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Recurrent respiratory papillomatosis (RRP) is a stubborn disease. Despite volumes of researches done for a definite cause and management, the scientific community offers only theories for causation and options for treatments. Bevacizumab has emerged as a promising solution to the fear of sufferers of RRP of undergoing repeated surgeries. The patients who received bevacizumab, either systemically or intralesionally, show decreased need for surgeries mostly and even remission in a few. Till date there are limited studies of use of bevacizumab, in adults, but only reports of its use in pediatric population. This is a report of two cases of juvenile onset RRP with use of systemic bevacizumab infusion in a child and intralesional injection in an adult.

Frailty has been shown to adversely impact outcomes in a number of surgical disciplines. In head and neck reconstructive surgery, frailty may represent a significant risk factor in predicting post-operative outcomes due to the common characteristics of the patient population undergoing these procedures.To summarize the available evidence about frailty as a predictor of post-operative complications, length of hospital stay and quality of life in patients undergoing head and neck reconstructive surgery.Systematic Review.The study protocol was registered with PROSPERO, registration CRD42022302899. Methodology was in keeping with the PRISMA Guidelines for Systematic Reviews. MEDLINE, SCOPUS, EMBASE, Web of Science and CENTRAL were the databases searched. Qualitative synthesis of the included studies was carried out, and quality assessment was performed.Nine studies that reported data on 10,457 patients undergoing reconstruction of the head and neck were included in the review. A number of different tools were used to assess frailty, with the modified frailty index being the most frequently used. In total, 8 studies reported increased rates of complications in patients with increased levels of frailty, irrespective of the frailty tool used, with varied levels of statistical significance across the studies.An association is observed between increased rates of perioperative complications and increased levels of frailty in patients undergoing head and neck reconstruction. Frailty tools may represent a useful method to risk stratify patients undergoing reconstructive head and neck surgery.

The study used machine learning to predict The American Spinal Injury Association Impairment Scale (AIS) scores for newly injured spinal cord injury patients at hospital discharge time from hospital admission data. Additionally, machine learning was used to analyze the best model for feature importance to validate the criticality of the AIS score and highlight relevant demographic details. The data used for training machine learning models was from the National Spinal Cord Injury Statistical Center (NSCISC) database of U.S. spinal cord injury patient details. Eighteen real features were used from 417 provided features, which mapped to 53 machine learning features after processing. Eight models were tuned on the dataset to predict AIS scores, and Shapely analysis was performed to extract the most important of the 53 features. Patients within the NSCISC database who sustained injuries were between 1972 and 2016 after data cleaning (n = 20,790). Outcomes were test set multiclass accuracy and aggregated Shapely score magnitudes. Ridge Classifier was the best performer with 73.6% test set accuracy. AIS scores and neurologic category at the time of admission were the best predictors of recovery. Demographically, features were less important, but age, sex, marital status, and race stood out. AIS scores on admission are highly predictive of patient outcomes when combined with patient demographic data. Promising results in terms of predicting recovery were seen, and Shapely analysis allowed for the machine learning model to be probed as a whole, giving insight into overall feature trends.

Introduction Indian medical schools contribute significantly to the global pool of doctors. To strengthen the medical education system further, this study was aimed at identifying existing lacunae and proposing solutions, from perspective of medical undergraduates. Methodology Medical undergraduates participated in group discussions under mentorship of an expert in medical education. Identification and prioritisation of problems was done based on demand and impact on medical education. After critically reviewing available literature, solutions were reached by consensus. Results Six interested students participated in discussions. Classification of identified problems was done into domains of knowledge, skill training, and research. Major problems in ‘knowledge' were: inefficient utilisation of time of students and teachers, and unyielding assessments; in ‘skill training': inadequate training in skills; and in ‘research': insufficient awareness and interest in research among undergraduates. Some solutions suggested for respective domains were prioritisation of teaching and training of medical students, advocation of innovative methods of teaching and assessment like flipped classroom approach and clinical vignettes respectively; early initiation of undergraduate training in skill development following the principle of competency-based education; and sensitization of undergraduates towards research by introducing it as basic core and advanced elective subject, besides setting up research advisory cells and organising research conclaves. Conclusion Problems were classified into domains of knowledge, skill training, and research. Proposed solutions involve a radical alteration in perception of students and teachers in terms of teaching-learning principles. There is a need to highlight these points before policy makers and implementers for a more robust medical education system.

This is a letter to the editor enlisting a few concerns and points of view which arose on reading a recently published article on the novel use of platelet rich fibrin membrane in transcanal myringoplasty.

Objectives Lung cancer is the most common cause of cancer mortality worldwide and, while tobacco smoke remains the primary cause, there is increasing concern that vaping and E-cigarette use may also increase lung cancer risk. This review concentrates on the current data, scholarship and active foci of research regarding potential cancer risk and oncogenic mechanisms of vaping and lung cancer. Materials and methods We performed a literature review of current and historical publications on lung cancer oncogenesis, vaping device/e-liquid contents and daughter products, molecular oncogenic mechanisms and the fundamental, potentially oncogenic, effects of electronic cigarette smoke/e-liquid products. Results E-cigarette devices and vaping fluids demonstrably contain a series of both definite and probable oncogens including nicotine derivatives (e.g. nitrosnornicotine, nitrosamine ketone), polycyclic aromatic hydrocarbons, heavy metals (including organometal compounds) and aldehydes/other complex organic compounds. These arise both as constituents of the e-liquid (with many aldehydes and other complex organics used as flavourings) and as a result of pyrolysis/complex organic reactions in the electronic cigarette device (including unequivocal carcinogens such as formaldehyde – formed from pyrolysis of glycerol). Various studies demonstrate in vitro transforming and cytotoxic activity of these derivatives. E-cigarette device use has been significantly increasing – particularly amongst the younger cohort and non-smokers; thus, this is an area of significant concern for the future. Conclusion Although research remains somewhat equivocal, there is clear reason for concern regarding the potential oncogenicity of E-Cigarettes/E-Liquids with a strong basic and molecular science basis. Given lag times (extrapolating from tobacco smoke data) of perhaps 20 years, this may have significant future public health implications. Thus, the authors feel further study in this field is strongly warranted and consideration should be made for tighter control and regulation of these products.

The treatment landscape of prostate cancer has evolved rapidly over the past five years. The explosion in treatment advances has been witnessed in parallel with significant progress in the field of molecular biomarkers. The advent of next-generation sequencing has enabled the molecular profiling of the genomic and transcriptomic architecture of prostate and other cancers. Coupled with this, is a renewed interest in the role of non-coding RNA (ncRNA) in prostate cancer biology. ncRNA consists of several different classes including small non-coding RNA (sncRNA), long non-coding RNA (lncRNA), and circular RNA (circRNA). These families are under active investigation, given their essential roles in cancer initiation, development and progression. This review focuses on the evidence for the role of RNAs in prostate cancer, and their use as diagnostic and prognostic markers, and targets for treatment in this disease.

This paper develops an adaptive control strategy for failure compensation of a single main-rotor helicopter. The adaptive controller addresses the problem of instability due to failure in the main rotor or tail rotor assemblies, or unexpected variations in thrust or yaw due to damage to the rotor blades. An abstracted model of the helicopter is used for the development of the adaptive control laws. The effects of instability due to damage to the helicopter are modeled. A set of adaptive controllers are developed to stabilize the helicopter's states under failure. Simulation results establish the efficacy of the adaptive control scheme over the conventional control laws in providing stability.

This paper presents a photovoltaic (PV) model estimation method from the PV module data. The model is based on a single-diode model of a PV cell. The cell model of a module is extended to estimate the parameters of arrays. The variation of the parameters with change in temperature and irradiance is also studied. Finally, estimation of the maximum power point from the estimated parameters under varying environmental conditions is presented.

This paper presents a comprehensive and robust strategy for the estimation of battery model parameters from noise corrupted data. The deficiencies of the existing methods for parameter estimation are studied and the proposed parameter estimation strategy improves on earlier methods by working optimally for low as well as high discharge currents, providing accurate estimates even under high levels of noise, and with a wide range of initial values. Testing on different data sets confirms the performance of the proposed parameter estimation strategy.

This paper examines the problem of decoding a unique CAPTCHA that has very high noise levels with only partially visible symbols with variable spacing but no skewing. An ingenious method for decoding is proposed that starts with preprocessing the image and identifies symbols first before removing them from the image, unlike a number of existing methods. The algorithm is expected to be very fast owing to a reduction of the image matrix into a number of small segments that codify information in a lossy way that still allows for template matching for symbol identification. Character identification is attempted using both a Neural Network based approach and a mean square error method and their performance is compared and it is shown that the latter is significantly faster without being much less accurate. The CAPTCHA decoding strategy should offer insight into better methods of designing CAPTCHA's and into decoding strategies for other applications such as OCR.

Perturb & Observe (P&O) maximum power point tracking (MPPT) strategies need to sense and effect changes in PV voltage faster to keep up with changing power due to rapidly varying environmental conditions, and thus track the MPP accurately. However, the effectiveness of such P&O methods is limited in a grid-connected PV station scenario by the switching frequency of power electronic components. This paper proposes a novel, easy-to-implement MPPT strategy that improves performance even at low operating frequencies and under fast changing environmental conditions by exercising a greater control over the transient PV voltage. The proposed MPPT strategy has also been made adaptive to reduce the oscillations around the MPP. In addition a special provision has been made to circumvent the problems encountered by the traditional adaptive P&O algorithms for scenarios with constantly decreasing output power due to changing atmospheric conditions. The proposed MPPT strategy was tested in simulations and was observed to perform far better than standard adaptive P&O strategy.

This paper develops a novel adaptive control based strategy for accurate simulation of solar panels under any given physical condition and for any general static or dynamic load. The method requires only PV datasheet values to set up the simulator for any given operating condition. Simulations confirm the efficacy of the proposed PV simulator. A novel controller for voltage buffering in applications requiring fast and accurate convergence is also proposed. The PV simulator is implemented in hardware using the proposed voltage buffer. The contributions of this paper are two-fold: (i) the development of a novel adaptive control based PV simulation methodology for dynamic tracking of any general load, and (ii) a novel control scheme for voltage buffering in DC applications.

This paper proposes the need for accurate and fast solar panel simulators for the purpose of accurately modeling the behavior of Smart Grids. A comprehensive strategy for accurate simulation of solar panels under different conditions and various kinds of loads is developed using adaptive control, based only on data from the solar panel datasheet. A novel method for more accurate solar panel simulation is proposed using a controller-based strategy. We demonstrate that with a set of adaptive controllers accurate tracking of any general load connected to the simulator is achievable. Simulations validate the efficacy of the proposed method for solar panel simulation.

This paper proposes an adaptive controller to provide for failure compensation for a coaxial rotor helicopter. It addresses the problem of instability due to damage to the rotors in a coaxial rotor helicopter, where degradation of or unexpected variations in thrust from the propellers are known to pose an especially serious risk. The key problems arising out of a situation of propeller failure are identified. A detailed description of the model of the coaxial rotor helicopter is provided, and a control scheme for the same is chosen. The problem of instability along the yaw and altitude axis for coaxial rotor helicopters under propeller failure is modeled. Finally, a suitable adaptive control scheme is proposed to stabilize the helicopter's states under propeller failure. Simulation results demonstrate that the adaptive control scheme is successful in stabilizing the aircraft in the event of damage.

We analyse questions of arbitrage in financial markets in which asset prices change in time as stationary stochastic processes. The main focus of the paper is on a model where the price vectors are independent and identically distributed. In the framework of this model, we find conditions that are necessary and sufficient for the absence of arbitrage opportunities. We discuss the relations between the results obtained and the phenomenon of “volatility-induced growth” in stationary markets.

We analyse questions of arbitrage in financial markets in which asset prices change in time as stationary stochastic processes. The main focus of the paper is on a model where the price vectors are independent and identically distributed. In the framework of this model, we find conditions that are necessary and su¢ cient for the absence of arbitrage opportunities. We discuss the relations between the results obtained and the phenomenon of "volatility-induced growth"in stationary markets.