Pazopanib, a multi-targeted tyrosine kinase inhibitor, has been explored for its efficacy in treating various subtypes of thyroid cancer, including differentiated thyroid carcinoma (DTC), medullary thyroid carcinoma (MTC), and anaplastic thyroid carcinoma (ATC). This systematic review assesses the efficacy and safety of pazopanib, focusing on the progression-free survival (PFS), overall survival (OS), and response rates and adverse events. A comprehensive search was conducted in databases including PubMed, Scopus, and Web of Science up to October 2024 to identify randomized controlled trials and phase II clinical trials that investigated the use of pazopanib in thyroid cancer. The PRISMA guidelines were followed for data extraction and quality assessment. The review included six studies encompassing 289 patients, presenting a comprehensive overview of pazopanib’s application across different thyroid cancer subtypes. The studies reported median PFS rates ranging from 2.1 to 11.7 months and median OS rates ranging from 5.7 months to not reached. The partial response rates varied from 5% to 49%. Adverse events were common, with hypertension occurring in up to 71.7% of patients, and fatigue and diarrhea were also frequently reported. Grade 3–5 adverse events led to treatment discontinuations in up to 14% of patients. Pazopanib shows variable efficacy across thyroid cancer types, offering significant benefits in MTC and refractory DTC in terms of PFS and OS but limited impact in ATC. The adverse event profile necessitates careful management, particularly regarding hypertension and other treatment-related toxicities. Further studies are required to refine the therapeutic protocols for pazopanib, exploring combination therapies that may enhance its efficacy and reduce the adverse outcomes.

Background and Objectives: Co-infection with Clostridioides difficile (C. difficile) in COVID-19 patients has emerged as a clinical challenge associated with increased morbidity and mortality. While both infections elicit systemic inflammation, the interplay between inflammatory markers, disease severity, and outcomes in patients with COVID-19 and concurrent C. difficile infection remains poorly characterized. This study aimed to evaluate the inflammatory status and clinical outcomes of patients hospitalized with COVID-19, with and without C. difficile co-infection, and to identify the inflammatory markers most predictive of severe disease. Methods: We conducted a retrospective cohort study of 200 hospitalized adults with confirmed COVID-19, of whom 92 had laboratory-confirmed C. difficile infection. Baseline demographic data, comorbidities, inflammatory markers (C-reactive protein [CRP], interleukin-6 [IL-6], ferritin, neutrophil-to-lymphocyte ratio [NLR], platelet count, albumin, and derived indices such as the CRP-to-Albumin Ratio [CAR] and Prognostic Nutritional Index [PNI]) were recorded. Clinical outcomes included ICU admission, need for mechanical ventilation, length of stay, and in-hospital mortality. Results: Patients with COVID-19 and C. difficile co-infection had significantly elevated inflammatory markers (CRP, IL-6, NLR) and higher CAR, alongside lower PNI, compared to those with COVID-19 alone (p < 0.001). Inflammatory indices correlated strongly with disease severity: elevated CAR and low PNI were associated with higher odds of ICU admission and mortality (p < 0.001). Multivariate analysis identified co-infection status, increased IL-6, and elevated CAR as independent predictors of severe outcomes. Conclusions: C. difficile co-infection in COVID-19 patients is associated with an intensified inflammatory response and worse clinical outcomes. Among the evaluated markers, CAR and PNI emerged as robust predictors of severe disease. Timely recognition of C. difficile co-infection and use of targeted anti-inflammatory and supportive therapies may improve patient management. Future studies should expand on these findings to optimize care and guide therapeutic strategies.

Background and Objectives: The interplay of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection and Clostridioides difficile infection (CDI) poses a critical clinical challenge. The resultant inflammatory milieu and its impact on outcomes remain incompletely understood, especially among vulnerable subgroups such as elderly patients, those with diabetes, and individuals with cancer. This study aimed to characterize inflammatory markers and composite inflammatory severity scores—such as Acute Physiology and Chronic Health Evaluation II (APACHE II), Confusion, Urea, Respiratory rate, Blood pressure, and age ≥ 65 years (CURB-65), National Early Warning Score (NEWS), and the Systemic Immune-Inflammation Index (SII)—in hospitalized Coronavirus Disease 2019 (COVID-19) patients with and without CDI, and to evaluate their prognostic implications across key clinical subgroups. Methods: We conducted a retrospective, single-center study of 240 hospitalized adults with Reverse Transcription Polymerase Chain Reaction (RT-PCR)-confirmed COVID-19 between February 2021 and March 2023. Of these, 98 had concurrent CDI. We collected baseline demographics, comorbidities, and laboratory parameters including C-reactive protein (CRP), Interleukin-6 (IL-6), ferritin, neutrophil and lymphocyte counts, albumin, platelet counts, and calculated indices (C-reactive protein to Albumin Ratio (CAR), Neutrophil-to-Lymphocyte Ratio (NLR), Prognostic Nutritional Index (PNI), SII). Patients were stratified by CDI status and analyzed for inflammatory marker distributions, severity scores (APACHE II, CURB-65, NEWS), and outcomes (Intensive Care Unit (ICU) admission, mechanical ventilation, mortality). Subgroup analyses included diabetes, elderly (≥65 years), and cancer patients. Statistical comparisons employed t-tests, chi-square tests, and logistic regression models. Results: Patients with CDI demonstrated significantly higher CRP, IL-6, SII, and CAR, coupled with lower albumin and PNI (p < 0.05). They also had elevated APACHE II, CURB-65, and NEWS scores. CDI-positive patients experienced increased ICU admission (38.8% vs. 20.5%), mechanical ventilation (24.5% vs. 12.9%), and mortality (22.4% vs. 10.6%, all p < 0.05). Subgroup analyses revealed more pronounced inflammatory derangements and worse outcomes in elderly, diabetic, and cancer patients with CDI. Conclusions: Concurrent CDI intensifies systemic inflammation and adverse clinical trajectories in hospitalized COVID-19 patients. Elevations in inflammatory markers and severity scores predict worse outcomes, especially in high-risk subgroups. Early recognition and targeted interventions, including infection control and supportive measures, may attenuate disease severity and improve patient survival.

Background & Objectives: Quantitative ultrasound (QUS) radiomics extracts microstructure-sensitive spectral features from radiofrequency data and may provide contrast-free, early indicators of neoadjuvant chemotherapy (NAC) response in breast cancer. This review synthesized open access human studies evaluating QUS radiomics for a priori prediction and early on-treatment monitoring. Methods: Following PRISMA-2020, we included English, free full-text clinical studies of biopsy-proven breast cancer receiving NAC that reported QUS spectral parameters (mid-band fit, spectral slope/intercept) ± textures/derivatives and machine learning models against clinical/pathologic response. Data on design, RF acquisition/normalization, features, validation, and performance (area under the curve (AUC), accuracy, sensitivity/specificity, balanced accuracy) were extracted. Results: Twelve cohorts were included. A priori baseline models achieved accuracies of 76–88% with AUCs 0.68–0.90; examples include 87% accuracy in a multi-institutional study, 82% accuracy/AUC 0.86 using texture-derivatives, 86% balanced accuracy with transfer learning, 88% accuracy/AUC 0.86 with deep learning, and AUC 0.90 in a hybrid QUS and molecular-subtype model. Early monitoring improved discrimination: week-1 results ranged from AUC 0.81 to 1.00 and accuracy 70 to 100%, noting that the upper bound was reported in a small cohort using combined QUS and diffuse optical spectroscopy features, while week 4 typically peaked (AUC 0.87–0.91; accuracy 80–86% in observational cohorts), and one series reported week-8 accuracy of 93%. Across reporting cohorts, mean AUC increased with a 0.05 absolute gain. A randomized feasibility study reported prospective week-4 model accuracy of 98% and demonstrated decision impact. Conclusions: QUS radiomics provides informative a priori prediction and strengthens by weeks 1–4 of NAC, supporting adaptive treatment windows without contrast or radiation. Standardized radiofrequency (RF) access, normalization, region of interest (ROI)/margin definitions, and external validation are priorities for clinical translation.

Background and Objectives: Sleep disruption and reduced physical activity are common in pregnancy and may impair sexual function through mood, body-image, and relational pathways. We prospectively examined whether sleep quality and physical activity predicted third-trimester sexual function in a Romanian antenatal cohort, and explored psychosocial correlates. Materials and Methods: In a single-center cohort, 102 pregnant adults were enrolled ≤ 20 weeks and followed to the third trimester. Sleep (Pittsburgh Sleep Quality Index, PSQI), sexual function (Female Sexual Function Index–Romanian version, FSFI-RO), physical activity (IPAQ-SF), depressive symptoms (PHQ-9), body-image avoidance (Body Exposure during Sexual Activities Questionnaire, BESAQ), and perceived social support (MSPSS) were assessed. Groups were defined by mid-/late-pregnancy sleep (good, PSQI ≤ 5; poor, PSQI > 5). Analyses used t-tests, Pearson correlations, multivariable linear regression for FSFI-Total, and logistic regression for FSFI-Total < 26.55. Results: Compared with good sleepers (n = 56), women with poor sleep (n = 46) had lower third-trimester FSFI-Total (24.4 ± 3.9 vs. 27.9 ± 4.3; p < 0.001) and higher odds of FSFI-defined dysfunction (adjusted OR 121.1; 95% CI 19.2–763.0; p < 0.001). FSFI-Total correlated with worse sleep (PSQI r = −0.42), depressive symptoms (PHQ-9 r = −0.36), social support (MSPSS r = 0.40), body-image avoidance (BESAQ r = −0.34) and physical activity (IPAQ-SF r = 0.24; all p ≤ 0.015). In adjusted models (R² = 0.42), higher MSPSS (β = 0.26; p = 0.004) was protective, whereas PSQI (β = −0.24; p = 0.008), ΔPHQ-9 (β = −0.19; p = 0.023), BESAQ (β = −0.17; p = 0.031), and higher BMI (β = −0.14; p = 0.049) predicted lower FSFI-Total. Conclusions: In this antenatal cohort, poor sleep was strongly and independently associated with lower sexual function, with meaningful contributions from social support, mood, body-image cognition, and physical activity, highlighting sleep as a clinically actionable target for preserving sexual well-being in pregnancy.

Background and Objectives: The current study aimed to compare the effectiveness of the Lung Imaging Reporting and Data System (Lung-RADS) Version 2022 and the British Thoracic Society (BTS) guidelines in differentiating lung metastases from de novo primary lung cancer on CT scans in patients without prior cancer diagnosis. Materials and Methods: This retrospective study included 196 patients who underwent chest CT scans between 2015 and 2022 without a known history of cancer but with detected pulmonary nodules. CT images characterized nodules based on size, number, location, margins, attenuation, and growth patterns. Nodules were classified according to Lung-RADS Version 2022 and BTS guidelines. Statistical analyses compared the sensitivity and specificity of Lung-RADS and BTS guidelines in distinguishing metastases from primary lung cancer. Subgroup analyses were conducted based on nodule characteristics. Results: Of the 196 patients, 148 (75.5%) had de novo primary lung cancer, and 48 (24.5%) had lung metastases from occult primary tumors. Lung-RADS Version 2022 demonstrated higher specificity than BTS guidelines (87.2% vs. 72.3%, p < 0.001) while maintaining similar sensitivity (91.7% vs. 93.8%, p = 0.68) in differentiating lung metastases from primary lung cancer. Lung metastases were more likely to present with multiple nodules (81.3% vs. 18.2%, p < 0.001), lower lobe distribution (58.3% vs. 28.4%, p < 0.001), and smooth margins (70.8% vs. 20.3%, p < 0.001), whereas primary lung cancers were associated with solitary nodules, upper lobe location, and spiculated margins. Conclusions: Lung-RADS Version 2022 provides higher specificity than the BTS guidelines in differentiating lung metastases from primary lung cancer on CT scans in patients without prior cancer diagnosis. Recognizing characteristic imaging features can improve diagnostic accuracy and guide appropriate management.