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Background Breast cancer is currently the most common neoplasm diagnosed in women globally. There is a growing body of evidence to suggest that human papillomavirus (HPV) infection may play a key role in invasiveness of breast cancer. The aim of this study was to determine the presence of HPV in patients with breast cancer and its possible association with cancer progression. Methods Breast specimens were collected from 72 patients with breast cancer and 31 healthy controls. The presence of HPV was investigated by polymerase chain reaction (PCR) and genotyping was performed for positive cases. We also evaluated the viral factors such as E6, E2, and E7 in HPV positive cases. Enzyme-linked immunosorbent assay (ELISA (and Real-time PCR techniques were used to measure the expression level of anti-carcinogenic genes, such as p53 , retinoblastoma ( RB ), breast and ovarian cancer susceptibility gene ( BRCA1 , BRCA2) and inflammatory cytokines, including tumor necrosis factor α (TNF-α), transforming growth factor β (TGF-β), nuclear factor-kB (NF-kB), and different interleukins [ILs] (IL-1,IL6, and IL-17). Results The HPV DNA was detected in 48.6% of breast cancer samples, whereas only 16.1% of controls were positive for HPV. We observed statistically significant differences between breast cancer patients and HPV presence ( P = 0.003 ). HPV type 18 was the most prevalent virus genotype in patients. The expression of P53 , RB , BRCA1 , and BRCA2 were decreased in patients with HPV-positive breast cancer as compared to HPV-negative breast cancer and healthy controls. (All P-values were less than 0.05). The presence of the HPV was associated with increased inflammatory cytokines (IL-1, IL-6, IL-17, TGF-β, TNF-α, and NF-kB) and tumor progression. Conclusion The present study demonstrated that HPV infection may implicate in the development of some types of breast cancer.

We aimed to construct a prediction model based on computed tomography (CT) radiomics features to classify COVID-19 patients into severe-, moderate-, mild-, and non-pneumonic. A total of 1110 patients were studied from a publicly available dataset with 4-class severity scoring performed by a radiologist (based on CT images and clinical features). The entire lungs were segmented and followed by resizing, bin discretization and radiomic features extraction. We utilized two feature selection algorithms, namely bagging random forest (BRF) and multivariate adaptive regression splines (MARS), each coupled to a classifier, namely multinomial logistic regression (MLR), to construct multiclass classification models. The dataset was divided into 50% (555 samples), 20% (223 samples), and 30% (332 samples) for training, validation, and untouched test datasets, respectively. Subsequently, nested cross-validation was performed on train/validation to select the features and tune the models. All predictive power indices were reported based on the testing set. The performance of multi-class models was assessed using precision, recall, F1-score, and accuracy based on the 4 × 4 confusion matrices. In addition, the areas under the receiver operating characteristic curves (AUCs) for multi-class classifications were calculated and compared for both models. Using BRF, 23 radiomic features were selected, 11 from first-order, 9 from GLCM, 1 GLRLM, 1 from GLDM, and 1 from shape. Ten features were selected using the MARS algorithm, namely 3 from first-order, 1 from GLDM, 1 from GLRLM, 1 from GLSZM, 1 from shape, and 3 from GLCM features. The mean absolute deviation, skewness, and variance from first-order and flatness from shape, and cluster prominence from GLCM features and Gray Level Non Uniformity Normalize from GLRLM were selected by both BRF and MARS algorithms. All selected features by BRF or MARS were significantly associated with four-class outcomes as assessed within MLR (All p values < 0.05). BRF + MLR and MARS + MLR resulted in pseudo-R 2 prediction performances of 0.305 and 0.253, respectively. Meanwhile, there was a significant difference between the feature selection models when using a likelihood ratio test ( p value = 0.046). Based on confusion matrices for BRF + MLR and MARS + MLR algorithms, the precision was 0.856 and 0.728, the recall was 0.852 and 0.722, whereas the accuracy was 0.921 and 0.861, respectively. AUCs (95% CI) for multi-class classification were 0.846 (0.805–0.887) and 0.807 (0.752–0.861) for BRF + MLR and MARS + MLR algorithms, respectively. Our models based on the utilization of radiomic features, coupled with machine learning were able to accurately classify patients according to the severity of pneumonia, thus highlighting the potential of this emerging paradigm in the prognostication and management of COVID-19 patients.

Background Missing data is a pervasive problem in longitudinal data analysis. Several single-imputation (SI) and multiple-imputation (MI) approaches have been proposed to address this issue. In this study, for the first time, the function of the longitudinal regression tree algorithm as a non-parametric method after imputing missing data using SI and MI was investigated using simulated and real data. Method Using different simulation scenarios derived from a real data set, we compared the performance of cross, trajectory mean, interpolation, copy-mean, and MI methods (27 approaches) to impute missing longitudinal data using parametric and non-parametric longitudinal models and the performance of the methods was assessed in real data. The real data included 3,645 participants older than 18 years within six waves obtained from the longitudinal Tehran cardiometabolic genetic study (TCGS). The data modeling was conducted using systolic and diastolic blood pressure (SBP/DBP) as the outcome variables and included predictor variables such as age, gender, and BMI. The efficiency of imputation approaches was compared using mean squared error (MSE), root-mean-squared error (RMSE), median absolute deviation (MAD), deviance, and Akaike information criteria (AIC). Results The longitudinal regression tree algorithm outperformed based on the criteria such as MSE, RMSE, and MAD than the linear mixed-effects model (LMM) for analyzing the TCGS and simulated data using the missing at random (MAR) mechanism. Overall, based on fitting the non-parametric model, the performance of the 27 imputation approaches was nearly similar. However, the SI traj-mean method improved performance compared with other imputation approaches. Conclusion Both SI and MI approaches performed better using the longitudinal regression tree algorithm compared with the parametric longitudinal models. Based on the results from both the real and simulated data, we recommend that researchers use the traj-mean method for imputing missing values of longitudinal data. Choosing the imputation method with the best performance is widely dependent on the models of interest and the data structure.

The use of anthropometric indices is one of the new and low-cost diagnostic methods of metabolic syndrome (MetS). The present study aimed to determine optimal cutoff points for the visceral adiposity index (VAI), body roundness index (BRI), and a body shape index (ABSI) in the prediction of MetS. This cross-sectional study was performed on 10,000 individuals aged from 35 to 65 years, recruited in Ravansar Non-Communicable Diseases (RaNCD) cohort study, in the west region of Iran, in 2019. MetS was defined according to International Diabetes Federation (IDF) criteria. The receiver operating characteristic (ROC) curve analysis was used to assess predictive anthropometric indices and determine optimal cutoff values. The optimal cutoff points for VAI were 4.11 (AUC: 0.82; 95% CI: 0.81-0.84) in men and 4.28 (AUC: 0.86; 95% CI: 0.85-0.87) in women to prediction of MetS. The optimal cutoff points for BRI were 4.75 (AUC: 0.75; 95% CI: 0.74-0.77) in men and 6.17 (AUC: 0.62; 95% CI: 0.61-0.64) in women to prediction of MetS. The optimal cutoff points for ABSI were 0.12 (AUC: 0.49; 95% CI: 0.47-0.51) in men and 0.13 (AUC: 0.49; 95% CI: 0.47-0.51) in women to prediction of MetS. The risk of MetS in men and women with a VAI higher than the optimal cutoff point was, respectively, 9.82 and 11.44 times higher than that in those with a VAI lower than the cutoff point. Although VAI might not be very cost-beneficial compared to IDF, our study showed VAI is a better predictor of MetS than BRI in adults. ABSI was not a suitable predictor for MetS.

Time-to-event data are very common in medical applications. Regression models have been developed on such data especially in the field of survival analysis. Kernels are used to handle even more complicated and enormous quantities of medical data by injecting non-linearity into linear models. In this study, a Multiple Kernel Learning (MKL) method has been proposed to optimize survival outcomes under the Accelerated Failure Time (AFT) model, a useful alternative to the Proportional Hazards (PH) frailty model. In other words, a survival parametric regression framework has been presented for clinical data to effectively integrate kernel learning with AFT model using a gradient descent optimization strategy. This methodology involves applying four different parametric models, evaluated using 19 distinct kernels to extract the best fitting scenario. This culminated in a sophisticated strategy that combined these kernels through MKL. We conducted a comparison between the Frailty model and MKL due to their shared fundamental properties. The models were assessed using the Concordance index (C-index) and Brier score (B-score). Each model was tested on both a case study and a replicated/independent dataset. The outcomes showed that kernelization enhances the performance of the model, especially by combining selected kernels for MKL.

Machine learning (ML) could have advantages over traditional statistical models in identifying risk factors. Using ML algorithms, our objective was to identify the most important variables associated with mortality after dementia diagnosis in the Swedish Registry for Cognitive/Dementia Disorders (SveDem). From SveDem, a longitudinal cohort of 28,023 dementia-diagnosed patients was selected for this study. Sixty variables were considered as potential predictors of mortality risk, such as age at dementia diagnosis, dementia type, sex, body mass index (BMI), mini-mental state examination (MMSE) score, time from referral to initiation of work-up, time from initiation of work-up to diagnosis, dementia medications, comorbidities, and some specific medications for chronic comorbidities (e.g., cardiovascular disease). We applied sparsity-inducing penalties for three ML algorithms and identified twenty important variables for the binary classification task in mortality risk prediction and fifteen variables to predict time to death. Area-under-ROC curve (AUC) measure was used to evaluate the classification algorithms. Then, an unsupervised clustering algorithm was applied on the set of twenty-selected variables to find two main clusters which accurately matched surviving and dead patient clusters. A support-vector-machines with an appropriate sparsity penalty provided the classification of mortality risk with accuracy = 0.7077, AUROC = 0.7375, sensitivity = 0.6436, and specificity = 0.740. Across three ML algorithms, the majority of the identified twenty variables were compatible with literature and with our previous studies on SveDem. We also found new variables which were not previously reported in literature as associated with mortality in dementia. Performance of basic dementia diagnostic work-up, time from referral to initiation of work-up, and time from initiation of work-up to diagnosis were found to be elements of the diagnostic process identified by the ML algorithms. The median follow-up time was 1053 (IQR = 516–1771) days in surviving and 1125 (IQR = 605–1770) days in dead patients. For prediction of time to death, the CoxBoost model identified 15 variables and classified them in order of importance. These highly important variables were age at diagnosis, MMSE score, sex, BMI, and Charlson Comorbidity Index with selection scores of 23%, 15%, 14%, 12% and 10%, respectively. This study demonstrates the potential of sparsity-inducing ML algorithms in improving our understanding of mortality risk factors in dementia patients and their application in clinical settings. Moreover, ML methods can be used as a complement to traditional statistical methods.

Background Disturbances in brain cholesterol homeostasis may be involved in the pathogenesis of Alzheimer’s disease (AD). Lipid-lowering medications could interfere with neurodegenerative processes in AD through cholesterol metabolism or other mechanisms. Objective To explore the association between the use of lipid-lowering medications and cognitive decline over time in a cohort of patients with AD or mixed dementia with indication for lipid-lowering treatment. Methods A longitudinal cohort study using the Swedish Registry for Cognitive/Dementia Disorders, linked with other Swedish national registries. Cognitive trajectories evaluated with mini-mental state examination (MMSE) were compared between statin users and non-users, individual statin users, groups of statins and non-statin lipid-lowering medications using mixed-effect regression models with inverse probability of drop out weighting. A dose-response analysis included statin users compared to non-users. Results Our cohort consisted of 15,586 patients with mean age of 79.5 years at diagnosis and a majority of women (59.2 %). A dose-response effect was demonstrated: taking one defined daily dose of statins on average was associated with 0.63 more MMSE points after 3 years compared to no use of statins (95% CI: 0.33;0.94). Simvastatin users showed 1.01 more MMSE points (95% CI: 0.06;1.97) after 3 years compared to atorvastatin users. Younger (< 79.5 years at index date) simvastatin users had 0.80 more MMSE points compared to younger atorvastatin users (95% CI: 0.05;1.55) after 3 years. Simvastatin users had 1.03 more MMSE points (95% CI: 0.26;1.80) compared to rosuvastatin users after 3 years. No differences regarding statin lipophilicity were observed. The results of sensitivity analysis restricted to incident users were not consistent. Conclusions Some patients with AD or mixed dementia with indication for lipid-lowering medication may benefit cognitively from statin treatment; however, further research is needed to clarify the findings of sensitivity analyses.

Background The prevalence of metabolic syndrome (MetS) is increasing worldwide. Early detection of MetS by valid and available indicators can help to prevent, control and reduce its complications. This study aimed to identify the most important anthropometric, biochemical and nutritional indices for the prediction of MetS using a machine-learning algorithm. Methods This study was conducted with 9602 participants from the baseline data of the Ravansar Non-Communicable Disease Cohort (RaNCD), which is part of the PERSIAN study including adults aged 35–65 years. The reference model for MetS was considered according to the International Diabetes Federation (IDF) criteria. The Boruta algorithm and ROC curve analysis were used to select and assess the most important predictors of MetS. Results The importance value (IV) for the components of the models predicting MetS was confirmed before the models were implemented. The identified model with components of age, waist circumference (WC), body mass index (BMI), fasting blood sugar (FBS), systolic–diastolic blood pressure (SBP–DBP), triglyceride, hip circumference and an AUC of 0.89 (95% CI 0.88–0.90) for men and 0.86 (95% CI 0.85–0.88) for women was the strongest model for predicting MetS risk. The AUC for the non-invasive model was 0.75 (95% CI 0.74–0.76) in the total population and has good predictive power for MetS risk with the components age, WC, BMI, SBP, DBP. Conclusions This study showed that in addition to aggressive models, non-invasive models (anthropometric indices, blood pressure and energy intake) can be a good and convenient screening tool for predicting MetS. The models can be used in clinical diagnosis as well as in research on large populations.

Pain management in chronic kidney disease (CKD) is challenging due to altered drug metabolism, impaired excretion, and higher opioid toxicity risk. Despite this, opioids are commonly prescribed, yet real-world data on dose escalation in CKD remain limited. To investigate patterns and timing of opioid dose escalation to ≥50 and ≥90 MME/day among new opioid users across kidney function levels. This population-based cohort study used data from the Stockholm Creatinine Measurements (SCREAM) project linking diagnoses, prescriptions, and laboratory records. Adult new opioid users (no prior opioid in 12 months) from 2012-2021 were categorized by baseline eGFR (≥60, 30-59, < 30 mL/min/1.73m²). Opioids were identified using ATC codes, and daily doses (MME/day) were calculated based on strength, quantity, and equianalgesic ratios. Fine-Gray competing-risks regression assessed time to dose escalation (≥50 and ≥90 MME/day), accounting for death as a competing event. Of 81,987 adult new opioid users, 5,987 (7.3%) escalated to ≥50 MME/day comprising 7.4%, 6.8%, and 8.1% of patients with eGFR ≥ 60, 30-59, and <30 mL/min/1.73m², respectively. For ≥90 MME/day, 2,067 (2.5%) escalated, 2.5%, 2.3%, and 2.9% across the same eGFR categories. Competing risks regression showed significantly lower risks of escalation among patients with reduced eGFR levels. For ≥50 MME/day, the sub distribution hazard ratios (SHRs) were 0.67 (95% CI: 0.56-0.81, p < 0.001) for eGFR 30-59 and 0.64 (95% CI: 0.42-0.99, p = 0.043) for those with eGFR < 30.For ≥90 MME/day, SHRs were 0.57 (95% CI: 0.43-0.75, p < 0.001)and 0.31(95% CI: 0.15-0.65, p = 0.002), respectively. Most escalation occurred within six months, with minimal increase thereafter. Opioid dose escalation occurred across all eGFR levels, underscoring the need for cautious, individualized prescribing and close monitoring, especially in patients with reduced kidney function.

Background Lung cancer is a leading cause of cancer morbidity and mortality worldwide. Several studies have suggested that Human papillomavirus (HPV) infection is an important risk factor in the development of lung cancer. In this study, we aim to address the role of HPV in the development of lung cancer mechanistically by examining the induction of inflammation and epithelial-mesenchymal transition (EMT) by this virus. Methods In this case-control study, tissue samples were collected from 102 cases with lung cancer and 48 controls. We examined the presence of HPV DNA and also the viral genotype in positive samples. We also examined the expression of viral genes (E2, E6 and E7), anti-carcinogenic genes (p53, retinoblastoma (RB)), and inflammatory cytokines in HPV positive cases. Results HPV DNA was detected in 52.9% (54/102) of the case samples and in 25% (12/48) of controls. A significant association was observed between a HPV positive status and lung cancer ( OR = 3.37, 95% C.I = 1.58–7.22, P = 0.001 ). The most prevalent virus genotype in the patients was type 16 (38.8%). The expression of p53 and RB were decreased while and inflammatory cytokines were increased in HPV-positive lung cancer and HPV-positive control tissues compared to HPV-negative lung cancer and HPV-negative control tissues. Also, the expression level of E-cad and PTPN-13 genes were decreased in HPV- positive samples while the expression level of SLUG, TWIST and N-cad was increased in HPV-positive samples compared to negative samples. Conclusion Our study suggests that HPV infection drives the induction of inflammation and EMT which may promote in the development of lung cancer.