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Passion fruit pests are characterized by their high species diversity, small physical size, and dense populations. Traditional algorithms often face challenges in achieving high detection accuracy and efficiency when addressing the complex task of detecting densely distributed small objects. To address this issue, this paper proposed an enhanced lightweight and efficient deep learning model, which is developed based on YOLOv5s, consists of the PLDIoU, four CBAM modules, and one newAnchors, termed P4CN-YOLOv5s, for detecting passion fruit pests. In P4CN-YOLOv5s, the Mosaic-9 and Mixup algorithms are initially used for data augmentation to augment the training dataset and enhance data complexity. Secondly, after analyzing the image set characteristics to be detected in this research, the point-line distance bounding box loss function is utilized to calculate the coordinate distance of the prediction box and target box, and aimed at improving detection speed. Subsequently, a convolutional block attention module (CBAM) and optimized anchor boxes are employed to reduce the false detection rate of the model. Finally, a dataset consisting of 6,000 images of passion fruit pests is used to evaluate the performance of the proposed model. The experimental data analysis reveals that the proposed P4CN-YOLOv5s model achieves superior performance, with an accuracy of 96.99%, an F1-score of 93.99%, and a mean detection time of 7.2 milliseconds. When compared to other widely used target detection models, including SSD, Faster R-CNN, YOLOv3, YOLOv4, YOLOv5, P4C-YOLOv5s, and YOLOv7 on the same dataset, the P4CN-YOLOv5s model demonstrates distinct advantages, such as a low false positive rate and high detection efficiency. Therefore, the proposed model proves to be more effective for detecting passion fruit pests in natural orchard environments.

Although the hippocampus is one of the most studied structures in the human brain, limited quantitative data exist on its 3D organization, anatomical variability, and effects of disease on its subregions. Histological studies provide restricted reference information due to their 2D nature. In this paper, high-resolution (∼200 × 200 × 200 μm³) ex vivo MRI scans of 31 human hippocampal specimens are combined using a groupwise diffeomorphic registration approach into a 3D probabilistic atlas that captures average anatomy and anatomic variability of hippocampal subfields. Serial histological imaging in 9 of the 31 specimens was used to label hippocampal subfields in the atlas based on cytoarchitecture. Specimens were obtained from autopsies in patients with a clinical diagnosis of Alzheimer’s disease (AD; 9 subjects, 13 hemispheres), of other dementia (nine subjects, nine hemispheres), and in subjects without dementia (seven subjects, nine hemispheres), and morphometric analysis was performed in atlas space to measure effects of age and AD on hippocampal subfields. Disproportional involvement of the cornu ammonis (CA) 1 subfield and stratum radiatum lacunosum moleculare was found in AD, with lesser involvement of the dentate gyrus and CA2/3 subfields. An association with age was found for the dentate gyrus and, to a lesser extent, for CA1. Three-dimensional patterns of variability and disease and aging effects discovered via the ex vivo hippocampus atlas provide information highly relevant to the active field of in vivo hippocampal subfield imaging.

Measures of change in hippocampal volume derived from longitudinal MRI are a well-studied biomarker of disease progression in Alzheimer's disease (AD) and are used in clinical trials to track therapeutic efficacy of disease-modifying treatments. However, longitudinal MRI change measures based on deformable registration can be confounded by MRI artifacts, resulting in over-estimation or underestimation of hippocampal atrophy. For example, the deformation-based-morphometry method ALOHA (Das et al., 2012) finds an increase in hippocampal volume in a substantial proportion of longitudinal scan pairs from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, unexpected, given that the hippocampal gray matter is lost with age and disease progression. We propose an alternative approach to quantify disease progression in the hippocampal region: to train a deep learning network (called DeepAtrophy) to infer temporal information from longitudinal scan pairs. The underlying assumption is that by learning to derive time-related information from scan pairs, the network implicitly learns to detect progressive changes that are related to aging and disease progression. Our network is trained using two categorical loss functions: one that measures the network's ability to correctly order two scans from the same subject, input in arbitrary order; and another that measures the ability to correctly infer the ratio of inter-scan intervals between two pairs of same-subject input scans. When applied to longitudinal MRI scan pairs from subjects unseen during training, DeepAtrophy achieves greater accuracy in scan temporal ordering and interscan interval inference tasks than ALOHA (88.5% vs. 75.5% and 81.1% vs. 75.0%, respectively). A scalar measure of time-related change in a subject level derived from DeepAtrophy is then examined as a biomarker of disease progression in the context of AD clinical trials. We find that this measure performs on par with ALOHA in discriminating groups of individuals at different stages of the AD continuum. Overall, our results suggest that using deep learning to infer temporal information from longitudinal MRI of the hippocampal region has good potential as a biomarker of disease progression, and hints that combining this approach with conventional deformation-based morphometry algorithms may lead to improved biomarkers in the future.

BackgroundAir pollution is among the top five environmental risk factors for human health worldwide. However, our understanding of the physiological responses to PM10 exposure over medium-term lag periods remains limited. This study aims to examine the medium-term lag–response associations—using lagging time windows of up to 21 days—between PM10 exposure and all-cause mortality in Valencia and London from 2002 to 2006.MethodsWe used a time-stratified case-crossover design; building on the methodologies of Tobias et al. and Bhaskaran et al., we applied a fixed-effects conditional quasi-Poisson regression model to quantify the association between PM10 exposure and all-cause mortality. We also analyzed three different temporal lag methodological models for the exposure–mortality relationships.ResultsWe found distinct differences in the relative risk (RR) patterns of PM10 exposure and all-cause mortality. In Valencia, the RR varied significantly, with confidence intervals that were wider than in London, where the RR remained more stable, fluctuating closely around 1. Significant associations were observed at early lag periods in both cities, consistent with Tobias et al. Notably, Valencia showed a significant peak in RR at lag 14, which was not observed in London. Subgroup analysis in Valencia also indicated delayed effects in younger populations. Scenario 3 (cumulative lag model) is conceptually closer to the cumulative progression of health risks associated with PM10 exposure and produces higher RR estimates compared to Scenario 1 and 2.ConclusionsThis study highlights the critical importance of addressing medium-term lag-response associations and methodological variations in environmental epidemiology. The findings have important clinical and public health implications and offer insights for risk assessment, healthcare planning, and the development of policies to mitigate the health impacts of PM10 exposure.

Background Only rigorously prepared analyses can provide the highest level of evidence to inform decision-making. Several recent systematic reviews (SRs) examined the hypothesis that the early introduction of specific allergenic complementary foods (CFs) to infants may lead to a lower incidence of one or more allergic outcomes. However, the methodological rigour and quality of reporting of SRs in this area has not yet been systematically evaluated. Methods We comprehensively searched PubMed, Medline (Ovid), and Web of Science Core Collection on 13th January 2022, using a pre-specified and tested search syntax for SRs with RCT evidence on the early introduction of allergenic CFs as a means for allergy prevention in infants and children. We examined the quality and risk of bias (RoB) using AMSTAR-2 and ROBIS tools, examined adherence to the Preferred Reporting Items for SRs and Meta-Analyses (PRISMA), and checked whether certainty of the evidence was assessed. Results Twelve SRs were included. Application of both tools resulted in similar overall judgements in terms of direction and extent for nine of the 12 SRs. Nine SRs were found to be of critically low to low quality according to AMSTAR-2 and to be at high RoB according to ROBIS. One SR received a moderate quality rating (AMSTAR-2) and high RoB rating (ROBIS). However, for two SRs, judgements between AMSTAR-2 and ROBIS were at stark variance. Only two SRs fully adhered to the PRISMA checklist. Six SRs evaluated the certainty of the body of RCT evidence. Several SRs failed to consider unpublished studies either by an explicit a priori exclusion or by inadequate search strategies. Conclusions Well-conducted SRs are important for decision-making and informing guideline development, the quality of their methodology should therefore be considered. The methodological rigour and the reporting quality of SRs on the timing of CF for allergy prevention must be improved. Registration https://osf.io/7cs4b .

Background Healthcare-associated infections (HAIs) and antimicrobial resistance (AMR) affect patients in acute-care hospitals worldwide. No systematic review has been published on adoption and implementation of the infection prevention and control (IPC) key components. The objective of this systematic review was to assess adoption and implementation of the three areas issued by the “National Health Commission of the People’s Republic of China” in acute-care hospitals in Mainland China, and to compare the findings with the key and core components on effective IPC, issued by the European Centre for Disease Prevention and Control (ECDC) and the World Health Organization (WHO). Methods We searched PubMed and the Chinese National Knowledge Infrastructure for reports on the areas “structure, organisation and management of IPC”, “education and training in IPC”, and “surveillance of outcome and process indicators in IPC” in acute-care facilities in Mainland China, published between January 2012 and October 2017. Results were stratified into primary care hospitals and secondary/tertiary care hospitals. Results A total of 6580 publications were retrieved, of which 56 were eligible for final analysis. Most of them were survey reports ( n  = 27), followed by observational studies ( n  = 17), and interventional studies ( n  = 12), either on hand hygiene promotion and best practice interventions ( n  = 7), or by applying education and training programmes ( n  = 5). More elements on IPC were reported by secondary/tertiary care hospitals than by primary care hospitals. Gaps were identified in the lack of detailing on organisation and management of IPC, education and training activities, and targets of surveillance such as central line-associated bloodstream infections, ventilator associated pneumonia, catheter-associated urinary tract infections, and Clostridium difficile infections. Information was available on adoption and implementation of 7 out of the 10 ECDC key components, and 7 out of the 8 WHO core components. Conclusion To variable degrees, there is evidence on implementation of all NHCPRC areas and of most of the ECDC key components and the WHO core components in acute care hospitals in Mainland China. The results are encouraging, but gaps in effective IPC were identified that may be used to guide future national policy-making in Mainland China.

An important challenge in segmenting real-world biomedical imaging data is the presence of multiple disease processes within individual subjects. Most adults above age 60 exhibit a variable degree of small vessel ischemic disease, as well as chronic infarcts, which will manifest as white matter hyperintensities (WMH) on brain MRIs. Subjects diagnosed with gliomas will also typically exhibit some degree of abnormal T2 signal due to WMH, rather than just due to tumor. We sought to develop a fully automated algorithm to distinguish and quantify these distinct disease processes within individual subjects' brain MRIs. To address this multi-disease problem, we trained a 3D U-Net to distinguish between abnormal signal arising from tumors vs. WMH in the 3D multi-parametric MRI (mpMRI, i.e., native T1-weighted, T1-post-contrast, T2, T2-FLAIR) scans of the International Brain Tumor Segmentation (BraTS) 2018 dataset ( = 285, = 66). Our trained neuroradiologist manually annotated WMH on the BraTS training subjects, finding that 69% of subjects had WMH. Our 3D U-Net model had a 4-channel 3D input patch (80 × 80 × 80) from mpMRI, four encoding and decoding layers, and an output of either four [background, active tumor (AT), necrotic core (NCR), peritumoral edematous/infiltrated tissue (ED)] or five classes (adding WMH as the fifth class). For both the four- and five-class output models, the median for whole tumor (WT) extent (i.e., union of AT, ED, NCR) was 0.92 in both training and validation sets. Notably, the five-class model achieved significantly ( = 0.002) lower/better Hausdorff distances for WT extent in the training subjects. There was strong positive correlation between manually segmented and predicted volumes for WT ( = 0.96) and WMH ( = 0.89). Larger lesion volumes were positively correlated with higher/better scores for WT ( = 0.33), WMH ( = 0.34), and across all lesions ( = 0.89) on a log(10) transformed scale. While the median for WMH was 0.42 across training subjects with WMH, the median was 0.62 for those with at least 5 cm of WMH. We anticipate the development of computational algorithms that are able to model multiple diseases within a single subject will be a critical step toward translating and integrating artificial intelligence systems into the heterogeneous real-world clinical workflow.

Introduction: Crowded Emergency and Trauma Department (ETD) have been associated with adverse patient outcomes and higher mortality rates. Crowding and lack of alcohol-based hand rubs (ABHRs) have been found to correlate with lower compliance to hand hygiene (HH) protocols among healthcare workers (HWs). This project aimed to improve and sustain HH compliance (HHC) among HWs in the ETD by adapting to the World Health Organization (WHO) HH Multimodal Improvement Strategy. Methodology: This is a cross-sectional study in ETD, Sarawak General Hospital, a university-affiliated, public tertiary-care hospital in Malaysia. It spanned 12 months, from Jan 2023 to Jan 2024. The intervention involved installing wall-mounted automated ABHR dispensers at multiple fixed locations in ETD. Pre-, during, and post-12 weeks intervention HHC audit were conducted according WHO’s gold-standard direct observation method. We conducted a sequential trend analysis and compared proportions across these periods using a linear logistic regression model to assess the improvement and sustainability of HHC. Results & Discussion: Mean HHC improved from 66% (383/579) (95% confidence interval [CI], 62.1%-70.0%) in the pre- intervention period to 81% (321/397) (95% CI, 76.6%-84.6%) in the intervention period, and further sustained at 85% (302/352) (95% CI, 81.7%-89.3%) in the post-intervention period (P value<0.05). The positive coefficient of 1.13 in the model, when moving from the pre- to the post-intervention period indicates a positive trend in HH compliance. The availability of adequate wall-mounted automated ABHR dispensers at multiple fixed locations at ETD created easy accessibility of ABHRs for HWs and acted as visual reminders for good HH behavior at the ETD. Conclusions: Having wall-mounted automated ABHR dispensers in various fixed locations proved effective in promoting good HH among HWs in emergency settings. It’s essential to have fixed ABHR dispenser placement in crowded - areas like the ETD to improve and sustain HHC among HWs.

Background/Aims: Our laboratory discovered a Kunming mouse with enormous electroretinogram (ERG) defects. Its auditory brainstem response (ABR) threshold was significantly elevated and closely resembled the features of Usher syndrome (USH). This study sought to cross these USH-like mice (named KM ush/ush mice) with CBA/CaJ mice to establish recombinant inbred strains and identify their phenotypes and genotypes. Methods: KM ush/ush mice were crossed with CBA/CaJ mice to establish inbred strains by sibling mating. ERG, ABR, ocular fundus morphology, histological examinations of the retina and inner ear, quantitative real-time polymerase chain reaction, western blotting, and exon sequencing were performed to assess the phenotypes and genotypes of the offspring strains. Results: The F1 hybrids from crossing KM ush/ush and CBA/CaJ mice had normal ERG and ABR responses. The F2 offspring from intercrossing the F1 mice showed a segregation of the retinitis pigmentosa (RP) and hearing loss phenotypes. The CBA-1 ush/ush mice had an RP phenotype that was characterized by a vanished ERG waveform and loss of the outer nuclear layer. Their Pde6b gene had a nonsense mutation that resulted in the failure of protein production in western blotting. However, the ABR threshold of this strain of mice was normal. The CBA-2 ush/ush mice had normal retinal function and architecture. Their ABR threshold was increased, with a dramatic degeneration of the stereocilia bundles in the outer hair cells of the inner ear. Whole exome sequencing and exon sequencing revealed a deletion of one base pair in exon 31 of the Adgrv1 gene, which would result in the premature termination of protein encoding. The level of Adgrv1 mRNA was reduced in the CBA-2 ush/ush mice. The CBA-3 ush/ush mice had phenotypes of RP, elevated ABR threshold, and degeneration of the stereocilia bundles in the outer hair cells. They were closely associated with the nonsense mutations of Pde6b and Adgrv1, respectively. Conclusion: We isolated a mouse strain with hearing loss from inbred mice with retinal degeneration and established it as a recombinant inbred strain with a spontaneous mutation in Adgrv1, the human Usher syndrome 2C gene. The retinal degeneration was cause by a mutation in Pde6b, while the hearing loss was caused by a mutation in Adgrv1.

Background Retinitis pigmentosa (RP) is a kind of inherited retinal degenerative diseases characterized by the progressive loss of photoreceptors. RP has been a conundrum without satisfactory countermeasures in clinic until now. Acetaldehyde dehydrogenase 2 (ALDH2), a major enzyme involved in aldehyde detoxification, has been demonstrated to be beneficial for a growing number of human diseases, such as cardiovascular dysfunction, diabetes mellitus and neurodegeneration. However, its protective effect against RP remains unknown. Our study explored the impact of ALDH2 on retinal function and structure in N-methyl-N-nitrosourea (MNU)-induced RP rats. Methods Rats were gavaged with 5 mg/kg Alda-1, an ALDH2 agonist, 5 days before and 3 days after MNU administration. Assessments of retinal function and morphology as well as measurement of specific proteins expression level were conducted. Results Electroretinogram recordings showed that Alda-1 administration alleviated the decrease in amplitude caused by MNU, rendering protection of retinal function. Mitigation of photoreceptor degeneration in MNU-treated retinas was observed by optical coherence tomography and retinal histological examination. In addition, Western blotting results revealed that ALDH2 protein expression level was upregulatedwith increased expression of SIRT1 protein after the Alda-1 intervention. Besides, endoplasmic reticulum stress (ERS) was reduced according to the significant downregulation of GRP78 protein, while apoptosis was ameliorated as shown by the decreased expression of PARP1 protein. Conclusions Together, our data demonstrated that ALDH2 could provide preservation of retinal function and morphology against MNU-induced RP, with the underlying mechanism at least partly related to the modulation of SIRT1, ERS and apoptosis.