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Abstract Background Cadherin 13 (CDH13) is a member of the cadherin superfamily that exerts tumor-suppressive effects on cancers derived from epithelial cells. Although hypermethylation of CDH13 promoter has been reported in various cancers, its prognostic value for colorectal cancer (CRC) is still controversial. The methylation alterations of CDH13 within exon 1 have not yet been investigated. Methods A total of 49 CRC patients were recruited for the prospective study. The methylation status of CpG sites was quantified by Bisulfite Amplicon Sequencing (BSAS) in malignant tissues and adjacent normal tissues. The primary endpoint of the study was overall survival (OS) after surgery. The relationship between methylation level with pathological stage and OS was also evaluated. Results Compared with adjacent normal tissues, the overall average methylation level within exon 1 was significantly increased in tumor tissues (p

Abstract Background Ciprofol, a novel intravenous general anesthetic with a chemical structure similar to propofol, exhibits significantly enhanced potency. It offers a rapid onset, reduced incidence of injection pain, and has comparable effects on heart rate and blood pressure to propofol. However, clinical data on its use for anesthesia induction in cardiac surgery remain limited. Methods Seventy-eight patients undergoing coronary artery bypass grafting or valve replacement surgery were randomly assigned to receive either ciprofol (N = 40) or propofol (N = 38) for anesthesia induction. Variables recorded included changes in mean arterial pressure and heart rate during anesthesia, alterations in the oxygenation index and lactic acid concentration before and 10 min after anesthesia induction, and the incidence of adverse events such as bradycardia, hypotension, and injection pain. Results The incidence of anesthesia-induced injection pain was significantly lower in the ciprofol group compared to the propofol group (3% vs. 18%, P

Longevity medicine is an emerging and iterative healthcare discipline focusing on early detection, preventive measures, and personalized approaches that aim to extend healthy lifespan and promote healthy aging. This comprehensive review introduces the innovative concept of the “Longevity Pyramid.” This conceptual framework delineates progressive intervention levels, providing a structured approach to understanding the diverse strategies available in longevity medicine. At the base of the Longevity Pyramid lies the level of prevention, emphasizing early detection strategies and advanced diagnostics or timely identification of potential health issues. Moving upwards, the next step involves lifestyle modifications, health-promoting behaviors, and proactive measures to delay the onset of age-related conditions. The Longevity Pyramid further explores the vast range of personalized interventions, highlighting the importance of tailoring medical approaches based on genetic predispositions, lifestyle factors, and unique health profiles, thereby optimizing interventions for maximal efficacy. These interventions aim to extend lifespan and reduce the impact and severity of age-related conditions, ensuring that additional years are characterized by vitality and wellbeing. By outlining these progressive levels of intervention, this review offers valuable insights into the evolving field of longevity medicine. This structured framework guides researchers and practitioners toward a nuanced strategic approach to advancing the science and practice of healthy aging.

BackgroundImproving health care in cities with a diverse, international population is crucial for ensuring health equity, particularly for foreigners facing challenges due to cultural and language barriers. This situation is especially relevant in China, a major destination for expatriates and travelers, where optimizing health care services and incorporating international standards in the public sector are vital. Achieving this involves understanding the operational details, cultural and linguistic nuances, and advancing medical digitalization. A strategic approach focusing on cultural competence and awareness of health care systems is essential for effectively navigating health care for foreigners and expatriates in China. ObjectiveThe aim of this study was to perform an in-depth analysis of the subjective and objective experiences of local and international patients in public hospitals in China to provide a basis for enhancing the medical experience of all patients. MethodsA structured questionnaire was provided to patients at an international outpatient service of a top-tier university hospital in China. Qualitative analysis of the survey responses was performed to methodically categorize and analyze medical treatment, focusing on patient demand and satisfaction across four main category elements (“high demand, high satisfaction”; “high demand, low satisfaction”; “low demand, high satisfaction”; and “low demand, low satisfaction”), enabling a detailed cross-sectional analysis to identify areas for improvement. ResultsElements falling under “high demand, high satisfaction” for both Chinese and international patients were primarily in the realms of medical quality and treatment processes. In contrast, elements identified as “high demand, low satisfaction” were significantly different between the two patient groups. ConclusionsThe findings highlight the importance of systematic, objective research in advancing the quality of international health care services within China’s leading academic medical centers. Key to this improvement is rigorous quality control involving both patients and providers. This study highlights the necessity of certifying such centers and emphasizes the role of digital platforms in disseminating information about medical services. This strategy is expected to cater to diverse patient needs, enhancing the overall patient experience. Furthermore, by developing comprehensive diagnosis and treatment services and highlighting the superior quality and costs associated with international health care, these efforts aim to foster a sense of belonging among international patients and increase the attractiveness of China’s medical services for this demographic.

Breakthroughs in medical research in the last century have led to a significant extension of the human lifespan, resulting in a shift towards an elderly population worldwide. Due to the ongoing progress of global development towards elevated standards of living, this study specifically examines Switzerland as a representative nation to explore the socioeconomic and healthcare ramifications associated with an ageing population, thereby highlighting the tangible impact experienced in this context. Beyond the exhaustion of pension funds and medical budgets, by reviewing the literature and analysing publicly available data, we observe a “Swiss Japanification”. Old age is associated with late-life comorbidities and an increasing proportion of time spent in poor health. To address these problems, a paradigm shift in medical practice is needed to improve health rather than respond to existing diseases. Basic ageing research is gaining momentum to be translated into therapeutic interventions and provides machine learning tools driving longevity medicine. We propose that research focus on closing the translational gap between the molecular mechanisms of ageing and a more prevention-based medicine, which would help people age better and prevent late-life chronic diseases.

While in the past technology has mostly been utilized to store information about the structural configuration of proteins and molecules for research and medical purposes, Artificial Intelligence is nowadays able to learn from the existing data how to predict and model properties and interactions, revealing important knowledge about complex biological processes, such as aging. Modern technologies, moreover, can rely on a broader set of information, including those derived from the next-generation sequencing (e.g., proteomics, lipidomics, and other omics), to understand the interactions between human body and the external environment. This is especially relevant as external factors have been shown to have a key role in aging. As the field of computational systems biology keeps improving and new biomarkers of aging are being developed, artificial intelligence promises to become a major ally of aging research.

The current, rapidly diversifying pandemic has accelerated the need for efficient and effective identification of potential drug candidates for COVID-19. Knowledge on host-immune response to SARS-CoV-2 infection, however, remains limited with few drugs approved to date. Viable strategies and tools are rapidly arising to address this, especially with repurposing of existing drugs offering significant promise. Here we introduce a systems biology tool, the PHENotype SIMulator, which -by leveraging available transcriptomic and proteomic databases-allows modeling of SARS-CoV-2 infection in host cells in silico to i) determine with high sensitivity and specificity (both>96%) the viral effects on cellular host-immune response, resulting in specific cellular SARS-CoV-2 signatures and ii) utilize these cell-specific signatures to identify promising repurposable therapeutics. Powered by this tool, coupled with domain expertise, we identify several potential COVID-19 drugs including methylprednisolone and metformin, and further discern key cellular SARS-CoV-2-affected pathways as potential druggable targets in COVID-19 pathogenesis.

Decentralized autonomous organizations (DAO) launched on a blockchain and governed by a smart contract promises to bring self-organization to a new technological level. Crisis management has no standard decentralized solution within DAO yet. A central authority is a natural component due to compliance reasons in certain domains, for example, special-purpose financing, in which the DAO governance model could be reasonably applied. More generally, a centralized DAO representative could streamline implementing DAO decisions that involve interactions with legacy systems. The article presents a perspective of modern technologies for organizing a foundation for special-purpose financing and considers longevity as a model example of the purpose.

BackgroundPrevious studies have demonstrated that inflammation and impaired microcirculation are key factors in post-resuscitation syndromes. Here, we investigated whether methylprednisolone (MP) could improve myocardial function and microcirculation by suppressing the systemic inflammatory response following cardiopulmonary resuscitation (CPR) in a rat model of cardiac arrest (CA).MethodsSprague-Dawley rats were randomly assigned to (1) sham, (2) control, and (3) drug groups. Ventricular fibrillation was induced and then followed by CPR. The rats were infused with either MP or vehicle at the start of CPR. Myocardial function and microcirculation were assessed at baseline and after the restoration of spontaneous circulation. Blood samples were drawn at baseline and 60-min post-resuscitation to assess serum cytokine (TNF-α, IL-1β, and IL-6) levels.ResultsMyocardial function [estimated by the ejection fraction (EF), myocardial performance index (MPI), and cardiac output (CO)] improved post-ROSC in the MP group compared with those in the control group (p < 0.05). MP decreased the levels of the aforementioned pro-inflammatory cytokines and alleviated cerebral, sublingual, and intestinal microcirculation compared with the control (p < 0.05). A negative correlation emerged between the cytokine profile and microcirculatory blood flow.ConclusionMP treatment reduced post-resuscitation myocardial dysfunction, inhibited pro-inflammatory cytokines, and improved microcirculation in the initial recovery phase in a CA and resuscitation animal model. Therefore, MP could be a potential clinical target for CA patients in the early phase after CPR to alleviate myocardial dysfunction and improve prognosis.

Amyotrophic lateral sclerosis (ALS) is a severe neurodegenerative disease with ill-defined pathogenesis, calling for urgent developments of new therapeutic regimens. Herein, we applied PandaOmics, an AI-driven target discovery platform, to analyze the expression profiles of central nervous system (CNS) samples (237 cases; 91 controls) from public datasets, and direct iPSC-derived motor neurons (diMNs) (135 cases; 31 controls) from Answer ALS. Seventeen high-confidence and eleven novel therapeutic targets were identified and will be released onto ALS.AI (http://als.ai/). Among the proposed targets screened in the c9ALS Drosophila model, we verified 8 unreported genes (KCNB2, KCNS3, ADRA2B, NR3C1, P2RY14, PPP3CB, PTPRC, and RARA) whose suppression strongly rescues eye neurodegeneration. Dysregulated pathways identified from CNS and diMN data characterize different stages of disease development. Altogether, our study provides new insights into ALS pathophysiology and demonstrates how AI speeds up the target discovery process, and opens up new opportunities for therapeutic interventions.

Aim: In patients with postmenopausal hormone receptor-positive breast cancer (ER + eBC), aromatase inhibitors (AIs) are widely used for effective relapse prevention. However, AIs reduce bone density and increase bone-related events (BREs). Alongside calcium and vitamin D3 supplementation, bisphosphonates and denosumab are well-known options for improving outcomes in bone health and breast cancer prognosis. This study aimed to evaluate the practice patterns of bone health guideline-based management in real-world patients with ER + eBC. Material and methods: In total, 68 patients with ER + eBC treated between 2009 and 2014 at the University Hospital Basel were included in this retrospective cohort study. Chart reviews were analyzed. Baseline, clinicopathological, treatment, and BRE data were extracted. Each patient was specifically reviewed for therapy adherence to the Swiss bone health guidelines (Swiss Association against Osteoporosis 2010 [SVGO]). Results: The mean patient age was 66.5 (range, 56–74) years, all post-menopausal. The most frequent tumor characteristics were tumor size of pT1–pT2 (N = 53, 77.9%) and treatment with letrozole (N = 35, 51.5%), followed by tamoxifen as a switch strategy (N = 27, 40.3%). The median treatment time with AIs was 47 (range, 30–60) months. Five patients (7.8%) experienced a fracture during or after AI treatment. Moreover, 51 (75%) patients were treated according to the SVGO recommendations. Conclusion: The fracture rate in our retrospective cohort was comparable to that in the larger phase III randomized trials. The adherence to bone health guidelines was satisfactory but still suboptimal. Clinicians should strictly adhere to the current bone health guidelines to ensure the best possible prevention of BREs and maintain bone health and cancer prognosis in patients with ER + eBC.

Abstract Background Emerging evidence has demonstrated the limited access to metabolic substrates as an effective approach to block cancer cell growth. The mechanisms remain unclear. Our previous work has revealed that miR-221/222 plays important role in regulating breast cancer development and progression through interaction with target gene p27. Results Herein, we determined the miRNA-mRNA interaction in breast cancer cells under induced stress status of starvation. Starvation stimulation attenuated the miR-221/222-p27 interaction in MDA-MB-231 cells, thereby increased p27 expression and suppressed cell proliferation. Through overexpression or knockdown of miR-221/222, we found that starvation-induced stress attenuated the negative regulation of p27 expression by miR-221/222. Similar patterns for miRNA-target mRNA interaction were observed between miR-17-5p and CyclinD1, and between mR-155 and Socs1. Expression of Ago2, one of the key components of RNA-induced silencing complex (RISC), was decreased under starvation-induced stress status, which took responsibility for the impaired miRNA-target interaction since addition of exogenous Ago2 into MDA-MB-231 cells restored the miR-221/222-p27 interaction in starvation condition. Conclusions We demonstrated the attenuated interaction between miR-221/222 and p27 by starvation-induced stress in MDA-MB-231 breast cancer cells. The findings add a new page to the general knowledge of negative regulation of gene expression by miRNAs, also demonstrate a novel mechanism through which limited access to nutrients suppresses cancer cell proliferation. These insights provide a basis for development of novel therapeutic options for breast cancer.

Aging is a major risk factor for cancer development. Indeed, 60% of people with cancer in Switzerland and worldwide are older than 65 years. However, the indication for treatment choice should not be based on chronological age alone but instead driven by the patient's biological age, physiological and functional status, and the biological and morphogenetic tumor signature. This article provides an overview of the current challenges in treating elderly and old cancer patients, specifically the reasons for a less-than-optimal treatment and care and the added value of performing comprehensive geriatric assessments in this unique population.

Summary: COVID-19 disproportionately affects older people, with likelihood of severe complications and death mirroring that of other age-associated diseases. Inhibition of the mechanistic target of rapamycin complex 1 (mTORC1) has been shown to delay or reverse many age-related phenotypes, including declining immune function. Rapamycin (sirolimus) and rapamycin derivatives are US Food and Drug Administration-approved inhibitors of mTORC1 with broad clinical utility and well established dosing and safety profiles. Based on preclinical and clinical evidence, a strong case can be made for immediate large-scale clinical trials to assess whether rapamycin and other mTORC1 inhibitors can prevent COVID-19 infection in these populations and also to determine whether these drugs can improve outcomes in patients with severe COVID-19.

Identifying prognostic biomarkers and risk stratification for COVID-19 patients is a challenging necessity. One of the core survival factors is patient age. However, chronological age is often severely biased due to dormant conditions and existing comorbidities. In this retrospective cohort study, we analyzed the data from 5315 COVID-19 patients (1689 lethal cases) admitted to 11 public hospitals in New York City from 1 March 2020 to 1 December. We calculated patients’ pace of aging with BloodAge—a deep learning aging clock trained on clinical blood tests. We further constructed survival models to explore the prognostic value of biological age compared to that of chronological age. A COVID-19 score was developed to support a practical patient stratification in a clinical setting. Lethal COVID-19 cases had higher predicted age, compared to non-lethal cases (Δ = 0.8–1.6 years). Increased pace of aging was a significant risk factor of COVID-related mortality (hazard ratio = 1.026 per year, 95% CI = 1.001–1.052). According to our logistic regression model, the pace of aging had a greater impact (adjusted odds ratio = 1.09 ± 0.00, per year) than chronological age (1.04 ± 0.00, per year) on the lethal infection outcome. Our results show that a biological age measure, derived from routine clinical blood tests, adds predictive power to COVID-19 survival models.