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Fulltext Linkage of SSR markers with rice blast resistance and development of partial resistant advanced lines of rice (Oryza sativa) through marker-assisted selection

Nihad SAI, Hasan MK, Kabir A, Hasan MA, Bhuiyan MR, Yusop MR, et al.

Physiol Mol Biol Plants, 2022 Jan;28(1):153-169.
PMID: 35221577 DOI: 10.1007/s12298-022-01141-3

Rice blast disease is one of the major bottlenecks of rice production in the world including Bangladesh. To develop blast resistant lines, a cross was made between a high yielding but blast susceptible variety MR263 and a blast resistant variety Pongsu seribu 2. Marker-assisted backcross breeding was followed to develop F1, BC1F1, BC2F1, BC2F2, BC2F3, BC2F4 and BC2F5 population. DNA markers i.e., RM206, RM1359 and RM8225 closely linked to Pb1, pi21 and Piz blast resistant genes, respectively and marker RM276 linked to panicle blast resistant QTL (qPbj-6.1) were used in foreground selection. Calculated chi-square (χ2) value of phenotypic and genotypic segregation data of BC2F1 population followed goodness of fit to the expected ratio (1:1) (phenotypic data χ2 = 1.08, p = 0.701; genotypic data χ2 = range from 0.33 to 3.00, p = 0.08-0.56) and it indicates that the inheritance pattern of blast resistance was followed by a single gene model. Eighty-nine advanced lines of BC2F5 population were developed and out of them, 58 lines contained Piz, Pb1, pi21, and qPbj-6.1 while 31 lines contained Piz, Pb1, and QTL qPbj-6.1. Marker-trait association analysis revealed that molecular markers i.e., RM206, RM276, and RM8225 were tightly linked with blast resistance, and each marker was explained by 33.33% phenotypic variation (resistance reaction). Morphological and pathogenicity performance of advanced lines was better compared to the recurrent parent. Developed blast resistance advanced lines could be used as donors or blast resistant variety for the management of devastating rice blast disease.
SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12298-022-01141-3.
Fulltext Point-of-care ultrasonography in diagnosing necrotizing fasciitis-a literature review

Gan RK, Sanchez Martinez A, Abu Hasan MA, Castro Delgado R, Arcos González P

J Ultrasound, 2023 Jun;26(2):343-353.
PMID: 36694072 DOI: 10.1007/s40477-022-00761-5

INTRODUCTION: Necrotizing fasciitis (NF) is a rapidly progressive necrosis of the fascial layer with a high mortality rate. It is a life-threatening medical emergency that requires urgent treatment. Lack of skin finding in NF made diagnosis difficult and required a high clinical index of suspicion. The use of ultrasound may guide clinicians in improving diagnostic speed and accuracy, thus leading to improved management decisions and patient outcomes. This literature search aims to review the use of point-of-care ultrasonography in diagnosing necrotizing fasciitis.
METHOD: We searched relevant electronic databases, including PUBMED, MEDLINE, and SCOPUS, and performed a systematic review. Keywords used were "necrotizing fasciitis" or "necrotising fasciitis" or "necrotizing soft tissue infections" and "point-of-care ultrasonography" "ultrasonography" or "ultrasound". No temporal limitation was set. An additional search was performed via google scholar, and the top 100 entry was screened.
RESULTS: Among 540 papers screened, only 21 were related to diagnosing necrotizing fasciitis using ultrasonography. The outcome includes three observational studies, 16 case reports, and two case series, covering the period from 1976 to 2022.
CONCLUSION: Although the use of ultrasonography in diagnosing NF was published in several papers with promising results, more studies are required to investigate its diagnostic accuracy and potential to reduce time delay before surgical intervention, morbidity, and mortality.
Two-year surveillance of tilapia lake virus (TiLV) reveals its wide circulation in tilapia farms and hatcheries from multiple districts of Bangladesh

Debnath PP, Delamare-Deboutteville J, Jansen MD, Phiwsaiya K, Dalia A, Hasan MA, et al.

J Fish Dis, 2020 Nov;43(11):1381-1389.
PMID: 32851674 DOI: 10.1111/jfd.13235

Tilapia lake virus (TiLV) is an emerging pathogen in aquaculture, reportedly affecting farmed tilapia in 16 countries across multiple continents. Following an early warning in 2017 that TiLV might be widespread, we executed a surveillance programme on tilapia grow-out farms and hatcheries from 10 districts of Bangladesh in 2017 and 2019. Among farms experiencing unusual mortality, eight out of 11 farms tested positive for TiLV in 2017, and two out of seven tested positive in 2019. Investigation of asymptomatic broodstock collected from 16 tilapia hatcheries revealed that six hatcheries tested positive for TiLV. Representative samples subjected to histopathology confirmed pathognomonic lesions of syncytial hepatitis. We recovered three complete genomes of TiLV from infected fish, one from 2017 and two from 2019. Phylogenetic analyses based on both the concatenated coding sequences of 10 segments and only segment 1 consistently revealed that Bangladeshi TiLV isolates formed a unique cluster within Thai clade, suggesting a close genetic relation. In summary, this study revealed the circulation of TiLV in 10 farms and six hatcheries located in eight districts of Bangladesh. We recommend continuing TiLV-targeted surveillance efforts to identify contaminated sources to minimize the countrywide spread and severity of TiLV infection.
Fulltext A machine learning model for early detection of diabetic foot using thermogram images

Khandakar A, Chowdhury MEH, Ibne Reaz MB, Md Ali SH, Hasan MA, Kiranyaz S, et al.

Comput Biol Med, 2021 10;137:104838.
PMID: 34534794 DOI: 10.1016/j.compbiomed.2021.104838

Diabetes foot ulceration (DFU) and amputation are a cause of significant morbidity. The prevention of DFU may be achieved by the identification of patients at risk of DFU and the institution of preventative measures through education and offloading. Several studies have reported that thermogram images may help to detect an increase in plantar temperature prior to DFU. However, the distribution of plantar temperature may be heterogeneous, making it difficult to quantify and utilize to predict outcomes. We have compared a machine learning-based scoring technique with feature selection and optimization techniques and learning classifiers to several state-of-the-art Convolutional Neural Networks (CNNs) on foot thermogram images and propose a robust solution to identify the diabetic foot. A comparatively shallow CNN model, MobilenetV2 achieved an F1 score of ∼95% for a two-feet thermogram image-based classification and the AdaBoost Classifier used 10 features and achieved an F1 score of 97%. A comparison of the inference time for the best-performing networks confirmed that the proposed algorithm can be deployed as a smartphone application to allow the user to monitor the progression of the DFU in a home setting.