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PubMed2026

Improving insurance deduction identification: a hybrid artificial intelligence model using machine learning and expert systems.

PURPOSE: Financial challenges in healthcare systems worldwide, especially in low- and middle-income countries like Iran, have increased hospitals' reliance on insurance reimbursements. Unrecognized insurance deductions often cause severe financial shortages, making efficient deduction management crucial. This study aimed to design a hybrid intelligent system for identifying and predicting insurance deductions by combining machine learning and expert system frameworks. DESIGN/METHODOLOGY/APPROACH: A mixed-methods design was applied in four stages. First, a scoping review identified the causes and patterns of insurance deductions. Second, interviews with 15 insurance experts produced a validated checklist and a dataset from inpatient billing records. Third, using the CRISP-DM methodology, machine learning algorithms were developed and tested in SPSS Modeler alongside a fuzzy expert system developed in MATLAB. Finally, the model was validated using the holdout method. FINDINGS: Four categories of deduction drivers were identified: service provision, registration errors, document submission issues, and revenue conversion processes. The CHAID decision tree outperformed other algorithms with a 99% precision rate and the lowest Mean Absolute Error (9.43). A brief assessment of potential overfitting was conducted to ensure that the CHAID model's high accuracy was interpreted cautiously and supported by the validation results. The fuzzy expert system with validated rules was adaptable for deduction classification, especially for cases unsuitable for quantitative modeling. ORIGINALITY/VALUE: The hybrid model improves detection and prevention of deductions, offering actionable insights for hospital administrators, insurers, and policymakers. Its implementation can enhance hospital information systems, streamline claims processing, and optimize revenue management amid financial constraints.

Machine Learning↗

PubMed2026

Comprehensive identification of carboxylic acids by using bromine isotope-based chemical isotope labelling and structure-guided molecular network.

Carboxylic acids (CAs) are important contributors to the flavor quality of sauce-flavor Chinese Baijiu, yet their comprehensive analysis remains challenging due to poor ionization efficiency, weak chromatographic retention, and limited annotation capability. Herein, we developed a workflow for the high-coverage discovery and annotation of CAs in Baijiu by coupling chemical isotope labeling-liquid chromatography-mass spectrometry with a structure-guided molecular network strategy (SGMNS). A bromine-containing derivatization reagent, 1-(3-aminopropyl)-3-bromoquinolin-1-ium bromide (APBQ), was designed and synthesized to exploit the natural isotope distribution of bromine and characteristic MS/MS fragmentation behavior. Following APBQ derivatization, the target CAs showed superior chromatographic retention and favorable analytical performance. Based on isotopic peak pairing in MS1 and diagnostic fragment validation in MS2, 372 potential CA derivatives were discovered from pooled Baijiu samples and 355 of them were validated by diagnostic fragments in MS2 spectra. To address the scarcity of derivatized spectral libraries, SGMNS was employed for annotation using a background network constructed from APBQ-labeled candidates derived from the Expanded Chinese Baijiu Compound Database. The developed method was further applied to profile Baijiu samples, revealing pronounced differences in CA composition across the seven fermentation rounds. Notably, rounds 3 to 5 exhibited the largest numbers of differential CAs. This study provided an effective analytical strategy for large-scale CA profiling, offering new insight into the chemical basis of flavor formation during multi-round fermentation of sauce-flavor Baijiu.

Isotope Labeling↗

PubMed2026

Cloning of two Hsp70 genes and association analysis between SNP haplotypes and high temperature tolerance trait in red swamp crayfish (Procambarus clarkii).

Aquaculture is suffering the challenge from high temperature climate. Two Hsp70 genes, PcHsp70-1 and PcHsp70-2, as key genes involved in the high temperature tolerance of red swamp crayfish (Procambarus clarkii) were identified and cloned in this study. Their molecular features and expression patterns were characterized, revealing the distinct tissue-specific upregulation expression under high temperature stress (33 °C). Two SNPs, PcHsp70-1 (SNP258) and PcHsp70-2 (SNP555) were examined to associate with high temperature tolerance in three populations (n = 675). The genotypes of PcHsp70-1-SNP258 (GA) and PcHsp70-2-SNP555 (TT) were significantly associated with stronger high temperature tolerance. Notably, individuals carrying the haplotype of Hap I (GG + TT) showed a survival rate exceeding 70% under high temperature stress, whereas, the Hap VIII (AA + CT) showed it at 5.2%. RNA interference of PcHsp70-1 resulted in a significant decrease expression of the gene GSH-Px and its encoding protein (glutathione peroxidase) activity, and damage in intestinal tissue under high temperature stress. The transcriptome result revealed that PcHsp70-1 participates in regulation of the pathways related to cytoskeletal construction, immune response, apoptosis, and antioxidant defense. These findings indicate that PcHsp70 genes are crucial for the cellular stress response under high temperature stress. The developed Kompetitive Allele Specific PCR (KASP) markers provide valuable tools for the marker-assisted selection of high temperature tolerant crayfish varieties, supporting the sustainable development of aquaculture under the challenge of global warming.

Animals↗

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

Automated CEAP Classification of Venous Duplex Reports Using Multimodal Artificial Intelligence.

OBJECTIVE: To develop and internally validate a prototype multimodal artificial intelligence system for automated CEAP (Clinical, Etiological, Anatomical and Pathophysiological) classification of venous duplex ultrasound (VDUS) reports, integrating natural language processing of free-text components with computer vision analysis of hand-drawn anatomical diagrams. METHODS: Single centre retrospective observational study using routinely collected clinical data. One thousand consecutive venous duplex ultrasound reports from Cambridge University Hospitals NHS Foundation Trust, UK (July 2024 - May 2025) were labelled according to the CEAP classification, excluding the Etiological component, which could not be reliably determined from duplex reports alone. Transfer learning was applied using ClinicalBERT for text and MobileNetV3 for diagrammatic data. Clinical classes were predicted from request line text. Text- and image-based pathophysiological models were developed for four anatomical territories (Great Saphenous Vein, Small Saphenous Vein, Deep system, Perforators), combined using late fusion with probability averaging. RESULTS: The clinical CEAP model achieved accuracy of 0.91, macro-F1 of 0.82, and macro-AUC of 0.98. Pathophysiological prediction varied, with text models broadly outperforming image models. Fusion yielded heterogeneous benefits, improving SSV performance but reducing Deep system accuracy. The performance of the final pathophysiological CEAP fusion models varied across anatomical territories: accuracy ranged from 0.70-0.92 and macro-AUC from 0.80-0.92. CONCLUSION: This study demonstrates the feasibility of automated CEAP classification from VDUS reports. Despite class imbalance affecting minority class predictions, the strong discriminatory performance validates this multimodal ML model for extracting clinically meaningful information from real-world data. This approach offers potential, pending external validation, to streamline vascular services through automated triage and guideline-compliant decision making.

Artificial intelligence↗

PubMed2026

Psychometric Evaluation of the Breast Inflammatory Symptom Severity Index Versions 2 and 3 Among Lactating Women.

OBJECTIVE: To evaluate the psychometric properties of Versions 2 and 3 of the Breast Inflammatory Symptom Severity Index (BISSI). DESIGN: Secondary data analysis of clinical trial data. SETTING: Private physiotherapy practices, a public tertiary hospital, and a community in Melbourne, Australia. PARTICIPANTS: Women more than 7 days after birth with inflammatory conditions of the lactating breast (N = 43). METHODS: We performed confirmatory factor analysis of the BISSI Version 2 to examine item loading, which informed development of the BISSI Version 3 (V3). We assessed convergent validity by comparing total BISSI V3 scores with human milk sodium to potassium ratio (Na+:K+) at Trial Days 1, 3, and 10 using Bland-Altman plots. We compared item-level scores for size of affected area with objective receiver operating characteristic curve analysis to assess discriminant validity for symptom severity and Cronbach's alpha for internal reliability. RESULTS: After confirmatory factor analysis, we removed two items, resulting in a six-item BISSI V3. All retained items demonstrated comparable loading on the overall scale. Limits of agreement for total BISSI V3 scores and item-level scores for size of affected area were acceptable at all time points, with more than 90% of observations falling within 2 standard deviations of the mean difference, supporting convergent validity. Discriminant validity of the BISSI V3 was supported. We found high internal reliability at both time points CONCLUSION: Our findings provide evidence for the validity and reliability of the BISSI V3 and support its continued development and for clinical use of the BISSI V3 and human milk Na+:K+ analysis to enhance management of inflammatory conditions of the lactating breast.

breastfeeding↗

PubMed2026

Transforming Curcuma longa leaf waste into cellulose scaffolds.

The constant dearth of transplantable tissues and organs in India required the development of substitute biomaterials for tissue engineering. Plant-based decellularized scaffolds have become attractive options because of their abundance, ethical acceptability, architectural diversity, and lower risks of zoonotic transmission. Curcuma longa leaves were investigated in this study as a possible source of cellulose-based scaffolding for use in biomedical applications. After cuticle removal, an immersion decellularization technique utilizing sodium dodecyl sulphate (SDS) and triton-X-100 was developed to successfully remove cellular and nuclear material while maintaining leaf parenchyma architecture. Histology, DAPI staining, scanning electron microscopy, and a notable decrease in leftover DNA content all demonstrated efficient decellularization. When contrasted with native leaves, the resultant decellularized C. longa leaf scaffolds showed significant increase in porosity, water vapor transmission rate and swelling percent, and significantly lower contact angle with an optimum surface roughness promoting cell adhesion. Mechanical test manifest higher tensile strength with decreased stiffness. Fourier transform infrared spectra of leaf scaffold reveals persistence of different components except cuticle but the intensity of different peaks was decreased. The leaf scaffolds showed superior hemocompatibility and excellent compatibility with Madin-Darby canine kidney cells (MDCK) which is demonstrated by cell attachment and proliferation. MTT assay of seeded scaffold showed significantly higher metabolically active cell. In vivo subcutaneous implantation of decellularized scaffolds showed host tissue incorporation, accumulation of collagen, and neovascularization. C. longa leaf scaffolds can be utilized as cost effective and sustainable biomaterials for soft tissue engineering and regenerative medicine.

Curcuma↗

PubMed2026

Safety of early discharge and abbreviated nimodipine course in patients with good-grade aneurysmal subarachnoid hemorrhage.

Aneurysmal subarachnoid hemorrhage (aSAH) remains a devastating cerebrovascular emergency associated with substantial morbidity and mortality. Current guidelines recommend 14-21 days of inpatient monitoring and a 21-day course of nimodipine following aSAH. This retrospective study evaluates early outcomes early discharge (≤14 days post-ictus) and an abbreviated nimodipine course in highly selected patients with good-grade aSAH managed under a standardized institutional protocol. Consecutive patients enrolled in the Vancouver Ruptured Aneurysm Database (VRAD) at Vancouver General Hospital between 2022 and 2025 were included. Inclusion criteria were good-grade aSAH (WFNS Grade I-III) and discharge home within 14 days of ictus. The primary outcome was re-presentation to emergency care within 30 days of discharge; secondary outcomes included hospital readmission and need for additional treatment. Of 333 total patients in VRAD, 49 patients met inclusion criteria. All patients received ≤ 14 days of nimodipine therapy. Forty-two patients (85.7%) were WFNS Grade I on presentation, 3 (6.1%) were WFNS Grade II, and 4 (8.2%) were WFNS Grade III. Radiographic vasospasm was reported in 18 cases (36.7%). No patients developed DCI or clinical vasospasm. Four patients (8.2%) re-presented to emergency care within 30 days of discharge, and only one patient (2%) required hospital re-admission within 30 days. While radiographic vasospasm was seen in over one third of patients, none developed clinical sequelae, supporting the premise that radiographic vasospasm alone may be insufficient to preclude early discharge in select good-grade patients.

Humans↗

PubMed2026

Expression profiles of miRNAs in ruminant intermediate hosts with cystic echinococcosis.

Cystic echinococcosis (CE), caused by the larval stage of Echinococcus granulosus sensu lato (s.l.), is a parasitic zoonotic disease recognized by the World Health Organization as a neglected tropical disease of significant public health concern. Despite ongoing control programs, CE remains endemic, underlining the need for integrated control strategies that involve new diagnostic and therapeutic tools. Recent investigations have spotlighted microRNAs (miRNAs) as key regulators in parasite development, immunomodulation, and as potential diagnostic and therapeutic targets. In the present research, a molecular study was conducted to investigate hydatid cyst samples (protoscoleces and germinal membranes) collected in southern Italy from different ruminant species (sheep, cattle, and water buffaloes), naturally infected with CE, with the ultimate goal of establishing a more comprehensive picture of miRNA expression patterns in these intermediate hosts. The bioinformatic analysis of hydatid cyst samples revealed 168 mature miRNAs. Among these, egr-miR-10-5p, egr-let-7-5p, and egr-miR-71-5p were the most abundant, with egr-miR-10-5p showing particularly high expression levels. No significant differences in miRNA abundance between host species were found. In contrast, when focusing on the comparison between protoscoleces and sterile germinal membranes, 24 miRNAs were found to be differentially expressed. Targeted qPCR of four selected miRNAs (egr-miR-71-5p, egr-let-7-5p, egr-miR-125-5p, and egr-miR-10-5p) showed clear overexpression in protoscoleces and in fertile germinal membranes compared with sterile ones. The differential miRNA expression patterns provide insight into the molecular mechanisms controlling the parasite's lifecycle and may guide the development of novel intervention methods to enhance CE control in endemic areas.

Animals↗

PubMed2026

Positive conversion of latent tuberculosis screening in patients with inflammatory bowel disease on antitumor necrosis factor alpha drugs: a systematic review and meta-analysis.

Inflammatory bowel disease (IBD) patients undergoing antitumor necrosis factor-alpha (anti-TNF) therapy are at increased risk of developing tuberculosis (TB), making screening before anti-TNF initiation mandatory. Repeated screening during treatment is not yet recommended because of a lack of studies to support this practice. We aimed to determine the proportion of patients who develop latent TB during anti-TNF therapy. We systematically searched studies from MEDLINE, Embase, and Lilacs, and performed a single-arm meta-analysis investigating the positive conversion rate in IBD patients under anti-TNF therapy with previous negative TB screening. We calculated the combined proportion with 95% confidence interval, using the random-effects model. A P value less than 0.05 was considered statistically significant for subgroup differences. We included 13 studies from nine countries with 1153 patients. The overall positive conversion rate was 9.20%. Portugal had 18.01% of positive conversion, Spain 4.51%, and the USA 1.11%. Tests for subgroup differences were statistically significant for subgroup analysis by country and consistency of test used (performig same test as baseline). Subgroup analyses by continent, study design, or specific test (tuberculin skin test or interferon-gamma release assay) showed no statistical difference. Meta-regression analysis showed a significant positive association between positive conversion and TB incidence. In conclusion, IBD patients on anti-TNF therapy can have a positive conversion rate of 9.20%. Higher conversion rates were seen in European and Asian studies compared with those in the Americas (particularly the USA). TB prevention strategies should, therefore, be individualized and based on geographic location and risk factors.

Humans↗

PubMed2026

The Animal Variant Classification Guidelines v2: An Update With New Criteria and Improved Clarifications.

The Animal Variant Classification Guidelines (AVCG) were developed to standardize and objectify the classification of putative disease-causing variants. These guidelines are sufficiently reproducible and are used to classify previously published and new disease-causing variants across species. Here, the guidelines are updated (AVCG.v2), based on a three-phase decision process. Overall, four new criteria and seven clarifying comments were added. The number of criteria has increased from 23 to 27, with three new criteria supporting pathogenicity and one new criterion supporting benign classification. Pharmacogenomic variants were determined to fall within the scope of the guidelines. These updated guidelines are being used by the Variant Pathogenicity Working Group (VPWG), part of the Animal Genetic Testing Standardization standing committee, which is a committee of elected members of the International Society for Animal Genetics (ISAG). Under the auspices of ISAG, the VPWG retrospectively classifies published putative disease-causing variants. The pathogenicity label for a variant will be presented in the variant tables of Online Mendelian Inheritance in Animals (OMIA; https://omia.org/). The AVCGv.2 criteria and recommendations were developed by the expertise of the animal genetics community and the ISAG Executive Committee through the Animal Genetics Testing Standardization Committee endorses and strongly encourages their use to evaluate the evidence supporting pathogenicity of putative disease-causing variants.

Animals↗

PubMed2026

MET expression by immunohistochemistry as a biomarker in pancreatic neuroendocrine tumours.

INTRODUCTION: MET (c-MET) is a receptor tyrosine kinase implicated in numerous cancers, including pancreatic neuroendocrine tumours (pNETs), by promoting cell proliferation, survival, invasion and angiogenesis. Recognizing its oncogenic potential, there is significant interest in MET-targeted therapies for malignancies like pNETs, which often develop treatment resistance. Immunohistochemistry (IHC) has become a practical method for detecting MET overexpression in cancers. This study evaluates MET expression in pNETs by IHC and assesses its correlation with prognostic variables and survival outcomes. METHODS AND RESULTS: Tissue microarrays containing well-differentiated neuroendocrine tumours from the gastrointestinal tract were analysed. The study included 125 pNET cores from 112 patients after application of inclusion criteria. MET expression was determined using the H-score system. Different variables were assessed for H-score distribution and cross-tables. Survival analyses were conducted based on progression-free survival and overall survival. Positive MET expression was found in 83.5% of cases. Higher MET H-scores were seen in patients with lymphovascular invasion (LVI), distant metastases and higher tumour grade (P < 0.05). When assessing different variables for higher MET H-scores, a significant association emerged at the 150-cut-off-point for LVI, perineural invasion, radiological evidence of progression and overall survival. For survival analysis, at a MET H-score threshold of 200, high MET expression was significantly associated with shorter progression-free survival (mean 8.7 versus 13.4 years, P < 0.05) and overall survival (mean 3.6 versus 7.7 years, P < 0.05). CONCLUSION: Elevated MET expression is linked to adverse histopathological features and worse clinical outcomes in pNET. Standardizing MET IHC evaluation is critical as anti-MET therapies develop, and identifying patients likely to benefit from these treatments remains essential.

MET protein↗

PubMed2026

Complementary feeding patterns in preterm and term infants.

Complementary feeding is essential for infants' nutritional status and development, marking the transition to solid foods when breast milk or formula alone is insufficient. Despite its importance, clear recommendations on which foods to introduce when initiating complementary feeding in preterm infants are lacking. By using data from our previously published randomized controlled trial on the timing of complementary feeding in preterm infants, the current study explores the complementary feeding patterns of preterm infants and compares them with those of term-born infants, providing insights into parental decision-making and potential long-term health impacts. Complementary feeding practices differed significantly between preterm (n = 255) and term (n = 159) infants, with preterm infants more often receiving vegetables as their first solid food (85.4% versus 68.8%, difference 17.6% with 95% CI 12-35%). The group with early introduction of vegetables had a lower BMI-for-age z-scores (β -0.28 [95% CI -0.55 - 0.02]) and weight-for-height z-scores (β -0.27 [95% CI -0.53 to -0.01]) at two years of age. Additionally, preterm infants showed a greater variety in the numbers of different fruits and vegetables consumed by six months (corrected) age than term-born counterparts (8.29 (SD 3.65) versus 6.26 (SD 3.47), p < 0.001). These results indicate that complementary feeding patterns in preterm infants differ from term-born infants, with potential positive implications on growth. These data contribute to the development of accurate feeding protocols for preterm infants. Given that feeding practices are culturally influenced, further multinational research is essential to refine complementary feeding guidelines for preterm infants and support caregivers in informed decision-making.

Humans↗

PubMed2026

Interface-dependent V. parahaemolyticus biofilm under varying temperatures, media, and oxygen conditions: implications for seafood safety.

Vibrio parahaemolyticus biofilms play a critical role in pathogen persistence in marine and seafood-processing environments, where oxygen availability, temperature, and surface interfaces vary widely. This study investigated biofilm development by three strains on partially submerged stainless-steel coupons under gas-liquid-wall (GLW) and fully submerged (SM) interfaces. Viable cell counts (log₁₀CFU/cm2) along with normalized protein concentration per viable cell (nProt) and normalized polysaccharide concentration per viable cell (nPol) were measured, under aerobic and anaerobic conditions across a temperature range of 15-30 °C, using tryptic soy broth with 3% NaCl (TSB) and seawater-based medium (SW). GLW biofilms consistently exhibited higher cell counts (6.4-7.3 log₁₀CFU/cm2) compared to SM biofilms (5.9-6.3 log₁₀CFU/cm2), suggesting that enhanced oxygen diffusion promotes bacterial proliferation. Conversely, SM biofilms exhibited significantly higher nProt and nPol levels (p < 0.001), indicating increased production of the extracellular polymeric substance (EPS) matrix under low-oxygen, high-nutrient conditions. Microscopy and three-dimensional surface plot analyses revealed relatively uniform biofilm layers at the GLW interface, whereas SM biofilms formed heterogeneous, tower-like structures. EPS production was further influenced by medium composition, oxygen, and temperature. SM biofilms grown in SW exhibited significantly higher nProt and nPol than those in TSB under aerobic conditions (p < 0.001), indicating enhanced matrix stabilization. Under anaerobic conditions at 15 °C, nProt and nPol were higher, whereas under aerobic conditions, peak nProt and nPol occurred at elevated temperatures. These findings highlight a trade-off between bacterial growth and matrix production and provide insight into biofilm adaptation and persistence in seafood-processing environments. These insights may help develop improved biofilm control and seafood safety management.

Biofilms↗

PubMed2026

Challenges and future directions in AI-driven biomaterials for microbiome-associated oral infectious diseases: A systematic review.

Oral biofilm-induced antimicrobial resistance is the core pathogenic mechanism of microbiome-associated oral infectious diseases (dental caries, periodontitis, peri-implantitis, and endodontic infection). Traditional therapies and biomaterials are limited by poor biofilm penetration, drug resistance induction, single functionality, and inadequate adaptation to dynamic oral microenvironmental changes (e.g., pH fluctuations, salivary rinsing, masticatory stimulation). Artificial intelligence (AI) has transformed the field by integrating materials science, microbiology, and stomatology data. Via machine learning, deep learning, and multi-physics simulation, AI optimizes biomaterial physicochemical properties, decodes microenvironmental signals, constructs precise sensing-response loops, and supports the full chain of material design, performance prediction, and action simulation, advancing treatment from empirical intervention to precision regulation. This systematic review retrieved literature from PubMed, Embase, and Web of Science (January 2016-January 2026) using keywords across three dimensions: AI, biomaterials, and oral microbiome. Following inclusion/exclusion criteria, 99 articles were included. It elaborates on five core mechanisms of AI-driven oral biomaterials (precise oral microbiome analysis, targeted material design/optimization, performance prediction/simulation, targeted delivery/intervention, effect evaluation/dynamic regulation), analyzes their applications in microbiome-targeted biomaterial research and development (R&D) and clinical practice for the four major oral infectious diseases, addresses technical bottlenecks (insufficient targeting specificity and precision of biomaterials, poor stability and durability in complex oral microenvironments, inadequate biofilm disruption capacity, and clinical translation obstacles), and proposes future directions (multimodal design to enhance targeting specificity, structural and component optimization to improve stability/durability, development of multi-mechanism synergistic biofilm disruption strategies, strengthening translational research for clinical application, and deep integration of AI in the full chain of biomaterial R&D). This work provides comprehensive theoretical and practical support for the R&D, optimization, and clinical translation of AI-driven microbiome-targeted oral biomaterials.

Humans↗

PubMed2026

Prenatal exposure to indoor PM2.5 and children's cognitive performance at 4 years of age: an observational analysis from the UGAAR randomized controlled trial.

Outdoor fine particulate matter (PM2.5) concentrations during pregnancy are linked to reduced cognitive performance in children. We previously reported that portable HEPA filter air cleaners use during pregnancy improved children's mean full-scale IQ (FSIQ), but no previous studies have evaluated the relationship between indoor PM2.5 during pregnancy and FSIQ in childhood. We conducted an observational analysis using data from the Ulaanbaatar Gestation and Air Pollution Research (UGAAR) randomized controlled trial. Using a previously developed model of weekly indoor PM2.5 concentrations, we estimated the average concentrations in participants' homes over the full pregnancy and in each trimester. When the children were four years old, we measured FSIQ using the Wechsler Preschool and Primary Scale of Intelligence (WPPSI-IV). We used multiple linear regression to assess the adjusted relationships between interquartile range (IQR) contrasts in indoor PM2.5 during pregnancy and FSIQ among 475 mother-child dyads. An 8.8 μg/m3 increase in indoor PM2.5 concentration over the full pregnancy was associated with a reduction of 1.4 points (95% CI: -3.4, 0.6) in mean FSIQ. The strongest association between PM2.5 concentrations and FSIQ was in the first trimester, when a 19.1 μg/m3 contrast was associated with a 2.8-point reduction (95% CI: -5.7, 0.2) in mean FSIQ. Indoor PM2.5, particularly during early pregnancy, may impair brain development, leading to lower mean FSIQ scores in four-year old children. These results, combined with our previous analysis of HEPA filter air cleaners, indicate that reducing PM2.5 exposure during pregnancy has beneficial effects on children's cognitive performance.

Humans↗

PubMed2026

A contextual activity score (CAS) for inferring ADAR-associated transcriptional activity across RNA-seq, single-cell, and spatial transcriptomics.

BACKGROUND AND OBJECTIVE: Adenosine-to-inosine RNA editing, catalyzed by Adenosine Deaminases Acting on RNA (ADARs), is a widespread modification involved in neural function, immune regulation, and cancer. The Alu Editing Index (AEI) is the standard metric to estimate ADAR activity but requires raw sequencing reads and is poorly suited for single-cell and spatial transcriptomic data. This study aimed to develop an alternative framework for inferring ADAR-associated transcriptional activity from gene expression data across diverse transcriptomic technologies. METHODS: We developed the Contextual Activity Score (CAS), a framework based on transcriptional signatures from ADAR perturbation experiments. Context-specific signatures were generated for human neurons, mouse neurons, and cancer models to infer ADAR1 and ADAR2 activity. CAS was computed from normalized gene expression matrices using regulon-based enrichment analysis. Performance was evaluated by comparing with the Alu Editing Index across bulk RNA sequencing datasets, simulated sequencing depths, and library preparation protocols. RESULTS: CAS showed strong concordance with the Alu Editing Index across multiple datasets, while remaining robust to reduced sequencing depth and different library protocols. Unlike the Alu Editing Index, CAS can be applied to single-cell and spatial transcriptomic data and enables the independent assessment of ADAR2 activity. In cancer and neuronal contexts, CAS captured biologically meaningful variations in ADAR-associated transcriptional activity at sample, cell-type, and spatial levels. CONCLUSION: CAS provides a scalable approach applicable across multiple RNA-seq protocols for estimating ADAR-associated transcriptional activity using gene expression data. This method, implemented in an open-source R package for broad adoption, expands the ability to study ADAR-associated transcriptional activity across transcriptomic modalities where direct editing quantification is challenging, such as single-cell and spatial transcriptomics.

Adenosine Deaminase↗

PubMed2026

Efficacy and safety of microwave ablation for the treatment of pulmonary osteosarcoma oligometastases.

PURPOSE: Evaluate efficacy and safety of microwave ablation (MWA) for pulmonary osteosarcoma oligometastases. METHODS: Twenty-two patients (median age, 16 years [range, 9-41 years]; 15 male) with pulmonary osteosarcoma oligometastases who underwent MWA from January 2018 to December 2023 were included, with 27 MWA sessions for 36 lung metastases. Technical success and complications were evaluated in all 22 patients, while efficacy and survival were evaluated in 19 patients with 24 MWA sessions in treatment of 32 tumors. Technical success was assessed for each tumor. Local tumor control, progression-free survival (PFS) and overall survival (OS) were estimated using Kaplan-Meier method. Complications were classified using Common Terminology Criteria for Adverse Events version 5.0. RESULTS: Technical success was achieved in all 36 tumors (100.0%). Local tumor progression occurred in five of 32 tumors (15.6%). The estimated local tumor control rates at 12, 24 and 36 months were 96.9%, 86.1% and 81.5%, respectively. No significant difference in local control was found between tumors ≤ 10 mm and > 10 mm (p = .470). Twelve of 19 patients (63.2%) developed new lung metastases outside the ablation area, including one with concurrent newly developed bone metastases and one with recurrence of primary osteosarcoma. The median PFS was 21.5 months. The estimated OS rates at 12, 24 and 60 months were 100.0%, 94.4% and 94.4%, respectively. Major complications occurred in five of 27 sessions (18.5%). CONCLUSIONS: MWA preliminarily demonstrates a high technical success rate, notable local tumor control, promising overall survival and acceptable safety for pulmonary osteosarcoma oligometastases.

Adolescent↗