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Che Suraya Zin,1 Nor Ilyani Nazar,1 Norny Syafinaz Rahman,1 Nor Elina Alias,1 Wan Rohaidah Ahmad,2 Nurul Sahida Rani,3 Mary Suma Cardosa,4 Kim Swan Ng,4 Felicia Loh Ye5 1Department of Pharmacy Practice, Kulliyah of Pharmacy, International Islamic University Malaysia, Kuantan, Malaysia; 2Department of Anaesthesiology and Intensive Care, Hospital Sultanah Nur Zahirah, Kuala Terengganu, Malaysia; 3Department of Pharmacy, Hospital Sultanah Nur Zahirah, Kuala Terengganu, Malaysia; 4Department of Anaesthesiology and Intensive Care, Hospital Selayang, Batu Caves, Malaysia; 5Department of Pharmacy, Hospital Selayang, Batu Caves, Malaysia Purpose: To examine the trends of analgesic prescribing at public tertiary hospital outpatient settings and explore the patterns of their utilization in nonsteroidal anti-inflammatory drugs (NSAIDs), tramadol, and opioid patients. Patients and methods: This cross-sectional study was conducted from 2010 to 2016 using the prescription databases of two tertiary hospitals in Malaysia. Prescriptions for nine NSAIDs (ketoprofen, diclofenac, celecoxib, etoricoxib, ibuprofen, indomethacin, meloxicam, mefenamic acid, and naproxen), tramadol, and five other opioids (morphine, fentanyl, oxycodone, dihydrocodeine, and buprenorphine) were included in this study. Annual number of patients and prescriptions were measured in repeat cross-sectional estimates. Descriptive statistics and linear trend analysis were performed using Stata version 13. Results: A total of 192,747 analgesic prescriptions of the nine NSAIDs, tramadol, and five other opioids were given for 97,227 patients (51.8% NSAIDs patients, 46.6% tramadol patients, and 1.7% opioid patients) from 2010 to 2016. Tramadol (37.9%, n=72,999) was the most frequently prescribed analgesic, followed by ketoprofen (17.5%, n=33,793), diclofenac (16.2%, n=31,180), celecoxib (12.2%, n=23,487), and other NSAIDs (

A large body of research establishes the efficacy of musical intervention in many aspects of physical, cognitive, communication, social, and emotional rehabilitation. However, the underlying neural mechanisms for musical therapy remain elusive. This study aimed to investigate the potential neural correlates of musical therapy, focusing on the changes in the topology of emotion brain network. To this end, a Bayesian statistical approach and a cross-over experimental design were employed together with two resting-state magnetoencephalography (MEG) as controls. MEG recordings of 30 healthy subjects were acquired while listening to five auditory stimuli in random order. Two resting-state MEG recordings of each subject were obtained, one prior to the first stimulus (pre) and one after the final stimulus (post). Time series at the level of brain regions were estimated using depth-weighted minimum norm estimation (wMNE) source reconstruction method and the functional connectivity between these regions were computed. The resultant connectivity matrices were used to derive two topological network measures: transitivity and global efficiency which are important in gauging the functional segregation and integration of brain network respectively. The differences in these measures between pre- and post-stimuli resting MEG were set as the equivalence regions. We found that the network measures under all auditory stimuli were equivalent to the resting state network measures in all frequency bands, indicating that the topology of the functional brain network associated with emotional regulation in healthy subjects remains unchanged following these auditory stimuli. This suggests that changes in the emotion network topology may not be the underlying neural mechanism of musical therapy. Nonetheless, further studies are required to explore the neural mechanisms of musical interventions especially in the populations with neuropsychiatric disorders., Competing Interests: The authors declare there are no competing interests., (©2024 Mohd Rashid et al.)

This study used smartphone captured RGB images of gooseberries to automatically sort into standard, premium, or rejected categories based on topology. Main challenges addressed include, separation of touching or overlapping fruits into individual entities and new method called 'TopoGeoFusion' that combines basic geometrical features with topology aware features computed from the fruits to assess the grade or maturity. Quality assessment helps in grading the fruit to determine market suitability and intelligent camera applications. Computer Vision-based techniques have been applied to automatically grade the quality of gooseberries as standard, premium, or rejected according to fruit maturity. Smartphone-captured images of 1697 Indian Star Gooseberries are contributed to the study. This work acquired images consisting multiple fruits with overlapping and non-overlapping boundaries for concurrent quality assessment. Multiple classifiers such as Random Forest, SVM, Naive Bayes, Decision Tree, and KNN were applied to grade the gooseberry fruit. Random Forest classification with a fusion feature model resulted in an accuracy of 100 % towards reject, standard, and premium classes for test sets with four training strategies. The proposed segmentation model proves reliable in fruit detection & extraction with an average mAP of 0.56, resulting in an acceptable model for grade assessment.•The study highlights the effectiveness of TopoGeoFusion in automating the grading process of gooseberry fruits using topologically computed features.•The developed models exhibit high accuracy and reliability, even in challenging scenarios such as overlapping and touching fruits.•The method provides the technique to detect and extract the occluded objects and compute the features based on the partial object's topology., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors. Published by Elsevier B.V.)

Stochastic Calculus-guided Reinforcement learning (SCRL) is a new way to make decisions in situations where things are uncertain. It uses mathematical principles to make better choices and improve decision-making in complex situations. SCRL works better than traditional Stochastic Reinforcement Learning (SRL) methods. In tests, SCRL showed that it can adapt and perform well. It was better than the SRL methods. SCRL had a lower dispersion value of 63.49 compared to SRL's 65.96. This means SCRL had less variation in its results. SCRL also had lower risks than SRL in the short- and long-term. SCRL's short-term risk value was 0.64, and its long-term risk value was 0.78. SRL's short-term risk value was much higher at 18.64, and its long-term risk value was 10.41. Lower risk values are better because they mean less chance of something going wrong. Overall, SCRL is a better way to make decisions when things are uncertain. It uses math to make smarter choices and has less risk than other methods. Also, different metrics, viz training rewards, learning progress, and rolling averages between SRL and SCRL, were assessed, and the study found that SCRL outperforms well compared to SRL. This makes SCRL very useful for real-world situations where decisions must be made carefully.•By leveraging mathematical principles derived from stochastic calculus, SCRL offers a robust framework for making informed choices and enhancing performance in complex scenarios.•In comparison to traditional SRL methods, SCRL demonstrates superior adaptability and efficacy, as evidenced by empirical tests., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Author(s).)

Alphonso Mango (Mangifera indica L.), is popularly known as king of mangoes in India. India is one of the leading countries in mango production. Automatic visual inspection systems for quality assessment using weight are intelligent interventions designed to evaluate fruit maturity based on various parameters. Automated systems utilize a combination of image analysis, computer vision, and artificial intelligence algorithms to estimate the weight of fruits precisely. One of the crucial quality parameters is weight, which measures the fruit's overall mass and potential quality. Integration of precision weighing mechanisms in fruit quality estimation leads to a quick and accurate method of measuring fruit weight in the marketplace. Furthermore, the fruit's demand in the market is directly connected to its size as it influences consumer preferences. Automatic precision weight estimation systems equipped with intelligent high-resolution assists in ensuring consistency in size across batches of fruits. The dataset samples consist of images of 71 Alphonso cultivars of mango fruit. The fruit is collected from the College of Horticulture Yalachahalli, Mysuru, India. The fruits were harvested in April/May 2022. The digital images of these fruits are captured using the acquisition setup with a controlled environment. Each image has a resolution of 2048×1536. The images include two orientations of each sample. The physical parameters such as the weight, fruit diameter, and width across the shoulder are also maintained. The digital images undergo pre-processing, and further, the vision-based features such as area, convex area, and minor axis for both orientations are captured., (© 2023 The Author(s).)

Mobile-captured images of medicinal plants are widely used in various research investigations. Machine vision-based tasks such as the identification of plant species types for intelligent imaging device applications take a significant part in it. Botanists, farmers and researchers can reliably identify medicinal plants with the help of images captured using smartphones.  Mobile captured images can be used for quality control to make sure that the right plant species are being used in pharmaceutical products. In the field of education, pictures of medicinal plants and their usage can be used to educate learners, medical professionals, and the general public. Further, various research investigations in the area of chemistry, pharmacology, the therapeutic potential of medicinal plants, images can be employed. In this paper, we contribute a dataset of Indian medicinal plant species. The dataset is collected from different regions of Karnataka and Kerala. Datasets include characteristics such as multiple resolutions, varying illuminations, varying backgrounds, and seasons in the year. The datasets consist of 5900 images of forty plant species and single leaf images of eighty plant species consisting of 6900 samples obtained from real-time conditions using smartphones. The datasets contributed would be useful to researchers to investigate on development of algorithmic models based on image processing, machine learning, and deep learning concepts to educate about medicinal plants. The dataset can be accessed by anybody, without charge, at DOI:10.17632/748f8jkphb.2, 10.17632/748f8jkphb.3., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships which have or could be perceived to have influenced the work reported in this article., (© 2023 The Author(s).)

Automated dewarping of camera-captured handwritten documents is a challenging research problem in Computer Vision and Pattern Recognition. Most available systems assume the shape of the camera-captured image boundaries to be anywhere between trapezoidal and octahedral, with linear distortion in areas between the boundaries for dewarping. The majority of the state-of-the-art applications successfully dewarp the simple-to-medium range geometrical distortions with partial selection of control points by a user. The proposed work implements a fully automated technique for control point detection from simple-to-complex geometrical distortions in camera-captured document images. The input image is subject to preprocessing, corner point detection, document map generation, and rendering of the de-warped document image. The proposed algorithm has been tested on five different camera-captured document datasets (one internal and four external publicly available) consisting of 958 images. Both quantitative and qualitative evaluations have been performed to test the efficacy of the proposed system. On the quantitative front, an Intersection Over Union (IoU) score of 0.92, 0.88, and 0.80 for document map generation for low-, medium-, and high-complexity datasets, respectively. Additionally, accuracies of the recognized texts, obtained from a market leading OCR engine, are utilized for quantitative comparative analysis on document images before and after the proposed enhancement. Finally, the qualitative analysis visually establishes the system's reliability by demonstrating improved readability even for severely distorted image samples., Competing Interests: Conflict of interestN. Shobha Rani reports administrative support and statistical analysis were provided by Amrita Vishwa Vidyapeetham. Nachappa C H reports a relationship with Amrita Vishwa Vidyapeetham that includes: education and non-financial support. Gokulnath M reports a relationship with Amrita Vishwa Vidyapeetham that includes: employment and non-financial support., (© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

Since its revelation over 14 centuries ago, the Holy Quran is considered as scriptural divine words of Islam, and it is believed to promote psycho-spiritual therapeutic benefits to its reciter and/or listener. In this context, the listening of rhythmic Quranic verses among Muslims is often viewed as a form of unconventional melodic vocals, with accompanied anecdotal claims of the 'Quranic chills' pleasing effect. However, compared to music, rhythm, and meditation therapy, information on the neural basis of the anecdotal healing effects of the Quran remain largely unexplored. Current studies in this area took the leads from the low-frequency neuronal oscillations (i.e., alpha and theta) as the neural correlates, mainly using electroencephalography (EEG) and/or magnetoencephalography (MEG). In this narrative review, we present and discuss recent work related to these neural correlates and highlight several methodical issues and propose recommendations to progress this emerging transdisciplinary research. Collectively, evidence suggests that listening to rhythmic Quranic verses activates similar brain regions and elicits comparable therapeutic effects reported in music and rhythmic therapy. Notwithstanding, further research are warranted with more concise and standardized study designs to substantiate these findings, and opens avenue for the listening to Quranic verses as an effective complementary psycho-spiritual therapy., Competing Interests: The authors declare no competing interests., (© 2022 The Author(s).)

Objective: This study examined opioid prescription initiation patterns and their association with short-term and long-term opioid use among opioid-naïve patients., Design: This study was designed as a retrospective cohort study., Setting and Participants: In this study, we analysed the prescription databases of tertiary hospitals in Malaysia. This study included patients aged ≥18 years with at least one opioid prescription (buprenorphine, morphine, oxycodone, fentanyl, dihydrocodeine or tramadol) between 1 January 2011 and 31 December 2016. These patients had no opioid prescriptions in the 365 days prior, and were followed up for 365 days after the initial opioid prescription., Main Outcome Measures: The main outcome measures were the number of short-term (<90 days) and long-term opioid users (≥90 days), initial opioid prescription period and daily dose., Results: There were 33 752 opioid-naïve patients who received opioid prescriptions (n=43 432 prescriptions) during the study period. Of these, 29 824 (88.36%) were short-term opioid users and 3928 (11.64%) were long-term opioid users. The majority of these short-term (99.09%) and long-term users (96.18%) received an initial daily opioid dose of <50 mg/day with a short-acting opioid formulation. Short-term opioid users were predominantly prescribed opioids for 3-7 days (59.06%) by the emergency department (ED, 60.56%), while long-term opioid users were primarily prescribed opioids for ≥7 days (91.85%) by non-ED hospital departments (91.8%). The adjusted model showed that the following were associated with long-term opioid use: increasing opioid daily doses, prescription period ≥7 days and long-acting opioids initiated by non-EDs., Conclusions: The majority of opioid-naïve patients in tertiary hospital settings in Malaysia were prescribed opioids for short-term use. The progression to long-term use among opioid-naïve patients was attributed to the prescription of higher opioid doses for a longer duration as well as long-acting opioids initiated by non-ED hospital departments., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Increasing incidence of dengue cases in Malaysia over the last few years has been paralleled by increased deaths. Mortality prediction models will therefore be useful in clinical management. The aim of this study is to identify factors at diagnosis of severe dengue that predicts mortality and assess predictive models based on these identified factors., Method: This is a retrospective cohort study of confirmed severe dengue patients that were admitted in 2014 to Hospital Kuala Lumpur. Data on baseline characteristics, clinical parameters, and laboratory findings at diagnosis of severe dengue were collected. The outcome of interest is death among patients diagnosed with severe dengue., Results: There were 199 patients with severe dengue included in the study. Multivariate analysis found lethargy, OR 3.84 (95% CI 1.23-12.03); bleeding, OR 8.88 (95% CI 2.91-27.15); pulse rate, OR 1.04 (95% CI 1.01-1.07); serum bicarbonate, OR 0.79 (95% CI 0.70-0.89) and serum lactate OR 1.27 (95% CI 1.09-1.47), to be statistically significant predictors of death. The regression equation to our model with the highest AUROC, 83.5 (95% CI 72.4-94.6), is: Log odds of death amongst severe dengue cases = - 1.021 - 0.220(Serum bicarbonate) + 0.001(ALT) + 0.067(Age) - 0.190(Gender)., Conclusion: This study showed that a large proportion of severe dengue occurred early, whilst patients were still febrile. The best prediction model to predict death at recognition of severe dengue is a model that incorporates serum bicarbonate and ALT levels.

A novel, simple and economic reverse phase high performance liquid chromatography (RP-HPLC) method has been developed for the quantification of Meloxicam in bulk and tablet dosage form with greater precision and accuracy. Separation was achieved on Develosil ODS HG-5 RP C18, (15 cm × 4.6 mm i.d. 5 μm) column in isocratic mode with mobile phase consisting of acetonitrile: phosphate buffer(pH 3.4) (60:40) with a flow rate of 1 mL/min. The detection was carried out at 268 nm. The retention time of Meloxicam was found to be 2.09 min. The method was validated as per ICH guidelines. Linearity was established for Meloxicam in the range 20 - 120 μg/ml with R(2) value 0.996. The percentage recovery of Meloxicam was found to be in the range 99.99-100.46%. The high recovery and low relative standard deviation confirm the suitability of the proposed method for the estimation of the drug in bulk and tablet dosage forms. Validation studies demonstrated that the proposed RP-HPLC method is simple, specific, rapid, reliable and reproducible for the determination of Meloxicam for Quality Control level.

Matrix metalloproteinases (MMPs) play an important role in breast cancer tumor invasion and progression. MMP-9 is a member of the MMP family and is also known as Gelatinase B or type IV collagenases (92 kDa) and possesses proteolytic activity against type IV collagen, a major component of the basement membrane. Our study aims to examine the association of Gelatinase B (-1562C > T) promoter polymorphism with breast cancer invasion and progression. The study involves 200 breast cancer patients and age-matched 191 healthy controls. The SNP-1562C > T (rs3918242) in MMP-9 promoter region was examined by allele-specific polymerase chain reaction and gel electrophoresis. The genotypes were determined and compared between patients and controls, and the influence of the polymorphism on clinicopathological data was analyzed. The T allele of the -1562C > T MMP-9 polymorphism was detected more frequently in breast cancer patients than controls (p < 0.001). Our results suggest the clinical importance of MMP-9 gene polymorphism (-1562C > T) in breast cancer patients. The study may also help in identifying individuals at risk of developing breast cancer.

With the ever-increasing global demand for high quality rice in both local production regions and with Western consumers, we have a strong desire to understand better the importance of the different traits that make up the quality of the rice grain and obtain a full picture of rice quality demographics. Rice is by no means a 'one size fits all' crop. Regional preferences are not only striking, they drive the market and hence are of major economic importance in any rice breeding / improvement strategy. In this analysis, we have engaged local experts across the world to perform a full assessment of all the major rice quality trait characteristics and importantly, to determine how these are combined in the most preferred varieties for each of their regions. Physical as well as biochemical characteristics have been monitored and this has resulted in the identification of no less than 18 quality trait combinations. This complexity immediately reveals the extent of the specificity of consumer preference. Nevertheless, further assessment of these combinations at the variety level reveals that several groups still comprise varieties which consumers can readily identify as being different. This emphasises the shortcomings in the current tools we have available to assess rice quality and raises the issue of how we might correct for this in the future. Only with additional tools and research will we be able to define directed strategies for rice breeding which are able to combine important agronomic features with the demands of local consumers for specific quality attributes and hence, design new, improved crop varieties which will be awarded success in the global market.

Starch, composed of amylose and amylopectin, greatly influences rice cooking and textural quality, which in turn is controlled by various isoforms of several enzymes. Activity of one or more isoforms of starch-synthesizing enzymes results in various forms of starch structure based on the amylopectin chain length and average external, internal and core chain length distribution and hence results in varying physicochemical and cooking quality. Since the synthesis of starch is highly complex, it is crucial but essential to understand its biosynthetic pathway, starch structure and effects on the physicochemical properties that control eating and cooking quality, and alongside conduct research on gene/QTL mapping for use in marker-assisted selection (MAS) with a view to improve and select cultivars with most desirable range and class of rice starch properties. This article presents the updates on current understanding of the coordination among various enzymes/isoforms towards rice starch synthesis in endosperm and their effect on rice grain physicochemical, cooking and eating qualities. The efforts in identifying regions responsible for these enzymes by mapping the gene/QTLs have provided a glimpse on their association with physicochemical and cooking properties of rice and, hence, improvement is possible by modifying the allelic pattern, resulting in down or nil regulation of a particular enzyme. The clear understanding of the tissue specific coordination between enzyme isoforms and their subsequent effect in controlling eating and cooking properties will enhance the chances to manipulate them for getting desired range of amylose content (AC) and gelatinization temperature (GT) in improved cultivars through combining desired alleles through MAS., (Copyright © 2012 Elsevier Inc. All rights reserved.)

Broadening of the genetic base for identification and transfer of genes for resistance to insect pests and diseases from wild relatives of rice is an important strategy in resistance breeding programs across the world. An accession of Oryza nivara, International Rice Germplasm Collection (IRGC) accession number 105710, was identified to exhibit high level and broad-spectrum resistance to Xanthomonas oryzae pv. oryzae. In order to study the genetics of resistance and to tag and map the resistance gene or genes present in IRGC 105710, it was crossed with the bacterial blight (BB)-susceptible varieties 'TN1' and 'Samba Mahsuri' (SM) and then backcrossed to generate backcross mapping populations. Analysis of these populations and their progeny testing revealed that a single dominant gene controls resistance in IRGC 105710. The BC(1)F(2) population derived from the cross IRGC 105710/TN1//TN1 was screened with a set of 72 polymorphic simple-sequence repeat (SSR) markers distributed across the rice genome and the resistance gene was coarse mapped on chromosome 7 between the SSR markers RM5711 and RM6728 at a genetic distance of 17.0 and 19.3 centimorgans (cM), respectively. After analysis involving 49 SSR markers located between the genomic interval spanned by RM5711 and RM6728, and BC(2)F(2) population consisting of 2,011 individuals derived from the cross IRGC 105710/TN1//TN1, the gene was fine mapped between two SSR markers (RMWR7.1 and RMWR7.6) located at a genetic distance of 0.9 and 1.2 cM, respectively, from the gene and flanking it. The linkage distances were validated in a BC(1)F(2) mapping population derived from the cross IRGC 105710/SM//2 × SM. The BB resistance gene present in the O. nivara accession was identified to be novel based on its unique map location on chromosome 7 and wider spectrum of BB resistance; this gene has been named Xa33. The genomic region between the two closely flanking SSR markers was in silico analyzed for putatively expressed candidate genes. In total, eight genes were identified in the region and a putative gene encoding serinethreonine kinase appears to be a candidate for the Xa33 gene.

Enormous sequence information is available in public databases as a result of sequencing of diverse crop genomes. It is important to use this genomic information for the identification and isolation of novel and superior alleles of agronomically important genes from crop gene pools to suitably deploy for the development of improved cultivars. Allele mining is a promising approach to dissect naturally occurring allelic variation at candidate genes controlling key agronomic traits which has potential applications in crop improvement programs. It helps in tracing the evolution of alleles, identification of new haplotypes and development of allele-specific markers for use in marker-assisted selection. Realizing the immense potential of allele mining, concerted allele mining efforts are underway in many international crop research institutes. This review examines the concepts, approaches and applications of allele mining along with the challenges associated while emphasizing the need for more refined 'mining' strategies for accelerating the process of allele discovery and its utilization in molecular breeding.

Bacterial blight (BB) is a serious disease of rice in India. We have used molecular marker-assisted selection in a backcross breeding program to introgress three genes (Xa21, xa13, and xa5) for BB resistance into Triguna, a mid-early duration, high yielding rice variety that is susceptible to BB. At each generation in the backcross program, molecular markers were used to select plants possessing these resistance genes and to select plants that have maximum contribution from the Triguna genome. A selected BC3F1 plant was selfed to generate homozygous BC(3)F(2) plants with different combinations of BB resistance genes. Plants containing the two-gene combination, Xa21 and xa13, were found to exhibit excellent resistance against BB. Single plant selections for superior agronomic characteristics were performed on the progeny of these plants, from BC(3)F(3) generation onwards. The selected plants were subjected to yield trials at the BC(3)F(8) generation and were found to have a significant yield advantage over Triguna. The newly developed lines are being entered into national multi-location field trials. This work represents a successful example of the application of molecular marker-assisted selection for BB resistance breeding in rice.