ranging from 0.58 to 0.84. Thirteen single nucleotide polymorphisms (SNPs) were identified, significantly associated with leaf scald resistance. Among these, eight were stable across multiple environments and association models. The candidate genes identified and validated based on RNA-seq and qRT-PCR included two genes that encode NB-ARC leucine-rich repeat (LRR)-containing domain disease-resistance protein. These findings provide a basis for developing marker-assisted selection strategies in sugarcane breeding programs.

Muscle density, but not size, is independently associated with cognitive health in older adults with hip fractures.

Emerging evidence indicates a complex interplay between skeletal muscle and cognitive function. Despite the known differences between muscle quantity and quality, which can be measured via computed tomography (CT), the precise nature of their associations with cognitive performance remain underexplored. To investigate the links between muscle size and density and cognitive impairment (CI) in the older adults with hip fractures, we conducted a post hoc, cross-sectional analysis within a prospective cohort study on 679 patients with hip fractures over 65. Mini-Mental State Examination (MMSE) and routine hip CT imaging were utilized to assess cognition function and muscle characteristics in older adults with hip fractures. The CT scans provided data on cross-sectional area and attenuation for the gluteus maximus (G.MaxM) and the combined gluteus medius and minimus (G.Med/MinM). Participants were categorized into CI and non-CI groups based on education levels and MMSE scores. Multivariate logistic regressions, propensity score (PS) methods, and subgroup analysis were employed to analyze associations and validate findings. This study included 123 participants (81.6 ± 6.8 years, 74% female) with CI and 556 participants (78.5 ± 7.7 years, 72% female) without. Compared to the non-CI group, muscle parameters, especially density, were significantly lower in the CI group. Specifically, G.Med/Min muscle density, but not size was robustly associated with CI (odds ratio (OR) = 0.77, 95% confidence interval = 0.62-0.96, P = 0.02), independent of other medical situations. Sensitivity analysis corroborated that G.Med/Min muscle density was consistently lower in the CI group than the non-CI group, as evidenced in the PS matched (P = 0.024) and weighted cohort (P = 0.033). Enhanced muscle parameters, particularly muscle density in the G.Med/MinM muscle, correlate with a lower risk of CI. Muscle density demonstrates a stronger association with cognitive performance than muscle size, highlighting its potential as a key focus in future cognitive health research.

Integrated analysis of transcriptome and miRNAome reveals the heat stress response of Pinellia ternata seedlings.

Pinellia ternata (Thunb.) Briet., a valuable herb native to China, is susceptible to the "sprout tumble" phenomenon because of high temperatures, resulting in a significant yield reduction. However, the molecular regulatory mechanisms underlying the response of P. ternata to heat stress are not well understood. In this study, we integrated transcriptome and miRNAome sequencing to identify heat-response genes, microRNAs (miRNAs), and key miRNA-target pairs in P. ternata that differed between heat-stress and room-temperature conditions. Transcriptome analysis revealed extensive reprogramming of 4,960 genes across various categories, predominantly associated with cellular and metabolic processes, responses to stimuli, biological regulation, cell parts, organelles, membranes, and catalytic and binding activities. miRNAome sequencing identified 1,597 known/conserved miRNAs that were differentially expressed between the two test conditions. According to the analysis, genes and miRNAs associated with the regulation of transcription, DNA template, transcription factor activity, and sequence-specific DNA binding pathways may play a major role in the resistance to heat stress in P. ternata. Integrated analysis of the transcriptome and miRNAome expression data revealed 41 high-confidence miRNA-mRNA pairs, forming 25 modules. MYB-like proteins and calcium-responsive transcription coactivators may play an integral role in heat-stress resistance in P. ternata. Additionally, the candidate genes and miRNAs were subjected to quantitative real-time polymerase chain reaction to validate their expression patterns. These results offer a foundation for future studies exploring the mechanisms and critical genes involved in heat-stress resistance in P. ternata.

Assembly and comparative analysis of the complete mitochondrial genome of Pinellia ternata.

Pinellia ternata is an important natural medicinal herb in China. However, it is susceptible to withering when exposed to high temperatures during growth, which limits its tuber production. Mitochondria usually function in stress response. The P . ternata mitochondrial (mt) genome has yet to be explored. Therefore, we integrated PacBio and Illumina sequencing reads to assemble and annotate the mt genome of P . ternata . The circular mt genome of P . ternata is 876 608bp in length and contains 38 protein-coding genes (PCGs), 20 tRNA genes and three rRNA genes. Codon usage, sequence repeats, RNA editing and gene migration from chloroplast (cp) to mt were also examined. Phylogenetic analysis based on the mt genomes of P . ternata and 36 other taxa revealed the taxonomic and evolutionary status of P . ternata . Furthermore, we investigated the mt genome size and GC content by comparing P . ternata with the other 35 species. An evaluation of non-synonymous substitutions and synonymous substitutions indicated that most PCGs in the mt genome underwent negative selection. Our results provide comprehensive information on the P . ternata mt genome, which may facilitate future research on the high-temperature response of P . ternata and provide new molecular insights on the Araceae family.

Unlocking the Secrets Behind Advanced Artificial Intelligence Language Models in Deidentifying Chinese-English Mixed Clinical Text: Development and Validation Study.

BACKGROUND: The widespread use of electronic health records in the clinical and biomedical fields makes the removal of protected health information (PHI) essential to maintain privacy. However, a significant portion of information is recorded in unstructured textual forms, posing a challenge for deidentification. In multilingual countries, medical records could be written in a mixture of more than one language, referred to as code mixing. Most current clinical natural language processing techniques are designed for monolingual text, and there is a need to address the deidentification of code-mixed text. OBJECTIVE: The aim of this study was to investigate the effectiveness and underlying mechanism of fine-tuned pretrained language models (PLMs) in identifying PHI in the code-mixed context. Additionally, we aimed to evaluate the potential of prompting large language models (LLMs) for recognizing PHI in a zero-shot manner. METHODS: We compiled the first clinical code-mixed deidentification data set consisting of text written in Chinese and English. We explored the effectiveness of fine-tuned PLMs for recognizing PHI in code-mixed content, with a focus on whether PLMs exploit naming regularity and mention coverage to achieve superior performance, by probing the developed models' outputs to examine their decision-making process. Furthermore, we investigated the potential of prompt-based in-context learning of LLMs for recognizing PHI in code-mixed text. RESULTS: The developed methods were evaluated on a code-mixed deidentification corpus of 1700 discharge summaries. We observed that different PHI types had preferences in their occurrences within the different types of language-mixed sentences, and PLMs could effectively recognize PHI by exploiting the learned name regularity. However, the models may exhibit suboptimal results when regularity is weak or mentions contain unknown words that the representations cannot generate well. We also found that the availability of code-mixed training instances is essential for the model's performance. Furthermore, the LLM-based deidentification method was a feasible and appealing approach that can be controlled and enhanced through natural language prompts. CONCLUSIONS: The study contributes to understanding the underlying mechanism of PLMs in addressing the deidentification process in the code-mixed context and highlights the significance of incorporating code-mixed training instances into the model training phase. To support the advancement of research, we created a manipulated subset of the resynthesized data set available for research purposes. Based on the compiled data set, we found that the LLM-based deidentification method is a feasible approach, but carefully crafted prompts are essential to avoid unwanted output. However, the use of such methods in the hospital setting requires careful consideration of data security and privacy concerns. Further research could explore the augmentation of PLMs and LLMs with external knowledge to improve their strength in recognizing rare PHI.

Thyrotropin induces atherosclerosis by upregulating large conductance Ca2+-activated K+ channel subunits.

Hypothyroidism is associated with elevated levels of serum thyrotropin (TSH), which have been shown to promote abnormal proliferation of vascular smooth muscle cells and contribute to the development of atherosclerosis. However, the specific mechanisms underlying the TSH-induced abnormal proliferation of vascular smooth muscle cells remain unclear. The objective of this study was to investigate the role of TSH in the progression of atherosclerosis. Our research findings revealed that hypothyroidism can trigger early atherosclerotic changes in the aorta of Wistar rats. In alignment with our in vitro experiments, we observed that TSH induces abnormal proliferation of aortic smooth muscle cells by modulating the expression of α and ß1 subunits of large conductance Ca2+-activated K+ (BKCa) channels within these cells via the cAMP/PKA signaling pathway. These results collectively indicate that TSH acts through the cAMP/PKA signaling pathway to upregulate the expression of α and ß1 subunits of BKCa channels, thereby promoting abnormal proliferation of arterial smooth muscle cells. These findings may provide a basis for the clinical prevention and treatment of atherosclerosis caused by elevated TSH levels.

Risk prediction models for deep venous thrombosis in patients with acute stroke: A systematic review and meta-analysis.

BACKGROUND: The number of risk prediction models for deep venous thrombosis (DVT) in patients with acute stroke is increasing, while the quality and applicability of these models in clinical practice and future research remain unknown. OBJECTIVE: To systematically review published studies on risk prediction models for DVT in patients with acute stroke. DESIGN: Systematic review and meta-analysis of observational studies. METHODS: China National Knowledge Infrastructure (CNKI), Wanfang Database, China Science and Technology Journal Database (VIP), SinoMed, PubMed, Web of Science, The Cochrane Library, Cumulative Index to Nursing and Allied Health Literature (CINAHL) and Embase were searched from inception to November 7, 2022. Data from selected studies were extracted, including study design, data source, outcome definition, sample size, predictors, model development and performance. The Prediction Model Risk of Bias Assessment Tool (PROBAST) checklist was used to assess the risk of bias and applicability. RESULTS: A total of 940 studies were retrieved, and after the selection process, nine prediction models from nine studies were included in this review. All studies utilized logistic regression to establish DVT risk prediction models. The incidence of DVT in patients with acute stroke ranged from 0.4 % to 28 %. The most frequently used predictors were D-dimer and age. The reported area under the curve (AUC) ranged from 0.70 to 0.912. All studies were found to have a high risk of bias, primarily due to inappropriate data sources and poor reporting of the analysis domain. The pooled AUC value of the five validated models was 0.76 (95 % confidence interval: 0.70-0.81), indicating a fair level of discrimination. CONCLUSION: Although the included studies reported a certain level of discrimination in the prediction models of DVT in patients with acute stroke, all of them were found to have a high risk of bias according to the PROBAST checklist. Future studies should focus on developing new models with larger samples, rigorous study designs, and multicenter external validation. REGISTRATION: The protocol for this study is registered with PROSPERO (registration number: CRD42022370287).

Unveiling the importance of VOCs from pesticides applicated in main crops for elevating ozone concentrations in China.

Volatile organic compounds (VOCs) are considered as important precursors of ozone in the air, while the contribution of VOCs from pesticide application (PVOCs) to ozone production is unknown. Utilizing data from the Ministry of Agriculture and Rural Affairs of the People's Republic of China and ChinaCropPhen1km, this paper developed PVOC emission inventories with a resolution of 1 km for the main crops (rice, maize, and wheat) from 2012 to 2019 in China. The results revealed that pesticide application is an important VOC emission source in China. Specially, the PVOC emissions from the major grain-producing regions in June accounted for approximately 30% of the annual total PVOC emissions in the local regions. The simulation with the Weather Research and Forecasting Community Multiscale Air Quality model (WRF-CMAQ) indicated that the PVOC emissions increased the mean maximum daily 8-hour average (MDA8) ozone concentration across China by 2.5 ppb in June 2019. During the same period, PVOCs in the parts of North China Plain contributed 10% of the ozone formation. Under the comprehensive emission reduction scenario, it is anticipated that by 2025, the joint implementation of measures including reducing pesticide application, improving pesticide utilization efficiency and promoting solvent substitution will decrease PVOC emissions by 60% compared with 2019, thereby mitigating ozone pollution.