Metabolically dysfunction-associated steatotic liver disease (MASLD) complicated by type 2 diabetes mellitus (T2DM) represents a clinically aggressive phenotype associated with accelerated hepatic fibrosis progression. The interplay among oxidative stress, systemic inflammation, and the risk of hepatic fibrosis in this context remains incompletely characterised. We conducted a single-centre observational study enrolling 110 adult MASLD patients, stratified into two groups: Group 1 (G1, n = 20), patients with concurrent T2DM, followed longitudinally at three successive time points, and Group 2 (G2, n = 90), non-diabetic controls. Serum oxidative stress biomarkers were assessed using malondialdehyde (MDA) and 8-isoprostaglandin F2α (8-iso-PGF2α). Systemic inflammatory status was quantified through the neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), and platelet-to-lymphocyte ratio (PLR). Hepatic fibrosis risk was estimated using the FIB-4 index. Diabetic MASLD patients exhibited significantly elevated levels of 8-iso-PGF2α (p = 0.014) and NLR (p = 0.016) compared with controls, indicating greater oxidative membrane damage and systemic neutrophilic inflammation. A robust inverse correlation between PLR and FIB-4 was observed across all analytical strata (combined cohort: Spearman r = -0.680, p < 0.001). MLR emerged as the only independent predictor of MDA in G1 (β = 841.78, p = 0.013). Longitudinal analysis demonstrated biomarker stability over time, except for a significant increase in ALT from T1 to T2 (p_adj = 0.014). These findings support the clinical utility of routinely available haematological inflammatory ratios and lipid peroxidation biomarkers for phenotypic characterisation of MASLD in the diabetic context, highlighting the need for larger prospective studies with histological validation.

Background: Malnutrition is highly prevalent among critically ill patients and has been associated with worse clinical outcomes, particularly in sepsis. Several nutritional risk scores have been proposed to identify patients at increased risk of mortality in the intensive care unit (ICU). This study aimed to evaluate the prognostic value of three commonly used nutritional indices-modified Nutrition Risk in the Critically Ill (mNUTRIC), Prognostic Nutritional Index (PNI), and Controlling Nutritional Status (CONUT)-for predicting mortality in septic ICU patients. Methods: In this prospective observational cohort study conducted at two ICUs, 155 critically ill patients at nutritional risk were evaluated, including 105 patients with sepsis and 50 without sepsis. The primary endpoint was ICU mortality. Nutritional risk scores (mNUTRIC, PNI, and CONUT) were calculated at ICU admission. Survival analysis was performed using Kaplan-Meier (KM) curves and log-rank tests to compare survival probabilities across nutritional risk categories. Cox proportional hazards regression analysis was used to assess the association between nutritional scores and ICU mortality. Of note, only 24 mortality events were recorded in the septic cohort, which limits the statistical power of the findings. Results: KM analysis revealed significantly reduced survival among patients with severe malnutrition, as measured by the PNI score (log-rank p = 0.044). Patients with high mNUTRIC scores showed a tendency toward lower survival probability compared with those with low nutritional risk, approaching statistical significance (log-rank p = 0.059). No significant survival differences were observed between CONUT categories (log-rank p = 0.380). In univariate Cox regression analysis, the mNUTRIC score was significantly associated with ICU mortality (HR 1.67, 95% CI 1.17-2.38, p = 0.005). Conclusions: In this selected cohort, mNUTRIC demonstrated the strongest univariate prognostic signal for ICU mortality; however, this association was attenuated and did not reach statistical significance after limited multivariable adjustment. These findings are exploratory and apply specifically to a cohort of septic ICU patients with confirmed nutritional risk and therefore should not be generalized to the broader population of critically ill septic patients.

Circulating irisin, a myokine implicated in energy expenditure and adipose tissue regulation, has been increasingly studied as a potential biomarker of metabolic dysfunction. This study evaluated the relationship between serum irisin and metabolic indices, including the atherogenic index of plasma (AIP), the lipid accumulation product (LAP), and hypertriglyceridemic-waist (HTGW) phenotype in individuals with prediabetes (PreDM) and newly diagnosed type 2 diabetes mellitus (T2DM). A total of 138 participants (48 PreDM, 90 T2DM) were assessed for anthropometric, glycemic, and lipid parameters. Serum irisin levels were measured by enzyme-linked immunosorbent assay (ELISA) and correlated with insulin resistance indices (Homeostatic Model Assessment of Insulin Resistance (HOMA-IR), Quantitative Insulin Sensitivity Check Index (QUICKI)), glycemic control (glycosylated hemoglobin A1c (HbA1c)), and composite lipid markers (total triglycerides-to-high-density lipoprotein cholesterol (TG/HDL-C)). Group differences were evaluated using non-parametric tests; two-way ANOVA assessed interactions between phenotypes and markers; multiple linear regression (MLR) and logistic regression models explored independent associations with metabolic indices and HTGW; receiver operating characteristic (ROC) analyses compared global and stratified model performance. Serum irisin was significantly lower in T2DM than in PreDM (median 140.4 vs. 230.7 ng/mL, p < 0.0001). Irisin levels remained comparable between males and females in both groups. Post hoc analysis shows that lipid indices and irisin primarily distinguish HTGW phenotypes, especially in T2DM. In both groups, irisin correlated inversely with HOMA-IR, AIP, and TG/HDL-C, and positively with QUICKI, indicating a possible compensatory role in early insulin resistance. MLR analyses revealed no independent relationship between irisin and either AIP or LAP in PreDM, while in T2DM, waist circumference remained the strongest negative predictor of irisin. Logistic regression identified age, male sex, and HbA1c as independent predictors of the HTGW phenotype, while irisin contributed modestly to overall model discrimination. ROC curves demonstrated good discriminative performance (AUC = 0.806 for global; 0.794 for PreDM; 0.813 for T2DM), suggesting comparable predictive accuracy across glycemic stages. In conclusion, irisin levels decline from prediabetes to overt diabetes and are inversely linked to lipid accumulation and insulin resistance but do not independently predict the HTGW phenotype. These findings support irisin's role as an integrative indicator of metabolic stress rather than a stand-alone biomarker. Incorporating irisin into multi-parameter metabolic panels may enhance early detection of cardiometabolic risk in dysglycemic populations.

Background/Objectives: Shift work is an essential component of modern occupational systems but represents a major source of chronic circadian disruption associated with adverse gastrointestinal outcomes. The biological pathways underlying these associations remain incompletely integrated across circadian, neuroendocrine, immune, epithelial, and microbial domains. This narrative review aimed to synthesize current evidence linking shift work with gastrointestinal dysfunction and disease and to examine its translational implications for occupational medicine. Methods: A structured literature search was conducted in PubMed/MEDLINE, Scopus, and Web of Science, focusing primarily on studies published between January 2020 and July 2026. Recent original studies, systematic reviews, meta-analyses, and mechanistic and translational investigations were prioritized, while relevant landmark studies were retained. Evidence was synthesized within a seven-stage mechanistic framework spanning occupational exposure, circadian clock disruption, neuroendocrine misalignment, immune dysregulation, intestinal barrier dysfunction, gut microbial and metabolic alterations, and gastrointestinal disease. Results: Current evidence supports a multidirectional pathway in which chronic circadian misalignment disrupts melatonin and cortisol rhythms, autonomic regulation, and innate and adaptive immune homeostasis. Persistent inflammatory signaling and oxidative stress may subsequently impair epithelial tight junction integrity and increase intestinal permeability, thereby promoting microbial translocation and gut dysbiosis. Alterations in microbial metabolites, including short-chain fatty acids, secondary bile acids, and tryptophan derivatives, may further reinforce barrier and immune dysfunction. These interconnected mechanisms provide biological plausibility for the increased burden of disorders of gut–brain interaction, gastroesophageal reflux disease, peptic ulcer disease, and potentially inflammatory bowel disease and colorectal neoplasia among shift workers. Emerging circadian, inflammatory, intestinal barrier, microbiome, and multi-omics biomarkers may enable earlier identification of biologically susceptible individuals. Conclusions: Gastrointestinal consequences of shift work appear to arise from interacting circadian, neuroendocrine, immune, epithelial, and microbial disturbances rather than from isolated mechanisms. Integrating occupational exposure assessment with multidimensional biological profiling may support biomarker-guided surveillance, individualized prevention, and the development of Precision Occupational Medicine for shift workers. Prospective longitudinal and interventional studies are required to validate biomarkers, clarify causal pathways, and determine whether mechanism-based interventions can prevent gastrointestinal disease.

The disproportionately severe disease course of diabetic patients with SARS-CoV-2 infection was repeatedly observed by clinicians during the COVID-19 pandemic. The overlap between metabolic impairment, viral pathophysiology, and chronic inflammation created a pattern that urged deeper examination. The aim of this paper was to review and synthesize evidence regarding the interaction between diabetes mellitus and COVID-19. We synthesized evidence across mechanistic pathways (immune dysregulation, chronic inflammation, ACE2/DPP-4-related signaling, endothelial dysfunction, and pancreatic involvement) and key clinical outcomes (severity, intensive care unit (ICU) admission, mortality, dysglycaemia/new-onset diabetes, and DKA). This systematic search was conducted in PubMed, Clinical Key, and Google Scholar. The eligibility criteria included papers on adults (≥18 years) with pre-existing diabetes mellitus (type 1 or type 2) or newly diagnosed diabetes/hyperglycemia and confirmed SARS-CoV-2 infection, published between January 2020 and October 2025, in English language. The PRISMA guidelines were used for data extraction. We identified 412 articles, out of which only 30 met all the inclusion criteria. Diabetes was consistently evoked as a major risk factor for severe COVID-19, being associated with higher susceptibility to pneumonia, respiratory failure, ICU admission, and mortality. The explanation lies in the impaired immune system, endothelial dysfunction, and metabolic repercussions imposed by hyperglycemia. Several antidiabetic drugs appeared protective in multiple cohorts. In conclusion, the accumulated evidence underscores the tight interplay between metabolic disease and COVID-19. Essentially, the clinical management of these patients would be a thoughtful selection of antidiabetic therapy and close metabolic monitoring.

Background/Objectives: Inflammatory and hematologic indices derived from routine blood tests have been increasingly investigated as prognostic biomarkers in multiple myeloma (MM). However, their clinical utility remains inconsistent, and data on novel composite indices, such as the mean corpuscular volume-to-lymphocyte ratio (MCVL) and the cumulative inflammatory index (IIC), are lacking in MM. Methods: We conducted a retrospective study including 122 patients with newly diagnosed MM. Hematologic and inflammatory indices were evaluated at baseline and after four cycles of induction therapy. Associations with progression-free survival (PFS) and overall survival (OS) were assessed using Kaplan-Meier analysis, Cox regression models, and receiver operating characteristic (ROC) curve analysis. Results: Baseline inflammatory biomarkers, including neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), systemic immune-inflammation index (SII), MCVL, and IIC, were not significantly associated with PFS or OS. ROC analysis demonstrated poor discriminative ability for all evaluated markers at both baseline and post-induction timepoints (AUC values close to or below 0.50). In contrast, post-induction inflammatory indices, particularly PLR, MLR, AISI, and SIRI, were significantly associated with PFS in both univariable and multivariable Cox regression analyses. Neither baseline nor post-induction MCVL and IIC showed independent prognostic value. Conclusions: Baseline inflammatory and erythrocyte-derived indices, including the novel composite markers MCVL and IIC, have limited prognostic utility in MM. In contrast, dynamic changes in inflammatory biomarkers during treatment may provide more clinically relevant information regarding disease progression. These findings support integrating longitudinal biomarker assessment into future risk stratification models for MM.

Preeclampsia involves an angiogenic imbalance, but circulating vascular endothelial growth factor A (VEGF A) remains inconsistently described, particularly in relation to maternal adiposity. We studied 90 second-trimester pregnancies, 30 uncomplicated and 60 with preeclampsia, recording maternal body mass index (BMI) and gestational age at sampling. Serum soluble fms-like tyrosine kinase 1 (sFlt1), placental growth factor (PlGF), and VEGF A were measured by enzyme-linked immunosorbent assay (ELISA), and the sFlt1-to-PlGF ratio was calculated. Preeclampsia was associated with higher pre-pregnancy and pregnancy BMI, lower PlGF, and an approximately threefold higher sFlt1-to-PlGF ratio, while sFlt1 alone was only borderline higher. VEGF A was elevated in preeclampsia and rose across higher sFlt1-to-PlGF ratio categories, supporting the interpretation of VEGF A within the integrated sFlt1,PlGF axis rather than as an isolated signal.

Bacterial infections remain a major global health burden, further exacerbated by the rapid emergence of antimicrobial resistance (AMR), which increases the need for accurate and timely etiological diagnosis. Conventional culture-based methods are limited by prolonged turnaround times, reduced sensitivity in patients receiving prior antimicrobial therapy, and restricted ability to characterize resistance mechanisms at the molecular level. Molecular diagnostic technologies have significantly transformed bacteriological diagnostics by enabling rapid, sensitive, and specific pathogen detection directly from clinical specimens. This review provides a structured comparative analysis of major molecular platforms, including polymerase chain reaction (PCR) and its variants, isothermal amplification technologies, next-generation sequencing (NGS), clustered regularly interspaced short palindromic repeats (CRISPR) based diagnostics, and digital PCR (dPCR). Key analytical parameters such as sensitivity, specificity, limit of detection (LOD), time to result, and multiplexing capacity are evaluated to highlight platform-specific strengths and limitations. In addition, the integration of artificial intelligence and machine learning (AI/ML) into molecular diagnostic workflows for AMR prediction and clinical decision support is critically examined. The translational potential of these technologies toward point-of-care (POC) implementation is also discussed, with consideration of clinical validation, operational constraints, and real-world applicability. Overall, this review provides an integrated perspective on current molecular diagnostic strategies, emphasizing the balance between analytical performance and clinical interpretability, and outlines key challenges and future directions for advancing culture-independent bacteriological diagnostics.

Background/Objectives: Osteoporosis is a major cause of skeletal fragility, yet occupational determinants of bone health remain comparatively underrecognized in clinical and occupational medicine. This review aimed to critically evaluate the relationships between occupational exposures and skeletal health, examine the biological mechanisms potentially linking workplace conditions to bone remodeling, and explore how occupational information could contribute to individualized osteoporosis risk assessment and prevention. Methods: A structured literature search was conducted focusing primarily on studies published between 2020 and June 2026, supplemented by relevant earlier landmark studies. Evidence concerning shift and night work, circadian and sleep disruption, indoor work and sunlight exposure, occupational physical activity and sedentary behavior, psychosocial stress, and selected chemical and environmental exposures was synthesized together with current approaches to osteoporosis diagnosis and fracture-risk assessment. Results: The available evidence suggests that night and rotating shift work, circadian disruption, limited daylight exposure, altered mechanical loading, and selected industrial exposures may be associated with changes in bone turnover, bone mineral density, osteoporosis, or skeletal fragility. Potential mechanisms converge on circadian and endocrine regulation, vitamin D–mineral homeostasis, inflammatory and oxidative pathways, RANK/RANKL/OPG signaling, and Wnt/β-catenin/sclerostin-mediated mechanotransduction. However, evidence remains heterogeneous, with limited prospective occupational cohorts and substantial variation in exposure assessment and skeletal endpoints. Occupational characteristics are not currently validated as independent criteria for diagnosing osteoporosis or as predictors of fracture risk. Based on the available evidence, we propose a Precision Occupational Medicine framework integrating cumulative occupational exposure, individual susceptibility, risk-enriched diagnostic assessment, personalized prevention, and longitudinal reassessment. Conclusions: Occupational factors may modify skeletal vulnerability, particularly when cumulative exposures interact with established osteoporosis risk factors. Current evidence supports risk-enriched assessment rather than universal occupational screening. Prospective studies are required to determine whether quantitative occupational exposure data improve prediction of longitudinal bone loss and fragility fractures beyond established clinical models and to validate the proposed framework for occupational practice.

BACKGROUND/OBJECTIVES:Microsatellite-stable colorectal cancer (MSS CRC) accounts for the vast majority of CRC cases and remains largely resistant to immune checkpoint inhibitors. Emerging evidence suggests that the gut microbiome is an important regulator of antitumor immunity and may contribute to immunotherapy resistance through multiple mechanisms involving the tumor microenvironment. This review aims to summarize current knowledge of the microbiome-immunity-therapy axis in MSS CRC and to explore microbiome-based strategies to enhance immunotherapy responsiveness. METHODS:A narrative review of the recent literature was conducted, focusing on studies published within the last five years that investigated gut microbiota composition, microbial metabolites, tumor immune regulation, immunotherapy response, and microbiome-targeted therapeutic interventions in CRC. Evidence from mechanistic studies, translational research, clinical investigations, and multi-omics analyses was integrated. RESULTS:Current evidence indicates that gut dysbiosis contributes to immune resistance in MSS CRC through immune exclusion, myeloid-driven immunosuppression, T-cell dysfunction, chronic inflammation, and altered microbial metabolite signaling. Specific microorganisms, including Fusobacterium nucleatum, enterotoxigenic Bacteroides fragilis, pks-positive Escherichia coli, and other CRC-associated pathobionts, have been implicated in tumor progression and modulation of antitumor immunity. Microbial metabolites such as short-chain fatty acids, tryptophan-derived compounds, bile acids, succinate, and inosine represent key functional mediators linking microbial communities to host immune responses. Emerging microbiome-targeted interventions, including fecal microbiota transplantation, next-generation probiotics, postbiotics, selective microbial depletion, and engineered bacterial therapeutics, have shown promising results in preclinical models and early translational or clinical studies, although robust clinical evidence remains limited. In parallel, advances in metagenomics, metabolomics, spatial transcriptomics, and artificial intelligence are facilitating the development of precision immuno-microbiome oncology approaches. CONCLUSIONS:The gut microbiome functions as a critical regulator of immune resistance in MSS CRC through coordinated effects on microbial composition, metabolite production, and tumor immune remodeling. Microbiome-targeted interventions, combined with multi-omics-based patient stratification, may provide new opportunities to overcome immunotherapy resistance and expand the clinical benefits of immune checkpoint blockade in this traditionally refractory disease.

Background: Oxidative stress plays a significant role in inflammatory bowel disease (IBD), yet the clinical relevance of specific lipid peroxidation markers remains insufficiently defined. This study evaluated serum levels of 8-epi-prostaglandin F2α (8-epi-PGF2α), an isoprostane generated through non-enzymatic lipid oxidation, and examined its relationship with antioxidant enzymes and clinical disease activity in ulcerative colitis (UC) and Crohn's disease (CD). Methods: Eighty-seven patients (55 UC and 32 CD) were assessed for serum 8-epi-PGF2α, superoxide dismutase 1 (SOD1), and glutathione peroxidase 1 (GPX1), and classified as having mild, moderate, or severe disease. Statistical analyses included comparative analysis, two-way ANOVA, multiple linear regression, and Ridge logistic regression. To address potential dietary confounding, total energy intake, Mediterranean Diet Score (MDS), and antioxidant supplement use were incorporated into the regression models. Results: Serum levels of 8-epi-PGF2α and GPX1 were significantly higher in UC than in CD (ρ = 0.001 and p = 0.042), and both increased with greater disease severity (p < 0.001 and p = 0.001). In UC, 8-epi-PGF2α positively correlated with high-sensitivity C-reactive protein (hs-CRP), white blood cells (WBC), and Truelove-Witts Index (TWI), and negatively with hemoglobin (False Discovery Rate (FDR)-adjusted q < 0.100). In CD, it correlated with the Harvey-Bradshaw Index (HBI) and disease duration (FDR-adjusted q < 0.050). Inter-biomarker analyses showed a strong association between 8-epi-PGF2α and GPX1 in UC (ρ = 0.677, p < 0.0001, FDR < 0.0001), suggesting coordinated activation of oxidative and antioxidant pathways. The observed associations remained consistent after adjustment for dietary factors, supporting the robustness of the findings. Because these results are cross-sectional, they cannot establish causality and should be interpreted with caution. Conclusions: Nevertheless, 8-epi-PGF2α emerges as a promising non-invasive biomarker for assessing oxidative stress and disease activity in IBD, with potential clinical applicability for patient monitoring and therapeutic evaluation.

Background and Objectives: Oxidative stress and chronic inflammation are closely interconnected processes involved in colorectal cancer (CRC) development and progression. However, the relationships between oxidative stress biomarkers, emerging hematological inflammatory indices, clinicopathological characteristics, and survival outcomes remain insufficiently characterized. This study evaluated circulating levels of 8-epi-prostaglandin F2α (8-epi-PGF2α), malondialdehyde (MDA), and superoxide dismutase 1 (SOD1), together with conventional and novel inflammatory indices, including the mean corpuscular volume-to-lymphocyte ratio (MCVL) and cumulative inflammatory index (IIC), in patients with CRC. Materials and Methods: This prospective observational study included 140 patients with histologically confirmed CRC and 40 healthy controls. Serum concentrations of 8-epi-PGF2α, MDA, and SOD1 were measured by ELISA, and hematological inflammatory indices were calculated from complete blood counts. Associations with clinicopathological characteristics were evaluated using non-parametric analyses with correction for multiple testing where appropriate. Spearman correlations with false discovery rate correction were used to assess oxidative–inflammatory associations. Prognostic analyses included 24-month time-dependent receiver operating characteristic (ROC) analysis accounting for censoring, Kaplan–Meier analysis, and Cox proportional hazards regression for overall survival (OS) and progression-free survival (PFS). Results: CRC patients exhibited significantly higher serum levels of 8-epi-PGF2α (p = 0.016), MDA (p < 0.001), and SOD1 (p < 0.001) than controls. Oxidative stress biomarkers differed significantly across TNM stage, nodal status, and histological grade, although the observed patterns were not uniformly progressive with disease stage. After false discovery rate correction, MDA retained significant correlations with multiple hematological inflammatory parameters, whereas SOD1 showed a more restricted correlation profile and 8-epi-PGF2α showed no significant correlations. At 24 months, MDA demonstrated the highest time-dependent discrimination for OS (AUC = 0.920; bootstrap 95% CI: 0.834–0.978), whereas its discrimination for PFS was modest (AUC = 0.636; bootstrap 95% CI: 0.487–0.773). High MDA, defined by the internally derived 24-month threshold, was associated with shorter PFS after adjustment for age, sex, and TNM stage (HR = 2.49, 95% CI: 1.29–4.80; p = 0.006) and, separately, metastatic status (HR = 2.40, 95% CI: 1.22–4.72; p = 0.011). However, when modeled continuously, MDA was no longer significantly associated with PFS after multivariable adjustment. Conclusions: CRC was associated with increased circulating oxidative stress biomarkers, with MDA showing the most consistent relationships with systemic inflammatory parameters and survival outcomes. Its prognostic association with PFS was dependent on the modeling approach, while its high discrimination for 24-month OS should be interpreted cautiously because of the limited number and metastatic restriction of death events. These findings identify MDA as a promising candidate oxidative–inflammatory marker warranting external validation rather than an independently established prognostic biomarker.

Adiponectin and omentin are adipose tissue-derived adipokines implicated in insulin sensitivity and cardiometabolic regulation. Their behavior across different stages of dysglycemia, as well as in relation to visceral adiposity and cardiometabolic phenotypes, remains incompletely understood. In this cross-sectional study, circulating adiponectin and omentin levels were evaluated in individuals with prediabetes (PreDM, n = 100) and newly diagnosed type 2 diabetes mellitus (T2DM, n = 128). Associations with insulin resistance-related indices, including the triglyceride-glucose (TyG) index and TyG-derived composites, the visceral adiposity index (VAI), cardiometabolic phenotypes, and cardiovascular risk categories, were assessed using correlation and multivariable regression analyses. Discriminatory performance for metabolically unhealthy obesity was evaluated using receiver operating characteristic (ROC) curve analysis. Both adiponectin and omentin levels were lower in T2DM compared with PreDM (22.05 vs. 30.30 and 25.72 vs. 38.84, p < 0.0001 for both). In PreDMs, omentin showed a significant inverse correlation with the TyG index (weak correlation, ρ = -0.197, p = 0.050), whereas adiponectin demonstrated only weak trends. In multivariable models, VAI and male sex were independent predictors of circulating omentin levels, whereas fasting insulin was not. In contrast, adiponectin did not retain independent associations with metabolic or visceral adiposity indices. In T2DM, adipokine-metabolic associations were largely absent. Neither adipokine differed substantially across cardiometabolic phenotypes or cardiovascular risk categories. ROC analyses revealed modest overall discriminatory performance for metabolically obese phenotypes, with poor discrimination after stratification by glycemic status (area under the ROC curve (AUC) of 0.704 for adiponectin and 0.710 for omentin, and AUC of 0.431 for adiponectin and 0.461 for omentin, respectively). Circulating adipokines appear to exhibit stage-dependent relationships with metabolic dysfunction, being more informative in PreDM than in established T2DM. Omentin may reflect visceral adiposity-related metabolic alterations in early dysglycemia, whereas adiponectin shows limited independent associations. Overall, these findings suggest that adipokines have limited diagnostic or cardiovascular risk-stratification utility when considered in isolation and may be better interpreted within multimarker cardiometabolic assessment frameworks.

Background/Objectives: Outborn neonates—those born outside tertiary perinatal centres and transferred postnatally—may experience higher mortality and morbidity than inborn infants. This systematic review without meta-analysis synthesised contemporary evidence on neonatal interfacility transfer to tertiary and quaternary neonatal centres, focusing on mortality, major morbidity, physiological instability, prognostic tools, and organisational determinants. Methods: Four electronic databases (PubMed/MEDLINE, Scopus, Web of Science, and the Cochrane Library) were searched for English-language publications from January 2010 to December 2025. Study selection and data extraction were performed independently by two reviewers, with disagreements resolved by consensus. Narrative synthesis followed PRISMA 2020 and SWiM principles. Of the 2456 records identified, 44 publications were retained: 31 primary studies and 13 contextual or methodological sources. Results: Across the primary evidence, outborn status was generally associated with higher mortality and major morbidity, particularly among very preterm infants, although effect magnitude varied across healthcare systems. Hypothermia, respiratory deterioration, and haemodynamic instability were recurrent transport-related complications. Specialised teams, standardised stabilisation, and thermal-management bundles were associated with better physiological stability. TRIPS and TRIPS-II showed prognostic utility. Conclusions: Predominantly observational evidence suggests that neonatal transport outcomes reflect interactions between biological vulnerability, transport-related stress, and system organisation. Strengthening regionalised transport pathways and standardising stabilisation practices may improve outcomes, but causal inference remains limited.

Background/Objectives: Type 2 diabetes mellitus (T2DM) and atherogenic dyslipidemia have been implicated in colorectal cancer (CRC) development, but their prognostic relevance after cancer diagnosis remains unclear. This study aimed to evaluate the association between T2DM, lipid-derived atherogenic indices, and survival outcomes in patients with CRC. Methods: We conducted a retrospective cohort study including 240 CRC patients, of whom 60 had coexisting T2DM. Overall survival (OS) and disease-free survival (DFS) were analyzed using the Kaplan-Meier (KM) method and log-rank tests. In the absence of recurrence-specific data, DFS was defined as time to death or last follow-up. Lipid-related indices, including the atherogenic index of plasma (AIP), atherogenic coefficient (AC), remnant cholesterol (RC), non-high-density lipoprotein cholesterol (non-HDL-C), triglyceride-glucose (TyG) index, and triglyceride-to-HDL cholesterol ratio (TG/HDL-C), were evaluated by tertiles in KM analyses. Multivariable Cox proportional hazards models were constructed to assess the independent prognostic value of AIP, AC, and RC (entered separately as a continuous variable standardized to 1 standard deviation), adjusted for age, sex, adjuvant chemotherapy, radiotherapy, and T2DM status. Sensitivity analyses were performed in stage III-IV patients. Results: During follow-up, 28 deaths occurred. OS did not differ significantly between CRC patients and those with CRC coexisting with T2DM (log-rank p-values = 0.220). DFS analyses showed no significant differences across tertiles of any lipid-related index (all log-rank p-values > 0.05), with overlapping survival curves and no consistent dose-response patterns. In adjusted Cox models, AIP (hazard ratio [HR] per 1 SD = 0.71, 95% CI 0.48-1.06), AC (HR = 0.72, 95% CI 0.44-1.20), and RC (HR = 0.66, 95% CI 0.39-1.12) were not independently associated with DFS. Results were consistent in advanced-stage disease (stage III-IV). Conclusions: In this cohort of patients with CRC, neither T2DM nor lipid-derived indices reflecting atherogenic dyslipidemia and insulin resistance were independently associated with OS or DFS. These findings help refine the clinical interpretation of lipid-derived biomarkers in CRC, suggesting limited prognostic utility beyond established oncologic factors.

Background: Interfacility neonatal transport is essential for regionalized perinatal care, yet its effects on systemic immune responses remain poorly characterized. This study evaluated longitudinal changes in hematologic and inflammatory biomarkers during neonatal transport and investigated the influence of transport modality on these responses. Methods: In this prospective observational study, 161 neonates undergoing air or ground transport to Level III/IV neonatal intensive care units were evaluated at three time points: before transport (T0), immediately after transport (T1), and 12 hours after admission (T2). Physiological stability parameters, complete blood count variables, and inflammatory indices, including neutrophil-to-lymphocyte ratio (NLR), systemic immune-inflammation index (SII), systemic inflammation response index (SIRI), and mean corpuscular volume-to-lymphocyte ratio (MCVL), were analyzed using repeated-measures and mixed-effects models adjusted for transport duration, sedation, and physiological stability. Results: Physiological stability was maintained throughout transport, with no significant changes in heart rate, oxygen saturation, or body temperature. In contrast, significant longitudinal changes were observed in leukocyte populations and inflammatory indices. Total leukocyte and neutrophil counts decreased over time, whereas lymphocyte counts increased. Mixed-effects analyses identified significant effects of time, transport modality, and Time × Transport interaction for WBC, neutrophils, lymphocytes, NLR, SII, and SIRI. These associations remained significant after adjustment for clinical and physiological confounders. MCVL also demonstrated significant effects of time, transport modality, and their interaction, emerging as a potential novel marker of transport-related immune adaptation. Conclusions: Neonatal transport is associated with significant hematologic and immune adaptations despite preserved physiological stability. Air and ground transport induce distinct patterns of leukocyte redistribution and inflammatory responses. MCVL may represent a promising biomarker of transport-related immune adaptation and warrants further investigation in neonatal transport medicine.

Oxidative stress biomarkers are elevated in liver cirrhosis, but their clinical utility for severity staging and complication prediction remains uncertain. This retrospective single-centre study enrolled 90 patients with decompensated cirrhosis (Child-Pugh classes B and C) to evaluate serum malondialdehyde (MDA) and 8-isoprostane (8-isoPGF2 alpha) as predictors of Child-Pugh severity, severe ascites, and severe hepatic encephalopathy, and to quantify their incremental value within supervised machine learning models. Four algorithms-logistic regression, Random Forest, Gradient Boosting, and Support Vector Machine-were evaluated using stratified 10-fold cross-validation; logistic regression models with and without oxidative stress biomarkers were compared for the prediction of ascites and encephalopathy. Routine biochemical parameters effectively discriminated Child-Pugh class B from C, with machine learning models achieving AUC-ROC values of 0.921-0.972. Neither MDA nor 8-isoPGF2 alpha differed between Child-Pugh classes or across ascites categories, and both failed to improve ascites prediction (Delta AUC = -0.015). For severe hepatic encephalopathy, the extended model showed modest but consistent improvements in accuracy (+3.4 percentage points), sensitivity (+6.4%), and model fit, suggesting an outcome-specific complementary role consistent with the established involvement of lipid peroxidation in ammonia neurotoxicity. These findings support the use of machine learning for automated cirrhosis severity classification and indicate that oxidative stress biomarkers hold selective relevance for hepatic encephalopathy rather than global disease staging.

Bacterial infections impose a substantial global health burden, with antimicrobial resistance (AMR) further compounding the urgency of accurate and timely etiological diagnosis. Conventional culture-based methods, limited by extended turnaround times of 48–96 hours, reduced sensitivity in the presence of prior antibiotic exposure, and an inability to characterize resistomes at the molecular level, are progressively insufficient in the face of contemporary clinical demands. Molecular technologies have transformed bacteriological diagnostics by enabling rapid, sensitive, and highly specific pathogen identification directly from clinical specimens. Despite a growing body of primary evidence, no current review synthesizes these platforms under a unified comparative analytical framework that simultaneously addresses three critical dimensions: (1) the quantitative performance benchmarking of principal molecular platforms, including polymerase chain reaction (PCR) and its variants, isothermal amplification technologies, next-generation sequencing (NGS), clustered regularly interspaced short palindromic repeats (CRISPR) based diagnostics, and digital PCR (dPCR), across standardized parameters of sensitivity, specificity, limit of detection (LOD), time-to-result, and multiplexing capacity; (2) the integration of artificial intelligence and machine learning (AI/ML) algorithms into molecular diagnostic workflows for AMR prediction and clinical decision support; and (3) the translational trajectory of these technologies toward point-of-care (POC) deployment in decentralized and resource-limited settings. This review addresses this gap by providing a structured, evidence-based comparative analysis of molecular platforms applicable to bacterial infection diagnostics, critically evaluating their clinical validation status, AMR genotyping capabilities, AI augmentation potential, and readiness for POC implementation. We further delineate regulatory, health-economic, and implementation considerations, and identify key research priorities for the next generation of culture-independent precision bacteriological diagnostics.

In the original publication [...].

Atopic dermatitis (AD) is a chronic inflammatory skin disease characterized by epidermal barrier dysfunction, immune dysregulation, and marked clinical heterogeneity. Growing evidence implicates the gut microbiome in AD-related pathways through microbial metabolites, intestinal barrier function, and systemic immune signaling. This narrative review synthesizes current evidence on gut microbial alterations in AD, with particular attention to short-chain fatty acids, tryptophan-derived aryl hydrocarbon receptor ligands, intestinal permeability, gut-skin microbiome interactions, and microbiota-targeted interventions. Human studies have reported associations between AD and altered abundance of selected microbial taxa, metabolite profiles, and markers of intestinal barrier dysfunction, whereas animal and in vitro studies provide complementary mechanistic evidence. However, findings remain heterogeneous across age groups, disease phenotypes, geographic populations, analytical platforms, and treatment exposures, and causality is incompletely established. Probiotic and synbiotic interventions have shown strain-specific and context-dependent effects, while postbiotics, fecal microbiota transplantation, washed microbiota transplantation, and metabolite-directed approaches remain investigational. AI-assisted multi-omics methods may improve biological stratification and hypothesis generation, but current applications are limited by small sample sizes, cohort heterogeneity, overfitting, insufficient external validation, and limited clinical implementation. Current evidence therefore supports the gut microbiome as a mechanistically plausible contributor, potential biomarker, and therapeutic target in AD while underscoring the need for longitudinal, phenotype-aware, and externally validated studies before routine clinical translation.