Tag: pubmed

Int J Surg. 2026 Feb 5. doi: 10.1097/JS9.0000000000004687. Online ahead of print.

ABSTRACT

BACKGROUND: Colorectal cancer (CRC) is the third most commonly diagnosed cancer and the second leading cause of cancer-related death globally. However, its macroeconomic burden remains underexplored.

METHODS: Using Global Burden of Disease (GBD) 2021 data, we quantified the economic welfare loss due to CRC across 204 countries using the Value of a Statistical Life Year (VSLY) approach. The Valuation of a Statistical Life (VSL) was derived by adjusting the U.S. benchmark ($11.8 million) based on per capita gross domestic product (GDP) and income elasticity (base case: 1.0). VSLY was calculated by dividing VSL by half the national life expectancy. Country-specific Value of Lost Welfare (VLW) was estimated and expressed as a percentage of GDP (VLW/GDP). Aggregated analyses were performed by sociodemographic index (SDI) and GBD regions. Sensitivity analyses used alternative elasticity values (0.55, 1.5) and applied a 3% discount rate.

FINDINGS: In 2021, global CRC-related VLW was estimated at $3.49 trillion (95% uncertainty interval [UI]: $3.02-$3.96 trillion), equivalent to 2.28% (95% UI: 1.97%-2.58%) of global GDP. VLW/GDP ratios were highest in high-SDI regions (2.81%) and the Central Europe, Eastern Europe, and Central Asia super-region (3.48%). At the national level, VLW ranged from <$500 million in small island states to >$0.6 trillion in China and the U.S. Discounting increased VLW estimates by 35%-67%.

CONCLUSIONS: CRC imposes a substantial and inequitable economic burden, particularly in economically developed and aging societies. Incorporating VSLY into cancer burden assessments underscores the urgency of investing in prevention, early detection, and surgical capacity strengthening, especially in middle-income and resource-limited settings.

PMID:41664847 | DOI:10.1097/JS9.0000000000004687

Immunotherapy. 2026 Feb 10:1-8. doi: 10.1080/1750743X.2026.2626241. Online ahead of print.

ABSTRACT

AIM: To determine the impact of antibiotic spectrum of activity and exposure timing on survival outcomes and development of gastritis/colitis.

METHODS: We conducted a single-center, retrospective cohort study of 214 patients with advanced, metastatic, or unresectable melanoma treated with immune checkpoint inhibitors. Antibiotic exposure was classified by spectrum of activity (with and without anaerobic coverage) and antibiotic timing. Primary outcomes were the effect of antibiotic administration 30-days prior to starting ICI therapy and during ICI therapy on overall survival (OS) and progression-free survival (PFS).

RESULTS: Antibiotic exposure during ICI was associated with improved OS (HR: 0.57, 95% CI (0.35-0.92), p = 0.023). Use of antibiotics without anaerobic coverage was associated with improved PFS (HR: 0.53, 95% CI (0.32-0.87), p = 0.013), and OS (HR: 0.47, 95% CI (0.24-0.92), p = 0.026). There was a trend toward increased risk of gastritis/colitis with antibiotics without anaerobic coverage during ICI therapy, although this did not reach statistical significance (OR 2.08, 95% CI (0.43-5.46), p = 0.069).

CONCLUSION: Antibiotic timing and spectrum of activity may be predictive of survival outcomes and risk of developing gastritis/colitis in ICI-treated patients with advanced-stage melanoma. Unlike previous studies, we found improved survival in patients receiving antibiotics during treatment and in those receiving antibiotics without anaerobic coverage.

PMID:41664838 | DOI:10.1080/1750743X.2026.2626241

J Periodontal Res. 2026 Feb 10. doi: 10.1111/jre.70086. Online ahead of print.

ABSTRACT

AIM: To evaluate clinical and patient reported outcomes after subgingival instrumentation at two different re-evaluation timings in stage III/IV periodontitis and the influence of clinical and radiographic variables on final outcomes.

METHODS: Forty participants were assigned to 3-month (control) or 6-month (test) re-evaluation after steps 1-2 of therapy. The primary outcome was the number of teeth reaching the Endpoints of Treatment (EoT). EoT was defined as a site with PPD < 6 mm or PPD = 4/5 mm without BoP. Secondary outcomes included changes in clinical parameters and in oral health-related quality of life (OHIP-14) scores. ANOVA, ANCOVA, mixed-effects models and multilevel models were applied.

RESULTS: Thirty-six patients completed the study. Patients allocated to the 3-month group had 15.8 ± 4.0 teeth reaching the EoT, accounting for 65.8% ± 14.3 of the teeth, while patients assigned to the 6-month group had 15.5 ± 5.9 (64.9% ± 21.5), without statistically significant differences. Percentages of sites achieving EoT were 68.5% ± 11.6 in the 3-month group and 71.9% ± 14.4 in the 6-month group, without statistically significant differences. Final OHIP-14 scores were 6.5 ± 8.9 in the 3-month group and 7.3 ± 7.5 in the 6-month group, without statistically significant differences. Risk for residual pockets at re-evaluation was influenced by higher baseline PPD (p < 0.0001), plaque (PI) at site level (p = 0.011), molar tooth (p = 0.012), furcation involvement (p < 0.0001), shallow intrabony defect (p = 0.018), deep intrabony defect (p = 0.002).

CONCLUSION: No difference in clinical and patient-centered outcomes was observed between groups. NSPT frequently failed to achieve EoT at pockets with intrabony defects, while EoT were frequently achieved at sites with mainly horizontal bony defects.

TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT06086821 (https://clinicaltrials.gov/study/NCT06086821?cond=re-evaluation%20periodontal&rank=1).

PMID:41664833 | DOI:10.1111/jre.70086

Int Conf Inf Sci Technol. 2025 Dec;2025:256-259. doi: 10.1109/ICIST66592.2025.11306560. Epub 2025 Dec 30.

ABSTRACT

Reconstructing missing data in high-dimensional time-series remains a challenging task, especially when the underlying signals exhibit complex temporal dynamics and non-linear relationships. While most traditional approaches can not model such intricacies, generative models-particularly conditional score-based diffusion methods-have emerged as powerful alternatives, offering significant improvements in imputation accuracy. Despite their success, these models typically rely on isotropic white noise during training, which treats all frequency components uniformly and fails to preserve critical frequency-dependent correlations. Relying solely on white noise can lead to the loss of fine-scale temporal patterns, compromising the accuracy and reliability of the reconstructed data. Our recent work introduces a time-varying blue noise-based conditional score-based diffusion model for imputation (tBN-CSDI) by incorporating a time-varying blue noise schedule into the diffusion process to address the limitations of existing methods in handling missing values in time series data. Experimental results on real-world datasets demonstrate that tBN-CSDI outperforms conventional methods based on white noise schedules. We also discuss the integration of pseudotime analysis with diffusion models as a promising direction for future research, particularly for applications in dynamic biological systems where temporal ordering is critical yet uncertain.

PMID:41664826 | PMC:PMC12883052 | DOI:10.1109/ICIST66592.2025.11306560

Neurooncol Adv. 2025 Nov 20;8(1):vdaf235. doi: 10.1093/noajnl/vdaf235. eCollection 2026 Jan-Dec.

ABSTRACT

ABSTRACT: BackgroundGlioblastoma (GBM) is an aggressive brain tumor. The authors of this study wanted to find out whether areas of reduced blood flow, called ischemia, that appear after surgery affect how long people live with this disease. To do this, they reviewed scans from 451 patients taken within three days after tumor removal and measured the size of any new areas with poor blood supply. Their results showed that larger areas of ischemia were linked to shorter survival. The prognostic relevance of postoperative ischemia in GBM remains unclear. This study investigated the association between infarct volume and survival in patients with Isocitrate Dehydrogenase (IDH)-wildtype GBM.

METHODS: We retrospectively reviewed 451 patients with IDH-wildtype GBM who underwent resection between 2013 and 2024 and had diffusion-weighted imaging within 72 h postoperatively. Ischemic changes were defined as new areas of diffusion restriction and stratified into none/rim-only, small (<5 cm³), and large (≥5 cm³) infarcts. Progression-free survival (PFS) and overall survival (OS) were analyzed using Kaplan-Meier and Cox regression models adjusted for age, preoperative KPS, tumor size, extent of resection, MGMT status, adjuvant therapy, and postoperative deficits.

RESULTS: Large infarcts were associated with shorter median PFS (7.0 months [95% CI: 5-9]) and OS (14.0 months [95% CI: 9-18]) compared to small infarcts and none/rim-only (PFS: P = .07; OS: P = .001). In multivariable models, infarct volume was independently associated with reduced OS (per cubic centimeter increase, HR = 1.02, 95% CI: 1.01-1.032; P = .003), while its association with PFS did not reach statistical significance (HR = 1.01, 95% CI: 1.0-1.02; P = .13). When modeled categorically, large infarcts remained predictive of shorter PFS (HR = 1.4, 95% CI: 1.01-1.9; P = .04) and OS (HR = 1.7, 95% CI: 1.2-2.4; P = .001).

CONCLUSIONS: Infarct volume is independently associated with survival in IDH-wildtype GBM. These findings highlight the clinical relevance of postoperative ischemia and may point toward ischemia-related mechanisms as targets for future therapeutic investigation.

PMID:41664819 | PMC:PMC12883208 | DOI:10.1093/noajnl/vdaf235

Arch Acad Emerg Med. 2025 Dec 30;13(1):e86. doi: 10.22037/aaem.v13i1.2720. eCollection 2025.

ABSTRACT

INTRODUCTION: Missed or delayed diagnosis of pulmonary embolism (PE) is associated with increased morbidity, mortality, and longer hospitalizations. This study aimed to evaluate the diagnostic accuracy of Artificial Intelligence (AI) models in detecting PE across imaging.

METHODS: We systematically searched PubMed/MEDLINE, Scopus, Embase and Web of Science from inception to 1 January 2025 without language or regional limits. After removing duplicate results, the remaining records were screened through titles/abstracts, and two reviewers independently assessed full texts. Risk of bias was evaluated in duplicate with the QUADAS-2 tool. Pooled sensitivity, specificity, positive and negative likelihood ratios, diagnostic odds ratio and area under the ROC curve were calculated with random-effects models in STATA 17. Heterogeneity was quantified with Cochran’s Q and I², while we explored its sources using subgroup analyses (for categorical moderators) and meta-regression (for continuous moderators). Publication bias was assessed with Deeks’ funnel plot and trim-and-fill, and we examined robustness through leave-one-out sensitivity analyses.

RESULTS: A total of 1,432 records were identified through database searches, with 654 duplicates removed. After screening titles and abstracts of 787 articles, 256 full-text articles were assessed for eligibility, and 28 studies met the inclusion criteria. Internal validation phases included 43,330 participants (4,866 PE-positive, 38,463 PE-negative), while external validation phases comprised 3,588 participants (1,699 PE-positive, 1,889 PE-negative). In the internal validation phase, the pooled sensitivity and specificity of AI in PE diagnosis across imaging were 0.91 (95% confidence interval (CI): 0.88-0.95; I²=9%) and 0.94 (95% CI: 0.86-0.98; I²=99.78%), respectively. The positive likelihood ratio (PLR) was 16.08, and the negative likelihood ratio (NLR) was 0.09, both statistically significant (P < 0.001). The pooled diagnostic odds ratio (DOR) was 163.55 (95% CI: 71.30-375.14, I²: 96.1), and the area under the curve (AUC) was 0.95 (95% CI: 0.93 to 0.97), indicating excellent accuracy. In external validation, the pooled sensitivity and specificity were slightly lower at 0.89 (95% CI: 0.79-0.95; I²=95.60%) and 0.88 (95% CI: 0.80-0.93; I²=91.48%), respectively. The DOR was 59.65 (95% CI: 23.53 to 151.17, I²: 89.6) and AUC was 0.94 (95% CI: 0.92 to 0.96, I²: 89.6). There was no significant publication bias detected.

CONCLUSION: AI models achieved high diagnostic accuracy in detecting PE through imaging. However, this performance tends to decrease from internal to external validation, highlighting limitations in generalizability. Additionally, substantial heterogeneity was observed across studies, as indicated by high I² values, which should be considered when interpreting the pooled estimates.

PMID:41664805 | PMC:PMC12883040 | DOI:10.22037/aaem.v13i1.2720

Cureus. 2026 Jan 9;18(1):e101182. doi: 10.7759/cureus.101182. eCollection 2026 Jan.

ABSTRACT

Introduction Substance use is frequently encountered in emergency department (ED) visits and may reflect underlying emotional or developmental challenges. Adverse childhood experiences (ACEs) are linked to both emotional dysregulation and later substance use, but this relationship is rarely examined in acute care settings. We investigated whether recent substance use, as measured by the Tobacco, Alcohol, Prescription medication, and other Substance use (TAPS) screening tool, is associated with ACEs, emotional dysregulation, and use of cognitive reappraisal (an adaptive emotion regulation strategy) among adult ED patients. Methodology In this cross-sectional study, 58 adult ED patients completed the ACE Inventory, the Difficulties in Emotion Regulation Scale (DERS), the Emotion Regulation Questionnaire (ERQ)-Cognitive reappraisal subscale, and the TAPS screening. Descriptive statistics characterized the sample. Pearson correlations and linear regression analyses (adjusting for age and gender) examined associations between TAPS scores and ACE, DERS, and ERQ scores. Results TAPS scores were positively correlated with ACEs (r = 0.23, P < 0.05) and DERS scores (r = 0.30, P < 0.01), and negatively correlated with ERQ-Cognitive reappraisal scores (r = -0.22, P < 0.05). In regression models, higher TAPS scores significantly predicted greater ACE burden, higher DERS scores, and lower ERQ-Cognitive reappraisal scores (all P < 0.05, with covariate adjustment). Participants reporting alcohol or marijuana use had significantly higher mean ACE scores than non-users. Conclusion Recent substance use identified during ED visits was associated with greater childhood trauma exposure and poorer emotion regulation. Substance use in the ED may serve as a marker of underlying emotional dysregulation and adversity. These findings support the incorporation of trauma-informed screening and referral practices into ED care as part of integrated care models to better address these underrecognized psychosocial factors.

PMID:41664779 | PMC:PMC12883253 | DOI:10.7759/cureus.101182

Cureus. 2026 Jan 9;18(1):e101153. doi: 10.7759/cureus.101153. eCollection 2026 Jan.

ABSTRACT

Background Knowledge of the ecological and soil differences between arid and savannah ecosystems is essential for assessing their biodiversity and potential for agricultural and disease-controlling applications. Arid zones are characterized by extreme temperatures and limited rainfall, supporting unique soil types that influence microbial communities. This study examined soil Streptomyces spp., known for their antibiotic properties, focusing on their inhibitory effects against Streptomyces sudanensis, a pathogen associated with actinomycetoma. Methodology Soil samples (n = 7) were collected, and physicochemical parameters, along with enzyme activities, were analyzed. Streptomyces spp. were isolated, characterized morphologically and phenotypically, and identified molecularly via 16S rRNA gene sequencing. Their inhibitory effects against S. sudanensis were evaluated using agar-based bioassays. Results Streptomyces spp. (n = 64), including Streptomyces griseostramineus, were identified. The results indicated significant ecological differences: arid sites had high temperatures (29.7°C) and low rainfall (70 mm) with nutrient-poor Yermosols, whereas savannah sites had higher rainfall (501 mm) and nutrient-rich Arenosols and Vertisols. Inhibition zones varied significantly, with arid ecosystem isolates showing a higher mean inhibition value (2.8411) compared to savannah isolates (1.7139). Statistical analysis verified a significant difference in mean inhibition (t = 2.589, p = 0.0126), suggesting distinct inhibitory capacities linked to the ecosystem. Conclusions This study emphasizes the significant ecological and soil differences between the ecosystems and proves that soil Streptomyces spp. in arid regions possess distinctly higher inhibition potential against S. sudanensis, suggesting their potential as biological agents in these environments.

PMID:41664777 | PMC:PMC12883073 | DOI:10.7759/cureus.101153

Neural Netw. 2026 Jan 24;199:108641. doi: 10.1016/j.neunet.2026.108641. Online ahead of print.

ABSTRACT

Simulating turbulent flows is crucial for a wide range of applications, and machine learning-based solvers are gaining increasing relevance. However, achieving temporal stability when generalizing to longer rollout horizons remains a persistent challenge for learned PDE solvers. In this work, we analyze if fully data-driven fluid solvers that utilize an autoregressive rollout based on conditional diffusion models are a viable option to address this challenge. We investigate accuracy, posterior sampling, spectral behavior, and temporal stability, while requiring that methods generalize to flow parameters beyond the training regime. To quantitatively and qualitatively benchmark the performance of various flow prediction approaches, three challenging 2D scenarios including incompressible and transonic flows, as well as isotropic turbulence are employed. We find that even simple diffusion-based approaches can outperform multiple established flow prediction methods in terms of accuracy and temporal stability, while being on par with state-of-the-art stabilization techniques like unrolling at training time. Such traditional architectures are superior in terms of inference speed, however, the probabilistic nature of diffusion approaches allows for inferring multiple predictions that align with the statistics of the underlying physics. Overall, our benchmark contains three carefully chosen data sets that are suitable for probabilistic evaluation alongside various established flow prediction architectures.

PMID:41662807 | DOI:10.1016/j.neunet.2026.108641

Comput Biol Med. 2026 Feb 8;204:111537. doi: 10.1016/j.compbiomed.2026.111537. Online ahead of print.

ABSTRACT

OBJECTIVE: Neonatal sepsis (NS) poses a significant global health challenge, with mortality rates ranging from 11% to 19%. Despite its substantial burden, there is currently a lack of systematic understanding of the global epidemiological trends and influencing factors of NS.

STUDY DESIGN: We conducted a comprehensive pan-database analysis integrating data from 18 international databases across 201 countries (1990-2021). Through advanced statistical modeling, including correlation analyses, risk parsimonious modeling, and confounder adjustments, we examined temporal trends, regional disparities, and key determinants of NS.

RESULTS: While NS prevalence increased annually due to improved detection, age-standardized rates showed consistent declines. For NS incidence, novel correlates included European ancestry (strongest), systolic/diastolic blood pressure, and inverse associations with Human Development Index. We developed a parsimonious model incorporating diastolic blood pressure, Global Hunger Index, and European ancestry, which showed strong cross-regional predictive capability (r = 0.727). For mortality, socioeconomic factors were primary correlates: positive associations with Global Hunger Index and food insecurity, and inverse associations with Inequality Adjusted HDI.

CONCLUSION: This first comprehensive global analysis reveals that NS outcomes are determined by both medical and socioeconomic factors. While blood pressure metrics and genetic factors influence incidence, mortality is primarily driven by socioeconomic determinants. These findings suggest that reducing NS burden requires a dual approach: enhancing medical care while addressing fundamental socioeconomic disparities, particularly in resource-limited regions.

PMID:41662778 | DOI:10.1016/j.compbiomed.2026.111537