Emergence of SARS-CoV-2 with Dual-Drug Resistant Mutations During a Long-Term Infection in a Kidney Transplant Recipient.

Introduction: Various therapeutic agents are being developed for the treatment of coronavirus disease 2019 (COVID-19). Therefore, it is crucial to accumulate information regarding the features of drug-resistant viruses to these antiviral drugs. Methods: We investigated the emergence of dual-drug resistance in a kidney transplant recipient who received sotrovimab (from day 0) and remdesivir (RDV) (from day 8 to day 17). We sequenced the whole viral genomes from nasopharyngeal swabs taken on day 0 and seven points after starting treatment (on days 12, 19, 23, 37, 43, 48, and 58). The genetic traits of the wild-type (day 0) and descendant viruses (after day 12) were determined by comparing the genomes with those of a Wuhan strain and the day 0 wild-type strain, respectively. Three viral isolates (from samples collected on days 0, 23, and 37) were investigated for their escape ability and growth kinetics in vitro. Results: The sotrovimab resistant mutation (S:E340K) and the RDV resistant mutation RdRp:V792I (nt: G15814A) emerged within 12 days (day 12) and 11 days (day 19) after the treatment, respectively. The day 23 isolate harboring S:E340K/RdRp:V791I was resistant to both sotrovimab and RDV, showing 364- and 2.73-fold higher resistance respectively, compared with the wild-type. Moreover, compared with the day 23 isolate, the day 37 isolate accumulated multiple additional mutations and had a higher level of resistance to both drugs. Conclusion: Drug-resistant variants with double mutations (S:E340K/RdRp:V791I) became dominant within 23 days after starting treatment, suggesting that even a combination therapy involving sotrovimab and RDV, dual-drug resistant viruses may emerge rapidly in immunocompromised patients. The dual-resistant variants had lower virus yields than those of the wild-type virus in vitro, suggesting that they paid a fitness cost.

Artificial intelligence-based forensic sex determination of East Asian cadavers from skull morphology.

Identification of unknown cadavers is an important task for forensic scientists. Forensic scientists attempt to identify skeletal remains based on factors including age, sex, and dental treatment remains. Forensic scientists commonly consider skull or pelvic shape to evaluate the sex; however, these evaluations require sufficient experience and knowledge and lack objectivity and reproducibility. To ensure objectivity and reproducibility for sex evaluation, we applied a gated attention-based multiple-instance learning model to three-dimensional (3D) skull images reconstructed from postmortem head computed tomography scans. We preprocessed the images, trained with 864 training data, validated the model with 124 validation data, and evaluated the performance of our model in terms of accuracy with 246 test data. Furthermore, three forensic scientists evaluated the 3D skull images, and their performances were compared with those of the model. Our model showed an accuracy of 0.93, which was higher than that of the forensic scientists. Our model primarily focused on the entire skull owing to visualization but focused less on the areas often investigated by forensic scientists. In summary, our model may serve as a supportive tool to identify cadaver sex based on skull shape. Further studies are required to improve the model's performance.

Detection of human chorionic gonadotropin (HCG) in fabric urine stains older than five years.

Human chorionic gonadotropin (HCG) is used in clinical medicine as a particularly important indicator to determine pregnancy. In this study, it was necessary to determine whether the urine spots on car seat fabric from a murder 5 years previously were from a pregnant woman. The HCG in the dried urine spot on a car seat was detected using an immunochromatography kit. It was found that the HCG in urine can be detected for much longer periods of time than the previously reported period of approximately 6 months.

Deep Neural Networks-Based Age Estimation of Cadavers Using CT Imaging of Vertebrae.

Although age estimation upon death is important in the identification of unknown cadavers for forensic scientists, to the best of our knowledge, no study has examined the utility of deep neural network (DNN) models for age estimation among cadavers. We performed a postmortem computed tomography (CT) examination of 1000 and 500 male and female cadavers, respectively. These CT slices were converted into 3-dimensional images, and only the thoracolumbar region was extracted. Eighty percent of them were categorized as training datasets and the others as test datasets for both sexes. We fine-tuned the ResNet152 models using the training datasets. We conducted 4-fold cross-validation, and the mean absolute error (MAE) of the test datasets was calculated using the ensemble learning of four ResNet152 models. Consequently, the MAE of the male and female models was 7.25 and 7.16, respectively. Our study shows that DNN models can be useful tools in the field of forensic medicine.

Estimating the age at death for forensic cases using quantitative computed tomography.

Estimation of the age at death is an important task for forensic scientists. Although the correlation between age and bone mineral density is already known, including for cadavers, to our knowledge, there are no published studies on age estimation with quantitative computed tomography. Quantitative computed tomography can be used to measure bone mineral density based on the mean computed tomography value of the cancellous bone. As this value cannot be calculated in putrefied cases, we modified quantitative computed tomography to calculate the bone mineral density from regions of the bone with mean computed tomography values of 50-350 Hounsfield units. We aimed to examine whether this method could be used for age estimation. We examined 171 male and 106 female cadavers, some of which were putrefied. We performed univariate linear regression analysis for age at death and bone mineral density. The resultant intercept, slope, and root mean square error were 91.3, - 0.20 (p < 0.0001), and 11.4, respectively, for male cadavers, and 96.1, - 0.23 (p < 0.0001), and 11.0, respectively, for female cadavers. We evaluated this regression formula by using 10-fold cross-validation, resulting in a coefficient of determination of 0.33 for male cadavers and 0.42 for female cadavers. The modified quantitative computed tomography method may be of assistance in estimating age at death, even in putrefied cases.

An autopsy case of COVID-19 with a sudden death: Clinico-pathological comparison.

Autopsy was performed on a COVID-19 patient, who suddenly died despite the extensive anti-viral and anti-inflammatory therapies. Although moderate subpleural fibrosis was seen, pathology of DAD, a well-known cause for pulmonary failure, was minimum. Instead, severe hemorrhage was observed. Therapeutic effects were indicated; however, why severe hemorrhage occurred was unclear.

Utilization of oral check-up data of autopsy cases.

The maintenance of oral health contributes to the overall health of an individual. Similar to the Adult Dental Health Survey in the UK, the Survey of Dental Diseases (SDD) is being conducted in Japan. However, our routine oral examination of autopsy cases showed that the oral cavities of the autopsied bodies were unhealthy as compared to the SDD data. Therefore, this study sought to understand the actual condition of the oral cavity and to clarify the factors related to the degradation of the oral status. This was achieved by comparing the data of the SDD subjects and autopsy cases in terms of the average number of teeth lost, the average total number of caries (number of decayed, missing, or filled teeth), percentage of people with untreated teeth, and 80-20 achievement rate (percentage of people aged 80 years with 20 or more teeth). Furthermore, this study investigated the relationship between the average number of missing teeth and smoking, psychiatric disorders, and body mass index (BMI) in autopsy cases. The oral condition of all autopsy cases was worse than that of SDD subjects. The 80-20 achievement rate was observed in more than half of the SDD subjects and only in less than 20% of the autopsy cases. Moreover, the average number of missing teeth was not significantly different depending on smoking status, BMI, and presence of psychiatric disorders in the autopsy cases. Unlike the SDD volunteers, there was no bias related to the inclusion of autopsy cases. Therefore, by analysing the data of both autopsy cases and SDD subjects, it may be possible to evaluate the oral condition more realistically. This could contribute to the realisation of improved health and quality of life.