Leishmaniasis: An Evolving Public Health Concern in Thailand

Leishmaniasis is a parasitic disease caused by flagellated protozoa of the genus Leishmania. It is transmitted by the bite of an infected sandfly. The 3 main clinical forms of the disease are cutaneous leishmaniasis (CL), visceral leishmaniasis (VL) and mucocutaneous leishmaniasis (MCL). Prior to 1996, all leishmaniasis cases were infected during the visit to the endemic areas. Thereafter, autochthonous leishmaniasis cases have been reported in Thailand. From 1996 to the present, at least 21 cases of autochthonous leishmaniasis have been confirmed in Thailand. Leishmania siamensis, a novel species of Leishmania, was suspected of being the causative pathogens in some of those cases, However, the data supporting the existence of this new species is limited. Until recently, in-depth investigation using molecular characterization and isoenzyme analysis revealed that this suspected novel species consists of 2 different, but closely related strains: L. siamensis and L. martiniquensis. L. martiniquensis, a rare species firstly discovered on Martinique Island, is the cause of leishmaniasis in the majority of cases. Meanwhile, L. siamensis, a true novel species firstly and only reported from Thailand, was confirmed as the cause of leishmaniasis in two autochthonous cases. Two clinical forms (CL and VL) have been observed in both L. martiniquensis and L. siamensis infection. The DNA of L. martiniquensis was found in black rats, suggesting their role as a natural reservoir. The presence of L. martiniquensis DNA in two sandfly species (Sergentomyia gemmea and Sergentomyia barraudi) that are commonly found in affected areas may also suggest their role as potential vectors. Here, we update the status of leishmaniasis in Thailand and its emergence as a potential public health concern

DC-SIGN (CD209) Mediates Dengue Virus Infection of Human Dendritic Cells

Dengue virus is a single-stranded, enveloped RNA virus that productively infects human dendritic cells (DCs) primarily at the immature stage of their differentiation. We now find that all four serotypes of dengue use DC-SIGN (CD209), a C-type lectin, to infect dendritic cells. THP-1 cells become susceptible to dengue infection after transfection of DC-specific ICAM-3 grabbing nonintegrin (DC-SIGN), or its homologue L-SIGN, whereas the infection of dendritic cells is blocked by anti–DC-SIGN antibodies and not by antibodies to other molecules on these cells. Viruses produced by dendritic cells are infectious for DC-SIGN– and L-SIGN–bearing THP-1 cells and other permissive cell lines. Therefore, DC-SIGN may be considered as a new target for designing therapies that block dengue infection

Parkinsonism associated with interferon alpha therapy for chronic myelogenous leukemia.

A 79 year-old man was treated with Interferon alpha for chronic myelogenous leukemia and developed severe parkinsonism that resolved after Interferon alpha was stopped. Carbidopa-levodopa was associated with early improvement, but discontinuation did not result in worsening of the parkinsonism

Leishmaniasis: An Evolving Public Health Concern in Thailand

Leishmaniasis is a parasitic disease caused by flagellated protozoa of the genus Leishmania. It is transmitted by infection from the bite of an infected sandfly. The 3 main clinical forms of the disease are cutaneous leishmaniasis (CL), visceral leishmaniasis (VL) and mucocutaneous leishmaniasis (MCL). Prior to 1996, all leishmaniasis cases were infected during the visit to the endemic areas. Thereafter, autochthonous leishmaniasis cases have been reported in Thailand. During 1996 to the present, at least 21 cases of autochthonous leishmaniasis have been confirmed in Thailand. Leishmania siamensis, a novel species of Leishmania, was suspected of being the causative pathogens in some of those cases, although the data supporting the existence of this new species is limited. Until recently, in-depth investigation using molecular characterization and isoenzyme analysis revealed that a suspected novel species consists of 2 different, but closely related strains: L. siamensis and L. martiniquensis. L. martiniquensis, a rare species firstly discovered on Martinique Island, is the cause of leishmaniasis in the majority of cases. Meanwhile, L. siamensis, a true novel species first and only reported from Thailand, was confirmed as the cause of leishmaniasis in two autochthonous cases. Two clinical forms (CL and VL) have been observed in both L. martiniquensis and L. siamensis infection. DNA of L. martiniquensis was found in black rats, suggesting their role as a natural reservoir. The presence of L. martiniquensis DNA in two sandfly species (Sergentomyia gemmea and Sergentomyia barraudi) that are commonly found in affected areas may also suggest their role as potential vectors. Here, we update the status of leishmaniasis in Thailand and its emergence as a potential public health concern

Automatic Detection, Classification, and Grading of Lumbar Intervertebral Disc Degeneration Using an Artificial Neural Network Model

Background and Objectives: Intervertebral disc degeneration (IDD) is a common cause of symptomatic axial low back pain. Magnetic resonance imaging (MRI) is currently the standard for the investigation and diagnosis of IDD. Deep learning artificial intelligence models represent a potential tool for rapidly and automatically detecting and visualizing IDD. This study investigated the use of deep convolutional neural networks (CNNs) for the detection, classification, and grading of IDD. Methods: Sagittal images of 1000 IDD T2-weighted MRI images from 515 adult patients with symptomatic low back pain were separated into 800 MRI images using annotation techniques to create a training dataset (80%) and 200 MRI images to create a test dataset (20%). The training dataset was cleaned, labeled, and annotated by a radiologist. All lumbar discs were classified for disc degeneration based on the Pfirrmann grading system. The deep learning CNN model was used for training in detecting and grading IDD. The results of the training with the CNN model were verified by testing the grading of the dataset using an automatic model. Results: The training dataset of the sagittal intervertebral disc lumbar MRI images found 220 IDDs of grade I, 530 of grade II, 170 of grade III, 160 of grade IV, and 20 of grade V. The deep CNN model was able to detect and classify lumbar IDD with an accuracy of more than 95%. Conclusion: The deep CNN model can reliably automatically grade routine T2-weighted MRIs using the Pfirrmann grading system, providing a quick and efficient method for lumbar IDD classification

Automatic Detection, Classification, and Grading of Lumbar Intervertebral Disc Degeneration Using an Artificial Neural Network Model

Background and Objectives: Intervertebral disc degeneration (IDD) is a common cause of symptomatic axial low back pain. Magnetic resonance imaging (MRI) is currently the standard for the investigation and diagnosis of IDD. Deep learning artificial intelligence models represent a potential tool for rapidly and automatically detecting and visualizing IDD. This study investigated the use of deep convolutional neural networks (CNNs) for the detection, classification, and grading of IDD. Methods: Sagittal images of 1000 IDD T2-weighted MRI images from 515 adult patients with symptomatic low back pain were separated into 800 MRI images using annotation techniques to create a training dataset (80%) and 200 MRI images to create a test dataset (20%). The training dataset was cleaned, labeled, and annotated by a radiologist. All lumbar discs were classified for disc degeneration based on the Pfirrmann grading system. The deep learning CNN model was used for training in detecting and grading IDD. The results of the training with the CNN model were verified by testing the grading of the dataset using an automatic model. Results: The training dataset of the sagittal intervertebral disc lumbar MRI images found 220 IDDs of grade I, 530 of grade II, 170 of grade III, 160 of grade IV, and 20 of grade V. The deep CNN model was able to detect and classify lumbar IDD with an accuracy of more than 95%. Conclusion: The deep CNN model can reliably automatically grade routine T2-weighted MRIs using the Pfirrmann grading system, providing a quick and efficient method for lumbar IDD classification