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PubMed2026

Machine learning-based prediction of unplanned readmission and construction of an online calculator for elderly patients with mild ischemic stroke.

OBJECTIVE: To screen for independent risk factors for unplanned readmission in elderly patients with mild ischemic stroke, and to construct and validate an online risk prediction calculator based on an interpretable machine learning model, thereby providing a promising practical tool for accurate clinical assessment of 30‑day all‑cause unplanned readmission risk in this population. METHODS: A prospective cohort study was conducted, including 1050 patients aged ≥ 60 years with mild ischemic stroke admitted between August 2023 and September 2024. Participants were randomly divided into a training set (840 cases) and a test set (210 cases) at a ratio of 8:2. Risk factors were screened by univariate analysis and multivariable Logistic regression. Four machine learning models, namely LightGBM, XGBoost, Random Forest, and K‑Nearest Neighbors (KNN), were developed and their performance was evaluated using AUC, accuracy, sensitivity, and specificity as metrics. The SHAP framework was used for interpretability analysis, and an online calculator was subsequently developed based on the optimal model. RESULTS: Univariate analysis showed significant differences (P < 0.05) in 13 factors including age, smoking, AIP, TyG index, HALP score, etc. Multivariable Logistic regression identified age (OR = 9.752), smoking (OR = 5.171), AIP (OR = 6.691), TyG index (OR = 4.393), HALP score (OR = 2.831), and ≥ 2 comorbidities (OR = 3.664) as independent risk factors. All four machine learning models demonstrated good predictive performance. Based on a comprehensive evaluation of multiple metrics and computational efficiency, the LightGBM model exhibited the best predictive performance (AUC = 0.884, accuracy = 0.829, sensitivity = 0.812, specificity = 0.875). SHAP analysis showed that age, AIP, TyG index, smoking, and HALP score were key predictors. An online calculator developed based on this model enables individualized risk predictions. CONCLUSION: Key risk factors associated with 30‑day unplanned readmission in elderly patients with mild ischemic stroke were identified. The LightGBM model demonstrated high predictive accuracy, and together with the interpretability analysis and online calculator, offers a practical tool to support clinical risk assessment. However, this tool requires future external validation.

Humans↗

PubMed2026

Unanticipated Effects of Parental Social Media Use: Guidance for Clinicians.

"Sharenting," the practice of parents posting photographs, videos, and information about their children on social media, has an ever-growing presence in modern society. However, researchers and the public are now recognizing the potential consequences of sharing, including creation of permanent digital footprints, strained familial relationships, and threats to children's safety. Despite this emerging evidence, no U.S. clinical or legal guidelines exist for parents on safe sharing. Visits with behavioral health providers and family physicians can serve as key points for intervention. This column aims to provide clinicians with a better understanding of sharenting and its potential effects on patients and families, guidance on discussing safe online sharing, and a tool for parents and other caregivers to use when deciding whether to post.

Humans↗

PubMed2026

Leveraging traveller genomics for LMIC diarrhoeal disease management.

Diarrhoeal pathogens impose a substantial global health burden, disproportionately affecting low- and middle-income countries (LMICs). However, in these settings, health-seeking behaviours, suboptimal microbiological capacity, and challenges in establishing genomics capacity constrain effective surveillance, including surveillance of antimicrobial resistance (AMR). In contrast, high-income countries routinely generate and share large volumes of diarrhoeal pathogen genomes through established systems, with a significant proportion originating from travellers returning from LMICs. These data reveal strong geographical structuring of lineages and clinically relevant AMR patterns, demonstrating untapped potential to support improvements in geographically granulated surveillance to support antimicrobial treatment recommendations. In this opinion article, we outline the potential to integrate traveller-derived microbial genomic data into LMIC public health decision-making and highlight the scientific, ethical, practical, and governance considerations for implementation.

antimicrobial resistance↗

PubMed2026

Evaluation of the clinical and mechanistic role of MCM2 expression in the prediction of meningioma recurrence after radiotherapy.

OBJECTIVE: Postoperative radiotherapy is an effective treatment for meningiomas; however, treatment response varies among patients. In addition, practical methods for predicting tumor recurrence after radiotherapy have not been well established. Minichromosome maintenance protein 2 (MCM2), a key regulator of DNA replication licensing, was recently implicated in highly proliferative molecular subtypes of meningioma. In this study, the authors evaluated whether MCM2 immunohistochemical expression predicts response to radiotherapy in patients with meningiomas. METHODS: The authors retrospectively analyzed the records of patients with WHO grade 1-3 meningiomas treated with resection followed by radiotherapy at a single institution between July 2003 and November 2023. The MCM2 labeling index was assessed immunohistochemically, and patients were stratified into MCM2-high and -low groups using a cutoff of 35%. Progression-free survival (PFS) was defined as the interval from the completion of radiation therapy to postoperative radiological tumor recurrence or regrowth. Patients who showed no progression were censored at their last follow-up. PFS was estimated using Kaplan-Meier analysis and subsequently evaluated with Cox proportional hazards models. To further investigate the biological mechanisms associated with MCM2 expression, comprehensive transcriptomic analyses, including gene set enrichment analysis, was performed to elucidate the molecular processes that occur within MCM2-high tumors. RESULTS: The study population included 15 men (42%) and 21 women (58%), with a mean age of 63 years. Ten tumors (28%) were classified as MCM2-high meningiomas and 26 (72%) as MCM2-low meningiomas. High MCM2 expression was significantly associated with WHO grades 2-3 histology and higher Ki-67 labeling indices. During a median follow-up of 2.52 years, tumor progression after radiotherapy occurred in 47% of the patients. High MCM2 expression (HR 8.34, p = 0.03) was significantly associated with shorter PFS and remained an independent predictor of recurrence after adjustment for WHO grade, tumor size, and Ki-67 labeling index. Transcriptomic analyses of MCM2-high tumors revealed upregulation of cell proliferation-related pathways, accompanied by increased signaling through the E2F8-CHEK1 axis associated with radiation resistance and suppression of the TNF-α signaling pathway implicated in radiosensitivity. CONCLUSIONS: In meningiomas, high MCM2 expression is associated with early recurrence following radiotherapy. The study findings suggest that this association is driven by diverse biological mechanisms related to cell cycle regulation and radioresistance. Immunohistochemical assessment of MCM2 expression may serve as a practical and accessible biomarker for risk stratification and may support the future development of individualized postoperative radiotherapy strategies.

Humans↗

PubMed2026

The landscape of pruning for large language models: A systematic review and unified taxonomy.

Confronting the inherent tension between the exceptional capabilities and the immense computational costs of Large Language Models (LLMs), pruning has become a crucial technique for achieving efficient deployment. However, a systematic analytical framework dedicated specifically to LLM pruning remains absent. In this paper, we aim to bridge this gap. We first elucidate the theoretical foundations that underpin the effectiveness of pruning, namely overparameterization and redundancy, and then propose a multidimensional taxonomy that organizes existing approaches along the axes of granularity, timing, and criteria. Building upon this unified perspective, we further analyze performance recovery mechanisms and the broader evaluation ecosystem, while also exploring forward-looking challenges such as interpretability, automation, and hardware-algorithm co-design. Through this comprehensive synthesis, we seek to provide an integrated and coherent analytical lens for advancing both research and practice in LLM pruning.

Large Language Models↗

PubMed2026

Nurse-Led Home-Based Mobile Health Cardiac Rehabilitation Program for Patients With Chronic Heart Failure: A Randomized Controlled Trial.

This 12-week randomized controlled trial evaluated a nurse-led mHealth intervention for patients with chronic heart failure, conceptually informed by Riegel's middle-range theory of self-care of chronic illness. The program integrated wearable activity tracking with weekly nurse-led behavioral coaching, reflecting the core self-care processes of monitoring, maintenance, and management. Compared with usual care, the intervention significantly improved daily step count, 6-minute walk distance, metabolic equivalents, and left ventricular ejection fraction. Findings highlight the effectiveness of theory-informed, nurse-delivered mHealth strategies in enhancing physical activity and cardiopulmonary function, while underscoring the critical role of advanced practice nurses in home-based chronic disease management.

Aged↗

PubMed2026

Smoke-Free Home Intervention in Permanent Supportive Housing: A Cluster Randomized Clinical Trial.

IMPORTANCE: Chronic diseases related to tobacco use and secondhand smoke exposure are the leading causes of death among formerly homeless adults living in permanent supportive housing (PSH) in the US. OBJECTIVE: To evaluate the efficacy of a brief, smoke-free home intervention in promoting voluntary smoke-free home adoption among PSH residents. DESIGN, SETTING, AND PARTICIPANTS: In this cluster randomized clinical trial, data collection occurred from January 11, 2022, to March 31, 2025. Participants were residents aged 18 years or older who smoked cigarettes at home in 40 multiunit PSH sites in the San Francisco Bay area, randomized to intervention or waiting list control clusters, and housing staff who worked at the study sites. INTERVENTION: Residents in intervention sites received one-on-one in-person coaching from research staff on adopting a smoke-free home; waiting list control site residents received no interventions during the study but were offered the intervention once the intervention group completed follow-up. Staff in both intervention and control sites received training on providing brief tobacco cessation coaching. MAIN OUTCOMES AND MEASURES: Primary outcomes were smoke-free home adoption for at least 90 days and 7-day carbon monoxide-verified point prevalence abstinence (PPA; expired carbon monoxide level ≤5 ppm) at 6 months. Secondary outcomes were any adoption (≥1 day) of a smoke-free home in the past 90 days among residents and changes in Smoking Knowledge, Attitudes, and Practices (S-KAP) scores among staff. RESULTS: The trial enrolled 400 residents (mean [SD] age, 54.5 [10.7] years; 251 [63.1%] male), 191 in the intervention and 209 in the control cluster. At 6 months, 13 residents (6.8%) in the intervention and 10 (4.8%) in the control group adopted a smoke-free home for at least 90 days (odds ratio [OR], 1.45; 95% CI, 0.69-3.07). Few residents achieved 7-day PPA, though more intervention residents (12 [6.3%]) achieved it compared with controls (2 [1.0%]) (OR, 6.94; 95% CI, 1.69-28.45). Intervention residents had greater odds than control residents of any smoke-free home adoption of at least 1 day (121 [63.4%] vs 77 [36.8%]; adjusted OR, 3.83 [95% CI, 2.63-5.57]). Among staff, mean (SD) S-KAP scores increased at 6 months vs baseline for beliefs (by 0.21 [0.55] points) and practices (by 0.24 [0.61] points) pertaining to providing cessation treatment. CONCLUSIONS AND RELEVANCE: In this cluster randomized clinical trial, the brief intervention did not result in a significant increase in smoke-free home adoption for at least 90 days, though more residents in the intervention than the control group attempted adoption for at least 1 day. These findings support the scalability of this approach to reduce smoking in PSH, but more intensive interventions may be needed to sustain intervention effects. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04855357.

Humans↗

PubMed2026

From fear to empowerment: the impact of employees AI awareness on workplace well-being - a new insight from the JD-R model.

PURPOSE: The primary purpose of the study was to explore the impact of health workers' awareness of artificial intelligence (AI) on their workplace well-being, addressing a critical gap in the literature. By examining this relationship through the lens of the Job demands-resources (JD-R) model, the study aimed to provide insights into how health workers' perceptions of AI integration in their jobs and careers could influence their informal learning behaviour and, consequently, their overall well-being in the workplace. The study's findings could inform strategies for supporting healthcare workers during technological transformations. DESIGN/METHODOLOGY/APPROACH: The study employed a quantitative research design using a survey methodology to collect data from 420 health workers across 10 hospitals in Ghana that have adopted AI technologies. The study was analysed using OLS and structural equation modelling. FINDINGS: The study findings revealed that health workers' AI awareness positively impacts their informal learning behaviour at the workplace. Again, informal learning behaviour positively impacts health workers' workplace well-being. Moreover, informal learning behaviour mediates the relationship between health workers' AI awareness and workplace wellbeing. Furthermore, employee learning orientation was found to strengthen the effect of AI awareness on informal learning behaviour. RESEARCH LIMITATIONS/IMPLICATIONS: While the study provides valuable insights, it is important to acknowledge its limitations. The study was conducted in a specific context (Ghanaian hospitals adopting AI), which may limit the generalizability of the findings to other healthcare settings or industries. Self-reported data from the questionnaires may be subject to response biases, and the study did not account for potential confounding factors that could influence the relationships between the variables. PRACTICAL IMPLICATIONS: The study offers practical implications for healthcare organizations navigating the digital transformation era. By understanding the positive impact of health workers' AI awareness on their informal learning behaviour and well-being, organizations can prioritize initiatives that foster a learning-oriented culture and provide opportunities for informal learning. This could include implementing mentorship programs, encouraging knowledge-sharing among employees and offering training and development resources to help workers adapt to AI-driven changes. Additionally, the findings highlight the importance of promoting employee learning orientation, which can enhance the effectiveness of such initiatives. ORIGINALITY/VALUE: The study contributes to the existing literature by addressing a relatively unexplored area - the impact of AI awareness on healthcare workers' well-being. While previous research has focused on the potential job displacement effects of AI, this study takes a unique perspective by examining how health workers' perceptions of AI integration can shape their informal learning behaviour and, subsequently, their workplace well-being. By drawing on the JD-R model and incorporating employee learning orientation as a moderator, the study offers a novel theoretical framework for understanding the implications of AI adoption in healthcare organizations.

Humans↗

PubMed2026

Statistical test to compare the linkage model and the admixture model based on central limit results.

In the Admixture Model, the probability that an individual carries a certain allele at a specific marker depends on the allele frequencies in K ancestral populations and the proportion of the individual's genome originating from these populations. The markers are assumed to be independent. The Linkage Model is a Hidden Markov Model that extends the Admixture Model by incorporating linkage between neighboring loci. We prove consistency and asymptotic normality of maximum likelihood estimators for the ancestry of individuals in the Linkage Model, complementing earlier results by (Pfaff et al., 2004; Pfaffelhuber and Rohde, 2022; Heinzel, 2025) for the Admixture Model. These results are used to prove that a statistical test that allows for model selection between the Admixture Model and the Linkage Model is an asymptotic level-α-test. Finally, we demonstrate the practical relevance of our results by applying the test to real-world data from The 1000 Genomes Project Consortium (2015).

Genetic Linkage↗

PubMed2026

Morphology-engineered NiFe@C nanocages boosting electrochemical quantification of ractopamine in meat samples.

It is essential to acquire efficient electrocatalysts to develop ractopamine (RAC) electrochemical sensors. Herein, we report the synthesis of a series of carbon coated NiFe alloy nanostructures (e.g., NiFe@C nanoparticles, nanocubes and nanocages) using NiFe Prussian blue analogue (PBA) as the precursor. The NiFe@C nanocages exhibited the best electrocatalytic performance for RAC sensing. This is attributed to the embedded NiFe alloy nanoparticles that provide abundant active sites, and the unique nanocage structure facilitates electron transfer pathways while offering a high specific surface area. The resulting sensor achieves a low detection limit (LOD) of 54 nM (S/N = 3) within a linear range of 0.2-12 μM. Moreover, the sensor demonstrates good reproducibility, stability, and excellent long-term stability. Practical applicability was confirmed in meat samples, yielding satisfactory recovery rates ranging from 98% to 108%. A feasible strategy was introduced herein for rational design of metal@carbon electrocatalysts.

Phenethylamines↗

PubMed2026

Recent advances in supramolecular macrocycle-based artificial light-harvesting systems.

Artificial light-harvesting systems (ALHSs) inspired by the antenna function of natural photosynthesis provide molecular platforms for collecting excitation energy and directing it to emissive or reactive acceptors. In many supramolecular ALHSs, however, practical performance is limited by poorly defined donor-acceptor orientation, aggregation-caused quenching (ACQ), interfacial defects, and limited stability in aqueous or complex media. Supramolecular macrocycles-particularly pillar[n]arenes (PAs), cucurbit[n]urils (CBs), calixarenes (CAs), cyclodextrins (CDs), and supramolecular coordination complexes (SCCs)-offer a useful design space because their cavities, pre-organized scaffolds, and reversible non-covalent interactions can confine chromophores, tune local donor/acceptor ratios, and modulate Förster resonance energy transfer (FRET). This Review systematically examines the unique structural advantages and assembly mechanisms of the five macrocyclic families, with an emphasis on their use in constructing ALHSs-from single-step to cascaded FRET-and in advancing aqueous photocatalysis, near-infrared bioimaging, panchromatic fluorescence modulation, and singlet oxygen generation. The resulting structure-property-application framework is intended to guide the rational design of macrocycle-assisted photofunctional materials while avoiding overextension of the photosynthesis analogy.

Journal Article↗

PubMed2026

Transcranial Photobiomodulation Variables Assessment Battery: Development and Validation.

Transcranial photobiomodulation (tPBM) response variability is partly driven by biophysical characteristics such as skin tone and hair properties that attenuate photon penetration, and by lifestyle factors including sleep quality, alcohol use, and nicotine consumption that disrupt the mitochondrial and vascular pathways on which tPBM acts. To date, no validated self-report tool exists to capture these moderators systematically. To address this gap, the tPBM Variables Assessment Battery was developed and psychometrically evaluated. It integrates adapted versions of established measures (Brief Pittsburgh Sleep Quality Index, E-cigarette Dependence Scale, Hair Scale Assessment PRO, Monk Skin Tone Scale, and Heaviness of Smoking Index), validated wellbeing evaluators (Ryff's Psychological Wellbeing), and custom measures (Hairstyle Classification, Hair Color Classification). Face and content validity met recommended expert thresholds, internal consistency was acceptable across adapted subscales, and criterion validity analyses confirmed meaningful associations between the lifestyle components and PROMIS-10 global health outcomes. The battery is low-burden, digitally deployable, and psychometrically defensible, offering a practical tool for characterizing the variables most likely to moderate tPBM response in home-use studies.

Humans↗

PubMed2026

Molecular mechanisms of natural de novo shoot organogenesis and their applications.

Natural de novo shoot organogenesis (DNSO) is the spontaneous regeneration of shoots from wound sites outside the shoot apical region through endogenous developmental programs. This regenerative capacity enables plants to recover from severe tissue damage by re-establishing the shoot-root axis. Here, we review current knowledge about the molecular mechanisms of natural DNSO, focusing on transcriptomic and physiological studies in model plants. Accumulating evidence suggests that natural DNSO proceeds through three sequential phases: (i) early wound responses, characterized by the activation of the WIND1-ESR1 module and the establishment of apical-basal auxin asymmetry; (ii) cellular proliferation driven by metabolic and cell-cycle reprogramming; and (iii) cytokinin-mediated establishment of shoot apical meristem identity. We also discuss how these mechanistic insights have been harnessed for practical applications, including tissue culture-free transformation systems such as the cut-dip-budding (CDB) method, and developmental reprogramming strategies that employ ectopic expression of developmental regulator (DR) genes to induce DNSO in otherwise recalcitrant species. Together, these advances illustrate how understanding natural regeneration can guide the development of simplified, broadly applicable plant transformation technologies.

Plant Shoots↗

PubMed2025

Examining Behavioral Interventions for Infancy and Early Toddlerhood: A Systematic Review of Intervention Effects, Parameters, and Participants.

Rapid advancement is paving the way to identify children who would likely benefit from early intervention during the first years of life, prior to the onset of significant delays in development. With the widely acknowledged benefits of early intervention, key questions arise: Does behavioral intervention targeted to infancy and early toddlerhood improve developmental outcomes? What procedures might be used, and under what circumstances? Who do these interventions work for? The current review comprehensively examined the literature on behavioral interventions based in operant learning, focused on key developmental areas with children in the first two years of life. We located and synthesized 69 studies with unique participant cohorts that included 1735 children. The search revealed many studies focused on the first year of life, of which a large proportion investigated approaches to increase communication. We provide implications, limitations, and future directions on how behavioral interventions for infants and young toddlers can inform current practice and future intervention research this population.

Humans↗

PubMed2026

Metal-organic frameworks nanozyme-integrated portable microneedle patch for visual bacterial monitoring in meat.

Foodborne microbial contamination is a major global health concern, with conventional methods often being time-consuming and complex. Herein, we developed a novel portable biosensor by integrating microneedle patch technology and a metal-organic framework (Fe/Cu-NBDC MOF) nanozyme, enabling rapid, on-site, visual detection of bacteria in meat. The sensing system works by encapsulating aptamer-functionalized MOF nanozymes within a hydrogel patch, where their catalytic sites are initially blocked by the aptamer. In the presence of Staphylococcus aureus (S. aureus) as the target, the specific aptamer's binding to bacteria exposes numerous catalytic sites, further activating the chromogenic reaction of the tetramethylbenzidine‑hydrogen peroxide (TMB-H₂O₂) system, enabling visual detection of S. aureus. The biosensor demonstrates a detection limit of 82 CFU/mL with excellent specificity to successfully apply to commercial mutton. By integrating sampling, enrichment, and visual detection into a single compact device, this platform offers a practical, efficient solution for rapid on-site screening of foodborne pathogens.

Biosensing Techniques↗

PubMed2026

Teaching Acute Coronary Syndrome High-Risk ECG Interpretation and Clinical Decision-Making Through FOAMed Videos and Podcast Versus Print-Based Materials Among Emergency Care Providers: Randomized Controlled Mixed Methods Trial.

BACKGROUND: Accurate interpretation of high-risk acute coronary syndrome (ACS) electrocardiograms (ECGs) is essential for early diagnosis and timely reperfusion, yet substantial deficits persist across health care professions. Digital self-learning formats such as FOAMed (Free Open Access Medical Education) are widely used, but their effectiveness has rarely been evaluated for complex, high-risk ACS ECG patterns. Existing ECG education studies often focus on students or single professional groups and established ST-segment elevation myocardial infarction (STEMI) criteria, leaving newer guideline-recognized STEMI equivalents, selected emerging occlusion myocardial infarction (OMI)-related patterns, and interprofessional emergency care underrepresented. OBJECTIVE: This study aimed to compare the effectiveness of FOAMed podcast and videos versus traditional print-based materials for teaching high-risk ACS ECG patterns and related clinical decision-making in emergency providers. METHODS: We conducted a prospective, interprofessional, controlled mixed methods trial across 5 training sites in Germany. Paramedics, prehospital emergency physicians, and emergency department clinicians received either a FOAMed multimedia module or print-based materials through concealed allocation; deviations from the intended 1:1 ratio resulted from participant no-shows. The intervention consisted of a 30-minute supervised self-learning session. In total, 103 participants were allocated to FOAMed (n=45) or print-based materials (n=58). Two coprimary outcomes were assessed: ECG interpretation accuracy and text-based ACS clinical decision-making. Secondary outcomes included subjective confidence, learning experience, and exploratory qualitative free-text responses. Outcome assessment was automated and blinded; mixed ANOVA was the primary analysis. The study was not prospectively registered because it assessed educational outcomes in health care professionals rather than patient health outcomes. RESULTS: All 103 participants completed the study. Both groups improved, with greater gains in the FOAMed group: ECG interpretation increased from 55% to 65.5% and text-based ACS clinical decision-making from 45% to 68%, versus 57% to 60% and from 47% to 63%, respectively, in the print-based group. Effect sizes were η²=0.055 for ECG interpretation and η²=0.044 for clinical decision-making. Exploratory subgroup analyses provided no evidence of differential effects across age, gender, or professional background and were likely underpowered. Qualitative responses (46 and 37 entries) provided contextual insights into perceived clarity, engagement, and practical relevance supporting the quantitative findings. CONCLUSIONS: This study is innovative in directly comparing a curated FOAMed multimedia module with selected print-based materials in an interprofessional emergency care population. It differs from existing research by focusing on subtle, emerging ischemic patterns and evaluating realistic, time-limited self-learning formats. The findings provide evidence that curated FOAMed resources can produce greater short-term improvements in ECG interpretation and text-based ACS clinical decision-making than traditional print-based materials in this setting. Although implications for clinical performance remain hypothetical, concise, high-quality digital modules may represent a practical supplement to structured continuing education in emergency care.

Humans↗

PubMed2026

PdIr bimetallic nanozyme engineered metal-organic frameworks integrated dual-mode sensor toward Stx2 detection in food.

Shiga toxin II (Stx2) has attracted extensive attention due to its toxicity and pathogenicity, making the development of sensitive detection methods urgent. This study constructed a dual-mode sensing platform for the sensitive detection of Stx2 in food. Composite material UIO-66@PdIr with peroxidase-like activity and fluorescent properties was synthesized and combined with cDNA as the signal probe, while aptamer-modified magnetic beads served as the capture probe. Specific binding of Stx2 to the aptamer triggered the release of the signal probe, enabling colorimetric and fluorescence signal readout. The colorimetric mode showed a linear range of 0.05-100 ng/mL with an LOD of 0.039 ng/mL, and the fluorescence mode exhibited 0.01-1000 ng/mL with an LOD of 0.0097 ng/mL. Additionally, this method was successfully applied to the detection of Stx2 in food, and the recovery rates were 94.33% ∼ 102.20%. It indicated that the constructed sensor holds great practical potential for Stx2 detection.

Food Contamination↗

PubMed2026

Metabolic engineering of Candida yeasts for biotechnological applications.

Candida yeasts represent a versatile yet underexploited platform for industrial biotechnology. These yeasts utilize a remarkably broad range of carbon sources, particularly for hydrophobic carbon sources, coupled with robust growth and diverse biosynthetic capacities, making them promising hosts for sustainable production of chemicals, fuels, and proteins. Despite these advantages, industrial deployment of Candida species has been hindered by concerns regarding opportunistic pathogenicity and the historical lack of efficient genetic manipulation tools, leading to a substantial gap between metabolic potential and practical utilization. Recent advances in functional genomics, genome editing, and systems metabolic engineering are rapidly overcoming these barriers, enabling more precise and efficient strain development. In this review, we systematically summarize recent progress in the metabolic engineering of Candida species as microbial cell factories, with particular emphasis on expanding genetic toolkits, utilizting renewable and non-conventional carbon sources, and biosynthesizing high-value compounds. In addition, we propose a biosafety-oriented classification framework to support their safe industrial deployment. Finally, we discuss current challenges and emerging opportunities, emphasizing that the synergy of synthetic biology and artificial intelligence-driven design holds the key to unlocking the biotechnological potential of Candida yeasts.

Candida↗