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Portable metagenomics for preventive surveillance and outbreak control in livestock and poultry: Pathogen detection, resistome profiling, and antimicrobial stewardship.

Conventional diagnostics for livestock and poultry outbreaks commonly rely on culture or targeted PCR panels, which may be too slow or too narrow to guide early control decisions. Portable metagenomics, particularly real-time nanopore sequencing, offers a route to broad pathogen detection, antimicrobial-resistance gene profiling, and outbreak investigation within an integrated workflow. This implementation-focused review evaluates how near-point-of-care metagenomics may support preventive veterinary medicine through earlier detection, surveillance, cohorting, biosecurity decisions, and antimicrobial stewardship. We synthesize sample-to-answer workflows for enteric and respiratory disease in food-producing animals, including sampling, nucleic-acid extraction, host depletion or target enrichment, library preparation, sequencing, bioinformatics, quality control, and interpretation. Applications in calf diarrhea, bovine respiratory disease, poultry outbreaks, mastitis, and resistome monitoring are considered alongside the central limitation that detection alone does not establish causation. Pathogen and resistance-gene signals must therefore be interpreted with clinical signs, lesions, epidemiology, controls, and confirmatory testing. We also propose a minimum reporting checklist, intended as a practical framework rather than a validated consensus standard. Portable metagenomics is not a replacement for conventional diagnostics, but appropriately validated workflows can reduce uncertainty during time-sensitive outbreaks and support more judicious antimicrobial use.

Animals

Urinary Small Extracellular Vesicle DNA as a Biomarker for the Non-Invasive Diagnosis of Bladder Cancer.

Existing diagnostic technologies for bladder cancer (BC) suffer from low sensitivity, low specificity, or a lack of validation. Therefore, validated, non-invasive diagnostic biomarkers with high sensitivity and specificity for early detection of BC are needed to complement and improve upon the limitations of existing diagnostic methods. We used low-pass whole genome sequencing (LP-WGS) technology to detect copy number variations (CNVs) in small extracellular vesicle (sEV) DNA isolated from urine samples of patients. Based on these results, we constructed and validated a diagnostic model to differentiate between benign and malignant bladder lesions. We conducted a receiver operating characteristic analysis and calculated the area under the curve (AUC) to evaluate the performance of the diagnostic model. The urine sEV-DNA LP-WGS data revealed CNV differences between benign and malignant samples. The diagnostic model achieved an AUC of 0.953, a sensitivity of 86.7%, and a specificity of 100% in the training cohort and an AUC of 0.985, a sensitivity of 90%, and a specificity of 100% in the validation cohort. Even at the lowest coverage depth of 0.01X, the performance of the diagnostic model remained relatively robust. Notably, the performance of this diagnostic model surpassed that of the biomarker neuron-specific enolase (sensitivity: 85.7% vs. 64.3%; specificity: 100% vs. 87.5%) and urinary cytology (sensitivity: 100% vs. 66.7%; specificity: 100% vs. 94.1%). Our study demonstrates that urine sEV-DNA exhibits high discriminatory power in distinguishing between benign and malignant bladder lesions, making it a promising tool for auxiliary diagnosis of BC.

Humans

Recent advances in electrode materials for electrochemical detection of zearalenone.

Zearalenone (ZEN) is an estrogenic mycotoxin commonly found in cereals, animal feed, and processed foods, making it an important concern for food safety and public health. Conventional chromatographic and immunological methods can detect ZEN; however, they often require expensive instruments, lengthy sample preparation, and skilled personnel, which restrict their use for rapid and on-site testing. Electrochemical sensors have attracted enormous interest of the scientific community because of their high sensitivity, rapid response, low cost, miniaturization potential, and compatibility with portable systems. The analytical performance of the electrochemical sensors is strongly influenced by electrode materials, morphology, conductivity, porosity, surface functionality, and the efficiency of bioreceptor immobilization. Despite several reviews on mycotoxin detection, a systematic assessment connecting electrode-material design, modification strategies, sensing mechanisms, and electroanalytical performance specifically for ZEN sensing remain limited. This review critically evaluates recent advances in metal oxides, carbon-based materials, metal-organic- and covalent organic frameworks, MXenes, polymers, and hybrid composites for electrochemical ZEN detection. Particular attention has been given to their roles in electron transfer, analyte enrichment, selectivity, and real-sample analysis. The review also compares the major limitations of current sensing systems, including complex fabrication, matrix interference, insufficient long-term stability, poor inter-electrode reproducibility, and limited scalability. Finally, future directions for developing robust, cost-effective, portable, and commercially viable ZEN sensors are discussed.

Journal Article

Diagnostic performance of panfungal PCR on tissue specimens for the diagnosis of invasive fungal diseases: a systematic review and meta-analysis of the Fungal PCR Initiative (FPCRI).

UNLABELLED: Invasive fungal diseases are difficult to diagnose because of the limited sensitivity of culture. Panfungal PCR amplicon sequencing assays (targeting ribosomal RNA, such as 18S, 28S, ITS) are recommended for fungal identification in histopathology samples showing fungal elements. However, data describing its overall performance and consistency are lacking. This systematic literature review and meta-analysis assessed the performance of panfungal PCR on formalin-fixed paraffin-embedded (FFPE) and non-fixed (fresh or frozen) tissue samples. A systematic literature search was performed to include studies reporting the use of panfungal PCR for fungal identification in FFPE or non-fixed tissue samples. PCR sensitivity and specificity were assessed using the reference standard of histopathology showing fungal elements. Quality assessment was performed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Pooled estimates were obtained using random-effects meta-analysis. Twenty-eight studies were included. In FFPE samples (18 studies, 852 samples), sensitivity and specificity were 75.4% (95% confidence interval [CI], 59.2-86.6) and 93.5% (70.2-98.9), respectively. Sensitivity in non-fixed samples (13 studies, 207 samples) was 86.5% (74.7-93.3), while specificity could not be assessed (insufficient data). Comparative analyses showed a significantly higher sensitivity of panfungal PCR over culture (88.2%; 76-94.7 vs 52.2%; 39-65, P = 0.001). Sub-analyses could not demonstrate the superiority of one PCR target over another due to limited data. Panfungal PCR exhibited adequate sensitivity and good specificity in FFPE samples. Sensitivity was even higher in non-fixed samples and largely superior to culture. Nevertheless, large interstudy variability was observed, warranting interlaboratory studies to define the optimal PCR target and standardized protocols. IMPORTANCE: Invasive fungal diseases are difficult to diagnose because of the low sensitivity of culture. Panfungal PCRs are widely used for fungal identification in tissue specimens but suffer from heterogeneous procedures and performance. This meta-analysis shows an acceptable sensitivity (75.4% and 86.5% in fixed and non-fixed samples, respectively) and good specificity (93.5%) of panfungal PCR, supporting its use, not only on histopathology-positive fixed samples but also in non-fixed samples concomitantly with other diagnostic tools (cultures and fungal-specific PCRs if available). These results provide a strong basis for further standardization of panfungal PCR techniques via interlaboratory assays to assess reproducibility and optimize analytical protocols. CLINICAL TRIALS: This study is registered with PROSPERO as CRD42023461148.

Humans

Can ChatGPT Replace Human Clinical Coders? A Comparative Study in Otology Billing.

OBJECTIVE: Evaluate the utility of the large language model (LLM), ChatGPT, for the analysis of operative notes and the generation of Current Procedural Terminology (CPT) codes in comparison to human clinical coders. STUDY DESIGN: CPT billing codes assigned by ChatGPT were compared to existing billing data. Otology practice within a tertiary academic center. METHODS: About 191 operative notes from a single surgeon (9/2022-10/2023) were analyzed. ChatGPT-3.5 and 4 models were prompted for CPT codes based on operative notes. Assessment included determining exact and partial match rates, sensitivity and specificity for targeted procedures, and work Relative Value Units (wRVU) differences between ChatGPT-generated and human-assigned codes. RESULTS: ChatGPT-3.5 achieved exact matches in 22% of cases and partial matches in 32%, while ChatGPT-4 achieved 14% exact and 33% partial matches. When cochlear implantation (CI) was excluded, performance dropped significantly. For CI, ChatGPT-3.5 demonstrated a sensitivity of 94% and specificity of 90%, while ChatGPT-4 showed a sensitivity of 96% and specificity of 92%. In contrast, performance on cartilage grafting was poor, with sensitivities of 4.2% for ChatGPT-3.5 and 0% for ChatGPT-4. ChatGPT-3.5 and 4 showed moderate CPT code matching accuracy among themselves, with slight agreement to human coders. Both models tended to underbill for wRVUs compared to human coders, with significant differences in the values generated. CONCLUSION: This study assessed ChatGPT's effectiveness in automating CPT code assignment for otologic surgeries. While the models achieved high sensitivity values for assigning codes related to cochlear implantation, both models struggled with complex cases, failed to apply modifiers, and often assigned fewer wRVUs. The findings highlight ChatGPT's potential in medical billing but indicate a need for further refinement.

Humans

A validated sensitive LC-MS/MS method and its application in elucidating the unique ocular pharmacokinetic profile of 0.01% atropine underpinning its clinical utility for myopia.

A sensitive liquid chromatography-tandem mass spectrometry (LC-MS/MS) method was developed and validated to quantify atropine in ten rabbit ocular tissues enabling systematic characterization of the ocular pharmacokinetic profile of 0.01% atropine sulfate eye drops after a single topical administration. The method demonstrated excellent linearity (coefficient of determination, R2&#xa0;&#x2265;&#xa0;0.9908) across all matrices, with lower limits of quantification (LLOQ) of 0.05&#xa0;ng/mL for most tissues and 0.10&#xa0;ng/mL for retina and lens; intra- and inter-day accuracy, precision, matrix effects, extraction recoveries, and stability all met the acceptance criteria. Following a single bilateral topical dose (50&#xa0;&#x3bc;L/eye) in New Zealand White rabbits, atropine distributed rapidly into all 12 ocular compartments (the sclera further divided into three anatomical regions) with marked heterogeneity-the highest exposures were found in conjunctiva and cornea, a distinct anterior-to-posterior concentration gradient was observed in the sclera, sustained retention was noted in the retina (mean residence time from zero to the last measurable time point, MRT0-t 3.30&#xa0;h), while aqueous and vitreous humor eliminated rapidly (elimination half-life, t&#x2081;/&#x2082;&#xa0;<&#xa0;0.7&#xa0;h), and all tissues except aqueous humor followed a two-compartment model. This validated method and the comprehensive pharmacokinetic data reveal that topically applied 0.01% atropine achieves sustained exposure in key myopia-regulating tissues (retina, choroid, posterior sclera) with low exposure in side-effect target tissues (iris, ciliary body, lens).

Animals

Development and Crossover Evaluation of an Artificial Intelligence-Assisted System for Solid Pancreatic Lesion Detection and Pancreatic Parenchyma Recognition in Endoscopic Ultrasonography (With Video).

BACKGROUND AND STUDY AIMS: Pancreatobiliary endoscopic ultrasonography (EUS) is technically demanding, and supervised training opportunities are limited. We developed an artificial intelligence (AI) overlay system for detecting solid pancreatic lesions (SPL) and recognizing pancreatic parenchyma (PP) and evaluated its effect on reader performance. PATIENTS AND METHODS: Across six centers, two deep learning-based models were trained using expert-annotated EUS frames. We then conducted a randomized, two-sequence, two-period crossover reader study in which eight endosonographers (five novices and three experts) interpreted image sets with and without AI assistance. The primary endpoint was superiority of sensitivity for SPL detection among novices; key secondary endpoints included specificity and PP recognition. RESULTS: From 118 patients, 120 SPL-positive/negative image sets and 160 PP-positive/negative image sets were constructed. Among novices, AI assistance improved SPL detection sensitivity (88.7% vs. 76.8%, p&#x2009;<&#x2009;0.001) and accuracy (86.4% vs. 78.7%), while specificity met the predefined noninferiority criterion (84.2% vs. 80.5%, p&#x2009;<&#x2009;0.001). For PP recognition, sensitivity increased numerically (86.3% vs. 83.3%) but did not meet the predefined superiority criterion (p&#x2009;=&#x2009;0.095); specificity met the noninferiority criterion (87.8% vs. 81.0%), and accuracy increased from 82.1% to 87.0%. Among experts, sensitivity was maintained for both tasks, whereas specificity increased with AI assistance. CONCLUSIONS: AI assistance improved SPL detection among novice endosonographers. For PP recognition, sensitivity increased without reaching statistical superiority, whereas specificity met the predefined noninferiority criterion. These findings support a potential adjunctive role for AI in EUS interpretation.

Humans

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&#x2011;day all&#x2011;cause unplanned readmission risk in this population. METHODS: A prospective cohort study was conducted, including 1050 patients aged&#xa0;&#x2265;&#xa0;60&#xa0;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&#x2011;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&#xa0;<&#xa0;0.05) in 13 factors including age, smoking, AIP, TyG index, HALP score, etc. Multivariable Logistic regression identified age (OR&#xa0;=&#xa0;9.752), smoking (OR&#xa0;=&#xa0;5.171), AIP (OR&#xa0;=&#xa0;6.691), TyG index (OR&#xa0;=&#xa0;4.393), HALP score (OR&#xa0;=&#xa0;2.831), and&#xa0;&#x2265;&#xa0;2 comorbidities (OR&#xa0;=&#xa0;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&#xa0;=&#xa0;0.884, accuracy&#xa0;=&#xa0;0.829, sensitivity&#xa0;=&#xa0;0.812, specificity&#xa0;=&#xa0;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&#x2011;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

Detoxifying biotransformation of chloramphenicol by Exiguobacterium sp. CAP4 and its bioaugmentation of chloramphenicol biodegradation in simulated wastewater.

The extensive use of chloramphenicol (CAP) in livestock leads the accumulation of CAP in livestock manures, threatening environmental and human health. Therefore, eliminating or reducing CAP concentration in manures before its re-utilization and application through microbial remediation is necessary. Exiguobacterium sp. CAP4, isolated from the plastisphere in duck manures, was capable of degrading CAP with the biodegradation efficiency of 97.8 % at initial CAP concentration of 5 mg/L within 4 days. A total of twenty-four biotransformation products were determined, including two novel transformation products, TP166 and TP203, enriched the integrity of CAP biodegradation pathways. Furthermore, the biotransformation process was proposed as a detoxifying process through biotransformation products toxicity evaluation. Notably, Exiguobacterium sp. CAP4 successfully colonized in the cow manures after inoculation, and bioaugmented the biodegradation of CAP in virgin cow manures. This study significantly extended our understanding of the CAP biotransformation fate, and provided a promising bacterial strain for bioremediation of CAP containing wastewater in situ.

Chloramphenicol

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&#x2011;hydrogen peroxide (TMB-H&#x2082;O&#x2082;) system, enabling visual detection of S. aureus. The biosensor demonstrates a detection limit of 82&#xa0;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

Diagnostic and prognostic value of fibroblast growth factor 23 in acute kidney injury: systematic review and meta-analysis.

Background: Acute kidney injury (AKI) is associated with high mortality and adverse outcomes. Fibroblast growth factor 23 (FGF23) has emerged as a potential biomarker for AKI; however, its diagnostic and prognostic utility remains inconsistent.Methods: We conducted a systematic review and meta-analysis of studies evaluating circulating intact FGF23 (iFGF23) or C-terminal FGF23 (cFGF23) (PROSPERO: CRD42022302659). PubMed, EMBASE, CNKI, and Wanfang databases were searched through June 9, 2026. QUADAS-2 was used for quality assessment. A random-effects bivariate model pooled sensitivity, specificity, positive/negative likelihood ratio (PLR/NLR), diagnostic odds ratio (DOR), and area under the summary receiver operating characteristic curve (SROC AUC).Results: Twenty-three studies were included: 17 diagnostic, 6 prognostic (one addressing both). For AKI diagnosis, the pooled sensitivity was 0.79 (95% CI 0.73-0.86), specificity 0.82 (95% CI 0.75-0.89), PLR 4.40 (95% CI 2.59-6.21), NLR 0.25 (95% CI 0.16-0.34), DOR 17.49 (95% CI 8.67-35.16), and SROC AUC 0.87 (95% CI 0.81-0.92). Substantial heterogeneity was observed (I2 = 67%), with iFGF23 demonstrating higher accuracy than cFGF23 (AUC 0.91 vs 0.81). For AKI mortality, pooled sensitivity was 0.77 (95% CI 0.69-0.84), specificity 0.76 (95% CI 0.70-0.82), DOR 10.89 (95% CI 6.86-17.30), and SROC AUC 0.77 (95% CI 0.70-0.83). Significant heterogeneity was noted (I2 = 86.2% for sensitivity, 80.4% for specificity). No significant publication bias was detected.Conclusions: Circulating FGF23 exhibits moderate-to-high diagnostic and moderate prognostic performance in AKI, though interpretation is limited by substantial heterogeneity. It may serve as a complementary biomarker for risk stratification, pending further validation with standardized protocols.

Humans

Efficacy of current approaches to non-invasive diagnosis of skin cancer and the potential impact of artificial intelligence: A systematic review and meta-analysis.

BACKGROUND: Skin cancer is one of the most prevalent malignancies worldwide, particularly within Caucasian populations. This systematic review and meta-analysis aimed to quantitatively review the current literature on non-invasive diagnosis of skin cancer and evaluate the current evidence to support the use of tools in addition to, or in replacement of clinician face-to-face assessment. METHODS: A literature search was conducted for publications in PubMed, Medline and Embase databases. Articles describing accuracy, sensitivity, specificity and outcomes of their mode of assessment were included. A total of 208 articles met the inclusion criteria. RESULTS AND CONCLUSION: This systematic review and meta-analysis showed that the diagnostic performance of artificial intelligence (AI) in the interpretation of dermatoscopic images was high for melanoma diagnosis, basal cell carcinoma or malignancy, in comparison to dermatoscopic assessment alone by clinicians and experts. Although AI interpretation of images demonstrated higher sensitivity for melanoma diagnosis in comparison to clinical assessment combined with dermatoscopic assessment, it is unclear if this is also the case for basal cell carcinoma and squamous cell carcinoma diagnosis. Reflectance confocal microscopy, a non-invasive high resolution imaging technique, is known to have a high sensitivity for diagnosing cutaneous malignancy, and this may have applications within secondary care. Therefore, AI could help reduce resource burden and aid in clinical assessment, particularly within primary care settings.

Humans

The effect of zalunfiban on high sensitivity cardiac troponin and the association with clinical outcomes in patients with STEMI.

BACKGROUND: Among individuals with ST-segment elevation myocardial infarction (STEMI), a single subcutaneous injection of the short-acting glycoprotein IIb/IIIa receptor blocker antagonist zalunfiban at first medical contact significantly improved the primary outcome including clinical endpoints. The impact of zalunfiban on Myocardial Infarction (MI) size and association with downstream outcomes remains unclear. METHODS: In a prespecified analysis, we studied results among study participants treated with 2 doses of zalunfiban who had core laboratory measurements concentrations of hs-cTnT. RESULTS: More elevated hs-cTnT concentrations at presentation were associated with less resolution of ST deviation (P = .006) and more frequent Q wave development (P < .001). At coronary angiography more elevated hs-cTnT at presentation was associated with higher thrombus grade and worse epicardial and myocardial perfusion (all P < .05). In multivariable analyses, higher hs-cTnT concentrations at 24 hours were associated with greater adjusted risk for all-cause death (odds ratio [OR] 1.83 per log unit increase; P = .03), cardiovascular death (OR 1.83 per log unit increase; P = .03), heart failure (OR 2.74 per log unit increase; P < .001) or the composite of death and heart failure (P < .001) by 30 days. At 24 hours, those treated with zalunfiban had lower hs-cTnT compared to placebo (P = .04) and across multiples &#x2265; 10 to &#x2265; 1,000 times elevation, treatment with zalunfiban resulted in smaller hs-cTnT determined MI size. CONCLUSIONS: Among patients with STEMI, more elevated concentrations of hs-cTnT are associated with worse measures of reperfusion and higher-risk for short-term death or heart failure. A single dose of zalunfiban at first medical contact reduced MI size. TRIAL REGISTRATION: A phase 3 study of zalunfiban in subjects with ST-elevation MI (CELEBRATE); NCT04825743.

Humans

Boosting kynurenic acid in kombucha via substrate selection: metagenomic and biochemical insights.

Kombucha is gaining global popularity for its health benefits. This study explored the use of chestnut honey, a rich source of kynurenic acid (KYNA), to produce kombucha enriched with this metabolite. Five variants were prepared using different green/black tea blends and carbon sources: white sugar or acacia honey (controls) versus chestnut honey. Samples were analyzed for tryptophan metabolites, physicochemical properties, and microbial diversity. Komagataeibacter and Enterobacter were predominant bacterial genera in SCOBY. Candida and Aspergillus were predominated in the single sample analyzed for fungi. During fermentation, tryptophan decreased, while kynurenine increased. KYNA levels remained largely stable during fermentation and were mainly influenced by the fermentation substrate. No melatonin pathway derivatives were detected. On day 7, chestnut honey yielded kombucha with 381.680-739.915&#xa0;&#x3bc;mol/L KYNA and elevated myricetin. Overall, chestnut honey-based kombucha represents a system in which substrate composition appears to be the main factor influencing KYNA levels in the final beverage.

Kynurenic Acid

Effects of an internet-based combined exercise and cognitive-behavioral therapy intervention on endocannabinoid system biomarkers and physical fitness in adults with mild-to-moderate depression: a SONRIE randomized controlled trial.

BACKGROUND: This SONRIE randomized controlled trial (NCT05849792) examined the effects of a 12-week combined physical exercise and internet-based cognitive-behavioral therapy (iCBT) intervention on endocannabinoid system (ES) biomarkers and physical fitness in adults with mild-to-moderate depression. METHODS: Eighty adults were randomly assigned 1:1 to an intervention (IG) or control (CG) group. Outcomes included nine ES biomarkers (2-arachidonoylglycerol, 2-AG; anandamide, AEA; seven analogues) and physical fitness, including cardiorespiratory fitness (CRF; 6-minute walking test) and muscular strength. Measurements were taken at baseline, post-intervention and 8-week follow-up. Primary analysis performed 2&#x2009;&#xd7;&#x2009;3 repeated-measures ANOVA on completers; mixed-effects intention-to-treat model as sensitivity analysis. RESULTS: No significant time &#xd7; group interaction was detected for any ES biomarker, including 2-AG (F(2,88)&#x2009;=&#x2009;0.17, p&#x2009;=&#x2009;0.844) and AEA (F(2,88)&#x2009;=&#x2009;1.20, p&#x2009;=&#x2009;0.306); both groups showed comparable within-group decreases in 2-AG, 2-LG and 2-OG. The intervention significantly improved CRF [between-group difference&#x2009;+&#x2009;81.6&#xa0;m at 12 weeks (F(2,78)&#x2009;=&#x2009;7.88, p&#x2009;=&#x2009;0.001)], exceeding the established minimal clinically important difference. None of the exploratory muscular fitness outcomes reached statistical significance; the arm curl test showed a borderline non-significant interaction (F(2,78)&#x2009;=&#x2009;2.83, p&#x2009;=&#x2009;0.065). CONCLUSION: A 12-week internet-based combined exercise and iCBT intervention significantly improved CRF in adults with mild-to-moderate depression. We did not find evidence of an intervention-specific effect on plasma ES biomarkers. These findings support the inclusion of internet-delivered exercise and psychological interventions in comprehensive treatment strategies for depression. TRIAL REGISTRATION: ClinicalTrials.gov NCT05849792 (registered 6 May 2023).

Humans

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

HRAS promotes mutant NRAS-driven transformation with codon and allele specificity.

Wild-type RAS family members determine the signaling and therapeutic response in cancers driven by mutant HRAS and KRAS because they activate alternate RAS effector pathways. Here, we found that the requirement for wild-type RAS to support mutant NRAS-driven transformation correlated with codon-specific differences in GTP hydrolysis. NRAS with mutations at either Gly12 (G12X) or Gly13 (G13X), which retained the GDP-GTP cycling function, had modest autonomous transforming potential. In contrast, NRAS with GTP-locking mutations at Gln61 (Q61X mutants) was uncoupled from receptor tyrosine kinase (RTK) input, rendering wild-type RAS an obligate partner for RTK-stimulated signaling and oncogenesis. In RASless cells expressing mutant NRAS, reintroduction of wild-type HRAS was sufficient to restore signaling and transformation. Global dependency mapping in human cancer cells revealed functional partitioning, wherein mutant NRAS promoted MAPK signaling and wild-type HRAS promoted PI3K-AKT survival signaling. Consequently, allele-specific or pan-RAS(ON) inhibitors synergized with inhibitors of proximal RTK signaling or of wild-type HRAS or KRAS to overcome this signaling plasticity. Pan-RAS(ON) and HRAS inhibition was synergistic for all NRAS mutants tested, with Q61X mutants showing greater sensitivity. These findings define the signaling partnership between mutant NRAS and wild-type HRAS as a targetable vulnerability and provide a biochemical blueprint for dual RAS inhibition in NRAS-mutated malignancies.

Humans

Changes in hemoglobin levels and cardiometabolic health in adults with metabolic syndrome - a secondary outcome analysis of a six-month randomized controlled trial.

BACKGROUND: Lower hemoglobin (Hb) levels within the normal range have been associated with favorable metabolic traits in cross-sectional studies. This study investigated whether changes in Hb levels correlated with changes in physiological and cardiometabolic parameters during a six-month behavioral intervention in individuals with metabolic syndrome. METHODS: The&#xa0;six-month randomized controlled trial aimed to reduce sedentary behavior in adults with metabolic syndrome (n&#x2009;=&#x2009;64). Key measurements included fasting blood samples, insulin sensitivity during a hyperinsulinemic-euglycemic clamp, insulin-stimulated liver glucose uptake, liver fat content (LFC), indirect calorimetry, cardiorespiratory fitness, and cardiac function. Correlations&#xa0;between changes in these variables and changes in Hb levels at baseline, three, and six months were examined. RESULTS: Cross-sectionally, higher Hb levels correlated with&#xa0;lower insulin sensitivity (r=-0.35, p&#x2009;=&#x2009;0.005), higher resting O2 consumption (r&#x2009;=&#x2009;0.41, p&#x2009;<&#x2009;0.001), higher resting energy expenditure (r&#x2009;=&#x2009;0.49, p&#x2009;<&#x2009;0.001), higher LFC (r&#x2009;=&#x2009;0.40, p&#x2009;=&#x2009;0.011), and greater&#xa0;left ventricular wall thickness (r&#x2009;=&#x2009;0.42, p&#x2009;=&#x2009;0.001). The intervention did not significantly impact Hb levels, and changes in Hb levels did not correlate with most cardiometabolic changes. However, reduced Hb levels correlated with reduced fasting blood glucose (r&#x2009;=&#x2009;0.29, p&#x2009;=&#x2009;0.032), improved insulin sensitivity (r = -0.26, p&#x2009;=&#x2009;0.045), and increased cardiorespiratory fitness (r = -0.29, p&#x2009;=&#x2009;0.033). CONCLUSIONS: Changes in Hb levels did not consistently correlate with changes in cardiometabolic markers during&#xa0;the intervention. However, reductions in Hb levels may relate to improved insulin sensitivity and fitness. Along&#xa0;cross-sectional correlations, this may be clinically relevant for individuals with metabolic syndrome. Further studies are merited to clarify&#xa0;the role of Hb levels in this high-risk group.

Humans