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Objective: The main purpose of this study is to assess the knowledge, attitude, experience, and perception about the management of medical emergencies in dental clinics amongst house officers at CMH LMC & IOD. Study Duration: This questionnaire-based descriptive cross-sectional study was conducted between May 2022 and June 2022 at CMH Lahore Medical College and Institute of Dentistry. Material & Methods: A previously tested questionnaire comprised of three parts was used for data collection. A total of 62 house officers were present. All of them responded to the questionnaire. The questionnaire consists of three sections; Section A: Contains the questions related to the understanding of the dental house officers in managing medical emergencies. Section B: Includes the evaluation of experiences and skills they have in managing medical emergencies if it occurs in dental clinics. Section C: Includes their perceptions related to the betterment of emergency management was evaluated. Data was analyzed using IBM SPSS Version 24. Data was analyzed using IBM SPSS Version 24. Results: Concerning Basic life support training, 64.5% had been given training during house job only. At least 67.7% of House officers came across 1-5 medical emergencies during their house job. Syncope was one of the most common (62.9%). Hypoglycemia was the second most common (35.5%) and asthma was reportedly the third most common medical emergency (1.6%). When facing a medical emergency, 90.3% report that they seek help. 96.8% reported that they can take a patient’s blood pressure, 72.4% can take a patient’s pulse, 27.4% can measure blood sugar level, only 38.7% can maintain an IV line, 41.9% can perform the Heimlich maneuver, 53.2% can carry out ventilation through a bag or mask and 35.5% are capable of performing CPR. Only 3.2% of house officers believe that their undergraduate training is sufficient to prepare them to manage medical emergencies. Though, 71.0 % think that house officers should undertake medical emergency training before starting their house job. Practical implication:There is scarcity of literature on how prepared dental house surgeons are to deal with medical emergencies so this study give future directions to dental house surgeons. Conclusion: It was concluded that practical training is lacking among dental house officers in managing medical emergencies and that there is a need to not only include medical emergency training at the undergraduate level but also allot a rotation to medical emergencies at the start of the house job. Keywords: Dental house officers, medical emergencies, management, training

Breast carcinogenesis is known to be instigated by genetic and epigenetic modifications impacting multiple cellular signaling cascades, thus making its prevention and treatments a challenging endeavor. However, epigenetic modification, particularly DNA methylation-mediated silencing of key TSGs, is a hallmark of cancer progression. One such tumor suppressor gene (TSG) RUNX3 (Runt-related transcription factor 3) has been a new insight in breast cancer known to be suppressed due to local promoter hypermethylation mediated by DNA methyltransferase 1 (DNMT1). However, the precise mechanism of epigenetic-influenced silencing of the RUNX3 signaling resulting in cancer invasion and metastasis remains inadequately characterized. In this study, a biological regulatory network (BRN) has been designed to model the dynamics of the DNMT1–RUNX3 network augmented by other regulators such as p21, c-myc, and p53. For this purpose, the René Thomas qualitative modeling was applied to compute the unknown parameters and the subsequent trajectories signified important behaviors of the DNMT1–RUNX3 network (i.e., recovery cycle, homeostasis, and bifurcation state). As a result, the biological system was observed to invade cancer metastasis due to persistent activation of oncogene c-myc accompanied by consistent downregulation of TSG RUNX3. Conversely, homeostasis was achieved in the absence of c-myc and activated TSG RUNX3. Furthermore, DNMT1 was endorsed as a potential epigenetic drug target to be subjected to the implementation of machine-learning techniques for the classification of the active and inactive DNMT1 modulators. The best-performing ML model successfully classified the active and least-active DNMT1 inhibitors exhibiting 97% classification accuracy. Collectively, this study reveals the underlined epigenetic events responsible for RUNX3-implicated breast cancer metastasis along with the classification of DNMT1 modulators that can potentially drive the perception of epigenetic-based tumor therapy.

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