Global Transitions最新文献

The global burden of cancer incidence, deaths and economic costs is steadily increasing since several decades. Despite a massive allocation of research funds since the 1970s, no significant (in terms of years) improvements of survival times have been achieved for most cancer types. In this article, I argue that the failure to effectively prevent and treat cancer is partly owing to the gene-centric paradigm of the somatic mutation theory of carcinogenesis. I outline and provide evidence for a new transdisciplinary evolutionary theory of carcinogenesis according to which cancer is a phylogenetic reversal towards unicellular lifeforms triggered by the breakdown of essential cooperative interactions on important levels of human organization. These levels include the genetic, cellular, tissue and psychosocial-spiritual level of human existence. The new theory considers the emergence of eukaryotes and metazoans and – of particular importance – human evolution and in this way explains why cooperation on these different levels is so essential to maintain holistic health. It is argued that the interaction between human’s slow natural evolution and the fast cultural evolution, especially during the current Anthropocene epoch, plays an important role in making individuals susceptible towards carcinogenesis. The implications of this insight and the theory of cancer as a phylogenetic reversal are discussed with respect to prevention and treatment, and concrete practical examples are provided. It is concluded that individuals could substantially reduce their risk of cancer by respecting certain biopsychosocial-spiritual lifestyle factors which are justified by human evolution.

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.

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.

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