Yoxas/statistical_literacy huggingface dataset:
Lung cancer is the leading cause of cancer death with the majority of cases being non-small cell lung cancer (NSCLC) [1]. A common complication of NSCLC is brain metastasis (BM) [2, 3], where the prognosis remains poor despite new treatments. Real world data complements data gained from clinical trials, providing information on patients excluded from prospective research [4]. However, information from patient notes may prove incomplete and difficult to extract. We developed an algorithm to identify patients in our clinical database with brain metastasis from the electronic health record (EHR). We retrospectively extracted data from the EHR of patients managed at a large teaching hospital between 2007 and 2018. Using the ICD-10 code C34, for lung cancer, our algorithm used phrases associated with BMs to search the unstructured text of radiology reports. Summary statistics and univariant analysis was performed for overall survival. 818 patients were identified as potentially having BM and 453 patients were confirmed on clinical review of their records. The median age of patients was 69 years, 50% were female and 66% had a performance status of >2. 12.2% had an identifiable mutation and 11.5% were identified as PD-L1 positive. In the first line setting, 65% of patients received symptomatic treatment, 23% received systemic anticancer therapy (SACT), 6.1% surgery and 10% radiotherapy, of which 6.5% had external beam and 3.5% stereotactic radiosurgery. Regarding those treated with SACT, 35% had an intracranial response to treatment (3% had complete response, 32% had a partial response). Median survival was 2 months (1.9 - 2.4 months 95% CI). The real-world prognosis for NSCLC patients with BMs is poor. By using an algorithm, we have reported outcomes on a comprehensive cohort of patients which helps identify those for whom an active treatment approach is appropriate.
Lung cancer is the most common cause of cancer-related death in Michigan. Most patients are diagnosed at advanced stages of the disease. There is a need to detect clusters of lung cancer incidence over time, to generate new hypotheses about causation and identify high-risk areas for screening and treatment. The Michigan Cancer Surveillance database of individual lung cancer cases, 1985 to 2018 was used for this study. Spatial and spatiotemporal clusters of lung cancer and level of disease (localized, regional and distant) were detected using discrete Poisson spatial scan statistics at the zip code level over the study time period. The approach detected cancer clusters in cities such as Battle Creek, Sterling Heights and St. Clair County that occurred prior to year 2000 but not afterwards. In the northern area of the lower peninsula and the upper peninsula clusters of late-stage lung cancer emerged after year 2000. In Otter Lake Township and southwest Detroit, late-stage lung cancer clusters persisted. Public and patient education about lung cancer screening programs must remain a health priority in order to optimize lung cancer surveillance. Interventions should also involve programs such as telemedicine to reduce advanced stage disease in remote areas. In cities such as Detroit, residents often live near industry that emits air pollutants. Future research should therefore, continue to focus on the geography of lung cancer to uncover place-based risks and in response, the need for screening and health care services.
Lung cancer is the second most common cancer worldwide, with over two million new cases per year. Early identification would allow healthcare practitioners to handle it more effectively. The advancement of computer-aided detection systems significantly impacted clinical analysis and decision-making on human disease. Towards this, machine learning and deep learning techniques are successfully being applied. Due to several advantages, transfer learning has become popular for disease detection based on image data. In this work, we build a novel transfer learning model (VER-Net) by stacking three different transfer learning models to detect lung cancer using lung CT scan images. The model is trained to map the CT scan images with four lung cancer classes. Various measures, such as image preprocessing, data augmentation, and hyperparameter tuning, are taken to improve the efficacy of VER-Net. All the models are trained and evaluated using multiclass classifications chest CT images. The experimental results confirm that VER-Net outperformed the other eight transfer learning models compared with. VER-Net scored 91%, 92%, 91%, and 91.3% when tested for accuracy, precision, recall, and F1-score, respectively. Compared to the state-of-the-art, VER-Net has better accuracy. VER-Net is not only effectively used for lung cancer detection but may also be useful for other diseases for which CT scan images are available.
Lung cancer remains one of the leading cause of global cancer-related mortality, posing a significant burden of disease. Tobacco exposure stands as the foremost risk factor for lung cancer. Since the 1960, global efforts have gradually been implemented to control tobacco exposure, consequently reducing tobacco exposure levels within populations. This shift in exposure levels may have altered the epidemiological characteristics of lung cancer globally. This study aims to describe global lung cancer incidence data across five dimensions: age, gender, region, stage at diagnosis, and survival status, using global cancer registry data and relevant research findings. The objective is to elucidate the current epidemiological features of lung cancer worldwide, providing a scientific basis for lung cancer prevention and control. Furthermore, this study offers corresponding measures and recommendations for lung cancer prevention and control, aligning with the three-tiered cancer prevention strategy. Findings indicate that the incidence and mortality burden of lung cancer is significantly higher among the elderly population (aged 65 years and above) compared to the working-age population (aged 15-64 years). The aged-standardized incidence rate of lung cancer remains higher in males than in females, but the overall aged-standardized incidence rate of lung cancer in males shows a declining trend, while that in females shows an increasing trend. Regions with high and very high human development index (HDI) exhibit a substantially higher incidence and mortality burden of lung cancer compared to regions with low and very low HDI. Japan ranks highest in the diagnosis of stage Ⅰ lung cancer, with a diagnosis rate of 38.6%. Its age-standardized 5-year net survival rate is relatively high at 32.9%. Despite improvements in the survival status of lung cancer in certain countries like China and Japan, the overall prognosis for lung cancer remains pessimistic. Given the current epidemiological characteristics of lung cancer, reinforcing tobacco control measures and reducing female-specific lung cancer risk factors stand as significant goals for primary prevention. Promoting low-dose computed tomography screening for high-risk population, minimizing false-positive rates in lung cancer screening, and promoting medical system reforms and standardized treatment constitute principal measures for secondary and tertiary lung cancer prevention, respectively.
Lung function tests are valuable in assessing respiratory health and disease, and the Healthy China Initiative clearly states that people over 40 years of age should have a lung function test once a year. To explore the influence of propensity factors, ability factors, and need factors on lung function detection behaviors of middle-aged and elderly Chinese, the following studies are conducted. A questionnaire was designed using Anderson's model, and multi-stage sampling was used to conduct a nationwide questionnaire survey based on geographical subdivisions and population distribution. Frequency and percentages were used for descriptive statistical analysis of lung function testing among middle-aged and elderly people in China, and chi-square tests and binary logistic regression analyses were used to investigate the factors influencing lung function testing behavior among middle-aged and elderly people in China. A total of 404 study participants were included in this study. Education level (relative to primary school and below, middle school and high school and secondary school OR = 2.652, P = 0.018; college and above OR = 4.566, P = 0.002), mode of health care affordability (relative to those who paid for the test, non-payers OR = 2.205, P = 0.004), dimensions of the European Five Dimensional Health Scale (mobility OR = 4.571, P = 0.006; pain or discomfort OR = 0.397, P = 0.003; anxiety or depression OR = 0.511, P = 0.028), and self-efficacy (medium group 0R = 0.294, P < 0.001; low group OR = 0.162, P = 0.003) had a significant impact on lung function testing behavior in our middle-aged and older adults. This study found that there is still room for improvement in the participation of middle-aged and elderly people in lung function testing. Among the propensity factors, the factor that affects the rate of lung function tests is the highest degree of education, which determines the degree of patients' attention to lung function tests. Among the need factors, the factors affecting the rate of lung function detection are the physical conditions of middle-aged and elderly people, and those with poor physical conditions need medical detection. Among the ability factors, the factor that affects the rate of lung function tests is the way of bearing medical expenses, and economic status is the key factor that determines whether patients can accept lung function tests.
Lung protective ventilation (LPV) is advocated for all patients requiring mechanical ventilation (MV), for any duration of time, to prevent worsening lung injury. Previous studies proved simple interventions can increase awareness of LPV and disease pathophysiology as well as improve adherence to LPV guidelines. To assess the impact of a multi-component LPV quality improvement project (QIP) on adherence to LPV guidelines. Tidal volume data for all patients requiring MV at a large, tertiary UK critical care unit were collected retrospectively over 3, 6 months, Plan-Do-Study-Act cycles between September 2019 and August 2022. These cycles included the sequential implementation of LPV reports, bedside whiteboards and targeted education led by a multispecialty working group. Adherence against predetermined targets of <5% of MV hours spent at >10 mL/kg predicted body weight (PBW) and >75% of MV hours spent <8 mL/kg PBW for all patients requiring MV. 408 949 hours (17 040 days) of MV data were analysed. Improved LPV adherence was demonstrated throughout the QIP. During mandated MV, time spent >10 mL/kg PBW reduced from 7.65% of MV hours to 4.04% and time spent <8 mL/kg PBW improved from 68.86% of MV hours to 71.87% following the QIP. During spontaneous MV, adherence improved with a reduction in time spent >10 mL/kg PBW from baseline to completion (13.2% vs 6.75%) with increased time spent <8 mL/kg PBW (62.74% vs 72.25%). Despite demonstrating improvements in adherence, we were unable to achieve success in all our predetermined targets. This multicomponent intervention including the use of LPV reports, bedside whiteboards and education improves adherence to LPV guidelines. More robust data analysis of reasons for non-adherence to our predetermined targets is required to guide future interventions that may allow further improvement in adherence to LPV guidelines.
Lyme disease is the most common vector-borne disease in the United States impacting the Northeast and Midwest at the highest rates. Recently, it has become established in southeastern and south-central regions of Canada. In these regions, Lyme disease is caused by Borrelia burgdorferi, which is transmitted to humans by an infected Ixodes scapularis tick. Understanding the parasite-host interaction is critical as the white-footed mouse is one of the most competent reservoir for B. burgdorferi. The cycle of infection is driven by tick larvae feeding on infected mice that molt into infected nymphs and then transmit the disease to another susceptible host such as mice or humans. Lyme disease in humans is generally caused by the bite of an infected nymph. The main aim of this investigation is to study how diapause delays and demographic and seasonal variability in tick births, deaths, and feedings impact the infection dynamics of the tick-mouse cycle. We model tick-mouse dynamics with fixed diapause delays and more realistic Erlang distributed delays through delay and ordinary differential equations (ODEs). To account for demographic and seasonal variability, the ODEs are generalized to a continuous-time Markov chain (CTMC). The basic reproduction number and parameter sensitivity analysis are computed for the ODEs. The CTMC is used to investigate the probability of Lyme disease emergence when ticks and mice are introduced, a few of which are infected. The probability of disease emergence is highly dependent on the time and the infected species introduced. Infected mice introduced during the summer season result in the highest probability of disease emergence.
Lymph node metastasis (LNM) is a significant prognostic factor in patients with head and neck cancer, and the ability to predict it accurately is essential for treatment optimization. PET and CT imaging are routinely used for LNM identification. However, uncertainties of LNM always exist especially for small size or reactive nodes. Radiomics and deep learning are the two preferred imaging-based strategies for node malignancy prediction. Radiomics models are built based on handcrafted features, and deep learning can learn the features automatically. We proposed a hybrid predictive model that combines many-objective radiomics (MO-radiomics) and 3-dimensional convolutional neural network (3D-CNN) through evidential reasoning (ER) approach. To build a more reliable model, we proposed a new many-objective radiomics model. Meanwhile, we designed a 3D-CNN that fully utilizes spatial contextual information. Finally, the outputs were fused through the ER approach. To study the predictability of the two modalities, three models were built for PET, CT, and PET& CT. The results showed that the model performed best when the two modalities were combined. Moreover, we showed that the quantitative results obtained from the hybrid model were better than those obtained from MO-radiomics and 3D-CNN.
Lymphoma is the sixth most common cancer in Australia and comprises 2.8% of worldwide cancer diagnoses. Research targeting development and evaluation of post-treatment care for debilitating complications resulting from the disease and its treatment is limited. This study aimed to assess the feasibility and acceptability of a nurse-led survivorship intervention, post-treatment in Hodgkin's and non-Hodgkin's lymphoma survivors. A single-center, prospective, 3-arm, pilot, randomized controlled, parallel-group trial was used. People with lymphoma were recruited and randomized to the intervention (ENGAGE), education booklet only, or usual care arm. Participants receiving ENGAGE received an educational booklet and were offered 3 consultations (via various modes) with a cancer nurse to develop a survivorship care plan and healthcare goals. Participant distress and intervention acceptability was measured at baseline and 12-wk. Acceptability was measured via a satisfaction survey using a 11-point scale. Feasibility was measured using participation, retention rates, and process outcomes. Data were analyzed using descriptive statistics. Thirty-four participants with HL and NHL were recruited to the study (11 = intervention, 11 = information only, 12 = usual care). Twenty-seven participants (79%) completed all time points from baseline to 12 wk. Seven (88%) of the 8 participants receiving ENGAGE completed all consultations using various modes to communicate with the nurse (videoconference 14/23, 61%; phone 5/23, 22%; face-to-face 4/23, 17%). Participants who completed the intervention were highly satisfied with ENGAGE. The ENGAGE intervention is feasible and highly acceptable for lymphoma survivors. These findings will inform a larger trial assessing effectiveness and cost effectiveness of ENGAGE.
Lysine acetylation has been primarily investigated in the context of transcriptional regulation, but a role for acetylation in mediating other cellular responses has emerged. Multiple studies have described global lysine acetylation profiles for particular biological states, but none to date have investigated the temporal dynamics regulating cellular response to perturbation. Reasoning that lysine acetylation may be altered in response to growth factors, we implemented quantitative mass spectrometry-based proteomics to investigate the temporal dynamics of lysine acetylation in response to growth factor stimulation in cultured carcinoma cell lines. We found that lysine acetylation changed rapidly in response to activation of several different receptor tyrosine kinases by their respective ligands. To uncover the effects of lysine acetylation dynamics on tyrosine phosphorylation signaling networks, cells were treated with an HDAC inhibitor. This short-term pharmacological inhibition of histone deacetylase activity modulated signaling networks involving phosphorylated tyrosine and thereby altered the response to receptor tyrosine kinase activation. This result highlights the interconnectivity of lysine acetylation and tyrosine phosphorylation signaling networks and suggests that HDAC inhibition may influence cellular responses by affecting both types of post-translational modifications.
Lysosomal acid sphingomyelinase (ASM), a critical enzyme in lipid metabolism encoded by the SMPD1 gene, plays a crucial role in sphingomyelin hydrolysis in lysosomes. ASM deficiency leads to acid sphingomyelinase deficiency, a rare genetic disorder with diverse clinical manifestations, and the protein can be found mutated in other diseases. We employed a structure-based framework to comprehensively understand the functional implications of ASM variants, integrating pathogenicity predictions with molecular insights derived from a molecular dynamics simulation in a lysosomal membrane environment. Our analysis, encompassing over 400 variants, establishes a structural atlas of missense variants of lysosomal ASM, associating mechanistic indicators with pathogenic potential. Our study highlights variants that influence structural stability or exert local and long-range effects at functional sites. To validate our predictions, we compared them to available experimental data on residual catalytic activity in 135 ASM variants. Notably, our findings also suggest applications of the resulting data for identifying cases suited for enzyme replacement therapy. This comprehensive approach enhances the understanding of ASM variants and provides valuable insights for potential therapeutic interventions.
Machine learning (ML) is widely used in classification tasks aimed at detecting various cognitive states or neurological diseases using noninvasive electroencephalogram (EEG) time series. However, successfully detecting specific cognitive skills in a healthy population, independent of subject, remains challenging. This study compared the subject-independent classification performance of three different pipelines: supervised and Riemann projections with logistic regression and handcrafted power spectral features with light gradient boosting machine (LightGBM). 128-channel EEGs were recorded from 26 healthy volunteers while they solved arithmetic, logical, and verbal tasks. The participants were divided into two groups based on their higher education and occupation: specialists in mathematics and humanities. The balanced accuracy of the education type was significantly above chance for all pipelines: 0.84-0.89, 0.85-0.88, and 0.86-0.88 for each type of task, respectively. All three pipelines allowed us to distinguish mathematical proficiency based on learning experience with different trade-offs between performance and explainability. Our results suggest that ML approaches could also be effective for recognizing individual cognitive traits using EEG.
Machine learning (ML) models are widely employed for crash severity modelling, yet their interpretability remains underexplored. Interpretation is crucial for comprehending ML results and aiding informed decision-making. This study aims to implement an interpretable ML to visualize the impacts of factors on crash severity using 5 years of freeways data from Iran. Methods including classification and regression trees (CART), K-nearest neighbours (KNNs), random forest (RF), artificial neural network (ANN) and support vector machines (SVM) were applied, with RF demonstrating superior accuracy, recall, F1-score and ROC. The accumulated local effects (ALE) were utilized for interpretation. Findings suggest that light traffic conditions (volume/capacity<0.5) with critical values around 0.05 or 0.38, and higher proportion of large trucks and buses, particularly at 10% and 4%, are associated with severe crashes. Additionally, speeds exceeding 90 km/h, drivers younger than 30 years, rollover crashes, collisions with fixed objects and barriers, nighttime driving and driver fatigue elevate the likelihood of severe crashes.
Machine learning (ML) offers potential opportunities to enhance the learning, teaching, and assessments within veterinary medical education including but not limited to assisting with admissions processes as well as student progress evaluations. The purpose of this primer is to assist veterinary educators in appraising and potentially adopting these rapid upcoming advances in data science and technology. In the first section, we introduce ML concepts and highlight similarities/differences between ML and classical statistics. In the second section, we provide a step-by-step worked example using simulated veterinary student data to answer a hypothesis-driven question. Python syntax with explanations is provided within the text to create a random forest ML prediction model, a model composed of decision trees with each decision tree being composed of nodes and leaves. Within each step of the model creation, specific considerations such as how to manage incomplete student records are highlighted when applying ML algorithms within the veterinary education field. The results from the simulated data demonstrate how decisions by the veterinary educator during ML model creation may impact the most important features contributing to the model. These results highlight the need for the veterinary educator to be fully transparent during the creation of ML models and future research is needed to establish guidelines for handling data not missing at random in medical education, and preferred methods for model evaluation.