Phytophthora infestans RXLR effector Pi23014 targets host RNA-binding protein NbRBP3a to suppress plant immunity.

Phytophthora infestans is a destructive oomycete that causes the late blight of potato and tomato worldwide. It secretes numerous small proteins called effectors in order to manipulate host cell components and suppress plant immunity. Identifying the targets of these effectors is crucial for understanding P. infestans pathogenesis and host plant immunity. In this study, we show that the virulence RXLR effector Pi23014 of P. infestans targets the host nucleus and chloroplasts. By using a liquid chromatogrpahy-tandem mass spectrometry assay and co-immunoprecipitation assasys, we show that it interacts with NbRBP3a, a putative glycine-rich RNA-binding protein. We confirmed the co-localization of Pi23014 and NbRBP3a within the nucleus, by using bimolecular fluorescence complementation. Reverse transcription-quantitative PCR assays showed that the expression of NbRBP3a was induced in Nicotiana benthamiana during P. infestans infection and the expression of marker genes for multiple defence pathways were significantly down-regulated in NbRBP3-silenced plants compared with GFP-silenced plants. Agrobacterium tumefaciens-mediated transient overexpression of NbRBP3a significantly enhanced plant resistance to P. infestans. Mutations in the N-terminus RNA recognition motif (RRM) of NbRBP3a abolished its interaction with Pi23014 and eliminated its capability to enhance plant resistance to leaf colonization by P. infestans. We further showed that silencing NbRBP3 reduced photosystem II activity, reduced host photosynthetic efficiency, attenuated Pi23014-mediated suppression of cell death triggered by P. infestans pathogen-associated molecular pattern elicitor INF1, and suppressed plant immunity.

Associations between the use of common nonsteroidal anti-inflammatory drugs, genetic susceptibility and dementia in participants with chronic pain: A prospective study based on 194,758 participants from the UK Biobank.

OBJECTIVE: To investigate the potential relationship between common nonsteroidal anti-inflammatory drugs (NSAIDs), genetic susceptibility and all-cause dementia (ACD), Alzheimer's disease (AD), and vascular dementia (VD) among individuals experiencing chronic pain. METHODS: This study was based on 194,758 chronic pain participants form UK biobank with a median follow-up of 13.7 years. Participants were categorized into different NSAIDs painkiller regimen groups: No NSAIDs group, Aspirin group, Ibuprofen group, Paracetamol group, and 2-3 NSAIDs group. Cox proportional risk models were used to examine the correlation between regular NSAIDs usage and the risk of ACD, AD, and VD. In addition, we further performed subgroup analyses and sensitivity analyses. RESULTS: 1) Compared to the No NSAIDs group, the aspirin group (HR = 1.12, 95% CI:1.01-1.24, P < 0.05), the paracetamol group (HR = 1.15, 95% CI:1.05-1.27, P < 0.01), and the 2-3 NSAIDs group (HR = 1.2, 95% CI:1.08-1.33, P < 0.05) showed a higher risk of ACD. Furthermore, the 2-3 NSAIDs group was also associated with a higher risk of VD (HR = 1.39, 95% CI: 1.08-1.33, P < 0.05). 2) At high dementia GRS participants with chronic pain, the paracetamol group (HR = 1.2, 95% CI: 1.03-1.43, P < 0.05) and the NSAIDs group (HR = 1.3, 95% CI: 1.07-1.59, P < 0.05) were associated with a higher risk of ACD compared to the no painkiller group. 3) There was no significant association between ibuprofen use and higher risk of dementia. CONCLUSION: In individuals with chronic pain, the use of aspirin and paracetamol was associated with a higher risk of ACD, whereas the use of ibuprofen was not significantly associated with a higher risk of ACD.

A Prospective Study of an Early Prediction Model of Attention Deficit Hyperactivity Disorder Based on Artificial Intelligence.

OBJECTIVE: To explore the relationship between the Parent Symptom Questionnaire (PSQ) and attention deficit hyperactivity disorder (ADHD) in China, and the application value of PSQ questionnaire. METHOD: Two hundred two children aged 3 to 14 years were enrolled in this study. Statistical methods were used to screen characteristic factors and explore the relationship between PSQ items and ADHD. Machine learning algorithms were used to evaluate the clinical application value of PSQ in screening ADHD. RESULTS: By Mean-Whitney U test, LASSO regression and decision tree, 44, 24 and 12 items were screened out from PSQ with high correlation with ADHD. Then the above items were classified, and the accuracy reached more than 90%. Moreover, the items of ADHD hyperactivity index of PSQ under artificial intelligence algorithm are different from those of PSQ. CONCLUSION: There are some differences in the items of hyperactivity index between the PSQ and ADHD in China. The artificial intelligence algorithm model of ADHD children based on PSQ scale has a high accuracy.

Associations of screen-based sedentary activities with all cause dementia, Alzheimer's disease, vascular dementia: a longitudinal study based on 462,524 participants from the UK Biobank.

BACKGROUND: Current drug treatments for dementia aren't effective. Studying gene-environment interactions can help develop personalized early intervention strategies for Alzheimer's disease (AD). However, no studies have examined the relationship between screen-based sedentary activities and genetic susceptibility to AD risk, and further understanding of the causal relationship is also crucial. METHODS: This study included 462,524 participants from the UK Biobank with a follow-up of 13.6 years. Participants' screen-based sedentary activities time was categorized into three groups based on recorded time: ≥ 4 h/day, 2-3 h/day, and ≤ 1 h/day. Cox proportional risk models were used to analyze the association between computer use/TV viewing groups and the risk of all-cause dementia, AD and vascular dementia (VD). Generalized linear model (GLM) were used to examine the relationship between screen-based sedentary activities and brain structure. Bidirectional Mendelian randomization (MR) was used to validate the causal relationship between TV viewing and AD. RESULTS: Compared to TV viewing ≤ 1 h/day, 1)TV viewing 2-3 h/day was correlated with a higher risk of all-cause dementia (HR = 1.09, 95% CI:1.01-1.18, P < 0.05), and TV viewing ≥ 4 h/day was associated with a higher risk of all-cause dementia (HR = 1.29, 95% CI: 1.19-1.40, P < 0.001), AD (HR = 1.25, 95% CI:1.1-1.42, P < 0.001), and VD (HR = 1.24, 95% CI: 1.04-1.49, P < 0.05); 2) TV viewing ≥ 4 h/day was correlated with a higher AD risk at intermediate (HR = 1.34, 95% CI: 1.03-1.75, P < 0.001) and high AD genetic risk score (AD-GRS) (HR = 2.18, 95% CI: 1.65-2.87, P < 0.001);3) TV viewing 2-3 h/day [ß = (-94.8), 95% CI: (-37.9) -(-151.7), P < 0.01] and TV viewing ≥ 4 h/day [ß = (-92.94), 95% CI: (-17.42) -(-168.46), P < 0.05] were correlated with a less hippocampus volume. In addition, a causal effect of TV viewing times was observed on AD analyzed using MR Egger (OR = 5.618, 95%CI:1.502-21.013, P < 0.05). CONCLUSIONS: There was a causal effect between TV viewing time and AD analyzed using bidirectional MR, and more TV viewing time exposure was correlated with a higher AD risk. Therefore, it is recommended that people with intermediate and high AD-GRS should control their TV viewing time to be less than 4 h/ day or even less than 1 h/day.

Impact of autologous platelet-rich plasma therapy vs. hyaluronic acid on synovial fluid biomarkers in knee osteoarthritis: a randomized controlled clinical trial.

Objective: Observe the effects of platelet-rich plasma (PRP) therapy on inflammatory cytokines in the synovial fluid of the knee joint of patients with KOA, and explore the effects of PRP intra-articular injection on the inflammation of the knee joint environment and the possible mechanism of action. Methods: Seventy patients were randomized to undergo three blinded weekly intra-articular injections of PRP or hyaluronic acid (HA). The concentrations of inflammatory cytokines, including interleukin (IL)-6, IL-1ß, tumor necrosis factor (TNF)-α, IL-8, IL-17A, IL-17F, IL-4, IL-5, and IL-10, in the synovial fluid were evaluated before the intervention and 1 month after the third injection. The Western Ontario and McMaster University (WOMAC) and visual analog scale (VAS) scores were used to assess pain and functional status of the knee joints in both groups before the intervention, immediately post-intervention, and 1, 3, 6, and 12 months after the intervention. Results: Baseline characteristics were similar in both groups with no statistical difference. The IL-6, IL-1ß, TNF-α, IL-17A, and IL-10 levels in the synovial fluid of the observation group decreased significantly after, vs. before, the intervention (p < 0.05), whereas the IL-8, IL-17F, and IL-4 levels decreased (p > 0.05) and IL-5 levels increased (p > 0.05). There was no statistically significant difference between inflammatory cytokine levels in the synovial fluid of the samples from the control group before and after the intervention (p > 0.05). There were no statistically significant differences between the two groups immediately after intervention (p > 0.05). At 1, 3, 6, and 12 months after intervention, the WOMAC and VAS scores were significantly better in the observation group than in the control group (p < 0.05). Conclusion: Platelet plasma therapy can reduce the concentrations of inflammatory cytokines IL-6, IL-1ß, TNF-α, IL-17A, and IL-10 in the synovial fluid of KOA patients, reduce the expression levels of IL-8, IL-17F, and IL-4, clear the pro-inflammatory factors, improve the inflammatory environment of the affected knee joint, and alleviate pain caused by inflammation. Thus, alleviating pain and improving knee function in patients with KOA.

Effects of emotion words activation and satiation on facial expression perception: evidence from behavioral and ERP investigations.

Objective: The present study investigated the impact of emotion concepts obtained from external environmental experiences on the perception of facial expressions by manipulating the activation and satiation of emotion words, which was based on the argument between basic emotion theory and constructed emotion theory. Methods: Experiment 1 explored the effects of emotion activation on happy, disgusted, emotion-label words and emotion-laden words in a facial expression judgment task through behavioral experimentation. Experiment 2 explored the effect of semantic satiation on emotion-label words and emotion-laden words using the event-related potential technique. Results: Experiment 1 found that facial expression perception was influenced by both types of emotion words and showed a significant emotional consistency effect. Experiment 2 found that N170 exhibited a more negative amplitude in the consistent condition compared to the inconsistent condition in the right hemisphere. More importantly, in the later stage of facial expression processing, emotion-label words and emotion-laden words both obstructed the perception of disgusted facial expressions and elicited more negative N400 amplitude in the emotion consistency condition, showing a reversed N400 effect. Conclusion: These results suggested that emotion concepts in the form of language influenced the perception of facial expressions, but there were differences between happy and disgusted faces. Disgusted faces were more dependent on emotion concept information and showed different performances in semantic activation and satiation conditions.

Application of vagus nerve stimulation on the rehabilitation of upper limb dysfunction after stroke: a systematic review and meta-analysis.

Objective: This study aimed to elucidate the efficacy, safety, and long-term implications of vagus nerve stimulation (VNS) as a viable therapeutic option for patients with upper limb dysfunction following a stroke. Methods: Data from the following libraries were searched from inception to December 2022: PubMed, Wanfang, Scopus, China Science and Technology Journal Database, Embase, Web of Science, China Biology Medicine Disc, Cochrane Library, and China National Knowledge Infrastructure. Outcomes included indicators of upper limb motor function, indicators of prognosis, and indicators of safety (incidence of adverse events [AEs] and serious AEs [SAEs]). Two of the authors extracted the data independently. A third researcher arbitrated when disputes occurred. The quality of each eligible study was evaluated using the Cochrane Risk of Bias tool. Meta-analysis and bias analysis were performed using Stata (version 16.0) and RevMan (version 5.3). Results: Ten trials (VNS combined with rehabilitation group vs. no or sham VNS combined with rehabilitation group) with 335 patients were included in the meta-analysis. Regarding upper extremity motor function, based on Fugl-Meyer assessment scores, VNS combined with other treatment options had immediate (mean difference [MD] = 2.82, 95% confidence interval [CI] = 1.78-3.91, I2 = 62%, p < 0.00001) and long-term (day-30 MD = 4.20, 95% CI = 2.90-5.50, p < 0.00001; day-90 MD = 3.27, 95% CI = 1.67-4.87, p < 0.00001) beneficial effects compared with that of the control treatment. Subgroup analyses showed that transcutaneous VNS (MD = 2.87, 95% CI = 1.78-3.91, I2 = 62%, p < 0.00001) may be superior to invasive VNS (MD = 3.56, 95% CI = 1.99-5.13, I2 = 77%, p < 0.0001) and that VNS combined with integrated treatment (MD = 2.87, 95% CI = 1.78-3.91, I2 = 62%, p < 0.00001) is superior to VNS combined with upper extremity training alone (MD = 2.24, 95% CI = 0.55-3.93, I2 = 48%, p = 0.009). Moreover, lower frequency VNS (20 Hz) (MD = 3.39, 95% CI = 2.06-4.73, I2 = 65%, p < 0.00001) may be superior to higher frequency VNS (25 Hz or 30 Hz) (MD = 2.29, 95% CI = 0.27-4.32, I2 = 58%, p = 0,03). Regarding prognosis, the VNS group outperformed the control group in the activities of daily living (standardized MD = 1.50, 95% CI = 1.10-1.90, I2 = 0%, p < 0.00001) and depression reduction. In contrast, quality of life did not improve (p = 0.51). Safety was not significantly different between the experimental and control groups (AE p = 0.25; SAE p = 0.26). Conclusion: VNS is an effective and safe treatment for upper extremity motor dysfunction after a stroke. For the functional restoration of the upper extremities, noninvasive integrated therapy and lower-frequency VNS may be more effective. In the future, further high-quality studies with larger study populations, more comprehensive indicators, and thorough data are required to advance the clinical application of VNS. Systematic review registration: https://www.crd.york.ac.uk/prospero/, identifier: CRD42023399820.

Machine learning for the prediction of cognitive impairment in older adults.

Objective: The purpose of this study was to develop and validate a predictive model of cognitive impairment in older adults based on a novel machine learning (ML) algorithm. Methods: The complete data of 2,226 participants aged 60-80 years were extracted from the 2011-2014 National Health and Nutrition Examination Survey database. Cognitive abilities were assessed using a composite cognitive functioning score (Z-score) calculated using a correlation test among the Consortium to Establish a Registry for Alzheimer's Disease Word Learning and Delayed Recall tests, Animal Fluency Test, and the Digit Symbol Substitution Test. Thirteen demographic characteristics and risk factors associated with cognitive impairment were considered: age, sex, race, body mass index (BMI), drink, smoke, direct HDL-cholesterol level, stroke history, dietary inflammatory index (DII), glycated hemoglobin (HbA1c), Patient Health Questionnaire-9 (PHQ-9) score, sleep duration, and albumin level. Feature selection is performed using the Boruta algorithm. Model building is performed using ten-fold cross-validation, machine learning (ML) algorithms such as generalized linear model (GLM), random forest (RF), support vector machine (SVM), artificial neural network (ANN), and stochastic gradient boosting (SGB). The performance of these models was evaluated in terms of discriminatory power and clinical application. Results: The study ultimately included 2,226 older adults for analysis, of whom 384 (17.25%) had cognitive impairment. After random assignment, 1,559 and 667 older adults were included in the training and test sets, respectively. A total of 10 variables such as age, race, BMI, direct HDL-cholesterol level, stroke history, DII, HbA1c, PHQ-9 score, sleep duration, and albumin level were selected to construct the model. GLM, RF, SVM, ANN, and SGB were established to obtain the area under the working characteristic curve of the test set subjects 0.779, 0.754, 0.726, 0.776, and 0.754. Among all models, the GLM model had the best predictive performance in terms of discriminatory power and clinical application. Conclusions: ML models can be a reliable tool to predict the occurrence of cognitive impairment in older adults. This study used machine learning methods to develop and validate a well performing risk prediction model for the development of cognitive impairment in the elderly.

Associations between dietary and blood inflammatory indices and their effects on cognitive function in elderly Americans.

Objective: To determine the correlations between dietary and blood inflammation indices in elderly Americans and their effects on cognitive function. Methods: This research extracted data from the 2011-2014 National Health and Nutrition Examination Survey for 2,479 patients who were ≥60 years old. Cognitive function was assessed as a composite cognitive function score (Z-score) calculated from the results of the Consortium to Establish a Registry for Alzheimer's Disease Word Learning and Delayed Recall tests, the Animal Fluency test, and the Digit Symbol Substitution Test. We used a dietary inflammatory index (DII) calculated from 28 food components to represent the dietary inflammation profile. Blood inflammation indicators included the white blood cell count (WBC), neutrophil count (NE), lymphocyte count (Lym), neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), neutrophil-albumin ratio (NAR), systemic immune-inflammation index [SII, calculated as (peripheral platelet count) × NE/Lym], and systemic inflammatory response index [SIRI, calculated as (monocyte count) × NE/Lym]. WBC, NE, Lym, NLR, PLR, NAR, SII, SIRI, and DII were initially treated as continuous variables. For logistic regression, WBC, NE, Lym, NLR, PLR, NAR, SII, and SIRI were divided into quartile groups, and DII was divided into tertile groups. Results: After adjusting for covariates, WBC, NE, NLR, NAR, SII, SIRI, and DII scores were markedly higher in the cognitively impaired group than in the normal group (p < 0.05). DII was negatively correlated with the Z-score when combined with WBC, NE, and NAR (p < 0.05). After adjusting for all covariates, DII was positively correlated with SII in people with cognitive impairment (p < 0.05). Higher DII with NLR, NAR, SII, and SIRI all increased the risk of cognitive impairment (p < 0.05). Conclusion: DII was positively correlated with blood inflammation indicators, and higher DII and blood inflammation indicators increased the risk of developing cognitive impairment.

Ageing threatens sustainability of smallholder farming in China.

Rapid demographic ageing substantially affects socioeconomic development1-4 and presents considerable challenges for food security and agricultural sustainability5-8, which have so far not been well understood. Here, by using data from more than 15,000 rural households with crops but no livestock across China, we show that rural population ageing reduced farm size by 4% through transferring cropland ownership and land abandonment (approximately 4 million hectares) in 2019, taking the population age structure in 1990 as a benchmark. These changes led to a reduction of agricultural inputs, including chemical fertilizers, manure and machinery, which decreased agricultural output and labour productivity by 5% and 4%, respectively, further lowering farmers' income by 15%. Meanwhile, fertilizer loss increased by 3%, resulting in higher pollutant emissions to the environment. In new farming models, such as cooperative farming, farms tend to be larger and operated by younger farmers, who have a higher average education level, hence improving agricultural management. By encouraging the transition to new farming models, the negative consequences of ageing can be reversed. Agricultural input, farm size and farmer's income would grow by approximately 14%, 20% and 26%, respectively, and fertilizer loss would reduce by 4% in 2100 compared with that in 2020. This suggests that management of rural ageing will contribute to a comprehensive transformation of smallholder farming to sustainable agriculture in China.

Emotion concept in perception of facial expressions: Effects of emotion-label words and emotion-laden words.

Emotion perception, the ability to detect and categorize the emotions of others, is an important component of social competence. The basic emotion theory and constructed emotion theory remain controversial over the role of emotional semantic information in the processing of facial expressions. In the present study, in order to explore the mechanism of emotional semantic information in different processing stages of facial expressions, we used the ERPs to investigate the effects of emotion-label words (e.g., happy, disgusted) and emotion-laden words (e.g., flower, maggot) on the perception of happy and disgusted faces. The results showed that disgusted faces were more susceptible to emotion words, and there were differences between word types and consistency conditions in the N170 component. More importantly, disgusted faces showed a significant N400 effect in the emotion-label words condition, that is, the amplitude in the emotion inconsistent condition was negative compared with that in the emotion consistent condition. The processing of happy faces was not significantly affected by the emotional information of words, and no significant effects related to consistency or to word types were found. These results found that disgusted faces exhibited emotional semantic conflict in the inconsistent emotion-label words condition, suggesting that emotion words moderated the perception of disgusted faces, supporting the hypothesis that emotional semantic information influenced the perception of facial expressions.

A prediction model for worsening diabetic retinopathy after panretinal photocoagulation.

BACKGROUND: As one of the severe complications of diabetes mellitus, diabetic retinopathy (DR) is the leading cause of blindness in the working age worldwide. Although panretinal photocoagulation (PRP) was standard treatment, PRP-treated DR still has a high risk of progression. Hence, this study aimed to assess the risk factors and establish a model for predicting worsening diabetic retinopathy (DR-worsening) within five years after PRP. METHODS: Patients who were diagnosed with severe non-proliferative diabetic retinopathy or proliferative diabetic retinopathy and treated with PRP were included, and those patients were randomly assigned to either a training or validation cohort. The multivariate logistic regression analysis was used to screen potential risk factors for DR-worsening in the training cohort. Then the model was established after including significant independent risk factors and further validated using discrimination and calibration. RESULTS: A total of 271 patients were included, and 56.46% of patients had an outcome of DR-worsening. In the training cohort (n = 135), age (odds ratio [OR] = 0.94, 95% confidence interval [CI] 0.90-0.98), baseline best corrected visual acuity (logMAR) (OR = 10.74, 95% CI 1.84-62.52), diabetic nephropathy (OR = 9.32, 95% CI 1.49-58.46), and hyperlipidemia (OR = 3.34, 95% CI 1.05-10.66) were screened out as the independent risk factors, which were incorporated into the predictive model. The area under the receiver operating characteristic curve and calibration slope in the training and validation cohort were 0.79, 0.96 (95% CI 0.60-1.31), and 0.79, 1.00 (95% CI 0.66-1.34), respectively. Two risk groups were developed depending on the best cut-off value of the predicted probability, and the actual probability was 34.90% and 82.79% in the low-risk and high-risk groups, respectively (P < 0.001). CONCLUSIONS: This study developed and internally validated a new model to predict the probability of DR-worsening after PRP treatment within five years. The model can be used as a rapid risk assessment system for clinical prediction of DR-worsening and identify individuals at a high risk of DR-worsening at an early stage and prescribe additional treatment.

Effectiveness of Response Inhibition Training and Its Long-Term Effects in Healthy Adults: A Systematic Review and Meta-Analysis.

Objective: This study aims to evaluate the effectiveness and long-term effects of response inhibition training as a therapeutic approach in healthy adults. Methods: The PubMed, Embase, Web of Science, China National Knowledge Infrastructure (CNKI), Wanfang, and China Science and Technology Journal Database (VIP) were searched for studies. Data on the improvement of Cognitive function and its long-term effect were extracted by two authors independently. The pooled data were meta-analyzed using a random-effects model, and the quality of each eligible study was assessed by The Cochrane Collaboration's tool. Results: Nine articles were included. 1 of the articles included 2 trials, so 10 eligible trials (response inhibition training group vs. control group) were identified. A total of 490 patients were included. Response inhibition training has beneficial effects on improving cognitive function in healthy adults compared to control treatment (SMD, -0.93; 95% CI, -1.56 to -0.30; Z = 2.88, P = 0.004), the subgroup analysis results showed that either GNG training alone (SMD, -2.27; 95% CI, -3.33 to -1.21; Z = 4.18, P < 0.0001) or the combination of both SST and GNG significantly improved cognitive function in healthy adults (SMD, -0.94; 95% CI, -1.33 to -0.56; Z = 4.80, P < 0.0001), whereas SST training alone did not have such an effect (SMD, -0.15; 95% CI, -0.76 to 0.47; Z = 0.47, P = 0.64). But its long-term effects are not significant (SMD, -0.29; 95% CI, -0.68 to 0.10; Z = 1.45, P = 0.15). The subgroup analysis results showed that neither GNG training alone (SMD, -0.25; 95% CI, -0.75 to 0.24; Z = 0.99, P = 0.32) nor SST training alone (SMD, 0.03; 95% CI, -0.42 to 0.48; Z = 0.14, P = 0.89) could improve the cognitive function of healthy adults in the long term. In contrast, the combination of both training (SMD, -0.95; 95% CI, -1.46 to -0.45; Z = 3.68, P = 0.0002) can have long-term effects on the improvement of cognitive function in healthy adults. Conclusion: The findings of our study indicate that response inhibition training can improve the cognitive function of healthy adults and that more RCTs need to be conducted to validate their usefulness in clinical cases.

A Weakly Supervised Deep Learning Approach for Leakage Detection in Fluorescein Angiography Images.

Purpose: The purpose of this study was to design an automated algorithm that can detect fluorescence leakage accurately and quickly without the use of a large amount of labeled data. Methods: A weakly supervised learning-based method was proposed to detect fluorescein leakage without the need for manual annotation of leakage areas. To enhance the representation of the network, a residual attention module (RAM) was designed as the core component of the proposed generator. Moreover, class activation maps (CAMs) were used to define a novel anomaly mask loss to facilitate more accurate learning of leakage areas. In addition, sensitivity, specificity, accuracy, area under the curve (AUC), and dice coefficient (DC) were used to evaluate the performance of the methods. Results: The proposed method reached a sensitivity of 0.73 ± 0.04, a specificity of 0.97 ± 0.03, an accuracy of 0.95 ± 0.05, an AUC of 0.86 ± 0.04, and a DC of 0.87 ± 0.01 on the HRA data set; a sensitivity of 0.91 ± 0.02, a specificity of 0.97 ± 0.02, an accuracy of 0.96 ± 0.03, an AUC of 0.94 ± 0.02, and a DC of 0.85 ± 0.03 on Zhao's publicly available data set; and a sensitivity of 0.71 ± 0.04, a specificity of 0.99 ± 0.06, an accuracy of 0.87 ± 0.06, an AUC of 0.85 ± 0.02, and a DC of 0.78 ± 0.04 on Rabbani's publicly available data set. Conclusions: The experimental results showed that the proposed method achieves better performance on fluorescence leakage detection and can detect one image within 1 second and thus has great potential value for clinical diagnosis and treatment of retina-related diseases, such as diabetic retinopathy and malarial retinopathy. Translational Relevance: The proposed weakly supervised learning-based method that automates the detection of fluorescence leakage can facilitate the assessment of retinal-related diseases.

A mitochondrial RNA processing protein mediates plant immunity to a broad spectrum of pathogens by modulating the mitochondrial oxidative burst.

Mitochondrial function depends on the RNA processing of mitochondrial gene transcripts by nucleus-encoded proteins. This posttranscriptional processing involves the large group of nuclear-encoded pentatricopeptide repeat (PPR) proteins. Mitochondrial processes represent a crucial part in animal immunity, but whether mitochondria play similar roles in plants remains unclear. Here, we report the identification of RESISTANCE TO PHYTOPHTHORA PARASITICA 7 (AtRTP7), a P-type PPR protein, in Arabidopsis thaliana and its conserved function in immunity to diverse pathogens across distantly related plant species. RTP7 affects the levels of mitochondrial reactive oxygen species (mROS) by participating in RNA splicing of nad7, which encodes a critical subunit of the mitochondrial respiratory chain Complex I, the largest of the four major components of the mitochondrial oxidative phosphorylation system. The enhanced resistance of rtp7 plants to Phytophthora parasitica is dependent on an elevated mROS burst, but might be independent from the ROS burst associated with plasma membrane-localized NADPH oxidases. Our study reveals the immune function of RTP7 and the defective processing of Complex I subunits in rtp7 plants resulted in enhanced resistance to both biotrophic and necrotrophic pathogens without affecting overall plant development.

Macular microcirculation changes after repair of rhegmatogenous retinal detachment assessed with optical coherence tomography angiography: A systematic review and meta-analysis.

Purpose: The aim of the study was to investigate microcirculation changes in the macula evaluated by optical coherence tomography angiography (OCTA)in patients receiving anatomical repair after surgery for rhegmatogenous retinal detachment (RRD). Methods: A literature search was conducted in PubMed, EMBASE, Web of Science and the Cochrane Library. Studies including patients with macula-on or macula-off RRD and repaired successfully through primary surgery were selected. Foveal avascular zone (FAZ) area and macular vascular density (VD) in both the superficial capillary plexus (SCP) and deep capillary plexus (DCP) were analyzed using RevMan 5.4 software. Results: Twelve studies including 430 RRD eyes and 430 control eyes were selected. In eyes with macula-on RRD, FAZ area, VD in the foveal SCP and DCP, and VD in the parafoveal SCP and DCP were not altered compared with control eyes, after the retina was reattached. In eyes with macula-off RRD that was repaired successfully through surgery, FAZ area in the DCP (0.13 mm2, 95% CI: 0.02 to 0.25, p = 0.02) remained enlarged compared with control eyes. Meanwhile, VD in the foveal DCP was also significantly reduced (-3.12%, 95% CI: -6.15 to -0.09%, p = 0.04), even though retinal reattachment was achieved by surgery in eyes with macula-off RRD. Conclusion: In patients with macula-off rhegmatogenous retinal detachment, foveal avascular zone area in the deep capillary plexuses was enlarged and vascular density in the foveal deep capillary plexus was reduced, even after the retina was successfully reattached through a primary surgery.

Influence of scene-based expectation on facial expression perception: The moderating effect of cognitive load.

Prior expectations play an important role in the process of perception. In real life, facial expressions always appear within a scene, which enables individuals to generate predictions that affect facial expression judgments. In the present study, using event-related potentials, we investigated the influence of scene-based expectation on facial expression processing. In addition, we used a cognitive task to manipulate cognitive load to interfere with scene-based expectation. Results showed that under the condition of sufficient cognitive resources, faces elicited more negative N170 amplitudes and more positive N400 amplitudes when the emotional valence of the scenes and faces was congruent. However, in the condition of cognitive load, no such difference was observed. The findings suggested that the effect of expectation on facial expression recognition emerges during both the early and late stages of facial expression processing, and the effect is weakened when cognitive resources are occupied by unrelated tasks.

Predictive model and risk analysis for diabetic retinopathy using machine learning: a retrospective cohort study in China.

OBJECTIVE: Aiming to investigate diabetic retinopathy (DR) risk factors and predictive models by machine learning using a large sample dataset. DESIGN: Retrospective study based on a large sample and a high dimensional database. SETTING: A Chinese central tertiary hospital in Beijing. PARTICIPANTS: Information on 32 452 inpatients with type-2 diabetes mellitus (T2DM) were retrieved from the electronic medical record system from 1 January 2013 to 31 December 2017. METHODS: Sixty variables (including demography information, physical and laboratory measurements, system diseases and insulin treatments) were retained for baseline analysis. The optimal 17 variables were selected by recursive feature elimination. The prediction model was built based on XGBoost algorithm, and it was compared with three other popular machine learning techniques: logistic regression, random forest and support vector machine. In order to explain the results of XGBoost model more visually, the Shapley Additive exPlanation (SHAP) method was used. RESULTS: DR occurred in 2038 (6.28%) T2DM patients. The XGBoost model was identified as the best prediction model with the highest AUC (area under the curve value, 0.90) and showed that an HbA1c value greater than 8%, nephropathy, a serum creatinine value greater than 100 µmol/L, insulin treatment and diabetic lower extremity arterial disease were associated with an increased risk of DR. A patient's age over 65 was associated with a decreased risk of DR. CONCLUSIONS: With better comprehensive performance, XGBoost model had high reliability to assess risk indicators of DR. The most critical risk factors of DR and the cut-off of risk factors can be found by SHAP method to render the output of the XGBoost model clinically interpretable.

An online diabetic retinopathy screening tool for patients with type 2 diabetes.

AIM: To develop a useful diabetic retinopathy (DR) screening tool for patients with type 2 diabetes mellitus (T2DM). METHODS: A DR prediction model based on the Logistic regression algorithm was established on the development dataset containing 778 samples (randomly assigned to the training dataset and the internal validation dataset at a ratio of 7:3). The generalization capability of the model was assessed using an external validation dataset containing 128 samples. The DR risk calculator was developed through WeChat Developer Tools using JavaScript, which was embedded in the WeChat Mini Program. RESULTS: The model revealed risk factors (duration of diabetes, diabetic nephropathy, and creatinine level) and protective factors (annual DR screening and hyperlipidemia) for DR. In the internal and external validation, the recall ratios of the model were 0.92 and 0.89, respectively, and the area under the curve values were 0.82 and 0.70, respectively. CONCLUSION: The DR screening tool integrates education, risk prediction, and medical advice function, which could help clinicians in conducting DR risk assessments and providing recommendations for ophthalmic referral to increase the DR screening rate among patients with T2DM.

Unsupervised domain adaptation model for lesion detection in retinal OCT images.

Background and objective.Optical coherence tomography (OCT) is one of the most used retinal imaging modalities in the clinic as it can provide high-resolution anatomical images. The huge number of OCT images has significantly advanced the development of deep learning methods for automatic lesion detection to ease the doctor's workload. However, it has been frequently revealed that the deep neural network model has difficulty handling the domain discrepancies, which widely exist in medical images captured from different devices. Many works have been proposed to solve the domain shift issue in deep learning tasks such as disease classification and lesion segmentation, but few works focused on lesion detection, especially for OCT images.Methods.In this work, we proposed a faster-RCNN based, unsupervised domain adaptation model to address the lesion detection task in cross-device retinal OCT images. The domain shift is minimized by reducing the image-level shift and instance-level shift at the same time. We combined a domain classifier with a Wasserstein distance critic to align the shifts at each level.Results.The model was tested on two sets of OCT image data captured from different devices, obtained an average accuracy improvement of more than 8% over the method without domain adaptation, and outperformed other comparable domain adaptation methods.Conclusion.The results demonstrate the proposed model is more effective in reducing the domain shift than advanced methods.