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Fulltext The dilemma of diagnosing wound botulism in an infant: A rare case of paralysis with topical application of honey

Mohd Tamrin MI

Int J Infect Dis, 2020 Jun;95:157-159.
PMID: 32220630 DOI: 10.1016/j.ijid.2020.03.044

Botulism is a form of paralysis caused by a neurotoxin produced by the bacterium Clostridium botulinum. It is well known that natural honey contains Clostridium botulinum spores; controversy arises when a honey-related product is being used for wound care, where the possibility occurs of applying these spores to an open wound. To our knowledge, no reported cases of medical-grade honey have been associated with wound botulism. Given this fact, do we feel secure regarding the safety of this product, and will it be enough to alleviate our concern? We present a case of an infant with an infected umbilical stump, which required a surgical wound debridement. This infant developed a sudden progressive flaccid paralysis a few days after the application of topical medical grade honey for wound care. Even though suspicion of wound botulism is high, confirmation of the diagnosis, detection of neurotoxin, and isolating the organism remains a challenge.
Fulltext COVID-19 Deep Learning Prediction Model Using Publicly Available Radiologist-Adjudicated Chest X-Ray Images as Training Data: Preliminary Findings

Che Azemin MZ, Hassan R, Mohd Tamrin MI, Md Ali MA

Int J Biomed Imaging, 2020;2020:8828855.
PMID: 32849861 DOI: 10.1155/2020/8828855

The key component in deep learning research is the availability of training data sets. With a limited number of publicly available COVID-19 chest X-ray images, the generalization and robustness of deep learning models to detect COVID-19 cases developed based on these images are questionable. We aimed to use thousands of readily available chest radiograph images with clinical findings associated with COVID-19 as a training data set, mutually exclusive from the images with confirmed COVID-19 cases, which will be used as the testing data set. We used a deep learning model based on the ResNet-101 convolutional neural network architecture, which was pretrained to recognize objects from a million of images and then retrained to detect abnormality in chest X-ray images. The performance of the model in terms of area under the receiver operating curve, sensitivity, specificity, and accuracy was 0.82, 77.3%, 71.8%, and 71.9%, respectively. The strength of this study lies in the use of labels that have a strong clinical association with COVID-19 cases and the use of mutually exclusive publicly available data for training, validation, and testing.
Fulltext Postsurgery Classification of Best-Corrected Visual Acuity Changes Based on Pterygium Characteristics Using the Machine Learning Technique

Jais FN, Che Azemin MZ, Hilmi MR, Mohd Tamrin MI, Kamal KM

ScientificWorldJournal, 2021;2021:6211006.
PMID: 34819813 DOI: 10.1155/2021/6211006

Introduction: Early detection of visual symptoms in pterygium patients is crucial as the progression of the disease can cause visual disruption and contribute to visual impairment. Best-corrected visual acuity (BCVA) and corneal astigmatism influence the degree of visual impairment due to direct invasion of fibrovascular tissue into the cornea. However, there were different characteristics of pterygium used to evaluate the severity of visual impairment, including fleshiness, size, length, and redness. The innovation of machine learning technology in visual science may contribute to developing a highly accurate predictive analytics model of BCVA outcomes in postsurgery pterygium patients.
Aim: To produce an accurate model of BCVA changes of postpterygium surgery according to its morphological characteristics by using the machine learning technique. Methodology. A retrospective of the secondary dataset of 93 samples of pterygium patients with different pterygium attributes was used and imported into four different machine learning algorithms in RapidMiner software to predict the improvement of BCVA after pterygium surgery.
Results: The performance of four machine learning techniques were evaluated, and it showed the support vector machine (SVM) model had the highest average accuracy (94.44% ± 5.86%), specificity (100%), and sensitivity (92.14% ± 8.33%).
Conclusion: Machine learning algorithms can produce a highly accurate postsurgery classification model of BCVA changes using pterygium characteristics.
10H-3,6-Diazaphenothiazines triggered the mitochondrial-dependent and cell death receptor-dependent apoptosis pathways and further increased the chemosensitivity of MCF-7 breast cancer cells via inhibition of AKT1 pathways

Ren G, Hao X, Yang S, Chen J, Qiu G, Ang KP, et al.

J Biochem Mol Toxicol, 2020 Sep;34(9):e22544.
PMID: 32619082 DOI: 10.1002/jbt.22544

Breast cancer is one of the leading causes of death in cancer categories, followed by lung, colorectal, and ovarian among the female gender across the world. 10H-3,6-diazaphenothiazine (PTZ) is a thiazine derivative compound that exhibits many pharmacological activities. Herein, we proceed to investigate the pharmacological activities of PTZ toward breast cancer MCF-7 cells as a representative in vitro breast cancer cell model. The PTZ exhibited a proliferation inhibition (IC50 = 0.895 µM) toward MCF-7 cells. Further, cell cycle analysis illustrated that the S-phase checkpoint was activated to achieve proliferation inhibition. In vitro cytotoxicity test on three normal cell lines (HEK293 normal kidney cells, MCF-10A normal breast cells, and H9C2 normal heart cells) demonstrated that PTZ was more potent toward cancer cells. Increase in the levels of reactive oxygen species results in polarization of mitochondrial membrane potential (ΔΨm), together with suppression of mitochondrial thioredoxin reductase enzymatic activity suggested that PTZ induced oxidative damages toward mitochondria and contributed to improved drug efficacy toward treatment. The RT2 PCR Profiler Array (human apoptosis pathways) proved that PTZ induced cell death via mitochondria-dependent and cell death receptor-dependent pathways, through a series of modulation of caspases, and the respective morphology of apoptosis was observed. Mechanistic studies of apoptosis suggested that PTZ inhibited AKT1 pathways resulting in enhanced drug efficacy despite it preventing invasion of cancer cells. These results showed the effectiveness of PTZ in initiation of apoptosis, programmed cell death, toward highly chemoresistant MCF-7 cells, thus suggesting its potential as a chemotherapeutic drug.
Prediction of Changes in Visual Acuity and Contrast Sensitivity Function by Tissue Redness after Pterygium Surgery

Hilmi MR, Che Azemin MZ, Mohd Kamal K, Mohd Tamrin MI, Abdul Gaffur N, Tengku Sembok TM

Curr Eye Res, 2017 Jun;42(6):852-856.
PMID: 28118054 DOI: 10.1080/02713683.2016.1250277

PURPOSE: The goal of this study was to predict visual acuity (VA) and contrast sensitivity function (CSF) with tissue redness grading after pterygium surgery.

MATERIALS AND METHODS: A total of 67 primary pterygium participants were selected from patients who visited an ophthalmology clinic. We developed a semi-automated computer program to measure the pterygium fibrovascular redness from digital pterygium images. The final outcome of this software is a continuous scale grading of 1 (minimum redness) to 3 (maximum redness). The region of interest (ROI) was selected manually using the software. Reliability was determined by repeat grading of all 67 images, and its association with CSF and VA was examined.

RESULTS: The mean and standard deviation of redness of the pterygium fibrovascular images was 1.88 ± 0.55. Intra-grader and inter-grader reliability estimates were high with intraclass correlation ranging from 0.97 to 0.98. The new grading was positively associated with CSF (p < 0.01) and VA (p < 0.01). The redness grading was able to predict 25% and 23% of the variance in the CSF and the VA, respectively.

CONCLUSIONS: The new grading of pterygium fibrovascular redness can be reliably measured from digital images and showed a good correlation with CSF and VA. The redness grading can be used in addition to the existing pterygium grading.