Re-exploring Dental Education During the COVID-19 Era: A Look Through the Challenges Faced, Solutions, and Their Impact on Traditional Methods.

December 2019 brought to the world one of the worst pandemic diseases to exist - COVID-19. The pandemic led to worldwide lockdowns and quarantine, affecting the entire cycle of the world. The education system was one of the severely affected systems. Dental education, which is a practice-based learning system, faced complications due to the pandemic. E-learning methods were adapted in order to restrict the spread of the virus. E-learning has its own advantages and disadvantages. Although dental education is not possible by virtual means, there exist some methodologies that could be incorporated into routine teaching methods. The mixture of traditional learning methods along with newer methods adopted during COVID-19 can prove to improve the understanding procedure. The main aim of this article is to highlight these hybrid methods and encourage their incorporation to enhance the learning experience.

Clustered regularly interspaced short palindromic repeats-Cas system: diversity and regulation in Enterobacteriaceae.

Insights into the arms race between bacteria and invading mobile genetic elements have revealed the intricacies of the clustered regularly interspaced short palindromic repeats (CRISPR)-Cas system and the counter-defenses of bacteriophages. Incredible spacer diversity but significant spacer conservation among species/subspecies dictates the specificity of the CRISPR-Cas system. Researchers have exploited this feature to type/subtype the bacterial strains, devise targeted antimicrobials and regulate gene expression. This review focuses on the nuances of the CRISPR-Cas systems in Enterobacteriaceae that predominantly harbor type I-E and I-F CRISPR systems. We discuss the systems' regulation by the global regulators, H-NS, LeuO, LRP, cAMP receptor protein and other regulators in response to environmental stress. We further discuss the regulation of noncanonical functions like DNA repair pathways, biofilm formation, quorum sensing and virulence by the CRISPR-Cas system. The review comprehends multiple facets of the CRISPR-Cas system in Enterobacteriaceae including its diverse attributes, association with genetic features, regulation and gene regulatory mechanisms.

Thriving or Simply Surviving? A Qualitative Exploration of STEM Community College Students' Transition to a Four-Year University.

Community colleges expand access to higher education and play a key role in efforts to increase and diversify the future science, technology, engineering, and mathematics (STEM) workforce. While community colleges increase access to higher education and millions of students attend them for some portion of their education, the experiences of transfer students remain relatively understudied. Transferring during an academic journey can compound the barriers that students already face when pursuing a STEM degree. This study uses Schlossberg's model for analyzing human adaptation to transition to understand how STEM community college transfer students navigate and adapt to the 4-year university. Five semistructured focus groups were conducted with STEM community college transfer students attending an urban university. Analysis of the focus groups resulted in a new model: the amended model of adaptation to transfer transition, or AMATT, which illustrates various factors that played a role in STEM community college transfer students' adaptation a university. Analyses illumined two broad pathways that students tend to diverge into during their transitions-thriving or simply surviving. This work provides a framework for understanding factors influencing the transfer process and ideally will inform institutions and students as they consider maximal transfer student success.

Present and pioneer methods of early detection of food borne pathogens.

Food-borne pathogens are a severe threat to human illness and death world-wide. Researchers have reported more than 250 food-borne diseases. Most of these are infections caused by a wide variety of bacteria, viruses, and parasites. It has a significant economic impact also. Detection of pathogenic microbes is thus essential for food safety. Such identification techniques could meet the following parameters viz., the accuracy of detection techniques that are quick, efficient, economical, highly sensitive, specific, and non-labor intensive. The various available methods for detecting food pathogens are classified into different groups, each having its advantages and disadvantages. The conventional methods are usually the first choice of detection even though they are laborious. Modern techniques such as biosensors, immunological assays, and macromolecule-based (nucleic acid) methods are being developed and refined to overcome traditional methods' limitations. Early detection of pathogens and secure food safety at each stage of food processing to storage, utilizing improved methodologies are mandatory. This review summarizes the deadly food pathogens leading to significant outbreaks and discusses the importance of early detection methods and advanced detection methods in comparison.

A Multidimensional Bioinformatic Platform for the Study of Human Response to Surgery.

OBJECTIVE: To design and establish a prospective biospecimen repository that integrates multi-omics assays with clinical data to study mechanisms of controlled injury and healing. BACKGROUND: Elective surgery is an opportunity to understand both the systemic and focal responses accompanying controlled and well-characterized injury to the human body. The overarching goal of this ongoing project is to define stereotypical responses to surgical injury, with the translational purpose of identifying targetable pathways involved in healing and resilience, and variations indicative of aberrant peri-operative outcomes. METHODS: Clinical data from the electronic medical record combined with large-scale biological data sets derived from blood, urine, fecal matter, and tissue samples are collected prospectively through the peri-operative period on patients undergoing 14 surgeries chosen to represent a range of injury locations and intensities. Specimens are subjected to genomic, transcriptomic, proteomic, and metabolomic assays to describe their genetic, metabolic, immunologic, and microbiome profiles, providing a multidimensional landscape of the human response to injury. RESULTS: The highly multiplexed data generated includes changes in over 28,000 mRNA transcripts, 100 plasma metabolites, 200 urine metabolites, and 400 proteins over the longitudinal course of surgery and recovery. In our initial pilot dataset, we demonstrate the feasibility of collecting high quality multi-omic data at pre- and postoperative time points and are already seeing evidence of physiologic perturbation between timepoints. CONCLUSIONS: This repository allows for longitudinal, state-of-the-art geno-mic, transcriptomic, proteomic, metabolomic, immunologic, and clinical data collection and provides a rich and stable infrastructure on which to fuel further biomedical discovery.

Evaluation of Sleep Architecture using 24-hour Polysomnography in Patients Recovering from Critical Illness in an Intensive Care Unit and High Dependency Unit: a Longitudinal, Prospective, and Observational Study.

BACKGROUND AND OBJECTIVE: The sleep architecture of critically ill patients being treated in Intensive Care Units (ICU) and High Dependency Units (HDU) is frequently unsettled and inadequate both qualitatively and quantitatively. The study aimed to investigate and elucidate factors influencing sleep architecture and quality in ICU and HDU in a limited resource setting with financial constraints, lacking human resources and technology for routine monitoring of noise, light and sleep promotion strategies in ICU. METHODS: The study was longitudinal, prospective, hospital-based, analytic, and observational. Insomnia Severity Index (ISI) and the Epworth Sleepiness Scale (ESS) pre hospitalisation scores were recorded. Patients underwent 24-hour polysomnography (PSG) with the simultaneous monitoring of noise and light in their environments. Patients stabilised in ICU were transferred to HDU, where the 24-hour PSG with the simultaneous monitoring of noise and light in their environments was repeated. Following PSG, the Richards-Campbell Sleep Questionnaire (RCSQ) was employed to rate patients' sleep in both the ICU and HDU. RESULTS: Of 46 screened patients, 26 patients were treated in the ICU and then transferred to the HDU. The mean (SD) of the study population's mean (SD) age was 35.96 (11.6) years with a predominantly male population (53.2% (n=14)). The mean (SD) of the ISI and ESS scores were 6.88 (2.58) and 4.92 (1.99), respectively. The comparative analysis of PSG data recording from the ICU and HDU showed a statistically significant reduction in N1, N2 and an increase in N3 stages of sleep (p<0.05). Mean (SD) of RCSQ in the ICU and the HDU were 54.65 (7.70) and 60.19 (10.85) (p-value = 0.04) respectively. The disease severity (APACHE II) has a weak correlation with the arousal index but failed to reach statistical significance (coeff= 0.347, p= 0.083). CONCLUSION: Sleep in ICU is disturbed and persisting during the recovery period in critically ill. However, during recovery, sleep architecture shows signs of restoration.

Roadmap for Transforming Preoperative Assessment to Preoperative Optimization.

Preoperative assessment typically equates to evaluating and accepting the presenting condition of the patient (unless extreme) and commonly occurs only a few days before the planned surgery. While this timing enables a preoperative history and examination and mitigates unexpected findings on the day of surgery that may delay throughput, it does not allow for meaningful preoperative management of modifiable medical conditions. Evidence is limited regarding how best to balance efforts to mitigate modifiable risk factors versus the timing of surgery. Furthermore, while the concept of preoperative risk modification is not novel, evidence is lacking for successful and sustained implementation of such an interdisciplinary, collaborative program. A better understanding of perioperative care coordination and, specifically, implementing a preoperative preparation process can enhance the value of surgery and surgical population health. In this article, we describe the implementation of a collaborative preoperative clinic with the primary goal of improving patient outcomes.

Delaying Elective Surgery in Geriatric Patients: An Opportunity for Preoperative Optimization.

Deciding whether to pursue elective surgery is a complex process for older adults. Comprehensive geriatric assessment (CGA) can help refine estimates of benefits and risks, at times leading to a delay of surgery to optimize surgical readiness. We describe a cohort of geriatric patients who were evaluated in anticipation of elective abdominal surgery and whose procedures were delayed for any reason. Themes behind the reasons for delay are described, and a holistic framework to guide preoperative discussion is suggested.

Optimizing the Feasibility and Scalability of a Geriatric Surgery Quality Improvement Initiative.

BACKGROUND: The American College of Surgeons Coalition for Quality in Geriatric Surgery is a multidisciplinary stakeholder group that aims to systematically improve the surgical care of older adults by establishing a verifiable quality improvement program with standards based on best evidence. Prior work confirmed the validity of a preliminary set of 308 standards to improve the quality of geriatric surgery, but concerns exist as to whether the standards are feasible for hospitals to implement. OBJECTIVE: Our aim was to utilize data gained from a multi-institutional survey and interview to improve the scalability and generalizability of a geriatric quality improvement program. METHODS: Using a survey followed by a targeted debrief interview, 15 hospitals gathered an interdisciplinary panel to answer whether each standard was already in place at their institution, and if not, the perceived difficulty of implementation according to a five-point Likert scale (from 1 [very easy] to 5 [very difficult]). The standards were then placed into categories according to the hospital responses. Standards were designated "duplicative" if 11 or more hospitals reported baseline implementation, "prohibitively difficult" if 6 or more hospitals rated the standard as such, and "high potential" if they were neither duplicative nor difficult. A targeted debrief interview was then conducted with each participating hospital. RESULTS: Fifteen participating hospitals evaluated the feasibility of 108 standards and found 28 (26%) duplicative, 35 (32%) too difficult, and 45 (42%) high potential. Of the 108 standards, 49 (45%) were selected for the next iteration of standards, and 59 were removed. Among the standards that were removed, the majority (64%) were rated duplicative and/or difficult. CONCLUSION: A multi-institutional survey and interview successfully identified care standards that were redundant or too difficult to implement on the hospital level. These data will help improve the generalizability and scalability of the program while maintaining the overall goal of improving care. J Am Geriatr Soc 67:1074-1078, 2019.

Development and validation of machine learning models to identify high-risk surgical patients using automatically curated electronic health record data (Pythia): A retrospective, single-site study.

BACKGROUND: Pythia is an automated, clinically curated surgical data pipeline and repository housing all surgical patient electronic health record (EHR) data from a large, quaternary, multisite health institute for data science initiatives. In an effort to better identify high-risk surgical patients from complex data, a machine learning project trained on Pythia was built to predict postoperative complication risk. METHODS AND FINDINGS: A curated data repository of surgical outcomes was created using automated SQL and R code that extracted and processed patient clinical and surgical data across 37 million clinical encounters from the EHRs. A total of 194 clinical features including patient demographics (e.g., age, sex, race), smoking status, medications, comorbidities, procedure information, and proxies for surgical complexity were constructed and aggregated. A cohort of 66,370 patients that had undergone 99,755 invasive procedural encounters between January 1, 2014, and January 31, 2017, was studied further for the purpose of predicting postoperative complications. The average complication and 30-day postoperative mortality rates of this cohort were 16.0% and 0.51%, respectively. Least absolute shrinkage and selection operator (lasso) penalized logistic regression, random forest models, and extreme gradient boosted decision trees were trained on this surgical cohort with cross-validation on 14 specific postoperative outcome groupings. Resulting models had area under the receiver operator characteristic curve (AUC) values ranging between 0.747 and 0.924, calculated on an out-of-sample test set from the last 5 months of data. Lasso penalized regression was identified as a high-performing model, providing clinically interpretable actionable insights. Highest and lowest performing lasso models predicted postoperative shock and genitourinary outcomes with AUCs of 0.924 (95% CI: 0.901, 0.946) and 0.780 (95% CI: 0.752, 0.810), respectively. A calculator requiring input of 9 data fields was created to produce a risk assessment for the 14 groupings of postoperative outcomes. A high-risk threshold (15% risk of any complication) was determined to identify high-risk surgical patients. The model sensitivity was 76%, with a specificity of 76%. Compared to heuristics that identify high-risk patients developed by clinical experts and the ACS NSQIP calculator, this tool performed superiorly, providing an improved approach for clinicians to estimate postoperative risk for patients. Limitations of this study include the missingness of data that were removed for analysis. CONCLUSIONS: Extracting and curating a large, local institution's EHR data for machine learning purposes resulted in models with strong predictive performance. These models can be used in clinical settings as decision support tools for identification of high-risk patients as well as patient evaluation and care management. Further work is necessary to evaluate the impact of the Pythia risk calculator within the clinical workflow on postoperative outcomes and to optimize this data flow for future machine learning efforts.

A study on the glycemic, lipid and blood pressure control among the type 2 diabetes patients of north Kerala, India.

AIM: The aim of the study was to detect the level of comprehensive diabetes control among the diabetic patients of Kerala, India. METHODS: Patients (1200) were randomly selected from a diabetes care center. Their blood sugar, biochemical and anthropometric measurements were done and statistically analyzed. RESULTS: Only 28.3% had their HbA1c at or below 7% and 45% above 9%. One-third of the female and one-fifth of the male patients had coronary artery disease. The prevalence of hypertension was almost equal in both sexes. However, there was a statistically significant higher systolic blood pressure (mean 162.12mmHg vs 147.49mmHg, p=0.01044) among females. The total cholesterol was above 200mg/dl in 42.1% of males and 45.61% of females. The triglyceride was >150mg/dl in 38.6% males and 50.88% females. Low high density lipoprotein (HDL) cholesterol levels were found in 20.07% of males and 41.12% of females (p=0.0445). The mean low density lipoprotein (LDL) was 121.75 (±32.29) CONCLUSION: The mean blood sugar values are found to be high, which will lead to a predictable increase in vascular disease, which in turn will affect the quality of health and productivity of the individual and the economic growth of the society as a whole. Studies suggest that therapeutic interventions to improve glycemic control may reduce the risk of cardiovascular disease and microvascular disease. This study shows that the level of diabetes control in Kerala is unsatisfactory. We need more medications, better strategies and more emphasis on glycemic management than we are currently able to apply.