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Classical mathematical models of tumor growth have shaped our understanding of cancer and have broad practical implications for treatment scheduling and dosage. However, even the simplest textbook models have been barely validated in real world-data of human patients. In this study, we fitted a range of differential equation models to tumor volume measurements of patients undergoing chemotherapy or cancer immunotherapy for solid tumors. We used a large dataset of 1472 patients with three or more measurements per target lesion, of which 652 patients had six or more data points. We show that the early treatment response shows only moderate correlation with the final treatment response, demonstrating the need for nuanced models. We then perform a head-to-head comparison of six classical models which are widely used in the field: the Exponential, Logistic, Classic Bertalanffy, General Bertalanffy, Classic Gompertz and General Gompertz model. Several models provide a good fit to tumor volume measurements, with the Gompertz model providing the best balance between goodness of fit and number of parameters. Similarly, when fitting to early treatment data, the general Bertalanffy and Gompertz models yield the lowest mean absolute error to forecasted data, indicating that these models could potentially be effective at predicting treatment outcome. In summary, we provide a quantitative benchmark for classical textbook models and state-of-the art models of human tumor growth. We publicly release an anonymized version of our original data, providing the first benchmark set of human tumor growth data for evaluation of mathematical models.

Background Running gait assessment has traditionally been performed using subjective observation or expensive laboratory-based objective technologies, such as three-dimensional motion capture or force plates. However, recent developments in wearable devices allow for continuous monitoring and analysis of running mechanics in any environment. Objective measurement of running gait is an important (clinical) tool for injury assessment and provides measures that can be used to enhance performance. Objectives We aimed to systematically review the available literature investigating how wearable technology is being used for running gait analysis in adults. Methods A systematic search of the literature was conducted in the following scientific databases: PubMed, Scopus, Web of Science and SPORTDiscus. Information was extracted from each included article regarding the type of study, participants, protocol, wearable device(s), main outcomes/measures, analysis and key findings. Results A total of 131 articles were reviewed: 56 investigated the validity of wearable technology, 22 examined the reliability and 77 focused on applied use. Most studies used inertial measurement units ( n  = 62) [i.e. a combination of accelerometers, gyroscopes and magnetometers in a single unit] or solely accelerometers ( n  = 40), with one using gyroscopes alone and 31 using pressure sensors. On average, studies used one wearable device to examine running gait. Wearable locations were distributed among the shank, shoe and waist. The mean number of participants was 26 (± 27), with an average age of 28.3 (± 7.0) years. Most studies took place indoors ( n  = 93), using a treadmill ( n  = 62), with the main aims seeking to identify running gait outcomes or investigate the effects of injury, fatigue, intrinsic factors (e.g. age, sex, morphology) or footwear on running gait outcomes. Generally, wearables were found to be valid and reliable tools for assessing running gait compared to reference standards. Conclusions This comprehensive review highlighted that most studies that have examined running gait using wearable sensors have done so with young adult recreational runners, using one inertial measurement unit sensor, with participants running on a treadmill and reporting outcomes of ground contact time, stride length, stride frequency and tibial acceleration. Future studies are required to obtain consensus regarding terminology, protocols for testing validity and the reliability of devices and suitability of gait outcomes. Clinical Trial Registration CRD42021235527.

AbstractSensory polyneuropathies, which are caused by dysfunction of peripheral sensory nerve fibers, are a heterogeneous group of disorders that range from the common diabetic neuropathy to the rare sensory neuronopathies. The presenting symptoms, acuity, time course, severity, and subsequent morbidity vary and depend on the type of fiber that is affected and the underlying cause. Damage to small thinly myelinated and unmyelinated nerve fibers results in neuropathic pain, whereas damage to large myelinated sensory afferents results in proprioceptive deficits and ataxia. The causes of these disorders are diverse and include metabolic, toxic, infectious, inflammatory, autoimmune, and genetic conditions. Idiopathic sensory polyneuropathies are common although they should be considered a diagnosis of exclusion. The diagnostic evaluation involves electrophysiologic testing including nerve conduction studies, histopathologic analysis of nerve tissue, serum studies, and sometimes autonomic testing and cerebrospinal fluid analysis. The treatment of these diseases depends on the underlying cause and may include immunotherapy, mitigation of risk factors, symptomatic treatment, and gene therapy, such as the recently developed RNA interference and antisense oligonucleotide therapies for transthyretin familial amyloid polyneuropathy. Many of these disorders have no directed treatment, in which case management remains symptomatic and supportive. More research is needed into the underlying pathophysiology of nerve damage in these polyneuropathies to guide advances in treatment.

Microsatellite instability determines whether patients with gastrointestinal cancer respond exceptionally well to immunotherapy. However, in clinical practice, not every patient is tested for MSI, because this requires additional genetic or immunohistochemical tests. Here we show that deep residual learning can predict MSI directly from H&E histology, which is ubiquitously available. This approach has the potential to provide immunotherapy to a much broader subset of patients with gastrointestinal cancer. A deep residual learning framework identifies microsatellite instability in histology slides from patients with cancer and can be used to guide immunotherapy.

Large language models such as ChatGPT can produce increasingly realistic text, with unknown information on the accuracy and integrity of using these models in scientific writing. We gathered fifth research abstracts from five high-impact factor medical journals and asked ChatGPT to generate research abstracts based on their titles and journals. Most generated abstracts were detected using an AI output detector, ‘GPT-2 Output Detector’, with % ‘fake’ scores (higher meaning more likely to be generated) of median [interquartile range] of 99.98% ‘fake’ [12.73%, 99.98%] compared with median 0.02% [IQR 0.02%, 0.09%] for the original abstracts. The AUROC of the AI output detector was 0.94. Generated abstracts scored lower than original abstracts when run through a plagiarism detector website and iThenticate (higher scores meaning more matching text found). When given a mixture of original and general abstracts, blinded human reviewers correctly identified 68% of generated abstracts as being generated by ChatGPT, but incorrectly identified 14% of original abstracts as being generated. Reviewers indicated that it was surprisingly difficult to differentiate between the two, though abstracts they suspected were generated were vaguer and more formulaic. ChatGPT writes believable scientific abstracts, though with completely generated data. Depending on publisher-specific guidelines, AI output detectors may serve as an editorial tool to help maintain scientific standards. The boundaries of ethical and acceptable use of large language models to help scientific writing are still being discussed, and different journals and conferences are adopting varying policies.

Inter-limb asymmetry refers to an imbalance in performance between the left and right limbs. Discrepancies throughout asymmetry research does not allow practitioners to confidently understand the effect of inter-limb asymmetries on athletic performance. Therefore, this review summarized the current literature using a meta-analytic approach, conforming to the Preferred Reporting Items for Systematic Review and Meta-Analyses guidelines to identify the association between inter-limb asymmetry and athletic performance. A literature search using PubMed, Web of Science and SPORTDiscus databases yielded 11-studies assessing the effect of inter-limb asymmetries, measured via unilateral jump performance, on bilateral jump, change of direction (COD) and sprint performance in adult sports players. The quality of evidence was assessed via a modified Downs and Black checklist and in compliance with the Grading of Recommendations Assessment Development and Evaluation. Correlation coefficients were transformed via Fishers z ( Z r ), meta-analysed and then re-converted to correlation coefficients. Egger’s regression presented no significant risk of bias. Vertical jump performance was not significantly affected by asymmetry ( Z r = 0.053, r = 0.05; P = 0.874), whereas COD and sprint both presented significant weak associations (COD, Z r = 0.243, r = 0.24; Sprint, Z r = 0.203, r = 0.2; P < 0.01). The results demonstrate that inter-limb asymmetries seem to present a negative impact to COD and sprint performance but not vertical jump performance. Practitioners should consider implementing monitoring strategies to identify, monitor and possibly address inter-limb asymmetries, specifically for performance tests underpinned by unilateral movements such as COD and sprint performance.

The Cancer Genome Atlas (TCGA) is one of the largest biorepositories of digital histology. Deep learning (DL) models have been trained on TCGA to predict numerous features directly from histology, including survival, gene expression patterns, and driver mutations. However, we demonstrate that these features vary substantially across tissue submitting sites in TCGA for over 3,000 patients with six cancer subtypes. Additionally, we show that histologic image differences between submitting sites can easily be identified with DL. Site detection remains possible despite commonly used color normalization and augmentation methods, and we quantify the image characteristics constituting this site-specific digital histology signature. We demonstrate that these site-specific signatures lead to biased accuracy for prediction of features including survival, genomic mutations, and tumor stage. Furthermore, ethnicity can also be inferred from site-specific signatures, which must be accounted for to ensure equitable application of DL. These site-specific signatures can lead to overoptimistic estimates of model performance, and we propose a quadratic programming method that abrogates this bias by ensuring models are not trained and validated on samples from the same site. Deep learning models have been trained on The Cancer Genome Atlas to predict numerous features directly from histology, including survival, gene expression patterns, and driver mutations. Here, the authors demonstrate that site-specific histologic signatures can lead to biased estimates of accuracy for such models, and propose a method to minimize such bias.

A model’s ability to express its own predictive uncertainty is an essential attribute for maintaining clinical user confidence as computational biomarkers are deployed into real-world medical settings. In the domain of cancer digital histopathology, we describe a clinically-oriented approach to uncertainty quantification for whole-slide images, estimating uncertainty using dropout and calculating thresholds on training data to establish cutoffs for low- and high-confidence predictions. We train models to identify lung adenocarcinoma vs. squamous cell carcinoma and show that high-confidence predictions outperform predictions without uncertainty, in both cross-validation and testing on two large external datasets spanning multiple institutions. Our testing strategy closely approximates real-world application, with predictions generated on unsupervised, unannotated slides using predetermined thresholds. Furthermore, we show that uncertainty thresholding remains reliable in the setting of domain shift, with accurate high-confidence predictions of adenocarcinoma vs. squamous cell carcinoma for out-of-distribution, non-lung cancer cohorts. Safe clinical deployment of deep learning models for digital pathology requires reliable estimates of predictive uncertainty. Here the authors describe an algorithm for quantifying whole-slide image uncertainty, demonstrating their approach with models trained to distinguish lung cancer subtypes.

Fourier Transform Infrared (FTIR) micro-spectroscopy is an emerging technique for the biochemical analysis of tissues and cellular materials. It provides objective information on the holistic biochemistry of a cell or tissue sample and has been applied in many areas of medical research. However, it has become apparent that how the tissue is handled prior to FTIR micro-spectroscopic imaging requires special consideration, particularly with regards to methods for preservation of the samples. We have performed FTIR micro-spectroscopy on rodent heart and liver tissue sections (two spectroscopically very different biological tissues) that were prepared by desiccation drying, ethanol substitution and formalin fixation and have compared the resulting spectra with that of fully hydrated freshly excised tissues. We have systematically examined the spectra for any biochemical changes to the native state of the tissue caused by the three methods of preparation and have detected changes in infrared (IR) absorption band intensities and peak positions. In particular, the position and profile of the amide I, key in assigning protein secondary structure, changes depending on preparation method and the lipid absorptions lose intensity drastically when these tissues are hydrated with ethanol. Indeed, we demonstrate that preserving samples through desiccation drying, ethanol substitution or formalin fixation significantly alters the biochemical information detected using spectroscopic methods when compared to spectra of fresh hydrated tissue. It is therefore imperative to consider tissue preparative effects when preparing, measuring, and analyzing samples using FTIR spectroscopy.