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METHODS: Systematic literature review.
OBJECTIVE: To develop a predictive model for osteoporotic vertebral compression fractures (OVCF) in the elderly, utilizing current tools that are sensitive to bone and paraspinal muscle changes.
METHODS: A retrospective analysis of data from 260 patients from October 2020 to December 2022, to form the Model population. This group was split into Training and Testing sets. The Training set aided in creating a nomogram through binary logistic regression. From January 2023 to January 2024, we prospectively collected data from 106 patients to constitute the Validation population. The model\'s performance was evaluated using concordance index (C-index), calibration curves, and decision curve analysis (DCA) for both internal and external validation.
RESULTS: The study included 366 patients. The Training and Testing sets were used for nomogram construction and internal validation, while the prospectively collected data was for external validation. Binary logistic regression identified nine independent OVCF risk factors: age, bone mineral density (BMD), quantitative computed tomography (QCT), vertebral bone quality (VBQ), relative functional cross-sectional area of psoas muscles (rFCSAPS), gross and functional muscle fat infiltration of multifidus and psoas muscles (GMFIES+MF and FMFIES+MF), FMFIPS, and mean muscle ratio. The nomogram showed an area under the curve (AUC) of 0.91 for the C-index, with internal and external validation AUCs of 0.90 and 0.92. Calibration curves and DCA indicated a good model fit.
CONCLUSIONS: This study identified nine factors as independent predictors of OVCF in the elderly. A nomogram including these factors was developed, proving effective for OVCF prediction.

OBJECTIVE: This review aims to provide clinical guidance and inform future research by examining risk factors and predictive models for early neurological deterioration in patients with ischemic stroke.
METHODS: Up to December 20, 2023, a comprehensive search was conducted across PubMed, Embase, Web of Science, MedLine, and The Cochrane Library for studies focusing on predictive models for early neurological deterioration in acute stroke patients. Included studies either developed or validated predictive models. The PROBAST tool was utilized to assess bias in these prediction models. Pooled area under the curve (AUC) values were calculated using DerSimonian and Laird random effects models.
RESULTS: Nineteen studies, each presenting an original model, were identified. Predominantly constructed through logistic multiple regression, these models demonstrated robust predictive performance (AUC ≥ 0.80). Key predictors of early neurological deterioration in acute ischemic stroke patients included blood glucose levels, baseline National Institute of Health Stroke Scale (NIHSS) scores, extent of cerebral infarction, and stenosis in the carotid and middle cerebral arteries.
CONCLUSIONS: Clinical practitioners should closely monitor high-frequency predictors of early neurological deterioration in patients. However, the varying quality of current models necessitates the selection of models that balance performance with operational simplicity in clinical practice.

Intrauterine growth restriction (IUGR) is defined as inadequate foetal growth during gestation. In response to placenta insufficiency, IUGR piglets prioritise brain development as a survival mechanism. This adaptation leads to a higher brain-to-liver weight ratio (BrW/LW) at birth. This study assessed the potential of using morphometric traits to estimate brain (BrW) and liver (LW) weights, enabling non-invasive diagnosis of IUGR in newborn piglets. At birth, body weight (BtW) of individual piglets (n = 144) was recorded. One day (± 1) after birth, BrW and LW were measured with computed tomography (n = 94) or by weighing the organs after natural death or euthanasia (n = 50). Additionally, 20 morphometric traits were captured from images of each piglet and correlated with the BrW and LW. The morphometric traits that showed a r ≥ 0.70 in linear correlation with the BrW or LW were selected. Each selected trait was combined as an independent variable with BtW to develop multiple linear regression models to predict the BrW and LW. Six models were chosen based on the highest adjusted R2 value: three for estimating BrW and three for LW. The dataset was then randomly divided into a training (75% of the data) and a testing (remaining 25%) subsets. Within the training subset, three equations to predict the BrW and three to predict the LW were extrapolated from the six selected models. The equations were then applied to the testing subset. The accuracy of the equations in predicting organ weight was assessed by calculating mean absolute and mean absolute percentage error (MAE and MAPE) between predicted and actual BrW and LW. To predict the BrW/LW, an equation including BtW and the two morphometric traits which better predicted BrW and LW was used. In the testing dataset, the equation combining ear distance and BtW better estimated the BrW. The equation performed with a MAE of 1.95 and a MAPE of 0.06 between the true and estimated weight of the brain. For the liver, the equation combining the abdominal area delimited by a square and BtW displayed the best performance, with a MAE of 9.29 and a MAPE of 0.17 between the true and estimated weight. Finally, the MAE and MAPE between the actual and estimated BrW/LW were 0.14 and 0.17, respectively. These findings suggest that specific morphometric traits can be used to estimate brain and liver weights, facilitating accurate and non-invasive identification of IUGR in newborn piglets.

BACKGROUND: Telecare and telehealth are important care-at-home services used to support individuals to live more independently at home. Historically, these technologies have reactively responded to issues. However, there has been a recent drive to make better use of the data from these services to facilitate more proactive and predictive care.
OBJECTIVE: This review seeks to explore the ways in which predictive data analytics techniques have been applied in telecare and telehealth in at-home settings.
METHODS: The PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist was adhered to alongside Arksey and O\'Malley\'s methodological framework. English language papers published in MEDLINE, Embase, and Social Science Premium Collection between 2012 and 2022 were considered and results were screened against inclusion or exclusion criteria.
RESULTS: In total, 86 papers were included in this review. The types of analytics featuring in this review can be categorized as anomaly detection (n=21), diagnosis (n=32), prediction (n=22), and activity recognition (n=11). The most common health conditions represented were Parkinson disease (n=12) and cardiovascular conditions (n=11). The main findings include: a lack of use of routinely collected data; a dominance of diagnostic tools; and barriers and opportunities that exist, such as including patient-reported outcomes, for future predictive analytics in telecare and telehealth.
CONCLUSIONS: All papers in this review were small-scale pilots and, as such, future research should seek to apply these predictive techniques into larger trials. Additionally, further integration of routinely collected care data and patient-reported outcomes into predictive models in telecare and telehealth offer significant opportunities to improve the analytics being performed and should be explored further. Data sets used must be of suitable size and diversity, ensuring that models are generalizable to a wider population and can be appropriately trained, validated, and tested.

With the rapid spread of the novel coronavirus (COVID-19), a sustained global pandemic has emerged. Globally, the cumulative death toll is in the millions. The rising number of COVID-19 infections and deaths has severely impacted the lives of people worldwide, healthcare systems, and economic development. We conducted a retrospective analysis of the characteristics of COVID-19 patients. This analysis includes clinical features upon initial hospital admission, relevant laboratory test results, and imaging findings. We aimed to identify risk factors for severe illness and to construct a predictive model for assessing the risk of severe COVID-19. We collected and analyzed electronic medical records of confirmed COVID-19 patients admitted to the Affiliated Hospital of Jiangsu University (Zhenjiang, China) between December 18, 2022, and February 28, 2023. According to the WHO diagnostic criteria for the novel coronavirus, we divided the patients into two groups: severe and non-severe, and compared their clinical, laboratory, and imaging data. Logistic regression analysis, the least absolute shrinkage and selection operator (LASSO) regression, and receiver operating characteristic (ROC) curve analysis were used to identify the relevant risk factors for severe COVID-19 patients. Patients were divided into a training cohort and a validation cohort. A nomogram model was constructed using the \"rms\" package in R software. Among the 346 patients, the severe group exhibited significantly higher respiratory rates, breathlessness, altered consciousness, neutrophil-to-lymphocyte ratio (NLR), and lactate dehydrogenase (LDH) levels compared to the non-severe group. Imaging findings indicated that the severe group had a higher proportion of bilateral pulmonary inflammation and ground-glass opacities compared to the non-severe group. NLR and LDH were identified as independent risk factors for severe patients. The diagnostic performance was maximized when NLR, respiratory rate (RR), and LDH were combined. Based on the statistical analysis results, we developed a COVID-19 severity risk prediction model. The total score is calculated by adding up the scores for each of the twelve independent variables. By mapping the total score to the lowest scale, we can estimate the risk of COVID-19 severity. In addition, the calibration plots and DCA analysis showed that the nomogram had better discrimination power for predicting the severity of COVID-19. Our results showed that the development and validation of the predictive nomogram had good predictive value for severe COVID-19.

UNASSIGNED: Thyroid cancer is the most common malignancy of the endocrine system. PANoptosis is a specific form of inflammatory cell death. It mainly includes pyroptosis, apoptosis and necrotic apoptosis. There is increasing evidence that PANoptosis plays a crucial role in tumour development. However, no pathogenic mechanism associated with PANoptosis in thyroid cancer has been identified.
UNASSIGNED: Based on the currently identified PANoptosis genes, a dataset of thyroid cancer patients from the GEO database was analysed. To screen the common differentially expressed genes of thyroid cancer and PANoptosis. To analyse the functional characteristics of PANoptosis-related genes (PRGs) and screen key expression pathways. The prognostic model was established by LASSO regression and key genes were identified. The association between hub genes and immune cells was evaluated based on the CIBERSORT algorithm. Predictive models were validated by validation datasets, immunohistochemistry as well as drug-gene interactions were explored.
UNASSIGNED: The results showed that eight key genes (NUAK2, TNFRSF10B, TNFRSF10C, TNFRSF12A, UNC5B, and PMAIP1) exhibited good diagnostic performance in differentiating between thyroid cancer patients and controls. These key genes were associated with macrophages, CD4+ T cells and neutrophils. In addition, PRGs were mainly enriched in the immunomodulatory pathway and TNF signalling pathway. The predictive performance of the model was confirmed in the validation dataset. The DGIdb database reveals 36 potential therapeutic target drugs for thyroid cancer.
UNASSIGNED: Our study suggests that PANoptosis may be involved in immune dysregulation in thyroid cancer by regulating macrophages, CD4+ T cells and activated T and B cells and TNF signalling pathways. This study suggests potential targets and mechanisms for thyroid cancer development.

Stargardt disease is the most common form of juvenile-onset macular dystrophy. Spectral-domain optical coherence tomography (SD-OCT) imaging provides an opportunity to directly measure changes to retinal layers due to Stargardt atrophy. Generally, atrophy segmentation and prediction can be conducted using mean intensity feature maps generated from the relevant retinal layers. In this paper, we report an approach using advanced OCT-derived features to augment and enhance data beyond the commonly used mean intensity features for enhanced prediction of Stargardt atrophy with an ensemble deep learning neural network. With all the relevant retinal layers, this neural network architecture achieves a median Dice coefficient of 0.830 for six-month predictions and 0.828 for twelve-month predictions, showing a significant improvement over a neural network using only mean intensity, which achieved Dice coefficients of 0.744 and 0.762 for six-month and twelve-month predictions, respectively. When using feature maps generated from different layers of the retina, significant differences in performance were observed. This study shows promising results for using multiple OCT-derived features beyond intensity for assessing the prognosis of Stargardt disease and quantifying the rate of progression.

Migratory fish are very important species from an ecological and socioeconomic point of view, but they suffer the effects of many threats such as climate change, pollution, or overfishing, thus contributing to the decline of these species. To study the main factors influencing these species, Partial Least Squares Path Modelling (PLS-PM) methodology has been used to analyse and quantify the main threats facing two highly relevant migratory species: the eel (Anguilla anguilla) and the sea lamprey (Petromyzon marinus). Based on this statistical approach, two models have been developed for a total of 14 rivers located in the Autonomous Community of Galicia (NW Spain), one for the eel and the other for the lamprey. For the construction of the models, the influence of environmental factors, surface water quality and anthropogenic impacts on the population of these species has been studied. Two scenarios have also been simulated to assess how the application of corrective measures to reduce the anthropogenic impact implies important improvements to the eel and lamprey populations. The results of the models developed indicate that the variables analysed predict 69% of the eel \"Population\", with the weight of the measured variables (MV) \'Water treatment plants\' having the most substantial weight (W=0.939) followed by the significant negative influence of \'Surface area of reservoirs and rivers\' (W=-0.746). Similarly, in the lamprey model, an R2 of 0.58 has been obtained, where the negative influence of the MV \"Agricultural nitrate discharge points\" (-0.938) stands out substantially. In relation to the scenarios developed for both species, we highlight that the application of measures aimed at reducing anthropogenic pressure manages to mitigate the impact by 4.82% in the case of eel and by 1.37% in the case of lamprey. The set of models and scenarios proposed will make it possible to design preventive and corrective measures to mitigate the impacts affecting these populations, guaranteeing the integrated management of these species, and improving future decision-making, thus strengthening environmental governance.

UNASSIGNED: Coronavirus disease 2019 (COVID-19) emerged as a global pandemic during 2019 to 2022. The gold standard method of detecting this disease is reverse transcription-polymerase chain reaction (RT-PCR). However, RT-PCR has a number of shortcomings. Hence, the objective is to propose a cheap and effective method of detecting COVID-19 infection by using machine learning (ML) techniques, which encompasses five basic parameters as an alternative to the costly RT-PCR.
UNASSIGNED: Two machine learning-based predictive models, namely, Artificial Neural Network (ANN) and Multivariate Adaptive Regression Splines (MARS), are designed for predicting COVID-19 infection as a cheaper and simpler alternative to RT-PCR utilizing five basic parameters [i.e., age, total leucocyte count, red blood cell count, platelet count, C-reactive protein (CRP)]. Each of these parameters was studied, and correlation is drawn with COVID-19 diagnosis and progression. These laboratory parameters were evaluated in 171 patients who presented with symptoms suspicious of COVID-19 in a hospital at Kharagpur, India, from April to August 2022. Out of a total of 171 patients, 88 and 83 were found to be COVID-19-negative and COVID-19-positive, respectively.
UNASSIGNED: The accuracies of the predicted class are found to be 97.06% and 91.18% for ANN and MARS, respectively. CRP is found to be the most significant input parameter. Finally, two predictive mathematical equations for each ML model are provided, which can be quite useful to detect the COVID-19 infection easily.
UNASSIGNED: It is expected that the present study will be useful to the medical practitioners for predicting the COVID-19 infection in patients based on only five very basic parameters.