Global Transitions最新文献

The incidence of diabetic retinopathy globally calls for advanced and more universally applicable computer-aided diagnosis (CAD) systems. This survey paper explores the current state of vision-based CAD techniques for the detection and classification of diabetic retinopathy, a diabetes-induced eye disorder that can lead to severe visual impairment or blindness. Characterized by a variety of manifestations including microaneurysms, exudates, hemorrhages, and macular detachment, diabetic retinopathy presents substantial challenges for automated detection. This is primarily due to the heterogeneity of retinal fundus images, which display diverse spatiotextural features and intricate vascular structures. Our exhaustive review indicates that most existing methodologies predominantly concentrate on isolated diabetic retinopathy types, employing localized spatiotextural feature analysis for classification. Such specificity often results in limited accuracy and generalizability, restricting practical real-world application. Furthermore, contemporary leading methods generally focus on single retinal characteristics, necessitating patients to undergo multiple CAD procedures, thereby increasing time, costs, and possibly intensifying retinal complexities. To overcome these obstacles, we propose the adoption of multi-trait-driven CAD solutions. Utilizing the potent capabilities of deep learning, these solutions could employ high-dimensional, multi-cue sensitive feature extraction and ensemble learning for classification. This approach is designed to improve the generalizability and dependability of CAD systems, offering a holistic solution capable of effectively managing the diverse manifestations of diabetic retinopathy. Our study highlights the need for a fundamental transformation in diabetic retinopathy CAD systems, motivating further research towards robust, multi-modal methods to enhance detection, classification, and grading of this widespread ailment.

Background

In Wuhan, China, a stringent lockdown was implemented to contain the spread of COVID-19, transitioning later to normalised prevention and control strategy. This study examines the trends in hospital visits for acute and chronic respiratory diseases, with a focus on outpatient, inpatient, and severe condition visits.

Methods

The study used administrative health insurance data spanning from January 2018 to August 2021, an interrupted time series analysis was conducted to assess the trend in hospital visits per million population for respiratory diseases. To confirm whether the change was exclusive to respiratory diseases, neoplasms and intracerebral haemorrhage were used as controls. The impact of the pandemic was estimated by comparing by weekly admissions to pre-pandemic levels. Subgroup analyses dissected variations by disease and visit types.

Results

Hospital visits for respiratory diseases declined significantly during the lockdown and exhibited a slower recovery in the later normalised prevention and control period compared to the control conditions. As of August 2021, outpatient visits increased by over 22.2% above the pre-pandemic level, while inpatient and severe condition visits witnessed significant reductions, falling to 46.7% and 80.6% of pre-pandemic levels, respectively. Compared to three other subgroups, visits for acute lower respiratory infections experienced the most significant decline, with inpatient and severe visits dropping to 23.9% and 25.7% of pre-pandemic levels.

Interpretation

Our study revealed a persistent reduction in inpatient and severe case visits for respiratory diseases throughout the ongoing pandemic. These findings suggested the possible role of non-pharmaceutical interventions in mitigating acute and chronic non-COVID respiratory diseases.

Objective

To assess changes in macrosomia prevalence following a two-stage lifestyle intervention program.

Methods

The study collected annual delivery data from singleton pregnant women at the Beijing Obstetrics and Gynecology Hospital in Beijing, China (2014–2023). The first intervention stage involved nutritional assessment and lifestyle management in pregnancy, and maternal weight and fetal growth monitoring were added in the second stage, with intensive management as necessary. Pre-intervention births (2014–2016) served as controls. The change in macrosomia and low birth weight prevalence following the intervention was assessed by an interrupted time series analysis.

Results

Among 126,824 pregnant women, macrosomia prevalence decreased from 7.11 % to 4.15 % over ten years, with an accelerated decrease post-intervention (p for slope = 0.050 and 0.004 for the first and second stages), primarily contributed by the reduction in excessive gestational weight gain (adjusted population attributable risk = 28.6 %, p for Granger cause = 0.0001). The change in the increasing rate of low-birth-weight prevalence was non-significant.

Conclusions

Macrosomia prevalence significantly decreased over a decade following the intensive intervention programs.

Background

Methods

Results

Conclusion

The aim of this study is to clarify the role of physical activity level and physical function on cognitive function of the elderly. A nested case–control investigation from a middle-aged and elderly cognitive health cohort was conducted. 103 mild cognitive impairment (MCI) patients were selected for the purpose of the study and matched 1:1 according to age and sex. Handgrip strength, gait speed and 5-time chair stand test were used to evaluate physical function. The physical activity scale for the elderly (PASE) was used to assess physical activity level. The Montreal cognitive assessment (MoCA) was used to quickly screen the mild cognitive impairment of the subjects. Body composition was estimated by bioelectrical impedance analysis. The total score of MoCA and the scores of different cognitive domains in two groups were different, handgrip strength was lower in the MCI group. Multivariate logistic regression model results showed that handgrip strength decreased could increase the risk of MCI(OR = 3.008, 95%CI: 1.421,6.369), higher PASE score was correlated with lower risk of MCI (OR = 0.402, 95%CI: 0.168,0.966). After combining body composition indices, handgrip strength and PASE score remained significantly associated with the occurrence of MCI, and there is an interaction. Logistic regression models were used for receiver operating characteristic curve analysis, all models demonstrate a good level of predictive performance for MCI. Physical activity level and physical function are associated with mild cognitive impairment. Higher physical activity level, normal handgrip strength are correlated with lower risk of MCI.

Objectives

Methods

Results

Conclusion

This paper delves into the critical issues of individual health, environmental health, and public health, which are all interconnected in the complex web of food security in emerging countries. Leveraging data from the top 10 countries with the lowest climate index values according to the Numbeo ranking, this article introduces a groundbreaking deep learning algorithm. This algorithm has the potential to revolutionize agricultural productivity and food security in the face of climate change, filling the gap in research on deep learning in agriculture. By enabling intelligent management, this algorithm could boost yields in agriculture, rendering it less dependent on climatic factors and ensuring the effectiveness of digital modernization. Furthermore, we explore the promising benefits of restoring ancient irrigation systems to elevate productivity levels. Our study provides definitive insights into deep learning techniques for yield prediction and productivity enhancement, offering a beacon of hope for the future of food security in emerging economies.

Background

Although the health impacts of ambient ozone (O₃) have been widely assessed, studies simultaneously investigating the acute and chronic effects of O₃ on mortality from respiratory diseases (RESP) are scarce.

Methods

We extracted personal information of all recorded deaths from RESP throughout 2013–2018 in six provinces in China. The daily, seasonal, and annual mean air pollutant concentrations at the township/subdistrict level were estimated by a random forest model. The acute association between the maximum daily average 8h ozone (MDA8 O₃) and RESP mortality was examined by a time-stratified case-crossover study design, and the chronic association was estimated by a difference-in-differences (DID) analysis approach. We also calculated the attributable fraction (AF) of RESP mortality attributable to MDA8 O₃.

Results

There were 1,034,226 RESP deaths included in this study. The excess risks (ERs) of overall RESP mortality for each 10 μg/m³ increase in short-term (lag03 days) and long-term (one-year average) exposure to MDA8 O₃ were 0.38 % (95%CI: 0.26 %, 0.50 %) and 4.37 % (3.91 %, 4.84 %), respectively. The AFs of overall RESP mortality ascribed to short- and long-term MDA8 O₃ exposures were 3.00 % (2.03 %, 3.95 %) and 29.45 % (26.86 %, 31.95 %), respectively. The average annual number of RESP deaths attributable to short-term MDA8 O₃ exposure was 30,790 and 302,254 were attributable to long-term MDA8 O₃ exposure across China during 2013–2018.

Conclusion

Both short- and long-term exposure to ambient MDA8 O₃ were positively associated with RESP mortality, and long-term exposure to MDA8 O₃ may lead to a greater RESP mortality burden than short-term exposure to MDA8 O₃.