Advances in predicting breast cancer driver mutations: Tools for precision oncology (Review).

In the modern era of medicine, prognosis and treatment, options for a number of cancer types including breast cancer have been improved by the identification of cancer­specific biomarkers. The availability of high­throughput sequencing and analysis platforms, the growth of publicly available cancer databases and molecular and histological profiling facilitate the development of new drugs through a precision medicine approach. However, only a fraction of patients with breast cancer with few actionable mutations typically benefit from the precision medicine approach. In the present review, the current development in breast cancer driver gene identification, actionable breast cancer mutations, as well as the available therapeutic options, challenges and applications of breast precision oncology are systematically described. Breast cancer driver mutation­based precision oncology helps to screen key drivers involved in disease development and progression, drug sensitivity and the genes responsible for drug resistance. Advances in precision oncology will provide more targeted therapeutic options for patients with breast cancer, improving disease­free survival and potentially leading to significant successes in breast cancer treatment in the near future. Identification of driver mutations has allowed new targeted therapeutic approaches in combination with standard chemo­ and immunotherapies in breast cancer. Developing new driver mutation identification strategies will help to define new therapeutic targets and improve the overall and disease­free survival of patients with breast cancer through efficient medicine.

[Uveal Melanoma: Molecular and Genetic Mechanisms of Development and Therapeutic Approaches].

Uveal melanoma (UM) is a neuroectodermal tumor that results from malignant transformation of melanocytes in the eye uvea, including the iris, the ciliary body, and the choroid. UM accounts for 5% of all melanoma cases and is extremely aggressive with half of the UM patients developing metastases within the first 1-2 years after tumor development. Molecular mechanisms of UM carcinogenesis are poorly understood, but are known to differ from those of skin melanoma. Activating mutations of the GNAQ and GNA11 genes, which code for the large G protein subunits Gq and G11, respectively, are found in 90% of UM patients. The Gaq/PKC/MAPK signaling pathway is a main signaling cascade that leads to the transformation of melanocytes of the uveal tract, and major regulators of the cascade provide targets for the development of drugs. Metastatic UM (MUM) is most often associated with mutations of BAP1, EIF1AX, GNA11, GNAQ, and SF3B1. A combination of a commercial expression test panel of 15 genes and a mutation panel of 7 genes, supplemented with data on the size of the primary tumor, is highly efficient in predicting the risk of metastasis. The risk of metastasis determines the choice of therapy and the patient follow-up regimen. However, no systemic therapy for MUM has been developed to date. New drugs undergoing clinical trials are mostly targeted drugs designed to inhibit the protein products of mutant genes or immunotherapeutic agents designed to stimulate the immune response against specific antigens. In addition to these approaches, potential therapeutic targets of epigenetic regulation of UM development are considered in the review.

RNA splicing junction landscape reveals abundant tumor-specific transcripts in human cancer.

RNA splicing is a critical process governing gene expression and transcriptomic diversity. Despite its importance, a detailed examination of transcript variation at the splicing junction level remains scarce. Here, we perform a thorough analysis of RNA splicing junctions in 34,775 samples across multiple sample types. We identified 29,051 tumor-specific transcripts (TSTs) in pan-cancer, with a majority of these TSTs being unannotated. Our findings show that TSTs are positively correlated with tumor stemness and linked to unfavorable outcomes in cancer patients. Additionally, TSTs display mutual exclusivity with somatic mutations and are overrepresented in transposable-element-derived transcripts possessing oncogenic functions. Importantly, TSTs can generate putative neoantigens for immunotherapy. Moreover, TSTs can be detected in blood extracellular vesicles from cancer patients. Our results shed light on the intricacies of RNA splicing and offer promising avenues for cancer diagnosis and therapy.

Evolutionary Modes of wtf Meiotic Driver Genes in Schizosaccharomyces pombe.

Killer meiotic drivers are a class of selfish genetic elements that bias inheritance in their favor by destroying meiotic progeny that do not carry them. How killer meiotic drivers evolve is not well understood. In the fission yeast, Schizosaccharomyces pombe, the largest gene family, known as the wtf genes, is a killer meiotic driver family that causes intraspecific hybrid sterility. Here, we investigate how wtf genes evolve using long-read-based genome assemblies of 31 distinct S. pombe natural isolates, which encompass the known genetic diversity of S. pombe. Our analysis, involving nearly 1,000 wtf genes in these isolates, yields a comprehensive portrayal of the intraspecific diversity of wtf genes. Leveraging single-nucleotide polymorphisms in adjacent unique sequences, we pinpoint wtf gene-containing loci that have recently undergone gene conversion events and infer their ancestral state. These events include the revival of wtf pseudogenes, lending support to the notion that gene conversion plays a role in preserving this gene family from extinction. Moreover, our investigation reveals that solo long terminal repeats of retrotransposons, frequently found near wtf genes, can act as recombination arms, influencing the upstream regulatory sequences of wtf genes. Additionally, our exploration of the outer boundaries of wtf genes uncovers a previously unrecognized type of directly oriented repeats flanking wtf genes. These repeats may have facilitated the early expansion of the wtf gene family in S. pombe. Our findings enhance the understanding of the mechanisms influencing the evolution of this killer meiotic driver gene family.

A heterogeneous graph transformer framework for accurate cancer driver gene prediction and downstream analysis.

Accurately predicting cancer driver genes remains a formidable challenge amidst the burgeoning volume and intricacy of cancer genomic data. In this investigation, we propose HGTDG, an innovative heterogeneous graph transformer framework tailored for precisely predicting cancer driver genes and exploring downstream tasks. A heterogeneous graph construction module is central to the framework, which assembles a gene-protein heterogeneous network leveraging the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and protein-protein interactions sourced from the STRING (search tool for recurring instances of neighboring genes) database. Moreover, our framework introduces a pioneering heterogeneous graph transformer module, harnessing multi-head attention mechanisms for nuanced node embedding. This transformative module proficiently captures distinct representations for both nodes and edges, thereby enriching the model's predictive capacity. Subsequently, the generated node embeddings are seamlessly integrated into a classification module, facilitating the discrimination between driver and non-driver genes. Our experimental findings evince the superiority of HGTDG over existing methodologies, as evidenced by the enhanced performance metrics, including the area under the receiver operating characteristic curves (AUROC) and the area under the precision-recall curves (AUPRC). Furthermore, the downstream analysis utilizing the newly identified cancer driver genes underscores the efficacy and versatility of our proposed framework.

Targeted and cytotoxic inhibitors used in the treatment of lung cancers.

Lung cancer is the leading cause of cancer deaths in the United States and the world. It is divided into two major types: small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC). In the tumor-node-metastasis (TNM) cancer-staging classification system (Stages I/II/III/IV), the severity of neoplastic growth is characterized by the size of the tumor (T1 to T4), the extent of lymph node involvement (N0 to N3), and whether (M1) or not (M0) distant metastasis has occurred. Surgery is the treatment of choice for medically fit patients with Stage I/II NSCLC. Combination chemoradiotherapy and immune checkpoint inhibitor therapy are used across all NSCLC types. Oncogene-addicted tumors with sensitizing EGFR or BRAF mutations or activating ALK, ROS1 or NTRK translocations are treated with their cognate orally active small molecule protein kinase blockers. On the order of 20 % of NSCLCs bear activating mutations in EGFR and are treated with osimertinib and other kinase antagonists. SCLC, which accounts for approximately 15 % of lung cancer cases, is a deadly high-grade neuroendocrine carcinoma with a poor prognosis. Limited-stage SCLC is confined to one hemi-thorax and one radiation port and extensive-stage disease signifies those cancers that do not meet the criteria for limited-stage disease. Local treatment options to control thoracic disease include radiotherapy and surgery. In patients with extensive-stage disease, a platinum agent (cisplatin or carboplatin) combined with etoposide and an anti-PDL1 inhibitor (atezolizumab or durvalumab) for four cycles followed by anti-PDL1 maintenance therapy is the recommended first-line regimen.

Psychiatric risk factors in Formula One and the importance of integrating mental health into driver science.

Formula One (F1) racing has recently grown in popularity, extending well beyond its traditional European roots. However, there has been a paucity of scholarly research dedicated to the health of drivers and even less discussion of the prevalence of psychiatric symptoms, risk factors, and types of psychopathology in F1. This is notable given advancements in sports psychiatry and evidence of psychiatric disorders emerging across other sports. Accordingly, this perspective paper details the physiological conditions in F1 and the socioenvironmental pressures that a driver may encounter during their career, including heat stress, weight restrictions, harassment, and other factors. These extreme physiological and psychological stressors, both in racing and non-racing environments, alongside sport-specific psychosocial pressures, may cause HPA axis dysregulation and other issues in drivers, heightening vulnerabilities for mental health concerns. Additionally, F1 is still affected by stigmatizing attitudes and regressive sociocultural norms, which could inhibit progress toward promoting sustainable wellbeing. Consequently, drivers may be at risk for mental disorder and a decrease in overall health and wellbeing. Against this background, we thereby recommend mental health programs and regulatory actions that could better address these challenges and promote mental wellbeing across F1.

Pilot Study on Gaze Characteristics of Older Drivers While Watching Driving Movies.

Objective: This study aims to clarify the gazing characteristics of older drivers while driving cars using a gaze analysis device. Methods: The participants included 16 older and 12 middle-aged drivers who drove cars daily. After conducting cognitive and attentional function tests, eye gaze while watching driving videos was measured using an eye tracker. Ten driving videos were prepared. In addition, a total of 34 hazard areas were analyzed. Results: The results of the gaze measurement parameters were statistically compared between the two groups. In the older group, the gaze analysis results indicated that while viewing driving videos, the search for areas close to the car was expanded. In addition, in several hazard areas, we observed a decrease in the number of drivers gazing at the driver, shortened total gazing time, delay in the timing of gazing, and decrease in the number of visits. Conclusions: Older drivers' eye movement is increased; however, it is characterized by gazing at unimportant areas, indicating an inefficient scanning pattern. Although these results do not indicate an obvious decline in driving ability among older drivers, the decline in hazard perception may become apparent in some situations. The data contain underpowered results and require revalidation in larger studies.

Clustering for mitigating subject variability in driving fatigue classification using electroencephalography source-space functional connectivity features.

While Electroencephalography (EEG)-based driver fatigue state classification models have demonstrated effectiveness, their real-world application remains uncertain. The substantial variability in EEG signals among individuals poses a challenge in developing a universal model, often necessitating retraining with the introduction of new subjects. However, obtaining sufficient data for retraining, especially fatigue data for new subjects, is impractical in real-world settings. In response to these challenges, this paper introduces a hybrid solution for fatigue detection that combines clustering with classification. Unsupervised clustering groups subjects based on their EEG functional connectivity in an alert state, and classification models are subsequently applied to each cluster for predicting alert and fatigue states. Results indicate that classification on clusters achieves higher accuracy than scenarios without clustering, suggesting successful grouping of subjects with similar functional connectivity characteristics through clustering, thereby enhancing the classification process. Furthermore, the proposed hybrid method ensures a practical and realistic retraining process, improving the adaptability and effectiveness of the fatigue detection system in real-world applications. .

Study protocol of the safe driving program for the prevention of car accidents: A randomized controlled trial protocol.

AIM: Driving cessation is strongly associated with adverse health outcomes in older adults. Although numerous studies have focused on driving skill interventions for safe driving, the effects of interventions on car accidents remain unclear. We designed a randomized controlled trial to examine the effects of driving skill training on the prevention of car accidents among community-dwelling older adults using on-road training. METHODS: A total of 1408 community-dwelling older drivers aged ≥65 years participated in the randomized controlled trial with blinded endpoint assessment. Participants randomized to the intervention group (n = 697) underwent four sessions, with 200 min re-training focused on the problem of driving in the older people. Controls (n = 697) received one classroom education session. On-road driving performance was assessed by certified driving school instructors in a driving school. The primary endpoint is an incident car accident, which will be detected based on the Japanese national car accident report data, after 24 months. DISCUSSION: This study has the potential to provide the first evidence of the effectiveness of on-road driving skill training regarding the prevention of car accidents. If our trial results show a lower number of car accidents due to driving skill training, this kind of intervention will provide an effective method for maintaining safe driving. These results will be disseminated to the appropriate national transportation agencies to improve or modify safe driving policies and possibly extend the validity of driver's licenses for older adults. TRIAL REGISTRATION: UMIN-CTR (identification number: UMIN000034709). Registered 31 October 2018. Geriatr Gerontol Int 2024; ••: ••-••.

Investigating risk factors associated with injury severity in highway crashes: A hybrid approach integrating two-step cluster analysis and latent class ordered regression model with covariates.

Highway crashes are responsible for a significant number of severe and fatal injuries drawing considerable attention from transportation authorities and safety researchers. This paper aims to investigate the unobserved heterogeneous effects of various risk factors, such as pre-crash circumstances, environmental and road conditions, vehicle-involved information, and driver attributes on injury severities. Our methodology uses a hybrid approach that combines two-step cluster analysis and latent class ordered regression model with covariates. The proposed approach extends traditional latent class model by elucidating potential relationships among predictors, covariates, and outcomes. A cross-sectional crash data covering a period of over five years (2011-2016) was obtained via the Dutch crash registration database for modeling injury severity outcomes. The results reveal substantial and statistically significant differences in injury severity between two latent classes. Moreover, we identify road lighting, time of crash, road surface conditions, weather, and season as covariates influencing class membership prediction. Factors such as high speed, alcohol involvement, frontal collision points, and older driver demographics increase the probability of serious injury and facility across all cases analyzed. Additionally, we observe notable heterogeneity effects between the two classes regarding temporal characteristics, the number and type of vehicles involved, as well as driver gender. Our findings provide specific and valuable insights into injury severity outcomes, which can inform the formulation of targeted safety countermeasures and regulatory strategies for traffic policies and relevant agencies.

Case report: a case of lung squamous cell carcinoma with a novel FGFR3-IER5L fusion mutation responding to anlotinib.

Lung squamous cell carcinoma (LUSC) is the second most common pathological type of non-small cell lung cancer (NSCLC). However, compared with lung adenocarcinoma (LUAD), the incidence of driver gene mutations in LUSC is relatively lower and treatment options for LUSC patients are very limited. We described a LUSC patient with a novel FGFR3-IER5L fusion revealed by next generation sequencing in this report. The patient refused surgery, radiotherapy or chemotherapy and received anlotinib treatment. Anlotinib is a small molecular multi-target tyrosine kinase inhibitor, which can inhibit the activity of kinases including vascular endothelial growth factor receptor 2/3 (VEGFR2/3), fibroblast growth factor receptor 1-4 (FGFR1-4), platelet-derived growth factor receptor α/ß (PDGFRα/ß), and c-Kit. The patient achieved partial response and the progression-free survival was 3.8 months.

Temporal Effect on PD-L1 Detection and Novel Insights Into Its Clinical Implications in Non-Small Cell Lung Cancer.

OBJECTIVES: Several studies rely on archived tissue blocks to assess the PD-L1 scores; however, a detailed analysis of potential variations of scores between fresh and archived tissue blocks still lacks. In addition, the prognostic implications of PD-L1 in lung cancers have not yet been completely understood. Here, we aimed to investigate the temporal variation in PD-L1 scores from clinical samples and the clinical implications of PD-L1 in non-small cell lung cancer (NSCLC). METHODS: NSCLC cases from January 2005 to June 2023 were considered for this study, and PD-L1 scores in archived and fresh tissue blocks were analyzed. Association of PD-L1 with various driver mutations was explored, and implications of PD-L1 in progression-free survival (PFS) and overall survival (OS) were analyzed. RESULTS: Our study revealed a significant disparity in PD-L1 scores between archived and fresh tissue blocks, and a temporal variation in scores within 6 months of tissue acquisition. Advanced-stage primary tumors, metastatic lymph nodes, and visceral pleural invasion revealed higher PD-L1 expression as presented by tumor proportion score (TPS). Notably, in fully resected stage I/II NSCLC cases, OS was better in the high PD-L1 (≥ 50% TPS) cohort with driver mutations compared to cases without driver mutations (hazard ratio-0.5129, 95% confidence interval 0.2058-1.084, p = 0.0779). In contrast, high PD-L1 was associated with worse OS compared to no PD-L1 (< 1% TPS) (hazard ratio-2.431, 95% confidence interval 1.144-6.656, p = 0.0242) in the cohort without driver mutations. Furthermore, the presence of a KRAS mutation favored the outcome of anti-PD-L1/PD1 immunotherapy in advanced NSCLC. CONCLUSION: PD-L1 detection from tissue blocks was found to vary temporally, urging for a prioritized consideration for patients with marginal scores when archived blocks are employed for its detection. Prognostic roles of PD-L1 were associated with driver mutations, and KRAS mutations favored the outcome of anti-PD-L1/PD1 therapy in advanced NSCLC.

Are Drivers Recurring or Ephemeral? Observations from Serial Mapping of Persistent Atrial Fibrillation.

RATIONALE: Rotational reentries and ectopic foci, or "drivers", are proposed mechanisms for persistent atrial fibrillation (persAF), but driver-based interventions have had mixed success in clinical trials. Selective targeting of drivers with multimonth stability may improve these interventions, but no prior work has investigated whether drivers can be stable on such a long timescale. OBJECTIVE: We hypothesized that drivers could recur even several months after initial observation. METHODS AND RESULTS: We performed serial electrophysiology studies on paced canines (n=18, 27-35 kg) at 1-, 3-, and 6-months post-initiation of continual persAF. Using a high-density 64-electrode catheter, we captured endocardial electrograms in the left atrium (LA) and right atrium (RA) to determine the presence of drivers at each major anatomical site. We defined drivers which were repeatedly observed across consecutive studies to be recurrent. Mean probability any driver would recur was 66% (LA: 73%, RA: 41%). We also found evidence of "multirecurring" drivers, i.e., those seen in all three studies. Multirecurring drivers constituted 53% of initially observed drivers with at least one found in 92% of animals, and we found more multirecurring drivers per animal than predicted by random chance (2.6±1.5 vs. 1.2±1.1, p<0.001). Driver sites showed more enhancement than non-drivers during late gadolinium enhancement-magnetic resonance imaging (p=0.04), but we observed no relationship between enhancement and driver recurrence type. CONCLUSIONS: We observed recurring drivers over a 6-month period at fixed locations, confirming our hypothesis. We also found drivers to be associated with fibrosis, implying a structural basis.

An integrated framework for driving risk evaluation that combines lane-changing detection and an attention-based prediction model.

OBJECTIVE: In recent years, the increase in traffic accidents has emerged as a significant social issue that poses a serious threat to public safety. The objective of this study is to predict risky driving scenarios to improve road safety. METHODS: On the basis of data collected from naturalistic driving real-vehicle experiments, a comprehensive framework for identifying and analyzing risky driving scenarios, which combines an integrated lane-changing detection model and an attention-based long short-term memory (LSTM) prediction model, is proposed. The performance of the 4 machine learning methods on the CULane data set is compared in terms of model running time and running speed as evaluation metrics, and the ultrafast network with the best performance is selected as the method for lane line detection. We compared the performance of LSTM and attention-based LSTM on the basis of the prediction accuracy, recall, precision, and F1 value and selected the better model (attention-based LSTM) for risky scenario prediction. Furthermore, Shapley additive explanation analysis (SHAP) is used to understand and interpret the prediction results of the model. RESULTS: In terms of algorithm efficiency, the running time of the ultrafast lane detection network only requires 4.1 ms, and the average detection speed reaches 131 fps. For prediction performance, the accuracy rate of attention-based LSTM reaches 96%, the precision rate is 98%, the recall rate is 96%, and the F1 value is 97%. CONCLUSIONS: The improved attention-based LSTM model is significantly better than the LSTM model in terms of convergence speed and prediction accuracy and can accurately identify risky scenarios that occur during driving. The importance of factors varies by risky scenario. The characteristics of the yaw rate, speed stability, vehicle speed, acceleration, and lane change significantly influence the driving risk, among which lane change has the greatest impact. This study can provide real-time risky scenario prediction, warnings, and scientific decision guidance for drivers.

Supporting driver performance in closely spaced tunnel-interchange structure: Traffic control devices and effects on driving behavior.

OBJECTIVES: With the rapid development of expressways in the mountainous regions of southwestern China, closely spaced tunnel-interchange structures have inevitably emerged due to topographical constraints and environmental limitations. Given the unfavorable road geometry and rapid cross-section transitions, drivers face significant safety concerns. This study aims to investigate drivers' safety performance at closely spaced tunnel-interchange sections and determine how safety risks can be mitigated through improved traffic control devices design. METHODS: Thirty-nine participants conducted an experimental study in a fixed-base simulator. The test scenario was modeled on the Xingyan Freeway-S3801 and accurately reproduced in the simulator. For each safety performance metric, the driving simulator experiments yielded a dataset with 780 observations. To address the idiosyncratic variation due to individual driver differences, a series of linear mixed effects models (LMM) were developed to analyze drivers' behavior responses. RESULTS: In closely spaced tunnel-interchange sections, a general impairment of both longitudinal and lateral performance was observed. This study identified potential critical impact variables in traffic control device systems. According to the LMM results: (a) Removing the 0.5 km interchange ramp exit advance guide sign located in the tunnel exit area reduces dangerous behavior in the corresponding impact area. (b) Replacing the 0.5 km interchange ramp exit advance guide sign with arrow pavement markers as an information source supports improved driver performance, promoting driver safety. (c) Adding tunnel exit distance signs within tunnels is recommended to enhance situation awareness for drivers. CONCLUSIONS: This study addresses the scientific issues related to traffic control devices setup for closely spaced tunnel-interchange sections, focusing on identifying potential critical impact variables. The findings provide guidance on the design of traffic control devices for such sections and support revisions to national engineering standards.

A model of dyadic merging interactions explains human drivers' behavior from control inputs to decisions.

Safe and socially acceptable interactions with human-driven vehicles are a major challenge in automated driving. A good understanding of the underlying principles of such traffic interactions could help address this challenge. Particularly, accurate driver models could be used to inform automated vehicles in interactions. These interactions entail complex dynamic joint behaviors composed of individual driver contributions in terms of high-level decisions, safety margins, and low-level control inputs. Existing driver models typically focus on one of these aspects, limiting our understanding of the underlying principles of traffic interactions. Here, we present a Communication-Enabled Interaction model based on risk perception, that does not assume humans are rational and explicitly accounts for communication between drivers. Our model can explain and reproduce observed human interactions in a simplified merging scenario on all three levels. Thereby improving our understanding of the underlying mechanisms of human traffic interactions and posing a step towards interaction-aware automated driving.

Landscape of C-MET overexpression in non-small cell lung cancer: a large-scale study of clinicomolecular features and prognosis based on Chinese data.

Background: Real-world data on C-MET protein overexpression in non-small cell lung cancer (NSCLC) patients, particularly among the Asian Chinese population, are limited. Objectives: This study aimed to evaluate the clinicomolecular characteristics and prognosis of C-MET overexpression in Chinese NSCLC patients, focusing on those with positive C-MET overexpression (immunohistochemistry (IHC) 3+). Design: A retrospective and observational study. Methods: Data were collected from NSCLC patients diagnosed at the First Affiliated Hospital of Guangzhou Medical University between November 2006 and April 2021. We identified C-MET overexpression using IHC and C-MET overexpression positivity was defined as IHC 3+ with ⩾50% tumor cells. Additionally, patient genotypes were collected for subgroup analysis. Results: Data from 9785 NSCLC patients were collected. C-MET (-) accounted for 5% (503/9785), C-MET (+) for 27% (2654/9785), C-MET (++) for 36% (3464/9785), and C-MET (+++) for 32% (3164/9785). Genetic testing was available for 4326 patients. Wild-type was observed in 37% (1591 cases), with epidermal growth factor receptor (EGFR) abnormalities being the most common at 49% (2127 cases). Positive C-MET overexpression correlated significantly with women (p < 0.001), early-stage (p = 0.003), adenocarcinoma (p < 0.001), and driver mutations (p < 0.001). Patients with anaplastic lymphoma kinase (ALK) alterations had a higher occurrence of C-MET overexpression positivity (57.1%). Positive C-MET overexpression was significantly associated with EGFR (p < 0.001), ALK (p < 0.001), and KRAS alterations (p = 0.024). Compared to C-MET overexpression (IHC 0), C-MET overexpression (IHC 2+) (hazard ratio (HR) = 0.455, p < 0.001) and C-MET overexpression (IHC 3+) (HR = 0.569, p < 0.001) were correlated with better overall survival in overall NSCLC patients, especially for C-MET overexpression (IHC 2+). Conclusion: Our study elucidates the clinicomolecular characteristics and prognosis of C-MET overexpression in NSCLC patients, particularly those with positive C-MET overexpression (IHC 3+). This provides insight into the prevalence of C-MET overexpression in Chinese NSCLC patients and offers a basis for considering C-MET overexpression as a prognostic and predictive marker in NSCLC.

A systematic literature review of driver behaviour in blind spot: Assessing risk and influencing factors for enhanced ergonomics in vehicle design.

Risk associated with vehicle blind spot is a critical concern for road traffic safety that poses a serious threat to drivers as well as vulnerable road users. While driving on the road, it is necessary for drivers to check their mirrors before attempting lane changing, overtaking, turning, or any kind of manoeuvring pattern. But still, there remains some areas around the vehicle that are not visible to driver's peripheral vision even through checking mirrors, known as vehicle blind spots. In this systematic literature, an overview of the blind spot of vehicles along with the associated risks are explained thoroughly by emphasising its importance by focusing on safety aspects. The considered factors included size and visibility of blind spot, demographic factors that influence drivers' awareness and behaviour in blind spot; vehicle characteristics associated with design of different components, and also the efficacy of mirrors in enhancing visibility of blind spots. A quick and concise summary of the literature is highlighted in the current review, and finally, some future directions are proposed to not only help the researchers for a better understanding of blind spot-related driver behavioural issues but also benefit the automotive industry and policymakers to make roads safer.

Insightful information on enhancing drivers' awareness while driving and affecting factors associated with the risk due to blind spot are explored comprehensively. Conducting this systematic review will not only help the researchers but also benefit the automotive industry and policymakers in developing better vehicle design and advanced road safety policy, respectively.