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The global rise in mental disorders presents a significant public health challenge, highlighting the need for a deeper understanding of their underlying risk factors. Existing studies on obesity and brain health primarily rely on metrics such as body mass index and waist-to-hip ratio, which inadequately capture central obesity, a fat distribution strongly associated with increased health risks. This underscores the need for more precise metrics, with the body roundness index (BRI) emerging as a promising option. 321,596 UK Biobank participants were analyzed in this study. Cox regression models assessed associations between BRI and incident mental disorders, while logistic and multiple linear regression models explored relationships with psychiatric symptoms and white matter integrity, respectively. Results indicated that higher BRI was significantly associated with increased risks of incident overall mental disorders (HR [95% CI] = 1.12 [1.09, 1.15]), as well as specific high-prevalence disorders including substance use disorder (1.05 [1.01, 1.11]), depressive disorder (1.34 [1.24, 1.45]), and anxiety disorder (1.09 [1.03, 1.15]). An overall positive association was also observed between elevated BRI and adverse psychiatric symptoms and reduced white matter integrity. These findings highlighted BRI as a potential indicator of mental health. BRI’s association with clinical and subclinical psychiatric outcomes emphasized its relevance in understanding the interplay between central adiposity and brain health.

Lung cancer remains the second most prevalent malignancy worldwide and is characterized by persistently high incidence and mortality rates. As the disease progresses, most patients develop immune evasion and metastatic dissemination, which represent major threats to overall survival. The advent of targeted therapies and immunotherapies has fundamentally reshaped the clinical management of lung cancer; however, therapeutic resistance and limited durability of response remain critical challenges. Lactylation has recently emerged not only as a novel post-translational modification but also as a potential therapeutic vulnerability in lung cancer. By modulating the activity, stability, and transcriptional functions of both histone and non-histone proteins, lactylation reshapes tumor metabolism, immune evasion, and resistance-associated signaling pathways. Importantly, growing evidence suggests that therapeutic strategies targeting lactylation-related pathways may offer new opportunities to improve outcomes in patients with advanced lung cancer and overcome acquired resistance to existing therapies. In this review, we systematically delineate the molecular mechanisms underlying lactylation, with particular emphasis on the enzymatic machinery governing lactylation dynamics and its regulatory network. We synthesize current evidence describing how lactylation-driven signaling programs contribute to lung cancer progression, immune escape, and treatment resistance, highlighting the complex interplay between lactylation pathways and lung cancer pathobiology. Furthermore, we critically evaluate the translational potential of lactylation sites and their downstream effectors as diagnostic and prognostic biomarkers, as well as actionable therapeutic targets. Collectively, these findings support the concept that targeting lactylation-associated regulatory circuits represents an emerging and potentially more selective therapeutic strategy for lung cancer. Nevertheless, clinical translation remains constrained by the lack of specific intervention tools, standardized detection methodologies, and robust human data. Future studies should prioritize the development of precise lactylation-targeted approaches and large-scale, longitudinal clinical investigations to validate their clinical value and overcome current translational bottlenecks.

As high-throughput sequencing tools have advanced in recent years, scientists have discovered that lung cancer tissues are not sterile. The intratumoral microbiota exists in the tumor parenchyma and stroma in a low-biomass form. This finding has overturned the traditional concept of “sterile tumors” and brought the intratumoral microbiota to the forefront of tumor research. In this review, we focus on elucidating the mechanisms by which intratumoral microbiota influence lung cancer cells and the tumor microenvironment (TME), with the aim of clarifying their role in lung cancer progression. The intratumoral microbiota does not exist as a passive resident. Instead, it may actively induce and maintain a chronic inflammatory state through the secretion of metabolites, activation of signaling pathways, immune suppressor cell recruitment, and upregulation of immune checkpoint molecule expression, thereby promoting tumor cell proliferation, invasion, and immune evasion. From a clinical translation perspective, we explore the potential of using intratumoral microbiota characteristics to predict immunotherapy efficacy. Additionally, we assess the application prospects of engineered bacteria and targeted nanobiotics, which are based on synthetic biology, in reshaping the immune microenvironment. However, the field still faces significant challenges, particularly as the low biomass nature of lung tissues makes sequencing data highly susceptible to reagent contamination and batch effects. Additionally, the synergistic role of non-bacterial components such as fungi and viruses in the tumor ecosystem is often overlooked. Future research needs to establish rigorous quality control standards and integrate multi-omics technologies to comprehensively analyze the dynamic interaction network between the microbiota and host immunity, which will drive the clinical implementation of microbiome-based precision diagnostic and therapeutic strategies for lung cancer.

Esophageal squamous cell carcinoma is a cancer with high morbidity and mortality. The advent of immune checkpoint inhibitors has significantly increased complete response rates and postoperative R0 resection rates after neoadjuvant therapy. These drugs can largely reverse the suppression of the immune system caused by the tumor microenvironment, allowing the reactivation of anti-tumor immune infiltrating cells, significantly improving the patient’s tumor microenvironment, and thus preventing tumor development. However, there are still some patients who respond poorly to neoadjuvant combined immunotherapy and cannot achieve the expected results. It is now found that exploring changes in the tumor microenvironment not only elucidates patient responsiveness to immunotherapy and identifies more reliable biomarkers, but also addresses the limitations of prediction with imaging examination such as CT and the instability of existing biomarkers. In light of these considerations, this review aims to delve into the alterations within the tumor microenvironment and identify potential predictive biomarkers ensuing from neoadjuvant immunotherapy in the context of esophageal squamous cell carcinoma.

Background Rare infectious diseases of poverty (rIDPs) involve more than hundreds of tropical diseases, which dominantly affect people living in impoverished and marginalized regions and fail to be prioritized in the global health agenda. The neglect of rIDPs could impede the progress toward sustainable development. This study aimed to estimate the disease burden of rIDPs in 2021, which would be pivotal for setting intervention priorities and mobilizing resources globally. Methods Leveraging data from the Global Burden of Disease Study 2021, the study reported both numbers and age-standardized rates of prevalence, mortality, disability-adjusted life-years (DALYs), years lived with disability, and years of life lost of rIDPs with corresponding 95% uncertainty intervals (UIs) at global, regional, and national levels. The temporal trends between 1990 and 2021 were assessed by the joinpoint regression analysis. A Bayesian age-period-cohort model was used to project the disease burden for 2050. Results In 2021, there were 103.76 million (95% UI: 102.13, 105.44 million) global population suffered from rIDPs with an age-standardized DALY rate of 58.44 per 100,000 population (95% UI: 42.92, 77.26 per 100,000 population). From 1990 to 2021, the age-standardized DALY rates showed an average annual percentage change of − 0.16% (95% confidence interval: − 0.22, − 0.11%). Higher age-standardized DALY rates were dominated in sub-Saharan Africa (126.35 per 100,000 population, 95% UI: 91.04, 161.73 per 100,000 population), South Asia (80.80 per 100,000 population, 95% UI: 57.31, 114.10 per 100,000 population), and countries with a low socio-demographic index. There was age heterogeneity in the DALY rates of rIDPs, with the population aged under 15 years being the most predominant. Females aged 15–49 years had four-times higher age-standardized DALY rates of rIDPs than males in the same age. The projections indicated a slight reduction in the disease burden of rIDPs by 2050. Conclusions There has been a slight reduction in the disease burden of rIDPs over the past three decades. Given that rIDPs mainly affect populations in impoverished regions, targeted health strategies and resource allocation are in great demand for these populations to further control rIDPs and end poverty in all its forms everywhere. Graphical Abstract

With the increasing demand for decentralized systems and the widespread usage of blockchain, low throughput and high latency have become the biggest stumbling blocks in the development of blockchain systems. This problem seriously hinders the expansion of blockchain and its application in production. Most existing smart contract scheduling solutions use static feature analysis to prevent contract conflicts during parallel execution. However, the conflicts between transactions are complex; static feature analysis is not accurate enough. In this paper, we first build the dependency between smart contracts by analyzing the features. After numerous experiments, we propose a conflict model to adjust the relationship between threads and conflict to achieve high throughput and low latency. Based on these works, we propose adaptive parallel scheduling for smart contracts on the blockchain. Our adaptive parallel scheduling can distinguish conflicts between smart contracts and dynamically adjust the execution strategy of smart contracts based on the conflict factors we define. We implement our scheme on ChainMaker, one of the most popular open-source permissioned blockchains, and build experiments to verify our solution. Regarding latency, our solution demonstrates remarkable efficiency compared with the fully parallel scheme, particularly in high-conflict transaction scenarios, where our solution achieves latency levels just one-twentieth of the fully parallel scheme. Regarding throughput, our solution significantly outperforms the fully parallel scheme, achieving 30 times higher throughput in high-conflict transaction scenarios. These results highlight the superior performance and effectiveness of our solution in addressing latency and throughput challenges, particularly in environments with high transaction conflicts.

Objectives: To evaluate excess deaths of gastrointestinal, liver, and pancreatic diseases in the United States during the COVID-19 pandemic. Methods: We retrieved weekly death counts from National Vital Statistics System and fitted them with a quasi-Poisson regression model. Cause-specific excess deaths were calculated by the difference between observed and expected deaths with adjustment for temporal trend and seasonality. Demographic disparities and temporal-spatial patterns were evaluated for different diseases. Results: From March 2020 to September 2022, the increased mortality (measured by excess risks) for Clostridium difficile colitis, gastrointestinal hemorrhage, and acute pancreatitis were 35.9%; 24.8%; and 20.6% higher than the expected. For alcoholic liver disease, fibrosis/cirrhosis, and hepatic failure, the excess risks were 1.4–2.8 times higher among younger inhabitants than older inhabitants. The excess deaths of selected diseases were persistently observed across multiple epidemic waves with fluctuating trends for gastrointestinal hemorrhage and fibrosis/cirrhosis and an increasing trend for C. difficile colitis. Conclusion: The persistently observed excess deaths of digestive diseases highlights the importance for healthcare authorities to develop sustainable strategies in response to the long-term circulating of SARS-CoV-2 in the community.

Low-grade inflammation (LGI) mainly acted as the mediator of the association of obesity and inflammatory diet with numerous chronic diseases, including neuropsychiatric diseases. However, the evidence about the effect of LGI on brain structure is limited but important, especially in the context of accelerating aging. This study was then designed to close the gap, and we leveraged a total of 37,699 participants from the UK Biobank and utilized inflammation score (INFLA-score) to measure LGI. We built the longitudinal relationships of INFLA-score with brain imaging phenotypes using multiple linear regression models. We further analyzed the interactive effects of specific covariates. The results showed high level inflammation reduced the volumes of the subcortex and cortex, especially the globus pallidus (β [95% confidence interval] = −0.062 [−0.083, −0.041]), thalamus (−0.053 [−0.073, −0.033]), insula (−0.052 [−0.072, −0.032]), superior temporal gyrus (−0.049 [−0.069, −0.028]), lateral orbitofrontal cortex (−0.047 [−0.068, −0.027]), and others. Most significant effects were observed among urban residents. Furthermore, males and individuals with physical frailty were susceptive to the associations. The study provided potential insights into pathological changes during disease progression and might aid in the development of preventive and control targets in an age-friendly city to promote great health and well-being for sustainable development goals.

Causality is an important element in decision-making and interventions are required to optimize results of target values. In this paper, based on the model of Causal Bayesian Optimization, a counter-noise version of acquisition function is proposed and new prior estimation algorithms including Support Vector Regression, Ridge Regression and Random Forest are evaluated. This paper provides an improved framework to facilitate causal inference and optimization processes.

Background: Dietary intake of polyunsaturated fatty acids (PUFA) plays a significant role in the onset and progression of neurodegenerative diseases. Since the neuroprotective effects of n-3 PUFA have been widely validated, the role of n-6 PUFA remains debated, with their underlying mechanisms still not fully understood. Methods: In this study, 169,295 participants from the UK Biobank were included to analyze the associations between dietary n-6 PUFA intake and neurodegenerative diseases using Cox regression models with full adjustments for potential confounders. In addition, multiple linear regression models were utilized to estimate the impact of n-6 PUFA intake on brain imaging phenotypes. Results: Results indicated that low dietary n-6 PUFA intake was associated with increased risks of incident dementia (hazard ratio [95% confidence interval] = 1.30 [1.13, 1.49]), Parkinson’s disease (1.42 [1.16, 1.74]), and multiple sclerosis (1.65 [1.03, 2.65]). Moreover, the low intake was linked to diminished volumes of various brain structures, including the hippocampus (β [95% confidence interval] = −0.061 [−0.098, −0.025]), thalamus (−0.071 [−0.105, −0.037]), and others. White matter integrity was also found to be compromised in individuals with low n-6 PUFA intake. Conclusions: These findings enhanced our understanding of how dietary n-6 PUFA intake might affect neurological health, thereby providing epidemiological evidence for future clinical and public health interventions.