Tag: pubmed

Am J Rhinol Allergy. 2026 Feb 16:19458924261418539. doi: 10.1177/19458924261418539. Online ahead of print.

ABSTRACT

BackgroundThe mechanisms driving chronic rhinosinusitis with nasal polyps (CRSwNP)-related olfactory loss remain largely unknown. Here we sought to identify novel modulators of olfactory function via the examination of nasal mucus biomarkers using an expansive 71-cytokine plex analyzed via machine learning models.MethodsOlfactory testing was performed via 40-question smell identify test (UPSIT). During endoscopic sinus surgery, sponges were placed in the middle meatus of individuals with CRSwNP (n = 15). Nasal mucus samples were screened by multiplex analysis for 71-cytokine/chemokines. Results underwent analysis with statistical and machine learning model approaches to assess whether protein concentrations were predictive of olfactory dysfunction.ResultsIn CRSwNP, multiple machine learning models revealed novel cytokines IL-21 and MIP-1δ as positive predictors of greater olfactory dysfunction. Other cytokines detected by more than one model as predictive of olfactory dysfunction were IL-18, MCP-1, IL-22, and BCA-1. Other cytokines identified to be predictive by at least one model were FLT-3L, LIF, IL-20, SCF, IL-23, and TPO.ConclusionUsing a 71-cytokine/chemokine plex analyzed via machine learning, we identified potentially novel roles for MIP-1δ and IL-21 as modulators of olfactory function in CRSwNP. Use of machine learning for the analysis of nasal mucus cytokines, may serve as powerful tool to analyze complex multiplex immune mediator data.

PMID:41699443 | DOI:10.1177/19458924261418539

BJOG. 2026 Feb 16. doi: 10.1111/1471-0528.70183. Online ahead of print.

NO ABSTRACT

PMID:41699415 | DOI:10.1111/1471-0528.70183

Stat Methods Med Res. 2026 Feb 16:9622802251415363. doi: 10.1177/09622802251415363. Online ahead of print.

ABSTRACT

Based on the test for equality of quantiles originally introduced by Kosorok (1999), we propose new power formulas for the comparison of one quantile between two treatment groups, as well as for the comparison of a collection of quantiles. Under the null hypothesis of equality of quantiles, the test statistic follows asymptotically a normal distribution in the univariate case and a ${\chi }^2$ with $J$ degrees of freedom in the multivariate case, with $J$ the number of quantiles compared. The variance of the test statistic depends on the estimation of the probability density function of the distribution of failure times at the quantile being tested. In order to apply the test on real data, we propose to estimate this quantity using a resampling-based method, as an alternative to Kosorok’s original kernel density estimator. The whole procedure provides a practical tool for designing and analyzing data arising from clinical trials using quantiles of survival as an endpoint. Simulation studies are performed to show the appropriateness of the power formulas. We illustrate the proposed test in a phase III randomized clinical trial where the proportional hazards assumption between treatment arms does not hold.

PMID:41699412 | DOI:10.1177/09622802251415363

Sci Rep. 2026 Feb 16. doi: 10.1038/s41598-026-36055-7. Online ahead of print.

ABSTRACT

Insecticide resistance complicates pest management by reducing chemical effectiveness and increasing environmental pollution from higher application rates. Monitoring resistance is essential before recommending chemicals in any agro-ecological region. The present study evaluated the extent and mechanisms of insecticide resistance in the cotton leafhopper, Amrasca biguttula biguttula (Ishida) (Hemiptera: Cicadellidae), across major cotton-growing regions of Maharashtra, India over five consecutive years, from 2015-16 to 2019-20 at ICAR-Central Institute for Cotton Research (CICR) in Nagpur. The bioassay studies using IRAC protocols revealed that populations collected from the Amravati district of Maharashtra exhibited higher LC₅₀ values for all tested insecticides, including Flonicamid 50WG, Thiamethoxam 25WG, Acetamiprid 20SP, Imidacloprid 17.8SL, Monocrotophos 36SL, Acephate 75SP, Clothianidin 50WDG, and Spiromesifen 22.9SC. The tentative discriminating doses were established based on a susceptible population of A. biguttula at 1, 0.2, 0.05, 0.01, 0.001, 0.0001, and a control of 0 ppm. Using standardized IRAC bioassay protocols, we assessed resistance levels for eight insecticides and noted significant increases in LC₅₀ values. Notably, the A. biguttula biguttula populations from Yavatmal, Chandrapur, and Amravati exhibited critical resistance to neonicotinoids and other classes of insecticides. Biochemical assays revealed elevated activities of detoxifying enzymes, including cytochrome P450 monooxygenases, carboxylesterases, and glutathione S-transferases (GST), indicating metabolic resistance as a key mechanism. Notably, Amravati populations displayed the highest enzyme activities (e.g., GST up to 555.56 pmol/min/mg protein), correlating with intense insecticide use. These findings emphasize the need for integrated pest management (IPM) strategies, including insecticide rotation, biological controls, and resistant cotton hybrids, to mitigate insecticide resistance and minimise environmental impact. Regular monitoring of resistance and enzyme activity is essential for sustainable pest control in cotton ecosystems.

PMID:41699402 | DOI:10.1038/s41598-026-36055-7

J Robot Surg. 2026 Feb 17;20(1):262. doi: 10.1007/s11701-025-03035-4.

ABSTRACT

We conducted this comprehensive systematic review and meta-analysis to assess surgical outcomes, kidney function preservation, and cancer control efficacy when comparing robot-assisted partial nephrectomy (RAPN) outcomes between renal hilar masses and peripherally located tumors. We performed an exhaustive search strategy utilizing four major electronic databases to capture all relevant comparative investigations published up to August 2025. Meta-analytical calculations were executed through Review Manager (RevMan) version 5.4 software platform. Our investigation synthesized data from eight research studies, including 7064 participants (1292 presenting hilar masses, 5772 with peripheral lesions). Comparative analysis revealed that RAPN procedures for hilar lesions demonstrated significantly longer surgical duration (WMD 22.18 min, 95% CI 16.86 to 27.51; p < 0.00001), greater intraoperative hemorrhage (WMD 31.66 ml, 95% CI 10.10 to 53.21; p = 0.004), higher blood transfusion requirements (OR 1.68, 95% CI 1.14 to 2.49; p = 0.009), prolonged renal clamping duration (WMD 4.89 min, 95% CI 2.80 to 6.97; p < 0.00001), increased severe adverse events (OR 1.44, 95% CI 1.03 to 2.01; p = 0.03), and diminished probability of optimal surgical outcomes (OR 0.45, 95% CI 0.25 to 0.84; p = 0.01). However, both patient cohorts exhibited equivalent outcomes regarding hospitalization duration, total adverse events, surgical approach conversion frequencies to open procedures or radical nephrectomy, postoperative kidney function deterioration, positive surgical margin (PSM), and tumor recurrence patterns, showing no statistically meaningful disparities. While technically more demanding and associated with increased perioperative morbidity, RAPN for hilar tumors is a safe and effective procedure that provides crucial renal functional and oncological outcomes comparable to those of RAPN for non-hilar tumors.

PMID:41699371 | DOI:10.1007/s11701-025-03035-4

Drug Deliv Transl Res. 2026 Feb 16. doi: 10.1007/s13346-026-02046-4. Online ahead of print.

ABSTRACT

Albumin-based nanoparticles (NPs) are typically synthesized by harsh conditions-based methods that limit their application in clinics and can seriously damage the entrapped drug and even their base material. Despite the potential of the use of chitosan (CS) as stabilizing agent by adapting the ionic gelation method or by adding CS as a coating to albumin NPs generated by desolvation, the influential factors of these methods have not yet been studied. In this article, these synthesis approaches have been optimized by a 2-step DoE-based methodology (a screening process with fractional designs plus a response surface methodology using central composite designs). The application of the ion gelation method to produce albumin-based NPs generates sizes from 66 to 1017 nm, PDI (polydispersity index) values of 0.3-0.6 and surface charges (ZP) from neutral to positive (> 20 mV). The fitted models of the responses depend on four factors (albumin and CS concentration, CS pH and CS:albumin mass ratio). On the other hand, the modification of the desolvation method using CS as a stabilizing coating generates 37-1305 nm NPs, with PDI between 0.4 and 0.7 and highly positive ZP (20-40 mV). In this case, the approximate models for the responses depend on four main effects (albumin and CS concentration, pH of CS and albumin:EtOH volume ratio). Furthermore, in this work the best combinations of factors and levels that allow minimizing PDI and obtaining the minimum and maximum expected values for mean size and ZP of NPs were determined for both synthesis methods. Focusing on the minimum possible PDI, the predicted values for the ion gelation- and desolvation-based methods are 0.363 and 0.341, respectively, which are achieved with values of [BSA] (mg/ml), [CS] (mg/ml), CS pH and CS:BSA or BSA:EtOH ratios (mL:mL) of {2.3,1.4,2.2,1:7.3} and {10,0.5,1.8,1:1}, respectively. These optimized conditions yield acceptable size and ZP values for the ion gelation-based (27.7 nm; 16.4 mV) and optimal values for the desolvation-based (146.2 nm; 29.5 mV).

PMID:41699362 | DOI:10.1007/s13346-026-02046-4

Environ Geochem Health. 2026 Feb 17;48(4):166. doi: 10.1007/s10653-026-03004-2.

ABSTRACT

Excess uranium (U) in the shallow aquifers of Punjab, India, has become a significant public health concern for the population dependent on groundwater for drinking and irrigation purposes. Although prior investigations have statistically established the control of geogenic and anthropogenic factors on U enrichment, a comprehensive and high-resolution spatial distribution of the extent of contamination remains lacking. To address this gap, we employed the Random Forest (RF) machine-learning classifier to model 1,852 data points of groundwater U concentrations compiled from different districts of Punjab. Spatial prediction and mapping were performed using spatially continuous predictor variables pertaining to meteorological, topographical, geological, soil, and other relevant parameters. A highly accurate prediction map of the occurrence probability of U surpassing the WHO drinking water limit of 30 µg L^-1 at a 250 m spatial resolution, with an accuracy of 85% for test data and 87% for validation data, was generated. The predicted U hazard was strongly influenced by potential evapotranspiration, elevation, and aquifer thickness, with a moderate to low influence from soil physical and chemical properties. Based on the predicted hazard map, the probability of U contamination was higher in the south and southwestern districts (Malwa region) than in other regions of Punjab, comprising approximately 1.7 million hectares (~ 35%) of the state’s total area. This study represents the first attempt to spatially predict the occurrence of high groundwater U levels, providing valuable insights for government agencies and policymakers to make informed decisions and manage groundwater sustainably.

PMID:41699351 | DOI:10.1007/s10653-026-03004-2

J Pharmacokinet Pharmacodyn. 2026 Feb 16;53(2):11. doi: 10.1007/s10928-026-10019-w.

ABSTRACT

The pharmacokinetic literature is rich in aggregated concentration data that contain valuable information, yet tools to extract this information remain limited. This work introduces distributional physics-informed neural networks (D-PINNs), a novel algorithm designed to enable statistical modelling within the PINN framework, allowing recovery of pharmacokinetic parameter distributions at the population level from published concentration means and variances. Unlike traditional PINNs, which often focus on point estimates, D-PINNs incorporate distributional assumptions directly into the optimisation process. The framework utilises neural networks for predicting the mean and variance of the concentration over time. These predictions are then incorporated into a sampling-based procedure within the residual network, which uses the governing ordinary differential equation (ODE) system to compute the physics-informed loss term. The methodology accounts for both interindividual variability through the parameter distribution and measurement noise through a residual error model. The capability of D-PINNs to infer population-level parameter distributions from concentration summary statistics was demonstrated through a simple proof-of-concept using simulated data from a one-compartment pharmacokinetic model of intravenous drug administration. The model achieved high accuracy in estimating both the parameter distribution and the residual error. Hyperparameter tuning highlighted important aspects of model development. The modelling framework was then applied to real-world data to demonstrate its ability to recover information on the distribution of kinetic parameters in the studied population. Specifically, a minimal physiologically-based pharmacokinetic (mPBPK) model for monoclonal antibodies (mAbs) was fitted to aggregated plasma concentration data reported in the literature using D-PINNs. The same aggregated data were also analysed using a Markov chain Monte Carlo (MCMC) analogue to benchmark the proposed methodology.

PMID:41699348 | DOI:10.1007/s10928-026-10019-w

Ann Biomed Eng. 2026 Feb 16. doi: 10.1007/s10439-026-04014-y. Online ahead of print.

ABSTRACT

PURPOSE: Deep vein thrombosis (DVT) poses significant health risks, including potentially fatal pulmonary embolism. Current clinical practice relies heavily on ultrasonography, requiring a skilled specialist. Alternative methods, such as light reflection rheography (LRR) and venous occlusion plethysmography (VOP), are non-invasive and simple; however, studies report limited consistency and standardization. The development of biosignal-based diagnostic tools is constrained by the inherent risks of DVT, including embolization, and challenges in patient recruitment. The ability to simulate DVT-like conditions would aid in developing and testing alternative screening methods. This study aims to present a simulation method of venous hemodynamic alterations resembling deep vein thrombosis using controlled external thigh compression with ultrasonic visualization.

METHODS: Data collection with thirty healthy volunteers was conducted in a laboratory using a commercially available system VasoScreen 5000-4000 to record LRR and VOP signals. Vein stenosis at varying levels was induced through controlled external thigh compression under ultrasonic guidance.

RESULTS: The experimental simulation showed statistically significant but small changes in LRR parameters across different stenosis levels. In comparison, VOP results showed greater differences across stenosis levels, with 70% and 100% performing the best. In these cases, 47% and 70% of the measurements, respectively, were below the normal reference limit, with a notably increased outflow time constant, compared to the baseline measurements, where it remained low despite varying venous capacity.

CONCLUSION: Presented hemodynamic alterations demonstrated to be a feasible option for simulating DVT-like conditions via controlled external pressure on the thigh.

PMID:41699339 | DOI:10.1007/s10439-026-04014-y

Eur J Pediatr. 2026 Feb 16;185(3):134. doi: 10.1007/s00431-026-06795-9.

ABSTRACT

This study aimed to describe the implementation and functioning of a regional hub-and-spoke model (“Gaslini Diffuso”) for managing pediatric bronchiolitis in Liguria, Italy, during the 2023-2024 season, focusing on severity stratification, resource allocation, and outcomes. A retrospective observational study was conducted across one tertiary hub (IRCCS Istituto Giannina Gaslini, Genoa) and four affiliated spoke hospitals. Medical records of all patients aged 0-2 years hospitalized with bronchiolitis (ICD-9-CM 466.19) between October 2023 and March 2024 were reviewed. Demographic, clinical, microbiological, and treatment data were analyzed. Predictors of centralization to the hub were identified through multivariable logistic regression. A total of 562 patients were included (median age 95 days; 40.4% female). Most cases were mild to moderate, with 56.6% requiring respiratory support-mainly low-flow oxygen or HFNC-and only 2% requiring mechanical ventilation. Thirteen patients (2.3%) were admitted to the PICU, and no deaths occurred. Centralized patients (n = 10) were significantly younger (median 43.5 days) and had higher severity indicators, including elevated CO₂ and CRP levels, and longer respiratory support (median 5 vs. 3-4 days, p < 0.001). Independent risk factors for centralization were age < 60 days (OR 23.1, p = 0.004) and HFNC use (OR 20.5, p = 0.006). Spoke centers showed homogeneous adherence to referral criteria, though some variability in ancillary treatments persisted.

CONCLUSIONS: The Ligurian hub-and-spoke model demonstrated internal consistency between referral criteria and observed patient severity, supporting appropriate case stratification within the regional network. This integrated framework enhanced regional coordination and represents a scalable, sustainable model for pediatric respiratory disease management.

WHAT IS KNOWN: • Bronchiolitis is the leading cause of hospitalization in infants under two, with seasonal surges that may overwhelm pediatric services; management remains largely supportive. • Hub-and-spoke models have been proposed to optimize care and resource allocation, but real-world data on their clinical and organizational impact, especially post-COVID, is limited.

WHAT IS NEW: • This study evaluates, for the first time in Italy, the real-world implementation of a regional hub-and-spoke model (Gaslini Diffuso) for bronchiolitis management. • The model enabled effective stratification of disease severity, with high specificity in centralizing only the most critical cases, ensuring efficient use of pediatric intensive care resources.

PMID:41699321 | DOI:10.1007/s00431-026-06795-9