Evaluation of Berlin-Frankfurt-Munster (BFM)protocols in Acute Lymphoblastic Leukemia and the role of Flow Cytometry in Minimal Residual Disease Monitoring: A Single Tertiary Centre Analysis from India.

INTRODUCTION : Acute lymphoblastic leukemia (ALL) is a malignant disease of immature lymphoid cells proliferating at an uncontrolled rate with a block in its early stage of differentiation.(1) It has been reported as one of the most common malignancy of the childhood, accounting for almost 25% of all pediatric tumours and about 80% of pediatric leukemia.(1,2) Its incidence shows a bimodal peak, with the initial and the highest peak seen between 2 to 5 years of age and then a continues decline in the incidence with increasing age till the age of 50 years, following which it again shows a second peak. AIMS AND OBJECTIVES : (1) To study the clinical profile of children and adults diagnosed with acute lymphoblastic leukemia in our institute and their treatment outcome, when treated with different BFM protocols. (2) To evaluate the clinical outcome in adolescent patients with acute lymphoblastic leukemia between 15 - 20 years of age, by using pediatric treatment regimens instead of adult regimens as currently used. (3) To assess the role of minimal residual disease status monitoring by flow cytometry at time of documenting remission, post induction phase of chemotherapy. METHODOLOGY : For retrospective analysis of adult patients, we included all newly diagnosed patients with acute lymphoblastic leukemia from January 2004 to February 2014. For adolescent patients, we compared adolescents treated by adult regimens from January 2004 to June 2012 with those adolescents treated with pediatric regimens from July 2012 to February 2014. We also compared standard and intermediate risk pediatric patients treated with the non BFM 95 regimens from January 2004 to those receiving BFM 95 based regimens from 2008. Lastly, since July 2012 we prospectively analyzed the role of flow cytometry in assessing the minimal residual status at the end of induction chemotherapy and compared the outcomes of those who tested positive with those tested negative. RESULTS : Among 455 adults analyzed, there were 331(72.7 %) standard risk and 124 (27.2 %) high risk adults. Median follow up duration was 65 months. There were 132 (29 %) relapses and 179 (39.3 %) deaths. The 5 year EFS was 50.1 ± 2.9 % and the 5- year OS was 51.6 ± 2.9 %. Among children with standard risk ALL, with an actuarial median follow up period of 25(1.5 - 65) months and 17(1- 64) months for those treated with the BFM 95 protocol and those with the Non MTx/Non RT based study protocol; the three year event free survival was 95 ± 4.9 % and 86.5 ± 6.5 % respectively. (P value= 0.391). Among children with intermediate risk ALL, with an actuarial median follow up period of 27 (1 - 47) months and 18 (1-116) months for those treated with the BFM 95 protocol and those with the radiation (RT) based protocol; the two year overall survival was 96.9 ± 3.1 % and 85.6 ± 2.4 % respectively. (P value = 0.103) With an actuarial median follow up period of 7.7 (1 - 19) months and 18(1-118) months those treated with intermediate risk pediatric protocol and the modified adult GMALL protocol; the one year event free survival was 82.3 ± 7.3 % and 75.9 ± 3.6 % respectively.(P value = 0.752) Among patients tested for the minimal residual disease, with an actuarial median follow up of 7.7 (1-19) months, the 6 months and 1 year EFS in MRD (-) cohort (n = 53) is 97.4 ± 2.6 %. With an actuarial median follow up of 7.7 (1-13) months, the 6 months and 1 year EFS in MRD (+) cohort (n = 16) is 75 ± 21.7 %. With a median follow up of 6 months, the 6 months OS in MRD strongly (+) cohort (n = 6) is 20 ± 17.9 %. (P = 0.000) CONCLUSION: Using the current modified GMALL and BFM 95 regimens in adults (≥ 15 years) and children (>1<15 years), treatment outcomes were comparable to those reported in the international literature. There was no significant difference as yet in the BFM 95 and the non BFM 95 regimens. Using pediatric regimens in adolescent age group (≥15 ≤ 20 years) did not reveal any significant difference in overall outcome as compared to adult regimens. Though the follow up is short, pediatric regimens are feasible in adolescents with minimal toxicity and there appears to be a trend towards improvement in their outcomes with pediatric regimens. Using flow cytometry in detecting minimal residual disease can significantly identify high risk patients and improve their outcome by timely intensification

Multiple hepatic lesions in a case of isolated hepatic tuberculosis simulating metastases on 18F-FDG PET/CT imaging

Hepatic tuberculosis is an unusual form of extrapulmonary tuberculosis and constitutes less than 1% of all cases of tuberculosis. Imaging studies for hepatic tuberculosis are nonspecific and mimic primary or metastatic carcinoma. Here we present 18F-FDG PET/CT images of a 25-year-old male patient with isolated hepatic tuberculosis

Impact of COVID-19 on cardiovascular testing in the United States versus the rest of the world

Objectives: This study sought to quantify and compare the decline in volumes of cardiovascular procedures between the United States and non-US institutions during the early phase of the coronavirus disease-2019 (COVID-19) pandemic. Background: The COVID-19 pandemic has disrupted the care of many non-COVID-19 illnesses. Reductions in diagnostic cardiovascular testing around the world have led to concerns over the implications of reduced testing for cardiovascular disease (CVD) morbidity and mortality. Methods: Data were submitted to the INCAPS-COVID (International Atomic Energy Agency Non-Invasive Cardiology Protocols Study of COVID-19), a multinational registry comprising 909 institutions in 108 countries (including 155 facilities in 40 U.S. states), assessing the impact of the COVID-19 pandemic on volumes of diagnostic cardiovascular procedures. Data were obtained for April 2020 and compared with volumes of baseline procedures from March 2019. We compared laboratory characteristics, practices, and procedure volumes between U.S. and non-U.S. facilities and between U.S. geographic regions and identified factors associated with volume reduction in the United States. Results: Reductions in the volumes of procedures in the United States were similar to those in non-U.S. facilities (68% vs. 63%, respectively; p = 0.237), although U.S. facilities reported greater reductions in invasive coronary angiography (69% vs. 53%, respectively; p < 0.001). Significantly more U.S. facilities reported increased use of telehealth and patient screening measures than non-U.S. facilities, such as temperature checks, symptom screenings, and COVID-19 testing. Reductions in volumes of procedures differed between U.S. regions, with larger declines observed in the Northeast (76%) and Midwest (74%) than in the South (62%) and West (44%). Prevalence of COVID-19, staff redeployments, outpatient centers, and urban centers were associated with greater reductions in volume in U.S. facilities in a multivariable analysis. Conclusions: We observed marked reductions in U.S. cardiovascular testing in the early phase of the pandemic and significant variability between U.S. regions. The association between reductions of volumes and COVID-19 prevalence in the United States highlighted the need for proactive efforts to maintain access to cardiovascular testing in areas most affected by outbreaks of COVID-19 infection

An explicit expression for velocity profile in presence of secondary current and sediment in an open channel turbulent flow

The present study revisits the determination of vertical distribution of streamwise velocity in an open channel turbulent flow considering the effect of secondary current in the presence of sediment together with a concentration dependent settling velocity and von Karman constant κs. The work mainly modifies a previous study that introduced a lot of assumptions to obtain an analytical solution of the velocity distribution. The present study overcomes those assumptions in the model and though not fully analytical, attempts to present a semi-analytical solution that is explicit and in the form of a convergent series. Homotopy analysis method is used for this purpose and it is validated with numerical solution as well as with available laboratory data from the literature. How the secondary current and concentration dependent κs influence the velocity profile, is also discussed.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Comparing a neural-fuzzy scheme with a probabilistic neural network for applications to monitoring in manufacturing systems

Abstract Introduction The success of unattended manufacturing depends largely on control mechanisms that monitor the machining state and take actions to rectify unsatisfactory performance. Direct sensing methods like quality inspection lack on-line capability, whereas indirect methods using sensors can be thwarted by noise and changes in operating conditions. While knowledge about these changes exists, it does not generally correspond with an available sensor. Two different techniques are applied to the problem of integrating data from multiple sensors in the manufacturing environment: one featuring the integration of fuzzy logic and neural networks, and one using a probabilistic neural network. These techniques are applied to monitor and diagnose tool wear in unattended milling machines -an application with implications toward extension to other manufacturing machines. Data from spindle motor current, acoustic emission, and vibration gathered in experiments on a Matsuura machining center are used as input to the two systems. In the case of the fuzzy-neural system, clusters for tool wear are established using the dendrogram method, then membership functions for these clusters are learned by a neural network. These clusters can be interpreted as fuzzy rules which are then applied to tool wear diagnosis using other principles of fuzzy logic. For the probabilistic neural network system, a network with fixed size is used for clustering of data and estimating the probability density function using a self-organizing probabilistic neural network (SOPNN). Both systems show promising results with regard to tool wear. The advantage of the fuzzy neural-fuzzy system is that its classification seems to exhibit high reliability due to its redundant structure and efficiency of the preclustering. The advantage of the probabilistic network, on the other hand, is that it allows the use of rigorous probabilistic analysis, supports Bayesian network models and provides a means for the continuous updating of the density functions. The neural-probabilistic system has been tested successfully on data from an industrial power generation plant for application to sensor validation. The need of manufacturers to produce inexpensive, quality products has resulted in increasing demand for unattended and/or automated manufacturing systems. One problem in automating machining is how to deal with common malfunctions and disturbances such as tool wear, chatter, and tool breakage. Tool wear is a process which is very difficult to deal with for a variety of reasons. It is not a linear process: a tool wears fast initially, then at a moderate rate for a longer period of time, and finally at an accelerated rate until total failure. To complicate things, tool life is not constant under the seemingly same operating conditions. Many factors affect the operating life up to the wear limit: slight variations in the material of the workpiece, the degree of inclusions in the workpiece and slight temperature changes are but a few. To avoid costly damage due to tool wear or breakage, manufacturers use conservative operating procedures to prevent these malfunctions (Rangwala and Dornfeld, 1989). However, these result in less efficient and more costly production because of premature tool replacement and excessive machine downtime. To increase operating efficiency, manufacturers can consider the use of sensors to diagnose tool status and control the system on-line. Since each sensor alone cannot reliably render the state of a tool in changing cutting conditions, integrating the information of various sensors becomes the major challenge. By using partly redundant information this sensor fusion can provide data for decision making about the process that will yield accurate diagnostic predictions and early warning of incipient failures. Early research focused on extracting relevant features from sensor data and inferring the tool status; others This paper summarizes two approaches to the problems outlined above: (1) a hybrid fuzzy-neural system and (2) system using a probabilistic neural network which can be 4

Membranoproliferative glomerulonephritis and acute renal failure in a patient with chronic lymphocytic leukemia: Response to obinutuzumab

Objective/background: Membranoproliferative glomerulonephritis (MPGN) is a common extramedullary renal presentation in chronic lymphocytic leukemia (CLL) and can present with either a frank renal failure or proteinuria. One of its etiologies has been attributed to a paraneoplastic, immune complex phenomenon occurring in CLL. Although there is no standard of care in such patients, use of anti-CD20 monoclonal antibodies like rituximab have been used before in such patients with variable responses. Obinutuzumab is a novel, type II, immunoglobulin-G1 monoclonal antibody with a higher efficacy than rituximab and has an established safely profile in patients with comorbidities and poor renal functions. There are no such reported cases of MPGN in CLL being treated with obinutuzumab. Methods: We used the standard doses of obinutuzumab in our elderly patient (78-year-old woman) with high-risk CLL due to an underlying TP53 mutation, along with a MPGN-related acute renal failure. Results: The patient achieved complete remission after six cycles of obinutuzumab; however, she remained positive for minimal residual disease on flow cytometry. Her renal function improved completely, suggesting a complete response of her underlying MPGN. Conclusion: Obinutuzumab has an established safety profile in patients with CLL, but our case is the first reported case of a paraneoplastic, immune complex-mediated MPGN in CLL being treated with obinutuzumab. Obinutuzumab should be explored as a potential option in patients with CLL and MPGN. Keywords: CLL, MPGN, Obinutuzumab, Renal failur

Abstract Comparing a Neural-Fuzzy Scheme with a Probabilistic Neural Network for Applications to Monitoring and Diagnostics in Manufacturing Systems

The success of unattended manufacturing depends largely on control mechanisms that monitor the machining state and take actions to rectify unsatisfactory performance. Direct sensing methods like quality inspection lack on-line capability, whereas indirect methods using sensors can be thwarted by noise and changes in operating conditions. While knowledge about these changes exists, it does not generally correspond with an available sensor. Two different techniques are applied to the problem of integrating data from multiple sensors in the manufacturing environment: one featuring the integration of fuzzy logic and neural networks, and one using a probabilistic neural network. These techniques are applied to monitor and diagnose tool wear in unattended milling machines- an application with implications toward extension to other manufacturing machines. Data from spindle motor current, acoustic emission, and vibration gathered in experiments on a Matsuura machining center are used as input to the two systems. In the case of the fuzzy-neural system, clusters for tool wear are established using the dendrogram method, then membership functions for these clusters are learned by a neural network. These clusters can be interpreted as fuzzy rules which are then applied to tool wear diagnosis using other principles of fuzzy logic. For the probabilistic neural network system, a network with fixed size is used for clustering of data and estimating the probability density function using a self-organizing probabilistic neural network (SOPNN). Both systems show promising results with regard to tool wear. The advantage of the fuzzy neural-fuzzy system is that its classification seems to exhibit high reliability due to its redundant structure and efficiency of the preclustering. The advantage of the probabilistic network, on the other hand, is that it allows the use of rigorous probabilistic analysis, supports Bayesian network models and provides a means for the continuous updating of the density functions. The neural-probabilistic system has been tested successfully on data from an industrial power generation plant for application to sensor validation.

Effect of parameter mismatch on the dynamics of strongly coupled self sustained oscillators

In this paper, we present an experimental setup and an associated mathematical model to study the synchronization of two self-sustained, strongly coupled, mechanical oscillators (metronomes). The effects of a small detuning in the internal parameters, namely, damping and frequency, have been studied. Our experimental system is a pair of spring wound mechanical metronomes; coupled by placing them on a common base, free to move along a horizontal direction. We designed a photodiode array based non-contact, non-magnetic position detection system driven by a microcontroller to record the instantaneous angular displacement of each oscillator and the small linear displacement of the base, coupling the two. In our system, the mass of the oscillating pendula forms a significant fraction of the total mass of the system, leading to strong coupling of the oscillators. We modified the internal mechanism of the spring-wound “clockwork” slightly, such that the natural frequency and the internal damping could be independently tuned. Stable synchronized and anti-synchronized states were observed as the difference in the parameters was varied in the experiments. The simulation results showed a rapid increase in the phase difference between the two oscillators beyond a certain threshold of parameter mismatch. Our simple model of the escapement mechanism did not reproduce a complete 180° out of phase state. However, the numerical simulations show that increased mismatch in parameters leads to a synchronized state with a large phase difference. We provide experimental and numerical evidence of phase locked synchronous states in a system of two strongly coupled mechanical oscillators. By designing an opto-electronic position tracking system using linear photosensor arrays, we track the motion of individual oscillators. Our mathematical model allows for independently detuning key metronome parameters like frequency and damping coefficient. We present two important results: (A) experimental evidence that two strongly coupled metronomes can be made to synchronize in states of in-phase and anti-phase synchrony by changing their relative parameter mismatch, and (B) numerical evidence of rapidly increasing phase difference of the two oscillators beyond a certain threshold of parameter mismatch. Our results demonstrate that increased mismatch of parameters can be a possible underlying reason behind out of phase synchronization. I. INTRODUCTIONby Nilaj Chakrabarty, Aditya Jain, Nijil Lal, Kantimay Das Gupta, and Punit Parmanand