Search Results (590)

Introduction: This study aimed to assess the accuracy of real-time polymerase chain reaction (PCR) as a diagnostic tool for Pneumocystis jirovecii pneumonia (PCP) in immunocompromised patients and evaluate the applicability of quantification cycle (Cq) data for PCP diagnosis. Methods: Clinical and laboratory data were collected from medical records of 96 immunocompromised patients hospitalized at the Hadassah hospital from 2018 to 2022, for lower respiratory tract infection. PCP diagnosis was independently categorized by two infectious disease specialists, blinded to PCR results, as either “definite” (confirmed by microscopic identification of P. jirovecii) or “probable” (compatible clinical data and negative microscopy). Clinical characteristics, PCR test performance, and Cq values were then compared between these PCP diagnostic groups and a control group of 85 patients who underwent bronchoscopy for indications unrelated to P. jirovecii infection. Results: The PCR test was found to be highly reliable for diagnosing PCP, with high sensitivity and specificity (93.1%, 98.7%, respectively), a positive predictive value (PPV) of 96.4%, a negative predictive value (NPV) of 97.1%, a negative likelihood ratio of 0.71, and a positive likelihood ratio of 46.5. A Cq cutoff value of 21.89 was found to discriminate between probable PCP and definite PCP. In addition, patients with probable PCP had lower in-hospital mortality than those with definite PCP or no PCP. Conclusions: PCR offers a promising approach for diagnosing PCP in immunocompromised patients with negative respiratory microscopy results. While further research may be warranted, its use may allow for more timely treatment and potentially improved outcomes. Full article

Crossover trials are specifically designed to evaluate treatment effects within individual participants through within-subject comparisons. In a standard AB/BA crossover trial, participants are randomly allocated to one of two treatment sequences: either the AB sequence (where patients receive treatment A first and then cross over to treatment B after a washout period) or the BA sequence (where patients receive B first and then cross over to A after a washout period). Asymptotic and approximate unconditional test procedures, based on two Wald-type statistics, the likelihood ratio statistic, and the score test statistic for the odds ratio (OR), are developed to evaluate the equality of treatment effects in this trial design. Additionally, confidence intervals for OR are constructed, accompanied by an approximate sample size calculation methodology to control the interval width at a pre-specified precision. Empirical analyses demonstrate that asymptotic test procedures exhibit robust performance in moderate to large sample sizes, though they occasionally yield unsatisfactory type I error rates when the sample size is small. In such cases, approximate unconditional test procedures emerge as a rigorous alternative. All proposed confidence intervals achieve satisfactory coverage probabilities, and the approximate sample size estimation method demonstrates high accuracy, as evidenced by empirical coverage probabilities aligning closely with pre-specified confidence levels under estimated sample sizes. To validate practical utility, two real examples are used to illustrate the proposed methodologies. Full article

Background: MRI-based radiomics has emerged as a promising approach to enhance the non-invasive, presurgical assessment of lymph node staging in rectal cancer (RC). However, its clinical implementation remains limited due to methodological variability in published studies. We conducted a systematic review and meta-analysis to synthesize the diagnostic performance of MRI-based radiomics models for predicting pathological nodal status (pN) in RC. Methods: A systematic literature search was conducted in PubMed, Web of Science, and Scopus for studies published until 31 December 2024. Eligible studies applied MRI-based radiomics for pN prediction in RC patients. We excluded other imaging sources and models combining radiomics and other data (e.g., clinical). All models with available outcome metrics were included in data analysis. Data extraction and quality assessment (QUADAS-2) were performed independently by two reviewers. Random-effects meta-analyses including hierarchical summary receiver operating characteristic (HSROC) and restricted maximum likelihood estimator (REML) analyses were conducted to pool sensitivity, specificity, area under the curve (AUC), and diagnostic odds ratios (DORs). Sensitivity analyses and publication bias evaluation were also performed. Results: Sixteen studies (n = 3157 patients) were included. The HSROC showed pooled sensitivity, specificity, and AUC values of 0.68 (95% CI, 0.63–0.72), 0.73 (95% CI, 0.68–0.78), and 0.70 (95% CI, 0.65–0.75), respectively. The mean pooled AUC and DOR obtained by REML were 0.78 (95% CI, 0.75–0.80) and 6.03 (95% CI, 4.65–7.82). Funnel plot asymmetry and Egger’s test (p = 0.025) indicated potential publication bias. Conclusions: Overall, MRI-based radiomics models demonstrated moderate accuracy in predicting pN status in RC, with some studies reporting outstanding results. However, heterogeneity in relevant methodological approaches such as the source of MRI sequences or machine learning methods applied along with possible publication bias call for further standardization and preclude their translation to clinical practice. Full article

Background and Objectives: The extraction of mandibular third molars poses challenges due to their proximity to the mandibular canal and risk of inferior alveolar nerve (IAN) injury. Accurate preoperative evaluation is essential to minimize complications. This study assessed the three-dimensional positional relationship between the mandibular canal and lower third molars using cone-beam computed tomography (CBCT), aiming to identify anatomical positions associated with increased surgical risk. Materials and Methods: This retrospective study analyzed 253 CBCT scans of fully developed lower third molars. The mandibular canal position was classified as apical (Class I), buccal (Class II), lingual (Class III), or interradicular (Class IV). Contact was categorized as no contact, contact with a complete or defective white line, or canal penetration. In no-contact cases, the apex–canal distance was measured. Statistical analysis included descriptive and contingency analyses using the Chi-Square Likelihood Ratio test. Results: Class I was most common (70.8%) and presented the lowest risk, while Classes III and IV showed significantly higher frequencies of canal contact or penetration. Class II exhibited shorter distances even in no-contact cases, suggesting residual risk. Statistically significant associations were found between canal position and both contact type (p < 0.001) and apex–canal distance (p = 0.046). Conclusions: CBCT offers valuable insight into the anatomical relationship between third molars and the mandibular canal. High-risk positions—particularly lingual and interradicular—require careful assessment. Even in the absence of contact, close proximity may pose a risk and should inform surgical planning. Full article

Artificial intelligence has attracted much attention due to its learning capability to solve versatile problems. Using a convolutional neural network in machine learning (ML), we investigated the binary hypothesis test, which is a fundamental problem in management and business. The simulation results showed that the proposed method is comparable with the conventional optimum likelihood ratio test for the aspect of type I and II errors. Moreover, the learning capability of ML is promising for complicated data, the properties of which, such as probability distribution and/or statistical data, i.e., mean, variance, and others, are not known. Full article

The Cox proportional hazards (PH) model is widely used because it models the covariates to the hazard through a log-linear effect. However, exploring flexible effects becomes desirable within the Cox PH framework when only a monotonic relationship between covariates and the hazard is assumed. This work proposes a partial-likelihood-based goodness-of-fit (GOF) test to assess the log-linear effect assumption in a univariate Cox PH model. Rejection of log-linearity suggests the need to incorporate monotonic and non-log-linear covariate effects on the hazard. Our simulation studies show that the proposed GOF test controls type I error rates and exhibits consistency across various scenarios. We illustrate the proposed GOF test with two datasets, breast cancer data and lung cancer data, to assess the presence of log-linear effects in the Cox PH model. Full article

Peripheral artery disease (PAD) is associated with an elevated risk of major adverse cardiovascular events (MACE). Despite this, few reliable biomarkers exist to identify patients at heightened risk of MACE. Growth differentiation factor 15 (GDF15), a stress-responsive cytokine implicated in inflammation, atherosclerosis, and thrombosis, has been broadly studied in cardiovascular disease but remains underexplored in PAD. This study aimed to evaluate the prognostic utility of GDF15 for predicting 2-year MACE in PAD patients using explainable statistical and machine learning approaches. We conducted a prospective analysis of 1192 individuals (454 with PAD and 738 without PAD). At study entry, patient plasma GDF15 concentrations were measured using a validated multiplex immunoassay. The cohort was followed for two years to monitor the occurrence of MACE, defined as stroke, myocardial infarction, or death. Baseline GDF15 levels were compared between PAD and non-PAD participants using the Mann–Whitney U test. A machine learning model based on extreme gradient boosting (XGBoost) was trained to predict 2-year MACE using 10-fold cross-validation, incorporating GDF15 and clinical variables including age, sex, comorbidities (hypertension, diabetes, dyslipidemia, congestive heart failure, coronary artery disease, and previous stroke or transient ischemic attack), smoking history, and cardioprotective medication use. The model’s primary evaluation metric was the F1 score, a validated measurement of the harmonic mean of the precision and recall values of the prediction model. Secondary model performance metrics included precision, recall, positive likelihood ratio (LR+), and negative likelihood ratio (LR-). A prediction probability histogram and Shapley additive explanations (SHAP) analysis were used to assess model discrimination and interpretability. The mean participant age was 70 ± SD 11 years, with 32% (n = 386) female representation. Median plasma GDF15 levels were significantly higher in PAD patients compared to the levels in non-PAD patients (1.29 [IQR 0.77–2.22] vs. 0.99 [IQR 0.61–1.63] pg/mL; p < 0.001). During the 2-year follow-up period, 219 individuals (18.4%) experienced MACE. The XGBoost model demonstrated strong predictive performance for 2-year MACE (F1 score = 0.83; precision = 82.0%; recall = 83.7%; LR+ = 1.88; LR− = 0.83). The prediction histogram revealed distinct stratification between those who did vs. did not experience 2-year MACE. SHAP analysis identified GDF15 as the most influential predictive feature, surpassing traditional clinical predictors such as age, cardiovascular history, and smoking status. This study highlights GDF15 as a strong prognostic biomarker for 2-year MACE in patients with PAD. When combined with clinical variables in an interpretable machine learning model, GDF15 supports the early identification of patients at high risk for systemic cardiovascular events, facilitating personalized treatment strategies including multidisciplinary specialist referrals and aggressive cardiovascular risk reduction therapy. This biomarker-guided approach offers a promising pathway for improving cardiovascular outcomes in the PAD population through precision risk stratification. Full article

Recently, there has been a rise in skin cancer cases, for which early detection is highly relevant, as it increases the likelihood of a cure. In this context, this work presents a benchmarking study of standard Convolutional Neural Network (CNN) architectures for automated skin lesion classification. A total of 38 CNN architectures from ten families (ConvNeXt, DenseNet, EfficientNet, Inception, InceptionResNet, MobileNet, NASNet, ResNet, VGG, and Xception) were evaluated using transfer learning on the HAM10000 dataset for seven-class skin lesion classification, namely, actinic keratoses, basal cell carcinoma, benign keratosis-like lesions, dermatofibroma, melanoma, melanocytic nevi, and vascular lesions. The comparative analysis used standardized training conditions, with all models utilizing frozen pre-trained weights. Cross-database validation was then conducted using the ISIC 2019 dataset to assess generalizability across different data distributions. The ConvNeXtXLarge architecture achieved the best performance, despite having one of the lowest performance-to-number-of-parameters ratios, with 87.62% overall accuracy and 76.15% F1 score on the test set, demonstrating competitive results within the established performance range of existing HAM10000-based studies. A proof-of-concept multiplatform mobile application was also implemented using a client–server architecture with encrypted image transmission, demonstrating the viability of integrating high-performing models into healthcare screening tools. Full article

The Tomographic Synthetic Aperture Radar (TomoSAR) technique is widely used for monitoring urban infrastructures, as it enables the mapping of individual scatterers across additional dimensions such as height (3D), thermal dilation (4D), and deformation velocity (5D). Retrieving this information is crucial for building management and maintenance. Nevertheless, accurately extracting it from TomoSAR data poses several challenges, particularly the presence of outliers due to uneven and limited baseline distributions. One way to address these issues is through statistical detection approaches such as the Generalized Likelihood Ratio Test, which ensures a Constant False Alarm Rate. While effective, these methods face two primary limitations: high computational complexity and the off-grid problem caused by the discretization of the search space. To overcome these drawbacks, we propose an approach that combines a quick initialization process using Fast-Sup GLRT with local descent optimization. This method operates directly in the continuous domain, bypassing the limitations of grid-based search while significantly reducing computational costs. Experiments conducted on both simulated and real datasets acquired with the TerraSAR-X satellite over the Spanish city of Barcelona demonstrate the ability of the proposed approach to maintain computational efficiency while improving scatterer localization accuracy in the third and fourth dimensions. Full article

Background/Objectives: The optimal surgical management of perforated gastric cancer (PGC) in emergency settings remains controversial. Urgent upfront one-stage gastrectomy (1SG) and two-stage gastrectomy (2SG) with damage-control surgery followed by elective gastrectomy have been proposed. The aim of the present systematic review is to compare short- and long-term outcomes between 1SG and 2SG in the treatment of PGC. Methods: A systematic review and individual patient data (IPD) meta-analysis of studies reporting data of patients undergoing 1SG vs. 2SG for PGC was conducted. The time-dependent effects of surgical interventions were assessed using a likelihood ratio test. Hazard function plots were generated via marginal prediction. Results: Ten retrospective series (579 patients) were included. Overall, 482 patients (83%) underwent 1SG, while 97 patients (17%) were treated with 2SG. A trend toward better short-term oncological outcomes and safety profiles for 2SG compared to 1SG was observed. Long-term outcomes were comparable between 1SG and 2SG, and the IPD meta-analysis showed no statistically significant difference between the two approaches in terms of OS or hazard for mortality at all time points. A trend towards a higher hazard for mortality was observed for 1SG in the first 20 months postoperatively. Conclusions: Our analysis suggests that 1SG and 2SG yield comparable short-term outcomes, although 2SG may be associated with a lower medium-term mortality risk. Further research is needed to identify key factors to improve clinical judgments and decision-making in PGC. Full article

Paired data often arise in medical studies, with a correlation between responses of paired organs or parts. Under an intra-correlated model, this paper proposes a generalized linear model to investigate probable confounding factors of the individual response rates in paired data. The main link functions include logistic, log–log, complementary log–log, probit, and double exponential. The estimators of model parameters are calculated through the Newton–Raphson, quadratic lower bound, and Fisher bounded algorithms. Then, three tests (i.e., likelihood ratio test, Wald-type test, and score test) are constructed to analyze whether covariates significantly affect the response rate. Finally, the proposed methods are illustrated by numerical simulation and visual impairment data from Iran. Full article

Background and Objectives: Spontaneous bacterial peritonitis (SBP) is both a prevalent and severe complication among individuals with cirrhosis. This systematic review and meta-analysis was designed to evaluate the diagnostic accuracy of procalcitonin (PCT) and compare it to C-reactive protein (CRP) in cirrhotic patients with suspected SBP. Materials and Methods: We performed an extensive literature review utilizing databases including MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials. Original investigations reporting PCT diagnostic accuracy for SBP in cirrhotic populations were included. We computed pooled measures of sensitivity, specificity, positive and negative likelihood ratios, diagnostic odds ratio, and SROC curve area under the curve, with corresponding 95% confidence intervals (CIs). Results: Meta-analytical synthesis encompassed twenty eligible studies. Diagnostic accuracy analysis revealed PCT sensitivity of 0.73 (95% CI, 0.61–0.83) and specificity of 0.88 (95% CI, 0.83–0.91). Likelihood ratio yielded positive values of 6.0 (95% CI, 4.1–8.8) and negative values of 0.30 (95% CI, 0.20–0.47). Overall discriminative ability, quantified through SROC curve analysis, demonstrated an AUC of 0.90 (95% CI, 0.87–0.92). Head-to-head comparisons between PCT and CRP were available from ten studies, demonstrating PCT’s superior diagnostic accuracy over CRP, with significantly higher AUC values (PCT: 0.89, 95% CI 0.86–0.91; CRP: 0.74, 95% CI 0.70–0.78, p < 0.01). Conclusions: Although PCT demonstrates higher diagnostic accuracy than CRP, it does not appear to provide sufficient accuracy to support treatment decisions for SBP. We recommend not relying solely on the PCT test and advise that it be interpreted in conjunction with clinical findings. Full article

Background/Objectives: Anemia and undernutrition are severe public health concerns in Ethiopia. These are the two most common nutritional disorders in pregnant women and frequently coexist. However, to our knowledge, there is little evidence of the coexistence of anemia and undernutrition among pregnant women. Therefore, this study aimed to examine the prevalence of coexisting anemia and undernutrition (CAU) and associated factors among pregnant women. Methods: A community-based cross-sectional study was conducted from 1 to 25 June 2024, on 515 pregnant women in the Hawela Lida district of Sidama, Ethiopia. We utilized a multi-stage sampling method to choose eligible study participants. A pre-tested and structured questionnaire was used to collect data via the online Open Data Kit mobile tool. We controlled the effect of confounders and clustering by using a multi-level mixed-effect modified Poisson regression analysis model. Results: The prevalence of CAU among pregnant women was 25.4% (95% CI: 21.9–28.9). The prevalence of CAU was associated with household food insecurity (adjusted prevalence ratio [APR]: 2.17; 95% CI: 1.43–3.28), training on model family (APR: 0.66; 95% CI: 0.45–0.96), inadequate dietary diversity (APR: 1.51; 95% CI: 1.18–1.95), and having poor knowledge of nutrition (APR: 1.55; 95% CI: 1.06–2.26) at individual levels. Low community-level women’s autonomy (APR: 6.19; 95% CI: 3.42–11.22) and community-level road accessibility (APR: 0.65; 95% CI: 0.43–0.98) were the identified determinants of CAU at the community level. Conclusions: One in four pregnant women had CAU in the study area. Household food insecurity, inadequate dietary diversity, and poor nutrition knowledge were associated with an increased likelihood of CAU, while participation in model family training and improved road accessibility were associated with reduced CAU. We have also indicated that low community-level women’s autonomy significantly increased the risk of CAU. Therefore, inter-sectorial collaboration should be required to comprehensively address CAU’s determinants at different levels. Additionally, any CAU prevention and intervention programs should provide model family training explicitly targeting women with poor nutritional knowledge and low autonomy in healthcare decision-making. Full article

Background/Objectives: The maternal mortality ratio is one of the global health indicators, and cardiopathies are the leading indirect causes of maternal deaths. Proper management of pregnant women with heart disease is crucial, as the severity of these conditions can lead to complications during the perinatal period. This study aimed to evaluate the rate of severe maternal morbidity and associated factors in pregnant women with heart disease. Methods: A retrospective cohort study was conducted at a referral hospital in São Paulo from 2008 to 2017, including pregnant women with heart disease who underwent procedures in the obstetric center (n = 345). Sociodemographic, obstetric, and pre-existing conditions were analyzed, along with life-threatening conditions, near-miss events, and maternal deaths. Heart diseases were classified according to the World Health Organization (WHO) guidelines, and health indicators were calculated using WHO-recommended formulas. The Chi-square test or Likelihood Ratio test (p < 0.05) was used to compare severe maternal morbidity among women with heart disease. Results: The mean age of participants was 29.1 ± 7.29 years; most were white (58.8%), had completed high school (37.9%), and were married (71.6%). The most frequent pre-existing conditions were hypertension (9.6%) and diabetes mellitus (9.3%). The mean gestational age at admission/delivery was 37 weeks. According to the WHO classification, most women were classified as “II/III” (31.6%). Life-threatening conditions included hemorrhagic complications (13.9%), hypertensive complications (5.8%), clinical complications (19.7%), and severe management conditions (31.6%). Near-miss events occurred in 6.4% of patients, with clinical criteria in 2.9%, laboratory criteria in 4.3%, and management criteria in 3.5%. The cesarean section rate was 51%. Patients classified as WHO III and IV presented more severe management conditions (p < 0.0001), and those in WHO IV had a higher occurrence of near-miss events (p = 0.0001). Maternal mortality was 0.9% (n = 3). Conclusions: The incidence of severe maternal morbidity was 25 cases (22 near-miss events + 3 maternal deaths), equivalent to 2.86 per 1000 live births, and was significantly associated with WHO classifications III and IV. Full article

This study aimed to identify the best model and method for the genomic selection of early growth traits in Inner Mongolian cashmere goats (IMCGs). Using data from 50,728 SNPs, the phenotypes (birth weight, BW; weaning weight, WW; daily weight gain, DWG; and yearling weight, YW) of 2256 individuals, and pedigree information from 14,165 individuals, fixed effects were analyzed using a generalized linear model. Four single-trait animal models with varying combinations of individual and maternal effects were evaluated using the ABLUP, GBLUP, and ssGBLUP methods. The best model was selected based on a likelihood ratio test. Five-fold cross-validation was used to assess the accuracy and reliability of the genomic estimated breeding values (GEBVs). Birth year and herd significantly affected BW (p < 0.05) and WW, DWG, and YW (p < 0.01), while sex, birth type, and dam age had highly significant effects on all traits (p < 0.01). Model 4, incorporating direct and maternal additive genetic effects, maternal environmental effects, and their covariance, was optimal. Additionally, ssGBLUP achieved the highest GEBV accuracy (0.61–0.70), outperforming the GBLUP and ABLUP methods. Thus, ssGBLUP is recommended for enhancing the genetic progress in IMCGs. Under the best method, the heritability estimates for BW, WW, DGW, and YW were 0.11, 0.25, 0.15, and 0.23, respectively. Full article