In vitro and in vivo studies revealed that Malaysian medicinal plants, Piper sarmentosum, Andrographis paniculata and Tinospora crispa produced considerable antimalarial effects. Chloroform extract in vitro did show better effect than the methanol extract. The chloroform extract showed complete parasite growth inhibition as low as 0.05 mg/ml drug dose within 24 h incubation period (Andrographis paniculata) as compared to methanol extract of drug dose of 2.5 mg/ml but under incubation time of 48 h of the same plant spesies. In vivo activity of Andrographis paniculata also demonstrated higher antimalarial effect than other two plant species.
We describe here a reverse transcriptase-polymerase chain reaction method for the detection of malaria parasites. Ten in vitro-cultured isolates of Plasmodium falciparum and 16 specimens from patients infected with P. falciparum were used to examine the specificity and sensitivity of the test. The sensitivity of the test was 0.3 parasites per microliter of blood. Specificity was determined by matching the sequences of the specimens' DNA to published sequences of 18S ribosomal RNA genes in the species-specific region. The test proved to be very sensitive and specific for the detection of P. falciparum infection.
Observing chromosomes is a time-consuming and labor-intensive process, and chromosomes have been analyzed manually for many years. In the last decade, automated acquisition systems for microscopic images have advanced dramatically due to advances in their controlling computer systems, and nowadays, it is possible to automatically acquire sets of tiling-images consisting of large number, more than 1000, of images from large areas of specimens. However, there has been no simple and inexpensive system to efficiently select images containing mitotic cells among these images. In this paper, a classification system of chromosomal images by deep learning artificial intelligence (AI) that can be easily handled by non-data scientists was applied. With this system, models suitable for our own samples could be easily built on a Macintosh computer with Create ML. As examples, models constructed by learning using chromosome images derived from various plant species were able to classify images containing mitotic cells among samples from plant species not used for learning in addition to samples from the species used. The system also worked for cells in tissue sections and tetrads. Since this system is inexpensive and can be easily trained via deep learning using scientists' own samples, it can be used not only for chromosomal image analysis but also for analysis of other biology-related images.
In a brachytherapy room irradiated with an Iridium-192 (192Ir) source, the spatial distributions of photon dose rates were measured and calculated for the dose distribution both inside and outside the room. The spatial distributions were measured using a thermoluminescent dosimeter (LiF-100) on the surfaces of the concrete walls and barriers of the irradiation room. The calculations were performed using the particle and heavy ion transport code system (PHITS) by considering the detailed model of the brachytherapy room and the radiation source used in the measurements. The measured and calculated doses exhibited a similar distribution pattern within and outside the brachytherapy room. To reduce the edge effect at the entrance door, the addition of a 3-mm thick lead layer on the surface of the concrete wall on the left doorstop is recommended. For the 60Co source, with the existing walls and lead door thickness, the dose at the control console and in front of the entrance maze increased by a factor of approximately 60.