Personalized Treatment Strategies Research Articles

Guiding treatment decisions in renal cell carcinoma: the role of biomarkers and clinical factors.

Immune checkpoint inhibitors (ICIs) have transformed the treatment landscape for metastatic renal cell carcinoma (mRCC), significantly improving overall survival and achieving durable responses. This review is timely due to the increasing number of ICI-based regimens now considered standard care for RCC. There is an urgent need to identify reliable biomarkers that can predict therapeutic responses and resistance, a key challenge in current research. While tumor-specific factors such as pathological characteristics, genomic mutations, and transcriptional profiles have been extensively studied, no definitive predictive biomarker has yet emerged. Additionally, advanced technologies are being explored to address tumor heterogeneity. Recent research has focused on novel areas such as the microbiome, radiomics, and spatial transcriptomics, which show promise as potential biomarkers. The translation of these emerging biomarker findings into clinical practice is essential to improving personalized treatment strategies for RCC. Until reliable biomarkers are clinically available, clinical factors may play a pivotal role in guiding individualized treatment decisions to optimize patient outcomes.

Integrated immunogenomic analyses of single-cell and bulk profiling construct a T cell-related signature for predicting prognosis and treatment response in osteosarcoma.

T cells play a crucial role as regulators of anti-tumor activity within the tumor microenvironment (TME) and are closely associated with the progression of osteosarcoma (OS). Nevertheless, the specific role of T cell-related genes (TCRGs) in the pathogenesis of OS remains unclear. First, we processed single-cell RNA sequencing (scRNA-seq) data of OS from the public databases and performed cell annotation. We identified highly variable genes in each cell type using the "FindAllMarkers" function, explored the distribution of different clusters, and investigated inter-cellular communication patterns via the "CellChat" framework. Then, we used multivariate Cox analysis to construct a TCRG and developed a nomogram to predict survival probabilities for OS patients. Finally, we validated the aforementioned results using various cell lines and investigated the immune cell infiltration, expression of immune checkpoints, chemotherapy sensitivity, and the efficacy of targeted therapies across different risk groups. From the scRNA-seq data, we identified 3,000 highly variable genes, presented the top 10 genes, and validated the expression of core genes across different cell lines.Moreover, our analysis delved into interactions between T cells and other cell types. Our analyses constructed a predictive T cell-related signature (TCRS) that incorporated these prognostic TCRGs, showing a clear prognostic separation between the high-risk and low-risk OS patient groups in multiple cohorts. Survival analysis indicated better outcomes for patients classified in the high-risk group. The low-risk group exhibited elevated levels of CD4 memory resting T cells, contrasting with the higher levels of macrophage M0 observed in the high-risk group via the CIBERSORT algorithm. Furthermore, we observed that the low-risk group exhibitedAQ1 significant up-regulation of immune checkpoint genes (ICGs) and lower Tumour Immune Dysfunction and Exclusion (TIDE) scores, suggesting that they may be suitable for immunotherapy. Conversely, the high-risk group appeared more responsive to chemotherapy and targeted therapies, according to our drug sensitivity analysis. In conclusion, our study identified TCRGs, constructed and validated a TCRS for OS, and assessed immune response and drug sensitivity in different risk groups of OS patients. These findings provide novel insights into personalized treatment strategies for OS, potentially guiding more effective therapeutic interventions.

Examining cardiac toxicity in HER2-positive breast cancer patients using trastuzumab and its influencing factors at Iran Hospital.

Trastuzumab is primarily utilized in the treatment of patients with human epidermal growth factor receptor 2 (HER2) positive breast cancer. This study aimed to investigate the incidence of cardiac toxicity associated with trastuzumab in HER2-positive breast cancer patients at Iran Hospital in 2023, as well as the factors influencing this toxicity. In this cross-sectional study, 200 patients diagnosed with HER2-positive breast cancer and receiving trastuzumab were included. The criteria for heart failure in this study were defined as an ejection fraction (EF) of less than 50% or a decrease of greater than 10% in EF. Descriptive statistics, the chi-square statistical test, Fisher's exact test, and logistic regression analyses were employed to assess the variables. A p-value of less than 0.05 was deemed statistically significant. The mean age of the participants was 51.5 ± 2.5 years. The odds ratio (OR) for the variable of anthracyclines was 1.3 (95% CI: 1.2-1.4); for opium use, the OR was 2.7 (95% CI: 0.9-8.5); for diabetes, the OR was 2.7 (95% CI: 1.2-5.9); for ischemic heart disease, the OR was 3.5 (95% CI: 1.6-7.7); and for hypertension, the OR was 4.8 (95% CI: 2.1-10.7). The OR for obesity was 1.45 (95% CI: 1.01-2.18), and the OR for age was 1.10 (95% CI: 1.01-1.12). No statistically significant association was found between opium use and cardiotoxicity (p = 0.07). This research contributes to the identification of factors that may predict responses to anthracyclines and the potential for cardiotoxicities. Ultimately, this information could inform the development of more personalized treatment strategies.

A Scoping Review on the Application of Artificial Intelligence in Treating Rare Diseases

Rare diseases affect a small percentage of the population but often present significant challenges in diagnosis, treatment, and management due to their complex and heterogeneous nature. Recent advancements in artificial intelligence (AI) have shown promising potential to address these challenges by improving early diagnosis, personalized treatment strategies, and drug discovery. This scoping review explores the current landscape of AI applications in rare disease treatment. We examine the role of AI-driven tools in enhancing diagnostic accuracy, optimizing treatment pathways, and facilitating drug repurposing for rare conditions. Additionally, we highlight the limitations and ethical concerns associated with AI implementation, such as data privacy, the need for high-quality datasets, and algorithmic transparency. The findings suggest that while AI holds transformative potential, its integration into clinical practice for rare diseases requires multidisciplinary collaboration, ongoing research, and regulatory oversight.

Neurophysiological markers of early cognitive decline in older adults: a mini-review of electroencephalography studies for precursors of dementia

The early detection of cognitive decline in older adults is crucial for preventing dementia. This mini-review focuses on electroencephalography (EEG) markers of early dementia-related precursors, including subjective cognitive decline, subjective memory complaints, and cognitive frailty. We present recent findings from EEG analyses identifying high dementia risk in older adults, with an emphasis on conditions that precede mild cognitive impairment. We also cover event-related potentials, quantitative EEG markers, microstate analysis, and functional connectivity approaches. Moreover, we discuss the potential of these neurophysiological markers for the early detection of cognitive decline as well as their correlations with related biomarkers. The integration of EEG data with advanced artificial intelligence technologies also shows promise for predicting the trajectory of cognitive decline in neurodegenerative disorders. Although challenges remain in its standardization and clinical application, EEG-based approaches offer non-invasive, cost-effective methods for identifying individuals at risk of dementia, which may enable earlier interventions and personalized treatment strategies.

Optimising hypoxia PET imaging and its applications in guiding targeted radiation therapy for non-small cell lung cancer: a scoping review.

Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related death. Definitive treatment includes chemotherapy and radiation therapy. Tumour hypoxia impacts the efficacy of these treatment modalities. Novel positron-emission tomography (PET) imaging has been developed to non-invasively quantify hypoxic tumour subregions, and to guide personalised treatment strategies. This review evaluates the reliability of hypoxia imaging in NSCLC in relation to various tracers, its correlations to treatment-related outcomes, and to assess if this imaging modality can be meaningfully applied into radiation therapy workflows. A literature search was conducted on the Medline (Ovid) and Embase databases. Searches included terms related to 'hypoxia', 'positron-emission tomography', 'magnetic resonance imaging' and 'lung cancer'. Results were filtered to exclude studies prior to 2011, and animal studies were excluded. Only studies referring to a confirmed pathology of NSCLC were included, while disease staging was not a limiting factor. Full-text English language and translated literature examined included clinical trials, clinical cohort studies and feasibility studies. Quantification of hypoxic volumes in a pre-treatment setting is of prognostic value, and indicative of treatment response. Dosimetric comparisons have highlighted potential to significantly dose escalate to hypoxic volumes without risk of additional toxicity. However, clinical data to support these strategies are lacking. Heterogenous study design and non-standardised imaging parameters have led to a lack of clarity regarding the application of hypoxia PET imaging in NSCLC. PET imaging using nitroimidazole tracers is the most investigated method of non-invasively measuring tumour hypoxia and has potential to guide hypoxia-targeted radiation therapy. Further clinical research is required to elucidate the benefits versus risks of dose-escalation strategies.

Bioinformatics investigation of the prognostic value and mechanistic role of CD9 in glioma

In recent years, CD9 has been extensively studied as a potential biomarker for cancer. However, the biological role of CD9 in gliomas remains unclear. This study investigates the function of CD9 in gliomas and its molecular mechanisms. Utilizing pan-cancer analysis with TCGA, CGGA, and GEO databases, differential expression of CD9 was observed in 11 tumor types within the TCGA cohort, and it was associated with patient survival rates. Analysis of the CGGA glioma database revealed that patients with high CD9 expression had lower survival rates. The area under the ROC curve (AUC) for GSE16011 was greater than 0.7, indicating a high discriminative ability. Through gene set enrichment analysis (GSEA), immune-related analysis, and CD9 mutation detection, CD9 was found to have the strongest correlation with neutrophil involvement (cor = 0.30, P < 0.05), and the high CD9 expression group exhibited higher rejection responses and TIDE scores, suggesting a lower likelihood of successful immunotherapy. The high CD9 expression group was more sensitive to 81 drugs, indicating potential therapeutic effects for gliomas. Furthermore, overexpression of CD9 in gliomas may be associated with gene mutations. Down-regulation or up-regulation of CD9 expression in the glioblastoma cell line LN229 showed that CD9 could positively regulate the migratory ability of LN229 cells. Further, several marker genes, such as VEGFR-2, TGF-β1, CASP1 and PI3K, were down regulated in CD9 knockdown cell lines and up regulated in CD9 overexpression cell lines, compared with control cell line. This study preliminarily explores the role of CD9 in gliomas and its prognostic value, providing new insights for personalized treatment strategies in glioma therapy.

Treatment preference archetypes in eosinophilic esophagitis and their implications for therapy.

Little is known about how patients make decisions about and prioritize, therapies and disease management in eosinophilic esophagitis (EoE). We aimed to systematically identify and characterize patient perspectives and attitudes that influence decision making for EoE management. To understand the diverse attitudes and values of EoE patients, we designed a study using the Q-method. We iteratively developed 31 statements related to EoE disease management. Participants sorted statements by ranking from +4 (most agree) to -4 (most disagree). By-person factor analysis, using 2- and 3- factor rotation, revealed distinct preference archetypes. Thirty-four adults with EoE (mean age 40.9, 51.4% male, 82.9% White) were recruited from gastroenterology and allergy clinics from a single center. We identified two treatment-centered archetypes: Medication preference, driven by symptoms and the desire to minimize risk of complications, and Natural treatment preference, focusing on identifying trigger foods and diet adherence. Three-factor analysis revealed an additional archetype: Treatment ambivalent, a view of EoE as a mild and episodic (not chronic) disease with low priority to treat. Comparison by factor revealed 54% of those in the natural preference archetype were recategorized as treatment ambivalent, suggesting that they see natural treatment as a less complicated or milder strategy and may be at risk of nonadherence and reduced treatment uptake. We identified three distinct treatment preference archetypes among individuals with EoE, underscoring the need for personalized treatment strategies, especially for those favoring natural approaches but masking ambivalence, and may be at risk for nonadherence or loss to follow-up.

Impact of extended endocrine therapy for patients with risk factors for late recurrence in estrogen receptor-positive, human epidermal growth factor receptor 2-negative breast cancer after 5years of endocrine therapy.

Extended endocrine therapy shows promise for reducing the recurrence of estrogen receptor (ER)-positive, human epidermal growth factor receptor 2 (HER2)-negative breast cancer. However, its benefits in patients with high-risk factors for late recurrence remain unclear, particularly in premenopausal patients. In this study, we aimed to explore the impact of extended endocrine therapy in patients with risk factors for late recurrence of postmenopausal and premenopausal ER-positive, HER2-negative breast cancer. We retrospectively analyzed data from patients with ER-positive, HER2-negative breast cancer at Tohoku Kosai Hospital who were disease-free after 5years of adjuvant endocrine therapy. The patients were classified as high risk based on lymph node positivity, tumor size > 2cm, or high tumor grade. The high-risk group was further divided into extended therapy and stop groups. Propensity score matching was applied to balance baseline characteristics. Disease-free survival (DFS) was the primary endpoint. Among the 1474 eligible patients, 224 received extended endocrine therapy, and 1250 stopped therapy. After propensity score matching, the high-risk group comprised 348 patients (n = 174 patients/group). The extended therapy group had significantly higher 10-year DFS and distant DFS rates than the stop group. The multivariate Cox model indicated a 69% reduction in recurrence risk in the extended therapy group. Extended endocrine therapy can substantially improve DFS in patients with high-risk ER-positive, HER2-negative breast cancer, especially in those with large tumors, lymph node involvement, and high tumor grade. These findings support personalized treatment strategies for enhancing long-term outcomes.

The Study Of Dermatoglyphics Pattern In Females With Primary Infertility

Background: Dermatoglyphics, the study of fingerprint ridge patterns, has been recognized for its potential in reflecting genetic and developmental variations. Primary infertility, defined as the inability to conceive after a year of regular, unprotected intercourse, is influenced by genetic, hormonal, and environmental factors. This study aims to investigate dermatoglyphic patterns in females with primary infertility to explore possible correlations between these patterns and infertility, providing insights for early diagnosis and management. Objective: The primary objective of this protocol is to systematically analyze dermatoglyphic patterns in females with primary infertility and compare them with those of fertile females. The study seeks to identify specific fingerprint characteristics that may serve as non-invasive markers for primary infertility. Methodology: This cross-sectional study will include two groups: females diagnosed with primary infertility and age-matched fertile controls. Dermatoglyphic data will be collected using the ink method, and the following parameters will be analyzed: total ridge count (TRC), patterns of arches, loops, and whorls, and other key qualitative features. Statistical analysis will be performed to evaluate significant differences between the two groups. Expected outcome: The study is expected to reveal distinct dermatoglyphic patterns in females with primary infertility compared to fertile females. Identifying such patterns may provide valuable markers for assessing genetic predispositions and could potentially assist in early diagnosis and personalized treatment strategies for infertility. Conclusion: This protocol outlines a method for studying the potential role of dermatoglyphic patterns in primary infertility, proposing a non-invasive, cost-effective diagnostic approach. Further research may help in establishing dermatoglyphics as a supplementary tool in infertility assessment and managemen.

Using interpretable machine learning to predict bloodstream infection and antimicrobial resistance in patients admitted to ICU: Early alert predictors based on EHR data to guide antimicrobial stewardship.

Nosocomial infections and Antimicrobial Resistance (AMR) stand as formidable healthcare challenges on a global scale. To address these issues, various infection control protocols and personalized treatment strategies, guided by laboratory tests, aim to detect bloodstream infections (BSI) and assess the potential for AMR. In this study, we introduce a machine learning (ML) approach based on Multi-Objective Symbolic Regression (MOSR), an evolutionary approach to create ML models in the form of readable mathematical equations in a multi-objective way to overcome the limitation of standard single-objective approaches. This method leverages readily available clinical data collected upon admission to intensive care units, with the goal of predicting the presence of BSI and AMR. We further assess its performance by comparing it to established ML algorithms using both naturally imbalanced real-world data and data that has been balanced through oversampling techniques. Our findings reveal that traditional ML models exhibit subpar performance across all training scenarios. In contrast, MOSR, specifically configured to minimize false negatives by optimizing also for the F1-Score, outperforms other ML algorithms and consistently delivers reliable results, irrespective of the training set balance with F1-Score.22 and.28 higher than any other alternative. This research signifies a promising path forward in enhancing Antimicrobial Stewardship (AMS) strategies. Notably, the MOSR approach can be readily implemented on a large scale, offering a new ML tool to find solutions to these critical healthcare issues affected by limited data availability.

Dual-energy computed tomography for predicting histological grading and survival in patients with pancreatic ductal adenocarcinoma.

We evaluated the value of dual-energy computed tomography (DECT) parameters derived from pancreatic ductal adenocarcinoma (PDAC) to discriminate between high- and low-grade tumors and predict overall survival (OS) in patients. Data were retrospectively collected from 169 consecutive patients with pathologically confirmed PDAC who underwent third-generation dual-source DECT enhanced dual-phase scanning before surgery between January 2017 and March 2023. Patients with prior treatments, other malignancies, small tumors, or poor-quality scans were excluded. Two radiologists evaluated three clinical and seven radiological features and measured sixteen DECT-derived parameters. Univariate and multivariate analyses were applied to select independent predictors. A prediction model and a corresponding nomogram were developed, and the area under the curve (AUC), calibration, and clinical applicability were assessed. The correlations between factors and OS were evaluated using Kaplan-Meier survival and Cox regression analyses. One hundred sixty-nine patients were randomly divided into training (n = 118) and validation (n = 51) cohorts, among which 43 (36.4%) and 19 (37.3%) had high-grade PDAC confirmed by pathology, respectively. The vascular invasion, normalized iodine concentration in the venous phase, and effective atomic number in the venous phase were independent predictors for histological grading. A nomogram was constructed to predict the risk of high-grade tumors in PDAC, with AUCs of 0.887 and 0.844 in the training and validation cohorts, respectively. The nomogram exhibited good calibration and was more beneficial than a single parameter in both cohorts. Pathological- and nomoscore-predicted high-grade PDACs were associated with poor OS (all p < 0.05). The nomogram, which combines DECT parameters and radiological features, can predict the histological grade and OS in patients with PDAC before surgery. Question Preoperative determination of histological grade in PDAC is crucial for guiding treatment, yet current methods are invasive and limited. Findings A DECT-based nomogram combining vascular invasion, normalized iodine concentration, and effective atomic number accurately predicts histological grade and OS in PDAC patients. Clinical relevance The DECT-based nomogram is a reliable, non-invasive tool for predicting histological grade and OS in PDAC. It provides essential information to guide personalized treatment strategies, potentially improving patient management and outcomes.

Rapid detection of drugs in blood using “molecular hook” surface-enhanced Raman spectroscopy and artificial intelligence technology for clinical applications

Accurate detection of cardiovascular drugs in blood is complicated by interference from serum biomolecules. This study introduces a novel surface-enhanced Raman spectroscopy (SERS) platform incorporating “molecular hooks” to capture small drug molecules while excluding larger biomolecules selectively. The self-assembled nanoparticles with the A13 molecule enhance Raman signals by creating dense electromagnetic “hotspot” regions, achieving detection limits of 10 pg/mL for dobutamine hydrochloride and 10 ng/mL for milrinone-substantially below therapeutic thresholds. Artificial intelligence (AI) integration enables automated spectral analysis, allowing rapid and precise drug detection in clinical blood samples. This approach offers a transformative solution for real-time diagnostics, significantly advancing personalized treatment strategies in clinical settings.

YAP1 Status Defines Two Intrinsic Subtypes of LCNEC with Distinct Molecular Features and Therapeutic Vulnerabilities.

Large cell neuroendocrine carcinoma (LCNEC) is a high-grade neuroendocrine malignancy that, like small cell lung cancer (SCLC), is associated with the absence of druggable oncogenic drivers and dismal prognosis. In contrast to SCLC, however, there is little evidence to guide optimal treatment strategies, which are often adapted from SCLC and non-small cell lung cancer approaches. To better define the biology of LCNEC, we analyzed cell line and patient genomic data and performed IHC and single-cell RNA sequencing of core needle biopsies from patients with LCNEC and preclinical models. In this study, we demonstrate that the presence or absence of YAP1 distinguishes two subsets of LCNEC. The YAP1-high subset is mesenchymal and inflamed and is characterized, alongside TP53 mutations, by co-occurring alterations in CDKN2A/B and SMARCA4. Therapeutically, the YAP1-high subset demonstrates vulnerability to MEK- and AXL-targeting strategies, including a novel preclinical AXL chimeric antigen receptor-expressing T cell. Meanwhile, the YAP1-low subset is epithelial and immune-cold and more commonly features TP53 and RB1 co-mutations, similar to those observed in pure SCLC. Notably, the YAP1-low subset is also characterized by the expression of SCLC subtype-defining transcription factors, especially ASCL1 and NEUROD1, and as expected, given its transcriptional similarities to SCLC, exhibits putative vulnerabilities reminiscent of SCLC, including delta-like ligand 3 and CD56 targeting, as is with novel preclinical delta-like ligand 3 and CD56 chimeric antigen receptor-expressing T cells, and DNA damage repair inhibition. YAP1 defines distinct subsets of LCNEC with unique biology. These findings highlight the potential for YAP1 to guide personalized treatment strategies for LCNEC.

The effect of antidiabetic drugs on bone metabolism: a concise review.

Diabetes mellitus (DM) is a complex metabolic disorder characterized by chronic hyperglycemia, which derives from either insufficient insulin production [type 1 diabetes mellitus (T1DM)] or both impaired insulin sensitivity along with inadequate insulin production [type 2 diabetes mellitus (T2DM)] and affects millions of people worldwide. In addition to the adverse effects of DM on classical target organs and tissues, skeletal health can also be adversely affected. There is considerable evidence linking DM with osteoporosis. The fracture risk in patients with DM differs upon the type of diabetes, and it appears to be related to the type of anti-diabetic treatment. Antidiabetic drugs may have various effects on bone health. Most of them have neutral or even favorable effects on bone metabolism with the exception of thiazolidinediones (TZDs). Some studies suggest that TZDs may have negative impact on bone health by decreasing bone formation and increasing the fracture risk. There are also limited studies linking the use of canagliflozin, a Sodium-glucose contransporter-2 inhibitor (SGLT2i), with increased fracture risk. On the other hand, therapies that are based on incretin effect, like Dipeptidyl peptidase-4 inhibitors (DPP-4i) and Glucagon-like peptide-1 receptor agonizts (GLP-1RAs) might have positive effects on bone health by promoting bone formation. Herein we review the impact of antidiabetic drugs on bone health, highlighting the potential benefits and risks associated with these medications in an attempt to contribute to the development of personalized treatment strategies for individuals with DM.

Multigene Panel Next-Generation Sequencing Techniques in the Management of Patients with Metastatic Colorectal Carcinoma: The Way Forward for Personalized Treatment? A Single-Center Experience

The efficacy and cost-effectiveness of Multigene Panel Next-Generation Sequencing (NGS) in directing patients towards genomically matched therapies remain uncertain. This study investigated metastatic colorectal cancer (mCRC) patients who underwent NGS analysis on formalin-fixed paraffin-embedded tumor samples. Data from 179 patients were analyzed, revealing no mutations in 39 patients (21.8%), one mutation in 83 patients (46.4%), and two or more mutations in 57 patients (31.8%). KRAS mutations were found in 87 patients (48.6%), including KRAS G12C mutations in 5 patients (2.8%), PIK3CA mutations in 40 patients (22.4%), and BRAF mutations in 26 patients (14.5%). Less common mutations were identified: ERBB2 in five patients (2.8%) and SMO in four patients (2.2%). Additionally, MAP2K1, CTNNB1, and MYC were mutated in three patients (2.4%). Two mutations (1.1%) were observed in ERBB3, RAF1, MTOR, JAK1, and FGFR2. No significant survival differences were observed based on number of mutations. In total, 40% of patients had druggable molecular alterations, but only 1.1% received genomically guided treatment, suggesting limited application in standard practice. Despite this, expanded gene panel testing can identify actionable mutations, aiding personalized treatment strategies in metastatic CRC, although current eligibility for biomarker-guided trials remains limited.

Unveiling the Complexities of Hereditary Angioedema

Hereditary angioedema (HAE) is a rare and potentially life-threatening genetic disorder, constituting approximately 2% of all clinical cases of angioedema, with a global prevalence estimated between 1 in 50,000 and 1 in 150,000 individuals. The condition affects individuals of all genders and ethnic backgrounds without significant variation. HAE is classified into three types. Type I HAE, which accounts for 85% of cases, is characterized by a deficiency of the C1 esterase inhibitor (C1-INH) gene. Type II HAE, making up 15% of cases, involves a dysfunctional C1-INH. Type III HAE, which represents about 5% to 10% of cases, is often estrogen-dependent and although several mutations have been identified, it typically involves normal C1-INH activity. Despite the differences in C1-INH functionality, all three types of HAE manifest with similar clinical symptoms. HAE leads to recurrent episodes of non-pruritic angioedema, which occurs in the absence of urticaria. Breakthroughs in understanding HAE pathophysiology have revolutionized treatment, leading to the development of highly targeted therapies for both acute management and long-term prevention. Meanwhile, cutting-edge advancements in omics technologies are unlocking new possibilities for biomarker discovery, paving the way for more precise diagnoses and personalized treatment strategies that could significantly enhance patient outcomes. This review will delve into the intricate pathophysiology, diverse clinical presentations, and diagnostic challenges of HAE while exploring emerging biomarkers and innovative approaches to therapeutic management and prevention strategies. Additionally, it will underscore the vital importance of screening family members of affected individuals, even when symptoms are not present.

Application and progress of 3D tumor models in breast cancer.

Due to its high heterogeneity and significant impact on women's health globally, breast cancer necessitates robust preclinical models to understand tumor biology and guide personalized treatment strategies. Three-dimensional (3D) in vitro tumor models hold immense promise in this regard. These tumor models not only mimic the spatial structure and growth environment of tumors in vivo, but also retain the pathological and genetic characteristics of solid tumors. This fidelity makes them powerful tools for accelerating advancements in fundamental research and translational medicine. The diversity, modularity, and efficacy of 3D tumor models are driving a biotechnological revolution. As these technologies become increasingly sophisticated, 3D tumor models are poised to become powerful weapons in the fight against breast cancer. This article expounds on the progress made in utilizing 3D tumor models for breast cancer research.

Image Analysis in Histopathology and Cytopathology: From Early Days to Current Perspectives

Both pathology and cytopathology still rely on recognizing microscopical morphologic features, and image analysis plays a crucial role, enabling the identification, categorization, and characterization of different tissue types, cell populations, and disease states within microscopic images. Historically, manual methods have been the primary approach, relying on expert knowledge and experience of pathologists to interpret microscopic tissue samples. Early image analysis methods were often constrained by computational power and the complexity of biological samples. The advent of computers and digital imaging technologies challenged the exclusivity of human eye vision and brain computational skills, transforming the diagnostic process in these fields. The increasing digitization of pathological images has led to the application of more objective and efficient computer-aided analysis techniques. Significant advancements were brought about by the integration of digital pathology, machine learning, and advanced imaging technologies. The continuous progress in machine learning and the increasing availability of digital pathology data offer exciting opportunities for the future. Furthermore, artificial intelligence has revolutionized this field, enabling predictive models that assist in diagnostic decision making. The future of pathology and cytopathology is predicted to be marked by advancements in computer-aided image analysis. The future of image analysis is promising, and the increasing availability of digital pathology data will invariably lead to enhanced diagnostic accuracy and improved prognostic predictions that shape personalized treatment strategies, ultimately leading to better patient outcomes.

Elucidating Clinical Queries for Tailored Therapy in Patients with Prolactinoma.

Prolactinomas are the most prevalent type of pituitary neuroendocrine adenomas, primarily affecting women of reproductive age. Unlike other pituitary tumors, the first-line management has traditionally been pharmacological rather than surgical. This preference is due to the effectiveness of dopamine agonists (DAs), which typically reduce tumor size and normalize prolactin levels in most patients. However, this does not imply that there is no room for improvement; the duration of treatment and medication side effects often lead to compliance issues among patients. Recent advances in surgical techniques and molecular biology have paved the way for the development of precision medicine, allowing for more flexible and personalized treatment strategies for prolactinomas. This review aims to enhance clinical decision-making and patient care for endocrinologists by focusing on several key factors: predictive markers of DA sensitivity, clinical characteristics and suitability for transsphenoidal adenomectomy as a potential first-line treatment, factors determining the successful withdrawal of DAs after prolonged use, safety concerns during pre/post-pregnancy and breastfeeding, and determinants of tumor aggressiveness. Through tailored therapy-a patient-focused, multidisciplinary approach- we aim to improve the management of prolactinoma patients.