Health crises across the globe bring dramatic changes to the lives of individuals and organizations. These crises have some psychological effects on society. The COVID-19 pandemic also caused some serious problems for individual and organizational life. Globally, the COVID-19 situation precipitated various economic and psychological issues that are far-reaching and exceptional. Health crises are increasing following the occurrence of COVID-19 due to its psychological effects on individuals worldwide. The current study highlighted the impact of COVID-19 fear on mental well-being (MWB). Most studies have examined the MWB of nursing staff and related their MWB to psychological factors. Few studies have considered the health crisis factors that are important in terms of bringing about variation in the MWB of nursing staff. Nursing staff MWB is impacted by various health crises (including COVID-19) at the global level and it has been ignored by researchers. In this study, a list of 1940 healthcare units with 6758 nursing staff was obtained. A total of 822 nurses were selected with the help of random sampling. The collected data were analyzed using correlation analysis, SPSS (statistical package for social sciences) version 23, and SEM. Thus, in this study we examined the effect of a health crisis (i.e., COVID-19) fear on the MWB of nurses. Moreover, we also examined the extent to which perceived stress (PS) influences the link between COVID-19 fear and MWB. The study’s findings confirmed that COVID-19 fear shown negative effect on MWB, while PS mediated the link between COVID-19 fear and MWB.

Artificial intelligence (AI) is rapidly transforming the medical oncology, by providing new tools for early diagnosis and personalized treatment planning. In mandibular oncology, where malignant transformation and metastatic progression can drastically alter prognosis, AI-driven systems show great promise in enhancing clinical decision-making. Convolutional neural networks and deep learning algorithms have already demonstrated high accuracy in analyzing imaging datasets, including CT scans and histopathological slides, for the detection of subtle changes that may indicate malignant transformation of initially benign lesions. These tools also aid in identifying early metastatic spread, allowing for timely intervention. One of the most aggressive head and neck cancers, oral squamous cell carcinoma (OSCC) usually affects the mandible and causes extensive anatomical disruption, neurological impairments, and functional deficits in speech, mastication, and facial symmetry. This article presents clinical and histopathological data on malignant lesions of the mandibular bone, primarily oral squamous cell carcinoma (OSCC). Tumours primarily affected the mandibular body and ramus, often causing facial asymmetry, tooth mobility, and bone loss. Histologically, keratin pearls and spindle-shaped atypical cells were typical characteristics. Neurological involvement from perineural invasion frequently led to pain, numbness, and impaired oral function. In addition to the complex consequences of mandibular cancers, such as their anatomical, functional, and psychological effects, the study underscores the importance of multidisciplinary treatment and rehabilitation. Our paper presents how new methods for improving the precision and effectiveness of oral oncology diagnosis have been made possible by developments in artificial intelligence (AI), especially deep learning approaches. By recognising both cellular and structural atypia, convolutional neural networks (CNNs) have shown excellent performance in the classification of histopathological images. The distinction between benign, dysplastic, and malignant oral lesions is further enhanced by hybrid techniques that blend deep and texture-based features. AI-assisted radiomics has shown promise in diagnostic imaging by detecting high-dimensional, subtle characteristics in CT, MRI, and ultrasound scans that are frequently impossible to interpret visually. According to analysed studies, these techniques can improve the identification of lymph node metastases and offer accurate risk assessment for oral malignancies. Further encouraging their inclusion into the oral oncology workflow, machine learning models have attained diagnostic accuracies that surpass standard radiological assessments by merging image texture analysis with clinical data. A triadic viewpoint is provided by integrating AI into mandibular oncology: (1) imaging-based tumour growth and metastasis detection, (2) histopathological evaluation of malignant potential, and (3) assessment of neurological outcomes following surgery. Also, this article presents a case study from the University of Craiova, guide surgical excision, and assess both histological progression and postoperative functional impact. AI technologies' ability to support individualized treatment plans, enable earlier diagnoses, and enhance clinical judgment is becoming more and more clear as they develop—especially in anatomically and neurologically complex areas like the mandible. In conclusion, AI represents a powerful ally in mandibular oncology, bridging imaging, histopathology, and neurological evaluation, and paving the way for more precise, patient-centered care.

Pleomorphic adenoma (PA) is the most common benign tumour of the salivary glands, accounting for up to two-thirds of all salivary gland tumors. It most frequently occurs in the parotid gland and typically presents as a slow-growing, asymptomatic mass, predominantly affecting females between the third and sixth decades of life. Histologically, PA exhibits a mixed composition of epithelial, myoepithelial, and mesenchymal components embedded in a myxoid or chondroid stroma, with eosinophilic cytoplasm in polygonal myoepithelial cells. Although often well-circumscribed and localized in the superficial lobe of the parotid, PA can extend into deeper regions, including the parapharyngeal space, which complicates surgical management due to the proximity of critical neurovascular structures. This study aims to highlight the histopathological features of pleomorphic adenoma and correlate them with intraoperative findings. Advanced imaging techniques, particularly MRI, are crucial for accurate preoperative assessment, as delayed surgical intervention increases the risk of malignant transformation into carcinoma ex-pleomorphic adenoma, a more aggressive entity associated with recurrence and metastasis. In recent years, machine learning and deep learning models applied to MRI and ultrasound imaging have demonstrated significant potential in differentiating pleomorphic adenoma from other benign and malignant parotid tumours, improving diagnostic accuracy and supporting earlier clinical decision-making. Microscopic findings confirmed characteristic features of PA, including epithelial proliferation, myoepithelial components, haemorrhagic zones, eosinophilic stroma, and incomplete tumour capsules. Additionally, recent AI-assisted radiomic and convolutional neural network approaches have been shown to complement classical histopathology by extracting high-dimensional imaging features that enhance tumour subtype classification and prognostic assessment. When integrated with clinical and imaging data, these machine learning techniques offer promising avenues for reducing diagnostic uncertainty, optimising surgical planning—particularly in relation to facial nerve preservation—and contributing to more personalised management strategies in parotid gland pathology.

Objectives. Evaluating the benefits of an early screening of combined maternal markers and ultrasound of the fetuses. Methods. Prospective study (1 January and 31 December 2018) enrolled 187 patients, evaluated at the end of the first trimester of pregnancy (nuchal scan). Biochemical markers: detection of free β human chorionic gonadotropin and serum pregnancy associated plasma protein A. The first trimester ultrasound examination included the assessment of the nuchal translucency, fetal crown rump length and fetal heart. Results. 9 cases with abnormal biomarkers, increased nuchal translucency and cardiac anomalies, including abnormal 4-chamber view (6 cases) and great vessel anomalies (3 cases). 1 case considered initially negative in the first trimester was diagnosed by morphological scan with tetralogy of Fallot in the second trimester. Conclusions. A first trimester combined ultrasound protocol and maternal serum biochemical markers is important in the early prenatal care screening.

Background: The rapid digitalisation of healthcare, the expanding use of artificial intelligence, and the development of cross-border medical services have significantly transformed medical practice. These developments have also intensified medico-legal risks and professional liability exposure for healthcare professionals. Despite these changes, legal education remains insufficiently integrated into medical training, creating a gap between regulatory requirements and clinical practice. Methods: This study is based on a narrative analysis of the international literature addressing medico-legal challenges in healthcare, including medical errors, malpractice litigation, patient safety, digital health, and artificial intelligence. Drawing on international regulatory standards and educational frameworks, a conceptual model was developed to support the integration of legal education into medical curricula. Results: The analysis highlights persistent deficiencies in legal literacy among healthcare professionals, regardless of speciality or level of experience. Evidence from the reviewed literature suggests an association between lower levels of legal knowledge and increased professional vulnerability, including reported malpractice litigation and patient safety incidents. The proposed conceptual framework outlines a structured and longitudinal approach to integrating legal education into medical training, based on horizontal and vertical curricular integration, alignment with regulatory standards, and continuous professional development. Conclusions: Integrating legal education into medical curricula may represent a relevant strategy for supporting patient safety, strengthening medico-legal risk awareness, and promoting professional accountability. The proposed framework offers a conceptual and adaptable structure for medical schools and healthcare institutions.