Pleural fluid adenosine deaminase to serum C-reactive protein ratio for diagnosing tuberculous pleural effusion.

BACKGROUND: Tuberculous pleural effusion (TPE) and malignant pleural effusion (MPE) may occasionally show similar cytological and biochemical picture including ADA. In such cases, differentiating TPE and MPE is challenging and needs histopathology of pleural tissue which may involve invasive procedures. The present study aims to evaluate the diagnostic accuracy of pleural fluid ADA to serum CRP (ADA/CRP) ratio to discriminate between tuberculous and malignant pleural effusion. In addition, we investigated whether the ratio ADA/CRP adds diagnostic value to ADA. METHODS: This cross-sectional study was conducted in the National Institute of Diseases of the Chest and Hospital (NIDCH), Mohakhali, Dhaka, from July 2021 to February 2022 on diagnosed patients of TPE and malignant pleural effusion MPE. A receiver operating characteristic curve (ROC) was constructed for identifying TPE. The added value of the ADA/CRP ratio to ADA was evaluated using the net reclassification improvement (NRI) and integrated discrimination improvement (IDI). A value of p < 0.05 was considered statistically significant for all tests. RESULTS: Fifty-nine patients were enrolled in this study, of which 31 had TPE, and 28 had MPE. Pleural fluid ADA to serum CRP ratio and pleural fluid ADA level was significantly higher in patients with TPE, but there was no significant difference in serum CRP levels between patients with TPE and MPE. At cut off value of > 1.25, pleural fluid ADA to serum CRP ratio had a sensitivity of 93.8%, specificity of 85.2%, and positive and negative predictive values were 88.2% and 92% respectively, in the diagnosis of TPE and area under ROC curve (AUC) was 0.94. The NRI and IDI analyses revealed added diagnostic value of ADA/CRP to ADA. CONCLUSION: This study shows that the ADA/CRP ratio improves the diagnostic usefulness of ADA for TPE.

Genomic and evolutionary study from SARS-CoV-2 virus isolates from Bangladesh during the early stage of pandemic strongly correlate with European origin and not with China.

The goal of this study was to identify the genomic variants and determine molecular epidemiology of SARS-CoV-2 virus during the early pandemic stage in Bangladesh. Viral RNA was extracted, converted to cDNA, and amplified using Ion AmpliSeq™ SARS-CoV-2 Research Panel. 413 unique mutants from 151 viral isolates were identified. 80% of cases belongs to 8 mutants: 241C toT, 1163A toT, 3037C toT, 14408C toT, 23403A toG, 28881G toA, 28,882 G toA, and 28883G toC. Observed dominance of GR clade variants that have strong presence in Europe, suggesting European channel a possible entry route. Among 37 genomic mutants significantly associated with clinical symptoms, 3916CtoT (associated with sore-throat), 14408C to T (associated with cough-protection), 28881G to A, 28882G to A, and 28883G to C (associated with chest pain) were notable. These findings may inform future research platforms for disease management and epidemiological study.

Acute dextran sulfate sodium (DSS)-induced colitis promotes gut microbial dysbiosis in mice.

UNLABELLED: The most widely used and characterized experimental model of ulcerative colitis (UC) is the epithelial erosion, dextran sulfate sodium (DSS)-induced colitis, which is developed by administration of DSS in drinking water. We investigated fecal and colonic mucosa microbial composition and functional changes in mice treated with DSS. C57Bl/6 mice received 5% DSS in drinking water for 5 days. Inflammation was evaluated clinically and by analysis of colonic tissue cytokine levels and C-reactive protein (CRP) in the serum. Colonic mucosa and fecal samples were used for DNA extraction and the V4 region of bacterial 16S rRNA gene was subjected to MiSeq Illumina sequencing. Alpha- and beta-diversities, and compositional differences at phylum and genus levels were determined, and bacterial functional pathways were predicted. DSS increased disease severity, serum CRP and cytokines IL-1ß and IL-6, but decreased bacterial species richness, and shifted bacterial community composition. Bacteroides, Turicibacter, Escherichia, Clostridium, Enterobacteriaceae, Clostridiaceae, Bacteroidaceae, Bacteroidales, among other taxa were associated with DSS treatment in fecal and colonic samples. Also, DSS altered microbial functional pathways in both colonic mucosa and fecal samples. CONCLUSIONS: The development of colitis in DSS model was accompanied with reduced microbial diversity and dysbiosis of gut microbiota at lower taxonomical levels.

Muscle synergy-informed neuromusculoskeletal modelling to estimate knee contact forces in children with cerebral palsy.

Cerebral palsy (CP) includes a group of neurological conditions caused by damage to the developing brain, resulting in maladaptive alterations of muscle coordination and movement. Estimates of joint moments and contact forces during locomotion are important to establish the trajectory of disease progression and plan appropriate surgical interventions in children with CP. Joint moments and contact forces can be estimated using electromyogram (EMG)-informed neuromusculoskeletal models, but a reduced number of EMG sensors would facilitate translation of these computational methods to clinics. This study developed and evaluated a muscle synergy-informed neuromusculoskeletal modelling approach using EMG recordings from three to four muscles to estimate joint moments and knee contact forces of children with CP and typically developing (TD) children during walking. Using only three to four experimental EMG sensors attached to a single leg and leveraging an EMG database of walking data of TD children, the synergy-informed approach estimated total knee contact forces comparable to those estimated by EMG-assisted approaches that used 13 EMG sensors (children with CP, n = 3, R2 = 0.95 ± 0.01, RMSE = 0.40 ± 0.14 BW; TD controls, n = 3, R2 = 0.93 ± 0.07, RMSE = 0.19 ± 0.05 BW). The proposed synergy-informed neuromusculoskeletal modelling approach could enable rapid evaluation of joint biomechanics in children with unimpaired and impaired motor control within a clinical environment.

Minimum number of inertial measurement units needed to identify significant variations in walk patterns of overweight individuals walking on irregular surfaces.

Gait data collection from overweight individuals walking on irregular surfaces is a challenging task that can be addressed using inertial measurement unit (IMU) sensors. However, it is unclear how many IMUs are needed, particularly when body attachment locations are not standardized. In this study, we analysed data collected from six body locations, including the torso, upper and lower limbs, to determine which locations exhibit significant variation across different real-world irregular surfaces. We then used deep learning method to verify whether the IMU data recorded from the identified body locations could classify walk patterns across the surfaces. Our results revealed two combinations of body locations, including the thigh and shank (i.e., the left and right shank, and the right thigh and right shank), from which IMU data should be collected to accurately classify walking patterns over real-world irregular surfaces (with classification accuracies of 97.24 and 95.87%, respectively). Our findings suggest that the identified numbers and locations of IMUs could potentially reduce the amount of data recorded and processed to develop a fall prevention system for overweight individuals.

M gene targeted qRT-PCR approach for SARS-CoV-2 virus detection.

Quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) is the gold standard method for SARS-CoV-2 detection, and several qRT-PCR kits have been established targeting different genes of the virus. Due to the high mutation rate of these genes, false negative results arise thus complicating the interpretation of the diagnosis and increasing the need of alternative targets. In this study, an alternative approach for the detection of SARS-CoV-2 viral RNA targeting the membrane (M) gene of the virus using qRT-PCR was described. Performance evaluation of this newly developed in-house assay against commercial qRT-PCR kits was done using clinical oropharyngeal specimens of COVID-19 positive patients. The limit of detection was determined using successive dilutions of known copies of SARS-CoV-2 pseudovirus. The M gene based assay was able to detect a minimum of 100 copies of virus/mL indicating its capacity to detect low viral load. The assay showed comparable accuracy, sensitivity and specificity with commercially available kits while detecting all the variants efficiently. The study concluded that the in-house M gene based assay might be an effective alternative for the currently available commercial qRT-PCR kits.

Evaluating the difference in walk patterns among normal-weight and overweight/obese individuals in real-world surfaces using statistical analysis and deep learning methods with inertial measurement unit data.

Unusual walk patterns may increase individuals' risks of falling. Anthropometric features of the human body, such as the body mass index (BMI), influences the walk patterns of individuals. In addition to the BMI, uneven walking surfaces may cause variations in the usual walk patterns of an individual that will potentially increase the individual's risk of falling. The objective of this study was to statistically evaluate the variations in the walk patterns of individuals belonging to two BMI groups across a wide range of walking surfaces and to investigate whether a deep learning method could classify the BMI-specific walk patterns with similar variations. Data collected by wearable inertial measurement unit (IMU) sensors attached to individuals with two different BMI were collected while walking on real-world surfaces. In addition to traditional statistical analysis tools, an advanced deep learning-based neural network was used to evaluate and classify the BMI-specific walk patterns. The walk patterns of overweight/obese individuals showed a greater correlation with the corresponding walking surfaces than the normal-weight population. The results were supported by the deep learning method, which was able to classify the walk patterns of overweight/obese (94.8 ± 4.5%) individuals more accurately than those of normal-weight (59.4 ± 23.7%) individuals. The results suggest that application of the deep learning method is more suitable for recognizing the walk patterns of overweight/obese population than those of normal-weight individuals. The findings from the study will potentially inform healthcare applications, including artificial intelligence-based fall assessment systems for minimizing the risk of fall-related incidents among overweight and obese individuals.

A muscle synergy-based method to estimate muscle activation patterns of children with cerebral palsy using data collected from typically developing children.

Preparing children with cerebral palsy prior to gait analysis may be a challenging and time-intensive task, especially when large number of sensors are involved. Collecting minimum number of electromyograms (EMG) and yet providing adequate information for clinical assessment might improve clinical workflow. The main goal of this study was to develop a method to estimate activation patterns of lower limb muscles from EMG measured from a small set of muscles in children with cerebral palsy. We developed and implemented a muscle synergy extrapolation method able to estimate the full set of lower limbs muscle activation patterns from only three experimentally measured EMG. Specifically, we extracted a set of hybrid muscle synergies from muscle activation patterns of children with cerebral palsy and their healthy counterparts. Next, those muscle synergies were used to estimate activation patterns of muscles, which were not initially measured in children with cerebral palsy. Two best combinations with three (medial gastrocnemius, semi membranous, and vastus lateralis) and four (lateral gastrocnemius, semi membranous, sartorius, and vastus medialis) experimental EMG were able to estimate the full set of 10 muscle activation patterns with mean (± standard deviation) variance accounted for of 79.93 (± 9.64)% and 79.15 (± 6.40)%, respectively, using only three muscle synergies. In conclusion, muscle activation patterns of unmeasured muscles in children with cerebral palsy can be estimated from EMG measured from three to four muscles using our muscle synergy extrapolation method. In the future, the proposed muscle synergy-based method could be employed in gait clinics to minimise the required preparation time.

Performance Evaluation of Developed Bangasure™ Multiplex rRT-PCR Assay for SARS-CoV-2 Detection in Bangladesh: A Blinded Observational Study at Two Different Sites.

In this study, we evaluated the performance of the in-house developed rRT-PCR assay for SARS-CoV-2 RNA targeting the envelope (E) and nucleocapsid (N) genes with internal control as human RNase P. A total of 50 positive samples and 50 negative samples of SARS-CoV-2 were tested by a reference kit at site 1 and a subset (30 positives and 16 negatives) of these samples are tested blindly at site 2. The limit of detection (LoD) was calculated by using a replication-deficient complete SARS-CoV-2 genome and known copy numbers, where Pseudo-virus samples were used to evaluate accuracy. On site 1, among the 50 SARS-CoV-2 positive samples 24, 18, and eight samples showed high (Ct < 26), moderate (26 < Ct ≤ 32), and low (32 < Ct ≤ 38) viral load, respectively, whereas in site 2, out of 30 SARS-CoV-2 positive samples, high, moderate, and low viral loads were found in each of the 10 samples. However, SARS-CoV-2 was not detected in the negative sample. So, in-house assays at both sites showed 100% sensitivity and specificity with no difference observed between RT PCR machines. The Ct values of the in-house kit had a very good correlation with the reference kits. LoD was determined as 100 copies/mL. It also displayed 100% accuracy in mutant and wild-type SARS-CoV-2 virus. This Bangasure™ RT-PCR kit shows excellent performance in detecting SARS-CoV-2 viral RNA compared to commercially imported CE-IVD marked FDA authorized kits.

Walking Speed Classification from Marker-Free Video Images in Two-Dimension Using Optimum Data and a Deep Learning Method.

Walking speed is considered a reliable assessment tool for any movement-related functional activities of an individual (i.e., patients and healthy controls) by caregivers and clinicians. Traditional video surveillance gait monitoring in clinics and aged care homes may employ modern artificial intelligence techniques to utilize walking speed as a screening indicator of various physical outcomes or accidents in individuals. Specifically, ratio-based body measurements of walking individuals are extracted from marker-free and two-dimensional video images to create a walk pattern suitable for walking speed classification using deep learning based artificial intelligence techniques. However, the development of successful and highly predictive deep learning architecture depends on the optimal use of extracted data because redundant data may overburden the deep learning architecture and hinder the classification performance. The aim of this study was to investigate the optimal combination of ratio-based body measurements needed for presenting potential information to define and predict a walk pattern in terms of speed with high classification accuracy using a deep learning-based walking speed classification model. To this end, the performance of different combinations of five ratio-based body measurements was evaluated through a correlation analysis and a deep learning-based walking speed classification test. The results show that a combination of three ratio-based body measurements can potentially define and predict a walk pattern in terms of speed with classification accuracies greater than 92% using a bidirectional long short-term memory deep learning method.

Food Security and the COVID-19 Crisis from a Consumer Buying Behaviour Perspective-The Case of Bangladesh.

Since COVID-19 was confirmed in Bangladesh in March 2020, the government have enacted stringent measures to prevent the spread of the coronavirus, which has had a significant impact on people's lives. Food consumption habits of consumers have shifted as a result of declining grocery shopping frequency, negative income shock, and food prices shooting up. This paper aims to explore Bangladeshi consumers' buying behaviour in association with the stress generated from a food supply shortage during the COVID-19 pandemic and the post-outbreak perception of the food industry, using a dataset with 540 online samples collected between July and August 2021. A two-stage cluster sampling method and self-administrated questionnaire techniques were adopted for collecting the data during the third wave of COVID-19. Using partial least squares path modelling (PLS-PM) and multivariate multiple ordered logit regression (MVORD) to reveal the pertinent structure between all the blocks, this study provides two key findings. First, a higher intensity of COVID-19 impact translates into higher food stress associated with income reduction and higher food prices. Second, food stress directly affects consumer buying and consumption behaviour. We strongly recommend connecting consumers with local producers and collective use of shared warehouses through institutions, policies, and reforms to prevent disruption in the food supply chain and to keep food prices stable. Additionally, food producers, distributors, stakeholders, and policy planners should strengthen the food supply chain to stabilize food security.

Genome Sequence of a SARS-CoV-2 P.1 Variant of Concern (20J/501Y.V3) from Bangladesh.

This study reports the coding-complete genome sequence, with variant identifications and phylogenetic analysis, of a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) P.1 variant (20J/501Y.V3), obtained from an oropharyngeal swab specimen from a female Bangladeshi patient diagnosed with coronavirus disease 2019 (COVID-19) with no travel history.

Non-negative matrix factorisation is the most appropriate method for extraction of muscle synergies in walking and running.

Muscle synergies provide a simple description of a complex motor control mechanism. Synergies are extracted from muscle activation patterns using factorisation methods. Despite the availability of several factorisation methods in the literature, the most appropriate method for muscle synergy extraction is currently unknown. In this study, we compared four muscle synergy extraction methods: non-negative matrix factorisation, principal component analysis, independent component analysis, and factor analysis. Probability distribution of muscle activation patterns were compared with the probability distribution of synergy excitation primitives obtained from the four factorisation methods. Muscle synergies extracted using non-negative matrix factorisation best matched the probability distribution of muscle activation patterns across different walking and running speeds. Non-negative matrix factorisation also best tracked changes in muscle activation patterns compared to the other factorisation methods. Our results suggest that non-negative matrix factorisation is the best factorisation method for identifying muscle synergies in dynamic tasks with different levels of muscle contraction.

Reports of Coding-Complete Genome Sequences of Five 2019 Novel Coronavirus (SARS-CoV-2) Strains Isolated in Bangladesh.

This study determined five coding-complete genome sequences of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) strains isolated from oropharyngeal swab specimens of Bangladeshi patients who were diagnosed with coronavirus disease 2019 (COVID-19) and had no travel history.

Investigation of the EMG activity of erector spinae and trapezius muscles during Islamic prayer (Salat).

This study aimed to investigate the electrical activity of two muscles located at the dorsal surface during Islamic prayer (Salat). Specifically, the electromyography (EMG) activity of the erector spinae and trapezius muscles during four positions observed while performing Salat, namely standing, bowing, sitting and prostration, were investigated. Seven adult subjects with an average age of 28.1 (± 3.8) years were included in the study. EMG data were obtained from their trapezius and erector spinae muscles while the subjects maintained the specific positions of Salat. The EMG signal was analysed using time and frequency domain features. The results indicate that the trapezius muscle remains relaxed during the standing and sitting positions while the erector spinae muscle remains contracted during these two positions. Additionally, during the bowing and prostration positions of Salat, these two muscles exhibit the opposite activities: the trapezius muscle remains contracted while the erector spinae muscle remains relaxed. Overall, both muscles maintain a balance in terms of contraction and relaxation during bowing and prostration position. The irregularity of the neuro-muscular signal might cause pain and prevent Muslims from performing their obligatory prayer. This study will aid the accurate understanding of how the back muscles respond in specific postures during Salat.