Objectives The aim of our study was to evaluate antimicrobial prescription behaviour amongst dentists performing oral implant surgery in India. Study Design Dentists performing oral implant surgery from different parts of India were personally approached during various national events such as conferences and academic meetings and information regarding their prescription habits for antimicrobial agents in routine oral implant surgery was collected using a structured questionnaire. Results Out of a total sample of 332 dentists, 85.5 % prescribed 17 different groups or combinations of antibiotics routinely for oral implant surgery in the normal healthy patient. Majority preferred the peri-operative protocol of drug therapy (72.2 %) with variable and prolonged duration of therapy after surgery, ranging from 3 to 10 days. An antimicrobial mouthwash was routinely prescribed by all the doctors (14.5 %) not in favour of prescribing antimicrobials in a normal healthy patient.Conclusions Our findings suggest that there is a trend of antimicrobial agent misuse by dentists performing oral implant surgery in India, both in terms of drugs used and the protocols prescribed. The majority of these dentists prescribed a variety of antimicrobial agents for prolonged durations routinely even in the normal, healthy patients.

The task of developing Intrusion Detection System (IDS) crucially depends on the preprocessing along with selecting important data features of it. Another crucial factor is design of efficient learning algorithm that classify normal and anomalous patterns. The objective of this research work is to propose a new and better version of the Naive Bayes classifiers that improves the accuracy of intrusion detection in IDS. The proposed classifier is also supposed to take less time as compared with the existing classifiers. To gain better accuracy and fast processing of network traffic, this study applied three standard methods of feature selection. This study tested the performance of the new proposed classifier algorithm with existing classifiers, namely Naïve bayes, J48 and REPTree thereby measuring different performance parameters using 10-fold cross validation. This study evaluates the performance of the new proposed classifier algorithm by using NSL-KDD data set. Empirical results of our study show that the proposed updated version of the Naive Bayes classifiers gives better results in terms of intrusion detection and false alarm rate.

Aim: This study was conducted to determine the preferred analgesic and anti-inflammatory drugs prescribed by oral implantologists in India. Methods: A structured questionnaire was distributed to 332 dentists to gather information regarding their prescription habits for analgesics and anti-inflammatory drugs. Frequency distributions were computed by type of drug being prescribed and the protocol followed. Results: Analysis of data showed that majority of dentists (85.8%, n = 285) prescribed conventional non-steroidal anti-inflammatory drugs (NSAIDs) for implant surgery. The most common prescription was ibuprofen with paracetamol combination (32.2%, n = 107) followed by diclofenac (20.2%, n = 67). Most dentists reported prescribing different NSAIDs for the same procedure in different patients (64.7%, n = 215). Only, 35.5% (n = 118) followed the peri-operative protocol. Adjunctive prescription of steroids was done by only 33.7% (n = 112). Conclusion: Our study illustrates that the general trend of analgesic and anti-inflammatory drug prescription for dental implant surgery among Indian dentists is mostly in accordance with the guidelines for pain management worldwide. However, it is noteworthy that a few dentists do prescribe drugs not primarily indicated for dental pain management and use widely varying protocols for the same. Therefore, in order to avoid potential complications, it is essential to raise awareness of among the dental practitioners of the appropriate indications and dosage regimen of specific drugs.

Syndrome of inappropriate anti-diuretic hormone secretion (SIADH) is a disease of unsuppressed secretion of anti-diuretic hormone (ADH) and its continued action on the Vasopressin (V2) receptors. It is characterized by hyponatremia in a setting of hypervolemia or euvolemia. In this study, we aimed to identify and compare demographic characteristics, adverse outcomes, and complications of SIADH hospitalizations with and without comorbid atrial fibrillation (AF). We analyzed the Nationwide Inpatient Sample (NIS) for 2019 to identify all adult (≥18 years) hospitalizations with a principal diagnosis of SIADH using the ICD-10-CM code (E22.2). Patients <18 years of age were excluded from the analysis. The study cohort was further divided based on the presence of absence of AF. Demographic and hospitalization characteristics were highlighted. Using a multivariate regression analysis adjusting for age, sex, race, primary payer, median household income by zip code, Carlson Comorbidity Index (CCI), hospital location, bed-size, and teaching status, inpatient outcomes such as inpatient mortality, rates of septic shock, acute myocardial infarction (AMI), acute respiratory failure (ARF), acute renal failure (AKI), and acute pulmonary edema (APE) were determined. All p-values< 0. 05 were considered statistically significant. In 2019, of the 39,110 hospitalizations for SIADH, 18.5% of the patients had comorbid AF. SIADH hospitalizations with AF were older (80.4 vs 70.1 years, p<0. 001, predominantly White and a higher proportion of patients with CCI >2 compared to the non-AF cohort. Although there was a higher proportion of females in the AF cohort (61.4% vs 63.1%, p=0.252), we did not find a statistically significant difference for gender between the two subgroups. SIADH hospitalizations with AF were associated with higher adjusted odds ratio (aOR) of inpatient mortality (2.1 vs 0.9%, aOR: 2.45, p<0. 001), increased length of stay (LOS), (5.8 vs 4.8 days, p<0. 001) and increased mean hospitalization cost (THC) ($12,284 vs $10,340, p <0. 001) compared to the non-AF cohort. Furthermore, compared to the non-AF cohort, we noted higher odds of septic shock (0.6 vs 0.3%, aOR: 3.35, p=0. 007) and ARF (11.2 vs 6.5%, aOR: 1.88, p<0. 001) for the AF cohort; however, we did not find a statistically significant difference in the odds of CVA, AKI or AMI between the two groups. Patients hospitalized with SIADH, and comorbid AF had higher inpatient mortality, LOS, THC, which may be secondary to multiple electrolyte abnormalities and fluid imbalances associated with SIADH leading to a worsening of the AF. Furthermore, these patients had higher odds of septic shock and ARF. Hence, it is vital to appropriately manage AF in patients with SIADH to prevent adverse outcomes and decrease the burden on the United States healthcare system. Presentation: No date and time listed

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