Category: Nevin Manimala

J Rural Health. 2025 Jun;41(3):e70073. doi: 10.1111/jrh.70073.

ABSTRACT

PURPOSE: Research has demonstrated many types of disparities in tobacco retailer density (TRD), but these analyses often fail to explore rural disparities. Given the substantial burden of rural tobacco use in the USA, this is a critical gap. The purpose of the present study was to estimate rural disparities in TRD across four US states.

METHODS: For the states of California, Connecticut, North Carolina, and Ohio, we used spatial statistical methods to model per capita TRD at the census tract level. Rurality was defined by the US Department of Agriculture Rural-Uran Commuting Area (RUCA) codes and categorized into Metropolitan, Micropolitan, Small Town, and Rural.

FINDINGS: Tobacco retailer count was highest in California (22,533), but TRD was highest in Connecticut (1.23 retailers per 1000 residents). In models for California, North Carolina, and Ohio (but not Connecticut), there was an association between rurality and TRD, such that rural census tracts had greater TRD than metropolitan census tracts. Micropolitan and small town (vs. metropolitan) census tracts also had greater TRD, although the association was not as strong. Models further showed associations between TRD and census tract poverty, racial and ethnic composition, and Appalachian designation.

CONCLUSIONS: Although there are notable state-level differences, TRD is clearly associated with rurality. Given the literature on the impacts of living in tobacco-retailer-dense areas, rural disparities in TRD likely contribute to rural disparities in tobacco use. There is a need for further policies in rural areas of the USA that address the tobacco retailer environment.

PMID:40817627 | DOI:10.1111/jrh.70073

Lab Med. 2025 Aug 16:lmaf033. doi: 10.1093/labmed/lmaf033. Online ahead of print.

ABSTRACT

INTRODUCTION: Platelet parameters are inexpensive and readily available biomarkers for platelet activation. This study investigated the differences and usefulness of platelet parameters in myeloproliferative neoplasms (MPNs), immune-mediated thrombocytopenia, and myelodysplastic syndrome (MDS), which are major hematologic disorders associated with platelet activation or dysfunction.

METHODS: We enrolled 418 patients: 186 with MPN, 109 with immune-mediated thrombocytopenia, and 123 with MDS. Platelet count and platelet parameters, including mean platelet volume (MPV), platelet distribution width (PDW), plateletcrit, and mean platelet component (MPC), were measured using an automated hematology analyzer.

RESULTS: Platelet parameters, particularly MPV and MPC, showed statistically significant differences in MPN compared with healthy control individuals, indicating the most significant platelet activation in primary myelofibrosis. We noted that MPV, plateletcrit, and MPC differed substantially between immune thrombocytopenic purpura and aplastic anemia compared with healthy control individuals, with statistically significant differences in MPV, PDW, and MPC between immune thrombocytopenic purpura and aplastic anemia. All parameters revealed statistically significant differences between MDS and healthy controls.

DISCUSSION: Platelet parameters demonstrated significant differences among patients with MPN, immune-mediated thrombocytopenia, and MDS compared with healthy control individuals, suggesting platelet activation in these disorders. They may also be useful markers for differentiating the causative disease in patients with thrombocytosis or thrombocytopenia.

PMID:40817623 | DOI:10.1093/labmed/lmaf033

Int J Rheum Dis. 2025 Aug;28(8):e70386. doi: 10.1111/1756-185x.70386.

ABSTRACT

OBJECTIVES: To understand the complexity of disease pathology through the prism of septic arthritis, especially the reliability of popular and, yet, arbitrary thresholds like synovial leucocyte counts of ≥ 100,000/μL suggestive of it, with the help of statistical analysis and logistic regression.

METHODS: An anonymized patient dataset comprising 360 swollen joint episodes was collated along with a range of patient attributes, including age, gender, comorbidity (e.g., diabetes, gout, pseudogout, immunosuppression), prior administration of antibiotics and washout of the affected joint, isolation of crystals from synovial aspirate, blood/synovial fluid culture growth, and synovial aspirate cell count. The dataset was subjected to statistical analysis (e.g., sensitivity, specificity, predictive and likelihood ratios) and logistic regression modeling, with results compared to the synovial leucocyte count thresholds of ≥ 100,000/μL and ≥ 50,000/μL.

RESULTS: The logistic regression model (sensitivity 50%, specificity 97.04%) outperformed the models based on arbitrary thresholds like a synovial leucocyte count of ≥ 100,000/μL (sensitivity 48.21%, specificity 88.16%) or ≥ 50,000/μL (sensitivity 64.29%, specificity 69.74%) in predicting septic arthritis. Independent variables like age, presence of gout, and autoimmune arthritis as comorbidities, hip joint involvement, synovial aspirate leucocyte count, and crystals in aspirated fluid demonstrated a significant (p < 0.05) correlation to septic arthritis.

CONCLUSION: Septic arthritis presents a multivariate correlation that deserves a holistic oversight rather than singling out individual factors. Data mining platforms like logistic regression can investigate the complex interplay among these individual variables while making a diagnosis not only in septic arthritis but also in other diseases with multisystem involvement, infective or non-infective alike.

PMID:40817605 | DOI:10.1111/1756-185x.70386

Proc Inst Mech Eng H. 2025 Aug 15:9544119251363649. doi: 10.1177/09544119251363649. Online ahead of print.

ABSTRACT

Lower extremity compression is effective in treating various vascular and wound conditions. Assessment of IP variations along limb length and under different compression applications are limited. This work quantified both local and gradient in vivo IP map with a piezoresistive (PR) sensor under three different compression applications when applied to the right leg of forty healthy subjects (n = 40). Compression applications included elastic stockinette, EdemaWear (EW), a pre-packaged compression set, CoFlex TLC (CF) and a combination application, CF applied over EW (Both = BO). Results showed statistical variations in local pressures and pressure gradients that varied by condition, body position, and post 10 min of exercise. Immediately post application significant differences between all compression conditions were observed at both distal and proximal measurement points, ranging from 10.8 ± 4.2 mmHg for supine EW to 38.2 ± 10.7 mmHg for standing BO. A non-uniform reduction in IP was observed post a brief period of wear under CF and BO, but not EW. The largest decrease was observed at the proximal measurement point under BO (37% reduction). Rate of change in IP proximal to distal ranging from -2.4 to 3.4 mmHg/in. Vertical patterning that mirrored the structural design of the EW was observed in some, but not all, of the pressure maps for the BO application only. The use of the PR sensor for capturing in vivo IP profiles may provide a more comprehensive understanding of the compression effect, highlighting the importance of considering variation in IP across the limb over a period of wear.

PMID:40817576 | DOI:10.1177/09544119251363649

Small Methods. 2025 Aug 15:e00782. doi: 10.1002/smtd.202500782. Online ahead of print.

ABSTRACT

When developing new materials for organic electronics, understanding how they will perform and change over time is critical. Typical bias stress exposure experiments provide limited information on the materials’ performance in applications which involve multiple charging and discharging steps. Here, organic thin film transistors (OTFTs) are characterized for 48-72 h straight in air and in N₂ using newly developed cyclic testing protocols that enable statistically significant evaluation of four different semiconductors by quantifying both, environmental and operational stress on their performance. It is demonstrated that the structure of the phthalocyanine leads to significant differences in response to bias stress, such as silicon bis(pentafluorophenoxy)phthalocyanine (F₁₀-SiPc) showing a much more air-stable p-type device compared to copper phthalocyanine (CuPc) and bis(pentafluorophenoxy) hexadecafluoro silicon(iv) phthalocyanine (F₅PhO)₂-F₁₆-SiPc showing much more air-stable n-type performance compared to Copper(II) 1,2,3,4,8,9,10,11,15,16,17,18,22,23,24,25-hexadecafluoro-29H,31H-phthalocyanine (F₁₆-CuPc). Raman microscopy of the films revealed no changes in morphology. The devices are also modeled using the 2D finite-element method, which suggests that most changes in device performance are due to fixed charges at the semiconductor/insulator interface. Overall, OTFT stress testing demonstrates, that important structure property relationships can be established between semiconductor molecular structure and device performance.

PMID:40817573 | DOI:10.1002/smtd.202500782

Psychol Rep. 2025 Aug 15:332941251370231. doi: 10.1177/00332941251370231. Online ahead of print.

ABSTRACT

The primary objective of this study is to create a measurement tool that is both valid and reliable for assessing parental regret among mothers and fathers regarding their roles as parents. The study sample comprised 721 participants residing in Turkey, each of whom had at least one biological child and willingly took part in the study. The results of the exploratory factor analysis (EFA) indicated that there is a two-factor structure consisting of 21 elements: intrinsic factors (items reflecting internal psychological aspects of parenting regret) and extrinsic factors (items reflecting external circumstantial aspects of parenting regret). The confirmatory factor analysis (CFA) yielded model fit indices that supported the existence of a two-factor structure. The validity analyses met the necessary criteria for convergent-divergent and criterion-related validity. Cronbach’s alpha (α) coefficient and the equivalent halves method were used to examine the reliability of the measurement tool. The Cronbach’s alpha (α) coefficient was calculated to be .943, the correlation between the two halves was found to be .637, and the Spearman Brown coefficient was calculated to be .778. We determined the item-total test correlation and t-values obtained during item analysis to be statistically significant. Upon evaluating the validity and reliability analyses holistically, we found that the Parenting Regret Scale (PRS-TR) was capable of producing accurate and dependable measurements.

PMID:40817447 | DOI:10.1177/00332941251370231

Int J Lang Commun Disord. 2025 Sep-Oct;60(5):e70105. doi: 10.1111/1460-6984.70105.

ABSTRACT

PURPOSE: Clinical reasoning is essential for speech-language pathologists (SLPs) when addressing ill-defined questions in various clinical settings. This study focuses on the acquisition of clinical reasoning skills in SLP students, particularly their evolution with clinical experience. To achieve this, the study developed and validated the first cloud-based script concordance test (SCT) tailored for assessing clinical reasoning skills in SSD diagnosis.

METHODS: An expert panel of 15 SLPs (average experience of 20.3 years) helped develop and score the SCT, which was administered to 51 undergraduate students (22 sophomores, 18 juniors, and 11 seniors). Statistical analyses examined the predictive role and trend of clinical experience in three dimensions (utility, interpretation, and diagnosis).

RESULTS: A significant difference in mean SCT scores was found between the expert panel and student groups (p < 0.05). Trend analysis showed a significant impact of clinical experience on SCT performance across all dimensions (all Fs > 9.91, p < 0.001), with greater experience linked to better reasoning skills. Low-scoring items highlighted challenges with stimulability testing, indicating a lack of clinical consensus.

CONCLUSIONS: This study demonstrates that clinical reasoning skills in SSD assessment become more refined with accumulated experience. The SCT developed effectively differentiates reasoning abilities between experts and students, offering a valuable tool for advancing clinical decision-making in speech-language pathology. These findings have practical implications, empowering SLP educators to design effective training programs and preparing students for the challenges they may face in clinical practice.

WHAT THIS PAPER ADDS: What is already known on this subject? The knowledge base of clinical practitioners includes both content and its organization. Clinical reasoning is the cognitive ability to integrate, organize, and interpret information as a key aspect of expertise in evidence-based practice. Recently, script concordance tests, which use case-based scenarios to reflect clinical decision-making processes, have become a popular method for assessing these reasoning skills. What this paper adds to existing knowledge? This study is the first to empirically show that clinical reasoning skills in speech-language pathology, particularly in assessing speech sound disorders, are progressively refined through clinical experience. It highlights that students’ reasoning skills during internships appear to be influenced by supervisory clinical approaches, potentially affecting diagnostic abilities. These reasoning skills may improve with further clinical experience or targeted continuing education. Additionally, the study developed the first SCT focused on SSD evaluation, demonstrating its effectiveness in distinguishing between expert and student clinical reasoning and decision-making abilities. What are the potential or actual clinical implications of this work? A recent goal in clinical education is to make clinical reasoning processes more explicit and structured during training. Creating comprehensive case assessment scripts and designing concrete clinical thinking guidance programs integrated with effective measurement, such as the SCT developed in this study, could serve as teaching objectives for the clinical diagnosis of SSD.

PMID:40817427 | DOI:10.1111/1460-6984.70105

Med Phys. 2025 Aug;52(8):e18067. doi: 10.1002/mp.18067.

ABSTRACT

BACKGROUND: Accurate segmentation of high-risk clinical target volume (HRCTV) and organs-at-risk (OARs) is crucial for optimizing gynecologic brachytherapy treatment planning. However, performing this segmentation on Computed Tomography (CT) images remains particularly challenging due to anatomical variability, limited soft-tissue contrast, and the scarcity of annotated datasets. Compared to other radiotherapy domains, CT-based gynecologic brachytherapy segmentation is notably underrepresented in benchmarking studies.

PURPOSE: This study aims to improve the segmentation of HRCTV and OARs in gynecologic brachytherapy by introducing GynBTNet, a multi-stage learning framework. Through large-scale self-supervised pretraining and progressive finetuning, the model is designed to enhance anatomical representation learning and adapt effectively to domain-specific gynecologic structures, addressing the challenges of limited training data and complex anatomical variability.

METHODS: GynBTNet employs a three-stage training strategy: (1) self-supervised pretraining on large-scale CT datasets using sparse submanifold convolution to capture robust anatomical representations, (2) supervised finetuning on a multi-organ segmentation dataset to refine feature extraction, and (3) task-specific finetuning on the gynecologic brachytherapy dataset to optimize segmentation performance for clinical applications. In the third stage, 116 cases were used for training, while 29 cases were reserved for independent testing. The model was evaluated against state-of-the-art methods using the Dice Similarity Coefficient (DSC), 95th percentile Hausdorff Distance (HD95%), and Average Surface Distance (ASD). Overall statistical significance across models was assessed using the Friedman test. Post hoc pairwise comparisons were conducted using two-tailed paired permutation tests, with multi-comparison correction via the Benjamini-Hochberg procedure to control the false discovery rate. Cohen’s effect sizes were calculated to quantify the performance differences.

RESULTS: GynBTNet demonstrated consistent superiority over nnU-Net across all structures and achieved overall favorable performance compared to Swin-UNETR. The most substantial improvement was observed in HRCTV segmentation, where GynBTNet achieved a DSC of 0.837 ± 0.068, significantly higher than nnU-Net (p < 0.05) with a large effect size of +1.25, and superior to Swin-UNETR with a moderate-to-large effect size of +0.57. Boundary precision for HRCTV also improved significantly, with effect sizes in HD95% (-0.81 vs. nnU-Net, -0.52 vs. Swin-UNETR) and ASD (-1.20 vs. nnU-Net, -0.61 vs. Swin-UNETR). For bladder segmentation, GynBTNet reached a DSC of 0.940 ± 0.052, significantly outperforming nnU-Net (p < 0.05) with a large effect size of +1.28 and showing a small advantage over Swin-UNETR (effect size +0.26). In rectum segmentation, GynBTNet achieved a DSC of 0.842 ± 0.070, significantly exceeding nnU-Net (p < 0.05) with a large effect size of +1.17, and surpassing Swin-UNETR with an effect size of +0.54. For the uterus, GynBTNet significantly improved boundary accuracy compared to both nnU-Net and Swin-UNETR (p < 0.05), with effect sizes in ASD of -0.99 and -0.64. Segmentation of the sigmoid colon remained challenging, as GynBTNet provided only marginal DSC gains over nnU-Net with negligible effect sizes.

CONCLUSIONS: The proposed multi-stage learning strategy effectively enhances segmentation accuracy for gynecologic brachytherapy, leveraging large-scale self-supervised pretraining and progressive finetuning. By improving HRCTV and OARs delineation, GynBTNet has the potential to enhance treatment planning precision, minimize radiation exposure to critical structures, and improve patient outcomes.

PMID:40817425 | DOI:10.1002/mp.18067

Sci Rep. 2025 Aug 15;15(1):29946. doi: 10.1038/s41598-025-16015-3.

ABSTRACT

Focal cortical dysplasia (FCD) is the leading cause of drug-resistant epilepsy (DRE) in pediatric patients. Accurate detection of FCDs is crucial for successful surgical outcomes, yet remains challenging due to frequently subtle MRI findings, especially in children, whose brain morphology undergoes significant developmental changes. Automated detection algorithms have the potential to improve diagnostic precision, particularly in cases, where standard visual assessment fails. This study aimed to evaluate the performance of automated algorithms in detecting FCD type II in pediatric patients and to examine the impact of adult versus pediatric templates on detection accuracy. MRI data from 23 surgical pediatric patients with histologically confirmed FCD type II were retrospectively analyzed. Three imaging-based detection algorithms were applied to T1-weighted images, each targeting key structural features: cortical thickness, gray matter intensity (extension), and gray-white matter junction blurring. Their performance was assessed using adult and pediatric healthy controls templates, with validation against both predictive radiological ROIs (PRR) and post-resection cavities (PRC). The junction algorithm achieved the highest median dice score (0.028, IQR 0.038, p < 0.01 when compared with other algorithms) and detected relevant clusters even in MRI-negative cases. The adult template (median dice score 0.013, IQR 0.027) significantly outperformed the pediatric template (0.0032, IQR 0.023) (p < 0.001), highlighting the importance of template consistency. Despite superior performance of the adult template, its use in pediatric populations may introduce bias, as it does not account for age-specific morphological features such as cortical maturation and incomplete myelination. Automated algorithms, especially those targeting junction blurring, enhance FCD detection in pediatric populations. These algorithms may serve as valuable decision-support tools, particularly in settings where neuroradiological expertise is limited.

PMID:40817382 | DOI:10.1038/s41598-025-16015-3

Sci Rep. 2025 Aug 15;15(1):29959. doi: 10.1038/s41598-025-15783-2.

ABSTRACT

With the rapid development of the global financial market, the problem of financial fraud is becoming more and more serious, which brings huge economic losses to the market, consumers and investors and threatens the stability of the financial system. Traditional fraud detection methods based on rules and statistical analysis are difficult to deal with increasingly complex and evolving fraud methods, and there are problems such as poor adaptability and high false alarm rate. Therefore, this paper proposes a financial fraud detection model based on Stacking ensemble learning algorithm, which integrates many basic learners such as logical regression (LR), decision tree (DT), random forest (RF), Gradient Boosting Tree (GBT), support vector machine (SVM) and neural network (NN), and introduces feature importance weighting and dynamic weight adjustment mechanism to improve the model performance. The experiment is based on more than 1 million real financial transaction data. The results show that the Stacking model is significantly superior to the traditional single model in accuracy (95%), recall (93%) and F1 score (94%), and has stronger generalization ability and stability. Although the Stacking model has challenges in computing cost and delay, its advantages in fraud detection accuracy and robustness make it a powerful tool for financial institutions to improve their risk control ability. In the future, its real-time adaptability can be further optimized through online learning and incremental update mechanism.

PMID:40817346 | DOI:10.1038/s41598-025-15783-2