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PubMed2026

Retinal microstructural alterations as early phenotypes of depression in radiogenomics analysis.

BACKGROUND: With the increasing prevalence of depression, there is an urgent clinical need for early screening in depression. The retina offers a promising window for early screening in depression due to its rapid, non-invasive, objective, eye-brain correlated characteristics, but previous research has yielded conflicting alterations in retinal microstructure in depression. METHODS: We screened retinal optical coherence tomography and brain magnetic resonance imaging data in the UK Biobank to enroll 23,225 participants for retinal study of depression occurrence, and 1475 participants for the eye-brain association study. We also used genetic data (ID: ebi-a-GCST90014267 and ukb-d-20,448) from the Integrative Epidemiology Unit Open Genome-Wide Association Study for Mendelian randomization analysis. We used Cox regression to assess the association between retinal microstructure and depression risk, Mendelian randomization to infer causality, and mediation analysis to explore retina-brain pathway association. RESULTS: The Cox regression analysis showed that retinal ganglion cell-inner plexiform layer (GCIPL) thickness remained a significant predictor of depression. The Mendelian randomization analysis indicated a positive statistical association between GCIPL thickness and depression. Moreover, there was a significant positive correlation (all p < 0.001) between the volume of specific depression-related brain regions and the GCIPL thickness. Adjusting for age, sex, and head size, the mediation analysis provided preliminary evidence for a potential anatomical pathway linking retinal GCIPL thickness to depression-related brain regions through primary visual cortex and secondary visual cortex volumes. CONCLUSION: Thickened retinal GCIPL is a potential early phenotype of depression and has a potential association pathway with depression-related brain regions using a radiogenomics approach.

Humans↗

PubMed2026

Three-dimensional porous nano-hydroxyapatite@gelatin composite as efficient adsorbent for uranyl ion removal from low-level radioactive wastewater.

The contamination of water resources by uranyl (UO22+) ions poses significant environmental and health risks, requiring the development of efficient and sustainable remediation strategies. Adsorption-based techniques have emerged as promising approaches in the field of UO22+ removal, but the design of cost-effective, high-capacity, and environmentally friendly adsorbents remains challenging. In this study, a three-dimensional porous nano-hydroxyapatite@gelatin (nHAP@Ge) composite was synthesized through glutaraldehyde cross-linking, combining the structural stability of Ge with the high uranium affinity of nHAP. The optimized nHAP@Ge, with a nHAP:Ge mass ratio of 1:0.5, exhibited exceptional UO22+ removal efficiency (97 %), along with high adsorption capacity (364.03 mg/g). Systematic characterizations using scanning electron microscopy (SEM), thermogravimetric analysis (TGA), Fourier transform infrared (FT-IR) spectroscopy, and X-ray photoelectron spectroscopy (XPS) methods revealed that the porous structure and surface functional groups (-OH, Ca2+, and PO43-) of the material synergistically contributed to binding UO22+ species. Furthermore, the incorporation of nHAP into the Ge framework resulted in enhanced thermal stability while significantly improving the UO22+ adsorption performance. This work presents a scalable, eco-friendly, and recyclable strategy for the effective treatment of uranium-contaminated water, with potential applications in nuclear wastewater treatment and environmental remediation.

Adsorption↗

PubMed2026

Prevalence of Theileria luwenshuni in goats (Capra hircus) on Tarama Island, Okinawa, Japan.

Caprine theileriosis is an economically important tick-borne disease caused by various Theileria species, particularly Theileria lestoquardi, Theileria luwenshuni, and Theileria uilenbergi, in goats (Capra hircus). Goat farming plays an economically and culturally important role on Tarama Island, Okinawa, Japan. Because goats on the island are mainly managed under an extensive grazing system, tick infestation is common. However, Theileria infections have not previously been investigated in goats on Tarama Island. To address this, archived DNA samples prepared from blood collected from 44 goats on Tarama Island were screened using a universal PCR assay targeting 18S rRNA sequences of Theileria and Babesia species. Two DNA samples were positive, and sequencing analysis of the amplicons identified T. luwenshuni. To further investigate the epidemiology of T. luwenshuni on Tarama Island, blood samples were subsequently collected from 96 goats across 19 farms. From each blood sample, a thin blood smear was prepared and genomic DNA was extracted. Microscopic examination of Diff-Quik-stained smears detected intraerythrocytic Theileria-like organisms in 35 (36.5%) goats. In addition, screening of DNA samples using a newly developed T. luwenshuni-specific PCR assay detected 77 (80.2%) positive goats, and the subsequent sequencing analysis confirmed the PCR results. Given that T. luwenshuni can cause severe disease in small ruminants, our findings highlight the importance of managing T. luwenshuni infection in goats on Tarama Island.

Animals↗

PubMed2026

Towards microplastic bioremediation: Fungal degradation of pristine and pretreated high-density polyethylene and polystyrene.

Microplastic (MP) contamination has become a significant ecological issue because of its enduring existence in the ecosystem and its possible negative impacts. Therefore, using degrading strategies to eliminate these stubborn polymers has been a subject of scientific research. However, the currently used degradation methods are relatively inefficient. Given the pervasiveness of High-Density Polyethylene (HDPE) and Polystyrene (PS) and their resistance to biodegradability, disposal strategies are critical and must be addressed. This manuscript examines the biodegradation of pristine and UV-treated HDPE and PS MPs by Aspergillus flavus species in minimal growth media over 70 days. The maximum weight loss observed at 70 days for pristine HDPE and PS in sole carbon source (SCS) media was (29.33 ± 0.28) % and (17.67 ± 0.35) %, respectively. Whereas, for UV-treated HDPE and PS MPs, the % weight reduction was (33 ± 0.21) % and (25 ± 0.19) %, respectively. UV-treated MPs exhibited greater weight reduction, as UV induced oxygenated functional groups enhance polymer susceptibility to enzymes, thereby promoting biodegradation. HDPE MPs typically show a higher proportion of particles in the lower size range compared to PS MPs. This assertion was based on the weight loss, particle size distribution, and SEM analysis. Furthermore, chemical changes were evaluated using Fourier transform Infrared Spectroscopy (FTIR) analysis, which also displayed chemical oxidation occurring during biodegradation. Liquid Chromatography-Mass Spectrometry (LC-MS) results indicate that UV pretreatment enhances biodegradability by promoting chain scission. These findings further suggest that this fungus's natural and ubiquitous occurrence in terrestrial and marine environments may actively contribute to MP biodegradation while requiring few nutrients.

Microplastics↗

PubMed2026

Integrated multi-omics profiling of amniotic fluid identifies predictive biomarkers for fetal growth restriction trajectories.

BACKGROUND: Fetal growth restriction (FGR) is a complex condition with highly heterogeneous clinical outcomes, making prenatal distinction between transient and persistent growth failure challenging. This study aims to identify amniotic fluid (AF) biomarkers capable of differentiating distinct FGR trajectories and characterizing persistent growth failure mechanisms. METHODS: Integrated proteomic and metabolomic profiling was performed on AF samples from transient FGR (n = 11), persistent FGR (n = 9), and healthy controls (n = 13). Diagnostic and prognostic models were developed using multivariate analysis. Selected protein candidates were validated via ELISA in an independent cohort (n = 69). RESULTS: Multi-omics analysis revealed distinct molecular signatures for FGR stratification. A two-protein diagnostic panel (PDGFA and phospho-STAT5A) achieved an AUC of 1.000 in the discovery stage and 0.780 in the external validation cohort. For prognostic assessment, a molecular signature including IREB2, HLA-C, and PLXNB2 accurately predicted persistent growth failure from transient recovery (AUC = 0.966). Cross-platform integration highlighted the mass spectrometry-derived WASHC2C as a central hub protein with a significant progressive increase across the control, transient, and persistent groups (p < 0.001). CONCLUSIONS: This study establishes a multi-omics framework for prenatal FGR stratification. Our findings identify distinct molecular signatures reflecting the intrauterine environment and provide high-performance molecular tools for predicting divergent fetal growth trajectories to guide personalized clinical decision-making.

Humans↗

PubMed2026

Decoding the spatiotemporal patterns of food spoilage microbial communities: Integrating multi-omics and artificial intelligence to enable precision preservation.

In the global food supply chain, food wastage caused by spoilage has resulted in significant economic losses, food shortages, and environmental pressure. This process is fundamentally driven by the spatiotemporal dynamics of microbial communities. However, traditional research methods struggle to elucidate the complex mechanisms of spatial heterogeneity, interspecies interactions, and functional succession. This limits the development of effective preservation strategies. This review systematically reviews the cutting-edge progress of integrating multi-omics technologies and artificial intelligence (AI) to study food spoilage microbial communities, breaking through this bottleneck. We propose an intelligent theoretical framework that could potentially analyze microbial metabolic activities and predict dynamic shelf life if implemented. The conceptual framework integrates multidimensional data, including spatial metabolomics, temporal metatranscriptomics, single-cell transcriptomics, and longitudinal metagenomics. It can also be combined with AI models, such as graph neural networks. The article elaborates on the principles and applications of spatio-temporal monitoring technologies, such as nano secondary ion mass spectrometry, hyperspectral imaging, and the Internet of Things sensing. Through illustrative cases of typical perishable foods, it also explores how such a multi-omics - AI system might be applied to spoilage warning and precise intervention. Additionally, the article addresses the current challenges in data coverage, model generalization, and federated learning implementation. Then the research further explores emerging areas such as engineered probiotics, edge AI, and microfluidic sensing. These areas are targeted at transforming food preservation from an empirical control approach to a data-driven, precise regulatory framework. This transformation provides theoretical support and technical approaches for developing a smart, sustainable food preservation system.

Multiomics↗

PubMed2026

Long-term mortality in pediatric sepsis: a systematic review and meta-analysis.

BACKGROUND: Pediatric sepsis represents a significant factor in the mortality rates among children, with survivors remaining highly fragile during the period following discharge. While in-hospital and short-term mortality have been widely studied, the long-term mortality of pediatric sepsis is not adequately synthesized or appreciated. This study aims to estimate the long-term mortality associated with pediatric sepsis, providing a basis for optimizing post-discharge surveillance and care protocols. METHODS: This systematic review and meta-analysis followed PRISMA guidelines and was registered in PROSPERO (CRD420251137504). Exhaustive searches were conducted in PubMed, Embase, the Cochrane Library, and Web of Science for studies published from the inception of each database to June 30, 2025. Studies reporting long-term mortality in pediatric sepsis patients diagnosed using international consensus criteria were included. After literature screening, long-term mortality was pooled using a random effects meta-analysis in R statistical software. RESULTS: A total of 72,065 records were identified through database searching. After removing duplicates and screening, six studies comprising 11,318 pediatric sepsis patients were included. The pooled long-term mortality in pediatric sepsis was 11% (95% CI: 7-16%), though significant heterogeneity was observed (I2 = 98.2%, p < 0.001). Sensitivity analyses yielded similar results, and evidence of publication bias was limited. CONCLUSION: Long-term mortality after pediatric sepsis was 11%, highlighting the persistent risk of mortality after hospital discharge. Further high-quality longitudinal studies are required to identify modifiable risk factors and guide evidence-based follow-up and personalized care.

Humans↗

PubMed2026

A conserved distal-tail helical extension defines a tailspike attachment architecture in Gram-negative siphophages.

Rapid growth of bacteriophage genome collections has outpaced functional annotation of tail-tip proteins, limiting comparative analysis of host-recognition structures. Starting from a shared distal-tail gene organization in the Salmonella phages 9NA and Jersey, I developed a morphogenetic bioinformatic framework integrating gene synteny, sequence comparison, profile hidden Markov model (HMM) screening, structural evidence, structure-aware searching, and AlphaFold modeling. Comparison with the experimentally characterized lambda and Sf11 tail assemblies identified a predominantly alpha-helical C-terminal extension of the distal-tail (DT) protein associated with tailspike attachment, termed the distal-tail helical extension (DT-helix). Screening 541,986 proteins from 5167 complete NCBI RefSeq tailed-phage genomes, followed by evidence-based evaluation of sequence, genomic context, and structural architecture, identified 165 curated DT-helical-extension-associated phages. Their DT proteins segregated into six sequence groups. In the four principal multi-member groups, cognate tailspikes showed group-specific conservation in proximal N-terminal regions but substantially greater downstream diversity, consistent with sequence constraint at the DT-tailspike attachment boundary. A complementary ProstT5/Foldseek search supported the established groups but revealed no convincing additional highly divergent family. Together with the experimentally characterized Sf11 attachment interface, these findings define a recurrent morphogenetic architecture linking conserved distal-tail scaffolds to more variable receptor-binding proteins across siphophages infecting Gram-negative bacteria. Although universal exchangeability is not established, the identified scaffold-receptor-binding boundaries provide a framework for molecular characterization and rational phage engineering. Accession-level information for the 165 curated phages is available through PhageTailDB.

Viral Tail Proteins↗

PubMed2026

Stable chloroform emissions in southeastern China: insights from recent observations.

Chloroform (CHCl3) is a short-lived ozone-depleting substance not currently regulated under the Montreal Protocol. Due to the unique meteorological conditions in East Asia, CHCl3 emitted in this region has a greater potential to reach the stratosphere and contribute to ozone depletion. As an essential component of national CHCl3 emissions, southeastern China has attracted increasing attention. However, long-term observational data in this region remain relatively scarce, with no updates since 2020. In this study, we continuously measured atmospheric CHCl3 concentrations at a remote monitoring station in southeastern China from 1 June 2023 to 31 May 2025. Frequent concentration enhancements were observed during the monitoring period, with mixing ratios ranging from 9.3 to 134.1 ppt and an average value of 36.8 ± 19.7 ppt. Back-trajectory analysis indicated that air masses associated with elevated CHCl3 levels primarily originated from coastal industrial provinces in eastern China. Using the Potential Source Contribution Function and Concentration Weighted Trajectory methods, we identified the Yangtze River Delta (Jiangsu, Anhui, and Zhejiang Provinces) and Jiangxi Province as dominant source regions. Emissions of CHCl3 in southeastern China were estimated using the Tracer Ratio Method to be approximately 30.1 ± 5.3 Gg/yr from 2023-06-01 to 2025-05-31, indicating overall stability relative to earlier estimates and no apparent upward trend. These findings provide updated insights into the current status of CHCl3 emissions in southeastern China and highlight the need for continued monitoring and emission assessment of CHCl3 in East Asia, given its unregulated status and implications for ozone layer recovery.

Air Pollutants↗

PubMed2026

In situ product monitoring in heterogeneous reaction of gaseous trimethylamine on Fe2O3/Fe(NO3)3: Effect of environmental factor and particle property.

Gas-particle reactions represent an important atmospheric heterogeneous transformation process for organic amines (OAs). Environmental factors and particle properties may impact the gas-particle reaction products. Although the products from gas-particle reactions can be monitored by various in situ techniques, related data remain scarce. Here, the interfacial and gaseous products from the reaction of trimethylamine on Fe2O3/Fe(NO3)3 particles under light irradiation with mixed NO2, O2, SO2 and H2O were monitored using in-situ diffuse reflectance Fourier transform infrared spectroscopy and proton transfer reaction time-of-flight mass spectrometry. Dark reaction of gaseous trimethylamine on Fe2O3/Fe(NO3)3 generated two interfacial products types: N-containing ones (CH3NCH2, CH3NO2, (CH3)2NCHO, and CH3N(OH)CHO) and N-free ones (alcohols, aldehydes and acids), both accumulating with reaction progression. Light irradiation and O2 oxidation enhanced formation of these products, while NO2 promoted the production of CH3NO2 and (CH3)2NCHO. H2O and SO2 occupied the active sites of particles to inhibit the formation of all products. Compared to Fe(NO3)3, Fe2O3 showed absolute dominance in contribution to the formation of products. Considering the smaller particle size of Fe2O3 and excess Fe(NO3)3, the physical mixing of them reduced the generation of interfacial products. Furthermore, gaseous products of CH3OH, HCHO, CH3CHO, HCOOH and CH3COOH detection clarified the N-free interfacial products. The presence of Fe(NO3)3 inhibited the formation of HCOOH and favored the formation of CH3CHO in the gas phase. By combining product information with thermodynamic calculations, the heterogeneous reaction pathways of trimethylamine were tentatively proposed. These findings provide a guiding significance for the migration of OAs in real atmospheric environment.

Methylamines↗

PubMed2026

Autophagy activation in granulosa cells as a mechanism of astaxanthin action: evidence from a pilot randomised trial in PMOS-associated infertility.

Astaxanthin (AST) has been reported to influence oxidative stress, endoplasmic reticulum stress, and apoptosis in women with polyendocrine metabolic ovarian syndrome (PMOS), formerly referred to as polycystic ovary syndrome (PCOS), but its effects on granulosa-cell (GC) autophagy remain unclear. Given the central role of autophagy in follicular development, this triple-blind, placebo-controlled pilot randomised trial evaluated whether AST modulates autophagy-related signalling in GCs and how these molecular effects relate to ovarian response. Fifty women with PMOS-related anovulatory infertility were enrolled between November 2023 and September 2024 and received AST (12 mg/day) or placebo for six weeks prior to oocyte retrieval; forty-four completed the study (21 AST, 23 placebo). Primary exploratory endpoints were molecular markers of adenosine monophosphate-activated protein kinase (AMPK)-autophagy signalling, and primary clinical outcomes included ovarian response indicators and cleavage stage embryo quality. AST supplementation increased autophagy-related gene 7 (ATG7) expression, enhanced autophagy flux, reduced apoptosis, and showed a trend toward increased AMPK activation. Before adjustment, AST improved oocyte maturity rate (OMR) and increased mature (metaphase II; MII) oocyte yield. After adjusting for age, body mass index, and anti-mullerian hormone level, total oocyte and MII oocyte yields remained significantly higher with AST, while OMR became non-significant. Among embryology outcomes, both the top-ranking embryo rate and the number of embryos suitable for cryopreservation were significantly higher with AST after adjustment. Pregnancy outcomes were numerically higher but not statistically significant. This pilot trial suggests that AST activates autophagy- and apoptosis-related pathways in GCs and may enhance oocyte competence and embryo quality in PMOS. Larger studies are needed to confirm these mechanistic and clinical effects.

Female↗

PubMed2026

Cerebrovascular involvement in Erdheim-Chester disease: a case report and systematic literature review.

BACKGROUND: Intracranial perivascular/vascular infiltrations and stenoses related to Erdheim-Chester disease (ECD), often associated with ischemic events, are rarely documented. This study aims to characterize intracranial perivascular/vascular infiltrations and stenoses. METHODS: We first report a new case of strokes revealing ECD with intracranial arterial involvement. We then searched all English- and French-language publications from database inception to November 2025 across 12 different search interfaces, including grey literature sources. Vascular involvement was defined by the presence of intracranial perivascular/vascular infiltrations and stenosis on imaging and/or histopathological evidence of small-vessel involvement. Cases with intracranial nodules or masses abutting vessels but without clear longitudinal perivascular infiltration were excluded. RESULTS: We present a case of recurrent strokes with intracranial vertebral and basilar artery wall stenosis, and aortitis. Initially diagnosed as giant cell arteritis, the patient was treated with corticosteroids, cyclophosphamide followed by methotrexate, but relapsed. The identification of tibial osteosclerosis led to the diagnosis of ECD, with a favorable response to anakinra. Twelve relevant articles were retrieved, in addition to our own case. Most patients exhibited focal cerebrovascular signs (11/13) and associated parenchymal involvement (11/13). Intracranial perivascular/vascular infiltrations and stenoses involved the carotid arteries (7/13), the vertebrobasilar arteries (1/13), or both territories (4/13). Aorta was involved in 8/12 cases. Among the nine patients with available follow-up data, five had poor overall or neurovascular outcomes. CONCLUSIONS: Intracranial perivascular/vascular infiltrations and stenoses, which leads to recurrent focal ischemic events, represents a likely underdiagnosed CNS pattern in ECD, referred to as "cerebrovascular ECD", which worsens overall prognosis. Vascular imaging should be included in brain MRI protocols for patients with ECD, given the overlap with parenchymal involvement.

Humans↗

PubMed2026

Artificial intelligence for anticancer drug discovery from natural products of macroalgae and sponges: A systematic review.

Marine natural products (MNPs) from macroalgae and marine sponges have inspired clinically important anticancer agents, including the cytarabine pharmacophore and the eribulin scaffold, while cyanobacterial dolastatin chemistry supplies the auristatin payloads of several marine-inspired antibody-drug conjugates (ADCs) such as brentuximab vedotin. Artificial intelligence (AI) methods, encompassing both classical machine learning (ML) with hand-engineered features and modern deep learning (DL) with many-layered neural networks, are increasingly supporting key decisions in natural-product anticancer drug discovery, including bioactivity prediction, target identification, absorption, distribution, metabolism, excretion and toxicity (ADMET) filtering, generative analogue design, and the selection of preclinical candidates. DL architectures relevant to this field include graph neural networks, transformer-based molecular generators, diffusion models for protein-ligand docking, and convolutional networks for mass spectrometry, while classical ML contributes interpretable fingerprint-based bioactivity models and molecular networking for dereplication. This review follows a systematic literature review methodology to organize the landscape of AI methods now applied to MNP anticancer discovery, distinguishing ML and DL approaches where relevant, situating them within the chemical context of macroalgal and sponge-derived oncology leads, and critically examining published case studies, including validation level (computational, in vitro, in vivo, clinical). The principal bottleneck for medical translation has shifted partly from algorithmic capability toward data infrastructure and experimental validation. Sparse, heterogeneous, and taxonomically biased bioactivity records limit what current models can learn and reduce the reliability of AI-prioritized candidates entering the preclinical pipeline. A roadmap is proposed that prioritizes open MNP-specific benchmarks, symbiont-aware modeling, and active learning loops with synthesizability and ADMET constraints. These AI workflows may accelerate the prioritization of marine-derived anticancer leads and support earlier, more evidence-based translational decisions in oncology drug development.

Biological Products↗

PubMed2026

Privacy, security, and reliability risks of artificial intelligence in healthcare: a systematic review of empirical evidence.

BACKGROUND: Artificial intelligence (AI) is increasingly integrated into healthcare information systems, supporting clinical decision-making, imaging analysis, and predictive modeling. While these applications offer operational and clinical benefits, they also introduce emerging risks to patient privacy, data security, and system reliability. OBJECTIVE: To systematically review empirical evidence on privacy breaches, security vulnerabilities, and misuse associated with AI applications in healthcare settings. METHODS: PubMed, Embase, Web of Science, Scopus, IEEE Xplore, and ACM Digital Library were searched for empirical studies published between January 2015 and November 2025 that evaluated AI use or misuse in clinical diagnosis, treatment, or decision-making. Two reviewers independently screened studies and extracted data using a standardized form. Findings were synthesized narratively due to heterogeneity in study designs, AI methods, and reported outcomes. RESULTS: Of 7,285 records identified through database searches and 205 through citation screening, 22 empirical studies met the inclusion criteria, spanning multiple clinical domains and data modalities, predominantly medical imaging applications. Five recurring threat categories were identified: patient re-identification, membership inference, unauthorized access and adversarial exploitation, input manipulation, and misuse or overinterpretation of AI outputs. Across studies, AI models were shown to encode latent biometric signals across diverse data types, limiting the effectiveness of traditional anonymization and synthetic data approaches. Adversarial attacks and input manipulation were also shown to compromise diagnostic performance and system integrity. CONCLUSION: This systematic review provides empirical evidence suggesting that contemporary AI systems in healthcare introduce privacy and security risks that may challenge traditional assumptions about data protection. These findings underscore the need for privacy- and security-by-design approaches and governance frameworks that address risks across the AI lifecycle.

Humans↗

PubMed2026

Identification and formation pathways of oxidation products of chlorinated paraffins during ozonation in municipal wastewater.

Chlorinated paraffins (CPs) cannot be efficiently removed by conventional water treatment processes and are continually discharged into the aqueous environment. Ozonation can effectively remove lipophilic and persistent pollutants. However, the degradation behaviors of short-chain CPs (SCCPs), medium-chain CPs (MCCPs), and long-chain CPs (LCCPs) in wastewater during the ozonation process remained unknown. In this study, ozonation treatment achieved removal efficiencies of 61 % for SCCPs, 66 % for MCCPs, and 51 % for LCCPs from wastewater within 30 min. Approximately 147 oxidative products of SCCPs, MCCPs, and LCCPs were non-targeted identified through Ph4PCl-enhanced ionization with ultra-high performance liquid chromatography-Orbitrap mass spectrometry. These oxidation products were structurally classified into three categories: carbon chain breakage (53 products), HCl-elimination (27 products), and hydroxylation (67 products). Twenty-three di-hydroxylated CPs were newly identified among the products. Hydroxylation was the predominant pathway for SCCPs, producing di-hydroxylated SCCPs ((OH)₂-SCCPs) with a higher generation rate constant (KG = 22.28 × 10⁻² min⁻¹) compared to other products. MCCPs and LCCPs mainly underwent carbon chain breakage and hydroxylation, generating shorter carbon chain congeners, (OH)2-SCCPs, and di-hydroxylated MCCPs ((OH)2-MCCPs). The KG values of (OH)2-SCCPs (10.56 × 10-2 min-1) and (OH)2-MCCPs (12.05 × 10-2 min-1) generated from the MCCPs were the highest, and the KG values of MCCPs (6.49 × 10-2 min-1), SCCPs (6.27 × 10-2 min-1), and (OH)2-SCCPs (4.74 × 10-2 min-1) generated from the LCCPs were higher than those of other products. These results comprehensively clarify the oxidation efficiencies and pathways of CPs during ozonation. Future studies must explore the potential risks associated with the oxidation products.

Water Pollutants, Chemical↗

PubMed2026

Data-centric, robust, and explainable multimodal deep learning for clinical decision support: A systematic review.

PURPOSE: Multimodal deep learning is increasingly proposed for clinical decision support (CDS) under a "data-centric" framing that prioritizes label quality, missing-modality robustness, distribution shift, calibration, and explainability. Prior reviews have examined multimodal medical AI, CDS, and data-centric methods separately, but none address their intersection. We mapped the modalities, fusion strategies, and data-centric and explainability techniques used in this recent literature, quantified how often each is implemented rather than merely mentioned, assessed deployment-relevant evidence (external validation, clinical-outcome measurement, equity), and formally appraised study-level risk of bias. METHODS: Following the PRISMA 2020 statement (PROSPERO CRD420261427815; registered retrospectively), we screened 150 records and included primary, clinical, multimodal studies that applied machine or deep learning to a decision-support task and reported at least one quantitative result. Two reviewers screened and extracted data with consensus adjudication. Each study was coded against pre-specified operational definitions, separating implemented or empirically evaluated techniques from those only mentioned. Study-level risk of bias was assessed with PROBAST + AI. Synthesis was narrative. RESULTS: Thirty-one studies met inclusion; 30 (97%) were published between 2024 and 2026, with a median of three modalities (range 2-6), most commonly structured EHR (71%) and imaging (39%). Data-centric techniques were frequently reported (74-84% across label-noise, distribution-shift, calibration, missing-modality and class-imbalance handling; equity 61%). However, external validation was reported in only 4/31 studies (13%), a clinical or provider outcome in 3/31 (10%), and no study reported routine deployment. Overall risk of bias was high in 27/31 studies (87%), driven by the analysis domain. CONCLUSION: Within this recent, self-selected slice of the field, technical robustness and explainability techniques are widely reported but rarely validated out-of-distribution or against clinical outcomes, and the underlying evidence is at high risk of bias. Progress requires external multi-site validation, clinical-outcome measurement, formal bias appraisal, and adherence to AI reporting standards (e.g., TRIPOD + AI) before deployment can be justified.

Deep Learning↗

PubMed2026

Effects of carbohydrate mouth rinse on psychophysiological responses, kinematic variables and technical actions during small-sided soccer games.

BACKGROUND: This study investigated the effects of carbohydrate mouth rinsing (CHOMR) on various psychophysiological, kinematic, and technical actions during 4-a-side small-sided soccer games (SSGs). METHODS: In a randomized, crossover, double-blind design, 16 young male soccer players (age: 15 ± 1 years; height: 172.6 ± 7.0 cm; body mass: 57.8 ± 7.9 kg) participated in two 4-a-side SSGs separated by one week following either CHOMR or placebo (PLA). During each SSGs, heart rate (HRmean, %HR, and HRmax), rating of perceived exertion (RPE), enjoyment, visual analogue scale (VAS) to perceived mental fatigue, mood states, total distance (TD), high-speed running distance (HSRD), average metabolic power (AMP), high metabolic load distance (HMLD), sprint distance (SD), acceleration (ACC), deceleration (DEC), and technical actions (e.g. passing, tackling, shooting, and interceptions) were assessed. RESULTS: Compared with PLA, CHOMR resulted in significantly higher RPE (p = 0.025, g = 0.86) and significantly lower VAS scores (p = 0.010, g = -0.71). In addition, the CHOMR condition showed significantly higher values for interceptions, successful tackles, unsuccessful tackles, unsuccessful shots, and goals (p < 0.05, g = 0.96-2.02), whereas block values were significantly higher in the PLA condition. There were no statistically significant differences in HRmean, %HR, HRmax, enjoyment, TD, HSRD, AMP, HMLD, SD, ACC, DEC, successful passes, unsuccessful passes, successful shots, or mood states between the conditions (p > 0.05). CONCLUSION: The current study findings suggest that CHOMR may enhance selected technical actions and lower VAS scores during 4-a-side SSGs, although it was also associated with higher RPE and did not alter the kinematic responses. In conclusion, CHOMR was associated with higher RPE, lower mental fatigue, and selective improvements in technical actions, with no significant effects on kinematic variables or HR.

Humans↗

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↗