The accurate diagnosis of small-cell lung cancer (SCLC) is crucial, as treatment strategies differ from those of other lung cancers. This systematic review aims to identify proteins differentially expressed in SCLC compared to normal lung tissue, evaluating their potential utility in diagnosing and prognosing the disease. Additionally, the study identifies proteins differentially expressed between SCLC and large cell neuroendocrine carcinoma (LCNEC), aiming to discover biomarkers distinguishing between these two subtypes of neuroendocrine lung cancers. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a comprehensive search was conducted across PubMed/MEDLINE, Scopus, Embase, and Web of Science databases. Studies reporting proteomics information and confirming SCLC and/or LCNEC through histopathological and/or cytopathological examination were included, while review articles, non-original articles, and studies based on animal samples or cell lines were excluded. The initial search yielded 1705 articles, and after deduplication and screening, 16 articles were deemed eligible. These studies revealed 117 unique proteins significantly differentially expressed in SCLC compared to normal lung tissue, along with 37 unique proteins differentially expressed between SCLC and LCNEC. In conclusion, this review highlights the potential of proteomics technology in identifying novel biomarkers for diagnosing SCLC, predicting its prognosis, and distinguishing it from LCNEC.

Hypertension is a pervasive and widespread health condition that poses a significant risk factor for cardiovascular disease, which includes conditions such as heart attack, stroke, and heart failure. Despite its widespread occurrence, the exact cause of hypertension remains unknown, and the mechanisms underlying the progression from prehypertension to hypertension require further investigation. Recent proteomic studies have shown promising results in uncovering potential biomarkers related to disease development. In this study, serum proteomic data collected from Qatar Biobank were analyzed to identify altered protein expression between individuals with normal blood pressure, prehypertension, and hypertension and to elucidate the biological pathways contributing to this disease. The results revealed a cluster of proteins, including the SRC family, CAMK2B, CAMK2D, TEC, GSK3, VAV, and RAC, which were markedly upregulated in patients with hypertension compared to those with prehypertension (fold change ≥ 1.6 or ≤−1.6, area under the curve ≥ 0.8, and q-value < 0.05). Pathway analysis showed that the majority of these proteins play a role in actin cytoskeleton remodeling. Actin cytoskeleton reorganization affects various biological processes that contribute to the maintenance of blood pressure, including vascular tone, endothelial function, cellular signaling, inflammation, fibrosis, and mechanosensing. Therefore, the findings of this study suggest a potential novel role of actin cytoskeleton-related proteins in the progression from prehypertension to hypertension. The present study sheds light on the underlying pathological mechanisms involved in hypertension and could pave the way for new diagnostic and therapeutic approaches for the treatment of this disease.

Background: Small cell lung cancer (SCLC) is an extremely aggressive form of lung cancer, characterized by rapid progression and poor survival rates. Despite the importance of early diagnosis, the current diagnostic techniques are invasive and restricted. Methods: This study presents a novel stacking-based ensemble machine learning approach for classifying small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) using metabolomics data. The analysis included 191 SCLC cases, 173 NSCLC cases, and 97 healthy controls. Feature selection techniques identified significant metabolites, with positive ions proving more relevant. Results: For multi-class classification (control, SCLC, NSCLC), the stacking ensemble achieved 85.03% accuracy and 92.47 AUC using Support Vector Machine (SVM). Binary classification (SCLC vs. NSCLC) further improved performance, with ExtraTreesClassifier reaching 88.19% accuracy and 92.65 AUC. SHapley Additive exPlanations (SHAP) analysis revealed key metabolites like benzoic acid, DL-lactate, and L-arginine as significant predictors. Conclusions: The stacking ensemble approach effectively leverages multiple classifiers to enhance overall predictive performance. The proposed model effectively captures the complementary strengths of different classifiers, enhancing the detection of SCLC and NSCLC. This work accentuates the potential of combining metabolomics with advanced machine learning for non-invasive early lung cancer subtype detection, offering an alternative to conventional biopsy methods.

Excessive skin scarring affects over 100 million patients worldwide, with effects ranging from cosmetic to systemic problems, and an effective treatment is yet to be found. Ultrasound-based therapies have been used to treat a variety of skin disorders, but the exact mechanisms behind the observed effects are still unclear. The aim of this work was to demonstrate the potential of ultrasound for the treatment of abnormal scarring by developing a multi-well device based on printable piezoelectric material (PiezoPaint™). First, compatibility with cell cultures was evaluated using measurements of heat shock response and cell viability. Second, the multi-well device was used to treat human fibroblasts with ultrasound and quantify their proliferation, focal adhesions, and extracellular matrix (ECM) production. Ultrasound caused a significant reduction in fibroblast growth and ECM deposition without changes in cell viability or adhesion. The data suggest that these effects were mediated by nonthermal mechanisms. Interestingly, the overall results suggest that ultrasound treatment would a be beneficial therapy for scar reduction. In addition, it is expected that this device will be a useful tool for mapping the effects of ultrasound treatment on cultured cells.

Extracellular vesicles (EVs) are known as potential biomarkers for several diseases; nevertheless, the degree of technical and biological variability is not yet adequately characterized. Because pre-analytical factors such as blood collection time and EV subpopulation could confound biomarker studies, we performed a pilot study systematically quantifying methodological and biological variability including EV-Array (surface proteins), and proteome characterization of cargo. Plasma samples from six healthy adults were collected at two time points (morning and afternoon) and plasma was analyzed with EV-Array, and isolated EVs were analyzed using nanoparticle tracking analysis (NTA), and label-free mass spectrometry (LC-MS/MS). Methodological repeatability was high for NTA particle size (3.3% CV) and LC-MS (8.2% CV), and lower for EV-Array surface markers (22.6% CV). Variations between samples were reasonable for NTA-size, EV-Array and LC-MS/MS (5–21%) and substantially lower than between-subject variation. No evidence of systemic morning–afternoon shifts in particle size and concentration or EV cargo was observed, although small effects cannot be excluded. The same was true for most surface markers, but minor but statistically significant reductions in a few specific surface markers occurred in afternoon EV-Array samples. In this pilot we therefore do not observe any major systemic diurnal bias in healthy individuals in samples collected a.m. vs. p.m. Despite the small sample size, this study underscores the importance of accounting for individual variability and methodological standardization when designing EV-based biomarker research.