The aim of this retrospective study was to determine the accuracy of postmortem computed tomography and different radiological signs for the determination of the bleeding source in cases with hemoperitoneum confirmed at autopsy.

Postmortem computed tomography data of consecutive cases with hemoperitoneum confirmed at autopsy were reviewed by two raters, blinded to the autopsy findings. The determination of possible bleeding sources was based on the presence of the sentinel clot sign, blood or sedimented blood surrounding an organ, intraparenchymal abnormal gas distribution, and parenchymal disruption. The bleeding source and the cause of hemoperitoneum (traumatic, surgical, natural, or resuscitation) as reported in the autopsy report were noted. The survival intervals of the deceased were calculated when information about the time of an incident related to death was available in the autopsy reports.

Eighty-five cases were included in the study. Postmortem computed tomography showed 79% sensitivity and 92.1% specificity for the detection of the bleeding source. The sentinel clot sign was associated with surgical or natural causes of hemoperitoneum and longer survival intervals. Sedimented blood around the bleeding source was associated with resuscitation. Abnormal gas distribution within organs and combination of multiple radiological signs provided higher sensitivity.

Postmortem computed tomography provides moderate sensitivity and high specificity for determining the bleeding source in cases with hemoperitoneum. Different PMCT signs are associated with different causes of hemoperitoneum and survival intervals.
Postmortem computed tomography provides moderate sensitivity and high specificity for determining the bleeding source in cases with hemoperitoneum. Different PMCT signs are associated with different causes of hemoperitoneum and survival intervals.
A detailed study of the response of wheat plants, inoculated with drought-tolerant PGPR is studied which would be beneficial to achieve genetic improvement of wheat for drought tolerance. Drought stress, a major challenge under current climatic conditions, adversely affects wheat productivity. In the current study, we observed the response of wheat plants, inoculated with drought-tolerant plant growth-promoting rhizobacteria (PGPR) Bacillus megaterium (MU2) and Bacillus licheniformis (MU8) under induced drought stress. In vitro study of 90 rhizobacteria exhibited 38 isolates showed one or more plant growth-promoting properties, such as solubilization of phosphorus, potassium, and exopolysaccharide production. Four strains revealing the best activities were tested for their drought-tolerance ability by growing them on varying water potentials (-0.05 to -0.73MPa). Among them, two bacterial strains Bacillus megaterium and Bacillus licheniformis showed the best drought-tolerance potential, ACC deaminase activit151%), fresh weight (35-192%), and dry weight (58-226%) of wheat under irrigated and drought stress. Moreover, these strains efficiently colonized the wheat roots and increased plant biomass, relative water content, photosynthetic pigments, and osmolytes. Upon exposure to drought stress, Bacillus megaterium inoculated wheat plants exhibited improved tolerance by enhancing 59% relative water content, 260, 174 and 70% chlorophyll a, b and carotenoid, 136% protein content, 117% proline content and 57% decline in MDA content. Further, activities of defense-related antioxidant enzymes were also upregulated. Our results revealed that drought tolerance was more evident in Bacillus megaterium as compared to Bacillus licheniformis. These strains could be effective bioenhancer and biofertilizer for wheat cultivation in arid and semi-arid regions. However, a detailed study at the molecular level to deduce the mechanism by which these strains alleviate drought stress in wheat plants needs to be explored.Autosomal short tandem repeats (asSTR) serve as genetic markers for discriminating individuals and have been extensively used for criminal investigations as well as the establishment of genetic relationships. Tri-allelic pattern usually occurs due to chromosomal duplication, trisomy, and chimerism during mitotic division, but a false tri-allelic pattern at the D7S820 locus was encountered in our laboratory during the analysis of a case exhibit. DNA isolation from exhibit for profiling was done as per manufacturer's protocol. This is the first report which observed false tri-allelic pattern (10, 11, 14.1 allele) on D7S820 locus by analysis with GlobalFiler™ PCR Amplification Kit in Indian population. Findings were re-confirmed using other available asSTR kits in the laboratory, viz., AmpFLSTR™ Identifiler™ Plus PCR Amplification Kit and PowerPlex® Fusion 6C System. Two alleles (10, 11) found at D7S820, apart from SE33 marker, showed homozygous condition, but one Off Marker (OMR) peak was observed before start of SE33 marker region with the analysis using PowerPlex® Fusion 6C System. As it has been confirmed that the OMR allele belongs to the SE33 locus, this could be possible because of the adjacent locations of the D7S820 and the SE33 in the GlobalFiler® PCR amplification kit. 14.1 allele appeared within the allelic window of D7S820. The false tri-allelic pattern was due to the overlapping of SE33 marker allele (1.2 repeat) with bin window of D7S820 Marker. This finding might create confusion for the establishment of genetic relationships. We, therefore, conclude that such uncommon observations with rare events should be carefully investigated and interpreted.
Dental age, one of the indicators of biological age, is inferred by radiological methods. Two of the most commonly used methods are using Demirjian's radiographic stages of permanent teeth excluding the third molar (Demirjian's and Willems' method). https://www.selleckchem.com/products/ted-347.html The major drawbacks of these methods are that they are based on population-specific conversion tables and may tend to over- or underestimate dental age in other populations. Machine learning (ML) methods make it possible to create complex data schemas more simply while keeping the same annotation system. The objectives of this study are to compare (1) the capacity of ten machine learning algorithms to predict dental age in children using the seven left permanent mandibular teeth compared to reference methods and (2) the capacity of ten machine learning algorithms to predict dental age from childhood to young adulthood using the seven left permanent mandibular teeth and the four third molars.

Using a large radiological database of 3605 orthopantomograms (1734 females and 1871 males) of healthy French patients aged between 2 and 24years, seven left permanent mandibular teeth and the 4 third molars were assessed using Demirjian's stages.