Background: Sleep is vital, but issues are mounting. Sleep deprivation is dangerous for hospital nurses. This reduces nurse productivity and increases patient-harming medical mistakes. Shift work affects sleep and circadian rhythms; therefore, night shift female nurses' sleep is important. Nurses and patients lose sleep amid disturbances. Objectives: The study examined night-shift female nurses' sleep quality. Methods: A cross-sectional study was conducted in Dhaka Medical College's Physiology Department from January to December 2019 among 160 female nurses aged 24-50 who work night shifts from 8 p.m. to 8 a.m. in In-patient department of Medicine, Surgery, Obstetrics, and Gynecology. Pittsburg Sleep Quality Index and nurse demographics were obtained. After informed consent was signed, the individual was thoroughly questioned and recorded in the predesigned data form. Statistics were done with Excel and SPSS-26. Results: Mean BMI was 23.91 ± 1.64 kg/m², and systolic and diastolic blood pressures were 105.63 ± 11.10 and 69.94 ± 6.68 mmHg, respectively, without statistical significance (p > 0.05). The Pittsburgh Sleep Quality Index (PSQI) components showed significant results (p < 0.001), with mean scores for subjective sleep quality (1.09 ± 0.35), latency (1.95 ± 0.84), duration (1.77 ± 0.72), and additional parameters resulting in a mean global PSQI score of 7.94 ± 1.76. Highest number of respondents were belonging to age group 31–40 and 68.75% were married and in medical wards (42.50%). The lowest sleep quality was seen in Obstetrics & Gynecological wards, with 54 nurses scoring over 5 on the global PSQI (p < 0.001). About 69% of nurses had a PSQI score > 5, suggesting poor sleep quality, associated with age, marital status, and ward type (p < 0.001). Conclusion: Female nurses need sleep hygiene instruction, shift schedule adjustments, and stress management to sleep better. These obstacles affect nurses' well-being and care quality.

Background: Diabetes mellitus (DM) is an endocrine disorder marked by insufficient insulin production, disrupting glucose metabolism and regulation. DM is classified into type I (DM I) and type 2 (DM II). Glycated haemoglobin (Hba1c) is a marker for long-term blood glucose levels. Benign tongue abnormalities (BTAs) associated with (DM II) include the fissured tongue (FT), benign migratory glossitis (BMG), black hairy tongue (BHT), median rhomboid glossitis (MRG), and oral lichen planus (OLP). Aim of the work: This study aimed to determine the prevalence of BTAs among Libyan patients with controlled and uncontrolled DM II. The study also evaluates the potential correlation between BTA and factors such as age and gender. Materials and Method: This study included 426 Libyan patients with DM II. Disease duration and complications were obtained from the patient's medical records. Dependent binary variables (BTAs) and independent variables (age, gender, glycemic control in controlled and uncontrolled DM II) were calculated using IBM-SPSS 26. Result: 77.2% of the 426 patients with type 2 DM exhibited BTAs. FT 96.2% was the most common BTA, followed by MRG 2.1%. BMG 0.9%. BHT 0.6%, and LP 0.3%. 79.3% were glucose-uncontrolled diabetic patients (GUCDPs) and 20.7% were glucose-controlled diabetic patients (GCDPs). Conclusion: BTAs such as FT, MRG, BMG, BHT and LP were the most frequent conditions. BTA have a high prevalence rate in GUCDPs. BTAs are equally observed in both genders.

Artificial Intelligence (AI) is the ability of machines to perform various tasks with smart work that normally requires human intelligence. It is not a new concept as it was introduced back in the 1950s. However, it has not become the practical tool until two decades ago. Artificial intelligence (AI) has obtained large interest and has long past via a transition level from being a pure statistical tool to being one of the main drivers of modern dentistry. In dentistry, the employment of synthetic intelligence continues to be at its start. Many radiographs are used to decide illnesses with the aid of using displaying the whole shape of the enamel and a few dental troubles that cannot be visible at once with the aid of using the human eye. The concepts of AI, including convolutional neural networks and/or synthetic neural networks, have proven a selection of applications in dentistry, forecasting the viability of stem cells. The dental pulp, measuring operating lengths, pinpointing root fractures and periapical lesions and forecasting the achievement of retreatment procedures. AI has established accuracy and precision in detection, evaluation and prediction. Thus, this review narrates the history, classification and its applications in dentistry.