Category: Nevin Manimala

Geohealth. 2026 May 6;10:e2025GH001501. doi: 10.1029/2025GH001501. eCollection 2026 May.

ABSTRACT

Extreme heat has been associated with adverse health outcomes, yet its impact on asthma exacerbations remains understudied. This is, in part, due to data limitations: research that relies on weather station records and aggregated health statistics cannot resolve fine-scale differences in heat impacts. This study investigates the association between heat wave definitions and summertime asthma-related emergency department visits in Baltimore, Maryland from 2016 to 2022, including 819 adult and 695 pediatric exacerbations. Using geocoded electronic health records and air temperature measurements at several spatial resolutions, we applied a case-crossover design with conditional logistic regressions at the census block group and tract levels. We found strong associations between asthma exacerbations and nighttime heat wave definitions based on relative thresholds of minimum temperatures when census block group or tract level temperature estimates were used. These relationships were significant for both age groups and showed elevated risks in socially vulnerable areas. In contrast, heat wave definitions derived from the city’s primary National Weather Service synoptic weather station show associations between asthma and daytime heat extremes, suggesting that the character of the heat hazard depends on the scale at which it is defined. The extreme heat event definition used by Baltimore City’s Code Red system showed no significant association with exacerbations. These findings highlight the importance of data resolution in shaping health inferences related to extreme heat in urban environments. Further, this study demonstrates that, regardless of spatial scale, extreme heat is associated with asthma exacerbations in both age groups.

PMID:42100719 | PMC:PMC13147955 | DOI:10.1029/2025GH001501

Front Microbiol. 2026 Apr 22;17:1815688. doi: 10.3389/fmicb.2026.1815688. eCollection 2026.

ABSTRACT

INTRODUCTION: Staphylococcus species, particularly Staphylococcus aureus, are leading opportunistic pathogens responsible for a wide range of infections, with antimicrobial resistance-including high rates of macrolide resistance-severely limiting treatment options. The msrA gene encodes the ABC-F protein MsrA, which mediates inducible resistance to 14-membered macrolides and type B streptogramins. Despite its clinical and epidemiological relevance, the evolutionary forces, selective pressures, and transmission routes shaping msrA in staphylococci remain insufficiently understood.

METHODS: Six hundred and one complete staphylococcal msrA coding sequences (CDSs) were retrieved from GenBank. Evolutionary analyses of msrA included nucleotide diversity (π), selection metrics (d_N-d_S , π_a/π_s, Tajima’s D, Fu’s Fs, FUBAR, MEME, and aBSREL), and conservation mapping using DnaSP in relation to MsrA functional domains (UniProt P23212). Linkage disequilibrium (LD) was assessed using ZnS, Za, ZZ, and Wall’s statistics. Recombination and transmission pathways were inferred using GARD, RDP4-embedded algorithms, SplitsTree network analysis, and the PHI test.

RESULTS: Forty-one msrA allelic variants were determined, with five predominant alleles accounting for approximately 90% of CDSs; allele 19 was almost exclusive to S. aureus. Nucleotide diversity was moderate (π ≈ 0.039-0.042), and strong purifying selection predominated (π_a/π_s ≈ 0.169; d_N-d_S = -0.138 ± 0.016; strongly negative Fu’s Fs), with only four codons showing evidence of episodic positive selection. Three highly conserved regions were identified, mainly overlapping the inter-domain linker and the second nucleotide-binding domain across MsrA. Moderate-to-high LD with minimal decay indicated the persistence of only a limited number of successful allelic variants. Predominant msrA alleles were largely plasmid-associated. Recombination analyses revealed frequent interspecies transfer within Staphylococcus, with S. aureus acting as a central donor to Staphylococcus chromogenes and Staphylococcus saprophyticus, as well as rare intergeneric transfers involving Citrobacter, Enterococcus, Corynebacterium, and Pseudomonas.

CONCLUSION: These findings support a dual evolutionary strategy for msrA: strong purifying selection preserves its essential ribosomal-protection function, while plasmid-mediated dissemination promotes the spread of fit alleles. S. aureus appears to be a key reservoir and vector, facilitating both interspecies and intergeneric transmission. Clinically, this underscores the need for surveillance of plasmid-borne msrA and targeted control of S. aureus reservoirs to limit resistance to macrolides and type B streptogramins.

PMID:42100691 | PMC:PMC13144154 | DOI:10.3389/fmicb.2026.1815688

Front Microbiol. 2026 Apr 22;17:1784126. doi: 10.3389/fmicb.2026.1784126. eCollection 2026.

ABSTRACT

This study dealt with developing a Raman spectroscopic method for estimating the degree of virulence of Streptococcus mutans bacteria isolated from clinical swab samples. Raman experiments aimed at establishing suitable spectroscopic parameters to quantify bacterial virulence and were conducted on a limited series of six clinical isolates three of which were genomically classified as Cnm-positive and three as Cnm-negative. Samples were characterized after biofilm purification and compared with cultures of the same bacteria in physiological state of equilibrium, namely, after long-term stabilization in vitro. Statistically significant series of ten Raman spectra were collected at different locations on each clinical sample, and their averages interpreted as multiomic snapshots of bacterial structure. Building upon the spectroscopic analyses described in the companion paper Part I, Raman characterizations of clinical isolates revealed a significant degree of variability in the bacterial structure, but also suggested clear classification criteria for clinical samples. These spectroscopic criteria reflected specific biochemical circumstances affecting the structure of bacteria in their pathophysiological state. Raman algorithms based on the fractional balance between proteins and peptidoglycans, and the degree of protein structural disorder vs. presence of oxysulfur compounds enabled insightful classifications of bacterial virulence, which matched genomic analyses. These structural characteristics, which allowed distinguishing between Cnm-positive and Cnm-negative bacteria, could provide fast and unbiased diagnostic criteria for risk assessments of endocarditis and hemorrhagic strokes as induced by Cnm-positive bacteria. In summary, the present study proposes a new spectroscopic approach to oral flora-related diagnostics and confirms the potential utility of Raman spectroscopy in chairside analyses of clinical isolates.

PMID:42100687 | PMC:PMC13148223 | DOI:10.3389/fmicb.2026.1784126

Stat Anal Data Min. 2025 Aug;18(4):e70026. doi: 10.1002/sam.70026. Epub 2025 Jul 2.

ABSTRACT

Atrial fibrillation (AF), a cardiac arrhythmia characterized by an abnormal and rapid heartbeat, has the potential to develop into stroke, heart failure, and, ultimately, mortality. The electrocardiogram (ECG) is a pivotal tool in the diagnosis of AF, offering a quick, cost-effective, and non-invasive mean to record the heart’s electrical activity. Recent studies are increasingly engaged in the implementation of deep learning techniques for ECG feature extraction for AF prediction. In addition, the application of Mendelian randomization (MR) methodologies has been investigated to identify causal associations between genetically imputed pre-defined ECG characteristics and cardiovascular diseases, such as AF. DeepFEIVR, a non-linear extension of the classical instrumental variable (IV) regression model, was designed with the objective of extracting disease-associated causal features from high-dimensional data, such as neuroimaging data. In this article, we applied DeepFEIVR as well as its variant (with residual inclusion), DeepFEIVR-RI, to the large UK Biobank dataset. The application of DeepFEIVR and DeepFEIVR-RI showed that the genetic components in ECGs could contribute to the development of AF statistically significantly (p values < 10^-8). Another contribution of this article is an extension to both DeepFEIVR and DeepFEIVR-RI to accommodate a large number of IVs. A comparison of results from DeepFEIVR and DeepFEIVR-RI, based on various choices of IVs, was conducted. Furthermore, we applied a recent algorithm called dnn-loc, enabling a visual examination on specific ECG components as extracted causal features for AF, thus advancing the understanding of the etiology of AF.

PMID:42100677 | PMC:PMC13148375 | DOI:10.1002/sam.70026

J Parasitol Res. 2026 May 6;2026:6617259. doi: 10.1155/japr/6617259. eCollection 2026.

ABSTRACT

BACKGROUND: Schistosomiasis, a parasitic waterborne infection, remains a major public health challenge in disadvantaged regions, with schoolchildren (5-14 years) at high risk due to frequent water exposure. The study is aimed at assessing the knowledge, attitudes, and practices (KAP) related to schistosomiasis among school-aged children in Nelson Mandela Bay (NMB) and examining how sociodemographic and environmental factors influence KAP outcomes.

METHODS: A quantitative, descriptive, cross-sectional study was conducted among 759 schoolchildren aged 5-14 years, enrolled in Grades 0-7. Data were collected using a structured, closed-ended, interview-administered questionnaire, which included sections on sociodemographic characteristics, clinical history, and KAP related to schistosomiasis. Bivariate and multivariate analyses were performed to evaluate associations and describe the data using R software (Version 4.3.1).

RESULTS: Only 11% participants were aware of schistosomiasis, mainly learning from school (62%) or home (35%). Key environmental factors included urinating in rivers (44%), living near water bodies (21.1%), and swimming (11.3%). Knowledge and attitude scores showed a moderate positive correlation (r = 0.33; p < 0.001). Gender and grade level significantly influenced KAP scores, with males and older children (Grades 4-7) exhibiting better knowledge (p = 0.015), attitudes (p = 0.023), and practices (p = 0.001). Females had lower knowledge scores (β = -0.15; p = 0.018), while older children displayed fewer positive attitudes (β = 0.07; p = 0.038) and poorer practices (β = 0.11; p = 0.001).

CONCLUSION: Significant gaps in knowledge, poor attitudes, and inadequate hygiene practices highlight the need for targeted education and community-based strategies to improve KAP and reduce schistosomiasis risk in NMB schoolchildren.

PMID:42100674 | PMC:PMC13147206 | DOI:10.1155/japr/6617259

Front Cell Infect Microbiol. 2026 Apr 22;16:1779186. doi: 10.3389/fcimb.2026.1779186. eCollection 2026.

ABSTRACT

BACKGROUND: Co-infection with Aspergillus and Mucorales in the intensive care unit (ICU) represents a devastating syndrome with high mortality that is frequently clinically occult. Clinically distinguishing this co-infection from invasive pulmonary aspergillosis (IPA) is challenging but critical for tailoring precise antifungal strategies.

METHODS: We conducted a single-center, retrospective observational study involving 93 critically ill patients (75 with Aspergillus infection and 18 with co-infection) admitted between 2017 and 2025. We compared clinical characteristics, inflammatory markers, and immunophenotypes between groups. A three-stage variable selection strategy integrating univariable regression pre-screening, multi-algorithm importance ranking (LASSO, Ridge, and Random Forest), and clinical applicability filtering was employed to identify predictors for a multivariable logistic regression nomogram.

RESULTS: The co-infection group exhibited substantially higher ICU mortality than the sole Aspergillus group, although the difference did not reach statistical significance (72.2% vs. 53.3%, p = 0.24).Kaplan-Meier analysis demonstrated that initiation of amphotericin B within <7 days of diagnosis or strong clinical suspicion was significantly associated with improved survival (log-rank p < 0.0001). A three-stage variable selection strategy integrating univariable regression, multi-algorithm importance ranking (LASSO, Ridge, and Random Forest), and clinical applicability filtering identified four key predictors. The resulting multivariable logistic regression nomogram – incorporating NK cell count, C-reactive protein, corticosteroid use history, and Gram-positive bacterial co-infection – demonstrated robust discrimination (AUC = 0.878, 95% CI: 0.789-0.967), with good calibration (Hosmer-Lemeshow p = 0.849) and stability on internal validation (cross-validated AUC = 0.860).

CONCLUSION: Aspergillus and Mucorales co-infection constitutes a distinct, high-mortality clinical entity in the ICU. The developed nomogram, integrating clinical, immunological, and inflammatory features, may facilitate the early identification of high-risk patients and guide timely initiation of Mucorales-active therapy to improve prognosis.

PMID:42100654 | PMC:PMC13144045 | DOI:10.3389/fcimb.2026.1779186

Stat Comput. 2026;36(3):131. doi: 10.1007/s11222-026-10866-0. Epub 2026 May 5.

ABSTRACT

In an effort to develop topic modeling methods that can be quickly applied to large data sets, we revisit the problem of maximum-likelihood estimation in topic models. It is known, at least informally, that maximum-likelihood estimation in topic models is closely related to non-negative matrix factorization (NMF). Yet, to our knowledge, this relationship has not been exploited previously to fit topic models. We show that recent advances in NMF optimization methods can be leveraged to fit topic models very efficiently, often resulting in much better fits and in less time than existing algorithms for topic models. We also formally make the connection between the NMF optimization problem and maximum-likelihood estimation for the topic model, and using this result we show that the expectation maximization (EM) algorithm for the topic model is essentially the same as the classic multiplicative updates for NMF. Our methods are implemented in the R package “fastTopics”.

PMID:42100650 | PMC:PMC13144203 | DOI:10.1007/s11222-026-10866-0

Cureus. 2026 May 6;18(5):e108357. doi: 10.7759/cureus.108357. eCollection 2026 May.

ABSTRACT

BACKGROUND: The fragility index (FI) is intended to quantify how many outcome changes would be required to convert a statistically significant two-arm trial result into a nonsignificant one. A reliable statistical metric should produce a result for every valid case it evaluates. This study examined whether a fragility value is always attainable for every statistically significant trial result.

METHODS: FI was analyzed as follows: baseline significance was required (p < 0.05), one-way movement only, and outcome changes were restricted to converting a nonevent to an event in the arm with fewer events, while keeping the arm size fixed. Nonattainability was assessed by determining whether valid 2×2 tables exist for which no finite FI can be obtained under these rules. Evidence is provided through formal counterexamples, complete enumeration of all valid nondegenerate 2 × 2 tables up to total sample size N = 60, and empirical evaluation of published two-arm trials with binary outcomes.

RESULTS: Valid baseline-significant 2 × 2 tables exist for which FI is not attainable. A simple counterexample is {3,0,4,11}: baseline two-sided Fisher’s exact p = 0.0429, the arm with fewer events is uniquely identified, but that arm has no nonevents available for the required toggle; thus, no legal FI path exists. Enumeration revealed that unattainable cases first appeared at N = 18 and then recurred at every larger sample size through N = 60; by N = 60, a total of 2,390 of 20,774 evaluable baseline-significant tables were unattainable (11.5%). In an empirical dataset of published trials, 2 of 82 baseline-significant evaluable trials (2.4%) were not attainable.

CONCLUSIONS: The FI is not universally attainable. This is a structural property of the FI algorithm, confirmed by mathematical proof, a complete table enumeration, and published trial data.

PMID:42100648 | PMC:PMC13148188 | DOI:10.7759/cureus.108357

Cureus. 2026 May 6;18(5):e108363. doi: 10.7759/cureus.108363. eCollection 2026 May.

ABSTRACT

Complex wound reconstruction may progress through stage-dependent clinical and economic trajectories in which wound failure leads to infection, reoperation, prolonged hospitalization, post-acute care, outpatient wound management, and possible readmission. Procedural-cost evaluation may not fully capture these downstream consequences in high-risk reconstructive settings. This paper presents the CASCADE (Cost Analysis of Surgical Complications and Downstream Expenditure) model, a conceptual, literature-derived decision framework rather than a primary data analysis or statistically validated predictive model. CASCADE applies an expected-value framework to evaluate the reconstructive strategy at the index operation, using three bounded inputs: failure probability (P), failure trajectory cost (C), and incremental reconstruction cost (ΔC). These inputs are structured into the decision rule ΔC < ΔP × C. A Clinical Risk Score (CRS) is used to standardize the assignment of cases into risk tiers. Across illustrative high-risk wound environments, failure trajectories may increase total episode expenditure from approximately $80,000-$150,000 after successful reconstruction to $400,000-$1,000,000+ after failure-driven care. These values are literature-informed illustrative estimates used to parameterize the framework, not observed case-level measurements. Sensitivity analysis demonstrates that at CRS ≥6, the CASCADE decision threshold often exceeds typical incremental reconstructive procedure costs across plausible input combinations. CASCADE provides a reproducible, bidirectional framework for trajectory-based cost evaluation in complex wound reconstruction. It may support surgical decision-making, institutional planning, and reimbursement analysis when total episode cost, rather than index procedural cost alone, is the appropriate unit of economic evaluation. The framework evaluates cost relationships but does not define reimbursement levels and requires future validation against institutional or claims-based datasets.

PMID:42100647 | PMC:PMC13148454 | DOI:10.7759/cureus.108363

Ecol Evol. 2026 May 5;16:e73605. doi: 10.1002/ece3.73605. eCollection 2026 May.

ABSTRACT

Forest structural complexity is critical for ecosystem functions, yet standardized metrics for its quantification remain elusive. This study compares two LiDAR-derived three-dimensional indices, the box dimension ( $D_b$ ) as a fractal-based measure, and canopy entropy ( $\mathrm{CE}$ ), an entropy-based metric, to evaluate their methodological, computational, and conceptual differences. Using mobile LiDAR scans from $15m\times 15m$ forest plots in Maine, USA, and Nova Scotia and New Brunswick, Canada, we analyzed 170 point clouds to assess correlation, computation time, and theoretical underpinnings. Statistical analysis revealed a strong linear relationship between $D_b$ and $\mathrm{CE}$ (Pearson’s $r=0.823,p<0.001$ ), with Deming regression indicating $\widehat{\mathrm{CE}}=4.75\times D_b–1.07$ . Also, $\mathrm{CE}$ computation averaged 40 times slower than $D_b$ , scaling roughly linearly with point cloud size. Conceptually, $D_b$ reflects fractal dimensionality linked to physiological process optimization, while $\mathrm{CE}$ quantifies biomass distribution homogeneity. ${\mathrm{CE}}^{‘}$ s unit dependence on plot size limits cross-study comparability, whereas ${D_b}^{‘}$ s dimensionless fractal interpretation offers broader intuitiveness. Both indices address sampling density bias but differ in parameterization and data efficiency. Despite ${\mathrm{CE}}^{‘}$ s theoretical novelty, it does not surpass $D_b$ in interpretability, precision, or speed, and its proposed advantage in capturing higher complexity remains unsubstantiated. Despite their conceptual distinctions, their strong correlation suggests competitive rather than complementary roles. Future research should explore biome-specific variability and physiological links to ecosystem functions to refine their utility in forest management under climate change.

PMID:42100628 | PMC:PMC13143574 | DOI:10.1002/ece3.73605