Correlations between Sagittal Parameters and Functional Scores in 65-Year-Old Osteoporotic Females with Vertebral Body Fracture under Low-Energy Mechanism.

Background: Vertebral compression fractures (VCFs) are the most common fragility fractures associated with low-energy injury mechanisms in postmenopausal women with osteoporosis. No clear consensus is currently available on the optimal timing for surgical intervention in specific cases. Methods: This study examined the correlations between sagittal parameters, functional scores, and the appropriate timing for surgical intervention during the recovery stage in patients with osteoporosis with thoracolumbar (TL) vertebral body fractures. A total of 161 women aged ≥ 65 years with osteoporosis were included in the study. Spinal sagittal parameters from standing plain films and functional outcomes as the Oswestry disability index (ODI) and the visual analogue scale (VAS) were collected. Results: We found that TL junction Cobb angle was significantly correlated with ODI > 30 (p < 0.001) and VAS > 6 (p < 0.001) and the discriminative values for predicting ODI > 30 and VAS > 6 were a TL kyphotic angle of 14.5° and 13.5°, respectively. Among women aged ≥ 65 years with osteoporosis, the back pain and functional impairment observed within 6 months following a compression fracture are associated with a greater TL kyphosis angle. Conclusions: This suggests that a more proactive approach may be necessary when addressing the conditions of these patients.

Role of metabolomic profile as a potential marker to discriminate membranous nephropathy from IgA nephropathy.

BACKGROUND: Membranous nephropathy (MN) and IgA nephropathy (IgAN) are the most common primary glomerulopathies worldwide. The systemic metabolic changes in the progression of MN and IgAN are not fully understood. METHODS: A total of 87 and 70 patients with MN and IgAN, respectively, and 30 healthy controls were enrolled in this study. Untargeted metabolomics was performed to explore the differential metabolites and metabolic pathways in the early stage of MN and IgAN. To judge the diagnostic ability of biomarkers, receiver operating characteristic curve analysis (ROC) were performed. RESULTS: Principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS-DA) suggested that patients with MN and IgAN showed an obvious separation trend from the healthy controls. In addition, 155 and 148 metabolites were identified to be significantly altered in the MN and IgAN groups, respectively. Of these, 70 metabolites were markedly altered in both disease groups; six metabolites, including L-tryptophan, L-kynurenine, gamma-aminobutyric acid (GABA), indoleacetaldehyde, 5-hydroxyindoleacetylglycine, and N-alpha-acetyllysine, showed the opposite tendency. The most affected metabolic pathways included the amino acid metabolic pathways, citrate cycle, pantothenate and CoA biosynthesis, and hormone signaling pathways. CONCLUSIONS: Substantial metabolic disorders occurred during the progression of MN and IgAN. L-tryptophan, L-kynurenine, GABA, indoleacetaldehyde, 5-hydroxyindoleacetylglycine, and N-alpha-acetyllysine may show potential as biomarkers for the identification of MN and IgAN.

Deciphering Intermediate Nugent scores: Utility of Real-time PCR for Bacterial Vaginosis diagnosis.

PURPOSE: To optimize real-time PCR assays for diagnosis of Bacterial Vaginosis (BV) and determine cut-off loads by ROC analysis for Gardnerella vaginalis, Atopobium vaginae and Lactobacillus spp. as compared to Nugent scoring (Gold standard) in clinical samples. RESULTS: Out of 125 women, 34 were positive, 26 intermediate and 65 negative for BV by Nugent scoring. All three real-time PCR assays were found to be highly sensitive & specific and AUC suggested excellent diagnostic accuracy. An optimal cut-off was >9.45 × 103 copies/ ml, >3.34 × 103 copies/ ml & ≤ 18.63 × 103 copies/ ml for G. vaginalis, A. vaginae and Lactobacillus spp. respectively, in BV positives. Gram staining and qPCR were discordant only in patients with intermediate scores (n = 26) where qPCR identified 15 (57.69%) as positive and 11 (42.3%) as negative. CONCLUSION: PCR-based molecular BV diagnosis is more accurate and can be used for deciphering intermediate Nugent scores.

Nonparametric bootstrap methods for interval estimation of the area under the ROC curve with correlated diagnostic test data: application to whole-virus ELISA testing in swine.

Developing and evaluating novel diagnostic assays are crucial components of contemporary diagnostic research. The receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC) are frequently used to evaluate diagnostic assays' performance. The variation in AUC estimation can be quantified nonparametrically using resampling methods, such as bootstrapping, and then used to construct interval estimation for the AUC. When multiple observations are observed from the same subject, which is very common in veterinary diagnostic tests evaluation experiments, a traditional bootstrap-based method can fail to provide valid interval estimations of AUC. In particular, the traditional method does not account for the correlation among data observations and could result in interval estimation that fails to cover the true AUC adequately at the desired confidence level. In this paper, we proposed two novel methods to calculate the confidence interval of the AUC for correlated diagnostic test data based on cluster bootstrapping and hierarchical bootstrapping, respectively. Our simulation studies showed that both proposed methods had adequate coverage probabilities which were higher than the existing traditional method when there were intra-subject correlations. We also discussed applying the proposed methods to evaluate a novel whole-virus ELISA (wv-ELISA) diagnostic assay in detecting porcine parainfluenza virus type-1 antibodies in swine serum.

Prevalence of Malnutrition and the Value of Predicting Pancreatic Fistula in Patients with Laparoscopic Pancreatoduodenectomy.

Background: Pancreatoduodenectomy is a standard surgical procedure for periampullary tumors. With recent improvements in perioperative management, postoperative mortality has decreased significantly in recent years; however, postoperative pancreatic fistula (POPF) is still one of the most prevalent and dangerous complications. The purpose of this study was to analyze the prevalence of malnutrition and the value of predicting POPF in patients with laparoscopic pancreatoduodenectomy (LPD). Methods: We retrospectively analyzed the perioperative data of 747 patients undergoing LPD in the Department of Hepatobiliary and Pancreatic Surgery, The First Hospital of Jilin University, China. Simultaneously, we analyzed the prevalence rate of malnutrition with three different nutritional assessment scores and explored the independent risk variables for POPF to identify potential predictive value. Results: Malnutrition was observed in 20.1% of patients with the prognostic nutritional index (PNI), 85.0% of patients with the controlling nutritional status (CONUT) score, and 73.1% of patients with the NRI score. Univariate and multivariate analyses all showed that the risk factors for POPF were pancreatic texture, pancreatic duct diameter, abdominal infection, body mass index (BMI), nomogram-revised risk index (NRI), and PNI. The receiver operating characteristic curve indicated that the BMI/PNI ratio was capable of predicting the occurrence of clinical POPF following LPD, with an area under the curve of 0.708. Conclusions: Compared with no malnourished patients, malnutrition is associated with a higher risk of POPF among patients with LPD. In addition, the BMI/PNI ratio has some predictive value in the development of POPF following LPD.

Two-phase stratified sampling and analysis for predicting binary outcomes.

The two-phase study design is a cost-efficient sampling strategy when certain data elements are expensive and, thus, can only be collected on a sub-sample of subjects. To date guidance on how best to allocate resources within the design has assumed that primary interest lies in estimating association parameters. When primary interest lies in the development and evaluation of a risk prediction tool, however, such guidance may, in fact, be detrimental. To resolve this, we propose a novel strategy for resource allocation based on oversampling cases and subjects who have more extreme risk estimates according to a preliminary model developed using fully observed predictors. Key to the proposed strategy is that it focuses on enhancing efficiency regarding estimation of measures of predictive accuracy, rather than on efficiency regarding association parameters which is the standard paradigm. Towards valid estimation and inference for accuracy measures using the resultant data, we extend an existing semiparametric maximum likelihood ethod for estimating odds ratio association parameters to accommodate the biased sampling scheme and data incompleteness. Motivated by our sampling design, we additionally propose a general post-stratification scheme for analyzing general two-phase data for estimating predictive accuracy measures. Through theoretical calculations and simulation studies, we show that the proposed sampling strategy and post-stratification scheme achieve the promised efficiency improvement. Finally, we apply the proposed methods to develop and evaluate a preliminary model for predicting the risk of hospital readmission after cardiac surgery using data from the Pennsylvania Health Care Cost Containment Council.

Development of a model for early differentiation of adenovirus pneumonia from Mycoplasma pneumoniae pneumonia.

Background: Adenovirus pneumonia (AVP) and Mycoplasma pneumoniae pneumonia (MPP) have similar clinical manifestations such as a high prevalence of lung consolidation, making the differential diagnosis difficult before the etiology is reported. This study aimed to compare AVP and MPP, and to build a predictive model to differentiate them early. Methods: We selected 198 cases of AVP and 876 cases of MPP. Clinical manifestations, computed tomography (CT) features, and biomarkers were compared. A logistic regression model was built to predict AVP. The area under the curve (AUC) of the receiver-operating characteristic was calculated to evaluate the discriminant ability of the prediction model. Results: Patients in the AVP group were mainly infants and toddlers, while the MPP group had more pre-school age children. The rate of hypoxemia and severe pneumonia was 3- and 11-times higher, respectively, in the AVP group than in the MPP group (5.6% vs. 1.8%, 27.8% vs. 2.5%, P<0.01). The proportion of patients with a Pediatric Logistic Organ Dysfunction-2 score ≥2 was 10 times higher in the AVP group than in the MPP group (17.4% vs. 1.7%, P<0.01). Bilateral pneumonia was present in 90.2% of the AVP group. Biomarkers, such as interleukin (IL)-2 receptor, IL-10 and lactic dehydrogenase (LDH), were considerably higher in the AVP group than in the MPP group (P<0.01). The predictive model included eight variables, namely: age, severe pneumonia, bilateral pneumonia, ground-glass attenuation, consolidation, atelectasis, C-reactive protein, and LDH. The AUC was 86.6%. Conclusions: Compared with MPP, AVP affects younger children, presents a more severe respiratory tract involvement, results in a larger range of lung lesions, and is associated with higher inflammatory biomarkers. Our predictive model includes a combination of clinical features, imaging findings, and biomarkers. It may help pediatricians in the early differentiation of AVP from MPP.

Determining the SARS-CoV-2 Anti-Spike Cutoff Level Denoting Neutralizing Activity Using Two Commercial Kits.

BACKGROUND: The viral neutralization assay is the gold standard to estimate the level of immunity against SARS-CoV-2. This study analyzes the correlation between the quantitative Anti-SARS-CoV-2 QuantiVac ELISA (IgG) and the NeutraLISA neutralization assay. METHODS: 650 serum samples were tested for both SARS-CoV-2 anti-spike (anti-S) immunoglobulin G (IgG) and neutralizing antibodies (nAbs) using kits by EUROIMMUN, Germany. RESULTS: There was a significant correlation between levels of anti-S and nAbs (Spearman's rho = 0.913). Among the positive samples for anti-S, 77.0% (n = 345) were positive for nAbs. There was a substantial agreement between anti-S and nAbs (Cohen's kappa coefficient = 0.658; agreement of 83.38%). Considering NeutraLISA as a gold standard, anti-S had a sensitivity of 98.57%, specificity of 65.66%, NPV of 97.5%, and PPV of 77.0%. When the anti-S titer was greater than 18.1 RU/mL (57.9 BAU/mL), nAbs were positive, with a sensitivity of 90.0% and specificity of 91%. CONCLUSIONS: A titer of SARS-CoV-2 anti-S IgG can be correlated with levels of nAbs.

Correlation between the Control of Blood Glucose Level and HbA1C and the Incidence of Surgical Site Infection after Emergent Surgery for the Lower Limb Fracture among Type II DM Patients Aged More Than 50 Years Old.

This is the first study focusing on perioperative blood glycemic monitoring for the incidence of surgical site infection (SSI) among patients with type II DM (T2DM) during the 1-year follow-up after emergent orthopedic surgery. We retrospectively collected the data of 604 patients who had received surgery for unilateral lower limb traumatic fracture from January 2011 to January 2021, including 215 men and 389 women with a mean age of 71.21 and a mean BMI of 25.26. In total, 84 (13.9%) of them developed SSI during the 1-year follow-up. Higher preoperative and postoperative -3-month hemoglobin A1c (HbA1c) and AC blood glucose and the presence of rheumatoid arthritis were all associated with increased rates of SSI. The thresholds for predicting SSI were the following: (1) preoperative HbA1c > 7.850% (area under curve [AUC] = 0.793); (2) postoperative HbA1c > 6.650% (AUC = 0.648); (3) preoperative AC blood glucose > 130.50 mg/dL (AUC = 0.773); and (4) postoperative AC blood glucose > 148.5 mg/dL (AUC = 0.709) by receiver-operating characteristic curve method. These findings may provide a useful control guideline for patients with T2DM older than 50 years old and who received surgery for a lower limb fracture in the prevention of postoperative SSI.

Characterizing the diagnostic sensitivity and specificity of pain biomarkers in cattle using receiver operating characteristic curves.

Biomarkers are used to assess pain and analgesic drug efficacy in livestock. However, often the diagnostic sensitivity and specificity of these biomarkers for different painful conditions over time have not been described. Receiver operating characteristic (ROC) curves are graphical plots that illustrate the diagnostic ability of a test as its discrimination threshold is varied. The objective of this analysis was to use area under the curve (AUC) values derived from ROC analysis to characterize the predictive value of potential pain biomarkers at specific time points following a painful stimulus. The biomarkers included in the analysis were plasma cortisol, salivary cortisol, hair cortisol, infrared thermography (IRT), mechanical nociceptive threshold (MNT), substance P, kinematic gait analysis, and a visual analog scale for pain. A total of 7,992 biomarker outcomes collected from 7 pain studies involving pain associated with castration, dehorning, lameness, and abdominal surgery were included in the analysis. Each study consisted of 3 treatments: uncontrolled pain (tissue damage), no pain (handled controls), and analgesic use (tissue damage, administered a nonsteroidal anti-inflammatory drug). Results comparing analgesic effects to uncontrolled pain consistently yielded AUC values >0.7 (95% confidence interval: 0.40 to 0.99) for plasma cortisol (time points: 1.5, 2, 3, 4, 6, and 8 h), hair cortisol (time point: 62 d), and IRT (time point: 72 h). Results comparing analgesic effects to uncontrolled pain consistently yielded AUC values <0.7 (95% confidence interval: 0.28 to 0.90) for salivary cortisol (6, 13, 20, 34, 48, and 62 d); MNT (6, 25, and 49 h); substance P (1, 2, 3, 4, 6, 8, 12, 18, 24, 48, 72, 96, 120, 144, 312, 480, 816, 1,152, and 1,488 h); kinematic gait analysis including area (8, 16, 48, 72, 96, and 120 h), force (8, 16, 24, 48, 72, 96, and 120 h), and pressure (8, 16, 24, 48, 72, 96, and 120 h); and a visual analog scale for pain (1, 2, 3, 4, 5, and 6 d). These results indicate that ROC analysis can be used to characterize the predictive value of pain biomarkers and provide new knowledge on the diagnostic accuracy of pain biomarkers within this data set. This analysis, using data from 7 studies, was a preliminary approach to identify biomarkers and collection time points that could inform additional analytical approaches or meta-analyses with larger sample sizes, which are needed to further validate these hypotheses and conclusions.

Evaluation of deep learning techniques for identification of sarcoma-causing carcinogenic mutations.

The abnormal growth of human healthy cells is called cancer. One of the major types of cancer is sarcoma, mostly found in human bones and soft tissue cells. It commonly occurs in children. According to a survey of the United States of America, there are more than 17,000 sarcoma patients registered each year which is 15% of all cancer cases. Recognition of cancer at its early stage saves many lives. The proposed study developed a framework for the early detection of human sarcoma cancer using deep learning Recurrent Neural Network (RNN) algorithms. The DNA of a human cell is made up of 25,000 to 30,000 genes. Each gene is represented by sequences of nucleotides. The nucleotides in a sequence of a driver gene can change which is termed as mutations. Some mutations can cause cancer. There are seven types of a gene whose mutation causes sarcoma cancer. The study uses the dataset which has been taken from more than 134 samples and includes 141 mutations in 8 driver genes. On these gene sequences RNN algorithms Long and Short-Term Memory (LSTM), Gated Recurrent Units and Bi-directional LSTM (Bi-LSTM) are used for training. Rigorous testing techniques such as Self-consistency testing, independent set testing, 10-fold cross-validation test are applied for the validation of results. These validation techniques yield several metrics such as Area Under the Curve (AUC), sensitivity, specificity, Mathew's correlation coefficient, loss, and accuracy. The proposed algorithm exhibits an accuracy of 99.6% with an AUC value of 1.00.

Assessment of Unresolved/Disorganized State of Mind in Relation to Attachment: A ROC Curve Study Using the Adult Attachment Interview and the Measure of Parental Style.

Objective: The main objective of the present study was to use the Receiver Operating Characteristic (ROC) curve analysis to identify cut-off points for a self-report measure assessing parental style, i.e., the Measure of Parental Style (MOPS), that are able to discriminate individuals with disorganized internal working models (IWMs) of attachment with adequate accuracy, in terms of sensitivity and specificity. Establishing cut-off points for the MOPS could provide clinicians and researchers with a valuable tool to investigate the role of disorganized IWMs as a link between parental styles and mental health. Method: A sample of 90 university students (mean age = 21.21 ± 2.05, females = 66) was enrolled in the study. We used the Adult Attachment Interview (AAI) to assess disorganized IWM and the MOPS to assess parental styles. Subsequently, we used ROC curve analysis to pursue the objective of the study. Results: The ROC curve analysis showed that the MOPS total score (i.e., the combination of maternal and paternal dimensions) was able to discriminate individuals with disorganized IWMs from individuals with organized IWMs (AUC= 0.77). Specifically, a score ≥ 25 (Youden index= 0.497) categorized individuals with a sensitivity of 0.69 (69% of participants with disorganized IWMs were correctly identified) and a specificity of 0.81 (19% of participants were incorrectly identified as having disorganized IWMs). Conclusions: Although the AAI has demonstrated high psychometric properties for assessing attachment representations in adulthood, its use is difficult when studies with large samples are to be conducted. As an alternative to the AAI, the MOPS can be used in studies with large populations, but no cut-off has yet been proposed. Here, we have identified cut-off points for the MOPS that are capable of detecting disorganized IWMs of attachment with adequate accuracy, and we suggest that this self-report is a useful brief instrument for detecting disorganized IWMs when time constraints prevent the use of the AAI (e.g., in studies with large samples or epidemiological studies).

Automatic Identification of Brain Injury Mechanism Based on Deep Learning.

OBJECTIVES: To apply the convolutional neural network (CNN) Inception_v3 model in automatic identification of acceleration and deceleration injury based on CT images of brain, and to explore the application prospect of deep learning technology in forensic brain injury mechanism inference. METHODS: CT images from 190 cases with acceleration and deceleration brain injury were selected as the experimental group, and CT images from 130 normal brain cases were used as the control group. The above-mentioned 320 imaging data were divided into training validation dataset and testing dataset according to random sampling method. The model classification performance was evaluated by the accuracy rate, precision rate, recall rate, F1-value and AUC value. RESULTS: In the training process and validation process, the accuracy rate of the model to classify acceleration injury, deceleration injury and normal brain was 99.00% and 87.21%, which met the requirements. The optimized model was used to test the data of the testing dataset, the result showed that the accuracy rate of the model in the test set was 87.18%, and the precision rate, recall rate, F1-score and AUC of the model to recognize acceleration injury were 84.38%, 90.00%, 87.10% and 0.98, respectively, to recognize deceleration injury were 86.67%, 72.22%, 78.79% and 0.92, respectively, to recognize normal brain were 88.57%, 89.86%, 89.21% and 0.93, respectively. CONCLUSIONS: Inception_v3 model has potential application value in distinguishing acceleration and deceleration injury based on brain CT images, and is expected to become an auxiliary tool to infer the mechanism of head injury.

Abnormal Voxel-Based Degree Centrality in Patients With Postpartum Depression: A Resting-State Functional Magnetic Resonance Imaging Study.

Postpartum depression (PPD) is a major public health concern with significant consequences for mothers, their children, and their families. However, less is known about its underlying neuropathological mechanisms. The voxel-based degree centrality (DC) analysis approach provides a new perspective for exploring the intrinsic dysconnectivity pattern of whole-brain functional networks of PPD. Twenty-nine patients with PPD and thirty healthy postpartum women were enrolled and received resting-state functional magnetic resonance imaging (fMRI) scans in the fourth week after delivery. DC image, clinical symptom correlation, and seed-based functional connectivity (FC) analyses were performed to reveal the abnormalities of the whole-brain functional network in PPD. Compared with healthy controls (HCs), patients with PPD exhibited significantly increased DC in the right hippocampus (HIP.R) and left inferior frontal orbital gyrus (ORBinf.L). The receiver operating characteristic (ROC) curve analysis showed that the area under the curve (AUC) of the above two brain regions is all over 0.7. In the seed-based FC analyses, the PPD showed significantly decreased FC between the HIP.R and right middle frontal gyrus (MFG.R), between the HIP.R and left median cingulate and paracingulate gyri (DCG.L), and between the ORBinf.L and the left fusiform (FFG.L) compared with HCs. The PPD showed significantly increased FC between the ORBinf.L and the right superior frontal gyrus, medial (SFGmed.R) compared with HCs. Mean FC between the HIP.R and DCG.L positively correlated with EDPS scores in the PPD group. This study provided evidence of aberrant DC and FC within brain regions in patients with PPD, which was associated with the default mode network (DMN) and limbic system (LIN). Identification of these above-altered brain areas may help physicians to better understand neural circuitry dysfunction in PPD.

Combined application of pharyngeal volume and minimal cross-sectional area may be helpful in screening persons suspected of obstructive sleep apnea (OSA).

BACKGROUND: Obstructive sleep apnea (OSA) is a common disease that seriously affects human health and daily life. However, the gold standard for its diagnosis, polysomnography (PSG), is expensive resulting in inadequate diagnosis of this disease in primary clinics. Therefore, a simple and rapid method for initial screening for OSA is needed. Acoustic pharyngometry (APh) is an FDA-approved noninvasive method that is gradually being applied to screening for OSA. MATERIALS AND METHODS: In this study, we applied analysis with receiver operating characteristic (ROC) curves to explore how APh may play a greater role in the screening of subjects with suspected OSA. Patients admitted into the departments of otolaryngology at our hospital from March 2017 to May 2019 were recruited into the study. All subjects underwent PSG monitor and were separated into two groups according to the apnea-hypopnea index (AHI) from the PSG results: OSA group (AHI ≥ 5) and control group (AHI < 5). APh measurements and other indicators of the subjects, including age, height, and weight; Epworth Sleepiness Scale (ESS) score; and the pharynx examination, including the degree of tonsil enlargement and tongue hypertrophy, were also be recorded. RESULTS: The t-test results showed that almost all indicators except age and height have significant differences between the OSA group and control group. Subjects with OSA had greater weight, BMI, ESS, higher degree of tonsil enlargement, and tongue hypertrophy, while they had smaller minimal cross-sectional area (mCSA) and pharyngeal volume than the subjects in control group. The correlation analysis revealed that pharyngeal volume and mCSA were two helpful indicators to screen for OSA. Furthermore, we established the ROC curve and calculated the combining predictors (combining predictors = pharyngeal volume + mCSA * (- 2.347)/(- 0.225)). The area under the ROC curve (AUC) of combining predictors was 0.917 (95% CI 0.842-0.991, P < 0.001), which was higher than combinations of other two independent indicators. The cutoff point of combining predictors was found to be 59.84 (AUC = 0.917, sensitivity = 0.80, 1-specificity = 0.06, P < 0.001). CONCLUSIONS: These findings suggest that APh is a simple, rapid, and economical detection method which may be useful in screening for OSA, especially in communities and primary clinics where PSG cannot be performed.

Development of Plantar Force Assessment Model for Patients with Patellofemoral Pain / 医用生物力学

Objective To develop plantar force model of patellofemoral pain (PFP), so as to provide theoretical references for the assessment of PFP rehabilitation. Methods The case-control study was conducted, and a total of 126 patients with PFP and 126 healthy controls matched by gender and age were enrolled in the study. The participants were tested for plantar force and pressure during level walking, and twelve plantar regions were divided and recorded. Whether the participants suffered PFP was analyzed as dependent variable, meanwhile the peak force and peak pressure in 12 plantar regions of participants at selected speed during level walking were analyzed as independent variables. Conditional logistic regression (CLR) equations of peak force and peak pressure with PFP were established, respectively. The receiver-operating characteristic (ROC) curve of the corresponding equations was derived, and the area under ROC curve was calculated to analyzed the validity of different equations on PFP assessment. Results The CLC equation of peak force in 12 plantar regions of the participants with FFP was constructed, and only peak force of lateral heel was in the equation. The CLC equation of peak pressure in each plantar region included medial heel, midfoot, 1st and 2nd metatarsals. Meanwhile, the area under ROC curve of the pressure equation was larger than that of the force equation. Conclusions Peak force and pressure at different plantar regions can be used to assess PFP during level walking, and peak pressure is more effective for assessment.

Automatic Identification of Brain Injury Mechanism Based on Deep Learning / 法医学杂志

OBJECTIVES@#To apply the convolutional neural network (CNN) Inception_v3 model in automatic identification of acceleration and deceleration injury based on CT images of brain, and to explore the application prospect of deep learning technology in forensic brain injury mechanism inference.@*METHODS@#CT images from 190 cases with acceleration and deceleration brain injury were selected as the experimental group, and CT images from 130 normal brain cases were used as the control group. The above-mentioned 320 imaging data were divided into training validation dataset and testing dataset according to random sampling method. The model classification performance was evaluated by the accuracy rate, precision rate, recall rate, F1-value and AUC value.@*RESULTS@#In the training process and validation process, the accuracy rate of the model to classify acceleration injury, deceleration injury and normal brain was 99.00% and 87.21%, which met the requirements. The optimized model was used to test the data of the testing dataset, the result showed that the accuracy rate of the model in the test set was 87.18%, and the precision rate, recall rate, F1-score and AUC of the model to recognize acceleration injury were 84.38%, 90.00%, 87.10% and 0.98, respectively, to recognize deceleration injury were 86.67%, 72.22%, 78.79% and 0.92, respectively, to recognize normal brain were 88.57%, 89.86%, 89.21% and 0.93, respectively.@*CONCLUSIONS@#Inception_v3 model has potential application value in distinguishing acceleration and deceleration injury based on brain CT images, and is expected to become an auxiliary tool to infer the mechanism of head injury.

Predictive value of the initial MELD score and its derivative scores for early survival rate after liver transplantation in patients with liver failure / 器官移植

Objective To explore the predictive values of the initial model for end-stage liver disease (MELD) score, MELD combined with serum sodium (MELD-Na) score and MELD combined with serum lactic acid (MELD-Lac) score for early survival rate after liver transplantation in patients with liver failure. Methods Clinical data of 135 recipients undergoing liver transplantation for liver failure were retrospectively analyzed. All patients were divided into the early survival group (n=110) and early death group (n=25) according to the survival at postoperative 28 d. Clinical data were compared between two groups. The optimal cut-off values of MELD, MELD-Na and MELD-Lac scores for predicting early survival rate after liver transplantation in patients with liver failure were determined by the receiver operating characteristic (ROC) curve. The predictive values of different scores for early survival rate after liver transplantation in patients with liver failure were evaluated. Results Significant differences were observed in the initial MELD, MELD-Na and MELD-Lac scores after liver transplantation between two groups (all P < 0.05). For the initial MELD, MELD-Na and MELD-Lac scores in predicting early survival rate after liver transplantation in patients with liver failure, the AUC were 0.653 [95% confidence interval (CI) 0.515-0.792], 0.648 (95%CI 0.514-0.781) and 0.809 (95%CI 0.718-0.900), the optimal cut-off values were 18.09, 18.09 and 19.97, Youden's indexes were 0.398, 0.380 and 0.525, the sensitivity was 0.680, 0.680 and 0.840, and the specificity was 0.720, 0.700 and 0.690, respectively. The AUC of MELD-Lac score was higher than those of MELD and MELD-Na scores, and the differences were statistically significant (both P < 0.05). Conclusions Compared with the initial MELD and MELD-Na scores after liver transplantation, the initial MELD-Lac score is a more reliable index for predicting early survival rate after liver transplantation in patients with liver failure.

Prediction of post-induction hypotension in young adults using ultrasound-derived inferior vena cava parameters: An observational study.

BACKGROUND AND AIMS: Inferior vena cava (IVC) diameter and its respiratory variability have been shown to predict post-induction hypotension with high specificity in a mixed population of patients. We assessed whether these parameters could be as reliable in healthy adult patients as in a mixed patient population. METHODS: In the present prospective observational study, 110 patients of either sex, aged between 18 and 50 years, belonging to American Society of Anesthesiologists class I and II, fasted as per the institutional protocol and scheduled for elective surgery under general anaesthesia were enroled. Prior to induction, ultrasound examination of IVC was done and variation in IVC diameter with respiration was assessed. Maximum and minimum IVC diameters [(dIVCmax) and (dIVCmin), respectively] over a single respiratory cycle were measured and collapsibility index (CI) was calculated. Vitals were recorded just before induction and at every minute after induction for 10 min. Episodes of hypotension (mean arterial pressure [MAP] <65 mmHg or fall in MAP >30% from baseline) during the observation period were recorded. The receiver operating characteristic (ROC) curve was constructed for determining optimum cut-off with sensitivity and specificity of IVC diameters and CI for development of hypotension. RESULTS: IVC was not visualised in 22 patients. Out of the remaining 88 patients, 17 (19.3%) patients developed hypotension after induction. The dIVCmax, dIVCmin and CI were comparable between patients who developed and who did not develop hypotension. The area under curve of ROC for CI, dIVCmax and dIVCmin was 0.51, 0.55 and 0.52, respectively, with optimum cut-off value of 0.46, 1.42 and 0.73, respectively. CONCLUSION: Ultrasound-derived IVC parameters demonstrate poor diagnostic accuracy for prediction of hypotension after induction in healthy adult patients.

Probing emotional recognition memory: how different response formats affect response behaviour.

Receiver-operating characteristic curves from confidence ratings and remember/know (R/K) judgments are often used to estimate the contribution of familiarity and recollection to recognition memory. Both coming with specific advantages and disadvantages, which could be reduced by their combination. Little is known how the combination of both methods impacts response behaviour. This could be particularly important for emotional memory research, which is susceptible to variation in meta-mnemonic processes. We obtained reference performance indices from the two methods, instructing individuals to give confidence ratings or R/K judgments in one step. Against these, we contrasted R/K judgments in a two-step format and two combined formats, confidence ratings followed by R/K judgments and vice versa. Regarding reference formats, confidence ratings resulted in more liberal response criteria and false alarm rates than R/K judgments. Two-step R/K judgments and confidence ratings followed by R/K judgments resulted in patterns similar to one-step R/K judgments. Reversing the order resulted in more liberal response biases, higher hit and false alarms rates. Recollection and familiarity were unaffected by response formats. Valence effects did not vary with response formats. The present results suggest that confidence ratings followed by R/K judgments provide the advantages of both without biasing response behaviour.