Expert Review of Molecular Diagnostics最新文献

Introduction: New research has shown an intriguing link between the gut bacteria and the thyroid. A gut-thyroid relationship affects energy production, immunological function, and inflammation. As a result, disrupted gut flora harmony is associated with an increased/altered risk of thyroid dysfunction, autoimmune disorders, and metabolic imbalance. In addition to current diagnostic technology, understanding the gut flora-thyroid relationship could assist in the detection of thyroid-related conditions and modify patient treatment.

Areas covered: This review explores state-of-the-art molecular techniques, e.g. metagenomics profiling and metabolomics, to uncover clinically relevant microbiota-driven biomarkers related to thyroid disorders.

Expert opinion: Revealing potential microbiota-driven biomarker candidates is pivotal in enhancing our understanding of the mechanisms of thyroid disorders more precisely and identifying diagnostic and prognostic markers with clinical potential. Precisely, the individualization in the approach to patients with thyroid disorder, inevitably considering the harmonization of the gut microbiota-thyroid hormone relationship, is the basis of rational pharmacotherapy.

Introduction: CRISPR-based genome editing has revolutionized molecular diagnostics and therapeutic applications, offering precise genetic modifications. However, off-target effects remain a significant challenge, potentially leading to unintended mutations and genomic instability. Addressing these concerns is crucial for ensuring the clinical safety and efficacy of CRISPR technologies.

Areas covered: This review provides a comprehensive analysis of strategies for detecting and mitigating off-target effects in CRISPR genome editing. We discuss in silico, in vitro, and in vivo detection methods, highlighting their strengths and limitations. Additionally, we examine advancements in guide RNA (gRNA) engineering, improved Cas variants, optimized delivery systems, base and prime editing technologies, and anti-CRISPR proteins as key approaches to enhance genome-editing precision.

Expert opinion: While significant progress has been made in off-target detection and mitigation, challenges remain in accurately assessing unintended genomic modifications in vivo and ensuring long-term safety in clinical applications. Future research should focus on developing high-fidelity CRISPR variants, refining computational prediction models, and integrating multi-modal detection systems. Advancing delivery mechanisms and regulatory strategies will be essential for translating CRISPR into safe and effective therapeutic interventions.

Purpose: To construct a nomogram for predicting metabolic syndrome (MetS) in women with polycystic ovary syndrome.

Methods: In this retrospective study, we analyzed clinical and biochemical data from 859 Chinese women diagnosed with PCOS. Univariable logistic regression and forward stepwise logistic regression were employed to identify independent predictors of MetS. A predictive nomogram was developed that integrates age, acne status, body mass index (BMI), fasting insulin levels (FINS), and the ApoB/ApoA ratio. The model's discriminative performance, calibration accuracy, and clinical utility were assessed using the area under the receiver operating characteristic curve (AUC), calibration curves accompanied by Brier scores, Hosmer - Lemeshow tests, decision curve analysis (DCA), and clinical impact curves (CIC). Internal validation was conducted through bootstrap resampling over 1,000 iterations.

Results: The nomogram exhibited strong discriminative capability with an AUC of 0.874 (95% CI: 0.850-0.899), surpassing BMI alone which had an AUC of 0.824 (p < 0.0001). Both the calibration curve and Hosmer - Lemeshow test indicated satisfactory model fit. DCA and CIC analyses suggested that the nomogram could provide net clinical benefits for risk stratification among PCOS patients.

Conclusions: The proposed nomogram accurately predicts MetS risk in PCOS patients, supporting early identification and individualized management.

Introduction: HER2-targeted antibody-drug conjugates (ADCs) have shown promising outcomes in the treatment of patients with various HER2-expressing solid tumors. The efficacy of ADCs is influenced by several factors, such as the antibody properties, linker design, payload type and potency, drug-to-antibody ratio, and target antigen expression.

Areas covered: This review specifically examines target antigen expression and the companion diagnostic (CDx) assays used to select patients for treatment with HER2-targeted ADCs. Pivotal clinical trials involving the two FDA-approved HER2-targeted ADCs, trastuzumab emtansine (T-DM1) and trastuzumab deruxtecan (T-DXd), were identified and assessed to determine whether they included data on efficacy outcomes associated with varying levels of HER2 expression. In addition, the clinical utility of the current FDA-approved HER2 CDx assays for selecting patients eligible for treatment with HER2-targeted ADCs is also discussed.

Expert opinion: High HER2 expression (IHC 3+) markedly affects the efficacy of T-DM1 and T-DXd in various solid tumors. Current data suggest that IHC should be used as the primary CDx assay to select patients for treatment with HER2-targeted ADCs. This recommendation aligns with the mechanism of action of ADCs, wherein increased receptor density facilitates receptor-mediated endocytosis of the drug molecules.

Introduction: Aromatase inhibitors (AI) plus CDK4/6 inhibitors are the current first-line standard-of-care for hormone receptor (HR)-positive/HER2-negative advanced breast cancer (ABC). However, mutations in ESR1 (ESR1m), the gene encoding the estrogen receptor, can drive resistance to AI; their detection in circulating tumor DNA from liquid biopsies may be used to guide treatment.

Areas covered: A literature search was performed using PubMed and Pharmaspectra to evaluate the current ESR1m testing landscape and compare analytical performance of commercially available ESR1m assays. Twenty-eight publications and ten congress abstracts detailing assays for ESR1m detection in liquid biopsies were identified. These included four quantitative polymerase chain reaction (qPCR) ESR1m assays, four digital PCR (dPCR) ESR1m assays, and twelve next-generation sequencing (NGS)-based assays that all varied by ESR1m coverage, sensitivity, and specificity.

Expert opinion: A range of commercial assays are available for ESR1m detection in liquid biopsies from patients with HR-positive/HER2-negative ABC, and more are in development, with increasing clinical demand. qPCR, dPCR, and NGS assays can detect common ESR1m with differing sensitivity and specificity depending on the assay and amount of input DNA. Assay selection will vary depending on analytical performance, turnaround times, lab infrastructure and expertise, healthcare setting, and regional variation in market costs.

Introduction: Pediatric tuberculosis (TB), especially among children co-infected with HIV, remains a significant global health challenge. Traditional sputum-based diagnostics are less effective in this population due to difficulties in sample collection and paucibacillary disease, necessitating alternative non-sputum diagnostic approaches.

Areas covered: This review examines recent advances in non-sputum biosignatures for pediatric TB diagnosis, including host- and pathogen-based biomarkers detectable in blood, urine, stool and breath.

Expert opinion: Despite rapid advances in TB biosignature discovery, translation into usable diagnostics lags behind - especially for children and those with HIV. Studies often exclude these key populations, lack diverse validation, and depend on complex laboratory platforms. Bridging this gap requires early integration of feasibility, usability, and health system factors into product development. Multimodal, point-of-care tools adapted for low-resource settings and inclusive of high-risk children are essential. Implementation science and technology adaptation are critical to ensure real-world impact of these promising innovations.To summarize, non-sputum biosignatures offer an opportunity for early diagnosis of TB in children with HIV. However, these signatures need to be validated and be translated into affordable point-of-care tools that can be integrated with current diagnostic approaches and implemented in low resource settings.

Background: This systematic review and meta-analysis of diagnostic test accuracy evaluated the performance of noninvasive liquid biopsy methods for diagnosis of meningioma, the most common primary intracranial neoplasm in adults.

Research design and methods: Following PRISMA-DTA guidelines, we searched three databases. Data for 2 × 2 contingency tables were extracted, methodological quality was assessed using QUADAS-2, and a bivariate meta-analysis was performed.

Results: A total of 23 studies with 27 datasets were included. Pooled sensitivity was 0.78 (95% CI: 0.71 - 0.83) and specificity was 0.82 (95% CI: 0.76 - 0.86), with an area under the curve (AUC) of 0.86. Subgroup analysis showed that seroreactivity assays had the highest accuracy (diagnostic odds ratio or DOR = 67), while emerging technologies like cfDNA methylation showed high potential but were supported by limited data. The evidence was limited by significant publication bias (Deeks' test, p < 0.001), and meta-regression revealed that studies with a lower risk of bias reported significantly lower specificity (p = 0.008).

Conclusions: Fluid-based biomarkers show good overall accuracy for meningioma diagnosis, but the current evidence is compromised by methodological bias, suggesting that true performance is more modest than reported. Rigorous, prospective validation of promising approaches like cfDNA methylation and seroreactivity is essential to clarify their clinical utility.

Introduction: Molecular diagnostics in Low- and Middle-Income Countries (LMICs) face significant barriers: limited expertise, geographical access, and infrastructure. These impede effective disease management and public health. Traditional workflows lack scalability and objectivity.

Areas covered: This review explores how Digital Pathology (DP) and Artificial Intelligence (AI) can enhance LMIC molecular diagnostics. DP, via whole slide imaging, enables remote expert consultation and quality control. AI integration automates quantitative analysis (e.g. cell counting, biomarker scoring), facilitating rapid interpretation and extending specialized diagnostic reach. We discuss successful telepathology pilots and their cost-effectiveness. The manuscript highlights DP/AI's capacity to bolster molecular screening and accelerate research, while addressing implementation barriers: infrastructure, cost, training, and regulation.

Expert opinion: Strategic integration of Digital Pathology (DP) and Artificial Intelligence (AI) offers an unparalleled opportunity to transform molecular diagnostics in LMICs. By providing scalable, objective, and accessible capabilities, these technologies can significantly improve public health outcomes and medical research. However, successful adoption demands targeted investment in digital infrastructure, capacity building, robust ethics, and public-private partnerships. Prioritizing direct clinical utility and molecular diagnostic applications is key to sustainable implementation and equitable access to advanced diagnostics.

Background: Oral squamous cell carcinoma, the most common head and neck cancer, is characterized by aggressive invasion, frequent lymph node metastasis, and high recurrence.

Objective: This review explores the role of microbial dysbiosis in shaping the immune landscape of oral squamous cell carcinoma. It aims to identify key microbial taxa altered in Oral squamous cell carcinoma, examine the immunological pathways they influence, and assess their impact on tumor progression and immune evasion.

Methods: A comprehensive literature search was conducted across three major databases: PubMed, Scopus, and Embase. A total of 300 records were initially identified (PubMed: n = 68; Scopus: n = 63; Embase: n = 169). After screening and eligibility assessment, 17 studies meeting inclusion criteria were selected for final analysis.

Results: Fusobacterium was the most frequently enriched genus in Oral squamous cell carcinoma, followed by Peptostreptococcus, Prevotella, Porphyromonas, Campylobacter, Treponema, Capnocytophaga, and Dialister. Streptococcus was the most consistently depleted, along with Rothia, Veillonella, Leptotrichia, Capnocytophaga, and Neisseria.

Conclusion: Cancer remains a leading cause of death. Oral microbial dysbiosis, with loss of beneficial species and enrichment of pathogens, has been linked to oral cancer, but its mechanistic role remains hypothetical and requires validation.