Objectives: The study’s goals are to evaluate the management of Stroke Mimics (SMs), conditions with stroke-like symptoms but non-vascular origins. It seeks to avoid the unnecessary intravenous thrombolysis, the target therapy delays and determine the best SMs diagnosis approach.Materials and methods: A review was conducted of all patients admitted to the Emergency Department under a “stroke code” from January 1, 2018, to January 31, 2019. Anamnestic and clinical data, along with information on neuroimaging protocols and findings, were collected. Advanced MRI sequences, such as Arterial Spin Labeling (ASL) MR perfusion and MR spectroscopy, were revised to confirm the diagnoses.Results: 513 stroke codes were found; a neurovascular disease was diagnosed in 414 cases: 282 ischemic strokes and 73 intracranial hemorrages and 59 TIAs. The 99 SMs included, 13 infections, 12 syncopes, 11 epileptic seizures, 11 hemodinamic conditions, 10 tumors, 9 metabolic disorders, 9 diziness, 7 migraines, 4 drug/alcohol intoxication, 3 functional disorders, 3 acute hydrocefalus, 2 multiple sclerosis, 2 arteriovenous malformations, 1 spinal cord compression and 2 unexplained conditions. Specific neuroimaging findings were evaluated for all cases. Positive and negative predictive values of clinical diagnosis of SM were respectively 0.23 and 0.35. 125 SM patients underwent MRI examination, 40 of which within 3 hours from the activation of stroke code. Advanced MRI sequences as ASL, MR perfusion, MR spectroscopy were used to reach the correct diagnosis.Conclusions: Advanced multimodal MRI can be a valuable tool in the assessment of, particularly in cases where conventional imaging techniques such as DWI-FLAIR mismatch are inconclusive. The novelty of this retrospective study is to demonstrate that the consistent use of arterial spin labeling perfusion in cases with stroke code leads to a rapid and accurate diagnosis of SMs. The implementation of an MRI-based pathway can expedite the diagnosis and treatment of underlying non-vascular causes such as SMs.

Forensic odontology’s evolution from manual to digital methods signifies a pivotal transition. Augmented Reality (AR) and Virtual Reality (VR) further this transformation by merging the physical and digital realms. This brief communication explores how AR and VR can enhance forensic odontology, offering precision, interactivity, and advanced analysis. It delves into the current landscape and envisions future possibilities, emphasizing their role in shaping precise and collaborative forensic practices. Additionally, challenges and considerations for implementing AR and VR in this field are discussed.

Background: The recruitment of experienced clinical nurses into vacant faculty positions is a global strategy for preparing, employing, and retaining competent nurse educators. As clinical nurses transition to academia, many struggle with unique challenges that affect their adaptation to the academic setting. Thus, understanding novice nurse educators’ transitional challenges is critical to addressing this threat. Aim: This study aimed to explore the challenges of novice nurse educators’ transition from clinical practice to academia in Ghana. Methods: A descriptive qualitative study design was employed for this study. A purposive sampling technique was used to recruit 12 novice nurse educators from three health training institutions in the Upper East Region of Ghana. A semi-structured interview guide was used to collect data through in-depth individual interviews. The interviews were audio-recorded, transcribed verbatim, and thematically analyzed.Results: Four themes emerged: poor administrative support, poor preparation, increased workload and stress, and role ambiguity. Novice nurse educators lacked formal orientation, formal mentorship, formal education in teaching, and post-graduate education. Novices were also challenged by increased workload and role ambiguity. Conclusion: Supporting novice nurse educators' transition from clinical practice would be better served by college administrators’ creation and implementation of formal orientation and mentorship programs.

Background: Liver cancer is a global health concern, with overweight and obese individuals exhibiting an increased risk of its development. Understanding the interplay between obesity-related factors and liver cancer incidence is crucial for early prediction and intervention.Aim: The aim of this investigation was to construct and validate an extreme gradient boosting (XGBoost) based machine learning model for the purpose of establishing a one-year liver cancer risk prediction system specifically tailored to overweight and obese patients. In addition, this study sought to compare the predictive performance of the XGBoost model with those of a random forest model and a logistic regression model, while also identifying the most influential predictive features for liver cancer incidence.Methods: A comprehensive retrospective analysis was conducted on MIMIC III data comprising 2,354 patients. To predict the risk of liver cancer development, three machine learning models were developed: XGBoost, random forest, and logistic regression. Feature selection was executed using a stepwise regression procedure encompassing both forward selection and backward elimination.Results: The stepwise regression technique unveiled 14 predictive factors for liver cancer incidence. Among the patient cohort, 132 individuals developed liver cancer within a year of follow-up, while 2,222 did not. Notably, most liver cancer cases occurred in male patients (60%). Statistically significant differences were observed between patients with liver cancer and those without, in terms of age, gender, total bilirubin, platelet, albumin, chloride, potassium, sodium, prothrombin time (PT) and alanine aminotransferase (ALT). The XGBoost model exhibited an impressive area under the receiver operating characteristic curve (AUROC) of 99%, Random Forest (RF) of 99%, and Logistic Regression (LR) of 90%. In a multivariate analysis, total bilirubin, creatinine levels, age, gender, ALT, alkaline phosphate (ALP), PT, calcium, and chloride emerged as independent predictors for liver cancer incidence.Conclusion: The XGBoost model demonstrated superior predictive performance when compared to the RF and LR models. If corroborated through prospective studies, the XGBoost model may prove to be a valuable tool for the early prediction of liver cancer risk in overweight or obese individuals. Such predictive capabilities could, in turn, facilitate the implementation of timely preventive interventions against liver cancer.

Introduction: The individual program of rehabilitation and (or) habilitation of children with disabilities (IPRH) is mandatory for execution by the relevant state authorities, local self-government bodies, as well as organizations regardless of organizational-legal forms and forms of ownership.Objective: To conduct a pilot analysis of the implementation of the IPRH contingent of patients of children with disabilities in an urban children’s clinic.Patients and methods: There were 366 reports on the implementation of measures provided for by an IPRH for a disabled person (disabled child) of 222 disabled. The organization of the study was in the nature of a continuous sample. The criterion for the inclusion of patients in it was the passage of an IPRH in a disabled child within a specified time frame. The following techniques were used: grouping, absolute and relative values, average values, detailing, and generalization. The threshold error probability for statistically significant differences was set at a level of 0.05.Results: The structure of the results of the control of the performance of IPRH in 222 disabled children according to the classes of diseases that caused the onset of disability (ICD) was as follows 1) G00-G99 - 35.47 ± 3.13%; 2) Q00-Q99 - 23.50 ± 2.77%; 3) 11.11 ± 2.05%; 4) C00-D48 - 10.25 ± 1.98%; 5) H60-H95 - 7.26 ± 1.67%; 6) M00-M99 - 2.99 ± 1.11%; 7-8) H00-H59 and P00-P96 - 2.14 ± 0.95%; 9-10) K00-K93 and S00-T98 - 1.29 ± 0.74% each; 11-12) I00-I99 and N00-N99 - 0.85 ± 0.60% each; 13-14) J00-J99 and L00-L99 - 0.43 ± 0.42% each.Conclusion: 1. In the structure of IPRH in 222 disabled children, according to the classes of diseases that caused disability (ICD), the following prevailed: 1) VI Diseases of the nervous system G00-G99 – 35.47%; 2) XVII Congenital anomalies, chromosomal disorders Q00-Q99 - 23.50%; 3) IV Diseases of the endocrine system, nutritional disorders, and metabolic disorders E00-E90 – 11.11%; 4) II Neoplasms C00-D48 - 10.25%; 5) VIII Diseases of the ear and mastoid process H60-H95 - 7.26%; 6) XIII Diseases of the musculoskeletal system and connective tissue M00-M99 - 2.99%; 7-8) VIII Diseases of the ear and mastoid process H60-H95 and VII Diseases of the eye and its adnexa H00-H59 - 2.14% each.2. The effectiveness of medical rehabilitation of disabled children was as follows: 1) Improvement - 23.26%; 2) Stabilization - 74.88%; 3) Deterioration - 1.86%. Dynamic observation was carried out on 94.26% of disabled children, drug therapy - 77.32%, non-drug therapy - 66.93%, and other types of medical rehabilitation were received by 14.48% of patients. Reconstructive operations were performed on 11.26% of disabled children.3. Prosthetics and orthotics were performed on 38.74% of disabled children. 32.43% of disabled children in need received sanatorium treatment, and 30.18% are currently in line to receive a voucher. For various reasons, 24.32% refused this type of rehabilitation; 3.60% of patients had contraindications at the time the voucher was provided.4. The obtained research results become the initial everyday statistical tool for objectifying the process of rehabilitation of patients and determining the strength and means of a medical institution to monitor and successfully implement an individual rehabilitation/habilitation program for a disabled person.

In this research, for the purpose of social implementation, we conducted a near-miss demonstration experiment using a car driving in the city and a drive-simulator. Next, we conducted a demonstration experiment to evaluate the reliability of biometric information measured on patients in a medical facility's recuperation ward and residents of a special nursing home. NBC-1100 emits radar waves from a distance of up to 3 meters from an object and uses the reflected waves to measure biological information such as pulse waves and breathing waves. The multi-element pyroelectric effect was used to measure body temperature by detecting infrared radiation emitted from distant objects. This device is unique in that it can measure biological information without being restrained while wearing clothes. In the demonstration test, simultaneous measurements were conducted on nine healthy men aged 45 to 65 using a pharmaceutically approved product (μBP-mp) and a prototype non-contact biological monitor (NBC-1100 manufactured by K&S Co., Ltd.). The demonstration experiments at medical institutions and nursing care facilities were conducted with the consent of residents and their families and were conducted on 30 men and women between the ages of 70 and 94 who were undergoing treatment or in need of care. The tests were conducted on residents with chronic diseases such as dilated gastrostomies, symptomatic epilepsy, hypertension, Alzheimer's disease, and progressive supranuclear palsy. The evaluation method was simultaneous measurement using a master meter (μBP-mp) and a test meter (NBC-1100).