Executive summary of the XXIV State Annual conference of Medicolegal Association of Maharashtra (MLAM): Forensicon 2021

The XXIV annual state conference of the Medicolegal Association of Maharashtra: Forensicon 2021 organized by the department of Forensic and Toxicology, Lokmanya Tilak Municipal Medical College and Hospital, Mumbai. Previous conference "Forensicon 2020” was held at TNMC & BYL Nair Ch. Hospital, Mumbai in the month of April 2021 as COVID pandemic prevented the continuity of the conference in the year 2020. Responsibility of organizing XXIV annual State conference was allotted to Department of Forensic Medicine and Toxicology, Lokmanya Tilak Municipal Medical College and General Hospital, Sion, Mumbai on 22nd November 2021. The organizing committee have magnificently conducted the annual state conference in the very short duration of 24 days on 17th and 18th December 2021 at main auditorium, Lokmanya Tilak Municipal Medical College and hospital, Mumbai. Dr Rajesh Dere, professor and head, Forensic Medicine Dept, LTMMC & hospital and Dean BKC Jumbo COVID 19 facility, Mumbai through his valiant efforts as team leader overcoming all the obstacles brought this conference to epitome of success. © 2022, Medicolegal Association of Maharashtra. All rights reserved.

Exposure to SARS-CoV-2 Aerosols in U.S. Residences: Sample analysis Using Two Detection Criteria

Uniform practices and quality control methods are needed to detect and quantify airborne viruses across sampling and analysis platforms. We compared detection of airborne SARSCoV-2 RNA in residences of individuals with COVID-19 using two commonly used criteria: environmental (at least one SARS-CoV-2-specific gene and internal control amplified by PCR with Ct ≤ 40) and clinical (at least two SARS-CoV-2-specific genes and internal control amplified with Ct ≤ 37). 24-hr total aerosol samples were collected in a self-isolation room and an additional room without manipulating subjects' behavior/activities. Under the environmental criterion, 7/16 samples in primary rooms and 7/15 samples in secondary rooms were positive. Comparable but lower positive sample proportions were observed using the more rigorous clinical criterion: 6/16 primary rooms and 5/15 secondary rooms. A consensus SARS-CoV-2 environmental sampling and analysis framework is needed for comparisons between studies. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

Nowcasting the output gap

We propose a way to directly nowcast the output gap using the Beveridge–Nelson decomposition based on a mixed-frequency Bayesian VAR. The mixed-frequency approach produces similar but more timely estimates of the U.S. output gap compared to those based on a quarterly model, the CBO measure of potential, or the HP filter. We find that within-quarter nowcasts for the output gap are more reliable than for output growth, with monthly indicators for a credit risk spread, consumer sentiment, and the unemployment rate providing particularly useful new information about the final estimate of the output gap. An out-of-sample analysis of the COVID-19 crisis anticipates the exceptionally large negative output gap of −8.3% in 2020Q2 before the release of real GDP data for the quarter, with both conditional and scenario nowcasts tracking a dramatic decline in the output gap given the April data. © 2022 The Authors

Covid-19 Detection Based on Lung Lesion Signs in Big Data Processing Environment

The world is going through a global health crisis known as the Covid-19 pandemic. Currently, the outbreak is still evolving in a complicated way with a high spreading speed and new variants appearing constantly. RT-PCR test is preferred to test a patient infected with Covid-19. However, this method depends on many factors such as the time of specimen collection and preservation procedure. The cost to perform the RT-PCR test is quite high and requires a system of specialized machinery for sample analysis. Using deep learning techniques on medial images provides promising results with high accuracy with recent technological advancements. In this study, we propose a deep learning method based on CasCade R-CNN ResNet-101 and CasCade R-CNN EfficientNet in a big data processing environment that accelerates the detection of Covid-19 infections on chest X-rays. Chest X-ray can quickly be performed in most medical facilities and provides important information in detecting suspected Covid-19 cases in an inexpensive way. Experimental results show that the classification of lung lesions infected with Covid-19 has an accuracy of 96% and mAP of 99%. This method effectively supports doctors to have more basis to identify patients infected with Covid-19 for timely treatment. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Smart Healthcare Ecosystems backed by IoT and Connected Biomedical Technologies

The Internet of Things (IoT) is a highly capable technology in the health industry. Research articles and upcoming technologies relating IoT to the healthcare industry are examined to determine the vast capabilities of technology in the future. This analysis would aid experts in devising solutions to issues and combating pandemics. IoT is emerging as a technology that enables many improvements in the health sector, such as accurate records maintenance, sample analysis, and illness root-cause analysis. The sensor-based technology can mitigate the potential risks associated with surgery. The study examines and correlates various applications of IoT in the industry, with a comprehensive analysis of their objectives, limitations, and outcomes. The study would help pave way for a technology-backed healthcare ecosystem in the future, which would include the best of all technologies. © 2022 IEEE.

New Psychoactive Substances Consumption in Opioid-Use Disorder Patients.

(1) Background: Since the beginning of the 21st century, the large number and wide chemical variety of new psychoactive substances (NPS) that enter the market every year has become a public health problem. Given the rapidity with which the drug market is changing, many NPS are not clinically investigated and their effects and health risks are unknown. Drug testing is a very useful tool for this purpose, but, unfortunately, it is not very widespread in individuals with opioid-use disorder under detoxification treatment. The aim of this study is to investigate the use of illicit drugs and NPS in opioid-use disorder (OUD) patients on opioid agonist treatment. (2) Methods: A multicenter, descriptive, cross-sectional study was conducted at two addiction care services in Barcelona and Badalona, Spain. Urine samples were collected from OUD individuals attending these two centers, who anonymously donated a urine sample at the time of a periodical visit. Samples were analyzed by high-sensitivity gas chromatography-mass spectrometry (GC-MS) and ultra-high-performance liquid chromatography-high -resolution mass spectrometry (UHPLC-HRMS). (3) Results: Out of the 187 collected and analyzed urine samples, 27.3% were positive for any type of NPS and 8.6% were positive for new synthetic opioids, including fentanyl and its derivatives (NSO). Other frequently detected substances were benzodiazepines in 46.0% of samples, antipsychotics in 27.8% of samples, or cocaine and cannabis in 23.5% of samples. (4) Conclusion: A wide number of NPS, including NSO, have been detected in urine samples from an OUD population. A lack of NPS detection in standard drug screening among drug users can hide the identification of a potential public health problem.

Voltammetric sensor based on bimetallic nanocomposite for determination of favipiravir as an antiviral drug.

A novel and sensitive voltammetric nanosensor was developed for the first time for trace level monitoring of favipiravir based on gold/silver core-shell nanoparticles (Au@Ag CSNPs) with conductive polymer poly (3,4-ethylene dioxythiophene) polystyrene sulfonate (PEDOT:PSS) and functionalized multi carbon nanotubes (F-MWCNTs) on a glassy carbon electrode (GCE). The formation of Au@Ag CSNPs/PEDOT:PSS/F-MWCNT composite was confirmed by various analytical techniques, including X-ray diffraction (XRD), ultraviolet-visible spectroscopy (UV-Vis), transmission electron microscopy (TEM), energy-dispersive X-ray spectroscopy (EDX), and field-emission scanning electron microscopy (SEM). Under the optimized conditions and at a typical working potential of + 1.23 V (vs. Ag/AgCl), the Au@Ag CSNPs/PEDOT:PSS/F-MWCNT/GCE revealed linear quantitative ranges from 0.005 to 0.009 and 0.009 to 1.95 µM with a limit of detection 0.46 nM (S/N = 3) with acceptable relative standard deviations (1.1-4.9 %) for pharmaceutical formulations, urine, and human plasma samples without applying any sample pretreatment (1.12-4.93%). The interference effect of antiviral drugs, biological compounds, and amino acids was negligible, and the sensing system demonstrated outstanding reproducibility, repeatability, stability, and reusability. The findings revealed that this assay strategy has promising applications in diagnosing FAV in clinical samples, which could be attributed to the large surface area on active sites and high conductivity of bimetallic nanocomposite.

Innovative electrochemical sensor for the precise determination of the new antiviral COVID-19 treatment Favipiravir in the presence of coadministered drugs.

Due the current pandemic of COVID-19, an urgent need is required for serious medical treatments of a huge number of patients. The world health organization (WHO) approved Favipiravir (FAV) as a medication for patients infected with corona virus. In the current study, we report the first simple electrochemical, greatly sensitive sensor using MnO2-rGO nanocomposite for the accurate determination of Favipiravir (FAV). The developed sensor showed a high improvement in the electrochemical oxidation of FAV comparing to the unmodified screen-printed electrode (SPE). The suggested platform constituents and the electrochemical measurements parameters were studied. Under optimal experimental parameters, a current response to the concentration change of FAV was found to be in the linear range of 1.0 × 10-8-5.5 × 10-5 M at pH 7.0 with a limit of detection 0.11 µM and a quantification limit of 0.33 µM. The developed platform was confirmed by the precise analysis of FAV in real samples including dosage form and plasma. The developed platform can be applied in different fields of industry quality control and clinical analysis laboratories for the FAV determination.