American Journal of Medical Research & Practice

Abstract: A hypertensive crisis is a critical medical situation characterized by a rapid spike in blood pressure that may result in severe outcomes like stroke, heart attack, or damage to organs. Immediate action is necessary to safely decrease blood pressure, typically through a combination of medications and medical intervention. Symptoms can include intense headaches, dizziness, chest discomfort, breathing difficulties, nausea, vision alterations, and feelings of anxiety. It is crucial to differentiate between hypertensive emergencies and hypertensive urgencies, as they necessitate distinct treatment strategies.

Abstract: Objective: This study aims to evaluate the demographic characteristics, seasonal distribution, and the impact of the February 6, 2023, Kahramanmaraş earthquake on the frequency of applications for scabies (ICD-10: B86) at the Hatay Mustafa Kemal University Training and Research Hospital emergency department between 2015 and 2026. Methods: Conducted as a retrospective cross-sectional study, 431 patients diagnosed with scabies in the emergency department were included. Case counts were examined in terms of gender, age group, application month, and year. To understand the earthquake’s impact, the average monthly application numbers for 2023, 2024, and 2025 were compared. Results: Of the cases, 62.4% (n=269) were male and 37.6% (n=162) were female, with a male-to-female ratio of 1.66. The highest number of applications was observed in the 18–29 age group (26.0%), with a median age of 25 years (min: 1, max: 94). In the seasonal distribution, the highest rate was observed in autumn (28.1%), followed by spring (26.0%), summer (23.2%), and winter (22.7%). Annual case numbers showed a significant increase after the earthquake; 38 cases in 2023, 125 in 2024, and 216 in 2025. The average monthly application number increased from 3.2 in the earthquake year of 2023 to 18.0 in 2025, an approximately 5.7-fold increase. Conclusion: It is considered that the number of scabies cases in the emergency department has continued to increase cumulatively following the February 6, 2023, earthquake, and this increase is associated with the ongoing socioeconomic and housing problems in the region. More comprehensive monitoring programs are recommended for disaster epidemiology and infectious skin diseases.

Abstract: Background: Renewed interest has recently grown around the relationship between autistic traits (AT) in childhood and adulthood and dementia symptoms in old age. In fact, it seems that individuals with AT are at greater risk of developing cognitive impairment in old age. The aim of this work is to examine, in a group of old individuals, the correlation between AT and cognitive impairment. Methods: In our study we enrolled a group of 122 old individuals (mean age=81.00±7.07 years, M=41, F=81). Participants were assessed for autism spectrum disorder (ASD) symptoms by the Adult Autism Subthreshold Spectrum (AdAS), the Ritvo Autism Asperger Diagnostic Scale-Revised (RAADS), and the Autism Questionnaire (AQ), while for cognitive profile by the Frontal Assessment Battery (FAB), Montreal Cognitive Assessment (MoCA), and Mini Mental State Examination (MMSE). We performed correlation analysis between scores reported in each item of the FAB and MoCA and global scores of the MMSE from one side and the score reported in each item of the AdAS and global scores of the RAADS and the AQ from the other. Results: A positive correlation emerged between the prevalence of AT and better cognitive functions, such as attention, orientation, and executive functions. From the other side, AT in childhood seem to impair subjects’ abilities to attend to conflicting tasks as well as orientation at the MoCA in old age. In addition, ASD non-verbal difficulties impair phonemic fluency and language abilities, respectively, at the FAB and the MoCA. As expected, adherence to routine and inflexibility are associated with difficulties in memory and executive functions of the MoCA as well as impairment in similarities and phonemic fluency at the FAB. Finally, hyperstimulation, typically associated with ASD, negatively affects “language abilities, as assessed by the FAB. Conclusion: Overall, intriguing results were pointed out about the relationship between AT and cognitive functions in old age. Particularly, if AT may preserve some cognitive functions, including attention, orientation, and executive functions from age-related decline, specific features of the disease, such as inflexibility, adherence to routine, and sometimes hyperstimulation may exert a potential negative effect on other aspects such as phonemic fluency, language abilities, conflicting instructions and orientation, and even memory. Further studies are needed to deepen the relationship between AT and cognitive decline in old age.

Abstract: Background: Diabetes mellitus is an important global metabolic disorder, and early identification remains important for reducing long-term cardiovascular, renal, and metabolic complications.Objective: This study evaluated clinical, biochemical, and machine-learning markers for differentiating normal, prediabetic and diabetic status focusing on prediabetes diagnosis and models performance on exclusion of HbA1c.Methods: The cleaned data set was analyzed for 1,000 participants of which 103 normal, 53 prediabetic, 844 diabetics. We used various statistics and tests to make comparisons across diabetes classes like Spearman Correlation and more. For analysis in machine-learning, repeats of clinical profiles were removed to obtain 826 records. A set of classical machine-learning models and neural networks were evaluated on binary and multiclass classification using accuracy, balanced accuracy, sensitivity/recall, F1-score, and ROC-AUC. To avoidany diagnostic circularity, we prioritized models that did not utilize HbA1c.Results: After cleaning, the dataset included 103 normal, 53 prediabetic, and 844 diabetic records. After clinical deduplication, 826 unique records remained for machine-learning analysis. HbA1c and BMI showed the strongest differences across diabetes classes, with large Kruskal-Wallis effect sizes. HbA1c correlated moderately with BMI and age. In multivariable logistic regression excluding HbA1c, BMI, cholesterol, and age independently predicted diabetic status. In binary machine-learning analysis without HbA1c, random forest achieved the best performance, with 95.6% accuracy, 94.7% balanced accuracy, 96.1% sensitivity, 93.4% specificity, 97.4% F1- score, and 98.2% ROC-AUC. Neural networks performed well but did not outperform random forest; the best neural network achieved 90.4% balanced accuracy and 95.9% ROC-AUC. Multiclass classification was more difficult, mainly due to the small prediabetic group.Conclusion: Information about BMI, age, cholesterol, triglycerides and TG/HDL ratio can help detect diabetes and prediabetes. Overall, the random forest models performed the best, but the identification of prediabetes was still difficult due to class imbalance and overlapping metabolic profiles.

Abstract: My previous Discovers of the Origin and Correct Cure of Maculopathy and Chronic Glaucoma has carried toLogic, Objective and Scientific Conclusions that both the Pathologies have the same Origin: the “Body - Over - Intoxication” ( and poisoning ).