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Top 10 statistical pitfalls: a reviewer’s guide to avoiding common errors
Get PDFMilestone articles have highlighted the frequency and types of statistical errors in research,1–5 yet fundamental errors persist across various disciplines. With a background in biostatistics and over 200 articles reviewed for journals such as Heart and Addiction since 2021, two differing and distinct research areas, I (DJG) have identified 10 common statistical mistakes that authors frequently make. Together with two academic colleagues, we present these issues in a concise, direct and accessible way to help researchers avoid them. This article will not repeat the pitfalls documented previously; rather, it reflects independent observations on statistical and presentational issues frequently made by authors across various medical fields. Ever wondered why your manuscript keeps getting rejected? It might be due to these common statistical mistakes. By highlighting these errors, we aim to save authors time with revisions and reviewers time in repeatedly reporting the same issues, ultimately advancing a research world that is transparent, specific and reproducible. Here is the upcoming list (each with their own merit and importance): 1) Incorrect use of causal language: avoid implying causation when only association is demonstrated. 2) Poorly formatted abstracts: ensure abstracts are concise, well-structured and accurately reflect the study. 3) Results in the methods section: keep results strictly within the results section to maintain clarity. 4) Inaccurate/incomplete statistical analysis presentation in the methods: provide detailed and accurate descriptions of statistical methods used. 5) Hypothesis tests for normality: understand when and how to appropriately assess for normality. 6) Absent or insufficient flow diagrams: use flow diagrams to clearly depict study design and participant flow. 7) Regression models issues (three-in-one): differentiate between multivariable and multivariate models, justify confounder inclusion, and ensure model assumptions are met. 8) Using univariable significance for multivariable models: avoid relying on univariable analysis to inform multivariable models. 9) Poor reporting of missing data: transparently report and handle missing data. 10) Insufficient attention
Naringenin Loaded Hydrogel Supports Wound Repair in a Cell Model of Diabetic Skin
Get PDFIntroduction: Diabetic foot ulcers are a major complication of diabetes, driven by inflammation, oxidative stress, and poor vascular function. Naringenin, a citrus flavonoid, addresses these factors but has low solubility and stability. We developed a Na-AMPS hydrogel dressing to enhance its delivery under diabetic-like conditions. Methods: A Na-AMPS hydrogel containing 0.02%(w/w) naringenin was formulated and assessed for rheological and adhesive properties, drug release, and biological activity in HUVEC and HDFa cells. Cytotoxicity (XTT), reactive oxygen species (ROS), mitochondrial membrane potential (TMRM), cytokine levels (IL-6, IL-8, MMP-9, TGF-β), and wound closure (scratch assay) were measured. Results/Discussion: Naringenin modestly reduced the hydrogel elastic modulus (15,791.5 ± 1965 Pa at 30 Hz) without affecting adhesion. Release studies showed rapid drug release from solution but sustained release from hydrogels (17.88 ± 2.61% over 24 h). Under hyperglycaemic and pro-inflammatory conditions, naringenin significantly decreased ROS in HUVECs (41,030.58 ± 2737 to 31,778.74 ± 1822 AU; p < 0.001) and HDFa cells (38,188.13 ± 4593 to 29,950.94 ± 1426 AU; p < 0.05). Naringenin improved mitochondrial membrane potential in both cell types (p < 0.05–0.01) and attenuated pro-inflammatory cytokines. IL-6 decreased in HUVECs (39.40 ± 5.02 to 27.15 ± 3.10 pg/mL; p < 0.01) and HDFa cells (40.05 ± 2.23 to 16.41 ± 1.27 pg/mL; p < 0.0001). In HDFa’s, MMP-9 was reduced (403.43 ± 18.70 to 195.33 ± 11.02 pg/mL; p < 0.0001), while in HUVECs, wound closure was enhanced. Conclusion: Naringenin-loaded Na-AMPS hydrogels demonstrated sustained release, suitable mechanical properties, and significant antioxidant, anti-inflammatory, and wound healing effects. These findings highlight their therapeutic potential for diabetic wounds treatment
'This thing we call 'Consideration' in English Contract Law: Stabilisation, 'value' and the Will theory'
No full textThis paper examines the doctrine of consideration in English Law and in particular tries to proffer an approach to the doctrine which aims to stabilise the same by ironing out inconsistent approaches to the doctrine. The article suggests a new approach to divining consideration in English Contract Law. In this regard this new approach seeks to build on the classical 'will theory' which traditionally underpins contract formation
Poly(styrene- alt -maleic acid)-assisted Membrane Solubilization for Improved Immobilization and Catalytic Performance of Soybean Lipolytic Enzymes in Electrospun Poly(vinyl alcohol) Fibers
Get PDFEfficient extraction and stabilization of plant-derived enzymes remain challenging due to their susceptibility to denaturation during processing. Soybean lipases, while exhibiting intrinsically high activity, lose functionality rapidly in the presence of salts, organic solvents, or elevated temperatures, thereby limiting their direct industrial use. To address these challenges, we developed a poly(styrene-alt-maleic acid) (PSMA)-assisted extraction and immobilization platform that simultaneously disrupts membranes and forms stable catalytic nanoparticles suitable for nanofiber fabrication. When applied to Glycine max (soybean) extracts, the PSMA-assisted process yielded the highest specific lipase activity of 16 mU/mg under optimized conditions (pH 7.5; mass-to-buffer volume ratio 1:25). Proteomic profiling identified 16 proteins showing significant abundance differences between conventional MOPS-buffered and PSMA/MOPS-assisted extractions, confirming selective stabilization of lipolytic enzymes. Morphological characterization revealed that the immobilized enzymes self-assembled into spherical, homogeneous nanoparticles with an average diameter of 227 nm. Incorporating 1% (w/v) of these nanoparticles into electrospun poly(vinyl alcohol) (PVA) fibers enhanced the enzyme activity by nearly 3-fold relative to the prespun solution, while maintaining comparable fiber size to the unloaded membranes (174 ± 65 nm vs 138 ± 31 nm, p > 0.05). By integrating the self-assembly behavior of PSMA with electrospun PVA nanofibers, this work demonstrates a scalable and effective route for preserving enzymatic function and fabricating ultrafine catalytic membranes for industrial biocatalysis
Blue-light-filtering spectacle lenses in managing vision-related symptoms: an updated review
Get PDFBlue light, emitted by natural and artificial sources such as digital screens, has raised concerns regarding its impact on ocular health, visual comfort, and circadian rhythms. Prolonged exposure has been linked to digital eye strain (DES), visual fatigue, potential retinal damage, and sleep disturbances. Blue-light-filtering spectacle lenses have been developed to mitigate these effects by reducing short-wavelength blue light transmission, but their efficacy remains debated. Studies indicate that these lenses have minimal or no significant impact on contrast sensitivity, color discrimination, and task performance, with visual outcomes comparable to standard lenses. While some research suggests minor benefits in reducing DES and visual fatigue in specific populations, most studies report no significant differences. This highlights the multifactorial nature of DES. Experimental evidence supports the potential for blue-light-filtering spectacle lenses to reduce oxidative stress and phototoxicity in retinal cells, which may offer protection against retinal damage and age-related macular degeneration (ARMD). Additionally, these lenses show promise in neurological and psychological domains, including reduced migraine frequency, alleviation of mania symptoms, and improved sleep quality through circadian rhythm regulation. However, subjective sleep improvements are often not supported by objective measures. In summary, blue-light-filtering spectacle lenses may provide benefits in retinal protection, sleep regulation, and neurological health. However, their effectiveness in reducing visual fatigue, enhancing task performance, and preventing ARMD remains inconclusive. Further research with standardized methodologies and larger sample sizes is needed to clarify their clinical and everyday utility
Dynamics of Nerve Conduction Studies in Patients With Guillain–Barré Syndrome
Get PDFIntroduction/Aims: The value of electrodiagnostic subtyping of Guillain–Barré syndrome (GBS) is still debated. This study aimed to determine the diagnostic yield, timing, and changes of the electrodiagnostic subtyping in patients with GBS in serial nerve conduction studies (NCS). Methods: Data were extracted from the International GBS Outcome Study (IGOS) database. Serial NCS were available for 469 patients. For the serial NCS analysis, the intervals between the first and second study were defined as ≥ 7 and ≤ 42 days after onset of weakness. All NCS were classified according to the electrodiagnostic criteria sets of Hadden et al. and Rajabally et al. Results: In NCS conducted within 3 days of onset of weakness, an axonal or demyelinating subtype could be demonstrated in 58.4% (Hadden) and 52.1% (Rajabally). NCS performed at a later timepoint demonstrated a similar yield of axonal and demyelinating subtypes. In patients with motor‐sensory and motor GBS, the electrodiagnostic subtype changed on serial NCS in 37.8% (Hadden) and 44.7% (Rajabally). As the subtypes changed in multiple and opposite directions, the total proportion of axonal and demyelinating subtypes remained stable across time points. In patients with motor GBS, both axonal and demyelinating subtypes were found. Discussion: This study demonstrates the highly dynamic disease course of GBS. The role of NCS remains to support the clinical diagnosis of GBS and should be performed as quickly as possible after onset of weakness. If these early NCS are non‐diagnostic, repeating the study should be considered. Electrodiagnostic subtyping offers no additional value
Advancing therapeutics with targeted formulations of hydrogen sulphide donors
Get PDFHydrogen sulphide (H2S), is a well described essential physiological molecule that is finely balanced to maintain cellular functions. Considering its important biological roles, H2S has promising therapeutic potential resulting in the development of many H2S donors. Such donors have proved to have therapeutic benefit in cognitive pathways, inflammation, reproduction, and the regulation of blood pressure. However, controlled delivery and targeted administration of this reactive and hazardous gas are necessary yet challenging due to its rapid diffusivity, and toxicity at high doses. Drug delivery systems are vital for the effective administration of many active pharmaceutical excipients, and H2S donors stands to benefit significantly from the tuneable physical, chemical, and pharmacokinetic properties of various formulation systems. To date, few studies have focused on the formulation and delivery aspects of H2S and its donors. Instead, H2S usually is administered either by inhalation or via site-specific injections of donor solution. Whilst therapeutic benefit has been observed following such administration, these are not patient friendly solutions. This review focuses on highlighting the advances in H2S donor formulations and their ability in sustaining the release of H2S as well as improving drug targeting
Human-Centered User Interface Design for Explainable AI in Chest Radiology: A Multi-Phase Co-Design Approach
Get PDFThe AI-powered computer vision is transforming medical imaging by improving analysis, diagnosis, and treatment. However, the opaque nature of deep learning often limits its adoption in critical clinical settings, where interpretability and trust are paramount. Explainable AI (XAI) aims at mitigating those limitations by creating visual and interpretable explanations of model decisions. However, poor user interface (UI) design frequently hampers the usability and clinical integration of these explanations. This paper addresses the critical need for human-centered UI design in XAI systems for chest radiology, a vital diagnostic domain for diseases such as pneumonia, lung cancer, and pulmonary embolism. Two deep learning–based XAI systems were developed for detecting pneumonia from chest X-rays and diagnosing COVID-19 from chest CT scans using post-hoc explanation methods, Gradient-weighted Class Activation Mapping (Grad-CAM) and Local Interpretable Model-Agnostic Explanations (LIME). Building on these systems, we introduce a novel multi-phase Human-Centered Design (HCD) methodology that actively involves radiologists and clinicians through participatory co-design, iterative prototyping, and multidisciplinary evaluation workshop. This process identified fifteen preliminary UI features tailored to clinical needs and led to a prototype XAI interface. Empirical evaluation from a multidisciplinary workshop revealed that radiologists preferred diagnostic prediction displays combining the original and AI-annotated images shown side-by-side or with adjustable overlays accompanied by explanatory text tailored to various audiences, including radiologists, clinicians, and patients. Participants agreed that highlighting the confidence scores of AI outputs aligned with clinical reasoning enhances perceived trust, diagnostic efficiency, and willingness to adopt XAI in daily practice. Our study demonstrates that collaborative multidisciplinary co-design is essential for bridging technical innovation and clinical utility, offering valuable insights for the future development of effective, trustworthy XAI user interfaces in medical imaging
CPFTransGAN: A Cross Perception Fusion Transformer-based Generative Adversarial Network for Head and Neck Cancer Dose Prediction in Radiotherapy
No full textRadiation therapy is one of the primary treatment modalities for head and neck (H&N) cancer in clinical practice, aiming to deliver sufficient dose to Planning Target Volume (PTV) while protecting surrounding Organs at Risk (OAR) from or minimizing exposure to radiation. Quantitative dose prediction of various tissues and organs is a prerequisite for implementing intelligent precision radiotherapy. In order to improve dose prediction accuracy, we propose a generative adversarial network CPFTrans- GAN based on Cross Perception Fusion Transformer (CPF Transformer). Specifically, we design a CPF Transformer module through deeply integrating CNN and Transformer. Using the CPF Transformer as basic unit, we constructed a generator with four-stage encoding-decoding structure called CPFTransGenerator. An adaptive weight loss is used to train the discriminator to alleviate the issues of imbalance training in adversarial learning. To further improve the prediction accuracy, a multiscale cross-window encoding network is designed, which can constrain the differences between predicted dose and the reference one at different granularity levels by calculating feature losses between them at different scales. The proposed method is evaluated on two public head and neck cancer datasets and a local clinical dataset. Extensive experiments demonstrate the superior performance of our method compared with the state-of-the-art ones
Evaluating the influence of grass distribution patterns on runoff and sediment yield dynamics: A flow path length perspective
No full textVegetation distribution patterns exert a first-order control on hillslope hydrology and erosion; however, the mechanisms by which spatial heterogeneity in vegetation regulates runoff generation and sediment yield remain inadequately understood. This knowledge gap constrains the development of physically based erosion models and effective soil conservation strategies. To elucidate how heterogeneous vegetation distributions govern hillslope hydrological connectivity and associated runoff–erosion processes, rainfall–runoff plot experiments were conducted under five vegetation distribution patterns—vertical strips (VS), horizontal strips (HS), X-shaped strips (XS), chessboard uniform distribution (CD), and random patchy distribution (RP)—with a bare slope (BS) serving as the control. Hydrological connectivity was quantified using relative flow path length (RFL), allowing systematic assessment of its influence on overland flow hydraulics, runoff and sediment yield. Results show that key hydrodynamic parameters respond nonlinearly to RFL and are well described by quadratic relationships (adjusted R² > 0.70). Mean flow velocity (v), stream power (ω), and unit energy (E) initially increased and subsequently declined with increasing RFL, reaching extreme values at RFL = 1. Under rainfall intensities of 60–120 mm·h⁻¹ , v, ω, and E increased by 100–114 %, 54–79 %, and 18–38 %, respectively. In contrast, flow resistance (f) and shear stress (τ) exhibited inverse responses, decreasing by 78–85 % and 11–29 % under the same conditions. Erosion rate (ER) also displayed a pronounced nonlinear response to RFL: as RFL increased from 0.513 to 1, ER rose by 65–118 %, with the sensitivity of ER to RFL diminishing at higher rainfall intensities. Building on these relationships, an erosion rate model coupling stream power ω and RFL was developed and validated using multi-source datasets. The model exhibited strong predictive skill and robustness, with adjusted R² and Nash–Sutcliffe efficiency (NSE) values exceeding 0.75, and substantially outperformed the Water Erosion Prediction Project (WEPP) model (adjusted R² = 0.316; NSE = −0.283). Overall, this study establishes a clear mechanistic link between vegetation-induced heterogeneity in hillslope hydrological connectivity and erosion dynamics, providing new insights for improving erosion modeling and designing vegetation-based soil conservation measures
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