Nevin Manimala

Rev Med Inst Mex Seguro Soc. 2025 Jul 1;63(4):e6168. doi: 10.5281/zenodo.15644313.

ABSTRACT

BACKGROUND: Acute appendicitis is the most common surgical emergency worldwide. Closure of the appendiceal stump is a critical step to prevent postoperative complications.

OBJECTIVE: To compare postoperative outcomes of appendectomy using the Pouchet technique versus other appendiceal stump closure techniques.

MATERIAL AND METHODS: This retrospective study analyzed medical records of patients over 18 years of age who underwent surgery for acute appendicitis at a secondary-level hospital. Postoperative outcomes were assessed based on the presence of infectious complications, operative time, and length of hospital stay, comparing the surgical techniques used: Pouchet, Halsted, Zuckerman, and Parker. Descriptive and inferential statistics were applied, using the chi-square test to estimate differences in postoperative outcomes, with a significance level of ≤ 0.05.

RESULTS: A total of 118 medical records were analyzed, of which 70 corresponded to female patients (59.3%), with a median age of 39 years (interquartile range: 18-92 years). The most commonly used surgical techniques were: Pouchet (74 cases; 62.7%), Halsted (27; 22.8%), Zuckerman (12; 10.1%), and Parker (5; 4.2%). The Pouchet and Halsted techniques showed statistically significant differences compared to other techniques in terms of shorter operative time and hospital stay (p = 0.000 and p = 0.011, respectively). Additionally, the Pouchet and Parker techniques were associated with statistically significant differences in the incidence of infectious complications (p = 0.030).

CONCLUSIONS: The Pouchet technique demonstrated the best postoperative outcomes in terms of operative time, hospital stay duration, and lower incidence of infectious complications.

PMID:40658474 | DOI:10.5281/zenodo.15644313

Bioinformatics. 2025 Jul 14:btaf400. doi: 10.1093/bioinformatics/btaf400. Online ahead of print.

ABSTRACT

MOTIVATION: A critical challenge in observational studies arises from the presence of hidden confounders in high-dimensional data. This leads to biases in causal effect estimation due to both hidden confounding and high-dimensional estimation. Some classical deconfounding methods are inadequate for high-dimensional scenarios and typically require prior information on hidden confounders. We propose a two-step deconfounded and debiased estimation for high-dimensional linear regression with hidden confounding.

RESULTS: First, we reduce hidden confounding via spectral transformation. Second, we correct bias from the weighted ℓ1 penalty, commonly used in high-dimensional estimation, by inverting the Karush-Kuhn-Tucker conditions and solving convex optimization programs. This deconfounding technique by spectral transformation requires no prior knowledge of hidden confounders. This novel debiasing approach improves over recent work by not assuming a sparse precision matrix, making it more suitable for cases with intrinsic covariate correlations. Simulations show that the proposed method corrects both biases and provides more precise coefficient estimates than existing approaches. We also apply the proposed method to a deoxyribonucleic acid methylation dataset from the Alzheimer’s disease (AD) neuroimaging initiative database to investigate the association between cerebrospinal fluid tau protein levels and AD severity.

AVAILABILITY: The code for the proposed method is available on GitHub (https://github.com/Li-Zhaoy/Dec-Deb.git) and archived on Zenodo (DOI: 10.5281/zenodo.15478745).

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:40658470 | DOI:10.1093/bioinformatics/btaf400

Eur J Cardiothorac Surg. 2025 Jul 14:ezaf232. doi: 10.1093/ejcts/ezaf232. Online ahead of print.

ABSTRACT

OBJECTIVES: We evaluated intimal damage at the histological level resulting from various clamp techniques in human pulmonary artery specimens obtained after lobectomy.

METHODS: We prospectively analysed patients who underwent anatomical lung resection at two centres between April 2021 and March 2025. The double-loop technique (DLT), DeBakey vascular clamp (3rd and 4th notches), Fogarty vascular clamp (2nd notch), endovascular clips (gold and silver), and vessel loop technique (VLT) were evaluated. Pulmonary artery specimens with an external diameter ≥10 mm were included. We measured the burst pressure and evaluated the intimal damage in the human pulmonary artery by using the modified Zhang’s score (MZS; 0 – 5).

RESULTS: Thirty-six patients were enrolled, and 70 pulmonary artery samples were obtained. DeBakey 3rd exerted a significantly higher burst pressure than did DLT (P = 0.022). No significant difference was found between DLT and VLT (P = 0.453). A burst pressure ≥30 mmHg was achieved in all DLT cases. None of the samples clamped with DLT and VLT exhibited MZS 3 – 5. The rate of MZS ≥2 with DeBakey 3rd, Fogarty 2nd, gold and silver clips, and VLT was statistically comparable to that for DLT, whereas DeBakey 4th resulted in significantly higher MZS values than did DLT (P = 0.029).

CONCLUSIONS: The DLT is a feasible and safe for thoracoscopic clamping. Additionally, DLT, VLT, and gold clip are appropriate for clamping the peripheral pulmonary artery. For DeBakey vascular clamp, notch selection should be carefully tailored to the vessel diameter.

PMID:40658467 | DOI:10.1093/ejcts/ezaf232

Bioinformatics. 2025 Jul 14:btaf399. doi: 10.1093/bioinformatics/btaf399. Online ahead of print.

ABSTRACT

MOTIVATION: High-throughput sequencing of single-cell data can be used to rigorously evaluate cell specification and enable intricate variations between groups or conditions to be identified. Many popular existing methods for differential expression target differences in aggregate measurement (mean, median, sum) and limit their approaches to detect only global differential changes.

RESULTS: We present a robust method for differential expression of single-cell data using a kernel-based score test, cytoKernel. CytoKernel is specifically designed to assess the differential expression of single-cell RNA sequencing and high-dimensional flow or mass cytometry data using the full probability distribution pattern. cytoKernel is based on kernel embeddings which employs the probability distributions of the single-cell data, by calculating the pairwise divergence/distance between distributions of subjects. It can detect both patterns involving changes in the aggregate, as well as more elusive variations that are often overlooked due to the multimodal characteristics of single-cell data. We performed extensive benchmarks across both simulated and real data sets from mass cytometry data and single-cell RNA sequencing. The cytoKernel procedure effectively controls the False Discovery Rate (FDR) and shows favourable performance compared to existing methods. The method is able to identify more differential patterns than existing approaches. We apply cytoKernel to assess gene expression and protein marker expression differences from cell subpopulations in various publicly available single-cell RNAseq and mass cytometry data sets.

AVAILABILITY AND IMPLEMENTATION: The methods described in this paper are implemented in the open-source R package cytoKernel, which is freely available from Bioconductor at http://bioconductor.org/packages/cytoKernel.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:40658464 | DOI:10.1093/bioinformatics/btaf399

Bioinformatics. 2025 Jul 14:btaf397. doi: 10.1093/bioinformatics/btaf397. Online ahead of print.

ABSTRACT

MOTIVATION: DNA methylation plays important roles in various cellular physiological processes in bacteria. Nanopore sequencing has shown the ability to identify different types of DNA methylation from individual bacteria directly. However, existing methods for identifying bacterial methylomes showed inconsistent performances in different methylation motifs in bacteria and didn’t fully utilize the different scale information contained in nanopore signals.

RESULTS: We propose a deep-learning method, called Nanoident, for de novo detection of DNA methylation types and methylated base positions in bacteria using Nanopore sequencing. For each targeted motif sequence, Nanoident utilizes five different features, including statistical features extracted from both the nanopore raw signals and the basecalling results of the motif. All the five features are processed by a multi-scale neural network in Nanoident, which extracts information from different receptive fields of the features. The LOOCV (Leave-One-Out Cross Validation) on the dataset containing 7 bacteria samples with 46 methylation motifs shows that, Nanoident achieves ∼10% improvement in accuracy than the previous method. Furthermore, Nanoident achieves ∼13% improvement in accuracy in an independent dataset, which contains 12 methylation motifs. Additionally, we optimize the pipeline for de novo methylation motif enrichment, enabling the discovery of novel methylation motifs.

AVAILABILITY AND IMPLEMENTATION: The source code of Nanoident is freely available at https://github.com/cz-csu/Nanoident and https://doi.org/10.6084/m9.figshare.29252264.

SUPPLEMENTARY INFORMATION: data are available at Bioinformatics online.

PMID:40658463 | DOI:10.1093/bioinformatics/btaf397

Nucl Med Commun. 2025 Jul 4. doi: 10.1097/MNM.0000000000002020. Online ahead of print.

ABSTRACT

INTRODUCTION: Sjögren’s syndrome is a chronic autoimmune disease characterized by lymphocytic infiltration and destruction of exocrine glands. Sjögren’s syndrome characteristically involves salivary glands with the presence of xerostomia in the majority (>93%) of patients. The severity of xerostomia can vary from mild to severe and debilitating. Labial histopathology and antinuclear antibodies (ANA) are commonly used in the diagnosis of Sjögren’s syndrome but do not correlate well with disease severity. Tests available for objective assessment of disease severity include sialometry and salivary gland scintigraphy (SGS). This study aims to correlate the severity of xerostomia with semiquantitative parameters on SGS.

MATERIALS AND METHODS: On the basis of clinical symptoms, the severity of xerostomia was graded into mild, moderate, and severe. Semiquantitative parameters (maximum uptake and excretion fractions) for all salivary glands were calculated on SGS. Spearman’s correlation coefficients were calculated to assess correlation with clinical disease severity.

RESULTS: One-hundred thirteen patients (94 females and 19 males) with a median age of 39 years (range: 4-85 years) were included. Of these, 74 had mild, 28 had moderate, while only 11 had severe disease. There was a statistically significant difference between the mean values of maximum uptake and excretion fractions across the three severity groups (P < 0.05).

CONCLUSION: Semiquantitative parameters on SGS show a reduction with an increase in the severity of xerostomia. In addition, maximum uptake and excretion fractions correlated well with the severity of xerostomia of Sjögren’s syndrome, whereas ANA levels showed no significant correlation with disease severity. SGS can serve as an objective parameter of clinical severity of xerostomia, which is otherwise difficult to determine clinically.

PMID:40658462 | DOI:10.1097/MNM.0000000000002020

Stat Med. 2025 Jul;44(15-17):e10258. doi: 10.1002/sim.10258.

ABSTRACT

Multivariate (average) equivalence testing is widely used to assess whether the means of two conditions of interest are “equivalent” for different outcomes simultaneously. In pharmacological research for example, many regulatory agencies require the generic product and its brand-name counterpart to have equivalent means both for the AUC and C_max pharmacokinetics parameters. The multivariate Two One-Sided Tests (TOST) procedure is typically used in this context by checking if, outcome by outcome, the marginal 100 ( 1 – 2 α ) % $$ 100left(1-2alpha right)% $$ confidence intervals for the difference in means between the two conditions of interest lie within predefined lower and upper equivalence limits. This procedure, already known to be conservative in the univariate case, leads to a rapid power loss when the number of outcomes increases, especially when one or more outcome variances are relatively large. In this work, we propose a finite-sample adjustment for this procedure, the multivariate α $$ alpha $$ -TOST, that consists in a correction of α $$ alpha $$ , the significance level, taking the (arbitrary) dependence between the outcomes of interest into account and making it uniformly more powerful than the conventional multivariate TOST. We present an iterative algorithm allowing to efficiently define α * $$ {alpha}^{ast } $$ , the corrected significance level, a task that proves challenging in the multivariate setting due to the inter-relationship between α * $$ {alpha}^{ast } $$ and the sets of values belonging to the null hypothesis space and defining the test size. We study the operating characteristics of the multivariate α $$ alpha $$ -TOST both theoretically and via an extensive simulation study considering cases relevant for real-world analyses-that is, relatively small sample sizes, unknown and possibly heterogeneous variances as well as different correlation structures-and show the superior finite-sample properties of the multivariate α $$ alpha $$ -TOST compared to its conventional counterpart. We finally re-visit a case study on ticlopidine hydrochloride and compare both methods when simultaneously assessing bioequivalence for multiple pharmacokinetic parameters.

PMID:40658428 | DOI:10.1002/sim.10258

Med Vet Entomol. 2025 Jul 14. doi: 10.1111/mve.12823. Online ahead of print.

ABSTRACT

As a tropical country, Colombia hosts a wide range of arthropods that can act as vectors of disease-causing pathogens, particularly those carrying hemopathogens. Ticks play a crucial role in the transmission of zoonotic pathogens, impacting both human and veterinary health. The pathogen load of ticks from wildlife is of particular concern, as it can contribute to the spillover of infectious agents to domestic animals and humans, highlighting the need for surveillance and control strategies to mitigate emerging tick-borne diseases. Therefore, this study aimed to determine the presence of microorganisms in ticks collected from wildlife in Antioquia (Colombia) through bioinformatic analysis. A prospective, cross-sectional, random, non-probabilistic, convenience-based study involving tick collection from animals in three different zones of Antioquia was conducted. Initially, vertebrate species were morphologically characterized via taxonomic keys and identification guides for amphibians, reptiles, birds, and mammals. Ticks were manually collected from these animals and preserved in absolute ethanol for later taxonomic identification. Genomic DNA was then extracted, and the resulting reads were processed through bioinformatic analysis, achieving taxonomic classification within DNA libraries of gram-positive bacteria, gram-negative bacteria, and parasites. Additionally, descriptive statistics were calculated for all variables of interest at the animal level (e.g., genus, species, sex, and age group, when applicable) and study zone. A total of 570 ticks, predominantly Amblyomma spp., were obtained from 46 host animals. Ticks from lizards presented the highest bacterial richness and diversity (based on 16S gDNA), whereas ticks from amphibians presented the lowest. Proteobacteria dominated most samples, as shown by taxonomic composition at the phylum, family, and genus levels. Ticks collected from mammals displayed lower diversity and richness than those collected from reptiles. For parasitic communities (18S gDNA), dominant eukaryotes were identified in ticks from mammals, excluding host-related taxa. Overall, lizard-associated ticks presented the most complex microbial diversity, whereas amphibian ticks were less diverse, highlighting the significant variation in microbial and parasitic communities across host species. This study highlights the microbial diversity of ticks from wild hosts in Colombia, focusing on the dominance of Francisella, Rickettsia, Aspergillus, and Penicillium. These findings underscore the need for further research on their ecological roles, transmission dynamics, and potential health risks, aiming to inform strategies to mitigate tick-borne diseases.

PMID:40658399 | DOI:10.1111/mve.12823

J Chem Inf Model. 2025 Jul 14. doi: 10.1021/acs.jcim.5c00893. Online ahead of print.

ABSTRACT

Molecular docking is a widely used technique in structure-based drug design for generating poses of small molecules in a protein receptor structure. These poses are then ranked to prioritize compounds for experimental validation. Numerous approaches to assessing the structural fit of a ligand exist, ranging from simple scoring functions to more elaborate free energy calculations. Regardless of the prioritization method chosen, its accuracy is limited by the quality of the protein-ligand pose. Here, we apply two established statistical approaches for quantifying atomic interaction preferences and torsional ligand strain, respectively, to compare poses generated by the docking algorithm Vina with crystallographic data from the PDB and CSD. This analysis allows us to identify potential deficiencies in the docking algorithm, such as underestimated electrostatic repulsion or high-energy hydroxyl conformations. By highlighting such inaccuracies, we aim to inspire improvements in future docking algorithms. Finally, a pose scoring approach is proposed that significantly improves the retrieval of the experimental pose from a set of docked poses.

PMID:40658398 | DOI:10.1021/acs.jcim.5c00893

Stat Med. 2025 Jul;44(15-17):e70111. doi: 10.1002/sim.70111.

ABSTRACT

Data collection procedures are often time-consuming and expensive. An alternative to collecting full information from all subjects enrolled in a study is a two-phase design: Variables that are inexpensive or easy to measure are obtained for the study population, and more specific, expensive, or hard-to-measure variables are collected only for a well-selected sample of individuals. Often, only these subjects that provided full information are used for inference, while those that were partially observed are discarded from the analysis. Recently, semiparametric approaches that use the entire dataset, resulting in fully efficient estimators, have been proposed. These estimators, however, have challenges incorporating multiple covariates, are computationally expensive, and depend on tuning parameters that affect their performance. In this paper, we propose an alternative semiparametric estimator that does not pose any distributional assumptions on the covariates or measurement error mechanism and can be applied to a wider range of settings. Although the proposed estimator is not semiparametric efficient, simulations show that the loss of efficiency to estimate the parameters associated with the partially observed covariates is minimal. We highlight the estimator’s applicability to real-world problems, where data structures are complex and rich, and complicated regression models are often necessary.

PMID:40658389 | DOI:10.1002/sim.70111