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Many novel therapies are being evaluated for the treatment of Multiple myeloma (MM). The cell-surface protein B-cell maturation antigen (BCMA, CD269) has recently emerged as a promising target for CAR-T cell and monoclonal-antibody therapies in MM. However, the knowledge of the BCMA expression-pattern in myeloma patients from the Indian subcontinent is still not available. We present an in-depth study of BCMA expression-pattern on abnormal plasma cells (aPC) in Indian MM patients.We studied BM samples from 217 MM patients (211-new and 6-relapsed) with a median age of 56 years (range, 30-78 yearsM:F-2.29) and 20 control samples. Expression levels/patterns of CD269 (clone-19f2) were evaluated in aPCs from MM patients and in normal PCs (nPC) from uninvolved staging bone marrow samples (controls) using multicolor flow cytometry (MFC). Expression-level of CD269 was determined as a ratio of mean fluorescent intensity (MFI-R) of CD269 in PCs to that of non-B-lymphocytes and expression-pattern (homogenous/heterogeneous) as coefficient-of-variation of immunofluorescence (CVIF).Median (range) percentage of CD269-positive abnormal-PCs in total PCs was 71.6% (0.49-99.29%). The MFI-R (median, range) of CD269 was significantly higher in aPCs (4.13, 1.12-26.88) than nPCs (3.33, 1.23-12.87), p .0001. Median (range) MFI of CD269 at diagnosis and relapse were 2.39 (0.77-9.57) and 2.66 (2.15-3.23) respectively. CD269 levels were similar at diagnosis and relapse, p = .5529.We demonstrated that BCMA/CD269 is highly expressed in aPCs from a majority of MM patients, both at diagnosis and relapse. Thus, BCMA is a valuable target for therapy for Indian MM patients.

A fall of an older adult often leads to severe injuries and is found to be a significant reason for the death due to post-traumatic complications. Many falls happen in the home atmosphere and prevail unrecognized. Thus, the need for reliable early fall detection is necessary for fast help. Lately, the emergence of wearables, smartphones, IoT, etc., made it possible to develop systems fall detection which aids in the remote monitoring of the elderly. The goal is to allow intelligent algorithms and smartphones to detect falls for elderly care and to monitor them regularly. This work presents the Artificial Intelligence of Things for Fall Detection (AIOTFD) system using a slime mould algorithm (SMA) to optimize the final data. The features extracted using SqueezeNet further CNN based SMA used for data optimization. The validation of the AIOTFD model performance is evaluated through the Multiple Cameras Fall Dataset (MCFD) and UR Fall Detection dataset (URFD). The empirical results accentuated the assuring realization of the model compared to other state-of the art methods.The obtained results shows our proposed AIOTFD attains accuracy of 99.82% and 99.79% and databases can be used for additional investigation and optimizations to increase the recognition rate to enhance the independent life of the elderly.

The risk of an outbreak of natural disaster increased in recent years due to climate change, pollution, and imbalance in the environment. As a result, sudden rise of infectious disease and damage to the ecosystem has occurred. The lack of potable water, sanitization and maintenance of hygiene, food and displacement of people led to increase in communicable disease. The catastrophic situation occurs due to natural calamities like flood, earthquake, cyclone, and volcano, which damages the environment, human life, and animals, reducing global economic growth and public health. Various strategies are used to manage the effect of natural disasters. Microbial engineering could be used as a supportive approach for various pre and post-disaster management strategies. For effective implementation of microbial engineering in management strategies requires knowledge of Microbiology, Ecology, biochemical mechanisms and field engineering. Due to microbes’ ubiquitous nature and small sizes, these can be successfully manipulated for desired functions in question.

Sustainable agriculture is a system of farming using a microbe that protects the environment, maintains natural resources, produces healthy foods, and minimizes the use of chemical pesticides or fertilizers. Soil microbes like bacteria and fungi play an essential role in maintaining soil fertility, decomposing organic matter, and recycling nutrients. The presence of microorganisms in soil depends on the soil properties like moisture, pH, temperature, oxygen, salinity, porosity. Rhizosphere microbial communities play important role in plant protection, growth promotion, and production of antibiotics. Soil horizon maintenance is also vital for soil health and quality. The soil environment consists of a variety of physical, chemical, and biological factors that directly affect the diversity of microbes. For sustainable agriculture, soil-microbial diversity is important for ensuring soil productivity, and increase in crop production. The microbial technologies, for instance, gene-editing system, RNAi mediated gene silencing, proteomics, Genetic engineering, Metagenomics, Metatranscriptomics, and microbial whole-genome sequencing are used for sustainable agriculture. In this review, we investigated magnitude of microbes in sustainable agricultural development.

BACKGROUND Measurable residual disease (MRD) assessment using multicolor flow cytometry (MFC) has become the center point of pediatric B-cell precursor acute lymphoblastic leukemia (BCP-ALL) risk stratification and therapeutic management. The addition of new markers can improve the accuracy and applicability of MFC-based MRD assay further. Herein, we evaluated the utility of a new marker, CD304/neuropilin-1, in the assessment of MFC-based MRD. METHODS Expression patterns of CD304 were studied in leukemic blasts from BCP-ALL patients and in normal precursor B cells (NPBC) from uninvolved non-BCP-ALL bone marrow samples using 10-color MFC. MRD was monitored at end-of-induction (EOI; Days 35-40) and end-of-consolidation (Day 78-80) time points. RESULTS We studied CD304 expression in 300 pediatric BCP-ALL patients and found it positive in BCP-ALL blasts in 41.7% of diagnostic samples. It was significantly associated with ETV6-RUNX1 (p

Now-a-days every institute needs automation. Automatic Voice Responding system (AVRS) is an automated communication system allowing parent to interact with a student database to know the defined result without using man power. The parent can get the information according to their free time from anywhere by dialing the specified mobile number. The AVRS uses the data stored in database. The parent’s mobile number is stored in the database and one mobile number of colleges is given to parent. When a parent dial the mobile number given by college, parent will get the student percentage attendance in the form of voice. So parents can get the student academic performance report from anyplace at their free time. For implementing this AVRS, we are using ARM 11 microcontroller Raspberry Pi and GSM module. The system programming is done by using python. AVRS is specially designed for parent to obtain the student academic performance and the system is highly reliable and scalable. Also the system made user friendly and operating on a 24*7 hour basis. Parent can obtain real time information of student using just mobile phone.

In India, everyday many lives are affected because the patients are not timely and properly operated. Also for real time parameter values are not efficiently measured in clinic as well as in hospitals. Sometimes it becomes difficult for hospitals to frequently check patients’ conditions. Also continuous monitoring of ICU patients is not possible. To deal with these types of situations, our system is beneficial. Our system is designed to be used in hospitals for measuring and monitoring various parameters like temperature, ECG, heart beat etc. The results can be recorded using Raspberry Pi displayed on a LCD display. Also the results can be sent to server using GSM module. Doctors can login to a website and view those results.