The gut microbiota produces a wide variety of metabolites that are essential for host-microbe communication and play a critical role in regulating host physiology, metabolism, and immunity. Among the most important of these metabolites are Short-Chain Fatty Acids (SCFAs), bile acid derivatives, tryptophan metabolites, polyamines, vitamins, and polyphenol-derived compounds. These bioactive metabolites regulate energy homeostasis, glucose and lipid metabolism, intestinal barrier integrity, immune signaling, and gene expression. Moreover, they influence systemic physiological processes, including cardiovascular and neuroendocrine functions, while playing a pivotal role in regulating hepatic and adipose tissue metabolism and maintaining intestinal homeostasis. Dysbiosis-induced alterations in microbial metabolic activity have been associated with the development of several chronic diseases, including obesity, type 2 diabetes mellitus, nonalcoholic fatty liver disease, cardiovascular diseases, cancer, autoimmune disorders, and neurological conditions. Consequently, therapeutic strategies aimed at modulating microbial metabolism, such as probiotics, prebiotics, postbiotics, dietary interventions, faecal microbiota transplantation, and synthetic biology-based approaches, are being extensively investigated, with microbial metabolites emerging as promising pharmacological targets. Despite these advances, significant challenges remain regarding their mechanistic understanding, standardisation, safety, and successful translation into clinical practice. The integration of multi-omics technologies, artificial intelligence, and precision microbiome-based interventions is expected to accelerate the development of personalized therapeutic strategies and enhance the clinical applicability of microbial metabolite research.

We investigated combinations of preterm birth, preeclampsia and offspring birthweight by gestational age in a first pregnancy and cross-over risk of gestational diabetes mellitus (GDM) in second pregnancy. The Medical Birth Registry of Norway provided data for 561 873 mothers with first and second births without diabetes prior to second pregnancy, 1985-2020. We combined first pregnancy preterm birth (<37 weeks), preeclampsia and birthweight by gestational age quartiles (Q1-4) into one exposure (16 categories). Relative risks with 95% confidence intervals for GDM in second pregnancy were estimated, keeping mothers with first term birth without preeclampsia and offspring in Q1 as reference. Women with first offsprings in Q4 had high risk of subsequent GDM, however, in combinations with preterm birth and preeclampsia the risk of GDM progressively increased: Compared to 0.9% GDM risk in the reference group, the risk increased to 1.8% for Q4 term births without preeclampsia (aRR 2.1 [95% confidence interval 2.0-2.3]), 2.5% for Q4 preterm births without preeclampsia (aRR 3.3 [2.8-3.8]), 4.2% for Q4 term birth with preeclampsia (aRR 5.6 [4.8-6.4]) and 7.8% for Q4 preterm birth with preeclampsia (aRR 10.1 [7.4-13.7]). Combinations of first pregnancy exposures were associated with a progressive cross-over risk of GDM in second pregnancy.

ObjectiveTo evaluate the clinical severity and microbiological characteristics of participants with type 2 diabetes and foot ulcers (DFUs) screened for eligibility into a bacteriophage therapy pilot study.Research Design And MethodsAdults aged (≥18 years) with type 2 diabetes mellitus (T2DM) and active DFUs presenting to a tertiary care centre were screened using predefined eligibility criteria. Ulcers were graded according to the University of Texas Diabetic Foot Classification System. Microbiological analysis included standard culture techniques and biochemical tests for identification of microorganism. Descriptive statistics were used to summarize ulcer severity, microbial patterns, and eligibility outcomes.ResultsA total of 595 individuals were screened. Grade 3 ulcers accounted for 51.1% of cases, followed by Grade 2 (29.1%) and Grade 1 (19.8%). Monomicrobial infections were identified in 63.7% of individuals, polymicrobial infections in 17.6%, and no growth in 18.7%. Gram-negative organisms predominated, including Pseudomonas spp. (n = 74), Escherichia spp. (n = 60), Klebsiella spp. (n = 50), Proteus spp. (n = 43), and Acinetobacter spp. (n = 25). Among Gram-positive organisms, Staphylococcus spp. (n = 81) and Enterococcus spp. (n = 43) were common.ConclusionsThe screened population demonstrated a high burden of advanced Grade 3B DFUs, predominantly associated with Gram-negative pathogens amenable to bacteriophage targeting. But this pilot study intentionally focused on Grade 1B and Grade 2B ulcers to assess the feasibility and safety of bacteriophage therapy in predefined, less severe DFUs. This pre-screening process supported the feasibility and enrollment of eligible participants for the subsequent bacteriophage therapy pilot study.

Patients with cancer often experience substantial fluctuations in psychological states during disease management. Traditional research tools are limited in capturing these dynamic changes in real time, constraining clinicians' understanding of patients' true conditions. Ecological momentary assessment (EMA) enables high-frequency, real-time data collection, providing patient-reported data with greater ecological validity. However, the effectiveness of EMA studies critically depends on patient compliance, and reported compliance rates vary widely, with a lack of systematic quantitative synthesis.

This study aims to systematically review and quantitatively analyze compliance with EMA among patients with cancer, and to examine whether EMA design characteristics were associated with compliance.

Web of Science, PubMed, Embase, Cochrane Library, CINAHL, PsycINFO, CNKI, and Wanfang databases were searched for literature published up to April 30, 2026. Compliance was defined as completed prompts divided by delivered prompts. Single-group proportions were pooled using logit transformation and random-effects models with the Hartung-Knapp-Sidik-Jonkman adjustment. Prediction intervals were calculated to describe the expected distribution of compliance in future comparable settings. Subgroup analyses, univariable meta-regressions, leave-one-out sensitivity analyses, and tests for small-study effects were performed. Risk of bias was assessed using the Joanna Briggs Institute Critical Appraisal Checklist for Studies Reporting Prevalence Data, methodological reporting quality was assessed using a modified Checklist for Reporting EMA Studies, and certainty of evidence was evaluated using the Grading of Recommendations Assessment, Development, and Evaluation approach.

Twenty-three studies involving 13,565 participants were included. The pooled compliance rate was 78.55% (95% CI 73.48%-82.87%), with a prediction interval of 48.59%-93.41%. Subgroup analyses identified no robust differences across study characteristics. Although study length showed a statistically significant subgroup test, the result was not stable after excluding singleton categories. Meta-regression analyses similarly found no significant linear associations for study length, prompts per day, items per prompt, or assessment window. Leave-one-out analyses showed that no single study drove the pooled estimate. Regarding the risk of bias, 2 studies were judged as low, while 21 were judged as moderate risk. Quality scores ranged from 6.5 to 9.0, and the certainty of evidence for the pooled compliance rate was rated as very low according to the Grading of Recommendations Assessment, Development, and Evaluation approach.

Overall compliance with EMA among patients with cancer was moderate to high, suggesting that repeated real-world assessment may be feasible in oncology research settings. Nevertheless, the very high heterogeneity, wide prediction interval, and very low certainty of evidence indicate that compliance is context-dependent. The pooled estimate should therefore be interpreted as an approximate benchmark rather than a universal expected rate. Future oncology EMA studies should use standardized compliance denominators, report missing prompts transparently, and prospectively evaluate patient-centered design strategies that reduce burden while preserving data quality.

Perioperative computed tomography (CT) imaging is essential for detecting postoperative recurrence and metastasis in cancer patients. However, large-scale automated extraction of oncological outcomes from CT reports remains limited by the unstructured nature of report text and wide variability in reporting styles. Radiology reports frequently contain linguistic ambiguities, including negations, hedging, and expressions conveying diagnostic uncertainty (eg, "cannot exclude recurrence" or "possibly metastatic"). Manual review is labor-intensive and constrains consistent extraction at scale. The inability to systematically account for diagnostic uncertainty represents a major barrier to reliable automated surveillance systems.

This study aimed to develop a semisupervised deep learning (DL) classification framework that explicitly captures diagnostic uncertainty by classifying postoperative recurrence and metastasis into 3 categories (positive, negative, and uncertain).

This retrospective study analyzed 288,076 postoperative CT reports from 86,083 cancer surgery patients at Asan Medical Center (2014-2021). After exact-match deduplication, model training and evaluation used 17,846 unique reports for recurrence and 63,766 for metastasis. Preprocessing identified presumed negatives through keyword filtering and unsupervised clustering. The semisupervised framework incorporated human-in-the-loop validation across 3 cycles-with clinicians reviewing approximately 2000 samples per cycle (<1% of total reports)-and integrated rule-based algorithms (RAs) and medical BERT (MedEmbed and PubMedBERT). A report-level train-validation split was used, as preprocessing reduces each report to sentence-level fragments that preclude patient-level linkage. Performance was evaluated against RAs and multiple BERT variants under both naive and simulated real-world class distributions. Maximum mean discrepancy testing confirmed distributional integrity of the sampled data. Model interpretability was assessed using Integrated Gradients.

The cohort included 11 cancer types, predominantly gastrointestinal (28,516/86,083, 33.1%), hepatobiliary and pancreas (14,715/86,083, 17.1%), and genitourinary (12,959/86,083, 15.1%). Under simulated conditions, PubMedBERT achieved 92.58% accuracy for recurrence in the multiclass, and MedEmbed achieved 93.25% for metastasis in the binary class. The framework achieved accuracies of 97.33% (multiclass) and 99.33% (binary class) for recurrence and 95.00% (multiclass) and 96.67% (binary class) for metastasis, compared with human intrarater consistencies of 96.88% and 93.80% (recurrence and metastasis, respectively, for multiclass), reflecting concordance with the clinician-derived consensus standard. The framework captured diagnostic uncertainty in 1.4% of recurrence cases and 6.9% of metastasis cases. Notably, the RA outperformed several sophisticated DL models in metastasis classification.

The proposed framework achieves clinician-concordant classification across the full 288,076-report corpus while requiring minimal expert annotation (<1% of reports). By explicitly modeling diagnostic uncertainty and combining rule-based and DL approaches, it demonstrates the potential for automated cancer surveillance and clinical decision support in real-world settings; however, generalizability to other institutions requires prospective multicenter validation.

ObjectiveThis study aimed to validate the prognostic discriminatory power of the revised International Federation of Gynecology and Obstetrics (FIGO) 2018 staging system and to evaluate the association between regional lymph node resection (RLNR) and survival in cervical cancer (CC) patients who underwent RLNR followed by chemotherapy and radiotherapy.MethodsIn this retrospective cohort study utilizing the Surveillance, Epidemiology, and End Results (SEER) database (2000-2018), patients were categorized into early-stage (IA/IB1-IB2/IIA1) and locally advanced-stage (IB3/IIA2-IIB/III/IVA) groups. Survival outcomes were analyzed using Kaplan-Meier, univariate and multivariate Cox regression (including time-dependent), and year-of-diagnosis stratified analyses, applied to crude, inverse probability of treatment weighting (IPTW)-weighted, and propensity score matching (PSM)-matched models.ResultsThe FIGO 2018 system revealed significant survival differences between IB1 vs IB2 and IIIC1 vs IIIA/IIIB (all p < 0.001). In early-stage patients, RLNR conferred no significant survival benefit. However, in locally advanced-stage patients, RLNR with primary surgery was associated with better survival than RLNR alone. In the cohort without primary surgery, RLNR alone was consistently identified as a prognostic factor versus non-surgery across all three analytical models (IPTW as primary, PSM as sensitivity; all p<0.001). The hazard ratios for RLNR alone versus non-surgery were all below 1.000, and year-of-diagnosis stratified analyses further supported this protective association, with consistently directional estimates.ConclusionThis study suggested the prognostic value of the FIGO 2018 staging system and hypothesized that RLNR is a prognostic factor for better survival compared with chemotherapy and radiotherapy alone in locally advanced-stage CC, although the limitations of a retrospective study must be acknowledged.

Accurate risk stratification is essential for guiding treatment decisions and preventing over treatment of prostate cancer, which remains one of the most prevalent cancers among adult men. While the Gleason score, obtained from prostate biopsies, is routinely used to assess tumor aggressiveness, the biopsy procedure carries risks such as pain, infection, and, in some cases, serious complications such as sepsis. In this study, we proposed an artificial intelligence-based framework that integrates mRNA expression profiles with functional interaction networks to classify prostate cancer patients into low-, medium-, and high-risk groups defined by Gleason scores. The pipeline comprised five steps: (1) data collection from The Cancer Genome Atlas (TCGA), (2) preprocessing of gene expression data, (3) two-stage feature selection to identify informative biomarkers, (4) risk classification using a dual-branch graph neural network (GNN) that combines gene-gene interaction graphs with sample-level expression features, and (5) model interpretation using SHAP to quantify feature contributions. Differentially expressed genes were identified in the High (ASPN, GMNN, PEBP4, C2, KNCK17), Medium (C2, IGSF1, ASPN, CDKN3, AMH), and Low (TNMD, VWA5B2, ST6GALNAC5, CYP3A5, PHGR1) risk groups, underscoring the molecular heterogeneity of disease progression. On an independent held-out test set, the model achieved AUCs of 0.86, 0.88, and 0.95 for the low-, medium-, and high-risk groups, respectively, with an overall accuracy of 80%. These results suggest that combining GNN-based modeling with explainable AI can capture both global and local molecular patterns relevant to tumor aggressiveness. However, as the model was developed and evaluated solely on the TCGA cohort, the findings should be regarded as exploratory, and external validation will be required to establish generalizability. Within these limitations, the proposed framework highlights the potential of molecular profiling and graph-based deep learning to support more precise, potentially less invasive, risk assessment and individualized treatment planning in prostate cancer.

Uterine leiomyoma, is a common benign neoplasm among women of reproductive age with a potential of causing significant health complications and financial burdens in severe cases. Recent research suggests a possible link between vitamin D deficiency and uterine leiomyoma development; however, this relationship has not been established in the Ugandan population. For this reason, there is no individualized or public health interventions targeting this modifiable risk factor. The study aimed to determine the relationship between hypovitaminosis D and uterine leiomyoma among women of reproductive age attending Jinja, Lira and Fort Portal Regional Referral Hospitals in Uganda.

A cross-sectional multicentre study was conducted at Jinja, Lira and Fort Portal Regional Referral Hospitals between 1st October 2022-31st January 2023. 246 non-pregnant women of reproductive age were included in the study. Questionnaires were administered to the participants, their serum vitamin D analysed and a transabdominal pelvic scan done. A p value of ≤0.05 was the threshold for statistical significance.

The prevalence of hypovitaminosis D among participants was high at 54.1%. There was no significant difference in the proportions of leiomyoma among those with and without hypovitaminosis D (χ2 = 0.503, p = 0.478 > 0.05). The mean serum vitamin D level was lower in individuals with uterine leiomyomas compared to those without (19.954 ± 9.77 versus 21.552 ± 9.54 [95% CI: -0.04 to 4.24, p = 0.235]). There was no statistically significant association between hypovitaminosis D and uterine leiomyoma (OR 1.22, 95% CI 0.70-2.12; p = 0.478). There was a weak negative correlation observed between serum vitamin D levels and leiomyoma site number (r = -0.2482, p = 0.0342 < 0.05).

The prevalence of hypovitaminosis D was high among women of reproductive age. There was no statistically significant relationship between hypovitaminosis D and uterine leiomyomas despite a weak negative correlation between vitamin D levels and leiomyoma site number which was statistically significant. High prevalence of hypovitaminosis D underscores the need for health education and public health measures by healthcare providers to manage and prevent vitamin D deficiency-related morbidity among women of reproductive age. Further research by the scientists is needed to investigate the potential association between hypovitaminosis D and uterine leiomyoma, in regards to increased site number, among the African population.

Clothing color can affect the psychological aspects in female patients and survivors of breast cancer; however, little is known regarding their preferences and reasons for choosing specific clothing color. This study aimed to explore the factors influencing their clothing color choices.

This qualitative study was conducted through face-to-face interviews. Fourteen adult female patients and survivors of breast cancer were recruited at a university hospital in Korea. An art therapist requested participants to indicate their usual choice of clothing colors and reasons. Participants were encouraged to talk about memories related to clothing colors in the past and present. Participants were asked if there were any changes in clothing colors after the diagnosis of breast cancer. Preferences and reasons regarding clothing colors were explored during one-hour interview. The interviews were audiotaped and analyzed using the Korean Computer Assisted Qualitative Data Analysis Software (CAQDAS), Blue Bird 2.0 (www.thebluebird.kr). The researchers crosschecked thematic coding results using the software.

All 14 patients and survivors had memorable experiences regarding their favorite clothing colors. Thematic coding identified four key themes: (1) social interactions and social image (39.5% of key sentences stated), (2) enhanced self-image and concealment of body shape (20.9%), (3) positive feelings (18.6%), and (4) changes in color preference after breast cancer diagnosis (14%).

All female patients and survivors of breast cancer had preferences and good memories for clothing color. In most participants, these colors supported social interaction, self-image, and positive feelings. It is needed to investigate if intervention through clothing colors would support quality of life.

BACKGROUND Oral squamous cell carcinoma (OSCC) is the most common malignant tumor in the head and neck. A free flap is often used to repair tissue defects left after surgery. Due to the existence of 2 surgical sites-the donor site and recipient site-cross-contamination should be prevented during the operation. This report presents a case of donor tumor implantation suspected to be caused by cross-contamination. CASE REPORT A 57-year-old Chinese man was admitted with a mass in the left floor of the mouth, first noticed 2 months before. The biopsy confirmed squamous cell carcinoma (SCC). Under general anesthesia, extended resection of the left floor of the mouth SCC and repair with a left anterolateral thigh free flap were performed. Five months after surgery, a mass was found in the scar on the left thigh. Extensive resection of the left thigh mass was performed, and postoperative pathology revealed metastatic SCC. At the same time, a tumor was found on the right back and another on the chest wall. These 2 tumors were resected at the same time and were also confirmed as SCC. Seven months after surgery, the patient gradually developed local recurrence and systemic multiple metastases, and he died 10 months after surgery. CONCLUSIONS The "no tumor left behind" principle should be strictly followed in malignant tumor surgery to avoid malignant tumor implantation. Oral and maxillofacial surgeons should be aware of this and use appropriate techniques to avoid such incidents.