Publications

Diagnostic technologies are critical for improving health outcomes and extending quality of life. Accurate and
timely diagnosis plays a critical role in preventing disease, guiding treatment, and improving clinical outcomes.
Over the past century, diagnostic technologies have advanced to enable earlier disease detection and more precise health monitoring, allowing more timely and efective intervention. Recently, smart diagnostics driven by artifcial intelligence (AI), portable and mobile systems integrated with the Internet of Things (IoT), and noninvasive yet highly accurate detection methods have promised a bright future, although signifcant technological challenges remain. […] (Read more online)

Key Points

Question  Considering their risk compared with current smokers, when should former smokers begin lung cancer screening?

Findings  In this cohort study of 86 035 former or current heavy smokers, former heavy smokers could be screened between 3 and 17 years later than current heavy smokers, depending on the length of smoking cessation, in order to more closely reflect their risk levels. Whereas the US Preventive Services Task Force currently recommends a unified starting age of 50 years, the derived risk-adapted screening start ages for former heavy smokers ranged between 53 and 67 years of age.

Meaning  This study’s findings suggest that using differentiated, risk-adapted starting ages of former smokers could allow for enhanced lung cancer screening strategies.

Screening heavy smokers by low-dose computed tomography (LDCT) can reduce lung cancer (LC) mortality, but defining the population that benefits most, a prerequisite for cost-effective screening, is challenging. In order to contribute to a more nuanced risk stratification of high-risk target populations, we developed and validated a blood-based protein marker model for LC. A two-stage design was implemented in this study, and the derivation set comprised 18 868 participants from the UK Biobank, which included 200 incident LC cases identified at 6 years of follow-up. The independent validation set included 101 LC cases identified at 6 years of follow-up. A total of 2025 protein markers measured by proximity extension assays available for both datasets were used for analysis. A risk prediction algorithm by least absolute shrinkage and selection operator regression with bootstrap method was developed in the derivation set and then externally evaluated in the independent validation set. The risk discriminatory performance of the protein marker model was compared with the established PLCO_m2012 model, USPSTF 2020 guidelines and trial criteria used in different LDCT trials. The protein marker model comprising of four protein biomarkers—CEACAM5, CXCL17, MMP12, and WFDC2—outperformed the PLCO_m2012 model, and the areas under the receiver operating curve (AUCs) for the protein marker model in the derivation and validation sets were 0.814 [95% confidence interval (95% CI), 0.785–0.843] and 0.814 (95% CI, 0.756–0.873), respectively. The addition of the protein marker model to the PLCO_m2012 model increased the AUCs up to 0.056 and 0.057 and yielded up to 16 and 12 percentage points higher sensitivities to identify future LC cases compared to the LDCT trial criteria, in the derivation and validation sets, respectively. The protein marker model improves the selection of high LC risk individuals for LDCT screening and thereby enhances screening efficacy.

Conventional electromagnetic communication systems face limitations in dense environments, including high energy consumption, signal attenuation, and interference. To overcome these challenges, we present a bioinspired molecular communication (MC) platform using spatiotemporally allied single-walled carbon nanotube (SWCNT) sensors for volatile organic compound (VOC)-based signal transmission. Inspired by nature’s chemical signaling, this system employs hierarchical functionalized SWCNT sensor arrays to detect and interpret data-specified VOC pulses with high precision, mimicking pheromone-based communication. The system employs hydrophobic and biodegradable polymer-functionalized SWCNTs on nanoporous cellulose paper for enhanced VOC selectivity and response dynamics, enabling spatial and temporal signal encoding for robust multibit data transmission. Integrated machine learning (ML) algorithms facilitate signal decoding, pattern recognition, and environmental adaptation, ensuring reliable communication under varying conditions. The hierarchical sensor architecture and selective VOC interactions enable applications in gas detection, environmental monitoring, industrial safety, and real-time communication in inaccessible areas. Chromatographic detection of VOC mixtures within the layered sensor network further expands data transmission capacity, offering a scalable, energy-efficient alternative to conventional methods. This study advances bioinspired molecular communication, integrating nanomaterials with spatiotemporal sensing for next-generation, low-power, high-fidelity communication.

Background

Nonsense-mediated mRNA decay (NMD) is a quality-control pathway that degrades mRNA bearing premature termination codons (PTCs) resulting from mutation or mis-splicing, and that additionally participates in gene regulation of unmutated transcripts. While NMD activity is known to differ between examples of PTCs, it is less well studied if human tissues differ in NMD activity, or if individuals differ.

Results

We analyzed exomes and matched transcriptomes from Human tumors and healthy tissues to quantify individual-level NMD efficiency, and assess its variability between tissues, tumors, and individuals. This was done by monitoring mRNA levels of endogenous NMD target transcripts, and additionally supported by allele-specific expression of germline PTCs. Nervous system and reproductive system tissues have lower NMD efficiency than other tissues, such as the digestive tract. Next, there is systematic inter-individual variability in NMD efficiency, and we identify two underlying mechanisms. First, somatic copy number alterations can robustly associate with NMD efficiency, prominently the commonly-occurring gain at chromosome 1q that encompasses two core NMD genes: SMG5 and SMG7 and additional functionally interacting genes such as PMF1 and GON4L. Second, deleterious germline variants in genes such as the KDM6B chromatin modifier can associate with higher or lower NMD efficiency in individuals. Variable NMD efficiency modulates positive selection upon somatic nonsense mutations in tumor suppressor genes, and is associated with cancer patient survival and immunotherapy responses.

Conclusions

NMD efficiency is variable across human tissues, and it is additionally variable across individuals and tumors thereof due to germline and somatic genetic alterations.

As a kind of node on the Internet of Things, gas sensor is specifically utilized for detecting gaseous chemical species and humidity. Functioning as a kind of discrete electronic components, gas sensors have been extensively implemented in many fields, including agricultural production, public safety, healthcare activity, dual-carbon strategic initiatives, new energy vehicles, food engineering, etc. Metals and metal hybrids are one of the most important classes of sensitive materials. In this review article, the advancements in gas sensors based on metals and metal-hybrid materials have been comprehensively introduced, focusing on their unique mechanisms, performance enhancements, and practical applications. Critical thinking and ideas regarding the orientation of the development of metals and metal hybrids-based gas sensors in the future are discussed.

Contemporary electronic devices generate substantial quantities of electronic waste (e-waste), presenting a significant environmental challenge and exerting considerable pressure on the health of the Earth’s ecosystems. Owing to the industry’s heavy reliance on limited resources and the use of nondegradable components, innovation is necessary in device design, use, and end-of-life management. This review delves into the growing field of advanced materials for sustainable electronics, emphasizing their critical role in transforming the industry toward sustainability. The use of advanced materials in a sustainable way offers promising opportunities for reducing environmental harm and health risks while enhancing device performance and longevity. This interdisciplinary review explores themes such as energy efficiency, sustainable materials, recycling potential, resource consumption reduction, and environmental and health monitoring. It aims to illuminate recent progress, highlight ongoing challenges, and examine future prospects and applications for advanced materials in the pursuit of responsible electronics.

Cancer is among the most common causes of mortality worldwide. Screening for cancer involves surveillance of populations towards identification of cancer that was unknown to the patient. Therapeutic treatments for cancer generally are more effective when applied earlier on in the condition’s development, as such, screening for cancer has the potential to improve the standard for patient care and improving mortality and morbidity associated with the disease. Cancer screening has increasingly become dependent on advanced technologies to assist in the identification and characterization of tumours. This article Collection showcases current research towards the development of new methods for cancer screening, including the development of new advanced technologies in this domain such as novel methods incorporating liquid biopsies, tailed primer isothermal amplification assays, and infrared spectroscopy. As is common in modern research, approaches make use of computational techniques as a critical component in cancer screening, and research is highlighted on the use of artificial intelligence, which is now a common technological innovation contributing to the overall cancer screening process in research studies. Future directions for cancer screening are discussed.

Background
Electronic cigarettes (e-cigarettes) have gained popularity in recent years. While initially introduced as a safe alternative for tobacco and a bridge for smoking cessation, subsequent studies found that they contain toxic substances. We aimed to assess the acute effect of a single session of e-cigarette smoking on cardiorespiratory parameters, exhaled volatile organic compounds (VOCs) and markers of inflammation.

Methods
A prospective single-centre study was carried out. Participants (healthy volunteers, former e-cigarette users) were assessed before and after a 30-min session of e-cigarette smoking. Evaluations included vital signs, pulmonary functions – spirometry and fractional exhaled nitric oxide (FeNO) – blood and exhaled breath condensate (EBC) cytokines and electronic nose (e-nose) for analysis of exhaled VOCs profile.

Results
30 participants aged 27.9±4.4 years were enrolled in the study. Post-smoking observations revealed a significant increase in heart rate (77.5±10.9 to 85.5±12.1 beats·min−1, p=0.002), respiratory rate (15.4±2.2 to 17.1±1.8 breaths·min−1, p=0.002) and blood pressure (systolic 118±8.1 to 123.5±11.9, p=0.017; diastolic 73.9±8.4 to 78.5±6.3 mmHg, p=0.011). FeNO decreased significantly (median of 11 (7.5–15.5) to 9.7 (7.3–17.3) ppb, p=0.024). Analysis of e-nose found a significant change of exhaled VOC pattern after e-cigarette smoking. No significant changes were found in spirometry and cytokine levels in blood or EBC.

Conclusions
A single session of 30 min of e-cigarette smoking caused significant cardiorespiratory effects, decreased FeNO and altered exhaled VOC pattern, similar to the effect seen with cigarette and water-pipe smoking.‏ The observed acute effects, together with the well-known chronic risks, highlight the importance of effective regulation of e-cigarettes.