Biomarker Changes during 20 Years Preceding Alzheimer's Disease.

BACKGROUND: Biomarker changes that occur in the period between normal cognition and the diagnosis of sporadic Alzheimer's disease have not been extensively investigated in longitudinal studies. METHODS: We conducted a multicenter, nested case-control study of Alzheimer's disease biomarkers in cognitively normal participants who were enrolled in the China Cognition and Aging Study from January 2000 through December 2020. A subgroup of these participants underwent testing of cerebrospinal fluid (CSF), cognitive assessments, and brain imaging at 2-year-to-3-year intervals. A total of 648 participants in whom Alzheimer's disease developed were matched with 648 participants who had normal cognition, and the temporal trajectories of CSF biochemical marker concentrations, cognitive testing, and imaging were analyzed in the two groups. RESULTS: The median follow-up was 19.9 years (interquartile range, 19.5 to 20.2). CSF and imaging biomarkers in the Alzheimer's disease group diverged from those in the cognitively normal group at the following estimated number of years before diagnosis: amyloid-beta (Aß)42, 18 years; the ratio of Aß42 to Aß40, 14 years; phosphorylated tau 181, 11 years; total tau, 10 years; neurofilament light chain, 9 years; hippocampal volume, 8 years; and cognitive decline, 6 years. As cognitive impairment progressed, the changes in CSF biomarker levels in the Alzheimer's disease group initially accelerated and then slowed. CONCLUSIONS: In this study involving Chinese participants during the 20 years preceding clinical diagnosis of sporadic Alzheimer's disease, we observed the time courses of CSF biomarkers, the times before diagnosis at which they diverged from the biomarkers from a matched group of participants who remained cognitively normal, and the temporal order in which the biomarkers became abnormal. (Funded by the Key Project of the National Natural Science Foundation of China and others; ClinicalTrials.gov number, NCT03653156.).

The Time-Dependent Association Between Irritable Bowel Syndrome and All-Cause and Cause-Specific Mortality: A Prospective Cohort Study Within the UK Biobank.

INTRODUCTION: Irritable bowel syndrome (IBS) is one of the most common functional gastrointestinal disorders, but few studies have evaluated mortality risks among individuals with IBS. We explored the association between IBS and all-cause and cause-specific mortality in the UK Biobank. METHODS: We included 502,369 participants from the UK Biobank with mortality data through 2022. IBS was defined using baseline self-report and linkage to primary care or hospital admission data. We estimated hazard ratios (HRs) and 95% confidence intervals (CIs) for all-cause and cause-specific mortality using multivariable Cox proportional hazards regression models within partitioned follow-up time categories (0-5, >5-10, and >10 years). RESULTS: A total of 25,697 participants (5.1%) had a history of IBS at baseline. After a median follow-up of 13.7 years, a total of 44,499 deaths occurred. Having an IBS diagnosis was strongly associated with lower risks of all-cause (HR = 0.70, 95% CI = 0.62-0.78) and all-cancer (HR = 0.69, 95% CI = 0.60-0.79) mortality in the first 5 years of follow-up. These associations were attenuated over follow-up, but even after 10 years of follow-up, associations remained inverse (all-cause: HR = 0.89, 95% CI = 0.84-0.96; all-cancer: HR = 0.87, 95% CI = 0.78-0.97) after full adjustment. Individuals with IBS had decreased risk of mortality from breast, prostate, and colorectal cancers in some of the follow-up time categories. DISCUSSION: We found that earlier during follow-up, having diagnosed IBS was associated with lower mortality risk, and the association attenuated over time. Additional studies to understand whether specific factors, such as lifestyle and healthcare access, explain the inverse association between IBS and mortality are needed.

Heavy metals and metalloids exposure and liver function in Chinese adults - A nationally representative cross-sectional study.

BACKGROUND: In China, the effects of heavy metals and metalloids (HMMs) on liver health are not consistently documented, despite their prevalent environmental presence. OBJECTIVE: Our research assessed the association between HMMs and liver function biomarkers in a comprehensive sample of Chinese adults. METHODS: We analyzed data from 9445 participants in the China National Human Biomonitoring survey. Blood and urine were evaluated for HMM concentrations, and liver health was gauged using serum albumin (ALB), alanine aminotransferase (ALT), and aspartate aminotransferase (AST) metrics. Various statistical methods were employed to understand the relationship between 11 HMMs and liver function, adjusting for multiple factors. We also explored interactions with alcohol intake, gender, and age. RESULTS: Among HMMs, selenium in blood [weighted geometric mean (GM) = 95.56 µg/L] and molybdenum in urine (GM = 46.44 µg/L) showed the highest concentrations, while lead in blood (GM = 21.92 µg/L) and arsenic in urine (GM = 19.80 µg/L) had the highest levels among risk HMMs. Manganese and thallium consistently indicated potential risk factor to liver in both sample types, while selenium displayed potential liver protection. Blood HMM mixtures were negatively associated with ALB (ß = -0.614, 95% CI: -0.809, -0.418) and positively with AST (ß = 0.701, 95% CI: 0.290, 1.111). No significant associations were found in urine HMM mixtures. Manganese, tin, nickel, and selenium were notable in blood mixture associations, with selenium and cobalt being significant in urine. The relationship of certain HMMs varied based on alcohol consumption. CONCLUSION: This research highlights the complex relationship between HMM exposure and liver health in Chinese adults, particularly emphasizing metals like manganese, thallium, and selenium. The results suggest a need for public health attention to low dose HMM exposure and underscore the potential benefits of selenium for liver health. Further studies are essential to establish causality.

Development and validation of dietary depression index in Chinese adults.

Objective: Previous studies have suggested diet was associated with depressive symptoms. We aimed to develop and validate Dietary Depression Index (DDI) based on dietary prediction of depression in a large Chinese cancer screening cohort.Methods: In the training set (n = 2729), we developed DDI by using intake of 20 food groups derived from a food frequency questionnaire to predict depression as assessed by Patient Health Questionnaire-9 based on the reduced rank regression method. Sensitivity, specificity, positive predictive value, and negative predictive value were used to assess the performance of DDI in evaluating depression in the validation dataset (n = 1176).Results: Receiver operating characteristic analysis was constructed to determine the best cut-off value of DDI in predicting depression. In the study population, the DDI ranged from -3.126 to 1.810. The discriminative ability of DDI in predicting depression was good with the AUC of 0.799 overall, 0.794 in males and 0.808 in females. The best cut-off values of DDI for depression prediction were 0.204 overall, 0.330 in males and 0.034 in females. DDI was a validated method to assess the effects of diet on depression.Conclusion: Among individual food components in DDI, fermented vegetables, fresh vegetables, whole grains and onions were inversely associated, whereas legumes, pickled vegetables and rice were positively associated with depressive symptoms.

Exome sequencing revealed PDE11A as a novel candidate gene for early-onset Alzheimer's disease.

To identify novel risk genes and better understand the molecular pathway underlying Alzheimer's disease (AD), whole-exome sequencing was performed in 215 early-onset AD (EOAD) patients and 255 unrelated healthy controls of Han Chinese ethnicity. Subsequent validation, computational annotation and in vitro functional studies were performed to evaluate the role of candidate variants in EOAD. We identified two rare missense variants in the phosphodiesterase 11A (PDE11A) gene in individuals with EOAD. Both variants are located in evolutionarily highly conserved amino acids, are predicted to alter the protein conformation and are classified as pathogenic. Furthermore, we found significantly decreased protein levels of PDE11A in brain samples of AD patients. Expression of PDE11A variants and knockdown experiments with specific short hairpin RNA (shRNA) for PDE11A both resulted in an increase of AD-associated Tau hyperphosphorylation at multiple epitopes in vitro. PDE11A variants or PDE11A shRNA also caused increased cyclic adenosine monophosphate (cAMP) levels, protein kinase A (PKA) activation and cAMP response element-binding protein phosphorylation. In addition, pretreatment with a PKA inhibitor (H89) suppressed PDE11A variant-induced Tau phosphorylation formation. This study offers insight into the involvement of Tau phosphorylation via the cAMP/PKA pathway in EOAD pathogenesis and provides a potential new target for intervention.

The association between poor oral health and risk of breast cancer in the UK Biobank.

PURPOSE: Specific oral health conditions may be risk factors for breast cancer. This study aimed to investigate the associations of oral health conditions with breast cancer risk. METHODS: A total of 234,363 women from the UK Biobank prospective cohort were included in this study. We examined the association of self-reported painful/bleeding gums, loose teeth, mouth ulcers, toothache, and use of dentures with the risk of breast cancer. Hazard ratios (HRs) and 95% confidence intervals (95% CIs) for the associations were calculated with adjustment for multiple confounders. RESULTS: No associations of self-reported painful/bleeding gums (HR = 1.04, 95% CI 0.98-1.10), loose teeth (HR = 0.92, 95% CI 0.82-1.02), mouth ulcers (HR = 0.99, 95% CI 0.93-1.06), toothache (HR = 1.03, 95% CI 0.92-1.14), or denture use (HR = 0.96, 95% CI 0.91-1.02) with breast cancer risk were found. No statistical heterogeneity was observed in analyses stratified by baseline smoking and menopausal status. CONCLUSION: We observed no association between self-reported oral health conditions with the risk of breast cancer. Additional research with clinical examinations or oral health biomarkers in diverse populations is warranted.

Identification of FGF13 as a Potential Biomarker and Target for Diagnosis of Impaired Glucose Tolerance.

Early identification of pre-diabetes provides an opportunity for intervention and treatment to delay its progression to type 2 diabetes mellitus (T2DM). We aimed to identify the biomarkers of impaired glucose tolerance (IGT) through bioinformatics analysis. The GSE76896 dataset, including non-diabetic (ND), IGT, and T2DM clinical samples, was deeply analyzed to identify 309 Co-DEGs for IGT and T2DM. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses indicated that inflammatory responses and the PI3K-AKT signaling pathway are important patho-physiological features of IGT and T2DM. Protein-protein interaction (PPI) network analysis and cytoHubba technolgy identified seven hub genes: namely, CCL2, CXCL1, CXCL8, EDN1, FGF13, MMP1, and NGF. The expression and ROC curves of these hub genes were validated using the GSE38642 dataset. Through an immunofluorescence assay, we found that the expression of FGF13 in islets of mice in the HFD and T2DM groups was significantly lower than in the control group. Similarly, the level of FGF13 in the sera of IGT and T2DM patients was lower than that in the healthy group. Together, these results suggest that FGF13 can be treated as a novel biomarker of IGT, which may provide new targets for the diagnosis and treatment of pre-diabetes and T2DM.

Changing trends and factors influencing anticoagulant use in patients with acute ischemic stroke and NVAF at discharge in the NOACs era.

OBJECTIVES: We sought to explore the trends and influencing factors of the use of anticoagulants in patients with acute ischemic stroke and non-valvular atrial fibrillation (NVAF) at discharge in the era of novel oral anticoagulants (NOACs). METHODS: We recruited consecutive inpatients with acute ischemic stroke and NVAF in a registered study (NCT04080830) from January 2016 to December 2021. The relevant data of patients were collected. We compared the proportions of anticoagulant treatment at discharge before and after NOACs entered China's medical insurance system. The proportion of each antithrombotic status as well as anticoagulant agents at discharge in every year were calculated, and the trends during the study period were analyzed. The relevant factors affecting anticoagulant use at discharge were further analyzed. RESULTS: The proportion of anticoagulation at discharge increased significantly after NOACs entered China's medical insurance system in 2018 versus before (χ2 = 42.828, P < 0.001). There were statistically significant differences in antithrombotic status (χ2 = 69.954, P < 0.001) and in the proportion of different anticoagulant drugs (χ2 = 63.049, P<0.001) by year. Anticoagulant therapy (χ2 = 1.55, P = 0.671) and NOACs (χ2 = .178, P = 0.243) increased over 2016-2018 but was relatively stable during 2018-2021. Multivariate logistic regression analysis showed that age ≥75 years, coexisting cerebral artery stenosis, massive cerebral infarction and hemorrhagic transformation were independent risk factors affecting anticoagulants use (all P < 0.05). CONCLUSION: NOACs have indeed improved anticoagulants use in patients with acute ischemic stroke and NVAF at discharge. However, some specific factors affect anticoagulation therapy use at discharge and hinder further improvement even in the NOACs era.

Prediction of Alzheimer's disease using multi-variants from a Chinese genome-wide association study.

Previous genome-wide association studies have identified dozens of susceptibility loci for sporadic Alzheimer's disease, but few of these loci have been validated in longitudinal cohorts. Establishing predictive models of Alzheimer's disease based on these novel variants is clinically important for verifying whether they have pathological functions and provide a useful tool for screening of disease risk. In the current study, we performed a two-stage genome-wide association study of 3913 patients with Alzheimer's disease and 7593 controls and identified four novel variants (rs3777215, rs6859823, rs234434, and rs2255835; Pcombined = 3.07 × 10-19, 2.49 × 10-23, 1.35 × 10-67, and 4.81 × 10-9, respectively) as well as nine variants in the apolipoprotein E region with genome-wide significance (P < 5.0 × 10-8). Literature mining suggested that these novel single nucleotide polymorphisms are related to amyloid precursor protein transport and metabolism, antioxidation, and neurogenesis. Based on their possible roles in the development of Alzheimer's disease, we used different combinations of these variants and the apolipoprotein E status and successively built 11 predictive models. The predictive models include relatively few single nucleotide polymorphisms useful for clinical practice, in which the maximum number was 13 and the minimum was only four. These predictive models were all significant and their peak of area under the curve reached 0.73 both in the first and second stages. Finally, these models were validated using a separate longitudinal cohort of 5474 individuals. The results showed that individuals carrying risk variants included in the models had a shorter latency and higher incidence of Alzheimer's disease, suggesting that our models can predict Alzheimer's disease onset in a population with genetic susceptibility. The effectiveness of the models for predicting Alzheimer's disease onset confirmed the contributions of these identified variants to disease pathogenesis. In conclusion, this is the first study to validate genome-wide association study-based predictive models for evaluating the risk of Alzheimer's disease onset in a large Chinese population. The clinical application of these models will be beneficial for individuals harbouring these risk variants, and particularly for young individuals seeking genetic consultation.

Food Insecurity and Health-Related Concerns Among Elementary Schoolteachers During the COVID-19 Pandemic.

INTRODUCTION: US school systems underwent major upheaval, including closures, implementation of virtual and/or hybrid learning, and stringent infection mitigation protocols, during the initial phase of the COVID-19 pandemic. We aimed to examine the association between food insecurity and perceived health, perceived stress, and social determinants of health concerns among elementary schoolteachers serving predominantly low-income children during the COVID-19 pandemic. METHODS: Brighter Bites, a nonprofit organization that weekly distributes fresh fruits and vegetables and nutrition education materials to more than 300 schools serving racial and ethnic minority populations with low income, conducts annual surveys of participating teachers to help determine subsequent efforts to support schools and families during the school year. We analyzed self-reported data collected electronically by the Brighter Bites teachers survey in 76 elementary schools during summer 2020. We used generalized linear mixed models to measure the association between food insecurity and health-related concerns. RESULTS: Of 862 teachers who responded to the survey, 685 answered the 2 questions about food insecurity status; of these, 199 (29.1%) reported experiencing food insecurity. Food insecurity was positively associated with poor perceived general health, greater perceived stress, concerns about various social determinants of health, and changes in fruit and vegetable consumption during the COVID-19 pandemic. CONCLUSION: Our study demonstrated the high prevalence of food insecurity and highlights its associated factors among elementary schoolteachers during the COVID-19 pandemic. It calls attention to the high correlation of various concerns among elementary schoolteachers during the COVID-19 pandemic. Further intervention and policy efforts are needed to relieve food insecurity-related concerns and enhance well-being among teachers.

A metabolite panel that differentiates Alzheimer's disease from other dementia types.

INTRODUCTION: Alzheimer's disease (AD) is associated with altered metabolites. This study aimed to determine the validity of using circulating metabolites to differentiate AD from other dementias. METHODS: Blood metabolites were measured in three data sets. Data set 1 (controls, 27; AD, 28) was used for analyzing differential metabolites. Data set 2 (controls, 93; AD, 92) was used to establish a diagnostic AD model with use of a metabolite panel. The model was applied to Data set 3 (controls, 76; AD, 76; other dementias, 205) to verify its capacity for differentiating AD from other dementias. RESULTS: Data set 1 revealed 7 upregulated and 77 downregulated metabolites. In Data set 2, a panel of 11 metabolites was included in a model that could distinguish AD from controls. In Data set 3, this panel was used to successfully differentiate AD from other dementias. DISCUSSION: This study revealed an AD-specific panel of 11 metabolites that may be used for AD diagnosis.

Prediction of P-tau/Aß42 in the cerebrospinal fluid with blood microRNAs in Alzheimer's disease.

BACKGROUND: The most common biomarkers of Alzheimer's disease (AD) are amyloid ß (Aß) and tau, detected in cerebrospinal fluid (CSF) or with positron emission tomography imaging. However, these procedures are invasive and expensive, which hamper their availability to the general population. Here, we report a panel of microRNAs (miRNAs) in serum that can predict P-tau/Aß42 in CSF and readily differentiate AD from other dementias, including vascular dementia (VaD), Parkinson disease dementia (PDD), behavioral variant frontotemporal dementia (bvFTD), and dementia with Lewy body (DLB). METHODS: RNA samples were extracted from the participant's blood. P-tau/Aß42 of CSF was examined for diagnostic purposes. A pilot study (controls, 21; AD, 23), followed by second (controls, 216; AD, 190) and third groups (controls, 153; AD, 151), is used to establish and verify a predictive model of P-tau/Aß42 in CSF. The test is then applied to a fourth group of patients with different dementias (controls, 139; AD,155; amnestic mild cognitive impairment [aMCI], 55; VaD, 51; PDD, 53; bvFTD, 53; DLB, 52) to assess its diagnostic capacity. RESULTS: In the pilot study, 29 upregulated and 31 downregulated miRNAs in the AD group were found. In Dataset 2, these miRNAs were then included as independent variables in the linear regression model. A seven-microRNA panel (miR-139-3p, miR-143-3p, miR-146a-5p, miR-485-5p, miR-10a-5P, miR-26b-5p, and miR-451a-5p) accurately predicted values of P-tau/Aß42 of CSF. In Datasets 3 and 4, by applying the predicted P-tau/Aß42, the predictive model successfully differentiates AD from controls and VaD, PDD, bvFTD, and DLB. CONCLUSIONS: This study suggests that the panel of microRNAs is a promising substitute for traditional measurement of P-tau/Aß42 in CSF as an effective biomarker of AD.

Machine learning research towards combating COVID-19: Virus detection, spread prevention, and medical assistance.

COVID-19 was first discovered in December 2019 and has continued to rapidly spread across countries worldwide infecting thousands and millions of people. The virus is deadly, and people who are suffering from prior illnesses or are older than the age of 60 are at a higher risk of mortality. Medicine and Healthcare industries have surged towards finding a cure, and different policies have been amended to mitigate the spread of the virus. While Machine Learning (ML) methods have been widely used in other domains, there is now a high demand for ML-aided diagnosis systems for screening, tracking, predicting the spread of COVID-19 and finding a cure against it. In this paper, we present a journey of what role ML has played so far in combating the virus, mainly looking at it from a screening, forecasting, and vaccine perspective. We present a comprehensive survey of the ML algorithms and models that can be used on this expedition and aid with battling the virus.

Knockdown of p62/sequestosome enhances ginsenoside Rh2-induced apoptosis in cervical cancer HeLa cells with no effect on autophagy.

p62/sequestosome is a multifunctional adaptor protein that participates in a wide variety of cellular processes. 20(S)-Ginsenoside Rh2 (G-Rh2) has various biological effects, including anticancer activity. We found that G-Rh2 can induce apoptosis and autophagy in HeLa cells. G-Rh2 significantly enhanced the transcriptional level of p62. A siRNA was constructed to knock down p62 and assess its effect on apoptosis induced by G-Rh2. p62 protein levels were successfully downregulated in cells transfected with the p62-specific siRNA. Silencing of p62 further decreased cell viability while also enhancing cell apoptosis, reactive oxygen species generation, the ratio of Bax to Bcl-2, and the cleavage of PARP. p62 knockdown decreased expression levels of Nrf2. Moreover, silencing of p62 had no significant effect on autophagy induced by G-Rh2. These results suggest that combining G-Rh2 treatment with inhibition of p62 may be a potential treatment strategy for cervical cancer.

DGAT1 K232A polymorphism is associated with milk production traits in Chinese cattle.

The production traits of cattle, especially milk trait, are of great significance to human life. A quantitative trait loci (QTL) associated with milk fat content was detected in the centromeric region of cattle chromosome 14. This QTL harbors a strong candidate gene called DGAT1 responsible for the milk quality. A non-conservative substitution of lysine by alanine (K232A) was found in DGAT1 gene producing a strong effect on milk composition and yield. The lysine (K allele) is associated with increased milk fat content, while the decreased milk fat content is linked to the alanine (A allele) amino acid. To estimate the frequencies of the DGAT1 K232A polymorphism in Chinese cattle breeds, PCR and DNA sequencing methods were used to investigate the polymorphism of DGAT1 K232A in a total of 682 individuals, including 655 Chinese cattle and 27 Holstein cattle. The results demonstrated that the frequency of K allele gradually elevated from the northern group to the southern group of native Chinese cattle, whereas the frequency of A allele showed a contrary pattern, displaying a significant geographical difference across native Chinese cattle breeds. Our results confirm that the southern cattle group has higher milk fat content than that of the northern group.

Blood neuro-exosomal synaptic proteins predict Alzheimer's disease at the asymptomatic stage.

INTRODUCTION: Exosomes are an emerging candidate for biomarkers of Alzheimer's disease (AD). This study investigated whether exosomal synaptic proteins can predict AD at the asymptomatic stage. METHODS: We conducted a two-stage-sectional study (discovery stage: AD, 28; amnestic mild cognitive impairment [aMCI], 25; controls, 29; validation stage: AD, 73; aMCI, 71; controls, 72), a study including preclinical AD (160) and controls (160), and a confirmation study in familial AD (mutation carriers: 59; non-mutation carriers: 62). RESULTS: The concentrations of growth associated protein 43 (GAP43), neurogranin, synaptosome associated protein 25 (SNAP25), and synaptotagmin 1 were lower in AD than in controls (P < .001). Exosomal biomarker levels were correlated with those in cerebrospinal fluid (R2  = 0.54-0.70). The combination of exosomal biomarkers detected AD 5 to 7 years before cognitive impairment (area under the curve = 0.87-0.89). DISCUSSION: This study revealed that exosomal GAP43, neurogranin, SNAP25, and synaptotagmin 1 act as effective biomarkers for prediction of AD 5 to 7 years before cognitive impairment.

CaO recovered from eggshell waste as a potential adsorbent for greenhouse gas CO2.

The growing number of industrial carbon emissions have resulted in a significant increase in the greenhouse gas carbon dioxide (CO2), which, in turn, will have a major impact on climate change. Therefore, the reduction, storage, and reuse of CO2 is an important concern in modern society. Calcium oxide (CaO) is known to be an excellent adsorbent of CO2 in a high-temperature environment. However, since deterioration of the adsorbent is likely to occur after repeated cycles of adsorption under high temperature conditions, it would be desirable to mitigate this phenomenon, in order to maintain the stability of CaO. In the present study, common eggshell waste was used as the starting material. The main component of eggshell waste is calcium carbonate (CaCO3), which was purified to produce CaO. Different surfactants and amino-containing polymers were added to synthesize CaO-based adsorbents with different configurations and pore sizes. The amount of CO2 adsorbed was determined using a thermogravimetric analyzer (TGA). The results showed that the CO2 adsorption capacity of the synthetic CaO recovered from purified eggshell waste could reach 0.6 g-CO2/g-sorbent, indicating a good adsorption capacity. CaO modified with a dopamine-containing polymer was shown to have an adsorption capacity of 0.62 g-CO2/g-sorbent. Moreover, it showed an excellent adsorption capacity of 0.40 g-CO2/g-sorbent, even after 10 cycles of CO2 adsorption. The present study suggests that using eggshell waste to synthesize CaO-based adsorbents for effective CO2 adsorption can not only reduce environmental waste, but also have the potential to capture greenhouse gas CO2 emissions, which conforms to the principles of green chemistry.

PSANet: Pyramid Splitting and Aggregation Network for 3D Object Detection in Point Cloud.

3D object detection in LiDAR point clouds has been extensively used in autonomous driving, intelligent robotics, and augmented reality. Although the one-stage 3D detector has satisfactory training and inference speed, there are still some performance problems due to insufficient utilization of bird's eye view (BEV) information. In this paper, a new backbone network is proposed to complete the cross-layer fusion of multi-scale BEV feature maps, which makes full use of various information for detection. Specifically, our proposed backbone network can be divided into a coarse branch and a fine branch. In the coarse branch, we use the pyramidal feature hierarchy (PFH) to generate multi-scale BEV feature maps, which retain the advantages of different levels and serves as the input of the fine branch. In the fine branch, our proposed pyramid splitting and aggregation (PSA) module deeply integrates different levels of multi-scale feature maps, thereby improving the expressive ability of the final features. Extensive experiments on the challenging KITTI-3D benchmark show that our method has better performance in both 3D and BEV object detection compared with some previous state-of-the-art methods. Experimental results with average precision (AP) prove the effectiveness of our network.

EPGNet: Enhanced Point Cloud Generation for 3D Object Detection.

Three-dimensional object detection from point cloud data is becoming more and more significant, especially for autonomous driving applications. However, it is difficult for lidar to obtain the complete structure of an object in a real scene due to its scanning characteristics. Although the existing methods have made great progress, most of them ignore the prior information of object structure, such as symmetry. So, in this paper, we use the symmetry of the object to complete the missing part in the point cloud and then detect it. Specifically, we propose a two-stage detection framework. In the first stage, we adopt an encoder-decoder structure to generate the symmetry points of the foreground points and make the symmetry points and the non-empty voxel centers form an enhanced point cloud. In the second stage, the enhanced point cloud is input into the baseline, which is an anchor-based region proposal network, to generate the detection results. Extensive experiments on the challenging KITTI benchmark show the effectiveness of our method, which has better performance on both 3D and BEV (bird's eye view) object detection compared with some previous state-of-the-art methods.

The APOE ε4 exerts differential effects on familial and other subtypes of Alzheimer's disease.

INTRODUCTION: The genetic risk effects of apolipoprotein E (APOE) on familial Alzheimer's disease (FAD) with or without gene mutations, sporadic AD (SAD), and normal controls (NC) remain unclear in the Chinese population. METHODS: In total, 15 119 subjects, including 311 FAD patients without PSEN1, PSEN2, APP, TREM2, and SORL1 pathogenic mutations (FAD [unknown]); 126 FAD patients with PSENs/APP mutations (FAD [PSENs/APP]); 7234 SAD patients; and 7448 NC were enrolled. The risk effects of APOE ε4 were analyzed across groups. RESULTS: The prevalence of the APOE ε4 genotype in FAD (unknown), FAD (PSENs/APP), SAD, and NC groups was 56.27%, 26.19%, 36.23%, and 19.54%, respectively. Further, the APOE ε4 positive genotype had predictive power for FAD (unknown) risk (odds ratio: 4.51, 95% confidence interval: 3.57-5.45, P < .001). DISCUSSION: APOE ε4 positive genotype may cause familial aggregation, and the investigation of multiple interventions targeting APOE pathological function to reduce the risk for this disease warrants attention.