Machine learning and radiomics analysis by computed tomography in colorectal liver metastases patients for RAS mutational status prediction.

PURPOSE: To assess the efficacy of machine learning and radiomics analysis by computed tomography (CT) in presurgical setting, to predict RAS mutational status in colorectal liver metastases. METHODS: Patient selection in a retrospective study was carried out from January 2018 to May 2021 considering the following inclusion criteria: patients subjected to surgical resection for liver metastases; proven pathological liver metastases; patients subjected to enhanced CT examination in the presurgical setting with a good quality of images; and RAS assessment as standard reference. A total of 851 radiomics features were extracted using the PyRadiomics Python package from the Slicer 3D image computing platform after slice-by-slice segmentation on CT portal phase by two expert radiologists of each individual liver metastasis performed first independently by the individual reader and then in consensus. Balancing technique was performed, and inter- and intraclass correlation coefficients were calculated to assess the between-observer and within-observer reproducibility of features. Receiver operating characteristics (ROC) analysis with the calculation of area under the ROC curve (AUC), sensitivity (SENS), specificity (SPEC), positive predictive value (PPV), negative predictive value (NPV) and accuracy (ACC) were assessed for each parameter. Linear and non-logistic regression model (LRM and NLRM) and different machine learning-based classifiers were considered. Moreover, features selection was performed before and after a normalized procedure using two different methods (3-sigma and z-score). RESULTS: Seventy-seven liver metastases in 28 patients with a mean age of 60 years (range 40-80 years) were analyzed. The best predictors, at univariate analysis for both normalized procedures, were original_shape_Maximum2DDiameter and wavelet_HLL_glcm_InverseVariance that reached an accuracy of 80%, an AUC ≥ 0.75, a sensitivity ≥ 80% and a specificity ≥ 70% (p value < < 0.01). However, a multivariate analysis significantly increased the accuracy in RAS prediction when a linear regression model (LRM) was used. The best performance was obtained using a LRM combining linearly 12 robust features after a z-score normalization procedure: AUC of 0.953, accuracy 98%, sensitivity 96%, specificity of 100%, PPV 100% and NPV 96% (p value < < 0.01). No statistically significant increase was obtained considering the tested machine learning both without normalization and with normalization methods. CONCLUSIONS: Normalized approach in CT radiomics analysis allows to predict RAS mutational status in colorectal liver metastases patients.

Plexiform Fibromyxoma in the Stomach: Immunohistochemical Profile and Comprehensive Genetic Characterization.

Plexiform fibromyxoma (PF), also referred to as plexiform angiomyxoid myofibroblast tumor, is an exceedingly rare mesenchymal neoplasm primarily affecting the stomach. Herein, we present a case of PF diagnosed in a 71-year-old male with a history of lung cancer, initially suspected to have a gastrointestinal stromal tumor (GIST) of the stomach, who subsequently underwent subtotal gastrectomy. The histopathological and molecular features of the tumor, including mutations in ABL1, CCND1, CSF1R, FGFR4, KDR, and MALAT1-GLI1 fusion, are elucidated and discussed in the context of diagnostic, prognostic, and therapeutic considerations.

Innovative Drug Modalities for the Treatment of Advanced Prostate Cancer.

Prostate cancer, a prevalent malignancy affecting the prostate gland, is a significant global health concern. Androgen-deprivation therapy (ADT) has proven effective in controlling advanced disease, with over 50% of patients surviving at the 10-year mark. However, a diverse spectrum of responses exists, and resistance to ADT may emerge over time. This underscores the need to explore innovative treatment strategies for effectively managing prostate cancer progression. Ongoing research endeavors persist in unraveling the complexity of prostate cancer and fostering the development of biologic and innovative approaches, including immunotherapies and targeted therapies. This review aims to provide a valuable synthesis of the dynamic landscape of emerging drug modalities in this context. Interestingly, the complexities posed by prostate cancer not only present a formidable challenge but also serve as a model and an opportunity for translational research and innovative therapies in the field of oncology.

Two-year Opioid Prescription Trends in Local Sanitary Agency Naples 3 South, Campania Region, Italy. Descriptive Analyses and AI-based Translational Perspectives.

Aims: This study delves into the two-year opioid prescription trends in the Local Sanitary Agency Naples 3 South, Campania Region, Italy. The research aims to elucidate prescribing patterns, demographics, and dosage categories within a population representing 1.7% of the national total. Perspectives on artificial intelligence research are discussed. Methods: From the original dataset, spanning from January 2022 to October 2023, we processed multiple variables including demographic data, medications, dosages, drug consumption, and administration routes. The dispensing quantity was calculated as defined daily doses (DDD). Results: The analysis reveals a conservative approach to opioid therapy. In subjects under the age of 20, prescriptions accounted for 2.1% in 2022 and declined to 1.4% in 2023. The drug combination paracetamol/codeine was the most frequently prescribed, followed by tapentadol. Approximately two-thirds of the consumption pertains to oral formulations. Transdermal formulations were 15% (fentanyl 9.8%, buprenorphine 5.1%) in 2022; and 16.6% (fentanyl 10%, buprenorphine 6.6%) in 2023. These data were confirmed by the DDD analysis. The trend analysis demonstrated a significant reduction ( p < 0.001) in the number of prescribed opioids from 2022 to 2023 in adults (40-69 years). The study of rapid-onset opioids (ROOs), drugs specifically used for breakthrough cancer pain, showed higher dosage (>267 mcg) consumption among women, whereas a lower dosage (<133 mcg) was calculated for men. Fentanyl pectin nasal spray accounted for approximately one-fifth of all ROOs. Conclusion: Despite limitations, the study provides valuable insights into prescribing practices involving an important study population. The findings underscore the need for tailored approaches to prescribing practices, recognizing the complexities of pain management in different contexts. This research can contribute to the ongoing discourse on opioid use, advocating for innovative strategies that optimize therapeutic outcomes while mitigating potential risks.

Predictive significance of FGFR4 p.G388R polymorphism in metastatic colorectal cancer patients receiving trifluridine/tipiracil (TAS-102) treatment.

BACKGROUND: TAS-102 (Lonsurf®) is an oral fluoropyrimidine consisting of a combination of trifluridine (a thymidine analog) and tipiracil (a thymidine phosphorylation inhibitor). The drug is effective in metastatic colorectal cancer (mCRC) patients refractory to fluorouracil, irinotecan and oxaliplatin. This study is a real-world analysis, investigating the interplay of genotype/phenotype in relation to TAS-102 sensitivity. METHODS: Forty-seven consecutive mCRC patients were treated with TAS-102 at the National Cancer Institute of Naples from March 2019 to March 2021, at a dosage of 35 mg/m2, twice a day, in cycles of 28 days (from day 1 to 5 and from day 8 to 12). Clinical-pathological parameters were described. Activity was evaluated with RECIST criteria (v1.1) and toxicity with NCI-CTC (v5.0). Survival was depicted through the Kaplan-Meyer curves. Genetic features of patients were evaluated with Next Generation Sequencing (NGS) through the Illumina NovaSeq 6000 platform and TruSigt™Oncology 500 kit. RESULTS: Median age of patients was 65 years (range: 46-77). Forty-one patients had 2 or more metastatic sites and 38 patients underwent to more than 2 previous lines of therapies. ECOG (Eastern Cooperative Oncology Group) Performance Status (PS) was 2 in 19 patients. The median number of TAS-102 cycles was 4 (range: 2-12). The most frequent toxic event was neutropenia (G3/G4 in 16 patients). There were no severe (> 3) non-haematological toxicities or treatment-related deaths. Twenty-six patients experienced progressive disease (PD), 21 stable disease (SD). Three patients with long-lasting disease control (DC: complete, partial responses or stable disease) shared an FGFR4 (p.Gly388Arg) mutation. Patients experiencing DC had more frequently a low tumour growth rate (P = 0.0306) and an FGFR4 p.G388R variant (P < 0.0001). The FGFR4 Arg388 genotype was associated with better survival (median: 6.4 months) compared to the Gly388 genotype (median: 4 months); the HR was 0.25 (95% CI 0.12- 0.51; P = 0.0001 at Log-Rank test). CONCLUSIONS: This phenotype/genotype investigation suggests that the FGFR4 p.G388R variant may serve as a new marker for identifying patients who are responsive to TAS-102. A mechanistic hypothesis is proposed to interpret these findings.

rs822336 binding to C/EBPß and NFIC modulates induction of PD-L1 expression and predicts anti-PD-1/PD-L1 therapy in advanced NSCLC.

Efficient predictive biomarkers are needed for immune checkpoint inhibitor (ICI)-based immunotherapy in non-small cell lung cancer (NSCLC). Testing the predictive value of single nucleotide polymorphisms (SNPs) in programmed cell death 1 (PD-1) or its ligand 1 (PD-L1) has shown contrasting results. Here, we aim to validate the predictive value of PD-L1 SNPs in advanced NSCLC patients treated with ICIs as well as to define the molecular mechanisms underlying the role of the identified SNP candidate. rs822336 efficiently predicted response to anti-PD-1/PD-L1 immunotherapy in advanced non-oncogene addicted NSCLC patients as compared to rs2282055 and rs4143815. rs822336 mapped to the promoter/enhancer region of PD-L1, differentially affecting the induction of PD-L1 expression in human NSCLC cell lines as well as their susceptibility to HLA class I antigen matched PBMCs incubated with anti-PD-1 monoclonal antibody nivolumab. The induction of PD-L1 expression by rs822336 was mediated by a competitive allele-specificity binding of two identified transcription factors: C/EBPß and NFIC. As a result, silencing of C/EBPß and NFIC differentially regulated the induction of PD-L1 expression in human NSCLC cell lines carrying different rs822336 genotypes. Analysis by binding microarray further validated the competitive allele-specificity binding of C/EBPß and NFIC to PD-L1 promoter/enhancer region based on rs822336 genotype in human NSCLC cell lines. These findings have high clinical relevance since identify rs822336 and induction of PD-L1 expression as novel biomarkers for predicting anti-PD-1/PD-L1-based immunotherapy in advanced NSCLC patients.

Treatments, prognostic factors, and genetic heterogeneity in advanced cholangiocarcinoma: A multicenter real-world study.

BACKGROUND AND AIMS: Cholangiocarcinoma (CCA), a rare and aggressive hepatobiliary malignancy, presents significant clinical management challenges. Despite rising incidence and evolving treatment options, prognosis remains poor, motivating the exploration of real-world data for enhanced understanding and patient care. METHODS: This multicenter study analyzed data from 120 metastatic CCA patients at three institutions from 2016 to 2023. Kaplan-Meier curves assessed overall survival (OS), while univariate and multivariate analyses evaluated links between clinical variables (age, gender, tumor site, metastatic burden, ECOG performance status, response to first-line chemotherapy) and OS. Genetic profiling was conducted selectively. RESULTS: Enrolled patients had a median age of 68.5 years, with intrahepatic tumors predominant in 79 cases (65.8%). Among 85 patients treated with first-line chemotherapy, cisplatin and gemcitabine (41.1%) was the most common regimen. Notably, one-third received no systemic treatment. After a median 14-month follow-up, 81 CCA-related deaths occurred, with a median survival of 13.1 months. Two clinical variables independently predicted survival: response to first-line chemotherapy (disease control vs. no disease control; HR: 0.27; 95% CI: 0.14-0.50; p < 0.0001) and metastatic involvement (>1 site vs. 1 site; HR: 1.99; 95% CI: 1.04-3.80; p = 0.0366). The three most common genetic alterations involved the ARID1A, tp53, and CDKN2A genes. CONCLUSIONS: Advanced CCA displays aggressive clinical behavior, emphasizing the need for treatments beyond chemotherapy. Genetic diversity supports potential personalized therapies. Collaborative research and deeper CCA biology understanding are crucial to enhance patient outcomes in this challenging malignancy.

Translational Insights in the Landscape of Salivary Gland Cancers: Ready for a New Era?

Salivary gland carcinomas (SGCs) are rare neoplasms, representing less than 10% of all head and neck tumors, but they are extremely heterogeneous from the histological point of view, their clinical behavior, and their genetics. The guidelines regarding their treatment include surgery in most cases, which can also play an important role in oligometastatic disease. Where surgery cannot be used, systemic therapy comes into play. Systemic therapy for many years has been represented by polychemotherapy, but recently, with the affirmation of translational research, it can also count on targeted therapy, at least in some subtypes of SGCs. Interestingly, in some SGC histotypes, predominant mutations have been identified, which in some cases behave as "driver mutations", namely mutations capable of governing the carcinogenesis process. Targeting these driver mutations may be an effective therapeutic strategy. Nonetheless, it is not always possible to have drugs suitable for targeting driver mutations-and targeting driver mutations is not always accompanied by a clinical benefit. In this review, we will analyze the main mutations predominant in the various histotypes of SGCs.

MiR-449a antagonizes EMT through IL-6-mediated trans-signaling in laryngeal squamous cancer.

MicroRNAs (miRNAs) are involved in post-transcriptional gene expression regulation and in mechanisms of cancer growth and metastases. In this light, miRNAs could be promising therapeutic targets and biomarkers in clinical practice. Therefore, we investigated if specific miRNAs and their target genes contribute to laryngeal squamous cell carcinoma (LSCC) development. We found a significant decrease of miR-449a in LSCC patients with nodal metastases (63.3%) compared with patients without nodal involvement (44%). The AmpliSeq Transcriptome of HNO-210 miR-449a-transfected cell lines allowed the identification of IL6-R as a potential target. Moreover, the downregulation of IL6-R and the phosphorylation reduction of the downstream signaling effectors, suggested the inhibition of the IL-6 trans-signaling pathway. These biochemical effects were paralleled by a significant inhibition of invasion and migration in vitro and in vivo, supporting an involvement of epithelial-mesenchymal transition. These findings indicate that miR-449a contributes to suppress the metastasization of LSCC by the IL-6 trans-signaling block and affects sensitivity to external stimuli that mimic pro-inflammatory conditions.

Machine learning-based radiomics analysis in predicting RAS mutational status using magnetic resonance imaging.

PURPOSE: To assess the efficacy of radiomics features, obtained by magnetic resonance imaging (MRI) with hepatospecific contrast agent, in pre-surgical setting, to predict RAS mutational status in liver metastases. METHODS: Patients with MRI in pre-surgical setting were enrolled in a retrospective study. Manual segmentation was made by means 3D Slicer image computing, and 851 radiomics features were extracted as median values using the PyRadiomics Python package. The features were extracted considering the agreement with the Imaging Biomarker Standardization Initiative (IBSI). Balancing was performed through synthesis of samples for the underrepresented classes using the self-adaptive synthetic oversampling (SASYNO) approach. Inter- and intraclass correlation coefficients (ICC) were calculated to assess the between-observer and within-observer reproducibility of all radiomics characteristics. For continuous variables, nonparametric Wilcoxon-Mann-Whitney test was utilized. Benjamini and Hochberg's false discovery rate (FDR) adjustment for multiple testing was used. Receiver operating characteristics (ROC) analysis with the calculation of area under the ROC curve (AUC), sensitivity (SENS), specificity (SPEC), positive predictive value (PPV), negative predictive value (NPV) and accuracy (ACC) were assessed for each parameter. Linear and non-logistic regression model (LRM and NLRM) and different machine learning-based classifiers including decision tree (DT), k-nearest neighbor (KNN) and support vector machine (SVM) were considered. Moreover, features selection were performed before and after a normalized procedure using two different methods (3-sigma and z-score). McNemar test was used to assess differences statistically significant between dichotomic tables. All statistical procedures were done using MATLAB R2021b Statistics and Machine Toolbox (MathWorks, Natick, MA, USA). RESULTS: Seven normalized radiomics features, extracted from arterial phase, 11 normalized radiomics features, from portal phase, 12 normalized radiomics features from hepatobiliary phase and 12 normalized features from T2-W SPACE sequence were robust predictors of RAS mutational status. The multivariate analysis increased significantly the accuracy in RAS prediction when a LRM was used, combining 12 robust normalized features extracted by VIBE hepatobiliary phase reaching an accuracy of 99%, a sensitivity 97%, a specificity of 100%, a PPV of 100% and a NPV of 98%. No statistically significant increase was obtained, considering the tested classifiers DT, KNN and SVM, both without normalization and with normalization methods. CONCLUSIONS: Normalized approach in MRI radiomics analysis allows to predict RAS mutational status.

Identification of Tissue miRNA Signatures for Pancreatic Ductal Adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC), a neoplasm of the gastrointestinal tract, is the most common pancreatic malignancy (90%) and the fourth highest cause of cancer mortality worldwide. Surgery intervention is currently the only strategy able to offer an advantage in terms of overall survival, but prognosis remains poor even for operated patients. Therefore, the development of robust biomarkers for early diagnosis and prognostic stratification in clinical practice is urgently needed. In this work, we investigated deregulated microRNAs (miRNAs) in tissues from PDAC patients with high (G3) or low (G2) histological grade and with (N+) or without (N-) lymph node metastases. miRNA expression profiling was performed by a comprehensive PCR array and subsequent validation by RT-qPCR. The results showed a significant increase in miR-1-3p, miR-31-5p, and miR-205-5p expression in G3 compared to G2 patients (** p < 0.01; *** p < 0.001; *** p < 0.001). miR-518d-3p upregulation and miR-215-5p downregulation were observed in N+ compared to N- patients. A statistical analysis performed using OncomiR program showed the significant involvement (p < 0.05) of two miRNAs (miR-31 and miR-205) in the histological grade of PDAC patients. Also, an expression analysis in PDAC patients showed that miR-31 and miR-205 had the highest expression at grade 3 compared with normal and other tumor grades. Overall, survival plots confirmed that the overexpression of miR-31 and miR-205 was significantly correlated with decreased survival in TCGA PDAC clinical samples. A KEGG pathway analysis showed that all three miRNAs are involved in the regulation of multiple pathways, including the Hippo signaling, adherens junction and microRNAs in cancer, along with several target genes. Based on in silico analysis and experimental validation, our study suggests the potential role of miR-1-3p, miR-31-5p, and miR-205-5p as useful clinical biomarkers and putative therapeutic targets in PDAC, which should be further investigated to determine the specific molecular processes affected by their aberrant expression.

Circulating vitamin D level before initiating chemotherapy impacts on the time-to-outcome in metastatic colorectal cancer patients: systematic review and meta-analysis.

BACKGROUND: Vitamin D (VD) is implicated in various health conditions, including colorectal cancer (CRC). To investigate potential relationships between pre-chemotherapy VD levels and the time-to-outcome in metastatic CRC patients, we conducted a systematic review and meta-analysis. METHODS: Following the PRISMA 2020 guidelines, we performed thorough searches in PubMed/MEDLINE and Scopus/ELSEVIER databases (covering the years 2002 to 2022). Inclusion criteria mandated studies to report on individuals aged 18 years and above with histologically confirmed stage IV CRC. Additionally, studies needed to provide data on VD levels before chemotherapy, along with hazard ratios (HR) and 95% confidence intervals (CIs) for overall survival (OS) and/or progression-free survival (PFS). Five articles were identified with the aim of establishing a combined risk estimate for death and progression based on pre-chemotherapy VD levels. Heterogeneity among studies and publication bias were evaluated using Tau2, I2 statistics, and a Funnel plot. RESULTS: Although no significant heterogeneity was observed in time-to-outcome among the selected studies, variations in technical assessments and serum VD concentration measurements were noted. The pooled analysis, involving 1712 patients for OS and 1264 patients for PFS, revealed a 47% increased risk of death (HR: 1.47, 95% CI: 1.21-1.79) and a 38% increased risk of progression (HR: 1.38, 95% CI: 1.13-1.70) for patients with lower VD levels, as indicated by fixed-effects models. CONCLUSIONS: Our results emphasize the adverse effects of low VD concentration on the time-to-outcome in metastatic CRC patients. This underscores the importance of investigating VD supplementation as an innovative approach in this clinical setting to enhance patient outcomes.

Machine Learning and Radiomics Analysis for Tumor Budding Prediction in Colorectal Liver Metastases Magnetic Resonance Imaging Assessment.

PURPOSE: We aimed to assess the efficacy of machine learning and radiomics analysis using magnetic resonance imaging (MRI) with a hepatospecific contrast agent, in a pre-surgical setting, to predict tumor budding in liver metastases. METHODS: Patients with MRI in a pre-surgical setting were retrospectively enrolled. Manual segmentation was made by means 3D Slicer image computing, and 851 radiomics features were extracted as median values using the PyRadiomics Python package. Balancing was performed and inter- and intraclass correlation coefficients were calculated to assess the between observer and within observer reproducibility of all radiomics extracted features. A Wilcoxon-Mann-Whitney nonparametric test and receiver operating characteristics (ROC) analysis were carried out. Balancing and feature selection procedures were performed. Linear and non-logistic regression models (LRM and NLRM) and different machine learning-based classifiers including decision tree (DT), k-nearest neighbor (KNN) and support vector machine (SVM) were considered. RESULTS: The internal training set included 49 patients and 119 liver metastases. The validation cohort consisted of a total of 28 single lesion patients. The best single predictor to classify tumor budding was original_glcm_Idn obtained in the T1-W VIBE sequence arterial phase with an accuracy of 84%; wavelet_LLH_firstorder_10Percentile was obtained in the T1-W VIBE sequence portal phase with an accuracy of 92%; wavelet_HHL_glcm_MaximumProbability was obtained in the T1-W VIBE sequence hepatobiliary excretion phase with an accuracy of 88%; and wavelet_LLH_glcm_Imc1 was obtained in T2-W SPACE sequences with an accuracy of 88%. Considering the linear regression analysis, a statistically significant increase in accuracy to 96% was obtained using a linear weighted combination of 13 radiomic features extracted from the T1-W VIBE sequence arterial phase. Moreover, the best classifier was a KNN trained with the 13 radiomic features extracted from the arterial phase of the T1-W VIBE sequence, obtaining an accuracy of 95% and an AUC of 0.96. The validation set reached an accuracy of 94%, a sensitivity of 86% and a specificity of 95%. CONCLUSIONS: Machine learning and radiomics analysis are promising tools in predicting tumor budding. Considering the linear regression analysis, there was a statistically significant increase in accuracy to 96% using a weighted linear combination of 13 radiomics features extracted from the arterial phase compared to a single radiomics feature.

Exploring the Spectrum of VEGF Inhibitors' Toxicities from Systemic to Intra-Vitreal Usage in Medical Practice.

The use of Vascular Endothelial Growth Factor inhibitors (VEGFi) has become prevalent in the field of medicine, given the high incidence of various pathological conditions necessitating VEGF inhibition within the general population. These conditions encompass a range of advanced neoplasms, such as colorectal cancer, non-small cell lung cancer, renal cancer, ovarian cancer, and others, along with ocular diseases. The utilization of VEGFi is not without potential risks and adverse effects, requiring healthcare providers to be well-prepared for identification and management. VEGFi can be broadly categorized into two groups: antibodies or chimeric proteins that specifically target VEGF (bevacizumab, ramucirumab, aflibercept, ranibizumab, and brolucizumab) and non-selective and selective small molecules (sunitinib, sorafenib, cabozantinib, lenvatinib, regorafenib, etc.) designed to impede intracellular signaling of the VEGF receptor (RTKi, receptor tyrosine kinase inhibitors). The presentation and mechanisms of adverse effects resulting from VEGFi depend primarily on this distinction and the route of drug administration (systemic or intra-vitreal). This review provides a thorough examination of the causes, recognition, management, and preventive strategies for VEGFi toxicities with the goal of offering support to oncologists in both clinical practice and the design of clinical trials.

Trend analysis of proton pump inhibitor consumption and expenditure: The real-world evidence.

INTRODUCTION: Proton pump inhibitors (PPIs) constitute a widely utilized pharmaceutical class, frequently associated with notable instances of therapeutic inappropriateness. Such patterns of misuse not only contribute to elevated healthcare expenditure, but may also exacerbate clinical conditions in certain patients. METHODS: A comprehensive analysis was conducted between 2019 and 2023 to assess all prescriptions dispensed using the Anatomical, Therapeutic and Chemical (ATC) classification system, which allowed trends among primary PPIs to be visualized. This was achieved by calculating the defined daily dose (DDD) and then defining the total expenditure incurred on these drugs. RESULTS: With regard to the prescription of PPIs, an upward trend in consumption was observed with a decreasing expenditure, due to the phenomena of drug generics and increased competition between pharmaceutical companies, ranging from €9,512,481.22 in the first six months of 2019 to €8,509,820.80 in the first six months of 2023. From 2019 to 2023, consumption increased by approximately 3 million DDDs for a total ranging from 18,483,167.59 DDDs to 21,480,871.00 DDDs. Pantoprazole and esomeprazole, the most expensive drugs compared to omeprazole, rabeprazole and lansoprazole, accounted for 61.4% of therapies in the first six months of 2023, up from 2019, where these two drugs were prescribed 54.9%. CONCLUSION: Within this analysis, we provide an illustrative representation of the prescribing trends for PPIs within a European context. Omeprazole, rabeprazole and lansoprazole appear to be the cheapest drugs compared to pantoprazole and esomeprazole. However, the results show that the most widely used PPIs, despite their therapeutic equivalence, are precisely the high-cost ones, thus generating higher expenditure for central governments.

Analysis of biosimilars consumption in an Italian Local Health Authority.

Biosimilar drugs offer an opportunity for all global healthcare systems because they provide significant cost savings while ensuring equal efficacy and safety in the treatment of chronic diseases. These savings can be allocated to support ongoing innovation. An analysis of the usage of major biosimilar drugs across various therapeutic areas has been conducted within an Italian healthcare company serving a population of over one million. Data on consumption, expenditure, and the number of treated patients has been extracted from the company's databases. Finally, a comparison with the year 2021 has been performed to determine if biosimilar drug usage increased in 2022. In 2022, the data reveals that a substantial portion of the analysed active ingredients are being used as biosimilar drugs, except in a few residual cases. However, among the most consumed drugs, resistance still exists in the case of Adalimumab and Etanercept, for which expenditure on originator drugs exceeds 2 million euros. The 2022-2021 comparison highlights the increasing use of biosimilar drugs. This data is encouraging and suggests that in the coming months, we may achieve total utilization, which would be to the benefit of the National Healthcare System (NHS) and the citizens who can rely on an efficient and sustainable healthcare policy that is continually improving.

Intrahepatic cholangiocarcinoma biomarkers: Towards early detection and personalized pharmacological treatments.

Cholangiocarcinoma (CCA) is a rare malignancy originating from the biliary tree and is anatomically categorized as intrahepatic (iCCA), perihilar, and extrahepatic or distal. iCCA, the second most prevalent hepatobiliary cancer following hepatocellular carcinoma (HCC), constitutes 5-20 % of all liver malignancies, with an increasing incidence. The challenging nature of iCCA, combined with nonspecific symptoms, often leads to late diagnoses, resulting in unfavorable outcomes. The advanced phase of this neoplasm is difficult to treat with dismal results. Early diagnosis could significantly reduce mortality attributed to iCCA but remains an elusive goal. The identification of biomarkers specific to iCCA and their translation into clinical practice could facilitate diagnosis, monitor therapy response, and potentially reveal novel interventions and personalized medicine. In this review, we present the current landscape of biomarkers in each of these contexts. In addition to CA19.9, a widely recognized biomarker for iCCA, others such as A1BG, CYFRA 21-1, FAM19A5, MMP-7, RBAK, SSP411, TuM2-PK, WFA, etc., as well as circulating tumor DNA, RNA, cells, and exosomes, are under investigation. Advancing our knowledge and monitoring of biomarkers may enable us to improve diagnosis, prognostication, and apply treatments dynamically and in a more personalized manner.

Revolutionizing KRAS p.G12C therapy in metastatic colorectal cancer: The triumph of dual inhibition.

The management of refractory metastatic colorectal cancer patients with the KRAS p.G12C mutation presents a significant unmet need, with limited success using standard therapies. The study by Fakih et al. highlights the potential of sotorasib and panitumumab combination therapy in this clinical context, paving the way for a promising personalized therapeutic approach.1.

Emerging treatment approaches for triple-negative breast cancer.

Approximately, 15% of global breast cancer cases are diagnosed as triple-negative breast cancer (TNBC), identified as the most aggressive subtype due to the simultaneous absence of estrogen receptor, progesterone receptor, and HER2. This characteristic renders TNBC highly aggressive and challenging to treat, as it excludes the use of effective drugs such as hormone therapy and anti-HER2 agents. In this review, we explore standard therapies and recent emerging approaches for TNBC, including PARP inhibitors, immune checkpoint inhibitors, PI3K/AKT pathway inhibitors, and cytotoxin-conjugated antibodies. The mechanism of action of these drugs and their utilization in clinical practice is explained in a pragmatic and prospective manner, contextualized within the current landscape of standard therapies for this pathology. These advancements present a promising frontier for tailored interventions with the potential to significantly improve outcomes for TNBC patients. Interestingly, while TNBC poses a complex challenge, it also serves as a paradigm and an opportunity for translational research and innovative therapies in the field of oncology.

Evaluating Nutritional Risk Factors for Delirium in Intensive-Care-Unit Patients: Present Insights and Prospects for Future Research.

Malnutrition, hypercatabolism, and metabolic changes are well-established risk factors for delirium in critically ill patients. Although the exact mechanisms are not fully understood, there is mounting evidence suggesting that malnutrition can cause a variety of changes that contribute to delirium, such as electrolyte imbalances, immune dysfunction, and alterations in drug metabolism. Therefore, a comprehensive metabolic and malnutrition assessment, along with appropriate nutritional support, may help to prevent or ameliorate malnutrition, reduce hypercatabolism, and improve overall physiological function, ultimately lowering the risk of delirium. For this aim, bioelectrical impedance analysis can represent a valuable strategy. Further research into the underlying mechanisms and nutritional risk factors for delirium is crucial to developing more effective prevention strategies. Understanding these processes will allow clinicians to personalize treatment plans for individual patients, leading to improved outcomes and quality of life in the intensive-care-unit survivors.