[Impact of complicated myocardial injury on the clinical outcome of severe or critically ill COVID-19 patients].

Objective: To analyze the clinical characteristics of the severe or critically ill patients with novel coronavirus pneumonia (COVID-19), and evaluate the impact of complicated myocardial injury on the prognosis of these patients. Methods: A retrospective study was conducted in 54 patients who admitted to Tongji hospital from February 3, 2020 to February 24, 2020 and met the criteria of severe or critical conditions of COVID-19. The clinical characteristics and hospital mortality rate were analyzed and compared between the patients with or without myocardial injury, which was defined with 3 times higher serum cardiac troponin value. Results: The age of the 54 patients was 68.0(59.8, 74.3) years. Among all the patients, 24 (44.4%) patients were complicated with hypertension, 13 (24.1%) with diabetes, 8 (14.8%) with coronary heart disease, and 3 (5.6%) with previous cerebral infarction. During hospitalization, 24 (44.4%) of the patients were complicated with myocardial injury and 26 (48.1%) patients died in hospital. In-hospital mortality was significantly higher in patients with myocardial injury than in patients without myocardial injury (14 (60.9%) vs. 8 (25.8%), P=0.013). Moreover, the levels of C-reactive protein (153.6 (80.3, 240.7) ng/L vs. 49.8 (15.9, 101.9) ng/L) and N-terminal pro-B-type natriuretic peptide (852.0 (400.0, 2 315.3) ng/L vs. 197.0 (115.3, 631.0) ng/L) were significantly higher than patients without myocardial injury (all P<0.01). Conclusions: Prevalence of myocardial injury is high among severe or critically ill COVID-19 patients. Severe or critically ill COVID-19 patients with myocardial injury face a significantly higher risk of in-hospital mortality. The study suggests that it is important to monitor and manage the myocardial injury during hospitalization for severe or critically ill COVID-19 patients.

Research Progress and Prospect of Machine Learning in Bone Age Assessment.

ABSTRACT: Bone age assessment has always been one of the key issues and difficulties in forensic science. With the gradual development of machine learning in many industries, it has been widely introduced to imageology, genomics, oncology, pathology, surgery and other medical research fields in recent years. The reason why the above research fields can be closely combined with machine learning, is because the research subjects of the above branches of medicine belong to the computer vision category. Machine learning provides unique advantages for computer vision research and has made breakthroughs in medical image recognition. Based on the advantages of machine learning in image recognition, it was combined with bone age assessment research, in order to construct a recognition model suitable for forensic skeletal images. This paper reviews the research progress in bone age assessment made by scholars at home and abroad using machine learning technology in recent years.

[Comparison of Three CNN Models Applied in Bone Age Assessment of Pelvic Radiographs of Adolescents].

ABSTRACT: Objective To compare the performance of three deep-learning models （VGG19, Inception-V3 and Inception-ResNet-V2） in automatic bone age assessment based on pelvic X-ray radiographs. Methods A total of 962 pelvic X ray radiographs taken from adolescents （481 males, 481 females） aged from 11.0 to 21.0 years in five provinces and cities of China were collected, preprocessed and used as objects of study. Eighty percent of these X ray radiographs were divided into training set and validation set with random sampling method and used for model fitting and hyper-parameters adjustment. Twenty percent were used as test sets, to evaluate the ability of model generalization. The performances of the three models were assessed by comparing the root mean square error （RMSE）, mean absolute error （MAE） and Bland-Altman plots between the model estimates and the chronological ages. Results The mean RMSE and MAE between bone age estimates of the VGG19 model and the chronological ages were 1.29 and 1.02 years, respectively. The mean RMSE and MAE between bone age estimates of the Inception-V3 model and the chronological ages were 1.17 and 0.82 years, respectively. The mean RMSE and MAE between bone age estimates of the Inception-ResNet-V2 model and the chronological ages were 1.11 and 0.84 years, respectively. The Bland-Altman plots showed that the mean value of differences between bone age estimates of Inception-ResNet-V2 model and the chronological ages was the lowest. Conclusion In the automatic bone age assessment of adolescent pelvis, the Inception-ResNet-V2 model performs the best while the Inception-V3 model achieves a similar accuracy as VGG19 model.

[Research Status of Postmortem MSCT Angiography in Forensic Science].

ABSTRACT: Virtual autopsy is a new technique for investigating the morphological changes of cadaveric tissues and organs by medical imaging technology. It has been widely used in the identification of causes of death. Multislice spiral computed tomography （MSCT） has become a routine inspection method in some identification institutions, although it cannot completely replace traditional autopsy, it plays a key auxiliary or substitute role in the identification of certain abnormal causes of death. Plain MSCT scan cannot visualize cadaveric vessels, but can perform cadaveric angiography to determine vascular injury or disease. This technology has entered a rapid development period in recent years, and has made a considerable progress in contrast agent, perfusion methods and imaging methods. This article summarizes several common cadaveric MSCT angiography methods, such as systemic angiography, angiography through cardiopulmonary resuscitation, targeted angiography, and angiography by cardiac puncture, and analyzes and compares the application prospects.

Molecular differentiation of three canine and feline hookworms in South China through HRM analysis.

To investigate the prevalence of canine and feline hookworms in South China, and to assess the risk of zoonotic hookworms to humans, one pair of primers (HRM-F/HRM-R) was designed to establish a high-resolution melting (HRM) method based on internal transcribed spacer 1 (ITS-1) rDNA for the detection of Ancylostoma ceylanicum, A. caninum and A. tubaeforme infection. The results showed that the HRM for the three hookworms produced different melting-curve profiles, where melting temperature (Tm) values were 84.50°C for A. ceylanicum, 82.25°C for A. caninum and 81.73°C for A. tubaeforme, respectively. The reproducibility of intra- and inter-assay melting curves was almost perfect. The lowest concentration detected was about 5.69 ×10-4 g/µl. The HRM detection results from 18 canine and feline hookworm samples were in complete accordance with their sequencing results. The HRM method was more sensitive than the polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) technique in the detection of 98 clinical samples. It is concluded that the HRM method can differentiate between A. ceylanicum, A. caninum, A. tubaeforme and their mixed infections, which may provide important technical support for the zoonotic risk assessment and molecular epidemiological survey of canine and feline hookworms.

Research Progress on Postmortem Changes of Computed Tomography Imaging Characteristics on Corpses.

ABSTRACT: Postmortem changes on corpses appear immediately after death, and can transform the original structure characteristics of the corpse to different degrees as well as show specific changes on computed tomography （CT） images, sometimes with false positives and false negatives, influencing the identification of injuries or diseases. This paper systematically summarizes the postmortem changes of computed tomography imaging characteristics on corpses, to further expand the application of virtopsy in the practices of forensic pathology identification, and provide reference for the identification of injuries, diseases and changes after normal death.

The mitochondrial genome of Ancylostoma tubaeforme from cats in China.

Ancylostoma tubaeforme may infect canids, felids and humans, and pose a potential risk to public health. Polymerase chain reaction (PCR) techniques were used to amplify the complete mitochondrial (mt) genome sequence of A. tubaeforme from cats and to analyse its sequence characteristics after molecular identification based on the internal transcribed spacer ITS1+ sequence. The results show that the complete mt genome sequence (GenBank accession number KY070315) of A. tubaeforme from cats was 13,730 bp in length, including 12 protein-coding genes, 22 transfer RNA (tRNA) genes, two ribosomal RNA (rRNA) genes, two non-coding regions and an AT-rich region. The nucleotide content of A and T was 77.93%, biased toward A and T. Twelve protein-coding genes used ATT, TTG and GTG as initiation codons, and TAA, TAG, TA and T as termination codons. The length of the 22 tRNA genes ranged from 52 to 62 bp, their predicted secondary structures were D loops and V loops. The lengths of the two rRNAs were 958 and 697 bp. Phylogenetic analyses showed that A. tubaeforme from cats was in the lineage of Ancylostoma, having a close phylogenetic relationship with A. caninum. This study reports for the first time the mt genome of A. tubaeforme from cats in China, which could enhance the mt genome database of Ancylostomatidae nematodes, and it offers the scientific basis for further studies in the genetic diversity of hookworms among different hosts.

The mitochondrial genome of Dipetalonema gracile from a squirrel monkey in China.

Dipetalonema gracile is a common parasite in squirrel monkeys (Saimiri sciureus), which can cause malnutrition and progressive wasting of the host, and lead to death in the case of massive infection. This study aimed to identify a suspected D. gracile worm from a dead squirrel monkey by means of molecular biology, and to amplify its complete mitochondrial genome by polymerase chain reaction (PCR) and sequence analysis. The results identified the worm as D. gracile, and the full length of its complete mitochondrial genome was 13,584 bp, which contained 22 tRNA genes, 12 protein-coding genes, two rRNA genes, one AT-rich region and one small non-coding region. The nucleotide composition included A (16.89%), G (20.19%), T (56.22%) and C (6.70%), among which A + T = 73.11%. The 12 protein-coding genes used TTG and ATT as start codons, and TAG and TAA as stop codons. Among the 22 tRNA genes, only trnS1AGN and trnS2UCN exhibited the TΨC-loop structure, while the other 20 tRNAs showed the TV-loop structure. The rrnL (986 bp) and rrnS (685 bp) genes were single-stranded and conserved in secondary structure. This study has enriched the mitochondrial gene database of Dipetalonema and laid a scientific basis for further study on classification, and genetic and evolutionary relationships of Dipetalonema nematodes.

Assessment of the Original Height of L1~2 after Vertebral Compression Fracture.

OBJECTIVES: To explore the assessment method of original height of L1-2 after vertebral compression fracture and its application value in forensic clinical practice. METHODS: A total of 154 normal thoracic and lumbar X-ray films were collected, and 140 cases were used as experimental group while 14 cases as validation group. The heights of anterior （Ha） and posterior （Hp） vertebral body of T12-L3 vertebrae in each X-ray image were measured. In the experimental group, the correlation analysis between HaL1 and HaT12, HpT12, HpL1, HaL2 and HpL2 was carried out, and regression equation was established via fitting. The correlation analysis between HaL2 and HaL1, HpL1, HpL2, HaL3, HpL3 was performed, and the regression equation was also established via fitting. The difference between the predicted and measured values of HaL1 and HaL2 in validation group was compared. RESULTS: In the 140 normal subjects, HaL1 （y1） was well correlated with HaT12 （x1） and HaL2（x2）, and the multiple linear regression equation was y1=2.545+0.423 x1+0.486 x2 （determining coefficient R²=0.712, P<0.05; F=169.206, P<0.05）. There was no significant difference between the predicted and actual measured values of HaL1 in the validation group （ P>0.05）. HaL2 （y2） was well correlated with HaL1 （x3） and HaL3 （x4）, and the multiple linear regression equation was y2=4.354+0.530 x3+0.349 x4 （determining coefficient R²=0.689, P<0.05; F=151.575, P<0.05）. There was no significant difference between the predicted and actual measured values of HaL2 in the validation group （ P>0.05）. CONCLUSIONS: It is more appropriate to evaluate the original height of L1 or L2 single vertebrae by comparing with the height of the anterior edge of the upper and lower adjacent vertebral bodies.

[Automated Assessment for Bone Age of Left Wrist Joint in Uyghur Teenagers by Deep Learning].

OBJECTIVES: To realize the automated bone age assessment by applying deep learning to digital radiography （DR） image recognition of left wrist joint in Uyghur teenagers, and explore its practical application value in forensic medicine bone age assessment. METHODS: The X-ray films of left wrist joint after pretreatment, which were taken from 245 male and 227 female Uyghur nationality teenagers in Uygur Autonomous Region aged from 13.0 to 19.0 years old, were chosen as subjects. And AlexNet was as a regression model of image recognition. From the total samples above, 60％ of male and female DR images of left wrist joint were selected as net train set, and 10％ of samples were selected as validation set. As test set, the rest 30％ were used to obtain the image recognition accuracy with an error range in ±1.0 and ±0.7 age respectively, compared to the real age. RESULTS: The modelling results of deep learning algorithm showed that when the error range was in ±1.0 and ±0.7 age respectively, the accuracy of the net train set was 81.4% and 75.6% in male, and 80.5% and 74.8% in female, respectively. When the error range was in ±1.0 and ±0.7 age respectively, the accuracy of the test set was 79.5% and 71.2% in male, and 79.4% and 66.2% in female, respectively. CONCLUSIONS: The combination of bone age research on teenagers' left wrist joint and deep learning, which has high accuracy and good feasibility, can be the research basis of bone age automatic assessment system for the rest joints of body.

[Advantages and Application Prospects of Deep Learning in Image Recognition and Bone Age Assessment].

Deep learning and neural network models have been new research directions and hot issues in the fields of machine learning and artificial intelligence in recent years. Deep learning has made a breakthrough in the applications of image and speech recognitions, and also has been extensively used in the fields of face recognition and information retrieval because of its special superiority. Bone X-ray images express different variations in black-white-gray gradations, which have image features of black and white contrasts and level differences. Based on these advantages of deep learning in image recognition, we combine it with the research of bone age assessment to provide basic datum for constructing a forensic automatic system of bone age assessment. This paper reviews the basic concept and network architectures of deep learning, and describes its recent research progress on image recognition in different research fields at home and abroad, and explores its advantages and application prospects in bone age assessment.

[Comparison of Measurement Methods of Exophthalmos and Its Forensic Significance].

OBJECTIVES: To compare the differences between Hertel exophthalmometry and CT on exophthalmos measurement, and explore its practical application value in forensic medicine. METHODS: Fifty-six normal individuals without eye injuries or diseases were selected as the control group. The absolute values of exophthalmos were measured in the standardized CT image workstation. The difference of binocular exophthalmos was compared in normal group. Forty-seven cases with unilateral orbital fracture were divided into injured eye group and normal eye group according to the presence of binocular orbital fractures. The differences of the absolute values of eophthalmos from the same eye and the relative values of eophthalmos between two eyes for same person measured by Hertel exophthalmometry and CT were analyzed. RESULTS: There was no statistical difference of exophthalmos between the normal eyes measured by CT method. In the normal eye group, the absolute value of exophthalmos measured by CT method was （16.66±5.41） mm, which was （16.16±4.45） mm when measured by the Hertel exophthalmometry method. There was no statistical difference between two groups （P>0.05）. In the injured eye group, there was statistical difference between the absolute values of exophthalmos measured by two measurement methods （ P<0.05）. There was no statistical difference of the relative values of exophthalmos between the injured eyes and the normal eyes by two measurement methods （P>0.05）. CONCLUSIONS: CT method has a good consistency with Hertel exophthalmometry, which can be applied into the practice of medicolegal expertise.