Specific and label-free biosensing with biological field-effect transistors (bioFETs) is highly pursued due to their high-end sensing performance, low-cost, and potential for multiplexed sensing in ultrasmall samples. Still, bioFET sensing in physiological samples, such as whole blood, presents two major hurdles: (1) short screening lengths due to high ionic strength and (2) nonspecific response due to high background population of various biological entities. Traditionally, the former challenge requires sample dilution or the employment of short receptors, and the latter requires multiple premeasurement washing steps for the removal of the nonspecific species. Hence, these required steps of sample preprocessing and multiple premeasurement washing are deleterious to the application of bioFET toward self-use, home-use, POC, bedside, etc. applications. To address this unmet need, we present a new method for low-cost, real-time, quantitative biosensing suitable for the above-mentioned applications. The presented approach is based on the Meta-Nano-Channel (MNC) bioFET. The MNC bioFET is specifically designed to detect minute concentrations of biomolecular targets in a label-free, specific, real-time, quantitative manner and in ultrasmall samples. This capability is enabled by the deterministic design of the MNC bioFET toward the identification of localized molecular interactions. The study presents sensing of L-Dopa in 0.5 μL of whole blood samples. Importantly, no preprocessing of the blood is required, and the sensing is performed directly in blood without premeasurement washing for the removal of nonspecific signals. L-Dopa is the cornerstone of the symptomatic treatment of Parkinson's disease, and its dose in clinical settings is titrated according to clinical response. Measurement of L-Dopa plasma levels is performed in processed blood using analytical methods, which are costly, time-consuming, and irrelevant for standard clinical settings. We demonstrate a limit-of-detection of 10 fg/mL and a dynamic range of 10 orders of magnitude with excellent sensitivity and linearity. The methods and mechanisms employed by the MNC bioFET to address the challenges of screening length and nonspecific adsorption are discussed. The MNC bioFET is a promising methodology for future self-use and point-of-care medical diagnostics.
Esophageal adenocarcinoma (EAC), the dominant subtype of esophageal cancer in developed countries, is a growing health problem, characterized by poor patient prognosis and dismal survival due to ineffective screening tools and a lack of efficacious options targeting the interception or treatment of EAC. Despite molecular advances, molecular targeting of EAC remains elusive, suggesting the need for identifying alternative targets with improved prognostic and therapeutic value. Herein, we performed RNA-sequencing analysis in EAC and Barrett's Esophagus (BE) precursor lesions to identify isoform switching events significantly linked with all-cause and cancer-specific mortality. Patients were stratified based on histopathology alone or in combination with TP53 mutation status, the most commonly mutated gene in EAC. To gain mechanistic insight, we performed isoform-specific siRNA knockdown of two isoforms, TTLL12 and HM13, both linked to patient survival, and investigated mechanisms associated with isoform dysregulation and whether targeting specific isoforms in EAC acts synergistically to improve therapeutic potential. Isoform-specific knockdown of TTLL12 and HM13 significantly decreased the viability of two EAC cell lines, sensitized EAC cell lines to standard-of-care chemotherapy agents (paclitaxel and carboplatin) with synergy, and inhibited EAC cell migratory potential. Knockdown of the TTLL12 isoform led to activation of chaperone-mediated autophagy, which, in turn, decreased expression of CHK1 and TP53; whereas knockdown of the HM13 isoform activated the unfolded protein response and induced endoplasmic reticulum stress-induced autophagy and apoptosis. In addition, HM13 isoform knockdown increased the response to an anti-PD-L1 agent, avelumab, in EAC cells, suggesting a role for isoform switching in immunosuppression. Taken together, study results suggest that isoform switching may provide novel insight for the identification of prognostic markers and inform new potential therapeutic targets for EAC treatment or prevention.
Up to one-third of people living with psoriasis develop psoriatic arthritis (PsA), and the majority have active psoriasis prior to the development of arthritis. Clinical risk factors, such as nail involvement, in conjunction with novel blood biomarkers, could improve PsA risk monitoring and early diagnosis. The aim of the HIPPOCRATES Prospective Observational Study (HPOS-www.hpos.study) is to follow a cohort living with psoriasis and identify risk factors for the development of PsA. HPOS is a patient-driven online prospective European observational cohort. Adult participants with psoriasis but with no prior diagnosis of PsA are eligible. Participants are invited to provide consent and join the study online. They complete a semi-structured questionnaire to collect data on demographics, psoriasis, comorbidities, risk factors for PsA, and the Psoriasis Epidemiology Screening Tool screening questionnaire. Follow-up is conducted through a questionnaire every 6 months. The primary outcome is the new onset of PsA confirmed by a diagnosis from their doctor. The study will also collect peripheral blood samples from a subset of participants for biomarker identification. This study follows the principles of the Declaration of Helsinki. To date, ethical approval has been granted by independent ethical committees in 10 countries. Studying a cohort of individuals with psoriasis will allow us to identify risk factors for arthritis development and to develop a risk calculator. This can support focused efforts on screening, patient education, and even studies looking to delay or prevent the onset of arthritis. This study, run via remote online data collection, provides an efficient way to recruit a large cohort (25,000) across multiple countries. However, challenges have had to be addressed with some key changes in study design, ethical review, and recruitment strategies required for each individual country. HPOS, Clinicaltrials.gov ID: NCT05858528, IRAS number 325080; https://clinicaltrials.gov/study/NCT05858528?locStr=United%20Kingdom&country=United%20Kingdom&cond=Psoriasis&term=HPOS&aggFilters=status%3Anot%20rec&rank=1. The HIPPOCRATES prospective observational study (HPOS) The HPOS Study, part of the HIPPOCRATES project, aims to find out what signs or factors can show which people with psoriasis might later develop Psoriatic Arthritis (PsA). PsA is a type of inflammatory arthritis that is related to the skin condition psoriasis. It occurs in about 1–2% of the general population but can develop in up to 30% of people who already have skin or nail psoriasis. Diagnosing PsA early can be difficult because symptoms can be vague or inconsistent, which means treatment often starts only after joint damage has already happened. By learning more about how psoriasis develops into PsA, researchers hope to find new ways to treat the disease earlier—or even prevent or delay it. The HPOS Study is an observational study that uses online questionnaires. Adults (aged 18 or older) who have psoriasis but not PsA can take part. Participants fill out a questionnaire every six months for three years. These questionnaires collect information about age, psoriasis details, lifestyle and health factors, early joint symptoms (using the PEST questionnaire), daily function, treatment satisfaction, disease impact, fatigue, and mental health. If early signs of PsA appear, participants are advised to contact a doctor for assessment. The study plans to recruit 25,000 people across 14 European countries (including the UK, Ireland, France, Germany, and others) and expects that around 675 participants will develop PsA each year. A smaller group of 3,000 participants will also provide a small finger-prick blood sample, which will help researchers look for blood markers that might predict PsA development. HPOS is the first large-scale European study to track how psoriasis progresses to PsA. The findings could lead to a “risk calculator” that helps doctors identify people at high risk of developing PsA earlier.
With the continuous advancement of research on life systems and disease mechanisms, analytical technologies are now moving toward the resolution of single molecules and individual genes. Among them, surface-enhanced Raman scattering (SERS) has garnered widespread interest because of its ultrahigh sensitivity, allowing even single-molecule detection. When integrated with microfluidics, SERS-based platforms combine the strengths of both techniques, offering complementary and synergistic effects. This integration enables rapid, non-invasive, ultrasensitive, and high-throughput analysis of biological samples, which is highly valuable for biomedical research and potential clinical applications. Consequently, this interdisciplinary approach has emerged as a major focus of current investigations. In this review, we outline recent developments and applications of microfluidic SERS systems in bioanalysis. The discussion first introduces the basic concepts and classifications of SERS-microfluidic strategies, such as continuous-flow, microarray, droplet-based, lateral flow assay (LFA), and digital formats. We then highlight their applications in biomolecular detection, cellular analysis, and disease diagnostics. Overall, the evidence suggests that microfluidic SERS platforms represent a powerful and promising tool for advancing bioanalytical science.
Colorectal Cancer (CRC) exhibits persistently high incidence and mortality rates worldwide, imposing a substantial socioeconomic burden. Early screening, early diagnosis, and early treatment can significantly improve patients’ survival rates while reducing mortality. However, there remains a lack of effective biomarkers to aid in early screening and diagnosis. As a branch of artificial intelligence, machine learning can automatically analyze large volumes of data, greatly saving human time and resources. The advancement of high-throughput sequencing technology has provided researchers with abundant gene expression data, offering rich data resources for the training and validation of machine learning models. With the development of artificial intelligence, integrating knowledge from bioinformatics, machine learning, molecular biology, and clinical medicine for analysis enables a more comprehensive understanding and exploration of the molecular biological mechanisms underlying CRC. In summary, this project aims to utilize machine learning techniques to screen five CRC signature genes (ABCG2, SCGN, USP2, CLDN1, and EPHX4) from GEO datasets, validate these signature genes using TCGA database, and perform RT-qPCR to detect the relative mRNA expression levels of these genes in CRC. Ultimately, this study seeks to provide novel biomolecular markers for the early diagnosis of CRC.
Early detection of hepatocellular carcinoma (HCC) remains a persistent worldwide challenge. Owing to its minimal invasiveness, liquid biopsy has emerged as a promising alternative for early screening. As key components of the tumor microenvironment (TME), platelets (PLTs) represent a rich source of biomolecular information that complements the data from conventional plasma and serum samples. Integrative multiomics analysis of such data offers a powerful strategy to deepen our understanding of hepatocarcinogenesis and accelerate the discovery of robust biomarker panels. Here, we described an integrative and ultrafast multiomics sample preparation (IAU-MOSP) strategy for the high-purity platelets. The optimized IAU-MOSP method shortened the multiomics workflow from over 24 to 6 h while yielding comparable biomolecule identifications to those of conventional methods. Then, the workflow was applied in an HCC cohort (n = 68) study. We quantified 6660 biomolecules with high reproducibility (median CVs: 0.31-0.39). The data exhibited strong cross-omics correlations, particularly between proteins and lipids (r = 0.75) as well as protein and metabolite (r = 0.68) groups. Differential analysis revealed 10 biomolecules significantly dysregulated in HCC platelets (TEK, citric acid, glycerol-3-phosphate (G3P), P2RX4, malic acid, ATP, PRG3, ITGAM, CXCR2, ITGB2) that participate in key pathways driving proliferation and metastasis. Accompanied by machine learning, the 10 biomolecules were ultimately identified as a potential biomarker panel for early diagnosis of HCC. It shows superior diagnostic efficacy (accuracy = 0.81, sensitivity = 0.74) over α-fetoprotein (AFP) (accuracy = 0.75, sensitivity = 0.45) for early HCC detection.
Highly sensitive point-of-care early screening for high-risk human papillomavirus (HPV) infections is urgently needed, particularly in resource-limited settings. Nucleic acid amplification methods, especially CRISPR/Cas-based biosensors, have emerged as promising tools for sensitive HPV detection; however, current approaches typically rely on tedious tube-based formats coupled with lateral flow assays for signal readout in point-of-care testing (POCT). Here, we developed customized microfluidic paper-based analytical devices (μPADs) with valves that seamlessly integrated recombinase polymerase amplification (RPA) with CRISPR/Cas12a biosensing (RPA-CRISPR/Cas12a) on the filter paper substrate. This innovation achieved sensitive and cost-effective high-risk HPV detection in POCT. The RPA-CRISPR/Cas12a system with a linear reporter on μPADs, enabled fluorescence detection of the E7 gene, achieving a sensitivity of 1 pM at approximately 1 h. The sensitivity was further enhanced by introducing a circular reporter into the fluorescence-based RPA-CRISPR/Cas12a system on μPADs, enabling detection of the E7 gene with a detection limit of 1 fM and an assay time of 35 min. The system was validated using 50 cervical swab clinical samples, demonstrating 95% sensitivity and 100% specificity when compared to qPCR. This sample-to-answer detection platform holds significant promise for early screening of high-risk HPV infections in point-of-care scenarios.
Familial Hypercholesterolemia (FH) is a genetic disorder of lipoprotein metabolism that causes an increased risk of premature atherosclerotic cardiovascular disease (ASCVD). Although early diagnosis and treatment of FH can significantly improve the cardiovascular prognosis, this disorder is underdiagnosed and undertreated. For these reasons the Italian Society for the Study of Atherosclerosis (SISA) assembled a Consensus Panel with the task to provide guidelines for FH diagnosis and treatment. Our guidelines include: i) an overview of the genetic complexity of FH and the role of candidate genes involved in LDL metabolism; ii) the prevalence of FH in the population; iii) the clinical criteria adopted for the diagnosis of FH; iv) the screening for ASCVD and the role of cardiovascular imaging techniques; v) the role of molecular diagnosis in establishing the genetic bases of the disorder; vi) the current therapeutic options in both heterozygous and homozygous FH. Treatment strategies and targets are currently based on low-density lipoprotein cholesterol (LDL-C) levels, as the prognosis of FH largely depends on the magnitude of LDL-C reduction achieved by lipid-lowering therapies. Statins with or without ezetimibe are the mainstay of treatment. Addition of novel medications like PCSK9 inhibitors, ANGPTL3 inhibitors or lomitapide in homozygous FH results in a further reduction of LDL-C levels. LDL apheresis is indicated in FH patients with inadequate response to cholesterol-lowering therapies. FH is a common, treatable genetic disorder and, although our understanding of this disease has improved, many challenges still remain with regard to its identification and management.
Nanoengineering of plasmonic/magnetic nanocrystals plays a pivotal role in advancing biomolecular detection, offering emerging strategies for cancer diagnostics and therapeutic monitoring. Early and precise detection of cancer biomarkers is critical for timely intervention. Yet conventional immunoassays are often hindered by limited sensitivity, nonspecific binding, and inefficient target enrichment, reducing their clinical reliability. Herein, we develop a self-reporting bimetallic plasmonic nanoflowers-based SERS immunoassay that integrates magnetic enrichment and plasmonic field amplification for ultrasensitive detection of total prostate-specific antigen (t-PSA). The bimetallic Ag-Au nanoflowers were synthesized through a novel nanocrystal engineering approach that integrates the structural stability of gold with the superior plasmonic enhancement of silver, while intrinsically incorporating self-reporting SERS labels on the surface. Serving as intrinsic Raman probes, these nanoflowers eliminate the need for external labeling, thereby simplifying the detection process and enhancing reproducibility. Simultaneously, antibody-functionalized magnetic nanoparticles facilitate efficient target separation and enrichment, significantly improving detection sensitivity. This synergistic strategy achieves an ultra-low detection limit of 100 fg mL-1 and enables precise quantification within the diagnostic gray zone (4.0-10.0 ng mL-1), a critical range for early prostate cancer screening. Furthermore, the reliability of the SERS immunoassay was further confirmed by testing t-PSA-spiked serum samples, showing consistent analytical performance and excellent agreement with conventional ELISA measurements. The combination of self-reporting plasmonic nanostructures, magnetic-assisted biomarker capture, and hotspot-driven SERS amplification offers promising and highly sensitive biosensing nanoprobes for early prostate cancer diagnosis.
Chronic kidney disease (CKD) is common and ranks among the leading causes of mortality and morbidity. This analysis aimed to present global CKD estimates using the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2023 to inform evidence-based policies for CKD identification and treatment. This analysis focused on adults aged 20 years and older over the period 1990 to 2023, from 204 countries and territories. Data sources used were published literature, vital registration systems, kidney failure treatment registries, and household surveys. Estimates of CKD burden, including deaths, incidence, prevalence, and disability-adjusted life-years (DALYs), were produced using a Cause of Death Ensemble model and a Bayesian meta-regression analytical tool. A comparative risk assessment approach estimated the proportion of cardiovascular deaths attributable to impaired kidney function and estimated risk factors for CKD. Globally, in 2023, 788 million (95% uncertainty interval 743-843) people aged 20 years and older were estimated to have CKD, up from 378 million (354-407) in 1990. The global age-standardised prevalence of CKD in adults was 14·2% (13·4-15·2), a relative rise of 3·5% (2·7-4·1) from 1990. The region with the highest age-standardised prevalence was north Africa and the Middle East (18·0%; 16·9-19·4). Most people had stage 1-3 CKD, with a combined prevalence of 13·9% (13·1-15·0). In 2023, CKD was the ninth leading cause of death globally, accounting for 1·48 million (1·30-1·65) deaths, and the 12th leading cause of DALYs, with an age-standardised DALY rate of 769·2 (691·8-857·4) per 100 000. Impaired kidney function as a risk factor accounted for 11·5% (8·4-14·5) of cardiovascular deaths. High fasting plasma glucose, body-mass index, and systolic blood pressure were all leading risk factors for CKD DALYs. CKD is a major global health issue, with rising prevalence and increasing importance as a cause of death and as a risk factor for cardiovascular death. A better understating of aetiology, appropriate screening, and implementation programmes are needed to translate advances in CKD treatment into improved patient outcomes. Gates Foundation, Wellcome, US National Kidney Foundation, and US National Institute of Diabetes and Digestive and Kidney Diseases.
Current treatments for multiple sclerosis (MS) are insufficient to delay the neurodegenerative process that is the main cause of disability progression in patients with MS. Therapeutics aimed at supporting myelin regeneration and neuroprotection are thus a major unmet medical need for the progressive forms of MS. To address this, we developed a strategy combining in silico screening of more than 1500 repurposed compounds with a validation pipeline of models, encompassing rodent and human in vitro assays as well as mouse models of demyelination/remyelination. From the initial library, 273 drugs were prioritized in silico on the basis of the predicted effects on myelination and neuroprotection, and among them, 160 were potentially nontoxic. We identified 32 molecules that exerted a promyelinating and a neuroprotective action on rodent and human oligodendroglia and neurons. Our data identified classes of compounds with potentially distinct mechanisms of action that may foster remyelination and neuroprotection. The therapeutic activity of one selected drug, the histamine receptor H3 antagonist bavisant, was further validated in mouse models of demyelination and axonal injury reproducing some key pathological features occurring in MS. Our in vivo studies demonstrated that bavisant promoted remyelination and neuroprotection when administered to LPC-treated, cuprizone-fed, or MOG-induced EAE mice, as well as in a human oligodendroglia chimeric mouse model of demyelination/remyelination. These findings provide proof-of-concept validation for bavisant as a candidate for neuroprotective clinical trials in MS.
High-risk HPV-16 infection is strongly linked to cervical cancer, making its quantitative detection vital for early screening and early diagnosis. However, accurate detection of HPV-16 DNA is challenging due to the low viral load and biomolecular interference in the cervical microenvironment. Here, we develop an electrochemiluminescence sensor using in situ-grown MXene quantum dots immobilized on N, P-codoped Ti3C2 sheets as a nanoprobe for HPV-16 DNA detection. This hybrid architecture enhances luminescence efficiency, charge transport, and coreactant catalysis, delivering a stronger and more stable ECL output in comparison to the individual components. To translate these material-driven gains into target-specific signal transduction, we couple the probe with CRISPR/Cas12a recognition. Upon target binding, Cas12a is activated and exhibits collateral transcleavage toward ssDNA auxiliaries tethered at the electrode, which disrupts probe retention and yields a robust signal-off ECL readout with high specificity. This biosensor achieves a detection limit of 0.56 fM with a wide linear range from 1.0 fM to 50 pM. Tests on cervical brush specimens show good agreement with PCR, while maintaining reliable performance at low target levels in clinical specimens. These results indicate that the N, P-Ti3C2-MQDs-Cas12a system is a promising platform for supporting early screening and diagnosis of cervical cancer.
With the introduction of early-onset colorectal cancer (EO-CRC), defined as diagnosis before the age of 50, research has increasingly focused on distinguishing it from late-onset colorectal cancer (LO-CRC). However, the majority of these studies have delved deeply into specific aspects of the condition, and there is still a limited number of articles that comprehensively review the overall differences between EO-CRC and LO-CRC. In this review, we conducted literature searches on PubMed, Embase, and ScienceDirect databases using keywords such as "early-onset colorectal cancer", and "late-onset colorectal cancer". The retrieved articles were further screened to select those related to clinical manifestations, pathological features, molecular mechanisms, and prognosis for detailed analysis. Our findings indicate that the potential pathogenesis of EO-CRC is closely associated with lifestyle and environmental changes of the younger population. Compared to LO-CRC, EO-CRC tends to present with more severe initial symptoms, is more often diagnosed at an advanced stage, and primarily affects the left half of the colon. Postoperative pathology shows greater malignancy and invasiveness. At the biomolecular level, PIK3CA mutation and TP53 deletion exhibits a higher mutation rate in EO-CRC compared to LO-CRC, while other common gene mutations such as APC, KRAS, and SMAD4 are relatively less frequent. Additionally, MSI-H is more prevalent in patients with EO-CRC. Differences in transcriptomics and metabolomics profiles have also been observed between EO-CRC and LO-CRC, which may account for their distinct biological characteristics. The prognosis of EO-CRC is a subject of controversy, with varying trends observed across different age groups at onset, as well as between genders and ethnicities. In this study, we aimed to uncover the potential mechanisms behind the continuous rise in EO-CRC incidence and to provide a basis for optimizing standardized screening and treatment strategies for EO-CRC through a comprehensive analysis of the differences between EO-CRC and LO-CRC.
Toxigenic Clostridioides difficile is the cause of C. difficile infection, highlighting the critical need for rapid and easy-to-use detection. In this study, a multiple cross displacement amplification (MCDA) assay was developed to detect toxigenic C. difficile and evaluated against real-time PCR and VIDAS C. difficile toxin A & B assay (CDAB). The results showed that MCDA targeting the C. difficile tcdB gene had a limit of detection (LOD) of 12.5 fg (2.5 copies of genomic DNA) per reaction with no non-specific amplification from other pathogens. The LOD for MCDA was the same as real-time PCR but had significantly faster detection time, detecting 500 pg and 12.5 fg per reaction in 5.7 ± 0.5 and 21.35 ± 2.2 min, respectively. Among 201 stool samples, MCDA identified 40 positive for toxigenic C. difficile with a shorter average detection time of 16.08 ± 4.62 min compared to real-time PCR (39.82 ± 4.44 min). Using cell cytotoxicity neutralization assays as reference, MCDA demonstrated a significantly higher specificity (92.4%) and a positive predictive value (67.5%) than real-time PCR (80.8 and 43.2%, respectively; P < 0.05). MCDA also had a significantly higher sensitivity (93.1%) and a negative predictive value (98.8%) compared to VIDAS CDAB (34.5 and 90.1%, respectively; P ≤ 0.001). This was also higher compared to real-time PCR (86.2 and 97.2%, P > 0.05). MCDA offers a promising screening alternative for point-of-care testing to detect toxigenic C. difficile and effectively rule out non-toxigenic C. difficile. It is rapid, easy to use, and has a balanced performance, making it applicable in clinical and community settings, particularly in resource-limited settings.IMPORTANCERapid detection of toxigenic Clostridioides difficile is important for early intervention and outbreak control of C. difficile infection in primary healthcare facilities. This study developed an MCDA assay and showed that it was rapid, sensitive, user-friendly, and a suitable point-of-care screening alternative for accurately detecting toxigenic C. difficile. MCDA had the same sensitivity as real-time PCR while providing significantly faster turnaround times and improved specificity along with positive and negative predictive values. Its balanced performance and rapid detection capability make it well-suited for clinical detection of toxigenic C. difficile and point-of-care testing in communities, particularly in resource-limited settings, and for potential self-testing.
Prostate Imaging Reporting and Data System (PI-RADS) score, a reporting system of prostate MRI cases, has become a standard prostate cancer (PCa) screening method due to exceptional diagnosis performance. However, PI-RADS 3 lesions are an unmet medical need because PI-RADS provides diagnosis accuracy of only 30-40% at most, accompanied by a high false-positive rate. Here, we propose an explainable artificial intelligence (XAI) based PCa screening system integrating a highly sensitive dual-gate field-effect transistor (DGFET) based multi-marker biosensor for ambiguous lesions identification. This system produces interpretable results by analyzing sensing patterns of three urinary exosomal biomarkers, providing a possibility of an evidence-based prediction from clinicians. In our results, XAI-based PCa screening system showed a high accuracy with an AUC of 0.93 using 102 blinded samples with the non-invasive method. Remarkably, the PCa diagnosis accuracy of patients with PI-RADS 3 was more than twice that of conventional PI-RADS scoring. Our system also provided a reasonable explanation of its decision that TMEM256 biomarker is the leading factor for screening those with PI-RADS 3. Our study implies that XAI can facilitate informed decisions, guided by insights into the significance of visualized multi-biomarkers and clinical factors. The XAI-based sensor system can assist healthcare professionals in providing practical and evidence-based PCa diagnoses.
Bladder cancer, when diagnosed at an advanced stage, often necessitates inevitable invasive intervention. Consequently, non-invasive biosensor-based cancer detection and AI-based precision screening are being actively employed. However, the misclassification of cancer patients as normal-referred to as false negatives-remains a significant concern, as it could lead to fatal outcomes in lifespan. Moreover, while ensemble techniques such as soft voting and other methods can improve model accuracy and reduce misclassification, their effectiveness is limited and not applicable to all diagnostic tasks. Here, we developed a double stage cancer screening system that utilizes a sensitive urinary electrical biosensor implemented with an AI model and XAI interpretation tools. This system is designed for screening bladder cancer, well-known for its notable recurrence and high tendency to advance from non-invasive muscle tumors to muscle-invasive tumors. Four urinary biomarkers (CK8, CK18, PD-1, PD-L1) were measured by a field-effect transistor biosensor, and along with gender and age information, patients underwent initial screening by the CatBoost classification model. Patients initially classified as normal were reclassified using local explanations from neural networks offering a different perspective than CatBoost. After the second-stage screening, all of the false negatives from the initial screening could be correctly reclassified as cancer patients. Furthermore, global explanation guides the improvement of the AI model to be trained on an appropriate set of biomarker features to achieve high accuracy.
Cervical cancer remains a significant global health concern, primarily associated with persistent infections by high-risk human papillomavirus (hr-HPV). As screening programmes evolve from traditional cytology to DNA-HPV testing, the need for a liquid medium that maintains the integrity of cervical samples for biomolecular analysis and cytology becomes critical. This study evaluated the performance of the candidate liquid preservation medium (PM) Cytoliq for cervical samples intended for DNA-HPV testing and liquid-based cytology (LBC), in comparison with the reference PM, PreservCyt-ThinPrep. A total of 112 women aged 18-64 years underwent routine gynaecological examinations, with paired cervical samples preserved in both PM for HPV testing and genotyping (Cobas HPV test), and LBC. The study aimed for a sensitivity greater than 90% in detecting cervical intraepithelial neoplasia grade 2 or worse (CIN2+), moderate to high agreement in HPV testing results (Kappa index > 0.70) and adequate performance in LBC. The candidate PM exhibited non-inferior performance relative to the reference PM. DNA-HPV testing showed a 94.5% agreement rate (Kappa = 0.88) and a sensitivity of 92.9% for CIN2+ detection. Additionally, the candidate PM performed well in LBC smear production, with no significant differences in cytological diagnoses. The agreement in LBC diagnoses was 94.0% (Kappa = 0.79) with the ThinPrep processor and 91.8% (Kappa = 0.63) with the Cytoliq processor. The Cytoliq PM demonstrated comparable efficacy to the reference for DNA-HPV testing and LBC, supporting its potential as an alternative preservation medium in cervical cancer screening programmes.
Meningitis remains the leading infectious cause of neurological disabilities globally, disproportionately affecting children younger than 5 years and populations in the African meningitis belt. Whereas previous global estimates focused on ten pathogen categories, this study presents the most comprehensive analysis to date, assessing the meningitis burden attributable to 17 causative pathogens based on the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2023 framework. GBD is a systematic, scientific effort aimed at quantifying the comparative magnitude of health loss caused by diseases, injuries, and risk factors across age groups, sexes, and geographical locations over time. We estimated meningitis mortality using the Cause of Death Ensemble model (CODEm) and morbidity using DisMod-MR 2.1, incorporating data from vital registration, verbal autopsy, surveillance, hospital data, and systematic reviews. Aetiology-specific estimates were generated with pathogen-linked case-fatality ratios and splined binomial regression models. Risk factor attribution was based on established risk-outcome pairs and population attributable fractions. In 2023, there were 259 000 (95% uncertainty interval 202 000-335 000) global deaths and 2·54 million (2·20-2·93) incident cases of meningitis. Children younger than 5 years accounted for more than a third of deaths (86 600 [53 300-149 000]). Streptococcus pneumoniae, Neisseria meningitidis, non-polio enteroviruses, and other viruses were the leading causes of death, while non-polio enteroviruses caused the most cases. The four WHO-defined preventable meningitis pathogens of interest (S pneumoniae, N meningitidis, Haemophilus influenzae, and Group B streptococcus) contributed to 98 700 deaths (77 000-127 000) and 594 000 cases (514 000-686 000). Low birthweight, short gestation, and household air pollution were the top risk factors for meningitis-related mortality. Although mortality and incidence have declined significantly since 1990, progress is insufficient to meet WHO 2030 targets. Despite marked progress in reducing bacterial meningitis via global vaccination campaigns, a substantial meningitis burden persists, attributable both to common pathogens such as S pneumoniae and N meningitidis and to emerging non-bacterial pathogens such as Candida spp and drug-resistant fungi. Achieving WHO goals will require sustained investment in surveillance, vaccination, maternal screening, and health-system strengthening, especially in high-burden settings. Gates Foundation, Wellcome Trust, and UK Department of Health and Social Care.
Diagnostic kits for the optical detection of bladder cancer in urine can facilitate effective screening and surveillance. However, the heterogeneity of urine samples, owing to patients with bladder cancer often presenting with haematuria, interfere with the transduction of the optical signal. Here we describe the development and point-of-care performance of a device for the detection of bladder cancer that obviates the need for sample processing. The device leverages the enzymatic release of organogel particles carrying solvatochromic fluorophores in the presence of urinary hyaluronidases-a bladder cancer biomarker. Owing to buoyancy, the particles transfer from the urine sample into the organic phase, where the change in fluorescence can be measured via a smartphone without interference from blood proteins. In a double-blind study with 80 unprocessed urine samples from patients with bladder cancer (including samples with haematuria) or other genitourinary diseases and with 25 samples from healthy participants, our system distinguished the cancerous samples, including those with early-stage bladder cancer, with accuracies of about 90%. Obviating the need for sample pretreatment may facilitate the at-home detection of bladder cancer.
Hit identification is a pivotal yet resource-intensive stage of early drug discovery, where large chemical libraries are screened to uncover compounds with target-specific activity. Traditional fluorescence- and luminescence-based high-throughput assays, while fast and automation-friendly, suffer from label-associated artifacts, limited biological relevance, and signal interference that can compromise data fidelity. These challenges, coupled with the growing scale of screening campaigns, have intensified the need for more robust and physiologically relevant label-free screening strategies. This review highlights the emergence of label-free detection technologies as powerful alternatives for hit identification. By enabling direct measurement of biomolecular interactions or cellular responses without secondary reporters, these modalities reduce false positives, improve assay reliability, and enhance mechanistic insight. The authors also summarize their operating principles, recent applications, and practical considerations, emphasizing how label-free approaches can strengthen screening accuracy and accelerate early drug discovery. Label-free assays have rapidly advanced, offering real-time measurements, improved physiological relevance, and expanding throughput for early drug discovery. While these methods reduce artifacts and broaden target compatibility, challenges remain in validating biological relevance and managing complex kinetic data. Recent software innovations, including automated kinetic modeling and high-throughput data pipelines, are accelerating analysis and enhancing scalability.