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Pesticides are extensively used by farmers in China. However, the effects of pesticides on farmers' health have not yet been systematically studied. This study evaluated the effects of pesticides exposure on hematological and neurological indicators over 3 years and 10 days respectively. A cohort of 246 farmers was randomly selected from 3 provinces (Guangdong, Jiangxi, and Hebei) in China. Two rounds of health investigations, including blood tests and neurological examinations, were conducted by medical doctors before and after the crop season in 2012. The data on pesticide use in 2009-2011 were collected retrospectively via face-to-face interviews and the 2012 data were collected from personal records maintained by participants prospectively. Ordinary least square (OLS), Probit, and fixed effect models were used to evaluate the relationship between pesticides exposure frequency and the health indicators. Long-term pesticide exposure was found to be associated with increased abnormality of nerve conductions, especially in sensory nerves. It also affected a wide spectrum of health indicators based on blood tests and decreased the tibial nerve compound muscle action potential amplitudes. Short-term health effects included alterations in complete blood count, hepatic and renal functions, and nerve conduction velocities and amplitudes. However, these effects could not be detected after 3 days following pesticide exposure. Overall, our results demonstrate that pesticide exposure adversely affects blood cells, the liver, and the peripheral nervous system. Future studies are needed to elucidate the specific effects of each pesticide and the mechanisms of these effects.

The existing flare prediction primarily relies on photospheric magnetic field parameters from the entire active region (AR), such as Space-Weather HMI Activity Region Patches (SHARP) parameters. However, these parameters may not capture the details of the AR evolution preceding flares. The magnetic structure within the core area of an AR is essential for predicting large solar flares. This paper utilizes the area of high photospheric free energy density (high-energy-density, hereafter HED, region) as a proxy for the AR core region. We construct two data sets: SHARP and HED data sets. The ARs contained in both data sets are identical. Furthermore, the start and end times for the same AR in both data sets are identical. We develop six models for 24 hr solar flare forecasting, utilizing SHARP and HED data sets. We then compare their categorical and probabilistic forecasting performance. Additionally, we conduct an analysis of parameter importance. The main results are as follows: (1) Among the six solar flare prediction models, the models using HED parameters outperform those using SHARP parameters in both categorical and probabilistic prediction, indicating the important role of the HED region in the flare initiation process. (2) The transformer flare prediction model stands out significantly in true skill statistic and Brier skill score, surpassing the other models. (3) In parameter importance analysis, the total photospheric free magnetic energy density (E free) within the HED parameters excels in both categorical and probabilistic forecasting. Similarly, among the SHARP parameters, the R_VALUE stands out as the most effective parameter for both categorical and probabilistic forecasting.

Background Postoperative delirium is a common complication following major surgeries, leading to a variety of adverse effects. However, there is a paucity of literatures studying the incidence and risk factors associated with delirium after primary elective total hip arthroplasty (THA) using a large-scale national database. Methods A retrospective database analysis was performed based on Nationwide Inpatient Sample (NIS) from 2009 to 2014. Patients who underwent primary elective THA were included. Patient demographics, preoperative comorbidities, length of hospital stay (LOS), total charges, in-hospital mortality, and major and minor perioperative complications were evaluated. Results A total of 388,424 primary elective THAs were obtained from the NIS database, and the general incidence of delirium after THA was 0.90%. Patients with delirium after THA presented more preoperative comorbidities, longer LOS, extra hospital charges, and higher in-hospital mortality rate ( P  < 0.001). Delirium following THA was associated with major complications during hospitalization including acute renal failure and pneumonia. Preoperative risk factors associated with postoperative delirium included advanced age, alcohol or drug abuse, depression, neurological disorders, psychoses, fluid and electrolyte disorders, diabetes, weight loss, deficiency anemia, coagulopathy, hypertension, congestive heart failure, valvular disease, pulmonary circulation disorders, peripheral vascular disorders, and renal failure. Both female and obesity were detected to be protective factors. Conclusions The results of our study identified a relatively low incidence of delirium after primary elective THA, which is as reported in the NIS and not necessarily the surgical population as a whole. Postoperative delirium of THA was associated with increased preoperative comorbidities, LOS, total charges, in-hospital mortality, and major perioperative complications including acute renal failure and pneumonia. It is of benefit to study risk factors associated with postoperative delirium to moderate its consequences.

Background Perilla frutescens is an oilseed crop rich in unsaturated fatty acids (UFAs). WRINKLED1 (WRI1) is an AP2/EREBP-type transcription factor (TF), which plays important regulation roles in fatty acid (FA) metabolism. Results In this study, 11 PfWRI1 members were identified in the perilla genome. qRT-PCR analysis revealed that PfWRI1-10 was expressed higher than other members in developing seeds of perilla, suggesting that PfWRI1-10 may play crucial roles in seed oil biosynthesis. Overexpression of PfWRI1-10 resulted in enhancement of total oil content by 9.75% and α-linolenic acid (C18:3) level by 0.43%, while the soluble sugar content decreased in the transgenic tobacco leaves. qRT-PCR showed that the expression levels of glycolysis gene ( NtPKp-β1 ) and FA biosynthesis genes ( NtACP1 and NtBCCP1 ) significantly upregulated, whereas NtKAS1-B expression was downregulated in transgenic tobacco plants, indicating that PfWRI1-10 reallocated carbon sources flow to facilitate lipid biosynthesis by regulating the expression of the target genes. Yeast one-hybrid (Y1H) assays demonstrate that PfWRI1-10 directly binds to the AW-box cis -element of the PfBCCP1 promoter. Overexpression of PfBCCP1 enhanced the lipid content in the transgenic yeast, confirming its function in lipid metabolism. Conclusions PfWRI1-10 transcription factor promotes lipid biosynthesis by upregulating PfBCCP1 and other lipid related genes. The current findings provide a scientific basis for the in-depth analysis of the regulatory network of perilla oil biosynthesis and also valuable genes for genetic improvement of perilla oil yield and quality.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease, which leads to muscle weakness and eventual paralysis. Numerous studies have indicated that mitophagy and immune inflammation have a significant impact on the onset and advancement of ALS. Nevertheless, the possible diagnostic and prognostic significance of mitophagy-related genes associated with immune infiltration in ALS is uncertain. The purpose of this study is to create a predictive model for ALS using genes linked with mitophagy-associated immune infiltration. ALS gene expression profiles were downloaded from the Gene Expression Omnibus (GEO) database. Univariate Cox analysis and machine learning methods were applied to analyze mitophagy-associated genes and develop a prognostic risk score model. Subsequently, functional and immune infiltration analyses were conducted to study the biological attributes and immune cell enrichment in individuals with ALS. Additionally, validation of identified feature genes in the prediction model was performed using ALS mouse models and ALS patients. In this study, a comprehensive analysis revealed the identification of 22 mitophagy-related differential expression genes and 40 prognostic genes. Additionally, an 18-gene prognostic signature was identified with machine learning, which was utilized to construct a prognostic risk score model. Functional enrichment analysis demonstrated the enrichment of various pathways, including oxidative phosphorylation, unfolded proteins, KRAS, and mTOR signaling pathways, as well as other immune-related pathways. The analysis of immune infiltration revealed notable distinctions in certain congenital immune cells and adaptive immune cells between the low-risk and high-risk groups, particularly concerning the T lymphocyte subgroup. ALS mouse models and ALS clinical samples demonstrated consistent expression levels of four mitophagy-related immune infiltration genes ( , , , and ) with the results of bioinformatics analysis. This study has successfully devised and verified a pioneering prognostic predictive risk score for ALS, utilizing eighteen mitophagy-related genes. Furthermore, the findings indicate that four of these genes exhibit promising roles in the context of ALS prognostic.

Evidence on the adverse effects of agricultural pesticide use by farmers under the actual field conditions on their peripheral nerve conduction in China is limited. This study was to investigate the association of agricultural pesticide use with the abnormalities of farmers’ peripheral nerve conduction based on two rounds of conventional nerve conduction studies. The level of pesticide exposure was assessed by measuring total amount of pesticides used by farmers in 2012. The logistic and negative binomial regression analyses were performed on a cohort study of 218 farmers. Results show that agricultural use of neither glyphosate nor non-glyphosate herbicides was not found to induce the abnormalities of farmers’ peripheral nerve conduction. However, agricultural use of organophosphorus compounds was significantly associated with increased risk of demylination disease of peripheral nerve conduction described by the reduced velocity. Moreover, the use of organonitrogen compounds by farmers would not only increase risk of demylination disease but axonal damages described by the reduced amplitude. By contrast, agricultural uses of organosulfur and pyrethroid compounds would not induce the abnormalities of farmers’ peripheral nerve conduction. The findings demonstrated the importance of developing health-friendly pesticides to replace organophosphorus and organonitrogen insecticides and fungicides in China.

Background Myasthenia gravis (MG) is an autoimmune disorder of the neuromuscular junction. Increasing evidence has suggested inflammation is involved in the pathogenesis of MG, but whether it is the cause or a downstream effect remains unclear. In this study, a two-sample Mendelian randomization (TSMR) analysis was performed to explore the causal relationship between 91 circulating inflammatory cytokines and MG. Method In this study, the data of 91 circulating inflammatory cytokines from 4824 Europeans and the largest GWAS database of MG (1873 patients and 36370 controls) were used to screen instrumental variables (IVs). Inverse variance weighting (IVW), Bayesian weighted MR (BWMR), MR-Egger regression, weighted median (WM), simple mode and weighted mode were used to evaluate the association between MG and inflammatory cytokines. The MR-Egger intercept test and Cochran’s Q test were used to test the pleiotropy and heterogeneity of IVs. Result Our results showed that adenosine deaminase (ADA) and CD40 Ligand‌ (CD40L) are positively associated with the risk of MG (OR = 1.16, 95%CI: 1.00-1.33, P  = 0.041; OR = 1.20, 95%CI: 1.02–1.40, P  = 0.025), while interleukin-1-alpha (IL-1α), glial-cell-line-derived neurotrophic factor (GDNF), Osteoprotegerin (OPG) and tumor necrosis factor-beta (TNF-β) are negatively associated with the risk of MG (OR = 0.80, 95% CI: 0.64 ~ 0.99, P  = 0.042; OR = 0.74, 95%CI:0.58 ~ 0.0.96, P  = 0.022; OR = 0.76, 95% CI: 0.61 ~ 0.94, P  = 0.013; OR = 0.76, 95% CI: 0.61 ~ 0.94, P  = 0.012; OR = 0.80, 95% CI: 0.68 ~ 0.93, P  = 0.006). In addition, genetically predicted MG affected the expression of seven cytokines. Sensitivity analysis showed no horizontal pleiotropy and significant heterogeneity of all results. Conclusions Our results provided promising clues for the treatment of MG. We evaluated the association between inflammatory cytokines and the disease by genetic informatics approach, which may help to better understand the underlying mechanisms of MG.

Background Associations between blood pressure (BP) with age at onset of Huntington’s disease (HD) have reported inconsistent findings. We used Mendelian randomization (MR) to assess effects of BP and lowering systolic BP (SBP) via the genes encoding targets of antihypertensive drugs on age at onset of HD. Methods Genetic variants from genome-wide association studies(GWAS) of BP traits and BP-lowering variants in genes encoding antihypertensive drugs targets were extracted. Summary statistics for age at onset of HD were retrieved from the GWAS meta-analysis of HD residual age at onset from the GEM-HD Consortium included 9064 HD patients of European ancestry (4417 males and 4,647 females). MR estimates were calculated using the inverse variance weighted method, supplemented by MR-Egger, weighted median, and MR-PRESSO methods. Results Genetically predicted SBP or diastolic BP increase was associated with a later age at onset of HD. However, after SBP/DBP was present as a covariate using multivariable MR method, no significant causal association was suggested. A 10-mm Hg reduction in SBP through variants in genes encoding targets of calcium channel blockers (CCB) was associated with an earlier age at onset of HD (β=-0.220 years, 95% CI =-0.337 to -0.102, P = 2.42 × 10 − 4 ). We did not find a causal association between angiotensin converting enzyme inhibitors and β-blockers with the earlier HD onset. No heterogeneity and horizontal pleiotropy were identified. Conclusions This MR analysis provided evidence that genetically determined SBP lowering through antihypertensive drugs might be associated with an earlier age at onset of HD. The results may have a potential impact on management of hypertension in the pre-motor-manifest HD population.

Objectives To analyze the factors affecting prognosis of patients with sporadic amyotrophic lateral sclerosis (ALS), to establish a nomogram predictive model. Methods A total of 236 patients with sporadic ALS hospitalized in the Department of Neurology of the First Medical Center, Chinese PLA General Hospital, from March 2011 to November 2021 were enrolled in the study. Basic information and clinical and laboratory data of patients were collected, including sex, age at onset, body mass index, disease duration, diagnostic grade, and serum levels of creatine kinase (CK), creatinine (Cr), uric acid (UA), and ferritin. Kaplan-Meier univariate and multivariate Cox proportional hazard regression models were used to analyze the prognostic factors, and a nomogram predictive model was established. Results Univariate analysis showed that ferritin, CK, Cr, age at onset, disease duration, and body mass index (BMI) were all correlated with prognosis of ALS. Multivariate analysis showed that ferritin, Cr, disease duration, age at onset, and BMI were the strongest predictors. ROC curve and correction curve analyses verified the accuracy of the nomogram prediction model. Conclusions Ferritin, Cr, disease duration, age at onset, and BMI are independent predictors of survival in patients with ALS. Based on these clinical and biological prognostic factors, we established a quantitative model for predicting survival probability, and may assist in the prognostic evaluation of ALS, pending further validation.

Background Previous studies have assessed the association between antidiabetic drugs and stroke risk, but the results are inconsistent. Mendelian randomization (MR) was used to assess effects of antidiabetic drugs on stroke risk. Methods We selected blood glucose-lowering variants in genes encoding antidiabetic drugs targets from genome-wide association studies (GWAS). A two-sample MR and Colocalization analyses were applied to examine associations between antidiabetic drugs and the risk of stroke. For antidiabetic agents that had effect on stroke risk, an independent blood glucose GWAS summary data was used for further verification. Results Genetic proxies for sulfonylureas targets were associated with reduced risk of any stroke (OR=0.062, 95% CI 0.013-0.295, P=4.65×10 －4 ) and any ischemic stroke (OR=0.055, 95% CI 0.010-0.289, P=6.25×10 －4 ), but not with intracranial hemorrhage. Colocalization supported shared casual variants for blood glucose with any stroke and any ischemic stroke within the encoding genes for sulfonylureas targets (KCNJ11 and ABCC8) (posterior probability>0.7). Furthermore, genetic variants in the targets of insulin/insulin analogues, glucagon-like peptide-1 analogues, thiazolidinediones, and metformin were not associated with the risk of any stroke, any ischemic stroke and intracranial hemorrhage. The association was consistent in the analysis of sulfonylureas with stroke risk using an independent blood glucose GWAS summary data. Conclusions Our findings showed that genetic proxies for sulfonylureas targets by lowering blood glucose were associated with a lower risk of any stroke and any ischemic stroke. The study might be of great significance to guide the selection of glucose-lowering drugs in individuals at high risk of stroke.