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Background: Conventional methods like Erich’s arch bars and eyelet wires are currently most common methods for achieving temporary inter-maxillary fixation (IMF). Objectives: The purpose of the present study was to compare the efficacy of Self-tapping screws over Erich’s arch bar wiring for inter maxillary fixation in the treatment of mandibular fractures. Methodology: This was a randomized control trial study. Study duration was one year from October 2014 to September 2015. This study involved patients with mandibular fractures aged 22 to 65 years who visited Department of Oral & Maxillofacial Surgery at Bangabandhu Sheikh Mujib Medical University, Dhaka, Bangladesh. After random sampling according to inclusion and exclusion criteria surgical treatment was done by miniplate osteosynthesis. Study population were divided into two groups. In group A, temporary MMF was done with Erich’s arch bar and in group B treatment was given with self-tapping screws. The study outcomes were total time required for achieving temporary MMF, number of wire stick injury, stability of occlusion and oral hygiene index. All the parameters were measured preoperatively and postoperatively for 14 days up to removal of screws or arch bars. Results: The mean time taken for temporary MMF was 11.31±2.46 min with screws as compared to 84.72±14.30 min with Erich’s arch bar. Mean number of perforations were significantly more in group A. Occlusion stability was 69.2% in group A and 84.6% in group B. The mean value of plaque index in group A was 2.85±0.25 and in group B was 1.15± 0.16. Conclusion: In conclusion, plaque deposition was more in group A. Journal of National Institute of Neurosciences Bangladesh, July 2022;8(2):175-180

Thoracic spine pain (TSP) is defined as pain perceived anywhere in the region bounded superiorly by a transverse line through the tip of the spinous process of T1, inferiorly by a transverse line through the tip of the spinous process of T12, and laterally by vertical lines tangential to the most lateral margins of the erector spine muscles. One year prevalence of TSP ranged from 8.3-38.1% in different Asian countries. A longitudinal observational study was conducted to observe the clinical feature, demographic profile and clinical course of patients, with TSP attending at the Department of Physical Medicine and Rehabilitation (PMR) of Shaheed Suhrawardy Medical College Hospital (ShSMCH). Among the 100 study patients mean age ±SD was 34.76±13.26. Highest number of the patients (45%) belong to 16-30 years age group and male-female vatioo was 3:1. Highest frequency in the level of education 28% belonged to higher secondary or diploma. Twenty six percent (26%) patients were housewife, manual labor 20%, students 19%, sedentary worker 15%, manufacturing and industrial worker 8%, health professional 4%, driver 3% and 5% were in others group. Most of them (71%) belong to 6-12 weeks) 16% and chronic (>12 weeks) 38%. Upper TSP was found among 51% of the patients. Onset of pain among the patients 66% was gradual. Mild intensity of pain was reported in 54% patients, moderate 44% and severe in only 2% patients and 64% patients had no radiation. Aggravating factors were found in patients with prolong sitting in 42%. More than one third (36%) patients relieving factor were lying, 24% rest, activity 16% and no relieving factors in 24% patients. Morning stiffness and depression was found 20% and 25% patients respectively. Associated conditions were found as diabetes mellitus (DM) 25%, sleep disturbance 16%, dyspepsia 10%, hypertension (HTN) were in 7% patients and 42% patients had no associated condition. Large number of the patients’ was occupational 46%; rest of the factors were MFPS 16%, degenerative 14% (dorsal spondylosis 5%, cervical spondylosis 6% and lumbar spondylosis 3%, Ankylosing Spondylitis (AS) 7% and traumatic were 6%. Pott‘s disease 4% and 7% patients’ cause were others. According to Numeric Rating Scale in the first visit mild causes were in 54% patients, 44% moderate and 2% severe. In the last visit 69% patients were found mild and rest had no pain (p value=0.001). Assessment of joint tenderness in first visit; 56% patients were in grade 1, 20% grade 2 and 3% in grade 3, 21% patients had no tenderness. In the last visit it was found that only 21% patients in grade 1 and rest 79% had no tenderness (p value=0.001). According to Pain Disability Index in the first visit mild disability was found in 67% patients, moderate 31% and 2% had no disability. In the last visit mild were 74% and 26% had no disability (p value=0.001). Teenager, young adults and adults were the most commonly affected patients with TSP with M:F=1.5:1. Most of the patient of upper TSP presented before 6 weeks; common presenting features were gradual onset, pain was constant in nature, mild to moderate in intensity without radiation, aggravated by prolong sitting and leaning forward, relieved by lying and rest, with no depression and significant morning stiffness. Most of the factors were occupational and MFPS. Occupations were commonly housewife and manual labor. They were improved significantly (p value=0.001) with conventional treatment. Bangladesh Med J. 2021 Sept; 50(3): 26-35

HighlightsRates of dry matter loss and lipid oxidation increased as moisture content and temperature increased.Temperature effects were smaller than moisture content effects.Sums of hexanal and 1-hexanol concentrations were used as lipid oxidation indicators during storage.Lipid oxidation products were correlated positively with dry matter loss (r = 0.88) and dry matter loss rates (r = 0.84).Abstract. After harvest, soybeans are susceptible to physical, chemical, and biological changes. Prolonged storage under unfavorable conditions, such as elevated temperature and moisture content (m.c.), is responsible for accelerating dry matter loss (DML) rates and lipid oxidation (LO). Knowledge of DML rates (vDML) is useful in developing maximum allowable storage time (MAST) guidelines for soybeans. In addition to DML, monitoring changes in the lipid fraction is essential to assess quality since soybeans are valued for their oil content. The objective of this study was to estimate DML and LO of soybeans during a 30-d period over a wide range of m.c. (14% to 22%wb m.c.) and temperature (25 to 35°C), which were chosen based on climate conditions from low latitude regions where major soybean producing countries are located. A series of respiration tests were conducted using a static grain respiration measurement system with a sensor unit to monitor carbon dioxide (CO2) concentration, which was used to estimate DML. After each respiration test, samples were collected for chemical analysis. Headspace-solid phase microextraction gas chromatography-mass spectrometry was employed to determine volatile compound concentrations, which served as LO indicators. Moreover, volatile compound analysis was also used to evaluate the formation of anaerobic respiration products and other compounds that can affect soybean quality during storage. There was an increase in vDML with increased m.c. and temperature. Across the temperature range chosen, vDML increased 40 to 46 times for soybeans at 18%wb m.c. compared to 14%wb m.c. and 2.7 to 3.7 times for soybeans at 22%wb m.c. compared to 18%wb m.c. Temperature effects on vDML were smaller than moisture effects. vDML increased 1.1 to 3.5 times across the temperature range tested at constant moisture. Changes in storage conditions also affected the formation of volatile compounds. The sum of hexanal and 1-hexanol concentrations (ppm), used as LO indicators, was higher in samples with elevated m.c. Concentrations of these compounds increased 1.3 to 5 times for 22%wb soybeans compared to 18%wb m.c. and 4.6 to 11.8 times for samples at 18%wb compared to 14%wb m.c. at the same temperature. LO products were positively correlated with DML (r = 0.88) and vDML (r = 0.84); this confirmed that lipids are degraded in addition to DML when soybeans are subjected to unfavorable storage conditions. This correlation can be useful to improve MAST guidelines based on qualitative and quantitative deterioration. Keywords: Dry matter loss, Grain storage, Lipid oxidation, Respiration, Soybeans, Volatile compounds.

Nanobubbles (NBs) stimulate seed germination; however, the mechanism of the promotion effect of NBs remains unclear. The impact of NBs on seed water absorption was investigated; we subsequently studied the genes associated with the response of radish seeds to NB water and used RNA sequencing to generate their expression profiles, especially those of aquaporin genes. NB water significantly promoted germination. The times at which 50% of the germinating seeds achieved germination (

Authenticity and adulteration detection are primary concerns of various stakeholders, such as researchers, consumers, manufacturers, traders, and regulatory agencies. Traditional approaches for authenticity and adulteration detection in edible oils are time-consuming, complicated, laborious, and expensive; they require technical skills when interpreting the data. Over the last several years, much effort has been spent in academia and industry on developing vibrational spectroscopic techniques for quality, authenticity, and adulteration detection in edible oils. Among them, Fourier transforms infrared (FT-IR) spectroscopy has gained enormous attention as a green analytical technique for the rapid monitoring quality of edible oils at all stages of production and for detecting and quantifying adulteration and authenticity in edible oils. The technique has several benefits such as rapid, precise, inexpensive, and multi-analytical; hence, several parameters can be predicted simultaneously from the same spectrum. Associated with chemometrics, the technique has been successfully implemented for the rapid detection of adulteration and authenticity in edible oils. After presenting the fundamentals, the latest research outcomes in the last 10 years on quality, authenticity, and adulteration detection in edible oils using FT-IR spectroscopy will be highlighted and described in this review. Additionally, opportunities, challenges, and future trends of FT-IR spectroscopy will also be discussed.

Meat tenderness is the most important attribute related to consumer satisfaction. No analytical tool is currently available to measure or predict tenderness in real-time. Therefore, direct measurement of meat tenderness is not possible in the meat industry. Several reviews are available on optical sensing technologies for meat; however, no comprehensive review is available on optical sensing for meat tenderness. The simplicity, accuracy, and limited sample preparation have made optical techniques the best tools for determining meat tenderness. This paper reviews the perspectives and aspects of conventional tenderness measurement tools along with some selected optical sensing technologies for meat tenderness. Additionally, the major challenges of optical sensing techniques and future trends will also be addressed. If adequately optimized, optical sensing techniques could be incorporated into the production line to measure tenderness in real-time for correct labeling, pricing, and screening of meat to better provide consumers with the products they desire.

Anthropological literature on health beliefs and practices related to COVID-19 is scarce, particularly in low and middle-income countries. We conducted a qualitative research on perceptions of COVID-19 among slum residents of Dhaka, Bangladesh from November 2020 through January, 2021. Methods included in-depth interviews and photo elicitation with community residents. Interviews were transcribed and analyzed thematically. Results show scientific explanations of COVID-19 conflicted with interviewees’ cultural and spiritual beliefs such as: coronavirus is a disease of rich, sinful people; the virus is a curse from Allah to punish sinners. Interviewees rejected going to hospitals in favor of home remedies, and eschewed measures such as mask-wearing or social distancing instead preferring to follow local beliefs. We have highlighted a gap between community beliefs about the pandemic and science-led interventions proposed by health professionals. For public health policy to be more effective it requires a deeper understanding of and response to community perceptions.

The study of bioactive compounds like food antioxidants is getting huge attention and curiosity by researchers and other relevant stakeholders (e.g., food and pharmaceutical industries) due to their health benefits. However, the currently available protocols to estimate the antioxidant activity of foods are time-consuming, destructive, require complex procedures for sample preparation, need technical persons, and not possible for real-time application, which are very important for large-scale or industrial applications. On the other hand, fluorescence spectroscopy and imaging techniques are relatively new, fast, mostly nondestructive, and possible to apply real-time to detect the antioxidants of foods. However, there is no review article on fluorescence techniques for estimating antioxidants in agricultural produces. Therefore, the present review comprehensively summarizes the overview of fluorescence phenomena, techniques (i.e., spectroscopy and computer vision), and their potential to monitor antioxidants in fruits and vegetables. Finally, opportunities and challenges of fluorescence techniques are described toward developing next-generation protocols for antioxidants measurement. Fluorescence techniques (both spectroscopy and imaging) are simpler and faster than available traditional methods of antioxidants measurement. Moreover, the fluorescence imaging technique has the potential to apply in real-time antioxidant identification in agricultural produce such as fruits and vegetables. Therefore, this technique might be used as a next-generation protocol for qualitative and quantitative antioxidants measurement after improvements like new material technologies for sensor (detector) and light sources for higher sensitivity and reduce the cost of implementing real-world applications.

Many studies have been conducted using NIR spectroscopy to predict corn constituents; however, a systematic investigation of the spectral sub-regions under the scope of overtones and combinations has not been performed. In this study, the corn spectra were divided into second overtones (1100-1388 nm), first overtones (1390-1852 nm), and combinations (1852-2498 nm). Then, using variable importance in projection and genetic algorithm, each region was inspected sequentially to identify the most informative sub-region for each attribute to improve interpretability. The identified spectral subsets were further tuned to select the most influential bands for each attribute. The sub-regions in combinations bands was most informative for predicting water (1908-2108 nm, 2 bands), oil (2176-2304 nm, 6 bands), and protein (2130-2190 nm, 3 bands), whereas the first overtones region was the best for predicting starch (1452-1770 nm, 5 bands). Results provided valuable information for potential hardware and software improvements.

In this era of network security, Intrusion Detection System plays an important role. It is used to predict network data traffic as normal or anomaly. Several machine learning models are used for building an accurate Intrusion Detection System. In this paper, a hybrid machine learning model with a new feature selection method is proposed for better performance of the Intrusion Detection System. In this proposed model, the Intrusion Detection System is built with a combination of supervised and unsupervised machine learning models. A brief comparison between the proposed model and the other machine learning models such as AdaBoost, XGBoost, Random Forest, Gaussian Naive Bayes, LGB is narrated in this paper. We applied the models on the NSL KDD dataset and experimentally proved that the accuracy level of our proposed model is approximately 11% higher than the others.

The authenticity of foods is an age-old problem. Food authenticity issues, especially adulteration, have been existed as long as food has been made and sold. Now it has become more sophisticated and widespread all over the world. Major food adulteration events seem to occur regularly, for example, Sudan Red dye adulteration with spices in 2003, melamine adulteration with milk powder in 2008, dioxins adulteration in pork in 2008, detergent, fat, and urea adulteration in milk in 2008, horse meat adulteration in processed beef in 2013, and many more. These are just a few of the major incidents, and the full scale of adulteration is not well known because most incidents go undetected or unreported. Moore and Lipp documented over 1300 cases of food adulteration occurred from 1980 to 2010. The occurrence of fraud is not natural to evaluate without using highly sophisticated analytical tools. Detecting adulteration is difficult, as most of the cases, the adulterants are very usually similar to the authentic product.

Variable selection is a critical step for designing a dedicated multispectral real-time system from multicollinearity spectral data. It improves the prediction ability of the calibration model and provides faster prediction by reducing the curse of dimensionality. The main objective of this study was to compare the effect of variables selection algorithms on model performance for predicting moisture content in red meat using visible and near-infrared (VNIR) hyperspectral imaging in the spectral range of 400-1000 nm and corn using near-infrared (NIR) spectroscopy in the spectral range of 1100-2498 nm. Six variable selection algorithms including the size of the regression coefficient (RC), variable importance in projection (VIP), genetic algorithm (GA), competitive adaptive reweighted sampling (CARS), successive projection algorithm (SPA), and stepwise regression (SWR) were tested and compared to realize their effects on the model performance for predicting moisture content in red meat and corn. The model based on competitive adaptive reweighted sampling-partial least squares regression (CARS-PLSR) was the best model to predict moisture content in red meat and corn. The results indicated the effectiveness of variable selection for providing the feature wavelengths to design a low-cost, real-time multispectral system.

Direct-contact ultrasonic drying is a novel approach to dehydrate fruits and vegetables to reduce microbial growth and post-harvest loss while preserving nutrients and the quality of the final product. Moisture content is a critical component for food behavior during drying, and its accurate evaluation in real-time is essential for food quality control. This study conveys the potential implementation of portable near-infrared spectroscopy (NIRS) combined with multivariate analysis for real-time assessment of moisture content in apple slices during direct-contact ultrasonic drying. Partial least squares regression (PLSR) and Gaussian process regression (GPR) models were developed, and their performances for different pre-treatments methods and data partitioning algorithms were evaluated with both internal cross-validation and an external dataset. Three wavelengths were selected by SPA (1359, 1517, and 1594 nm) which were then used to introduce a closed-form equation for moisture content prediction with R

Fruits and vegetable powders are gaining attention due to their flavor, color, high nutritional content, and consumers’ demand for compact and lightweight foods. This study was undertaken to explore their commercial applications as an edible coating onto sliced apples to incorporate various functional and nutritional characteristics to apple chips. The subsequent aim of this work was to investigate miniature NIR spectroscopy as a tool to rapidly monitor and develop a predictive model for the drying of edible coating on these apple slices. The apple slices coated with selected fruit powders were dried and compared with uncoated samples. NIR spectra were collected at different drying times, and multivariate calibration models were developed using partial least-squares regression (PLSR) with raw and various pre-treated spectra. Instead of selecting different sets of feature wavelengths for coated and uncoated apple slices, a set of 7 key wavelengths was selected for convenient application to monitor moisture content during drying of apples with or without edible coatings. The results showed that the miniature NIR spectroscopy was able to monitor the drying process and discriminate between the coated and uncoated apple slices and drying times, primarily by the differences in sugar and water absorption bands.

Monitoring dissolved organic matter (DOM) content in aquatic environments is crucial for not only understanding the dynamics of heavy metals but also predicting their bioavailability. Fluorescence spectroscopy is typically employed to characterise DOM. Here, the interaction between DOM and trace metals was investigated by combining excitation-emission matrix (EEM) quenching with two-dimensional Savitzky-Golay second-order differentiation (2D-SG-2nd-df) analysis. The 2D-SG-2nd-df analysis decomposed the EEM spectra of commercial humic acid (HA) samples into six separate fluorescence peaks, which agreed with the results obtained through conventional parallel factor (PARAFAC) analysis. Compared with PARAFAC modeling, the 2D-SG-2nd-df approach provided more valid and reliable results when the dataset contained distinct samples. Moreover, since the results obtained from 2D-SG-2nd-df for each sample are independent, shifts in the peak wavelength can be reproduced more efficiently using this method. Triplicate titration experiments showed clear differences in HA-copper interactions for samples with different HA composition and molecular weight. The binding strength between copper and low-molecular-weight DOM in water was weaker than that observed for high-molecular-weight DOM. The results obtained in this study will serve as a basis for applying 2D-SG-2nd-df not only to DOM but also to other samples studied using EEM measurements.

A hyperspectral imaging system in the spectral range of 400-1000 nm was investigated to develop a multispectral real-time imaging system allowing the meat industry to determine moisture content in red meat including beef, lamb, and pork. Multivariate calibration models were developed using partial least-squares regression (PLSR) and least-squares support vector machines (LS-SVM) in the full spectral range. Instead of selection of different sets of feature wavelengths for beef, lamb, and pork, a set of 10 feature wavelengths was selected for convenient industrial application for the determination of moisture content in red meat. A quantitative linear function was then established using MLR based on these key feature wavelengths for predicting moisture content of red meat in an online system and creating moisture distribution maps. The results reveal that the combination of hyperspectral imaging and multivariate has great potential in the meat industry for real-time determination of moisture content.

A hyperspectral imaging system was investigated for determination of feature wavelengths to be used in a design of a multispectral system for real-time monitoring of water holding capacity (WHC) in red meat. Hyperspectral images of different red meat samples were acquired in the spectral range of 400–1000 nm and partial least-squares regression (PLSR) and least square support vector machine (LS-SVM) models were developed. Feature wavelengths were selected using regression coefficients (RCs) and competitive adaptive reweighted sampling (CARS). The best set of feature wavelengths was determined using RCs and the best calibration model obtained was based on RCs-LS-SVM. The model obtained an R2p of 0.93 and RPD of 4.09, indicating that the model is adequate for analytical purposes. An image processing algorithm was developed to transfer this model to each pixel in the image. The results showed that instead of selecting different sets of wavelengths for beef, lamb, and pork, a subset of feature wavelengths can be used for convenient industrial application for the determination of WHC in red meat. The pixel wise visualization of WHC obtained with the aid of image processing was another advantage of using hyperspectral imaging that cannot be obtained with either imaging or conventional spectroscopy.