Chlorogenic acids (caffeoylquinic acid isomers, CQAs) are major phenolic constituents of Ilex guayusa, but their comprehensive profiling in complex plant matrices is hindered by co-elution, overlapping UV spectra, and isomeric similarity in MS/MS. Rather than aiming to fully resolve isomer-specific quantification by MS, here we present an integrated workflow that couples validated HPLC-UV quantification of the major CQA (5-CQA) with an optimized UPLC-MS/MS strategy designed to improve MS1 peak integrity and expand MS/MS coverage for higher-confidence structural annotation. The HPLC-UV method showed excellent performance for targeted quantification of 5-CQA, including strong linearity (r² = 0.998), selectivity, sensitivity (LOQ = 0.25 mg/L), precision, and recovery. For LC-MS/MS, FastDDA acquisition (top-5 vs. top-15 precursors) revealed the expected trade-off between fragmentation depth and MS1 peak quality; however, post-acquisition raw-data merging restored MS1 fidelity and increased the number of detected features by 43%, enabling high-confidence annotation rather than quantitative discrimination of 16 metabolites and the propagation of oxidized CQA-related derivatives using feature-based molecular networking. Multivariate analyses (PCA, volcano plots, HCA) indicated that geographic location exerted the strongest influence on the metabolite composition, followed by sunlight exposure and plant age. Overall, the proposed workflow provides a practical framework that integrates robust chromatographic quantification with MS acquisition and data-processing optimization, thereby enhancing structural characterization and biological interpretation, rather than complete isomer-resolved quantification, of chlorogenic-acid-related chemistry across complex plant-derived and natural product matrices.
Clinical trial recruitment faces significant challenges, with 55% of trials terminated due to low enrolment and more than 80% failing to reach targets on time. While digital recruitment strategies show promise, standardised implementation frameworks using digital health informatics approaches remain underdeveloped. Referral partnerships combined with multi-platform analytics offer potential solutions but lack systematic implementation methodologies. To demonstrate a structured methodology for implementing and measuring multi-channel digital recruitment campaigns for clinical trials using integrated analytics platforms and referral partnerships. A six-month multi-channel digital recruitment campaign was implemented across seven channels to support two ongoing Phase III clinical trials (EAGLE studies, NCT04020341, NCT04187144), from May to October 2022. The campaign was integrated with an analytics platform to track performance across mass emails, website announcements, browser notifications, Instagram posts and three email automations. The implementation utilised both direct and indirect funnel architectures, with real-time performance optimisation. The integrated analytics framework successfully tracked 4829 clicks across seven channels, achieving an overall click-through rate (CTR) of 2.79%, substantially exceeding clinical trial banner advertisement benchmarks (0.1-0.3%) and healthcare industry Facebook advertisement standards (0.83%). Website announcements generated the highest volume (52.54% of total clicks), followed by mass emails (28.00%). This study provides a replicable informatics framework for implementing analytics-driven digital recruitment campaigns for clinical trials. The methodology demonstrates how clinical trial recruiters can integrate analytics platforms and referral partners to optimise outreach and achieve performance substantially above industry benchmarks.
There is a lack of breast radiologists in Norway and in Europe. Artificial intelligence (AI) offers an alternative to solely human readers and has demonstrated promising results in cancer detection in mammographic screening. We aimed to estimate the potential reduction in radiologists' workload by replacing one of the two radiologists with AI in screen-reading mammograms in BreastScreen Norway. BreastScreen Norway targets about 680,000 women aged 50-69 who are invited biennially. The participation rates for each screening round are about 75%. All the screening mammograms are independently read by two radiologists. We collected information about the number of radiologist positions from all 16 breast centers in the country in 2024, while the number of screening examinations performed and the time spent on screen-reading and consensus were extracted from the screening database. We used 1 min for each screen-reading to estimate the screen-reading workload performed by the radiologists and calculated the time saved if one reader were replaced by AI. Screen-reading required a total of 6.5 man-years in BreastScreen Norway. Implementing AI as one of the two readers is thus able to reduce the screen-reading workload by 50%, from 6.5 to 3.3 man-years. The workload reduction corresponds to a reduction from 9% to 4.5% of the total workload for radiologists at the Norwegian breast centers. Implementation of AI in mammographic screening has the potential to reduce the screen-reading workload for breast radiologists. The reduced screening volume is of moderate influence on the overall workload for breast radiologists. Question How much radiology time is expected to be saved if AI were used as one of the two readers of screening mammograms? Findings Use of AI as one of the two readers, reducing the screen reading volume by 50%, was of moderate influence on the total workload for breast radiologists. Clinical relevance Implementing AI was shown to have limited potential in saving radiologists' time in screen-reading mammograms. The main benefit of implementing AI in screen-reading might thus be related to increased sensitivity of the screening test.
Written communication has changed dramatically in the computer era. Email use has exploded in the last three decades, from the inter-individual level through to mass dissemination. In the workplace, it has become the default mode of communication despite very limited critical appraisal. Specifically in the healthcare setting, its utility as a communication strategy for broadcasting information has barely been examined. The aim of this study was to quantify the use of all-staff emails (ASE) across a public hospital and its umbrella Hospital and Health Service (HHS). An audit of one year's ASE was performed to determine the number, word count and readability using validated metrics. The median was used as a reference text. Seventy five randomly selected volunteers from the five occupation groups in the health service were timed whilst reading the reference text to enable an estimate of time and financial cost. Five hundred and six ASE were identified, with a median length of 622 words (95% CI 570 to 694). The median Flesch-Kincaid (FK) Grade score was 8.9 (95% CI 8.8 to 9.1). The calculated predicted salary costs if all staff read all ASE in 1 year were AU$6 116 386.47 (range AU$3 441 365.32 to AU$13 424 704.98) at the hospital level and AU$14 555 209.62 (range AU$8 258 984.03 to AU$31 778 109.75) at the HHS level. A modest reduction in annual ASE burden could result in considerable savings for hospitals and hospital health services, which could be reinvested into patient care, procedures and staff wellbeing. Further research would improve the limited understanding of the impacts of ASE and build an evidence base for how to optimise their use.
Asphalt is frequently used as road pavement and consists of bitumen as a binder, and fillers. Bitumen consists of a complex mixture of hydrocarbons, where a minor component is polycyclic aromatic hydrocarbons (PAHs). Many PAHs are classified as carcinogenic to humans. Bitumen fumes from road paving have been classified as possibly carcinogenic. Paving and milling are open processes generating asphalt fumes, mechanically generated dust particulate matter and diesel exhaust, which the asphalt workers are exposed to. Ultrafine particles (UFPs) are present in both asphalt fumes and diesel exhaust. The aim was to characterize occupational exposure of milling and road paving with a novel multi-metric approach by using real-time monitors and offline methods. Additional aims were to monitor asphalt workers' skin contamination of PAHs by skin wiping, and to biologically monitor their systemic exposure to PAH in urine. Personal exposure measurements of lung deposited surface area (LDSA), particle number concentration (PNC), particulate mass (PM0.3), average particle size, organic carbon (OC), elemental carbon (EC), equivalent black carbon, 16 US Environmental Protection Agency (EPA) PAHs, and nitrogen dioxide (NO2) were performed on millers and pavers in a field study. Skin wipe samples (palm) and urine samples were collected before and after workshifts and were analysed for PAH and PAH metabolites, respectively. Repeated self-administered samplings of 16 US EPA PAHs and NO2 were performed twice by the millers and pavers. The pavers had the highest average exposure to all exposure metrics, except for OC and NO2. Their geometric mean (GM) exposures to PNC and LDSA were 31,000/cm3 and 80 µm2/cm3, respectively. The GM exposure to 16 US EPA PAHs, OC, EC, and NO2 were 0.29, 21, 0.75, and 31 µg/m3, respectively. The millers' GM exposures to PNC and LDSA were 29,000/cm3 and 67 µm2/cm3, respectively. Their GM exposure to 16 US EPA PAHs, OC, EC, and NO2 were 0.053, 40, 0.40, and 83 µg/m3, respectively. The self-administrated sampling of 16 US EPA PAH and NO2 showed that the exposures were in the same range as in the field study, increasing the validity of the results. Pavers showed significantly higher levels of PAH on the palm after the workshift compared with millers. Millers showed higher levels of benzo[a]pyrene on their palm after the workshift compared with pavers. The urinary levels of PAH metabolites were significantly increased in pavers after the workshift. This study showed that millers and pavers were exposed to airborne 16 US EPA PAHs, UFPs, OC, and diesel exhaust. With a study design that involved repeated exposure measurements for each participant, more accurate exposure characterization and assessment of PAHs and NO2 were obtained. By using portable aerosol monitors, valuable exposure data for novel metrics, including UFPs, could be obtained. Operators of, eg, rollers and milling machines were exposed to multiple peak exposures during the workshift. Millers were exposed to somewhat elevated levels of the carcinogenic particulate PAHs. As biomonitoring generally is measuring metabolites of gaseous and intermediate molecular mass PAHs, particulate PAH exposure could not be detected. Air and skin exposure measurements were vital in order to detect this exposure. Recommendations for reducing occupational exposure are proposed.
Translation initiation and termination are critical regulatory checkpoints in protein synthesis, yet accurate computational prediction of their sites remains challenging due to training data biases and the complexity of full-length transcripts. To address these limitations, we present TRANSAID (TRANSlation AI for Detection), a novel deep learning framework that accurately and simultaneously predicts translation initiation (TIS) and termination (TTS) sites from complete transcript sequences. TRANSAID's hierarchical architecture efficiently processes long transcripts, capturing both local motifs and long-range dependencies. Crucially, the model was trained on a human transcriptome dataset that was rigorously partitioned at the gene level to prevent data leakage and included both protein-coding (NM) and non-coding (NR) transcripts. This mixed-training strategy enables TRANSAID to achieve high fidelity, correctly identifying 73.61% of NR transcripts as non-coding. Performance is further enhanced by an integrated biological scoring system, improving "perfect ORF prediction" for coding sequences to 94.94% and "correct non-coding prediction" to 82.00%. The human-trained model demonstrates remarkable cross-species applicability, maintaining high accuracy on organisms from mammals to yeast. Beyond annotation, TRANSAID serves as a powerful discovery tool for novel coding events. When applied to long-read sequencing data, it accurately identified previously unannotated protein isoforms validated by mass spectrometry (76.28% validation rate). Furthermore, homology searches of high-scoring ORFs predicted within NR transcripts suggest a strong potential for identifying cryptic translation events. As a fully documented open-source tool with a user-friendly web server, TRANSAID provides a powerful and accessible resource for improving transcriptome annotation and proteomic discovery.
PURPOSE: To evaluate whether AI can substitute for the first reader in a double-reading workflow for lung-cancer detection on screening chest radiographs. METHODS: A retrospective analysis was conducted in a screening cohort at Ishikawa Health Service Association that included 155,503 participants undergoing 320,329 examinations between January 2018 and September 2020. From examinations initially identified as suspected lung cancer by the conventional double-reading system (n = 2,882), prespecified exclusions were applied, yielding 1,847 examinations for detection-performance analysis. AI-based lesion detection was retrospectively performed using three AI models, and the localization accuracy of the AI outputs was evaluated. Detection performance (AI vs. first readers) was compared using McNemar’s test with a non-inferiority margin of − 0.05 (AI deemed non-inferior if the lower bound of the 95% CI exceeded − 0.05) in two settings: (1) all lesions and (2) pulmonary nodule/mass only. The false-positive rate per examination was estimated using 5,784 normal examinations (5,689 participants) performed between January and June 2018 with ≥ 2-year negative follow-up. RESULTS: For all abnormalities, each AI model met the non-inferiority criterion relative to first readers and showed higher detection rates (AI detection, 62.5–77.3%; first readers, 59.3%). Similar findings were observed when the analysis was limited to nodule/mass only (AI, 64.5–76.5%; first readers, 59.2%). False-positive frequencies per examination were 0.081 (Software A), 0.065 (Software B), and 0.147 (Software C), versus 0.002 for first readers. CONCLUSIONS: In a retrospective screening cohort, three AI models achieved non-inferior, overall higher detection performance compared with first readers for suspected lung cancer on chest radiographs. Despite higher false-positive rates, AI could feasibly assume the first-reader role within a conventional double-reading workflow while maintaining diagnostic quality. Prospective, multi-center studies are warranted to confirm effectiveness, quantify workflow impact, and assess downstream consequences of AI-assisted single reading.
Trophoblast cell surface antigen 2 (TROP2) is a popular current antibody-drug conjugate target due to its high expression in various solid tumor types. With the recent FDA-approval of sacituzumab govitecan and datopotamab deruxtecan in breast cancer, TROP2-targeting therapies showed promising clinical outcomes. Despite the prevalent expression of TROP2 in breast cancer (about 90%), the objective response rates from clinical trials are around 30% with nonsignificant hazard ratios for benefit from sacituzumab govitecan in low TROP2-expressing tumors. This suggests that there may be value in a companion diagnostic assay to measure TROP2 protein expression in tumor samples, but H-score assessment is not required. Here, we develop a quantitative immunofluorescence (QIF) assay and a quantitative hematoxylin-DAB (QH-DAB) assay for potential use as a future companion diagnostic test. TROP2 peptide concentrations were measured in cell lines using mass spectrometry to convert fluorescent signal or chromogen optical density to protein concentrations in amol/mm 2 . Coupled with QuPath-based image analysis tool Qymia, our QIF and QH-DAB assays have limits of detection of 90 amol/mm 2 and 667 amol/mm 2 , and limits of quantifications of 272 amol/mm 2 and 2021 amol/mm 2 , respectively. Using these assays, we measured the TROP2 expression in a breast cancer serial cohort (N=264) and a triple-negative breast cancer cohort (N=100) from Yale New Haven Hospital, identifying a broad dynamic range of TROP2 expression in breast cancer samples. Since the H-score method is the current standard of practice in the clinics, we selected 68 breast cancer biopsies and compared the measurements from our assays to H-scores evaluated by 5 certified pathologists. There is an overall agreement between our QIF and QH-DAB measurements and pathologists' readings. However, the quantitative assay has better sensitivity and less subjectivity. In summary, these assays allow for an accurate and reproducible TROP2 protein measurement that could be incorporated into a clinical workflow. This work represents only analytic validation, but work on clinical validation has begun. In the future, this assay may help identify patients who are more likely to benefit from TROP2-targeted therapies.
Thousands of short open reading frames (sORFs) are translated outside of annotated coding sequences. Recent studies have pioneered searching for sORF-encoded microproteins in mass spectrometry (MS)-based proteomics and peptidomics datasets. Here, we assessed literature-reported MS-based identifications of unannotated human proteins. We find that studies vary by three orders of magnitude in the number of unannotated proteins they report. Of nearly 10,000 reported sORF-encoded peptides, 96% were unique to a single study, and 12% mapped to annotated proteins or proteoforms. Manual curation of a benchmark dataset of 406 manually evaluated spectra from 204 sORF-encoded proteins revealed large variation in peptide-spectrum match (PSM) quality between studies, with immunopeptidomics studies generally reporting higher quality PSMs than conventional enzymatic digests of whole cell lysates. We estimate that 65% of predicted sORF-encoded protein detections in immunopeptidomics studies were supported by high-quality PSMs versus 7.8% in non-immunopeptidomics datasets. Our work stresses the need for standardized protocols and analysis workflows to guide future advancements in microprotein detection by MS towards uncovering how many human microproteins exist.
Despite the availability of advanced data processing tools for single particle inductively coupled plasma mass spectrometry (SP-ICP-MS), users cannot fully trust on them to obtain reliable and accurate information due to their lack of validation. Along this work, current approaches for data processing have been evaluated in depth, paying special attention to the criteria and expressions used for calculation of critical values concerning the discrimination of baseline and particle readings, the counting of particle events and the determination of their total intensities, in order to promote their harmonization within the field, although focusing on quadrupole instruments. Baseline intensity was the most critical variable, since its magnitude determines which approach, Poisson or Gaussian, must be applied for discrimination of baseline and particle readings depending on its magnitude. Application of the corresponding approaches with a coverage factor of 5 led to the occurrence of less than 10 false positives (baseline readings considered as particle events) in a variety of experimental conditions (baseline intensities, number of readings, dwell times). The use of less demanding coverage factors (e.g., 3) led to increased false positives, particularly in the presence of nano- and microparticles and working at short dwell times, due to the higher occurrence of low-intensity particle events. Therefore, such conditions should be avoided. Processing data from nano and microparticle suspensions measured at different dwell times and baseline levels with the free-access and open-source tool SPCal, resulted in reliable counting numbers and total intensities when the adequate critical values were applied. Consequently, this tool allowed the validation of a proprietary software as a proof of concept, confirming comparable results, except for the counting of particle events with high baseline levels or when using short dwell times, as long as the proposed approaches for the calculation of critical values, which were not originally implemented in such proprietary software, were applied.
The vast majority of small-molecule active pharmaceutical ingredients (APIs) are formulated in the crystalline state, for reasons including thermodynamic stability, ease of purification and characterisation, and better control over polymorphism. However, the selective crystallisation of polymorphic APIs provides a significant hurdle to overcome, especially in the case of API co-crystals. Herein we report a series of low-molecular-weight organogels (LMWGs) which can be used to selectively crystallise APIs. In solution, these LMWGs (2-10 mg mL-1) self-assemble through hydrogen bonding to form stable gels which feature nano-structured morphologies. When utilised as crystallisation media, these LMWGs can influence crystal growth, as evidenced by the discovery of two novel 1 : 1 co-crystals of chlorzoxazone with nicotinamide and chlorzoxazone with isonicotinamide. This work highlights the potential of LMWGs as another means of controlling API crystallisation.
The functional roles of non-conventional peptides (NCPs) encoded by short open reading frames (sORFs) are increasingly recognized. However, their evolutionary conservation among closely related species remains largely unexplored. This study presented a genome-wide identification of NCPs in the hybrid poplar 84K (P. alba × P. glandulosa), and analyzed NCPs' sequence conservation across six sections of the Populus genus. Using LC-MS/MS with a custom six-frame-translated genome database, 516 conventional peptides (CPs), and 337 NCPs were indentified. NCPs exhibited distinct properties, including shorter length and lower molecular weight, compared to CPs. Tissue-specific expression patterns were prominent, with peptides functionally linked to photosynthesis in leaves, cell wall biosynthesis in stems, and nutrient uptake in roots. Allelic analysis revealed a parent-of-origin expression bias for over 10% of peptides, each set enriched in distinct metabolic pathways. Notably, NCP sequences were significantly less conserved than CPs across the genus, though specific conserved motifs were identified. This work provides the first systematic NCP resource for a model hybrid tree, establishing a foundational platform for leveraging peptide biology in molecular forestry and hybrid breeding.
Pancreatic islet transplantation (ITx) improves glycemic management and prevents severe hypoglycemia in select individuals with type 1 diabetes (T1D). However, insulin independence cannot be guaranteed due to transplant and recipient-specific factors, limiting broad application. Exercise improves insulin sensitivity, reduces chronic inflammation, increases insulin-independent glucose uptake, and can decrease the risk of surgery-related complications. Therefore, we examined the utility of pretransplant exercise in the context of ITx on metabolic and immune outcomes. Streptozotocin (STZ)-induced diabetic rats performed voluntary, 1-h wheel-running exercise in an overnight-fasted state on three nonconsecutive days weekly for 4 wk preceding transplantation. Exercised (RUN, n = 7) and sedentary (SIT, n = 6) rats underwent marginal syngeneic ITx. Graft efficacy was compared with nonfasted blood glucose readings and a 4-wk intraperitoneal glucose challenge. At 4 wk posttransplant, 100% (7/7) of RUN rats became euglycemic compared with 66% (4/6) in the SIT condition (P < 0.05). RUN recipients demonstrated superior nonfasting blood glucose and weight gain (P < 0.05) and comparable glucose tolerance to naïve rats, whereas SIT rats had inferior clearance (P < 0.05), despite comparable proportions of insulin and glucagon graft-positive cells. Reduction in RUN adipose tissue macrophages suggests lower inflammation levels alongside greater insulin sensitivity based on the quantitative insulin-sensitivity check index (QUICKI) (P < 0.01). Moreover, the soleus muscle of RUN recipients had lower levels of pyruvate dehydrogenase phosphorylation at serine 232 (P < 0.05), and increased levels of phosphorylated glycogen synthase kinase (P < 0.05), suggestive of increased carbohydrate oxidation and insulin signaling, respectively. Altogether, we demonstrate that pretransplant exercise may enhance glycemic outcomes in ITx due to lower inflammation levels and increased carbohydrate oxidation.NEW & NOTEWORTHY To our knowledge, there have yet to be any formal investigations into the role of prehabilitation exercise in ITx. Our exciting preliminary work highlights a noninvasive and feasible approach that may improve the outcomes of this functionally curative procedure. Our findings presented support additional investigation into prehabilitation in ITx, which can encompass a wide array of exercise regimens.
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.
The continuous increase of population dose due to ever-rising Computed Tomography (CT) examinations has called for more personalized dose estimations in medical imaging - a far from trivial task. This study aims to demonstrate a GPU-enabled pipeline combining automatic segmentation with GPU Monte Carlo Dose (GPUMCD) simulations to provide patient-specific dose-to-organ CT dosimetry reports using existing patient CT images. A dynamic representation of the CT imaging process was reproduced within GPUMCD using information in DICOM headers, complemented by in-house exposure measurements, and validated in homogeneous and anthropomorphic phantoms. A dose pipeline was implemented using GPUMCD and a pre-trained open-source nnU-net model (TotalSegmentator). Dose-to-organ dosimetry was obtained for images from a lung cancer screening program and stored in DICOM-compliant Structured Reports. GPUMCD calculated dose values were within 5.5% of measurements for all phantoms and investigated conditions. Utilizing one A100-SXM4-40GB GPU, the average pipeline runtime was 6 min and 06 s per CT study. The GPU-driven simulation and segmentation operation took 46% (2 min and 7 s) of the total runtime, and data processing (file reading, conversion, and writing) occupied the remaining 54%. This work demonstrates the ability to generate patient-specific three-dimensional dose distributions in CT within a few seconds and the subsequent feasibility of performing fully automated mass personalized dose-to-organ calculations. The pipeline ingests and produces DICOM-compliant data compatible with clinical and research environments, enabling routine imaging dosimetry and large-scale retroactive dosimetry studies.
Micropeptides are emerging as a previously hidden layer of the human proteome, redefining the long-standing separation between coding and noncoding genomic regions. Once dismissed as translational noise, sORFs embedded within lncRNAs, circRNAs, pseudogenes, and UTRs are now recognized as a reservoir of functional peptides that regulate core cellular programs, including metabolism, mitochondrial function, immune signaling, and stress adaptation. In cancer, micropeptides exert dual and context-dependent roles: oncogenic micropeptides such as SMIM30, circPDHK1-241aa, and PDL1P41 promote proliferation, angiogenesis, immune escape, and therapeutic resistance, whereas tumor-suppressive peptides, including HOXB-AS3, CIP2A-BP, SPAR, and ASRPS, restore metabolic homeostasis, reactivate the PP2A axis, inhibit mTORC1, and block tumor vascularization. Their small size, modular structure, and tissue specificity make them ideal biomarkers for liquid biopsies and attractive substrates for peptide-based and mRNA-encoded therapeutics. Emerging frameworks integrating single-cell proteogenomics, Ribo-seq, mass spectrometry, and deep-learning-based structural inference are accelerating micropeptide discovery and annotation. Synthetic biology now enables the rational design of micropeptide-based therapeutic constructs, including tumor-specific mRNA-encoded peptides, CRISPR-activated peptide circuits, and targeted peptide chimeras. Collectively, micropeptides represent a transformative paradigm, bridging genomics and proteomics, and establishing the hidden proteome as a new frontier in precision oncology, immunotherapy development, and programmable cancer therapeutics.
A major scientific drive is to characterize the protein-coding genome, which is a primary basis for studying human health. But the fundamental question remains of what has been missed in previous analyses. Over the past decade, the translation of non-canonical open reading frames (ncORFs) has been observed across human cell types and disease states1-3, with major implications for biomedical science. However, a key gap in knowledge has been which ncORFs produce small microproteins or alternative protein molecules that contribute to the human proteome. Here we report the collaborative efforts of the TransCODE Consortium4 to produce a consensus landscape of protein-level evidence for ncORFs. We show that about 25% of a set of 7,264 ncORFs gives rise to detectable peptides in a large-scale analysis of 95,520 proteomics experiments. We develop an annotation framework for ncORF-encoded microproteins as human proteins and codify the new conceptual model of 'peptideins' as microproteins that have indeterminate potential as functional proteins. To probe the biological implications of peptideins, we create an evolutionary analysis approach, termed ORF relative branch length (ORBL), and determine that evolutionary constraint is common and associates with observation of ncORF-derived peptides. We then characterize a pan-essential cellular phenotype for one peptidein from the OLMALINC long non-coding RNA. Overall, we generate public research tools supported by GENCODE and PeptideAtlas and advance biomedical discovery for understudied components of the human proteome.
In recent years, the frequent occurrence of public health emergencies has posed serious threats to human health, leading to a broad international consensus on the necessity of enhancing national health literacy. In response, the Chinese government has integrated health education into the national education system as a key strategic measure aimed at improving population health. The health literacy level of physical education (PE) teachers, who serve as primary agents in implementing school-based health education, is essential for ensuring the delivery of high-quality health education. Functional health literacy, as the foundational component of overall health literacy, plays a critical role in the development and enhancement of individual health competencies. In this study, we developed a structural model of functional health literacy specific to Chinese PE teachers, aiming to evaluate their own literacy levels and their contributions to school-based health education. Qualitative data were primarily collected through semi-structured interviews. A total of 16 PE teachers from 11 provinces across China mainland were purposively selected as study participants. We employed a grounded theory technical approach to analyze the data using NVIVO 20.0 qualitative analysis software. The functional health literacy model for PE teachers comprises four core dimensions: reading and understanding health knowledge, numerating and calculating health data, communicating and appreciating health performance, recognizing and valuing health values. Reading and understanding health knowledge contains four categories: Health and safety emergency method, health concepts and general knowledge, sports-related health knowledge, medical health knowledge. Numerating and calculating health data include two categories: body mass index and indicators, physical fitness test data. Communicating and appreciating health performance contains three categories: health communication, health identification, health performance. Recognizing and valuing health values contains two categories: health responsibility awareness, significance and value of health. The proposed model offers a theoretical framework to support the professional transition of Chinese PE teachers into dual-role educators specializing in both physical education and health instruction. Applying this model with teacher education programs is expected to enhance the quality and effectiveness of health education in Chinese primary and secondary schools.
Parkinson's disease is associated with amyloid aggregation of alpha-synuclein, which could be affected by the proteins of the SARS-CoV-2 coronavirus, possibly accelerating and provoking neurodegeneration. The purpose of this work was to compare the effects of the N-protein and the receptor binding domain (RBD) of the S protein on fibrillization of the alpha-synuclein preparation produced using an original technique that excludes presence of non-native forms of alpha-synuclein that alter kinetics of the process. Presence of an elongated form of alpha-synuclein in the previously studied protein preparations is associated with the erroneous reading of the rare for E. coli TGA stop codon in the pET33b(+) expression plasmid as tryptophan, which led to the continued translation to the next stop codon. To prevent this effect, a new plasmid design was suggested with replacement of the original stop codon with a double stop codon TAA, which made it possible to obtain a homogeneous protein preparation without the admixture of alpha-synuclein with increased molecular weight. It has been shown that the N-protein is able to accelerate alpha-synuclein fibrillization, while the RBD of the S protein inhibits aggregation. According to the electron microscopy data, structure of the fibrils formed in the presence of viral proteins is also different. The obtained data are important for understanding the mechanisms of development of post-covid synucleinopathies, as well as consequences of vaccination with the viral proteins.
Enteric infectious diseases claim more than 1 million lives annually and are among the top ten causes of death in children younger than 5 years. Remarkable global investment has been dedicated to enteric infectious disease prevention and control; however, the shifting global health landscape is testing the continuance of progress. To evaluate the current status and guide future interventions, we present the latest epidemiological estimates of enteric infectious diseases from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2023 and assess progress towards the Global Action Plan for the Prevention and Control of Pneumonia and Diarrhoea (GAPPD) mortality target of fewer than 20 deaths per 100 000 children younger than 5 years by 2025. We quantified the incidence, mortality, and disability-adjusted life-years (DALYs) of enteric infectious diseases by age, sex, and year across 204 countries and territories from 1990 to 2023. In GBD 2023, the following were considered under the category of enteric infectious diseases: diarrhoeal diseases, enteric fever (typhoid and paratyphoid), invasive non-typhoidal Salmonella spp (iNTS) infections, and other intestinal infectious diseases. We also examined 15 aetiologies contributing to diarrhoeal diseases. Incidence and prevalence were estimated with DisMod-MR (version 2.1), a Bayesian meta-regression tool, drawing on data from systematic reviews, population-based surveys, claims data, and hospital sources. Cause-specific mortality was modelled with Cause of Death Ensemble Modelling based on data from sources including vital registration, mortality surveillance, verbal autopsy, and minimally invasive tissue sampling. Years of life lost and years lived with disability were computed and combined to derive DALYs. For aetiology-specific estimation, population-attributable fractions (PAFs) for 15 pathogens were derived with a counterfactual framework. Point estimates and 95% uncertainty intervals (UIs) were generated from 250 draws from the posterior distribution. In 2023, enteric infectious diseases resulted in an estimated 1·27 million (95% UI 0·963-1·68) deaths globally, declining from 3·69 million (3·04-4·56) in 1990. The global age-standardised mortality rate (ASMR) decreased from 74·1 (62·0-92·9) per 100 000 population to 16·4 (12·6-21·3) per 100 000 population during the same period. Diarrhoeal diseases accounted for most deaths in 2023 (1·11 million [0·811-1·54]), followed by enteric fever and iNTS. South Asia and sub-Saharan Africa remained the most affected regions in 2023, with 599 000 (441 000-882 000) and 501 000 (373 000-648 000) deaths due to enteric infectious diseases, respectively, predominantly from diarrhoeal disease. Rotavirus was the leading cause of all-age diarrhoeal disease deaths (PAF 16·3% [12·0-21·5]), followed by norovirus (10·2% [2·4-17·0]) and Shigella spp (9·3% [5·4-15·2]). Among children younger than 5 years, PAFs of deaths due to diarrhoeal diseases were 40·2% (32·5-48·5) for rotavirus, 24·0% (15·1-36·7) for Shigella spp, and 23·4% (13·7-34·3) for adenovirus. Across 204 countries and territories, 141 met the GAPPD mortality target in 2023. The driving aetiologies among countries that did not meet the target in 2023 varied slightly by GBD super-region, but the highest or second-highest number of deaths in children younger than 5 years were consistently attributed to rotavirus. Astrovirus and sapovirus, newly included in GBD 2023, were responsible for 24 600 (6290-49 000) and 18 800 (4650-44 400) deaths, respectively, in 2023, mainly in children younger than 5 years. Our findings show that mortality and ASMRs of enteric infectious diseases declined substantially between 1990 and 2023. This decline is consistent with the expansion of public health measures and broader socioeconomic development. However, the burden in 2023 remains considerably high, with the highest mortality concentrated in sub-Saharan Africa and south Asia. Considering that more than a quarter of all countries had yet to meet the GAPPD mortality target in 2023, sustained efforts are needed to address the persistent burden in affected countries and to adapt to the changing global health landscape. Gates Foundation.