Tag: nevin manimala

Br Med Bull. 2025 Sep 22;156(1):ldaf014. doi: 10.1093/bmb/ldaf014.

ABSTRACT

INTRODUCTION: Empirical evidence on migrant morbidity suggests that migrant populations have a higher burden of disease compared to non-migrants in high-income destination countries. Yet, empirical evidence on migrant mortality typically shows a lower risk of death compared to non-migrants. Migrants might be living longer lives in worse health-a ‘migrant “morbidity-mortality” paradox’.

SOURCES OF DATA: Peer-reviewed, English-language publications.

AREAS OF AGREEMENT: The paradox has been reported in different destinations, across different migrant groups, and across different health outcomes. It presents most consistently among migrants and women born in low and middle-income countries, and/or when morbidity is self-reported.

AREAS OF CONTROVERSY: The majority of the evidence is based upon unlinked, aggregated, cross-sectional prevalence data that has well-known limitations. Nearly all the studies to date have been descriptive, and there is a lack of understanding concerning what might explain this paradox among migrants.

GROWING POINTS: That migrants are living longer subject to a higher burden of diseases is a social and public health concern that needs to be further explored and understood through more research.

AREAS TIMELY FOR DEVELOPING RESEARCH: We need more evidence of the paradox based upon linked individual-level, incidence-based data that compares the morbidity and mortality risks of the same migrant and non-migrant populations using objective data on morbidity from primary care (general practitioners) or secondary care (hospitalizations). We need to know how widespread the paradox is, which migrant populations are most affected by it, and the potential mechanisms responsible for it.

PMID:40986280 | DOI:10.1093/bmb/ldaf014

Biometrics. 2025 Jul 3;81(3):ujaf125. doi: 10.1093/biomtc/ujaf125.

ABSTRACT

Microbiome research has immense potential for unlocking insights into human health and disease. A common goal in human microbiome research is identifying subgroups of individuals with similar microbial composition that may be linked to specific health states or environmental exposures. However, existing clustering methods are often not equipped to accommodate the complex structure of microbiome data and typically make limiting assumptions regarding the number of clusters in the data which can bias inference. Designed for zero-inflated multivariate compositional count data collected in microbiome research, we propose a novel Bayesian semiparametric mixture modeling framework that simultaneously learns the number of clusters in the data while performing cluster allocation. In simulation, we demonstrate the clustering performance of our method compared to distance- and model-based alternatives and the importance of accommodating zero-inflation when present in the data. We then apply the model to identify clusters in microbiome data collected in a study designed to investigate the relation between gut microbial composition and enteric diarrheal disease.

PMID:40986279 | DOI:10.1093/biomtc/ujaf125

Pharmacoeconomics. 2025 Sep 23. doi: 10.1007/s40273-025-01528-6. Online ahead of print.

ABSTRACT

BACKGROUND AND OBJECTIVE: Aztreonam-avibactam (ATM-AVI) is a novel combination antibiotic approved in Europe for the treatment of complicated intra-abdominal infection, hospital-acquired pneumonia, including ventilator-associated pneumonia; complicated urinary tract infection, including pyelonephritis and for infections due to aerobic Gram-negative organisms with limited treatment options. This analysis assessed the cost effectiveness of ATM-AVI ± metronidazole versus colistin + meropenem (COL + MER) for the treatment of patients with complicated intra-abdominal infection and hospital-acquired pneumonia/ventilator-associated pneumonia, including infections with suspected metallo-β-lactamase-producing Enterobacterales from the public payer perspective in Italy using phase III trial data.

METHODS: The cost-effectiveness analysis adopted a decision tree model to simulate the clinical pathway of complicated intra-abdominal infection and hospital-acquired pneumonia/ventilator-associated pneumonia, followed by a Markov model to capture lifetime health outcomes on cured patients, with costs valued in 2024 Euros and discounted at 3%. The model captures the impact of resistant pathogens and side effects (i.e. nephrotoxicity). Model uncertainty was assessed using a probabilistic and deterministic sensitivity analysis.

RESULTS: The ATM-AVI treatment sequence (ATM-AVI ± metronidazole followed by cefiderocol after treatment failure) had improved clinical outcomes and higher cure rates, shorter hospital stays and higher quality-adjusted life-year gains compared with the COL + MER sequence (COL + MER followed by cefiderocol after treatment failure). The incremental cost-effectiveness ratio in the ATM-AVI sequence was dominant for complicated intra-abdominal infection and was €1552 per quality-adjusted life-year for hospital-acquired pneumonia/ventilator-associated pneumonia, well below the willingness-to-pay threshold of €30,000 in Italy.

CONCLUSIONS: Our analysis suggests that ATM-AVI is expected to be a cost-effective use of Italian healthcare resources for treating suspected metallo-β-lactamase-producing Enterobacterales, including complicated intra-abdominal infection and hospital-acquired pneumonia/ventilator-associated pneumonia.

PMID:40986278 | DOI:10.1007/s40273-025-01528-6

Clin Rheumatol. 2025 Sep 23. doi: 10.1007/s10067-025-07647-x. Online ahead of print.

ABSTRACT

INTRODUCTION: Rheumatoid Arthritis (RA) is a chronic autoimmune disease that affects the oral cavity, contributing to the development of periodontal disease (PD), an inflammatory condition that has a bidirectional relationship with various systemic conditions and can lead to tooth loss (TL).This study aimed to evaluate whether the number of missing teeth could serve as an additional indicator for the medical team in assessing the association with systemic disease exacerbations.

METHODS: This pilot study assessed patients with rheumatoid arthritis (RA) using specific protocols, including medical record analysis and a systematic orofacial examination to calculate the Decayed, Missing, and Filled Teeth (DMFT) index. Validated questionnaires were applied, and the disease activity (DAS-28) and functional capacity (HAQ) indices were collected.

RESULTS: The study included 21 patients with a mean DAS28 of 3.12 and a mean HAQ of 1.077. Of these, 7 (33.4%) were in remission, while 14 (66.6%) had some level of disease activity. 10 (47%) had moderate to severe disability due to RA. Statistical analysis identified polypharmacy as a clinically relevant factor associated with tooth loss (p = 0.029; r = 0.48). Additionally, patients with disease activity had higher DMF-T scores. Correspondence analysis indicated that polypharmacy was associated with a higher prevalence of moderate to severe disability (HAQ) and higher DMF-T scores.

CONCLUSION: It is concluded that there is a relationship between the DMF-T index, RA activity, the number of missing teeth, and the need for polypharmacy, making these data important to be assessed in the clinical routine of RA patients. Additional studies are necessary to explore this association in greater depth and strengthen the evidence base.

PMID:40986267 | DOI:10.1007/s10067-025-07647-x

Rheumatol Ther. 2025 Sep 23. doi: 10.1007/s40744-025-00792-4. Online ahead of print.

ABSTRACT

INTRODUCTION: Sjögren’s disease (SjD) is often characterized by the presence of anti-SSA/Ro and anti-SSB/La autoantibodies. The Clinical European Alliance of Associations for Rheumatology (EULAR) Sjögren’s Syndrome Disease Activity Index (ClinESSDAI) and Patient-Reported Index (ESSPRI) assess disease activity and patient-reported symptomatology; however, their association with patient-reported outcome measures (PROMs) remains unclear. We aimed to describe systemic disease activity in seropositive and seronegative SjD patients and evaluate the association between proxy ClinESSDAI and ESSPRI scores with PROMs.

METHODS: Data were drawn from the Adelphi Real World SjD Disease Specific Programme™, a cross-sectional survey conducted in France, Germany, Italy, Spain and the United States between June and October 2018. Physicians reported patient demographics and clinical characteristics. Patients completed the EQ-5D-3L and Visual Analogue Scale (EQ-VAS), and the Functional Assessment of Chronic Illness Therapy-Fatigue Scale (FACIT-F). Proxy ClinESSDAI and ESSPRI scores were calculated using physician-reported organ activity and averaged patient ratings of dryness, pain, and fatigue, respectively. Associations between ClinESSDAI, ESSPRI, physician-reported disease severity, and PROMs were determined using linear and logistic regression modeling. Statistical significance was p < 0.05 for all tests.

RESULTS: Overall, 319 rheumatologists provided data on 1879 patients with SjD. Mean (standard deviation) patient age was 53.2 (12.2) years, 89% were female, and 89% were White. Of patients who received serum antibody testing for both anti-SSA/Ro and anti-SSB/La antibodies (n = 1344), 69% were double seropositive and 6% were double seronegative. The most common symptoms experienced by double seropositive and double seronegative SjD patients, respectively, included dry eyes (94% and 74%), and physical fatigue (82% and 60%). ClinESSDAI and ESSPRI were significantly associated with EQ-5D-3L, EQ-VAS, and FACIT-F (all p < 0.001).

CONCLUSIONS: Systemic disease activity and patient-reported symptomatology were significantly associated with health-related quality of life measures, highlighting the need for disease management that considers both clinical outcomes and the patient experience.

PMID:40986255 | DOI:10.1007/s40744-025-00792-4

Dermatol Ther (Heidelb). 2025 Sep 23. doi: 10.1007/s13555-025-01547-3. Online ahead of print.

ABSTRACT

INTRODUCTION: Atopic dermatitis (AD) is a common chronic inflammatory disease during adolescence, with severe forms having a particularly high impact on patients’ quality of life. Several therapeutic options are currently approved for the treatment of AD in individuals over 12 years of age. This study evaluated the efficacy and safety of tralokinumab in adolescent patients, with a particular focus on identifying which patient profiles may derive the greatest benefit from this therapy.

METHODS: A retrospective multicenter study was conducted across nine Spanish hospitals, including patients from 12 to 17 years old with moderate-to-severe AD treated with tralokinumab.

RESULTS: A total of 27 patients were included, with a mean age of 14.8 years. Nine had previously received treatment with dupilumab, five due to primary or secondary failure, and four due to adverse events. One patient had been treated with upadacitinib, which was discontinued because of primary failure and acne. A statistically significant reduction was achieved in Eczema Area and Severity Index (EASI), pruritus visual analog scale (VAS), and Investigator’s Global Assessment (IGA) scores. Palmoplantar involvement was observed in 44.4% of patients; after 24 weeks of treatment, 83.3% of those with palmoplantar involvement experienced complete resolution. Additionally, 37.0% of patients were overweight or obese, with no statistically significant differences in treatment efficacy.

CONCLUSION: Tralokinumab demonstrated efficacy and safety in the treatment of moderate-to-severe AD in patients aged 12-17 years. Notably, the treatment was effective in adolescent patients with palmoplantar involvement and/or obesity.

PMID:40986238 | DOI:10.1007/s13555-025-01547-3

Environ Sci Pollut Res Int. 2025 Sep 23. doi: 10.1007/s11356-025-36925-z. Online ahead of print.

ABSTRACT

The removal of contaminants of emerging concern (CECs) from water is vital due to their persistence and harmful effects on ecosystems and human health. This study developed a titanium-coconut shell based minerals (Ti-CSM) composite for the simultaneous removal of bisphenol S (BPS), carbamazepine (CBZ), and clonazepam (CZP) from water. Carbon material produced with coconut shell-a low-cost biomass-was used as support for titanium oxide in varying mass/mass Ti/biomass ratios (25:75, 50:50, and 75:25), with the Materials being calcined at 400 °C and 600 °C. The response surface methodology with central composite rotatable design (RSM-CCRD) optimized pH (5-9), adsorbate/adsorbent ratio (2.5-7.5 mg g⁻¹), and temperature (16-34 °C), while an artificial neural network (ANN) model was applied for performance prediction. The Ti-CSM 25:75 composite calcined at 600 °C achieved up to 99% removal for BPS and CZP, and 98.7% for CBZ under optimal conditions (pH 7.0, adsorbate/adsorbent ratio 2.5, and temperature 16 °C). Adsorption capacities reached 12.31 mg g⁻¹ (BPS), 8.02 mg g⁻¹ (CBZ), and 7.13 mg g⁻¹ (CZP). Kinetic studies followed a non-linear pseudo-second-order model, while Freundlich and Sips isotherms indicated monolayer adsorption. ANN model revealed higher predictive accuracy compared to RSM-CCRD (R² > 0.98 vs. R² > 0.85). Removal rates in ultrapure water exceeded 98%, while real wastewater treatment removed 89.5 ± 2.5%, 68.7 ± 1.9%, and 57.3 ± 2.0% of BPS, CBZ, and CZP, respectively. This result highlights the material’s potential in treating complex matrices and lessens risks of environmental toxicity, particularly for an endocrine disruptor like BPS. By minimizing titanium use and leveraging a biomass precursor for contaminants adsorption, Ti-CSM composites offer a sustainable, efficient solution for CECs removal, showcasing the potential of biomass modification in eco-friendly water treatment.

PMID:40986226 | DOI:10.1007/s11356-025-36925-z

Insights Imaging. 2025 Sep 23;16(1):201. doi: 10.1186/s13244-025-02086-3.

ABSTRACT

OBJECTIVES: To develop and validate an MRI-derived radiomics model for the prediction of intratumoral tertiary lymphoid structures (TLSs) status of soft tissue sarcoma (STS) and explore its prognostic value.

MATERIALS AND METHODS: This study retrospectively included 302 patients of three cohorts who underwent surgical resection of STS from two medical centers. Radiomics features were derived for both intratumoral and peritumoral regions from preoperative axial fat-suppressed T2-weighted and T1-weighted imaging sequences. Intratumoral, peritumoral, and combined radiomics models were constructed using a logistic regression algorithm. The area under the receiver operator characteristic curve (AUC) and the DeLong test were utilized to assess and compare the performances of three radiomics models. By applying a linear combination of the chosen features, the Rad-score for the optimal radiomics model was computed.

RESULTS: TLS positivity was identified in 114 (38%) of the 302 patients. No clinical, radiological, or pathological variable was found to show a statistically significant association with TLSs status. The combined radiomics model showed superior performance compared to both the intratumoral and peritumoral models, with an AUC of 0.878 (95% CI 0.812-0.927) in the development cohort, 0.778 (95% CI 0.649-0.876) in the internal validation cohort, and 0.772 (95% CI 0.679-0.850) in the external validation cohort. In the cohort for all patients, the 36-month cumulative PFS rate was 66.1% in the high Rad-score (≥ 0.5) group vs. 37.2% in the low Rad-score group (p < 0.05, log-rank test).

CONCLUSION: An MRI-derived radiomics model could predict intratumoral TLS status in patients with STS and demonstrated a correlation with PFS.

CRITICAL RELEVANCE STATEMENT: The MRI-derived radiomics model could predict intratumoral TLSs status in patients with STS accurately, which may help to screen patients who will benefit from immunotherapy and have a better prognosis.

KEY POINTS: Intratumoral tertiary lymphoid structure status in patients with soft tissue sarcomas was accurately predicted by an MRI-derived radiomics model. The combined radiomics model showed superior performance compared to both the intratumoral and peritumoral radiomics models. Progression-free survival was significantly longer in patients with a high Rad-score (≥ 0.5) in the development, internal validation, and external validation cohorts.

PMID:40986220 | DOI:10.1186/s13244-025-02086-3

Biol Trace Elem Res. 2025 Sep 23. doi: 10.1007/s12011-025-04823-7. Online ahead of print.

ABSTRACT

Factors affecting iron chelation therapy outcomes are complex and should be identified to tailor interventions to the needs of individuals with beta-thalassemia major (TM). The purpose of the study was to determine the effects of PON-1 or UGT1A1 single-nucleotide polymorphisms on therapeutic outcomes via deferasirox (DFX) and the antioxidant status. PON-1 (rs662) or UGT1A1 (rs887829) polymorphisms, iron chelation therapy outcomes (cardiac iron T2*, serum ferritin (SF)), and antioxidant-related nutritional indices (PON-1 activity, zinc, 25-hydroxyvitamin D) were determined in 44 Taiwanese TM patients receiving chronic blood transfusion and DFX therapy. Patients’ cardiac iron T2* values were negatively correlated with SF levels (r = – 0.38, p < 0.01). PON-1 AA/AG carriers had significantly greater PON-1 activity, whereas PON-1 GG carriers were prescribed significantly higher DFX doses. UGT1A1 CT and TT carriers had marginally significantly greater SF levels. Only four patients had normal levels of 25-hydroxyvitamin D (25(OH)D > 30 ng/mL). PON-1 activity in those with SF > 2500 (6.4 ± 1.9 units/mL) was significantly lower than that (7.7 ± 1.7 units/mL; p < 0.03) in patients with SF ≤ 2500. Although not statistically significant, variants in PON-1 or UGT1A1 were associated with increased odds ratios (2.44 and 2.899, respectively) for lower cardiac iron T2* values < 30 ms. Taiwanese TM patients with moderate iron overload status had significantly lower PON-1 activity and vitamin 25(OH)D levels, particularly those with T2* < 30 ms. Patients with PON-1 GG and UGT1A1 TT carriers may have an increased risk of cardiac iron overload.

PMID:40986213 | DOI:10.1007/s12011-025-04823-7

J Imaging Inform Med. 2025 Sep 23. doi: 10.1007/s10278-025-01670-9. Online ahead of print.

ABSTRACT

The application of artificial intelligence (AI) in medical imaging has revolutionized diagnostic practices, enabling advanced analysis and interpretation of radiological data. This study presents a comprehensive evaluation of radiomics-based and deep learning-based approaches for disease detection in chest radiography, focusing on COVID-19, lung opacity, and viral pneumonia. While deep learning models, particularly convolutional neural networks (CNNs) and vision transformers (ViTs), learn directly from image data, radiomics-based models extract handcrafted features, offering potential advantages in data-limited scenarios. We systematically compared the diagnostic performance of various AI models, including Decision Trees, Gradient Boosting, Random Forests, Support Vector Machines (SVMs), and Multi-Layer Perceptrons (MLPs) for radiomics, against state-of-the-art deep learning models such as InceptionV3, EfficientNetL, and ConvNeXtXLarge. Performance was evaluated across multiple sample sizes. At 24 samples, EfficientNetL achieved an AUC of 0.839, outperforming SVM (AUC = 0.762). At 4000 samples, InceptionV3 achieved the highest AUC of 0.996, compared to 0.885 for Random Forest. A Scheirer-Ray-Hare test confirmed significant main and interaction effects of model type and sample size on all metrics. Post hoc Mann-Whitney U tests with Bonferroni correction further revealed consistent performance advantages for deep learning models across most conditions. These findings provide statistically validated, data-driven recommendations for model selection in diagnostic AI. Deep learning models demonstrated higher performance and better scalability with increasing data availability, while radiomics-based models may remain useful in low-data contexts. This study addresses a critical gap in AI-based diagnostic research by offering practical guidance for deploying AI models across diverse clinical environments.

PMID:40986191 | DOI:10.1007/s10278-025-01670-9